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ava i l ab l e a t www.sc i enced i rec t . com

www.e l sev i e r. com/ l oca te /eco l econ

ANALYSIS

Do emotions matter? Coherent preferences under anchoringand emotional effects☆

Jorge E. Araña⁎, Carmelo J. LeónUniversidad de Las Palmas de Gran Canaria, Edificio de Ciencias Economicas y Empresariales, 35017 Las Palmas de Gran Canaria,Canary Islands, Spain

A R T I C L E I N F O

☆ We would like to thank the support of prVEM2004–08558 and SEJ2005–09276 of theEducation. We also would like to thank MichAriely, Barbara Mellers and Teck Ho for prearlier drafts that improved the paper. SeminXXIII European Environmental Economics As(Budapest, June 2004) provided comments tthe piece. The usual disclaimer applies.⁎ Corresponding author.E-mail address: jarana@daea.ulpgc.es ( J.E.

0921-8009/$ – see front matter © 2007 Elsevidoi:10.1016/j.ecolecon.2007.11.005

A B S T R A C T

Article history:Received 24 August 2007Received in revised form31 October 2007Accepted 8 November 2007Available online 14 January 2008

Emotions can affect individuals' preferences and economic behavior. In this paper weconsider the relationship between emotions and anchoring effects in non-market valuation.The findings show that although anchoring effects are relevant, elicited preferences arecoherent, in the sense that they are sensitive to changes in the dimension of the good.Additionally, it is found that the relationship between emotional intensity and the level ofanchoring is U-shaped, with anchoring declining as emotional intensity rises until aminimum is reached. Thus, preferences can be substantially less affected by anchoringeffects if emotional intensity deviates from extreme values. Finally, it is found that thedegree of sensitivity to scope is influenced by the level of emotional load involve in thevaluation task.

© 2007 Elsevier B.V. All rights reserved.

Keywords:Emotions;Anchoring;Non-market valuation;Decision making

JEL classifications:D0; Q51; Q26

1. Introduction

A relevant issue in Economics is to provide a reliable answer tothe question of how individuals do make choices. Thetraditional model is based on the assumption that individualshave stable andwell-defined preferences, and their choices aredrivenby consistent optimization (Sen, 1982). The idea is that ifagents are motivated enough (normally through monetary

1 This is also the main argument claimed by some researchersagainst the validity of the use of stated preference data. Ameliorating

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incentives) they are going to do the best for themselves, that is,maximize their utility function. The failure of thismotivationalrequisite (or incentive compatibility) is the most widespreadexplanation for the observed deviations between real andpredicted behavior with the traditional economic model.1

This general framework constitutes a simple, intuitive andpowerful way to explain a wide range of economic behavior.However, “while this model of individual behavior dominates

theeffect of this biashasplayeda central role innonmarket valuationliterature (see for instance Carson et al., 2001, for a detailed review).

.

5

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contemporary economic analysis there is a longhistory amongeconomists of questioning its behavioral validity and seekingalternatives” (McFadden, 1999). Some of the most relevant“anomalies” have been found in terms of its deviations fromthe transitivity assumption (Allais, 1953), monotonicity (Kah-neman et al., 1982) and procedural invariance (Tversky andKahneman, 1986; Arrow, 1982).

This paper considers the role of human emotions in theprocedural invariance observed in what has been termed“the anchoring effect”. Our empirical evidence focuses on therelationships between emotions and anchoring effects inthe context of the valuation of non-market goods utilizing thedouble-bounded dichotomous choice (DBDC) contingent valua-tion 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 theemotional dimensions; and iii) the sensitivity to scope whenboth the emotional and the cognitive dimensions are present.

In general, anchoring effects are the most relevant and welldocumented behavioral responses in eliciting judgments ofwillingness to pay for public goods with the DBDC method(Green et al., 1998; Kahneman and Knetsch, 1992). This methodwas proposed as a potentially advantageous technique over thesingle bounded dichotomous choice (SBDC) method, because ofthe larger amount of information requested from theindividual.2 Thus, the technique represents a good example ofhow an undesirable cognitive dimension (i.e. anchoring effects)may outweigh the desirable economic and statistical advan-tages of a methodology (Carson et al., 2001; McFadden andLeonard, 1993; Bateman et al., 2001; DeShazo, 2002; Whitehead,2002; Burton et al., 2003).

From a theoretical standpoint, anchoring effects can beconceived as the “pervasive judgment biases in which decisionmakers are systematically influenced by random and unin-formative random points” (Chapman and Johnson, 1999). Sincewidespread anchors can have an influence on human prefer-ences and values, this would question the assumptions ofunique and stable preferences. Without these assumptions,there can be significant doubts about the ability of standardpreference elicitation techniques, such as DBDC, to capturehuman preferences (Slovic, 2000).

Anchoring effects have been found in a wide array of othercontexts.3 They are also a main component of theories ex-plaining several other “anomalies” of the economic model ofconsumer choice, suchas thepreference reversals and theWTP/WTA discrepancy.4 Tversky and Kahneman (1974) argue thatanchoring effects can be explained because of a cognitiveheuristic by which decision makers first focus on the anchorand then make a series of dynamic adjustments toward their

2 Hanemann et al. (1991) demonstrated that it raises the level ofstatistical 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, theestimation of probabilities, social judgments, knowledge ques-tions, 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).

final estimate. Because these adjustments are insufficientlydeveloped, the final answer is biased toward the anchor.

