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Elasticities of Road Traffic and Fuel Consumption with Respect to Price and Income: A Review

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This paper gives the main results of a literature review of new empirical studies, published since 1990, updating work on the effects of price and income on fuel consumption, traffic levels, and where available other indicators including fuel efficiency and car ownership. The results are broadly consistent with several earlier reviews, though not always with current practice. The work was carried out as one of two parallel ‘blind’ literature reviews, the other being summarized in a companion paper by Graham and Glaister: the results are broadly, though not in every respect, consistent.
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0144-1647 Print/1464-5327 Online/04/030275-18 © 2004 Taylor & Francis Ltd
DOI: 10.1080/0144164042000181725
Transport Reviews, Vol. 24, No. 3, 275–292, May 2004
Correspondence Address: Phil Goodwin, ESRC Transport Studies Unit, University College London,
Gower Street, London WC1E 6BT, UK. Email: goodwin@transport.ucl.ac.uk
Elasticities of Road Traffic and Fuel Consumption with
Respect to Price and Income: A Review
PHIL GOODWIN, JOYCE DARGAY and MARK HANLY
ESRC Transport Studies Unit, University College London, London, UK
(Received 24 March 2003; revised 2 June 2003; accepted 26 September 2003)
A
BSTRACT
This paper gives the main results of a literature review of new empirical
studies, published since 1990, updating work on the effects of price and income on fuel
consumption, traffic levels, and where available other indicators including fuel efficiency
and car ownership. The results are broadly consistent with several earlier reviews, though
not always with current practice. The work was carried out as one of two parallel ‘blind’
literature reviews, the other being summarized in a companion paper by Graham and
Glaister: the results are broadly, though not in every respect, consistent.
Introduction
This is a companion paper to Graham and Glaister (2004). Both papers were
commissioned by the UK Department of the Environment, Transport and the
Regions (now called the Department for Transport), with the same project brief,
but to be carried out separately and independently as a means of ensuring the
robustness of the conclusions. The published versions were amended following
sight of each other’s draft reports, but these amendments were minor. The two
projects identified an overlapping but not identical source literature, used
different selection criteria when drawing from that literature, gave different
weights to meta-analysis and to earlier literature reviews as source material. They
had different emphases especially in relation to new evidence on freight (to which
Graham and Glaister give greater attention) and to new evidence on traffic
volumes and forecasting implications (to which the present paper gives greater
attention). The core results are strongly consistent, but there are some interesting
and illuminating differences.
Definitions
The present paper will not repeat the standard and well-rehearsed definitions and
caveats relating to the estimation and use of demand elasticities, except in relation
276 P. Goodwin et al.
to the distinction made between short- and long-term effects. Dynamic methods
of estimation are thosealways using time series data in which allowance is
made for a progressive build-up of effects over an explicitly identified time scale.
This is now standard in the fuel consumption literature and increasingly common
in the traffic literature. Static (or equilibrium) methods are thoseeither using
cross-section or time series datain which there is no explicit allowance for any
time scale of response, which their users hope relate to an end state, of
indeterminate date, when all responses have been completed.
Using this definition, the distinctions between short term and long term are
well-defined empirical results of the estimation, not assumptions based on
conjectures about behaviour. Short term is defined as responses made within one
period of the data used for the study, most commonly, in this context, within 1
year. Long term refers to the asymptotic end state when responses are (as close as
may be estimated) completed, and might vary according to what sort of
behaviour is under consideration: for much of the transport literature, periods of
510 years are estimated empirically, within which the greatest part of the
response is in the first 35 years. The present authors do not support the common
practice of using the phrases short term and long term as legitimate labels for
either cross-section equilibrium models, or unlagged time series models,
distinguished by whether they include big or small dimensions of behaviour,
which has been common in the literature (and which was indirectly, but wrongly,
applied in a previous literature review by Goodwin, 1992).
New Data
Published studies, confined to those carried out in the UK or other countries
broadly comparable with the UK, were collected from academic journals,
government reports, researchers and consultants (including, but not giving
special attention to, studies carried out by the present authors). Although some
attention was paid to old but previously unnoticed studies, these were few: the
main emphasis was on papers published since the reviews carried out by
Goodwin (1992) and Oum et al. (1992), which this exercise was intended to
update, but not treating other cumulative reviews published during this period as
independent source material. Altogether, 69 new empirical studies of this type
were collected after filtering to ensure that the same results were not included
more than once as a result of repeated publication in different forms or minor
variants, or progressive updating of the same base material. (This often happens.)
They were reinforced by a larger, and wider, literature adding other useful
evidence, earlier reviews, etc., although these were not used as sources in their
own right, and no literature review results were counted as data, since this would
have double-counted the sources used. These 69 studies produced 175 different
equations, containing 491 elasticities, based on data covering different periods
spread over the 62 years from 1929 to 1991. Over 100 results dealt with fuel
consumption, over 30 dealt with traffic levels, and others covered car sales and
fuel efficiency. Nearly all were either for cars only, or for cars and lorries added
together. At the aggregate level of interest to the review, there was very little
evidence related to commercial traffic as a whole, and no region- and sector-
specific freight studies were included.
