Public Values for Biodiversity
Conservation Policies in the Oregon
Coast Range
Brian Garber-Yonts, Joe Kerkvliet, and Rebecca Johnson
ABSTRACT. This study uses a choice experiment framework to estimate Oregonians’
willingness to pay (WTP) for changes in levels of biodiversity protection under different
conservation programs in the Oregon Coast Range. We present biodiversity policy as an
amalgam of four different conservation programs: salmon and aquatic habitat conservation,
forest age-class management, endangered species protection, and large-scale conservation
reserves. The results indicate substantial support for biodiversity protection, but significant
differences in WTP across programs. Oregonians indicate the highest WTP for increasing the
amount of forest devoted to achieving old-growth characteristics. On average, respondents
indicate an annual household WTP of $380 to increase old-growth forests from 5% to 35% of
the age-class distribution. Conversely, WTP for increasing conservation reserves peaks at
$45 annually to double the current level to 20% of the landscape, whereas WTP is negative
for any increase over 32%. We also find resistance to any change in conservation policy,
which substantially offsets WTP for increases in all four conservation programs. FOR. SCI.
50(5):589 – 602.
Key words: Choice experiment, conjoint analysis, ecosystem management, willingness to
pay, Oregon forests.
I
NCREASED RECOGNITION OF NATURAL RESOURCES’
noncommercial values has sparked a shift toward ecosystem management of forest land in the Pacific Northwest. Examples include the USFS Northwest Forest Plan,
the Oregon Plan for Salmon and Watersheds, Washington
State’s Timber, Fish, and Wildlife Agreement, and the current emphasis on sustainability in federal land policy. A
principal objective of ecosystem management is biodiversity conservation.
However, biodiversity is difficult to define (Baydack et
al. 1999, Haufler 1999), and, because limited budgets require land managers to set priorities in a milieu of close
public scrutiny, even more difficult to implement. Although
the public broadly supports biodiversity conservation, designing specific policies that invite public support and participation requires more understanding of preferences over
management alternatives. In this article, we use a welfare
economics framework to measure public preferences and
willingness to pay (WTP) for four biodiversity conservation
programs in the Oregon Coast Range (OCR), including:
(1) area and distribution of different age classes of trees,
from young stands to old growth; (2) protection of coastal
salmon and aquatic and riparian habitat; (3) protection of
threatened and endangered species’ habitats on public and
private land; and (4) designation of reserves managed for
biodiversity and ecological functions.
Background and Objectives
A review of some of the literature relevant to operationalizing biological conservation and ecosystem management
on a landscape scale (Oliver 1992, Levin 1993, Noss 1993,
Alverson et al. 1994, Noss et al. 1997, Baydack et al. 1999)
Brian Gerber-Yonts, Research Economist, USDA Forest Service, Pacific Northwest Research Station, 3200 SW Jefferson
Way, Corvallis, OR 97331—Phone: 541-758-7756; FAX: 541-750-7329, Brian.Garber-Yonts@orst.edu. Joe Kerkvliet,
Professor, Department of Economics, Oregon State University, Corvallis, OR 97331—Phone: 541-737-1482;
Joe.Kerkvliet@orst.edu. Rebecca Johnson, Professor, Department of Forest Resources, Oregon State University, Corvallis, OR 97331—Phone: 541-737-1492; Rebecca.Johnson@orst.edu.
Manuscript received October 21, 2002, accepted November 14, 2003.
Copyright © 2004 by the Society of American Foresters
Forest Science 50(5) 2004
589
identifies four broad approaches to ecosystem management:
(1) species-based (“fine filter”) approaches: focus on protection of umbrella and keystone species as indicators of
ecosystem health and biodiversity; (2) reserve-based
(“coarse filter”) approaches: focus on networked reserves
within which ecological processes, including natural disturbance regimes, are allowed to function largely free from
commercial constraints; (3) freshwater and riparian zone
protection: focus on protection of corridors, for both connectivity to facilitate species dispersion over the landscape,
and for protecting diversity “hotspots” given characteristics
of riparian zones as steep environmental gradients and loci
of disturbance processes; and (4) integrated structure-based
management of unreserved land: focus on integrating diversity objectives into management of developed and semi-developed land to increase resident diversity and improve
connectivity functions of managed portions of landscapes.
Each approach is discussed in the literature largely in
terms of its scientific attributes. However, each also defines
the core element of an institutional response to conservation
and is represented by management initiatives currently being implemented within the OCR. Both the federal and state
endangered species processes exist and federal recovery
plans for listed species have been adopted as well as numerous Habitat Conservation Plans (US Fish and Wildlife
Service 1982, 1983, 1986, 1991, 1992, and 1997). The
reserve-based approach is represented by the network of
designated Wilderness Areas, administratively withdrawn
areas, and late successional reserves designated under the
Northwest Forest Plan (NWFP) (USDA Forest Service
1994). Other elements of the NWFP include riparian zone
management and integrated management of unreserved
land. The Oregon State Plan for Salmon and Watersheds
(Oregon Coastal Salmon Restoration Initiative 1997) focuses on aquatic and riparian habitat in the OCR. Finally,
the structure-based management approach being taken by
the State of Oregon northern OCR lands (Oregon Department of Forestry 2001) focuses on maintaining a diversity
of forest age classes and structures, including late seral
conditions, on land actively managed for timber production.
The process of defining a landscape scale strategy for
conserving biodiversity is complex and demanding in terms
of required time, expertise, and public expenditure. Although different approaches are advocated by the authors
cited above, all advocate integrated approaches using a
variety of methods, and all advocate clear definition of goals
and objectives. It seems essential, then, that Noss’s (1993)
first step—setting priorities and identifying objectives—
incorporates the interests of stakeholders if the outcome of
planning efforts is to be useful in terms of managing resources
in the public interest. In discussing ten key “themes” of ecosystem management, Grumbine (1997) concludes by emphasizing the need for scientists and resource managers to step
away from purely technical considerations and confront the
nature and variety of human values that principally drive
management decisions. The research documented here is intended to provide one measure of (public) stakeholder prefer590
Forest Science 50(5) 2004
ences to assist policy makers in setting objectives for management in the OCR.
