PRIVATE CAR: THE ANGEL OR DEVIL TO CHINA’S URBAN DEVELOPMENT? Jason Z. Yin, Ph.D. W. Paul Stillman School of Business Seton Hall University South Orange, NJ 07079 Tel: 973-761-9360 Fax: 973-761-9217 Email: [email protected]David F. Gates, Ph.D. Consultant, Markets and Countries The petroleum Finance Company 1300 Connecticut Avenue, NW, Suite 800 Washington, D.C. Tel: 202-872-1199 Fax: 202-8721219 Email: [email protected]ABSTRACT This paper will review the outlook for growth in the number of cars in China and then explore the relationship between growth in the number of private cars and urbanization including especially the effects of income, density, roads and public transportation on the growth of private cars in urban areas. The paper concludes with a discussion of possible strategic and policy measures to encourage the favorable consequences of growth in the number of cars in urban areas while discouraging the unfavorable consequences. 03/15/04 1
Transcript
PRIVATE CAR: THE ANGEL OR DEVIL TO CHINA’S URBAN DEVELOPMENT?
Jason Z. Yin, Ph.D. W. Paul Stillman School of Business
This paper will review the outlook for growth in the number of cars in China and then
explore the relationship between growth in the number of private cars and urbanization including especially the effects of income, density, roads and public transportation on the growth of private cars in urban areas. The paper concludes with a discussion of possible strategic and policy measures to encourage the favorable consequences of growth in the number of cars in urban areas while discouraging the unfavorable consequences.
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PRIVATE CAR: THE ANGEL OR DEVIL TO CHINA’S URBAN DEVELOPMENT?
Most analysts who have examined the prospects for cars in China see huge potential for
growth in the number of vehicles over the next ten to fifteen years. But realizing that
potential will depend on a number of things including not only continued economic
growth and reform but also China’s ability to manage the integration / harmonization of
increased numbers of cars into what are already large and congested urban areas. In effect
China must find a way to take advantage of the mobility enhancing effects of increased
numbers of cars while limiting the adverse consequences that too often seem to
accompany such increases in the number and concentration of vehicles.
Looking at the prospects for economic growth in China over the next twenty years, it
is impossible to ignore the potential for strong growth in the number of private cars and
by extension the industries that build, service and fuel these vehicles. Despite China’s
economic accomplishments since reform began, the number of passenger cars today is
still only about 5 per thousand people, a figure that is lower than almost any other
emerging market. It is also lower than almost any other country historically, i.e., when
other countries were at a comparable level of economic development. However, the
number of cars is expected to grow more rapidly and this growth is likely to continue for
years to come. This potential growth in private cars has important implications not only
for China’s overall economic development but also for the economic wellbeing of its
urban centers, which are likely to continue to attract a disproportionate share of this
growth. Properly managed increased numbers of cars can contribute to increased mobility
without the unfortunate traffic and health effects that too often seem to accompany the
growth in the number of cars in urban areas in other parts of the world. Not properly
managed the risk is that problems with traffic and air quality will lead to counter
measures that will in effect choke off potential improvements in mobility while at the
same time limiting the growth in employment opportunities and closing off opportunities
for increases in production that should lead to economies of scale and by extension
increased exports.
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This paper will first consider the demand for private cars in China over the next ten to
fifteen years and then will analyze the relationship between the growth of the number of
cars and urbanization including the effects of income, population density, road
development, and public transportation. It will also consider the effects of cars and
urbanization on traffic and the environment. It will discuss possible strategic and policy
measures for a well-aligned development for both cars and urban areas so as to avoid the
downside consequences that have too often characterized this relationship in too many
parts of the world.
The Growth Potential of Private Cars
In developing a long-term outlook for the number of cars in China, it is helpful to
begin with an objective / statistical projection that can then be modified to take account
of real world considerations. We all believe that cars in China are in the beginning stages
of what is likely to be an extended period of rapid growth. But how rapid is rapid? The
1994 State Council’s automotive industrial policy (AIP) plan for 2000-2010: shows total
vehicle production capacity of 6 million units, of which 4 million will be passenger cars.
Is this realistic? The next few paragraphs will provide a basis for answering this question.
Among the possible approaches to this type of projection, we chose an approach that
compares the cars per capita in China with that in other countries. The key to the
projection is an assumption that China will respond in more or less the same way as other
countries to likely future changes in income and other factors. The forecast of cars per
1000 people, that was the basis for the quantitative comments in this assessment, was
developed using a multiple regression approach, which was a combination of time series
and cross section analysis.
