# Chapter 6

ATTIWWTE-BASED METHODS

Thomas P. Holmes and Wiktor i.,. Adamowicz ( r .s f--ol.ESt SL’I1.ICF .So~~tlwrt~ Resenrch Stnt~on. and i ‘tr/versrt>, oj.4fberta



## 1 . 1NTRODUCTlON

Stated preference methods of en\:ironrnental valuation have been used by
economists for decades v\ here behavioral data have limitations. The contingent valuation method (Chapter 5) is the oldest stated preference approach, and hundreds of contingent valuation studies have been conducted. More recently, and especially over the last decade. a rrem class of stated preference methods has been developed, \vhich Me genericall> refer to as attribute-based methods (ABMs). As with contingent valu;ltion. numerous ABM variants exist, employing, for example. different constructs for eliciting preferences. In this chapter, ivedescribe the various AB~lscurrently used, explain how to construct

an attribute-based experiment. and recommend methods for environmental
valuation
T-he objective of an ABM stated preference study is to estimate economic
values for a technically~ di\,isible set of attributes of an environmental good. Responses to survey questions I-egnrdillg\,crsions ofan environmental good that vary in levels of its attributes can provide r-esource managers and policy makers
with detailed information about public preferences for multiple states of the environment. The inclusion of price as an attribute permits a multi-dimensional
valuation surface to be estimated for use in benefit-cost analysis. The focus on
economic vcelfare and willingness to pay (WTP) distinguishes the
environmental economists‘ use of .AI3i\ls fr-on1 other applications of conjoint analysis.

171,4l3Ms can offer bevera advantages relative to other valuation methods:

- The euperimcntal stimuli are under the control ofthe researcher, as opposed
10 the lack ofcontrol generalI> afforded b\ obser\,ing the real market place.
l-his includes the introduction of ne\\, attributes and attributes associated \\ ith passive uses that cannot be obsewed in the marketplace. The use of‘stntisticai design theor\, !,iefds sreater statistical efficient\. and
eliminates collinenriQ bet\\een explanatory variables.

- A rntilti-diriieilsiollnl response surface is modeled that pro\.ides a richer description of preferences than can he obtained by the valuation of single “\vifh \wsus 1 ~ ithout” scenarios. This richness enhances the ap$ication of
i-1BRls to managerinl decision making.

- Salient attributes of the \alriation problem are clearI>. circumscribed. Attributes are traded off in the process of value elicitation? so that a reduction in one attribute ma> be compensated by an increase in another
attribute.
hlodern applications of ,113i\,Is are based on theoretical and empirical foundations spanning several decades. To con\.c); a sense of the richness of ARMS as developed in a variety of academic disciplines, this chapter provides

an overvie\\

of the conceptual foundations that Support contemporar), applicatiorlsof;\13hls. flfier pro\.idinga historical perspecti\,e, \\e describethe basic steps for conductin, 1~ an attribute-based experiment Then \\e expand upon a set of selected topics in experimental desiy that are important tc
understand \\ hen developing an attribute-based experiment. Next, \\e re\.ie\\
the three n~osr popular- response formats for conducting .4BMs: ratings, rankings. and choice. :\n application nfa choice experiment to a forestiy issue is presented to illustrate the implementation and interpretation of a choicebased model. \Ve then probide descriptiorls of models that relax the standard assumprions. \\ hich are the subject of much current research LL’e end \\ith an overvie\\ of the future directions of ,4BM research.



## 2 . AN INTERPRETIVE HISTORY

The origins of cut

rent I> popular ABiVls are found in various social science disciplines. This creati\ e mcr-~in~ofdisciplirles has generated some confilsion
in terminoIo~~;Ind classification. my presenting an interpreri~eoverview~fthe
literature. \ve hope to clnril), the main concepts needed to appl\ ARMS to non-market v-aluation. and to distinguish hetvveen non-market v.aluation and other

applications of ABfvls. $‘ithin economics. the conceptual foundation for ARhls finds its SOUKX in
the “hedonic” method that v ie\\s the demand for goods as derived from the demand for attributes. This approach can be traced to Court ( 1939) who used hedonic regressions to stud> the demand for automobiles. and Griliches (1961)
\v ho used hedonic regressions in the construct ion of hedonic price indices. The hedonic model was put on a firm theoretical foundation by Lancaster (1966) using household production theoq,. .~lthough theories of information ‘processing in the .judgment anti decision makin g literature in pqchology (Harnmo11d 1955: Anderson 1970) have also included discussions of how
consumers evaluate characteristics of items and use these evaluations in choosing hetn een itenls. Lnncastcr‘s theory of consumer demand provjides the basic conceptual structure that underlies economic applications of ABMs. 

### At the same time that Lancaster \vas writing about consumer demand being
driven by commodit~~attribrttes. a ne\\~measurement technique in mathematical psychology was a1ticulated for decomposing overall judgments regarding a set ofcomplex alternativ~es into the sum of \veights on attributes ofthe alternatives (Lute and Tukq 1964). This method. known as “conjoint measurement”, was rapidly embraced b) marketin, 0 researchers who recognized the value of
information about the relative impor-lance ofcornmodi~attributes in thedesign of new products (Green and Kno I97 I: Green and Wind 1975). This new marketing research method became

~ ~renernll\ kno\\ II as ~~conjoint analysis“.’ Many commercial applications for conjoint snal~sts \\ere soon found. particularly the predictinn of mar-hct share for ne\v products (Catti11 and Wittink 1982). The t>-pical procedure \\orrld ask respondents to rate the attractiveness of a set of products and then model the preferences of each respondent (see Section 9).’ Pr-edicted utilities forcompeting products \\ould then be computed
for each individual and entered into a choice simulator to estimate the market share. computed over the sample. for each competing product (e.g., see Green et al. I98 I),’ This approach emphasized the imporfnnce ofcapturing individuallevel preference hetero~cneit\ as a kev element in predicting marhet share. Despite these ad\,nnces. t\w primary concerns arose regarding the typical conjoint procedure. First. it \vas not clear that the infbnnation contained in rating data VV~S the same as the information contained in choice data. Second,
imPlementation of choice simulators was cumbersome and ofien confusing to
managers who used the predict ions of market share models.A simpler, more direct approach to predicting choices in the market place

was provided by discrete choice theory, particularly as formulated fortcorlomic analysis by McFadden (1973). The conceptual foundation for McFadden‘s anal>,sis of economic choice Ia> in Thurstone‘s (1917) idea of random utilit) (discussed in greater detail in Section 6). By positing that indi\,iduals make choices that maximize their utility, and that utility is .‘subject to the vagaries of whim and perception“. McFadden (I 986, p. 278) was able to place choice theory on a strong economic foundation that included a richness ofbehavior not found in standard Hicks-Samuelson theory.“ In addition, starting with Lute‘s

’ choice axiom (I 959). as linked to the random utility model by Marschak ( 1960),
McF&len developed an econometric model that combined hedonic analysis of
alternatives and random utility maximization.’ J-his model is kno\vn as the
multinomial logit (conditional logit) model. A further advance identified by McFadden and others is the linkage behveen
random utili9. models and welfare economics. ‘The utility function in random utility models is actually a conditional indirect utility function (conditional on the choice of the alternative). Thus. including price. or more formally income minus price, as an attribute in the conditional indirect utility function allo\\s
one to assess economic welfare measures (e.?.. compensating variation: see Small and Rosen, 1981). ‘This differentiates random utility applications of
ABMs in economics from other non-economic applications since economists are often interested in \veIfare 1neasnres and are always cognizant of the need
to be consistent \vith theor),. The conceptual richness of random utilit! therw?. and the practical advantages of the multinomial logit (MNL) model. \vere embraced by marketing researchers ivho piornoted the use of MNL to anall,ze aggregate marketing data (I,ouviere and Woodworth 1983: Louviere and Hensher 1983;
Louviere 1988a). The rando1n utility model also found wide application in modeling transportation demand (a comprehensive treatment is provided in
Ben-Akiva and Lerrnan 1985)’ Initial work using the MNL model was based
on the analyis ofaggregate data but recent methodological developments have. focused on understanding sources of individual preference heterogeneity in random utility models (see Section I?), reminiscent of the focus on individuallevel modeling used in early applications of conjoint analysis. In addition to rating and choice response formats, another variant of ABMs

.- developed III marketin g and transportation research v as to ask respondents to
rank bundles of attributes from most preferred to least preferred Ranking datahave the advantage of not requiring the assumption of cardinal utility that \vas

typically relied on to analyze rating data. A popular interpretation of ranking data is based on a random utility model of choice behavior in which respondents make a sequence of choices. and the number of alternatives in the choice set decreases as ranking depth increases (this model is described in greater detail in Section 8). Thus, ranking data could be anal)xd using a special form of the MNL model (Be,,. . 0~~s Car-dell and Hausman 198 I ; Chapman and Staelin 1982). The ability to decompose values of environmental programs into implicit

. values associated withparticular attributes of those programs has made ABMs
attfactive to environmental &onomists. Although the three major response formats (rating, ranking and choice) have all been used b~,economists. the first application of ABMs .to 

### eu\ ironmental valuation that \ve are alLare of was Rae‘s (1983) lvork using rankings to value visibility impairments at hlesa Verde and Great Smoky hlouritains National Parks. However, 

### only a weak empirical association bet\\een rankings and visibility \cas observed. Stronger empirical support for ranking models was later provided by Smith and Desvousges( 1986) who evaluated water qualit), in the Monongaheln Ri\,er, and
Lareau and Rae (1989) \vho evaluated U’TP for diesel odor reductions. After a hiatus of nearly a decade. a number of recent studies have been conducted using the ranking model.for non-market valuation of environmental amenities (Garrod and Willis 1996 and 1998: f:oster and Mourato 2000; Layton 2000; Morrison and Boyle 200 I ). ABMs using rating data to value environmental qualit-y began growing in popularit) during the earl! I99O’.s. hlnckenrie ( 1993) sho\+ed ho\\ rating data
could be converted to rank 

### and choice data. Gan 

### and Luzar ( 1993) used ratings
to model waterfo\t.l hunting site decisions. Roe, Boyle: 

### and Teisl (1996) showed how compensating variation can be estimated from rating data. During the same period that [rating models for environmental valuation were being developed, a number of studies \vere reported using random utilit),
models ofchoice. Adamolvicr. Louviere. and.Williams (1994) recognized that random utility theory pro\,idcs 

### a COI~ITWI~ conceptual foundation for a class ot stated preference and rc\ealcd preference models and demonstrated how revealed and stated preference data can be combined.’ At present, choice-basc,d

ABMs are receiving the most attention.

## 3. STEPS IN CONDUCTING AN ATTRIBI_JTE-BASED

EXPERIMENT

Implementation of an attribute-based experiment should follow the seven steps outlined in Table 1 (Adamowicz, Louviere, and Swait 1998: Louviere. Hensher, and Snrait 2000). Each step is briefly described he/o\\.

