Proceedings of the Eastern Asia Society for Transportation Studies, Vol.6, 2007TASK COMPLEXITY IN MULTI-ATTRIBUTE STATED CHOICE
ENVIRONMENTAL VALUATION
Michelle PARUMOG
Research fellow
Institute for Transport Policy Studies
3-18-19 Toranomon, Minato-ku, Tokyo
105-0001 Japan
Fax: +81-3-5470-8419
E-mail: parumog@jterc.or.jp

Shoshi MIZOKAMI
Professor
Department of Civil and Environmental
Engineering, Kumamoto University
2-39-1 Kurokami, Kumamoto City
860-8555 Japan
Fax: +81-96-342-3507
Email: smizo@gpo.kumamoto-u.ac.jp

Ryuji KAKIMOTO
Associate Professor
Department of Civil and Environmental
Engineering, Kumamoto University
2-39-1 Kurokami, Kumamoto City
860-8555 Japan
Fax: 81-96-342-3507
Email: kakimoto@gpo.kumamoto-u.ac.jp
Abstract: This study aims to investigate task complexity problems in stated choice valuationof non-market environmental goods. Data from a web-based survey on the valuation of theenvironmental impacts of motor-vehicle use in Metro Mania were used for this purpose. Two
factors leading to task complexity problems were considered: (1) non-linearity in the utility
function indicating uncertainty in preferences, and (2) parameterization of the scale of thestochastic error of the utility indicating decision complexity. The results of empiricalinvestigations show strong suggestions that degree of complexity of the choice problem isaffected by not only by the number of alternatives but also by the range and the description of
the attribute.
Key Words: state choice methods, preference uncertainty, decision complexity,
environmental valuation
1.

INTRODUCTION

Stated preference data has been an important tool in predicting behavior in the absence ofmarkets. The valuation of non-market environmental goods is critical in many policy- anddecision-marking particularly in this age where resources are limited in many aspects. Various
issues surround the contextualization of environmental goods for valuation. One of thoseissues is defining the goods in terms of it quantitative dimensions such as its quality level or
its functional dimensions such as its role in ecosystem processes. The concept ofenvironmental resource and systems has its complexity that must be taken into consideration
when implementing valuation. Dimensions of environmental goods vary according to context.
Most applied stated choice survey have dealt with single dimension goods, meaning asurvey aimed to get specific WTP for an environmental resource or amenity, say value ofclean air. However, most often, environmental systems cannot be simply aggregated into asingle good as most of them come naturally as a basket of goods. Clean air, for instance, as aresource, may be broken down into attributes like visibility, degradation to vegetation, impact
to soil and streams, and impact to human health. Attribute-based SCM is an approach whereProceedings of the Eastern Asia Society for Transportation Studies, Vol.6, 2007

respondents are asked to choose from a set of alternatives with an array of attributes (Louviere
et al., 2000). It is analyzed using multinomial disaggregate model such as the multinomial
logit (MNL). In contrast, contingent valuation method (CVM) elicits willingness-to-pay(WTP) value in open or closed form on the context of made-believe circumstances that
improve or damage to existing environmental conditions. Environmental valuation studies
using attribute-based SCM are limited compared to CVM and other environmental valuation
methods such as hedonic pricing and travel cost. If price is the only attribute, the SCM reverts
back to CVM. There are some advantages in using attribute-based SCM over CVM:(1) theability to deduce behavioral tendencies of respondents over variation of goods or goods’
attributes; (2) policy implications are less suggestive, thus less susceptible to context biases or
behavioral heuristics (Herriges and Kling, 1999); and (2) avoid zero protest votes present in
CVM (Wardman and Bristow, 2004).
This paper aimed to empirically investigate the decision complexity in situations whereenvironmental goods have varying goods dimension. We used an internet survey data on thecontext of WTP valuation of the social costs of motor-vehicle use in Metro Manila. The
result of the empirical test aims to contribute to the design of multi-attribute stated choiceinternet survey.
2.

