How Consumers Value Fuel Economy:

A Literature Review
EPA-420-R-10-008

March 2010

Assessment and Standards Division

Office of Transportation and Air Quality

U.S. Environmental Protection Agency

Prepared for EPA by

Oak Ridge National Laboratory

EPA Contract No. DE-AC05-00OR22725

How Consumers Value Fuel Economy:

A Literature Review

NOTICE

This technical report does not necessarily represent final EPA decisions or 

positions. It is intended to present technical analysis of issues using data 

that are currently available. The purpose in the release of such reports is to 

facilitate the exchange of technical information and to inform the public of 

technical developments.

iii 

CONTENTS 

Page 

LIST OF FIGURES ...................................................................................................................v 

LIST OF TABLES .................................................................................................................. vii 

EXECUTIVE SUMMARY ..................................................................................................... ix 

ABSTRACT ........................................................................................................................... xix 

1. INTRODUCTION ...............................................................................................................1 

2. ALTERNATIVE MODELS OF CONSUMERS’ EVALUATION OF FUEL 

ECONOMY .........................................................................................................................4 

 2.1 SUPPLY SIDE ..............................................................................................................5 

 2.2 DEMAND SIDE ...........................................................................................................7 

3. LITERATURE REVIEW: VALUE OF FUEL ECONOMY ..............................................9 

 3.1 DISCRETE CHOICE MODELS ..................................................................................9 

 3.2 HEDONIC PRICE MODELS .....................................................................................29 

 3.3 OTHER METHODS ...................................................................................................37 

4. SUMMARY AND DISCUSSION .....................................................................................49 

REFERENCES ........................................................................................................................59 

Appendix: REFERENCE ASSUMPTIONS ABOUT VEHICLE USE, LIFETIME 

AND DISCOUNTING.......................................................................................................63 

v 

LIST OF FIGURES 

Figure Page 

 ES-1 Distribution of 25 Distinct Studies by Model Type and Value of Fuel 

Economy Relative to the Reference Value ........................................................... xii 

ES-2 Distribution of 25 Distinct Studies by Date of Study and Value of Fuel 

Economy Relative to Reference Value ................................................................. xii 

ES-3 Distribution of 25 Distinct Studies by Form of Fuel Economy Variable and 

Value of Fuel Economy Relative to Reference Value ......................................... xiii 

 1 Passenger Car and Light Truck Fuel Economy, Fuel Economy Standards 

and the Price of Gasoline, 1978-2009 ......................................................................7 

 2 Histogram of Estimated Coefficients of Dollars per Mile from Klier and 

Linn (2008) ............................................................................................................16 

 3 Estimated Values of a 1 Cent per Mile Decrease in Fuel Costs Based on 

Brownstone, Bunch, Golob and Ren (1996) ..........................................................24 

 4 Estimated Willingness to Pay for a 1 MPG Increase in Fuel Economy by 

Vehicle Class .........................................................................................................30 

 5 Annual Estimates of the hedonic Value of a 1 MPG Increase in Fuel 

Economy and the Percent of Estimated Lifetime Fuel Savings Each 

Represents ..............................................................................................................36 

 6 Trend of Nominal Gasoline Prices over the Period of Sallee, West and Fan’s 

(2010) Study...........................................................................................................47 

 7 Distribution of 25 Distinct Studies by Model Type and Value of Fuel 

Economy Relative to the Reference Value ............................................................57 

 8 Distribution of 25 Distinct Studies by Date of Study and Value of Fuel 

 Economy Relative to Reference Value ..................................................................58 

 9 Distribution of 25 Distinct Studies by Form of Fuel Economy Variable and 

Value of Fuel Economy Relative to Reference Value ...........................................58 

vii 

LIST OF TABLES 

Table Page 

 ES-1 Summary of Consumers’ Evaluation of Fuel Economy Improvements Based on 

27 Recent Studies ................................................................................................. xiv 

ES-2 Summary of Key Features of 27 Econometric Studies ........................................ xvi 

 1 Estimated Unadjusted Discount Rates .....................................................................3 

 2 Estimated Parameters of the Demand and Pricing Equations: Berry, Levinsohn 

and Pakes’ Specification, 2,217 Observations .......................................................11 

 3 Impacts of Gas Tax Increases Calculated by Bento et al. (2005) ..........................21 

 4 Estimated Value of Fuel Costs in Vehicle Ownership Choice Model of Feng, 

Fullerton and Gan (2005) .......................................................................................22 

 5 Actual Versus Estimated Value of Fuel Economy ................................................32 

 6 Fifer and Bunn’s (2009) Hedonic Regression Results ...........................................34 

 7 Elasticity of Household Vehicle Choice with Respect to Fuel Cost per Mile .......40 

 8 Manufacturer Prices and Fuel Costs ......................................................................41 

 9 Gasoline Price Coefficient Estimates: New Car Price Equation ...........................42 

 10 Gasoline Price Coefficient Estimates: Used Car Price Equation ...........................43 

 11 Coefficients on the Expected Present Value of Real Remaining Fuel Costs .........48 

 12 Summary of Consumers’ Evaluation of Fuel Economy Improvements Based on 

27 Recent Studies ...................................................................................................50 

 13 Summary of Key Features of 27 Econometric Studies ..........................................52 

 A1 Annual Miles and Discounted Miles for Light-Duty Vehicles ..............................64 

ix 

EXECUTIVE SUMMARY 

Fuel economy or CO2 emissions standards are a core component of governments’ policy 

strategies to address global climate change and energy security. Standards have been adopted by 

the United States, the European Union, Japan and China, among others. The annual costs and 

benefits of these standards easily amount to tens of billions of dollars. How consumers’ value 

future fuel savings in making car buying decisions has been shown to be a crucial determinant of 

the consequences of such standards for economic welfare (Fischer et al., 2007). Yet surprisingly 

little is known about this vitally important subject. This review examines empirical evidence 

from 28 econometric studies that directly or indirectly estimated the value consumers place on 

fuel economy. 

The available econometric evidence is inconclusive. The 28 studies reviewed are approximately 

equally divided between those that imply that consumers significantly undervalue future fuel 

savings in their car buying decisions and those that find that they either approximately fully 

value or significantly over-value them. The studies span a wide range of model formulations, 

data sources, premises and estimation methods (Table ES-1). Yet there is no clear association 

between these distinguishing features and the conclusions reached by researchers. Furthermore, 

the econometric studies reviewed are, in general, technically well executed. 

Because the estimates of consumers’ willingness to pay for fuel economy improvements vary so 

greatly it would not be meaningful to try to identify a consensus value from the literature at the 

present time. Moreover, the appropriate theory of consumer decision making is also in doubt. 

Many of the studies reviewed assume that consumers follow the rational economic model: 

consumers make an estimate of future fuel prices, consider how long they will own a vehicle and 

how much driving they will do, calculate fuel savings per mile and, applying a discount rate, add 

up the present value of fuel savings over the life of the vehicle. Yet the little empirical evidence 

that exists about the fuel economy decision processes of actual consumers indicates that this 

model is very rarely used. The field of behavioral economics has recently provided a plausible 

alternative theory. Because future fuel savings are inherently uncertain, consumers will discount 

them heavily relative to certain initial costs. While this theory, referred to as loss aversion, is 

firmly established in behavioral economics, its application to decisions about fuel economy has 

only recently been proposed. At the present time, there is very substantial uncertainty about how 

consumers make decisions about fuel economy, as well as how much they value expected future 

fuel savings. 

Of the 28 studies reviewed in this report, 25 can be used to derive estimates of consumers’ 

willingness to pay for fuel economy improvements. Two of the studies by the same author are 

essentially the same, and two provide estimates of fuel price elasticities rather than the value of 

fuel savings, leaving 25 distinct estimates, explicit or implicit, of the value consumers attach to 

future fuel savings. The studies utilize a range of methods, including discrete choice models 

based on aggregate sales or disaggregate survey data, hedonic price models, asset price models 

and other methods. They are based on a wide range of data sources and cover varying time 

periods from 1970 to 2010. Ten of the studies are unpublished manuscripts, 13 are peerreviewed journal articles and five are other published reports. In this author’s opinion, there is 
x 

no important difference in the quality of the analyses between the manuscripts or reports and the 

published journal articles. 

Key findings of this literature review are: 

1. Of the 25 distinctly different estimates, 12 studies indicate that consumers significantly 

undervalue future fuel savings relative to a reference values based on U.S. Department of 

Transportation data, 8 indicate that consumers’ values are approximately equal to the 

reference expected value, and 5 indicate that consumers significantly overvalue fuel 

savings. 

2. With a very few exceptions, there are no obvious flaws in the methods or data used by 

these studies. This finding applies equally to the published and unpublished studies. 

3. There does not appear to be an obvious explanation for the widely divergent results. 

Neither model type, formulation of the variable representing fuel economy, data type, 

time period, nor any other readily identifiable factor shows a strong association with 

inferences about the values consumers place on fuel economy (Table ES-2). 

4. Fifteen of the studies are based on some form of discrete choice model. These are evenly 

divided between under, equal and over-valuing fuel economy; studies using hedonic 

price, asset price and other models more often indicate undervaluing (Figure ES-1). 

5. The studies are evenly divided between those dated 2008 or later (12) and those dated 

between 1994 and 2007. Six of the earlier studies and six of the 2008-2010 studies 

conclude that consumers significantly undervalue fuel economy. Seven of the earlier 

studies and six of the later studies imply that consumers roughly equally value or 

significantly over value fuel economy (Figure ES-2). 

6. Consumers’ expectations about future fuel prices are an important factor in all studies. 

Almost all of the studies assume that consumers will use the current price of fuel as a best 

estimate of future fuel prices, either due to static expectations or because they perceive 

fuel prices will follow a random walk. Five of the studies explore alternative price 

expectations models. However, none of the models allows consumers to project trends of 

increasing or decreasing prices into the future. Given the importance of price 

expectations to the evaluation of future fuel savings, a better understanding of how 

consumers form price expectations might provide useful insights. 

7. Most of the studies (15) represent fuel economy as the price of fuel divided by miles per 

gallon, i.e., fuel cost per mile. These studies are evenly divided between undervaluing 

(5), equally valuing (5) and overvaluing (5) (Figure ES-3). Six included fuel economy 

without interaction with the price of fuel, either as miles per gallon or gallons per mile. 

Of these, five found undervaluing and one equally valuing. Four used a calculated 

discounted present value of future fuel costs based on assumptions about vehicle lifetime, 

usage and fuel price expectations to represent fuel economy. These studies were evenly 

divided between undervaluing and approximately equally valuing. These differences 

suggest that there may be some insights to be gained by testing hypotheses about whether 

consumers respond differently to fuel price changes as opposed to fuel economy 

differences, and whether responses to rising fuel prices differ from responses to falling 

fuel prices. 

8. Several studies point out the empirical challenges to inferring the value of vehicle 

attributes to consumers, in general: (1) vehicle attributes such as weight, size, 
xi 

performance, luxury and fuel economy are correlated, (2) there are important difficulties 

in defining and measuring the many relevant attributes of vehicles, and (3) there are 

important differences (heterogeneity) in tastes among consumers. These problems can 

lead to errors in variables and omitted variables and, together with correlations among 

variables they can result in seriously unstable, biased parameter estimates. More recent 

studies, exploiting massive data sets, have attempted to address these problems with 

detailed fixed effect coefficients formulations that recognize consumer heterogeneity and 

other methods. The persistent differences in results even among these studies suggest 

that even these efforts may not have successfully addressed the empirical challenges. 

These findings are consistent with earlier literature reviews of implicit discount rates for fuel 

economy based on discrete choice models. The consistency with which the literature has yielded 

widely varying, inconsistent estimates over a period of more than three decades suggests that 

there is either a fundamental empirical problem in estimating the value consumers place on fuel 

economy, or that the presumed theory of consumer behavior is incorrect, or both. Recent but 

very limited in-depth survey evidence indicates that the rational economic model of consumer 

behavior is very likely not an accurate description of consumers’ decision making about fuel 

economy. 

Given the importance of understanding how the market values fuel economy and makes 

decisions about it, it might be worthwhile to convene qualified researchers with differing results 

to jointly investigate why those results differ so greatly. Such an effort would require sharing of 

data sets among researchers, who would then execute a mutually agreed upon set of statistical 

analyses, (1) to validate the results produced by others, and (2) to test a specified set of 

alternative model formulations using the different data sets. Such a structured test of model 

formulations against alternative data sets might lead to important insights about why apparently 

carefully and competently done analyses can lead to widely differing results. 

It is at least as important to investigate the possibility that it is the rational economic consumer 

model that is incorrect. This line of inquiry might best be pursued in two steps. First, conduct 

more in-depth interviews, surveys and experiments, such as reported in the seminal paper by 

Turrentine and Kurani (2007), to discover what decision criteria and algorithms real consumers 

actually employ when considering fuel economy and valuing fuel savings. Second, test these 

alternative models using experimental methods and empirical market data. 
xii 

Figure ES-1. Distribution of 25 Distinct Studies by Model Type and Value of Fuel Economy 

Relative to the Reference Value. 

Figure ES-2. Distribution of 25 Distinct Studies by Date of Study and Value of Fuel Economy 

Relative to Reference Value. 

0

1

2

3

4

5

6

Under Equal Over

Frequency

Relative Value of Fuel Economy

Distribution of 25 Distinct Studies by Model Type and Value

of Fuel Economy Relative to Reference

Discrete Choice Hedonic Other

0

1

2

3

4

5

6

7

Under Equal Over

Frequency

Relative Value of Fuel Economy

Distribution of 25 Distinct Studies by Date of Study and

Value of Fuel Economy Relative to Reference

< 2008 2008 +
xiii 

Figure ES-3. Distribution of 25 Distinct Studies by Form of Fuel Economy Variable and Value 

of Fuel Economy Relative to Reference Value. 

 

0

1

2

3

4

5

6

Under Equal Over

Frequency

Relative Value of Fuel Economy

Distribution of 25 Distinct Studies by Form of Fuel Economy

Variable and Value of Fuel Economy Relative to Reference

w Price interaction w/o Price Disc. PV
xiv 

Table ES-1. Summary of Consumers’ Evaluation of Fuel Economy Improvements 

Based on 27 Recent Studies

Authors Model Type Data / Time W-T-P as % of Discounted PV Implied Annual 

Discount Rate 

Alcott & Wozny 

(2009) 

Mixed NMNL Aggregate U.S., 

1999-2008 

25% > 60% 

Gramlich (2008) NMNL Aggregate U.S., 

1971-2007 

287% to 823% 

Berry, Levinsohn & 

Pakes (1995) 

NMNL Aggregate US, 

1971-1990 

<1% 

Non-significant 

Sawhill (2008) Mixed NMNL Aggregate U.S., 

1971-1990 

140%, range of 

-360% to 1,410% 

Train & Winston 

(2007) 

Mixed NMNL Survey, U.S., 

2000 

1.3% 

Non-significant 

Dagupta, Siddarth and 

Silva-Risso (2007) 

NMNL Survey, CA, 

1999-2000 

 15.2% 

Bento, Goulder, 

Henry, Jacobsen & 

von Haefen (2005) 

NMNL Survey, U.S., 

2001 

No direct estimate but MPG 

insensitive to price of gasoline 

Feng, Fullerton & Gan 

(2005) 

Klier and Linn (2008a) 

NMNL 

Logit 

CES, U.S., 1996-

2000 

Aggregate U.S., 

1970-2007 

0.03% to 1.3% 

Very approximately 69% 

Brownstone, Bunch & 

Train (2000) 

Mixed NMNL 

Stated & 

Revealed 

Preference 

CA Survey, 1993 132% to 147% 

Brownstone, Bunch, 

Golob & Ren (1996) 

NMNL Stated & 

Revealed 

Preference 

CA Survey, 1993 -420% to 402% 

Goldberg (1996, 1998) NMNL U.S. CES, 1984-

1990 

Consumers “not myopic” 

Goldberg (1995) 

Vance & Mehlin 

(2009) 

NMNL 

NMNL 

U.S. CES, 1983-

1987 

Germany, 

Aggregate New 

Car Sales 

Approximately 1,000% 

Cambridge 

Econometrics (2008) 

Mixed logit UK survey, 2004 

to 2009 

196% but uncertain of 

estimate. Authors contacted 

for clarifications. 

Eftec (2008) NMNL UK 2001 to 2006 TBD – authors contacted for 

clarifications. 

Fan & Rubin (2009) 

Fifer & Bunn (2009) 

Hedonic Price 

Hedonic Price 

State of Maine, 

2007 

U.S., 1996-2005 

Cars: 25% 

Lt. Trucks: 16% 

Cars: 52%, Pickups: 283% 

SUVs: 44%, Vans: 240% 

Cars: 37% 

Lt. Trucks: 77% 

McManus (2007) Hedonic Price U.S., 2002 90% 

Espey & Nair (2005) Hedonic Price U.S., 2001 109% 

Arguea, Hsiao & 

Taylor (1994) 

Hedonic Price U.S., 1969 to 

1986 

3% to 46% 

   
xv 

Bhat & Sen (2006) 

Sallee, West & Fan 

(2010) 

Choice model 

Price Regression 

San Francisco 

Bay Area, 2000 

Aggregate U.S., 

Used Cars, 1978-

2009 

Elasticities of vehicle choice 

with respect to fuel costs 2% 

to 3% of purchase price 

elasticities. 

79%, not statistically different 

from 100% 

Langer & Miller 

(2008) 

Price Regression U.S., 2003 to 

2006 

Approx. 15% of PV of fuel 

cost changes reflected in 

vehicle price changes. 

Busse, Knittel & 

Zettelmeyer (2009) 

Kilian and Sims 

(2006) 

Price Regression 

Price Regression 

U.S., 1999 to 

2008 

Aggregate U.S., 

Used Cars, 1978-

1984 

Transaction prices adjust by 

1.2 years worth of fuel savings 

for new cars. 

11% to 25% 

Li, Timmins & von 

Haefen (2009) 

Vehicle sales by 

fuel economy 

quantile 

U.S. Metro 

Areas 1997 to 

2005 

Short-run price elasticity of 

MPG with respect to sales mix 

+0.02, long-run +0.2. 
xvi 

Table ES-2. Summary of Key Features of 27 Econometric Studies 

Study 

Publication 

Status Model 

Dependent 

Variable 

Type of 

Data 

Time 

Period 

Fuel 

Economy 

Measure 

Price 

Expectations 

Transaction 

Prices? 

Heterogeneour 

Tastes? 

Berry, Levinsohn & 

Pakes 1995 Journal NMNL Sales shaes Aggregate U.S. 1971-1990 Miles/Pg Random Walk No Yes 

Allcott & Wozny 2009 Manuscript NMNL New & used vehicle prices Aggregate U.S. 1999-2008 Disc. PV of Fuel Cost RW + alternatives Yes No 

Klier & Linn 2008 Manuscript Logit New vehicle shares Aggregate U.S., monthly 1970-2007 Disc. PV of Fuel Cost Random Walk n.a. Yes 

Gramlich 2008 Manuscript NMNL New vehicle shares Aggregate U.S. 1971-2007 Pg/MPG & MPG Random Walk No No 

Sawhill 2008 Manuscript NMNL New vehicle shares Aggregate U.S. 1971-1990 Pg/MPG ARIMA No Yes 

Train & Winston 2007 Journal Mixed Logit Indiv. Vehicle Choices U.S. Household Survey 2000 1/MPG Static No Yes 

Dasgupta, Siddarth & 

Silva-Risso 2007 Journal Mixed Logit Indiv. Vehicle Choices So. CA Vehicle Transactions 1999-2000 Pg/MPG Static Yes Yes 

Bento, Goulder, 

Jacobsen & von 

Haefen 2005 & 2008 

Journal Random Coef. Logit Indiv. Vehicle Choices Nat. HH. Travel Survey U.S. 2001 Pg/MPG Static No Yes 

Feng, Fullerton & Gan 

2005 Manuscript NMNL Indiv. Vehicle Choices CES 1996-2000 Pg/MPG Static No No 

Brownstone, Bunch & 

Train 2000 Journal Mixed Logit Indiv. Vehicle Choices CA survey 1993 Pg/MPG Static Yes, via respondents Yes 

Brownstone, Bunch, 

Golob & Ren 1996 Journal NMNL Indiv. Vehicle Choices CA survey 1993 Pg/MPG Static Yes, via respondents No 

Goldberg 1995 Journal NMNL Indiv. Vehicle Choices CES 1983-1987 Pg/MPG Static No Yes Goldberg 1996 Report NMNL Indiv. Vehicle Choices CES 1985-1990 Pg/MPG Static No Yes Goldberg 1998 Journal NMNL Indiv. Vehicle Choices CES 1984-1990 Pg/MPG Static No Yes 

Cambridge 

Econometrics 2008 Report Mixed Logit Indiv. Vehicle Choices UK Survey 2005-2006 £/100 km Static No Yes 

eftec 2008 Report Mixed Logit Indiv. Vehicle Choices UK Survey 2004 & 2007 l/100 km & £/100km Static No Yes 

Vance & Mehlin 2009 Report NMNL New Vehicle Shares Sales data, Germany 1995-2007 €/100 km Static No No 

Fan & Rubin 2010 Manuscript 2-stage hedonic New vehicle prices Maine, sales data 2007 log(MPG) Static No Yes 

Espey & Nair 2005 Journal 1-stage hedonic New vehicle prices U.S. vehicle data 2001 1/MPG Static No No 

McManus 2007 Journal 1-stage hedonic New vehicle prices U.S. vehicle data 2002-2005 Pg/MPG Static Yes No 

Fifer & Bunn 2009 Thesis 1-stage hedonic New vehicle prices U.S. vehicle data 1996-2005 1/MPG Random Walk No Yes 

Arguera, Hsaio & 

Taylor 1994 Journal 2-stage hedonic New vehicle prices U.S. vehicle data 1969-1986 MPG n.a. No No 

Killian & Sims 2006 Manuscript Asset Price Used Car Prices U.S. 1978-1984 PV fuel costs Random Walk + No No 
xvii 

Table ES-2. Summary of Key Features of 27 Econometric Studies (continued) 

Study 

Publication 

Status Model 

Dependent 

Variable 

Type of 

Data 

Time 

Period 

Fuel 

Economy 

Measure 

Price 

Expectations 

Transaction 

Prices? 

Heterogeneour 

Tastes? 

