EC O LO GIC A L E CO N O M ICS 6 6 ( 2 00 8 ) 7 0 0 –7 11
a v a i l a b l e a t w w w. s c i e n c e d i r e c t . c o m
w w w. e l s e v i e r. c o m / l o c a t e / e c o l e c o n
ANALYSIS
Do emotions matter? Coherent preferences under anchoring
and emotional effects☆
Jorge E. Araña⁎, Carmelo J. León
Universidad de Las Palmas de Gran Canaria, Edificio de Ciencias Economicas y Empresariales, 35017 Las Palmas de Gran Canaria,
Canary Islands, Spain
AR TIC LE I N FO
ABS TR ACT
Article history:
Emotions can affect individuals' preferences and economic behavior. In this paper we
Received 24 August 2007
consider the relationship between emotions and anchoring effects in non-market valuation.
Received in revised form
The findings show that although anchoring effects are relevant, elicited preferences are
31 October 2007
coherent, in the sense that they are sensitive to changes in the dimension of the good.
Accepted 8 November 2007
Additionally, it is found that the relationship between emotional intensity and the level of
Available online 14 January 2008
anchoring is U-shaped, with anchoring declining as emotional intensity rises until a
minimum is reached. Thus, preferences can be substantially less affected by anchoring
Keywords:
effects if emotional intensity deviates from extreme values. Finally, it is found that the
Emotions;
degree of sensitivity to scope is influenced by the level of emotional load involve in the
Anchoring;
valuation task.
Non-market valuation;
© 2007 Elsevier B.V. All rights reserved.
Decision making
JEL classifications:
D0; Q51; Q26
1.
Introduction
A relevant issue in Economics is to provide a reliable answer to
the question of how individuals do make choices. The
traditional model is based on the assumption that individuals
have stable and well-defined preferences, and their choices are
driven by consistent optimization (Sen, 1982). The idea is that if
agents are motivated enough (normally through monetary
incentives) they are going to do the best for themselves, that is,
maximize their utility function. The failure of this motivational
requisite (or incentive compatibility) is the most widespread
explanation for the observed deviations between real and
predicted behavior with the traditional economic model.1
This general framework constitutes a simple, intuitive and
powerful way to explain a wide range of economic behavior.
However, “while this model of individual behavior dominates
☆
We would like to thank the support of projects BEC2000–0435,
VEM2004–08558 and SEJ2005–09276 of the Spanish Ministry of
Education. We also would like to thank Michael Hanemann, Dan
Ariely, Barbara Mellers and Teck Ho for providing remarks on
earlier drafts that improved the paper. Seminar participants at the
XXIII European Environmental Economics Association Conference
(Budapest, June 2004) provided comments that helped to shape
the piece. The usual disclaimer applies.
⁎ Corresponding author.
E-mail address: jarana@daea.ulpgc.es ( J.E. Araña).
1
This is also the main argument claimed by some researchers
against the validity of the use of stated preference data. Ameliorating
the effect of this bias has played a central role in non market valuation
literature (see for instance Carson et al., 2001, for a detailed review).
0921-8009/$ – see front matter © 2007 Elsevier B.V. All rights reserved.
doi:10.1016/j.ecolecon.2007.11.005
EC O L O G IC A L E C O N O M IC S 6 6 ( 2 0 08 ) 70 0 –7 11
contemporary economic analysis there is a long history among
economists of questioning its behavioral validity and seeking
alternatives” (McFadden, 1999). Some of the most relevant
“anomalies” have been found in terms of its deviations from
the transitivity assumption (Allais, 1953), monotonicity (Kahneman et al., 1982) and procedural invariance (Tversky and
Kahneman, 1986; Arrow, 1982).
This paper considers the role of human emotions in the
procedural invariance observed in what has been termed
“the anchoring effect”. Our empirical evidence focuses on the
relationships between emotions and anchoring effects in
the context of the valuation of non-market goods utilizing the
double-bounded dichotomous choice (DBDC) contingent valuation method. The main tested hypotheses are the following: i)
the role of human emotional intensity on welfare estimates; ii)
the independence of the cognitive (i.e. anchoring) and the
emotional dimensions; and iii) the sensitivity to scope when
both the emotional and the cognitive dimensions are present.
In general, anchoring effects are the most relevant and well
documented behavioral responses in eliciting judgments of
willingness to pay for public goods with the DBDC method
(Green et al., 1998; Kahneman and Knetsch, 1992). This method
was proposed as a potentially advantageous technique over the
single bounded dichotomous choice (SBDC) method, because of
the larger amount of information requested from the
individual.2 Thus, the technique represents a good example of
how an undesirable cognitive dimension (i.e. anchoring effects)
may outweigh the desirable economic and statistical advantages of a methodology (Carson et al., 2001; McFadden and
Leonard, 1993; Bateman et al., 2001; DeShazo, 2002; Whitehead,
2002; Burton et al., 2003).
From a theoretical standpoint, anchoring effects can be
conceived as the “pervasive judgment biases in which decision
makers are systematically influenced by random and uninformative random points” (Chapman and Johnson, 1999). Since
widespread anchors can have an influence on human preferences and values, this would question the assumptions of
unique and stable preferences. Without these assumptions,
there can be significant doubts about the ability of standard
preference elicitation techniques, such as DBDC, to capture
human preferences (Slovic, 2000).
Anchoring effects have been found in a wide array of other
contexts.3 They are also a main component of theories explaining several other “anomalies” of the economic model of
consumer choice, such as the preference reversals and the WTP/
WTA discrepancy.4 Tversky and Kahneman (1974) argue that
anchoring effects can be explained because of a cognitive
heuristic by which decision makers first focus on the anchor
and then make a series of dynamic adjustments toward their
2
Hanemann et al. (1991) demonstrated that it raises the level of
statistical efficiency of parameter estimates and welfare measures.
