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DRAFT
Efficient Alignment and Survival in the U.S. Automobile Industry
Lyda S. Bigelow*Olin School of Business
Campus Box 1133Washington University
St. Louis, MO 63130-4899(314) 935-6318
[email protected] January 2000
This paper prepared for presentation at the Organization Science Winter Conference, to
be held in Breckenridge, CO, February, 2000
* I would like to thank Glenn Carroll, John Freeman, Patrick Moreton, Jack Nickerson, Michael Thayer, and Oliver Williamson for helpful comments on this draft.
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Efficient Alignment and Survival in the U.S. Automobile Industry
Abstract
Various authors have criticized transaction cost economics for its implied assertion that getting
the governance right, i.e. organizing transactions so as to economize on transaction costs, is of
paramount concern to organizations. They argue that if economizing on transactions is of such
importance, then there should be some way of measuring the effect of getting the alignment right,
from a TCE perspective, on firm performance. To date, there have been only a handful of
empirical papers which test this link. This paper offers evidence that, yes, getting the alignment
right does have profound effects on firm performance. Misalignment has a positive effect on the
likelihood of firm failure. Perhaps of even greater interest, this paper also demonstrates that the
effect of misalignment varies with firm age. Timing is everything. Getting the alignment right
does matter, but depending on the age of the firm, getting to market may matter as well.
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I. Introduction:
Understanding the trade-offs involved in relying on internal or external sourcing
of components and/or services has been a central focus of the transaction cost economics
research program. And indeed, many of the hazards associated with governing highly
asset specific transactions by relying on an external market rather than vertical
integration have been theoretically elaborated (e.g. Williamson, 1991, 1996). A recent
review of the empirical transaction cost literature lists nearly 200 studies which support
the central prediction that highly asset specific transactions are more likely to be
vertically integrated than outsourced (Klein and Shelanski, 1995). However, in that same
review, there were only a handful of studies which connected this governance decision
with firm performance. In order to demonstrate the usefulness of the transaction cost lens
to other organizational and strategy scholars, it is necessary to demonstrate this
connection.
Of this handful of studies which investigate the relationship between transaction
alignment and firm performance, most rely on survey measures of outcomes and a few
look at comparative costs (e.g. Masten et al. 1991; Walker and Poppo, 1991). Very few
have analyzed the link between alignment and financial performance, though early
studies tested whether the M-form of organization was more profitable for diversified
firms (e.g. Armour and Teece 1978). To date this author knows of only one study which
has attempted to link alignment with survival (Silverman et al.,1997) and the results were
inconclusive.1
Why this lack of empirical work in an area that many have recognized as a
serious gap in the literature (e.g. Gulati, 1999, Winter, 1990)? First, there is the
theoretical issue of how to link events at the transaction level of analysis with outcomes
at the firm level of analysis. (See Masten, 1991 for a good summary of these concerns.)
Even the simplest firm must organize multiple transactions simultaneously. How do we
know which transaction is most likely to effect firm performance? In its strictest
1 Although their study found the predicted positive relation between asset specificity and governance, the results for the test of the effects of alignment on survival were inconclusive. The sign on the coefficient for misalignment was in the expected direction but was statistically insignificant.
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interpretation, transaction cost reasoning would suggest that each transaction could be
assessed in isolation for its ability to economize on transaction costs. But recent work
(e.g. Argyris and Liebeskind,1998?) suggests that there may be bundling problems (some
transactions are inherently tied to related transactions) or there may be path dependent
problems, i.e how transactions were organized in the past may constrain how future
transactions can be organized. For the moment, carefully choosing a focal transaction is
one way of controlling for these level of analysis problems. That is the argument that will
be developed in this paper, though intriguing theoretical issues remain to be sorted out in
future work.
Another serious impediment to undertaking empirical work in this area is the
substantial data required. Gathering data at both the transaction and firm level of analysis
may not always be feasible. Building in the additional requirement of creating a
complete, longitudinal time series becomes a non-trivial task. Yet, it is imperative if we
are to empirically test the effects of transaction alignment on firm performance.2
This paper uses survival as the outcome measure for two reasons.3 The first has to
do with assessing the fundamental nature of transaction alignment. If governance really
matters, we ought to be able to empirically demonstrate this in a fundamental way.
Survival is a significant performance measure. Though not as fine-grained as financial
measures, it nonetheless represents the cumulative success of the firms operations and
decisions over time in an unambiguous way.4 Second, in the course of attempting to
understand the effects of misalignment, it is difficult to ignore the case of firms that
attempt to remedy their misalignment by undergoing change. This paper takes a
perspective on change from organizational ecology theory’s structural inertia hypothesis.
As will be argued in a following section, firms may face competing risks from either
remaining misaligned or undergoing core structural change. Testing for these effects in a
way that compliments the existing change literature requires using the hazard rate of
failure as the dependent variable.2 See Bowen and Wiersema, 1999 for an analysis of the limitations of prevailing methods of cross-sectional empirical strategy research.3 Still, future work is underway to examine the impact of alignment decisions on market share, an alternate measure of firm performance.4 This depends, of course, on how we code failure. In this study, eleven different types of exit events are coded and only those which represent failure, e.g. bankruptcy or liquidation as opposed to being acquired or merging, are included in the analysis. More on this in the results section.
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The primary goal of this paper is to demonstrate that getting the alignment right
does indeed matter. Over time, those firms that are misaligned are more likely to fail than
those which are aligned more efficiently. Therefore, although the analysis which follows
is preliminary it has the potential to make a contribution simply because there are no
studies to date which have empirically established this assertion.5 Further, this paper
develops the argument that integrating ideas from structural inertia perspectives on
change may influence the way we perceive the viability of and the constraints on the
realignment process, another area of transaction cost research which has yet to be
explored.
The paper is organized as follows: The next section reviews the theoretical basis
for predicting vertical integration decisions and briefly reviews the extant empirical
literature on vertical integration decisions in the U.S. automobile industry. Section III
describes the basis for predicting the effect of misalignment on survival and compares
this to the predicted effect of core structural change. Section IV describes the industry,
section V describes the data used in this analysis, section VI presents results, and section
VII concludes.
