02 McGahan, A y M E Porter (2002)

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What Do We Know about Variance in Accounting Profitability?
Author(s): Anita M. McGahan and Michael E. Porter
Source: Management Science, Vol. 48, No. 7 (Jul., 2002), pp. 834-851
Published by: INFORMS
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 What Do We Know About Variance
 in Accounting Profitability?
 Anita M. McGahan * Michael E. Porter
 Boston University School of Management, 595 Commonwealth Avenue,
 Boston, Massachusetts 02215
 Harvard Business School, Soldiers Field, Boston, Massachusetts 02163
 amcgahan@bu.edu * mporter@hbs.edu
 In this paper, we analyze the variance of accounting profitability among a broad cross-
 section of firms in the American economy from 1981 to 1994. The purpose of the analysis is
 to identify the importance of year, industry, corporate-parent, and business-specific effects on
 accounting profitability among operating businesses across sectors. The findings indicate that
 industry and corporate-parent effects are important and related to one another. As expected,
 business-specific effects, which arise from competitive positioning and other factors, have a
 large influence on performance. The analysis reconciles the results of previous studies by
 exploring differences in method and data. We also identify the broad contributions and limi-
 tations of the research, and suggest avenues for further study. New approaches are necessary
 to generate significant insights about the relationships between industry, corporate-parent,
 and business influences on firm profitability.
 (Performance; Sustainability; Industry Structure; Corporate Strategy)
 1. Introduction
 Researchers in the economics and strategy fields have
 long been interested in understanding the determi-
 nants of firm profitability. During the 1960s and 1970s,
 a large empirical literature in industrial organiza-
 tion employed cross-sectional regression analysis to
 explain firm performance based on industry character-
 istics, including seller concentration, advertising, and
 R&D intensity. The aim was to explore the relationship
 between structural entry barriers, tacit collusion, and
 industry performance. These studies were challenged
 in the 1980s because they tended to assume that indus-
 try structure is fixed independently of firm perfor-
 mance. In a review of the literature, Schmalensee
 (1989) reinterpreted the structure-performance find-
 ings as descriptive of empirical regularities rather than
 as conclusive evidence of causal relationships. Viewed
 in this way, the literature of the 1960s and 1970s on
 firm performance generated important insights about
 the variation in accounting profitability.
 MANAGEMENT SCIENCE ? 2002 INFORMS
 Vol. 48, No. 7, July 2002 pp. 834-851
 Partly in response to the limits of the early research,
 a new style of work emerged in the 1980s. This new
 approach, pioneered by Schmalensee (1985), decom-
 posed the variance in profitability across business seg-
 ments into components associated with year, industry,
 the corporate-parent, and business-specific effects.1
 Over the past dozen years, several studies in this
 research stream have explored profit variance (Rumelt
 1991, Roquebert et al. 1996, McGahan and Porter
 1 During the mid-1980s, questions were raised about the informa-
 tion contained in accounting returns about real economic activ-
 ity. The classic expression of concern by Fisher and McGowan
 (1983) emphasized that accounting returns do not capture the net
 present value of all returns on investment. A famous debate, which
 included comments by Horowitz (1984), Long and Ravenscraft
 (1984), Martin (1984), Van Breda (1984), and a reply by Fisher (1984),
 raised questions about whether or not accounting rates of return
 reflect monopoly rents and whether or not booked assets are fairly
 depreciated. In this study, we investigate the importance of year,
 industry, business-specific, and corporate-parent effects on account-
 ing profitability, but do not address the sources of the effects.
 0025-1909/02/4807/0834$5.00
 1526-5501 electronic ISSN
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 McGAHAN AND PORTER
 Variance in Accounting Profitability
 1997a), yielding somewhat different conclusions. The
 purpose of these studies was to describe the impor-
 tance of industry, corporate-parent, and business
 influences on profitability. Despite the advances asso-
 ciated with the new approach, the generality of results
 has been limited by breadth of data and statistical
 limitations.
 This first objective of this study is to reconcile results
 from various studies in the recent literature on vari-
 ation in profitability. A second objective is to obtain
 new results based on a broad dataset and methods
 that are less restrictive than those used in the previous
 studies. The analysis relies on a dataset that includes
 all publicly traded firms in the American economy
 over an extended period. The methods employed in
 this study also drop certain restrictive assumptions
 about the underlying economic processes that had
 been incorporated in the methodologies of the earlier
 studies.
 2. Studies on the Decomposition
 of Variance in Business-Specific
 Accounting Profitability
 The studies of variance in business-specific account-
 ing profitability were developed in response to limita-
 tions in the predecessor research on structural models.
 The structural models incorporated the assumption
 that industry structure shaped firm conduct, which
 in turn drove firm performance. This assumption
 implicitly ruled out alternative directions of causal-
 ity. No provisions were made for the possibility that
 conduct was driven by performance, or that conduct
 influenced industry structure. By the mid-1980s, a
 "new empirical industrial organization" gained favor
 over the classical "structure/conduct/performance"
 models (Bresnahan 1989). The new approach empha-
 sized detailed industry studies that could account for
 feedback between performance, conduct, and struc-
 ture.2
 Separately, a small group of authors continued the
 large-scale statistical analyses, but with methodolo-
 gies that did not impute causal relationships between
 2For example, principal-agent studies examined the influence of
 managerial conduct on performance simultaneously with the influ-
 ence of performance on managerial conduct.
 industry structure, business conduct, and firm per-
 formance (Schmalensee 1985, Rumelt 1991, Werner-
 felt and Montgomery 1988). These studies sought to
 document relationships without stipulating causal-
 ity. The advantage was the robustness of the find-
 ings. The variance-decomposition literature was an
 important complement to studies in the "new empiri-
 cal industrial organization" because it established the
 general importance of industry, corporate, and busi-
 ness effects on firm performance. A disadvantage of
 the approach was an inherent limitation in the power
 of the claims about the drivers of performance.
 Table 1 summarizes studies that decompose the
 variance of accounting profitability.3 The first col-
 umn of Table 1 reproduces Schmalensee's seminal
 results. Schmalensee (1985) used the 1975 FTC Line-
 of-BusinessSurvey to study the effects of indus-
 try on variance in business-unit accounting profit
 in manufacturing industries. Because his data cov-
 ered a single year, he could not identify either year
 or business-specific effects on variance in profitabil-
 ity. Instead, Schmalensee (1985) tested for evidence
 of business-specific differences through a single,
 exogenous measure of market share. His analy-
 sis also included corporate-parent effects, which he
 called "firm effects." Schmalensee found that industry
 effects accounted for about 20% of variance, market-
 share effects accounted for less than 1% of variance,
 and corporate-parent effects did not significantly con-
 tribute to variance. He concluded that managerial
 influences were not important compared to differ-
 ences in industry structure.
 The second major advance in recent research on the
 components of accounting profit was Rumelt (1991).