3
For instance, in the assessment of the willingness to pay for
public goods with bidding games and other elicitation methods,
the pricing and rating of gambles, the risk assessment, the
estimation of probabilities, social judgments, knowledge questions, and the predictions of future performance.
4
For a detailed review of anchoring effects see for instance
Chapman and Johnson (1999) or Ariely et al. (2003).
701
final estimate. Because these adjustments are insufficiently
developed, the final answer is biased toward the anchor.
Although Tversky and Kahneman's work has had a tremendous influence on economics and psychology, providing a better
understanding of how individuals make choices, it seems that it
has partially obstructed the inclusion of emotional aspects5 into
the economic model. Economists have been aware of the role of
emotions in individual's behavior since early works (Smith,
1759; Commons, 1934). However, until recently little attention
has been paid to the role of the emotional dimension in
individual economic behavior6 (some exceptions are Frank,
1988; Kauffman, 1999; Slovic et al., 2002; and Gifford, 2002,
among others). Several arguments have been used to explain
this phenomenon. For instance, Loewenstein (2000) pointed out
that emotions have been perceived as transient and unimportant, and therefore too unpredictable and complex to be
included in a formal model. Although many economists would
agree that emotions have a significant influence on behavior,
most would leave them out either because they have nothing to
do with rational decision making or because they only produce
noise around some average behavior (which is the one predicted
by the neoclassical model).
A common characteristic of most economic models that
incorporate emotions is the implicit assumption that the
cognitive and the emotional dimensions are independent.
This assumption has been a constant source of controversy in
research on emotions (Hilgard, 1980; Zajonc, 1980), which has
been revitalized in recent years as the “cognition–emotion
debate” (Lazarus, 1984 and Leventhal and Scherer, 1987). More
recently, a new literature has emerged that considers choices as
a result of a dual-process (Hsee and Rottenstreich, 2004;
Kahneman and Frederick, 2002; Chaiken and Trope, 1999;
Sloman, 1996), resulting from a combination of a deliberative
and an affective dimension. Hsee and Rottenstreich (2004)
propose the terms “valuation by calculations” and “valuation by
feelings” to refer to these two dimensions. These authors argue
that under the valuation by calculations system, changes in
scope have a relatively constant influence on value throughout
the entire range, while under the valuation by feelings system,
the value is highly sensitive for a change from 0 to some positive
value, but is largely insensitive to further variations of scope.
The plan of the paper is as follows. In the next section we
present the details of the experimental design that provided us
with the source data for the study of the relationships between
the emotional and cognitive dimensions in the contingent
valuation method. Section 3 outlines the econometric model
utilized to estimate the anchoring effects in the DBDC model.
5
Emotions have been also termed in the literature as “affect”
(Slovic et al., 2002), “visceral factors” (Loewenstein, 2000) or
“passions” (Frank, 1988).
6
There is a long tradition of research in other decision sciences
(e.g. psychology, sociology and neurosciences) suggesting that
emotions may play a significant role in several aspects involved
in the decision-making processes. For instance, emotions may
affect memory (Heuer and Reisberg, 1990), perceptions (Zajonc,
1980; Lerner and Keltner, 2001), creativity (Isen et al., 1985),
problem solving abilities (Isen et al., 1987), motivated cognition
(Camerer and Lovallo, 1999; Dovidio et al., 1995), purchase
intentions (Brown et al., 1998), variety seeking (Kahn and Isen,
1993) and performance (Damasio, 1994).
702
EC O LO GIC A L E CO N O M ICS 6 6 ( 2 00 8 ) 7 0 0 –7 11
Section 4 discusses the results regarding the hypotheses of the
relationships between the emotional load facing the individual
in the valuation task and the degrees of anchoring and scope
effects, as well as the relationships between anchoring and
scope effects. Finally, Section 5 summarizes the main findings of
the paper and sets up some of the implications for further
research.
2.
Experimental design
2.1.
The good to be valued
The application focused on the valuation of the rehabilitation of
a network of walking paths in the island of Gran Canaria, Spain.
The network is an ancient infrastructure which was used in
previous centuries for communications between villages. In
recent times, these paths have been abandoned and new roads
have been built using modern techniques, at times replacing the
old paths. The ancient path structure was mainly used for
walking, although it also could allow carriage and animals
transit in many parts of the network. The extension of the
network is about 1000 km. In the last years rehabilitation work
has been accomplished on 300 km using European funds.
The rehabilitated network is currently used by the local
population and also by some tourists and visitors for hiking and
walking. Due to its rural origin, most paths go through natural
areas and allow users to enjoy nature and magnificent landscapes. The primary objective of the study was to determine
how much would be the benefits for the resident population of
the island to be obtained from the expansion of the rehabilitated
network. The old abandoned paths are practically impenetrable,
thus the expansion of the services of the network requires
rebuilding more routes. All the construction techniques have
been done following the old traditions and involving the original
materials, and were supposed to continue to be so for the
expansion of the network.7
2.2.
The computer-based questionnaire
Using the taxonomy proposed by Harrison and List (2004) our
study is defined as an artefactual field experiment. The data
collection was conducted in 2002 to the resident population of
Gran Canaria. The questionnaire was administered via
personal interviews at the subject's home. The interviews
were conducted by professional interviewers of a survey firm,
previously trained by the authors to standardize survey
environment and subject attention. The interviews were
supported by a computer-based questionnaire implemented
on personal laptops computers. The computer-based survey
instrument, while holds most of the desirable characteristics
of lab experiments (e.g. control of the environment), has
several advantages over the alternatives: it can reduce sample
selection bias commonly observed in lab experiments, reduce
interviewer bias effects, allow the consideration of additional
7
For more up to date information regarding conservation of
walking paths in Gran Canaria you can visit: http://www.
grancanaria.com/patronato_turismo/1860.0.html.
covariates, and potentially permit improvements in the
statistical design.8
2.3.
