Evaluating the Consumer Response to Fuel Economy: A 

Review of the Literature 

Gloria Helfand and Ann Wolverton 

Working Paper Series

Working Paper # 09-04 

August, 2009 

U.S. Environmental Protection Agency 

National Center for Environmental Economics 

1200 Pennsylvania Avenue, NW (MC 1809) 

Washington, DC 20460 

http://www.epa.gov/economics
Evaluating the Consumer Response to Fuel Economy: A Review of 

the Literature 

Gloria Helfand and Ann Wolverton 

NCEE Working Paper Series 

Working Paper # 09-04 

August, 2009

DISCLAIMER

The views expressed in this paper are those of the author(s) and do not necessarily represent 

those of the U.S. Environmental Protection Agency. In addition, although the research described 

in this paper may have been funded entirely or in part by the U.S. Environmental Protection 

Agency, it has not been subjected to the Agency's required peer and policy review. No official 

Agency endorsement should be inferred.
1

Evaluating the Consumer Response to Fuel Economy: A Review of the Literature

By Gloria Helfand and Ann Wolverton1

U.S Environmental Protection Agency

Abstract

How consumers evaluate trade-offs between the cost of buying additional fuel economy 

and the expected fuel savings that result is an important underlying determinant of the 

overall cost of national fuel economy standards. Models of vehicle choice are a means to 

predict the change in consumers’ vehicle purchase patterns, as well as the effects of these 

changes on compliance costs and consumer surplus. This paper surveys the literature on 

vehicle choice models and finds a wide range in methods and results. A large puzzle 

raised is whether automakers build into their vehicles as much fuel economy as 

consumers are willing to purchase. This paper examines possible reasons why there may 

be a gap between the amount consumers are willing to pay for fuel economy and the 

amount that automakers provide, though there is insufficient evidence on the relative 

roles of these various hypotheses. Further research on the role of fuel economy in 

consumer vehicle purchases is needed to assist in understanding the welfare effects of 

fuel economy regulation.

Key Words: Consumer behavior, vehicle purchase decisions, fuel economy, energy 

paradox, vehicle choice

JEL Codes: D11, D12, D22, R41

 1 Authors can be contacted via email at helfand.gloria@epa.gov and wolverton.ann@epa.gov. The views 

expressed in this paper are those of the authors and do not necessarily represent those of the U.S.

Environmental Protection Agency. This paper has not been subjected to EPA’s formal review process and 

therefore does not represent official policy or views. We are very grateful for the suggestions and 

contributions of Matt Massey, Chris Moore, two anonymous reviewers, and participants at our presentation 

at the World Congress of Environmental and Resource Economics. 
2

1. Introduction

On April 1, 2010, the U.S. Environmental Protection Agency (EPA) and the 

Department of Transportation (2010a) issued regulations to reduce greenhouse gas 

(GHG) emissions from vehicles and increase the stringency of Corporate Average Fuel 

Economy (CAFE) standards.

2

In analyses of the potential economic impacts of fuel economy and GHG 

standards for cars and trucks, U.S. Federal agencies have commonly assumed that the 

market shares of the vehicle fleet stay constant. For rules that lead to small changes in 

vehicle attributes and prices, this may not be a bad approximation, since we would not 

expect consumers to make large changes in vehicle purchase patterns in response. 

Proposals to increase fuel economy substantially, on the other hand, may cause producers 

to change both vehicle attributes and the price of vehicles enough that consumers 

noticeably alter their decisions about what vehicles to purchase.

A key analytic question that was discussed in the 

evaluation of the standards was the role that fuel economy plays in consumers’ vehicle 

purchases. Do consumers properly account for the fuel savings from more fuel-efficient 

vehicles when making purchase decisions? Will the requirement of additional fuel 

economy increase or reduce consumer and producer welfare? This topic is also likely to 

be highly relevant in other countries as they contemplate setting or tightening their own 

fuel efficiency and/or GHG tailpipe standards. For instance, the International Council on 

Clean Transportation (ICCT) finds that a number of countries are contemplating

increasingly stringent mandatory GHG standards for vehicles (ICCT 2010). 

 2 Because the primary source of GHG emissions from vehicles is burning fuel, reducing fuel consumption 

will reduce emissions. CAFE standards require the sales-weighted fuel economy of a manufacturer’s fleet 

to achieve minimum levels. 
3

In addition, whether consumers accurately evaluate trade-offs between the costs

of consuming more fuel economy and the expected fuel savings is a matter of some 

debate in the literature. Government intervention in markets is commonly justified by the 

existence of one or more market failures, such as environmental externalities associated 

with a private market transaction or behavior. For instance, vehicle tailpipe emissions 

contribute to climate change, an impact not priced into consumer purchase or driving 

decisions. If significant numbers of consumers and/or producers also routinely make 

mistakes when factoring fuel economy into vehicle purchase decisions, then regulation 

may save consumers money in addition to reducing externalities.

3

Consumers should be interested in the fuel economy of the vehicles they purchase 

apart from what is induced by government regulation: An increase in fuel economy 

reduces the private cost of driving. Empirical studies of the effects of higher fuel prices 

indicate that higher fuel costs do seem to trigger efforts on the part of consumers to 

reduce their gasoline consumption through changes in driving behavior and vehicle 

purchase decisions. However, simple present value calculations comparing upfront costs 

to future fuel savings indicate that there appear to be many low cost opportunities to 

reduce fuel consumption that are not undertaken, even when they could pay off over 

relatively short time periods. This observation is commonly termed the “Energy 

Paradox.” It is unclear, however, whether such a paradox by itself justifies additional 

fuel economy requirements. Some vehicle purchasers may derive pure financial gains 

from the additional fuel savings if, for instance, they made mistakes in calculating fuel 

savings or relied on rule of thumb calculations at the time of purchase. Other consumers 

 3 The effect of fuel economy regulations on consumer and producer surplus would need to be combined 

with these external effects to estimate the total benefits and costs of fuel economy regulation. 
4

may be made worse off in their vehicle purchase decision if, for instance, they incur 

additional costs for an attribute that they view as uncertain or risky or that they were 

unwilling to buy.

This paper reviews the large but inconclusive literature on the role of fuel 

economy in consumers’ vehicle purchase decisions. A primary objective of this review is 

to highlight key gaps in the existing research that make accurate modeling and 

quantification of the welfare effects associated with raising CAFE standards difficult. 

Section 2 reviews the state of the art in vehicle choice modeling, with a focus on the role 

of fuel economy in these models. Section 3 examines the various explanations posited in 

the literature for why consumers appear to undervalue fuel economy. Section 4 discusses 

the few studies that focus on the producer side of the equation: why, even if consumers 

actually would buy vehicles with greater fuel economy, automakers may not supply 

vehicles with the mix of attributes consumers want. Section 5 concludes.

2. State of the Art in Vehicle Choice Modeling

There are a variety of tools available to examine consumers’ valuation of different 

vehicle attributes. Hedonic models, for instance, regress vehicle model attributes, such as 

fuel economy and horsepower, on the market price of the vehicle to estimate consumers’ 

implicit willingness-to-pay for each individual feature (e.g., Court 1939, Arguea et al. 

1994, Espey and Nair 2005).

4

 4 Freeman (2003) is a classic reference on nonmarket valuation, including hedonic models. 

 Because a simple hedonic regression is a reduced-form 

equation where market price and quantity are jointly determined by supply and demand, 

separately identifying the demand function requires additional modeling. Papers that use 

a two-stage approach can examine how changes in external conditions affect demand for 
5

a particular attribute. For instance, Ohta and Griliches (1986) examine how changes in 

gasoline prices affect the shadow price for particular vehicle characteristics, including 

fuel economy, power, and weight. Fan and Rubin (2010) estimate consumers’ 

willingness-to-pay for fuel economy while accounting for demographic differences such 

as age and education. Agarwal and Ratchford (1980) estimate a hedonic price function as 

the first step to predicting how consumers would rank order different makes and models. 

When evaluating the potential impacts of fuel efficiency or GHG standards, it is 

important to understand how changes in stringency could affect the number and types of 

vehicles purchased based on their new (post-policy) mix of attributes. Investigating this 

empirical question requires the use of models that examine consumers’ choices of 

vehicles. For instance, many studies econometrically estimate vehicle choice as a 

function of prices, consumer characteristics (such as income, family size, and age), and 

vehicle attributes (such as a vehicle’s power and fuel economy). Once estimated, these 

models are often used to examine how consumers’ vehicle purchase decisions are 

affected by marginal changes in vehicle or personal characteristics. While the focus is on 

the consumer in these papers, many also model production decisions in recognition that it 

is the interaction between producers and consumers that leads to observed market 

outcomes. Other papers evaluate large-scale policy changes by simulating how 

consumers and producers in the vehicle market would respond to policies such as an 

emissions tax or substantially tighter fuel economy standards. These models often do not

uniquely estimate their own parameters, instead borrowing from the vehicle choice

literature. While we discuss both types of models in this paper, we focus mainly on issues 

related to the econometric estimation of vehicle choice models.
6

Reflecting the complexity of consumers’ decisions, models of vehicle choice vary 

widely. This section begins with a discussion of the range of research questions explored 

by the literature to set the stage for the issues that arise in the models. Next, we discuss 

the modeling frameworks that have been developed to examine vehicle choice in the 

context of these questions. In particular, we review the modeling approaches, data 

sources, and individual buyer and vehicle characteristics included in the models. Vehicle 

choice is a rich area of research both because of its direct policy relevance – for instance, 

the importance of the auto industry in the U.S. economy, the increasing attention to the 

contributions of vehicles to greenhouse gas emissions, and large fluctuations in fuel 

prices over time - as well as a multitude of technical challenges. The reader also will note 

the large number of working papers referenced throughout the document. Reliance on 

only published work for this discussion would neglect a large portion of the literature that 

offers innovative new estimation techniques and intriguing results. With or without the 

inclusion of these new papers, a review of the literature suggests that the models vary 

greatly on a number of dimensions. In particular, and of special interest for policy 

analysis, they continue to produce widely varying estimates of the role of fuel economy 

in consumers’ purchase decisions.

2a. Research Questions

The auto market has attracted a great deal of research interest because of its size, 

its market structure, the availability of data, and its role in a number of significant policy 

areas, including international trade and environmental quality. Vehicle choice models 

have been developed to analyze many of these research and policy questions. These 

models have also served to advance the state of economic modeling: the work of Berry et 
7

al. (1995), for instance, is often cited outside the motor vehicle context for its 

incorporation of multiple new modeling issues into its framework.5

 In the public policy 

arena, topics have included the effects of voluntary export restraints on Japanese vehicles 

compared to tariffs and quotas (e.g., Goldberg 1995), the market acceptability of 

alternative-fuel vehicles (e.g., Brownstone et al. 1996; Brownstone and Train 1999; 

Brownstone et al. 2000; Greene 2001; Greene et al. 2004), the effects of introducing and 

removing vehicles from markets (e.g., Petrin 2002; Berry et al. 2004), causes of the 

decline in market shares of U.S. automakers (e.g., Train and Winston 2007), and the 

effects of gasoline taxes (e.g., Austin and Dinan 2005; Bento et al. 2005, Feng et al. 

