Download Fuel & Traffic Elasticities w/ Price & Income: A Review and more Study notes English Literature in PDF only on Docsity!
0144-1647 Print/1464-5327 Online/04/030275-18 © 2004 Taylor & Francis Ltd DOI: 10.1080/
Correspondence Address: Phil Goodwin, ESRC Transport Studies Unit, University College London, Gower Street, London WC1E 6BT, UK. Email: [email protected]
Elasticities of Road Traffic and Fuel Consumption with
Respect to Price and Income: A Review
PHIL GOODWIN, JOYCE DARGAY and MARK HANLY
(Received 24 March 2003; revised 2 June 2003; accepted 26 September 2003)
ABSTRACT 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 those—always 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 those—either using
cross-section or time series data—in 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
5 – 10 years are estimated empirically, within which the greatest part of the
response is in the first 3–5 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.
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:
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 Box–Cox
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 1974– 81 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. Standard deviation 0.15 0. Range – 0.01, – 0.57 0, – 1. Number of estimates 46 51
Fuel consumption (per vehicle) Mean elasticity – 0.08 – 1. Standard deviation n/a n/a Range – 0.08, – 0.08 – 1.1, – 1. Number of estimates 1 1
Vehicle-km (total) Mean elasticity – 0.10 – 0. Standard deviation 0.06 0. Range – 0.17, – 0.05 – 0.63, – 0. Number of estimates 3 3
Vehicle-km (per vehicle) Mean elasticity – 0.10 – 0. Standard deviation 0.06 0. Range – 0.14, – 0.06 – 0.55, – 0. Number of estimates 2 3
Vehicle stock Mean elasticity – 0.08 – 0. Standard deviation 0.06 0. Range – 0.21, – 0.02 – 0.63, – 0. Number of estimates 8 8
n/a = Not available
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. Standard deviation 0.25 0. Range 0.00, 0.89 0.27, 1. Number of estimates 45 50
Fuel consumption (per vehicle) Mean elasticity 0.07 0. Standard deviation n/a n/a Range 0.07, 0.07 0.93, 0. Number of estimates 1 1
Vehicle-km (total) Mean elasticity 0.30 0. Standard deviation 0.21 0. Range 0.05, 0.62 0.12, 1. Number of estimates 7 7
Vehicle-km (per vehicle) Mean elasticity – 0.005 0. Standard deviation 0.01 0. Range – 0.02, 0.005 0.00, 0. Number of estimates 3 4
Vehicle stock Mean elasticity 0.32 0. Standard deviation 0.21 0. Range 0.08, 0.94 0.28, 1. 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. Standard deviation 0.40 0.39 0.39 0. Range 0.02, 1.44 0.15, 1.25 0.22, 1.44 0.02, 1. 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. Range 0.07, 1.14 0.07, 1. Number of estimates 19 19
Vehicle-km (total) Mean elasticity 0.49 0.47 0.46 0. Standard deviation 0.42 0.02 0.51 0. Range 0.05, 1.44 0.46, 0.48 0.05, 1.44 0.15, 1. Number of estimates 15 2 8 5
Vehicle-km (per vehicle) Mean elasticity 0.06 0.07 no observations 0. Standard deviation 0.03 0.01 – Range 0.03, 0.08 0.06, 0.08 0.03, 0. Number of estimates 3 2 1
Vehicle stock Mean elasticity 1.09 1.89 0.78 1. Standard deviation 0.56 – 0.40 – Range 0.49, 1.89 1.89, 1.89 0.49, 1.23 1.22, 1. Number of estimates 5 1 3 1
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. ( n = 277)
( n = 363)
( 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.
Fuel consumption – 0.2 to – 0.3 – 0.6 to – 0.8 0.35 to 0.55 1.1 to 1.
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. 1974 – 81 – 0.35 – 0.93 – 0.36 0.37 1.08 0. Post-1981 – 0.16 – 0.43 – 0.28 0.38 1.04 0.
