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Strategic Management Journal, Vol. 12, 167-185 (1991)
HOW MUCH DOES INDUSTRYMATTER?
RICHARD . RUMELT
Anderson Graduate School of Management, University of
California,
Los
Angeles,
California, U.S.A.
This study partitions
the
total
variance
in rate of return among FTC Line of Business
reporting units into industry factors
(whatever
their nature),
time
factors, factors associated
with the corporate parent,
and
business-specific factors.
Whereas
Schmalensee (1985)
reported
that
industry factors
were the
stronlgest, corporate
and
market
share
effects being
extremely weak, this study distinguishes between stable and fluctuating effects and reaches
markedly different conclusions.
The data
reveal negligible corporate effects,
small stable
indlustryeffects,
and
very large
stable business-unit
effects. These results imlply that
the most
itnportant
sources
of
economic rents
are
businiess-specific; ndustry membership
is a
much
less
itmportant
ource and
corporate parentage
is
quite unimportant.
Because
competition
acts to
direct
resources
towards uses
offering the
highest
returns,
persist-
ently
unequal
returns mark the
presence of
either
natural or
contrived
impediments to
resource
flows.
The
study of such
impediments is a
principal
concern of
industrial
organization eco-
nomics and
the
dominant unit of
analysis
in
that field
has been
the
industry. The
implicit
assumption
has been
that the
most
important
market
imperfections
arise
out
of
the
collective
circumstances
and behavior
of
firms.
However,
the
field of
business
strategy offers
a
contrary
view: it
holds that
the most
important
impedi-
ments
are not
the common
property
of
collections
of
firms,
but
arise
instead
from the
unique
endowments
and
actions
of
individual
corpo-
rations
or-
business-units.
If
this is
true, then
industry
may not
be the
most useful
unit of
analysis.
Consequently,
there should
be
consider-
able
interest in
the relative
sizes of
inter-industry
and
intra-industry dispersions
in
long-term
profit
rates.
Despite
these
arguments for
this
issue's sali-
ence,
surprisingly
little work
addressed
it until
Schmalensee's
(1985)
estimation of
the
variance
components of
profit
rates in the
FTC
Line of
Business
(LB) data.
Schmalensee
decomposed
0143-2095/91/030167-19$09.50
? 1991
by
John
Wiley
& Sons,
Ltd.
the total variance
of
rates
of return on assets
in
the
1975 LB data into
industry,
corporate,
and
market-share
components. He reported that:
(1)
corporate
effects did not exist; (2)
market-share
effects accounted
for
a
negligible
fraction
of
the
variance in
business-unit rates
of return; (3)
industry effects
accounted for 20
percent of the
variance in
business-unit returns;
(4) industry
effects accounted for at
least 75
percent
of
the
variance in
industry returns.'
He concluded
"the
finding
that
industry effects are
important
supports the classical
focus on
industry-level
analysis as against the
revisionist
tendency
to
downplay
industry differences"
(1985: 349).
Schmalensee's
study
was
innovative and
techni-
cally
sophisticated.
Nevertheless,
there are
diffi-
culties with it traceable to
the use of a
single
year of data.
In
this article
I
perform
a new
variance
components analysis of
the FTC LB
data that corrects this
weakness. I
analyze the
four years
(1974-1977) of
data available
and
'
Industry and
corporate
'effects'
are
(unobserved) com-
ponents of
business-unit
returns
that are
associated
with
membership
in
each
particular
industry
and
corporation. An
'industry return'
is the
calculated
average
return of
the
business-units in that
industry.
Received 16 Februiary
1990
Revised 28 December
1990
-
8/10/2019 How Much Does Industry Matter
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168
R. P. Rumelt
iniclude components for
overall business cycle
effects, stable
and
transient
industry effects, as
well as stable and transient business-unit effects.2
Like Schmalensee, I find that corporate effects
are negligible.
However,
I
draw dramatically
different conclusions about the importance of
industry effects, the existence and importance of
business-level effects,
and the validity of industry-
level analysis.
The most
straightforward
way to review my
analysis
is to start
with
what
Schmalensee's results
left
undecided.
The first
major incertitude is
that,
although 20 percent
of
business-unit returns are
explained by 'industry effects',
we
do not know
how much of this 20 percent is due
to stable
industry
effects rather
than
to
transient
phenom-
ena. For example, in 1975 the return on assets
of the
passenger
automobile
industry
was 6.9
percent
and that of the
corn wet
milling industry
was 35
percent.