II. Determining choice of organizational form using transaction cost logic
Clearly it is important to describe the logic of transaction cost reasoning which
drives the determination of efficient alignment in this study. This section, then, presents a
straightforward summary of transaction cost predictions on vertical integration decisions
with particular emphasis on the automobile industry.6 Transaction cost economics has
emerged as the dominant theoretical framework for addressing various questions related
to the make-or-buy decision, the degree of vertical integration, and specifically vertical
integration within the automobile industry (Langlois and Robertson, 1989; Masten et al.,
1989; Helper, 1991). Indeed, the paradigmatic case of investigating the impact of
transaction characteristics on governance structure is that of vertical integration in the 5 It should be noted that this is a rapidly developing area of research interest and this author knows of several projects which are underway. See proceedings from the Western Economic Association meetings, July 1999 for working papers on these issues.6 While there are alternative organizational and economic perspectives on this topic (e.g. isomorphic arguments (Fligstein, 1990); economies of scale arguments (Stigler, 1951);etc.) , the goal here is to focus on one perspective.
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automobile industry. Below we provide a brief review of empirical research to date as
well as relevant theoretical analyses and case studies. Contributions and gaps in the
literature are summarized.
Theory and Case Studies
There have been several case studies of vertical integration in the US automobile
industry, many of which focus on the 1920’s and 1930’s. Klein et al. (1978) provide the
most well-known case study, that of GM’s decision to acquire Fisher Body in 1924.
Others include Langlois and Robertson (1989), Helper (1991), Katz, (1977) and Raff
(1991). One advantage of the case study approach is that several hypotheses concerning
the observed pattern of vertical integration have been created. As each author revisits the
phenomena, cumulativity reveals the importance of subtle distinctions in the emphasis
each author puts on certain features or hazards of the transaction.
Helper (1991) offers an illustrative example. She argues that integration was the
preferred mode of organization not because of asset specificity alone, but because it
fostered asset specific learning. She then differentiates her argument from Chandler and
Salisbury (1971) who disregard asset specificity but emphasize the acquisition of
managerial and production capabilities (learning) and Langlois and Robertson (1989)
who emphasize the specificity of the knowledge (learning) but suggest that it is the
geographic proximity of integration which supports this process.
Edmonds (1923) is the first of many to use the case study approach to study the
pattern of vertical integration in the US auto industry. His work identifies three firms
GM, Ford, and Durant Motors as large enough to support extensive integration, but
suggests that integration decisions within the over one hundred independents (e.g.
Studebaker, Dodge, Willys, Hudson) varied considerably. He notes that this observed
heterogeneity in form suggests that more than economies of scale are driving the
integration decision. His work emphasizes hold-up hazards (though no mention of asset
specificity) as a primary motivation but hypothesizes that quality control (monitoring
hazard) and marketing benefits (reputation effects) may have had as much to do with the
decision to integrate. He also argues that market segment has an effect as well.
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Empirical Research
Several studies have empirically tested transaction cost predictions on vertical
integration in the auto industry. In general, measures of asset specificity, uncertainty,
frequency, complexity are modeled to assess their predicted effect on the dependent
variable, often a dichotomous measure of integration, occasionally a continuous measure.
Overall, the results from these studies support the theory. Asset specificity, particularly
specialized technical know-how, uncertainty, as well as the size of appropriable quasi
rents, all have positive effects on the integration decision.
Monteverde and Teece (1982a) collected information on 133 components to
reveal a statistically significant positive relationship between the engineering effort
(proxy for size of appropriable quasi rents), the specificity of a component and the
likelihood that the component is produced in-house as opposed to procured from an
external supplier.
The dependent variable was a dichotomous dummy variable which measured
whether a component was sourced internally or externally. Since GM, Ford, and other
auto producers often use both sourcing options for a given component, the authors chose
a cutoff point of 80% to determine the value of the dependent variable. In other words, if
80% or more of a given component were manufactured in-house, then that component
was considered to be sourced internally.7
Independent variables were devised as follows. To measure engineering effort,
the authors surveyed two experienced design engineers asking them to rate the
development cost of each component on a 10 point scale from 1 = ‘none’ to 10 = ‘a lot’.
They then measured specificity by asking officials of a replacement parts wholesaler to
categorize each of the components as either common across many manufacturers or
relatively unique8. Roughly 75% of the components were considered firm-specific. The
authors also argue that the complexity and degree of mechanical ties between
components, i.e. systems effects, should also have a positive effect on integration due to
7 Further analysis revealed that the results held even if cutoff point shifted plus/minus 10%.8 From Monteverde and Teece (1982a), p.209: “ The actual question was : ‘Please examine the following list of 133 automotive components and indicate which of the noncaptive items on the list could be procured as replacement units without necessarily having to know the manufacturer, make, and model of the vehicle for which the replacement is sought. That is, which of the following categories of parts may be expected to be largely common across several manufacturers’ vehicles.”
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greater coordination capability of hierarchy over market transactions. Unable to construct
such a measure, the authors simply control for the type of sub-system to which each
component belongs instead.
Engineering effort, firm-specificity, and company identity (in this case, GM) had
a positive effect on the probability of vertical integration. Subsystem identity had
virtually no significant effect. From these results the authors argue that as engineering
know-how increases, firms are more likely to take production in-house because of the
hazard of potential high supplier switching costs.
In a second study, using data on 28 components from two divisions of one auto
manufacturer, Monteverde and Teece (1982b) find support for a hypothesized positive
relationship between appropriable quasi rents and quasi-vertical integration. This
hypothesis is derived from work by Williamson (1975) and Klein, Crawford, and
Alchian (1978).The authors construct a proxy for appropriable quasi rents from measures
of tooling cost and the degree of specialization of tooling (the percentage of tooling cost
needed to convert tooling to next best alternative use). Again, the results support the
notion that specialized assets create hazards which may be mitigated, in part, through
increase in vertical integration.
A study by Walker and Weber (1984) uses a structural equation model to estimate
the relationship between stages of the decision-making process as well as measures of
transaction characteristics. Data on the decision to make-or-buy 60 components within
one firm were gathered from the discussions of a committee comprised of engineering
and purchasing managers entrusted with the As one pathway in their model, the authors
found support for a positive relationship between volume uncertainty and a firm’s
decision to make rather than buy a component. Of particular interest to this research, they
also use a measure of supplier competition which is based in theory, on the number of
available suppliers for a given component. The number of suppliers is considered a proxy
for asset specificity. Unfortunately, rather than a true measure of supplier density, the
authors rely on answers to a survey question which asks managers to estimate the extent
to which there is supplier competition.
