EAI

Economic Analysis, Inc.

PO Box 3462 Peace Dale, Rhode Island 02883

(401)789-0050

FINAL REPORT

Recreational and Resource Economic Values

for the Peconic Estuary System

Final Report on Phase II 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
Estuarine Use and Economic Value of the Peconic Estuary System

Phase II: A Perspective on Nonmarket Values.

This is the final report of the Phase II 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-associated use,

(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 II was to collect primary data to quantify non-market uses and non-market values

of the Peconic estuary. Phase II 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 various recreational 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 III is to draw together these various studies in order to prioritize specific

management actions for the Peconic estuary. Phase III, 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.
1

Recreational and Resource Economic Values 

for the Peconic Estuary System

EXECUTIVE SUMMARY

I.A. PURPOSE AND SCOPE

The environmental and natural resources (“natural assets”) of the Peconic Estuary System--the bay

waters, beaches, wetlands, ecosystems, habitats, and parks and watershed lands--provide many

services to the public. Outdoor recreation, scenic views, and the productivity of wetlands that

contribute to stocks of fish, birds, and other species used for commercial and recreational purposes

are but a few examples of 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 II of a series of economic studies being done by EAI for the Peconic

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

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

estuarine-related sectors, and (2) summarized available information on recreational uses. Subsequent

work by EAI will include benefit-cost analyses of resource management actions and sustainable

financing options for the selected, preferred actions. 
2

I.B. ESTUARINE-RELATED USES, RESOURCES AND ECONOMIC VALUES

I.B.1. Introduction and Overview

No single method can capture the value of the variety of services provided by the natural assets of

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

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

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

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

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

vicinity.

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

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

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

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

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

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

abundance.

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

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

sand/mud bottoms, based on the value of the fish, shellfish and bird species that these

ecosystems help “produce”. The primary focus is on the nursery and habitat services of

the wetland ecosystems in the production of commercial fisheries.

(4) The Resource Value study uses original survey results to estimate the relative preferences

that residents and second homeowners have for preserving and restoring key PES natural

and environmental resources: Open space, farmland, unpolluted shellfish grounds, eelgrass

beds, and intertidal salt marsh. This study also provides a perspective on the economic

value the public has for these resources, as indicated in their stated willingness to pay for

programs to preserve and restore them. 

Key results for each of these studies are outlined below. A detailed discussion of each method, its

purpose, the data used, assumptions and limitations, and results is given in the chapters that follow.

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

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

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

interested, Appendixes present the technical details of the methodologies used.
3

I.C. SUMMARY OF SELECTED RESULTS

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

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

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

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

impact on nearby property values. 

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

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

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

increase adjoining property values by $410,907. 

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

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

value than a similar parcel located elsewhere (i.e., in a 1/2-acre zone). Conversely, property located

on a main road (highway 25 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:

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

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

or hunting trips

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

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

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

hiking/walking and bicycling.

 

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

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

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

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

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

estimate the economic value per person, per trip, for four key recreation activities: Swimming,
1

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

4

boating1, fishing, and for viewing of birds and wildlife. The estimated values per trip range from

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

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

actually pay to participate. 

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

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

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

for these key recreational activities range 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 (i.e., SCDH field

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

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

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

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

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

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

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

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

 

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

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

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

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

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

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

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

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

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

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

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

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

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

45 percent of these activities are PES-related (Grigalunas and Diamantides, 1996), the annual PESrelated expenditures for these two activities are $8.7 and $2.4 million, respectively. 
5

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 value per acre for existing wetlands of approximately $12.4 thousand

for eelgrass, $4.3 thousand for salt marsh, and $786 for mud flats, using a discount rate of 7 percent

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

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

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

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

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

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

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

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

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

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

used often by participants in EAI’s focus groups. 

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

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

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

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

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

possible, to compare with other study results. 

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

protect resources, and indicated they would financially support such actions. The relative priorities

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

We also estimated the monetary value that the survey 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 resources are more reliable than are the dollar

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

process of selecting management actions. 

.
6

I.D. REFERENCES 

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

Methods. Washington, D.C.: Resources for the Future.

Grigalunas, T.A. and J. Diamantides, 1996. The Peconic Estuary System: Perspective on Uses,

Sectors, and Economic Impacts. Peace Dale, 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. Environmental and Natural Resource Economics (4th ed.) New York: John

Wiley & Sons 
7

II. BACKGROUND AND INTRODUCTION

II.A. BACKGROUND 

Located within Suffolk County 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 adjoining watershed

lands (Figure II.1). Included within the PES are five towns: East Hampton, Southampton,

Riverhead, Southold, and Shelter Island, as well as a small part of a sixth town, Brookhaven. In

total, the five 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 characteristics of the PES include: (1) the generally high quality of its coastal estuarine

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

particularly tourism and recreation, and (3) the population, which is highly seasonal and for yearround residents is comprised of smaller, more elderly, and lower-income households than for the

County or Long Island as a whole (Long Island Business News, 1994; Grigalunas and Diamantides,

1996). 

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

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

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

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

(“interest”) over time, if maintained.

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

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

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

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

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

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

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

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

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

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

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

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

economic benefits and cost of such actions.
8

II.B. NATURE AND IMPORTANCE OF NON-MARKET RESOURCE VALUES 

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

Economic benefits provided by natural asset services show up in at least two ways:

(1) As market benefits to the owners, operators and employees of over 1,000 businesses that

engage in or support recreation and tourism, commercial fishing and seafood activities, and

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

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

renters, and others who use or otherwise enjoy the quality of the PES’s natural amenities.

EAI’s Phase I study summarized available information for the first of these two categories by

quantifying “economic impacts” of estuarine-dependent economic sectors (Figure II.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 10,000 residents who own, operate, or are

employed in more than 1,000 marine and tourism-related businesses (Grigalunas and Diamantides,

1996). 

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

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

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

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

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

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

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

This Phase II report focuses on non-market benefits provided by amenities in the Peconic Estuary

System (“PES”). We adopt the view that the natural resources and environmental amenities of the

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

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

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

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

outset. 
2

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

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 equivalent today. The

equivalent, lump sum value today arrived at by discounting is called the “present value”. 

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

annual flow using the formula: 

Present Value = V1/(1+ r)1 + ....+ VT/(1+r)T

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

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

$1,000 in year 2, and the discount rate is 10 percent, 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, Vt

, (2) when they occur, and (3) the discount rate, r, used. We estimate

the annual monetary values, Vt

 for non-market services in the chapters that follow, employ a time
3

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

10

horizon, T, of 25 years, and use a discount rate of 7 percent, a standard rate recommended for use

in many resource management projects3.

II.C. PURPOSE AND SCOPE

This report describes the four non-market valuation studies comprising Phase II of a series of studies

being carried out by EAI for the Peconic Estuary Program. The Phase II studies provide estimates

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

other services provided by key PES environmental and natural resources. 

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

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

II.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 sectors identified as estuarine-related. Phase

II provides estimates of non-market benefits. Our studies were carried out for the purpose of

eventually contributing to benefit-cost analyses of proposed management actions by the PES

Management Committee in Phase III of the project.

 

Phase III analyses will (1) provide cost analyses for specific management actions designed to protect

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

I and II to provide benefit-cost analyses of potential management actions, and (3) 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 II studies are the property value study, the recreation survey, the wetlands

productivity analysis, and the resource survey. The property value study was designed to estimate

benefits that PES amenities provide to individuals who own adjacent properties. The recreational

survey allows us to estimate recreational use and use values for a variety of outdoor activities. This
11

survey was designed fill data gaps identified in Phase I report, and to estimate recreational uses and

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

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

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

food web and habitat services and focuses on the “production” of commercial species, and to a lesser

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

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

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

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

information for each study is give in Table II.1; each study is described in detail in the chapters and

appendices that follow.

II.D. REFERENCES

Grigalunas, T.A. and J. Diamantides, 1996. The Peconic Estuary System: Perspective on Uses,

Sectors, and Economic Impacts. Peace Dale, RI: Economic Analysis, Inc. 

Long Island Business News, 1994. Long Island Almanac 1994. Ronkonkoma, NY: Long Island

Business News. 

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

Management Program. 3 volumes. Suffolk County Dept. of Health Services (November).
12

Table II.1. Summary of EAI’s Phase II Non-Market Valuation Studies for PES 

Study Purpose Data and Method(s) 

Recreational Uses and Use Values 1. Estimate Resident, Second Home

Owners & Visitor Recreational Uses:

 ! Activities

 i 8 Key Activities 

 i 8 Locations

 5 PES Bays

 Atlantic Ocean

 LI & BI Sounds

 i 5 Towns

2. Estimate Use Value for: 

 ! Swimming

 ! Boating

 ! Fishing

 ! Bird & Wildlife Viewing 

Convenience sampling/

Intercept Survey

Self administered

1,354 completed surveys

Travel Cost Method

Demand for swimming

and recreational fishing

depends on quality

Resource Values Estimate Residents’ and Second

Homeowners’ Preferences and

Economic Value for preserving and

restoring PES:

 ! Open space

 ! Farmland preservation

 ! Unpolluted shellfish beds

 ! Wetlands

 ! Eelgrass beds

Convenience sampling/

Intercept Survey

Self administered

968 completed surveys

Contingent Choice

Method

Wetlands Productivity Values Estimate the Economic Value for

Nursery and Habitat Productivity

functions for:

 ! Saltmarsh

 ! Eelgrass

 ! Intertidal mud bottom

Based on Productivity

approach and results in

available literature.

Property Value Estimate the effect on Property Value in

Town of Southhold of attributes,

including:

 ! Site

 i Lot & home size

 i Garage

 i Special features

 ! Neighborhood

 i Main road

 ! Environmental

 i Wetlands 

 i Open Space

 i Farmlands

 i Zoning Size

All property sales in

Town for 1996

374 sales transactions

Geographical

Information System

Property Value

(“Hedonic”) Model

III. ENVIRONMENTAL AMENITIES AND PROPERTY VALUES: 
13

A CASE STUDY OF THE TOWN OF SOUTHOLD

III.A. INTRODUCTION

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

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

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

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

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

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

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

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

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

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

if all of these important 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 market prices for property. 

We use the Town of Southold, located on the North Fork of the PES, as a case study (Figure III.1).

Southold is an interesting example, for several reasons. First, it has a wide range of environmental

amenity levels and neighborhood conditions. For example, Southold has large areas of 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.
14

The impacts of environmental amenities on Southold property values, and the impacts resulting from

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

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

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

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

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

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

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

environmental amenities and property values estimated using the property value model will continue

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

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

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

III.B. RELATIONSHIP BETWEEN ENVIRONMENTAL AMENITIES AND PROPERTY

VALUES

Environmental amenities provide valued services to residents of local communities. For example,

open space contributes, in a physical sense, to the character of local communities, while providing

a wide variety of services, including scenic views, outdoor recreation, insulation from noise and

other aspects of the urban landscape, and protection from erosion, flooding, and other physical

hazards (Johnston 1997a). These amenities are valued by local homeow ners, making communities

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

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

for land or housing with higher levels of environmental quality. 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
15

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

cluster-zoned housing with permanently protected open space appreciates at a faster rate that similar

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

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

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

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

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

the per-acre selling price of that land. This study also shows that agricultural pasture land 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 4-11% 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

economic methods (hedonic methods) to a database comprised of GIS parcel coverage data and real

estate sales data. In simple terms, the property value model estimates correlations between property

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

data used are described next.

III.C. APPLICATION OF PROPERTY VALUE MODEL TO SOUTHOLD

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

analysis estimates how various site, neighborhood and environmental characteristics affect the value

of individual properties. Property value analysis is a common means of estimating the effect of

environmental changes on land values, as described in detail by Freeman (1993) and Garrod and

Willis (1992), among many others.

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

People buying homes in effect purchase many site, neighborhood, and environmental characteristics.

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

of baths, the square footage of the structure, the zoning classification of the land, 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
 4

 The accuracy of hedonic analysis depends on a well-functioning real estate market, in which consumers

have accurate information regarding all home characteristics. The technique also assumes that a large number of

different housing types are available for purchase, so that consumers can choose the “package” of housing characteristics that is most to their liking, and can “mix and match” different types of characteristics. Finally, the

technique assumes the availability of appropriate data, concerning all characteristics that influence property values. Bias can result from the application of hedonic techniques to ill-functioning real estate markets, in which

few different types of housing options are available, and/or in which consumers do not have accurate information regarding housing characteristics (Johnston 1997a). 

16

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

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

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

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

works by statistically comparing values 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 characteristics of the property. The Property Value method uses

a statistical technique called “multiple regression” to assess the impact of each characteristic on the

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

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

characteristics are associated with higher values, which are associated with lower values, and which

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

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

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

nearby property values can be estimated.4

 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 Southold data set used in the analysis has full information on 374 parcels.

III.D. STATISTICAL ANALYSIS OF SOUTHOLD LAND VALUES
 5 Wichelns and Kline (1996), Chicoine (1981), Shonkwiler and Reynolds (1986), and Garrod and Willis (1992).

17

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 on per-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 estimates the direction, magnitude, and statistical significance of correlations between each

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

(per-acre property value). 

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

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

applies a “transcendental” or “translog functional form”.5

 The transcendental form 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.

III.E. ANALYSIS OF SOUTHOLD LAND VALUES: RESULTS

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

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

indicate high levels of correlation between land characteristics and property values. An R2

 value

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

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

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

associated with model variables are consistent with prior expectations and with the findings of

earlier hedonic studies (e.g., Bockstael 1996; Johnston 1997a; Des Rosiers and Theriault 1992;

Garrod andWillis 1992).Of those variables with demonstrated impacts on property values, seven

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

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

defined by the GIS database. A parcel of land located next to open space has,

on average, 12.83% higher per-acre value than a similar parcel located
18

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 of additional distance between

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

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

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

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

than a similar parcel located elsewhere. 

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

(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

freshwater wetland. For every one percentage point increase in the percent of

a parcel classified as wetlands, average per-acre property value increases by

0.27%. (However, this variable is only significant at the 14% level.) 

III.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 for policy, the following section simulates the

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

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

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

Southold. 
6

In this regard, we note that recent support for an open space initiative in Suffolk County suggests that the electorate

has a strong preference for open space as a public good. 