706/e I Steps in an Attributed-Based Experment

I Charactersze the decision problen>

2 IdrntlfL and describe the at&ihutes

3 Decelop an ezpertntenral design

-I De\,elop the questionnaire

5 Coilec~ data

6 Esrirnate model

7 Interpret results for policy analgis or decision support

The initial step is to clearly identify the economic and environmental problem. This requires thinking about two key issues: (I ) the geographic and temporal scope of the change in en\.ironmental quality, and (2) the types of values that are associated with changes in environmental quality. Regrding the first key issue, several questions should be considered: Are changes in environmental quality limited to a single site or will they impact multiple sites? Are there any possible spill-avers between changes at one site and changes at other sites? Will changes be implemented instantaneously OI- \\ ill they take time to be fully realized? ‘The second key issue focuses attention on the types of \fnlues that are
affected by changes in environmental quality. This requires consideralion of the following questions: Who will benefit from changes in en\ ironmental cjuality? Will passive uses be affected? And, if the changes in environmental quality affect use value, what is the behavior that best captures this value‘? Consider, for example, valuation of the benefits from improving a specific beach recreation site. The relevant values would be associated with changes in various beach attributes (such as Lvater clarity, sho\vers, picnic areas, and soforth). The relevant beha\ ior to model is beach choice (from a set of beaches)

and the frequfnc\’ of‘ trips. And. if chanses in en\ ironmental quality also impact people \\ho do not use the bench. passive use \:alucs need to be
considered as \\ell. \ Once the decision problem isspecified. the relevant attributesare identified

and characterized (step 2). Continuin g \x ith the beach choice example, the
researcher must identif!, the most important attributes ofbeaches that influence decisions regarding \\ hich site(s) to visit. Focus groups, or structured con\ersntions \vith people whoare broadly representati\eofthe population that will be sampled. are used to identib. the important attributes. For example, \\‘e might ask members of a focus group ‘.Clo\\ \\,ould you describe an excellent
beach. or a poor beach’?” or -.lVhat thing do you .consider when choosing a
beach to \,isit?” At this stage, it is also necessary to decide ho\9 many attributes to include in the experiment as \\ell as the particular le\.els that each attribute
can take. l-lo\+ people respond to highl!.cornples SUI-\-cJ’ questions is unknoivn (e.g.. see Mazrotta and Opnluch 1995: S\\ait and Adamo~ icz 200 I a and
200 I b). so it is good to keep the set of attributes as simple as possible. Steps I and 2 are critically important to the successful application of ABMs but these steps are often not given the due consideration that they require. Ifthe
researcher eitljer inappropriateI> frames the choice problem or omits important
attributes. the entire esperiment isjeopardized. LVe encourage practitioners to spend significant time and effort in scopin, ~7 the problem. using focus groups and pre-tests. and making sure that the choice context and scenario descriptions are well developed. After attributes and levels hale been determined, in step 3 experimental design procedures are used to construct the alternati\,es that 1 4 ill be presented to the respondents. 3s mentioned above. the objective of an ABM stated preference stud\, is to identif! LV1.P for the attributes ofan en\.ironmental good WTP values are constructed from econometric estimates of the preference or taste parameters (coefficients of a utllitj model). I he scenarios presented to respondents must provide sufficient variation o\‘cr the attributes to allow the
researcher to identi@ the taste parameters. In most cases. presenting all combinations of attributes and levels I\ ill be impossible. 1.hus. experimental design procedures must be used to identif) subsetsofthe possiblecombinatic)ns
of attributes and levels that \\ill “best” idelltifi the attribute pfeferences.
Economists tend not to recei\,e formal trainin? in experimental de;ign becausethey seldom construct controlled experiments. Therefore. \\e present Section

4 as a primer to this important topic.
In step 4 the questionnaire is developed. All ABiLls involve sur\eys of some sort. As with other stated preference methods. Lnrious modes of
administration are available:

- mail-out. mail-back surveys

- telephone recruitment, mail-out, mail-back surveys

- telephone recruitment, mail-out: telephone surveys

- computer-assisted surveys at centralized facilities

- intercept suwe\‘s. \vhich may be paperGnd-pencii or computer assisted

- internet-based surveys. To date. the performance characteristics associated \\ith various administration modes (in terms ofoverall response rate 

### and item non-response)
are not 

### kno~~n. Thus. selection of the mode of administration is usualI\: based on pragmatic concerns such as geographic specificitJr of the target populaticin and budget limitations. Various methods can be used to communicate information about the
attributes ofthe valuation problem. 

### In addition to verbal descriptions. graphic displays such as maps. photographs, and line dra\vings should be considered. As in any survey-based research. pi-e-testing of the questionnaire is absolutely

necessary to as,sure that respondents clearl~.u~~derstand the information being communicated (see Chapter 3 for more detail 

### 011 sur\‘e\ methods). In step 5. the data are collected using the best survey practices (e.g.,
Dillman 1978). Chapter 5 outlines a number of issues in data collection for contingent valuarion studies that appl], as \ttll to the implementation of ABhls. In step 6. the taste parameters in the utility model are estimated
econometricall> The choice of econometric method depends on the response format (choice. ranking, 

### or- rating) and on a \,ariet), of econometric considerations. as discussed in Sections 7 through 9. Finally: the results are interpreted for policy analysis 

### alnd decision support. ABM applications are targeted to generating \\elfare measur-es, predictions of behavior, or both 7.hese models are used to simulate otltcomes that 

### car1 be used
in polic), analysis or as components of decision-support tools. Estimation of welfare measur-es is descr-ibed in Section IO.

### 179



## 4. EXPERIMENTAL, DESIGN

;\ strength of attribute-based experiments is that they allo\v the researcher to manipulate the set of explanatory \xinbles associated Lvith the attributes of
the en\.ironmental \.aluation problem. Ho~sever. \\ ithout a proper understanding ofexperimental design. this asset can become a IiahiliQ,. The design determines
both the t! pes ofeffects that can be identified in the data and the interpretation
ofthose effects. Without a proper design. an improperly specified model \vith biased parameter estimates and collinear variables may result. .’ Designed experiments are \cidel?. used in biological, physical, and behavioral sciences but are not as familiar to economists, \\ho have historicall), fa\,ored the analIsis of secondar!, data generated by social p’rocesses. .A designed experiment involves the manipulation of independent variables. called fu.c/o~~.s. 0~ er pre-specified factor le\.els. Factors that represent feattires or characteristics of a consumer _gnotl or serbice are Q,picall\, referred to as
nllrihllles.



## 4.1 Factorial Designs

bl factorial design combines e\‘er>’ Ic\.el of each attribute with e\er\ le\-el of all other attributes (e.g., Cochrane and Cou 1957; Snedecor and Cochrane
1973: Wirier 197 I ). Each combination of attribute levels is called an
ullernrrlf vc. yr-ojiie. 01 l~t’Ullllc’lll COllih/llU~iOl7. We use these terms interchangeably (although profile is more commonly used in conjoint anal\ sis. since combinations ofattributes are often examined one-at-a-time and, illus. arc not truly “alternatives”). As yw can anticipate. a problem of the full factorial design is that a large number of alter-natives are gener-ated as the numbers of
attributes and levels are increased. To set the stage, consider a ststc parks and recreation agency that is evaluating various designs for a ne\r campground. Suppose that agency managers need to decide \+ hether or not to build picnic shelters. playgrounds.
and showers at the new campground. Each of the three “facilit\,” attributes
takes two levels (“build‘A cx “do not build”). Thus. there are 2’ possible
combinations of facilities. This is referred to as an I,” design, \vhere 1.+ r-efer to the number of levels and II refers to the number of attributes. in this case. the
full factorial design includes S possible combinations of attributes.The primq ad\,antase of a factorial desi_gn is that all “main” and

“interaction” effects are independent (orthogonal) and can be identified. A
“main effect” is the difference hct\\een the axraee (mean) response to each attribute level awl the overall a\crage (or “grand mean..). In multiple reo,ression, main effects are represented by parameter estimates for the attribute te\els and the pr-and mean is the 

### intercept. An “interaction ef‘fect” occur-s ifthe response to the le\-el ofone attribute is affected by the level ofanother attribute. Interaction effects are represented b>, the parameter estimates for the interact ion (cross-product) of t\vo (or more) \,ariables in a multiple regression model. Interaction effects are important in economics because the\; represent the concepts of 

### cor~r~~ic~t~~et~/trr.i~?~ and 

### .~ub.rtitutNbility. In the example above. the aberage consumer may respond more fa\,orably to a new campground ix-ith picnic shelters it‘pla).grounds are also included in the campground description’. If so. picnic shelters and pla>gounds are complements. A less thaii full
factorial design may fail to detect the interaction between picnic shelters and pla\.prounds and could possibly confound the interaction ~ 4 ith one of the main effects. The reasoning behind this result follo\vs.



### 4.2 Fractional Factorial Designs

Fractional designs reduce the numbe,r of profiles or nlterna!i\,es included in a design to redtlce the co3nitk.e burden faced by respondents. t+o\ve\,er, information is potentially lost \\ hen fractional designs are used. P O understand \vli\, fractional designs t\picall> omit information on interactions among attributes. and the potential impact of fractional designson par-ameler estimates.

an understanding ofr~linsi/~g (or 

### cor$~unc/ir7g) is required. The alias ofan included effect consists of the correlated omitted effects in a fractional factorial design Attribute codes that are completely uncorrelated are useful for identifying correlated effects. Orthogonal polynomial codes are used for this purpose. Pito-Iwet iarinbles are r-epresented b\ I and + 1 rather

than 0 and +I used for dummy variables (for details see Lou\,ierc 1988a; Lou\ iere. Hensher. and S\xait 2000). The concept of aliasin, 0 can be illustrated with a one-half fraction of a 

### 2’ design. Returnins to our camp~rnund example. let A.1 represent .‘picnic shelters”, A2 represent -‘shonws”. and A3 represent “playgrounds”. Table 2
sho\\s the main and interaction effects for the full-factorial and t\\o 5’; fractions of the full design. The main effects in the full-factdrial are specified using all possible combinations of attributes. Interaction effects are defined bymirltiplq~ing columns (cross-products) of the orthogonal polynomial codes for

each attribute.’ No\v, note that in the first one-half fraction of the full factorial (combinations 1

through ~1). the vectorof2-way interactions A 1 A2 [+ 1.

- I,

- I. +I ] is exacti), the same as the vector of main effects for A3. Thus, A 1 A2 is perfectly collinear (confounded) with ‘43 (A3 is an alias for the A IA2 interaction). Ifonly the first four attribute combinations in Table 2 were used for a 2’ factorial, and if {egression analysis showed that the parameter estimate
on A3 \+as significantly different from zero, we could not be certain whether playgrounds \\ere significant, the combination of picnic shelters and showers was significant, or both. The parameter estimate on A3 is unbiased only if the
A 1 A2 interaction equals zero.

Tnbir _’ Orrhopm~l codes sho\\ln_g two ‘,/2 fractions of a 2’ factorial design

2 I t I -I - I t1 -1 +I

3 -I- 1 -I - I -I . - I 41 +I

6 I +I +I -I -1 +I -I

7 +I - I +I -I +I -I I

We also note that the 3-way interactions in the first I/r fraction in Table 2 abays take the value “+ I ‘-. Thus, the intercept in a regression model is perfectly collinear with the three-way interaction A I A2A3.9 From a practical perspective, it is generally not kno\vn a ~~riori which attributes are complements or substitutes. ‘To shed some light on the issue,first-order (2-\\ny) interactions can be evaluated during the focus group stage of

sure; development. If focus group participants indicate that the attractiveness of a particular attribute depends in part on the level of other attributes, then “main effects plus selected interaction” designs can be constructed (Carmone
and Green 1981). 1-tigher order interaction effects typically have little explanatory power and probably can be ignored. If focus groups and pre-tests reveal that interactions can be safely omitted. design catalogues are available for orthogonal fractional factorial main effects plans (e.g.. Adelman 1962; Hahn and Shapiro 1966; McLean and Anderson 1984). However, if complements and substitutes are important elements of preferences for the environmental good(s) under consideration, specialized software that supports “main effects plus interactions“ plans can be used.



## 4.3 Randomized Desigm

In addition to factorial and fractional factorial designs, other strategies are available for designedexperiments.” In principle, random samplingofattribute levels from the full factorial design space \vill maintain orthogonalit? of the design. Of course. this result is valid only for large samples. For small samples. random sampling may induce unwanted correlation among attributes. For example. consider a design for 5 attributes each with 4 levels. The full
factorial for a 4i design yields 1024 possible profiles. Consider cor!structing a l/32 fraction design by random sampling of the design space until 32 profiles are selected. Because 32 random profiles represent a small fraction of the design space, the randomly generated profiles could be clustered by the “luck of the dra\\“. If all respondents are sho\\n profiles from the same sample of profiles, sampling asmall proportion of the design space may result in a set of correlated attributes, which reduces the efficient>. of the design. The ability of computers to randornly sample and store large amounts of data offers a second random sampling technique. the completely randomized design, \vherein a randomly sampled profile is generated for each respondent in the sample. If, for example, a researcher anticipates that I,000 people will respond to an ABM questionnaire, random assignment of attribute levels to profiles for each respondent would near11 span the entire design space in a 4’ full factorial. Of course, it is not guaranteed that every randomly generated prof?le will be unique. However, ifeach respondent is presented with 2 or more profiles. as is usual practice in an attribute-based experiment, then it is likely
that the entire design space Lvill be sampled by randomly generated profiles.After rn;IdOlllI~ generating profiles, it is a good idea to e\.aluate the

experimelltal design b\ exan;i;;i;;g the correlation matrix of rnai;; effects to

assure that the design IS orthogonal. In addition, the correlation matrix oftnai;;
effects and 2-\vay interactio;; effects sh~uid be examined for evidence of

cO~fOundiiig.



