Vehicle Ownership and Income Growth, Worldwide: 1960-2030

Abstract

The speed of vehicle ownership expansion in emerging market and developing countries has important implications for transport and environmental policies, as well as the global oil market. The literature remains divided on the issue of whether the vehicle ownership rates will ever catch up to the levels common in the advanced economies. This paper contributes to the debate by building a model that explicitly models the vehicle saturation level as a function of observable country characteristics: urbanization and population density. Our model is estimated on the basis of pooled time-series (1960-2002) and cross-section data for 45 countries that include 75 percent of the worldÕs population. We project that the total vehicle stock will increase from about 800 million in 2002 to more than two billion units in 2030. By this time, 56% of the worldÕs vehicles will be owned by non-OECD countries, compared with 24% in 2002. In particular, ChinaÕs vehicle stock will increase nearly twenty-fold, to 390 million in 2030. This fast speed of vehicle ownership expansion implies rapid growth in oil demand.

Full text

1
Vehicle Ownership and Income Growth, Worldwide: 1960-2030
Joyce Dargay, Dermot Gately and Martin Sommer
January 2007
Abstract:
The speed of vehicle ownership expansion in emerging market and developing countries
has important implications for transport and environmental policies, as well as the global
oil market. The literature remains divided on the issue of whether the vehicle ownership
rates will ever catch up to the levels common in the advanced economies. This paper
contributes to the debate by building a model that explicitly models the vehicle saturation
level as a function of observable country characteristics: urbanization and population
density. Our model is estimated on the basis of pooled time-series (1960-2002) and cross-
section data for 45 countries that include 75 percent of the world’s population. We
project that the total vehicle stock will increase from about 800 million in 2002 to over 2
billion units in 2030. By this time, 56% of the world’s vehicles will be owned by non-
OECD countries, compared with 24% in 2002. In particular, China’s vehicle stock will
increase nearly twenty-fold, to 390 million in 2030. This fast speed of vehicle ownership
expansion implies rapid growth in oil demand.
Keywords: vehicle ownership, transport modeling, transport oil demand
JEL Classification: R41 - Transportation: Demand, Supply, and Congestion;
Q41 – Energy Demand and Supply.
Joyce Dargay
Institute for Transport Studies, University of Leeds
Leeds LS2 9JT, England
j.dargay@its.leeds.ac.uk
Corresponding Author:
Dermot Gately
Dept. of Economics, New York University
19 W. 4 St., New York, NY 10012 USA
Dermot.Gately@NYU.edu
Telephone: 212 998 8955 Fax: 212 995 3932
Martin Sommer
International Monetary Fund
700 19th St. NW, Washington, DC 20431 USA
MSommer@imf.org

2
1. INTRODUCTION
Economic development has historically been strongly associated with an increase in the
demand for transportation and particularly in the number of road vehicles (with at least 4
wheels, including cars, trucks, and buses). This relationship is also evident in the
developing economies today. Surprisingly, very little research has been done on the
determinants of vehicle ownership in developing countries. Typically, analyses such as
IEA(2004) or OPEC(2004) make assumptions about vehicle saturation rates – maximum
levels of vehicle ownership (vehicles per 1000 people) – which are very much lower than
the vehicle ownership already experienced in the most of the wealthier countries.
Because of this, their forecasts of future vehicle ownership in currently developing
countries are much lower than would be expected by comparison with developed
countries when these were at comparable income levels.
This paper empirically estimates the saturation rate for different countries, by formalizing
the idea that vehicle saturation levels may be different across countries. Given data
availability, we limit ourselves to the influence of demographic factors, urban population
and population density. A higher proportion of urban population and greater population
density would encourage the availability and use of public transit, and could reduce the
distances traveled by individuals and for goods transportation. Thus countries that are
more urbanized and densely populated could have a lower need for vehicles. In this
study we attempt to account for these demographic differences by specifying a country’s
saturation level as a function its population density and proportion of the population
living in urban areas. There are, of course, a number of other reasons why saturation may
vary amongst countries. For example, the existence of reliable public transport
alternatives and the use of rail for goods transport may reduce the saturation demand for
road vehicles. Alternatively, investment in a comprehensive road network will most
likely increase the saturation level. Such factors, however, are difficult to take into
account, as they would require far more data than are available for all but a few countries.
This paper examines the trends in the growth of the stock of road vehicles (at least 4
wheels) for a large sample of countries since 1960 and makes projections of its
development through 2030. It employs an S-shaped function – the Gompertz function –
to estimate the relationship between vehicle ownership and per-capita income, or GDP.
Pooled time-series and cross-section data are employed to estimate empirically the
responsiveness of vehicle ownership to income growth at different income levels. By
employing a dynamic model specification, which takes into account lags in adjustment of
the vehicle stock to income changes, the influence of income on the vehicle stock over
time is examined. The estimates are used, in conjunction with forecasts of income and
population growth, for projections of future growth in the vehicle stock.
The study builds on the earlier work of Dargay and Gately (1999), who estimated vehicle
demand in a sample of 26 countries - 20 OECD countries and 6 developing countries –
for the period 1960 to 1992, and projected vehicle ownership rates until 2015.

3
The current study extends that work in four ways. Firstly, we relax the 1999 paper’s
assumption of a common saturation level for all countries. In our previous study, the
estimated saturation level was constrained to be the same for all countries (at about 850
vehicles per thousand people); differences in vehicle ownership between countries at the
same income level were accounted for by allowing saturation to be reached at different
income levels.
Secondly, the data set is extended in time to 2002 and adds 19 countries (mostly non-
OECD countries) to the original 26; these 45 countries comprise about three-fourths of
world population. The inclusion of a large number of non-OECD countries – more than
one-third of the countries, with three-fourths of the sample’s population – provides a high
degree of variation in both income and vehicle ownership. This allows more precise
estimates of the relationship between income and vehicle ownership at various stages of
economic development. In addition, the model is used for countries not included in the
econometric analysis to obtain projections for the “rest of the world”.
The third extension we make to our earlier study concerns the assumption of symmetry in
the response of vehicle ownership to rising and falling income. Given habit persistence,
the longevity of the vehicle stock and expectations of rising income, one might expect
that reductions in income would not lead to changes in vehicle ownership of the same
magnitude as those resulting from increasing income. If this is the case, estimates based
on symmetric models can be misleading if there is a significant proportion of
observations where income declines. This is the case in the current study, particularly for
developing countries. In most countries, real per capita income has fallen occasionally,
and in Argentina and South Africa it has fallen over a number of years. In order to
account for possible asymmetry, the demand function is specified so that the adjustment
to falling income can be different from that to rising income. Specifically, the model
permits the short-run response to be different for rising and falling income without
changing the equilibrium relationship between the vehicle stock and income. The
hypothesis of asymmetry is then tested statistically.
Finally, the fourth extension is to use the projections of vehicle growth to investigate the
implications for future transportation oil demand. This is based on a number of
simplifying assumptions and comparisons are made with other projections.
Section 2 summarizes the data used for the analysis, and explores the historical patterns
of vehicle ownership and income growth. Section 3 presents the Gompertz model used in
the econometric estimation, and the econometric results are described in Section 4.
Section 5 summarizes the projections for vehicle ownership, based upon assumed growth
rates of per-capita income in the various countries. Section 6 presents the implications
for the growth of highway fuel demand. Section 7 presents conclusions.

4
2. HISTORICAL PATTERNS IN THE GROWTH OF VEHICLE
OWNERSHIP
Table 1 summarizes the various countries’ historical data1 in 1960 and 2002, for per-
capita income (GDP), vehicle ownership, and population. Comparisons of the data for
1960 and 2002 are graphed below (in Section 5, we present similar graphic comparisons
between 2002 and the projections for 2030).
The relationship between the growth of vehicle ownership and per-capita income is
highly non-linear. Vehicle ownership grows relatively slowly at the lowest levels of per-
capita income, then about twice as fast as income at middle-income levels (from $3,000
to $10,000 per capita), and finally, about as fast as income at higher income levels, before
reaching saturation at the highest levels of income. This relationship is shown in Figure
1, using annual data over the entire period 1960-2002 for the USA, Germany, Japan and
South Korea; in the background is an illustrative Gompertz function that is on average
representative of our econometric results below. Figure 2 shows similar data for China,
India, Brazil and South Korea – with the same Gompertz function, but using logarithmic
scales. Figure 3 shows the illustrative Gompertz relationship between vehicle ownership
and per-capita income, as well as the income elasticity of vehicle ownership at different
levels of per-capita income.
1 All OECD countries are included, excepting Portugal and the Slovak Republic. Portugal was excluded
because we could not get vehicles data that excluded 2-wheeled vehicles, and the Slovak Republic because
comparable data were unavailable for a sufficiently long period. Among the non-OECD countries with
comparable data, we excluded Singapore and Hong Kong because their population density was 10 times
greater than any of the other countries, and we excluded Colombia because of implausible 25% annual
reductions in vehicle registrations in 1994 and 1997.

