A Comparison of Elasticities Derived from Multinomial Logit, Nested Logit and Heteroscedastic Extreme Value SP-RP Discrete Choice Models

Developments in the estimation of discrete choice models which relax elements of the independence of irrelevant alternatives (IIA) property of the multinomial logit model (MNL) provide opportunities to explore the richer behavioural sensitivity of a choice model to changes in the levels of attributes influencing choice. Surprisingly, the literature offers limited evidence on the variations in sensitivity (ie elasticity) as we move from an MNL model based on revealed preference (RP) data, to MNL based on stated preference (SP) data, to combined RP-SP data estimated sequentially and jointly with partial relaxation of the differential variance in the unobserved effects by the ‘nested logit’ method, and then as free variance across all RP and SP alternatives by heteroscedastic extreme value (HEV) estimation. This paper draws on a data set collected in 6 Australian capital cities in 1994 to estimate a series of commuter mode choice models in the presence and absence of two ‘new’ alternatives (light rail and busway systems), to derive matrices of direct and cross point elasticities for travel cost and travel time. The evidence suggests that constraining the variance of the unobserved effects to varying degrees tends to over-estimate the elasticities sufficiently to distort the real behavioural sensitivity of specific attributes influencing choice. Furthermore, we seriously question the usefulness of studies which rely solely on SP data

The Use of Mixtures of Market and Experimental Choice Data in Establishing Guideline Weights for Evaluating Competitive Bids in a Transport Organisation

The government sector is increasingly using competitive bidding for service deliveries such as the provision of bus and rail services as well as the purchasing of professional engineering services such as project planning, design, and project supervision. As part of a program to simplify and introduce consistency in the tender evaluation process, one government transport agency in New Zealand financed a study to investigate the potential of combined revealed and stated preference methods as a way of establishing weights to attach to the criteria used to evaluate offers of engineering services. These techniques have mainly been used in the study of travel choices yet they have a much broader appeal in studying the decision making process of organisations. In this paper we use a data-mixing model to capture the decision expertise of a transport organisation through the revelation of preference weights for each evaluation criterion. Using choice information based on both market-driven and experimentally-derived choice sets, we should be able to increase the robustness of the evaluation weights in comparison to the weights obtained from single data-sourced models. The resulting parameterised tool can be used in subsequent tender evaluations to provide an additional source of advise to supplement or replace that provided by current members of a bid evaluation team

Estimating preferences for a dermatology consultation using Best-Worst Scaling: Comparison of various methods of analysis

Background: Additional insights into patient preferences can be gained by supplementing discrete choice experiments with best-worst choice tasks. However, there are no empirical studies illustrating the relative advantages of the various methods of analysis within a random utility framework. Methods: Multinomial and weighted least squares regression models were estimated for a discrete choice experiment. The discrete choice experiment incorporated a best-worst study and was conducted in a UK NHS dermatology context. Waiting time, expertise of doctor, convenience of attending and perceived thoroughness of care were varied across 16 hypothetical appointments. Sample level preferences were estimated for all models and differences between patient subgroups were investigated using ovariateadjusted multinomial logistic regression. Results: A high level of agreement was observed between results from the paired model (which is theoretically consistent with the 'maxdiff' choice model) and the marginal model (which is only an approximation to it). Adjusting for covariates showed that patients who felt particularly affected by their skin condition during the previous week displayed extreme preference for short/no waiting time and were less concerned about other aspects of the appointment. Higher levels of educational attainment were associated with larger differences in utility between the levels of all attributes, although the attributes per use had the same impact upon choices as those with lower levels of attainment. The study also demonstrated the high levels of agreement between summary analyses using weighted least squares and estimates from multinomial models. Conclusion: Robust policy-relevant information on preferences can be obtained from discrete choice experiments incorporating best-worst questions with relatively small sample sizes. The separation of the effects due to attribute impact from the position of levels on the latent utility scale is not possible using traditional discrete choice experiments. This separation is important because health policies to change the levels of attributes in health care may be very different from those aiming to change the attribute impact per se. The good approximation of summary analyses to the multinomial model is a useful finding, because weighted least squares choice totals give better insights into the choice model and promote greater familiarity with the preference data

Modeling the effects of including/excluding attributes in choice experiments on systematic and random components

Train on an earlier draft of this paper. 2 Modeling the Effects of Including/Excluding Attributes in Choice Experiments on Systematic and Random Components Abstract This paper examines the impact of attribute presence/absence in choice experiments using covariance heterogeneity models and random coefficient models. Results show that attribute presence/absence impacts both mean utility (systematic components) and choice variability (random components). Biased mean effects can occur by not accounting for choice variability. Further, even if one accounts for choice variability, attribute effects can differ because of attribute presence/absence. Managers who use choice experiments to study product changes or new variants should be cautious about excluding potentially essential attributes. Although including more relevant attributes increases choice variability, it also reduces bias

Using a market-utility-based approach to designing public services: A case illustration from United States Forest Service

Government and public services have to not only enforce the requirements of the regulatory policies, but also have to satisfy the needs of preferences of their clients and customers. In this paper, we summarize the results of a multi-year case study conducted to assess the choices of campground users at the Shawnee National Forest (Illinois), which is managed by United States Forest Service. The results show how users' tradeoff between location, capacity-related and pricing attributes when choosing a campground. The case study provides guidance for design and development of new services and more effective management of effective resources within the national forest. © 2005 Elsevier B.V. All rights reserved

Rescaling quality of life values from discrete choice experiments for use as QALYs: a cautionary tale

Background: Researchers are increasingly investigating the potential for ordinal tasks such as ranking and discrete choice experiments to estimate QALY health state values. However, the assumptions of random utility theory, which underpin the statistical models used to provide these estimates, have received insufficient attention. In particular, the assumptions made about the decisions between living states and the death state are not satisfied, at least for some people. Estimated values are likely to be incorrectly anchored with respect to death (zero) in such circumstances. Methods: Data from the Investigating Choice Experiments for the preferences of older people CAPability instrument (ICECAP) valuation exercise were analysed. The values (previously anchored to the worst possible state) were rescaled using an ordinal model proposed previously to estimate QALY-like values. Bootstrapping was conducted to vary artificially the proportion of people who conformed to the conventional random utility model underpinning the analyses. Results: Only 26% of respondents conformed unequivocally to the assumptions of conventional random utility theory. At least 14% of respondents unequivocally violated the assumptions. Varying the relative proportions of conforming respondents in sensitivity analyses led to large changes in the estimated QALY values, particularly for lower-valued states. As a result these values could be either positive (considered to be better than death) or negative (considered to be worse than death). Conclusion: Use of a statistical model such as conditional (multinomial) regression to anchor quality of life values from ordinal data to death is inappropriate in the presence of respondents who do not conform to the assumptions of conventional random utility theory. This is clearest when estimating values for that group of respondents observed in valuation samples who refuse to consider any living state to be worse than death: in such circumstances the model cannot be estimated. Only a valuation task requiring respondents to make choices in which both length and quality of life vary can produce estimates that properly reflect the preferences of all respondents