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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 Economic Value 

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 contribution by 

Applied Science Associates 

Narragansett, RI 

Prepared for: 

Peconic Estuary Program 

Suffolk County Department of Health Services 

County Center 

Riverhead, NY 11901 

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 of the study is to provide economic information to support management 

decisions for the Peconic estuary under the National Estuaries Program. 

Phase I of the study used available data to summarize the level of key 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 of the potential for, and constraints on, aquaculture in 

the Peconic estuary. 

The goal of Phase I1was to collect primary data to quantify non-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 amenity valuation survey. The recreation study 

collected data on the level participation in variousrecreational activities. The recreational study also 

estimated the economic value the public has for key activities and determined how the value of 

recreation activities changes with key indicators of the quality of the 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 

estimates biological “production” of important fish and bird species by wetlands based 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 and natural resources (“natural assets”) of the 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 species used for commercial and recreational purposes 

are but a few examples of these services. 

While the quality of PES coastal amenities is widely recognized, little information exists about the 

uses and users of PES natural resources. Even less is known about the value that the public holds 

for the natural asset services of the PES--that is, what they are “worth” to the public. This is 

because recreation, scenic views, and ecosystem productivity are not directly valued in markets. 

Lacking information on recreational and resources values, comparisons cannot be made of the 

benefits and costs of prospective actions to preserve or restore PES natural assets. This report is 

designed to help fill this major gap. 

In this report, we present the results of four non-market valuation studies carried out by Economic 

Analysis Inc. (“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 values of key recreational activities, and (2) other resource values provided by the natural 

assets of the PES. The economic valuation studies described herein were done to contribute to 

benefit-cost analyses of proposed management actions. 

This document is Phase I1 of a series of economic studies being done by EA1 for the Peconic 

Estuary Program. A Phase I report (Grigalunas and Diamantides, 1996)(1) provided an assessment 

of the “economic impacts” of the PES, in terms of business revenues, employment, and wages of 

estuarine-relatedsectors, and (2) summarized availableinformation on recreationaluses. 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-RELATED USES, 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 assets of 

the PES. Recognizing the many uses of PES natural resources, we designed and implemented a suite 

of four non-market valuation studies in order to provide estimates of the value of particular services: 

(1) A Property Value study examines the contribution of environmental amenities to the 

market price of property. Using the Town of Southold as a case study, the Property Value 

study was designed to measure values of amenities to residents living in the immediate 

vicinity. 

(2) A Travel Cost study uses original survey results to estimate outdoor recreational uses in 

the PES and the economic value that users have for four, key PES outdoor recreation 

activities: swimming, boating, fishing, and bird and wildlife viewing. This study also 

examines the impact that (A) water quality has on the number of trips and the value of 

swimming and (B) the effect of the catch rate on recreational fishing, important 

recreational uses of the estuary and activities much affected by water quality and resource 

abundance. 

(3) A Wetlands Productivity Value study, carried out in collaboration with Applied Science 

Associates, gives estimates of the economic value of eelgrass, intertidal salt marsh, and 

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 relative preferences 

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 in the chapters that follow. 

A note on the style adopted for this document. We believe that the results of the economic analyses 

presented in this report provide a wealth of data to decision makers and to the public. To make the 

report accessible to a wide audience, we deliberately adopt a non-technical style. For those 

interested, Appendixes present the technical details of the methodologies used. 

2 
I.C. SUMMARY OF SELECTED RESULTS 

I.C. 1. Property Value Study (Chapter 111). 

Data for property sales in the Town of Southold were used to estimate the contribution of specific 

environmental attributes to the market value of nearby property. Using a property value model, we 

found that proximity to open space and other environmental attributes had a significant, positive 

impact on nearby property values. 

For example, a parcel of land located next to open space has, on average, a 12.83% higher per-acre 

value than a similar parcel located elsewhere. To illustrate the how this result might be used, we 

estimate that a hypothetical contribution of a parcel of approximately 10 acres of open space would 

increase adjoining property values by $410,907. 

The model results also show that density of development and other attributes affect property values. 

For example, 2 or 4 acre zoning (Le., R-80 or R-120) has, on average, a 16.65% higher per-acre 

value than a similar parcel located elsewhere (Le., in a 1/2-acrezone). Conversely, property located 

on a main road (highway 25 or 48), or property adjoining a farm, has a lower value, after taking into 

account other property attributes. 

I.C.2. Recreational Uses and Economic Value (Chapter IV). 

A recreational survey adminstered to residents, second homeowners, and visitors allowed us to 

estimate (1) participation in outdoor recreation, and (2) the economic value the public has for four 

key PES recreational activites. We estimate that in 1995: 

127,762people took some 3.3million swimming, boating, fishing, or shell fishing, outings 

156,184 people engaged in about 5.2 million beach use, bird watching, wildlife viewing, 

or hunting trips 

Swimming and beach use were the most popular activities, followed by bird and wildlife viewing, 

boating, and fishing. Shell fishing and hunting had the fewest estimated number of trips. 

Measurement problems prevented us from including other common activities, such as 

hikinglwalking and bicycling. 

Among the PES Bays, Great Peconic Bay is the most popular, accounting for 28% of all recreational 

trips to the PES. Flanders Bay is the least frequently used, with 8% of PES trips. Great Peconic Bay 

is the most used for swimming (30%), fishing (29%), and boating (25%). For shell fishing, 

Gardiners Bay is the most popular PES location, with 33%.of all PES shell fishing trips. 

Outdoor recreation in the PES is enormously valuable to users. Using a Travel Cost model, we 

estimate the economic value per person, per trip, for four key recreation activities: Swimming, 

3 
boating', fishing, and for viewing of birds and wildlife. The estimated valuesper trip range fiom 

$49.83 for viewing birds and wildlife to $8.59 for swimming (in 1995dollars). These are estimates 

of what participants would be willing to pay, per trip, above and beyond the amount that they 

actually pay to participate. 

Adding across all trips, in 1995Viewing of Birds and Wildlife ($49.3 million) has the highest total 

annual value, followed by Recreational Fishing ($22.4 million), Boating ($18.1 million), and 

Swimming ($12.1 million). The correspondingasset values of the PES (over 25 years at 7 percent) 

for these key recreational activities range from $318 million for Bird Watching and Wildlife 

Viewing to $141 million for Swimming. The PES has an asset value of $276 million for 

Recreational Fishing and $210 million for Boating. 

Quality is important to PES recreationists. Swimming was found to depend upon perceptions of 

water quality, and swimmers perceptions, in turn, were related to objective (Le., SCDH field 

sampling) measures of water quality for nitrogen, water clarity, Brown Tide, and coliformbacteria. 

To illustrate how the results might be used in a Benefit-Cost analysis, we simulated hypothetical, 

uniform improvements in water quality. For example, a 10percent uniform improvement in water 

quality in each Bay increases the estimated number of annual swimming trips by 151thousand and 

adds yearly benefits of $1.3 million. Most of the benefits ($754 thousand) are due to improvements 

in water clarity (as measured by Secchi depth). Swimming benefits increase further with a 

hypothetical, 20 percent uniform water quality improvement, although the added benefits from the 

second 10 percent improvement is slightly less than that due to the first 10 percent improvement. 

The present value of the $1.3 million increase in benefits due to the 10 percent hypothetical water 

quality improvement is $15.1 million, using a 7 percent discount rate and a 25-year time horizon. 

Thas, if the present value of the costs of the policy (or set of policies) to improve water quality did 

not exceed $15.1 million over the same period, it would be a good investment of scarce resources. 

Recreational fishing also was found to depend upon the quality of the experience--in particular, the 

catch rate. A hypothetical 10 percent increase in catch rates raises the economic value per trip by 

$0.80, the number of trips by 11,249, and total annual fishing benefits by $472,359. The present 

value of this increase in catch rates--the increase in the asset value of the PES in providing this 

service-- is $5.5 million, using the 7 percent rate and time horizon of 25 years used for all cases. 

On another issue, to address a data gap identified in EAI's Phase I study, Phase I1 survey 

respondents were asked how much they spent at road side farmstandsand at vineyards and for rental 

accomodations. In total, in 1995, the public spent some $19.4 million at East End roadside 

farmstands and $5.4 million at vineyards. Using the same assumption as in the Phase I study that 

45 percent of these activitiesare PES-related (Grigalunasand Diamantides, 1996),the annual PESrelated expenditures for these two activities are $8.7 and $2.4 million, respectively. 

'Boating for the primary purpose of fishing is valued under recreational fishing. 

4 
I.C.3. Wetlands Productivity Values (Chapter V) 

Eelgrass, saltmarsh and intertidal mud bottoms provide food web and habitat services for many 

species of fish, shell fish, waterfowl and birds, and thus directly or indirectly benefit people who 

consume, hunt, or view these species. A simple food web model was developed that used available 

biological data, commercial fish and shell fish prices, and viewing values for birds to provide a 

perspective on the economic value of the productivity of these habitat types. 

The results suggest an asset valueper awe for existing wetlands of approximately$12.4 thousand 

for eelgrass, $4.3thousand for salt marsh, and $786 for mud flats, using a discount rate of7 percent 

and 25 year period for the servicesvalued. Restored wetlands are worth somewhat less since it may 

take several years to restore fully their natural functions as a food source or habitat. 

I.C.4. Resource Preferences and Economic Values (Chapter VI) 

Early discussions revealed that the public has a strong attachment to environmental and amenity 

resources of the PES, even if they do not use these resources directly. As outlined above, the 

Property Value study captures the value of amenities (or disamenities) to nearby properties, the 

Recreation study estimates use value for key outdoor recreational activities, and the Wetlands 

Productivity study yields estimates of the value of wetland ecosystems in the “production” of fish, 

shell fish, and birds. However, none of these studies reflects the value residents and second 

homeowners hold for the general ambience of the PES--the “sense of place” it provides, a phrase 

used often by participants in EAI’s focus groups. 

To try to capture these elusive but important non-market resource values, a survey developed and 

administered to 968 residents and second homeowners was used to learn which amenities are most 

important to people in the study area, and the amount that they would pay to preserve or restore 

them. Our purpose was (1) to account for the preferences many residents have for resources, even 

if they do not use these resources directly, and (2) to measure these preferences in dollars, if 

possible, to compare with other study results. 

Most respondents to the resource survey (97 percent) supported at least one hypothetical action to 

protect resources,and indicatedthey would financiallysupport such actions. The relative priorities 

of respondents, in order, were for farmland, eelgrass, wetlands, shellfish, and undeveloped land. 

We also estimated the monetary value that the survey responses 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 

believe that the resource priorities or relative values of resourcesare more reliablethan are the dollar 

estimates of values and recommend that relative values, rather than dollar values, be used in the 

process of selecting management 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 Economic Impacts. Peace Dale, RI:Economic Analysis, Inc. 

Kopp, R. and V.K. Smith, 1993. Valuing Natural Assets: The Economics of Natural Resource 

Damage Assessment. Washington, D.C.: Resource for the Future. 

Tietenberg,Tom, 1995. Environmentaland Natural ResourceEconomics (4th ed.) New York: John 

Wiley & Sons 

6 
11. BACKGROUND AND INTRODUCTION 

1I.A. BACKGROUND 

Located within SuffolkCounty at the East End of Long Island, the PES is comprised of the PeconicFlanders Bays system, Gardiners Bay and part of Block 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 Business News, 1994). 

Important characteristicsof the PES include: (1) the generally high quality of its coastal estuarine 

environment, (2) its economy,which is strongly influenced by seasonal,estuarine-relatedactivities, 

particularly tourism 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 or Long Island as a whole (Long Island BusinessNews, 1994;Grigalunasand Diamantides, 

1996). 

Water quality in the PES generally is very good, and the study area contains many beaches, parks, 

open space, and habitat for birds and other wildlife, including some endangered species. These and 

other environmentaland natural resources of the PES can be viewed as natural assets. A significant 

feature of assets--natural or otherwise--is that they can provide a stream of valuable services 

(“interest”) over time, if maintained. 

Some of the natural assets of the PES, however, are not being maintained, and others are at risk. 

Localized water quality problems exist due to runoff, failing septic systems, and inadequate sewage 

treatment. Substantial areas of open space and farms have been lost due to development; and 

pollution has closed thousands of acres of shellfish grounds used for commercial and recreational 

purposes. Further, large areas of ecologically productive eelgrass and saltmarsh ecosystems have 

been lost due to development, Brown Tide, and water pollution. Fin fish and shell fish landings 

have declined substantiallyover time, in part due to these problems. The combination of all of these 

adverse developmentsthreaten the quality of life of residents of, and visitors to, the PES. 

Management programs being developed by the Peconic Estuary Program would preserve or restore 

key PES environmental or natural resources. None of these programs is free, however, and some 

could be very costly. Deciding whether, where, how much, and when to invest in preserving or 

restoring PES natural resources requires consideration of many factors. One of these is the 

economic benefits and cost of such actions. 

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 1,000 businesses that 

engagein or supportrecreation and tourism, commercial fishing and seafood activities, and 

other estuarine-related economic operations (Grigalunas and Diamantides, 1996). 

(2) As non-market benefits to the thousands of recreationists, visitors, property owners, 

renters, and others who use or otherwise enjoy the quality of the PES’Snatural 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 of the 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-related businesses (Grigalunas and Diamantides, 

1996). 

Market data alone, however, give only a very limited view of the overall benefit or value of the 

services provided by PES estuary-related resources. This is because much of what people enjoy in 

the PES--for example, natural amenities, like open space, attractive views, good beaches and high 

levels of water quality--are not bought and sold in markets. Benefits cannot be counted in economic 

impact studies if they are not bought and sold on markets; and many benefits that do show up on 

markets are indirect and hence not linked to the natural asset that provides the service (e.g., fish or 

shell fish “produced” by PES wetlands and later harvested by fishermen). 

This Phase 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 of the 

PES can be looked at as natural assets (Freeman, 1993; Kopp and Smith, 1993; Tietenberg, 1995) 

that provide a flow of benefits over time, if maintained. 

Several standard economic concepts are used throughout this report. Two of these are Consumer 

Surplzis and Present Value. To avoid later confusion, these concepts should be explained at the 

outset. 

8 
Consumer Surplus. People buy goods or engage in activities like recreation when the benefit they 

receive is at least as great as the cost to them. For example, if I can buy a discount airplane ticket 

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 $15 to use a beach, but if using 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 case is the difference between the most I would pay, less what I actually pay. 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. 

Of course, the “trick” is to use sound methods to discover the Consumer Surplus the public receives 

for the services provided by the natural assets of the PES. Fortunately, standard methods are 

available to do just that. These methods, and how they were used to value the services of PES 

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

Discounting and Present Value. A second important concept involves discounting. Many benefits 

or costs occur over time, 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”. 

To estimate the present value of a stream of dollar flows over years 1through “T”, we discount each 

annual flow using the formula: 

Present Value = V,/(l+ r)’ + ....+V,/(l+r)T 

where V is a dollar value and r is the discount rate--the interest rate used to convert future flows into 

a value today. For example, if the total Consumer Surplus from swimming is $1,000 in year 1 and 

$1,000 in year 2, and the discount rate is 10percent, then the present value of these annual amounts 

is $1,735 (= $909 +$826) today. 

The present value calculated using the above formula depends upon three factors: (1) the size of the 

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”. 

9 
horizon, 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 

This report describesthe four non-market valuation studiescomprising Phase I1of a seriesof studies 

being carried out by EA1 for the Pec.onic Estuary Program. The Phase 11studies provide estimates 

of recreational uses and the economic values that the public holds recreation and for a variety of 

other services provided by key PES environmental and natural resources. 

The Phase I1 studies reported on here are a major component of a larger program to assess the 

benefits and costs of proposed management actions as part of the Peconic Estuary Program (Figure 

11.3). Phase I was designed to estimate market effects provided by the PES. As discussed above, 

Phase I measures “economic impacts” of the PES, including employment, sales, wages, and the 

number of establishments associated with 29 economic sectorsidentifiedas estuarine-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. 

Phase I11analyses will (1) provide cost analyses for specificmanagement actions designed to protect 

or restore PES amenities, (2) combine this cost information and the benefit information from Phases 

I and I1 to provide benefit-cost analyses of potential management actions, and (3) compare various 

financing options for the proposed management actions. 

We designed a suite of non-market valuation studies in order to be able to estimate the value of key 

services provided by amenities in the PES. Multiple non-market valuation studies are needed 

because of the many different types of services provided by PES environmental and natural 

resources. For example, recreational participants directly use Bay waters and beaches; hence, for 

these activities we want to measure participants’ recreational use values. In other cases, PES 

resources may provide amenity or aesthetic benefits to residents, which will be captured in property 

values, another type of use value. Additionally, some may also enjoy just knowing that PES 

environmental amenities, such as open space and salt marsh, are being preserved or restored, 

whether or not they directly use these resources. For these resources, other values, not involving 

direct use, may also be important and should be included. In sum, our use of a variety of methods 

was designed to allow us to gain a better understanding of the non-market values provided by 

different resource services, information that will prove useful for later benefit-cost studies. 

The key Phase 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 amenities provide to individuals who own adjacent properties. The recreational 

survey allows us to estimate recreational use and use values for a variety of outdoor activities. This 

’7 % is the representative value used in this report. In later efforts, the effects of different rates will be examined. 