19

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

total property value loss is equal to $27,394×15=$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 increase in nearby property

value associated with this open space parcel. 

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

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

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

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

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

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

section do not capture general amenity benefits enjoyed by all local residents, regardless of the location

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

 

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

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

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

property values. However, the illustrated results do indicate the general types of benefits and costs that

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

assumptions as noted above, they are robust with respect to the quantity of open space preserved. 
20

III.G. LIMITATIONS IN PROPERTY VALUE ANALYSIS

As noted, the model provides a good fit for sales data for the town, and the results of the Southold

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

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

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

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

property values. Although numerous environmental variables may influence property purchase

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

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

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

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

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

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

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

important for 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

existing environmental attributes on current per-acre land values. 

A second limitation is that increased property values, related to improved environmental quality,

may not be favored by all residents. For example, residents may resist open space preservation on

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). 

III.H. SUMMARY
21

Environmental amenities increase property values of nearby parcels, reflecting the valuable services

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

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

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

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

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

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

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

III.I REFERENCES

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

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

Chicoine, D. L., 1981. Farmland Values at the Urban Fringe: 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. "Integrating Geographic Information Systems to Hedonic

Price Modeling: An Application to the Quebec Region. Property Tax Journal 11(1): 29-57.

Edwards, Steven and Glenn Anderson, 1984. “Land Use Conflicts in the Coastal Zone: An

Approach for the Analysis of the Opportunity Costs of Protecting Coastal Resources.”

Journal of the Northeastern Agricultural and Resource Economics Association (April). 

Freeman, M. A., 1979. The Benefits of Environmental Improvement: Theory and Practice.

Baltimore: Johns Hopkins University Press.

Freeman, M.A. 1993. The Measurement of Environmental and Natural Resource Values.

Washington, DC: 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. 
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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 Appreciation for Clustered Housing with Permanently

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

University of Massachusetts.

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

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

Parks and Recreation.

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

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

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

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

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

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.
23

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 Characteristics on the Cost of Development

Rights to Farmland”. Agric. and Resource Economics Review: 150-158.
24

APPENDIX A. Variables in the Hedonic Analysis

All model variables were generated from one of two sources: 1] Official Southold Property Record

Cards for all parcels sold during 1996; 2] GIS coverages maintained Suffolk County

Planning Department, updated 1995.

Dependent Variable:

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

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.
25

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.

PECON100: dummy variable: value of 1 assigned if the parcel is within 100 meters of the

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

meters from the estuary. Based on GIS coverages.

SOUND100: dummy variable: value of 1 assigned if the parcel is within 100 meters of

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

linear meters from the Sound. Based on GIS coverages.

ONROAD: dummy variable: value of 1 assigned if the parcel is within 20 meters of a

main road (Rts. 25 and 48); value of 0 assigned if the parcel is further than

20 meters from a main road. Based on GIS coverages.

FARMDISTANCE: the linear distance between the parcel and the nearest farmland (farmland),

in meters. Based on GIS coverages.

ONFARM: dummy variable: value of 1 assigned if the parcel is contiguous (distance=0)

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
26

parks; other wild or conservation lands; 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.1 Mean Values of Model Variables

Variable Mean Value

LVALACRE 12.8362

LNACRE -0.7521

WATFRONT 0.2340

BULKHEAD 0.0718

PORCH 0.5266

YEAR 16.7500

GARSQFT 265.1861

PATIO 0.4734

BATHS 1.6303

NOHEAT 0.1197

FIRE 0.5372

SQFT 1270.3200

SPECIAL 0.1064

PECON100 0.0053

SOUND100 0.0848

ONROAD 0.0585

ONFARM 0.0452

DFARM 2395.1400

OPENSPACE 0.050398

WETLAND 2.4548

LARGEZONE 0.1011
27

#

#

#

#

#

pond

Long Island Sound

100-meter radius

ROUTE 25

Agricultural Land

 Distance > 100-m

Agricultural Land

 Distance < 100-m

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 illustrates the type of analysis applied. In this case, the map illustrates the

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

sales database. 
28

Wetlands

pond

Long Island Sound

ROUTE 25

The following GIS map covers the same area of Southold, yet illustrates the distribution of wetlands,

used to assess the percentage of each sales parcel covered by freshwater wetlands.
29

APPENDIX B. The Statistical Model

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

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

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

applying a transcendental or translog functional form to the property price – characteristics

equation

Vi = β0Xi1

β1

exp[ΣβjXij]

 j=2...n

where Vi is the 1996 selling price of the ith parcel, in dollars per-acre, Xi1 is parcel size in acres,

and Xij are measures of the j = 2...n other characteristics that affect land value. The β’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 ln(.) 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 of homoskedasticity at the 5% level. Although multicollinearity is ubiquitous in hedonic

models, tests for multicollinearity indicate that it does not have severe impacts on model results in

the present application. As the data is cross-sectional and data order has been randomized, tests

for autocorrelation are unnecessary.
30

APPENDIX C. OLS Model Results

Dependent Variable: LVALACRE

 

 Sum of Mean

 Source DF Squares Square F Value 

Prob>F

 

 Model 20 152.03723 7.60186 89.930 

 Error 355 30.00860 0.08453

 C Total 375 182.04583

 

 Root MSE 0.29074 R-square 0.8352

 Dep Mean 12.83621 Adj R-sq 0.8259

 C.V. 2.26502

 

Parameter Estimates

 

 Parameter Standard T for H0: 

 Variable Estimate Error Parameter=0 Prob > |T|

 

 INTERCEP 11.409748 0.08174613 139.575 0.0001

 LNACRE -0.905956 0.02606823 -34.753 0.0001

 WATFRONT 0.333735 0.04497479 7.420 0.0001

 BULKHEAD 0.200245 0.06780928 2.953 0.0034

 YEARSQ 0.000302 0.00011945 2.528 0.0119

 YEAR -0.012017 0.00484814 -2.479 0.0136

 GARSQFT 0.000165 0.00006797 2.428 0.0157

 PATIO 0.103269 0.03166099 3.262 0.0012

 BATHS 0.109587 0.02678655 4.091 0.0001

 NOHEAT -0.099710 0.04189498 -2.380 0.0178

 FIRE 0.117428 0.03516537 3.339 0.0009

 SQFT 0.000264 0.00004262 6.193 0.0001

 SPECIAL 0.123980 0.05349404 2.318 0.0210

 SOUND100 0.294474 0.07076478 4.161 0.0001

 PECON100 0.671739 0.30810728 2.180 0.0299

 ONROAD -0.176308 0.06582347 -2.678 0.0077

 FARMDISTANCE 0.000017320 0.00000374 4.625 0.0001

 ONFARM -0.142932 0.07661228 -1.866 0.0629

 WETLAND 0.002729 0.00181653 1.502 0.1339

 LARGEZONE 0.154045 0.06636055 2.321 0.0208

 OPENSPACE 0.120691 0.07154488 1.687 0.0925
31

IV. OUTDOOR RECREATIONAL USES AND USE VALUES IN THE PES

IV.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 II recreational survey is designed to fill these data gaps.

Characteristics and preferences of users. Prior to the recreational use survey, only anecdotal

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

accommodations, recreational preferences, etc., are provided by EAI’s Phase II recreational survey.

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

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

in recreational value resulting from changes in water quality.

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

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

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

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

Economic data for these sectors--sales-- is presented in this report.
7

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

participation.

32

IV.B. IDENTIFICATION OF RECREATIONAL USES AND LOCATIONS

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

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

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

identified:

Fishing

Boating

Swimming

Shell fishing

Beach Use

Bird Watching

Wildlife Viewing

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.

Southold.

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

. 
33

IV.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 from the resource survey will be discussed here. For a full

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

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

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

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

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

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

limits on budgets for this project.

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

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

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

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

relevant questions, wording of questions, and survey presentation. 

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

at the site on a four point scale from “excellent” to “poor”. As is described below, this approach

worked satisfactorily in that we found a statistical association between respondents’ subjective

estimates of water quality and objective measures of water quality based on field sampling

undertaken by the County. 
34

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

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

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

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

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

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

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

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

is available from the authors.

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

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

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

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

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

and miscellaneous public places throughout the study area.

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

process. These activities include: walking/hiking, biking, sightseeing, jet skiing, jogging,

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

questionnaire for comprehensiveness. However, these activities are considered less significant than

the key activities identified above, and data collected on these “non-key” activities are limited to

a single question on participation.

IV.D. DESCRIPTION OF THE RECREATIONAL USE SURVEY QUESTIONNAIRE

The survey booklet cover provides a map of the East End to orient respondents. The first set of

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

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

and the type of accommodations used, if any. 

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

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

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

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

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

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

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

early on, allowing respondents to focus on the questions that follow. We asked respondents whether

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

The next set of questions asked respondents to indicate their annual participation in key recreational
35

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

The other chart identified recreation locations by town. Respondents indicated the number of times

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

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

Respondents were asked to identify their most recent recreation day at the east End. They then were

asked to identify all the recreational activities participated during that day. Shell fishing, swimming

and beach use, boat use, fishing, bird watching, and wildlife viewing were selected as key activities.

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

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

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

After the participation questions, respondents were asked about selected expenditures made at the

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

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

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

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

questions was asked in order to obtain socio-demographic data, such as residency, household status,

income, age, education, and employment status. 

IV.E. DESCRIPTIVE RESULTS: DEMOGRAPHICS

A total of 1,354 respondents provided usable survey questionnaires, although not every respondent

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

follows:

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

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

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

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

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

Compared to 1990 Census data, the sample has a slightly higher share of women and people

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

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

those responding (59%) were visitors from outside the East End (Table IV.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 Owners - Place of Residency. Residents and second home

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

(30%) and Southold (28%) had the largest percentages of resident respondents. The remainder was
36

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

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

significant effect on survey results.

Table IV.1 - Residency Status of Survey Respondents

Residency Number of Respondents Percentage of all

Respondents

East End Residents 358 28%

Second Home Owners 159 13%

Visitors 750 59%

Total 1267 100%

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

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

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

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

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

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

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

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

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

11%.

Visitor Classification and Accommodations. Most visitors in our sample (60%) were

overnight visitors. Day trippers (those who returned to a home outside the East End at the end of

the day) accounted for 39% of visitors surveyed. 

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

type of accommodation was with friends or relatives. Rental (24%) and hotel or motel (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.
37

For all residency and visitor categories, the share of female respondents was only slightly more

than in the resident population. The age distribution of the sample reflects the survey

implementation practice of not including individuals less than 18 years of age. 

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

indicates that 49% of the resident population had some level of post-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: comparison among resident and visitor categories. The distribution

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

the most respondents under 24 years of age (10%), and overnight visitors have the most

respondents in the 25-44 age group (60%). Second homeowners have the largest percentage of 45-

74 year olds (62%). Second homeowners are also the most highly educated (95% post-secondary

schooling) and have the highest incomes (80% above $50,000 household income).

Sample characteristics: East End Town of Residence. Residents and second home owners

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

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

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

Hampton and also in Southampton. This disproportionate distribution of the sample most likely has

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

strongly related to the East End town the respondent lives in.
38

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

1990

Population

Resident

Sample

Second

Homewner

Sample

Overnight

Visitor

Sample

Day

tripper

Sample

Gender

Female 51.83% 53.57% 54.67% 53.63% 55.34%

Male 48.17% 46.43% 45.33% 46.37% 44.66%

Age

up to 20 23.89% 3.83% 3.36% 1.49% 2.76%

21-24 4.53% 1.91% 1.34% 4.95% 7.09%

25-34 16.23% 14.48% 8.72% 25.50% 22.83%

35-44 12.84% 21.86% 19.46% 34.65% 24.02%

45-54 11.01% 18.85% 30.87% 21.29% 20.87%

55-64 11.04% 13.39% 18.79% 8.42% 12.20%

65-74 11.17% 21.31% 12.08% 2.97% 7.87%

75-84 7.25% 3.83% 4.70% 0.50% 1.97%

85 and up 2.04% 0.55% 0.67% 0.25% 0.39%
39

Table IV.2 - Population Demographics vs. Survey Respondent Demographics, cont.

1990

Population

Resident

Sample

Second

Homewner

Sample

Overnight

Visitor

Sample

Day tripper

Sample

Education

<High

School

7.44% 1.27% 0.00% 0.28% 0.46%

Some HS 11.55% 2.55% 0.77% 0.84% 2.78%

HS Grad. 31.77% 16.56% 4.62% 7.58% 14.35%

Some Coll. 18.35% 21.66% 13.85% 17.42% 26.39%

Assoc. Deg. 6.66% 12.74% 4.62% 7.30% 12.04%

Bachelor’s

Deg.

13.51% 21.97% 23.85% 29.78% 19.44%

Advanced

Deg.

10.72% 23.25% 52.31% 36.80% 24.54%

Income

<$15,000 19.17% 9.42% 1.57% 5.16% 3.70%

$15,000 -

$24,999

14.60% 12.01% 3.15% 4.01% 8.80%

$25,000 -

$34,999

14.40% 13.31% 5.51% 10.32% 8.33%

$35,000 -

$49,999

17.52% 17.53% 10.24% 16.62% 20.37%

$50,000 -

$74,999

18.90% 23.38% 13.39% 19.48% 26.85%

$75,000 -

$99,999

7.04% 11.36% 14.96% 11.75% 17.59%

$100,000 -

$149,999 4.93% 6.82% 18.11% 17.19% 9.26%

$150,000 

and up 3.41% 6.17% 33.07% 15.47% 5.09%
40

IV.F. DESCRIPTIVE RESULTS: KEY RECREATION ACTIVITY PROFILE

Respondents were asked the number of times that they performed each of the following

outdoor recreational activities at various East End locations (Bays and towns) this year (1995):

Activities: Fishing, Boating, Swimming, Shell fishing

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

Gardiners Bay, Block Island Sound, Long Island Sound, and Atlantic Ocean

Activities: Beach Use, Bird Watching, Wildlife Viewing, and Hunting

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

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

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

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

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

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

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

for location by both water body and town.

