EXPLAINING	THE	U.S.	

PETROLEUM	CONSUMPTION	

SURPRISE

June	2015

 

**Draft**
1

Contents

Executive Summary.......................................................................................................................................2

I. Introduction: Overview of the U.S. Consumption Surprise ....................................................................... 4

II. Historical and International Perspectives................................................................................................. 6

Historical Context for the Consumption Surprise..................................................................................... 6

The U.S. Consumption Surprise from an International Perspective......................................................... 7

III. Decomposing the U.S. Consumption Surprises........................................................................................ 9

Decomposition by Sector and Fuel ........................................................................................................... 9

Decomposition of the Vehicle Fuel Consumption Surprises................................................................... 13

Summary of the Consumption Surprises................................................................................................ 14

IV. A Closer Look at Vehicle Fuel Consumption .......................................................................................... 15

Decomposition of Vehicle Fuel Consumption by Vehicle Type .............................................................. 15

Gasoline Prices, Fuel Economy Standards, and Light‐Duty Fuel Consumption Rates ............................15

Why Have Household Vehicle Miles Traveled Started to Decline? ........................................................ 19

V. Conclusion...............................................................................................................................................22

References ..................................................................................................................................................23

Appendix .....................................................................................................................................................24

Comparison of EIA and Other Projections.............................................................................................. 24

Methodology for Decomposing EIA Projections..................................................................................... 25

Sum of Parts Decomposition............................................................................................................... 25

Log‐Product Decomposition ............................................................................................................... 25

Overview of the Recent Household VMT Literature............................................................................... 26

Household VMT Data .............................................................................................................................. 27

VMT Methodology ..................................................................................................................................28

   
2

Executive Summary		

U.S. oil production has transformed fundamentally in the past decade. After two decades of

steady decline, domestic crude oil production grew by nearly 75 percent between 2008 and 2014,

and each month since October 2013 production has exceeded net imports, which hadn’t

happened since the mid‐1990s. While developmentsin oil production have been widely reported

and appreciated, far less attention has been paid to the remarkable and surprising decline in U.S.

petroleum consumption relative to both recent levels and past projections. This report identifies

the mechanisms behind the surprising decline. Quantifying the underlying causes improves

understanding of why the consumption path has changed, how it might evolve in the future, and

the role that the President’s clean energy strategy and other public policies might play in further

change. We find that:

 U.S. petroleum consumption was lower in 2014 than it was in 1997, despite the fact that

the economy grew almost 50 percent over this period. Petroleum consumption peaked in

2004 and the subsequent decline was one of the biggest surprises that has occurred in global

oil markets in recent years. Actual consumption in 2014 was 6.4 million barrels per day (b/d)

below the 2003 projection of 2014 consumption, which we refer to as the 25 percent

consumption surprise for 2014. This consumption surprise is nearly twice as large as the 3.4

million b/d U.S. production surprise in 2014, and it frees up roughly $150 billion for spending

on other purposes (2014 dollars).  

 The U.S. petroleum consumption surprise is expected to grow over the next decade, from

a 25 percent downward revision in petroleum consumption in 2014 to a projected 34

percent downward revision in projected consumption for 2025, both relative to the

projections made in 2003. Just about a decade ago, petroleum consumption was expected

to increase steadily through at least 2025. After falling in actual terms, the trajectory for

consumption going forward is now expected to be flat in overall terms and declining as a

share of the economy. As a result, the petroleum consumption surprise is expected to grow

from 6.4 million b/d in 2014 to 10.0 million b/d in 2025.

 The U.S. petroleum consumption surprise exceeds that of nearly all other advanced

economies. The consumption surprise for European OECD countries is about half that of the

United States, amounting to about 13 percent in 2015 and declining slightly to a projected 12

percent in 2025. For other OECD countries, the surprises are even smaller, at 7 and 11 percent

in 2015 and 2025. The consumption surprises for Japan lie just below those of the United

States.

 The transportation sector accounts for 80 to 90 percent of the 2014 and 2025 petroleum

consumption surprises. The industrial sector accounts for most of the remainder, at around

20 percent in 2014 and 7 percent in 2025, while the residential and commercial sectors

account for a combined 4 percent of the surprise in both years. While other sectors have
3

experienced similar percentage surprises asthe transportation sector, because they consume

considerably far less petroleum, they do not contribute nearly as much to the overall story.

 Within the transportation sector, declining vehicle miles traveled (VMT) has had a greater

effect on consumption than rising fuel economy to date, but the role of fuel economy grows

through 2025. In 2014, rising fuel economy explains roughly 25 percent of the consumption

surprise, and VMT explains the remaining three quarters. In 2025, however, the factors are

more comparable, accounting for 45 percent (fuel economy) and 55 percent (VMT).

o Fuel economy standards and fuel prices explain large shares of the increase in light‐

duty fuel economy. Fuel economy standards explain up to 43 percent of the increase

in fuel economy between 2003 and 2014 and will explain an even larger portion going

forward. Higher‐than‐projected gasoline prices explain at least 17 percent of the fuel

economy increase through 2014.

o Demographics and economic factors explain most of the household VMT decrease. Our

new analysis suggests that demographics and economic factors more than fully

explain the decline and, if anything, other factors like preference shifts slightly offset

these changes and by themselves would have led to more VMT.

 The surprise reduction in the consumption of oil derived from fossil fuelsis even larger than

the surprise reduction in petroleum consumption because of the increasingly large portion

of petroleum derived from renewables like ethanol. The main analysis includes renewables

such as ethanol in total petroleum products consumption, finding that the United States

consumed 6.4 million b/d less petroleum in 2014 than projected. But 0.8 million b/d of that

consumption wasin the form of ethanol and otherrenewables, as compared to the 0.2 million

b/d of this fuel that were consumed in 2003. At the time ethanol consumption was expected

to remain constant; the increase waslargely due to the Renewable Fuels Standard. As a result,

the total surprise reduction in oil consumption is closer to 7 million b/d in 2014 when

accounting for ethanol.

The increasing importance of fuel economy over time in lowering petroleum consumption

demonstratesthe vital role of public policy in shaping energy consumption. For example, the fuel

economy standards for cars, light trucks, and heavy‐duty trucks implemented by President

Obama have already reduced the amount of petroleum that our economy is expected to

consume in coming years. More broadly, other policies championed by the President like the

Clean Power Plan, emissions reductions commitments at the G7, and tax credits for renewable

energy are helping to shift the U.S. economy to a low‐carbon future.   
4

I. Introduction: Overview of the U.S. Consumption Surprise

U.S. oil production has transformed fundamentally in the past decade. Between 1970 and 2008,

U.S. crude oil production fell by nearly half as conventional wells were depleted. Since 2008,

however, production has rebounded from 5 million barrels per day (b/d) to an average of 8.7

million b/d in 2014. The almost entirely unexpected increase—largely attributable to

technological innovationssuch as advancesin horizontal drilling, hydraulic fracturing, and seismic

imaging—has helped the United States become the world leader in oil production.  

Whereasthe developmentsin oil production have been widely reported and appreciated, far less

attention has been paid to U.S. petroleum consumption’s remarkable decline relative to both

recent levels and past projections. Petroleum consumption in 2014 was actually slightly below

consumption in 1997, despite the fact that the U.S. economy grew nearly 50 percent over the 17‐

year period. This levelling off of consumption was largely unanticipated. In 2003 the U.S. Energy

Information Administration (EIA) projected that consumption would steadily grow at an average

annual rate of 1.8 percent over the subsequent two decades; consumption in 2025 was projected

to be 47 percent percent higher than its 2003 level. The actual path that consumption has taken,

however, diverges dramatically from this projection. Consumption in 2014 was actually slightly

below 2003 consumption, and about 25 percent below the projections for 2014 that were made

in 2003. Moreover, the difference between the actual and projected consumption in 2014—6.4

million b/d—is almost double the unexpected production increase over the same period (3.4

million b/d). The surprise reduction frees up roughly $150 billion dollars for spending on other

purposes (2014 dollars). There is even greater divergence in longer‐term projections; the most

recent projection for 2025 is 34 percent lower than the projection made in 2003 (freeing up

roughly $250 billion).  