## 4.4 Correlated Attributes

Attributes encountered in e;;~~iro;i;;ie;;tal \ aluation problems may be highly
correlated b!, natural processes and, tht;s. they are not intrinsically separable.
Lf t\vo correlated attr/butes \\.ere treated as independent in a valuation experilnent, respondents ;;;ight become confused, reject the scenario, and fail
to ans\ser the question. Although so;ne e;npirical studies indicate that treating correlated attributes as independent factors does not cause serious pr-oblems (Huber and McCann 1982: Moore and flolbrook 1990) it is safest to use only
feasible combinations of attribtltes. In ge;;eralt the problem of correlated
attributes is best solved by selecti;;g attributes that represerlt separable
dimensions of the \.al;;ation problern.



## 4.5 Designs for Choice Experiments

When the rating response for;nat is used in an attribute-based experiment, the efficiency of an experimental design is maintained by construct ins profiles that are independent (uncorrelated) over the iterations (sequence ofrating tasks)
lfthe experiment. However, the design of a choice experiment is complicated 5y requiring respondents to coinpare t\vo or more alternatives simultaneously.

~larimt~nl design eff?cienc> requires selection of attribtrte levels that are independent of o;;e another both \\ ithi;; arid bet\vee;; alternatiwz,. This results in a L”“‘ factorial design. lvhere ITI refers to the number- of designed (non-status quo) alternatives in each choice set presented to respondents. Let‘s revisit the ca;npgrou;;d design problern where the fuli factorial design is represented by 2’ (L,“) possible cowbinations of attributes. If a rating scale resporlse format \vere used, then the full factorial for this prohle;n would be represented by 8 profiles (as in Table 2). It is possible that people could meaningfully respond to all pr-ofiles in the factorial design, and ~‘011 could test h~~potheses about all main and interaction effects. Hov~ever, ifa t\vo-a1ter;;ativ.e choice response format \+ere used. the full factorial v\,ould incl;;de 2’ x 2’ (I.~”

x 1-l’ = L’“,v\,here 11; = 2)co;nbinations ofattribute levels and choice alternatives.or 6-I (8 x 8) possible pairs of profiles (choice sets). Choice formats u ith more

alternatives \vouId clearly require even larger designs. Although there is no definitive number ofchoices that people can respond to Lvithout being fatigued, most researchers use no more than 8 or sometimes 16 choice sets. A design v,ith 6-1 choice sets would be too large a design for people to respond to. A
main ef-fects design could be selected from this collective factorial if one assumed that there were no interaction effects. An example choice set is shown
in Table 3.

T&/e 3 A campgrow~d choice set taken from a 2 6 (2’ * 2’) factorial

Pla) grounds Yes No

I’icn~c shelters N O \r’es

I ~vould choose please check one ho\ 0 cl

In actuality, the choice experiment presented above would not be \:ery
useful to economists because no price variable (distance) is included and because choosing neither of the alternatives (opting out) is not allo\ved. Let us expand the example to include a 3 lh attribute, distance. that also has t\\o levels (Table 4). Now the campground problem contains 4 two-level attributes in each alternative and the overall problem can be represented by a 2’ * 2’ or 2’ main effects plan.
What is the smallest main effects plan (design with no interaction effects) that could be selected for this campground choice problem? This is determined by first evaluating the number of degrees of freedom needed to estimate the
entire set of main effects (Lout-iere. Hensher, and Swait 2000). In our example \\:ith L=2 and n=4, there are 8 main effects (L x n). and each main effect 113s I (or Z,

- 1) degree(s) of freedom T-here are 8 main effects because each level
of each attribute constitutes a main effect. Thus, there are a total of (L x 17) X (L

- 1 ) = 8 degrees of freedom plus one degree of freedom for the equation intercept. Next, the number oforthogonal choice sets in the fractional factorial must exceed the number of degrees of fi-eedom. An orthogonal main effects 2@-“) fraction of the 2’ factorial satisfies this requirement. Thus, the smallest orthogonal main effects plan for this example requires 16 choice sets.” Thenttrnber of choice sets offered defines the number of iterations or replications of the choice experiment.

Tohle 3 r\ campground choice set taksn from 3 2’ factorial

.I\itribute

Distance

Showers

Pla! grounds Camp site .A

50 miles

N o

Yes .+.lternarl\e

Camp site B

100 rTlI/CS

Yes

N o Stay at home. I

~~oulti not choose
either camp site

A

or B and Lvould stay at home
instead

Picnic shelters N o Yes

I \rould choose plrxc
check one box 0 Cl cl

Suppose that in the campgound choice problem it was decided that 16
choice sets lvere too many for people to reasonabl>- consider. In this case,
choice sets could be assigned to “blocks” (independent subsets of the overall design) and each respondent assigned randomly to a particular block. Two methods can be used for blocking. The first method is to list the choice sets in
random order and then subdivide the list to obtain blocks of “reasonable” size. For example, 16 choice sets may be reordered and separated into 4 blocks of 4
choice sets each. \Vith the second method. blocks are considered another attribute in the experimental design. \\here the number of le\,els is represented by the number of desired blocks. Including blocks as attribtttes in an orthogonal.design assures that e\‘e!-j’ level of all attributes \\ill be present in every block (Adamo\\,icz. Louviere, and SU ait 1998). When considet-ing the number of choice alter-nnti\,es to present in a choice set, one attracti\.e option is the binary choice esperimcnt. This is simply the
ABM version of the binat->- (or dichotomous) choice model used in contingent valuation. A binary choice experjment can be posed as a referendum (“Would

YOU vote for a gi\,en profile’?“) gi\-en a certain specification of environmental
attributes. The binar\. choice experiment reduces the full factorial design in a choice experiment from L”‘” to L” because in this case 1)) = I Econometric models are I\ idel! a\,ailable for anal\*zinF binary choice experiments that are
not generally available for mul(inomial choice experiments.”186

Another issue to consider xchen designing choice alternati\,es is Lihether

choice alternatives should be generic or “branded”. A branded alternative in the camping example \vould include catnp site natnes as labels for the alternatives (such as “Jasper National Park”), and ask respondents to consider
choices from the labeled alternati\‘es. \vith the attributes and levels as specified However, it is important TO recognize that Ihe brand name migllt be highI>,
collinear Icith attributes otnitted from the choice problem If brand name is
collinear \i,ith omitted attributes and is not included in the model specification, then parameter estimates are affected b> omitted variable bias. Fortunately, this can be sitnply handled by including alternative-specific constants in ttie econometric specification to account for the utility associated \xith the alternative that is independent of the attributes (see Section 5). In the design of a choice experiment, a common recommendation (e.2.. Lou\.ierel Hensher. and S\tait. 2000) is to mitnic an actual market situation bl including a constant opt-out option (e.g., “I \vOuld not choose an>; of the
available alternatives“). Continuing \vith the catnpground selection problem shown in Table 3, a t),pical question \vould ask the respondent to choose among camp site A, catnp site B. ot- the option to sta)’ at home. In this case. adding the choice to not go camping \\ould allo\\, for the possibiliry that when indi\,iduals are presented u ith camping alternntites that are not satisfactory to them. they
kvill respond by choosing not to go catnping. Without the sta>-at-home option, respondents are required to choose a camping alternative. even in cases where they \vouId never choose to go catnping under the specified conditions. In practice, the opt-out alternative is not modeled in a sophisticated fashion (for a discussion of inclusion of the opt-out alternative see Lou\-iere. l-lensher: and Sivait, 2000, and the discussion of alternative-specific constants in the section itntnediately folio\\ ing). If an ‘-opt-out” alternative is not presented, the choice provides information on preferences, conditional on choosing one of the alternatives, but it does not pro\Tide information on whether the individual
lvould choose one of the alternati\,es or not. We believe that choice scenarios should include opt-out options because in most real \vorld choice situations,
individuals are not in a situation of “forced choice” and the> have the option to
choose not to choose. ‘4 more important issue for debate may be the form of the opt-out option and the econometric modeling of this option.”

## 5 . ATTRIBUTE CODING SCHEMES

Coding quantitative attributes. such a~ travel distance. is straightforward because the attribute level is a quantity. HoLvever, qualitative attributes pose a problem Of course. dumm!, \,ariables can be defined for I. I qualitati\,e attribute levels in the usual manner and, for ease of interpretation. the status
quo le\,el can be designated as the “omitted“ level so that parameter estimates on included levels represent changes from the status quo. The problem. ho\\-e\.er. is that when dummy, variables are used to code attribute lev,els. the
attribute level associated \vith the omitted category is perfectly collinear \vith the intercept in a regression model. ‘Thus. no information is recov wed about preferences regarding the omitted level.
This limitation can be overcome by using effects codes. &cause effects codes are uncorrelated with the intercept, the values of omitted levels for- each
attribute can be estimated (Louview. Hensher, and Swait 2000).
Effects codes are created as follo\\s. 13egin by creating an effects-coded
LariatlIe. EC,, for the first attribute using 3 criteria:
I. If the profile contains the first level of the attribute. set EC, = I
7 &. If the profile contains the l.,lh level of the attribute, set EC, = I.
3. If neither step I or 2 apply, set EC, = 0.
If an attribute has two levels. \xe only need to create one effects-coded
v-ariable using the precedin, 0 3 criteria for that attribute. Ho\ve\,er. if an
attribute has three levels. 1%~ continue the coding process b!, creating a second
effects coded variable, EC-. for that attt ibute using 3 additional criteria: J If the profile contains the seco~~ci le\,el of the attribute. set EC, = I.
5. If the profile contains the I,lh level of the attribute, set EC, = ---I.
6. If neither step 4 or 5 apply, set EC, L = 0. if an attribute has tnore than three levels, vve continue creating effects codes in
this manner until L- 1 effects codes are created for each L- level attr-ibute. (ising this coding scheme, the parameter value fat- the omitted attribute le\ cl can be simply computed. For example. the value of the parameter for the
1-l”-level of an attribute is the SIII~~ b,( -- I) + b?(- I) + + b, ,(

- I ). L\ here b, is the parameter estimate on the 17”’ level (it * L) of an effects coded variable. An example of effects coding is presented in Section I I belowJfnn attribute-based experiment contains branded alternatives or an opt- out

option (e.2.. sta)’ at home option in a recreation choice experiment). it is
necessary to use durnm~ L ariables hno\k n as nlfe~,7aril,c-.r/7CClfiC. cn/~.s/i1~7t.r (,\SCs). As preLiousl\ suggested. people might respond in some degree to a brand II~ITW independent of the attribute levels. ASCs identif?. the utilit!’ of
branded alternatives not accounted for b\ the attributes of those alternatives.
It is also essential to create an ASC for the opt out option to capture the utility
associated with that option. Since the opt-out alternative iisriall~~ has no attributes, an AX

is necessar>’ to model this alternatix‘s utilit),. lfthere are K alternati\.es in the choice set. then (ti

- 1 ) ASCs are included ill the econometric specification.



## 6 . RANDOM UTILITY

Models used to implement an attribute-based experiment for en\ ironmental \;aluation should be based on an explicit utility theor). Much ofthe recent \\ork
in environmental valuation is based cm random utility maximization (RLJhr).” The RUM model ass~~mes that uti(it) is the SUIII of sqxtemntic (\,) anti random components:

(1) q = +,,p,:p) + c,

ishere CJ, is the true hut unobser\.able inclirect utility associated \\ ith profilej,

x, is a vector of attributes associated \\ ith profilej, p, is the cost of pi-ofilej, /I is a vector of preference parameters. and E , is a random error term \\ith zero mear~.‘~ Choice beha\sior is assumed to be deterministic (\\.ithout error) from
the perspective of the indi\ idual, hut stochastic from the perspcctii e of the
researcher because the researcher does not observe e\ et-l,thing nbollt the
individual. l‘hus the error term in the random utilih. expression reflects researcher uncertnint)r. about the choice. It is wunll~~ assumed that utility is linear-in-parameters:



## ‘(2) k=l\\ here pi. is the preference parameier associated with attribute 

### k, s,~ is attribute



### k in profile/. and p, is the parameter on profile cost. However, if interactions are included in the experimental design, a utility function that includes interactions (quadratic terms) can be specified as:

\t here pl,” is a vector of preference parameters for interactions behveen
attributes 

### k and 

### tn in profile/. and x,~ and xlm are attributes 

### k and 171 in profilej. Equation (3) includes all possible substitute/complementaT relations between attributes. In practice, only a subset ofall possible attribute interactions would likely be specified in an attribute-based model. i-3~ differentiating equation 

### (2). it is seen that parameter estimates (p’s) in

an additjvel! separable linear utility 1nodel represent marginal utilities: J& = ~?U/L3x,. ‘The parameter estimate on profile cost. p,, has a special interpretation. Because an increase in profile price decreases income, /3, registers the change
in utilit: associated with a marginal decrease in income. Thus, the negative of the parameter estimate on pi-ofile cost. p,. is interpreted as the marginal utility
of mane\‘. The marginal rates of substitution bet\\een any two attributes 

### k and nz is easily computed as the ratio of hvn parameter estimates (MRS,,,,= Pk / p,). The marginal value (implicit price) of attribute 

### k is computed as the ratio pL@, = (dU/o’x,)~(~3Ul~?p,). Differentiation of equation (3) shows that the marginal utility. of attribute xL in a quadratic utility function depends on the level of s,,,: dliidx, = [$ -+- flk,,,S ),,.