Although Tversky and Kahneman's work has had a tremen-dous influenceoneconomics andpsychology, providinga betterunderstanding of how individualsmake choices, it seems that ithas partially obstructed the inclusion of emotional aspects5 intothe economicmodel. Economists have been aware of the role ofemotions in individual's behavior since early works (Smith,1759; Commons, 1934). However, until recently little attentionhas been paid to the role of the emotional dimension inindividual economic behavior6 (some exceptions are Frank,1988; Kauffman, 1999; Slovic et al., 2002; and Gifford, 2002,among others). Several arguments have been used to explainthis phenomenon. For instance, Loewenstein (2000) pointed outthat emotions have been perceived as transient and unim-portant, and therefore too unpredictable and complex to beincluded in a formal model. Although many economists wouldagree that emotions have a significant influence on behavior,most would leave themout either because they have nothing todo with rational decision making or because they only producenoise aroundsomeaveragebehavior (which is theonepredictedby the neoclassical model).

A common characteristic of most economic models thatincorporate emotions is the implicit assumption that thecognitive and the emotional dimensions are independent.This assumption has been a constant source of controversy inresearch on emotions (Hilgard, 1980; Zajonc, 1980), which hasbeen revitalized in recent years as the “cognition–emotiondebate” (Lazarus, 1984 and Leventhal and Scherer, 1987). Morerecently, a new literature has emerged that considers choices asa 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 deliberativeand an affective dimension. Hsee and Rottenstreich (2004)propose the terms “valuation by calculations” and “valuation byfeelings” to refer to these two dimensions. These authors arguethat under the valuation by calculations system, changes inscope have a relatively constant influence on value throughoutthe entire range, while under the valuation by feelings system,the value ishighly sensitive for a change from0 to somepositivevalue, but is largely insensitive to further variations of scope.

The plan of the paper is as follows. In the next section wepresent the details of the experimental design that provided uswith the source data for the study of the relationships betweenthe emotional and cognitive dimensions in the contingentvaluation method. Section 3 outlines the econometric modelutilized to estimate the anchoring effects in the DBDC model.

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 thaemotions may play a significant role in several aspects involvedin the decision-making processes. For instance, emotions mayaffect memory (Heuer and Reisberg, 1990), perceptions (Zajonc1980; 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), purchaseintentions (Brown et al., 1998), variety seeking (Kahn and Isen1993) and performance (Damasio, 1994).

t

,,

,

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Section 4 discusses the results regarding the hypotheses of therelationships between the emotional load facing the individualin the valuation task and the degrees of anchoring and scopeeffects, as well as the relationships between anchoring andscopeeffects. Finally, Section5summarizes themain findingsofthe paper and sets up some of the implications for furtherresearch.

8 As it was noted by a reviewer, in contexts in which the use of

2. Experimental design

2.1. The good to be valued

The application focused on the valuation of the rehabilitation ofa network ofwalking paths in the island of Gran Canaria, Spain.The network is an ancient infrastructure which was used inprevious centuries for communications between villages. Inrecent times, these paths have been abandoned and new roadshave beenbuilt usingmodern techniques, at times replacing theold paths. The ancient path structure was mainly used forwalking, although it also could allow carriage and animalstransit in many parts of the network. The extension of thenetwork is about 1000 km. In the last years rehabilitation workhas been accomplished on 300 km using European funds.

The rehabilitated network is currently used by the localpopulation and also by some tourists and visitors for hiking andwalking. Due to its rural origin, most paths go through naturalareas and allow users to enjoy nature and magnificent land-scapes. The primary objective of the study was to determinehow much would be the benefits for the resident population ofthe island to beobtained fromtheexpansionof the rehabilitatednetwork. Theold abandonedpaths arepractically impenetrable,thus the expansion of the services of the network requiresrebuilding more routes. All the construction techniques havebeendone following theold traditionsand involving the originalmaterials, and were supposed to continue to be so for theexpansion of the network.7

2.2. The computer-based questionnaire

Using the taxonomy proposed by Harrison and List (2004) ourstudy is defined as an artefactual field experiment. The datacollection was conducted in 2002 to the resident population ofGran Canaria. The questionnaire was administered viapersonal interviews at the subject's home. The interviewswere conducted by professional interviewers of a survey firm,previously trained by the authors to standardize surveyenvironment and subject attention. The interviews weresupported by a computer-based questionnaire implementedon personal laptops computers. The computer-based surveyinstrument, while holds most of the desirable characteristicsof lab experiments (e.g. control of the environment), hasseveral advantages over the alternatives: it can reduce sampleselection bias commonly observed in lab experiments, reduceinterviewer bias effects, allow the consideration of additional

7 For more up to date information regarding conservation ofwalking paths in Gran Canaria you can visit: http://www.grancanaria.com/patronato_turismo/1860.0.html.

covariates, and potentially permit improvements in thestatistical design.8

2.3. The design

A sample of households was randomly screened from thecensus population of Gran Canaria published by the CanaryIslands Statistical Institute (ISTAC). The interviewers partici-pated actively in several training sessions on the specifics ofthe questionnaire. They did work also for the pre-test surveys,providing comments and suggestions for improving the finalquestionnaire. Up to three focus groups of 5–10 subjects andthree pre-tests of 20–30 subjects were needed in order to reachthe final version of the survey instrument. In the process ofsuccessive revision of the pre-test questionnaires we con-sidered critical issues such as the payment vehicle, the good tobe valued and the information content of the market design.The number of valid interviews was 574, with a response rateof approximately 84%.