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

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

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

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

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

Most residents (95 %) participated in key recreational activities

Swimming was the most popular activity 

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

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

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

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

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

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

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

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

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

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

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

More than 12,000 beach visits at the five East End towns were reported. 
41

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

Residents

(n=358)

SHO

(n=159)

Overnight

(n=427)

Day

trippers

(n=273)

Total

(n=1217)

Fishing 88 (25%) 41 (26%) 62 (15%) 13 (5%) 204 (17%)

Boating 132 (37%) 63 (40%) 88 (21%) 39 (14%) 322 (26%)

Swimming 186 (52%) 80 (50%) 153 (36%) 28 (10%) 447 (37%)

Shell fishing 49 (14%) 19 (12%) 14 (3%) 6 (2%) 88 (7%)

Beach use 282 (79%) 103 (65%) 235 (55%) 74 (27%) 694 (57%)

Bird

watching 36 (10%) 13 (8%) 23 (5%) 32 (12%) 104 (9%)

Wildlife

viewing 73 (20%) 39 (17%) 45 (11%) 40 (15%) 185 (15%)

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

Any key

activity* 342 (96%) 132 (83%) 287 (67%) 117 (43%) 878 (72%)

Multiple

key

activities

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 95% of the residents surveyed

engaged in at least one, key water-based activity in the PES or one key land-based activity in one

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

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

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

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

PES and beach use on the Atlantic Ocean or Long Island Sound shoreline at the East End.
42

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

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

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

more residents (70%) and second homeowners (65%) swim, boat, fish, and shellfish than overnight

visitors (45%) and day trippers (22%). Among these activities, the highest participation rates

across all residency status categories are for swimming (37%) followed by boating (26%) and

fishing (17%). Shell fishing is more popular with residents (14%) and second home owners (12%)

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

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

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

homeowners (70%) engage in these activities than overnight visitors (59%) and day trippers (37%).

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

followed by wildlife viewing (21%) and bird watching (19%). Only 2% of survey respondents hunt.

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

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

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

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

Fishing Shell

fishing

Boating Swimming Totals

Block Island

Sd.

320 (6%) 71 (5%) 467 (5%) 380 (2%) 1238 (5%)

Long Island Sd. 1422

(25%)

266 (20%) 1760

(20%)

2801 (18%) 6249 (24%)

Atlantic Ocean 1125

(20%)

201 (15%) 1056

(12%)

5685 (37%) 7116 (27%)

PES 2732

(49%)

766 (59%) 5470

(62%)

6538 (42%) 15506 (60%)

Totals 5599

(100%)

1304

(100%)

8753

(100%)

15404

(100%)

26060

(100%)

* Percentages may not sum precisely due to rounding

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

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

Island was the most used for bird watching (567) and hunting (92), and East Hampton had the most
43

(1123) wildlife viewing experiences. On the other hand, Riverhead was the least used town for

beach use (1075), bird watching (68), and wildlife viewing (220). Southampton had the least

number of hunting occasions (26).

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

Beach Use

Bird

Watching

Wildlife

Viewing Hunting

Total

Trips

East

Hampton

3139 (26%) 300 (20%) 1123 (29%) 50 (20%) 4612 (25%)

Riverhead 1075 (9%) 68 (5%) 220 (6%) 56 (22%) 1419 (7%)

Shelter Island 2461 (20%) 567 (37%) 1030 (27%) 92 (36%) 4150 (25%)

Southampton 3334 (27%) 386 (25%) 690 (18%) 26 (10%) 4436 (24%)

Southold 2239 (18%) 215 (14%) 764 (20%) 31 (12%) 3249 (18%)

Totals 12248

(100%)

1536

(100%)

3827

(100%)

255

(100%)

17866 (100)

*Percentages may not sum precisely due to rounding

Location of PES Water-Based Activities. 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. 
44

Table IV. 6 - PES Key Recreational Activity Outings - (n=1267)

Fishing Shell

fishing

Boating Swimming Totals

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

Bay

486 (18%) 109 (14%) 1021 (19%) 1298 (20%) 2914 (19%)

Shelter Isl.

Sound 511 (19%) 158 (21%) 1321 (24%) 1410 (22%) 3400 (22%)

Gardiners

Bay 654 (24%) 255 (33%) 1204 (22%) 1473 (23%) 3586 (23%)

Totals 2732

 (100%)

766

 (100%)

5470

 (100%)

6538 

(100%)

15506 

 (100%)

* Percentages may not sum precisely due to rounding

PES Key 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 

(100%)

3506 

(100%)

4328 

(100%)

324 

(100%)

14542 

(100%)

* Percentages may not sum precisely due to rounding
45

Locations of PES Activities - By Residency Status. For residents, Shelter Island Sound

(27%) is the most popular location in the PES, followed closely by Great Peconic Bay (25%)

(Table V.8). Gardiners Bay (31%) is the most frequented destination for second home owners, with

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

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

Flanders Bay is by far the least often visited location for residents (7%), second home owners (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 SHO Overnight Day tripper Total

Flanders

Bay 451 (7%) 114 (3%) 446 (10%) 54 (17%) 1065 (7%)

Gt. Peconic

Bay 1589 (25%) 1048 (30%) 1304 (30%) 110 (34%) 4051 (28%)

Ltl.

Peconic

Bay

1260 (20%) 740 (21%) 736 (17%) 60 (19%) 2796 (19%)

Shelter Isl.

Sound 1752 (27%) 525 (15%) 1031 (24%) 58 (18%) 3366 (23%)

Gardiners

Bay 1332 (21%) 1079 (31%) 811 (19%) 42 (13%) 3264 (22%)

Total 6384

 (100%)

3506

(100%)

4328

(100%)

324

 (100%)

14542

(100%)

* Percentages may not sum precisely due to rounding

IV.G. DESCRIPTIVE RESULTS: PARTICIPATION AND TRIP ESTIMATES

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

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

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

Thus, we have: 

Trips = No. of Potential Participants x Percent Participating x No. of Times Participate
8

Day tripper are excluded because we have no reliable way of estimating the total number in this group. This

omission slightly understates total participation in PES recreation (see text).

46

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

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

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

swimming trips or outings is 4 million: 

= 2 million x 20 % x 10

= 400,000 x 10 = 4 million trips

The data and assumptions used to estimate trips for each PES recreation activity is explained below.

Estimation of Total Number of Trips. The population of potential participants include all East End

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

we used 1990 census data from the Long Island Regional Planning Board (“LIRPB”). The LIRPB

analysis provides estimates of the year-round resident population; the seasonal population,

including second homeowners and guests; year-round resident guests; and hotel, motel, and

campsite capacities. The number of participants in each recreational activity was estimated

separately for each of these residency category. The resident population over age 15 is estimated

by the LIRPB to be 84,871.

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

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

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

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

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

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

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

homeowners and residents as renters or guests. 

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

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

participation. 

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

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

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

trippers8

) for 1995 can be summarized as follows:
47

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

taking 3.3 million outings.

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

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

Table IV.9 - Average Annual Outings per Participant by Activity and Residence*

Residents SHO Overnight Day trippers

Fishing 12.66 9.78 15.27 2.31

Boating 17.83 20.24 16.48 2.82

Swimming 13.55 20.95 12.02 4.71

Shell fishing 8.08 8.11 6.57 8.67

Beach Use 22.50 25.85 9.20 6.11

Bird Watching 37.55 23.49 8.83 3.28

Wildlife Viewing 34.36 25.39 9.99 3.80

Hunting 13.20 16.33 7.00 5.50

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

The total number 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 (i.e., residents, second homeowners,

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

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

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

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

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

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

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

trips. 

Total trip estimates slightly understate actual recreation trips because day tripper 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. 
48

Comparison with Phase I Total Outdoor Recreational Trip Estimates: Introduction. The Phase I

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

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

sources. No original data was collected during Phase I. In Phase II original data was assembled

and it is important to note and to explain the major differences between the two sets of results

(Table IV.11).

Table IV.10 - Estimated Annual Key Recreation Activity Outings*

Residents SHO Overnight Totals

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

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

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

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

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

Bird Watching 219,008 32,139 41,699 292,846

Wildlife

Viewing

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

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

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

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

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

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

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

survey-based estimates 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 II for bird watching

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

wildlife viewing) for respondents and a large number of trips per participant (37.55 bird watching

trips and 34.36 wildlife viewing trips for residents). The Phase I survey, in contrast, was based on

NFWS statewide estimates for New York. These statewide participation rates (9.4%) and number

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 II results is not surprising (at least in

retrospect). Residents and seasonal visitors to the PES are attracted to the area precisely because

they are interested in estuary-related outdoor recreation. We thus would expect this group to be
49

much more interested in outdoor recreation than the general population of the state. Similar large

differences between the Phase I and II results were found for other activities (see Table IV.11 and

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

 Table IV.11 - Comparison of Participation Estimates Phase I Versus Phase II 

PES - Phase II PES - Phase I*

Beach Use 2,624,794 NA**

NonConsumptive547,317 91,713

Hunting 52,498 77,300

Fishing 588,493 113,589

Boating 1,198,983 NA

Swimming 1,409,970 714,600

* Grigalunas and Diamantides (1996).

** NA = Not available

IV.H. DESCRIPTIVE RESULTS: SELECTED EXPENDITURE ESTIMATES

Introduction. During the early survey development phase of our work, members of the

Citizens Advisory Committee and officials at the Suffolk County Dept. of Health Services

expressed interest in our collecting information on sectors of the PES economy for which data are

difficult to 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 on Farm stands, Wineries, and

Rental Lodging collected in this survey are household (not per person) expenditures. The number

of East End resident households (44,241) and seasonal households (29,183) are based on 1995

Long Island Lighting Company estimates.
50

The number of overnight visitor households cannot be adequately estimated with available

data. LIRPB seasonal population estimates account for occupancy of some rental lodgings, such

as hotels, motels, and campsites. However, this component of the population estimate does not

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 --$5,400--make it evident

that seasonal rentals are a major source of income for PES rental property owners and owners of

hotels, motels, and inns. 

Estimated Total Participation and Expenditures. Table IV.13: Estimated Farm Stand and

Winery Expenditures presents estimates (in 1995 dollars) of participation and expenditures in these

sectors for resident and 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 %) 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.
51

Table IV.12 - Survey Farm Stand, Winery, and Rental Lodging Expenditures (1995 dollars)*

Farm stand Winery Rentals

Residents

Participation 316 (88%) 114 (32%) ---

Total Expenditures $86,400 $20,871 ---

Average Expenditures $273 $183 ---

Second Home

Owners

Participation 131 (82%) 67 (42%) ---

Total Expenditures $48,122 $15,350 ---

Average Expenditures $367 $229 ---

Overnight Visitors

Participation 225 (53%) 114 (27%) 103 (26%)

Total Expenditures $41,228 $18,360 $556,000

Average Expenditures $183 $161 $5,398

Day Trippers

Participation 153 (56%) 94 (34%) ---

Total Expenditures $12,755 $9,121 ---

Average Expenditures $83 $97 ---

Totals

Participation 825 (68%) 389 (32%) 103 (9%)

Total Expenditures $188,505 $63,702 $556,000

Average Expenditures $228 $164 $5,398

 * Figures in parentheses indicate the percent who made such expenditures
52

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

Expenditures $8,734,671 $2,428810

An interesting finding is that PES-related Farm stand expenditures are greater than earlier

estimates for several other food-related sectors presented in the Phase I Report. For example, 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).
53

IV.I. DESCRIPTIVE RESULTS: REFERENDA AND BROWN TIDE RESULTS

IV.I.1. Hypothetical Referenda

Introduction. One section of the recreational use survey posed referendum-type policy

questions to East End residents and second home owners. These questions were added to “test the

waters” on selected policy issues and were not designed as a basis for resource valuation, which

would have required considerably more effort and data than was possible for this project.

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

(1) reseeding scallops following Brown Tide, 

(2) planting hard clams, 

(3) upgrading the Riverhead sewage treatment plant, and 

(4) reducing road runoff. 

Respondents were presented with one of these programs and were asked whether they support the

program, if they had to pay a given annual cost. Annual costs were varied for each of the proposed

policy actions as described in Table IV.14.

Referenda Results. Respondents overwhelmingly supported the policy actions and indicated

a willingness to pay the annual costs. Support ranged from a high of 100% for reseeding scallops

following Brown Tide at an annual cost of $10, to a low of 75% for upgrading the Riverhead

sewage treatment plant at an annual cost of $100. This high level of support for PES management

programs was consistent across residents and second home owners. Overall, 87% of respondents

indicated they support the policy actions at the given costs (Table IV.14).

IV.I.2. Descriptive Results: Effects of Brown Tide

Respondents were asked whether they had heard of Brown Tide (“BT”). Those who had heard of

BT were asked about their level of concern and about any activities that may have been affected

by BT. Overall, most respondents (83%) had heard of BT, and of those, most (71%) were also very

concerned (Table IV.15). 

BT had the biggest effect on swimming. Fifty-eight percent of those who had heard of BT indicated

that it had affected their swimming. Recreational fishing (34 percent) and shell fishing (33 percent)

also were affected by BT. 

Residents and second home owners had the most knowledge of and the highest concern for BT.