10 
survey was designed fill data gaps identifiedin Phase I report, and to estimaterecreational uses and 

use values to contribute to assessment of management actions in Phase 111. The wetlands 

productivity analysis uses available data to estimate the productivity of specific habitat types, and 

to place a dollar measure on the ecosystem servicesprovided. The productivity study includes only 

food web and habitat servicesand focuses on the “production” of commercialspecies,andto a lesser 

extent,on non-commercial values (viewingand hunting values). Finally,the resource survey is used 

to identify public priorities and values for specific environmental amenities. The resource survey 

used a contingent choice model to learn which amenities are most important to the public and the 

amount that respondents indicate that they would pay to preserve or restore them. Summary 

informationfor each study is give in Table 11.1;each study is described in detail in the chaptersand 

appendices that 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 

Business News. 

Suffolk County Department of Health Services, 1992.Brown Tide ComprehensiveAssessment and 

Management Program. 3 volumes. SuffolkCounty Dept. of Health Services(November). 

11 
j, 

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 

1,354completed 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: 

fI 
A CASE STUDY OF THE TOWN OF SOUTHOLD 

1II.A. INTRODUCTION 

Property values in coastal communities depend upon many factors. These include not only the size 

of the property and the characteristics of the home on the property, but also on environmental 

amenities, such as open space and proximity to the shoreline. For example, we would expect a 

home located near the PES waterfront to be more valuable than another home, identical in all 

respects, but located a greater distance from the shore. If we could find two such homes, we could 

simply compare the prices at which they sell and, by that, calculate the value ofbeing near the shore. 

Of course, rarely are two homes identical in all respects except for one attribute. Instead, as anyone 

who has bought a home knows, the price that a property commands on real estate markets reflects 

a great many factors. These include: lot size, size of the home and its location, the characteristics 

of the surrounding neighborhood, and a wide range of other environmental factors. Nevertheless, 

if all of these important 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 several reasons. First, it has a wide range of environmental 

amenity levels and neighborhood conditions. For example, Southoldhas large areas of farmland and 

open space, although its population density (0.67 persons per acre) is highest among all five PES 

towns. In addition, Southold has a long and varied coastline, both on the Peconic Estuary and on 

Long Island Sound, and is characterized by a variety of development densities and types. About a 

quarter (26%) of the town is currently in agricultural use, compared with 30% in residential use, 

12% is preserved as open space, and 18% is classified as vacant. Less than 3% is classified as 

commercial or industrial (Suffolk County Department of Planning 1997a). 

Southold’s population increased 8.9% between 1980 and 1990, yet the number of housing units 

increased at nearly twice this level (16.6%). Thus, the growth of housing and the associated loss of 

undeveloped lands has far out paced population growth (Suffolk County Department of Planning 

1997b). Given the recent rapid pace of development, the protection of open space, 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 

i 
The impacts of environmental amenities on Southoldproperty values, and the impacts resulting from 

possible PES management actions, will depend upon the unique characteristics of the town (and 

perhaps the quality of surrounding waters and land areas). To assess these impacts, EA1 conducted 

a property value analysis specific to the town of Southold. We apply economic methods using the 

property value (or “hedonic” method) to a database comprised of all Southoldreal estate transactions 

in 1996 and GIS parcel coverage data for the town. Briefly, the analysis estimates correlations 

between property values and levels of valued environmental attributes, including open space. 

The policy relevance of the results lies in the assumption that established relationships between 

environmentalamenities andproperty values estimated using the property value model will continue 

to hold in the future, as future events (including policies) lead to changes in the level of these 

amenities. Through the detailed study of existing property values, we seek the best possible 

statistical estimate of the environmental impacts on local property values, given the available data. 

1II.B. RELATIONSHIPBETWEEN ENVIRONMENTALAMENITIESAND PROPERTY 

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 valued by local homeow ners, making communities 

with a high level of valued environmental amenities (Le., environmental quality) more attractive 

than similar communities without such amenities. As a result, home buyers are willing to pay more 

for land or housing with higher levels of environmental quality. 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 

evidenced by their actual willingness to pay higher prices for properties with higher levels of desired 

environmental characteristics. 

A well-documented example of the relationship among environmental amenities and property values 

involves open space. Many studies show that nearby open space increases property values, 

reflecting home buyers’ values for the services and character offered by open space (e.g., Freeman, 

1993). In a recent, coordinated property value/geographic information system study conducted in 

Middletown, Rhode Island, Johnston (1997) shows that positive property values impacts of open 

space can range from less than 1%to greater than 13%,depending on the size of nearby contiguous 

open space parcels, the total amount of open space in a region, and the distance of property from 

open space parcels. Larger property value impacts are associated with larger contiguous parcels, 

larger acreage of open space, and a closer proximity between valued parcels and 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 $2,000 more than similar houses located more than 2000 feet from the park (More, et 

14 
al., 1982). In an analysis of cluster zoning in Amherst, Massachusetts, Lacy (1990) estimates that 

cluster-zonedhousingwith permanentlyprotected open space appreciatesat a fasterrate that similar 

housing in conventional subdivisions. As of 1989,this difference had created an average $17,100 

difference between the average selling price in the two types of developments. 

Comparable results concerningthe value of open space also have been found in other coastal areas. 

For example, a study of land values adjacent to Chesapeake Bay shows a positive relationship 

between the percentage of preserved open space in a 250 acre area surrounding a parcel of land, and 

the per-acre sellingprice of that land. This study also shows that agriculturalpasture land and forest 

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) find that, on average, residential housing prices in Boulder, Colorado decrease 

by $4.20 for each additional foot of distance from a central green way, up to a distance of 3200 feet. 

Of course, the impacts of environmental amenities on Southold property values depend on the 

unique characteristics of the town, and may be greater or less than those cited above. To assess 

these impacts, we conducted a property value analysis specific to the town of Southold. We apply 

economicmethods (hedonicmethods)to a database comprised of GIS parcel coverage data and real 

estate sales data. In simpleterms, the property value model estimates correlationsbetween property 

values and levels of valued environmental attributes, including open space. The steps involved and 

data used are described next. 

PI1.C. APPLICATION OF PROPERTY VALUE MODEL TO SOUTHOLD 

This study applies the property value model to real estate parcels sold in Southoldduring 1996. The 

analysis estimates how various site,neighborhood and environmental characteristicsaffect the value 

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 (1992), among many others. 

1II.C.1. Explanation of The Property Value Method 

People buying homes in effectpurchase many site,neighborhood,and environmentalcharacteristics. 

For example, the characteristicsof a residential property include: the size of the house, the number 

of baths, the square footage of the structure,the zoning classification of the land, whether the land 

is served by public sewers and water, the existence of wetlands on the property, and the proximity 

of the property to amenities such as open space, coastlines, and farms. These and other 

characteristics define the property, and make it more or less attractive to potential buyers. Other 

things being the same, home buyers are willing to pay more for properties that they find have 

15 

I

e 
desirable characteristics,and less for properties with undesirable characteristics. These likes and . 

dislikes across many prospective buyers and sellers are reflected in market prices. The Property 

Value Method attempts to estimate the portion of a property’s value related to each relevant 

characteristic,by that providing an estimateof the (implicit)value of each characteristic. The model 

works by statisticallycomparing values of a large number of properties with differing levels of the 

identified characteristics (e.g. lot sizes, number of rooms, proximity to roads, farms, open space, 

etc.) (Freeman, 1993; Johnston, 1997a). 

The Property Value technique is based on the assumption that a relationship exists between the 

market value of a property, and the characteristicsof the property. The Property Value method uses 

a statisticaltechnique called “multipleregression” to assess the impact of each characteristicon the 

market value of the property. The technique simultaneously compares a large number of properties 

with different prices and different levels of each characteristic. The method establishes which 

characteristicsare associatedwith higher values, which are associated with lower values, and which 

have no significant impact on values. The model also estimates the dollar magnitude of these 

impacts--that is, it estimates how large an impact is likely to be caused by a specific level of a 

specific characteristic. Using this technique, the impact of different environmental amenities on 

nearby property values can be 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 available for 401 parcels, complete GIS coverage data exists for only 374 

of these parcels. Thus, the Southolddata set used in the analysis has full informationon 374 parcels. 

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). 

16 
Model variables are the characteristics that influence buyers’ willingness to purchase a land parcel, 

or pay higher prices for that parcel. These include proximity to amenities such as open space and 

coastlines; the size of the parcel; the size and other characteristics of the structures on the parcel; 

applicable use restrictions such as zoning codes; and the existence of features such as wetlands on 

the parcel. These variables may be grouped into three general categories: parcel characteristics, 

neighborhood characteristics, and environmental characteristics. To distinguish the effects of 

parcel size on land values, we consider the effect of the characteristics onper-acre value. This is 

calculated by dividing selling price by the number of acres in the parcel. Note that these per-acre 

land values include the value of all structures built on the land. For a full description of the 23 

variables included in the final analysis, see Appendix A. 

The statistical analysis uses the ordinary least squares (OLS) multiple regression approach. This 

approach estimatesthe direction,magnitude, and statisticalsignificance of correlationsbetween each 

of a set of independent variables (the characteristics of the property), and a single dependent variable 

(per-acre property value). 

To establish the nature of the specific functional relationship between these variables and assessed 

land values, the study relies on the findings of prior research. In particular, the regression analysis 

applies a “transcendental” or “translog functional The transcendentalform is chosen for its 

ability to capture realistic relationships between parcel characteristics and land values (Chicoine 

1981). Such functional forms are also preferred for their statistical properties in cases where data 

concerning certain relevant variables may not exist in the data set (Garrod and Willis 1992). For a 

technical description of the statistical model and its properties, see Appendix B. 

1II.E. ANALYSIS OF SOUTHOLD LAND VALUES: RESULTS 

The hedonic analysis provides information on the effect of 20 characteristics on per-acre land values 

in Southold. Final model results are illustrated in Appendix C. Overall measures of model fit 

indicate high levels of correlation between land characteristics and property values. An R2value 

of 0.8352 indicates that over 83% of the variation in per-acre value is “explained” by the model, or 

is correlated with model variables. An F-statistic of 75.945 indicates only a .01% chance that the 

set of correlations reported by the model could be observed through random chance alone. The signs 

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).0f those variables with demonstrated impacts on property values, seven 

have potential implications for environmental policy. These include the following: 

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

defined by the GIs database. A parcel of land 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 space parcel. As defined by the GIS for 

Suffolk County, open space includes: parks; nature and wildlife preserves; 

recreational fields; unbuildable swamps or wetlands; large tracts of land 

associated with schools, cemeteries, or other institutions; and selected large 

parcels of preserved farmland. 

farmdistance: This variable represents the distance between a specific parcel 

and the nearest farmland (farmland on which agricultural crops or nursery 

products are grown), in meters. For every meter ofadditional distance between 

a parcel and the nearestfarmland, average per-acre property value increases 

by 0.0017% (i.e., the magnitude of the impact is quite small). 

onfarm: This variable identifies parcels contiguous to farmland. A parcel of 

land located next tofarmland has, on average, a 13.32% lower per-acre value 

than a similar parcel located elsewhere. 

onroad: This variable identifies parcels within 20 meters of a major road 

(defined as Rts. 25 and 48). A parcel of land located next to a major road has, 

on average, a 16.16% lower per-acre value than a similar parcel located 

elsewhere. 

largezone: This variable identifies parcels located in districts zoned R-80 or 

R-120 (two- or three-acre zoning). A parcel of land located within a district 

zoned R-80 or R-120 has, on average, 16.65% higher per-acre value than a 

similar parcel located elsewhere. 

wetland: This variable indicates the percentage of a parcel classified as a 

freshwaterwetland. For every onepercentage point increase in thepercent of 

a parcel classiJed as wetlands, average per-acre property 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 space variable, the impact of open space on neighboring property 

values will differ depending on the characteristics of the surrounding property. However, to 

illustrate the basic implications of the model results �or policy, the following section simulates the 

predicted property value impact of the hypothetical loss of 10 acres of open space, in a highly 

simplified scenario. Given specific locations for the open space land under consideration, and the 

characteristics of the surrounding parcels, similar results could be generated for actual areas in 

Southold. 

18 

t 
According to the model results, properties adjacent to open space land (or within 25 ft.) are an 

average of 12.83%more valuable than those not adjacent to open space. Within the Southold data 

sample, the average parcel sales price was $213,514, with an average parcel size of 2941.95 square 

meters (0.72 acre). Given this average value, a loss of 12.83% (related to the loss of our 

hypothetical 10 acres of adjacent open space) implies that average property value for the adjacent 

parcels would decrease by $27,394, all else held constant. 

To estimate a hypothetical total impact in a simple case, assume that the lost open space was ten acres 

in a square shape, surrounded by parcels of average 0.72 acre size (the average in our sample), also 

square in shape. Assuming a square shape for the open space parcel, it would have a perimeter of 805.2 

meters that would be bordered by other parcels. Given an average per-parcel area of 2941.95 square 

meters (also assumed square), the length per-side of these bordering parcels would be approximately 

54 meters. Given these measurements, approximately 15parcels would fit adjacentto the 10 acre open 

space parcel, assuming each had a full 54 meter side bordering the open space (805.2 f54 = 14.9). 

Accordingly,for this simple example,we assume that 15 averageparcels would be affected by the loss, 

or development, of our square ten acres of open space. 

As statedpreviously, development of the open space parcel would result in a loss of 12.83%of average 

value per lot or $213,514, according to the calculations made above. Thus, the average parcel would 

lose $27,394 in total value. Multiplied by the 15 parcels adjacent to the newly developed parcel, the 

total property value loss is equal to $27,394~15=$410,907.This equates to an average loss of $41,090 

in total property value per-acre of open space lost. Looked at another way, the estimated benefits of 

preserving the ten acres of open space thus would be $410,907, as this is the increasein nearby property 

value associated with this open space parcel. 

For this illustration we do not compare costs with benefits since we do not have information on the 

costs of acquiring specific undeveloped property for open space. However, in the illustration, if the 

10 acres of undeveloped property for open space could be acquired for less than $410,907 or $41,097 

per acre, then the benefits would be greater than costs. Note that even if the property to be acquired 

for open space costs more than $410,907,benefits still may exceed costs. This is because not all of the 

public benefits of open space are captured in our hedonic analysis. For example, our results in this 

section do not capture general amenity benefits enjoyedby all local residents,regardlessof the location 

of their homes (see Chapter VI for discussion of this issue).6 

We note that the impacts illustratedabove should not be regarded as exact results expected in any single 

instance, and are included for illustration purposes only. A full benefit-cost analysis would require 

much more detailed analysis of the proposed project and its impacts, including impacts on surrounding 

property values. However, the illustratedresults do indicate the generaltypes ofbenefits and costs that 

may be expected, on average, over the entire community. Although the results require various 

assumptions as noted above, they are robust with respect to the quantity of open space preserved. 

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 the town, and the results of the Southold 

property value analysis correspond to prior expectations and with the results of similar analyses. 

Nevertheless several potential limitations should be noted. The most important of these limitations 

is that the results of the statistical analysis are sensitive to characteristics of the data set. The 

combined property card-GIS database may exclude information on certain variables that may affect 

property values. Although numerous environmental variables may influence property purchase 

decisions, data limitations allowed us to include only a sub-set of these variables in the model. For 

example, the data set includes water quality monitoring data for the Peconic Estuary, conducted at 

numerous sites. However, no parcels in the 1996 sales database are located within 100 meters of 

the Peconic Estuary. Thus, for those parcels in the current database, water quality is not expected 

to have an impact. However, were data to exist for parcels close to the estuary, one would expect 

that water quality might have an impact on property values. Other variables for which data was 

unavailable included fresh water (including groundwater) quality; beach erosion (likely only 

important for beachfront properties); and proximity to creeks, all of which would be expected to 

affect property values. If in fact important variables are excluded from the model, statistical results 

may show upward or downward bias. Omission of important variables can either increase or 

decrease the estimated effects of environmental amenities on property values, based on the extent 

to which these variables are correlated with environmental variables. (If these omitted variables are 

uncorrelated with the environmental policy variables of interest, then their absence should not 

influence reported results.) 

For all of the above reasons, the estimated Property Value model results should not be interpreted 

as "exact". Rather, they provide the best possible estimate, given available data, of the impacts of 

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 space preservation on 

the grounds that it tends to increase property values, thereby increasing the costs of housing for 

young home buyers. Others may reject increases in property taxes that accompany higher property 

values. Still others may object that higher property values tend to ''squeeze out" low income 

residents. Real estate agents may oppose additional open space on the basis that fewer housing 

units are available, despite the act that average property values in the area affected will be higher 

with the additional open space. Such arguments should be acknowledged when considering 

environmental policy changes using the results of property value methods. 

Finally, we note that many people beyond the immediate area may derive benefits from open space 

but their benefits are not included in numbers given in the above illustration. For example, residents 

of the town who live outside of the grid used in the illustration may value open space elsewhere in 

town (see Chapter V). 