 By studying the business-unit accounting profit of
 the manufacturing firms covered in the FTC Survey
 for more than one year (i.e., for 1974-1977), Rumelt
 reported on the amount of variance that persisted
 over the entire period but that could not be attributed
 3 The studies also generated interest in the decomposition of other
 measures of firm performance, including Tobin's q (Wernerfelt and
 Montgomery 1988, McGahan 1999a) and market share (Chang and
 Singh 2000). Mauri and Michaels (1997) supplement findings in the
 research stream by decomposing variance in advertising and R&D
 ratios across undiversified manufacturers. Only studies on account-
 ing profit are reviewed here.
 MANAGEMENT SCIENCE/Vol. 48, No. 7, July 2002 835
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 McGAHAN AND PORTER
 Variance in Accounting Profitability
 Table 1 Results on the Decomposition of Variance in Business-Specific Accounting Profit
 (1) (2) (3) (4) (5) (6)
 Schmalenseea Rumeltb Rumeltc Roquebert et al.d McGahan & Portere McGahan & Porterf
 Method COVg COV Nested ANOVAh COV COV Nested ANOVA
 Source of data FTC FTC FTC Compustat Compustat Compustat
 Years covered 1975 1974-1977 1974-1977 1985-1991 1981-1994 1981-1994
 Sectoral coverage Manuf. Manuf. Manuf. Manuf. All All
 No. of observations 1,775 6,931 6,931 16,596 58,132 58,132
 Year N/A N/A 0.03 N/A 2.39 0.3
 Industry 19.59 8.32 17.9 10.2 18.68 9.4
 Corporate-parent N/A 0.80 14.8 17.9 4.33 9.1
 Segment-specific N/A N/A N/A 37.1 31.71 35.1
 Business-unit N/A 46.37 33.9 N/A N/A N/A
 Industry-market-share covariance -0.62 N/A N/A N/A N/A N/A
 Corporate-parent-industry covariance N/A N/A N/A N/A -5.51 N/A
 Market share 0.62 N/A N/A N/A N/A N/A
 Industry-year N/A 7.84 9.8 2.3 N/A N/A
 Model 19.59 63.33 76.5 68.0 51.60 66.8'
 Error 80.41 36.87 23.5 32.0 48.40 33.2
 Total 100.00 100.00 100.0 100.00 100.00 100.00
 Notes.
 aResults from Schmalensee (1985, Table 1, p. 348).
 bResults from Rumelt (1991, Dataset A as reported in Table 3, p. 178).
 CResults from Rumelt (1991, Dataset A as reported in Table 2, Panel (a), p. 177).
 dResults from Roquebert et al. (1996, as reported in Table 4, p. 661).
 eResults from McGahan and Porter (1997a, as reported in Table 2).
 'Results from McGahan and Porter (1997a, as reported in Table 5, Panel (a)).
 gComponents-of-variance technique.
 hNested analysis-of-variance technique.
 'Includes 12.9% of variance explained by a null model accounting for serial correlation in errors.
 to year, industry, or corporate-parent effects. This sta-
 ble portion of variance, attributable to "business-unit
 effects," captured the entire effect of competitive-
 positioning differences, and was more general than
 the market-share measure used by Schmalensee
 (1985). (Rumelt's (1991) fixed business-unit effects
 captured not only competitive-positioning effects but
 also any other persistent source of idiosyncratic dif-
 ferences.) A central purpose of Rumelt's (1991) paper
 was to refute Schmalensee's (1985) suggestion about
 management by showing that business-unit effects
 were more important than industry effects.
 Columns (2) and (3) of Table 1 show the results
 of Rumelt's (1991) analyses. Using both components-
 of-variance and ANOVA techniques, Rumelt found
 that business-unit effects accounted for substantially
 more of variance than either industry or corporate-
 parent effects. Unlike Schmalensee (1985), Rumelt
 (1991) found that corporate-parent effects contributed
 to variance, although Rumelt (1991) concluded that
 the corporate-parent effect was small based on
 his components-of-variance result. Rumelt's (1991)
 ANOVA, reproduced in Column (3), received little
 attention at first partly because the discussion in
 Rumelt's (1991) paper highlighted the methodology
 that was directly comparable to Schmalensee (1985):
 the components-of-variance approach.
 Schmalensee's (1985) and Rumelt's (1991) studies
 raised several questions about data and method. First,
 some researchers expressed concern about the gen-
 erality of the results because the period of study-
 the mid-1970s-coincided with unusual macroeco-
 nomic conditions that might have affected industry,
 corporate-parent, and business-specific performance.
 MANAGEMENT SCIENCE/Vol. 48, No. 7, July 2002 836
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 McGAHAN AND PORTER
 Variance in Accounting Profitability
 Second, Schmalensee (1985) reported that industry
 effects accounted for about 20% of variance in profits,
 whereas Rumelt (1991) reported that stable indus-
 try effects accounted for only about 8% of variance.
 Rumelt's (1991) specification also included a transient
 industry effect. The difference in result and in specifi-
 cation fueled some controversy about the true impor-
 tance of industry to differences in accounting profit,
 although Rumelt's (1991) specification of industry
 effects received less attention than merited by the
 importance of the results. Third, the absence of impor-
 tant corporate-parent effects suggested that corporate
 headquarters might not contribute to the performance
 of business units. Concern about the result was com-
 pounded by confusion about definition and measure-
 ment. Fourth, the results covered only manufactur-
 ers, which raised questions about the generality of the
 results to other sectors. Last, concerns were raised that
 transient corporate-parent or business-specific effects
 might be important,4 that serial correlation may have
 complicated the results, and that the reported results
 may have been sensitive to outliers and thus not rep-
 resentative (see Brush and Bromiley 1997).5
 Recent studies by Roquebert et al. (1996) and
 McGahan and Porter (1997a) addressed a number of
 the questions raised by the previous studies. Both
 studies relied on a much broader dataset-the Com-
 pustat Business-Segment Reports. Compustat con-
 tains data on the accounting profitability of corpo-
 rations that are publicly traded on United States
 exchanges by four-digit SIC category. Each four-digit
 category for a firm is called a "business segment."6
 Roquebert et al. (1996) used the Compustat reports
 for the 7-year period from 1985-1991. McGahan and
 Porter (1997a) used the reports for the 14-year period
 from 1981-1994.
 4Rumelt's (1991) specification had included a transient industry
 effect, but not a transient corporate-parent or business-specific
 effect. Thus, transient industry effects may have acted as proxies
 for transient corporate-parent or business-specific effects.
 5Brush and Bromiley (1997) used simulation analysis to show
 that the decomposition of variance is sensitive to the presence of
 outliers.
 6 Analysis suggests that business segments are considerably larger
 than operating business units on average, although the data are also
 substantially disaggregated from the corporate level for diversified
 firms (see McGahanand Porter 1997a).
