Job Market Paper
Market Structure and Pricing Strategy of China's Automobile Industry
Haiyan Deng1
Economics Department University of California, Davis
October, 2003
Abstract
In this study, using market-level data on quantities, prices, and automobile
characteristics from 1995 to 2001, I conduct the market analysis of China's automobile
market assuming imperfect competition. On the demand side, a nested multinomial logit
model is applied to the national market share data in order to ascertain the demand
features of China’s automobile market. On the supply side, Bertrand behavior is assumed
in order to uncover the mark-ups set by automobile manufacturers. My results show that
some large automobile manufacturers set high mark-ups, indicating their strong market
power in China’s automobile market. However, their declining mark-ups from the mid
1990s imply the reduction in market control by oligopolies in this industry during the
period.
JEL Classification: L13, L62, D43
Keywords: oligopoly markets, differentiated products, nested logit model, Bertrand
Equilibrium
1
This paper is the second chapter of my Ph.D. dissertation. I am most indebted to my advisor, Robert C.
Feenstra, for his insightful guidance and comments. I am grateful to Deborah L. Swenson, James E.
Prieger for valuable discussions and comments on the earlier version of the paper. My thanks also go to
Wing T. Woo for keeping me updated about China’s automobile industry development. Comments from
participants in Western Economic Association International 78th Annual Conference in Denver, July 2003
are greatly appreciated. I also thank Taaru Chawla for editorial help. The remaining errors are mine.
1
Market Structure and Pricing Strategy of China's Automobile Industry
1.
Introduction
My earlier paper, “How Integrated Is China's Market?—Evidence From the
Automobile Industry in China”, found clear quantitative evidence of inter-provincial
trade barriers in China’s automobile market from 1988-1992. Moreover, it claimed that
China’s regional markets progressed towards integration during the study period,
indicated by substantial reduction of inter-provincial non-trade barriers. The extension of
this work to more recent years is impeded by the absence of comparable data. This paper
takes a different approach to study whether China’s automakers continued to enjoy
sheltered local market and exercise monopoly powers for the more recent years.
Specifically, by taking advantage of market-level data on quantities, prices, and
automobile characteristics from 1995 to 2001, this study investigates the market structure
and pricing strategy of China’s automobile industry, which also looks for indirect
evidence on China regional market protection.
The automobile market is best characterized as an oligopoly market with
differentiated products, given the small number of competing firms and the various
automobile production lines. Oligopoly markets with differentiated products have
received considerable attention by both theoretical and empirical economists. In
particular, research on the U.S. automobile industry has been prominent in the empirical
work on market structure and pricing to market over the last two decades. However, I am
unaware of any equivalent research of China’s automobile market. The lack of empirical
results is unfortunate since optimal policy depends critically on detailed understanding
about market structure and conduct. This understanding is of great importance for
2
China’s policymakers as the automobile market continues to dramatically change
following China’s succession to the World Trade Organization (WTO) on December 11,
2001. Appropriate government intervention based on accurate understanding of the
automobile market is important in determining the fate of this industry in China. To the
best of my knowledge, this work is the original attempt of applying state-of-the-art
econometric methods to quantitatively analyze China’s automobile market structure.
Although there is little detailed quantitative analysis on China’s automobile
market, a brief review of existing literature on the market structure and conduct of the
U.S. automobile market is helpful in directing the research of China’s automobile market.
There are two main groups investigating the US automobile market equilibrium. The
first group takes the spatial approach on products differentiation. Bresnahan (1981,
1987) first adopts a pure vertical differentiation model assuming uniform distribution of
consumers’ tastes over a quality line, as in a Hotelling (1921) model. He assumes that
autos are differentiated along one dimension. This assumption has been relaxed in
Feenstra and Levinson (1995), who allow a much richer structure of neighbors for each
product variety. This extension of the Hotelling model enables a less restrictive pattern
of the cross-price elasticity, which is a big step closer to reality.
The second group of study is primarily composed of discrete-choice-based models
that estimate demand parameters. This group can be further divided into two branches
depending on the type of data used. The first branch uses data on individual consumer
purchase. The representative work is Goldberg’s (1995) paper on investigating the
effects of the Voluntary Export Restraint (VER) and the exchange rate pass-through. By
applying a sequential logit model to individual purchase data for the U.S. automobile
3
market during 1983-1987, she estimates demand elasticities and markups. Using
disaggregate data, Goldberg avoids the problem of price endogeneity by assuming that a
single consumer has no impact on market prices. However, this approach requires
detailed consumer level survey data.
The demand for an applicable model on market level data leads to the second
branch of the discrete choice model approach. Berry's (1994) theoretical framework
based on the discrete choice model resolved the problem of the lack of consumer choice
data. By inverting the market share function, this highly structural approach is able to
infer the demand taste parameters from the price, the characteristics, and the quantity
purchased of each product. Similarly, Berry, Levinsohn, and Pakes (1995) use the
market-level data to analyze the U.S. automobile industry. They adopt simulation
methods to estimate a random coefficients logit model. Other authors have extended
Berry’s method to study oligopoly markets with differentiated products in a variety of
industries. For example, Irwin and Pavcnik (2001) apply Berry's technique to estimate
the elasticities and markups of the commercial aircraft industry in order to examine
international competition in this industry. Ohashi (2000) builds on the model in order to
study the impact of the EC-Japan VER on the U.S. VCR market in 1983-1985.
In comparison, this paper applies the nested logit model similar to Goldberg, but
to aggregate national level data for China from 1995-2001 instead of individual consumer
level data. Moreover, in contrast to the empirical work in this field, the demand model in
this study introduces varying within group correlations of consumer utilities across
market segments. By relaxing the assumption of constant within group correlation, this
approach places less structure on the car selection process.
4
The average markup of Chinese automobile market inferred by this study is
relatively high compared to the U.S. automobile market, indicating that the leading
automakers in China have been exercising a certain degree of market power. However,
the markups are declining over time which may suggest increasing competition in
China’s automobile market. The strong market power may reflect local trade protection
in China. However, the decreasing market power of the automakers indicates increasing
competition in this market starting from the late 1990s due to the pressure of entering the
WTO.
The paper is organized as follows. In Section 2, I present the theoretical
framework of the paper. I first describe the demand side, and then I elaborate on the
supply side. Section 3 gives the data description. Section 4 analyzes the estimating
results and discusses the empirical interpretation. Finally, Section 5 presents the
conclusions.
2.
The Model
I adopt Berry’s (1994) theoretical framework to study the supply-and-demand
system of China's passenger car industry in order to examine its market structure.
