Compared to many applied areas of economics, health economics has a strong tradition in eliciting and using stated preferences (SP) in policy analysis. Discrete choice experiments (DCEs) are one SP method increasingly used in this area. Literature on DCEs in health and more generally has grown rapidly since the mid-1990s. Applications of DCEs in health have come a long way, but to date few have been 'best practice', in part because 'best practice' has been somewhat of a moving target. The purpose of this paper is to briefly survey the history of DCEs and the state of current knowledge, identify and discuss knowledge gaps, and suggest potentially fruitful areas for future research to fill such gaps with the aim of moving the application of DCEs in health economics closer to best practice.
Since the early 1990s there has been much progress in understanding and taking into account preference heterogeneity in probabilistic discrete choice models (e.g., Wedel and Kamakura '1999;McFadden and Train 2000). The vast majority of models applied in marketing and applied economics try to represent heterogeneity as some type of discrete or continuous distribution of preferences. These relatively new types of statistical models have done well in comparisons against simpler model forms like conditional multinomiallogit in terms of inand out-of-sample !fits,with fit performance often assessed against so-called hold-out sets. It is fair to say that these models are long on statistical theory, but short on behavioral theory; the latter aspect is the focus of this paper.
The use of stated preference (SP) techniques for estimating environmental values has increased substantially in recent years. However, criticism about the most widespread SP technique used for valuing environmental resources, the contingent valuation method (CVM), suggests that there is a need to not only refine the CVM, but to develop alternative SP techniques. In this paper the CVM is compared with four other SP techniques: contingent rating, contingent ranking, paired comparison and choice modelling. The techniques are compared in terms of their methodologies and the validity and reliability of the results they produce. The appropriateness of using each of the SP techniques in different environmental valuation applications is also discussed. It was concluded that while the CVM is prone to bias and has some practical limitations, when applied appropriately it can be used to produce theoretically valid results. Three of the other techniques- contingent rating, contingent raking, and paired comparison- are found to have weak theoretical bases and do not produce economically valid valuation estimates. The final SP technique examined, choice modelling, appears to have considerable potential for providing useful and valid estimates of environmental values.
This chapter provides the basic framework for stated preference (SP) and stated choice (SC) methods. We first provide a brief rationale for developing and applying SP theory and methods. Then we briefly overview the history of the field. The bulk of attention in this chapter is devoted to an introduction to experimental design, with special reference to SP theory and methods. The next and subsequent chapters deal specifically with the design of (stated) choice experiments, which are briefly introduced in this chapter.
Applications of discrete choice experiments to study consumer choice behavior have grown significantly over the past decade. Typically, discrete choice experiment data are analyzed using McFadden’s Multinomial Logit (MNL) model or a more sophisticated extension that relaxes some of MNL’s restrictive assumptions. Advantages of MNL for analyzing discrete responses are well-known to marketing academics and practitioners, but arguably the latent preference structure that explains discrete responses has yet to be clearly articulated. For example, integration of latent variable systems and discrete choice experiments has several advantages. First, it is possible to model the variances associated with consumers’ discrete choices by introducing latent preference variables, which can provide useful information on the consistency of consumers’ choices. Second, one can specify fully generalized models whereby brand characteristics explain variation in consumers’ latent brand preferences to provide insight into processes that drive consumers’ preferences. Third, the resulting latent variable systems generalize to all of the characteristics of brands one chooses to study. We illustrate the integration in an application to consumer preferences for brands in the “last minute” hotel accommodation market. For convenience, we use Joreskog’s LISREL model to fit the latent structure, although other latent variable systems are considered. The study has immediate implications for researchers analyzing stated preference data, with additional implications for potential applications to revealed preference data.
This paper critically reviews the report by Green, DeSarbo, and Kedia “on the insensitivity of brand‐choice simulations to attribute importance weights”. The review suggests that results from two of their four weight distortion conditions should be viewed with suspicion and that their procedure for selecting brand scores biased the sensitivity analysis and inflated individual‐level hit rates. The most unfortunate problem is that their simulation‐experimental design precludes a global test of the main research issue. Results are presented from an alternative simulation approach that has the power to detect the sensitivity of brand shares to a wide range of conditions that affect the shape of attribute weight distributions. The results suggest that conclusions of Green, DeSarbo, and Kedia apply only to a limited domain and would not obtain in many likely market situations. Extensions to nonadditive decision structures are suggested and several unresolved issues are outlined.
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