Bhat & Sen 2005 

Journal 

MDCEV 

random 

utility 

Individual vehicle 

choice 

Survey: San 

Francisco, CA 2000 Pg/MPG Static Price not included Yes 

Langer & Miller 2008 Manuscript Asset Price New vehicle prices U.S. regions weekly 2003-2006 Pg/MPG Random Walk + No Yes 

Busse, Knittel & 

Zettelmeyer 2009 Manuscript Sales shares by quartile New & Used vehicle prices Sample, U.S. transactions 1999-2008 MPG Quartiles RW Yes Yes 

Li, Timmins & von 

Haefen 2009 

Journal Vehicle demand Sales by Quantile New & used sales in 20 U.S. metro areas 1997-2005 Pg/MPG & Pg Random Walk No Yes 

Sallee, West & Fan 

2010 

Manuscript Asset Price Used vehicle prices 

Sample of U.S. 

auction 

transactions 

1990-2009 Disc. PV of fuel costs Random Walk + Yes Yes 

* Indicates whether study generally implies that consumers undervalue (—), over-value (+) or equally value (0) fuel econo

above (X). 
xviii 

 
xix 

ABSTRACT 

The extent to which consumers value the expected future fuel savings from fuel economy 

improvements to new passenger cars and light trucks is a key determinant of the levels of fuel 

economy achieved in unregulated markets and the effects of regulatory standards on consumers’ 

surplus. This paper reviews 28 recent quantitative analyses of consumers’ willingness to pay for 

automotive fuel economy. Some of the studies estimate discrete choice models with random or 

fixed coefficients, some are based on aggregate market data while others use disaggregate survey 

data. Other studies make use of hedonic price analysis or other methods. Their inferences about 

willingness to pay span a very broad range with roughly equal numbers finding significant 

under-valuing, significant over-valuing or approximately valuing the full present value of 

expected fuel savings over the lifetime of a typical vehicle. Although the methodologies or 

model formulations of a few of the studies are questionable, there do not appear to be clear 

associations among methods or data sources and the resulting inferences. It is suggested that 

such conflicting results may be attributable to the statistical problems caused by omitted 

variables, errors in variables and correlated variables, the complexity of consumers’ vehicle 

choice decisions, and the likelihood that the rational economic consumer model does not 

adequately describe the decision-making of consumers in the real world. Additional, empirical 

behavioral research appears to be needed to resolve the issue. 

 
xx 
1 

1. INTRODUCTION 

Passenger cars and light trucks account for 44% of the petroleum consumed in the United States 

each year and produce 16% of total U.S. greenhouse gas emissions (Davis, Diegel and Boundy, 

2009, tables 1.13, 1.14, 11.3 and 11.7). Given the importance of the automotive sector to the 

U.S. economy, understanding the costs and benefits of fuel economy and greenhouse gas 

standards for light-duty vehicles is of great importance. The economic impacts of vehicle 

standards depend on the functioning of the market for fuel economy. In particular, carefully 

chosen regulatory standards have been shown to increase or decrease private welfare depending 

on how consumers value future fuel savings (Fischer, Harrington and Parry, 2007). The question 

has been controversial for decades, with some analysts assuming the market functions efficiently 

(e.g., Kleit, 1990; Austin and Dinan, 2005) while other assert that it does not (e.g., Greene, 

German and Delucchi, 2009). This paper surveys the recent literature of studies addressing 

consumers’ willingness to pay for fuel economy improvements, trade-offs between capital costs 

and fuel costs, and related economic research. Despite a substantial body of significant new 

econometric evidence, the topic remains unresolved. In short, there is evidence supporting both 

sides and some in the middle, as well. 

More than a quarter century ago, Greene (1983) reviewed the evidence on implicit consumer 

discounting of future fuel savings arising from the burgeoning literature in a new area of research 

applying discrete choice models to the problem of automobile choice.1

 Based on eight studies, 

Greene estimated implicit discount rates by assuming that consumers were trading off capital 

cost (vehicle price) and future fuel savings (mostly represented by fuel cost per mile). For all but 

one model, the implied discount rates ranged from 2% to 73% per year (Table 1).2 

Greene summed up his findings as follows. 

 “Eight recent studies are examined and estimates of asset price/operating cost 

discount rates are derived for each. A critical comparison of results suggests that 

most are implausible. Plausible estimates range from 4% to 40% as a function of 

household income.” (Greene, 1983, p. 491) 

In a comment to Greene’s paper, Train (1983) suggested that the assumption that consumers are 

trading off capital and operating costs according to the model of rational economic behavior 

might be the problem. 

“There are many plausible reasons for believing that Greene’s assumptions about 

consumer behavior are perhaps not correct. On the one hand, consumers might 

not be rational in their time allocation of money, in an economist’s sense of the 

                                                            1

 Discrete choice models typically estimate an indirect utility function, V(p,e,x), in which p is vehicle price, 

e is fuel use or fuel cost per mile. Discount rates can be estimated by noting that –(∂V/∂e)/(∂V/∂p) is the implied 

value in dollars of a 1 unit change in e. Given assumptions about vehicle use over time, vehicle lifetime and the 

price of fuel, traditional discounting formulas can be used to estimate an implied discount rate. 2

 The model excepted, Manski and Sherman (1980), produced implied discount rates ranging from 2840% 

per year to -164% per year, depending consumers’ on income and education levels. 
2 

word “rational”....Given the difficulty my economics students have in calculating 

present values of future savings and holding costs for assets, I would not find this 

at all surprising.” (Train, 1983, p. 498) 

In light of subsequent research on how consumers actually consider fuel economy in their car 

buying decisions (Turrentine and Kurani, 2007), Train’s comment appears to be prophetic. 

Recent historically high fuel prices, combined with renewed interest in fuel economy and 

greenhouse gas emissions standards for automobiles have engendered a number of new 

assessments, many specifically aimed at understanding the effects of fuel prices and fuel 

economy on consumers’ vehicle choices. This paper reviews those studies, published and 

unpublished, with the objectives of determining whether a consensus now exists on the value 

consumers place on fuel economy, and of gleaning insights into how consumers use fuel 

economy information in their car buying decisions. The intent of the review is to be 

comprehensive with respect to studies of the U.S. market for fuel economy that make inferences 

about consumers’ willingness to pay for fuel economy improvements in both the new and used 

vehicle markets. A number of recent studies from the gray (not peer reviewed) literature are 

included because, in the authors judgment they are of publishable quality. There are so many of 

these studies and their quality is sufficiently high that, in the author’s opinion, leaving them out 

would substantially reduce the value of the review. Unpublished studies are clearly indicated as 

such, however. 

This review attempts to compare the inferences of different studies on a consistent basis: a 

typical consumer’s willingness to pay for a reduction in the present value of fuel costs through 

improved fuel economy. This is never more than an approximation; however, since a variety of 

assumptions about vehicle use, consumer expectations and discount rates are necessary to make 

the calculation. In addition, consumers’ behavior and preferences are heterogeneous due to 

different vehicle usage rates, and discount rates among other factors. Still, national averages 

provide a useful basis for comparisons. The assumptions used here are taken from the National 

Highway Traffic Safety Administration’s (U.S. DOT/NHTSA, 2006) most recent assessment of 

vehicle usage and life expectancy, and are explained in detail in the appendix. In brief, the 

NHTSA analysis implies that passenger cars and light trucks in the United States can be 

expected to last 14 years, on average. Over 14 years, passenger cars are expected to travel 

168,853 miles and light trucks 188,104 miles. Most studies assume that consumers expect fuel 

prices to follow a random walk, implying that the current price of fuel is the best predictor of 

future prices. That assumption is used here, as well, although there is little behavioral research 

to support it. If future fuel price is constant at the current level, then it is convenient to discount 

future miles traveled to present value, so that different values for fuel prices and fuel economy 

can be applied. Assuming a discount rate of 7% per year and discounting miles over the life of 

the vehicle results in 112,600 discounted lifetime miles for passenger cars and 125,891 miles for 

light trucks. These standard assumptions are used throughout the report to compare results from 

different studies. At the same time, it is recognized that consumers’ preferences for fuel 

economy are heterogeneous, as several studies conclude. 

 
3 

Table 1. Estimated Unadjusted Discount Rates 

1. Lave and Train (1978) 

 Auto Price (1977$) 

 2500 3500 5000 

 Income 10,000 0.23 0.21 0.19 

 (1977$) 20,000 0.12 0.12 0.11 

 25,000 0.10 0.10 0.09 

 30,000 0.08 0.08 0.08 

 50,000 0.05 0.05 0.05 

 

2. Cardell and Dunbar (1980) 

 Median = 0.43 Mean = 0.25 

 

3. Beggs and Cardell (1980) 

 Base Model Financial and Size Variables Only 

 Household 10,000 0.59 0.73 

 Income 20,000 0.35 0.35 

 25,000 0.31 0.31 

 30,000 0.29 0.28 

 50,000 0.24 0.23 

 

4. Boyd and Mellman (1980) 

 Simple logit 0.06 

 Hedonic Median = 0.09 Mean = 0.02 

 

5. Manski and Sherman (1980) 

 a) One-vehicle households 

 Urban Rural 

 Low I High I Low I High I 

 College 0.10 0.06 0.18 0.19 

 No College 0.17 0.18 0.54 -0.16 

 b) Two-vehicle households 

 Urban Rural 

 Low I High I Low I High I 

 College 0.64 0.09 -1.64 0.19 

 No College 28.4 0.26 -0.61 2.26 

 

6. Beggs, Cardell and Hausman (1981) 

 Common tastes Individual tastes 0.30 

 10,000 0.36 

 20,000 0.30 

 Income 25,000 0.29 

 30,000 0.29 

 50,000 0.28 

 

7. Sherman (1982) 

 One-vehicle households 0.13 [dependent on ln (miles annually) here 10,000] 

 Two-vehicle households 

 Annual Miles (both 

cars) 

 

 10,000 20,000 25,000 

 Income 10,000 0.02 

 (1978$) 20,000 0.01 0.00 

 30,000 0.01 0.00 0.00 

 

8. Train and Lohrer (1982) 

 One-vehicle households 

 0.12 if I ≤ 12,000 0.09 if I > 12,000 

 Two-vehicle households 

 0.12 if I ≤ 12,000 0.09 if 12,000 < I ≤ 20,000 0.05 if I > 20,000 

Source: Greene (1983, Table 3) 
4 

This paper is organized as follows. Section 2 summarizes views of how the market for fuel 

economy functions, including both supply and demand. The body of the report is Section 3 

which reviews recent empirical estimates of the value of fuel economy based on aggregate and 

disaggregate data, discrete choice models and hedonic demand analyses. Section 4 discusses the 

implication of those estimates and Section 5 contains concluding observations. 

2. ALTERNATIVE MODELS OF CONSUMERS’ EVALUATION 

 OF FUEL ECONOMY

There is no doubt that consumers do care about fuel costs, do value fuel economy, and that their 

interest in fuel economy increases when fuel prices increase. Past evidence of this has been 

reviewed by Mahadi and Gallagher (2009) who also provide an analysis of the effects of gasoline 

price increases since 2000 on consumers’ interest in fuel economy. The question is not whether 

consumers value fuel economy but how much? The issue is not whether the market for fuel 

economy responds to higher fuel prices but whether it responds efficiently. To be more precise, 

does the market value fuel economy improvements at society’s discounted expected value of 

future fuel savings over the lifetime of a new vehicle, or significantly less or more? Fischer, 

Harrington and Parry (2007) have demonstrated that the answer to this question has profound 

implications for public policy concerning automotive fuel economy and carbon dioxide 

emissions. Considering only private costs and benefits, if consumers already fully value 

expected lifetime fuel savings, fuel economy standards lead to private welfare losses.3

 Studies 

that assume efficient markets inevitably arrive at this conclusion (e.g., Austin and Dinan, 2005). 

On the other hand, if consumers are myopic and consider only the first three years of fuel 

savings, for example, fuel economy standards can increase welfare even based solely on private 

costs and benefits. 

Given the importance of fuel economy standards to manufacturers, consumers and society, it is 

surprising that there has been almost no behavioral research on how consumers consider fuel 

economy in their car buying decisions. Larrick and Soll (2008) found that measuring fuel 

economy in miles per gallon rather than gallons per mile leads to confusion about the value of 

increasing fuel economy. In general, consumers expect fuel savings to increase linearly with 

miles per gallon, leading to overvaluing of fuel economy increases for high MPG vehicles 

relative to lower MPG vehicles. Turrentine and Kurani (2007) conducted in-depth, semistructured interviews with a stratified sample of 57 California households concerning the entire 

history of their car buying decisions. Their findings are well worth quoting at some length. 

 “We found no household that analyzed their fuel costs in a systematic way in 

their automobile or gasoline purchases. Almost none of these households track 

gasoline costs over time or consider them explicitly in household budgets. These 

households may know the cost of their last tank of gasoline and the unit price of 

gasoline on that day, but this accurate information is rapidly forgotten and 

replaced by typical information. One effect of this lack of knowledge and 

                                                            3

 Fischer, Harrington and Parry (2007) consider external costs as well as private costs in their study. The 

above comment pertains only to the private costs calculations in their report. 
5 

information is that when consumers buy a vehicle, they do not have the basic 

building blocks of knowledge assumed by the model of economically rational 

decision-making, and they make large errors estimating gasoline costs and 

savings over time. 

 “Moreover, we find that consumer value for fuel economy is not only about 

private cost savings. Fuel economy can be a symbolic value as well, for example 

among drivers who view resource conservation or thrift as important values to 

communicate. Consumers also assign non-monetary meaning to fuel prices, for 

example seeing rising prices as evidence of conspiracy. This research suggests 

that consumer responses to fuel economy technology and changes in fuel prices 

are more complex than economic assumptions suggest.” (Turrentine and Kurani, 

2007, p. 1213) 

Turrentine and Kurani’s final observation, that consumers’ fuel economy decision making is 

more complex than any single economic model, echoes Train’s (1983) observation and is 

important to keep in mind as one reviews the empirical studies of vehicle choice and fuel 

economy. Very likely, there is not one correct model or one correct set of parameters waiting to 

be discovered. Instead, the goal should be to find a useful generalization of a set of complex 

behaviors that likely vary from one individual to another, perhaps bearing little resemblance to 

the model of perfectly rational economic behavior. 

2.1 SUPPLY SIDE 

Although the functioning of the supply side for fuel economy is not the central focus of this 

review, appropriately representing the supply of fuel economy is important both for 

understanding how the market functions, as well as for estimating models of fuel economy 

demand. There are three key issues: (1) the competitive structure of the industry, (2) the nature 

of short- and long-run supply responses, and (3) the effect of regulatory standards versus market 

decisions on fuel economy. 

Most econometric studies of the U.S. market assume the industry is an oligopoly (e.g., Bento 

et al., 2005; Berry, Levinsohn and Pakes, 1995; Goldberg, 1995; Austin and Dinan, 2005; Busse, 

Knittel and Zettelmeyer, 2009). For example, Eftec (2008, p. vi) considered the United 

Kingdom (UK) automobile market to be a non-collusive oligopoly, characterized by a relatively 

small number of manufacturers all of whom take account of the actions of the others in making 

product and pricing decisions. Goldberg modeled the U.S. automobile industry as an oligopoly 

with multiproduct firms. Goldberg (1998) assumed that domestic and imported manufacturers 

formed two groups, within which there was collusion but between which there was Bertrand 

competition. However, she notes that this assumption was based on computational constraints 

rather than on observation of actual business practices. 

The globalization of the world economy raises doubts about the characterization of the U.S. auto 

market as an oligopoly. There are now approximately 25-30 significant vehicle manufacturers in 

the global marketplace, not considering partial ownership of one firm by another. Although all 
6 

these firms are not now participating in the U.S. market, the barriers to entry are not great.4 With 

this many firms participating and relatively low barriers to entry, it may be more appropriate to 

characterize the current and future supply side as monopolistically competitive. In a 

monopolistically competitive market, firms sell differentiated products and strive to capture rents 

thereby, but in the long run products sell at their long-run average costs, and normal rates of 

profit prevail. This assumption is consistent with profit rates observed in the industry in recent 

years (Rogozhin, Gallaher and McManus, 2009). 

Manufacturers’ ability to respond to changes in consumer demand for fuel economy or 

regulatory standards differs depending on the length of time available (NRC, 2002; Klier and 

Linn, 2008). In the short run (less than two years) manufacturers can change the prices of 

vehicles to induce shifts in sales and make very minor changes to vehicles themselves (e.g., tires, 

lubricants, engine control algorithms). Within 2-7 years, manufacturers will have an opportunity 

for major design changes to every vehicle they produce. In general, vehicles are redesigned over 

a 4-5 year cycle, with 20% to 25% of any given manufacturer’s product line being redesigned 

each year in order to evenly distribute capital investments and use of engineering expertise. 

Engine and transmission lines are typically amortized over a somewhat longer period, on the 

order of 10 years. Studies vary in the degree to which they take account of these cycles of 

change. 

For studies that attempt to estimate the demand for fuel economy taking into consideration the 

simultaneous effects of supply and demand, it is critically important to recognize the impacts of 

fuel economy regulations. Although there is some disagreement about the relative effectiveness 

of the Corporate Average Fuel Economy Standards versus market responses to the price of 

gasoline (e.g., see NRC, 2002; Gerard and Lave, 2003; Greene, 1990), there can be no doubt that 

the standards had a major impact on the level of fuel economy observed in the market (Figure 1). 

Failure to recognize fuel economy constraints on manufacturers is likely to seriously bias 

estimates of consumers’ willingness to pay for fuel economy especially in hedonic price 

regressions where identification of the demand function is a critical issue. Some studies do 

explicitly incorporate fuel economy constraints while others do not. 

                                                            4

 Chief among these are certification to U.S. safety and environmental standards. Even in these respects 

there is increasing harmonization in world markets. 
7 

Figure 1. Passenger Car and Light Truck Fuel Economy, Fuel Economy Standards and the Price 

of Gasoline, 1978-2009. 

2.2 DEMAND SIDE 

While there is no doubt that consumers respond to fuel prices and value fuel economy to some 

degree, there is also little doubt that very few consumers compare discounted present values of 

fuel costs when making vehicle choices. Turrentine and Kurani’s (2007) research demonstrates 

only that the strict, rational economic model of consumer behavior probably does not apply to 

consumers’ decision making about fuel economy.5 By itself, this does not tell us whether 

consumers under- or over-value fuel economy. There is still the possibility that consumers 

approximate an efficient market response based on experience and intuition. 

Some analysts contend that the market for fuel economy reasonably approximates a perfectly 

competitive market from the demand side. For example, Austin and Dinan (2005) assume that 

consumers fully value lifetime fuel savings when considering fuel economy in their vehicle 

choices. 

 “We argue, though, that there is no such market failure – that the information on 

new-vehicle window stickers, reporting the EPA’s city and highway mileage 

rating and the vehicle’s estimated annual fuel cost, is sufficient to allow 

consumers to make informed decisions about fuel economy.” (Austin and Dinan, 

2005) 

                                                            5

 This finding is definitive for the 57 households in their study. However, it cannot necessarily be 

extrapolated to the United States as a whole because the households are from only California and were selected by a 

stratified random sampling method. Turrentine and Kurani defined 10 household types of interest and then 

randomly selected six households in each group. In addition, gasoline prices were relatively low when the surveys 

were conducted. 

$0.00

$0.50

$1.00

$1.50

$2.00

$2.50

$3.00

0

5

10

15

20

25

30

35

1978 1981 1984 1987 1990 1993 1996 1999 2002 2005 2008 Real Price

Combined MPG

Model Year

Passenger Car and Light Truck Fuel Economy, Fuel Economy 

Standards and the Price of Gasoline, 1978-2009

Combined (Cars)

Car Standard

Combined (Truck)

Truck Standard

Gasoline Price
8 

Consumers make decisions that involve present capital cost and future energy costs in many 

areas and behavioral researchers have voiced doubts about the efficiency of markets for energy 

efficiency for decades (e.g., Stern and Aronson, 1984). Economic analyses of consumer choices 

of other types of energy using equipment have produced estimates of implicit discount rates that 

are several times average rates of return on capital (Allcott and Wozny, 2009; Hausman, 1979; 

Train, 1985; Howarth and Sanstad, 1995; U.S. DOE/EIA, 1996, table 3). A variety of 

explanations have been proposed for the apparent undervaluing of energy savings by consumers, 

ranging from irrationality and imperfect information to the rational assessment of the value of 

waiting in a market in which energy prices are generally rising but highly uncertain (Hassett and 

Metcalf, 1993). Energy analysts have identified several types of market failures6 that affect the 

market for energy efficiency (e.g., Howarth and Sanstad, 1995; ACEEE, 2007): 

 Principal agent conflicts 

 Information asymmetry 

 Transaction costs 

 Bounded rationality 

 External costs and benefits 

With the exception of externalities, there is little quantitative evidence of the impact of these 

failures on consumers’ choices of energy using durable goods (ACEEE, 2007). 

Two recent analyses have quantified the potential impacts of uncertainty and risk or loss adverse 

behavior on the market for fuel economy. Delucchi (2007) quantified the uncertainty in the key 

elements of the fuel economy decision, and by assuming consumers would make uniformly 

conservative estimates for each factor, demonstrated that the resulting decisions would appear to 

reflect a high discount rate for future fuel savings. For example, consumers with a real discount 

rate of 5.5% would appear to have a discount rate of 19% if they conservatively estimated factors 

such as the life of the vehicle, lifetime vehicle miles, the price of fuel, and the fuel economy that 

would be realized in actual use (in distinction to the official fuel economy rating). 

Greene, German and Delucchi (2009) and Greene (2009) applied the theory of context dependent 

loss aversion from behavioral economics to the question of valuing fuel economy and concluded 

that a typically loss averse consumer would behave as if he or she required a simple 3-year 

payback for increased fuel economy. Their method quantified uncertainties about realized in-use 

MPG, fuel price, vehicle life expectancy, annual miles of travel, and the cost of increased fuel 

economy in order to derive a probability distribution of present value rather than a single 

number. Because the net present value is the difference of savings-cost, there is a chance the 

consumer will lose money on the deal. By applying loss aversion functions derived by Tversky 

and Kahnemann (1992) they showed that a 25% improvement in passenger car fuel economy that 

had an expected present value of +$400 would be perceived by loss averse consumers to have a 

present value of -$30. A potentially significant aspect of the theory of loss aversion is that it is 

context dependent and allows consumers to undervalue fuel economy at the time of the purchase 

                                                            6

 In the author’s view, the term “market failure” is unfortunate because it implies an inability to perform a 

function rather than the impairment of a function. Market perfection is a high standard indeed and it is doubtful that 

any market fully satisfies the criteria for rational economic decision making (Rubenstein, 1998). What matters then 

is how far the market for energy efficiency is from an efficient solution. This requires quantification. 
9 

decision but fully value fuel savings afterward. While these calculations are based on empirical 

data, there has been no testing of the theories either via experiments with consumers or by 

statistical inference from market transactions. 

3. LITERATURE REVIEW: VALUE OF FUEL ECONOMY 

This section reviews 27 recent empirical studies that produced quantitative inferences about the 

value of fuel economy to consumers. Helfand and Wolverton (2009) provide a qualitative 

analysis of the recent literature, and raise most of the issues considered here. The studies reflect 

a wide range of data sources, model formulations and estimation methods. Approximately half 

of the research reviewed has been published in refereed journals, however, most of the recent 

research which takes advantage of the very large fuel price increases in 2008, is found in 

unpublished manuscripts. In this reviewer’s opinion, the quality of the unpublished research is 

equal to that of the published research. 