The survey instrument implemented the constructed mar-ket aimed at valuing in monetary terms the benefits that thepopulationwould enjoy from the expansion of thenetwork. Thequestionnaire was structured in threemain parts. The first partasked questions about the relative importance of a range ofobjectives of public policy in general, and recorded informationon the various recreational activities that the subject made inleisure time. These questions preceded the presentation of theelements of themarket scenario andwere intended to introducethe subject to the valuation context. The second sectionpresented the valuation scenario and asked the subject'swillingness to pay for the proposed policy. The policy proposalwas presented by a descriptive paragraph, and by means of aninteractive map on the computer, showing simulated picturesand drawings. The final section obtained information onsocioeconomic variables such as employment status, educationlevel, income level, family size, and year of birth.

Key elements of the scenario are the payment vehicle, theelicitation method and the provision rule. The elicitationmethod was the double-bounded dichotomous choice basedon a bid design of alternative prices that were randomlydistributed across the sample. Each individual in the samplerandomly received one of the several initial prices. The bidvector was designed utilizing Cooper's (1993) methods for apredetermined number of bids and based on the informationprovided by an open ended pre-test question. A second follow-up bid vector was defined by the next successive price in theinitial bid vector with an upper and a lower limit equallyspanned. If the individual answered ‘yes’ to the first price, thisprice was increased; if the answer to the first price wasnegative then the price were lowered. The final elicitation treeis presented in Fig. 1.

computers is restricted to some portion of the population(normally younger and more educated people), the use ofcomputer-based questionnaires may invoke some selection bias.In this study, this potential issue was tested by employinginformation obtained in pilot surveys and focus groups. Thisanalysis found no selection bias effect.

Fig. 1 – DBDC structure.

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The payment vehicle was a contribution to a special fundfor the specific purpose of carrying out the expansion of thewalking paths network. In order to enhance the incentivecompatibility of the payment vehicle we tested alternative

provision rules in the initial stagesof thestudy (i.e. focusgroups,in-depth surveys and pilot surveys). The most satisfactoryoption was to follow a wording structure similar to the oneproposed in Rondeau et al. (1999) and Poe et al. (2002). This

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consists of a provisionpointmechanism (PPM)withmoneybackguarantee (MBG) and a proportional rebate of excess contribu-tions (PR). Subjects were told that the public good is providedonly if the sum of contributions equals or exceeds its cost (theprovision point). If contributions fall short of costs, they arecompletely refunded (the money back guarantee), whereas ifthey exceed costs, the excess is returned to each contributorproportionally to the share of their individual contribution inthe total amount contributed (the proportional rebate).9 Inaddition, the chosen payment vehicle was perceived as feasiblefrom a policy perspective, since some local facilities arecommonly financed by special contributions.

2.4. Sensitivity to scope

The proposed expansion of the network of ancient walkingpaths is designedwith the aim of increasing the leisure optionsof the objective population andpotential visitors to the island ofGranCanaria. The accomplishment of the project involves costswhich are expected to increase linearly with the size of thenetwork. Thus, an interesting question from a policy perspec-tive is how preferences would change across different projects.This would allow us to test for the coherency of the preferencesas the subject evaluates expansions of different scales whichcould raise different benefits. Three alternative projects werepresented to the subjects in split samples, and variedonly in thenumber of kilometers of the proposed expansion, above thecurrently rehabilitated 300 km, i.e. 30, 100 and 300 km. All theseprojects were feasible in the island according to experts andwould serve for the purposes of increasing the amount ofservices provided by the network. Subjects were presentedwiththe three alternatives and randomly asked to value only one ofthe proposed projects, conditional on the relative benefits thatthe other two alternatives would provide them.

2.5. The measure of thee emotions intensity: the EIS-Rscale

Since emotions are omnipresent in everyday life and play animportant role in several scientific theories, a particularchallenge is agreeing upon a concrete definition of thisphenomenon, including its conceptualisation and operationa-lisation. A large part of the disagreement between differenttheories can be subscribed to different definitions of what con-stitutes an emotion. There is a distinction between emotions,moods and emotional disorders (Ben-Ze'ev, 2000).10 In particu-lar, we are interested in emotion intensity, because it is arguedto be an important predictor ofmood experience, and therefore,of individual decision making. Affect or emotion intensity, as

9 In the context of controlled experiments and treating subjectsin groups, Rondeau et al. (1999) found out that this paymentmechanism can closely approximate demand revelation. Thisevidence was supported in the context of our experiment for theresults of a split sample comparison between alternative pay-ment vehicles in the pre-tests studies.10 In the context of stated preference methods, there is anextensive literature that includes attitudinal scales and scalesoperationalising WTP as behavioural intention that reflectsvariables from social-psychology. Some examples are Brouweret al. (1999), Heberlein et al. (2005), or Fischer and Hanley (2007).

usedhere, can be defined as the “stable individual differences inthe strengthwithwhich individuals experience their emotions”(Larsen and Diener, 1987).

