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.
 

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

By Gloria Helfand and Ann Wolverton,1

U.S Environmental Protection Agency 

Abstract

In modeling how the U.S. market responds to changes in national fuel economy 

standards, the question of how consumers evaluate trade-offs between the cost of 

consuming more fuel economy than they would otherwise choose and the expected fuel 

savings that result is potentially quite important. Consumer vehicle choice models are a 

means to predict the change in vehicle purchase patterns, as well as the effects of these 

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

consumer 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. 

Key Words: Consumer behavior, vehicle purchase decisions 

NCEE Subject Area Classification: Transportation, benefit-cost analysis, modeling 

 

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. 

1
 

1. Introduction 

Thus far, at the national level the primary means of reducing greenhouse gas 

(GHG) emissions from vehicles has been via increasing the stringency of Corporate 

Average Fuel Economy (CAFE) standards.2 Because the major source of GHG emissions 

from vehicles is burning fuel, reducing fuel consumption will reduce emissions.3

 

Consumers have reasons to 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. Higher fuel prices do seem to trigger efforts 

on the part of consumers to reduce their gasoline consumption. However, there still 

appear to be many low cost opportunities to reduce fuel consumption that are not 

undertaken, in spite of the likelihood that they will pay off in the end. Possible 

explanations for why consumers may under-value energy efficiency include uncertainty, 

risk aversion, lack of interest in fuel savings, bounded rationality, confusing information, 

loss aversion, and unavailability of preferred choices. From a public policy perspective, 

it is unclear whether these problems justify additional fuel economy requirements. Some 

consumers are likely to appreciate the additional fuel savings that they did not believe 

would actually occur; other consumers are likely to be made worse off by incurring the 

additional costs for an attribute of little interest to them. 

In regulatory analyses, the U.S. Environmental Protection Agency (EPA) and the 

National Highway Traffic Safety Administration (NHTSA) often assume that the market 

 

2

 CAFE standards require the sales-weighted fuel economy of its fleet to achieve minimum levels 3

 Government intervention in markets is commonly justified by market failures, such as reducing the 

environmental externalities associated with vehicle tailpipe emissions. These types of benefits from such 

intervention are in addition to any deficiencies on the part of consumers and producers that have been the 

subject of this review. 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. 

2
 

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

attributes and prices – that is, changes that consumers are unlikely to notice – this 

procedure is not a bad approximation, since the changes may not affect consumer vehicle 

purchase patterns. Proposals to increase fuel economy substantially in the next few 

years, on the other hand, may change both vehicle attributes and the price of vehicles 

enough that consumers may rearrange what vehicles they purchase. Consumer vehicle 

choice models are ways to estimate the changes in fleet mix. In addition, the models can 

be used to estimate changes in consumer surplus associated with these changes. 

 This paper reviews the literature on the role of fuel economy in consumers’ 

vehicle purchase decisions. Section 2 reviews the state-of-the-art in consumer vehicle 

choice modeling, with a focus on the role of fuel economy in these models. Sections 3 

and 4 examine consumer and producer behavior in the market for fuel economy. Is 

consumers’ valuation of fuel economy comparable to the value of the savings they get 

from lower fuel use? If not, why might they not pay for money-saving improvements? 

And if consumers actually would buy vehicles with greater fuel economy, then why 

hasn’t competition among automakers to capture greater market share led to the supply of 

those vehicles? Finally, this paper assesses the potential contributions of consumer 

vehicle choice modeling to regulatory analysis, as well as some of their limitations. 

2. State of the Art of Consumer Vehicle Choice Modeling 

Consumer vehicle choice models (CCMs) are one tool used by analysts to 

examine how consumers weigh different attributes when deciding what vehicles they 

buy. The models typically take into consideration both consumer characteristics (such as 

income, family size, and age) and vehicle characteristics (including a vehicle’s power, 

3
 

price, and fuel economy). Once estimated, these models are often used to examine how a 

consumer’s vehicle purchase decision is affected by a change in a particular vehicle or 

personal characteristic. While the focus is on the consumer, many of these models also 

specify aspects of the auto producers’ side, in recognition that it is the interaction 

between producers and consumers that leads to observed market outcomes. 

Reflecting the complexity of consumers’ decisions, CCMs involve a number of 

modeling and data choices. This section discusses the range of CCMs that have been 

developed in recent decades. It reviews the methods, data sources, characteristics 

included in the models, and research questions asked. As the analysis shows, it has been 

and continues to be a rich area of research, because of the significance of the auto 

industry in the U.S. economy, large fluctuations in fuel prices over time, and a multitude 

of technical challenges. A review of the literature also suggests that consumer vehicle 

choice models have produced widely varying estimates of the role of fuel economy in 

consumers’ purchase decisions. 

2a. Methods 

Consumer choice models (CCMs) of vehicle purchases typically model choices as 

unordered and discrete rather than continuous.4

 The choice is typically defined as 

whether to purchase a specified vehicle. The result of these models is an estimate of the 

probability that a consumer will purchase a specified vehicle. A minor variant on 

discrete choice models estimates the market share for each vehicle. Because the market 

share is, essentially, the probability that consumers will purchase a specific vehicle, these 

 

4

 An example of a continuous decision is how many pounds of food a farm might grow: the pounds of food 

can take any numerical value. 

4
 

approaches are similar in process; they differ mostly in the kinds of data that they use and 

in the interpretation of results. 

The primary methods used to model vehicle choices are nested logit and mixed 

logit.5 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. The analyst restricts 

substitution patterns among alternatives by the way the data are organized into nests. 

There is no definitive way to test whether one nesting structure is superior to another, 

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

remain unsettled even when different nesting structures result in significant differences in 

results. 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 make the model 

converge to a unique solution. Examples of nested logit models include Goldberg (1995, 

1998), Greene, Duleep, Elliott, and Ahmad (2005), and McManus (2006). 

A mixed logit relaxes the restrictions on substitution patterns imposed in a nested 

logit structure by allowing for random variation in tastes. The addition of a random 

variable as an explanatory variable can capture the effects of unobserved characteristics 

that are randomly distributed in the population. Multiplying a (different) random variable 

by another variable in the analysis (such as a consumer’s income) similarly is intended to 

capture variation across the population in the effects of that variable on consumer 

 

5

 Discrete-choice equations often model the choice as Yes = 1, No = 0. A common tool for estimating 

discrete choices is a logit; its defining characteristics include a dependent (endogenous) variable whose 

values are between 0 and 1, and a specific distribution for the error term. 