1II.H. SUMMARY 

20 
Environmental amenitiesincreaseproperty values of nearby parcels, reflecting the valuable services 

offered by these amenities. An analysis of Southold property values supports this contention, and 

corresponds with the results of prior studies. In Southold, existing open space increases the values 

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

higher in parcels located further from farms and major roads. Finally, higher property values appear 

to be associated with the existence of wetlands on the property, and with large lot (2- and 3-acre) 

zoning. These results indicate that environmental policies can have significant impacts on property 

values, reflecting the influence of local environmental amenities on local quality of life. 

1II.IREFERENCES 

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

Perspective,” American Journal of Agricultural Economics 78: 1168-1180. 

Chicoine, D. L., 1981. Farmland Values at the Urban Fringe: An Analysis of Sales Prices.” 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 Application to 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 the Northeastern 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. College Park, MD: Center for Agricultural and Natural Resource Policy. 

21 
Geoghegan, J., N. Bockstael, and L. Wainger. 1995. “Spatial Landscape Indices in a Hedonic 

Framework: An Ecological Economics Analysis Using GIS.” presented at the annual 

meetings of the Association of Environmental and Resource Economics, Indianapolis, IN. 

Hammer, T.R., R.E. Coughlin and E.T. Horn IV. 1974. Research Report: The Effect of a Large 

Park on Real Estate Values. Journal of the American Institute of Planners, July. 

Johnston, R.J. 1997a Aquidneck Island and Open Space: An Economic Perspective: Technical 

Manual Narragansett, RI: The Aquidneck Island Partnership and the Coastal Resources 

Center, University of Rhode Island. 

Johnston, R.J. 1997b. Aquidneck Island and Open Space: An Economic Perspective. Narragansett, 

RI: The Aquidneck Island Partnership and the Coastal Resources Center, University of 

Rhode Island. 

Kask, S.B. and S.A. Maani, 1992. “Uncertainty, Information, and Hedonic Pricing.” Land 

Economics 68(2): 170-184. 

Kimmel, M.M., 1985. Parks and Property Values: An Empirical Study in Dayton and Columbus, 

Ohio. Thesis. Oxford, OH: Miami Univ., Inst. of Environmental Sciences. 

Lacy, J. 1990. An Examination of Market Appreciationfor Clustered Housing with Permanently 

Protected Open Space. Center for Rural Massachusetts Monograph Series. Amherst, MA: 

University of Massachusetts. 

Lardaro, L., 1993. Applied Econometrics. New York: Harper Collins College Publishers. 

More, T. A., Thomas Stevens and P. Geoffrey Allen, 1992. “The Economics of Urban Parks.” 

Parks and Recreation. 

National Park Service, 1995. The Economic Impacts of Protection Rivers, Trails, and Greenway 

Corridors. Washington, D.C.: Rivers, Trails, and Conservation Assistance Program. 

Nelson, A. C., 1985. “A Unifying View of Greenbelt Influences on Regional Land Values and 

Implications for Regional Planning Policy.” Growth and Change 16(2): 43-48. 

Parsons, G.R., 1992. “The Effect of Coastal Land Use Restrictions on Housing Prices: A Repeat 

Sale Analysis.” Jour. of Environmental Economics and Management 22: 25-37. 

Rosen, S., 1974. Hedonic Prices and Implicit Markets: Product Differentiation in Pure 

Competition.” Journal of Political Economy 82(1): 34-55. 

SAS Institute Inc. 1996. SAS User’s Guide: Statistics. Cary: NC: SAS Institute Inc. 

22 
Shonkwiler, J.S. and J.E Reynolds, 1986. “A Note on the Use of Hedonic Models in the Analysis 

of Land Prices at the Urban Fringe.” Land Economics 62(1). 

Wichelns, D. and J. Kline, 1993. “The Impact of Parcel Characteristicson the Cost of Development 

Rights to Farmland”. Agric. and Resource Economics Review: 150-158. 

23 
APPENDIX A. Variables in the Hedonic Analysis 

All model 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. 

Dependent Variable: 

LVALACRE: The natural log of per-acre sales price, in dollars. Based on Southold property 

record cards for properties sold during 1996. 

Independent Variables: 

LNACRES: the natural log of acres in each parcel. Estimated using GIS coverages. 

WATFRONT: dummy variable: value of 1 assigned if property is located on the waterfront 

(Long Island Sound or Peconic); value of value of 0 assigned if property is 

not located on a waterfront. Value assigned based on waterfront 

classification of property record cards. 

BULKHEAD: dummy variable: value of 1 assigned if property has a bulkhead on the 

waterfront, value of 0 assigned if no bulkhead. Value assigned based on 

property record cards. 

LARGEZONE: dummy variable: value of 1 assigned for parcels located in districts zoned 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 if the house has a patio or deck; value 

of 0 assigned if there is no patio or deck, based on property records. 

BATHS: the number of bathrooms, based on property records. 

NOHEAT: dummy variable: value of 1 assigned if the house has no heat, value of 0 

assigned if house has heat, based on property records. 

24 
FIRE: 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 on property records. 

SPECIAL: dummy variable: value of 1 assigned if the house has a “special” feature 

(tennis court, swimming pool, etc.); value of 0 assigned if no special features 

are present, 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 1 assigned if the parcel is within 100meters of the 

Peconic Estuary; value of zero assigned if the parcel is furtherthan 100linear 

meters from the estuary. Based on GIS coverages. 

SOUNDl00: dummy variable: value of 1 assigned if the parcel is within 100 meters of 

Long Island Sound; value of zero assigned if the parcel is further than 100 

linear meters from the Sound. 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 1assigned if the parcel 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 GIS coverage classifies the following land uses 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 

25 
parks; otherwild or conservationlands;and taxable state owned conservation 

easements. 

Farms and undeveloped private property, in general, are not considered open 

space. 

Mean values of variables included in the analysis, for the 374 observations in the database, are 

illustrated below. 

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 

0.0718 

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 coverages of Southold. 

The following GIS map illustratesthe type of analysis applied. In this case, the map illustratesthe 

process used to determine the existence of agricultural land within 100 meters of each parcel in the 

sales database. 

27 
The followingGIS map coversthe same area of Southold,yet illustratesthe distributionofwetlands, 

used to assess the percentage of each sales parcel covered by freshwater wetlands. 

28 

t 
APPENDIX B. The Statistical Model 

To establish the functional (mathematical) relationship between model variables and assessed land 

values, the analysis on the findings of prior research. In particular, the analysis follows Wichelns 

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 Vi is the 1996 selling price of the ith parcel, in dollars per-acre, Xi, is parcel size in acres, 

and X, are measures of the j = 2...n other characteristics that affect land value. The p's are 

parameters to be estimated, and represent the effects of each of the characteristics on per-acre land 

price. Estimated parameters are used to calculate the effects of different open space 

characteristics on the value of land in Southold. The transcendental form is chosen for its ability 

to capture realistic relationships between parcel characteristics and land values (Chicoine 1981). 

For example, the transcendental form permits a positive or negative marginal relationship between 

value and parcel size (acres), requires value to be zero when parcel size is zero, can detect 

proportional value-size relationships, and allows for increasing or decreasing returns to scale for 

land parcel characteristics (Chicoine 1981; Wichelns and Kline 1993). Moreover, functional 

forms such as the semi-log or transcendental are preferred (to the more flexible functional forms) 

when there is potential omitted variable bias (Garrod and Willis 1992). 

The empirical version of the translog model is as follows: 

where the variables are as defined above, and In(.) represents the natural log. This is the 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 significance levels indicate good model fit, and trials with alternate functional forms (e.g., 

linear, semi-log) indicate that these alternate forms result in lower significance levels and 

explanatory power. A White test for heteroskedasticity (White 1980) fails to reject the null 

hypothesis ofhomoskedasticityat the 5% level. Although multicollinearity isubiquitous inhedonic 

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 

PECON100 

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 

0.00011945 

0.00484814 

0.00006797 

0.03166099 

0.02678655 

0.04189498 

139.575 0.0001 

-34.753 0.0001 

7.420 0.0001 

2.953 0.0034 

2.528 0.0119 

-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 and location choice for key recreational uses, and (2) estimates of the economic value 

of those uses. Also presented is a summary of expenditures at PES farm stands, wineries, and rental 

housing, data that was identified as a gap in EAI’s Phase I study and is of interest to decision 

makers. 

The main tool for providing information for this analysis is a recreational use survey carried out 

in August, 1995. The recreational survey was designed to: 

identify key outdoor recreational activities and locations in the PES 

estimate the level of participation in key recreational activities 

identify key characteristics and preferences of users 

estimate the value of recreational uses of the PES 

fill data gaps identified in the Phase I study. 

Key recreational uses and locations. More than 15 recreational uses of the PES were identified 

through focus groups and discussions with local citizens and officials. One key component of the 

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

Participation in key recreational uses. EAI’s Phase I study (Diamantides and 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 and preferences of users. Prior to the recreational use survey, only anecdotal 

information existed concerning recreational users of the PES. Information, such as residency, 

accommodations, recreational preferences, etc.,are providedby EAI’sPhase I1recreational survey. 

Recreational use values. Data from the Phase I1 recreational survey is used to estimate the 

economic value of key recreational activities in the PES and, when possible, to estimate the change 

in recreational value resulting from changes in water quality. 

Phase I economic impact data gaps. Key data gaps identified in the Phase I study include the 

economic impact of specialized sectors of the local economy. These sectors include: summer 

rentals, winery tours and sales, and farm stand sales. Each of these sectors was identified by local 

citizens and officials as playing an important, but unquantified role, in the local economy. 

Economic data for these sectors--sales-- is presented in this report. 

31 
1V.B. IDENTIFICATION OF RECREATIONAL USES AND LOCATIONS 

Outdoor recreation is a major activity in the PES. Based on recreational uses identified during the 

survey development process (see Sec. C, Survey Development), the survey questionnaire included 

16 major natural resource-based recreational uses. Of these, eight key recreational activities were 

identified: 

Fishing Beach Use 

Boating Bird Watching 

Swimming Wildlife Viewing 

Shell fishing Hunting 

Data were collected for these eight key recreational uses, including annual participation during the 

past year. Detailed information also was obtained on these activities for the respondent’s most 

recent recreational outing to the East End. 

Annual participation data on key recreational activities include the number of times the respondent 

had done each activity at specific East End locations during that year (1995). The survey instrument 

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 for the remaining key recreational activities (beach use, bird watching, wildlife viewing, 

and hunting) were categorized by the town in which the activity took place. Five East End towns 

were identified by the survey instrument. The five recreation locations identified by town are: 

East Hampton 

Riverhead 

Shelter Island 

Southampton. 

SouthoId. 

Single-day data on key recreational activities include information on activities the respondent 

participated in during their most recent recreation day at the East End. This question was followed 

by occasion-specific, write-in questions concerning the: (1) location of the activity, (2) perceived 

water quality, (3) number of people in the recreation party, (4) travel time, and (5) time on site. 

Other activity-specific data collected included fishing catch, shellfish harvest, choice of marina or 

ramp, perception of beach facility quality, and number of birds or type of wildlife sighted7. 

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

participation. 

32 
1V.C. SURVEY DEVELOPMENT AND IMPLEMENTATION 

The development and implementation of the recreational use survey parallels the development and 

implementation of the resource survey. Meetings, interviews, and focus groups were instrumental 

in the development of both surveys. Similarly, implementation of the two surveys occured at the 

same time and places using the same survey staff. Only significant aspects of the development of 

the recreational survey that differ fiom the resource survey will be discussed here. For a full 

discussion of the resource survey development and implementation including similarities with the 

recreational survey (see Chapter VI, Resource Value Survey). 

A major challenge in the development of the recreational use survey was the creation of a question 

format. A single instrument was selected to cover all the major recreational uses and locations of 

the PES. The single-instrumentformat allowed collection of data for multiple current and past uses 

and was a much easier, and less costly, method for acquiring data, an important issue givenrealistic 

limits on budgets for this project. 

Questionnaire development began with meetings with the Management Committee, the Citizen’s 

advisory group, and “expert informants” from major stakeholder groups, such as marina operators 

and Baymen. Based on issues raised by the three groups, questionnairedevelopment then proceeded 

with informal interviews with the public, focus groups, and pretests in order to determine the most 

relevant questions, wording of questions, and survey presentation. 

Informal interviews with the public were a critical component of questionnaire development. From 

these interviews we gained insight into who the users are and their perspective on recreation in the 

PES. For example, during these interviews, it became apparent that many recreational users did not 

think in terms of the PES as an estuary system. Instead individuals more easily identified the East 

End or individual bays within the PES as geographic entities. The questionnaire, therefore, asks 

about recreational uses at locations at the East End with which individuals could easily identify. 

Focus groups and pretests were used to hone the questionnaire down to an efficient presentation 

that would allow us to collect all the required data. For example, one issue we used focus groups 

to refine was the questionnaire’s approach to eliciting water quality perceptions. Originally we had 

hoped to use the questionnaire to ask very specific water quality questions, such as clarity, smell, 

debris, etc., so that we might be able to estimate the contribution of these different attributes to an 

individual’s overall perception of water quality. However, during focus groups we found that 

recreational users can have very complex and inconsistent ways of assessing water quality, which 

include numerous additional attributes such as beach condition, signs, and plant abundance. 

Eliciting information on all of these attributes became too cumbersome, and a more simplified 

approach was eventually used. The simplified approach asks the respondent to rank water quality 

at the site on a four point scale from “excellent” to “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. 

33 
A key objective in the survey development phase was to keep the questioning simple and consistent 

across uses. Many versions of the recreational use survey were pretested, to assess the ease with 

which respondents could understand and answer the questions. The survey pretests were 

instrumental in reducing the size of the questionnaire. One of our concerns was that if the 

questionnairewas too long, only the first few questions would be answered, or respondents would 

only answer selected questions. Another important concern was that the survey be clear and 

relatively brief in order to allow us to use an intercept-survey, self-administration approach, to 

economize on scarceresearch funds. The final version of the recreational use survey questionnaire 

is available from the authors. 

The recreational use survey and the resource use survey were implemented simultaneously during 

the week of August 22-29,1995, in pre-selected, public places in each of the five East End towns. 

Interviewers solicited potential respondents by simply approaching them and asking if they would 

be willing to spend 10-15 minutes filling out a survey to be used to help develop a plan to protect 

and manage the bays. Survey locations included beaches, shopping areas, libraries, Post Offices, 

and miscellaneous public places throughout the study area. 

Other natural resource-based recreational activities were identified in the survey development 

process. These activities include: walkinghiking, biking, sightseeing, jet skiing, jogging, 

windsurfing, art work, and photography. Questions on these activities were retained in the 

questionnaire for comprehensiveness.However,these activities are considered less 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 

questions asks whether the respondent is an East End resident, owns a second home in the East End, 

or is a visitor. Visitors are also asked the mode of transportation they used to travel to the East End 

and the type of accommodations used, if any. 

Subsequent questions address the issue of Brown Tide (“BT”). BT refers to the discoloration of 

the waters in and around the PES due to an algae that sporadically grows in enormous numbers. 

Recent incidents of BT may have contributed to a significant decline in ecologically productive 

eelgrass beds and caused major disruptions in the formerly valuable scallop industry. BT also was 

a significant concern to recreationists, as described in some detail later. 

Throughout the survey development process, the issue of BT repeatedly surfaced. Questions 

concerning the BT were placed in the beginning of the questionnaire in part to diffuse this issue 

early on, allowingrespondents to focus on the questions that follow. We asked respondentswhether 

they were aware of BT and which, if any, of their recreational activities were affected by BT. 

The next set of questions asked respondents to indicate their annual participation in key recreational 

34 
activities by filling out two brief charts. One chart identified recreation locations by water body. 

The other chart identifiedrecreation locationsby town. Respondentsindicatedthe number of times 

that they did each activity in each location during the past year (1995). 

The annual participation questions were followed by single-day recreation activity questions. 

Respondentswere askedto identifytheir most recent recreation day at the east End. They then were 

asked to identify allthe recreationalactivitiesparticipatedduringthat day. Shell fishing,swimming 

and beach use, boat use, fishing,bird watching, and wildlifeviewingwere selectedas key activities. 

For each of these recreational pursuits, a separate page of questions asked for activity-specific 

information. Activity-specific questions included: location, travel distance, time on site, number 

of people in the party, catch or harvest, and water quality and site quality ratings. 

After the participationquestions,respondents were asked about selected expenditures made at the 

East End during the past twelve months. As noted, these questions were designed to fill data gaps 

in the Phase I study for farm stands, wineries, and housing rentals. Residents and second home 

owners were also asked a referendum-type question about their willingness to support selected 

water quality initiatives (see section on water quality initiative referendum). Finally, a. set of 

questionswas asked in orderto obtain socio-demographic data, such as residency,household status, 

income, age, education, and employment status. 

1V.E. DESCRIPTIVE RESULTS: DEMOGRAPHICS 

A total of 1,354respondents provided usable survey questionnaires,althoughnot every respondent 

answered all questions. Selected, major demographic results of the survey are summarized as 

follows: 

Residents and second homeowners from all East End towns were represented in the sample 

A majority (59 %) of respondents were visitors to the East End 

Most visitors (76%) came from either Long Island or New York City 

Most visitors (60 %) stayed at the East End overnight 

The most common overnight accommodation was with friends or relatives (39%) 

Compared to 1990Census data,the samplehas a slightlyhigher share of women and people 

in the middle age groups; is better educated; and is wealthier than the resident population. 