2005), “feebates”6

The research question of interest often affects the structure of the model used to 

investigate the choices consumers make in the vehicle market. For instance, some studies 

consider only the market shares of new vehicles (e.g., Train and Winston 2007), while 

others include the choice between new vehicles and a generic outside good (e.g., Berry et 

al. 1995; Klier and Linn 2010a; Whitefoot et al. 2011), and some explicitly consider the 

relationship between the new and used vehicle markets (e.g., Bento et al. 2005, 2009; 

Busse et al. 2010; Allcott and Wozny 2010). Focusing on market share is sufficient, for 

instance, to examine the decline in market shares of domestic U.S. auto producers, but it 

would not be appropriate for estimating the effect of a gasoline tax on total vehicle sales. 

(e.g., Greene et al. 2005b; Feng et al. 2005; Greene 2009), and fuel 

economy standards (e.g., Goldberg 1998; Austin and Dinan 2005; Klier and Linn 2010a,

2010b; Jacobson 2010; Whitefoot et al. 2011). 

 5 For instance, Bresnahan et al. (1997) apply the Berry et al. approach to analyze personal computer 

purchases, and Nevo (2001) uses it to evaluate ready-to-eat cereals purchases. 6 A feebate system subsidizes fuel-efficient cars with revenue collected by taxing fuel-inefficient vehicles.
8

Including a generic outside good allows estimation of total market size, but it cannot look 

at the effect of changed new vehicle sales on the used vehicle market.

Some studies focus on estimating the role of a specific vehicle attribute in 

consumer purchase decisions (e.g., Alcott and Wozny 2010, Busse et al. 2010, Sallee et 

al. 2010, Kilian and Sims 2006, Sawhill 2008, Langer and Miller 2011). For instance, 

several of these papers examine how vehicle markets adjust in response to changes in 

gasoline prices. These studies often rely on panel data approaches and highly

disaggregated data on individual consumer vehicle purchases but do not analyze them in 

the context of the alternatives not chosen.

Studies that seek to evaluate a consumer’s choice of a particular vehicle over 

other available alternatives often use discrete choice models (e.g., Goldberg 1995, 1998;

Berry et al. 1995, 2004; Train and Winston 2007). These models allow the researcher to 

evaluate the relative roles of price, vehicle attributes, and - at times – consumer 

characteristics in the vehicle purchase decision. While some discrete choice studies 

include only consumer behavior (e.g., Brownstone et al. 1996, 2000; Mohammedian and 

Miller 2003; Train and Winston 2007), many jointly estimate demand and supply (Berry 

et al. 1995, 2004; Bento et al. 2005, 2009; Whitefoot et al. 2011), and others jointly 

model vehicle purchase decisions and vehicle miles traveled (e.g., Goldberg 1998; Bhat 

and Sen 2006; Bento et al. 2005, 2009; Feng et al. 2005; Spissu et al. 2009). Empirical 

strategies, estimation challenges, and the relative importance of fuel economy in discrete 

choice modeling are the main focus of the remainder of Section 2.

As previously mentioned, studies that examine the overall welfare implications of 

a new approach or policy (e.g. such as a gasoline tax) often use a market equilibrium 
9

model to simulate the effects of the policy on vehicle demand and supply (e.g. Austin and 

Dinan 2005; Goldberg 1998; Bento et al. 2005, 2009). These models may involve 

original estimation of the demand side, the supply side, or both, or they may be wholly or 

partially constructed using parameters borrowed from the literature regarding the 

relationship between changes in price and shifts in the demand and supply for different 

vehicle attributes. 

2b. Empirical Estimation Methods 

In models that empirically investigate vehicle purchase behavior, choices are 

typically unordered and discrete (e.g., whether to purchase a vehicle; which type of 

vehicle to purchase). Underlying the theory of discrete choice modeling is the 

assumption that a consumer’s choice will result in higher utility relative to other available 

options. Using this theoretic construct, the analyst can build an empirical model that

estimates what factors influence the probability that a consumer will purchase a particular 

vehicle. A minor variant estimates the market share for each vehicle type based on 

aggregate market data instead of micro-level data. Because market share is based on the 

same underlying utility theory, the estimated equations can also be interpreted as 

predicting the probability that consumers will purchase a specific vehicle. 

The primary methods used in the literature to model discrete vehicle choices,

nested logit and mixed logit, were developed to avoid a pitfall of the simple multinomial

or conditional logit. A simple multinomial logit has the embedded assumption of 

“independence of irrelevant alternatives” (IIA): a change in one choice does not affect 

the relative preferences for other choices. For instance, under IIA, if the price of a large 

truck goes up, the consumer’s preference for switching to another alternative is governed 
10

by market shares, not by similarity across vehicles. Thus, the model might predict that 

the consumer switches to a mid-size car that sells well instead of to another large truck. 

The IIA assumption is generally not considered plausible in the vehicle market. 

The nested logit structures consumer choices into “nests” or layers. For instance, 

the first layer may be the choice of whether to buy a new vehicle or some other good, 

including a used vehicle; given that the person chooses a new vehicle, the second layer 

may be whether to buy a car or a truck; given that the person chooses a car, the third layer 

may be the choice among an economy, midsize, or luxury car. Within a nest IIA holds, 

but the assumption does not apply across nests. In other words, the nested logit accounts 

for the fact that some vehicles are closer substitutes than others. If a consumer has sorted 

into the midsize nest and the price of her chosen vehicles goes up, she is more likely to 

choose another midsize vehicle (which is in the same nest) than an economy car (which is 

in a different nest), though her relative preferences among midsize cars remain 

unchanged. There is no definitive way to test whether one nesting structure is superior to 

another, however, which means that the question of how best to model consumer 

decisions may remain unsettled even when alternative nesting structures result in 

significant differences in results (Greene 2008).

7

 7 The sequence of choices made by the consumer at the time of purchase often dictates an intuitive nesting 

structure (e.g., first a consumer decides whether to buy a vehicle, then what type of vehicle to buy). Some 

advocate the use of likelihood dominance criterion to choose between models with alternative nesting 

structures. If the difference between log-likelihood functions is large, then the test allows one to determine 

the superior nesting structure. If the difference is small, then the test cannot be used to distinguish between 

two possible nesting structures. See Haab and McConnell (2002) for a more detailed discussion.

 It is also important to keep in mind that 

the complexity of the model increases rapidly with the number of nests, which can make 

it challenging to achieve model convergence to a unique solution. Examples of nested 

logit models used to examine vehicle choice decisions include Goldberg (1995, 1998), 
11

Mohammadian and Miller (2003), Greene et al. (2005b), Eftec (2008), Gramlich (2010), 

Allcott and Wozny (2010), and Klier and Linn (2010a).

8

A mixed logit model (also known as a random coefficients logit) relaxes the IIA 

assumption by allowing for a more general substitution pattern among alternatives. The 

relative importance and even the direction of a vehicle attribute's influence on purchase 

decisions is allowed to vary over consumers. As with the nested logit model, the mixed 

logit captures average preferences for particular vehicle attributes. However, the mixed 

logit also estimates a distribution of preferences over observed vehicle attributes (which 

can also be interacted with consumer characteristics). For example, the mixed logit 

allows the influence of income on the probability of purchasing a luxury car to vary, 

characterizing this heterogeneity by estimating a distribution for that coefficient. The 

mixed logit also allows for random variation in preferences for particular vehicle 

characteristics that influence the purchase decision but are unobserved by the researcher. 

For example, some consumers may have a range of preferences for luxury apart from any 

associations with observable characteristics such as income.

 

9

 8 Brenkers and Verboven (2006) examine the effects of liberalizing the European market on 

competitiveness in the vehicle market using a nested logit and panel data on sales and vehicle 

characteristics from five European countries.

It also is worth noting that 

vehicle price is often correlated with the unobserved vehicle or consumer attributes (since 

they influence the purchase decision, they also likely influence price) in these type of 

models, presenting identification problems. Examples of mixed logit models of consumer 

vehicle choice decisions include Berry et al. (1995, 2004), Bento et al. (2005, 2009), 

9 Because the mixed logit does not produce a closed form solution, the probability of purchasing a 

particular vehicle cannot be directly estimated by maximizing the likelihood function. Instead it must be 

simulated. Knittel and Metaxoglou (2008) find that the results of mixed logit models appear to be sensitive 

to the solution algorithm and start values: in other words, there may not be a well defined set of results 

from the model. Their study uses data from Berry et al. (1995) and Nevo (2000).
12

Train and Winston (2007), Cambridge Econometrics (2008), Sawhill (2008), and 

Whitefoot et al. (2011).10

While discrete choice models appears to be the primary method for modeling 

vehicle choice, some (e.g., Kleit 2004, Austin and Dinan 2005, McManus and Kleinbaum 

2009) have used a matrix of demand elasticities to simulate the effects of changes in 

vehicle cost on behavior. At times, these are borrowed from other studies that have 

estimated them using discrete choice models (e.g., Berry et al. (1995) report a number of

elasticities). In addition, Bordley (1993) proposes a method for calculating cross-price 

elasticities using own-price elasticities combined with information on the vehicles people 

would have bought if their first-choice vehicle was not available; the second-choice 

information provides specific evidence on the substitutions that people would have made. 

General Motors (GM) has collected information on second choices in surveys to vehicle 

buyers for a number of years. Kleit (2004) uses GM data and an approach similar to 

Bordley to estimate elasticities that he then uses in model simulations; Austin and Dinan 

(2005) base their consumer model on Kleit’s elasticities.

What is assumed about how manufacturers respond to changes in fuel costs also 

affects consumers’ choices of what vehicles to buy. Langer and Miller (2011) find that 

discrete choice models that do not consider the supply side result in inconsistent 

predictions of consumer demand response and that this problem is particularly acute for 

nested logit approaches. Berry et al. (1995) and Goldberg (1995, 1998) were among the 

first to incorporate oligopolistic behavior on the part of automakers into econometrically-

 10 It is worth noting that interacting choice-invariant characteristics with product attributes is a common 

way to avoid dropping relevant information about the consumer from a logit regression. It is also worth 

noting that the mixed logit reduces to a simple multinomial logit when particular assumptions are imposed 

on consumer preferences. Train (2009) is one source for further explanation of these methods. 
13

estimated models, though this is now relatively common (e.g., Berry et al. 2004; Bento et 

al. 2005; Allcott and Muehlegger 2008; Klier and Linn 2010a; Jacobsen 2010; Gramlich 

2010; Langer and Miller 2011; Whitefoot et al. 2011). These papers often pair a discrete 

choice model on the demand side with a supply-side model that assumes auto makers

have market power but compete on the basis of price (e.g. Bertrand competition).11 12

2c. Methodological Challenges

 In 

addition, because many improvements in the efficiency of engine technology have led, 

not to increases in fuel economy, but to increases in power and vehicle weight, several 

papers (e.g., Klier and Linn 2010a; Allcott and Muehlegger 2008; Whitefoot et al. 2011) 

have built these tradeoffs into the supply side and found that results are sensitive to the 

way that tradeoffs are incorporated. 