But this
difference was far from
stable:
in
the
following
year
the
industries
virtually reversed
positions,
auto's return
rising
to
22.1
percent
and corn wet
milling's
return
falling
to 11.5
percent
(Federal
Trade
Com-
mission,
1975,
1976).
The presence
of
industry-
specific fluctuations
like these adds to the variance
in
industry
returns observed
in
any
one
year.
Thus, Schmalensee's snapshot estimate of the
variance of 'industry effects' is the variance
among
stable
industry
effects
plus
the
variance
of annual fluctuations.
But
the 'classical
focus' is
surely
on the
stable differences among
industries,
rather
than on
random
year-to-year
variations
in
those differences.
My analysis of
the
FTC
LB
data shows
that
stable
industry effects
account
for only
8
percent of
the
variance
in business-unit returns.
Furthermore, only about 40 percent of the
dispersion
in
industry
returns
is
due
to
stable
industry effects.
The
second incertitude concerns
the
variance not
explained by industry
effects.
Schmalensee
noted
(p. 350)
'it is
important
to
recognize
that
80
percent
of
the
variance
in
business-unit
2
'Stable'
industry effects are the (unobserved) time-invariant
components of business-unit returns associated with member-
ship in each industry. 'Stable'
business-unit effects are the
(unobserved) time-invariant components
of business-unit
returns that are not due to industry
or corporate membership.
profitability is unrelated
to
industry
or share
effects. While industry differences
matter,
they
are clearly not all that matters.' If this
intra-
industry
variance is due to
transient
disequilib-
rium
phenomena,
then
the 'classical focus on
industry' would still be a contender; although it
explains only 8 percent
of
the variance,
it would
be the only stable pattern in the data.
But, if a
large portion of the intra-industry variance is due
to stable differences among business-units
within
industries, then the 'classical focus
on industry'
may
be
misplaced.
In this
study,
I find that the majority of this
'residual' variance is due to stable lotng-term
differences among business-units rather
than
to transient phenomena. Using Schmalensee's
sample,
I
find
that stable
business-unit
effects
account for 46 percent of the variance. Indeed,
the stable business-unit
effects
are six times
more
important
than
stable
industry effects
in
explaining
the
dispersion of
returns. Business-
units
differ from
one
another withiin
ndustries
a
great
deal
more
than industries
differ from
one another.
The
conceptual conclusions are straightfor-
ward. The 'classical focus on industry analysis'
is mistaken because these industries are
too
heterogeneous to support
classical
theory.
It
is
also mistaken because the most important
impediments
to
the equilibration
of long-term
rates
of return
are
not associated with industry,
but with the
unique endowments,
positions,
and
strategies
of individual businesses.
The
empirical warning
is
equally striking.
Most
of
the observed
differences
among industry
returns
have
nothing
to do with
long-term
industry effects; they are due to the random
distribution
of
especially high
and
low-performing
business-units across industries.
As will be
shown,
an FTC
industry
return
must
be
at least 15.21
percentage points
above
the
mean to
warrant a
conclusion
(95 percent confidence)
that
the true
stable
industry
effect
is
positive.
Fewer
than
one
in
forty industry returns
are
high enough
to pass
this test.
BACKGROUND
Most
industrial
organization
research
on
business,
corporate,
and
industry profitability
tests
prop-
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How Much Does Industry
Matter?
169
ositions about
the causes of differential
perform-
ance. The primary tradition
made industry
the
unit
of analysis and sought
a link between
industry
concentration (and
entry barriers) and
industry profitability
(usually measured
with
pooled data).3 A second tradition focused on
inter-firm
differences in
performance,
seeking
explanation
first
in
terms
of
firm size and
later
in terms of market
share.4 The early
reaction
against the mainline
tradition viewed
the concen-
tration-profitability
correlation as
an artifact
induced
by the deeper
share-profitability link.5
Finally,
the stochastic
and
efficiency
views explain
both
firm
profitability
and market-share,
and
thus
concentration,
in terms of exogenous
differential
firm efficiencies.6
In contrast to economics, business strategy
research
began with the presumption
of hetero-
geneity
within industries
and has
only recently
come to
grips
with
the question
of
how differences
in efficiency are
sustained in the face
of compe-
tition. Thus,
the earliest case research
informed
by the
'strategy' concept focused
on the different
approaches
to competition adopted
by
firms
within
the
same industry.
As the field matured,
attention
turned towards developing quantitative
measures of
this
diversity7
and,
more
recently,
to its explanation in economic terms.8
Each
of
these
streams of
work
presumes
different
causal mechanisms
and employs differ-
ent units
of
analysis.
