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Accommodating attribute processing strategies in stated choice analysis: do respondents do what they say they do? Danny Campbella a

Victoria S. Lorimerb

Institute for a Sustainable World, Queen’s University Belfast, Email: [email protected] b Gibson Institute, Queen’s University Belfast, Email: [email protected]

European Association of Environmental and Resource Economists Annual Conference, Amsterdam, June 2009

Abstract Data from a discrete choice experiment on restoration of environmental damage caused by illegal dumping are used to investigate the implications of attribute processing strategies. Using random parameters logit models, where we account for heterogeneity in both preferences and attribute processing strategies, this paper explores whether the responses to follow-up questions on attribute nonattendance are consistent with those picked up analytically. Results from the analysis provide evidence that allowing for rationally adaptive behaviour leads to significant improvements in goodness-of-fit and has repercussions for willingness to pay estimation and policy appraisal. Our findings also call into question the accuracy of respondent’s statements of attribute non-attendance. Keywords: attribute processing strategies, discrete choice experiments, random parameters logit model JEL classifications: C25, Q24, Q51, Q53

1

Introduction

Since its introduction by Louviere and Hensher (1982) and Louviere and Woodworth (1983) there has been a growing number of studies using the discrete choice experiment methodology. Discrete choice experiments are appealing as value derivation techniques because they are consistent with the Lancasterian microeconomic approach (Lancaster, 1966), whereby individuals derive utility from the different characteristics, or attributes, that a good possesses, rather than directly from the good per se. Accordingly, a change in the level of an attribute

Danny Campbell and Victoria S. Lorimer

Attribute processing strategies in stated choice analysis

describing a given alternative may cause the respondent to favour that alternative over another that is perceived as providing an inferior combination of attributes. In discrete choice experiments, respondents are asked to select their preferred alternative from a given set (the choice set), and are typically asked to perform a sequence of such choices (Alp´ızar et al., 2003) giving rise to a panel of discrete choices. Experimental design theory is used to construct the alternatives, which are defined in terms of their attributes and the levels these attributes could take (see Louviere et al., 2000). This type of analysis has been widely used to derive welfare estimates for ecological and environmental goods. Despite the widespread use of discrete choice experiments, evidence presented in numerous papers indicates that responses made by some respondents are inconsistent with standard economic assumptions. This has led to continuing scepticism about stated preference approaches. As noted by Sugden (2005), if stated preference methods are to become more generally accepted, a defensible strategy for coping with such anomalies is essential. This paper is intended to contribute to this active debate. A basic assumption, which gives rise to the continuity axiom, within the discrete choice experiment framework is that of unlimited substitutability between the attributes used to succinctly describe the alternatives in the choice set. This implies passive bounded rationality, whereby respondents make tradeoffs between all attributes across each of the alternatives, and are expected to choose their most preferred alternative. Thus, the continuity axiom rules out rationally adaptive behaviour, whereby respondents focus solely on a subset of attributes, ignoring all other differences between the alternatives. Ignoring attributes in the choice set implies non-compensatory behaviour because no matter how much an attribute level is improved—if the attribute itself is ignored by the respondent—then such improvement will fail to compensate for worsening in the levels of other attributes (e.g., Spash, 2000; Rekola, 2003; Sælensminde, 2002; Lockwood, 1996). Therefore, respondents using such attribute processing strategies pose a problem for neoclassical analysis as they cannot be represented by a conventional utility function (Lancsar and Louviere, 2006). Without continuity, there is no trade-off between two different attributes (e.g., McIntosh and Ryan, 2002; Rosenberger et al., 2003; Gowdy and Mayumi, 2001). This is a key issue because without a trade-off, there is no computable marginal rate of substitution and, crucially for non-market valuation, no computable relative implicit price. For these reasons, following the sequence of choice tasks, respondents are often asked to state the attributes they attended to during the experiment thus enabling the parameters to be conditioned on the basis of attribute neglect or consideration. The standard practice (e.g., Hensher, 2008; Hensher et al., 2005; Campbell et al., 2008) is to restrict the parameters to zero for the attributes respondents have stated they ignored. This approach ensures that unnecessary weight is not placed on the attributes ignored by respondents. However, if there


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are any discrepancies between what respondents say and what they actually do, restricting the parameters to zero for the attributes they state to ignore is inappropriate. Using a random parameter framework—where we account for heterogeneity in both preferences and attribute processing strategies—this paper analytically investigates the role of attribute processing strategies and explores whether the respondent’s self-stated responses are consistent with those picked up by the model. This is a novel approach which tests the aptness of standard practice for dealing with self-stated attribute processing strategies used in stated choice studies. Results from the analysis provides evidence of significant improvements in goodness-of-fit and suggests that the standard approach for dealing with attribute neglect may be inappropriate. The paper uses data from a study that was used to elicit the economic benefits associated with restoring environmental damage caused by illegal dumping. Our study focuses on an area close to Belfast, where illegal dumping activities are prevalent. The paper is organized as follows. Section 2 defines attribute processing strategies and discusses possible causes. Section 3 outlines the empirical application, followed by Section 4 in which the random parameters logit models used in the analysis are presented. Section 5 presents the relevant results. Finally, Section 6 provides a discussion and offers a number of conclusions.