## 7 . CHOICE

RlJM provides the theoretical foundation for a class of empirical models
based on consumer choices bet\\-eencompeting alternatives. The choice problern asks respondents to choose the 1nost preferred alternative from a choice set.
This response format mimics actual market behavior, such as choosing a brand of cereal from among brands with different attributes. The choice for1nat
focuses the consumer’s attention on the tradeoffs between attributes that areimplicit in making a choice. klodei estimates are based on utility differences

across the alternatives contained in choice sets. The stochastic term in the random utility function sholvn in equation (I ) allo\\.s probabilijtic statements to be made about choice behavior. The probabilit), that a consumer \vill choose alternative i from a choice set containing competing alternatives can be expressed as:


## (J) P(i)C)=I’(lii>lij)=P(~~i+t-i>\‘,j’EJ),’JEC

\vhere C contains all ofthe alternatives in the choice set. Different probabilistic choice models can be derived depending on the specific assumptions that are
made about the distribution of the random error term. If errors are assumed to be distributed according to a bivariate normal distribution, a binary probit model can be specified (Thurstone 1927) I\hich can be generalized to the multi\.ariate case cia a multinomial probit nlodel.‘b A type I extreme value (Cumbel)distriblltion ),ieids the conditional or multinomial logit (MNL) model (McFadden 1974). A generalized extreme \,alue distribution gives rise to the
nested MNL model (McFadden i 98 I ). The standard assumption in using RUM has been that errors are independerttI!,nnd iderlticallydistribtlted (ilD)follo\vinga type 1 exfreme\:alw
distribution. tiowever. theassociated MNL model imposes the restrictions thzit. (I) preference structure is homogeneous over respondents (this assumption is relaxed in Section 12. I below), (2) choices conform to the Independence from
Irrelevant Aiternati\;es(Il,~)assumption”(thisassumption is relaxed in Section
12.2 beio\v), and (3) all errors have the same scale parameter.‘8 Equation (3) can be rearranged to sho\v that, in RIJM, choices are made based on utility differences across alternati\,es’

(j) P(iIC)= P(\,, --

~ j >c:j-Ei),v ;~c.

Thus. an!’ \,ariable that remains the same across profiles, such as respondent income, drops out of the model. If errors are distributed as h’pe 1 extreme \,alue. the hlNL model applies and the choice probability is written as:v\

here tr is the scale parameter.” Gi\,en an additively separable speciftcation of utilin’, and assuming that 11 = I. the probability of choosing profile i from the
set C is vcritten as:

(7) eti t [3hu,h +i+b) p(i /C)= -A------.-

5 exdPkxjh +Pppl) JEC

If vve let N represent the sample size and define

1 if respondent n chose profile i Y,,, = 

### 0 otherwise

then the likelihood function for the MNL model is:

N 

### (8) L= f-l j-j Pn(i)!‘n

Substituting equation (7) into equation (8) and taking the natural logarithm, the
MN1 model is estimated by finding the values of the p’s that maximize the loglikelihood function:

Choice based ABhls have been found to be useful for modeling use values (Adamouicz et al. 1997) and the), were found to be useful in measuring passive use values as vtell (Adamo\vicr et al. 1998). Random utility models of
choice have been used in a number of other studies including recreational site
choice (Boxail et al. 1996) and policviprogram evaluation (Viscusi et al. 1991; Opaluch et al. 1993: Ilanlev et al. 1998; Hanley, Wright. and Adamowicz 

### 1998).

### 192 C-h&v, 6

8. 

## RANKING

Contingent ranking questions ask respondents to rank a set of profiles from most preferred to feast preferred. This question format rresufts in a series of responses from 1

to J for a set of J profiles. Ranking of responses ostensibly provides more information than a single choice because. in addition to the most preferred choice from a choice set. rankingsprov,ide information on preferences
for all of the profiles included in the set. The standard interpretation of a ranking task vietis the ranking problem as a series of choices, and indeed a series of carefully constructed individual choice questions could provide the same information as a single ranking task.‘O Analysis of ranking responses is typicall\, conducted using random utilitl; theory. Consider the problem of ranking profiles from the set b, k, 1:

_... J).
Marscftak ( 1960) sfmvved that a ranking problem can be modeled as a sequence of choices that. in turn, can be considered the product of independent probabilities:

Equation ( IO) is based on the assumption tftat the respondent chooses the most preferred profile frorn the entire choice set. then the second ranked profile is chosen from the remaining choice set. and SO forth. ffit is assumed that tfte J- I choices in a set of J profiles are independent, and if the additively separable linear utility model adequately represents the data. then the rank-ordered Jogit
model (Beggs, Cardelf, and ffausman 1981) describing the probability of a given ranking is Ivritten as a function of the probability of the utility of afternativej being greater than that ofalternative k, the utiIit!,ofk being greater than that of I, and so on:From a statistical perspectiv,e. the additional information provided by

rankings should lead to smaller standard errors for parameter estimates or, equivalentl\~. smaller sample sizes for a gi\,en level ofprecision (Hnusman and
Ruud 1987). tfo\\ever. practical experience has sho\\n that this is not always
the case. Rankings are cogniti\el>- more demanding than a single choice, and respondents ma> become fatigued or confused as the\, proceed through the sequence of choices required in rankins. Consequent I>, parameter estimates

ma>. lack stabilit!, and “noise” ma) increase for lo\\er ranks (Chapman and
Staelin 1982: Ben-Akiva, hlorika\va. and Shiroishi 1992: Holmes and Bo>,le 2002).



# 9. RATI,NG

Rating scale questions require indi\ iduals to make judgments about the magnitude of’ utiIit>. associated \\ith profiles presented in an attribute-based experiment. It is implicitly assumed that ,judgmenr: directI\, transform utility
to the ratin_e scale. Rating models can be simpl> esttmnted by regressing the
vector of ratinp responses on the attribute levels included in each profile. Errors
in ratins models are often treated as additive nuisance parameters rather than having a structural interpretation as in RUhl models. Rating data are most often assumed to contatn tnformation on ordinal, not
cardinal. prefer-ences. An ordinal interpretation ofrating data onI> requires, for example. that a response of -I on a ratiri z scale represents a higher intensit) of preference than a 3, but does not neccw~r II\ treprcsent the same cardinal
difference as a score of 2 relative to a score of /. Tahin~ this vie\v of rating data, it is appropriate to use an ordered probit or or-dered logit model” although many analysts ernplo!~ ordinary least squaws procedures that can be implemented easil\, \vith rating data.
The use of ratings is appealing becattse of the sirnplicit?, ofthe econometric analysis and the ease \\ ith \I hich respondents can ans\zer rating questions.
flowever. problems arise in using such an approac-h First. ratmgs must be adjusted so that a common metric is used across indiv iduals (T‘orgerson 1958:
Mackenzie 1903: Roe. Bo>.le, and Teisl 1996: tlolmes et al. 1998). Second. a status quo or base situation (current choice) must be e\ aluated using the rating to judge v~hether- an indi\,idual would rate a ne\c alter-native hi$er than the status quo or base situation (Q hich vsould iiiiplv choice of the alternative o\‘erthe current situation) (Roe, Boyle, and ‘l‘eisl 1996). Respondents may srrggest

that alternatives have equal ratings (ties), which presents problems when one is attempting to estimate ordinal econometric models and predict demand behavior. Decisions to include or exclude ties can effect parameter estimates (Boyle et al. 2001). Most of these challenges can be addressed using econometric procedures or by restructuring the data. However, despite potential econometric “fixes” to rating data, we do not recommend their use for environmental valuation. Choice or ranking methods provide information on choice directly and do not require such econometric and data restructuring steps. Rating scales do not have a.naturai anatogue in actua/
markets. &onom.ic theory, in its most basic form, involves the prefer-ence of one object over.another. Thus, choice methods correspond most dir-ectly with such a theory and form the most direct method of eliciting preference information. While ratings data may be used to deicelop v\elfare measures, choices or rankings are more direct.*’



## 10. POLICY ANALYSIS

The goal ofmany ABM nonmarket valuation studies is to estimate lvelfare impacts so they can be used in policy analysis. Welfare measures for the random utility model underlying stated choice methods are relatively \vell founded and presented in the literature (Small and Rosen I98 I : Hanemann 1999; Morey 1999). Since utility is random, the evaluation of\\elfare measures involves examination of the systematic components of utility, as well as the
stochastic etements. ARMS provide quantitative measures oftradeoffs between attributes (including price). Thus. they can be used to examine, after an attribute change, how much money would be required to make a person as well off as they were before the change. The fact that ABMs provide estimates of the indirect utility function allows one to calculate these Lselfare measures for gains, losses, or any combination of change in attributes (assuming that the specification is accurate.) As defined in equation (I), utility is characterized by systematic (v) and stochastic (E) components. The maximal elements of the utilities over the set
of alternatives is defined as max ( II, ) = max (v, + F, ) Yj. Following Morey (I 999), we can express the expected value of the maximum as:195



### (12) 

## E(U)= ij

. . . ~rimx(“, +I-

**, I,___**  **,vJ +**  **&J)f(F, I,...,**  **&J )d&i...&J** 

**El=-= “J**  **=--7’**

where equation (12) integrates utilities over all stochastic terms (densities defined by f(.) associated with each alternati\,e).” If a t)‘pe I extreme value distribution is assumed for the stochastic elements. the expected value of the maximum can be specified as

where expression (13) is the “log sum’* plus a term knowri as Euler’s constant (D).” This expression forms the basis for welfare measurement in the multialternative case. In a simple situation where the marginal utility of mane), is constant (and expressed as Iv), an expression for compensating variation can be formulated as follows. Compensating variation (CV) is the amount of money that must be given to or taken away from a person to make him or her as \~eil off after a change as they were before a change. Thus, let “before the change” be expressed as the expected value ofthe maximum utility in the base case: E(IJ)O

= E(U(Y’, PO, X0)), where Y is income, P is price, X is the set of attributes. and E is the expectation operator. Let “after the change” be represented by E(U)’

= E(U(Y”. P’, X’)), where for genernlib either price, or attributes, or both can change. Compensating variation is calculated by solving the expression E(U(YO, PO, X0)) = E(U(Y’

- CV, P’. X’)) for the value CV. Using the expression for the expected value ofthe maximum, and assuming zero income effects, CV becomes

which is simply the difference in the t\ko expected values of maximum utility (change in utility), divided by the marginal utility of money.25 The marginal utilityofmoney parameter, in this simple case, isjust the parameter on the price variable,with the sign change to reflect increasing utility with income increases. Note that the welfare measure described in equation (14) is for single choiceoccasio~~s (e.9.. one campin g trip) or is per choice occasion.“’ That is. the

random utilit\’ model is implicitI) specified for a given time per-iod (such a5 a \\eek or da)) and the \xetfare measure applies to this time pel-iod. ,(\ model of camping dcstinatioll choice. for example. ma) be applied to the choice of site each \\eeh. \\here in man> \\eeks the choice \\ill be to “sta\, at home” or not 30 camping..
Jhc c.ypression in equation (11) is rrele\w~t to cases i!ith multiple atternati\xs as in the case of recreation sites. alternative products. and so for-th. tlo\vever, choice experiments are also used to compare “states of the \\orld“.

or- a base case described b> a single alternative against an altered case described
b>. a single alternative. For example. t\vo riew sta!es of the world described h>
attributes could be presented alon, ( 7 \\.ith the current situation. These ne\\’ states of the \\orld could involve improved attribute levels and a positi\,e pa! merit
amount. reduced attribute le\:els and a negative payment amount (refund). or

some combination of these conditions. Expression (13) then reduces to 

# (IS) CV=(l/.,,)[V’

- V”)

\\here V’ and V” are the expressions ot‘utilit) for the base and altered cases. Finall\.. if V’ and Vn are linear in attr-ibutes, and the goal is to e\.aluate a chang
in a sin& attribute, equatioil (I 5) reduces to the ratio of the attribute coefficient and the marginal utilit? ofmonej .” The resultinp \-allies ar-e knou ii as “implicit prices” or marginal \\ iltingness to pay. Note that in most simple ABMs. income is not included in the utilie function (since income drops out of the utility difference expression). This means that income effects, to the extent that the) exist. are ignored. In this
case. the **utiiih,** function specified can be used to measure compensating or equivalent variation, and the! \\ ill be identical. More co~nple\; forms of random utility models do include income effects. Readers M ill notice that \ie ha\,e not discrwed u,illingness toaccept (\\‘TA)

nor t1aL.e \\e discussed the difference bet\\een \\iIlingness to pa! (LC’TP) and WT,\ as this relates to AUiLls. That is bccatlse A13Ms result in specification of indirect utility f’unctions and the specification of the indirect utilit\ function
\viil dictate the difference bct\\een Lj.7..4 and WTP, if any. If ;I simple linear i utility function is specified. income effects are assumed to be zero. Furthermore, these simple utilit> filrrctions seldom contain anv reference point
measures or endowment effects. ‘I hercfore. in this case it is assumed that thereis no difference bet\\een LWP and WT.4. Some researchers hale examined

indirect utilit! functions \Iith income effects and \\ith reference points (e.g. i4damo\\ icz et al. 1998) and 1i.e espect this to become more common practice in the future.