Several methodological issues arise in the context of vehicle choice modeling. 

One problem is the possible endogeneity of some explanatory variables, especially 

vehicle price. If consumers’ choices of vehicles affect the price charged for the vehicle, 

or if the price of a vehicle is correlated with characteristics that are not observable to the 

 11 In spite of the popularity of this assumption, it is possible that firms successfully differentiate their 

product to set a price that is greater than marginal cost or that steep barriers to entry allow firms to maintain 

significant market power. For example, Langer and Miller (2011) find that manufacturers respond to 

gasoline price shocks by changing the rebates and incentives offered to consumers to favor less fuel 

efficient vehicles. They point out that this could effectively dampen the price signal that gets passed onto 

consumers: fuel costs have risen but vehicle costs for the most fuel inefficient vehicles have fallen. Busse et 

al. (2010) also find evidence that the new vehicle market adjusts to changes in fuel prices on the basis of 

market share rather than price (e.g., higher gasoline prices result in a shift by consumers toward buying 

more fuel efficient vehicles), while the used vehicle market competes on the basis of price with little 

change in market share. 

12 Fischer (2004) examines the policy implications of assuming imperfect competition by suppliers. She 

constructs a case where producers offer consumers with a preference for low-end appliances too little 

energy efficiency, so that they can charge higher prices to consumers that prefer higher-end (and more 

energy efficient) appliances. She shows that minimum energy efficiency standards could be welfare 

improving under this theoretical construct. Energy efficiency standards limit the ability of the firm to 

extract rents from high-end users (thus lowering the price these users pay), making these consumers better 

off. And, low-end consumers have choices that more closely align with their preferences for greater energy 

efficiency, though they also have to pay more, making them no worse off than before.
14

researcher, then the analysis may produce biased estimates of the role of price in 

consumers’ purchase decisions. As a result, some studies (e.g., Berry et al. 1995, 

Goldberg 1995, Klier and Linn 2010a, Allcott and Muehlegger 2008) use instrumental 

variable methods to correct for endogeneity.13

More recent literature addresses endogeneity in vehicle choice and technology 

decisions in novel ways. Bento et al. (2005) and Jacobsen (2010) incorporate not only 

the decision of what new vehicle to purchase, but also the decision on how long to hold 

and whether to buy a used vehicle, and how many miles to drive a vehicle each year. 

Market prices for the vehicles are also treated as endogenous variables in these models. 

In Klier and Linn (2010a) the fact that a manufacturer often uses the same engine 

platform across different vehicle models is used to develop an instrument for fuel 

economy, since vehicles in different classes that use the same engine tend to exhibit 

similar power and fuel economy characteristics. Likewise, Allcott and Muehlegger 

The results in these papers indicate that 

ignoring endogeneity leads to different results than in the full models. In Berry et al.

(1995), for instance, the effect of miles per dollar (miles per gallon divided by fuel price) 

on vehicle purchase is smaller once the model is corrected for endogeneity. The full 

model (including random coefficients) also finds that variation around miles per dollar is 

an important factor: that is, some people find high miles per dollar a desirable factor in 

their vehicle purchase decision, while others find it a disincentive for purchase (i.e., the 

standard deviation for miles per dollar is wide and encompasses both positive and 

negative numbers). Thus, model specification can affect the results and their 

interpretations.

 13 For instance, Berry et al. (1995) use instruments based on own-vehicle characteristics, the characteristics 

of other vehicles produced by the same manufacturer, and the characteristics of vehicles produced by other 

manufacturers.
15

(2008) combine macro- and micro-level data to resolve the problem of endogenous fuel 

economy, prices, power, and weight. In their model, vehicle manufacturers adjust 

vehicle characteristics in response to both consumer demand and other manufacturers’ 

vehicle plans.14

Omitted variable bias also has the potential to play a significant role in models of 

vehicle purchase behavior. Many vehicle characteristics are or have historically been 

strongly correlated with each other: vehicle size and fuel economy, or fuel economy and 

vehicle power, for instance, are negatively correlated. Because of these strong 

associations, including all measurable vehicle characteristics in the regression may result 

in inefficient results due to collinearity, but omitting some may bias coefficients. In 

consequence, the results from vehicle choice models may be difficult to interpret. For 

example, Gramlich (2010) includes variables for both dollars per mile and miles per 

gallon (mpg) in his regression. The coefficient on dollars per mile is negative, indicating 

that people are less likely to choose a vehicle that is expensive to drive per mile, all else 

equal; this result is expected. However, he also finds that the coefficient on mpg is 

negative: all else equal, people prefer low-mpg vehicles. He interprets this finding as an 

indication that miles per gallon measures some omitted vehicle characteristics: for 

instance, perhaps high-mpg vehicles are, on average, not as well made or not as luxurious 

Whitefoot et al. (2011) instruments for price, fuel economy, and 

acceleration by using vehicle attributes that are fixed in the medium run, such as vehicle 

dimensions, power train architecture, and drive type.

 14 Related papers on vehicle demand that do not use discrete choice models also grapple with endogeneity 

issues. For instance, Allcott and Wozny (2010), in their model of new and used vehicles, expect more fuelefficient new cars to be sold in years when gasoline prices are high; the larger number of these vehicles as 

well as their fuel economy affects their prices in the future as used vehicles. To separate the effect of the 

vehicle’s fuel economy from the numbers of the vehicles originally sold, they use the expected lifetime fuel 

costs of the vehicle when it was new as an instrument. Sallee et al. (2010) examine differences in the 

expected future cost of the vehicle by utilizing individual vehicle transaction prices, the predicted odometer 

reading based on age and type of vehicle, and the price of fuel at the time a vehicle is sold. 
16

as low-mpg vehicles. In practice, because it is impossible to identify all the factors that 

influence people’s vehicle choices, researchers try to capture the key variables, and hope 

that omitted variables do not bias other coefficients by a substantial amount. It also has 

become increasingly common for researchers to rely on high-frequency panel data of 

vehicle sales in order to include fixed effects in the regression (e.g. Busse et al. 2010). 

Because fuel economy is correlated with other vehicle attributes (such as power), some 

researchers (e.g., Berry et al. 1995, Allcott and Wozny 2010) use variation in gasoline 

prices to identify preferences for fuel economy, since sales and/or prices of higher milesper-gallon vehicles should increase when gasoline prices are higher.

Errors in variables may arise in models of vehicle choice if the attributes that are 

included in the regression are only approximations of the attributes truly of interest to 

consumers at the time of vehicle purchase. For instance, horsepower may only be a 

proxy for acceleration or towing capacity or some other feature that a consumer seeks. 

Because the error in the independent variable may be correlated with the error term for 

the regression, the coefficient may be biased on the inaccurately measured variable. 

Using instruments for the regressors, as in Berry et al. (1995), is one method to address 

this problem. 

A possible additional complication in modeling vehicle choices is that fuel 

economy is regulated: auto makers have been subject to corporate average fuel economy 

(CAFE) standards for several decades. The presence of these regulations has influenced 

the technology, production, and pricing strategy of auto makers; as a result vehicle fuel 

economy is not the same as it would be in an unregulated market. It is possible that 

models that do not account for this restriction may produce biased results. Some models 
17

(e.g., Goldberg 1998; Bento et al. 2005, 2009; Gramlich 2010; Klier and Linn 2010a; 

Whitefoot et al. 2011) explicitly account for fuel economy standards; others (e.g., Berry 

et al. 1995; Petrin 2002) do not. 

2d. Data Sources

Models of how consumers choose a vehicle for purchase commonly rely on 

detailed information about the consumer, vehicle purchased, and - in a discrete choice 

setting - other vehicles available to the consumer at the time of purchase that were not 

chosen. As already mentioned, the parameters in these models can be developed either 

from estimations based on original data sources (estimated models) or borrowed from 

other studies (calibrated simulation models). 

Estimated models use datasets on consumer purchase patterns, consumer 

characteristics, and vehicle characteristics to develop original sets of parameters. The 

datasets used in these studies sometimes come from surveys of individuals’ behaviors 

(e.g., Mohammadian and Miller 2003; Bento et al. 2005; Train and Winston 2007; 

Whitefoot et al. 2011). Micro-level data drawing on the behavior of individual

households can be very valuable because of the high level of detail about households’ 

traits that may be relevant to vehicle purchase decisions. Other studies estimate market 

shares instead of discrete purchase decisions using aggregated data (e.g., Berry et al. 

1995; Vance and Mehlin 2009). Aggregate data tend to be more easily accessible than 

micro-level data, which often rely on surveys that may not be publicly available. In 

addition, aggregate data are often available over longer time periods, thus allowing for 

the examination of trends or changes in tastes. It is also possible to combine micro-level 

data with aggregate data (e.g., Berry et al. 2004, Allcott and Muehlegger 2008). The 
18

quality of the results depends on the quality of the underlying data and the appropriate 

use of statistical methods. Finally, a recent set of papers rely on detailed vehicle 

registration or transaction-level data (e.g., Allcott and Wozny 2010; Sallee et al. 2010; 

Busse et al. 2010). These papers do not model vehicle purchase decisions explicitly, but 

rather use other approaches to examine the role of fuel economy in consumer decisions. 

Information on the purchaser is generally not available in these data sets, though some 

researchers (e.g., Sallee et al. 2010) attempt to account for differences in driving behavior 

(for example, through predicted odometer readings) within vehicle type. 

Calibrated models largely rely on existing studies for their parameters. 

Researchers may draw on results from estimated models or other research to select the 

parameters for the models. For instance, Austin and Dinan (2005) use elasticities in their 

analysis that were developed by Kleit (2004) based on survey data that included 

information on purchasers’ second choices as well as their actual choices.. The Fuel 

Economy Regulatory Analysis Model developed for the Energy Information 

Administration (Greene et al. 2005a) and the New Vehicle Market Model developed by 

NERA Economic Consulting (2008) are other examples of calibrated models. The 

quality of calibrated models depends on the parameter estimates they use. The advantage 

of calibrated models is that they can use best estimates from the range of existing studies, 

though they do not themselves provide new estimates of the parameters. 

2e. Results from Vehicle Choice Models on Fuel Economy

In vehicle choice models, the effect of fuel economy on vehicle purchase 

decisions can appear in various forms. Some models (e.g., Austin and Dinan 2005)

incorporate fuel economy through its effects on the cost of owning a vehicle. With 
19

assumptions on the number of miles traveled per year and the cost of fuel, it is possible to 

estimate the fuel savings (and perhaps other operating costs) associated with a more fuelefficient vehicle. In practice, those savings are incorporated as a reduction in the 

purchase price. This approach relies on the assumption that, when purchasing vehicles, 

consumers can estimate the fuel savings that they expect to receive from a more fuelefficient vehicle and consider the savings equivalent to a reduction in purchase price. 

