George Perkins Marsh Institute

Clark University • Worcester, MA 01610 • http://www.clarku.edu/departments/marsh

GPMI

GPMI Working Papers No. 2009-06

 July 2009

Improving the Ecological Validity of Non-Market 

Valuation: Development and Application of 

Bioindicator-Based Stated Preference Valuation for 

Aquatic Restoration

Robert J. Johnston

Clark University; 950 Main Street; Worcester, MA 01610-1477

rjohnston@clarku.edu

+01 508.751.4619 tel

+01 508.751.4600 fax

Kathleen Segerson

University of Connecticut

Kathleen.Segerson@uconn.edu

+01 860.486.4567 tel

+01 860.486.4463 fax 

Elena Y. Besedin

Abt Associates

Elena_Besedin@abtassoc.com

+01 617.349.2770 tel

+01 617.349.2660 fax

Eric T. Schultz

University of Connecticut

eric.schultz@uconn.edu 

+01 860.486.4692 tel

+01 860.486.6364 fax 
WP 2009-06 1

Improving the Ecological Validity of Non-Market Valuation:

Development and Application of Bioindicator-Based

Stated Preference Valuation

for Aquatic Restoration

Robert J. Johnston1, Eric T. Schultz

2

, Kathleen Segerson2,

and Elena Y. Besedin3

1 Clark University; 950 Main Street; Worcester, MA 01610-1477

2 University of Connecticut; 75 N. Eagleville Road, Storrs, CT 06269-3043

3 Abt Associates; 55 Wheeler Street; Cambridge, MA 02138-1168

Abstract: This paper introduces a variant of choice experiment valuation 

designed to ameliorate common limitations related to a lack of ecological clarity 

in stated preference survey scenarios. This approach, which we call BioindicatorBased Stated Preference Valuation (BSPV), is distinguished by guidelines that

inform the structure and use of ecological indicators to define policy scenarios in 

valuation surveys. The approach begins with a formal basis in ecological science

and extends to relationships between attributes in respondents’ preference

functions and those used to characterize policy outcomes. The resulting welfare

measures are designed to be unambiguously linked to models and indicators of

ecosystem function, are based on measurable and unambiguous ecological

outcomes, and are more easily incorporated into benefit cost analysis.

Performance of the developed methods is illustrated through an application to

diadromous fish passage restoration in a Rhode Island watershed. Results suggest

that less structured treatments of ecological change within stated preference

surveys can omit information that is essential for the estimation of nonuse or

indirect use values and relevant to respondents when determining WTP.
WP 2009-06 2

Introduction

Many recent studies apply stated preference (SP) techniques to assess willingness to pay (WTP)

for policies that affect the ecology of aquatic systems (e.g., Bateman et al. 2006; Boyer and

Polasky 2004; Flores and Shafran 2006; Hanley et al. 2006; Holmes et al. 2004; Johnston et al.

2002; Loomis et al. 2000; Morrison and Bennett 2004; Morrison et al. 2002). The validity of

resulting welfare estimates depends on an appropriate integration of ecological and economic

information. To date, however, the integration of ecological information within SP methods has

been limited. A key concern is that SP surveys often communicate ecological changes using ad 

hoc, oversimplified representations of ecological systems (Johnston et al. 2007).

i

Measures of

change in aquatic living resources presented in SP surveys, for example, are rarely developed 

within the context of established models or formal indicators from the ecological literature, and 

are frequently based on arbitrary, vague or inadequately defined measurement units.

A case in point is the common use of ecologically indistinct terms such as “low,”

“medium” or “high” biodiversity (e.g., Carlsson et al. 2003; Garrod and Willis 1997) or “unique”

ecological quality (e.g., McGonagle and Swallow 2005; Opaluch et al. 1993). Similarly, Hanley

et al. (2006, p. 186) characterize ecological condition using descriptors such as a “wide” versus

“poor” “range of water plants, insects and birds,” noting that “none of these attributes are

necessarily consistent with what an ecologist would choose in terms of either indicators of the

ecological health of a waterbody, or underlying factors driving changes in ecological status.”

Although representations of ecological change within SP surveys should reflect the cognitive

capacity of survey respondents, weak correspondence between ecological and economic model

components can render survey results of limited use for policy evaluation, bias associated 

welfare measures, and promote skepticism among natural scientists and policy analysts.

This paper introduces a variant of SP valuation designed to address these and other

criticisms. This approach, which we call Bioindicator-Based Stated Preference Valuation 

(BSPV), is distinguished by guidelines that, among other things, inform the structure and use of

ecological indicators to define policy scenarios in valuation surveys. Specifically designed for

applications to ecological systems, the guidelines underlying BSPV promote ecological clarity

and closer integration of ecological and economic information. The approach begins with a

formal basis in ecological science and extends to relationships between attributes in respondents’

preference functions and those used to characterize policy outcomes. The resulting welfare

measures can be unambiguously linked to models and indicators of ecosystem function, are

based on measurable ecological outcomes, and are more easily incorporated into benefit cost

analysis. BSPV also provides a means to estimate values for ecological outcomes that individuals

might value, even though they may not fully understand all relevant ecological science.

Performance of the developed methods is illustrated through an application to migratory 

(diadromous) fish passage restoration in a Rhode Island watershed.ii Results provide sometimes

unexpected insights into public preferences for aquatic ecosystem restoration, many of which 

might be obscured by more traditional approaches to welfare evaluation. These and other

findings suggest that less structured treatments of ecological change within SP surveys can omit

information that is both essential for the estimation of nonuse or indirect use values and highly

relevant to respondents when determining WTP.
WP 2009-06 3

Valuation of ecological restoration: Conceptual and theoretical challenges

Stated preference methods, including choice experiments, use carefully designed surveys to

estimate public, often non-market values for well-defined changes in the quantity or quality of a

bundle of goods and services. Choice experiments present individuals with an opportunity to 

select one out of a set of available multiattribute options or to reject all of the presented options

in favor of the status quo (Adamowicz et al. 1998; Louviere et al. 2000). Choice data over many 

sets of possible choice options, where each option is defined by component attribute levels,

enables the probability of choice to be modeled as a function of attribute levels (Bennett and

Blamey 2001).

iii

Model results reveal tradeoffs made by respondents when choosing over

options; respondents’ willingness to tradeoff non-monetary attributes (e.g., ecological attributes)

for changes in household cost provides the theoretical and empirical basis for WTP estimation.