5
 

purchases. For instance, if there is variation in how people’s income affects their choices 

– some people, for instance, buy luxury cars when they get wealthier, while others do not 

change their choices – the mixed logit structure allows for this variation. Because a 

mixed logit approach allows unobserved personal characteristics of consumers to enter 

the model, it allows the analyst to evaluate the effects of a change in one factor on a 

range of consumers that respond in different ways (for instance, only some individuals 

change their purchases when their income changes). Nested logit can only look at the 

effects of a change in average consumer characteristics (how, on average, income affects 

consumer purchases). Examples of mixed logit models include Berry et al. (1995, 2004), 

Bento et al. (2005), and Train and Winston (2007).6 

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

choice modeling, others (such as Kleit (2004), Austin and Dinan (2005), and McManus 

and Kleinbaum (2009)) have used a matrix of demand elasticities to estimate the effects 

of changes in cost. At times these are borrowed from other studies that have estimated 

them using discrete choice models (e.g. from Train and Winston 2007). In addition, 

Bordley (1993) proposes a method for calculating cross-price elasticities (the effect of 

increasing the price of one vehicle on sales of other vehicles) 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. GM has collected information on second 

choices in surveys to vehicle buyers for a number of years. Kleit (2004) uses GM data 

 

6

 Train (2003) is one source for further explanation of these methods. 

6
 

and an approach similar to Bordley to estimate elasticities that he then uses in model 

simulations; Austin and Dinan base their consumer model on Kleit’s elasticities. 

In addition to the modeling approach, several other methodological issues arise in 

the context of consumer 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 vehicle characteristics not otherwise included in the analysis (for instance, 

if the distinction between luxury cars and others is omitted), then the analysis may 

produce biased - that is inaccurate - estimates of the role of price in consumers’ purchase 

decisions. As a result, some studies (Berry et al. 1995, Goldberg 1995, Klier and Linn 

2008, Allcott and Muehlegger 2008) use instrumental variable methods to correct for 

endogeneity.7

 The results in these papers indicate that ignoring the endogeneity leads to 

different results than in their full models. In Berry et al., 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. Thus, model specification can affect the results 

and their interpretations. 

More recent literature addresses endogeneity in vehicle choice and technology 

decisions in novel ways. Bento et al. (2005) and Jacobsen (2008) incorporate, not only 

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

 

7

 Berry et al. and Goldberg are notable as well for their incorporation of oligopolistic behavior on the part 

of automakers, a relatively common practice afterwards (e.g., Bento et al. 2005; Austin and Dinan 2005, 

Allcott and Muehlegger 2008). 

7
 

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

Market prices for the vehicles are also treated endogenously in the models. For instance, 

in Klier and Linn (2008) 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 (2008) combine macroand 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. 

2b. Data Sources 

CCMs that model how consumers choose a vehicle for purchase commonly rely 

on detailed information on the consumer, vehicle purchased, and other vehicles available 

to the consumer at the time of purchase that were not chosen. The CCMs identify the 

effects of changing either consumer or vehicle characteristics on a household’s 

purchasing decision. These effects are typically called the parameters or coefficients of 

the models. For instance, the model parameters may predict that a 10 percent increase in 

a household’s disposable income would increase the probability of purchasing vehicle A 

by 5%, and decrease the probability of purchasing vehicle B by 10%. The parameters in 

CCMs can be developed either from estimations based on original data sources 

(estimated models) or values taken from other studies (calibrated 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 

8
 

(e.g., Bento et al., 2005; Train and Winston, 2007). Because they draw on the behavior 

of individual households, they provide what is sometimes called micro-level data. Microlevel data can be very valuable because of the high level of detail about the role of 

households’ traits in their purchase decisions. Other studies estimate market shares 

instead of discrete purchase decisions using aggregated data that can cover long time 

periods (e.g., Berry et al., 1995). Aggregate data tend to be more easily accessible than 

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

addition, the ability to look at data over long time periods allows for examination of 

trends or changes in tastes. It is also possible to combine micro-level data with 

aggregated data (Berry et al. 2004, Allcott and Muehlegger 2008). Each of these 

estimated CCMs provides information about the factors that influence vehicle choice. 

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

appropriate use of statistical methods. 

Calibrated models rely on existing studies for their parameters. Researchers may 

draw on results from estimated CCMs or other research to choose the parameters of the 

models. Kleit (2004) develops a set of elasticities, based on survey data that included 

information on purchasers’ second choices as well as their actual choices; Austin and 

Dinan (2005) use these elasticities in their analysis. The Fuel Economy Regulatory 

Analysis Model developed for the Energy Information Administration (Greene, Duleep, 

Elliott, and Ahmad, 2005) 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 

9
 

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. 

2c. Factors Included in the Models 

Consumer choice models have been developed to analyze many different research 

and policy questions. In part, these models have been developed to advance the state of 

economic modeling: the work of Berry et al. (1995), for instance, is often cited outside 

the motor vehicle context for its incorporation of multiple new modeling issues into its 

framework. In addition, because the vehicle sector is a major part of the U.S. economy 

and a stakeholder in many public policy discussions, research questions cover a wide 

gamut. These topics have included the effects of voluntary export restraints on Japanese 

vehicles compared to tariffs and quotas (Goldberg 1995), the market acceptability of 

alternative-fuel vehicles (Brownstone and Train 1999; Brownstone, Bunch, and Train 

2000; Greene 2001; Greene et al., 2004), the effects of introduction and removal of 

vehicles from markets (Petrin 2002; Berry et al. 2004), causes of the decline in market 

shares of U.S. automakers (Train and Winston 2007), and the effects of gasoline taxes 

(Bento et al., 2005, Feng et al. 2005) and “feebates” (subsidizing fuel-efficient cars with 

revenue collected by taxing fuel-inefficient vehicles) (Greene, Patterson, Singh, and Li, 

2005; Feng et al. 2005; Greene et al. 2009a). 