CHOICES IN MULTI-ATTRIBUTES ALTERNATIVES

The intricacy of environmental systems can often be captured by the broad array of theresource attributes. Preferences over an environmental good such as forestland uses, forinstance, can be categorized into old-growth forest conservation, hardwood native timber
production, and recreation (Ananda and Herath, 2006). Moreover, policy involving economicand environmental systems often consists of multiple objectives. Policies involving
agricultural non-point source pollution may require trade-off between contradicting objectives
on soil erosion and water pollution (Lakshminarayan et al., 1995). The impacts ofmotorization on the environment likewise comes in many dimensions such as air and noise
pollutions, accidents, and environmental dacay(e.gSælensminde, 2001, Iraguen and de Dios
Ortuzar, 2004). To address multi-dimensionality of valuation problems, researchers have used
multiple contingent valuation (CV) (Protiere et al., 2004, Gonzalez and Leon, 2003, Feitelson
et al., 1996, Parumog et al., 2003) and choice experiments. While it has been agued that
attribute valuation methods such as conjoint analysis provides a powerful alternate to CV,
challenges arises as to the complexity of decision-making.
This section presents two frameworks on how get inference on the preferences uncertainty of
the respondents and the decision complexity of the choice questions. Both these factors
influence the task complexity of the choice problems.
2.1. Multi-attribute utility
This section discusses ecosystem valuation, which consists of variety of environmental
services or goods broken down into attributes or objectives. Here, we introduce themulti-attribute utility (MAU) theory that considers preferences over a range of attribute. Let
us assume an environmental good q with M attributes or services Q(q1 ,q2, …qM). This
assumes that the subject has a well-behaved utility function that follows U(x, Q, s, p). The
utility is quasi-concave with respect to market goods X, environmental good Q, andsocioeconomic variables s. Utility specifications with multi-attribute preferences wereProceedings of the Eastern Asia Society for Transportation Studies, Vol.6, 2007

initially articulated by Debreu (1960) in his discussion of goods partitioning in stochastic
choice models utilities, and by Fishburn (1964) in terms of expected utility. Keeney (1993)
extended the utility theory to describe decisions involving preferential and utility
independence, which decompose the multi-attribute utility function to more practical form forelicitation.
The very flexible additive multi-attribute utility model based on the environmental good Q
can be defined as in equation 1.
Mu (q 1 , q 2 ,...q M ) = ∑ k i u i q i

(1)

i =1

Additive utility assume that individual has strong preference independence. In cases, however,where individual experiences uncertainty in preferences, multiplicative utility structure persist.
The kis are the scale parameters, u(qM) are the single-attribute utility associated with qM. We
follow Keeney and Raffia (1976) in showing that the condition of preference independence
among attribute that presents the multiplicative multi-attribute utility structure shown inequation 2.
M

(

( ) )

Ku (q 1 , q 2 ,...q M ) + 1 = ∏ Kk i u i q i + 1

(2)

i =1

In this notation, K is the scaling constant that is a function of ki. The multiplicative utility
function can represent powerful preference structure as it can represent nonlinearities in utility
and interaction between attributes. Note that if K=0, then the utility goes back to equation 1.
The specification of the multi-attribute utility has great implications on environmental
valuation. Additive utility assumes preference independence and assume that values are
simply the marginal utility of attribute divided by the marginal utility of cost, which is simply
the utility parameter of attribute over costs. In case of multiplicative utility, the marginalutility becomes a function of the assumed function (e.g., quadratic) and interactions.
Uncertainty in preferences may be inferred from the functional form of the value functions.
2.2. Decision complexity

Simon (1957) present the idea that consumer approach simplification of its cognitive burden
by deciding only based on a part of the attributes of an alternative. Most applications of
choice modeling assume that respondents have perfect information-processing capacity.
Econometric models usually fail to acknowledge common knowledge in behavioral decision
theory that choice environment, the inability of individual to make complex decision, andchoice context affects decision-making.
A conventional RUM model relies on the random utility to interpret preferences. The utility
disturbances are the basis of the probabilistic inference on utilities. However, they are given
very little consideration in the interpretation of the model. Deterministic variables observableto the researchers are main concerns and the underlying nature of the errors are critical to the
explanation of the choice behavior are often overlooked. Psychological models or behavioral
decision theory touched on task complexity and the environment. Furthermore, more andProceedings of the Eastern Asia Society for Transportation Studies, Vol.6, 2007

