How_Much Does Industry Matter-Rumelt

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Precis of

How Much Does Industry Matter?

Richard P. Rumelt

October 10, 2003

This is an abridged and slightly edited version of the paper: Richard

P. Rumelt, How Much Does Industry Matter?, Strategic ManagementJournal,12 (1991):167-185.

Because competition acts to direct resources towards uses offering the highestreturns, persistently unequal returns mark the presence of either natural orcontrived impediments to resource flows. The study of such impediments is aprincipal concern of industrial organization economics and the dominant unitof analysis in that field has been the industry. The implicit assumption hasbeen that the most important market imperfections arise out of the collectivecircumstances and behavior of firms. However, there is a contrary view: itholds that the most important impediments are not the common property of

collections of firms, but arise instead from the unique endowments and actions ofindividual corporations or business- units. If this is true, then industry may notbe the most useful unit of analysis. Consequently, there should be considerableinterest in the relative sizes of inter-industry and intra-industry dispersions inlong-term profit rates.

In the business strategy field these contrary views are represented by industryanalysis, as first espoused by Porter [1980], and by the resource- based view, asarticulated by Wernerfelt [1984], Rumelt [1984], and Barney [1986].1 Proponentsof industry analysis see business strategy as the art of picking good industries,whereas proponents of the resource-based view place much more importanceon the development and effective utilization of firm-specific difficult-to-imitateresources.

Given this debate, it is surprising that so little research was directly ad-

dressed to this issue until Schmalensees [1985] estimation of the variance com-ponents of profit rates in the FTC Line of Business (LB) data. Schmalenseedecomposed the total variance of rates of return on assets in the 1975 LB datainto industry, corporate, and market-share components. He reported that (1)corporate effects did not exist; (2) market-share effects accounted for a negligi-ble fraction of the variance in business-unit rates of return; (3) industry effectsaccounted for 20 percent of the variance in business-unit returns; (4) industryeffects accounted for at least 75 percent of the variance in industry returns.2 Heconcluded [p. 349] the finding that industry effects are important supports theclassical focus on industry-level analysis as against the revisionist tendency todownplay industry differences.

Schmalensees study was innovative and technically sophisticated. Neverthe-less, there are difficulties with it traceable to the use of a single- year of data.

1Industry analysis admits heterogeneitywithin industriesin the form of strategicgroups.For a survey of the resource-based perspective, see Conner [1991] or Grant [1991].


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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 dataavailable and include components for overall business cycle effects, stable andtransient industry effects, as well as stable and transient business-unit effects.3

Like Schmalensee, I find that corporate effects are negligible. However, I drawdramatically different conclusions about the importance of industry effects, theexistence 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 whatSchmalensees results left undecided. The first major incertitude is that, al-though 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 effectsrather than to transient phenomena. For example, in 1975 the return on assetsof the passenger automobile industry was 6.9 percent and that of the corn wetmilling industry was 35 percent. But this difference was far from stable; in thefollowing year the industries virtually reversed positions, autos return risingto 22.1 percent and corn wet millings return falling to 11.5 percent [FederalTrade Commission, 1975, 1976]. The presence of industry-specific fluctuationslike these adds to the variance in industry returns observed in any one year.Thus, Schmalensees snapshot estimate of the variance of industry effects isthe variance among stable industry effects plus the variance of annual fluc-tuations. But the classical focus is surely on the stable differences amongindustries, 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 re-

turns is due to stable industry effects.

The second incertitude concerns the variance not explained by industry ef-fects. Schmalensee noted [p. 350] it is important to recognize that 80 percentof the variance in business-unit profitability is unrelated to industry or share ef-fects. While industry differences matter, they are clearly not all that matters.If this intra-industry variance is due to transient disequilibrium phenomena,then the classical focus on industry would still be a contender; although itexplains only 8 percent of the variance, it would be the only stable pattern inthe data. But, if a large portion of the intra- industry variance is due to stabledifferences among business-units within industries, then the classical focus onindustry may be misplaced.

In this study, I find that the majority of this residual variance isdue to stable long-term differences among business-units rather than

to transient phenomena. Using Schmalensees 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 within industries a great deal

more than industries differ from one another.

The conceptual conclusions are straightforward. The classical focus onindustry analysis is mistaken because these industries are too heterogeneousto support classical theory. It is also mistaken because the most importantimpediments to the equilibration of long-term rates of return are not associated

with industry, but with the unique endowments, positions, and strategies ofindividual businesses.