The research question of interest affects the structure of the consumer choice 

model. Some studies focus only on the new vehicle market (e.g., Train and Winston, 

2007); others consider the choice between new vehicles and an outside good (possibly 

including a used vehicle) (Berry et al., 1995); others explicitly consider the relationship 

between the new and used vehicle markets (e.g., Bento et al., 2005). Some models also 

10
 

consider vehicle miles traveled (e.g., Goldberg, 1998; Bento et al., 2005; Feng et al., 

2005), though most do not. A model focused on market share, such as Train and Winston 

(2007), does not consider the choice of whether or not to buy a vehicle, or whether to buy 

a new or used vehicle. This approach is sufficient, for instance, to focus on the question 

of why there has been a decline in market shares of domestic U.S. auto producers, but it 

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

Some models (e.g., Berry et al., 1995; Goldberg, 1995) include a generic “outside good” 

as a way to incorporate the decision on whether to buy a new vehicle; this approach can 

estimate total market size, but it cannot look at the effect of changed new vehicle sales on 

the used vehicle market. Bento et al. (2005) is unusual in its incorporation of different 

vehicle vintages as well as consumers’ choices on miles driven, so that it can examine the 

effect of policies on both the used vehicle market and total miles driven. 

In a statistical analysis, it is important to include all the variables in a demand 

model that are likely to affect a consumer’s choices. Otherwise, it is possible that the 

relationship between an included variable and the consumer’s choice will be incorrect, 

through what is termed “omitted variable bias.” For instance, a vehicle’s size may be 

related to its price: all else equal, larger vehicles tend to be more expensive. If size is not 

included in the model, then the coefficient on price will also measure, in part, the effect 

of a vehicle’s size. As a result, the model cannot predict the effects of a price change 

separately from the effects of changing size. For this reason, the results from consumer 

vehicle choice models may be difficult to interpret. Gramlich (2008) 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 

11
 

is expensive to drive per mile, all else equal; this result is expected. He also finds, 

though, 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 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.8 The choice of key variables depends on 

the research question on which the model focuses. For instance, to incorporate vehicle 

miles traveled into her model, Goldberg (1998) uses a more aggregated version of the 

model in Goldberg (1995): she does not include individual model characteristics, but 

rather only vehicle classes differentiating between foreign and domestic. NERA 

Economic Consulting has used different specifications of its New Vehicle Market Model: 

in its July 2008 comments on the National Highway Traffic Safety Administration’s 

proposed fuel economy standards, it included only price as a dependent variable; in its 

April 2009 study, it includes price divided by income as a dependent variable. Both 

models are calibrated, with alternative-specific coefficients picking up all other vehicle 

attributes besides price; income is addressed as a random variable with a known 

distribution. Most other models include a wide range of consumer and vehicle 

characteristics, such as vehicle size, fuel economy, and family characteristics. 

 

8

 Mixed logit models seek to account for omitted variables through the use of random variables. This 

method assumes that the omitted variables are not associated with the error term -- for instance, that an 

omitted characteristic such as the quality of the stereo system is distributed randomly among vehicles in the 

population. If, however, the quality of the stereo system is associated with, e.g., the price of the vehicle, 

then the mixed logit will still lead to biased coefficients. 

12
 

2d. Results from CCMs on Fuel Economy 

In consumer 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 

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. Those savings are considered to reduce the cost of owning a vehicle: 

effectively, they reduce 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 fuel-efficient 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 remains, then, 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, most estimated consumer choice models use data on consumer 

behavior to identify that effect. In some models, a vehicle’s miles per gallon is 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: since consumers pay for gallons of fuel, then 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 fuel consumption to get miles per dollar (e.g., 

13
 

Berry et al., 1995; Petrin, 2004). 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. 

Among others, Greene et al. (2009b) and Klier and Linn (2008) note that many 

improvements in the efficiency of engine technology have led, not to increases in fuel 

economy, but to increases in power and vehicle weight. Klier and Linn, as well as 

Allcott and Muehlegger (2008), seek to build into their models the tradeoffs among fuel 

economy, power, and weight. Klier and Linn find that their results are sensitive to the 

way that these tradeoffs are incorporated. It is possible, then, that the effect of fuel 

economy on consumers’ choices may be sensitive to model specification. If so, then 

models may not predict consumer choices or preferences accurately without careful 

attention to these relationships. 

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

and estimated for each one how much consumers would be willing to pay at time of 

purchase to reduce vehicle operating costs by $1 per year. They find that people were 

willing to pay between $0.74 and $25.97 for a $1 decrease in annual operating costs of a 

vehicle. This is clearly a very wide range: the lowest estimate suggests that people are 

not willing to pay $1 once to get $1 per year reduced costs of operating their vehicle; the 

maximum suggests a willingness to pay 35 times as much, almost twice as much as the 

undiscounted fuel savings over the typical 15-year lifetime of a vehicle. For comparison, 

the present value of saving $1 per year for 15 years at a 3% discount rate is $11.94, while 

a 7% discount rate produces a present value of $8.78. While this study is quite old, it 

14
 

suggests that, at least as of that time, consumer vehicle choice models produced widely 

varying estimates of the value of reduced vehicle operating costs. 

More recent studies suggest continued disagreement on the value of increased fuel 

economy to consumers. For instance, some papers (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 

(2008) 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 may be 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 undesirable attributes, while fuel cost picks up the 

consumer’s demand for improved fuel economy. 

Busse et al. (2009) focus solely on the relationship between 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. In the used vehicle market, on the other hand, prices of high 

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

15
 

rise. These results suggest that consumers pay attention to the fuel economy of the 

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

markets respond in different ways. 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. Given 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 report their findings on this parameter. Espey and Nair (2005) find, using 

data from model year 2001, that consumers might be willing to pay roughly $500 for a 1-

mpg increase in city driving, approximately $250 for a 1-mpg 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. McManus (2006) finds, in 2005, that consumers 

were willing to pay $578 for a 1-mpg increase in fuel economy. Gramlich (2008) reports 

his results in terms of an increase from 25 mpg to 30 mpg; his estimates of willingness to 

pay range between $4100 for this 5-mpg increment (for luxury cars, when gasoline costs 

$2/gallon) to $20,600 (for SUVs when gasoline costs $3.50/gallon). 

