MEASURING PUBLIC VALUES AND PRIORITIES FOR NATURAL RESOURCES: 

AN APPLICATION TO THE PECONIC ESTUARY SYSTEM 

BY 

MARISA J. MAZZOTTA 

A DISSERTATION SUBMITTED IN PARTIAL FULFILLMENT OF THE 

REQUIREMENTS FOR THE DEGREE OF 

DOCTOR OF PHILOSOPHY 

IN 

ENVIRONMENTAL AND NATURAL RESOURCE ECONOMICS 

UNIVERSITY OF RHODE ISLAND 

1996
SHORT TITLE: 

VALUES AND PRIORITIES FOR NATURAL RESOURCES 
DOCTOR OF PHILOSOPHY DISSERTATION 

OF 

MARISA J. MAZZOTTA 

APPROVED: 

 Dissertation Committee 

 Major Professor 

 

 DEAN OF THE GRADUATE SCHOOL 

UNIVERSITY OF RHODE ISLAND 

1996
ABSTRACT

This study focuses on measurement of public values and priorities for protecting and 

enhancing natural resources of the Peconic Estuary system; and explores issues related to 

economic valuation methods, including theoretical validity and a comparison of model 

specifications. 

A contingent choice survey was administered to 968 residents of the area surrounding the 

Estuary. Respondents were presented with sets of hypothetical alternatives, described in 

terms of their effects on natural resources and the cost to each household, and asked to 

select their preferred alternative. The baseline was represented by a “no new action” 

alternative, which was compared to two different programs to protect or enhance natural 

resources. Five natural resources were included—farmland, undeveloped land, wetlands, 

shellfishing areas, and eelgrass. 

Three model specifications are compared: the conditional logit model with and without 

alternative-specific constants, and the nested logit model. The two latter models allow for 

effects related to the options themselves rather than their attributes, and their results indicate 

that choices between the two protection/enhancement alternatives are correlated, and that, 

ceteris paribus, respondents are more likely to select Program A than Program B. 

Although dollar value estimates vary, estimated ordinal priorities and relative values of 

resources are robust to different specifications. Estimated priorities for protection or 
enhancement of resources are: farmland, eelgrass, wetlands, shellfish, and undeveloped land. 

For an infinite time horizon, and the official 7.625 percent discount rate, present values 

range from around $90,000 to $168,000 per acre of farmland; $17,000 to $32,000 per acre of 

undeveloped land; $54,000 to $128,000 per acre of wetlands; $32,000 to $71,000 per acre of 

shellfishing areas; and $64,000 to $157,000 per acre of eelgrass. 

Tests of hypotheses related to theoretical validity indicate that estimated economic values 

pass basic tests of consistency with economic theory, with mixed results for the most 

rigorous tests. Thus, although respondents may not be able to express their values precisely 

enough to pass the most rigorous tests of economic rationality, responses provide robust 

estimates of people’s priorities and relative values for the natural resources, which is most 

important for policy analysis that aims to prioritize actions. 
ACKNOWLEDGMENTS

First, I would like to thank the faculty members in the Department of Environmental and 

Natural Resource Economics, and the members of my committee. I am grateful to my 

advisor, Professor Jim Opaluch, for his constant guidance and support, and for always 

expecting the best from me. I would also like to thank Professor Tom Grigalunas for his 

encouragement and assistance over the years; and my other committee members, Professors 

Grant Willis, Tim Tyrrell, and Peter August, as well as the other faculty members in the 

Department of Environmental and Natural Resource Economics, who have been 

consistently supportive and encouraging. I feel privileged to have known Professor Thomas 

Weaver, who was chairman of the Department until his death this spring. Tom was a 

wonderful sounding board, and his open-mindedness, broad interests, and unconventional 

viewpoints were a constant inspiration. 

I am grateful for the funding support that I have received over the years from the 

Department, the University, and Rhode Island Sea Grant, and specifically for funding from 

the Peconic Estuary Program and U.S. Environmental Protection Agency for my dissertation 

research. 

I would also like to thank all the people who helped me with the project, in many different 

ways, and without whom it would not have been possible. Jerry Diamantides provided the 

initial impetus, and much of the work, to create the opportunity for this research, and has 

been a great colleague and friend. Vito Minei and Walt Dawidiack of the Suffolk County 

Health Department, Kevin McDonald and Gayle Marriner-Smith of the Citizen’s Advisory 
Council for the Peconic Estuary Program, and Cynthia Decker of the New York State 

Department of Environmental Conservation were all especially helpful, and provided a great 

deal of useful information. The survey could not have been completed without the hard 

work of the members of the survey teams: Wendy Mitchell, Mike McNair, Paul Lamb, 

Maureen Devitt, Helen Besedina and Jeff Kline, with an especially big thanks to Mike 

McNair. His research assistance, persistence, and organizational and communications skills 

were tremendously helpful. I would also like to thank all of the focus group participants, the 

people who participated in interviews, and the survey respondents, who gave their time and 

opinions. 

Finally, I want to thank my friends and family. My friends in the department, especially 

Vishwanie Maharaj, Penporn Janekarnkif, Quentin Fong, Daniele Perrot-Maitre, Jeff Kline, 

Cilla Brooks and Kathryn Bisack, among many others, have been like a second family, 

providing support and sharing the stresses and successes of student life. My other friends 

and family have also been extremely supportive and patient, and I offer a big thanks to my 

parents, Wendy Walker, Terri Bisonnette, the Monday night group, and most especially, Paul 

Lamb, who has been a constant source of strength, humor and support. 
vi

TABLE OF CONTENTS

CHAPTER 1: INTRODUCTION......................................................................................1

 Statement of the Problem...........................................................................1 

 Objectives of the Study ...............................................................................2 

Justification and Significance ...................................................................2 

 Policy Analysis for Estuary Management..........................................2 

 Economic Valuation.............................................................................4 

Approach and Methodology......................................................................6 

Dissertation Outline ....................................................................................8 

CHAPTER 2: THE PECONIC ESTUARY SYSTEM.................................................9 

The Peconic Estuary System ....................................................................9 

The National Estuary Program and the 

 Peconic Estuary Program ....................................................................... 13 

Environmental Problems and Recommended Actions.................. 14 

 Brown Tide ......................................................................................... 15 

 Nutrient Loading................................................................................ 16 

 Coliforms............................................................................................. 18 

 Other Pollutants................................................................................. 19 

CHAPTER 3: LITERATURE REVIEW....................................................................... 20 

Introduction ................................................................................................ 20 

Philosophies of Nature ............................................................................ 22 

 Historical Views of Nature............................................................... 22 

 The Conservationist and Preservationist Philosophies ................ 23 
vii

 Nonanthropocentric Philosophies .................................................. 25 

 Americans’ Current Views................................................................ 29 

Goals of Management.............................................................................. 30 

Neoclassical Theory of the Natural Environment .......................... 33 

 Natural Resources as a Portfolio of Assets.................................... 33 

 Neoclassical Duality Theory and Revealed Preference Theory .. 35 

 Welfare Theory................................................................................... 40 

Economists’ Categories of Natural Resource 

 and Environmental Values ..................................................................... 45 

Nonmarket Valuation .............................................................................. 49 

 Methods............................................................................................... 49 

 Reliability and Validity....................................................................... 50 

Motivations for Choice............................................................................. 54 

 Means and Ends................................................................................. 56 

 Commitment and Sympathy............................................................. 57 

 Consumer/Citizen ............................................................................. 62 

 Sense of Place..................................................................................... 64 

 Nonanthropocentric Motivations.................................................... 66 

Violations of Neoclassical Theory........................................................ 69 

Alternative Theories ................................................................................. 74 

 Kopp’s Theory of Ethical Motivations........................................... 74 

 Socio-Economics ............................................................................... 79 

Implications for Policy Analysis............................................................ 86 
viii

CHAPTER 4: THEORETICAL FRAMEWORK....................................................... 92 

Introduction ................................................................................................ 92 

The Discrete Choice Model for Contingent Choices...................... 94 

The Nested Logit Model....................................................................... 100 

Welfare Measurement ............................................................................ 105 

Tests for Economic “Rationality”...................................................... 107 

 Moral Commitments ....................................................................... 107 

 Preference Uncertainty.................................................................... 110 

 The Adding Up and Scope Hypotheses ....................................... 111

CHAPTER 5: SURVEY DEVELOPMENT AND IMPLEMENTATION ..... 115 

Introduction .............................................................................................. 115 

Steps in Survey Development .............................................................. 116 

 Initial Planning and Information Gathering ................................ 117 

 Informal Interviews With the Public ............................................ 118 

 Focus Groups and Preliminary Survey......................................... 119 

 Survey Pretests and Implementation............................................. 124 

Selection of Survey Format................................................................... 127 

Selection of Natural Resources and Levels ..................................... 129 

 Natural Resources Included in the Survey ................................... 129 

 Levels of Natural Resources in the Survey................................... 131 

Description of Survey Questionnaire................................................. 137 

CHAPTER 6: DATA ANALYSIS AND RESULTS ................................................. 147 

Summary Statistics.................................................................................. 147 

 Participation in Recreational Activities......................................... 148 
ix

 Support for Management Actions................................................. 149 

 Opinions About Brown Tide......................................................... 152 

 Membership in Organizations........................................................ 154 

 Demographics .................................................................................. 155 

 Responses to Contingent Choice Questions................................ 158 

Data Analysis of Contingent Choice Questions ............................. 159 

 Models With Additional Variables ................................................ 160 

 Effects of Respondents’ Characteristics....................................... 163 

Calculation of Representative Values................................................ 169 

 Valuation ........................................................................................... 175 

Model Extensions.................................................................................... 179 

 Choice-Specific Constant Model ................................................... 180 

 The Nested Logit Model................................................................. 184 

Comparisons of Models and Sensitivity Analysis .......................... 188 

 Comparison of Model Results ....................................................... 189 

 Sensitivity Analysis of Economic Values...................................... 190 

Tests for Economic “Rationality”...................................................... 192 

 Initial Validity Tests......................................................................... 194 

 Qualitative Evidence........................................................................ 195 

 Scope and Adding Up Tests........................................................... 196 

 Summary............................................................................................ 201 

CHAPTER 7: CONCLUSIONS AND RECOMMENDATIONS ...................... 203 

Summary of Main Findings.................................................................. 204 

 Importance of the Peconic Estuary............................................... 204 
x

 Public Priorities and Values for Natural Resources.................... 205 

 Model Extensions ............................................................................ 207 

 Tests for Economic “Rationality” ................................................. 210 

Implications and Recommendations for Further Research ....... 212 

 Economic Theory and Methods for 

 Environmental Valuation................................................................ 213 

 Environmental Policy Analysis ...................................................... 216 

APPENDIX A: EXAMPLES OF PRELIMINARY SURVEY VERSIONS......... 219 

 MONTAUK PRELIMINARY SURVEY ............................................. 220 

 FOCUS GROUP SURVEY, VERSION 1............................................ 228 

 OTHER PRELIMINARY FOCUS GROUP QUESTIONS ............ 236 

 SURVEY, VERSION 1 ............................................................................ 241 

APPENDIX B: CONTINGENT CHOICE QUESTION 

 DESIGN .................................................................................................... 247 

APPENDIX C: EXAMPLE SURVEY BOOKLET..................................................... 253 

APPENDIX D: PRELIMINARY MODEL RESULTS ............................................. 262 

BIBLIOGRAPHY..................................................................................................................... 265 
xi

LIST OF TABLES 

Table 3-1 - The Neoclassical Model and Violations ................................................................. 70 

Table 5-1 - Focus Groups and Pretests .................................................................................... 119 

Table 5-2 - Concerns of Preliminary Survey Respondents .................................................... 120 

Table 5-3 - Survey Locations...................................................................................................... 126 

Table 5-4 - Past, Present, and Projected Natural Resource Levels....................................... 134 

Table 5-5 - Levels of Natural Resources and Cost in the Survey.......................................... 134 

Table 6-1 - Participation in Recreational Activities................................................................. 148 

Table 6-2 - Support for Actions................................................................................................. 150 

Table 6-3 - Responses to Brown Tide Questions.................................................................... 153 

Table 6-4 - Membership in Organizations................................................................................ 154 

Table 6-5 - Respondent Demographics, Full Sample ............................................................. 156 

Table 6-6 - Number of Choice Questions Answered............................................................. 159 

Table 6-7 - Base Model Results.................................................................................................. 160 

Table 6-8 - Likelihood Ratio Tests for Demographic Variables........................................... 162 

Table 6-9 - Likelihood Ratio Tests for Education and Income Categories......................... 163 

Table 6-10 - Likelihood Ratio Tests for Respondent Characteristics................................... 164 

Table 6-11 - Statistically Significant Explanatory Variables in Full Model.......................... 165 

Table 6-12 - Results for Conditional Logit Model With All Characteristics ....................... 166 

Table 6-13 - Population Demographics vs. Survey Respondent Demographics................ 171 

Table 6-14 - Year-Round Resident Model, Weighted Coefficients ...................................... 173 

Table 6-15 - Seasonal Resident Model, Weighted Coefficients............................................. 174 

Table 6-16 - Combined Model Weighted Coefficients........................................................... 174 
xii

Table 6-17 - Estimated Economic Values for Natural Resources, 

 for Year-Round and Seasonal Residents................................................. 175 

Table 6-18 - Estimated Economic Values for Natural Resources, 

 Combined Model........................................................................................ 176 

Table 6-19 - Discounted Present Values for Weighted Model, 

 7.625% Discount Rate............................................................................... 177 

Table 6-20 - Sensitivity Analysis for Discount Rates, Weighted Model............................... 178 

Table 6-21 - Comparison of Weighted Model and Simple Model Results.......................... 179 

Table 6-22 - Model With Alternative-Specific Constants ...................................................... 181 

Table 6-23 - Nested Logit Model Results, Main Effects Only.............................................. 185 

Table 6-24 - Nested Logit Model Results, Extended Model ................................................. 187 

Table 6-25 - Comparison of Different Model Results............................................................ 190 

Table 6-26 - Comparison of Discounted Present Values for Different 

 Models, 7.625% Discount Rate................................................................ 191 

Table 6-27 - Sensitivity Analysis for Discount Rates, Alternative-Specific 

 Constants Model and Nested Logit Model ............................................ 192 

Table 6-28 - Model Results for Adding Up and Scope Tests................................................ 197 

Table 6-29 - Wald Tests for Scope ............................................................................................ 198 

Table 6-30 - Wald Tests for Adding Up ................................................................................... 201 
xiii

LIST OF FIGURES 

Figure 2-1 - Map of Peconic Estuary System............................................................................. 10 

Figure 4-1 - Sequential Choice Structure.................................................................................. 102 

Figure 5-1 - Overview of Project............................................................................................... 116 

Figure 5-2 - First Page of Survey ............................................................................................... 139 

Figure 5-3 - Survey Question #1 ............................................................................................... 140 

Figure 5-4 - Survey Question #2 ............................................................................................... 141 

Figure 5-5 - Survey Question #3 ............................................................................................... 142 

Figure 5-6 - Background Information Presented in the Survey............................................ 143 

Figure 5-7 - Survey Instructions and Sample Contingent Choice Question ....................... 144 

Figure 5-8 Survey Demographic Questions ............................................................................. 145 
xiv
1

CHAPTER 1

INTRODUCTION 

Statement of the Problem

In recent years, concern over degradation of the natural environment has grown and, 

reflecting this concern, many environmental laws and policies have been enacted. Although 

environmental policies may achieve important goals, they may also be costly, and may 

conflict with other policies or goals. Therefore, in implementing environmental policies it is 

necessary to establish priorities and balance competing goals. Economists can contribute 

important information to environmental policy analysis, including estimates of economic 

benefits and costs associated with a policy, and information about public preferences and 

priorities. 

The National Estuary Program was established under the Clean Water Act to protect and 

manage nationally significant estuaries that are threatened by pollution, development, or 

overuse. Under this program, a Management Committee for each participating estuary must 

develop and implement a Comprehensive Conservation and Management Plan that 

considers both the integrity of the ecosystem and benefits to people. This study is part of a 

comprehensive economic value assessment undertaken for the Peconic Estuary Program, 

one of 21 such programs funded under the National Estuary Program to date. It focuses on 

assessing the public’s priorities and values for protecting and enhancing important natural 

resources of the Peconic Estuary System. 
2

Objectives of the Study

The first objective of this study is to measure public preferences, priorities and values for 

protecting and enhancing natural resources of the Peconic Estuary system. This includes an 

analysis of values and priorities for a set of important natural resources of the area—

farmland, undeveloped land, wetlands, shellfishing areas, and eelgrass—and estimation of 

economic benefits that would result from protecting or enhancing these resources. This 

information will assist policy makers in prioritizing alternative actions to create the 

Management Plan. 

The study’s second objective is to explore several issues related to economic valuation 

methods. These include consideration of respondents’ motivations, and the importance of 

these motivations for survey development and analysis; tests of hypotheses related to the 

theoretical validity of survey results; and a comparison of different estimation models, 

including two specifications of the conditional logit model, and the nested logit model. 

Justification and Significance 

Policy Analysis for Estuary Management 

Almost half the population of the United States lives in coastal areas, and on average, 

populations of coastal counties in the United States are growing three times faster than any 

other areas (U.S. Environmental Protection Agency 1993). The marine and estuarine 
3

resources of coastal areas contribute greatly to the quality of life, and help to define the 

community. In addition to providing aesthetic, recreational, and economic benefits to 

people, estuaries are important ecosystems. They provide spawning areas and nurseries for 

fish, birds and other wildlife; marshes that filter sediment and pollution; and storm and flood 

barriers for the surrounding coastline (U.S. Environmental Protection Agency 1993). 

The Peconic Estuary system, located at the eastern end of Long Island, New York, 

comprises over 100 bays, harbors, embayments, and tributaries. The area surrounding the 

Estuary is a major recreation area for residents of New York City and western Long Island, 

and has a large seasonal population. Its economy is largely resource-based, and the most 

important economic sectors are agriculture, fishing, wine making, recreation, and services 

related to second homes (East End Economic and Environmental Task Force 1994). 

Although environmental quality in the Estuary has historically been good to excellent, rapid 

development and associated environmental problems threaten water quality and other 

amenities such as wetlands, farmland and other open space; commercial and recreational fish 

stocks; aquifers; and fish and wildlife habitat. Therefore, planning and actions to maintain 

and improve environmental quality are important. 

This study is one part of a multidisciplinary process leading to the creation of a Management 

Plan for the Estuary. Natural science and other technical studies are simultaneously being 

carried out to determine the Estuary’s most important environmental problems, their causes, 

and potential solutions. Based on this information, a set of actions to improve 

environmental quality and ecosystem integrity will be proposed. 
4

Because management actions are costly, and resources of government agencies and the 

public must be devoted to their implementation, assessing public priorities and values is an 

important component of the planning process. Information on public preferences and 

economic values from this study will be combined with other components of the economic 

assessment, and with the science and technical information, in order to prioritize proposed 

actions for the final Management Plan. 

 

Economic Valuation 

As the number and scope of environmental policies has increased, economic methods have 

also expanded to encompass a broader range of issues and values related to the natural 

environment. The primary motivation for this has been the desire to quantify costs and 

benefits of policies in order to make informed decisions about implementation. However, 

economic valuation methods are often controversial and may encounter a variety of 

problems. 

One important issue is whether expressions of value elicited by surveys are consistent with 

the underlying utility theoretic framework in which they are analyzed. A problem frequently 

encountered is that people find placing monetary values on environmental amenities to be 

difficult or even morally offensive (Stevens, et al. 1992; Grigalunas and Opaluch 1993). In 

such cases, choices may not reveal economic value, defined as the amount of money that 

would make an individual indifferent between conditions before and after the environmental 

change occurs. When this is true, economic values cannot be inferred directly from choices. 
5

The possibility of “ill-defined” preferences that are not consistent with neoclassical utility 

theory has a variety of implications for economic methods, and especially for benefit-cost 

analysis where valuation of environmental amenities is required. 

Two important reasons for ill-defined preferences have been hypothesized—the existence of 

moral commitments related to the choice, and the complexity of such choices (see, for 

example Brookshire, et al. 1987; Diamond, et al. 1993; Edwards 1986; Etzioni 1988; 

Kahneman and Knetsch 1992a; Mazzotta and Opaluch 1995; Opaluch and Grigalunas 1993). 

These may be especially important in the case of public goods, because they are more likely 

than most market goods to invoke moral concerns, and choices among these goods are also 

more likely to be unfamiliar to many people. 

Ill-defined preferences can lead to a variety of inconsistent results from valuation surveys. 

Two of these are referred to in the literature as the “embedding” problem and the “adding 

up” problem. These are related to the theoretical validity of survey results, and occur when 

dollar values from surveys are not related to quantities of natural resources as predicted by 

economic theory. 

“Embedding” refers to cases where estimated values are independent of the scale of the 

resource being valued, so that the value of a good depends on whether it is valued alone or 

as part of a more inclusive category (Kahneman and Knetsch 1992a). The “adding up” 

property, refers to the fact that the value elicited for a high level of a natural resource should 

be equal to the sum of values for two or more lower levels purchased in sequence. Both of 

these problems have been hypothesized to result from the fact that individuals, when 
6

responding to survey valuation questions, may focus on means rather than ends, and thus 

express approval or disapproval of the action resulting in the specified environmental 

changes rather than the value of the environmental changes themselves (Hausman 1993; 

Etzioni 1988). This study addresses these issues through the survey development process, 

and in tests of the survey results for embedding and adding up. 

Approach and Methodology

This study relies on primary data, collected through a survey of year-round and seasonal 

residents of the area surrounding the Peconic Estuary. The survey was designed to 

complement scientific and technical studies, using data available at the time, in order to 

provide information that will be useful in the final policy analysis. 

The survey was developed through an extensive process that included individual interviews, 

focus groups, and pretests. Based on information from the science and technical studies and 

concerns of the public, five natural resources were selected for inclusion in the survey—

farmland, undeveloped land, wetlands, shellfishing areas, and eelgrass. 

The contingent choice, rather than contingent valuation, format was chosen in an attempt to 

minimize some of the problems discussed above, particularly those related to respondents 

focusing on means rather than ends. This method is based on presenting respondents with a 

set of hypothetical alternatives with various attributes—in this case different levels of natural 

resources and cost—and requiring them to choose among or rank the alternatives. Given a 
7

large number of observed choices, discrete choice techniques are used to identify the relative 

importance of the attributes. 

Contingent choice does not require that values be expressed in monetary terms, but elicits 

choices among alternative outcomes of actions. Respondents are not asked directly to 

express monetary values, although these can be inferred from the analysis. Therefore, 

responses are more likely to focus on tradeoffs among resources. In addition, the survey 

used in this study presented only outcomes of generic actions, or ends, so that respondents 

are not given the opportunity to focus on specific means of achieving results. Thus, 

responses are less likely to be expressions of approval or disapproval for actions, and more 

likely to focus on the specific natural resources. 

In addition, survey methods are believed to be better at estimating relative demand than 

absolute demand, even for market goods (National Oceanic and Atmospheric 

Administration 1993). 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. 

The survey was completed by 968 respondents in August, 1995, in public places throughout 

the area surrounding the Peconic Estuary. The resulting data were analyzed using both a 

conditional logit model and a nested logit model. The analysis included tests of hypotheses 

related to adding up and embedding, and comparisons of different model specifications. 
8

Finally, a welfare analysis was carried out, to estimate the economic benefits of protecting or 

enhancing natural resources. 

Dissertation Outline

This dissertation is organized as follows. Chapter 2 provides background information on the 

Peconic Estuary and the Peconic Estuary Program. Chapter 3 provides an overview of 

philosophies of the natural environment and their relation to management goals and 

methods of analysis; a discussion of economic analysis of environmental policies; and a 

review of related literature. Chapter 4 describes the theoretical framework for models used 

in the analysis. Chapter 5 discusses the process of survey development. Chapter 6 describes 

the data analysis and hypothesis tests, and presents the results; and Chapter 7 provides 

conclusions and recommendations for further research. 
9

CHAPTER 2 

THE PECONIC ESTUARY SYSTEM 

This chapter provides background information on the Peconic Estuary and the Peconic 

Estuary Program. The first section gives an overview of the area and its major 

characteristics. This is followed by a discussion of the National Estuary Program and the 

Peconic Estuary Program. The final section describes the most important environmental 

problems of the Peconic Estuary, and actions that might be taken to protect or enhance the 

Estuary’s natural resources. 

The Peconic Estuary System

The Peconic Estuary system, located at the eastern end of Long Island, New York (see 

Figure 0-1) comprises over 100 bays, harbors, embayments, and tributaries, covering an area 

of over 100,000 acres. It extends from the Peconic River in the west to Block Island Sound 

in the east, and includes Flanders Bay, the Peconic Bays, Gardiners Bay, and a portion of 

Block Island Sound, as well as many smaller bays. The Estuary’s watershed drains a land 

area of around 110,000 acres (Suffolk County Department of Health Services 1994; 

Economic Analysis, Inc. 1995). 
10

Figure 0-1 - Map of Peconic Estuary System

Atlantic Ocean

Long Island Sound Block Island

Sound

Riverhead

Southold

Southampton East Hampton

Flanders

Bay

Great

Peconic

Bay

Little 

Peconic Bay

Gardiners

Bay Gardiners

Island

Plum Is.

CT RI

Long Island

Shelter

Island

Peconic River

Orient

Point

Montauk

Point

Shinnecock Bay

Moriches Bay

Brookhaven

Study Area

Town Boundary

Legend:

(map not drawn to scale)
11

The five towns surrounding the Estuary–Riverhead, Southampton, East Hampton, Shelter 

Island and Southold–are collectively known as the East End of Long Island. The Peconic 

Estuary, Long Island Sound and the Atlantic Ocean, along with many farms, vineyards, and 

historical sites, contribute to the East End’s unique character and strongly resource-based 

economy. The East End is a major recreation area for residents of New York City and 

western Long Island, and has a large seasonal population. The area also draws many artists. 

The East End’s most important economic sectors are agriculture, fishing, wine making, 

recreation, and services related to second homes (East End Economic and Environmental 

Task Force 1994). 

Estuaries are coastal areas where fresh water from rivers and streams mixes with salt water 

from the ocean. In addition to providing aesthetic, recreational, and economic benefits to 

people, estuaries are important ecosystems. They provide spawning areas and nurseries for 

fish, birds and other wildlife; marshes that filter sediment and pollution; and storm and flood 

barriers for the surrounding coastline (U.S. Environmental Protection Agency 1993). 

The Peconic Estuary system includes extensive tidal and freshwater wetlands, 15 ecosystems 

designated as rare by the New York Natural Heritage Program, and over forty areas 

designated as significant coastal fish and wildlife habitats under the New York State Coastal 

Management Program. The Peconic Estuary has the largest and most diverse concentration 

of habitats of any coastal area in New York, and many endangered species are found in and 

around the Estuary. Because of its importance to both wildlife and people, the area was 

designated by the Nature Conservancy as one of the “Last Great Places” in the Western 

Hemisphere (Suffolk County Department of Health Services 1992). 
12

The East End’s natural beauty and rural character, combined with its proximity to a large 

population center and the growing trend of telecommuting, have led to strong development 

pressures. In 1980, the population of year-round residents of the East End was 98,661. By 

1995, the year-round population was 108,783, with around 126,000 seasonal residents (Long 

Island Lighting Company 1995; Suffolk County Department of Planning 1991). On average, 

populations of coastal counties in the United States are growing three times faster than any 

other areas (U.S. Environmental Protection Agency 1993). 

Although environmental quality in the Estuary has historically been good to excellent, rapid 

development and associated environmental problems threaten the estuary’s waters and other 

amenities such as wetlands, farmland and other open space; commercial and recreational fish 

stocks; aquifers; and fish and wildlife habitat. The tidal portion of the Peconic River, the 

western portion of the Estuary system, and many of the smaller, enclosed bays face the most 

severe pollution problems. The western area of the Estuary routinely suffers from high 

nitrogen concentrations and occasionally experiences low levels of dissolved oxygen. The 

small, enclosed bays are adversely affected by bacteria from failed or outdated septic systems, 

road runoff, and discharges from boats. Farmland and other open space surrounding the 

Estuary is faced with development pressures (Suffolk County Department of Health Services 

1992). 

The most significant environmental problem to date has been an algal bloom known as the 

Brown Tide, which first occurred in 1985-1988, and has subsequently recurred for shorter 

periods. The impacts of brown tide motivated local citizens and government officials to 

propose that the Peconic Estuary be included in the National Estuary Program. 
13

The National Estuary Program and the Peconic Estuary Program

The National Estuary Program was established by Congress in 1987 under section 320 of the 

Clean Water Act. Its goal is to protect and manage nationally significant estuaries that are 

threatened by pollution, development, or overuse, and it is administered by the U.S. 

Environmental Protection Agency. Estuaries are nominated for inclusion in the program by 

the governor(s) of the state(s) where they are located. Under the program, a Management 

Conference made up of representatives of stakeholders associated with the estuary are asked 

to identify and propose solutions to the estuary’s major environmental problems, by 

developing a Comprehensive Conservation and Management Plan (U.S. Environmental 

Protection Agency 1992). As of 1995, 21 estuaries were included in the Program. 

The Peconic Estuary was added to the National Estuary Program priority list in October 

1988, and was accepted into the program in September 1992. Its Management Conference is 

made up of a Policy Committee, Management Committee, Technical Advisory Committee, 

Citizens Advisory Committee, and Local Government Committee. These committees must 

work together to develop and implement the management plan, through “collaborative 

decision making and consensus building” (U.S. Environmental Protection Agency 1992, p. 

4). 

The general goals of the Peconic Estuary Program are: 

a. To establish a comprehensive water quality policy which ensures the 

integrity of marine resources, habitat and terrestrial ecosystems while 

supporting human activities in the Peconic Estuary study area. 

b. To ensure an effective technical, regulatory and administrative framework 

for the continued monitoring and management of the Peconic Estuary 

study area. 
14

 

c. To broaden and generalize the recommended water quality policy and 

administrative and regulatory framework so that the lessons learned from 

the Peconic Estuary system will serve as a local, nationwide, and global 

model for other estuaries which may experience similar problems. Such 

an approach would focus on program development and coordination as 

well as involvement at all levels of government and citizenry (Peconic 

Estuary Management Conference 1993, p.1). 

More specific goals of the program include conservation and management of resources in 

the estuary, optimizing recreation opportunities, and promoting social and economic 

benefits stemming from the estuary (Peconic Estuary Management Conference 1993). 

Because it would be difficult to fund all possible actions to protect and enhance 

environmental quality of the Estuary, it is important to establish priorities for potential 

actions. It is not only necessary to scientifically determine which resources are most 

important, but to consider public priorities and support as well. According to USEPA 

Journal, “[i]nforming and involving the public and getting its support can be the most 

difficult aspect of an estuary program, yet it is the cornerstone of a successful NEP program” 

(quoted in Peconic Estuary Management Conference 1993, p. 21). 

Environmental Problems and Recommended Actions

The most significant environmental threats to the Peconic Estuary system are increased 

nutrient loading of the water, contamination of shellfish beds, declines in habitat quality and 

quantity, reductions in finfish and shellfish catches, potential toxic contamination, and 

brown tide (Suffolk County Department of Health Services 1992). At the time of this study, 
15

an Action Plan describing a set of preliminary recommendations for specific actions to 

protect and restore natural resources of the estuary had been completed (Suffolk County 

Department of Health Services 1994). This will be updated and finalized in the Management 

Plan. The most important environmental problems of the Estuary and potential actions are 

described briefly below. 

Brown Tide 

The most severe problem for the Peconic Estuary to date is brown tide, an algal bloom of 

the species Aureococcus anophagefferens that appeared in the Peconic Estuary system for 

extended periods in 1985-1988, and shorter periods in recent years. The causes of brown 

tide are unknown. 

Although it is not toxic to humans, the major brown tide episodes virtually eradicated the 

scallop population in the estuary, decimated eelgrass beds, and adversely affected other 

shellfish. For example, before the brown tide, scallop harvests were between 150,000 and 

500,000 pounds per year, about 28% of the United States landings. After the brown tide, by 

1987 and 1988, harvests dropped to around 300 pounds per year. Through effects on 

eelgrass, which is an important habitat, finfish populations were also affected, and may 

continue to be affected in the future (Suffolk County Department of Health Services 1992, 

1994). 
16

Restoration and monitoring of resources adversely affected by brown tide is recommended 

in the Action Plan. This includes scallop reseeding and eelgrass replanting, which would 

help restore the stocks lost as a result of the brown tide and other pollution. 

Nutrient Loading 

Excessive nutrients, particularly nitrogen and phosphorus, cause an overabundance of 

vegetation and algae, resulting in eutrophication of the water. Eutrophication occurs when 

sunlight entering the water is blocked by vegetation, and oxygen levels are depleted by its 

decomposition. Decreased oxygen levels are harmful to fish and shellfish, and decreased 

light is harmful to eelgrass, and therefore indirectly affects fish, shellfish and other wildlife 

that use the eelgrass for food and habitat. 

Nitrogen levels in the Peconic River and western Flanders Bay are currently above 

government guidelines, especially in the summer, and scientists predict that an increase in 

nitrogen could lead to serious water quality problems for the Estuary’s western areas. East 

of Flanders Bay, water quality with respect to nitrogen levels is generally excellent (Suffolk 

County Department of Health Services 1992). 

The sources of nitrogen, in order of importance, are: sediment flux (34%), or nitrogen that 

has collected over the years in sediments; groundwater underflow from septic systems and 

fertilizers (27%); Meetinghouse Creek, which was formerly the site of many duck farms 

(17%); atmospheric deposition from fossil fuel combustion (7%); the Riverhead Sewage 
17

Treatment Plant (6.5%); the Peconic River (6%); other creeks (2%); and stormwater runoff 

from the land (1.4%) (Suffolk County Department of Health Services 1992). 

Several actions can be taken to control nitrogen levels. Improvements in the Riverhead 

sewage treatment plant (STP) could reduce nitrogen levels to near the guideline in most 

areas, except for small creeks and tributaries. This could be done by installing a groundwater 

discharge system, relocating the surface water discharge to central or eastern Flanders Bay, 

or decreasing the nitrogen levels at the current discharge location (Suffolk County 

Department of Health Services 1992, 1994). 

 

Limiting development or limiting new hookups to the STP would prevent future increases in 

nitrogen discharges, through both the STP and groundwater sources. Some proposals to 

limit development include zoning for larger lot sizes, cluster development, transfer of 

development rights programs, and land preservation, acquisition, or enhancement. 

Controlling commercial, industrial and institutional uses of land has also been proposed. 

Finally, fertilizer use could be modified, by encouraging or requiring low maintenance lawns, 

use of slow-release nitrogen fertilizers, decreased use of fertilizer, or changing the timing of 

fertilizer application. Decreases in point sources would eventually change the sediment flux 

rate, but the ultimate effect of this is not well-known (Suffolk County Department of Health 

Services 1992, 1994). 
18

Coliforms 

Coliforms are bacteria that are harmful to animals, plants, and humans. The main impact of 

coliforms in the Peconic Estuary System is closure of shellfish beds, because the shellfish are 

not safe to eat. There are no areas where coliforms are high enough to close swimming 

beaches. In 1975 around 1,100 acres of the estuary were closed to shellfishing. By 1995, 

this had risen to over 4,500 acres (Suffolk County Department of Health Services 1992). 

The largest and most significant source of coliforms is stormwater runoff, which is 

correlated with intensity of land use. The Riverhead STP and duck farms along 

Meetinghouse Creek have also contributed coliform, although both of these sources have 

decreased recently. Only one duck farm remains in the area, where there were many in the 

past. Other sources are domestic and wild animal waste; improperly installed or poorly 

functioning septic systems; and marinas and boats, especially in enclosed and/or poorly 

flushed areas (Suffolk County Department of Health Services 1992). 

