A joint initiative of Ludwig-Maximilians University’s Center for Economic Studies and the Ifo Institute for Economic Research
Area Conference on
Applied
Microeconomics
5 – 6 March 2010 CESifo Conference Centre, Munich
Competition and Innovation in the
US Automobile Market
Vivek Ghosal
CESifo GmbH
Poschingerstr. 5
81679 Munich
Germany
Phone:
Fax:
E-mail:
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+49 (0) 89 9224-1410
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Competition and Innovation in the US Automobile Market
Vivek Ghosal*
March 2010
VERY PRELIMINARY AND INCOMPLETE!
Prepared for presentation at CESifo Applied Micro workshop
Abstract
This paper studies competition and innovation in the US automobile market. The data used in
this paper are over the period (approximately) 1969-present. The firms included in my sample
are: General Motors, Ford, Chrysler, BMW, Daimler, Volkswagen, Toyota, Nissan, Honda, and
some other smaller firms. My dataset includes information on market shares, patents, number of
models, composition of the automobile fleet such as smaller versus larger cars and engines,
engine characteristics including horsepower, capacity and fuel-efficiency, quality of cars
reflecting cost of ownership and repair records, among several other key variables. While much
research has been done on this industry, no paper has examined this industry over such a longperiod of time and with a such rich dataset to fully understand the dynamics of competition and
innovation. Specifically, in this paper, I present the results on examining the responsiveness of
firms’ market shares to income and fuel price shocks, and relate them to some underlying
characteristics of the firms. My preliminary results show wide variation in the relevant income
and fuel price elasticities, and show suggestive and interesting links to the producers’
characteristics.
* Affiliations: Professor, School of Economics, Georgia Institute of Technology, Atlanta, GA
30332, USA; Research Fellow, CESifo.
Contact: [email protected]
1. Introduction
The paper’s central objectives are to examine the nature of competition and innovation in
the US automobile markets over a long period of time, 1969 to recent years. The US has had one
of the highest per capita incomes in the world, a relatively large population, and lack of public
transportation. These factors in combination have resulted in a relatively high demand for
automobiles (currently the US averages about 22% of global sales), and a market where almost
all the major firms in the world seek to compete for market share and profits. The US has also
had a relatively open markets, allowing entry by a wide range of foreign producers. In a sense,
the US market serves as a microcosm of the global automobile market, and the dynamics in US
market have implications for the global industry.
Before 1973, US automobile market was a relatively serene playground. The 1973-80 oil
price increases changed rules of the game. US fuel-inefficient cars lost competitive advantage
and automobile imports increased. The US manufacturers faced significant actual and potential
competition from foreign brands. A more open playground lead to a race to compete and
innovate. Figure 1 displays the oil prices and import-shares in the US market.
The 1970s oil price spikes acted as a significant technology shock for the US producers.
The existing larger engines and designs become obsolescent. My data reveals that the US
producers first rushed to introduce smaller-engined cars and fill new market segments. Second,
the data shows that they made composition changes with less larger-engined cars and more
smaller-engined cars. They also switched their product lines to include more light trucks and
sports utility vehicles (SUVs), as well as sharp increase in new models and smaller cars. Larger
engine sizes constituted a much smaller share of the market.
2
Based on an extensive dataset for a wide range of producers selling in the US market, my
estimation results reveal the following:
1. The income and fuel price elasticities vary widely across the producers in my sample.
The income elasticities are negative for the US firms, and generally positive for all other
producers. The fuel price elasticities are generally negative for the US producers and
positive for the foreign producers.
2. My results suggest that superior quality was an important driver of the long-run gains in
market share by the Japanese firms in the US market. Popular belief is that it was the
higher fuel-efficiency of the Japanese cars that drove their supremacy. The data shows
that the US firms had largely caught up with the Japanese firms by the late-1980s (Figure
2). Further, the Japanese firms had to make their cars bigger and heavier to meet with the
demands of the US consumers and US geography. So fuel efficiency related arguments
are not a consistent factor explaining the Japanese supremacy. My results suggest that the
primary race was in product quality. In contrast to the Japanese firms, the German firms
had mixed fortunes in the US market: BMW was the most successful; VW and Daimler
had mixed fortunes. Once again, my data suggests that the primary cause of the
shortcoming of the German firms in the US (mass) market segment is related to their
lower quality on average.