more research incorporating decision-maker’s limitation in processing information aboutalternatives in choice problems and in econometric modeling are done.
In the following discussion, we follow Swait and Adamowics (2001) in presenting complex
decision and apply it to a multi-attribute discrete choice valuation framework. The framework
is a heteroscedastic discrete choice model where variance or scale of the stochastic error isparameterized. The model presents a relaxation on the neoclassical perfect decision-maker byincorporating assumption on the basis of information theory. Consider an individual
maximizing his or her utility according to discrete commodities x with attributes q and prices
p. The numeraire here is z. The following formulation based on Hanemann (1982) shows theutility maximization based on factors: (1) budget constraint; (2) mutually exclusive alternative
constraint; and (3) optimal quantity control.

MaxU ( xi ...x j , qi ...q j , z ) subject toJ∑p x +z=M
i =1

i

i

(3)

y i ⋅ y j = 0∀i ≠ j
y i = y i*∀iThis is reflects the perfect decision-maker and can be directly implemented throughconditional indirect utility function readily estimated through MNL. In the framework, therespondent, in its task to make choices, exert an amount of effort to understand each attributeof the alternative, and that when effort is not applied, all attributes appear the same to the
respondents. As effort is an object directly conditional on the individuals, it represents thecomplexity of the choice environment. Swait and Adamowicz (2001) presented a way how
integrate the complexity of the thinking process in the equation. They assume an Ek
representing the effort, where the k indexes the choice problem that the respondents faces
within the planning horizon identified as k=1 … K. Let B be the effort budget. Quantities B
and E are unobservable latent variables. Finally, Hk represent the complexity of the task of
selecting an option in choice set K. The consumer’s decision problem is then represented by
the following notations:
Max U ( y11 ... y j1 , q11 ...q J 1 , E1 H 1 ;...; y1K ... y Jk ,E K H K ; z , E K +1 , H K +1 ) subject toJ∑p y ≤M
i =1

i

i

K +1

(4)

∑E ≤ Bk =1

ky ik ⋅ y jk = 0∀i ≠ jy ik = y ik* ∀i
By employing the random utility theory, with certain assumption of error distribution theprobability of choosing an alternative becomes:

Proceedings of the Eastern Asia Society for Transportation Studies, Vol.6, 2007

Pr( j = 1) =exp[μ i ( Ei H i ) ⋅ Vi1 ]∑ exp μ i ( Ei H i ) ⋅ Vij

[

]

(5)

j∈C

where µi is the scale factor related to scale factor that is inversely proportional to the error
variance in the RUM.
Complexity may be formally represented though the concept of entropy which is formalizedusing the information theory by Shannon (1948). This was initially applied in processing
language or information in communications. The theory assumes that a probability
distribution П= (π1, π2, π3,…πJ) has an associated entropy (or lack of predictability):J

H ( X ) = H (π x ) = − k ∑ π ( x j ) log π ( x j )

(6)

j =1

which is at maximum when all the probabilities, Hmax=ln(J), when all the probabilities havethe same values Pi=1/J and a minimum of 0 when the probability has a value of 1. The
complexity of the situation is assumed to affect the stochastic utility the term specifically its
variance a function of entropy of the decision. To capture nonlinearities in entropy, a quadratic
function is assumed for the entropy. The complexity function is formulated as:

μ i ( Ei H i ) = μ n (C n ) = exp(θ1 H n+θ 2H n2 )where H n = − ∑ Q jn ln Q jnj∈Dand Qin =exp( β X in )for all i ∈ D∑ exp(βX in )

(7)

(8)

(9)

j∈D

Swait and Adamowicz (2001) infer that the these equations (3-9) allows the scale variance tocapture how a consumer make effort up to a degree of certain complexity after which they
resort to a plethora of simplifying decision heuristics that generate greater preference
inconsistencies across decision makers. If this were correct, we would expect that θ1 ≤ 0 andθ1 ≥ 0, or opposite in case of the variance.
3.