 These studies confirmed the presence of significant
 industry effects in the manufacturing sector during
 the 1980s and early 1990s, showing that earlier results
 had not been an artifact of unusual macroeconomic
 conditions in the mid-1970s. Roquebert et al. (1996)
 argued that since the sum of Rumelt's (1991) stable
 and transient industry effects (from his components-
 of-variance analysis) are comparable to the indus-
 try effects in Schmalensee (1985), there was no dis-
 crepancy in the importance of industry between
 Schmalensee (1985) and Rumelt (1991).7 Thus, the
 studies largely resolved questions about the impor-
 tance of industry to manufacturers.
 Both Roquebert et al. (1996) and McGahan and
 Porter (1997a) found evidence of corporate-parent
 effects among the business segments in the Compu-
 stat data. Using components-of-variance techniques,
 McGahan and Porter (1997a) found that corporate-
 parent effects accounted for 4% of the variance in
 profits over the 14-year period covered in the study.
 Segments of both diversified and undiversified firms
 were included in the analysis, with the corporate-
 parent effect held to zero for single-segment firms.
 The study by Roquebert et al. (1996) was restricted to
 manufacturing. The authors reported that corporate-
 parent effects accounted for 17.9% of variance among
 manufacturing segments over the 7-year period from
 1985-1991. The authors attributed the difference with
 Rumelt (1991) to shifts in opportunities for corpo-
 rate parents between the mid-1970s and the mid-
 1980s. However, the Roquebert et al. (1996) data also
 included only those corporations reporting on at least
 two segments. Estimates of the influence of indus-
 try, for example, were based on the performance of
 only diversified industry members.8 Only about half
 7 Indeed, Rumelt (1991) had also made this point. Roquebert et al.
 (1996) also discuss a supplemental approach used by Schmalensee
 (1985) to study variance in industry-average profitability.
 8 Table 2 in Roquebert et al. (1996) suggests that the authors did not
 screen their dataset to exclude four-digit SIC categories identified
 as "not elsewhere classified," and "miscellaneous." Because these
 categories typically contain segments that are not related in busi-
 ness practice, there is no economic or strategic basis for an industry
 effect. Thus, the inclusion of these four-digit SIC categories may
 diminish the importance of industry in their results independently
 of any distortion associated with the inclusion of only diversified
 firms.
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 McGAHAN AND PORTER
 Variance in Accounting Profitability
 of industry members in the Compustat Business-
 Segment data belong to diversified corporations, and
 diversified companies tend to perform differently on
 average than nondiversified competitors. This choice
 to exclude data on a large number of companies sig-
 nificantly distorted the Roquebert et al. (1996) results
 on both industry and corporate-parent effects.
 McGahan and Porter (1997a) also expanded the
 analysis beyond manufacturing, and included all sec-
 tors of the economy except government and finance.
 The results indicated strong differences in the impor-
 tance of effects by sector. In manufacturing, the influ-
 ence of industry and corporate-parent effects was
 lower than in any other sector, and, of business-
 specific effects, was higher.
 Finally, McGahan and Porter (1997a) also addressed
 several methodological issues. Rumelt's (1991) model
 included a term representing industry-year interac-
 tions. McGahan and Porter (1997a) argued that the
 term might proxy for interactions in the other types of
 effects and modeled a general first-order autoregres-
 sive process on the error term. By running Rumelt's
 (1991) model on manufacturing data, McGahan and
 Porter (1997a) were able to reconcile results of the two
 studies. With regard to method, McGahan and Porter
 (1997a) decomposed variance using both components-
 of-variance and nested ANOVA techniques. The core
 result, obtained using the COV method, indicated that
 year, industry, corporate-parent, and business-specific
 effects accounted for 2%, 19%, 4%, and 32%, respec-
 tively, of variance. The nested ANOVA indicated
 that the effects explained 0.3%, 7%-9%, 9%-12%, and
 35%, respectively, of variance. These differences sug-
 gested the need for further research on the restrictive
 assumptions inherent in the COV and nested ANOVA
 methods.
 Seen as a whole, this body of research left a num-
 ber of questions unanswered. First, the recent stud-
 ies compound methodological questions because of
 the difference between COV and nested ANOVA
 results. The components-of-variance approach, dis-
 cussed extensively in both previous papers, does
 not generate estimates of each effect, but uses sum-
 mary statistics to assess the influence of variance
 in year, industry, business-specific, and corporate-
 parent effects. The approach requires the assump-
 tion that each of the effects on a particular business
 is drawn independently of the others. Similarly, the
 nested ANOVA approach does not model covari-
 ance between effects. The strong covariance between
 industry and corporate-parent effects reported in
 McGahan and Porter (1997a) suggests flaws in the
 assumptions required under both approaches.
 Here, we employ a simultaneous ANOVA imple-
 mented using regression analysis. The simultaneous
 ANOVA allows for a full set of covariance effects but
 does not assume randomness in the model errors. In
 particular, we have not imposed an assumption that
 the dispersion of business-segment effects around an
 industry mean is unrelated to the industry mean.
 In previous studies, the methods either assumed
 that the effects and their covariances were randomly
 generated or imputed all of the covariance either
 to the industry or to the corporate-parent effects.
 In some of the papers, the imputation of covari-
 ance to industry or corporate-parent effects was an
 artifact of reporting: Nested-ANOVA tables partly
 obscure the relative influence of each set of effects on
 the total variance. The covariance between industry
 and corporate-parent effects is potentially important
 because, for example, a diversified firm may be more
 likely to expand into particular types of industries.
 Thus, the main difference between our model and
 those in previous studies is that it reveals the incre-
 mental contribution to explanatory power of the cor-
 porate and industry effects while allowing for rela-
 tionships in the processes that generate the effects.
 This is possible because we estimate each of our mod-
 els separately using fixed-effects techniques that allow
 for covariance. The model allows us to address the
 implications of Brush and Bromiley's (1997) study
 about the sensitivity to outliers by reporting the dis-
 tribution of the dependent variable and the results of
 alternative specifications to serial correlation.9
 9 Note that we report the rate of serial correlation in the unmod-
 elled error term in our report of results on each specification. We
 do not impose any structure on the process that generates serial
 correlation. In particular, we do not model interactions between
 the various classes of effects directly. Our study does not address
 one criticism in Brush and Bromiley (1997). Brush and Bromiley
 (1997) argue that the decomposition of variance-through any
 technique-involves dealing with the sum of squared differences
 and thus involves "nonlinear" treatment of outliers. We take the
 MANAGEMENT SCIENCE/Vol. 48, No. 7, July 2002 838
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 McGAHAN AND PORTER
 Variance in Accounting ProfitabilitySecond, McGahan and Porter (1997a) found that
 the importance of effects in manufacturing did not
 broadly represent the importance in other sectors of
 the economy. In this study, we replicate the simultane-
 ous ANOVA for manufacturers to test the robustness
 of this finding. This analysis also controls for part of
 the differences with Roquebert et al. (1996), who stud-
 ied only manufacturers. The result is important for
 distinguishing whether broad conclusions from the
 studies on manufacturers are relevant for companies
 in other sectors.