5
Country Code
first data
year (if
not 1960)
1960
or
first
year
2002
Average
annual
growth rate
1960
or
first
year
2002
Average
annual
growth rate
1960
or
first
year
2002
Average
annual
growth rate millions density
per
sq.KM
%
urbanized
OECD, North America
Canada Can 10.4 26.9 2.3% 292 581 1.6% 5.2 18.2 3.0% 0.72 31 3 79
United States USA 13.1 31.9 2.1% 411 812 1.6% 74.4 233.9 2.8% 0.76 288 31 78
Mexico Mex 3.7 8.1 1.9% 22 165 4.9% 0.8 16.7 7.5% 2.58 101 53 75
OECD, Europe
Austria Aut 8.1 26.3 2.8% 69 629 5.4% 0.5 5.1 5.8% 1.91 8 97 68
Belgium Bel 8.2 24.7 2.7% 102 520 4.0% 0.9 5.3 4.3% 1.48 10 315 97
Switzerland Che 15.4 27.7 1.4% 106 559 4.0% 0.6 4.0 4.8% 2.89 7 184 67
Czech Republic Cze 1970 8.9 13.6 1.3% 82 390 5.0% 0.8 4.0 5.1% 3.79 10 133 75
Germany Deu 9.0 23.5 2.3% 73 586 5.1% 5.1 48.3 5.5% 2.20 83 236 88
Denmark Dnk 10.6 25.9 2.1% 126 430 3.0% 0.6 2.3 3.4% 1.38 5 127 85
Spain Esp 4.8 19.3 3.3% 14 564 9.2% 0.4 22.9 9.9% 2.74 41 82 78
Finland Fin 7.4 24.3 2.9% 58 488 5.2% 0.3 2.5 5.6% 1.82 5 17 59
France Fra 8.5 23.7 2.5% 158 576 3.1% 7.2 35.3 3.9% 1.26 61 108 76
Great Britain GBr 9.7 23.6 2.1% 137 515 3.2% 7.2 30.6 3.5% 1.50 59 246 90
Greece Grc 4.5 16.1 3.1% 10 422 9.4% 0.1 4.6 10.1% 3.03 11 82 61
Hungary Hun 1963 4.2 12.3 2.8% 15 306 8.1% 0.1 3.0 8.1% 2.87 10 110 65
Ireland Ire 5.3 29.8 4.2% 78 472 4.4% 0.2 1.9 5.2% 1.05 4 57 60
Iceland Isl 8.3 26.7 2.8% 118 672 4.2% 0.0 0.2 5.4% 1.50 0.3 3 93
Italy Ita 7.2 23.3 2.8% 49 656 6.4% 2.5 37.7 6.7% 2.25 57 196 67
Luxembourg Lux 10.9 42.6 3.3% 135 716 4.0% 0.05 0.3 4.7% 1.23 0.4 173 92
Netherlands Nld 9.6 25.3 2.3% 59 477 5.1% 0.7 7.7 5.9% 2.19 16 477 90
Norway Nor 7.7 28.1 3.1% 95 521 4.1% 0.3 2.4 4.7% 1.33 5 15 75
Poland Pol 4.0 9.6 2.1% 8 370 9.5% 0.2 14.4 10.3% 4.51 39 127 63
Sweden Swe 10.2 25.4 2.2% 175 500 2.5% 1.3 4.5 3.0% 1.15 9 22 83
Turkey Tur 2.5 6.1 2.1% 4 96 7.7% 0.1 6.4 10.0% 3.62 67 90 67
OECD, Pacific
Australia Aus 10.4 25.0 2.1% 266 632 2.1% 2.7 12.5 3.7% 0.99 20 3 91
Japan Jpn 4.5 23.9 4.1% 19 599 8.6% 1.8 76.3 9.4% 2.12 127 349 79
Korea Kor 1.4 15.1 5.8% 1.2 293 13.9% 0.03 13.9 15.7% 2.40 48 483 83
New Zealand NZL 11.1 19.6 1.4% 271 612 2.0% 0.6 2.4 3.2% 1.45 4 15 86
Non-OECD, South America
Argentina Arg 1962 9.7 9.6 -0.05% 55 186 3.1% 0.9 7.1 5.4% -67.8 38 13 88
Brazil Bra 1962 2.7 7.1 2.5% 20 121 4.6% 1.0 20.8 7.8% 1.87 171 21 82
Chile Chl 1962 1.8 9.2 4.2% 17 144 5.4% 0.1 2.2 7.5% 1.29 16 21 86
Dominican Rep. Dom 1962 2.3 6.0 2.4% 7 118 7.3% 0.02 1.0 10.7% 3.04 9 178 67
Ecuador Ecu 1969 1.7 2.9 1.6% 9 50 5.2% 0.03 0.7 10.1% 3.16 13 46 64
Non-OECD, Africa and Middle East
Egypt Egy 1963 1.2 3.5 2.8% 4 38 6.0% 0.1 2.5 8.4% 2.16 68 67 43
Israel Isr 1961 3.3 17.9 4.2% 25 303 6.2% 0.1 1.9 9.3% 1.49 6 318 92
Morocco Mar 1962 2.1 3.6 1.3% 17 59 3.2% 0.2 1.8 6.0% 2.44 30 66 57
Syria Syr 1.2 3.1 2.4% 6 35 4.1% 0.03 0.6 7.5% 1.71 17 92 52
South Africa Zaf 1962 6.7 8.8 0.7% 66 152 2.1% 1.1 6.9 4.7% 3.17 45 37 58
Non-OECD, Asia
China Chn 1962 0.3 4.3 6.5% 0.38 16 9.8% 0.2 20.5 12.0% 1.51 1285 137 38
Chinese Taipei Twn 1974 3.8 18.5 5.0% 14 260 9.5% 0.2 5.9 12.4% 1.89 23 701 81
Indonesia Idn 0.7 2.9 3.3% 2.1 29 6.4% 0.2 6.2 8.6% 1.93 216 117 43
India Ind 0.9 2.3 2.3% 1.0 17 6.8% 0.4 17.4 9.1% 2.92 1051 353 28
Malaysia Mys 1967 2.2 8.1 3.8% 25 240 6.7% 0.2 5.9 9.6% 1.77 25 74 59
Pakistan Pak 0.9 1.8 1.8% 1.7 12 4.7% 0.1 1.7 7.4% 2.57 145 188 34
Thailand Tha 1.0 6.2 4.4% 4 127 8.7% 0.1 8.1 11.0% 1.98 64 121 20
Sample (45 countries) 3.4 8.6 2.3% 53 166 2.8% 118 728 4.4% 1.21 4346 68 48
Other Countries 2.2 3.1 0.8% 5 45 5.2% 4 83 7.4% 6.73 1891 28 45
OECD Total 8.1 22.12 2.4% 150 550 3.1% 115 617 4.1% 1.30 1127 34 78
Non-OECD Total 1.4 3.6 2.3% 4 39 5.6% 9 195 7.5% 2.39 5110 53 41
Total World 3.1 7.0 2.0% 41 130 2.8% 122 812 4.6% 1.41 6237 48 47
Population, 2002
per-capita income
(thousands, 1995 $ PPP)
Vehicles per 1000
population
ratio of
growth rates:
Veh.Own. to
per-cap.
income
Total Vehicles
(millions)
Table 1. Historical Data on Income, Vehicle Ownership and Population, 1960-2002

6
0 1 10
per-capita income, 1960-2002 (thousands 1995 $ PPP, log scale)
0.1
1
10
100
1000
Vehicles
per 1000
people
1960-2002
(log scale)
India
China
S.Korea
Brazil
Brazil 2002
China
2002
1962
S.Korea 1960
India
1960
S.Korea 2002
Brazil 1960
India 2002
Gompertz
function
010 20 30
per-capita income, 1960-2002 (thousands 1995 $ PPP)
0
200
400
600
800
1000
Vehicles
per 1000
people
1960-2002
USA
Japan
S.Korea
Germany
S.Korea
2002
2002
USA
2002
USA
1960
Germany
1960
Japan
1960
Gompertz
function
Figure 1. Vehicle Ownership and Per-Capita Income for USA, Germany, Japan, and
South Korea, with an Illustrative Gompertz Function, 1960-2002
Figure 2. Vehicle Ownership and Per-capita Income for South Korea, Brazil, China, and
India, with the Same Illustrative Gompertz Function, 1960-2002
3.

7
3. THE MODEL
As illustrated above, we represent the relationship between vehicle ownership and per-
capita income by an S-shaped curve. This implies that vehicle ownership increases
slowly at the lowest income levels, and then more rapidly as income rises, and finally
slows down as saturation is approached. There are a number of different functional
forms that can describe such a process—for example, the logistic, logarithmic logistic,
cumulative normal, and Gompertz functions. Following our earlier studies, the
Gompertz model was chosen for the empirical analysis, because it is relatively easy to
estimate and is more flexible than the logistic model, particularly by allowing different
curvatures at low- and high-income levels.2
Letting V* denote the long-run equilibrium level of vehicle ownership (vehicles per 1000
people), and letting GDP denote per-capita income (expressed in real 1995 dollars
evaluated at Purchasing Power Parities), the Gompertz model can be written as:
t
t
GDP
Ve
e
β
α
γ
=
* (1)
where γ is the saturation level (measured in vehicles per 1000 people) and α and β are
negative parameters defining the shape, or curvature, of the function.
The implied long-run elasticity of the vehicle/population ratio with respect to per-capita
income is not constant, due to the nature of the functional form, but instead varies with
income. The long-run income elasticity is calculated as:
t
t
LR
tGDP
GDP e
β
αβη
= (2)
This elasticity is positive for all income levels, because α and β are negative. The
elasticity increases from zero at GDP=0 to a maximum at GDP=-1/β, then declines to
zero asymptotically as saturation is approached. Thus β determines the per-capita
income level at which vehicle ownership becomes saturated: the larger the β in absolute
value, the lower the income level at which vehicle ownership flattens out. Figure 3
depicts an illustrative Gompertz function, similar to what we have estimated
econometrically, together with the implied income elastictity for all income levels3.
2 See Dargay-Gately (1999) for a simpler model, using a smaller set of countries. Earlier analyses are
summarized in Mogridge (1983), which discusses vehicle ownership being modelled by various S-shaped
functions of time, rather than of per-capita income, some with saturation and some without. Medlock and
Soligo (2002) employ a log-quadratic function of per-capita income.
3 As discussed below, there can be differences across countries in the saturation levels of a country’s
Gompertz function and its income elasticity. Figure 3 plots an illustrative function for the median
country’s saturation level. Differences across countries are illustrated in Figure 6.

