The Demand for Automobile Fuel

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The Demand for Automobile Fuel: A Survey of ElasticitiesAuthor(s): Daniel J. Graham and Stephen GlaisterSource: Journal of Transport Economics and Policy, Vol. 36, No. 1 (Jan., 2002), pp. 1-25Published by: University of Bath and The London School of Economics and Political ScienceStable URL: http://www.jstor.org/stable/20053890 .

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Journal of Transport Economics and Policy, Volume 36, Part 1, January 2002, pp. 1-26

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 priceeffects. 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 priceelasticities of fuel consumption.

Date of receipt of final manuscript: September 2000

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Journal of Transport Economics and Policy Volume 36, Part 1

Introduction

This review is concerned with vehicle fuel demand elasticities. It gathersevidence on responses to fuel price changes, reporting empirical evidence

from a number of different countries. It looks at the effect of price on fuel

consumption and on motorists' demand for road travel, emphasisingdifferences 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

demandliterature, giving

an assessment of thegeneral 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. Thepaper

thengoes

on to draw out some recent

work, which by focusing on specific issues or by using innovative data or

methodology, has added substantial content to the field.

Major review articles

Survey articles on the characteristics of fuel demand are noted here in

chronological order. In most cases these studies provided new empiricalestimates 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 for

European countries in the 1980s. The author cites price and income

elasticities from previous studies predominantly estimated for the US. His

survey spans different modelling techniques including static cross-sectional

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

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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 effects. 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 from

0.86 to 1.90 respectively.The authors express concerns over the demand specifications 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 different 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.

Foos's results give a short-run price effect of ?0.28 and income effect of

0.25. The short-run price elasticity is of fairly typical magnitude but the

income effect is smaller than commonly reported. Blum et al explain this

result by pointing out that the model also contains variables reflecting 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 greatlyover-estimated the pure income elasticity.

Other interesting results reported include the cross-elasticity of gasolinedemand 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 percent increase in fuel efficiency brings about a decrease in fuel consumptionof 6.1 per cent: motorists compensate by driving more. The authors also

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

bearing on fuel demand, although they determine only a small impactfrom weather conditions.

Sterner (1990) examines the pricing and consumption of gasoline inOECD countries. His survey finds long-run price elasticities falling in the

interval ?0.65 to ?1.0 and for income between 1.0 and 1.3. Using data for

the OECD between 1962 and 1985 Sterner provides his own set of esti

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The Demand for Automobile Fuel Graham and Glaister

mates. He finds long-run price elasticities of between ?1.0 and ?1.4 using

pooled 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, and

1.1 to 1.3 for income. The short-run elasticities for dynamic models appearto 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 elasticityestimates. 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 of ?0.1 to ?0.4.

Goodwin highlights differences between recent studies by categorisingestimates 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 generallyrefers to less than one year and the ambiguous category refers to estimates

obtained from models with no explicit consideration of the time dimen

sion. Goodwin's summary of results is reproduced in Table 1.

Table 1

Summary of Evidence from Studies of Elasticity of Gasoline Consumptionwith Respect to Price

Explicit Ambiguous

Short term Long term

Time-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).

The results in Table 1 illustrate the difference in magnitude that existsbetween the short- and long-term effects 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 differences in methodo

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

methods, only marginally affected the magnitude of the elasticities.

The review also considers the effects of gasoline prices on traffic levels.

An earlier paper by Dix and Goodwin (1982) hypothesised that the shortrun elasticities of traffic 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 traffic 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 efficiency would have a strong effect

on consumption while preserving mobility.Goodwin's evidence of elasticity effects of traffic levels with respect to

fuel prices is shown in Table 2. Table 2 does not support the Dix and

Goodwin hypothesis. While it is the case that long-term elasticities are

larger than short-term, both short- and long-term effects of gasoline priceson traffic levels are much less than their effects on gasoline consumption.

Goodwin notes that this is indicative of rapid behavioural responses that

affect gasoline consumption more than traffic. He suggests that they may

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

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

price manipulation might be a more effective tool where the objective is to

decrease fuel consumption rather than to reduce road congestion.

Table 2

Summary of Evidence from Studies of Elasticity of Traffic 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)

With respect to the time effect 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


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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 effect on the pattern of travel demand.

Sterner et al (1992) examine the price sensitivity of transport gasolinedemand. 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 difference in the short- and long-term magnitudeof price and income elasticities. The short-run price elasticity of gasolinedemand varies between ?0.10 to ?0.24 depending on the model estimated.

The equivalent long-run figure is between ?0.54 and ?0.96. Averagingthese estimates gives a short-run value of ?0.23 and a long-run figure of

almost three-and-a-half times as large, ?0.77. The average national

income 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 elasticityestimates generated from lagged endogenous variable models for 20

OECD countries. These figures are shown in Table 3.

