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

1996SHORT TITLE:

VALUES AND PRIORITIES FOR NATURAL RESOURCESDOCTOR OF PHILOSOPHY DISSERTATION

OF MARISA J. MAZZOTTA

APPROVED:

Dissertation Committee Major Professor

DEAN OF THE GRADUATE SCHOOL

UNIVERSITY OF RHODE ISLAND

1996ABSTRACT

This study focuses on measurement of public values and priorities for protecting and enhancing natural resources of the Peconic Es tuary system; and explores issues related to economic valuation methods, including theo retical validity and a comparison of model specifications.

A contingent choice survey was administered to 968 residents of the area surrounding the Estuary. Respondents were pres ented with sets of hypothetical alternatives, described in

terms of their effects on natural resources an d the cost to each household, and asked to select their preferred alternative. The base line was represented by a “no new action” alternative, which was compared to two differe nt programs to protect or enhance natural

resources. Five natural resources were incl uded—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 specificati ons. Estimated priorities for protection orenhancement of resources are: farmland, eelgra ss, 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 ac re 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 va lidity 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 ec onomic rationality, responses provide robust estimates of people’s priorities and relative va lues 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 memb ers 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 lik e 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 Natu ral 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-mind edness, 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 an d 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 Mi nei and Walt Dawidiack of the Suffolk County Health Department, Kevin McDonald and Gayl e Marriner-Smith of the Citizen’s AdvisoryCouncil for the Peconic Estuary Program, an d Cynthia Decker of the New York State Department of Environmental Conservation were a ll especially helpful, and provided a great deal of useful information. The survey coul d 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, Quen tin 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 su ccesses 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.v i 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 ................ 23v ii 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 ............................................................ 86v iii 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......................................... 148ix 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............................................... 204x 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 ..................................................................................................................... 265xi 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 Demogra phic Variables........................................... 162

Table 6-9 - Likelihood Ratio Tests for Educati on and Income Categories......................... 163 Table 6-10 - Likelihood Ratio Tests for Respondent Characteristics................................... 164 Table 6-11 - Statistically Significant Explanator y 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........................................................... 174xii 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 ............................................................................................ 19 8 Table 6-30 - Wald Tests for Adding Up ................................................................................... 201xiii 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 ............................................................................................... 1 40 Figure 5-4 - Survey Question #2 ............................................................................................... 1 41 Figure 5-5 - Survey Question #3 ............................................................................................... 1 42 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 ............................................................................. 145xi v1 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 la ws 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 balan ce competing goals. Economists can contribute important information to environmental poli cy analysis, including estimates of economic benefits and costs associated with a policy, and information about public preferences and priorities.

The National Estuary Program was establishe d 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 be nefits to people. This study is part of a comprehensive economic value assessment unde rtaken for the Peconic Estuary Program, one of 21 such programs funded under the Nati onal 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 th e 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, shellfishi ng areas, and eelgrass—and estimation of economic benefits that would result from pro tecting or enhancing these resources. This information will assist policy makers in prio ritizing 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 re spondents’ motivations, and the importance of these motivations for survey development and an alysis; 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 Sta tes lives in coastal ar eas, and on average, populations of coastal counties in the United St ates are growing three times faster than any other areas (U.S. Environmental Protection Ag ency 1993). The marine and estuarine3 resources of coastal areas contribute greatly to the quality of life, an d help to define the community. In addition to providing aestheti c, recreational, and economic benefits to people, estuaries are important ecosystems. They provide spawning areas and nurseries for fish, birds and other wildlife; ma rshes 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 th e 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 reside nts of New York City and western Long Island, and has a large seasonal population. Its econom y is largely resource-based, and the most important economic sectors are agriculture, fish ing, wine making, recreation, and services related to second homes (East End Economic and Environmental Task Force 1994).

Although environmental quality in the Estuary ha s 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 an d 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 Es tuary’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 proce ss. Information on public preferences and economic values from this study will be comb ined with other components of the economic assessment, and with the science and technical in formation, 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 inform ed decisions about implementation. However,

economic valuation methods are often cont roversial 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 wh ich 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 va lue, defined as the amount of money that

would make an individual indifferent between c onditions before and after the environmental change occurs. When this is true, economic va lues cannot be inferred directly from choices.5 The possibility of “ill-defined” preferences th at 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 preferen ces have been hypothesized—the existence of moral commitments related to the choice, an d 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 Opal uch 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 con cerns, 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 literatur e as the “embedding” problem and the “adding up” problem. These are related to the theoreti cal validity of survey results, and occur when dollar values from surveys are not related to qua ntities 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 g ood depends on whether it is valued alone or as part of a more inclusive category (Ka hneman and Knetsch 1992a). The “adding up” property, refers to the fact that the value elici ted 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 re sult from the fact that individuals, when6 responding to survey valuation questions, may focus on means rather than ends, and thus express approval or disapproval of the acti on resulting in the specified environmental changes rather than the value of the envi ronmental changes themselves (Hausman 1993; Etzioni 1988). This study addresses these i ssues 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 Pec onic Estuary. The survey was designed to complement scientific and technical studies, usi ng 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 informati on from the science and technical studies and concerns of the public, five natural reso urces were selected for inclusion in the survey— farmland, undeveloped land, wetlands, shellfishing areas, and eelgrass.

The contingent choice, rather than contingent va luation, 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 choos e among or rank the alternatives. Given a7 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 ge neric actions, or ends, so that respondents are not given the opportunity to focus on speci fic 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 good s (National Oceanic and Atmospheric Administration 1993). For a policy analysis th at 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, rath er 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 Peconi c 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 comp arisons of different model specifications.8 Finally, a welfare analysis was carried out, to estimate the economic bene fits of protecting or enhancing natural resources.

Dissertation Outline

This dissertation is organized as follows. Cha pter 2 provides background information on the Peconic Estuary and the Peconic Estuary Prog ram. Chapter 3 provides an overview of philosophies of the natural environment an d 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 descri bes the theoretical framework for models used in the analysis. Chapter 5 discusses the proce ss 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 informat ion 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 disc ussion 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 P econic 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 Cou nty 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 BayBrookhaven

Study Area Town Boundary Legend :

(map not drawn to scale)11 The five towns surrounding the Estuary–Rive rhead, 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 O cean, 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 popula tion. 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 wher e fresh water from rivers and streams mixes with salt water from the ocean. In addition to providing aest hetic, recreational, and economic benefits to people, estuaries are important ecosystems. They provide spawning areas and nurseries for fish, birds and other wildlife; ma rshes 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 Natura l Heritage Program, and over forty areas designated as significant coastal fish and wild life habitats under the New York State Coastal Management Program. The Peconic Estuary ha s 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 importa nce 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 chara cter, combined with its proximity to a large population center and the growing trend of teleco mmuting, 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, wi th around 126,000 seasonal residents (Long Island Lighting Company 1995; Suffolk County De partment of Planning 1991). On average, populations of coastal counties in the United St ates are growing three times faster than any other areas (U.S. Environmental Protection Agency 1993).

Although environmental quality in the Estuary ha s historically been good to excellent, rapid development and associated environmental probl ems 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 experi ences low levels of dissolved oxygen. The small, enclosed bays are adversely affected by ba cteria from failed or outdated septic systems, road runoff, and discharges from boats. Fa rmland and other open space surrounding the Estuary is faced with development pressures (S uffolk 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 1985-198 8, 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 includ ed in the National Estuary Program.13 The National Estuary Program a nd 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 ma nage nationally significant estuaries that are threatened by pollution, development, or ov eruse, and it is administered by the U.S. Environmental Protection Agency. Estuaries ar e 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 stake holders associated with the estuary are asked to identify and propose solutions to the es tuary’s major environmental problems, by developing a Comprehensive Conservation an d Management Plan (U.S. Environmental Protection Agency 1992). As of 1995, 21 es tuaries were included in the Program.

The Peconic Estuary was added to the Nationa l Estuary Program priority list in October 1988, and was accepted into the program in Se ptember 1992. Its Management Conference is made up of a Policy Committee, Management Committee, Technical Advisory Committee, Citizens Advisory Committee, and Local Go vernment Committee. These committees must work together to develop and implement th e 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, habita t and terrestrial ecosystems while supporting human activities in the Peconic Estuary study area. b. To ensure an effective technical, re gulatory and administrative framework for the continued monitoring and ma nagement 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 c onservation and management of resources in the estuary, optimizing recreation opportuni ties, and promoting social and economic benefits stemming from the estuary (P econic 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 im portant to establish priorities for potential actions. It is not only necessary to scient ifically 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 prelimin ary recommendations for specific actions to protect and restore natural resources of the estuary had been completed (Suffolk County Department of Health Services 1994). This w ill be updated and finalized in the Management Plan. The most important environmental probl ems of the Estuary and potential actions are described briefly below.

Brown Tide

The most severe problem for the Peconic Estuar y to date is brown tide, an algal bloom of the species Aureococcus anophagefferens that appeared in the P econic Estuary system for extended periods in 1985-1988, and shorter pe riods 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 (Suffo lk County Department of Health Services 1992, 1994).16 Restoration and monitoring of resources adve rsely 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 eutrophicati on of the water. Eutrophication occurs when sunlight entering the water is blocked by vegeta tion, 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 indi rectly affects fish, shellfish and other wildlife that use the eelgrass for food and habitat.

Nitrogen levels in the Peconic River an d 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 ni trogen levels is genera lly 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; gr oundwater underflow from septic systems and fertilizers (27%); Meetinghouse Creek, which wa s formerly the site of many duck farms (17%); atmospheric deposition from fossil fu el combustion (7%); the Riverhead Sewage17 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 nitrog en levels. Improvements in the Riverhead sewage treatment plant (STP) could reduce nitrog en 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 di scharge to central or eastern Flanders Bay, or decreasing the nitrogen levels at th e 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 an d groundwater sources. Some proposals to limit development include zoning for larger lo t sizes, cluster development, transfer of development rights programs, and land pres ervation, acquisition, or enhancement. Controlling commercial, industrial and instituti onal uses of land has also been proposed. Finally, fertilizer use could be modified, by en couraging or requiring low maintenance lawns, use of slow-release nitrogen fertilizers, decrease d use of fertilizer, or changing the timing of fertilizer application. Decreases in point sour ces 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 anim als, 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 co liforms are high enough to close swimming beaches. In 1975 around 1,100 acres of th e 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 colif orm, although both of these sources have decreased recently. Only one duck farm rema ins 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, localiz ed 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 si gnificantly increase stormwater runoff be prohibited; and that vegetative buffers and se diment and erosion control plans be required for new development (Suffolk County Department of Health Services 1994).19 The Riverhead STP made improvements to co liform discharges in 1991, but could be further improved by converting to groundwater discharge. Stringen t review of new marina construction is recommended, as well as encour aging 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. Constru ction of new pump-out facilities would allow

for the establishment of additional no di scharge zones for boats (Suffolk County Department of Health Services 1994).