The changes in production and consumption have had significant impacts on the U.S. economy.

The dramatic turnaround in U.S. production has boosted GDP growth. The combination of

increased production and reduced consumption has reduced net petroleum imports, improving

U.S. energy security, and the reduced consumption has contributed to the nearly 10 percent

reduction in greenhouse gas emissions since 2005 (CEA 2015).

The objective of this report is to identify the mechanisms behind the large decrease in U.S.

petroleum consumption relative to past projections. Quantifying the underlying causes helps us

understand why the consumption path has changed, and also has implications for what is likely

to happen in the future and for public policy.

Petroleum consumption accounts for about 40 percent of U.S. energy‐related carbon dioxide

emissions, and projections of long‐run greenhouse gas emissions from petroleum consumption

are highly dependent on the drivers of the consumption decline. If the Great Recession were the

primary driver, for instance, we would expect petroleum consumption to return to past

trajectories as the economy recovers, causing consumption‐related emissions to rebound. On

the other hand, if fuel economy standards were the primary driver, consumption and associated

greenhouse gas emissions might continue to decline.
5

For this exploration, we focus on two specific quantities: the gap between consumption that was

projected in 2003 and actual 2014 consumption, to which we refer as the 2014 consumption

surprise; and the gap between the 2003 and 2015 projections of 2025 consumption, the 2025

consumption surprise. We focus on projections made by EIA, not to criticize those projections,

but because of EIA’s prominence as a highly‐respected source of analysis in this area. In fact, EIA

projections are qualitatively similar to other projections made at around the same time (see

Appendix). Moreover, the conclusions are broadly robust to the choice of projection year—

consumption in 2014 fell below all EIA projections made in the early 2000s. In this analysis we

focus on the 2003 projections because it is the earliest year that consistent and sufficiently

detailed data are available. This allows us to consider the full array of factors that have affected

consumption.  

Before attempting to document the source of the consumption surprises, we begin by providing

historical and international perspectives on the U.S. experience. In percentage and absolute

terms, the 2014 and 2025 surprises in the United States are much larger than those observed in

many other countries and regions of the world. By source, declining vehicle gasoline consumption

is the biggest source of both surprises, followed by industrial consumption of petroleum

products.  

Focusing on vehicle fuel consumption, we compare the effects of vehicle miles traveled (VMT)

and fuel economy. While VMT per household was projected to increase steadily with rising

income, in fact, VMT per household peaked in the mid‐2000s and has since declined slightly.

Rising fuel economy explains a smaller portion of the 2014 consumption surprise than does VMT,

but fuel economy and VMT explain similar shares of the 2025 consumption surprise,

demonstrating the growing role of public policies, like the fuel economy standards implemented

by the Obama Administration, in shaping fuel use. Throughout the anlaysis, petroleum

consumption includes fossil fuel and renewable fuels, but we also note that ethanol has further

reduced U.S. consumption of fossil fuel‐based petroleum products.

After documenting the sources of the consumption surprises, we further explore the factors

underlying the rise in vehicle fuel economy and the drop in household VMT relative to past

projections. Based on recent estimates of the effect of gasoline prices on passenger vehicle fuel

economy, we estimate that high gasoline prices—relative to 2003 projections—explain a

substantial share of the overall increase in passenger vehicle fuel economy. Fuel economy

standards explain a large and growing share of the increase in vehicle fuel economy. Changes in

demographics and economic variables appear to explain much of the change in the trajectory of

VMT, but there is also evidence that the effects of demographics on VMT have changed as well.

For example, individuals below 40 years old drove 5 percent lessin 2009 than did individuals who

were below 40 years old in 1990. Because demographics themeselves are easier to project than

the changes in their impacts, the importance of these changes presents a challenge to projecting

future petroleum consumption.
6

II. Historical and International Perspectives		

Historical Context for the Consumption Surprise

We begin the analysis by placing the consumption surprises in a historical context. Figure 1a

shows total U.S. petroleum consumption since 1949 and illustrates that consumption grew at a

steady rate from 1949 through the early 1970s, but decreased when oil prices were high in the

late 1970s and early 1980s. Consumption began rising again in 1984, but through the early 2000s

the pace of growth was much slower than it had been historically. Consumption peaked in 2004

and decreased in conjunction with rising oil prices, but since the end of the recession

consumption has been flat even though the economy has grown and oil prices have remained

steady or declined. Therefore, although economic growth is strongly correlated with petroleum

consumption growth (e.g., Hughes et al. 2008), the fact that consumption remained flat while

income grew suggests that other factors besides the recession explain the surprises.  

Figure 1a shows three broad regimes: 1) consumption and GDP grow proportionately (1949

through the early 1970s); 2) consumption grows at a slower rate than GDP (late 1980s through

the early 2000s) and 3) consumption decreases (2004 through 2014). Illustrating the dramatic

turnaround in the most recent period is the fact that petroleum consumption in 2014 was lower

than it was in 1997, despite nearly 50 percent real GDP growth over the 17‐year period. This has

been possible because, as Figure 1b shows, petroleum intensity (barrels of petroleum

consumption per dollar of GDP) has decreased steadily since the mid‐1970s.

Focusing on the third of our three regimes, 2004 to the present, and on consumption projections

for the next 10 years, Figure 2a shows a) projections made in 2003 for total U.S. petroleum

consumption through 2025; and b) actual consumption between 2003 and 2014, and updated

2015 projections for consumption between 2015 and 2025. Projections and data are from EIA’s

Annual Energy Outlook (AEO) (throughout the analysis we use the AEO Reference Case, which

contains EIA’s baseline assumptions). In 2003, EIA projected steady growth in petroleum

consumption, but actual consumption has fallen since 2004. Looking ahead, the 2015 AEO

projects that consumption will remain virtually steady through 2025. The 2014 consumption

0

5

10

15

20

25

1949 1957 1965 1973 1981 1989 1997 2005 2013

Figure 1a: U.S. Petroleum Consumption, 1949‐2014

Million Barrels per Day

Source: Energy Information Administration.

OPEC Oil

Embargo

Iranian

Revolution

1st Gulf War

2nd Gulf War

Financial

Crisis

‐120

‐80

‐40

0

40

80

120

‐1.00

‐0.75

‐0.50

‐0.25

0.00

0.25

0.50

0.75

1.00

1949 1957 1965 1973 1981 1989 1997 2005 2013

Figure 1b: Petroleum Intensity of GDP and

Real Oil Prices, 1949‐2014

Source: Energy Information Administration.

Index, 1949=0 Dollars per Barrel

OPEC Oil

Embargo

Iranian

Revolution

1st Gulf War

2nd Gulf

War

Financial

Crisis

Real Oil Prices

(right axis)

Petroleum

Intensity

(left axis)
7

surprise reflects a 6.4 million b/d reduction in petroleum consumption, relative to the 2003

projection of 2014 consumption, a 25 percent decline. The 2025 surprise amounts to a 10.0

million b/d, or 34 percent, difference between the 2003 and 2015 projections of consumption in

2025.

