Ecological Economics 35 (2000) 289–302

ANALYSIS

Choice modelling and its potential application to tropicalrainforest preservation

John Rolfe a,*, Jeff Bennett b, Jordan Louviere c

a Central Queensland Uni6ersity, P.O. Box 197, Emerald, Qld 4720, Australiab National Centre for De6elopment Studies, Australian National Uni6ersity, Canberra 2600, Australia

c Memetrics Proprietary Limited, Sydney 2000, Australia

Received 16 April 1999; received in revised form 19 May 2000; accepted 19 May 2000

Abstract

Loss of rainforest occurs in part because of a global market failure problem. Markets routinely transmit signalsabout international demands for forest products such as timber, but fail to transmit signals about demands forpreservation. To make efficient resource allocation choices, decision makers need some framework to estimate thenon-use values that might be held by an international community for rainforest preservation. Non-market valuationtechniques can be used for this purpose, but framing and other problems limit the application of the contingentvaluation method (CVM). An alternative technique, choice modelling (CM), appears to hold some promise becauseit can be used to model complex situations and to frame choices consistent with ‘real life’ choices. In this paper a CMexperiment to assess the values that Australians hold for rainforest conservation in Vanuatu is reported. Theexperiment disguises effectively the issue of interest, rainforest conservation in Vanuatu, within a pool of otherconservation locations. Also, the experiment demonstrates that choice tradeoffs may involve a variety of environmen-tal and socio-economic factors, rather than be concentrated on some monetary tradeoff. The results demonstrate thata variety of predictive and valuation tools can be gained from a CM experiment, and suggest that furtherdevelopment is warranted. © 2000 Elsevier Science B.V. All rights reserved.

Keywords: Choice modelling; Rainforest; Non-market valuation

www.elsevier.com/locate/ecolecon

1. Introduction

Environmental awareness and concerns are nowmainstream in western developed countries. Inpart, these environmental concerns mark a more

sophisticated awareness on the part of the popula-tion that many production and developmentchoices have environmental impacts, and that asimplistic focus on production issues might gener-ate environmental losses. Despite this growth inawareness of environmental issues, there is still nowidespread consensus about how decisions involv-* Corresponding author. Tel.: +66-749-822904.

0921-8009/00/$ - see front matter © 2000 Elsevier Science B.V. All rights reserved.

PII: S 0921 -8009 (00 )00201 -9

J. Rolfe et al. / Ecological Economics 35 (2000) 289–302290

ing environmental choices should be made. Ques-tions about appropriate value systems and deci-sion criteria provide an ideological flavour tomany arguments. Debates within the broad fieldof economics reflect this uncertainty. There iswidespread agreement that the environment mat-ters (Harris, 1996), but much less agreementabout the role and relevance of economic analysis.

The primary role of economic analysis is topresent information to decision makers abouthow society might balance the tradeoffs inherentin resource allocation decisions, and how thecosts, benefits and incentives might be distributedin any allocation of resources and property rights.The field of Ecological Economics has arisen outof concerns that traditional economic frameworksmay be too narrowly focused when applied toenvironmental issues (Costanza, 1989). As a con-sequence, they may not be appropriate for captur-ing some of the complexity and richness ofecological processes and systems.

There seems to be a widespread consensus thatpeople have awareness, although perhaps not adetailed understanding of a need to treat ecologi-cal systems and processes with respect and con-cern. However, these concerns may not becaptured in a narrow economic focus on produc-tion and consumption issues. One of the majorchallenges in Ecological Economics is to developan appreciation of the non-consumptive valuesthat a population might hold for ecological sys-tems, and to incorporate them into an analysis ofchoices about production tradeoffs.

The issue of forest protection provides an ex-ample where the concerns of people for thepreservation of forests, sometimes expressed inethical frameworks, are not incorporated into thestandard economic analysis of production choices(Tacconi, 1995; Pearce, 1996). The clearing andlogging of rainforests provides perhaps the bestexample of failures to consider non-use values inmaking resource allocation decisions (Repetto,1990; Brown and Pearce, 1994; Capistrano andKiker, 1995; Barbier and Burgess, 1997; Barbieret al., 1991).Rainforests are widely recognisedthroughout the world for their biological richnessand ecological importance, but their conservationand management are focused at the national and

local level in the countries where they exist. Notsurprisingly, there is concern that although inter-national demands for timber and other productsare well recognised through export markets, thereis no corresponding mechanism to recognise inter-national demands for conservation and preserva-tion (Repetto, 1992; Pearce, 1996). While this canbe classified as a market failure problem, it isdifficult to address because non-use values (largelyflowing to the international community) must bebalanced against use values (largely flowing to thelocal and/or national community).