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. 1974 – 81 n/a n/a – 0.32 n/a 0.21 0. Post– 1981 – 0.10 – 0.29 – 0.24 0.30 0.73 0.
n/a = Not available
290 P. Goodwin et al.
Cairns, S. (2000) Reactions and developments following the report: ‘Traffic Impact of Highway Capacity Reductions: Assessment of the Evidence’, Summary of Work in Progress. Paper presented at the Reallocating Roadspace Conference, 10 February 2000 (Landor Conferences) (London: ESRC Transport Studies Unit, University College London). Cairns, S., Hass-Klau, C. and Goodwin, P. B. (1998) Traffic Impact of Highway Capacity Reductions: Assessment of the Evidence (London: Landor). Chambers, M. J. (1999) Consumers’ Demand and Excise Duty Receipts Equations for Alcohol, Tobacco, Petrol and DERV (London: HM Treasury). Coto-Mill´an, P., Ba nos-Pino,˜ J. and Inglada, V. (1997) Marshallian demands of intercity passenger transport in Spain: 1980–1992 an economic analysis, Transportation Research , 33E, pp. 79 – 96. Dahl, C. (1979) Consumer adjustment to a gasoline tax, Review of Economics and Statistics , 61, pp. 427 – 432. Dahl, C. (1982) Do gasoline demand elasticities vary?, Land Economics , 58, pp. 373–382. Dargay, J. (1995) Measuring the components of dependence, in: P. B. Goodwin (Ed.) Car Dependence: A Report for the RAC Foundation for Motoring and the Environment , pp. 53–86 (London: RAC Foundation for Motoring and the Environment). Dargay, J. and Gately, D. (1997) The demand for transportation fuels: imperfect price-reversibility?, Transportation Research , 31B, pp. 71–82. Dargay, J., Madre, J. and Berri, A. (2000) Car ownership dynamics seen through the follow-up of cohorts: a comparison of France and the United Kingdom, Transportation Research Record , pp. 31 – 38. Dargay, J. and Vythoulkas, P. (1998) Estimation of dynamic transport demand models using pseudo- panel data. Paper presented at the 8th World Conference on Transport Research, Antwerp, Belgium. Dargay, J. M. (1991) The Irreversible Demand Effects of High Oil Prices Motor Fuels in France, Germany and the UK (Oxford: Oxford Institute for Energy Studies). Dargay, J. M. (1993) The Demand for Fuels for Private Transport in the UK. Discussion Paper No. 72 (Guildford: BIEE/Surrey Energy Economics Centre). Dargay, J. M. (1999) Car ownership and motor fuel demand: a pseudo-panel analysis. Paper presented at the 22nd Annual Conference of International Association of Energy Economists, 9–12 June 1999, Rome, Italy. Dargay, J. M. (2000) Pseudo-panel analysis of dynamic transport relationships. Paper presented at the International Conference on the Analysis of Repeated Cross-sectional Surveys, 15 – 16 June, Nijmegen, The Netherlands. Dargay, J. M. (2001) The effect of income on car ownership: evidence of asymmetry, Transportation Research A , 35(9), pp. 807–821. Dargay, J. M. (2002) Determinants of car ownership in rural and urban areas: a pseudo-panel analysis, Transportation Research E , 38(5), pp. 351–366. Dargay, J. M. and Goodwin, P. B. (1994) Transport evaluation in a disequilibrium world: some problems in dynamics. Paper presented at the 11th Annual Conference on Transport Research, Link oping, Sweden.¨ Dargay, J. M. and Vythoulkas, P. C. (1999) Estimation of a dynamic car ownership model: a pseudo- panel approach, Journal of Transport Economics and Policy , 33, pp. 283–302. Department for Transport (2002) Delivering Better Transport: Progress Report (London: Department for Transport). Dewees, D. N., Hyndman, R. M. and Waverman, L. (1975) The demand for gasoline in Canada, 1956 – 1972, Energy Policy , June, pp. 116–123. Donnelly, W. A. (1982) The regional demand for petrol in Australia, Economic Record , 58, pp. 317 – 327. Drollas, L. P. (1984) The demand for gasoline: further evidence, Energy Economics , pp. 71–82. Eltony, M. N. (1993) Transport gasoline demand in Canada, Journal of Transport Economics and Policy , 27, pp. 193–208. Espey, M. (1998) Gasoline demand revisited: an international meta-analysis of elasticities, Energy Economics , 20, pp. 273–295. Fridsdtrøm, L. (1998) An econometric model of aggregate car ownership and road use, paper presented at the 8th World Conference on Transport Research (WCTR), Antwerp, Belgium, July 12 – 17 1998. Gallini, N. (1983) Demand for gasoline in Canada, Canadian Journal of Economics , 16, pp. 299–324. Gately, D. (1990) The U.S. demand for highway travel, Energy Journal , 11, pp. 59–73.