The main properties of the database are summarized in Table 1.
Elasticities of Road Traffic and Fuel Consumption with Respect to Price and Income 277
The full report by Hanly et al. (2002) from which this summary is drawn also
includes new empirical statistical evidence for the UK, from which some of the
findings of the literature review were checked, and a discussion of the indirect
evidence in other published work, which, while not producing elasticities,
nevertheless provided evidence on related responses such as induced and
suppressed traffic. These results are not included here.
Methods of Analysis
It might seem unnecessary to state, but all the analysis was based on actually
reading the original source studies, not summaries of them. After filtering for
redundancy and repetition, all their relevant results were transferred onto a
computer database. It was then used to calculate the range of results, the average
and whether the results were different according to the methods, definitions and
scope the original papers had used. Using a similar approach, it was then
considered whether there were any definite patterns in the results, especially
whether the elasticities had been changing over time, whether short-term effects
were different from long-term effects, and whether differences in the elasticities
found were themselves, in turn, influenced by combinations of other factors, and
if so, which factors had the greatest influence. A meta-analysis was carried out,
but it was not very useful. Finally, some of the patterns were checked by statistical
analysis of the UK data to see if the same effects were noticed.
Main Results: An Overview
All the following figures are the average of quite a wide range of different
answers. Nearly all the studies are symmetrical, i.e. they assume that the effects
Table 1. Properties of the 175 estimation equations
Property Coverage
Geography Countries included in the equations are the USA (n= 63), the UK
(29), Canada (12), France (7), Germany (7), Belgium (6), OECD 12
countries (6) plus Denmark, Italy, the Netherlands, Austria, Sweden,
Norway, Spain, Australia, Japan, specific US states and various
multicountry groupings (14 each)
Data Run from 1929 to 1998, with an average duration per study of 19
years (SD = 10 years); the mid-point of the data collected is 1974
Data type Time series (n= 83), cross-section/time series (77), cross-section only
(15)
Data interval Annual (n= 145), quarterly (15), monthly (7) and other (15)
Dependent variable Fuel consumption (n= 101), vehicle-km (34), vehicles (20), fuel
efficiency (16) and other (4)
Type of vehicles/fuel Cars (n= 141), cars plus trucks (29), other (5), petrol (92), petrol plus
diesel (43), diesel only (1)
Equations and estimation Static (n= 89), dynamic (86), constant elasticity (138), linear (26),
other (8), ordinary least squares (113), full information maximum
likelihood (19), generalized least squares (18) and other (19)
278 P. Goodwin et al.
of a reduction are equal and opposite to the effects of an increase, both for price
and income. There is some empirical evidence that this assumption might not be
true, and the problem is particularly plausible if price rises induce changes in the
car fleet through earlier scrappage of inefficient vehicles. Increased scrappage of
fuel-inefficient vehicles for price rises would then not be balanced by an extra
cheap available car stock for price falls.
Price Effects
Taking what were judged to be the best defined results, the overall picture
implied is as follows. (According to the assumption of symmetry, all the
statements might be reversed by replacing up and down.) If the real price of
fuel rises by 10% and stays at that level, the result is a dynamic process of
adjustment such that the following occur:
(a) Volume of traffic will fall by roundly 1% within about a year, building up to
a reduction of about 3% in the longer run (about 5 years or so).
(b) Volume of fuel consumed will fall by about 2.5% within a year, building up to
a reduction of over 6% in the longer run.
The reason why fuel consumed falls by more than the volume of traffic is
probably because price increases trigger a more efficient use of fuel (by a
combination of technical improvements to vehicles, more fuel-conserving driving
styles and driving in easier traffic conditions). A further probable differential
effect is between high- and low-consumption vehicles, since with high prices, gas-
guzzlers are more likely to be the vehicles left at home or scrapped.
Therefore, further consequences of the same price increase are as follows:
(c) Efficiency of the use of fuel rises by about 1.5% within a year, and around 4%
in the longer run.
(d) Total number of vehicles owned falls by less than 1% in the short run, and by
2.5% in the longer run.
At face value, the results imply that the sensitivity of car ownership with respect
to fuel price is rather large, constituting a larger part of the effect of price on traffic
levels. Attention is drawn to a strong caveat: many studies only assess the effects
on car ownership, on traffic or on use per car, but not at the same time or when
using the same data. Therefore, this conclusion is based on drawing together quite
different studies. Considerations of sample sizes suggest that the two effects (c)
and (d) are somewhat less well supported than (a) and (b). At this stage, the
authors view is that the results do support the idea that the effects of prices on car
ownership are important enough to take seriously, but are not necessarily such an
overwhelmingly large part of the overall effect.
Income Effects
If real income goes up by 10%, the following occurs:
Number of vehicles, and the total amount of fuel they consume, will both rise
by nearly 4% within about a year, and by over 10% in the longer run.
Elasticities of Road Traffic and Fuel Consumption with Respect to Price and Income 279
However, the volume of traffic does not grow in proportion: 2% within a year
and about 5% in the longer run.