In sum, the role of asset specificity is of primary importance in determining
choice of organizational form. According to Williamson (e.g. 1985, 1991), of the three
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characteristics which drive integration decisions- asset specificity, frequency and
uncertainty- asset specificity often has the greatest impact on the magnitude of
contracting hazards and thus should be of paramount importance in any analysis. In brief,
as asset specificity increases, appropriability and hold-up hazards intensify, encouraging
firms to bring in-house highly asset specific transactions. Thus, based on the theory and
prior empirical evidence, we predict a positive relationship between asset specificity and
vertical integration. It is a simple exercise to construct the first hypothesis:
H1: As the asset specificity of a primary component increases, the likelihood of
the integration of that component increases.
However, in an effort to incorporate recent concerns about the ability to support the idea
that transactional governance is designed in isolation we consider that the governance of
other related transactions may influence the organization of the focal transaction. Hence
we hypothesize:
H2: The integration of a related component is likely to have a positive effect on
the integration of the focal component.
III. Predicting the effect of misalignment and core structural change on firm
survival
An underlying premise of transaction cost reasoning is that, except in cases of
societies with extraordinary institutional constraints, we can assume the existence of a
selection mechanism based on efficiency. As Williamson describes it: “Inefficiency
invites its own demise, (e.g. Williamson, 1985). As long as weak-form efficiency
selection mechanisms operate, inefficient alignments will not hold. Either they will be
selected out, or they will be replaced.
We can infer that the strength of the selection environment may vary with
individual populations of organizations, or may vary within populations over time, but
ultimately firms that have failed at getting the alignment right will suffer some penalty
vis a vis firms that are better aligned. The penalties will vary depending on the nature of
the misalignment. For example, a firm which produces in-house a generalized component
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will likely face an efficiency disadvantage compared to rivals who correctly rely on the
market for this transaction. Alternately, a firm which contracts out for a specialized
component is likely to face approproiability, monitoring or other similar hazards.
Although lags may delay the impact, eventually misaligned firms will either be selected
out of the population or they will undergo some sort of adaptive re-alignment.
Based on this argument it is logical to assume that firms which are misaligned
will find the latter option preferable. However, although economic actors are assumed to
be far-sighted (if boundedly rational), i.e. we can assume that economic actors perceive
the risks associated with misalignment, the theory is relatively silent on how firms
manage the misalignment problem. Thus, there is a gap in the theory as to how the costs
of re-alignment are to be calculated, how these costs may be factored into a firm’s
decision to adjust its governance structure, and under what conditions such re-alignment
is more or less likely. Transaction cost theory is not alone in its silence on this issue.
Many other organizational theories implicitly assume that successful change is feasible,
though not always inevitable.
Organizational ecology theory provides a rich alternative theoretical perspective
on the likelihood of successful adaptation. The theory acknowledges that firms will
undertake change. However, the probability of implementing successful change is
assumed to be low (Hannan and Freeman, 1989). This low probability is a function of the
difficulty of making timely adjustments and accurate ex ante predictions of the effects of
both the content and process of change (Barnett and Carroll, 1995). For theoretical
reasons related to the benefits of structural inertia, the theory predicts that firms which
undertake change are likely to face a higher risk of failure than firms which do not.
Further, the theory predicts differential effects on the risk of failure depending on the
type of change. Core change entails greater risk to the firm’s survival chances than does
peripheral change.9
Hannan and Freeman’s (1984, 1989) structural inertia theory provides the
framework for analyzing change used in this study. Their principle argument suggests
that the probability of organizational change diminishes over time as a result of both
9 This is a broad implication of the theory. Research in this area has continued to refine theoretically-motivated hypotheses about change. Relevant refinements will be discussed in a later section. (See Barnett and Carroll (1995) for further discussion)
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internal and external pressures. To survive, organizations develop routines which
facilitate interactions within the organization as well as with external agents. As these
routines become institutionalized, they contribute to an increase in structural inertia.
While evolutionary theorists such as Nelson and Winter (1982) suggest that these
routines and procedures are difficult to change for reasons of learning costs and the
costs of delay, and Cohen and Levinthal (1990) suggest that these routines might be
difficult to change for lack of absorptive capacity or organizational recognition of their
value, Hannan and Freeman’s argument suggests that the loss of reliability and
accountability in rationally justifying their actions is likely to be a primary obstacle to
change.10
Of particular importance here, the theory predicts that there are real hazards
associated with core change. Thus, core change, as specified in the theory to include
changes in the goals, authority structure, technology or marketing strategy of a firm, will
have a serious effect on the firm’s likelihood of survival. From this we predict that core
change in the boundaries of the firm will be riskier than less extensive or peripheral
change in the boundaries of the firm.
Several recent empirical studies have supported and extended the structural inertia
argument. In a test of the effect of change on firm survival among Finnish newspapers,
Amburgey, Kelly, and Barnett (1993) found that change is generally disruptive. Their
analysis included an organizational age term which proved to be critical in clarifying
some previously ambiguous results (e.g. Haveman, 1992) on the effect of change. While
change can be both disruptive and adaptive, they find that the net effect of change,
including age effects, appears to be detrimental to survival.
This study attempts to compare the risks of changing mode of organization with
the risks of misalignment. It is an effort to unpack the as yet unidentified costs of
realignment. Undertaking core change, for reasons elaborated in organizational ecology
theory, could comprise a substantial proportion of these costs.
The predicted effects of change and misalignment on a firm’s survival chances
are summarized in Figure 1.
10 Usurping institutionalized practices makes it more difficult for external and internal agents to predict and understand firm behavior. This in turn makes it more difficult for the firm to attract and retain resources.
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Figure 1: Predicted effects on firm’s survival chances
Change (at t2) No Change (at t2)
Aligned (at t1) TCE -
OE -
TCE +
OE +
Misaligned (at t1) TCE +
OE -
TCE -
OE +
Interestingly, the theories offer both competing and complementary predictions.
If we assume a firm is aligned at t1, then the theories offer similar predictions regarding
firm survival regardless of whether or not the firm changes in t2. However, if we start
with the condition that the firm is misaligned, the theories offer opposite predictions.
