Consumption process and multiple valuation of landscape attributes

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: [email protected] (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

mailto:[email protected]

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.


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


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.


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.


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.


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


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)


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.

References

Abello, R.P., Bernaldez, F.G., 1986. Landscape preference and

personality. Landscape and Urban Planning 13, 19�/28.

Adamowicz, V., Boxall, P., Williams, M., Louviere, J., 1998.

Stated preference approaches for measuring passive use

values: choice experiments and contingent valuation. Amer-

ican Journal of Agricultural Economics 80, 64�/75.

Amir, S., Gidelson, E., 1990. Expert based method for the

evaluation of visual absorption capacity of the landscape.

Journal of Environmental Management 30, 151�/163.

Arrow, K., Solow, R., Portney, P., Leamer, E., Radner, R.Y.,

Schuman, H., 1993. Report of the national oceanic and

atmospheric administration panel on contingent valuation.

Federal Register 58, 4602�/4614.

Beggs, G., Cardell, S., Hausman, J.A., 1981. Assessing the

potential demand for electric cars. Journal of Econometrics

16, 1�/19.

Bergland, O., 1998. Valuation of Landscape Elements Using a

Contingent Choice Method. Working Paper, Norwegian

University of Agricultural Sciences.

Bishop, I., Hulse, D., 1994. Prediction of scenic beauty using

mapped data and geographic information systems. Land-

scape and Urban Planning 30, 59�/70.

Boxall, P., Adamowicz, W., Swait, J., Wiliams, M., Louviere,

J., 1996. A comparison of stated preference methods for

environmental valuation. Ecological Economics 18, 243�/

253.

Briggs, D., France, J., 1980. Landscape evaluation: a compara-

tive study. Journal of Environmental Management 10, 263�/

275.

Buhyoff, G., Miller, P.A., Roach, J.W., Zhou, D., Fuller, L.G.,

1994. An AI methodology for landscape visual assessments.

AI Applications 8, 1�/13.

Cameron, T., 1988. A new paradigm for valuing non-market

goods using referendum data. Maximum likelihood estima-

tion by censored logistic regression. Journal of Environ-

mental Economics and Management 15, 355�/379.

Carson, R.T., Hanemann, W.M., Kopp, R.J., Krosnick, J.A.,

Mitchell, R.C., Presser, S., Ruud, P.A., Smith, V.K., 1997.

Temporal reliability of estimates from contingent valuation.

Land Economics 73, 151�/163.

Cooper, J.C., 1993. Optimal bid selection for dichotomous

contingent valuation surveys. Journal of Environmental

Economics and Management 24, 25�/40.

Cummings, R.G., Ganderton, P.T., McGuckin, T., 1994.

Substitution effects in CVM values. American Journal of

Agricultural Economics 76, 205�/214.

Daniel, T., Vinig, J., 1983. Methodological issues in the

assessment of landscape quality. In: Altman, I., Wohwill,

J. (Eds.), Behaviour and the Natural Environment. Plenum

Press, pp. 39�/83.

Einhorn, H.J., Hogarth, R.M., 1987. Decision making: going

forward in reverse. Harvard Business Review 65, 66�/70.

Fischhoff, B., 1991. Value elicitation: is there anything in there.

American Psychologist 46, 835�/847.

Hanemann, M.W., 1984. Welfare evaluations in contingent

valuation experiments with discrete responses. American

Journal of Agricultural Economics 66, 103�/118.

Hanemann, W., Loomis, J., Kanninen, B., 1991. Statistical

efficiency of double-bounded dichotomous choice contin-

gent valuation. American Journal of Agricultural Econom-

ics 73, 1255�/1263.

Hanley, N., Ruffell, R.J., 1993. The contingent valuation of

forest characteristics. Journal of Agricultural Economics 44,

218�/229.

Hanley, N., MacMillan, D., Wright, R.E., Bullock, C.,

Simpson, I., Parsisson, D., Crabtree, B., 1998. Contingent

valuation versus choice experiments: estimating the benefits

Table 4

WTP estimates with interaction effects (t) (confidence intervals in brackets)

Program Contingent valuation Contingent ranking


A 14.7 (11.3, 18.1) 14.4 (11.2, 17.6) 36.3 (32.1, 40.5) 33.2 (29.8, 37.6)

B 12.3 (8.5, 16.0) 14.0 (10.7, 17.3) 25.2 (21.42, 29.1) 22.9 (36.2, 25.5)

C 6.7 (2.9, 10.4) 11.6 (8.2, 14.6) 23.9 (19.9, 27.2) 30.5 (27.9, 33.2)

D 11.4 (7.5, 15.3) 13.7 (10.4, 16.9) 30.8 (26.7, 34.0) 30.8 (26.2, 34.4)


of environmentally sensitive areas in Scotland. Journal of

Agricultural Economics 49, 1�/15.

Hoehn, J.P., 1991. Valuing the multidimensional impacts of

environmental policy: theory and methods. American


Hoehn, J., Randal, A., 1989. Too many proposals pass

the benefit cost test. American Economic Review 79, 544�/

551.

Hoehn, J., Loomis, J., 1993. Substitution effects in the

valuation of multiple environmental programs. Journal of

Environmental Economics and Management 25, 56�/75.

Keyly, M.J., Montgomery, M., Dovidio, J.F., 1990. Reliability

and predictive validity of contingent valuation: does the

nature of the good matter. Journal of Environmental

Economics and Management 19, 244�/263.

Krinsky, I., Robb, L.A., 1986. On approximating the statistical

properties of elasticities. The Review of Economics and

Statistics 68, 715�/719.

Loomis, J.B., 1990. Comparative reliability of the dichotomous

choice and open-ended contingent valuation techniques.

Journal of Environmental Economics and Management 16,

78�/85.

March, J.M., 1978. Bounded rationality, ambiguity, and the

engineering of choice. Bell Journal of Economics 9 (2), 587�/

608.

Price, C., 1978. Landscape Economics. McMillan, London.

Reiling, S.D., Boyle, K.J., Phillips, M.L., Anderson, M.W.,

1990. Temporal reliability of contingent values. Land

Economics 66, 128�/134.

Stevens, T.H., More, T.A., Glass, J., 1994. Intertemporal and

temporal stability of CV bids for wildlife existence: a panel

study. Land Economics 70, 355�/363.

Teils, M.F., Boyle, K.J., McCollum, D.W., Reiling, S.D., 1995.

Test�/retest reliability of contingent valuation with indepen-

dent sample pretest and posttest control groups. American


Tversky, A., Simonson, I., 1993. Context dependent prefer-

ences. Management Science 39, 1179�/1189.

Santos, J., 1998. The Economic Valuation of Landscape

Change. Edward Elgar, Cheltenham, UK.

Stevens, T.H., Belkner, R., Dennis, D., Kittredge, D., Willis,

C., 2000. Comparison of contingent valuation and conjoint

analysis in ecosystem management. Ecological Economics

32, 63�/74.

Whitehead, J.C., Hoban, T.J., 2000. Testing for temporal

reliability in contingent valuation with time for changes in

factors affecting demand. Land Economics 75, 453�/465.

Wildavsky, A., 1987. Choosing preferences by constructing

institutions: a cultural theory of preference formation.

American Political Science Review 81 (1), 3�/21.

Willis, K., Garrod, G., 1993. Valuing landscape: a contingent

valuation approach. Journal of Environmental Manage-

ment 37, 1�/22.


Documents

Consumption process and multiple valuation of landscape attributes