 Finally, Roquebert et al. (1996) and McGahan and
 Porter (1997a) came to radically different conclusions
 about the importance of corporate-parent effects to
 variance in profitability. In this paper, we address the
 difference by analyzing how the inclusion of only
 diversified firms in Roquebert et al.'s (1996) analy-
 sis affected results.10 A replication for the diversified
 firms in manufacturing largely reconciles McGahan
 and Porter (1997a) with Roquebert et al. (1996). The
 comparison nonetheless exposes deeper limitations in
 the entire line of work about the corporate-parent
 effect.
 3. The Model and Data
 The analysis relies on the following model of
 business-specific accounting profit:
 ri,k, t = A + yt + i + k + i, k + i, k, t (1)
 In this equation, ri k, t is the accounting profit in year
 t of corporate-parent k's business unit in industry i.
 Profit is the ratio of operating income to identifiable
 assets in percent. The right-hand-side variables are ,u,
 the economic average profit over the entire period; yt,
 the premium associated with year t; ai, the premium
 associated with participation in industry i; /k, the
 premium conferred by membership in a diversified
 view that variance is well established as an important statistic for
 describing a population, and that this type of research is comple-
 mentary to other types of theoretical and empirical work on firm
 performance.
 10 In an earlier study based on a COV analysis on the same sec-
 toral data (McGahan and Porter 1997a), we argued that the indus-
 try, corporate-parent, and business-specific effects in manufacturing
 firms differed from effects on performance in other sectors.
 corporate-parent k in year t; i, k, the premium asso-
 ciated with business segment i, k given the presence
 of all of the other effects; and si, k, t the residual. We
 allow for first-order serial correlation in the residu-
 als and report the rate of serial correlation with our
 results. This model is estimated using dummy vari-
 ables to represent classes of effects.
 In the model, corporate-parent effects are defined to
 arise only if a corporation belongs to more than one
 included segment. We adopt this convention because
 the corporate-parent and the business-segment effect
 cannot be separated for firms that participate in just
 one segment. For undiversified firms, the corporate-
 parent effect is assumed to be zero. Thus, a mod-
 eled corporate-parent effect reflects the tendency of a
 firm's business segments to perform differently from
 the average given the industries in which the firm
 participates.
 Rather than report the thousands of coefficients
 estimated for the full model, results are presented in
 the form of an ANOVA in which each class of effects
 is restricted.1 Rates of serial correlation in the error
 term are presented for the full model and for each
 restricted model. When the model is restricted to omit
 one of the classes of effects, then rates of serial cor-
 relation are higher because the residual captures the
 omitted effects.
 Year effects are defined to capture the general
 impact of macroeconomic fluctuations in business
 activity, and are therefore restricted to be equal
 for all segments. When an industry or corporate-
 parent effect is omitted from the full model, the
 business-specific effect picks up the variation that
 would have been ascribed to the industry or to the
 corporate-parent effect. The importance of industry
 and corporate-parent effects is evaluated by assessing
 1 The estimation procedure accounts for imbalance in the panel.
 Imbalance arises because of entry and exit into the Compustat data
 by various segments. Note that entry and exit from Compustat does
 not necessarily constitute actual entry and exit from the economy
 (see McGahan and Porter 1997a). To deal with imbalance, we gener-
 ated, calculated, and inverted the matrix implied by our regression
 equation using new software designed for large, sparse matrices
 by MATLAB. Thanks to Arthur Schleifer for extensive discussions
 regarding this process.
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 McGAHAN AND PORTER
 Variance in Accounting Profitability
 their explanatory power in models that exclude
 business-specific effects.12
 To explore the results for manufacturers, we iden-
 tify manufacturing segments based on the first digit
 of the SIC codes (we consider businesses classified in
 SICs from 3000-3999 to be manufacturers). A subtle
 question arises with regard to identification of diver-
 sified corporate parents. For the aggregate analysis,
 diversified corporate parents are identified as corpo-
 rations with two or more segments in the dataset.
 In their replication of results by economic sector,
 McGahan and Porter (1997a) reclassify corporations
 within specific sectors. For example, corporations in
 the manufacturing sector are considered diversified
 only if they include two or more manufacturing
 segments. The purpose of this classification was to
 make the results comparable to Schmalensee (1985)
 and Rumelt (1991). For consistency with the previous
 studies (including Roquebert et al. 1996), this analysis
 12The nature of an ANOVA involves examining the incremental
 explanatory power of a specific set of effects and, hence, there is
 an inherent "nesting" quality to an ANOVA. There is no difference
 in the estimation procedure between the simultaneous ANOVA
 and nested ANOVA when business-specific effects are introduced
 into the model. The reason is technical: Business-specific effects
 are linear by design with both the industry and the corporate-
 parent effects. This occurs because the industry effect is numerically
 equivalent to the average of business-specific profits among indus-
 try members; similarly, the corporate-parent effect is equivalent to
 the average of business-specific profits among corporate members.
 There is a subtle difference between the simultaneous ANOVA
 and the nested ANOVA, however, in the models that incorporate
 the industry and corporate-parent effects. Industry and corporate-
 parent effects are not linear by design, and there may be covariance
 between the effects in the data. The nested ANOVA approaches
 impute all of the covariance to the first introduced effect. By esti-
 mating a simultaneous model that includes both industry and
 corporate-parent effects and comparing the results with models
 that include either industry or corporate-parent effects, we can bet-
 ter understand whether relationships between the industry and
 corporate-parent effects influence the results. The components-of-
 variance models often generate estimates of the covariance between
 the industry and corporate effects, but under an unusual assump-
 tion: COV models assume that the covariances as well as the effects
 are randomly generated. Thus, the approach does not account for
 systematic relationships such as the tendency of high-performance
 industries to host a disproportionate number of diversified firms.
 This understanding is crucial for establishing the importance of
 industry and corporate-parent influences on performance.
 treats corporations as diversified, based on their num-
 bers of segments within the manufacturing sector.
 The data come from the Compustat Business-
 Segment reports,which include information on com-
 panies with equity that is publicly traded in American
 markets. For each corporate parent, the Compustat
 reports identify up to 10 lines of business. Each line is
 identified by a segment number, which allows track-
 ing of performance between years even if the name
 or primary SIC of the segment changed. Companies
 are allowed to adopt reasonable conventions for allo-
 cating operating income and assets to particular seg-
 ments, with segment results included in the com-
 pany's audited financial statements.13
 The Compustat database was screened as in
 McGahan and Porter (1997a).14 We excluded about
 15% of the original observations because they
 belonged to four-digit categories that do not cor-
 respond to economically meaningful industries (i.e.,
 "not elsewhere classified," etc.). Segments in such
 categories are often not direct rivals, and including
 them would understate industry effects and over-
 state business-specific effects. Following Schmalensee
 (1985) and Rumelt (1991), we also excluded segments
 with less than $10 million in sales or assets because
 they are often created for the disposition of assets
 prior to exit, for example. Segments that are the only
 organizations within an SIC (analogous to monop-
 olies) are omitted because a business-specific effect
 13 We used Compustat's conventions for dealing with the SIC revi-
 sions that took place in 1981, 1987, and 1992.