Taken together, these would imply that use per car declines as income increases,
although (as with the price effect above) this depends on the comparison of
different studies and is not yet well supported by direct evidence. (A small
number of studies show a direct hint of this in the short run, but not in the long
run.) It is possible that as incomes increase, successive new car owners are
attracted into the car market who have less inclination to drive much. An
additional effect implied is that rising income has generally been associated
with a fall in the efficiency of the use of fuel, for which a possible reason might
be that as incomes grow, people buy newer, but larger, vehicles. Such decisions
can also raise the numbers of multiple cars per driver (e.g. sports vehicles) in
wealthy countries/households, while in poorer countries/households, it may be
more associated with the first acquisition of cars by non-workers who typically
use them less.
One strong, repeated and consistent result is that studies using methods that
allow explicit estimation of short- and long-run elasticities separately nearly
always find that the long-run effect is substantially higher than the short-run
effect, for both price and income, and for all measures of demand.
The present authors did not have sufficient information in the studies to
calculate an overall freight transport effect at the aggregate level separately, but
there are three pieces of relevant evidence. First, the effects of a price increase for
diesel plus petrol cause a smaller reduction in the total amount of fuel bought
than for petrol alone. Second, the effect of an overall fuel price increase has a
smaller effect on the total traffic level (including lorries) than petrol prices have on
the private car traffic. Third, as Graham and Glaister show, results of studies in
particular freight sectors must also imply an aggregate effect. However, there are
reasons to suppose that the influence of price on freight operators decisions can
be different from those affecting individuals, in particular because commercial
vehicle operators are less likely to ignore or misperceive categories of cost such as
labour, depreciation, etc., and because freight costs are part of a wider production
and distribution process. These considerations mean that the direct fuel costs are
likely to be a smaller proportion of (perceived) total costs for freight than for
passenger transport.
Although not all goods vehicles use diesel and not all cars use petrol, these
results taken together suggest that goods traffic is less sensitive to price, and
private cars more sensitive. The difference is large enough to be important, but
not well defined enough in the data to provide a definite figure, because the
proportion of lorries and cars varies greatly, but is not recorded in most
studies.
The same is not true for effects of changes in income, for which the effect on
personal transport and goods transport seems to be rather similar in size.
Sources of Variation in Elasticities
Certain features are now well established and can be taken as strong results.
These relate to the differences between elasticities based on traffic or fuel
consumption, the effect of dynamic process, and the relative size of income and
price effects:
280 P. Goodwin et al.
Fuel consumption elasticities are greater than traffic elasticities, mostly by
factors of 1.52.
Long-run elasticities are greater than short run elasticities, mostly by factors of
23.
Income elasticities are greater than price, mostly by factors of 1.53.
Espey (1998) carried out a meta-analysis using linear regression to investigate 32
potential causes of variation in the estimated elasticity coefficients for fuel
consumption, including measures of demand specification, data characteristics,
geographical and other contexts, and estimation technique. These factors together
explained between one-quarter and one-third of the variation found in the
elasticities, which is perhaps disappointing given the number of independent
variables and the fact that the estimated elasticities themselves already are highly
aggregated in character. Many of the factors included in Espeys published
equations had effects that were individually not statistically significant or which
did not seem to relate to any particular hypothesis, or both.
A similar approach was applied to the studies in the present database, although
not with exactly the same definitions as Espey. In particular, the static results
were we separated out from the dynamic results. (Espey included each static
result in both short- and long-run dynamic results, which bypasses the need to
classify them in terms of their time scale, but which will weaken the statistical
association.) The resulting full equations are given in Hanly et al. (2002). The main
results are shown qualitatively in Table 2.
The results, shown in detail in the full report, can explain a high proportion of
the variation, albeit remembering that the segmented form of analysis has already
taken out many important sources of variation, e.g. between short- and long-run
effects or between effects on fuel consumption and traffic volume. Such statistical
explanatory power is at the expense of including coefficients that, taken on their
own, have very poor significance, and usually fail to show a systematic pattern:
it is not useful to know that, for example, maximum likelihood estimation
sometimes produces significantly higher, and sometimes significantly lower,
elasticities with no readily apparent reason to explain which case is which. It is
possible that more detailed analysis would produce hypotheses that make sense,
but this form of meta-analysis is not very revealing, and the present authors have
chosen not to spend further time on it.
Implications for Practice
The full report (Hanly et al., 2002) made a number of recommendations for
changes in the assumptions and relationships used for forecasting, especially in
the 10-year frame of interest to the Department for Transport. Particular points of
interest included the following:
Effects of price on traffic levels, although not huge, were bigger than had been
assumed in earlier forecasts.
These elasticities did not appear to decline over time as much as would be
assumed when using the generalized cost framework as defined by the
Department for Transport, or perhaps at all. (Income elasticities, however, have
declined over time.)
Elasticities of Road Traffic and Fuel Consumption with Respect to Price and Income 281
Table 2. Summary of meta-analysis results for sources of variation in estimated
elasticities
Petrol price:
Pence per litre (p/litre)
Pence per km (p/km)
Few direct results, so most inferences have to be indirect.