However, this framework on the demand side differs from Berrry’s nested logit model in
that the within group heterogeneity is modeled to be different rather than constant across
market segments. The advantage of relaxing the constant heterogeneity is to obtain more
realistic cross-price elasticities of demand. On the supply side, by solving the Bertrand
equilibrium for the multi-product firms, I infer the markup of China’s automobile
manufacturers.
5
2.1
The Demand Side - Nested Logit Model
Starting from the demand side, I apply a nested logit model to infer the demand
parameters. The advantage of using a nested logit model is that it does not have the
traditional problem of independent of irrelevant alternatives (IIA) found in the simple
logit model. Unlike the simple logit model, the nested logit model preserves the
assumption that consumers’ tastes for passenger cars have an extreme value distribution
but are correlated across models within a market segment (Berry, 1994).
A consumer’s decision of buying a car can be nested as shown in Figure I. Each
consumer i at time t chooses among J+1 alternatives, where J denotes the number of car
models available in China’s automobile market. At the first level of the tree, a consumer
chooses between buying a domestic car or an outside good. At the second level, the
buyer decides which market segment to purchase the car from. At the last level, the
buyer decides which model s/he wants to buy in that market segment.2
outside goods
mini
models
consumer
subcompact
passenger
cars
compact
luxury
models
models
models
Figure I. Automobile Choice Model
2
As discussed in Goldberg (1995), the structure of the tree reflects the correlation patterns among
unobserved factors across alternatives rather than consumers sequential decisions.
6
Passenger cars are grouped into four exhaustive and mutually exclusive sets,
g = 1, 2, 3, 4, denoting mini, subcompact, compact, and luxury cars respectively. I
denote the set of car models in group g as Mg. g ∈ G, where G = {0, 1, 2, 3, 4}. The
outside good, j=0, is assumed to be the only element of group 0. For product j∈ Mg,
assume that the utility of consumer i is:
u ij = x j β − αp j + ξ j + γ ig (σ g ) + (1 − σ g )ε ij ,
(1)
where xj, ξj, and pj are observed model attributes, unobserved model attributes, and price,
respectively. β and α are demand parameters. For consumer i, εij is an identically and
independently distributed extreme value over each different models j=1, 2,…, J, while γig
follows a unique distribution such that [γ ig (σ g ) + (1 − σ g )ε ij ] is also an extreme value
random variable when εij is an extreme value random variable (Cardell, 1997) . σg
roughly captures the correlation of consumers’ tastes within a group g. One can also
think of (1-σg ) as the degree of heterogeneity of consumers’ tastes within a group g. The
correlation of utility levels within group g goes to unity as the parameter σg approaches
one and goes to zero as σg approaches zero.
Unlike other research in this field, I allow the degree of heterogeneity of
consumers’ tastes to be different across groups. The reason is that it gives more realistic
cross-price elasticity estimation. The more models available in a group, the more diverse
the group is likely to be. As a result, consumers in this segment also tend to have more
heterogeneous tastes than consumers in other groups. Moreover, consumers who prefer
larger cars in terms of engine size (henceforth, larger cars) usually also prefer more
luxurious features of autos compared to consumers who choose smaller engine sized cars
(henceforth, smaller cars). Hence, consumers’ tastes are likely to vary more among
7
expensive cars. For instance, some buyers may prefer “cool” features like a moon roof,
while others may desire safety measures such as ABS and curtain airbags. For the
reasons discussed above, it is more realistic to assume different correlation of consumers’
tastes across market segments.
Equation (1) of consumer i’s utility function can be rewritten as:
u ij = δ j + γ ig (σ g ) + (1 − σ g )ε ij ,
(2)
where, δj, the mean utility level of product j, is defined as:
δ j = x j β − αp j + ξ j .
(3)
The nested logit model allows us to model correlation between groups of similar
products in a simple way. If model j is in group g, the market share of model j as a
fraction of this total group share is:
s j g (δ , σ g ) =
e
δ j (1−σ g )
,
Dg
(4)
where the denominator of this expression for a model in group g is:
Dg =
∑e
δ j (1−σ g )
.
(5)
j∈Μ g
This is called the “inclusive value”, which summarizes the utility obtained from all cars
in a group g. Similarly, the group share, the probability of choosing one of the group g,
is:
(1−σ g )
s g (δ , σ g ) =
Dg
∑ Dg
(1−σ g )
.
(6)
g
8
Then, the market share, sj , is the multiplication of the within group share and the group
share:
s j (δ , σ g ) = s j g (δ , σ g ) s g (δ , σ g ) =
e
δ j /(1−σ g )
D gσ ∑ D g
(1−σ g )
.
(7)
g
When a consumer chooses the outside good, j=0, δ0 ≡ 0, and D0=0, thus the market share
of j=0 is:
s 0 (δ , σ g ) =
1
∑ Dg
(1−σ g )
.
(8)
g
I denote the relative market share, the market share of model j relative to the market share
of the outside good, as sj/ s0. Taking the log of the relative market share sj/ s0 and solving
for the unknown value of Dg by taking log of equation (4), the relative market share in
log term becomes:
ln s j − ln s 0 = δ j − σ g ln s j
g
= x j β − αp j + σ g ln s j g + ξ j
(9)
or ln s j − ln s 0 = δ j − σ g ln s j
g
= x j β − αp j + σ g ln s j g + γ ln(m j ) + ξ j .
(10)
In equation (10), mj is the number of models included in market share sj , when only
combined market share is available. Refer to appendix for detailed derivation.
Since market price and the within market share are endogenous, I adopt
traditional instrument variables to deal with this problem. The instruments are cost
variables and the characteristics of the other car models in this group. While the
correlation of cost variables with its own price and market share is obvious, the choice of
the other instrument variables deserve further explanation. For an individual
manufacturer, the characteristics of the other manufacturers’ models affect the prices and
9
the market shares of its own products through market strategic interaction. The cost
shifter variables that I adopt in this paper are: average manufacturing wage rate, price
index for fuels, and price index for the purchase of equipment, tools, and instruments.
After obtaining the demand parameters, I am able to calculate the price elasticities
of demand derived from the market share equation. By the chain rule, the own price
elasticity and the cross-price elasticity of demand are:
ε j, j =
ε j ,k =
∂s j p j
∂p j s j
=
3
∂δ j ∂p j s j
j ∈ M g, g ∈G
,
∂s j p k ∂s j ∂δ k p k
=
,
∂p k s j ∂δ k ∂p k s j
After solving for
ε j, j =
∂s j ∂δ j p j
∂s j
∂p k
and
j ≠ k, j ∈ M g , k ∈ M h , g, h ∈ G
∂s j
∂p j
(12)
, equations (11) and (12) become: 3
⎛ σg
1
= αp j ⎜
sj/g + sj −
⎜
∂p j s j
1−σ g
⎝1− σ g
∂s j p j
In order to find the expression for
(11)
∂s j
∂p k
and
∂s j
∂p j
⎞
⎟,
⎟
⎠
j ∈ M g, g ∈G
(13)
, one needs to look at the market share functions.