3Stable industry effects are the unobserved time-invariant components of business-unit


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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- performingbusiness-units across industries. As will be shown, an FTC industry return mustbe at least 15.21 percentage points above the mean to warrant a conclusion (95percent confidence) that the true stable industry effect is positive. Fewer thanone in forty industry returns are high enough to pass this test.

I Background

All of the lines of research concerning industry and business profitability are con-nected with claims about whether profit-rate dispersion reflects collusion, share-based market power, or difficult-to-imitate resources and are also connected with

claims that more aggregate phenomena are spurious or counter-claims that lessaggregate phenomena are noise. My intention here is to suppress concern withcausalmechanisms and focus instead on the question of locus. Put differently,my concern here is with the existence and relative importance of time, corporate,industry, and business-unit effects, however generated, on the total dispersionof reported rates of return.

Gort and Singamsetti [1976] were apparently the first to explicitly ask whetheror not the profit rates of firms cluster around industry means. Assigning firmsto 3-digit and 4-digit industries, they found to their surprise that the data failedto support the hypothesis that industries have different characteristic levels ofprofitability. Furthermore, they noted that the proportion of the total varianceexplained by industry was low (approximately 11 percent, adjusted), did notincrease as they moved from 3- digit to 4-digit industry definitions, and did not

increase as the sample was restricted to more specialized firms.Schmalensees [1985] study was the first published work aimed squarely at

this issue and is the direct ancestor of the work presented here. Looking atthe 1975 FTC LB data, Schmalensee used regression to conclude that corporateeffects were non-existent and performed a variance components decompositionto measure the relative importances of market share and industry effects. Hefound that both were statistically significant, but that industry effects were verymuch more substantial.

II Data

Data on the operations of large U.S. corporations are available from a vari-

ety of sources. However, there is only one source of disaggregate data on theprofits of corporations by industrythe FTCs Line of Business Program. TheFTC collected data on the domestic operations of large corporations in eachof 261 4-digit FTC manufacturing industry categories. Information on a totalof 588 different corporations was collected for the years 1974-1977; because oflate additions, deletions, acquisitions, and mergers, the number of corporationsreporting in any one year ranged from 432 to 471. The average corporationreported on about 8 business-units.

Schmalensees sample was constructed by starting with Ravenscrafts [1983]data-set of 3186 stable and meaningful business-unitsthose which were not inmiscellaneous categories and which were neither newly created nor terminatedduring the 1974-1976 period. He then dropped business-units in 16 FTC in-dustries judged to be primarily residual classifications, dropped business-units

with sales less than 1 percent of 1975 FTC industry total sales, and excludedone outlier.


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1976, and 1977 files. After this expansion, one business-unit was judged to

have unreliable asset measures (in 1976-77), and was dropped. Eight otherobservations were eliminated because assets were reported as zero. Sample Athen 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 Schmalensees 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 changesin sales. Sample B then contained 10,866 observations provided by 463 corpora-tions 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 andtaxes to total assets, expressed as a percentage. In sample A the average returnwas 13.92 and the sample variance was 279.35. In sample B, the average andsample variance of return were 13.17 and 410.73 respectively.

The FTC defined operating income as total revenues (including transfersfrom other units) less cost of goods sold, less selling, advertising, and generaland administrative expenses. Both expenses and assets were further dividedinto traceable and untraceable components, the traceable component beingdirectly attributable to the line of business and the untraceable componentbeing allocated by the reporting firm among lines of business using reasonableprocedures. In 1975, 15.8 percent of the total expenses and 13.6 percent oftotal assets of the average business- unit were allocated.

III A Variance Components Model

To reduce the ambiguity in what follows I avoid the term firm. Instead, I usethe term business-unitto denote that portion of a companys operations whichare wholly contained within a single industry. I use the term corporation todenote 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 todenote what I call corporate effects. Thus, his first proposition, firm effectsdo not exist [p. 349] refers to what are here termed corporate effects. Conse-quently, as he noted, finding insignificant corporate effects does not rule out thepresence of substantial intra-industry effects. However, unless more than oneyear of data are analyzed, intra-industry effects pool with the error and cannotbe 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 membershipand its corporate ownership. Let rikt denote the rate of return reported in timeperiodtby the business-unit owned by corporation k and active in industryi. Aparticular business-unit is labeledik, highlighting the fact that it is simultane-ously a member of an industry and a corporation. Working with this notation,I posit the following descriptive model:

rikt = +i+k+t+it+ik+ikt, (1)

where the i are industry effects (i = 1, . . . , ), the k are corporate effects(k = 1, . . . , ), the t are year effects (t= 1, . . . , ), the it are industry-year

interaction effects ( distinct it combinations), and the ik are business-uniteffects ( distinct ik combinations). The ikt are random disturbances (one for


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industries as given and is essentially descriptive. In particular, it offers no causal

or structural explanation for profitability differences across industries, years,corporations, or business- unitsit simply posits the existence of differences inreturn associated with these categories.