There are several scales available that may be used tomeasure emotion intensity. In this paper we adopt a reducedversion of the Emotional Intensity Scale (EIS) first proposed byBraaten and Bachorowski (1993). The main drawback ofstandard emotion scales is that they often combine frequencyand intensity of emotions in the same scale. The EIS overcomesthis problem by measuring only intensity of emotions. In ad-dition to this, “the EIS is adequately developed and shows ev-idence 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 advan-tage of EIS-R is that “it provides amore practical instrument forstudies investigating the relationship of the EIS to cognitive,affective, or behavioral, at the same time that minimizematuration and fatigue effects in respondents”.11

3. The double-bounded dichotomouschoice 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 servicesprovided by an environmental good from q0 to q1. Let ei(·) be theexpenditure function for individual i, that is, the inverse of theindirect utility function with respect to income. Under thetypical assumption of consumer rationality, the answers wouldbe yes if ei(q1,V⁎)+Bi1≤ei(q0,V⁎) and no otherwise, where V⁎ issome fixed level of utility. The expenditure difference could beseen as the individual's willingness to pay (WTP) for the offeredservices, that is, WTPi1=ei,(q1,V⁎)−ei(q0,V⁎). Therefore, the ob-servedanswer {yi1} to the firstbidprice {Bi1} takes thevalueof oneor zero ifWTPi1 ishigheror lower thanthebidprice, respectively.

Traditional CVmodels assume that the expenditure functionis not fully observed by the researcher. Thus, the latent variableWTP can be viewed as a function of two components, adeterministic μ and a random component ɛ. In general, we canassume a linearWTP function (Cameron, 1988),12 that is, WTPi1=μi1+σ1ɛi1, where μi1 and σ1 are, respectively, the mean and thestandard 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. Thismethod was first proposed by Carson (1985) and Hanemann(1985). The second bid offered (Bi2) is assumed to be higherthan (Bi1) if individual i answers positively to the first price andvice-versa. Let us assume that WTP from the first question isthe true WTP (see for example, Herriges and Shogren, 1996 orWhitehead, 2002). Following the general setting developed inAraña and León (2007) for repeated elicitation formats (whichincludes DBDC), we consider here a simultaneous equation

11 Definition of EIS-R is presented in Appendix B. Results of thePCA and validity and reliability of the scale are available by theauthors upon request.12 Alternatively, the model can be specified directly in terms ofthe indirect utility function (Hanemann, 1984). McConnell (1990)shows how Cameron's model may be seen as the dual of theHanemann's.

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model with anchoring effects that allows us to consider theinterdependencies between the stages in the elicitation process.That is,

WTP1i ¼ A1i þ e1i

WTP2i ¼ A2i þ giB1i þ e2i with gia 0;1½ � 8i; i ¼ 1;2; :::n

ð1Þ

where μik=αik+xikβk (k=1,2) are the linear predictors associatedwith l×1 regression parameter vectors βk and covariate vectorsxik, αi

k is the intercept term, and ηi captures the potential exis-tence of an anchor effect of the first bid on WTP of individual i(Herriges and Shogren, 1996). The linear predictors are linked tothe probability of a positive response by a bivariate normalcumulative distribution (BVN) called the link function. Simulta-neity between responses is captured by the lower triangle com-ponent ofΣ (e.g.σ2,1). This is amodel of simultaneous equationswith limited dependent variables (SLDV), which reduces to ageneral 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 formerprovides more information on individual's preferences. Hane-mann et al. (1991) showed that it leads to more efficient welfareestimates. This additional information may potentially allowthe researcher to conduct studiesat a lower cost (smaller samplesizes)whileholding theprecision of theWTP estimates constant(same variance).13 However, the DBDC has been questionedbecause of the empirical support to the argument that thedistribution ofWTP is incoherent between both steps (e.g. Greenet al., 1998; Cameron and Quiggin, 1994).14 In other words, themean or median WTP estimated using responses to the firstvaluation question differs empirically from the one estimatedusing the responses to the second question.15

The presence of potential behavioral responses to the follow-up question has been argued in non-market valuation as theprimary explanation of the incoherency between DBDC andSBDC (Alberini, 1995; Carson et al., 2001; Burton et al., 2003;DeShazo, 2002; Bateman et al., 2001). Innovative efforts tomodeleconometrically some of the reactions to the second bid offeredcan be found in Cameron and Quiggin (1994), Herriges andShogren (1996), Alberini et al. (1997), Whitehead (2002), Flachaireand Hollard (2006) and Araña and León (2007). The argumentscommonly raised for explaining these behavioral responsesinclude anchoring effects, strategic behavior, yea-saying, nea-

13 Alternatively, it would allow us to increase the precision of theestimates (lower variance) holding sample size constant.14 The inconsistency between first and second responses leads tothe rejection of the maintained assumption of the restricteddouble bounded model (Hanemann et al. 1991) that the mean andvariances are constant across both bounds, and that the correla-tion coefficient between the standard errors is equal to one,which essentially means that WTPi1=WTPi2. The rejection of thishypothesis implies a reduction in the efficiency gains of thedouble bounded elicitation procedure.15 The number of bounds can be increased successively in theelicitation process, leading to what has been denominated thetriple 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 thenumber of binary steps in the elicitation process is increased.

saying, uncertainty cost, weighted average, bargaining, guilty/indignation and quality/quantity shift, among others. Forsimplicity, in this paper we focus on the anchoring effects,which can be seen as a general cognitive heuristic implicitlylinked to other behavioral responses.

