JANUARY 2008

Effects of 

Gasoline Prices on 

Driving Behavior and 

Vehicle Markets

CONGRESS OF THE UNITED STATES

CONGRESSIONAL BUDGET OFFICE

A

S T U D Y

CBO

© JupiterImages Corp.
Pub. No. 2883
The Congress of the United States O Congressional Budget Office

A

STUDY

CBO

Effects of Gasoline Prices on 

Driving Behavior and Vehicle Markets

January 2008
Notes

Numbers in the text and tables may not add up to totals because of rounding.

Photographs for the cover were taken by David Austin (lower left) and Maureen Costantino 

(lower right). 
Preface

Over the past several years, gasoline prices have risen well above their historic average. 

In many parts of the United States, gasoline prices were above $3 per gallon for much of 

2007. Although consumers in the past did not respond very much to small fluctuations in the 

price of gasoline, the recent large increases have led many people to make adjustments, for 

example, in the way they drive and in the kinds of vehicles they buy. 

This Congressional Budget Office (CBO) study—prepared at the request of the Ranking 

Member of the Senate Budget Committee—relates rising gasoline prices to changes in how 

fast people drive, the volume of highway traffic, and rail transit ridership. It also examines the 

effects on market shares, fuel economy, and pricing of cars and light trucks purchased over 

the past several years. With the worldwide price of oil continuing to rise, this study provides 

an indication of the kinds of adjustments consumers would make if gasoline prices continue 

to rise, and of the implications of rising gasoline prices for policies that would discourage 

gasoline consumption and thus limit the growth in carbon dioxide emissions. In keeping 

with CBO’s mandate to provide objective, impartial analysis, the study makes no 

recommendations.

David Austin of CBO’s Microeconomic Studies Division wrote the study, with input from 

Tyler Kruzich on total gasoline consumption, and with supervision from Joseph Kile and 

David Moore. Sarah West of Macalester College provided valuable comments, as did Juan 

Contreras, Robert Dennis, Kim Kowaleswki, John Peterson, Robert Sunshine, Adam Weber, 

and G. Thomas Woodward of CBO; and Arlene Holen and Donald Marron, formerly of 

CBO. (The assistance of external reviewers implies no responsibility for the final product, 

which rests solely with CBO.) Kate Kelly edited the study with assistance from John Skeen. 

Christine Bogusz proofread the study, and Angela McCollough prepared the final draft of the 

manuscript. Maureen Costantino designed the cover and prepared the report for publication 

with assistance from Allan Keaton. Lenny Skutnik printed the initial copies, Linda Schimmel 

coordinated the print distribution, and Simone Thomas prepared the electronic version for 

CBO’s Web site (www.cbo.gov).

Peter R. Orszag

Director

January 2008

Contents

Summary ix

1 Gasoline Prices and Driving Behavior 1

Volume of Traffic 1

Speed of Traffic 4

Applicability of Findings to Other Regions of the United States 10

2 Gasoline Prices and Vehicle Markets 13

Market Shares for Cars and Light Trucks 14

Gasoline Prices and Vehicle Market Shares 16

Changes in New-Vehicle Fuel Economy and Pricing 19

Changes in the Used-Vehicle Market 20

A Study Data 25

B Analytical Approach and Econometric Results 31

References 39
VI EFFECTS OF GASOLINE PRICES ON DRIVING BEHAVIOR AND VEHICLE MARKETS

Tables

1-1. Estimated Effect of a 20 Percent Increase in Gasoline Price on 

Relative Traffic Volume 3

1-2. Value of Fuel Saved by Slowing from 70 Miles per Hour, as a Function of the 

Price of Gasoline 8

1-3. Average Weekend Speeds on Three California Highways, 2003 to 2006 9

1-4. Estimated Effect of a 50 Cent 

Increase in the Price of Gasoline on Highway Speeds 12

2-1. Market Shares of Different Types of New Vehicles, 2004 to 2006 15

2-2. Estimated Effect of a 20 Percent Increase in the Price of Gasoline on 

U.S. Market Shares of New Passenger Vehicles 18

2-3. National Average Gasoline Prices, 2000 to 2006 20

2-4. Average Monthly Change in Price of Used Vehicles, 2002 to 2006 20

A-1. Vehicle Detector Stations in Sample 28

B-1. Sample Means and Vehicle Speeds, January 2003 to December 2006 34

B-2. Vehicle Speeds and Gasoline Prices, Primary Econometric Results 35

B-3. Sample Means for Total Daily Vehicles, April 2003 to December 2006 36

B-4. Total Trips and Gasoline Prices, Primary Econometric Results 37

Figures

S-1. Personal Consumption Expenditures for Gasoline and the Average Price of 

Gasoline in the United States x

1-1. Fuel Consumption and Vehicle Speed 7

2-1. Market Share of Cars versus Light Trucks, 1976 to 2007 14

2-2. Passenger Vehicle Market Shares and the Real Price of Gasoline, 2004 to 2006 16

2-3. Average Rated Fuel Economy for New U.S. Passenger Vehicles, 1975 to 2006 19

2-4. Average Wholesale Prices for Used Vehicles, July 2002 to July 2006 21

A-1. Daily Traffic Volume, I-8, San Diego, California 26

A-2. Weekend Speeds on I-680, San Ramon, California, April 2006 27

A-3. Median Weekend Speeds on I-405, Orange County, California, and 

Gasoline Prices 30
CONTENTS VII

Boxes

1-1. Empirical Results and the Value of Time and Safety 6

1-2. Declining Purchases of Midgrade and Premium Gasoline 10

2-1. Modeling the Influence of the Price of Gasoline on Vehicle Market Shares 17

2-2. CAFE Implications of Rising Average Fuel Economy 22

Summary

In January 2003 the average retail price for a gallon of 

gasoline in the United States was $1.50—roughly equal 

to the real (inflation-adjusted) price during much of the 

preceding half-century. Since then, the price of gasoline 

has risen sharply. It was last below $2 per gallon in February 2005, and for much of 2007, prices topped $3 per 

gallon (see Summary Figure 1).

This Congressional Budget Office (CBO) study examines 

the scope and intensity of consumers’ responses to the 

upward trend in gasoline prices that began in 2003. 

Those responses have been large enough to interrupt a 

pattern of steady growth in total gasoline consumption 

dating back to 1990, the last time U.S. gasoline prices 

rose substantially.1 If current high prices—and consumers’ responses to them—persist, the effect on overall gasoline consumption will grow stronger as older, less-fuelefficient vehicles are retired and as consumers consider 

other, less easily implemented adjustments to their patterns of consumption. 

The 100 percent increase in real U.S. gasoline prices since 

2003, which is larger even than the record increases of the 

early 1980s, has induced motorists to adjust their driving 

habits and the types of vehicles they purchase. Those 

responses have important implications for the future fuel 

efficiency of the passenger vehicle fleet, for the way vehicles are driven, and for the use of the nation’s highway 

and mass transit networks should higher gasoline prices 

persist. The findings of this study are thus relevant in 

assessing the impact of policies that seek to encourage 

greater fuel economy and promote more-efficient patterns of driving.

In preparing this study, CBO analyzed data on trip frequencies and speeds on several California highways from 

2003 to 2006. CBO also gathered and analyzed data on 

U.S. sales of new and used vehicles over the same period.

The data show that consumers have responded in a variety of ways to higher gasoline prices. The effect has thus 

far been small, which is consistent with current estimates 

of the short-run responsiveness of gasoline consumption 

to changes in price. That effect would be expected to 

increase if prices remained high.2

CBO has found the following specific effects in its 

analysis:

B Freeway motorists have adjusted to higher prices by 

making fewer trips and by driving more slowly. CBO 

analyzed data collected at a dozen metropolitan 

highway locations in California, along with data on 

gasoline prices in California, to identify changes in 

driving patterns. On weekdays in the study period, for 

every 50 cent increase in the price of gasoline, the 

number of freeway trips declined by about 0.7 percent 

in areas where rail transit is a nearby substitute for 

driving; transit ridership on the corresponding rail systems increased by a commensurate amount. Median 

speeds on uncongested freeways declined by about 

1. Similar episodes also occurred in 1974 and 1979 in conjunction 

with Mideast oil supply interruptions. The current increase has 

several origins, including higher prices for crude oil caused by 

increased global demand, higher-than-usual refinery costs, and 

larger price markups. See, for example, Federal Trade Commission, Gasoline Price Changes: The Dynamic of Supply, Demand, and 

Competition (2005), www.ftc.gov/reports/gasprices05/

050705gaspricesrpt.pdf; and Investigation of Gasoline Price 

Manipulation and Post-Katrina Gasoline Price Increases (2006), 

www.ftc.gov/reports/060518PublicGasolinePricesInvestigation 

ReportFinal.pdf. 

2. The short-run responsiveness, or elasticity, of consumption to 

changes in price reflects adjustments that consumers can easily (in 

some cases, spontaneously) make, without any major investment. 

By contrast, the long-run elasticity is greater because it reflects the 

effects of additional choices that arise over a period of years, 

including vehicle purchases and decisions about where to live in 

relation to one’s place of work.
X EFFECTS OF GASOLINE PRICES ON DRIVING BEHAVIOR AND VEHICLE MARKETS

Summary Figure 1.

Personal Consumption Expenditures for Gasoline and the Average Price of 

Gasoline in the United States

(Billions of dollars) (Dollars per gallon)

Source: Congressional Budget Office based on data from the Department of Commerce, Bureau of Economic Analysis.

Notes: Consumer expenditures are for gasoline and motor oil through October 2007 (motor oil is about 1.5 percent of the total). 

Consumer expenditures were adjusted by CBO using the Bureau of Economic Analysis’s (BEA’s) chained price index for gasoline and

other motor fuel. Changes in expenditures reflect changes in gallons consumed. Real gasoline prices were calculated by CBO using 

BEA’s consumer price index for all urban consumers.

three-quarters of a mile per hour for every 50 cents the 

price of gasoline has increased since 2003. 

B After increasing steadily for more than 20 years, the 

market share of light trucks (including sport–utility 

vehicles and minivans), relative to all new passenger 

vehicles, began to decline in 2004. As a result, the 

average fuel economy of new vehicles has increased by 

more than half a mile per gallon since 2004 (because 

light trucks tend to be less fuel efficient than cars).

B Used-vehicle prices have shifted, reflecting changing 

demand, particularly with respect to fuel economy: 

The average prices for larger, less-fuel-efficient models 

have declined over the past five years as average prices 

for the most-fuel-efficient automobiles have risen.

Consumers’ Responses to 

Higher Gasoline Prices

Recent research suggests that consumers are not very 

responsive to changes in the price of gasoline, at least in 

the short run. (Increased expenditures on gasoline have, 

however, reduced consumers’ saving, real income growth, 

and probably other forms of consumption.)3

 For a variety 

of reasons, consumers are currently only about one-fifth 

as responsive to short-run changes in gasoline prices as 

they were several decades ago. That decline in sensitivity 

has been attributed to growth in real income, which has 

rendered gasoline a smaller share of consumers’ purchases 

from disposable income. Price sensitivity also has 

1959 1963 1967 1971 1975 1979 1983 1987 1991 1995 1999 2003 2007

0

80

120

160

200

240

280

320

0

0.50

1.00

1.50

2.00

2.50

3.00

3.50

Personal Consumption

Expenditures for Gasoline

(Left scale)

Price per Gallon (2007 dollars)

(Right scale)

Price per Gallon (Nominal dollars)

(Right scale)

3. See Congressional Budget Office, The Economic Effects of Recent 

Increases in Energy Prices (July 2006).
SUMMARY XI

declined because a gallon of gasoline takes a car farther 

than it did in the past, in part because of fuel economy 

standards. Finally, the development of distant suburbs 

also has contributed by making some consumers more 

reliant on the automobile. The longer commutes are balanced by lower housing costs.4

The research suggests that a 10 percent increase in the 

retail price of gasoline would reduce consumption by 

about 0.6 percent in the short run.5

 Over a longer period, 

consumers would be much more responsive to an increase 

in the price of gasoline (should the higher price persist) 

because they would have more time to make choices that 

took longer to put in place, such as buying an automobile 

that gets better gasoline mileage. Estimates of the longrun elasticity of demand for gasoline indicate that a sustained increase of 10 percent in price eventually would 

reduce gasoline consumption by about 4 percent.6

 That 

effect is as much as seven times larger than the estimated 

short-run response, but it would not be fully realized 

unless prices remained high long enough for the entire 

stock of passenger vehicles to be replaced by new vehicles 

purchased under the effect of higher gasoline prices—or 

about 15 years. Over that time, consumers also might 

adjust to higher gasoline prices by moving or by changing 

jobs to reduce their commutes—actions they might take 

if the savings in transportation costs were sufficiently 

compelling. Those long-term effects would be in addition 

to consumption savings from short-run behavioral 

adjustments attributable to higher fuel prices.

Driving Behavior

Underlying the market’s overall response to higher gasoline prices are some specific short-run adjustments in the 

way people drive. To estimate the importance of those 

adjustments and how they contribute to the response to 

higher gasoline prices, CBO analyzed two sets of data: 

One consisted of detailed information on traffic flows at 

multiple freeway locations in California; the other contained measurements of vehicle speeds at some of those 

locations. 

California was chosen because its highway system has an 

extensive network of automatic data collection devices 

that have recorded large quantities of traffic data from 

many locations over long periods. (More information 

about the data and how they were collected is provided in 

Appendix A.) Although CBO’s analysis is based on data 

from California, the findings should apply to metropolitan areas in other states to the extent that those areas 

are similar in terms of drivers’ ages and income, the vehicle stock, highway configuration, and enforcement of 

speed limits. 

Many of the freeways CBO studied run parallel to lightor heavy-rail transit systems, so it is possible to discern 

the effects of gasoline prices on daily vehicle flow in the 

presence or absence of an accessible rail transit alternative 

to driving. CBO’s study also took into account the time 

4. For a discussion of increased reliance on the automobile, see 

Matthew E. Kahn, “The Environmental Impact of Suburbanization,” Journal of Policy Analysis and Management, vol. 19, no. 4 

(2000), pp. 569–586. For information on the decline in fuel costs’ 

share of disposable income, see Jonathan E. Hughes, Christopher 

R. Knittel, and Daniel Sperling, Evidence of a Shift in the ShortRun Price Elasticity of Gasoline Demand, Research Report 

UCD-ITS-RR-06-16 (University of California, Davis, Institute of 

Transportation Studies, 2006); and Kenneth A. Small and 

Kurt Van Dender, “Fuel Efficiency and Motor Vehicle Travel: 

The Declining Rebound Effect,” Energy Journal, vol. 28, no. 1 

(2007), pp. 25–51. For information on the decline of fuel costs as 

a share of total driving expenditures, see Ian W.H. Parry and Kenneth A. Small, “Does Britain or the United States Have the Right 

Gasoline Tax?” American Economic Review, vol. 95, no. 4 (2005), 

pp. 1276–1289; and Hughes, Knittel, and Sperling, Evidence of a 

Shift in the Short-Run Price Elasticity of Gasoline Demand.

5. See Hughes, Knittel, and Sperling, Evidence of a Shift in the 

Short-Run Price Elasticity of Gasoline Demand, and Small and 

Van Dender, “Fuel Efficiency and Motor Vehicle Travel.” With 

the relatively small price elasticity, moderate price increases may 

cause the growth in consumption to decline even as total consumption continues to rise, because gasoline consumption 

depends on factors other than price: Growth in the population 

and in the number of registered vehicles and drivers, for example, 

leads to increases in total consumption. A sizable increase in price 

would be required to completely neutralize those factors and cause 

total consumption to decline.

6. A 1996 federal report proposed a value of -0.38 for long-run price 

elasticity. See Department of Energy, Office of Policy and International Affairs, Policies and Measures for Reducing Energy Related 

Greenhouse Gas Emissions: Lessons from Recent Literature, DOE/

PO-0047 (July 1996). That value was used by the Congressional 

Budget Office in Reducing Gasoline Consumption: Three Policy 

Options (November 2002) and in The Economic Costs of Fuel Economy Standards Versus a Gasoline Tax (December 2003). Kenneth 

A. Small and Kurt Van Dender, in “Fuel Efficiency and Motor 

Vehicle Travel,” estimate a similar long-run price elasticity value 

of -0.43. Higher estimates exist, but they come primarily from the 

1970s and 1980s and from international studies. For a survey, 

see Daniel Graham and Stephen Glaister, “The Demand for 

Automobile Fuel: A Survey of Elasticities,” Journal of Transport 

Economics and Policy, vol. 36 (2002), pp. 1–26.
XII EFFECTS OF GASOLINE PRICES ON DRIVING BEHAVIOR AND VEHICLE MARKETS

of day, day of the week, season, road characteristics, and 

other factors that influence the way motorists drive. 

CBO’s analysis indicates that, since 2003, for every 

nominal increase of 50 cents per gallon in the price of 

gasoline, median driving speeds on uncongested urban 

freeways have declined by about three-quarters of a mile 

per hour, on average, and the amount of weekday traffic 

on freeways next to commuter rail systems has declined 

by about seven-tenths of a percent. Those adjacent commuter rail systems also are affected by higher gasoline 

prices: The increase in the number of passengers per day 

on those systems is approximately equal to the decline in 

the number of vehicles on the adjoining freeways. Those 

effects, although fairly small, are highly consistent with 

recent estimates of the short-run elasticity of demand for 

gasoline.

