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This paper presents new evidence that the error in estimating the economic welfare of a transport scheme can be very large. This is for two reasons. Firstly when cost changes are large the income effect can be significant. This means the change in consumer surplus is no longer a good estimate of the compensating variation — the true measure of welfare benefit. Secondly, in the presence of large cost changes estimating the change in consumer surplus using the Rule of Half can lead to large errors. The paper uses a novel approach based on stated choice and contingent valuation data to estimate the size of this error for the situation of the provision of fixed links to islands in the Outer Hebrides of Scotland.

An assumption made in many applications of stated preference modeling is that preferences remain stable over time and over multiple exposures to information about choice alternatives. However, there are many domains where this assumption can be challenged. One of these is where individuals learn about new products. This paper aims to test how attribute preferences as measured in an experimental choice task shift when respondents are exposed to new product information. The paper presents results from a study investigating consumer preferences for a new consumer electronics product conducted among 400 respondents from a large consumer panel. All respondents received several choice tasks and were then able to read additional information about the new product. After this they completed an additional set of choice tasks. All choices were from pairs of new product alternatives that varied across eight attributes designed according to an orthogonal plan. Using heteroscedastic logit modeling, the paper analyses the shifts in attribute utilities and scale variances that result from the exposure to product information. Results show that as respondents become better informed about a new attribute the attribute has a greater influence on their choices. In addition a significant shift in scale variance is observed, suggesting an increase in preference heterogeneity after information exposure.

This paper deals with choice set generation for the estimation of route choice models. Two different frameworks are presented in the literature: one aims at generating consideration sets and one samples alternatives from the set of all paths. Most algorithms are designed to generate consideration sets but fail in general to do so because some observed paths are not generated. In the sampling approach, the observed path as well as all considered paths is in the choice set by design. However, few algorithms can be actually used in the sampling context.

In this paper, we present the two frameworks, with an emphasis on the sampling approach, and discuss the applicability of existing algorithms to each of the frameworks.

In this paper, we analyze statistical properties of stated choice experimental designs when model attributes are functions of several design attributes. The scheduling model is taken as an example. This model is frequently used for estimating the willingness to pay (WTP) for a reduction in schedule delay early and schedule delay late. These WTP values can be used to calculate the costs of travel time variability. We apply the theoretical results to the scheduling model and design the choice experiment using measures of efficiency (S-efficiency and WTP-efficiency). In the simulation exercise, we show that the designs based on these efficiency criteria perform on average better than the designs used in the literature in terms of the WTP for travel time, schedule delay early, and schedule delay late variables. However, the gains in efficiency decrease in the number of respondents. Surprisingly, the orthogonal design performs rather well in the example we demonstrated.

The presence of respondents with apparently extreme sensitivities in choice data may have an important influence on model results, yet their role is rarely assessed or even explored. Irrespective of whether such outliers are due to genuine preference expressions, their presence suggests that specifications relying on preference heterogeneity may be more appropriate. In this paper, we compare the potential of discrete and continuous mixture distributions in identifying and accommodating extreme coefficient values. To test our methodology, we use five stated preference datasets (four simulated and one real). The real data were collected to estimate the existence value of rare and endangered fish species in Ireland.

Mixed logit models can represent heterogeneity across individuals, in both observed and unobserved preferences, but require computationally expensive calculations to compute probabilities. A few methods for including error covariance heterogeneity in a closed form models have been proposed, and this paper adds to that collection, introducing a new form of a Network GEV model that sub-parameterizes the allocation values for the assignment of alternatives (and sub-nests) to nests. This change allows the incorporation of systematic (nonrandom) error covariance heterogeneity across individuals, while maintaining a closed form for the calculation of choice probabilities. Also explored is a latent class model of nested models, which can similarly express heterogeneity. The heterogeneous models are compared to a similar model with homogeneous covariance in a realistic scenario, and are shown to significantly outperform the homogeneous model, and the level of improvement is especially large in certain market segments. The results also suggest that the two heterogeneous models introduced herein may be functionally equivalent.

There have always been concerns about task complexity and respondent burden in the context of stated choice (SC) studies, with calls to limit the number of alternatives, attributes and choice sets. At the same time, some researchers have also made the case that too simplistic a design might be counterproductive given that such designs may result in issues of omitting important decision variables. This paper aims to take another look at the effects of design complexity on model results. Specifically, we make use of an approach devised by Hensher (2004)¹ in which different respondents in the study are presented with designs of different complexity, and look specifically at effects on model scale in a UK context, adding to previous Chilean evidence by Caussade et al. (2005). The results of our study indicate that the impact of design complexity may be somewhat lower than anticipated, and that more complex designs may not necessarily lead to poorer results. In fact, some of the more complex designs lead to higher scale in the models. Overall, our findings suggest that respondents can cope adequately with large number of attributes, alternatives and choice sets. The implications for practical research are potentially significant, given the widespread use, especially in Europe, of stated choice designs with a limited number of alternatives and attributes.

Discrete choice models based on cross-sectional data have the important limitation of not considering habit and inertia effects and this may be especially significant in changing environments; notwithstanding, most demand models to date have been based on this type of data. To avoid this limitation, we started by building a mode choice panel around a drastically changing environment, the introduction of a radically new public transport system for the conurbation of Santiago de Chile. This paper presents the formulation and estimation of a family of discrete choice models that enables to treat two main elements: (i) the relative values of the modal attributes, as usual, and (ii) the shock resulting from the introduction of this radical new policy. We also analyse the influence of socioeconomic variables in these two forces.

We found that introducing this drastic new policy may even modify the perception of attribute values; in fact, the changes can be different among individuals, as socioeconomic characteristics act as either enhancers or softeners of the shock effects generated by the new policy.