Weak evidence that short run price elasticity is higher for
p/km and long run higher for p/litre. p/km gives lower
income elasticity then p/litre for vehicle-km, higher for vehicle
stock
Functional form:
Log-linear
Linear
Non-linear
Semilog
BoxCox
No strong consistent pattern of effect of model form.
Miscellaneous hints (e.g. log-linear gives lower elasticities of
car ownership with respect to income than do other non-linear
forms), but the effect is not strong
Model specification:
Partial adjustment
Error Correction Model
Inverted-v lag
Some significant differences, but with no systematic or
well-supported pattern that would relate to useful hypotheses
or repeatable results
Quantity measure:
Per capita
Aggregate
Per household
Some cases indicating that per capita measures give lower
price elasticities and higher income elasticities for fuel
consumption. Sample sizes too small for other demand
measures
Data interval:
Annual
Quarterly
Monthly
Annual data gives lower price elasticity and higher income
elasticity for fuel consumption. A number of other statistically
significant but non-systematic results
Data type:
Time series
Cross-section
Cross-section/time series
Pooled time series/cross-section analysis (usually comparisons
of countries) has some tendency to give lower elasticities
when using dynamic specification
Country:
Europe
USA
OECD
Australia, Canada, Japan
Other
USA has lower fuel consumption elasticities than Europe with
respect to both price and income. The OECD seems to have
higher elasticities, although this fact is not supported by
consideration of the countries within the OECD. Other results
are not very consistent
Time:
Data set ends before 1974
Data set ends 197481
Data set ends after 1981
Several results show that the middle period has higher price
elasticities and lower income elasticities than early or late
periods. There is no evident systematic decline except,
perhaps, for long run income effect on fuel consumption
Estimation method:
Ordinary least squares
(two-stage least squares,
three-stage least squares,
maximum likelihood, error
components, generalized
least squares, iterative,
instrumental variables,
seemingly unrelated least
squares)
Many significant differences, but unrevealing as in every case
there was little or no consistency about whether differences
were positive or negative
282 P. Goodwin et al.
Further, but less firm, evidence related to fuel efficiency that could have a big
effect on how technical changes have an impact on traffic levels.
It is interesting that early results of congestion charging in London also seemed to
indicate that the price elasticities were higher than expected, so that traffic
reductions were greater, but revenue less, than forecast.
The results of the present review were used to inform changes to the
Department for Transport forecasting procedures implemented during 2002 and
contributed to substantial amendments in forecasts (Department for Transport,
2002), of which the most notable is that the level of traffic congestion is now
expected to increase between 2000 and 2010 rather than decline as had
previously been expected. This is prompting a reconsideration of several
important policy areas, including the role of road-user charging and fuel prices.
However, it should be stated that revised price elasticities were not the only
new element in this change of forecasts, and a reconsideration is currently in
progress for the effects of other policy instruments to which similar considera-
tions may apply.
Table 3. Overall results: elasticities of various measures of demand with
respect to fuel price per litre produced by dynamic estimation using time
series data
Dependent variable Short-term Long-term
Fuel consumption (total)
Mean elasticity 0.25 0.64
Standard deviation 0.15 0.44
Range 0.01, 0.57 0, 1.81
Number of estimates 46 51
Fuel consumption (per vehicle)
Mean elasticity 0.08 1.1
Standard deviation n/a n/a
Range 0.08, 0.08 1.1, 1.1
Number of estimates 1 1
Vehicle-km (total)
Mean elasticity 0.10 0.29
Standard deviation 0.06 0.29
Range 0.17, 0.05 0.63, 0.10
Number of estimates 3 3
Vehicle-km (per vehicle)
Mean elasticity 0.10 0.30
Standard deviation 0.06 0.23
Range 0.14, 0.06 0.55, 0.11
Number of estimates 2 3
Vehicle stock
Mean elasticity 0.08 0.25
Standard deviation 0.06 0.17
Range 0.21, 0.02 0.63, 0.10
Number of estimates 8 8
n/a = Not available
Elasticities of Road Traffic and Fuel Consumption with Respect to Price and Income 283
Appendix: Detailed Results
Using a simple classification adapted from the form that has become common in
earlier reviews, results of the analysis are shown in Tables 37, where each cell
shows the elasticities produced by different studies. Mean results, standard
deviations and sample sizes (i.e. the number of estimated coefficients cited) are
given to help assess the backing for the results, although each datum point in
this case is a statistically derived result with its own statistical properties, i.e. at
least two stages away from real data, and it would not be useful to apply
conventional significance tests.
Note that in all cases the range of results is quite wide, and the standard
deviations are large in relation to the means. This is not surprising because the
estimates come from such a wide range of different sources and contexts, whose
separate influence is considered in later sections. The exclusion of some multiple
results from single studies also has the effect of revealing a wider variance than
if they had all been included.