## 11. APPLICATION

The foregoing concepts lay out the basic methods used in designing. analyzing. and interpreting 

### an attribute-based experiment. To clarify the concepts and fill in some of the details that \ve hn\:e omitted so far, we present an empirical example based on data collected in a 

### mail surve\’ regarding Maine residents‘ preferences for alternative timber harvesting practices. For purposes of this chapter. the follo\\ing example is modified from data descriptions and analyses presented else\\,hcr-e (Boyle et al. 200 I : 

### ~Iolmes and Bol le 2002).‘”
Timber har\,esting practices in Maine have received a great deal of public attention. In 1989, the Maine legislature passed a Forest Practices Act that provides rules regarding timber har\,esting standards. i-foi\ever, public concern
about some pro\,isions ofthe Act, particularly regarding clearcutting (removing
all trees from a har\,est area), led to a number of initiatives to modify the Act. Although none of the initiati\.es ha\,e succeeded to date, it is clear that 

### man> among the voting public are dissatisfied \\ith status quo forest practices and are seeking alternati\.es that reduce timber harvesting impacts on the goods and
services pro\-ided b>, Maine forests.
After discussions 1% ith forestry experts. stakeholders, and focus groups. a policy proposal and a set of timber har\,esting attributes \rere selected for the experiment. The policy proposal was for the state of Maine to purchase a laige tract of forest 

### land from the timber industry 

### ant! to manage a set of forestqr
attributes 

### 011 the tract. Table 5 presents a set of forestry attributes and the
anribute levels used for our example. In addition. thirteen different tax prices. ranging from $1 to $1 ,600 for a one-time tax pa\ merit. were included in the experimental desigrl. Alternatives Lterecreated bir-~lndoml},selectinganribute
levels for each individual in the sample. The data here consist of N = I56 observations ill \\hich the choice set incIudxxl J nltcrnntive management plans plus the option to select the status quo (no public purchase of pri\.ate forest land). An example choice question is sho\vn in Table 6.T~I/J/~ 5 Forcstr? attrlburcs and levels for Maine timber hnrlesting example

Attribute Level

Live trees lcfi after harvest No trees (clcarcut)

I53 trees’acre (heat? sclectmn harvest)

-i59 treestacre (light selectIon harvest)

Dead trees left

after harvest Rernow all

5 trees/acre

IO trees/acre

Percent of forest set aslde from hwesting 20% set asidc from har%ting

50% set asIde from harvesting

80% set aside from harvesting

For each L-level, non-price attribute, 1,

- 1 variables were constructed to specify the qualitative timber harvesting attributes. Table 7 presents the effects codes associated with each attribute level. Using effects codes, a base level is chosen. If dummy variables were used, this would be the level assigned zeros throughout the data set. With j levels only 2 unique parameters can be estimated. When using effects codes.‘the two unique parameters are summed minus one (the omitted level would be coded as 0 if we used dummy codes)

rob/e 6 A timber harvestlny plan choice set

Attribute

Live trees

Dead trees Alternative

Plan A Plan B Plan C Plan D I \b’OU Id
not tote No trees 459 No trees I53 for any of the plans Rcmo\e all Remove all 5 IO

Percent set asIde 80% 20% 50% 20%

Tax %-IO $200 $10 $80

- t would vote 0 cl cl cl 0 for. (please
choose one box)and multiplied by

- I to create the parameter value for the base level. When

using dummy variables, the parameter value for the base level is assumed to be zero. Note how the base (omitted) levels for the attributes are coded using -1 in the effects codes. In addition. an ASC \\as included in the specification to estimate the change in utility associated with choosing the status quo
alternative. The results from maximum likelihood estimation of a MNL model are
shown in Table 8. All ofthe preference M-eight parametersofthe indirect utility function have t-statistics greater than 1.64 (90 percent confidence level) except “selection harvest-light” and “5 dead trees/acre”. Preference Lveights for the base (omitted) attribute levels \\ere computed as the sum of

- 1 times the preference iteights on the included lewls f’or each attribute. Marginal WTP values (the WTP for a marginal change in the attribute) were then computed by dividing the preference weights by the marginal utility of money (- I times the preference weight for the tax attribute). As can be seen, clear-cutting, feaving no dead trees after harvest and setting aside 80 percent of the forest from

T&de 7 Effects codes for rhe rlmher hanesling airributcs

Attribute Effects code I Effects code 2

Live trees left after harvest

Clear cut

Selrction harvest

- hea\? (base level)

Selection harvest

- I~ghr

Dead trees left after harvest

Remove all (base Imel)

5 dead rreeslacre

IO dead Irees,‘acre

Percent forest set aside from harvest


### 20% set asldc (base le\el)

50% set as1de

80% set aside I 0

I 1

0 I

I -I

I 0

0 I

I -1

I 0

0 Iharvest have relatively large negative impacts on indirect utilit). Conversely,

a heaq sejection harvest, lea\.ing IO dead trees per acre and setting aside 50 percent of the forest from harvest have relative!), large positive impacts on indirect utility. An interesting interpretation can be made for the preference \\eight on the ASC. Recall that, as defined here. the ASC represents the utility of choosing the status quo alternative, everythin, 0 else held constant. The negative sign indicates that choosing the status quo decreases indirect utility (choosing alternatives to the status quo increase indirect utility). This result is consistent with the degree of political activit), in Maine seeking alternatives to current timber harvesting practices. The respondents would prefer to see a change from the status quo even if all attributes \\ere held constant. This indicates a significant desire to have some change in the policy environment. If a positive sign on the ASC \vere found. it \\ould indicate a positive preference for the status quo (everything else held COJIS~JI~) and would be consistent \vith the
more common status quo “bias” found in the literature. in Lvhich individuals attach some positive utilit>. to the status quo situation. The pseudo R’ for the overall model. computed as I minus the ratio of loglikelihood at convergence and log-likelihood at zero, is 0.14. The ASC accounts for 0.03 of the pseudo R’ value. The attributes included in the example clearly had a’ dominant role in explaining choice among the timber harvesting alternatives.



## 12. RELAXING THE ASSUMPTIONS OF THE CONDITIONAL LOGIT MODEL

Up to this point. t\\o assumptions have been made to simplify the econometric analysis of the conditional logit model. First. \\e assumed that everyone in the population has the same prefercncc structure. T-his assumption
restricts the p’s to be the same for all members of the population. Second, we assumed that the ratio of pl-obabilities bet\veen any t\ro alternatives was unaffected by other alternatives in the choice set. This propem’ (IIA. section 7) results in limited substitution possibilities.t-‘,t;LlIstIc

--I 36

-2 79

0 57 h4arginal

WTP

-

-22105

32.1 I

178 95

j dead trees:acrc’ 0 09 0 66 47 37

IO dead trcwxrc 0 32 2 12 173.68



## 12.1 Relaxing the Assumption of Common Preferences: Heterogeneity

‘The basic conditional logit model described in equation (6) implicitlq assumes that preferences are identical, for all respondents (the parameters in the
conditional indirect utility, function are constant). This simplifying assumption
can be altered bj, three modifications: (I) including interaction effects, (2) estimating a latent class/finite mixture model. and (3) using a random ,Parameter/rni~ed logit approach.202



### 12.1.1 Interaction Effects

Individual (respondent) specific variables (age. wealth. etc.) cannot be examined directly in a conditional logit 

### ~nodcl because Fhese variables do 

### 110t
var\ across alternatives. Thus, indi\ idunl specific variables drop out of the utility difference. However, individual specific variables can interact \\,ith alternative-specific attributes to provide some identification of attribute parameter differences in response to chnrqes in individual factors. 

### Forexample. interacting age with the price attribute \~ould generate information 

### on the marginal utility of money (price) a~ a function of age. This is a simple approach that provides insight into heterogeneity of consumers. but it 

### assu~nes the researcher already knows the elements that lead to heterogeneit>, (those items included as interaction effects) and results in many parameters 

### and potential collinearity problems.



### 12.1.2 Latent Class/Finite Mixture Approach

A better, although somelvhat more complicated, approach is to 

### LJSC a latent class/finite mixture model. Suppose S segments exist in the population, each with different preference structures, and that individual n belongs to se:gment

s (s = I ,....S). The conditional indirect utilit)t f-unction presented abovc can no\C be expressed as U,,!, = p,X,, + tl,,,,$ The preference paralneters (p) \nr1 ty segment. The probability ofchoosing alternative i depends 

### 011 the segment that

one belon_gs to and can be expressed as:



### (16) 

## P,ls (9 = eMD,xi > cetiPsxk) kd'

where [3, are segment-specific utility parameters (and scale is fixed at I). Now let there be a process describing the probability of being in a particular segment, as a function of demographic (and other) information. Fcllo\ving
Boxall and Adarnowicz (1999), Swait (1994), and Gupta and Chintagtmta (1993). that process can be specified as a separate logit model to identify segment membership as:

### 203

where Z is a set of indi\;idual characteristics and 1. is a vector of parameters. Let P,,(i) be the joint probability that indi\~idual n belongs to segment s and chooses alternative i. This is also the product of the probabilities defined in equations (I 6) and (I 7): P,,(i) = P,, P,,,(i). The probability that individual n chooses i becomes the key component in the finite mixture or latent class approach:

Note that this approach provides information on factois that affect or result in preference differences. That is. the parameters in the segment membership
function indicate ho\\, the probability of being in a specific segment is affected by age, wealth. or other elements included in the segment membership function. Further detail on this approach to heterogeneity can be found in Suait ( 199G):
Boxall and Adarno\\ icz ( 1999) or Shank\+ i ler and Sha\t ( 1997).



### 12.1.3 Random Parameter/Mixed Logit Approach

Another approach to identieing preference heterogeneity is based on the assumption that parameters are randomly distributed in the population. Then,
the heterogeneit? in the sample can be captured by estimating the mean and variance of the random parameter distribution. ‘This approach is referred to as random parameter logit (RPL) or mixed logit (Train 1999) modeling.
Let the conditional indirect utilit\: function be as specified in equation (I). Assume that the parameters (p) are not fixed coefficients but rather are random coefficients that follow a predetermined distributional for-m. The probability expression from ti;e conditional logit modelis modified to reflect the fact that 1 has a distribution. Folio\\ in? Tr-ain (1999)

the overall probnbilitl, is eup~-essed as the conditional prohabilitl (conditional

on p) integrated o\‘fr \,nlucs of 13. or:



### (20)

Given a choice ofn specific distribution for P(or assumptions on g(p)) such as normal or log-normal, estimation of the choice probabilities proceeds \vith pro\.iding estimates of the mean and 1 ariance of those pnrarneiers assumed to
be random. Note that if g( 0) is constant or degenerate. this model reduces to
the standard conditional losit model. Also. note the similarity ofequation (20) to equation (IS). Both are essentially \\eighted collditionsl lo2it models. Equation (I 8) reflects a finite \\eighting or mixture. \\hereas equation (20) is
a continuous mixture. .See Train (1999). Revelt and Train (1998). or La! ton (2000) for details.