We estimate demand curves for Oregon residents measuring WTP for incremental changes in distinct programs or
attributes of OCR biological conservation plans. We provide continuous measures of public values for biodiversity
conservation actions to integrate with value estimates for
other resources in evaluation of policy and management
scenarios in the Coastal Landscape Analysis and Modeling
Study (CLAMS) [1] simulation model of the OCR. To
estimate demand curves for attributes of conservation plans,
we use the choice experiment valuation approach, combining the multiattribute approach of conjoint analysis with the
utility theoretic foundation of dichotomous choice contingent valuation. In a broader context, the study provides
information on public preferences for competing objectives
of the NWFP. This is of particular relevance given recent
initiatives to increase timber production on federal forests
covered by NWFP (Seattle Post-Intelligencer 2003).
Theoretical Model of Preferences for
Biodiversity Conservation Programs
Consumers signal their preferences for marketed goods
through prices paid and quantities bought. These signals are
used by private and public entities to make resource-allocation decisions. Unfortunately, consumers holding preferences for nonmarketed goods often do not take actions
resulting in meaningful market signals. Still, there is little
doubt that consumers derive value from nonmarketed goods
from three sources: direct use (e.g. recreation), indirect use
(e.g. ecosystem services such as waste assimilation), and
nonuse. Nonuse values stem from preferences for a good’s
existence or from fulfilling the perceived responsibility of
preservation for future generations. Of these sources of
value, measuring nonuse values presents the greatest challenge. Motivated in part by the directed use of cost benefit
analysis, economists have developed survey-based “stated
preference” methods to measure nonuse values in terms
comparable to market prices. These methods create hypothetical markets for nonmarketed goods and elicit values
from responses to WTP questions or stated choices in hypothetical decision scenarios [2].
In this article, we use random utility theory to estimate
continuous demand functions for biodiversity programs
based on discrete choice observations (McFadden 1974,
Ben-Akiva and Lerman 1985). We present individuals with
sets of alternative biodiversity plans and ask them to choose
the plan they most prefer. Given a set of alternatives, A n ,
presented to individual n, the random utility model posits
that the probability that individual n chooses alternative i is
Pn 共i兩An 兲 ⫽ Pr共Uin ⱖ Ujn , ᭙ j 僆 An 兲, i ⫽ j,
(1)
where U in is the utility (i.e., satisfaction) that n obtains from
alternative i in set A n , and similarly for U jn . The utility
function is known to the individual but stochastic to the
investigator because of unmeasured attributes of the good
being valued and other measurement errors (Ben-Akiva and
Lerman 1985). So, for analytical purposes,
U in ⫽ V in ⫹ ␧ in ,
(2)
where V in is mean utility and ␧ in is a random error. V in
includes a vector, X in , of the extent of application of the
biodiversity conservation programs and associated costs,
and a vector, R n , of individual characteristics of the
respondent,
U in ⫽ ␤ ⬘f共X in , R n 兲 ⫹ ␧in ,
(3)
where f(⵺) is a single valued function and ␤ is a vector of
unknown parameters.
The individual chooses the alternative providing the
greatest utility, such that Equation 1 can be written as
Pr共␤⬘f共Xin , Rn 兲 ⫹ ␧in ⬎ ␤⬘f共Xjn , Rn 兲 ⫹ ␧jn
⫽ Pr共␤⬘f共Xin , Rn 兲 ⫺ ␤共Xjn , Rn 兲 ⱖ ␧jn ⫺ ␧in 兲;
i, j 僆 An , i ⫽ j.
(4)
Specification of the errors’ distribution allows estimation
of ␤. We assume that ␧ in and ␧ jn are independently and
identically extreme-value distributed, so ␧* ⫽ ␧ jn ⫺ ␧ in is
logistically distributed (Ben-Akiva and Lerman 1985) and
use the multinomial logit regression model, as presented
below.
To obtain information on X in and R n , we used the conjoint method (Louviere 1988). This survey-based method,
originally used in marketing and transportation, is increasingly used to simultaneously investigate preferences for
complex resource-management options where alternative
scenarios encompass multiple attributes or programs (Boxall et al. 1996, Li et al. 1996, Adamowicz et al. 1999, Xu et
al. 2003). In a conjoint survey, survey respondents are asked
to state their preferences between alternative specifications
of a composite good, often framed as consumer goods with
associated prices. In the present case, the specifications are
framed as conservation plans, which vary in the spatial
extent to which conservation programs are applied on the
OCR landscape and with associated costs specified as annual household income taxes paid into a biodiversity trust
fund to support a given plan.
We use choice experiment analysis (CEA), a variant of
conjoint analysis. CEA asks survey respondents to indicate
the single most preferred composite rather than rankings of
all composites. CEA has the advantage of being a more
general case of the choice model that provides the foundation of the commonly used dichotomous choice contingent
valuation method (see, for examples, Loomis and GonzalezCaban 1997, and Winter and Fried 2001). Although controversial, the dichotomous choice contingent valuation
method has well-developed theoretical foundations linking
CEA with the valuation literature (Mitchell and Carson
1989, Bateman and Willis 1999).
Observing respondents’ choices over alternative composites, the analyst can statistically estimate the relative
importance of each program and compute estimates of WTP
for program changes (Hanemann 1984). The results provide
estimated aggregate and marginal demand curves for each
biodiversity program, depicting the public’s desire for incremental increases in conservation programs as incremental costs change.
Survey Design and Administration
In planning the valuation survey, we identified a list of
biodiversity indicators produced by CLAMS models for
which economic values were desired. Following discussions
with CLAMS scientists and the results from several layperson focus groups held throughout Oregon, we determined
that the salient biodiversity indicators could be nested
within four programs: aquatic habitat and anadromous fish,
threatened and endangered species’ habitats, forest structural and age-class diversity, and biological reserve networks. We also determined that CEA allowed us to address
the need for continuous value estimates rather than point
estimates of value, especially given the multiattribute nature
of the valuation problem [3].
Despite its potential nonresponse problem (Arrow et al.
1993), a mail survey was necessary, given the combination
of time and budget limitations, which precluded personal
interviews, and the need to present survey respondents with
a large amount of information accompanied by visual aids.