The studies have been grouped into five categories: 

1. Discrete choice random utility models using aggregate data 

2. Discrete choice random utility models using disaggregate survey data 

3. Discrete choice models using non-U.S. data 

4. Hedonic price regressions 

5. Asset price models 

Of these studies, twelve generally indicate that consumers significantly undervalue fuel 

economy, nine conclude that consumers value fuel economy at approximately its discounted 

present value of future fuel savings (three are very similar papers by the same author), and four 

find that consumers significantly over-value fuel economy. There is no clear association 

between a study’s findings and its choice of model, data source, or estimation method. It is 

suggested that this may be attributable to the complexity of vehicle choices which lead to very 

difficult problems for statistical inference, combined with the likelihood that the rational 

economic consumer model may be an inappropriate representation of consumers’ fuel economy 

decision making. 

3.1 DISCRETE CHOICE MODELS 

3.1.1 Aggregate Data 

In a seminal paper, Berry, Levinsohn and Pakes (1995) develop a new method for estimating 

both demand functions with random coefficients and cost functions, based on aggregate sales 

data for over 2,000 makes and models of vehicles over the period 1971-1990. The model 

assumes a utility function that is linear in the logarithm of u, in which y is the income of 

consumer I, pj the price of vehicle j, X is a vector of observed attributes of vehicle j, v a fixed 
10 

effect, ij a random error, and the term in summation the sum of cross products of unobserved 

vehicle and consumer attributes. 

௝௜ߝ ൅ ௞௝ݒ௞௝ݔ௞ߪ෍൅ ௝ݒ൅ߚ௝ܺ൅൯௝݌ െ௜ ݕ൫݃݋݈ ߙ ൌ௜௝ ݑ

௞

(1) 

Because prices are endogenously determined in their model, Berry, Levinsohn and Pakes (1995) 

estimate the consumer utility functions using instrumental variables. 

Fuel economy enters the consumers’ utility function not as miles per gallon but as miles per 

dollar, MPG divided by price per gallon (more precisely, the variable is the number of ten mile 

increments one could drive for $1 worth of gasoline). This formulation accounts for the very 

substantial variations in the price of gasoline over the 1971 to 1990 period. However, it also 

implies a linear relationship between increasing fuel economy and utility. This is less than ideal 

because fuel economy is the inverse of fuel consumption and fuel expenditures are linearly 

related to fuel consumption. Representing fuel economy by miles per dollar with a constant 

marginal utility parameter implies that consumers purchasing vehicles with higher levels of fuel 

economy place a higher value on the same quantity of fuel savings than purchasers of vehicles 

with lower levels of fuel economy. This makes it all the more surprising that the authors’ 

analysis of elasticities shows decreasing elasticity of vehicle choice with respect to miles/$ with 

increasing miles/$. 

 “The elasticity of demand with respect to MP$ declines almost monotonically 

with the car’s MP$ rating. 

 “Hence we conclude that consumers who purchase the high mileage cars care a 

great deal about fuel economy while those who purchase cars like the BMW 735i 

or Lexus LS400 are not concerned with fuel economy.” (Berry, Levinsohn and 

Pakes, p. 878) 

Berry, Levinsohn and Pakes find that the average value of fuel economy (MP$) is not 

significantly different from zero, regardless of whether the scale of production of each vehicle is 

included in the equation or not. On the other hand, the variance of the value of fuel economy is 

statistically significant. The estimated parameters of the authors’ preferred model are shown in 

Table 2. The average values of the coefficient estimates for MP$ are negative, implying that 

more miles per dollar is undesirable, but are not close to being statistically significant. This 

result, which will appear again in the random coefficient mixed logit model of Train and 

Winston (2007) implies that while some car buyers value fuel economy as a positive attribute 

others find it to be a negative factor. On average, the market is approximately indifferent. 

 
11 

Table 2. Estimated Parameters of the Demand and Pricing Equations: 

Berry, Levinsohn and Pakes’ Specification, 2,217 Observations

Demand Side Parameters Variable 

Parameter 

Estimate 

Standard 

Error 

Parameter 

Estimate 

Standard 

Error 

Means ( ’s) Constant -7.061 0.941 -7.304 0.746

 HP/Weight 2.883 2.091 2.185 0.896

 Air 1.521 0.891 0.579 0.632

 MP$ -0.122 0.320 -0.049 0.164

 Size 3.460 0.610 2.604 0.285

Std. Deviations (σβ’s) Constant 3.612 1.485 2.009 1.017

 HP/Weight 4.628 1.88 1.586 1.186

 Air 1.818 1.695 1.215 1.149

 MP$ 1.050 0.272 0.670 0.168

 Size 2.056 0.585 1.510 0.297

Term on price (α) ln (y – p) 43.501 6.427 23.710 4.079

 

Cost Side Parameters Constant 0.952 0.194 0.726 0.285

 ln (HP/Weight) 0.477 0.056 0.313 0.071

 Air 0.619 0.038 0.290 0.052

 ln (MPG) -0.415 0.055 0.293 0.091

 ln (Size) -0.046 0.081 1.499 0.139

 Trend 0.019 0.002 0.026 0.004

 ln (q) -0.387 0.029

Source: Berry, Levinsohn and Pakes (1995, table IV) 

Because of the inverse relationship between miles per dollar and fuel expenditures ($/mile) the 

value of fuel economy in the Berry, Levinsohn and Pakes’ model varies with both the price of 

gasoline and the level of MPG. Berry, Levinsohn and Pakes report an average value of 20.86 

miles per 1983 $ for their sample. The average price of gasoline from 1971 to 1990 in 1983 

dollars was $1.03 per gallon (U.S. DOE/EIA, 2009, table 5.24), thus the average fuel economy of 

cars in the sample was 20.2 miles per gallon. Since the average value of MP/$ is negative 

(implying fuel economy is a bad) it is more interesting to calculate the value of fuel economy to 

consumers whose attribute value is one standard deviation above the mean. 

∂u

-∂MP$

∂u

∂P

൙ =

(-0.122+1.050)

-0.0435 =21.33 ൭$

MP$ ൗ ൱

(2) 

Since MP$ is in units of tens of miles per dollar, the value of a 1 mile per dollar increase in fuel 

economy is $2.13. Using the standard assumptions for discounting future fuel savings described 

in the appendix, the present value of a 1 MPG increase from 20 MPG to 21 MPG would be $254. 

Thus, even consumers who are interested in fuel economy appear to be undervaluing it by 

roughly two orders of magnitude. 
12 

Uij = utility of vehicle choice j to consumer i 

uij = log[Uij] 

Pj = price of vehicle j 

Xj = a vector of attributes of vehicle j 

Yi = income of consumer i 

Zi = a vector of characteristics of consumer i that may interact with the attributes of vehicles 

β = a parameter vector of mean consumer utility values for vehicle attributes indexed k = 1, n 

σ = a vector of variances for consumers’ values of vehicle attributes, k = 1, n 

εij = a random utility parameter varying across consumers and vehicle choices 

νi = a vector of unobserved components of utility varying across vehicle choices 

The stability of parameter estimates for models like the random coefficient logit model of Berry, 

Levinsohn and Pakes (1995) has been studied in depth by Knittel and Metaxoglou (2008). The 

objective function optimized to estimate the coefficients of such models is generally highly 

nonlinear, and thus prone to multiple, local optima. One of the data sets analyzed by Knittel and 

Metaxoglou was the automobile choice data base of Berry, Levinsohn and Pakes (1995). They 

tested 10 different optimization algorithms, using 50 different starting values for each. Their 

findings call for caution not only in estimating such models, but in interpreting any set of 

estimated parameters. 

 “We find that convergence may occur at a number of local extrema, at saddles 

and in regions of the objective function where the first-order conditions are not 

satisfied. We find own-and cross-price elasticity estimates that differ by a factor 

of over 100 depending on the set of candidate parameter estimates.” (Knittel and 

Metaxoglou, 2008) 

Allcott and Wozny (2009) estimated a nested logit discrete choice model using an extensive 

data set of both new and used vehicles up to 25 years old in use in the United States between 

1999 and 2008. The central purpose of their analysis is to test whether the effect of a $1 change 

in the price of a vehicle is the same as the effect of a $1 change in the discounted present value 

of fuel costs. Their estimating equation is derived from the consumer’s utility function in the 

nested logit framework. 

݌௔௝௧ ൌ െܩߙ௔௝௧ െ ߚ݈݊൫ݏ௔௝௧൯ ൅ ߛ݈݊൫ݏ௔௝௧⁄ݏ௡௧൯൅ߜ߶ ൅ ௔௝௧ ൅ ߝ௔௝௧

(3) 

In equation (3) pjat is the price of model j of age a, in year t, G is the discounted present value 

cost of future gasoline use, sjat is the market share of model j, and snt is the market share of the 

nest to which vehicle j belongs. The log ratio of market shares enters the equation due to the 

nested logit structure which allows differing price effects within nests and across nests. The 

constants δ and φ are fixed effects representing, respectively, the other attributes of model j of 

age a, and shifts in the desirability of the outside good from year to year. Finally, εjat is a random 

error term. The random error term is assumed to be uncorrelated with G, while the model and 

age fixed effects (δja) can be correlated with G. Because of the simultaneity of vehicle prices and 

market shares, equation (3) is estimated using instrumental variables and two-stage least squares. 
13 

Calculating the discounted present value of fuel costs requires a number of assumptions 

including expectations about future fuel prices. Allcott and Wozny’s approach was to choose 

assumptions that were supported by credible data and that were biased towards accepting their 

null hypothesis. 

 “We formulate our assumptions to conservatively bias us against finding that 

consumers undervalue gasoline costs. We find, however, undervaluation for any 

plausible set of assumptions about gasoline cost expectations, vehicle survival 

probabilities, vehicle miles traveled, and other parameters. We conservatively 

estimate that U.S. auto consumers are willing to pay only twenty-five cents to 

reduce expected discounted gas expenditures by one dollar.” (Allcott and Wozny, 

2009, p. 5) 

For example, the authors assumed that consumers would discount future fuel costs at 15% per 

year; if this discount rate is too high, the present value of fuel costs will be understated and the 

leverage of fuel costs on market shares will be overestimated. This is consistent with their intent 

to bias their analysis in favor of finding that consumers fully value or overvalue fuel economy. 

Used vehicle prices came from a data base of auction prices that tracks 5 million transactions 

annually. New vehicle prices came from a data base of 2.5 million new vehicle transaction 

prices. 

Under a wide variety of model formulations and assumptions, Allcott and Wozny found that 

consumers substantially undervalue future fuel costs in their choices of new and used vehicles. 

The preferred form of their nested logit model, using instrumental variables to account for 

potential simultaneous equations bias, produced an estimate of 0.25 for the ratio of the 

coefficient of present value fuel costs to that of vehicle price. This implies that consumers count 

a present value dollar of fuel costs as only $0.25 relative to a dollar of purchase price. The 

nested logit estimated by ordinary least squares (OLS) produced a ratio of 0.15, while the simple 

(un-nested) logit generated an estimate of 0.33 and a simpler reduced form model yielded a ratio 

of 0.23. Testing the sensitivity of the results to alternative nesting structures, Allcott and Wozny 

found a range of estimates from 0.22 to 0.35. 

Alternative assumptions about gas price expectations produce a range of estimates for the value 

of $1 present value of fuel costs of $0.20 to $0.46. Although Alcott and Wozny’s models of 

consumers expectations span a range from random walk to the use of NYMEX futures prices, 

there is still no consensus as to which of the alternatives, if any, accurately represents the way 

consumers form their expectations about future fuel prices. At the same time, this is a critical 

element in the analysis. These sensitivity tests suggest that the inference that consumers 

underweight fuel costs relative to purchase price in both new and used car purchase decisions is 

relatively robust. Increasing the assumed discount rate used to calculate the present value of fuel 

costs (in the preferred nested simultaneous model) drove the ratio closer to one, as expected. At 

an assumed 60% annual discount rate, the estimated ratio was 0.74, still lower than 1.0 implying 

that consumers are using an even higher discount rate. 

The undervaluing of fuel economy found by Allcott and Wozny is very large relative to vehicle 

prices and the cost of fuel. It suggests a very serious departure from the rational economic 

consumer model. The authors conclude that correcting this market imperfection would increase 
14 

the privately optimal level of fuel economy by 9 miles per gallon by sales mix shifts alone. They 

also conclude that the market failure of undervaluing fuel costs is even more significant than 

externalities associated with fuel use. 

 “Comparing these figures shows that the welfare gains from reducing negative 

externalities are dwarfed by the welfare gains from reducing the “internality” by 

inducing consumers to make the privately-optimal choice. Perhaps the most 

important take-away from this analysis then is that behavioral misoptimization 

can be a more powerful justification for fuel economy policies than internalizing 

environmental externalities.” (Allcott and Wozny, 2009, p. 33) 

Klier and Linn (2008) use a data base of new vehicle sales covering an unusually long time 

period from 1970 to 2007 by detailed model and model year to estimate the effects of fuel price 

changes on vehicle sales. They do not directly estimate a measure of willingness to pay, nor can 

one be derived from their model without making a number of important assumptions. 

Nonetheless, their model does indicate a relative insensitivity of vehicle sales and fleet average 

fuel economy to fuel price. Their analysis is entirely focused on the effect of fuel economy on 

sales of different makes and models and the impact of those salesmix changes on average fuel 

economy. It does not address changes in vehicle technology, engineering or design made by 

manufacturers to improve fuel economy. The paper also does not address the impacts of the 

Corporate Average Fuel Economy (CAFE) standards on sales or fuel economy. 

The form of their model is similar to that used by other studies. The utility, Uijt, of vehicle model 

j, within a given model year, to individual I, in month t is assumed to be the sum of services 

flows from observed, Xj, and unobserved, ωj, vehicle attributes and the cost of obtaining those 

services, which is the sum of the vehicle’s purchase price, Pj, maintenance costs, mj, and fuel 

costs, fjt. 

ܷ௜௝௧ ൌ ߙ൫ܲ௝ ൅ ݉௝ ൅ ݂௝௧൯൅ܺ௝ߚ൅߱ ൅ ௝ߝ௝௜௧

(4) 

In equation (4) α and β are coefficients to be estimated and εijt is a random error. The form 

chosen for equation (4) allows a model-specific intercept to be defined as the sum of all the 

components that vary only over j, that is everything except the cost of fuel and the error term. 

Assuming the error term has the extreme value distribution; the share of model j will be a logit 

function of its fuel costs, those of other models and the attractiveness of the outside good, which 

is assumed to be an arbitrary used vehicle. The difference between the log market share of 

model j and the log market share of the outside good is given by the following. 

݈݊൫ݏ௝௧൯ െ ݈݊ሺݏை௧ሻ ൌ ߙ݂௝௧ ൅ ߶௝

(5) 

In equation (5), Ԅj is a model specific constant measuring the relative value of model j in 

comparison to the outside good (used car) except for the effect of fuel costs on the market share 

of j. To eliminate the used car share from equation (5) the authors add dummy variables to time 

periods and index Ԅjy, by model year, y. Like others, they specify fuel costs as the discounted 
15 

present value, but assume that the price of gasoline follows a random walk, so that the expected 

price is equal to the current price. 

1 ෍൥ ߙ௧൯ൌ௝ݏ൫݈݊

ሺ1൅ݎሻ௦

௧݌

௬௝ܩܲܯ

௦ܯ

்ା௧

௦ୀ௧

൩ ൅ ߜ௧ ൅ ߶௝௬ ൅ ݁௝௧ ൌ ߙ́ ݌௧

௬௝ܩܲܯ

൅ ߜ௧ ൅ ߶௝௬ ൅ ݁௝௧

(6) 

Note that the coefficient α’ now includes α times discounted lifetime miles. It should therefore 

be possible to approximately recover the original α by dividing by an appropriate estimate of 

discounted lifetime miles. However, this would give the value of fuel costs in utils per dollar. 

One would need a purchase price coefficient to convert to dollars of present value fuel cost per 

dollar of purchase price, and this model does not have one. While it would be possible to assume 

a purchase price coefficient based on other studies, the uncertainty would be great since price 

coefficients vary substantially from study to study. However it may be useful to try and bound 

the implications of Klier and Linn’s analysis. In the logit model framework they use, the 

coefficient of purchase price (b) is the following function of a vehicle’s market share (σ), its own 

price (P), and the own price elasticity of its market share (β). 

ߚ  ൌ ܾ

ܲሺ1െߪሻ

(7) 

In general, with hundreds of models available in any given model year, σ for any given model 

will be negligible, so that the price slope is chiefly a function of the own price elasticity and own 

price. For a typical car, an average price is approximately $20,000 to $25,000, and a reasonable 

range for model level price elasticities is -2 to -6. Using an average price of $25,000, this 

produces a range of price slope estimates from -0.00008 to -0.00024, with a midpoint value of 

-0.00016. Klier and Linn’s overall estimates of α’ range from -10 to -15 (Klier and Linn, 2008a, 

table 2). Applying our estimate of discounted miles for a passenger car of 112,600, produces a 

range for α of -0.0000888 to -0.000133, with a midpoint of -0.000111. Recall that this α is the 

coefficient of expected lifetime fuel costs. Dividing α by the purchase price slope gives 0.69, 

implying that a new car buyer would be willing to pay $0.69 for a reduction in expected lifetime 

operating costs of $1. However, given the number of assumptions required to arrive at this 

estimate, all that it is meaningful to say is that its general magnitude is plausible. Klier and Linn 

also estimate individual coefficients for each model in their sample. The estimates range from -

65 to +35, following a skewed distribution with a mode around -15 (Figure 2). Like other 

studies that allow for heterogeneity, Klier and Linn find very substantial heterogeneity in 

consumers’ apparent valuation of future fuel savings. 
16 

Figure 2. Histogram of Estimated Coefficients of Dollars per Mile from Klier and Linn (2008). 

 

Gramlich (2008) proposed a model of U.S. automobile supply and demand in which 

manufacturers decided on the level of fuel economy to include in vehicle designs by trading off 

increased fuel economy against increased cost and a measure of quality. New car demand is 

represented by a nested logit model, estimated using U.S. sales data for 1971-2007. Consumer i 

is assumed to choose the vehicle model (j) that maximizes utility. Utility is a function of vehicle 

price (pj), fuel economy (econj), other quality (qualj), which “...includes things such as power, 

weight, acceleration, electronics, sportiness, interior room, etc. – in short, the collection of other 

vehicle attributes that must be traded off with fuel efficiency.” (Gramlich, 2008, p. 6). Other 

variables common to all vehicles in a given year, such as macroeconomic factors, and vehicle 

specific fixed effects are also included. 

Gramlich makes the important observation that the correlation of fuel economy with other 

vehicle attributes creates estimation biases. 

“Previous models, including seminal work, have had difficulty finding parameter 

estimates that show consumers care about fuel efficiency. Parameters on 

preference for fuel efficiency have been biased towards zero. The reason for this 

is that in automobiles, fuel efficiency (MPG) is negatively correlated with other 

characteristics that provide utility. Some of these characteristics are observed and 

easily controlled for, such as horsepower and weight. Others, however, are not.” 

(Gramlich, 2008, p. 4) 

Surprisingly, the measure of quality selected by Gramlich was precisely fuel economy in miles 

per gallon. The idea is that fuel economy will represent negative quality, that is, it will represent 

the trade-off between fuel economy and quality. The positive value of fuel economy is 

represented by the price of fuel divided by miles per gallon. 

 “Fuel efficiency (MPG) affects both the fuel economy (econ) and “other quality” 

(qual) of a vehicle through the technological tradeoff. ...The measure I use for 
17 

qual is MPG itself. This may seem unusual but, conditional on the economic 

effects of fuel efficiency (dpm), higher MPG is strongly associated with lower 

“other quality.” (Gramlich, 2008, p. 7) 

This formulation raises two questions. The first is whether fuel economy is an adequate 

(negative) proxy for quality. The numerous excluded dimensions of quality include not only 

factors that are clearly negatively related to fuel economy, such as size, power, and energy using 

accessories, but also items seemingly unrelated to fuel economy, such as reliability, fit and 

finish, and safety features. Indeed, the two manufacturers with the highest fuel economy given 

the mass and power of their passenger cars are Toyota and Honda, manufacturers also having 

among the highest reputations for quality (Knittel, 2009). Thus, using MPG as the sole proxy for 

quality would seem to create problems of omitted variables and errors in variables, conditions 

likely to lead to biased estimates. It is highly unlikely that MPG itself could serve solely as a 

negative proxy for other quality attributes and not in any way function as a positive attribute 

itself. The formulation also requires that any increase in fuel economy necessitates a reduction 

in quality in other areas. That is, does not permit fuel economy to be purchased at a higher price 

without degrading quality. 

The second issue is that using fuel price divided by MPG does not allow for distinction between 

consumers’ valuation of fuel economy versus their reaction to changes in gasoline prices, since 

the effects are constrained to be equal and opposite effect. Although a presumption of rational 

behavior implies that the effects of price and fuel economy should be equal and opposite, this 

hypothesis can be tested and, indeed, that should be one of the objectives of an analysis of the 

effect of fuel economy on consumers purchase decisions. Including MPG by itself as a measure 

of quality also confuses the interpretation of price/MPG. 

Another difficulty with Gramlich’s model formulation is that he assumes that decisions regarding 

the designs of vehicles are taken the year before the current model year. This does not match the 

consensus understanding of how vehicle designs are changed over time. According to the NRC 

(2002) report on the Corporate Average Fuel Economy standards, manufacturers typically lock 

in vehicle designs two years in advance to allow time for tooling, certification and testing. In 

addition, only about 20% of a manufacturer’s makes and models are substantially redesigned in 

any given year, in order to allow more efficient use of engineering resources and to distribute 

capital expenditures more evenly over time. Gramlich (2008) does test two alternative 

formulations with 3 and 5 year lags in design, but neither reflects the continuous, gradual 

redesign process manufacturers follow in reality. 

Gramlich (2008) calculated willingness to pay for an increase in MPG from 25 to 30 miles per 

gallon. With gasoline at $2/gal., luxury car purchasers were willing to pay $4,098, compact car 

owners were willing to pay $7,377 and SUV owners $11,749. If the price of gasoline increased 

to $3.50, luxury car owners would pay up to $7,172, compact car owners $12,910 and SUV 

buyers, $20,560. These values are high relative to the discounted present value of fuel savings. 

Using the standard assumptions of this assessment, an improvement in fuel economy from 25 to 

30 MPG is worth $1,428 present value, with gasoline at $2/gal. and $2,500 with gasoline at 

$3.50/gal. 
18 

Gramlich also used his model to make predictions about the impact of the increases in gasoline 

prices from 2007 to 2008 on the average fuel economy (identical to fuel efficiency in the quote 

below) of new light-duty vehicles and on vehicle sales. 