Table 4. Overall results: elasticities of various measures of demand with respect
to fuel price per litre produced by static estimation
Dependent variable Total
Of which
Cross-section
data
Cross-section/time
series data
Time series
data
Fuel consumption (total)
Mean elasticity 0.43 0.55 0.28 0.48
Standard deviation 0.23 0.32 0.10 0.16
Range 0.11, 1.12 0.23, 1.12 0.45, 0.11 0.77, 0.28
Number of estimates 24 7 9 8
Fuel consumption (per vehicle)
Mean elasticity 0.30 no observations 0.30 no observations
Standard deviation 0.22 0.22
Range 0.89, 0.04 0.89, 0.04
Number of estimates 22 22
Vehicle-km (total)
Mean elasticity 0.31 0.38 0.27 0.32
Standard deviation 0.14 0.23 0.12
Range 0.54, 0.13 0.54, 0.21 0.41, 0.13 0.32, 0.32
Number of estimates 7 2 4 1
Vehicle-km (per vehicle)
Mean elasticity 0.51 no observations 0.33 0.69
Standard deviation 0.25 ––
Range 0.69, 0.33 0.33, 0.33 0.69, 0.69
Number of estimates 2 1 1
Vehicle stock
Mean elasticity 0.06 0.03 0.11 no observations
Standard deviation 0.08 0.03
Range 0.13, 0.03 0.03, 0.03 0.13, 0.09
Number of estimates 3 1 2
284 P. Goodwin et al.
The price elasticities for fuel consumption are higher than the elasticities for
vehicle-km, i.e. when fuel price rises, people reduce their fuel consumption more
than their mileage. The methods available to do so are (1) change driving styles
(less heavy acceleration and breaking, more fuel economical speeds; (2) a shift in
the pattern of journeys such that more of them are in fuel-efficient contexts (e.g.
light traffic at moderate speeds as compared with very low or very high speeds);
(3) changing to more fuel-efficient vehicles, e.g. newer, better maintained, smaller
or more technically advanced.
To a first approximation for small quantities, the relationship is as follows:
Elasticity of fuel efficiency = elasticity of fuel consumption + elasticity of vehicle-km.
Given the results in Tables 2 and 3, this suggests that the effect of price changes
on efficiency is quite large.
The elasticity of the response of vehicle ownership to fuel price is smaller
than the elasticity of vehicle-km, but not much smaller. At face value, this
suggests that a larger component (perhaps 80%) of the change in traffic level is
Table 5. Overall results: elasticities of various measures of demand with respect
to income using dynamic estimation
Dependent variable Short-term Long-term
Fuel consumption (total)
Mean elasticity 0.39 1.08
Standard deviation 0.25 0.35
Range 0.00, 0.89 0.27, 1.71
Number of estimates 45 50
Fuel consumption (per vehicle)
Mean elasticity 0.07 0.93
Standard deviation n/a n/a
Range 0.07, 0.07 0.93, 0.93
Number of estimates 1 1
Vehicle-km (total)
Mean elasticity 0.30 0.73
Standard deviation 0.21 0.48
Range 0.05, 0.62 0.12, 1.47
Number of estimates 7 7
Vehicle-km (per vehicle)
Mean elasticity 0.005 0.17
Standard deviation 0.01 0.19
Range 0.02, 0.005 0.00, 0.41
Number of estimates 3 4
Vehicle stock
Mean elasticity 0.32 0.81
Standard deviation 0.21 0.43
Range 0.08, 0.94 0.28, 1.62
Number of estimates 15 15
n/a = Not available
Elasticities of Road Traffic and Fuel Consumption with Respect to Price and Income 285
brought about by a change in vehicle ownership. This is somewhat at odds
with a widespread assumption that car ownership is relatively insensitive to
fuel price, and a whole literature demonstrating that travel demand responses
other than car ownership do have an importance of their own. Since the result
arises from relatively few studies in this review, it should be treated as less
well founded than the stronger effects noted above. Nevertheless, this is an
indication that car ownership is influenced, to some extent, by fuel price, and
this should not be dismissed.
Comparison with Earlier Reviews
Previous generations of literature reviews had been carried out by Oum et al.
(1992), Sterner and Dahl (1992), Goodwin (1992), then by Lee (1998), Espey (1998),
Graham and Glaister (2002), and others. These reviews substantially overlap,
making use of various subsets of the same primary sources, and updated by
accumulation: this naturally blurs any tendency for the estimates to change.