Other Pollutants

Other pollutants have not been a major problem for the Peconic Estuar y. 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; an d 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 to xic chemicals (Suffolk County Department of Health Services 1994).20 CHAPTER 3

LITERATURE REVIEW

Introduction

Until recently, nature has been viewed by Wes tern society as a virtually unlimited resource to be used by humans not only to support life, bu t to fuel technological progress, leading to increased standards of living and consumpt ion 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 po llution 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 envir onmental 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 environmenta l 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 of21 ecosystems. There has also been a growing r ecognition of the limitations of technology in solving environmental problems. Nevertheless, people also desire the amenit ies 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 conf licting 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 en vironment, 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 multitud es that must be fed, clothed, and sheltered—and a commitment to ma intenance of the present liberaldemocratic value system, then nature m ust be managed: there is no apparent alternative. By all indications the quest ion 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 literat ure related to environmental policy analysis, focusing on economic theory and methods. The chapter begins with a discussion of philosophies about nature, how they have chan ged 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 fo r 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 re lation to nature. These views, along with our understanding of the effects of human actions, ha ve 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 secti on 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 na ture in Western culture have been dominated by the philosophies of anthropocentrism, or th e 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 inanim ate, governed by natural laws of cause and

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

scientific and industrial revolutions of the se venteenth and eighteenth centuries, and underlie the belief that nature can be mastered through science and technology.

The theories and methods of neoclassical economic s, and its primary goal of efficiency, are grounded in the philosophical viewpoints of th e scientific and technological revolutions. Thus, natural resources have been viewed in ec onomic theory primarily as inputs and waste repositories to be used by humans; and environmental economics has focused on the23 efficient allocation of natural resources and en vironmental amenities. However, as Herman Daly points out, when economics was first deve loped “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 fo cus 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 envi ronment 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 th e management of natural resources in the United States—conservationism and preservationi sm—are both anthropocentrically focused. Conservationism was formalized by Gifford Pinc hot and Theodore Roosevelt at around the turn of the century. Conservationists view huma ns 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 prim ary 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 fini te, 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 primarily24 focused on transferring public land holdings to private ownership in order to assist

landowners and businesses. The conserva tion 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 Mu ir, began at around the same time as conservationism, but focused on nonmaterial benefits of nature—what economists now refer to as nonconsumptive use values an d nonuse values. Preservationists were

instrumental in the creation of the National Pa rk Service, which focused on preservation of natural areas for nonconsumptive uses, rather th an for consumptive uses of resources, as in

the national forests. The first environmenta l 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 influe ntial in the policy realm since the conflicts between John Muir and Gifford Pinchot at the tu rn of the century. An important difference between the preservationist and conservationist philosophies is that, while conservationism is reductionist, focused on individual component s 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 de rived from human interests, so that economic efficiency remains an important goal (Oelschlaeger 1991).25 Nonanthropocentric Philosophies

Although anthropocentric concerns have domi nated 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 fo rms 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 acqui re those rights which never could have been withholden from them but by the hand of tyr anny” (quoted in Nash 1989, p. 23). However, although Bentham believed that utility of all cr eatures 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 preservati onist ideas—including Henry David Thoreau, John Muir, and Aldo Leopold—personally held nonanthropocentric values for nature. Thoreau considered all of nature as a sing le 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 figu res in the evolution of thinking about the ethics of human relationships with the envir onment. Leopold proposed the establishment of a land ethic, and the extension of ethics to th e environment so that the environment would26 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 eco logical 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 littl e 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 thei r cause. Booth (1994b) believes that this is because such arguments were not effective, and th us 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” (Le opold 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 nonanthropo centric philosophies have been defined, including biocentrism, ecocentrism, deep ecology, social ecology, and ecofeminism (Oelschlaeger 1991; Zimmerman 1993; Ma zzotta 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 th eories, ecocentrism originated more recently, with Aldo Leopold’s land ethic. Biocentrists de fine value in relation to life, so that each27 living organism is seen as important. Humans are thought of as members of the Earth’s community of life, and natural ecosystems ar e viewed as a complex web of interconnected elements, with the sound biological functi oning 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 th e basis of all organic existence, and therefore possess intrinsic value. Ethical human acti ons necessarily promote all life on earth by preserving intrinsic values such as diversity, st ability, and beauty. Ecocentrists define value in relation to natural systems, thus including i norganic components and setting life in a larger framework. To an ecocentrist each ecosystem, including both its living and nonliving components, is important. Therefore, ecocentr ism may be defined as a broader philosophy that includes biocentrism, based on the fact th at 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 th e 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 policie s related to basic economic, ideological, and technological structures must be changed. Fina lly, they believe that those who subscribe to28 the beliefs of deep ecology are obligated to try to implement these social changes (Naess 1993a, b).

Social ecologists believe that ecological problem s stem from underlying social problems, thus necessitating major social changes. Social eco logists 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, develope d from the joining of the post-World War II

feminist and environmental movements. Al though they hold many different viewpoints, ecofeminists generally believe that “there are important connections between the domination of women and the domination of nature” (Wa rren 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 issue s. He found an increase in testimony relating to nonanthropocentric values in the 19 70s 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 be fore the Endangered Species Committee in 199229 concerning preservation of spotted owl habitat, Booth found that 31.5 percent of advocates for preservation made nonanthropocentric argume nts. Thus, there is evidence that such concerns are important to people, and are b ecoming increasingly acceptable in terms of policy justification.

Americans’ Current Views

An anthropological study of Americans’ envi ronmental values by Willett Kempton, James Boster, and Jennifer Hartley (1995) provides further evidence that people’s views are changing. After reviewing existing studies, th ey conclude that there is “little doubt that a substantial change is taking place in the way Am ericans conceive of the environment. . . . the

fact that the indicators we reviewed include vo ting records and market purchases means that the proenvironment sentiments carry some fo rce—they are not just empty platitudes. Further, they are widespread across strata of so ciety” (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 envir onmental values: religion, anthropocentric values, and nonanthropocentric (which they call bi ocentric) values. In expressing religious values, many respondents described nature in religious terms, invoked religious teachings, or expressed a spiritual connection to nature. An thropocentric values included both concern for self and concern for future generations, and were expressed in terms of both material30 utility and aesthetic utility to humans. Concern for the future of children and descendants was the strongest of the anthropocentric valu es 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 envir onmental 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. Although31 nonanthropocentric views have been expressed to some extent over the last three hundred years, they have not been the basis for policy d ecisions 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 th e realization of the lim its of both nature and technology, the movement towards holism in scie nce, and the emergence of the science of ecology.

Thus, many programs and laws are also expre ssing more ecocentric goals, most notably the Endangered Species Act, which protects enda ngered species regardless of most humancentered considerations. Both the Com prehensive Environmental Response and Compensation Act (CERCLA) and the Oil Pollu tion Act (OPA) focus on restoring natural resources, rather than paying monetary compen sation to people, as the primary remedy for damages resulting from spills of oil or ha zardous substances. However, restoration may focus on making the environment whole based on ecocentric considerations, or on making the public whole based on anthropocentric cons iderations. 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 balanc ing of human and environmental concerns. While economic efficiency remains a priority, an d in fact many laws require benefit-cost analysis, other considerations are also importa nt, and we may be moving in the direction of Leopold’s land ethic. Leopold suggested that we “[e]xamine each question in terms of what32 is ethically and esthetically right, as well as wh at is economically expedient. A thing is right when it tends to preserve the integrity, stabili ty, and beauty of the biot ic community. It is wrong when it tends otherwise” (1966, p. 26 2). Consequently, multidisciplinary approaches to policy analysis are becoming increasingly important.

For example, as discussed in Chapter 2, go als of the Peconic Estuary Program focus on “ensur[ing] the integrity of marine resources , habitat and terrestrial ecosystems while supporting human activities” (P econic Estuary Management Conference 1993, p.1). In order to meet these goals, the Program includes na tural 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 with in a larger biophysical system, the latter focuses on the economy within a larger social sy stem. This will be discussed in greater detail

below. The next sections describe standard ne oclassical 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 en vironmental 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 thin gs; 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 servi ces to the human economy: material inputs, lifesupport services, amenity services, and was te dispersion, transformation, and storage

services.

Management of natural assets, or environmen tal 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, alterin g, 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 onl y ones who can consciously act to preserve, protect and manage natural assets.

Like a portfolio of financial assets, natural a ssets may be managed in a variety of ways, to fulfill a broad spectrum of possible goals. These might include, for example, human34 enjoyment or financial gain, fairness, biodiversi ty, 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, partic ular ecosystems, or even inanimate features of the environment. Thus, management go als may be anthropocentrically focused or nonanthropocentrically focused. Simply because th e 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 stea dy flow of income, or social responsibility. Consequently, the important questions for manageme nt of natural assets are, first, defining management goals; and then defining, selecti ng 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 differe nt philosophies. “Whatever an individual’s idea of wilderness, mere stockpile of natu ral 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 pra ctice” (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 cri teria 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 wh ere a particular cultural tradition is affected, such as subsistence whaling, cultural standard s and values might take precedence over other concerns. Although historically the primary go al of most neoclassical economic analysis of

natural resource issues has been economic effi ciency, other goals are beginning to be recognized and included in economic analysis, pa rticularly 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 dist inguishes it from other social sciences, is its conceptual framework, the neoclassical para digm. 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 ta stes or preferences and the relevant opportunities or constraints. Constraints in clude incomes and the prices of goods; institutional constraints, such as laws and pr operty rights; and production constraints, such36 as available technology. While constraints ma y be more or less observable and measurable, tastes and preferences are not as easily obs erved or measured. Thus, economists form assumptions or hypotheses about preferences in order to explain and predict behavior (Silberberg 1990).