2

Attribute processing strategies

The estimation of discrete choice experiments assumes that every individual evaluates each and every attribute when choosing their preferred alternative. This implies passive bounded rationality in which individuals are capable of processing all available information (Puckett and Hensher, 2008). The concept of passive bounded rationality recognizes that the disparity in attention that individuals allocate to particular attributes is a consequence of the perceived cost and benefits associated with information evaluation and the opportunity cost of their attention (DeShazo and Fermo, 2004). Thus a model based on passive bounded rationality assumes that when individuals are presented with complicated choice sets they will continue to evaluate all the information provided, however they are more likely to make mistakes when processing the information (DeShazo and Fermo, 2004). This typical assumption lends itself to the continuity axiom, which is based on the notion of unlimited substitutability between attributes. Specifically, individuals are assumed to consider—and make trade-offs—between all attributes within the choice set. However, recent survey evidence (e.g., Rosenberger et al., 2003; DeShazo and Fermo, 2002; Sælensminde, 2001; Gelso and Peterson, 2005) suggests that many respondents exhibit signs of having discontinuous preference structures (i.e., rationally adaptive behaviour). Attribute processing strategies imply non-compensatory decision-making behaviour such as lexicographic or-


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dering and prevent the marginal rate of substitution between attributes being estimated. In such cases, respondents have a tendency to rank alternatives solely with reference to a subset of attributes, ignoring all other differences between the alternatives. Such orderings can be classified according to either ‘strict’ lexicographic procedures—where respondents have an absolute order of preferences which precludes any degree of substitution between attributes—or ‘modified’ lexicographic preferences—where choice is based on thresholds and minimum levels of an attribute are necessary (e.g., Lockwood, 1996; Scott, 2002). Literature suggests a number of reasons as to why individuals might adopt attribute processing strategies. Attribute processing strategies are likely to be an indication that there are some attributes within the choice set that are not behaviourally relevant to certain respondents (Sælensminde, 2006). In particular, these respondents are indifferent with respect to the attributes in the choice set which they ignore. However, the literature has identified that there is a range of other factors that may give rise to discontinuous preferences in discrete choice experiments. The choice tasks respondents are expected to perform require a significant cognitive effort. Hence, respondents may be unclear how to trade one attribute against another, and this may well be exacerbated in the case of complex and unfamiliar ecological and environmental goods. Individuals may, therefore, be inclined to impose confines when making trade-offs between attributes to ease the task. Indeed, a common procedure for some respondents is to consistently discriminate between the attribute(s) they perceive to be more important and those they perceive to be less important (e.g., Luce et al., 2000; Blamey et al., 2002; Caussade et al., 2005). Other strategies include establishing thresholds on attribute levels, conditioning one attribute on the levels of others or choosing to ignore a subset of the attributes (Hensher et al., 2005). Furthermore, it is thought that the decision for individuals to employ a coping strategy to deal with the volume of information they are presented with is intrinsically related to the perceived complexity of the task. As choice complexity increases—identified in terms of the number of attributes, the number of choice sets, the number of levels, the ranges of the attributes and the presentation format—respondents may further restrict the range of factors that they consider and their precision in evaluation decreases (e.g., Heiner, 1983; DeShazo and Fermo, 2002; Hensher, 2006; Puckett and Hensher, 2008). As complexity increases, and decision making becomes more difficult, it is premised that noise is added to the variation in the choices made by respondents and as a result estimation outputs are derived with less certainty (Puckett and Hensher, 2008). Evidence provided by Arentze et al. (2003) also confirms that complexity has significant effects on data quality. Therefore, it is misleading to assume that all individuals have an unlimited capacity to process information to make a utility-maximizing choice. Subsequently it is important to recognize how receptive individual respondents are towards complex information and their inclination to use attribute processing strategies (Hensher et al., 2005). There is also a range of external factors which may explain the use of heuristics.


