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ANALYSIS
Consumption process and multiple valuation of landscapeattributes
Matıas Gonzalez, Carmelo J. Leon *
University of Las Palmas de Gran Canaria, Edificio de Ciencias Economicas, Modulo D-3.16, Las Palmas de Gran Canaria 35017, Spain
Received 21 December 2001; received in revised form 26 September 2002; accepted 20 November 2002
Abstract
Environmental valuation can be seen as a process which evolves as individuals reflect on their consumption
experience. In this paper we consider how environmental values could change as the subject has reached the end of the
consumption process. A split sample comparison is conducted for estimating the values tourists give to a set of
landscape attributes. The first subsample was taken on-site as subjects were on a tour contemplating the landscape of
the island of Gran Canaria (Canary Islands). The second subsample was contacted at the airport when individuals were
about to leave the tourist enclave. Since landscapes are complex environmental goods, we utilize and compare a
multiple contingent valuation (CV) approach with the stated preference approach of contingent ranking. The results
show that the influence of the consumption experience depends on the valuation method and the consideration of
interaction effects in the model. The valuation functions were not stable across both sites for most of the models
considered and some of the attributes changed their values between the two points of the consumption process.
Interaction effects were more relevant with the contingent ranking model, suggesting that CV is a more limited
approach in a context of multiple valuation.
# 2003 Published by Elsevier Science B.V.
Keywords: Multiple contingent valuation; Consumption process; Landscape attributes; Preference formation; Stated preference
1. Introduction
Valuation of environmental goods utilize direct
and indirect methods which are based on the
assumption that preferences are stable throughout
the consumption process. In general, neoclassical
economics assumes that preferences are ‘exogen-
ous, stable, and known with adequate precision’
(March, 1978). The implication is that individuals
hold well develop preferences which can be elicited
by direct approaches such as contingent valuation
(CV) or stated preference techniques. Environ-
mental values and preferences can be studied at
different points of the consumption process with-
out expecting significant changes in individual
behavior.
However, authors such as Fischhoff (1991) and
Tversky and Simonson (1993); Wilson et al. (1993)
have argued that consumers do not have well-
developed preference functions except for the most* Corresponding author.
E-mail address: carmelo@empresariales.ulpgc.es (C.J. Leon).
Ecological Economics 45 (2003) 159�/169
www.elsevier.com/locate/ecolecon
0921-8009/03/$ - see front matter # 2003 Published by Elsevier Science B.V.
doi:10.1016/S0921-8009(02)00279-3
basic goods, and that consumers construct their
preferences by making choices and reacting to
products after consumption or purchase. Simi-
larly, Einhorn and Hogarth (1987) suggest that
learning and preference formation requires both
forward and backward thinking, while Wildavsky
(1987) goes further by claiming that preference
formation emanates from a cultural context, and
therefore, cannot be considered as a process
external to social constructs.
The influence of the consumption process on
preference formation could be a relevant issue in
the valuation of some environmental goods1. A
related aspect is the question of the distance from
the good to be studied in the design of non-market
scenarios following stated preference methods. For
instance, visitors to a National Park can respond
differently when they are interviewed on-site than
when they are interviewed at their homes after
visits had been completed. This presumption
would imply that some non-market goods could
be subject to the distance hypothesis, i.e. indivi-
duals might hold different values depending on
which phase in the consumption process they are
approached. Consumption can be seen as a
process in which values are formed, evolving
from the initial stages until the subject has reached
a final conclusion when consumption is finished,
or is kept only in memory. The approach of
interviewing subjects when they are consuming a
non-market good could limit the reflection process
on which monetary values should be based. The
lack of previous experience in market contexts for
most environmental goods makes it more likely to
obtain biased values from the early stages of the
consumption process.