To yield valid estimates of value, SP surveys and estimation methods must have certain 

features (cf. Bateman et al. 2002; Bennett and Blamey 2001; Louviere et al. 2000). For example,

individuals can only make well-informed choices if provided with sufficient information 

(Hanemann 1994). In the context of ecological restoration, welfare estimates are contingent upon

information that allows respondents to predict the expected influence of ecological changes on 

their welfare (Bingham et al. 1995). One of the associated challenges in the design of surveys for

valuation of aquatic ecosystem restoration is characterization of the condition and change in

ecosystem function and related impacts on valued goods and services (Flores and Shafran 2006).

Within the following discussion, we distinguish between two terms. The term “attribute”

here refers either to an argument in a utility function or to a corresponding element

characterizing a stated preference choice option. The term “ecological indicator,” in contrast,

refers to a measure that characterizes, and may be used to communicate, the condition of an 

ecosystem or one of its components (Jackson et al. 2000).

While not made explicit in most SP studies

iv

, ecological change is most often 

communicated through indicators. That is, ecological indicators—either formal or ad hoc—are

often used as attributes in choice experiments. Ecologists have developed indicators such as

ecological diversity and integrity indices, among many others, to represent the status and 

function of natural systems (Bortone 2005; Davis and Simon 1995; Jorgensen et al. 2004).

v

Within SP valuation, the role of such indicators is to communicate changes in welfare-relevant

qualities or quantities, such that meaningful expressions of value can be elicited (Spash and 

Hanley 1995). This ecological information must not only be placed in a format that is readily 

understood by respondents, but that also provides an accurate representation of the change being 

valued. As stated by Schiller et al. (2001), “effective communication of ecological indicators

involve[s] more than simply transforming scientific phrases into easily comprehensible words. [It

requires] language that simultaneously fit[s] within both scientists’ and nonscientists’...frames of

reference, such that resulting indicators [are] at once technically accurate and understandable.”

In contrast to the care devoted to indicator development and interpretation in the

ecological literature, indicators used in SP surveys often lack documented reference to empirical

findings regarding ways in which natural systems respond to changes or stresses, and have often 
WP 2009-06 4

minimal grounding in prior ecological research. As a result, indicators of ecological change

presented in SP surveys: (1) rarely correspond to formal indicators presented in the published 

ecological literature, (2) are often ambiguously linked to quantifiable policy impacts, (3) are

often based on arbitrary or vague measurement units, and (4) have often incomplete or

ambiguous links to ecosystem goods or services valued by respondents.

For example, SP studies often define policy changes solely in terms of convenient units

such as the number organisms or acres of habitat affected. While descriptions such as these may 

relate to welfare-relevant attributes in direct or indirect ways, is unlikely that such 

characterizations by themselves promote valid expressions of welfare. For example, it is not

clear whether, when answering valuation questions of this type, respondents are directly valuing 

the commodity over which they have preferences. Do respondents care directly about the loss of

a specific number of fish, birds or other organisms, or do they instead care about other

environmental attributes or outcomes, such as ecosystem condition or biodiversity, and use the

number of fish or birds as a proxy for the attributes or outcomes of real concern? In the latter

case, respondents might assume ecological production function relationships that do not

correspond with those quantified by ecologists. For example, respondents might use presented 

information on organism numbers or habitat area to make speculative inferences regarding other

omitted but welfare relevant ecological attributes.

A related criticism of SP methods, and economic valuation is general, is that lay

individuals might not understand or appreciate fully the ecological importance of certain species

or processes, and hence estimated economic values will not reflect the true value of these

ecological changes (e.g., U.S. EPA 2009). This is most likely to be a concern for nonuse values

or use values for which the implications for use are very indirect, such as values stemming from

contributions of microorganisms or insects to soil formation, primary productivity, and overall

ecological integrity or condition (e.g., Nichols et al. 2008; Weslawski et al. 2004).

vi

Such 

contributions are often captured poorly if at all by the types of ecological variables often 

included in SP studies, such as changes in water quality, acreage of wetlands or habitat, or even

species abundance. Unless such contributions can be expressed in terms that survey respondents

can understand and value, and that are consistent with ecological science, welfare estimates will

not reflect the full range of potential values derived from ecological systems.

vii

Ambiguous correspondence between SP survey attributes and measurable policy impacts

can also render welfare estimates of limited use for policy evaluations. Ecological outcomes of

policy implementation are quantified, both in ex ante predictions and ex post evaluations,

through effects on measurable outcomes, often called measurement endpoints (U.S. EPA 1998).

An emerging literature discusses ways in which these endpoints correspond to well-defined

ecosystem services and other valued outputs (Boyd and Banzhaf 2006). SP valuation, in contrast,

often derives welfare estimates as a function of attributes that have little systematic relationship 

to measurable outcomes. As noted above, the use of ecologically ambiguous descriptors such as

“unique wildlife habitat,” “high quality groundwater,” or “high biodiversity” is widespread 

among SP surveys in the literature. A consequence is that associated welfare estimates cannot be

integrated into benefit cost analyses without ad hoc and often questionable assumptions required 

to reconcile ecologically ambiguous SP attributes with measurable policy outcomes.

viii
WP 2009-06 5

The theoretical issue

As with all economic values, WTP estimated using SP methods is contingent upon the

information available to respondents—either as provided by the survey or derived from other

sources (Bergstrom and Stoll 1989; Bergstrom et al. 1989; Cameron and Englin 1997; Hoehn 

and Randall 2002). In the view of many ecologists and economists, SP survey instruments often 

fail to provide sufficient information to enable respondents to understand potential effects of

ecological change on welfare (Spash and Hanley 1995).

In formal terms, the question regards the content and structure of the utility function. For

example, assume that respondent i’s utility is of the form Ui(E(X)), where E(⋅) is ecosystem

integrity, biodiversity or some other valued ecological outcome and X is the number of migratory

fish passing upstream, or some other quantifiable measure. If the respondent cares about X solely

because of its impact on E(⋅), then a valuation question framed solely in terms of X effectively 

asks the respondent to directly value changes in the input rather than changes in the output that

directly influences utility. This is akin to asking an individual to value a change in the labor or

capital that is used in the production of a good or service, rather than a change in the output of

the good or service that results from the input change (Boyd and Banzhaf 2006).

The first implication of this is a potential for bias. A preference function of the form

Ui(E(X)) can be mathematically collapsed to Ui(X). However, if individuals value E(⋅) directly,

the specification of valuation scenarios solely in terms of X will likely lead to biased WTP

estimates, because this requires respondents to assume their own (almost certainly incorrect)

ecological production function (i.e., to assume a relationship between X and E). As noted by 

Carson (1998, p. 23), “[r]espondents will tend to fill in whatever details are missing in the ...

survey with default assumptions. These may differ considerably from what the researcher

perceives.” This is particularly true for ecological resources and functions, for which respondents

often have little baseline information (e.g., Carson 1998; Spash and Hanley 1995).