Given mean standard errors the 95% confidence interval for the

average short-run effect is from ?0.06 to ?0.42, and for long-run ?0.21 to? 1.37. The long-run income effect is about 2.8 times as large as the shortrun. Excluding Germany, Spain, and Switzerland, which have extremelylow /-ratios, and re-calculating the figures, increases the confidence intervals

for average price elasticities. For short-run effects, the confidence interval is

from ?0.12 to ?0.42, and for long-run effects from ?0.38 to ?1.38. The

long-run mean price elasticities for the OECD countries are approximately3.3 times as large as the short-run effects. The difference in order of magnitude 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 methodologicalissues, reviewing a large number of different models that have been

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

other variables. They find that different model specifications can give verydifferent estimates, and they compare model results by applying them to

the same OECD data set (1960-1985). Long-run elasticities can be esti

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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?

Boni (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 0.47.38Dahl & Sterner (199la, 199lb) -0.26 -0.86 0.48.21

Goodwin (1992) -0.27 -0.71 to -0.84 ?

Source: Dahl (1995).

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 effectivemeans 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 finds slightly lower long-run price and income elasticities from

studies based on recent data (-0.16/0.46) compared to (-0.53/1.16) from

previous estimates. However, static analyses tend to produce intermediate

run, rather than long-run, price elasticity estimates, and Dahl's 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 of?0.19/?0.66 and 0.27/0.28, and those based on the inverted V model show

long-run price and income effects of ?1.20 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. The

reliability of these results, however, is tempered by the small number of

estimates reviewed in Dahl's update, and by the predominance of static

models.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

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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 effects typically fall

between ?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 specific 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 is

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

both commercial and consumer demand. Some authors have recentlyshown that the use of micro-level data, which reflects 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 behaviouralresponses that give rise to negative price elasticities of demand for gasoline. He estimates household gasoline demand in Canada using pooledtime-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

efficient vehicles. Eltony estimates five separate equations that attempt to

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

sales per household; new car fuel efficiency; and the sales ratio of new cars.

Using pooled time-series and cross-section data on the Canadian provinces from 1969-1988 he estimates short-run gasoline price elasticities per

car, holding fuel economy constant, of ?0.21, and a short-run income

elasticity of 0.15.

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From these estimates Eltony goes on to determine dynamic priceelasticities 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

Dynamic Price Elasticities of Gasoline Demand in Canada

Year Year

1 -0.3120 7

2 -0.4673 8

3 -0.5370 9

4 -0.5981 105 -0.6984 11

6 -0.8132 12

Source: Eltony (1993).

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

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

own-price elasticity of gasoline is estimated at ?0.31. He finds that almost

75 per cent of household response to price changes in the first year can be

attributed to driving fewer miles. A further 10 per cent results from analteration in the composition of the fleet to more fuel-efficient vehicles, and

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

fleet. Eltony also finds intermediate term (5-year) price elasticities rangingfrom ?0.689 to ?0.709, and the long-term elasticities from ?0.975 to? 1.059. Table 5 also shows a rapid response to price increases within the

first four years. Eltony also interprets these results as pointing to the

importance of improving fuel efficiency as an effective means of reducinghousehold gasoline consumption.

Rouwendal (1996) seeks direct verification of the validity of short-termbehavioural 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

-0.8935

-0.9478

-0.9839

-1.0073-1.0192

-1.0239

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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, classifications 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 specifications 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 less

fuel-efficient than others and diesel cars to be more fuel efficient. Gender

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

being less fuel-efficient. 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 regardsthis central result as verification of the significant effect of gasoline priceson fuel use in the short run. Surprisingly, the income of the main driver is

found to be insignificant, although the type of employment is not. Rou

wendal points out that this result conflicts with the commonly held beliefthat there are short-run income effects. It is, however, perhaps consistent

with the finding of Blum et al (1988) that some explicit consideration of"economic activity" in gasoline demand models substantially reduces the

magnitude of the income effect.

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 for

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

specification of the type choice model.

Hensher et ?z/.'s results are consistent with Rouwendal's findings on

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

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but only small and insignificant effects from household income in the short

term. The estimated short-run price elasticities of vehicle use are ?0.26 for

1-vehicle households, ?0.33 for 2-vehicle households, and ?0.39 for 3vehicle households. However, the authors find that income is not con

firmed as an important empirical influence 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 ofmicro-level data to identify the intricacies of temporal response to short

run gasoline price changes. They review short-run estimates of priceelasticities of gasoline demand from a number of previous studies based on

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

Table 6.