A total of 413 observations from 19 countries over a period of twenty-six years
were used in the analysis. Since not all years were available for all countries the number
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of observations was limited to those available. The countries included in the sample
include: Australia, Indonesia, South Korea, Austria, Ireland, Spain, Belgium, Italy,
Taiwan, Canada, Japan, Thailand, China, Malaysia, U.K., France, Pakistan, U.S., and
India. Note that in all cases the countries included are from East, South and Southeast
Asia, North America and Europe. The reason for this is that these countries appeared to
be the most plausible models for the type of car population growth that we would expect
to see in China. Other regions and countries including most in South America, Africa and
Eastern Europe do not show the type of consistent positive relationship between income
growth and numbers of vehicles that we would expect to see in China and thus were
excluded from the analysis.
Figure 1
0
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400
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0 5000 10000 15000 20000 25000 30000
CARS PER 1000 PEOPLE
PPP GDP PER CAPITARegression: Cars / 1000 People = f(PPP GDP per Capita, PopulationDensity, and IMF Debt / GDP)
Japan
South Korea
Thailand
China
Figure 1 illustrates one of the basic building blocks for this type of approach. The
chart shows cars per 1000 people for a sample of the 19 counties, including China,
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plotted against their Purchasing Power Parity (PPP) GDPs – expressed in real dollars per
capita. The data on numbers of cars are from the country level surveys that until recently
were compiled by the American Automobile Manufacturers Association. The data on
PPP GDP per capita are from the World Bank. Note that a foreign exchange rate based
measure of GDP per capita could have been used, but the PPP approach offers some
slight advantages in making comparisons. The graph in Figure 1 shows the “S”-shape
relationship between cars / 1000 people and PPP GDP using the coefficients generated in
the regression analysis and holding all variables other than PPP GDP constant. Historical
data for several countries have been highlighted in Figure 1: Thailand, South Korea and
Japan in addition to China, which is buried in the collection of data points at the lower
left. As mentioned China has less than 5 cars per 1000 people and its PPP GDP war about
$3500 per capita in 1998.
The full set of independent variables included PPP GDP per capita, population
density, calculated as each country’s population divided by its geographic area, and the
ratio of IMF debt to GDP. PPP GDP per capita is the most important causal factor and is
positively related to the demand for cars, which is consistent with expectations. The
second variable, population density, is negatively related to car demand - consistent with
expectations - because countries with fewer people per square kilometer, like Australia,
tend to have more driving and more cars per capita. The third variable, the ratio of IMF
debt to GDP, is a rough but not unreasonable measure of IMF involvement. The idea
here is that countries that are in serious enough difficulty to require IMF involvement and
loans, especially over an extended period of time, generally have fewer cars per 1000
people than other countries with similar incomes and population densities but no IMF
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involvement. This is not a factor in China but this variable is helpful to take account of
variations in cars per 1000 people in countries that have had extended periods of
economic difficulty that required IMF assistance. We have used other variables from time
to time but these make sense and also have the advantage of being reasonably easy to
forecast or to use to test sensitivities.
We also included in the regression a few dummy variables that were designed to take
account of major changes in government policy that is expected to impact the cars and
car fuel industries (the number of cars per 1000). i.e., Korea in the early 1980s and China,
in 1983 and again in 1994. We feel that the changes in policy in both these countries
were sufficiently profound that the use of dummy variables would increase the overall
explanatory power. For example, Korea appears to have eased up on its traditional
government constraints on car ownership in about 1983 and there was an immediate
inflection in the trend in cars per 1000 people. Policy changes in China were more
general and less specific to cars. Even so the shift in the reform program from agriculture
to industry in 1983 and the further shift toward structural reform in 1994 were
sufficiently dramatic that it is reasonable to expect that each would contribute to a shift in
the trend demand for cars. A further reason for using dummy variables for China is a
practical consideration that estimating the equation without dummy variables would have
resulted in a forecast that was much higher than actual values for China. This problem
could have been dealt with by using a convergence equation to bridge between the last
actual observation for China and the equation-based trend. Both approaches – use of
dummy variables or use of a convergence formula - are somewhat arbitrary but the
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convergence approach seemed somewhat more arbitrary and also would have implied a
larger number of cars per 1000 for China in 2010 and 2015.1
A matrix of simple correlation coefficients are shown in Table 1 and statistical results
for the regression are shown in Table 2. The signs of the correlation coefficients are
consistent with expectations and the sizes are such as to imply minimal problems with
multicolinearity. In terms of the regression results, all of the standard statistical measures
R-square, t-values, etc. are satisfactory, as are the signs and magnitudes of the
coefficients, which are consistent with our hypotheses. Indeed, the coefficients that
warrant comment are those for the dummy variables. The coefficients for the Korea
variable and for the three China variables can best be interpreted in comparison with the
intercept for the whole sample, which was estimated as “1” for all years and all countries
except Korea before 1983 and China, which were entered as “0”. In the dummy variables
“1”s were entered for Korea before 1984 (with “0”s for Korea after 1983 and for all other
countries and years), for China before 1984 (with “0”s for China after 1983 and all other
1 The key independent variable was per capita income, specifically real PPP GDP per
capita, which was expected to be a positive determinant of cars per 1000 people. Other measures of income such as a simple foreign exchange based real GDP per capita could have been used but PPP GDP, as published by the World Bank had the advantage of at least attempting to compensate rigorously for differences in purchasing power across different countries.