Overnight visitors had the least knowledge and indicated that their activities were affected the least.
54

Table IV.14. Management Action Referenda Results

Proposal; Annual Cost

Resident

s

Y/N

SHO

Y/N

Total

s

Y/N

Total

%Yes

Reseed Scallops Following Brown Tide; $5 21/2 6/0 27/2 93%

Reseed Scallops Following Brown Tide; $10 14/0 10/0 24/0 100%

Reseed Scallops Following Brown Tide; $25 17/2 5/1 22/3 88%

Plant Hard Clams; $5 21/4 12/0 33/4 89%

Plant Hard Clams; $10 18/3 5/0 23/3 88%

Plant Hard Clams; $25 14/2 2/2 16/4 80%

Upgrade Riverhead Sewage Treatment Plant;$5 16/2 6/0 22/2 92%

Upgrade Riverhead Sewage Treatment Plant;$25 18/3 9/1 27/4 87%

Upgrade Riverhead Sewage Treatment Plant;$50 15/4 6/0 21/4 84%

Upgrade Riverhead Sewage Treatment

Plant;$100

73/28 30/6 103/34 75%

Reduce Road Run Off; $10 19/1 11/0 30/1 97%

Reduce Road Run Off; $25 22/1 15/1 37/2 95%

Reduce Road Run Off; $50 22/1 6/0 28/1 97%

Totals 290/53 123/11 413/64 87%
55

Table IV.15 Awareness of Brown Tide

Second Overnight Day

Residents Home Owners Visitors Trippers Total

Heard of Brown 370 159 279 202 1010

97.11% 91.91% 67.72% 78.29% 82.52%

Have NOT Heard of 11 14 133 56 214

Brown Tide 2.89% 8.09% 32.28% 21.71% 17.48%

Very Concerned 295 113 174 131 713

about Brown Tide* 80.16% 71.52% 62.37% 65.17% 70.87%

A Little Concerned 70 44 100 64 278

about Brown Tide* 19.02% 27.85% 35.84% 31.84% 27.63%

NOT Concerned 3 1 5 6 15

about Brown Tide* 0.82% 0.63% 1.79% 2.99% 1.49%

* Includes only respondents who have heard of Brown Tide
56

Table IV.16. Activities Affected By Brown Tide

Second

Resident

s

Home

Owners

Overnght

Visitors

Day

Trippers Total

Swimming 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

fishing

168 52 62 58 340

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

IV.J. ESTIMATES OF THE ECONOMIC VALUE OF RECREATION

IV.J.1. Introduction

Our intention was to use data from the recreational survey to assess the use value for many waterbased and shore-based recreational activities. The survey collected activity-specific data on fishing,

Shell fishing, boating, and swimming in the PES and surrounding waters. Information also was

collected on beach use, 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
9

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

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, substantial research on recreational fishing, including the results of this

study, shows the importance of the quality of the experience--the catch rate--on the number of trips

and their economic value (e.g., Freeman, 1995; McConnell and Strand, 1994). However,

establishing the necessary cause-and-effect scientific links between policies to preserve or restore

habitat, for example, with changes in salt water recreational catch rates appears to be beyond the

state of the art9

. Nevertheless, we can simulate how benefits to recreation fishing would change

if the catch rate increased by, say, 10 percent. 

Individual recreation trip value estimates and annual aggregate value estimates are given for three

cases. First, we present the existing (“baseline”) conditions for swimming, fishing, boating, and for

birdwatching and wildlife viewing. For the baseline, we show consumer surplus per trip, total

estimated number of trips in 1995, and total benefits for the year for the four key recreation

activities studied. 

Second, we show how economic value for swimming changes for illustrative, hypothetical policies

affecting water quality. One such policy is assumed to increase water quality by 10 percent in each

of the five PES bays. We also simulate a 20 percent uniform improvement in water quality for

comparative purposes. Finally, a 10 percent improvement in the recreational fishing catch rate is

simulated.

IV.J.2. Methodology

Value estimates for each activity are derived using the Travel Cost method of valuation (see, for

example, Freeman, 1993). The rationale behind this methodology is that, although there is no

market price that reveals the value of a recreational activity, an implicit price can be observed

through the cost of traveling to the activity’s location. Included in this implicit price is the cost

of transportation, such as gas, wear and tear on the automobile, etc., and the value of the

individual’s travel time. In theory, an individual’s opportunity cost is valued as his or her next best

use of their time. However, a fraction of the individual’s wage is generally used, in practice, to

represent income foregone in favor of recreation.

In the travel cost model, the number of trips a person takes for recreation at a specific site depends

on the costs of getting to that site, the comparative costs of getting to substitute sites, and the

quality of the recreational experience at the sites. In other words, the cheaper it is to get to a site,

the cheaper travel to that site is relative to other sites, and the better the quality of the recreational

experience at that site, the more times the individual will visit that site, all else equal. Thus,

observed recreation choices reveal tradeoffs between cost of participation, as measured by travel
10We 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

cost, and participation rates. In addition, participants might reveal values for site quality by

participating more at sites of higher quality, and less at sites of lower quality, all else being the

same. 

Carrying out the travel cost analysis requires data on participation rates, cost of travel to sites, and

site quality. Data on participation rates is collected in the recreational survey. Data on travel costs

to the target and substitute sites, including the opportunity cost of travel time, are collected from

the survey and augmented, as necessary, with distance information from road maps. Data on water

quality comes from two sources. First, we use field measurements of various water quality

parameters from the SCDHS PES water sampling program, including nitrogen, coliform counts,

water clarity (Secchi disk measurements) and Brown Tide cell counts. These are objective

measures of quality. Second, we use data from the recreation survey which asked respondents their

subjective evaluation of water quality (excellent, good, fair or poor). These evaluations of course

are subjective.

For swimming, our analysis combines the subjective assessments by individuals and the objective

field measurements of water quality. Ultimately, we are interested in whether we can provide

sequential linkages between:

(1) management actions that effect water quality parameters, 

(2) the resultant perceptions of water quality, and 

(3) participation in specific recreation activities. 

If we can establish such a linkage we can evaluate water quality policy alternatives.10

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 experience more; the other is that they will make more trips. This is the basis

of our approach for estimating the economic benefits of water quality improvements in the PES for

swimming. 

For example, consider an assessment of proposed improvements in the Riverhead sewage treatment

plant. Upgrading the sewage treatment plant will result in improvements in water clarity,

reductions in coliform counts, etc., with the largest effect in Flanders Bay and progressively small

changes in bays to the east. Our model allows us to predict how these changes in water quality
59

parameters effect the subjective assessment of water quality by recreational users, and the resultant

changes in participation rates and in recreational values at various sites. Thus, we can identify

benefits to recreational swimming that result from management options that improve water quality.

In order to implement this methodology, we will coordinate our assessments with ongoing water

quality modeling efforts in the PES. Thus, we anticipate that water quality modeling will be used

to project changes in quality parameters from prospective management policies. Our recreational

modeling will then be used to estimate recreational benefits resulting from these changes in quality.

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

depend upon fish populations. Our recreational fishing model includes catch rates as a predictor

of participation rates and recreational values. Thus, 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 scientists to

predict changes if salt water fish populations that would likely result from management actions.

Nevertheless, the opportunity exists for estimating recreational fishing benefits from management

actions, if resultant changes in populations can be estimated. Even in cases where precise

predictions are not possible, it may be possible to provide possible ranges of population changes,

which will enable us to provide order-of-magnitude estimates of possible benefits.

The economic benefits from improving (or maintaining) water quality could be long-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. 

IV.L. ESTIMATES OF ECONOMIC VALUE: RESULTS

IV.L.1. Value (Consumer Surplus) Per Trip

The methodology described above yields a consumer surplus estimate per person, per trip ranging

from $8.59 for swimming to $49.83 for non-residential bird watching and wildlife viewing (Table

IV.17). Fishing and boating values per 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 unpaid for benefit that

individuals receive, on average, from a recreational trip. The relative sizes of these values are

consistent with the literature (e.g. Walsh et al., 1988; Freeman, 1995). 

IV.L.2. Total Annual Economic Value for All Trips

Total annual benefits from each of the four recreational activities studied are given in Table IV.17.

The total benefits are estimated by multiplying the average consumer surplus for an 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
60

activities, recreational fishing is the most highly valued ($23.7 million). 

We emphasize that total values rarely are useful for policy analyses since most policies involve

small changes in an activity or its quality and are not “all or nothing” choices. Nevertheless, we

recognize that information on total values, such as that given in Table IV.17 helps put the scale of

these activities in some perspective. 

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

Quality)

Swimming Boating*

Recreational

Fishing

Bird Watching&

Wildlife

Viewing

Total Trips/Year 1,409,970 937,387 588,493 547,317

Consumer Surplus

Per Trip $8.59 $19.23 $40.25 $49.83

Total Annual 

Consumer Surplus $12,113,216 $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 illustrate how the economic benefits estimated in this report can be used to contribute to estuary

management policy, consider a hypothetical policy or set of policies that improves all water

quality measures by 10 percent in all five PES Bays. That is, the policy reduces field

measurements of Nitrogen, Total Coli, Brown Tide cell counts by 10 percent and increases Secchi

depth by 10 percent throughout the PES. Note that since water quality measures differ among

individual bays (and indeed within Bays), the absolute water quality changes due to a 10 percent
11Subsequent research by EAI will address benefit-cost analysis of proposed water quality actions. 12Recall, 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

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 report11. 

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 available12 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. If maintained for 25 years, this improvement has 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).
62

Table IV.18 Benefits to Swimmers of 10% Improvements in Each Water Quality Indicator

at Each PES Water Body

PES Water

Body

Total Kjeldahl

Nitrogen

Total

Colifrm

Brown Tide

Cell Counts

Secchi

Disk

Depth

Water

Body

Total

Flanders Bay $65,278

7,598

$71,310

8,300

$14,424

1,679

$125,753

14,637

$276,766

32,215

Great Peconic

Bay

$24,801

2,887

$3,522

410

$48,095

5,598

$126,362

14,708

$202,779

23,603

Little Peconic

Bay

$20,584

2,396

$1,140

133

$70,207

8,172

$139,929

16,288

$231,860

26,988

Shelter Is. Sd. $22,598

2,630

$4,553

530

$109,790

12,779

$175,093

20,381

$312,033

36,320

Gardiners Bay $14,138

1,646

$129

15

$76,863

8,947

$185,286

21,567

$276,416

32,174

PES Total $147,399

17,156

$80,653

9,387

$319,378

37,175

$752,423

87,581

$1,299,854

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 million)of the second 10

percent water quality improvement (i.e. going from 10 percent to 20 improvement). The issue of

incremental benefits from alternative management actions that affect different PES bays differently

will be addressed more comprehensively in Phase III economic studies. 
63

Table IV.19. Benefits of 20% Improvements in Each Water Quality Indicator at Each PES

Water Body

PES Water

Body

Total Kjeldahl

Nitrogen

Total

Colifrm

Brown Tide

Cell Counts

Secchi

Disk

Depth

Water

Body

Total

Flanders Bay $130,030

15,135

$141,908

16,518

$28,888

3,362

$253,807

29,543

$554,633

64,558

Great Peconic

Bay

$48,869

5,688

$6,200

722

$95,937

11,1678

$249,204

29,152

$400,201

46,729

Little Peconic

Bay

$41,319

4,809

$2,510

292

$139,892

16,283

$279,957

32,262

$463,678

53,647

Shelter Is. Sd. $44,207

5,145

$7,907

920

$235,633

27,427

$315,939

36,775

$603,685

70,268

Gardiners Bay $28,555

3,324

$5567

648

$151,556

17,641

$356,181

41,459

$541,859

63,071

PES Total $292,979

34,102

$164,653

19,100

$651,905

75,881

$1,455,088

169,191

$2,564,065

298,273

Italics indicate number of trips

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.
64

Table IV.20. Benefits of Improving Expected Recreational Catch Rate by 10 Percent

Baseline

10 Percent Increase

in Expected Catch

Rate

Incremental

Benefits

Number of Fishing

Trips 588,493 599,742 11,249

Consumer

Surplus/Trip $40.25 $41.05 $0.80

Total Consumer

Surplus $23,685,985 $24,158,344 $472,359

We re-emphasize that the results presented here are for benefits only; the cost of water quality

improvements, or of improvements that might increase recreational fishing catch rates, have not

been considered. Thus, we do not know the net benefits of potential management actions. Later

Benefit-Cost analyses to be done by EAI of proposed program actions will address these issues. 

 

IV.M. REFERENCES

Freeman. A. Myrick III, 1993. The Measurement of Environmental and Resource Values: Theory

and Methods. Washington, D.C.: Resources for the Future.

Grigalunas, Thomas A. and Jerry Diamintides, 1996. “The Peconic Estuary System: Perspective

on Uses, Sectors and Economic Impacts”. Peacedale, RI: Economic Analysis, Inc. 

McConnell, Kenneth and Ivar Strand, 1996. “Marine Recreational Sportfishing”. College Park:

University of Maryland. 

Walsh, Richard G., Donn M. Johnson, and John R. McKean, 1988. “Review of Outdoor Recreation

Economic Demand Studies with Nonmarket Benefit Estimates, 1968-1988". Fort Collins,

CO: Water Resources Center, Colorado State University. 
65

Supplementary Tables and Appendices

Supplementary Tables

Table S.1

East End Resident Respondent Distribution by Town

Town

Number of

Respondents

Percentage of All

Respondents

Percentage of Yearround Population-a

East Hampton 50 14% 15%

Riverhead 42 12% 22%

Shelter Island 57 16% 2%

Southampton 107 30% 43%

Southold 102 28% 19%

Totals 358 100% 100%*

*adjusted to account for rounding

a- Source: Long Island Regional Planning Board

Table S.2

Second Home Owner Primary Residence

Area Number of Respondents

Percentage of All SHO

Respondents

New York City 72 45%

Nassau County 30 19%

Suffolk County 22 14%

Other Areas 1 35 22%

Totals 159 100%

1

Includes Westchester and Rockland Counties in New York and parts of Northeastern New Jersey.
66

Table S.3

Second Home Owner Distribution by Town

Number of

Respondents

Percentage of all

Respondents

Percentage of

Seasonal Home

Population-a

East Hampton 29 22% 29%

Riverhead 6 5% 5%

Shelter Island 17 13% 3%

Southampton 39 29% 49%

Southold 42 32% 14%

Totals 133 100%* 100%*

*adjusted for rounding

a- Source: Long Island Regional Planning Board

Table S.4

Primary Residency of Visitors to the East End

Area Number of Respondents

Percentage of All

Respondents

New York City 146 19%

Nassau County 118 16%

Suffolk County 300 40%

Other New York State and

New Jersey 63 8%

Connecticut and

Massachusetts 39 5%

Other 84 11%

Totals 750 100%*

*adjusted for rounding
67

Table S.5

Accommodations for Overnight Visitors

Accommodation Number of Respondents

Percentage of All

Overnight Visitor

Respondents

Hotel or Motel 95 22%

Rental 101 24%

Friends or Relatives 168 39%

Other 63 15%

Totals 427 100%*

* Adjusted for rounding

Table S.6

Overall Participationa

 in Key Recreation Activities - All Respondents (n=1354)

Number of Participants Percentage of all

Respondents

Any Activity* 944 70%

Multiple Activities 648 48%

Water Based Activities 820 61%

Land Based Activities 644 48%

*Any of the eight activities listed in the survey questionnaire. Water Based Activities: Swimming,

Boating, Fishing, Shell fishing; Land Based Activities: Beach Use, Bird Watching, Wildlife

Viewing, Hunting.

a

 Includes water-based recreation at PES, BIS, LIS, and Atlantic Ocean.
68

Table S.7

Participation in PES Water Based Recreation - All Respondents (n=1354)

Activity

Number of

Participants

Percentage of

Total

Participation

Number of

Experiences

Average

Experiences

Per

Participant

Fishing 224 35% 2732 12

Boating 338 52% 5470 16

Swimming 470 73% 6538 14

Shell fishing 92 14% 766 8

Totals 644 100% 15506 24 *

* Includes more than one activity.