Neoclassical duality theory provides the basi c framework for explai ning and predicting consumer choices and for deriving welfare mea sures. 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) prefer ence 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 pers on is indifferent between bundles. Transitivity requires that, if bundle A is preferred to bund le 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, an d 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, individual s 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 satisf action of preferences, and utility is assumed to37 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 commo dities; 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 margin al rate of substitution. Nonsatiation says that more of a good is always preferred to le ss. Substitution says that individuals are willing to make tradeoffs between goods, and that th e maximum amount of one good that will be

traded for another is the amount that will mainta in the individual’s existing level of utility. The diminishing marginal rate of substitution a ssumption 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 th e Marshallian, or money income held constant, demand functions,

x i = x i M ( P , Y).38 Substituting these functions back into the ut ility function gives the indirect utility function,

U * (x 1 M ( P , Y), ..., x n M ( 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 mi nimizing expenditures subject to maintaining a fixed level of utility,

minimize E = P'X subject to U 0 = U( X

), where E is total expenditures and U 0 is the initial level of utili ty. The solution to this gives the Hicksian, or real income held constant, demand functions,

x i = x i U ( P , U 0 ). Substituting these into the expenditure function results in the indirect expenditure function,

E * ( P , U 0 ) = P' X U , 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 dema nd 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 resp ect 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 consisten t with utility theory. Thus, it links utility39 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. Th us, 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 p 0 and income Y 0 , where p 0 x 0 =Y 0 , 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 cons istent with utility theory, are implied by the weak axiom of revealed preferences. Firs t, the Marshallian demand relations are homogeneous of degree zero in all prices and inco me. Thus, if all prices and income change by a constant factor, the level of demand w ill 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 th e Hicksian cross-price effects, which is necessary for a utility

function to exist. This is because the weak ax iom allows intransitivity of preferences, since only pairs of bundles are compar ed. The strong axiom of revealed preference was developed40 to force transitivity of preferen ces, 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 x k , then x k is not revealed preferred to x 1 (Varian 1984).

Choices that conform to the axioms of reveal ed 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” th ey do, allows economists to use market data to infer preference orderings and values for mark et goods (Silberberg 1990). However, in the case of nonmarketed goods, pri ces 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 maxi mum well-being for the individuals in society” (Just, Heuth and Schmitz 1982, p. 3). Theo retically, the well-being of individuals is represented by their level of utility. However, ut ility 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 the41 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 Schm itz 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—com pensating and equivalent variation, and compensating and equivalent surplus—that do pr ovide 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 individu al 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 ch ange. 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 minimu m 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( P 1 , U 0 ) - E( P 0 , U 0 ),42 where U( P 1 , Y 0 + CV) / U( P 0 , Y 0 ) / U 0 and CV is the compensating variation, E( @ ) is the expenditure function, P 1 is the vector of prices after the change, P 0 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 th at must be paid to a person when a change does not occur, so that the person achieves th e 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( P 0 , U 1 ) - E( P 0 , U 0 ), where U( P 1 , Y 0 ) / U( P 0 , Y 0 + EV), Hence, compensating and equivalent variations are income adjustments that maintain a given level of welfare, and compensating variation maintains initial welfa re while equivalent variation maintains subsequent welfare.

Compensating and equivalent surplus are simila rly defined, with the additional restriction that consumption of the good in question must re main at a fixed level, as defined above. In terms of expenditure functions, these are represented as: CS = E( P 1

, X 1 , U 0 ) - E( P 0

, X 1

, U 0 ),

and ES = E( P 1

, X 0 , U') - E( P 0

, X 0

, U 1 ).

However, Hicksian measures of compensati on 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. R obert Willig (1976) showed that, under certain conditions, consumer’s surplus can provide a clos e 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 im possibility 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 ar e 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, criter ion, 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 b etter 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 th ere 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 sa ys that a state is preferred if gainers can potentially compensate losers, so that every one 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 dua lity theory and its underlying assumptions, economists generally define value as “the amount of money the in dividual 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 assum ed 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 simp ly 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 an d summing values for diverse goods. In general, the economic definition of value is wh at 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 resour ce management problems, in order to help policy makers determine the best use of natu ral 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 posit ive and negative impacts of a project are45 compared, generally in dollar terms. In so me cases, cost-effectiveness analysis, which determines the least costly way to achieve a go al, may be more appropriate or more easily carried out. However, as Freeman points ou t, “a welfare measure should answer the questions posed by policymakers,” and other go als, such as equity, intergenerational effects, sustainability, and social risk aversion might al so be important (Freeman 1993, p. 40).

Because most of the services of natural re sources and the environment are not marketed, their economic values are not straightforwar d 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 valu e that people might derive from natural

resources or environmental amenities. Th e 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 bird watching, hiking and other such activities. Robert Mitchell and Richard Carson (1989) ca tegorize 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 th e 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 intr oduced to economics by John Krutilla (1967), who suggested that people derive benefits fr om preserving scenic wonders or important ecosystems, for at least two reasons: preservi ng the option for future use, and bequeathing the resources to future generations. Since Kr utilla’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 defini tions (see, for example, Boyle and Bishop 1987; Fisher and Raucher 1984; Freeman 19 93; 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 ab le to use a resource. “Stewardship values involve a desire to see public resources use d 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 base d on the utility received from knowing that the resource is preserved “regardle ss of whether it will ever be used by anyone” (p. 65). The

values that Mitchell and Carson refer to as inhe rent 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 clea rest 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 argu ment in the utility function. He defines a vector of market goods, X , and a nonmarket resource, q, wh ere q is defined as a scalar

measure of some characteristi c 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 expendit ure 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, x 1 . 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 an d 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 conven ient 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 valu e, pure nonuse value, user’s nonuse value, and pure existence value. Use valu e occurs when the price of x 1 , p 1 , is less than the choke price p*, and q is greater than some threshold value, q min , where x 1 is the market good and q is the

nonmarket resource. This threshold is defi ned 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 complement arity hold, then use value would be measured by the change in expenditures that would compensate for an increase in the price of x 1 to the choke price.

Pure nonuse value occurs when the price of x 1 , p 1 , 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 wh en 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 di scontinuous in q at the threshold level, and that the derivative of the expenditure fun ction with respect to q becomes zero below the

threshold.49 Nonmarket Valuation

Methods

Because most natural resources and environm ental amenities are not traded in markets, economists have devised a variety of methods fo r measuring their value, several of which rely on survey data. These methods include di rect or indirect; and obs erved or hypothetical methods (Mitchell and Carson 1989). Dir ect 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, wher e 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 mark ets; for example, how far they travel to a recreational site and how much money they spen d 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 continge nt behavior and contingent choice, involve questioning people about their intended actions based on a specific scenario. For example, using the contingent behavior method, a pers on might be asked how many more times they50 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 anal ytical 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 contin gent 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 consis tent with rationality as defined by economists; that respondents51 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 controve rsy, the National Oceanic and Atmospheric Administration (NOAA) convened a “blue-ribbon” panel, including two Nobel Laureates, to consider whether contingent valuation ca n reliably estimate nonuse values, and to

recommend standards for its use. The Pa nel 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 valu e than they would if they believed the effects described in the scenario. Other respondents mi ght 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 could52 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. Fo r 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 rela ted problem is violation of the adding up property, which states that the value elicited fo r 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 res ponses are not reliable estimates of willingness to pay for the specific resource, but might indi cate 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 ca used by the “warm glow” motivation, “willingness to pay might well exceed compensation required b ecause 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 that53 the survey contain enough contex t to achieve a realistic background, that the survey design should explicitly address the “warm glow” pr oblem, 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 othe r 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 bu dget constraint emphasized; that the payment scenario be realistic, preferably using a refere ndum format; and that all choices in designing the survey be made in the conser vative 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 valuati on question; inadequate responsiveness of values to the scope of the change; lack of unde rstanding 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 va lue 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 use d 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 c onclude that “hypothetical markets tend to overstate willingness to pay. . . .” However, “[n]o automatic or mechanical calibration of54 responses seems to be possible” (National Oceanic and Atmospheric Administration 1993, p. 4610).

Motivations for Choice

In the case of natural resource valuation, partic ularly 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 ex ample, 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, re presented 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 di fferent countries or ethnic groups, is a discipline outside of economics. These are certainly interesting questions. They are also exceedingly diffi cult 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 partic ular, 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, th e “warm glow” motivation can result in dollar values that remain constant over a wide rang e of resource quantities. Additionally, choices based on moral commitments may lead to non-scal ar utility, where a deci sion 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 le ad 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 preferen ce theory, welfare measures derived in the usual way from these choices are not meaningful.

Consequently, it is important to consider mo tivations, and how they affect value estimates. While a variety of motivations other than pers onal 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 gene rally are based on the assumption that value derives from the goods and services that are c onsumed [i.e., the ends], not from the process by which these goods are allocated [i.e., th e means]” (National Oceanic and Atmospheric Administration 1993, p. 4613). However, eth ical motivations for choice may often make means important, as well as affecting pref erences 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 mo stly 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, indi viduals’ concerns about policies might include not only the policy goal, but also how it is im plemented. For example, a large number of people might value the improved air quality resul ting 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, determi ning what is acceptable to people, and why, can be an important step in policy implementa tion, especially when controversial policies, like siting a hazardous waste facility, are being considered.

The distinction between means and ends is also im portant to valuation, because it is crucial to the definition of exactly what is being valu ed, 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 “tra nsaction value;” and that related to ends, or57 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 valu e,” or may elicit some combination of the two types of value. The CV Panel states that [w]e do not question the validity of ‘tra nsaction 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 charit able activities that can generate the same ‘warm glow’ and there are many ot her 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 “rea l 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 measur e of damages. However, disregarding the validity of “transaction value” for damage assessment, its measurement in terms of neoclassical welfare measures is likely to be pr oblematic, 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 assumed58 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 prefer ences simply by observing choices, which is the economist’s preferred means of analysis. Th erefore, the revealed preference approach, based on observed choices, disregards th e non-choice sources of information on preferences, and also fails to recognize th at choice “may reflect a compromise among a variety of considerations of which persona l 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. Commitmen t refers to cases where a person chooses to act in a certain way even if it results in a d ecrease 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 ot her 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 ha ve some interest in the natural objects.

Commitment can also occur in cases where th e choice maximizes personal welfare, but the maximization of personal welfare is not the reason for the choice.

For example, suppose loggers in the old gro wth forest are given the opportunity to make choices about preserving or logging the fore st. Besides feeling concern for their own59 welfare, the loggers might feel sympathy for ot her 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 re quire 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 choi ce, and thus “drives a wedge between personal choice and personal welfare,” which are often rega rded 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 19 77, 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 ar ises this is supposed to reflect his interests, represent his welfare, summarize his idea of what should be done, and describe his actual choices and be havior. Can one preference ordering do all these things? A person thus descri bed 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-ranki ng 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 theories60 may use this structure. Use of this structu re 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 wo uld have implications for the inclusion of all empirical estimates of existence value within nor mative 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. Therefor e, efficiency is no longer the underlying criterion for choice, and measures of willingne ss to pay that include such motivations are not consistent with welfare theory.