The potential for bias is compounded if utility takes a more complex form such as

Ui(E(X),H(X),X), where H(X) represents a second valued ecological outcome influenced by X.

For example, H(⋅) might represent expected quality of recreational fishing for non-diadromous

species (e.g., largemouth bass), where diadromous fish could have a positive (

0 ( ) > !

! "

X

H

) or

negative (

0 ( ) < !

! "

X

H

) marginal effect on recreational fisheries, depending on ecological

relationships.

ix Here, X influences utility directly, as well as indirectly through its influence on 

both E(X) and H(X). Moreover, the indirect effects may not be universally positive. In this

context, appropriate modeling of utility is critical to obtaining understanding and unbiased 

estimates of values for policies that influence X, E(X) and H(X).

The situation is further complicated if one cannot observe ecological outcomes directly.

Assume, for example, that ecologists have developed multimetric indicators of overall ecosystem

condition that are designed to proxy for true underlying ecosystem condition, E(⋅), that we

assume influences individuals’ well being. As a simple illustration, assume that the multimetric

indicator W is a formal indicator or measure of E(⋅) developed by ecologists. However, the
WP 2009-06 6

survey designer, concerned that respondents will not be able to understand W (or unaware of the

appropriate ecological literature), instead uses a simplified, ad hoc indicator (e.g., high, medium,

low), which we denote

W

~ . In this case, respondents must infer E(⋅) based on 

W

~ , with no

provided function or information to make this inference; there is no quantitative relationship 

between 

W

~

and either W or E(⋅).

x Hence, the model estimates WTP for a derived indicator, W

~ ,

which has no formal ecological interpretation or link to measurable policy outcomes.

The remainder of this paper presents and illustrates the use of a variant of the SP method 

designed to ameliorate these limitations. In particular, it allows for the incorporation of

indicators designed to capture nonuse or indirect use values in a manner that is both grounded in

the relevant ecological science and able to distinguish among the various ways in which 

ecological changes influence respondents’ welfare.

xi

Structured use of ecological indicators within stated preference valuation

Outcomes of ecological restoration may be measured and represented using ecological indicators

developed by natural scientists. Some of these indicators may be simple and direct, such as the

use of laser/optical fish counter data to measure the number of fish passing upstream through a

fish ladder. Others are more complex, less direct, and can require additional interpretation. For

example, the ecological literature offers numerous integrative, multimetric indicators that

formally assimilate multiple ecosystem components and are widely used as indicators of

ecosystem health or condition (e.g., Bortone 2005; Davis and Simon 1995; Jorgensen et al.

2004). These indicators (e.g., Index of Biotic Integrity (IBI) and the Estuarine Biotic Integrity

Index (EBI)) are calculated from ecological data and provide a quantitative if controversial

means to characterize changes in ecosystem condition (Bortone 2005; Karr 1981; 1991).

In contrast to traditional valuation, BSPV employs a more structured use of ecological

indicators to characterize and communicate welfare-relevant changes. Specific guidelines ensure

that survey scenarios and resulting welfare estimates are characterized by (1) a formal basis in 

established and measurable ecological indicators, (2) a clear structure linking these indicators to

attributes influencing individuals’ well-being, (3) consistent and meaningful interpretation of

ecological information, and (4) a consequent ability to link welfare measures to measurable and 

unambiguous policy outcomes. Specific guidelines to ensure (1) through (4) include:

1) Indicators used within BSPV survey scenarios must be associated with specific and welldefined ecological changes over which respondents express preferences. Results of

preliminary research such as focus groups and cognitive interviews (Johnston et al. 1995;

Kaplowitz et al. 2004; Powe 2007) should be combined with findings from the relevant

ecological literature to first determine a comprehensive set of ecological outputs with

potential influences on respondents’ utility, then to identify ecological indicators best

able to characterize those outputs. This requires differentiation between intermediate

ecological changes valued solely as inputs into other final ecological outcomes versus

those valued at least in part due to direct influences on utility (Boyd and Banzhaf 2006).

Resulting information is then used to provide a one-to-one match between ecological

indicators (serving as attributes) in SP scenarios and final welfare-relevant ecological

outputs. This guideline requires mapping of relationships between underlying ecological
WP 2009-06 7

changes, formal indicators of those changes, and attributes in respondents’ utility 

functions.

2) Indicators used within BSPV survey scenarios must have a systematic relationship to 

ecological field data or quantitative model results. All indicators within survey scenarios

should reflect a measure that is empirically quantifiable, e.g., as the output of a

recognized ecological model or standard field measurement. If ordinal categories (e.g.,

high, medium, low) are used to represent ecological change, the empirical basis for these

categories must be defined quantitatively and presented to survey respondents. In 

addition, there must be justification for the applied categorization within the ecological

literature. That is, survey attributes must be traceable to unambiguous ecological

measurements.

3) The quantitative basis for survey attributes must be understandable to respondents and 

meaningful to scientists. Survey pretesting must ensure that respondents understand the

units and definition of ecological indicators included in survey scenarios, and that these

correspond to meaningful outcomes from the perspective of individual utility.

Respondents’ understandings’ of indicator units, definitions and interpretations must

coincide, at least broadly, with those of natural scientists.

4) Indicators used within BSPV survey scenarios should be specified such that respondents

can identify baselines (i.e., status quo), reference conditions (i.e., the best possible

outcome or level of the indicator in an undisturbed system), and changes in both relative

and cardinal units where applicable. Findings from the valuation literature demonstrate

that WTP often depends on available (or visible) choice sets, reflecting the full set of

policy options possible within a given choice experiment or policy context (Bateman et

al. 2004). Similarly, ecological reference conditions may be thought of as characterizing

the range of possible substitute goods or policy outcomes that might be available,

regardless of whether these are available in presented survey scenarios. Reference

conditions also provide a means to interpret quantities such as “200,000 birds” that might

be otherwise largely meaningless to lay respondents (e.g., Desvousges et al. 1993; see

also comments by Hanemann 1994). The communication of ecological information in 

this more comprehensive manner provides respondents’ with an increased capacity to 

comprehend policy scope and to anticipate effects on welfare.