Figure 2b shows that the two consumption surprises have emerged because of gradual changes

in the AEO projections over time. The horizontal axis represents the AEO years 2003 through

2015. The vertical axis plots the projected consumption in the corresponding AEO year. For each

projection year, the blue curve shows projected consumption in 2015 and the red curve shows

projected consumption in 2025. Projections for 2014 consumption declined fairly steadily

between the 2003 and 2013 projection years. Projections for 2025 consumption show a similar

downward trend, with an additional noticeable drop between the 2007 and 2008 projection

years. Starting in 2008, the consumption projection flattens, despite projected growth in real

GDP, implying an ongoing decline in the petroleum intensity of U.S. output. Some of the larger

year‐to‐year declines in forecasts can be attributed to changes in fuel economy standards, which

are discussed in a later section.  

The U.S. Consumption Surprise from an International Perspective

Looking at petroleum consumption from an international perspective shows that the U.S.

experience has been different from that of Europe and many other OECD countries. Figures 3a

through 3d compare projections and actual data from the EIA International Energy Outlook (IEO),

which projects petroleum consumption for the United States and other regions. Data availability

allows us to compare the 2006 and 2014 projections for the United States, Europe, Japan, and

other OECD countries. Figure 3a shows that using the IEO data, the surprise between the 2006

and 2014 projections for 2025 consumption in the United States—i.e., the 2025 consumption

surprise—is about 27 percent, which is consistent with the AEO data used in the other parts of

this analysis. The surprises for European, as well as non‐U.S. and non‐Japan, OECD countries,

however, are less than half as large in percentage terms—just 12 and 11 percent, respectively. In

percentage terms, the 2025 surprise for Japan lies just below that of the United States. Table 1

shows the 2015 and 2025 consumption surprises for each region, in percentages.  

15

18

21

24

27

30

2000 2005 2010 2015 2020 2025

Figure 2a: Total U.S. Petroleum Consumption, 2000‐2025

Source: Energy Information Administration.

Million Barrels per Day

2003 AEO

2015 AEO

15

18

21

24

27

30

2003 2006 2009 2012 2015

Figure 2b: Changing Projections for Petroleum

Consumption in 2014 and 2025

Million Barrels per Day

Source: Energy Information Administration

Projection Year

2014 Consumption

2025 Consumption
8

0

5

10

15

20

25

30

2000 2005 2010 2015 2020 2025

Million Barrels per Day

Figure 3a: U.S. Petroleum Consumption:

Historical and Projected, 2002‐2025

2014 IEO

2006 IEO

Source: Energy Information Administration.

0

5

10

15

20

25

30

2000 2005 2010 2015 2020 2025

Million Barrels per Day

Figure 3b: Europe OECD Petroleum Consumption:

Historical and Projected, 2002‐2025

2014 IEO

2006 IEO

Source: Energy Information Administration.

0

5

10

15

20

25

30

2000 2005 2010 2015 2020 2025

Million Barrels per Day

Figure 3d: Other OECD Petroleum Consumption:

Historical and Projected, 2002‐2025

2014 IEO

2006 IEO

Source: Energy Information Administration.

0

5

10

15

20

25

30

2000 2005 2010 2015 2020 2025

Million Barrels per Day

Figure 3c: Japan Petroleum Consumption:

Historical and Projected, 2002‐2025

2014 IEO

2006 IEO

Source: Energy Information Administration.

United

States

Europe

OECD Japan

Other

OECD

2015 19.7 12.9 19.5 6.7

2025 27.3 12.1 24.0 11.3

Table 1: Percent Decline in Consumption by Region
9

III. Decomposing the U.S. Consumption Surprises

Decomposition by Sector and Fuel		

Returning to the United States, we next decompose the overall consumption surprises into the

contributions of individual sectors and fuels. Figures 4a and 4b illustrate the contributions to the

2014 and 2025 surprises of the four major energy‐consuming sectors. The two black curves show

the AEO projections from 2003 and 2015 and actual petroleum consumption (repeated from

Figure 2a).  

Total consumption equals the sum of consumption by sector, and we decompose the

consumption surprise into the effects of the individualsectors. The detailed methodology for this

“sum of parts” decomposition can be found in the Appendix, the basic notion being that we can

express the change in overall consumption as the sum of the change in consumption in each of

the individual sectors.  

Figure 4a shows the contributions of each sector to the consumption surprises. The vertical

height of the blue region is the difference between the two transportation projections, and the

interpretation isthe same for the red (industrial) and green (residential and commercial) regions.  

The sum of the vertical height of the three regions is equal to the difference between the 2003

and 2015 total consumption projections. For example, the total vertical height of the 3 regions in

2014 equals the 2014 consumption surprise. The figure shows that industrial consumption

explains a larger share of the two surprises than residential and commercial consumption, but

the transportation sector is clearly the biggest contributor (see Table 2 for the percentages of

each surprise accounted for by each sector).  

Though the transportation sector clearly represents the largest share of the 2014 and 2025

surprises, Figure 4b shows that the size of the surprises in each sector, relative to projected

consumption, are more similar to one another in 2014 than in 2025. In the transportation,

industrial, and residential and commercial sectors, between 20 and 30 percent less petroleum

was consumed in 2014 than had been projected in 2003. By 2025, however, while the size of the

surprises for transportation and residential and commercial increase to about 40 percent each,

the magnitude of the surprise actually falls for the industrial sector. Overall, the transportation

sector accounts for a large share of the 2014 surprise, and the contribution of the transportation

sector to the consumption surprise is even larger in 2025.
10

Breaking consumption down by fuel type and sector, Figures 4c and 4d show the contributions

of specific fuels to the total consumption surprises. Gasoline consumption in the transportation

sector is responsible for the biggest contribution to the surprises, accounting for a larger share

of the 2014 surprise than industrial petroleum and transportation diesel fuel combined.

Gasoline’s contribution is even greater in 2025, illustrated by the widening of the blue region.

The red and green sections, on the other hand, show that petroleum consumed in the industrial

sector and transportation sector diesel fuel consumption explain roughly equal amounts of both

the 2014 and 2025 surprises. The “other” category shows the contribution of other

transportation petroleum products, such as jet fuel, as well as petroleum products consumed in

the residential and commercial sectors. Table 3 shows the percentages of each surprise

accounted for by each fuel. Asin Figure 4b, above, Figure 4d showsthe relative size of the surprise

for each fuel. Although the size of the surprise for the “other” category is comparable to that of

gasoline, the size of the gasoline surprise increases the most of any of the fuels between 2014

and 2025.

The preceding decomposition has focused on the consumption of fossil fuel‐based fuels, and Box

1 discusses the effect of ethanol consumption on gasoline consumption. In short, actual ethanol

consumption has been higher than the 2003 projections, and ethanol consumption has reduced

fossil fuel‐based gasoline consumption. So accounting for ethanol, if anything, would make the

petroleum consumption surprise even larger than the featured magnitudes in this report.

15

18

21

24

27

30

2000 2005 2010 2015 2020 2025

Transportation

Industrial Residential +

Commercial

Figure 4a: Decomposition by Sector

Million Barrels per Day

Source: Energy Information Administration; CEA Calculations.

2003 AEO

2015 AEO

27

40

21

12

24

38

0

5

10

15

20

25

30

35

40

45

2014 2025

Transportation Industrial Residential + Commercial

Figure 4b: Percent Consumption Surprise by Sector

Percent

Source: Energy Information Administration; CEA Calculations

2014 2025

Residential and commercial 4 4

Industrial 18 7

Transportation 78 89

Table 2: Percentages of 2014 and 2025 Surpises by Sector
11

15

18

21

24

27

30

2000 2005 2010 2015 2020 2025

Transportation  

Gasoline

Industrial

Petroleum Transportation

Diesel

Figure 4c: Decomposition by Fuel in Select Sectors

Million Barrels per Day

Source: Energy Information Administration; CEA Calculations.