Differentiating equation 1)a)i)(12) with respect to δj and δk yields:
⎛ σg
1
= −s j ⎜
sj/g + sj −
⎜
∂δ j
1−σ g
⎝1− σ g
∂s j
∂s j
∂δ k
= −s j (
σg
s k / g + s k ),
1-σ g
⎞
⎟,
⎟
⎠
j ∈ M g, g ∈G ,
k ≠ j, j ∈ M g , k ∈ M h , g , h ∈ G
①
②
where group g and h could be either different or the same. If g ≠ h , then sk/g = 0, and equation Error!
Reference source not found.② becomes - sj sk . In addition, from the derivation of the single-product
pricing equation, I obtained that the derivative of mean utility with respect to price is -α. Therefore,
together with equations Error! Reference source not found. and Error! Reference source not found., I
can solve equations 1)a)i)(12) and 1)a)i)(12).
10
ε j ,k =
∂s j p k
⎞
⎛ σh
s k / g + s k ⎟⎟ ,
= αp k ⎜⎜
∂p k s j
⎠
⎝1− σ h
j ≠ k, j ∈ M g , k ∈ M h , g, h ∈ G
(14)
In equation (14), if g ≠ h , then the cross group cross-price elasticity of demand becomes
αpksk.
2.2
The Supply Side - Bertrand Equilibrium
I assume China’s automobile market to be characterized by N price setting firms.
Following Berry’s (1994) assumption, the marginal cost of production is both
independent of the output levels and is linear in a vector of cost characteristics. The cost
characteristics are decomposed into an observed subset, vector wj, and an unobserved
component, ωj. Therefore, the marginal cost of producing good j is given as:
mc j = w jη + ω j ,
(15)
where η is a vector of cost parameters to be estimated.
The firm’s profit from selling model j is then given as:
π j = ( p j − mc j ) Ms j ,
(16)
where M is the market size. When a multi-product firm raises the price for model j, it
will not only shift the demand toward the vehicles of its competitors, but also toward its
other models. For this reason, enforcing the assumption of single-product firm would
underestimate the markups if the automakers are indeed multi-product firms. Since most
automobile manufacturers in China produce more than one model, I consider multiproduct firms in the framework. Therefore, the firm’s profit from selling all its
automobile models is:
π f = ( Pf − C f )' S f M ,
(17)
11
where Pf , Cf , and Sf are the price, marginal cost, and market share vectors for firm f.
Assuming Bertrand equilibrium exists, the first-order condition for profit maximizing
firm f with respect to model j is:
s j + ( Pf − C f )'
∂S f
∂p j
= 0.
(18)
In the simple case of single-product firms, equation (18) becomes:
⎛ 1 ∂s j
p j = mc j + ⎜
⎜ s ∂p
j
⎝ j
−1
⎞
⎟ .
⎟
⎠
(19)
The last term of the above equation is semi-elasticity, which can be derived by
differentiating the market share equation with respect to the mean utility and by
differentiating the mean utility equation with respect to the price. Combining equations
(3) and (7), semi-elasticity becomes:
⎛ 1 ∂s j
⎜
⎜ s ∂p
j
⎝ j
⎞
⎟
⎟
⎠
−1
−1
⎫⎪
1 ∂s j ∂δ j ⎪⎧ − α
[1 − σ g s j / g − (1 − σ g ) s j ]⎬ , j ∈ M g , g ∈ G .
=
=⎨
s j ∂δ j ∂p j ⎪⎩1 − σ g
⎪⎭
(20)
Substitute the marginal cost equation (15) and the above semi-elasticity equation (20)
into equation (19), the pricing equation for a single-product firm becomes:
⎧1 − σ g
⎫
p j = w jη + ⎨
/ 1 − σ g s j / g − (1 − σ g ) s j ⎬ + ω j ,
⎩ α
⎭
[
]
j ∈ M g , g ∈ G . (21)
In order to find the pricing solution for multi-product firms, it is convenient to
introduce matrix expressions. Matrix E and D are defined as J×J matrixes, whose
elements in row k and column j are defined by:
12
E kj =
∂s j
∂p k
Dkj = 1, if model k and j are made by the same company,
Dkj = 0, otherwise.
I have solved
∂s j
∂p k
and
∂s j
∂p j
while deriving the price elasticities of demand. Thus the
kj-th element in matrix E is:
⎛ σg
⎞
E kj = αs j ⎜
sk / g + sk ⎟ ,
⎜1− σ
⎟
g
⎝
⎠
j ≠ k, j ∈ M g , k ∈ M h , g, h ∈ G
⎛ σg
1
E jj = αs j ⎜
s + sj −
⎜1− σ j / g
1−σ g
g
⎝
⎞
⎟,
⎟
⎠
j ∈ M g, g ∈G
(22)
(23)
Again, in equation (22), If g ≠ h , it becomes αsjsk. In conclusion, the pricing equation
for multi-product firms is:
P = C − ( E * D) −1 S ,
(24)
in which, P, C, and S are J×1 vectors, D is the ownership matrix defined before, E is a
matrix define by equation (22) and (23), * is an operator defined as element-by-element
matrix multiplication.
3.
Data
This paper studies the China’s automobile market for the years 1995-2001. I
model the imported automobiles as the outside good at the first level of the tree illustrated
in Figure I. This simplification is inevitable due to the data limitation; the data on prices
and automobile characteristics by model is not available for imports. Although this
treatment is far from perfect, it is justified by the excessive price lag between imported
cars and the domestic cars. Since China imposed eighty to one hundred percent tariff on
13
the imported passenger cars in the study period, foreign cars were much more expensive
than domestic cars. Therefore, consumers in China’s automobile market considered
imports quite different than the domestically produced cars.
For the domestic cars, the primary data source is the China Automotive Industry
Yearbooks (CAIY) from 1996-2002. Another data source is the Automotive Industry of
China of the following years: 1998, 1999, and 2002. The main role of the latter data
source is to supplement the former one. Most characteristics of passenger cars and some
sales data are from CAIY. A few automobile attributes data is from Automotive News—
Market Data Book. When sales data is not available, I assume automobile manufacturers
sold all their production in that year.