Turrentine and Kurani (2007) question this assumption; they find that consumers do not 

make this calculation when they purchase a vehicle. The question then remains of how or 

whether consumers take fuel economy into account when they purchase their vehicles.

See Section 3 of this paper for further discussion.

Instead of making assumptions about how consumers incorporate fuel economy 

into their decisions, a number of econometric studies use data on consumer behavior to 

identify this effect. In some models, a vehicle’s miles per gallon are included to explain 

purchase decisions. Other models (e.g., Espey and Nair 2005; Train and Winston 2007) 

use fuel consumption per mile, the inverse of miles per gallon, as a measure: because 

consumers pay for gallons of fuel, this measure can assess fuel savings relatively directly

(Larrick and Soll 2008). Yet other models multiply fuel consumption per mile by the 

cost of fuel to get the price of driving a mile (e.g., Goldberg 1995), or they divide fuel 

economy by the price of gasoline to get miles per dollar (e.g., Berry et al. 1995; Petrin 

2002). It is worth noting that these last two measures assume that consumers respond the 

same way to an increase in fuel economy as they do to a decrease in the price of fuel 

when each has the same effect on cost per mile driven: in other words, it assumes that

consumers are indifferent to the source of fuel savings.
20

Greene and Liu (1988) review 10 papers using vehicle choice models and 

estimate for each one how much consumers would be willing to pay at time of purchase 

to reduce vehicle operating costs by $1 per year. This translates to roughly $9-$12 in 

present-value savings for a vehicle with a 15-year lifespan and discount rates of 7 percent

and 3 percent, respectively. They find that people are willing to pay between $0.74 and 

$25.97 for that $1 decrease in annual operating costs. This is clearly a very wide range: 

the lowest estimate suggests that people are not willing to pay $1 up front to reduce the 

costs of operating their vehicle by $1 in each subsequent year; the maximum suggests a 

willingness to pay 35 times as much, more than twice as much as the undiscounted fuel 

savings over the typical 15-year lifetime of a vehicle. While this study is quite old, it 

suggests that vehicle choice models produced widely varying estimates of the value of 

reduced vehicle operating costs.

A recent review by Greene (2010) suggests continued disagreement over the 

value of increased fuel economy to consumers, in spite of access to larger, more 

disaggregated data sets and sophisticated empirical techniques. Table 1 summarizes 

Greene’s categorization of the results of these studies with regard to how consumers

value fuel economy. It is interesting to note that the heterogeneity of results is not limited 

to studies of the U.S. vehicle market. For instance, of two studies of the British vehicle 

market included in his survey, one finds evidence of overvaluation (Cambridge 

Econometrics 2008) and the other of undervaluation of fuel economy on the part of 

consumers (Eftec 2008).

15

 15 Greene also includes Vance and Mehlin (2009) in his 2010 survey. This study examines the new vehicle 

market in Germany using a nested logit framework. Mohammadian and Miller (2003), which is not 

included in Greene’s survey, examines new vehicle purchases in Toronto, Canada. 
21

There is also disagreement regarding the relationship between fuel cost and other 

vehicle attributes. For instance, some papers (e.g., Goldberg 1995; Berry et al. 1995) find 

that the role of fuel cost (price per gallon divided by miles per gallon, or the cost of 

driving one mile) in purchase decisions is lower for larger vehicles, while Gramlich

(2010) finds that owners of large, fuel-inefficient vehicles have the greatest willingness to 

pay for improved fuel economy. Part of the difficulty may be, as these papers note, that 

fuel economy is correlated (either positively or negatively) with other vehicle attributes, 

such as size, power, or quality, not all of which may be included in the analyses; as a 

result, “fuel economy” may in fact represent several characteristics at the same time. 

Indeed, as noted above, Gramlich includes both fuel cost (dollars per mile) and miles per 

gallon in his analysis, with the argument that miles per gallon is positively correlated 

with unobserved attributes, while fuel cost picks up the consumer’s demand for improved 

fuel economy. 

Research suggests that consumers pay attention to the fuel economy of the 

vehicles that they purchase, both in the new and used vehicle markets, even if the two 

markets respond in different ways. Busse et al. (2010) focus on the relationship between 

changes in the price of gasoline and the fuel economy of vehicles sold, and find a very 

strong correlation. This result is consistent with consumers basing their expectation of 

future fuel price on the current fuel price (known as a random walk expectation). They 

find that the new and used vehicle markets adjust differently. With new vehicles, prices 

of vehicles change relatively little when fuel prices rise, but high fuel prices lead to much 

higher market shares for more fuel-efficient vehicles.16

 16 Berry et al. (1995), Allcott and Wozny (2010), and Sallee et al. (2010) attempt to model expectations 

about future fuel prices.

 Manufacturers have some ability 
22

to adjust the vehicles they produce, rather than the price, in the new vehicle market. In 

the used vehicle market, on the other hand, where there is less ability to change quantity, 

prices of high miles-per-gallon (mpg) vehicles rise, and those of low-mpg vehicles fall, 

when fuel prices rise. Langer and Miller (2011) find that increases in gasoline prices lead 

to higher new vehicle incentives (reduced suggested retail price for consumers), and that 

these incentives are higher for fuel-inefficient vehicles; indeed, a few especially fuelefficient vehicles actually see price increases. Li et al. (2009) find that high gasoline 

prices lead people to buy more fuel-efficient vehicles, keep more fuel-efficient used 

vehicles in operation, and encourage them to scrap older, fuel-inefficient vehicles. The 

Congressional Budget Office (2008) also finds that higher gasoline prices increase fuel 

economy, in part by decreasing the market share of light trucks (which are in general less 

fuel-efficient than cars). Preliminary results from West (2008) suggest that consumers 

pay more attention to past gasoline prices than they do to present prices when making 

vehicle purchase decisions, suggesting that accounting for medium or long term 

behavioral response may matter for the accurate modeling of vehicle purchase decisions. 

Not many studies in this literature directly report willingness to pay for fuel 

economy.

17

 17 The willingness to pay for fuel economy in a simple logit is the ratio of the marginal utility of fuel 

economy to the marginal utility of income. In a mixed logit, the inclusion of a random variable associated 

with consumer characteristics requires the use of Monte Carlo methods to calculate the willingness to pay. 

When the fuel economy variable is inverted, or interacted with gasoline price, the calculation develops 

additional sources of uncertainty.

 For some of the methods utilized – nested and mixed logit – these values are 

difficult to back out of the models without having direct access to the detailed results. A 

few studies, including some hedonic price studies, report their findings on this parameter. 

Espey and Nair (2005) find, using data from model year 2001, that consumers are willing 

to pay roughly $500 for a 1-mpg increase in city driving, approximately $250 for a 1-mpg 
23

increase in highway driving, or approximately $600 for an increase in combined fuel 

economy; they argue that these values approximately correspond to the fuel savings that 

consumers might expect over the lifetime of the vehicle at low discount rates. McManus

(2006) finds, in 2005, that consumers are willing to pay $578 for a 1-mpg increase in fuel 

economy.18 Fan and Rubin (2009) estimate that consumers are only willing to pay $208 

for an additional mpg for a car, and $233 per additional mpg for a light truck, much less 

than the expected fuel savings from the fuel economy improvement.19

2f. Assumptions of Market Efficiency in Vehicle Choice Models

 Allcott and 

Wozny (2010) estimate that consumers consider about 60 percent of fuel savings when 

they consider fuel economy at the time of vehicle purchase, though initial results from 

Sallee et al. (2010) suggest that, in the used vehicle market, wholesale prices reflects 80

percent (or more) of the fuel cost differences among vehicles. It is worth noting the role 

of discount rate assumptions: Allcott and Wozny use a discount rate of 9 percent, while 

Sallee et al. use a discount rate of 5 percent (in a sensitivity analysis, they find that the 

market fully accounts for fuel savings at a discount rate of 10 percent). See Section 3a for 

a more detailed discussion on the role of discount rates.

Most vehicle choice models assume that consumers are the best judges of how to 

improve their own welfare. If consumers want more fuel economy, they will seek it in 

the vehicles they buy; automakers, sensitive to consumer desires, will provide more fuel 

 18 Gramlich (2010) reports estimates of willingness to pay for a 20 percent improvement in a vehicle 

segment’s average fuel economy (mpg) that range between zero for luxury cars and $7,000 for SUVs when 

gasoline costs $3.50 per gallon. Because Gramlich focuses on a fairly large change in mpg, it is difficult to 

compare his results with those from other papers that examine marginal changes, though Greene (2010) 

characterizes Gramlich’s results as evidence of overvaluation of fuel economy (See Table 1). 19 Fan and Rubin (2010) examine demand for fuel economy in Maine. A number of studies have focused 

on the California vehicle market including Brownstone, et al. (1996), Brownstone et al. (2000), Bhat and 

Sen (2006), and Dasgupta et al. (2007).
24

economy in their vehicles if the cost of the additional fuel economy is less than or equal 

to what consumers are willing to pay for it. Consider the following example. 

Improved fuel economy reduces operating costs per mile. To minimize the costs 

of owning and operating a vehicle, a person can calculate the expected fuel savings per 

year from a more fuel-efficient vehicle and compare it to the additional cost of that 

vehicle. For instance, consider a vehicle that gets 20 miles per gallon (mpg), and an 

otherwise identical vehicle that gets 25 mpg. If a person drives 12,000 miles per year, the 

first vehicle uses 600 gallons per year (12,000 divided by 20), while the latter uses 480 

gallons per year (12,000 divided by 25), a savings of 120 gallons. At a gasoline price of 

$2.50 per gallon, those savings are worth $300 per year. Over the 14-year median 

lifespan of a vehicle, the present value of those savings is $3,388 with a 3% discount rate; 

at a 7% discount rate, they are worth $2,624. In principle, then, a consumer should be 

willing to spend at least $2,600 to buy a 25 mpg vehicle instead of an otherwise identical

20 mpg vehicle. If the costs of improving the fuel economy in the vehicle cost less than 

$2,600, the automakers should be willing to include it in the vehicle.

As previously mentioned, it is fairly common for vehicle choice models (e.g., 

Kleit 2004; Austin and Dinan 2005; Klier and Linn 2010a; Jacobsen 2010) to start with 

the assumption that the market for fuel economy works efficiently, absent an accounting 

for externalities. If this assumption is true, then consumers are not willing to pay for 

additional fuel economy, and new requirements will make them worse off. A few papers 

(e.g., Gramlich 2010; McManus 2006; McManus and Kleinbaum 2009) find that both 

automakers and consumers would be better off with increased fuel economy; and Austin 

and Dinan note that they have to adjust their model to eliminate such gains. If the amount 
25

that consumers are willing to pay to get additional fuel economy exceeds the costs to 

automakers of that addition, then both would be better off with more fuel-efficient 

vehicles. In these studies, consumers appear not to buy fuel economy for which savings 

in gasoline expenses would easily cover the costs. Possible reasons why this could occur 

are discussed in Section 3.