2e. Assumptions of Market Efficiency in CCM 

Most consumer 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 

16
 

economy in their vehicles if the cost of the additional fuel economy is less than 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 12,000/20 = 600 gallons per year, while the latter uses 12,000/25 = 480 

gallons per year, 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 a 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. 

Most consumer choice models (e.g., Kleit 2004; Austin and Dinan 2005; Klier 

and Linn 2008; Jacobsen 2008) start with the assumption that the market for fuel 

economy works efficiently, absent an accounting for externalities: that is, producers are 

offering as much fuel economy on vehicles as consumers are willing to buy. 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 (Gramlich 2008; 

McManus 2006; McManus and Kleinbaum, 2009) explicitly find that both automakers 

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

17
 

note that they have to adjust their model to eliminate such gains. If the amount 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 other 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. 

2f. Potential contributions of consumer vehicle choice modeling to regulatory 

analysis 

In modeling the impacts of vehicle regulation, EPA and NHTSA typically assume 

that the fleet mix – the market shares of specific vehicles – stays constant in response to 

new regulation. There may be overall impacts on the number of new vehicle sales, due to 

changes in vehicle costs, but, in the absence of consumer choice modeling, they are 

unable to predict changes in market shares. For small changes in vehicle prices and 

attributes, the market shares may not adjust very much, and the omission of consumer 

vehicle choice modeling may not miss notable market effects. 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. 

Consumer vehicle choice models allow for the possibility that the fleet mix will 

respond to changes in costs and vehicle attributes. In response to new fleet-wide fuel 

economy requirements, automakers may sell more existing high-mpg vehicles -- for 

instance, 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 

18
 

standards. Automakers are expected to pursue the combination of changes in vehicle 

prices and additions of technologies that they expect to maximize their profits. If 

inducing consumers to buy a 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. 

Work currently underway by Resources for the Future is aimed at shedding light 

on whether it is much more costly to rely solely on the application of technologies to the 

vehicle to meet a given fuel standard than when consumer behavior is taken into account. 

Initial work used a market-share model, which, unlike a true demand model, does not 

examine the effect on total fleet sales. One result from this work (Coleman and 

Harrington 2008) is that doubling fuel economy improvement appears to more than 

double the average compliance cost when consumer demand is fixed; when consumers 

are allowed to adjust, however, the average compliance cost is much closer to a linear 

function, reflecting consumers’ tendencies to respond to increases in vehicle prices by 

shifting to less expensive models. Also, since regulations can change the structure of the 

automobile market and the types of vehicles produced, consumer response may matter 

when evaluating the costs associated with different forms of the standard (e.g., whether 

the standard varies for different auto producers based on the characteristics of the 

vehicles they produce). Work currently underway has evolved to modeling the vehicle 

market in a true demand modeling framework with an outside good available for 

purchase (e.g., a used vehicle). The producer side of the market is also modeled as an 

oligopolistic market in the newer work. 

19
 

An additional output from consumer choice modeling is the effect of changes on 

consumer surplus. Consumer surplus is the benefit that a consumer gets over and above 

the market price paid for the good. For instance, if a consumer is willing to pay up to 

$30,000 for a car but is able to negotiate a price of $25,000, the $5000 difference is 

consumer surplus. Because consumer choice models are able to estimate the maximum 

willingness to pay for a vehicle, these models are capable of calculating effects on 

consumer surplus. Current modeling by EPA and NHTSA of changes in fuel economy 

standards, for instance, compares the fuel savings with the technology costs of additional 

fuel economy. 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 (or no vehicle) with a different set of attributes compared to what they would 

have purchased in the absence of higher standards. Consumer surplus estimated from 

consumer choice models could measure these combined effects and provide a more 

encompassing benefits estimation. 

Because consumer 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 only a few years’ time -- in response to changing market 

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

20
 

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. 

In summary, consumer 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. There has been relatively little systematic 

comparison of consumer choice models: while Greene and Liu (1988) compare the value 

of reducing vehicle operating costs across a number of models, the study has not been 

updated despite substantial modeling developments that have occurred since that time. It 

appears that the studies conducted since Greene and Liu 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, might not spend as much on fuel 

economy as an objective evaluation of the resulting financial savings would suggest. 

3. Do Consumers Value Fuel Economy “Correctly?” 

EPA and NHTSA analyses of increasing the fuel economy standard for light duty 

vehicles routinely find that 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 up-front 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 

21
 

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 energyconserving cost savings and what consumers actually spend on energy conservation is 

often referred to as the Energy Paradox (IEA 2007; Jaffe, Newell, and Stavins 2001; 

Metcalf and Hassett 1999; Tietenberg 2009), since consumers appear to routinely 

undervalue a wide range of investments in energy conservation; it has been most studied 

in consumers’ decisions related to energy-saving products and appliances, such as light 

bulbs and washing machines. Possible explanations for the paradox include: consumers 

who put little weight on the future; consumer disinterest in fuel economy; the inability to 

find a specific vehicle with improved fuel economy; the difficulty in calculating expected 

fuel savings; bounded rationality; uncertain fuel savings contrasted with certain and 

immediate increased costs; loss aversion; and variation in consumer benefits from 

improved fuel economy. We discuss each of these in turn. 

Recognizing that consumers do not buy as much energy conservation as a simple 

present value calculation would suggest, government agencies have 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 Agency (1996) has used discount rates as high as 111 percent for water 

heaters and 120 percent for electric clothes dryers in their analyses. 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 

22
 

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

longer (on average, 5 years).9 Rubin and Fin (2009) also find some evidence that 

consumers undervalue the stream of future fuel savings that would result from 

improvements in fuel economy. While this explanation points to similarly heavy 

discounting of future returns in fuel savings, it says little about the underlying reasons 

why consumers may be doing so. Various explanations have been proposed for the 

reluctance of the private market to invest in energy efficiency. It is worth noting that 

little empirical evidence exists to support one hypothesis over another. 