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These are discussed in Payne et al. (1993) and Rosenberger et al. (2003), and include the cognitive ability of the respondent, the strength of attitudes, beliefs, or dispositions that the respondent holds, demographic characteristics of the respondent, and the social and economic environment and situation (e.g., distractions and time pressures during the experiment). The existence of attribute processing strategies has significant repercussions for both the design of choice tasks and their estimation. In cases where attribute processing strategies are not accounted for it is inherently assumed that all the choice tasks were within the cognitive ability of the respondents, that all attributes were relevant to respondents and the design was of sufficient complexity so that the choice experiment was meaningful (Hensher et al., 2005). However, DeShazo and Fermo (2004) brings the validity of this assumption into question. They argue that if rationally adaptive behaviour is evident it is necessary to account for such to mitigate the significant misspecification bias that would be present if the model was estimated under the assumption of passive bounded rationality. Thus, it becomes necessary to condition the choice made by individuals on the information that they claim influenced their choice rather than assuming attendance to all information. As a result, analysts are required to identify how respondents evaluate information and the consideration they give to each attribute and acknowledge that respondents may only consider a subset of the information provided (DeShazo and Fermo, 2004). Therefore, when designing choice sets it is necessary to deliberate the type and quantity of information which will be considered by respondents to produce an adequately extensive set of choice tasks which will permit the identification of the attribute processing strategy adopted. From this it can be established if attribute neglect is employed as a coping strategy or as a process of evaluating alternatives (Hensher et al., 2005). It is important to note, however, that if a respondent chooses to ignore a particular attribute this does not necessarily imply that the actual marginal disutility is zero, but instead the cost of fully considering that attribute may be perceived to outweigh the benefits (Hensher et al., 2005; Campbell et al., 2008). By better understanding how respondents attend to information within choice tasks, there is potential to greatly improve the design of choice models. Furthermore, from an econometric perspective there are obvious benefits in estimating choice models which condition the choices only on the basis of information that actually influences respondent’s choices rather than assuming attendance to all information (DeShazo and Fermo, 2004). Welfare estimates are also likely to be biased under modelling specifications that neither assume nor allow for violations of the continuity axiom and attribute processing strategies. Therefore, evidence strongly advocates the use of models which have the capacity to accommodate violations of the continuity axiom and limit potential bias which could lead to subsequent inaccurate policy implications (Puckett and Hensher, 2008).


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For instance, when comparing a baseline model, which assumes passive bounded rationality with a rationally adaptive model, DeShazo and Fermo (2004) and Campbell et al. (2008) demonstrate significant differences in willingness to pay (WTP) estimates derived from the passive bounded rationality assumption. Such findings indicate important policy implications for models based on the standard passive bounded rationality assumption as they can be distinctly different from those derived from a rationally adaptive model which conditions parameter estimates on the attribute processing strategies adopted by respondents (Puckett and Hensher, 2008). Swait (2001) also argues that accommodating such strategies can considerably improve the ability of the analyst to predict behavioral changes associated with proposed policy changes.

3

Empirical application

The introduction of the EU Environmental Liability Directive (2004/35/CE) establishes a common framework for the prevention and restoration of environmental damage. Using discrete choice experiments, this research aimed to examine public preferences for environmental restoration activities as permitted by the Directive. The Belfast Hills, where environmental damage arising from illegal dumping is prevalent, is used as a case study. The discrete choice experiment exercise reported here involved several rounds of design and testing. This process began with the gathering of opinions from stakeholders. Having identified the initial attributes, a series of focus group discussions with members of the public were held. The aims of the focus group discussions were fourfold: to highlight the criteria and issues that the general public felt were of importance to the countryside surrounding Belfast; to produce and refine a list of interpretable attributes, and levels thereof, that could later be used in a discrete choice experiment survey; to shed light on the best way to introduce and explain the choice tasks; and, finally, to provide a platform to test draft versions of the questionnaire. Following the focus group discussions, the questionnaire was piloted. This pilot testing had the objective of checking whether the wording and format of the questionnaire was appropriate and if respondents were able to understand the discrete choice experiment exercises. In the final version of the questionnaire four attributes were decided upon to describe the restoration activities. Restorative attributes were categorised as improvements that could take place at the illegal dump sites or general improvements that could take place elsewhere within the Belfast Hills boundary. This distinction was made to coincide with the EU Environmental Liability Directive which stipulates remediation to take place either at the damaged site (i.e., on site) or at an alternative location geographically linked to the damaged site (i.e., offsite). The discrete choice experiment contained one on-site restoration attribute: improvement at the Dump Sites, and three complementary, or off-site, restoration