8
010 20 30 40 50
per-capita income (thousands 1995 $ PPP)
0
100
200
300
400
500
600
700
800
900
1000
vehicle
ownership:
vehicles
per 1000
people
010 20 30 40 50
per-capita income (thousands 1995 $ PPP)
0
1
2
3
income
elasticity
of
vehicle
ownership
010 20 30 40 50
average per-capita income,
(thousands 1995 $ PPP), 1960-2002
0
1
2
3
4
5
ratio of
vehicle
ownership
growth
to
per-capita
income
growth,
1960-2002 Chn
Ind
USA
Idn Bra
Pak
Jpn
Mex
Deu
Egy
Tur
Tha
Fra
GBr
Ita
Kor
Zaf
Esp
Pol
Can
Mar
Mys Twn
Aus
long-run
income elasticity
of vehicle ownership
Figure 3. Illustrative Gompertz function and its implied income elasticity
Shown in Figure 4 are the historical ratios of vehicle ownership growth to per-capita
income growth (which approximates the income elasticity), compared to the countries’
average level of per-capita income (for the largest countries, with population above 20
million in 2002). Also graphed is the income elasticity of vehicle ownership for our
illustrative Gompertz function. One can observe the pattern across countries of the
income elasticity increasing at the lowest levels of per-capita income, then peaking in the
per-capita income range of $5,000 to $10,000, followed by a gradual decline in the
income elasticity at higher income levels.
Figure 4. Historical Ratios of Vehicle Ownership Growth to Income Growth,
by Levels of per-capita Income:1960-2002

9
We assume that the Gompertz function (1) describes the long-run relationship between
vehicle ownership and per-capita income. In order to account for lags in the adjustment
of vehicle ownership to per-capita income, a simple partial adjustment mechanism is
postulated:
(3)
where V is actual vehicle ownership and θ is the speed of adjustment (0 < θ <1). Such
lags reflect the slow adjustment of vehicle ownership to increased income: the necessary
build-up of savings to afford ownership; the gradual changes in housing patterns and land
use that are associated with increased ownership; and the slow demographic changes as
young adults learn to drive, replacing their elders who have never driven. Substituting
equation (1) into equation (3), we have the equation:
1
)1( −
−+= t
t
tV
GDP
Ve
e
θ
β
θγ
α
(4)
In Dargay and Gately (1999), we had assumed that only the coefficients βi were country-
specific, while all the other parameters of the Gompertz function were the same for all
countries: the saturation level γ, the speed of adjustment θ, and the coefficient α. Thus,
differences between countries in that paper were reflected in the curvature parameters βi ,
which determined the income level for each country at which the common level of
saturation is reached (620 cars and 850 vehicles per 1000 people). In this paper we relax
this restriction of a common saturation level. Instead, we assume that the maximum
saturation level will be that estimated for the USA, denoted MAX
γ
. Other countries that
are more urbanized and more densely populated than the USA will have lower saturation
levels. The saturation level for country i at time t is specified as:4
otherwise
UUifUUU
and
otherwise
DDifDDD
where
UD
tUSAittUSAit
it
tUSAittUSAit
it
it
it
MAXit
0
0
,,
,,
=
>−=
=
>−=
++=
ϕλγγ
(5)
4 Population density and urbanization are normalised by taking the deviations from their means over all
countries and years in the data sample. Since population density and urbanization vary over time, so too
does the saturation level.
)( 1
*
1−− −+= tttt VVVV
θ

10
020 40 60 80 100
% Urbanized, 2002
1
10
100
1000
Population
Density
2002
(per sq. KM,
log scale)
Chn
Ind
USA
Idn
Bra
Pak
Jpn
Mex
Deu
Egy Tur
Tha Fra
GBr
Ita
Kor
Zaf
Esp
Pol
Arg
Can
Mar
Mys
Twn
Aus
where λ and ϕ are negative, and Dit denotes population density and Uit denotes
urbanization in country i at time t..
Figure 5. Countries’ Population Density and Urbanization, 2002
Figure 5 plots the 2002 data on population
density and urbanization, for countries
with population greater than 20 million.
The most urbanized and densely populated
countries are in Western Europe and East
Asia: Germany, Great Britain, Japan and
South Korea. Some countries are highly
urbanized but not densely populated, such
as Australia and Canada. Others are
densely populated but not highly
urbanized, such as China, India, Pakistan,
Thailand, and Indonesia.
The dynamic specification in equations (3) and (4) assumes that the response to a fall in
income is equal but opposite the response to an equivalent rise in income. As mentioned
earlier, there is evidence that this may not be the case, and that assuming symmetry may
lead to biased estimates of income elasticities. Many of the countries in the sample have
experienced periods of negative changes in per-capita income, some for several years,
such as Argentina and South Africa, whose experience is graphed in Figure 6. Thus it is
important that we take such asymmetry into consideration.5 To do so, the adjustment
coefficient relating to periods of falling income,
θ
F , is allowed to be different from that
to rising income,
θ
R. This is done by creating two dummy variables defined as:
otherwiseandGDP GDP ifF
otherwiseandGDP GDP ifR
ititit
ititit 001
001
1
1
=<−=
=
>−=
−
− (6)
and replacing θ in (4) with:
itFitR FR
θ
θ
θ
+= (7)
5 Note that this asymmetry differs from the long-run asymmetric price responsiveness of oil demand, used
in papers by Dargay, Gately and Huntington: see Gately-Huntington (2002); an alternative approach has
been proposed by Griffin and Schulman (2005). The asymmetry used here relates to the short-run income
elasticity and affects the speed of adjustment, while the long-run elasticities are symmetric..

11
678910 11 12
per-capita income, 1962-2002
(thousands 1995 $ PPP)
0
100
200
300
400
500
Vehicles
per 1000
people
1962-2002
1962
1974
1981
1993
2002
South
Africa
Figure 6. Asymmetric Response of Vehicle Ownership
to Increases and Decreases in Income: South Africa, 1962-2002.
This specification does not change the
equilibrium relationship between the
vehicle stock and income given in
equation (1), nor the long-run income
elasticities. Only the rate of adjustment
to equilibrium is different for rising and
falling income, so that the short-run
elasticities and the time required for
adjustment will be different. Since it is
likely that vehicle ownership does not
decline as quickly when income falls as it
increases when income rises6, we would
expect
θ
R >
θ
F . The hypothesis of
asymmetry can be tested statistically
from the estimates of
θ
R and
θ
F. If they are not statistically different from each other,
symmetry cannot be rejected and the model reverts to the traditional, symmetric case.
Substituting (5) and (7) into (4), the model to be estimated econometrically from the
pooled data sample becomes:
itititFitR
iti
itFitRititMAXit VFR
GDP
e
eFRUDV
εθθ
β
α
θθϕλγ
+−−++++= −1
)1())(( (8)
where the subscript i represents country i and εit is random error term. The adjustment
parameters,
θ
R and
θ
F , and the parameters α,MAX
γ
, ϕ and λ are constrained to be the
same for all countries, while βi is allowed to be country-specific, as is each country’s
saturation level from equation (5). The long-run income elasticities for each country are
calculated as
iti
iti
LR
ti GDP
eGDP
β
βαη
= (9)
which are the same as in the symmetric model (2). The short-run income elasticities are
also determined by the adjustment parameter, θ, and are
iti
iti
SR
it GDP
eGDP
β
βαθη
=. (10)
6 In the graph for South Africa, vehicle ownership does not decline when income falls; it continues
increasing, albeit more slowly, because of the long lags of adjusting vehicle ownership to extended periods
of increasing income.

12
where
θ
=
θ
R for income increases and
θ
=
θ
F for income decreases.
The rationale for pooling time-series data across countries is the following. Although it
is possible, in theory, to estimate a separate vehicle ownership function for each country,
the short time periods and relatively small range of income levels that are available for
each country make such an approach untenable. Reliable estimation of the saturation
level requires observations on vehicle ownership which are nearing saturation.
Analogously, estimation of the parameter α, which determines the value of the Gompertz
function at the lowest income levels, necessitates observations for low income and
ownership levels. Thus it would not be sensible to estimate the saturation level for low-
income countries separately, because vehicle ownership in these countries is far from
saturation. Similarly, one could not estimate the lower end of the curve, i.e. the
parameter α, on the basis of data only for high-income countries with high vehicle-
ownership, unless historic data were available for many years in the past. For these
reasons, we use a pooled time-series cross-section approach, with all countries being
modeled simultaneously.
We had considered utilizing additional explanatory variables in the model, such as the
cost of vehicle ownership, or the price of gasoline.7 However, the unavailability of data
for a sufficient number of countries and periods prevented such an attempt.
7 Storchmann (2005) uses fuel price, the fixed cost of vehicle ownership, and income distribution – but not
per-capita income – to explain vehicle ownership across countries. His data set includes more countries
(90) but only a short time series, 1990-1997. Medlock and Soligo (2002), with a smaller set of countries,
utilize the price of highway fuel to model the cross-country fixed effects within a log-quadratic
approximation of vehicle ownership.