Elasticities of Road Traffic and Fuel Consumption with Respect to Price and Income 291
Goodwin, P. B. (1992) A review of new demand elasticities with special reference to short and long run effects of price changes, Journal of Transport Economics and Policy , 26, pp. 155–163. Goodwin, P. B. (1996) Empirical evidence on induced traffic, Transportation , 23, pp. 35–54. Graham, D. and Glaister, S. (2002) The demand for automobile fuel: a survey of elasticities, Journal of Transport Economics and Policy , 36(1), pp. 1–26. Graham, D. J. and Glaister, S. (2004) Road traffic demand elasticity estimates: a review, Transport Reviews , 24(3), pp. 261–274. Greene, D. L. (1992) Vehicle use and fuel economy: how big is the ‘rebound’ effect?, Energy Journal , 13, pp. 117–143. Greene, D. L. and Hu, P. S. (1986) A functional form analysis of the short-run demand for travel and gasoline by one-vehicle households, Transportation Research Record , 1092, pp. 10–15. Greene, D. L., Kahn, J. R. and Gibson, R. C. (1999) Fuel economy rebound effect for US household vehicles, Energy Journal , 20, pp. 1–31. Hanly, M., Dargay, J. and Goodwin, P. (2002) Review of Income and Price Elasticities in the Demand for Road Traffic, Report 2002/13 (London: ESRC Transport Studies Unit, University College London); available at http://www.cts.ucl.ac.uk/tsu/elasfinweb.pdf Hansen, M. and Huang, Y. (1997) Road supply and traffic in California urban areas, Transportation Research , 31A, pp. 205–218. Haughton, J. and Sarkar, S. (1996) Gasoline tax as a corrective tax: estimates for the United States, 1970 – 1991, Energy Journal , 17, pp. 103–128. Houthakker, H. S., Verleger, P. K. and Sheehan, D. P. (1974) Dynamic demand analysis for gasoline and residential electricity, American Journal of Agricultural Economics , 56, pp. 412–418. Hsing, Y. (1990) On the variable elasticity of the demand for gasoline, Energy Economics , 12, pp. 132 – 136. Johansson, O. and Schipper, L. (1997) Measuring the long run fuel demand of cars: separate estimations of vehicle stock, mean fuel intensity, and mean annual driving distance, Journal of Transport Economics and Policy , 31, pp. 277–292. Jones, C. T. (1993) Another look at U.S. passenger vehicle use and the ‘rebound’ effect from improved fuel efficiency, Energy Journal , 14, pp. 99–109. Jong, G. de and Gunn, H. (2001) Recent evidence on car cost and time elasticities of travel demand in Europe, Journal of Transport Economics and Policy , 35, pp. 137–160. Kayser, H. A. (2000) Gasoline demand and car choice: estimating gasoline demand using household information, Energy Economics , 22, pp. 331–348. Kennedy, M. L. (1974) An econometric model of the world oil market, Bell Journal of Economics and Management Science , 5, pp. 540–577. Koshal, R., Rajindar, K. and Bradfield, Jr, J. (1977) World demand for gasoline: some empirical findings, Keio Economic Studies , 14, pp. 41–48. Kouris, G. (1983) Fuel consumption for road transport in the USA, Energy Economics , pp. 88–99. Lin, A., Botsas, E. N. and Monroe, S. A. (1985) State gasoline consumption in the USA: an econometric analysis, Energy Economics , 7, pp. 29–36. Mannering, F. and Winston, C. (1985) A dynamic empirical analysis of household vehicle ownership and utilization, Rand Journal of Economics , 16, pp. 215–236. Mannering, F. L. (1983) An econometric analysis of vehicle use in multivehicle households, Transportation Research , 17A, pp. 183–189. Mannering, F. L. (1986) A note on endogenous variables in household vehicle utilisation equations, Transportation Research , 20B, pp. 1–6. Mayo, J. W. and Mathis, J. E. (1988) The effectiveness of mandatory fuel efficiency standards in reducing the demand for gasoline, Applied Economics , 20, pp. 211–219. Mehta, J., Narasimham, G. and Swamy, P. (1978) Estimation of a dynamic demand function for gasoline with different schemes of parameter estimation, Journal of Econometrics , 7, pp. 263 – 269. Noland, R. B. (2001) Relationships between highway capacity and induced vehicle travel, Transportation Research , 35A, pp. 47–72. Noland, R. B. and Lewison, L. L. (2002) A review of the evidence for induced travel and changes in transportation and environmental policy in the US and the UK, Transportation Research , 7D, pp. 1 – 26. Orasch, W. and Wirl, F. (1997) Technological efficiency and the demand for energy (road transport), Energy Policy , 25, pp. 1129–1136. Oum, T. H. (1989) Alternative demand models and their elasticity estimates, Journal of Transport Economics and Policy , 23, pp. 163–187.