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attributes: improvement to Water Quality, Wildlife Habitats and Outdoor Recreation. For each restoration attribute, three possible levels of improvement were available. To lessen the cognitive burden on the respondent, these levels were consistent for each attribute. They were described as A Lot of Improvement, Some Improvement and No Improvement. Each of which was explained in terms of the level of improvement that would be achieved through their implementation. The Cost attribute was described as a one-off cost (in Pounds Sterling) that the respondent would personally have to pay to implement the alternative. An orthogonal design was used to generate a panel of six repeated choice tasks. For each choice task respondents were asked firstly to indicate their preferred alternative between two experimentally designed alternatives—labelled OPTION A and OPTION B. Secondly, respondents were asked to choose between their chosen alternative and a DO NOTHING option—which portrayed all the restoration attributes at the No Improvement level with zero cost to the respondent. An example of a choice task presented to respondents during the discrete choice experiment is given in Figure 1. When making their choices, respondents were asked to consider only the attributes presented in the choice task and to treat each choice task independently. In an attempt to minimize hypothetical bias, respondents were also reminded to take into account whether they thought restoring the environmental damage was worth the payment asked of them and were made aware that environmental protection is embedded in an array of substitute and complementary goods. In total, 3234 observables were obtained from a random sample of 556 respondents.

4

Empirical models

In this paper, we use a random parameters logit model specification to account for unobserved taste heterogeneity. Random parameters logit models provide a flexible and computationally practical econometric method, which, as described in McFadden and Train (2000), may in principle be used to approximate any discrete choice model derived from random utility maximization. Starting with the conventional specification of utility, we have: Uni = β0n xni + ni ,

(1)

where Uni is the utility that respondent n obtains from alternative i; βn is a vector of parameters of variables for respondent n representing the respondent’s tastes; xni is a vector of observed explanatory variables that relate to alternative i and to respondent n; and, ni is a Gumbel-distributed and independently and identically distributed (iid) random term, with constant variance π2 /6, and where we have assumed a linear in parameters specification of the observed utility function. A treatment of repeated choices, with preferences varying across respondents,


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Figure 1: Example of a choice task presented to respondents OPTION A

OPTION B

SOME improvement at dumping sites

A LOT of improvement at dumping sites

A LOT of improvement in water quality

NO improvement in water quality

A LOT of improvement in wildlife habitats

SOME improvement in wildlife habitats

SOME improvement in outdoor recreation

NO improvement in outdoor recreation

Cost to you £10

Cost to you £20

I prefer OPTION A [ ]

I prefer OPTION B [ ]

X X

If instead, there was a ‘DO NOTHING’ option available—which would mean nothing would be done to deal with the unauthorised dumping and would cost you nothing extra—would the ‘DO NOTHING’ option be your most preferred? Yes [ ]

No [ ]

but not across observations from the same respondent, can also be accommodated. In this case, we work with a sequence of choices for each individual and treat to be respondent-specific, thus addressing the intrinsic correlation among observations from the same respondent. Denoting the respondent’s chosen alternative in choice occasion t as ynt and their sequence of choices over the T n choice

occasions as yn = yn1 , yn2 , . . . , ynTn , then, conditional on βn , the probability of respondent n’s sequence of choices is the product of logit formulas: Tn Y exp (Vnit (βn )) P (yn |βn , xn ) = , J P t=1 exp Vn jt (βn )

(2)

j=1


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where Vnit = β0n xnit . As βn is not given, the unconditional choice probability becomes the integral of the logit probability, L (yn |βn , xn ), over all values of βn , weighted by the density of βn , f (β): P (yn |βn , xn ) =

Z

L (yn |βn , xn ) f (βn ) dβn .

(3)

βn

A further advantage of random parameters logit models—which we exploit in this paper—is that they also accommodate the estimation of respondent-specific distributions of preferences by deriving the conditional distribution based (within sample) on their known choices (i.e., prior knowledge) (e.g., Train, 2003; Hensher and Greene, 2003; Sillano and Ortúzar, 2005; Hensher et al., 2006). These conditional parameter estimates are strictly same-choice-specific parameters, or the mean of the parameters of the sub-sample of respondents who, when faced with the same choice tasks, made the choices. Using Bayes’ rule, the conditional probability is given by Equation 4 (Hensher and Greene, 2003): h (βn |yn , xn , θ) =

L (yn |βn , xn ) g (βn |θ) , P (yn |xn , θ)

(4)

where L (yn |βn , xn ) is now the likelihood of an individual’s sequence of choices if they had this specific βn ; θ are the parameters of this distribution; g (βn |θ) is the distribution in the population of βn s; and, Pnit (yn |xn , θ) is the choice probability defined as: Z Pnit (yn |xn , θ) = L (yn |βn , xn ) g (βn |θ) dβn . (5) βn

