Consumer willingness to pay for vehicle attributes: What do we Know?

Abstract

As standards for vehicle greenhouse gas emissions and fuel economy have become more stringent, concerns have arisen that the incorporation of fuel-saving technologies may entail tradeoffs with other vehicle attributes important to consumers such as acceleration performance. Assessing the effects of these tradeoffs on consumer welfare requires estimates of both the degree of the tradeoffs, and consumer willingness to pay (WTP) for the foregone benefits. This paper has two objectives. The first is to review recent literature that presents, or can be used to calculate, marginal WTP (MWTP) for vehicle attributes to describe the attributes that have been studied and the estimated MWTP values. We found 52 U.S.-focused papers with sufficient data to calculate WTP values for 142 different vehicle attributes, which we organized into 15 general groups of comfort, fuel availability, fuel costs, fuel type, incentives, model availability, non-fuel operating costs, performance, pollution, prestige, range, reliability, safety, size, and vehicle type. Measures of dispersion around central MWTP values typically show large variation in MWTP values for attributes. We explore factors that may contribute to this large variation via analysis of variance (ANOVA) and find that, although most have statistically significant effects, they account for only about one third of the observed variation. Case studies of papers that provide estimates from a variety of model formulations and estimation methods suggest that decisions made by researchers can strongly influence MWTP estimates. The paper's second objective is to seek consensus estimates for WTP for fuel cost reduction and increased acceleration performance. Meta-analysis of MWTP for reduced fuel cost indicates that estimates based on revealed vs. stated preference data differ, as do estimates from models that account for endogeneity and those that do not. We find greater consistency in estimates of MWTP for acceleration despite substantial uncertainty about the overall mean. We conclude with recommendations for improving the understanding of consumers' MWTP for vehicle attributes.

A Novel Approach in Evaluating the Impact of Vehicle Age on Road Safety

“…roadside hazard, horizontal curve, traffic volume, speed limit, etc.) variables and its impact on AVC, assuming that the age of the vehicle strongly affects the level of in-built safety technology of the vehicle and the adapted improvements in its safety designs [12].…”

Simpler is better: Predicting consumer vehicle purchases in the short run

“…The parsimonious model design avoids adding additional uncertainty from projecting changes in other vehicle characteristics and consumers' marginal willingness to pay for changes in vehicle characteristics. Estimates of consumers' marginal willingness to pay for vehicle attributes vary greatly and may be sensitive to model formulation and estimation (Greene et al, 2018).…”

“…Great debate exists about the role of fuel savings in consumer vehicle purchases(Greene, 2010;Greene et al, 2018). Some researchers find that vehicle buyers consider the expected full lifetime of fuel savings in deciding on how much fuel economy to buy (e.g.,Busse et al, 2013), while others argue that buyers put much less emphasis on fuel economy(Gallagher and Muehlegger, 2011).18 Formally, the probability is P( j ∩ k) = P( j | k)P(k) .19 As noted above, as a robustness check, we aggregated the data to the manufacturer-class level.…”

Proposing effective strategies for meeting an environmental regulation with attainable technology improvement targets

“…Based on these investigations, the Japanese government has decided to adopt the CAFE standard by 2020 to encourage automobile manufacturers to improve fuel efficiency through the flexible competition of vehicle sales (Kaneko, 2019;Ministry of Economy, Trade, and Industry, 2014). However, vehicle performance should be evaluated not only according to fuel efficiency, but also by economic efficiency, driving performance, and safety, since consumers choose vehicles by comprehensively considering all these characteristics (Greene et al, 2018;Hess et al, 2012). Data Envelopment Analysis (DEA) is a well-known method for evaluating comprehensive performance.…”

“…However, vehicle performance should be evaluated not only according to fuel efficiency, but also by economic efficiency, driving performance, and safety, as consumers choose vehicles by comprehensively considering all these characteristics (Greene et al, 2018; Hess et al, 2012).…”