The design
A sample of households was randomly screened from the
census population of Gran Canaria published by the Canary
Islands Statistical Institute (ISTAC). The interviewers participated actively in several training sessions on the specifics of
the questionnaire. They did work also for the pre-test surveys,
providing comments and suggestions for improving the final
questionnaire. Up to three focus groups of 5–10 subjects and
three pre-tests of 20–30 subjects were needed in order to reach
the final version of the survey instrument. In the process of
successive revision of the pre-test questionnaires we considered critical issues such as the payment vehicle, the good to
be valued and the information content of the market design.
The number of valid interviews was 574, with a response rate
of approximately 84%.
The survey instrument implemented the constructed market aimed at valuing in monetary terms the benefits that the
population would enjoy from the expansion of the network. The
questionnaire was structured in three main parts. The first part
asked questions about the relative importance of a range of
objectives of public policy in general, and recorded information
on the various recreational activities that the subject made in
leisure time. These questions preceded the presentation of the
elements of the market scenario and were intended to introduce
the subject to the valuation context. The second section
presented the valuation scenario and asked the subject's
willingness to pay for the proposed policy. The policy proposal
was presented by a descriptive paragraph, and by means of an
interactive map on the computer, showing simulated pictures
and drawings. The final section obtained information on
socioeconomic variables such as employment status, education
level, income level, family size, and year of birth.
Key elements of the scenario are the payment vehicle, the
elicitation method and the provision rule. The elicitation
method was the double-bounded dichotomous choice based
on a bid design of alternative prices that were randomly
distributed across the sample. Each individual in the sample
randomly received one of the several initial prices. The bid
vector was designed utilizing Cooper's (1993) methods for a
predetermined number of bids and based on the information
provided by an open ended pre-test question. A second followup bid vector was defined by the next successive price in the
initial bid vector with an upper and a lower limit equally
spanned. If the individual answered ‘yes’ to the first price, this
price was increased; if the answer to the first price was
negative then the price were lowered. The final elicitation tree
is presented in Fig. 1.
8
As it was noted by a reviewer, in contexts in which the use of
computers is restricted to some portion of the population
(normally younger and more educated people), the use of
computer-based questionnaires may invoke some selection bias.
In this study, this potential issue was tested by employing
information obtained in pilot surveys and focus groups. This
analysis found no selection bias effect.
EC O L O G IC A L E C O N O M IC S 6 6 ( 2 0 08 ) 70 0 –7 11
703
Fig. 1 – DBDC structure.
The payment vehicle was a contribution to a special fund
for the specific purpose of carrying out the expansion of the
walking paths network. In order to enhance the incentive
compatibility of the payment vehicle we tested alternative
provision rules in the initial stages of the study (i.e. focus groups,
in-depth surveys and pilot surveys). The most satisfactory
option was to follow a wording structure similar to the one
proposed in Rondeau et al. (1999) and Poe et al. (2002). This
704
EC O LO GIC A L E CO N O M ICS 6 6 ( 2 00 8 ) 7 0 0 –7 11
consists of a provision point mechanism (PPM) with money back
guarantee (MBG) and a proportional rebate of excess contributions (PR). Subjects were told that the public good is provided
only if the sum of contributions equals or exceeds its cost (the
provision point). If contributions fall short of costs, they are
completely refunded (the money back guarantee), whereas if
they exceed costs, the excess is returned to each contributor
proportionally to the share of their individual contribution in
the total amount contributed (the proportional rebate).9 In
addition, the chosen payment vehicle was perceived as feasible
from a policy perspective, since some local facilities are
commonly financed by special contributions.
2.4.
Sensitivity to scope
The proposed expansion of the network of ancient walking
paths is designed with the aim of increasing the leisure options
of the objective population and potential visitors to the island of
Gran Canaria. The accomplishment of the project involves costs
which are expected to increase linearly with the size of the
network. Thus, an interesting question from a policy perspective is how preferences would change across different projects.
This would allow us to test for the coherency of the preferences
as the subject evaluates expansions of different scales which
could raise different benefits. Three alternative projects were
presented to the subjects in split samples, and varied only in the
number of kilometers of the proposed expansion, above the
currently rehabilitated 300 km, i.e. 30, 100 and 300 km. All these
projects were feasible in the island according to experts and
would serve for the purposes of increasing the amount of
services provided by the network. Subjects were presented with
the three alternatives and randomly asked to value only one of
the proposed projects, conditional on the relative benefits that
the other two alternatives would provide them.
2.5.
scale
The measure of thee emotions intensity: the EIS-R
Since emotions are omnipresent in everyday life and play an
important role in several scientific theories, a particular
challenge is agreeing upon a concrete definition of this
phenomenon, including its conceptualisation and operationalisation. A large part of the disagreement between different
theories can be subscribed to different definitions of what constitutes an emotion. There is a distinction between emotions,
moods and emotional disorders (Ben-Ze'ev, 2000).10 In particular, we are interested in emotion intensity, because it is argued
to be an important predictor of mood experience, and therefore,
of individual decision making. Affect or emotion intensity, as
9
In the context of controlled experiments and treating subjects
in groups, Rondeau et al. (1999) found out that this payment
mechanism can closely approximate demand revelation. This
evidence was supported in the context of our experiment for the
results of a split sample comparison between alternative payment vehicles in the pre-tests studies.
10
In the context of stated preference methods, there is an
extensive literature that includes attitudinal scales and scales
operationalising WTP as behavioural intention that reflects
variables from social-psychology. Some examples are Brouwer
et al. (1999), Heberlein et al. (2005), or Fischer and Hanley (2007).
used here, can be defined as the “stable individual differences in
the strength with which individuals experience their emotions”
(Larsen and Diener, 1987).