13
4. MODEL ESTIMATION
The model described in equation (8) was estimated for the pooled cross-section time-
series data on vehicle ownership for the 45 countries. The period of estimation is
generally from 1960 to 2002, but is shorter for some countries due to early data being
unavailable (see Table 1). In all, we have 1838 observations. In order to allow larger
countries to have more influence on the estimated coefficients, the observations were
weighted with population. As mentioned above, the maximum saturation level, MAX
γ
, the
speed-of-adjustment coefficients,
θ
R and
θ
F, and the lower-curvature parameter α were
constrained to be the same for all countries. The upper-curvature parameters βi were
estimated separately for each country. The model was estimated using iterative least
squares.
The resulting estimates are shown in Table 2. A total of 51 parameters are estimated,
including 45 country-specific βi. All the estimated coefficients are of the expected signs:
θ
R ,
θ
F , and MAX
γ
are positive and α, λ, ϕ and βi are negative. All coefficients are
statistically significant, except for the βi coefficients for Luxembourg, Iceland, Ecuador,
and Syria. From the Adjusted R2, we see the model explains the data very well; however,
this is to be expected in a model containing a lagged dependent variable. Several
alternative specifications were also estimated – respectively dropping from the equation
population density, or urbanization, or asymmetry; these results are compared with our
standard specification, and with those of Dargay-Gately (1999), in Appendix B.
The estimated adjustment parameter is larger for rising income than for falling income,
0.095 versus 0.084. Testing the equality
θ
R =
θ
F yields an F-statistic of 4.76 (with
probability value=0.03) so that symmetry is rejected. This implies that the vehicle stock
responds less quickly when income falls than when income rises. With increasing
income, 9.5% of the complete adjustment occurs in one year, but when income falls only
8.4% of the long-term adjustment occurs in one year. Thus a fall in per-capita income
reduces vehicle ownership about 11% less in the short run (1-year) than an equivalent
rise in income increases vehicle ownership. The long-run elasticity is the same for both
income increases and decreases.
The vehicle saturation levels vary across countries –– from a maximum of 852 for the
USA (and for Finland, Norway, and South Africa) to a minimum of 508 for Chinese
Taipei. All the OECD countries have saturation levels above 700 except for the most
urbanized and densely populated: Netherlands (613), Belgium (647), and South Korea
(646). Similarly, most of the Non-OECD countries have saturation levels in the range of
700 to 800 vehicles per 1000 people.

14
coef. P-value
Speed of adjustment θ
income increases 0.095 0.0000
income decreases 0.084 0.0000
max. saturat
i
on
l
eve
l
γ
max
852 0.0000
population density λ-0.000388 0.0000
urbanization φ-0.007765 0.0001
alpha α-5.897 0.0000
Country beta coef. P-value vehicle ownership
saturation
(per 1000 people)
per-capita income
(thousands 1995 $ PPP) at
which vehicle ownership =
200
OECD, North America
Canada -0.15 0.00 845 9.4
United States -0.20 0.00 852 7.0
Mexico -0.17 0.00 840 7.9
OECD, Europe
Austria -0.15 0.00 831 9.4
Belgium -0.20 0.00 647 8.1
Switzerland -0.11 0.00 803 13.3
Czech Republic -0.17 0.00 819 8.3
Germany -0.18 0.00 728 8.5
Denmark -0.12 0.00 782 12.0
Spain -0.17 0.00 835 8.1
Finland -0.13 0.00 852 10.6
France -0.15 0.00 823 9.4
Great Britain -0.17 0.00 707 8.9
Greece -0.15 0.00 836 9.4
Hungary -0.17 0.00 831 8.1
Ireland -0.15 0.01 841 9.4
Iceland -0.17 0.87 779 8.3
Italy -0.18 0.00 800 8.1
Luxembourg -0.16 0.78 706 9.6
Netherlands -0.16 0.00 613 10.1
Norway -0.13 0.00 852 10.6
Poland -0.23 0.00 821 6.2
Sweden -0.13 0.00 825 10.6
Turkey -0.18 0.00 820 7.7
OECD, Pacific
Australia -0.19 0.00 785 7.7
Japan -0.18 0.00 732 8.3
Korea -0.20 0.00 646 8.1
New Zealand -0.19 0.01 812 7.3
Non-OECD, South America
Argentina -0.13 0.00 800 10.6
Brazil -0.17 0.00 831 8.5
Chile -0.17 0.00 810 8.3
Dominican Rep. -0.24 0.02 777 6.2
Ecuador -0.25 0.13 845 5.6
Non-OECD, Africa and Middle East
Egypt -0.22 0.00 824 6.3
Israel -0.13 0.00 630 12.6
Morocco -0.25 0.00 830 5.6
Syria -0.22 0.22 807 6.5
South Africa -0.14 0.00 852 10.1
Non-OECD, Asia
China -0.14 0.00 807 10.1
Chinese Taipei -0.16 0.00 508 11.7
Indonesia -0.23 0.00 808 6.3
India -0.24 0.00 683 6.5
Malaysia -0.23 0.00 827 6.0
Pakistan -0.21 0.01 725 7.3
Thailand -0.22 0.00 812 6.3
Adjusted R-squared 0.999821
Sum of Squared Residuals 0.038947
Table 2. Estimated Coefficients of Equation (8)

15
Chn
Ind
USA
Idn
Bra
Pak Jpn
Mex
Deu
Egy Tur
Tha Fra
GBr
Ita
Kor
Zaf
Esp
Pol Arg
Can
Mar Mys
Twn
Aus
4 6 8 10 12
per-capita income (thousands 1995 $ PPP)
at which vehicle ownership = 200 in long run
500
600
700
800
900
vehicle
ownership
saturation
level
(vehicles
per 1000
people)
The estimated maximum saturation level is 852 vehicles per 1000 people – for the USA
and for those countries which are less urbanized and less densely populated: Finland,
Norway, and South Africa. The coefficients for population density and urbanization are
both negative and statistically significant, indicating that the saturation level declines
with increasing population density and with increasing urbanization. The lowest
saturation levels among the largest countries are for Germany, Great Britain, Japan,
South Korea and India8. Figure 7 plots for each country (with population greater than 20
million in 2002) the estimated saturation level and the income level at which it would
reach vehicle ownership of 200 vehicles per 1000 people. The latter measures reflects
the country’s curvature parameter βi. Some countries would reach vehicle ownership of
200 quickly, at relatively low income levels (USA, India, Indonesia, Malaysia), while
others would reach it more slowly, at much higher income levels (China, Netherlands,
Denmark, Israel, Switzerland).
Figure 7. Countries’ Estimated Vehicle Ownership Saturation Levels
and Income Levels at which Vehicle Ownership = 200.
8 In Medlock-Soligo (2002), there is much wider cross-country variation in vehicle-ownership saturation
levels estimated – nearly tenfold, from lowest (China) to highest (USA). Their estimated ownership-
saturation levels (for passenger vehicles only) range from 600 in the USA and Italy, 400-500 in the most of
the OECD, 150-200 in Mexico, Turkey, S. Korea and most of Non-OECD Asia, but less than 100 for
China. This large variability is due to the fact that saturation levels in the Medlock-Soligo model are
closely related to the estimated fixed effects— therefore, the calculated saturation levels do not take into
account as much cross-country information as in our framework. For comparison, our estimated
ownership-saturation levels estimates are almost all within 10% of the average saturation level. Only those
countries that are most urbanized and densely populated have estimated saturation levels that are
substantially lower; the lowest saturation level (Twn) is 60% of the highest (USA). At the other extreme,
there was no cross-country variation in vehicle ownership saturation levels in Dargay-Gately (1999), which
assumed a shared saturation level across countries that was estimated to be 850 vehicles (652 cars) per
1000 people.

16
The value of α determines the maximum income elasticity of vehicle ownership rates9,
which in this case is estimated to be 2.1. The value of βi determines the income level
where the common maximum elasticity is reached: the smaller the βi in absolute value,
the greater the per-capita income at which the maximum income elasticity occurs – for
the different countries respectively, at income levels between $4,000 and $9,600. The
vehicle ownership level at which the maximum income elasticity occurs is about 90
vehicles per 1000 people. The values of α and βi also determine the income level at
which vehicle saturation is reached. The estimates imply that 99% of saturation is
reached, for the different countries respectively, at a per-capita income level of between
$19,000 and $46,000.
The graphs in Figure 8 illustrate the cross-country differences in saturation levels and
low-income curvature for 6 selected countries. Countries can differ in their saturation
level, or their low-income curvature (measured by income level at which vehicle
ownership of 200 is reached), or both. USA and France have similar saturation levels but
different low-income curvatures: USA reaches 200 vehicle ownership at per-capita
income of $7,000 while France reaches it at $9,400. France and Netherlands reach 200
vehicle ownership at similar income levels, but France has a much higher saturation level
(823) than does Netherlands (613). Similarly, India and Indonesia have similar low-
income curvatures – reaching vehicle ownership of 200 at about $6,500 – but India’s
saturation level (683) is lower than Indonesia’s (808) because India is more urbanized
and has higher population density. By contrast, China reaches vehicle ownership of 200
more slowly (at about $10,000) than India but it has a higher saturation level.10
9 The maximum elasticity is derived by setting the derivative of the long-run elasticity with respect to GDP
equal to zero, solving for the value of GDP where the elasticity is a maximum and replacing this value of
GDP (=-1/β) in the original elasticity formula. This gives a maximum elasticity of -αe-1 = -0.367α.
10 Although China is more urbanized than India, it has much lower population density as we have measured
it, using land area. Since much of western China is virtually uninhabitable, it would have been preferable
to use habitable land area rather than total land area when calculating population density, but such data are
unavailable. This would have the effect of lowering China’s estimated saturation level to something closer
to that of India (683). The effect of this on China’s projections is discussed in the next section.