 “To check the predictive ability of the model, I compare out-of-sample 

predictions to 2008 actual figures. The model matches sales composition changes 

well. Actual aggregate sales are down 12% through August from 2007 levels, 

compared to a prediction of 11.9%. The model also predicts an increase in salesweighted fuel efficiency of 28%, a number consistent with the large 2008 

reductions in purchases of SUVs and light trucks.” (Gramlich, 2008, p. 4) 

Indeed, according to U.S. EPA (2009) data, sales of passenger cars and light trucks declined 

from 15.277 million units in model year 2007 to 13.900 million units in model year 2008, a 

reduction of 9.0%, quite close to Gramlich’s model’s prediction. The average fuel economy of 

new light-duty vehicles increased from 25.8 to 26.3 MPG (combined, unadjusted test value), a 

change of only +1.9%. Using the city MPG measure Gramlich used in his model estimation, the 

increase was from 21.8 to 22.1, or +1.4%. Fleet average MPG numbers reported by the NHTSA 

(U.S. DOT/NHTSA, 2009) are very similar to the EPA numbers: 27.0 for 2008 versus 26.6 for 

2007, an increase of 1.5%. Thus, while Gramlich’s model did a reasonable job of predicting the 

decline in vehicle sales it was off by more than an order of magnitude in over-predicting the 

impact of higher fuel prices. 

Sawhill (2008) estimated a model to quantify the trade-off between automobile operating cost 

and purchase price using a virtually identical data base (1971-1990 model years) and a very 

similar methodology to Berry, Levinsohn and Pakes (1995). The key differences are that 

Sawhill’s model includes heterogeneous consumer preferences and explicitly represents 

expectations about future fuel prices. Vehicle prices are list prices, although they are assumed to 

be endogenously determined along with unobserved vehicle attributes and operating costs are 

represented by the price of gasoline divided by fuel economy. The authors do not state so 

explicitly, but given the source and the data shown in their table 2, the fuel economy data is 

almost certainly the EPA adjusted, combined MPG measure. Consumers’ expectations about 

future fuel prices are based on an autoregressive model in price differences fitted to annual 

historical gasoline price data. This implies that consumers form an analogous model via 

observation of fuel price changes. The coefficient of the lagged price difference is 0.35 in the 

expectations model, indicating that the change from last year is expected to persist, with a 

rapidly declining rate. The author states that fuel cost is used as a proxy for vehicle operating 

cost, asserting that fuel costs are the largest share of operating costs. However, no other future 

costs (e.g., insurance, maintenance and repairs, etc.) are included in the model. According to 

data from the publication Motor Vehicle Facts and Figures (Davis, Diegel and Boundy, 2009, 

table 10.14) depreciation and financing costs are about 56% of the total costs of owning and 

operating a motor vehicle. The issue is whether fuel costs are truly a proxy for all costs paid out 

over time, as opposed to vehicle purchase price, or whether the other costs should be considered 

one of the many unobserved vehicle attributes. If fuel costs are a proxy for (and therefore 

correlated with) other future costs, then one might expect the coefficient of fuel costs to be 

biased upwards. If non-fuel future costs are more correctly interpreted as unobserved attributes, 

then the coefficient of fuel costs could be unbiased. 
19 

The key equation of Sawhill’s model relates consumer i’s utility for automobile j to the price of 

vehicle j, the expected present value operating cost of j over the car’s lifetime, and vectors of 

observed (xj) and unobserved (zj) attributes of vehicle j, as well as a random error term, εij. 

௝࢞ ൅ ൯ቁ௜௝ܿ൫ܧ௜ߛ ൅ ௝݌ቀ௜ ߙെ ൌ௜௝ ܷ

௝௜ߝ ൅ ௝ݖ ൅ ௝ࢼ்

(8)

The parameter α represents consumer i’s sensitivity to price (or income) while a value of γ

different from 1 indicates that consumers value fuel savings differently from purchase price. 

E(cij) is a key function representing the consumer’s expected present value of future operating 

costs. For γ to equal 1, E(cij) must be measured accurately. This requires that price expectations 

be accurately represented. It also requires that fuel economy and the distribution of annual miles 

be correctly estimated. Sawhill uses data from the Department of Energy’s 1991 RTECS survey 

to estimate the distribution of vehicle miles. Data for all model years rather than just new 

vehicles were used but mileage was adjusted by a weighting factor intended to represent the 

frequency of new vehicle purchases for each car as a function of its annual mileage. Vehicles 

were assumed to be driven different numbers of miles per year by different households but to all 

have the same total lifetime miles. This is clearly not the case, but it is also not correct to assume 

that all vehicles last for the same number of years. Thus, the expected life of a vehicle is an 

inverse function of annual use equal to the average lifetime miles divided by the household’s 

annual mileage. Based on the adjusted RTECS data an estimated average lifetime mileage of 

90,000 was used (p. 15). The expected annual miles driven for a typical new car in Sawhill’s 

study is 13,612. At this rate the vehicle’s expected life would be 6.6 years. These numbers are 

far lower than the reference lifetime mileage estimates used by the Department of Transportation 

(DOT): 152,137 miles for passenger cars and 179,954 for light trucks (U.S. DOT/NHTSA, 

2006), based on expected lifetimes of just over 13 years for passenger cars and 14 years for light 

trucks (see appendix). The fuel cost calculation also assumes an annual real discount rate of 5% 

for the base model. Finally, consumers are able to adjust their annual driving rates as fuel prices 

increase or decrease, although sensitivity testing showed that this factor had a minor influence on 

the estimated coefficients. 

Sawhill (2008) first estimates a simple model that includes neither heterogeneous consumer 

preferences nor price expectations. The OLS and two-stage least squares versions of the simple 

model imply that consumers undervalue the discounted present value of future fuel savings. In 

contrast, versions of the full model including heterogeneity of consumers’ preferences and price 

expectations indicate that most consumers overvalue fuel costs (representing operating costs) by 

a factor of about 1.4. A ratio of 1.0 would imply that consumers equally value a dollar change in 

vehicle price and a dollar change in the expected present value of lifetime fuel savings. 

Increasing average lifetime mileage to 110,000 from 90,000 miles reduced the ratio to 1.3. 

Increasing the expected lifetime miles to 152,137 would presumably bring the ratio closer to 1. 

The estimated variability of valuation of fuel costs is considerable; a 95% confidence interval for 

the distribution of fuel costs in the population is -3.6 to 14.1. This implies that some consumers 

prefer higher fuel costs but this could be a consequence of the assumed symmetrical distribution 

of consumer preferences. 
20 

The only vehicle attributes included in the base model formulation were horsepower, a measure 

of car size whose average varies from 1.5 to 1.25 over the sample period, and air conditioning. 

As a test of robustness to the specification of vehicle attributes, the authors also estimated a 

model including a dummy variable for an automatic transmission, the number of cylinders in the 

vehicle’s engine and the number of doors in the vehicle. Including these variables has little 

effect on the coefficient of price, which changes from -3.78 to -3.64. The coefficient of fuel 

costs, however, changes from -5.26 to -3.27. The standard deviation of the fuel cost coefficient 

measuring its distribution in the population also changes from 4.5 to 2.8. While these are not 

dramatic changes they do indicate a nontrivial sensitivity of the estimated fuel cost coefficient to 

both the included variables and the specification of the present value of fuel costs. 

Because Sawhill’s model is focused on fuel costs as a proxy for operating costs, it is not possible 

to separate the influence of fuel economy from that of fuel price expectations on consumers’ car 

buying decisions. It may be that consumers respond differently to fuel price changes and 

changes in fuel economy that produce equivalent changes in expected present value operating 

costs. Different influences for fuel economy and fuel price have been found in econometric 

analyses of vehicle miles of travel, for example (e.g., Small and Van Dender, 2007; Greene, 

2010). 

3.1.2 Survey Data 

Train and Winston (2007) formulated a mixed logit choice model that allows the estimation of 

average values, values that vary systematically with consumer attributes, and values that are 

randomly distributed in the population. They estimated the model using a random sample of 458 

U.S. consumers who acquired a 2000 model year vehicle. The consumer data included the make 

and model of the vehicles they purchased, other vehicles they seriously considered, their vehicle 

ownership histories and socioeconomic characteristics. They found that the effect of fuel 

consumption (gallons per mile) in the average utility equation, was negative as expected but not 

statistically significant. The estimated coefficient was reported to be -0.0032 with a standard 

error of 0.0023. Fuel consumption was not included in the estimation of utilities that varied 

across consumers as a function of consumers’ socioeconomic attributes. There was a statistically 

significant component that varied randomly across the population, comprised of the coefficient 

-0.0102 (std. err. = 0.0020) times a standard normal random variable N(0,1). This implies that 

about 62% of the respondents considered lower fuel consumption (higher fuel economy) to be a 

positive attribute for a new vehicle while about 48% considered it to be a disadvantage. On 

average, consumers considered better fuel economy to be slightly, and not statistically 

significantly, advantageous. Taking the value -0.0032 at face value and using the standard 

assumptions for fuel cost and vehicle use, a 0.01 gal./mi. change in fuel consumption (equivalent 

to 20 MPG to 25 MPG) would be worth only $30 to the average consumer. Using the reference 

assumptions about vehicle use, lifetime, a 7% real annual discount rate and gasoline at $2/gallon, 

an improvement to 25 MPG should be worth $2,250 to owners of a typical passenger car, and 

$2,500 to the owner of a light truck 

Dasgupta, Siddarth and Silva-Risso (2007) estimated a mixed logit model of consumers’ 

choice of vehicle and lease-buy option using data from vehicle transactions in Southern 

California for the period September 1999 to October 2000. Only 15 vehicle models were 
21 

included in the study, all were in the entry-luxury market segment. Although the contract choice 

equation included fuel cost per mile as a variable, the structure of the model is such that its 

coefficient should be interpreted as the distribution of annual miles traveled. The authors report 

the estimated mean of that distribution as 16,380 miles with a standard deviation of 1,914 miles. 

Although this prohibits estimating an implied discount rate from the ratio of the net price (net 

present value of acquisition costs) and fuel cost coefficient, consumers’ implicit discount rates 

were inferred from consumers’ choices among purchase and leasing options. The structure of 

the model implies that the discount rate for lease or loan payments should be the same as the 

discounting of future fuel costs. The authors report an estimated discount rate of 15.2%, higher 

than the 9% annual market rate prevailing at the time the transactions occurred. The inference 

that this is the discount rate for future fuel savings also depends on the accuracy of the average 

annual mileage estimate of 16,380. 

Bento, Goulder, Henry, Jacobsen and von Haefen (2005) estimated a model of vehicle choice 

and use using data from the 2001 National Household Travel Survey, augmented with data on 

vehicle attributes and operating costs. They estimated a random coefficient model of 

households’ vehicle and vehicle travel choices. Although estimated coefficients and mean 

values of some variables are available in an extended on-line version of the paper (Bento et al., 

2008; http://www4.ncsu.edu/~rhhaefen/auto051808.pdf) the information is not sufficient to 

estimate trade-off rates between fuel economy and rental cost. The model’s estimates of the 

impacts of higher gasoline prices on vehicle travel and vehicle fuel efficiency shed some light on 

how consumers value fuel economy. Although it is not explicitly discussed in the paper, the fuel 

economy impacts presumably come about via consumers’ choices among a fixed set of vehicles 

and do not include manufacturers’ decisions about the technological content and design of 

vehicles. Their estimates of the impacts of gasoline price increases of $0.10, $0.30 and $0.50 per 

gallon over a base price of $1.45 per gallon (6.9%, 20.7%, and 34.5%), in cases with and without 

income-based revenue recycling are shown in Table 3. The change in MPG is very small, 

ranging from 0.04% (0.01 MPG) for the $0.10 increase without revenue recycling to 0.24% (0.06 

MPG) for the $0.50 per gallon increase with revenue recycling. Implied elasticities of MPG with 

respect to fuel prices are shown in parentheses. 

Table 3. Impacts of Gas Tax Increases Calculated by Bento et al. (2005) 

 Base $0.10 $0.30 $0.50 

Fuel Price $1.45 +6.9% +20.7% +34.5% 

MPG no recycling 24.2 +0.04% 

(+0.006) 

+0.11% 

(+0.005)

+0.18% 

(+0.005) 

MPG with recycling 24.2 +0.05% 

(+0.007) 

+0.13% 

(+0.006) 

+0.24% 

(+0.007) 

Bento et al., 2005, table 1. 

Feng, Fullerton and Gan (2005) estimated a simultaneous model of vehicle use and choice of 

ownership using data from the 1996 to 2000 Consumer Expenditure Survey. The detail on 

vehicle type was limited to passenger car or SUV, and only households owning 1, 2 or no 

vehicles were included. A nested multinomial logit model structure was assumed, however, the 

requirement for simultaneous estimation of vehicle use and ownership choice required a two-
22 

stage estimation method. There are six fuel cost per mile coefficients in their model: (1) single 

car or first car in two-car household, (2) second car in two car household, (3) single SUV or first 

SUV in two-SUV household, (4) second SUV in two SUV household, (5) car in car+SUV 

household, and (6) SUV in car+SUV household. Four of the six estimated coefficients of vehicle 

capital cost and fuel cost per mile were statistically significant and all had appropriate negative 

signs. The coefficients, their ratio to the coefficient of capital cost, the implied value of a $0.01 

per mile change in fuel cost per mile, and that value as a percent of the full lifetime discounted 

present value of a $0.01 per mile change in operating costs are shown in Table 4. For estimation, 

Feng et al. (2005) represented capital costs in units of thousands of dollars and they state that 

they represented fuel cost per mile in units of dollars per mile (table 6 and p. 6). As seen in 

Table 4, the estimated willingness to pay for reduced fuel cost per mile is on the order of 1% of 

the discounted present value of lifetime value. If Feng et al. actually converted fuel cost per mile 

in dollars per mile to fuel cost per mile in cents per mile but did not report that in their paper, 

then the values of fuel costs implied by their model’s estimates would be comparable to lifetime 

discounted present value. 

Table 4. Estimated Value of Fuel Costs in Vehicle Ownership Choice Model 

of Feng, Fullerton and Gan (2005) 

Variable 

Coefficient 

Estimate 

Implied Value 

$/Unit 

Willingness to Pay as a % 

of Lifetime Discounted PV 

Only Car/1st Car -0.433 -1069 1.0% 

2nd Car -0.045 -111 0.1% 

Only SUV/2nd

 SUV -0.526 -1299 1.0% 

2nd SUV -0.013 -32 0.03% 

Car in Car/SUV HH -0.399 -985 0.9% 

SUV in Car/SUV HH -0.662 -1635 1.3% 

Source: Feng, Fullerton and Gan (2005, table 7). The coefficient of capital cost in dollars rather than 

1,000s of dollars is -0.000405. Discounted lifetime miles are 112,600 for cars and 125,891 for SUVs. 

An additional interesting finding of Feng et al.’s study was that coefficient estimates varied 

considerably depending on the estimation method used. 

Brownstone, Bunch and Train (2000) estimated a mixed logit choice model for automobiles 

using a combination of stated and revealed preference data. Use of stated preference data was 

necessary in order to include in the choice set alternative fuel vehicles, such as battery electric 

and compressed natural gas vehicles, which were generally unavailable in the market at the time. 

The data base was a multi-wave survey that began with a telephone survey of 7,387 California 

households carried out in 1993. Households were not only asked about the vehicles they owned, 

but about vehicles they intended to purchase, if any. A total of 4,747 households completed a 

more detailed mailed questionnaire. Households to whom a questionnaire was mailed were also 

contacted by phone and asked about their preferences for alternative fuel vehicles. More than a 

year after the above surveys were completed, most of the original households were reinterviewed. Of these, 874 had purchased a vehicle and these became the revealed preference 

data set for the logit model estimation. In the end, there were two stated preference and one 

revealed preference observation for each of these households. 
23 

First, models were estimated using the stated and revealed preference data sets separately. In the 

preferred stated preference mixed logit model, fuel economy entered as the denominator of fuel 

cost per mile. The coefficient of fuel cost per mile was negative and statistically significant, as 

expected. Vehicle price entered as price in thousands of dollars, divided by the natural logarithm 

of household income in thousands. The mean of log(income) was 4. The average willingness to 

pay for fuel cost reduction is therefore: -(-0.236/((-0.503/1000)/4)) = $1,877 per penny per mile 

change in fuel cost per mile. The normalized coefficient estimates also provided by Brownstone 

et al. in their table 2 indicate a willingness to pay of $1,710 for a 1 cent decrease in fuel cost per 

mile. Using the standard assumptions about lifetime miles (112,600 discounted lifetime miles 

for passenger cars, 125,891 for light trucks), a one cent per mile change in fuel costs would be 

worth $1,126 for passenger cars and $1,259 for light trucks, indicating that in the stated 

preference survey consumers tend to overvalue lifetime fuel savings. However, the value of fuel 

cost per mile has a large variance, which led Brownstone et al. to make the following 

observation. 

 “Unfortunately, the relatively large error component for operating cost implies 

that the model will generate an (implausible) positive price effect for one third of 

the respondents.” (Brownstone et al., 2000, p. 327) 

The willingness to pay in Brownstone et al.’s (2000, tables 3 and 4) revealed preference model is 

even greater, $2,240 per 1 cent per mile. The mixed logit model estimated on the combined 

stated and revealed preference data indicated a willingness to pay for reduced fuel cost per mile 

of $1,660. 

Brownstone, Bunch, Golob and Ren (1996) estimated a nested logit choice model of household 

ownership and use that included modeling of vehicle acquisition and disposal transactions. The 

data came from a stated preference survey of California urban households conducted in June and 

July of 1993. Households were asked to choose among hypothetical vehicles that included 

alternative fuel vehicles. Choice models were estimated for households owning one and two 

vehicles. Both the vehicle price (in dollars) and operating cost (in cents per mile) coefficients 

were varied according to income, the presence of children in the household and ownership of a 

luxury vehicle. Estimates of the willingness to pay for a reduction in fuel cost per mile for these 

groups are shown in Figure 3. There is a wide range of implied willingness to pay, with only 

about four out of the ten estimates being close to our reference willingness to pay estimates of 

$1,126 per penny per mile for passenger cars and $1259 for light trucks. The greatest 

willingness to pay ($4,718) was for households with incomes of more than $30,000 per year, no 

luxury vehicle but with children in the household. The lowest value was -$4,526 for households 

with incomes exceeding $75,000 per year and no children. 
24 

Figure 3. Estimated Values of a 1 Cent per Mile Decrease in Fuel Costs Based on Brownstone, 

Bunch, Golob and Ren (1996, tables 3 and 4). 

Goldberg (1995) estimated a nested multinomial logit model of consumers’ choices among 

passenger car and light truck makes and models. Three equations of make and model choice 

were estimated using pooled data from the 1983 to 1987 Consumer Expenditure Surveys: 

(1) small cars comprised of subcompacts and compacts, (2) luxury automobiles including sports 

cars, and (3) all other vehicles. A likelihood ratio test rejected the hypothesis that the three 

classes of vehicles had the same coefficient values. Fuel efficiency was represented by the 

regional price of gasoline times the rate of fuel consumption in gallons per mile, i.e., fuel cost 

per mile. In the small car choice equation, fuel cost per mile was decidedly statistically 

significant; the coefficient of -7.143 being almost ten times its standard error. The coefficient 

estimate of fuel cost per mile in the “Big Car” equation, -1.381, was only 1.86 times its standard 

error, making it significant at the 10% but not the 5% level. In the luxury vehicle equation fuel 

cost per mile had the wrong sign (+0.231) but was not close to being statistically significant. 

Calculating the value of fuel economy using Goldberg’s (1995) results is uncertain because she 

is not precise about the units used when estimating her model. The variable definition for price, 

for example is purchase price and vehicle specific dummy variables, and while the mean values 

shown in her table A5 are in dollars, it is impossible to reconcile the estimated price coefficients 

ranging from -0.5 to -4.7 with elasticities reported in table II of -1.1 to -5.2 (depending on class) 

assuming price is in dollars. Goldberg (1996) use vehicle purchase price as a proxy for 

annualized cost and fuel cost per mile, and reports a price coefficient of -2.991 and a fuel cost 

coefficient of -0.425. Fuel cost is measured in cents per mile while purchase price is presumably 

in dollars. The ratio of the fuel cost to purchase price coefficients is $0.14 per 1-cent-per-mile. 

$247

$1,151

$1,964

‐$4,526

$142

$1,151

‐$3,699‐$3,998

$4,718

$2,044

‐$5,500

‐$4,500

‐$3,500

‐$2,500

‐$1,500

‐$500

$500

$1,500

$2,500

$3,500

$4,500

$5,500

Estimated  Values of a  1 Cent per Mile Decrease in Fuel Costs

Reference Values are $1,126 for Cars, $1259 for Light Trucks
25 

Applying the standard estimate of discounted lifetime miles implies a present value of 1-centper-mile of $1,126. Thus, it does not appear likely that purchase price is in simple dollars per 

vehicle. The reported elasticities can only be reconciled with the reported parameter estimates 

by assuming that vehicle price was in units of 10,000 dollars for estimation purposes. This 

leaves the question of what units fuel cost is in, dollars, cents or some other units. 

Goldberg’s (1998) analysis is specifically focused on the short-run effects of the CAFE 

standards, which limited manufacturers’ strategies to pricing and domestic/import content 

decisions. Changes to vehicle design and technology content were excluded. She estimated a 

nested multinomial logit model of vehicle choice among nine car classes. Within each class, 

consumers had a choice between a foreign or domestic car. The vehicle attributes included were 

price, horsepower/weight, size, and fuel cost per mile. These were often interacted with 

household characteristics such as income, household size, education level, etc. The principle 

data source was the Consumer Expenditures Survey 1985 to 1989. Although Goldberg does not 

report the empirical results of the estimation of her consumer choice model in her 1998 paper, 

she does make specific observations about consumer valuation of fuel economy. 

“A question of particular interest for environmental regulation is whether consumers 

appear to be “myopic,” in the sense that they respond more to current changes in 

vehicle prices, than to changes in fuel costs that are felt over several periods. 

“Assuming a discount rate of 5%, and an average vehicle holding period of 7 years, 

produces vehicle price, and fuel costs semi-elasticity estimates that are very similar in 

magnitude. Even though these calculations are sensitive to assumptions about vehicle 

holding periods and discount rates, the numbers used above seem realistic enough to 

safely say that we do not have any reason to believe that consumers are myopic.” 