The survey instrument was carefully designed, tested, and
administered to minimize problems of nonresponse and
other sources of bias.
The 16-page survey instrument contained five sections.
Section 1 provided background information, the survey’s
purpose, introduced the concept of biodiversity, and asked
respondents to identify the personally important benefits of
OCR forests. Section 2 described the four biodiversity conservation programs, discussed causal factors for the decline
of some species and habitats, and current state, federal, and
private actions to improve their status. Each program was
described in terms of a management approach for protecting
one of the elements of biodiversity and specified the current
or baseline level of implementation (as of the time of the
survey). Graphic icons visually depicted quantitative program changes by incorporating either a pie chart or histogram with a representative icon for the program. The icons
were used in the subsequent choice questions to present
variation in the program levels in visual and textual terms.
Extensive color graphics helped maintain respondents’ interest and condense information. Throughout section 2,
attitudinal questions were posed to maintain respondents’
interest and prepare them for the choice experiment questions. Section 3 contained a summary in which the four
biodiversity programs were contrasted, and the choice situation motivated (see Figure 1). The survey set up a hypothetical biodiversity trust fund to finance biodiversity improvement, funded by general income taxes and fees paid by
industrial and recreational forest users. To motivate serious
consideration, respondents were asked to rank the four
programs and then to compare the biodiversity programs’
importance to other social programs, including education,
crime prevention, and rural development. Respondents were
reminded to consider trade-offs to biodiversity conservation
Forest Science 50(5) 2004
591
Figure 1. Conservation programs.
592
Forest Science 50(5) 2004
and household spending constraints before proceeding to
the choice experiment questions [4].
In section 4, each respondent was presented with four
choice questions, potentially yielding four observations
from each respondent. The choices were described as hypothetical Oregon ballots and the income tax and fee payment was reiterated. Each of four choice sets (an example of
which is shown in Figure 2) asked the respondent to indicate
his or her first choice of three alternatives: a status quo
alternative and two alternative conservation plans with various levels of the four programs and “bid levels” described
as an annual household cost. Table 1 gives the bid levels,
the descriptions used in the survey for each program, and
the four different levels of application for each program [5].
An asterisk indicates the current OCR baseline level of each
program.
Section five concluded the survey with questions regarding respondents’ demographic characteristics, including
age, sex, length of residency, occupation, environmental
group membership, political alignment, years of education,
household size, and income. Mean values for selected demographic variables are provided by strata in Table 2.
We used a stratified random sample of 3000 Oregon
households drawn by Affordable Samples, Inc., with 1000
addresses each from the OCR, Willamette Valley (WV),
and Southern, Central, and Eastern Oregon (EO). Following
Dillman (1978), the survey booklet was mailed in June
1999, accompanied by a cover letter and a $1 bill. After 10
days, nonrespondents were sent a postcard and a second
survey one month later. Seventy-nine percent of the returned surveys were received before the mailing of the
second survey. Of the 2,540 deliverable surveys (460 mailings were returned by the post office as undeliverable),
1,372 were ultimately returned, for an effective response
rate of 54%. Of these, 1,090 surveys yielded usable results,
with 20% of respondents failing to complete the valuation
questions or providing unusable responses.
We designed the survey to present a balanced view of
resource management and the relative trade-offs required in
identifying OCR conservation plans. Oregonians have a
long history of controversy over natural-resource use and
several recent state ballot measures concerning resource
policy have attracted extensive media coverage and public
debate. Thus, Oregonians are familiar with these issues
(Vaidya 1999), and respondents are unlikely to regard the
choice scenarios as purely hypothetical (a common criticism
of survey-based economic studies, e.g., McKillop 1992) [6].
Analysis
Using the survey data, we estimated numerous alternative multinomial logit. Equation 5 gives the model specification that appeared to achieve the best fit to the data [7]:
Pn 共i兩An 兲 ⫽
exp共␤⬘Xni ⫹ ␣Cni ⫹ ␶Qni ⫹ ␥⬘Rn Xni 兲
冘 exp共␤⬘X
nj
⫹ ␣Cnj ⫹ ␶Qnj ⫹ ␥⬘Rn Xnj 兲
,
j僆A
(5)
where X ni is the vector of linear and quadratic terms for the
four conservation program levels; ␤ ni is the vector of associated regression coefficients; C ni is the plan cost and ␣ is
the cost coefficient; Q ni is an alternative-specific term with
value set to zero for the status quo alternative and unity for
the other two alternatives, and ␶ is the associated parameter;
R n is a vector of demographic variables and X ni is the linear
component of the vector X ni , so that R n X n is a vector of
interactions between the demographic terms and the program attributes, and ␥ is the associated parameter vector.
Results
Table 2 presents the parameter estimates for each region.
These generally have expected signs and high degrees of
significance [8]. The conservation program level coefficients are significant, with the exception of the linear effects
of salmon and old-growth forest among OCR residents, and
the linear effect of reserves across all three regions. All
quadratic-term coefficients are significant at the 95% level
or above. The cost coefficients are negative and highly
significant for all three regions. The coefficient (␶Q) is also
negative and significant for all regions. This status quo
effect (SQE) indicates a tendency for respondents to refuse
any of the actions offered as alternatives for increasing or
decreasing biodiversity conservation, regardless of cost or
the degree of increase or decrease. Interpreting this effect
raises technical questions and is discussed below. All quadratic terms for the program levels are negative, and with
the exception of the salmon habitat program among OCR
residents [9], all of the linear effects are positive.
We performed experiments with additional demographic
terms, including income and length of regional residence,
but found insignificant estimates and dropped them from the
final model. Note also that variables for the forest age
management program (F and F2 in Tables 1 and 3) describe
the proportion of old-growth forest in the OCR forest age
class distribution. The survey descriptions included the proportions of young and mid-age forest, but collinearity and
insufficient degrees of freedom dictated that we include
only old-growth proportion in the final model.
Validity and Reliability
We conducted several statistical tests for potential biases.
Context effects, such as the specified scale of decision
variables (anchoring) and the order in which the survey
presented information and key questions, may result in
biased estimates (Tversky and Kahneman 1974, Mitchell
and Carson 1989, Payne et al. 1992, Schkade and Payne
1994). The results of three statistical tests indicate that
respondents display no significant anchoring or order biases
[10].