Table S.8

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

Activity

Number of

Participants

Percentage of

Total

Participation

Number of

Experiences

Average

Experiences

Per

Participant

Beach Use 736 90% 12248 17

Bird Watching 243 30% 5371 22

Wildlife

Viewing

275 34% 5628 20

Hunting 23 3% 255 11

Totals 820 100% 23502 29 *

* Includes more than one activity.
69

S.9 - Estimated Trips for Key Recreation Activities for Sample*

Residents SHO Overnight Totals

Fishing 21,218 9,520 14,850 45,588

Boating 31,402 14,647 20,791 66,840

Swimming 44,133 18,309 35,641 98,083

Shell fishing 11,882 4,394 2,970 19,246

Beach Use 67,048 23,801 54,452 145,301

Bird Watching 8,555 3,032 5,346 16,933

Wildlife Viewing 17,313 6,225 10,098 33,636

Hunting 2,546 732 990 4,268

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

Appendix B - Technical Appendix: Swimming Trips and Value Estimation

Overview

A “count data” model is used to estimate the demand for recreational activities in the PES. Count

data models are appropriate for estimating recreational demand because the number of recreational

trips taken by an individual must take on an integer value. The Poisson distribution is used to

estimate the number of trips to each site in the PES and to estimate the economic value (consumer

surplus) of those trips. 

For swimming, the number of trips to each site is modeled as a function of the travel cost to the site,

costs of travel to substitute sites, the number of boating occasions at the same site, and a subjective

measure of water quality at the site. The measure of water quality is the probability that water

quality at the site will be ranked either good or excellent by survey respondents who recreate at that

site. 

This model can be used to estimate the effect of management actions to improve water quality on

the number of recreational trips to each site, and to the value of a recreational trip to a site. In order

to do so, we need to link management actions to changes in water quality, and to link objective field

measures of water quality to subjective perceptions of water quality by recreational users. 

Predictions of changes in water quality due to management actions is done as part of ongoing water

quality modeling work, carried out as part of the Peconic Estuary Program (Tetra Tech, 1998). The

relationship between objective water quality measures and subjective perceived water quality is
70

estimated by linking data collected in the recreational survey with field measure of water quality.

Specifically, for those respondents who indicate that they went swimming on their most recent

recreation day, the survey asked where they went swimming and how highly they rated water

quality (excellent, good, fair or poor) at that site. We also obtained data on measures of key water

quality variables, including brown tide cell counts, total coliform bacteria, total Kjeldahl nitrogen,

and Secchi disk depth readings from sampling locations distributed across the PES. The data was

grouped into ten sites - north fork and south fork sites for Flanders, Great Peconic, Little Peconic,

Shelter Island Sound and Gardners bay.

An ordered LOGIT model was used to estimate the probability that an individual would rate water

quality high (good or excellent), fair or poor as a function of the objective field measures of water

quality, discussed above. We then include these predicted probabilities into the travel cost

estimation, so that the number of trips to a site is related to the cost of traveling to the site, the cost

of traveling to key substitute sites, and the predicted probabilities of the subjective rankings of

water quality. These steps are described in detail below. 

Water Quality Model

The water quality model links individuals’ perception of water quality - expressed in a ranking of

poor, fair, good, and excellent - to physical measures of water quality collected at sampling stations

by the SCDHS. An ordered logit model is used to estimate the probabilities for each rank based on

physical measures of water quality and on the effects of Brown Tide on the individual’s swimming

that year. The purpose of the water quality model is to provide input into the travel cost model i.e.,

the probability that the water quality of a site would be ranked good or excellent.

Data for the water quality model consists of site readings during August 1995 for the following

water quality indicators:

1. Brown Tide, Aureococcus anophagefferens, 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

identify 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
71

Pr ( ) .

'

ob Y e

e

x

= = x +1

1

β

β '

the survey respondent. Only observations that indicate an identifiable swimming location for trips

taken during August 1995 were included in the data set. Water quality sampling stations were

allocated to the ten sites according to SCDHS sampling station maps, as described in Table 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 100 to preclude the use of fractions. Average input values for each site are presented

in table A2.

The modeling procedure uses an ordered logit model that predicts water quality rank based on the

Brown Tide Index, Nitrogen-Bacteria Index, and Secchi Disk depth readings. The ordered logit

model is based on the logistical distribution,

where β 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
72

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* = β’x + ε.

Y* is a continuous measure of site quality, not constrained by the discrete ranking, β is the vector

of coefficients estimated by the ordered logit model, x is the vector of field measures of water

quality, and ε is a vector of unknown factors that might affect the individual’s rank order choice

(e.g., the individual’s mood or the prevailing weather conditions). The unknown factors are

assumed to be logistically distributed across individuals with a mean of zero and a variance of one.

Although y* is unobserved, the rank order selections indicated in the survey questionnaire are

observed. These selections can be modeled as

y = 0 (poor) if y* 0,

y = 1 (fair) if 0 < y* μ1,

y = 2 (good) if μ1 < y* μ2,

y = 3 (excellent) if μ2 y*.

where the μi are threshold values that determine when water quality ranking would change for a

“representative” individual. The ordered logit model estimates the μ threshold parameters along

with the vector of β 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) = Λ (-β’x),

Prob (y = 1; fair) = Λ (μ1 -β’x) - Λ (-β’x),

Prob (y = 2; good) = Λ (μ2 -β’x) - Λ (μ1 -β’x),

Prob (y=3; excellent) = 1 - Λ (μ2 -β’x),

where Λ 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 substituting the site-specific

water quality indicator values for the mean values of the entire sample. An average of each

probability weighted by the number of observations for each site is calculated for the five PES

water bodies. For example, the probability that water quality at Gardiners bay will be ranked good
73

is based on the weighted average of the probability for good at Gardiners bay north and Gardiners

bay south. The probabilities for the nine sites are presented in table A4. The probabilities for the

five PES water bodies are presented in table A5.

The effects of management actions on the rank order probability at any or all of the nine sites can

be simulated by changing the water quality measures for the sample station readings. Changes in

these values will in turn change the value of the water quality indices and/or Secchi Disk readings

used as input into the ordered logit model. These simulated values for objective water quality field

measures are used to provide at a new set of probabilities for subjective ratings of water quality for

the sites, which in turn affect 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 objective water quality parameters used

in the 10% improvement model are Brown Tide cell counts, Total Kjeldahl Nitrogen readings,

Total Coliform readings, and Secchi Disk depth.

For example, under baseline water quality levels in 1995, Great Peconic Bay (Table A5) is

predicted to obtain a “poor” rating for water quality by 8% of sample respondents, a “fair” rating

by 37% of sample respondents, a “good” rating by 45% of sample respondents and an “excellent”

rating by 10% of sample respondents. These probabilities are the weighted averages of the ranking

probabilities for Great Peconic Bay North and Great Peconic Bay South (Table A4). If, continuing

our example, Brown Tide Cell counts were to improve (decrease) by 10% at sampling stations in

Great 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
74

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 Distance/40mph * 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
75

New CS = {T + T P} ∆ ∆ ∗

−( ) C

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) = eλi λi

 yi / yi

 ! 

Where yi = 0,1,2,3,....; ln λi = β’xi; β 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:

.

Where T represents the number of trips taken prior to the quality change, ∆T 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, ∆P 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.
76

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 A1. PES Sampling Station Locations

Location Sampling Station

Flanders Bay North 170 (Central), 220, 240

Flanders Bay South 170 (Central)

Great Peconic Bay North 101, 130 (Central)

Great Peconic Bay South 130 (Central)

Little Peconic Bay North 102, 103, 105, 113 (Central)

Little Peconic Bay South 113 (Central)

Shelter Island Sound North 106,107,108,109,111,112,114,

115,119,122

Shelter Island Sound South 118,121,126,127,131

Gardiners Bay North 116

Gardiners Bay South 132,133,134
77

Table A2. Average Input Value for the Water Quality Model (Ordered Logit)

Site

Brown Tide

Index 

Nitrogen - Bacteria

Index

Secchi Disk

Depth (Feet)

Flanders Bay North 5.57 818.09 3.2

Great Peconic Bay North 18.33 169.85 3.13

Great Peconic Bay South 14.81 117.75 3.5

Little Peconic Bay North 28.04 126.39 3.5

Little Peconic Bay South 16.21 126.76 3.5

Shelter Is. Sd. North 39.51 136.44 4.35

Shelter Is. Sd. South 47.5 256.26 4.37

Gardiners Bay North 32.41 94.21 5.75

Gardiners Bay South 33.37 124.7 5.08
78

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

Observations 199 ChiSquared27.50

Log Likelihood -225.26 D. Frdm. 3

Res. L. Likelihood -239.00 Sig. Level .000005

Variable Coefficient Std. Err. T Stat. Sig. Level Mean of X

Constant 2.0679 1.1136 1.857 0.06332

Brown Tide Index -0.02297 0.00499 -4.605 0.00000 33.83

Nitro. - Bact. Index -0.00147 0.00089 -1.652 0.09860 173.4

Secchi Disk 0.34515 0.23232 1.486 0.13737 4.397

μ 1 2.2812 0.25915 --- ---

μ 2 4.6968 0.3522 --- ---
79

Table A4. Baseline Probabilities for Nine Sites

Site Prob. Poor Prob. Fair Prob.Good Prob.

Excellent

Flanders Bay North 0.14 0.47 0.34 0.05

Great Peconic Bay North 0.08 0.37 0.45 0.10

Great Peconic Bay South 0.06 0.32 0.49 0.13

Little Peconic Bay North 0.08 0.38 0.45 0.10

Little Peconic Bay South 0.06 0.33 0.49 0.12

Shelter Is. Sd. North 0.08 0.38 0.45 0.10

Shelter Is. Sd. South 0.11 0.43 0.39 0.07

Gardiners Bay North 0.04 0.25 0.53 0.18

Gardiners Bay South 0.05 0.30 0.50 0.14

*Probabilities may not sum to one due to rounding

Table A5. Baseline Probabilities for PES Water Bodies

PES Water Body Prob.

Poor

Prob.

Fair

Prob.

Good

Prob.

Excellent

Flanders Bay 0.14 0.47 0.34 0.05

Great Peconic Bay 0.08 0.37 0.45 0.10

Little Peconic Bay 0.08 0.37 0.45 0.10

Shelter Is. Sd. 0.08 0.39 0.44 0.09

Gardiners Bay 0.05 0.28 0.52 0.16
80

Table A6. Probabilities at Nine Sites for 10% Improvements in Brown Tide Cell Count

Site Prob.

Poor

Prob. Fair Prob.Good Prob.

Excellent

Flanders Bay North 0.14 0.47 0.34 0.05

Great Peconic Bay North 0.07 0.37 0.46 0.10

Great Peconic Bay South 0.06 0.32 0.50 0.13

Little Peconic Bay North 0.08 0.37 0.46 0.10

Little Peconic Bay South 0.06 0.32 0.49 0.13

Shelter Is. Sd. North 0.07 0.36 0.46 0.10

Shelter Is. Sd. South 0.10 0.42 0.41 0.08

Gardiners Bay North 0.04 0.24 0.53 0.19

Gardiners Bay South 0.05 0.29 0.51 0.15

Table A7. Probabilities at Nine Sites for 10% Improvements in Total Kjeldahl Nitrogen

Readings

Site Prob.

Poor

Prob. Fair Prob.Good Prob.

Excellent

Flanders Bay North 0.13 0.46 0.35 0.06

Great Peconic Bay North 0.08 0.37 0.45 0.10

Great Peconic Bay South 0.06 0.32 0.49 0.13

Little Peconic Bay North 0.08 0.38 0.45 0.10

Little Peconic Bay South 0.06 0.33 0.49 0.12

Shelter Is. Sd. North 0.08 0.37 0.45 0.10

Shelter Is. Sd. South 0.11 0.43 0.39 0.07

Gardiners Bay North 0.04 0.25 0.53 0.18

Gardiners Bay South 0.05 0.30 0.51 0.14
81

Table A8. Probabilities at Nine Sites for 10% Improvements in Total Coliform

Readings

Site Prob.

Poor

Prob. Fair Prob.Good Prob.

Excellent

Flanders Bay North 0.13 0.46 0.35 0.06

Great Peconic Bay North 0.08 0.37 0.45 0.10

Great Peconic Bay South 0.06 0.32 0.49 0.13

Little Peconic Bay North 0.08 0.38 0.45 0.10

Little Peconic Bay South 0.06 0.33 0.49 0.12

Shelter Is. Sd. North 0.08 0.38 0.45 0.10

Shelter Is. Sd. South 0.11 0.43 0.39 0.07

Gardiners Bay North 0.04 0.25 0.53 0.18

Gardiners Bay South 0.05 0.30 0.50 0.14

Table A9. Probabilities at Nine Sites for 10% Improvements in Secchi Disk Depth

Site Prob.

Poor

Prob. Fair Prob.Good Prob.

Excellent

Flanders Bay North 0.12 0.46 0.36 0.06

Great Peconic Bay North 0.07 0.35 0.47 0.11

Great 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 0.11

Shelter Is. Sd. South 0.09 0.41 0.41 0.08

Gardiners Bay North 0.03 0.22 0.54 0.21

Gardiners Bay South 0.05 0.27 0.52 0.16
82

Table A10. Probabilities at PES Water Bodies for a 10% Improvement in Brown Tide

Cell Counts

PES Water Body Prob.

Poor

Prob.

Fair

Prob.

Good

Prob.