 14 None of our screens on data involved an exclusion based on the
 dependent variable, as in Roquebert et al. (1996). Before screen-
 ing, the dataset contained 151,929 records, each of which con-
 tained information on a single business segment in a particular
 year between 1981 and 1994. From this dataset, we dropped 2,743
 records that do not contain a primary SIC designation. A total of
 22,041 records were excluded because they are in SICs identified
 as "not elsewhere classified," "nonclassifiable establishments," or
 "government, excluding finance." We also dropped 15,689 records
 on "depository institutions" and 2,529 records on the only organiza-
 tions in their primary SIC classifications in specific years (analogous
 to monopolies). Another 1,433 observations were excluded because
 they were associated with segments that were in the database for
 only one year. We then excluded 29,077 very small segments with
 less than $10 million in sales and an additional 5,675 segments with
 less than $10 million in assets.
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 Variance in Accounting Profitability
 Table 2 Average Profitability of Business Segments
 Avg. Assets Avg. Assets
 No. ($mil) Avg. Profita Median Profit No. ($mil) Avg. Profita Median Profit
 All yrs 72,742 829 10.2 9.8
 1981 4,643 507 14.3 12.6 1988 5,112 865 10.4 9.9
 1982 5,200 528 11.3 10.9 1989 5,030 948 9.8 9.4
 1983 5,285 556 11.4 10.8 1990 5,029 1,028 9.5 9.0
 1984 5,205 598 11.9 11.6 1991 5,114 1,040 8.4 8.4
 1985 5,195 660 10.3 10.3 1992 5,232 1,051 9.0 8.7
 1986 5,249 699 9.2 10.0 1993 5,396 1,127 9.1 8.7
 1987 5,319 772 10.0 9.8 1994 5,733 1,161 9.1 9.0
 Note. aAverage ratio in percent of operating income to identifiable assets.
 cannot be distinguished from an industry effect.
 Roquebert et al. (1996) report that their analysis
 encompasses 746 different four-digit SIC categories
 with designations between 2000-3999 (manufactur-
 ing). In our data, after exclusions, we report on 390
 different four-digit SIC categories with comparable
 designations between 2000-3999. Thus, the finding of
 lower industry influence in Roquebert et al. (1996)
 may be at least partly attributable to this difference.
 Our screened dataset includes 72,742 observations
 for an average of 5,196 per year.15 The dataset rep-
 resents the activities of 13,660 distinct business seg-
 ments in a total of 668 different industries, which are
 represented by their four-digit SIC codes. The aver-
 age business segment posts 5.3 years of data, a period
 somewhat longer than the 4 years covered by Rumelt
 (1991).16 Each industry in each year includes the activ-
 ities of 10.1 business segments on average. We have
 information on 7,793 corporations, with 1,943 partic-
 ipating in more than one industry in at least one
 year. In years of diversification, these corporations
 5Outliers are omitted as we drop observations in the screening
 process. In particular, we drop data on three reports of extraor-
 dinarily high return on assets among a group of oil-and-gas-
 exploration companies that have moderate amounts of income but
 hardly any assets (i.e., less than $1 million in assets). Note that at no
 point do we screen on the dependent variable, however. After the
 screens, the resulting distribution on the dependent variable is sta-
 tistically indistinguishable from normal. Thanks to Arthur Schleifer
 for extensive discussions to verify the screening process.
 16 The lengthening of the series tends to depress the stable portions
 of the effects and to increase the transient portions of the effects. If
 the time series were long enough, then all stable effects would be
 eliminated.
 post information on 2.8 business segments on aver-
 age. As a result, 47.8% of observations are associated
 with diversified firms. The mean profit (expressed as
 the ratio of operating income to identifiable assets)
 is 10.2% with a variance of 260%. Table 2 shows the
 yearly number of observations, average size in assets,
 average profit, and median profit.
 The typical business segment, with assets of $829
 million, is considerably larger than an operating busi-
 ness unit.17 There are several implications for our
 results. It is likely that a typical Compustat segment
 reflects actual operating activity in three or four SIC
 codes (at the four-digit level). As a consequence, the
 operations posted to each SIC in the Compustat data
 are probably more diverse than the actual operations
 in each SIC. The broadening of industry definition
 beyond the real four-digit level probably dampens
 industry and corporate-parent effects in our results.
 (Corporate-parent effects will be dampened if the typ-
 ical parent participates in a greater variety of indus-
 tries than reported in the segment data.) These prob-
 lems are exacerbated because the SIC system does not
 map closely to actual economic activity in some sec-
 tors, particularly the computing and medical device
 industries. As a result, we interpret our results cau-
 tiously. A finding of high business-specific effects
 with low industry and low corporate-parent effects
 may reflect artificial aggregation in Compustat rather
 17 Roquebert et al. (1996) describe Compustat Business Segments as
 "SBUs," or strategic business units. We believe that this represen-
 tation is somewhat misleading.
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 McGAHAN AND PORTER
 Variance in Accounting Profitability
 than real differences in economic activity. This prob-
 lem is fundamentally related to general questions of
 appropriate industry definition.18
 There are several advantages of our data. First, the
 14-year time series allows us to identify effects that
 are truly stable over a long period. (The longer time
 series tends to depress our estimates of stable effects
 compared to Rumelt's four-year time series and the
 Roquebert et al. (1996) seven-year series, however.)
 Second, the period we study spans several phases
 of the business cycle. We also provide a benchmark
 for prior results on the period immediately follow-
 ing the first oil shock. Third, Compustat captures a
 large portion of activity in all sectors of the American
 economy. Business segments in the raw Compustat
 Business Segment data (afterscreening only for miss-
 ing observations and for financial firms) account for
 about two-thirds of the corporate sales and 45% of
 the corporate assets reported to the Internal Revenue
 Service for nonfinancial sectors from 1985-1992, the
 last year for which data is available. After the appli-
 cation of our screens, the data covers slightly more
 than half of corporate sales and slightly more than
 a quarter of corporate assets in nonfinancial sectors.
 Schmalensee (1985) reports that the FTC data in his
 study accounted for about half of manufacturing sales
 and two-thirds of manufacturing assets in 1975.
 4. Are Results Robust
 to Analytical Method?
 The first outstanding question raised by recent stud-
 ies is whether results are robust to analytical method.