A first step in assessing the problem in eco-nomic terms is to estimate the extent of non-usevalues held by the international community forrainforest conservation. The measurement of non-use values is restricted to stated preference tech-niques, such as the contingent valuation method(CVM), which focuses on valuing a specific trade-off.1 While there have been some advocates of thesuitability of CVM for this purpose (e.g. Carson,1998), there has been some criticism from envi-ronmentalists and economists (e.g. Sagoff, 1988;Vatn and Bromley, 1994; Bromley, 1995). How-ever, the general consensus reported by Binghamet al. (1995) was that ecosystem valuation had animportant role to play if limitations and underly-ing assumptions were recognised.

Choice modelling (CM), an emerging non-mar-ket valuation technique, appears to have somepotential for successful application in situationswhere practical issues limit the CVM. In contrastto the single tradeoff approach of CVM, CMallows for the simultaneous analysis of severalinfluences on choice. More complex models ofvaluation can thus be constructed.

The focus of this paper is on the argument thateconomic techniques have substantial potential toprovide for the analysis of rainforest conservationissues. The complexity of the issues involved doesimpose limitations on the use of different tech-niques, and indicates that new approaches to eco-nomic analysis should be welcomed. While issuessuch as the commensurability of environmental,

1 See Kramer et al. (1996), Kramer and Mercer (1997) forexamples of attempts to use the CVM to estimate the interna-tional rainforest conservation values.

J. Rolfe et al. / Ecological Economics 35 (2000) 289–302 291

economic and social items will continue to bedebated, there is still scope for valuation ap-proaches to be developed that encompass thesevarious factors (Bingham et al., 1995).

To illustrate one approach, the results of anapplication of the CM technique are reported. InSection 2, an economic analysis of tropical defor-estation is presented, followed in Section 3 by asummary of some of the difficulties in applyingnon-market valuation techniques to rainforestconservation proposals. An overview of the CMtechnique is presented in Section 4, and the exper-iment reported in Section 5, followed by Section6.

2. Preservation of tropical rainforests

The problem of rainforest depletion and conser-vation helps to illustrate some of the issues in-volved in the application of non-market valuationtechniques. Tropical deforestation is often de-picted as a major environmental issue for a num-ber of reasons, including potential impacts onclimate, loss of plant and animal species, andimpacts of ecosystem loss (Barbier et al., 1991;Brown and Pearce, 1994). In many cases, defor-estation causes severe production problems in theshort term (Repetto, 1992), while long-term prob-lems can be caused by indirect effects on erosion,ecosystems and climate (Pearce, 1996).

Tacconi and Bennett (1997b) outline the case ofVanuatu, where, in common with many PacificIslands, deforestation continues at a high rate. Itis likely that short-term commercial gains may notmeet economic efficiency criteria when the marketand non-market impacts of environmental dam-age are considered. Decisions to clear forests of-ten only reflect narrow commercial interests,ignoring other benefits that accrue to local land-holders, domestic residents, and internationalpopulations. While causes of deforestation arecomplex, many can be classified as problems oflocal market failure, government failure andglobal market failure (Pearce, 1996).

Poor property rights, undeveloped institutionalstructures and consequent missing markets areoften cited as reasons for local market failures

(Deacon, 1994; Paulson, 1994). Market prices forgoods from tropical forests or land cleared oftropical forests are based almost exclusively onmarginal opportunity costs of labour and capital,and include little economic return for other fac-tors of production (Panayotou, 1993; Pearce,1996). This may reflect deficiencies in propertyrights and other institutional structures, as well asbeing a part-consequence of high discount rates.

Tropical rainforests also exhibit many charac-teristics of public goods. These include non-ex-cludable goods, such as biodiversity and impactson micro-climate, as well as goods and servicesthat have dual (joint) production outcomes, suchas local protection areas used as food sources byindigenous people. The recognition of these publicgoods has led many tropical countries to intro-duce systems of reserves and protected areas forthe purpose of forest conservation.

At the same time, the impact of government onrates of forest clearing are not always benign, andproblems of government failure count as anothermajor cause of deforestation (Repetto, 1988,1990; Pearce, 1996). One group of problems re-lates to the distortion of market signals by gov-ernment, including failures to tax loggingcompanies, which encourages excessive levels ofclearing. Low stumpage fees meant that economicrent was effectively transferred to logging compa-nies in many countries, providing an even greaterstimulus to clearing (Repetto, 1988, 1990; Repettoand Gillis, 1988; Capistrano and Kiker, 1995;Pearce, 1996).

Government failure can also occur through im-pact on institutional factors. The imposition ofgovernment controls over previously effective lo-cal customs and ownership, together with changesin institutional rules and increases in transactioncosts, helps to explain some causes of deforesta-tion. Many tropical underdeveloped countries arecomprised of a large number of cultures, andthere is often an inherent tradeoff between devel-oping an effective national government and main-taining the customs and institutions of thedifferent internal cultures (Pradhan and Parks,1995). In some cases, governments actively en-courage clearing for establishing agriculturethrough a variety of institutional and incentivemechanisms.