2g. Potential contributions of vehicle choice modeling to regulatory analysis

As noted earlier, in modeling the impacts of vehicle regulation, Federal agencies 

in the United States have typically assumed that the fleet mix – the market shares of 

specific vehicles – stays constant. However, recently discussed increases in fuel 

economy standards and the costs associated with them may be significant enough to lead 

to more substantial effects on the fleet mix. 

Vehicle choice models allow for the possibility that the fleet mix will respond to 

changes in vehicle prices and attributes. When coupled with a supply-side model, models 

of vehicle choice can illustrate the mechanisms through which the market will respond to 

new fleet-wide fuel economy requirements. For example, automakers may sell more 

existing high-mpg vehicles -- by increasing the prices of low-mpg vehicles and lowering 

those of high-mpg vehicles – or they can add technology to their vehicles that will 

improve fuel economy standards.

20

 20 Available evidence suggests that automakers respond to new standards largely by adding technology to 

the vehicles rather than changing prices to influence the vehicles consumers purchase. For instance, in the 

context of feebates, Greene et al. (2005b) found that 95 percent of fuel economy improvements were 

accomplished by adding technology, while in the context of fuel economy standards Whitefoot et al. (2011) 

found that vehicle redesign accounted for 62 percent of required fuel economy improvements.

 Automakers are expected to pursue the combination 

of changes in vehicle prices and additions of technologies that maximize their profits

once consumer preferences are taken into account. If inducing consumers to buy a 
26

different vehicle is cheaper than applying a given technology, then manufacturers should 

do so; not accounting for such a response could overstate the costs of the rule. 

An additional output from vehicle choice modeling of particular relevance to 

regulatory analyses conducted by government agencies is the effect that a particular 

policy is expected to have on consumer and producer surplus. U.S. Federal agencies 

estimate the effects of fuel economy standards ex-ante, by calculating the technology 

costs of additional fuel economy and comparing these to the social benefits and fuel 

savings (U.S. EPA and Department of Transportation 2010a). This approach does not 

take into account changes in consumer satisfaction as they trade off higher prices with

better fuel economy and potentially buy a vehicle with a different set of attributes (or no 

vehicle) compared to what they would have purchased in the absence of higher standards. 

It also does not account for changes in profits to producers due to changes in total vehicle 

sales and changes in the fleet mix. Consumer plus producer surplus, which could be

estimated from consumer choice models with a producer component, could measure these 

combined effects and provide a more encompassing benefits estimation.

Because vehicle choice models – and econometric models in general – rely on 

historical data to predict responses, they are most useful for evaluating the effects of 

small changes. They are not necessarily well suited to predicting larger scale 

compositional changes in the vehicle fleet. Berry et al. (1995) note, for instance, that 

their model predicted fuel economy changes reasonably well after the oil shock of 1973, 

but that its predictive ability declined markedly starting in 1976, due to wide introduction 

of new vehicles into the market. Given that vehicle models and characteristics may 

change substantially within a few years’ time -- in response to changing market 
27

conditions or new legal settings -- vehicle choice models used to predict responses 

several years into the future may be more useful when makes and models are aggregated 

by class of vehicle than when they are treated individually. Of course, even classes of 

vehicles can change over time: the introduction of the minivan, for instance, led to 

significant increases in consumer welfare in the mid-1980s (Petrin 2002). 

In summary, vehicle choice models are a continuing area of research and 

development. Existing models vary in a number of dimensions, in part because of 

different research intentions behind the models and in part because different methods can 

be used to analyze similar questions. Table 2 summarizes the main methodological 

sources of variation. There has been relatively little systematic comparison of vehicle 

choice models: while Greene (2010) updates the summary provided by Greene and Liu 

(1988), researchers rarely conduct a given experiment across models in a consistent 

matter to facilitate comparisons. It also appears that the studies conducted since Greene 

and Liu, many of which are reviewed in Greene (2010), continue to show notable 

disparities in results. In particular, there are significant differences across models in 

predicting whether consumers and automakers will benefit from additional fuel economy 

improvements to their vehicles. The lack of consensus makes it difficult to determine the 

effects of new fuel economy regulations. The following section reviews the evidence on 

why consumers, at the time of vehicle purchase, may not spend as much on fuel economy 

as present value calculations of the resulting savings would suggest.

3. Do Consumers Value Fuel Economy “Correctly?”

U.S. Environmental Protection Agency and the Department of Transportation 

(2010a) analyses of increasing the fuel economy standard for light duty vehicles find that 
28

there are a myriad of relatively low-cost technologies available, and that these 

technologies are expected to result in fuel savings to consumers that more than make up 

for the upfront cost of the technology over the lifetime of the vehicle. The quandary,

then, is why does the market not already take advantage of these low cost technologies? 

Why aren’t consumers demanding these vehicle improvements and manufacturers 

supplying them when they appear to pay for themselves even in the absence of 

regulation? This disconnect between net present value estimates of energy-conserving 

cost savings and what consumers actually spend on energy conservation is often referred 

to as the Energy Paradox (e.g., IEA 2007; Jaffe et al. 2001; Metcalf and Hassett 1999; 

Tietenberg 2009), since consumers appear to routinely undervalue a wide range of 

investments in energy conservation. Possible explanations for the paradox cited in the 

literature include: consumers who put little weight on the future; consumer disinterest in 

fuel economy; bundling of fuel economy with other attributes; consumer difficulty 

calculating expected fuel savings; uncertain fuel savings contrasted with certain and 

immediate increased costs; consumer heterogeneity; and the role of vehicles and fuel 

economy in signaling a consumer’s social status. These explanations range from those 

that suggest the absence of a genuine paradox – e.g, that if costs or other factors that are 

omitted when analysts calculate energy savings are properly accounted for, they may

make the apparent paradox disappear - to those that point to the existence of a gap 

between savings and valuation - for instance, due to widespread behavioral failures on the 

part of consumers. While the empirical evidence in support of some of these 

explanations is relatively thin, the attention they receive in the literature – and sometimes,

in the popular press - leads us to discuss each of these in turn.
29

3a. The Private Discount Rate

A key challenge in quantifying the possible welfare effects of proposed regulation 

is estimating the rate at which consumers make trade-offs across time (i.e., the private 

discount rate). Recognizing that consumers do not buy as much energy conservation as a 

simple present value calculation would suggest, government agencies have at times 

assumed consumers have very high discount rates absent government intervention. For 

instance, when modeling consumers’ choices of appliances, the U.S. Department of 

Energy’s Energy Information Administration (1996) used discount rates as high as 111 

percent for water heaters and 120 percent for electric clothes dryers. Kubik (2006) offers 

some evidence for the notion that consumers are impatient or myopic (e.g., use a high 

discount rate) when it comes to fuel saving returns from automobile purchases. On 

average, consumers from the Kubik survey indicate that fuel savings would have to pay 

back the additional cost in 2.9 years to persuade them to buy a higher fuel-economy 

vehicle, even when evidence shows that consumers tend to hold onto their vehicles for 

longer (on average, 5 years).21 22 Dreyfus and Viscusi (1995) estimate rates of time 

preference of 11-17 percent for vehicle purchases in the United States.

23

 21 Dasgupta et al. (2007) find that consumers are also myopic when it comes to the decision of whether to 

lease or buy a new vehicle, preferring contracts with lower payment streams even when they imply an 

overall higher cost.

Attanasio et al.

(2008), using data from the Consumer Expenditure Survey, find that consumers who 

financed the purchase of a vehicle took out a loan with an average real interest rate of 

22 Whether the consumer values higher fuel economy beyond the 5 year timeframe depends on whether fuel 

economy is valued appropriately in the resale market. A well-recognized phenomenon in the used vehicle 

market is the “lemons” problem – quality is uncertain because of lack of information on the part of the 

buyer relative to the seller (Akerloff 1970). Since the buyer cannot observe the seller’s maintenance record 

or her driving style, both of which can affect fuel economy, it is possible that the reported fuel economy for 

a used vehicle is not a good predictor of its actual fuel economy.

23 Cambridge Econometrics (2008) finds evidence that the private discount rate ranges from 6 to 19 percent 

for U.K. car buyers.
30

about 9 percent. Those buying new cars tended to have a lower interest rate (on average,

7.6 percent), while those buying a used car tended to have a slightly higher interest rate 

(on average, 10.1 percent).

As the discussion in Section 2e indicates, researchers’ attempts to estimate the 

relative importance of fuel economy in consumer vehicle purchase decisions lead to 

markedly different conclusions regarding how consumers trade off upfront costs and 

future fuel savings, and often are predicated on particular assumptions about driving 

behavior, gasoline price expectations, and the private discount rate.

24

If purchase decisions represent optimal consumer choices, high observed private 

discount rates can reflect factors such as credit constraints, the irreversibility of 

investment, or uncertainty about the future. However, mistakes due to imperfect 

Use of a high 

implicit discount rate may capture variation across consumers in these other factors that 

are embedded in a calculation of expected lifetime operating costs. For instance, as 

mentioned in Section 2e, with a real discount rate of 9 percent, Allcott and Wozny (2010) 

estimate that consumers of new and used vehicles consider about 61 percent of fuel 

savings at the time of vehicle purchase. However, their results are sensitive to the choice 

of discount rate: they find that a discount rate between 18 and 27 percent would lead to a 

finding that consumers fully account for fuel savings in their purchase decisions. Initial 

results from Sallee et al. (2010), using a discount rate of 5 percent, suggest that used 

vehicle purchase prices reflect 79 percent of the fuel cost differences among vehicles.

When they use a discount rate of 10 percent, consumers appear to fully account for fuel 

savings in their purchase decisions. 

