|
Items in cart [0] |
Questions? Call 888-624-8373 |
| Copyright © 2009. National Academy of Sciences. All rights reserved. Terms of Use and Privacy Statement |
Below are the first 10 and last 10 pages of uncorrected machine-read text (when available) of this chapter, followed by the top 30 algorithmically extracted key phrases from the chapter as a whole.
Intended to provide our own search engines and external engines with highly rich, chapter-representative searchable text on the opening pages of each chapter.
Because it is UNCORRECTED material, please consider the following text as a useful but insufficient proxy for the authoritative book pages.
Do not use for reproduction, copying, pasting, or reading; exclusively for search engines.
OCR for page 122
8
Technology and Competition
in the U.S. Automobile Market
The crisis in the U.S. auto industry reflects in part the vigorous
competitive challenge of the Japanese, in part a rapid shift in
market preferences. We have argued that a recapture of com-
petitive cost and quality performance will require fundamental
changes In the way the manufacturing process in the U.S. auto
industry is managed. At the same time the industry is faced with
the problem of developing new products to meet changing market
demands. "Small" and "fuel efficient" are the words most often
used to describe desired characteristics, and, however superficial
those descriptions may be, it is becoming increasingly clear that
competitive vehicle design requires product technology different
from that found in the standard American sedan of the post-World
War II period. Throughout that era, competition in any given
segment of the U.S. automobile market occurred largely on the
basis of economies of scale, styling, and sales and service net-
works. As befits a maturing industry, innovation became increas-
ingly incremental in nature and, in marketing terms, virtually
invisible. Has that situation now changed?
Clearly, some major changes in product technology have
occurred in the last few years, most notably the introduction of
front-wheel drive, the trans-axle, and the increased use of the
diesel engine. Yet it is not clear whether such developments
reflect the beginning of new technological ferment or the end of a
technological transition. Indeed, some suggest that the small,
front-wheel-drive car, with its transverse mounted, four-cylinder
engine, already constitutes a new dominant design. If so, future
changes in automobile technology are likely to be incremental to
the new design, and competition will occur much as before on the
basis of styling, scale economies, and dealer networks. This
reading of events places the industry farther along its path toward
maturity.
If, however, innovation in technology is again becoming of vital
competitive significance, not only are we likely to see con-
. . ..
.
122
OCR for page 123
123
tinuing functional innovations but the overall pace of innovation is
likely to increase as well. These developments, in turn, may have
far-reaching implications for the structure of the industry, for
individual strategic decisions, and for the pattern of international
trade. The question of technology's role in competi tion is thus
central to interpreting current events and is an important aspect
of the larger question of the industry's future.
In marketing terms the notion of "competitive significance"
has both a quantity and a price dimension. A given technology
(e.g., front-wheel drive) becomes significant in a competitive
sense either if consumers are willing to pay a premium for cars-
embodying the technology or if the market share of such models
increases. Accordingly, our analysis in this chapter will focus on
both sales and price effects of a few major technological char-
acteristics in the context of a fairly simple model of consumer
demand (at the compact and subcompact end of the market). Our
hypothesis is that the oil shock of 1979 altered demand patterns,
thus increasing both the visibility of technology and its competi-
t~ve significance. If true, we should observe very different market
valuations of those characteristics before and after 1979.
A FRAMEWORK FOR ANALYSIS
The framework we use to test the competitive significance of
technology is designed to identify the market's valuation (sales and
price) of a given characteristic. There are few difficulties with
sales effects. In fact the only major issue is the problem of
capacity constraints. In a given year the sales of a particular
model may be more a reflection of the capacity of the firm to
produce it than of underlying consumer demand. But since our
purpose is to estimate the average effect on sales of a given
characteristic, and since each characteristic tends to be found on
m ore than one model, it follows that only if all models with a
given characteristic are subject to capacity constraints will sales
reflect those constraints and not market demand.
The sales model we use is presented in Appendix C. Our
approach simply is to identify the impact of a particular tech-
nological feature (e.g., front-wheel drive) by statistically holding
constant the effects of other characteristics.
Estimating the market value attached to specific character-
~stics is a somewhat trickier process, for it relies on a set of
assumptions that must be spelled out clearly. The problem here
lies in the fact that there is no market for individual character-
istics as such. A given model contains by definition a bundle of
characteristics or attributes, and its price in the market is the
price of the bundle as a whole.