Though a direct test for nonresponse bias (i.e., where
survey nonrespondents hold systematically different views
than respondents) was infeasible, we performed an indirect
test by including a dummy variable for late responders and
interacting this with terms for the four program levels. The
implicit assumption is that late responders, defined as those
who responded after the third mailing (consisting of a
Forest Science 50(5) 2004
593
Figure 2. Choice scenario.
594
Forest Science 50(5) 2004
Table 1. Model terms and descriptions.
Terms
Coefficient
C
␣
S
␤s
S2
E
␤s 2
␤E
E2
F
␤E 2
␤F
F2
B
␤F 2
␤B
B2
STATUSQ
␤B 2
␶Q
SXOCCN
EXOCCN
FXOCCN
BXOCCN
SXGRN
EXGRN
FXGRN
BXGRN
EXLOR
FXLOR
BXLOR
␥SxO
␥ExO
␥FxO
␥BxO
␥SxG
␥ExG
␥FxG
␥BxG
␥SxL
␥ExL
␥FxL
SXPOL
EXPOL
FXPOL
BXPOL
␥BxP
␥SxP
␥ExP
␥FxP
SXED
EXED
FXED
BXED
SXAGE
EXAGE
FXAGE
BXAGE
␥BxEd
␥SxEd
␥ExEd
␥FxEd
␥BxA
␥SxA
␥ExA
␥FxA
Description
Bid level: two bid sets were specified
in the design, (0*, 22, 48, 86, 145,
236, 325, 648, 1272) for the
Willamette Valley, and (0*, 10, 22,
48, 86, 145, 236, 325, 648) for the
Coast and E. Oregon regions
Percent of coastal salmon habitat
protected at highest protection
standard: (5, 15*, 40, 90)
Squared value of S
Percent of endangered species habitat
in the Coast Range in which species
are protected at highest protection
standard: (5, 15*,
25, 75)
Squared value of E
Percent of forest age-class distribution
⬎150 years (0, 5*, 33, 50)
Squared value of F
Percent of Coast Range land
designated as biodiversity reserves
(5, 10*, 20, 40)
Squared value of B
Dummy variable for status quo
alternative
Dummy variable indicating forest
products occupation* multiplied by
S, E, F, and B
Dummy variable indicating
environmental group membership
multiplied by S, E, F, and B
Quantitative variable indicating length
of residence in years in Oregon
multiplied by E, F, and B (the
interaction with S could not be
estimated because of collinearity in
the model)
Interaction Likert scale rating for
political alignment multiplied by S,
E, F, and B; 1 ⫽ liberal . . . 5 ⫽
conservative; response values 1–2
recoded to ⫺1, and 4–5 recoded to
1.
Quantitative variable for years of
education multiplied by S, E, F, and
B
Quantitative variable for age of
respondent multiplied by S, E, F,
and B
* SIC codes 081, 083, 085, 241, 242, 243, 261, 262, 263.
second copy of the survey booklet and a cover letter), are
more similar in their preferences to nonrespondents than are
early responders. That is, assuming individuals with more
well-defined preferences are more likely to respond to the
survey, and to do so early, we test whether late respondents
express higher or lower WTP, on average. We conducted
twelve likelihood ratio tests, one for each program and each
stratum, of the null hypothesis of no difference for late
response. In only one of the tests did we reject the null at the
95% level. However, this estimated effect was miniscule
and we do not include it in the WTP calculations.
To address the potential that the respondents were not
representative of the populations of the respective sample
strata, sample mean values for key demographic variables
were compared to mean demographic values from more
broad-based sources (see Table 2). These comparisons indicate that survey respondents tend to be more educated and
have a higher level of environmental group affiliation, both
associated positively with environmental preference but
also having a higher level of timber sector employment,
average age, conservative political alignment, and average
length of residence, all of which were associated with lower
likelihood of choosing higher levels of conservation. To
render results more representative of public values, WTP
results reported below were calculated using means of demographic variables from the more broad-based sources
rather than from the survey sample.
Calculation of Willingness to Pay for
Conservation Programs
Using the estimates in Table 3, mean total willingness to
pay (TWTP) for the four conservation programs is given by
TWTP共Xk 兲
⫽⫺
冋 冉
1
␶ ⫹ ␤X k ⫹
␣
冘 ␥ R៮ 冊共X
kl
l
kj
册
⫺ Xki 兲 ⫹ ␤X k2共Xkj2 ⫺ Xki2 兲 ,
l
(6)
where ␶ is an alternative specific constant for the status quo
choice, the ␤’s control for positive and negative variation in
the levels of the conservation programs between the three
choice set alternatives; X k denotes the conservation programs k ⫽ 1, . . . , 4; j, i subscripts denote program levels
where i identifies the baseline level; and R៮ l denotes the
regional “population” mean values of demographic covariates l ⫽ 1, . . . , 7 from Table 2 (see Hanemann [1984] for
derivation; see also Beenstock et al. [1998], Hanemann
[1989], and Li et al. [1996] for further notes on calculating
demand curves based on discrete observations).
The results suggest that, in general, Oregonians have a
positive WTP for moderate increases in conservation programs, with declining marginal (incremental) values as program levels increase [11]. At the same time, respondents
tend to reject changes in any of the four programs, choosing
instead the status quo alternative regardless of the changes
of any program or costs. Although the WTP results produce
continuous WTP curves for changes in the four programs,
this status quo result produces a discrete shift in WTP that
partially offsets the estimated benefits of changing conservation levels.
Figure 3 depicts the population-weighted estimated mean
TWTP of Oregon households for each of the four conservation programs, adjusted and unadjusted for the SQE. With
the WV stratum representing 75% of Oregon’s population,
the weighted average values are largely representative of the
preferences of this most populous region. At current levels,
Forest Science 50(5) 2004
595
Table 2. Population and survey sample mean values for selected demographics.