5) Indicators used within BSPV survey scenarios must be described so that respondents can 

understand potential linkages between ecological attributes and how these are, or are

not, captured in illustrated scenarios. To prevent respondent speculation regarding 

ecological production functions and implications for valued ecological outputs, surveys

should provide sufficient information regarding relationships between ecological

functions, e.g., as inputs and outputs according to ecological production functions. This,

together with other BSPV guidelines, can help prevent problems in which, for example,

respondents’ choices reflect a speculated (likely incorrect) value for an omitted

ecological outcome E(X) caused by a presented change in X.

6) Indicators used within BSPV survey scenarios must provide a comprehensive perspective
WP 2009-06 8

on welfare-relevant policy outcomes, including both direct and indirect effects. As noted

above, incomplete coverage of welfare-relevant outcomes in survey scenarios can 

encourage respondents to speculate regarding omitted outcomes. For the case of complex 

ecological systems, such speculation will almost certainly lead to incorrect assumptions

regarding the value of these outcomes. For many restoration policies this guideline

requires specification of outcomes (using appropriate ecological indicators) at three

levels: (1) the direct effect of proposed policies on targeted species or habitats (e.g., an 

increase in the number of fish able to travel upstream to spawning areas due to fish run 

restoration); (2) resulting indirect effects on other specific ecosystem commodities or

human uses (e.g., indirect effects of fish passage on other wildlife species); (3) broader

indirect effects on ecosystem condition (e.g., as might be measured using a multimetric

index of biotic integrity). The inclusion of ecological changes at these three levels also

provides a basis for investigating the structure of preferences. For example, it allows a

test of whether utility takes the form

U(E(X ), X )

or simply 

U(E(X )). That is, it allows

for a distinction between utility gained from specific elements of the ecological system

and utility associated with overall system condition, the latter of which may be a source

of perhaps substantial nonuse values.

As noted above, there are many examples of SP survey instruments addressing aquatic

restoration that incorporate ecological indicators—either simple or complex—in a less structured 

manner. Some of these surveys may have generated valid welfare estimates. However, the lack 

of a clear framework specifying linkages among different types of ecological impacts, welldefined ecological indicators and the structure of respondents’ utility can lead to ambiguity

regarding the interpretation of model results and potential bias in welfare estimates. BSPV is

designed to at least partially ameliorate these concerns and thereby promote more valid 

estimation of non-market values.

Random utility model

The theoretical model for the present application is adapted from a standard random utility 

specification (Hanemann 1984). We assume that the utility of household h from ecological

restoration program k (Uhk) is given by

Uhk(Xk, Wk(Xk), Yh- Chk) = vhk(Xk, Wk(Xk), Yh- Chk) +εhk (1)

where:

Xk = vector of indicators characterizing direct ecological outcomes of

program k;

Wk(·) = vector of indicators characterizing indirect ecological outcomes of

program k (outcomes related to direct outcomes Xk);

Yh = disposable income of household h;

Chk = mandatory cost to the respondent of preservation plan k;

vhk (.) = function representing the empirically measurable component of utility;

εhk = unobservable component of utility, modeled as econometric error.
WP 2009-06 9

Given the above specification, household h chooses among three policy plans, (j=A,B,N). The

household may choose option A, option B, or may reject both options and choose the status quo 

(neither plan, j=N). A choice of neither plan would result in no restoration and zero household 

cost, Chk=0. The model assumes that household h assesses the utility that would result from

choice options (j=A,B,N) and chooses that which offers the greatest utility. That is, given (1),

household h will choose plan A if

UhA(XA, WA(XA), YA- ChA) ≥ Uhz(Xz, Wz(Xz), Yz- Chz) for z=B,N, (2)

so that

vhA(XA, WA(XA), YA- ChA) + εkA ≥ vhz(Xz, Wz(Xz), Yz- Chz) + εkz . (3)

If the εhk are assumed independently and identically drawn from a type I extreme value

distribution, the model may be estimated as a conditional logit (CL) model (Maddala 1983). The

mixed logit (ML) model applied here is a generalization allowing alternative error structures, a

relaxation of certain assumptions imposed by the CL model, and expanded possibilities to model

response heterogeneity (Greene 2003).

Empirical application and data

The SP methods outlined above were developed for a case study addressing public preferences

for the restoration of migratory fish passage in the Pawtuxet and Wood-Pawcatuck watersheds of

Rhode Island. The present illustration is drawn from a subset of surveys addressing restoration of

the Pawtuxet Watershed. The Pawtuxet Watershed currently provides no spawning habitat for

migratory fish; access to all 4,347 acres of potential habitat is blocked by 22 dams and other

obstructions (Erkan 2002). The choice experiment questionnaire Rhode Island Rivers: Migratory

Fishes and Dams estimated willingness to pay of Rhode Island residents for options that would

provide fish passage over dams and access to between 225 and 900 acres of historical, but

currently inaccessible, habitat. Choice scenarios and restoration options were informed in part by 

data and restoration priorities in the Strategic Plan for the Restoration of Anadromous Fishes to 

Rhode Island Coastal Streams (Erkan 2002). Additional information was drawn from the

ecological literature on fish passage restoration, interviews with ecologists and policy experts,

and other sources described below. Consistent with the strategic plan, the choice experiment

addressed restoration methods that neither require dam removal nor would cause appreciable

changes in river flows; considered options included fish ladders, bypass channels and fish lifts.

The SP questionnaire was developed and tested over 21⁄2 years through a collaborative

process involving interactions of economists and ecologists; meetings with resource managers,

natural scientists, and stakeholder groups; and 12 focus groups with 105 total participants.

xii

Economist and ecologist researchers were intimately involved in all aspects of survey design,

and the ecologist researcher had “veto power” over any aspect of the survey that did not

incorporate sufficient ecological clarity. In addition to survey development and testing in focus

groups, individual interviews were conducted with both ecological experts and non-experts.

These included cognitive interviews (Kaplowitz et al. 2004), verbal protocols (Schkade and 

Payne 1994) and other pretests conducted to gain additional insight into respondents’

understanding and interpretation of the questionnaire.

xiii

Careful attention to development and

testing helped ensure that the survey language and format would be easily understood by

respondents, that respondents would have similar interpretations of survey terminology and 
WP 2009-06 10

scenarios, and that the survey scenarios captured restoration outcomes viewed as relevant and 

realistic by both respondents and natural scientists. Survey development paid particular attention

to the use and interpretation of ecological indicators and related information.

The choice experiment asked respondents to consider alternative options for the

restoration of migratory fish passage in the Pawtuxet Watershed. Respondents were provided 

with two multiattribute restoration options, “Restoration Project A” and “Restoration Project B,”

as well as a status quo option that would result in no policy change and zero household cost.

