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

doi:10.1016/j.tra.2018.09.013

Transportation Research Part A: Policy and Practice

Volume 118, December 2018, Pages 258-279

https://doi.org/10.1016/j.tra.2018.09.013 Get rights and content

Highlights

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Willingness to pay (WTP) for vehicle attributes from 52 US studies: 1995 to 2015.
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Across-study variance is large: sample std. dev. generally > sample mean.
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Factors like type of model or data explain minor fraction of the variability.
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Case studies of suggest choices made by analysts can strongly affect results.
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Meta-analysis of WTP for fuel cost reduction indicates large uncertainty.

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.

Section snippets

Introduction: problem formulation

Cars and light trucks are significant contributors to air pollution and to greenhouse gas (GHG) emissions in the U.S. and around the world. The incorporation of technologies to reduce emissions may not only increase vehicle costs but may also entail tradeoffs in other vehicle attributes important to consumers such as safety, comfort, or performance. Assessing the effects of these tradeoffs on consumer welfare requires estimates of both the degree of the tradeoffs, and consumer willingness to

Literature review

A rich literature on consumers’ vehicle choices has developed over the last 50 years from innovations in the theory and empirical estimation of consumer demand, going back to Lancaster’s (1966) conception of consumer goods deriving their value from their attributes, rather than the good itself. Early applications of Lancaster’s theory included efforts to predict transportation choice: Quandt and Baumol (1966) defined the value of a transportation mode by its speed, frequency of service,

Data collection

For this study, data collection began with a systematic literature search for peer-reviewed publications and grey literature from academic or research institutions that suggested relevance to the following set of search terms.

Search parameters include the following:

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Types of literature: peer reviewed publications, grey literature from academic/research institutions
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Search engines: Google Scholar, Econlit, Science Direct
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Sample journals: Energy Economics, Econometrica, American Economic Review,

Estimating MWTP: Data evaluation and abstraction

Our goal was to calculate MWTP estimates for vehicle attributes that represent the central tendency of the sample upon which they were estimated. To do this, we use mean parameter estimates except when models assume random parameters with lognormal distributions, in which case we use medians. When attributes or price are interacted with consumer attributes we use central tendency measures of the attributes provided in the paper in question or obtained from other sources when the necessary data

Data preparation and descriptive statistics for MWTP

For each paper, MWTP calculations were made in a separate spreadsheet. The spreadsheets contain the coefficient values, units in which attributes were measured, values of potential explanatory variables, assumptions used in the calculations, and all formulas used to calculate MWTP. All dollar measures were converted to 2015 dollars using the Consumer Price Index. Sources are provided for any external data we introduced to calculate MWTP. In addition to checking our own work, we e-mailed all

Meta-analysis of MWTP estimates

The meta-analysis of MWTP estimates has two objectives: (1) to discover factors that may help explain the large variances of MWTP estimates and (2) to attempt to generate consensus central tendency estimates for MWTP for two of the vehicle attributes, reduced fuel costs and acceleration times. These attributes were selected based on policy relevance.

We begin by using Stata’s™ ANOVA procedure to identify factors that might explain differences in MWTP estimates across studies. ANOVA and

Concluding observations

Estimating consumers’ willingness to pay for vehicle attributes is a very difficult problem. Automobiles are multidimensional with dozens of relevant attributes. Researchers face challenges of variables measured with error, omitted variables, and correlations among included and omitted variables. Measuring relevant vehicle attributes is often difficult, and often only possible with a loss of precision. Consumers’ preferences vary, but accurately measuring that heterogeneity remains a challenge.

Funding

This research was funded by the U.S. Environmental Protection Agency under contracts EP-C-11-045 and EP-C-16-021. This paper’s content is the responsibility of the authors and does not necessarily reflect the views of the Environmental Protection Agency.

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View full text

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

Highlights

Abstract

Section snippets

Introduction: problem formulation

Literature review

Data collection

Estimating MWTP: Data evaluation and abstraction

Data preparation and descriptive statistics for MWTP

Meta-analysis of MWTP estimates

Concluding observations

Funding

Transp. Res. Part B

Res. Transport. Econ.

Resource Energy Econ.

Transport. Res. A

Transp. Res.

Resource Energy Econ.

J. Statist. Plann. Inference

Transport. Res. Part A – Policy Pract.

J. Public Econ.

Energy Policy

Automobile prices in market equilibrium

Econometrica

Meta-analysis of subgroups

Prevent. Sci.

Consumer valuation of changes in driving range: a meta-analysis

Transp. Res. Part A

Automobile fuel economy: What is it worth?

Contemp. Econ. Policy

Two-stage hedonic price model for light-duty vehicles consumer valuations of automotive fuel economy in Maine

Transp. Res. Record

Product differentiation and oligopoly in international markets: The case of the U.S. Automobile Industry

Econometrica

TAFV Alternative Fuels and Vehicles Choice Model Documentation