Were this a fixed-effectsmodel, the usual assumption would be that the iktare random disturbances, drawn independently from a distribution with meanzero and unknown variance 2. In this model I make the additional assumptionthat all of the other effects, like the error term, are realizations of randomprocesses with zero means and constant, but unknown, variances 2 ,

2

,2

,2

,

and 2 .Note that this random effects assumption does not mean that the various

effects are inconstant. Instead, for example, each business-unit effect ikis seenas having been independently generated by a random process with variance2 ,and, having once been set, remaining fixed thereafter.

The random-effects assumption says nothing about why effects differ fromone anothereffects may differ from one another in either fixed-effects or ran-dom effects models. The real substance of the random-effects assumption isthat the differences among effects, whatever their source, are natural, nothaving been controlled or contrived by the research design, and are independentof other effects. That is, the effects in the data represent a randomsample ofthe effects in the population. Independence implies that knowing the value ofa particular ik, for example, is of no help in predicting the values of otherbusiness-unit effects or the values of any industry, corporate, or year effects.An important exception to this assumption, involving an association betweenindustry and corporate effects, is discussed below.

The i represent all persistent industry-specific impacts on observed rates

of return. Differences among the i reflect differing competitive behavior, con-ditions of entry, rates of growth, demand-capacity conditions, differing levelsof risk, differing asset utilization rates, differing accounting practices, and anyother industry-specific impacts on the rate of return. The fundamentally de-scriptive model used here offers no hypotheses as to the nature of these industrydifferencesthe i represent their total collective impact.

Corporate effectskshould arise from differences in the quality of monitoringand control, differences in resource sharing and other types of synergy, anddifferences in accounting policy. Total corporate returns will, of course, also beaffected by the industry memberships of their constituent businesses. However,the unit of analysis here is the business-unit, not the corporation.

The ik represent persistent differences among business-unit returns otherthan those due to industry and corporate membership. That is, they are due to

the presence of business-specific skills, resources, reputations, learning, patents,and other intangible contributions to stable differences among business-unitreturns. Such differences may also arise from persistent errors in the allocationof costs or assets among a corporations business-units. (Note, however, thatcorporate-wide or industry-wide biases in accounting will appear as corporateor industry effects.)

Are the differences among business-unit returns within industries simply dis-equilibrium phenomena? Until recently, rates of return were thought to convergefairly rapidly to normal levels. Consequently, the idea of business-unit effectshad little currency. If they surfaced empirically, they were treated as an auto-correlation problem. However, researchers using more disaggregate data havediscovered that abnormal profit rates do not rapidly fade away; Mueller [1977,1985] and Jacobson [1988] have found them to be extraordinarily persistent.

This consideration, and the fact that the FTC LB data covers only four years,leads to modeling the business-unit effects as fixed. If this assumption is incor-

-


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a more complex autoregressive model. As will be seen, no such autocorrelation

was found in the data studied here.Thet represent year-to-year fluctuations in macroeconomic conditions that

influence all business-units equally. The it represent industry-specific year-to-year fluctuations in return. Finally, there is an ikt associated with eachobservation. Although these effects have been named error, they may equallywell be thought of as year-to-year variations that are specific to each business-unit.

In an important exception to the independence assumption, Schmalensee[1985, p. 344] argued that corporations which are more skillful at operatingbusinesses might also be more skillful at having identified and entered more prof-itable industries, thereby inducing a dependence between the values ofandobserved across business-units. Incorporating this presumption, and maintain-ing elsewhere the assumption of independence, the total variance 2r of returnsmay be decomposed into these variance-covariance components:

2r =2+

2

+2 +

2

+2

+2 + 2C, (2)

whereCis the covariance betweeniandk, given that corporationkis activein industry i (i.e., E(ik) =C if business-unit ik exists, and 0 otherwise).

