Fuel Elasticities

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The Demand for Automobile Fuel

A Survey of Elasticities

Daniel J. Graham and Stephen Glaister

Address for correspondence: Daniel J. Graham, Research Fellow, Department of Civil

Engineering, Imperial College of Science, Technology and Medicine, London SW7 2BU.

Professor Glaister is also at Imperial College.

Abstract

A survey is made of the international research on the response of motorists to fuel price

changes and an assessment of the orders of magnitude of the relevant income and price

effects. The paper highlights some new results and directions that have appeared in the

literature. The evidence shows important differences between the long- and short-run price

elasticities of fuel consumption.

Date of receipt of nal manuscript: September 2000

Journal of Transport Economics and Policy, Volume 36, Part 1, January 2002, pp.126

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Introduction

This review is concerned with vehicle fuel demand elasticities. It gathers

evidence on responses to fuel price changes, reporting empirical evidencefrom a number of diVerent countries. It looks at the eVect of price on fuel

consumption and on motorists demand for road travel, emphasising

diVerences that are found between the long- and short-run price elasti-

cities. The paper also reviews estimates of income elasticities of demand

for fuel and for car use.

The purpose is to provide an up-to-date survey of the international fuel

demand literature, giving an assessment of the general magnitude of the

relevant elasticities. The paper is not a methodological review. Instead, it

focuses on identifying the main themes in the literature and seeks to

illustrate some of the new results and directions that have appeared in

recent research.

Earlier extensive surveys of this literature are now well known (see, for

example, Drollas, 1984; Oum, 1989; Dahl and Sterner, 1991a, 1991b;

Goodwin, 1992). The most informative of these surveys are noted here to

provide a general view about the orders of magnitude of the elasticities

relevant to fuel demand. The paper then goes on to draw out some recent

work, which by focusing on specic issues or by using innovative data ormethodology, has added substantial content to the eld.

Major review articles

Survey articles on the characteristics of fuel demand are noted here inchronological order. In most cases these studies provided new empirical

estimates as well as review material. Where this is the case both con-

tributions are reported. By focusing on comprehensive reviews, which

collectively cover hundreds of individual studies, this section seeks to

arrive at a balanced view of the likely orders of magnitude of fuel demand

elasticities.

Drollas (1984) provides an early comprehensive review of fuel demand

characteristics. He surveys a variety of academic and non-academic studies

of gasoline demand elasticities and also provides his own estimates forEuropean countries in the 1980s. The author cites price and income

elasticities from previous studies predominantly estimated for the US. His

survey spans diVerent modelling techniques including static cross-sectional

specications and time-series and pooled cross-section time-series models

Journal of Transport Economics and Policy Volume 36, Part 1

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with a variety of lagged structures. While a range of estimates is found in

the literature, the consensus view is that the long-run price elasticity of

demand is around 0:80, while the long-run income elasticity is slightlybelow unity. Only some of the studies reviewed by Drollas distinguish

short- from long-run eVects. Those that do typically nd short-run price

elasticities to be one-third the magnitude of the long run, and short-run

income eVects to range from a quarter to a half of the long-run estimate.

The review of limited existing evidence on other countries suggests no

substantial diVerences from the US.

Drollas also provides his own price elasticity estimates for European

countries over the period 1950 to 1980. The motivations for his empirical

work are to extend the analysis beyond the timeframe of the previous

studies he reviews to include the mid 70s oil crisis, to incorporate a widerrange of nations, and to implement a vehicle-stock adjustment model that

he believes to be well specied yet economical in data requirements.

Specically, Drollas estimates a vehicle stock adjustment model in its

reduced form without explicit consideration of the vehicle stock itself. He

estimates dynamic models in log-linear form that relate gasoline con-

sumption to income, the real price of gasoline, the real price of other

transport services, and the price of vehicles. The models are estimated with

endogenous and exogenous lags according to geometric and inverted-V lagschemes.

The authors results yield long-run price elasticity estimates of

approximately0:6 for the UK, 0:8 to 1:2 for West Germany, 0:6 forFrance, and 0:8 and 0:9 for Austria. These compare to short-run g-ures of around 0:26 for the UK, 0:41 and 0:53 for West Germany,0:44 for France, and 0:34 and 0:42 for Austria.

Thus, he nds that while gasoline demand may be inelastic in the short

run it is less so in the long run, and these results are consistent with thoseof the previous studies he reviews. However, Drollas believes his estimates

give evidence that the true long-run price elasticities, particularly for

European countries, may well be above unity. He attributes these higher

than expected long-run elasticities to substitute types of transport fuels

(diesel, liqueed petroleum gas), substitute forms of transport, and the fact

that consumers can switch expenditure to activities or goods that compete

with transport. Other important ndings of this study are that similarity

rather than diversity exists between countries in the characteristics of fuel

demand, and that inertia in gasoline consumption can be explained by the

slowly changing vehicle stock and by the persistence of inecient habits.

Blum et al. (1988) review studies on aggregate time-series gasoline

demand models for West Germany and Austria. The authors set out a

The Demand for Automobile Fuel Graham and Glaister

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typology of gasoline demand studies based on the formal econometric

structure of the models used and provide a commentary on the results

obtained. Models are distinguished with respect to the form of the demand

function, the treatment of time, the structure of the error component, and

the estimation technique. The paper emphasises short-term eVects. The

studies they review for Germany and Austria give short-run price and

income elasticities over very large ranges, from 0:25 to 0:83 and from0.86 to 1.90 respectively.

The authors express concerns over the demand specications used to

estimate these elasticities. They argue that while many previous studies

have interesting model characteristics and estimation techniques, they are

also typically characterised by diVerent restrictive functional forms, which

have given rise to much of the variation between estimates.Blum et al. go on to review some results for Germany by Foos (1986),

which examines a much larger number of variables than commonly found

in gasoline demand studies, including important exogenous variables such

as the level of economic activity, the prices of other goods, weather con-

ditions, and the availability of infrastructure. The data used by Foos are

for West Germany and are monthly from January 1968 to December 1983.