A similar hypothesis which has been the object
of substantial research is the temporal reliability of
CV estimates. The NOAA panel protocol (Arrow
et al. (1993)) recommends that CV studies should
be carried out after some time of a natural disaster
in order to allow for people to reflect on the
consequences of the damages. There is confluence
among test�/retest and split sample tests that CV
results are stable over time2. However, this cannot
be interpreted as the conclusion that consumption
of an environmental good has no effect on the
final value. For comparability reasons, temporal
reliability studies are conducted at the same point
in the consumption process, either on-site or when
the good is not being consumed. Some studies
focus on non-use values, thus by definition con-
sumption has not even taken place. Therefore,
temporal reliability studies cannot provide insights
as to how the stage of consumption could affect
the values elicited in constructed market scenarios.
The objective of this paper is to investigate the
relevance of the distance hypothesis for the case of
the valuation of landscape preferences. In general,
we can consider that landscapes are information
that human beings receive from the ecological
systems. Abello and Bernaldez (1986) point out
that human beings are part of the landscapes and
they are sensitive to their ecological relationships.
That is, there is a network of social and environ-
mental factors interacting in the conformation of
landscapes.
In order to evaluate landscapes with a policy
purpose, there are two general approaches. In the
objectivist approach, landscapes are valued by
their objective and intrinsic characteristics (Daniel
and Vinig, 1983). On the opposite side, the
subjectivist approach considers that landscapes’
values depend on the characteristics of the ob-
server (Briggs and France, 1980). Thus, the land-
scapes would refer to those properties of the
1 By definition, non-use values for environmental goods are
exempt from the consumption process, since no previous
experience is assumed when assessing these types of values.
However, in some sense non-use value formation might also
respond to similar processes in which information and social
contexts can be relevant.
2 Test�/retest studies compare the same survey instrument
addressed with the same sample at two points in time. In order
to avoid recall bias, it is common to vary the valuation question
or to span the time lag. For instance, Keyly et al. (1990),
Loomis (1990) and Stevens et al. (1994) found that there were
no changes across time. Split-halves reliability test can also be
conducted if different samples are used at the two points in
time. Reliable results are found, among others, in Reiling et al.
(1990), Teils et al. (1995), Carson et al. (1997) and Whitehead
and Hoban (2000).
M. Gonzalez, C.J. Leon / Ecological Economics 45 (2003) 159�/169160
environment which can be visually perceived
(Amir and Gidelson, 1990), and the value of a
particular landscape would be given by the satis-
faction experienced in its contemplation. There are
also holistic approaches integrating both the sub-
jective and objective ideas (Bishop and Hulse,
1994; Buhyoff et al., 1994), which are particularly
focused on predicting the value of landscape
changes because the impact of human activities.
CV methods have been applied to landscapes
features by Price (1978), Willis and Garrod (1993)
and Hanley and Ruffell (1993), among others.
Since landscapes are complex environmental
goods involving several attributes, there has been
a more recent interest in the application of multi-
ple valuation methods. Bergland (1998) presented
an example which shows that conjoint methods
such as contingent ranking are useful for valuing
landscape attributes. Similarly, Santos (1998) de-
rived substitution and complementarity effects
utilizing CV for landscape programs based on
the framework proposed by Hoehn and Loomis
(1993) and Cummings et al. (1994). We consider
both approaches for valuing landscape preferences
at two points of the consumption process3. The
application focuses on a policy to rehabilitate the
landscape of Gran Canada (Canary Islands),
which is a popular tourist destination in the
European market.
2. Data sources
The data were obtained from a survey con-
ducted in Gran Canaria to the population of
European tourists visiting the island for holidays.