The simple conditional logit model prodllces pl-obabilities of the form expressed ill equation (19). tlo\\ever, the ratio of probabilities for an\ t\\o
alternati\‘es (i and j) results in:



## f-Y;> ezp ( Y)

-- z -.- P(i) exp ( I,:)

Thus the ratio of probabilities between i and j is unaffected by any other
alternative in the choice set: and the conditional logit model depends on the II,4 property. This property results in elasticities that are limited in flesibilit) and _eenerally produces substitution patterns that arc simplistic (the elasticities of the probability of choosin, ~7 alternative j \\ ith respect to a change in an attribute in alternatives other than j are all equal). In the simple camping choice experiment, for example. the t\vo camping alternatives \\ould likelv be,nlore
similar or there would be unobser-\,ed correlation bet\\een these alternatives, relative to the opt-out alter-native Ho\tever, in the conditional logit f&rnulation,there is no correlation between the unobserved effects (errors) of the

alter.nati\‘es. A further implication of choosing the conditional logit model is that the cross elasticities (the percent change in probability of choosing i for a percent change in an attribute level in any alternative j) are identical. This is

a hiphI> restr.icli\e form of preference.



### 12.2.1 Nested Logit

An approach IO address these issues is to estimate a nested logit model (McFadden 198 I : Ben-Aki\,a and Lerman 1985; Louviere, liensher, and S\\ait 2000). Suppose \\e consider the campin g example above, but assume that camping alternnti\~es (A or B) are similar relative to the alternative ofnot going camping (C). The choice of alternati\.es A. B. or C could be specified as the prohabilit?, of choosing an alternative, conditional on the probability of going

**camping** (,A

or B) Lersus (C). Utility Lvould be decomposed into utility
associated \\ ith camping versus not camping. and utilit)’ arising from camping
sites (:I or f3) conditional on going camping. In terms of probability expl-essions. this is reflected as follo\\s. L-et j index alternative sites and **111** index activities (going camping or not). The utility ofchoosing sitej in activit!

m (camping) can be expressed as:



### (22) 

## LJ,,,, = lj,l,n + IJ,, = V,!, + V,,, + eliln - + e,,,

The t\\o error terms (e,,, and e,, ) retlect the unobserved variation in alternati\,ec i (conditional on **m)** and **m.** Assuming independence between the t\io er-t-or terms. **mt’** can she\\, that theJoint probability of choosing alternative jm is

14iere V “,. is ( l/a,,,) log C exp (V,,,,) or the “inclusive value” or “log-sum“ and a,” is the parameter on the inclusive value. The inclusive value term captures the utilities (the expected \,alue of the maximilm uti.lity) of the camping alternati\w \\ ithin the utility associated 1 5 ith the activity camping. If a,, = I

_then the expression collapses to the simple logit expression. An inclusive value

parameter of I corresponds to equal correlation between the alternatives and an inclusive value parameter bet\\een zero and I indicates the degree of correlation (or similarip) betlbecn alternatives ivithin a particular acti\,ity. Expression (22) can also be considered to bc the product of probabilities. The probability, ofchoosing alternati\.e j and activity m can be expressed as the product of the probability of choosing alternative j. conditional on choosing activity m, times the probability of choosing activity m. 

## III other \\-ords. the probability of choosing camping alternatkse j is the product of the probability of choosing camping (versus not camping) times the probability of choosing alternative j conditional on choosing camping, or:



### (24) P(j.m) = P(jI nr) P(m)

The nested logit model (nesting the decision of \vhere to go camping \x ithin the decision to go camping or not) does not have the IIA proper? and relaxes the assumption of identical substitution elasticities. tlo\xever. a more interesting interpretation of the nested logit model, in terms of error variance components. is provided below through the description of the mixed
multinomial logit model.



### 12.2.2 Mixed Multinomial Logit Models: Error Components

Random parameter models \wre described in Section 12. I .3 as one outcome of a mixed logit structure. An alternative interpretation of mixed logit can he used to construct nested logit models. as well as a variety of other models that
involve correlation between the unobserved elements of the alternatives. Follo\vingTrain (1998, 1999): let the conditional indirect utility ofalternative j be expressed as



### (25) 

## v, I= px, + pz, + F,

where c,, is an IID extreme value error term (extreme value is chosei~ to be consistent with the logit framework), and LIZ, represents an additional stochastic component of the utility. Let )-I be a mean zero term. The inclusion of pZ, in the stochastic component of the utility function allows alternative-specific elements to enter the stochastic portion of utility, and thus allows for theexamination of \,ariotts correlations of unobserved effects. As Train (1999)

illustrates. defining Z, as a **dummy** variable for a subset of the overall set of
alternatives (e.9.. camping alternatives) provides an estimate of the error correlation among this subset ofalternatives. and the variance on 1-1 becomes an estimate of the correlation or the inclusive value parameter. Note that if 1 1 is

zero **and** non-random, the conditional logit model results.

Estimation of such a model relies onthe relationship bet\\een the mixed logit/rantfom pat-ameters model specified above and the error components
model. If X, = %, , the parameter p can be interpreted as the mean while p can be interpreted as the \,ariance. In an error components interpretation, one is most interested in the correlations between alternatives (as in nested logit models) as captured by the stochastic terms (Brownstone and Train 1996;
Revelt and Train 1996). Nevertheless, the estimation of these models follows the approach presented in equations (i 9) and (20) above.



## 13. FUTURE DIRECTIONS

These are still earl? da>s in the application of ABMs to environmental
valuation. Researchers continue to evaluate the effectiveness ofthese methods.
Efforts to improve design and analysis of data generated by ABMs are **on** going. The current literature can be divided into these components: evaluating and testing ABM performance, improving econometric analysis of ABM data,
and improv ing ABM designs.



## 13.1 E\~aluation and Testing of ABM Performance

Many writers have speculated that ARMS may outperform contingent
valuation I\ ith respect to strategic behavior. hypothetical bias. or a variety of other challenging issues associated with stated preference methods. However,
very few tests 

### of-ARM performance have been conducted. Recent results from Carlsson and hlar-tinsson (2001) suggest that ABMs perfonn very v~ell relative to market or experimental market choices. In addition, studies like that off-Taener. Boxall. and Adamowicz (2000) show that ABMs do a good job in Predicting‘*out ofsample” (data not included in the sample used for estimation)
choices. Ne\.ertheless. additional research is required to evaluate ABM Performance and subject ARMS to the same level of scrutiny as contin_gent
vafuation methods i1nv.e received in the past.208



## 13.2 Econometric Analysis

Attribute-based methods are often administered such that individuals respond to several ranking, rating, or choice tashs. Presenting respondents \vith

as man>’ as 16 such tasks is not unusual. In simple econometric analysis. these? tasks are assumed to be independent. l-lo\ve\er. some empirical and tnuch
anecdotal e\ idence suggests that these responses are not independent. Respondents ma) learn about their preferences, or the\, may become fatigued during the sur\‘ey. In general, the responses map’ be serially correlated, or at
least shoutd be treated ag arising from panel data. Mixed multinornial logit
models offer econometric methods to address correlations between choice sets and panel data considerations within discrete choice!randotn utility data (e.g.,
Train 1999: Revelt and Train 1998, LlcFaddcn and Train 2000). Ho\cever. in addition to simple correlation bet\\een alternatives, issues of fatigue and learning ma> be better represented as s\stemntic preference changes in response to sequences ofquestions. S\\ ait and Adamo\c icz(200 I a) provide one approach to such an issue by examining preference variation with a finite mixture model operating on question order and task complexity. CertainI!:
other approaches also could be explored to assess the implications of question order. serial correlation. and stated preference question response. In addition to serial correlation, research on cotnbining data types. or data fusion, is on-going. If revealed preference responses suffer 

### from collinear-it?. or from limited data range, ABMs can facilitate the estimation of parameters
that ar-e dif‘ficult or impossible to tneasure using revealed preference data alone. Evidence suggests thatJoint revealed and stated preference models outperform re\ealed prel’erence methods within samples (Adamowicz, Louviere. and
Williams 1994) as \\ell as in out-of-sample prediction tests (Haener, Adatno\\ icz and Bosall 2000). l-lo\\e\-er. many unanswered questions in data fusion remain including the following 

### three: \Vhat weight should be placed on each data type‘? Are there more efficient \va}‘s to combine data? Can combining Af3M data with small satnples of revealed preference data pro\,ide
better beriefits transfers than transfers of re\.eaIed preference data from 

## other regions?

### 209



## 13.3 Design Issrtes

PSJ chologists **and** 

### researchers in human judgment and decision making have Ions focused on the effeci of changes in decision context on response and implied behavior. Similar issues arise in ABhl survqs. Dochanges in context afftxt I-esponses’? Are these effects s>sternatic and could they be examined econometricalIS”’ Currently rules-of-thumb are used to determine the number of attributes, alternatives, and questions, and orthogonal designs are heavily relied upon to generate 

### the correlation structure between alternatives. tfo\\everl these rules-of-thumb have not been rigorously examined. In addition
to complesit\. other context effects arise. such as the respondent’s reference
group (farnil!,. peer group. etc.) and the degree to which these elements affect preferences. iIlthough economists hax historically focused on individual responses. there is illcr-easins interest in examining demand and preference as arising from g_r-oups such as hotlseholds (Smith and van t-foutven 1998 among other-s) or as being affcctcd b\ reference groups (Manski 2000 or Rrock and rhhf 1995

among otllers).



## 14. CONCLIJSIONS

/r\BMs habe emerged from a creatice linkage of research across disciplines including marketin?. ps>cholog\,. transportation and economics. Through this process. the hedonic frame\rorh articulated b> l.ancaster more than 3 decades
a_go has been r-eiined b\, de\,elopments in random utility theory econometrics and experimental design into a set of po\x,erful tools that provide economists \k ith ne\\

methods for en\,ironmental valuation. If carefully designed and administered. :\Bhls can pro\ ide defensible estimates of environmental value for behavioral anal! sis (such a5 recreational choice) or passive use valuation. tfo\+wcr. \\ ithout

careful attention to fr-aming the decision contest, applying an appropriate experimental design. developrnz a focused survey instrttrnent and implementil;~ robust embirical procedures, AI3M applications will not provide the desired inf(>rmation.These are still earl), and exciting da\-s in the application of ADhIs to

environmental \raluation As stated preference methods. i\Bhls are close!>, related to contingent valilation methods and face similar issues relating to the
validi& of responses. Asse5srnent of the \alidit>, and consistent> of ABhl
responses \vilI undotlbtedl~~ be an important avenue of ftrttlre Iresearch. lHo\\ever. research to date combining stated and revealed preference data indicates that ABMs. I\ hen properI), applied. can pro\,ide infor-rnstion on preferences that is consistent with actual behavior. We anticipate that future
research on ARMS will not only provide a deeper understanding of environmental preferences but \\,i!l also enhance other applications of stated preference methods.



## ACKNOWLEDGMENTS

The development of this book has been a wonderful collesial experience.
We \vould like to thanA Patt\, Champ. TOI?> Bro\\n and Ke\,in [3oyle folassembling this group of authors and for inviting us to be part of this team. \i’e
are indebted to George Peterson for- his leadership over the tears in promoting the scientific assessment ofnonmar-ket values and hope that this book makes a positive contribution to his intellectual legacy. We gratefull\ nckno\\ ledge the authors of other chapters in this text for their comments during ollr \\ot-kshop sessions. In particular, many thanks are due to Mark Dickie for his insi@tful
review ofour chapter- during its development and to Tom Bra\\ n \\ hose editing of our chapter substantiali> improved its quality. Vie i2da~no~\ icr. wotlld like
to ncknowled_ee that this chapter \tas completed I\ hile he ~\as n Gilbert S’hite Visiting Fellow at Resources for the Future. Washington DC.