Claims about whether
profit-
rate dispersion
reflects collusion,
share-based
market power,
or difficult-to-imitate
resources
are coupled with
claims
that the
more
aggregate
phenomena
are spurious
or counter-claims
that
less
aggregate
phenomena
are
noise.
My
intention
here
is to
suppress
concern
with causal mechan-
isms
and
focus
instead
on the
question
of
locus.
Put differently, my concern here is with the
existence
and relative
importance
of
time, corpo-
rate,
industry,
and business-unit
effects,
however
generated,
on the
total
dispersion
of
reported
rates
of return.
'
See
Weiss'
(1974)
survey of this line of
work.
4See
Scherer's (1980)
review of
prior
work on this
topic.
I
Ravenscraft (1980,
1983) is the best example of this line.
6
See Demsetz (1973)
and Mancke (1974), as well as Lippman
and Rumelt (1982).
7Hatten
and Schendel (1977) provided early contributions;
see McGee and Thomas (1986) for a review
of the strategic
groups literature.
8
See Teece (1982),
Rumelt (1984) and Wernerfelt (1984).
Most
prior
work touching
on the issue
of locus
has done so
tangentially,
rough
measures
of
intra-
industry
dispersions
in
return being
mentioned
in passing
within
a study
on a
different
topic.
Stigler,
for example, studying
the
convergence
of profit rates over time, used the relative
proportions
of
positive-profit
and
loss corpo-
rations
to
construct rough
estimates
of
intra-
industry
variances
in the
rate of
return by
IRS
size class
(his
estimates
unavoidably
confound
inter-period
and
inter-firm
variances).
He
remarked
in passing
(1963:
48) that these
values
were
much larger
than inter-industry
variances,
but
drew no implications.
Fisher
and Hall (1969)
measured
the long-term
(1950-1964)
dispersion
in
rates of
return
about industry
averages
in
order to obtain a measure of risk that could be
regressed
against
industry
profitability.
Although
they
did not remark
the fact,
they
obtained
estimates
that
were
approximately
double their
reported standard
deviation
in
inter-industry
rates
of return.
McEnally (1976),
in an
analysis
of results
obtained by
Conrad
and Plotkin
(1968),
showed
that
industries
with
larger
average
return
tend
also
to have
larger dispersions
in
long-term
inter-
firm rates
of return.
His figures9
show inter-firm
variances that are two to five times as large as
inter-industry
variances.
As
part
of a re-examination
of
the
concentration-profitability
relationship,
Gort
and
Singamsetti
(1976)
were
apparently
the
first
to
explicitly
ask
whether
or not 'the
profit
rates
of
firms cluster
around
industry
means.'
Assigning
firms
to
3-digit
and 4-digit
industries, they
found
to their
surprise
that
the
data failed
to
support
the
hypothesis
that
industries
have different
characteristic
levels
of
profitability.
Furthermore,
they noted that the proportion of the total
variance explained
by
industry
was low
(approximately
11
percent.
adjusted),
did
not
increase
as
they
moved
from
3-digit
to
4-digit
industry
definitions,
and did not increase
as
the
sample
was restricted
to
more
specialized
firms.
9
Conrad
and
Plotkin computed intra-industry variances
directly from deviations about industry averages. Because
they
are
not based on
true
variance components estimation,
their results may overestimate intra-industry variances and
produce substantially upwards biased estimates of inter-
industry variances (although the latter was not of direct
interest to them or to McEnally).
-
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170 R. P. Rumelt
In
an
unpublished
working paper
I
performed
a
variance components
analysis
of
corporate
returns
using
20
years
of
Compustat
data
(Rumelt,
1982).
Although problems
of
industry definition
and
firm diversification prevented definitive
results, here again the intra-industry effect
dominated
the
inter-industry
effect:
the
measured
intra-industry
variance in
long-term
firm
effects
was three
to
ten times as
large as
the
variance
due to industry-specific effects.
Schmalensee's (1985)
study
was the
first
pub-
lished
work
aimed
squarely
at
these issues and
is
the direct ancestor of the
work presented here.