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SUMMARY OUTPUT
Regression StatisticsMultiple R 0.977R Square 0.954
countries and years), from 1984-93 (with “0”s for China before 1984 and after 1993 all
other countries and years) and for 1994 forward (with “0”s for China before 1994 and all
other countries and years). Given this construct, the intercept for the overall sample does
not apply to Korea before 1983 or for China in any of the years. Rather the coefficients
for the dummy variables perform this function. The intercept for the overall sample is -
3.99. The coefficient for the dummy variable for Korea before 1983 is –5.5 and the
coefficients for China –8.2 before 1984, -6.8 from 1984 to 1993, and –5.9 after 1994. The
coefficients for all of the dummy variables are lower (more negative) than that for the
overall sample suggesting that cars per 1000 people were consistently lower than that for
the overall sample during those periods other things equal. After the 1984 policy change
the number of cars per 1000 in Korea shifted upward toward the sample average. China
showed a similar pattern starting off well below the sample average with a dummy
variable coefficient of –8.2, that shifted up to –6.8 with the first set of policy changes in
1984 and then shifted up once again to –5.9 with the next set of policy changes in 1994.
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In other words consistent with our hypothesis the policy changes in Korea and in China
all contributed to upward shifts in the number of cars per 1000 people other things equal.
The last China dummy was kept in place for the forecast even though a case could
probably be made that at some point during the forecast period China could reach the
point in which cars per 1000 people will be on the same trajectory as the sample average.
Making this adjustment would result in a somewhat higher forecast than that shown here
but since the forecast is already high enough to imply tremendous increases in activity, it
seemed prudent to continue with what can readily be characterized as a conservative
forecast.
Once the equation had been derived, preparing the projection of possible future cars
per 1000 people in China was simply a matter of combining the coefficients from the
equation with actual and forecast values for China for the various independent variables.
The resulting forecast was then combined with a standard depreciation formula to derive
an estimate of likely retirements by year and by difference, a possible profile of future
growth in sales of new cars. This in turn was compared with government and automobile
manufacturers' forecasts of future production capacity to provide a further check on the
reasonableness of the overall fleet and sales profile. Such a projection can also be
broken down into implied numbers of new car sales and retirements – an exercise that
can serve as a useful check for overall feasibility and reasonableness.
Figure 2 below shows the results of the statistical analysis for China’s cars per
1000 people as projected though 2010 --- the solid black line--- in comparison with the
results of some other published projections: one by the Institute for Energy Economics –
Japan --- the dashed black line --- that was done in the mid 1990s; two by economists for
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the World Bank that were done in the mid 1990s --- the brown and green lines; and two
by economists at the Baker Institute at Rice University that were done last year --- the
blue and red lines. Note that the actuals and the projections are shown on a log scale. This
makes it easier to make comparisons with other countries but it tends to make differences
in the forecasts for China look smaller than they actually are – unless you pay close
attention to the vertical scale. For 2010, the projections for China range from 30 cars per
1000 people in the Institute for Energy Economics – Japan study (12% annual growth)
and 25 cars per 1000 people in the World Bank High Case (10% annual growth) down to
about 10 cars per 1000 in our calculation (8% annual growth). Ten is just slightly lower
than what would be implied by the Low Case prepared by the economists at the Baker
Institute (which showed 15 for 2015--- 8% annual growth for 15 years).