Fooss results give a short-run price eVect of0:28 and income eVect of

0.25. The short-run price elasticity is of fairly typical magnitude but theincome eVect is smaller than commonly reported. Blum et al. explain this

result by pointing out that the model also contains variables reecting the

level of economic activity (employment, retail sales, industrial activity):

adding the elasticities of these variables to the elasticity of income gives a

total elasticity of 1.22. Thus the authors argue that by not explicitly spe-

cifying dimensions of the level of economic activity in gasoline demand

models, which ultimately generates travel, previous studies have greatly

over-estimated the pure income elasticity.Other interesting results reported include the cross-elasticity of gasoline

demand with respect to the price of mass transit, estimated at 0.39, and the

elasticity of fuel consumption rate of cars, estimated at 0.61. Thus, a 10 per

cent increase in fuel eciency brings about a decrease in fuel consumption

of 6.1 per cent: motorists compensate by driving more. The authors also

nd that the availability of infrastructure and its quality has an important

bearing on fuel demand, although they determine only a small impact

from weather conditions.

Sterner (1990) examines the pricing and consumption of gasoline in

OECD countries. His survey nds long-run price elasticities falling in the

interval 0:65 to 1:0 and for income between 1.0 and 1.3. Using data forthe OECD between 1962 and 1985 Sterner provides his own set of esti-


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mates. He nds long-run price elasticities of between 1:0 and 1:4 usingpooled data. The corresponding income elasticities vary from 0.6 to 1.6.

Using time series data the price elasticities are between 0:6 to 1:0, and1.1 to 1.3 for income. The short-run elasticities for dynamic models appear

to be around 0:2 to 0:3 for price, and 0.35 to 0.55 for income.Thus, the treatment of time and the particular methodological

approach can have a crucial bearing upon the magnitude of elasticity

estimates. Goodwin (1992) explores these issues, updating previous work

on gasoline price elasticities in his review of academic and non-academic

studies undertaken in the 1980s and 1990s. His paper shows that more

recent work has generally revised the magnitude of elasticity estimates

upwards. The unweighted mean value of 120 elasticities of gasoline con-

sumption with respect to fuel prices considered in the review is 0:48,compared with similar values from previous reviews of0:1 to 0:4.

Goodwin highlights diVerences between recent studies by categorising

estimates of the elasticity of gasoline consumption with respect to fuel

price into cross-section or time series, and subdividing this distinction into

short-term, long-term, or ambiguous. The short-term period generally

refers to less than one year and the ambiguous category refers to estimates

obtained from models with no explicit consideration of the time dimen-

sion. Goodwins summary of results is reproduced in Table 1.

The results in Table 1 illustrate the diVerence in magnitude that existsbetween the short- and long-term eVects of fuel price increases on gasoline

consumption. Long-term elasticities tend to be between one-and-a-half

and three times higher than the short term. However, having reviewed a

wide range of studies, Goodwin also shows that diVerences in methodo-

Table 1Summary of Evidence from Studies of Elasticity of Gasoline Consumption

with Respect to Price

Explicit Ambiguous

Short term Long termTime-series 0:27 0:71 0:53

(0.18, 51) (0.41, 45) (0.47, 8)

Cross-section 0:28 0:84 0:18

(0.13, 6) (0.18, 8) (0.10, 5)

Note: Figures in parentheses are standard deviations and the number of quoted elasticities in the

average.

Source: Goodwin (1992).


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logical approach, in this case between time-series and cross-section

methods, only marginally aVected the magnitude of the elasticities.

The review also considers the eVects of gasoline prices on trac levels.

An earlier paper by Dix and Goodwin (1982) hypothesised that the short-

run elasticities of trac levels and of gasoline consumption with respect to

fuel price would be identical, but that they would diverge over time as the

long-run gasoline consumption elasticity grew faster than the trac elas-

ticity. The reasoning here was that changes in trip rates, car ownership,

destination choice, and location decisions would take some time to occur,

and that changes in vehicle size and eciency would have a strong eVect

on consumption while preserving mobility.

Goodwins evidence of elasticity eVects of trac levels with respect to

fuel prices is shown in Table 2. Table 2 does not support the Dix andGoodwin hypothesis. While it is the case that long-term elasticities are

larger than short-term, both short- and long-term eVects of gasoline prices

on trac levels are much less than their eVects on gasoline consumption.

Goodwin notes that this is indicative of rapid behavioural responses that

aVect gasoline consumption more than trac. He suggests that they may

be due to changes in driving style or speed, or by modifying the least

energy-ecient journeys. If this is true, then it would seem that gasoline

price manipulation might be a more eVective tool where the objective is todecrease fuel consumption rather than to reduce road congestion.

With respect to the time eVect in the magnitude of elasticities Goodwin

draws three important conclusions. First, behavioural responses to cost

changes take place over time and this implies that time-independent esti-

Table 2Summary of Evidence from Studies of Elasticity of Trac with

Respect to Price

Explicit Ambiguous

Short term Long term

Time-series 0:16 0:33 0:46

(0.08, 4) (0.11, 4) (0.40, 5)

Cross-section 0:29 0:5

(0.06, 2) (N/A., 1)

Note: Figures in parentheses are standard deviations and the number of quoted elasticities in the

average.

Source: Goodwin (1992)


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mates are subject to error. Second, the range of responses considered

credible has to be extended to include changes in car ownership, vehicle

type, location decisions, and the use of public transport. Third, policy

options are wider than perceived by earlier studies and pricing has a

powerful cumulative eVect on the pattern of travel demand.

Sterner et al. (1992) examine the price sensitivity of transport gasoline

demand. They report results from earlier surveys (Dahl and Sterner 1991a,

1991b), which stratify a wide variety of previous results by the type of

model and data used, and calculated average elasticities for each category.