3.2. Anchoring effects

An empirical result of the DBDC is the fact that, in follow-upquestion the distribution function of WTP could be influencedby precedent stage, implying some type of anchoring effect orbehavioral process (e.g. Herriges and Shogren, 1996; Aadlandand Caplan, 2004). In a general setting, Tversky and Kahne-man's (1974) describe the anchoring effect as “the process inwhich people make estimates by starting from an initial valuethat is adjusted to yield a final answer.”

Following previous models of DBDC (Herriges and Shogren,1996;Whitehead, 2002), ourmodel collects the influence of thestarting bid amount on WTP in the term ηiBi1. Parameter ηimeasures the importance of the anchor effect of the first bidon WTP at an individual level. Thus, the anchoring effecthypothesis may be tested for each individual of the sample byconsidering 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 byArañaand León (2005).16 This approachhas basically threemainadvantages over standardmaximum likelihood estimation: i) itallows for more flexibility and unobserved heterogeneity in themodel through the random parameters specification; ii) itallows for an easy and efficient comparison between modelsthrough the use of the Bayes Factor; iv) it relies on an exacttheory of probability even with small samples, leading to moreaccurate results in this context. The detailed description of theeconometric model and the components of the Bayesianapproach are explained in detailed in the Appendix A.

4. Results

The estimation of the simultaneous equation model outlinedin the previous section is particularly intended to raise furtherevidence on the anchoring effects produced by the first bidprices offered in the double-bounded dichotomous choicemodel. Although this model centers on anchoring effects, thedata collected in our field experiment also allows us toinvestigate i) the potential relationships between anchoringeffects and the emotional state of the individual, and ii) thepotential relationships between anchoring effects and thescope of the environmental good to be valued, as represented

16 In order to test the sensitivity of the results to the econometricapproach, maximum likelihood estimations of a bivariate probimodel have been carried out. The results show no significandifferences in terms of the hypotheses proposed in this studyThe sensitivity analysis results, data set and the GAUSS programcodes for the Bayesian estimation are available from the authorsupon request.

tt.

Table 1 – Estimation results of Bayesian bivariate modelsfor a naïve and anchored individual (posterior standarddeviations in parentheses)

Naïve model Anchored model

β1 β2 β1 β2

Intercept 18.9460(4.5615)

0.7664(3.6249)

18.8234(4.4138)

0.7878(3.5954)

EIS 12.9748(0.8970)

15.0844(0.6515)

12.9463(0.8947)

15.0560(0.6486)

Age −0.2461(0.0463)

−0.2005(0.0353)

−0.2450(0.0446)

−0.2006(0.0351)

Log (KIL) 5.1005(1.7401)

7.1897(1.3784)

5.0873(1.7373)

7.1883(1.3571)

EDU 0.4274(0.1714)

0.5997(0.1259)

0.4333(0.1683)

0.6005(0.1288)

INC 0.0062(0.0018)

0.0030(0.0014)

0.0063(0.0018)

0.0030(0.0014)

BID 1 – – – 0.2694(0.0379)

σ1 13.0619 (0.5807) 13.0302 (0.5603)σ2 11.0802 (0.6310) 11.0647 (0.6313)σ21 70.4236 (8.2962) 59.6298 (10.3180)Mean WTP 19.18 [18.43, 19.98] 19.16 [18.37, 20.01]Marginal likelihood −1113.97 −1057.26

18 Ariely et al. (2003) found coherent preferences within indivi-duals, i.e. by asking an individual about various dimensions of a

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by the number of kilometers to be rehabilitated in the policyprogram presented in the market construct.

Table 1 shows the estimation results for the simultaneousequation model for two alternative assumptions. Under thenaïve 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 priceof the first question in the equation for the second response.Anchoring effects as induced by the first bid offered are relevantin this application, as is evident by the significance of the firstbid price in the second equation. This result has been obtainedin most applications of the double-bounded dichotomouschoice format (e.g. Green et al., 1998).

The results in Table 1 show also the relevance of some otherexplanatory variables that are significant in explaining WTP atboth stages of the elicitation process. WTP rises with income(INC) and the years of education (EDU), while decreases with theageof the individual (AGE).Thesignificanceof these explanatoryvariables can be interpreted as giving support to the constructvalidity of the contingent valuation study that was designed tovalue the rehabilitation of a network of walking paths.

But for our purposes, the main variables of interest in theoutput regression of the bivariate model are the emotionalintensity scale (EIS) and the logarithm of the number ofkilometers presented in the valuation task of the contingentvaluation scenario (KIL).17 These variables are both signifi-cant at the 95% level, supporting the following two empiricalresults:

1. There was significant sensitivity of WTP to the size of theenvironmental good being offered. This relationship was

17 After testing for the validity and reliability of the EIS, the PCAreported that a model with only one factor provide the mostsatisfactory solution (e.g. reported Cronbach's alpha was 0.92).

logarithmic: when the number of kilometers of the walkingpaths network was increased, mean WTP also increased,but at a decreasing rate. Thus, the scope effect or theabsence of sensitivity to the dimensions of the good to bevalued can be rejected for this particular application.

2. The emotional state of the individual played a significantrole on the elicited values of the environmental good inquestion. This relationship was positive, i.e. the higher theemotional state the large becomes WTP.