As Sen notes, simply observing choices in suc h cases is not sufficient. Therefore, in developing and implementing a survey of na tural 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 th e 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 to61 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 mo tivations are not unique to inherent, or intrinsic, values, but could also underlie ot her types of nonuse values, which Brookshire, et al. accept as legitimate values. However, if mo ral 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 pers onal welfare as they conceive it. Far from being ‘counterpreferential,’ in properly c onducted CV studies choices based on these preferences are motivated by self-interested an d 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 cand y 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 ca ndy 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—rat her than their value for the commodity62 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 th e 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 preferen ce 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 Broml ey, 1994). Whereas the consumer decides based on personal preferences and ends, the ci tizen 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, consum er choices are those made in markets, and citizen choices are those made in the politic al arena. However, in many decisions, both consumer and citizen preferences may be considered by an individual.

Sagoff states that “[t]hrough the political proce ss, 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 on63 citizens’ desires, thus allowing people to make choices as consumers that would not have otherwise been possible. In turn, policy choi ces 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 ge nerally based on consumer preferences. In addition, many environmental laws require cons iderations of costs and benefits in their implementation, and again these measur es are based on consumer preferences.

The consumer/citizen concept is compatib le 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, wher e different contexts may invoke different choice motivations—consumer, citizen, or a co mbination of the two—and it is not clear how respondents will answer.64 Sense of Place

Another important concept related to environmen tal values is the “sense of place” that people experience because of the unique chara cteristics 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 da ys about saving the environment. We must, for the environment sustains ou r bodies. But as humans we also require support for our spirits, and this is what certain kinds of places provide. The catalyst that converts an y 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 impor tant component of the sense of place. For example, people in a shoreline community are lik ely 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 likel y 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 ther efore less concerned for, the unique characteristics of a particular place. This may result in increased environmental degradation as well as a weaker community. Both Sago ff and Kellert argue that environmental degradation often results from a lack of a fee ling of rootedness to a particular place, and a65 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 en vironmental degradation—congestion, aesthetic deterioration, habitat dest ruction and fragmentation—are also associated with increasing alienation an d separation from the coastal context. This phenomenon of human estrangement from the coastal landscape is further exacerbated by declines in re gional economies, the increase in what could be termed the ‘suburbanization and commuterization’ of the coast, even the expanding number of isola ted and restricted protected areas. Collectively, these factors all contribute to the separation and distancing of people from the coast as a secure an d meaningful place of human habitation (1995, pp. 17-18).

A sense of place can create moral and cultur al 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 an d cultural commitments provide stronger motivations for people to preserve the natural environment than economic rationales, although people may often use economic argu ments to support a view that is primarily ethical. He argues that “those who love natu re 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 subst antive terms—in terms of the quality of life and the sustainability of cultures and communiti es, for example—[rather] than in terms of66 aggregate income, social wealth, consumer surplus, or any such abstraction of economic theory” (Sagoff 1992b, p. 409).

Nonanthropocentric Motivations

As discussed above, questions about defini ng nonuse values, their measurement, and whether they should be included in environm ental 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 va lues 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 natura l 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 po ssessing 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 preferen ces. Mitchell and Carson say that “economic theory . . . reject[s] the notion that ‘trees have rights.’ The statement that is consistent with67 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 th at 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, belie ving instead that some or all elements of nature have truly intrinsic values that transcend human values.

There is an important distinction between a nona nthropocentric 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 fr om just knowing a particular species or ecosystem exists, or from the desire to pr eserve a species or ecosystem for future generations. Nevertheless, these are base d 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 ph ilosophy, the economist may determine that protecting a particular species or ecosystem is necessary even though humans do not use the associated environmental services, because th ere 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 sur prising that the economics profession has been68 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 va lues 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 th e 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 obj ect to [nature] being placed in the stream of commerce and treated strictly as an instrume nt” (Booth 1992, p. 59). At a minimum, valuation of natural resources by huma ns may be viewed by nonanthropocentric philosophies as inadequate to address the que stion 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 valuat ion 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 willingne ss 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 theo ry and welfare theory provide a systematic method of inferring economic values for goods from observable behavior. However, numerous violations of neoclassical assumpti ons and postulates have been empirically observed, and a variety of reasons for such viol ations 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 al ternative theories that might better explain

choices.70 Table 0-1 summarizes the neoclassical model and po tential 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 th at 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 va riety 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 invalid71 Such motivations as moral commitments, re gret or disappointment, valuing of gains differently from losses as outlined by pros pect 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; Kahne man and Tversky 1979; Loomes and Sugden 1982; Opaluch and Segerson 1988, 1989; Opaluc h 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 explai n the large differences often found between willingness to accept compensation and willingn ess 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 pro test 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 (Mazzo tta and Opaluch 1995). Question complexity, combined with unfamiliarity, can result in be havior that violates basic assumptions of neoclassical utility theory, for two reasons. Firs t, respondents may not fully comprehend the72 question or the information presented in a surv ey. Alternatively, when faced with tradeoffs among commodities that are viewed as in congruous, respondents may experience uncertainty with regard to their preferences. Ei ther of these problems ma y 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 moti vations are nonanthropocentric in nature, then standard economic theory cannot account fo r 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 preferen ces 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 inco me), rather than changes in preferences. Economists generally assume that preferences ar e stable, at least for the period of the analysis. However, preferences may not be stab le over time, even when the time period considered is relatively short. Instead, preferences may be dynamic, changing as new information is acquired. In addition, pref erences might be endogenous, dependent on the choice context or on past choices. This is especi ally 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 deci sion rule: maximization of utility (or expected utility) subject to a budget constraint. Howev er, 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 le ad to lexicographic choices, or they might result from cognitive limitations that make it di fficult 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 maxi mizing 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 determi ned. 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 part icular 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-be ing 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 ut ility; 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 co mmitments or by maximizing self-interested75 utility. Kopp recognizes the distinction between means and ends, saying that “[w]hile the normal arguments of utility provide satisfacti on by providing ‘ends’ (i.e., consumption), ethics leads to enhanced well-being through ‘mea ns’ (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 d ecreases 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 consis tent 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 pe rceive 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(E 1 ', E 2 , ..., E n ) > f(E 1 , E 2 , ..., E n ) if E 1 '=1 and E 1 =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 perf ect tradeoffs between them. However, this is not a universally accepted idea, and, as noted a bove, 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-be ing decreases in either case. Although Kopp’s model assumes that it is possible for the individu al 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 E i 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 pers onal utility can be offset by a gain in ethical well-being, so that it is no longer a compl etely no-win situation. In addition, Kopp points out that f( E ) can be specified in different ways, fo r example as in prospect theory, where gains are less valuable than losses. Kopp point s 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 be77 indifferent over a wide range of utility losse s. 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 de fined for ethical behavior by measuring the maximum loss in self-interested utility that the individual will accept before violating their ethical codes. Again, this onl y 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 neoc lassical 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, un der 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 deat hs. Indeed, such a demand curve is wholly inappropriate since the value of the policy re sts 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 con sumption. 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 foc us on the policy—preserving wildlife—rather than on the specific numbers of w ildlife saved. This provides information about the value of a policy, or the ethical considerations of a poli cy. However, if the objecti ve 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 questi ons. He says that different sources of motivation should not be aggregated unde r something called “preferences,” but he aggregates them under something called “well-be ing.” 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 star ting point for the consideration of ethical79 values in standard neoclassical theory, but his assumption of additive separability may be too restrictive in many cases, and he does not di rectly 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 pa radigm, society is not seen as simply the aggregation of individuals, but as having it s own structure that affects the actions of individuals. Thus, the economy cannot be stud ied without considering social, political and cultural factors. Methodologically, soci o-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 interdisc iplinary 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 an d goals primarily based on their values and emotions. Third, social collectives, rather th an individuals, are assumed to be the primary decision-making units. Such collectives include ethnic groups, peer groups, and80 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 ten sion and perpetual search for balance between two primary forces—those of individuals, an d 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, th e I&We paradigm is holistically focused, and considers both types of preferences and the interactions between them.

Etzioni critiques the neoclassical idea of a sing le 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-moti vations 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 mean s of attaining a certain consequence (e.g., buying or stealing the good). Etzioni poi nts out that “the difference between the mono-81 utilitarian paradigm and the one advanced he re comes down to alternative assumptions about human nature” (1988, p. 22).

Etzioni describes three forms of neoclassical ut ility, 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 “commitmen t,” 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 systemat ically 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 that82 accompanies discharging one’s moral commitm ents” (1988, pp. 23-24, emphasis in the original). Therefore, moral and pleasure “prefe rences” may be qualitatively different, so that they cannot be traded off or substituted fo r one another; and if people violate moral commitments to satisfy pleasure or forgo plea sures 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 conseque nces. 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 prov ide 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 subst itutes (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 cost s and benefits or other factors. In such cases,

certain means are considered to be “unthinkable ,” even if they may be acceptable in terms of83 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 prin ciples have already been violated, moral considerations may be compared with other c onsiderations when making a choice. Thus, there are absolute and expedient moralities, where absolute moralities preclude all but the “right” choice, and expedient moralities imply th at 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 stic ky 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 substit utions, complementarity, or relative prices” (Etzioni 1988, p. 71).

If certain behavior is seen as “sacred,” th en choices are limited or predetermined, and calculative approaches like cost-benefit analysis are rejected. Such c hoices would violate the continuity axiom of revealed preference theo ry. There is evidence for this in many responses to CV surveys, especially in those th at ask for willingness to accept compensation. Etzioni compares the idea of moral commitments with that of an externality, in that the84 market price of a good does not always fully reflect its moral standing. Conversely, willingness to pay responses in contingent va luation surveys may reflect moral standing in addition to or instead of the personal satisfa ction 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 assert s that “[t]he root cause of many of these is

the absence of markets for the items involved b ecause 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 commitmen ts are important, there are behavioral consequences that can be explained and predi cted by understanding the conflicts that people experience about their preferences, or how peopl e feel about their actions. In Etzioni’s theory, choices are viewed as “multi-staged, mu lti-level events, in which moral and pleasure considerations are combined in complex manne rs,” 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 cer tain tradeoffs. If this is the case, the neoclassical axioms of behavior—continuity, re flexivity, 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 over arching utility, people are assumed to “seek a

balance between their moral commitments and their plea sures (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. Th us, 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 policin g;” but also at formation of preferences through moral education, peer culture, commu nity 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 difficu lt 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 po licy 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 straight forward, 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 co mmodities other than those intended by the researcher. Therefore, the efficient solution may be difficult to determine.