J. Rolfe et al. / Ecological Economics 35 (2000) 289–302292

The third broad area of economic failure con-tributing to widespread clearing of rainforests canbe termed as global market failure (Pearce, 1996).Just as forests provide a range of direct, indirectand non-use values at a local and national level(and market failures at these levels explain manyproblems of overclearing), they also provide arange of benefits across an international setting.Market failure in these cases is usually associatedwith indirect benefits and with non-use values.Capistrano and Kiker (1995) demonstrate thatmuch of the tropical forest depletion is linked todemands in international markets. However, al-most no mechanisms exist to balance commercialdemands for deforestation with other demands forpreservation. Market failure exists because directdemands for forest products are not being balancedwith indirect and non-use demands.

Many of these factors are relevant to highclearing rates in Vanuatu, where the local villagersare able to grant overseas timber companies loggingconcessions for forests in their area. Tacconi andBennett (1997) note that the forest ecosystems ofa proposed protected area in Vanuatu had limitedimportance to the subsistence of the local people,and that they therefore had little economic interestin conserving the forests. The benefits and oppor-tunity costs of forest conservation though arespread across a wider population, and to assess theoverall welfare situation, other benefits have to beassessed.

There have been limited attempts to use non-market valuation techniques to estimate preserva-tion values for rainforests. Carson (1998) exploressome of the issues involved. Kramer and Mercer(1997) and Kramer et al. (1996) report one CVMstudy of the values that citizens of United Statesof America were able to place on tropical rainforestprotection. They found that respondents were will-ing to make a one-off payment of between US$21and US$31 to protect an additional 5% of theworld’s rainforests, but that focus group respon-dents were unable or unwilling to allocate theirWTP bids between specific rainforests or acrossregions. This meant that it would be difficultto incorporate the results within specific CBAstudies.

The second type of approach is where the poten-tial respondents have high levels of knowledgeabout the rainforest area and issues of interest, andthus can be expected to frame the tradeoffs pre-sented with some accuracy. For issues of globalmarket failure, this means that only internationalissues that are familiar to potential respondents canbe targeted. An example of this approach is thestudy reported by Flatley and Bennett (1996) of thepreservation values that Australian tourists toVanuatu might have for some specific rainforestprotection options. In this case, the tourists wereasked to complete a CVM survey at the end of theirvisit to Vanuatu. This helped to minimise theinformation transfer process in the experimentbecause the survey respondents were already famil-iar with Vanuatu and many of the issues of concern.

Demonstrating that significant values exist forindirect and non-use benefits of tropical rainforestswill be an important stage in ensuring that thosevalues are brought to the attention of decisionmakers. Even if those values are not includedwithin a project assessment, recognition of theirexistence may prove a significant factor in evaluat-ing decisions where projects are finely balancedbetween costs and benefits. As well, estimates ofinternational WTP for rainforest preservation willprovide some guide to financial transfers that mightbe made between industrialised countries and thosewilling to preserve their rainforests.

To address these issues of global market failure,the primary need is to be able to assess the valueof preferences that are not currently being recog-nised by markets or decision makers. To generateestimates of non-use values, an appropriate non-market valuation mechanism needs to be devel-oped. However, numerous methodologicalproblems are involved in applying non-marketvaluation techniques to issues of international rain-forest preservation. These are discussed in moredetail below.

3. Non-market valuation and rainforestpreservation: some challenges

The case study reported in this paper wasfocused on the estimation of non-use values that

J. Rolfe et al. / Ecological Economics 35 (2000) 289–302 293

Australians might hold for the preservation ofrainforests in Vanuatu. Because Vanuatu is notvery well known to Australians, (apart from beinga Pacific Island holiday destination), substantialdifficulties are associated with the potential use ofnon-market valuation techniques, especially theCVM. These difficulties are summarised asfollows.

3.1. Ignorance

To establish hypothetical tradeoffs that generaterealistic estimates of value, it is essential that thetradeoffs are accurately defined (Carson, 1998).This includes the item or commodity being valued,the stream of benefits and outcomes that willresult, and the payment and provision mecha-nisms. Respondents being asked to assess tradeoffsinvolving rainforest conservation in another coun-try may have very little knowledge about therainforest area (and the country in which it issituated), the benefits that the rainforest area mayproduce, and the institutional and payment mech-anisms associated with the preservation option.

3.2. Complexity

Associated with issues of ignorance are theproblems of complexity. Rainforest depletion andassociated issues, such as impacts on greenhousegas levels and biological diversity, are complexissues involving uncertainty and often generatingsignificant levels of scientific debate. These factors,together with the magnitude and detail of theissues involved, means that it is difficult for a layperson to gain a clear understanding of particularissues.

3.3. Information transfer

These issues of ignorance and complexity poseproblems regarding the transfer of information torespondents in non-market valuation surveys. Thedefinition and delineation of the environmentalamenity to be valued, the relevant tradeoffs, andthe institutional and payment mechanisms are alllikely to involve substantial transfers of new infor-mation. On the one hand, this causes problems

because respondents become ‘super-informed’ andhence are no longer representatives of the generalcommunity (Rolfe, 1996).