I follow CAIY’s convention for market segmentation criteria, except that I
merged the intermediate cars and the limousines into one group and named it the luxury
group.4,5 In this study, therefore, there are four groups of passenger cars in China’s
automobile market. Table 1 lists the Chinese vehicle brands for each market segment
during 1995-2001. The criterion of grouping is the engine size of the autos. Hence, the
luxury cars group refers to larger engine sized cars relative to the engine size of the cars
in the other three groups, which is not the traditional definition.
China Business Update is an important data source. It provides price information
from the State Price Bureau. This data source has price data of leading brands in 15 large
cities in China from 1995 to 2001. Other sources of price data are: China Price (19962001), China Automotive Market Outlook (1998, 1999), Shanghai Auto News (1998,
4
The justification is that over the seven year period, China only produced limousines in 1995 and 2001.
The total production and sale of limousines are also negligible. The total production of limousines in these
two years is 552; the total sales are 397. The only three models of limousines are Redflag CA7560, CA
7460, and Cherokee BJ2021V8.
5
The regression results remain very similar if dropping limousine from the sample.
14
1999), China Auto (Nov 1999, Volume 9, Number 8), and various Internet websites.6 I
convert these prices into current US dollars in order to have comparable results with U.S.
markups.
The China Statistical Yearbook (1996-2002) is another important data source for
this paper. Data used in this study from the China Statistical Yearbook are: exchange
rate, average manufacturing wage rate, price index for fuels, and price index for the
purchase of equipment, tools, and instruments.
4.
Empirical Results
The demand coefficients are obtained by estimating equation (10) with the full
data sample from 1995-2001. I report estimates from four different regressions: OLS and
IV regressions, each with constant, and then variant within group heterogeneity. In order
to calculate the price elasticities by year, I estimate the same demand equation for each
year, using IV regressions with different heterogeneity coefficients. The own price
elasticity and the cross-price elasticities are then calculated according to equation (13)
and (14).
On the supply side, due to the data shortage of China’s automakers’ cost
variables, the price equation (24) is not estimated. Instead, I use the estimates from
demand equation (10) to imply the markups using the pricing equation. Similar to the
calculation of price elasticities, the estimates used to imply automakers’ markups are
obtained from IV regressions by year with different within group heterogeneity.
4.1
Demand Parameters
6
Important Internet websites include: auto.sina.com.cn, auto.sohu.com , www.carcn.com,
www.autostarry.com, www.wlqp.com, www.cheshi.com.cn, etc.
15
The estimates of equation (10) are given in Table 2. In this table, I report
parameter estimates from both the OLS regression and IV regressions. They are obtained
from regressions of log of relative market shares on: prices, within group shares, and
product characteristics. The first set of results is based on nested logit specifications with
one constant σ for all market segments. The second set of regressions estimate different
σg for each automobile group, assuming that the correlation of consumer tastes for
models within each automobile market segment is different from any other vehicle group.
This allows to obtain estimates that are closer to reality. The results under this
assumption are reported under the columns “different correlation”, where I use market
segment dummy variables interacted with within group shares as the independent
variables to capture σg for the four automobile groups.
The first row reports the estimates of the semi-elasticity. The coefficients are
–0.05 for the OLS models and –0.14 for the IV models approximately. The smaller
absolute value of semi-elasticity for the OLS models reflects the problem of endogenous
prices discussed before. The magnitude of these estimates are insensitive to the different
treatment of the within group correlation or the selection of automobile characteristics.
All the regressions lead to very elastic demand for cars in China’s automobile market,
which is as expected.
The second row of the “constant correlation” model reports one single estimate of
σ. The positive estimate of 0.614 suggests autos within the same group are better
substitutes for each other than autos across groups. Under the “different correlation”
regression, I obtain the highest σg for the mini cars, followed by the subcompact cars,
then the compact cars, and finally, the luxury cars. The magnitude of these σg indicates
16
that consumers’ tastes for mini cars in China are fairly similar. Consumers’ preferences
for luxury cars in China, however, are quite different. The reason is that the degree of
heterogeneity of consumers’ preferences is directly correlated to the number of different
features available for that group. Moreover, the larger a car is, the more emphasis a
manufacturer is likely to put on different features and hence create a distinctive car. The
pattern of the estimates of σg also depicts the reality in China well. China’s mini car
group has less variety than other automobile market segments.7 Table 1 gives the
detailed brands information for each automobile group.
The remaining rows of Table 2 report the estimates on automobile characteristics.
Estimated coefficients on size and maximum power are of the expected sign and
significant. However, none of the other estimates of miles per gallon (mpg), maximum
speed, and displacement are positive and significant. The imprecisely estimated
coefficients on automobile characteristics are due to the correlation between these
characteristics variables. Another possible reason for this is that Chinese consumers
prefer smaller cars due to a lower average income level. This is reflected by the very low
market share of the luxury cars group in China’s automobile market. In Table 6c, the
average market share of luxury cars during 1995-2001 is only four percent. Although the
luxury cars show an increasing trend, it is still relatively low.
4.2
Demand Elasticities
The price elasticities of demand by year and by group are summarized in Table
3a-3c. In Table 3a, the average own price elasticity is -9.2. The magnitude of the own
price elasticity is about three times than that of the US automobile market found by
7
The leading brands in the mini car group are Xiali and Alto (Suzuki), which make up 64% and 30% of the
mini car market respectively.
17
Koujianou Goldberg (1995), where she found the average price elasticity of demand is 3.28 for 1983-1987. The reason for this large difference is that autos in China are
probably considered as more expensive consumption than in the US. Even including
other passenger vehicles, autos per capita in China is less than one seventieth of that of
the US.8 Therefore, it is not surprising to find that the Chinese consumers are more
responsive to automobile price changes than consumers in the US.
The magnitudes of the own price elasticity are quite different across groups.
Averaging over 1995-2001, the most price elastic group is the subcompact cars, followed
by the mini cars, with the elasticities of -15.6 and -9.6 respectively. The compact and the
luxury cars are the least price elastic – elasticites for these groups are -6.6 and -7
respectively.