Coast range
Length of residence*
Education†
Environmental group membership‡
Political alignment§
Age¶
Forest products employment㛳
Willamette Valley
E. Oregon
Population
Sample
Population
Sample
Population
Sample
20.0355
13.1709
0.0400
⫺0.0687
40.6737
5.97%
38.1890
13.8850
0.1024
0.1457
56.0299
17.23%
19.4480
13.9073
0.1065
⫺0.0474
35.7637
3.51%
35.2108
14.7528
0.1325
0.0476
51.1735
0.06%
19.8179
13.1068
0.1115
0.0427
37.6606
7.78%
33.9482
14.3254
0.0986
0.0822
54.8546
12.00%
* Oregon Progress Board (1998, Table All-R1).
†
Oregon Progress Board (1998, Table All-EL1).
‡
Oregon Forest Resources Institute (1999).
§
Oregon Secretary of State Office (1998). These figures were compared to data from a Likert scale of political alignment coded to ⫺1 for liberal and
⫹1 for conservative.
¶
US Bureau of the Census (1999).
㛳
Oregon Employment Department (1999).
TWTP is zero by definition. For changes decreasing the
level of a program (left of lower x-intercept), mean TWTP
is negative, indicating that Oregonians would pay to avoid
reductions in the current levels of conservation programs.
Negative WTP in this context is typically interpreted as
willingness to accept economic compensation, or the
amount of compensation required to offset the perceived
loss in benefits relative to the baseline [12]. For abovebaseline changes, respondents on average indicated positive
WTP up to a threshold level, represented by the peaks of the
Table 3. Regression analysis results.
Estimated coefficients
Term
Coefficient
C
S
S2
E
E2
F
F2
B
B2
STATUSQ
SXOCCN
EXOCCN
FXOCCN
BXOCCN
SXGRN
EXGRN
FXGRN
BXGRN
EXLOR
FXLOR
BXLOR
SXPOL
EXPOL
FXPOL
BXPOL
SXED
EXED
FXED
BXED
SXAGE
EXAGE
FXAGE
BXAGE
Obs
Log L
Chi-Squared
Pseudo-R 2
␣
␤S
␤S 2
␤E
␤E 2
␤F
␤F 2
␤B
␤B 2
␶Q
␥SxO
␥ExO
␥FxO
␥BxO
␥SxG
␥ExG
␥FxG
␥BxG
␥SxL
␥ExL
␥FxL
␥BxP
␥SxP
␥ExP
␥FxP
␥BxEd
␥SxEd
␥ExEd
␥FxEd
␥BxA
␥SxA
␥ExA
␥FxA
Coast range
⫺0.003214**
⫺0.006982
⫺0.000278**
0.037968**
⫺0.000251*
0.026838
⫺0.001198**
0.026756
⫺0.000692*
⫺0.776431**
0.003812
⫺0.016453**
⫺0.012441*
0.006422
0.002325
⫺0.003116
0.005829
0.003473
⫺0.00014*
⫺0.00026*
0.00009194
⫺0.000651
⫺0.004939*
⫺0.00691**
⫺0.010671*
0.002344**
⫺0.000122
0.003414**
0.000425
0.000185*
⫺0.000147
0.0000255
⫺0.0000915
3987
⫺1239.4455
771.013**
0.2486
* Significant at S 95% level; **significant at S 99% level.
596
Forest Science 50(5) 2004
Willamette Valley
⫺0.002153**
0.016116
⫺0.000193**
0.058958**
⫺0.000638**
0.06135**
⫺0.000904**
0.034725
⫺0.000871*
⫺0.30116*
⫺0.002281
⫺0.00103
⫺0.014782
⫺0.0078
0.005406
0.004046
⫺0.004745
0.016448
0.000105
⫺0.000144
⫺0.000129
⫺0.001983
⫺0.001225
⫺0.008663**
⫺0.007922
0.000436
0.000165
0.001612*
⫺0.000254
⫺0.0000323
⫺0.0000950
⫺0.000356*
0.000258
3303
⫺1010.687
417.179**
0.1862
E. Oregon
⫺0.004297**
0.034209**
⫺0.000226**
0.044707**
⫺0.000415**
0.037894*
⫺0.000837**
0.019884
⫺0.001054**
⫺0.745023**
⫺0.00102
⫺0.00747
⫺0.007399
⫺0.01366
0.003889
0.010723
0.007948
0.023427*
0.00001560
⫺0.000342**
0.00000899
⫺0.003053
0.000361
⫺0.005904*
⫺0.00997*
0.000272
⫺0.00057
0.002236**
0.001371
⫺0.000199*
0.0000059
⫺0.0000168
0.000131
3756
⫺1041.901
429.456**
0.1856
Figure 3. Weighted average total annual household WTP for conservation programs with adjustment for status quo effect.
respective curves. Beyond these thresholds, additional increments in the level of resources devoted to the programs
have negative value, decreasing the TWTP. Ultimately, the
perceived cost of allocating more land and resources to
conservation overwhelms the gains and TWTP again becomes negative.
Boxes on the respective curves in Figure 3 indicate the
levels specified in the experimental design. Portions of the
curves extending beyond the endpoints of the design represent extrapolation, are not fully supported by the data, and
should be viewed with caution. With the exception of biodiversity reserves, TWTP is positive at all above-baseline
levels included in the design [13]. But because the slope of
the TWTP curve corresponds to the marginal WTP, i.e., the
WTP for the marginal percentage-point increase, the corresponding marginal WTP is negative for the highest levels of
each program included in the experimental design. This
suggests that, at very high levels of increase in the conservation programs, respondents on average impute additional
costs above the annual household costs specified in the
survey instrument.
The WTP calculation of the SQE is ⫺␶/␣, with values
⫺$242, ⫺$140, and ⫺$173 annually for the OCR, WV, and
EO regions [14], with a population-weighted average of
⫺$153. This should be deducted from the summed TWTP
for any conservation alternative that deviates from current
program levels. Ideally, we would be able to associate a
fraction of the SQE with each of the programs. However,
there is insufficient information to associate the SQE differentially. In considering an isolated change in any one
conservation program, a downward vertical shift of the
respective TWTP curve in Figure 3 by the amount of the
SQE value is a conservative interpretation of preferences
and may be a lower bound on WTP.