Large system-wide programs for reducing runoff are not expected to be worth the 

improvement that would result. However, localized actions could result in large benefits in 

certain areas, so site-specific remediation efforts are suggested. These include wetland 

restoration and construction to create a natural filtering system for runoff. It is also 

recommended that any actions that would significantly increase stormwater runoff be 

prohibited; and that vegetative buffers and sediment and erosion control plans be required 

for new development (Suffolk County Department of Health Services 1994). 
19

The Riverhead STP made improvements to coliform discharges in 1991, but could be 

further improved by converting to groundwater discharge. Stringent review of new marina 

construction is recommended, as well as encouraging increased use of holding tanks, shorebased toilets and pump-out stations by boaters, especially in environmentally sensitive areas 

and areas where boat traffic is high. Construction of new pump-out facilities would allow 

for the establishment of additional no discharge zones for boats (Suffolk County 

Department of Health Services 1994). 

Other Pollutants 

Other pollutants have not been a major problem for the Peconic Estuary. There is some 

pesticide runoff from agriculture, which has not been quantified. Other potential pollution 

sources are the North Sea Landfill and the Rowe Industries site in Sag Harbor, which could 

leak into the Bays; industrial pollution from Brookhaven National Laboratory and the 

Grumman Industries site, on the Peconic River; and discharges of oil, gas, paints, and debris 

from boats. There is no detailed information on any of these possible sources. Proposed 

actions include cleanup of the Rowe Industries site; and increasing public awareness of 

pesticide use and the disposal of household toxic chemicals (Suffolk County Department of 

Health Services 1994).
20

CHAPTER 3 

LITERATURE REVIEW

Introduction 

Until recently, nature has been viewed by Western society as a virtually unlimited resource to 

be used by humans not only to support life, but to fuel technological progress, leading to 

increased standards of living and consumption for many people. However, as the 

population grew and technology became more complex, producing more harmful waste 

products in greater quantities, the impacts of human actions could no longer be ignored. By 

the late 1960’s, environmental degradation and pollution began to be widely recognized as 

serious problems. Rachel Carson’s The Silent Spring (1962) is often cited as one of the most 

important catalysts for this increased environmental awareness. Carson’s book warned of 

the dangers of pesticides, particularly DDT, to wildlife and ultimately to humans. 

In the 1970’s the first of many environmental laws and regulations were passed, beginning 

with the National Environmental Policy Act of 1969. In 1970, the Environmental 

Protection Agency was created by Congress to help implement the growing number of 

environmental policies. Since that time, environmental concern and environmental 

programs in the United States have increased dramatically. Citizens have indicated that they 

expect careful consideration of and limitations on human interference with or disruption of 
21

ecosystems. There has also been a growing recognition of the limitations of technology in 

solving environmental problems. 

Nevertheless, people also desire the amenities of technology and civilization, such as 

automobiles, consumer products, shopping malls, new housing, and industry. There is an 

increasing recognition that people’s quality of life, health, and livelihoods, as well as the 

health of many natural resources, depend on both economic prosperity and a healthy natural 

environment. Therefore, these seemingly conflicting needs and desires must be balanced. 

Max Oelschlaeger discusses this from a philosopher’s point of view, stating that 

Life for any species is possible only in relation to an environment on which 

its continued existence depends. All species, except one, live in a naturally 

determined relationship with their environment, subject to change only 

through the workings of evolutionary process. The human animal, in 

distinction from all others, interposes culture between itself and 

environment, which is to say that Homo sapiens is a culture-dwelling animal. 

Given the cultural status quo—the multitudes that must be fed, clothed, and 

sheltered—and a commitment to maintenance of the present liberaldemocratic value system, then nature must be managed: there is no apparent 

alternative. By all indications the question is not whether advanced industrial 

society can choose to govern nature but how to do so (1991, pp. 284-285). 

This chapter discusses topics and reviews literature related to environmental policy analysis, 

focusing on economic theory and methods. The chapter begins with a discussion of 

philosophies about nature, how they have changed over time, and how they influence policy 

formulation and implementation. The next sections present an overview of neoclassical 

economic theory of the natural environment and environmental valuation. This is followed 

by a discussion of individuals’ motivations for choices, and their implications for economic 

analysis of public preferences and values for the natural environment. Next, two alternative 

models that broaden neoclassical theory are described. The chapter concludes with a 

discussion of the implications for policy analysis. 
22

Philosophies of Nature

The choice of means and goals for managing the natural environment depend on our views 

of nature and of the place of humans in relation to nature. These views, along with our 

understanding of the effects of human actions, have been changing over time, especially in 

the last fifty years or so. As a result, the goals and means of managing the natural 

environment are also changing. This section discusses how Western views of the natural 

environment have changed over time, and describes current views in the United States. 

Historical Views of Nature 

For the last three hundred years, ideas about nature in Western culture have been dominated 

by the philosophies of anthropocentrism, or the idea that humans are the measure of all 

value, and dualism, the idea that humans are separate from the rest of nature (Nash 1989). 

Nature has been viewed as mechanistic and inanimate, governed by natural laws of cause and 

effect. These views are rooted in the Protestant reformation of the sixteenth century and the 

scientific and industrial revolutions of the seventeenth and eighteenth centuries, and underlie 

the belief that nature can be mastered through science and technology. 

The theories and methods of neoclassical economics, and its primary goal of efficiency, are 

grounded in the philosophical viewpoints of the scientific and technological revolutions. 

Thus, natural resources have been viewed in economic theory primarily as inputs and waste 

repositories to be used by humans; and environmental economics has focused on the 
23

efficient allocation of natural resources and environmental amenities. However, as Herman 

Daly points out, when economics was first developed “the environment was considered an 

infinite source and sink because it was so large relative to the economy” (Daly 1991, p. 185). 

These views have provided the dominant focus for the management and use of natural 

resources to this day, although people have recently begun to recognize that the economy 

has grown so large relative to the natural environment that the consideration of throughput, 

both to and from the environment, is crucial (Daly 1991). 

The Conservationist and Preservationist Philosophies 

The primary philosophies that have guided the management of natural resources in the 

United States—conservationism and preservationism—are both anthropocentrically focused. 

Conservationism was formalized by Gifford Pinchot and Theodore Roosevelt at around the 

turn of the century. Conservationists view humans as separate from nature, and natural 

resources as existing solely to fulfill human ends, with the primary goal being economic 

efficiency. Benefit-cost analysis is the primary tool for determining efficient resource use, 

and nature is valued strictly in human terms (Oelschlaeger 1991). 

Oelschlaeger (1991) attributes the rise of the conservation movement to a growing 

recognition that the supply of resources is finite, prompted by the publications of Darwin’s 

Origin of the Species in 1859, and George Marsh’s Earth as Modified by Human Action in 1863. 

Douglas Booth (1994b) describes conservationism as beginning in response to the policies 

of distributing public land in the nineteenth century, when U.S. resource policy was primarily 
24

focused on transferring public land holdings to private ownership in order to assist 

landowners and businesses. The conservation movement focused on the efficient and 

sustainable use of public resources. One of the most important accomplishments of the 

conservationists was the creation of the U.S. Forest Service, whose primary goal is to 

manage national forests for “wise use” of natural resources. 

Preservationism, often attributed to John Muir, began at around the same time as 

conservationism, but focused on nonmaterial benefits of nature—what economists now 

refer to as nonconsumptive use values and nonuse values. Preservationists were 

instrumental in the creation of the National Park Service, which focused on preservation of 

natural areas for nonconsumptive uses, rather than for consumptive uses of resources, as in 

the national forests. The first environmental groups, the Sierra Club and the Wilderness 

Society, were preservationist in orientation, arguing for the preservation of wilderness for 

nonconsumptive, yet anthropocentric, reasons. 

Preservationism has been increasingly influential in the policy realm since the conflicts 

between John Muir and Gifford Pinchot at the turn of the century. An important difference 

between the preservationist and conservationist philosophies is that, while conservationism 

is reductionist, focused on individual components of a system, preservationism is holistic, 

with the whole of nature being seen as greater than the sum of its parts. Preservationism is 

thus an ecologically-oriented view. However, preservationism, like conservationism, is 

anthropocentrically-based because value is derived from human interests, so that economic 

efficiency remains an important goal (Oelschlaeger 1991). 
25

Nonanthropocentric Philosophies 

Although anthropocentric concerns have dominated Western thought about nature for the 

last three hundred years, the idea that the rest of nature does not exist solely for the benefit 

of humans has been expressed in various forms throughout this time. In fact Jeremy 

Bentham extended his idea of utilitarianism to non-humans, saying that “the day may come 

when the rest of the animal creation may acquire those rights which never could have been 

withholden from them but by the hand of tyranny” (quoted in Nash 1989, p. 23). However, 

although Bentham believed that utility of all creatures must be considered, he also believed 

in a hierarchy of importance, with humans at the top, followed by other creatures, and thus 

remained anthropocentrically motivated. 

Both Booth (1994b) and Roderick Nash (1989) discuss the fact that those who were most 

important in inspiring and shaping preservationist ideas—including Henry David Thoreau, 

John Muir, and Aldo Leopold—personally held nonanthropocentric values for nature. 

Thoreau considered all of nature as a single community, and its nonhuman members as his 

neighbors. Thoreau wrote that “[i]f some are prosecuted for abusing children, others 

deserve to be prosecuted for maltreating the face of nature committed to their care” (quoted 

in Nash 1989, p. 37). John Muir also advocated rights for nature, feeling that nature existed 

for itself and for God, rather than for humans. 

Aldo Leopold was one of the most important figures in the evolution of thinking about the 

ethics of human relationships with the environment. Leopold proposed the establishment of 

a land ethic, and the extension of ethics to the environment so that the environment would 
26

be given certain rights, rather than being viewed simply as property to be expediently used, 

based solely on economic or other anthropocentric considerations. Such an ethic “may be 

regarded as a mode of guidance for meeting ecological situations so new or intricate, or 

involving such deferred reactions, that the path of social expediency is not discernible to the 

average individual” (Leopold 1966, p. 239). 

However, because there was little or no organized public support for such ideas at the time, 

the preservationist leaders did not often express these values in public, or use 

nonanthropocentric arguments in fighting for their cause. Booth (1994b) believes that this is 

because such arguments were not effective, and thus did not make sense politically, at a time 

when society was primarily anthropocentrically oriented. Aldo Leopold wrote: “When one 

of these noneconomic categories is threatened, and if we happen to love it, we invent 

subterfuges to give it economic importance” (Leopold 1966, p. 247). In the 1950’s “the idea 

of an ecological consciousness and that nature had value in itself was beginning to enter 

preservationist thinking, but it had yet to significantly penetrate public discourse on 

wilderness issues” (Booth 1994b, p. 15). 

Recent years have seen the rise of public discussion and organized movements based on 

nonanthropocentric values. Several nonanthropocentric philosophies have been defined, 

including biocentrism, ecocentrism, deep ecology, social ecology, and ecofeminism 

(Oelschlaeger 1991; Zimmerman 1993; Mazzotta and Kline 1995). Biocentrism and 

ecocentrism are similar in many ways, and some have used these terms interchangeably. 

While biocentrism is founded in Darwin’s theories, ecocentrism originated more recently, 

with Aldo Leopold’s land ethic. Biocentrists define value in relation to life, so that each 
27

living organism is seen as important. Humans are thought of as members of the Earth’s 

community of life, and natural ecosystems are viewed as a complex web of interconnected 

elements, with the sound biological functioning of each being dependent on the sound 

biological functioning of the others. Therefore, biocentrists reject the claim that humans are 

superior to other species (Taylor 1981). 

Ecocentrists believe that natural systems are the basis of all organic existence, and therefore 

possess intrinsic value. Ethical human actions necessarily promote all life on earth by 

preserving intrinsic values such as diversity, stability, and beauty. Ecocentrists define value 

in relation to natural systems, thus including inorganic components and setting life in a larger 

framework. To an ecocentrist each ecosystem, including both its living and nonliving 

components, is important. Therefore, ecocentrism may be defined as a broader philosophy 

that includes biocentrism, based on the fact that life cannot exist outside of its ecosystem; 

and ecocentric values encompass human, biological, and ecosystemic values. 

Like most ecocentrists, deep ecologists do not believe that human actions should be based 

on anthropocentric values. However, while deep ecology embraces ecocentrism, it also cites 

problems with modern industrial culture and proposes societal changes. Deep ecologists 

believe that all life on earth has intrinsic value, that the richness and diversity of life itself has 

value, and that human life is privileged only to the extent of satisfying vital needs. They also 

believe that humans interfere excessively with the non-human world, that human 

populations should be reduced, and that policies related to basic economic, ideological, and 

technological structures must be changed. Finally, they believe that those who subscribe to 
28

the beliefs of deep ecology are obligated to try to implement these social changes (Naess 

1993a, b). 

Social ecologists believe that ecological problems stem from underlying social problems, thus 

necessitating major social changes. Social ecologists view the capitalist system as immoral in 

its imperative for growth at all costs, and believe that social changes that eliminate 

hierarchical and class structures are needed (Bookchin 1993). 

Ecological feminism, or ecofeminism, developed from the joining of the post-World War II 

feminist and environmental movements. Although they hold many different viewpoints, 

ecofeminists generally believe that “there are important connections between the domination 

of women and the domination of nature” (Warren 1993, p. 256). Karen Warren defines 

ecofeminism in general as “the name for a variety of positions expressly committed to 

exploring woman-nature connections . . . and to developing feminist and environmental 

philosophies based on these insights” (1993, p. 262). Ecofeminists advocate social changes 

that foster egalitarianism both between men and women, and between humans and 

nonhumans. 

Booth shows how nonanthropocentric values have been increasingly expressed in the policy 

arena, using the example of old growth forest management, by analyzing testimony at 

Congressional hearings on forest preservation issues. He found an increase in testimony 

relating to nonanthropocentric values in the 1970s and 1980s, with such testimony presented 

by five to six percent of wilderness advocates, compared to almost none in the 1950s and 

1960s (Booth 1994b). In public hearings before the Endangered Species Committee in 1992 
29

concerning preservation of spotted owl habitat, Booth found that 31.5 percent of advocates 

for preservation made nonanthropocentric arguments. Thus, there is evidence that such 

concerns are important to people, and are becoming increasingly acceptable in terms of 

policy justification. 

Americans’ Current Views 

An anthropological study of Americans’ environmental values by Willett Kempton, James 

Boster, and Jennifer Hartley (1995) provides further evidence that people’s views are 

changing. After reviewing existing studies, they conclude that there is “little doubt that a 

substantial change is taking place in the way Americans conceive of the environment. . . . the 

fact that the indicators we reviewed include voting records and market purchases means that 

the proenvironment sentiments carry some force—they are not just empty platitudes. 

Further, they are widespread across strata of society” (p. 7). In their own study, Kempton, et 

al. interviewed people representing a wide range of interest groups, and conclude that “there 

is a remarkably strong consensus across this wide spectrum on a core set of environmental 

values” (p. 12). 

They found three important sources of environmental values: religion, anthropocentric 

values, and nonanthropocentric (which they call biocentric) values. In expressing religious 

values, many respondents described nature in religious terms, invoked religious teachings, or 

expressed a spiritual connection to nature. Anthropocentric values included both concern 

for self and concern for future generations, and were expressed in terms of both material 
30

utility and aesthetic utility to humans. Concern for the future of children and descendants 

was the strongest of the anthropocentric values expressed by respondents. Only a small 

minority of respondents held a purely utilitarian view, that nature’s only function is to serve 

humans, although less extreme utilitarian values were common. 

Nonanthropocentric values were expressed by a substantial minority of respondents. These 

include the idea that humans are part of nature, and thus should not harm it, the idea that 

other species have a right to exist, and the idea that nature has intrinsic rights beyond the 

survival of species. However, even those with the strongest nonanthropocentric views also 

expressed anthropocentric utilitarian concerns, so that there were no sharp distinctions 

between respondents, but a gradation in views. 

Kempton, et al. conclude that “current environmental values represent a major change in 

how our culture conceives of the relationship between humanity and nature. The speed of 

this change is stunning—it has surely occurred since the end of the nineteenth century and 

perhaps mostly over the last thirty years, since Rachel Carson published Silent Spring” (p. 

226). Taken together, Booth’s and Kempton, et al.’s results imply that nonanthropocentric 

viewpoints are becoming more common among members of the American public. 

Goals of Management 

Based on the dominant philosophical viewpoints, the predominant goals in managing natural 

resources in the United States have been anthropocentric in nature. Although 
31

nonanthropocentric views have been expressed to some extent over the last three hundred 

years, they have not been the basis for policy decisions regarding the natural environment. 

Recently, however, there has been an increase in the expression of ecocentric goals and 

values. Some reasons for this may include the realization of the limits of both nature and 

technology, the movement towards holism in science, and the emergence of the science of 

ecology. 

Thus, many programs and laws are also expressing more ecocentric goals, most notably the 

Endangered Species Act, which protects endangered species regardless of most humancentered considerations. Both the Comprehensive Environmental Response and 

Compensation Act (CERCLA) and the Oil Pollution Act (OPA) focus on restoring natural 

resources, rather than paying monetary compensation to people, as the primary remedy for 

damages resulting from spills of oil or hazardous substances. However, restoration may 

focus on making the environment whole based on ecocentric considerations, or on making 

the public whole based on anthropocentric considerations. Even the Endangered Species 

Act provides for a Cabinet-level review board, the so-called “God Squad,” that may allow 

the extinction of species in special cases (Booth 1994b, p. 210; Plater, Abrams and Goldfarb 

1992, p. 670). 

These examples provide evidence of a trend in law and policy away from an entirely 

anthropocentric approach and towards a balancing of human and environmental concerns. 

While economic efficiency remains a priority, and in fact many laws require benefit-cost 

analysis, other considerations are also important, and we may be moving in the direction of 

Leopold’s land ethic. Leopold suggested that we “[e]xamine each question in terms of what 
32

is ethically and esthetically right, as well as what is economically expedient. A thing is right 

when it tends to preserve the integrity, stability, and beauty of the biotic community. It is 

wrong when it tends otherwise” (1966, p. 262). Consequently, multidisciplinary approaches 

to policy analysis are becoming increasingly important. 

For example, as discussed in Chapter 2, goals of the Peconic Estuary Program focus on 

“ensur[ing] the integrity of marine resources, habitat and terrestrial ecosystems while 

supporting human activities” (Peconic Estuary Management Conference 1993, p.1). In order 

to meet these goals, the Program includes natural science studies, economic studies, and 

public participation and outreach in a consensus-building approach. 

In summary, management of the natural environment seems to be evolving towards a more 

holistic approach. At the same time, economics is also evolving, and the last few years have 

seen a growth in the multidisciplinary fields of ecological economics and socio-economics. 

While the former focuses on the economy within a larger biophysical system, the latter 

focuses on the economy within a larger social system. This will be discussed in greater detail 

below. The next sections describe standard neoclassical theory of the natural environment, 

and some limitations and extensions of that theory. 
33

Neoclassical Theory of the Natural Environment 

Natural Resources as a Portfolio of Assets 

Economists often view natural resources and environmental attributes as a portfolio of 

assets, which provide a flow of services to both the ecosystem in general and to people 

(Clark 1990; Mazzotta, Opaluch and Grigalunas 1994). These services include life support—

food, air, water, and shelter—for all living things; as well as inputs to fulfill more complex 

needs and desires of humans. Myrick Freeman (1993) describes natural resources and the 

environment as providing four types of services to the human economy: material inputs, lifesupport services, amenity services, and waste dispersion, transformation, and storage 

services. 

Management of natural assets, or environmental policy, may be seen as analogous to 

management of a trust fund of financial assets, where humans are the trustees of the planet’s 

resources. Although we may not “own” the planet’s natural resources or environment, 

humans are capable of controlling, altering, and destroying natural resources and 

environmental attributes, or “natural assets,” on a large scale. We are also the only creatures 

who do so consciously, and thus we are the only ones who can consciously act to preserve, 

protect and manage natural assets. 

Like a portfolio of financial assets, natural assets may be managed in a variety of ways, to 

fulfill a broad spectrum of possible goals. These might include, for example, human 
34

enjoyment or financial gain, fairness, biodiversity, or sustainability. Natural assets may be 

managed for the benefit of humans, both current generations and future generations, or for 

the benefit of non-human species or biota, particular ecosystems, or even inanimate features 

of the environment. Thus, management goals may be anthropocentrically focused or 

nonanthropocentrically focused. Simply because the analogy to financial assets is made does 

not mean that the overriding goal must be to maximize monetary benefits, or even benefits 

to humans. In fact, financial assets may also be managed for goals other than maximizing 

monetary returns, such as risk reduction, a steady flow of income, or social responsibility. 

Consequently, the important questions for management of natural assets are, first, defining 

management goals; and then defining, selecting and implementing acceptable policies to 

fulfill those goals. 

The goals espoused by an individual will depend on that person’s philosophy of nature. In 

turn, the means of managing natural assets will also be grounded in a certain philosophy, or 

perhaps in a combination of ideas from different philosophies. “Whatever an individual’s 

idea of wilderness, mere stockpile of natural resources or Mother Earth, the mass of 

humanity so fundamentally alters nature that no laissez-faire position is rational. ... Rather 

the conservation question arises at a more fundamental level: the issue involves the theorie

upon which the praxis will rest—the idea of wilderness itself. Whatever this idea, the 

conceptual difference will be reflected in practice” (Oelschlaeger 1991, p. 285). Hence, in 

order to formulate and implement sound policies, it is important not only to determine the 

best means of management, but also to be clear about the goals. 
35

Of course, there are many criteria for comparing policies, including economic efficiency, 

fairness, or majority vote. The criteria might be established by law or other institutional 

requirements, or might be context dependent. Political, social, and cultural concerns might 

affect the decision. For example, in a case where a particular cultural tradition is affected, 

such as subsistence whaling, cultural standards and values might take precedence over other 

concerns. Although historically the primary goal of most neoclassical economic analysis of 

natural resource issues has been economic efficiency, other goals are beginning to be 

recognized and included in economic analysis, particularly by the newly developing fields of 

ecological economics and socio-economics. 

Neoclassical Duality Theory and Revealed Preference Theory 

Economics is often defined as “the study of the allocation of scarce resources among competing end 

uses” (Nicholson 1989 p. 3, emphasis in the original). Eugene Silberberg (1990, p. 2) states 

that the important aspect of economics, which distinguishes it from other social sciences, is 

its conceptual framework, the neoclassical paradigm. He claims that problems that cannot 

be analyzed with this paradigm are noneconomic problems. 

According to Silberberg, while social sciences in general study human behavior, neoclassical 

economics studies this behavior in terms of individual choice. Economic choices are 

defined as the interaction between people’s tastes or preferences and the relevant 

opportunities or constraints. Constraints include incomes and the prices of goods; 

institutional constraints, such as laws and property rights; and production constraints, such 
36

as available technology. While constraints may be more or less observable and measurable, 

tastes and preferences are not as easily observed or measured. Thus, economists form 

assumptions or hypotheses about preferences in order to explain and predict behavior 

(Silberberg 1990). 

Neoclassical duality theory provides the basic framework for explaining and predicting 

consumer choices and for deriving welfare measures. This framework relies on very specific 

assumptions, grounded in the utilitarian ethic, about people’s preferences, how they make 

choices, and how to interpret individuals’ choices. Individuals are assumed to have fixed (at 

least for the duration of the analysis) preference orderings that are complete, transitive, 

reflexive, and continuous (Silberberg 1990; Varian 1984). 

Completeness requires that any two bundles of commodities can be compared, so that one 

bundle is preferred to the other or the person is indifferent between bundles. Transitivity 

requires that, if bundle A is preferred to bundle B, and B is preferred to C, then A is 

preferred to C, so that a scalar measure of the relative ranking over bundles can be 

constructed. Reflexivity simply says that a person is indifferent between any bundle and 

itself. A continuous ordering is one where, if A is preferred to B, and B is preferred to C, 

then some linear combination of A and C exists, which is indifferent to B (Nicholson 1989; 

Varian 1984). 

Based on their preference orderings, individuals are assumed to maximize their utility subject 

to a budget constraint, by choosing to consume the most preferred bundle of goods that is 

affordable. Thus, utility is equated with satisfaction of preferences, and utility is assumed to 
37

be greater when a more preferred bundle is consumed than when a less preferred bundle is 

consumed. Utility is generally defined as “the consumer’s own subjective evaluation of the 

satisfaction . . . derived from consuming . . . commodities” (Silberberg 1990, p. 299). 

Standard neoclassical theory defines this in ordinal terms, so that consumers are only 

assumed to be able to rank bundles of commodities; as opposed to cardinal measures, where 

it would be necessary to measure the actual level of utility achieved by consuming a 

particular bundle of commodities. 

The utility function is assumed to have certain mathematical properties: to be monotonically 

increasing and continuous. Three other basic assumptions of neoclassical utility theory are 

nonsatiation, substitution, and diminishing marginal rate of substitution. Nonsatiation says 

that more of a good is always preferred to less. Substitution says that individuals are willing 

to make tradeoffs between goods, and that the maximum amount of one good that will be 

traded for another is the amount that will maintain the individual’s existing level of utility. 

The diminishing marginal rate of substitution assumption implies that, as more of a good is 

consumed, its marginal value relative to other goods decreases (Nicholson 1989). 

The solution of utility maximization subject to a budget constraint, 

maximize U(X) 

subject to P'X = Y, 

where U(A) is the utility function, X is a vector of goods consumed, P is a vector of prices 

of goods, and Y is total income, leads to the Marshallian, or money income held constant, 

demand functions, 

xi = xi

M(P, Y). 
38

Substituting these functions back into the utility function gives the indirect utility function, 

U*(x1

M(P, Y), ..., xnM(P, Y)) = U(P, Y), 

which gives the maximum level of utility for a given set of prices and income (Silberberg 

1990; Varian 1984). 

The dual problem is that of minimizing expenditures subject to maintaining a fixed level of 

utility, 

minimize E = P'X

subject to U0 = U(X), 

 where E is total expenditures and U0 is the initial level of utility. The solution to this gives 

the Hicksian, or real income held constant, demand functions, 

xi = xi

U(P, U0). 

Substituting these into the expenditure function results in the indirect expenditure function, 

E*(P, U0) = P'XU, 

or the amount of money that allows a person to attain a given level of utility at a given set of 

prices (Silberberg 1990; Varian 1984). 

The partial derivatives of the Hicksian demand functions with respect to prices give 

measures of pure substitution effects, as utility is held constant, while the partial derivatives 

of the Marshallian demand functions with respect to prices measure the sum of income and 

substitution effects (Silberberg 1990; Varian 1984). 

The theory of revealed preference shows how, under certain conditions, preferences as 

revealed through market exchanges are consistent with utility theory. Thus, it links utility 
39

theory with observable behavior, and provides a means for economists to use market data to 

infer values for goods. In economic terms, the marginal value of a good is measured by the 

amount of income that must be given up to obtain the last unit of the good, holding utility 

constant. Utility theory says that people will continue to make trades until the marginal 

utility of all goods is equal. Thus, if all of the assumptions of utility theory hold, and if the 

economy is competitive, prices can be used to measure the marginal value of market goods. 

Revealed preference theory states that if the bundle or vector of goods x

0

 is chosen, given 

the price vector p0

 and income Y0, where p0x

0

=Y0, then x

0

 is revealed preferred to all 

affordable bundles that were not chosen. The weak axiom of revealed preferences says that, 

if x

0

 is revealed preferred to x

1

, where x

1

 is also affordable, then x

1

 will never be revealed 

preferred to x

0

 (Varian 1984). 

Certain restrictions on behavior, which are consistent with utility theory, are implied by the 

weak axiom of revealed preferences. First, the Marshallian demand relations are 

homogeneous of degree zero in all prices and income. Thus, if all prices and income change 

by a constant factor, the level of demand will not change. Second, the budget constraint 

holds for the whole system of demand. Third, the matrix of second derivatives of the 

expenditure function is negative semidefinite (Varian 1984). 

The one consequence of utility theory that is not implied by the weak axiom of revealed 

preference is the symmetry of the Hicksian cross-price effects, which is necessary for a utility 

function to exist. This is because the weak axiom allows intransitivity of preferences, since 

only pairs of bundles are compared. The strong axiom of revealed preference was developed 
40

to force transitivity of preferences, so that revealed preferences can be shown to imply all of 

the consequences of utility theory. The strong axiom of revealed preference says that, for 

any finite set of bundles, x

1

,...,x

k

, if x

1

 is revealed preferred to x

2

, and x

2

 revealed preferred to 

x

3

, ... , x

k-1 revealed preferred to xk

, then x

k

 is not revealed preferred to x

1

 (Varian 1984). 

Choices that conform to the axioms of revealed preference theory and utility theory are 

defined by economists as “rational” choices. Assuming that individuals follow the principles 

of rational choice, or at least act “as if” they do, allows economists to use market data to 

infer preference orderings and values for market goods (Silberberg 1990). However, in the 

case of nonmarketed goods, prices do not exist, and other methods, which will be discussed 

below, must be used to measure value. 

Welfare Theory 

Economists analyze policies using the tools of welfare economics, which “focuses on using 

resources optimally so as to achieve the maximum well-being for the individuals in society” 

(Just, Heuth and Schmitz 1982, p. 3). Theoretically, the well-being of individuals is 

represented by their level of utility. However, utility is unobservable and, even if it could be 

observed and measured, there is no means of objectively comparing the utility of different 

people. Thus, another means of measurement is needed. This is provided through the 

duality framework and the concept of Pareto optimality (Just, Heuth and Schmitz 1982). 

Duality theory shows that, if people maximize utility through their consumption decisions, 

the area under observable Marshallian demand functions can provide information in the 
41

form of dollar measures of consumer’s surplus. This “willingness to pay” is assumed to 

represent a person’s utility level, given their tastes and preferences, income level and the 

existing price structure (Just, Heuth and Schmitz 1982). However, consumer’s surplus does 

not generally provide a unique measure, except under very restrictive conditions. Hence, 

Hicks defined a set of four measures—compensating and equivalent variation, and 

compensating and equivalent surplus—that do provide unique measures of value. These are 

measured by areas under the Hicksian demand functions. 

Compensating measures assume that the individual maintains the initial level of utility; while 

equivalence measures assume that the individual maintains the subsequent level of utility. 

Surplus measures constrain the quantity of the good to either the quantity that would be 

purchased at the new price if no compensation is given, in the case of compensating surplus, 

or the quantity that would be purchased at the old price if no compensation is given, in the 

case of equivalent surplus. Variation measures do not constrain the quantity of the good 

purchased (Mitchell and Carson 1989, p. 25). 

Thus, compensating variation is the amount of money that, when taken from a person after 

a change in price, quality or quantity (in the case of public goods) of a good or set of goods, 

leaves the person as well off as before the change. In cases where a person faces a welfare 

gain, this would be the maximum willingness to pay (WTP) for the gain; and in cases where a 

welfare loss is faced, it would be the minimum willingness to accept (WTA) for the loss. 

Compensating variation can be calculated as the difference between expenditure functions. 

In the case of a price change, this would be 

CV = E(P1

, U0) - E(P0, U0), 
42

where U(P1, Y0 + CV) / U(P0, Y0) / U0 

and CV is the compensating variation, E(@) is the expenditure function, P1 is the vector of 

prices after the change, P0

 is the vector of prices before the change, U(@) is the utility 

function, and Y is the person’s income. 

Equivalent variation is the amount of money that must be paid to a person when a change 

does not occur, so that the person achieves the level of utility that would have been reached 

with the change. Thus, it is the minimum WTA to forgo a gain or the maximum WTP to 

avoid a loss. This can be represented as 

EV = E(P0

, U1) - E(P0, U0), 

where U(P1, Y0) / U(P0, Y0 + EV), 

Hence, compensating and equivalent variations are income adjustments that maintain a given 

level of welfare, and compensating variation maintains initial welfare while equivalent 

variation maintains subsequent welfare. 

Compensating and equivalent surplus are similarly defined, with the additional restriction 

that consumption of the good in question must remain at a fixed level, as defined above. In 

terms of expenditure functions, these are represented as: 

CS = E(P1

, X1, U0) - E(P0, X1, U0), 

and 

ES = E(P1, X0, U') - E(P0, X0, U1). 

However, Hicksian measures of compensation cannot be directly observed from market 

data, because market data only allows for the derivation of Marshallian demand functions. 
43

Again, duality theory provides a solution. Robert Willig (1976) showed that, under certain 

conditions, consumer’s surplus can provide a close approximation to the Hicksian measures. 

Jerry Hausman (1981) and Yrjo Vartia (1983) developed mathematical methods for deriving 

Hicksian measures from consumer’s surplus measures. 

Another concern in calculating social welfare is the fact that interpersonal comparisons of 

welfare measures are not possible. In his impossibility theorem, Arrow showed that, under 

reasonable requirements, a general rule cannot be devised to rank social states based only on 

individuals’ preference rankings (Arrow 1951; Just, Hueth and Schmitz 1982). Consequently, 

creating a social welfare function is a subjective exercise that requires value judgments related 

to equitable distributions of wealth. There are many ethical tenets, such as Rawls’ theory of 

justice, that might determine such weightings, but economists generally avoid the issue by 

focusing on efficiency criteria, leaving equity considerations to policy makers (Just, Hueth 

and Schmitz 1982). 

Consequently, the Pareto, or efficiency, criterion, can be used to determine the optimal 

economic solution to a policy problem. This criterion says that a state of the economy is 

unambiguously preferred if at least one person is better off and no one is worse off. If a 

point is reached where no one can be made better off without making someone else worse 

off, a Pareto optimum, or efficient state, has been achieved (Just, Hueth and Schmitz 1982). 

The efficient solution is thus contingent upon a given income distribution, which may or 

may not be considered to be equitable; and there are many possible efficient solutions, each 

associated with a given income distribution. 
44

Because the Pareto criterion requires that no one be made worse off by a policy, it restricts 

the domain of options that may be considered. To avoid this problem, the Kaldor-Hicks 

compensation principle was developed. This says that a state is preferred if gainers can 

potentially compensate losers, so that everyone could be better off, although actual 

compensation need not occur (Just, Hueth and Schmitz 1982). This is the principle 

underlying benefit-cost analysis, where options are evaluated by comparing total benefits to 

total costs. 

In summary, based on the principles of duality theory and its underlying assumptions, 

economists generally define value as “the amount of money the individual is willing to pay or 

accept to move from one situation to another” (Just, Hueth and Schmitz 1982, p. 10). These 

money measures of willingness to pay are assumed to provide a proxy measure of a person’s 

level of utility because they indicate what someone is willing to give up to maintain a given 

level of utility. However, dollar measures are not necessary, and may not be the best 

measure in some circumstances. Money is simply a convenient representation of intensity of 

preferences, or the level of utility, because it can often be calculated from market data, and 

because it provides a means of comparing and summing values for diverse goods. In 

general, the economic definition of value is what someone will give up to get something, 

while holding their utility fixed, whether it is money, time, or other goods. 

Economists are often asked to analyze resource management problems, in order to help 

policy makers determine the best use of natural resources. In general, economists have 

focused on the goal of economic efficiency, using tools of benefit-cost analysis. In benefitcost analysis, economic values of the positive and negative impacts of a project are 
45

compared, generally in dollar terms. In some cases, cost-effectiveness analysis, which 

determines the least costly way to achieve a goal, may be more appropriate or more easily 

carried out. However, as Freeman points out, “a welfare measure should answer the 

questions posed by policymakers,” and other goals, such as equity, intergenerational effects, 

sustainability, and social risk aversion might also be important (Freeman 1993, p. 40). 