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

Household Data

Short Run Price Elasticity

Archibald and Gillingham (1980) -0.43Greene and Hu (1986) -0.5 to 0.6

Walls et al. (1993) -0.51

Greening et al. (1995) 0.00 to 0.67

Dahl and Sterner (1991a) -0.52

Puller and Greening examine household adjustment to changes in the

real price of gasoline using a panel of US households over nine years. Theybelieve their work differs 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 gasolinein 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 figure they believe to be consistent with estimates from the lit

erature. However, when they use different specifications of quarterly lag

ged prices they estimate a much larger price elasticity of ?0.8. This, they

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argue, indicates that the initial immediate response of consumers to a pricerise 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 temporalnature of behaviour response. The consensus from these studies is that

short-term price elasticity effects do exist and are of the order of magnitude suggested by the main survey articles reviewed above. There is evi

dence, however, that income effects are more difficult 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 specificationthan the aggregate studies and, as Blum et al (1988) suggest, this may well

account for the absence or reduction of the income effect.

Vehicle Technology and Fuel Efficiency

Many recent studies have investigated fuel efficiency and vehicle technol

ogy characteristics in gasoline demand models. Typically, the gasolineelasticities studies, and particularly those using aggregate data, have either

not explicitly modelled fuel efficiency or have accorded the issue inade

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

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

traffic 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 Griffin (1983) provide an early example of the explicittreatment of fuel efficiency effects in gasoline demand estimation. They are

interested in the magnitude of the price elasticity of demand for gasolineand review earlier studies that show wide variation in the magnitude of

price elasticity estimates. For instance, Houthakker et al (1974), in a studyof the US, indicate very low price elasticities of demand ranging from

?0.04 to ?0.24 using quarterly data for a cross-section of states. Sweeney

(1978), on the other hand, using a model that incorporated the efficiencycharacteristics of the automobile fleet, finds a higher long-run price elas

ticity of -0.73.

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

believing them to be symptomatic of the methodology and data used. Theywish to obtain more consistent estimates and to understand the implications 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 proposeexplains gasoline consumption per vehicle by income per capita, gasoline

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

of vehicle efficiency.

Following the application of these different estimation methods Baltagiand Griffin find that the long-run price elasticity of gasoline demand

typically falls within the range ?0.6 and ?0.9? a range consistent with

the 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 find a slow adaptation rate with the major

response being due to the efficiency characteristics of the automobile fleet.

Approximately 60 per cent of the adjustment to the long-run equilibriumtakes place within the first five years

?previous studies had claimed it was

almost instantaneous. Thus they find that adaptations in the gasoline

efficiency of the fleet and driving conditions require long periods for

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

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

nological change in influencing 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 fleet fuel efficiency, km/

litre) by fuel prices, per capita income, an automobile purchase and

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

logical change.

Espey's results indicate a price elasticity of fuel economy of around

0.20, but an income elasticity not significantly different from zero. The

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

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

not explained by changes in fuel prices and income. The influence 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 coefficient on the

lagged dependent variable is 0.94, indicating that only 6 per cent of theeffect of a change in fuel prices, income, or vehicle taxation takes place in

the first year.

Espey considers the implications of her results for transport policy in

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

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 differences in fuel economy.The issue of how fuel efficiency affects gasoline demand is explored

directly by Orasch and Wirl (1997). Their investigation ismotivated by adesire to explain the asymmetry of gasoline demand with respect to energy

prices. For the US, they note that the dramatic reduction in gasoline prices

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

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

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

They estimate an energy demand model with efficiency explicitly treated

within an asymmetric framework and a second model excluding efficiency.

They find that the explicit consideration of energy efficiency proves less

important than previously thought, with little noticeable difference in price

elasticity effects. The income elasticities are found to differ?

being higherwith efficiency included in the model. The authors are sceptical about the

importance of technical efficiency to fuel demand. They conclude that

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

efficiency 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 efficiency for 12 OECD

countries 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 different esti

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

estimates for long-run car fuel and travel demand.

The results confirm the importance of increasing fuel efficiency in

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

approximately -0.7, in which the largest portion, just under 60 per cent, is

due to changes in fuel intensity. The gasoline demand figure 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 efficiency effect is found to arise from both increasedtechnical efficiency and the imposition of environmental standards.

Johansson and Schipper also consider the effects of different taxation

measures on fuel and travel demand. They find 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 efficiency in gasoline demand studies, although

yielding some quite different results, does indicate that increasing efficiencyis crucial in explaining the long-run price elasticity. Most studies show a

slow rate of adaptation, but nonetheless a strong and identifiable effect.

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 different 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 techniquesthat 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 theshort- 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 the

process 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 effect, 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-runtrends.

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-stationarythe 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 bymeans of an Error Correction Model (ECM).

Bentzen (1994) estimates short- and long-run elasticities of gasolinedemand for Denmark using annual time-series data for the economy

covering the period 1948 to 1991. The model estimated explains gasoline

consumption per capita by the price of fuel, vehicle stock per capita, and

increasing fuel efficiency represented by a time trend.