Results from dynamic models for OECD countries over the period 1960

85 show great degrees of diVerence in the short- and long-term magnitude

of price and income elasticities. The short-run price elasticity of gasoline

demand varies between 0:10 to 0:24 depending on the model estimated.The equivalent long-run gure is between 0:54 and 0:96. Averagingthese estimates gives a short-run value of0:23 and a long-run gure ofalmost three-and-a-half times as large, 0:77. The average nationalincome short-run elasticity is given as 0.39 and the long-run as 1.17.

Sterner et al. note that the indication that the absolute value of the income

elasticity is higher than for price suggests that gasoline prices must rise

faster than the rate of income growth if gasoline consumption is to be

stabilised at existing levels.Sterner et al. present the short- and long-run price and income elasticity

estimates generated from lagged endogenous variable models for 20

OECD countries. These gures are shown in Table 3.

Given mean standard errors the 95% condence interval for the

average short-run eVect is from 0:06 to 0:42, and for long-run 0:21 to1:37. The long-run income eVect is about 2.8 times as large as the short-run. Excluding Germany, Spain, and Switzerland, which have extremely

low t-ratios, and re-calculating the gures, increases the condence intervalsfor average price elasticities. For short-run eVects, the condence interval is

from 0:12 to 0:42, and for long-run eVects from 0:38 to 1:38. Thelong-run mean price elasticities for the OECD countries are approximately

3.3 times as large as the short-run eVects. The diVerence in order of mag-

nitude for the UK between the short- and long-run is, however, much

greater, with an elasticity of about 4.1 times as large in the long term.

Sterner and Dahl (1992) extend the investigation into methodological

issues, reviewing a large number of diVerent models that have been

developed to explain how gasoline demand is related to price, income, and

other variables. They nd that diVerent model specications can give very

diVerent estimates, and they compare model results by applying them to

the same OECD data set (19601985). Long-run elasticities can be esti-


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mated with either dynamic models on ordinary time-series data or with

static models on cross-section data. The dynamic models give estimated

price elasticities within the range 0:80 to 0:95, and income elasticities ofbetween 1.1 and 1.3. Static models for cross-section data give roughly

unitary elasticities for both price and income. Sterner and Dahl also note

that models using pooled data estimate price elasticities as high as 1:3 or1:4. Short-run estimates from dynamic models generally fall in the range

0:1 to 0:3 for price and 0.15 and 0.55 for income.Dahl (1995) reviews a number of previous gasoline demand surveys

conducted since 1977 and updates this work with evidence from the most

recent US studies. Table 4 summarises the results reviewed by Dahl.

Table 3Price and Income Elasticity Estimates of Gasoline Demand Estimates,

OECD Countries, 19601985

Price Elasticities Income Elasticities

SR LR SR LR

Canada 0:25 (0.06) 1:07 (0.24) 0.12 (0.09) 0.53 (0.40)

US 0:18 (0.03) 1:00 (0.15) 0.18 (0.07) 1.00 (0.38)

Austria 0:25 (0.11) 0:59 (0.26) 0.51 (0.23) 1.19 (0.54)

Belgium 0:36 (0.05) 0:71 (0.09) 0.63 (0.19) 1.25 (0.39)

Denmark 0:37 (0.06) 0:61 (0.10) 0.34 (0.08) 0.71 (0.17)

Finland 0:34 (0.15) 1:10 (0.47) 0.39 (0.24) 1.26 (0.76)

France 0:36 (0.08) 0:70 (0.15) 0.64 (0.23) 1.23 (0.43)Germany 0:05 (0.07) 0:56 (0.82) 0.04 (0.16) 0.48 (1.92)

Greece 0:23 (0.11) 1:12 (0.52) 0.41 (0.19) 2.03 (0.93)

Ireland 0:21 (0.04) 1:62 (0.33) 0.12 (0.14) 0.93 (1.06)

Italy 0:37 (0.13) 1:16 (0.40) 0.40 (0.17) 1.25 (0.52)

Netherlands 0:57 (0.11) 2:29 (0.46) 0.14 (0.13) 0.57 (0.52)

Norway 0:43 (0.13) 0:90 (0.28) 0.63 (0.20) 1.32 (0.42)

Portugal 0:13 (0.07) 0:67 (0.34) 0.37 (0.18) 1.93 (0.94)

Spain 0:14 (0.17) 0:30 (0.37) 0.96 (0.45) 2.08 (0.98)

Sweden 0:30 (0.09) 0:37 (0.11) 0.51 (0.30) 0.99 (0.59)

Switzerland 0.05 (0.16) 0.09 (0.28) 0.85 (0.29) 1.54 (0.53)UK 0:11 (0.07) 0:45 (0.27) 0.36 (0.20) 1.47 (0.81)

Australia 0:05 (0.02) 0:18 (0.07) 0.18 (0.07) 0.71 (0.29)

Japan 0:15 (0.03) 0:76 (0.17) 0.15 (0.01) 0.77 (0.06)

Turkey 0:31 (0.06) 0:61 (0.11) 0.65 (0.16) 1.29 (0.32)

Mean 0:24 (0.09) 0:79 (0.29) 0.41 (0.18) 1.17 (0.62)

Note: Standard errors are given in parentheses.

Source: Sterner et al. (1992).


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The studies reviewed were concerned with price elasticities in the

industrialised world and they generally found long-run price elasticities

between 0.7 and 1.0 and long-run income elasticity greater than 1.0.Dahl notes that these results suggest that taxes may well be an eVective

means of reducing pollution from gasoline use, but to keep use constant

fuel prices would have to rise faster than income.