Because most of the services of natural resources and the environment are not marketed, 

their economic values are not straightforward to determine. The next sections discuss 

categories of value, methods of nonmarket valuation and issues surrounding them, and how 

other goals might be considered in a policy analysis. 

Economists’ Categories of Natural Resource and Environmental Values 

Economists define several categories of value that people might derive from natural 

resources or environmental amenities. The two primary categories are use values and 

nonuse values. Use values may result from consumptive or nonconsumptive uses of a 

natural resource. Consumptive uses involve a reduction in the resource, for example 

hunting, fishing, or withdrawing water from a reservoir for drinking. Nonconsumptive uses 

do not diminish the resource, and include birdwatching, hiking and other such activities. 

Robert Mitchell and Richard Carson (1989) categorize use values into direct and indirect 

uses, where direct uses require contact with the resource, and indirect uses are those that are 

enhanced by the existence of the resource. An example of an indirect use value would be 

the value of wetlands as a nursery for fish that are later caught by anglers. 
46

The concept of nonuse values was first introduced to economics by John Krutilla (1967), 

who suggested that people derive benefits from preserving scenic wonders or important 

ecosystems, for at least two reasons: preserving the option for future use, and bequeathing 

the resources to future generations. Since Krutilla’s article was written, several types of 

nonuse values have been defined by economists, and there has been a great deal of 

discussion and disagreement over these definitions (see, for example, Boyle and Bishop 

1987; Fisher and Raucher 1984; Freeman 1993; Mitchell and Carson 1989; Randall 1991; 

Randall and Stoll 1983; Smith 1987). 

Mitchell and Carson define nonuse values as providing “utility from an amenity for various 

reasons other than . . . expected personal use” (1989, p. 63). They define four types of 

nonuse benefits, in two categories that they call vicarious consumption and stewardship. 

Vicarious consumption values are defined as the utility received by knowing that others, 

either specific or unknown individuals, are able to use a resource. “Stewardship values 

involve a desire to see public resources used in a responsible manner and conserved for 

future generations” (Mitchell and Carson 1989, p. 64). Two types of stewardship values are 

defined by Mitchell and Carson—bequest values and inherent values. They define bequest 

values as based on the utility received from making a resource available for future 

generations’ use; and inherent values as based on the utility received from knowing that the 

resource is preserved “regardless of whether it will ever be used by anyone” (p. 65). The 

values that Mitchell and Carson refer to as inherent values have also been termed intrinsic 

values. 
47

Generally, economists attempt to measure the “total value” of a natural resource or 

environmental amenity. “Total value is viewed as an individual’s willingness to pay to 

preserve or maintain a resource in its present state” (Freeman 1993, p. 145). Thus, if total 

value is greater than use value, the difference is defined as nonuse value. 

Myrick Freeman (1993) provides one of the clearest expositions of the standard neoclassical 

model for resource valuation. Freeman assumes that the resource or environmental attribute 

provides utility to people, and thus is an argument in the utility function. He defines a 

vector of market goods, X, and a nonmarket resource, q, where q is defined as a scalar 

measure of some characteristic of the environment (e.g., quantity or quality of a natural 

resource or other environmental amenity). Thus, if an individual maximizes utility, U(X, q), 

subject to their budget constraint, the value of changes in q can be measured in the usual 

way, as the difference between the two expenditure functions. Freeman defines this as the 

total value of q, which includes use plus nonuse values. 

To distinguish between use and nonuse values in the model, Freeman assumes that use 

values require weak complementarity of the resource, q, with some market good, x1. Weak 

complementarity implies that a nonmarket good is consumed together with a market good, 

so that if the market good is not consumed, demand for use of the nonmarket good is 

assumed to be zero (Mäler 1974; Mitchell and Carson 1989; Freeman 1993). While Freeman 

points out that consumption of a market good is not necessary for all use values, he adopts 

this assumption because it provides a convenient distinction between use and nonuse values, 

and allows for measurement of use values through observations of market transactions. 
48

Freeman presents four types of value: use value, pure nonuse value, user’s nonuse value, and 

pure existence value. Use value occurs when the price of x1, p1, is less than the choke price 

p*, and q is greater than some threshold value, qmin, where x1 is the market good and q is the 

nonmarket resource. This threshold is defined as the minimum level of q where the 

resource exists and can be used, or a “minimum viable level of the resource” (Freeman 1993, 

p. 151). If the conditions of weak complementarity hold, then use value would be measured 

by the change in expenditures that would compensate for an increase in the price of x1 to the 

choke price. 

Pure nonuse value occurs when the price of x1, p1, is greater than the choke price p*, so that 

the individual will not use the resource q. This is measured by the change in expenditure 

necessary to maintain the initial utility level when q changes. User’s nonuse value would be 

measured by the change in expenditure needed to maintain the initial utility level of a user, 

after the price has risen beyond the choke price. Finally, pure existence value is the loss in 

nonuse value when the resource falls below the threshold level. This relies on the 

assumption that the expenditure function is discontinuous in q at the threshold level, and 

that the derivative of the expenditure function with respect to q becomes zero below the 

threshold. 
49

Nonmarket Valuation

Methods 

Because most natural resources and environmental amenities are not traded in markets, 

economists have devised a variety of methods for measuring their value, several of which 

rely on survey data. These methods include direct or indirect; and observed or hypothetical 

methods (Mitchell and Carson 1989). Direct observed methods involve observations of 

people’s actual behavior, such as purchases of marketed resources. Direct hypothetical 

methods ask people to express their values based on a hypothetical scenario. Contingent 

valuation is a direct hypothetical method, where people are asked their willingness to pay for 

hypothetical changes in resources or environmental quality. 

Indirect observed methods, including the travel cost method, infer values from observations 

of people’s actions in closely related markets; for example, how far they travel to a 

recreational site and how much money they spend on equipment. These methods are based 

on the concept of weak complementarity, which implies that a nonmarket good is consumed 

together with a market good, so that if the market good is not consumed, demand for use of 

the nonmarket good is assumed to be zero (Mäler 1974; Mitchell and Carson 1989; Freeman 

1993). 

Indirect hypothetical methods, including contingent behavior and contingent choice, involve 

questioning people about their intended actions based on a specific scenario. For example, 

using the contingent behavior method, a person might be asked how many more times they 
50

would visit a beach if the water were cleaner. In a contingent choice survey, respondents are 

asked to choose a preferred alternative, or rank two or more hypothetical scenarios. Thus, 

unlike contingent valuation, contingent choice does not directly ask for willingness to pay, 

but derives it from responses, using an analytical model. Survey responses are analyzed 

using a discrete choice, random utility model, and the marginal rates of substitution between 

attributes of the alternatives are calculated. If one attribute is the cost of the alternative, then 

willingness to pay for the other attributes may be inferred from the results (Freeman 1993). 

This model is described in detail in Chapter 4. 

Reliability and Validity 

The reliability and validity of the resulting data are major concerns related to the use of 

hypothetical valuation methods; and a great deal of effort has gone into developing and 

testing these methods, particularly the contingent valuation method. Contingent valuation 

(CV), the primary method that has been used to estimate nonuse values, and nonuse values 

themselves, have been the subject of a great deal of controversy, most notably in the context 

of litigation over assessment of natural resource damages under CERCLA and the Oil 

Pollution Act (see, for example, Cameron 1992; Cicchetti and Wilde 1992; Harrison 1992; 

Kahneman and Knetsch 1992a, 1992b; Larson 1992; Lazo et al. 1992; National Oceanic and 

Atmospheric Administration 1993; Nickerson 1993; Smith 1992). 

Criticisms of contingent valuation methods include arguments that respondents often do not 

answer in a way that is consistent with rationality as defined by economists; that respondents 
51

do not understand the good(s) being valued; and that hypothetical surveys are not taken 

seriously by respondents (National Oceanic and Atmospheric Administration 1993; 

Hausman 1993). Because of this controversy, the National Oceanic and Atmospheric 

Administration (NOAA) convened a “blue-ribbon” panel, including two Nobel Laureates, to 

consider whether contingent valuation can reliably estimate nonuse values, and to 

recommend standards for its use. The Panel commented on several important issues 

(National Oceanic and Atmospheric Administration 1993). 

First, willingness to pay amounts may be implausibly high when the total number of 

environmental problems that a household might pay for is not considered by respondents, or 

if respondents do not consider substitutes for the resources in question. Second, 

respondents may not consider their budget constraint when stating their hypothetical 

willingness to pay, and thus could overstate their true willingness to pay. 

Third, respondents may not understand exactly what they are being asked to value, and thus 

may be expressing their willingness to pay for something other than intended by the 

researcher. For example, if respondents believe that damage will be greater than that stated 

in the survey, they might express a higher value than they would if they believed the effects 

described in the scenario. Other respondents might believe that the scenario is exaggerated 

and thus give lower values than they would for the actual effects described. 

Fourth, the extent of the market may not be easily defined. While it might be defined by 

law, it often must be defined by the researcher. For example, in the case of damages to the 

population of one state, losses might extend to individuals outside the state, and thus could 
52

be included in the analysis. However, if those outside the area are expected to have very low 

values, then it could be appropriate to omit them from the analysis. 

Fifth, there is a concern that CV studies can produce results that appear to be inconsistent 

with the assumptions of rational choice. For example, in what is referred to as the 

embedding problem, willingness to pay might not increase as the quantity of the good 

increases (Kahneman and Knetsch 1992a). A related problem is violation of the adding up 

property, which states that the value elicited for a high level of a natural resource should be 

equal to the sum of values for two or more lower levels purchased in sequence, where the 

purchase of the second good is conditional on already having purchased the first (Diamond, 

et al. 1993). These types of problems are often thought to result from the Panel’s final 

concern, that individuals, when responding to CV surveys, may be expressing their concern 

for the issue, referred to as the “warm glow” of giving (Andreoni 1989), or their disapproval 

for some aspect of the scenario, rather than their economic value for the resources in 

question. Under these circumstances, CV responses are not reliable estimates of willingness 

to pay for the specific resource, but might indicate approval or disapproval for the scenario 

described. 

The Panel states that the embedding problem is “perhaps the most important internal 

argument against the reliability of the CV approach” (National Oceanic and Atmospheric 

Administration 1993, p. 4607). If it is caused by the “warm glow” motivation, “willingness 

to pay might well exceed compensation required because the former contains an element of 

self-approbation. It might be real but not properly compensable” (National Oceanic and 

Atmospheric Administration 1993, p. 4607). To address this, the Panel recommends that 
53

the survey contain enough context to achieve a realistic background, that the survey design 

should explicitly address the “warm glow” problem, and that tests be conducted to assure 

that willingness to pay is responsive to the quantity of the resource. 

The Panel provides a set of guidelines for surveys, placing the burden of proof on survey 

designers to show “through pretesting or other experiments that their survey does not suffer 

from the problems that these guidelines are intended to avoid” (National Oceanic and 

Atmospheric Administration 1993, p. 4609). The guidelines require that respondents be 

given adequate information about the resources to be valued and the available substitutes; 

that the payment vehicle be clear and the budget constraint emphasized; that the payment 

scenario be realistic, preferably using a referendum format; and that all choices in designing 

the survey be made in the conservative direction. The Panel also lists several conditions that 

would render survey results “unreliable” in their judgment. These include a high 

nonresponse rate to the survey or the valuation question; inadequate responsiveness of 

values to the scope of the change; lack of understanding of the task by respondents; lack of 

belief in the scenario; and answers to the valuation question that are not followed up by 

comments that refer to the cost or the value of the resources (National Oceanic and 

Atmospheric Administration 1993, p. 4609). 

The Panel concludes that, although there are problems with contingent valuation, it is 

reliable enough to provide values that can be used as “the starting point of a judicial process 

of damage assessment,” if their guidelines are followed (National Oceanic and Atmospheric 

Administration 1993, p. 4610). They also conclude that “hypothetical markets tend to 

overstate willingness to pay. . . .” However, “[n]o automatic or mechanical calibration of 
54

responses seems to be possible” (National Oceanic and Atmospheric Administration 1993, 

p. 4610). 

Motivations for Choice 

In the case of natural resource valuation, particularly with regard to nonuse values, there has 

been a great deal of discussion about whether it is important to consider peoples’ 

motivations for their choices (see, for example, Edwards 1986; Etzioni 1988; Kopp 1992; 

Opaluch and Grigalunas 1993; Peterson, Driver and Gregory 1988; Sagoff 1988b; Vatn and 

Bromley 1994). The NOAA CV Panel’s concerns and recommendations regarding the 

“warm glow” effect imply that it is important to consider respondents’ motivations when 

designing a valuation survey. 

However, standard neoclassical economic theory is based on the assumption that all 

motivations can be reduced to self-interest, represented by a single utility function, so that 

motivations for choice need not be considered in the analysis of demand. For example, 

Silberberg says that 

[t]he assertion that consumers possess utility functions is a statement that 

people do in fact have preferences. How these preferences came to be, and 

why they might differ among people of different countries or ethnic groups, 

is a discipline outside of economics. These are certainly interesting 

questions. They are also exceedingly difficult to grapple with. The specialty 

of economics arose precisely because it was fruitful in many problems to 

ignore the origins of individuals’ tastes and explain certain events on the basis 

of changes in opportunities, assuming that individuals’ tastes remain constant 

in the interim (1990, p. 303). 
55

Freeman believes that “motivations do not play an important role in the empirical analysis of 

the demands for market goods. There is little talk of ‘prestige value’ and ‘speed value’ in the 

literature on the demand for automobiles. So why should motivations be important in the 

case of nonuse values?” (1993, p. 145). He thus does not consider the possibility that 

different motivations, moral commitments in particular, could result in violations of standard 

utility theory, and thus make welfare measurement based on this theory invalid. 

For example, as noted by the CV Panel, the “warm glow” motivation can result in dollar 

values that remain constant over a wide range of resource quantities. Additionally, choices 

based on moral commitments may lead to non-scalar utility, where a decision is not made by 

making tradeoffs and balancing, as implied by neoclassical theory, but by some other means, 

such as lexicographic ordering; or to choices that are incommensurable, where a decision 

may be difficult or impossible to make (Edwards 1986; Etzioni 1988; Opaluch and 

Grigalunas 1993). The latter case might lead to protest responses in surveys, because 

respondents might feel that these types of tradeoffs are inappropriate, amounting to 

coercion or bribery (Opaluch and Grigalunas 1993). Because all of these examples lead to 

violations of the axioms of revealed preference theory, welfare measures derived in the usual 

way from these choices are not meaningful. 

Consequently, it is important to consider motivations, and how they affect value estimates. 

While a variety of motivations other than personal preferences have been considered, moral 

commitments have often been singled out as the most important. The following sections 

discuss several concepts related to motivations that are relevant to valuation and policy 

analysis of the natural environment. 
56

Means and Ends 

An important concept for economic valuation is the distinction between means and ends. 

“Economic models of consumer behavior generally are based on the assumption that value 

derives from the goods and services that are consumed [i.e., the ends], not from the process 

by which these goods are allocated [i.e., the means]” (National Oceanic and Atmospheric 

Administration 1993, p. 4613). However, ethical motivations for choice may often make 

means important, as well as affecting preferences over the goods themselves. 

As the NOAA CV Panel notes in its discussion of the “warm glow” problem, “happiness 

that derives from charitable giving may come mostly from the act of giving rather than from 

the material changes that follow from the gift” (National Oceanic and Atmospheric 

Administration 1993, p. 4613). Similarly, individuals’ concerns about policies might include 

not only the policy goal, but also how it is implemented. For example, a large number of 

people might value the improved air quality resulting from decreasing emissions from power 

plants; but some people may support certain means, such as emissions trading or taxes, while 

others see them as unacceptable. Thus, determining what is acceptable to people, and why, 

can be an important step in policy implementation, especially when controversial policies, 

like siting a hazardous waste facility, are being considered. 

The distinction between means and ends is also important to valuation, because it is crucial 

to the definition of exactly what is being valued, as well as to the validity of the values 

themselves. The NOAA CV Panel refers to the value related to means, or the “process or 

transaction that establishes ownership,” as “transaction value;” and that related to ends, or 
57

products of consumption, as “consumption value” (National Oceanic and Atmospheric 

Administration 1993, p. 4614). 

In some cases, a survey that purports to value a particular quantity of a natural resource, the 

“consumption value,” may actually be eliciting the value of the policy that is presented to 

achieve the improvement, the “transaction value,” or may elicit some combination of the 

two types of value. The CV Panel states that 

[w]e do not question the validity of ‘transaction value’ or differentiate it from 

‘consumption value’ as far as damage assessment is concerned. But for both 

forms of value, respondents need to be thinking clearly about the substitutes, 

since the closer are the substitutes the less the damage that is done. In the 

case of ‘transaction value,’ there are many close substitutes to cleaning up oil 

spills since there are many other charitable activities that can generate the 

same ‘warm glow’ and there are many other ways to express hostility toward 

big business and modern technology (National Oceanic and Atmospheric 

Administration 1993, p. 4614). 

This statement appears to contradict the Panel’s earlier assertion that willingness to pay that 

is motivated by the “warm glow” may be “real but not properly compensable,” because the 

responsible party is liable for natural resource damages, not for spill prevention. Thus, the 

“consumption value” is the appropriate measure of damages. However, disregarding the 

validity of “transaction value” for damage assessment, its measurement in terms of 

neoclassical welfare measures is likely to be problematic, as the following sections elaborate. 

Commitment and Sympathy 

In his paper “Rational Fools,” Amartya Sen (1977, as reprinted in Mansbridge 1990) 

proposes that personal choice and personal welfare are not necessarily the same, as assumed 
58

by standard neoclassical theory. He argues that, because of the way revealed preference 

theory is defined “you can hardly escape maximizing your own utility, except through 

inconsistency” (in Mansbridge 1990, p. 29). However, it is not possible to distinguish 

inconsistency from changes in underlying preferences simply by observing choices, which is 

the economist’s preferred means of analysis. Therefore, the revealed preference approach, 

based on observed choices, disregards the non-choice sources of information on 

preferences, and also fails to recognize that choice “may reflect a compromise among a 

variety of considerations of which personal welfare may be just one” (Sen 1977, in 

Mansbridge 1990, p. 30). 

Sen defines two important considerations other than personal welfare—sympathy and 

commitment. Sympathy refers to cases where an individual’s utility is affected by others’ 

welfare, or utility is interdependent. Commitment refers to cases where a person chooses to 

act in a certain way even if it results in a decrease in personal welfare. Thus, sympathy would 

be directly related to the personal welfare of the individual, who might improve his or her 

welfare by considering the welfare of others; and commitment implies that the individual 

chooses a course of action based on something other than their own individual welfare. In 

the case of the natural environment, this would include moral commitments to both natural 

objects themselves, and to other people who have some interest in the natural objects. 

Commitment can also occur in cases where the choice maximizes personal welfare, but the 

maximization of personal welfare is not the reason for the choice. 

For example, suppose loggers in the old growth forest are given the opportunity to make 

choices about preserving or logging the forest. Besides feeling concern for their own 
59

welfare, the loggers might feel sympathy for other loggers, and thus choose to log the forest 

because they feel good about protecting fellow workers’ jobs. Alternatively, the loggers 

might feel a commitment to preserving old growth forest for future generations, to the trees 

themselves, or to the spotted owls that require the old growth habitat, and choose to 

preserve the forest even though they will be out of a job and thus worse off in terms of 

personal welfare. 

Sympathy is analogous to an externality, and can be modeled as one. Commitment, 

however, can involve counterpreferential choice, and thus “drives a wedge between personal 

choice and personal welfare,” which are often regarded as identical in economic theory (Sen 

1977, in Mansbridge 1990, p. 33). Consequently, “[t]he basic link between choice behavior 

and welfare achievements in the traditional models is severed as soon as commitment is 

admitted as an ingredient of choice” (Sen 1977, in Mansbridge 1990, p. 34). Sen believes 

that, if commitment is important, 

traditional theory has too little structure. A person is given one preference 

ordering, and as and when the need arises this is supposed to reflect his 

interests, represent his welfare, summarize his idea of what should be done, 

and describe his actual choices and behavior. Can one preference ordering 

do all these things? A person thus described may be ‘rational’ in the limited 

sense of revealing no inconsistencies in his choice behavior, but if he has no 

use for these distinctions between quite different concepts, he must be a bit 

of a fool. The purely economic man is indeed close to being a social moron 

(in Mansbridge 1990, p. 37, emphasis in the original). 

Sen recommends using a ranking of preference rankings to express moral judgments, or a 

meta-ranking of actions. Such a meta-ranking may not be complete, e.g., it requires 

separability between different motivations, which may not always be possible, but can give 

more information than simply specifying a dual utility framework of ‘ethical’ and ‘subjective’ 

preferences. Sen describes this as a structure, not a theory, and says that alternative theories 
60

may use this structure. Use of this structure requires more information than simple 

observation of choices. “It gives a role to introspection and to communication” (Sen 1977, 

in Mansbridge 1990, p. 40). 

Based on Sen’s ideas, David Brookshire, Larry Eubanks, and Cindy Sorg (1987) state that 

“an individual willingness to pay for the existence of natural assets, in part, may be the 

expression of a commitment to a nonefficiency-based ethic. That is, individuals may be 

willing to pay for a commitment. If so, this would have implications for the inclusion of all 

empirical estimates of existence value within normative benefit-cost analysis” (p. 22). If 

nonuse values are motivated by commitment, as defined by Sen, so that choices are made 

based on personally-held ethics and utility is not maximized, then a wedge is driven between 

personal choice and personal welfare. Therefore, efficiency is no longer the underlying 

criterion for choice, and measures of willingness to pay that include such motivations are not 

consistent with welfare theory. 

As Sen notes, simply observing choices in such cases is not sufficient. Therefore, in 

developing and implementing a survey of natural resource values, respondents might be 

asked about their motivations for choices, in order to determine whether they are based on 

commitment. This is consistent with the NOAA Panel’s recommendation that survey 

designs should explicitly address the “warm glow” problem. 

Mitchell and Carson (1988) argue that Brookshire, et al. have based their conclusions on 

faulty assumptions. First, they say that welfare theory does not restrict benefits only to 

human uses. However, Brookshire, et al. do not argue that welfare theory is restricted to 
61

human uses. Their argument is that certain motivations for nonuse values may result in 

violations of welfare theory. 

Second, Mitchell and Carson say that ethical motivations are not unique to inherent, or 

intrinsic, values, but could also underlie other types of nonuse values, which Brookshire, et 

al. accept as legitimate values. However, if moral commitments do underlie other types of 

nonuse values, then these values must also be considered differently from efficiency-based 

values. 

Third, Mitchell and Carson say that choices based on ethical beliefs do not necessarily 

involve self-sacrifice, but may increase a person’s utility by satisfying internal social norms. 

“For some people such things as preserving wilderness for its own sake . . . are amenities 

whose provision genuinely improves their personal welfare as they conceive it. Far from 

being ‘counterpreferential,’ in properly conducted CV studies choices based on these 

preferences are motivated by self-interested and egoistic considerations” (p. 66). However, 

in this case the preference expressed is likely to be for the action, rather than for a specific 

good, and thus must be carefully interpreted. 

For example, a person might purchase a candy bar to support a neighborhood child’s Little 

League team, even though they do not want the candy bar itself and thus do not value it. In 

fact, they may have a negative value for the candy bar if they are diabetic or allergic to 

chocolate. Therefore, by purchasing the candy bar, the person is revealing their value for the 

action itself—supporting Little League—rather than their value for the commodity 
62

purchased—the candy bar. Thus, if they were subsequently deprived of the candy bar, they 

would not suffer a loss in utility. 

Additionally, even if the person does value the commodity, expressions of monetary value 

when such beliefs are important are likely to overstate the value of the good, and also will 

often violate the axioms of revealed preference theory, as Brookshire, et al. point out. 

Consequently, the value of the candy bar cannot necessarily be inferred from the price paid 

without further investigation into the person’s motives. 

Consumer/Citizen 

Mark Sagoff and others have suggested that people make decisions in two different ways—

as “consumers” and as “citizens”—and that these decisions will differ, depending on the 

context (Sagoff 1992a, 1981; Vatn and Bromley, 1994). Whereas the consumer decides 

based on personal preferences and ends, the citizen decides as a member of the community 

and society, based on what is seen as best for the community as a whole rather than for the 

individual. As Sagoff describes them, consumer choices are those made in markets, and 

citizen choices are those made in the political arena. However, in many decisions, both 

consumer and citizen preferences may be considered by an individual. 

Sagoff states that “[t]hrough the political process, citizens are able to change the conditions 

under which they then make consumer choices. Where should we say their true values lie?” 

(1992, p. 200). In other words, markets may be regulated by policy decisions based on 
63

citizens’ desires, thus allowing people to make choices as consumers that would not have 

otherwise been possible. In turn, policy choices are often made based on market data that 

infers the preferences of individuals. 

This distinction between consumer and citizen can be significant in the environmental arena, 

where both types of choices can be important. As citizens, we make decisions that affect the 

formulation of environmental laws and policies. As consumers we are affected by these laws 

and policies, as well as by the actions of those who violate them. Many of these laws require 

that the public be compensated for harms that they suffer if the laws are violated, and the 

standards for determining compensation are generally based on consumer preferences. In 

addition, many environmental laws require considerations of costs and benefits in their 

implementation, and again these measures are based on consumer preferences. 

The consumer/citizen concept is compatible with Etzioni’s socio-economics theory, 

described below, where individuals are seen as having two sources of “utility,” and individual 

decisions are assumed to be largely influenced by social context. It may also explain 

embedding and related phenomena in surveys, where different contexts may invoke different 

choice motivations—consumer, citizen, or a combination of the two—and it is not clear 

how respondents will answer. 
64

Sense of Place 

Another important concept related to environmental values is the “sense of place” that 

people experience because of the unique characteristics of a particular location and their 

emotional connection to it (Sagoff 1992b, 1995; Kellert 1995). As Alan Gussow says, 

There is a great deal of talk these days about saving the environment. We 

must, for the environment sustains our bodies. But as humans we also 

require support for our spirits, and this is what certain kinds of places 

provide. The catalyst that converts any physical location . . . into a place, is 

the process of experiencing deeply. A place is a piece of the whole 

environment that has been claimed by feelings (quoted in Sagoff 1992b, p. 

359). 

An area’s natural environment can be an important component of the sense of place. For 

example, people in a shoreline community are likely to have a strong attachment to the 

water, and fishermen might be an important part of the local lifestyle and culture. Sagoff 

argues that people who have strong community ties and commitments to a place, and those 

whose livelihoods depend on local resources, will be more likely to respect nature and 

support efforts to preserve its environment. Conversely, those who are more transient and 

can take their business anywhere are more likely to put more emphasis on financial rather 

than environmental considerations (Sagoff 1992b). 

Technology and the globalization of business create more uniformity across places, thus 

making people less dependent on, and therefore less concerned for, the unique 

characteristics of a particular place. This may result in increased environmental degradation 

as well as a weaker community. Both Sagoff and Kellert argue that environmental 

degradation often results from a lack of a feeling of rootedness to a particular place, and a 
65

lack of a strong connection between humans and nature (Sagoff 1992b; Kellert 1995). This 

degradation then leads to further separation from the place. 

Referring to coastal areas, Kellert says 

The various elements of coastal environmental degradation—congestion, 

aesthetic deterioration, habitat destruction and fragmentation—are also 

associated with increasing alienation and separation from the coastal context. 

This phenomenon of human estrangement from the coastal landscape is 

further exacerbated by declines in regional economies, the increase in what 

could be termed the ‘suburbanization and commuterization’ of the coast, 

even the expanding number of isolated and restricted protected areas. 

Collectively, these factors all contribute to the separation and distancing of 

people from the coast as a secure and meaningful place of human habitation 

(1995, pp. 17-18). 

A sense of place can create moral and cultural obligations to preserve the natural 

environment. “The motive for saving ecosystems . . . may fundamentally lie in our need to 

feel at home—to attach ourselves to what becomes safe and secure because it retains its 

aesthetic and cultural characteristics in the midst of change” (Sagoff 1995, p. 7). 

Sagoff believes that these types of moral and cultural commitments provide stronger 

motivations for people to preserve the natural environment than economic rationales, 

although people may often use economic arguments to support a view that is primarily 

ethical. He argues that “those who love nature and care about the environment— who care 

about the integrity of places—should not need to substitute weak instrumental arguments 

for strong moral and cultural ones” (Sagoff 1992b, p. 382). Instead, he suggests that we 

“measure human welfare or well-being in substantive terms—in terms of the quality of life 

and the sustainability of cultures and communities, for example—[rather] than in terms of 
66

aggregate income, social wealth, consumer surplus, or any such abstraction of economic 

theory” (Sagoff 1992b, p. 409). 

Nonanthropocentric Motivations 

As discussed above, questions about defining nonuse values, their measurement, and 

whether they should be included in environmental policy-making have been subjects of a 

great deal of debate. However, much of this debate has not considered the possibility that 

many individuals may view nonmarket valuation as irrelevant to the more fundamental issue 

of whether humans have obligations to nature beyond purely anthropocentric concerns. 

Anthropocentrism is the belief that humans are the most significant entity of the universe, 

thus interpreting the world in terms of human values and experiences, and defining all value 

as human-oriented. This implies that all of nature is to be managed to benefit humans, 

sometimes at the expense of other species. Conversely, nonanthropocentric philosophies 

view humans as only one part of the natural community, and not the central or most 

important part. Nonanthopocentrists believe that nature is the source of all values, including 

human values. They see natural systems as possessing values that are truly intrinsic, 

independent of human values, including human-determined nonuse values. 

The methodology of neoclassical economics is inherently anthropocentric, measuring 

nonmarket benefits in terms of human preferences. Mitchell and Carson say that “economic 

theory . . . reject[s] the notion that ‘trees have rights.’ The statement that is consistent with 
67

economic theory is that ‘trees have rights to the extent that humans are willing to pay for 

those rights’” (1988, p. 66, fn. 18). This is based on the belief that natural resources have 

value strictly because they benefit humans, whether through use or nonuse services. 

Neoclassical valuation methods thus assume that all people embrace this philosophy, and 

welfare theory restricts value to anthropocentric value. However, it is possible that some 

people do not view the world in this way, believing instead that some or all elements of 

nature have truly intrinsic values that transcend human values. 

There is an important distinction between a nonanthropocentric concept of intrinsic value 

and the concept of nonuse value as defined by economists. Nonuse values are determined 

by people, and are measured by economists as people’s willingness to pay to preserve 

resources. These values might be derived from just knowing a particular species or 

ecosystem exists, or from the desire to preserve a species or ecosystem for future 

generations. Nevertheless, these are based on human values for preservation. A 

nonanthropocentric concept of value may go beyond the economic notion of nonuse value, 

by placing natural resource values outside of human determination. 

Therefore, based on an anthropocentric philosophy, the economist may determine that 

protecting a particular species or ecosystem is necessary even though humans do not use the 

associated environmental services, because there are significant nonuse values. However, a 

nonanthropocentric philosophy may reject the notion that humans even have a choice 

regarding whether to protect a particular species or ecosystem, believing instead that it is a 

moral obligation. Consequently, it is not surprising that the economics profession has been 
68

criticized by people in other disciplines for not adequately addressing environmental issues 

(see, for example, Sagoff 1994, 1988b; Norton 1991). 

As discussed above, neoclassical theory of resource economics is based on the 

anthropocentric philosophies of conservationism and preservationism. The primary goal is 

to efficiently allocate natural resources by measuring the value humans place on those 

resources. Even the recognition of nonuse values and the resulting attempts to measure 

them remain anthropocentrically motivated. For example, people may desire to protect 

some natural asset because they enjoy knowing that it exists, or may wish to preserve 

biodiversity because it will enhance future options for the development of medicines. 

Other environmental philosophies believe that appropriate environmental policy should be 

nonanthropocentrically motivated. For example, biocentrists and ecocentrists believe that 

human interests must yield to the interests of biota and ecosystems, and that natural assets 

should be preserved for reasons independent of their values as expressed by people. 

Because they do not view resources in anthropocentric terms, placing a dollar value on 

natural resources is not logically consistent with their philosophy. “Much as those who hold 

human beings to be morally considerable object to the institution of slavery, those who hold 

the natural world to be morally considerable object to [nature] being placed in the stream of 

commerce and treated strictly as an instrument” (Booth 1992, p. 59). At a minimum, 

valuation of natural resources by humans may be viewed by nonanthropocentric 

philosophies as inadequate to address the question of their appropriate management, since 

resource values transcend human values, including human-determined nonuse values. 
69

Consequently, economists may find that individuals who embrace nonanthropocentric 

philosophies may reject the monetary valuation of natural resources, in a variety of ways. 

For example, respondents to contingent valuation surveys may give responses that do not 

reflect their value for the resource as a commodity. They may respond with protest zeros, or 

extremely high values, or with some other expression of their environmental philosophy. 

Questions that ask people for their willingness to accept compensation might be viewed as 

attempts at bribery (Opaluch and Grigalunas 1993). These types of responses are not 

correctly interpreted using standard economic analysis, and therefore require a broader 

approach. 

Violations of Neoclassical Theory

As outlined above, neoclassical duality theory and welfare theory provide a systematic 

method of inferring economic values for goods from observable behavior. However, 

numerous violations of neoclassical assumptions and postulates have been empirically 

observed, and a variety of reasons for such violations have been suggested. The previous 

section discussed several concepts related to moral commitments that might explain 

anomalous answers to valuation surveys. This section briefly discusses other possible 

explanations, and some of the implications of such violations, focusing on nonmarket 

valuation. The next section presents two alternative theories that might better explain 

choices. 
70

Table 0-1 summarizes the neoclassical model and potential violations of neoclassical theory. 

The theory of consumer choice can be broken down into four components: preferences, 

decision rules, observable behavior, and value or welfare measures. Neoclassical theory 

assumes that preferences are well-behaved, in that they are complete, transitive, reflexive and 

continuous. Preferences are also assumed to be stable over time, and to be known with 

certainty by the individual. However, for a variety of reasons, preferences may not be wellbehaved and/or well-defined. 

Table 0-1 - The Neoclassical Model and Violations 

Neoclassical Violations of Neoclassical

Preferences

Well-behaved Preferences: 

complete 

transitive 

reflexive 

continuous 

Stable Preferences 

Ill-Behaved and/or Ill-Defined 

Preferences due to: 

moral commitments 

regret/disappointment 

prospect theory 

ambivalence 

complexity 

etc. 

Endogenous and/or Dynamic 

Preferences 

Decision Rules 

Link Preferences 

and Behavior

Maximize utility 

or expected utility 

Use decision heuristics 

Observable 

Behavior

Revealed preferences - 

estimate demand 

functions or WTP 

Preferences may not be revealed 

(e.g. symbolic WTP answers) 

Welfare Measures Valid according to theory Welfare measures may be invalid 
71

Such motivations as moral commitments, regret or disappointment, valuing of gains 

differently from losses as outlined by prospect theory, or ambivalence, might result in 

preferences that are uncertain or that violate the axioms of utility theory (see, for example, 

Akerlof and Dickens 1982; Bell 1982; Kahneman and Tversky 1979; Loomes and Sugden 

1982; Opaluch and Segerson 1988, 1989; Opaluch and Grigalunas 1993). For example, 

moral commitments might lead to lexicographic preferences, which are not continuous; or to 

symbolic responses to survey questions rather than expressions of economic value for a 

specific good. 

Opaluch and Segerson (1989) show that choices based on anticipated feelings of regret or 

disappointment can be intransitive. Prospect theory’s hypothesis that the value function is 

steeper for gains than for losses may explain the large differences often found between 

willingness to accept compensation and willingness to pay (Kahneman and Tversky 1979). 