There are several scales available that may be used to
measure emotion intensity. In this paper we adopt a reduced
version of the Emotional Intensity Scale (EIS) first proposed by
Braaten and Bachorowski (1993). The main drawback of
standard emotion scales is that they often combine frequency
and intensity of emotions in the same scale. The EIS overcomes
this problem by measuring only intensity of emotions. In addition to this, “the EIS is adequately developed and shows evidence for reliability and validity” (Bachorowski and Braaten,
1994). Therefore, we use a reduced version of EIS (EIS-R)
proposed by Geuens and Pelsmacker (2002). The main advantage of EIS-R is that “it provides a more practical instrument for
studies investigating the relationship of the EIS to cognitive,
affective, or behavioral, at the same time that minimize
maturation and fatigue effects in respondents”.11
3.
The double-bounded dichotomous
choice model
Consider the first stage in an elicitation process involving a yes/
no question to pay a bid price (Bi1) for an increase in the services
provided by an environmental good from q0 to q1. Let ei(·) be the
expenditure function for individual i, that is, the inverse of the
indirect utility function with respect to income. Under the
typical assumption of consumer rationality, the answers would
be yes if ei(q1,V⁎) +Bi1 ≤ei(q0,V⁎) and no otherwise, where V⁎ is
some fixed level of utility. The expenditure difference could be
seen as the individual's willingness to pay (WTP) for the offered
services, that is, WTPi1 =ei,(q1,V⁎) −ei(q0,V⁎). Therefore, the observed answer {yi1} to the first bid price {Bi1} takes the value of one
or zero if WTPi1 is higher or lower than the bid price, respectively.
Traditional CV models assume that the expenditure function
is not fully observed by the researcher. Thus, the latent variable
WTP can be viewed as a function of two components, a
deterministic μ and a random component ɛ. In general, we can
assume a linear WTP function (Cameron, 1988),12 that is, WTPi1 =
μi1 +σ1ɛi1, where μi1 and σ1 are, respectively, the mean and the
standard deviation of WTP1, and ɛi1 is a random error term.
The double-bounded dichotomous choice format (DBDC)
consists of the inclusion of a second binary question. This
method was first proposed by Carson (1985) and Hanemann
(1985). The second bid offered (Bi2) is assumed to be higher
than (Bi1) if individual i answers positively to the first price and
vice-versa. Let us assume that WTP from the first question is
the true WTP (see for example, Herriges and Shogren, 1996 or
Whitehead, 2002). Following the general setting developed in
Araña and León (2007) for repeated elicitation formats (which
includes DBDC), we consider here a simultaneous equation
11
Definition of EIS-R is presented in Appendix B. Results of the
PCA and validity and reliability of the scale are available by the
authors upon request.
12
Alternatively, the model can be specified directly in terms of
the indirect utility function (Hanemann, 1984). McConnell (1990)
shows how Cameron's model may be seen as the dual of the
Hanemann's.
EC O L O G IC A L E C O N O M IC S 6 6 ( 2 0 08 ) 70 0 –7 11
model with anchoring effects that allows us to consider the
interdependencies between the stages in the elicitation process.
That is,
WTP1i ¼ A1i þ e1i
WTP2i ¼ A2i þ gi B1i þ e2i
ð1Þ
with gi a½0; 1 8i; i ¼ 1; 2; :::n
where μki = αki + xki βk (k = 1,2) are the linear predictors associated
with l × 1 regression parameter vectors βk and covariate vectors
xki , αki is the intercept term, and ηi captures the potential existence of an anchor effect of the first bid on WTP of individual i
(Herriges and Shogren, 1996). The linear predictors are linked to
the probability of a positive response by a bivariate normal
cumulative distribution (BVN) called the link function. Simultaneity between responses is captured by the lower triangle component of Σ (e.g. σ2,1). This is a model of simultaneous equations
with limited dependent variables (SLDV), which reduces to a
general triangular system (Zellner, 1971) for complete data sets.
3.1.
Inconsistency in elicited preferences between answers
The main advantage of DBDC over SBDC is that the former
provides more information on individual's preferences. Hanemann et al. (1991) showed that it leads to more efficient welfare
estimates. This additional information may potentially allow
the researcher to conduct studies at a lower cost (smaller sample
sizes) while holding the precision of the WTP estimates constant
(same variance).13 However, the DBDC has been questioned
because of the empirical support to the argument that the
distribution of WTP is incoherent between both steps (e.g. Green
et al., 1998; Cameron and Quiggin, 1994).14 In other words, the
mean or median WTP estimated using responses to the first
valuation question differs empirically from the one estimated
using the responses to the second question.15
The presence of potential behavioral responses to the followup question has been argued in non-market valuation as the
primary explanation of the incoherency between DBDC and
SBDC (Alberini, 1995; Carson et al., 2001; Burton et al., 2003;
DeShazo, 2002; Bateman et al., 2001). Innovative efforts to model
econometrically some of the reactions to the second bid offered
can be found in Cameron and Quiggin (1994), Herriges and
Shogren (1996), Alberini et al. (1997), Whitehead (2002), Flachaire
and Hollard (2006) and Araña and León (2007). The arguments
commonly raised for explaining these behavioral responses
include anchoring effects, strategic behavior, yea-saying, nea13
Alternatively, it would allow us to increase the precision of the
estimates (lower variance) holding sample size constant.
14
The inconsistency between first and second responses leads to
the rejection of the maintained assumption of the restricted
double bounded model (Hanemann et al. 1991) that the mean and
variances are constant across both bounds, and that the correlation coefficient between the standard errors is equal to one,
which essentially means that WTPi1 = WTPi2. The rejection of this
hypothesis implies a reduction in the efficiency gains of the
double bounded elicitation procedure.
15
The number of bounds can be increased successively in the
elicitation process, leading to what has been denominated the
triple bounded dichotomous choice model (Langford et al. (1996).
Cooper and Hanemann (1995) and Scarpa and Bateman (2000)
showed that the efficiency gains are likely to diminish when the
number of binary steps in the elicitation process is increased.