2003 AEO

2015 AEO

Other

28

45

16

21 21

12

34

44

0

5

10

15

20

25

30

35

40

45

50

2014 2025

Gasoline Diesel Industrial Petroleum Other

Figure 4d: Percent Consumption Surprise by Fuel

Percent

Source: Energy Information Administration; CEA Calculations

2014 2025

Transportation gasoline 50 61

Industrial petroleum 18 7

Transportation diesel 10 10

Other 21 21

Table 3: Percentages of 2014 and 2025 Surprises by Fuel
12

BOX 1: ETHANOL AND GASOLINE CONSUMPTION

In recent years, fossil fuels have been increasingly displaced with renewable fuels such as ethanol and biodiesel, which

can have energy security and environmental benefits. The displacement is largely due to the Renewable Fuels Standard

(RFS), which the EPA administers under the Energy Independence and Security Act, and which mandates usage of

increasing volumes of renewable fuels. For various reasons, our analysis focuses on total petroleum consumption, which

includes petroleum products refined from fossil fuel feedstocks, such as diesel fuel and jet fuel, as well as fuels refined

from renewable feedstocks, such as ethanol. This box discusses how displacement of fossil fuel‐based fuels by renewable

fuels fits into the rest of the story, in particular, focusing on fuel ethanol and gasoline consumption.  

Currently, most gasoline issold in the form of E10, which contains 10 percent ethanol by volume. The 2003 AEO projected

very little ethanol consumption by 2025, but the RFS and other factors have dramatically changed the outlook for ethanol

consumption. The figure on the left, below, plots the actual and projected ethanol consumption from the 2015 AEO,

illustrating both the percentage of ethanol in total consumption (left axis) and the total ethanol consumption itself (right

axis). Between 2000 and 2025, ethanol consumption is projected to increase by roughly 0.8 million b/d. Thus, the rising

ethanol consumption has caused a small but noticeable additional reduction in U.S. fossil fuel consumption. The figure

on the right shows the same decomposition as in Figure 4c, but illustrates the ethanol displacement assuming ethanol

displaces gasoline one‐for‐one on an energy‐equivalent basis. Unlike the surprises for other fuels and sectors that we

discuss, the ethanol surprise is negative meaning that we actually consumed more ethanol than had been expected. This

impliesthat, excluding ethanol, the size of the overall petroleum consumption surprise isincreased by the amount shown

in the green shaded region—a roughly 0.6 million b/d addition to each of the 6.4 million b/d and 10.0 million b/d surprises

in 2014 and 2025.  
13

Decomposition of the Vehicle Fuel Consumption Surprises

Because vehicle fuel consumption contributes so much to the 2014 and 2025 surprises (Figure

4), we turn our attention to vehicle fuel consumption. We define the 2014 and 2025 vehicle fuel

consumption surprises analogously to the petroleum consumption surprises, as the difference

between projected and actual fuel consumption in 2014 and the difference between 2003 and

2015 projections for fuel consumption in 2025.

We begin by breaking down these surprises into two factors: 1) rising vehicle fuel economy; and

2) changes in VMT. The fuel consumption rate, in gallons per mile, is the reciprocal of fuel

economy, in miles per gallon; an increase in fuel economy implies a reduction in the fuel

consumption rate. To extract the relative contribution of each of the two factors to the change

in vehicle fuel consumption, we employ what we refer to as a “log‐product decomposition.” The

Appendix provides the details of the methodology, but, generally speaking, the calculation

exploits the fact that, by definition, fuel consumption can be written as the product of VMT and

the fuel consumption rate. Using properties of the natural logarithm, this definition allows us to

rewrite the log of the fuel consumption surprise as the sum of the change in log VMT over time

and the change in the log fuel consumption rate over time, thereby illustrating the factors’

relative contributions to the log consumption change.  

Figure 5a illustrates this decomposition. The vertical axis is the log of transportation sector

gasoline and diesel fuel consumption. The light blue section in Figure 5a shows the contribution

of VMT to the fuel consumption surprise, and the dark blue section shows the contribution of

fuel consumption rates. Comparing the two factors, VMT explains a larger share of the 2014

surprise than does the fuel consumption rate. However, the effect of the fuel consumption rate

increases over time. Table 4 shows that the fuel consumption rate explains 25 percent of the

2014 consumption surprise and about 45 percent of the 2025 consumption surprise (the

percentages refer to the ratio of the vertical height of the corresponding region to the total log

consumption surprise, multiplied by 100).  

2

2.5

3

2000 2005 2010 2015 2020 2025

Figure 5a: Transportation Sector Fuel Consumption Rate

and VMT Decompositions

Source: Energy Information Administration; CEA Calculations.

Log Million Barrels per Day

2003 AEO

2015 AEO

Fuel Consumption Rate

VMT

8

10

3

8

0

2

4

6

8

10

12

2014 2025

VMT Fuel Consumption Rate

Figure 5b: Percent Transportation Consumption Surprise by

VMT and Fuel Consumption Rate

Percent

Source: Energy Information Administration; CEA Calculations.
14

Summary of the Consumption Surprises		

Summarizing the results so far, Figure 6 illustrates the contributions of each sector to the overall

petroleum consumption surprises. In the figure, the transportation sector petroleum

consumption surprise is decomposed further into the vehicle fuel consumption rate, VMT, and

other factors such as aircraft consumption (as an approximation, for the transportation sector

we multiply the percentages in Figure 5b by the total gasoline and diesel fuel contribution in

Figure 4c). In 2014, VMT is the single most important factor, accounting for just under half of the

total petroleum consumption surprise. The importance of the fuel consumption rate doubles (in

percentage terms) between 2014 and 2025. Because the transportation sector accounts for 80‐

90 percent of the two consumption surprises, and vehicle fuel consumption dominates

consumption of other fuels in the transportation sector, the remainder of this report focuses on

the factors that explain vehicle fuel consumption rates and miles traveled.  

2014 2025

VMT 75 55

Fuel Consumption Rate 25 45

Table 4: Percentages of 2014 and 2025 Transportation Fuel

Consumption Surprises by VMT and Fuel Consumption Rate

VMT

Fuel Consumption

Rate

Other

Transportation

Industrial

Res. &

Comm.

0

20

40

60

80

100

2014 2025

Figure 6: Overview of Contributions to the

Percent 2014 and 2025 Consumption Surprises

Source: Energy Information Administration; CEA Calculations.

45 40

15

17

32

17

18

7

44

4

4
15

IV. A Closer Look at Vehicle Fuel Consumption		

Decomposition of Vehicle Fuel Consumption by Vehicle Type

We can further refine the focus on transportation sector fuels by decomposing the fuelssurprises

into the contributions by vehicle type—light, medium, and heavy‐duty. These categories are

defined based on vehicle weight, with light‐duty vehicles weighing below 8,500 pounds and

heavy‐duty vehicles weighing above 10,000 pounds. This decomposition is similar to the sectoral

decomposition above, using the “sum of parts” decomposition described in the Appendix. Figure

7a shows the results of this decomposition and indicates that, at around 90 percent, light‐duty

vehicles make a far greater contribution to the consumption surprises than do the other vehicle

types (see Table 5). That said, as Figure 7b illustrates, the magnitude of the surprise as a

percentage of consumption for light‐duty vehicles was actually slightly smaller than the size of

the percentage for medium‐duty trucks. With the new fuel economy standards for heavy‐duty

trucks announced in June 2015, fuel consumption by vehicle type may further change in the

coming years.