Finally, ambivalence might result in preferences that are not clearly defined due to an inner 

conflict (Opaluch and Segerson 1989). This could lead to an inability to choose if the 

choices are seen as incommensurable; to protest responses in surveys; or to the use of 

simplified decision rules. In these cases, a person’s “true” preferences may not be 

measurable using standard techniques. 

Additionally, survey respondents may simply be unsure of their preferences, possibly due to 

unfamiliarity with valuing natural resources; or may have trouble making “rational” choices 

due to the complexity of the questions (Mazzotta and Opaluch 1995). Question complexity, 

combined with unfamiliarity, can result in behavior that violates basic assumptions of 

neoclassical utility theory, for two reasons. First, respondents may not fully comprehend the 
72

question or the information presented in a survey. Alternatively, when faced with tradeoffs 

among commodities that are viewed as incongruous, respondents may experience 

uncertainty with regard to their preferences. Either of these problems may lead to the use of 

simplified decision rules for making choices. 

Thus, consideration of motivations, complexity and uncertainty may be crucial to the analysis 

of revealed preferences. Additionally, if motivations are nonanthropocentric in nature, then 

standard economic theory cannot account for them because they represent a different 

concept of value, which is not based on human utility. Therefore alternative theories and 

means of analysis are needed. 

The neoclassical assumption of stable preferences allows for welfare measurement based on 

revealed preferences because it can be assumed that observed choices result from changes in 

the decision environment (e.g., prices or income), rather than changes in preferences. 

Economists generally assume that preferences are stable, at least for the period of the 

analysis. However, preferences may not be stable over time, even when the time period 

considered is relatively short. Instead, preferences may be dynamic, changing as new 

information is acquired. In addition, preferences might be endogenous, dependent on the 

choice context or on past choices. This is especially likely in the case of valuation of natural 

resources or environmental amenities using surveys, where it has been suggested that 

preferences may be constructed by respondents during the elicitation process (Gregory, 

Lichtenstein, and Slovic 1993). 
73

Decision rules, or the way that individuals make choices, link preferences and behavior. 

Thus, knowledge of decision rules is necessary to infer value from behavior. Neoclassical 

theory assumes that people use a single decision rule: maximization of utility (or expected 

utility) subject to a budget constraint. However, in many cases people may use decision 

heuristics, or simplified decision rules. Such heuristics might result from non-neoclassical 

motivations, such as moral commitments that lead to lexicographic choices, or they might 

result from cognitive limitations that make it difficult to balance all attributes in a complex 

choice (Heiner 1983; Mazzotta and Opaluch 1995). 

Observable behavior in markets, related markets, or hypothetical markets provides 

economists with clues to people’s preferences. Economic models are applied to this data in 

order to explain or predict consumer choices, and to infer economic value for goods. In 

neoclassical analysis, it is assumed that well-defined preferences are revealed by observed 

choices, and that such choices are made by maximizing utility. If violations occur, as 

discussed above, preferences may not be revealed by choices, or alternative methods may be 

needed to infer preferences from choices. 

Finally, valid welfare measures require that preferences be well-defined, and that proper 

models of decision making be used to analyze choices, so that behavior can be linked to 

preferences from which, in turn, economic values can be inferred. If neoclassical models are 

applied blindly without considering motivations, preference orderings, and decision rules, the 

validity of welfare measures cannot be determined. Thus, it may be necessary to collect 

additional data about preferences and decisions, in order to determine whether standard 

means of analysis and interpretation of results are appropriate. 
74

As a result of the concerns related to moral commitments, a variety of alternative theories 

and methods have been proposed. Most of these view moral commitments as fundamentally 

different than personal preferences. However, they vary widely in their hypotheses about 

the form of the relationship between moral commitments and personal preferences, and 

therefore in how they would modify standard theory to account for moral commitments. 

The next section briefly discusses two theories that are relevant to natural resource valuation. 

Alternative Theories 

Kopp’s Theory of Ethical Motivations 

One model that extends neoclassical theory to account for ethical motivations is presented 

by Raymond Kopp (1992), who focuses on ethical motivations for nonuse values. He 

believes that “[t]he reasons underlying a particular choice become important when we seek 

to say something normative about the choice or when we need to empirically measure value” 

(Kopp 1992, p. 37). 

In his model, Kopp distinguishes between well-being and utility. He defines well-being as 

preference satisfaction, with self-interested utility maximization as only one means of 

achieving well-being. Altruism that results from interdependent utility functions, or Sen’s 

“sympathy” is included in his definition of utility; while commitment, as defined by Sen, is 

included as a separate source of well-being. Thus, in Kopp’s theory, well-being might be 

increased by making choices based on moral commitments or by maximizing self-interested 
75

utility. Kopp recognizes the distinction between means and ends, saying that “[w]hile the 

normal arguments of utility provide satisfaction by providing ‘ends’ (i.e., consumption), 

ethics leads to enhanced well-being through ‘means’ (i.e., by actions)” (Kopp 1992, p. 10, fn. 

23). 

Kopp distinguishes between individual ethics and social ethics, and assumes that well-being 

increases when a person acts in accordance with their ethics, and also when others or society 

act consistently with the person’s ethics; and decreases when a person or others violate the 

person’s ethics. He models this in two ways: using discrete measures of ethical codes, 

termed “binary ethics;” and using continuous measures, termed “continuous ethics.” Under 

binary ethics, if a person’s conduct is consistent with their ethical code, their well-being 

increases. Under continuous ethics people may achieve different degrees of changes in wellbeing, depending on the degree to which they perceive themselves as acting in accordance 

with their ethical codes. 

In his binary ethics model, Kopp defines E as an n-dimensional vector of ethical codes, 

whose value is 1 if a person acts in accordance with a code, and 0 if they do not. F(E) is a 

function that measures the well-being associated with E; this is not assumed to have any 

particular curvature, only that f(E1', E2, ..., En) > f(E1, E2, ..., En) if E1'=1 and E1=0. U(X) is 

the utility function for self-interested pleasure from the consumption of a vector of goods 

and services, X; and is assumed to be continuous, monotonically increasing, and quasiconcave. 
76

Well-being for individual j is defined as W = M[f(E), u(X)], where f(E) and u(X) are assumed 

to be additively separable, and M[•] is assumed to be monotonically increasing, so 

W=f(E)+u(X). Thus, Kopp assumes that ethical codes and personal utility are completely 

commensurable, and that people can make perfect tradeoffs between them. However, this is 

not a universally accepted idea, and, as noted above, there is evidence that people often have 

difficulty making such trades (Etzioni 1988; Opaluch and Grigalunas 1993). 

Based on the assumption that people can trade ethical codes for personal utility, Kopp gives 

an example of what he considers to be a “no-win” situation, where a person must choose 

between an ethical loss where utility remains constant, or a utility loss, where ethical codes 

are satisfied. Thus, as Kopp defines it, well-being decreases in either case. Although Kopp’s 

model assumes that it is possible for the individual to make this choice, he implies otherwise 

in his example of people who cross the street to avoid a panhandler, where “many avoid the 

encounter since it would result in a no-win choice” (Kopp 1992, p. 14, fn. 27). 

Kopp’s model of continuous ethics differs from the binary ethics model in that each Ei

becomes an “ethic bank,” where well-being is enhanced by consistent behavior and 

diminished by inconsistent behavior. Thus, when making a choice that impacts both 

personal utility and ethical codes, a loss in personal utility can be offset by a gain in ethical 

well-being, so that it is no longer a completely no-win situation. In addition, Kopp points 

out that f(E) can be specified in different ways, for example as in prospect theory, where 

gains are less valuable than losses. Kopp points out that this type of ethical value implies 

that a person may vote yes for a policy that results in a utility loss, or may seem to be 
77

indifferent over a wide range of utility losses. This is consistent with the embedding 

phenomenon, where equal values are estimated for a range of quantities of the good. 

Kopp asserts that an economic value can be defined for ethical behavior by measuring the 

maximum loss in self-interested utility that the individual will accept before violating their 

ethical codes. Again, this only makes sense if a person can trade utility for ethics, so that the 

utility that is given up to remain consistent with ethical codes can be defined. This is an 

important empirical question, and cannot simply be assumed to be true. 

In comparing his model to the standard neoclassical model, Kopp states that “unlike the 

self-interested model, the well-being model is freed from the constraints imposed by utility 

theory” (1992, p. 28). He points out that 

[t]he careful reader will note that our use of the indirect utility function leads 

to an empirical inconsistency if one were to actually estimate such a function 

from observable behavior. Since, under the assumption of the well-being 

model, observed behavior results not only from simple self-interest, but from 

ethics as well, the estimated indirect utility function will be contaminated 

with the ethical motivations. . . . Since the theory we are presenting does not 

rest on the existence of an empirical indirect utility function, we will assume 

that one can be found and move on (p. 23, fn. 32). 

However, because this model is a departure from neoclassical utility theory, welfare measures 

for goods cannot be derived from it in the usual way. Kopp recognizes this in an example, 

saying that, if ethical motivations are important, an individual may not “have a well-defined, 

self-interested ‘demand curve’ for bird deaths. Indeed, such a demand curve is wholly 

inappropriate since the value of the policy rests not only on the magnitude of deaths, but 

also on the importance that is attached to acting in a particular manner” (1992, p. 28, fn. 34). 
78

This is related to the fact that self-interested utility maximization, as defined by economists, 

focuses on ends, e.g., the products of consumption. However, moral commitments often 

focus on means. For example, if a person is asked about values for wildlife, moral 

commitments for preserving wildlife may be so important that the specific wildlife itself is 

not considered. Thus, the person might focus on the policy—preserving wildlife—rather 

than on the specific numbers of wildlife saved. This provides information about the value of 

a policy, or the ethical considerations of a policy. However, if the objective is to measure the 

value of a particular number of birds, then the fact that there is no well-defined demand 

curve for those birds implies that measurement of the value is not possible using standard 

methods. 

Kopp argues against creating an overall utility function u(X, E), in favor of the welfare 

function W = M[f(E), u(X)], because the utility approach implies that there must be specific 

relationships between the E’s and the X’s. However, his welfare approach, while more 

general than the standard neoclassical model of utility, makes some strong assumptions, such 

as additive separability between utility and ethical values, that do not distinguish it 

completely from the utility function approach. Thus, Kopp separates preferences and ethics, 

but does not go far enough in considering how they are related in the overall well-being 

function, and other important empirical questions. He says that different sources of 

motivation should not be aggregated under something called “preferences,” but he 

aggregates them under something called “well-being.” The only distinction he makes is that 

well-being is not required to follow the axioms of revealed preference theory. This makes 

sense but has major implications for Hicksian measures of welfare, which Kopp does not 

consider. In summary, Kopp has provided a starting point for the consideration of ethical 
79

values in standard neoclassical theory, but his assumption of additive separability may be too 

restrictive in many cases, and he does not directly address the questions of empirical use of 

the model or welfare measurement. 

Socio-Economics 

Amitai Etzioni (1988) proposes a new paradigm that includes the neoclassical paradigm as a 

special case. He calls this paradigm the I&We paradigm, and his theory based on the 

paradigm “socio-economics.” In Etzioni’s paradigm, society is not seen as simply the 

aggregation of individuals, but as having its own structure that affects the actions of 

individuals. Thus, the economy cannot be studied without considering social, political and 

cultural factors. Methodologically, socio-economics “seeks to integrate elements of 

economics and of other social sciences into one theoretical system, but not to fuse them” 

(Etzioni 1988, p. 15). Thus, it is interdisciplinary in nature, and attempts to have greater 

explanatory and predictive power than neoclassical theory. 

Etzioni suggests three basic changes to the core assumptions of the neoclassical paradigm. 

First, he assumes that, rather than seeking to maximize one utility, people seek to maximize 

“at least two irreducible ‘utilities,’ and have two sources of valuation: pleasure and morality” 

(Etzioni 1988, p. 4). Second, Etzioni assumes that people do not always decide rationally, as 

defined by economists, but select both means and goals primarily based on their values and 

emotions. Third, social collectives, rather than individuals, are assumed to be the primary 

decision-making units. Such collectives include ethnic groups, peer groups, and 
80

neighborhood groups. Thus, individual decisions are seen as occurring within a specific 

context defined by the social collective(s) that the individual identifies with. 

Etzioni believes that there is a “creative tension and perpetual search for balance between 

two primary forces—those of individuals, and those of the community, of which they are 

members,” (1988, p. 8) which is similar to Sagoff’s consumer/citizen distinction. A basic 

difference between this theory and neoclassical economic theory lies in the characterization 

of community. In the neoclassical paradigm the community is viewed in a reductionist 

manner as an aggregation of individuals, where all choices are seen as fulfilling only 

individual preferences, which, when summed, indicate the preferences of the community. 

Thus, the theory cannot explain acts that are motivated by the needs of the community 

rather than individual needs. Conversely, the I&We paradigm is holistically focused, and 

considers both types of preferences and the interactions between them. 

Etzioni critiques the neoclassical idea of a single utility function that encompasses all actions, 

arguing that it does not make sense as a scientific theory because it does not differentiate, 

and therefore can not explain why certain kinds of behavior are different than others. His 

underlying premise is that “moral acts (such as authentic altruism) provide a fundamentally 

different source of valuation and explanation of the reasons why people behave the way they 

do, than that provided by consumption and other sources of pleasure” (Etzioni 1988, pp. 21-

22). Etzioni also points out that pleasure-motivations are generally concerned only with the 

consequences of an action (e.g., consuming a particular good), while moral actions are 

generally concerned with intentions and means of attaining a certain consequence (e.g., 

buying or stealing the good). Etzioni points out that “the difference between the mono-
81

utilitarian paradigm and the one advanced here comes down to alternative assumptions 

about human nature” (1988, p. 22). 

Etzioni describes three forms of neoclassical utility, and argues that none are sufficient to 

properly explain behavior. The first, which he calls pleasure utility, or P-utility, is utility as 

originally conceived by Bentham—self-oriented and hedonistic, where all means are justified 

by the ends served. However, if a pleasurable act is not performed because it is immoral, 

such an action cannot be explained by P-utility. 

The second utility, interdependent, or I-utility, allows an individual to receive pleasure from 

the consumption of others. Thus, a person is seen as getting personal pleasure from 

altruistic acts. While I-utility can explain impure altruism, i.e. Sen’s “sympathy,” it cannot 

explain purely altruistic acts, or “commitment,” because such acts involve self-denial or 

sacrifice, and thus do not provide personal pleasure. 

The third type of utility is formal utility, or X-utility. This utility is merely a common 

denominator that allows preferences to be rank ordered. However, X-utility requires the 

assumption that utility is scalar, and that all values are compatible and commensurable. If 

moral commitments are not always compatible with, or comparable to, pleasure-motivated 

preferences, then utility may not be scalar, so that a single X-utility would not make sense. 

Etzioni believes that behavior is systematically different when motivated by moral 

commitments than when motivated by pleasure. He distinguishes between the feeling of 

“satisfaction (doing what one likes, enjoys, finds pleasurable), and the sense of affirmation that 
82

accompanies discharging one’s moral commitments” (1988, pp. 23-24, emphasis in the 

original). Therefore, moral and pleasure “preferences” may be qualitatively different, so that 

they cannot be traded off or substituted for one another; and if people violate moral 

commitments to satisfy pleasure or forgo pleasures to live up to moral commitments, then 

they are likely to feel guilt or shame, or a sense of affirmation. 

Such feelings may have behavioral consequences. For example, conflicts between pleasure 

motivations and moral commitments may lead to dissonance, and dissonance is expected to 

lead either to inaction (until the dissonance is reduced), or denial (acting one way but 

pretending to act differently, or believing that one is acting differently). Conflict can also 

lead to stress, which diminishes the capacity of the person to act rationally (Etzioni 1988). 

Moral commitments might result in ill-defined preferences, so that ambivalence occurs. 

Moral commitments may also set a context or create a “climate,” where a moral obligation 

may be fulfilled in a variety of ways that provide the same symbolic value. Thus, several 

actions may be morally “equivalent,” even though their costs are different. In such instances 

moral values provide a context by defining a range of acceptable actions (Etzioni 1988). 

This idea is consistent with the NOAA CV Panel’s statement, quoted above, that a charitable 

action is likely to have many close substitutes (National Oceanic and Atmospheric 

Administration 1993). 

In certain contexts, moral commitments may completely determine a person’s actions, so 

that it is not viewed as valid to consider costs and benefits or other factors. In such cases, 

certain means are considered to be “unthinkable,” even if they may be acceptable in terms of 
83

rational choice. Thus, some or all other options are not considered as part of the choice set, 

and mere consideration of these options may be seen as taboo. Choices are made on the 

basis of what is believed to be right. These might be considered to be “sacred” moral 

principles. 

In other cases, or when these sacred principles have already been violated, moral 

considerations may be compared with other considerations when making a choice. Thus, 

there are absolute and expedient moralities, where absolute moralities preclude all but the 

“right” choice, and expedient moralities imply that certain moral means are preferred but not 

at all costs. 

Consequently, morally motivated choices are often irreversible or asymmetrical, so that they 

are very difficult to reverse; such choices may be lumpy or discontinuous; and there may be a 

high “notch-effect,” so that behavior is sticky up to a threshold, and the resistance is 

decreased once the threshold is crossed (Etzioni 1988). Such choices are also likely to be 

lexicographic. Because of moral commitments, “[p]roducts turn out to be related in 

patterns that have nothing to do with substitutions, complementarity, or relative prices” 

(Etzioni 1988, p. 71). 

If certain behavior is seen as “sacred,” then choices are limited or predetermined, and 

calculative approaches like cost-benefit analysis are rejected. Such choices would violate the 

continuity axiom of revealed preference theory. There is evidence for this in many 

responses to CV surveys, especially in those that ask for willingness to accept compensation. 

Etzioni compares the idea of moral commitments with that of an externality, in that the 
84

market price of a good does not always fully reflect its moral standing. Conversely, 

willingness to pay responses in contingent valuation surveys may reflect moral standing in 

addition to or instead of the personal satisfaction received from the good being valued, and 

thus may measure values a commodity other than that intended by the survey. 

Etzioni believes that it is thus not surprising that neoclassical cost-benefit analysis of public 

goods “run[s] into severe difficulties,” and asserts that “[t]he root cause of many of these is 

the absence of markets for the items involved because it is considered immoral to trade in 

them” (1988, p. 79). He goes on to say that “in any given society and period, certain items 

are not considered legitimate material for exchange. . . . Most likely, this means that up to a 

point they are not traded at all, and beyond that they may be traded once the taboo is 

violated, and only in encumbered ways. . . As a result, market models are expected to depict 

the relations poorly at best; socio-economic conceptions that include moral as well as 

economic factors are necessary” (Etzioni 1988, p. 81). 

Therefore, in cases where moral commitments are important, there are behavioral 

consequences that can be explained and predicted by understanding the conflicts that people 

experience about their preferences, or how people feel about their actions. In Etzioni’s 

theory, choices are viewed as “multi-staged, multi-level events, in which moral and pleasure 

considerations are combined in complex manners,” and moral commitments are seen as an 

irreducible category of motivation that is not always commensurable with other motivations. 

Thus, people might reject the notion of making certain tradeoffs. If this is the case, the 

neoclassical axioms of behavior—continuity, reflexivity, completeness, and transitivity—are 

likely to be violated. 
85

Although pleasure and moral commitments are not always in conflict, they often compete 

for resources. Instead of maximizing one overarching utility, people are assumed to “seek a 

balance between their moral commitments and their pleasures (a judicious ‘mix’), rather than 

seeking to maximize either” (Etzioni 1988, p. 67). In cases where balancing occurs, choices 

may be consistent with neoclassical theory. However, although people may be making 

tradeoffs, the researcher may not observe what they are trading, so that the specific 

commodity being valued may not be clear. Thus, valuation of certain commodities may not 

be possible. 

Because of this conflict between personal desires and moral commitments and its 

consequences for valuation, an important empirical question is defining the circumstances 

where each one dominates, and determining how context affects the balance between the 

two motivations. Etzioni suggests that it might be useful to think in terms of 

competition/cooperation ratios, and ratio differences among areas of behavior. 

In summary, Etzioni asserts that, for policy analysis, it is not only important to look at “costs 

and benefits, deterrence, incentives and policing;” but also at formation of preferences 

through moral education, peer culture, community values, mobilization of public opinion, 

etc. Because neoclassical theory provides no analytical framework for preference formation 

and changes in preferences, a broader paradigm is needed, such as Etzioni’s I&We paradigm. 

However, many of these concepts are difficult to quantify, and must be made more 

operational. 
86

Implications for Policy Analysis 

The role of economists in policy analysis is commonly viewed as determining the optimal, or 

economically efficient, solution to a particular policy question. This is seen as one input into 

the broader policy analysis, which encompasses other concerns, including distributional and 

equity issues. However, as discussed above, economic analysis of natural resource and 

environmental issues is not always straightforward, and choices that consider moral 

commitments as well as personal utility are likely to violate neoclassical axioms of behavior, 

or result in the expression of value for commodities other than those intended by the 

researcher. Therefore, the efficient solution may be difficult to determine. 

Nonmarket valuation techniques are controversial and cannot be objectively validated. They 

may involve a variety of responses that are not consistent with neoclassical assumptions of 

self-interested utility maximization. Some of the most important issues, which were 

discussed in previous sections, include uncertain preferences for natural resources due to 

unfamiliarity or question complexity; elicitation of values for means (e.g., actions) rather than 

ends (e.g., commodities); moral or ethical motivations that affect choices and the way they 

are made, including “citizen” motivations, “commitment,” and a “sense of place;” and 

nonanthropocentric values, which are not considered by economic theory. As discussed 

above, these are likely to lead to violations of neoclassical axioms of behavior. 

Arild Vatn and Daniel Bromley (1994) believe that valuing or pricing environmental goods 

and services is neither necessary nor sufficient for coherent and consistent choices about the 

environment. They argue that, because of the fundamental complexities and multiple 
87

attributes of the environment, trying to reduce it to monetary values results in important 

losses of information, and “twists” the information about the individual and collective 

significance of environmental goods and services. They believe that economic efficiency is 

not the most important component of social choices; and more importantly, that if 

hypothetical prices do not convey the relevant information, then they cannot lead to 

efficiency. They conclude that attempting to measure economic values for environmental 

goods without accounting for moral commitments is biased, and cannot be resolved by 

simply aggregating individual bids. Thus, focusing on benefit-cost analysis without 

considering these broader issues may be inappropriate. 

The primary solution that has been suggested is that societal norms and standards for 

environmental management be developed. Vatn and Bromley believe that first we must 

advance common ways of understanding what the pertinent issues are about, and then 

develop a basis for collective choice based on the elicitation of individual choice. Because 

individual preferences are context dependent, it is necessary to define the context for a 

particular choice problem. Thus, there are two kinds of interrelated choice processes— 

choosing sets of norms and common values, and making policy decisions given specific 

values or constraints. 

As Mark Sagoff points out, individuals are both consumers and citizens, and environmental 

choices span both domains. Preferences are the driving force for consumers, and judgments 

are the driving concept for citizens when choosing basic norms or modifying existing 

constraints. Therefore, “[i]f the choice can be categorized as a ‘mere’ consumer choice, 
88

individual preferences and bids are relevant. If choices are about formulating common 

norms and values, such individual bids have little to offer” (Vatn and Bromley 1994, p. 142). 

Vatn and Bromley suggest that environmental goods need to be comprehended more 

holistically, in which case it does not make sense to attempt to value discrete units, because 

each part of a holistic system is as valuable as the whole. Because the value of most 

environmental goods and services is derived from keeping them working in their existing 

functional relation, they suggest that attention should be focused on ecosystems or 

bioregions (Vatn and Bromley 1994). 

Vatn and Bromley believe that the core of the problem is the desire for “development paths 

that will reduce competition between the ‘man-made’ (internal) economy and the 

environment.” One approach is to think in what they term “co-evolutionary terms” by 

attempting to choose development paths that allow both outcomes, such as multiple-use 

strategies or zoning policies. However, this may be difficult to implement in cases where it is 

not possible to predict the results of a given change in complex and interdependent systems. 

Based on the arguments summarized here, Vatn and Bromley conclude that pricing may not 

be appropriate for many environmental choices, that norms may be used instead, and that 

pricing is not sufficient to insure informed and coherent collective choices about 

environmental goods and services because of uncertainty about effects of actions and 

because values change over time. Thus contingent valuation may not provide decisive 

information about what is “efficient” and “welfare enhancing.” 
89

Similarly, Mark Sagoff suggests that values related to the environment include cultural, 

historical, aesthetic, and moral values, and that these are public values, not individual values, 

just like values for worker health and safety, so that they transcend individual self interest. 

He proposes that, when public values are involved, the best way to balance efficiency against 

these other values is by legislative debate and voting, rather than benefit-cost analysis. 

Economic considerations can be considered in such cases through cost-effectiveness analysis 

and “sensible recognition of economic constraints” (Sagoff 1981). 

The appropriate compromise must lie somewhere between the reductionism 

of markets and idealism of politics, between what we seem willing to pay for 

as consumers and what we nevertheless insist upon as citizens. Earlier, we 

asked whether the individual should consent to risks through consumer or 

citizen choice. The answer may be both; the finer-grained decisions may be 

appropriate to markets, but some of the basic conditions of choice may be 

appropriately decided in the political process (Sagoff, 1992, p. 201). 

However, legislative debate and voting has certain limitations as well, such as the power of 

special interest groups, and the low rate of participation among many groups of people. 

Several other authors discuss the use of standards, applied in various ways. Douglas Booth 

(1992) concludes that ecocentric and biocentric perspectives require that policy choices be 

made based on “moral standards of conduct and priority principles,” rather than benefit-cost 

analysis (p. 59). Holmes Rolston (1986) suggests a reversibility maxim and a scarcity maxim, 

where human interests are explicitly made subordinate in certain cases. The reversibility 

maxim states that humans should avoid introducing irreversible changes in natural processes. 

The scarcity maxim states that humans should take special measures to protect rare 

ecosystems and endangered species. 
90

Alan Randall (1992, 1994) suggests that ethical standards can be combined with benefit-cost 

analysis, for example by establishing safe minimum standards that constrain economic 

choices. Once the constraint is set, Randall proposes a benefit-cost analysis of the 

constrained choice set. However, there is still the problem of eliciting economic values for a 

benefit-cost analysis through surveys if some responses do not reflect the monetary value of 

the resource. 

Additionally, several complicated issues arise when setting standards. First, the level of 

“acceptable” implementation costs must be identified. There are also no clear answers to 

such questions as whether third world populations are more important than natural systems; 

how to reconcile culture and nature; and the determination of intrinsic value, if any, of things 

like the smallpox virus or poison ivy (Oelschlaeger 1991, p. 300). A more encompassing 

policy question is how values are to be determined if nonanthropocentric goals are 

important. Who would represent the interests of nature, and how would they decide what 

those interests are, apart from human standards? In the end, all choices made by humans are 

based on human values, whether the values are purely anthropocentric or ecocentric. 

In summary, these issues suggest that a more holistic and multidisciplinary approach to 

environmental policy analysis is required. Economists often point out that economic 

analysis is not concerned with issues of equity, distribution, or other moral or ethical 

questions, but that the role of economists is to provide input on economic benefits and costs 

and economic efficiency, conditional on a given income distribution, to policy makers and 

politicians, who then consider this as one part of the information available to make their 

decision. 
91

However, in certain contexts, such as measurement of nonuse values, it may be difficult to 

produce defensible estimates of economic benefits and costs. In such cases, recognizing that 

values may be uncertain, and that moral commitments and nonanthropocentric values may 

be important, can be vital to performing relevant and useful policy analysis. Although 

estimation of precise economic benefits and costs may be difficult and, in certain cases, 

inappropriate, economists can still provide information on relative values and priorities. 

This information can provide one means of considering the public interest as part of a 

multidisciplinary approach to policy analysis that combines natural sciences and social 

sciences, and considers ecosystem integrity, public priorities, and other social and cultural 

concerns. 
92

CHAPTER 4 

THEORETICAL FRAMEWORK 

Introduction 

The overall goal of the Peconic Estuary Program, which is described in Chapter 2, is to 

develop a Comprehensive Conservation and Management Plan for the Estuary. As part of 

the planning process, the various committees that are studying the Estuary will suggest 

actions to protect or enhance natural resources. Because of limited sources of funding, it 

will most likely be impossible to implement all of the suggested actions. Therefore, in order 

to formulate the final Management Plan, the Management Committee will be required to 

choose between alternative programs, or sets of actions, that will provide different levels of 

protection and/or enhancement of natural resources. For example, one possible 

combination of actions might preserve a large amount of farmland and not address eelgrass 

restoration. Another program might focus heavily on wetlands and shellfishing areas, but 

have little impact on controlling development and preserving farmland. 

The goal of this study is to provide information on public priorities and values for natural 

resources of the Peconic Estuary to help managers make choices among actions. This 

information was gathered primarily through a survey of year-round and seasonal residents of 

the area. The survey, which is described in detail in Chapter 5, included a set of contingent 

choice questions, where respondents were asked to select one of three hypothetical options: 
93

“No New Action,” “Program A,” or “Program B.” Each program was described in terms of 

its impacts on natural resources, and the associated annual cost to each East End household. 

The “No New Action” option does not protect or enhance any resources, and does not cost 

the respondent any additional amount per year. Thus, it provides a baseline level for the 

resources, indicating what would likely occur if no action is taken to protect or enhance 

them. 

By observing large numbers of such choices, a statistical model can be estimated that 

determines the relative importance of the natural resources to the representative respondent. 

This information can be used to rank different programs in order of importance to the 

public, without placing dollar values on the individual natural resources. Alternatively, dollar 

values for resource changes may be calculated and used in benefit-cost analysis of different 

programs. 

This chapter explains the conceptual and theoretical framework of the study. First, the 

discrete choice and conditional logit models for contingent choices are described. Next, the 

nested logit model is presented. This is followed by a discussion of welfare estimation based 

on these models. The final section describes possible violations of revealed preference 

theory, and hypotheses that can be tested to determine whether results are consistent with 

the neoclassical utility theoretic framework. 
94

The Discrete Choice Model for Contingent Choices 

Based on neoclassical consumer theory, it is assumed that respondents to contingent choice 

questions choose the option that maximizes utility received from attributes of the option, 

subject to their budget constraint. In this study, the options are described in terms of a set 

of natural resources and the cost of the option. Thus, the utility function can be specified as 

U(R), where R is a vector of natural resource levels. Solving the maximization problem 

results in the demand functions for the natural resources, Ri(P, M), where P is the vector of 

prices and M is total income. Substituting the demand functions into the utility function 

gives the indirect utility function, U(P, M), and the conditional indirect utility function, U(R, 

M). 

Described in terms of the conditional indirect utility function, the utility received from the 

chosen option is assumed to be greater than the utility received from the other available 

options, or 

U MC U MC ij i j ik i k (, ) ( , ) R R −> − ≠ for all k j (1) 

where U(A) is the utility function for individual i, j represents the program that is chosen by 

individual i, k represents the other options (k≠j), R is a vector of natural resource levels for 

each program, Mi is the individual’s household income, and Cj is the annual cost to each 

household for program j. 
95

Because of errors in measurement by the researcher, the utility function is assumed to 

consist of a fixed component and a random component (Maddala 1983; McFadden 1973), so 

that 

U MC V MC ij i j ij i j j (, ) (, ) R R −= −+ ε (2) 

where V(@) is the systematic, or measurable, component of utility, and g is the random, or 

unobservable, component. This is referred to as the random utility model. The random 

component of the utility function may be a result of unobserved attributes of the choice, 

unobserved taste variations, measurement errors, or the use of instrumental variables to 

replace variables that are not observable (Ben-Akiva and Lerman 1985). 

Program j, a preservation or enhancement program that provides the vector of natural 

resources Rj

, will be chosen if 

V MC V MC ij i j j ik i k k (, ) ( , ) R R − +> − + ≠ ε ε for all k j (3) 

 or 

V MC V MC ij i j ik i k k (, ) ( , ) R R −− −> ≠ ε - for all k j. ε j (4) 
96

The level of utility obtained by the ith individual from the jth choice can be represented by 

Uij*, so that 

U V MC ij ij i j j

* = −+ (, ) . R ε (5) 

However Uij* is not directly observed. Instead we observe the option chosen by the 

respondent. If there are three options, as in this study, the observed choice variables, Yij, are 

defined as 

Y UU

Y

ij

ij

= =

=

1

0

 if U Max(U

 otherwise.

ij

*

i0

*

i1

*

i2

* ,,)

 (6) 

If the respondent selects the choice that maximizes utility, then the probability that 

individual i will select option j is 

Prob( ), * * U U ij ik > (7) 

where k represents all other choices (k≠j). 

In order to estimate the model, a distribution for the disturbance terms must be chosen. 

The most commonly used distributions are the normal distribution, which leads to the probit 

model; or the Weibull distribution, which leads to the logit model. The logit model is usually 
97

selected for multinomial models, because it is easier to estimate (Ben-Akiva and Lerman 

1985; Maddala 1983). 

The logit model is derived by first assuming that the disturbance terms are independent and 

identically distributed with Weibull distribution, so that 

 F( ) exp( ) ij ε ε = − −

e ij (8) 

and 

P = Prob ij () . Y

e

e ij

V

V

k

ij

ik

= = ∑ 1 (9) 

If the systematic component of the utility function, Vij, is assumed to be linear in parameters, 

then 

P =ij

e

e

ij

ik

k

′

′

∑

β

β

z

z . (10) 

where z is a vector of attributes of the choice. In this case, zj = (Rj, Cj). This is generally 

referred to as the conditional logit model, and is distinguished from the multinomial logit 

model by the fact that characteristics of the choices are considered as determinants of choice 

probabilities, while the multinomial logit model considers only characteristics of the 

individuals (Greene 1993; Maddala 1983). 
98

If utility depends on both characteristics of the individual and attributes of the choice, then 

the model becomes 

Pij

k

e

e

ij i

ik i

=

′ + ′

′ + ′

∑

β α

β α

z w

z w . (11) 

where zij are attributes of the choice (e.g., the levels of natural resources and the cost), which 

may also vary across individuals, and wi are characteristics of the individual, which are the 

same for all options faced by an individual. However, in this model, terms that do not vary 

across alternatives fall out of the model, so the coefficients on the individual characteristics 

cannot be estimated. 

One way to solve this is to create a choice-specific dummy variable for each of the options, 

and multiply by each characteristic of the individual. Thus, if there are three options, each 

individual characteristic, such as income, would appear as three separate variables that are 

the product of the dummy variable for the option and the individual’s income. The resulting 

variables explain how the individual’s characteristics affect their choice of a particular option, 

for example whether a person with higher income is more likely to drive to work rather than 

taking the bus. However, if preferences over attributes of an alternative vary systematically 

over individuals, this model will not explain those variations. 

In this study, outcomes of generic programs are presented to respondents without specifying 

the actions that would achieve them. Thus, one would expect that respondents choose 

based on the attributes of the alternatives—the levels of natural resources—rather than 
99

choosing a specific alternative as in the travel demand example. Therefore, interaction terms 

were created for each attribute, or resource level, multiplied by categorical variables for 

individual characteristics. Thus, the model becomes 

Pij

j ji

k ki

k

= ′ + ′

∑ ′ + ′

exp( )

exp( ). β α

β α

z zw

z zw

 (12) 

The parameters of the model, $ and ", are estimated using maximum likelihood techniques. 