Purchases of New Vehicles

If sustained, higher gasoline prices would increasingly 

influence consumers’ automobile-buying habits. Consumers typically own a vehicle for several years, during 

which time little can be done to affect that vehicle’s fuel 

economy. Consumers who are considering replacing a 

vehicle are more likely to buy a more-fuel-efficient vehicle 

the higher they expect gasoline prices to be during the 

time they own their next vehicle. 

Thus, with rising gasoline prices, in 2004 the market 

share of light trucks began to decline relative to that of 

cars. That year, light trucks constituted about 55 percent 

of the passenger vehicle market; by 2006, that proportion 

had slipped below 52 percent. The decline occurred 

despite slight increases in financial incentives to promote 

the purchase of light trucks in 2006 and a slower average 

rate of increase in suggested retail prices compared with 

those for cars. 

Consistent with a shift in the mix of vehicles sold, the 

higher gasoline prices of the past several years have contributed to an increase of more than half a mile per gallon 

in the combined average fuel economy ratings of new cars 

and light trucks since 2004. A primary cause has been the 

decreased market share of light trucks, which average 

fewer miles per gallon than cars do. But light trucks also 

have become more fuel efficient, a trend that began in 

2003 and that has been spurred on by the adoption of 

more-stringent fuel economy standards beginning with 

the 2005 model year. The average fuel economy of new 

cars also has increased somewhat in nearly every model 

year since 2000—the first sustained increase since the 

mid-1980s. (In 2000, average real gasoline prices also 

reached levels not seen since the mid-1980s.) That such 

an increase occurred despite slightly larger annual price 

increases for more-fuel-efficient vehicles (a pattern that 

carried over to used-vehicle markets) is a further indication of an increase in consumer demand for fuel 

economy.

The increase in fuel economy among new vehicles over 

the past few years has partially offset the 1.9 mile per gallon (mpg) decline that occurred from 1987 through 

2004.7 Should higher gasoline prices persist over the next 

decade or more, and should consumers continue to 

respond to those prices as they have done over the past 

few vehicle model years, the increase in fuel economy 

eventually will be reflected throughout the fleet rather 

than, as currently, only in passenger vehicles from the 

past few model years. 

Gasoline Prices, Policy, and 

Total Gasoline Consumption

The nationwide increase in gasoline prices since 2003 has 

not merely slowed the rate of growth in gasoline consumption. Through the third quarter of 2007, real consumer purchases of gasoline—which can be thought of as 

a measure of quantities consumed—had fallen slightly in 

8 of the preceding 10 quarters, compared with purchases 

the year before (see Summary Figure 1). Such declines, 

although small, occurred despite continued population 

growth, changing patterns of residential development and 

job location, and technological change, all of which have 

encouraged the increasing consumption of gasoline, particularly in recent decades.

The increase in gasoline prices and the response of consumers to higher prices have important implications for 

government policies that would reduce gasoline consumption. Such policies can produce better outcomes for 

society than would result from pure market forces, 

because the consumption of gasoline imposes social 

costs—environmentally damaging emissions, for example—that are not reflected in the price of gasoline. 

7. In some of the intervening years, average fuel economy increased, 

but never by more than 0.2 mpg. CBO’s calculations of average 

fuel economy are based on annual vehicle sales and closely mirror 

the Environmental Protection Agency’s calculations based on 

model year sales. (See Environmental Protection Agency, Fuel 

Economy, www.epa.gov/fueleconomy.)
SUMMARY XIII

Two important policy tools that encourage people to 

drive more-fuel-efficient vehicles (and thus reduce gasoline consumption) are the federal corporate average fuel 

economy (CAFE) standards and federal and state gasoline 

taxes.8 CAFE standards require manufacturers to design 

and sell larger numbers of fuel-efficient cars and light 

trucks than the market would otherwise demand. Higher 

taxes reduce gasoline consumption by raising the retail 

price of gasoline, with the same effects on driving 

behaviors and vehicle choices that would result from a 

market-driven price increase. In contrast, stricter CAFE 

standards, while reducing gasoline consumption, also 

reduce the per-mile costs of driving and thus partially offset the effects of higher gasoline prices on total miles 

driven and on freeway speeds. 

Higher prices for gasoline affect both types of policies. By 

increasing the market demand for fuel-efficient vehicles, 

higher gasoline prices reduce the economic costs—to 

manufacturers and to consumers—of achieving stricter 

CAFE standards. Also, with higher gasoline prices, the 

average gasoline tax—or any given increase in that tax—

is now a smaller share of the price of gasoline than it was 

in the past. (The average gasoline tax, including state levies, is currently about 46 cents per gallon, of which 18.4 

cents is the federal tax.) Consequently, a given (cents per 

gallon) increase in the gasoline tax would have a smaller 

effect on fuel economy in new vehicles and on fuel-saving 

changes in the way motorists drive. However, because the 

higher gasoline prices would themselves encourage 

greater fuel efficiency, a smaller tax would be needed if 

that were the policy goal.

8. For an analysis of the correspondence between gasoline taxes and 

CAFE standards, in terms of fuel savings, see Congressional 

Budget Office, The Economic Costs of Fuel Economy Standards 

Versus a Gasoline Tax. That analysis is based on a gasoline price of 

$1.50 per gallon, but the same methodology can be used with different gasoline prices. For national average gasoline taxes as of 

March 2007, see American Petroleum Institute, www.api.org/

policy/tax/stateexcise/upload/March_2007_gasoline_and 

_diesel_summary_pages.pdf.

CHAPTER

1

Gasoline Prices and Driving Behavior

The effects of rising gasoline prices can be seen in 

changing highway traffic volumes and speeds and 

in shifting consumers’ choices about the kinds of vehicles 

to drive. In the short run, rising gasoline prices affect the 

number of vehicles on the highway and the speeds at 

which those vehicles are driven in free-flow conditions. It 

is a simple matter for motorists who make such behavioral adjustments to undo them if gasoline prices decline. 

Other adjustments motorists could make—including 

changing the kinds of vehicles to buy or where to live or 

work—are not as easily reversed. The greater price sensitivity of gasoline consumption in the long run reflects 

those other adjustments.

This Congressional Budget Office (CBO) study illustrates both kinds of effects that rising gasoline prices have 

had on consumers, and it suggests the kinds of consumer 

effects that could be expected from policies that would 

seek to discourage gasoline consumption and, by extension, limit the associated carbon dioxide emissions.

CBO’s analysis of the influence of gasoline prices on 

motorists’ behaviors is based on four years of data collected from metropolitan freeways in California between 

2003 and 2006 and on statewide average gasoline prices 

and wages over that period. (Appendix A describes the 

data used, and Appendix B explains CBO’s analytical 

approach and presents the econometric results of the 

analysis.)

Volume of Traffic

One way motorists can reduce transportation costs is to 

drive less, for example by using public transportation, 

alternative modes of transportation, or car pools; by consolidating trips; or by telecommuting to work. They also 

might make shorter trips, substituting nearby recreation 

or shopping locations for more-distant alternatives that 

they otherwise prefer or, in the long run, they might 

move closer to work or choose jobs closer to home.

The likelihood that a driver will make one of those 

changes depends on the price of gasoline and on other 

factors that determine how attractive driving is compared 

with the alternatives. For a motorist who routinely faces 

heavy traffic or high parking fees, the benefit of switching 

to public transportation can be quite large. But motorists 

who previously had been willing to accept those costs 

without switching must therefore place a relatively high 

value on driving. That said, work commuters are more 

likely to switch to public transportation—especially to 

rail, which is usually less affected by traffic congestion—if 

the available transit alternatives are convenient to workplaces and commuting routes.

Expected Effects of Higher Gasoline Prices

Recent empirical research suggests that total driving, or 

vehicle miles traveled (VMT), is not currently very 

responsive to the price of gasoline. A 10 percent increase 

in gasoline prices is estimated to reduce VMT by as little 

as 0.2 percent to 0.3 percent in the short run and by 

1.1 percent to 1.5 percent eventually.1 A 2003 study of 

corporate average fuel economy (CAFE) standards, published by the National Research Council, cited slightly 

1. The VMT effect is slightly smaller, therefore, than the more 

general effect of gasoline prices on the demand for gasoline, 

estimated in recent research to be about -0.6. See Kenneth A. 

Small and Kurt Van Dender, “Fuel Efficiency and Motor Vehicle 

Travel: The Declining Rebound Effect,” Energy Journal, vol. 28, 

no. 1 (2007), pp. 25–51. Other estimates of VMT elasticity (in 

the literature reviewed in that study) were higher, ranging from 

-0.10 to -0.16 in the short run and -0.26 to -0.31 in the long run. 

Small and Van Dender’s estimates are based on recent data, and 

they attribute their estimates’ being lower than those in the 

reviewed literature to growth of real (inflation-adjusted) income 

and lower real fuel prices, which have combined to make the cost 

of driving a smaller share of personal disposable income.
2 EFFECTS OF GASOLINE PRICES ON DRIVING BEHAVIOR AND VEHICLE MARKETS

older estimates of the responsiveness of VMT to fuel costs 

that ranged from about 1 percent to 2 percent.2

Some of the VMT response comes from drivers who 

switch to commuter rail. An increase in gasoline prices 

raises the relative cost of driving compared with rail transit. As an illustration—in the opposite direction—of the 

price sensitivity of the demand for rail transit, a 10 percent increase in transit fares is estimated to reduce ridership by about 5 percent in the short run and by about 

10 percent in the long run.3

 Survey research has indicated 

that a change in the cost of driving is the most important 

factor motorists consider when deciding whether to continue to drive or to switch to some other mode of transportation.4 CBO’s findings suggest, however, that a large 

increase in the price of gasoline might cause only a small 

shift from automobiles to public transportation, at least 

in the short run.

CBO’s analysis is based on traffic volume (total vehicles 

per day) on metropolitan highways, rather than on total 

vehicle miles traveled. Those measures should be correlated, however: A general decline in VMT should reduce 

the volume of highway traffic, and most of that traffic 

occurs in metropolitan areas.5 However, because recent 

research indicates that VMT is relatively insensitive to 

gasoline prices, the higher prices of the past several years 

should not be expected to cause large changes in freeway 

traffic volume. 

When faced with an increase in gasoline prices, motorists 

should most readily curtail their lowest value trips. If they 

consider weekend trips generally less important than 

weekday trips, then weekend traffic volumes should be 

more sensitive to the price of gasoline. (CBO has no 

information about how weekday driving is valued compared with weekend driving.) However, freeway traffic 

volume should be more responsive to changing gasoline 

prices in places where transit rail service is available, particularly on weekdays. That is because rail service is probably a better substitute for weekday driving to work than 

it is for weekend driving, when transit service often is less 

frequent, some destinations (such as sports fields or places 

of worship) may be less well served by public transportation, and trips are more likely to involve hauling purchased items or recreational gear. The relative sensitivity 

of weekend versus weekday traffic to the price of gasoline 

ultimately is an empirical question, which is addressed by 

CBO’s analysis. 

Findings

Average weekday traffic volumes on some freeways have 

declined slightly in response to higher gasoline prices, 

CBO’s analysis shows. The routes on which that response 

was detected are adjacent to commuter rail systems. 

Weekly average gasoline prices appear to have had little 

effect on traffic volume at other freeway locations or on 

weekends.6 In the California data that CBO analyzed, 

higher gasoline prices also are associated with slightly 

greater ridership on transit rail systems.

The data consist of daily traffic counts for a dozen diverse 

freeway locations in metropolitan areas of California. The

2. Technically, what is estimated is the VMT elasticity with respect to 

fuel costs, or the percentage change in VMT that results from a 

1 percent change in per-mile fuel costs. See National Research 

Council, Effectiveness and Impact of Corporate Average Fuel 

Economy (CAFE) Standards (Washington, D.C.: National 

Academy Press, 2002), available from www.nap.edu/

catalog.php?record_id=10172. For VMT elasticity estimates, that 

report cites David L. Greene, James Kahn, and R. Gibson, “Fuel 

Economy Rebound Effect for U.S. Household Vehicles,” Energy 

Journal, vol. 20, no. 3 (1999), pp. 1–31; J. Haughton and 

S. Sarker, “Gasoline Tax as a Corrective Tax: Estimates for the 

United States. 1970–1991,” Energy Journal, vol. 17, no. 2 (1996), 

pp. 103–126; and C.T. Jones, “Another Look at U.S. Passenger 

Vehicle Use and the ‘Rebound’ Effect from Improved Fuel 

Efficiency,” Energy Journal, vol. 14, no. 4 (1993), pp. 99–110.

3. Richard Voith, “Fares, Service Levels, and Demographics: What 

Determines Commuter Rail Ridership in the Long Run?” Journal 

of Urban Economics, vol. 41 (1997), pp. 176–197. In earlier 

research, Voith reported a somewhat lower elasticity of ridership; 

see “The Long-Run Elasticity of Commuter Rail Demand,” 

Journal of Urban Economics, vol. 30 (1991), pp. 360–372.

4. The primary costs considered in the survey were tolls and parking 

charges, but gasoline prices also affect driving costs. Lesser factors 

included increases in the time to drive a given distance and 

improvements in the quality of other modes of transportation. 

See Kevin Washbrook, Wolfgang Haider, and Mark Jaccard, 

“Estimating Commuter Mode Choice: A Discrete Choice 

Analysis of the Impact of Road Pricing and Parking Charges,” 

Transportation, vol. 33, no. 6 (2006), pp. 621–639.

5. The correlation would be lower to the extent that the reduction in 

VMT attributable to higher gasoline prices occurred less on 

metropolitan highways and more on surface streets or rural highways. (That would be the case, for example, if urban-area residents 

responded to higher gasoline prices by cutting back on their outof-town highway travel.)

6. CBO’s sample did not include rural highways. A decline in out-oftown automobile travel, such as weekend recreational trips, would 

not necessarily be detectable in the data used for this analysis.
CHAPTER ONE GASOLINE PRICES AND DRIVING BEHAVIOR 3

Table 1-1.

Estimated Effect of a 20 Percent 

Increase in Gasoline Price on Relative 

Traffic Volume

(Percent)

Source: Congressional Budget Office based on data from the 

Freeway Performance Measurement Project, 

https://pems.eecs.berkeley.edu.

Notes: * = significant at <5 percent; ** = significant at 

<1 percent. 

A 20 percent increase would be 50 cents per gallon if 

gasoline costs $2.50 per gallon or 60 cents if the price is 

$3.00.

data cover the period from early 2003 through the end of 

2006 and come from the state’s four primary metropolitan areas (Sacramento, the San Francisco Bay Area, Los 

Angeles and Orange County, and San Diego County). 

For each area, CBO collected data at representative freeway locations adjacent to the commuter rail system in 

that region and at other locations in the region where rail 

transit was not available. (The figures and tables in 

Appendix A give details on the data.)

On average, over all locations, the price of gasoline in a 

given week had a negligible effect on the volume of weekend traffic, but on weekdays, higher gasoline prices had a 

small but statistically significant effect (see Table 1-1). A 

20 percent increase in price, or 50 cents if the base price 

is $2.50 per gallon, would reduce weekday freeway traffic 

by an average of 0.4 percent. The effect would occur 

entirely in the response at rail-accessible freeway locations 

(as shown in the last two rows of the table). At those 

places, a 20 percent price increase would reduce weekday 

traffic by an average of 0.69 percent. That result is 

strongly statistically significant, although it amounts only 

to about 730 fewer vehicles out of an average of more 

than 106,000 vehicles per weekday at those locations. 

Gasoline prices did not affect weekend traffic volume at 

any of the locations, nor did they affect weekday traffic 

counts where rail commuting was not an option.7

The weekday response for rail-accessible freeway routes 

implies a traffic volume elasticity of -0.035 with respect 

to the price of gasoline. That result is roughly consistent 

with the short-run VMT elasticity estimate of -0.03 

noted earlier, although that estimate represents all driving, not just driving on rail-accessible freeways. However, 

both elasticities indicate that gasoline prices have had a 

measurable, although small, effect on total driving.

Relationship Between Traffic Volumes and 

Rail Ridership

Reductions in traffic volume correspond closely to 

increases in transit ridership. CBO analyzed monthly 

ridership totals for the municipal light-rail systems in 

Sacramento, Los Angeles, and San Diego and for the subway systems in Los Angeles and the San Francisco Bay 

Area. (In order to combine the data from different-sized 

transit systems into the same analysis, CBO expressed 

each system’s ridership as a percentage of its average in a 

baseline period—the same treatment it applied to freeway 

traffic volumes.) Adjusting for long-term ridership trends 

on each system, seasonal effects, and inertia (the tendency 

for ridership totals to persist from one month to the 

next), CBO estimates that the same increase of 20 percent in gasoline prices that affects freeway traffic volume 

is associated with an increase of 1.9 percent in average 

system ridership. That result is moderately statistically 

significant: It can be asserted with 95 percent confidence 

that higher gasoline prices are associated with increased 

ridership.