0
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1970 1975 1980 1985 1990 1995 2000 2005 2010 2015
SOUTH KOREA
CHINA
WORLD BANKHIGH CASE (25)
BAKER INSTITUTELOW CASE(15 IN 2015)
REGRESSION
CHINA CARS PER 1000 PEOPLEPOSSIBLE PROFILES THROUGH
2010/15
CA
RS
PE
R 1
000
PE
OP
LE
IEEJ (30)
Since all of the projections for China imply substantial increases in the number of
cars , between now and 2010, it is not worth spending time arguing over which projection
makes more sense. Some of the other forecasts assume higher real GDP growth (8 % or
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so versus 7 %) and the forecasts of Institute for Energy Economics and World Bank were
done before the Chinese economy began to grow more slowly as happened during 1998-
99. But for various reasons, we think a number closer to 10 cars per 1000 people is
somewhat more likely than a number that is much higher than 10. Ten cars per 1000 in
2010 would imply that it will have taken about 10 years (from 2000) for the number of
cars in China to have increased by about two times from less than 5 cars per 1000 people
in 1999 according to data from China Statistical Yearbook. An increase of this magnitude
looks fairly impressive. According to our conservative estimation, there will be a fleet of
13 million cars on the street by 2010 in comparison of 6 million cars in 1999. Note to JY
– this should probably come later: Majority of the cars will concentrate in big cities.
Therefore the density of car ownership in big cities is expected to 50 per 1,000 people by
2010 (Zhou Ganshi, 1996)
However, if we compare it with what happened historically in Korea, we probably
need to question whether a forecast of 10 or so for China in 2010 might be too
conservative. As shown here, it took Korea on the order of 16 years to go from where
China is today in terms of cars per 1000 to 100 cars per thousand, an increase of more
than 20 times, many times the increase we show for China over roughly the same number
of years. Though not shown, it took Japan only about 12 years starting in the-mid 1950s
to achieve an increase in the number of cars per 1000 that was similar to that for Korea.
The bottom line is that an increase to 10 cars per 1000 or so by 2010 is possible and
the increase could be a higher if we look at the results of other forecasts or the experience
of other countries like Korea and Japan. But whether this potential is realized or not will
depend on a lot of other constraints including the ability of China to integrate and
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rationalize increased numbers of cars in its already large, congested and air quality
impacted urban areas. This is the focus of the rest of this paper.
Car Projection at the National level According to Zhou Ganshi (1995), there are two strategic changes taking place in China’s auto industry: (1) car is replacing track to be in the dominant position of auto manufacturing; (2) private or household car is becoming the focus of the car market, replacing business cars including buses and taxis.
• Private auto ownership in China was about zero in 1978, 50,000 in 1993 and was about 3 million in 1998 (excluding farm-vehicles and motorcycles)
• An optimistic estimation: private car ownership will reach 1.2 million in 2000, 4.6
million in 2005 and 13.2 million in 2010. By 2010, private car ownership will be 15.8 per 1,000 people. According to this calculation, by 2010, Chinese large cities will have an additional 15 million cars. It is then necessary to build another 300 square km area of road and parking spaces. As the land has been almost used up in large cities, this is no doubt a serious problem (Zhou Ganshi, 1996).
• In 1999, China produced 571,000 passenger cars with capacity of 900,000.
• It will take at least another 10 years before large numbers of ordinary Chinese
families purchase cars for personal use. For the foreseeable future, the family car in China will have to be able to function both for business and for personal use (Wayne W.J. Wang, 1999, CBR)
. • Given projected population of 1.5 billion by 2015, if only 10% of the population
achieves incomes and auto ownership comparable to the developed nations, the stock of automobiles could increase by 40 to 60 million in China alone.
At national level, the variable income elasticities of motor vehicle ownership are statistically significant and decrease with income. The elasticities are only weakly nonlinear. At the urban level income elasticities of motor vehicle ownership are linear. Overall, linearity is the most parsimonious specification consistent with the data. Although elasticity estimates vary depending on the functional specification, a good point estimate is approximately 1 for the elasticity of national fleet growth with respect to per capita income and population. These value mean that country motor vehicle fleets grow at roughly the same rates as country incomes (Ingram and Liu, 1999, p.8)
Table 1: Income Elasticity of Passenger Vehicle Demand