Relying on the strong form of both theoretical arguments, we suggest that from a
transaction cost perspective, misalignment presents a greater risk to the firm than
change11. Firms should pay a penalty (in this case decreased survival chances) for
misalignment. Yet, from an organizational ecology perspective, we know that
organizations are subject to strong structural inertial pressures, and we know that core
change is likely to increase the chance of firm failure. Here the risk of change is likely to
outweigh the benefit of re-alignment.
Thus we hypothesize:
H3: Misalignment will have a negative effect on a firm’s survival chances.
H4: Core change will have a negative effect on a firm’s survival chances.
Clearly, for purposes of testing both the transaction cost and structural inertia hypotheses
the definition of a core transaction is critical. Aware of the transaction cost level of
analysis problem described in the introduction as well as the need for identifying a
change of appropriate magnitude for the structural inertia hypothesis , we argue that
engines can be defended in this case as a core transaction. This assertion relies on expert
11 Assuming change improves alignment, or that the content of change is beneficial and, as Barnett and Carroll (1995) would suggest, outweighs process effects.
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accounts of the importance of this transaction (e.g. Abernathy) and by using theoretically
identified variables such as the magnitude of production cost. Thus, changes in the
arrangement of core transactions such as engine production will have greater effect on
firm than changes in arrangement of peripheral transactions such as tires. Additionally,
we can categorize both a change in mode of organization as a core change based on the
definition in Hannan and Freeman (1989). If a shift in governance occurs, it is defensible
as a core change based on attendant disruptions in authority structure and the possibility
of changes in technology or even marketing strategy.12 From a transaction cost
perspective, the misalignment of the component which accounts for such a significant
proportion of production costs is arguably going to effect the performance of the firm if
weak form efficiency selection mechanisms operate.
IV. History of Auto industry, emphasis on period between 1917-1933
Experimentation with self-propelled vehicles goes back at least to the early 19th c.
in the US when a Pennsylvania inventor produced a steam-engine vehicle designed for
land and water use in 1806. In 1885, German engineers Karl Benz and Gottlieb Daimler
produced the world’s first internal combustion automobile, but it wasn’t until 1893 that
the Duryea brothers of Massachusetts produced their own American version. Other firms
quickly entered the field, each with slightly different products. Some had three wheels,
others four, some used tiller steering, some had controls on the right, propulsion
technologies included electric, steam, gas, and others, some placed the engine below the
passenger compartment, others in front, others in the rear (one innovative engineer
placed an engine over each wheel, cite std cat) (See Epstein p.89 for further options). All
were produced on a relatively small scale, demanded high-skill from those who produced
them, relied on both outside parts from suppliers (often carriage, bicycle, industrial
engines) and their own specialized production.
As the automobile industry grew, there was a shift from workshop means of
assembly (what Hounshell (1984) calls the American system of manufacture) to adoption
12 For example, certain auto manufacturers advertised the fact that they produced their own components and emphasized the quality of craftsmanship.
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of mass production. Arguably, the most famous development of the period was Ford’s
introduction of assembly line technique mass production in 1913-14 at its Highland Park
plant, on the northern edge of Detroit. This method of assembly combined the use of
interchangeable parts with an assembly line which brought the work to the worker. This
enabled managerial control of the speed of production through the speed of the line,
experimentation with configurations of the work process, and the inception of the five-
dollar day when it was recognized that high wages were still needed to entice workers to
withstand the lack of autonomy that came with the process.
According to most historians, the technique diffused widely and in a relatively
short period of time13 In addition to written accounts in the engineering and popular
press, Ford sponsored plant tours for competitors, suppliers, and journalists to publicize
its achievement. In 1915 the company literally took the assembly line on the road when it
set up a replica of its Highland park line at the Pan-Pacific Exposition in San Francisco.
Testifying to the revolutionary quality of the assembly line and its power to capture the
public imagination crowds waited hours in line for tickets to watch the replica line in
action.
The end of WWI ushered in a period of high growth in the industry in terms of
number of cars produced, number of new entrants (as well as number of exits), number
of new product features. From an industry evolution perspective, one of the most
important developments was the emergence of the all-steel closed car as the dominant
design. This design was the result of the organizational factors such as the refinement of
production techniques related to steel stamping as well as environmental factors such as
the expansion of road systems and the use of asphalt and concrete to create smoother
road surfaces which reduced stress on all-steel closed bodies. In 1917, closed car
production accounted for four percent of total car production and most of these cars were
coupes and limousines destined for the wealthy (Epstein, 1928).14 Ten years later, closed
13 Raff describes the diffusion process as slow because it wasn’t until after WWI that the entire Ford system including high wages, control features and complete interchangeability was adopted by other firms. Even Raff acknowledges however that within only a year or two , most other firms had adopted many of the features of Ford’s system. All historians agree that Ford actually encouraged and took steps to hasten the diffusion process. Also see Hounshell (1984) for argument that assembly line production diffused rapidly.14 Closed cars cost approximately 30-50 percent more than open cars, a price differential which collapsed over the next few years.
15
bodies accounted for 70 % of total passenger car production. By the mid-thirties,
virtually all sedans were all-steel closed body.
Between 1924 and 1929, with the uncertainty over the establishment of a
dominant design removed and the threat of Ford’s dominance reduced, expectations of
growth for both new entrants and incumbents were largely positive. There may have been
less differentiation among firms in this period than in the previous period, but the
selection environment was weaker than what was to follow.
Following the Great Depression, automobile production fell (65% drop from
1929 to 1933),the number of firm failures increased and the rate of entry slowed
significantly although it did not cease altogether. During this period the Big Three firms-
GM, Ford, and Chrysler established their dominant positions. However, roughly a dozen
or so mid-sized firms continued to compete directly with the Big Three (referred to as the
independents) and a larger number of small specialist producers survived. Indeed, the
FTC pursued an investigation of industry practices, with special reference to retail
distribution, prompted in part by lobbying and with the cooperation of the remaining
independents.