Weekends Weekdays

Average Effect, 

All Sampled Routes 0.12 -0.40

Statistical Significance Not significant 1.4 percent*

No Rail Option 0 0

Statistical Significance Not significant Not significant

Parallel Rail 0.20 -0.69

Statistical Significance Not significant 0.04 percent**

7. Some results in Table 1-1 are sensitive to the inclusion of a 

location on eastbound Interstate 80 that often carries significant 

weekend recreational traffic toward Lake Tahoe. With I-80 in the 

sample, the “no rail option” weekend response is positive 

(indicating more driving in response to higher gasoline prices) and 

statistically significant, suggesting that the model is misspecified 

(in particular, it lacks indicator variables for weekends in summer 

and in ski season).
4 EFFECTS OF GASOLINE PRICES ON DRIVING BEHAVIOR AND VEHICLE MARKETS

For an average-sized system, that result translates into 

about 1,870 additional rail trips per day in each direction, throughout the system.8

 For a transit system that 

has several branches running alongside freeways—as the 

systems in CBO’s sample have—that implies an additional 625 to 935 riders per line for systems with, respectively, three or two such lines. Thus, as gasoline prices 

have increased, the average number of riders gained by 

the rail transit systems in CBO’s sample has been reasonably consistent with the reduction in the number of vehicles per weekday, about 730, on the adjacent freeways. In 

CBO’s analysis, all five transit systems exhibited positive 

relationships between ridership and gasoline prices, 

although for the two (interconnected) Los Angeles systems, the effect was small and not statistically different 

from zero.

Speed of Traffic

Another way that motorists can reduce their fuel costs is 

to drive more slowly. The incentive to slow down will 

depend on how much gasoline prices have increased, 

how much fuel would be saved by slowing down, and 

how much motorists value their time while driving. The 

value of the potential fuel savings from slowing down is 

rather small compared with reasonable measures of many 

motorists’ value of time, so the likely effect of gasoline 

prices on highway speeds also should be rather small. 

For any given reduction in speed, however, the 

fuel savings are greater at faster speeds and for less-fuelefficient vehicles.

The development of freeway congestion-pricing 

projects—which charge tolls that rise with the amount of 

traffic congestion—has enabled researchers to estimate 

motorists’ value of time during congested commuting 

hours. Estimates for California’s high-occupancy toll 

lanes along State Route 91, west of Riverside, and Interstate 15, north of San Diego, indicate, on the basis of 

tolls and travel time savings in toll lanes versus free lanes, 

that motorists value their time between $20 and $45 per 

hour of reduced travel time.9

In other contexts, where toll-based estimates are not 

available, economists typically use average hourly aftertax wage rates as a proxy for motorists’ value of time.10

8. The systems in CBO’s sample averaged about 4.1 million trips per 

month in 2006, ranging from 8.8 million on Bay Area Rapid 

Transit to 1.2 million on Sacramento’s light-rail system. Thus, 

1.9 percent translates to about 78,000 additional trips per month, 

or 1,870 trips per (nonholiday) weekday, under an assumption 

that each added rider takes two trips (one round-trip) per day— 

21 days a month, on average.

9. The range of values is attributable to differences in the commuter 

populations on the two freeways. The estimates may slightly overstate true values of time, as they may also reflect motorists’ willingness to pay for greater reliability and perceived safer conditions 

on toll roads. See David Brownstone and others, “Drivers’ 

Willingness-to-Pay to Reduce Travel Time: Evidence from the San 

Diego I-15 Congestion Pricing Project,” Transportation Research, 

Part A: Policy and Practice, vol. 37 (2003), pp. 372–387. For 

results from SR-91 in Riverside, see Kenneth A. Small, Clifford 

Winston, and Jia Yan, “Uncovering the Distribution of Motorists’ 

Preferences for Travel Time and Reliability,” Econometrica, vol. 73, 

no. 4 (2005), pp. 1367–1382.

10. On the basis of his review of the economic literature, Kenneth 

Small concluded that “a reasonable average value of time for the 

journey to work is 50 percent of the [motorist’s] gross wage rate.” 

See Kenneth A. Small, Urban Transportation Economics, vol. 51 of 

Fundamentals of Pure and Applied Economics (Newark, N.J.: 

Harwood Academic Publishers, 1992), p. 44. For value-of-time 

estimates not based on road tolls, see Orley Ashenfelter and 

Michael Greenstone, “Using Mandated Speed Limits to Measure 

the Value of a Statistical Life,” Journal of Political Economy,

vol. 112, no. 1 (2004), pp. S226–S267. See also Robert T. 

Deacon and Jon Sonstelie, “Rationing by Waiting and the Value 

of Time: Results from a Natural Experiment,” Journal of Political 

Economy, vol. 93, no. 1 (1985), pp. 627–647. Deacon and 

Sonstelie report that drivers who voluntarily queued for lowerpriced gasoline implicitly valued their waiting time at amounts 

similar to their after-tax wages. Finally, a long-ago survey of 

empirical studies of choices of commuting modes showed that, on 

average, the estimated value of time spent commuting was only 

about 20 percent to 30 percent of wages, although that analysis 

examined commuting by all modes of transit, not just private 

automobiles. See Nils Bruzelius, The Value of Travel Time: Theory 

and Measurement (London: Croom Helm, 1979).
CHAPTER ONE GASOLINE PRICES AND DRIVING BEHAVIOR 5

Consistent with that, motorists’ preferred driving speeds 

have been found to be positively associated with 

income.11

There have been studies that link driving speeds and 

motorists’ value of time, but CBO’s analysis is among the 

few published studies on the relationship between driving 

speeds and gasoline prices. Previous work, now decades 

old, also found a link between higher gasoline prices and 

slower driving.12 And new research has identified a relationship between higher gasoline prices and lower motor 

vehicle fatality rates, although the researchers attributed it 

to a reduction in vehicle miles traveled and did not consider whether slower driving also could have contributed 

to the decline in fatalities.13

In response to higher gasoline prices, drivers optimally 

would slow just to the speed at which the value of the fuel 

saved equaled the value of time lost to slower driving. By 

that logic, motorists who valued their time more would 

slow down less, or not at all, than drivers who valued 

their time at a lower rate per hour. At any given gasoline 

price, a motorist’s preferred speed also depends on factors 

that are unrelated to gasoline prices or the value of time, 

such as the local speed limit and its enforcement, the 

time of day, the time of year, the physical characteristics 

of the road at that location, and traffic density.14 However, in keeping with the evidence cited earlier, speed 

should be correlated with the value that motorists place 

on their time.

CBO’s analytical results are consistent with that observation. In all likelihood, few if any motorists know what 

their optimal response should be (with respect to driving 

speed) when gasoline prices change. But CBO’s analysis 

suggests that drivers’ responses may be proportionate 

to their value of time. That is, if motorists can be said to 

have particular speed preferences, then those who tended 

to drive more slowly than average before gasoline prices 

increased appear to have slowed slightly more than other 

drivers did, and faster drivers have not reduced their 

speeds at all.15 Overall, as described with the rest of the 

findings, the amount of fuel saved as a result is consistent 

with recent estimates of the price elasticity of the demand 

for gasoline. (The variation in motorists’ driving speeds as 

a response to higher gasoline prices may have some implication for highway safety; see Box 1-1.)

How Much Slowing Is “Sensible” When Fuel 

Prices Rise?

A study conducted by Oak Ridge National Laboratory 

(ORNL) showed that slowing from 70 miles per hour 

(mph) to 65 mph—a 7.1 percent reduction—would 

reduce a typical vehicle’s fuel consumption from 3.7 to 

3.4 gallons per 100 miles, an 8.2 percent reduction. At 

$3 per gallon, the fuel savings would be worth 0.9 cents 

per mile. Travel time would increase by about 4 seconds 

per mile.16

Figure 1-1 shows the average relationship between speed 

and fuel consumption for speeds from 15 mph to 75 mph. 

11. Young-Jun Kweon and Kara Kockelman, “Driver Attitudes and 

Choices: Speed Limits, Seat Belt Use, and Drinking-andDriving,” Journal of Transportation Research Forum, vol. 45, no. 3 

(2006), pp. 39–56.

12. A 1979 report (based on 1972 data) found that a 10 percent 

increase in gasoline prices would have induced a 3.5 percent 

decrease in annual statewide average highway speeds. See Carol A. 

Dahl, “Consumer Adjustment to a Gasoline Tax,” Review of Economics and Statistics, vol. 61, no. 3 (1979), pp. 427–432. Morerecent work finds a weak (not statistically significant) link between 

higher gasoline prices and slower speeds (Nicholas E. Burger and 

Daniel T. Kaffine, “Gas Prices, Traffic, and Freeway Speeds in 

Los Angeles,” University of California at Santa Barbara, Department of Economics, unpublished working paper). The lack of significance may stem from a failure to account for correlations in 

highway speeds along different routes and at different times and 

in considering speeds only within a pair of two-hour nighttime 

periods.

13. See David Grabowski and Michael Morrisey, “Do Higher 

Gasoline Taxes Save Lives?” Economics Letters, vol. 90 (2006), 

pp. 51–55. A more in-depth exposition of that research—albeit 

with a focus on declining real gasoline prices—appears in 

Grabowski and Morrisey, “Gasoline Prices and Motor Vehicle 

Fatalities,” Journal of Policy Analysis and Management, vol. 23, 

no. 3 (2004), pp. 575–593.

14. Those factors are accounted for in CBO’s analysis. Weather and 

daylight conditions can greatly influence speed; the analysis does 

not explicitly control for those factors, but they are correlated with 

seasons, which the analysis does consider.

15. CBO does not have data on individual motorists, so it is not 

possible to determine the extent to which individual drivers tend 

to drive faster or more slowly nor to observe differences in how 

such motorists respond to gasoline prices. However, CBO’s 

findings are consistent with that suggestion. 

16. See B.H. West and others, Development and Validation of LightDuty Modal Emissions and Fuel Consumption Values for Traffic 

Models, FHWA-RD-99-068 (Federal Highway Administration, 

1999).
6 EFFECTS OF GASOLINE PRICES ON DRIVING BEHAVIOR AND VEHICLE MARKETS

Table 1-2 is based on the data underlying Figure 1-1. It 

shows average fuel savings per hour of additional travel 

time caused by slower driving, compared with the fuel 

and time consumed when driving at 70 mph. For example, when the price of gasoline is $2.50 per gallon, slowing from 70 mph to 69 mph would generate fuel savings 

of $7.47 for every hour “lost” to slower driving. Thus, for 

motorists who value their time at less than $7.47 per 

hour—and who would prefer to drive 70 mph or faster if 

gasoline were less expensive—the value of fuel saved by 

slowing to 69 mph would exceed the cost in terms of 

additional travel time. For drivers who value their time at 

more than $7.47 per hour, the financial benefit of slowing would be less than the time cost.

More generally, Table 1-2 suggests that an increase in the 

price of gasoline will cause some motorists, but not all, to 

slow down when they are driving on uncongested freeways. For example, no motorist with a value of time 

above $10.45 per hour and a preference for driving 70 

mph would slow down as long as the price of gasoline 

remained below $3.50 per gallon. The table also suggests 

that a motorist’s optimal response should vary inversely 

with that motorist’s value of time. For example, if the 

price of a gallon of gasoline rose from $2.50 to $3.00, a 

motorist who prefers to drive 70 mph would slow by anywhere from 1 mph to 10 mph for values of time ranging 

from around $9 per hour to below $7 per hour, respectively. In September 2005, when the price of gasoline first 

reached $3 per gallon in California, the median after-tax 

wage rate in that state was $11.27 per hour—close 

enough to the values in Table 1-2 that the price should 

have caused motorists to drive more slowly if their value 

Box 1-1.

Empirical Results and the Value of Time and Safety

If the speeds at which motorists drive are positively 

correlated with how much they value their time, then 

a disproportionate number of slower-driving motorists would have lower-than-average values of time and 

faster-driving motorists would have higher values.1

 If 

that is the case, then the findings of the Congressional Budget Office (CBO) about the effects of the 

price of gasoline on highway speeds are consistent 

with the prediction that motorists with lower values 

of time will be more responsive to an increase in gasoline prices than will drivers with higher values of 

time. (CBO has no information about whether driving speeds and values of time are correlated.) The 

results also could be explained if all motorists have a 

similar distribution of driving speeds and a similar 

response to gasoline prices, or if drivers of less-fuelefficient vehicles tend to drive more slowly than do 

drivers whose vehicles get better mileage. However, 

those explanations would require that all motorists 

value their time about the same. Given the wide 

variation in motorists’ effective wage rates, that 

premise seems unlikely.

If different motorists do have different propensities to 

drive faster or slower, the findings also imply that 

higher gasoline prices increase the variance in highway driving speeds at a given time. That would mean 

more interactions, such as passing. In that case, 

motorists who wish to maintain a given level of safety 

would need to devote slightly more attention to tasks 

such as monitoring other vehicles and maintaining 

their desired following distance.2 However, because 

higher gasoline prices also would cause drivers to 

reduce vehicle speeds slightly, the effect on safety is 

indeterminate but probably negligible because the 

changes in speed are small.

1. One useful way of thinking about the concept of motorists’ 

value of time is the amount they would be willing to pay to 

reduce their travel time by one hour. 

2. For a discussion of the possibility of a negative link between 

variance of speed and highway safety, see Charles A. Lave, 

“Speeding, Coordination, and the 55-MPH Limit,” American Economic Review, vol. 75, no. 5 (1985), pp. 1159–1164; 

and Theodore E. Keeler, “Highway Safety, Economic Behavior, and Driving Environment,” American Economic Review,

vol. 84, no. 3 (1994), pp. 684–693.
CHAPTER ONE GASOLINE PRICES AND DRIVING BEHAVIOR 7

Figure 1-1.

Fuel Consumption and Vehicle Speed

(Gallons per hundred miles)

Source: Congressional Budget Office based on data from the 

Bureau of Transportation Statistics. See Stacy C. Davis, 

Transportation Energy Data Book: Edition 21-2001, ORNL6966 (prepared by Oak Ridge National Laboratory for the 

Department of Energy Office of Transportation Technologies, October 2001), www.ornl.gov/~webworks/cppr/

y2001/rpt/111858.pdf, Tables 7.21 and 7.22; B.H. West 

and others, Development and Validation of Light-Duty 

Modal Emissions and Fuel Consumption Values for Traffic 

Models, FHWA-RD-99-068 (Federal Highway Administration, 1999). 

Note: The results are based on nine representative vehicles 

selected and tested by Oak Ridge National Laboratory.

of time was several dollars (or more) below the statewide 

median wage.17

It would be difficult to detect the effect of gasoline prices 

on speeds in heavy traffic, because motorists already must 

drive more slowly than they prefer in such conditions. 

For that reason, CBO analyzed driving speeds only for 

weekends, when freeways are demonstrably less congested 

than they are on weekdays.18 If motorists value their time 

less on the weekends, their weekend driving speeds also 

should be more sensitive to gasoline prices. Thus, if no 

slowdown could be detected in weekend driving, probably none could be detected in weekday travel either.

Findings

Higher gasoline prices from 2003 through the end of 

2006 caused many motorists to drive a little more slowly 

on uncongested highways. Median speeds in free-flow 

conditions declined slightly as gasoline prices increased. 

The slowdown was more pronounced for vehicles moving 

at the somewhat lower 5th percentile speeds; there was no 

discernible effect on 95th percentile speeds. The median 

effect is consistent with recent estimates of gasoline price 

elasticity, which indicate that short-run demand declines 

by around 0.6 percent when the price rises by 10 percent, 

all else being equal.19 The diverse effects of gasoline 

prices on vehicles traveling at different speeds are consistent with the notion that motorists who set a lower value 

on their time may be more willing to trade (slightly) 

longer travel times for (slightly) lower fuel costs.

The data CBO examined consist of distributions (percentile values) of weekend vehicle speeds over a month, with 

a separate distribution for each hour of day and each 

month.20 CBO collected data for three locations, 

17. The California Energy Commission tracks weekly gasoline prices; 

see www.energy.ca.gov/gasoline/graphs/GASOLINE_1996-

PRESENT.XLS. According to the Bureau of Labor Statistics, in 

2005, the median hourly wage rate for all occupations, including 

salaried positions, was $15.80 in California (archived data; see 

ftp://ftp.bls.gov/pub/special.requests/cew/2005/state/; for current 

values, see www.bls.gov/oes/current/oes_ca.htm); the national 

median wage was nearly the same. Thus, the median after-tax 

wage rate would have been $11.27, on the basis of a marginal tax 

rate of 28.65 percent (15 percent for federal income tax, 7.65 percent for Social Security and Medicare payroll tax, and 6 percent 

for state income tax). For an analysis of effective tax rates, allowing 

for deductions, see Congressional Budget Office, Effective Marginal Tax Rates on Labor Income (November 2005). See also California Franchise Tax Board, 2006 California Tax Table, 

www.ftb.ca.gov/forms/06_forms/06_540tt.pdf. 

15 20 25 30 35 40 45 50 55 60 65 70 75

0

2.0

2.5

3.0

3.5

4.0

4.5

Miles per Hour

18. CBO’s comparison of weekend and weekday freeway speeds at all 

sampled locations revealed significant regular slowdowns during 

weekday commutes that did not occur in weekend traffic.

19. The 0.6 percent represents the midpoint of a range of recent 

estimates. Jonathan E. Hughes, Christopher R. Knittel, and 

Daniel Sperling, Evidence of a Shift in the Short-Run Price Elasticity 

of Gasoline Demand, Research Report UCD-ITS-RR-06-16 

(University of California, Davis: Institute of Transportation 

Studies, 2006), estimate that the short-run demand elasticity for 

gasoline ranges between about -0.3 and -0.8; for Small and 

Van Dender, “Fuel Efficiency and Motor Vehicle Travel,” the 

range of estimates is from about -0.5 to -0.9.