In this paper such probabilities are approximated in estimation by simulating the log-likelihood with 500 pseudo-random draws. A key element of the random parameters logit model is the assumption regarding the distribution of each of the random parameters. After evaluating the results from various specifications and distributional assumptions, in estimation we impose each of the K random parameters to be normally distributed with mean βnk and standard deviation σnk . Estimation of discrete choice models generally assume respondents are fully rational, fully informed and behave in utility-maximizing manner. However, as the previous discussion highlighted, it has become increasingly recognized that actual respondent behaviour may be somewhat different. Despite this realization, almost all estimation of discrete choice experiment applications fail to accommodate non-compensatory behaviour. Therefore, as part of the debriefing, we asked respondents a series of questions that would help identify the attribute processing strategy they adopted during the discrete choice experiment. This information can be used within the econometric model to account for the heterogeneity in attribute processing. The standard approach in previous literature is to specify the


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attribute parameters as a function of a dummy variable representing whether or not the attribute was said to be considered (Hensher et al., 2005; Campbell et al., 2008). In so doing, the choice probabilities are constructed in such a way that the actual elements of βn that enter the likelihood function are set to zero in cases where the element is associated with an attribute that was reported to be ignored by respondent n. However, this approach is quite stringent and does not account for the fact that there may be some discrepancies between the attribute processing strategies that respondents said they adopted and those they actually did adopt. We, therefore, introduce a further model where we do not place any restrictions on the parameters for attributes reported to be ignored. Essentially this leads to a model with separate attribute parameters estimated for respondents who said they considered and ignored the attribute. This provides a convenient approach for assessing the accuracy of the self-stated attribute processing strategies (i.e., if the attribute parameter for respondents who stated they ignored the attribute is found to be significantly different from zero it implies that the attribute was not completely ignored by these respondents and, thus, the standard approach may not best fit the data). We compare this model against the standard approach for accommodating attribute processing strategies and a model that assumes passive bounded rationality.

5 5.1

Results Incidence of discontinuous preferences

Subsequent to the discrete choice experiment, respondents were asked to state whether they considered or ignored each of the attributes. These self-stated attribute processing strategies are summarized in Table 1. As may be seen, 141 (25%) respondents stated they considered all attributes in the discrete choice experiment. Inspection of Table 1 reveals that 8 (1%) respondents said they ignored all attributes and a further 13 (2%) said they focused solely on only one attribute, thus providing no information on their willingness to make trade-offs among the attributes. When reaching their decisions 74 (13%) respondents indicated that they took into account two attributes. Three and four attributes were said to be considered by 155 (28%) and 165 (30%) respondents respectively. With 488 (88%) respondents, Water Quality is the attribute reported to be most considered by respondents. Overall, 454 (82%), 473 (85%) and 324 (58%) respondents said they considered the Dump Sites, Wildlife Habitats and Outdoor Recreation attributes respectively. The Cost attribute was said to be considered by 252 (45%) respondents. Accordingly, the Cost attribute is the attribute that was indicated to be least taken into account in the discrete choice experiment, which is an important finding in a study that was primarily concerned with deriving WTP estimates. This result would suggest that the Cost attribute was the least relevant factor in influencing the respondent’s choices. Further scrutiny of Table 1 reveals European Association of Environmental and Resource Economists Annual Conference, Amsterdam, June 2009

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Table 1: Self-stated attribute processing strategies Attributes and combinations of attributes considered Dump Sites, Water Quality, Wildlife Habitats, Outdoor Recreation and Cost Dump Sites, Water Quality, Wildlife Habitats and Outdoor Recreation Dump Sites, Water Quality, Wildlife Habitats and Cost Dump Sites, Water Quality, Outdoor Recreation and Cost Dump Sites, Wildlife Habitats, Outdoor Recreation and Cost Water Quality, Wildlife Habitats, Outdoor Recreation and Cost Dump Sites, Water Quality and Wildlife Habitats Dump Sites, Water Quality and Outdoor Recreation Dump Sites, Water Quality and Cost Dump Sites, Wildlife Habitats and Outdoor Recreation Dump Sites, Wildlife Habitats and Cost Dump Sites, Outdoor Recreation and Cost Water Quality, Wildlife Habitats and Outdoor Recreation Water Quality, Wildlife Habitats and Cost Water Quality, Outdoor Recreation and Cost Wildlife Habitats, Outdoor Recreation and Cost Dump Sites and Water Quality Dump Sites and Wildlife Habitats Dump Sites and Outdoor Recreation Dump Sites and Cost Water Quality and Wildlife Habitats Water Quality and Outdoor Recreation Water Quality and Cost Wildlife Habitats and Outdoor Recreation Wildlife Habitats and Cost Outdoor Recreation and Cost Dump Sites Water Quality Wildlife Habitats Outdoor Recreation Cost None Total