Prior to administration of choice questions, the survey provided information (1) describing the

current status of Rhode Island river ecology and migratory fish compared to historical baselines,

(2) characterizing affected ecological systems and linkages, (3) describing methods and details of

fish passage restoration, and (4) providing the definitions, derivations and interpretations of

ecological indicators used in survey scenarios, including the reason for their inclusion. All

survey language and graphics were pretested carefully to ensure respondent comprehension.

Using ecological indicators to characterize restoration outcomes

As noted above, the primary distinguishing feature of BSPV is the set of guidelines for the use

and structure of ecological indicators within survey scenarios. Here, survey choice options are

characterized by seven attributes (Table 1). These include five ecological indicators, one attribute

characterizing public access, and one attribute characterizing unavoidable household cost. Table

2 illustrates associated attribute levels included in the choice experiment design.

Ecological indicators included in each choice option characterize:

1) the quantity of river habitat accessible to migratory fishes (acres), based upon 

restorable Pawtuxet habitat acreage in Erkan (2002);

2) the probability that the restored fish run will exist in 50 years, reflecting 

numerical results that could be made available through population viability 

analysis (PVA)

xiv

;

3) the abundance of fish suitable for recreational harvest (catch), reflecting 

abundance measures available from ongoing statewide river samplingxv;

4) the abundance of fish-dependent wildlife (wildlife), reflecting the common

appearance of identifiable species within restored areas; and 

5) overall ecological condition (IBI), reflecting the output of a multimetric aquatic

ecological condition score (i.e., index of biotic integrity).

Following BSPV guidelines, all indicators have a systematic linkage to ecological field

data or quantitative model results. Attribute levels (within the experimental design) are grounded

in feasible restoration outcomes identified by ecological models, field studies or expert

consultations. Choice scenarios represent each ecological attribute in relative terms with regard

to upper and lower reference conditions (i.e., best and worst possible in the Pawtuxet) as defined

in survey informational materials. Scenarios also present the cardinal basis for relative scores

where applicable.

xvi

Relative scores represent percent progress towards the upper reference

condition (100%), starting from the lower reference condition (0%). This also implies bounds on
WP 2009-06 11

the potential attribute levels that might occur in choice questions, following guidance in the

literature to provide visible choice sets (Bateman et al. 2004).

Consider, for example, the aquatic ecological condition score (IBI), described in the

survey as “0 - 100 score representing how close a river is to the most natural, undisturbed area

found in Rhode Island.” This indicator was included following guidance from preliminary focus

groups suggesting that respondents would be willing to pay to restore overall ecological

condition in the Pawtuxet Watershed, above and beyond WTP related to all other identifiable

restoration outcomes (guideline #1 above). Prior to the illustration of IBI scores in choice

questions, the survey described the calculation and quantitative basis for this multimetric

indicator, including characterization of the eight component uni-metric indicators included

within the combined IBI score (guideline #2). Also included were descriptions of ecological

linkages through which the restoration of fish passage influences ecological condition (guideline

#5). Each choice question specified the status quo level for this indicator (65%) calculated to

reflect current watershed conditions, as well as the maximum possible value in the watershed 

(100%) (guideline #4). Focus groups and pretests were used to ensure shared and accurate

interpretation of this information among potential respondents, and that respondents’

interpretations of this indicator were broadly similar to those of policy experts and scientists

(guideline #3). Survey pretests and verbal protocols (Schkade and Payne 1994) were also used to

ensure correspondence between the presented IBI and the associated welfare-relevant policy 

outcome (i.e., the effect of restoration on overall ecological condition), and that this outcome was

not confused with other restoration outcomes (guideline #6). Parallel guidelines were applied to

all ecological indicators used within choice questions.

Survey implementation

From choice attributes (Table 1) and levels (Table 2), a fractional factorial experimental design

was created using a D-efficiency criterion for main effects and selected two-way interactions,

resulting in 180 profiles optimally blocked into 60 booklets (Lusk and Norwood 2005). Figure 1

illustrates a sample survey scenario and choice question. Each respondent was provided with

three choice experiment questions and was instructed to consider each as an independent, nonadditive choice. Detailed instructions were also provided, including reminders to consider budget

constraints and statements highlighting survey consequentiality (Carson and Groves 2007).

Surveys were implemented using a dual wave phone-mail approach during June, July and 

August 2008. An initial random digit dial (RDD) sample of Rhode Island households was

contacted via telephone and asked to participate in a survey addressing Rhode Island 

“environmental issues and government programs.” Those agreeing to participate were sent the

questionnaire via postal mail, with repeated follow-up mailings as suggested by Dillman (2000)

to increase response rates. A total of 2,400 surveys were mailed to Rhode Island residents; 1,157 

surveys were returned (48.2% response rate). Of the total surveys mailed, 600 addressed 

restoration in the Pawtuxet watershed.xviiThe following analysis is based on the 277 usable

returns for the Pawtuxet questionnaire, providing 803 completed responses to choice questions.
WP 2009-06 12

Empirical model

Choice experiment research has increasingly applied mixed logit (ML) models of choice among 

discrete alternatives, replacing traditional conditional logit models (Hensher and Greene 2003).

ML models allow for coefficients on attributes to be distributed across sampled individuals

according to a set of estimated parameters and researcher-imposed restrictions (Hu et al. 2005).

While such models require a greater number of choices regarding model specification, they have

greater flexibility and can approximate any random utility model (Hensher and Greene 2003).

The random utility model is estimated using maximum likelihood ML with Halton draws

in the likelihood simulation. Coefficients on acres, PVA, access, and IBI are specified as random

with a normal distribution. The coefficient on annual household cost (cost) is specified as

random with a lognormal distribution, ensuring a positive marginal utility of income. Signreversal is applied to the cost variable prior to estimation (Hensher and Greene 2003).

Coefficients on other variables are specified as fixed. These include the coefficients on the

alternative specific constant (neither), catch and wildlife. The final model was chosen after the

estimation of preliminary models with varying specifications of fixed and random coefficients.

Preliminary models show a high degree of robustness to variations in model specification.

Results

Model results are reported in Table 3. Estimated coefficients are jointly significant at p<0.0001 (-

2 log likelihood χ

2 = 537.18; df = 13), with a pseudo-R2 of 0.31. Seven out of eight model

coefficients are statistically significant at p<0.10; six of these are significant at p<0.01.