Discussion
Comparison of the four TWTP curves in Figure 3 provides information on the relative value Oregonians place on
the four conservation programs. On average, Oregonians
indicate the highest WTP for increasing the amount of forest
devoted to achieving old-growth characteristics (i.e.,
⬎150-year-old trees and more complex structure). The survey explained that this could not be achieved in the short
run and would require an extended period to accomplish,
given current conditions. On average, respondents indicated
an annual household WTP of $380 (not including the SQE)
to increase old-growth forests from 5% to 35% of the
age-class distribution. In attitudinal questions preceding the
survey’s choice experiment, respondents were asked to select a preferred age-class distribution. Sixty percent of respondents indicated that they preferred a distribution that
Forest Science 50(5) 2004
597
evenly split forest between young, mid-age, and old forest
age classes. Apart from this high level of support for increasing old-growth forest to roughly one-third of the ageclass distribution, there is little information to explain the
apparent preference for this program over the other three. A
possible explanation, discussed further below, is a strong
tendency to reject the below-baseline level of the oldgrowth program, which eliminated the last 5% of oldgrowth forest. Because the functional form of the regression
does not allow for a discontinuity at the baseline level, the
fitted model may overestimate above-baseline WTP and
underestimate below-baseline values.
In contrast, the results indicate a much lower WTP for
increasing biodiversity reserves, with a peak TWP of $45
annually to double the current level to 20% of the OCR
landscape. Any increase over 32% is regarded as worse than
the current level, resulting in negative TWTP. The survey
described reserve designation as representing a wholesale
change of land use on large areas of land (40 –180 square
miles), sharply limiting logging, road building, grazing,
residential development, and other intensive uses, and leaving low-impact recreation as the principal direct-use benefit.
The other three programs are more mitigative in nature, at
least in principle, and are intended to protect elements of
biodiversity on land that is otherwise intensively used. This
may explain why respondents would view greater than 20%
of the OCR in reserves as excessive. Apart from the range
of increase that respondents regard as acceptable, it is also
notable that, at any given level, reserves appear to be the
least preferred conservation mechanism. This is a significant implication, given the scientific support for “coarse
filter” reserve-based approaches to conservation and their
role in maintaining ecological processes (Noss et al. 1997).
These results suggest that management approaches that emphasize large-scale reserves may meet a greater degree of
public resistance than more “fine filter” approaches.
The WTP for the endangered species habitat program is
intermediate relative to the old-growth and reserve programs, with a maximum TWTP of $250/yr/household to
increase habitat protected from 15% to 47%. The curve for
salmon habitat is somewhat flatter, peaking at a maximum
TWTP value of $144/year/household to increase protected
habitat from 15% to 60%, but with positive WTP up to
nearly 100% of habitat streams protected. Although the
analysis indicated that there was no significant difference in
response to program levels across the three regional strata
[15], it is notable that WTP for moderate increases in
salmon habitat protection was substantially higher among
OCR residents than in the other two regions, and was also
the preferred conservation program among residents of the
region. This is consistent with a strong association of OCR
residents with salmon, both as a cultural icon and as a
commodity and recreational resource.
The SQE is represented in the choice modeling as an
alternative-specific constant. Interpreting this as an aversion
to changing current conservation policy may oversimplify.
There are also three instrumental effects that may contribute
to the magnitude of the SQE. First, the status quo alternative
598
Forest Science 50(5) 2004
may be chosen as a proxy for a “don’t know” response by
some respondents who felt that their understanding or the
information provided was insufficient. Second, respondents
reject alternatives to the baseline policy to protest elements
other than cost or changes in program levels. Examples
include a distrust of public land managers or general opposition to tax increases [16]. In other valuation studies, protest responses are typically censored with the justification
that they reveal nothing about preference for the good being
valued. In the context of this study, however, the goods
being valued are inseparable from policy mechanisms, and
such responses do reveal something about relevant preferences. Third, respondents may choose the status quo as a
proxy for another preferred alternative known to be in the
respondent’s choice set, but is not offered (DeShazo et al.
2001). However, this is unlikely in the public-referendum
context used in this survey, because voters are typically
required to vote either for or against a referendum, rather
than choose among many alternatives [17].
Table 4 presents estimated household and aggregated
TWTP for two hypothetical conservation plans: (1) a 5%
reduction from baseline levels of all four programs, including elimination of the last 5% of old-growth forest, and (2)
a 10% increase in all four programs (i.e., in absolute terms,
not proportion of baseline). Note that these WTP figures are
also based on the population demographic values reported
in Table 2. In the far right column, which displays weighted
average TWTP figures for a 10% increase, with the exception of the old-growth program, an isolated increase in any
one program does not achieve sufficiently high WTP to
compensate for the SQE, producing negative TWTP for
changes in program levels. It is only when other program
changes are combined with a 10% increase in old growth
that moderate increases in all program levels produce a
positive TWTP.
The negative WTP values estimated for decreases in the
conservation programs’ below-baseline levels are also notable. Assuming that the current baseline levels are supported by law and the current allocation of property rights,
the negative WTP values represent the amount by which
Oregon households would have to be compensated to make
them indifferent between the current level of conservation
efforts and the reduced level. This so-called willingness to
accept (WTA) can be quite difficult to measure because
many respondents reject the notion of accepting compensation, often by stating an infinite WTA value. The CEA
offers a certain advantage in this regard. By combining
reductions in one or two programs with increases in other
programs, respondents are less inclined to reject the valuation scenario. It is a common result of valuation studies that
there is a sharp disparity between WTP for increases in a
valued resource and willingness to accept compensation for
decreases, and there is ample evidence of a discontinuity
between WTP and WTA for changes in the provision of
public and private goods (Knetsch 1990, Hanemann 1991,
Beenstock et al. 1998). Evidence from these and other
studies suggest that there may be a kink or vertical shift in
the conservation program WTP curves at the status quo
Table 4. Total annual household WTP for two hypothetical scenarios.