Number

Percent

141 113 33 6 3 10 76 8 13 12 4 2 21 16 2 1 18 10 1 9 23 2 4 2 5 0 5 2 3 0 3 8 556

25.36 20.32 5.94 1.08 0.54 1.80 13.67 1.44 2.34 2.16 0.72 0.36 3.78 2.88 0.36 0.18 3.24 1.80 0.18 1.62 4.14 0.36 0.72 0.36 0.90 0.00 0.90 0.36 0.54 0.00 0.54 1.44 100.00

that only 249 (45%) respondents said they made trade-offs between at least one of the restoration attributes and the Cost attribute. 5.2

Estimation results

Reported in Table 2 are the parameter estimates for three models. Model 1 pertains to the estimation of the data assuming full attribute attention (i.e., passive bounded rationality). Models 2 and 3 account for the heterogeneity in respondent’s self-stated attribute processing strategies by allowing the parameters to take different values based on whether or not the attribute was said to be considered. Whilst the attribute parameters for respondents who said they ignored the attribute are constrained to zero under Model 2 (i.e., the standard approach), they


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b

a

2.833 2.277 2.323 0.796 −0.034

critical value equal to 18.31 χ210,0.05 . critical value equal to 31.41 χ220,0.05 .

L (0) L βˆ χ2 ρ¯ 2 AIC BIC

Considered Dump Sites Water Quality Wildlife Habitats Outdoor Recreation Cost Ignored Dump Sites Water Quality Wildlife Habitats Outdoor Recreation Cost

βˆ 2.093 1.729 1.970 1.287 0.107

σ ˆ

−3552.91 −1773.23 3559.36a 0.500 3566.46 3627.27

14.018 13.220 13.071 6.619 −4.417

t-ratio

Model 1

10.367 9.171 9.692 6.576 9.485

t-ratio

Fixed Fixed Fixed Fixed Fixed

0.000 0.000 0.000 0.000 0.000

1.818 1.506 1.451 1.114 0.102

σ ˆ

−3552.91 −1750.31 3605.20a 0.507 3520.62 3581.44

16.829 15.414 15.551 8.113 −5.145

t-ratio

2.780 2.169 2.262 1.131 −0.052

βˆ

Model 2

Table 2: Parameter estimates

10.245 8.955 8.193 6.309 8.164

t-ratio

1.330 0.945 0.495 0.185 −0.004

3.223 2.514 2.664 1.244 −0.066

βˆ

0.690 0.560 0.894 0.979 0.070

2.111 1.796 1.863 1.257 0.131

σ ˆ

−3552.91 −1678.83 3748.16b 0.526 3397.67 3519.30

6.631 4.137 2.201 1.243 −0.534

14.519 13.330 13.274 7.774 −5.218

t-ratio

Model 3

1.636 1.284 2.408 3.958 6.142

10.100 8.745 8.557 5.310 8.027

t-ratio

Danny Campbell and Victoria S. Lorimer Attribute processing strategies in stated choice analysis

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are freely estimateable under Model 3. The restoration attributes included in the models are the two levels (i.e., A Lot of Improvement and Some Improvement) specified as dummy (1,0) variables, with parameters constrained equal relative to No Improvement. Although this does not differentiate the effects of the degree of policy action on the given restoration attribute, it has the advantage of being parsimonious and sufficient for the purpose at hand, which is assessing whether or not respondent’s self-stated attribute processing strategies are consistent with the strategies they actually adopted. To allow heterogeneous preferences among respondents for all attributes, they are all specified as random with normal distributions. Under Model 1, the restoration attributes are statistically significant, with positive signs—implying that respondents, all else held constant, prefer environmental damage caused by illegal dumping to be restored (relative to the no improvement condition)—and the Cost parameter is negative, and significant, which is in line with a priori expectations. The standard deviations are also significant— indicating that there is heterogeneity in respondent’s tastes. Turning to Model 2, we find that the parameters estimated for the subsets of respondents who said they considered the attributes are significant and have the expected signs. While the standard deviations are also significant, we observe that they are markedly lower (in relative terms) compared to those estimated in Model 1. This finding complies with the finding in Campbell (2008) and suggests that attribute processing strategies play an important role in the unobserved heterogeneity. Similarly, under Model 3, the parameters estimated for respondents who said they considered the attributes are significant and have the expected signs. The standard deviations are significant, and of the same relative magnitude to those derived in Model 2. Of greatest interest in Model 3, are the parameters and standard deviations estimated for respondents who said they ignored the attributes. We remark that these are not zero and are estimated with the expected signs. Moreover, further inspection reveals that, with the exception of Outdoor Recreation and Cost the estimated parameters are significantly different from zero. This is an important finding, because for these attributes it suggests that, on the whole, respondents who stated they ignored these attributes did not completely ignore them. Thus, fixing the parameters for these respondents to zero is inappropriate. We also note the surprising fact that there is significant heterogeneity in taste intensities among respondents who said they ignored attributes, for three of the attributes, namely Wildlife Habitats, Outdoor Recreation and Cost. Contrasting the parameters estimated for respondents who said they ignored the attributes against those for respondents who stated they considered the attributes, we find, as expected, that they are lower in the case of the restoration attributes and higher in the case of the Cost attribute. Further analysis leads to the rejection of the null that they are equal. A further expected finding, as revealed by the standard deviations (in relative terms), is that there is substantially less