Two split samples were taken with the intention of
testing the hypothesis of invariant willingness to
pay (WTP) after the consumption experience. The
good considered was a wide landscape-recovering
program which was going to be conducted by the
local authorities, funded jointly with European
sources.The first subsample was taken while tourists
were traveling on a bus-tour around the island. On
this tour tourists focused on enjoying the varied
and somewhat unique features of the landscape of
the volcanic island, with scenic views and endemic
vegetation. Thus, these individuals were asked
about the valuation for a policy agenda while
they were enjoying consumption of the present
status of the good to be valued. Another sub-
sample was collected while tourists were at the
airport before taking their flight back to their
origin countries. It is supposed that individuals in
the first subsample would be conditioned by the
act of consuming the landscape, while those in the
second subsample had more time to reflect on their
consumption experience.In winter 1998 both subsamples were taken
randomly from both the buses and the departure
areas of the airport. Only subjects with enough
time to answer the questionnaire were considered
for the study, which was handed out for self-
administration in five different languages (Dutch,
English, German, Spanish and Swedish). A total
sample of 2109 respondents was obtained, with
888 individuals for the bus subsample and 1221 for
the airport subsample4.The design of the final questionnaire was
improved with the results from two pre-test studies
and two focus groups with tourists and with
3 There are several contributions on the comparison between
CV and other stated preference methods, some of them
involving landscape policies. For instance, Boxall et al. (1996)
found out that CV led to higher values than a choice experiment
(CE) for a particular attribute in forest recreation. Hanley et al.
(1998) obtained that CE results were not significantly different
than dichotomous choice CV but substantially larger than open
ended CV in a split sample exercise involving landscape and
wilderness protection. Adamowicz et al. (1998) concluded that
both CV and CE led to statistically similar results for attribute
parameters, while Stevens et al. (2000) argued that conjoint
techniques (ratings) lead to upwardly biased estimates of
welfare changes. These studies fail to provide insights on the
performance of a multiple valuation approach to CV, which
could take account of possible relationships between the goods
included in the valuation bundle.
4 For the airport subsample, 67% of the subjects had made
an excursion around the island. This is 827 individuals in this
subsample. This group was not different from the excursion
subsample in terms of socioeconomic characteristics such as
education and income.
M. Gonzalez, C.J. Leon / Ecological Economics 45 (2003) 159�/169 161
professional bus-tour tourist guides working in the
field. The landscape recovery program contained a
number of 122 specific policy measures of inter-
vention in the landscape, which were grouped in
four sets5:
. Program A: Reforestation and road-side gar-
dening.
. Program B: Rehabilitation of old houses.
. Program C: Decorating and painting the out-
side of houses.
. Program D: Waste and rubbish removal.
The valuation scenario included a map of the
island of Gran Canaria showing some of the places
where measures were going to be implemented. In
addition, two sets of four colorful pictures, one for
each specific program (or group of measures) were
included in order to illustrate the present state of
the landscape and its status after the measure is
fulfilled. These pictures were manipulated with the
advice of experts in the programs and utilizing
available specialized software.
The four programs lead to a full factorial design
of 16 possible combinations. Excluding the no
programs combination, we were left with 15
potential profiles for the CV questions. Since it
was very demanding from the subject to answer 15
different questions, five groups of four profiles
were randomly generated and distributed across
the sample. Each group contained the combina-
tion including all programs as the reference
agenda. Thus, each individual was faced with
four dichotomous choice questions referring to a
subset of the potential combinations. After the
binary questions the subject was asked to rank the
four alternatives from one to five, with the
addition of a status quo options involving zero
cost and no policy measures.
The bid vector was designed utilizing the
responses to the open ended question in the pretest
samples. A four bid vector design was adopted
following Cooper (1993) approach.
The vector describing the baseline prices for thesingle program agendas was 750, 1500, 2250, and
4000 pesetas6. These prices were increased propor-
tionally with the number of programs in the
valuation question and were randomly distributed
across the sample, such that each individual
answered one of the four possible levels. In order
to facilitate the valuation process by foreign
visitors used to buy and sell in their own currency,the amounts were converted to the origin country
currencies at market exchange rates.
3. Methods
Let us consider a complex environmental good z
such as landscape, which is a vector including a
number of attributes or services (z1,. . ., zi ,. . . zn ).