Residency Status. Of the 1,267 respondents who provided this information, over half of 

those responding (59%) were visitors from outside the East End (Table IV.1). Of the remainder, 

28% indicated that their primary residence was at one of the five East End towns, and 13% owned 

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

residents considered themselves East End residents but were not counted as such in this study. 

Residents and Second Home Ownevs - Place of Residency. Residents and second home 

owners from each of the five East End towns were represented in the survey sample. Southampton 

(30%) and Southold(28%) had the largest percentages of resident respondents.The remainder was 

35 
closely distributed among Shelter Island (16%), East Hampton (14%), and Riverhead (12%). The 

relatively high proportion of Shelter Island resident respondents is not expected to have a 

significant effect on survey results. 

Table IV.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 second home owners,New York City was the dominant primary place of residence (45 

% of second home owners surveyed), followed by Nassau (19 %) and Suffolk counties (14 %). 

Other locations, largely distributed across Westchester and Rockland counties in New York and 

northeastern New Jersey, were the primary residences of the remaining 22%. 

Visitors-Place ofliesidency. Visitors to the East End predominately came from Long Island 

and New York City. 40% of visitors came from Suffolk county, 20% came from New York City, 

and 16% from Nassau county. Only 8% came from other locations in New York State and New 

Jersey, and just 5% came from Connecticut and Massachusetts. Other locations, such as Florida, 

California, and several foreign countries accounted for the primary residences of the remaining 

11%. 

Visitor 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. 

Of the 700 overnight visitors who provided accommodation information, the most common (39%) 

type of accommodation was with friends or relatives. Rental (24%) and hotel or motel (22%) 

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

reported that they stayed at accommodations other than friends or relatives, hotels or motels, or 

rental properties. Presumably these other accommodations are predominately boats and camp sites. 

Sample characteristics: comparison with resident census data. Gender, age, education, and 

income characteristics of survey respondents can be compared to the characteristics of the resident 

population as identified by the most recent (1990) census. Only comparisons to the resident 

population are possible, since characteristics of the second homeowner and visitor populations are 

unknown. 

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 individuals less than 18 years of age. 

Our survey sample is much more educated than the resident population. For example, census data 

indicatesthat 49% of the residentpopulation had some level of post-secondary education. The same 

level of education was achieved by 80% of the resident sample, 95% of second homeowners, 91% 

of overnight visitors, and 82% of day trippers. 

The survey sample also has higher household incomes than the resident population. According to 

the census, 34% of the resident population has a household income of $50,000 or more. However, 

nearly half (48%) the residents in the survey sample have household incomes of $50,000 or more, 

as do 80% of second homeowners, 64% of overnight visitors, and 59% of day trippers. 

Sample characteristics: comparisonamong resident and visitor categories. The distribution 

among female and male respondents slightly favored females in all categories. Day trippers have 

the most respondents under 24 years of age (IO%), and overnight visitors have the most 

respondents in the 25-44 age group (60%). Second homeowners have the largest percentage 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 of Residence. Residents and second home owners 

from Shelter Island and Southold were-over represented in the survey sample, based on 

comparisons to year-round and seasonal population statistics.Residentswere under represented in 

Riverhead and Southampton. Second home owners were under represented slightly in East 

Hampton and also in Southampton. This disproportionate distributionof the samplemost likelyhas 

little, if any, effect on survey results since the type and frequency of recreational activity is not 

strongly related to the East End town the respondent lives in. 

37 
Table IV.2 - Population Demographics vs. Survey Respondent Demographics 

Gender 

Female 

Male 

Age 

up to 20 

21-24 

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% 

11.04% 

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% 

21.29% 

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. Survey RespondentDemographics, 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% 

31.77% 

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% 

11.36% 

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% 

29.78% 

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 (1995): 

Activities: Fishing, Boating, Swimming, Shell fishing 

Locations: Flanders Bay, Great Peconic Bay, Little Peconic Bay, Shelter Island Sound, 

GardinersBay, Block Island Sound, Long Island Sound,and Atlantic Ocean 

Activities: 

Locations: 

Beach Use, Bird Watching, Wildlife Viewing, and Hunting 

East Hampton, Riverhead, Shelter Island, Southampton, and Southold. 

The water bodies of the Peconic Estuary Systemare identified as Flanders Bay, Great Peconic Bay, 

Little Peconic Bay, Shelter Island Sound, and Gardiners Bay. Substitute water bodies included in 

the survey were Long Island Sound, Block Island Sound, and the Atlantic Ocean. 

Swimming and beach use were treated as separate activities in the questionnaire in order 

to identify swimming locations by body of water and identify the number of beach goers by town. 

Since each of the five towns has multiple water body options for swimming, it was necessary to ask 

for location by both water body and town. 

Figures for swimming and beach use should not be summed. This is because the activities are not 

mutually exclusive, and it is expected that most respondents gave a positive answer to both 

categories even though they may be referring to a single outing. 

Major characteristics of respondents’ annual recreational activity during the year are: 

Most (83%) engaged in at least one key recreational activity, half in more than one activity. 

Most residents (95 %) participated in key recreational activities 

Swimming was the most popular activity 

The PES was the most popular swimming location (42% of all swimming trips) 

Boating, fishing, and Shell fishing were predominantly done in the PES. 

Sample Participation Overall. Most respondents (83%) engaged in at least one outdoor 

recreational activity at the East End and during the past year averaged 38 recreational experiences 

per respondent. In total, respondents engaged in more than 48,400 outdoor recreational experiences 

during the past year. Of this number, more than 26,000 were water-based outdoor recreational 

experiences, and the majority of these (15,506) were in the PES. Land-based outdoor recreational 

experiences the five East End towns amounted to 22,300. 

Sample Participation By Activity. Among all respondents, swimming and beach use were 

the most popular activities. Swimming was done on more than 15,000 occasions by respondents. 

The most popular area for swimming was the PES, which accounted for 42% of all swimming trips. 

More than 12,000 beach visits at the five East End towns were reported. 

40 
Shell fishing was the least frequent water- based activity (1,304 trips). More than half (59%) of 

these Shell fishing trips took place in the PES. Hunting was the least popular land-based activity 

(255 occasions). Note that bird watching and wildlife viewing responses given in Table IV.3 were 

adjusted to include only trips for viewing, i.e., activity less than one mile from home was not 

included. 

Table IV.3 - Participation in Key Recreation Activities - By Residency Status 

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 

of the East End towns. By comparison, 83% of second homeowners and 67% of overnight visitors 

also participated in outdoor recreation. Day trippers have the lowest rate of participation (43%). 

A direct comparison between the number of people engaging in land-based and water-based 

activities cannot be made. This is due to the inability to tell the difference between beach use in the 

PES and beach use on the Atlantic Ocean or Long Island Sound shoreline at the East End. 

41 
However, the majority of residents (73%) and second home owners (65%)--and 50% of all survey 

respondents-- engaged in more than one key recreational activity during the past year. 

Participation Rate in Key Recreational Activities-By Residency Status. Proportionately 

more residents (70%) and secondhomeowners (65%) swim,boat, fish, and shellfish than 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%). Shell fishing is more popular with residents (14%) and secondhome owners (12%) 

as compared with the overall sample (only 7 %). 

Residents and second homeowners are more likely to go to the beach, watch birds, view wildlife, 

and hunt in the East End than other groups. Proportionately more residents (85%) and second 

homeowners(70%) engage in these activitiesthan overnightvisitors (59%) and day trippers (37%). 

Among these activities, the highest participation rates across all groups are for beach use (57%) 

followedby wildlifeviewing (21%0)and bird watching (19%). Only 2% of survey respondentshunt. 

Location ofKey Recreational Activities. Respondents used the PES more times (15,506 

occasions) for all key outdoor recreation activities--swimming,boating, fishing,and Shell fishing--

than any other water body at the East End (Table IV.4). 

Table IV.4 -Water Body Selection by Activity - (n=1267) 

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%) (12%) 

2732 766 (59%) 5470 

(49%) (62%) 

5599 1304 8753 

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

Swimming Totals 

380 (2%) 1238 (5%) 

2801 (18%) 6249 (24%) 

5685 (37%) 7116 (27%) 

6538 (42%) 15506 (60%) 

15404 26060 

(100%) (1OO%> 

* Percentages may not sum precisely due to rounding 

No single town is the dominant site for all categories of recreation by respondents. For example, 

beach use occurs most often at Southampton (3334 occasions) and East Hampton (3139). Shelter 

Island was the most used for bird watching (567) and hunting (92), and East Hampton had the most 

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 

Shelter Island 

Southampton 

Southold 

Totals 

Table IV.5 - Town Selection by Activity - (n=1267) 

Bird Wildlife Total 

Beach Use Watching Viewing Hunting Trips 

3139 (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%) (100%) (100%) (100%) 

*Percentages may not sum precisely due to rounding 

Location ofPES Water-BasedActivities. Great Peconic Bay is the most popular water body 

in the PES for recreational activity, with 28% of recreational trips in the PES, while Flanders Bay 

is the least frequently used, with 8%. Great Peconic Bay is also the most popular location in the 

PES for swimming (30%), fishing (29%), and boating (25%). Gardiners Bay is the most popular 

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

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 

(100%) 

5470 

(100%) 

6538 

(100%) 

15506 

(100%) 

* Percentages may not sum precisely due to rounding 

PESKey Recreational Activities by Residency Status. Swimming is the most popular 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 of their PES trips to Shell fishing (16%) than to fishing (9%), 

although Shell fishing overall is a modest activity (5% of PES all trips). 

Table IV.7 - PES Key Recreational Activity Outings by Residency Status - (n=1217) 

Residents SHO Overnight Day tripper Total 

Fishing 1114 (17%) 401 (11%) 947 (22%) 30 (9%) 2492 (17%) 

Boating 2354 (37%) 1275 (36%) 1450 (34%) 110 (34%) 5189 (36%) 

Swimming 2520 (39%) 1676 (48%) 1839 (43%) 132 (41%) 6167 (42%) 

Shell fishing ,396 (6%) 154 (4%) 92 (2%) 52 (16%) 694 (5%) 

Totals 6384 3506 4328 324 14542 

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

* Percentages may not sum precisely due to rounding 

44 
Locations 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%) 

(TableV.8). GardinersBay (31YO)is the most frequenteddestination for secondhome owners, with 

Great Peconic Bay (30%) again nearly as popular. Over night visitors and day trippersprefer Great 

Peconic Bay (30% and 34%, respectively) over all other locations. 

Flanders Bay is by far the least often visited location for residents (7%), second home owners (3%), 

and overnight visitors (10%). The least visited location by day trippers is Gardiners Bay (13%), 

presumably because it is the furthest of the bays from their homes. Flanders Bay is more popular 

with day trippers (17%) than with any other group. 

Table IV. 8 - PES Key Recreational Activity Locations by Residency Status (n=1217) 

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 (31Yo) 

3506 

(100%) 

Overnight Day tripper Total 

446 (10%) 54 (17%) 1065 (7%) 

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

736 (17%) 60 (19%) 2796 (19%) 

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

811 (19%) 42 (13%) 3264 (22%) 

4328 324 14542 

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

* Percentages may not sum precisely due to rounding 

[V.G. DESCRIPTIVE RESULTS: PARTICIPATION AND TRIP ESTIMATES 

Introduction The total number of trips (or “outings”) for outdoor recreation is the product 

of three factors: (1) The potential number of participants, (2) the proportion who actually 

participate, and (3) the number of times those who participate engage in an recreation activity. 

Thus, we have: 

Trips = No. of Potential Participants x Percent Participating x No. of Times Participate 

45 
For example, assume that there are 2 million potential participants (i.e., residents, second home 

owners, over night visitors, and day trippers) for swimming, that 20 % of these actually swim, and 

that of these participants, each swims an average of 10 times per year. In this case, the number of 

swimming trips or outings is 4 million: 

= 2 million x 20 % x 10 

= 400,000 x 10 = 4 million trips 

The data and assumptionsused to estimate trips for each PES recreation activity is explainedbelow. 

Estimation of Total Number of Trips. Thepopulation ofpotentialparticipants include all East End 

residents, second homeowners, overnight visitors, and day trippers. To estimate this population, 

we used 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 84,871. 

Data are not available for the age breakdown of the seasonal population. Lacking authoritative 

data, we assume that the seasonal population has the same percentage over the age of 15 (80%) as 

the resident population. This implies an estimated seasonal population over age 15 of 135,620. 

The seasonal population is split into two groups, based on EAI’s recreational use survey results: 

Second Home Owners (27%) and Overnight Visitors (73%). Our survey results indicate that the 

two components of the seasonal population have different participation rates and therefore should 

be considered separately, when possible. Note that many overnight visitors stay with second 

homeowners and residents as renters or guests. 

Population data is not available for day trippers; therefore this group is excluded from our 

participation estimates. The absence of this information will slightly understate outdoor recreation 

participation. 

Information concerning the percent of potential participants that participates in one or more 

recreational activity was presented earlier. The trips per participant are given in Table IV.9. 

The major results of estimated annual participation in PES outdoor recreation (excluding day 

tripped) for 1995 can 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). 

46 
127,762people participate in either swimming, boating, fishing, or Shellfishing in the PES, 

taking 3.3 million outings. 

156,184people participated in either beach use, bird watching, wildlife viewing, or hunting 

in the East End towns, engaging in 5.2 million trips. 

Table IV.9 - 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 8.11 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 

The total number of trips by each group, for each activity, is estimated by multiplying the estimated 

number of trips per participant (Table IV.9) for each group (Le., residents, second homeowners, 

etc.) by the estimated total number of participants by group. The results are shown in Table IV. 10. 

Overall, there are 5.15 million beach use, bird watching, wildlife viewing, and hunting outings 

taken in East End towns and 3.35 million swimming fishing, boating, and Shell fishing outings 

taken in the PES in 1995 (Table IV.10). Most outdoor recreational activity is accounted for by 

residents, who engaged in 3.3 million land-based and 1.5 million water-based occasions locally. 

Second homeowners took 1.1 million land-based trips and more than 800 thousand water-based 

trips. Overnight visitors engaged in more than 780 thousand land-based, and 1million water-based, 

trips. 

Total trip estimates slightly understate actual recreation trips because day tripper trip estimates are 

not included due to the lack of population data for this group, as noted earlier. According to EAI’s 

recreational use survey, day trippers account for 3% of land-based and 2% of water-based 

recreational trips taken by the sample. 
Comparison with Phase I Total Outdoor Recreational Trip Estimates: Introduction. The Phase I 

study (Grigalunas and Diamantides, 1996) gave estimates of total recreation trips to the area for 

selected activities based on available “off the shelf’ data and were comprised of a variety of 

sources. No original data was collected during Phase I. In Phase 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. 11). 

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 

268,617 93,110 226,766 588,493 

559,902 296,451 342,630 1,198,983 

598,001 383,563 428,406 1,409,970 

96,006 35,636 19,513 151,155 

1,508,582 615,257 500,955 2,624,794 

2 19,008 32,139 41,699 292,846 

174,861 39,218 40,392 254,471 

33,609 11,959 6,930 52,498 

3,458,586 1,507,333 1,607,291 6,573,210 

*Includes activities at the five East End towns and PES water bodies only 

In general, we found major that the Phase I estimates of recreational activity were understated--in 

some cases, greatly understated-- as compared to the Phase I1 results. The major reason for the 

large differences in estimates between the Phase I and I1 reports can be explained as follows. Our 

survey-based estimates find much high participation rates and trips per participant than the Phase 

I report, which used a state-wide average for New York. For example, in Phase I1for bird watching 

and wildlife viewing we found a high participation rate (19% for bird watching and 21% for 

wildlife viewing) for respondents and a large number of trips per participant (37.55 bird watching 

trips and 34.36 wildlife viewing trips for residents). The Phase I survey, in contrast, was based on 

NFWS statewide estimates for New York. These statewide participation rates (9.4%) and number 

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 

48 
much more interested in outdoor recreation than the general population of the state. Similar large 

differences between the Phase I and I1 results were found for other activities (see Table IV. 11 and 

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

Table IV.ll- Comparison of Participation Estimates Phase I Versus Phase I1 

PES - Phase I1 

Beach Use 2,624,794 

Non- 547,317 

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 EXPENDITURE ESTIMATES 

Introduction. During the early survey development phase of our work, members of the 

Citizens Advisory Committee and officials at the Suffolk County Dept. of Health Services 

expressed interest in our collecting information on sectors of the PES economy for which data are 

difficult to find using standard economic sources. Specific sectors of the economy targeted for 

special consideration were Farm Stands, Wineries, and Rental Lodging. 

The survey questionnaire asked respondents to estimate the amount that their household 

spent at 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. 

49 
The number of overnight visitor households cannot be adequately estimatedwith available 

data. LIRPB seasonal population estimates account for occupancy of some rental lodgings, such 

as hotels, motels, and campsites. However, this component of the population estimate does not 

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 for rental accommodations per household from our survey --$5,40O--makeit evident 

that seasonal rentals are a major source of income for PES rental property owners and owners of 

hotels, motels, and inns. 