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heterogeneity among respondents who said they ignored the attributes vis-à-vis those who said they considered the attributes. While all models are found to be statistically significant and have acceptable ρ¯ values, as reflected by the increases in L βˆ and ρ¯ 2 and reductions in the AIC and BIC statistics, there is an overall increase in model performance as one moves from Model 1 to Model 3—indicating that failing to accommodate noncompensatory behavior in discrete choice analysis may lead to biased estimates. 2

To further highlight the features of the model assumptions we report in Figure 2 the box-plots of the conditional distributions derived from Equation 4. The box-plots show the median, notches to indicate the 95% confidence interval of the median, ‘hinges’ corresponding with the first and the third quartile (i.e., the 25th and the 75th percentile points in the cumulative distribution), limits of the plots (using the standard multiple of 1.5 times the interquartile range) and the resulting outliers. The box-plots show quite clearly that the conditional distributions are different under each of our modelling assumptions. In line with observations from Table 2, the box-plots confirm that, with the sole exception of the Cost attribute (Figure 2(e)), parameter estimates for respondents who stated they ignored an attribute are (on average) greater than zero. The box-plots also illustrate that these distributions also have less spread and variability compared to those produced by respondents who sated they considered the attribute. Moreover, non-overlapping notches reflect rejection of the null that the median values for respondents who said they considered an attribute are equal to respondents who said they ignored an attribute. An alternative way of teasing out the effect of axiomatic violations of compensatory decision-making, which is likely to be of greater interest to policy makers is to consider the effects on the WTP estimates. Table 3 reports the marginal WTP estimates (using the point estimates given in Table 2). Similar to Campbell et al. (2008), we find stark differences in the WTP estimates derived from the model that assumes passive bounded rationality (i.e., Model 1) to those derived when this assumption is relaxed (i.e., Models 2 and 3). Whilst the implied rank remained constant, we find that the WTP estimates obtained from respondents who said they considered both the restoration and Cost attributes are much lower than those where it is assumed that respondents considered all attributes. We also note that the WTP estimates for these respondents are slightly lower under Model 3 compared to Model 2. Using Model 3, separate WTP estimates are derived to identify differences between the four possible self-stated attribute processing strategies: (i) restoration attribute and Cost considered, (ii) restoration attribute considered but Cost ignored, (iii) restoration attribute ignored but Cost considered, and (iv) restoration attribute and Cost attribute ignored. Irrespective of whether or not respondents said they considered restoration attributes, we find that very high WTP estimates—which are a direct consequence of the lowly estimated denominator (i.e., the Cost parameter) in the WTP calculation—are


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Figure 2: Conditional distributions of βˆ n Model 1 Model 2 (considered)

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● ● ●● ● ● ●● ●● ● ● ●●

● ● ●●

●●● ●●● ●●●●● ●

● ● ●● ●● ●●●

●●●●●

● ● ●●

● ●

−1

●

0

●●

●●

1

2

3

(d) Outdoor Recreation Model 1

●

●●●●●●●

●

Model 2 (considered) Model 3 (considered)

●

●

Model 3 (ignored)

● ●●

−0.3

−0.2

● ●● ● ●●●

−0.1

●

0.0

●●

0.1

(e) Cost


Page 15



Table 3: Willingness to pay estimates (once-off payment in Pounds Sterling) Model 1 βˆ

t-ratio

Model 2 βˆ

t-ratio

Considered restoration attribute and considered Cost attribute Dump Sites 83.41 4.576 53.65 5.237 Water Quality 67.05 4.520 41.86 5.198 Wildlife Habitats 68.40 4.515 43.66 5.164 Outdoor Recreation 23.43 4.010 21.82 4.621 Considered restoration attribute and ignored Cost attribute Dump Sites Water Quality Wildlife Habitats Outdoor Recreation Ignored restoration attribute and considered Cost attribute Dump Sites Water Quality Wildlife Habitats Outdoor Recreation Ignored restoration attribute and ignored Cost attribute Dump Sites Water Quality Wildlife Habitats Outdoor Recreation Weighted averagea Dump Sites 83.41 4.576 20.00 5.237 Water Quality 67.05 4.520 16.64 5.198 Wildlife Habitats 68.40 4.515 16.43 5.164 Outdoor Recreation 23.43 4.010 6.36 4.621 a