Table 6. Overall results: elasticities of various measures of demand with respect
to income using static estimation
Dependent variable Total
Of which
Cross-section
data
Cross-section/time
series data
Time series
data
Fuel consumption (total)
Mean elasticity 0.49 0.51 0.51 0.44
Standard deviation 0.40 0.39 0.39 0.52
Range 0.02, 1.44 0.15, 1.25 0.22, 1.44 0.02, 1.34
Number of estimates 20 6 9 5
Fuel consumption (per vehicle)
Mean elasticity 0.55 no observations 0.52 no observations
Standard deviation 0.35 0.35
Range 0.07, 1.14 0.07, 1.14
Number of estimates 19 19
Vehicle-km (total)
Mean elasticity 0.49 0.47 0.46 0.55
Standard deviation 0.42 0.02 0.51 0.40
Range 0.05, 1.44 0.46, 0.48 0.05, 1.44 0.15, 1.18
Number of estimates 15 2 8 5
Vehicle-km (per vehicle)
Mean elasticity 0.06 0.07 no observations 0.03
Standard deviation 0.03 0.01
Range 0.03, 0.08 0.06, 0.08 0.03, 0.03
Number of estimates 3 2 1
Vehicle stock
Mean elasticity 1.09 1.89 0.78 1.22
Standard deviation 0.56 0.40
Range 0.49, 1.89 1.89, 1.89 0.49, 1.23 1.22, 1.22
Number of estimates 5 1 3 1
286 P. Goodwin et al.
Key results from the earlier reviews are summarized in Tables 810, in order of
publication, and slightly rearranging the published results to fit into the format
used for the present authors results.
Espey (1998) carried out an analysis of 101 citations on fuel consumption,
mostly built on earlier reviews and data collated by Sterner and Dahl (1992),
published between 1966 and 1997 with data from 192993. Table 9 shows Espeys
comparable results.
Graham and Glaister (2002) came to the general conclusions shown in Table 10,
which were based substantially on the earlier reviews together with some later
empirical sources.
These reviews give results that are of broadly similar general magnitude as the
earlier results.
Results Related to Modelling and Forecasting
There is a widely used facility, provided by the conventions of generalized cost,
for building logical extensions that go beyond the results of empirical research.
Table 7. Overall results: elasticities of various measures of demand with respect
to car purchase cost: whole database
Dependent variable Short-term Long-term Static
Fuel consumption (total)
Mean elasticity 0.12 0.51 0.45
Standard deviation 0.08 0.24 0.25
Range 0.26, 0.00 0.88, 0.00 0.66, 0.15
Number of estimates 11 10 4
Fuel consumption (per vehicle)
Mean elasticity n/a n/a n/a
Standard deviation n/a n/a n/a
Range n/a n/a n/a
Number of estimates n/a n/a n/a
Vehicle-km (total)
Mean elasticity 0.19 0.42 0.35
Standard deviation 0.12 0.21 0.42
Range 0.33, 0.11 0.62, 0.20 0.65, 0.05
Number of estimates 3 3 2
Vehicle-km (per vehicle)
Mean elasticity n/a n/a n/a
Standard deviation n/a n/a n/a
Range n/a n/a n/a
Number of estimates n/a n/a n/a
Vehicle stock
Mean elasticity 0.24 0.49 0.38
Standard deviation 0.15 0.19 0.29
Range 0.44, 0.03 0.78, 0.13 0.59, 0.05
Number of estimates 11 11 3
n/a = Not available
Elasticities of Road Traffic and Fuel Consumption with Respect to Price and Income 287
Using a mathematical derivation reported in Hanly et al. (2002), the main
implications are as follows:
Price elasticities will be positively related to price level, and will rise and fall as
real price rises and falls.
Price elasticities will be negatively related to income, and therefore would tend
to fall over time.
Price elasticities will have a definite relationship with travel time elasticities.
Not derived directly from the generalized cost argument, but nevertheless
overlapping with its results, is the expectation from ideas of saturating car
ownership that elasticities of demand for cars should come down as income
increases, and therefore over time, with a consequent, possibly weaker, effect for
traffic volume.
Table 8. Review results from Goodwin (1992)
Dependent variable
Elasticity with respect to fuel price
Short Long
Vehicle-km 0.16 (n= 4) 0.32 (n= 6)
Fuel consumption 0.27 (n= 57) 0.73 (n= 53)
Results originally identified as ambiguous or unspecified are not included
Table 9. Review results from Espey (1998)
Dependent variable
Elasticity with respect
to fuel price
Short Long
Elasticity with respect
to income
Short Long
Fuel consumption 0.26
(n= 277)
0.58
(n= 363)
+ 0.47 + 0.88
(n= 345)
Table 10. Review results from Graham and Glaister (2002)
Dependent variable
Elasticity with respect
to fuel price
Short Long
Elasticity with respect
to income
Short Long
Vehicle-km 0.15 0.3
Fuel consumption 0.2 to 0.3 0.6 to 0.8 0.35 to 0.55 1.1 to 1.3
288 P. Goodwin et al.
The literature database contains several sources of variation for incomes and
prices, primarily variation between places, and over time. Some results are shown
in Tables 11 and 12. Only the short-run fuel consumption price elasticity behaves
in a way fully in accordance with both hypotheses. The long-run elasticity is
supportive of the expectation that price elasticity is related to price level, but not
the overall trend of income. The static results appear to demonstrate the opposite.
In general, the expectation of a decline in income elasticities over time is mildly
supported.