17
010 20 30 40 50
per-capita income (thousands 1995 $ PPP)
0
100
200
300
400
500
600
700
800
900
1000
vehicles
per 1000
people
USA
France
Netherlands
010 20 30 40 50
per-capita income (thousands 1995 $ PPP)
0
100
200
300
400
500
600
700
800
900
1000
vehicles
per 1000
people
Indonesia
India
China
010 20 30 40 50
per-capita income (thousands 1995 $ PPP)
0
1
2
3
income
elasticity
of
vehicle
ownership
010 20 30 40 50
per-capita income (thousands 1995 $ PPP)
0
1
2
3
income
elasticity
of
vehicle
ownership
USA
France
Netherlands
Indonesia
India
China
Figure 8. Long-run Gompertz Functions for Six Selected Countries, and the
Implied Income Elasticity of Vehicle Ownership

18
5. PROJECTIONS OF VEHICLE OWNERSHIP TO 2030
On the basis of assumptions concerning future trends in income, population and
urbanization, the model projects vehicle ownership for each country.11 These are shown
in Table 3.
Within the OECD countries, projected growth in vehicle ownership is relatively slow,
about 0.6% annually, because many of these countries are approaching saturation. The
only exceptions to slowly growing vehicle ownership in the OECD are Mexico and
Turkey, whose vehicle ownership will grow faster than income. However, due to
population growth, the annual OECD growth rate for total vehicles is somewhat higher,
at 1.4%. For the USA, we project only a slight increase in vehicle ownership (from 812
to 849 per 1000 people) but a large absolute increase in the total vehicle stock of 80
million, due to population growth of nearly 1% annually. This 80 million increase for
the USA is larger than the projected 2030 total of vehicles in any European country, and
is almost as large as the total number of vehicles in Japan.
For the non-OECD countries12, we project much faster rates of growth: vehicle
ownership growth of about 3.5% annually, and total vehicles growth of 6.5% annually –
four times the rate for the OECD. The most rapid growth is in the non-OECD
economies with high rates of income growth, and per-capita income levels ($3,000 to
$10,000) at which the income elasticity of vehicle ownership is the highest. China has by
far the highest growth rate of vehicle ownership, 10.6% annually, followed by India (7%)
and Indonesia (6.5%). By 2030, China will have 269 vehicles per 1000 people –
comparable to vehicle ownership levels of Japan and Western Europe in the early 1970’s
– and it will have more vehicles than any other country: 24% more vehicles than the
USA. China’s vehicle ownership is projected to grow rapidly for two reasons: (1) its
projected high growth rate for per-capita income during 2002-2030, 4.8% (which is
actually much slower than its recent rapid growth), and (2) vehicle ownership is growing
2.2 times as fast as per-capita income, as it passes through the middle level of per-capita
income ($3,000 to $10,000) with the highest income-elasticity of vehicle ownership.
Similarly for India and Indonesia, whose per-capita income is not projected to grow as
11 Population density is assumed to grow at the same rate as population. Projections for urbanization are
obtained by estimating a model relating urbanization to per-capita income and lagged urbanization for all
countries over the sample period and creating forecasts on the basis of this model and the projected per-
capita income values. The model used and the estimates obtained are available upon request.
12 For the “Other” (non-sample) countries in the rest of the world, we projected vehicle ownership from
our estimated Gompertz function’s parameters, adapted to this “Other” group’s characteristics. In 2002
this group had per-capita income of about $3000 and owned 44 vehicles per 1000 people. We estimated
the group’s βi coefficient by regressing the sample countries’ βi values against the levels of per-capita
income at which the respective countries had 44 vehicles per 1000 people; this produced a value of βi=-
0.21 for “Other” countries. Using the sample countries’ median saturation value (812), we assumed 1.7%
annual per-capita income growth for “Other” countries, and projected their vehicle ownership to 2030.

19
0% 1% 2% 3% 4% 5% 6% 7%
annual growth rate, 1960-2002: GDP per capita
0%
2%
4%
6%
8%
10%
12%
14%
annual
growth rate
1960-2002:
Vehicles
per 1000
people
Chn
Ind
USA
Idn
Bra
Pak
Jpn
Mex
DeuEgy
Tur
Tha
Fra
GBr
Ita
Kor
Zaf
Esp
Pol
Can
Mar
Mys
Twn
Aus
equally fast
twice as fast
0% 1% 2% 3% 4% 5% 6% 7%
annual growth rate, 2002-2030: GDP per capita
0%
2%
4%
6%
8%
10%
12%
14%
annual
growth rate
2002-2030:
Vehicles
per 1000
people
Chn
Ind
USA
Idn
Bra
Pak
Jpn
Mex
Deu
Egy Tur Tha
Fra GBr
Ita
Kor
Zaf
Esp
Pol
Arg
Can
Mar
Mys
Twn
Aus
equally fast
twice as fast
fast as China’s, but whose vehicle ownership also is projected to grow nearly twice as
fast as per-capita income.
The faster growth of total vehicles in the non-OECD countries will more than double
their share of world vehicles – from 24% in 2002 to 56% by 2030. Non-OECD countries
will acquire over three-fourths of these additional vehicles – nearly 30% will be from
China alone. By 2030, there will be 2.08 billion vehicles on the planet, compared with
812 million in 2002; this total is 2.5 times greater than in 2002.
Shown in Figure 9 (for the countries with population above 20 million in 2002) are the
historical growth rates in vehicle ownership and per-capita income (1970-2002), and the
projected growth rates for 2002-2030. The historical results for 1970-2002 show that
vehicle ownership in most countries grew twice as fast as per-capita income, and in a few
countries more than twice as fast. Such large income-elasticities for vehicle ownership
(two or higher) are consistent with the non-linear Gompertz function we have estimated,
for countries whose per-capita income is increasing through the middle-income range of
$3,000 to $10,000. The projected results to 2030 show that most OECD countries’
vehicle ownership growth will decelerate in the future, growing at a rate lower than per-
capita income. However, the non-OECD countries whose per-capita income is increasing
through the middle-income range will experience growth in vehicle ownership that is at
least as rapid as their growth in per-capita income. In some of the largest countries,
vehicle ownership will grow twice as rapidly as per-capita income – in China, India,
Indonesia, and Egypt.
Figure 9. Growth Rates for Vehicle Ownership and Per-Capita Income
History: 1970-2002 Projections: 2002-2030
By 2030, the six countries with the largest number of vehicles will be China, USA, India,
Japan, Brazil, and Mexico. China is projected to have nearly 20 times as many vehicles
in 2030 as it had in 2002. This growth is due both to its high rate of income growth and
the fact that its per-capita income during this period is associated with vehicle ownership
growing more than twice as fast as income.

20
Country 2002 2030
Average
annual
growth rate
2002 2030
Average
annual
growth rate
2002 2030
Average
annual
growth rate
2002 2030
Average
annual
growth rate
OECD, North America
Canada 26.9 46.2 2.0% 581 812 1.2% 18.2 30.0 1.8% 0.62 31 37 0.6%
United States 31.9 56.6 2.1% 812 849 0.2% 234 314 1.1% 0.08 288 370 0.9%
Mexico 8.1 19.3 3.1% 165 491 4.0% 16.7 65.5 5.0% 1.26 101 134 1.0%
OECD, Europe
Austria 26.3 49.8 2.3% 629 803 0.9% 5.1 6.4 0.8% 0.38 8 8 -0.1%
Belgium 24.7 45.3 2.2% 520 636 0.7% 5.3 6.7 0.8% 0.33 10 11 0.1%
Switzerland 27.7 54.3 2.4% 559 741 1.0% 4.0 4.9 0.7% 0.41 7 7 -0.3%
Czech Republic 13.6 40.2 4.0% 390 740 2.3% 4.0 7.1 2.1% 0.59 10 10 -0.2%
Germany 23.5 38.1 1.7% 586 705 0.7% 48.3 57.5 0.6% 0.38 83 82 0.0%
Denmark 25.9 46.7 2.1% 430 715 1.8% 2.3 3.9 1.9% 0.86 5 5 0.1%
Spain 19.3 39.0 2.5% 564 795 1.2% 22.9 31.7 1.2% 0.48 41 40 -0.1%
Finland 24.3 46.1 2.3% 488 791 1.7% 2.5 4.2 1.8% 0.75 5 5 0.0%
France 23.7 41.2 2.0% 576 779 1.1% 35.3 50.3 1.3% 0.54 61 65 0.2%
Great Britain 23.6 43.1 2.2% 515 685 1.0% 30.6 44.0 1.3% 0.47 59 64 0.3%
Greece 16.1 33.0 2.6% 422 725 2.0% 4.6 7.7 1.8% 0.75 11 11 -0.1%
Hungary 12.3 40.0 4.3% 306 745 3.2% 3.0 6.4 2.7% 0.75 10 9 -0.5%
Ireland 29.8 54.0 2.1% 472 812 2.0% 1.9 3.9 2.7% 0.91 4 5 0.7%
Iceland 26.7 49.5 2.2% 672 768 0.5% 0.2 0.3 1.0% 0.21 0 0 0.5%
Italy 23.3 44.5 2.3% 656 781 0.6% 37.7 40.2 0.2% 0.27 57 52 -0.4%
Luxembourg 42.6 63.8 1.4% 716 706 -0.1% 0.3 0.4 1.1% -0.04 0 1 1.1%
Netherlands 25.3 42.3 1.8% 477 593 0.8% 7.7 10.2 1.0% 0.42 16 17 0.2%
Norway 28.1 47.5 1.9% 521 805 1.6% 2.4 4.0 1.9% 0.83 5 5 0.3%
Poland 9.6 30.7 4.2% 370 746 2.5% 14.4 27.4 2.3% 0.60 39 37 -0.2%
Sweden 25.4 48.1 2.3% 500 777 1.6% 4.5 7.0 1.6% 0.69 9 9 0.0%
Turkey 6.1 14.1 3.0% 96 377 5.0% 6.4 34.7 6.2% 1.67 67 92 1.2%
OECD, Pacific
Australia 25.0 47.6 2.3% 632 772 0.7% 12.5 18.4 1.4% 0.31 20 24 0.7%
Japan 23.9 42.1 2.0% 599 716 0.6% 76.3 86.6 0.5% 0.31 127 121 -0.2%
Korea 15.1 39.0 3.5% 293 609 2.6% 13.9 30.5 2.8% 0.77 48 50 0.2%
New Zealand 19.6 39.1 2.5% 612 786 0.9% 2.4 3.5 1.3% 0.36 4 4 0.4%
Non-OECD, South America
Argentina 9.6 25.5 3.6% 186 489 3.5% 7.1 23.8 4.4% 1.0 38 49 0.9%
Brazil 7.1 15.9 2.9% 121 377 4.1% 20.8 83.7 5.1% 1.43 171 222 0.9%
Chile 9.2 23.7 3.4% 144 574 5.1% 2.2 11.7 6.1% 1.47 16 20 0.9%
Dominican Rep. 6.0 13.6 3.0% 118 448 4.9% 1.0 5.1 5.9% 1.65 9 11 1.0%
Ecuador 2.9 7.0 3.1% 50 182 4.7% 0.7 3.2 5.6% 1.50 13 17 0.9%
Non-OECD, Africa and Middle East
Egypt 3.5 6.6 2.3% 38 142 4.9% 2.5 15.5 6.7% 2.09 68 109 1.7%
Israel 17.9 25.9 1.3% 303 454 1.5% 1.9 4.1 2.7% 1.10 6 9 1.3%
Morocco 3.6 7.5 2.7% 59 228 4.9% 1.8 9.7 6.3% 1.83 30 43 1.3%
Syria 3.1 4.9 1.6% 35 80 3.0% 0.6 2.3 4.9% 1.89 17 29 1.8%
South Africa 8.8 18.6 2.7% 152 395 3.5% 6.9 16.7 3.2% 1.27 45 42 -0.3%
Non-OECD, Asia
China 4.3 16.0 4.8% 16 269 10.6% 20.5 390 11.1% 2.20 1285 1451 0.4%
Chinese Taipei 18.5 46.2 3.3% 260 477 2.2% 5.9 13.6 3.1% 0.66 23 29 0.8%
Indonesia 2.9 7.3 3.4% 29 166 6.5% 6.2 46.1 7.4% 1.89 216 278 0.9%
India 2.3 6.2 3.5% 17 110 7.0% 17.4 156 8.1% 1.98 1051 1417 1.1%
Malaysia 8.1 19.8 3.2% 240 677 3.8% 5.9 23.8 5.1% 1.16 25 35 1.3%
Pakistan 1.8 3.4 2.2% 12 29 3.2% 1.7 7.8 5.6% 1.48 145 272 2.3%
Thailand 6.2 18.3 3.9% 127 592 5.7% 8.1 44.6 6.3% 1.43 64 75 0.6%
Sample (45 countri
e
8.6 18.3 1.8% 166 316 1.5% 728 1765 3.2% 0.85 4346 5379 0.8%
Other Countries 3.0 6.0 1.7% 44 112 2.2% 83 315 4.9% 1.34 1891 2820 1.4%
OECD Total 22.3 41.6 1.5% 548 713 0.6% 617 908 1.4% 0.42 1127 1272 0.4%
Non-OECD Tota
l
3.6 9.1 2.2% 38 169 3.6% 195 1172 6.6% 1.61 5110 6927 1.1%
Total World 7.0 14.1 1.7% 130 254 1.6% 812 2080 3.4% 0.94 6237 8199 1.0%
Population (millions)
per-capita income
(thousands, 1995 $ PPP)
Vehicles per 1000
population Total Vehicles
(millions)
ratio of
growth rates:
Veh.Own. to
per-cap.
Income
Table 3. Projections of Income and Vehicle Ownership, 2002-2030