On the other hand, respondents are limited intheir ability and opportunity to absorb new infor-mation, and the researcher has to choose the ‘levelof ignorance’ at which tradeoffs between scenariosand opportunity costs can be made. For complexand multi-faceted goods such as international rain-forest preservation, the challenge is to delineatewhich are the core layers of information that needto be transferred to respondents. An associatedissue here relates to the various levels of under-standing that might be found across a spectrum ofrespondents, and the resulting impact on choices.

3.4. Issues of framing

One outcome of the problems associated withignorance and information transfer may be that‘super informing’ and drawing respondents’ atten-tion to the good to be valued means that resultingvalues are biased upwards. This is a problemrelating to inadequate framing — the choicesmade in a valuation experiment do not reflect thechoice framework existing in a real world situa-tion. There are two groups of associated problems.The first is that it is difficult to give respondentsthe same level of information about all the alterna-tive goods needed to frame a particular issueproperly. The second is that there are many differ-ent ways of framing a complex good such astropical rainforest conservation (e.g. a variety ofdifferent locations, vegetation types, or other envi-ronmental causes), and defining the best way offraming a particular issue represents a major chal-lenge.

3.5. Problems of scoping

Another symptom of the problems involved infocusing on a particular issue relate to issues ofscope, where respondents are insensitive to thequantity of the particular item involved. Someapparent problems of scope are not necessarilyvalid. For example, respondents who are valuingbiodiversity preservation may be indifferent to thearea of preserved areas above some minimum

J. Rolfe et al. / Ecological Economics 35 (2000) 289–302294

standard. The apparent insensitivity to area mayoccur because a different good, biodiversity, isbeing valued. A valid test for scope insensitivity inthis case would focus on attributes such as thenumber of species observed rather than otherindicators that are not important to respondents.Modelling the choice processes of respondents anddeveloping valid tests for scope is another challengefor researchers.

3.6. Modelling choice processes

Understanding how respondents make choices isimportant in developing tests for framing andscoping issues. As well, it is one option for devel-oping validation tests. Many choices are pathdependent, and the statistical interpretation ofresults often must model these decision pathways.The development of decision models gives someidea about how choices have been framed. How-ever, for complex and little-known goods, such asrainforest conservation, there is a wide variety ofdecision paths that can be followed, and modellingthese can be a difficult process.

3.7. Ethics

Ethical issues often trigger responses along setdecision pathways. However, there are a widevariety of ethical and other institutional issuesinvolved with rainforest preservation. This includesissues relating to environmental factors, the in-volvement of people indigenous to rainforest areas,different payment and preservation structures, andinstitutional arrangements relating to national andinternational boundaries. Defining how these relateto the particular good to be valued is a majorchallenge, particularly when ethics and other insti-tutional rules become default decision mechanismsfor issues involving ignorance and uncertainty.

4. The choice modelling technique

Increased interest in the development of choice-based techniques has occurred in recent years.Development began with Louviere and Hensher(1982), Louviere and Woodworth (1983), and ap-

plications have since emerged in the fields ofmarketing, transportation and tourism (Carson etal., 1995; Morrison et al., 1996). There have beenlimited applications of CM to environmental issues,and only recent studies (e.g. Rolfe and Bennett,1996; Adamowicz et al., 1998) have begun toexplore the use of the technique for estimatingnon-use values.

In contrast to the single tradeoff approach ofCVM, the CM technique requires respondents tochoose only one resource use option from each ofseveral sets of multiple resource use options. Theresulting statistical model predicts choice be-haviour as a function of the attributes and labelsthat identify the different choice sets. As informa-tion about choice is the basic requirement forgenerating data about compensating marginal ad-justments in attributes, choice based models arepreferred for theoretical validity. As well, the useof a constant base in the application of the CMtechnique ensures that responses to the variousprofiles are evaluated against a set standard and arethus comparable. This use of the set base (often a‘no choice’ option), together with an opportunitycost attribute in dollar terms, allows estimates ofconditional and absolute value to be made. Theseestimates can be used as Hicksian welfare measures(McConnell, 1995).

The random utility approach underlying the CMtechnique provides the theoretical basis for inte-grating choice behaviour with economic valuation.The random utility approach describes the utilityof a choice as being comprised of a systematic(explainable) component and an error (unexplain-able) component. The following equation for-malises the basic relationship where Vij representsthe measurable component of utility and eij cap-tures the effect of unobserved and omitted influ-ences on choice.2

2 More formally, a stochastic error term is associated withthe utility of choice to represent the effect of random responseshocks, while another error term is associated with the influ-ence of unobserved characteristics. The latter error term isunique to each individual respondent, and may be reduced byintroducing heterogeneity into choice models. Alberini et al.(1997) demonstrate this in relation to CVM. In this analysis,the two error components are confounded.

J. Rolfe et al. / Ecological Economics 35 (2000) 289–302 295

Uij=Vij+eij (1)

The systematic component of choice can bedisaggregated further, as in the following examplewhere utility is held to be a function of thecharacteristics of the relevant good (represented byZij) and the characteristics of the individual (repre-sented by Si), together with the error term.