Hoehn and Randal (1989) showed that indepen-
dent valuation and summation of the effects of
policy measures on individual welfare could leadto overestimation because of possible substitution
effects that are left out under this approach.
It is assumed that the subject has a well behaved
utility function U�/U (x , z , c ), i.e. strictly increas-
ing and quasi-concave in x and z , where x is a
composite market good and c represents other
socioeconomic variables. The indirect utility func-
tion follows from solving the consumer maximiza-tion problem subject to the budget constraint, i.e.
V (p , z , y , c )�/U (x(p , z , y , c), z , c), where y is
personal income and p is the market price of x .
Considering a change in attribute qualities from
the baseline z0 to alternative zt , the maximum
WTP is given by solving the following:
V (z0; y; c)�V (zt; y�WTP; c) (1)
Hoehn (1991) and Hoehn and Loomis (1993)showed that the WTP function can be interpreted
as a linear approximation involving first and
second order effects.
Following the dichotomous choice CV method,
the individual is asked a yes/no question about
voting for a policy option which is defined by a set
5 The survey instrument is available from the authors on
request.
6 These prices are converted to EUROS at the official rate of
166 386 pesetas for the empirical analysis.
M. Gonzalez, C.J. Leon / Ecological Economics 45 (2003) 159�/169162
of attribute levels, i.e. a possible combination,including a given price. Hoehn and Loomis (1993)
and Santos (1998) modeled the responses to the
valuation questions following Cameron’s (Ca-
meron, 1988) approach for censored dependent
variable models. The alternative approach by
Hanemann (1984) and Hanemann et al. (1991) is
compatible with the general structure of random
utility models.In a multiple valuation setting, the subject is
asked a number of binary questions involving
different combinations of the possible choice set
defined by all possible policy agendas. Let us
assume agenda zt with price brt for individual r ,
and let us define rrt and drt as indicator variables
which take the value of 1 if the individual receives
agenda zt and gives a positive answer, respectively,and 0 otherwise. The probability of an affirmative
answer to this question is given by:
Pr(WTP�brt)�Pr(drt�1)
�Pr(V (z0; y; c)�o0BV (zt; y�brt; c)�ot)
�Pr(o0�otBDV ) (2)
where o0 and ot are identically and independently
distributed error terms for each alternative, DV is
the utility difference in both states, and brt is the
price the subject is asked to pay for this change
involving profile zt , against the no policy baseline
z0.
Let us consider a successive binary question
involving another policy agenda. For instance,define a new bundle such that zu �/zt and bru �/
brt . Assuming monotonic preferences, the answer
to this second question could give us more
information about the bounds of WTP. Let
pru �/1 if the individual answers affirmatively to
bundle zt with price brt and negatively to bundle zu
with price bru , and 0 otherwise. If pru �/1 then
brt B/WTPB/bru , while pru �/0 indicates WTP�/
bru . Similarly, consider that prv�/1 if the indivi-
dual answers negatively to bundle zt with price brt
but positively to zv with price brv , where zvB/zt
and brvB/brt , and 0 otherwise. It is clear that if
prv�/1 then brvB/WTPB/brt while prv�/0 indi-
cates WTPB/brv .
The sample log likelihood function is as follows:
log lCV �XM
r�1
�XN
t�1
drt(1�pru)logf(1�8 (azt�bbrt))
�drtpru log(8 (azu�bbru))
�8 (azt�bbrt))�(1�drt)
� (1�prv)log(8 (azu�bbru))
�(1�drt)prvlog(8 (azt�bbrt)
�8 (azv�bbrv)) (3)
where a and b are parameters to be estimated, and
8 is the logistic cumulative distribution. The
parameters can be estimated by maximum like-lihood, similarly to Hanemann et al. (1991) for the
double bounded dichotomous choice model.