OCR for page 124
124
To identify the "price" of a particular attribute, we assume
that observed prices are a reflection of an implicit market in
characteristics. Consumers place a value on specific attributes
that have been aggregated or bundled in different ways to form
different models. To infer the value of a specific characteristic,
we take a range of model prices, some of which have the charac-
teristic and some of which do not. By observing how the overall
price changes with variation in characteristics, we can identify
the value consumers place on specific attributes. The details of
this approach are presented in Appendix C.
A difficulty arises, however, in interpreting the prices of these
attributes. What we are after is the market's or the consumer's
valuation of each characteristic, but the market price of a given
m odel is determined by the interaction of supply as well as
demand forces. On the supolY side the Rev determinants are the
. . . . , , , _. _ .. _
costs ot production, the pricing policy (e.g., relationship of one
model's price to another), and the strategy of the firm. The
demand side is determined by the Dreferences of c'~n~'m~rc i ~
.
by their assessments of the value of a given characteristic.
Though only demand considerations are relevant for our purposes,
the price of a model reflects both market valuation, which
interests us, and the manufacturers cc,rtc anal nri~ina mail ire
which do not.
r ^~- ~ r~~~~~
One solution to the problem of isolating the demand effects is
to specify a structural model of consumer demand and the costs of
production. Given appropriate exogenous variables one can use
advanced statistical techniques to disentangle supply and demand
effects.2 But a solution much less demanding of the data may be
available. The lack of identifiability of the demand function may
not affect inferences about changes in demand. Costs of
reduction may be more stable than consumer valuation, particu-
larly over short periods of time when the sales mix of
characteristics shifts rapidly. If this is true, then any differences
in coefficients estimated for two closely paired years will reveal
the influence of changes in consumer demand. Furthermore, the
availability of both list and transaction prices may provide another
means of correcting for supply side effects.
Existing evidence on automobile pricing suggests that list
prices are determined by product-line policy and standard costs.3
While the markup over standard cost may be influenced by
strategic considerations and estimates of consumer valuation,
variations in list prices are likely to reflect variations in cost.
Inclusion of the list price in an equation explaining transaction
prices may therefore provide a control for the supply side. It is of
course possible that such a procedure will "overcontrol" and
thereby obscure demand effects. But estimation with and without
the list price in a comparative context should provide a basis for
inferences about shifts in demand.
-
OCR for page 125
125
Empirical Specification
The framework developed above requires data on sales, list and
transaction prices, and model characteristics that capture the
effect of technology and innovation. List prices and sales data are
readily available for most models sold in the United States, but
transaction prices for new cars are not. As a proxy for them we
have chosen to use the price of the model one year later as
determined in the used-car market. Use of the year-old price
introduces a further complication in the analysis, since discounts
based on used-car prices will reflect the effects of deterioration
as well as the market's valuation of technological character-
istics. However, as long as deterioration is not a function of those
characteristics, it is fair to assume that the effects of general
deterioration will be reflected in the overall average for the
market and will not obscure relevant model differences.
The characteristics we have chosen to include in the analysis
are determined by our hypothesis about changes in the role of
technology in the market for automobiles. To begin, we distin-
guish between performance characteristics and technological
attributes, although both are closely related. Thus, miles per
gallon measures fuel-efficiency performance, but the size of the
engine is a characteristic of the model, and both kinds of vari-
ables are included in the analysis.5
In the compact and subcompact markets on which we focus,
the key performance characteristics are fuel efficiency, driving
range, repair frequency, and package efficiency (efficient use of
space). The relevant definitions and measures are presented in
Table 8.1. The key technological characteristics include engine
type (gas or diesel), drive train (front or rear wheel), and age.
This last variable is meant to test the notion that newness itself is
valued independent of specific characteristics.
We are aware that this scheme leaves out, by necessity, a
number of alternatives that may be important in the market.
Since the omitted factors may be reflected in the analysis if they
are correlated with variables that are included, care must be
taken in interpreting the result. The drive train, for example, may
pick up some of the effects of differences in handling and
m aneuverability.
To complete the framework we have added a set of variables
that control for the country of origin and the market segment.
V ariables indicating whether a car is produced in Japan (we
distinguish captive imports from others), Europe, or the United
States are intended to pick up any otherwise unmeasured differ-
ences in quality of attributes correlated with the country of
origin. Finally, we have allowed the average discount to be
different in the subcompact and compact model categories.
_
OCR for page 126
126
TABLE 8.1 Basic Variables: Definitions
Variables
Symbols and Definitions
Sales Si: number of vehicles of ith model sold in specific year.
List price P: fist price.
Transaction price P*: one-year-old used-car price.
Fuel efficiency MPG: EPA miles per gallon rating for city driving.