Dahl reviews 18 recent studies on gasoline demand from the US to

explore how elasticity estimates have changed. For studies based on static

models, she nds slightly lower long-run price and income elasticities from

studies based on recent data 0:16/0.46) compared to (0:53=1:16) fromprevious estimates. However, static analyses tend to produce intermediate-

run, rather than long-run, price elasticity estimates, and Dahls review of

dynamic models shows no substantial reduction in the magnitude of the

elasticity estimates. For instance, estimates based on lagged endogenous

variable models shows short-/long-run price and income elasticities of0:19/0:66 and 0.27/0.28, and those based on the inverted V model showlong-run price and income eVects of1:20 and 1.22.

Dahl believes on balance that elasticities have become less over time,

particularly for income. While previous studies show long-run price and

income elasticities of around 0.8 and 1.0, recent studies suggest a priceresponse of around 0.6 and a slightly inelastic income response. Thereliability of these results, however, is tempered by the small number of

estimates reviewed in Dahls update, and by the predominance of staticmodels.

On the basis of the surveys reviewed in this section, which have

assimilated many hundreds of studies, there is a clear indication that

despite variation in elasticities of fuel demand there are fairly narrow

Table 4Demand Elasticity Estimates Reported by Dahl (1995)

Price Elasticity Income Elasticity

short run long run short run long run

Taylor (1977) 0:1 to 0:5 0:25 to 1:0

Bohi (1981) 0:2 0:7 1:0

Kouris (1983) 1:09

Bohi & Zimmerman (1984) 0.0 to 0:77 0.0 to 1:59 0:18 to 1.20 0:34 to 1.35

Dahl (1986) 0:29 1:02 0.47 1.38

Dahl & Sterner (1991a,1991b) 0:26 0:86 0.48 1.21

Goodwin (1992) 0:27 0:71 to 0:84

Source: Dahl (1995).


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ranges within which the values typically fall. Short-term price elasticities

tend to be between 0:2 and 0:3, while the long-run eVects typically fallbetween 0:6 and 0:8. For income, the long-run elasticity is usuallyestimated as slightly higher than unity (1.1 to 1.3) and the short-run

elasticity in the range 0.35 to 0.55.

However, while the overwhelming evidence points towards values

within these ranges the review articles do not categorically account for the

variation in the estimates that exists. The following sections attempt to

shed some light on this issue. They draw upon recent studies that have

added substantially to our understanding of elasticity estimates by

exploring specic themes, or by explicitly setting out to explain the var-

iation in elasticity estimates.

Micro-level Data: Individual and Household DemandStudies

One important issue surrounding gasoline demand elasticity estimates isthe analytical diVerences permitted by the use of dissaggregate as opposed

aggregate data. Most of the estimates reviewed above, and the vast

majority of gasoline demand studies in general, are based on aggregate

level data at the country or sub-national level. Thus, these studies consider

both commercial and consumer demand. Some authors have recently

shown that the use of micro-level data, which reects individual and

household behaviour more closely, can add detail to our understanding of

the temporal nature of consumer response.Eltony (1993) uses household data to quantify the behavioural

responses that give rise to negative price elasticities of demand for gaso-

line. He estimates household gasoline demand in Canada using pooled

time-series and cross-sectional provincial household data. His model

recognises three main behavioural responses of households to changes in

gasoline prices: drive fewer miles, purchase fewer cars and buy more

ecient vehicles. Eltony estimates ve separate equations that attempt to

explain: gasoline demand per car; the stock of cars per household; new car

sales per household; new car fuel eciency; and the sales ratio of new cars.Using pooled time-series and cross-section data on the Canadian pro-

vinces from 19691988 he estimates short-run gasoline price elasticities per

car, holding fuel economy constant, of 0:21, and a short-run incomeelasticity of 0.15.


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From these estimates Eltony goes on to determine dynamic price

elasticities of gasoline demand for Canada by simulating the model over

the period 1989 to 2000. He assumes a base case in which real household

income, the unemployment rate, the real price of new cars, the interest

rate, and the real price of gasoline per gallon in Canada and the US are

equal to 1988 values and remain constant for the rest of the time horizon.

In an alternative solution to the model the real prices of gasoline in

Canada and the US are assumed to increase by 10 per cent. The two model

solutions are obtained and the percentage change in gasoline consumption

computed.

His results for the short term (one year) and the long term (two to ten

years) are given in Table 5.

Table 5 demonstrates a number of important points about short-run

and long-run eVects of increasing the price of fuel. The short-run dynamic

own-price elasticity of gasoline is estimated at 0:31. He nds that almost75 per cent of household response to price changes in the rst year can be

attributed to driving fewer miles. A further 10 per cent results from an

alteration in the composition of the eet to more fuel-ecient vehicles, and

the remaining 15 per cent can be attributed to changes in the size of the

eet. Eltony also nds intermediate term (5-year) price elasticities ranging

from 0:689 to 0:709, and the long-term elasticities from 0:975 to1:059. Table 5 also shows a rapid response to price increases within therst four years. Eltony also interprets these results as pointing to the

importance of improving fuel eciency as an eVective means of reducing

household gasoline consumption.Rouwendal (1996) seeks direct verication of the validity of short-term

behavioural responses to fuel price increases using individual consumer

data. The author obtained information about fuel use per kilometre driven

from the Dutch Private Car Panel, a rotating panel in which car drivers

Table 5Dynamic Price Elasticities of Gasoline Demand in Canada

Year Year

1 0:3120 7 0:8935

2 0:4673 8 0:9478

3 0:5370 9 0:9839

4

0:5981 10

1:0073

5 0:6984 11 1:0192

6 0:8132 12 1:0239

Source: Eltony (1993).


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participate for three months. Rouwendal seeks to investigate the rela-

tionships between fuel use and other recorded information about cars and

their drivers in the short run. With respect to cars, he is able to observe

weight, cylinder volume, year of construction, and type of fuel. Known

driver characteristics include gender, classications of age and income,

total number of kilometres driven each year by the main car user, infor-

mation about business, whether the driver receives compensation for the

cost of the car, and, for employed people, the distance between residential

and work location. Monthly information about fuel prices in Holland is

available.