EMPIRICAL APPLICATION: MULTI-ATTRIBUTE ROUTE CHOICE

To empirically investigate task complexity, a stated preference route choice survey of private
work trips in Metro Manila is analyzed. A route choice experiment where, apart from thecurrent route taken, two alternative routes with varying environmental improvements in air
and noise quality, greenery and streetscape, road safety. Attributes were assigned to varyrandomly and stated choice problem is repeated six times. The choice is complicated as aside
from deterministic attributes travel time and travel cost, subjective attributes such as thementioned environmental quality improvements were incorporated. We focused on subjective
variables corresponding to environmental qualities we wanted to investigate (i.e. air pollution,
noise, accident risk). As air quality is a complex concept to grasp when done inpollutant-specific manner, we present it in percentages of improvement. This readily translateProceedings of the Eastern Asia Society for Transportation Studies, Vol.6, 2007

into unit reductions or scaling of regional air quality index which is an aggregate scale
describing concentrations of major pollutants such as carbon monoxide (CO), sulphur dioxide(SO2), nitrogen dioxide (NO2), ozone (O3) and fine particular matter in policy context. Noiseis likewise offered in percentages of reduction. Greenery and streetscape are with or without
scenario. In the current route, the number of fatalities a year is pegged at 150.
Table 1 shows how the attributes are determined for the hypothetical route alternatives.
Random noise is added to actual inputted travel time TT and actual travel cost TC. The noise
is deemed as a random draw from normal distribution with standard deviation that is one-third
the value of TT. On the other hand, noise for TC entails addition of the absolute value of arandom draw from normal distribution with standard deviation that is one-third the value of
TC. In the other attributes, embedded scripts performed random drawing of the attributes
levels.
Table 1 Attributes levels
Attributes
Travel time:
Travel cost:
Air Quality Improvement:
Reduction in Noise Pollution:
Greenery and streetscape:Current
TT
TC
Base
Base
Base

Road fatalities/year:

150

Attribute levels
TT + R ~N(0,TT/3)
TC + | R ~N(0,TC/3) |20% improvement, 30% improvement, 80% improvement
20% reduction, 30% reduction, 80% reduction
greenery and streetscape improvements, greenery improvement,
streetscape improvement, and no improvements
20, 50, 75 and 100

In the six repetitions of the choice experiment, the dimensions of the attributes were varied toinvestigate changes in respondent preferences as the complexity of alternatives deepens. Table
2 shows the dimensions of the choice problem.
Table 2 Dimensions of the choice problems
Choicedim. type
SC 1
SC 2
SC 3
SC 4
Total

No. ofAttributes
7
4
5
8

Obs.

Attributes

Repetitions

190
64
61
65

Price, Time, Air quality, Noise, Greenery, Streetscape, Fatalities
Price, Time, Air quality, Noise
Price, Time, Greenery, Streetscape, Fatalities
Price, Additional cost for environmental amenity, Time, Air
quality, Noise, Greenery, Streetscape, Fatalities

3
1
1
1
6

Web survey of private work trips in Metro Manila was conducted for about three weeks, from
June 5 to July 1, 2005. Samples were drawn by sending e-mails to human resource
department heads of different private offices, government offices, non-government offices,
and institutions listed in various online directories. They were informed of the purpose andtimeframe of the study and were asked to forward the website to the personnel of their offices.
One follow-up email was done for each request.
The questionnaire has five parts: work trip characteristics; environmental quality perception
in commonly used route; environmental attitudes; the experimental choice problems; and thesocioeconomic characteristics of respondents. Characteristics of the work trip asked include
home and work location, motor vehicle characteristics, travel time and cost to work. A 5-level
scale of commonly used route’s environmental quality perception, which consists of air
pollution, noise pollution, greenery and streetscape, and road safety, as well as a 10-level
perception scale of the general environmental quality rating composed the second part ofquestionnaire. Ordinal rating questions on government spending on environment, stance about
the environment, and subsidies made up the attitude questions. We received 83 filledProceedings of the Eastern Asia Society for Transportation Studies, Vol.6, 2007