If consumers are well informed but relatively indifferent to increased fuel savings 

compared to improvements in other vehicle attributes they may purchase, then it is 

possible that no market failure exists. If so, then seemingly low cost opportunities to 

improve fuel economy simply do not pay for themselves in spite of appearances that they 

should. The American Council for an Energy-Efficient Economy (ACEEE) (2007) finds 

in interviews with consumers that, while fuel economy is mentioned as a concern when 

making a vehicle purchase, it ranks below reliability, price, features, and safety in order 

of importance. Busse et al. (2009) provide evidence that the role of fuel economy in 

people’s vehicle purchases varies with fuel prices: high fuel prices lead to increased sales 

of high-mpg new vehicles, and higher prices for high-mpg used vehicles. West (2008) 

offers preliminary evidence that consumers pay more attention to past gasoline prices 

than present ones when making vehicle purchase decisions, while the Congressional 

Budget Office (CBO) (2008) notes that the longer higher gasoline prices are maintained, 

the more likely that they will affect consumers expectations about future gasoline prices 

 

9

 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. 

23
 

and thus consumers’ longer term decisions, such as which vehicle to purchase. It is 

worth noting that the degree to which demand for fuel economy varies with fuel prices or 

other related characteristics is only beginning to be studied. 

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

economy can also be used to improve vehicle performance or increase vehicle size. 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 this 

occurrence in passenger cars. 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. (2009b) 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. 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 met the cost. 

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 to vary some individual attributes when contemplating a 

particular vehicle – for instance, a consumer can often choose between manual and 

automatic transmission for the same make and model -- fuel economy is not usually one 

24
 

of them;10 there is no independent 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. 2009b). 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 (Nye 2002). This result is consistent with Gramlich’s (2008) 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, in 

consumers’ minds, something other than fuel economy. 

Recent evidence points out that consumers may not understand how to calculate 

fuel savings correctly when making vehicle purchase decisions and thus are not making 

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

concept of “bounded rationality,” that consumers optimize their behavior without full 

information by resorting to imprecise but convenient rules of thumb. This allows for the 

possibility that consumers may make mistakes when evaluating fuel economy relative to 

other vehicle attributes available for purchase. 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 

 

10 There are a couple exceptions. For instance, one can purchase a hybrid or non-hybrid version of the 

Honda Civic. 

25
 

they would be willing to pay for their preferred vehicle with a 50% 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 gasoline savings than improving a 35 mpg vehicle 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. As a result, participants appeared to 

substantially undervalue improvements in fuel efficiency for relatively fuel-inefficient 

vehicles and to overvalue those improvements for relatively fuel-efficient vehicles.11 

Another potential explanation for the Energy Paradox is the notion of 

irreversibility in investment (Metcalf and Rosenthal 1995). The argument is as follows: 

 Energy conservation investments require up-front capital investments that are 

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

the return never materializes. 

 The return (e.g. the reduction in operating costs) is uncertain due to 

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

 

11 In their experiment, participants were asked to assess fuel savings in a gallons-per-mile framework or in 

a mpg framework. A substantially larger number were able to identify a more fuel efficient option with 

gallons per mile than when the same scenario was expressed in mpg. The authors argue for reporting fuel 

efficiency in gallons per mile, or per hundred miles, to aid consumers in accurately assessing the savings 

from investing in increased fuel efficiency 

26
 

 Purchasers have flexibility with regard to the timing of their investment. They 

can always postpone it until they have more and better information on 

potential returns. 

These characteristics imply that a more cautious approach to investment is 

optimal for the consumer, and that the required rate of return for the investment is often 

substantially higher than the underlying discount rate applied by consumers. 

Specifically, Metcalf and Rosenthal show, for two energy efficiency examples, that 

estimated discount rates are biased upward when irreversibility and uncertainty are 

ignored. They estimate that the consumer discount rate is somewhere between 5 and 10 

percent for these examples – previous studies found much higher discount rates. Thus, 

they argue that apparently high discount rates appear to be at least partially attributable to 

not accounting for uncertainty and irreversibility. 

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

a theory related to that of Metcalf and Rosenthal (1995). They argue for uncertainty with 

regard to fuel savings 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. Empirical evidence 

points to a 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). 

Thus, because consumers evaluate decisions not in terms of their impact on uncertain 

future wealth, but in terms of potential changes from present, known wealth, they have a 

preference for maintaining the status quo -- avoiding increased vehicle costs in the 

present -- and would have to be offered a premium over the expected value of fuel 

27
 

savings to invest in the new vehicle technology. When taking these aspects of consumer 

decision making into account, Greene et al. (2009b) 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 regarding the future price of gasoline is less important than 

uncertainty with regard to vehicle lifetime. While the median lifetime of a passenger 

vehicle is 14 years, this implies that half of the vehicles on the road will not last that long 

due to mechanical failure or accidents. 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.12 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. 

 

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, EPA states 

on its fuel economy sticker that actually fuel economy is likely to vary from EPA 

12 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. 

28
 

estimates with maintenance and use (e.g., 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 empirical evidence that this is indeed 

the case. Relying on self-reported data, they find that EPA fuel economy estimates listed 

on labels at the time of purchase are unbiased, but that they are also highly inaccurate 

indicato

 of the actual discount rates that people have, 

and thu

 

rs of the actual fuel economy achieved.13 

Another alternative explanation for the Energy Paradox that is discussed in the 

literature is variation across consumers in their discount rates, expected uses of the 

product, the lifespans of the investment, the purchase prices, and other costs. This 

variation can lead to differences in the expected value that individual purchasers attach to 

energy-efficient products. For example, it may not make sense for someone who rarely 

uses an air conditioner to spend more money on an energy-efficient model: they will not 

recoup their investment through energy savings. In a study of consumer air conditioning 

purchases, such a person may appear instead to have a very high discount rate. 

Averaging the discount rates that people appear to have, without accounting for consumer 

heterogeneity, may lead to overestimates

s exaggerate the Energy Paradox. 

In sum, 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 paradox include: consumers who put little weight on the future; consumer 

disinterest in fuel economy; the inability to find a specific vehicle with improved fuel 

economy; the difficulty in calculating expected fuel savings; bounded rationality; 

13 Using drivers’ own estimates of the fraction of driving done in city versus highway driving conditions 

improved the ability to predict fuel economy for a particular vehicle by 19 percent. 