This is counter intuitive since luxury cars usually has the highest own price
elasticity. However, this special pattern of elasticity for China can be explained by the
scarce domestic supply of luxury cars. China only produced few models in the luxury
group compared to the number of models produced in the other three groups. This is
particularly true for the years 1995-1998.9 The correlation between own price elasticity
and the number of models available in the group is positive. This indicates that the less
the competition in a group, the less elastic the cars in the group tend to be. In addition,
the consumers in the luxury cars segment of China’s automobile market are primarily
government agencies or enterprises. Given the short supply of luxury cars and the
8
US figure does not include buses. In year 2000, the number of passenger vehicles, including vans and
buses, per 1000 people is 6.74 for China; while the number of passenger cars per 1000 people is 474.81 for
the US. Figures of autos in use for China and the US are from Automotive Industry of China, 2000, pp69,
pp84; the US population figure is from the webpage: http://www.factmonster.com/ipka/A0764220.html .
9
In the luxury group, the number of models available in each year (1995-2001) is: 2, 2, 2, 2, 5, 4, and 9,
respectively.
18
specific purchasers in this group, lower value of price elasticity is expected for the luxury
car group.
Table 3b and Table 3c report the cross-price elasticities within group and cross
group respectively. Comparing the two sets of cross-price elasticities, the magnitudes of
within group cross-price elasticities are much larger than the cross group cross-price
elasticity, which reflects stronger substitution effects within each group than cross
groups.
Each automaker’s price elasticities are listed in the first three columns of Table 4.
Shanghai Volkswagen (SHVW), has the lowest own price elasticity of -5.3 among
China’s automakers. Accordingly, the price increment of Shanghai VW does not affect
its sales as much as sales of other automakers if they had the same percentage price rise.
This shows that SHVW has been exercising monopoly power to a certain degree, which
also reflected by its highest market share and markup among China’s automakers.
Except for Tianjin Xiali, the own price elasticity increases from -5.3 to
-17.3 as the markup margins decreases from 42% to 7%. In the next subsection, I will
discuss the markups and their implication of automaker’s market power.
4.3
Markups
From equation (24), I infer the markups of China’s automobile market during
1995-2001 under Bertrand Equilibrium. The markups by year and by group, both in
dollar value and in percentage, are summarized in Table 6a and Table 6b. Table 6a
reports the sales weighted average markup values by year and by group. The average
price over marginal cost markup is $4200 which corresponds to a 25% markup margin.
The markup values range from $900 to $8200 depending on the market segment and
19
year. The last row of Table 6a presents the markup values for the four market segments
during 1995-2001. This table shows that while mini cars and the subcompact cars have
the same lowest level of markup values of $1400, the markup values rise dramatically to
$6500 and $6900 for the compact and luxury cars respectively. This pattern of higher
markup values on larger cars is consistent with that of the U.S. automobile market
(Feenstra and Levinsohn, 1995).
Since the percentage of markups is of more importance, I shift the focus to Table
6b. Table 6b reports the sales weighted average markup margins in percentage by year
and by group. The last column summarizes average markups for the seven years
(1995-2001) which is also illustrated in Figure 1. Before 1999, the yearly average
markup fluctuated over time, ranging from 23% to 33%; from 1999 onwards, it dropped
dramatically from 33% to 15%. This time trend indicates that the competition in China’s
automobile market has intensified over time, especially after 1999.
Switching from time series to cross section, the last row gives average markups
for the four groups. On average, compact cars have the highest markups (36%), followed
by mini cars (17%), luxury cars (15%), and subcompact cars (9%). Equation (21)
suggests that this result is driven by market shares. The lower markups of luxury cars
and subcompact cars are driven by their low market shares. Table 6c gives the market
shares by year and by group. Subcompact cars and luxury cars have lower market shares
averaging at 21% and 4% respectively over the seven years. Compact cars, however,
capture more than half of the automobile market share.
There is another important reason for the high markups of the compact vehicles.
The vehicles produced by the top manufacturer, Shanghai Volkswagen, belong to this
20
market segment. Across the seven years, automobile models produced by SHVW
accounted for 41% of the domestic automobile market and 80% of the domestic compact
automobile market. Figure 5 illustrates the market shares and within market shares of
SHVW for the years 1995-2001. The largest market share of SHVW indicates its
strongest market power resulting in the highest markups in China’s automobile market.
Since SHVW is the main player in the compact car group, its high markup drives up the
sales weighted markup of the compact car segment. Therefore, it is not surprising to see
that the highest sales weighted markup goes to the compact cars instead of the luxury
cars.
Figure 2 shows that the markups for all the automobile groups, except for luxury
cars, have been decreasing over time. The downward trend for these other groups can be
regarded as the result of more intense competition within these segments. Starting from
1999, manufacturers in China introduced more models into the automobile market. For
example, before 1999, the mini car segment had only three models, later, six more were
added.10 In the luxury car segment, the Guangzhou Honda series were introduced in
1998 and Shanghai GM Buick series were introduced in 1999. While only two brands
were added to this segment, the market share of luxury cars increased from 0.45% in
1998 to 4.31% in 1999. The newly introduced Buick has been dominating the luxury car
segment ever since.11 Therefore, Buick’s high markup drives up the markup of the whole
luxury car segment.
10
Before 1999, the three models in mini car group are: Xiali, Alto, and Yunque. The added six models are:
Zhonghua, Xiaofuxing, Jeely-Haoqing Jeely-Merrie, Baili, and Flyer.
11
Buick had 79%, 78%, and 70% market share of the luxury car segment in years 1999, 2000, and 2001
respectively.
21
The last three columns of Table 4 list the average prices, market shares, and
markups of all manufacturers in descending order.12 Figure 3 provides a visual
presentation of the markups of these manufacturers. It is obvious from Figure 3 that the
most successful automaker in China’s automobile market is Shanghai Volkswagen. Its
average markup of 42% during 1995-2001 is more than twice that of the second largest
producer, Tianjin Xiali. This clearly demonstrates the strong market power of the
Shanghai Volkswagen. However, Figure 5 also shows that both the market share and the
within group share of the Shanghai Volkswagen are declining, which gives the decreased
markups as shown in Figure 4. This declining pattern is more apparent after 1999. Its
eroded market shares and thus decreased markup is due to the introduction of new models
not only in the compact group, but also in other groups. This is especially true of the
introduction of the Buick which is also produced in Shanghai.
While Shanghai Volkswagen dominates the compact group, Tianjin Xiali
dominates the mini car group. Although Xiali has high within market share of
approximately 65%, its market share in the overall automobile market is only 16.8%
compared to the 41% of the Shanghai VW. In addition, smaller cars are expected to have
lower markups than larger ones. This explains Tianjin Xiali’s second position of the
markup ranking in China’s automobile market.
The firm that ranked the third in markup is Shanghai GM. As discussed
previously, its brand, Buick, has dominated the luxury car segment ever since its
introduction in 1999. Similar to Tianjin Xiali, the Buick, despite its dominance within
12
This table excludes the manufacturers whose sales are less than 10000 cars over seven years.