In the CR experiment, the subject receives a set
of alternatives which she has to order according to
her preferences. For each alternative there is a
policy agenda involving different sets of landscape
actions and an accompanying price which the
subject has to pay for the agenda to be carriedout. One of the agendas in the choice set involves
no cost and no policy actions. Let urt �/V (zb , y�/
brt , c ) be the maximum utility that subject r
receives from agenda zt at the price brt and
consider that t�/1, . . ., N , where N is the number
of policy agendas in the choice set. For simplicity,
as in the binary choice model, it is assumed that urt
is composed by a deterministic component and arandom component. That is:
urt�V (zt; y�brt; c)�ot�Vrt�ot (4)
Assuming Gumbel distributions for ot , the
probability that the subject provides a particularorder of preference for the alternatives in the set is
given by (Beggs et al., 1981):
Pr[ur1�ur2� . . .�urN ]
�YN
j�1
�exp
�Vrj�XN
k�j
exp(Vrk)
���
: (5)
Thus the likelihood function follows from
M. Gonzalez, C.J. Leon / Ecological Economics 45 (2003) 159�/169 163
evaluating the joint probability across all indivi-duals. That is:
lR�YMr�1
YN
j�1
�exp
�Vrj�XN
k�j
exp(Vrk)
���
(6)
The parameters of the utility function determin-
ing individual behavior result from maximizing
this likelihood function with respect to these
parameters. This can be done by any iterativemethod, similarly to the CV model. The optimal
parameters are comparable and have the same
interpretation than the CV model because they
arise from the same utility maximization frame-
work. For a linear utility model, such as Vrt �/
azt�/bbrt , the marginal value of attribute zti is
given by �/ai /b, where ai , is the marginal utility of
the attribute i and b is the marginal utility ofincome.
4. Results
The results for the valuation of complex envir-
onmental goods critically depend on the consid-eration of the interaction or second order effects,
as previously shown by Hoehn and Randal (1989).
These effects can be considered in our experiments
for valuing landscape attributes because we have
chosen a simple design task involving four attri-
butes with two levels, giving us a reduced number
of potential combinations for a design with inter-
actions. These combinations could be feasiblydistributed across the sample. Most comparisons
between stated preference methods have not
reported results with interaction effects. However,
both the CV and the CR methods are capable of
considering substitution and complementary ef-
fects. These effects might be particularly relevant
for welfare assessment in the case of multiple
valuation. The models have been estimated bymaximum likelihood using LIMDEP and SAS
routines. For the CV model, we have assumed a
logistic distribution for the random term of the
indirect utility function.
When interaction effects are omitted, it can be
seen in Table 1 that parameter estimates are close
between the excursion and airport subsamples,
particularly for the CV data7. In general, para-
meters for landscape attributes are larger for the
excursion data. This means that landscape attri-
butes have higher impacts on utility for those
subjects experiencing the landscape on the bus
tour around the island of Gran Canaria. For the
CV method, the differences in the estimated
parameters across both subsamples are statistically
significant, as can be demonstrated by using a
likelihood ratio test. The LR statistic takes the
value of 11.96, which is larger than the corre-
sponding critical value at the 95% level (9.49). The
differences between both sites are more accentu-
ated for the results with the CR valuation method.
In this case the LR statistics takes the value of
34.98, which is much larger than the critical value.
Further, programs B and D are not significant for
the airport data with this method. Thus, the results
with both valuation methods suggest that a simple
valuation framework, which omits interaction
effects is sensitive to the distance from the land-
scape attributes.
Comparing the two approaches to multiple
valuation utilized in this paper, the parameter
values for the specific landscape programs ob-
tained from the CV responses are found to be
larger than those generated for the CR data8.
These divergences become more evident for the
excursion subsample. The cost parameter is also
substantially much lower*/in absolute value*/for
7 The analysis is conducted on the group of individuals who
had been in the excursion and were interviewed at the airport.