(Goldberg, 1998, p. 21) 

On the other hand, using our standard assumption of a 14 year lifetime, 112,600 discounted 

miles, together with a 1987 fuel price of $0.96 and an average MPG of 18, produces a discounted 

present value of $6,000. Goldberg reports a price elasticity of vehicle choice of -3.01 and a fuel 

cost elasticity of vehicle choice of -0.5, implying that purchase price has six times the leverage of 

fuel cost, in terms of a percent change. She also gives the average price of a new car as $12,000. 

If a present value dollar of fuel cost were equal to a dollar of purchase price, the elasticity of fuel 

cost (given our standard assumptions) should be half the purchase price elasticity. Since it is 

one-sixth as large, the implication is that consumers undervalue fuel savings by 1:3. 

3.1.3 Studies from the EU 

Cambridge Econometrics (2008) carried out an econometric analysis of demand for cars and 

their attributes for the Department for Transport, UK. The researchers estimated a mixed logit 

model of vehicle choice using survey data on households in the UK who had purchased a new or 

less than 12 month old car during the years 2004 and 2007. Households identified the 

manufacturer, model and engine size of the vehicles they purchased. A separate data base on 

vehicle attributes was mapped to the survey data to add information on vehicle attributes such as 

transmission, purchase price and fuel consumption. It also provided an estimate of fuel costs for 
26 

12,000 kms of travel. Interestingly, both fuel costs in pounds per 100 km, and fuel consumption 

in liters/100km were included as vehicle attributes in the model estimation. Fuel consumption 

was not statistically significant but fuel costs were. This may indicate a greater sensitivity to fuel 

price than fuel economy but the authors did not explore this question. 

The report estimates the value of a reduction in fuel costs by the ratio of the mean value equation 

coefficients of vehicle price (-0.380) and fuel cost per 100km (-0.745), obtaining a ratio of 

0.380/0.745 = 0.510. This is interpreted as a willingness to pay £510 per £1/100km reduction in 

operating costs. This calculation implies that vehicle price is measured in thousands of pounds. 

But this is not stated in the report, indeed, it is stated in several places that purchase price is 

measured in units of pounds. Moreover, the ratio used is the marginal utility of a pound in 

purchase price to the marginal utility of a pound per 100km in fuel costs. This ratio (actually the 

negative of this ratio) is the marginal change in fuel costs that equates to a £1 change in purchase 

price, rather than the change in purchase price that equates to a £1/100km change in fuel costs. 

The latter value should be calculated as follows. 

-

∂P

∂F =-

∂U

∂F

∂U

∂P

=- -0.745

-0.380 =-1.96 

(9) 

Thus, a decrease of £1/100km in fuel costs equates to a £1.96 increase in purchase price. That is, 

consumers would be indifferent between no change in vehicle attributes and a decrease of 

£1/100km in fuel costs coupled with a £1.96 increase in purchase price. Put another way, 

consumers would be willing to pay up to £1.96 in increased purchase price for a £1/100km 

reduction in fuel costs. If price is indeed measured in thousands of pounds, the willingness to pay 

for a £1/100km reduction in fuel costs would be £1,960.7

 An average annual vehicle use of 

15,000 km is used in the study, implying a discounted lifetime kilometers traveled on the order 

of 100,000 or more. This value appears to imply that consumers in the UK somewhat overvalue 

fuel cost savings, since 100,000 km × £1/100 km = £1,000. 

Eftec (2008) estimated an aggregate discrete choice model for purchases of automobiles by 

households in the UK. Their data consisted of counts of the numbers of new vehicle 

registrations of 2,190 different vehicle types, by households by region of the UK, from 2001 to 

2006, a total of 847,689 records. Data on vehicle attributes were matched to the new registration 

records, as were demographic and income data for the 11 Government Office Regions of Great 

Britain in which the vehicles were registered. 

The report states that “...we scale all cost related variables by the market average income...” and 

in table 5.1 fuel cost per 100km is listed as one of the variables scaled by average income. 

                                                            7

 Although it does not appear to be clearly stated in the text, vehicle price was apparently in units of 

£1,000s rather than pounds. Also, in the text of the report (p. 55) the willingness to pay for fuel economy is 

calculated by dividing the price coefficient by the coefficient of fuel costs, the inverse of the correct procedure. An 

e-mail request for clarification has been sent to the authors but no response has been received at time of writing. 
27 

However, this assumption leads to the inference that households in the UK place very little value 

on fuel costs, as will be shown. On the other hand, the mathematical derivation of the model 

presented in chapter 3 states that fuel costs are treated differently from the fixed costs of vehicle 

ownership and are included among the other physical attributes of the vehicle. This implies but 

does not clearly state that fuel costs may not have been scaled by average income. 

If one assumes that fuel costs have been scaled by average income, the ratio of the coefficients of 

Fuel Cost/Purchase Price -((du/dFC)/(du/dPP)) yields an implied value of -£0.385 per increase of 

£1 per 100km in fuel cost. Since, over the life of a car, there are likely to be on the order of 

100,000 kms of travel, one would expect the present value of a change in fuel cost for a 

£1/100km change in fuel costs to be on the order of £1,000, more than three orders of magnitude 

larger. Average household incomes by region range from £26,921 to £40,540 in 2005, with the 

median region at £30,189. Recalculating the value of a £1/100km change in fuel costs while 

scaling just the purchase price coefficient by median income implies a value of 

–((-0.3850)/(-1.0165))30189=- £11,434 per £1/100km. This estimate, on the other hand seems 

roughly one order of magnitude too high.8

 Neither result seems satisfactory. 

The authors of the Eftec report are well aware of the statistical difficulties in estimating a vehicle 

choice model as detailed as theirs. 

“The estimation data set consisted of some 70,850 observations, where each 

observation relates the market share of a particular type of vehicle in a particular 

GOR in a particular year to the physical and cost attributes of that vehicle. This 

represents one of the most comprehensive data sets of its kinds ever compiled. 

All the same, it remains unlikely that the variables available to describe the 

physical attributes are sufficient to capture the myriad details that differentiate 

one vehicle from another. 

“The existence of unobserved vehicle attributes is a real problem for the robust 

estimation of the parameters of the utility function. Indeed, if unobserved 

attributes are not controlled for in the estimation procedure, the estimated 

parameters will in all likelihood be biased.” (Eftec, 2008, p.xiii- xiv) 

The authors attempt to control for the problem of unobserved attributes by including fixed effects 

for every make, model, body type, and fuel combination. However, even this may not have been 

enough. 

Vance and Mehlin (2009) estimated a nested multinomial logit model of new car registrations in 

Germany using the method of Berry, Levinsohn and Pakes (1995). The data were obtained from 

R.L. Polk Europe and provide total sales by over 6,000 makes, models, configurations and body 

styles each year, aggregated to 681 individual models spanning the years from 1995-2005 (5,007 

observations). Two alternative nesting structures were estimated: (1) vehicles grouped into 

eight classes by size, body style and price, and (2) a further subdivision within each class into 

foreign and domestic brands. 

 

                                                            8

 A request for clarification of this point was sent to the authors via e-mail. Receipt of the e-mail was 

acknowledged and a substantive response is expected soon but was not available in time for inclusion in this draft. 
28 

Fuel economy was represented by the product of fuel price (€/liter) multiplied by the New 

European Drive Cycle fuel consumption rate in liter per 100 km, or €/100km. Vehicle purchase 

price was represented by the retail price divided by the national disposable income level per 

capita. Other variables included in the model were: 

1. The annual circulation tax (e.g., € 5.11 to € 6.75 per 0.1 liter for gasoline passenger cars) 

2. Engine power in kW 

3. Vehicle size, measured by length times width 

4. Curb weight 

5. Dummy variables for each year 

The estimated coefficients of fuel costs per 100 km and car price/income were similar in the two 

nested logit models. The model nesting by vehicle class only produced a fuel cost coefficient of 

-0.197 and a vehicle price coefficient of -0.272, while the model nesting by vehicle class 

produced corresponding estimates of -0.186 and -0.255. The ratio of the two, which determines 

the willingness to pay for fuel economy, is nearly identical. Calculation of the willingness to pay 

for fuel economy requires the average income per capita for the sample period (€ 16,017, kindly 

provided by the corresponding author) and data on vehicle use and expected lifetime. Annual 

usage is assumed to be 16,000 kms per year for a new vehicle declining at 4% per year over a 15 

year vehicle life. This yields 117,520 discounted lifetime kilometers or 1,175 segments of 100 

kms each. The willingness to pay for a € 1 reduction in fuel cost per 100 kms is given by the 

following equation, in which E is fuel cost in Euros per 100 km, P is vehicle retail price in Euros 

and Y is per capita income in Euros. 

ቌ

∂U

∂E

∂U

∂PൗY

ቍ= Euros

1 Euro 100km ⁄ = -0.197-0.272 16017 ⁄ =11601 

(10) 

This implies that a typical German new car buyer would be willing to pay € 11,601 to reduce the 

fuel costs of a new vehicle by € 1/100 km. How reasonable this is requires knowing how many 

hundreds of kilometers a new vehicle is likely to travel over its lifetime. Multiplying the 

hundreds of kilometers by € 1 and discounting to present value gives the present value of the 

future savings in fuel produced by a € 1 / 100 km reduction in fuel costs. Let Ko be the 

kilometers traveled by a new vehicle and q be the annual rate of decline in kilometers traveled. 

Let L be the lifetime of a vehicle in years and r be the consumer’s annual discount rate. If a 

vehicle in Germany travels an average of 16,000 kilometers in its first year, declining at 4% per 

year over a 15 year life expectancy, and assuming a discount rate of 7% per year, a €1 change in 

fuel costs per 100 km should be worth approximately € 1,175 in present value. 

ܸܲ ൌ

€ 1

100 න ܭ௢݁ି௤௧

௅

଴

݁ି௥௧݀ݐ ൌ €

16000

100 න ݁ିሺ.଴ସା.଴଻ሻ௧

ଵହ

௢

€ 1175 ൌ ݐ݀

(11) 
29 

The willingness to pay per present value Euro in fuel savings calculated from Vance and 

Mehlin’s (2009) model in this way is almost ten Euros, and varies little between the vehicle class 

(€ 9.87) and the class/origin (€ 9.94) nesting strategies. It is not obvious why rational consumers 

would be willing to pay an order of magnitude more for an improvement in fuel economy than it 

is worth in present value. 

3.2 HEDONIC PRICE MODELS 

Fan and Rubin (2009) estimated a hedonic demand regression for the state of Maine in 2007, 

using a two-stage method that allowed them to estimate a demand function for fuel economy. 

Their first stage regression is of greatest interest here because it can be compared with the studies 

by Espey and Nair (2005) and McManus (2007) discussed below. Fan and Rubin’s data base 

contained information on 523 passenger cars and 2,100 light trucks, with vehicle attributes 

obtained from Ward’s Automotive Group. A Box-Cox test for functional form indicated that the 

log-log form should be used for estimation. Their first-stage model regressed log of 

manufacturer’s suggested retail price (MSRP) against the logs of MPG, vehicle weight, and 

horsepower to weight ratio, and dummy variables representing vehicle class, transmission type 

and manufacturer. A second first stage regression was run adding interactions between the 

vehicle class dummy variables and the vehicle attributes, in order to explore heterogeneity in 

consumer preferences across classes. 

The results for passenger cars and light trucks indicated a willingness to pay for a 1 MPG 

increase in fuel economy of $208 for cars and $233 for light trucks. The authors note that these 

values are small in comparison to their estimated undiscounted lifetime fuel savings of $823 for 

passenger cars and $1,461 for light trucks. They state that the implied discount rates are 48% for 

cars and 13% for light trucks, but these estimates are not consistent with the numbers for lifetime 

savings. However, recalculating the implied discount rates using national data on vehicle usage 

and survival rates (U.S. DOT/NHTSA, 2006) and national average model year 2007 MPG 

numbers (U.S. EPA, 2009), and assuming a national average gasoline price of $2.85 per gallon 

(2007 $), this author obtains implied discount rates of 37% for cars and 77% for light trucks. 

The estimates of willingness to pay for fuel economy by vehicle class, shown in Figure 4, raise 

additional issues. They show negative willingness to pay for some significant vehicle classes 

and substantial variability across classes that is not easy to explain. The authors’ suggested 

interpretation is consistent with omitted variables bias, that is, MPG is aliasing other vehicle 

attributes which are not included in the regression equation but with which MPG is correlated. 

“Although luxury and large car consumers may not be intentionally unwilling to 

pay for higher fuel economy, luxury car consumers’ preferences for luxury 

features, and large car buyers’ primary needs for large sizes might compromise 

their value on fuel economy.” (Fan and Rubin, 2009, p. 9) 
30 

Figure 4. Estimated Willingness to Pay for a 1 MPG Increase in Fuel Economy by Vehicle 

Class. Source: Fan and Rubin (2009). 

Espey and Nair (2005) used a hedonic model applied to 2001 automobiles to estimate the value 

of fuel economy to consumers and concluded that consumers fully value lifetime vehicle fuel 

savings, and perhaps more. The authors note the relative scarcity of studies (at that time) of the 

value of fuel economy despite the importance of the issue. 

 “Surprisingly few economic studies have attempted to determine consumers’ 

willingness to pay for improvements in automobile fuel economy.” (Espey and 

Nair, p. 317) 

The authors cite the method of Rosen (1974) as their methodological approach for regressing 

vehicle price on attributes including fuel economy. However, Rosen’s method explicitly 

recognizes the simultaneity of the supply and demand for vehicle attributes and, therefore their 

price whereas the authors use a single-stage estimation method based on least square corrected 

for heteroskedasticity. A critical issue is whether the analysis is identifying the supply or 

demand curve for fuel economy. The authors point out that their method depends on the market 

achieving equilibrium. 

“Given an equilibrium market, this value reflects both the consumer’s marginal 

willingness to pay for an additional unit of that attribute and the producer’s marginal cost 

of providing another unit of that attribute in that vehicle.” (Espey and Nair, 2005, p. 318) 

Given the existence of fuel economy standards that were a binding constraint on at least some 

manufacturers during this period, it is not clear whether the analysis has identified the 

$657

$41

‐$419 ‐$324

$379

$1,822

‐$154

‐$500

$0

$500

$1,000

$1,500

$2,000

Small Car Midsize Car Large Car Luxury Car SUV Van Pickup

EstimatedWillingnessto Pay for a 1 MPGIncrease in Fuel

Economy

(Fan and Rubin, 2009)
31 

consumers’ willingness to pay or the manufacturer’s marginal cost. The full Rosen method, as 

implemented by Fan and Rubin, does assure identification of the consumer’s demand curve. 

The data used in the study represented 130, 2001 automobiles whose attributes were obtained 

from standard sources, such as Consumer Reports and Ward’s Automotive Report web sites. In 

addition to fuel economy, seven vehicle attributes were included in the regression analysis: size, 

power, performance, safety, comfort, reliability and whether or not the vehicle is classified as a 

luxury vehicle. In the models estimated, these factors were represented by curb weight, 0-60 

mph acceleration, turning circle radius, braking distance in feet, the sum of NHTSA’s front and 

side crash test ratings, and a 1-5 comfort rating, a 1-5 reliability rating and a luxury designation 

(presumably from Consumer Reports). Fuel economy was measured as its inverse, gallons per 

mile, and city, highway and the combined average fuel economy ratings were tested separately. 

In addition, four categories of the gas guzzler tax were also included.9

 Four models were 

estimated using least squares corrected for heteroscedasticity, each using a different fuel 

economy variable: (1) city gpm, (2) highway gpm, (3) both city and highway gpm, and 

(4) combined gpm. It is assumed that adjusted fuel economy numbers were used. The estimated 

value of fuel economy ranged from $282/MPG for the highway MPG variable, to $613/MPG for 

the combined MPG variable. The authors compute undiscounted values of fuel saving, assuming 

vehicles travel 145,000 miles. This is lower than the NHTSA’s estimate of undiscounted 

lifetime miles (168,853) but higher than discounted lifetime miles (112,600). At $1.50 per 

gallon, the undiscounted fuel savings for a vehicle driving 145,000 miles at the average 

automobile MPG of 20.4 would be $561, while at $2 per gallon the undiscounted value would be 

$747/MPG. Thus, Espey and Nair (Table 5) conclude, 

 “...model 4 estimates a higher value of an incremental change in fuel economy 

than the actual fuel cost savings at a price of $1.50 per gallon. 

 “However, for higher fuel prices, the estimate from model 4 using average fuel 

economy suggests only moderate discounting by consumers, at a rate of about 1% 

for fuel prices of $1.75 per gallon and 4% for fuel prices of $2.00 per gallon.” 

(Espey and Nair, pp. 321-322) 

 

                                                            9

 Espey and Nair include the gas guzzler tax as if it were paid by consumers. In fact, the gas guzzler tax is 

paid by manufacturers. However, the tax is required to be shown on the vehicle’s window sticker.
32 

Table 5. Actual Versus Estimated Value of Fuel Economy 

 1 MPG increase in: 

 City 

MPG 

Highway 

MPG 

Average 

MPG 

Model 1 $531 

Model 2 $282 

Model 3 $440 $242 

Model 4 $613 

Actual undiscounted fuel savings assuming: 

145,000 miles, $.150/gal $514 $110 $561 

145,000 miles, $1.75/gal $600 $128 $654 

145,000 miles, $2.00/gal $686 $146 $747 

Source: Espey and Nair (2005, Table 4) 

McManus (2007) also estimated a hedonic demand model including the price of gasoline 

divided by fuel economy. His dependent variable was transaction prices for 445 vehicles during 

the period 2002. Other explanatory variables were horsepower per ton of vehicle weight, vehicle 

weight, real disposable income per capita and dummy variables for the brand of each vehicle. 

The estimated coefficient of fuel price divided by MPG was -768. According to the variable 

definitions, this should mean that an increase of 1 in fuel cost per mile would reduce the price of 

a new vehicle by $768. Although McManus doesn’t state so in his paper, it appears that the 

measure of fuel cost is cents, rather than dollars per gallon. Translating to dollars per mile, the 

coefficient would be 76,800. Since this coefficient translates dollars per mile into present value 

dollars, its units are discounted lifetime miles. Using the standard assumptions in the appendix 

to this paper, the discounted lifetime miles of a typical U.S. light-duty vehicle are 85,161, very 

close to McManus’ coefficient estimate.10 Like the results of Espey and Nair, McManus’ results 

imply that consumers would, approximately, somewhat under-value the lifetime fuel savings due 

to fuel economy improvements, by about 10%. The two studies use a similar methodology. As 

noted below in the discussion of Espey and Nair’s work, no attempt is made to correct for the 

potential endogeneity of vehicle prices and vehicle attributes, thus, there is a question as to 

whether the supply or demand curve is being estimated. Also, by using fuel cost per mile as the 

explanatory variable, the constraint that consumers respond equally and in opposite directions to 

fuel price and fuel economy is enforced. 

Fifer and Bunn (2009) estimated willingness to pay for reduced fuel consumption per mile by 

means of hedonic regression analysis and compared that willingness to pay with calculated 

present values of fuel consumption for passenger cars, SUVs, vans and pickup trucks. A key 

feature of their analysis was accounting for the variation in annual miles traveled across 

households and the implications for willingness to pay for fuel economy. Their data on 2,054 

makes and models of new cars and light trucks for the period 1996 to 2005 included vehicle 

price, fuel consumption (gallons per mile), weight, horsepower, engine displacement and 

presence of airbags. Different coefficients for the fuel consumption variable were estimated for 

                                                            10 These assumption are, 15,600 miles per year when new declining at 4.5% per year, an expected rate of 

return on an investment in fuel economy of 12% and an expected 14 year lifetime (NRC, 2002, p. table 4-1).
33 

four broad market segments: (1) passenger cars, (2) SUVs, (3) vans, and (4) pickup trucks. 

Dummy variables were also included for model year, manufacturer and “detail” market segment. 

Detail market segments are subsets of the four segments for which different fuel consumption 

coefficients were estimated (e.g., for passenger cars: subcompact car, compact car, midsize car, 

large car, luxury car). 

The authors note that 1996-2005 was a period over which there was very little variation in the 

average fuel economy of new vehicles. In their sample, the average fuel economy of a new 

vehicle was 21 MPG and was relatively constant over all ten years. Not only was there little 

variation in new vehicle fuel economy (see Figure 1), but there was no variation in the fuel 

economy standards until 2005, when the light truck standards increased from 20.7 to 21.0 MPG; 

the passenger cars standards remained at 27.5 throughout the period. This is potentially 

significant because if the fuel economy standards were a binding constraint on some or all 

manufacturers for some or all of this period, hedonic regression estimates of consumers’ 

willingness to pay for fuel economy improvements could reflect the shadow price of the 

standards rather than true willingness to pay. 

To account for the effect of heterogeneity in vehicle use on the value of improved fuel economy, 

the authors used data from the 2001 National Household Travel Survey (NHTS) to estimate 

annual mileage for each of the four vehicle types. Median annual usage varied across the four 

vehicle types as follows: cars, 9,185; pickup trucks, 9,500; vans, 11,400; SUVs, 11,800. The 

NHTS does not measure the actual use of each vehicle over a full year. Instead, it develops a 

“best guess” estimate based on a combination of odometer readings and survey responses. 

Vehicles up to five years old were included in the calculation of the expected present value of 

fuel savings. 

 

Fifer and Bunn (2009) assumed consumers would perceive fuel prices to be a random walk, and 

thus take the current price of fuel to be the best predictor of fuel price over the life of a new 

vehicle. Indeed, fuel prices varied little over the sample period. Fuel prices increased from a 

median (over states) of $1.23 in 1997 to $1.40 in 2001. The expected present value of fuel 

savings was calculated as the current price of fuel time the sum over the vehicle’s expected 

lifetime (assumed to be 14 years for all vehicle types) of the product of miles traveled, multiplied 

by the rate of fuel consumption per mile, times a discounting factor for the year in question. 

Discount rates of 3% and 7% were used. Using this method and the 3% discount rate, they 

calculated the expected present value of a 0.001 gallon per mile reduction in the rate of fuel 

consumption to be $167.42 for cars, $193.97 for pickups, $194.67 for vans and $197.78 for 

SUVs. 

The hedonic regression results are shown in Table 6. The regression is linear in levels of the 

variables, and all variables are statistically significant except the airbags dummy, and the 

coefficient of gallons per mile for cars in Regression 1. Regression 2 combines cars and SUVs 

to estimate a single coefficient for both variables. The willingness to pay for a reduction in fuel 

consumption of 0.001 gallons per mile equals the coefficient estimate in Table 6 divided by 

1,000 (for example, for cars & SUVs in Regression 2, that would be 87,349.1/1,000 = $87.35). 

The most striking feature of the regressions is the very large differences in willingness to pay 

between purchasers of cars or SUVs and vans or pickup trucks. According to Regression 2, on 
34 

average, pickup truck buyers will pay more than six times as much to reduce their vehicle’s rate 

of fuel consumption (gallons per mile) as car or SUV buyers, and purchasers of new vans will 

pay more than five times as much. These differences cannot be accounted for by the somewhat 

higher rates of usage for pickups and vans, about 20%, on average. 