22
group, has an overall market share of only four percent. Hence, it produces a markup of
only 14%.
The leading brand in the subcompact car segment is Jetta, which is produced by
the First Auto Works – Volkswagen (FAW – VW). However, the FAW – VW ranks only
eighth with a markup of 10%. Several reasons lead to this phenomenon. First, the
subcompact group is the market segment clustered with the most number of models (see
Table 1). This obviously leads to greater competition in the group. Second, Jetta has the
weakest market power compared to the other three main brands, even within the group.
Although Jetta has an average of 60% of market share within the subcompact group, it
still has the lowest within group share among the other three models discussed above. Its
10% market share cannot compete with that of Shanghai VW or Tianjin Xiali. Although
its market share is higher than Buick, subcompact cars are expected to have lower
markups than luxury cars. Finally, FAW – VW also produces two other series of models,
the Redflag and the Audi. However, these two series of models have very small market
shares of approximately 2.5%. The low market shares of these two series drive down
their markups as suggested by equation (21), hence also driving down the overall
markups of FAW –VW.
5.
Conclusion
There is an increasing number of studies in this field over the last two decades
both theoretically and empirically. However, most research concentrates on the U.S.
automobile markets. This paper is the first attempt to estimate the quantitative features of
the oligopoly automobile market in China.
23
Two of the estimates on automobile characteristics obtained from the demand
equation are significant and positive as expected. However, the remaining attributes that
are strongly correlated to luxury features are negative or insignificant. This can be
explained by consumers’ preference leaning toward less expensive cars caused by low
income per capita. The average within group correlation of consumer utilities is 0.61,
suggesting autos within the same group are better substitutes for each other than autos
across groups. Across automobile groups, the within group correlations were shown to
decrease from 0.91 for the mini cars to 0.06 for the luxury cars. The automobiles are
grouped by engine size. This relationship of σg with the automobile groups suggests
increasing diversity of consumer tastes along the line of automobile engine sizes.
The estimates of the demand enable us to obtain price elasticities. Own price
elasticities range from negative four to negative twenty depending on the firm and the
year, implying that market share is very sensitive to price changes. Much larger crossprice elasticity within group than that across groups also supports the nesting structure
presented in Figure I.
The implied markups range from eight percent to fifty-four percent. The most
successful player in China’s automobile market is Shanghai VW, which has the highest
markup among China’s automobile producers. This strong market power may reflect
local trade protection in China. Anecdotal evidence shows that in Shanghai, additional
purchasing fees were levied on cars produced outside Shanghai, although these were
gradually removed as time passed.
Shanghai VW’s dominance in China’s automobile market has declined from
1999. Its market share was eroded by many newly introduced models among which
24
Buick, produced by Shanghai GM, was most noticeable. Similar to Shanghai VW, the
leading models in other automobile market segments also possess declining within group
shares, which lead to decreasing markup values. All the above phenomena suggest
increasing competition in China’s automobile market starting from the late 1990s. In the
late 1990s, both producers and consumers expected that China would join the WTO soon.
This expectation put pressure on the domestic manufacturers as they competed fiercely
for market presence. It is widely believed that Chinese automakers have higher average
unit cost compared to their international counterparts of a similar make due to lack of
economies of scale (Liu and Woo, 2001). Only with certain market shares could the
automakers reduce their average costs. Under the WTO agreement, the Chinese
government was committed to reduce its protection of domestic market, including the
automobile vehicle market. This would make it even harder for the domestic models to
compete with imported cars. The market presence of domestic models directly relates to
whether they could survive after the foreign brands flood China’s automobile market.
Relative to the other Chinese industries, the automobile industry is one of the
most open to global challenges. The future of this industry is determined by the current
performance of its participants. I hope this initial study on China’s automobile market
will help in attaining an in-depth understanding of the industry, and thus provide a
foundation for optimal policies.
25
Appendix—Combined Market Share
The China Automotive Industry Yearbooks (CAIY) occasionally list the sum of
the productions or the sales of two or more models for an automaker. In this case, a
combined market share is calculated instead of one market share for each model. This
appendix solves for the relative combined market share in equation (10).
Assume the exact market share for each of mj models is not available. Instead,
mj
only the combined market share s j = ∑ s k is given. It is obvious that:
k =1
⎛ 1
⎞
1
ln⎜
s j ⎟ = ln(
⎜m
⎟
mj
⎝ j ⎠
1
⎛ mj ⎞ mj
1
s k ) ≅ ln⎜⎜ ∏ s k ⎟⎟ =
∑
mj
k =1
⎝ k =1 ⎠
mj
The equation equals when s k =
mj
∑ (ln s )
k =1
k
1
s j for all model k which are jointly listed. Similarly
mj
mj
for the within group market share s j / g = ∑ s k / g :
k =1
⎛ 1
⎞
1
ln⎜
s j / g ⎟ = ln(
⎜m
⎟
mj
⎝ j
⎠
1
⎞ mj
⎛ mj
1
s k / g ) ≅ ln⎜⎜ ∏ s k / g ⎟⎟ =
∑
mj
k =1
⎠
⎝ k =1
mj
The l.h.s equals the r.h.s. when s k / g =
mj
∑ ln s
k =1
k/g
1
s j / g for all model k which are jointly listed.
mj
26
Then solve for the relative combined market share:
⎞
⎛ 1
s j ⎟ − ln s 0
ln s j − ln s 0 = ln m j + ln⎜
⎟
⎜m
⎝ j ⎠
≅ ln m j +
1
mj
mj
∑ (ln s ) − ln s
k =1
k
0
m
⎤
1 ⎡ j
⎢∑ (ln s k − ln s 0 )⎥
m j ⎣ k =1
⎦
mj
⎞
⎛ 1 mj
⎞
⎛ 1 mj
⎞
⎛ 1
⎟
⎜
⎟
⎜
xk − α
p k + σ g ln⎜
ln s k / g ⎟
= ln m j + β '
∑
∑
∑
⎟
⎜ m k =1
⎜ m k =1 ⎟
⎜ m k =1 ⎟
⎠
⎝ j
⎠
⎝ j
⎠
⎝ j
⎞
⎛ 1
⎞
⎛ 1 mj
⎛ 1 mj ⎞
⎜
⎟
x
α
p k ⎟ + σ g ln⎜
sj/g ⎟
−
≅ ln m j + β ' ⎜
∑
∑
k
⎟
⎜m
⎜ m k =1 ⎟
⎜ m k =1 ⎟
⎠
⎝ j
⎠
⎝ j
⎠
⎝ j
= x j β − αp j + σ g ln s j g + (1 − σ g ) ln(m j )
= ln m j +
= x j β − αp j + σ g ln s j g + γ ln(m j ),
where xj and pj are the average characteristics and the average price for the mj models
respectively. The equation equals when each model k has the same market share. The
coefficient of (1-σg) on the number of models requires that each jointly listed model
belongs to the same market group and has the same fraction of market share. This
constraint is too strong to impose. Therefore, the last equation relaxes the constraint by
assuming that the combined market share is a function of the number of jointly listed
models.