Change in Program Level
Coast range
Conservation programs
⫺5%
⫹10%
$ Mean Household Total
WTP
Salmon
⫺40
67
Endangered species
⫺29
46
Old growth
⫺81
150
Reserves
⫺34
36
Status quo cost
⫺242
SUM
⫺426
58
Population
78,353
$ Aggregate Total WTP (Thousands)
Salmon
⫺3,141
5,266
Endangered species
⫺2,269
3,620
Old-growth
⫺6,349 11,787
Reserves
⫺2,673
2,817
Status quo cost
⫺18,927
SUM
⫺33,360
4,564
Willamette Valley
⫺5%
⫹10%
⫺38
⫺102
⫺117
⫺55
⫺140
⫺453
966,010
63
160
235
49
367
⫺36,725
60,488
⫺98,857
154,793
⫺113,349
226,578
⫺53,083
47,552
⫺135,125
⫺437,138
354,28
point [18], with the curve being much steeper for decreases
below the baseline. As such, the WTA values in Table 4,
particularly if the SQE value is excluded, are probably
conservative estimates.
An examination of the TWTP figures in Table 4 reveals
that, under the 10% above-baseline scenario, mean TWTP
of WV households for increases in all conservation programs is in excess of $500/year. Including the deduction for
SQE decreases household TWTP to about $360/year. This
is 0.75% of median WV household income. From Figure 3,
average Oregon household TWTP for increased old-growth
forest alone is almost as high as $280 per year, even when
adjusted for the full SQE.
Given the general aversion of Oregonians to income and
property tax increases, and numerous competing demands
for public funding and private spending, estimates of these
magnitudes should be considered carefully. There is evidence that the estimates suffer to some extent from misspecification bias in the regression analysis, which is problematic in most dichotomous choice valuation studies (Creel
and Loomis 1997, Haab 1997). Because the smooth parabolic shape of the curves is an artifact of the quadratic
specification of the regression model, the magnitude of the
estimates may be less with the use of a functional form
permitting more complex curvature. Of particular concern is
the inability to model the disparity between WTP for increases in program attributes and WTA for decreases below
the baseline. The failure to incorporate this discontinuity, if
it indeed exists in the data, would likely have the effect of
biasing above-baseline estimates upward and belowbaseline estimates downward.
Having stated these caveats, however, these estimates are
consistent with other evidence. First, the values presented
here are not exclusively nonuse values, but most likely also
include perceived recreational and other amenity values,
apart from the values associated with biodiversity conservation. In a telephone survey of Oregon residents, the Or-
E. Oregon
⫺5%
State-weighted average
⫹10%
⫺29
⫺33
⫺50
⫺35
⫺140
⫺321
966,010
51
52
98
34
61
⫺7,891
13,654
⫺9,013
14,120
⫺13,503
26,398
⫺9,4945
9,063
⫺46,765
⫺86,667
16,4171
⫺5%
⫹10%
⫺36
⫺84
⫺101
⫺50
⫺153
⫺424
1,314,113
286
269,750
⫺47,757
⫺110,138
⫺133,201
⫺65,250
⫺200,816
⫺557,165
79,409
172,533
264,764
59,433
⫺46,765,287
375,325
⫺173
60
131
201
45
egon Forest Resources Institute (1999) found that a majority
of Oregonians placed the highest value of the state’s forest
resources on wildlife protection and ecosystem services
(principally protection of water resources). Although 84%
of respondents stated that private property rights and financial returns to forest owners were important, these values
were attenuated by the public goods values of forests. Fortyeight percent of respondents rated “tax and other (voluntary)
incentives” as the best means of promoting environmental
protection. Haynes and Horne (1997) estimated that 89% of
economic benefits produced on federal land in the Columbia
basin in 1995 were environmental amenity values. Also, the
scale of benefits measured in this study is similar to that
obtained by Xu et al. (2003). In a study similar to ours, these
authors estimated average household annual WTP to increase 50-year-old and older forests from 15% to 25% of the
forest landscape. Values ranged from $188 among Washington timber rural residents to $502 for urban residents.
Regardless of the absolute magnitude of WTP for biodiversity conservation, the relative values of the conservation
programs found in this study provide guidance to policy
makers regarding the public acceptability of changes in
OCR conservation plans. Current efforts to increase the
level of harvest under the NWFP (Seattle Post-Intelligencer
2003), particularly to the extent that this requires reducing
the current quantity of late seral forests standing on federal
land, should be considered in light of the level of public
support for old growth. The methodology used here to
estimate public demands for conservation measures surpasses the standards required by the federal government for
valuation of nonmarket benefits in cost-benefit analyses
(Office of Management and Budget 1996), and is most
likely a more conservative valuation technique. Even without submitting increased harvest to a strict cost-benefit test,
however, the results here suggest that support for biological
conservation in the OCR is high and should be considered in
future resource-management decisions.
Forest Science 50(5) 2004
599
Conclusions
With competing demands for resource use in the OCR,
there is a clear need to investigate the preferences Oregonians hold regarding priorities for conservation, and commercial use, of resources. This research empirically maps
the structure of Oregonians’ preferences for four distinct
approaches to conserving biological diversity, and uses an
economic model to estimate public willingness to pay
(WTP) for changes in conservation policies. The use of a
multiattribute valuation framework allows a more accurate
and flexible characterization of the policy and management
scenario than the more commonly used dichotomous
choice-contingent valuation method, and permits more detailed measurement of respondents’ choice behavior. Our
key findings are substantial differences in WTP across
conservation programs and, to a lesser degree, regional
populations. All regions expressed substantial positive WTP
for biodiversity conservation at least at intermediate program levels. Despite expressing general approval of conservation objectives, respondents also indicated substantial
aversion to policy changes. This reduces the estimated WTP
for conservation plans and increases the compensation required for decreases in current protections. The findings will
be useful to policy makers in planning for landscape-wide
initiatives to improve viability of species and ecosystems in
the Oregon Coast Range or, alternatively, to diminish the
level of protection currently in place on public lands.
[9]
[10]
[11]
Endnotes
[1]
[2]
[3]
[4]
[5]
[6]
[7]
[8]
600
CLAMS (www.fsl.orst.edu/clams/first.htm; Spies et al. 2002), an
interdisciplinary project, models the social, economic, and ecological
effects of alternative forest management and land-use policies involving the full array of ownership classes on the landscape. CLAMS
provides tools for policy analysis and primary research in landscape
management and ecological change, using geographic information
systems, remote sensing, spatial simulations of landowner behavior,
vegetation dynamics, and species– habitat relationships. This article is
ancillary to CLAMS and provides measures of economic value for
potential biodiversity conservation scenarios, for comparison with
values of alternative economic OCR landscape outputs.