Estimated standard deviations of all random parameter distributions are statistically significant,

suggesting that a ML specification is warranted. Results of log-likelihood tests of conditional

(suppressed for conciseness) versus mixed logit results yield the same conclusion at p<0.0001 (χ

2

= 118.08; df = 5). Signs of estimated coefficients match prior expectations.

Even before welfare estimates such as WTP are calculated, the model specification 

provides a convenient means to interpret and contrast results. As detailed in Table 1, all model

variables except access and cost represent percent progress towards the upper reference

condition (100%). Hence, model coefficients may be directly compared as the relative weight (or

marginal utility) given to a one percentage point change in each attribute. Viewed from this

perspective, marginal utility is greatest (per percentage point change) for increases in restored 

acres (acres) and changes in overall ecological condition in the restored area (IBI).

xviii

Changes

in marginal utility associated with the probability of fish run survival (PVA) and increases in the

number of fish-dependent wildlife species (wildlife) are statistically significant and roughly half

the size of those associated with acres and IBI. Marginal utility is lowest—and not statistically 

significant—for changes in the abundance of catchable fish (catch).

These results are mirrored by WTP estimates derived from model results. Because the

ML model includes random coefficients, we estimate WTP using the welfare simulation

approach of Johnston and Duke (2007; 2009) following the framework of Hensher and Greene

(2003). The procedure begins with a parameter simulation following the parametric bootstrap of

Krinsky and Robb (1986), with R=1000 draws taken from the mean parameter vector and 
WP 2009-06 13

associated covariance matrix. For each draw, the resulting parameters are used to characterize

asymptotically normal empirical densities for fixed and random coefficients. For each of these R

draws, a coefficient simulation is then conducted for each random coefficient, with S=1000

draws taken from simulated empirical densities.

xix Welfare measures are calculated for each 

draw, resulting in a combined empirical distribution of R×S observations from which summary 

statistics are derived. The resulting empirical distributions accommodate both the sampling 

variance of parameter estimates and the estimated distribution of random parameters.

Using this procedure, we simulate WTP associated with each restoration policy attribute.

While the lognormal distribution of the program cost coefficient in this case ensures a negative

impact of cost on utility, and hence is the most commonly chosen distribution for SP payment

vehicle parameters, it can also lead to unrealistic mean WTP estimates over simulated 

distributions, particularly given the long right-hand tail of the distribution (Hensher and Greene

2003). Accordingly, we follow Hu et al. (2005) and Johnston and Duke (2007; 2009) and 

simulate welfare estimates as the mean over the parameter simulation of median WTP calculated 

over the coefficient simulation (i.e., mean of median WTP).

Resulting WTP distributions are summarized in Table 4, along with p-values for the null

hypothesis of zero (mean of median) WTP. For all attributes except access, results are

interpreted as WTP for a marginal, one percentage point increase in the indicator, holding all else

constant. For access, results indicate WTP for the provision of public access in the restored area,

relative to the default of no access. Statistical significance levels (p-values) in Table 4 are

determined through reference to the percentiles on the empirical distributions (Poe et al. 2005).

Welfare estimates may be interpreted in either relative (i.e., per percentage point increase

in the indicator) or cardinal terms. For example, the reported welfare results in Table 4 imply that

mean of median, per household annual WTP for a one percentage point increase in river acres

accessible to migratory fish is $0.78. Given that each percentage increase in acres within the

Pawtuxet watershed represents 43.47 cardinal acres restored,xx marginal WTP per acre is $0.018,

ceteris paribus. In contrast, annual WTP for a one percentage point increase in the probability of

50-year fish run survival is $0.29, while that for a one percentage point increase in the aquatic

ecological condition index (IBI) is $0.79. Based on simulated empirical welfare distributions

(Poe et al. 2005), all WTP estimates except that on catch are statistically significant.

Estimated welfare measures demonstrate that respondents were able to distinguish 

between anticipated welfare effects of different ecological outcomes, with WTP higher for some

outcomes (e.g., increases in ecosystem condition) than for others (e.g., increases in catchable fish 

abundance). Moreover, results suggest substantial WTP both for restoration outcomes that are

straightforward and direct (e.g., the number of acres accessible to migratory fish), as well as

more complex and indirect (e.g., effects on ecological condition). Moreover, while public access

(access) to restored areas is valued highly, the relationship of ecological outcomes to human uses

is not sufficient to guarantee non-zero WTP. Changes in catchable fish abundance (catch), for

example, are not associated with statistically significant WTP. Taken together, results suggest

that respondents are able to make systematic choices over complex restoration possibilities, and 

that the design of restoration options can have non-trivial impacts on public welfare.
WP 2009-06 14

Implications and discussion

Results presented in this paper suggest that ad hoc, oversimplified representation of ecological

outcomes is not a compulsory aspect of survey-based welfare estimation; SP choice experiment

scenarios can use indicators that are meaningful from both ecological and economic

perspectives. More complete, structured specifications of ecological outcomes within SP

questionnaires—while demanding greater attention to survey design and testing—can provide

welfare results that are both more comprehensive from a utility perspective and more easily 

linked to measurable policy outcomes.

Results for the case study discussed here, that of migratory fish passage restoration in 

Rhode Island watersheds, provide insight into ways in which respondents value outcomes of

aquatic ecosystem restoration. Survey responses suggest nuanced preferences for aquatic

ecosystem change. Welfare estimates reveal differences in WTP across restoration attributes.

Interestingly, some of the more direct effects of fish passage restoration, such as increases in the

probability of fish run survival (PVA), are associated with modest WTP compared to some

indirect restoration effects. This suggests that past surveys in which policy effects have been 

characterized solely in terms of the direct effects on individual species might have omitted some

of the primary outcomes through which restoration influences utility.

For example, SP survey scenarios often omit effects on overall ecosystem condition,xxias

measured here using an index of biotic integrity. In the present study, this indicator has a larger

associated marginal welfare estimate than any other ecological attribute (Table 4). These results

suggest that respondents can gain significant utility from changes in overall ecosystem condition,

even after one holds constant effects on other primary restoration outcomes. Welfare evaluations

should therefore consider the possibility that ecosystem condition can yield significant nonmarket values that are distinct from the values of ecological attributes or outcomes that might

influence that condition (such as species abundance). Model results also imply that some past SP

surveys in the literature may have suppressed exactly the type of information that is most

relevant to respondents when determining WTP for ecological restoration.