After excluding protest and missing responses the total number
of observations was 802 for the airport and 808 for the
excursion.8 An anonymous reviewer pointed out that there can be
some correlation between both sets of responses which might
lead to biased results from separate estimation, However, the
fact that the CR question comes after the CV question does not
preclude a comparison between both question formats. Focus
group experiments showed that the potential difficulty
associated with the CR method could be reduced by
introducing an early stage of more simple binary questions.
Thus, the CR data can be interpreted as final observations
generated by the elicitation process after discarding the first set
of responses to binary questions. On the other hand,
comparisons within the same individuals avoids potential
sample errors which can be found in split sample.
M. Gonzalez, C.J. Leon / Ecological Economics 45 (2003) 159�/169164
the CR model. Hence, the latter model gives a
smaller marginal utility of money than the CV
model. A likelihood ratio test was conducted by
estimating the joint CV and CR model and
comparing its results with those derived from
separate models. The results confirmed that both
models were statistically dissimilar, and could not
be considered as resulting from the same sample
information. These statistical differences will lead
to divergent value estimates for the mean WTP
obtained with these alternative models.
If second order interaction effects are taken into
account9, there are relevant changes in parameter
values with respect to the models in Table 1. The
main effects parameters for the individual pro-
grams become larger, producing also higher con-
tributions to individual utility in the excursion
than in the airport (Table 2). But we find that there
are no statistical differences across both sites using
the CV data. For this valuation model, the LR
statistics takes the value of 16.4 which is slightly
lower than the critical value at the 95% confidence
level (18.31). The corresponding statistics for the
CR responses raises to 39.6, indicating that there
are significant differences in the statistical para-
meters across both subsamples with this valuation
technique.
The negative signs of the interaction effects
reveal substitution between landscape programs.
These substitution effects seem more relevant and
more significant for the CR model. In the CV
model, most of the interaction effects are not
significant. The interaction between programs A
and B is significant for both subsamples, and the
interaction between programs B and D is signifi-
cant for the excursion subsample. However, in the
CR model all substitution effects are very signifi-
cant. This suggests that the CR model could be
more capable of measuring substitution effects
between programs than the CV model10.
Table 3 presents the results of WTP estimates
for the specific programs obtained with the model
without interactions11. This is the type of model-
ing most employed in the comparisons between
CV and alternative stated preference methods.
Table 1
WTP Models without interaction terms (standard errors in brackets)
Variable Contingent valuation Contingent ranking
Airport Excursion Airport Excursion
A 0.7171* (0.0733) 0.7664* (0.0734) 0.1723* (0.0498) 0.2868* (0.0508)
B 0.5913* (0.0754) 0.6806* (0.0743) 0.0154 (0.0493) 0.1496* (0.0498)
C 0.5132* (0.0757) 0.6914* (0.0722) 0.1262* (0.0477) 0.3218* (0.0482)
D 0.6387* (0.0765) 0.7130* (0.0246) 0.0340 (0.0525) 0.1694* (0.0523)
Cost �/0.06806* (0.001866) �/0.07672* (0.00189) �/0.01096* (0.001315) �/0.01417* (0.00128)
log L �/2844.39 �/3035.13 �/3812.75 �/3826.71
n 802 808 802 808
*Significant at 0.01 level.
9 We also considered models with covariates, but did not
produce substantial changes in parameter estimates. For the
pooled model as well as for both subsamples we found out to be
relevant explanatory variables income, education, age, the
degree of satisfaction with the actual state of the landscape, and
the place where the interviews were conducted (excursion vs.
airport). Personal income and the education level of the
individual had a positive effect on the probability of
acceptance an option, while age had a negative effect.
10 For each subsample, a joint CV�/CR model was estimated
and compared with the split models with a LR test. As expected
by the differences in the parameter estimates, both models are
not statistically alike. The most important difference is for the
cost parameter, which is found about six times larger with the
CV data.11 Results for the combination pairs can be obtained from
the authors upon request.