29
 

uncertain fuel savings contrasted with certain and immediate increased costs; loss 

aversion; and variation in consumer benefits from improved fuel economy. It is worth 

noting, as discussed above, that the empirical evidence from econometrically estimated 

consumer choice models varies widely regarding the existence of an Energy Paradox in 

fuel economy. Greene and Liu (1988) find widely varying estimates of the amount 

consumers are willing to pay for increased fuel economy. More recently, Espey and Nair 

(2005) argue that consumers appear to purchase approximately the cost-minimizing 

amount of fuel economy. The wide variation in estimates suggests that the value of 

increas

ch as reduced air pollution, that may outweigh any 

private loss in consumer welfare. 

ed fuel economy to consumers deserves increased research. 

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, it is likely to give consumers actual savings in costs that, because of the 

complexity of the calculation and uncertainties about future payback, they did not 

appreciate at the time of purchase; the requirement may compensate for a boundedly 

rational but not globally rational decision. On the other hand, in this situation the 

government is imposing a requirement that consumers may not want; fuel economy may 

not be important at the time of purchase for a variety of reasons. It is also important to 

keep in mind that there may be other policy justifications -- not discussed here -- for 

decreasing fuel consumption, su

30
 

4. Reasons why producers may not provide as much fuel efficiency as 

consumers want to buy 

The empirical literature has spent very little time focusing on 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 in 

consumer decision making. Manufacturers also play a role, particularly in how they view 

consumers and what they would be willing to pay for various attributes. The following 

discussion posits several hypotheses on the automakers’ role in the market for fuel 

economy. Little research has been done to date to investigate their relative merits. 

As previously mentioned, several recent analyses (Greene et al. 2009a; Klier and 

Linn 2008) argue that improvements in engine efficiency have been channeled into other 

vehicle characteristics, such as power and acceleration, rather than fuel economy. 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 the additional fuel 

economy and for the additional acceleration or power. 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. 

One possible explanation for seemingly suboptimal behavior on the part of 

automakers is analogous to the effect of risk aversion on consumers. If large auto 

31
 

manufacturers are risk averse, then investments viewed as inherently more risky are less 

likely to be undertaken. Since the fuel savings that accrue to consumers in the future are 

uncertain, it is less likely that manufacturers will invest in fuel economy (Greene et al. 

2009b), and more likely that these technologies will be put towards attributes for which 

consumers have shown a strong and consistent preference and from which consumers 

will get 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 certain 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. 

Vehicle design and development also can take several years. In response to 

regulations and to market conditions, automakers may want to change the attributes of the 

vehicles that they sell, but those changes cannot happen as quickly as fluctuations in fuel 

price. As a result, the fuel economy that automakers include in their vehicles may lag 

unexpected changes in the market by several years. 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 

32
 

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 (2008) attempts to explicitly account for the 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, 

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. 

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. They reject alternative 

hypotheses, such as brand loyalty, dealership networks, and health care and other legacy 

costs. According to their results, U.S. manufacturers had provided relatively less value 

relative to the cost for 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 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 

33
 

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 a 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, as discussed above, by risk aversion. It is also possible that 

U.S. manufacturers made a rational decision to specialize in larger, less fuel efficient 

vehicles and that this became a large disadvantage as fuel prices increased, given their 

inability to quickly change the vehicles that they produce. 

In sum, possible non-profit-maximizing behavior on the part of auto 

manufacturers has received even less attention than the consumers’ Energy Paradox. 

Possible reasons for the alleged non-profit-maximizing behavior include risk aversion; 

the decision to focus, in designing model variations, on characteristics other than fuel 

economy; time lags inherent in the production process; and internal decision-making 

processes that have not been responsive to changes in consumer preferences. As with 

consumers, uncertainty is a normal part of markets, and risk aversion is one way of 

coping with it: though risk averse behavior is likely to reduce average profits, it should 

also reduce the variance of profits and thus make a company more stable. Economic 

theory predicts that manufacturers have strong incentives to undertake all profitable 

34
 

product development; the market would be expected to reward companies that make 

correct predictions, and punish producers whose products do not adequately fulfill 

consumers’ desires.14 The state of major change in the auto industry in 2009 is due both 

to short-term shocks associated with high gasoline prices in 2007 and 2008 and the 

subsequent recession, and to longer-lasting market forces that has led to industry 

restructuring. The relative proportions of these changes is 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 

regarding possible sub-optimality in how producer make vehicle production decisions. 

On the one hand, many policymakers are reluctant to let the market provide harsh 

discipline for non-profit-maximizing behavior in the auto industry, due to the importance 

of the industry and the large effects on workers and other affiliated industries, especially 

in a recession. On the other hand, if automakers, who have a profit motive to make 

vehicles that people want, have trouble finding the best set of attributes to offer to 

consumers, are government agencies likely to have better insights? 

5. Conclusion 

In regulatory analyses, EPA and NHTSA often hold the market shares of the 

vehicle fleet constant. For rules that lead to small changes in vehicle attributes and prices 

– that is, changes that consumers are unlikely to notice – this procedure is not a bad 

approximation, since the changes may not affect consumer vehicle purchase patterns. 

Proposals to increase fuel economy substantially in the next few years, on the other hand, 

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

 

14 Of course, GM and Chrysler recently have gone through bankruptcy proceedings. 

35
 

rearrange what vehicles they purchase. Consumer vehicle choice models are ways to 

estimate the changes in fleet mix. In addition, the models can be used to estimate 

changes in consumer surplus associated with these changes. 

As this review has shown, a number of researchers have developed consumer 

vehicle choice models. These models vary along a number of dimensions, including 

analytical methods, data sources, and research questions addressed. Of particular interest 

for fuel economy-related rules, the models appear to produce widely varying estimates of 

the value that consumers place on fuel economy. This value plays a major role in the 

issues surrounding consumer vehicle choice modeling: its magnitude affects the 

estimates of consumer surplus as well as debates over the efficiency of the market for 

vehicle fuel economy. As discussed, this parameter deserves additional research. 

Consumer vehicle choice modeling has the potential to provide useful information 

for regulatory analysis of the effects of vehicle changes. To make the modeling useful in 

practice, it is important to identify the factors in the models around which there appears 

to be consensus, as well as the important factors that lead to variation in model results. 