Even though these variables are important for explainingWTP at an aggregate level, they can be also related to thedegree of anchoring which is likely to be found in theelicitation mechanism of the DBDC model. Thus, let usconsider the relationships between the anchoring effectsand i) the scope effect, and ii) the state of emotional loadfacing the individual.

4.1. Scope and anchoring effects

In order to ascertain whether scope effects are also present forthe various anchoring bids utilized in the experiment, Table 2reports the mean WTP for the subsamples of the lowest andhighest bids utilized in the first dichotomous choice question.It can be seen that the size of the walking paths network has asimilar influence on WTP across the lowest and highest bidsoffered to the individuals. This result is similar to the onefound by Ariely et al. (2003) in what these authors called“coherent arbitrariness”. That is, although preferences arelikely to be influenced by initial anchors or bids, they can becoherent in economic terms for the different dimensions ofthe good to be valued.18

However, the scope effect giving support to the coherenceof preferences under anchoring, could be dependent on theemotional status of the individual facing the task of valuingdifferent dimension of a given good. Thus, this potentialrelationship would raise the need to consider the role ofemotions in both the anchoring and scope effects.

4.2. Emotional load and anchoring effects

The extent of the anchoring effects can be also influenced bythe emotional status of the individual. In general, thishypothesis implies that the cognitive aspects of the valuationtask, i.e. the commonly found recurrence to some anchor inorder to base a valuation response, can be influenced by theemotional aspects involved. As can be seen in Table 3, theparameter of the anchoring effect ηi is related with the level ofemotional load posed by the individual in the valuation task.

The relationship between the anchor parameter ηi and theEIS is depicted in Fig. 2. Low and high values of EIS correspondwith significantly high levels of anchoring. This relationship is

given good. Since we used split samples for the sizes of thewalking path network, our results can be interpreted as support-ing coherent preferences at an aggregate level, rather than at anindividual one, i.e. at a social welfare function rather than atindividual utility functions.

Fig. 2 – Estimated relationship between anchoring effectsand EIS.

Table 4 – Mean WTP (€) of the size of the network by EIS

Table 2 –Mean WTP (€) for different sizes of the networkby starting bids (standard deviation in parenthesis)

Starting bid Size of the walking path

30 km 100 km 300 km

Lowest (6.01€) 14.88 (2.38) 16.37 (2.51) 21.36 (2.49)Total sample 19.94 (2.44) 25.83 (3.86) 28.31 (3.76)Highest (48.04 €) 21.94 (3.69) 37.93 (4.05) 40.82 (4.01)

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not linear but U-shaped. The degree of anchoring declines asemotional intensity increases, reaching a minimum for anaverage value of EIS. At this point, anchoring effects are notsignificantly different from zero at the 99% level. Thus, eventhough anchoring effects are significant across the sample,those subjects with average EIS are not influenced by the firstbid in the valuation process.

These results can be related to the evidence that purport aU-shaped relationship between human performance andemotional intensity (Ashcraft and Faust, 1994; Idzikowski andBaddeley, 1983). The “Yerkes–Dodson law” (Yerkes and Dod-son, 1908) states that performance requires an intermediatelevel of emotional intensity (Leibenstein, 1987; Kauffman,1999).When emotional intensity is too low there is insufficientattention and mental arousal, and short-term memory isblocked (Kahneman, 1973). When emotional intensity is toohigh thinking becomes disorganized, and there is difficulty torationally 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 havean influenceon his ability to perform according to coherent preferences, i.e.successfully passing the scope test. This hypothesis can beappreciated by looking at the relationships between the EIS andthe size of the walking paths to be rehabilitated in the policyproposal. Table 4 shows themeanWTP for thedifferent levels ofthe walking paths network according to three groups ofindividuals as bunched by their level of emotional state (low,average, and high). Fig. 3 depicts the general relationshipsbetween themeanWTP and the variable KIL for the three typesof emotional profiles considered.

It can be seen thatWTP is less sensitive for initial changes inscope (from 0 to 30 km) when the emotional scale is low. Thissensitivity risesas theemotional scale increases, from15.20€ to31.21 €. In addition, for further increases in the size of thewalking paths (beyond 30 km) WTP remains invariant for thegroups of high and low emotional scales. Thus, the scope test isfailed for subjectswith extremeemotional scales (lowandhigh).These subjects also posed a large degree of anchoring effects.However, the group of individuals with average emotional stateshowed a steeper valuation function in relation to scope (i.e. to

Table 3 – Anchoring effects parameter by EIS levels

Low EIS Avg EIS High EIS

Anchoring effect (ηi) 0.42 (0.10) 0.15 (0.07) 0.43 (0.11)Confidence interval (99%) [0.16, 0.67] [−0.01, 0.30] [0.17, 0.68]

the level of km). Thus, only those subjects with an averageemotional state are likely to behave according to coherentpreferences with no anchoring effect.

These results can be seen as giving some support to thehypothesis claimed by Hsee and Rottenstreich (2004) thatunder “valuation by feelings” preferences tend to be verysensitive to changes in the initial values of the good and veryinsensitive to changes for higher values; and under “valuationby calculations” preferences are less influenced by changes ininitial values, and more sensitive to further values.