20. A distribution is, in effect, a continuous histogram that describes 

the complete range of observed vehicle speeds and the frequency 

with which they are observed. That information is reported as 

percentile values: For example, the 5th percentile or 50th percentile (median) speeds are those faster than, respectively, the speeds 

at which 5 percent or 50 percent of the vehicles were driven.
8 EFFECTS OF GASOLINE PRICES ON DRIVING BEHAVIOR AND VEHICLE MARKETS

Table 1-2.

Value of Fuel Saved by Slowing from 70 Miles per Hour, as a Function of the 

Price of Gasoline

(Dollars)

Source: Congressional Budget Office based on Oak Ridge National Laboratory (ORNL) estimates of average fuel consumption versus speed.

a. At 70 miles per hour (mph) and at a given gasoline price, under ORNL’s assumption of an average of 26.8 miles per gallon at 70 mph.

b. At target speed compared with 70 mph. Value determined by gasoline price times the quantity of fuel saved.

c. Value of fuel savings per hour of lost time (not clock time). At 65 mph, 4 minutes and 37 seconds of travel time is lost per hour of clock 

time, compared with the amount of travel time at 70 mph.

recording each month’s 5th, 50th (median), and 95th 

percentile weekend speeds at each location for each hour 

of the day. Table 1-3 reports sample statistics for those 

data and median traffic volumes for each location. Interstate 405 in Orange County is the busiest of the three 

sampled locations, with a median volume of 5,530 vehicles per hour over the entire sample.21 That location also 

has the slowest 5th percentile speeds, averaging 59 mph 

over all hours of the day, compared with a little less than 

63 mph on I-680 in San Ramon and nearly 67 mph on I8 in San Diego. Median speeds are more similar across 

the three locations, ranging from 66.4 mph to 69.5 mph, 

on average. The averages in Table 1-3 include some congested traffic; as described in the appendix, data that 

appear to indicate congested travel were excluded from 

the analysis.

From 2003 to 2006, the monthly average (nominal) price 

of gasoline doubled, from $1.66 to a peak of $3.34 in 

May 2006. Over that time, average nominal hourly wages 

rose by only 11 percent, from $15.36 to $17.02 per 

hour.22

For this study, CBO developed a statistical model of driving speed as a function of the price of gasoline and other 

factors, including seasonal and freeway-specific effects. 

Holding those other factors constant, the model indicates 

that a 50 cent increase in the price of gasoline would 

cause median freeway speeds in the sample to decline by a 

little more than three-quarters of a mile per hour. (For 

example, at the study’s mean gasoline price of $2.35 per 

gallon and the median freeway speed of 67.8 mph on 

uncongested freeways, when the price of gasoline reached 

$2.85 per gallon, the median speed would have declined, 

on average, to 67.0 mph.) The effect on slower vehicles is 

50 percent greater: Fifth percentile speeds would decline 

by about 1.2 mph. By contrast, higher gasoline prices do 

not appear to have affected 95th percentile speeds: Fastermoving traffic appears not to have slowed down as gasoline prices increased, at least over the period observed in 

the data (see Table 1-4 on page 12).

The median result translates into an elasticity of speed of 

about -0.05 with respect to the real price of gasoline—

which is to say that a 10 percent increase in the price of 

gasoline would cause the median speed to decline by 

about 0.5 percent. That result, applied to the findings of 

Price per 

Gallon

2.50 0.093 69 0.0015 0.75 4.9 7.47

69 0.0019 0.75 4.0 8.96

3.00 0.112 65 0.0092 3.96 4.3 8.37

60 0.0164 8.57 5.2 6.89

3.50 0.131 69 0.0022 0.75 3.4 10.45

Value of 

Fuel Saved

per Mileb Per Mile

Time Lost (Seconds)

Hourc

Fuel Cost Savings per

per Milea 

Target Speed

(Miles per hour) Per Penny Saved

21. Each location has four lanes in each direction; I-405 in Orange 

County also has a fifth, HOV (high-occupancy vehicle) lane, but 

its traffic flow and speed data are not included in CBO’s sample. 

Median traffic volumes in Table 1-3 are for one direction, for 

hours from 6 a.m. through 1 a.m. 

22. Data on average gasoline prices come from the Department of 

Energy, Energy Information Administration; data on average 

hourly wages come from the Bureau of Labor Statistics.
CHAPTER ONE GASOLINE PRICES AND DRIVING BEHAVIOR 9

Table 1-3.

Average Weekend Speeds on Three 

California Highways, 2003 to 2006

(Miles per hour)

Source: Congressional Budget Office based on data from 

the Freeway Performance Measurement Project, 

https://pems.eecs.berkeley.edu.

Notes: I = Interstate.

Excluding 1 a.m to 6 a.m. In the bottom three rows, each 

figure is the average of the given percentile speed in each 

month for each hour on Saturdays and Sundays. With 

19 hours between 6 a.m. and 1 a.m., each figure is an 

average of 48 months × 19 hours = 912 values. 

the ORNL study, implies that for an average vehicle 

driven at highway speed, fuel consumption would decline 

by about 1.2 percent, or slightly less than one-twentieth 

of a gallon per 100 miles. Those savings—a teaspoon of 

gasoline every 2.6 miles—imply a gasoline demand elasticity in urban highway driving of about -0.06 with 

respect to the price of gasoline. 

Thus, along with conventionally understood sources of 

elasticity in the demand for gasoline—changes in the 

length and frequency of automobile trips and in the types 

of vehicles people drive—the way vehicles are operated 

could be a meaningful source of short-run elasticity in the 

demand for gasoline. In particular, although the implied 

elasticity of -0.06 is objectively small, it is consistent with 

current estimates of the overall short-run elasticity, which 

range from about -0.03 to -0.09.23

The analysis of driving speeds shows that if gasoline costs 

$2.85 per gallon, motorists with average fuel economy 

vehicles who slow from the median speed by 0.75 mph 

would cut their fuel expenditures by about 0.13 cents per 

mile. Those savings would accumulate at a rate of about 

$8 per hour of additional travel time—about 30 percent 

less than the median hourly after-tax wage rate for California in 2005.24 At the 5th percentile speed, slowing by 

1.2 mph would cut fuel expenditures by about 0.16 cents 

per mile, or about $5.24 per additional hour of travel 

time compared with the baseline speed of 62.8 mph. 

Motorists who travel at the 95th percentile speed, and 

who do not slow in response to higher gasoline prices, 

would be implicitly valuing their driving time at $8.60 

per hour or more.25 (Another way motorists can reduce 

the per-mile cost of driving is to switch to a lower grade 

of gasoline; see Box 1-2).

Such small responses are unlikely to result from conscious 

calculations. Few motorists would have the information 

required to gauge their responses so acutely, nor the time 

or inclination to do so. However, higher prices make drivers pay more attention to speed. The modest reductions 

in speed suggest that drivers may have responded by easing off slightly on the gasoline pedal or dialing back 

cruise-control settings a notch. If only a minority of drivers have that response, their reduced speeds could cause 

nearby drivers to slow down as well, even if gasoline 

prices alone would not have that effect. Both kinds of 

response contribute to elasticity in the demand for 

gasoline.

Vehicles per Hour 3,460 5,530 3,620

62.7 59.0 66.8

66.4 67.6 69.5

70.3 70.9 71.3

Median Number of

5th Percentile Speed 

95th Percentile Speed

(North)

San Ramon

Median Speed

I-680

(West)

San Diego

 Orange

County

I-405

(South) I-8

23. In “Fuel-Efficiency and Motor Vehicle Travel,” Small and 

Van Dender used U.S. market data for 1997 to 2001 to estimate 

the elasticity of fuel consumption with respect to price at -0.066 

(with a standard error of 0.01). They report that higher fuel costs 

(58 percent above the sample’s average) yield a slightly higher elasticity (-0.074, with a standard error of 0.007). Hughes and his 

colleagues, in Evidence of a Shift in the Short-Run Price Elasticity of 

Gasoline Demand, estimate a range for short-run demand elasticity 

(on the basis of data from 2001 to 2006) of -0.034 to -0.077. See 

also Michael Morris, “Short-Run Motor Gasoline Demand 

Model” (presentation at the Energy Outlook, Modeling and Data 

Conference, Department of Energy, Energy Information Administration, Washington, D.C., March 28, 2007, www.eia.doe.gov/

oiaf/aeo/conf/pdf/morris.pdf). He estimates, on the basis of data 

from 1977 to 1989, that the short-run elasticity of demand for 

gasoline ranged from -0.05 to -0.08; data from 1994 to 2006 led 

to an estimate of -0.02 to -0.04.

24. If a motorist slowed from 67.8 mph to 67 mph, it would take 

about 90 hours (and more than 6,000 miles) of driving to accumulate 1 hour of additional travel time. 

25. By comparison, Small and colleagues, in “Uncovering the Distribution of Motorists’ Preferences,” report that along California’s 

SR-91 the median value of time for weekday morning commuters 

(4 a.m. to 10 a.m.) is $21.46 per hour, about 93 percent of the 

surveyed motorists’ average wage rate. The 90 percent confidence 

interval around that estimate extends down to $11.47. That value 

is higher than CBO’s statewide average value, perhaps because 

workers who accept the kinds of long-distance commutes that 

commuters along SR-91 have would tend to do so for jobs that 

pay higher wages.
10 EFFECTS OF GASOLINE PRICES ON DRIVING BEHAVIOR AND VEHICLE MARKETS

Applicability of Findings to Other 

Regions of the United States

Although they are based on California data, the findings 

of this study are more or less applicable to other, similar 

metropolitan areas in the United States. Motorist populations, highway and mass transit infrastructure, and vehicle stocks differ somewhat from one part of the country 

to another, and gasoline prices vary from state to state 

because of differences in state taxes and regional supply.26

Retail gasoline prices tend to be higher in California not 

only because gasoline pipelines that serve many other 

parts of the country do not extend into California but 

also because the gasoline sold in that state’s metropolitan 

areas is reformulated as required by the Clean Air Act,

Box 1-2.

Declining Purchases of Midgrade and Premium Gasoline

Higher gasoline prices have induced many drivers to 

make small changes in the way they operate their 

vehicles. The higher prices also may have caused 

some consumers to switch to lower octane formulations, which generally are sold at slightly lower prices. 

There currently tends to be a difference of about 

20 cents per gallon between the prices of regular and 

premium gasoline; the price for midgrade gasoline 

tends to fall in the middle.1

Anecdotal evidence suggests that some consumers are 

satisfied with their vehicles’ performance using a 

lower octane gasoline than that recommended by the 

manufacturer. As gasoline prices rise, consumers may 

become more willing to ignore manufacturers’ recommendations and switch to a less expensive grade of 

gasoline. It is also the case, however, that if the price 

of each grade increases by the same amount, the relative price of the higher grade falls in comparison to 

the price for each lower grade. That shift can cause 

some consumers to substitute toward a higher grade, 

although the empirical evidence for that phenomenon is mixed and rather sparse.2 In either case, given 

the low elasticity of overall demand for gasoline, 

grade switching has little effect on total U.S. gasoline 

consumption.

Consumption of midgrade and premium gasoline has 

been declining in absolute terms since 2000 (see the 

accompanying figure on the facing page); consumption of regular fuel has increased. Some of the decline 

in the use of higher octane fuels might reflect a 

change in vehicle designs and in sales of vehicles that 

require higher grades of gasoline. However, as with 

grade switching, the Congressional Budget Office did 

not analyze the extent to which changing engine 

designs and consumer preferences have contributed 

to the decline in sales of higher octane fuels.

1. In 2006, the U.S. average prices for regular and premium 

grades were $2.59 and $2.80 per gallon, respectively. See 

Department of Energy, Energy Information Administration, 

Monthly Energy Review (October 2007), p. 132, Table 9.4, 

Motor Gasoline Retail Prices, U.S. City Average, 

www.eia.doe.gov/emeu/mer/pdf/pages/sec9_6.pdf.

2. See Robert Lawson and Lauren Raymer, “Testing the 

Alchian–Allen Theorem: A Study of Consumer Behavior in 

the Gasoline Market,” Economics Bulletin, vol. 4, issue 35 

(2006), pp. 1–6, http://economicsbulletin.vanderbilt.edu/

2006/volume4/EB-06D00021A.pdf; and Todd M. Nesbit, 

Excise Taxation and Product Quality: The Gasoline Market,

Working Paper 05-11 (Morgantown: West Virginia University, Department of Economics, 2005), www.be.wvu.edu/

div/econ//work/pdf_files/05-11.pdf. 

The theorem was originally explained by Armen Alchian and 

William Allen in Exchange and Production: Competition, 

Coordination, and Control (Belmont, Calif.: Wadsworth, 

1983; originally published as University Economics: Elements 

of Inquiry, 1964) and further developed by Yoram Barzel in 

“An Alternative Approach to Analysis and Taxation,” Journal 

of Political Economy, vol. 84, no. 6 (1976), pp. 1177–1197.

26. In 2006, California’s state tax on gasoline was 40.1 cents per 

gallon, the nation’s third highest gasoline tax. Nationwide, the 

average tax was 27.1 cents per gallon; New York’s was highest 

(41.7 cents per gallon) and Alaska’s was lowest (8 cents per 

gallon). See American Petroleum Institute, “Gasoline Taxes, 

October 2006,” www.api.org/policy/tax/stateexcise/upload/

october_2006_gasoline_and_diesel_summary_pages.pdf.
CHAPTER ONE GASOLINE PRICES AND DRIVING BEHAVIOR 11

adding about three cents to the retail price.27 Despite the 

differences, changes in gasoline prices tend to be highly 

correlated, so consumers throughout the country have 

had similar financial incentives to reduce their consumpion of gasoline.28 This study’s findings on highway speeds 

may be more generally applicable than are its findings on 

the volume of traffic, which apply to areas served by rail 

transit systems.

Higher gasoline prices could have a smaller effect on 

VMT in rural areas, in California or elsewhere, because 

there are fewer alternatives to driving and because trip 

distances may be greater. However, the largest share of 

total VMT occurs on highways in metropolitan areas.29

This study’s findings on vehicle speeds in urban highway 

driving may, if anything, understate the effect of higher

Box 1-2.

Continued

Retail Prices and Consumption of Gasoline

(Millions of gallons per day) (Dollars per gallon)

Source: Congressional Budget Office based on data from the Department of Energy, Energy Information Administration.

Notes: Prices are nominal at-the-pump prices and include all taxes, ending in May 2007. The smoothed price series is a six-month

moving average, computed by CBO.

Consumption totals were converted to logarithms (left scale) by CBO.

2000 2001 2002 2003 2004 2005 2006 2007

0

1

2

3

4

5

6

0

0.50

1.00

1.50

2.00

2.50

3.00

3.50

Regular

Consumption

(Left scale)

Smoothed Price

(Right scale)

Actual Price

(Right scale)

Midgrade

Consumption

(Left scale)

Premium

Consumption

(Left scale)

27. For information about reformulated gasoline, see Environmental 

Protection Agency, Gasoline Fuels, Reformulated Gasoline Emission 

Facts, EPA420-F-99-040 (November 1999), www.epa.gov/otaq/

f99040.htm. According to data compiled by the American Automobile Association, average retail gasoline prices are often higher in 

California than in any other state (see www.fuelgaugreport.com/ 

sbsavg.asp for current statewide average prices). Hawaii also tends to 

have relatively high gasoline prices; Idaho, Wyoming, and Utah 

often have relatively low prices.

28. See Department of Energy, Energy Information Administration, 

U.S. Retail Gasoline Prices, www.eia.doe.gov/oil_gas/petroleum/

data_publications/wrgp/mogas_home_page.html.

29. Urban highways such as those in this study carry 35 percent of 

total VMT. Rural interstate highways and other highways 

(“principal arterials”) carry about 18 percent of VMT. The 

remainder of VMT, in comparatively smaller shares, occurs on 

urban and rural “minor arterial” routes, collector roads, and local 

roads. See Marilouise Burgess, Contrasting Rural and Urban Fatal 

Crashes, 1994–2003, NHTSA Technical Report DOT HS 

809 896 (National Highway Traffic Safety Administration, 

Department of Transportation, December 2005), 

www.nrd.nhtsa.dot.gov/Pubs/809896.PDF, Table 3, p. 19.
12 EFFECTS OF GASOLINE PRICES ON DRIVING BEHAVIOR AND VEHICLE MARKETS

Table 1-4.

Estimated Effect of a 50 Cent 

Increase in the Price of Gasoline on 

Highway Speeds

Source: Congressional Budget Office based on data from the 

Freeway Performance Measurement Project, 

https://pems.eecs.berkeley.edu.