Gasoline Prices, Fuel Economy Standards, and Light‐Duty Fuel Consumption

Rates

Because of the importance of light‐duty vehicles in explaining the consumption surprises (Table

5) the remainder of the analysis focuses on fuel consumption by light duty vehicles. As discussed

above, fuel consumption is the product of VMT and the fuel consumption rate. In this section we

investigate the underlying causes behind the decrease in the fuel consumption rate relative to

5

10

15

20

2000 2005 2010 2015 2020 2025

Figure 7a: Decomposition of Motor Vehicle Fuel

Consumption by Vehicle Type

Source: Energy Information Administration; CEA Calculations.

Million Barrels per Day

2003 AEO

2015 AEO

Heavy

Light

Medium

28

45

37

50

7

14

0

10

20

30

40

50

60

2014 2025

Light Medium Heavy

Figure 7b: Percent Motor Vehicle Fuel

Percent Consumption Surprise by Vehicle Type

Source: Energy Information Administration; CEA Calculations.

2014 2025

Light 89 89

Medium 5 4

Heavy 6 7

Table 5: Percentages of 2014 and 2025 Motor Vehicle Fuel

Consumption Surprises by Vehicle Type
16

projections, and the next section focuses on changes in light‐duty VMT. Of course, the two can

be interrelated; for example higher gasoline prices would be expected to increase fuel economy

and reduce VMT.

For background, Figure 8a shows actual and projected fuel consumption ratesfor the entire light‐

duty fleet between 2000 and 2014. Whereas the EIA projected a steady increase in the fuel

consumption rate, the actual fuel consumption rate has decreased since 2003. Figure 8b shows

that real gasoline prices began increasing—both in absolute terms and relative to the 2003

projection—around 2003, decreased in 2008, and then increased to roughly pre‐recession levels

from 2009‐2014. We briefly discussthe factorsthat could cause light‐duty fuel consumption rates

to fall below 2003 projections. Box 2 provides background on vehicle fuel economy standards in

the 2000s, which caused fleet‐wide light‐duty vehicle fuel economy to increase starting in 2005

and caused fleet‐wide medium and heavy‐duty vehicle fuel economy to increase starting in 2014.

Changes in gasoline prices may have affected the fleet‐wide average vehicle fuel economy since

2003 by inducing: a) households to scrap older vehicles with low fuel economy; b) households to

purchase new vehicles with high fuel economy and c) manufacturers to increase the fuel

economy of the vehicles they offer. The gasoline price increase since 2003 may have raised new

vehicle fuel economy for all three reasons.

0.00

0.50

1.00

1.50

2.00

2.50

3.00

2000 2002 2004 2006 2008 2010 2012 2014

Figure 8b: Real Price of Motor Gasoline

1996 Dollars per Gallon

Source: Energy Information Administration.

Actual

Projected

4.2

4.4

4.6

4.8

5

5.2

5.4

2000 2002 2004 2006 2008 2010 2012 2014

Figure 8a: Light‐Duty Fleet Average Fuel Consumption Rate

Gallons per 100 Miles

Source: Energy Information Administration.

Actual

Projected
17

In principle, other factors besides fuel prices and fuel economy standards may have affected

vehicle fuel economy over this time period. For example, because vehicles with high fuel

economy tend to be smaller and less expensive than vehicles with low fuel economy, the Great

Recession may have induced consumersto purchase vehicles with higher fuel economy than they

would have otherwise. An example of other policiesthat may have affected fuel economy is Cash‐

for‐Clunkers, which President Obama implemented in July and August of 2009, offering subsidies

to consumers who scrapped old vehicles and purchased new vehicles with high fuel economy.

Isolating the independent effects of gasoline prices, fuel economy standards, and these other

factors is challenging and we are not aware of any research that has attempted to do this.

BOX 2: VEHICLE FUEL ECONOMY STANDARDS

In 2012, the Administration finalized light‐duty fuel economy standards that, together with the Administration’s first

round of standards (implemented in 2011), will roughly double fuel economy of light‐duty vehicles from 2010 levels. The

figure below shows that fuel economy standards for cars and light trucks were roughly constant between the mid‐1980s

and the mid‐2000s, at about 27.5 miles per gallon (mpg) for cars and 20.7 mpg for light trucks. The Department of

Transportation began increasing the standards for light trucks in 2005, and the light‐truck standard reached 24.1 mpg in

2011. Starting in 2011, the Environmental Protection Agency and Department of Transportation began jointly regulating

greenhouse gas emissions rates and fuel economy of new light‐duty vehicles. The new car and light‐truck standards will

be fully phased in by 2025, and are expected to achieve an average fuel economy of 47 mpg in 2025.

Fuel economy standards for medium and heavy‐duty trucks, first applied in 2014, are tightening through 2018, and post‐

2018 standards are currently under development. The standards are projected to increase fuel economy by 10 to 20

percent by 2018. Because they first applied in 2014, these standards should not affect the 2014 surprise, but they could

affect the 2025 surprise because they would not have been part of the 2003 AEO projection, but are included in the 2015

projection.
18

However, we can obtain some rough estimates of the independent effects of gasoline prices and

fuel economy standards on average fuel consumption rates.

A recent literature has linked gasoline prices and vehicle fuel economy, which allows us to

estimate the contribution of gasoline prices to the declining fuel consumption rate displayed in

Figure 8a. Klier and Linn (2010) and Busse et al. (2013) estimate that a gasoline price increase of

$1 per gallon raises new vehicle fuel economy by approximately 1 mile per gallon. Li et al. (2009)

estimate that a 10 percent gasoline price increase raises average fleet fuel economy by 0.2

percent in the short run and by 2 percent in the long run.

These estimates allow us to quantify the contribution of gasoline prices to changes in fuel

consumption rates over time. For each year, we multiply the difference between the actual and

projected gasoline price from Figure 8b by the estimated effect of gasoline prices on new vehicle

fuel economy. We estimate retirement rates from the full fleet using EIA data on the size of the

fleet and new vehicle sales, and assume that gasoline prices do not affect the average fuel

consumption rate of retired vehicles. This represents a conservative assumption because, in

reality, an increase in gasoline prices is likely to delay scrappage of vehicles with low fuel

consumption rates (Jacobsen and van Benthem 2015). Combining these estimates and

assumptions, we compute the effect of the gasoline price changes from 2003‐2014 on the fleet‐

wide average consumption rate. We find that gasoline prices explain at least 17 percent of the

decline in 2014 fuel consumption rate relative to the 2003 projection. Using the same approach,

we estimate that gasoline prices explain at least 11 percent of the difference between the 2003

and 2015 projections of the fuel consumption rate in 2025.

For comparison, using the same set of assumptions on vehicle retirements, combined with the

actual fuel economy standards for cars and light trucks, we estimate the effect of the standards

on average fuel consumption rates. This estimate represents an upper bound if we assume that—

absent any deviations from projections in gasoline prices, income, or other factors that affect

vehicle fuel economy—actual average fuel consumption rates would have been equal to the 2003

projection shown in Figure 8a. This is an upper bound because higher‐than‐projected gasoline

prices have likely contributed to the fuel economy increase, as the preceding calculations

suggest. We estimate that the fuel economy standards explain up to 43 percent of the overall

decrease in fuel consumption rates between 2003 and 2014. The gasoline price estimates imply

that gasoline prices may explain much of the remainder, but other factors such as the recession

may have played a role. Nonetheless, if gasoline prices remain low and stable, as is currently

projected, tightening passenger vehicle standards between 2015 and 2025 will become

increasingly important and will continue to reduce average fuel consumption rates. In fact, the

standards are responsible for up to 89 percent of the overall decrease in the projected 2025 fuel

consumption rate that occurred between the 2003 and 2015 projections; Table 6 summarizesthe

results.
19

  

Why Have Household Vehicle Miles Traveled Started to Decline?