Uij=V(Zij, Si)+eij (2)

Choices made between alternatives will be afunction of the probability that the utility associ-ated with a particular option ( j ) is higher than foralternatives, as in Eq. (3).

Pij=Prob(Vij+eij\Vih+eih)

for all h in choice set C, j"h (3)

The parameters for the relationship can be intro-duced by assuming that the relationship betweenutility and characteristics follows a linear path inthe parameters and variables function, and byassuming that the error terms are distributed ac-cording to a double log (Gumbel) distribution; thechoice probabilities have a convenient closed-formsolution known as the multinominal logit model(MNL) (McFadden, 1974). The MNL model isgenerally preferred because it is computationallyeasier to use (Stern, 1997), and takes the generalform:

Pij=exp(lVij)

% exp(lVih)(for all h in choice set C)

(4)

where l represents a scale parameter which iscommonly normalised to 1 for any particular dataset. The MNL model generates results for a condi-tional indirect utility function of the form:

Vij=l(b+b1Z1+b2Z2+ ... bnZn+baS1+bbS2

+ ... bmSj) (5)

where b is the constant term, and b1 to bn and ba

to bm are the vector of coefficients attached to thevector of attributes (Z) that influence utility. Theconstant term b can be partitioned into alternatespecific constants (ASCs) that are unique for eachof the alternatives that are considered in the choice

sets. These ASCs capture the influence on choice ofunobserved attributes relative to specific alterna-tives.

The assumptions about the distribution of errorterms3 implicit in the use of the MNL modelimpose a particular condition known as the inde-pendence from irrelevant alternatives (IIA) condi-tion. This states that the probability of a particularalternative being selected is independent of theother alternatives, and can be tested by droppingan alternative from the choice sets and comparingparameter vectors for significant differences.

Welfare estimates can be estimated from MNLmodels through the use of the following formula:

CS=−1

a [ln % exp Vi0− ln % expVi1](7)

where CS is the compensating surplus welfaremeasure, a is the marginal utility of income (gener-ally represented by the coefficient for the monetaryopportunity cost attribute in an experiment), andVi0 and Vi1 represent indirect utility functionsbefore and after the change under consideration.The marginal value of a change within a singleattribute can be represented as a ratio of coeffi-cients, where Eq. (7) reduces further to:

W= −1�battribute

bmoney

�(8)

This part-worth formula provides effectively themarginal rate of substitution between incomechange and the attribute in question.

5. Using the choice modelling technique to valuerainforest conservation in Vanuatu

CM experiments were designed to assess valuesthat Australians might hold for rainforest preser-

3 The error terms (ei) are distributed according to the ex-treme value distribution with a mean of 0 and a variance ofm2. This variance of the error terms is impossible to isolatefrom a single data set because changes in b and m2 such thatthe ratio b/m2 remains constant have no effect on probability(Stern, 1997). The scale parameter is inversely proportional tothe variance of the error term distribution.

J. Rolfe et al. / Ecological Economics 35 (2000) 289–302296

vation in Vanuatu. The results of focus groupsessions indicated that while Australians wereaware of rainforest preservation issues in general,and had some knowledge about the existence ofVanuatu, there was little to suggest that they hadspecific knowledge and concern about rainforestpreservation in Vanuatu. To frame choices in anappropriate context then, it was necessary topresent options for rainforest preservation inVanuatu in the context of other close substitutes.As well, it was important for contextual andpolicy purposes to frame the choices according tothe key variables appropriate to the Vanuatusituation.

To address these framing goals in thisexperiment, the issue of interest, international

rainforest conservation, was described with theaid of six key attributes:� location;� rarity;� effect on local people;� potential for future visits;� size;� possession of special features;� amount of donation needed to preserve option

described.The payment vehicle was presented as a volun-

tary once-off donation to capture the presentvalue of preferences for preservation, and to avoidpotential protests associated with compulsorypayment mechanisms such as taxes. Levels foreach attribute were used to build the choice profi-les to be presented to respondents. For example,the location attribute was split into eight levels, sothat each profile used included one of those eightlocations. The pool of attributes and levels se-lected is presented in Table 1.

These seven attributes could be combined in81×46 ways to form 32 768 possible differentprofiles of rainforest protection options. An ex-perimental design process was used to select thesets of profiles that were presented to surveyrespondents. An example of one of the choice setsused in the experiment is set out in Fig. 1.

An introductory section4 had explained to re-spondents the context in which choices were to bemade, stating that several possible conservationsites had been selected from around the worldthat would be cleared within 2 years unless thenecessary funding could be found to conservethem. It was explained that the key attributes ofthe sites had been selected and pooled togetherartificially in the choice sets that followed. Re-spondents were informed that completion of theexercise would help policy makers determine thekey factors of importance, and to prioritise con-servation efforts.