Model results also raise the prospect—following similar findings of Bulte et al. (2005)

and Johnston and Duke (2007)—that respondents may have well-defined WTP for an attribute

reflecting the status of an ecological process (the condition of a functioning ecological system),

apart from the outcomes of that process. Some economists might argue that the condition of an 

ecosystem represents the status of an ecological process that should only be valued in as much as

it contributes to other valued outputs. Others, however, might interpret ecological condition as an 

output that can be valued apart from other ecosystem services or outputs. While results here

cannot resolve this debate, they do indicate clearly that respondents are willing to pay for

improvements in ecological condition, holding other outputs constant. From an empirical

perspective, respondents view ecological condition as directly welfare relevant.

Results also suggest the potential hazards in less comprehensive coverage of ecological

outcomes in SP scenarios. For example, contingent valuation and choice experiment surveys

often emphasize direct or indirect effects on recreational fishing, sometimes with relatively

sparse descriptions of other ecological outcomes. Associated models frequently estimate
WP 2009-06 15

statistically significant WTP for improvements in recreational fishing. In contrast, results here

suggest that if one accounts for other ecological impacts, respondents may not always express a

significant WTP for recreational fishery improvements (i.e., the coefficient on catch is not

statistically significant). This raises the possibility that the incorporation of recreational fishery 

effects in SP scenarios that omit other related ecosystem effects might cause respondents to use

fishery impacts as a proxy for other welfare-relevant ecological outcomes.

At a minimum, results suggest that WTP for aquatic ecosystem restoration can reflect a

wide range of direct and indirect effects. In the absence of details on such effects and related 

ecosystem services, respondents might “fill in whatever details are missing ... with default

assumptions” (Carson 1998, p. 23). Some of these default assumptions might involve erroneous

speculations regarding ecological production functions, with a concomitant likelihood of bias in 

survey responses and welfare estimates. Avoidance of speculation requires careful survey and

model development to ensure a comprehensive perspective on welfare-relevant outcomes.

Conclusion

SP valuation methods may in some cases provide insufficient ecological information to enable

meaningful, unbiased WTP estimation. While there are exceptions (e.g., Horne et al. 2005), the

large number of valuation surveys implemented with ad hoc treatments of ecological outcomes

suggests the need for more structured guidance on the use of ecological information within nonmarket valuation. The research presented above illustrates a method that addresses the need for a

more systematic treatment of ecological indicators within SP valuation and related structural

linkages between ecological and economic frameworks. Despite prior efforts to merge ecological

and economic information for economic valuation, SP valuation still commonly retains often ad

hoc treatments of ecological information, with little reference to the extensive work by ecologists

to model and measure ecosystem condition and function.

The broader purpose of the research presented in this paper is to better ground SP welfare

evaluation in the types of ecological indicators and models developed within the ecological

sciences, as well as in measurable data available to policymakers. This paper formalizes an 

approach to SP valuation characterized by a set of guidelines and criteria for use of ecological

indicators within models and surveys. These guidelines promote a more defensible structure for

ecological valuation and a more comprehensive representation of ecological change within SP

scenarios, yielding more valid welfare estimation.

We emphasize that this paper addresses only a few of the many challenges involved in

the coordination of economics and ecology for welfare estimation. Reported findings are limited

by the policy case study from which they are drawn, and additional verification in other

valuation contexts will be required to assess the broader applicability of the proposed methods.

These limitations aside, results presented here suggest the potential benefits of closer and more

meaningful collaboration among natural and social scientists. Potential benefits for economists

include enhanced validity and relevance of welfare estimates, increased ability to link nonmarket values to measurable policy outcomes, and improved validity of results.
WP 2009-06 16

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Table 1. Choice Experiment Variables and Descriptive Statistics

Variable Definition Mean

(Std. Dev.)

a

acres The number of acres of river habitat accessible to migratory

fish, presented as a percentage of the reference value for the 

watershed (Erkan 2002). Range 0-100.

8.1794

(8.1550)

PVA Population viability analysis (PVA) score: Estimated

probability, in percentage terms, that migratory species will

migrate the river in 50 years. Reference condition is estimated

based from surveys of experts in fish restoration. Range 0-100.

33.4413

(28.1265)

access Binary (dummy) variable indicating whether the restored area 

is accessible to the public for walking and fishing; a value of 1

indicates that the public can access the area. Range 0-1.

0.3296

(0.4702)

IBI Index of biotic integrity (IBI) score: A linear multimetric index

of aquatic ecological condition, reflecting the similarity of the

restored area to the most undisturbed watershed area in Rhode 

Island. Index components include overall fish abundance,

number of mussel species, number of native species, number of 

sensitive species, number of feeding types, percentage of native 

individuals, percentage of migratory individuals, and

percentage of fish that are tumor free. Presented as a percentage

of the reference condition. Range 0-100.

71.6978

(6.0762)

cost Household annual cost, described as the mandatory increase in

annual taxes and fees required to implement the restoration

plan. Household cost for the status quo is zero. Range 0-25.

11.9762

(14.1019)

neither Alternative specific constant (ASC) associated with the status

quo, or a choice of neither plan.

0.3333

(0.4715)

catch The number of catchable-size fish in restored areas, measured

as the number of fish per hour caught by scientific sampling

crews. Presented as a percentage of the reference value for the 

watershed, defined as the highest average level sampled in any

Rhode Island river (from Rhode Island Department of 

Environmental Management sampling data). Range 0-100.

79.9087

(7.5807)

wildlife Number of fish-eating species that are common in restored

areas, such as egrets, osprey, otters, eagles, turtles and mink.

Presented as a percentage of the reference value for the 

watershed, quantified from surveys of regional experts in

wildlife biology. Range 0-100.

65.0125

(10.3920)

a Means and standard deviations include status quo option of no restoration.
WP 2009-06 21

Table 2. Attribute Levels in Choice Experiment Design

Variable Levels

acres 1. 0% (0 acres accessible to fish)a

2. 5% (225 acres accessible to fish)

3. 10% (450 acres accessible to fish)

4. 20% (900 acres accessible to fish)

PVA 1. 0% (probability of 50 year fish run survival)

a

2. 30% (probability of 50 year fish run survival)

3. 50% (probability of 50 year fish run survival)

4. 70% (probability of 50 year fish run survival)

access 1. Public Cannot Walk and Fish in Area

a

2. Public Can Walk and Fish in Area

IBI 1. 65% (aquatic ecological condition score)

a

2. 70% (aquatic ecological condition score)

3. 75% (aquatic ecological condition score)

4. 80% (aquatic ecological condition score)

cost 1. $0 (cost to household per year)

a

2. $5 (cost to household per year)

3. $10 (cost to household per year)