IV Empirical Results

Table 1 displays the estimated variance-covariance components for the full model.The procedure used does not prohibit negative estimates. The normal practiceis to replace small negative estimates with zero and take large negative estimates

as an indication of specification error. In sample A, 2

=

2.82, and in sampleB, 2C = 0.01, results surely indistinguishable from zero.

The results strongly suggest that 2 = 0 and C = 0 in both samples.Accordingly, the model was re-estimated with these restrictions. The results areshown in Table 2. The restrictions produce only slight changes in the estimatesof the remaining variance components.4

The standard errors of the estimates offer strong evidence that the estimatesof the larger variance components are not overly noisy.5 With regard to thecorporate effects, I conclude that there is no evidence of non-zero corporateeffects in sample A, whereas the inclusion of the smaller business-units (sampleB) provides some evidence of (small) corporate effects.

V Discussion and ImplicationsThe variance in business-unit profitability in sample A (B) may be partitionedapproximately as follows: 8 (4) percent industry effects, 1 (2) percent corporateeffects, 46 (44) percent business-unit effects, 8 (5) percent industry-year effects,and 37 (45) percent residual error.

The fundamental differences between the two samples is that in sample Bthe non-industry variances are substantially larger, making industry relativelyless important. Whereas the industry and industry-year components are com-parable in both samples, in sample B business-unit variance component is 40percent larger than in sample A and the residual error is 80 percent larger.

4Because the sample variances2r

is computed about the sample average, rather than aboutthe true mean , the sum of the variance components is not the sample variance. However, it

is very close in both cases and the difference will be ignored in what follows.5These estimates were obtained by simulation: taking the variance components at their

estimated values, a realization of each effect in the model was generated by a draw from the


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(The corporate variance component is three times as large but its magnitude

is small in both samples.) If the components are expressed as percentages, theopposite pattern emerges: the contribution of industry falls from 8.29 percentin sample A to 4.01 percent in sample B, whereas the percentage contributionof the business- unit component is virtually unchanged.

Table 3 compares the variance partition for sample A with that reported bySchmalensee [1985]. Schmalensee estimated that 19.59 percent of the total vari-ance was due to industry effects. In this study, I find that somewhat less, 16.12percent, is due to all industry effects (stable plus year-to-year fluctuations). Thedifference between the estimates arises mainly because 1975 was an abnormalyearrepeating Schmalensees one-year analysis in 1976 and 1977 yields smallerindustry components. More importantly, I find that only one-half of this vari-ance is due to stable effects. Long-term industry effects account for only 8.28percent of the observed variance among sample A business-unit returns.

Turning to the intra-industry variance, Schmalensee reported that 80.41 per-cent of the variance was unexplained by industry; the comparable figure in thisstudy is 83.08 percent. However, my partition of this intra- industry varianceinto stable and year-to-year components reveals that over one-half is due to sta-ble business-unit effects. Indeed, the variance among stable business-unit effectsis six times as large as the variance among stable industry effectsbusiness-unitsdiffer from one another within industries much more than industries differ fromone another.

Despite the fact that this is a descriptive study, some strong general resultscan be reported:

1. There are significant business-unit effects in U.S. manufacturing activitiesthat strongly outweigh industry and corporate membership as predictors

of profitability. The variance among business-unit effects is much largerthan the variance among industry effects (six times larger in sample Aand eleven times larger in sample B).

2. Corporate effects, although present in sample B, are not important inexplaining the dispersion in observed rates of return among business-units.

Business-Unit Effects

The large observed variance component for business-unit effects overshadows theother variance components. Although this model cannot reveal the sources ofthis dispersion, some insight can be gained by examining Schmalensees resultson the importance of market share. His study of sample A for 1975 measured

a variance component due to share of 2.2. This amounts to 1.7 percent of thebusiness-unit variance component estimated for sample A data in this study.Hence, it seems safe to conclude that only a very small part of the large business-unit effects can be associated with differences in the relative sizes of business-units.

