Revealing Differences in Willingness to Pay due to the Dimensionality of Stated Choice Designs: An Initial Assessment
Stated choice (SC) methods are now a widely accepted data paradigm in the study of the choice responses of agents. Their popularity has spawned an industry of applications in fields as diverse as transportation, environmental science, health economics and policy, marketing, political science and econometrics. With rare exception, empirical studies have used a single SC design, in which the numbers of attributes, alternatives, choice sets, attribute levels and ranges have been fixed across the entire design. As a consequence the opportunity to investigate the influence of design dimensionality on behavioural response has been denied. Accumulated wisdom has promoted a large number of positions on what design features are specifically challenging for respondents; and although a number of studies have assessed the influence of subsets of design dimensions, there exists no single study (that we are aware of) that has systematically varied all of the main dimensions of SC experiments. This paper reports some initial findings on what influences, in aggregate, specific design configurations have on the mean willingness to pay for specific attributes using a Design of Designs (DoD) SC experiment in which the ‘attributes’ of the design are the design dimensions themselves. The design dimensions that are varied are the number of choice sets presented, the number of alternatives in each choice set, the number of attributes per alternative, the number of levels of each attribute and the range of attribute levels. The empirical evidence, using a sample of respondents in Sydney choosing amongst trip attribute bundles for their car commuting trip, suggests that, within the boundaries of design dimensionality investigated, mean estimates of WTP for travel time savings in the aggregate cover a range that is appropriate for reporting a global mean and a set of meaningful values for sensitivity testing in project appraisal and demand prediction. When these aggregated mean estimates are conditioned on all design dimensions we do not find any systematic differences due to specific design dimensions; however when each design dimension is assessed without controlling for the other dimensions we find evidence to support differences in aggregate mean WTP attributable to the number of attributes per alternative and the number of alternatives in a choice set.