People are driving less than they used to, and economic developments do not fully explain this

phenomenon. This reduction in VMT was largely unexpected by the 2003 AEO model which,

based on past history, projected VMT largely based on income and unemployment. As Figure 9

shows, however, starting in the late 1990s there has been a striking divergence between VMT

per licensed driver and the paths of per capita disposable income and employment.  

The dramatic change in the VMT trajectory has been noted by researchers and the public. In

2014, EIA published a short study examining changes in light‐duty VMT. Although EIA concluded

that economic factors still play a dominant role in driving behavior, shifting demographic,

technological,social, and environmental factors may have caused a decoupling of travel behavior

and traditional correlates. In general we are aware of very little quantitative analysis of this

phenomenon, and only a few studies have asked whether changesin preferences, demographics,

or economic factors are the main explanations.

A popular demographic hypothesis hasto do with the aging population. The elderly typically drive

fewer miles than younger adults, and the share of elderly in the population has increased over

the past decade as the baby boom generation has aged. The aging population could help explain

the slowdown and reversal of VMT growth. Another possibility is that driving patterns,

2014 2025

Fuel prices

(lower bound) 17 11

Fuel economy standards

(upper bound) 43 89

Table 6: Percentages of 2014 and 2025 Fuel Consumption Rate

Improvements from Fuel Prices and Fuel Economy Standards

0.5

0.7

0.9

1.1

1.3

1.5

1970 1980 1990 2000 2010

Figure 9: VMT, Income, and Employment, 1970‐2014

Index, 1995=1

Note: Adapted from EIA (2014).

Source: Federal Highway Administration; Bureau of Labor Statistics;

Bureau of Economic Analysis.

VMT per Driver

Real Disposable Income

per Capita

Non‐Farm

Employment
20

conditional on age and other demographics, have changed. For example some have speculated

that “millennials” (commonly defined asindividuals born after 1980, CEA 2014a) may drive fewer

milesthan young adults did in past generations because of culturalshifts. Other possible changes

in behavior that affect household VMT are tied to the internet: greater online shopping, for

example, reduces shopping trips (and may induce a partially offsetting increase in commercial

truck miles traveled); social networking reduces travel associated with social visits; and greater

telecommuting reduces driving too and from work.

Economic factors such as gasoline prices, income, or employment status could also affect the

amount people drive. Figure 8b illustrated that gasoline prices have been higher than previously

projected. In addition, real incomes for many households fell in the 2001 to 2007 recovery and

then dramatically fell during the most recent recession. In recent years, the sharp decline in

employment during the Great Recession and its aftermath may also have affected levels of travel

(CEA 2014b), as commuting to and from work accounts for roughly one‐quarter of VMT.

Although recent studies on household VMT have identified some correlations between VMT and

economic factors and demographics, the studies have not clarified whether changes in

demographics and economic factors explain the changes in VMT (the Appendix contains a more

detailed discussion of the literature). More specifically, do the changes in demographics

themselves,such asthe aging of the U.S. population, explain the turnaround? Or have the effects

of demographics and economic factors on VMT changed, such as a decrease in driving by

millennials compared to previous generations of young adults?  

CEA used panel data for the driving patterns of households from 1990 through 2009. The

Appendix describes the analysis in greater detail. The analysis projects the path of VMT since

1990 given only information on demography and economic factors like income and gasoline

prices. Absent such information a linear trend would have simply predicted a continued increase

in VMT from 1990 forward as shown in Figures 10a and 10b. But because of the aging of the

population and other demographic factors, the model predicts that VMT levels out in the late

1990s and starts to fall after 2001. The age structure explains a large share of this turnaround, as

changes in the age distribution between 1990 and 2009 roughly offset the growth in real income

and vehicle ownership over the same time period. But changing demographics and economic

factors are not the entire story. In fact, actual VMT was slightly higher than this prediction

suggesting that, if anything, the factors that are omitted from this model—including preference

changes—have led to more driving and not less. There is also some evidence that driving behavior

by age group has changed. For example, in 2009 individuals below 40 years old drove 5 percent

less than did individuals below 40 years old in 1990. Thus, using data from 1990 to 2009, we find

that changes in demographics and economic factors explain much, but not all, of the VMT

turnaround. Nevertheless, more recent household data than are currently available may indicate

larger changes in driving behavior by demographic group, or greater effects of telecommuting

and online shopping on household VMT and overall petroleum consumption; these are questions

for future research.
21

15

20

25

1990 1995 2000 2005 2010

Figure 10a: Actual and Predicted VMT per Household Using

Changes in Household Demographics and Economic Factors

Thousand Miles

Source: National Household Travel Survey; CEA Calculations.

Linear Trend

Actual

Predicted

8

9

10

11

1990 1995 2000 2005 2010

Figure 10b: Actual and Predicted VMT per Person Using

Changes in Household Demographics and Economic Factors

Thousand Miles

Source: National Household Travel Survey; CEA Calculations.

Linear Trend

Actual

Predicted
22

V. Conclusion

This report documents the surprising decline in U.S. petroleum consumption, both in absolute

terms and relative to past projections. We show that the transportation sector explains most of

the surprise between previous projections of petroleum consumption and actual data, as well as

the surprise between previous and the most recent projections. Miles traveled by passenger

vehicles plays an important role in the transportation sector, but rising fuel economy (and

declining fuel consumption rates) has a large influence that is growing over time.  

Between 2003 and 2014 gasoline prices explain a large share of the fuel economy increase of the

light‐duty fleet. However, over time we expect fuel economy standards to have a growing

influence on fuel economy—both for the light and heavy‐duty fleets. Fuel consumption from the

heavy‐duty fleet accounts for one‐fifth of total transportation sector consumption, and is the

fastest growing component of the transportation sector. New heavy‐duty fuel economy

standards that the Administration is announcing will reduce actual transportation sector

consumption relative to even the 2015 projections, which do not reflect these new standards.

Given the importance of milestraveled in explaining the surprises, we use household survey data

to present new evidence on the factors underlying the changes. Demographics appear to explain

a very large portion of the VMT developments, but we also find evidence that the effects of

demographics and economic variables on VMT have changed over the past 20 years. Such

changes present a major challenge to projecting future petroleum consumption.

Turning to ethanol, we report a large unexpected increase in ethanol consumption relative to

2003 projections. Given the large and growing importance of fuel economy standards and

ethanol, this analysis suggests that Federal policy has and will continue to have a substantial

effect on U.S. petroleum consumption and on overall carbon emissions reductions by the

transportation sector.

As discussed above, President Obama’s clean energy strategy has made concrete strides to

reduce petroleum consumption and shift the U.S. economy to a low carbon future through

policies like fuel economy standards and tax credits for renewable energy (CEA 2015). The

President remains deeply committed to providing a clean and secure energy future for our

children and will continue to act to reduce carbon emissionsthrough policieslike the Clean Power

Plan and engagement with international allies.
23

References

Baxandall, P. (2013). Moving off the Road: A State‐by‐State Analysis of the National Decline in

Driving.

Blumenberg, E., B. D. Taylor, M. Smart, R. Kelcie, M. Wander, and S. Brumbagh (2012). What’s

Youth Got to Do with It? Exploring the Travel Behavior of Teens and Young Adults.

University of California Transportation Center, UCTC‐FR‐2012‐14.

Busse, M., C. Knittel and F. Zettelmeyer (2013). Are Consumers Myopic? Evidence from New and

Used Car Purchases. American Economic Review, v103: 220‐256.

Council of Economic Advisers (CEA, 2014a). 15 Economic Facts About Millennials.

CEA (2014b). The Labor Force Participation Rate Since 2007: Causes and Policy Implications.

CEA (2015). The Economic Report of the President.

Energy Information Administration (EIA, various years). Annual Energy Outlook.

EIA (2014). Light‐duty vehicle energy demand: demographics and travel behavior.