M. Gonzalez, C.J. Leon / Ecological Economics 45 (2003) 159�/169 165
Confidence intervals were calculated utilizing
Krinsky and Robb (1986) procedure12. The results
from the CR model show that the values of all
landscape programs are substantially smaller for
the airport subsample. For the CV model, results
are quite close for both points of the consumption
process, with only a small difference observed
between the airport and the excursion for program
C. Therefore, although the estimated CV models
reveal significant differences between both sites
(Table 1) for at least one of the parameters, it is
clear that there are no differences for the values of
most landscape attributes. These contrasting re-
sults for the valuation functions and for the value
measures can not be seen as contradictory. It
reflects the fact that landscape values are a
function of the estimated parameters. The com-
parison of the estimated parameters from the
utility functions for both sites does not necessarily
lead to the same results for the corresponding
value measures13. Looking at the differences
between valuation methods, CR leads to larger
values than CV for all significant programs and
their combinations. The relative values between
programs depend on the valuation method and the
sample context. For the CV method, program A is
the most valued followed by program D. These are
the programs concerned with the actions leading
to reforestation and waste removal. The CR
method leads to programs C and A as the most
valued. Thus, both valuation methods do not lead
to the same ordering of the values for the
attributes if interaction terms are omitted from
the estimation process.
Considering the evaluation of the welfare mea-
sures with the inclusion of interaction terms, Table
4 shows that none of the landscape programs
become significantly different across both sites for
the CV method. However, most interaction terms
reflecting substitution between programs were not
significant for the CV model. The results for theCR data improved substantially, since all interac-
tion effects were significant. In this case, although
the valuation functions were significantly different,
the results for the valuation measures do not
deviate as much across both subsamples.
5. Concluding remarks
Environmental valuation can be seen as a
process by which individuals could develop their
preferences as they distance themselves from the
good to be valued. In this paper we have con-
sidered how the consumption process could influ-
ence the values elicited in a non-market valuation
context. The experiment has been based on the
comparison of tourist’s values for a landscapepolicy program at two points of the consumption
process. The first was when subjects were enjoying
the landscape attributes on a tourist bus tour, and
the second at the airport before taking their return
flight. These are two points of the tourist trip,
which involve a different distance from the land-
scape attributes. It is presumed that if values are
developed along the consumption process, theresults for both subsamples might not be exactly
matched. Since landscapes are complex environ-
mental goods, we consider two alternative multiple
valuation approaches which allow us to study the
implications of the interaction effects.
The results of the comparison between the two
points of the consumption process gives some
support to the distance hypothesis. Valuationfunctions are not interchangeable between the
airport and the excursion subsamples, except for
the CV model with interaction effects. However,
the welfare estimates for the individual programs
and their combinations are not significantly dif-
ferent between both sites for the CR model with
interaction effects. The distance hypothesis is
supported for the welfare estimates only if inter-action terms are omitted with the CR model and
only for program C if interaction effects are taken
into account. Most applications of stated prefer-
ence techniques to environmental goods have
omitted the possibility of interaction effects.
Hence, the pursue of this practice would lead to
13 The reason is that welfare measures are a function of the
estimated parameters. In our case, if all parameters shift by the
same proportion there is no change in the welfare estimates.13 The reason is that welfare measures are a function of the
estimated parameters. In our case, if all parameters shift by the
same proportion there is no change in the welfare estimates.
M. Gonzalez, C.J. Leon / Ecological Economics 45 (2003) 159�/169166
the conclusion that the distance hypothesis is
relevant, i.e. the values arising in the airport would
be significantly lower than those elicited when the
individual was enjoying the landscape attributes.