Driving range RNG: MPG (reported fuel-tank capacity).
Repair frequency (two REP1: has value of 1 if Consumer Reports' survey of repair
variables entered) frequency placed model in "substantially below average"
category; zero otherwise.
REP2: has value of 1 if Consumer Reports' survey of repair
frequency placed model in "substantially above average"
category; zero otherwise.
Package efficiency (two IVTV: internal volume (defined below) . total volume
measures) (length x width x height).
VOLWT: internal volume . vehicle curb weight.
Engine type DIESEL: has value of 1 if model has diesel engine; zero
otherwise.
Drive train F WD: has value of 1 if model has front-wheel drive; zero
otherwise.
Age of model AGE: years since last major model redesign.
NOTE: Using the diagrams and specifications in the figure below (taken from Consumer
Reports' Annual Auto Issue, April 1980), the internal volume is obtained by adding the in-
ternal volume of the front and rear compartments of the car. The internal volume of the
front compartment is the product of the front shoulder room and the cross-section area of
the front compartment. The internal volume of the rear compartment is similarly obtained.
The formulas are as follows:
Front compartment volume V1 = J [(0.5) (G - 18)2 + (24) (E) + 0.18 (E)2]
Rear compartment volume V2 = K [(H- 18) (G- 18) (0.71) + (F) (H)]
Internal Volume = V1 + V2
The following assumptions were made in the calculations:
· distance between tester's hip and knee = 18 inches
· tester's leg was inclined at 45°
· seats were inclined at 10° to the vertical
The dimensions E and F were defined as the height above seats rather than the clearances
measured by Consumer Reports' tester. Consumer Reports' E and F were modified by
adding a constant of 34 inches to obtain the heights above seat levels. Data for the E adjust-
ments were obtained from Automotive News Almanac.
W:~
OCR for page 127
127
- -~1~1
:,r,,. . =~
1 . ~-
!
Table 8.2 presents average values of the basic variables
included in our analysis of sales and discounts. A comparison of
1977 and 1979 data reveals that most characteristics are quite
similar in the two years.
-
Because of the introduction of new
models and substantial redesign efforts, the average age fell from
almost four years to a little over two and one-half.
Among the other characteristics, however, we find little
difference. The percentage of models with front-wheel drive
Increased Slightly, as did driving range. But the changes are small,
and in general the average characteristics are similar in the two
years. The model-age data suggest, however. that substantial
. . . . .
. . . . ..
. , _
changes may nave taken place both in the way in which the
technology was packaged and in the quality of the technology.
It is important to realize that the subcompact market in 1977
included models (in comparable numbers and at comparable levels
of performance) offering the characteristics we hypothesize were
relevant in the 1979-1980 market. The date suggest that a com-
parison of market results in these two years might be a useful test
of the role of technology in competition. Had we found gross
dissimilarities in attributes, the validity of the test would have
been suspect.
COMPARISONS OF DISCOUNTS DURING 1977 AND 1979
To compare the impact of given attributes on the transaction
price (statistical results for discounts are presented in Appendix
C, Tables C.3 and C.4), we have developed what we call the basic
effect of each characteristic. We first calculate how much a
"typical" difference in the characteristic would have raised or
lowered the price.6 For VOLWT (package efficiency), for
example, a typical or average difference between a given model
and the average for all models was 8.7 cubic inches per pound in
1979. We estimate that such a difference would raise the
OCR for page 128
128
TABLE 8.2 Means and Standard Deviations of Basic Variables,
1977 and 1979
1977
1979
Typical Typical
Difference Difference
From the From the
Variable Mean Mean Mean Mean
Sales per (S) model (000s) 92.6 93.4 102.8 95.1
List price (P) 4484 1209 5657 963
Transaction price (P*) 3674 901 4591 832
Fuel efficiency (MPG) 21.8 5.3 21.0 4.8
Driving range (RNG) 318.0 57.7 326.5 70.8
Package efficiency
VOLWT (cubic inches per
pound) 49.0 7.0 49.3 8.7
IVIV (percent) 22.5 1.7 22.3 2.8
Engine type (percept diesel) 2.3 14.9 3.5 18.5
Drive tra~n(~;WD) (percent) 25.0 43.3 27.1 44.4
Age ofmodel (AGE) 3.7 3.5 2.3 2.1
Repair frequency (percent)
REP 1 15.9 36.6 3.5 18.5
REP 2 22.7 41.9 15.3 36.0
Subcompact (percent) 65.9 47.4 50.6 50.0
SOURCE: Automotive News Almanac, 1978, 1979; Consumer Reports (annu~il auto issue
1978, 1980).
transaction price by about $30. This is the basic effect. In the
case of characteristics that are either present or absent (e.g.,
diesel engines) the basic effect is the effect of having the
characteristic. To find out whether the basic effect is a large or
small one, we compare it to the typical difference in transaction
prices, which in 1979 was $832. Thus, the basic effect of VOLWT
, ~ ~
mu wan equal To '.o percent or the typical difference in the
transaction price in 1979. We call this the relative effect.