Note: mph = miles per hour; ** = significant at 1 percent. The 

differences between effects also are significant at 1 percent.

a. Estimate applies to an average vehicle traveling at baseline 

speed. Total gasoline price elasticity would reflect additional 

types of adjustments.

gasoline prices on driving speeds in rural areas because 

median household income—and presumably motorists’ 

valuation of their time—tends to be considerably higher 

in urban areas.30

95th

Percentile

62.8 67.8 70.8

- 1.2 ** - 0.8 ** No change

Respect to the 

Price of Gasoline -0.09 -0.05 0

Fuel Consumptiona -0.08 -0.06 0

5th

Baseline Speed (mph)

Change in Speed (mph)

Elasticity of Speed with

Median 

Implied Elasticity of 

Percentile 

30. The Census Bureau reported that in 2005 the median household 

income for metropolitan areas was $48,474, compared with 

$37,564 outside of those areas. See Carmen DeNavas-Walt, 

Bernadette D. Proctor, and Cheryl Hill Lee, Income, Poverty, 

and Health Insurance Coverage in the United States: 2005, Current 

Population Reports P60-231 (Bureau of the Census, August 

2006), p. 6, Table 1, “Income and Earnings Summary Measures 

by Selected Characteristics: 2004 and 2005,” www.census.gov/

prod/2006pubs/p60-231.pdf.
CHAPTER

2

Gasoline Prices and Vehicle Markets

The longer gasoline prices remain high, the broader 

the scope of actions consumers will take in response—in 

part because the longer high prices are sustained, the 

more they affect consumers’ expectations about future 

prices. Those expectations influence consumers’ longterm choices in several areas—including their decisions 

about what kinds of automobiles to drive and how many 

miles they are prepared to commute to work. All of those 

choices impinge on gasoline consumption and are in contrast to consumers’ immediate, short-term, largely behavioral adjustments to high gasoline prices, which involve 

how fast or how much to drive, for example. 

A shift in the kinds of vehicles consumers buy can affect 

overall gasoline consumption, but only gradually because 

many vehicles already in operation when the shift occurs 

remain in use for a dozen years or more. In contrast, signs 

of a shift can be detected relatively quickly in vehicle sales 

data, as was apparently the case with automobile sales 

after the average price of a gallon of gasoline first rose 

above $3, in September 2005. That price threshold was 

exceeded again in the spring and summer of 2006 and yet 

again through much of 2007. Since 2005, the sale of cars 

relative to light trucks has increased, after declining for 

several decades. After stagnating for a comparable period, 

the average fuel economy of new cars has increased, as has 

that for light trucks.

Whether those effects will be sustained in future vehicle 

model years depends on whether real gasoline prices 

remain substantially above their historic average, how 

those prices affect automakers’ future product decisions 

and the demand for those products, and how quickly 

consumers’ real income grows in relation to the growth in 

their fuel costs of driving. If gasoline prices drop back to 

earlier levels, the effect of high prices on the overall passenger vehicle fleet might be limited to vehicles sold while 

consumers expected gasoline prices to remain high. If 

high gasoline prices persist, however, and if consumers 

continue to respond as they have in the past few years, the 

composition of the passenger vehicle fleet eventually 

could resemble that of the vehicles sold in the 2005 and 

2006 model years, as described below. 

Some of the recent decline in market share for light 

trucks could be attributable to the imposition of stricter 

fuel economy standards for those vehicles, which include 

crossover and sport–utility vehicles (SUVs).1 The new 

standards could have led to price or attribute changes in 

some light-truck models that induced some consumers to 

purchase cars instead. The phase-out of some of the 

favorable tax treatment for business use of light trucks 

also could have changed some companies’ buying patterns. Some tax benefit is still provided for those purchases, however, and the effect of the phased-out benefit 

may not have been large.2 Still, the contribution of either 

factor to the loss in the light-truck market share remains 

uncertain.

The relationship between gasoline prices and the demand 

for automobiles was the subject of several economic 

studies in the 1980s in the aftermath of the gasoline price 

shocks of the 1970s. Those studies found that higher 

1. For a concise introduction to corporate average fuel economy 

standards, see Congressional Budget Office, The Economic Costs of 

Fuel Economy Standards Versus a Gasoline Tax (December 2003), 

p. 2.

2. See Congressional Budget Office, Budget Options (February 

2005), Option 32, p. 308. The phaseout was mandated by the 

American Jobs Creation Act of 2004.
14 EFFECTS OF GASOLINE PRICES ON DRIVING BEHAVIOR AND VEHICLE MARKETS

Figure 2-1.

Market Share of Cars versus 

Light Trucks, 1976 to 2007

(Percent, new passenger vehicle sales)

Source: Congressional Budget Office based on data from the 

Bureau of Economic Analysis.

gasoline prices increased the demand for smaller, morefuel-efficient vehicles relative to larger, less-efficient vehicles.3 That relationship continues to hold with recent 

vehicle sales, according to several current economic 

studies.4

Market Shares for Cars and 

Light Trucks

Sales of new cars as a share of all passenger vehicle sales 

increased noticeably in late 2005. Through August of 

that year, the share of cars was unchanged from its yearto-date share in August 2004. But light-truck sales in 

autumn 2005 were weaker than usual as businesses and 

consumers first experienced gasoline prices in excess of 

$3 per gallon after Hurricane Katrina.5

 For the full year 

of 2005, cars constituted 45 percent of new-vehicle sales, 

almost a full percentage point higher than in 2004. The 

share since then has been even higher, around 47 percent 

(see Figure 2-1).

The upturn in the market share of cars since 2004 is particularly noteworthy because that share had been in 

decline for 25 years, falling lower every year after 1981, 

when more than 80 percent of new passenger vehicles 

were cars. The decline reflects the increasing popularity of 

minivans and, later, of SUVs and crossover vehicles, and 

the declining popularity of some types of cars, particularly station wagons. The adoption of corporate average 

fuel economy regulations contributed to that shift by 

requiring higher average fuel economy for cars than for 

light trucks, which led to the development of the minivan, a light-truck alternative to the station wagon. 

Because most minivans had relatively good fuel economy 

compared with that of other light trucks, and most station wagons had relatively poor fuel economy compared 

with that of other cars, the shift in the demand for highcapacity passenger vehicles from station wagons toward 

3. John Greenlees discussed the effects on new-vehicle demand in 

“Gasoline Prices and Purchases of New Automobiles,” Southern 

Economic Journal, vol. 47, no. 1 (1980), pp. 167–178. For 

evidence that used-vehicle demand responds similarly to gasoline 

prices, see George G. Daly and Thomas H. Mayor, “Reason and 

Rationality During Energy Crises,” Journal of Political Economy,

vol. 19, no. 1 (1983), pp. 168–181; and James Kahn, “Gasoline 

Prices and the Used Automobile Market: A Rational Expectations 

Asset Price Approach,” Quarterly Journal of Economics, vol. 101, 

no. 2 (1986), pp. 323–340.

1976 1980 1984 1988 1992 1996 2000 2004

0

40

45

50

55

60

65

70

75

80

85

4. See, for example, Joshua Linn and Thomas Klier, “Gasoline Prices 

and the Demand for New Vehicles: Evidence from Monthly Sales 

Data” (working paper, March 2007), http://tigger.uic.edu/~jlinn/

vehicles.pdf; and Sarah West, “The Effect of Gasoline Prices on 

the Demand for Sport Utility Vehicles,” (prepared for a session on 

“Demand Estimation in Environmental Economics,” Midwest 

Economics Association Meetings, March 2007), 

www.macalester.edu/~wests/SarahWestMEA2007.pdf. For a more 

general treatment, see Walter McManus, “The Link Between 

Gasoline Prices and Vehicle Sales: Economic Theory Trumps 

Conventional Detroit Wisdom,” Business Economics (January 

2007), pp. 53–60.

5. According to data published in Automotive News, a trade publication, in other recent years, the share of light-truck sales was higher

in the autumn than in the rest of the year. For 2003, 2004, and 

2006, the August year-to-date market share for light trucks was 

1 to 2 percentage points lower than the full-year share, indicating 

stronger light-truck sales for September through December. 
CHAPTER TWO GASOLINE PRICES AND VEHICLE MARKETS 15

Table 2-1.

Market Shares of Different Types of New Vehicles, 2004 to 2006

(Percent)

Source: Congressional Budget Office based on data published in Automotive News.

Notes: SUV = sport–utility vehicle.

Market shares total 100 percent.

the more popular minivan helped automakers satisfy 

CAFE standards both for cars and for light trucks.6

Between 2004 and 2006, every major category of car 

gained and every category of light truck lost market share 

(see Table 2-1). The biggest gain was for large cars, which 

went from 8.0 percent to 9.4 percent of the market. The 

share of midsize cars also increased considerably, and 

although the market shares of compact and subcompact 

cars also increased, those gains were smaller. The underlying sales data indicate that those gains in market share 

for the most part reflect a decline in sales of light trucks 

(in every category) rather than an increase in car sales. 

Overall, the number of cars sold actually declined by 

about 1.5 percent each year from 2004 to 2006, or by a 

little more than 100,000 cars per year. (Sales in the 

large-car category increased by 10 percent.) However, 

over that same period, sales of light trucks fell by 

10 percent, with nearly 1 million fewer new vehicles sold 

per year. (For 2007, August year-to-date unit sales of light 

trucks were down an additional 2 percent compared with 

the same period in 2006. However, car sales were 

4 percent lower.)

Despite the relatively large decline in light-truck sales, the 

data do not indicate the extent to which the increase in 

market share for cars occurred because would-be truck 

buyers merely delayed their truck purchases versus buying 

a car instead. The small decline in sales of cars could even 

be consistent with a large number of would-be truck buyers switching to cars and averting what might otherwise 

have been a larger decline in car sales. Such a possibility 

also is consistent with the considerable gains that 

occurred for large-car sales, because among all car types, 

large cars are the closest substitute for light trucks in 

2006

1.3 1.3 1.4 0.1 3.8

19.2 19.8 19.4 0.2 0.8

16.3 16.4 17.0 0.7 4.3

8.0 8.5 9.4 1.4 14.4

7.6 7.8 7.5

-1.2 2.2 -3.6

Light Trucks

6.3 6.4 6.0 -0.3 -4.4

27.2 26.6 26.8 -0.4 -1.3

18.4 18.6 17.8 -0.6 -3.3

3.3 2.3 2.2 -1.1 -32.0

9.4 9.1 8.4

Change from Previous Year (Percent) 6.0 -3.3 -7.8

(Percentage points) Change

Pickup truck

Passenger or cargo van

Calendar Year Sales (Millions of vehicles)

Minivan

SUV

Compact

Midsize

Large

Calendar Year Sales (Millions of vehicles)

Change from Previous Year (Percent)

Subcompact and two-seater

Change 2004 to 2006

Cars

Absolute Percentage

2004 2005

6. See Congressional Budget Office, The Economic Costs of Fuel Economy Standards Versus a Gasoline Tax. Automakers’ CAFE ratings 

depend not only on the mix of vehicles they sell but also on the 

fuel economy rating of each model. As automotive technologies 

have become more fuel efficient, automakers have been able either 

to improve fuel economy or to add fuel-consuming features (such 

as more horsepower) without reducing fuel economy. With lower 

gasoline prices, consumers tended to favor other attributes over 

fuel economy, and many manufacturers’ CAFE ratings stayed flat, 

despite technological advances. Higher gasoline prices have led to 

a shift in consumer preferences toward greater fuel economy.
16 EFFECTS OF GASOLINE PRICES ON DRIVING BEHAVIOR AND VEHICLE MARKETS

Figure 2-2.

Passenger Vehicle Market Shares and the Real Price of Gasoline, 2004 to 2006

(Percent, new passenger vehicle sales) (2007 Dollars per gallon)

Sources: Congressional Budget Office based on data from Automotive News and the Department of Energy, Energy Information 

Administration.

Note: The right-hand scale shows the average inflation-adjusted price per gallon for all grades and formulations of gasoline. 

terms of seating, storage capacity, and engine size. To the 

extent that some truck buyers have delayed purchasing a 

truck rather than switching to a car, sustained high gasoline prices could eventually lead to more car sales because 

consumers must eventually replace their existing vehicles.

Gasoline Prices and Vehicle 

Market Shares 

A comparison of monthly data on the market shares of 

different types of vehicles against monthly average retail 

gasoline prices suggests that some light-truck buyers may 

have briefly delayed purchasing a new truck while gasoline prices were increasing. Price spikes in the spring of 

2005, in October 2005 (after Hurricane Katrina), and 

in the spring of 2006 all coincided with sharp increases in 

the new-car market share (see Figure 2-2). Market shares 

for leading categories of light trucks—especially SUVs—

went the opposite way, dipping as gasoline prices rose. 

When the price of gasoline has dropped, the market share 

of each vehicle type has tended to return to previous levels. Some of those movements in market share could be 

2004 2005 2006

0

40

42

44

46

48

50

52

0

1.50

2.00

2.50

3.00

3.50

Gasoline Price

(Right scale)

2004 2005 2006

0

24

26

28

30

32

34

0

1.50

2.00

2.50

3.00

3.50 SUVs

Car Share

(Left scale)

SUV Share

(Left scale)

Gasoline Price

(Right scale)

Cars
CHAPTER TWO GASOLINE PRICES AND VEHICLE MARKETS 17

influenced by manufacturers’ sales incentives, which vary 

from one month to the next and sometimes are relatively 

stronger for cars, sometimes for light trucks. However, 

exploratory analysis of recent data on incentives, 

described later, suggests the incentives account for little of 

the variation in the relative market shares of cars and light 

trucks.

To assess the degree to which gasoline prices and vehicle 

market shares are related statistically, the Congressional 

Budget Office analyzed 36 months of data from January 

2004 through December 2006 on sales of each type of 

vehicle. CBO computed the market shares from monthly 

sales data for specific vehicle models (for example, Toyota 

Camry), aggregated into total sales by vehicle category 

(for example, midsize cars). The aggregations are based 

on vehicle classifications from the fuel economy ratings 

published by the Environmental Protection Agency 

(EPA).

Current gasoline prices and the market shares of specific 

types of vehicles should be related to the extent that 

current prices influence consumers’ expectations about 

future prices and to the extent that vehicle operating costs 

(primarily fuel costs) influence consumers’ decisions 

about which vehicles to purchase.7 CBO’s analysis of the 

relationship between gasoline prices and vehicle market 

shares accounts for seasonal effects, a possible source of 

correlation between market shares and gasoline prices if 

both exhibit seasonal variation.8

On the basis of that reasoning, CBO constructed a model 

in which the market shares of different vehicle types are 

influenced by the current average inflation-adjusted price 

of gasoline and the current season (see Box 2-1).9 According to the model, the effect of gasoline prices on 

Box 2-1.

Modeling the Influence of the 

Price of Gasoline on Vehicle 

Market Shares

The gasoline price that the Congressional Budget 

Office (CBO) used in its analysis is the U.S. average monthly retail price for all grades and formulations. CBO’s statistical model consists of a 

system of equations, one for each vehicle category 

(the sum of all of the market shares is necessarily 

100 percent, so one vehicle category must be 

excluded to prevent the system of equations from 

being linearly dependent and thus inestimable). 

It is estimated as a “seemingly unrelated regression” model:

where j is a type of vehicle (subcompact, compact, midsize, large, minivan, sport–utility vehicle, or pickup; the omitted category is cargo–

passenger van). The terms represent 

percentage-point changes in market share that are 

attributable to changes in gasoline prices. As a 

check, CBO also estimated each equation separately, with an autoregressive error term ε of 

degree 1 (to account for the tendency for market 

shares to remain above or below their long-term 

averages for more than a month at a time). Each 

approach yielded similar results.

[vehicle category market share]j =

αj β1j + + × ( ) real gasoline price

β2j × β ( ) winter 3j + + × ( ) spring

β4j × ε ( ) summer + j

β1j

7. Current gasoline prices could reflect consumers’ best forecast of 

likely future prices—what economists call “static” expectations. 

(For empirical evidence, see Daly and Mayor, “Reason and 

Rationality During Energy Crises,” an examination of vehicle 

prices in the wake of the 1973 and 1979 oil price increases.) 

Consumers also could implicitly make more sophisticated, 

“dynamic” price projections by, in effect, averaging prices 

observed in recent months. It is straightforward to model such 

expectations, but it is not considered here. Given the recent 

upward trend in gasoline prices and the downward trend in the 

market share for light trucks, such a model would probably yield 

results similar to those of the static model.

8. Gasoline prices typically rise in the warmer months as longer daylight hours and vacation travel increase demand. Light-truck sales 

recently have tended to be higher in the autumn, when gasoline 

prices usually are below their annual peak.

9. A more thorough analysis would incorporate the effects of relative 

vehicle prices. Automakers have responded to rising gasoline 

prices by raising the prices of smaller vehicles relative to those for 

larger ones. Thus, CBO’s analysis could understate the effects of 

gasoline prices on the market share of a given type of vehicle, 

because some of those effects would have been partly neutralized 

by changing vehicle prices.
18 EFFECTS OF GASOLINE PRICES ON DRIVING BEHAVIOR AND VEHICLE MARKETS

Table 2-2.

Estimated Effect of a 20 Percent Increase in the Price of Gasoline on 

U.S. Market Shares of New Passenger Vehicles

(Percent)

Source: Congressional Budget Office based on vehicle sales data from Automotive News and gasoline price data from the Department of 

Energy, Energy Information Administration.

Note: ** = significant at 1 percent; * = significant at 5 percent; SUV = sport–utility vehicle; n.a. = not applicable.

a. Market share averages are not sales weighted.

b. Over all vehicle categories, the net change in market share must be zero. Thus, a value of -0.1 for the market share of “passenger or cargo 

van” is not estimated directly but is derived from changes in the market shares of other vehicles.

c. Percentage increase in market share given in first column when percentage points in second column are added to it.

U.S. new-vehicle market shares has been such that a price 

increase of 60 cents per gallon (a 20 percent increase if 

the base price is $3 per gallon) is associated with an average increase in the market share of new cars of 2.6 percentage points (the sample average is 46.4 percent). That 

result reflects recent experience, although consumers’ 

future responses also would be affected by changes in 

automakers’ product offerings and pricing (which are also 

responsive to rising gasoline prices).