questionnaires, from which we have gathered 380 stated route choices of different dimensions
after eliminating lexicographic and unreasonable responses. The choice experiment format
SC 1 to SC 4 has 190, 64, 61, and 65 usable observations, respectively.
Internet is becoming a very popular survey media because of the rapid growth in the numberof internet users all over the world particularly in developing counties in Asia. The
disadvantages in internet survey like low response rate and presence of unreasonable
responses are redeemed by some of its inherent advantages which include fast response rate,
less interviewer effects, and more intelligent questionnaire. Internet survey can be designed tobe more realistic as web pages can be embedded with script that increase interactivity such asrecalling prior inputs, performing mathematical calculations, and calling random variables.
Unique IDs can identify respondents for easy response verification. Consideration inquestionnaire length, keeping respondents’ interest, complexity, and presentation are some ofnecessary consideration critical to the success of a web-based survey.
4.

RESULTS OF ANALYSIS AND FINDINGSThis section shows the results of the estimation of the models incorporating task complexity.
We estimated the basic MNL model with additive and multiplicative utility, and the MNL
considering task complexity. At first, we define a linear in-utility specification in the form:
V j =α j + β M X M + β TC X TC + ε j(10)where the βM corresponds to the attribute parameter matrix and βTC is the price vector
parameter. The parameter estimates are presented in Table 3. The models show that estimatesare greatly affected by the number of samples since among the repetitions only SCM1 isfound to be robust. SCM3 follows with marginal significance mainly carried by the variables
related to traffic safety.
Table 3 MNL Estimates of attribute-based SCM with additive utility
Variable
1. Alternative 1 constant
2. Alternative 2 constant
3. Price (PhP)
4. Time (Minutes)
5. Air quality (1-% of improvement)
6. Noise (1-% of reduction)
7. Greenery (with or without)
8. Streetscape (with or without)
9. Number of fatalities a year
10. General rating of quality of road and roadside(1 worst -10 best)
11. Income (1=>20,000, 0-otherwise)
No. of observationsl( β )
χ 2 (p-level)

SCM 1
1.497
(-1.94)
1.277(-1.62)-0.010(-3.14)-0.043(-4.36)-0.544(-1.10)-0.095(-0.22)0.040(-0.15)-0.287(-0.86)-0.011(-3.25)SCM 2
0.447 (-0.44)
0.598 (-0.54)-0.024 (-2.90)-0.059 (-4.01)-0.490 (-0.43)-0.640 (-0.76)

0.340(-3.34)0.150-0.410
190
-170
125

(-1.07)

ρ

0.27

2

Estimated WTP
Travel time savings (in mins)
100% air pollution reduction
100% noise reduction
GreeneryLandscapeTraffic fatality reduction a year

4.41
55.24
9.67
-4.07
29.2
1.11

(0.000)(-2.87)(-1.13)(-0.22)(-0.15)(-0.86)(-2.14)-0.054 (-0.09)-1.548 (-1.64)-0.020 (-3.60)

SCM 4
0.060 (-0.04)
0.019 (-0.01)-0.003 (-0.63)-0.040 (-3.98)
0.378 (-0.47)-0.642 (-0.84)
0.103 (-0.16)-0.723 (-1.25)-0.010 (-1.68)

0.280(-1.20)0.210(-0.85)0.070 (-0.12) 0.250 (-0.35)
64
61
-52
-54
7
(-0.546) 28 (-0.001)-0.71065-59
13(-0.93)

0.06

0.1

2.47
20.68
27.01

(-0.94)SCM 3-0.600 (-0.30)-0.706 (-0.36)-0.003 (-0.61)-0.019 (-1.14)

0.21(-3.55)(-0.43)(-0.75)

5.6(-0.57)

15.88
455.1
5.76

(-0.09)(-0.59)(-0.60)13.53-128.04
217.66-34.8
245.21
3.29(-0.323)(-0.66)(-0.42)(-0.51)(-0.16)(-0.66)(-0.60)Proceedings of the Eastern Asia Society for Transportation Studies, Vol.6, 2007