The author presents OLS estimates for specications that are linear in

parameters with the logarithm of the number of kilometres driven per litre

of fuel as the dependent variable. His results show heavier cars to be lessfuel-ecient than others and diesel cars to be more fuel ecient. Gender

eVects are not found but age is important with older drivers generally

being less fuel-ecient. As regards the gasoline prices, Rouwendal esti-

mates that a 10 per cent increase in fuel price will induce drivers to increase

the average distance per litre of fuel by 1.5 per cent. Rouwendal regards

this central result as verication of the signicant eVect of gasoline prices

on fuel use in the short run. Surprisingly, the income of the main driver is

found to be insignicant, although the type of employment is not. Rou-wendal points out that this result conicts with the commonly held belief

that there are short-run income eVects. It is, however, perhaps consistent

with the nding of Blum et al. (1988) that some explicit consideration of

economic activity in gasoline demand models substantially reduces the

magnitude of the income eVect.

Short-term response is also investigated by Hensher et al. (1990) in an

earlier study, but in this case with respect to vehicle use and fuel price. The

authors develop a model to explain vehicle kilometres per annum forhouseholds in the Sydney metropolitan area in terms of a range of vehicle

characteristics as well as household price and income attributes. They are

able to distinguish elasticities on the basis of household car ownership

characteristics. Their data cover the period 1981 to 1982 for 1,172

households. Hensher et al. start from the premise that households face a

set of alternative vehicle technologies and select the one that is consistent

with the maximisation of the joint utility of vehicle choice and use.

Parameter estimates are presented in the absence of selectivity of vehicles,

and in the presence of selectivity where that is derived from the non-linear

specication of the type choice model.

Hensher et al.s results are consistent with Rouwendals ndings on

short-term responses. They show a substantial price eVect on vehicle use


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but only small and insignicant eVects from household income in the short

term. The estimated short-run price elasticities of vehicle use are 0:26 for1-vehicle households, 0:33 for 2-vehicle households, and 0:39 for 3-vehicle households. However, the authors nd that income is not con-

rmed as an important empirical inuence on vehicle use, except for 2-

vehicle households, with an estimated elasticity of 0.14.

Puller and Greening (1999) provide a recent example of the use of

micro-level data to identify the intricacies of temporal response to short-

run gasoline price changes. They review short-run estimates of price

elasticities of gasoline demand from a number of previous studies based on

dissagregated household data. A summary of this review is provided in

Table 6.

Puller and Greening examine household adjustment to changes in the

real price of gasoline using a panel of US households over nine years. They

believe their work diVers from the studies they review in two ways. First,

they allow household vehicle stock to change over time and therefore areable to capture long-run adjustments. Second, they decompose demand

into a vehicle usage and a vehicle stock component. The authors present a

basic demand framework that explains the household demand for gasoline

in terms of contemporaneous and lagged real prices of gasoline, the real

income of the household, and a vector of household demographic char-

acteristics.

Puller and Greening apply a variety of estimation techniques and lag-

ged structures to their data. Using one-year lags, as previous studies have,the short-run price elasticity of gasoline demand is estimated to be around

0:35, a gure they believe to be consistent with estimates from the lit-erature. However, when they use diVerent specications of quarterly lag-

ged prices they estimate a much larger price elasticity of0:8. This, they

Table 6Estimates of Short-Run Price Elasticities from Studies Based on

Household Data

Short Run Price Elasticity

Archibald and Gillingham (1980) 0:43

Greene and Hu (1986) 0:5 to 0.6Walls et al. (1993) 0:51

Greening et al. (1995) 0.00 to 0.67

Dahl and Sterner (1991a) 0:52


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argue, indicates that the initial immediate response of consumers to a price

rise involves a much larger decrease in gasoline consumption compared to

the total annual short-run elasticity.

This section has looked at how the gasoline demand studies using

disaggregated data have been used to shed more light on the temporal

nature of behaviour response. The consensus from these studies is that

short-term price elasticity eVects do exist and are of the order of magni-

tude suggested by the main survey articles reviewed above. There is evi-

dence, however, that income eVects are more dicult to determine in the

short run using disaggregated data. However, the models used at the micro

level tend to be much less restrictive in exogenous variable specication

than the aggregate studies and, as Blum et al. (1988) suggest, this may well

account for the absence or reduction of the income eVect.

Vehicle Technology and Fuel Eciency

Many recent studies have investigated fuel eciency and vehicle technol-ogy characteristics in gasoline demand models. Typically, the gasoline

elasticities studies, and particularly those using aggregate data, have either

not explicitly modelled fuel eciency or have accorded the issue inade-

quate attention. Interest in the role of fuel eciency has grown in recent

years as researchers try to understand the implications of scal policy for

trac levels, vehicle emissions, and environmental externalities (see, for

example, Hall, 1995; Koopman, 1995; Small and Kazimi, 1995; Crawford

and Smith, 1995; Eyre, 1997; McCubbin and Delucchi, 1999; Delucchi,2000). This section draws together some prominent research from the

elasticities literature that considers this particular dimension of fuel

demand.

Baltagi and Grin (1983) provide an early example of the explicit

treatment of fuel eciency eVects in gasoline demand estimation. They are

interested in the magnitude of the price elasticity of demand for gasoline

and review earlier studies that show wide variation in the magnitude of

price elasticity estimates. For instance, Houthakker et al. (1974), in a study

of the US, indicate very low price elasticities of demand ranging from0:04 to 0:24 using quarterly data for a cross-section of states. Sweeney(1978), on the other hand, using a model that incorporated the eciency

characteristics of the automobile eet, nds a higher long-run price elas-

ticity of0:73.


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Baltagi and Grin are unhappy with such a wide range in estimates,

believing them to be symptomatic of the methodology and data used. They

wish to obtain more consistent estimates and to understand the implica-

tions for estimates of the method and data used. Applying eight alternative

estimation techniques to pooled cross-section time-series data, they set out

to quantify the magnitude of the price elasticity of gasoline demand in

OECD countries for the period 1960 to 1978. The model they propose

explains gasoline consumption per vehicle by income per capita, gasoline

prices, the stock of cars per capita, and a proxy variable reecting the level

of vehicle eciency.