It may be useful to restate our basic hypothesis in terms of the
variables in the analysis. In the price analysis the issue of
competitive significance is essentially a question of the size and
magnitude of the basic and relative effects. If a given technology
characteristic, e.g., front-wheel drive, is a positive factor in
competition, we would expect models with that characteristic to
be higher priced. Thus, the sign of the basic effect should be
positive; of course, an important factor will have-a larger relative
effect.
Table 8.3 contains the basic effect and the relative effect of
, ~ q ~ ~ . _ . ~ ~ , . ~ .. . . ..
OCR for page 129
129
technological characteristics for 1977 and 1979. The results are
striking. A line-by-line comparison of the effects of the various
characteristics reveals a sharply different pattern of market
valuation in these two years. While MPG was a positive impact in
1977, its effect in 1979 is swamped by the technology variables,
and the estimated fuel-economy effect turns negative. The
negative effect of MPG is puzzling but may reflect collinearity
with the technology variables. Collinearity may also affect
estimates of parameters on the technology variables. This seems
to be especially true for VOLWT (package efficiency), where a
large standard error precludes any strong conclusions about the
sign of the price effect in either 1977 or 1979. For FWD (front-
wheel drive) and AGE, however, collinearity does not seem to be a
serious problem. If, for example, we drop the variables for diesel
engine, age, and package efficiency (VOLWT) from the analysis,
leaving only front-wheel drive as a measure of technology, we
obtain basic and relative effects of front-wheel drive very similar
to those reported in Table 8.3. And even if we reduced the 1979
F WD coefficient by two standard deviations, the resulting
estimate (1 1 1 ) is still positive. In general, we find clear
differences between the results for 1977 and those for 1979.
TABLE 8.3 Basic and Relative Effects on Price Discounts of Performance
and Technology Characteristics, 1977 and 1 979a
1977
1979
Basic Basic
Effect Relative Effect Relative
(current Effect (current Effect
Characteristics dollars) (percentage) dollars) (percentage)
Performance Characteristics
Fuelefficiency (MPG) 192 21.3 -67 -8.1
Driving range (RNG) 5 0.6 65 7.8
Package efficiency -57 -6.3 30 3.6
(VOLWT)
Technology Characteristics
Engine type (DIESEL) -853 -94.6 302 36.3
Drive train (FOOD) -316 -35.0 417 50.1
Model age (AGE) -40 -4.4 -85 10.2
Market Segment
Subcompact (SUB) - 174 - 19.6 313 37.6
a Effects for 1977 and 1979 are based on the coefficients given in equation 6 of Table C.3 in
this volume.
OCR for page 130
130
The evidence suggests, for example, that consumers were
willing to pay a premium for a diesel engine over and above
premiums for greater range and greater fuel efficiency. In
contrast, consumers in 1977 heavily discounted cars with front-
wheel drive and diesel engines. Moreover, even the performance
characteristics valued in 1979--range, package efficiency--had
small or negative effects in 1977. While the market placed a high
value on fuel efficiency in 1977, the performance and technology
characteristics associated with market success in 1979 had little
market appeal in 1977. If features that were considered inno-
vative in 1979 had been introduced in 1977, they would have been
greeted with above-average discounts. Introduction in such a con-
text could only be interpreted as an attempt to force the market,
for it seems clear that innovation, at least as defined in 1979
terms, was not valued in 1977.
Perhaps the clearest indication of the value of innovation in
1977 and 1979 is the effect of model age. In 1979 the basic effect
of a typical difference in age (2.3 years) was to lower the price by
$85. Thus, newer cars received a premium even after controlling
f or other attributes. Since performance and technology are
already accounted for, the result implies that newness per se
carried a premium in 1979.
The effect of age in 1977 has the same sign but is much
smaller and, as Appendix C shows, is not statistically significant.
The results on the age variable in 1979 stand in contrast to the
patterns of competition that have prevailed in the auto industry
during most of the postwar era. Older models tend to be debug-
ged, refined, and developed to meet a relatively stable set of
~~ User aemanas. ~ major model change may introduce new
features and above-average performance, but if innovation is not
itself valued the effects of bugs and introduction problems are
likely to outweigh any value attached to the new features. This
situation reversed in the late 1970s.