3. My estimation results reveal that the Japanese ratcheted up their innovation profile to
meet the demands of the US market and to shore up their competitive position. Their
overall patenting profiles increased significantly, and their patenting profile in critical
segments such as engines and motors increased sharply. The US firms saw some increase
in patenting and overall innovation, but the rate of increase was clearly less than the
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Japanese. The German firms’ patenting profile (in the US) was relatively low and flat. I
model feedback effects and find that Japanese increases in innovation an important driver
of the innovation race.
4. Finally, my results shed some light on the current crisis in the industry. The current
financial crisis hit the automobile industry hard. While the big-3 US firms, General
Motors, Chrysler and Ford, were affected the most, the crisis has had a profound effect on
virtually all the global players in the market. Perhaps the defining moment of the current
crisis was the declaration of bankruptcy by General Motors (henceforth, GM), the
world’s largest automaker, on June 1, 2009. Following this, Chrysler filed for bankruptcy
on May 1 2009, and Ford was barely surviving. This collapse of the US industry opened
the markets for the Japanese firms, as well as other players such as Volkswagen, to take
on even more dominant positions in the US and global markets. My results show that the
current problems faced by the US firms are largely unrelated to the ongoing crisis. The
problem lies in the fundamental inability of the US firms to meet the Japanese, and to
some extent, German, competitive, quality and innovation challenges in the 1980s.
2. Theoretical Modeling
* To be completed later
Automobiles are highly differentiated products. The elements of differentiation include
product quality, design, engine characteristics, safety, environmental characteristics, among
others. Consistent with previous modeling in the literature, the industry is best characterized as
oligopolistic with Bertrand price competition with differentiated products.
4
In terms of consumers’’ decision making, the factors that influence purchase decision
include: price (and prices of competing brands/models); income; costs of ownership – fuel,
routine maintenance, repairs; reliability; resale value; comfort; features; performance; and design
One of the most critical variables is quality. Enhancing quality entails costly investments,
a significant fraction of which are likely to be sunk. Given the importance of this, I model costly
investments and technology adoption within a Bertrand price competition framework.
3. Data and Related Information
In my study of competition and innovation in this industry, I use both quantitative data as
well as qualitative information gathered from my visits to various factories and meeting
executives in different countries.
3.1. Quantitative Data
I have compiled data on every single model sold in the US by every single US and
foreign manufacturer from 1969 to 2006. (I am in the process of completing this to 2008/09.)
The main brands I consider are: the US firms Ford, GM and Chrysler; German firms
BMW, Daimler and VW; the Japanese firms Toyota, Nissan and Honda. I have data on several
other brands like Mazda, Subaru, Mitsubishi, Volvo, Saab, among others.
For each brand, I have data on all the models. The data on each model includes: all the
engine characteristics including engine size, fuel efficiency and horsepower; safety features;
quality – as measured by repair records and cost of maintenance; number of units sold in the US;
list prices; among several other variables. The dataset also includes the market shares.
5
3.2. Qualitative Information
To complement my quantitative data and obtain a deeper perspective of the firms, I have
visited BMW and Audi production facilities in Germany, and Toyota and Nissan production
facilities in Japan. During these visits, I interviewed executives as well as got a detailed tour and
description of the production process. (** The key findings from these visits will be detailed.)
In my interviews with executives, I sought to understand the important factors that
influence purchase decisions. The Executives, in different countries and companies, repeatedly
referred to consumers falling into two broad groups:
1. “Rational” – those who primarily seek value, reliability.
2. “Emotional” – those who seek performance, design, and technology.
While companies like BMW seek to serve a niche market, firms like Toyota, GM, etc,
seek to serve the mass markets. In the mass market segments, reliability of brands and models is
a critical variable.
From the perspective of the typical consumer, a car is the most expensive durable product
they will buy (probably even more expensive than a house over their lifetime). The current
median US household income is about $60K. Average new car costs between $20-$25K plus
financing cost. Around 90K miles, maintenance costs can rise sharply, and resale values become
quite low. The average US driver drives about 12.5K miles per year and on average, this gives
roughly 7.5 years of effective life. The average car replacement rate in US is in the 6-7 year
range. This cost works out to a significant fraction of annual income, plus expenditures related to
operating costs – fuel, maintenance, repairs – add more
Largely due to cost (purchase and ownership), consumers search for information on
quality of car before making purchase. Cars, therefore, tend to be an experience good. If you
6
purchased a brand/model and had a good experience, you are likely to continue with same brand.
This explains the (historically) relatively strong brand loyalty.
We assume that the automobile producers know this and they strive to deliver reliability
and quality. With change in (foreign) competition since the 1970s, we assume this aspect became
more important to attract and retain buyers. To deliver reliability and quality, producers have to
make costly investments. They also have to make costly investments in, for example, product
design, engine design, optimize production line dynamics, optimize functions and efficiency of
robots. These have significant implications for firms’ business strategy and implementation of
strategy.