Following the application of these diVerent estimation methods Baltagi

and Grin nd that the long-run price elasticity of gasoline demand

typically falls within the range 0:6 and 0:9 a range consistent withthe orders of magnitude given in most survey articles. However, in con-

trast to previous studies (Houthakker et al. 1974; Ramsey et al. 1975;

Mehta et al., 1978) they nd a slow adaptation rate with the major

response being due to the eciency characteristics of the automobile eet.

Approximately 60 per cent of the adjustment to the long-run equilibrium

takes place within the rst ve years previous studies had claimed it was

almost instantaneous. Thus they nd that adaptations in the gasoline

eciency of the eet and driving conditions require long periods foradjustment.

Broader aspects of fuel eciency are considered by Espey (1996b). She

analyses the role of fuel prices, income, government taxation and tech-

nological change in inuencing the consumers choice of fuel economy.

The study uses an international data set that comprises observations on

eight countries: USA, Japan, France, Germany, the UK, Norway, Swe-

den, and Denmark, between 1975 and 1990. The equation estimated

explains the demand for fuel economy (average eet fuel eciency, km/litre) by fuel prices, per capita income, an automobile purchase and

registration tax index, and a time trend that is thought to reect techno-

logical change.

Espeys results indicate a price elasticity of fuel economy of around

0.20, but an income elasticity not signicantly diVerent from zero. The

time trend in the model is also found to be statistically signicant,

implying a 2.8 per cent annual increase in fuel eciency over time that is

not explained by changes in fuel prices and income. The inuence of time

declines over time from 5 per cent in 1975 to under 2 per cent by 1990.

Espey indicates that the time trend captures a combination of pure tech-

nological improvements in fuel economy and the impact of implicit and

explicit environmental standards. The elasticity of fuel economy with


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respect to vehicle taxation is estimated at 0.09, and the coecient on the

lagged dependent variable is 0.94, indicating that only 6 per cent of the

eVect of a change in fuel prices, income, or vehicle taxation takes place in

the rst year.

Espey considers the implications of her results for transport policy in

the USA. She argues that fuel prices account for around half the diVer-

ences in fuel economy between the US and other countries in her study.

There is however, no strong relationship between income and fuel econ-

omy. The author also believes that purchase and registration taxation

regimes have an important bearing on diVerences in fuel economy.

The issue of how fuel eciency aVects gasoline demand is explored

directly by Orasch and Wirl (1997). Their investigation is motivated by a

desire to explain the asymmetry of gasoline demand with respect to energyprices. For the US, they note that the dramatic reduction in gasoline prices

during 1986 did not have an eVect on demand comparable to the previous

price increases of 1974 and 1979/1980. The authors investigate the eVect of

technical fuel eciency on gasoline demand for the UK, France, and Italy.

They estimate an energy demand model with eciency explicitly treated

within an asymmetric framework and a second model excluding eciency.

They nd that the explicit consideration of energy eciency proves less

important than previously thought, with little noticeable diVerence in priceelasticity eVects. The income elasticities are found to diVer being higher

with eciency included in the model. The authors are sceptical about the

importance of technical eciency to fuel demand. They conclude that

energy and environmental taxes are unlikely to give rise to R & D eVorts in

eciency unless they are very high. Otherwise, any response will be modest

and come about only through consumer adjustments.

Johansson and Schipper (1997) examine aspects of car fuel in relation

to decreasing overall travel and increasing fuel eciency for 12 OECDcountries over the period 1973 to 1992: US, UK, Japan, Australia, Ger-

many, France, Italy, The Netherlands, Sweden, Denmark, Norway, and

Finland. Their fuel-use data are disaggregated in such a way that it allows

them to conduct separate estimations for vehicle stock, mean fuel inten-

sity, and mean annual driving distance. Using a variety of diVerent esti-

mation techniques and models, the authors use their results to obtain

estimates for long-run car fuel and travel demand.

The results conrm the importance of increasing fuel eciency in

gasoline demand. They calculate a long-run fuel price elasticity of

approximately0:7, in which the largest portion, just under 60 per cent, isdue to changes in fuel intensity. The gasoline demand gure is more than

double the estimated price elasticity of travel demand. The long-run


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income elasticity of fuel demand is approximately 1.2, almost all due to the

number of cars, and is of identical magnitude with respect to travel

demand. The fuel eciency eVect is found to arise from both increased

technical eciency and the imposition of environmental standards.

Johansson and Schipper also consider the eVects of diVerent taxation

measures on fuel and travel demand. They nd a fuel tax increase will

reduce overall long-run fuel consumption much more than an increase in

the other car related taxes, for example, taxing car ownership.

The focus on fuel eciency in gasoline demand studies, although

yielding some quite diVerent results, does indicate that increasing eciency

is crucial in explaining the long-run price elasticity. Most studies show a

slow rate of adaptation, but nonetheless a strong and identiable eVect.

An important and consistent implication of these studies is that the impactof fuel price changes has a greater impact on fuel demand and vehicle

emissions than on vehicle use and congestion, particularly in the long run.

Non-stationary Data and the Cointegration Technique

The appropriateness of diVerent data types (cross-section, time-series,

pooled) and the methodologies applied to each has proved a source of

constant debate in gasoline demand research. Many recent studies have

expressed concern over the customary treatment of time-series data and

particularly the lack of recognition of the non-stationary nature of these

data. This has given rise to the widespread use of cointegration techniques

that seek to model the non-stationary nature of time-series data explicitly.The use of this method is employed both as a means of distinguishing the

short- from the long-run gasoline demand characteristics, and for calcu-

lating the speed of adjustment towards the long-run values. The results

obtained in this way often give estimates that are outside the range

reported in the major reviews.