Estimated Total Participation and Expenditures. Table IV.13: Estimated Farm Stand and 

Winery Expenditurespresents estimates (in 1995dollars)ofparticipation and expendituresin these 

sectors for resident and seasonal households. Table IV. 13 also presents estimates of PES-related 

expenditures in these sectors. PES-related expenditures were estimated using the same seasonal 

adjustment (45 YO)used in the Phase I Report to estimate the PES-related impacts of tourism and 

recreation sectors. Total rental lodging expenditures could not be estimated due to incomplete 

information on the number of participants. 

50 
rable IV.12 -Survey Farm Stand, Winery, and Rental Lodging Expenditures (1995 dollars)" 

Residents 

Participation 

Total Expenditures 

Average Expenditures 

Second Home 

Owners 

Participation 

Total Expenditures 

Average Expenditures 

Overnight Visitors 

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 $8,781,943 $2,806,624 

Estimated PES-Related 

Expenditures $3,951,874 $1,262,981 

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 $2,428810 

An interesting finding is that PES-related Farm stand expenditures are greater than earlier 

estimates for several other food-related sectors presented in the Phase I Report. For example, PESrelated farm stand expenditures are greater than expenditures (in 1995 dollars) in Meat and Fish 

Markets ($6.5 million), Fruit and Vegetable Markets ($3.8 million), Retail Bakeries ($2.9 million), 

and Drinking Establishments ($6.0 million) (Grigalunas and Diamantides, 1996). 

52 
IV.1. DESCRIPTIVE RESULTS: REFERENDA AND 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 added to “test the 

waters” on selected policy issues and were not designed as a basis for resource valuation, which 

would have required considerably more effort and data than was possible for this project. 

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

(1) reseeding scallops following Brown Tide, 

(2) planting hard clams, 

(3) upgrading the Riverhead sewage treatment plant, and 

(4) reducing road runoff. 

Respondents were presented with one of these programs and were asked whether they support the 

program, if they had to pay a given annual cost. Annual costs were varied for each of the proposed 

policy actions as described in Table IV. 14. 

Referenda Results. Respondents overwhelmingly supportedthe policy actions and indicated 

a willingness to pay the annual costs. Support ranged from a high of 100% for reseeding scallops 

following Brown Tide at an annual cost of $10, to a low of 75% for upgrading the Riverhead 

sewage treatment plant at an annual cost of $100. This high level of support for PES management 

programs was consistent across residents and second home owners. Overall, 87% of respondents 

indicated they support the policy actions at the given costs (Table IV.14). 

IV.I.2. Descriptive Results: Effects of Brown Tide 

Respondents were asked whether they had heard of Brown Tide (“BT”). Those who had heard of 

BT were asked about their level of concern and about any activities that may have been affected 

by BT. Overall, most respondents (83%) had heard of BT, and ofthose, most (71%) were also very 

concerned (Table IV.15). 

BT had the biggest effect on swimming. Fifty-eight percent of those who had heard of BT indicated 

that it had affected their swimming. Recreational fishing (34 percent) and shell fishing (33 percent) 

also were affected by BT. 

Residents and second home owners had the most knowledge of and the highest concern for BT. 

Overnight visitors had the least knowledge and indicated that their activities were affected the least. 

53 
Table IV.14. Management Action 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 Tide 

Second Overnight Day 

Residents Home Owners Visitors Trippers Total 

Heard of Brown 

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 

97.11% 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% 71.52% 62.37% 65.17% 70.87% 

70 44 100 64 278 

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 98 151 118 591 

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 to use data from the recreational survey to assess the use value for many waterbased and shore-basedrecreational activities. The survey collected activity-specific data on fishing, 

Shell fishing, boating, and swimming in the PES and surrounding waters. Information also was 

collected on beach use, 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 affected by water quality and hence by management actions aimed at enhancing water 

.56 
quality (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 on recreational fishing, including the results of this 

study, shows the importance of the quality of the experience--the catch rate--on the number of trips 

and their economic value (e.g., Freeman, 1995; McConnell and Strand, 1994). However, 

establishing the necessary cause-and-effect scientific links between policies to preserve or restore 

habitat, for example, with changes in salt water recreational catch rates appears to be beyond the 

state of the art’. Nevertheless, we can simulate how benefits to recreation fishing would change 

if the catch rate increased by, say, 10 percent. 

Individual recreation trip value estimates and annual aggregate value estimates are given for three 

cases. First, we present the existing (“baseline”) conditions for swimming, fishing, boating, and for 

birdwatching and wildlife viewing. For the baseline, we show consumer surplus per trip, total 

estimated number of trips in 1995, and total benefits for the year for the four key recreation 

activities studied. 

Second, we show how economic value for swimming changes for illustrative, hypothetical policies 

affecting water quality. One such policy is assumed to increase water 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 10 percent improvement in the recreational fishing catch rate is 

simulated. 

IV.J.2. Methodology 

Value estimates for each activity are derived using the Travel Cost method of valuation (see, for 

example, Freeman, 1993). The rationale behind this methodology is that, although there is no 

market price that reveals the value of a recreational activity, an implicit price can be observed 

through the cost of traveling to the activity’s location. Included in this implicit price is the cost 

of transportation, such as gas, wear and tear on the automobile, etc., and the value of the 

individual’s travel time. In theory, an individual’s opportunity cost is valued as his or her next best 

use of their time. However, a fraction of the individual’s wage is generally used, in practice, to 

represent income foregone in favor of recreation. 

In the travel cost model, the number of trips a person takes for recreation at a specific site depends 

on the costs of getting to that site, the comparative costs of getting to substitute sites, and the 

quality of the recreational experience at the sites. In other words, the cheaper it is to get to a site, 

the cheaper travel to that site is relative to other sites, and the better the quality of the recreational 

experience at that site, the more times the individual will visit that site, all else equal. Thus, 

observed recreation choices reveal tradeoffs between cost of participation, as measured by travel 

’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. 

57 
cost, and participation rates. In addition, participants might reveal values for site quality by 

participating more at sites of higher quality, and less at sites of lower quality, all else being the 

same. 

Carrying out the travel cost analysis requires data on participationrates, cost of travel to sites, and 

site quality. Data on participationrates is collected in the recreational survey. Data on travel costs 

to the target and substitute sites, including the opportunity cost of travel time, are collected from 

the survey and augmented, as necessary,with distance informationfromroad maps. Data on water 

quality comes from two sources. First, we use field measurements of various water quality 

parameters from the SCDHS PES water sampling program, including nitrogen, coliform counts, 

water clarity (Secchi disk measurements) and Brown Tide cell counts. These are objective 

measures of quality. Second,we use data from the recreation surveywhich asked respondents their 

subjective evaluation of water quality (excellent, good, fair or poor). These evaluations of course 

are subjective. 

For swimming, our analysis combines the subjective assessments by individuals and the objective 

field measurements of water quality. Ultimately, we are interested in whether we can provide 

sequential linkages between: 

(1) management actions that effect water quality parameters, 

(2) the resultantperceptions of water quality, and 

(3) participation in specific recreation activities. 

If we can establish such a linkage we can evaluate water quality policy alternatives." 

To attempt to do so, we relate participation to subjective ratings of water quality, as indicated in 

the recreational survey, and in turn, we relate these subjective water quality ratings to field 

measurements of water quality. These are: water clarity, nitrogen concentration,coliform counts, 

and Brown Tide cell counts for each location and time period. This allows us to estimate the 

recreational benefits associated with water quality improvements resulting from management 

options. 

The annual benefits of a policy improving water quality has two parts. One is that individuals will 

enjoy the swimming experiencemore; the other is that they will make more trips. This is the basis 

of our approach for estimating the economic benefits of water quality improvements in the PES for 

swimming. 

For example,consider an assessment ofproposed improvements in the Riverhead sewagetreatment 

plant. Upgrading the sewage treatment plant will result in improvements in water clarity, 

reductions in coliform counts, etc., with the largest effect in Flanders Bay and progressively small 

changes in 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. 

58 
parameters effect the subjectiveassessment of water qualityby recreationalusers, and the resultant 

changes in participation rates and in recreational values at various sites. Thus, we can identify 

benefits to recreationalswimming that result from management optionsthat improve water quality. 

In order to implement this methodology, we will coordinate our assessments with ongoing water 

quality modeling efforts in the PES. Thus, we anticipate that water quality modeling will be used 

to project changes in quality parameters from prospective management policies. Our recreational 

modelingwill thenbe used to estimaterecreationalbenefitsresultingfromthese changes in quality. 

Similarlyfor recreationalfishing, quality of the experiencedependsupon catch rates, which in turn 

depend upon fish populations. Our recreational fishing model includes catch rates as a predictor 

of participation rates and recreational values. Thus, if predictions can be made concerning the 

effect of management actions on fish populations,we could use our recreational model to estimate 

resultant benefits to recreational anglers. However, it may not be feasible for natural scientiststo 

predict changes if salt water fish populations that would likely result from management actions. 

Nevertheless, the opportunityexists for estimating recreational fishingbenefits from management 

actions, if resultant changes in populations can be estimated. Even in cases where precise 

predictions are not possible, it may be possible to provide possible ranges of population changes, 

which will enable us to provide order-of-magnitudeestimates of possible benefits. 

The economic benefits from improving (or maintaining) water quality could be long-run benefits, 

if the policy is sustained. To recognize the long-run benefits, we sum the annual benefits over 

time, discounting them as appropriate to get an equivalent value today--the asset value of the 

resource in providing services supporting that activity. 

1~7.~. ESTIMATES OF ECONOMIC VALUE: RESULTS 

IV.L.1. Value (Consumer Surplus) Per Trip 

The methodology described above yields a consumer surplus estimateper person, per trip ranging 

from $8.59 for swimming to $49.83 for non-residentialbird watching and wildlife viewing (Table 

IV.17). Fishing and boating values per trip fall within that range at $40.25 and $19.23, respectively. 

These estimates of consumer surplus are the average value individuals receive per trip over and 

above the cost of their recreational trip. Looked at another way, it is the unpaidfor 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 annualbenefits from each of the four recreational activities studied are given in Table IV.17. 

The total benefits are estimated by multiplying the average consumer surplus for an activity by the 

estimated total number of trips to engage in that activity during the year (1995). Viewing of Birds 

and Wildlife is the most valued of the activities studied ($27.3 million). Of the water-based 

59 
activities, recreational fishing is the most highly valued ($23.7 million). 

We emphasize that total values rarely are useful for policy analyses since most policies involve 

smaZZ changes in an activity or its quality and are not “all or nothing” choices. Nevertheless, we 

recognize that information on total values, such as that given in Table IV.17 helps put the scale of 

these activities in some perspective. 

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

.~ ~ 

Recreational Bird Watching& 

Swimming Boating” Fishing Wildlife 

Viewing 

Total Tripsmear 1,409,97 937,387 588,493 547,317 

Consumer Surplus 

Per Trip $8.59 $19.23 $40.25 $49.83 

Total Annual 

Consumer Surdus $12.113.2 I , 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 annual estimates from the above table, the asset values range from $318 

million for Bird Watching and Wildlife Viewing to$141 million for Swimming. The PES has an 

asset value of $276 million for Recreational Fishing and $210 million for Boating. 

IV.L.4. Change in Swimming Trips and Benefits Due to Hypothetical Water Quality Changes 

To illustratehow the economic benefits estimated in this report can be 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 of Nitrogen, Total Coli, Brown Tide cell counts by 10percent and increases Secchi 

depth by 10 percent throughout the PES. Note that since water quality measures differ among 

individual bays (and indeed within Bays), the absolute water quality changes due to a 10 percent 

60 
improvement will also differ among and within PES Bays. Only benefits are considered; the costs 

of achieving the water quality improvements are not taken into account in this 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 affecting recreational fishing is described below. 

The hypothetical uniform 10 % improvement for all quality measures for all Bays increases the 

number of yearly swimming trips in the PES as a whole by about 151 thousand and has annual 

benefits of $1.3 million dollars (Table IV. 18). This is an increase of about 11 percent in use and 

in benefits. Ifmaintained for 25 years, this improvementhas a present value of $15.1 million,using 

a discount rate of 7 percent. This number represents the change in the asset value of the PES for 

swimming due to the quality improvement, all else remaining the same. Thus, a policy (or set of 

policies) that created and maintained such an improvement water quality would be a good 

investment of scarce resources if the present value of these costs was less than $15.1 million. 

Most of the incremental benefit to swimmers ($752 thousand of the $1.3 million annual benefit) 

is due to increased water clarity (Secchi depth). The lowest benefit is due to the 10 percent 

improvement in Coli counts ($81 thousand per year). Among the five PES Bays, the 10 percent 

uniform water quality improvement has the highest incremental benefit for those who swim in 

Shelter Island Sound (an annual increase in swimming benefits of $312 thousand). 

"Subsequent research by EA1 will address benefit-cost analysis of proposed 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 
Fable IV.18 Benefits to Swimmersof 10% Improvements in Each Water Quality 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 $312,033 

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 of the increase 

in benefits ($1.46 million) is attributable to greater water clarity. However, benefits due to reduced 

Brown Tide cell count (about $652 thousand) also are important. 

In policy analyses, it is important to examine the incremental benefits of policies in.order to know 

what the public gets from stricter policies and the higher costs that they entail. The annual, 

incremental swimming benefits of going 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 10 percent to 20 improvement). The issue of 

incrementalbenefits hom alternative management actions that affect different PES bays differently 

will be addressed more comprehensively in Phase 111economic studies. 

62 
Table IV.19. Benefits of 20% Improvements in Each Water Quality Indicator at Each PES 

Water Body 

Total Kjeldahl 

PES Water Nitrogen 

Body 

Flanders Bay $130,030 

15,135 

Great Peconic $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 of trips 

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 

$2,510 $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 17,641 41,459 63,O71 

$164,653 $651,905 $1,455,088 $2,564,065 

19,100 75,881 I 69,I91 298,273 

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 10 percent increase in catch rates raises the benefit (Consumer 

Surplus) per trip by $0.80, increases the number of recreation trips by 11,249, and boosts total 

annual benefits by $472,359. The 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 discount rate 

and time horizon of 25 years used for all cases. 

63 
Number of Fishing 

Trips 

Consumer 

SurplusRrip 

Total Consumer 

Surplus 

1 

10 Percent Increase Incremental 

Baseline in Expected Catch Benefits 

Rate 

588,493 599,742 11,249 

$40.25 $41.05 $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. The Measurement ofEnvironmental 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 of Outdoor Recreation 

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% 1OO%* 

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% 

11 
Table 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 
Accommodations for Overnight Visitors 

Accommodation Number of Respondents 

Hotel or Motel 95 

I 

Rental 101 

Friends or Relatives 168 

I Other 63 

Totals I 427 I 

* Adjusted �or rounding 

Table S.6 

Percentage of All 

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 3yo 255 11 

Totals 820 100% 23502 29 * 

68 
S.9 - Estimated Trips for Key Recreation Activities for Sample* 

Residents SHO Overnight 

Fishing 21,218 9,520 14,850 

Boating 31,402 14,647 20,791 

Swimming 44,133 18,309 35,641 

Shell fishing 11,882 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 demand because the number of recreational 

trips taken by an individual must take on an integer value. The Poisson distribution is used to 

estimate the number of trips to each site in the PES and to estimate the economic value (consumer 

surplus) of those trips. 

For swimming, the number of trips to each site is modeled as a function of the travel cost to the site, 

costs of travel to substitute sites, the number of boating occasions at the same site, and a subjective 

measure of water quality at the site. The measure of water quality is the probability that water 

quality at the site will be ranked either good or excellent by survey respondents who recreate at that 

site. 

This model can be used to estimate the effect of management actions to improve water quality on 

the number of recreational trips to each site, and to the value of a recreational trip to a site. In order 

to do so, we need to link management actions to changes in water quality, and to link objective field 

measures of water quality to subjective perceptions of water quality by recreational users. 

Predictions of changes in water quality due to management actions is done as part of ongoing water 

quality modeling work, carried out as part of the Peconic Estuary Program (Tetra Tech, 1998). The 

relationship between objective water quality measures and subjective perceived water quality is 

69 

I 
estimated by linking data collected in the recreational survey with field measure of water quality. 

Specifically, for those respondents who indicate that they went swimming on their most recent 

recreation day, the survey asked where they went swimming and how highly they rated water 

quality (excellent, good, fair or poor) at that site. We also obtained data on measures of key water 

quality variables, including brown tide cell counts, total coliform bacteria, total Kjeldahl nitrogen, 

and Secchi disk depth readings 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 or poor as a function of the objective field measures of water 

quality, discussed above. We then include these predicted probabilities into the travel cost 

estimation, so that the number of trips to a site is related to the cost of traveling to the site, the cost 

of traveling to key substitute sites, and the predicted probabilities of the subjective rankings of 

water quality. These steps are described in detail below. 

Water Quality Model 

The water quality model links individuals’ perception of water quality - expressed in a ranking of 

poor, fair, good, and excellent -to physical measures of water quality collected at sampling stations 

by the SCDHS. An ordered logit model is used to estimate the probabilities for each rank based on 

physical measures of water quality and on the effects of Brown Tide on the individual’s swimming 

that year. The purpose of the water quality model is to provide input into the travel cost model 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 100 milliliters. 

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 from the questionnaire are included. One question asks individuals if their swimming was 

affected by Brown Tide. Another question asks respondents to rank the water quality - poor, fair, 

good, excellent - at their last swimming outing in the PES. The third question asks respondents to 

identi6 the swimming location of their last swimming outing in the PES. 