3.3 Quantitative Data on Reliability
Given the importance of quality, I collected data from the Consumer Reports. For each
model sold in the US, they use consumer surveys, testing, and other information, e.g., repair
records, cost of maintenance. The Consumer Reports is a consistent and reliable publication of
data available over a long time period. These data are cited in academic, and industry
publications, and touted by manufacturers. In Figure 3 I display the aggregated quality measures
by country. These data show that the US firms consistently have the lowest quality ratings. The
Japanese have the highest quality ratings. And the German firms are in-between. In the
underlying disaggregated data, GM, Ford and Chrysler have the lowest quality ratings.
Volkswagen quality ratings are also low. In contrast, virtually all the models by Toyota, Honda
and Nissan are above average quality.
Discuss Tables 1 and 2.
7
3.4. Quantitative Data on Innovation
As the US firms faced competitive challenges, their incentives to innovate increased. As
the Japanese firms took on a stronger foothold in the US, their incentive to innovate also
increased – in part to meet the competitive challenges and to make improvements in their
products and engines to meet US conditions. To address these innovation issues, I collected data
on total patent counts for all the firms in my sample, as well as by specific categories. The data
reveal the following:
1. Patents by the US firms first increase, then are stable, and then show additional increases;
overall a mixed pattern.
2. German patent count is relatively low and stable.
3. Patents by the Japanese firms show a steady upward trend.
In my econometric analysis. I estimate dynamic equations with feedback effects to study
the interrelationships. I find that increases in US and Japanese patents feed of each other, in the
sense that increase in one leads to an increase in the other. The patents by the German firms in
contrast in basically non-responsive to changes in US or Japanese patents. In a sense, these
results are consistent with what we observe during the early-1970s to early-2000s. The main
battle is between the US firms trying to maintain their market share; the Japanese firms making
significant inroads; and the German firms mainly playing a peripheral role.
4. Market Share Dynamics: Specification and Estimation.
I begin by examining the dynamics of market shares using an Autoregressive-Distributed
lag, AD(n,m), specification (1): 1
1
The AD(n.m) specification is derived from a standard partial-adjustment model where firms’ decisions are subject
to adjustment and disequilibrium costs; see Jorgensen, 1989.
8
( 1 ) st = c + ∑i2=1 st −i + ∑ 2j =0 rpcdit − j + ∑ 2j =0 rpumpt − j + ε t ,
where s represents the firm’s market share, rpcdi is real disposable per capita income and rpump
is the real price per gallon of regular unleaded fuel. Due to constraints imposed by the total
number of time-series observations per firm, I only used a maximum of 2 lags to estimate (1).
Once this parsimonious specification was estimated for each firm, I reduced the lag-length if the
longer lags were insignificant. In this manner, I derive an optimal lag-length structure for each
firm. Apart from each firm in the sample, I also estimate (1) for country-wide averages for the
US, Japan and Germany.
Discuss the results in the two tables: Table 3 presents the country averages regressions
and Table 4 presents a sampling of the firms’ regressions.
Based on the estimates from the firms’ regressions, and the mean values of the market
shares and the income and fuel price variables, I compute the implies income and fuel price
elasticities. Discuss Table 5. The results in Table 5 show:
1. The US firms have negative income elasticities, implying that as per capita disposable
income increases in the US, GM, Ford and Chrysler see their market shares drop.
2. Toyota and Nissan have positive and statistically significant income elasticities. Honda’s
income elasticity, while positive, is imprecisely measured.
3. The German cars also have positive elasticities.
When we examine the fuel price elasticities, the estimates are more dispersed. The main
findings are as follows:
9
1. While Ford and Chrysler have meaningful negative elasticities, GM’s shares are not
responsive.
2. Toyota and Nissan see their market shares increase when fuel prices rise. Honda’s shares
are not responsive.
3. VW’s market shares benefit from increase in fuel prices, but the effect for the other
German car makers are not statistically significant.
To examine the fuel price effects in more detail, I estimated some ancillary specifications
with the following feature: the fuel price time-series was decomposed into meaningful positive
and negative changes. The objective was to examine whether fuel price decreases tend to restore
the lost market shares by the US firms when the fuel prices went up. My initial findings are that
when fuel prices increase, the US car makers see noticeable drop in shares, but they do not gain
back as much when fuel prices drop. Discuss implications.