Excellent

Flanders Bay 0.14 0.47 0.34 0.05

Great Peconic Bay 0.07 0.36 0.46 0.10

Little Peconic Bay 0.07 0.36 0.46 0.10

Shelter Is. Sd. 0.08 0.37 0.45 0.10

Gardiners Bay 0.04 0.26 0.52 0.17

Table A11. Probabilities at PES Water Bodies for a 10% Improvement in Total

Kjeldahl Nitrogen Readings

PES Water Body Prob.

Poor

Prob.

Fair

Prob.

Good

Prob.

Excellent

Flanders Bay 0.13 0.46 0.35 0.06

Great Peconic Bay 0.07 0.37 0.46 0.10

Little Peconic Bay 0.08 0.37 0.45 0.10

Shelter Is. Sd. 0.08 0.38 0.44 0.09

Gardiners Bay 0.05 0.27 0.52 0.16
83

Table A12. Probabilities at PES Water Bodies for a 10% Improvement in Total

Coliform Readings

PES Water Body Prob.

Poor

Prob.

Fair

Prob.

Good

Prob.

Excellent

Flanders Bay 0.13 0.46 0.35 0.06

Great Peconic Bay 0.08 0.37 0.46 0.10

Little Peconic Bay 0.08 0.37 0.45 0.10

Shelter Is. Sd. 0.08 0.39 0.44 0.09

Gardiners Bay 0.05 0.28 0.52 0.16

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

Depth

PES Water Body P(Poor) P(Fair) P(Good) P(Excellent)

Flanders Bay 0.12 0.46 0.36 0.06

Great Peconic Bay 0.07 0.35 0.47 0.11

Little Peconic Bay 0.07 0.35 0.47 0.11

Shelter Is. Sd. 0.07 0.36 0.46 0.11

Gardiners Bay 0.04 0.25 0.53 0.19
84

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

Observations 995 ChiSquared1846.693

Log Likelihood -3707.225 D. Frdm. 4

Res. L. Likelihood -4630.572 Sig. Level .000000

Variable Coefficient Std.

Err.

T Stat. Sig. Level Mean of X

Constant -0.76984 0.16607 -4.636 0.00000

Travel Cost -0.028321 0.00210 -13.494 0.00000 23.29

Boating 0.0781 0.00157 49.757 0.00000 0.9246

Travel Cost to

Substitute Locations 0.00978 0.00119 5.282 0.00000 31.74

Water Quality Rank 2.688 0.28209 9.529 0.00000 0.5404

Scale Factor 1.5002

Marginal Effect for

Water Quality Rank 4.0325

Appendix C. Comparison of Recreation Activity Estimates: Phase I and Phase II 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 II report are combined to

create a “non-consumptive wildlife use” estimate of 547 thousand trips (Table V.11). This estimate

greatly exceeds the PES Phase I estimate of non-consumptive wildlife use (92 thousand trips). 

The major reason for the large differences in estimates between the Phase I and II reports is

explained as follows. Our survey-based estimate finds a high participation rate (19% for bird

watching and 21% for wildlife viewing) for respondents and a large number of trips per participant

(37.55 bird watching trips and 34.36 wildlife viewing trips for residents). The Phase I survey, in
85

contrast, was based on NFWS statewide estimates for New York. These statewide participation

rates (9.4%) and number of trips per participant (8.3) are very much lower than those found in the

PES sample. This major difference is not surprising since residents and seasonal visitors to the PES

would be expected to be more interested in outdoor recreation than the general population of the

state.

Comparison with Phase I Total Outdoor Recreational Trip Estimates: Recreational Fishing.

Not surprisingly the recreational use survey data yields a much larger estimate (588 thousand trips)

of recreational fishing trips in the PES than the Phase I study (114 thousand). The Phase I estimate

is based on the NFWS survey covering all of New York State. In the NFWS study, the average

estimates of participation rate (9%) and trips per participant (8 for residents, 4.3 for visitors) is

much lower than found in the PES survey. This estimated participation rate is 17% and 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 (715 thousand) is simply the sum of all available beach attendance data

for the PES. The Phase II survey-based estimate (1.4 million) includes participation not only at

official beaches but also at the many unadministrated beaches throughout the PES, and in addition

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

Comparison with Phase I Total Outdoor Recreational Trip Estimates: Hunting. The discrepancy

between the Phase I (77 thousand trips) and Phase II (52 thousand trips) hunting estimates also is

based on differences in participation rate and number of hunting days per participant, as estimated

by the two surveys. The NFWS estimates that 11% of the New York State population over 16

hunts, and that participants average 18.6 hunting trips per year. On the other hand, the PES survey

indicates that only 2% of residents and visitors to the PES hunt in the PES. These participants also

hunt less than the state average, ranging from 16.33 trips per second homeowner participant to 5.5

trips per participating day tripper. The Phase I hunting estimate was based only on residents and

did not attempt to account for participation by seasonal residents and visitors since such data are

unavailable.
13Two 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

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 adapts13 estimates by French and Schuttenberg (1998) of the marginal value of PES

eel grass beds, salt marshes, and intertidal mud flats. Two types of wetland productivity

(biological) gains are considered: 

(1) The increase in food produced by the habitat which is utilized by higher trophic

levels (such as fish and shellfish) in the PES, and 

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

availability of habitat. 

The biological gains from restoring or protecting increments of each wetland type (eelgrass,

saltmarsh, and intertidal mud flats) are assigned an economic value. This value is based on the (1)

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

value of waterfowl ultimately “produced” by wetlands. Other direct and indirect services and

values wetlands may provide are not considered. 
14A summary of the productivity approach and examples for coastal areas is given in Grigalunas and Congar (1995).

87

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 ecosystems14. For example,

Lynne et al. (1981) estimated the relationship between mangrove area, fishing effort, and landings

of a single species, blue crab in Florida. They estimated a marginal value for blue crab of several

dollars. Bell (1989) also used a time series of data on mangrove area, effort, and catch to evaluate

the marginal productivity of Florida mangroves in the production of principal Florida commercial

and recreational fish. He estimated a marginal value of several thousand dollars per acre of

mangrove over all commercial and recreational species. 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
88

productivity of PES wetlands, and it uses biological information and economic values specific to

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

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

the other hand, the method used does not allow for statistical tests of significance and hence heavily

relies upon professional judgement. 

The methodology used is described next. First, we explain how the food web estimates were made.

Then, the methodology and data used for habitat values is presented. A detailed statement of the

methodology, data and assumptions is given in French and Scuttenberg (1998).

 

V.B.2. Food Web Estimates

Saltmarsh and eelgrass beds benefit the entire food web by primary (plant) production. Similarly,

the net gain in lower trophic level, animal production is passed up the food web. Ultimately this

production via the food web results in the production of species of economic value to people. 

To estimate the economic value of these food-web effects, several pieces of information are

needed. First, it is necessary to quantify the amount of food produced by a habitat. To do this,

primary (plant) and bottom (amphipods, worms, etc., in and on the sediments) production rates

were estimated for PES wetland categories, using results in the literature. Then, the fraction 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 estuaries by Nixon (1982). Finally, the estimated fish and shell

fish landings are valued using species-specific fishery values for PES landings. 

Thus, lower trophic levels are 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
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 restoration/preservation. That is, resources used in these activities

to some extent will be drawn away from private and public investments. 

89

flats. Softshell clams are assumed to all have been produced in intertidal mudflats and shoals in

the PES. 

Abundance of birds depends upon habitat type. An average abundance per unit area of habitat is

assumed, based on the results for the coastal area including the PES, as given in the Natural

Resource Damage Assessment Model (Version 2.4, April, 1996) developed by the authors for the

US Department of the Interior (French, et al, 1996a,b,c). Birds that are specifically benefitted by

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 shorebirds use marsh and mud flats. Brants specifically feed on eelgrass. Black

ducks are know to require structured habitat, marsh and eelgrass. 

The value of bird species usage of the habitat is based on the benefits human receive from viewing

or hunting (waterfowl) birds. The values are marginal values, that is, the additional value people

obtain by seeing additional birds per day. The values per animal per year are proportional to the

number of viewing trips and the rareness of the species in the local area (see French et al.

(1996a,b,c) for a full description of the 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.1 shows the marginal value for existing and restored wetlands. The value for existing

wetlands would be used for policy issues dealing with preservation decisions; results for

restoration are critical for assessing policies to restore wetlands. 

Two values are calculated: An annual value and an asset value. The annual value is the sum of the

food web values and the habitat values. Asset values were calculated by discounting the annual

value over a 25 period using a discount rate of 7 percent, the same time frame and discount rate

used elsewhere in this 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
90

becomes fully functional. Thus, in the table it is noted that eelgrass, saltmarsh, and intertidal mud

flats take 15, 10, and 3 years, respectively, to become fully functional. 

The marginal asset values of PES wetlands appear to be substantial, especially in light of the fact

that other services wetlands may provide, such as protection from erosion and storms, aesthetics

and existence value, are not considered. 

Table V.1. Marginal Values of PES Wetlands

Existing Habitats Created Habitats

Wetland

Type

Annual

Value

per Acre a

Asset Value

per Acre b

Years to

Become

Fully

Functinal a

Asset Value

per Acre b

Estimated

Number of

Acres in

PES

(millions)

Eelgrass $1,065 $12,412 10 $9,996 6.04

Saltmarsh $338 $4,291 15 $3454 13.51

Intertidal

Mud Flat $67 $786 3 $626

14.05

a French and Schuttenberg (1998) estimate habitat values using a discount rate of 3 % and time horizon of 25

years. Our use of 7 % and 25 years reduces the estimated wetland values by almost one half.

b Using a discount rate of 7 percent and a time horizon of 25 years. Assumes linear recovery to full (99%)

restoration over the period estimated by French and Schuttenberg (1998). French and Schuttenberg used a

sigmoid function to approximate the time path or recovery.

V.D. SUMMARY AND CONCLUSIONS

This chapter presents estimates of the marginal economic value of the productivity services

provided eelgrass, salt marsh, and intertidal mud flats in the PES. The productivity services

covered are food web and habitat services. Economic values estimated are for commercial fishing

value for crab, scallop, and clams; viewing values for birds; and hunting values for waterfowl.

Other possible values provided by PES wetland services, such as erosion and storm protection,

aesthetics, and existence value are not considered. 
91

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 in Florida. Sea Grant Program. Tallahassee: Florida State Univeristy

(June).

French, et al., 1996. Natural Resource Damage Assessment Model for Coastal and marine

Environments. Version 2. 

French, Deborah and Heidi Shuttenberg, 1998. Estimated Food Web and Habitat Values for

Habitats in the Peconic Estuary System. Submitted to Economic Analysis Inc. (January 23).

Grigalunas, Thomas and Richard Congar, 1995. Environmental Economics for Integrated Coastal

Area Management. Nairobi: United Nations Environmental Programme.

Kahn, James and W.M. Kemp, 1985. ”Economic Losses Associated With the Degredation of An

Ecosystem: The Case of Submerged Aquatic Vegetation in 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. “Nutrient Dynamics, Primary production and Fisheries Yields of Lagoons”.

Narragansett: Graduate School of Oceanography, University of Rhode Island.

Pornpinatapong, 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. 
16 Other important goals were to create a survey that would minimize some of the problems often associated with

valuation surveys; would be easily understandable to members of the public; could be answered in a reasonable

amount of time; and could be administered in a variety of public places.

92

VI. 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 III of the Comprehensive Economic

Valuation Study), that will help policy makers prioritize alternative management actions. 

The remainder of this section is organized as follows: Part B describes the survey development and

implementation, and the questionnaire design; Part C gives descriptive results of the survey; Part

D presents the estimates of economic values; and Part E is a summary and conclusions. 

VI. B. THE NATURAL RESOURCE SURVEY

VI.B.1. Survey Development and Implementation

We developed the survey over a six-month period, from February to August 1995, in an extensive

process that included individual interviews, focus groups, and pretests. The primary goal of the

survey was to learn about the public’s preferences, priorities and values for natural resources of the

Peconic Estuary that might be affected by preservation and restoration actions.16
93

VI.B.1.a. Initial Meetings, Interviews, and Information Gathering

We began the survey development process in February, 1995 by meeting with members of the

Management Committee. Shortly afterwards, we met with representatives of different interests,

including the head of the Local Government Committee; the Chairman of the Citizen’s Advisory

Group, who is also president of a local environmental group; a representative of the Nature

Conservancy; a biologist 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.1.b. Informal Interviews With the Public

Next, we conducted a set of informal interviews with members of the public, where we asked about

their uses of and concerns about the Estuary. A total of sixteen randomly selected people were

interviewed in Montauk, Springs, Sag Harbor, Shelter Island and Greenport. They included business

owners, store clerks, a police officer, visitors, and residents. 

These interviews provided information on some of the most important natural resources and related

concerns, and how people think and talk about the bays. Most people interviewed were very

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. 

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

The next step was a series of focus groups, and a short preliminary survey. Table VI.1 lists the dates

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

focus groups, we asked the participants general questions about how they define the study area; their

familiarity with the Peconic Estuary Program; the characteristics of the area that are most and least

important to them; their uses of the local waters; the attributes they look for in choosing recreational

sites; their perceptions of water quality; and their concerns about the natural environment and natural

resources of the Estuary. 

A short preliminary survey was carried out in Montauk on March 19, before the St. Patrick’s Day

parade, an event that draws a large number of visitors and residents. This survey included both

closed- and open-ended questions, including questions about:

(1) 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.
94

Table VI.1 - Focus Groups and Pretests

Date Location

Number of

Participants

March 18 Montauk Focus Group 1 6

March 19 Montauk Preliminary Survey 35

March 22 Jamesport Focus Group 2 10

March 31 Riverhead Focus Group 3 7

April 19 Shelter Island Focus Group 4 3

April 20 Southampton Focus Group 5 9

April 29 East Hampton Focus Group 6 6

May 2 Springs Focus Group 7 7

May 11 Rhode Island Focus Group 8 5

May 18 Springs Pretest 1 10

June 21 Rhode Island Pretest 2 5

June 27 Rhode Island Pretest 3 5

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, concerns, and actions were difficult for

people to answer, for two important reasons. First, many people in the focus groups believed that

almost everything was important, and therefore resisted ranking their priorities. Instead, they wanted
17 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

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

Concern Mean Ranka

Declining stocks of shellfish 3.09

Declining stocks of finfish 3.34

Water quality 3.43

Trash on beaches 3.59

Areas closed to shellfishing 3.66

Overcrowding of recreational sites 4.23

Declining open space 4.57

Crowding in boating harbors 4.74

Too little public access to the water 4.88

Need to improve public access points 4.90

Less wildlife available for viewing 5.21

a - Ranked from 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 terms of the natural resources that would be

protected or restored and the cost. 17 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. 
18 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.