Using the main effects variables [with subscripts TC (travel time), TC (travel cost), AI (airquality, NS (Noise), and AC (traffic fatalities)], we likewise estimated the multiplicative
utility function in the form to see interaction and nonlinear effects in the utility. In thisanalysis, we eliminated the variables landscape and greenery due to the low significant of
estimate and high correlation of the variables.
22
V j = α j + β1 X TT + β 2 X TT
+ β 3 X TC + β 4 X TC
+ β 5 X AI + β 6 X NS(11）2
+ β 7 X AI ⋅ X NS + β 8 X AC ⋅ + β 9 X AC + β 10 X AC

The estimates of the nonlinear in utility equation are shown in Table 4. Using the nonlinear
utility specifications, and the elimination of some variables, the fit of the models, except for
SCM 2, improved significantly (based on adjusted ρ2). It can be seen form these models thatwhile nonlinear effects and interaction effects are insignificant in most models, it can be
deduced that its effect in the general fit of the model is significant.
Table 4 MNL Estimates of SCM with multiplicative utility
Variable
1. Alternative 1 constant
2. Alternative 2 constant
3. Price (PhP)
4. Price (PhP)^2
5. Time (Minutes)
6. Time (Minutes)^2
7. Air quality (1-% of improvement)
8. Noise (1-% of reduction)
9. Air quality ⋅ Noise
10. Number of fatalities a year
11. Number of fatalities a year^2
12. General rating of quality of roadand roadside (1 worst -10 best)
13. Income (1=>20,000, 0-otherwise)Nl( β )
χ 2 (p-level)ρ2

Travel time savings
100% air pollution reduction
100% noise reduction
Traffic fatality reduction a year

SCM 1
SCM 2
SCM 3
1.935
(2.29)
0.609(0.56)
1.931(1.30)
1.724(2.02)
0.719(0.58)
1.844(1.26)-0.015(-2.34)-0.032(-1.97)-0.012(-0.83)
6.40E-06 (0.88) 1.34E-05 (0.51) 7.46E-06 (0.36)-0.060(-2.55)-0.102(-1.81)-0.005(-0.12)
1.45E-04 (0.85) 4.22E-04 (0.83) -1.07E-04 (-0.44)-1.434(-1.33)-0.573(-0.26)-0.995(-1.03)-0.720(-0.31)
1.532(0.95)
0.066(0.02)-0.008(-7.53)-0.037(-1.55)-2.10E-05 (-0.27)
1.12E-04 (0.79)

SCM 4
0.364
(0.20)
0.346(0.19)-0.020(-1.61)
2.09E-05 (0.99)-0.120(-2.74)
5.71E-04 (2.38)
2.179(0.92)
0.849(0.35)-2.337(-0.64)0.018(0.81)-1.72E-04 (-1.29)

0.321(3.20)

0.138(0.86)

0.357(1.60)

0.245(0.20)-0.379
190
-169
127

(-0.99)(0.27)(0.000)-0.79765-57
48(0.93)(0.208)

0.239
61
-55
71

(0.33)(0.000)

0.163
64
-51
14

(0.000)

0.27
3.98
24.69
-6.27
0.71

(2.00)(0.26)(-0.08)(2.15)

0.12
2.77
16.88
21.69

(1.23)(0.24)(0.29)

0.39
0.83(0.24)

2.48(0.52)

0.29
5.59-42.7939.68-0.14(1.28)(-0.42)(0.37)(-0.13)To investigate the decision complexity, we estimated the models incorporating choicecomplexity in the equations (3-9). A quadratic form for the complexity function was used in
the estimation. Using the additive utility specifications, the results of models considering
choice complexity is shown in Table 5. In all models, the entropy parameters both at linear
and quadratic terms follows right signs for complex decision and are significant. SCM3, withvariables greenery and landscape, shows the highest scale of complexity followed by SCM4
and SCM1 with eight and seven attributes, respectively. The value estimates tends to becomeless significant as the scale of the entropy parameters become bigger.