4.1. Income and Fuel Price Elasticities and Firms’ Characteristics
I examine what might be some of the underlying reasons for the wide differences in the
income and fuel price elasticities. The specific factors I examine related to: (1) quality of the
cars; (2) number of models in the fleet; and (3) innovative activity as captured by patents.
Discuss the reasoning behind this.
The data limitations are quite severe and this precludes a detailed analysis at this pint. For
a quick look, in Table 6 I present the Pearson and Spearman correlation coefficients. Discuss the
findings.
10
Figure 1. Oil Prices and Market Shares.
Levels: Oil Prices and Car Market Shares
Oil Price (Real $)
Import Share (%)
100
90
80
70
60
50
40
30
20
10
0
1965
1970
1975
1980
1985
Year
1990
1995
2000
Oil Price
2005
Import Share
11
Figure 2. Fuel Efficiency (miles per gallon).
Year
1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
Federal Chrysler
27.5
27.8
26.0
27.8
26.0
27.5
26.0
28.5
26.5
28.0
27.5
27.4
27.5
27.5
27.5
27.8
27.5
27.8
27.5
26.3
27.5
28.4
Ford
26.6
27.0
26.9
26.6
26.6
26.3
27.6
27.4
28.3
27.7
27.4
GM Japanese
25.8
40.5
26.6
35.2
26.9
35.2
27.6
34.4
27.3
32.7
27.1
32.9
27.1
32.8
26.7
32.7
27.4
32.5
27.7
32.4
27.4
31.6
Japanese cars got bigger in size and heavier, and their
engines got bigger and less fuel efficient. US firms largely
caught up with fuel efficiency differences.
12
Figure 3. Quality Rating of Cars – Country Averages.
Figure 3. Quality Comparisons by Country
3.00
Poor
Quality
Germany
US
Japan
Sw eden
Good
1.00
13
2010
2005
2000
1995
1990
1985
1980
1975
1970
1965
Year
Table 1. Quality Index.
Indicator
Air Conditioning
Body, Exterior
Body, Hardware
Body, Integrity
Brakes
Clutch
Drive Line
Electrical, Chassis
Engine, Cooling
Engine, Mechanical
Exhaust System
Fuel System
Ignition System
Steering
Suspension
Transmission, Automatic
Transmission, Manual
Overall Record
Score
Weighted Score
14
Table. 2. Quality Patterns: Examples by Manufacturer.
Manufacturer
VW
GM
Toyota
Volvo
Summary Statistics
Trend Regressions
Mean
C.V. (%)
const.
t
t2
2.21
13.1
1.453*
0.073*
(0.001)
(0.002)
2.29
3.6
1.766*
0.062*
(0.001)
(0.001)
1.12
5.1
1.501*
-0.046*
(0.001)
(0.001)
1.91
20.1
2.051*
-0.033
(0.001)
(0.326)
Note:
1. The quality (trouble) index is as follows:
Score=3 is below average
Score=2 is average
Score=1 is above average
Therefore, a higher value of the index implies lower quality.
2. Observations:
VW:
Low mean quality with high variance. Quality trending down.
GM:
Low mean quality and high variance. Quality trending down.
Toyota:
High mean quality with low variance. Quality trending up.
Volvo:
Average mean quality with very high variance. Quality trending up.
* All trends are marginal in nature.
15
-0.001*
(0.025)
-0.001*
(0.001)
0.001*
(0.001)
0.001
(0.208)
Table 3. Regression of Market Share on Income and Fuel Prices, 1970-2006.
By Country
Japan
US
const.
sharet-1
sharet-2
rpcdit
Income
rpcdit-1
Income
rpcdit-2
Income
rfuelt
Pump Price
rfuelt-1
Pump Price
rfuelt-2
Pump Price
Adj-R2
Germany
124.2428*
(0.001)
0.1103
(0.279)
-
-10.2338*
(0.001)
0.6435*
(0.001)
-
0.0020*
(0.001)
-0.0021*
(0.011)
-0.0025*
(0.001)
-3.4435*
(0.001)
-1.7432*
(0.045)
-
-0.0013*
(0.017)
0.007
(0.360)
0.0013*
(0.011)
1.4075*
(0.022)
-
-2.1084
0.534
1.2194*
(0.001)
-0.4128*
(0.017)
-0.0048
(0.326)
0.0021*
(0.003)
-0.0014*
(0.005)
0.2789
(0.718)
-
-
-
0.98
0.96
0.88
Notes:
1. Estimated regression is: st = c + ∑ i = 1 st − i + ∑ j = 0 rpcdit − j + ∑ j = 0 rpumpt − j + ε t .
2
2
2
Insignificant deeper lags were dropped to determine optimal lag-length of each variable.