19 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

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.

VI.B.1.d. Survey Pretests and Implementation

In May and June, we conducted several pretests of preliminary survey instruments.18 This series of

pretests helped to refine the question wording and layout of the survey, resulting in large

improvements in respondents’ ability to easily make the comparisons. In July, a fifth draft of the

survey was pretested on Long Island, with a group of people at the First Parish Church in Jamesport,

and with randomly selected people on the beach in Mattituck. In these pretests, people were able to

easily comprehend and answer the survey questions. In early August, after meeting with members

of the Management Committee, we made final modifications to the survey, and conducted final

pretesting. An example of the final survey is included in Appendix 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
97

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.
98

Table V1.3 - Survey Locations and Number Collected

Location

Number

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 Ferry 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
99

expressions of approval or disapproval for actions, and more likely to focus on the specific natural

resources. However, people may still express symbolic values for protecting the environment,

beyond their values for the natural resource improvements. This is more easily detected and

corrected for in contingent choice than contingent valuation, as discussed below.

Finally, it has been pointed out that “survey methods are better at estimating relative demand than

absolute demand,” even for market goods, and “absolute willingness to pay is hard to pin down”

(National Oceanic and Atmospheric Administration 1993, p. 4609). Thus, the relative values or

priorities for natural resources elicited by a contingent choice survey may be valid, even if the

absolute dollar values of specific resource changes are not. For a policy analysis that focuses on

prioritizing actions, information on relative values and priorities for resources is more important than

the estimation of total dollar values. Hence, contingent choice, rather than contingent valuation, may

be more effective in obtaining the information needed to compare alternative policies.

Based on the results of focus groups and past research (Mazzotta and Opaluch 1995), each

comparison was designed to include only three attributes: Two of the five natural resources included

in the survey, and the cost. 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 restoration/protection programs. Each option was described by different levels

of resulting natural resources, and the annual cost to each household. In early focus groups, only the

two programs were presented, but focus groups participants indicated that a “no new action” option

should be added, for two reasons. First, it allowed people to express a preference for no action if

they did not support either program. Second, it provided a baseline from which to judge the benefits

of each of the programs.

VI.B.2.b. Selection of Natural Resources and Levels

VI.B.2.i. Natural Resources Included in the Survey

Based on concerns expressed by participants in focus groups and natural resources identified as

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
20 Brown Tide is an algal bloom of the species Aureococcus 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).

21 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

of life, or “sense of place,” (Sagoff 1992; Kellert 1995) for many residents of the East End, who

enjoy the rural quality of the area and shopping at numerous local farm stands. The amount of

development also affects environmental quality of the Estuary. Thus, it is important to consider uses

of the surrounding land as well as resources more directly associated with the Estuary.

Many people were aware of the importance of wetlands to water quality and as wildlife habitat,

and expressed 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 tide20 and the recent large increases in areas closed to

shellfishing. 

Finally, we included eelgrass for two reasons. First, much of the Estuary’s eelgrass was destroyed

by the brown tide, and one proposed action is restoring eelgrass areas. Second, eelgrass serves as

a proxy for fish and shellfish populations. Many participants in the preliminary interviews and focus

groups expressed great concern over declines in fish over the years. However, the 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.21 Next, we projected

the levels in 2020 based on information and judgments provided by members of the Technical

Advisory Committee regarding the anticipated change in the resource if no new actions were to be

taken. In cases where no technical projections were available, we based the projection on

extrapolation of past trends. 
101

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.
102

Table VI.4 - Past, Present and Projected Natural Resource Levels

Natural Resource 1981 Level 1995 Level

Projected

2020 Level

Farmlanda 13,500 acres 12,000 acres 9,000 acres

Undeveloped Landa 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.
22 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

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

No Action,

Baseline Level

(% Change)

Level with Moderate

Preservation or

Restoration

 (% Change)

Level with High

Preservation or

Restoration

(% Change)

Farmland 9,000 acres

(-25%)

10,000 acres

(-15%)

12,000 acres

(current)

Undeveloped Land 50,000 acres

(-25%)

59,000 acres

(-10%)

63,000 acres

(-5%)

Wetlands 12,000 acres

(-25%)

16,000 acres

(current)

17,500 acres

(+10%)

Safe Shellfishing

Areas

25,000 acres

(-5%)

26,000 acres

(current)

29,000 acres

(+10%)

Eelgrass 8,000 acres

(-10%)

9,000 acres

(current)

11,000 acres

(+25%)

Cost Levels $0 $50 $100 $200 $300 $500

In order to statistically estimate values for each resource, we chose a statistical design that required

sixty different comparisons.22 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
23 Note that census data are only available for year-round residents, so these results are for year-round residents in

the sample.

104

VI. C.1. Who Responded to the Survey?

Table VI.6 reports demographic information for all respondents, and Table VI. 7 compares the

sample demographics to actual population demographics for gender, age, education, and income.

Table VI. 8 gives the number of respondents by town.

The 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 1⁄2 mile or less from the water (51%).

• A majority have lived in the area for more than 10 years (63%).

• The average household size is just under 3 members, and 46% have no children under

age 18 in their household.

• Most are employed (67%), and 21% are retired.

• Compared to 1990 population figures, the sample contains a slightly higher percent of

women and people in the middle age groups, and the sample population is better

educated and more wealthy than the general population.23
105

Table VI.6 - Respondent Demographics, Full Sample

Question Category Number

Percent

of Sample

9: Primary Home 673 73.1%

Type of Second Home 248 26.9%

residence Did Not Answer 47 4.86%

10: Own 773 84.7%

Own or Rent 140 15.3%

rent Did Not Answer 55 5.7%

12: Peconic Bays 564 58.3%

Closest Long Island Sound 216 22.3%

water body Atlantic Ocean 219 22.6%

12: Waterfront 143 14.8%

Distance #1/2 Mile From Water 347 35.8%

from any >1/2 Mile From Water 382 39.5%

water body Did Not Answer 96 9.9%

12: Waterfront 116 12.0%

Dist. from #1/2 Mile From Water 234 24.2%

Peconic Bays >1/2 Mile From Water 618 63.8%

13: 0-10 Years 297 30.7%

Length of 11-20 Years 245 25.3%

residence 21-30 Years 176 18.2%

>30 Years 185 19.1%

Did Not Answer 65 6.7%

14: Female 540 57.8%

Gender Male 394 42.2%

Did Not Answer 34 3.5%

15: 1-2 Household Members 481 49.7%

Size of 3-6 Household Members 430 44.4%

household >6 Household Members 24 2.5%

Did Not Answer 33 3.4%

Average hh size 2.94 

16: No Children 440 45.5%

Number 1-2 Children 254 26.2%

of children >2 Children 66 6.8%

Did Not Answer 208 21.5%

Table VI.6 (Continued)
106

Question Category Number

Percent 

of Sample

17: up to 20 23 2.5% 

Age 21-24 36 3.9% 

25-34 121 13.1% 

35-44 207 22.5% 

45-54 194 21% 

55-64 162 17.6% 

65-74 144 15.6% 

75-84 33 3.6% 

85 and older 2 .2% 

Did Not Answer 46 4.8% 

18: Less Than High School 5 .5% 

Education Some High School 16 1.7% 

High School Graduate 126 13.6% 

Some College 167 18.1% 

Associate’s Degree 90 9.7% 

Bachelor’s Degree 236 25.5% 

Advanced Degree 284 30.7% 

Did Not Answer 44 4.6% 

19: Employed Full Time 497 53.9% 

Employment Employed Part Time 123 13.3% 

Full Time Homemaker 66 7.2% 

Full Time Student 28 3% 

Retired 197 21.4% 

Unemployed 11 1.2% 

Did Not Answer 46 4.8% 

20: <$15,000 43 5.1% 

Income $15,000-$24,999 77 9.1% 

$25,000-$34,999 102 12.1% 

$35,000-$49,999 111 13.2% 

$50,000-$74,999 201 23.8% 

$75,000-$99,999 113 13.4% 

$100,000-$149,999 86 10.2% 

$150,000 or more 111 13.2% 

Did Not Answer 124 12.8% 
107

Table V.7 - Population Demographics vs. Survey Respondent Demographics

1990

Populationa

 Year 

 Round

Resident

Sampleb

Seasonal

Resident

Sampleb

Gender Female 51.83% 60.85% 51.36%

Male 48.17% 39.15% 48.64%

Age up to 20 23.89% 3.17% 0.45%

21-24 4.53% 4.76% 3.62%

25-44 29.07% 40.39% 26.24%

45-54 11.01% 19.05% 24.89%

55-64 11.04% 14.99% 22.62%

65-74 11.17% 14.11% 19.00%

75-84 7.25% 3.17% 3.17%

85 up 2.04% 0.35% 0.00%

Education < High School 7.44% 0.00% 0.00%

(over Some H.S. 11.55% 1.53% 0.94%

age 24) H.S. Graduate 31.77% 15.33% 2.83%

Ed. categories 1-3 50.76% 16.86% 3.77%

Some College (Ed. cat. 4) 18.35% 21.26% 8.49%

Assoc. Degree 6.66% 11.30% 7.08%

Bachelor's Deg. 13.51% 23.18% 31.60%

Advanced Deg. 10.72% 27.39% 49.06%

Ed. categories 5-7 30.90% 61.88% 87.74%

Income < $15,000 19.17% 5.45% 1.81%

$15,000-$24,999 14.60% 11.60% 1.81%

Inc. categories 1&2 33.77% 17.05% 3.62%

$25,000-$34,999 14.40% 14.94% 4.98%

$35,000-$49,999 17.52% 15.99% 5.88%

Inc. categories 3&4 31.93% 30.93% 10.86%

$50,000-$74,999 18.90% 27.94% 14.48%

$75,000-$99,999 7.04% 11.07% 18.55%

$100,000-$149,999 4.93% 7.38% 18.10%

Inc. categories 5-7 30.86% 46.40% 51.13%

$150,000 and up (Inc. cat. 8) 3.41% 5.62% 34.39%

a - The 1990 population figures are for year-round residents only; sources of data are Long Island Lighting Company 1995

and Suffolk County Department of Planning 1991, based on 1990 census data and projections. No demographic data are

available for seasonal residents.

b - Sample percents refer to percentages of those who answered the question.
108

Table VI.8 - Numbers of Respondents by Town 

Town Number of

Respondents

Percent

of 

Sample

Brookhaven 19 1.9%

Riverhead 110 11.4%

Southold 247 25.5%

Shelter Island 136 14.1%

East Hampton 182 18.8%

Sag Harbor 66 6.8%

Southampton 139 14.4%

Other 25 2.6%

Did not Answer 44 4.5%

VI.C. 2. Participation in Recreational Activities

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

(11%).

More people participate in activities on or around the Peconic Estuary than any other water body.

This result is consistent with results of a telephone survey of residents conducted in the fall of 1993

by the Center for Community Research of Suffolk Community College (the SCC survey), which also

found that the 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.
109

Table VI.9 - Participation in Recreational Activities

Activity Peconic Bays

Atlantic

Ocean L.I. Sound Other

All

Locations

Fishing 391 (40.4%) 167 (17.3%) 216 (22.3%) 74 (7.6%) 516

(53.3%)

Shellfishing 261 (27.0%) 18 (1.9%) 55 (5.7%) 39 (4.0%) 311

(32.1%)

Walking/

Hiking 518 (53.5%) 339 (35.0%) 306 (31.6%) 69 (7.1%) 684

(70.7%)

Swimming 610 (63.0%) 468 (48.3%) 362 (37.4%) 79 (8.2%) 833

(86.1%)

Other

Beach Use 201 (20.8%) 169 (17.5%) 131 (13.5%) 44 (4.5%) 324

(33.5%)

Boating 432 (44.6%) 144 (14.9%) 224 (23.1%) 67 (6.9%) 520

(53.7%)

Artwork 204 (21.1%) 120 (12.4%) 129 (13.3%) 40 (4.1%) 252

(26.0%)

Other 68 (7.0%) 46 (4.8%) 51 (5.3%) 24 (2.5%) 103

(10.6%)

All

Activities 779 (80.5%) 586 (60.5%) 534 (55.2%) 205 (21.2%) 936

(96.7%)

As expected, all of the actions are supported by a majority of respondents. The actions with the

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 
110

Table VI.10 - Support for Actions

Strongly Strongly No Average

Action Support Support Neutral Oppose Oppose Opinion Rank

Prohibit sewage discharge

from boats. (requires building

more pumpout stations) 79.0% 10.5% 1.0% 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% 1.0% 6.2% 3

More research on water

quality issues. 64.9% 19.4% 6.9% 0.7% 1.1% 6.9% 4

Better enforcement of

existing regulations. 66.3% 17.0% 3.2% 0.4% 0.9% 12.1% 5

Restrictions on waterfront

property, including requiring

buffer zones (areas left

untouched) between

development and the water,

drywells for roof runoff, etc.

61.9% 16.8% 8.5% 2.5% 2.5% 7.9% 6

Zoning to limit future

development. 62.3% 14.0% 11.1% 2.4% 2.2% 8.1% 7

Limiting removal of 

vegetation on newly

developed land.

58.5% 18.2% 9.9% 2.8% 2.6% 8.1% 8

Requiring repair or upgrade

of septic systems when

property is sold or improved.

51.4% 21.8% 12.4% 2.5% 2.6% 9.3% 9

Controlling stormwater

runoff with diversion, catch

basins, etc.

47.6% 26.1% 12.9% 1.7% 0.9% 10.7% 10

Restricting use of fertilizer

and lawn chemicals for

residential property.

43.4% 24.8% 16.7% 5.4% 2.2% 7.5% 11

Requiring pumpouts of

existing septic systems every

4 years.

32.7% 21.3% 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.
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

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 1⁄2 mile from the water, and 82.5

percent of people who live more than 1⁄2 mile from the water supporting restrictions on waterfront

property. However, there was no statistically significant difference in the mean strength of support

across these groups.