The notion that market valuation of performance and technol-
ogy was different after the oil shock of 1979 can be examined
more rigorously using standard statistical tests. The null hypoth-
esis in this connection is that the effects in the two years are
identical. Appendix C provides technical details, but the statisti-
cal tests confirm the apparent differences in Table 8.3. We are
able to reject with a high degree of confidence the hypothesis that
the effects are equal. In short, the evidence suggests that market
valuation changed sharply after the oil shock of 1979, with
technology playing a much more critical role.
~ ~ ~ . ~ .
OCR for page 131
131
COMPARISON OF SALES PER MODEL DURING 1977 AND 1979
The effects of performance and of technology characteristics on
model sales in 1977 and 1979 are examined in Table 8.4. In con-
trast to the results for price discounts, we find remarkable
stability in the pattern of effects for 1977 and 1979. For most
variables the direction of the effects are identical, and in several
cases the orders of magnitude are roughly comparable. Even
where there are differences the evidence in Appendix C suggests
that little should be made of these results. The quality of the
statistical evidence is poor, and the estimated effects are not
statistically different from one another.
Thus, while the more negative effects of diesel and front-
wheel drive cast doubt on the hypothesis, and the evidence on age
supports it, these changes are in fact more apparent than real.
The data available provide no evidence that technology (as defined
TABLE 8.4 Basic and Relative Effects on Sales of Performance and
Technology Characteristics, 1977 and 1979a
1977
Characteristics
Basic
Effect
(thousands
of vehicles)
1979
Relative
Effect
(percentage)
Basic
Effect
(thousands
of vehicles)
Relative
Effect
(percentage)
Performance Characteristics
Fuel efficiency (MPG) 35.0 37.5 10.1 10.6
Driving range (RNG) -46.2 -49.5 -6.6 -6.9
Package efficiency 32.9 35.2 36.5 38.4
(VOLWT)
Technology Characteristics
Engine type (DIESEL) -0.6 -0.6 -97.8 - 102.8
Drive train LEWD) -23.4 -25.1 -53.1 -55.8
Age (AGE) 4.9 5.2 -1.8 1.9
Repair Record
Much worse than
average (REP 1)
Much better than
average (REP 2)
Market Segment
Subcompact (SUB)
7.9 8.5 -61.3 -64.5
82.2 88.0 65.2 68.6
-84.8 94.0
3.0 3.2
a Effects are based on the coefficients given in equation 3 of Table C.4 in this volume.
OCR for page 132
132
here) had a positive effect on sales in either year or even that the
sales relationships shifted dramatically. It may be that market
shares are more stable than prices and that most of the effects of
market shifts in 1979 were felt on the price side of the market.
The results of the price analysis provide evidence that technology
was an important aspect of competition in the 1979-1980 market.
Compared with the situation in 1977, we find that technology
characteristics and innovation were highly valued. At least as far
as market premiums are concerned, changes in the market created
an incentive for innovation consistent with the hypothesis
advanced at the beginning of this chapter. In terms of their effect
on prices, technology became more visible and competitively
significant.
N OTES
1. There is a large literature on the estimation of hedonic
price equations. A review of the literature and an application can
be found in Toder ( 1978~.
2. In effect what is required is a structural model of the
implicit market for characteristics. With appropriate exogenous
variables and exclusion restrictions, the model can be identified
and estimated using one of several simultaneous equation methods.
3. It is obvious that this is a strong assumption, since the
firm's pricing policy may attempt to estimate consumer
valuation. Thus, the mode is only an approximation.
4. Although identification of estimated discounts effects
depends on the assumption that list prices are based on standard
costs and standard markups, the change in the estimated effects
between two periods is likely to be more affected by demand
considerations since relative costs of characteristics tend to be
more stable.
5. This allows us to identify the effect of the technc~lc,av-
holding performance constant.
Of
6. In statistical terms, "typical difference" is the standard
deviation of the variable in question.
7. Evaluation of the results should be tempered by the fact
that the standard errors (in Appendix C of this volume) are
sizeable in some cases. The lack of precision precludes strong
conclusions for some variables. However, the overall pattern of
effects is what is important, and it appears that overall the two
patterns are different.
8. Discount equations with average repair records also were
estimated, but the results were insignificant and were not
reported in the test; see Appendix C of this volume.
Representative terms from entire chapter:
package efficiency