2. Two-tailed significance levels, computed from robust standard errors, are in parentheses.
3. Variables:
rpcdi: real per capita disposable income (net of taxes).
rfuel: real price per gallon of regular unleaded fuel (US average per year).
16
Table 4. Regression of Market Share on Income and Fuel Prices, 1970-2006.
By Manufacturer
US
const.
sharet-1
sharet-2
rpcdit
Income
rpcdit-1
Income
rpcdit-2
Income
rfuelt
Pump Price
rfuelt-1
Pump Price
rfuelt-2
Pump Price
Adj-R2
Japan
GM
Ford
38.5678*
(0.001)
0.4500*
(0.001)
-
37.6177*
(0.001)
0.3532*
(0.008)
-0.2187*
0.045
0.0006*
(0.017)
-0.0011*
(0.001)
-
0.0012*
(0.082)
-0.0022*
(0.006)
-0.0025*
(0.001)
0.6958
(0.301)
-
Toyota
0.97
Nissan
-5.5050*
(0.001)
0.5621*
(0.001)
-0.4128*
(0.017)
-0.0003
(0.288)
0.0000
(0.896)
0.0007*
(0.002)
1.1892*
(0.001)
0.0323
(0.949)
-0.7087*
(0.049)
0.96
-2.7485*
(0.005)
-1.3780
(0.201)
-1.5982*
(0.085)
0.87
Germany
-1.9291*
(0.025)
0.3123*
(0.038)
0.2724*
(0.031)
-0.0009
(0.001)
0.0006
(0.073)
0.0005*
(0.065)
1.2277*
(0.001)
-0.8276
(0.057)
0.6285*
(0.047)
0.83
VW
-1.7739
(0.534)
0.8141
(0.001)
0.0005
(0.042)
0.0000
(0.900)
-0.0005
(0.011)
1.0301
(0.001)
-0.6658
(0.013)
0.87
Notes:
1. Estimated regression is: st = c + ∑i =1 st −i + ∑ j =0 rpcdit − j + ∑ j =0 rpumpt − j + ε t .
2
2
2
Insignificant deeper lags were dropped to determine optimal lag-length of each variable.
2. Two-tailed significance levels, computed from robust standard errors, are in parentheses.
3. Variables:
rpcdi: real per capita disposable income (net of taxes).
rfuel: real price per gallon of regular unleaded fuel (US average per year).
17
Other
-2.026
(0.534)
1.0953
(0.001)
-0.4776
(0.002)
-0.0010
(0.072)
0.0020
(0.001)
-0.0009
(0.032)
0.1465
(0.814)
0.84
Table 5. Implied Long-Run Elasticities of Market Shares to
Income and Fuel Price
Income
Fuel Price
By Country
US
Japan
Germany
-1.16*
2.44*
6.55*
-0.17*
0.37*
0.94†
By Manufacturer
GM
Ford
Chrysler
Toyota
Honda
Nissan
Volkswagen
Other
-1.07*
-0.63*
-1.34*
2.75*
2.09†
1.56*
2.55*
1.77*
0.06†
-0.57*
-0.41*
0.28*
-0.02†
0.89*
1.27*
0.10†
Notes:
1. The above elasticities were computed from the regression specification (XX) estimated for each
manufacturer as well as country averages, and the sample means of the respective variables.
2. An * indicates that the elasticity is significant at conventional levels; an † indicates the estimate is not
significant.
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Table 6. Correlations between Income and Fuel Elasticities and Producer Characteristics
Income
Pearson
Income
Fuel
Spearman
Income
Fuel
Fuel
1.00
0.66
(0.98)
1.00
0.62
(0.06)
Quality
Models
Patents
0.66
(0.98)
1.00
-0.69
(0.05)
-0.18
(0.65)
-0.81
(0.01)
-0.45
(0.25)
-0.19
(0.64)
-0.17
(0.68)
0.62
(0.06)
1.00
-0.74
(0.03)
-0.18
(0.65)
-0.61
(0.10)
-0.45
(0.25)
-0.19
(0.66)
-0.17
(0.68)
Notes:
1. Above are the Pearson product-moment correlation and Spearman rank correlation coefficients
between the income and fuel price elasticities repotted earlier, and three indicators of the producer
characteristics: quality of cars, number of models, and patents. The three characteristics variables used are
the means for each producer over the sample period.
2. The significance levels are reported in parentheses.
3. The sample size (8 obs for 8 producers) is very small, so the above correlations are only suggestive of
some patterns.
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