 In this section, we show the results of a simultane-
 ous ANOVA approach for Equation (1) in compar-
 ison with the results for a variety of related mod-
 els where restrictions have been imposed. We also
 show the incremental explanatory power associated
 with year, industry, corporate-parent, and business-
 specific effects, respectively, and show the robust-
 ness of results to serial correlation. If the results are
 similar to those of the previous studies, then we
 have evidence that the entire body of work is robust
 18 Chang and Singh (2000) find that their results on market share
 are closely related to the narrowness of industry definition.
 to method. If the results are dissimilar, then addi-
 tional research is needed either to find the definitive
 methodology or to reconcile the approaches.
 The results of our analysis-of-variance on Equa-
 tion (1) are shown in Figure 1, which is broadly com-
 parable to Figure 1 in Schmalensee (1985). The model
 at the bottom of the figure corresponds to the fully
 specified model in Equation (1). All other entries in
 Figure 1 correspond to model in which at least one
 class of effects is restricted to zero. The serial corre-
 lation in residuals (p), and the ordinary and adjusted
 R2 are shown for each model. Each line is accompa-
 nied by the probability at which an F-test rejects the
 corresponding restriction.
 Consider first the fully specified model at the bot-
 tom of the figure. Each of the lines immediately
 above this model points to a model in which one
 type of effect is omitted.19 The first two of these
 lines are associated with restrictions on corporate-
 parent and industry effects, respectively. In each case,
 the level of the F-test does not reject the restric-
 tion because industry and corporate-parent effects are
 linear by design with business-specific effects. The
 third line points to a model in which business-specific
 effects are restricted. The F-test rejects the exclusion
 with 1% confidence. Note that by comparing models
 we are invoking the inherent "nested" nature of an
 ANOVA. The description of our models as "simulta-
 neous" ANOVA derives from the fact that each model
 reported in the figure is estimated while accounting
 for covariance between the estimated effects.
 The next-highest group of lines corresponds to var-
 ious restrictions on models in which three of the
 four effects are present. The first group of three
 lines is associated with restrictions on the model
 that includes year, industry, and business-specific
 effects. The F-tests cannot reject the restriction on
 industry effects (because of linearity by design with
 business-specific effects), although they do reject
 restrictions on business-specific effects. Similarly, the
 second group of three lines is associated with restric-
 tions on the model that includes year, business-
 specific, and corporate-parent effects. The F-tests can-
 not reject the restriction on corporate-parent effects,
 19 For simplicity, the chart does not depict the exclusion of year
 effects. Year effects are significant.
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 McGAHAN AND PORTER
 Variance in Accounting Profitability
 Figure 1 Analysis of Variance on Equation (1)
 Null Model
 >o^93^^ /^o.99 N^>0.9^ p>0.99
 Year Effects Industry Effects Business-Specific Effects Corporate-Parent Effects
 est. p = 0.340 est. p = 0.299 est. p = 0.112 est. p = 0.231
 R2= 0.008 R2= 0.095 R2= 0.591 R2= 0.160
 adj. R2= 0.008 adj. R2= 0.078 adj. R2= 0.497 adj. R2= 0.135
 Industry Effects Business-Specific Effects
 est. p = 0.297 est. p = 0.102
 R2= 0.104 R2= 0.601
 adj. R2 = 0.097 adj. R2 = 0.510
 Year, Industry, & Year, Industry, &
 Business-Specific Effects Corporate-Parent Effects
 est. p = 0.102 est. p = 0.217
 R2 = 0.601 R2 = 0.224
 adj. R2= 0.501 adj. R2= 0.185
 Corporate-Parent Effects
 est. p = 0.230
 R2= 0.167
 adj. R2= 0.141
 Year, Business-Segment, &
 Corporate-Parent Effects
 est. p = 0.102
 R2 = 0.601
 0
 Year, Industry, Business-Specific, & Corporate-Parent Effects
 est. p=0.102, R2= 0.601, adj. R2 = 0.510
 Note. This figure shows the estimated rate of correlation, the R, and the adjusted R2 in models that include various sets of effects. Each line is accompanied
 by a figure that represents the probability with which the model rejects the restriction indicated by comparing the two models. For example, the model at the
 bottom of the figure includes year, industry, business-specific, and corporate-parent effects, and generates an R2 of 0.601. The model immediately above it
 excludes the business-specific effects, and generates an R2 of 0.224. The difference in the explanatory power of the two models is significant at the 99% level,
 as indicated by the "> 0.99" that accompanies the restriction. Thus, the analysis shows that business-specific effects add significant explanatory power even
 in a model that already includes year, industry, and corporate-parent effects.
 but can reject the restrictions on business-specific
 effects. These results provide additional support for
 business-specific effects. The third group of three lines
 is especially important because it is associated with
 restrictions on the model that includes year, indus-
 try, and corporate-parent effects, but not business-
 specific effects. Industry and corporate-parent effects
 significantly contribute to explanatory power when
 business-specific effects are excluded. These results
 support the inclusion of industry and corporate-
 parent effects.
 The third-highest group of lines corresponds to
 restrictions on models with two sets of effects.
 Business-specific effects have important explanatory
 power in the fixed-effects model. The remaining mod-
 els also reject the exclusion of all effects, except in
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 McGAHAN AND PORTER
 Variance in Accounting Profitability
 the cases of linearity by design. The results also sug-
 gest relationships between industry and corporate-
 parent effects. Adjusted R2 drops by a relatively small
 amount when either industry or corporate-parent
 effects are removed from the model with year, indus-
 try, and corporate-parent effects.
 The final group of restrictions at the top of the fig-
 ure provide information about the explanatory power
 of each type of effect on its own. When only one of the
 classes of effects is present, the F-statistic never rejects
 the restriction to the null model. In sum, Figure 1
 confirms that all four types of effects-year, industry,
 corporate-parent, and business-specific-are justified
 for inclusion in the full model.
 Table 3 summarizes the results from Figure 1 on the
 increment to explanatory power by type of effect. To
 construct Table 3, we calculated the increment to the
 ordinary and adjusted R2 with effects introduced in
 the following order: year, industry, corporate-parent,
 and business-specific.20 Year effects add lessthan 1%
 to both ordinary and adjusted R2. Industry effects
 add 9%-10%, corporate-parent effects add 9%-12%,
 and business-specific effects add 33%-38%. Thus,
 business-specific effects are more important than any
 other type of effect. Industry and corporate-parent
 effects are about equally important, and year effects
 are relatively unimportant (although significant). Both
 industry and corporate-parent effects are comparable
 to those obtained in McGahan and Porter's (1997a)
 nested ANOVA. Thus, the findings in this literature
 generally prove robust and invariant to method.
 For each of the models, Figure 1 also reports the rate
 of serial correlation in residuals, p. For the full model,
 the rate of serial correlation is 10.2%. In restricted
 models, the rate of serial correlation is higher because
 the residuals include the omitted effects. In the full
 model, the rate of serial correlation represents the ten-
 dencies of shocks in a specific year to influence returns
 in the subsequent year. Because the effects of indus-
 try, the specific business, and the corporate parent are
 20 If business-specific effects were introduced before industry and
 corporate-parent effects, then industry and corporate-parent effects
 would have no explanatory power because all profit differences in
 industries and in corporate parents would be previously captured
 in the business-specific effects. We judged that this order of intro-
 duction would be minimally informative.