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

his model and goes on to estimate the error correction model to distinguishshort- and long-run effects. The estimated short-run price elasticity is

?0.32 and the long-run, ?0.41. The short-run vehicle per capita income

elasticity is 0.89 and the long-run 1.04.

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

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

believes that the lower value can be at least partly explained by the particular 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 Australia's road transportsector. He has quarterly data for the Australian road transport sector from

1980 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 of ?0.02 in

the short run and ?0.12 in the long run. The estimated income elasticities

are 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 difference in the long-run price effect by hypothesising that

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

also argues that use of different time periods or different econometric

specifications would yield different 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 inKuwait for the period 1970-1989 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 price

elasticity of ?0.46. The estimated short- and long-run income elasticities

are 0.47 and 0.92 respectively. Again the long-run price elasticities are

outside 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 author's results for India estimate a short-run price elasticity of

gasoline demand of ?0.21 and a short-run income elasticity of 1.18. The

cointegration model indicates that the adjustment of gasoline consumption towards its long-run equilibrium occurs at a relatively slow rate with

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

price elasticity of demand estimate is ?0.32 and the long-run income

elasticity estimate is 2.68.

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

rather inelastic price effect. The author believes that the low level of

gasoline consumption in India and the gradual increase in economic

growth can explain the differences between his results and those obtained

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

ment is unlikely to have an influential effect on gasoline demand in India.

The cointegration studies of time-series data estimate long-run priceelasticities that are often substantially lower than those reported in the

major reviews. Researchers adopting this particular technique frequentlystate 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 efficiency, a factor that has often received insuffi

cient attention in many of the cointegration studies.

Meta-analysis of Gasoline Demand Elasticities

Espey (1998) carries out "meta-analyses" of international gasolinedemand elasticities to explain the variation in the magnitude of estimated

price and income effects. 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, Espey's work seeks to explain it. The paper extends and updatesearlier work that focused on variation in elasticity estimates of gasolinedemand for the United States alone (Espey, 1996a).

Espey's study is based on an extensive review of articles publishedbetween 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 author's analysis provides four models that seek to explain separatelyvariation in the short- and long-run income and price elasticities. The basic

hypothesis is that variation in elasticity estimates can be explained bydemand specification, data characteristics, "environmental" character

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

estimation method.

Espey's results indicate that elasticity estimates are sensitive to a

number of different aspects of model structure. In terms of price effects,

the inclusion of vehicle ownership and fuel efficiency 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 longrun price elasticities, indicating that perhaps these models produce inter

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

across different dynamic specifications, and no differences 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 longrun elasticity has tended to grow. The author believes this temporal effect

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

people made some initial adjustments in driving habits and bought morefuel efficient vehicles, there were fewer options for further short-run

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responses to price changes. However, as automobile fuel efficiency

improved during the late 1970s and early to mid-1980s, long-run responsesto fuel price changes were larger than before 1974." (Espey, 1998; 290)

As regards income effects Espey's analysis finds that the inclusion of

vehicle ownership and vehicle characteristics substantially influences

results. Models that include some measure of vehicle ownership estimate

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

significant differences are found for long-run estimates between static and

dynamic models, or between different dynamic specifications. Nor are anydifferences found for long-run estimates in studies based on cross-sec

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

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

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

declining.The author concludes that the exclusion of vehicle ownership in

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

effects. The finding 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 and

estimation techniques.

Conclusions

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

about the magnitude of price elasticity effects on gasoline consumptionand private travel demand. Figure 1 illustrates differences 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. Shortrun price elasticities range from ?0.2 to ?0.5.

The Figure illustrates the important influences 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

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

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Figure 1

Petrol Price Elasticities

-1 -0.8 -0.6

H Short-run Long Run

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 specific data or methodological approaches can

create crucial differences 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 find similarities and not differences between

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

apply a variety of different estimation techniques to the same data (Baltagiand Griffin, 1983; Eltony, 1990) also produce long-run estimates within

the 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 between

2.5 and 3.5 times lower magnitude than the long-run effects. Again, this is

fairly consistent across different empirical environments.

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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 differences between the short- and long-run elasticities of

fuel consumption with respect to price. Typically, short-term

elasticities are in the region of ?0.3 and long-term between ?0.6

and ?0.8. Therefore, itmay be right to say that "it won't make much

difference" 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 significant response, albeit a less than proportionate one.

(ii) Both long- and short-term effects of gasoline prices on traffic levels

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

short-term elasticity of traffic with respect to price is about ?0.15

and long-term about ?0.30. So motorists do find ways of

economising on their use of fuel, given time to adjust. Raising fuel

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

used than in reducing the volume of traffic.

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

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

The Demand for Automobile Fuel