Even with the ability to model consumer choice, the literature still leaves open the 

question of how consumers value fuel economy, and why their willingness to pay for 

more of it does not appear to equal the expected value of the fuel savings. In the general 

context of energy efficiency, this observation is known as the Energy Paradox. 

Explanations, which typically center on why consumers may under-value energy 

efficiency, include uncertainty, risk aversion, lack of interest in fuel savings, bounded 

rationality, confusing information, loss aversion, and unavailability of preferred choices. 

From a public policy perspective, it is an open question whether these problems justify 

36
 

additional fuel economy requirements. Some consumers are likely to appreciate the 

additional fuel savings that they did not believe would actually occur; other consumers 

are likely to be made worse off by incurring the additional costs for an attribute of little 

interest to them. 

Much less research exists on whether or why producers might provide less fuel 

economy than consumers are willing to buy. Some possible explanations include risk 

aversion, investing in characteristics other than fuel economy, time lags in responding to 

market changes, and flawed decision-making processes within the companies. Economic 

theory argues that markets should discipline companies that do not provide the products 

people want at suitable prices; whether government intervention is warranted if auto 

companies make mistakes is a current policy debate. 

Consumer decisions on vehicle purchases involve complex tradeoffs among many 

dimensions. For environmental and energy policy, the dimension of most interest is fuel 

economy of vehicles. This review has shown that consumers pay attention to the fuel 

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’ decisions. 

37
 

References 

ACEEE (2007). “Environmental Performance Labels for Vehicles: Context and Findings 

of Market Research for the U.S. Environmental Protection Agency.” Report T071. 

Allcott, H., and E. Muehlegger (2008). “Refining Equilibria in Counterfactual 

Simulations: An Application to Corporate Average Fuel Economy Standards.” Working 

paper, available at http://www.nber.org/~confer/2008/si2008/EEE/allcott.pdf, accessed 

7/24/09. 

Austin, D., and T. Dinan (2005). “Clearing the Air: The Costs and Consequences of 

Higher CAFE Standards and Increased Gasoline Taxes.” Journal of Environmental 

Economics and Management 50: 562-582. 

Bento, A., L. Goulder, E. Henry, M. Jacobsen, and R. von Haefen (2005). “Distributional 

and Efficiency Impacts of Gasoline Taxes: An Econometrically Based Multi-Market 

Study,” American Economic Review 95(2): 282-287. 

Benartzi, S. and R. Thaler (1995). “Myopic Loss Aversion and the Equity Premium 

Puzzle.” Quarterly Journal of Economics 110(1): 73-92. 

Berry, S., J. Levinsohn, and A. Pakes (1995). “Automobile Prices in Market 

Equilibrium,” Econometrica 63(4): 841-940. 

Berry, S., J. Levinsohn, and A. Pakes (2004). “Differentiated Products Demand Systems 

from a Combination of Micro and Macro Data: The New Car Market,” Journal of 

Political Economy 112(1): 68-105. 

Bordley, R. (1993). “Estimating Automotive Elasticities from Segment Elasticities and 

First Choice/Second Choice Data,” Review of Economics and Statistics 75(3): 455-462. 

Brownstone, D., D. Bunch, and K. Train (2000). “Joint Mixed Logit Models of Stated 

and Revealed Preferences for Alternative-Fuel Vehicles,” Transportation Research Part B 

34: 315-338 

Brownstone, D., and K. Train (1999). “Forecasting New Product Penetration with 

Flexible Substitution Patterns,” Journal of Econometrics 89: 109-129. 

Busse, M., C. Knittel, and F. Zettelmeyer (2009). “Pain at the Pump: How Gasoline 

Prices Affect Automobile Purchasing in New and Used Markets,” Working paper 

(accessed 6/30/09), available at 

 http://www.econ.ucdavis.edu/faculty/knittel/papers/gaspaper_latest.pdf, 

Congressional Budget Office (2008). Effects of Gasoline Prices on Driving Behavior 

and Vehicle Markets. 

38
 

Coleman, C., and W. Harrington (2008). “A Model of New Vehicle Purchases by 

Households.” Final Interim Report for EPA. September. 

Dasgupta, S., S. Siddarth, and J. Silva-Risso (2007). “To Lease or to Buy? A Structural 

Model of a Consumer’s Vehicle and Contract Choice Decisions.” Journal of Marketing 

Research 44: 490 – 502. 

Energy Information Administration, U.S. Department of Energy (1996). Issues in 

Midterm Analysis and Forecasting 1996, DOE/EIA-0607(96), Washington, D.C., 

http://www.osti.gov/bridge/purl.cover.jsp?purl=/366567-BvCFp0/webviewable/, 

accessed 7/7/09. 

Espey, M., and S. Nair (2005). “Automobile Fuel Economy: What is it Worth?” 

Contemporary Economic Policy 23(3): 317-323. 

Feng, Y., D. Fullerton, and L. Gan, “Vehicle Choices, Miles Driven and Pollution 

Policies,” National Bureau of Economic Analysis Working Paper 11553, available at 

http://econweb.tamu.edu/gan/w11553.pdf , accessed 5/12/09. 

Goldberg, P. (1995). “Product Differentiation and Oligopoly in International Markets: 

The Case of the U.S. Automobile Industry,” Econometrica 63(4): 891-951. 

Goldberg, P. (1998). “The Effects of the Corporate Average Fuel Efficiency Standards in 

the U.S.” Journal of Industrial Economics 46(1): 1-33. 

Gramlich, J., “Gas Prices and Endogenous Product Selection in the U.S. Automobile 

Industry,” http://www.econ.yale.edu/seminars/apmicro/am08/gramlich-081216.pdf , 

accessed 5/11/09. 

Greene, D. (2009a). “Feebates, Footprints and Highway Safety,” Transportation Research 

Part D 14: 375-384. 

Greene D. (2001). “TAFV Alternative Fuels and Vehicles Choice Model 

Documentation,” prepared by the Oak Ridge National Laboratory for the U.S. 

Department of Energy. 

Greene, D., K. Duleep, D. Elliott, and S. Ahmad (2005). “A Fuel Economy Regulatory 

Analysis Model (FERAM) For the Energy Information Administration,” prepared by the 

Oak Ridge National Laboratory for the U.S. Department of Energy under contract No. 

DE-AC0500OR22725. 