 Table 3 Increment to Explanatory Power by Type of Effect
 Ordinary R2 Adjusted R2
 Yeara 0.8% 0.8%
 Industryb 9.6 8.9
 Corporate-parentc 12.0 8.8
 Business-specificd 37.7 32.5
 Full model 60.1 51.0
 Notes.
 alncrement in model of year effects over null model.
 blncrement in model of year and industry effects over model of year
 effects.
 Clncrement in model of year, industry, and corporate-parent effects over
 model of year and industry effects.
 dincrement in full model over model of year, industry, and corporate-parent
 effects.
 defined to apply across the entire 14-year period, the
 serial correlation in the residuals reflects any shock
 with intertemporal influence greater than one year but
 less than the full 14-year period under study. Analysis
 of the residuals in estimation of both the fully spec-
 ified model and the model with year and business-
 specific effects yields no evidence of heteroscedasticity
 in either sales or assets. For the full model, the corre-
 lation between the residuals and the inverse of assets
 is 0.7%.
 Table 4 contains the results from models corrected
 for serial correlation.21 The first column shows the
 results from the uncorrected model for reference. The
 second column shows results when each of the con-
 stituent models (i.e., the model of year effects, the
 model of year and industry effects; the model of year,
 industry, and corporate-parent effects; and the full
 model with all effects) is corrected for its own esti-
 mated serial correlation. The estimates are substan-
 tially similar to those in the uncorrected model. This
 approach, which is standard for dealing with serial
 correlation, generates R2 that are based on different
 sums of squares because observations are corrected
 for different rates of serial correlation in each model.
 The results reported in the third column of Table 4
 are based on the estimates generated by the standard
 21 Previous studies-particularly McGahan and Porter (1997a)-
 dealt with the presence of serial correlation by stipulating a cor-
 rected model. In this paper, take a more general approach and show
 the robustness of results in models that are both uncorrected and
 corrected.
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 McGAHAN AND PORTER
 Variance in Accounting Profitability
 Table 4 Increment to Ordinary R2 by Type of Effect in Models Dealing
 with Serial Correlation
 Correction
 Uncorrected Standard on full
 model correction model
 Yeara 0.8 0.4 0.4
 Industryb 9.6 9.2 10.3
 Corporate-parentc 12.0 11.7 11.6
 Business-specificd 37.7 39.3 36.0
 Full model 60.1 60.6 58.3
 Notes.
 alncrement in model of year effects over null model.
 bIncrement in model of year and industry effects over model of year
 effects.
 Clncrement in model of year, industry, and corporate-parent effects over
 model of year and industry effects.
 dincrement in full model over model of year, industry, and corporate-parent
 effects.
 serial correlation (from Column 2) and the total sums
 of squares on the uncorrected data. The R2 in the
 models of Column 3 are directly comparable with
 one another because the total sum of squares in each
 model is the same. The results are similar to those in
 the previous columns. In the subsequent sections of
 the paper, we use uncorrected models because they
 involve the simplest of assumptions. In cases where
 our results would differ substantially, we also report
 on models corrected for serial correlation.
 The analyses presented in Tables 3 and 4 resolve a
 central question regarding previous studies on vari-
 ance in business-segment accounting profit regarding
 the robustness of results to differences in method.
 Previous studies employed methods of analysis-
 namely, components-of-variance techniques and
 nested ANOVA-that required restrictive assump-
 tions about the absence of meaningful relationships
 between the economic processes generating year,
 industry, corporate-parent, and business-specific
 effects. As a result, estimates of the importance of
 corporate-parent effects differed substantially when
 the same data were analyzed using different statisti-
 cal approaches (see Rumelt 1991 and McGahan and
 Porter 1997a). The methodology on which the results
 are based allows for a full set of covariance effects,
 and generates results that are robust to serial corre-
 lation in the data. The comparison with McGahan
 and Porter (1997a) suggests that small differences in
 results obtained under different analytical methods
 should not be interpreted as meaningful.
 5. Are Results for Manufacturers
 Representative?
 A second major issue in the literature on variance
 decomposition concerns whether the results of the
 early studies on manufacturers were representative.
 This question carries implications for future research
 because much more data is normally available on
 manufacturing companies than on firms in other sec-
 tors. If manufacturers are representative in the aggre-
 gate, then the results of detailed studies of manufac-
 turing may hold for a broader cross-section of firms.
 McGahan and Porter (1997a) suggested that the
 importance of industry, corporate-parent, and
 business-specific effects in manufacturing did not
 broadly represent the importance of effects among
 businesses in other sectors. This suggestion was based
 on comparison of a components-of-variance analysis
 of businesses in manufacturing with a components-
 of-variance analysis of businesses in other sectors of
 the economy. To test the robustness of this claim, we
 replicated results of our simultaneous ANOVA on the
 manufacturing segments in the screened Compustat
 dataset for 1981-1994. The results are presented in
 Table 5. The first two columns, labeled "uncorrected
 model" show the results under the simple assump-
 tions of regression analysis without correction for
 serial correlation. The decomposition of variance for
 manufacturers is shown in the first column and the
 decomposition for all of the covered sectors is shown
 in the second column. The results for the influence
 of corporate-parent effects are the same, although
 the influence of industry and business-specific effects
 differs somewhat. The columns labeled "standard
 correction" and "correction on the full model" show
 the analyses corrected for serial correlation in the
 same manner as in the previous section.The results
 on corporate-parent effects are similar for manufac-
 turers and for businesses in all of the covered sectors,
 although the results on the influence of industry
 differ in the corrected models.
 Table 5 supports McGahan and Porter's (1997a)
 earlier claim on the importance of sectoral analysis,
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 McGAHAN AND PORTER
 Variance in Accounting Profitability
 Table 5 Incremental Contribution to Ordinary R2 for Manufacturers
 Correction on
 Uncorrected model Std. correction full model
 (1) (2) (3) (4) (5) (6)
 Mfg. All Mfg. All Mfg. All
 Yeara 1.1 0.8 0.7 0.4 0.6 0.4
 Industryb 7.1 9.6 6.9 9.2 7.6 10.3
 Corporate-parentc 12.0 12.0 11.0 11.7 10.8 11.6
 Business-specificd 35.2 37.7 38.7 39.3 35.1 36.0
 Model 55.4 60.1 57.2 60.6 54.1 58.3
 Error 44.6 39.9 42.8 39.4 45.9 41.7
 Total 100.0 100.0 100.0 100.0 100.0 100.0
 Estimated serial correlation 0.165 0.102
 Notes.
 alncrement in model of year effects over null model.
 blncrement in model of year and industry effects over model of year effects.