 24 Recent work by Anderson et al. (2011a) suggests that the gasoline price expectations that consumers 

report appear to follow a random walk. Note also that preliminary work by Anderson et al. (2011b) 

suggests that consumers do a reasonable job projecting future gasoline prices over a five year time period. 
31

information or bounded rationality can also be modeled as higher implicit private 

discount rates. Studies that back out implicit discount rates based on consumer decisions 

generally conflate all of these factors, making it difficult to identify from the empirical 

literature the driving factors underlying the “energy efficiency paradox.” Thus, while the 

literature proposes various explanations for the seeming reluctance of the private market 

to invest in energy efficiency, little empirical evidence exists to support one hypothesis 

over another.25

3b. Fuel Economy Ranks below Other Vehicle Attributes in Preference

One hypothesis for why consumers may be reluctant to invest in fuel economy is 

that they are well informed but relatively indifferent to increased fuel savings compared 

to improvements in other vehicle attributes they may purchase. For instance, consumers 

may care more about the vehicle type and only then make comparisons on the basis of 

fuel economy, or they may care more about carrying capacity or power than fuel 

economy. If so, then they may not be willing to purchase seemingly low cost 

opportunities to improve fuel economy; and, if consumers do not seek those 

opportunities, then auto makers have little incentive to provide them. In other words, 

there may not be an energy paradox if we accurately account for these trade-offs between 

vehicle attributes. The American Council for an Energy-Efficient Economy (ACEEE)

(2007) offers some anecdotal support for this hypothesis in interviews with consumers 

that, while fuel economy is mentioned as a concern when making a vehicle purchase, it 

 25 Fischer et al. (2007) integrate the question of whether consumers are short or long-sighted directly into a 

theoretical vehicle choice model as a parameter that can be adjusted up or down to reflect the analyst’s 

priors in this regard and then can be used to inform intuition on when fuel efficiency standards are likely to 

be welfare improving. In simulations, they find that, as expected, consumers with short time horizons or 

low discount rates tend to benefit from tightening fuel economy standards, while those with long time 

horizons or high discount rates do not.
32

ranks below reliability, price, features, and safety in order of importance. This also leads 

to the possibility that, given fuel economy’s relative unimportance in purchasing 

decisions, consumers may be using rules of thumb or gathering only enough information 

to assess whether a given vehicle has a sufficient level of fuel economy (also known as 

“satisficing,” a term coined in Simon 1955), instead of maximizing utility.

Even if we take as given the relative unimportance of fuel economy, a number of 

recent studies (e.g., Busse et al. 2010; Klier and Linn 2010b) provide evidence that fuel 

economy plays some role in people’s vehicle purchases, particularly when fuel prices

increase (as economic theory would predict). As discussed in Section 2e, Busse et al.

find that high fuel prices mainly lead to increased market share for high-mpg new 

vehicles and higher relative prices for high-mpg used vehicles. Klier and Linn find 

evidence that almost half of the decline in market share for large SUVs in the United 

States from 2002 to 2007 can be explained by the increase in the price of gasoline. West 

(2008) offers preliminary evidence that consumers pay more attention to past gasoline 

prices than present ones when making vehicle purchase decisions.

26

As mentioned above, the same technology that can be used to improve fuel 

economy can alternatively be used to improve vehicle performance or increase vehicle 

size.

 

27

 26 A strand of literature also has investigated whether the response to gasoline prices is asymmetric. Kilian 

and Sims (2006) find evidence of asymmetric responses to real gasoline price changes in the used vehicle 

market - consumers are much more responsive to increases in gasoline prices than they are to decreases -

though Sallee et al. (2010) find no evidence for asymmetry.

 If consumers value these other attributes more highly than they do fuel economy, 

then it makes sense that these technologies are applied to that end. Historical trends point 

to the market’s emphasis on characteristics other than fuel economy in passenger cars. 

27 Arguea et al. (1994) find that horsepower and fuel economy appear to be substitutes, whereas comfort 

and horsepower appear to be complements.
33

Fuel economy standards for these vehicles were constant over a roughly 20-year time 

period. Over the same time period, almost all technology was applied for the purposes of 

increasing acceleration, weight, and automatic transmission instead of for improving 

vehicle fuel economy. Greene et al. (2009) calculate that the average 2006 passenger car 

would have achieved 38 miles per gallon instead of 29 miles per gallon had these 

technologies been applied exclusively toward fuel economy improvements. Knittel 

(forthcoming) finds that fuel economy for passenger cars and light trucks could have 

increased by nearly 50 percent between 1980 and 2006, instead of the 15 percent that 

occurred, if technological progress had been applied to fuel economy instead of weight, 

horsepower, and torque. That said, the relative preference for performance over fuel 

economy still does not explain the seeming paradox that fuel savings appears to exceed 

the cost of adding additional fuel economy to the vehicle. One would expect from 

economic theory that consumers would continue to demand fuel economy improvements 

until the benefits of a marginal improvement just meets the cost. Only if there are limits 

on the total amount of efficiency that can go in a vehicle does economic theory predict 

that the marginal benefit of fuel economy should not equal its marginal cost.

3c. Bundling of Vehicle Attributes

Another reason why consumers may appear relatively indifferent to fuel economy 

compared to other attributes is that a vehicle is actually a bundle of attributes sold 

together. While it is common for consumers to have a menu of options for some vehicle

attributes when contemplating the purchase of a particular vehicle – for instance, a 

consumer can often choose between manual and automatic transmission for the same 
34

make and model -- fuel economy is not usually one of them (Greene 2011).

28

3d. Misunderstanding Fuel Economy

There is 

rarely an ability, for instance, to spend an additional $500 to improve the fuel economy 

on a particular vehicle. Instead, consumers must trade off between fuel economy and 

other attributes included in the bundle, several of which are likely to differ 

simultaneously across automakers and models in the same vehicle class (Greene et al. 

2009). In fact, there is some evidence that consumers associate better gas mileage with 

smaller, lighter cars, and that, as a result, they expect to pay less, not more, for such a 

vehicle (PRR 2005; Teisl et al. 2009). This result is consistent with Gramlich’s (2010)

finding that increasing mpg, holding cost of travel per mile constant, decreases the 

likelihood that a consumer will buy a particular vehicle. He attributes this to mpg 

representing more than just fuel economy.

Recent evidence suggests that consumers may not understand how to calculate 

fuel savings correctly when making vehicle purchases and thus are not making well 

informed decisions. Sanstad and Howarth (1994) explain this phenomenon with the 

concept of “bounded rationality,” that consumers resort to imprecise but convenient rules 

of thumb when making decisions. This allows for the possibility that consumers may 

make mistakes when evaluating fuel economy relative to other vehicle attributes 

available for purchase.29

 28 For 6-cylinder minivans in 2010, for instance, the full range of combined fuel economy is 18-20 mpg 

(www.fueleconomy.gov). On the other hand, it is possible to purchase a hybrid or non-hybrid version of 

the Honda Civic. Whether consumers consider these to be otherwise identical vehicles with different fuel 

economy is as yet unstudied.

 For repeated purchases, one might expect that consumers will, 

on average, approximate the more sophisticated calculation as they learn and correct for 

29 Note that the idea of bounded rationality is also consistent with utility satisficing due to the relative 

unimportance of fuel economy in vehicle purchasing decisions (Simon 1955). See section 3b.
35

past mistakes. In the case of vehicles, learning may be hindered by the relative 

infrequency of purchase and large fluctuations in fuel prices that make it difficult to 

predict returns very far into the future. 

Turrentine and Kurani (2007) present the results of a survey of 57 households in 

northern California, asking a variety of questions related to the role of fuel economy in 

their vehicle purchasing decisions. They find no evidence that households track and 

analyze fuel costs related to automobile or gasoline purchases over time. Households 

appear aware of the cost of gasoline today but do not incorporate this cost into their 

budgets. As a result, when asked to consider how much they would be willing to pay for 

their preferred vehicle with a 50 percent improvement in fuel economy, the majority of 

households could not answer, seemed to guess at a number, or made basic errors in 

estimating gasoline savings over time when constructing an answer. Larrick and Soll 

(2008) argue that measuring fuel economy in mpg is one source of consumer 

miscalculations. Potential fuel savings are non-linearly associated with mpg but linearly 

associated with gallons per mile (the inverse of mpg). Thus, while reducing fuel use by 

one gallon per hundred miles is independent of the efficiency of a vehicle – one gallon is 

one gallon – improving a 10 mpg vehicle by 1 mpg results in more than ten times higher 

fuel savings than improving a 35 mpg vehicle by 1 mpg. Through a series of surveys, 

they find that consumers regularly and incorrectly assume that mpg is directly associated 

with the amount of gasoline consumed.30

 30 Every new vehicle has a label with information on estimated annual fuel costs and miles per gallon. 

While, in principle, this information should reduce consumer misinformation, the problem seems to persist. 

One explanation is the nonlinearity of mpg; another may be that the assumptions embedded in the fuel cost 

calculation – for instance, the prevailing gasoline price and vehicle miles – do not reflect driver experience. 

EPA and DOT (2010b) are currently revising the vehicle label and proposing to add fuel economy 

information in gallons per mile.

 As a result, participants appeared to 
36

substantially undervalue improvements in fuel efficiency for relatively fuel-inefficient 

vehicles and to overvalue those improvements for relatively fuel-efficient vehicles. 

Allcott (2010) combines revealed choice data with self-reported information on 

consumer’s perceived fuel savings associated with varying fuel economy from a 

nationally representative survey to test the extent of consumer misperception. 

Preliminary results suggest that there is a substantial difference between actual and 

perceived cost savings, consistent with the misinterpretation of the relationship between 

mpg and fuel savings observed by Larrick and Soll. However, the welfare implications of 

these miscalculations are relatively small.

3e. Irreversibility of Investment, Uncertainty, and Loss Aversion

Another potential explanation for the apparent Energy Paradox is that consumers 

are rationally incorporating uncertainty in future returns and irreversibility in investment

into their decisions (Metcalf and Rosenthal 1995). The argument is as follows:

• Energy conservation requires additional upfront capital investments that are 

“sunk” and therefore essentially irreversible – they have a low salvage value if 

the return for investing in energy efficiency never materializes.

• The return (e.g. the reduction in operating costs) occurs in the future and is

uncertain due to fluctuations in (and an inability to anticipate) energy prices.

• Purchasers have flexibility with regard to when they invest in energy 

efficiency. 

If the energy efficient alternative is more expensive (all other attributes equal), 

then the difference in price between two alternatives can be interpreted as the 
37

“investment,” while the return is dependent on future fuel prices. If the consumer 

chooses to invest in energy efficiency, she will recoup her investment in fuel savings if 

gasoline prices are high, but she may lose money if they turn out to be low. If the 

consumer forgoes this investment and instead purchases the less energy-efficient 

alternative, she does not benefit when gasoline prices are high but also risks nothing 

additional upfront if they turn out to be low. These characteristics imply that the 

consumer has good reason to pursue a more cautious approach than what is implied when 

the additional upfront cost and future returns are calculated with certainty: to avoid the 

outcome in which the additional cost of investing in energy efficiency does not pay for 

itself. This implies that the required rate of return for investing in energy efficiency is 

often substantially higher than the underlying discount rate applied by consumers. 

Specifically, Metcalf and Rosenthal argue that it is incorrect to interpret high discount 

rates as evidence of an Energy Paradox due to consumer myopia when they can be 

partially explained by the effects of uncertainty and irreversibility on investment 

decisions.

Looking specifically at the automotive market, Greene et al. (2009, 2011) put 

forward a theory related to that of Metcalf and Rosenthal (1995). They argue for 

uncertainty with regard to fuel savings and upfront costs combined with loss aversion as 

the main explanation for why consumers do not adopt energy efficiency improvements in 

this market. Loss aversion means that consumers are more sensitive to losses than to 

gains.31

 31 Empirical evidence points to a general loss aversion of 2: “the disutility of giving something up is twice 

as great as the utility of acquiring it” (Benartzi and Thaler 1995; Tversky and Kahneman 1991).

