Labels and choice Directory UMM :Data Elmu:jurnal:E:Ecological Economics:Vol32.Issue2.Feb2000:

One way of circumventing the IIA property is to allow for correlations among the error terms within different subsets or classes of alternatives by estimating a nested logit model McFadden, 1978; Daganzo and Kusnic, 1993. In a two-level nested logit model, the probability of an individ- ual choosing the hth alternative in the rth branch P hr is represented as: P hr = PhrPr 5 where Phr is the probability of an individual choosing the hth alternative conditional on choos- ing the rth class of outcome, located in the rth branch of the tree. Pr is the probability that the individual chooses the rth branch. Following Kling and Thomson 1996: Phr = exp[V hr a r ] exp[I r ] 6 P = exp[a r I r ] R k = 1 exp[a k I k ] 7 where I r = log H r i = 1 expV ir a r n 8 is referred to as the inclusive value. This is a measure of the expected maximum utility from the alternatives associated with the rth class of alternatives. H r is the number of alternatives in branch r, and V hr is the utility of the hth alterna- tive in the rth branch. The coefficient of the inclusive value, a r , measures substitutability across alternatives. When substitutability is greater within rather than between alternatives, 0 B a r B 1. In this case, respondents will shift to other alternatives in the branch more readily than they will shift to other branches Train et al., 1987. The popularity of the nested logit model is in part due to the way in which nested decision structures lend themselves to behavioural interpretations. Welfare estimates are obtained in CM studies using the following general formula described by Hanemann 1984: W = − 1 m ln i C e V i 0 − ln i C e V i 1 n 9 where m is the marginal utility of income, V i0 and V i1 represent the indirect observable utility before and after the change under consideration, and C is the choice set. In CM, the absolute value of the coefficient of the monetary attribute in the choice model is taken as an estimate of m. Changes in V i0 or V i1 can arise from changes in the attributes of alternatives or the removal or addition of alter- natives altogether. For example, in recreational site studies where alternatives are substitutes in consumption, the removal of an alternative from the choice set might correspond to a site closure, which one would expect to result in a welfare loss. When alternatives are substitutes in ‘production’, such as when a single solution has to be chosen from a set of feasible solutions, the removal of alternatives can be used to estimate selection probabilities and welfare implications based on different choice sets. When the choice set includes a single before and after policy option, Eq. 9 reduces to: W = − 1 m [lne V i 0 − lne V i 1 ] = − 1 m [V i0 − V i1 ] 10 In the case of changes in a single attribute, this further reduces to − b j m when a linear in parameters utility function is employed. This is equivalent to calculating the ratio of marginal utilities for the attribute in question and the mon- etary attribute, or the marginal rate of substitu- tion MRS Hensher and Johnson, 1981. Kling and Thomson 1996, Herriges and Kling 1997, Choi and Moon 1997 consider the application of Eq. 9 in the nested logit case.

3. Labels and choice

The specification shown in Eq. 4 is consistent with some popular models of consumer be- haviour, in addition to that of Lancaster 1991. According to Fishbein and Ajzen 1975, the atti- tudes of consumers to a good, and hence the subjective expected utility of the good, is deter- mined by the sum of the person’s salient beliefs about the good’s attributes, multiplied by his or her evaluations of these attributes. Additionally, brand names and other labels may exist that are imbued with specific emotional associations that are not obviously tied to specific product at- tributes. Such emotional associations are some- times referred to as ‘freestanding emotions’ Rossiter and Percy, 1997, p. 123. They are dis- tinct from the weights that are associated with specific attributes. In the terminology of Eq. 4, the variables X 1 − X n reflect the product attribute values and the b 1 − b n reflect the weights of the attributes. Differences between perceived and objective product attribute values can be accounted for when substituting values into Eq. 4 to calculate market share andor compensating surplus Adamowicz et al., 1996. The influence on choice of freestanding emo- tions is captured by the ASCs in Eq. 4, along with any other systematic unobserved effects. Dif- ferent labels may impose different demand arte- facts on the task in the form of inferences that cannot be observed, thereby producing differences in the ASCs. The ASCs may also capture the influence of any ‘presentation effects’. For exam- ple, the tendency of some respondents to favour middle alternatives in the choice set, on the basis that this represents some form of compromise between conflicting goals, would be reflected in the ASCs Blamey et al., 1997; Morrison et al., 1997. Although individuals are assumed above to sum the weighted benefit beliefs, and any freestanding emotions, to form an attitude and purchase inten- tion, they can, of course, employ any of a number of compensatory and non-compensatory choice rules. As observed by Simon 1955, they may seek to simplify their decisions by either restrict- ing the scope of the decision problem, for exam- ple, by limiting the number of attributes to be evaluated, or by simplifying the decision rule used to evaluate them. In the context of the present study, respondents may develop choice rules based around the demand artefacts contained within the labels but also reflecting certain limited attribute contingencies. For example, a respon- dent may decide to adopt a choice rule in which one labelled option is always preferred unless the level of a selected attribute exceeds a given threshold. A labelled approach may make it easier for respondents to process the options, by reduc- ing the number of difficult attribute trade-offs requiring consideration. A consequence may be lower weights on the attribute parameters.

4. Research design and hypotheses