705
saying, uncertainty cost, weighted average, bargaining, guilty/
indignation and quality/quantity shift, among others. For
simplicity, in this paper we focus on the anchoring effects,
which can be seen as a general cognitive heuristic implicitly
linked to other behavioral responses.
3.2.
Anchoring effects
An empirical result of the DBDC is the fact that, in follow-up
question the distribution function of WTP could be influenced
by precedent stage, implying some type of anchoring effect or
behavioral process (e.g. Herriges and Shogren, 1996; Aadland
and Caplan, 2004). In a general setting, Tversky and Kahneman's (1974) describe the anchoring effect as “the process in
which people make estimates by starting from an initial value
that is adjusted to yield a final answer.”
Following previous models of DBDC (Herriges and Shogren,
1996; Whitehead, 2002), our model collects the influence of the
starting bid amount on WTP in the term ηiBi1. Parameter ηi
measures the importance of the anchor effect of the first bid
on WTP at an individual level. Thus, the anchoring effect
hypothesis may be tested for each individual of the sample by
considering these two alternatives: H0: ηi = 0; and H1: no H0.
3.3.
The econometric model
In order to estimate the model, we utilize a Bayes approach
(Chib, 1992; Albert and Chib, 1993), similar to the one applied by
Araña and León (2005).16 This approach has basically three main
advantages over standard maximum likelihood estimation: i) it
allows for more flexibility and unobserved heterogeneity in the
model through the random parameters specification; ii) it
allows for an easy and efficient comparison between models
through the use of the Bayes Factor; iv) it relies on an exact
theory of probability even with small samples, leading to more
accurate results in this context. The detailed description of the
econometric model and the components of the Bayesian
approach are explained in detailed in the Appendix A.
4.
Results
The estimation of the simultaneous equation model outlined
in the previous section is particularly intended to raise further
evidence on the anchoring effects produced by the first bid
prices offered in the double-bounded dichotomous choice
model. Although this model centers on anchoring effects, the
data collected in our field experiment also allows us to
investigate i) the potential relationships between anchoring
effects and the emotional state of the individual, and ii) the
potential relationships between anchoring effects and the
scope of the environmental good to be valued, as represented
16
In order to test the sensitivity of the results to the econometric
approach, maximum likelihood estimations of a bivariate probit
model have been carried out. The results show no significant
differences in terms of the hypotheses proposed in this study.
The sensitivity analysis results, data set and the GAUSS program
codes for the Bayesian estimation are available from the authors
upon request.
706
EC O LO GIC A L E CO N O M ICS 6 6 ( 2 00 8 ) 7 0 0 –7 11
Table 1 – Estimation results of Bayesian bivariate models
for a naïve and anchored individual (posterior standard
deviations in parentheses)
Naïve model
Anchored model
β1
β2
β1
BID 1
18.9460
(4.5615)
12.9748
(0.8970)
−0.2461
(0.0463)
5.1005
(1.7401)
0.4274
(0.1714)
0.0062
(0.0018)
–
0.7664
(3.6249)
15.0844
(0.6515)
− 0.2005
(0.0353)
7.1897
(1.3784)
0.5997
(0.1259)
0.0030
(0.0014)
–
18.8234
(4.4138)
12.9463
(0.8947)
−0.2450
(0.0446)
5.0873
(1.7373)
0.4333
(0.1683)
0.0063
(0.0018)
–
σ1
σ2
σ21
Mean WTP
Marginal likelihood
13.0619 (0.5807)
11.0802 (0.6310)
70.4236 (8.2962)
19.18 [18.43, 19.98]
− 1113.97
Intercept
EIS
Age
Log (KIL)
EDU
INC
β2
0.7878
(3.5954)
15.0560
(0.6486)
− 0.2006
(0.0351)
7.1883
(1.3571)
0.6005
(0.1288)
0.0030
(0.0014)
0.2694
(0.0379)
13.0302 (0.5603)
11.0647 (0.6313)
59.6298 (10.3180)
19.16 [18.37, 20.01]
− 1057.26
by the number of kilometers to be rehabilitated in the policy
program presented in the market construct.
Table 1 shows the estimation results for the simultaneous
equation model for two alternative assumptions. Under the
naïve assumption, we omit the bid price from the first response
(Bi1). Here the anchoring effects are not modeled (Whitehead,
2002). The second assumption explicitly considers the bid price
of the first question in the equation for the second response.
Anchoring effects as induced by the first bid offered are relevant
in this application, as is evident by the significance of the first
bid price in the second equation. This result has been obtained
in most applications of the double-bounded dichotomous
choice format (e.g. Green et al., 1998).
The results in Table 1 show also the relevance of some other
explanatory variables that are significant in explaining WTP at
both stages of the elicitation process. WTP rises with income
(INC) and the years of education (EDU), while decreases with the
age of the individual (AGE). The significance of these explanatory
variables can be interpreted as giving support to the construct
validity of the contingent valuation study that was designed to
value the rehabilitation of a network of walking paths.
But for our purposes, the main variables of interest in the
output regression of the bivariate model are the emotional
intensity scale (EIS) and the logarithm of the number of
kilometers presented in the valuation task of the contingent
valuation scenario (KIL).17 These variables are both significant at the 95% level, supporting the following two empirical
results:
1. There was significant sensitivity of WTP to the size of the
environmental good being offered. This relationship was
17
After testing for the validity and reliability of the EIS, the PCA
reported that a model with only one factor provide the most
satisfactory solution (e.g. reported Cronbach's alpha was 0.92).
logarithmic: when the number of kilometers of the walking
paths network was increased, mean WTP also increased,
but at a decreasing rate. Thus, the scope effect or the
absence of sensitivity to the dimensions of the good to be
valued can be rejected for this particular application.
2. The emotional state of the individual played a significant
role on the elicited values of the environmental good in
question. This relationship was positive, i.e. the higher the
emotional state the large becomes WTP.