The empty cells in Table 11 arise because the new dynamic studies included in
the present review mostly update the data series, therefore there are no new
studies only relating to the earlier period. However, it is notable that the dynamic
results for fuel price, at 0.1 for short-run and at 0.29 for long-run effects on
traffic volume, show a slightly lower short-run elasticity but virtually the same
long-term elasticity as reported 10 years ago in, for example, Goodwin (1992),
whose own results seemed similar to comparable results 10 years earlier. If one
were only to look at the static results, there is an appearance of a downward
movement, although this is based on only seven studies. The most important
figure for forecasting, 0.29 for the price elasticity in the latest period, is as high
as has ever been estimated. There is no obvious trend effect for income
elasticities.
Similar analyses were carried out for effects on the vehicle stock and fuel
efficiency not reported here.
Collating these results, one finds that quite a number of indications that
demand elasticities with respect to income have declined over the period of the
Table 11. Elasticities of fuel consumption with respect to fuel price and income
Period
Average elasticity with respect to
fuel price
Short Long Static
Average elasticity with respect to
income
Short Long Static
Pre-1974 0.29 0.45 0.56 0.52 1.28 0.63
197481 0.35 0.93 0.36 0.37 1.08 0.43
Post-1981 0.16 0.43 0.28 0.38 1.04 0.14
Table 12. Elasticity of vehicle-km with respect to fuel price and income
Period
Average elasticity with respect to
fuel price
Short Long Static
Average elasticity with respect to
income
Short Long Static
Pre-1974 n/a n/a 0.54 n/a n/a 0.30
197481 n/a n/a 0.32 n/a 0.21 0.57
Post1981 0.10 0.29 0.24 0.30 0.73 0.49
n/a = Not available
Elasticities of Road Traffic and Fuel Consumption with Respect to Price and Income 289
studies, but there is a much more mixed picture about price elasticities. Some
analyses suggest they have declined (notably for short-term effects) and others
that they have increased (notably for long-term effects). Perhaps the most
intriguing feature here is the absence of strong, clear evidence of a systematic
decline, since the effect, if price elasticities are indeed inversely related to income,
would be a large one over such a long period of continued income growth.
These results are supported by other studies, and some new empirical analysis
of UK data from 1960 to 2000, for different functional forms, model specifications
and periods. The results are shown in Hanly et al. (2002). These strongly
supported a declining income elasticity for both fuel consumption and vehicle-
km. Specifications using (1) a constant price elasticity and (2) a declining price
elasticity as demand increases are indistinguishable on statistical grounds. In
addition, there is no evidence that price elasticity is related to price level.
However, dividing the period into three indicates that price elasticity has increased
over time, not reduced as the generalized cost hypothesis would suggest.
Thus, the area of current practice that seems least consistent with the results is
the assumption that price elasticities will come down as income increases. This in
turn puts pressure either on the presumption that generalized cost behaves in the
way assumed, or on the presumption that values of time increase as income
increases.
Bibliography
Main source reference: Hanly, M., Dargay, J. & Goodwin, P. (2002). Review of
Income and Price Elasticities in the Demand for Road Traffic. Report 2002/13
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encyclopaedia (available at: http://www.vtpi.org/tdm/) and to an earlier
literature review by TRACE (1998) (also as Jong and Gunn, 2001), which was
not used as separate source evidence itself, but whose sources overlap those
used here.
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The dominance of motorcycles in mixed transport systems in developing cities and countries might lead to several problems such as traffic congestion and accident. To solve these challenges and increase the modal share of public transport (PT), several new PT projects have been invested in these countries. However, there seems to be very little evidence on evaluation methods of all transport modes to analyse the feasibility of a new PT mode and identify the most cost-effective mixed transport system. Therefore, it is essential to have a comprehensive evaluation method for motorcycle, car, Demand Responsive Transit (DRT) and PT in mixed transport environments. Hence, the main aim of this thesis is to develop a comparative economic assessment for evaluating the feasibility of a new PT mode and choosing the best mixed transport system based on the PT technologies’ characteristics and the conditions of local transport networks. The comparative economic assessment is integrated from four models: Social Cost Model, Incremental Elasticity Analysis, Incremental Multinomial/Nested Logit Model and Microscopic Simulation Model. The Social Cost Model calculates the social costs of exclusive private transport (PRV), segregated PT, exclusive DRT and mixed transport at a strategic planning level. The Incremental Elasticity Analysis evaluates endogenous changes in total general demand of all transport modes by using the demand elasticity with respect to a composite cost (a logsum). The Incremental Multinomial/Nested Logit Model estimates the choices of passengers in favour of all transport modes with respect to generalised costs. The Microscopic Simulation Model simulates all existing transport modes’ flows on the local network by using a microscopic simulation model in VISSIM, which is developed, calibrated and validated based on the data collected from one real urban corridor in Hanoi, the capital of Vietnam. The comparative economic assessment was applied to compare the existing mixed transport situation and twelve transport infrastructure options with a new PT technology (Bus Rapid Transit, elevated Metro or Monorail) replacing the existing bus services; either wholly or partially, and with or without a congestion charge scheme for PRV on the chosen corridor in Hanoi, in terms of average social cost, total general demand and PT share. The results show that eight options with Bus Rapid Transit or Monorail or Metro are feasible. In addition, the BRT option thatreplaces all existing buses with a congestion charging scheme is the best alternative in terms of average social cost. Transport planners and decision makers in Hanoi can draw on the findings of this thesis. Moreover, the methodology of the comparative economic assessment might be applied and modified to various transport networks with an abundance of motorcycles to assess the costs and benefits of each new PT technology and mixed transport systems with or without the congestion charge. However, various limitations are identified and further work is suggested.