Greene, D., K. Duleep, and W. McManus (2004). “Future Potential of Hybrid and Diesel 

Powertrains in the U.S. Light-Duty Vehicle Market,” prepared by the Oak Ridge National 

Laboratory for the U.S. Department of Energy. 

39
 

Greene, D., J. German, and M. Delucchi (2009b). “Fuel Economy: The Case for Market 

Failure” in Reducing Climate Impacts in the Transportation Sector, Sperling, D., and J. 

Cannon, eds. Springer Science. 

Greene, D., L. Goeltz, J. Hopson, and E. Tworek (2006). “Analysis of In-Use Fuel 

Economy Shortfall by Means of Voluntarily Reported Fuel Economy Estimates.” 

Transportation Research Record No. 1983. Transportation Research Board, Washington, 

DC. 

Greene, D., and J. Liu (1988). “Automotive Fuel Economy Improvements and 

Consumers’ Surplus.” Transportation Research Part A 22A(3): 203-218. 

Greene, D., P. Patterson, M. Singh, and J. Li (2005). “Feebates, Rebates, and GasGuzzler Taxes: A Study of Incentives for Increased Fuel Economy,” Energy Policy 33: 

757-775. 

IEA. 2007. “Mind the Gap: Quantifying Principal-Agent Problems in Energy Efficiency.” 

Paris, France: International Energy Agency. 

Jacobsen, M. “Evaluating U.S. Fuel Economy Standards In a Model with Producer and 

Household Heterogeneity,” 

http://www.econ.ucsd.edu/~m3jacobs/Jacobsen_CAFE.pdf, accessed 5/11/09. 

Jaffe, A, R. Newell, and R. Stavins (2001). "Energy Efficient Technologies and Climate 

Change Policies: Issues and Evidence." In Climate Change Economics and Policy, 

Toman, Michael A., ed., Washington, D.C.: Resources for the Future. p.171 - 181. 

Kleit, A. (2004). “Impacts of Long-Range Increases in the Fuel Economy (CAFE) 

Standard.” Economic Inquiry 42(2): 279-294. 

Klier, T., and J. Linn (2008). “New Vehicle Characteristics and the Cost of the Corporate 

Average Fuel Economy Standard,” working paper. 

http://www.chicagofed.org/publications/workingpapers/wp2008_13.pdf 

Kubik, M. (2006). Consumer Views on Transportation and Energy. Second Edition. 

Technical Report: National Renewable Energy Laboratory. 

Larrick, R. P., and J.B. Soll (2008). “The MPG illusion.” Science 320: 1593-1594. 

McManus, W. (2006). “Can Proactive Fuel Economy Strategies Help Automakers 

Mitigate Fuel-Price Risks?” University of Michigan Transportation Research Institute. 

McManus, W. (2007). “The Impact of Attribute-Based Corporate Average Fuel Economy 

(CAFE) Standards: Preliminary Findings.” University of Michigan Transportation 

Research Institute paper UMTRI-2007-31. 

40
 

McManus, W. and R. Kleinbaum (2009). “Fixing Detroit: How Far, How Fast, How Fuel 

Efficient.“ Working Paper, Transportation Research Institute, University of Michigan. 

Metcalf, G., and K. Hassett (1999). "Measuring the Energy Savings From Home 

Improvement Investments: Evidence From Monthly Billing Data." The Review of 

Economics and Statistics 81(3): 516-528. 

Metcalf, G., and D. Rosenthal (1995). "The ‘New’ View of Investment Decisions and 

Public Policy Analysis: An Application to Green Lights and Cold Refrigerators,’’ Journal 

of Policy Analysis and Management 14: 517–531. 

NERA Economic Consulting (2009). “Appendix B: New Vehicle Market Model,” 

“Impacts of the California Greenhouse Gas Emission Standards on Motor Vehicle Sales,” 

comments submitted to the U.S. Environmental Protection Agency at Regulations.gov, 

document number EPA-HQ-OAR-2006-0173-9053.1. 

NERA Economic Consulting (2008). “Evaluation of NHTSA’s Benefit-Cost Analysis of 

2011-2015 CAFE Standards,” available at 

http://www.heartland.org/policybot/results/23495/Evaluation_of_NHTSAs_BenefitCost_

Analysis_Of_20112015_CAFE_Standards.html. 

Nye, R. (2002). Qualitative Research Report: ORNL/NTRC Focus Groups,” conducted 

by the Looking Glass Group. Knoxville, TN. 

Petrin, A. (2002). “Quantifying the Benefits of New Products: The Case of the Minivan,” 

Journal of Political Economy 110: 705-729. 

Rubin, J., and Q. Fin (2009). “Hedonic Price Model for Light-Duty Vehicles: 

Consumers’ Valuation of Automotive Fuel Economy.” Working Paper, University of 

Maine. 

Sanstad, A., and R. Howarth (1994). “’Normal’ Markets, Market Imperfections, and 

Energy Efficiency.” Energy Policy 22(10): 811-818. 

Tietenberg, T. (2009). “Reflections – Energy Efficiency Policy: Pipe Dream or Pipeline 

to the Future?” Review of Environmental Economics and Policy. Vol.3, 2: 304 -320. 

Train, K. (2003). Discrete Choice Methods with Simulation. New York: Cambridge 

University Press. 

Train, K., and Clifford Winston (November 2007). “Vehicle Choice Behavior and the 

Declining Market Share of U.S. Automakers,” International Economic Review 48: 1469-

1496. 

Turrentine, T. and K. Kurani (2007). “Car Buyers and Fuel Economy?” Energy Policy 

35: 1213-1223. 

41
 

42

Tversky and Kahneman (1991). “Loss Aversion in Riskless Choice: A Reference 

Dependent Model.” The Quarterly Journal of Economics. V. 106, 4: 1039 – 61. 

Welch, D. “CAFE: A Prize for Making Gas-Guzzlers?” Business Week April 16, 2009, 

http://www.businessweek.com/magazine/content/09_17/b4128048030307.htm?chan=ma

gazine+channel_what's+next , accessed 7/7/09. 

West, S. (2008). “The Effect of Gasoline Prices on the Demand for Sport Utility 

Vehicles.” Working Paper. Macalester College.