Nonmarket valuation techniques are controversia l 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 mo tivations that affect choices and the way they are made, including “citizen” motivations, “c ommitment,” and a “sense of place;” and nonanthropocentric values, which are not consid ered by economic theory. As discussed above, these are likely to lead to violat ions of neoclassical axioms of behavior.

Arild Vatn and Daniel Bromley (1994) believe that valuing or pricing environmental goods and services is neither necessary nor sufficien t for coherent and consistent choices about the environment. They argue that, because of the fundamental complexities and multiple87 attributes of the environment, trying to redu ce it to monetary values results in important losses of information, and “twists” the info rmation about the individual and collective significance of environmental goods and servi ces. 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 releva nt information, then they cannot lead to efficiency. They conclude that attempting to measure economic values for environmental goods without accounting for moral commitmen ts 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. Va tn 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 con text 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 bot h consumers and citizens, and environmental choices span both domains. Preferences are th e 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 environmenta l goods need to be comprehended more holistically, in which case it does not make se nse 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 fr om keeping them working in their existing functional relation, they suggest that atten tion should be focused on ecosystems or bioregions (Vatn and Bromley 1994).

Vatn and Bromley believe that the core of th e 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 inform ed and coherent collective choices about environmental goods and services because of uncertainty about effects of actions and because values change over time. Thus cont ingent 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 involv ed, 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 c onsent to risks through consumer or citizen choice. The answer may be bot h; the finer-grained decisions may be appropriate to markets, but some of th e 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 pa rticipation among many groups of people.

Several other authors discuss the use of standard s, 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 pr iority principles,” rather than benefit-cost analysis (p. 59). Holmes Rolston (1986) sugge sts a reversibility maxim and a scarcity maxim, where human interests are explicitly made su bordinate in certain cases. The reversibility

maxim states that humans should avoid introducin g irreversible changes in natural processes. The scarcity maxim states that humans shou ld 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, Randa ll 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 iden tified. 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 determi nation of intrinsic value, if any, of things like the smallpox virus or poison ivy (Oelschl aeger 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 equi ty, 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 measuremen t 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 re levant and useful policy analysis. Although estimation of precise economic benefits and cost s may be difficult and, in certain cases, inappropriate, economists can still provide inform ation on relative values and priorities. This information can provide one means of c onsidering the public interest as part of a multidisciplinary approach to policy analysis that combines natural sciences and social sciences, and considers ecosystem integrity, pub lic priorities, and other social and cultural

concerns.92 CHAPTER 4

THEORETICAL FRAMEWORK

Introduction

The overall goal of the Peconic Estuary Progra m, which is described in Chapter 2, is to develop a Comprehensive Conservation and Manageme nt 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 he avily 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 Estu ary 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 deta il 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 associa ted 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 differe nt programs in order of importance to the public, without placing dollar values on the indi vidual natural resources. Alternatively, dollar values for resource changes may be calculated an d used in benefit-cost analysis of different

programs.

This chapter explains the conceptual and theo retical 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 fo llowed 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 a ssumed that respondents to contingent choice questions choose the option that maximizes utili ty received from attributes of the option, subject to their budget constraint. In this stud y, 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 leve ls. Solving the maximization problem results in the demand functions for the natural resources, R i ( 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 indir ect utility function, the ut ility 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, M i is the individual’s household income, and C j is the annual cost to each household for program j.95 Because of errors in measurement by the re searcher, 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 measur able, 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 resul t 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 pr ogram that provides the vector of natural

resources R j , 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 ε - for all k j. j (4)96 The level of utility obtained by the ith individu al from the jth choice can be represented by

U ij *, so that

U V MC ij ij i j j * (, ) .

= −+ R ε (5)

However U ij * is not directly observed. Instead we observe the option chosen by the respondent. If there are thr ee options, as in this study, the observed choice variables, Y ij , 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 max imizes 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 usually97 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, V ij , 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, z j = ( R j , C j ). This is generally referred to as the conditional logit model, an d is distinguished from the multinomial logit model by the fact that characteristics of the c hoices are considered as determinants of choice probabilities, while the multinomial logit mo del considers only characteristics of the individuals (Greene 1993; Maddala 1983).98 If utility depends on both characteristics of the in dividual and attributes of the choice, then

the model becomes

P ij

k e

e ij i

ik i = ′ + ′

′ + ′ 

## ∑ β α

β α z w

z w . (11)

where z ij are attributes of the choice (e.g., the levels of natural resources and the cost), which may also vary across individuals, and w i are characteristics of th e individual, which are the same for all options faced by an individual. Ho wever, in this model, terms that do not vary across alternatives fall out of the model, so th e coefficients on the individual characteristics

cannot be estimated.

One way to solve this is to create a choice-sp ecific dummy variable for each of the options, and multiply by each characteristic of the indi vidual. Thus, if there are three options, each individual characteristic, such as income, woul d appear as three separate variables that are the product of the dummy variable for the opti on and the individual’s income. The resulting variables explain how the individual’s characterist ics 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 a ttributes 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—t he levels of natural resources—rather than99 choosing a specific alternative as in the travel demand example. Therefore, interaction terms were created for each attribute, or resource level, multiplied by ca tegorical variables for individual characteristics. Thus, the model becomes

P

ij 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

ln L YP

Y i j ij ij

i j ij ij i k ik =

= ′

− ′



## ∑ ∑ ∑∑ ∑ ∑ ln ( ) ln( exp( ) γ γ X X (13)

where Y ij is defined by Equation (6), P ij is defined by Equation (12), X includes both z ij and

z j w i , and ( includes both $ and " . The first-order condition for a maximum is

∂

∂γ

ln ( ). L Y P j i ij ij ij = −= 

## ∑ X 0 (14)

The matrix of second-order conditions has J 2 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 P

ij ik i i i X X ′ 

## ∑ (16)

The estimated coefficients are interpreted as th e 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-Aki va and Lerman 1989). This is termed the101 independence of irrelevant alternatives prope rty, or IIA, and follows from the assumption that the disturbances are independent (Greene 1993).

The most widely cited example of the signific ance of this assumption is the “red bus/blue bus” paradox. In this example, a commuter c hooses between driving and taking a bus, with equal probabilities, so that P(ca r)=.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 bu s)=.25, and P(blue bus)=.25. This problem

occurs because the different colored buses sh are all of the unobserved characteristics of buses, and thus the disturbances are correla ted (Ben-Akiva and Lerman 1989). Therefore, the standard logit model is only valid for choi ce sets with distinct alternatives, where the unobserved components of utility can be assumed to be independent.

Several alternative models may be used when th e disturbances are likely to be correlated. These include the multinomial probit model, th e elimination-by-aspects model, the nested logit model, and the generalized extreme-va lue 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 attr ibutes 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 choose102 between “No New Action,” “Program A,” and “P rogram B.” It is possible that the two alternatives that represent actions, “Progr am 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 whethe r 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 Figu re 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 B103 Thus, respondents face a multidimensional choi ce consisting of three combinations of choices: (No Action, No Action), (Action, Prog ram 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 U jk is the total utility; j represents the choi ce to take action or not; k represents the

alternative chosen—No Action, Pr ogram 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 lowe st 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” altern ative 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 β

x

x x J j (20)104 where k is one of the two alternatives, Progr am 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 J j 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 x n 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 al ternatives, 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 Progra m 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-theo retic 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 re spondent. 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 C j =0; k is the option selected; and C k is the minimum willingness to pay for option k. For the lin ear approximation of the utility function presented above, this can be solved for C k 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 ca lculated as the ratio of the coefficient on the

resource, $ , to the coefficient on cost, (

.

The calculation of dollar values for the nes ted 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 attrib utes 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

1 1 γ 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 un certainty about preferences related to the natural environment, due either to unfamiliari ty with the choice situat ion, or the complexity

of such choices.

In the context of this study, several hypothese s about responses may be considered. First, a respondent may behave according to neoclassica l 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 th is chapter would be appropriate, and welfare

measures derived from this model would be valid.

Second, survey responses may symbolically ex press 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 commitm ents 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 defi ned 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 in crease 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, w hose value is 1 if a person acts in accordance with a code, and 0 if they do not. Thus, F(E 1 ', E 2 , ..., E n ) > F(E 1 , E 2 , ..., E n ) if E 1 '=1 and

E 1 =0. In the continuous ethics model, each E i 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 acco rding 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 ra tional 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 ha ve already been violated, moral considerations may be balanced with personal utility when maki ng a choice. Etzioni calls these expedient moralities, which imply that certain moral me ans are preferred but not at all costs. Consequently, this model has a variety of imp lications 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 commitmen ts 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. Anothe r person may express an environmental commitment by always choosing to take action, without considering costs and benefits. Nevertheless, the person may compare the natu ral resource levels when choosing between Program A and Program B. Re spondents may also express strong support for taking action by always choosing the most expensive acti on. A respondent also may feel a moral commitment to preserving a particular natu ral 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 a bout moral commitments, it is very difficult to determine if they are important. It is po ssible to test whether a significant number of people always select the “no acti on” option, or always select the most expensive option. However, these tests only provide evidence re lated 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 (Mazzo tta 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 th e 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, whos e 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 repr esents 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 simp lify complex choices, by applying decision heuristics. For example, they may use lexico graphic 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 by111 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 commitmen ts, without more information on how people choose, statistical tests cannot provide defi nitive evidence to support or refute these hypotheses. However, a variety of statisti cal and qualitative tests may provide evidence related to consistency of responses with reveal ed preference theory. Two hypotheses related to theoretical validity of valuation surveys ma y be tested—the adding up hypothesis, and the scope hypothesis. The conceptual framework fo r these tests is presented in the following

sections. The results of these tests and addi tional evidence related to the above hypotheses is discussed in Chapter 6.

The Adding Up and Scope Hypotheses

The adding up property requires that values fo r 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 th e 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 ut ility received from a vect or of market goods, X , and a vector of natural resources, R , subject to their budget constraint, P x X + P r R # M,

where M is total income. The resul ting 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( R 1 - R 0 ) = WTP 1 = E( P , R 0 , U * ) - E( P , R 1 , U * ) (29)

where R 1 > R 0 . If two additional changes are defined as

WTP( R 2 - R 1 ) = WTP 2 = E( P , R 1 , U * ) - E( P , R 2 , U * ) and (30) WTP( R 2 - R 0 ) = WTP 3 = E( P , R 0 , U * ) - E( P , R 2 , U * ),

where R 2 >R 1 >R 0 , then

WTP 1 + WTP 2 / E( P , R 0 , U * ) - E( P , R 1 , U * ) + E( P , R 1 , U * ) - E( P , R 2 , U * ) (31) = E( P , R 0 , U * ) - E( P , R 2 , U * ) / WTP 3 .