21
1 2 3 4 5 6 7 8 9 10 20 30 40 50 60
per-capita income: historical 1960-2002 & projections 2003-2030
(thousands 1995 $ PPP, log scale)
1
10
100
1000
Vehicles
per 1000
people:
historical
1960-2002
and
projections
2003-2030
(log scale)
S.Korea 1960-2002
Gompertz
function China 2030
India
1970-2002
India 2030
USA
1960-2002 S.Korea
2030
China 1984-2002
China 2002
Japan 1960-2002
Japan 2030
USA 2030
S.Korea 1960-2002
S.Korea 2002
Figures 10 and 11 put into historical context the rapid growth that we are projecting for China.
In 2002, China’s vehicle ownership was 16 per 1000 people, similar to that of India, but at a
higher per-capita income. This rate of vehicle ownership was comparable to the rate in 1960
for Japan, Spain, Mexico and Brazil, and in 1982 for South Korea. We project that China’s
vehicle ownership will rise to 269 by 2030, increasing 2.2 times faster than its growth rate for
per-capita income13. This projection for China, as its per-capita income increases from $4,300
to $16,000, is comparable to the 1960-2002 experience of Japan, Spain, Mexico and Brazil,
and since 1982 for South Korea. Although these other countries’ per-capita incomes grew at
different rates historically (slower in Brazil and Mexico, faster in Spain, Japan, and South
Korea), their ratios of growth in vehicle ownership to per-capita income growth over the
1960-2002 period were at least as high as the 2.2 that we project for China.14
Figure 10. Historical and Projected Growth for China, India, South Korea, Japan and
USA: 1960-2030
13 As noted above, we assume China’s per-capita income will grow at an average annual rate of 4.8% (see
Appendix A for details). This is lower than the 5.6% growth rate for 2003-2030 that is assumed in DoE’s
International Energy Outlook 2006.
14 As observed in the previous section, China’s estimated saturation level for vehicle ownership (807) is higher
than that for India (683). This is because China’s population density is only one-third of India’s, given the fact
that we divide population by land area rather than habitable land area (90% of China’s population lives in only
30% of the land area). If we used India’s lower saturation level for China, our projections for China in 2030
would be vehicle ownership of 228 rather than 269 vehicles per 1000 people, and 331 million total vehicles
rather than 390 million. This would represent a reduction in the annual growth rate of vehicle ownership from
10.6% to 10.0%; the ratio to growth in per-capita income would be 2.07 rather than 2.2.

22
2 3 4 5 6 7 8 9 10 20 30 40 50 60
per-capita income: historical & projected (thousands 1995 $ PPP, log scale)
10
20
30
40
50
60
70
80
90
100
200
300
400
500
600
700
800
900
1000
Vehicles
per 1000
people:
historical
&
projected
(log scale)
USA 2030
India 2030
S.Korea
1982
China
2002
China 2030
Brazil
1962
Mexico
1960 Japan
1960
Brazil 2002
Mexico 2002
USA 1960
USA 2002
Korea 2030
Japan 2030
Japan
'02
India
2002
Brazil 2030
Spain
1960
Spain
2002
Spain 2030
S.Korea 2002
Mexico 2030
Figure 11. Projected Growth for China and India, compared with Historical and
Projected Growth for USA, Japan, South Korea, Brazil, Mexico, and Spain.

23
1960 1970 1980 1990 2000 2010 2020 2030
0
200
400
600
800
1000
1200
1400
1600
1800
2000
2200
Total
Vehicles
(millions)
USA
Rest
of
OECD
China
History Projections India
Brazil
Rest of Sample
Rest of World
Total Income Total Vehicles
0
20
40
60
80
100
% Share
of Increase
2002-2030
USA USA
Rest of OECD
Rest of OECD
China
China
India
India
Brazil Brazil
Rest of Sample Rest of Sample
Rest of World Rest of World
Figure 12. Total Vehicles, 1960-2030
Figure 12 summarizes historical and projected regional values for total vehicles. The world
stock of vehicles grew from 122 million in 1960 to 812 million in 2002 (4.6% annually), and
is projected to increase further to 2.08 billion by 2030 (3.4% annually). The implications for
highway fuel use are discussed in the following section.
Figure 13. Regional Shares of the Absolute Increase in Income and Total Vehicles,
2002-2030
Figure 13 shows the non-
OECD’s disproportionately high
share of additional Total
Vehicles relative to their share
of additional Total Income
during 2002-2030. The non-
OECD countries will produce
62% of the absolute increase in
Total Income, but will constitute
77% of the increase in Total
Vehicles.

24
Region D-G-S
to 2030 IEA(2004)
to 2030 IEA(2006)
to 2030 OPEC(2004)
to 2025 SMP(2004)
to 2030
M
e
dl
oc
k
&
Soligo(2002):
1995-2015
B
utton et a
l
.
(1993)
2000-2025
OECD 0.42 0.57 0.39 0.40
N
on-OECD 1.61 1.12 0.97 1.13
China 2.20 1.38 1.96 1.28 1.42 2.02
India 1.98 0.39 2.25 1.23 2.89
Indonesia 1.89 2.94
Malaysia 1.16 1.96 0.92
Pakistan 1.48 4.00 0.73
Thailan
d
1.43 2.63
Worl
d
0.94 0.61 0.86 0.57 0.59
5.1 Comparison to Previous Studies
Our vehicle ownership projections for the OECD are comparable to others in the literature, but
are much higher for non-OECD countries. Since comparisons among projections are
complicated by differences in income growth rates assumed, we compare the projected ratios
of average annual growth rate of vehicle ownership to average annual growth rate of per-
capita income for 2002-2030. Table 4 compares our projections (D-G-S) with those of IEA
(2004), IEA (2006), OPEC (2004), the Sustainable Mobility Project (SMP, 2004), Button et
al. (1993) and Medlock-Soligo(2002).
Table 4. Projected Ratios of Vehicle Ownership Growth to Per-capita Income Growth,
2002-2030
The respective ratios for the OECD are similar across the studies. However, for the Non-
OECD countries, our projected ratios are substantially higher than those of all the others15 –
except Medlock-Soligo (2002), which is discussed separately below. For the world as a
whole, we project that vehicle ownership will grow almost as rapidly as per-capita income,
while IEA (2004)16, OPEC (2004) and SMP (2004) project that it will grow only about six-
tenths as rapidly.
15 Exxon Mobil (2005) projections to 2030 for OECD Europe and North America are similar to those in Table 4:
growth in “light duty” vehicles (cars and light trucks) about half as rapid as income growth. However, for Asia-
Pacific (both OECD and non-OECD combined) they project 4.7% annual growth in “light duty” vehicles, while
we project 6.1% annual growth in total vehicles for those countries, using comparable income growth
assumptions. Details of the underlying model are not provided. Wilson et al. (2004), using the Dargay-
Gately(1999) model, make projections for China and India that are similar to ours: (car) ownership growth twice
as rapid as per-capita income.
16 The latest IEA projections, IEA (2006), are much closer to ours than to IEA (2004); details of the underlying
models are not provided.