Even though these variables are important for explaining
WTP at an aggregate level, they can be also related to the
degree of anchoring which is likely to be found in the
elicitation mechanism of the DBDC model. Thus, let us
consider the relationships between the anchoring effects
and i) the scope effect, and ii) the state of emotional load
facing the individual.
4.1.
Scope and anchoring effects
In order to ascertain whether scope effects are also present for
the various anchoring bids utilized in the experiment, Table 2
reports the mean WTP for the subsamples of the lowest and
highest bids utilized in the first dichotomous choice question.
It can be seen that the size of the walking paths network has a
similar influence on WTP across the lowest and highest bids
offered to the individuals. This result is similar to the one
found by Ariely et al. (2003) in what these authors called
“coherent arbitrariness”. That is, although preferences are
likely to be influenced by initial anchors or bids, they can be
coherent in economic terms for the different dimensions of
the good to be valued.18
However, the scope effect giving support to the coherence
of preferences under anchoring, could be dependent on the
emotional status of the individual facing the task of valuing
different dimension of a given good. Thus, this potential
relationship would raise the need to consider the role of
emotions in both the anchoring and scope effects.
4.2.
Emotional load and anchoring effects
The extent of the anchoring effects can be also influenced by
the emotional status of the individual. In general, this
hypothesis implies that the cognitive aspects of the valuation
task, i.e. the commonly found recurrence to some anchor in
order to base a valuation response, can be influenced by the
emotional aspects involved. As can be seen in Table 3, the
parameter of the anchoring effect ηi is related with the level of
emotional load posed by the individual in the valuation task.
The relationship between the anchor parameter ηi and the
EIS is depicted in Fig. 2. Low and high values of EIS correspond
with significantly high levels of anchoring. This relationship is
18
Ariely et al. (2003) found coherent preferences within individuals, i.e. by asking an individual about various dimensions of a
given good. Since we used split samples for the sizes of the
walking path network, our results can be interpreted as supporting coherent preferences at an aggregate level, rather than at an
individual one, i.e. at a social welfare function rather than at
individual utility functions.
707
EC O L O G IC A L E C O N O M IC S 6 6 ( 2 0 08 ) 70 0 –7 11
Table 2 – Mean WTP (€) for different sizes of the network
by starting bids (standard deviation in parenthesis)
Starting bid
Lowest (6.01€)
Total sample
Highest (48.04 €)
Size of the walking path
30 km
100 km
300 km
14.88 (2.38)
19.94 (2.44)
21.94 (3.69)
16.37 (2.51)
25.83 (3.86)
37.93 (4.05)
21.36 (2.49)
28.31 (3.76)
40.82 (4.01)
not linear but U-shaped. The degree of anchoring declines as
emotional intensity increases, reaching a minimum for an
average value of EIS. At this point, anchoring effects are not
significantly different from zero at the 99% level. Thus, even
though anchoring effects are significant across the sample,
those subjects with average EIS are not influenced by the first
bid in the valuation process.
These results can be related to the evidence that purport a
U-shaped relationship between human performance and
emotional intensity (Ashcraft and Faust, 1994; Idzikowski and
Baddeley, 1983). The “Yerkes–Dodson law” (Yerkes and Dodson, 1908) states that performance requires an intermediate
level of emotional intensity (Leibenstein, 1987; Kauffman,
1999). When emotional intensity is too low there is insufficient
attention and mental arousal, and short-term memory is
blocked (Kahneman, 1973). When emotional intensity is too
high thinking becomes disorganized, and there is difficulty to
rationally evaluate the benefits and costs of alternatives
(Eysenck, 1982; Yates, 1990; Lazarus, 1991; Oatley, 1992).
4.3.
Emotional load and scope effect
The emotional state of the individual can also have an influence
on his ability to perform according to coherent preferences, i.e.
successfully passing the scope test. This hypothesis can be
appreciated by looking at the relationships between the EIS and
the size of the walking paths to be rehabilitated in the policy
proposal. Table 4 shows the mean WTP for the different levels of
the walking paths network according to three groups of
individuals as bunched by their level of emotional state (low,
average, and high). Fig. 3 depicts the general relationships
between the mean WTP and the variable KIL for the three types
of emotional profiles considered.
It can be seen that WTP is less sensitive for initial changes in
scope (from 0 to 30 km) when the emotional scale is low. This
sensitivity rises as the emotional scale increases, from 15.20 € to
31.21 €. In addition, for further increases in the size of the
walking paths (beyond 30 km) WTP remains invariant for the
groups of high and low emotional scales. Thus, the scope test is
failed for subjects with extreme emotional scales (low and high).
These subjects also posed a large degree of anchoring effects.
However, the group of individuals with average emotional state
showed a steeper valuation function in relation to scope (i.e. to
Table 3 – Anchoring effects parameter by EIS levels
Anchoring effect (ηi)
Confidence interval (99%)
Fig. 2 – Estimated relationship between anchoring effects
and EIS.
the level of km). Thus, only those subjects with an average
emotional state are likely to behave according to coherent
preferences with no anchoring effect.
These results can be seen as giving some support to the
hypothesis claimed by Hsee and Rottenstreich (2004) that
under “valuation by feelings” preferences tend to be very
sensitive to changes in the initial values of the good and very
insensitive to changes for higher values; and under “valuation
by calculations” preferences are less influenced by changes in
initial values, and more sensitive to further values.
5.
Conclusions
Emotions are present everyday in human life. Several theories
in neurosciences, psychology and sociology acknowledge its
central role in explaining human behavior. Although some
economists have recognized the role of emotions in explaining
human behavior (e.g. Smith, 1759, Commons, 1934), the overall
significance of emotions has been virtually ignored in the
economics literature in general, and in environmental valuation
in particular, until recent times. In this paper we have tried to
empirically test the role of emotions in non-market valuation
and the potential trade-offs between cognitive and emotional
intensity. The study utilizes a reduced emotional intensity scale
score which has been successfully used in other sciences to
measure the emotional intensity of the individual.