Therefore, WTP 1 + WTP 2 . WTP 3 if income effects are assum ed 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 M 1 , and the level after paying for the change ( R 1 - R 0 ) can be defined as M 2 , so that M 2 < M 1 . Because utility is maximized subject to the income constraint, the maximum level of utility that may be

achieved at income level M 1 , U * 1 , is greater than that which can be achieved at income level

M 2 , U * 2 .

Therefore, if a person has already paid for ( R 1 - R 0 ), and is then asked for their willingness to pay for the change ( R 2 - R 1 ), the effect of the change in inco me on willingness to pay is given by

[E( P , R 1 , U * 1 ) - E( P , R 2 , U * 1 )] - [E( P , R 1 , U * 2 ) - E( P , R 2 , U * 2 )]. (32)

If the amount paid for the first change is sma ll 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 assumpti on 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 a114 normal good and they are not satiated. Thus, using the above example, WTP 3 > WTP 1 , so

that E( P , R 0 , U * ) - E( P , R 2 , U * ) > E( P , R 0 , U * ) - E( P , R 1 , U * ). (33) This implies that E( P , R 1 , U * ) > E( P , R 2 , 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 pa y is greater for a higher level of the natural resource. Therefore, when valu es are elicited for different size changes of the same natural resource, they should conform to this proper ty, 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 Chap ter 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 fina ncing alternatives. Data from Phases I and II will be used to provide inform ation 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 recr eational use values for the Estuary. The resource survey, which is the subject of this dissert ation, elicited relative values and priorities for important natural resources of the Peconic Es tuary. 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 surv ey. The primary goal was to elicit public preferences, priorities and values for natural re sources 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 environm ent. Other important goals were to create a survey that would minimize some of the problems associated with valuation surveys; would117 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 cons isted of meetings with the Management Committee, the Citizens’ Advisory Group, and k nowledgeable representatives of stakeholder groups. This was followed by informal inter views of the public, focus groups, a brief preliminary survey, and pretesting of the fina l 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 differe nt 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 Februa ry, 1995 with a visit to the area and an initial meeting with members of the Manage ment Committee. Shortl y afterwards, meetings were held with representatives of different interests, including the head of the Local Government Committee; the Chairman of the Ci tizen’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 wi th the Citizen’s Advisory Group, information was gathered118 about the area and about the con cerns of various interest groups. At the same time, in order to get an understanding of the most impor tant problems and concerns related to the Estuary, available information on the area was gathered, including studies and other documents related to the Peconic Estuary Progra m, 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 ta lk about the bays. Most people interviewed were very concerned about water quality, declin es 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 wa s 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, an d 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 Pr ogram; 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 percepti ons 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 17120 A short preliminary survey was implemented in Montauk on March 19, before the St. Patrick’s Day parade, an event that draws a larg e number of visitors and residents. This survey included both closed- and open-ended quest ions, and is reproduced in Appendix A. The thirty-five usable responses gave some preliminary information about important

attributes of the area and its recreational si tes; 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 Rating a 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, participan ts were asked general questions about their concerns, their perceptions of water quality, and actions they would like to see; and some121 preliminary survey questions and question form ats 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 preliminar y 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 impor tant to them; the third question asked participants which of a list of environmenta l problems they believed were most serious; and the fourth question asked which potential actions should be given highest priority.

Participants found these three questions difficu lt to answer, for two reasons. First, they believed that almost everything was important, an d 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 poten tial actions, but believed that priorities should be set by experts. Nonetheless, these questions provid ed 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 wo uld be good for the overall community.122 However, this question did not work as inten ded, but instead tended to generate discussion of septic systems and political concerns surroun ding 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, wa s 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, undeve loped land, shellfish closures, and fish population. Developed land, farmland and unde veloped land were all included because the total acres of land were intended to add up to th e total area of the East End. Participants found this confusing, however, and also had tr ouble 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 th ree most important reasons for living on the East End, from a list of fifteen reasons given by participants in earlier focus groups. This123 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 attitu de questions was included, with the intention of correlating respondents’ attitudes with their values for natural resources. These questions were adapted from an anthropological surv ey 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 a bout 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 difficu lties, 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 each124 comparison; graphics were added to the quest ions; and the option of choosing to take no

action was added.

Survey Pretests and Implementation

The first pretest of an actual survey instrume nt 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, wh ich 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 over all 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 draf t 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 pr etests, 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 samp ling. 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 an d the recreation survey together, and because125 names and addresses of visitors were not availa ble. 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 lo cation 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 th e East End. Only year-round and seasonal

residents were asked to fill out the resource surv ey. 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 10126 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) 5127 Selection of Survey Format

The contingent choice format, rather than c ontingent 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 ti me the survey was developed and implemented, the survey was designed to elicit public pref erences for generic natural resources that were most important to the public, and would mo st likely be affected by restoration and preservation programs. Therefore, the survey id entifies public support for many different combinations of improvements in resources th at 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 th at 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 pr esented 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, although128 the contingent choice format may still invoke sym bolic 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 m ethods are better at estimating relative demand than absolute demand,” even for mark et goods, and “absolute willingness to pay is hard to pin down” (National Oceanic and At mospheric Administration 1993, p. 4609). Thus, the relative values or priorities for na tural resources elicited by a contingent choice survey may be valid, even if the absolute dolla r values of specific resource changes are not precise. One potential problem with the continge nt choice format is that it may lead to the use of simplified decision rules, such as le xicographic ordering or choices based on qualitative rather than quantitative balancing of attributes. However, even if this occurs, priorities over attributes may still be reve aled. For a policy analysis that focuses on prioritizing actions, information on relative va lues and priorities for resources is more important than the estimation of total dollar valu es. 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 enhancemen t/protection programs. Each option was129 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” opt ion 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 reso urce changes resulting from different actions were not complete when the survey was created , the natural resources and their levels described in the survey were chos en 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 Commi ttee, five natural resources were selected to be included in the survey: farmland, undevel oped 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 important130 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 st ands. 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. Th erefore, in order to av oid 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 shellf ishing, 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 sm all bays are the most productive areas, and most shellfishing takes place in these areas. These are also the areas most in danger of131 pollution from stormwater runoff, septic sys tems, and boat discharges. Therefore, in consultation with the Technical Advisory Co mmittee, 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 she llfish populations. Many participants in the preliminary interviews and focus groups expresse d great concern over declines in fish over the years. However, potential changes in thes e populations resulting from proposed actions could not be easily determined by the technical c onsultants. Therefore, it was hoped that by including eelgrass, which serves as fish and sh ellfish habitat, some of these concerns would be captured.

Levels of Natural Resources in the Survey

The objective of the survey was to elicit rela tive values for improvements in natural

resources above a baseline level. The baseline wa s 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 an d 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 19 81); and information provided by the New132 York State Department of Environmenta l Conservation and other members of the Technical Advisory Committee. The acres of fa rmland and undeveloped land in the Long Island Regional Planning Board’s land use re port were presented for the entire East End. Therefore, the number of acres within th e Peconic Estuary Prog ram study area were calculated by assuming that the fraction of fa rmland 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 judgment s provided by members of the Technical Advisory Committee regarding the anticipated chan ge 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 wi th 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 surv ey as the anticipated results of hypothetical133 programs that would preserve or restore the resources to different levels. Because actual results of programs were not known when th e survey was created, the resource changes were selected based on judgment, in consultation wi th 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 wo uld 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 Farmland a 13,500 acres 12,000 acres 9,000 acres Undeveloped Land a 74,000 acres 66,000 acres 50,000 acres Wetlands b 18,000 acres 16,000 acres 12,000 acres Safe Shellfishing Areas b 28,000 acres 26,000 acres 25,000 acres Eelgrass b 10,000 acres 9,000 acres 8,000 acres a - Calculated based on Long Island Regiona l 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 pe rcent. The results of hypothetical preservation progra ms 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. Beca use it did not seem realistic to preserve undeveloped land at the current level, hypot hetical 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 $500135 programs might maintain the current level or increase safe areas by ten percent. The baseline for eelgrass is a ten percent decrease, an d it might be preserved at the current level or increased by twenty-five percent.

The costs of the hypothetical programs were desi gned to elicit the amount that people would pay for the resource changes presented above, an d 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 coe fficients, 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 ort hogonal 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 se nse to respondents, Addelman’s design was modified. For example, in some cases follo wing 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 of136 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 comparis ons 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 correlati ons were below 50%. The sixty comparisons created and the comparisons assigned to ea ch booklet are listed in Appendix B. The comparisons were assigned to booklets rand omly, 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 firs t 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 th e 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 Questi on 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. Therefor e, 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 pa rticipants in focus groups wanted to voice opinions on proposed actions; and because res pondents 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 or der to demonstrate public support for proposed

actions.138 The third question asked people for their opi nions 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 br own tide, and because the Management Committee was interested in obtaining this information.

Next the background information for the c ontingent 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 quest ions (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 pump out stat ions)....................... 

### 1 2 3 4 5 NA Improving sewage tr eatment pl ants. ................. 

### 1 2 3 4 5 NA Zoning to limit fu ture 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 runo ff, 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 residentia l proper ty...................................... 

### 1 2 3 4 5 NA Controlling stormwater runoff with diversion, catch basi ns, 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 wa ter quality issues. ............. 

### 1 2 3 4 5 NA142 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 surv ey of residents of the East End conducted in 1993 (Suffolk Community College 1994). The s econd section describes the estimation of the conditional logit model, and the resulting estima tes of relative values for natural resources.