Table 6. Fifer and Bunn’s (2009) Hedonic Regression Results 

Regression 1 Regression 2 

Variable Coefficient Std. Err. t-stat. Coefficient Std. Err. t-stat 

 

cars*GPM -73022.64 43490.2 -1.68 

vans*GPM -471770.3 103601.6 -4.55 -468153 103508.6 -4.52 

SUVs*GPM -126319.9 60676.48 -2.08 

pickups*GPM -553761.6 95299.28 -5.81 -549569 95167.13 -5.77 

(cars+SUVs)*GPM -87349.1 40142.99 -2.18 

 

Hp 28.78605 6.204614 4.64 29.90907 6.064106 4.93 

Wt 7.693808 0.521927 14.74 7.609789 0.5125924 14.85 

Displacement 3986.338 408.7145 9.75 3954.065 406.9469 9.72 

Airbags 130.4298 447.148 0.29 138.2672 447.0245 0.31 

 

Detail Segment Dummies 

Year Dummies 

Manufacturer Dummies 

 

_cons -9576.885 3910.963 2.45 -11244.6 3391.639 -3.32 

 

Number of obs 2048 2048 

F(54, 1993) 216.4 220.5 

Prob > F 0 0 

R-squared 0.8543 0.8542 

Adj R-squared 0.8503 0.8504 

Root MSE 5697.1 5696.8 

On the other hand, when the authors aggregate all vehicle types, they obtain results very similar 

to those of Espey and Nair (2005) and McManus (2007), namely that consumers’ willingness to 

pay for fuel economy improvement, on average, is approximately equal to the present value of 

fuel saved. 

 

“Combining observations from all four vehicle segment (sic), there is a net underpayment 

of $4.93 per capita for the low-end discount rate and a net overpayment of $37.17 per 

capita for the high-end rate. While this combination of all vehicle segments suggests a 

small to negligible net overpayment, car and SUV buyers generally reap large benefits 

while van and pickup buyers incur large losses from their investments in fuel economy. 

Aggregating the results by summing across all vehicle segments misses the more nuanced 

result that “rationality” depends greatly on the type of vehicle purchased and driven by 

the consumer.” (Fifer and Bunn, 2009, p. 20) 

The authors, however, are not able to come up with an explanation that satisfies them as to why 

apparent rationality should vary by vehicle type. 
35 

“This raises an interesting question that is beyond the scope of our analysis: unless it is 

technologically infeasible, why don’t van and pickup producers introduce more fuel 

efficient vehicles to capture the profits suggested by these hedonic prices?” (Fifer and 

Bunn, 2009, p. 21) 

In fact, analyses of the technological potential and cost of improving fuel economy have found 

that it is no more, and perhaps less costly to improve the fuel economy of vans and pickup trucks 

than of passenger cars and SUVs (e.g., NRC, 2002). 

The data used by Fifer and Bunn (2009) presents the usual challenges for statistical inference of 

the value of automobile attributes. The three included continuous variables (horsepower, weight 

and engine displacement) are strongly correlated with fuel consumption and with each other. For 

example, Knittel (2009) found that weight and horsepower, together with dummy variables 

representing diesel engines and manual transmissions, achieved an R2

 of 0.848 in a double log 

regression on fuel consumption. In addition, manufacturers generally package other features 

with engine options (sunroofs, alloy wheels, premium floor mats or upholstery, special paint, 

etc.). The extent of packaging can vary considerably. For example, the 2010 Camry SE’s 

6-cylinder package adds $5,395 to the price of the vehicle while the Chevrolet Malibu’s 

6-cylinder option adds only $1,795. The two 4-cylinder and the two 6-cylinder engines are 

almost identical in size and power. Presumably, there is more in the Camry’s 6-cylinder 

package. Such differences will not be captured by make and model dummy variables, let alone 

vehicle class dummies. In addition, there is the question of the impacts of fuel economy 

standards. Fifer and Bunn do not attempt to represent the effects of the standards in their model. 

This leaves open the possibility that if the standards were a binding constraint on manufacturers, 

what Fifer and Bunn have estimated is the supply function for fuel economy rather than 

consumers’ willingness to pay. If the standards were binding on some manufacturers and not 

other, or if they were binding in some years and not other, the identification of the estimates as 

consumers’ willingness to pay becomes even more complicated. 

Arguea, Hsiao and Taylor (1994) estimated the marginal value of fuel economy using hedonic 

regression analysis and eighteen years of data from 1969 to 1986. Fuel economy was 

represented by Consumer Reports magazine’s open-road miles per gallon estimates. Ordinary 

least squares were used to estimate a linear hedonic price function in current dollars. The 

resulting year-by-year hedonic price coefficients for fuel economy represent the value of an 

increase of 1 MPG, and are illustrated by the gray bars in Figure 4. The present value of lifetime 

fuel savings resulting from a 1 MPG increase in the highway fuel economy of an average 

passenger car (U.S. EPA, 2009, table 1, adjusted highway MPG) was calculated using the 

standard vehicle usage and discounting assumptions (see appendix), and the current dollar price 

of gasoline in each year (U.S. DOE/EIA, 2009, table 5.24). The ratio of the hedonic price 

estimate to the estimated present value of fuel savings, expressed as a percent, is shown by the 

larger unshaded bars in Figure 5. In general, the apparent willingness to pay for fuel economy is 

in the vicinity of 5% to 10% of the calculated present value of fuel savings, except for the final 

year, 1986, in which the estimated willingness to pay is 46% of lifetime fuel savings. 
36 

Figure 5. Annual Estimates of the Hedonic Value of a 1 MPG Increase in Fuel Economy and 

the Percent of Estimated Lifetime Fuel Savings Each Represents (Arguea, Hsiao and Taylor, 

1994). 

Espey and Nair (2005) use data from a single year, 2001, to estimate their model. As Arguea, 

Hsiao and Taylor (1994) point out, this poses a potential problem because the estimated hedonic 

prices of attributes represent an assumed equilibrium of supply and demand. 

 “From a single shadow price representing equilibrium of market demand and 

supply, it is not possible to separately identify the structural demand and supply 

functions for these characteristics.” (Arguea, Hsiao and Tayler, 1009, pp. 6-7) 

The potential problem arises if for some reason supply and demand are not in equilibrium. There 

is a strong possibility that what McManus, Espey and Nair, and Fifer and Bunn have estimated is 

the supply function for fuel economy and other vehicle attributes or a mixture of supply and 

demand. Fuel economy standards for automobiles were in effect for the years covered by both 

analyses. What studies finding cost-effective willingness to pay for fuel economy may be 

showing is that the fuel economy standards were binding and that they required manufacturers to 

provide levels of fuel economy that were approximately cost-effective based on expected 

lifetime fuel savings. In the event the fuel economy standards were not binding, then market 

forces may have led manufacturers to provide cost-effective levels of fuel economy. In the latter 

case, one could reasonably assert that the marginal cost of fuel economy should equal the 

marginal willingness to pay, and thus should be a relevant estimator of it. If the former, then 

marginal cost would equal or exceed marginal willingness to pay. Without a clear identification 

of the supply and demand curves, the question cannot be resolved definitively. Determining 

when fuel economy standards were binding and on which manufacturers might be useful for 

identifying the consumers’ demand function. 

Fan and Rubin’s (2009) results, however, were inconsistent. They found that MPG coefficients 

for luxury cars, large cars and pickup trucks all indicated a negative value for higher fuel 

$0

$10

$20

$30

$40

$50

$60

$70

$80

0%

5%

10%

15%

20%

25%

30%

35%

40%

45%

1970 1975 1980 1985

EstimatedHedonicValue ($/MPG) of a 1 MPG Increase in Fuel

Economy and Percent of PresentValue Lifetime Savings

% of PV

$/MPG
37 

economy. These results should probably be interpreted as coefficient bias as a result of fuel 

economy aliasing unobserved attributes. Other interpretations seem implausible: (1) purchasers 

of these vehicles are averse to saving money and prefer to make extra trips to the refueling 

station, and (2) for purchasers of these vehicles, there is a snob effect associated with lower fuel 

economy, everything else equal. This last interpretation seems to be favored by Fan and Rubin 

but it is inconsistent with the assumption that other factors are held constant. It is difficult to 

believe that even if consumers could have all the other attributes associated with luxury and size 

and improved fuel economy they were willing to pay for, they would still prefer lower fuel 

economy. This implies a negative utility for saving money, which seems implausible for large 

market segments. 

3.3 OTHER METHODS 

Kilian and Sims (2006) analyzed the effect of changes in fuel cost per mile on the prices of 

individual makes and models of used cars in the United States for the years 1978-1984. Their 

partial equilibrium model of automobile pricing considers automobiles to be assets producing a 

flow of services to households over the lifetime of the vehicle. The supply of used vehicle 

services is assumed to be fixed, which is not entirely correct since vehicle lifetimes can be 

extended by investment in maintenance and repair. The annual rental rate (Rijt = value of flow of 

services for vehicle i of age j in year t) is a function of services (U) derived from observable and 

unobservable attributes (Z) and operating costs, represented by fuel cost. Fuel costs are equal to 

the price of gasoline (pt) times annual miles of travel by vehicle i (mit) divided by the fuel 

economy of vehicle I (MPGi). 

ܴ௜௝௧ ൌ ܷሺܼ௜, ݉௜௧ሻ െ ݌௧݉௜௧

௜ܩܲܯ

(12) 

The current price of a used vehicle (P) is assumed to be the present, discounted value of future, 

expected service flows, or rental flows over the vehicle’s lifetime (L). 

ܲ௜௝௧ ൌ ෍ሺ1െݎሻ௝

௅ି௝

௝ୀ଴

ܧ௧ൣܴ௜௝௧൧ כ

(13) 

From the equation for used car prices it is clear that a decrease in the present value of expected 

fuel costs of a certain amount should increase the price of a used car by the same amount. If the 

other attributes of a vehicle are assumed to remain constant, then the effect of an increase in the 

price of gasoline on the price of a used car is just the discounted present value of the increase in 

expected fuel costs. 

 
38 

ܺ௜௝௧ ൌ ߂ܲ௝௜௧ ൌ ෍ሺ1െݎሻ௝

௅ି௝

௝ୀ଴

൤ ௧ܧ

െ݌߂௧݉௜௧

௜ܩܲܯ

൨

(14) 

In the form of the model used for estimation, the change in used car prices and all other variables 

are expressed relative to the initial used car price. In the equation below, δt represents fixed time 

effects and ωijt represents the flow of vehicles services (other than fuel cost) in time period t. 

ܲ௜௝ାଵ௧ାଵ െ ܲ௜௝௧

ܲ௜௝௧

ൌ െ൫ߜ௧ ൅ ߱௜௝௧൯൅ߛ ܺ௝௜௧

ܲ௜௝௧

൅ ݁௧ାଵ

(15) 

Kilian and Sims assume that all vehicles have the same lifetime (L = 10 years), the same usage 

rates (mit = m = 10,000 miles per year) and that all consumers have the same discount rate (5% 

per year). In general, these assumptions appear to be low relative to other data sources (see 

appendix) which would tend to underestimate the present value of a change in fuel prices and 

overestimate the impact of fuel price changes on used car prices. In addition to these 

assumptions, the model also requires a way to represent consumers expectations about future fuel 

prices. The reference assumption is static expectations, an optimal method if gasoline prices are 

a random walk. However, a variety of other price expectation models were tested, including 

fitted ARIMA and GARCH models, as well as lagged updating of expectations. 

Data on fuel economy for 2,780 light-duty vehicles for the period 1978-1984 were obtained from 

official EPA/DOE estimates. Used car price data were collected from the National Automobile 

Dealers’ Association Used Car Price Guides for the years 1979-1989, since used cars up to five 

years of age were included in the analysis. Gasoline price data were obtained from the Energy 

Information Administration’s Monthly Energy Review. 

 

The coefficient of the present value of the change in gasoline price relative to the lagged used 

vehicle price (γ) is predicted to be -1 by Kilian and Sims’ theory. Results from the baseline 

model employing static expectations finds a coefficient of -0.1096, indicating that only 11% of 

the change in present value expected fuel costs translates into a change in used vehicle prices. 

Interpreted as a willingness to pay for fuel economy, it would imply that in used vehicle 

transactions, consumers are willing to pay for only 11% of a reduction in fuel costs per mile. 

The authors provide a sample calculation for the effect of a $0.25 per gallon price change for two 

vehicles costing $10,000 each, with one getting 15 MPG and the other 25 MPG. Their theory 

predicts that the 15 MPG “gas guzzler” should experience a reduction in price of $1,000 while 

the 25 MPG vehicle should lose only $600, for a net advantage of $400 for the more fuel 

efficient car. The estimated model, however, predicts only a $50 relative price change. 

Including different formulations of fixed effects did not change the resulting estimates in any 

important way. Excluding all foreign autos, light trucks and diesel autos, however, produced a 

coefficient estimate of -0.288, still far from -1.0. Fitting an ARIMA model to prices and using it 

in place of static expectations resulted in uniformly lower impact estimates. The authors then 

systematically varied their assumptions about vehicle life, usage and discount rates. Only by 
39 

decreasing annual use to 5,000 miles per year, lowering the expected lifetime to 6 years and 

assuming discount rates in excess of 10% could coefficient estimates of -1 be obtained. 

“We thus conclude that the baseline model cannot be rationalized for reasonable 

calibrations of the parameters or assumptions regarding expectations formation. ...Either 

individuals are extremely myopic and do not factor the near future into their decisions or 

the theory and/or empirical specification is flawed in a more fundamental sense.” (Kilian 

and Sims, 2006, p. 16) 

The authors then try several other models of gasoline price expectations, including a twelve 

month average and a random walk model with a GARCH error term. This produced coefficient 

estimates as high as -0.25 in one case. Finally, the authors test for asymmetric response to 

gasoline price increases and decreases. In years in which gasoline prices rose, the coefficient of 

the cost per mile variable was quite close to -1, -0.887, and did not differ significantly from -1. 

However, in years in which gasoline prices fell the estimated γ was +0.08 and was statistically 

significantly different from zero. This result was generally consistent across alternative 

formulations and estimation methods. 

“In particular, a robust finding is that gasoline price increases have a relatively strong 

effect on used automobile prices while decreases do not.” (Kilian and Sims, 2006, p. 25.) 

The results on asymmetry may be relevant to the willingness to pay for increased fuel economy, 

since an increase in fuel economy decreases fuel cost per mile. In fuel economy terms, the 

asymmetry results imply that consumers will pay nothing or close to nothing for increased fuel 

economy but would pay close to expected present value to avoid decreased fuel economy. If this 

result proved to be valid, it would imply that consumers respond differently to fuel price and fuel 

economy. 

Bhat and Sen (2006) constructed a model of household vehicle holdings and use by vehicle 

type, using data from a 2000 survey of consumers in the San Francisco Bay area. Their multiple 

discrete-continuous extreme value (MDCEV) model did not include the price of vehicles but did 

include their fuel economy via a variable representing fuel cost per mile divided by household 

income. Because vehicle price did not appear in their model, the value of fuel economy cannot 

be directly calculated. However, Bhat and Sen did compute the elasticities of vehicle choice by 

vehicle type with respect to fuel cost per mile by varying fuel cost from $1.40 per gallon to $2.00 

per gallon (a 35% increase based on the midpoint formula). These elasticities are quite low 

compared to typical estimates of the price elasticity of vehicle choice, which are on the order of 

-2 to -3. Very roughly, the discounted present value of total lifetime fuel costs is on the order of 

$10,000, while a typical vehicle costs on the order of $20,000. Thus, a given percent change in 

discounted present value fuel costs (represented by a percent change in fuel cost per mile) should 

have approximately half the impact on vehicle holdings as the same percent change in own price. 

Given this, Bhat and Sen’s elasticity estimates shown in table 7 suggest a significant undervaluing of fuel economy. 

 
40 

Table 7. Elasticity of Household Vehicle Choice with Respect to Fuel Cost per Mile 

 Pass. Car. SUV Pickup Minivan Van 

% change in vehicle holdings -0.1 -5.9 -2.1 -4.9 -3.4 

Fuel cost per mile elasticity -0.002 -0.169 -0.006 -0.140 -0.097 

In an analysis of the short-run effects of fuel prices on vehicle prices, Langer and Miller (2008)

found that when gasoline prices rise, the prices of inefficient vehicles fall while those of 

especially efficient vehicles rise. Using 300,000 observations of weekly automobile price data 

over the period 2003-2006, they measured the extent to which the prices of new light-duty 

vehicles responded to changes in the price of gasoline. Their dependent variable was a 

constructed “manufacturer price,” comprised of the manufacturers’ suggested retail prices minus 

the average incentives offered by GM, Ford, Chrysler and Toyota in for each week in five 

geographic regions during 2003-2006. 

Their fundamental equation for empirical estimation expresses the price of vehicle j, in time 

period t, in region r (pjtr) as a function of the price of gasoline (gptr) divided by fuel economy 

(mpgj), the sales-weighted average fuel cost per mile of other vehicles made by competitors and 

the weighted average fuel cost per mile of vehicles made by the same manufacturer. A vehicle is 

defined as a make/model (e.g., Ford Taurus) in a specific model year. The equation also includes 

fixed effects for time periods (δt) and vehicles (κj) as well as third order polynomials of variables 

representing the length of time vehicle j, vehicles produced by other manufacturers and vehicle 

produced by the same manufacturer other than j have been on the market, and the weighted 

average lengths of time other vehicles have been on the market. The functions involving length 

of time in the market are not shown in equation 5. 

݌௝௧௥ ൌ ܾଵ

௥௧௚݌

௝݃݌݉

൅ ܾଶ ෍ ݓଶ௝௞௧

௥௧௚݌

௝݃݌݉

൅ ܾଷ ෍ ݓଵ௝௟௧

௥௧௚݌

௟݃݌݉

௥݊ ൅ ௝݇ ൅ ௧ߜ ൅

௞׍ெ ௟׌ெ,௟ஷ௝

(16) 

Equations were estimated with fixed values of the b’s for all manufacturers, as well as allowing 

the b parameters to vary across manufacturers. In the regressions with fixed b values across 

manufacturers, own fuel cost, b1, was consistently statistically significant and negative in sign. 

The effect of the average competitor fuel cost was similar in magnitude, statistically significant 

but opposite in sign, as might would be expected (Table 8). The effect of same firm fuel costs 

was generally not statistically significant. Langer and Miller point out two caveats concerning 

their manufacturer price variable. First, it is not a transaction price, and the negotiations during 

transactions could push the transaction price in either direction from the manufacturer price. 

Second, the manufacturer price is based on the average incentive program on offer during the 

period rather than the actual incentives taken by consumers. If manufacturers allow more 

combining of incentives during high gasoline prices than during other periods, the impacts of 

gasoline prices may be underestimated. 

It is convenient to measure the magnitude of the effect of fuel costs on vehicle prices relative to 

the change in expected fuel costs caused by a change in the price of gasoline. Langer and Miller 
41 

call this the “offset percentage.” In calculating expected lifetime fuel costs, they assume static 

expectations in their base model but explore alternative price expectation models. The median 

offset percentages in their base model are 18% for cars, 15% for SUVs, and much less for light 

trucks and vans. For nearly all vehicles, higher gasoline prices reduced manufacturer prices. For 

especially efficient vehicles, however, higher gasoline prices actually increased the manufacturer 

prices. For example, among SUVs the Ford Escape Hybrid, Mercury Mariner Hybrid, Toyota 

Highlander Hybrid and Lexus RX 400 Hybrid were the only SUVs whose prices increased when 

gasoline prices increased. 

Table 8. Manufacturer Prices and Fuel Costs 

 Incentive Level 

Variables Regional+ 

National 

(1) 

Regional 

Only 

(2) 

National 

Only 

(3) 

Fuel cost -55.40 -56.96 -63.75 

 (7.73) (7.86) (8.77) 

Average competitor fuel cost 50.76 50.16 50.09 

 (7.15) (7.39) (8.12) 

Average same-firm fuel cost 1.15 2.62 1.31 

 (2.29) (1.78) (2.30) 

R2 0.5260 0.6763 0.5289 

# of observations 299,855 299,855 59,971 

# of vehicles 681 681 681 

Source: Langer and Miller (2008, table 3) 

While Langer and Miller’s results clearly indicate that manufacturers adjust vehicle prices when 

fuel prices change, they do not indicate whether consumers are over- or undervaluing the 

potential fuel savings. This would require an understanding of how consumers form price 

expectations for the life of a vehicle, an issue that Langer and Miller do not believe could be 

resolved with the data used in their study. It would also depend on the short-run price elasticities 

of vehicle demand and supply. If new vehicle supply were perfectly elastic, a change in fuel 

costs would affect quantities sold but not prices. If supply were perfectly inelastic, price would 

change by the full present value of the change in expected lifetime fuel costs. This question is 

also not addressed by Langer and Miller. However, their results suggest that either new vehicle 

supply is very elastic or consumers undervalue fuel economy. 

Busse, Knittel and Zettelmeyer (2009) estimated the effects of changes in gasoline prices on 

the market shares and prices of new and used automobiles. Using data from a 15-20% sample of 

actual vehicle transactions at new car dealers from September 1, 1999 to June 30, 2008, they 

estimated regressions relating gasoline prices and a variety of control variables to market shares 

and transaction prices, separately for new vehicle purchases and used vehicle purchases. Fuel 

economy was brought into the analysis by ranking vehicles’ fuel economy and estimating 

separate equations by quartile. They found that both market shares and vehicle prices responded 

to fuel price changes in ways that were predictable by economic theory. 
42 

Busse et al.’s (2009) findings concerning the effect of gasoline prices on new and used car prices 

are most relevant for this study. The estimated coefficients for gasoline price for new cars by 

MPG quartile are shown in Table 9. For new cars, a $1 increase in the price of gasoline reduced 

the price of the average vehicle in the least efficient quartile by $246, and increased the price of a 

vehicle in the most efficient quartile by $136, for a net effect of $382. Although two of the four 

coefficients are not statistically significant, they all follow a logical pattern of increasing cost 

with decreasing fuel economy. According to estimates made by the authors, the difference 

between the 1st and 4th quartile values is equal to 1.2 years of (undiscounted) fuel savings (based 

on an average MPG of 27.9 for the 1st quartile and 16.2 MPG for the 4th, and an average annual 

mileage of 12,000). These results do not necessarily imply that new car buyers undervalue fuel 

economy, since the change in price also depends on the elasticities of supply and demand and on 

the formulation of price expectations. 