All major car categories—from two-seaters and subcompacts to large sedans and wagons—have gained market 

share as the price of gasoline has risen, with gains of 

between 4.5 percent and about 9 percent for every 

60 cent increase in the price of gasoline above $2.30 per 

gallon. At the same time, the market shares of all types of 

light trucks, from minivans and SUVs to pickup trucks 

and passenger or cargo vans, have fallen by 4 percent to 

6 percent. For example, at average values, a 60 cent 

increase in the price of gasoline would have increased the 

market share of midsize cars by about 0.8 percentage 

points, which is a 5 percent increase over its average value 

of 16.6 percent. That price increase also would be associated with a decline of 1.2 percentage points or 

4.5 percent in the share of new SUVs, on average, from a 

baseline share of about 27 percent (see Table 2-2). 

The results in Table 2-2 might not reflect the full shift 

in demand toward greater fuel economy, because 

automobile manufacturers can respond to higher gasoline 

prices—and to slower sales of light trucks—by offering 

incentives to purchasers to reduce inventories of new 

vehicles. CBO examined data on finance rate and price 

incentives for February 2006 through October 2006 and 

Cars (Versus light trucks) 46.4 +2.6 ** +5.6

Cars

Subcompact or two-seater 1.4 +0.1 * +6.8

Compact 19.7 +0.9 * +4.5

Midsize 16.6 +0.8 * +5.0

Large 8.7 +0.8 ** +9.4

Light Trucks

Minivan 6.2 -0.3 * -5.0

SUV 26.9 -1.2 ** -4.5

Pickup truck 18.2 -1.0 * -5.7

Passenger or cargo van 2.3 -0.1 b -3.9 ____ ____

Total Cars and Light Trucks 100 0 n.a n.a.

Market Sharea (Percentage points)b Significance In Market Sharec

Average Statistical Relative Change

Average Effect of 

Increase
CHAPTER TWO GASOLINE PRICES AND VEHICLE MARKETS 19

Figure 2-3.

Average Rated Fuel Economy for New 

U.S. Passenger Vehicles, 1975 to 2006

(Miles per gallon)

Sources: Congressional Budget Office based on data from the 

Environmental Protection Agency (EPA).

Note: Data are for sales-weighted fuel economy. Although a 

vehicle’s fuel economy performance is determined by EPA, 

either through EPA’s laboratory results or in test data 

submitted by the manufacturer, it is the National Highway 

Traffic Safety Administration (NHTSA) that determines 

compliance with corporate average fuel economy (CAFE) 

standards. NHTSA considers CAFE credits the automaker has 

earned, including those for hybrid and dual-fuel vehicles. 

NHTSA’s CAFE data are similar to those illustrated here, 

although the averages are slightly higher because of the 

credits.

found that, during the summer of 2006, when gasoline 

prices were above $3 per gallon, the average financing 

rates that automakers were offering on some models of 

light truck did decline somewhat relative to the rates 

offered on some cars. (Incentives were not offered on all 

models every month, and for some models, no incentives 

were offered in any month.) Some consumers thus might 

have been encouraged to purchase a light truck when 

they otherwise would have chosen a car, partially counteracting the effect of higher gasoline prices on consumers’ 

decisions about which new vehicle to purchase and leading to slightly smaller gasoline price effects.

Changes in New-Vehicle Fuel 

Economy and Pricing

The 2005 model year might have marked a turning point 

not only in the long, steady decline in the new-car market 

share but also in the declining average fuel economy of 

new passenger vehicles. The recent increase in market 

share for new cars is responsible for some of the increase 

in the average fuel economy for the vehicle fleet; on average, a car can travel about 6.5 miles farther on a gallon of 

gasoline than a light truck can, according to data from 

EPA (see Figure 2-3). 

However, the average fuel economy of new cars has itself 

been rising. Beginning with the 2001 model year, when 

the average fuel economy rating for new cars was 

28.4 miles per gallon, the average began to increase, peaking at 29.2 mpg for the 2005 model year before slipping 

back slightly in 2006.10 Another reason for the increase 

in average new-vehicle fuel economy is that since 2005, 

automobile manufacturers have had to meet stricter 

CAFE standards for light trucks. An increasingly popular 

new kind of light truck, often called a crossover vehicle 

because it is built on a car chassis but with the higher 

clearance of a light truck (and thus is so classified for 

CAFE purposes), also is partly responsible for the rise in 

the average fuel economy of light trucks and may reflect, 

in part, a response by automakers to higher CAFE standards for light trucks. The CAFE standard for cars did 

not change. (Legislation signed in December 2007 

1975 1979 1983 1987 1991 1995 1999 2003

0

12

14

16

18

20

22

24

26

28

30

Total Fleet

Cars

Light Trucks

10. From 2003 to 2006, midsize cars made the biggest gain in salesweighted average fuel economy, increasing from 23.9 mpg to 

25.3 mpg. Among light trucks, the biggest increase was for SUVs, 

which went from 18.3 mpg in 2003 to 19.5 mpg in 2006. Sizable 

improvements also were made by compact cars and minivans. The 

averages reflect EPA’s adjustment of about 15 percent below 

the vehicles’ CAFE ratings to account for estimated differences 

between laboratory-measured and actual fuel economy. EPA will 

begin using a new method of adjustment in 2008. See Robert M. 

Heavenrich, Light-Duty Automotive Technology and Fuel Economy 

Trends: 1975 Through 2006, EPA420-R-06-011 (Environmental 

Protection Agency, Office of Transportation and Air Quality, 

Advanced Technology Division, July 2006), p. A-10, 

www.epa.gov/otaq/cert/mpg/fetrends/420r06011.pdf; and Environmental Protection Agency, Office of Transportation and Air 

Quality, Compliance and Innovative Strategies Division and 

Transportation Compliance Division, Light-Duty Automotive 

Technology and Fuel Economy Trends: 1975 Through 2007, 

EPA420-R-07-008 (September 2007), p. A-9, www.epa.gov/otaq/

cert/mpg/fetrends/420r07008a.pdf.
20 EFFECTS OF GASOLINE PRICES ON DRIVING BEHAVIOR AND VEHICLE MARKETS

Table 2-3.

National Average Gasoline Prices, 

2000 to 2006

(Dollars per gallon)

Source: Congressional Budget Office based on data from the 

Department of Energy, Energy Information Administration 

(for nominal prices) and from the Department of 

Commerce, Bureau of Labor Statistics (real prices 

calcluated from the consumer product index for all 

urban consumers). 

Note: Prices are for all grades and all formulations.

requires a substantial increase in the average fuel economy of all new passenger vehicles sold in the United 

States. By 2020, the CAFE standard will increase to 

35 mpg.11)

Although the average fuel economy ratings of cars and 

light trucks alike have been increasing since 2002, the 

average fuel economy for all new passenger vehicles 

declined through 2004 because the market share for light 

trucks was still rising at that time. Higher gasoline prices 

cannot explain why average new-car fuel economy ratings 

began to increase in 2001, because gasoline prices did not 

start rising until 2003 (see Table 2-3). However, gasoline 

prices have risen steadily since then, helping to increase 

the demand for vehicles that are more fuel efficient.

That increased demand and the resulting improvement in 

vehicle fuel economy appear to have affected the pricing 

of some new vehicles. In the past, some automakers 

might have set lower prices on fuel-efficient vehicles—

and higher prices on other vehicles—as part of their overall strategy to meet CAFE standards; an increase in 

demand for fuel-efficient vehicles would permit automakers to rely less on pricing to meet the standards. If so, 

prices of new fuel-efficient vehicles should have increased 

relative to prices of larger, less-efficient vehicles. There is 

some evidence that that has occurred (see Box 2-2), 

meaning that the rise in average fuel economy for new 

vehicles does not fully reflect the shift in consumer preferences toward greater fuel efficiency.

Changes in the Used-Vehicle Market 

The recently observed price shifts for new vehicles are 

reflected in used-vehicle prices as well. Average prices of 

fuel-efficient used vehicles have been rising, and those 

of less-efficient vehicles have been falling. That is as 

expected: In both markets, consumers’ preferences for 

fuel-efficient vehicles should be similarly affected by rising gasoline prices—which should affect prices similarly 

in both markets. Even if consumers’ preferences were not 

affected in exactly the same way by gasoline prices, price

Table 2-4.

Average Monthly Change in Price of 

Used Vehicles, 2002 to 2006

Source: Congressional Budget Office based on monthly average 

used-vehicle prices and vehicle classifications from 

Automotive News. 

Note: *** = significant at <0.01 percent; ** = significant at 

<5 percent; * = significant at <10 percent; n.s. = not 

significant at conventional levels; SUV = sport–utility 

vehicle.

a. Includes SUVs, luxury sport wagons, and small sport wagons.

11. See the Energy Independence and Security Act of 2007. For 

model years 1996 to 2004, the CAFE standard for light trucks was 

20.7 mpg. It was raised to 21 mpg for 2005 and 21.6 mpg for 

2006. The standard is now 22.2 mpg for light trucks in model 

years 2007 and later. The CAFE standard for cars, unchanged 

since 1990, is 27.5 mpg. See National Highway Traffic Safety 

Administration, “CAFE Overview, Frequently Asked Questions,” 

www.nhtsa.dot.gov/cars/rules/cafe/overview.htm.

2000 1.52 1.82

2001 1.47 1.70

2002 1.38 1.58

2003 1.60 1.79

2004 1.89 2.06

2005 2.31 2.43

2006 2.62 2.66

Nominal 2007 Dollars

Average Price

Statistical 

Significance

Full-Size Car 0.58 47 ***

Sporty Car 0.54 64 ***

Compact or 

Midsize Car 0.47 33 ***

Minivan 0.15 12 **

Small Pickup Truck 0.12 8 *

“Other” SUVa 0.01 2 n.s.

Full-Size Pickup Truck -0.04 -5 n.s.

Small or Midsize SUV -0.07 -7 *

Luxury Car -0.20 -28 ***

Large SUV -0.49 -72 ***

Monthly Mean

Price Change

(Percent)

Average

Change

(Dollars)
CHAPTER TWO GASOLINE PRICES AND VEHICLE MARKETS 21

Figure 2-4.

Average Wholesale Prices for Used Vehicles, July 2002 to July 2006

(Thousands of dollars)

Source: Congressional Budget Office based on data published in Automotive News.

Notes: SUV = sport–utility vehicle.

Automobile category names are from Automotive News. Data are monthly sales figures. Straight lines are trend lines added by CBO.

increases on fuel-efficient new vehicles would cause the 

prices on similar used vehicles to rise as some consumers 

shifted from buying new vehicles to buying similar used 

vehicles. Similarly, smaller price increases on less-fuelefficient new vehicles would limit how quickly prices 

could rise for comparable used vehicles. New-vehicle 

prices serve as a ceiling on consumers’ willingness to pay 

for used (noncollectible) vehicles.12

Consistent with a shift in consumer demand toward 

greater fuel economy, monthly average prices in the 

United States for used vehicles in the large-SUV and luxury car categories declined steadily over the 49 months 

for which CBO collected data, through July 2006. Over 

the same period, average prices increased for sporty, fullsize, and compact or midsize used cars (see Table 2-4).13

It is not possible to attribute those price changes definitively to vehicles’ fuel economy ratings, because the data 

do not include information on other characteristics (such 

as the age of the vehicles or prices when new) that also 

could have contributed to the price trends. However, 

those trends are consistent with the new-vehicle market: 

Prices for small vehicles have been rising while those for 

larger vehicles have fallen or, for new cars, have risen 

more slowly (see Figure 2-4).

Figure 2-4 displays average prices for the vehicle categories with the strongest and most statistically significant 

price trends. Although the data do not include information about the average size or weight of vehicles in each 

category, the category names are an indication of likely 

differences in vehicle size. Based on category names 

alone, the price trends in Figure 2-4 suggest that vehicles 

with declining prices are larger than average and thus are 

likely to have fuel economy ratings below the applicable 

CAFE standard. The vehicle categories with increasing 

prices seem likely to include somewhat smaller-thanaverage vehicles with higher fuel economy ratings. (Compliance with CAFE standards is determined on the basis 

of an automaker’s fleetwide average fuel economy, so 

vehicles with ratings lower than the applicable standard 

do not subject the automaker to penalties if they are balanced by sales of more-fuel-efficient vehicles.) The average monthly dollar changes in vehicle prices corresponding to the sloping trend lines in Figure 2-4 are given in 

Table 2-4.

2003 2004 2005 2006

0

2

4

6

8

10

12

14

16

18

Compact or Midsized Cars

Sporty Cars

Full-Sized Cars

Luxury Cars

Large SUVs

12. Although CAFE standards apply only to new vehicles, they also 

can affect prices of used vehicles: Where some manufacturers’ 

pricing is affected by the standards, as when just-compliant manufacturers offer lower prices on their fuel-efficient vehicles to boost 

sales and CAFE ratings, those lower prices will attract some potential buyers of used vehicles, thus reducing the demand for (and 

average prices of) fuel-efficient used vehicles.

13. The used-vehicle classifications are from Automotive News.
22 EFFECTS OF GASOLINE PRICES ON DRIVING BEHAVIOR AND VEHICLE MARKETS

Box 2-2.

CAFE Implications of Rising Average Fuel Economy

For the 2006 model year, list prices of new vehicles 

with higher fuel economy ratings rose more quickly, 

in relation to 2005 list prices, than did prices of other 

new vehicles. For models sold in both years, there is a 

positive correlation, which is statistically significant at 

the 1 percent level, between a model’s fuel economy 

rating (in either year) and the percentage increase in 

its MSRP, the manufacturer’s suggested retail price.1

That correlation implies that a fuel economy difference of one mile per gallon between any two car 

models is associated with a 0.24 percentage-point 

bigger price increase for the car that is more fuel efficient. That difference is considerable, given that the 

average price increase for new cars was 1.2 percent for 

2006 models (see the table on the facing page). Similar, if slightly weaker, relationships exist for SUVs 

(sport–utility vehicles) and for minivans. (The 

correlations for SUVs and minivans are just as large 

but are statistically weaker because they are based on 

fewer models.)

Automakers that achieve higher average fuel economy 

ratings have more latitude in maintaining compliance 

with corporate average fuel economy (CAFE) 

standards. By contrast, companies that just meet the 

standards might not be able to raise prices for their 

more-fuel-efficient vehicles as much as they otherwise 

would, for example, because their CAFE ratings 

would fall if the higher prices caused too many 

purchasers to choose other vehicles instead. 

To see whether vehicle list prices are consistent with 

the notion that automakers constrained by CAFE 

standards have less latitude in setting prices, the 

Congressional Budget Office compared the MSRPs 

from four manufacturers for the 2005 and 2006 

model years. Two of the companies, Ford and 

General Motors (GM), had relatively little latitude in 

maintaining compliance with one or both types of 

CAFE standard (for cars and for light trucks). Honda 

and Toyota, by contrast, had CAFE ratings well 

above the standards in both years. The differences in 

CAFE compliance margins were reflected in all four 

automakers’ list prices for the 2005 and 2006 

model years.

1. The Congressional Budget Office (CBO) adjusted the yearto-year price changes for differences in standard equipment, 

using equipment and vehicle price data from the 2005 and 

2006 editions of the Automotive News Market Data Book.

The MSRP is not the same as the actual transaction price, 

which will include any manufacturers’ incentives and is the 

result of negotiation between the dealer and the customer. 

CBO does not have transaction prices.
CHAPTER TWO GASOLINE PRICES AND VEHICLE MARKETS 23

Box 2-2.

Continued

The prices Ford and GM set for their 2006 cars, relative to the 2005 prices for the same models, were significantly and negatively correlated with the cars’ fuel 

economy ratings. That is, Ford and GM raised prices 

less for models that got better gas mileage than they 

did for their other models. Honda and Toyota raised 

prices more for their more-fuel-efficient cars. The 

price increases for Honda and Toyota light trucks followed a similar pattern. The changes from 2005 to 

2006 in the list prices of Ford and GM light trucks 

showed no statistical correlation with the fuel 

economy ratings of those vehicles. 

Given the increased demand for fuel-efficient vehicles, that difference in pricing strategies is consistent 

with the CAFE standards’ having induced Ford and 

GM to sell larger numbers of fuel-efficient vehicles 

than they otherwise would. However, the longer gasoline prices stay above their historic levels, and the 

greater the shift in consumers’ preferences toward 

improved fuel efficiency, the easier it will become for 

automakers to comply with the current standards. 

Average Change in MSRP, 2005 to 2006, for Models Sold Both Years

Source: Congressional Budget Office based on data from Automotive News.

Notes: MSRP = manufacturer’s suggested retail price; p = probability; SUV = sport–utility vehicle.

Analysis based on 2005 fuel economy ratings yields similar results.

a. Nominal prices for identical models, adjusted for differences in standard equipment. Nominal prices are used because there was 

little change in the consumer price index for automobiles from 2005 to 2006. Averages weighted by August 2006 year-to-date 

sales.

b. Not a measure of average price increase for all vehicles, only for those included in this analysis.