If the dependent and independent variables are trending variables the

time-series data are said to be non-stationary, and if there is a long-term

relationship between them then they are cointegrated. Then the mean and

variance of the time series are non-constant over time and the value of theprocess at any point depends on the time period itself. The cointegration

technique is designed to distinguish the long-run relationship, the manner

in which the two variables drift together, from the short-run eVect, the

relationship between deviations of the dependent variable from its long-


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run trend, and deviations of the independent variables from their long-run

trends.

The cointegration method typically follows three basic steps. First, the

time series under consideration are examined to determine if the variables

are non-stationary. Second, if the variables are found to be non-stationary

the cointegration of the variables is investigated. If the variables do indeed

possess a long-run relationship the long-run elasticities may be estimated

from the cointegrated regression. Third, the short-run elasticities and the

rate of adjustment towards the long-run equilibrium can be estimated by

means of an Error Correction Model (ECM).

Bentzen (1994) estimates short- and long-run elasticities of gasoline

demand for Denmark using annual time-series data for the economy

covering the period 1948 to 1991. The model estimated explains gasolineconsumption per capita by the price of fuel, vehicle stock per capita, and

increasing fuel eciency represented by a time trend.

The author nds a stable long-run relationship between the variables in

his model and goes on to estimate the error correction model to distinguish

short- and long-run eVects. The estimated short-run price elasticity is

0:32 and the long-run, 0:41. The short-run vehicle per capita incomeelasticity is 0.89 and the long-run 1.04.

The short-run price elasticity estimated by Bentzen is of similar mag-nitude to values reported in other studies. The long-run value, however, is

somewhat lower. Besides diVerences in data and models, the author

believes that the lower value can be at least partly explained by the par-

ticular statistical technique used, with explicit treatment of the non-sta-

tionary properties of the variables.

Samimi (1995) uses cointegration techniques to examine the short- and

long-run characteristics of energy demand in Australias road transport

sector. He has quarterly data for the Australian road transport sector from1980 to 1993. The model estimated has a lagged endogenous structure. The

dependent variable is road transport energy demand, which includes

gasoline and diesel oil. The independent variables are fuel prices, the lag of

road transport energy demand, and road transport output, which is

measured as the revenue generated by carrying goods and passengers for

hire and reward and provision of other road transport services.

The cointegration estimates yield price elasticity estimates of0:02 inthe short run and 0:12 in the long run. The estimated income elasticitiesare 0.25 in the short-run and 0.48 in the long run.

Samimi notes that the long-run income and price elasticities for Aus-

tralia are of much lower magnitude than found previously. The author

explains the diVerence in the long-run price eVect by hypothesising that


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more ecient vehicle technology is built into his long-run estimate. But he

also argues that use of diVerent time periods or diVerent econometric

specications would yield diVerent estimates, mainly due to changes in

market structure. On this basis the author questions the existence of stable

price elasticities.

Eltony and Al-Mutairi (1995) estimate the demand for gasoline in

Kuwait for the period 19701989 using a cointegration and error correc-

tion model. The model they estimate, which is identical to that of Bentzen

(1994), explains per capita gasoline consumption in Kuwait by the real

price of gasoline and real per capita income. Their cointegrated results

show a short-run price elasticity estimate of 0:37 and a long-run priceelasticity of0:46. The estimated short- and long-run income elasticities

are 0.47 and 0.92 respectively. Again the long-run price elasticities areoutside the range typically reported in the literature.

Gasoline demand in India is examined by Ramanathan (1999) using a

cointegration methodology to analyse long- and short-run behaviour. The

model estimated in the paper explains national per capita gasoline con-

sumption (in tonnes) as a function of real per capita GDP and the price of

gasoline. Time-series data are used for estimation covering the period

1972/73 to 1993/94.

The authors results for India estimate a short-run price elasticity ofgasoline demand of0:21 and a short-run income elasticity of 1.18. Thecointegration model indicates that the adjustment of gasoline consump-

tion towards its long-run equilibrium occurs at a relatively slow rate with

28 per cent of the adjustment occurring within the rst year. The long-run

price elasticity of demand estimate is 0:32 and the long-run incomeelasticity estimate is 2.68.

Ramanathan thus derives a very high long-run income elasticity and a

rather inelastic price eV

ect. The author believes that the low level ofgasoline consumption in India and the gradual increase in economic

growth can explain the diVerences between his results and those obtained

elsewhere. He concludes that overpricing of gasoline as a policy instru-

ment is unlikely to have an inuential eVect on gasoline demand in India.

The cointegration studies of time-series data estimate long-run price

elasticities that are often substantially lower than those reported in the

major reviews. Researchers adopting this particular technique frequently

state that this is due to the application of a more appropriate treatment of

the non-stationary nature of time-series data. However, the generality of

these results is still open to question because it is not clear why the use of a

long time series, regardless of treatment, yields lower price elasticity esti-

mates. Certainly, as is illustrated in the next section, there may be reason


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to believe that price elasticities have grown over time at least partly as a

result of increased fuel eciency, a factor that has often received insu-

cient attention in many of the cointegration studies.

Meta-analysis of Gasoline Demand Elasticities

Espey (1998) carries out meta-analyses of international gasoline

demand elasticities to explain the variation in the magnitude of estimated

price and income eVects. This work forms a particularly important and

novel contribution to the literature because it examines empirically whyvariation in estimates exists. Thus while the major reviews identify the

variation, Espeys work seeks to explain it. The paper extends and updates

earlier work that focused on variation in elasticity estimates of gasoline

demand for the United States alone (Espey, 1996a).

Espeys study is based on an extensive review of articles published

between 1966 and 1997, which gave 277 estimates of long-run price elas-

ticity, 245 estimates of long-run income elasticities, 363 estimates of short-

run price elasticity, and 345 estimates of the short-run income elasticity.The authors analysis provides four models that seek to explain separately

variation in the short- and long-run income and price elasticities. The basic

hypothesis is that variation in elasticity estimates can be explained by

demand specication, data characteristics, environmental character-

istics (the level of the data, the setting, time span analysed), and the

estimation method.