The log-likelihood function is 

lnL YP

Y

i j

ij ij

i j

ij ij

i k

ik

=

= ′ − ′

∑ ∑

∑∑ ∑ ∑

ln

( ) ln( exp( ) γ γ X X

 (13) 

where Yij is defined by Equation (6), Pij is defined by Equation (12), X includes both zij and 

zjwi, and ( includes both $ and ". The first-order condition for a maximum is 

∂

∂γ

ln

( ).

L

Y P

j i

= −= ∑ Xij ij ij 0 (14) 

The matrix of second-order conditions has J2 blocks, and the jth diagonal block is 

− − ′ ∑ P P ij ij i i

i

( ) 1 X X (15) 
100

and the jkth off-diagonal block is 

 () P Pij ik i i

i

X X ′ ∑ (16) 

The estimated coefficients are interpreted as the marginal utilities of the attributes. In order 

to determine the marginal effect of a change in one of the attributes on the probability of 

selecting an alternative, Equation (12) must be differentiated with respect to the attributes, to 

obtain 

∂

∂ β

∂

∂ α

∂

∂ β

∂

∂ α

P

P P

P

P P

P

P P

and

P

P P

j

j

j j

j

j i

j j

j

k

j k

j

k i

j k

z

z w

z

z w

= −

= −

= −

= −

( ),

( ),

,

.

1

1

 (17) 

The Nested Logit Model 

An important assumption of the multinomial and conditional logit models is that, for a given 

individual, the ratio of the choice probabilities of any two alternatives is not affected by the 

availability of any other alternative (Ben-Akiva and Lerman 1989). This is termed the 
101

independence of irrelevant alternatives property, or IIA, and follows from the assumption 

that the disturbances are independent (Greene 1993). 

The most widely cited example of the significance of this assumption is the “red bus/blue 

bus” paradox. In this example, a commuter chooses between driving and taking a bus, with 

equal probabilities, so that P(car)=.5 and P(bus)=.5. If a second bus service is added, with 

identical characteristics to the first except for the color of the buses, the choice probabilities 

in the standard logit model become P(car)=.33, P(red bus)=.33, and P(blue bus)=.33. 

However, this model is unrealistic, leading to counterintuitive predictions. Most likely, 

individuals who previously chose to travel by car would continue to do so, and riders on the 

new bus would come from those who previously traveled by bus. Hence, the actual choice 

probabilities would likely be P(car)=.5, P(red bus)=.25, and P(blue bus)=.25. This problem 

occurs because the different colored buses share all of the unobserved characteristics of 

buses, and thus the disturbances are correlated (Ben-Akiva and Lerman 1989). Therefore, 

the standard logit model is only valid for choice sets with distinct alternatives, where the 

unobserved components of utility can be assumed to be independent. 

Several alternative models may be used when the disturbances are likely to be correlated. 

These include the multinomial probit model, the elimination-by-aspects model, the nested 

logit model, and the generalized extreme-value model (Maddala 1983). The nested logit 

model is a special case of the generalized extreme-value model, and is appropriate for 

multidimensional choices where unobserved attributes of elements of the choice set are 

equal across subsets of alternatives (Ben-Akiva and Lerman 1989; Kling and Thomson 

1996). For example, in the survey used in this study, respondents were asked to choose 
102

between “No New Action,” “Program A,” and “Program B.” It is possible that the two 

alternatives that represent actions, “Program A” and “Program B,” may share unobserved 

attributes that would differ from those of the “No New Action” alternative, because both 

reflect taking action. Thus, the error terms for the “action” choices would be correlated. 

One intuitively appealing explanation for such correlations is that respondents may use a 

sequential choice process, first deciding whether to select “No Action” or “Action.” If 

“Action” is selected, a choice of “Program A” or “Program B” is made. This can be 

represented by a tree structure, illustrated in Figure 0-1, with two levels. It has two branches, 

and three sub-branches. 

Figure 0-1 - Sequential Choice Structure

Choice

No Action Action

No Action Program A Program B
103

Thus, respondents face a multidimensional choice consisting of three combinations of 

choices: (No Action, No Action), (Action, Program A), and (Action, Program B). The total 

utility of an element of this choice set is defined as 

U VVV jk j k jk j jk = + + ++ ~~~ ~ ~ ε ε , (18) 

where Ujk is the total utility; j represents the choice to take action or not; k represents the 

alternative chosen—No Action, Program A, or Program B; the ~

V ’s represent the systematic 

components of total utility; and the ~ε ’s represent the unobserved components of utility. 

Thus, ~ε k is assumed to equal zero, implying that there is no correlation across branches 

(Ben-Akiva and Lerman 1985). 

The probability that an individual chooses alternative k is 

 P P(k|j)P(j) kj = . (19) 

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. If they choose to take action, 

the probability of selecting “Program A” or “Program B” is 

P(k|j)=

e

e

e

e

e

e

V

V

nj nj

k j

n j

k j

n j

| k j

|

|

|

|

| |

, ∑ ∑= =

′

′

β ′

β

x β

x

x

Jj (20) 
104

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 represents the 

expected maximum utility from the choice of an alternative in a sub-branch, termed the 

inclusive value for choice j. This is defined as 

J j

n j

= log( exp( )). ∑ ′ |

|

β xn j (21) 

The probability of choosing to take action or not is 

P j j jj

m mm

m

( ) exp( )

exp( = , ′ +

∑ ′ +

α τ

α τ

Y J

Y J (22) 

where m is the number of branches, which in this case is two. 

The parameter J is related to the correlation between alternatives, and is termed the inclusive 

value coefficient. The condition 0#J#1 is sufficient for the nested logit model to be 

consistent with utility maximization (McFadden 1981). However, values greater than 1 can 

also be consistent with utility maximization if the marginal choice probabilities of subsets of 

similar alternatives are sufficiently large (Börsch-Supan 1990; Kling and Herriges 1995), or 

τ ≤ −

1

1 P j ( ). (23) 
105

A value of J 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 there is greater 

substitutability between Program A and Program B than between either program and the 

“No New Action” alternative. If J is equal to 1, then all alternatives are equally 

substitutable, and the model becomes identical to the standard multinomial or conditional 

logit model, where the IIA property holds for all alternatives. Therefore, the hypothesis that 

J=1 can be tested in order to determine if there is significant correlation between 

unobserved attributes of the alternatives (Kling and Thomson 1996). 

Welfare Measurement 

Michael Hanemann (1982, 1984) derives utility-theoretic welfare measures for discrete choice 

models, where the objective is to calculate the maximum willingness to pay for a change in 

each of the natural resources presented to the respondent. This is measured by the cost, C, 

that would make a person indifferent between the choice selected and the baseline, no 

action, which has zero cost. Thus, for the conditional logit model, 

U M U MC ij i ik i k (, ) ( , ) R R =− ≠ for all k j, (24) 

where j represents the baseline, so that Cj=0; k is the option selected; and Ck is the minimum 

willingness to pay for option k. For the linear approximation of the utility function 

presented above, this can be solved for Ck as follows: 
106

β γ β γ

β

γ

R R

R R

ij i ik i k

k ik ij

M MC

and

C

+ =+ −

=− −

() ( )

( ).

 (25) 

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, $, to the coefficient on cost, (. 

The calculation of dollar values for the nested logit model must account for the nested 

structure and the inclusive value parameter, J. 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 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( / ) (26) 

where V is the utility function, the superscripts on V indicate whether the attributes are set at 

the new level or the old level, and ( is the coefficient on cost. 

Tests for Economic “Rationality” 

Chapter 3 described several ways that responses to valuation surveys might violate the 

axioms of revealed preference theory, and thus not be “rational” as defined by economists. 
107

Two of the most important hypothesized reasons for such violations are the expression of 

moral commitments in survey responses, and uncertainty about preferences related to the 

natural environment, due either to unfamiliarity with the choice situation, or the complexity 

of such choices. 

In the context of this study, several hypotheses about responses may be considered. First, a 

respondent may behave according to neoclassical theory, and balance the options and their 

attributes quantitatively to maximize personal utility. In this case, the standard random 

utility model presented in the first section of this chapter would be appropriate, and welfare 

measures derived from this model would be valid. 

Second, survey responses may symbolically express moral commitments or other emotional 

responses to the survey. Third, because of unfamiliarity or complexity, respondents may 

simplify the choice, applying decision heuristics. 

Moral Commitments 

This section presents a model of moral commitments based on Kopp (1992), combined with 

ideas from Etzioni (1988) and others, which were discussed in Chapter 3. An individual’s 

total well-being is assumed to be made up of two components, ethical values and personal 

utility, so that 

 W = W[F(E), U(X)], (27) 
108

where F(E) is a function that measures the well-being associated with E, a vector 

representing ethical values; and U(X) is the utility function for self-interested pleasure from 

the consumption of a vector of goods and services, X (Kopp 1992). Note that in this model 

well-being is not synonymous with utility, as defined by neoclassical theory, but is a broader 

concept that encompasses both personal utility and moral commitments. 

The utility function, U(X) is assumed to possess the usual neoclassical properties; thus it is 

continuous, monotonically increasing, and quasi-concave. However, F(E) is not assumed to 

have any particular curvature, but only to increase with adherence to ethical values. E may 

be modeled in two ways—as binary or continuous. In the binary ethics model, E is defined 

as an n-dimensional vector of ethical codes, whose value is 1 if a person acts in accordance 

with a code, and 0 if they do not. Thus, F(E1', E2, ..., En) > F(E1, E2, ..., En) if E1'=1 and 

E1=0. In the continuous ethics model, each Ei becomes an “ethic bank” where well-being is 

enhanced by consistent behavior and diminished by inconsistent behavior. F(E) can be 

specified in different ways, for example as in prospect theory, where gains are less valuable 

than losses (Kopp 1992). 

One of the most important components of this model is specification of the relationship 

between F(E) and U(X). This is likely to vary according to the choice context, from 

complete incommensurability, to the ability to make perfect tradeoffs. As Etzioni points 

out, moral commitments may be absolute, and thus make certain choices “unthinkable,” 

even if they may be acceptable in terms of rational choice. In such cases, the well-being 

function would be dominated by F(E), and the individual’s choice set would be constrained. 
109

In other cases, or when absolute principles have already been violated, moral considerations 

may be balanced with personal utility when making a choice. Etzioni calls these expedient 

moralities, which imply that certain moral means are preferred but not at all costs. 

Consequently, this model has a variety of implications for observable behavior and its 

interpretation. Most important, it implies that additional data must be collected in order to 

specify the F(E) function, and to model the relationship between F(E) and U(X). 

In the context of this study, moral commitments might be expressed in a variety of ways. 

For example, a person who disapproves of government actions may select the “No New 

Action” option for every choice. Another person may express an environmental 

commitment by always choosing to take action, without considering costs and benefits. 

Nevertheless, the person may compare the natural resource levels when choosing between 

Program A and Program B. Respondents may also express strong support for taking action 

by always choosing the most expensive action. A respondent also may feel a moral 

commitment to preserving a particular natural resource, for example farmland, and thus 

always choose the option that preserves the most farmland without balancing the cost of the 

alternative against the resulting increase in farmland. 

However, without directly asking respondents about moral commitments, it is very difficult 

to determine if they are important. It is possible to test whether a significant number of 

people always select the “no action” option, or always select the most expensive option. 

However, these tests only provide evidence related to absolute commitments, and thus are 

not definitive tests for the effects of moral commitments. 
110

Preference Uncertainty 

When faced with complex or unfamiliar decisions, individuals may be uncertain about the 

utility obtained from their choices (Mazzotta and Opaluch 1995). Thus, the random 

component of the utility function defined in Equation (2) may be made up of two 

components—a component that is known to the respondent but unknown to the researcher, 

:, and a component that is unknown to both the respondent and the researcher, J. This 

second component is a random variable, whose variance may depend on the complexity and 

unfamiliarity of the decision. In this case, the variance of the utility difference defined in 

Equation (4) becomes 

σσσ μ τ

2 22 = + ( ) c, u , (28) 

where c represents choice complexity, and u represents unfamiliarity. If this variance is 

constant across choices, its value is unimportant to the model estimation, because the model 

is based on an ordinal measure of utility, and the size of the variance will only affect the scale 

of the utility function. However, if the variance differs systematically across choices, g will 

not be identically distributed across observations, resulting in statistically inconsistent logit 

estimates (Greene 1993). 

Alternatively, individuals may attempt to simplify complex choices, by applying decision 

heuristics. For example, they may use lexicographic ordering, choosing based only on the 

resource that is most important to them and ignoring the other resource and the cost. They 

might also simplify the choice by ignoring the level of cost or another attribute, or by 
111

ignoring the quantitative changes and deciding only based on whether a resource increases or 

decreases (Mazzotta and Opaluch 1995). 

As with the hypothesis of moral commitments, without more information on how people 

choose, statistical tests cannot provide definitive evidence to support or refute these 

hypotheses. However, a variety of statistical and qualitative tests may provide evidence 

related to consistency of responses with revealed preference theory. Two hypotheses related 

to theoretical validity of valuation surveys may be tested—the adding up hypothesis, and the 

scope hypothesis. The conceptual framework for these tests is presented in the following 

sections. The results of these tests and additional evidence related to the above hypotheses 

is discussed in Chapter 6. 

The Adding Up and Scope Hypotheses 

The adding up property requires that values for the same incremental change in a natural 

resource should be equal, whether asked as one or two questions. For example, if one 

sample is asked their maximum willingness to pay for a cup of coffee, and a second sample is 

asked their maximum willingness to pay for a doughnut, given that they have already 

purchased a cup of coffee, then the sum of the mean responses for the two samples should 

equal the mean of the third sample’s maximum willingness to pay for a cup of coffee and a 

doughnut (Diamond, et al. 1993). This can be derived as follows. 
112

An individual is assumed to maximize the utility received from a vector of market goods, X, 

and a vector of natural resources, R, subject to their budget constraint, PxX + Pr

R # M, 

where M is total income. The resulting indirect utility function is U*(M, X, R), and the 

expenditure function for this level of utility is E(P, R, U*). This is equivalent to the amount 

of income needed to achieve utility U*. Therefore, the willingness to pay to go from a given 

level of a natural resource to a higher level can be represented as 

 WTP(R1- R0) = WTP1 = E(P, R0, U*) - E(P, R1, U*) (29) 

where R1> R0. If two additional changes are defined as 

 WTP(R2 - R1) = WTP2 = E(P, R1, U*) - E(P, R2, U*) 

 and (30) 

 WTP(R2 - R0) = WTP3 = E(P, R0, U*) - E(P, R2, U*), 

where R2 >R1 >R0, then 

 WTP1 + WTP2 / E(P, R0, U*) - E(P, R1, U*) 

 + E(P, R1, U*) - E(P, R2, U*) (31) 

 = E(P, R0, U*) - E(P, R2, U*) / WTP3. 

Therefore, WTP1 + WTP2 . WTP3 if income effects are assumed to be close to zero 

(Diamond, et al. 1993). 
113

Equations (29) through (31) assume that there is no income effect. However, if a person has 

already purchased a given level of the natural resource, their income will have decreased by 

the amount paid. If the same person is then asked to value another resource change, their 

willingness to pay will be based on the new level of income, after paying for the first change. 

Using the example above, the initial income level can be defined as M1, and the level after 

paying for the change (R1 - R0) can be defined as M2, so that M2< M1. Because utility is 

maximized subject to the income constraint, the maximum level of utility that may be 

achieved at income level M1, U*

1, is greater than that which can be achieved at income level 

M2, U*

2. 

Therefore, if a person has already paid for (R1 - R0), and is then asked for their willingness to 

pay for the change (R2 - R1), the effect of the change in income on willingness to pay is given 

by 

 [E(P, R1, U*

1) - E(P, R2, U*1)] 

 - [E(P, R1, U*

2) - E(P, R2, U*2)]. (32) 

If the amount paid for the first change is small compared to total income, the income effects 

can be expected to be negligable and thus not likely to affect the result presented in 

Equation (31) (Diamond, et al. 1993). 

The scope hypothesis is based on the assumption that a person’s willingness to pay for a 

larger quantity of a good should be greater than that for a smaller quantity, assuming it is a 
114

normal good and they are not satiated. Thus, using the above example, WTP3 > WTP1, so 

that 

 E(P, R0, U*) - E(P, R2, U*) > E(P, R0, U*) - E(P, R1, U*). (33) 

This implies that 

 E(P, R1, U*) > E(P, R2, U*), (34) 

or the amount of income needed to maintain a given level of utility is greater for a lower 

level of the natural resource, and willingness to pay is greater for a higher level of the natural 

resource. Therefore, when values are elicited for different size changes of the same natural 

resource, they should conform to this property, and this can be tested by determining 

whether estimates of willingness to pay increase with the quantity of the resource. 
115

CHAPTER 5 

SURVEY DEVELOPMENT AND IMPLEMENTATION 

Introduction 

This research is part of a three-phase comprehensive economic value assessment for the 

Peconic Estuary Program (see Chapter 2). As illustrated in Figure 0-1, the first phase is an 

economic impact analysis of Estuary-related sectors; the second phase focuses on nonmarket 

values; and the third phase includes a benefit-cost analysis of potential restoration and 

preservation programs, and an evaluation of financing alternatives. Data from Phases I and 

II will be used to provide information on benefits of proposed actions for the analysis in 

Phase III. 

Two surveys were conducted during Phase II to collect data on nonmarket values of the 

Estuary’s natural resources: a recreation survey and a resource survey. The data from the 

recreation survey will be used to estimate recreational use values for the Estuary. The 

resource survey, which is the subject of this dissertation, elicited relative values and priorities 

for important natural resources of the Peconic Estuary. The remainder of this chapter gives 

a detailed description of the survey development process for the resource survey. 
116

Figure 0-1 - Overview of Project 

Market Values / 

Economic Impacts

Phase I

Recreational 

Values

Natural Resource

Values

Phase II

Program 

Benefits

Program

Costs

Benefit-Cost

Analysis

Phase III

Financing

Options

Policy

Analysis

Steps in Survey Development 

Several goals were important in creating the survey. The primary goal was to elicit public 

preferences, priorities and values for natural resources of the Peconic Estuary that might be 

affected by preservation and restoration actions. A secondary goal was to explore people’s 

motivations for choices related to the environment. Other important goals were to create a 

survey that would minimize some of the problems associated with valuation surveys; would 
117

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. 

The first stage of survey development consisted of meetings with the Management 

Committee, the Citizens’ Advisory Group, and knowledgeable representatives of stakeholder 

groups. This was followed by informal interviews of the public, focus groups, a brief 

preliminary survey, and pretesting of the final draft survey. In addition, ten in-depth 

personal interviews were conducted in order to learn more about people’s environmental 

attitudes and motivations for supporting different types of actions. Thus, the final survey 

instrument was based on the needs of the Management Committee, combined with input 

from representatives of stakeholder groups and the general public. 

Initial Planning and Information Gathering 

The survey development process began in February, 1995 with a visit to the area and an 

initial meeting with members of the Management Committee. Shortly afterwards, meetings 

were held 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. From 

these meetings, and a meeting with the Citizen’s Advisory Group, information was gathered 
118

about the area and about the concerns of various interest groups. At the same time, in order 

to get an understanding of the most important problems and concerns related to the 

Estuary, available information on the area was gathered, including studies and other 

documents related to the Peconic Estuary Program, and articles in local newspapers. This 

information is summarized in Chapter 2. 

Informal Interviews With the Public 

The next step was a set of informal interviews with members of the public. Two days were 

spent talking with people in several towns to ask about their uses of the Estuary and 

concerns about the Estuary. Sixteen people in Montauk, Springs, Sag Harbor, Shelter Island 

and Greenport were interviewed briefly. 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. Two important findings were that 

most people interviewed were not familiar with the word estuary; and most people in 

Montauk did not consider themselves to be located on the Peconic Bay. 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. 
119

Focus Groups and Preliminary Survey 

The next step was a series of focus groups, and a short preliminary survey. Table 0-1 lists the 

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

focus groups, participants were asked 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. 

Table 0-1 - 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 
120

A short preliminary survey was implemented 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, and is reproduced in Appendix A. 

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 rate a list of concerns on a scale of 1 to 10, where 1 

indicates the highest level of concern. Table 0-2 lists these concerns, in order of importance 

to respondents. 

Table 0-2 - Concerns of Preliminary Survey Respondents 

Concern Mean Ratinga

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 - Rated from 1 to 10, with 1 denoting the highest rating. 

In the next several focus groups, participants were asked general questions about their 

concerns, their perceptions of water quality, and actions they would like to see; and some 
121

preliminary survey questions and question formats were tested. The questions and formats 

were revised between groups. 

The first set of written questions was tested in Focus Groups 3 through 5, with slight 

changes made between groups. These preliminary questions are shown in Appendix A. The 

first four questions were primarily intended to stimulate discussion. The first question asked 

about participation in recreational activities, and was used to generate a discussion of these 

activities and related concerns. The second question asked participants which of a list of 

human impacts on the Bays were most important to them; the third question asked 

participants which of a list of environmental problems they believed were most serious; and 

the fourth question asked which potential actions should be given highest priority. 

Participants found these three questions difficult to answer, for two reasons. First, they 

believed that almost everything was important, and therefore resisted ranking their priorities. 

 Instead, many participants wanted to rank 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 set by 

experts. Nonetheless, these questions provided a good focus for the discussion of people’s 

concerns. 

The final three questions asked about priorities related to natural resources, in order to begin 

testing possible question formats for the survey. The first, labeled Example 1 (see Appendix 

A), was intended to explore consumer-citizen issues (Sagoff 1992a, 1981; Vatn and Bromley 

1994; see Chapter 3 for discussion), by asking people how they would vote in a referendum 

on septic systems; how the personal financial impact of the referendum would affect their 

vote; and whether they thought the program would be good for the overall community. 
122

However, this question did not work as intended, but instead tended to generate discussion 

of septic systems and political concerns surrounding the issue of regulating them. Several 

respondents rejected these questions, stating that they thought it would be illegal to upgrade 

sewer requirements. 

The second question, labeled Example 2, was a standard open-ended willingness to pay 

question. Many of the participants found this difficult to answer. The third, Example 3, was 

an initial version of a paired comparison question. Participants were able to answer this, but 

did not like the question itself, primarily because each plan included one resource that did 

not improve, and they found the presentation of information too complicated. In general, 

participants found these last three questions to be too wordy. 

The next version of focus group questions developed the paired comparison method further. 

 More background information was given, and each comparison included five natural 

resources: developed land, farmland, undeveloped land, shellfish closures, and fish 

population. Developed land, farmland and undeveloped land were all included because the 

total acres of land were intended to add up to the total area of the East End. Participants 

found this confusing, however, and also had trouble answering comparisons with so many 

attributes. 

This version also tested several other questions (see Appendix A for examples). 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 
123

question was intended to explore people’s “sense of place.” However, it was later dropped 

because it did not provide enough information to justify the space and time it took in the 

survey. 

Another set of questions asked respondents to make tradeoffs between sets of two natural 

resources. However, the format was confusing to respondents, so these questions were also 

dropped. Finally, a set of environmental attitude questions was included, with the intention 

of correlating respondents’ attitudes with their values for natural resources. These questions 

were adapted from an anthropological survey of Americans’ environmental attitudes 

(Kempton, et al. 1995, see Chapter 3). 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 a long time to answer; and finally, the 

Management Committee felt uncomfortable about including them because of their 

controversial nature. 

The next version simplified the background information and instructions, and added 

graphical depictions of the levels of natural resources to the questions. Only one or two 

natural resources, plus the cost, were included in each comparison. This version was easier 

to answer, but respondents still had some difficulties, and made suggestions for changes in 

the question formats, wording and graphics. The most important suggestion was to include 

the option of choosing neither program. In summary, through the process of focus groups, 

the survey became less complicated and less wordy, with fewer attributes included in each 
124

comparison; graphics were added to the questions; and the option of choosing to take no 

action was added. 

Survey Pretests and Implementation 

The first pretest of an actual survey instrument was at the May 18 group in Springs (see 

Appendix A for a copy of this survey). This version refined the wording of previously tested 

versions, and added a “Do Nothing” option, which was subsequently revised to read “No 

New Action.” The respondents found the graphics helpful, and were able to understand and 

answer the survey questions. However, the overall layout remained somewhat confusing for 

some people. The next pretests, conducted in Rhode Island, refined the question formats, 

graphics and page layout, 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, final modifications were made to the survey, and final pretesting 

on Long Island was conducted. An example of the final survey is included in Appendix C. 

The survey was implemented during the week of Aug. 22-29 in a variety of public locations 

around the East End, using convenience sampling. This method was selected 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 
125

names and addresses of visitors were not available. The surveys were therefore designed to 

be administered in public places where visitors to the area could be intercepted. An effort 

was made to select a wide variety of locations, in order to intercept a heterogeneous 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 5-3. A total of 968 resource 

surveys were collected from year round and seasonal residents of the East End. 

In implementing the survey, interviewers approached people and said 

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? 

People who agreed to fill out the survey were then asked whether they were visitors, yearround residents, or seasonal residents of the East End. Only year-round and seasonal 

residents were asked to fill out the resource survey. All visitors, and every third resident, 

were given the recreational use survey. When the survey was handed to the person, they 

were told that there are no right or wrong answers, and that all answers would be 

confidential. Interviewers were instructed to give brief and neutral answers to any questions. 

Table 0-3 - Survey Locations 

Location 

Number 

Collected

Beaches: Orient State Beach, Southold 3 

 Southold Town Beach 10 
126

 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 

 Miscellaneous (unidentified) 5 
127

Selection of Survey Format

The contingent choice format, rather than contingent valuation, was selected for several 

reasons. Based on responses in focus groups, the contingent choice framework seemed to 

be the most effective means of eliciting the required information. Because specific actions 

and their results were not defined at the time the survey was developed and implemented, 

the survey was designed to elicit public preferences for generic natural resources that were 

most important to the public, and would most likely be affected by restoration and 

preservation programs. Therefore, the survey identifies public support for many different 

combinations of improvements in resources that might be obtained by management actions. 

 This allows for assessment of programs, either by ranking or valuation of benefits, that 

affect any combination of the natural resources evaluated in the survey. 

Contingent choice may also minimize some of the problems associated with contingent 

valuation. 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. 

In addition, the survey used in this study presented only outcomes of generic actions, or 

ends, so that respondents are not given the opportunity to focus on the means of achieving 

results. Thus, responses are less likely to be expressions of approval or disapproval for 

actions, and more likely to focus on the specific natural resources. Consequently, although 
128

the contingent choice format may still invoke symbolic values, it is less likely to do so than 

contingent valuation, because of the focus on tradeoffs and on ends rather than means. 

The NOAA CV Panel points 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 

precise. One potential problem with the contingent choice format is that it may lead to the 

use of simplified decision rules, such as lexicographic ordering or choices based on 

qualitative rather than quantitative balancing of attributes. However, even if this occurs, 

priorities over attributes may still be revealed. 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 two of the five natural resources included in the 

survey. This simplifies the choice, so that it is more likely that choices will be based on 

considering and balancing all of the attributes rather than using a simplified decision rule. 

In the final survey, respondents were asked to select from a set of three hypothetical choices: 

no new action or one of two enhancement/protection programs. Each option was 
129

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. 

Selection Of Natural Resources And Levels 

Because scientific studies of anticipated resource changes resulting from different actions 

were not complete when the survey was created, the natural resources and their levels 

described in the survey were chosen to bracket the feasible range of resource changes under 

various policy actions, including the “no new action” option. 

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, five natural resources were selected to 

be included in the survey: farmland, undeveloped land, wetlands, safe shellfishing areas, and 

eelgrass. Farmland and undeveloped land were included 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 
130

components of the quality of life, or “sense of place,” (Sagoff 1992b; Kellert 1995; see 

Chapter 3 for discussion) for many residents of the East End, who enjoy the rural quality of 

the area and shopping at numerous local farm stands. The rate 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. The 

historical significance of shellfishing to the local economy, and its importance as a 

recreational activity, combined with the declines in shellfish due to brown tide and the recent 

large increases in areas closed to shellfishing, resulted in many people expressing concern for 

reopening closed shellfishing areas. 

In initial versions, the survey listed the number of acres closed to shellfishing, and thus 

described improvements in terms of decreases in closed areas. However, this presentation 

was confusing to people because the changes described were opposite those for all of the 

other resources; i.e., a decrease in closed areas is desirable for shellfishing, while a decrease in 

each of the other resources is undesirable. Therefore, in order to avoid confusion, and to be 

consistent with the rest of the survey, the number of acres that are safe for shellfishing, 

rather than the number of acres closed to shellfishing, was presented. However, because 

most of the Estuary’s area is safe for shellfishing, potential changes based on total acres open 

to shellfishing are very small, and thus difficult to distinguish. In addition, although most of 

the open waters are safe for shellfishing, the small bays are the most productive areas, and 

most shellfishing takes place in these areas. These are also the areas most in danger of 
131

pollution from stormwater runoff, septic systems, and boat discharges. Therefore, in 

consultation with the Technical Advisory Committee, it was decided to present only the 

areas safe for shellfishing in small bays around the Estuary. 

Finally, eelgrass was chosen 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, potential changes in these populations resulting from proposed actions 

could not be easily determined by the technical consultants. Therefore, it was hoped that by 

including eelgrass, which serves as fish and shellfish habitat, some of these concerns would 

be captured. 

Levels of Natural Resources in the Survey 

The objective of the survey was to elicit relative values for improvements in natural 

resources above a baseline level. The baseline was defined as the level that would exist in the 

year 2020 if no action is taken to preserve or enhance the resource, and was determined 

based on historical declines and the judgment of experts. 

First, the level of each resource in 1981 and the current level were obtained 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 
132

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, the number of acres within the Peconic Estuary Program study area were 

calculated by assuming 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. 

 

Once the past and present levels of resources were determined, projections of baseline levels 

in 2020 were based on information and judgments provided by members of the Technical 

Advisory Committee regarding the anticipated change in the resource if no action were to be 

taken. In cases where no technical projections were available, linear extrapolation of past 

trends was used. 

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 actions are taken. These levels are shown in Table 0-4, and the survey 

pages are shown in Figure 5-6. Respondents were told that “trends indicate approximate 

conditions in 2020,” in order to make it clear that these are not scientifically-based 

projections. 

Finally, hypothetical changes from the baseline were chosen, with the objective of bracketing 

the reasonable range of changes that might occur if actions to preserve or restore resources 

are taken. These were presented in the survey as the anticipated results of hypothetical 
133

programs that would preserve or restore the resources to different levels. Because actual 

results of programs were not known when the survey was created, the resource changes were 

selected based on judgment, in consultation with the Technical Advisory Committee, of the 

largest realistic and feasible changes. Although, in some cases, anticipated results of 

enhancement 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 enhance 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 0-5. 

Table 0-4 - 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. 
134

Table 0-5 - Levels of Natural Resources and Cost in the Survey 

If no action is taken, farmland is projected to decrease by twenty-five percent. The results of 

hypothetical preservation programs were projected to reduce the loss of farmland to fifteen 

percent or maintain it at the current level. Undeveloped land was presented at the baseline 

level of a twenty-five percent decrease. 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. 

Wetlands were projected to degrade by twenty-five percent if no action is taken, or 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 

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 
135

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 elicit the amount that 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 program costs. 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. 

In order to statistically estimate all of the coefficients, a large number of such comparisons is 

needed. However, so that the survey could be answered in a reasonable amount of time (10-

15 minutes), each booklet contained only five comparisons. A total of sixty different 

comparisons were created, with five in each of twelve different survey booklets. 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 efficient. 

In order to create comparisons that make sense to respondents, Addelman’s design was 

modified. For example, in some cases following Addelman’s plan exactly results in a 

comparison where one program might dominate the other, in the sense that it had a lower 

cost for higher levels of the natural resources. In other cases, the resource levels for one of 
136

the hypothetical programs were the same as the baseline, No New Action, level; or Program 

A and Program B had identical resource levels with different costs. 

Two methods were used to insure that all comparisons made sense. If possible, either the 

costs or the levels of one of the resources were switched between Program A and Program 

B. Otherwise, the level of one attribute of Program A or Program B was replaced with a 

randomly selected level that resulted in a sensible comparison. After adjusting the 

comparisons, the design was tested for correlations between attributes by examining the 

correlation matrix to ensure that all correlations were below 50%. The sixty comparisons 

created and the comparisons assigned to each booklet are listed in Appendix B. The 

comparisons were assigned to booklets randomly, while making sure that each booklet 

included each resource in at least one comparison. 

Description Of The Survey Questionnaire

An example of the full survey booklet is shown in Appendix C, and selected pages are 

shown in Figures 5-2 through 5-8. The first page described the goal of the survey and 

showed a map of the relevant area (see Figure 5-2). Next, some preliminary questions were 

included, in order to learn something about the respondents as well as to allow them to 

answer some relatively easy questions before the contingent choice questions (see Figures 5-

3 through 5-5). 
137

The first question asked about participation in recreational activities in the area, and the 

second question asked people how strongly they support different actions. Focus group 

responses to different versions of Question 2 indicated that responses were primarily 

symbolic of general support for environmental action. In the focus groups and pretests 

most respondents tended to support almost any action proposed, and when questioned said 

that they simply wanted something to be done to preserve the environment of the East End. 

 Respondents also indicated that they were not knowledgeable enough to make informed 

choices about how to prioritize specific actions, and that this should be done by experts 

based on effectiveness of the actions. Therefore, they tended to rank every proposed action 

as high priority. 

Thus, although it was not expected to provide reliable data for policy analysis, Question 2 

was included in the survey primarily because participants in focus groups wanted to voice 

opinions on proposed actions; and because respondents to early versions of the survey 

wanted to know what types of actions might be taken to produce the results described in the 

contingent choice questions. Focus group results indicated that this question allowed 

respondents to separate their concerns about actions from their priorities for preserving and 

restoring natural resources when answering the contingent choice questions. Thus, it 

allowed them to focus on the ends of proposed policies when answering the contingent 

choice questions, rather than the means of achieving them. The Management Committee 

also wanted to include this question in order to demonstrate public support for proposed 

actions. 
138

The third question asked people for their opinions about brown tide, which is a very 

important concern for many people in the area. Like Question 2, it was included mostly to 

allow people to express opinions about brown tide, and because the Management 

Committee was interested in obtaining this information. 

Next the background information for the contingent choice questions were presented (see 

Figure 5-6), followed by a page of instructions and the five comparisons (Figure 5-7). The 

survey ended with a set of demographic questions (Figure 5-8), in order to gather 

information that could be used to adjust the results to reflect the values of a representative 

member of the public. 
139 

Figure 5-2 - First Page of Survey 

 

The goal of this study is to include public opinions in decisions 

to manage the Peconic Bays System. 

By the Peconic Bays System, we mean the area inside the 

dashed lines below. 

Your opinions are very important. 

All answers are confidential.
140 

Figure 5-3 - Survey Question #1

 

1. Please check the activities that you participate in around the water, for each 

location on the East End. (Check all that apply.) 

 Peconic Atlantic Long Island Other 

Activity Bays System Ocean Sound Water Body (specify) 

Fishing ..............................“.................“.................“.................“ 

Shellfishing .......................“ ............. “.............. “..............“ 

Walking/Hiking .... ......“ ............. “.............. “..............“ 

Swimming... ......... ......“ ............. “.............. “..............“ 

Other Beach Use.. ......“ ............. “.............. “..............“ 

Boating ....... ......... ......“ ............. “.............. “..............“ 

Other Activities - please specify activity(ies) and location(s)below: 

“ ............“........... “.............“ 

“ ............“........... “.............“ 
141 

Figure 5-4 - Survey Question #2 

 

2. The following actions would affect water quality in the Peconic Bays System. 

Please indicate how strongly you support or oppose each action, by circling a 

number for each one. 