If used cars supply is perfectly inelastic, then the shift in the demand curve caused by higher fuel 

prices would exactly equal the reduction in market price. If supply is highly price elastic, the 

observed change in market equilibrium prices would be only a small fraction of the change in 

lifetime operating costs even if consumers fully valued lifetime fuel costs because the adjustment 

to a shift in the demand curve would be primarily a quantity adjustment. On the other hand, if 

the supply of used vehicles is almost perfectly inelastic, then the change in price would be nearly 

equal to the change in lifetime operating costs. Price expectations matter because if consumers 

expect continuing increases whenever fuel prices increase, the calculated change in fuel costs 

based on static expectations would greatly understate consumers’ expected change in fuel costs, 

and overestimate their willingness to pay for fuel economy. 

Table 9. Gasoline Price Coefficient Estimates: New Car Price Equation

Variable Coefficient (std. error) 

Gasoline Price * MPG Quartile 1 Dummy -246 (75) 

Gasoline Price * MPG Quartile 2 Dummy -81 (40) 

Gasoline Price * MPG Quartile 3 Dummy 5.2 (30) 

Gasoline Price * MPG Quartile 4 Dummy 136 (43) 

Source: Busse et al. (2009, p. 19) 

The results for used car market shares and prices were essentially the reverse of the new car 

results: market shares changed little while prices changed a great deal. Table 10 shows the 

gasoline price coefficients by MPG quartile for the used car price equation. The pattern is the 

same but the magnitude of price changes is almost an order of magnitude greater. For used cars, 

the difference between the 1st and 4th quartiles is $2,723, equivalent to nine years of 

undiscounted fuel savings. Considering that used cars have a shorter expected life than new cars, 

the results for used cars suggest an overvaluing of fuel costs by used car buyers. 

 
43 

Table 10. Gasoline Price Coefficient Estimates: Used Car Price Equation

Variable Coefficient (std. error) 

Gasoline Price * MPG Quartile 1 Dummy -1096 (38) 

Gasoline Price * MPG Quartile 2 Dummy -936 (57) 

Gasoline Price * MPG Quartile 3 Dummy 76 (71) 

Gasoline Price * MPG Quartile 4 Dummy 1627 (56) 

Source: Busse et al. (2009, p. 22) 

In general, Busse et al. (2009) found that new car market shares changed more than used car 

market shares in response to fuel price changes, while used car prices changed more than used 

car prices. The explanation focused on the supply side and market efficiency. The used car 

market was believed to function most efficiently, on the premise that there are many used car 

buyers and sellers (individuals), frequent transactions, and that buyers and sellers alike 

experience the same change in operating costs when gasoline prices rise, which helps to equate 

the changes in willingness to pay and willingness to accept. The authors argue that the new car 

market differs primarily in that the new car manufacturers have market power, and therefore 

choose not to lower prices when the demand curve shifts downward but rather to accept loss of 

volume, in part due to the potential damage to their brand’s reputation. An alternative 

explanation could be that the supply of new cars is more price elastic than the supply of used 

cars. In this case, a downward shift in the demand curve caused by an increase in operating costs 

would produce a smaller change in price in the new car market but a greater change in sales, or 

market shares. 

The authors performed a variety of sensitivity tests as a check on the robustness of their results. 

In general, these changes did not fundamentally alter the results described above. A question 

that may not have been satisfactorily answered is whether price expectations play an important 

role. The authors tested their model with and without a variable that interacted gasoline prices 

with an indicator of whether prices had been rising, falling or constant over the past three 

months. Given the historical volatility of gasoline prices, three months may not be enough time 

for consumers purchasing a long-lived asset like a car to form a strong view about future price 

trends. Gasoline prices were generally trending upward over the study period, but especially 

during the final three years. If consumers’ price expectations took longer to develop, this could 

have an effect on the coefficient estimates. 

Robustness tests with and without controls for seasonal and fixed effects show the greatest 

differences in the estimated coefficients (Busse et al., 2009, table 2). For example, with 

year*region and month-of-year*region dummies included, the coefficient of gasoline price for 

the fourth MPG quartile is 136; if the year*region dummies are dropped, that coefficient changes 

to -796. In the used car equation, the same coefficient switches from 1,627 to -4,053 under the 

same change in dummy variables. However, in both equations the difference between the first 

and fourth quartile coefficients is about the same (382 versus 386 in the new car equations and 

2,723 versus 2,919 in the used car equations). Still, there is a suggestion in these results that the 

differences in gasoline price coefficients across equations may be aliasing other factors that vary 

by quartile. 
44 

In the regressions for the probability of vehicle purchases within a fuel economy quartile the 

authors include an extensive set of control variables for demographics, region and timing of 

purchase, but they do not include explicit measures of other vehicle attributes. (In other 

regressions, such as for the probability a purchase will be a new car, they do include fixed effects 

for detailed vehicle types: make, model, model year, trim level, number of doors, body type, 

displacement, number of cylinders and transmission type). Since a variety of other vehicle 

attributes are closely correlated with fuel economy (e.g., acceleration performance, size, luxury 

features) it is not clear that the fuel economy quartile-specific price coefficients are not aliasing 

the effect of other vehicle attributes. In this regard, it may be significant that the new and used 

car price coefficients by quartile are estimated in a single equation without quartile-specific 

intercepts or other quartile specific variables (tables A-3 and A-7). On the other hand, the 

authors estimate separate equations for market segments, so not only intercepts but all other 

parameters are specific to the market segment. In these equations, the market segment-specific 

constants are significant at the 1% level, and the coefficients of many other variables appear to 

vary significantly across car classes. These results suggest that there may be unobserved 

attributes that affect car prices that may be correlated with MPG, and therefore may be biasing 

the estimated gasoline price coefficients in the MPG quartile equations. 

The effect of gasoline prices on fuel economy via two “channels,” changes in the distribution of 

new vehicle purchases and in the composition of the on-road stock of vehicles, was investigated 

by Li, Timmins and von Haefen (2009). In general, they found that fleet fuel economy was 

relatively insensitive to the price of gasoline. 

 “We find that gasoline prices have statistically significant effects on both 

channels, but that their combined effect results in only modest impacts on fleet 

fuel economy. The short-run and long-run elasticities of fleet fuel economy with 

respect to gasoline prices were estimated at 0.022 and 0.204 in 2005.” (Li, 

Timmins and von Haefen, 2009., p. 135) 

Using model and vintage-specific data on vehicle registrations and scrappage from 20 major 

metropolitan areas in the United States, they estimated models of (1) the distribution of vehicle 

sales, and (2) vehicle survival rates by fuel economy level. They did not attempt to estimate the 

impacts of fuel prices on manufacturers’ decisions about vehicle technology and design. 

The dependent variable of their vehicle sales model is the number of new vehicles registered in 

fuel economy quantile q, in year t, in metropolitan area m. In the new vehicle model, vehicles 

were classified into 68 fuel economy quantiles. The key explanatory variables were the price of 

gasoline and gasoline cost per mile (price divided by MPG). Several other metropolitan area 

attributes were included, both individually and divided by fuel economy, in order to allow 

heterogeneity in parameter values across metropolitan areas. Dummies were included for 

quantiles, year, and region. A lagged adjustment formulation was used and tests for serial 

correlation and heteroscedasticity were conducted. 

ln൫Nqtm൯ =b0+b1 ln൫Nqt-1m൯ +b2

Ptm

MPGqtm

+b3Ptm+Other Controls+εctm

 (17) 
45 

In models including sufficient control variables, autocorrelation in the error term was not 

statistically significant. In the preferred models, the coefficient on the lagged dependent variable 

was quite small (0.068 to 0.074) indicating that adjustment of new vehicle purchases to gasoline 

prices was almost entirely complete within one year. In the preferred model, an increase in 

gasoline prices was found to increase the sales of vehicles in the 60th percentile of the MPG 

distribution above, and decrease sales of vehicles below the 60th percentile. 

In the vehicle scrappage model, the survival probability of a vehicle was assumed to be a logistic 

function of fuel economy, the price of gasoline and other variables controlling for metropolitan 

area and vehicle attributes. Two data sets were combined using a method of moments estimation 

technique: one with make/model detail covering the period 1997 to 2000 and the other with only 

vehicle class detail for the period 2001 to 2005. The preferred models included only vehicles 

older than 10 years, which were assumed to be more sensitive to changes in fuel prices and less 

affected by migrations of vehicles into and out of metropolitan areas. In the preferred model, 

higher gasoline prices increased the survival probabilities of vehicles with greater than 28.7 

MPG, and reduced survival rates for vehicles with lower MPG. The average elasticity of vehicle 

survival with respect to the price of gasoline was low, -0.026 in the preferred model, but 

elasticities across vehicle types and areas were up to 5 times as large. Nonetheless, relative 

insensitivity of survival rates to gasoline price suggests that the fuel economy of the on-road 

vehicle stock will also be relatively insensitive to the price of gasoline. 

Simulations using the new vehicle sales and scrappage models confirmed that gasoline price had 

a very small impact on the fuel economy of the on-road vehicle stock (via its composition). The 

estimated short-run elasticities of MPG with respect to the price of gasoline were 0.191 for new 

vehicles, 0.006 for used vehicles and 0.022 for all vehicles. In the long run, the greatest change 

in fleet MPG will come about via newer more efficient vehicles replacing the used vehicle stock. 

In the long run, the simulations indicate a price elasticity of 0.204, that is, a 10% increase in the 

price of gasoline would eventually result in about a 2% increase in fuel economy. These 

elasticities were calculated for 2005 when the price of gasoline was $2.34 per gallon. The model 

implies that elasticities will increase with increasing gasoline price. At $4 per gallon, the 

elasticities increase by about 50% to 0.033 in the short run and 0.330 in the long run. These 

elasticities include only the effects of fuel price on consumers’ choices among existing new 

vehicles and not manufacturers’ decisions about the technological content and design of vehicles. 

For the existing fleet, only the impact on the composition of the fleet and not its operation (e.g., 

speed, maintenance, driving style) are included. Still, this paper offers a rare quantification of 

both the new vehicle sales mix and on-road fleet composition impacts of gasoline price and fuel 

economy. 

The most recent study of the impact of gasoline price changes on vehicle prices also uses the 

most detailed data. Sallee, West and Fan (2010) estimated a model using approximately 

8 million wholesale market transactions for used vehicles. Because the wholesale transactions 

take place at auction houses, the market is likely to be well-informed and efficient. While the 

data do not represent retail transactions with final purchasers, it seems reasonable to believe that 

the wholesale market reflects the likely responses of the retail market. The model is similar to 

other but includes odometer readings for each vehicle, as well as time-period fixed effects for 

detailed vehicle types (make, model, model year, cylinder count, engine displacement, 
46 

transmission and trim style). Let Pijt represent the price of the ith vehicle record of type j, in year 

t, let Cijt represent the estimated discounted present value of its remaining fuel costs, Oijt

represent the vehicle’s odometer reading at time of sale, δjt represent a fixed effect for that 

vehicle type and time period and εijt represent random errors. 

ܲ௜௝௧ ൌ ܥߚ௝௜௧ ൅෍ߙ௝௔ܱ௝௜௧

௔

ସ

௔ୀଵ

௧௜௝ߝ ൅ ௧௝ߜ ൅

(18) 

In equation (18) the polynomial odometer terms and the time period fixed effects are intended to 

represent the value of future services from the vehicle, as well as shocks not reflected in gasoline 

prices, such as economic conditions. 

Expected future fuel cost is the central variable in the analysis. For fuel price expectations, the 

authors assume buyers consider fuel prices to follow a random walk so that the current price 

becomes the best estimate of future prices. Other formulations were tested, however, with 

similar results. Consumers were assumed to discount future costs at r = 5% per year, although 

rates of 10% and 15% were also tested. Fuel economy is assumed to be constant over the life of 

the vehicle. 

Cijt=Et ൥෍H൫Oijt,Xj൯ 1

ሺ1+rሻs-t

mjsps

MPGj

R

s=t

൩

(19) 

The function Et[] denotes expected value, which could vary by time period. The function H() 

represents the probability of survival, which is a function of the vehicle’s odometer reading and 

whether it is a passenger car or light truck. 

The most important determinant of expected present value fuel costs is the expected remaining 

vehicle mileage; since it identifies the cost effect versus the time period fixed effects. The key 

assumption is that mileage, rather than age, is assumed to be the relevant measure of the position 

of a vehicle in its lifecycle. While other studies have determined remaining expected lifetime 

using a fixed number of years (e.g., 15), Sallee et al. assume vehicles will travel a fixed number 

of miles but that cumulative lifetime miles vary by vehicle type (cars versus light trucks and 

make). Clearly, both age and mileage matter in vehicle scrappage decisions, since used vehicle 

prices depend on both and not only one or the other. Since other studies rely on age, Sallee et 

al.’s estimation based on mileage provides a different perspective. 

Estimating future fuel costs requires knowing how many more miles remain in a vehicle’s 

lifetime and their distribution over time. The approach is to estimate lifetime mileage 

distributions separately for passenger cars and light trucks, by make. First, parameters 

describing the relative intensity of use by vehicle class and make were estimated by regressing a 

predicted odometer reading as a function of age against each vehicle’s actual odometer reading 

(c indexes passenger cars versus light trucks). 
47 

௠௖௜ߝ ൅ ௠௖௜ܱ௠௖ߠ ൌ௖௔ ݀

(20) 

The predicted odometer readings were obtained from a U.S. Department of Transportation 

(DOT) report (U.S. DOT/NHTSA, 2006) that provides an equation for mileage by age. A value 

of θ < 1 indicates that vehicles of that make are used more than expected based on the NHTSA 

function. Next the NHTSA functions that predict vehicle scrappage and annual mileage as a 

function of age were transformed to be functions of odometer reading.11 

Data for the study come from an enormous database of over 90 million used vehicle transactions 

at wholesale auction houses from 1990 to 2009. Using truncated Vehicle Identification Numbers 

(VIN) these transactions are matched to EPA fuel economy ratings using make, model, model 

year, cylinder count, engine displacement, body type, transmission and trim style. The EPA 

combined, adjusted fuel economy ratings (the estimates reported to the public) were used to 

estimate fuel economy. Model years prior to 1978 were dropped from the sample since EPA 

estimates are not generally available for these years. A 10% sample of the records was taken, 

leaving still about 8 million cases. The price of gasoline was represented by the monthly 

national retail price series of the Energy Information Administration (Figure 6), deflated using 

the CPI-U. The undeflated price series shows a generally rising trend with considerable ups and 

downs after 2001, with a sudden decline in 2009. Gasoline price varies only by month, while the 

time-period fixed effects vary by month and vehicle type. What identifies the effect of cost, Cijt , 

from the fixed effects is therefore the variation in odometer readings from vehicle to vehicle, the 

differences in expected life implied thereby, and differences in fuel economy. 

Figure 6. Trend of Nominal Gasoline Prices over the Period of Sallee, West and Fan’s (2010) 

Study.

                                                            11 The report states that, “The base mileage function is then multiplied by the estimated theta for each make 

to allow for heterogeneity, but we currently impost the same scrappage function on all vehicles.” Equation 2, 

however, implies that the mileage functions should be divided by theta in order to recover the empirical odometer 

readings. 

0

50

100

150

200

250

300

350

400

450

Price (Nominal Cents per Gallon)

Nominal MonthlyGasoline Prices, AllGrades: 8/90 to 4/09
48 

The estimation results indicate that used vehicle purchasers come very close to fully 

incorporating future fuel costs into the prices of used cars. The overall estimated beta coefficient 

for the full sample is -0.788 with a standard error of 0.011, indicating that on average buyers 

incorporate almost four fifths of the expected costs of future fuel expenditures into the price of 

the vehicle. The null hypothesis that β = -1 could not be rejected, however. The reference 

discount rate is 5%. If that is raised to 10%, the β coefficient for all vehicles becomes -1.01. 

The study also found considerable variation in β values between passenger cars and light trucks 

and across makes (Table 11). Purchasers of Ford passenger cars, for example, are estimated to 

incorporate only about 50% of the expected future fuel costs, while purchasers of Toyota cars are 

estimated to account for more than 90%. 

Table 11. Coefficients on the Expected Present Value of Real Remaining Fuel Costs

Full Sample 

-0.788 

(0.011) 

Makes Passenger Cars Light Trucks 

All -0.851 

(0.018) 

-0.736 

(0.014) 

Chevrolet -0.599 

(0.045) 

-0.727 

(0.033) 

Ford -0.513 

(0.030) 

-0.735 

(0.033) 

Honda -0.787 

(0.034) 

-0.619 

(0.048) 

Toyota -0.914 

(0.054) 

-0.792 

(0.043) 

There are several areas in which this analysis differs from others. Possibly a key difference is 

the assumption that the expected remaining vehicle life will be determined by the odometer 

reading rather than the age of the vehicle. Other studies assume that age will determine 

remaining vehicle lifetime. Certainly both matter, since vehicles deteriorate with ageing of the 

materials of which they are made, and their value also declines with technical obsolescence. 

Another key difference is the use of variation in remaining vehicle use specific to individual 

vehicles to help identify the valuation of future fuel costs. When the authors restricted their 

estimation to impose the same odometer coefficients and the same time period effects (both vary 

by vehicle type in the base model), they find that their beta estimates fall dramatically and are 

closer to zero than one. A key question for future research is therefore to understand why 

vehicle type-specific polynomial coefficients and fixed effects have such a pronounced impact 

on the estimated willingness to pay. The authors also have not yet estimated a model with 

asymmetric price effects. Kilian and Sims (2006) found a similar valuation of future fuel costs 

during periods of rising prices (about -0.8) but no value during periods of falling prices. The 

authors also intend to examine this in future work. 

 
49 

4. SUMMARY AND DISCUSSION 

Twenty-seven recent studies have been reviewed for evidence on the value consumers place on 

fuel economy in their vehicle purchase decisions. The resulting estimates are highly variable. 

They are summarized in Table 12 in terms of willingness to pay as a fraction of discounted 

present value of fuel savings or in terms of implied discount rates. There are studies indicating 

that consumers significantly undervalue fuel economy relative to its expected present value, and 

there are others indicating that consumers value fuel economy at approximately its expected 

present value, or much more. This wide disparity of results mirrors the highly variable estimates 

of implicit consumer discount rates for future fuel savings found by Greene (1983) more than 25 

years ago. The persistence of this lack of consensus over a period of decades, despite significant 

improvements in data and advances in methodology, calls out for an explanation. First, the 

evidence will be reviewed. 

Among the studies using discrete choice modeling to draw inferences from aggregate vehicle 

sales data, Allcott and Wozny concluded that consumers significantly undervalue future fuel 

savings at the rate of about $0.25 on the expected present value dollar. Gramlich’s (2008) results 

contradict this finding. His results imply that consumers overvalue fuel savings by a factor of 2 

to 3. However, Gramlich’s model over-predicted the increase in light-duty vehicle fuel economy 

from 2007 to 2008 as a result of higher fuel prices by an order of magnitude. Sawhill’s (2008) 

results also imply that consumers overvalue fuel economy but by a factor of 1.3 to 1.4, on 

average. Berry, Levinsohn and Pakes’ (1995) model found the miles per dollar was not a 

significant factor in consumers’ vehicle choices. Taking their estimated coefficient at face value 

implies that consumers undervalue fuel economy, counting only about $0.01 on the dollar. 

Among discrete choice models estimated using survey data, Train and Winston (2007) found that 

fuel cost per mile was not significant on average, but that the variation across households in 

preferences for fuel economy was significant. About 60 percent of the population preferred 

higher fuel economy while 40% preferred lower fuel economy. Like BLP, fuel economy was 

undervalued, on average, by about two orders of magnitude. Dasgupta, Siddarth and Silva-Risso 

(2007) found only a slightly high implicit discount rate for fuel economy: 15.2% per annum. 

Bento et al. (2005) did not directly estimate the value of fuel economy, but the very low 

elasticities of vehicle choice with respect to fuel costs implied by their model indicate significant 

undervaluation relative to vehicle price. Feng et al. (2005) found four out of six fuel cost per 

mile coefficient were significant, and that these consumers valued fuel economy at 

approximately its expected present value. Estimates made by Brownstone et al. (1996) and 

Brownstone et al. (2000) using stated preference survey data in general suggest that households 

overvalue fuel economy by 50% to 67%, but are highly variable across income groups and 

household demographics, with certain groups preferring less fuel economy. Goldberg’s (1995, 

1998) estimates show statistical significance for small cars but not large cars. Overall she 

concludes there is no reason to believe the consumers are myopic. Two studies from the UK 

appear to show that car buyers there somewhat overvalue fuel economy, although there are 

questions about the units key variables are in that remain to be resolved. 
50 

Table 12. Summary of Consumers’ Evaluation of Fuel Economy Improvements 

Based on 27 Recent Studies

Authors Model Type Data / Time W-T-P as % of Discounted PV Implied Annual 

Discount Rate 

Alcott & Wozny 

(2009) 

Mixed NMNL Aggregate U.S., 

1999-2008 

25% > 60% 

Gramlich (2008) NMNL Aggregate U.S., 

1971-2007 

287% to 823% 

Berry, Levinsohn & 

Pakes (1995) 

NMNL Aggregate US, 

1971-1990 

<1% 

Non-significant 

Sawhill (2008) Mixed NMNL Aggregate U.S., 

1971-1990 

140%, range of 

-360% to 1,410% 

Train & Winston 

(2007) 

Mixed NMNL Survey, U.S., 

2000 

1.3% 

Non-significant 

Dagupta, Siddarth and 

Silva-Risso (2007) 

NMNL Survey, CA, 

1999-2000 

 15.2% 

Bento, Goulder, 

Henry, Jacobsen & 

von Haefen (2005) 

NMNL Survey, U.S., 

2001 

No direct estimate but MPG 

insensitive to price of gasoline 

Feng, Fullerton & Gan 

(2005) 

Klier and Linn (2008a) 

NMNL 

Logit 

CES, U.S., 1996-

2000 

Aggregate U.S., 

1970-2007 

0.03% to 1.3% 

Very approximately 69% 

Brownstone, Bunch & 

Train (2000) 

Mixed NMNL 

Stated & 

Revealed 

Preference 

CA Survey, 1993 132% to 147% 

Brownstone, Bunch, 

Golob & Ren (1996) 

NMNL Stated & 

Revealed 

Preference 

CA Survey, 1993 -420% to 402% 

Goldberg (1996, 1998) NMNL U.S. CES, 1984-

1990 

Consumers “not myopic” 

Goldberg (1995) 

Vance & Mehlin 

(2009) 

NMNL 

NMNL 

U.S. CES, 1983-

1987 

Germany, 

Aggregate New 

Car Sales 

Approximately 1,000% 

Cambridge 

Econometrics (2008) 

Mixed logit UK survey, 2004 

to 2009 

196% but uncertain of 

estimate. Authors contacted 

for clarifications. 

Eftec (2008) NMNL UK 2001 to 2006 TBD – authors contacted for 

clarifications. 