The large business-unit effects indicate that there is more intra-industry het-erogeneity than has been commonly recognized. Whereas economists are quickto refer to inframarginal rentswhen this issue arises, the unspoken presumptionis that these effects are small, or related to scale. The results are otherwise. Thebusiness-unit effects are large and owe only a small fraction of their strengthto market share. Some portion of these effects may, of course, be due to mea-surement biases. But the most obvious sources of bias, differences in industryaccounting and differences in corporate policy, should appear as industry or

corporate effects.The presence of strong business-unit effects is consonant with the presump-


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size, [p. 250] ideas in this area have evolved in the direction of recognizing

increasingly disaggregate sources of resource immobility or specificity.6 Accord-ing to this view, product-specific reputation, team-specific learning, a varietyof first- mover advantages, causal ambiguity that limits effective imitation, andother special conditions permit equilibria in which competitors earn dramati-cally different rates of return. Although this study cannot discriminate amongthe various theories regarding the sources of intra- industry heterogeneity, itnecessarily gives broad support to this class of theory and should encouragefurther work in this vein.

What Do Industry Returns Measure?

If business-units within industries have large and persistent differences in re-turn, it becomes necessary to ask what the industry returns measures used in

many industrial organization studies actually represent. That is, when indus-tries exhibit differing levels of overall return, to what extent are such differencesdue to systematic industry effects and to what extent are such differences theveiled result of differences in individual business-unit performance? The actualvariance among average industry returns was 61.9 in sample A and 58.1 in sam-ple B. Using the variance components model, this variance can be broken intoits constituent parts. The results are shown in Table 4. This partition revealsthat only about forty percent of the variance among industry returns is actuallydue to stable industry effects. In sample A an additional forty percent is dueto business-unit effects which randomly combine to affect industry averages; insample B the corresponding proportion is close to one-half. The remaining vari-ance (one-fifth in sample A, one-eighth in sample B) is due to various industry-year and business-unit-year fluctuations. (This portion would be smaller hadthe averages been taken over more than four years.) Because only forty percentof the variance in industry returns is due to industry effects, industry returnsare noisy estimates of the true industry effects.

How large does an industry return have to be in order to justify a conclusionthat the corresponding industry effect is positive? Additional analysis showsthat industry returns are such noisy measures of industry effects that only aboutsix of the 242 FTC industries studied could be judged (95 percent confidence)to have positive industry effects.

Corporate Effects

Turning to the issue of corporate effects, corporations exhibit little or no (dif-

ferential) ability to affect business-unit returns. It is not that corporate effectsdo not existit appears that 2 > 0 in sample Bbut rather that corporateeffects are astonishingly small. Put differently, if one business- unit within acorporation is very profitable, there is little reason to expect that any of thecorporations other business-units will be performing at other than the normsset by industry, year, and industry-year effects.

Corporate returns will, of course, differ from one another for reasons otherthan corporate effects. Corporate returns will differ because of their differingpatterns of participation in industries. More importantly, corporate returns willdiffer because their portfolios of business-units differ. But the results indicatethat the dispersion among corporate returns can be fully explained by the dis-persions of industry and business-unit effects; there is no evidence of synergy.

Given the extent of the literature on corporate strategy, corporate culture,

the number of consulting firms that specialize in corporate management, andthe focus on senior corporate leaders in the business world, it is surprising tofind only vanishingly small corporate effects in these data. This result, first


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Implications

To the extent that accounting returns measure the presence of economic rents,the results obtained here imply that by far the most important sources of rentsin U.S. manufacturing businesses are due to resources or market positions thatare specific to particular business-units rather than to corporate resources or tomembership in an industry. Put simply, business-units within industries differfrom one another a great deal more than industries differ from one another.

Empirical results are rarely definitive and there are a number of issues leftunresolved in this study. It may be, for example, that the FTC 4-digit industriesare simply too broad to reveal the true strength of industry effects. Or, itmay be that the assumption of a constant 2 is unjustified, some industriesbeing much more heterogeneous than others. Nevertheless, most empirical workwithin the industrial organization paradigm has been conducted on data at this

or higher levels of aggregation and persistent intra-industry heterogeneity hasbeen generally assumed away rather than measured. Consequently, it seemsworthwhile to sharply and clearly state the implications of this study:

1. The neoclassical model of industry as composed of firms that are homoge-neous (but for scale) does not describe 4-digit industries: these data showreal industries to be extremely heterogeneous.

2. The simple revisionist model in which business-units differ in size due todifferences in manufacturing efficiency is incorrectonly a small portion ofthe large observed variance among business-unit effects can be associatedwith differences in relative size.

3. Theoretical or statistical explanations of business-unit performance that

use industry as the unit of analysis can, at best, explain only about eightpercent of the observed dispersion among business-unit profit rates.

4. Theoretical or statistical explanations of business-unit performance thatuse the corporation as the unit of analysis can, at best, explain only abouttwo percent of the observed dispersion among business-unit profit rates.