25
010 20 30 40 50
per-capita income (thousands 1995 $ PPP)
0
1
2
3
income
elasticity
of
vehicle
ownership
Medlock-Soligo(2002)
D-G-S
Lower projections of Non-OECD vehicle ownership by OPEC (2004) and SMP (2004) can be
explained by their assumption of low saturation levels and low income-elasticities of vehicle
ownership. For OPEC (2004), the developing countries’ vehicle ownership saturation level
was assumed to be 425 vehicles per 1000 people17 – considerably lower than our saturation
estimates of 700 to 800 for most countries. For SMP (2004), the relatively low projections of
non-OECD vehicle ownership are due to their assumption of relatively low income-elasticity
of vehicle ownership (1.3) for low-to-middle levels of per-capita income (through which most
Non-OECD countries will be passing in the next two decades) – which is one-third lower than
our estimated income elasticity for those income levels. SMP (2004) assumes similarly low
income-elasticities of vehicle ownership for all income levels, which implies much lower
saturation levels than we have estimated.
Figure 14. Comparison of Income Elasticities
The highest projected ratios for low-income
Non-OECD Asian countries are those of
Medlock-Soligo (2002). They employ a log-
quadratic functional specification, which has an
income-elasticity of vehicle ownership that is
very high at the lowest levels of per-capita
income but which declines rapidly as per-capita
income increases (Figure 14). However, the
data in Figure 4 suggest that the income-
elasticity of vehicle ownership follows a non-
monotonic pattern: increasing over the lowest
income levels and decreasing over higher
income levels, but remaining above 1.0 for
income levels in the range of $3,000 to
$15,000.
To sum up these comparisons, the considerably lower projections of non-OECD vehicle
ownership in OPEC (2004) and SMP (2004) are due to their assumption of significantly lower
income-elasticities and saturation levels of vehicle ownership for these regions. Such
assumptions raise the important question of why developing countries – once they achieve
levels of per-capita income within the range of OECD countries over the past few decades –
would not have comparable levels of vehicle ownership. On what other goods would
consumers in developing countries be spending their incomes instead?
17 See Brennand (2006). Similarly low saturation levels were assumed by Button et al. (1993), for car
ownership (300 to 450 cars per 1000 people). By contrast, Dargay-Gately(1999) estimated saturation levels of
620 cars (and 850 vehicles) per 1000 people. Button et al. (1993) made car ownership projections for ten low-
income countries, two of which are included in our sample: Pakistan and Malaysia. They also made projections
for 1986-2000, which underestimated by 50% the ratio of car ownership growth to per-capita income growth that
actually occurred from 1986 to 2000 for these two countries.

26
0.1 110 100 1000
Vehicles per 1000 people, 1971-2002
(log scale)
0.01
0.1
1
10
Total
Gasoline
Demand
1971-2002
(million
barrels
per day,
log scale)
China Germany
Japan
USA
S.Korea
India
Brazil
Mexico
110 100 1000
Vehicles per 1000 people, 1971-2002
(logarithmic scale)
0
1
2
3
4
5
Gasoline
per
Vehicle
1971-2002
(gallons
per day)
China
Germany
Japan
USA
S.Korea
India
Brazil
Mexico
6. IMPLICATIONS FOR PROJECTIONS OF HIGHWAY FUEL DEMAND
Projections of increasing vehicle ownership suggest that highway fuel use may also increase
significantly. However, the rate of increase in highway fuel demand depends upon the
changes over time in fuel use per vehicle as vehicle availability increases.
Figure 15. Gasoline Usage and Vehicle Ownership for Selected Countries, 1971-2002
Figure 15 summarizes, for several large countries, the 1971-2002 relationship between
gasoline usage18 and vehicle ownership, both per-vehicle (left graph) and total (right graph).
At the lowest levels of vehicle ownership, fuel use per vehicle is relatively high; a relatively
small number of vehicles (mostly buses and trucks) are used intensively. As vehicle
ownership grows, more cars and other personal vehicles are available; these additional
vehicles are used less intensively than buses and trucks, so that fuel use per vehicle declines,
while total use grows.
Highway fuel use per vehicle also changes over time for other reasons than vehicle
availability, namely vehicle usage, and fuel efficiency. With a given vehicle stock, fuel price
and income can affect vehicle usage (distance driven) in a given year. Fuel-efficiency
improvements can reduce fuel use per vehicle, as it takes less fuel to travel a given distance.
Based on judgment and historical patterns, OPEC (2004) makes assumptions about different
regions’ rates of decline in highway fuel per vehicle. Using those projected rates of decline19
18 The ratio of gasoline consumption to total vehicles is an imperfect measure of highway fuel use per vehicle,
because some vehicles use diesel fuel instead of gasoline, and some gasoline is not used by vehicles. We use
only gasoline consumption because we have no data for diesel fuel consumption for non-OECD countries, or for
OECD countries before 1993. Some recent reductions in German gasoline usage reflect fuel-switching to diesel
and in Brazil reflect the use of ethanol.
19 OPEC (2004) projects the following annual rates of decline for highway fuel per vehicle: OECD North
America: -0.5%, OECD Europe: -0.6%, OECD Pacific: -0.4%, China: -2.1%, Southeast Asia: -0.9%, South Asia:

27
together with our projected growth rates for total vehicles, we project that world consumption
of highway fuel will grow by 2.5% annually by 2030: 0.9% in the OECD and 5.2% in the rest
of the world. By comparison, OPEC (2004) projects 2000-2025 annual growth in world
highway fuel of 1.9%. Our higher rate of growth in highway fuel is due to higher projections
of the vehicle stock. If instead we were to assume slower projected rates of decline in fuel per
vehicle – closer to those experienced in 1990-2000 (-0.1% for OECD, -2% for China and
South Asia, -1% for other non-OECD) – then world highway fuel consumption would grow at
2.8% annually.
If our high projected long-term growth rates of highway fuel demand turn out to be correct,
this may test the ability of producers to increase production. Given limited incentives for the
OPEC countries to increase production quickly (Gately, 2004), as well as restrictions on
investment in many countries, it is not clear whether there will be enough oil in the market to
match rising demand at prices typical over the past several decades. If prices indeed turn out
to be considerably higher than in the past, highway fuel demand will grow more slowly than
our projections, due to lower use of vehicles, higher fuel efficiency, use of alternative fuels
such as bio-diesel and possibly also due to reduced vehicle ownership rates. This last effect
would not be captured by our partial equilibrium model of vehicle ownership. However, our
results clearly support the view that, with current policies, oil demand will continue to rise
significantly over the coming decades and there are significant risks that the oil market
balances will often be tight; see IMF (2005) for a detailed discussion.
7. CONCLUSIONS
We use a comprehensive data set covering 45 countries over 1960-2002 to explain historical
patterns in the vehicle ownership rates as an S-shaped, Gompertz function of per-capita
income. Our model specification exploits the similarity of response in vehicle ownership rates
to per-capita income across countries over time, while allowing for cross-country variation in
the speed of vehicle ownership growth and in ownership saturation levels.
The relationship between vehicle ownership and per-capita income is highly non-linear. The
income elasticity of vehicle ownership starts low but increases rapidly over the range of
$3,000 to $10,000, when vehicle ownership increases twice as fast as per-capita income.
Europe and Japan were at this stage in the 1960’s. Many developing countries, especially in
Asia, are currently experiencing similar developments and will continue to do so during the
next two decades. When income levels increase to the range of $10,000 to $20,000, vehicle
ownership increases only as fast as income. At very high levels of income, vehicle ownership
growth decelerates and slowly approaches the saturation level. Most of the OECD countries
are at this stage now.
We project that the world’s total vehicle stock will be 2.5 times greater in 2030 than in 2002,
increasing to more than two billion vehicles. Non-OECD countries’ share of total vehicles
-2.2%, Latin America: -0.7%, Africa and Middle East: -1.4%. We use estimates of these regions’ highway fuel
per vehicle from Brennand (2006) to calculate highway fuel consumption in 2002.

28
will rise from 24% to 56%, as they acquire over three-fourths of the additional vehicles.
China’s vehicle stock will increase nearly twenty-fold, to 390 million by 2030 – more vehicles
than the USA – even though its rate of vehicle ownership (about 270 vehicles per 1000
people) will be only at levels experienced by Japan and Western Europe in the mid-1970’s,
and by South Korea in 2001. As in most countries, vehicle ownership in China, India,
Indonesia and elsewhere will grow twice as rapidly as its per-capita income, as these countries
pass through middle-income levels of $3,000 to $10,000 per capita. By 2030, vehicle
ownership in virtually all the OECD countries will have reached saturation, but in most of
Asia it will still only be at 15% to 45% of ownership saturation levels.
Our results also suggest that the future strong growth in the vehicle stock in developing
countries will lead to significant increases in oil demand from the transport sector. We project
annual worldwide growth in highway fuel demand to be in the range of up to 2.5-2.8%. Our
work has a number of other broad policy implications. For example, developing countries will
face the challenge of building the infrastructure (roads, bridges, fuel delivery, etc.) needed to
support the growth in vehicle ownership. Moreover, many of the environmental concerns
associated with the greater use of vehicles could presumably be strengthened by our
projections, especially since future vehicle ownership growth will mostly take place in
developing countries that have so far been able to deal with the environmental issues less
successfully than advanced economies (World Bank, 2002). However, while the historical
patterns in vehicle ownership rates suggest that growing wealth is a powerful determinant of
vehicle demand, policymakers may be able to slow the expansion of the vehicle stock through
tax policies, promotion of public transport, and appropriate urban planning – an important area
for future research.
ACKNOWLEDGEMENTS
The authors wish to thank Karl Storchmann, two anonymous referees and the editor for
helpful suggestions.
Paul Atang, Stephanie Denis, and Angela Espiritu provided excellent research assistance.