Proceedings of the Eastern Asia Society for Transportation Studies, Vol.6, 2007

Table 5 Estimates of MNL of SCM with complexity parameters
Variable
Parameter Estimates(t-statistics)
1.
Alternative 1 constant
2.
Alternative 2 constant3.
Price (PhP)
4.
Time (Minutes¥)
5.
Air quality (1-% ofimprovement)
6.
Noise (1-% of reduction)
7.
Greenery (with or without)
8.
Streetscape (with orwithout)
9.
Number of fatalities a year
10. General rating of quality ofroad and roadside (1 worst-10 best)
11. Income (1=>20,000,
0-otherwise)θ1
θ2
No. of observationsl( β )
χ 2 (p-level)

SCM1

SCM2

SCM3

5.135
4.902
-0.023-0.084(3.92)(3.82)(-6.50)(-8.90)

0.752
0.683
-0.009-0.044(1.39)(1.27)(-1.79)(-2.39)-0.032(-0.05)0.254(0.52)-0.772-0.111(-2.04)(-0.44)-0.243(-0.84)-0.9134.773
4.290-0.016-0.071SCM 4(1.36)(1.30)(-1.29)(-1.87)

4.217
3.605
-0.019-0.062(1.26)(1.10)(-2.36)(-3.06)0.465(0.32)

1.695(1.50)-0.186
0.979(-0.14)(0.98)(-2.03)-8.786(-2.20)-2.280(-2.19)-0.028(-7.53)-0.049(-6.17)-0.040(-3.92)

0.93(6.34)

0.11(1.39)

0.69(1.45)

0.72(1.40)-0.35(-0.98)0.28(1.13)

3.63(2.38)

0.34(0.28)

-6.47
7.64
190
-163
125

(-6.51)(6.45)

-3.56
4.64
64
-48
40

(-1.67)(2.13)-11.39
12.75
61
-50
42

(-2.23)(1.80)

-9.07
10.26
65
-57
48

(-1.62)(1.24)

ρ2

0.28

Estimated WTP
Travel time savings
100% air pollution reduction
100% noise reduction
GreeneryLandscapeTraffic fatality reduction a year

3.67
1.40
33.80
4.85
39.96
1.21

(0.000)(0.000)0.29(.78)(0.05)(1.94)(0.44)(2.07)(4.99)

4.85-27.6926.53(0.001)0.30(2.03)(-0.55)(0.88)(0.000)

0.29

4.34

(1.17)-103.52536.652.99(-1.10)(1.23)(1.27)

3.26-24.649.83-51.90120.832.09(1.87)(-0.32)(0.14)(-0.97)(2.01)(2.09)The Figure 1 below shows the entropy functions (equation 7) with respect to the probabilityof choosing the first alternative, or choosing current route and not the new alternatives withimproved environmental attributes. The trend is that, as complexity of the question increases,
i.e. less level of attributes, the respondent tends not to choose the improved alternatives andchoose the default alternative. The results also shows that choice problem involving mainly
non-use, dummy attributes such as greenery and landscape (i.e. SCM3) appear to be more
complex for respondents than attributes posing direct effects.
1.2
SCM1
1

SCM2
SCM3

Entropy

0.8

SCM4

0.6
0.4
0.2
0
0

0.1

0.2

0.3
0.4
0.5
0.6
0.7
Probability of choosing current route

0.8

0.9

1

Figure 1 Entropy as a function of probability remaining in current routeProceedings of the Eastern Asia Society for Transportation Studies, Vol.6, 2007

5.

SUMMARY AND CONCLUSIONSIn this paper, we considered two factors leading to task complexity problems: (1)non-linearity in the utility function indicating uncertainty in preferences, and (2)parameterization of the scale of the stochastic error of the utility indicating decisioncomplexity. The results of estimations show presence of preference uncertainty and decisioncomplexity in the empirical application which leads to inferior WTP estimates. Empiricalapplication also shows a strong suggestion that degree of complexity is affected by not onlyby the number of alternatives but also by the range and the description of the attribute.
Based on the results of the study, the following factors should be considered in preparing
multi-attribute stated questions. First is the length or the number of attributes in the design ofchoice alternatives. While results for up to four attributes are found acceptable in theempirical examples in this study, the number of attributes may vary according to thecomplexity of the good in question. Second is the range and description of attributes. Fromthe experiment, it is shown that attributes presented in crisp numbers, percentage or quantities,
are better understood by the respondents than qualitative descriptions. The dimension of theattribute levels is likewise a critical factor to take into account. A small sample pre-testingshould be able to reveal the points in avoiding complexity in stated choice valuation
problems.
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