The data is grouped into ten 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 

70 
the survey respondent. Only observations that indicate an identifiable swimming location for trips 

taken during August 1995 were included in the data set. Water quality sampling stations were 

allocated to the ten sites according to SCDHS sampling stationmaps, as described in Table A1.The 

number of samples recorded from each sampling station varies. An average of the data recorded 

at each station during August 1995 (the period during which our survey was administered) was 

taken. Sampling stations with a central position, as opposed to north or south, were included in the 

averages of both north and south sites. 

Data for Brown Tide, total Kjeldahl nitrogen, and total coliform bacteria were used to construct 

indices of water quality relative to threshold values for each parameter to make the measures more 

comparable. 

The average Brown Tide cell count for each of the ten sites was divided by 250,000cells/ml- the 

visibility threshold for Brown Tide. The resulting value was then multiplied by 1 if the respondent 

indicated that their swimming was affected by Brown Tide, or by 0 if not. In this way, the Brown 

Tide cell count is modeled to affect the water quality ranking of only those respondents who 

indicated they had knowledge of and were affected by Brown Tide. 

The average total Kjeldahl nitrogen reading for each of the ten sites was divided by .5mg/l, the 

nitrogen guideline established by the SCDHS. The average total coliform bacteria reading for each 

of the ten sites was divided by 400MPN/100 ml, a threshold for bathing water quality. The total 

Kjeldahl nitrogen index and the total coliform bacteria index were added together to form a joint 

nitrogen-bacteria index. This joint index was used in the ordered logit model instead of the 

individual indices because high colinearity in field measurements of nitrogen and coliform bacteria 

precluded separate estimation of the effects of these two water quality variables. All indices were 

multiplied by 100to preclude the use of fractions. Average input values for each site are presented 

in table A2. 

The modeling procedure uses an ordered logit model that predicts water quality rank based on the 

Brown Tide Index, Nitrogen-Bacteria Index, and Secchi Disk depth readings. The ordered logit 

model is based on the logistical distribution, 

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 is reasonable to talk about the probability that 

any ranking would be chosen based on the levels of the water quality indicators encountered by an 

individual. Since the individual is faced with a predetermined ranking order - poor, fair, good, 

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 

71 
by 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 continuous measure of site quality, not constrained by 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’s rank order choice 

(e.g., the individual’s mood or the prevailing weather conditions). The unknown factors are 

assumedto be logistically distributed across individuals with a mean of zero and a variance of one. 

Although y* is unobserved, the rank order selections indicated in the survey questionnaire are 

observed. These selections can be modeled as 

y = 0 (poor) 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 

would result in higher water quality rankings. Similarly,an increase in Secchi disk depth (increased 

water clarity) would raise the subjective water quality ranking by survey respondents. 

The probabilities that water quality would be ranked poor, fair, good, or excellent are calculated 

from the ordered logit model results. These probabilities are: 

Prob (y = 0; poor) = A (-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 at Flanders Bay south. These probabilities 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 

72 
is based on the weighted average of the probability for good at Gardiners bay north and Gardiners 

bay south. The probabilities for the nine sites are presented in table A4. The probabilities for the 

five PES water bodies are presented in table A5. 

The effects of management actions on the rank order probability at any or all of the nine sites can 

be simulated by changing the water quality measures for the sample station readings. Changes in 

these values will in turn change the value of the water quality indices and/or Secchi Disk readings 

used as input into the ordered logit model. These simulated values for objective water quality field 

measures are used to provide at a new set of probabilities for subjectiveratings of water quality for 

the sites, which in hunaffect predicted participation rates for swimming in each of the PES water 

bodies. 

Tables A6-A9 contain estimated water quality ranking probabilities i.e., the probability that water 

quality will be ranked poor, fair, good, or excellent, for each of nine PES sites at a 10% 

improvement in an objective water quality parameter. The objectivewater quality parameters used 

in the 10% improvement model are Brown Tide cell counts, Total Kjeldahl Nitrogen readings, 

Total Coliform readings, and Secchi Disk depth. 

For example, under baseline water quality levels in 1995, Great Peconic Bay (Table A5) is 

predicted to obtain a “poor” rating for water quality by 8% of sample respondents, a “fair” rating 

by 37% of sample respondents, a “good” rating by 45% of sample respondents and an “excellent” 

rating by 10%of sample respondents. These probabilities are the weighted averages of the ranking 

probabilities for Great Peconic Bay North and Great Peconic Bay South (Table A4). If, continuing 

our example, Brown Tide Cell counts were to improve (decrease) by 10% at sampling stations in 

Great Peconic 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 also used to estimate the increased number of trips and increased value per 

trip that results due to management actions that increase water quality. This is done in three steps. 

First, the changes in water quality resulting from specific management actions (e.g. upgrading the 

Riverhead sewage treatment plant) are to be simulated using the water quality modeling results 

(Tetra Tech, 1998). Next, changes in subjective probabilities are predicted using the methods 

discussed above. Finally, predicted perceptions of water quality by users are substituted into the 

73 6 
Travel cost model, to calculated the estimated number of trips and value per trip under the new 

level of water quality. 

Data for the travel cost model comes from the Recreational Use Survey and from the results of the 

water quality model. The survey questionnaire asks respondents to indicate the number of times 

they went swimming this year at a variety of water bodies on the East End. These water bodies 

include Flanders Bay, Great Peconic Bay, Little Peconic Bay, Shelter Island Sound, Gardiners Bay, 

Long Island Sound, Block Island Sound, and the Atlantic Ocean. 

The price, or marginal cost, of swimming in the PES is not identified in any existing market. Since 

there is no readily available market data on the price of swimming in the PES, a proxy for price is 

used. In the travel cost 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 double the one-way highway distance between the respondent’s 

point of origin and each swimming location. This distance was determined on a case by case basis 

using a road atlas and assessing the most direct travel route. Verification of distance traveled was 

made possible when respondents answered the question concerning distance traveled on their most 

recent swimming occasion. Travel time was estimated by dividing the round trip distance by 40 

miles per hour. This speed was used as a reasonable compromise between highway speeds of 55 

mph and more and local speeds between 25 and 40 mph. The opportunity cost of travel time was 

estimated at 40% of the wage rate, a common practice in benefit estimation. For respondents who 

did not indicate household income, income was estimated using a simple OLS regression that 

modeled income as a function of a constant value and education. 

In the case of substitute swimming locations, the same travel cost formulation used for the PES 

locations was applied to the substitute swimming locations. The average of travel costs to Long 

Island Sound, Block Island Sound, and the Atlantic Ocean, for each respondent, was used as the 

cost of travel to substitute sites and input into the travel cost model. Data on the number of trips to 

the PES swimming location comes directly from the survey questionnaire. The water quality 

variable used in the travel cost model is the sum of the probabilities that the PES swimming 

location would be ranked good and excellent. These probabilities are generated by the water quality 

model. 

Overall, 199 respondents 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. 

The travel cost modeling procedure is based on a Poisson regression model. The Poisson 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 

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

phenomenon occurs, such as swimming trips to Flanders Bay. 

The Poisson regression 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 of the 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 of travel to a substitute site increases; increases 

the more times the individual participates in swimming at the same location; and increases as water 

quality at the site increases. 

The value of swimming trips to PES locations is estimated from the results of the travel cost model 

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 same variables that influence the number of trips 

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

swimming locations due to differences in water quality. 

The value of a swimming trip to a PES location will be affected by management actions to improve 

water quality. The results of the travel cost model can be 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 of water 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 T represents the number of trips taken prior to the quality change, AT represents the change 

in the number of trips that occurs due to a 1% change in the probability of obtaining a “good” or 

“excellent” water quality rating, AP represents the change in the probability of obtaining a “good” 

or “excellent” rating that results from the management action, and C represents the coefficient on 

the travel cost variable. 
Increases in the number of swimming days to PES locations and increases in consumer surplus due 

to a hypothetical 10% improvement in water quality measures are presented in Table A15. 

Aggregate benefits are based on the estimated baseline number of swimming trips to the PES. This 

number of trips - 1,409,970 - was estimated by extrapolating the number of trips respondents 

identified in the Recreational Use Survey to the population of residents, 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 

swimming in 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) 

113 (Central) 

106,107,108,109,111,112,114, 

115J 19,122 

118,121,126,127,131 

116 

132,133,134 

76 
Table A2. Average Input Value for the Water Quality Model (Ordered Logit) 

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 

16.21 126.76 3.5 

39.5I 136.44 4.35 

47.5 256.26 4.37 

32.41 94.21 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 0.25915 

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 

*Probabilities may 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 0.51 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 

0.11 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 

0.11 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 0.31 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 0.41 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 
Table A12. 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 0.11 

0.07 0.35 0.47 0.11 

0.07 0.36 0.46 0.11 

0.04 0.25 0.53 0.19 

83 I 
rable A14. Results of the 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 0.00157 49.757 0.00000 0.9246 

0.00978 0.00119 5.282 0.00000 31.74 

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 Outdoor Recreational Trip Estimates: Beach Use. Beach 

use, in this report, refers to any beach in the five East End towns, including Long Island Sound and 

Atlantic Ocean beaches, as well use of PES beaches. The Phase I report did not estimate beach use 

on Long Island Sound or the Atlantic Ocean and therefore is not presented for comparison. 

Comparison with Phase I Total Outdoor Recreational Trip Estimates: Non-Consumptive 

Wildlife Use. Bird watching and wildlife viewing estimates in this Phase I1report are combined to 

create a “non-consumptive wildlife use” estimateof 547 thousand trips (Table V. 11). This estimate 

greatly exceeds the PES Phase I estimate of non-consumptive wildlife use (92 thousand trips). 

The major reason for the large differences in estimates between the Phase I and I1 reports is 

explained as follows. Our survey-based estimate finds a high participation rate (19% for bird 

watching and 2 1% for wildlife viewing) for respondents and a large number of trips per participant 

(37.55 bird watching trips and 34.36 wildlife viewing trips for residents). The Phase I survey, in 
contrast, was based on NFWS statewide estimates for New York. These statewide participation 

rates (9.4%) and number of trips per participant (8.3) are very much lower than those found in the 

PES sample. This major difference is not surprising since residents and seasonalvisitors to the PES 

would be expected to be more interested in outdoor recreation than the general population of the 

state. 

Comparisonwith PhaseI Total OutdoorRecreational TripEstimates: Recreational Fishing. 

Not surprisinglythe recreational use swey data yields a much larger estimate (588 thousand trips) 

of recreational fishing trips in the PES than the Phase I study (114thousand). 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 Outdoor Recreational Trip Estimates: Swimming. The 

Phase I swimming estimate (7 15thousand) is simply the sum of all available beach attendance data 

for the PES. The Phase I1 survey-based estimate (1.4 million) includes participation not only at 

official beaches but also at the many unadministrated beaches throughout the PES, and in addition 

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

Comparison with Phase I Total Outdoor Recreational Trip Estimates: Hunting. The discrepancy 

between the Phase 1(77 thousand trips) and Phase I1 (52 thousand trips) hunting estimates also is 

based on differences in participation rate and number of hunting days per participant, as estimated 

by the two surveys. The NFWS estimates that 11% of the New York State population over 16 

hunts, and that participants average 18.6 hunting trips per year. On the other hand, the PES survey 

indicates that only 2% of residents and visitors to the PES hunt in the PES. These participants also 

hunt less than the state average, ranging from 16.33 trips per second homeowner participant to 5.5 

trips per participating day tripper. The Phase I hunting estimate was based only on residents and 

did not attempt to account for participation by seasonal residents and visitors since such data are 

unavailable. 
V. WETLAND PRODUCTIVITY VALUES IN THE PES 

V.A. INTRODUCTION 

Eelgrass, saltmarsh, and intertidal mud bottom (“wetlands”) provide many services to the public. 

These services include contributing to the production of commercial and recreational harvests of 

fin fish and shell fish, and of birds and other wildlife used for viewing and for hunting. Other 

services include protection of shoreline property from storm damage and erosion. 

Wetland services may occur on site or off site and may or may not be valued in markets. For 

example, some wetland services, such as scallop harvests, occur on site, while others are realized 

offsite, for example, when fin fish or birds “produced” by an eelgrass bed of intertidal salt marsh 

are harvested or viewed many miles away. Some wetland services are valued in the market place 

(e.g., 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 address relatively small changes in wetlands--not whether or not 

to preserve all wetlands. 

This chapter adapts13estimates by French and Schuttenberg (1998) of the marginal value of PES 

eel grass beds, salt marshes, and intertidal mud flats. Two types of wetland productivity 

(biological) gains are considered: 

(1) The increase in food produced by the habitat which is utilized by higher trophic 

levels (such as fish and shellfish) in the PES, and 

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

availability of habitat. 

The biological gains from restoring or protecting increments of each wetland type (eelgrass, 

saltmarsh, and intertidal mud flats) are assigned an economic value. This value is based on the (1) 

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

value of waterfowl ultimately “produced” by wetlands. Other direct and indirect sewices and 

values wetlands may provide 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. 

86 
Two assumptions are critical to the analysis that follows. One is that food and habitat are 

biologically limiting factors for the species considered; that is, fish,,shell fish and birds depend 

upon the availability of wetlands, so that small changes in wetlands will causes changes in the 

populations of these species. If wetlands are not limiting for these species, then an increment of 

wetlands would have no productivity value for the species concerned, although it may have other 

values (e.g., shoreline erosion protection, esthetics, or existence value) 

The second critical assumption concerns effort and its cost. Fishing, viewing or hunting require 

the use of labor, capital and other inputs used for harvesting, viewing or hunting; the net gain from 

these activities is the benefit (e.g., value of fish landings) minus the costs of the effort required. 

However, very small changes in the abundance of fish, shellfish or birds due to a small change in 

wetland areas will lead to only a very slight increase in harvests per unit of effort. Slight increases 

in 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 to preservation or restoration actions are presumed to be 

small enough so that fishing,hunting, or viewing effort remains the same. This assumption implies 

that we do not need to net out the cost of any change in effort due to small changes in abundance 

of fish, or birds arising from marginal changes in wetlands. Under these assumptions, the value of 

additional harvests, viewing or hunting is the gross value. 

V.B. METHODOLOGY AND DATA 

V.B. 1. Introduction 

Several studies estimate the economic values provided by natural 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 of principal Florida commercial 

and recreational fish. He estimated a marginal value of several thousand dollars per acre of 

mangrove over all commercial and recreational species. 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 

(1986) 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 (1997) used the results in the Natural Resource Damage Assessment Model 

(French, et al, 1996) to estimate the asset value of coastal wetland services in the Northeast. 

The approach used in this chapter derives estimates of wetland productivity using data specific to 

the PES. The virtue of this approach is that it captures the underlying biological structure and 

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

87 

:­ .."..,I3 
productivity of PES wetlands, and it uses biological information and economic values specific to 

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

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

the other hand, the method used does not allow for statisticaltests of significanceand hence heavily 

relies upon professional judgement. 

The methodology used is described next. First, we explain how the food web estimates were made. 

Then, the methodology and data used for habitat values is presented. A detailed statement of the 

methodology, data and assumptions is given in French and Scuttenberg (1998). 

V.B.2. Food Web Estimates 

Saltmarsh and eelgrass beds benefit the entire food web by primary (plant) production. Similarly, 

the net gain in lower trophic level, animal production is passed up the food web. Ultimately this 

production via the food web results in the production of species of economic value to people. 

To estimate the economic value of these food-web effects, several pieces of 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 PESwetland categories, using results in the literature. Then, the fraction of the 

additional production passed up the food web was estimated. Next, this additional production is 

translated into commercial fin fish and shell fish production and landings using average 

relationships estimated across many estuariesby Nixon (1 982). 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 

production harvested and valued by people. Specifically, fishery production is estimated to be 0.16 

percent of primary production and 4 % percent of bottom (benthic macrofaunal) production. 

Details of these calculations are given in French and Schuttenberg (1998). 

V.B.3. Habitat Estimates 

Habitat values are estimated for species (bay scallops,blue crab, softshell clams, and birds) having 

human use values. Habitat values are based on (1) the expected yield of fish or shellfish dependent 

upon the habitat, and (2) the abundances of wildlife (birds) that utilize the habitat. Fish and 

shellfish values are commercial values; wildlife values are for hunting (waterfowl) and viewing 

(waders). 

Bay scallops depend upon eelgrass as nursery habitat for juveniles. The grass provides a refuge 

from predators for juvenile scallops. It is assumed that eelgrass is a limiting factor for scallops, so 

that the entire PES scallop fishery depends upon eelgrass beds. Blue crab use saltmarsh and 

eelgrass habitats preferentially. Again, it is assumed that the entire blue crab fishery depends upon 

the saltmarsh and eelgrass beds of the estuary. Softshell clams prefer intertidal mud flats and sand 

88 
flats. Softshell clams are assumed to all have been produced in intertidal mudflats and shoals in 

the PES. 