5. Conclusions

Emotions are present everyday in human life. Several theoriesin neurosciences, psychology and sociology acknowledge itscentral role in explaining human behavior. Although someeconomists have recognized the role of emotions in explaininghuman behavior (e.g. Smith, 1759, Commons, 1934), the overallsignificance of emotions has been virtually ignored in theeconomics literature ingeneral, and inenvironmental valuationin particular, until recent times. In this paper we have tried toempirically test the role of emotions in non-market valuationand the potential trade-offs between cognitive and emotionalintensity. The study utilizes a reduced emotional intensity scalescore which has been successfully used in other sciences tomeasure the emotional intensity of the individual.

The cognitive dimension of the formation of individual'spreferences is studied by considering the anchoring effects in theDBDC elicitation process in the contingent valuation method. Asimultaneous equation Bayesian approach has been used toestimate the individual's anchor to the first bid price. The resultsshow that the answers to the second question are anchored bythe bid price offered in the first question. Thus, anchoring is a

levels (standard deviation in parenthesis)

30 km 100 km 300 km

Low EIS 15.29 (1.19) 16.64 (2.55) 16.87 (2.18)Avg EIS 19.83 (3.73) 25.80 (2.46) 28.29 (4.07)High EIS 31.21 (5.97) 38.77 (2.01) 37.68 (4.72)

See Li (1998) for details.

Fig. 3 – Estimated relationships between sensitivity to scope(km) and WTP for three EIS levels.

708 E C O L O G I C A L E C O N O M I C S 6 6 ( 2 0 0 8 ) 7 0 0 – 7 1 1

relevant empirical effect that influences the elicitation of thevalue of non-market goods using DBDC.

However, in our empirical application we found that pre-ferenceswere sensitive to the scope of the good, suggesting thatthe embedding effect was not relevant in this context, since thesubject reacted significantly to the different dimensions of thegood to be valued. Further, the sensitivity ofWTP to the scope ofthe good was also found for the various levels of the anchorgiven by the first bid price; in other words, the “coherent” rela-tionship between WTP and the dimension of the good was notaffected by the bid price offered in the first binary question.

Thus, we can conclude that anchoring effects do not seem tohave a relevant influence on the individual's ability to discernamongdifferent dimensionsof anon-market good in avaluationscenario. A useful implication might be that relative WTP couldbe successfully elicited. Nevertheless, anchoring effects are stillpresent in our application and raise serious concerns about theunderlying nature of human values and the capability ofpreference elicitation techniques to capture them. If individualchoices are influenced by external anchors, it can be questionedhow can preferences be defined and how can elicitationtechniques be able to seize them. As pointed out by Ariely et al.(2003), “even if there are no clear violations of the transitivityaxiom, the researcher cannot ascertain whether elicited choicesreveal a set of unique and well-defined preferences”.

In order to shed light on the conditions under which bidanchors could have an influence on the formation of individualpreferences, we looked at the potential role of individual'semotions. In particular,we focused on the relationship betweenthe EIS and the degree of anchor raised by the bid offered in thefirst binary question. The results show that anchoring effectsdecline as emotional intensity increases, reaching a minimumfor anaverage valueof EIS. After this point, anchoringeffects areagain significant. Themajor implication of these findings is thatindividuals tend to improve their ability for the non-marketvaluation task when their emotional intensity is moderate.

On the other hand, the omission of EIS in the valuationfunction could bias non-market valuation results. The correla-tion between cognitive and emotional intensity implies thatsome indexof the latterneeds tobeconsidered in thesystematicpart of the utility or expenditure functions. To our knowledge,previous work has generally assumed independence betweenthe cognitive and emotional dimensions. The common incor-poration of unexplained emotional conditions as part of thestochastic term introduces correlation between the systematicand the stochastic parts, leading to biased results.

Some researchers have claimed that the finding of insensi-tivity to scope in some applications is a clear evidence of theinabilityof statedpreferencemethods to capturepreferences forpublic goods (Kahneman et al., 1999; Diamond and Hausman,1994) Our results suggest that the degree of sensitivity to scopecan be also related to the emotional load involved in thevaluation task. This might prompt a need for further evidenceon the role of emotions in the valuation of private and public orenvironmental goods.

Our results concurwith thenotion that theremightbea trade-off between the emotional and cognitive dimensions in non-market valuation tasks. That is, some degree of emotionalintensity might help reduce the cognitive load and enhanceperformance in human decisionmaking. Nevertheless, it shouldbe acknowledged that this relationship is complex because of themultivariate factors that can influence individual's emotionsandthe cognitive aspects involved in the survey instrument. Furtherresearch should explore the relationships of other emotional andcognitive factors that might play a role in the decision makingtask and the formation of individual's preferences.

Appendix A. Estimation of the Bayesian model forDBDC

In this appendix we illustrate the application of the modeloutlined in Section 3 for the double-boundeddichotomous choicedata. For simplicity, let us decompose the joint bivariate normaldistribution for (εi1, εi2) into the product of the marginal distribu-tion 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−xi1β1, σ2=σ22−σ212 , 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 areassumed:

f að ÞfMVN a0;w�10

� � ðA1:3Þ

f r21ð ÞfN r0; b�10

� � ðA1:4Þ

f r2� �

fIGa02;c02

� �ðA1:5Þ

where MVN and N are a multivariate and univariate normaldistribution respectively, and IG is the inverted gamma distribu-tion. Since we have no prior information on model parameters,very non-informative diffuse priors are assumed by consideringα0=c0=r0=α0=0, and largevalues for theparameters collecting thevariance (b0, ψ0). Therefore, the joint posterior distribution takesthe following form,