29
REFERENCES
Button, Kenneth, Ndoh Ngoe, and John Hine (1993).
“Modeling Vehicle Ownership and Use in Low Income Countries.”
Journal of Transport Economics and Policy. January: 51-67.
Brennand, Garry. “Transportation sector modeling at the OPEC Secretariat.”
Workshop on Fuel Demand Modeling in the Transportation Sector.
Vienna. 20th January; 2006.
Dargay, Joyce (2001). “The effect of income on car ownership: evidence of asymmetry”.
Transportation Research, Part A. 35:807-821.
----- and Dermot Gately (1999). “Income's effect on car and vehicle ownership, worldwide:
1960-2015.” Transportation Research, Part A 33: 101-138.
Gately, Dermot (2004). “OPEC’s Incentives for Faster Output Growth.”
The Energy Journal 25(2): 75-96.
----- and Hillard G. Huntington (2002). The Asymmetric Effects of Changes in Price and
Income on Energy and Oil Demand. The Energy Journal 23(1): 19-55
Exxon Mobil. 2005 Energy Outlook. December 2005.
http://www.exxonmobil.com/Corporate/Citizenship/Imports/
EnergyOutlook05/2005_energy_outlook.pdf
Griffin, James M., and Craig T. Schulman (2005). “Price Asymmetry in Energy Demand
Models: A Proxy for Energy Saving Technical Change.”
The Energy Journal 26(2): 1-21.
International Energy Agency (IEA). World Energy Outlook 2004. Paris.
-----, World Energy Outlook 2006. Paris.
International Monetary Fund. World Economic Outlook, Chapter IV. April 2005.
http://www.imf.org/Pubs/FT/weo/2005/01/pdf/chapter4.pdf
Medlock, Kenneth B. III, and Ronald Soligo (2002). “Automobile Ownership and Economic
Development: Forecasting Passenger Vehicle Demand to the year 2015.”
Journal of Transport Economics and Policy. 36(2): 163-188.
Mogridge MJH (1983).
The Car Market: A Study of the Statics and Dynamics of Supply-Demand Equilibrium.
London: Pion.
Organization of the Petroleum Exporting Countries (OPEC, 2004). Oil Outlook to 2025.
OPEC Review paper.
Storchmann, Karl (2005).
“Long-run Gasoline Demand for Passenger Cars: The role of income distribution.”
Energy Economics. January, 27 (1): 25-58.
Sustainable Mobility Project (2004). Mobility 2030: Meeting the Challenges to Sustainability.
World Business Council for Sustainable Mobility.
Wilson, Dominic, Roopa Purushothaman, and Themistoklis Fiotakis (2004).
“The BRICs and Global Markets: Crude, Cars and Capital.”
Global Economics Paper No. 118. New York: Goldman Sachs.
World Bank. Cities on the Move. August 2002.
http://www.worldbank.org/transport/urbtrans/cities_on_the_move.pdf
US Department of Energy (2006). International Energy Outlook 2006. Washington.

30
APPENDIX A: Data Sources
This appendix provides further details on the datasets used in the analysis of vehicle
ownership.
Data on vehicles (at least 4 wheels, including cars, trucks, and buses) are primarily from the
United Nations Statistical Yearbook. The data for a few country-years are from the national
statistical offices.
Historical data on Purchasing-Power-Parity (PPP) adjusted gross domestic product are from
the OECD’s SourceOECD database. The data are expressed in thousands of 1995 PPP-
adjusted dollars. Where necessary, the series were spliced with real income data from IMF’s
World Economic Outlook database using the assumption that growth in the PPP GDP rate
equals real income growth.
Data on the real income growth projections for 2005-09 are from the IMF’s World Economic
Outlook. For 2010-30, the main data source is the U.S. Department of Energy (DoE)
International Energy Outlook April 2004. An adjustment was made to the DoE’s growth
projection for China and India. In both cases, the long-term income growth rates were reduced
by 1 percentage point (specifically for China, the growth rate is 5 percent annually over 2010-
14, 4.4 percent over 2015-2019, and 4.1 percent over 2020-2030; for India, the growth rate
assumption is 4.3 during 2010-2014, 4.1 percent during 2015-2019, and 3.9 during 2020-
2030). This adjustment was made to reduce the PPP-weighted world growth rate to its
historical average of about 3.5 percent a year. This adjustment may create a downward bias in
our vehicles projection if, in the future, world income growth will turn out to be higher than
the historical average.
The data on urbanization and land area are from the World Bank’s World Development
Indicators database. Urbanization is expressed in percentage points and land area is expressed
in square kilometers. The data on population, including projections, are from the United
Nations database (median scenario). Population density was calculated by dividing total
population by land area; it is measured by persons per square kilometer.

31
APPENDIX B: Alternative Specifications
This appendix compares in the results of our Reference Case with four alternative
specifications of the vehicle ownership equation:
Reference Case but without using Population Density;
Reference Case but without using Urbanization;
Reference Case but without allowing for Asymmetric Income Responsiveness;
Common Saturation Levels: Dargay-Gately(1999), Symmetric Income Response.
Table B1 displays the estimated values for all coefficients (except country-specific βi) and
their Probability Values, as well as the Adjusted R2 and Sum of Squared Residuals. Also
shown are projections of Total Vehicles in 2030 (millions), for all countries with more than 10
million vehicles, as well as World totals – assuming the same projected level for non-sample
“Other” countries (315 million) across all specifications.
The econometric results are similar across all specifications. All the coefficients have the
expected sign and are statistically significant (except for country-specific βi for some of the
smaller countries, as in Table 2). All have comparable summary statistics: high Adjusted R2
and low Sums of Squared Residuals.
Projections of Total Vehicles in 2030 for the World are very similar across specifications,
although country-specific projections may differ by more, especially for those countries with
extreme values of density or urbanization. World totals within 2% of the Reference Case are
projected by the specifications which exclude (respectively) Density or Urbanization or
Income Asymmetry. The largest differences from the Reference Case result from the
Common Saturation specification (Dargay-Gately 1999); world projections that are 95% of
Reference Case, with OECD projections higher (103%) and Non-OECD projections lower
(89%). Compared with the differences across projections in Table 4, however, these
alternative specifications generate remarkably similar projections.

32
coef. P-value coef. P-value coef. P-value coef. P-value coef. P-value
Speed of adjustment θ0.095 0.00 0.080 0.00 0.093 0.00 0.098 0.00 0.075 0.00
0.084 0.00 0.066 0.00 0.083 0.00 0.098 0.075
max. saturat
i
on
l
eve
l
γ
max
852 0.00 853 0.00 852 0.00 852 0.00 852 0.00
population density λ-0.000388 0.00 -0.000476 0.00 -0.000400 0.00
urbanization φ-0.007765 0.00 -0.015297 0.00 -0.007445 0.00
alpha α-5.897 0.00 -5.613 0.00 -5.814 0.00 -5.912 0.00 -5.362 0.00
Adjusted R-squared
Sum of Sq. Residuals
OECD, North America
Canada
United States
Mexico
OECD, Europe
Germany
Spain
France
Great Britain
Italy
Netherlands
Poland
Turkey
OECD, Pacific
Australia
Japan
Korea
Non-OECD, South America
Argentina
Brazil
Chile
Non-OECD, Africa and Midd
l
Egypt
South Africa
Non-OECD, Asia
China
Chinese Taipei
Indonesia
India
Malaysia
Thailand
Sample (45 countries)
Other Countries
OECD Total
Non-OECD Total
Total World
Sample (45 countries)
Other Countries
OECD Total
Non-OECD Total
Total World
0.999820
0.041131
Reference Case X-Density X-Urbanization X-Asymmetry
Common Saturation
0.999828
0.039229 0.999828
0.039039
0.999825
0.038947 0.999825
0.039843
Total Vehicles: % of
Reference Case Total Vehicles: % of
Reference Case Total Vehicles: % of
Reference Case Total Vehicles: % of
Reference Case
100.1%
94.2%
100.0%
103.2%
88.8%
95.1%
100.1%
100.0%
99.9%
100.2%
98.0%
98.5%
100.0%
100.0%
97.7%
98.7%
97.6%
100.0%
102.1%
94.8%
19782080 2038 2054 2082
937
1172 1112 1146 1175 1041
908927908907
1663
315 315 315 315 315
1765 1723 1739 1767
44.5 43.9 44.7 43.1
23.9 23.6 23.8 23.4
142.1 147.4 155.8 123.4
42.7 44.7 46.1 38.7
20.1 12.2 13.4 20.2
349.8 376.5 392.0 309.8
15.8 16.5 16.8 15.1
14.6 15.1 15.5 13.5
11.0 11.8 11.7 11.0
78.1 83.9 83.7 76.1
22.3 23.9 24.0 22.1
36.1 29.8 30.3 37.5
96.7 84.6 86.3 97.1
17.0 19.7 18.5 19.5
34.0 34.1 34.6 32.3
27.6 27.1 27.4 27.2
11.9 10.3 10.2 12.7
42.2 39.7 40.2 42.1
45.6 45.6 44.0 49.9
51.2 49.9 50.3 50.9
31.9 31.7 31.8 31.8
59.2 59.4 57.4 64.1
63.3 64.8 65.5 60.5
314.3 314.4 314.4 313.8
Total Vehicles 2030
(millions) Total Vehicles 2030
(millions) Total Vehicles 2030
(millions) Total Vehicles 2030
(millions)
29.6 30.2 30.1 29.7
155.5
23.8
44.6
390.2
13.6
46.1
16.7
15.5
23.8
83.7
11.7
86.6
30.5
34.7
18.4
10.2
27.4
40.2
50.3
44.0
57.5
31.7
Total Vehicles 2030
(millions)
314.4
30.0
65.5
income increases
income decreases
yes no
urbanization yes yes no yes no
population density yes no yes
Alternative Specifications
asymmetric income
response yes yes yes no no
Table B1. Econometric Results and Projections from Alternative Specifications