Table 1Attributes and levels used in the CM experiment

LevelsAttribute

VanuatuLocationFar North QueenslandQueensland/NSW borderPapua New GuineaSouth AmericaThailandIndonesia

Area 100 ha1000 ha10 000 haNot rare at allRarityFairly rareExtremely rareNo visits allowedVisitsVisits possible but moderate access andfew facilitiesEasy to visit with full facilities

Local people Protection of rainforest means localpeople will be worse offNo local peopleProtection of rainforest means localpeople will be better off

Special features No special featuresSpecial plants and animalsSpecial landscapes as well as plants andanimals

Price $5$10$20$50

4 The survey also consisted of an earlier framing session ofquestions about environmental tradeoffs and visitation to rain-forests. The final part of the survey collected informationabout respondent characteristics. The survey form is availablefrom the authors on request.

J. Rolfe et al. / Ecological Economics 35 (2000) 289–302 297

Fig. 1. A sample choice set from the CM experiment.

The choice profile set demonstrates several ad-vantages in utilising this CM technique as com-pared with the CVM. The first of these relates tothe ability of the researcher to model the issue ofinterests in ways that:� frame the amenity of interest within a pool of

substitutes;� describe complex scenarios and tradeoffs;� focus on more than one tradeoff simulta-

neously; and� make respondents aware that differing

amounts of each attribute might be available.For example, in the experiment described

above, the issue of rainforest conservation in Van-uatu was only one location within a pool ofinternational rainforest conservation, minimisingpotential problems of framing. Because the exper-iments were held in Australia, two Australianlocations were included within the experiment todetermine how domestic conservation issues com-pared with international ones.

The attributes used allow a mixture of environ-mental and socio-economic tradeoffs to be mod-elled. There were four attributes, which describedrainforest conservation (location, area, rarity andspecial features), and three attributes which de-scribe socio-economic tradeoffs (the potential tovisit, the effect on local (indigenous) people, and

the willingness-to-pay variable). This has the ef-fect of de-emphasising price as a tradeoff, andmodelling more realistic choices where the trade-offs may, for instance, be between varying quanti-ties of environmental attributes.

Another broad advantage of using the CMtechnique for this case study is that a much richerlevel of statistical information is available thancan be obtained with the CVM. For example, thesurvey above was designed with the assumptionthat the observable utility function would follow astrictly additive form, as in the following:

Vij=b+b1(Zlocation)+b2(Zarea)+b3(Zrarity)

+b4(Zvisits)+b5(Zlocal)+b6(Zspecial features)

+b7(Zmoney) (9)

where b refers to some constant and b1–7 is avector of coefficients associated with the vector ofattributes describing rainforest conservation.

For the experiment reported here, 105 respon-dents in Brisbane, Australia, completed the sur-veys in July 1995. Each respondent answered 16choice sets, giving a total of 1680 observations.The results of a basic MNL model were estimatedusing LIMDEP, with the results presented inTable 2.

J. Rolfe et al. / Ecological Economics 35 (2000) 289–302298

All attributes in the model were statisticallysignificant at conventional levels, and their signswere as expected a priori; the overall fit of themodel as measured by McFadden’s r2 was alsovery good by conventional standards used to de-scribe probabilistic discrete choice models (Ben-Akiva and Lerman, 1985). The results indicatethat the indirect utility function takes the follow-ing form5

Vij=6.1722−0.1050(Zlocation)+0.00005(Zarea)

+0.3100(Zrarity)+0.0732(Zvisits)

+0.3083(Zlocal)+0.1629(Zspecial features)

−0.0094(Zprice) (10)

The IIA tests performed indicated that themodel did not fully conform to the underlyingIIA/IID conditions. The tests followed the Haus-man procedure contained within LIMDEP. For aversion of the general model without an inter-cept,6 the test results are shown in Table 3.

While the IIA conditions are not violated whenthe ‘no choice alternative’ is dropped, the viola-tions are just significant at the 10% level when thefirst alternative is dropped from the choice set.

5.1. Impro6ements in model fit

There are several possibilities for improvingmodel fit and removing the IIA/IID violations.The inclusion of socio-economic attributes is oneimportant step for estimating more accurate mod-els of choice. This can be done by including, asattributes in the choice model, information aboutrespondents’:� education;� membership in environmental groups;� association with the timber industry;� occupation;� income;� age; and� sex.

As well, parameters need to be estimated forindividual levels of some attributes. While a part-worth can be generated for Area from the datareported in Table 2 because both Area and Priceare continuous variables, more information isneeded to be able to calculate other part-worths.This is because the other attributes are discretevariables, and a single parameter was estimated todescribe a linear relationship between these vari-ables and utility.

For example, the part-worth that can be calcu-lated for Area from the model reported above isequal to:

−1� b2(Zarea)

b7(Zmoney)�

= −1� 0.00005

−0.009437�

=0.005298.