Stated preference methods and applications have met with criticism
from economists and others in a most vigorous methodological debate
(Diamond and Hausman 1993, Portney 1994, Kopp and Pease 1997).
CEA is a more general technique for eliciting and measuring public
preferences. It encompasses the more familiar (in natural resource
economics) dichotomous choice contingent valuation technique.
Methodological advantages of CEA relative to contingent valuation
technique are discussed in Adamowicz et al. (1999).
A dichotomous choice valuation question was also included. The
results from this question and comparison to the choice experiment
are left to a future paper.
We used SAS OPTEX (SAS Institute 1995) to identify a set of
experimental contrasts to maximize statistical efficiency in the regression analysis (Kuhfeld et al. 1994).
Carson et al. (2000) presents evidence that respondents typically take
hypothetical referenda quite seriously, and that the conjecture of
hypothetical bias is generally unfounded. See Vossler et al. (2003) and
Vossler and Kerkvliet (2003) for further evidence in an Oregon context.
As seen by inspection of Equation 5, entering demographic terms R n
additively would result in cancellation. We interacted demographic
terms with attribute terms to prevent this. An alternative specification
interacted R n with alternative specific constants for each of the three
alternatives, with similar qualitative results.
For all three strata, the null hypothesis H0: ␤ ⫽ 0 is rejected with p ⫽
0.0001, and the Pseudo-R 2 statistics are quite high. Pseudo-R 2 is used
as a somewhat analogous measure of model fit to the R 2 measure used
in linear regression models (Greene 1993), though it does not measure
the proportion of variation in the dependent variable explained by the
Forest Science 50(5) 2004
[12]
[13]
[14]
[15]
[16]
[17]
regressors as the R 2 does. Noting, as Hanemann and Kanninen (1999)
point out, that there is no standard threshold that indicates a satisfactory model fit, the values derived from our estimation performed in
this analysis are relatively high.
Summing the direct linear effect with the demographic interaction
terms results in a positive linear effect of salmon habitat protection
among OCR residents.
Two tests for anchoring effects were implemented by including
parameters in the choice model for the program and bid level specified in the dichotomous choice-contingent valuation technique section of the survey. Likelihood ratio tests of the effect of these
parameters found that there was no significant effect (90% confidence
level). The third test, which tested the effect of the order of presentation of the conservation programs in the survey instrument, found
that a statistically significant, but negligibly small effect was observed in the OCR strata.
A reviewer suggested that, in cases involving ecological systems,
there are correlations between conservation programs. As such, estimates of WTP may be overstated for one amenity and understated for
another. For example, old-growth forest is habitat for many species of
threatened or endangered salmon and for the northern spotted owl.
Thus, respondents may view old-growth protection as preserving the
environmental amenity and protecting endangered species. Clearly
non-zero correlations exist between programs, because of the inherent
interconnectedness of ecological processes. Indeed, our survey points
out that these correlations may exist, by presenting the following text
before the choice experiment: “Each of the four programs on the
previous pages is one part of a possible overall conservation strategy.
The four programs overlap somewhat, but each has different advantages and no single approach is adequate to protect all the elements of
biodiversity.” If respondents view the choice of one program as
leading to the enhancement of another, then there is a potential for
biased estimates of WTP for a specific program. The direction of bias
depends on respondents’ view of these interactions. As a practical
matter, these views are not known, but the potential for bias could be
mitigated by specifying more flexible functional forms for the indirect utility function, with interactions between programs. In doing so,
response surfaces could be estimated, so that WTP for one program
depends on the levels of the other programs. We attempted this
strategy by estimating models with interaction terms between program levels. However, singularity problems precluded us from obtaining meaningful results.
Estimation of willingness to accept compensation is particularly
difficult. Estimates are commonly hampered by anomalous results
associated with protest responses and other cognitive difficulties, and
the disparity between WTP and WTA is an issue of considerable
debate within the literature (Hanemann 1991, Champ and Loomis
1998, Brown and Gregory 1999, Brown 2000). The choice experiment approach used in this survey offers an improved method for
WTA estimation that avoids many of the problems associated with
the mode of elicitation used in other valuation techniques (Adamowicz et al. 1999).
The TWTP curve estimated for old-growth forest conservation among
the OCR sample was negatively sloped at both above-baseline design
levels. This is obscured when averaged with the results from the other
two sample strata.
A likelihood ratio test indicated statistically different status quo
effects between regions at a 99% confidence level.
Using terms that interacted program levels with dummy variables for
strata, likelihood ratio tests were conducted to test for significant
difference between coefficients across strata. Results did not reject
the null hypothesis of equal coefficients across strata (␣ ⫽ 0.10).
Demographic differences between regions, however, give rise to
substantial differences in calculated WTP functions.
Seventy-six respondents indicated in a follow-up question that they
chose the status quo because of distrust of the government, opposition
to property rights interference, opposition to taxes in general, or other
forms of protest relating to the policy mechanism or payment vehicle.
Most respondents left the follow-up question blank, and it is likely
that many protest responses were made without an explanation from
the respondent.
We thank an anonymous reviewer for referring us to DeShazo et al.
(2001). The authors of this article develop a test for the effect of
unoffered alternatives on respondent choice of the status quo alternative. The test is developed in the context of a consumer choice
experiment wherein alternative real-product choices are available,
which may plausibly affect respondent choice behavior. We feel that
this hypothesis is less plausible in the context of public-choice referenda. However, it does point out that further research is needed to
fully understand the status quo effect and to better control for the
choice behavior that gives rise to it.
[18] A model was tested that included a dummy variable for decreases
below baseline for each of the conservation programs. Although the
parameters on these terms were highly significant and negative,
suggesting a discontinuity in demand at the baseline and proportionately higher WTA for decreases, there were insufficient degrees of
freedom to estimate more than one of these terms in the model, and
none could be estimated simultaneously with the status quo term.
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