Car 104 1.2 0.24 0.013

Minivan 16 0.3 0.54 0.032

SUV 63 0.3 0.25 0.044

Pickup Truck 13 -2.3 0.08 0.795

Overall 196 0.2 b 0.33 <0.0001

Number of 

Models

Compared

Statistical 

Average

Change in MSRP,

2005 to 2006a Significance

(Percent) (p value)

Correlation 

(Change in MSRP and

fuel economy)

APPENDIX

A

Study Data

The Congressional Budget Office (CBO) constructed the highway traffic data set for this study from 

data gathered through an extensive network of electronic 

sensors embedded in urban and suburban highway travel 

lanes throughout the state of California.1

 The available 

data describe traffic flow (vehicle counts), lane occupancy 

rates, and vehicle speeds in every lane at thousands of 

locations. In the sample that CBO constructed from 

those data, sensor readings date back at least to 2003, and 

for many locations, they begin several years earlier. The 

data can be assembled for periods of as little as five 

minutes or for much longer intervals. In this study, CBO 

analyzed 24-hour vehicle totals for Wednesdays, Saturdays, and Sundays, and the distribution of speeds 

observed over an entire month, on Saturdays and Sundays only, by hour of the day.

CBO collected data on total vehicle flows at 13 representative locations around California; speed data were gathered from 3 of those locations. The locations were chosen 

to represent traffic conditions in all of California’s major 

metropolitan areas. They were not chosen randomly, but 

the data were not inspected before inclusion in the 

sample.2

The locations selected represent areas of moderate to relatively high (for California) population density. They 

include locations that are adjacent to rail transit systems 

as well as those with no nearby rail option. The data 

exclude locations with the potential for localized congestion, as from lane reduction bottlenecks or merges ahead. 

The sample includes interstate, U.S., and state highways. 

Most locations have four lanes in each direction; two 

locations have three lanes, and two have five lanes. Except 

where noted, measurements were made on inbound lanes 

only, where the direction of travel is toward city centers 

(see Table A-1 on page 28).

Total Trips

CBO collected traffic volume data for Wednesdays, Saturdays, and Sundays through the end of 2006. The sample includes data for Saturdays and Sundays because their 

typical traffic volumes differ considerably. In CBO’s 

highway sample, there is about 10 percent less highway 

travel on Saturdays and 20 percent less on Sundays than 

on a typical weekday. Wednesday totals are representative 

of weekday traffic, with Wednesdays least affected by 

three-day weekend travel. CBO’s sample excludes other 

weekdays to avoid needlessly introducing holiday-related 

variation into the data. In urban areas and outlying suburbs, highway travel demand tends to be relatively high 

on Fridays and Mondays around three-day holiday 

weekends and lower on the weekends. Travel also varies 

seasonally (it is typically highest in summer, lowest in 

winter). CBO’s analysis accounts for all of those effects 

on highway travel.

Figure A-1 shows daily vehicle flows since 1999 in the 

westbound lanes of I-8 in San Diego. The data show traffic rising to a peak of about 100,000 vehicles per weekday 

in mid-2002 and then gently declining through 2005. 

That could have been caused by shifts in regional development patterns and in economic conditions at that 

1. The data are provided by the Freeway Performance Measurement 

Project, a joint effort of the University of California at Berkeley, 

the California Department of Transportation, California Partners 

for Advanced Transit and Highways, and Berkeley Transportation 

Systems. See Freeway Performance Measurement System, https://

pems.eecs.berkeley.edu.

2. In some cases, data from a location were of insufficient quality, 

so a nearby location was chosen instead. Data quality suffers 

when electronic detector stations go offline. In those cases, the 

Freeway Performance Measurement Project imputes values from 

nearby detectors. For lengthy outages, however, the imputed data 

are not usable for CBO’s purposes because their day-to-day variance is too low (often zero). 
26 EFFECTS OF GASOLINE PRICES ON DRIVING BEHAVIOR AND VEHICLE MARKETS

Figure A-1.

Daily Traffic Volume, I-8, San Diego, California

(Thousands of vehicles)

Source: Congressional Budget Office based on data from the Freeway Performance Measurement Project, https://pems.eecs.berkeley.edu.

Note: Daily traffic recorded on westbound Interstate 8, Lake Murray Boulevard, San Diego, California.

location. CBO accounts for such influences by including 

a trend line for each of the 13 locations in the analysis 

and allowing those lines to curve upward or downward as 

dictated by the data.3

The analysis also accounts for other factors that could 

explain differences in volume at different locations and 

on different days. Many potential factors, such as number 

of lanes, population density, or proximity to employment 

centers, are accounted for by including a fixed factor for 

each location–day. Including those factors in the model 

allows the effect of gasoline prices on traffic volume to be 

estimated independently of other factors that also affect 

the demand for passenger vehicle travel.

The several unusual one-day drops or increases in the figure mostly indicate holidays. The extended drop in late 

2005 was caused by a detector outage of several weeks. 

The outliers are included in the analysis, but are attributed to holidays or the effects of offline detectors (thus, 

not to gasoline prices) as appropriate.

The analysis expresses traffic volume as a percentage of 

the average baseline volume (from a period before the 

study began) at each location. That accounts for the likelihood that a change in weekly average gasoline prices will 

have a similar percentage effect on daily traffic volumes 

that week at locations with different amounts of traffic, as 

opposed to affecting similar numbers of trips at locations 

carrying different numbers of vehicles. Gasoline prices 

also are expressed in relative terms, as a percentage change 

from a baseline price, to allow a given percentage price 

change to have a consistent effect on traffic volumes at 

different times.

CBO’s analysis assumes that gasoline prices are independent of local demand for highway travel. Changes in the 

price of gasoline are determined largely by changes in global supplies of and demand for oil and in the cost of 

refining that oil into gasoline. In recent years, the price of 

gasoline in U.S. markets has been substantially influenced 

by growth in the demand for oil in countries with rapidly 

developing economies, such as China.4

 Regional constraints on supplies can cause gasoline prices to be higher 

2000 2001 2002 2003 2004 2005 2006

0

20

40

60

80

100

120

Wednesday

Saturday

Sunday

3. Technically, the analysis fits a second-degree trend line to the 

data, so that the trend can be a straight line or have the shape of 

an upward or downward “U.” Restricting the trend to be a straight 

line (constraining it from curving) resulted in very similar 

conclusions. 

4. See Congressional Budget Office, China’s Growing Demand for 

Oil and Its Impact on U.S. Petroleum Markets (April 2006).
APPENDIX A STUDY DATA 27

Figure A-2.

Weekend Speeds on I-680, San Ramon, California, April 2006

(Miles per hour)

Source: Congressional Budget Office based on data from the Freeway Performance Measurement Project, https://pems.eecs.berkeley.edu. 

Note: Data come from sensors located on northbound Interstate 680 at Montevideo Drive, San Ramon (in East Bay, San Francisco). 

in some areas than in others, but within California, supply constraints do not differ substantially.

Vehicle Speed

CBO analyzed four years of monthly average gasoline 

prices against the same months’ characteristic vehicle 

speeds at different locations and times of day. Figure A-2 

provides an example, showing vehicle speed data for 

April 2006 for a northbound section of Interstate 680 

near Montevideo Drive in San Ramon, California—one 

of the three sampled locations. For each location, CBO 

collected vehicle speed data from January 2003 through 

December 2006. Figure A-2 shows the median, 5th percentile, and 95th percentile speeds observed at each hour 

of the day on Saturdays and Sundays in April 2006. 

The figure indicates, for example, that between 2 p.m. 

and 3 p.m., the 5th percentile weekend speed was 

68.1 miles per hour (mph). The median speed was 

70.7 mph. The 95th percentile speed was 71.4 mph. 

Tests of hypotheses that drivers react differently to gasoline prices according to their value of time actually are 

tests for whether prices affect speeds more at the slow end 

of the distribution than at the median or at the faster end. 

Support for that interpretation is given with the results.5

Sometimes there is mild traffic congestion on weekends. 

Figure A-1 shows that speeds peak in the predawn hours 

and gradually decline through 2 p.m. Another peak 

occurs at 6 p.m. Other months and other locations 

exhibit slightly different patterns. The 5th percentile 

speeds in Figure A-2 provide stronger evidence for congestion: They are as much as 10 mph slower than the 

median speeds. That pattern could be due to a one-time 

slowdown (as from an accident, road work, poor weather, 

special event, or simply a random surge in traffic), or it 

could originate in recurring congestion that affects only a 

fraction of vehicles.6

CBO’s analysis accounts for the effects of congestion so 

that they do not influence the estimated effect of gasoline 

prices on speeds. The analysis accounts for slowdowns 

caused by relatively severe congestion and for differences

2 4 6 8 10 Noon 2 4 6 8 10

0

55

60

65

70

75

80

5th Percentile

95th Percentile

Median

5. The results are also consistent with the possibility that drivers of 

less-fuel-efficient vehicles tend to drive more slowly and are more 

responsive to increases in gasoline prices. However, such a possibility also would imply that those drivers have lower values of time 

than do owners of more-fuel-efficient vehicles.

6. Most months have eight weekend days, so a one-time congestion 

event lasting 45 minutes would affect observed speeds only up to 

the 10th percentile (45 minutes out of 8 hours of traffic 

observed), and only for that time of day.
28 EFFECTS OF GASOLINE PRICES ON DRIVING BEHAVIOR AND VEHICLE MARKETS

Table A-1.

Vehicle Detector Stations in Sample

Continued

in traffic volume from one month to the next.7 It also 

accounts for congestion that is related to the time of day 

by incorporating fixed factors for location and hour into 

the statistical model. (Those factors also account for differences by location in speed limits and in the physical 

characteristics of the roadway, such as grade, curvature, 

distance from ramps, number and width of lanes, and 

type and condition of pavement.8

) Time-of-day variation 

in traffic speed cannot be attributed to fluctuating gasoline prices, because prices generally do not change much 

over a single day. However, accounting for time-of-day 

effects reduces the amount of unexplained variation in 

the data, thus improving the precision of the analysis. 

Route 

(Direction) Location Comments

Type of 

Data

Rail 

Option?

Sacramento

I-80 (E)a Mace Boulevard, Davis Eastern edge of Davis, 9 miles west of Sacramento Trips No

CA 99 (N) 47th Avenue, Sacramento Adjacent, parallel to Sacramento RT (light rail) 4 miles 

from city center

Trips Yes

US 50 (W) Folsom Boulevard and La Riviera 

Drive, Sacramento

Adjacent, parallel to Sacramento RT (light rail) 6 miles 

from city center

Trips Yes

San Francisco Bay Area

I-680 (N) Montevideo Drive, San Ramon 

(San Ramon Valley)

Lateral route (neither inbound nor outbound) Trips, Speed No

CA 24 (W) El Nido Ranch Road, Lafayette Adjacent, parallel to BART (heavy rail) 7 miles east of 

Oakland, 11 miles east of San Francisco

Trips Yes

I-880 (N) 98th Avenue, San Leandro Adjacent, parallel to BART (heavy rail) 7 miles south of 

Oakland, 11 miles from San Francisco

Trips Yes

CA 101 (N) Poplar and Peninsula Avenues, 

San Mateo

Parallel to CalTrain (commuter train) 17 miles south of 

San Francisco (downtown)

Trips Nob

Los Angeles and Orange County

CA 101 (S) Barham Boulevard, 

Universal City and Hollywood

Adjacent, parallel to L.A. Metro Red Line (heavy rail) 

9 miles northwest of downtown Los Angeles

Trips Yes

I-105 (W) South Central Avenue, 

South-Central Los Angeles

Adjacent, parallel to L.A. Metro Green Line (light rail) 

11 miles south of downtown Los Angeles

Trips Yes

I-405 (S) Newland Street, Westminster 

(Orange County)

Outbound for Long Beach and Los Angeles but inbound 

for Orange County (Costa Mesa, Irvine, and Newport 

Beach). High-occupancy-vehicle lane not analyzed.

Trips, Speed No

7. For the purposes of its analysis, CBO defined congestion as 

5th percentile speeds below 55 miles per hour, median speeds of 

60 mph or slower, and 95th percentile speeds slower than 

65 mph. Between 1 percent and 3 percent of observed speeds are 

below those thresholds. Of the three locations surveyed, I-405 in 

Orange County experienced the greatest frequency of temporary 

slowdowns: Its slowest 5th percentile speed (for all times of day) 

was below 40 mph in 32 of 48 months, and it was faster than 

55 mph only twice. On I-680 in San Ramon there were 7 months 

below 40 mph, 27 months above 55 mph, and 12 months above 

60 mph. In contrast, I-8 in San Diego had only 1 month below 

40 mph and only 5 months below 50 mph; it had 41 months 

above 55 mph and 31 months with 5th percentile speeds above 

60 mph.

8. Fixed effects also would control for differences in the stringency 

with which speed limits are enforced, if the agency charged with 

that enforcement, the California Highway Patrol, consistently 

allocates its enforcement resources on the basis of historic 

differences in accident rates or other criteria.
APPENDIX A STUDY DATA 29

Table A-1.

Continued

Source: Congressional Budget Office.

Notes: I= Interstate; CA = California state route; US = U.S. route; E = east; N = north; W = west; S = south; NCTD = North County 

(San Diego) Transit District; RT = rapid transit; BART = Bay Area Rapid Transit; LA = Los Angeles.

According to statistics published by the American Public Transportation Association (www.apta.com), in the third quarter of 2006, 

average weekday ridership totals for the transit systems were as follows: BART (heavy rail), 355,400; LA Metro (light rail), 129,000; 

LA Metro (heavy rail), 125,900; San Diego Trolley (light rail), 107,300; Sacramento Regional Transit (light rail), 54,400; CalTrain 

(commuter train), 36,200; NCTD (commuter train), 6,300. Those figures are not directly comparable because of differences in track 

miles, but on the basis of passengers per mile of track, ridership on the two commuter train systems is relatively low: LA Metro (heavy 

rail), 3,690; BART, 1,330; LA Metro (light rail), 1,110; San Diego Trolley, 1,110; Sacramento Regional Transit, 870; CalTrain, 260; 

NCTD, 80. Because the freeways adjacent to the two train systems (CalTrain and NCTD) carry about as much traffic as the other 

freeways in the sample, those figures imply that the two train systems carry a much lower fraction of commuters in those locations 

compared with the light- and heavy-rail systems.

a. Carries substantial Sierra-bound weekend recreation traffic; reported results exclude that route.

b. CBO analysis treats routes served by commuter train as “no transit” routes: Ridership is lower than on light- and heavy-rail systems 

because of lower capacity and less frequent service.

c. Inbound I-15 (S) has a notorious traffic bottleneck.

Seasonal differences in weather, amount of daylight, and 

weekend recreational travel can affect driving speeds, 

and inasmuch as they are related to the demand for gasoline, those differences can also affect the price of gasoline. 

CBO’s analysis accounts for seasonal effects.

Figure A-3 shows the overall structure of the data. It 

reports median weekend speeds from 2003 to 2006 (for 

the sake of clarity it shows only one location and two 

periods: 6 a.m. to 7 a.m. and 1 p.m. to 2 p.m.) and the 

statewide monthly average gasoline price for all grades 

and formulations.9

 At the I-405 location, median speeds 

are often 4 mph to 5 mph slower in the afternoon than 

they are in the early morning. The difference could result 

from higher traffic volume in the middle of the day. It 

also could be that traffic enforcement is more rigorous at 

that time of day.

Median speeds at the I-405 location appear to have fallen 

slightly from 2004 to 2006, as gasoline prices were rising. 

CBO’s analysis estimates the influence of higher gas 

prices on vehicle speeds, controlling for other possible 

factors that also could have caused a change in median 

(and other percentile) vehicle speeds. Those factors are 

described in Appendix B.

The figure shows that median speeds at the I-405 location dropped sharply in mid-2003 and again in mid2006. Such a pattern (also found at different times at the 

other two locations) can be caused by chronic congestion 

(lasting a month or more), as from road construction. 

That data pattern also could have been caused by an 

offline vehicle detector station (in which case imputed 

data are substituted). In all such cases, CBO flagged the 

data, thus neutralizing their effect on the analysis. For 

technical reasons, CBO did not exclude those observations altogether, because doing so would have removed all 

of the contemporaneous data from the other locations.

Route 

(Direction) Location Comments

Type of 

Data

Rail 

Option?

San Diego County

I-15 (N) Scripps Poway Parkway, Poway 

(North San Diego County)

Outbound direction, 10 miles north of San Diegoc Trips No

I-5 (S) Lomas Santa Fe Drive, Solana 

Beach (North San Diego County)

Parallel to NCTD Coaster (commuter train), 17 miles 

north of San Diego (downtown)

Trips Nob

I-8 (W) Lake Murray Boulevard, 

San Diego

Adjacent, parallel to San Diego Trolley (light rail), 

7 miles east of San Diego (downtown)

Trips, Speed Yes

9. The prices are averages of nominal posted prices from a survey of 

gasoline stations around California. CBO’s analysis adjusts prices 

for inflation. Prices vary slightly by metropolitan area in this 

study, but price movements are highly correlated.
30 EFFECTS OF GASOLINE PRICES ON DRIVING BEHAVIOR AND VEHICLE MARKETS

Figure A-3.

Median Weekend Speeds on I-405, Orange County, California, and 

Gasoline Prices

(Miles per hour) (Dollars per gallon)

Sources: Congressional Budget Office based on data from the Freeway Performance Measurement Project, https://pems.eecs.berkeley.edu.