V. Description of Data
As stated in the introduction, one of the primary roadblocks to doing empirical
work on the effect of misalignment on firm survival has been the need for detailed,
longitudinal and comprehensive information on individual firms and transactions. This
paper combines data collected on the automobile manufacturing industry by the research
team headed by Carroll and Hannan (see Hannan et al., 1995; Carroll et al., 1994) with
data on components and suppliers I have collected separately. The database includes
information on all firms which engaged in automobile production in the U.S. from the
inception of the industry until reliable data ends in 1981.
In constructing the database, we primarily relied on three sources: The New
Encyclopedia of Motorcars (Georgano at al., 1982), The World Guide to Automobile
Manufacturers (Baldwin et al., 1987), and the three volumes of The Standard Catalog of
American Cars (Kimes and Clark, 1989; Flammang et al., 1989; Gunnell et al., 1987).
16
These sources provide comprehensive information on automobile producers including
descriptions of the models produced, start and end dates of production, and historical
accounts of organizational and production processes. In many cases, information on
technical specifications and the source of components is provided.
Augmenting this database, I have collected archival data from various issues of
Motor Age, The Automotive Trade Journal, and the volume: Automobile Specifications
1915-1945 (Lester-Steele, 1960) which breaks out information by firm on component
characteristics, including technical specifications, whether the component is produced in-
house or outsourced, and the identity of the supplier. From various years of The
Automotive Trade Journal and Chilton’s Automobile Suppliers Directory, I have
collected information on the location (city and state), type of parts produced, and start
and end dates for selected suppliers.
Initial comparison of the list of firms for which we have component data with the
list of firms in our database of manufacturers suggests that approximately 80% of firms
have information on the source and type for a selected set of components. (The Standard
Catalogue and other sources increase this percentage). For reasons of comparability
across firms, only firms that employ internal combustion propulsion technology, i.e. gas
engines, are included in the analysis (i.e. steam and electric car manufacturers are
excluded.)
Automobile producer density ranges from about 150 firms in the beginning of the
observation period (1917), increases to about 200 in 1922, then declines to about 25
firms by the end of the observation period (1942). For each firm, I have information on
whether or not the firm produced the following components: engine, axles, carburetor,
transmission, clutch, ignition system, tires, tire rims, steering gear, battery, crankshaft,
camshaft, horn, speedometer. There is even more technical information as year of
observation increases (In large part because cars become more complex, not because of
systematic changes in archival records, as far as I can tell.) I have coded firms for each
component as follows: 1 if the firm makes the component, 0 if the firm purchases the
component from a supplier, 2 if the firm does both. As explained further below, this
information is collected at the model level, although information on firms that produce
multiple models can be aggreagated through the use of a firm identification number.
17
VI. Results and Discussion
.
As stated above, in order to produce the misalignment measure which will be
used in the hazard rate models it is necessary to first run a discrete choice model
predicting the effect of asset specificity on the probability of vertical integration.15 This
analysis provides a compliment to cross-sectional work such as Monteverde and Teece
(1982). The basic question here is: Can we make predictions about the firm’s choice to
integrate or rely on suppliers based on theoretically derived notions of the importance of
certain variables (e.g. asset specificity, size, etc)?
Producing a measure of asset specificity that can be compiled over hundreds of
firms for twenty-five annual observations is a difficult but necessary first step. Clearly
the conventional operationalization via survey is not feasible. Instead I propose a
simplified operationalization based on the notion that asset specificity can be understood
as a measure of uniqueness. I utilize information on the technical specifications16 of each
engine to construct this proxy for asset specificity. Thus I can test the asset specificity
hypothesis using this one transaction, but across all firms in my sample. I use the
distribution of the relevant metric then compare the specifications for the engine for firm
i to the distribution for engines across either all firms, or across a relevant sub-population
of firms, by year. It is important to emphasize that the distribution from which either of
these proxies for asset specificity is derived includes the entire population of automobile
manufacturers. In other words, although there may be other possible technical
configurations of engines, we can assert that such configurations are hypothetical ideals
not realistic alternatives. Thus, the problem of sample selection bias is mitigated in the
construction of the scale for determining asset specificity.
Turning to Table 1a&b I describe below the variables used in the analysis. The
dependent variable, engine_m, is a dummy variable coded 1 if the automobile
manufacturer produces this engine in-house, 0 if it uses a supplier to provide the engine,
15 See Bigelow (1999) for further discussion of the implications of this analysis.16 These technical specifications include: engine material, position of cylinders, number of cylinders, engine displacement, measurements of bore and stroke, and horsepower.
18
or 2 if it does both. Only a small number of observations ( 42 out of 1872) are coded as
2. These are not included in the analysis.
Information is coded at the model level. Thus GM, for example, produces several
marques (e.g. Buick, Oakland, Cadillac, etc) and within these marques they may offer
different models. Since the transaction is the relevant unit of analysis, the information on
engines is captured at the model level.
The independent variables include transaction (in this case engine) and firm level
variables. The engine variables are designed to capture characteristics of the engine
which proxy in some way for asset specificity. Three variables are continuous measures:
cubic displacement (cc), horsepower (hp), and the diameter of the cylinder (bore). Both
cc and hp are measures of potential performance and rely on slightly different formulas,
utilizing dimensions of the cylinders. Bore is measured in inches.
In an effort to distinguish those engines that are particularly unusual in their size
and power, a measure called unique_eng is constructed. This is the proxy for asset
specificity which is constructed from the distribution of cubic displacement. This
variable is a dummy variable coded 1 if the standardized measure of cc for that engine
for that year is greater than or equal to 2, 0 otherwise. Since engines that fall in this
category must either be physically larger or more unusual (in terms of their bore and
stroke measures, which in part comprise cc) this is considered a rough proxy for asset
specificity. It also allows for the maximum number of factors that can be rearranged in
order to produce similar levels of cc. In other words, the lower end of the distribution is
not considered highly unique because these low levels each utilize the same number of
cylinders (4) whereas the high end of the distribution could use (and does use) 8, 12, or
16 cylinders to produce the same level of cc. The implication here is that, given that the
inter-connectedness of other drivetrain components depends in part on the number of
cylinders, redeployability is greatly reduced for engines at this end of the distribution
compared to those in the middle or left tail of the distribution.