Table 2Results for the survey with a basic MNL modela

Variable ProbabilityCoefficient

0.000006.172226Constant (b)−0.105048Location (b1) 0.00000

0.000000.000050Area (b2)0.310042Rare (b3) 0.00000

0.03016Visit (b4) 0.0731700.00000Locals (b5) 0.3082770.000000.162872Special (b6)0.00001Price (b7) −0.009437

a Log likelihood, −1557; x2 (7)=268 (significant at the0.00000 level); and r2=0.15630 (the r2 value in MNL func-tions is similar to R2 in conventional analysis except thatsignificance occurs at lower levels. Hensher and Johnson(1981) comment that values of r2 between 0.2 and 0.4 areconsidered extremely good fits so that the analyst should notbe looking for values in excess of 0.9 as is often the case whenusing R2 in ordinary regression).

Table 3IIA/IID tests for the general model

ProbabilityAlternative Degrees of Fx2

dropped

Alternative 1 0.107132711.8057145.9876 0.0000007Alternative

(none)

5 The coefficients and standard errors for area and priceboth appear low relative to the other coefficients, but this isbecause actual values (100, 500, 2500, 10 000) (5, 10, 20, 50)have been coded in for these attributes.1 The other attributeshave been coded 0, 1, 2, or 3 for the separate levels.

6 The intercept has to be dropped from the model to avoidsingularity problems. Because the ratios of model parametersshould remain consistent, the Hausman test is still valid underthese conditions.

J. Rolfe et al. / Ecological Economics 35 (2000) 289–302 299

Table 4MNL model results incorporating levels and socioeconomicfactorsa

ProbabilityVariable Coefficient

Constant (C1) −0.3559 0.92610.0050Vanuatu 0.46790.00001.4120Far North Queensland0.0000Qld/NSW border 1.19360.02210.3962Papua New Guinea

0.7629South America 0.00000.01500.3754Africa

0.3078Indonesia 0.09290Thailand

0.000005.95E-05AreaNot Rare at all 0

0.00010.4279Somewhat rare0.6648Fairly rare 0.0000

0.00001.1330Extremely rare0No visits allowed0.1843Visits allowed 0.0999

0.00220.3498Visits possible0.1941Easy to visit 0.0986

Locals worse off 00.00000.6672No locals

0.9120Locals can stay 0.00001.1344Locals better off 0.00000No special features0.3669Special landscapes 0.00090.5221Special plants and animals 0.00000.6446 0.0000Special landscapes as well

as P&APrice 0.0000−0.0112

0.02010.0452C1×Occupation−0.0364C1×Age 0.0000

a Log likelihood, −1483; x2 (25), 415 (significant at the0.00000 level); and r2=0.19619.

model at zero. Positive coefficients for the otherlocations would indicate that they are preferred toThailand, while negative coefficients would indi-cate the reverse.

The levels used in this case as a base are:� Thailand;� not rare at all;� no visits allowed;� locals will be worse off; and� no special features.

The model parameters are reported in Table 4.The socioeconomic variables have been includedas interactions with the ASC (C1), and only inter-actions that were significant had been included inthe model estimation.

The parameters of the model generally accordwith a priori expectations. For example, the highpositive coefficients for the two Australian loca-tions imply that these are highly favoured com-pared with Thailand as a base. In comparison, thecoefficients for the other overseas locations indi-cate that they rate more highly than Thailand, butlower than the Australian locations. Thus FarNorth Queensland rates as the most desirable Aus-tralian location, and South America rates as themost desirable non-Australian location for rain-forest conservation.

The coefficients for the other non-price at-tribute levels are all positive, indicating that thenon-location bases chosen have the smallest con-tributions to utility. For example, Extremely rareis preferred to Fairly rare, which is preferred toSomewhat rare, which in turn is preferred to thebase level of Not rare at all. The coefficient forMoney is negative, which confirms that increasinglevels of donation make negative contributions toutility.

There are a number of notable features aboutthis specification of the model as compared withthat reported in Table 2. First, the model has ahigher level of parametric fit compared with thegeneral model, with improvements in log-likeli-hood values and both the x2 and the r2 statistics.

These improvements can be tested for signifi-cance with a version of a Swait–Louviere log-likelihood ratio test. The test statistic isasymptotically distributed as x2, and is expressed:

This means that each one unit (1 ha) increase inthe area of rainforest conservation has a marginalvalue of $0.0053.

In the case of qualitative and/or logically dis-crete variables like Location with L levels, we useddummy variables to represent L-1 of the levels inorder to avoid perfect linear dependence. Themodel to be calculated is defined with the omittedlevel from each attribute set as a base. The modelparameters so estimated then reflect differences inchoice probabilities between those base levels andspecific attribute levels. For example, Thailand hasbeen used as a base level for the Location at-tribute, thus setting its coefficient in the resulting

J. Rolfe et al. / Ecological Economics 35 (2000) 289–302300

x2= −2(LL1−LL2), (11)

where LLx refers to the log-likelihood statistics forthe different models. The calculated statistic is2(1557−1483)=148, which compares with a x2

statistic of 32.671 at 21 degrees of freedom.7 Theresult indicates that the improvement in model fitis significant.