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Regulating the production and incentivizing the purchase of energy-efficient cars has long been a primary policy recommendation to curb the world's ever-increasing energy usage. However, as cars become increasingly energy efficient, the cost of using them decreases, and cheaper usage costs provide a strong economic motivation to increase the usage of these cars. A rich body of literature examining this ‘rebound effect’ under the fuel efficiency standards regulating manufacturers (e.g., CAFE) has largely concluded that the effect diminishes over time. However, research on policies targeting consumers remains limited, and the magnitude and trend over time of a rebound associated with such policies remain unclear. We empirically estimated a ten-year rebound following ongoing policy measures targeting consumers in Israel, in a research setting highly suitable for disentangling the complex effects affecting distance traveled and the demand for energy efficiency of cars. The empirical results indicated a fairly large rebound effect of 62% emerging shortly after the initial introduction of the policy. Unexpectedly, this rebound effect gradually intensified over time, reaching the point at which all potential energy savings were lost to increased driving.
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What happens when motor fuel prices rise? In the US, the National Household Travel Survey (NHTS) collects information about car ownership and use, and travel during a typical day, for over 100,000 households. It is conducted only once every eight years, and does not include a longitudinal component, making it difficult to observe drivers’ adjustments to changing gasoline prices. We experiment with combining the 2017 NHTS with eight waves of the American Time Use Survey (ATUS), which tracks trips, time spent traveling, and other characteristics of each trip. We find that the two datasets document remarkably similar behaviors—whether we use the eight waves of the ATUS or limit the comparison to the period when the two surveys overlap (April 2016 to April 2017). They also document similar responsiveness to prices—at least for the decision to take a car or a public transit trip. By contrast, minutes on the road and miles in the NHTS appear to be strongly responsive to gasoline prices, whereas minutes on the road from the ATUS are unrelated to fuel prices, despite the much greater price variation therein. The results are robust to extensive checks and efforts to reduce measurement error in gasoline prices. A 25-cent increase in the price of gasoline is predicted to reduce CO2 emissions by 1–5%.
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The price and income elasticities of highway gasoline and automobile travel demand are useful for forecasting gasoline tax revenues and highway investment needs and evaluating policies to reduce automobile use, improve fuel efficiency, or reduce greenhouse gas emissions. Gasoline and travel demand elasticities are calculated using 1950 to 1994 time series data for the United States and 1988 to 1992 pooled data for states of the United States. Gasoline demand was found to be price inelastic in the short run, but in the long run, it was found to be —0.7. Even in the United States, gasoline price has a significant impact on gasoline use. The response to price changes is divided among driving, fuel efficiency, and the size of the vehicle stock, although the latter is the smallest. The Corporate Average Fuel Economy (CAFE) program was found to be associated with an average 1 percent annual decline in per capita fuel consumption. The elasticity of driving with respect to fuel efficiency— the rebound effect—was found to be —0.3, confirming previous results. The state-level data produce inconclusive results; it is hypothesized that this is the result of the confounding effect of CAFE.
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An international collection of twenty papers with three themes: energy demand, modelling energy supply and models of specific markets.
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There is considerable scope for energy conservation. Examines the effect of energy prices on demand. For motor gasoline, both vehicle miles travelled and automobile stock respond to changing prices. Consumption of diesel, aviation fuel and electrical energy respond to energy prices as well as to the level of economic activity. The magnitude of the price responsiveness is typically small, since energy costs are only a minor portion of truck, bus, air and rail transport costs.-Author
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Transport fuels account for an increasing share of oil consumption, and savings appear to be both technically and socially harder to achieve than in many other sectors where substitutes are more easily available. Large sums of money are invested in trying to improve efficiency of vehicles but the really most relevant issue is that of whole transport systems. These systems cannot be planned in detail, however, but are the result of many individual actions. It is therefore of particular interest to study the economics of the transport fuel market and thereby to evaluate the efficiency of the price mechanism as an instrument of policy in this area. Taxes and hence domestic prices of transport fuels vary considerably between countries (Sterner, 1989a, Sterner, 1989b; Angelier and Sterner, 1990) and thus high demand elasticities would imply considerable differences in consumption patterns.
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This paper presents a new approach to the estimation of dynamic transport demand models by using information from independent samples of cross-sections of the population collected over time. A 'pseudo-panel' approach is used to estimate a dynamic model of car ownership, based on data from annual Family Expenditure Surveys in the UK. The determinants of car ownership investigated include income, the costs of car ownership and use, public transport fares, and the socio-demographic characteristics of the households. The results obtained suggest that the applied methodology can provide a very fruitful basis for exploring the dynamics of household transport behaviour.