Finally, the strength of support for all actions was compared for members of environmental groups

versus those who are not members.24 Although the average level of support was slightly greater for

members of environmental groups, the means were not statistically significantly different. 

From these results, it can be concluded that the majority of respondents are in favor of

environmental actions, with slightly lower support for some actions that are personally

inconvenient. However, as expected, the results appear to be primarily symbolic of a general desire

for action. That is, people seem to support most actions. Thus, these results do not provide adequate

information with which to make policy decisions. 

VI.C.4. Opinions About Brown Tide

Question 3 asked respondents whether they have heard of brown tide, and if they have, about their

level of concern, whether their activities have been affected, and whether they support funding for

further study. The results are reported in Table V.1.

 

Most people (90%) had heard of brown tide, and 75 percent said that at least one activity was

affected by the brown tide. The most commonly affected activity was swimming, with 59 percent

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
112

of brown tide are concerned (21%) or very concerned (76%) about it. Most of those who had heard

of brown tide (86%) also support more funding to study brown tide and possible remedies for it. 

Table V.11 - Responses to Brown Tide Questions

Heard of 

Brown Tide

Heard of and Affected

by Brown Tide

872 (90.1%) 725 (74.9%)

Q. 3B - How concerned are you about brown tide?

 No Opinion 22 (2.5%) 8 (1.1%) 

 Not Concerned 5 (0.6%) 1 (0.1%) 

 A Little Concerned 183 (21.0%) 120 (16.6%) 

 Very Concerned 662 (75.9%) 596 (82.2%) 

Q. 3D - Do you support or oppose more funding to study 

the causes of brown tide and how to end it?

 No Opinion 26 (3.0%) 14 (1.9%) 

Opposed 17 (1.9%) 13 (1.8%) 

Neutral 79 (9.1%) 61 (8.4%) 

Support 750 (86.0%) 637 (87.9%) 

Q. 3C - Activities Affected by Brown Tide

Swimming 512 (58.7%)

Boating 121 (13.9%)

Fishing 319 (36.6%)

Shellfishing 349 (40.0%)

Other 89 (10.2%)

VI.C.5. Membership in Organizations

Question 21 asked respondents whether they are members of different types of organizations. The

choices listed are sports, community service, environmental, political, business-related, farm or

agricultural, religious, and civic organizations. The purpose of this question was to supplement the

standard demographic questions by providing information about respondents’ interests and level

of commitment to different causes. Table VI.12 shows the statistics for membership in

organizations. 

Sixty-five percent of respondents belong to at least one organization, and 31 percent belong to an

environmental organization. This is comparable to the results of the SCC survey, which found that

57 percent of respondents had donated to environmental organizations, and 21 percent had been

active in an environmental group. 
113

Table VI.12 - Membership in Organizations

 Organization Number (%)

Sports 171 (17.7%)

Community Service 182 (18.8%)

Environmental 300 (31.0%)

Political 146 (15.1%)

Business 94 (9.7%)

Agricultural 24 (2.5%)

Religious 275 (28.4%)

Civic 152 (15.7%)

Other 91 (9.4%)

Any Organization 628 (64.9%)

VI.D. ESTIMATES OF ECONOMIC VALUE

VI.D.1. 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.
25 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. 26 These calculations are described in detail the Technical Appendix.

114

Table VI.13 - Number of Choice Questions Answered

Number

Answered

Number of

Respondents

Percent of

Total

Sample

0 71 7.3% 

1 8 .8% 

2 8 .8% 

3 31 3.2% 

4 60 6.2% 

5 790 81.6% 

Older respondents, and those with lower education and income levels, were slightly less likely to

answer all of the choice questions. Of those who answered at least one choice question, almost all

(91.6%) chose an action, rather than no action, for more than half of their answers. Only 27 people

(3.0%) chose no action for all of the choice questions answered. These results demonstrate the

strong environmental concern expressed by survey respondents, but also may suggest some

symbolic bias, if respondents voted to “take action,” irrespective of the associated levels of resource

protection and cost. Below we discuss some methods that we employed to identify and control for

this possible bias.

Of those who chose either Program A or Program B, more people chose Program A than Program

B.25 This may indicate a tendency to select the option placed in the center, or may simply indicate

that the options presented as Program A were preferred more often. This is discussed further in the

Technical Appendix.

VI.D.2. Results and Discussion

The statistical models used to estimate the results are described in detail in the Technical Appendix.

These methods calculate the relative weights, or values, for an additional acre of each natural

resource, and for an additional dollar of cost to each household. These weights are measured by the

estimated coefficients for each resource and cost. From these coefficients, relative values for the

different resources, and dollar values for protecting an additional acre of each resource, can be

calculated.26
27 Model 1 is the standard conditional logit model, and Model 2 is the nested logit model.

115

The results for two different models are reported in Table VI.14.27 The model results indicate that

the order of priorities for protection or restoration of resources is as follows: farmland, eelgrass,

wetlands, shellfish, and undeveloped land.

Table VI.14 - Estimation Results

Coefficient

Value

/Acre/hh/

year

95% Confidence

Intervala

Avg. Value

/Acre/Yearb

Model 1:c

Farmland 0.000511 $0.136 $0.122 $0.150 $ 9,979

Undeveloped Land 0.000107 $0.028 $0.025 $0.032 $ 2,080

Wetlands 0.000336 $0.089 $0.079 $0.100 $ 6,560

Shellfish Areas 0.000233 $0.062 $0.053 $0.071 $ 4,555

Eelgrass 0.000419 $0.111 $0.098 $0.125 $ 8,186

Cost -0.003765

Model 2:d

Program B -.1586

Farmland .000300 $0.087 $0.073 $0.101 $6,398

Undeveloped Land .000056 $0.016 $0.013 $0.019 $ 1,203

Wetlands .000228 $0.066 $0.056 $0.077 $ 4,863

Shellfish Areas .000128 $0.037 $0.031 $0.044 $ 2,724

Eelgrass .000281 $0.082 $0.069 $0.094 $6,003

Cost -.003441

a - The 95% confidence interval indicates the range within which the “true” value is likely to fall, with a 95%

probability.

b - Calculated based on 73,423 households.

c - Conditional Logit model.

d - Nested Logit model.
28 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 (U.S. Department of the Interior 1995).

116

As discussed above, symbolic values are often an issue for both contingent valuation and contingent

choice methods. The estimation results show that this is an issue in this survey, and that respondents

to the survey are more likely to choose to take action, independent of the specific results of the

action. Thus, these respondents may be expressing a symbolic willingness to pay to take action, as

opposed to revealing values for the specific natural resources of concern. The higher probability of

choosing to take action is not surprising, given the level of concern among residents of the area for

the environment of the Estuary.

However, Model 2 provides a correction for such values, by separating the probability of taking

action vs. no action, from the probability of selecting either Program A or Program B. Thus, the

estimated dollar values for Model 2 are approximately half to two-thirds as large as those estimated

from Model 1. The estimated dollar values range from around $2.1 thousand per acre per year for

undeveloped land, to around $10 thousand for farmland for Model 1; and around $1.2 thousand to

$6.4 thousand for Model 2. The values from Model 2 might be interpreted as the portion of

respondents’ willingness to pay to take action which can be 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, “[t]he 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 (U.S. Department of the

Interior 1995).28 The 3 percent rate is included for comparison, because it is often argued that the

social discount rate should be lower. The 25 year time horizon was chosen to match the time period
117

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

PV/Acre

r=.03, t=100*

PV/Acre

r=.03, t=25*

PV/Acre

r=.07, t=100*

PV/Acre

r=.07, t=25*

Model 1:

Farmland $315,314 $173,759 $142,387 $116,286

Undeveloped Land $65,728 $36,221 $29,681 $24,240

Wetlands $207,286 $114,228 $93,605 $76,446

Shellfish Areas $143,945 $79,323 $65,001 $53,086

Eelgrass $258,675 $142,547 $116,811 $95,398

Model 2:

Farmland $202,175 $111,412 $91,297 $74,562

Undeveloped Land $38,028 $20,956 $17,172 $14,024

Wetlands $153,659 $84,676 $69,388 $56,669

Shellfish Areas $86,070 $47,430 $38,867 $31,742

Eelgrass $189,703 $104,539 $85,665 $69,962

 * r is the discount rate; t is the time horizon in years.

VII.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.
118

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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120

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

P (1) ij

j ji

j ji

k

= + ′

∑ + ′

exp( )

exp( ).

'

'

β α

β α

z zw

z zw

Pij is the probability that individual i will select option j; zj is a vector of attributes of the choice (e.g.,

the levels of natural resources and the cost), which may also vary across individuals; wi

 is a vector

of characteristics of the individual; and υ and α 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

P PkjP j (2) kj = ( | ) ( ).

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

Pkj (3)

V

V

k j

n j

n j

k j

n j

n j

k j

j

(|) exp( )

exp( )

exp( )

exp( )

exp( )

exp( ) , |

|

|

|

|

|

| = = ′

′ = ′

∑ ∑

β

β

x β

x

x

J
14 Note that, alternatively, this could also suggest use of the incorrect functional form, although the Box Cox test

shows that the linear form is the most appropriate of the commonly used functional forms.

121

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

J (4) j nj

n j

= log( exp( )). ∑ ′ |

|

β x

The probability of choosing to take action or not is

P j (5)

Y J

Y J

j jj

m mm

m

( ) exp( )

exp( ) = , ′ +

∑ ′ +

α τ

α τ

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 τ 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 τ 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
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significant coefficient for “Program B” indicates that, even if Program A and Program B produced

the same results in terms of preservation of natural resources, respondents are more likely to choose

Program A. The coefficient on Action indicates that there is an 87 percent probability that the

average respondent would select action over no action, if that action cost nothing and provided zero

resource protection. Similarly, conditional on taking action, there is a 55 percent probability that

the representative respondent would choose Program A over Program B if their costs and levels of

resource protection were identical.

Table B-1 - Comparison of Model Results

Coefficient

Value

/Acre

95% Confidence

Interval

Avg. Value

/Acre/Year*

Conditional Logit Model:

Farmland 0.000511 $0.136 $0.122 $0.150 $ 9,979

Undeveloped Land 0.000107 $0.028 $0.025 $0.032 $ 2,080

Wetlands 0.000336 $0.089 $0.079 $0.100 $ 6,560

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

Ѓ2

=1-(L (υ)/L (0)) .138

Model with AlternativeSpecific Constants:

Action 1.2866

Program B -0.1799

Farmland .000300 $0.094 $0.078 $0.109 $ 6,872

Undeveloped Land .000057 $0.018 $0.014 $0.022 $ 1,304

Wetlands .000179 $0.056 $0.045 $0.066 $ 4,090

Shellfish Areas .000108 $0.034 $0.023 $0.044 $ 2,467

Eelgrass .000214 $0.067 $0.052 $0.081 $ 4,909

Cost -.003207

Ѓ2 .171

Nested Logit Model:

Program B -.1586

Farmland .000300 $0.087 $0.073 $0.101 $6,398

Undeveloped Land .000056 $0.016 $0.013 $0.019 $ 1,203

Wetlands .000228 $0.066 $0.056 $0.077 $ 4,863

Shellfish Areas .000128 $0.037 $0.031 $0.044 $ 2,724

Eelgrass .000281 $0.082 $0.069 $0.094 $6,003

Cost -.003441

τ .3397

Ѓ2 .309

* - Calculated based on 73,423 households.
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These constant terms for taking action and for differences in probability of selecting A and B may

be interpreted as representing a qualitative or symbolic dimension of respondents’ preferences, while

the coefficients on the natural resources represent the quantitative dimension that can be attributed

to the stated levels of resource protection. Thus, if respondents exhibit a tendency to a resource

protection action, rather than “No New Action,” beyond that which can be associated with the stated

levels of resource protection and the cost, they may be expressing a symbolic willingness to pay to

take action to protect the environment of the East End. Similarly, the coefficient on the constant

term for Program B indicates that there is some qualitative reason that people choose Program A,

and measures the degree of preference for A over B which cannot be explained by the described

attributes of the two programs. Note that this constant term is statistically significant, but

quantitatively small.

The higher probability of choosing to take action is not surprising, given the level of 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 implies that an action/no action bias

might exist, but that the bias is randomly distributed across choices. For example, some

individuals might exhibit a bias towards taking action, while others might exhibit a bias against

taking action.

The random effects model can be implemented using the nested logit approach, which captures the

correlation of the random components of utility associated with the two action alternatives. Tests

of the inclusive value parameter in the nested logit model indicate that there is greater

substitutability between Program A and Program B than between taking action or not, and that there

is significant correlation between the Program options. The constant term for Program B is similar

in magnitude to that estimated in the previous model.

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,
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(6) UR M UR M C ij i ik i k (, ) (, ) = − for all k j, ≠

where j represents the “No New Action” alternative, or the baseline levels of the resources, so that

Cj=0; k is the option selected; and Ck is the maximum willingness to pay for option k. For the linear

approximation of the utility function presented above, this can be solved for Ck as follows:

βγ β γ (7) β

γ

R M R MC C R R ij i ik i k k ik ij + = + − =− − ( ) ( ) ( ). and 

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, β, to the

coefficient on cost, γ.

The calculation of dollar values for the nested logit model must account for the nested structure and

the inclusive value parameter, τ. 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):

CV V V (8) jk j

k

K

j

J

jk j

k

K

j

j J j j j

= − 



 





















− 



 





































 = = = =  ∑ ∑ ∑ ∑ 1 2

1 1

1

γ 1 1

τ τ

τ τ

ln exp( / ) ln exp( / )

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 γ 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. 
125

These results indicate, however, that there may be a statistically significant symbolic component to

choices, which is comprised of an effect that is unrelated to the described levels of resource

protection provided by the hypothetical programs and the associated cost. The similarity of results

from the nested logit and conditional logit with constants models, and the fact that the results are not

statistically different, indicate that these biases are likely overwhelmingly in one direction—towards

taking action rather than no action. Thus, both of these models appear to account for a “symbolic”

aspect of values, and to separate that from estimated values for specific natural resource

improvements. Note, however, the Nested Logit model provides considerable improvement in fit,

as measured by the ρ2 statistic.
126