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 data
have 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 so
forth). 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 because
they 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. PO 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 by
mirltiplq~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.” The
nttrnber 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 below
Jfnn 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 are
implicit 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 1exfreme\: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\‘er
the 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 choice
occasio~~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 there
is 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 I
harvest 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 model
is 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 the
examination 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 ongoing. 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 bundlec
Rating 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 II 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

25
26 @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

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

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.\/c/hoiis FInal Repor IO Resource \‘aluntlon Branch. Damage .Asscssm&nr C‘entcr. NO:\;1.

I! S Deparlmenr of Commerce

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