Second, the coefficient for the intercept hasdecreased in absolute value, implying that theattributes (and levels) included in the expandedmodel (where two heterogeneous factors, occupa-tion and age, are included) are explaining a muchhigher proportion of choice than in the simplemodel. The result of the expanded model is thatthe probability of choice can be largely predictedaccording to attributes and socio-economic vari-ables, and that non-observed variables (repre-sented by the constant term) are insignificant.

The value of particular changes in the supply ofrainforest conservation can be calculated by esti-mating the appropriate part-worths. For example,the part-worth for a change from fairly rare toextremely rare is given by:

Part-worth ( fairly rare to extremely rare)

=−1

−0.0112(1.1330−0.6648)=$41.80

In a similar way, the part-worths for changesbetween locations can also be represented. Forexample, the value of a change from Thailand toSouth America as a location is given by:

Part-worth (Thailand to South America)

=−1

−0.0112(0.7629−0.0)=$68.12

while the part-worth of a change from Far NorthQueensland to the Qld/NSW border is givenby:

Part-worth�Far North Qld to Qld

NSW border�

=−1

−0.0112(1.1936−1.412)= −$19.50

The negative sign indicates that utility has beenreduced. The caveat that must be applied to thesepart-worth results is that they are framed accord-ing to the attributes and levels used in the choicemodels, and may not be relevant to differentpolicy situations.

The model parameters can also be used toestimate value differences between profiles. Forexample, the difference between the two profilespresented in Fig. 1 can be represented as:

−1bprice

[Dblocation+Dbarea+Dbrare+Dbvisits

+Dblocals+Dbspecial]

=−1

−0.0112[(1.412−0.375)

+ (0.0000595(500−10 000))

+ (1.133−0.6648)+ (0.1941−0.1843)

+ (0.6672−1.1344)+ (0.5221−0.3669)]

=$56.94

This indicates that there is a difference of$56.94 between the values of the profile involvingFar North Queensland and the value of the profileinvolving Africa.

The model can be used to generate differencesin value between any two profiles that can beconstructed from the attributes and levels used inthe experiment. For example, if Vanuatu is substi-tuted for Africa in the previous example (and theremaining profile features held constant), the re-sulting value difference is $48.65.

These values though must be expressed withone important caveat; they are relative valuesrather than absolute values. Because the initialwelfare conditions were not defined for surveyrespondents in the choice sets, the values of theindividual profiles are only accurate in relation toeach other.8 In this experiment, the value of aprofile is not appropriate in absolute terms.

However, the relative value information ap-pears robust because of the statistical strength of

8 Absolute values could be calculated if the current statusquo condition were defined to respondents in the choice setswithin an experiment.

7 The degrees of freedom are given by the difference in thenumbers of parameters estimated in the two models.

J. Rolfe et al. / Ecological Economics 35 (2000) 289–302 301

the model. The model shows how different rain-forest conservation options relate to each other,and gives some idea of the relative value of inter-national rainforests relative to Australian ones.For policy makers, the results of the experimentindicate how different rainforest preservation op-tions competing for funds may be prioritised. Theresults show that preservation locations in SouthAmerica are preferred by Australians to otherparts of the world framed in the experiment.However, other locations may achieve higher pri-ority if they rate highly on other attributes (suchas area and the number of rare species protected).

6. Conclusion

Rainforest conservation is typical of many envi-ronmental issues where non-use values may behighly significant. The lack of recognition of non-use and other components of value by marketsystems and other decision forums is a majorcause of resource misallocation. One example ofthis in rainforest conservation issues is globalmarket failure where international signals for di-rect use values of forests are transmitted routinelythrough world markets, but no mechanism existsto register non-use values.

It is important that all components of value beassessed to generate efficient resource allocationdecisions. Choice modelling is one developingnon-market valuation technique that has somepotential for this purpose. One recent applicationof the technique, to assess the values that Aus-tralians might hold for rainforest conservation inVanuatu, has been reported here. The exampledemonstrates three important conclusions. First,the CM technique can be used to construct appro-priately framed and detailed models of choice.Second, the technique can be used to model com-plex choice situations where more than one vari-able is an important influence on preferenceformation. Third, the technique can be used tomodel a variety of simultaneous tradeoffs, whichmay be a mixture of environmental and socio-eco-nomic factors.

The results confirm that, depending on the cir-cumstances of the conservation proposal, Aus-

tralians can hold substantial non-use values forrainforest preservation in other countries relativeto preservation options in Australia. The resultsprovide a tool for decision makers to use inprioritising rainforest preservation options. Aswell, the results demonstrate the importance ofsocial factors, such as the influence of policy onindigenous peoples, in the overall assessment ofpreservation proposals.

While the CM technique is in its infancy andmuch development work still needs to occur, itsapplication looks promising. Its potential tomodel complex and simultaneous tradeoffs willfind many applications in the ecological econom-ics framework, and will help to address someperceived shortcomings with more traditional eco-nomic assessment techniques.

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