Looking
at the
1975 FTC
LB
data,
Schmalensee
estimated
the
following random-effects model:"'
rik - +
,i
+
Pk
+
qSik
+
Eik
(1)
where
rik
is the
rate
of
return of
corporation
k's
activity
in
industry i,
Sik
is
the
corresponding
market
share,
ai
and
Pk
are industry and
corporate effects
respectively,
and
Eik
is a
disturbance. Schmalensee used
regression
to
conclude that
corporate effects
were non-existent
(Pk
=
0),
and variance
components
estimation
to
show that
industry
effects
were
significant
and
substantial
(Co2
>
0), and that share effects
were
significant but not substantial (-q
>
0 and
uJ2 >
q2or2)
Kessides
(1987)
re-analyzed Schmalensee's
data,
excluding corporations
active in
less than
three industries. He found
statistically significant
corporate
effects in
the restricted
sample, suggest-
ing
that inclusion
of the less-diversified
corpo-
rations had lowered
the
power
of Schmalensee's
test. In
a
related
vein,
Wernerfelt
and
Montgom-
ery
(1988)
estimated
a
model
patterned
after
Schmalensee's, replacing
return on
assets with
Tobin's q and replacing the numerous corporate
dummy
variables with a
single continuous meas-
ure
of
'focus'
(the
inverse of
diversification).
They
found
industry
effects and share
effects of
about
the same
magnitudes
as
Schmalensee
found,
and also
found
a
small,
but
statistically
significant,positive
association
between
corporate
focus
and
performance.
Cubbin
and
Geroski
(1987)
attacked the
question
of the
relative
strength
of
industry
and
firm
effects
with a
different
methodology. Using
"'
I have
altered his notation
to preserve consistency
within
this paper.
a sample
of 217
large U.K. firms, they measured
how much of firms' profitability movements over
time were unique, how much were related to
other firms' movements, and how much were
related to common industry movements. Nearly
one-half of the companies in their sample
exhibited
no common
industry-wide response
to
dynamic
factors.
Hansen
and Wernerfelt
(1989)
studied the
relative importance
of
economic and
organi-
zational
factors
in
explaining
inter-firm
differences
in profit
rates.
They
found that
industry explained
19 percent
of the variance in
profit rates,
but
that organizational characteristics
were
roughly
twice as important.
DATA
Because
the
impetus
for
this
study comes
from
the existence
of
the
unique
FTC LB
data,
and
because
the statistical work
performed
is
fundamentally descriptive rather than hypothesis
testing,
I
break with
convention and discuss
the
data
before introducing
the
model.
Data
on the
operations
of
large U.S. corpo-
rations are
available
from a
variety
of
sources.
However, there is only one source of disaggregate
data on
the
profits
of
corporations by industry-
the
FTC's
Line
of
Business
Program. The
FTC
collected
data on
the domestic
operations
of
large corporations in each
of
261 4-digit FTC
manufacturing industry categories.
Information
on a total
of
588
different
corporations was
collected for the
years 1974-1977;
because of late
additions, deletions, acquisitions,
and
mergers,
the
number
of
corporations reporting
in
any
one
year ranged
from 432 to
471. The average
corporation reported on about 8 business-units.
Schmalensee's
sample
was constructed
by
starting
with Ravenscraft's
(1983)
data-set
of
3186 stable and
meaningful
business-units-those
which were
not
in
miscellaneous
categories
and
which were neither
newly
created nor terminated
during
the
1974-1976 period.
He
then dropped
business-units
in
16
FTC
industries judged
to
be
primarily residual classifications, dropped
business-units
with
sales
less
than 1
percent
of
1975 FTC
industry total sales,
and
excluded one
outlier.
Two data
sets were used in this research,
labeled A and B.
Sample
A
was constructed by
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How
Much
Does
Industry
Matter?
171
starting
with
Schmalensee's sample of 1775
business-units
from
the
1975
file and
appending
data
on
the same business-units from the 1974,
1976,
and
1977 files. After this expansion, one
business-unit
was
judged to have unreliable asset
measures (in 1976-77) and was dropped. Eight
other observations were eliminated because
assets
were reported as zero. Sample A then contained
6932
observations
provided by 457 corporations
on
1774
business-units
operating
in a total of 242
4-digit FTC industries.
Sample
B was constructed
by adding
to
Sample
A the 1070 'small' business-units which had
failed
Schmalensee's size criterion.
After
adjoining
the
1974, 1976
and
1977 data
for
these business-
units,
34 were
excluded
due to
(apparent)
measurement problems: negative or zero assets,
sales-to-assets
ratios over
30,
and
extreme
year-
to-year
variations
in
assets that were
unconnected
to
changes
in
sales. Sample
B
then contained
10,866
observations
provided by
463
corporations
on
2810 business-units operating
in
a total
of 242
4-digit
FTC industries.