1=strongly support; 2=support; 3=neutral; 

4=oppose; 5=strongly oppose; NA=no opinion 

Better enforcement of existing regulations. 1 2 3 4 5 NA 

Prohibit sewage discharge from boats (requires 

building more pumpout stations)....................... 1 2 3 4 5 NA 

Improving sewage treatment plants. ................. 1 2 3 4 5 NA 

Zoning to limit future development. ................. 1 2 3 4 5 NA 

Restrictions on waterfront property, including 

requiring buffer zones (areas left untouched) 

between development and the water, drywells 

for roof runoff, etc............................................. 1 2 3 4 5 NA 

Limiting removal of natural vegetation on newly 

developed land................................................... 1 2 3 4 5 NA 

Requiring repair or upgrade of septic systems 

when property is sold or improved. 1 2 3 4 5 NA 

Requiring pumpouts of existing septic systems 

every 4 years...................................................... 1 2 3 4 5 NA 

Restricting use of fertilizer and lawn chemicals 

for residential property...................................... 1 2 3 4 5 NA 

Controlling stormwater runoff with diversion, 

catch basins, etc. ................................................ 1 2 3 4 5 NA 

Public education to teach people how to reduce 

their impacts on the environment. .................... 1 2 3 4 5 NA 

More research on water quality issues. ............. 1 2 3 4 5 NA 
142 

Figure 5-5 - Survey Question #3 

 

3. Brown Tide

< Brown tide is caused by an algae that grows in enormous numbers, 

turning the water brown. 

< Scientists are studying the causes of brown tide, and what can be 

done to control it. 

1. Have you heard of brown tide? 

“ no (Skip to the next page.) 

“ yes (Go on with this page.) 

2. How concerned are you about brown tide? “ no opinion 

“ not concerned “ a little concerned “ very concerned 

3. Which, if any, of your activities have been affected by brown tide? 

“ swimming “ boating “ fishing “ shellfishing 

“ other (specify) 

4. Do you support or oppose more funding to study the causes of brown tide 

and how to end it? 

“ support “ neutral “ opposed “ no opinion 

5. Please add any other comments you would like to make about brown tide 

and how it affects you. 
145 

Figure 5-8 - Survey Demographic Questions 

 

These last questions will ensure that all groups of people are fairly 

represented in the survey results. Your answers are anonymous and 

strictly confidential. 

9. Is your home on the East End your primary or second home? 

“ primary home “ second home 

10. Do you own or rent your home on the East End? 

“ own “ rent 

11. Where is your home on the East End? 

town zip code 

12. a. Which of the following water bodies is closest to your home? 

“ Long Island Sound “ Peconic Bays System “ Atlantic Ocean 

b. How far is your home from this water body? 

13. How long have you lived, and/or had a second home, on the East End? 

 years. 

14. Are you: “ female “ male 

15. Including yourself, how many people live in your household? 

number of people in household 

16. How many people under the age of 18 live in your household? 

number of people under 18 

Figure 5-8 - Survey Demographic Questions (Continued) 
146 

17. What is your age? (check one) 

“ 20 or under “ 55 to 64 

“ 21 to 24 “ 65 to 74 

“ 25 to 34 “ 75 to 84 

“ 35 to 44 “ 85 or over 

“ 45 to 54 

18. Please check the highest level of school that you have completed. 

“ Less than high school “ Associate=s degree 

“ Some high school “ Bachelor=s degree 

“ Completed high school or GED “ Graduate or other 

“ Some college advanced degree 

19. Are you currently . . . 

“ employed full-time “ a full-time student 

“ employed part-time “ retired 

“ a full-time homemaker “ unemployed 

20. What is your annual household income before taxes? 

“ Less than $15,000 “ $50,000 to $74,999 

“ $15,000 to $24,999 “ $75,000 to $99,999 

“ $25,000 to $34,999 “ $100,000 to $149,999 

“ $35,000 to $49,999 “ $150,000 or more 

21. Please check all organizations that you belong to on the list below: 

“ Sports organizations (hunting or fishing clubs, etc.) 

“ Community service organizations (Rotary, Elk=s Club, League of Women 

Voters, etc.) 

“ Environmental organizations (Sierra Club, Nature Conservancy, Save the 

Peconic Bays, Group for South Fork, etc.) 

“ Political organizations 

“ Business-related organizations (Chamber of Commerce, etc.) 

“ Farm or agricultural organizations (local grange, Farm Bureau, etc.) 

“ Religious organizations 

“ Civic organizations 

“ Other (please specify) 

22. Do you have any other comments? (continue on the back)
147

CHAPTER 6 

DATA ANALYSIS AND RESULTS 

This chapter describes the data, and presents the results of the data analysis. The first 

section presents summary statistics for each survey question. Where applicable, these are 

compared to results of a public opinion survey of residents of the East End conducted in 

1993 (Suffolk Community College 1994). The second section describes the estimation of the 

conditional logit model, and the resulting estimates of relative values for natural resources. 

The third section describes the calculation of economic values of a representative member of 

the public for changes in the natural resources, with sensitivity analysis for different discount 

rates. The fourth section presents two alternative model specifications that address possible 

correlation between alternatives, order effects, and responses that may have a qualitative 

dimension. These are the nested logit model and a model with alternative-specific constants. 

 The results of these models are discussed and compared to the conditional logit results. 

The final section presents results of tests for theoretical validity. 

Summary Statistics 

A total of 968 natural resource surveys were completed, and the data was professionally 

coded and verified by a data entry company. After data entry was complete, minor data 

cleaning was performed, and additional variables were created for the analysis. The 
148

following tables describe and summarize the full sample of survey respondents and their 

answers to the survey questions. An example of a complete survey booklet is reproduced in 

Appendix C. 

Participation in Recreational Activities 

The responses to Question 1, which asked about participation in recreational activities, are 

shown in Table 0-1. 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; followed by walking 

and hiking (71%), boating (54%), fishing (53%), other beach use (34%), shellfishing (32%), 

artwork (26%) and other activities (11%). 

Table 0-1 - 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%) 
149

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). 

Support for Management Actions 

Table 0-2 summarizes the responses to Question 2, which asked about support for specific 

management actions. Although the focus group results suggested that this question would 

provide primarily symbolic information, as discussed in Chapter 5, it was included 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. 

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. It is interesting to note that these are 

the actions that are likely to be the most inconvenient and costly for the average person. 
150

Table 0-2 - Support for Actions 

Strongly Strongly No Averag

e 

Action Support Support Neutral Oppose Oppose Opinio

n 

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 
151

The SCC 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 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 chemicals. While 75 percent of the SCC sample stated that they were 

willing to improve their own septic system, 73.2 percent of this sample support required 

upgrades when property is sold or improved. However, only 54 percent support required 

pumpouts every four years. 

It was hypothesized that certain groups of people would be 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 
152

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. 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. However, 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, and thus do not provide adequate information with which to make policy 

decisions. 

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 0-3. 
153

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 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 0-3 - 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%) 
154

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 0-4 shows 

the statistics for membership in organizations. 

Table 0-4 - 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%)

Sixty-five percent of respondents belong to at least one organization, and 31 percent belong 

to an environmental organization. The SCC survey found that 57 percent of respondents 

had donated to environmental organizations, and 21 percent had been active in an 

environmental group. 
155

Demographics 

Questions 9 through 20 asked for demographic information, and the results for all 

respondents are reported in Table 0-5. These questions were included in order to adjust the 

sample for representativeness in terms of demographic characteristics of the population, and 

to test hypotheses about differences in respondents’ values. 

The first set of questions asked about residence on the East End, and were included 

primarily to test hypotheses about different types of residents, locations of residence, and 

length of residence. Question 9, which asked whether the person is a full-time or seasonal 

resident, was also important in adjusting the results to reflect population demographics, as 

described below. Question 10 asked whether the respondent owns or rents their home, and 

Question 11 asked for the person’s town and zip code. Question 12 asked how far the 

person lives from the water and which water body they are closest to, and Question 13 asked 

how long the person has been either a full-time or seasonal resident of the East End. The 

remaining questions asked for standard demographics, including gender, household size, age, 

education, employment, and income. The comparison of sample demographics to 

population demographics is discussed below with the estimation results. 
156

Table 0-5- 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: 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% 

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% 
157

Table 6-5, Continued

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% 
158

Responses to Contingent Choice Questions 

As discussed in Chapter 5, 60 different choice questions were created, and five were included 

in each booklet, to create twelve different booklets. Each of the twelve booklets was 

replicated between 73 and 85 times. The demographic groups—year-round vs. seasonal 

residence, gender, age, education, employment, and income—were fairly evenly distributed 

across booklets. 

Table 0-6 shows how many of the contingent choice questions were answered by 

respondents. Seventy-one respondents (7.3% of the sample) did not answer any of the 

choice questions, and therefore were dropped from the analysis. 897 (92.7%) answered at 

least one choice question, and 790 (81.6%) answered all of the choice questions. 

Of the people who did not answer any of the choice questions, the highest percentages were 

between the ages of 45 and 74. A total of 6.4 percent of respondents in these age groups did 

not answer any of the choice questions. People with lower education and income levels were 

also more likely to leave the choice questions unanswered. A total of 15 percent of 

respondents with high school education or less; 18.6 percent of respondents with household 

incomes of less than $15,000; and 6.5 percent of respondents with incomes between $15,000 

and $25,000 did not answer any of the choice questions. 

The initial data set for the analysis included 4,307 observations, one for each choice question 

answered. Of those who answered at least one choice question, most (91.6%) chose an 

action, rather than no action for more than half of their answers. Only 27 people (3.0%) 
159

chose no action for all of the choice questions answered. Of those who chose either 

Program A or Program B, more people chose Program A than Program B. 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. 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. This is discussed 

further below. 

Table 0-6 - 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% 

Data Analysis Of Contingent Choice Questions

The first model estimated includes only the attributes of the choices—the levels of natural 

resources and cost. The results for this model are reported in Table 0-7. All estimation was 

performed with the discrete choice model in LIMDEPTM (Greene, 1995). 
160

Table 0-7 - Base Model Results (N=4307) 

Variable Coefficient Std. Error T-Stat.

Farmland .0005113 .000028 17.94 

Undeveloped Land .0001066 .000006 18.06 

Wetlands .0003361 .000017 20.04 

Shellfish Areas .0002334 .000019 12.38 

Eelgrass .0004195 .000024 17.35 

Cost -.003762 .000237 -15.91 

Log-Likelihood 

Restricted Log-Likelihood 

Chi-Squared (Sig. Level)

D2=1-(L ($)/L (0)) 

-4079.37 

-4731.72 

1304.71 (.00000) 

.138 

First, Box-Cox tests of different functional forms were carried out. A set of discrete 

transformations was created, using the formula (x8 - 1)/8. Values for 8 of .5, .25, .1, and .01 

were tested, and the linear model was found to be the best fit, based on comparing the 

likelihood ratio index for the different models. This is calculated as D2=1-(L ($)/L (0)) (BenAkiva and Lerman 1989). All subsequent models were estimated using a linear functional 

form. 

Models With Additional Variables 

The primary objective of the analysis is to determine the economic values and relative 

priorities, for a representative member of the public, of the five natural resources in the 

survey (farmland, undeveloped land, wetlands, shellfishing areas, and eelgrass). The sample 

results may be adjusted to better reflect the values a representative member of the 

population by including characteristics of individuals in the model, comparing these 
161

characteristics with those of the general public, and adjusting the estimation results based on 

the differences between a representative individual in the sample and a member of the 

general public. 

Unfortunately, only specific demographic variables—gender, age, income, and education—

are available for the general public, so the results can only be adjusted with regard to these 

variables. Other variables that may also be important determinants of value can be included 

in the analysis in order to learn more about the values of respondents. However, 

generalizations to the general public concerning these characteristics cannot be made, and 

therefore they cannot be included in the welfare analysis for policy purposes unless it is 

known that the survey sample reflects the characteristics of the actual population. 

In order to determine the effects of individuals’ characteristics on their values for the natural 

resources, interactions between characteristics of respondents and the attributes of each 

choice are included in the model. First, dummy variables for each category of the individual 

characteristics are created, with a one indicating inclusion in that category. These are then 

multiplied by the level of each attribute of the choice—the natural resources and cost. 

After deleting observations with missing values for any of the demographic variables, this 

data set contained 3,837 observations. This is 79.3 percent of the 4,840 total possible 

observations and 89 percent of the 4,307 observations where the contingent choice question 

was answered. Likelihood ratio tests, reported in Table 0-8, were performed to test each 

demographic variable as a group. Only education and income were statistically significant at 
162

better than the ten percent level, so the other demographic variables were dropped from the 

model. 

Table 0-8 - Likelihood Ratio Tests for Demographic Variables 

Next, likelihood ratio tests were performed to determine whether income and education 

categories could be aggregated. To do this, models were estimated with consecutive levels of 

the categories combined. Because of small numbers of observations, education categories 

one and two, and income categories one and two were combined before testing. The results 

of these tests are reported in Table 0-9. 

Based on these tests, the education variables were reduced from seven to three categories; 

and the income variables were reduced from eight to four categories. Education categories 

one through three and five through seven were combined, and income categories one and 

two, three and four, and five through seven were combined, to create the demographic 

variables used in the final model. The lowest levels of each were used as the base case, and 

were eliminated from the model in the estimation. 

Variables 

Tested 

LogLikelihood# of 

Variables

Test 

Statistic

Degrees of 

Freedom 

Chi Square 

p- value 

Full Model -3490.66 144 

Gender -3495.37 138 9.418 6 .151399 

Education -3517.49 132 53.658 12 .0000003 

Age -3520.42 96 59.514 48 .1232 

Employment -3507.56 114 33.802 30 .2888 

Income -3519.98 102 58.648 42 .0454 
163

Table 0-9 - Likelihood Ratio Tests for Education and Income Categories 

Variables 

Tested 

LogLikelihood# of 

Variables

Test 

Statistic

Degrees of 

Freedom 

Chi Square 

p- value 

Full Model -3577.76 72 

Ed123 -3581.61 66 7.7 6 .2609 

Ed34 -3585.82 66 16.12 6 .0131 

Ed45 -3583.32 66 11.12 6 .0847 

Ed56 -3580.02 66 4.52 6 .6072 

Ed67 -3581.21 66 6.90 6 .3298 

Inc123 -3581.03 66 6.54 6 .3653 

Inc34 -3579.25 66 2.98 6 .8114 

Inc45 -3580.19 66 4.86 6 .5624 

Inc56 -3578.56 66 1.60 6 .9527 

Inc67 -3580.00 66 4.48 6 .6125 

Inc78 -3585.60 66 15.69 6 .0155 

Effects of Respondents’ Characteristics 

It was hypothesized that other variables, besides demographics, would affect people’s values 

for natural resources. These include whether a person fishes, shellfishes, uses the beach, 

boats, or uses the waters in other ways; whether they are a year-round or seasonal resident; 

whether they rent or own their home; the distance lived from the Peconic Estuary; whether 

they belong to an environmental group; whether they are a member of any other 

organization; their level of support for the actions listed in Question 2; whether they were 

affected by brown tide; their level of concern for brown tide; and the number of years lived 

on the East End. Each of these variables was interacted with each attribute, as described 

above, and each group of interactions was tested for statistical significance using likelihood 

ratio tests. These tests are reported in Table 0-10. 
164

Table 0-10- Likelihood Ratio Tests for Respondent Characteristics (N=3386) 

Variables 

Tested 

LogLikelihood# of 

Variables

Test 

Statistic 

Degrees of 

Freedom 

Chi Square 

p- value 

Full Model -2945.27 126 

Fishing -2949.93 120 9.318 6 .156 

Shellfishing -2951.71 120 12.872 6 .045 

Beach Use -2947.33 120 4.122 6 .660 

Boating -2953.12 120 15.692 6 .016 

Other Use -2947.67 120 4.788 6 .571 

Second Home -2948.14 120 5.734 6 .454 

Renter -2949.79 120 9.036 6 .172 

Distance -2954.57 114 18.6 12 .099 

Envir. Member -2961.34 120 32.142 6 .000002 

Other Member -2948.25 120 5.964 6 .427 

Action Score -2987.32 120 84.096 6 .000000 

Affected by BT -2950.73 120 10.922 6 .091 

Concern for BT -2957.41 120 24.276 6 .0005 

Years Lived -2951.71 120 12.882 6 .045 

Shellfishing, boating, distance lived from the Peconic Estuary, membership in an 

environmental group, support for actions, affected by brown tide, concern about brown tide, 

and years lived on the East End were all statistically significant at at least the 10 percent level. 

 A model was estimated with these variables plus the aggregated income and education 

categories, in order to examine the effects of these characteristics on preferences for natural 

resources. The descriptions of variables in this model are presented in Table 0-11, and 

estimation results are presented in Table 0-12. 

However, as noted above, except for education and income, there are no statistics for the 

general public for any of the individual characteristics included in this model. Therefore, this 

model cannot be used to determine values for a representative resident of the East End. 
165

Nevertheless, the results provide some interesting information about how survey 

respondents’ values are affected by their characteristics. 

Table 0-11 - Statistically Significant Explanatory Variables in Full Model 

Variable 

Abbreviation Variable Definition

Ed123 Education categories 1-3 (High School or Less); base case, omitted 

from model. 

Ed4 Education category 4 (Some College). 

Ed567 Education categories 5-7 (Associate’s, Bachelor’s or Advanced 

Degree). 

Inc12 Income categories 1 and 2 (Less than $15,000 and $15,000 to $24,999); 

base case, omitted from model. 

Inc34 Income categories 3 and 4 ($25,000 to $34,999 and $35,000 to 

$49,999). 

Inc567 Income categories 5-7 ($50,000 to $74,999, $75,000 to $99,999, and 

$100,000 to $149,.999). 

Inc8 Income category 8 ($150,000 or more). 

Shell Person who participates in shellfishing. 

Boat Boater. 

Pbd0 Person whose home is on the Peconic Estuary waterfront. 

Pbd1 Person who lives up to 1⁄2 mile from the Peconic Estuary. 

Pbd2 Person who lives more than 1⁄2 mile from the Peconic Estuary; base 

case, omitted from model. 

Envir Member of an environmental organization. 

Ascor Support for actions, calculated as the sum of responses to question 2 

(lower number indicates greater support). 

Btaff Person who said they were affected by brown tide. 

Btcon Level of concern for brown tide (higher number indicates greater 

concern). 

Years Number of years as resident of the East End. 
166

Table 0-12 - Results for Conditional Logit Model With All Characteristics (N=3386) 

Variable Coefficient Std. Error T Statistic P-Value 

FARM 0.000591 0.000240 2.463 0.01379 

UNDEV 0.000118 0.000047 2.482 0.01305 

WET 0.000235 0.000125 1.883 0.05975 

SHELL 0.000478 0.000158 3.028 0.00246 

EEL 0.000581 0.000202 2.878 0.00401 

COST -0.004586 0.002040 -2.248 0.02459 

ED4_F 0.000136 0.000123 1.105 0.26915 

ED4_U 0.000066 0.000026 2.524 0.01159 

ED4_W 0.000165 0.000069 2.383 0.01718 

ED4_S 0.000155 0.000082 1.881 0.05998 

ED4_E 0.000086 0.000105 0.823 0.4103 

ED4_C -0.002631 0.001082 -2.432 0.01502 

ED567_F 0.000026 0.000106 0.247 0.80519 

ED567_U 0.000046 0.000023 2.0 0.04547 

ED567_W 0.000100 0.000060 1.667 0.09551 

ED567_S 0.000043 0.000069 0.615 0.53832 

ED567_E 0.000265 0.000090 2.956 0.00312 

ED567_C -0.000101 0.000905 -0.111 0.91135 

INC34_F 0.000006 0.000120 0.046 0.96297 

INC34_U 0.000019 0.000026 0.729 0.46627 

INC34_W 0.000070 0.000070 0.993 0.32072 

INC34_S -0.000075 0.000080 -0.935 0.35003 

INC34_E -0.000181 0.000106 -1.711 0.08704 

INC34_C 0.000061 0.001020 0.06 0.95236 

INC567_F 0.000053 0.000115 0.462 0.64444 

INC567_U -0.000025 0.000024 -1.062 0.28822 

INC567_W -0.000071 0.000064 -1.11 0.26681 

INC567_S -0.000099 0.000075 -1.31 0.19018 

INC567_E -0.000274 0.000101 -2.705 0.00682 

INC567_C 0.000758 0.000962 0.788 0.43075 

INC8_F 0.000225 0.000150 1.497 0.13428 

INC8_U 0.000014 0.000031 0.461 0.64448 

INC8_W 0.000006 0.000087 0.067 0.94683 

INC8_S -0.000072 0.000096 -0.75 0.45337 

INC8_E -0.000208 0.000129 -1.604 0.10862 

INC8_C 0.001704 0.001204 1.416 0.1569 

SHELL_F 0.000085 0.000079 1.067 0.28617 

SHELL_U 0.000009 0.000017 0.514 0.60692 

SHELL_W 0.000019 0.000046 0.409 0.68277 

SHELL_S 0.000136 0.000053 2.559 0.0105 

SHELL_E 0.000159 0.000068 2.32 0.02032 

SHELL_C -0.001119 0.000660 -1.696 0.08988 

BOAT_F -0.000191 0.000076 -2.509 0.0121 

BOAT_U 0.000004 0.000016 0.271 0.78655 
167

BOAT_W 0.000035 0.000043 0.8 0.42372 

BOAT_S -0.000052 0.000049 -1.047 0.29508 

BOAT_E 0.000071 0.000064 1.104 0.26943 

BOAT_C -0.000086 0.000633 -0.136 0.89196 

PBD0_F -0.000189 0.000101 -1.868 0.06177 

PBD0_U -0.000016 0.000022 -0.728 0.4667 

PBD0_W -0.000116 0.000061 -1.893 0.05832 

PBD0_S -0.000114 0.000070 -1.632 0.10265 

PBD0_E -0.000118 0.000092 -1.275 0.20217 

PBD0_C 0.001572 0.000856 1.836 0.06634 

PBD1_F 0.000150 0.000084 1.784 0.07436 

PBD1_U 0.000044 0.000018 2.393 0.01671 

PBD1_W 0.000007 0.000049 0.147 0.88299 

PBD1_S 0.000010 0.000055 0.185 0.85344 

PBD1_E -0.000033 0.000070 -0.466 0.64113 

PBD1_C -0.000578 0.000697 -0.829 0.40684 

ENVIR_F 0.000098 0.000078 1.26 0.20757 

ENVIR_U 0.000014 0.000016 0.845 0.39791 

ENVIR_W 0.000137 0.000049 2.817 0.00484 

ENVIR_S 0.000043 0.000051 0.834 0.40416 

ENVIR_E 0.000122 0.000067 1.831 0.06706 

ENVIR_C 0.000620 0.000633 0.981 0.32661 

ASCOR_F -0.000002 0.000004 -0.544 0.58644 

ASCOR_U -0.000002 0.000001 -2.643 0.00822 

ASCOR_W -0.000005 0.000002 -2.231 0.02571 

ASCOR_S -0.000005 0.000003 -1.873 0.06103 

ASCOR_E -0.000011 0.000004 -3.077 0.00209 

ASCOR_C -0.000075 0.000038 -1.995 0.046 

BTAFF_F -0.000002 0.000110 -0.02 0.9844 

BTAFF_U -0.000027 0.000023 -1.179 0.23859 

BTAFF_W -0.000106 0.000064 -1.647 0.09952 

BTAFF_S -0.000019 0.000073 -0.256 0.79798 

BTAFF_E 0.000037 0.000092 0.404 0.686 

BTAFF_C -0.000607 0.000907 -0.669 0.50368 

BTCON_F 0.000016 0.000066 0.239 0.81082 

BTCON_U 0.000017 0.000013 1.331 0.18329 

BTCON_W 0.000101 0.000034 2.991 0.00278 

BTCON_S -0.000009 0.000043 -0.2 0.8418 

BTCON_E 0.000035 0.000055 0.64 0.52243 

BTCON_C 0.000441 0.000559 0.787 0.43103 

YEARS_F -0.000003 0.000002 -1.408 0.15917 

YEARS_U -0.000001 0.000000 -2.663 0.00775 

YEARS_W -0.000003 0.000001 -2.454 0.01411 

YEARS_S -0.000002 0.000002 -1.506 0.13218 

YEARS_E -0.000004 0.000002 -2.026 0.04281 

YEARS_C 0.000052 0.000019 2.731 0.00632 

 Chi-Squared (Sig. Level) = 1511 (.00000) D2 = .203 
168

For the variables that are statistically significant at the 10 percent level or better, the 

following conclusions may be drawn about the sample population. People with some college 

education have higher values for undeveloped land, wetlands and shellfishing areas, and are 

less concerned about cost than people with a high school education or less. People with an 

associate’s, bachelor’s or advanced degree have higher values for undeveloped land, 

wetlands, and eelgrass than people with a high school education or less. People with income 

between $25,000 and $49,999 have lower values for eelgrass than people with income up to 

$24,999. People with the highest income levels ($50,000-$149,999) have lower values for 

eelgrass than people with income up to $24,999. 

People who shellfish have higher values for shellfishing areas and eelgrass, and less concern 

for cost than people who do not shellfish. People who boat have lower values for farmland 

than people who do not boat. People who live on the waterfront of the Peconic Estuary 

have lower values for farmland and wetlands, and more concern for cost than people who 

live more than 1⁄2 mile from the Estuary. People who live up to 1⁄2 mile from the Estuary 

have higher values for farmland and undeveloped land than people who live more than 1⁄2 

mile from the Estuary. 

People who belong to environmental groups have higher values for wetlands and eelgrass 

than people who do not belong to environmental groups. People who express less support 

for the actions listed in Question 2 (i.e., have higher action scores) have lower values for all 

of the natural resources than people who express greater support for the actions. People 

who say they were affected by brown tide have lower values for wetlands than people who 

were not affected by brown tide. People who are more concerned about brown tide have 
169

higher values for wetlands than people who are less concerned. People who have lived on 

the East End longer have lower values for undeveloped land, wetlands, and eelgrass. 

Calculation of Representative Values 

This section describes the estimation of economic values of a representative resident of the 

East End for the natural resources in the survey. As discussed in Chapter 5, the survey was 

implemented using convenience sampling. Thus, any non-representativeness of the sample 

must be corrected for by weighting the model coefficients using demographic characteristics 

of the population of the East End. The weighting process results in a model with six 

“collapsed” coefficients that include the effects of individual demographic characteristics on 

relative values of the natural resources. 

As noted above, population statistics are only available for standard demographic variables, 

so all of the other characteristics were deleted from the valuation model. Additionally, 

population demographics are not available for seasonal residents. Consequently, the 

adjustments for the seasonal resident sample were made using a procedure that is discussed 

below. 

First, the sample was divided into two subsamples—one for year-round residents and one 

for seasonal residents—and the model, including attributes and interactions of attributes and 

income and education levels, was estimated for each subsample. After deleting observations 

with missing values for income, education, and type of residence, the data set contains 3,819 
170

observations—2,734 for year-round residents and 1,085 for seasonal residents. This is 78.9 

percent of the 4,840 total possible observations, and 88.7 percent of the 4,307 observations 

where the choice question was answered. Demographics for this usable sample, compared 

to actual population demographics for year-round residents of the East End, are reported in 

Table 0-13, and the model results before weighting are listed in Appendix D. 

Comparing the percent of year-round resident respondents to the 1990 population figures, 

(and assuming proportions in the population have not changed significantly since 1990), the 

sample contains a slightly higher percent of women and people in the middle age groups. As 

is often found in surveys of this type, the sample population is better educated and more 

wealthy than the general population. 

The year-round resident model was weighted by the actual population demographics from 

1990 census data (Long Island Lighting Company 1995; Suffolk County Department of 

Planning 1991). Education census data is only available for people age 25 and over, so the 

sample will still be biased slightly upward for education level, since approximately 7 percent 

of the sample is under age 25. 

Table 0-13 - Population Demographics vs. Survey Respondent Demographics 
171

 

1990 

Populationa

YearRound 

Resident 

Sampleb

Seasonal 

Resident 

Sampleb

Adjustment 

for Seasonal 

Residentsc

Gender Female 51.83% 60.85% 51.36% 42.24% 

Male 48.17% 39.15% 48.64% 57.76% 

Age up to 20 23.89% 3.17% 0.45% 4.31% 

21-24 4.53% 4.76% 3.62% 4.36% 

25-44 29.07% 40.39% 26.24% 23.89% 

45-54 11.01% 19.05% 24.89% 18.20% 

55-64 11.04% 14.99% 22.62% 21.07% 

65-74 11.17% 14.11% 19.00% 19.02% 

75-84 7.25% 3.17% 3.17% 9.15% 

85 up 2.04% 0.35% 0.00% 0.00% 

Education < High School 7.44% 0.00% 0.00% 11.91% 

(over Some H.S. 11.55% 1.53% 0.94% 11.38% 

age 24) H.S. Graduate 31.77% 15.33% 2.83% 9.39% 

Ed. categories 1-3 50.76% 16.86% 3.77% 18.18% 

Some College (Ed. cat. 4) 18.35% 21.26% 8.49% 11.72% 

Assoc. Degree 6.66% 11.30% 7.08% 6.67% 

Bachelor's Deg. 13.51% 23.18% 31.60% 29.48% 

Advanced Deg. 10.72% 27.39% 49.06% 30.73% 

Ed. categories 5-7 30.90% 61.88% 87.74% 70.10% 

Income < $15,000 19.17% 5.45% 1.81% 8.69% 

$15,000-$24,999 14.60% 11.60% 1.81% 3.11% 

Inc. categories 1&2 33.77% 17.05% 3.62% 9.79% 

$25,000-$34,999 14.40% 14.94% 4.98% 6.55% 

$35,000-$49,999 17.52% 15.99% 5.88% 8.80% 

Inc. categories 3&4 31.93% 30.93% 10.86% 15.30% 

$50,000-$74,999 18.90% 27.94% 14.48% 13.37% 

$75,000-$99,999 7.04% 11.07% 18.55% 16.09% 

$100,000-$149,999 4.93% 7.38% 18.10% 16.49% 

Inc. categories 5-7 30.86% 46.40% 51.13% 46.42% 

$150,000 and up (Inc. cat. 

8)

3.41% 5.62% 34.39% 28.49% 

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. 

c - See text for explanation. 
172

Because there is no census data for seasonal residents, the seasonal resident model was 

adjusted by the same proportion as was used for the year round population. Thus, in the 

absence of demographic data for seasonal residents, it was assumed that education and 

income levels for the sample population of seasonal residents are higher than those for the 

actual population of seasonal residents, and that the appropriate weighting factor is the same 

as that used for year-round residents. The weighting factor is 

WSH Pop

PH

SH

Pop

PH k SH

k

k

k

k

K

k

k

= k 



 

































 



 



 





=

∑

100

1

%

%

* %

*

%

%

* % 

where k is the category for a given demographic variable; K is the total number of categories 

for the given demographic variable; WSHk is the weighting factor applied to each seasonal 

resident model coefficient; Pop%k is the percent of the 1990 year-round population in 

category k; PH%k is the percent of the year-round sample in category k; and SH%k is the 

percent of the seasonal resident sample in category k. The adjustment in the first set of 

brackets was necessary to insure that the adjusted categories would add up to 100 percent. 

Table 0-14 gives the weighted coefficients for the year-round resident model, and Table 0-15 

gives the weighted coefficients for the seasonal resident model. The calculation of these 

coefficients proceeded as follows. The full model, before weighting (see Appendix D) has 

thirty-six variables—one for each of the six choice attributes and thirty for the six choice 

attributes multiplied by two education and three income dummy variables. The lowest 
173

income and education categories were selected as base cases for the dummy variables, and 

were omitted from the model. 

First, a 6x36 weighting matrix, W, was created. This is made up of six 6x6 submatrices, each 

of which contains the weighting factor for one demographic category on its diagonal, and 

zeros elsewhere. The weights used for each model are listed in Table 0-14 and Table 0-15. 

The weight for the education and income base cases is 1, because their values are reflected in 

the coefficients on the attributes alone (not interacted with any demographic variables). 

Next, the 6x36 matrix W was multiplied by the 36x1 vector of coefficients, resulting in a 6x1 

vector of weighted, or “collapsed” coefficients. Finally, variances for the weighted 

coefficients were calculated in a similar manner, by pre- and post-multiplying W by the 

variance-covariance matrix from the model estimation (Judge, et al., 1985). These weighted 

variances were used to calculate t-statistics for the weighted coefficients. 

Table 0-14 - Year-Round Resident Model, Weighted Coefficients 

Variable Coefficient Std. Error T Statistic P-Value Weighting Factors

FARM 0.0005037 0.0000472 10.68 0.00004 ED123INC12 1.0 

UNDEV 0.0000904 0.0000097 9.34 0.00009 ED4 0.1835 

WET 0.0003267 0.0000264 12.37 0.00002 ED567 0.3090 

SHELL 0.0002661 0.0000320 8.31 0.00016 INC34 0.3194 

EEL 0.0003829 0.0000400 9.57 0.00007 INC567 0.3087 

COST -0.0044858 0.0004054 -11.06 0.00003 INC8 0.0341 

Log-Likelihood 

Restricted Log-Likelihood 

Chi-Squared (Sig. Level) 

D2 

 -2551.71 

 -3003.61 

 903.8 (.00000) 

 .151 
174

Table 0-15 - Seasonal Resident Model, Weighted Coefficients 

Variable Coefficient Std. Error T Statistic P-Value Weighting Factors

FARM 0.0006550 0.00008826 7.42 0.0003 ED123INC12 1.0 

UNDEV 0.0001250 0.00001863 6.71 0.0005 ED4 0.1172

WET 0.0003840 0.00005404 7.11 0.0004 ED567 0.701 

SHELL 0.0001990 0.00004899 4.06 0.0066 INC34 0.153 

EEL 0.0004840 0.00006656 7.27 0.0003 INC567 0.4642

COST -0.0039400 0.00070993 -5.55 0.0014 INC8 0.2849

Log-Likelihood 

Restricted Log-Likelihood 

Chi-Squared (Sig. Level) 

D2 

 -957.56 

 -1191.99 

 469 (.00000) 

 .197 

Once the two sets of weighted coefficients were calculated, the models were combined by 

taking a weighted average of the coefficients, based on the estimated number of year-round 

and seasonal households for the East End. The combined model results are reported in 

Table 0-16. 

Table 0-16 - Combined Model Weighted Coefficients 

Weights for Year-Round and Seasonal Homesa Variable Coefficient

Estimated FARM 0.000564 

 1995 Percent UNDEV 0.000104 

 Households of Total WET 0.000349 

Year-Round Residents 44,241 60.255% SHELL 0.000239 

Seasonal Residents 29,182 39.745% EEL 0.000423 

Total 73,423 100% COST -0.004269

a - based on Long Island Lighting Company 1995 and Suffolk County 

Department of Planning 1991; 1995 seasonal population extrapolated 

from 1990 figures using same growth rate as year-round population. 
175

Valuation 

Based on the formula presented in Chapter 4, economic values per acre for each resource 

were calculated by dividing each resource coefficient by the cost coefficient (Hanemann 

1984). The values per acre for a representative year-round and seasonal household are 

reported in Table 0-17. Standard errors for these estimates were calculated using the analytic 

method presented in Cameron (1991). For both segments of the sample, an acre of 

farmland has the highest value, followed by eelgrass, wetlands, shellfishing areas, and 

undeveloped land. 