Model 3

Percent of

βˆ

t-ratio

respondents

49.19 38.37 40.66 18.99

5.309 5.280 5.300 4.664

37.95 40.47 38.31 29.68

722.60 563.70 597.25 278.94

0.535 0.535 0.534 0.534

43.71 47.30 46.76 28.60

20.35 14.43 7.55 2.82

4.324 3.358 2.065 1.218

7.37 4.86 7.01 15.65

298.91 211.95 110.90 41.42

0.534 0.532 0.520 0.500

10.97 7.37 7.91 26.08

20.17 16.23 16.10 5.63

5.324 5.294 5.305 4.664

100.00 100.00 100.00 100.00

Based on attribute processing strategies adopted by respondents. Insignificant WTP estimates treated as zero.

obtained from respondents who said they ignored the Cost attribute. These estimates exceed what we expect a member of the Belfast public would be willing to pay to restore environmental damage caused by illegal dumping. Fortunately, none of these WTP estimates are found to be significant. Interestingly, with the exception of the Outdoor Recreation attribute, we find that respondents who stated they ignored the restoration attribute but considered the Cost attribute have a significant WTP value. We note that the remaining WTP estimates for these respondents are not as high as those who said they consider both the restoration and Cost attributes, which is an expected finding. In Table 3 we also report the sample weighted average WTP estimates— weighted by the percentage (given in the final column) of respondents who said they adopted the attribute processing strategy. In the case of Model 1, where it is assumed that all attributes were attended to by all respondents, no weighting is necessary. However, for Models 2 and 3, based on the self-stated processing strategies, less than half of the respondents made a trade-off between the Cost attribute and any of the restoration attributes, and thus the WTP estimates need to be weighted accordingly. The weighted WTP estimates derived under Model 3 European Association of Environmental and Resource Economists Annual Conference, Amsterdam, June 2009

Page 16



also account for the proportions of respondents who said they ignored the Dump Sites, Water Quality and Wildlife Habitats attributes but said they considered the Cost attribute. Since the remaining self-stated attribute processing strategies produced WTP estimates that were not significantly different from zero, they were assigned zero in the weighted average calculation. Whilst we find that the weighted WTP estimates obtained under Models 2 and 3 are comparable, they are all approximately 75% lower than those derived under Model 1.

6

Discussion and conclusions

This study was designed to provide straightforward insight into the public’s preferences to restore environmental damage caused by illegal dumping within the Belfast Hills and, as addressed in this paper, the implications of attribute processing strategies. This was achieved using random parameters logit models, where we accounted for heterogeneity in both preferences and attribute processing strategies. Results from this analysis signified that respondents do not attend to all attributes within discrete choice experiments and are likely to adopt an attribute processing strategy to ease their decision-making. Moreover, from a modelling point of view, compared to models which accounted for the self-stated attribute processing strategies, parameters obtained from the base model were found to be erroneous and biased. As a result, significant improvements in model performance and, thus, more accurate utility expressions were achieved when such strategies were accounted for in estimation. However, the fact that improvements in model fit were observed when the parameters for attributes said to be ignored were freely estimateable, suggests that the standard approach for accommodating attribute processing strategies may be inappropriate. Furthermore, we find that most of these parameters are significantly different from zero (with significant heterogeneity in tastes), implying that there is some discrepancy between the self-stated responses and the attribute processing strategies picked up by the model. This provides further evidence that the standard approach does not adequately deal with the heterogeneity in processing strategies. In relation to the welfare estimates, in this empirical application, we do not find any substantial differences between the two approaches for accommodating self-stated attribute processing strategies. However, we do find that the na¨ıve model, which does not account for the heterogeneity in attribute processing, results in an over estimation of the WTP values—in the order of three times the magnitude. Deciding whether or not to account for attribute processing strategies is a judgment that should not be based on statistical criteria alone. However, such strategies do not satisfy the underlying continuity axiom and are a departure from the use of compensatory decision-making. The fact that a significant proportion of respondents state they use these simple decision-making heuristics, combined


Page 17



with the reported effect that their underlying preferences are found to be significantly different and have a major bearing on WTP estimates, suggests some caution when this issue is neglected in estimating discrete choice models. The evidence presented herein provides compelling evidence for further research in this area. Future studies should incorporate procedures for identifying and dealing with attribute processing strategies so that the sensitivity on model performance and welfare estimates can be further evaluated.

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