27
References
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30
Table 1. Market Segment of China's Auto Market
Groups
Brands
Displacement (cc)
Mini
Alto, Xiali, Yunque, Jeely, Baili
<1000
Sail, Citroen, Jetta, Chery, Xiali, Fiat,
Subcompact
1000<d<1600
Kia, Mazda
Audi, Honda, Redflag, Golf, Santana,
1600<d<2500
Compact
Passat, Bora, Nissan
Luxury Audi, Buick, Honda, Redflag, Cherokee
>2500
Table 2. Estimate of Demand Equation
Dependent Variable: Relative Market Share (ln (s j ) -ln (s 0 ))
price (1000$)
ln(Sj/g)
all cars
mini
subcompact
compact
luxury
mpg
2
Size (m )
maximum speed
(km/h)
displacement (L)
maximum power
(hp)
maximum torque
(Nm)
ln(model#)
constant
2
R
observation
CONSTANT CORRELATION
OLS
IV
estimate t-stat estimate t-stat
-0.055
-4.90 -0.143
-5.08
0.833
25.06
0.614
DIFFERENT CORRELATION
OLS
IV
estimate t-stat estimate t-stat
-0.054
-4.17 -0.147
-2.41
6.21
-0.045
0.465
-3.21
4.34
0.003
0.704
0.14
4.31
0.963
0.946
0.780
0.808
-0.033
0.295
16.35
13.72
19.70
3.63
-1.73
2.15
0.907
0.884
0.622
0.055
-0.011
0.695
1.44
2.60
4.52
0.16
-0.44
1.47
-0.016
-2.99
0.003
0.40
-0.012
-1.71
0.002
0.11
-0.003
-4.82
-0.002
-2.07
-0.002
-3.32
-0.003
-2.46
0.026
2.94
0.035
2.66
0.025
2.31
0.046
3.07
0.011
1.12
0.010
0.55
0.010
0.91
0.004
0.19
0.261
3.364
0.843
222
2.04
2.31
0.361
-4.056
0.759
222
2.23
-1.60
0.117
3.529
0.851
222
0.91
1.77
0.113
-1.159
0.762
222
0.30
-0.17
Note: "Constant Correlation" reports estimates under the assumption of one constant within group correlation
of consumer tastes. Estimates reported under "Different Correlation" are obtained by assuming different
correlation of within group consumer tastes across automobile market segment.
31
Table 3a. Own Price Elasticity by Year & Group
Year\Group
1995
1996
1997
1998
1999
2000
2001
Average
Mini
-11.3
-10.3
-9.7
-8.6
-8.6
-9.3
-10.3
-9.6
Subcompact Compact
-8.6
-5.8
-9.1
-5.2
-11.5
-5.4
-11.7
-5.3
-18.4
-6.0
-17.1
-8.1
-17.4
-9.1
-15.6
-6.6
Luxury Average
-4.9
-7.5
-10.0
-7.1
-8.3
-7.5
-7.5
-7.5
-6.6
-9.8
-6.3
-10.6
-6.5
-11.8
-7.0
-9.2
Note: Averages are sales weighted average
Table 3b. Within Group Cross-Price Elasticity by Year & Group
Year\Group
1995
1996
1997
1998
1999
2000
2001
Average
Mini
4.4
5.1
4.1
3.6
3.3
2.1
1.5
3.4
Subcompact Compact Luxury
15.2
2.5
0.3
13.0
2.1
0.9
9.8
2.2
0.3
6.9
1.8
0.4
2.1
1.5
0.3
3.0
0.8
0.5
1.9
1.0
0.1
4.5
1.7
0.4
Average
4.0
3.8
3.8
3.3
2.1
1.7
1.3
2.6
Note: Averages are sales weighted average
Table 3c. Cross Group Cross-Price Elasticity by Year & Group
Year\Group
1995
1996
1997
1998
1999
2000
2001
Average
Mini
0.09
0.12
0.10
0.09
0.08
0.04
0.02
0.08
Subcompact Compact Luxury
0.10
0.56
0.02
0.12
0.51
0.38
0.18
0.53
0.05
0.17
0.43
0.03
0.07
0.32
0.12
0.10
0.18
0.25
0.07
0.21
0.07
0.10
0.38
0.17
Average
0.39
0.37
0.35
0.29
0.19
0.13
0.13
0.24
Note: Averages are sales weighted average
32
1
2
3
4
5
6
7
8
9
10
11
12
13
14
Table 4. Price Elasticities of Demand and Markup Margins by Manufacturer
Market
Own
Within
Cross Group
Price
Mark-up
Firm
Share
Price Group Cross- Cross-Price
(1K$)
(%)
(%)
Elasticit
Price
Elasticity
Shanghai VW Auto Co., Ltd.
-5.3
1.9
0.44
19
41.1
42
Tianjin Auto Xiali Company Ltd.
-10.1
4.0
0.09
9
16.8
19
Shanghai GM Auto Co. Ltd.
-8.5
0.8
0.15
34
3.0
14
Guangzhou (Peugeot) Auto Co., Ltd
-7.4
0.1
0.03
19
0.4
14
Beijing Jeep Co. Ltd.
-7.4
0.5
0.12
22
2.8
14
Chang'an Suzuki Auto Co., Ltd
-9.2
2.0
0.05
7
6.3
12
Jeely Automobile (Group) Co., Lt
-10.7
0.6
0.01
7
0.6
11
Guizhou Yunque Auto Co., Ltd.
-9.6
0.5
0.01
6
0.4
10
FAW-VW Auto Co., Ltd.
-14.7
5.5
0.13
21
13.3
10
Xi'an Qinchuan Auto Co., Ltd.
-10.3
0.3
0.01
7
0.7
10
China FAW Group Corporation
-11.2
0.6
0.15
37
3.7
9
Guangzhou Honda Auto Co., Ltd.
-13.6
0.8
0.16
38
2.5
8
1.5
0.06
12
0.8
7
Shanghai Auto Group-Chery Auto Co -13.6
Dongfeng Citreon Auto Co., Ltd
-17.3
2.6
0.07
17
6.6
7
Note: 1. This table only lists automakers with sales over 10,000 during 1995-2001.