Abundance of birds depends upon habitat type. An average abundance per unit area of habitat is 

assumed, based on the results for the coastal area including the PES, as given in the Natural 

Resource Damage Assessment Model (Version 2.4, April, 1996) developed by the authors for the 

US Department of the Interior (French, et al, 1996a,b,c). Birds that are specificallybenefitted by 

saltmarsh, eelgrass or mud flats were selected and included in the present analysis. These species 

are waders (herons, egrets, and ibis), shorebirds,brant and black ducks. Waders use all three of the 

habitat types, while shorebirdsuse marsh and mud flats. Brants specificallyfeed on eelgrass. Black 

ducks are know to require structured habitat, marsh and eelgrass. 

The value of bird species usage of the habitat is based on the benefits human receive from viewing 

or hunting (waterfowl) birds. The values are marginal values, that is, the additional value people 

obtain by seeing additional birds per day. The values per animal per year are proportional to the 

number of viewing trips and the rareness of the species in the local area (see French et al. 

(1 996a,b,c) for a full description of the development of these values. 

V.C. RESULTS 

V.C. 1. Introduction 

Results are provided for the (1) Marginal value of existing wetlands, and (2) the marginal value for 

restored wetlands. Restored wetlands have a lower value than existing wetlands since it may take 

years for an existing wetland to become fully functional. 

V.C.2. Results 

Table V.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 of the 

food web values and the habitat values. Asset values were calculated by discounting the annual 

value over a 25 period using a discount rate of 7 percent, the same time frame and discount rate 

used elsewhere in this 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 

*-
becomes fully functional. Thus, in the table it is noted that eelgrass, saltmarsh, and intertidal mud 

flats take 15, 10, and 3 years, respectively, to become fully functional. 

The marginal asset values of PES wetlands appear to be substantial, especially in light of the fact 

that other services wetlands may provide, such as protection from erosion and storms, aesthetics 

and existence value, are not considered. 

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 $4,291 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 Steven Farber, 1986 “The Economic Value of Coastal Louisiana Wetlands”. 

Baton Rouge: Lousiana State University. 

Bell, Frederick W., 1989. Application of Wetland valuation Theory to Commercial and 

Recreational Fisheries inFlorida. SeaGrant Program. Tallahassee: Florida StateUniveristy 

(June). 

French, et al., 1996. Natural Resource Damage Assessment Model for Coastal and marine 

Environments. Version 2. 

French, Deborah and Heidi Shuttenberg, 1998. Estimated Food Web and Habitat Valuesfor 

Habitats in thePeconic Estuary System. Submittedto Economic Analysis Inc. (January23). 

Grigalunas, Thomas and Richard Congar, 1995.Envivonmental Economicsfor Integrated Coastal 

Area Management. Nairobi: United Nations Environmental Programme. 

Kahn, James and W.M. Kemp, 1985. ”Economic Losses Associated With the Degredation of An 

Ecosystem: The Case of Submerged Aquatic Vegetation in Chesapeake Bay”, Journal of 

Environmental Economics and Management 

Lynne, G., P. Conroy, and F. Prohaska, 1981. “Economic Valuation of Marsh Areas for Marine 

Production Processes”, Journal of Environmental Economics and Management 8(2):175­

186. 

Nixon, Scott W., 1982.“NutrientDynamics,Primary production and Fisheries Yields of Lagoons”. 

Narragansett: Graduate School of Oceanography, University of Rhode Island. 

Pompinatapong, K., 1997.“Valuing Coastal Wetlands: Insigths from the Type A Natural Resource 

Damage Assesment model”. Unpublished M.S. Major Paper. Kingston: Department of 

Environmental and Natural Resource Economics, University of Rhode Island. 

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 final policy analysis. This information will be 

used in a cost-benefit analysis of policy alternatives (Phase I11 of the Comprehensive Economic 

Valuation Study), that will help policy makers prioritize alternative management actions. 

The remainder of this section is organized as follows: Part B describes the survey development and 

implementation, and the questionnaire design; Part C gives descriptive results of the survey; Part 

D presents the estimates of economic values; and Part E is a summary and conclusions. 

VI. B. THE NATURAL RESOURCE SURVEY 

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 of the 

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 of public places. 

92 
V1.B. 1.a. Initial Meetings, Interviews, and Information Gathering 

We began the survey development process in February, 1995 by meeting with members of the 

Management Committee. Shortly afterwards, we met with representatives of different interests, 

including the head of the Local Government Committee; the Chairman of the Citizen’s Advisory 

Group, who is also president of a local environmental group; a representative of the Nature 

Conservancy; a biologist from the NY State Department of Environmental Conservation; a marina 

owner who was head of the local Marine Trades Association; a commercial fisherman who 

represented the 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 interviews with members of the public, where we asked about 

their uses of and concerns about the Estuary. A total of sixteen randomly selected people were 

interviewed in Montauk, Springs, Sag Harbor, ShelterIsland and Greenport. They included business 

owners, store clerks, a police officer, visitors, and residents. 

These interviews provided information on some of the most important natural resources and related 

concerns, and how people think and talk about the bays. Most people interviewed were very 

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 short preliminary survey. Table VI. 1 lists the dates 

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

focus groups, we asked the participants general questions about how they define the study area; their 

familiarity with the Peconic Estuary Program; the characteristics of the area that are most and least 

important to them; their uses of the local waters; the attributesthey look for in choosing recreational 

sites; their perceptions ofwater quality; and their concerns about the natural environment and natural 

resources of the Estuary. 

A short preliminary survey was carried out in Montauk on March 19, before the St. Patrick’s Day 

parade, an event that draws a large number of visitors and residents. This survey included both 

closed- and open-ended questions, including questions about: 

(1) 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 

Date Location Participants

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 about people’s greatest concerns for the Estuary. Respondents 

were asked to rank a list of concerns on a scale of 1 to 10, where 1 indicates the highest level of 

concern. Table V.2 lists these concerns, in order of importance to respondents. The most important 

were declining stocks of shellfish and finfish, water quality, trash on beaches, and areas closed to 

shellfishing. The least important were crowding in boating harbors, public access to the water and 

availability of wildlife for viewing. 

In the next several focus groups, we asked participants general questions about their concerns, their 

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 of these questions were 

primarily intended to stimulate discussion. For example, one question asked about participation in 

recreational activities, and was used to generate a discussion of these activities and related concerns. 

Other questions asked participants which of a list of human impacts on the Bays were most 

important to them; which 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, and therefore resisted ranking their priorities. Instead, they wanted 

94 
to rate everything as most important. Second, many participants stated that they did not know 

enough to rank the causes of environmental problems and the effects of potential actions, but 

believed that priorities should be decided by experts. Nonetheless, these questions provided a 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 1 to 10,with 1 denoting the most important. 

Other questions in these focus groups asked about priorities related to natural resources, and tested 

possible question formats for the survey, including standard contingent valuation questions and 

contingent choice questions. Contingent valuation asks people directly to state their willingness to 

pay to preserve or improve a natural resource. Contingent choice asks people to make tradeoffs 

between alternative actions with results specified in terns of the natural resources that would be 

protected or restored and the cost. l7 Contingent choice questions are similar to marketing surveys 

often done by businesses to allow them to understand tradeoffs that customers are willing to make 

among product 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 to justify the 

space and time it took in the survey. A set of environmental attitude questions was also tested, with the intention of 

correlating respondents' attitudes with their values for natural resources. The attitude questions generated quite a bit 

of controversy, as they were designed to elicit strong opinions, and were dropped from the final survey for several 

reasons. First, they tended to draw respondents' focus from the more important questions in the survey; second, 

they took too long to answer; and finally, it was feared that the controversial nature of the questions might result in 

a negative reaction to the entire survey. 

95 
As discussed in more detail below, the contingent choice method appeared to be the most promising 

format, and thus we developed this method further in the next focus groups. As the contingent 

choice questions were refined through the focus group process, the survey became less complicated 

and less wordy, with fewer 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 several pretests of preliminary 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 was pretested on Long Island, with a group of people at the First Parish Church in Jamesport, 

and with randomly selected people on the beach in Mattituck. In these pretests, people were able to 

easily comprehend and answer the survey questions. In early August, after meeting with members 

of the Management Committee, we made final modifications to the survey, and conducted final 

pretesting. An example of the final survey is included in Appendix A19. 

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

locations around the East End, using convenience intercept sampling. We selected this method over 

a mail survey with probability sampling primarily because budget limitations made it necessary to 

administer both the natural resource survey and the recreation survey together, and because names 

and addresses of visitors were not available. Thus, we designed the surveys to be administered in 

public places where visitors to the area could be intercepted. We selected a wide variety of 

locations, in order to intercept a representative sample in terms of demographic characteristics and 

location of residence. The survey locations, and the number of surveys collected at each, are listed 

in Table 3. A total of 968 resource surveys were collected from year-round and seasonal residents 

of the East End. 

In implementing the survey, interviewers were instructed to approach people and say: 

Hi, my name is ,and I’m working for Suffolk County. We’re doing a 

survey of the public to help develop a plan to protect and manage the bays. Would 

you be willing to fill out a survey, which will take about 5-10 minutes? 

The interviewer then asked those who agreed to fill out the survey if they were visitors, year-round 

residents, or seasonal residents of the East End. Only year-round and seasonal residents were asked 

to fill out the resource survey. All visitors, and every third resident, were given the recreational use 

’*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 12 different survey booklets, each of which contains 

five different contingent choice questions. 

96 
survey. When the survey was handed to the person, they were told that there are no right or wrong 

answers, and that all answers would be confidential. Interviewers were instructed to give brief and 

neutral answers to any questions. 

VI.B.2. Questionnaire Design 

VI.B.2. a. Selection of Survey Format 

As mentioned above, we considered two methods for asking the public about their values for 

protecting and restoring natural resources--contingent valuation and contingent choice. We selected 

the contingent choice format for several reasons. At the time the survey was developed and 

implemented, the Management Committee had not yet determined specific actions that might be 

taken to protect and restore natural resources of the Estuary, and their results. Therefore, the survey 

needed to ask the public about their preferences for important natural resources that would most 

likely 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 of programs that would 

affect any combination of the natural resources evaluated in the survey, either by ranking alternative 

programs or valuing benefits of specific programs. 

Contingent choice may also minimize some of the problems associated with contingent valuation. 

For example, people often have trouble putting dollar values on specific natural resources. 

Contingent choice does not require that values be expressed in monetary terms, but elicits choices 

among alternative outcomes of actions. Therefore, responses are focused on tradeoffs among 

resources, and respondents are not asked to directly express monetary values, although these can be 

inferred from the analysis. Focus group participants found this type of question to be easier to 

answer. 

An additional issue, which may be a factor in both contingent valuation and contingent choice 

surveys is the expression of “symbolic” values. This might be related to expressions of approval or 

disapproval of the actions to be taken to protect natural resources, or expressions of the importance 

of improving or protecting the environment in general, versus values for specific levels of natural 

resources. For example, a survey might ask respondents to state how much they would be willing 

to pay to protect a particular species of birds from oil spills. A person answering the survey may not 

care about those specific birds, but may state a positive willingness to pay because they think that 

oil spills should be prevented. Thus, their response may not reveal the value of the birds, per se. 

97 
Table V1.3 - Survey Locations 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 

King Kullen Shopping Center, Mattituck, Southold 11

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 

98 
expressions of approval or disapproval for actions, and more likely to focus on the specific natural 

resources. However, people may still express symbolic values for protecting the environment, 

beyond their values for the natural resource improvements. This is more easily detected and 

corrected for in contingent choice than contingent valuation, as discussed below. 

Finally, it has been pointed out that “survey methods are better at estimating relative demand than 

absolute demand,” even for market goods, and “absolute willingness to pay is hard to pin down” 

(National Oceanic and Atmospheric Administration 1993, p. 4609). Thus, the relative values or 

priorities for natural resources elicited by a contingent choice survey may be valid, even if the 

absolute dollar values of specific resource changes are not. For a policy analysis that focuses on 

prioritizing actions, information on relative values and priorities for resources is more important than 

the estimation of total dollar values. Hence, contingent choice, rather than 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 

comparison was designed to include only three attributes: Two of the five natural 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 asked to select from a set of three hypothetical choices: no new 

action or one of two restoratiodprotection programs. Each option was described by different levels 

of resulting natural resources, and the annual cost to each household. In early focus groups, only the 

two programs were presented, but focus groups participants indicated that a “no new action” option 

should be added, for two reasons. First, it allowed people to express a preference for no action if 

they did not support either program. Second, it provided a baseline from which to judge the benefits 

of each of the programs. 

VI.B.2.b. Selection of Natural Resources and Levels 

VI.B.2.i. Natural Resources Included in the Survey 

Based on concerns expressed by participants in focus groups and natural resources identified as 

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 

99 
of life, or “sense of place,” (Sagoff 1992; Kellert 1995) for many residents of the East End, who 

enjoy the rural quality of the area and shopping at numerous local farm stands. The amount of 

development also affects environmental quality of the Estuary. Thus, it is important to 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 

a proxy for fish and shellfishpopulations. Many participants in the 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 that by including eelgrass, which serves as fish and shellfish 

habitat, the survey would capture some of these concerns. 

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

The objective of the survey was to determine respondents’ values for improvements in natural 

resources above a baseline level. We defined the baseline as the level that would exist in the year 

2020, if no action is taken to preserve or restore the resource. We determined the baseline in 

consultation with the Technical Advisory Committee, based on historical declines and the judgment 

of experts, for each resource. 

First, we determined the level of each resource in 1981 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 actions were 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 species Aureococctis anophagefferens, which first occurred in 1985-1988, and 

has subsequently recurred for shorter periods. The major brown tide episodes severely affected scallops, eelgrass, and 

other shellfish in the estuary (see Suffolk County Department of Health Services 1992).

’’We compiled this information from the recent brown tide study (Suffolk County Department of Health Services 

1992); a 1981 land use report (Long Island Regional Planning Board 1981); and information provided by the New 

York State Department of 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. 

100 
In the survey, respondents were presented with two pages of background information, which 

described the level of each resource in 1981, the current level, and projections of levels in 2020 if 

no new actions are taken. These levels are shown in Table 4,and the survey pages are shown in 

Appendix A. Respondents were told that “trends indicate approximate conditions in 2020,” in order 

to make it clear that these are not precise, scientifically-based projections. 

When the survey was created, the Technical Advisory 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,” or baseline, scenario), and two levels associated with 

hypothetical programs that would preserve or restore the resource. In order to make the hypothetical 

context clear, survey respondents were told: 

“The following programs are hypothetical. We are trying to learn which resources are most 

important to you and how much you would pay to protect them.” 

The levels for each resource and for cost are shown in Table IV.5. 

If no action is taken, farmland is projected to decrease by twenty-five percent. The results of 

different hypothetical preservation programs were projected to reduce the loss of farmland to fifteen 

percent or maintain it at the current level. Undeveloped land was also projected to decrease by 

twenty-five percent if no new action is taken. Because it did not seem realistic to preserve 

undeveloped land at the current level, hypothetical preservation programs were projected to reduce 

the loss of undeveloped land to ten percent or five percent. 

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 if no new action is taken; or they might 

be preserved at the current level or restored to ten percent above the current level. The baseline for 

safe shellfishing areas is a twenty-five percent decrease, and hypothetical programs might maintain 

the current level or increase safe areas by ten percent. The baseline for eelgrass is a ten percent 

decrease, and it might be preserved at the current level or increased by twenty-five percent. 

The costs of the hypothetical programs were designed to learn how much people would pay for the 

resource changes presented above, and to bracket a range of costs that seemed feasible to members 

of the Management Committee. However, they are not intended to reflect actual costs of some 

particular set of programs. The no new action option was always presented at no additional cost to 

each household. The other levels of annual household cost included were $50, $100, $200, $300, 

and $500. 

102 
Table V1.5 - Levels of Natural Resources and 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 

(YOChange) ("! Change) (YOChange) 

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 first three questions asked about participation in recreational activities in the area; 

support for different possible actions; and opinions about brown tide. 

Next the background information for the 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. 

103 
VI. 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 Sound or 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 10 years (63%). 

The average household size is just under 3 members, and 46% have no children under 

age 18 in their household. 

Most are employed (67'3'0)~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 -< 1/2 Mile From Water 

from any >1/2 Mile From Water 

water body Did Not Answer 

12: Waterfront 

Dist. from I 1/2 Mile From Water 

Peconic Bavs >1/2 Mile From Water 

13: 0-10 Years 

Length of 11-20 Years 

residence 21-30 Years 

>30 Years 

Did Not Answer 

14: Female 

Gender Male 

Did Not Answer 

15: 1-2 Household Members 

Size of 3-6 Household Members 

household >6 Household Members 

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 21-24 

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 21.4% 

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% 

1I.O4Yo 14.99% 22.62% 

11.17% 14.11% 19.00% 

7.25% 3.170/0 3.17% 

11.55% 1.53% 

31.77% 15.33% 

50.76% 16.86% 

18.35% 21.26?/0 

6.66% 11.30% 

13.51YO 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% 

31.93% 30.93% 10.86% 

18.90% 27.94% 14.48% 

7.04% 11.07% 18.55% 

4.93% 7.38%0 18.10% 

30.86% 46.40% 51.13% 

3.41% 5.62% 34.39% 

a - The 199U population 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 - Sanple percents 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 11.4% 

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 of the respondents (97%) participate in at least one of the listed activities, 

and 81% participate in at least one activity in the Peconic Estuary. Swimming is the most popular 

activity, with 86 percent participating. Swimming is followed by walking and hiking (71%), boating 

(54%), fishing (53%), other beach 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 of residents conducted in the fall of 1993 

by the Center for Community Research of Suffolk Community College (the SCC survey), which also 

found that the Peconic Estuary System was used more frequently than any other water body (Suffolk 

Community College 1994). 