pPWTP

1;PWTP

2; r12;r

2; ajY1;Y2� �

¼ jn

i¼1Pnni� � 1�y1ið Þ 1�y2ið Þ Pyyi

� �y1i y2i Pnyi� � 1�y1ið Þy2i Pyni

� �y1i 1�y2ið Þ� �

� f að Þf r21ð Þf r2� �

ðA1:6Þ

709E C O L O G I C A L E C O N O M I C S 6 6 ( 2 0 0 8 ) 7 0 0 – 7 1 1

where Pinn, Piyy, Piny, Piyn are the probabilities of individual i responds no/no, yes/yes, no/yes and yes/no respectively, and Y1=(y11,…,yn1), Y2=(y12,…,yn2), WTP‾‾‾‾‾‾‾ 1=(WTP11,…, WTPn1), WTP‾‾‾‾‾‾‾ 2=(WTP12,…, WTPn2). Since thedependent variables follow normal distributions the posteriorconditional distributions are as follows:

f WTP1i jY1;PWTP

2; h

� �f

/ WTP1i jA1j2; r1j2� �

l 0;Bi½ � if y1i ¼ 1

/ WTP1i jA1j2; r1j2

� �l Bi;l½ � if y1i ¼ 0

8<:

ðA1:7Þ

f WTP2i jY2;PWTP

1; h

� �f

/ WTP2i A2j1; r2j1j� �

l 0;Bi½ � if y2i ¼ 1

/ WTP2i jA2j1; r2j1

� �l Bi;l½ � if y2i ¼ 0

8<:

ðA1:8Þ

f ajY1;Y2;

PWTP

1;PWTP

2; r21;r

2� �

fMVN a0;W�10

� �ðA1:9Þ

f r21jY1;Y2;PWTP

1;PWTP

2; a;r2

� �fN r; b

�1� �ðA1:10Þ

f r2jY1;Y2;PWTP

1;PWTP

2; a; r21

� �fIG

a12;c12

� �ðA1:11Þ

where ϕ(·) l[a,b] is the truncated normal distribution in interval[a,b]. If θ(0)=(γ(0), β1

(0), β2(0), σ2, σ12

(0)) is the starting value of θ, then theGibbs samplingworks by iteratively replacing the initial valueonthe conditional distributions, and Eqs. (A1.7)–(A1.11) completethe MCMC algorithm. The algorithm is repeated t times, leadingto the final values (WTP1(t), WTP2(t), γ(t), β1

(t), β2(t), σ2(t), σ21

(t)) obtainedfrom the joint distribution (WTP1, WTP1,α, σ21, σ2)|Y1, Y2. Thissequence of t algorithms is conducted overH times, leading toHvalues for each parameter of the posterior distribution. Theseseries of simulated values are utilised to generate the posteriormoments 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 choosethe answer that best describes how you usually feel.

1. Someone compliments me. I feel:1. It has little effect on me2. Mildly pleased3. Pleased4. Very pleased5. Ecstatic—on top of the world

2. I am happy. I feel:1. It has little effect on me2. Mildly happy3. Happy4. Extremely happy5. Euphoric—so happy I could burst

3. Someone I am very attracted to ask me out for coffee. I feel1. Ecstatic—on top of the world2. Very thrilled3. Thrilled4. Mildly thrilled5. It has little effect on me

4. I am at a fun party. I feel:1. It has little effect on me2. A little light-hearted

3. Lively4. Very lively5. So lively that I almost feel like a new person

5. Something wonderful happens to me. I feel:1. Extremely joyful–exuberant2. Extremely glad3. Glad4. A little glad5. It has little effect on me

6. I have accomplished something valuable. I feel:1. It has little effect on me2. A little satisfied3. Satisfied4. Very satisfied5. So satisfied it's as if my entire life was worthwhile

7. A person with whom I am involved preparesme a candlelight dinner. I feel:1. It has little effect on me2. Slightly romantic3. Romantic4. Very romantic5. 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 else2. Very passionate3. Passionate4. Mildly passionate5. It has little effect on me

9. Someone surprises me with a gift. I feel:1. It has little effect on me2. A little grateful3. Grateful4. Very grateful5. 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 me2. A little frustrated3. Frustrated4. Very frustrated5. 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 me2. A twinge of guilt3. Guilty4. Very guilty5. Extremely guilty

12. Someone criticizes me. I feel:1. It has little effect on me2. I am a bit taken aback3. Upset4. Very upset5. So extremely upset I could cry

13. I have an embarrassing experience. I feel:1. It has little effect on me2. A little ill at ease3. Embarrassed4. Very embarrassed5. So embarrassed I want to die

14. Someone I know is rude to me. I feel:1. So incredibly hurt I could cry2. Very hurt3. Hurt4. A little hurt5. It has little effect on me

15. I see a sad movie. I feel:1. So extremely sad that I feel like weeping2. Very sad3. Sad

710 E C O L O G I C A L E C O N O M I C S 6 6 ( 2 0 0 8 ) 7 0 0 – 7 1 1

4. A little sad5. It has little effect on me

16. I am involved in a situation in which I must do well, such asan important exam or job interview. I feel:1. It has little effect on me2. Slightly anxious3. Anxious4. Very anxious5. So extremely anxious I can think of nothing else

17. I am in an argument. I feel:1. It has little effect on me2. Mildly angry3. Angry4. Very angry5. Extremely angry

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