## NOTES

The lerm “conto~nl“ arow from earl! nliempls to c\;uiilne n se1 of;Irirlbulcs ;3I lhe Same lime or “consider jo~riti)' Trsdlltonal contoint analysis vn\c,t\ed r;rlln_es Some authors refer IO the melhods \\c descrlb’e In thus chapter as being s~amplcs of conlolnt a~int~sis Others refer 1 0 choice nlclhods as “choice-based ~OIIJOII~I‘ \\ c preler ihe term “artrrhute-bat-d method bccnuse it more e.~plrcr~l~ h~ghlrghts the ioc~rs on examinallon airt bundlcl 0 1 allrlbntes associated \\llh a good ;111d bccnusc 11 teaies open the choice ofel~c~iar~on approach. \vherher II bc choices bc~\\ccn bundtc~ ranking of bundles. or r.?tings of Intill 1du;11 bundlecRating scale approaches. or traditional conJoint analysis. are based on Torgcrson‘s (I 958) La\\ oCComparatl\s .Iutf_ement This approach presents Andy\ ~duals \rlth protiles (altrrnatr\es) or bundles ofattr~butes and asks them lo pro\,ldc a rating of cnch profile (e g I 10 IO. t\ here IO !s \cr~ good. and I 1 s \ cry poor) The de\elopmcnt of rating-based contotnt 1 3 discussed 11, (;rcen and Srrni\aran (1978) rind Loti\~erz (1988b) .A\iomatlc theorlcts ofcon~o~nt andl\s~s ha\e also been de\elopetl (KrantLet al 1969. Barron 1977) that deal \\lth the relatlonshlp between the ordinal numerical scores provided in conIolnt rating lasks and 1 artous forms of preferences or utriit! One
of the earliest empIrIcal studlcs usIn rarin, 0 scales lo measure preference parameters concerned preferences for the \ isunl appearance of rcsldenlinl nelghhorhoods (Peierson 1967) Individual prediction models I! p~cally assumed that the best or first choice alternative
\\ould be (he product that recel\ed the highest predicted uIIIII~ Summation of first ,choices across the sample pro\ lded ~SII~~ICS of market share :\n alternative approach
Introduced b! Lou\fere and L~~ood\~orth (1983) used the preillcted ut1111ies tn n modified Lute model to predrct the prob;lbility that an individual \\ould choose competing products The summ;~t~on of predicted probahllltirss over the snmplc then generated choice frequenctes. \\htch \\erc used in a wtghted lenst squares regression of the multinomlal loeistic mods1 McFadden ( 1986) goes on IO state that “. II is tlnnecessory I<> proi DDE accuratcr behavioral models indl\ldrlal-b!-Irldivld~r;ll 10 obt;lin.gooti market forec;tsIs I I IS sufficient 10 determIne the dt\trlbution of beha\lor in the twpulatron” up 778) T‘he multlnomial log11 model IS based on lhc nssiimptwn lhal bclin\ Ior in the pnpril;lllon tollo\\s an e\trrme-value lypc I d~slr~hut~on See also subsequenI \\ork b> hlanski (1977) and Ye1101 (1977)
In addlllon. Hensher ( 19%1) pro\ ides an owr\‘le\\ 0 f RUS,? models In the transportation
literature \\hlle LouLic‘re (199.1) rc\Ic\vs applications tn m;trhellng (sez also the case
stud> chapters tn Lou\ tere. tlznsher. and S\\alI. 2000)
See also SM 311 and l.ou\ iere ( 1993) In the marketlniL research Ilterature In Table 2. note that the element5 of each colclrnn vector ~111 IO Icro and that. \\~th~n
sets of marn effects and 2-\\:1\ tnteractions. the lniler product of ti\o tzolumn Lectors quals 7cro 7 hrs second propert!’ delines ortho~onnl!t) or sI;ttistlcal tndeprndence [he alias of an\ fhclorinl cfrccl can he delcrm~ncd using \‘. 1131 the c\pel Imc‘ntJI dtwgn
llleralilre refer< to ns n delinlng conlrwl In 1 able 2. note thn~ the 3-\+a! Intt’ractlon
A I .A2A3 coiiIn1n5 3 1 actor 01 + I’s 1.01 the first I: fraction (and 3 \c‘ctor 01‘~ l’s li)r the
second in: fraction). .A 1 :12.43 IS the “defining conIrast” because II \\ as used IO @II the
factortal Into t\\o liactlons In a 2” design. (he allas ofan! factortnl eff’ect is found by multIpI> tn_e the effect h\ tl~c defintng contrast (C‘ochran and (‘ok 1057) So. Ihr example. the allas of ;\3 I< rhr: gencrallzed tnteractton .\1:22:2>’ Squared terms are canceled 111 InIerpretlng gcnernlircd Interactton\. <o ,\ 1:22.4i IS ICA JS .A I:\:! \vhtch.

as shoit n pre\ 10usl\ IS the alias for A3 for a good discusston of 1 arlous design srratc~~es. wz Lou\ ic‘re. I icnsher, and S\va~t 

### G390)
The smallest ortho_eonal mate effects tles~gns for \.arlous co~nh~nat~onr of attrthutes.
levels and choice oprlons 1 s plesenIed 111 Loultere. tlrnsher. :md S\\att (2000. I’;lble
5 3. p 121)
For ehample. methods for mal! zing pant1 datij \\ hen the responsc5 arc’ blnrtr! are \\ell kno\$n (e g tlslao 1986)1 7

l-1
1 5

I4

1 7

1 8

IY

20

21

22

23

2 3
2526 @ernlls on rnlegrattng !wIfare measures from choice occasions \\irh changtnp frequencies of choice occasions (number of trips) can be found 111 I\lorc! and \Valtirnan ( 199s). Hausrnan. Leonard, and hlcfadden ( 1995)

27 This stall assumes zero mcome effects.
28 In the oriPtnal experiment. sur\ex respondents wsre asked to consider sebcn rimher har\esrln,g attributes Here we reduce the number of attributes to three ~1lro. In the original e\perirnent. rhe “opl-out-- optlon \\as presented in a sequenlral manner I fere \\c treat the opt-out option as helng presenled slmullaneously \\ith ihe other

allcrnaii\es



## REFERENCES

.-\damo\rrcz. W
, P. Boxall. IN Williams. and J Lou\ iere 1998 Stated Prcfercnce rlppronches
for Pleasurlnp Passive Use Salutes. Choice Expcrlments and Contmgent \‘aluntlon

.~rrrr~~rccrn Jol~rnnl of.4gr1cul1uroi Eco/romics 80 h-1-75

,-\cl;tmn\\ ILL. \\’ J Lou\ iere. and J Sit a11 I998 /n/j-odrrctlon io :llirthu/e-l(nsc~d S~cl~ftr’(‘hotce
.\/c/hoiis FInal Repor IO Resource \‘aluntlon Branch. Damage .Asscssm&nr C‘entcr. NO:\;1.

I! S Deparlmenr of Commerce

..\darno\ricr. \L’ J. S\\alt, P Boxall. 1 Lou\ iere. and bl Williams. 1997 Percepttons \crsus Objsctlve hleasures ofEnvironmcnlal Quality m Cornbined Revealed and Slated Preference
Rlodzls of Environmental Valuation Jor~rnol of Env~ronmentnl Econon71c.s oml .\/n,rngrm~nt 32 65-83

Ad;tmo\\ IcL. \\’ 1 . J Lou\iere. and hl L’+‘llllarns 199-l Combintng Stared and Re\ calcd
Prsfcrence hlrrhods for Valuing Eni Ironmcnral .&nenifies Jorcmnl o/- k~ni.rro~n,c,/,fnl
Ecorrortrtcs owc~ .\Ionng”“rent 26.27 l-292

Adelman. S I962 Ortho_eonal Main-Ef-feet Plans for Asymmetrical Factortal f~\perlrnents
Technomcrrrc~s 4

2 I-46

Anderson. N H I970 FunctIonal hleasu~cmcnl anti f’qchophysical Jutlpem~nt /‘.s!:ci~olopicill
Rr\~rrM 7: 1 j3- I70

Bnnzhaf. %l R . F R Johnson. and K f j\lalthtz\\s 2001 Opt-out Alternatives **and** :‘irtglcrs’
Stated Preferences. ln The Chotce .ttodelllng 

### .-lppronch 10 Envlronmentol I ‘i~!rrc?tron
Edlied b> J 13ennert. and R. DInme! Northampton. MA. Edward Elgar

Barron. F tl 1977 A.\tomallc ConjoInt hleasuremcnt Decrsiorr Soenc~es 8 548-559

Begs. S S t‘ardell. and J Hausman I98 I Assessing the f’otential Demand for Electrtc Car\ 

### Journnl (f Econometrtcs I6 I

- 19

Ben-AkiLn_ Kl and S R Lerman I985 l)frc-/-c~re Cllo~ce .-lnnlysrs Theor\, uncl .lpp/~co/ron 70
Twrl Demtrnd Cambridge. b1.4 hll 1 Press

Ben-.Akiva. hl
, 1‘ h4orlhawa, and F Shlroishl 1992 **Analysts** of the Reltabllrr! of Pleferencr Ranking Data Journal o/ Buslne.ss RcJcurch 24 l49- I63

### 2 14 ~~‘llopler- 6

Boxall. P 1V .4darno\\tc7. 34 L\~~ll~arns. J S\\a~t. and J Louvtere 1996 :\ Comparison of
Statsd Preference ,4pproaches to the Measurement of En\ ironmental \‘alues Ecoiog~col
Economrrs 18 ‘43-253

Boall. I’. C and W. 1. Adamotvtcz 1999 Ltnderstandtnp Hcterogcneou5 Preferencec In
fiantiom Lttiltty Models The llse of Latent Class Anai>sts Staff Pnpcr 09-02. IIepartmcnt
of Rural Economy. IJnt\srs~t! of Alberta. Edmonton. AIberIa

Bo>Ic. K J , P P Ilolmes. RI F T’e~!,l. and B Roe 3001 .A Compnrtson ofCoil!o~irt Analbs~c Response Formats .Imerrco,l Jorirnnl o,/ .-lgricl~ic~rrni 15orromlss X.3 4-l 1 -‘t 5 1

Brock. W ii, and S N Durlauf 1995 Discrete Chotce \c~th Social Interacttons I- Theor) NBER Working Paper Series. 5291 October Cambrtdge. hi.4

Brownstone. D , and K. Train 1996, Forecasting Ne\\ Product Penetration \\ith Fleurblz
Substitution Patterns Paper presented at $merican Economtc ,\ssoclatton meetings. Ne\\
Orleans.

Carlsson. F and P tilartlnsson 7001 Do Hypothetical and .-\c~ual hlarptnal \\‘llltngncss to Pa!
Differ.in Choice Eupertmcnts” .-\~pltsntion to Valuation of the En\ ironment Jozirnoi q/
En~~ronmen~ul &-o~~o~n~cs ~rnd Ilnnngem~n/ 4 I 179. I92

Carmone. F J and P E Green 19X1 hlodel Mlsspectfication in Rlulitattrtbule Parameter
Estimation Jorrrr~<z/ o/.tlorkr~rrlp Researcl~ IX X7-91

Cattin. P . and D R \Vltttnk 1982 (..ommerclal Use ofConJotnt :\nal\<I\ .4 Sur\cy .lo~i,-nc~/ o,fhfarke/,ng 46-41-53

Chapman. R G and R Staeltn 19X2 Exploiting Rank-Ordered Chotce Set Data \\ IthIn the
Stochastic Utility Model JOIII.M/ ql,tlorLr/1)7g Rcscorclr 19 258-301

Chow, K-H and C-G Moon 1997 (;eneraltzed Extreme \‘aluc L+odzl and .-\tid~t~\el! Separable
Generator Function Jot/,-nal q/ t’c.or~omelrrcs 76 I29- I JO

Cochran. W G., and G bl Cos 1957 Lpcrrmeritnl &srgt?s Neo Yorh lottn \Vtlc!, NL Sons

Court, ,\ T 1939 tledonrc f’rlce Indc\es \vcth ;\utomotr\e f!\nmplr\ In 7i,t* ~I~YI~I~II~.\ o/’
l~trontoblle Demand Ne\\ \I’orh General Motors

Dillman. D A 197X ,Ilirl/ nrrci L~icplrone .Trr~~vj~.r

- /lx 101nl lIc.ctg~~ ll?iltod Nel< York:
Wale?