To control for some firm characteristics that might influence the vertical
integration decision apart from the nature of the transaction, I’ve included a variable for
the age of the firm (firmage) and two variables that indicate whether the firm has
experience with vertically integrating other components. Clutch_m and trans_m are
19
dummy variables coded 1 if the firm produces its own clutch or transmission, 0
otherwise. As in the case of engine_m, scores of 2 (the firm both produces and contracts
out for the component) are omitted form the analysis and information is captured at the
model level.
A measure of firm size based on annual production is also included in order to
control for economies of scale- an important alternative explanation of integration. The
construction of this size measure represents a labor- and time- intensive data collection
procedure that relied on a combination of using available precise production data as well
as using historical descriptions of size that were less precise. For example, exact annual
production figures for GM are easily obtained over the observation period, but for
smaller firms the only extant information may be an account in the Standard Catalogue
which indicates that the firm produced only a few cars. Rather than completely discard
this information, we came up with coding rules which assigned a number to these verbal
accounts and also came up with rules for making assumptions about the distribution of
lifetime production over annual periods. In general, given the many varied distributions
of production we observed in the empirical data, we opted to assume a uniform
distribution, unless there was additional information to decide otherwise (see Hannan et
al. (1997) for a complete description).
To test for whether or not firms through out the population are more likely to
integrate those engine transactions which are more asset specific, a discrete choice model
is used. The results of the probit model (either probit or logit are applicable and yield
nearly identical results) are presented in Table 2. To control for a potential unobserved
relation between year of observation and vertical integration, I use robust estimators
adjusted for clustering on year.
Because these results are discussed in greater detail in another paper I simply
highlight the results of interest to understanding the hazard rate models in Tables 3-6.
Models 1-4 each show that as predicted, unique_eng has a significant and positive effect
on the decision to produce the engine in-house. Horsepower also shows a similar effect.
However, as firm-level control variables are added in models 2, 3 and 4, the hp effect
disappears while the unique_eng effect remains. One potential explanation of this pattern
of results is that it is not performance alone which accounts for the decision to make the
20
engine. Rather it is some combination of technical dimensions (which, as reflected in the
unique_eng variable, may be more idiosyncratic or asset specific) to deliver that level of
performance which seems to be driving the results.
It is worth noting however, that firmage as well as additional experience with
integration have an effect on the decision to integrate the manufacture of engines. The
decision to integrate clutches as well as transmissions has a significant and positive effect
on the decision to integrate engines. One possible explanation is that firms develop some
competence in taking these important component transactions in-house. Or it may be the
case that these firms have specialized needs prompted by a specialized engine.
Obviously, there are still alternative explanations which need to be addressed. Overall,
however, these estimates support the hypothesis that all firms, not just the largest or the
oldest, are likely to adhere to transaction cost predictions regarding choice of
governance.
Effects of misalignment and change
In order to empirically test for the effects of misalignment on survival we need to
construct a measure which can be updated over time. For this we incorporate the results
from the analysis in Table 2. Misalignment is operationalized as the interaction of the
uniqueness of a component with the residual of the integration decision. Using the
measure of determination of alignment from this analysis, we include this variable in the
survival analysis.
Tables 3-6 show the results from piece-wise constant rate mortality models which
were run using a subroutine developed by Jesper Sorensen for use in Stata 6.0. Given the
importance in recent organizational ecology research to correctly specify age
dependence, we have to have models that will allow mortality rates to vary across age
groups (but the rate is assumed to hold constant within whatever age group is defined).
Tables 3 and 4 use the age split of (0,5,10,15) while Tables 5 and 6 use splits of
(0,3,7,10)17. The results of the misalignment main effect is similar no matter which split
is used. Comparing models 1-8 across Tables 3 and 5 it is clear that misalignment has a
strong significant positive affect on the likelihood of failure. Even after important control
17 These splits were arrived at after experimenting with and comparing various alternate time periods.
21
and other independent variables are added to the model, the main effect of misalignment
holds.
Firm size has the expected negative effect on mortality but note that it is a rather
small effect. Consistent with a prior analysis of this industry (Carroll et al.,1996), firms
which have had experience in another industry prior to entering automobile
manufacturing have better survival chances than start-ups. But given that I am looking at
only a limited number of years in the industry, I need to ascertain that there is no left-
censoring bias. The control for this is a dummy variable which is never significant in any
of the models, indicating that left-censoring is not biasing the results.
Model 8 includes a dummy variable which codes whether or not a firm has
undergone a change in the procurement of its engines. The effect is positive and
significant, but only at a relatively low level of significance. This may be that at the
moment, this variable does not distinguish between either the type of change, i.e. from
contracting to integration or the reverse, or the direction of the change, i.e. is this change
improving or exacerbating alignment. Future work will be able to sort out these
important effects. But for the moment, these results indicate that, indeed, firms may face
competing mortality risks from either misalignment or undergoing core structural
change.
Comparing Tables 4 and 6 we see an intriguing pattern of results regarding the
effect of misalignment over time, depending on the age of the firm.18 From Table 4, it
appears that firms under the age of 5 have an increased mortality risk from misalignment,
but when we look at the initial age split in Table 6 we see that firms under the age of 3
have no such risk from misalignment. Additional analyses not included here (but
available from the author) confirm that there is no effect of misalignment for firms age 1,
2, or 3 but a huge effect in years 4 and 5. Then firms appear to settle into a middle-age
period of relative immunity from misalignment. In old age, firms again seem to be
subject to an increased mortality risk from misalignment but note that the increase is not
as great as that faced by the first wave of selection in years 4 and 5, in most models.
Further, as seen in the separate age split variables, there is a clear pattern of negative age
dependence and firms reap some buffer to misalignment through this effect. Obviously,
18 Note that as these subroutines are run in stata, the main effects of misalignment are run separately.
22
there are several plausible explanations for this pattern of results and more work needs to
be done to begin to sort these out. One possible scenario might be that firms feel the
pressure to get to market and at time of entry, getting the alignment right is a secondary
concern. In a relatively short period of time, perhaps as competition intensifies, firms
discover that efficiency is in fact a first order priority. Throughout most of the
observation period, i.e. prior to 1930, getting to market and thereby establishing viability
among customers, suppliers and investors was a substantial concern. Still, it may be that
what we are seeing in these results is merely a lag in the efficiency selection mechanism.
Further analysis of the data, particularly controlling for changes in degree of
misalignment, will help clarify these results.