Note: Speeds were recorded at 6 a.m. and 1 p.m. each Saturday and Sunday from 2003 to 2006 by sensors located on southbound 

Interstate 405 at Newland Street, Westminster, California. Prices are nominal average California retail gasoline prices for all grades 

and formulations.

2003 2004 2005 2006

0

50

60

70

80

0

1.50

2.00

2.50

3.00

3.50

Gasoline Price

(Right scale)

Median Speed 6 a.m.

(Left scale)

Median Speed 1 p.m.

(Left scale)
APPENDIX

B

Analytical Approach and Econometric Results

For this analysis, the Congressional Budget Office 

(CBO) modeled preferred driving speeds at a given location as a function of the price of gasoline, time of day, 

month (time of year), average value of time, fixed physical characteristics of the freeway at that location (including grade, curvature, speed limit, and distance to nearest 

on- and off-ramps), and the overall demand for weekend 

travel in that month at that location. The time-of-day 

and time-of-year factors control for the effects of variation in the amount of daylight and in average weather 

conditions, as well as possible variations in the types of 

trips motorists make at different times of day or season. 

The average wage rate is a proxy for motorists’ value of 

time.1

The travel demand term captures changes over time in 

average traffic density, or the median number of vehicles 

per day on weekends at each location, per month. That 

factor controls for the effect that overall traffic density, or 

proximity to other vehicles, might have on motorists’ preferred speeds even under relatively free-flowing weekend 

driving conditions. Thus, the model estimates the effect 

of gasoline prices on vehicle speeds independently of the 

effects of increased travel demand and other factors. The 

model also includes dummy variables for high and low 

outliers associated with imputed data and for speeds that 

are slow enough to indicate possible congestion. Finally, 

as a measure of data quality, the model includes the percentage of time the vehicle detection equipment was 

online that month, in case the measurements the equipment provides are correlated with the fraction of time 

that the equipment is functioning properly.

The data are organized as a panel, with each location–

hour constituting a cross section. The main analysis 

examines the median (or 5th or 95th percentile) speeds 

for 11 one-hour periods of the day, observed over every 

Saturday and Sunday each month, at each of three locations. Thus the panel comprises 33 cross-sectional observations, with a time-step of 1 month and 48 months of 

observations. The percentile speed statistic for one cross 

section (summarizing observed speeds within a given 

hour of the day at a given location) might not be independent of that for another cross section (a different hour 

at the same location, or the same or a different hour at 

another location). CBO fit the data to an ordinary leastsquares (OLS) model of the following form:

1. Using the average gross hourly wage in the analysis yields the same 

outcome as would using the hourly wage, net of taxes (because the 

analysis is not done at the level of the individual motorist). Both 

are statewide values that vary only over time, at approximately the 

same rate: The effect of any changes in marginal tax rates on the 

average net hourly wage would be very small. (1)

yit Xitkβk + uit

k = 1

K

= 

i = 1 . . . , ,N ; t = 1 . . . , T
32 EFFECTS OF GASOLINE PRICES ON DRIVING BEHAVIOR AND VEHICLE MARKETS

but computed panel-corrected standard errors (PCSE) ûit

that allow for such a structure among the errors.2

 In 

Equation (1), the yit term is the Qth percentile (for example, the median) vehicle speed on the weekend at location–hour i in month t. N is the number of location–

hour cross sections (here, 33), T is the number of months 

observed (48), K is the number of exogenous regressors X 

in the model, and β is a vector of parameters to be estimated. The statistical significance of the fitted parameters 

 depends on the PCSE term ûit, which contains the 

square roots of the diagonal terms in the following 

expression:

In Equation (2), is an NT × NT block-diagonal matrix 

formed from the panel structure of N cross sections and T 

time periods, with each block comprising an N × N 

matrix of terms of the form eitejt, each term the product of the OLS residuals for cross sections i and j

at time t:

The results indicate that the model fits the data reasonably well, with R2

 values in excess of 0.5. Sample statistics 

are reported in Table B-1 on page 34; results are reported 

in Table B-2 on page 35.3

Total Trips

The vehicle count data have a substantially different 

structure from the data on vehicle speeds, which are 

expressed as a distribution summarizing the speeds that 

were observed each month and the frequency of occurrence for each speed. In contrast, vehicle counts are 

observed directly and pertain to travel on a single day. 

The data consist of total daily vehicle counts at each of a 

dozen locations around California on every Wednesday, 

Saturday, and Sunday from April 2003 through December 2006.4 Because of differences in capacity, local population density, and patterns of travel demand, vehicle 

counts vary substantially more from one location to the 

next than do vehicle speeds. To pool the data into a 

panel, CBO expressed the vehicle counts as a fraction of 

the baseline mean vehicle count at each location, using 

nonholiday Wednesdays in January 2003 (several months 

before the period covered in the analysis) as the baseline.

With vehicle counts, there is less likelihood of contemporaneous correlation between cross sections than there is 

with the speed data, because no two cross-sectional (location–day) counts are collected on the same day at the 

same location. Also, as the analysis shows, counts at different locations respond slightly differently to changes in 

gasoline prices, depending partly on the ease of access to 

public transportation at each location. 

Thus, CBO fit a one-way, fixed-effects model to the data, 

with a separate fixed effect for each location–day. The 

fixed effects control not only for day-of-week differences 

in volume of traffic at each location relative to the baseline but also for differences among locations in population density, proximity to residential or employment 

locations, and existence of alternative routes or modes of 

travel. As an alternative specification, a two-way, fixedeffects model would fit separate effects also for each week 

(183 weeks total). However, CBO takes the more parsimonious approach described below.

The analysis models the daily relative vehicle count at 

each location as a function of the price of gasoline, with 

separate price effects estimated for weekdays and weekends and for locations where rail transit either is or is not 

accessible. The model also allows for quadratic locationspecific trends in vehicle totals, and it includes fixed 

effects for the month and indicators for holiday travel 

(estimated separately as effects for the first, middle, or 

final day of multiple-day holiday travel periods, as appropriate); summer weekends (which can feature aboveaverage travel); nonholiday outliers; and, to control for 

differences in data quality, a continuous measure of the 

2. See Nathaniel Beck and Jonathan N. Katz, “What to Do (and Not 

to Do) with Time-Series Cross-Section Data,” American Political 

Science Review, vol. 89 (September 1995), pp. 634–647.

(2)

(3)

3. CBO also analyzed the data using the Parks method, a related but 

older and now less common technique that produces unbiased 

parameter estimates but that can underestimate the standard 

errors (thus overestimating the precision of the estimates). See 

Richard Parks, “Efficient Estimation of a System of Regression 

Equations When Disturbances Are Both Serially and Contemporaneously Correlated,” Journal of the American Statistical Association, vol. 62 (1967), pp. 500–509. In CBO’s analysis, the Parks 

method yielded qualitatively similar results.

βˆ

k

( ) X′X –1X′Ωˆ X X( ) ′X –1

Ωˆ

Σ

ˆ

ij

Σ

ˆ

ij

eitejt

t = 1

T



T = -----------------

4. Earlier data can be analyzed for most routes, but at the cost of 

excluding, for computational reasons, routes lacking any earlier 

data. However, results are generally unchanged if such routes are 

excluded from the analysis.
APPENDIX B ANALYTICAL APPROACH AND ECONOMETRIC RESULTS 33

percentage of time each vehicle detector station was not 

operating and an indicator for zero percent observed 

(meaning that the observation in question was imputed 

entirely).

Up to the error term, the model for daily total vehicles 

has same the form as that used for Equation (1). Here, 

the error term has the following form:

where, as before, the εit are mean-zero, independent, 

identically distributed errors (although in this model, the 

cross-term covariances are assumed to be zero), and is 

the fixed-effect term for each location–day cross section i.

The model fits the data well, with R2

 terms of nearly 0.9. 

A standard statistical F test strongly rejects the hypothesis 

of no route–day fixed effects, and a Hausman test 

strongly rejects an alternative specification with random 

effects as opposed to fixed effects.5 Sample statistics are 

reported in Table B-3 on page 36; results are reported in 

Table B-4 on page 37. (4) uit = νi + εit

5. See J.A. Hausman, “Specification Tests in Econometrics,” Econometrica, vol. 46, no. 6 (1978), pp. 1251–1271.

νi
34 EFFECTS OF GASOLINE PRICES ON DRIVING BEHAVIOR AND VEHICLE MARKETS

Table B-1.

Sample Means and Vehicle Speeds, January 2003 to December 2006

Source: Congressional Budget Office based on data from the Freeway Performance Measurement Project, https://pems.eecs.berkeley.edu.

Note: Hours analyzed are 6, 8, 9, and 11 a.m.; noon; and 1, 4, 5, 7, 8, and 10 p.m. Analysis results are not dependent on that specific set of 

hours.

a. Miles per hour.

b. Thousands of vehicles per hour.

c. Average monthly retail price for all grades and formulations, adjusted for inflation (base period January 2006). Data from the Department 

of Energy, Energy Information Administration.

d. Adjusted for inflation (base period January 2006). Data from the Department of Commerce, Bureau of Labor Statistics.

e. Denotes sustained periods of low- or high-speed anomalies in the data (congestion indicators capture brief, temporary slowdowns only).

5th Percentile Speeda 62.8 8.3 8.7 78.1

Median Speeda 67.8 4.1 40.1 78.8

95th Percentile Speeda 70.8 3.3 53.9 79.1

Median Traffic Densityb 4.2 1.72 0.47 7.86

95th Percentile Traffic Densityb 5.06 1.75 0.74 8.99

Real Retail Gasoline Price (Dollars per gallon)c 2.38 0.40 1.75 3.28

Real Wages (Dollars per hour)d 16.46 0.26 15.78 16.85

Daily Percent Uptime, Detector 86.1 18.6 12.5 100

Month Effects 1/12 0.3 0 1

Early Morning (6–8 a.m., by route) 0.06 0.24 0 1

Prime I (9 a.m.–1 p.m., by route) 0.12 0.33 0 1

Prime II (2–6 p.m., by route) 0.06 0.24 0 1

Evening (7–9 p.m., by route) 0.06 0.24 0 1

Night (10 p.m.–midnight., by route) 0.03 0.17 0 1

5th Percentile Speed <55 mph 0.12 0.32 0 1

Median speed <60 mph 0.02 0.14 0 1

95th Percentile Speed <65 mph 0.02 0.14 0 1

Low-Speed Outlierse 0.07 0.27 0 1

High-Speed Outlierse 0.06 0.23 0 1

Hour-Specific Monthly Percentiles (Analyzed Hours Only)

Other Continuous Variables

Indicator Variables

Congestion and Data Anomaly Indicators

Standard

Mean Deviation Minimum Maximum
APPENDIX B ANALYTICAL APPROACH AND ECONOMETRIC RESULTS 35

Table B-2.

Vehicle Speeds and Gasoline Prices, Primary Econometric Results

(Miles per hour)

Source: Congressional Budget Office based on data from the Freeway Performance Measurement Project, https://pems.eecs.berkeley.edu.

Notes: Std. = standard; ** = significant at 1 percent; * = significant at 5 percent; I = Interstate.

Hours analyzed are 6, 8, 9, and 11 a.m.; noon; and 1, 4, 5, 7, 8, and 10 p.m. Results are very similar for an analysis of entirely different 

hours, for the exclusion of prime hours, or both. Panel structure is 33 cross-sections (3 locations × 11 hours) for 48 months.

a. Average monthly price for all grades and formulations.

b. Thousands of vehicles per hour, either median (middle column) or 95th percentile densities (outside columns) for a given time of day on 

the weekend, in the same month, at a given location. Panel structure is 33 cross-sections (3 locations × 11 hours) for 48 months.

Intercept 85.2 ** 17.4 55.3 ** 16.3 61.6 ** 14.0

Real Retail Gasoline Pricea -0.024 ** 0.005 -0.015 ** 0.004 -0.001 0.004

Traffic Densityb -0.33 * 0.15 -0.49 ** 0.10 -0.14 0.10

Real Wages -1.04 1.06 0.91 1.00 0.54 0.86

I-680, San Ramon 1.20 0.92 0.94 0.60 2.44 ** 0.46

I-405, Westminster 3.61 ** 0.66 2.39 ** 0.56 3.77 ** 0.60

I-8, San Diego 3.56 ** 0.81 1.21 * 0.61 1.64 ** 0.64

I-680, San Ramon -0.14 0.63 -0.46 0.33 1.08 * 0.45

I-405, Westminster 0.23 0.83 0.90 0.65 1.54 * 0.64

I-8, San Diego 2.79 ** 0.82 1.65 ** 0.59 1.27 * 0.59

I-680, San Ramon

I-405, Westminster -0.85 1.14 0.13 0.71 0.95 0.64

I-8, San Diego 3.34 ** 0.82 1.85 ** 0.57 1.21 * 0.59

I-680, San Ramon 0.35 0.59 0.45 0.24 0.02 0.30

I-405, Westminster 2.32 ** 0.81 1.30 * 0.65 1.45 * 0.62

I-8, San Diego 2.94 ** 0.81 0.99 0.57 0.48 0.64

I-680, San Ramon 0.75 0.77 -0.15 0.47 0.19 0.43

I-405, Westminster 1.81 * 0.86 1.47 * 0.66 2.48 ** 0.63

I-8, San Diego 1.78 0.94 -0.58 0.59 -0.03 0.66

Month Effects

Congestion, Outlier Flags   ** 

Detector Percent Online 0.02 * 0.01 0.02 * 0.01 0.003 0.009

R-squared 0.718 0.613 0.523

Prime I (9 a.m.–1 p.m.)

Jointly significant Jointly significant Not significant

Std. Error

95th Percentile

(Time-of-Day × Route) Effects

Early Morning (6–8 a.m.)

Prime II (2–6 p.m.)

Median

Std. Error

5th Percentile

Speed Speed Speed Std. Error

(Omitted Factor)

Evening (7–9 p.m.)

Night (10 p.m.–Midnight)

Significance
36 EFFECTS OF GASOLINE PRICES ON DRIVING BEHAVIOR AND VEHICLE MARKETS

Table B-3.

Sample Means for Total Daily Vehicles, April 2003 to December 2006

Source: Congressional Budget Office based on data from the Freeway Performance Measurement Project, https://pems.eecs.berkeley.edu.

Note: n = 6,558

a. Includes Wednesdays on the eve of Thanksgiving, Christmas, and New Year’s Day. (There were no Wednesday holidays from April 2003 to 

December 2006.)

b. Includes Saturday of Thanksgiving week.

c. Includes Sunday of Thanksgiving week.

d. Veterans Day is a fixed-date holiday that was observed on a Friday in 2005 and 2006.

e. May 23 to September 6, the dates of the earliest Memorial Day Saturday and latest Labor Day Sunday from 2003 to 2006. Weekends 

constitute two-thirds of the data; one-third of the data fall between May 23 and September 6 because the data that CBO analyzed date 

from April 2003.

(Relative to baseline average, 1:1 = 100) 88.6 13.2 28.2 184.2

(Relative to baseline period; all grades, all formulations; 1:1 = 100) 111.2 18.6 79.4 152.5

(All grades, all formulations, dollars per gallon) 2.42 0.40 1.73 3.32

Daily Percent Uptime, Detector Station 81.5 34.8 0 100

Detector Offline All Day 0.13 0.33 0 1

Day 1 of 3-Day Holidaya 0.05 0.22 0 1

Day 2 of 3-Day Holidayb 0.05 0.22 0 1

Day 3 of 3-Day Holidayc 0.02 0.12 0 1

Veterans Day Weekendd 0.02 0.15 0 1

Summer Weekende 0.22 0.41 0 1

Total Vehicles 

Real Average Weekly Retail Gasoline Price 

Real Average Weekly Retail Gasoline Price 

Mean

Standard

Deviation Minimum Maximum

Indicator Variables
APPENDIX B ANALYTICAL APPROACH AND ECONOMETRIC RESULTS 37

Table B-4.

Total Trips and Gasoline Prices, Primary Econometric Results

Source: Congressional Budget Office based on data from the Freeway Performance Measurement Project, https://pems.eecs.berkeley.edu.

Notes: p > F = statistical significance; **= significant at 1 percent; Sat = Saturday; Sun = Sunday.

Exclusion of squared-trend terms yields similar results. Panel structure is 36 cross sections (12 locations × 3 days) for 183 weeks.

a. Relative to baseline (1:1 = 100).

b. Average weekly retail price, all grades and formulations (relative to 2003 baseline price).

Intercept 94.9 ** 1.08

Real Relative Price of Gasolineb -0.0002 0.011 0.00 0.99

Price × Weekend 0.0003 0.009 0.0001 0.00 0.99

Price × Rail -0.034 ** 0.013 -0.034 12.42 ** 0.004

Price × Weekend × Rail 0.044 ** 0.012 0.01 1.33 0.25

Summer Weekends 0.475 0.22

Day 1, Holiday Period -2.65 ** 0.26

Day 2, Holiday Period -3.16 ** 0.27

Day 3, holiday period -3.02 ** 0.48

Veterans Day Weekend 3.19 ** 0.44 0.03 (Sat), 0.17 (Sun)

Percent Uptime 0.024 ** 0.004

Location Trends 5.95 ** 0.0001

Location Squared Trends 14.78 ** 0.0001

Month Effects 27.49 ** 0.0001

Cross-Section Effects

Vehiclesa

Total Daily

Dependent Variable

Standard

Significance

All significant at 1 percent

F Test (p > F )

Jointly significant

Jointly significant

Jointly significant

Error Net Effect

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