Empirical and methodological challenges in choice experiments

Economic values are usually revealed in the market place. However, no such mechanism exists to reveal people's relative values for goods and services that are currently not being bought and sold in the marketplace. Still, scientists would like to know the monetary value people attribute to them. We want to be able to carry out cost-benefit analysis to determine the welfare effects of technological innovations or public policy, to forecast new product success, and to understand the degree to which behavior is consistent with preferences and beliefs. Choice experiments (CEs) are arguably the most popular method currently used in preference and willingness to pay (WTP) elicitation studies, both in hypothetical and non-hypothetical settings. Originally, the method was developed for marketing and transport studies, but in the last two decennia, it has spread to environmental and resource economics, agricultural and food economics, and health economics. The ever growing body of literature on CEs emphasizes the increasing role they are playing. In this elicitation method, respondents are generally asked to make choices between multiple alternatives, also called profiles, which are described by a number of attributes with different levels. Consequently, through nonlinear regression models, generally based on random utility theory (RUT), the utility each attribute (level) contributes to the good or service under study can be quantified and translated into (marginal) willingness to pay. To a large extent, the design of the CE drives the precision and the validity of the conclusions and it is therefore considered to be a key aspect of the planning of a CE. Designing a CE involves selecting the profiles to be used in the experiment The current state of the art is the Bayesian optimal design method. However, the design and analysis methods for CEs are constantly improving, which goes along with the improvement of the discrete choice models and the increasing number of applications in different fields. Research on empirical and methodological advances in CEs faces the following challenges. First, RUT assumes the respondent to act in a fully compensatory manner based on stable preferences. This has been found to be a demanding assumption. Hence, it is up to empirical research to determine what causes these assumptions to be violated and how sensitive the obtained estimates are to them. Second, the debate concerning what drives (out) hypothetical bias, being the difference between what people say they are willing to pay in a hypothetical survey question and what they will actually pay in a non-hypothetical experiment when money is really on the line or in real-life situations, has not been closed. Third, most CEs are hitherto single-site and/or single-case studies. By consequence, spatial and socio-cultural effects are often ignored, which impedes generalization. Despite the vast amount of studies, findings often remain context-specific and cross-case comparisons are limited. Researchers from various applied economic disciplines continuously keep improving the way of designing, collecting and analyzing choice data in search of behavioral insights as well as efficient policy development. While some informal connections between several of the participating groups are already in place, a more formal setup would provide a driving force for more rapid knowledge dissemination and state of the art development of expertise. Therefore, it is important for Flanders to create a united and multi-disciplinary platform to keep up to date with the latest developments on CEs and to gather sufficient critical mass to be able to compete with other consortia for publications and project funding. Moreover, with this scientific research network, we aim to provide a platform for postdoctoral researchers to exchange knowledge and to more easily and intensively collaborate intra- and internationally.

Keywords:CHOICE BASED CONJOINT, CHOICE MODELING

Disciplines:Applied mathematics in specific fields, Statistics and numerical methods, Applied economics, Economic history, Macroeconomics and monetary economics, Microeconomics, Tourism

Project type:Collaboration project