 

Table 0-17 - Estimated Economic Values for Natural Resources, 

for Year-Round and Seasonal Residents

Year-Round Residents Seasonal Residents

Resource 

Value/Acre/

HH/Year 

Std. Error

 of Value 

Value/Acre/ 

HH/Year 

Std. Error

 of Value

Farmland $0.11 0.009 $0.17 0.024 

Undeveloped Land $0.02 0.002 $0.03 0.005 

Wetlands $0.07 0.007 $0.10 0.015 

Shellfishing Areas $0.06 0.006 $0.05 0.012 

Eelgrass $0.09 0.009 $0.12 0.019 

The values are combined into a weighted average, as described above for the coefficients, 

based on the estimated numbers of year-round and seasonal households of the East End. 

The average values, and their confidence intervals, for a representative resident of the East 

End are reported in Table 0-18. These confidence intervals account for the variance in the 
176

parameter estimates, but do not account for variance in the demographic statistics obtained 

from the census data. Because the census data are not exact measures, and data from 1990 

were extrapolated to 1995 the true confidence intervals will be larger than those presented 

here. However, there is no information available to adjust these estimates. 

Table 0-18 - Estimated Economic Values for Natural Resources, Combined Model 

a - Total values calculated based on 73,423 households. 

Discounting is a means of aggregating welfare measures 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, calculated using the official water resources planning 

discount rate for fiscal year 1996, 7.625 percent (U.S. Department of the Interior 1995), are 

reported in Table 0-19. This is the rate required by the government for Federal water and 

Values per Household Total Valuesa

Resource

Value/Acre/

Year 

95% Confidence 

Interval

Value/Acre/ 

Year 

95% Confidence 

Interval

Farmland $0.134 $0.112 $0.156 $9,819 $8,212 $11,425 

Undeveloped Land $0.025 $0.020 $0.030 $1,817 $1,454 $ 2,180 

Wetlands $0.083 $0.069 $0.097 $6,066 $5,033 $ 7,099 

Shellfishing Areas $0.056 $0.044 $0.068 $4,099 $3,221 $ 4,977 

Eelgrass $0.100 $0.082 $0.119 $7,361 $6,014 $ 8,708 
177

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). A sensitivity analysis for 2 

percent, 5 percent and 10 percent discount rates is shown in Table 0-20. The 25 year time 

horizon was chosen to match the time period presented in the survey. The infinite time 

horizon was chosen to avoid the choice of an arbitrary cutoff point for consideration of 

future benefits. 

Table 0-19 - Discounted Present Values for Weighted Model, 

7.625% Discount Rate 

Resource

PV/Acre 

T=4

PV/Acre 

T=25 

Farmland $128,771 $108,260 

Undeveloped Land $ 23,831 $ 20,035 

Wetlands $ 79,557 $ 66,885 

Shellfishing Areas $ 53,754 $ 45,192 

Eelgrass $ 96,538 $ 81,161 

For the 7.625 percent discount rate and an infinite time horizon, the present values range 

from $23,831 to preserve an acre of undeveloped land, to $128,771 to preserve an acre of 

farmland. The value to restore an acre of shellfishing grounds is $53,754; to preserve or 

restore an acre of wetlands is $79,557; and to restore an acre of eelgrass is $96,538. A 2% 

discount rate increases these values to $90,855-$490,940, and a 10% discount rate reduces 

the values to $18,171-$98,188. 
178

Table 0-20 - Sensitivity Analysis for Discount Rates, Weighted Model 

2% Discount Rate 5% Discount Rate 10% Discount Rate

Resource 

PV/Acre 

T=4

PV/Acre 

T=25 

PV/Acre 

T=4

PV/Acre 

T=25 

PV/Acre 

T=4

PV/Acre 

T=25 

Farmland $490,940 $191,697 $196,376 $138,386 $98,188 $89,126 

Undeveloped Land $ 90,855 $ 35,476 $ 36,342 $ 25,610 $18,171 $16,494 

Wetlands $303,312 $118,434 $121,325 $ 85,497 $60,662 $55,063 

Shellfishing Areas $204,939 $ 80,022 $ 81,976 $ 57,768 $40,988 $37,205 

Eelgrass $368,053 $143,713 $147,221 $103,746 $73,611 $66,817 

The results of the weighted model are compared to a simple model that includes only the 

choice attributes, for the entire sample (4,307 observations) in Table 0-21. The natural 

resource coefficients for the two models are almost identical. The cost coefficient is smaller 

for the weighted model, resulting in lower mean dollar values for the natural resources in this 

model, which is to be expected, given that the survey sample is wealthier and better educated 

than the general population. Based on a Wilcoxan signed rank test (Neave and Worthington 

1988), the difference in values is significant at the 10 percent level. However, the confidence 

intervals on the dollar estimates overlap, indicating that the differences are not great. 

 
179

Table 0-21 - Comparison of Weighted Model and Simple Model Results 

Coefficient Value/Acre 95% Confidence Interval

Weighted Model: 

Farmland 0.000564 $0.134 $0.112 $0.156 

Undeveloped Land 0.000104 $0.025 $0.020 $0.030 

Wetlands 0.000349 $0.083 $0.069 $0.097 

Shellfish Areas 0.000239 $0.056 $0.044 $0.068 

Eelgrass 0.000423 $0.100 $0.082 $0.119 

Cost -0.004269 

Simple Model: 

Farmland 0.000511 $0.136 $0.122 $0.150 

Undeveloped Land 0.000107 $0.028 $0.025 $0.032 

Wetlands 0.000336 $0.089 $0.079 $0.100 

Shellfish Areas 0.000233 $0.062 $0.053 $0.071 

Eelgrass 0.000419 $0.111 $0.098 $0.125 

Cost -0.003765 

Model Extensions

Preliminary analysis of the data, discussed above, showed that most respondents chose to 

take action, and that more respondents chose Program A than chose Program B. Therefore, 

two extensions of the base model were estimated in order to examine effects related to the 

options themselves rather than their attributes. These include possible correlations between 

options, and other effects that might lead respondents to select a particular option 

independent of the levels of its attributes. The first model extension discussed in this 

section includes choice-specific constants for choice of action versus no action, and for 

choice of Program B. The second extension discussed is the nested logit model, which 

allows for correlation of the error terms for Programs A and B. 
180

Both of these models were estimated using only the main effects—the levels of attributes 

and cost, and thus are compared to the base model results presented in Table 0-7. The 

demographic variables were not included because they complicate comparisons between 

models, and because, as discussed in the previous section, the value estimates from the 

weighted model are within the confidence intervals on the value estimates for the base 

model. 

Choice-Specific Constant Model 

In order to examine general effects related to the choice of a particular option, independent 

of its attributes, a model with choice-specific constants was estimated. This model includes 

two dummy variables, one for choice of either Program A or Program B, and another for the 

choice of 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. For example, respondents may be expressing their willingness to pay something to 

protect the environment of the East End in general. 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, for example an order effect. The results of this model 

are presented in Table 0-22. 

Both constant terms are statistically significant, indicating that there is an effect on choices 

that is not related to the quantities of the individual attributes, but is instead related to the 

choices themselves. The positive and significant coefficient for Programs A & B indicates 
181

that people are more likely to choose an action rather than “No New Action,” independent 

of the action’s specific effects on natural resources. Similarly, the negative and significant 

coefficient for Program B indicates that, even if Program A and Program B produced the 

same results in terms of preservation of natural resources, respondents are more likely to 

choose Program A. Using Equation (9) from Chapter 4 to calculate the probability of 

selecting an option, the probability that the average respondent will choose either Program A 

or Program B rather than “No New Action,” independent of the levels of the attributes, is 

78 percent. The probability that the average respondent will choose Program A rather than 

Program B, if their attributes were equal, is 54 percent. 

Table 0-22 - Model With Alternative-Specific Constants (N=4307) 

Model Results Values per Household

Variable Coefficient

Std. 

Error T-Stat.

Value/Acre

/Year 

95% Confidence 

Interval

Programs A&B 1.2866 .0743 17.33 

Program B -0.1799 .0351 -5.13 

Farmland .000300 .000030 9.92 $0.094 $0.078 $0.109

Undev. Land .000057 .000006 8.77 $0.018 $0.014 $0.022

Wetlands .000179 .000018 9.56 $0.056 $0.045 $0.066

Shellfish Areas .000108 .000020 5.34 $0.034 $0.023 $0.044

Eelgrass .000214 .000027 7.98 $0.067 $0.052 $0.081

Cost -.003207 .000234 -13.73 

Log-Likelihood

Restricted Log-Likelihood

Chi-Squared (Sig. Level) 

D2

-3921.39 

-4731.72 

1621 (.00000) 

.171 

 
182

These constant terms for taking action and for differences in probability of selecting A and 

B may be interpreted as representing a qualitative dimension of respondents’ preferences, 

while the coefficients on the natural resources represent the portion of responses that is 

related to the quantitative comparison between options. Thus, if people are more likely to 

choose Program A or Program B than No New Action because of a desire to take action 

that is not related to the specific natural resources and their levels, or to the cost, they may 

be expressing a symbolic willingness to pay to protect the environment of the East End, in 

general. Similarly, the coefficient on the constant term for Program B indicates that there is 

some qualitative reason that people prefer Program A and measures the degree of preference 

for A over B. 

If people’s answers were a purely quantitative evaluation of attribute levels, then the 

coefficients on the alternative-specific constants would not be statistically significant. 

Alternatively, if answers were purely qualitative, then the coefficients on the attributes would 

not be statistically significant. Since both attribute levels and constant terms are significant, 

it may be concluded that people are expressing both a quantitative and qualitative 

component of preferences in their responses. It is also possible that respondents may 

simplify their choices by not rigorously considering the quantitative resource levels when 

making their choice, but instead choose based on relative levels of the resources, e.g., 

whether the resource increases or decreases. 

The higher probability of choosing to take action is not surprising. Most people interviewed 

during the survey development process expressed a great deal of concern for the 
183

environment of the Estuary, and wanted something to be done to protect it, and thus might 

express this concern partly by choosing an action independent of its specific effects. 

However, the preference of one program over another independent of effects is not 

expected, and could occur for a variety of reasons. There might be an order effect, whereby 

A is more likely to be chosen because it is in the center of the page; or respondents may 

assume that A indicates a better program than B because it is labeled “A” (i.e., analogous to a 

grading system). Alternatively, respondents may determine that Program A is the most 

environmentally desirable alternative in the first question, and assume that A is meant to 

represent the most environmentally desirable alternative throughout the survey. 

 

In order to further test this last hypothesis, Program A and Program B were compared for 

each of the sixty different comparisons, to see which option is better in terms of the level of 

natural resources protected or enhanced. Program A dominates Program B in terms of the 

levels of environmental attributes in 23 of the 60 questions, while Program B dominates in 

18 questions. In addition, Program A dominates Program B in six of the twelve booklets for 

the first of the five contingent choice questions, while Program B is not dominant for the 

first question in any booklets. This information provides support for the hypothesis that 

some people might prefer A for the first choice, and simplify later choices by again choosing 

A. However, without debriefing of respondents, there is no way to further verify this 

hypothesis. 

The dollar values for this model are approximately half to two-thirds as large as those 

estimated from the base model (see Table 0-21). These values might be interpreted as the 
184

quantitative portion of people’s values for the natural resources, which is smaller than their 

total value for preserving the Estuary’s resources. In other words, a portion of people’s 

value is a symbolic expression of willingness to pay to protect the environment. Thus, the 

base model captures both portions of value. 

The Nested Logit Model 

The alternative-specific constant in the previous model for the choice of action versus no 

action accounts for qualitative reasons that respondents are more likely to choose an action 

rather than “No New Action.” However, it is estimated as a constant term and thus assumes 

that this is a fixed effect. Alternatively, the nested logit model allows for non-constant 

correlations between alternatives. For example, some respondents might place a positive 

value on taking action, while others might place a negative value on taking action. This 

section describes results of a nested logit model, and compares them to results from the base 

model. 

The nested logit model was set up as described in Chapter 4, with choices of (No Action, No 

Action), (Action, Program A), and (Action, Program B). The choice between Program A 

and Program B was thus conditional on the choice to take action. The model was estimated 

by full information maximum likelihood, using the NLOGIT package with LIMDEPTM 

(Greene, 1995). Results of the model estimated with main effects only are reported in Table 

0-23. 
185

The dollar values were calculated based on the formula given in Equation (26) in Chapter 4. 

The estimated dollar values from the nested logit model are larger than those from any of 

the other models, although they are close to the upper bounds given by the confidence 

intervals of the weighted model. The constant term for Program B is similar in magnitude to 

that estimated in the previous model, although it is slightly smaller. 

Table 0-23 - Nested Logit Model Results, Main Effects Only 

Variable Coefficient

Std. 

Error T-Stat.

Value/Acre

HH/Year 

Program B -.1586 .03511 -4.52 

Farmland .000300 .000032 9.38 $0.174 

Undev. Land .000056 .000006 9.17 $0.033 

Wetlands .000228 .000023 10.10 $0.132 

Shellfish Areas .000128 .000014 8.93 $0.074 

Eelgrass .000281 .000026 10.91 $0.164 

Cost -.003441 .000264 -13.02 

J .3397 .0285 11.92 

Log-Likelihood

Restricted Log-Likelihood

Chi-Squared (Sig. Level) 

D2

-3919.449 

-5674.796 

3511 (.00000) 

.309 

As explained in Chapter 4, the parameter J is related to the correlation between alternatives, 

and the condition 0#J#1 is sufficient for the nested logit model to be consistent with utility 

maximization (McFadden 1981). A value of J between 0 and 1 indicates that there is greater 

substitutability within, rather than across, groups of alternatives. 

Based on t-tests, the hypotheses that J=0 and J=1 can both be rejected at a high level of 

significance. This indicates that there is greater substitutability between Program A and 
186

Program B than between not taking action versus either Program A or Program B, and that 

there is significant correlation between the Program options. 

The nested logit model was also estimated with a set of explanatory variables to explain the 

choice of Action versus No Action, where the choice between Action and No Action is 

hypothesized to depend on characteristics of respondents that are significant in the 

conditional logit model. These are income, education, participation in shellfishing, 

participation in boating, distance lived from the Estuary, affected by brown tide, concern for 

brown tide, years lived on the East End, membership in environmental groups, and level of 

support for specific actions. 

Level of use of the Peconic Estuary was also included, because the SCC survey found that 

people who used the Estuary more frequently were more likely to support actions; and a 

constant term was included to account for other factors that might affect the choice of 

action versus no action. The level of use was calculated by adding up the number of uses 

that the respondent reported for the Peconic Bays. The support for specific actions was 

calculated by summing the level of support for all actions in Question 2, where 1 indicates 

strongly support, and 5 indicates strongly oppose. Thus a lower score indicates stronger 

support for taking action. 

Based on t-tests for individual variables and likelihood ratio tests for the sets of income and 

education variables and distance from the Peconic Bay, the constant term, use of the Peconic 

Estuary, participation in boating, and level of concern for brown tide are not statistically 

significant at the 10 percent level or better and were not included in the final model. 
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The choice between Program A and Program B is hypothesized to depend on the levels of 

the natural resources, and the cost. All of these variables are highly significant. The results 

of this model, after deleting the variables that are not statistically significant, are reported in 

Table 0-24. 

Table 0-24 - Nested Logit Model Results, Extended Model 

Variable Coefficient Std. Error T Statistic P-Value

Choice of Program A or B: 

Constant for Prog. B -.163 .037464 -4.35 .00001 

Farmland .000282 .000032 8.94 .00000 

Undeveloped Land .000054 .000006 8.85 .00000 

Wetlands .000218 .000023 9.39 .00000 

Shellfish Areas .000124 .000014 8.69 .00000 

Eelgrass .000293 .000028 10.60 .00000 

Cost -.003479 .000280 -12.42 .00000 

Choice of No Action or Action: 

Shellfishing .273 .1364 2.00 .045 

Waterfront Home -.320 .1852 -1.73 .084 

<1/2 mile from PES .256 .1472 1.74 .082 

Affected by B.T. -.400 .1620 -2.47 .014 

Action Score -.038 .0043 -8.84 .00000 

Envir. Grp. Member 1.197 .1721 6.96 .00000 

Years on E.E. -.0068 .00396 -1.72 .087 

Ed4 .331 .184 1.80 .072 

Ed567 .517 .165 3.13 .0018 

Inc34 -.117 .192 -.61 .542 

Inc567 -.213 .184 -1.16 .247 

Inc8 .725 .298 2.44 .015 

J .4691 .0626 7.49 .00000 

Log-Likelihood

Restricted Log-Likelihood

Chi-Squared (Sig. Level) 

D2

-3258.254 

-4962.934 

3409 (.00000) 

.343 

 

As expected, people who indicate stronger support for specific actions, and members of 

environmental groups are more likely to choose to take action. People who shellfish are also 
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more likely to choose to take action. The variables for distance lived from the Estuary are 

dummy variables, where the base case represents people who live more than 1⁄2 mile from 

the Estuary. People who live on the waterfront are less likely to choose an action than 

people who live more than 1⁄2 mile away; and people who live less than 1⁄2 mile from the 

Estuary are more likely to choose an action than those who live farther away. 

People who have lived on the East End longer, and people who were affected by brown tide 

are less likely to choose to take action. People with some college, or a college degree, are 

more likely to choose an action than people with high school or less. People with the 

highest income level ($150,000 or more) are more likely to choose an action than people 

with lower incomes. These variables explain why people are more likely to choose to take 

action, independent of the action’s effects. 

Comparisons of Models and Sensitivity Analysis

This section presents comparisons of the conditional logit models estimated with and 

without alternative-specific constants, and the nested logit model. Each of the models 

compared here were estimated with the choice attributes only. A sensitivity analysis of 

estimated dollar values is also presented, to provide a range of values based on the highest 

estimated values, from the nested logit model, and the lowest estimated values, from the 

conditional logit model with alternative-specific constants. 
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Comparison of Model Results 

Table 0-25 compares the coefficients, estimated dollar values, and relative values for the 

conditional logit model, the conditional logit model with alternative-specific constants, and 

the nested logit model. The relative values for the natural resources are calculated as ratios 

between the coefficients on each pair of resources. 

The dollar values and relative values for the three models were compared using Friedman’s 

test for more than two related samples (Neave and Worthington, 1988). The hypothesis that 

the estimated dollar values for the three models are equal is rejected at better than the 1 

percent significance level, with a test statistic of 20 and a critical value of 8.4. However, the 

hypothesis that the relative values are equal for the different models cannot be rejected at the 

5 percent level, with a test statistic of 2.6 and critical value of 6.2. 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 the model is robust with respect to priorities and relative values, 

although the estimated dollar values vary with model specification. This occurs because the 

largest difference between the models is the estimated coefficient on cost. The estimated 

dollar values for the three models are within the same general magnitude. Therefore, it may 

be concluded that the model is relatively robust to different specifications. 
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Table 0-25 - Comparison of Different Model Results

Coefficient Value/Acre Relative Values

Conditional Logit Model: F/E 1.22 

Farmland 0.000511 $0.136 F/W 1.52 

Undeveloped Land 0.000107 $0.028 F/S 2.19 

Wetlands 0.000336 $0.089 F/U 4.80 

Shellfish Areas 0.000233 $0.062 E/W 1.25 

Eelgrass 0.000419 $0.111 E/S 1.80 

Cost -0.003765 E/U 3.94 

D2=1-(L ($)/L (0)) .138 W/S 1.44 

 W/U 3.15 

 S/U 2.19 

Model with AlternativeSpecific Constants: F/E 1.40 

Programs A&B 1.2866 F/W 1.68 

Program B -0.1799 F/S 2.79 

Farmland .000300 $0.094 F/U 5.27 

Undeveloped Land .000057 $0.018 E/W 1.20 

Wetlands .000179 $0.056 E/S 1.99 

Shellfish Areas .000108 $0.034 E/U 3.77 

Eelgrass .000214 $0.067 W/S 1.66 

Cost -.003207 W/U 3.14 

D2 .171 S/U 1.89 

Nested Logit Model: F/E 1.07 

Program B -.1586 F/W 1.32 

Farmland .000300 $0.174 F/S 2.35 

Undeveloped Land .000056 $0.033 F/U 5.32 

Wetlands .000228 $0.132 E/W 1.23 

Shellfish Areas .000128 $0.074 E/S 2.20 

Eelgrass .000281 $0.164 E/U 4.99 

Cost -.003441 W/S 1.79 

J .3397 W/U 4.04 

D2 .309 S/U 2.26 

Sensitivity Analysis of Economic Values 

This section presents comparisons of discounted present values for the different models. In 

Table 0-26 the discounted present values for each model, at the 7.625% discount rate, is 
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shown. The values for the weighted model, discussed above, would fall within the range of 

values shown here. For an infinite time horizon, estimated values range from around 

$90,000 to $168,000 for an acre of farmland; from $17,000 to $32,000 for an acre of 

undeveloped land; from $54,000 to $128,000 for an acre of wetlands; from $32,000 to 

$71,000 for an acre of shellfishing area; and from $64,000 to $157,000 for an acre of 

eelgrass. 

Table 0-26 - Comparison of Discounted Present Values 

for Different Models, 7.625% Discount Rate

 Constants Model Base Model Nested Logit 

Resource

PV/Acre

T=4

PV/Acre 

T=25 

PV/Acre

T=4

PV/Acre 

T=25 

PV/Acre 

T=4

PV/Acre 

T=25 

Farmland $ 90,122 $ 75,767 $130,867 $110,022 $167,821 $141,090 

Undeveloped Land $ 17,098 $ 14,375 $ 27,280 $ 22,934 $ 31,566 $ 26,538 

Wetlands $ 53,643 $ 45,098 $ 86,031 $ 72,328 $127,549 $107,232 

Shellfishing Areas $ 32,360 $ 27,205 $ 59,742 $ 50,226 $ 71,444 $ 60,064

Eelgrass $ 64,380 $ 54,125 $107,360 $ 90,259 $157,467 $132,385 

Table 0-27 presents a sensitivity analysis for different discount rates for the model with 

alternative-specific constants and the nested logit model, in order to indicate the range of 

values that might occur if the discount rate were changed. For an infinite time horizon, 

these values range from around $69,000 to $640,000 for an acre of farmland; from $13,000 

to $120,000 for an acre of undeveloped land; from $41,000 to $486,000 for an acre of 

wetlands; from $25,000 to $272,000 for an acre of shellfishing area; and from $49,000 to 

$600,000 for an acre of eelgrass. The lowest values are for the model with alternative-
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specific constants and a 10% discount rate, and the highest are for the nested logit model 

and a 2% discount rate. These results show that the choice of discount rate is more 

important in determining the magnitude of values than the choice of model specification.

Table 0-27 - Sensitivity Analysis for Discount Rates 

Alternative-Specific Constants Model and Nested Logit Model 

 2% Discount Rate 10% Discount Rate

Resource

PV/Acre

T=4

PV/Acre 

T=25 

PV/Acre 

T=25 

PV/Acre 

T=25 

Constants Model

Farmland $343,590 $134,161 $ 68,718 $ 62,376 

Undeveloped Land $ 65,188 $ 25,454 $ 13,038 $ 11,834 

Wetlands $204,512 $ 79,856 $ 40,902 $ 37,127 

Shellfishing Areas $123,371 $ 48,173 $ 24,674 $ 22,397 

Eelgrass $245,449 $ 95,840 $ 49,090 $ 44,559 

Nested Logit 

Farmland $639,816 $249,828 $127,963 $116,153 

Undeveloped Land $120,344 $ 46,991 $ 24,069 $ 21,847 

Wetlands $486,279 $189,877 $ 97,256 $ 88,279 

Shellfishing Areas $272,381 $106,356 $ 54,476 $ 49,448 

Eelgrass $600,344 $234,416 $120,069 $108,987 

Tests for Economic “Rationality”

Chapter 4 discussed several hypotheses related to how respondents might make choices in 

the survey. First, respondents may behave perfectly “rationally,” as defined by neoclassical 

theory, and balance the alternatives and their attributes quantitatively to maximize personal 

utility. Second, survey responses may symbolically express moral commitments or other 
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emotional responses to the survey. Third, because of unfamiliarity or complexity, 

respondents may simplify the choice, applying decision heuristics. Finally, respondents 

might make choices using some combination of the above. 

In general, given the unfamiliarity and difficulty of choices related to natural resources and 

the environment, it would not be surprising to find that respondents exhibit some form of 

bounded rationality (Simon 1955; Conlisk 1996; Heiner 1983; Mazzotta and Opaluch 1995). 

However, because respondents are very familiar with the area, and very concerned about 

protecting the local environment, one might expect that responses to this survey would be 

better-behaved than choices for totally unfamiliar environmental amenities, although perhaps 

less well-behaved than choices for market purchases of familiar goods. 

Without information on how and why people choose, statistical tests cannot provide 

definitive evidence to support or refute specific hypotheses related to economic rationality. 

However, a variety of statistical and qualitative tests may be utilized to provide evidence 

related to reasonableness and precision of responses, and consistency with revealed 

preference theory. A set of such tests are described in this section. 

Validity may be tested at different levels of rigor. At the most basic level, explanatory 

variables must be statistically significant, and model coefficients must have the appropriate 

sign, as predicted by economic theory. At a more rigorous level, values should be robust to 

model specification. At a still more rigorous level, the value for a larger incremental change 

in an attribute must be greater than that for a smaller change. Finally, at the most rigorous 

level the adding up property must hold, so that the value of a given natural resource change 
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is the same whether the change is evaluated as a single unit, or whether the same change is 

considered as two separate incremental steps. 

Initial Validity Tests 

The model results presented in the previous sections all meet the minimum conditions for 

validity, with statistically significant coefficients of the expected signs—positive for 

environmental attributes and negative for the cost of programs. The results are also 

consistent with theory at next level of validity. As discussed above, results are robust to 

model specification, with equal priorities and relative values for different models, and dollar 

values that are relatively stable over different specifications. 

The results of the alternative-specific constant model and nested logit model indicate that 

part of people’s expression of value may be symbolic of general concern for the 

environment of the East End, and that some respondents may use decision heuristics in 

making their choices. As discussed above, the preference for Program A over Program B, 

independent of effects on natural resources, likely indicates that some respondents may have 

attempted to simplify their choices. 

The large and statistically significant constant term for action indicates that respondents are 

more likely to choose to take action, independent of the action’s effects on natural resources. 

 This is consistent with expression of a symbolic concern for the environment. As reported 

above, most (91.6%) chose an action, rather than no action for more than half of their 
195

answers. 706 respondents (79%) chose an action for all of their answers, and only 27 people 

(3.0%) chose no action for all of the choice questions answered. 

Respondents also may symbolically express strong concern for the environment by always 

choosing the most expensive program, based on the assumption that the most expensive 

program must provide the greatest environmental benefit. However, 112 respondents (13%) 

chose the higher cost program for all of their choices, and 97 (11%) chose the lower cost 

program for all of their choices. This is fairly balanced, and represents a small percent of 

respondents, so does not provide strong evidence for this type of symbolic response, 

although it could indicate that a small number of respondents might simplify their choices by 

selecting either the higher or lower cost program every time. 

Qualitative Evidence 

Open-ended comments by respondents provide additional information on how people 

responded to the survey. Only 82 people (9% of all respondents) included written 

comments on their surveys. Most of the comments (71% of those who commented) did not 

refer specifically to the contingent choice question. Sixteen of these expressed general 

approval of the survey; and 42 included a comment about the environment, local politics, or 

specific actions that the respondent was concerned about. These comments provide 

evidence that the issues are salient to people and that they are thus likely to think carefully 

about their answers. Eighteen (22%) specifically mentioned the cost, indicating that these 

respondents were considering the cost along with the outcomes of programs in making their 
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choices. For example, several said that they wanted to protect the environment, but could 

not afford to pay. 

The remaining 6 comments (7% of people who commented and 0.7% of all respondents) 

expressed disagreement with the scenario, stating that some part of the scenario was 

unrealistic or that they wanted more information on exactly how the money would be spent. 

 This small number of protests compared to a larger number who expressed concern for the 

issues or considered costs, combined with results of focus groups and pretests, provide 

evidence that a majority of respondents accepted the scenario, and did not have difficulty 

answering the questions or making the required tradeoffs. 

Scope and Adding Up Tests 

Two statistical tests that can provide information about the consistency of survey results 

with economic theory at the most rigorous levels are the scope test and adding up test. The 

scope test determines whether the value for a larger incremental change in an attribute is 

greater than the value for a smaller change. The additivity test determines whether the value 

of a given natural resource change is the same whether the change is evaluated as a single 

unit, or whether the same change is considered as two separate incremental steps. 

In order to conduct these tests, the model was estimated using dummy variables to represent 

each of the three attribute differences for each natural resource. The dummy variable for a 

difference was set equal to 1 if the level for Program A was greater than the level for 
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Program B, and -1 if the level for Program A was less than that for Program B. Cost was 

included as a continuous variable. These tests were conducted using a model including only 

attributes of choices, without the demographic variables. In addition, only responses that 

selected either Program A or Program B were included in the estimation. The increments 

and estimated coefficients are listed in Table 0-28. 

Table 0-28 - Model Results for Adding Up and Scope Tests 

Variable

Increment 

(acres) Coefficient Std. Error T Statistic P-Value

Farm1 9,000-10,000 .49324 .1158 4.26 .00002 

Farm2 10,000-12,000 .46085 .0579 7.97 .00000 

Farm3 9,000-12,000 .55652 .0730 7.62 .00000 

Undev1 50,000-59,000 .27008 .0607 4.45 .00001 

Undev2 59,000-63,000 .20775 .0585 3.55 .00038 

Undev3 50,000-63,000 .48107 .0618 7.78 .00000 

Wetland1 12,000-16,000 .86718 .0834 10.39 .00000 

Wetland2 16,000-17,500 .000013 .0557 0.00 .99981 

Wetland3 12,000-17,500 .45964 .0856 5.37 .00000 

Shellfish1 25,000-26,000 .04861 .0712 .683 .49459 

Shellfish2 26,000-29,000 .10603 .0505 2.10 .03562 

Shellfish3 29,000-25,000 .12795 .0620 2.06 .03907 

Eelgrass1 8,000-9,000 .29242 .0671 4.36 .00001 

Eelgrass2 9,000-11,000 .25412 .0575 4.42 .00001 

Eelgrass3 8,000-11,000 .32122 .0552 5.82 .00000 

Cost — -.00349 .00029 -11.94 .00000 

First, scope tests were carried out to determine whether expressed values increase with 

quantity. In order to be a valid test of scope, each of these comparisons requires the 

assumption of nonsatiation to hold. In other words, if respondents are satiated at the 
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intermediate level, so that there is no value for additional increments, then the value for the 

incremental change from the baseline to the intermediate level would be expected to be the 

same as the value for the change from the baseline to the highest level. Therefore, each 

scope test must consider the statistical significance of the coefficients along with the 

comparisons of coefficients for different increments. 

The hypotheses tested, using Wald tests, are whether $ i1 = $ i3 and $i

2 = $ i3, for each 

attribute, i. The superscripts indicate the incremental change, with superscript 1 indicating 

the change from the baseline to intermediate level, superscript 2 indicating the change from 

intermediate to highest level, and superscript 3 indicating the change from the baseline to the 

highest level. The results of the Wald tests are reported in Table 0-29. 

Table 0-29 - Wald Tests for Scopea

Test Chi-Squared Significance Level 

Farm1=Farm3 .25 .62 

Farm2=Farm3 1.07 .30 

Undev1=Undev3 6.27 .01 

Undev2=Undev3 11.79 .0006 

Wet1=Wet3 13.21 .0003 

Wet2=Wet3 22.97 .00000 

Shell1=Shell3 .82 .36 

Shell2=Shell3 .09 .76 

Eel1=Eel3 .14 .71 

Eel2=Eel3 .09 .76 

a - These tests compare differences between attribute levels, as described in the text.
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As reported in Table 0-28, the coefficients for all increments of farmland, undeveloped land, 

and eelgrass are statistically significant, indicating that nonsatiation holds for each of these 

resources. Thus, the Wald tests may be interpreted alone as valid scope tests. For both 

farmland and eelgrass, the hypotheses of equality between increments are not rejected, 

implying that the values are not significantly different for different size increments. Thus, 

the values for farmland and eelgrass are not consistent with theory in terms of scope. The 

hypotheses of equality between increments for undeveloped land are both rejected, 

indicating that values for undeveloped land do increase with quantity and thus are consistent 

with theory. 

The results for wetlands contain several inconsistencies. First, the coefficient, $W2, on the 

increment from the intermediate to highest level for wetlands, is not statistically significant, 

indicating that an increase above 16,000 acres is not valued by respondents. Thus, it might 

be expected that the hypothesis of equality of values between the increment from the 

baseline level to the intermediate level and the baseline to highest level would not be 

rejected. It would also be expected that the hypothesis of equality of values between the 

increment from intermediate to highest and baseline to highest levels should be rejected, 

since the value for the intermediate to highest increment is not significantly different from 

zero, and the value for the baseline to highest level is positive. 

Both of these hypotheses are rejected, indicating that there are significant differences in 

value for the different wetlands increments. However, there is an additional inconsistency. 

The coefficient for the largest incremental change, from the baseline to the highest level, is 

smaller by almost half than the coefficient for the change from the baseline to the 
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intermediate level. These results indicate that, for wetlands, a larger increment is actually 

valued less than a smaller increment. Thus, the values for changes in wetlands are not 

consistent with economic theory in terms of scope. 

Neither scope hypothesis is rejected for shellfishing areas, indicating that values for all 

increments are the same. In addition, the coefficient on the increment from the baseline to 

the intermediate level is not statistically significant. Thus, the results for shellfishing areas 

are not consistent with economic theory in terms of scope. 

As described in Chapter 4, the adding up property requires that, if income effects are small, 

the value for a given change in quantity of a natural resource should be approximately the 

same, whether it is presented as a single increment or in two smaller increments, where the 

second is conditional on the first. In this survey, each natural resource is included at three 

different levels—the baseline level and two improvements from the baseline. Therefore, 

assuming the income effects are small, the estimated value for the largest improvement—the 

highest level compared to the baseline level—should be approximately equal to the sum of 

the two smaller improvements—the baseline level compared to the intermediate level, and 

the intermediate level compared to the highest level. This is the most rigorous test of 

economic rationality. 

The hypotheses to be tested are $i

1 + $ i2 = $ i3 for each natural resource, i, or that the sum 

of each pair of coefficients on the two smaller increments is equal to the coefficient on the 

largest increment. These were tested as linear restrictions on the model using Wald tests. 
201

Each attribute was tested separately, and a joint test was performed for all five sets of 

restrictions. The results are reported in Table 0-30. 

Tested together, the hypothesis that all attributes simultaneously conform to the adding up 

property is rejected. Tested separately, the adding up hypothesis can be rejected for 

farmland, wetlands, and eelgrass, but cannot be rejected for undeveloped land and shellfish. 

Table 0-30 - Wald Tests for Adding Upa

Test Chi-Squared Significance Level 

Farm1+Farm2=Farm3 8.26 .004 

Undev1+Undev2=Undev3 0.00 .974 

Wet1+Wet2=Wet3 10.43 .001 

Shell1+Shell2=Shell3 0.08 .783 

Eel1+Eel2=Eel3 5.58 .018 

Joint Test 25.24 .0001 

a - These tests compare differences between attribute levels, as described in the text.

Summary 

Taken together, these tests indicate that the minimum conditions for economic rationality 

are met, with mixed results for the more rigorous tests. The estimated coefficients for all 

models have the correct sign and are statistically significant. Relative values and priorities are 

consistent across model specifications, and estimates of dollar values are not highly sensitive 

to specification. There is some evidence of symbolic values and decision simplification, and 

scope and additivity tests indicate that answers to the survey do not completely satisfy the 
202

most rigorous tests, so that values do not always change in expected ways with changes in 

quantity. 