The third section describes the calculation of ec onomic values of a representative member of the public for changes in the natural resources, wi th sensitivity analysis for different discount rates. The fourth section presents two alternati ve model specifications that address possible correlation between alternatives, order effects, and responses that may have a qualitative dimension. These are the nested logit model an d 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 vari ables were created for the analysis. The148 following tables describe and summarize the full sample of survey respondents and their answers to the survey questions. An example of a complete survey bookl et 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 responden ts (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 ar ound 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 Peconi c 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 Questi on 2, which asked about support for specific management actions. Although the focus group results suggested that this question would provide primarily symbolic information, as disc ussed 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 plan ts; public education; and more research on water quality issues. The actions with the le ast support are required pumpouts of septic systems, and restrictions on the use of lawn chemic als. 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% 12151 The SCC survey also asked about support for a ctions. Although their questions were more general and not perfectly comparable to those pr esented here, their results are similar. For example, 83 percent of their sample suppor t 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 pe rcent who support restrictions on vegetation. The SCC survey found that 71 percent of re spondents 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 pe rcent of the SCC sample stated that they were willing to improve their own septic system, 7 3.2 percent of this sample support required upgrades when property is sold or improv ed. 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 wo uld 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 in convenience in exchange for cleaner water. This is consistent with the SCC survey findin g that 63 percent of boaters are willing to change their boating habits to improve water quality.

Based on similar reasoning, waterfront resident s were expected to be less likely to support restrictions on waterfront proper ty. There is some support for this hypothesis, with 76.2152 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, th ere 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 to tal 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 scor e 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 a ppear to be primarily symbolic of a general desire for action, and thus do not provide ad equate 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 a ctivities 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 he ard 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 w ho 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 demo graphic questions by providing information about respondents’ interests and level of commi tment 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 demographi c information, and the results for all respondents are reported in Table 0-5. These que stions were included in order to adjust the sample for representativeness in terms of demo graphic characteristics of the population, and to test hypotheses about differences in respondents’ values.

The first set of questions asked about resi dence 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 th e results to reflect population demographics, as described below. Question 10 asked whether th e 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 st andard 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 de mographic 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 contin gent 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 ans wer 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 questi ons 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 Pr ogram A than Program B. Of respondents who answered more than one question, 434 (4 8%) 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 chos e Program B for all of the questions they answered. This may indicate a tendency to se lect 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 attr ibutes 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) D 2 =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 crea ted, using the formula (x 8 - 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 D 2 =1-( L ( $ )/ L ( 0 )) (BenAkiva and Lerman 1989). All subsequent mode ls 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, she llfishing areas, and eelgrass). The sample results may be adjusted to better reflect th e values a representative member of the population by including characteristics of in dividuals in the model, comparing these161 characteristics with those of the general pub lic, 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 vari ables—gender, age, income, and education— are available for the general public, so the resul ts 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 concerni ng 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 categ ory. 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 obser vations 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 educati on and income were statistically significant at162 better than the ten percent level, so the ot her 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, mo dels 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 wer e reduced from seven to three categories; and the income variables were reduced from eigh t to four categories. Education categories one through three and five through seven wer e combined, and income categories one and two, three and four, and five through seve n were combined, to create the demographic

variables used in the final model. The lowest le vels 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 .0454163 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, besi des 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 dist ance 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 a ctions 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 fo r 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 characteris tics 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 0.78655167 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) D 2 = .203168 For the variables that are statistically signif icant 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 hi gh 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 sc hool 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 leve ls ($50,000-$149,999) have lower values for eelgrass than people with income up to $24,999.

People who shellfish have higher values for sh ellfishing 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 liv e 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. Pe ople who live up to 1⁄2 mile from the Estuary have higher values for farmland and undeve loped land than people who live more than 1⁄2 mile from the Estuary.

People who belong to environmental groups ha ve higher values for wetlands and eelgrass than people who do not belong to environm ental 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 ex press 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 have169 higher values for wetlands than people who ar e 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 surv ey. As discussed in Cha pter 5, the survey was implemented using convenience sampling. Th us, any non-representativeness of the sample must be corrected for by weighting the model co efficients using demographic characteristics of the population of the East End. The weigh ting 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 statis tics 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 fo r 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 sub samples—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 3,819170 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. Demo graphics 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 Compa ny 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 Demographics171 1990 Population a YearRound Resident Sample b Seasonal Resident Sample b Adjustment for Seasonal Residents c 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 ar e available for seasonal residents. b - Sample percents refer to percentage s of those who answered the question. c - See text for explanation.172 Because there is no census data for seasonal re sidents, 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 seas onal 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 SH k 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; WSH k 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 categ ory 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 th e 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 lowest173 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 mode l are listed in Table 0-14 and Table 0-15. The weight for the education and income base ca ses 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” coeffici ents. Finally, variances for the weighted

coefficients were calculated in a simila r manner, by pre- and post-multiplying W by the

variance-covariance matrix from the model esti mation (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) D 2 -2551.71 -3003.61 903.8 (.00000) .151174 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) D 2 -957.56 -1191.99 469 (.00000) .197

Once the two sets of weighted coefficients wer e 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 Homes a 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 co efficient by the cost coefficient (Hanemann 1984). The values per acre for a represen tative year-round and seasonal household are reported in Table 0-17. Standard errors for th ese estimates were calcul ated 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 averag e, 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 confid ence intervals account for the variance in the176 parameter estimates, but do not account for va riance 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 calculat ed 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 a ll of the services. The benefit of any public policy that increases the flow of one type of se rvice is the increase in the present value of that service” (Freeman 1993, p. 5).

The discounted present values, calculated usi ng the official water resources planning discount rate for fiscal year 1996, 7.625 per cent (U.S. Department of the Interior 1995), are reported in Table 0-19. This is the rate requi red by the government for Federal water and Values per Household Total Values a

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 $ 8,708177 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 in finite time horizon, the present values range from $23,831 to preserve an acre of undevel oped land, to $128,771 to preserve an acre of

farmland. The value to restore an acre of sh ellfishing grounds is $53,754; to preserve or restore an acre of wetlands is $79,557; and to restore an acre of eel grass 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 fo r 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 (4,3 07 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 Wilcox an 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, indica ting 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 abov e, showed that most respondents chose to take action, and that more respondents chose Program A than chose Pr ogram 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 fo r 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 esti mated 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 prev ious section, the value estimates from the weighted model are within the confidence inter vals 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, th e 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 expr essing their willingness to pay something to protect the environment of the East End in genera l. The coefficient on the second constant term reflects any difference in preference for Pr ogram B versus Program A that is unrelated to the levels of attributes of A and B, for exam ple 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 indi vidual attributes, but is instead related to the choices themselves. The positive and signific ant coefficient for Programs A & B indicates181 that people are more likely to choose an acti on 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 pres ervation 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,” inde pendent of the levels of the attributes, is 78 percent. The probability th at 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) D 2 -3921.39 -4731.72 1621 (.00000) .171182 These constant terms for taking action and for differences in probability of selecting A and B may be interpreted as representing a qualit ative dimension of respondents’ preferences, while the coefficients on the natural resources represent the portion of responses that is related to the quantitative comparison between opt ions. 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 consta nt term for Program B indicates that there is some qualitative reason that people prefer Prog ram A and measures the degree of preference

for A over B.

If people’s answers were a purely quantitativ e evaluation of attribute levels, then the coefficients on the alternative-specific constant s would not be statistically significant. Alternatively, if answers were purely qualitative, then the coefficients on the attributes would not be statistically significant. Since both attrib ute levels and constant terms are significant, it may be concluded that people are expr essing both a quantitative and qualitative component of preferences in their responses. It is also possible that respondents may simplify their choices by not rigorously consid ering 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 acti on is not surprising. Most people interviewed during the survey development process expr essed a great deal of concern for the183 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 pr ogram 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 fi rst question, and assume that A is meant to represent the most environmentally desira ble alternative throughout the survey.

In order to further test this last hypothesi s, Program A and Progra m 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. Prog ram 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 questi ons, 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 responden ts, there is no way to further verify this hypothesis.

The dollar values for this model are approxim ately half to two-thirds as large as those estimated from the base model (see Table 0-21). These values might be interpreted as the184 quantitative portion of people’s values for the na tural resources, which is smaller than their total value for preserving the Estuary’s reso urces. 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 pr evious 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 exampl e, some respondents might place a positive value on taking action, while others might pla ce 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 onl y are reported in Table

0-23.185 The dollar values were calculated based on the fo rmula 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) D 2 -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 and186 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 th e Estuary, affected by brown tide, concern for brown tide, years lived on the East End, member ship in environmental groups, and level of support for specific actions.

Level of use of the Peconic Es tuary 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 ot her factors that might affect the choice of

action versus no action. The level of use was ca lculated 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 fo r all actions in Question 2, where 1 indicates strongly support, and 5 indicates strongly oppos e. Thus a lower score indicates stronger support for taking action.

Based on t-tests for individual variables and like lihood ratio tests for the sets of income and

education variables and distance from the Peconi c 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.187 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) D 2 -3258.254 -4962.934 3409 (.00000) .343 As expected, people who indicate stronger su pport for specific actions, and members of environmental groups are more likely to choose to take action. People who shellfish are also188 more likely to choose to take action. The va riables 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 wi th 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 c onditional logit models estimated with and without alternative-specific constants, and th e nested logit model. Each of the models compared here were estimated wi th 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 mode l, and the lowest estimated values, from the conditional logit model with alternative-specific constants.189 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 fo r 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 valu e of 8.4. However, the hypothesis that the relative values are equal fo r 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 es timated coefficient on cost. The estimated

dollar values for the three models are within th e same general magnitude. Therefore, it may be concluded that the model is relatively robust to different specifications.190 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 D 2 =1-( L ( $ )/ L (0)) .138 W/S 1.44 W/U 3.15 S/U 2.19Model 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 D 2 .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 D 2 .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, is191 shown. The values for the weighted model, di scussed above, would fall within the range of

values shown here. For an infinite time hor izon, estimated values range from around $90,000 to $168,000 for an acre of farmla nd; 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 fo r different discount rates for the model with alternative-specific constants and the nested lo git 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 $6 40,000 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 lowes t values are for the model with alternative-192 specific constants and a 10% discount rate, an d 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 va lues 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 pe rfectly “rationally,” as defined by neoclassical theory, and balance the alternatives and their a ttributes quantitatively to maximize personal utility. Second, survey responses may symbo lically express moral commitments or other193 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 difficul ty 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 familia r with the area, and very concerned about protecting the local environment, one might ex pect that responses to this survey would be better-behaved than choices for totally unfamilia r 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 specifi c hypotheses related to economic rationality. However, a variety of statistical and qualitativ e 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 ri gor. At the most basic level, explanatory variables must be statistically significant, an d 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 leve l, 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 change194 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 coe fficients of the expected signs—positive for environmental attributes and negative for th e cost of programs. The results are also consistent with theory at next level of validi ty. As discussed above, results are robust to model specification, with equal priorities and re lative 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, th e 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, independ ent of the action’s effects on natural resources. This is consistent with expression of a symbo lic concern for the environment. As reported above, most (91.6%) chose an action, rather than no action for more than half of their195 answers. 706 respondents (79%) chose an acti on 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 st rong concern for the environment by always choosing the most expensive program, based on the assumption that the most expensive program must provide the greatest environmenta l benefit. However, 112 respondents (13%) chose the higher cost program for all of thei r choices, and 97 (11%) chose the lower cost program for all of their choices. This is fair ly balanced, and represents a small percent of respondents, so does not provide strong ev idence 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 lo wer 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 comme nts (71% of those who commented) did not refer specifically to the contingent choice que stion. Sixteen of these expressed general approval of the survey; and 42 included a comme nt about the environment, local politics, or specific actions that the respondent was c oncerned about. These comments provide evidence that the issues are salient to people an d 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 their196 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, stat ing that some part of the scenario was unrealistic or that they wanted more informat ion 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 inform ation 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 larg er 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 fo r Program A was greater than the level for197 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 tes ts were conducted using a model including only attributes of choices, without the demographic variables. In addition, only responses that selected either Program A or Pr ogram 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 the198 intermediate level, so that there is no value fo r 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 base line to the highest level. Therefore, each scope test must consider the statistical sign ificance of the coefficients along with the comparisons of coefficients for different increments.