VII. Conclusion
It is clear to both transaction cost researchers and their critics that if this particular
theoretical lens is to be of use to the strategy field, a link needs to be made between the
governance of transactions and firm outcomes. To date, extensive data requirements have
deterred empirical research. This study is one of the first to offer empirical evidence that
in fact the alignment of transactions does matter at the firm level. In this case,
misalignment has a strong positive effect on the hazard rate of failure. But as soon as we
begin to discuss misalignment from a strategic perspective, it is difficult to disregard the
issue of how firms remedy misalignment. By incorporating ideas from organizational
ecology theory on the risks of change, we can begin to get a sense of what might
constrain organizational adaptation and thus allow misalignment to persist. We see some
evidence in this study that indeed undertaking core structural change is risky. We also see
patterns of misalignment that vary with the age of the firm which indicate that either
change is a significant deterrent, or getting to market is of greater priority than getting
the alignment right. Clearly much more work needs to be done to sort out these potential
explanations. But this study is an encouraging beginning.
23
Table 1aDescriptive Statistics
Variable Mean Std Dev Min Max
1 engine mb 0.633 0.527 0 22 cc 262.26 85.05 45.62 824.673 hp 27.806 7.625 7.74 62.54 bore 3.384 0.379 2.2 5.255 firm age 8.970 8.120 0 33.126 firm size 29830 156404 1 22823717 unique_eng 0.17 0.37 0.00 1.008 clutch_m 0.349 0.499 0 29 trans_m 0.499 0.529 0 2
10 num models 2.62 2.54 0 12
Number of obs. = 1746
Table 1b Correlations
Variable 1 2 3 4 5 6 7 8
1 engine mb 12 std cc 0.147 13 bore 0.103 0.568 14 hp 0.181 0.913 0.425 15 firm age -0.022 0.062 -0.025 0.048 16 firm size -0.009 -0.021 -0.029 -0.008 0.222 17 unique_eng 0.145 0.622 0.439 0.576 0.038 -0.028 18 clutch_m 0.354 0.014 0.069 0.028 -0.026 -0.098 0.088 19 trans_m 0.478 0.028 0.008 0.074 -0.031 0.054 0.065 0.607
10 num models 0.272 -0.077 -0.162 0.059 0.022 -0.002 -0.034 0.328
24
Table 2
Probit Models of Integrated Engine Manufacture(standard errors- in parentheses- adjusted for clustering on year)
Model 1 Model 2 Model 3 Model 4constant -.912*
(.417)-1.871**(.511)
-1.765**(.521)
-1.191*(.556)
cc -.003(.0016)
.00001(.001)
.002(.002)
-.0003(.002)
hp .0567*(.021)
.0016(.021)
-.013(.023)
.0174(.026)
bore .1211(.140)
.3626*(.165)
.311(.171)
-.0314(.157)
unique_eng .9338**(.216)
1.008**(.263)
.9567**(.258)
1.215**(.357)
firmage .066**(.007)
.0565**(.007)
.0443**(.004)
size 1.83e-06**(5.66e-07)
7.65e-07 *(3.93e-07)
make_clutch .5754**(.122)
make_trans .8799**(.135)
No. of obs. 1746 1746 1746 1481Log likelihood -1121.75 -990.24 -959.18 -675.97Prob > chi2 .000 .000 .000 .000Pseudo r2 .038 .151 .177 .305* p < .05** p < .001
25
Table 3
Piece-Wise Constant Rate Mortality Models for U.S. Automobile Manufacturers, 1917-1933(standard errors in parentheses)
Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7 Model 8Age < 5 years -1.589***
(.107)-1.947***(.223)
-1.765***(.233)
-1.665***(.242)
-1.552***(.251)
-1.821***(.298)
-2.213**(1.166)
-2.381**(1.172)
Age 5-10 years -1.444***(.136)
-2.021***(.260)
-1.624***(.262)
-1.351***(.319)
-1.191***(.331)
-1.472***(.368)
-2.164*(1.208)
-2.343*(1.214)
Age 10-15 years -2.186***(.218)
-3.286***(.411)
-2.726***(.425)
-2.355***(.498)
-2.148***(.511)
-2.511***(.554)
-3.199**(1.287)
-3.405**(1.294)
Age >15 years -2.391***(.162)
-2.948***(.248)
-2.213***(.270)
-1.814***(.390)
-1.611***(.406)
-2.024***(.481)
-3.004**(1.313)
-3.179**(1.319)
misalign1 1.079**(.423)
1.044**(.448)
.981**(.452)
1.009**(.461)
.947**(.463)
.926**(.459)
.852*(.460)
firm size -.0001***(.00004)
-.0001***(.00004)
-.0001***(.00004)
-.0001***(.00004)
-.0001***(.00004)
-.0001***(.00004)
left censored -.374(.265)
-.333(.266)
-.293(.264)
.066(.275)
.047(.277)
de alio (entrant from other industry)
-.369*(.198)
-.405**(.199)
-.446**(.203)
-.416**(.204)
number of models .271*(.160)
.128(.175)
.111(.175)
gnp -.003(.010)
-.002(.010)
total industry car production
5.37e-07**(2.01e-07)
5.31e-07**(2.00e-07)
change in mode of governance, engine
.539*(.314)
No. of obs. 1511 978 978 978 978 978 978 978Log likelihood -202.17 -119.11 -97.82 -96.81 -95.06 -93.77 -86.61 -85.31Prob > chi2 .000 .000 .000 .000 .000 .000 .000 .000No. of firms 218 183 183 183 183 183 183 183* p < .10 ** p < .05*** p < .001
26
Table 4
Piece-Wise Constant Rate Mortality Models for U.S. Automobile Manufacturers, 1917-1933, with the effect of misalignment allowed to vary by age.(standard errors in parentheses)
Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7Age < 5 years -1.589***