Overall, there is no strong evidence that people are unable to make tradeoffs that express 

their priorities for resources. Open-ended comments and other qualitative evidence suggest 

that respondents were concerned about the issues, accepted the scenarios, and made an 

effort to provide thoughtful answers. The mixed results for the most rigorous tests most 

likely result from a combination of symbolic expressions of concern and decision 

simplification which, as mentioned above, might be expected for choices that are difficult 

and unfamiliar. 

Although respondents may not be able to express their values precisely enough to pass the 

most rigorous tests of economic rationality, responses still provide robust estimates of 

people’s priorities and relative values for the natural resources, which is most important for 

policy analysis that aims to prioritize actions. Although the dollar values may not be precise, 

they may be appropriately used to indicate the general magnitude of benefits of different 

programs as part of a decision-making process that considers a variety of factors, including 

economic benefits. 
203

CHAPTER 7

CONCLUSIONS AND RECOMMENDATIONS 

The objectives of this study are to measure public priorities and values for protecting and 

enhancing natural resources of the Peconic Estuary system; and to explore several issues 

related to economic valuation methods. The results of this study will be combined with 

information from other studies of the Peconic Estuary System to help develop a 

Management Plan for the Estuary. 

This study relies on primary data, collected in August, 1995 through a survey of 968 yearround and seasonal residents of the area surrounding the Peconic Estuary. The resulting 

data were analyzed using both a conditional logit model and a nested logit model. The 

nested logit model results are compared to two specifications of the conditional logit model, 

with and without alternative-specific constants. The analysis also includes tests of 

hypotheses related to the precision and economic rationality of responses; and estimation of 

economic values, relative values, and priorities for protecting or enhancing natural resources. 

Five natural resources were included in the survey—farmland, undeveloped land, wetlands, 

shellfishing areas, and eelgrass. The selection of natural resources was based on the needs of 

the Peconic Estuary Management Committee, and concerns of the public. The contingent 

choice format was chosen for the survey in an attempt to minimize the expression of 

symbolic values and other problems that may occur in contingent valuation surveys; and 
204

because the format is most appropriate for the policy analysis, which focuses on prioritizing 

actions to protect or enhance natural resources. 

Summary of Main Findings

Importance of the Peconic Estuary 

The interviews and focus groups conducted as part of the survey development process 

revealed that residents of the East End have a very strong “sense of place,” and that two of 

the most important factors that contribute to this are the rural character of the area and the 

area’s waters, including the Atlantic Ocean, Long Island Sound, and the Peconic Estuary. 

Therefore, the survey focused on both resources directly related to the Estuary—wetlands, 

shellfishing areas, and eelgrass—and land uses surrounding the Estuary—farmland and 

undeveloped land. 

Most survey respondents (81%) use the Estuary in some way, and it is used by more people 

than any other water body of the East End. Most also express support for actions to 

improve water quality, and are very concerned about brown tide, an algal bloom that has 

affected water quality in recent years. The lowest level of support for actions is for those 

actions that are most likely to be personally inconvenient and costly—required pumpouts of 

septic systems and restrictions on use of lawn chemicals. However, these actions are still 

supported by a majority of respondents (54 percent and 68 percent, respectively). 
205

Public Priorities and Values for Natural Resources 

Of the 968 people who completed the survey, 897 (92.7%) answered at least one of the five 

contingent choice questions in each survey booklet; and of the 4,840 total possible choices, 

4,307 (89%) were answered. Older respondents, and those with lower education and income 

levels, were less likely to answer all of the choice questions. Of those who answered at least 

one choice question, 91.6 percent chose either Program A or Program B, rather than “No 

New Action,” for more than half of their answers, and 79 percent chose an action for all of 

their answers. Only 27 people (3.0%) chose “No New Action” for all of the choice 

questions answered. 

Based on the random utility model, relative values and priorities for the natural resources 

were estimated using the conditional logit method. 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 model results indicate that priorities for protection or 

enhancement of resources are as follows: farmland, eelgrass, wetlands, shellfish, and 

undeveloped land. 

In order to adjust the results to reflect the values of a representative member of the public, 

demographic characteristics of respondents were included in the model. Of the standard 

demographic variables, only education and income are statistically significant determinants of 

preferences over resources. After weighting the model results to reflect population 

demographics, priorities over resources remain the same as in the model estimated without 

demographics, and relative values of the resources are not statistically different. Estimates of 
206

dollar values for the natural resources decrease slightly, although the 95 percent confidence 

intervals overlap. Thus, the adjustments do not change the results substantially. 

The estimated economic values for a representative resident of the East End are as follows: 

farmland is valued by the average household at $.13 per acre, per year; eelgrass at $.10; 

wetlands at $.08; shellfishing areas at $.06; and undeveloped land at $.03. These values imply 

total values for the entire East End population of 73,423 households of $1,817 per acre per 

year for undeveloped land to $9,819 per acre per year for farmland. 

In order to aggregate these values over time, the discounted present value was calculated, 

with sensitivity analysis for different discount rates and for two time horizons—25 years and 

infinity. The base case uses the official water resources planning discount rate for fiscal year 

1996, 7.625 percent, with sensitivity analysis for 2 percent, 5 percent and 10 percent discount 

rates. 

For the 7.625 percent discount rate and an infinite time horizon, the present values range 

from $23,831 to preserve an acre of undeveloped land, to $128,771 to preserve an acre of 

farmland. The value to restore an acre of shellfishing grounds is $53,754; to preserve or 

restore an acre of wetlands is $79,557; and to restore an acre of eelgrass is $96,538. A 2 

percent discount rate increases these values to around $91,000-$491,000, and a 10 percent 

discount rate reduces the values to around $18,000-$98,000. 
207

Model Extensions 

The results of the conditional logit model were compared to two alternative specifications. 

The first is the conditional logit model with two alternative-specific constants, one for the 

choice of an action versus no action, and one for Program B. The second 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. Thus, the choice between Program A and 

Program B is conditional on the choice to take action. Both of these alternative models 

account for effects that are related to the options themselves rather than their specific 

attributes. 

The results of both of these models indicate that there are effects on choices that are not 

related to the quantities of individual attributes. In the alternative-specific constant model, 

both constant terms are statistically significant, indicating that the average respondent is 

more likely to choose an action rather than “No New Action,” independent of the action’s 

specific effects on natural resources, and that there is a 56 percent probability that the 

average respondent would choose Program A rather than Program B if their effects on 

natural resources were identical. 

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. Thus, if people are more likely to choose to take action, rather than “No New 
208

Action,” independent of the specific results of actions, they may be expressing a symbolic 

willingness to pay 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 

prefer Program A, and measures the degree of preference for A over B. 

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 independent of effects is not expected, and could occur for a 

variety of reasons, including order effects or an attempt by some respondents to simplify the 

decision process. For example, respondents may carefully examine the first of the five 

contingent choice questions, and determine which program is better in terms of natural 

resources protected. Choices for the remaining questions may then be based on the option 

chosen in the first question. This might occur because Program A dominates Program B, in 

terms of natural resources protected, in six of the twelve booklets for the first contingent 

choice question; while Program B is not dominant for the first question in any booklets. 

This might lead some respondents to assume that Program A represents a higher level of 

resource protection throughout the survey. 

The dollar values for this model are approximately half to two-thirds as large as those 

estimated from the base model. These values might be interpreted as the quantitative 

portion of people’s values for the natural resources, which is smaller than their total value 

for preserving the Estuary’s resources. In other words, a portion of people’s value is 

expressed quantitatively, while a portion of the value is expressed qualitatively or 

symbolically. The base model captures both portions of value. 
209

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. 

An alternative approach to modeling is to use a random effects model, which assumes that a 

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 exhibit a bias against 

taking action. The random effects model can be implemented using a nested logit approach, 

where the random components of utility associated with the two action alternatives are 

correlated. 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. 

The estimated dollar values and relative values, calculated as ratios between the coefficients 

on each pair of resources, for the conditional logit model, model with alternative-specific 

constants, and nested logit models were compared 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. 

The model with alternative-specific constants produces the lowest estimated dollar values, 

while the nested logit model produces the highest values. For an infinite time horizon, and 

the 7.625 percent discount rate, estimated values for the different models range from around 
210

$90,000 to $168,000 for an acre of farmland; from $17,000 to $32,000 for an acre of 

undeveloped land; from $54,000 to $128,000 for an acre of wetlands; from $32,000 to 

$71,000 for an acre of shellfishing area; and from $64,000 to $157,000 for an acre of 

eelgrass.

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 the model is robust with 

respect to priorities and relative values, but that the estimated dollar values vary with model 

specification. However, the estimated dollar values for the three models are close in 

magnitude. The sensitivity analysis with respect to discount rates produces greater variations 

in dollar estimates than the different model specifications. Therefore, it may be concluded 

that the model is relatively robust to different specifications. 

Tests for Economic “Rationality” 

In the context of this study, several hypotheses about responses were considered. First, 

respondents may behave perfectly “rationally,” as defined by neoclassical theory, and balance 

the alternatives and their attributes quantitatively to maximize personal utility. Second, 

survey responses may symbolically express moral commitments or other emotional 

responses to the survey. Third, because of unfamiliarity or complexity, respondents may 
211

simplify the choice, applying decision heuristics. Finally, respondents might make choices 

using some combination of the above. 

Validity may be tested at different levels of rigor. At the most basic level, explanatory 

variables must be statistically significant, and model coefficients must have the appropriate 

sign, as predicted by economic theory. At a more rigorous level, values should be robust to 

model specification. At a still more rigorous level, the value for a larger incremental change 

in an attribute must be greater than that for a smaller change. Finally, at the most rigorous 

level the adding up property must hold, so that the value of a given natural resource change 

is the same whether the change is evaluated as a single unit, or whether the same change is 

considered as two separate incremental steps. 

Several qualitative and statistical tests were conducted to provide evidence related to 

reasonableness and precision of responses, and consistency with revealed preference theory. 

The model results all meet the minimum level of validity, with statistically significant 

coefficients of the expected signs—positive for environmental attributes and negative for the 

cost of programs. The results are also consistent with theory at next level of validity, 

robustness to model specification, with equal priorities and relative values for different 

models, and dollar values that are relatively stable over different specifications. 

The results of the alternative-specific constant model and nested logit model indicate that 

part of people’s expression of value may be symbolic of general concern for the 

environment of the East End, and that some respondents may use decision heuristics in 

making their choices. However, open-ended comments provide evidence that respondents 
212

accepted the scenario, and did not have difficulty answering the questions or making the 

required tradeoffs. 

The results of the most rigorous tests, the scope and additivity tests, are mixed. Values for 

farmland, wetlands, shellfishing areas and eelgrass are not consistent with theory in terms of 

scope, while values for undeveloped land do increase with quantity, and thus are consistent 

with theory. Tested together, the hypothesis that all attributes conform to the adding up 

property is rejected. Tested separately, the adding up hypothesis can be rejected for 

farmland, wetlands, and eelgrass, but cannot be rejected for undeveloped land and 

shellfishing areas. The mixed results for the most rigorous tests most likely result from a 

combination of symbolic expressions of concern and decision simplification, which might be 

expected for choices that are difficult and unfamiliar. 

In summary, the survey results pass basic tastes of consistency with economic theory, with 

mixed results for the most rigorous tests. Thus, although respondents may not be able to 

express their values precisely enough to pass the most rigorous tests of economic rationality, 

responses still provide robust estimates of people’s priorities and relative values for the 

natural resources, which is most important for policy analysis that aims to prioritize actions. 

Implications and Recommendations for Further Research 

The results of this study imply that, even when a survey is carefully designed and 

respondents provide thoughtful answers, more confidence can be placed on measures of 
213

public priorities over natural resources than on dollar estimates of economic values. 

However, informed decisions do not necessarily require precise measures of benefits and 

costs. Therefore, although dollar estimates may provide a starting point for judging the 

magnitude of benefits and costs, policy analysis might do better to place more focus on 

measures of public priorities rather than on strict benefit-cost analysis. 

These results support Vatn and Bromley’s (1994) assertions that economic efficiency is not 

the most important component of social choices, and that, if hypothetical prices do not 

convey the relevant information, they cannot lead to efficiency. Therefore, focusing on 

benefit-cost analysis without considering broader issues related to people’s environmental 

philosophies, moral commitments, and whether they can precisely express preferences, may 

be inappropriate. 

These ideas point to two areas for further research. The first relates to economic theory and 

methods for measuring public priorities and values for the environment. The second relates 

to environmental policy analysis in general, and the role that economics can and should play. 

Economic Theory and Methods for Environmental Valuation 

One of the most important issues related to economic valuation is whether expressions of 

value elicited by surveys are consistent with the underlying utility theoretic framework in 

which they are analyzed. The possibility of “ill-defined” preferences that are not consistent 
214

with neoclassical utility theory has a variety of implications for economic theory and 

methods. 

Two important reasons for ill-defined preferences have been hypothesized—the existence of 

moral commitments related to the choice, and the complexity of such choices. One area for 

further research is the design of surveys that explicitly recognize different environmental 

philosophies so that respondents are able to express their preferences in ways that are 

acceptable to them. Another is to design surveys that guide respondents to focus on the 

specific goods being valued rather than the ethical dimension of the choice. The survey 

described in this study was an attempt to accomplish the latter. 

Although this appears to have been relatively successful, the survey data did not allow for a 

definitive test for the importance of moral commitments. To do so, a question could have 

been included to learn about people’s moral commitments, and thus to provide a statistical 

test for whether such motivations were important to choices. Early versions of the survey 

included a set of environmental attitude questions, with this intention, but they were later 

deleted for three reasons. First, they tended to draw respondents’ focus from the more 

important questions in the survey; second, they took a long time to answer; and finally, the 

Management Committee felt uncomfortable about including them because of their 

controversial nature. Questions about people’s sense of place and consumer versus citizen 

motivations were also tested in early versions, but were dropped primarily because they were 

not central to the policy analysis and they would have made the survey too long. 
215

These issues point to a drawback of using a survey intended to provide policy information to 

test methodological issues. Because the survey must focus primarily on eliciting policy 

relevant information, it is difficult, for several reasons, to include questions solely to test 

methodological issues. First, adding more questions can make the survey too long, which 

can limit the contexts in which the survey may be implemented, make it more difficult to get 

a large number of responses, and possibly tire respondents so that they will not give 

thoughtful answers. Second, inclusion of controversial or unusual questions can draw 

respondents’ focus from the more important questions for policy analysis, or evoke 

emotions that affect their answers to the policy questions. Third, controversial questions 

may not be politically feasible to include in a survey that is part of a highly publicized public 

program. Thus, care must be exercised in including such questions. One way to do so 

would be through the use of pilot surveys. However, these require additional time and 

money to implement, and thus may not be feasible. 

A second area for further research is development of methods that put into practice 

theoretical models of moral commitments, such as the well-being model presented in 

Chapters 3 and 4. This requires the recognition that economic values and personal utility are 

only one dimension of people’s motivations for choices, and that in many cases moral 

commitments and norms are also important. However, their level of importance and 

influence depends on the context, so that, as Etzioni (1988) points out, there are absolute 

moralities that preclude certain tradeoffs, and expedient moralities that may be balanced with 

other concerns. In order to make this theory operational, more information about 

respondents and their motivations must be collected, and analytical methods that include 

such information must be developed. 
216

A third area for future survey research is related to the fact that, even in cases where moral 

commitments are not important, people may still find choices related to the natural 

environment to be unfamiliar and complicated. Therefore, they simply may not be able to 

express preferences “crisply” or precisely. The results of this study indicate that focusing on 

developing measures of priorities, rather than attempting to elicit precise economic values, 

may be a productive avenue for additional research. Additionally, methods of analyzing 

“fuzzy” preferences may be considered. 

Another area to consider is the development of new types of measures that allow for 

consideration of public preferences in the policy process. For example, Mark Sagoff 

suggests that we “measure human welfare or well-being in substantive terms—in terms of 

the quality of life and the sustainability of cultures and communities, for example—[rather] 

than in terms of aggregate income, social wealth, consumer surplus, or any such abstraction 

of economic theory” (1992b, p. 409). There is already work being done in this area, which 

attempts to create indicators of quality of life and sustainability at the community level. 

Environmental Policy Analysis 

Although economic valuation surveys may be limited in their ability to provide precise 

measures of economic values, they may still provide important information for analysis of 

environmental policies. While survey methods may be further developed as suggested 

above, they may also be combined with other, more qualitative, methods to create a 

multidisciplinary approach. Such an approach might combine expert priorities with public 
217

priorities in a process based on judgment, rather than attempting to perform a strict benefitcost analysis. Thus, survey data might be augmented with interviews with representatives of 

stakeholder groups, public outreach and hearings, and the advice of experts. 

As Vatn and Bromley (1994) suggest, the core of the problem is the desire for “development 

paths that will reduce competition between the ‘man-made’ (internal) economy and the 

environment.” In order to accomplish this, they suggest that first we must advance common 

ways of understanding what the pertinent issues are about, and then develop a basis for 

collective choice based on the elicitation of individual choice. Because individual 

preferences are context relative it is necessary to define the context for a particular choice 

problem. Thus, there are two kinds of interrelated choice processes—decisions from within 

sets of given values or constraints and choosing these sets of norms and common values. 

Mark Sagoff proposes that the best way to balance efficiency against moral, cultural, and 

other values is by legislative debate and voting, rather than benefit-cost analysis. Thus, “the 

finer-grained decisions may be appropriate to markets, but some of the basic conditions of 

choice may be appropriately decided in the political process” (Sagoff, 1992, p. 201). 

Economic considerations can be considered in such cases through cost-effectiveness analysis 

and “sensible recognition of economic constraints” (Sagoff 1981). However, the political 

process is not without its drawbacks as well, for example, the potential influence of powerful 

interest groups. 

In summary, these issues suggest that a holistic approach to policy analysis related to the 

natural environment is required. Because, in many contexts, it may be difficult to produce 
218

defensible estimates of benefits and costs, recognizing that values may be uncertain, and that 

moral commitments and nonanthropocentric values may be important can be vital to 

performing relevant and useful policy analysis. The recognition and inclusion of these other 

dimensions in economic analysis will enrich the analysis and provide more useful 

information to policy makers. 
219

APPENDIX A 

EXAMPLES OF PRELIMINARY SURVEY VERSIONS 
220

MONTAUK PRELIMINARY SURVEY
221
222
223
224
225
226
227
228

FOCUS GROUP SURVEY, VERSION 1
229
230
231
232
233
234
235
236

OTHER PRELIMINARY FOCUS GROUP QUESTIONS
237
238
239
240
241

SURVEY, VERSION 1
242
243
244
245
246
247

APPENDIX B 

CONTINGENT CHOICE QUESTION DESIGN
248

Question Design for the 60 Contingent Choice Questions

Question Resources/Cost No Action Program A Program B

 Farmland 9,000 acres 12,000 acres 10,000 acres 

1 Undeveloped Land 50,000 acres 59,000 acres 50,000 acres 

 Cost $0 $200 $50 

 Farmland 9,000 acres 10,000 acres 12,000 acres 

2 Undeveloped Land 50,000 acres 50,000 acres 59,000 acres 

 Cost $0 $200 $300 

 Farmland 9,000 acres 9,000 acres 10,000 acres 

3 Wetlands 12,000 acres 16,000 acres 16,000 acres 

 Cost $0 $50 $100 

 Farmland 9,000 acres 10,000 acres 9,000 acres 

4 Shellfish Areas 25,000 acres 25,000 acres 26,000 acres 

 Cost $0 $50 $100 

 Farmland 9,000 acres 12,000 acres 10,000 acres 

5 Eelgrass 8,000 acres 9,000 acres 8,000 acres 

 Cost $0 $300 $200 

 Farmland 9,000 acres 10,000 acres 9,000 acres 

6 Undeveloped Land 50,000 acres 59,000 acres 63,000 acres 

 Cost $0 $100 $100 

 Farmland 9,000 acres 9,000 acres 10,000 acres 

7 Undeveloped Land 50,000 acres 59,000 acres 59,000 acres 

 Cost $0 $50 $300 

 Undeveloped Land 50,000 acres 63,000 acres 50,000 acres 

8 Wetlands 12,000 acres 16,000 acres 17,500 acres 

 Cost $0 $50 $200 

 Undeveloped Land 50,000 acres 63,000 acres 59,000 acres 

9 Shellfish Areas 25,000 acres 29,000 acres 26,000 acres 

 Cost $0 $200 $50 

 Undeveloped Land 50,000 acres 59,000 acres 50,000 acres 

10 Eelgrass 8,000 acres 9,000 acres 9,000 acres 

 Cost $0 $300 $50 

 Farmland 9,000 acres 9,000 acres 12,000 acres 

11 Wetlands 12,000 acres 16,000 acres 12,000 acres 

 Cost $0 $200 $200 

 Undeveloped Land 50,000 acres 63,000 acres 50,000 acres 

12 Wetlands 12,000 acres 17,500 acres 17,500 acres 

 Cost $0 $100 $50 

 Wetlands 12,000 acres 16,000 acres 17,500 acres 

13 Eelgrass 8,000 acres 9,000 acres 9,000 acres 

 Cost $0 $100 $300 

 Wetlands 12,000 acres 16,000 acres 17,500 acres 

14 Shellfish Areas 25,000 acres 29,000 acres 25,000 acres 

 Cost $0 $300 $100 

 Wetlands 12,000 acres 16,000 acres 17,500 acres 

15 Eelgrass 8,000 acres 11,000 acres 8,000 acres 
249

Question Resources/Cost No Action Program A Program B

 Cost $0 $50 $50 

 Farmland 9,000 acres 12,000 acres 10,000 acres 

16 Shellfish Areas 25,000 acres 25,000 acres 29,000 acres 

 Cost $0 $300 $300 

 Undeveloped Land 50,000 acres 59,000 acres 63,000 acres 

17 Shellfish Areas 25,000 acres 26,000 acres 26,000 acres 

 Cost $0 $50 $100 

 Wetlands 12,000 acres 17,500 acres 17,500 acres 

18 Shellfish Areas 25,000 acres 26,000 acres 29,000 acres 

 Cost $0 $50 $200 

 Farmland 9,000 acres 10,000 acres 12,000 acres 

19 Shellfish Areas 25,000 acres 25,000 acres 26,000 acres 

 Cost $0 $50 $200 

 Shellfish Areas 25,000 acres 29,000 acres 29,000 acres 

20 Eelgrass 8,000 acres 11,000 acres 8,000 acres 

 Cost $0 $100 $50 

 Farmland 9,000 acres 12,000 acres 10,000 acres 

21 Eelgrass 8,000 acres 9,000 acres 8,000 acres 

 Cost $0 $100 $50 

 Undeveloped Land 50,000 acres 59,000 acres 59,000 acres 

22 Eelgrass 8,000 acres 8,000 acres 11,000 acres 

 Cost $0 $100 $200 

 Wetlands 12,000 acres 17,500 acres 17,500 acres 

23 Eelgrass 8,000 acres 11,000 acres 9,000 acres 

 Cost $0 $300 $50 

 Shellfish Areas 25,000 acres 25,000 acres 29,000 acres 

24 Eelgrass 8,000 acres 11,000 acres 8,000 acres 

 Cost $0 $50 $300 

 Shellfish Areas 25,000 acres 29,000 acres 25,000 acres 

25 Eelgrass 8,000 acres 9,000 acres 9,000 acres 

 Cost $0 $200 $100 

 Farmland 9,000 acres 12,000 acres 12,000 acres 

26 Eelgrass 8,000 acres 8,000 acres 9,000 acres 

 Cost $0 $200 $300 

 Farmland 9,000 acres 10,000 acres 10,000 acres 

27 Undeveloped Land 50,000 acres 63,000 acres 50,000 acres 

 Cost $0 $300 $50 

 Farmland 9,000 acres 9,000 acres 12,000 acres 

28 Wetlands 12,000 acres 16,000 acres 12,000 acres 

 Cost $0 $200 $300 

 Farmland 9,000 acres 9,000 acres 10,000 acres 

29 Shellfish Areas 25,000 acres 29,000 acres 26,000 acres 

 Cost $0 $50 $100 

 Farmland 9,000 acres 12,000 acres 12,000 acres 

30 Eelgrass 8,000 acres 9,000 acres 11,000 acres 
250

Question Resources/Cost No Action Program A Program B

 Cost $0 $50 $100 
251

Question Resources/Cost No Action Program A Program B

 Farmland 9,000 acres 9,000 acres 12,000 acres

31 Undeveloped Land 50,000 acres 59,000 acres 50,000 acres

 Cost $0 $50 $300 

 Undeveloped Land 50,000 acres 50,000 acres 63,000 acres 

32 Eelgrass 8,000 acres 11,000 acres 11,000 acres

 Cost $0 $100 $200 

 Undeveloped Land 50,000 acres 63,000 acres 59,000 acres

33 Wetlands 12,000 acres 12,000 acres 17,500 acres

 Cost $0 $300 $50 

 Undeveloped Land 50,000 acres 63,000 acres 50,000 acres

34 Shellfish Areas 25,000 acres 29,000 acres 29,000 acres

 Cost $0 $200 $50 

 Undeveloped Land 50,000 acres 50,000 acres 59,000 acres

35 Eelgrass 8,000 acres 11,000 acres 8,000 acres 

 Cost $0 $200 $50 

 Farmland 9,000 acres 9,000 acres 10,000 acres

36 Wetlands 12,000 acres 17,500 acres 16,000 acres

 Cost $0 $50 $50 

 Undeveloped Land 50,000 acres 59,000 acres 59,000 acres

37 Wetlands 12,000 acres 17,500 acres 12,000 acres

 Cost $0 $200 $50 

 Wetlands 12,000 acres 16,000 acres 17,500 acres

38 Eelgrass 8,000 acres 9,000 acres 8,000 acres 

 Cost $0 $50 $50 

 Wetlands 12,000 acres 16,000 acres 16,000 acres

39 Shellfish Areas 25,000 acres 26,000 acres 29,000 acres

 Cost $0 $100 $300 

 Wetlands 12,000 acres 16,000 acres 12,000 acres

40 Eelgrass 8,000 acres 9,000 acres 11,000 acres

 Cost $0 $300 $100 

 Farmland 9,000 acres 12,000 acres 10,000 acres

41 Shellfish Areas 25,000 acres 29,000 acres 25,000 acres

 Cost $0 $100 $50 

 Undeveloped Land 50,000 acres 59,000 acres 59,000 acres

42 Shellfish Areas 25,000 acres 26,000 acres 29,000 acres

 Cost $0 $100 $300 

 Wetlands 12,000 acres 17,500 acres 16,000 acres

43 Shellfish Areas 25,000 acres 25,000 acres 26,000 acres

 Cost $0 $50 $100 

 Shellfish Areas 25,000 acres 25,000 acres 29,000 acres

44 Eelgrass 8,000 acres 11,000 acres 8,000 acres 

 Cost $0 $200 $50 

 Shellfish Areas 25,000 acres 29,000 acres 26,000 acres

45 Eelgrass 8,000 acres 11,000 acres 9,000 acres 
252

Question Resources/Cost No Action Program A Program B

 Cost $0 $200 $50 

 Farmland 9,000 acres 12,000 acres 9,000 acres 

46 Eelgrass 8,000 acres 9,000 acres 9,000 acres 

 Cost $0 $200 $100 

 Undeveloped Land 50,000 acres 59,000 acres 50,000 acres

47 Eelgrass 8,000 acres 11,000 acres 9,000 acres 

 Cost $0 $100 $50 

 Wetlands 12,000 acres 16,000 acres 12,000 acres

48 Eelgrass 8,000 acres 11,000 acres 11,000 acres

 Cost $0 $200 $100 

 Shellfish Areas 25,000 acres 25,000 acres 26,000 acres

49 Eelgrass 8,000 acres 11,000 acres 9,000 acres 

 Cost $0 $300 $50 

 Shellfish Areas 25,000 acres 29,000 acres 29,000 acres

50 Eelgrass 8,000 acres 9,000 acres 8,000 acres 

 Cost $0 $300 $50 

 Farmland 9,000 acres 12,000 acres 9,000 acres 

51 Undeveloped Land 50,000 acres 63,000 acres 59,000 acres

 Cost $0 $500 $50 

 Farmland 9,000 acres 12,000 acres 12,000 acres

52 Wetlands 12,000 acres 16,000 acres 17,500 acres

 Cost $0 $200 $500 

 Farmland 9,000 acres 12,000 acres 9,000 acres 

53 Shellfish Areas 25,000 acres 29,000 acres 26,000 acres

 Cost $0 $500 $50 

 Farmland 9,000 acres 10,000 acres 12,000 acres

54 Eelgrass 8,000 acres 8,000 acres 11,000 acres

 Cost $0 $100 $500 

 Undeveloped Land 50,000 acres 63,000 acres 59,000 acres

55 Wetlands 12,000 acres 17,500 acres 17,500 acres

 Cost $0 $500 $300 

 Undeveloped Land 50,000 acres 59,000 acres 63,000 acres

56 Shellfish Areas 25,000 acres 26,000 acres 29,000 acres

 Cost $0 $100 $500 

 Undeveloped Land 50,000 acres 63,000 acres 63,000 acres

57 Eelgrass 8,000 acres 11,000 acres 8,000 acres 

 Cost $0 $500 $300 

 Wetlands 12,000 acres 12,000 acres 17,500 acres

58 Shellfish Areas 25,000 acres 29,000 acres 29,000 acres

 Cost $0 $50 $500 

 Wetlands 12,000 acres 17,500 acres 17,500 acres

59 Eelgrass 8,000 acres 11,000 acres 8,000 acres 

 Cost $0 $500 $200 

 Shellfish Areas 25,000 acres 25,000 acres 29,000 acres

60 Eelgrass 8,000 acres 11,000 acres 11,000 acres
253

Question Resources/Cost No Action Program A Program B

 Cost $0 $50 $500 
254

Contingent Choice Questions in Each of the 12 Survey Booklets

Booklet # Q4 Q5 Q6 Q7 Q8 

1 27 60 47 3 14 

2 5 43 53 8 22 

3 9 33 50 54 31 

4 44 32 2 39 52 

5 45 40 29 7 55 

6 49 30 56 11 12 

7 35 13 34 58 36 

8 10 26 17 4 59 

9 15 24 51 46 41 

10 38 25 19 1 57 

11 20 16 21 37 23 

12 28 42 18 48 6 
255

APPENDIX C 

EXAMPLE SURVEY BOOKLET
256
257
258
259
260
261
262
263
264

APPENDIX D 

PRELIMINARY MODEL RESULTS
265

Full Model Before Weighting, Year-Round Resident Sample

Variable Coefficient Std. Error T Statistic P-Value 

FARM 0.000480 0.000105 4.587 0.00000 

UNDEV 0.000055 0.000021 2.613 0.00897 

WET 0.000242 0.000058 4.192 0.00003 

SHELL 0.000282 0.000071 3.983 0.00007 

EEL 0.000405 0.000090 4.514 0.00001 

COST -0.004520 0.000888 -5.089 0.00000 

ED4_F 0.000094 0.000117 0.809 0.41832 

ED4_U 0.000080 0.000024 3.328 0.00087 

ED4_W 0.000161 0.000066 2.446 0.01443 

ED4_S 0.000105 0.000079 1.329 0.18396 

ED4_E 0.000133 0.000099 1.349 0.17727 

ED4_C -0.001748 0.001016 -1.721 0.08529 

ED567_F 0.000015 0.000099 0.153 0.87829 

ED567_U 0.000066 0.000021 3.156 0.0016 

ED567_W 0.000153 0.000056 2.731 0.00632 

ED567_S 0.000011 0.000066 0.165 0.86913 

ED567_E 0.000300 0.000083 3.589 0.00033 

ED567_C 0.000253 0.000842 0.3 0.7641 

INC34_F 0.000003 0.000112 0.031 0.97543 

INC34_U 0.000023 0.000024 0.973 0.33059 

INC34_W 0.000070 0.000068 1.026 0.3049 

INC34_S -0.000051 0.000076 -0.674 0.50008 

INC34_E -0.000199 0.000097 -2.05 0.04032 

INC34_C -0.000031 0.000950 -0.032 0.97433 

INC567_F -0.000004 0.000108 -0.039 0.96853 

INC567_U -0.000017 0.000023 -0.773 0.43955 

INC567_W -0.000045 0.000064 -0.703 0.48198 

INC567_S -0.000060 0.000072 -0.824 0.41003 

INC567_E -0.000212 0.000095 -2.245 0.02477 

INC567_C 0.000607 0.000911 0.666 0.50559 

INC8_F 0.000062 0.000187 0.334 0.73836 

INC8_U -0.000052 0.000034 -1.551 0.12093 

INC8_W -0.000029 0.000110 -0.262 0.79296 

INC8_S -0.000105 0.000133 -0.792 0.42861 

INC8_E -0.000284 0.000163 -1.746 0.08077 

INC8_C 0.002901 0.001490 1.947 0.0515 

Log-Likelihood

Restricted Log-Likelihood

Chi-Squared (Sig. Level)

D2

 -2551.71 

 -3003.61 

903.80 (.00000) 

.151 
266

Full Model Before Weighting, Seasonal Resident Sample

Variable Coefficient Std. Error T Statistic P-Value 

FARM 0.000488 0.000433 1.126 0.26023 

UNDEV 0.000093 0.000096 0.971 0.33152 

WET 0.000386 0.000256 1.505 0.13221 

SHELL -0.000053 0.000253 -0.208 0.83518 

EEL 0.000117 0.000358 0.326 0.74439 

COST -0.002058 0.003704 -0.556 0.57848 

ED4_F -0.000033 0.000466 -0.072 0.94275 

ED4_U -0.000022 0.000091 -0.242 0.80859 

ED4_W -0.000128 0.000282 -0.455 0.64934 

ED4_S 0.000337 0.000238 1.414 0.15736 

ED4_E -0.000137 0.000336 -0.408 0.68337 

ED4_C -0.001261 0.003659 -0.345 0.73028 

ED567_F -0.000581 0.000430 -1.353 0.17609 

ED567_U -0.000022 0.000084 -0.261 0.79391 

ED567_W -0.000401 0.000252 -1.593 0.11127 

ED567_S 0.000005 0.000200 0.024 0.98049 

ED567_E -0.000230 0.000275 -0.838 0.40197 

ED567_C 0.004640 0.003211 1.445 0.14844 

INC34_F 0.000365 0.000377 0.967 0.33361 

INC34_U -0.000013 0.000072 -0.177 0.85925 

INC34_W 0.000169 0.000172 0.988 0.32329 

INC34_S 0.000108 0.000233 0.463 0.6436 

INC34_E 0.000601 0.000328 1.831 0.0671 

INC34_C -0.005320 0.003139 -1.695 0.09013 

INC567_F 0.000627 0.000352 1.78 0.075 

INC567_U 0.000041 0.000064 0.64 0.52249 

INC567_W 0.000318 0.000152 2.089 0.03675 

INC567_S 0.000238 0.000214 1.111 0.26643 

INC567_E 0.000577 0.000310 1.863 0.06246 

INC567_C -0.005586 0.002885 -1.936 0.05284 

INC8_F 0.000813 0.000364 2.235 0.02541 

INC8_U 0.000117 0.000067 1.749 0.0803 

INC8_W 0.000424 0.000160 2.645 0.00816 

INC8_S 0.000287 0.000221 1.298 0.19428 

INC8_E 0.000650 0.000319 2.036 0.04178 

INC8_C -0.005538 0.002965 -1.868 0.06179 

Log-Likelihood

Restricted Log-Likelihood

Chi-Squared (Sig. Level)

D2

 -957.56 

 -1191.99 

468.87 (.00000) 

.197 

 
265

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