However, interaction effects are relevant for the
CR model and substantially less important with
the CV model. These results would be in favor of
the idea that CR and related conjoint or choice
experiment techniques are more capable of mea-
suring substitution or complementary effects be-
tween environmental attributes than the more
traditional CV techniques. The significance of the
interaction terms makes the CR model superior to
the CV model in the context of multiple landscape
attributes. Therefore, we might conclude that the
distance hypothesis is relevant for the valuation
function but not for all the welfare measures. That
is, it is clear that the contributions to individual
utility obtained from the landscape attributes are
larger in the excursion than at the airport point,
but most money measures are very similar. This
means that individual behavior changes signifi-
cantly between both sites, but this change does not
necessarily affect all the values elicited in a multi-
ple valuation context. Therefore, although overall
preferences are not stable in the consumption
process, some but not all of the attribute values
could be found to be stable.
The importance of the distance hypothesis
should be further investigated in the contexts of
the value formation and the consumption process.
There is need for more insights into how values are
formed, and what is the role of the consumption
process in value formation. The sensitivity ob-
Table 2
WTP Models with interaction effects (standard errors in brackets)
Contingent valuation Contingent ranking
Variable Airport Excursion Airport Excursion
A 1.0312* (0.1277) 1.0951* (0.1420) 0.3603* (0.0764) 0.4309* (0.0784)
B 0.8608* (0.1435) 1.0685* (0.1474) 0.2501* (0.0776) 0.2969* (0.0728)
C 0.4700* (0.1433) 0.8823* (0.1348) 0.2375* (0.0768) 0.3963* (0.0773)
D 0.7972* (0.1483) 1.0430* (0.1457) 0.3062* (0.0772) 0.3991* (0.0751)
AB �/0.4028$ (0.1925) �/0.3638# (0.1988) �/0.2055$ (0.1063) �/0.1773# (0.0950)
AC �/0.1480 (0.2113) �/0.1295 (0.2087) �/0.1506* (0.0651) �/0.1572* (0.0656)
AD �/0.2901 (0.2140) �/0.2444 (0.2173) �/0.2248$ (0.0959) �/0.2773* (0.0963)
BC 0.1271 (0.2147) �/0.1224 (0.2173) �/0.0760$ (0.0347) �/0.1511* (0.0351)
BD �/0.3305 (0.2128) �/0.3844# (0.2102) �/0.2230$ (0.0952) �/0.1576# (0.0954)
CD 0.1972 (0.1965) �/0.1270 (0.1974) �/0.1493$ (0.073) �/0.2513* (0.0743)
COST �/0.06989* (0.001936) �/0.0758* (0.001987) �/0.00991* (0.00143) �/0.01295* (0.00141)
log L �/2830.33 �/3009.16 �/3556.68 �/3500.43
N 802 808 802 808
*, Significant at 0.01 level; $, significant at 0.05 level; #, significant at 0.10 level.
Table 3
WTP Estimates without interaction terms (t) (confidence intervals in brackets)
Program Contingent valuation Contingent ranking
Airport Excursion Airport Excursion
A 10.5 (8.1, 12.4) 9.9 (7.6, 11.2) 15.7 (13.91, 17.50) 20.2 (18.2, 22.8)
B 8.6 (6.3, 10.8) 8.8 (7.0, 10.6) 1.4 (�/2.9, 3.7) 10.5 (7.1, 13.3)
C 7.5 (5.1, 9.6) 9.0 (7.2, 10.7) 11.5 (9.7, 13.2) 22.7 (20.2, 24.9)
D 9.3 (7.8, 12.1) 9.2 (7.5, 11.0) 3.1 (1.4, 5.6) 11.9 (9.8, 13.4)
M. Gonzalez, C.J. Leon / Ecological Economics 45 (2003) 159�/169 167
served with respect to the valuation function
indicates that some monetary values could evolve
in a declining fashion along the consumption
process. It would be interesting to know whether
there is a limit for these values and to what extentthey are upwardly biased by the earlier stages of
the consumption experience. On the other hand,
the investigation of the linkages between the
valuation and consumption processes should con-
sider the scope of alternative valuation methods
provided by the development of stated preference
techniques.
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