Hughes, J. E., C. R. Knittel, and D. Sperling (2008). Evidence of a Shift in the Short‐Run Price

Elasticity of Gasoline Demand. The Energy Journal, v29: 93‐114.

Jacobsen, M. and A. van Benthem (2015). Vehicle Scrappage and Gasoline Policy. American

Economic Review, v105: 1312‐38.

Klier, T. and J. Linn (2010). The Price of Gasoline and New Vehicle Fuel Economy: Evidence from

Monthly Sales Data. American Economic Journal: Economic Policy, v2: 134‐53.

Li, S., C. Timmins and R. H. von Haefen (2009). How Do Gasoline Prices Affect Fleet Fuel Economy?

American Economic Journal: Economic Policy, v1: 113‐137.

Li, S., J. Linn, and E. Muehlegger (2014). Gasoline Taxes and Consumer Behavior. American

Economic Journal: Economic Policy, v6: 302‐42.

McGuckin, N. (2014). Analysis Brief: Trends in VMT.   
24

Appendix

Comparison of EIA and Other Projections

This report focuses on the EIA petroleum consumption projections because EIA’s projections are

some of the most influential and well‐regarded. Importantly, the projections of rising petroleum

consumption that we remark upon in this analysis were not unique to EIA. At around the same

time as the EIA published the 2003 AEO, other organizations made similar projections of global

petroleum consumption, several of which are shown in Table A1. At the individual country level,

the reports are not standardized in their inclusion of changes in inventories to country

consumption, so, for the purpose of comparing the projections, we focus on global consumption.

Although EIA’s projected annual growth rate for consumption is at the high end of the range of

projections, most organizations expected similar increases of around 2 percent per year. Despite

the differences in projected growth rates, the projected 2020 consumption levels are quite

similar across the projections shown in the table.

2010

(million b/d)

2020

(million b/d)

2000‐2020*

(percent

annual

growth)

2010‐2020

(percent

annual

growth)

Source

International Energy

Agency (IEA) 90 106 1.8 1.8

World Energy Outlook

(2004)

Energy Information

Administration (EIA) 91 110 1.8 2.1

International Energy

Outlook (2004)

Organization of the

Petroleum Exporting

Countries (OPEC)

89 106 ‐‐ 1.9 Oil Outlook to 2025

Institute of Energy

Economics, Japan

(IEEJ)

‐‐ 102 1.9 ‐‐ Asia/World Energy

Outlook (2004)

European

Commission

87 104 1.9 2

World Energy,

Technology, and

Climate Policy Outlook

(2003)

*Average annual growth rate from 2000‐2020, except IEA, which is 2002‐2020.

Source: IEA (2004), Comparison of World Oil Demand Projections.

Table A1: Comparison of World Oil Demand Projections
25

Methodology for Decomposing EIA Projections		

We provide additional details on the decompositions of the EIA projections. There are two types

of decompositions: a decomposition into the sum of parts and a log‐product decomposition. We

use the first decomposition in cases where total consumption is expressed as the sum of two or

more terms, such as a decomposition into consumption by sector. We use the second

decomposition in cases where consumption is expressed as the product of two terms, such as

the product of VMT and the fuel consumption rate.

Sum of Parts Decomposition

We use a sum of parts decomposition when consumption is expressed asthe sum of consumption

across components. We express the 2003 projection of consumption in year ݐ, ܥ଴ଷ;௧, as:

ܥ଴ଷ;௧ ൌ ܶ଴ଷ;௧ ൅ ܰ଴ଷ;௧ ൅ ܴ଴ଷ;௧,                 (A1)

where ܥ଴ଷ;௧ is the 2003 projection of petroleum consumption in year ݐ ;ܶ, ܰ, and ܴ represent

consumption by the transportation, industrial, and commercial plus residential sectors. Similarly,

the 2015 projection of petroleum consumption in yearݐ is ܥଵହ;௧. We decompose the consumption

surprise in year ݐ,	ܥ଴ଷ;௧ െ	ܥଵହ;௧ as:

ܥ଴ଷ;௧ െ	ܥଵହ;௧ ൌ ൣܶ଴ଷ;௧ െ	ܶଵହ;௧൧ ൅ ൣܰ଴ଷ;௧ െ	ܰଵହ;௧൧ ൅ ሾܴ଴ଷ;௧ െ	ܴଵହ;௧ሿ.     (A2)

The three terms in square brackets show the contributions of the individual sectors to the

consumption surprise. That is, the consumption surprise in year ݐ, ܥ଴ଷ;௧ െ	ܥଵହ;௧, is equal to the

sum across the three sectors of the difference between the 2003 and 2015 projections.  

Log‐Product Decomposition

By definition, the 2003 projection of vehicle fuel consumption in year ݐ can be written as the

product of VMT and the fuel consumption rate:  

ܥ଴ଷ;௧ ൌ ܸ଴ଷ;௧ܨ଴ଷ;௧,

where ܸ଴ଷ;௧ is VMT and ܨ଴ଷ;௧ is the fuel consumption rate in gallons per mile (i.e., the reciprocal

of fuel economy, measured in miles per gallon). Taking the natural logarithm of the 2003

projection of fuel consumption in year ݐ and subtracting the natural logarithm of the 2015

projection of fuel consumption in year ݐ, we decompose the difference between the 2003 and

2015 consumption projections into the contributions of VMT and the fuel consumption rate:  

lnሺܥ଴ଷ;௧ሻ െ ln	ሺܥଵହ;௧ሻ ൌ ሾln൫ܸ଴ଷ;௧൯ െ ln	ሺܸଵହ;௧ሻሿ ൅ ሾln൫ܨ଴ଷ;௧൯ െ ln	ሺܨଵହ;௧ሻሿ.     (A3)

The left‐hand side of equation (A3) is the surprise between the 2015 and 2003 log consumption

projections for year ݐ. The first term in square brackets on the right‐hand side is the contribution

of VMT to the consumption surprise; and the second term is the contribution of the fuel

consumption rate to the consumption surprise. Forsmall differences between the 2003 and 2015
26

consumption projections, the difference in the log of the two projections is approximately equal

to the percentage by which the 2003 projection exceeds the 2015 projection, divided by 100.  

Equation (A3) shows that, for small differences, the consumption surprise is approximately equal

to the percentage difference between the two VMT projections, plus the percentage difference

between the two fuel consumption rate projections (i.e., the percentage divided by 100). Note

that for larger changes, the difference in logs overstates the percentage differences.

Overview of the Recent Household VMT Literature

Thissection summarizesthe few existing quantitative studies of which we are aware that attempt

to explain the recent decrease in household VMT. Baxandall (2013) documents a decrease in VMT

per person since 2004, both on average and for millennials compared with other age groups.

Most states have experienced a decline in average VMT per person and Baxandall analyzes

whether economic or demographic factors explain the overall decline across states. The paper

reports weak correlations between the change in a state’s average VMT per person between

2005 and 2011, and factors such as unemployment rates, labor force participation rates (the

share of working‐age population in the workforce or seeking employment), and urbanization

rates. The weak correlations suggest that demographics and economic conditions alone do not

explain the overall decrease in VMT. Indeed, the paper is not able to identify which factors have

caused this decline, which may be due to the poor measurement ofstate‐level VMT (Li et al. 2014

discuss the VMT data quality).  

Blumenberg et al. (2012) ask whether travel behavior for young adults has changed relative to

older adults. That is, younger adults typically drive fewer milesthan older adults, and the authors

explore whether this difference is larger for millennials than for previous generations. Rather

than examining VMT, their analysis focuses on person miles traveled, which is the total annual

miles each individual travels. They conclude that economic factors such as income and

employment have a positive and fairly stable effect over time on person milestraveled. They also

find evidence that individuals born in the 1980s drive 8 percent fewer milesthan individuals born

prior to the 1980s after controlling for observable differences; individuals born in the 1990s drive

even less—18 percent fewer miles than previous generations.  

