how much does industry matter

8/10/2019 How Much Does Industry Matter

1/20

John Wiley & Sons http://www.jstor.org/stable/2486591.

Your use of the JSTOR archive indicates your acceptance of JSTOR's Terms and Conditions of Use, available at.http://www.jstor.org/page/info/about/policies/terms.jsp. JSTOR's Terms and Conditions of Use provides, in part, that unlessyou have obtained prior permission, you may not download an entire issue of a journal or multiple copies of articles, and you

may use content in the JSTOR archive only for your personal, non-commercial use.

Please contact the publisher regarding any further use of this work. Publisher contact information may be obtained at .http://www.jstor.org/action/showPublisher?publisherCode=jwiley..

Each copy of any part of a JSTOR transmission must contain the same copyright notice that appears on the screen or printed

page of such transmission.

JSTOR is a not-for-profit service that helps scholars, researchers, and students discover, use, and build upon a wide range of

content in a trusted digital archive. We use information technology and tools to increase productivity and facilitate new forms

of scholarship. For more information about JSTOR, please contact [email protected].

John Wiley & Sonsis collaborating with JSTOR to digitize, preserve and extend access to Strategic

Management Journal.

http://www.jstor.org
http://www.jstor.org/action/showPublisher?publisherCode=jwileyhttp://www.jstor.org/stable/2486591?origin=JSTOR-pdfhttp://www.jstor.org/page/info/about/policies/terms.jsphttp://www.jstor.org/action/showPublisher?publisherCode=jwileyhttp://www.jstor.org/action/showPublisher?publisherCode=jwileyhttp://www.jstor.org/page/info/about/policies/terms.jsphttp://www.jstor.org/stable/2486591?origin=JSTOR-pdfhttp://www.jstor.org/action/showPublisher?publisherCode=jwiley


2/20

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


3/20

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-


4/20

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).


5/20

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


6/20

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.


7/20

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,

how much does industry matter

Documents