The cognitive dimension of the formation of individual's
preferences is studied by considering the anchoring effects in the
DBDC elicitation process in the contingent valuation method. A
simultaneous equation Bayesian approach has been used to
estimate the individual's anchor to the first bid price. The results
show that the answers to the second question are anchored by
the bid price offered in the first question. Thus, anchoring is a
Table 4 – Mean WTP (€) of the size of the network by EIS
levels (standard deviation in parenthesis)
30 km
100 km
300 km
15.29 (1.19)
19.83 (3.73)
31.21 (5.97)
16.64 (2.55)
25.80 (2.46)
38.77 (2.01)
16.87 (2.18)
28.29 (4.07)
37.68 (4.72)
Low EIS
Avg EIS
High EIS
Low EIS
Avg EIS
High EIS
0.42 (0.10)
[0.16, 0.67]
0.15 (0.07)
[−0.01, 0.30]
0.43 (0.11)
[0.17, 0.68]
See Li (1998) for details.
708
EC O LO GIC A L E CO N O M ICS 6 6 ( 2 00 8 ) 7 0 0 –7 11
Fig. 3 – Estimated relationships between sensitivity to scope
(km) and WTP for three EIS levels.
relevant empirical effect that influences the elicitation of the
value of non-market goods using DBDC.
However, in our empirical application we found that preferences were sensitive to the scope of the good, suggesting that
the embedding effect was not relevant in this context, since the
subject reacted significantly to the different dimensions of the
good to be valued. Further, the sensitivity of WTP to the scope of
the good was also found for the various levels of the anchor
given by the first bid price; in other words, the “coherent” relationship between WTP and the dimension of the good was not
affected by the bid price offered in the first binary question.
Thus, we can conclude that anchoring effects do not seem to
have a relevant influence on the individual's ability to discern
among different dimensions of a non-market good in a valuation
scenario. A useful implication might be that relative WTP could
be successfully elicited. Nevertheless, anchoring effects are still
present in our application and raise serious concerns about the
underlying nature of human values and the capability of
preference elicitation techniques to capture them. If individual
choices are influenced by external anchors, it can be questioned
how can preferences be defined and how can elicitation
techniques be able to seize them. As pointed out by Ariely et al.
(2003), “even if there are no clear violations of the transitivity
axiom, the researcher cannot ascertain whether elicited choices
reveal a set of unique and well-defined preferences”.
In order to shed light on the conditions under which bid
anchors could have an influence on the formation of individual
preferences, we looked at the potential role of individual's
emotions. In particular, we focused on the relationship between
the EIS and the degree of anchor raised by the bid offered in the
first binary question. The results show that anchoring effects
decline as emotional intensity increases, reaching a minimum
for an average value of EIS. After this point, anchoring effects are
again significant. The major implication of these findings is that
individuals tend to improve their ability for the non-market
valuation task when their emotional intensity is moderate.
On the other hand, the omission of EIS in the valuation
function could bias non-market valuation results. The correlation between cognitive and emotional intensity implies that
some index of the latter needs to be considered in the systematic
part of the utility or expenditure functions. To our knowledge,
previous work has generally assumed independence between
the cognitive and emotional dimensions. The common incorporation of unexplained emotional conditions as part of the
stochastic term introduces correlation between the systematic
and the stochastic parts, leading to biased results.
Some researchers have claimed that the finding of insensitivity to scope in some applications is a clear evidence of the
inability of stated preference methods to capture preferences for
public goods (Kahneman et al., 1999; Diamond and Hausman,
1994) Our results suggest that the degree of sensitivity to scope
can be also related to the emotional load involved in the
valuation task. This might prompt a need for further evidence
on the role of emotions in the valuation of private and public or
environmental goods.
Our results concur with the notion that there might be a tradeoff between the emotional and cognitive dimensions in nonmarket valuation tasks. That is, some degree of emotional
intensity might help reduce the cognitive load and enhance
performance in human decision making. Nevertheless, it should
be acknowledged that this relationship is complex because of the
multivariate factors that can influence individual's emotions and
the cognitive aspects involved in the survey instrument. Further
research should explore the relationships of other emotional and
cognitive factors that might play a role in the decision making
task and the formation of individual's preferences.