Fan & Rubin (2009) 

Fifer & Bunn (2009) 

Hedonic Price 

Hedonic Price 

State of Maine, 

2007 

U.S., 1996-2005 

Cars: 25% 

Lt. Trucks: 16% 

Cars: 52%, Pickups: 283% 

SUVs: 44%, Vans: 240% 

Cars: 37% 

Lt. Trucks: 77% 

McManus (2007) Hedonic Price U.S., 2002 90% 

Espey & Nair (2005) Hedonic Price U.S., 2001 109% 

Arguea, Hsiao & 

Taylor (1994) 

Hedonic Price U.S., 1969 to 

1986 

3% to 46% 

Bhat & Sen (2006) Choice model San Francisco 

Bay Area, 2000 

Elasticities of vehicle choice 

with respect to fuel costs 2% 

to 3% of purchase price 

elasticities. 
51 

Sallee, West & Fan 

(2010) 

Price Regression Aggregate U.S., 

Used Cars, 1978-

2009 

79%, not statistically different 

from 100% 

Langer & Miller 

(2008) 

Price Regression U.S., 2003 to 

2006 

Approx. 15% of PV of fuel 

cost changes reflected in 

vehicle price changes. 

Busse, Knittel & 

Zettelmeyer (2009) 

Kilian and Sims 

(2006) 

Price Regression 

Price Regression 

U.S., 1999 to 

2008 

Aggregate U.S., 

Used Cars, 1978-

1984 

Transaction prices adjust by 

1.2 years worth of fuel savings 

for new cars. 

11% to 25% 

Li, Timmins & von 

Haefen (2009) 

Vehicle sales by 

fuel economy 

quantile 

U.S. Metro 

Areas 1997 to 

2005 

Short-run price elasticity of 

MPG with respect to sales mix 

+0.02, long-run +0.2. 

Hedonic price studies also disagree. Fan and Rubin estimate very high implicit discount rates for 

consumers in the state of Maine: 37% for car buyers, 77% for purchasers of light trucks. 

McManus’ (2007) results indicate that consumers undervalue fuel economy by 30%, or less, 

depending on assumptions. Espey and Nair (2005) find very low implied discount rates, 1% to 

4% for most of their models. Arguea, Hsiao and Taylor (1994) produce estimates of willingness 

to pay that range from 5% of expected present value to 46%, depending on the year. 

Using other methods Bhat and Sen, 2006 found that consumers undervalue fuel economy by up 

to an order of magnitude. Evidence from Langer and Miller (2008) and Busse, Knittel and 

Zettelmeyer (2009) is more difficult to interpret but tends to indicate that consumers either 

undervalue fuel savings or value it at close to its expected present value. 

Key attributes of the 28 studies are summarized in Table 13. Less than half of the studies were 

published in peer reviewed journals. Most of those in manuscript form are very recent (2008-

2010) and are likely to be published soon. Four are reports and one a thesis. There is a relatively 

even distribution among the model types: 5 aggregate discrete choice, 12 disaggregate discrete 

choice (only 9 are U.S. studies and three of these are slightly different versions of the same 

study, leaving 7 different U.S. studies), 5 are hedonic price analyses, 3 are asset price models 

with two using other modeling frameworks. Six studies use aggregate sales or sales shares as the 

dependent variable, 11 use individuals’ vehicle choices from surveys, 9 make new or used 

vehicle prices the dependent variable, and two use sales by fuel economy quantile. There is a 

similarly wide array of survey, aggregate sales, and vehicle price data sources. Some studies are 

based on a single year’s data, others make use of data covering decades. The time periods range 

from the 1970s to 2009. 

 

Fuel economy appears in a variety of forms, most commonly fuel cost per mile or fuel 

consumption per mile. Only one study, however, tested the hypothesis that markets might 

respond asymmetrically to rising and falling fuel prices. That study concluded that they do. This 

subject is worthy of further attention, since there is evidence that markets in other areas respond 

asymmetrically to petroleum price increases and decreases. In addition, it is possible that 

consumers respond differently to fuel economy than they do to fuel prices. There is evidence 

that vehicle travel responds differently to fuel costs than to miles per gallon (Small and van 
52 

Table 13. Summary of Key Features of 27 Econometric Studies 

Study 

Publication 

Status Model 

Dependent 

Variable 

Type of 

Data 

Time 

Period 

Fuel 

Economy 

Measure 

Price 

Expectations 

Transaction 

Prices? 

Heterogeneour 

Tastes? 

Berry, Levinsohn & Pakes 1995 Journal NMNL Sales shaes Aggregate U.S. 1971-1990 Miles/Pg Random Walk No Yes Allcott & Wozny 2009 Manuscript NMNL New & used vehicle prices Aggregate U.S. 1999-2008 Disc. PV of Fuel Cost RW + alternatives Yes No Klier & Linn 2008 Manuscript Logit New vehicle shares Aggregate U.S., monthly 1970-2007 Disc. PV of Fuel Cost Random Walk n.a. Yes Gramlich 2008 Manuscript NMNL New vehicle shares Aggregate U.S. 1971-2007 Pg/MPG & MPG Random Walk No No Sawhill 2008 Manuscript NMNL New vehicle shares Aggregate U.S. 1971-1990 Pg/MPG ARIMA No Yes Train & Winston 2007 Journal Mixed Logit Indiv. Vehicle Choices U.S. Household Survey 2000 1/MPG Static No Yes Dasgupta, Siddarth & Silva-Risso 2007 Journal Mixed Logit Indiv. Vehicle Choices So. CA Vehicle Transactions 1999-2000 Pg/MPG Static Yes Yes Bento, Goulder, Jacobsen & von Haefen 2005 & 2008 

Journal Random Coef. Logit Indiv. Vehicle Choices Nat. HH. Travel Survey U.S. 2001 Pg/MPG Static No Yes 

Feng, Fullerton & Gan 2005 Manuscript NMNL Indiv. Vehicle Choices CES 1996-2000 Pg/MPG Static No No Brownstone, Bunch & Train 2000 Journal Mixed Logit Indiv. Vehicle Choices CA survey 1993 Pg/MPG Static Yes, via respondents Yes Brownstone, Bunch, Golob & Ren 1996 Journal NMNL Indiv. Vehicle Choices CA survey 1993 Pg/MPG Static Yes, via respondents No Goldberg 1995 Journal NMNL Indiv. Vehicle Choices CES 1983-1987 Pg/MPG Static No Yes Goldberg 1996 Report NMNL Indiv. Vehicle Choices CES 1985-1990 Pg/MPG Static No Yes Goldberg 1998 Journal NMNL Indiv. Vehicle Choices CES 1984-1990 Pg/MPG Static No Yes Cambridge Econometrics 2008 Report Mixed Logit Indiv. Vehicle Choices UK Survey 2005-2006 £/100 km Static No Yes eftec 2008 Report Mixed Logit Indiv. Vehicle Choices UK Survey 2004 & 2007 l/100 km & £/100km Static No Yes Vance & Mehlin 2009 Report NMNL New Vehicle Shares Sales data, Germany 1995-2007 €/100 km Static No No Fan & Rubin 2010 Manuscript 2-stage hedonic New vehicle prices Maine, sales data 2007 log(MPG) Static No Yes Espey & Nair 2005 Journal 1-stage hedonic New vehicle prices U.S. vehicle data 2001 1/MPG Static No No McManus 2007 Journal 1-stage hedonic New vehicle prices U.S. vehicle data 2002-2005 Pg/MPG Static Yes No Fifer & Bunn 2009 Thesis 1-stage hedonic New vehicle prices U.S. vehicle data 1996-2005 1/MPG Random Walk No Yes Arguera, Hsaio & Taylor 1994 Journal 2-stage hedonic New vehicle prices U.S. vehicle data 1969-1986 MPG n.a. No No Killian & Sims 2006 Manuscript Asset Price Used Car Prices U.S. 1978-1984 PV fuel costs Random Walk + No No 
53 

Table 13. Summary of Key Features of 27 Econometric Studies (continued) 

Study 

Publication 

Status Model 

Dependent 

Variable 

Type of 

Data 

Time 

Period 

Fuel 

Economy 

Measure 

Price 

Expectations 

Transaction 

Prices? 

Heterogeneour 

Tastes? 

Bhat & Sen 2005 

Journal 

MDCEV 

random 

utility 

Individual vehicle 

choice 

Survey: San 

Francisco, CA 2000 Pg/MPG Static Price not included Yes 

Langer & Miller 2008 Manuscript Asset Price New vehicle prices U.S. regions weekly 2003-2006 Pg/MPG Random Walk + No Yes Busse, Knittel & Zettelmeyer 2009 Manuscript Sales shares by quartile New & Used vehicle prices Sample, U.S. transactions 1999-2008 MPG Quartiles RW Yes Yes Li, Timmins & von Haefen 2009 

Journal Vehicle demand Sales by Quantile New & used sales in 20 U.S. metro areas 1997-2005 Pg/MPG & Pg Random Walk No Yes 

Sallee, West & Fan 2010 

Manuscript Asset Price Used vehicle prices 

Sample of U.S. 

auction 

transactions 

1990-2009 Disc. PV of fuel costs Random Walk + Yes Yes 

* Indicates whether study generally implies that consumers undervalue (—), over-value (+) or equally value (0) fuel econo

above (X). 
54 

Dender, 2007; Greene 2010) and it would be interesting to see if vehicle purchases do also. The 

premise that consumers respond equivalently to a change in the price of fuel or a change in fuel 

use per mile is a logical consequence of the rational economic model, yet it may not actually 

reflect how consumers make decisions. It is a maintained hypothesis. If it is incorrect, then 

inferences about how consumers value fuel economy could be largely a reflection of a different 

kind of behavior: how consumers respond to fuel prices. 

Although a variety of fuel price expectation models are employed, there is a clear preference for 

static expectations or random walk, both of which imply that current prices are the best predictor 

of future prices. Undoubtedly the fact that, historically, oil and gasoline prices are consistent 

with the random walk model (e.g., Hamilton, 2009) influenced analysts’ decisions. Other 

models tested were models that implied that shocks would die away over time. While these 

models are reasonable from a statistical viewpoint, they may not match consumers’ expectations. 

A possibly useful area for future analysis would be to explore models that allowed consumers to 

project trends into the future, or to base their expectations on learning from past price excursions. 

Most studies did not have access to actual transaction prices and relied on manufacturer’s 

suggested retail prices or other standard values. Very few studies attempted to explicitly 

estimate the effects of fuel economy standards on the market for fuel economy. In some cases, 

such as discrete choice models for a single year, this should not be a problem. In other cases, 

such as hedonic price models based on a times series, this could make it very difficult to identify 

the demand function. Many studies allowed preferences to vary across individual consumers or 

market segments. Methods of incorporating heterogeneity in preferences varied considerably, 

however. Some models estimate distributions of preference coefficients, others use dummy 

variables or interact consumer attributes with vehicle attributes. Many of the models use 

estimation methods appropriate for simultaneous determination of attributes (such as fuel 

economy) and their values. This is not necessary for all model formulations, however, and those 

not using simultaneous equation methods generally explain the assumptions that make it 

unnecessary (e.g., approximately fixed supply and attributes of used vehicles). 

In the final analysis, however, none of these factors appears to be able to explain the different 

conclusions reached by the 27 studies. Given the variety of models, data and estimation 

methods, combined with the generally high level of technical expertise demonstrated by the 

researchers, one is led to look elsewhere for an explanation. In particular, fruitful areas to 

investigate appear to be the appropriateness of the classical rational economic decision making 

model for explaining consumers’ fuel economy choices, and the difficulties of making statistical 

inferences given the complexity of the vehicle purchase decision. 

Although a completely satisfactory explanation for these widely diverging results does not exist 

at present, several factors can be identified that clearly play a role in creating the confusion. 

First, and perhaps most importantly, Turrentine and Kurani’s work has shown that real 

consumers almost certainly do not make their decisions according to the strict model of rational 

economic behavior. Instead, they found that households employed numerous, different decision 

rules. Many of the economic models reviewed in this report allow for heterogeneity of tastes; 

Turrentine and Kurani’s work implies heterogeneity of decision rules, as well. This could be 

what causes estimates in mixed logit models to indicate no significant value of fuel economy on 

average, but significant variance across individuals. Certainly, the mere fact that vehicle use 
55 

varies considerably across households would dictate a certain degree of variability in the value 

given to fuel economy. Heterogeneity of decision rules further complicates the question. 

Consumers may not be continuously trading off the multiple attributes of motor vehicles. 

Estimating models that imply that they are could lead to a misspecification bias. 

Second, the choice of a motor vehicle is itself a complex problem. Vehicles have multiple 

attributes, many of which are difficult to define, let alone measure. Modelers cannot be expected 

to include all variables that are relevant, nor to measure them precisely in all cases. Style, 

reliability, prestige, and safety are examples of important vehicle attributes that themselves have 

many dimensions and are difficult to define and measure. These and other omitted or 

imprecisely measured attributes are likely to be correlated with fuel economy to a greater or 

lesser extent. The combination of omitted variables, errors in variables and correlated variables 

can easily lead to seriously biased coefficients. Most modelers do their best to deal with the 

problem with liberal use of fixed effects. This should help a great deal but may not be enough to 

prevent serious bias in the estimates of certain coefficients. 

The literature on the economics of energy efficiency has only recently begun to appreciate that 

the consumer faces substantial uncertainty in making choices about the energy efficiency of 

energy-using durable goods. Uncertainty about future energy prices is the most obvious but may 

not be the most important source of uncertainty. Although vehicles are clearly labeled with fuel 

economy ratings, the credibility of the miles per gallon estimates is seriously questioned by 

many consumers. Moreover, as the label itself states, “your mileage may vary.” Driving style, 

traffic conditions, trip lengths and even climate have significant impacts on realized fuel 

economy. There are also non-trivial uncertainties about vehicle use, vehicle lifetime, and the 

cost of increased fuel economy either in dollars or foregone other attributes. Uncertainty further 

complicates both consumers’ decision making and quantitative inferences about it. 

Vehicle fuel economy is primarily determined by two different processes. There is the 

consumer’s choice among a set of vehicles with fixed attributes, and then there is the 

manufacturer’s decision about how to design vehicles and which technologies to use. These are 

very different processes. Some studies are based solely on consumers’ choices, while others 

attempt to combine the two. This in itself can lead to different inferences. Potentially even more 

important is the possibility that manufacturers’ decision making and consumers’ decision making 

about fuel economy may not be as perfectly synchronized as the rational economic model 

implies. 

Key findings of this literature review can be summarized as follows: 

1. Of the 25 distinctly different study estimates, 12 indicate that consumers significantly 

undervalue future fuel savings relative to a reference expected value based on average 

U.S. statistics, 8 indicate that consumers’ values are approximately equal to the reference 

expected value, and 5 indicate that consumers significantly overvalue fuel savings. 

2. With a very few exceptions, there are no obvious flaws in the methods or data used by 

these studies. This finding applies equally to the published and unpublished studies. 

3. There does not appear to be an obvious explanation for the widely divergent results. 

Neither model type, formulation of the variable representing fuel economy, data type, 
56 

time period, nor any other readily identifiable factor shows a strong association with 

inferences about the values consumers place on fuel economy (Table 13). 

4. The fifteen studies using discrete choice models are evenly divided between under, equal 

and over-valuing fuel economy; studies using hedonic price, asset price and other models 

more often indicate undervaluing (Figure 7). 

5. The studies are also evenly divided between those dated 2008 or later (12) and those 

between 1994 and 2007. Six of the earlier studies and six of the 2008-2010 studies 

conclude that consumers significantly undervalue fuel economy. Seven of the earlier 

studies and six of the later studies imply that consumers roughly equally value or 

significantly over value fuel economy (Figure 8). 

6. Price expectations are an important factor in all studies. Almost all of the studies assume 

that consumers will use the current price of fuel as a best estimate of future fuel prices, 

either due to static expectations or because they perceive fuel prices will follow a random 

walk. Five of the studies explore alternative price expectations models. However, none 

of the models allows consumers to project trends of increasing or decreasing prices into 

the future. Given the importance of price expectations to the evaluation of future fuel 

savings, a better understanding of how consumers form price expectations might provide 

useful insights. 

7. Most of the studies (15) represent fuel economy interacted with the price of fuel, 

generally as fuel cost per mile. These studies are evenly divided between undervaluing 

(5), equally valuing (5) and overvaluing (5) (Figure 9). Six included fuel economy 

without interaction with the price of fuel, either as miles per gallon or gallons per mile. 

Of these, five found undervaluing and one equally valuing. Four calculated a discounted 

present value based on assumptions about vehicle lifetime, usage and fuel price 

expectations. These studies were evenly divided between undervaluing and 

approximately equally valuing. These differences suggest that there may be some 

insights to be gained by testing hypotheses about whether consumers respond differently 

to fuel price changes as opposed to fuel economy differences, or whether responses to the 

two variables are symmetric or asymmetric. 

8. Several studies point out the empirical challenges to inferring the value of vehicle 

attributes to consumers: (1) correlations among attributes, (2) difficulties in defining and 

measuring the many relevant attributes, and (3) differences (heterogeneity) in tastes 

among consumers. These problems can lead to errors in variables and omitted variables 

and, together with correlations among variables they can result in seriously unstable, 

biased parameter estimates. More recent studies, exploiting massive data sets, have 

attempted to address these problems with detailed fixed effect coefficients and other 

methods. The continued differences in results suggest that even these efforts may not 

have successfully addressed the empirical challenges. 

Fuel economy or CO2 emissions standards are a core component of governments’ policy 

strategies to address global climate change and energy security. Standards have been adopted by 

the United States, the European Union, Japan and China, among others. The annual costs and 

benefits of these standards easily amount to tens of billions of dollars. How consumers’ value 

future fuel savings in making car buying decisions has been shown to be a crucial determinant of 

the economic consequences of such standards (Fischer et al., 2007). Yet surprisingly little is 

known about this vitally important subject. 
57 

Given the importance of understanding how the market values fuel economy and makes 

decisions about it, it might be worthwhile to convene top researchers with differing results to 

jointly investigate why those results differ so greatly. Such an effort would require sharing of 

data sets among researchers, who would then execute a mutually agreed upon set of statistical 

analyses, (1) to validate the results produced by others, and (2) to test a specified set of 

alternative model formulations using the different data sets. Such a structured test of model 

formulations against alternative data sets might lead to important insights about why apparently 

carefully and competently done analyses can lead to widely differing results. 

It is at least as important to investigate the possibility that it is the rational economic consumer 

model that is incorrect. This line of inquiry might best be pursued through in two steps. First, 

conduct more in-depth interviews, surveys and experiments, such as reported in the seminal 

paper by Turrentine and Kurani (2007), to discover what decision criteria and algorithms real 

consumers actually employ when considering fuel economy and valuing fuel savings. Second, 

test these alternative models using experimental methods and empirical market data. 

Figure 7. Distribution of 25 Distinct Studies by Model Type and Value of Fuel Economy 

Relative to the Reference Value. 

0

1

2

3

4

5

6

Under Equal Over

Frequency

Relative Value of Fuel Economy

Distribution of 25 Distinct Studies by Model Type and Value

of Fuel Economy Relative to Reference

Discrete Choice Hedonic Other
58 

Figure 8. Distribution of 25 Distinct Studies by Date of Study and Value of Fuel Economy 

Relative to Reference Value. 

Figure 9. Distribution of 25 Distinct Studies by Form of Fuel Economy Variable and Value of 

Fuel Economy Relative to Reference Value. 

 

0

1

2

3

4

5

6

7

Under Equal Over

Frequency

Relative Value of Fuel Economy

Distribution of 25 Distinct Studies by Date of Study and

Value of Fuel Economy Relative to Reference

< 2008 2008 +

0

1

2

3

4

5

6

Under Equal Over

Frequency

Relative Value of Fuel Economy

Distribution of 25 Distinct Studies by Form of Fuel Economy

Variable and Value of Fuel Economy Relative to Reference

w Price interaction w/o Price Disc. PV
59 

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63 

APPENDIX 

REFERENCE ASSUMPTIONS ABOUT VEHICLE USE, 

LIFETIME AND DISCOUNTING 

Most econometric studies that estimate the value of fuel economy in consumers’ automobile 

choices represent vehicle price in dollars and fuel economy either as fuel cost per mile (dollars or 

cents per mile) or gallons of fuel per mile. To compare estimates from different studies on a 

common basis, it is necessary to have a standard set of assumptions about annual vehicle use, life 

expectancy and the discount rate. This allows one to compute expected discounted lifetime 

miles. Multiplying expected discounted lifetime miles times fuel cost per mile yields the 

expected discounted present value of future fuel savings. 

The calculations used throughout this report rely on data produced by the National Highway 

Traffic Safety Administration (NHTSA, 2006). NHTSA’s data, shown in Table A.1, are 

estimates of the average annual miles of use for passenger cars and light trucks by age. The 

same NHTSA document also provides estimated survival probabilities for the two vehicle types 

as a function of age. These were used to determine expected vehicle life. The median expected 

lifetime of a passenger car in the United States was estimated at just over 13 years for passenger 

cars and just over 14 years for light trucks. In the calculations below, we assume an average 

expected lifetime of 14 years for both vehicle types. 

A real discount rate of 7% per annum is used throughout. The following discounting formula 

was applied to produce the estimates in Table A.1. 

  

  

14

1

5.0

a 07.01 a Miles M a Expected Discounted Lifetime

(A1) 

The discounted lifetime miles are 112,600 for passenger cars and 125,891 for light trucks. 

When fuel economy is represented as gallons per mile in a model, it is also necessary to assume 

a price of fuel. In all cases a constant fuel price has been assumed. Data for the year appropriate 

for the study in question are obtained from the Energy Information Administration’s Annual 

Energy Review. That source provides prices in both current and constant dollars which are used 

as appropriate to the study. 

 
64 

Table A1. Annual Miles and Discounted Miles for Light-Duty Vehicles 

Vehicle Age 

(Years) 

Discounting 

Factor 

Passenger 

Car Miles 

Passenger 

Car Disc. 

Miles 

Light Truck 

Miles 

Light Truck 

Disc. Miles 

1 0.96674 14,231 13,758 16,085 15,550 

2 0.90349 13,961 12,614 15,782 14,259 

3 0.84439 13,669 11,542 15,442 13,039 

4 0.78914 13,357 10,541 15,069 11,892 

5 0.73752 13,028 9,608 14,667 10,817 

6 0.68927 12,683 8,742 14,239 9,815 

7 0.64418 12,325 7,939 13,790 8,883 

8 0.60203 11,956 7,198 13,323 8,021 

9 0.56265 11,578 6,514 12,844 7,227 

10 0.52584 11,193 5,886 12,356 6,497 

11 0.49144 10,804 5,310 11,863 5,830 

12 0.45929 10,413 4,783 11,369 5,222 

13 0.42924 10,022 4,302 10,879 4,670 

14 0.40116 9,633 3,864 10,396 4,170 

Total 168,853 112,600 188,104 125,891 

Source: U.S. DOT/NHTSA (2006, tables 9 & 10).