However, the results do not directly address the question of why VMT has decreased. Although

person milestraveled is highly correlated with VMT, factorsthat affect person milestraveled may

not affect VMT, and vice versa. For example, if millennial teenagers are less likely to accompany

their parents on tripsthan previous generations of teenagers, person milestraveled forteenagers

would fall whereas VMT may not change. Therefore, the estimated differences in person miles

traveled across cohorts do not necessarily translate to differences in household VMT caused by

differences in driving preferences for millennials compared to other adults. More importantly,

although the changes in person miles traveled for teenagers and young adults are striking, the

report does not quantify how much these changes contribute to the overall household VMT

trends.
27

Finally, McGuckin (2014) notes that VMT per capita decreased substantially between 1995 and

2009 for individuals below 40 years old. In addition, the greatest decline in driving for young

people occursin urban areas, and has been more pronounced for men than for women. However,

labor force participation rates have declined at a faster rate for men than for women (CEA 2014b)

and McGuckin notesthe need to control for economic factorsto determine whether these trends

reflect shifts in preferences among young adults, or economic factors.

Household VMT Data

We describe in greater detail the National Household Travel Survey (NHTS) data used for the VMT

analysis. We use the 1990, 1995, 2001 and 2009 surveys (the 1990 and 1995 waves are referred

to as the National Personal Travel Survey). We harmonize the demographic and income variables

across survey years and for each survey we include all household observations with non‐missing

demographic variables. For households with missing income levels, we impute household income

using the mean income of other households in the same state and demographic category

(defined below). Table A2 reportsthe number of householdsincluded in the finalsample for each

year, the share of sample households in total households, and the share of sample households

with imputed income.  

The 1990 survey reports total household VMT, and in other survey years we compute household

VMT as the sum of VMT across the household’s vehicles. A few other variables, including MSA

size, population density, and race of the survey respondent, are coded differently across survey

years. Typically, the 1990 survey includes the fewest categories, in which case we aggregate

across categories for other survey years.

Because the NHTS reports household income by category (0‐$5,000, $5,000‐10,000, etc.) rather

than as a number, accounting for inflation represents the main challenge to harmonizing income

across survey years. We address this challenge using the following procedure. First, we assign to

each household an income equal to the midpoint of its income range—for example, households

with income in the range $20,000 – 25,000 are assigned an income of $22,500. Second, we assign

each household to a demographic category based on its number of household members, number

of workers, Census division, MSA size, and the race and education level of the reference person.

We compute the mean income across households in each category and year, and assign that

income level to the household, as its imputed income. Using the consumer price index for all

urban consumers from the Bureau of Labor Statistics, we convert the imputed income to 1990

1990 1995 2001 2009

Number of households in final

sample 17,594 33,958 58,997 144,305

Share of surveyed households in

final sample

0.79 0.81 0.85 0.96

Share of sample households with

imputed income

0.23 0.15 0.08 0.08

Table A2: NHTS Sample Size by Survey Year
28

dollars and assign households to income categories based on their imputed income in 1990

dollars.  

Note that alternatively we could convert the boundaries of the income categoriesto 1990 dollars,

and reassign households based on the income range in 1990 dollars. For example, households

surveyed in 2009 that have nominal income between $5,000 and $10,000 have income in 1990

dollars between roughly $3,000 and $6,000. We could assign 2009 households in the $5,000 –

10,000 income range based on their nominal income to the 0 – $5,000 based on their income in

1990 dollars. That is, after the reassignment the 1990 households with income between 0 and

$5,000 would be in the same final income category as 2009 households with income between 0

and $10,000. Therefore, this approach assigns all households in a particular nominal income

category to the same final income category. Our preferred approach, in contrast, accounts for

the fact that households with certain demographics (e.g., having two workers rather than one)

are more likely to be at the upper end of their nominal income range than are other households;

our reassignment accounts for this tendency by using imputed income rather than the income

range to reassign households to income categories.

VMT Methodology

We use a Oaxaca‐Blinder style decomposition to distinguish the effects of economic factors and

demographics on average VMT per household or person. The decomposition consists of two

steps, and the first is to specify VMT per household as a linear function of explanatory variables:

ܸ௜௧ ൌ ߙ௧ ൅ ܺ௜௧ߚ௧ ൅ ߝ௜௧.                 (A4)

In equation (A4), the dependent variable is the total VMT for household ݅ in survey year ݐ, ߙ௧ is a

(possibly time‐varying) intercept, ܺ௜௧ includes a set of independent variables described next, ߚ௧

is a vector of coefficients to be estimated, and ߝ௜௧ is an error term. The independent variables

include economic factors (income and gasoline prices); geographic and urbanization factors (size

of the Metropolitan Statistical Area [MSA], an urban area indicator, Census division, and

population density); number of drivers, number of vehicles, and number of workers in the

household; and the age and education level of the survey respondent. All of these variables,

including income, are categorical, and equation (A4) includes a fixed effect (that is, a distinct

dummy variable) for each set of categorical variables. We also include the interactions of the

MSA size with the urban indicator. Importantly for the subsequent decompositions, a separate

set of coefficients,	ߚ௧, is estimated each survey year. We estimate equation (A4) weighting

observations using the household survey weights, so that the coefficients represent national

sample averages. We perform an analogous estimation using VMT per person as the dependent

variable in equation (A4), which is computed as the total household VMT divided by the number

of people living in the household.

In the second step, we use the estimates of equation (A4) to predict VMT per household each

survey year, ܸ෠

௜௧. The change in predicted VMT per household between time ݐ and ݐ ൌ 1990 is:
29

ܸ෠

௜௧ െ ܸ෠௜;ଽ଴ ൌ ൣܺ௜௧ െ ܺ௜;ଽ଴൧ߚመଽ଴ ൅ ܺ௜௧ሾߚ௧

෡ െ ߚ෢ଽ଴ሿ           (A5)

The first term on the right‐hand side represents the effect of the change in economic factors and

demographics on average VMT per household. The economic factors and demographics variables

are weighted by the coefficients estimated for the 1990 survey year, ߚመ

ଽ଴. The second term

represents the changes in the effects of the explanatory variables on VMT as captured by the

estimated coefficients. For example, if the highest income group drives fewer miles in 2009 than

in 1990, this would be reflected as a change in the coefficient for the highest income group

between the 1990 and 2009 survey years. The change in each coefficient is weighted by the level

of the corresponding explanatory variable in the later time period, ݐ. For shorthand, we refer to

the first term as the effect of demographics and economic factors on VMT.  

We note two important considerations that apply to any decomposition of this sort. The first is

that the decomposition identifies the underlying components of variation in mean VMT per

household or person over time, and does not have a causal interpretation. For example, the

resultsshow that the aging population is correlated with declining VMT after controlling for other

factors and holding constant the coefficients, but this does not necessarily mean that the aging

of the population caused the turnaround; otherfactorsthat are omitted from equation (A4) could

be correlated with age. Second, this decomposition is not unique, meaning that we could use

baseline years other than 1990 in constructing the two terms in equation (A5).

We use equation (A5) to construct Figure 10, where 10a shows results using VMT per household

as the dependent variable and 10b shows results using VMT per person as the dependent

variable. The data series labeled “actual” represents the mean of the dependent variable in the

survey year. The “predicted” series is the first term in equation (A5), plus the mean of the

dependent variable in 1990, ܸ෠

௜;ଽ଴. The “linear trend” is a simple extrapolation based on the

annual growth rate between the 1990 and 1995 surveys.