Espeys results indicate that elasticity estimates are sensitive to a

number of diV

erent aspects of model structure. In terms of price eV

ects,the inclusion of vehicle ownership and fuel eciency variables serves to

lower estimates of the short-, but not the long-run, price elasticity. Static

models tend to produce larger short-run price elasticities and lower long-

run price elasticities, indicating that perhaps these models produce inter-

mediate-run elasticities. No diVerences are found for price elasticities

across diVerent dynamic specications, and no diVerences in long-run

price elasticity estimates among time-series, cross-sectional, and cross-

sectional-time-series studies. The paper does show, however, that the

short-run price elasticity has tended to decrease over time, while the long-run elasticity has tended to grow. The author believes this temporal eVect

is due to increased fuel eciency. As prices rose during the 1970s and

people made some initial adjustments in driving habits and bought more

fuel ecient vehicles, there were fewer options for further short-run


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responses to price changes. However, as automobile fuel eciency

improved during the late 1970s and early to mid-1980s, long-run responses

to fuel price changes were larger than before 1974. (Espey, 1998; 290)

As regards income eVects Espeys analysis nds that the inclusion of

vehicle ownership and vehicle characteristics substantially inuences

results. Models that include some measure of vehicle ownership estimate

signicantly lower short- and long-run income elasticities. No statistically

signicant diVerences are found for long-run estimates between static and

dynamic models, or between diVerent dynamic specications. Nor are any

diVerences found for long-run estimates in studies based on cross-sec-

tional, time-series, or cross-sectional-time-series data. Finally, the author

nds that the short-run income elasticity has remained fairly constant over

time, while there is evidence to show that the long-run elasticity may bedeclining.

The author concludes that the exclusion of vehicle ownership in

demand models would be expected to bias results, particularly short-run

eVects. The nding that elasticity estimates are changing over time

prompts Espey to warn against using elasticity estimates from the 1970s or

even 1980s to extrapolate into the future. But the author also argues that

in many ways price elasticity estimates are relatively robust, having a fair

degree of consistency across data types and across functional forms andestimation techniques.

Conclusions

On one level, our survey shows that there is a range of diV

erent viewsabout the magnitude of price elasticity eVects on gasoline consumption

and private travel demand. Figure 1 illustrates diVerences in magnitude,

showing estimates of long- and short-run price elasticities of gasoline

consumption from various studies. These estimates vary greatly both

between and within geographical areas of study for long- and short-run

elasticities. For instance, long-run price elasticity estimates range from

0:23 in the US to 1:35 in the OECD countries, and within the US itselffrom 0:23 to 0:8, and within the OECD from 0:75 to 1:35. Short-run price elasticities range from 0:2 to 0:5.

The Figure illustrates the important inuences that particular data and

methods of estimation can have on the results obtained. Whether the data

used for estimation are cross-section, time-series, or pooled, has an

inuence on the magnitude of the estimates obtained. For this reason,


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discussion of individual gasoline price elasticity estimates has to be based

on a clear understanding of the method used and of the empirical context

for estimation.

But while the use of specic data or methodological approaches can

create crucial diVerences in the magnitude of elasticity estimates, the

overwhelming evidence from our survey suggests that long-run price

elasticities will typically tend to fall in the 0:6 to 0:8 range. This orderof magnitude is indicated by those papers we have reviewed that are

themselves extensive surveys, and which have considered hundreds of

individual estimates across a range of empirical contexts (Drollas, 1984;

Sterner, 1990; Goodwin, 1992; Sterner and Dahl, 1992). In many cases

authors explicitly claim to nd similarities and not diVerences between

countries in the size of long-run price elasticities. Individual studies, which

apply a variety of diVerent estimation techniques to the same data (Baltagi

and Grin, 1983; Eltony, 1990) also produce long-run estimates withinthe same range. These same studies show that short-run price elasticities

normally range from 0:2 to 0:3. In other words they tend to be between2.5 and 3.5 times lower magnitude than the long-run eVects. Again, this is

fairly consistent across diVerent empirical environments.

Figure 1

Petrol Price Elasticities

US

US

US

OECD

OECD

OECD

OECD

OECD

UK

France

Austria

Germany

Canada

Various Countries

Various Countr ies

-1.6 -1.4 -1.2 -1 -0.8 -0.6 -0.4 -0.2 0

Short-run Long Run


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Thus, concentrating on evidence that has proved to be consistent across

studies, we can draw out three central conclusions from our survey of the

literature and highlight some of their implications.

(i) There are diV

erences between the short- and long-run elasticities offuel consumption with respect to price. Typically, short-term

elasticities are in the region of 0:3 and long-term between 0:6and 0:8. Therefore, it may be right to say that it wont make muchdiVerence or people will use their cars just the same, but only in

the short run. The evidence is clear and remarkably consistent

over a wide range of studies in many countries that in the long run

there is a signicant response, albeit a less than proportionate one.

(ii) Both long- and short-term eVects of gasoline prices on trac levels

tend to be less than their eVects on the volume of fuel burned. The

short-term elasticity of trac with respect to price is about 0:15and long-term about 0:30. So motorists do nd ways of economising on their use of fuel, given time to adjust. Raising fuel

prices will therefore be more eVective in reducing the quantity of fuel

used than in reducing the volume of trac.

(iii) The demand for owning cars in heavily dependent on income. The

long-run income elasticity of fuel demand is typically found to fall in

the range 1.1 to 1.3. Short-run income elasticities are between justbelow one-third and just above one-sixth in magnitude: elasticities

normally estimated in the range 0.35 to 0.55. The implication is that

fuel prices must rise faster than the rate of income growth, even to

stabilise consumption at existing levels.

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