Because consumers evaluate decisions in terms of potential changes from 

present, known wealth rather than in terms of their impact on uncertain future wealth, 
38

they have a preference for maintaining the status quo -- avoiding increased vehicle costs 

in the present. This preference for the status quo means that consumers would have to be 

offered a premium over the expected value of fuel savings to invest in the new vehicle 

technology.

32 When taking these aspects of consumer decision-making into account, 

Greene et al. (2009, 2011) find that the expected net present value of increasing the fuel 

economy of a passenger car from 28 to 35 miles per gallon falls from $405 when 

calculated using standard methods to very close to zero. In their results, uncertainty 

about the actual fuel economy consumers achieve is most important, followed by 

uncertainty about the upfront cost, vehicle lifetime, and the price of gasoline.33 34

Bernatzi and Thaler (1995) also point out that the willingness of an investor to 

take on a risk is related to the time horizon of the investor. The shorter the time horizon, 

the less likely they are to invest in the risky asset. This argument could apply in the 

context of vehicle purchases, given the short payback period and uncertainty with regard 

to vehicle lifetime.35

 32 An open question in the literature is to what extent loss aversion is context dependent (see Greene 2011 

for a brief discussion). In other words, do consumers revise their status quo after making a purchase 

decision? If this is the case, then, while they initially had to be offered a premium to invest in a more 

energy efficient vehicle, now that they have purchased the vehicle they value the net fuel savings without 

regard to the premium.

 Dasgupta et al. (2007) provide evidence that consumers are aware 

of differences in relative risk across vehicles: they find that consumers are more likely to 

lease than buy a vehicle with higher maintenance costs because it provides them with the 

option to return it before those costs become too high. 

33 It is worth noting that these results are based on Monte Carlo simulations and as such are dependent on 

the assumed probability distributions for a variety of factors. 34 Studies talked about in section 2 illustrate that accounting for uncertainty in future fuel prices does not 

resolve the question of whether consumers undervalue fuel economy. For example, Sallee et al. (2010) 

suggest that consumers largely value fuel economy as theory would predict, while Allcott and Wozny 

(2010) suggest some degree of undervaluation is present.

35 Bernatzi and Thaler (1995) examine myopic loss aversion in the context of the “equity premium puzzle”: 

relatively risky stocks have outperformed bonds by a far larger margin than expected, even though 

investors have relatively low values of relative risk aversion. 
39

Another significant source of uncertainty is the fuel economy that consumers will 

actually achieve. If consumers believe that the fuel economy that they will get is lower 

than the vehicle’s rated value, then their future gains are overestimated. Engineering 

returns estimated in a controlled or lab setting often do not materialize – and are not 

expected – in a household context (Metcalf and Hassett 1999). For instance, actual fuel 

economy is likely to vary with maintenance and use: households that drive with 

underinflated tires or in a particularly aggressive manner would not get the same fuel 

savings as those predicted in a lab setting. Greene et al. (2006) find, using self-reported 

data, that EPA fuel economy estimates listed on labels at the time of purchase are 

unbiased, but that they are also highly inaccurate indicators of the actual fuel economy 

achieved. 

3f. Consumer Heterogeneity

Another possible reason for the appearance of an Energy Paradox is that analysts 

may fail to account for variation in tastes across consumers when examining vehicle 

purchase decisions. Consumers may vary across many dimensions, including their

discount rate, risk preferences, liquidity constraints, expected use of the product, lifespan

of the investment, purchase price, and other costs, which can lead to differences in the 

expected value that individual purchasers attach to energy-efficient products. For 

instance, a survey by the Energy Information Administration (2001) found that vehicles 

were driven on average about 12,000 miles per year. However, miles traveled for rural 

vehicles were 9 percent higher than for urban vehicles, and vehicles driven by households 

with children were driven 24 percent more miles than those in households without 

children. It may not make sense for people who drive less to spend more money on 
40

energy-efficient models: they will not recoup their investment through energy savings. 

On the other hand, for people who put many more miles on their car than the average, the 

additional fuel savings on energy-efficient models may be worth the initial investment.

Mixed logit estimation allows for both observed and unobserved heterogeneity in 

tatses across consumers. Papers that use this estimation technique find that controlling for 

heterogeneity matters (e.g., Berry et al. 2004; Train and Winston 2007; Bento et al. 2005, 

2009; Sawhill 2008; Whitefoot et al. 2011). For example, Sawhill (2008) demonstrates 

that vehicle buyers tend to undervalue future fuel costs in purchase decisions when he 

uses a simplified model, but once he accounts for uncertainty in future gasoline prices 

and heterogeneity in tastes and driving patterns, he finds no evidence for undervaluation 

by the average buyer. Fifer and Bunn (2009) distinguish the marginal value of fuel 

economy by vehicle type, and compare this value to the distribution for miles driven. 

They find that the average car and SUV owner underpays for fuel economy, but that the 

average van and truck buyer overpays.

3g. Vehicles as Positional Goods

Finally, people may buy vehicles for reasons unrelated to their use as a means of 

transportation, such as status. If a consumer seeks to invest in status, goods or attributes 

that are observable become more important than goods or attributes that are not 

observable (Frank 1985). Carlsson et al. (2007), in a stated preference survey in Sweden, 

find that what vehicle someone owns, which is easily observable to others, conveys social 

status more effectively than vehicle safety, a less visible characteristic. Likewise, fuel 

economy is a less visible attribute, though how a consumer’s focus on vehicle status will 

affect the demand for fuel economy depends on whether they are positively or negatively 
41

correlated, which may vary by region, income class, and other household characteristics. 

To the extent that bigger, more powerful vehicles signal status, consumers may underinvest in fuel economy relative to what they would have done absent the ability to signal 

position via their vehicle purchase. And because there is nothing to prevent someone else 

from buying the same vehicle, the consumer never captures the expected welfare gain –

the positional good loses its value over time due to the inability to exclude others from 

making the same purchase. Frank (1985) and Frank and Sunstein (2001) argue that 

regulation of goods used to convey status may improve social welfare as it can correct for 

underinvestment in attributes such as safety or fuel economy while maintaining relative 

economic position.36 

The Energy Paradox asks why consumers do not buy as much energy 

conservation as a simple present value calculation would suggest. Possible explanations 

for the appearance of a paradox include: consumers who put little weight on the future; 

consumer disinterest in fuel economy when buying a vehicle; bundling of fuel economy 

with other attributes; difficulty in calculating expected fuel savings or bounded 

rationality; uncertainty and irreversibility of energy efficiency investments; loss aversion; 

variation in consumer benefits from improved fuel economy; and the role of vehicles in 

signaling social status. These widely varying hypotheses have not been rigorously 

examined in tandem to determine which, if any, can be rejected. It is also worth noting, 

as discussed above, that the empirical evidence from econometrically estimated vehicle 

 36 Many of the studies cited above (e.g., Alcott and Wozny 2010; Sallee et al. 2010; Langer and Miller 

2011; Whitefoot et al. 2011) use panel-data approaches that control for time-invariant or fixed effects

which could include status, in their estimation. These models cannot be used, however, to independently 

verify the role that status may play in vehicle purchasing decisions. 
42

choice models varies widely regarding the existence of an Energy Paradox in fuel 

economy. This wide variation in results suggests that the value of increased fuel 

economy to consumers is an area deserving of attention by the research community.

Another question arising from this discussion is its implications for policy. Price 

volatility and uncertainty about the future are natural occurrences in markets, and

consumers regularly face decisions under these circumstances. Does a requirement for 

improved fuel economy that pays for itself under reasonable conditions, but that 

consumers might not buy in the absence of that requirement, make consumers better off? 

On the one hand, if it is likely to give consumers actual savings in costs that they did not 

fully consider at the time of purchase, the requirement may compensate for a boundedly 

rational or otherwise suboptimal decision. On the other hand, the government is 

mandating increased consumption of an attribute that many consumers appear not to 

want, potentially imposing additional welfare costs on those consumers. It is also 

important to keep in mind that there are usually other policy justifications -- not discussed 

here -- for decreasing fuel consumption, such as reduced air pollution, that form the basis 

for regulation.

4. Why producers may not provide as much fuel efficiency as consumers 

want to buy

The empirical literature has spent relatively little time investigating the efficiency 

of the producer side of the new vehicle market; it is typically assumed that automakers 

are taking advantage of all profit-making opportunities. However, the seeming 

disconnect between the existence of low cost opportunities to improve fuel economy and 

the failure of the market to invest in such improvements may not be entirely due to errors 
43

in consumer decision making. Manufacturers also play a role, particularly in how they 

perceive what consumers would be willing to pay for various attributes. The following 

discussion posits several hypotheses for why automakers may not supply the expected 

level of fuel economy to consumers, including uncertainty, attribute bundling, and the 

vehicle design process. Little research has been done to date to investigate their relative 

merits. 

As previously mentioned, several recent analyses (Greene et al. 2009; Klier and 

Linn 2010a; Knittel, forthcoming) argue that improvements in engine efficiency have 

been channeled into other vehicle characteristics, such as power and acceleration, rather 

than fuel economy (with the exception of when CAFE was first introduced). If producers 

are maximizing profits, then they should add improvements in efficiency to both fuel 

economy and to other vehicle characteristics until the cost of additional energy efficiency 

just equals the amount that consumers are willing to pay for it. If producers have been 

channeling the increased engine efficiency into acceleration and power rather than fuel 

economy, then they must expect that consumers are willing to pay more for increased 

acceleration and power than they are for increased fuel economy. As previously noted in 

Section 3a, this argument does not explain why auto makers do not add more fuel-saving 

technology when it continues to be cost-effective to do so.

One possible explanation for seemingly suboptimal behavior on the part of 

automakers is analogous to the effect of uncertainty on consumers. If large auto 

manufacturers are risk averse, then investments with inherently more uncertain returns

are less likely to be undertaken due to irreversibility of investment. If consumers are 

somewhat less likely to invest in fuel savings because in the future they are uncertain, it 
44

follows that automakers also will invest less in fuel economy (Greene et al. 2009) and 

more in attributes for which consumers have shown a strong and consistent preference 

and from which consumers will get more immediate and certain benefits, such as 

performance and weight. 

