Combining stated and revealed choice research to simulatethe neighbor effect: The case of hybrid-electric vehicles

摘要

According to intuition and theories of diffusion, consumerpreferences develop along with technological change. However,most economic models designed for policy simulation unrealisti-cally assume static preferences. To improve the behavioral realismof an energy-economy policy model, this study investigates the“neighbor effect,” where a new technology becomes more desirableas its adoption becomes more widespread in the market. Wemeasure this effect as a change in aggregated willingness to payunder different levels of technology penetration. Focusing onhybrid-electric vehicles (HEVs), an online survey experimentcollected stated preference (SP) data from 535 Canadian and 408Californian vehicle owners under different hypothetical marketconditions. Revealed preference (RP) data was collected from the samerespondents by eliciting the year, make and model of recentvehicle purchases from regions with different degrees of HEVpopularity: Canada with 0.17% new market share, and Californiawith 3.0% new market share. We compare choice models estimatedfrom RP data only with three joint SP-RP estimation techniques,each assigning a different weight to the influence of SP and RP datain coefficient estimates. Statistically, models allowing more RPinfluence outperform SP influenced models. However, resultssuggest that because the RP data in this study is afflicted bymulticollinearity, techniques that allow more SP influence in thebeta estimates while maintaining RP data for calibrating vehicle class constraints produce more realistic estimates of willingness topay. Furthermore, SP influenced coefficient estimates also translateto more realistic behavioral parameters for CIMS, allowing moresensitivity to policy simulations.