The rate of return was taken to
be the ratio
of
profit
before
interest
and taxes to
total
assets,
expressed
as
a
percentage.
In
sample
A
the
average return
was
13.92 and the sample variance
was 279.35. In sample B, the average and
sample
variance
of
return were
13.17 and 410.73
respectively.
The FTC defined
operating income
as
total
revenues
(including transfers
from other
units)
less cost
of
goods sold,
less
selling, advertising,
and
general
and administrative
expenses.
Both
expenses
and assets
were further
divided
into
'traceable'
and
'untraceable'
components,
the
traceable
component being directly
attributable
to
the
line of business and the
untraceable
component being allocated by the reporting
firm
among
lines of
business
using
'reasonable
procedures.'
In
1975,
15.8
percent
of the total
expenses
and 13.6
percent
of total
assets
of
the
average
business-unit were
allocated.
A
number
of scholars have
advanced
arguments
that
accounting
rates
of return are
systematically
biased measures
of
true internal rates of return.
I
I
Whatever the merits of this
position,
the
purpose
of this
study
is
to
partition
the variance in
reported
business-unit rates of return. If
different
industry practices or corporate policies do induce
"
In particular, see Fisher
and McGowan (1983).
systematic biases in reported
returns, the esti-
mated variance components
will reflect these
facts and, therefore, help in
estimating their
importance.
A
VARIANCE COMPONENTS MODEL
In discussing the heterogeneity
within industries
the term
'firm' has an
ambiguity that easily leads
to
confusion.
In
economics a 'firm'
is usually an
autonomous competitive unit within
an industry,
but the term
is also
often used
to indicate a legal
entity: a 'company'
or
'corporation'. Because
most empirical studies are
of
large corporations,
and because most
large corporations
are substan-
tially diversified, legal or corporate 'firms' are,
at
best, amalgams
of individual theoretical
competitive units. Confusion
can arise
if
one
author uses the
term 'firm effects' to indicate
intra-industry dispersion
among theoretical
'firms',
and another author uses the same term
to
denote
differences
among corporations
which
are not
explained by
their
patterns
of
industry
activities.
To reduce the
ambiguity
in what follows
I
avoid the term
'firm'.
Instead,
I use
the term
business-unit to denote that portion of a com-
pany's operations
which
are wholly
contained
within a
single industry.
12
I
use the
term
corporation
to
denote
a
legal company
which
owns and operates one
or
more
business-units.
Thus,
both industries
and
corporations
are
considered to
be
sets
of
business-units.
In this
regard,
note
that
Schmalensee
(1985)
used
the term 'firm-effects' to denote
what I
call
corporate
effects.
Thus,
his first
proposition,
'firm effects
do not
exist' (p. 349)
refers
to what
are here termed corporate effects. Consequently,
as he
noted, finding insignificantcorporate
effects
does
not rule out the
presence
of
substantial
intra-industry
effects.
However,
unless more than
one
year
of data are
analyzed, intra-industry
effects pool
with
the
error and cannot be detected.
Taking
the
unit of
analysis
to be the business-
unit,
assume
that
each business-unit is observed
over
time
and is classified
according
to its
industry
12
It is common practice
among
FTC
LB researchers
to
refer
to a business-unit as an 'LB'. I avoid this usage because
many others
naturally,
but erroneously,
believe that the term
'Line
of Business' refers
to an industry group
rather than to
an individual
business-unit
within a larger firm.
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8/10/2019 How Much Does Industry Matter
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172
R.
P.
Rumelt
membership and
its
corporate ownership. Let
rik,
denote the rate of return
reported
in
time
period
t by the
business-unit owned by corporation
k
and active in
industry
i. A
particular
business-
unit
is
labeled ik, highlighting
the fact that
it
is
simultaneously a member of an industry and a
corporation. Working
with this
notation,
I
posit
the following descriptive model:
rikt
+
-i
+
Pk
+
t
+
8it
+
ik
+
Eikt
(2)
where the
(i
are
industry
effects
(i
=
1,
I the
Pk
are corporate
effects
(k
=
1,
. .
.,
the
-y
are
year
effects
(t
=
1,
. .
.,
L)
the
sit
are industry-year interaction
effects
(l distinct
it
combinations),
and the
4ik
are business-unit
effects (4,, distinct ik combinations). The
Eikt
are
random disturbances
(one
for each of
the
N
observations).
Each
corporation
is
only
active in
a
few
industries,
so
l,