VI. C.3. Support for Management Actions 

Table VI.10 summarizes the responses to Question 2, which asked about support for specific 

management actions. We included this question in order to allow respondents to express their 

opinions about proposed actions, and to give them an idea of the types of actions that might be taken 

to obtain the natural resource changes described in the contingent choice 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%) 

311 

(32.1%) 

684 

(70.7%) 

833 

(86.1%) 

33.5% 

a

53.7%

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 sewage treatment plants; public education; and more research on water quality 

issues. The actions with the least support are required pumpouts of septic systems, and restrictions 

on the use of lawn chemicals. 

The Suffolk Community College survey also asked about support for actions. Although their 

questions were more general and not perfectly comparable to those presented here, their results are 

similar. For example, 83 percent of their sample support stronger building regulations, which is 

comparable to the 76.3 percent of our sample who support zoning regulations, the 78.7 percent who 

support waterfront restrictions, and the 76.7 percent who support restrictions on vegetation. The 

SCC survey found that 71 percent of respondents were willing to change fertilizers, which is 

consistent with the 68.2 percent of this sample who support restrictions on fertilizers and lawn 

109 
Table VI.10 - Support j r 

-

Actio

I

Strongly Strongly No iverage

Action support support Neutral Oppose Oppose Opinion Rank

Prohibit sewage discharge 

from boats. (requires building I

more pumpout stations) 79.0% 10.5% -

1.O% -1.2% -

1.4% 6.7% -

1 ~~ 

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% -

32% 0.4% 0.9% 12.1% 5

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 51.4% 21.8% 

property is sold or improved. 

12.4% 2.5% 2.6% 9.3% 9 

Controlling stormwater 

runoff with diversion, catch 47.6% 26.1Yo

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 of the SCC sample stated that they were willing to improve their own 

septic system, 73.2 percent of our sample support required upgrades when property is sold or 

improved. However, only 54 percent support required pumpouts every four years. 

110 
It 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 of boaters, 

versus 88.2 percent of non-boaters support no-discharge regulations ( although, based on a test of 

equality of the means, the difference in the mean strength of support for discharge prohibitions 

between boaters and non-boaters is not statistically significant). Thus, boaters appear to be willing 

to undergo some inconvenience in exchange for cleaner water. This is consistent with the SCC 

survey finding that 63 percent of boaters are willing to change their boating habits to improve water 

quality. 

Based on similar reasoning, waterfront residents were expected to be less likely to support 

restrictions on waterfront property. There is some support for this hypothesis, with 76.2 percent of 

waterfront residents, 79.8 percent of residents who live up to % mile fi-om the water, and 82.5 

percent of people who live more than ?4mile from the water supporting restrictions on waterfront 

property. However, there was no statistically significant difference in the mean strength of support 

across these groups. 

Finally, the strength of support for all actions was compared for members of environmental groups 

versus those who are not members.24Although the average level of support was slightly greater for 

members of environmental groups, the means were not statistically significantly different. 

From these results, it can be concluded that the majority of respondents are in favor of 

environmental actions, with slightly lower support for some actions that are personally 

inconvenient.However, as expected, the results appear to be primarily symbolic of a general desire 

for action. That is, people seem to support most actions. Thus, these results do not provide adequate 

information with which to make policy decisions. 

VI.C.4. Opinions About Brown Tide 

Question 3 asked respondents whether they have heard of brown tide, and if they have, about their 

level of concern, whether their activities have been affected, and whether they support funding for 

further study. The results are reported in Table V. 1. 

Most people (90%) had heard of brown tide, and 75 percent said that at least one activity was 

affected by the brown tide. The most commonly affected activity was swimming, with 59 percent 

of those who had heard of brown tide affected, followed by shellfishing (40%) and fishing (37%). 

A small number of people said that boating or other activities were affected. The most commonly 

mentioned “other” activity affected was eating seafood. The majority (97%) of those who had heard 

24 A total score for all actions was calculated by summing the level of support for all actions, where 1 indicates 

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 
of brown tide are concerned (21%) or very concerned (76%) about it. Most of those who had heard 

of brown tide (86%) also support more funding to study brown tide and possible remedies for it. 

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. The purpose of this question was to supplement the 

standard demographic questions by providing information about respondents' interests and level 

of commitment to different causes. Table VI.12 shows the statistics for membership in 

organizations. 

Sixty-five percent of respondents belong to at least one organization, and 31 percent belong to an 

environmental organization. This is comparable to the results of the SCC survey, which found that 

57 percent of respondents had donated to environmental organizations, and 21 percent had been 

active in an environmental group. 

112 
Table VI.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 (18.8%) 

300 (3 1.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 85 responses to each of the twelve 

booklets. The demographic groups-year-round vs. seasonal residence, gender, age, education, 

employment, and income-were fairly evenly distributed across booklets. 

Table VI. 13 shows how many of the contingent choice questions were answered by respondents. 

Of the 968 people who completed the survey, 897 (92.7%) answered at least one of the five 

contingent choice questions in their survey booklet, and 790 (81.6%) answered all of the choice 

questions. Of the 4,840 total choice questions, 4,307 (89%) were answered. Seventy-one 

respondents (7.3% of the sample) did not answer any of the choice questions. 

113 

, 
Table VI.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 of their answers. Only 27 people 

(3.0%) chose no action for all of the choice questions answered. These results demonstrate the 

strong environmental concern expressed by survey respondents, but also may suggest some 

symbolic bias, if respondents voted to “take action,” irrespective of the associated levels of resource 

protection and cost. Below we discuss some methods that we employed to identify and control for 

this possible bias. 

Of those who chose either Program A or Program B, more people chose Program A than Program 

B.” This may indicate a tendency to select the option placed in the center, or may simply indicate 

that the options presented as Program A were prefened more often. This is discussed further in the 

Technical Appendix. 

VI.D.2. Results and Discussion 

The statistical models used to estimate the results are described in detail in the Technical Appendix. 

These methods calculate the relative weights, or values, for an additional acre of each natural 

resource, and for an additional dollar of cost to each household. These weights are measured by the 

estimated coefficients for each resource and cost. From these coefficients, relative values for the 

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. 

114 
The results for two different models are reported in Table VI. 14.27The model results indicate that 

the order of priorities for protection or restoration of resources is as follows: farmland, eelgrass, 

wetlands, shellfish, and undeveloped land. 

Table VI.14 - Estimation Results _______~~ 

Value 

IAcreIhhl 95% Confidence Avg. Value 

Coefficient year Interval” lAcreNearb 

Model 1:‘ 

Farmland 0.000511 $0.136 $0.122 $0.150 $9,979 

Undeveloped Land 0.000107 $0.028 $0.025 $0.032 $2,080 

Wetlands 0.000336 $0.089 $0.079 $0.100 $6,560 

Shellfish Areas 0.000233 $0.062 $0.053 $0.071 $4,555 

Eelgrass 0.000419 $0.111 $0.098 $0.125 $ 8,186 

Cost -0.003765 

Model 2:d 

Program B -.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 

a - The 95% confidence interval indicates the range within which the “true” value is likely to : ,with a 95%

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 for both contingent valuation and contingent 

choice methods. The estimation results show that this is an issue in this survey, and that respondents 

to the survey are more likely to choose to take action, independent of the specific results of the 

action. Thus, these respondents may be expressing a symbolic willingness to pay to take action, as 

opposed to revealing values for the specific natural resources of concern. The higher probability of 

choosing to take action is not surprising, given the level of concern among residents of the area for 

the environment of the Estuary. 

However, Model 2 provides a correction for such values, by separating the probability of taking 

action vs. no action, from the probability of selecting either Program A or Program B. Thus, the 

estimated dollar values for Model 2 are approximately half to two-thirds as large as those estimated 

from Model 1. The estimated dollar values range from around $2.1 thousand per acre per year for 

undeveloped land, to around $10 thousand for farmland for Model 1;and around $1.2 thousand to 

$6.4 thousand for Model 2. The values from Model 2 might be interpreted as the portion of 

respondents’ willingness to pay to take action which can be 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 dollar values differ, both models result in the same ordering of priorities and 

relative values for the natural resources. These results indicate that priorities and relative values are 

robust with respect to different model specifications, and are independent of symbolic effects, but 

that the estimated dollar values vary somewhat, although they are close in magnitude. Therefore, 

it may be concluded that the model is relatively robust to different specifications, and that the 

proportion of value that is “symbolic” is not great. 

3. Discounted Present Values 

Discounting is a means of aggregating dollar values over time, and is based on the idea that natural 

resources are assets that provide a flow of services over time. Thus, “[tlhe economic value of a 

resource-environment system as an asset can be defined as the sum of the discounted present values 

of the flows of all of the services. The benefit of any public policy that increases the flow of one type 

of service is the increase in the present value of that service” (Freeman 1993, p. 5). 

The discounted present values for two discount rates are presented in Table IV. 15.The 7 percent rate 

is used to approximate a “typical” interest rate 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).18The 3 percent rate is included for comparison, because it is often argued that the 

social discount rate 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 

15 years or more remaining to maturity (US. Department of the Interior 1995). 

116 
presented in the survey. The 100 year time horizon was used to identify values associated with a 

longer time horizon. 

Table IV.15 - Comparison of Discounted 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 $116,286 

$65,728 $36,221 $29,681 $24,240 

$207,286 $114,228 $93,605 $76,446 

$143,945 $79,323 $65,001 $53,086 

$258,675 $142,547 $116,811 $95,398 

$202,175 $111,412 $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 $31,742 

$189.703 $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 of public values 

for important natural resources of the Peconic Estuary System. We developed the survey through 

an extensive six-month process of interviews, focus groups, and pretests. We selected the contingent 

choice method to elicit values for five important resources: farmland, undeveloped land, wetlands, 

safe shellfishing areas, and eelgrass. The survey was implemented in August, 1995, to 968 yearround and seasonal residents of the East End of Long Island. 

In summary, residents of the East End are very concerned about protecting of the area’s natural 

resources, and are willing to pay a significant amount to do so. Our statistical results indicate that 

respondents are so concerned about protecting the East End’s environment that they would be 

willing to pay to take action, independent of specific results of the action. We corrected for this 

“symbolic” value in our estimation models, and present the results of both the standard and 

“corrected” model. 

117 
Overall priorities for the five natural resources included in this survey are for farmland, followed 

by wetlands, eelgrass, shellfish areas, and undeveloped land. Discounted present values for the most 

conservative model range from around $14 thousand to preserve an acre of undeveloped land, to 

around $75 thousand to preserve an acre of farmland. The results of this survey will be combined 

with those of several other studies to evaluate benefits of a set of proposed management actions for 

the Estuary. 

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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‘zjt a’zjwi>‘ 

k 

P, is the probability that individual i will select option j; zjis a vector of attributes of the 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 is the nested logit model, which allows for correlations between 

the two action alternatives. The nested logit model was structured so that respondents are assumed 

to first choose whether to take action or not, and then to choose a specific program conditional on 

taking action. In the nested logit model, the probability that an individual chooses alternative k is 

The choice probability for each of the three lowest level alternatives is conditional on the choice to 

take action or not. In this case, if the person chooses not to take action, then the probability of 

selecting the “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 

120 
where k is one of the two alternatives, Program A or Program B; j is the choice to take action; n is 

the number of alternatives in choice j, which would be 2; and Jj is the inclusive value for choice j, 

which represents the expected maximum utility from the choice of an alternative that sub-branch. 

This is defined as 

The probability of choosing to take action or not is 

exp(a‘? t zjJ 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 of this study, this would 

indicate that Program A and Program B are closer substitutes than either program and the “No New 

Action” alternative. If z is equal to 1, then all alternatives are equally substitutable, and the model 

becomes identical to the standard conditional logit model (McFadden 1981; Kling and 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 on the first constant term reflects factors other than the levels of attributes that affect 

the choice of an action versus the choice of no 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 is unrelated to the levels of attributes 

of A and B, such as an order effect. 

The results of both of these models indicate that there may be effects on choices unrelated to the 

described attributes. In the alternative-specific constant model, both constant terms are statistically 

significant, indicating that there is an effect on choices unrelated to the quantities of the individual 

attributes, but is instead related to the choices themselves. The positive and significant coefficient 

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 COXtest 

shows that the linear form is the most appropriate of the commonly used functional forms. 

121 
significant coefficient for "Program B" indicatesthat, even if Program A and Program B produced 

the same results in terms of preservation of natural resources, respondents are more likely to choose 

Program A. The coefficient on Action indicates that there is an 87 percent probability that the 

average respondent would select action over no action, if that action cost nothing and provided zero 

resource protection. Similarly, conditional on taking action, there is a 55 percent probability that 

the representative respondent would choose Program A over Program B if their costs and levels of 

resource protection were identical. 

Table B-1 - Comparison of Model Results 

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 

Shellfish Areas 

Eelgrass 

Cost 

p2 

Nested Logit Model: 

Program B 

Farmland 

Undeveloped Land 

Wetlands 

Shellfish Areas 

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 $0.111 $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 $0.031 $0.044 $2,724 

.000281 $0.082 $0.069 $0.094 $6.003 

-.003441 

,3397 

.309 

* - Calculated based on 73,423 households. 

122 
These constant terms for taking action and for differences in probability of selecting A and B may 

be interpreted as representing a qualitativeor symbolic dimension of respondents’ preferences, while 

the coefficients on the natural resources represent the quantitative dimension that can be attributed 

to the stated levels of resource protection. Thus, if respondents exhibit a tendency to a resource 

protection action,rather than “NoNew Action,” beyond that which can be associated with the stated 

levels of resource protection and the cost, they may be expressing a symbolic willingness to pay to 

take action to protect the environment of the East End. Similarly, the coefficient on the constant 

term for Program B indicates that there is some qualitative reason that people choose Program A, 

and measures the degree of preference for A over B which cannot be explained by the described 

attributes of the two programs. Note that this constant term is statistically significant, but 

quantitatively small. 

The higher probability of choosing to take action is not surprising, given the level of concern among 

residents of the area for the environment of the Estuary. However, the preference of one program 

over another beyond the described effects is not expected, and could occur for a variety of reasons. 

For example, the effect could be related to the ordering of the two programs, their placement on the 

page, or could possibly indicate that respondents infer some preference from the labels (e.g., an A 

is better than a B). 

The constant for the choice of action versus no action accounts for qualitative aspects of the decision 

to choose an action rather than no new action. However, it is estimated as a constant term and thus 

assumes a fixed effect, where the constant represents a mean “bias” towards action versus no action, 

beyond that which can be explained by the described levels of resource protection and cost. An 

alternative approach to modeling is to use a random effects model, where the random components 

of preferences for the two action programs are correlated. This’impliesthat an actiodno action bias 

might exist, but that the bias is randomly distributed across choices. For example, some 

individuals might exhibit a bias towards taking action, while others might exhibit a bias against 

taking action. 

The random effects model can be implemented using the nested logit approach, which captures the 

correlation of the random components of utility associated with the two action alternatives. Tests 

of the inclusive value parameter in the nested logit model indicate that there is greater 

substitutabilitybetween ProgramA and Program B than between taking action or not, and that there 

is significant correlation between the Program options. The constant term for Program B is similar 

in magnitude to that estimated in the previous model. 

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, 

123 
where j represents the “No New Action” alternative, or the baseline levels of the resources, so that 

Cj=O;k is the option selected; and C, is the maximum willingness 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 of the natural resources is calculated as the ratio of the coefficient on the resource, p, to the 

coefficient on cost, y. 

The calculation of dollar values for the nested logit model must account for the nested structure and 

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 dollar values and relative values, calculated as ratios between the coefficients on each 

pair of resources, were compared for each model using Friedman’s test for more than two related 

samples (Neave and Worthington, 1988). Based on this test, the hypothesis of equality of the 

estimated dollar values for the three models is rejected. However, a comparison of the estimated 

dollar values for the nested logit model and the alternative-specific constants model using the 

Wilcoxon signed rank test does not reject the hypothesis of equality of values for these two models. 

The Friedman test does not reject the hypothesis that the relative values for natural resources are 

equal for all three models. Additionally, the ordinal priorities for all three models are the same, with 

farmland most important, followed by eelgrass, wetlands, shellfishing areas and undeveloped land. 

These results indicate that priorities and relative values are robust with respect to different model 

specifications, and are independent of symbolic effects, but that the estimated dollar 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. 

124 
These results indicate,however, that there may be a statisticallysignificant symbolic component to 

choices, which is comprised of an effect that is unrelated to the described levels of resource 

protection provided by the hypotheticalprograms and the associated cost. The similarity of results 

from the nested logit and conditional logit with constants models, and the fact that the results are not 

statisticallydifferent,indicatethat these biases are likely overwhelminglyin one direction-towards 

taking action rather than no action. Thus, both of these models appear to account for a “symbolic” 

aspect of values, and to separate that from estimated values for specific natural resource 

improvements. Note, however, the Nested Logit model provides considerable improvement in fit, 

as measured by the p2statistic.