The hypotheses tested, using Wald tests, are whether $ i 1 = $ i 3 and $ i 2 = $ i 3 , for each attribute, i. The superscripts indicate the incr emental 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 Scope a 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 a ttribute levels, as described in the text.199 As reported in Table 0-28, the coefficients for all increments of farmland, undeveloped land, and eelgrass are statistically significant, indica ting that nonsatiation holds for each of these resources. Thus, the Wald tests may be inter preted 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 si ze increments. Thus, the values for farmland and eelgrass are not c onsistent 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, $ W 2 , 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 th e hypothesis of equality of values between the increment from intermediate to highest and ba seline 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. Ho wever, 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 coefficien t for the change from the baseline to the200 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 shellfis hing areas, indicating that values for all increments are the same. In addition, the co efficient on the increment from the baseline to the intermediate level is not statistically signific ant. Thus, the results for shellfishing areas are not consistent with economic theory in terms of scope.

As described in Chapter 4, the adding up prope rty 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 incr ement or in two smaller increments, where the

second is conditional on the first. In this sur vey, 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 es timated value for the largest improvement—the highest level compared to the baseline level—s hould 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 + $ i 2 = $ i 3 for each natural resource, i, or that the sum of each pair of coefficients on the two smalle r 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 jo int test was performed for all five sets of restrictions. The results are reported in Table 0-30.

Tested together, the hypothesis that all attrib utes simultaneously conform to the adding up property is rejected. Tested separately, th e 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 Up a 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 a ttribute levels, as described in the text.

Summary

Taken together, these tests indicate that th e minimum conditions for economic rationality are met, with mixed results for the more rigo rous tests. The estimated coefficients for all models have the correct sign and are statistically si gnificant. 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 sy mbolic values and decision simplification, and scope and additivity tests indicate that answer s to the survey do not completely satisfy the202 most rigorous tests, so that values do not alwa ys change in expected ways with changes in quantity.

Overall, there is no strong evidence that peopl e 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 i ssues, 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 symbo lic expressions of concern and decision simplification which, as mentioned above, migh t be expected for choices that are difficult

and unfamiliar.

Although respondents may not be able to expr ess 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 ge neral magnitude of benefits of different programs as part of a decision-making proce ss that considers a variety of factors, including

economic benefits.203 CHAPTER 7

CONCLUSIONS AND RECOMMENDATIONS

The objectives of this study are to measure pub lic priorities and values for protecting and enhancing natural resources of the Peconic Es tuary system; and to explore several issues

related to economic valuation methods. The re sults of this study will be combined with

information from other studies of the P econic 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 surro unding the Peconic Estuary. The resulting data were analyzed using both a conditional lo git 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 constant s. 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 th e 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; and204 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 th is are the rural character of the area and the area’s waters, including the Atlantic Ocean, L ong 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 lowes t 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 book let; 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 ans wers, and 79 percent chose an action for all of their answers. Only 27 people (3.0%) c hose “No New Action” for all of the choice questions answered.

Based on the random utility model, relative valu es and priorities for the natural resources were estimated using the conditional logit met hod. Box-Cox tests were carried out to select an appropriate functional form and, based on th ese 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: fa rmland, eelgrass, wetlands, shellfish, and undeveloped land.

In order to adjust the results to reflect the valu es 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 of206 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 representati ve resident of the East End are as follows: farmland is valued by the average household at $.13 per acre, per yea r; 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 populati on 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 discou nt 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 fo r 2 percent, 5 percent and 10 percent discount

rates.

For the 7.625 percent discount rate and an infi nite time horizon, the present values range from $23,831 to preserve an acre of undevel oped land, to $128,771 to preserve an acre of

farmland. The value to restore an acre of sh ellfishing grounds is $53,754; to preserve or restore an acre of wetlands is $79,557; and to restore an acre of eel grass 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 th e 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 progra m. 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 opt ions themselves rather than their specific

attributes.

The results of both of these mo dels 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 significan t, indicating that th e 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 ra ther 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 qualitat ive or symbolic dimension of respondents’ preferences, while the coefficients on the natural resources represent the quantitative dimension. Thus, if people ar e more likely to choose to take action, rather than “No New208 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 de gree 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 environmen t of the Estuary. Ho wever, the preference of one program over another independent of e ffects 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 wh ich 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 o ccur 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 domina nt for the first question in any booklets. This might lead some respondents to assume th at Program A represents a higher level of resource protection throughout the survey.

The dollar values for this model are approxim ately half to two-thirds as large as those

estimated from the base model. These valu es 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, wh ere 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 associ ated with the two action alternatives are correlated. Tests of the inclusive value param eter in the nested logit model indicate that there is greater substitutability between Prog ram A and Program B than between taking action or not, and that there is significan t 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 compar ed 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 high est values. For an infinite time horizon, and the 7.625 percent discount rate, estimated values for the different models range from around210 $90,000 to $168,000 for an acre of farmla nd; 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 hypothe sis that the relative values for natural resources are equal for all three models. Addi tionally, 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 sp ecifications. 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 hypot heses about responses were considered. First, respondents may behave perfectly “rationally,” as defined by neoclassical theory, and balance the alternatives and their attributes quantitat ively to maximize personal utility. Second, survey responses may symbolically expre ss moral commitments or other emotional responses to the survey. Third, because of unfamiliarity or complexity, respondents may211 simplify the choice, applying decision heuristi cs. Finally, respondents might make choices using some combination of the above.

Validity may be tested at different levels of ri gor. At the most basic level, explanatory variables must be statistically significant, an d 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 leve l, 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 consis tent with theory at next level of validity, robustness to model specification, with equal pr iorities 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 respondents212 accepted the scenario, and did not have difficu lty 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 eelgra ss are not consistent with theory in terms of scope, while values for undeveloped land do in crease with quantity, and thus are consistent with theory. Tested together, the hypothesis th at all attributes conform to the adding up property is rejected. Tested separately, th e 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 th e most rigorous tests most likely result from a combination of symbolic expressions of concer n 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 peopl e’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 of213 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 anal ysis 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 choi ces, and that, if hypothetical prices do not convey the relevant information, they cannot le ad to efficiency. Therefore, focusing on benefit-cost analysis without considering br oader 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 resear ch. The first relates to economic theory and methods for measuring public priorities and valu es 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 po ssibility of “ill-defined” prefer ences that are not consistent214 with neoclassical utility theory has a variety of implications for economic theory and

methods.

Two important reasons for ill-defined preferen ces 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 sur veys that guide respondents to focus on the specific goods being valued rather than the ethical dimension of th e 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 quest ions, 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 a bout including them because of their controversial nature. Questions about people’ s sense of place and consumer versus citizen motivations were also tested in early versi ons, 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 sur vey intended to provide policy information to test methodological issues. Because the survey must focus primarily on eliciting policy relevant information, it is di fficult, 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 ti re 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 poli cy questions. Third, controversial questions may not be politically feasible to include in a sur vey 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 deve lopment 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 recogniti on that economic values and personal utility are only one dimension of people’s motivations fo r 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 ex pedient 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 rela ted to the fact that, even in cases where moral commitments are not important, people may st ill 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 re search. Additionally, methods of analyzing “fuzzy” preferences may be considered.

Another area to consider is the developmen t 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 we ll-being in substantive terms—in terms of the quality of life and the sustainability of cult ures and communities, for example—[rather] than in terms of aggregate income, social wea lth, 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 prov ide important information for analysis of environmental policies. While survey method s may be further developed as suggested above, they may also be combined with ot her, more qualitative, methods to create a multidisciplinary approach. Such an approach might combine expert priorities with public217 priorities in a process based on judgment, rath er than attempting to perform a strict benefitcost analysis. Thus, survey data might be augm ented 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 i ssues 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 interre lated 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 ma rkets, but some of the basic conditions of choice may be appropriately decided in the po litical process” (Sagoff, 1992, p. 201).

Economic considerations can be considered in such cases through cost-effectiveness analysis and “sensible recognition of economic constrai nts” (Sagoff 1981). However, the political process is not without its drawbacks as well, fo r 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 ma ny contexts, it may be difficult to produce218 defensible estimates of benefits and costs, reco gnizing that values may be uncertain, and that moral commitments and nonanthropocentric valu es 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 enri ch the analysis and provide more useful information to policy makers.219 APPENDIX A

EXAMPLES OF PRELIMINARY SURVEY VERSIONS220 MONTAUK PRELIMINARY SURVEY221222223224225226227228 FOCUS GROUP SURVEY, VERSION 1229230231232233234235236 OTHER PRELIMINARY FOCUS GROUP QUESTIONS237238239240241 SURVEY, VERSION 1242243244245246247 APPENDIX B CONTINGENT CHOICE QUESTION DESIGN248 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 acres249 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 acres250 Question Resources/Cost No Action Program A Program B Cost $0 $50 $100251 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 6 3,000 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 acres252 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 acres253 Question Resources/Cost No Action Program A Program B Cost $0 $50 $500254 Contingent Choice Questions in Ea ch 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 6255 APPENDIX C

EXAMPLE SURVEY BOOKLET256257258259260261262263264 APPENDIX D

PRELIMINARY MODEL RESULTS265 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) D 2 -2551.71 -3003.61

903.80 (.00000) .151266 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) D 2 -957.56 -1191.99

468.87 (.00000) .197265 BIBLIOGRAPHY

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