Foster. V and S hIourato 2000 \:~I~JIII~ the hlult~ple Impacts ofl’e~t~c~de I:>e 111 the t!K A Contingent Ranktng ?,pproach Jourrlol o/,lpvrctrlr~rral Ilcorzomi~-..r 5 I I-7 I

Gan. C . and E. J Luzar 1993 .‘\ C‘on\otnt :\nnl~srs of \h’aterfo\\ I 1 lunt~ng tn I oi,l\rann .Jowna~ 

### of .Igrmltut-c~l ant/ ..lpplrrd ECO~OW~IC-S 25 36-I 5

Garrod. C; D. and ti G \5’1ll1s I996 The Non-Use Benefits of.t:nha&~ng f (>rcxt RmdlLersity-

.4 Contingent Rank Stud! I:c.olo~~col L~wnon~rc.r 2 1 35-6 I

Garrod. G 1) and K G W’ill~s 1998 Using C‘ont~n_eent Ranking to Fstlrriate the Loss of IZmenity Value fat-Inland Water\ra)s from Publtc Utilit> Servlcz Structures L‘~~~~ronmen~a~
and Resourw Econotn~cs I2 24 I-247

### 215

Green. P E and \’ R Rao 197 I. Conjoint Measurement for Quantifyins Judgmental Data Journal of .\~arkcrtng Research 8 355-363

Green. P E.. and V Srinr\asan 1978 Conjoint .Anslysis in Consumer Research Issues and
Outlook Jozrrnnl o/Con.wmer Research 5 103- I23

Green. P E and ‘I’ \Vrnd 1975 Ne\i \‘~‘a! to i\lcnsure Consumers‘ Judgmsnts /lono/-d Btrsrness Ret~iew 53 IO7- I 17

(ireen. P E J. D Carroll. and S M Goldberg. I981 .A General :\pproach to Product Desrgn Optrmrzation \ ia Conjoint Anal!,srs Journa/ of.Waarketrng 35.1 7-37

Grilrches. Z 1961 Hedonic f’rrce Indexes for Automobiles. An Econometric ,Inalysts of Qualit! Change In Price Indrres and Quallrj Chaqe .Sllldte,s rn .\‘ew Melhod, oj
.\Ieensuwmr/?/. Edrtcd b> Z. Criliches Cambridge. hlA Hnr\ar,d kUnt\,er,it! Press

(;upta. S . and P K. Chintagunta. 1993 On Using Demog,raphrc \;arrables tonerermine Segment Membership in Logrt hlixture hlodels. Journnl o/.llorketrng Researrh 3 I l28- I36

Haener. hl
, I’. C Bosall. and W L Adarno\vicz. 2000 Modeling Recreatron Site Choice- Do Hypothetical Chotces Reflect Actual Beha\,ior? .4mer1cn11 .Jourrial o/ .Qwcul~urai
Econonrrc.s 83(33 629-642

Hahn. G J.. and S S Shapiro. 1966. if Cntnlog Land C‘ompufrr. Progmnr jor rhe Des~gr~ and .4tfnl~~sfs q/- Or/ho~o~~al kSjv77n7elric and .I.ymmelric I-~ructtotinl F~aclortnl Erperimenrs

Pechnical Report 66-c‘ 165. Schnectady. NY General Electrrc Research and LDevclopment
Center

Hammond, K R. 1955 Probabilistic Functroning and the Clrnical blcthod P.q~cholug~col Review 62-255-262

flanernann. W M 1999 \VeIfare .Ana&srs with Discrete Chorce Models In I ‘~/u/n~ Recwn/torr and rhc Er1t.o-oumeni Resealed f’refirence .\lerhods ,II Throq anti /‘I-acirce Edited by C Kling and J Herrrgcs Northampton h1.4 Cdtvard Elear

Ilanlcy. N D hlacm~llan. R \\:rrght. C‘ Bullock. I Srrnpson. 1.1 f’arr\rssorr. ;ind B Crahtree
1998 Contrn_eent Valuarron Lcrsus Choice Exprrrrnsnts Estimatrny the Benefits of Environmentally Sensrtrxe Areas rn Scotland. Jo~lrnc~l oj.-lgrrc‘rdliurc~l I-Jconomrcs -19 I- I5

Hanle!;. N., R \Vright. and \V. Adnrno\\rcz 1998 t!sing Choice Experiments to Value the
Environment P~rc/ronnz~~rlal rind Kesource Economrcs 1 I -,I 13--12X

Hausman. J A
, G. K Ixonard. and D McFadden 1995 A [Jtilrt\-Conststent Combrned Discrete
Choice and Count Data &lodel Assessing Recreatronal list I.osscs Due to Natural Resource Darnage .Jowr~ai q/ Public Ecmornrc~s 56 I-30

Hausman. J A . and P A. Ruuti 1987 Specif\,rn_e and Tcstrng f-conometrrc Models for RankOrdered Data .Jourm/ ofErot~omerr~cs 34 83-101

Hensher. D A 1991 Stated f’reference Analysis of Travel Chorces The State of Practrcc Transporlatrorl 2 I 107. I33

Holmes. T P K Aleer. C Zrnkhan, and E Mercer 1998 The Effect of Response T~rnr on Conjoint Xnal\s~s Estimates of RaInforest Protectron Values Jorwnoi o/Ii-ores/ Ecwron~rcs 4-7-28llolmes. T P.. and K I 130) le. 2002 lising Stated Preference hterhotls to Estimate rhe Value
ot‘Forest AttrIbutes In Fores/s t/z n .tfmkct Ecconor~t~~ Edited b! K .-\ I.e. ;tnif E< 0 Sills
Dordrecht. Netherlands Kluwer .Academic Publtsherr Forthcoming

tis~ao. c ‘ 1986 1no11st.s qffnnt-l .&till Ne\\ York Carnbrttfec Unt\ersity Press

tfuher. J and J RlcCann 1982 The Impact of Infcrentral Rellcfs on Product E\nluations

Journal oj,2/nrkr/lug Neseorch 19.324-333

lluher. 1 1997 What 1 lave We Learned from 20 Years ofConjoInt Research- When to Use SelfExplicated. Graded Pairs. Full Protiles, or Choice Fspertments Paper presenled at
Sawtooth Soft\vvre Conference, A u g u 5 1 .Accessed at
M\\U sa\~toothsofr\\are.com!do\\nload:’techpap/~~hatlrnd pdT

Johnson, F I? and W Dcsvousges 1997 Estimattng SIated.Preferences with Rated-Pair Dn~a
En\ tr~nt~xm~;~l. I+calth. and Emplo),ment Effecls 6f Energ Programs .lournn/ o/
En\.ironmt=nrnl Economtcs nerd .ifonclgemerfl 34 19-99

Krantz. D H R D Lucc. P Suppes. and A T\ersk> I969 Fofrndcl/ron.s qf \leil.rLlrrmt~rrc

Nw York- .Academic Press.

Lancaster. K 1966 A Ne\\ Approach IO Consumer Theor? .Jowni~l o/ Polir~cal .Ecown~~
71 132-157

Lareau. T J and D A Rae 1989 Valuing \WP for Diesel Odor Reductions An -\ppl~cart~>n
ofConttn:ent Ranking Technique Soutlwrn Econo~n~c .Jo~~rnn/ 55 72%741

La) ton. D F. 2000 Random Coefficient Models for Stated Preference Sur\ey Jour~trl cvEnvrronmt~ntol Ecorromics nnd ,IIonagemrn/ JO 2 I-36

Louwere. J.. D flensher. and J St\ nit. 2000 StnredChorce .Ifethotfs .-tnn/~~rs ond:lpp/rcir/ror, Carnbridge. Cambridge lJni~ersrt> Press

Louviere. J J 1988a. .411n(\:-r~g /ndrv~r(trmi Dectsron IlcrXtng .\leri-rc Coryc/o?[ .Ana/)sls Sage
Itnr\ersit\ Serlrs on Quantitn~~~e .Applica~mns 11‘) the Soctal Sciences. Serlcs No 67
Ne\\ bur\ Park. Ca Sage Publications. Inc

L.ouvlerc. J J 1988b. Contotnr Anal)s~s hlodcltng of Stated Preferences .A Re\~e\\ oi~l~heor~,
hlethods. Recent Developmentsand External l’al~d~r\ Journn/o/TI-nnsl~orr Econc-lrn,i,sn,rd Policy IO 93-I 19

Lou\ irre. J J 1991 ConjoInt Analbxis In .-l&ctwe.s ,n .\lclrXc/ln,g Rpscn,-cl7 Edrted h!. R Bagozz~ CambrIdge. hlA Black~~rll Publishers

Louviere. f 1 . and D A flensher 1983 UsIns Discrete Choice hlodels wth E\perimentaf D?sl,ori Data lo Forecast Consumer Demand fbf ;I LJnique Cultural f<t,erif Jo?i).lx7/ 01
Corwutner Rcxorch IQ 348-36 I

l.ouvtere, J L and G \Vood\\orth 1983. Design and Analysis of Stmulated Consumer Choice or Allocation Experiments ,-In Approach Based on .Aggregare Data ./o~rnc~/ o/ .Clorbf’flng
Resror-ch 20-350-367

Lute. K

D 1959 Indwrdrrc~l Chorce Behcrwor Ne\\ Lark John W~lcy 8: Sons

Lute. K. D and J W Tukey 1964 Stmultaneous ConJoint Measurement A Nelv TYe of
Fundamental ~leasuremenr .Jorrmal o/;~loriirmntlc,cjl fs~clrolog~~)’ I I-27

### 217

hlnckenzle. J 1993 A Comparison of Conttngent Preference Slotlrls 4mcl~irorr J01rrnn/ o y ..t,gr~u/mr-U/ FCO~TO~IC-s 75 593-603

%l,tnski. C I977 -The Structure of Random UIIIII!, hlodels 7/7coq (1)1d necrs/o,, 8 -729-25 1

I\lanshi. C F 2000 Economic Analysts of Social Interacttons .Jor~l.~t~/ cif t-CConomtc

P2rsp2cr/ws I-I 1

IS-136

Ilarschak. .I 1960 Binar), Choice Constraints 011 Random Utillt> Indicators In S/t7~~/qjivc/

S~~mpos1un7 on ,ttfltllemn/rcnl.Ilerhods rn [heSocrof,Sr:CIetxxx Edited b> K -\rroi\ Stanford.
C.4- Stanford Universit) Press.

Mazzotta. Iv1 ! . and J i Opaluch 1995. Decision hlaktn g When Choices :Zrc CornpIe\: A Test
of tleiner‘s tl\ pothesis Land Econornrcs 71 500-3 15.

hlcfadden. D 1973 Conditional Logit Analysis ofQuaiitati\r Choice Behavior In F~‘vo,?/ier-s
IPI Econowwrws Edited by P. Zarembka. Ne\\ York. :Icademic Press

I\lcFndden. D 1951. Econometric Models of Probahtlisttc Chow In .Soxc.~wa~ .-lno/ws OJ

nrscrere Ontn >t,/lh Economerrrc .4pp//ca/ro~s. Edtted b! C hlnnski. and D hlcf-‘adrJTn Cambrrdge. h,IA MIT Press.

hlcFadden. 0 I986 -The Choice Theorb llpproach I O blarket Research \/c~rk~~t~~r,~ ,J;-wnw j 275-297

hlclean. R . and V Anderson. 1984 App’lred Fuc/orro/ trnd I-i~nc~tonn/ D~.T/~~K~ Ne\x York
Marcel Dekker

bloore. W L . and M B Holbrook. 1990. Conjotnl Anal>s~s on obyxts \r~th En\lrc~nmcntall)
Correlated .-jttributes: J-he Questionable Importance of Represrntntl\e Destgn ./o~ir-no/ o/
Consumer Research 16: 490-397

hfore!. E R I999 T\co RUMS uncloaked: nested log~t models of site choice and nested losit
models of parttcrpatron and site choice In ~rrllriy Kecrcciooll nrd IliC t~il~lrotv?7cr,t Reueoled f’re,&rence .tferhods in Theor)* and Pracrrce Ed~retl h? J A Hcrrlees. and C I. Klmg Northampton MA- Edward Elgar

Morey, E R and D M Waldman. 1998 hfeasurement Error 111 Recreatton Demand h%)drls The Joint Estimation of Participation. Site Choice and Site C-haractcrt\ttcs Jorrr-no/ m/
Environmerlrcll Economrcs and dtanagemenr 35 262-276

Morrison. hl.. and K Bo\,le. 2001. Comparative Relrabllity of Ranh and Chotce Data In Stated
Preference h~lodels. Paper presented at the European :Issocmrlon of En\ tronmcntal and
Resource Economics 2001 Conference. Southampton. UK. June

opaluch. J J S S~\allow. 1. Weaver. C. Wessels. and D \Vtchlens 1903 fxnlua~ing Impact?
from NOXKXJS Waste Facilities Includtng Publtc Prercrences 111 Current Sprang hlcchanlsms 

### Jmmi of Enl.lronmentol Economics and Afanagem~~n, 24 -I I-59

Peterson CI 1 , 1967. A Rlodel of Preference: Quantttattvr .?.nal\sls of the Perceptton of the
visual .4ppearnnce of Residential Neighborhoods .lournal of Regional scrence 7 19-j I

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