(.107)-2.167***(.347)
-1.935***(.351)
-1.811***(.356)
-1.713***(.363)
-2.727***(.444)
-2.361**(1.215)
Age 5-10 years -1.444***(.136)
-1.341***(.379)
-1.140**(.403)
-.822*(.455)
-.716*(.454)
-2.032***(.563)
-1.648(1.308)
Age 10-15 years -2.186***(.218)
-3.801***(.820)
-2.984***(.819)
-2.597**(.871)
-2.426**(.881)
-3.680**(.936)
-3.302**(1.493)
Age >15 years -2.391***(.162)
-3.210***(.362)
-2.376***(.387)
-1.980***(.471)
-1.760***(.493)
-3.271***(.631)
-2.892**(1.327)
Age <5 x misalign1
1.612**(.746)
1.444**(.754)
1.331*(.751)
1.381*(.762)
1.529**(.745)
1.528**(.744)
Age 5-10 x misalign1
-.644(.936)
-.253(1.011)
-.395(1.026)
-.253(1.015)
-.174(1.034)
-.209(1.041)
Age 10-15 x misalign1
2.327(1.639)
1.623(1.629)
1.568(1.659)
1.651(1.674)
1.468(1.680)
1.466(1.669)
Age >15 x misalign1
1.746**(.739)
1.421*(.792)
1.421*(.792)
1.373*(.818)
.971(.783)
.993(.788)
firm size -.0001***(.00004)
-.0001***(.00004)
-.0001***(.00004)
-.0001***(.00004)
-.0001***(.00004)
left censored -.395(.268)
-.354(.269)
.067(.280)
.059(.281)
de alio (entrant from other industry)
-.351*(.199)
-.418**(.204)
-.415**(.204)
total industry car production
5.11e-07***(1.25e-07)
5.62e-07**(2.01e-07)
gnp -.003(.009)
No. of obs. 1511 978 978 978 978 978 978Log likelihood -202.17 -116.19 -96.59 -95.48 -93.92 -85.89 -85.84Prob > chi2 .000 .000 .000 .000 .000 .000 .000No. of firms 218 183 183 183 183 183 183* p < .10 ** p < .05*** p < .001
27
Table 5
Piece-Wise Constant Rate Mortality Models for U.S. Automobile Manufacturers, 1917-1933(standard errors in parentheses)
Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7 Model 8Age < 3 years -1.663***
(.141)-1.777***(.244)
-1.602***(.253)
-1.548***(.258)
-1.439***(.266)
-1.724***(.311)
-2.228**(1.161)
-2.421**(1.168)
Age 3-7 years -1.457***(.128)
-2.195***(.258)
-1.901***(.263)
-1.734***(.288)
-1.607***(.295)
-1.892***(.335)
-2.601**(1.193)
-2.798**(1.203)
Age 7-10 years -1.465***(.182)
-1.898***(.303)
-1.476***(.306)
-1.154**(.387)
-.988**(.401)
-1.288**(.432)
-2.245*(1.234)
-2.492**(1.246)
Age >10 years -2.322***(.130)
-3.020***(.232)
-2.330***(.256)
-1.949***(.386)
-1.742***(.404)
-2.163***(.473)
-3.281**(1.302)
-3.513**(1.314)
misalign1 1.058**(.422)
1.001**(.447)
.938**(.451)
.977**(.459)
.905**(.462)
.899**(.460)
.833*(.460)
firm size -.0001***(.00004)
-.0001***(.00004)
-.0001***(.00004)
-.0001***(.00004)
-.0001***(.00004)
-.0001***(.00004)
left censored -.362(.276)
-.331(.279)
-.288(.276)
.115(.283)
.115(.286)
de alio (entrant from other industry)
-.364*(.198)
-.404**(.199)
-.452**(.203)
-.421**(.205)
number of models .288*(.160)
.119(.175)
.101(.175)
gnp -.002(.010)
-.0005(.010)
total industry car production
5.53e-07**(2.01e-07)
5.50e-07**(2.01e-07)
change in mode of governance, engine
.516*(.315)
No. of obs. 1524 980 980 980 980 980 980 980Log likelihood -202.11 -118.14 -97.69 -96.82 -95.12 -93.66 -85.51 -84.32Prob > chi2 .000 .000 .000 .000 .000 .000 .000 .000No. of firms 218 183 183 183 183 183 183 183* p < .10 ** p < .05*** p < .001
28
Table 6
Piece-Wise Constant Rate Mortality Models for U.S. Automobile Manufacturers, 1917-1933, with the effect of misalignment allowed to vary by age.(standard errors in parentheses)
Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7Age < 3 years -1.663***
(.141)-1.796***(.410)
-1.590***(.419)
-1.515***(.420)
-1.407***(.427)
-2.415***(.485)
-2.269*(1.202)
Age 3-7 years -1.457***(.128)
-2.457***(.456)
-2.351**(.478)
-2.136***(.501)
-2.029***(.454)
-3.367***(.583)
-3.211**(1.316)
Age 7-10 years -1.465***(.182)
-.977**(.469)
-.644(.501)
-.323(.560)
-.269(.552)
-1.671**(.639)
-1.513(1.359)
Age >10 years -2.322***(.130)
-3.331***(.333)
-2.512***(.355)
-2.161***(.453)
-1.962***(.473)
-3.551***(.597)
-3.396**(1.316)
Age <3 x misalign1
1.107(.910)
.922(.932)
.851(.926)
.859(.936)
.896(.938)
.904(.939)
Age 3-7 x misalign1
1.683*(.958)
2.078**(1.011)
1.885*(1.014)
1.937**(1.021)
2.257**(.996)
2.243**(1.001)
Age 7-10 x misalign1
-1.375(1.258)
-1.292(1.368)
-1.382(1.370)
-1.115(1.347)
-1.346(1.396)
-1.365(1.408)
Age >10 x misalign1
1.843**(.681)
1.427**(.718)
1.410**(.720)
1.400*(.739)
1.027(.711)
1.034(.713)
firm size -.0001***(.00004)
-.0001***(.00004)
-.0001***(.00004)
-.0001***(.00004)
-.0001***(.00004)
left censored -.351(.281)
-.315(.283)
.147(.286)
.142(.289)
de alio (entrant from other industry)
-.326*(.199)
-.418**(.204)
-.417**(.205)
total industry car production
5.62e-07***(1.24e-07)
5.83e-07**(2.01e-07)
gnp -.001(.009)
No. of obs. 1524 980 980 980 980 980 980Log likelihood -202.11 -114.83 -95.21 -94.41 -93.07 -83.29 -83.28Prob > chi2 .000 .000 .000 .000 .000 .000 .000No. of firms 218 183 183 183 183 183 183* p < .10 ** p < .05*** p < .001
29
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