2. Averages are sales weighted average
33
1
2
3
4
5
6
7
8
9
10
11
12
13
14
Table 5a. Own Price Elasticity by Year & Firm
Firm\Year
1995
1996
1997
1998
1999
Shanghai VW Auto Co., Ltd.
-4.5
-4.5
-4.7
-4.6
-5.0
Tianjin Auto Xiali Company Ltd.
-11.5
-10.2
-9.5
-9.0
-9.4
Shanghai GM Auto Co. Ltd.
-6.6
Guangzhou (Peugeot) Auto Co., Ltd
-7.9
-8.0
-7.8
-5.3
Beijing Jeep Co. Ltd.
-6.9
-7.5
-7.8
-8.3
-6.9
Chang'an Suzuki Auto Co., Ltd
-11.1
-11.1
-10.0
-8.2
-7.6
Jeely Automobile (Group) Co., Lt
Guizhou Yunque Auto Co., Ltd.
-9.7
-10.6
-10.7
-8.4
FAW-VW Auto Co., Ltd.
-6.3
-6.9
-10.1
-8.8
-20.1
Xi'an Qinchuan Auto Co., Ltd.
-11.6
-11.8
-11.7
-10.7
-10.8
China FAW Group Corporation
-14.0
-10.7
-10.0
-11.0
-11.0
Guangzhou Honda Auto Co., Ltd.
-16.9
-16.4
Shanghai Auto Group-Chery Auto Co
Dongfeng Citreon Auto Co., Ltd
-19.7
-17.6
-13.7
-17.6
-18.5
2000
-6.8
-10.6
-6.0
2001
-7.1
-11.4
-10.5
-6.9
-8.5
-6.9
-11.3
-10.7
-9.6
-17.8
-9.6
-11.2
-12.7
-13.5
-17.4
-8.8
-15.9
-8.5
-10.7
-14.2
-14.9
-17.6
Average
-5.3
-10.1
-8.5
-7.4
-7.4
-9.2
-10.7
-9.6
-14.7
-10.3
-11.2
-13.6
-13.6
-17.3
Note: 1. This table only lists automakers with sales over 10,000 during 1995-2001.
2. Averages are sales weighted average
34
1
2
3
4
5
6
7
8
9
10
11
12
13
14
Table 5b. Auto Markups by Year & Firm (Percent)
Firm\Year
1995
1996
1997
1998
1999
Shanghai VW Auto Co., Ltd.
32
46
46
54
49
Tianjin Auto Xiali Company Ltd.
18
25
22
20
16
Shanghai GM Auto Co. Ltd.
16
Guangzhou (Peugeot) Auto Co., Ltd
13
13
13
19
Beijing Jeep Co. Ltd.
15
13
13
12
15
Chang'an Suzuki Auto Co., Ltd
9
9
10
12
14
Jeely Automobile (Group) Co., Lt
Guizhou Yunque Auto Co., Ltd.
10
9
9
12
FAW-VW Auto Co., Ltd.
16
15
10
12
9
Xi'an Qinchuan Auto Co., Ltd.
9
9
9
9
9
China FAW Group Corporation
7
10
10
9
9
Guangzhou Honda Auto Co., Ltd.
6
6
Shanghai Auto Group-Chery Auto Co
Dongfeng Citreon Auto Co., Ltd
5
6
7
7
7
2000
36
17
17
15
15
11
11
12
10
7
7
7
2001 Average
28
42
12
19
13
14
14
16
14
12
12
11
11
10
10
8
10
11
10
10
9
9
8
7
7
7
7
Note: Averages are sales weighted average
35
Table 6a. Auto Markup Values by Year & Group
unit: 1000 US $
Year\Group
1995
1996
1997
1998
1999
2000
2001
Average
Mini
1.6
2.2
1.7
1.4
1.2
1.2
0.9
1.4
Subcompact Compact
2.6
5.4
2.2
7.3
1.5
7.4
1.4
8.2
1.3
7.3
1.4
5.8
1.1
4.6
1.4
6.5
Luxury Average
7.0
4.2
7.1
5.5
6.8
5.0
7.0
5.0
6.9
4.3
7.0
3.7
6.9
3.0
6.9
4.2
Note: Averages are sales weighted average
Table 6b. Auto Markup Margins by Year & Group (Percent)
Year\Group
1995
1996
1997
1998
1999
2000
2001
Average
Mini
15
22
19
18
16
17
13
17
Subcompact Compact Luxury Average
14
27
21
23
13
40
10
32
9
40
12
29
10
49
14
33
8
43
16
26
9
30
16
21
7
22
17
15
9
36
15
25
Note: Averages are sales weighted average
Table 6c. Auto Market Shares by Year & Group (Percent)
Year\Group
1995
1996
1997
1998
1999
2000
2001
Average
Mini
27
27
27
27
24
21
17
24
Subcompact
7
8
15
20
25
26
32
21
Compact
65
60
56
53
46
46
45
51
Luxury
1
5
1
0
4
6
6
4
36
Figure 1. Bertrand Markups by Year
35
29
26
25
23
21
20
15
15
10
5
0
1995
1996
1997
1998
1999
2000
2001
Year
Figure 2. Auto Markups by Year & Group
60
50
Markups (%)
Markups (%)
33
32
30
40
30
20
10
0
1995
1996
1997
1998
1999
2000
2001
Year
mini
subcompact
compact
luxury
37
42
10
9
8
7
7
FAW
GZ Honda
SH Chery
HB Citreon
10
FAW-VW
10
GZ Yunque
11
ZJ Jeely
12
CQ Suzuki
14
BJ Jeep
14
GZ Peugeot
SH GM
TJ Xiali
14
XA Alto
19
SH VW
45
40
35
30
25
20
15
10
5
0
Firm
Figure 4. Auto Markups by Year & Firm
60.0
50.0
Markups (%)
Markups (%)
Figure 3. Auto Markups by Firm
SH VW
40.0
Tianjin Xiali
30.0
FAW-VW
20.0
BJ Jeep
10.0
SH GM
0.0
1995
1996
1997
1998
1999
2000
2001
Year
38
Within Group Share/ Market
Share(%)
Figure 5. Shanghai VW Market share and Within Group Share,
1995-2001
100
90
83
80
70
84
88
86
78
74
68
60
50
48
50
47
46
40
40
36
30
31
Within
Group
Share
Market
Share
20
10
0
1995
1996
1997
1998
1999
2000
2001
Year
39