5. Theoretical or statistical work seeking to explain an important portion ofthe observed dispersion in business-unit profit rates must use the business-unit (or even less aggregate entities) as the unit of analysis and must focuson sources of heterogeneity within industries other than relative size.

References

Barney, Jay B., Strategic Factor Markets: Expectations, Luck, and BusinessStrategy,Management Science, October 1986,32, pp. 1231-1241.Caves, R. E. and M. E. Porter, From Entry Barriers to Mobility Barriers: Con-

jectural Decisions and Contrived Deterrence to New Competition, QuarterlyJournal of Economics, May 1977,91, 241-61.Conner, Kathleen R., A Historical Comparison of Resource-Based Theory andFive Schools of Thought Within Industrial Organization Economics: Do WeHave a New Theory of the Firm? Journal of Management, 1991, 17, pp.121-54.Federal Trade Commission,Statistical Report: Annual Line of Business Reports,1975, 1976. Published in 1981 and 1982.Gort, Michael and Rao Singamsetti, Concentration and Profit Rates: New Ev-idence on an Old Issue,Occasional Papers of the National Bureau of EconomicResearch: Explorations in Economic Research, Winter 1976,3, 1-20.Grant, Robert M. The Resource-Based Theory of Competitive Advantage.


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Mueller, Dennis C., The Persistence of Profits above the Norm, Economica,

November 1977,44, 369-80.Mueller, Dennis C., Profits in the Long Run, Cambridge University Press, 1985.Porter, Michael E. Competitive Strategies: Techniques for Analyzing Industriesand Competitors. New York: Free Press, 1980.Ravenscraft, David J., Structure-Profit Relationships at the Line of Businessand Industry Level, Review of economics and Statistics, February 1983, 65,22-31.Rumelt, R. P., Toward a Strategic Theory of the Firm, in Lamb, Robert B.,ed. Competitive Strategic Management, Englewood Cliffs, N.J.: Prentice-Hall,1984, 557-570.Rumelt, R. P., Theory, Strategy, and Entrepreneurship, in David Teece (ed.)The Competitive Challenge: Strategies for Industrial Innovation and Renewal,

Cambridge, Mass.: Ballinger, 1987, 137-158.Schmalensee, Richard, Do Markets Differ Much? American Economic Review,June 1985, 75, 341-51.Searle, S. R., Linear Models, New York: Wiley & Sons, 1971.Wernerfelt, Birger, A Resource-Based View of the Firm, Strategic Manage-ment Journal, April-June 1984,5, 171-80.


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Table 1

Variance-Covariance Components Estimates: Full Model

Component Sample A Sample BYear -2.82 0.20Industry-Year 24.74 21.89Industry 20.49 16.62Corporation 0.19 6.75Business-unit 131.69 181.492C 2.13 -0.01Error 102.51 184.06

Table 2Variance Components Estimates: Restricted Model

(Year Effects and C Removed)

Sample A Sample B

Std. Std.Component Est. Error Percent Est. Error PercentIndustry-Year 21.92 2.04 7.84 22.09 2.31 5.38Industry 23.26 4.72 8.32 16.55 4.26 4.03Corporation 2.25 3.84 0.80 6.74 3.31 1.64Business-unit 129.63 6.91 46.37 1 81.50 7.04 44.17Error 102.51 2.18 36.87 184.06 3.04 44.79Total 279.56 100.00 410.95 100.00


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Table 3

Comparison with Schmalensees Results(Percentage of Total Variance by Source)

This Study SchmalenseeSource Sample A [1985]Corporate 0.80 (x)

Industry 8.28 (x)Industry-Year 7.84 (x)All Industry 16.12 19.46

Share (x) 0.63Share-Industry Covariance (x) -0.62

Business-Unit 46.38 (x)Business-Unit-Year 36.70 (x)All Intra-Industry 83.08 80.54

Total 100.00 100.00

(x) Component not estimated.

Table 4Estimated Components of Sample Variance Among

Industry Average Returns

Sample A Sample BSource Component Percent Component Percent

Industry 23.3 39.3 16.6 29.8Industry*Year 5.5 9.3 5.6 10.0Business-Unit 25.3 42.7 26.6 47.7Error 5.2 8.7 7.0 12.5Total 59.27 100.0 55.8 100.0

Actual Sample Variance 61.9 58.1

How_Much Does Industry Matter-Rumelt

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