Appendix A. Estimation of the Bayesian model for
DBDC
In this appendix we illustrate the application of the model
outlined in Section 3 for the double-bounded dichotomous choice
data. For simplicity, let us decompose the joint bivariate normal
distribution for (εi1, εi2) into the product of the marginal distribution of εi1 and the conditional distribution εi1/εi1, that is,
WTP1i ¼ x1i b1 þ e1i
ðA1:1Þ
WTP2i ¼ x2i b2 þ B1i g21 þ e1i r21 þ mi
ðA1:2Þ
where εi1 =WTPi1 −x1i β1, σ2 =σ22 −σ221, and vi ∼N(0, σ2), ε1 ∼N(0,1)
are independents. Thus, the set of unknown parameters is θ={α,
σ21, σ2}, where α=(η21, β1, β2). The following independent priors are
assumed:
f ðaÞfMVN a0 ; w0 1
f ðr21 ÞfN r0 ; b0 1
ðA1:3Þ
ðA1:4Þ
a c
0
0
f r2 fIG
;
2 2
ðA1:5Þ
where MVN and N are a multivariate and univariate normal
distribution respectively, and IG is the inverted gamma distribution. Since we have no prior information on model parameters,
very non-informative diffuse priors are assumed by considering
α0 =c0 =r0 =α0 =0, and large values for the parameters collecting the
variance (b0, ψ0). Therefore, the joint posterior distribution takes
the following form,
P P
1
2
p WTP ; WTP ; r12 ; r2 ; ajY 1 ; Y 2
n
ð1 y1 Þð1 y2 Þ yy y1i y2i ny ð1
i
i
Pi
Pi
Pnn
¼j
i
i¼1
f ðaÞf ðr21 Þf r2
y1i Þy2i
yn y1i ð1 y2i Þ
Pi
ðA1:6Þ
EC O L O G IC A L E C O N O M IC S 6 6 ( 2 0 08 ) 70 0 –7 11
yy ny yn
where Pnn
i , Pi , Pi , Pi are the probabilities of individual i responds no/
no, yes/yes, no/yes and yes/no respectively, and Y1 =(y11,…,yn1 ), Y2 =(y21,
WTP 1 =(WTP11,…, WTPn1 ), ‾‾‾‾‾‾‾
WTP 2 =(WTP12,…, WTPn2). Since the
…,yn2), ‾‾‾‾‾‾‾
dependent variables follow normal distributions the posterior
conditional distributions are as follows:
8
< / WTP1i jA1j2 ; r1j2 l½0; Bi if y1 ¼ 1
i
1 1 P2
f WTPi jY ; WTP ; h f
: / WTP1 jA ; r1j2 l½Bi ; l if y1 ¼ 0
1j2
i
i
ðA1:7Þ
8
< / WTP2i A2j1 ; r2j1 j l½0; Bi if y2 ¼ 1
P1
i
f WTP2i jY 2 ; WTP ; h f
: / WTP2 jA ; r2j1 l½Bi ; l if y2 ¼ 0
2j1
i
i
ðA1:8Þ
P1 P2
1
f ajY 1 ; Y 2 ; WTP ; WTP ; r21 ; r2 fMVN ã0 ;W̃0
ðA1:9Þ
1
P1 P2
f r21 jY 1 ; Y 2 ; WTP ; WTP ; a; r2 fN r̃; b̃
ðA1:10Þ
a c
P1 P2
1
1
f r2 jY 1 ; Y 2 ; WTP ; WTP ; a; r21 fIG
;
2 2
ðA1:11Þ
where ϕ(·) l[a,b] is the truncated normal distribution in interval
(0) (0)
(0)
[a,b]. If θ(0) = (γ(0), β1 , β 2 , σ2, σ12 ) is the starting value of θ, then the
Gibbs sampling works by iteratively replacing the initial value on
the conditional distributions, and Eqs. (A1.7)–(A1.11) complete
the MCMC algorithm. The algorithm is repeated t times, leading
(t) (t)
(t)
to the final values (WTP1(t), WTP2(t), γ(t), β1 , β 2 , σ2(t), σ21) obtained
1
1
2
from the joint distribution (WTP , WTP ,α, σ21, σ )|Y1, Y2. This
sequence of t algorithms is conducted over H times, leading to H
values for each parameter of the posterior distribution. These
series of simulated values are utilised to generate the posterior
moments for the parameters after discarding the first d values.
Appendix B. The Reduced Emotion Intensity Scale
(EIS-R)
Imagine yourself in the following situations and then choose
the answer that best describes how you usually feel.
1. Someone compliments me. I feel:
1. It has little effect on me
2. Mildly pleased
3. Pleased
4. Very pleased
5. Ecstatic—on top of the world
2. I am happy. I feel:
1. It has little effect on me
2. Mildly happy
3. Happy
4. Extremely happy
5. Euphoric—so happy I could burst
3. Someone I am very attracted to ask me out for coffee. I feel
1. Ecstatic—on top of the world
2. Very thrilled
3. Thrilled
4. Mildly thrilled
5. It has little effect on me
4. I am at a fun party. I feel:
1. It has little effect on me
2. A little light-hearted
3. Lively
4. Very lively
5. So lively that I almost feel like a new person
5. Something wonderful happens to me. I feel:
1. Extremely joyful–exuberant
2. Extremely glad
3. Glad
4. A little glad
5. It has little effect on me
6. I have accomplished something valuable. I feel:
1. It has little effect on me
2. A little satisfied
3. Satisfied
4. Very satisfied
5. So satisfied it's as if my entire life was worthwhile
7. A person with whom I am involved prepares
me a candlelight dinner. I feel:
1. It has little effect on me
2. Slightly romantic
3. Romantic
4. Very romantic
5. So passionate nothing else matters
8. I am involved in a romantic relationship. I feel:
1. So consumed with passion I can think of nothing else
2. Very passionate
3. Passionate
4. Mildly passionate
5. It has little effect on me
9. Someone surprises me with a gift. I feel:
1. It has little effect on me
2. A little grateful
3. Grateful
4. Very grateful
5. So grateful I want to run out and buy them a gift in return
10. Something frustrates me. I feel:
1. It has little effect on me
2. A little frustrated
3. Frustrated
4. Very frustrated
5. So tense and frustrated that my muscles knot up
11. I say or do something I should not have done. I feel:
1. It has little effect on me
2. A twinge of guilt
3. Guilty
4. Very guilty
5. Extremely guilty
12. Someone criticizes me. I feel:
1. It has little effect on me
2. I am a bit taken aback
3. Upset
4. Very upset
5. So extremely upset I could cry
13. I have an embarrassing experience. I feel:
1. It has little effect on me
2. A little ill at ease
3. Embarrassed
4. Very embarrassed
5. So embarrassed I want to die
14. Someone I know is rude to me. I feel:
1. So incredibly hurt I could cry
2. Very hurt
3. Hurt
4. A little hurt
5. It has little effect on me
15. I see a sad movie. I feel:
1. So extremely sad that I feel like weeping
2. Very sad
3. Sad
709
710
EC O LO GIC A L E CO N O M ICS 6 6 ( 2 00 8 ) 7 0 0 –7 11
4. A little sad
5. It has little effect on me
16. I am involved in a situation in which I must do well, such as
an important exam or job interview. I feel:
1. It has little effect on me
2. Slightly anxious
3. Anxious
4. Very anxious
5. So extremely anxious I can think of nothing else
17. I am in an argument. I feel:
1. It has little effect on me
2. Mildly angry
3. Angry
4. Very angry
5. Extremely angry
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