4. $15 (cost to household per year)

5. $20 (cost to household per year)

6. $25 (cost to household per year)

catch 1. 70% (102 fish/hour sampling abundance)

2. 80% (116 fish/hour sampling abundance)

a

3. 90% (130 fish/hour sampling abundance)

wildlife 1. 55% (20 species common)a

2. 60% (22 species common)

3. 70% (25 species common)

4. 80% (28 species common)

a Status quo value.
WP 2009-06 22

Table 3. Mixed Logit Results: Pawtuxet Restoration Choice Experiment

Variable Coefficient Std. Error Pr > |t|

Random Parameters

acres 0.0520 0.0150 0.0005

PVA 0.0196 0.0067 0.0035

access 1.2435 0.2354 0.0001

IBI 0.0525 0.0219 0.0163

cost (lognormal) -2.9738 0.1724 0.0001

Fixed Parameters

neither -1.7598 0.5038 0.0005

catch 0.0027 0.0105 0.7957

wildlife 0.0275 0.0099 0.0057

Standard Deviations of Random Parameter Distributions

std_acres 0.1089 0.0257 0.0001

std_PVA 0.0452 0.0083 0.0001

std_access 1.3564 0.4572 0.0030

std_IBI 0.1531 0.0380 0.0001

std_cost (lognormal) 0.8351 0.2347 0.0004

-2 Log Likelihood χ

2 537.18 0.0001

Pseudo-R2 0.31

Observations (N) 803
WP 2009-06 23

Table 4. Marginal Willingness to Pay Estimates (Implicit Prices): Empirical Distributions

a

Variable WTP Standard

Deviation

Skewness Kurtosis Percentiles

(1%, 99%)

Pr > |t|

b

acres 0.7841 0.2658 0.37 3.17 (0.23, 1.44) <0.01

PVA 0.2881 0.1143 0.39 3.34 (0.05, 0.58) <0.01

access 19.9620 4.5110 0.53 3.59 (10.71, 33.46) <0.01

IBI 0.7916 0.3749 0.28 3.52 (-0.03, 1.70) 0.02

catch 0.0497 0.2109 -0.32 3.61 (-0.52, 0.53) 0.76

wildlife 0.5489 0.2024 0.20 3.12 (0.12, 1.04) <0.01

a

Results reflect the mean over the parameter simulation of median WTP over the coefficient simulation (see text).

Estimates are per household, per year. For all variables except access, estimates represent WTP for a one percentage

point increase.

b P-values are two-tailed, for the null hypothesis of zero mean of median WTP, calculated directly from percentiles

in simulated empirical WTP distributions (see text).
WP 2009-06 24
WP 2009-06 25

Figure 1. Sample Choice Experiment Question

Endnotes

 i Similar limitations are found in other areas of economic analysis (Simpson 1998).

ii

Affected species include river herring (blueback herring and alewife), shad and American eel.

iii For example, respondents might select among different options for coastal wetland restoration,

each with distinct effects on wetland attributes, related ecosystem services and household costs

(Johnston et al. 2002).

iv For an exception, see Holmes et al. (2004).

v For example, Population Viability Analysis (PVA) is an approach that yields benchmarks on

the status of individual species in the system. PVA is a process wherein demographic features

(abundance, population structure), genetic characteristics, and environmental variability are

modeled to yield predictions of the probability that a population will persist for a specified 

period of time under different scenarios, bounded by 0 and 1 (Boyce 1992). Indices of Biotic

Integrity provide comparable benchmarks on a system-wide level representing ecological

condition; they are conventionally scaled relative to least-impacted, relatively pristine reference

sites (Jackson et al. 2000).

vi

Many of these fall under the category of “supporting services” in the typology of ecosystem

services used in the Millennium Ecosystem Assessment (2005).

vii

An alternative approach would be to educate the survey respondents about the relevant

ecological science. However, the ability to do this within the confines of a survey is limited.

viii

For example, in the absence of other defining information, WTP for attainment of “high”

biodiversity cannot be linked to measurable policy outcomes without assumptions regarding the

combinations of observable conditions, or measurement endpoints, that one might associate with 

this otherwise ambiguous descriptor.

ix For example, some species of diadromous fish compete with juveniles of valued recreational

species and also are prey for the adult stages of the same species. The balance of such positive

and negative effects depend on other factors (Yako et al. 2000).

x This lack of formalized relationship may be realized at either the researcher level (i.e.,

researchers have not specified any formal relationship) or at the respondent level (i.e.,

researchers may have specified a relationship, but this is unknown or poorly understood by 

respondents.

xi

While not explored here, the proposed method also has the potential to expand possibilities for

benefit transfer. Consider, for example, a utility function of the form U(E(X,Y)), where E is an 

ecological outcome determined by two (or more) relevant variables X and Y. Suppose that in a

given context a change in X leads to a change in E, and this change in E is valued using the

illustrated methods. Provided the affected population is sufficiently similar, this value could then 

be transferred and used in a context where E changes as a result of a change in Y rather than a

change in X. This would be valid provided that individuals care only about the change in E and 

not independently about the source of any change in E, as posited in a utility function of this

form. Whether utility, in fact, takes this form is an empirical question, which the presents

methods can help to answer.
WP 2009-06 26

 xii

This total includes one focus group conducted with respondents from an undergraduate

economics course (11 participants); all other groups included respondents recruited from the

general public.

xiii

No formal count was made of individual interviews used in survey design, but we

approximate that a minimum of 60 in-person and telephone interviews were conducted with 

expert and non-expert respondents between September 2005 and June 2008.

xiv For an illustration of population viability analysis applied to diadromous fish, see Lee and 

Rieman (1997).

xv This reflects the effect of fish passage on the abundance of other recreationally harvested fish 

such as largemouth bass (Yako et al. 2000).

xvi

For example, river acres available to fish migrations (acres) are presented both as a cardinal

number of acres and as a percentage relative to historical habitat available in the watershed.

xvii

Other versions of the survey included variations in the definition or set of included ecological

indicators or addressed restoration in other Rhode Island watersheds.

xviii

This excludes access, which is measured as a dummy variable and is hence not comparable

in this manner.

xix Because the coefficient on cost is specified with a lognormal distribution, an exponential

transformation is required subsequent to the coefficient simulation to obtain a simulated 

empirical density for the coefficient itself (Hensher and Greene 2003).

xx There are 4,347 total acres of potential river habitat in the watershed, of which zero are

currently accessible to migratory fish.

xxi

For an exception, see Jakus and Shaw (2003).