As we mentioned earlier, automakers also bundle attributes. To control the 

number of variations of any given make and model, they only provide consumers with 

choices over particular vehicle attributes, those for which they believe consumer 

preferences are highest and heterogeneous. These choices may include, for instance, 

manual vs. automatic transmissions or different body styles. Different fuel economy 

options for a given make and model is not typically offered as a choice. Automakers may 

consider choices in other attributes to be more salient to consumers at the time of vehicle 

purchase than the choice of additional fuel economy. Thus, consumers may not have the 

opportunity to select a particular model with additional fuel economy due to producers’ 

desire to control the number of variations of models that they build (which may well be 

optimal from the producer’s perspective). Fifer and Bunn (2009) hypothesize that 

attribute bundling may explain their results for pickup trucks and vans, where they find 

that consumers are willing to overpay for fuel economy. They point out that this form of 

“irrationality” may actually reflect lack of choice: consumers are not able to purchase 

more fuel economy without giving up some other essential attribute such as the hauling 

capacity.

Vehicle design and development can take several years. In response to market 

conditions and to regulations, automakers may want to change the attributes of the 

vehicles that they sell, but those changes cannot happen as quickly as fluctuations in fuel
45

price. As a result, the fuel economy that automakers include in their vehicles may lag 

unexpected changes in the market by several years.37

Train and Winston (2007) argue that U.S. manufacturers may have been slow to 

respond to changes in consumer preferences. This is a key reason why they have seen 

their market share consistently decline since the 1970s. The authors reject alternative 

hypotheses, such as brand loyalty, dealership networks, and health care and other legacy 

costs. According to their results, U.S. manufacturers had not provided as much value 

 A period of relatively stable and 

low gasoline prices, such as the 1990s, did not encourage development of new high-mpg 

vehicles; when prices rose sharply in 2008, vehicle manufacturers could not suddenly 

improve the fuel economy of the vehicles that they offered. In addition, because it takes 

time and significant cost to change the vehicle that a manufacturing facility produces, 

many automakers struggled to increase the capacity to produce their existing high-mpg 

vehicles. Gramlich (2010) attempts to explicitly account for this disconnect between the 

timing of an automaker’s production decision and the market conditions at the time of 

sale. He models the supply decision in two distinct stages. In the first stage, the 

automaker decides on vehicle characteristics based on current gasoline prices, costs, and 

demand information. However, sales do not occur until the second stage (in his model, 

this occurs one year later). Gasoline prices are allowed to change between the first and 

second stages, but automakers are already committed to producing a vehicle with the set 

of characteristics decided upon in the first stage. Automakers can only adjust the prices 

of the vehicles they sell to consumers based on the new information.

 37 EPA and DOT assume that automakers redesign a vehicle platform every five years. This is the point at 

which substantial changes can be made to the vehicles (U.S. EPA 2010a). Prior to a major redesign, it is 

assumed that automakers can only make relatively small adjustments, such as improving the aerodynamics 

of a vehicle. 
46

relative to the cost of fuel consumption, horsepower, and other attributes, compared to 

Japanese manufacturers. The general slowness of U.S. manufacturers to respond to 

consumer desires may have contributed to the slow response to changes in consumer 

demand brought about by much higher fuel prices. 

McManus and Kleinbaum (2009) argue that U.S. manufacturers have 

underestimated the value of fuel economy to consumers; in particular, they argue that 

GM regularly applied a lower value to the willingness to pay for fuel economy than was 

estimated by its own researchers. Given what may have been the sub-optimal nature of 

decision-making among U.S. manufacturers, they find that tightening fuel economy

standards could result in increased profits for U.S. automakers: consumers may be willing 

to pay for additional fuel economy improvements, and the sales for those vehicles could 

actually increase relative to those of Japanese automakers. This result is somewhat 

sensitive to the value that consumers place on fuel economy; if consumers value fuel 

economy at half of what is deemed “rational,” then profits for U.S. manufacturers could 

decline. Automakers’ possible underestimation of consumers’ valuation of fuel economy 

may be partially explained by a variety of factors, including that U.S. manufacturers may 

have made a deliberate decision to specialize in larger, less fuel efficient vehicles. This 

degree of specialization has become a large disadvantage as fuel prices increased, given 

their inability to quickly change the vehicles that they produce.

In sum, the possibility that auto manufacturers provide less fuel economy to 

consumers than what seems mutually advantageous has received far less attention in the 

literature than the existence of an Energy Paradox on the demand side. Possible reasons 

for the appearance of an Energy Paradox that could be driven by producer behavior 
47

include uncertainty and irreversibility of investment; the decision to focus on 

characteristics other than fuel economy when designing model variations; time lags 

inherent in the production process; and internal decision-making processes that have not 

been responsive to changes in consumer preferences. As with the consumer side of the 

market, some of the reasons listed above and the way producers respond to them may be

sound business strategy – for instance, avoiding inherently more uncertain investments or 

bundling attributes to minimize design costs – while others may suggest poor decisionmaking. Economic theory predicts that manufacturers have strong incentives to 

undertake all profitable product development; the market is expected to reward 

companies that make correct predictions and punish producers whose products do not 

adequately measure up to consumers’ desires. Major changes in the auto industry in 

2009 were due both to short-term shocks due to high gasoline prices in 2007 and 2008 

and the subsequent recession, and to longer-lasting market forces that led to industry 

restructuring. The relative proportions of these changes are clearly a topic of great 

debate.

In the same way that the implications of how consumers evaluate tradeoffs when 

making vehicle choices are unclear, it is difficult to reach clear policy conclusions based 

on how producers make vehicle production decisions. It is possible that manufacturers 

are accurately assessing consumers’ willingness-to-pay for fuel economy and other 

vehicle attributes, but that government involvement (commonly motivated by 

externalities) may help alleviate errors in decision-making on the demand side. On the 

other hand, if consumers do not substantially undervalue fuel economy but some 

automakers, who have a profit motive to make vehicles that people want, have trouble 
48

finding the best set of attributes to offer to consumers, are government agencies likely to 

have better insights? 

5. Conclusion

Government mandates that are intended to increase fuel economy substantially in 

the next few years may change both vehicle attributes and the price of vehicles 

substantially. This may, in turn, affect consumers’ choices regarding the type of vehicles 

they purchase. Vehicle choice models are one way to estimate changes in fleet mix. In 

addition, these types of models can be used to estimate changes in consumer surplus 

associated with new regulation.

As this review has shown, vehicle choice models vary along a number of 

dimensions, including analytical methods, data sources, and research questions evaluated. 

Vehicle choice modeling estimation must also overcome a variety of challenges, 

including omitted variable bias, collinearity, endogeneity, and measurement issues. 

Despite increasingly sophisticated methods and access to highly disaggregated panel 

data, the models appear to produce widely varying estimates of the value that consumers 

place on fuel economy. This value plays a major role in how analysts estimate the 

impacts of fuel economy and greenhouse gas emission standards for vehicles: its 

magnitude affects the estimates of consumer surplus and informs assessments of the 

efficiency of the market for vehicle fuel economy. Policymakers would benefit from 

additional research to estimate with greater precision the direction and magnitude of this 

effect. This review has not examined variation in estimates of the values for other 

parameters; it is possible that values for power, safety, and other vehicle attributes are 

also not yet robustly estimated.
49

Even with the ability to model vehicle choice, the literature still leaves open the 

question of how consumers value fuel economy, and why their willingness to pay for 

more of it may not equal the expected value of the fuel savings. From a public policy 

perspective, it is an open question whether these problems justify additional fuel 

economy requirements. Consumers may receive additional fuel savings even if they did 

not account for them at the time of purchase: this could result in a welfare gain. On the 

other hand, consumers may incur the additional costs for an attribute of little interest to 

them or view fuel savings as inherently uncertain: this could result in a welfare loss.

Shogren and Taylor (2008) point to a need to evaluate potential behavioral failures using 

evidence-based and testable hypotheses to identify when rational choice theory may fail 

to fully explain observable behavior. The literature in the area of vehicle choice is still 

relatively thin, making this an area ripe for future research.

Another area where researchers could help shed light is whether or why producers 

may provide less fuel economy than consumers are willing to buy. This may merely be a 

market response to the “energy paradox” observed in the consumer market. However, it 

is possible that suppliers provide less than what consumers would buy. Economic theory 

argues that markets should discipline companies that do not provide the products people 

want at suitable prices; it is an open question whether government intervention is 

warranted if auto companies consistently make mistakes.

Consumer decisions regarding vehicle purchases involve complex tradeoffs along 

many dimensions. For environmental and energy policy, the dimension of most interest 

is fuel economy of vehicles because of its direct relationship to greenhouse gas emissions 

and other air pollution. This review has shown that consumers pay attention to the fuel 
50

economy of the vehicles that they buy, but that there is still much to be learned about how 

to model the role of fuel economy in consumers’ and producers’ decisions. 
51

Table 1. Evidence on Consumer Valuation of Fuel Economy38

Undervaluation of Fuel 

Economy

"About Right” Valuation 

of Fuel Economy

Overvaluation of Fuel 

Economy

Allcott and Wozny 

(2010)

39

Arguea et al. (1994)

Berry et al. (1995)

Bhat and Sen (2006) 

Busse et al. (2010)

Eftec (2008) 

Fan and Rubin (2010)

Feng et al. (2005)

Fifer and Bunn (2009) 

Kilian and Sims (2006) 

Langer and Miller (2011) 

Train and Winston (2007)

Brownstone et al. (1996) 

Dasgupta et al. (2007) 

Espey and Nair (2005)

Goldberg (1995)

Goldberg (1998) 

Klier and Linn (2010b) 

McManus (2007) 

Sallee et al. (2010)

Brownstone et al. (2000)

Cambridge Econometrics 

(2008) 

Gramlich (2010)

Sawhill (2008)

Vance and Mehlin (2009)

Source: Greene (2010).

 38 Note that many of these papers – for instance, Sawhill (2008),Fifer and Bunn (2009), and Klier and Linn 

(2010b) - account for heterogeneity in consumer preferences and as such find much more nuanced results 

with regard to under- or overvaluation of fuel economy than suggested by how they are categorized in the 

table.

39 Note that Greene reviewed earlier versions of the papers by Allcott and Wozny, Busse et al., Langer and 

Miller, Klier and Linn, and Gramlich. Of these revised papers, Busse et al. should move to the “about 

right” category.
52

Table 2. Summary of Main Sources of Variation in Vehicle Choice Models

Model Characteristics

Methods

Estimated models

Nested Logit

Mixed Logit

Two-stage methods to address endogeneity

Calibrated models

Consumer Data

Individual purchaser (micro) data

Vehicle sales data

Aggregate (macro) data

Combined micro and aggregate data

Choices included in model *

Only market shares

New vehicle sales

Used vehicle sales

Vehicle miles traveled

Market Assumptions

Supply side market structure

Competitive

Oligopoly

Fuel economy market structure

Equilibrium in fuel economy choice

Possible disequilibrium in fuel economy choice

Inclusion of Fuel Economy in Regression

Fuel economy as explanatory variable 

Fuel cost (price per mile or miles per dollar) as explanatory variable

Vehicle purchase price less exogenously calculated fuel costs (both 

in either present value or annualized terms) as explanatory variable 

Tradeoffs with other attributes explicitly modeled

* Note: There is wide variation in other included and excluded variables.
53

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