 Clncrement in model of year, industry, and corporate-parent effects over model of year and industry effects.
 dlncrement in full model over model of year, industry, and corporate-parent effects.
 although the results are somewhat different than the
 COV. Components-of-variance techniques, which do
 not allow for relationships in the processes that gen-
 erate industry and corporate-parent effects, detected
 no influence of the corporate parent within manufac-
 turing. In contrast, the simultaneous ANOVA detects
 corporate-parent effects among manufacturers that
 are similar to those that arise for businesses in all
 sectors. The COV technique did identify differences
 in the importance of industry for manufacturers and
 firms in other sectors, however.
 The simultaneous ANOVA in Table 5 reveals that
 the idiosyncratic portion of profits for manufacturers
 (captured in the residual) persists at a higher rate in
 manufacturing than in other sectors. When models
 are corrected for serial correlation, industry effects are
 affected significantly. Thus, manufacturing is not rep-
 resentative both because the magnitude of effects is
 somewhat different, but also because the correction
 for serial correlation suggests different rates of persis-
 tence for manufacturers and for businesses in other
 sectors.
 6. How Important Are
 Corporate-Parent Effects?
 Perhaps the greatest controversy arising in this
 research regards the robustness of findings about
 corporate-parent effects. Some authors have sug-
 gested that corporate-parent effects are much more
 important than most of the studies suggest. In par-
 ticular, a recent study by Roquebert et al. (1996) sug-
 gests that corporate-parent effects account for a large
 part (17.9%) of variance in the accounting profit of
 manufacturers covered in the Compustat Reports for
 1985-1991. This result conflicts with the results from
 McGahan and Porter (1997a) and with results of the
 current study that indicate a smaller corporate-parent
 influence.
 Table 6 reconciles the difference between Roquebert
 et al. (1996) and this study. The first four columns
 of Table 6 show the results of a standard ANOVA
 on all sectors. The first column is a reproduction of
 results from Table 3 for reference. The second through
 fourth columns replicate results for 1985-1991, the
 period covered in Roquebert et al. (1996). A com-
 parison between the first and second columns indi-
 cates that industry, corporate-parent, and business-
 specific effects explain somewhat more of variance in
 profitability during the 7-year period from 1985-1991
 than during the full 14-year period from 1981-1994.
 This result is fully consistent with the idea that the
 effects dissipate and change over time, thus dimin-
 ishing their contribution to variance over a longer
 period.
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 McGAHAN AND PORTER
 Variance in Accounting Profitability
 Table 6 Models of Various Types with Corporate-Parent Effects
 (1) (2) (3) (4) (5) (6) (7) (8)
 Sectoral coverage All All All All Mfg. Mfg. Mfg. Mfg.
 Period of coverage 1981-1994 1985-1991 1985-1991 1985-1991 1981-1994 1985-1991 1985-1991 1985-1991
 Corporate screens All All 2+ segs 3+ segs All All 2+ segs 3+ segs
 N 72,742 36,048 20,677 16,213 58,340 11,233 5,379 3,430
 Yeara 0.8 0.2 0.3 0.4 1.1 0.2 0.5 0.5
 Industryb 9.6 11.4 14.6 16.3 7.1 8.2 9.9 11.0
 Corporate-parentc 12.0 14.2 19.5 17.8 12.0 13.7 23.7 17.1
 Business-specificd 37.7 41.1 33.6 33.8 35.2 44.3 34.9 43.0
 Full model 60.1 66.9 68.0 68.2 55.4 66.4 70.0 71.6
 Notes.
 alncrement in model of year effects over null model.
 blncrement in model of year and industry effects over model of year effects.
 Clncrement in model of year, industry, and corporate-parent effects over model of year and industry effects.
 dlncrement in full model over model of year, industry, and corporate-parent effects.
 The third column of Table 6 shows results when the
 data is screened to include only corporations with two
 or more included segments, and the fourth column
 shows results when the data is screened to include
 only corporations with three or more included seg-
 ments. The screens substantially change estimates of
 both industry and corporate-parent effects.22 The esti-
 mated influence of corporate-parent effects is maxi-
 mized when the data are screened to include corpo-
 rations with two or more segments.
 The Columns 5-8 of Table 6 show the same anal-
 yses for manufacturers, thus facilitating comparison
 with Roquebert et al. (1996). Column 5 replicates the
 results from Table 5 for reference. The Columns 6-8
 cover the variance in profits among manufacturers
 from 1985-1991. Again, the influence of the corporate-
 parent effect is maximized when the data is screened
 to include corporations with two or more segments.
 Indeed, the results in Column 8 of Table 6 indicate
 an even greater influence of corporate-parent effects
 (at 23.7%) than reported by Roquebert et al. (at 17.9%).
 There are two reasons why corporate-parent effects
 have maximal influence when the data is screened to
 include corporations with two or more segments. First,
 the corporate-parent effects are defined to be zero for
 undiversified firms. Undiversified firms account for
 22 A test of equality in means rejects at the 1% level the hypothesis
 that the industry and corporate-parent effects are the same across
 all of the columns.
 about half of the observations in the screened Compu-
 stat data. All else equal, the exclusion of undiversified
 firms should about double the reported influence of
 corporate-parent effects. Second, diversified corporate
 parents with more than two segments have corporate-
 parent effects that are defined to reflect the common
 tendencies of a larger group of member segments.
 With an increase in the size of the group, the com-
 mon tendency tends to diminish (all else equal). Thus,
 corporate-parent effects explain a smaller portion of
 variance when the data is screened to include only
 firms with three and more segments, for example.
 Indeed, Roquebert et al. (1996) show that the influence
 of corporate parents consistently diminishes when the
 data on diversified firms is screened to include par-
 ents with greater numbers of segments. (Roquebert
 et al. 1996 do not report on the influence of corporate
 parents when undiversified firms are included.)
 Thus, differences between the results reported by
 Roquebert et al. (1996) and those reported in this
 study can be attributed to choices regarding screens
 on the data. The comparison also indicates impor-
 tant relationships between diversification and theesti-
 mated influence of industry and business-specific
 effects. In our view, the superior approach is to esti-
 mate the importance of year, industry, corporate-
 parent, and business-specific effects on a dataset that
 includes both undiversified and diversified firms. The
 exclusion of undiversified firms leads to spurious
 estimates of industry influence, for example, because
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 McGAHAN AND PORTER
 Variance in Accounting Profitability
 industry effects are imputed from the performance
 of only diversified members. The results in Table 6
 suggest that diversified corporate parents have sys-
 tematically different industry and business-specific
 effects than undiversified firms. Furthermore, these
 systematic differences appear to differ for manufac-
 turing and other sectors of the economy. This anomaly
 deserves further research.
 7. What Have We Learned?
 This stream of research based on the decomposi-
 tion of variance in accounting profitability leads to
 several important conclusions. First, business-specific
 effects are more important than year, industry, and
 corporate-parent effects in the variance of business-
 specific profitability. Year effects account for a small
 but significant portion of variance. Second, the rel-
 ative importance of year, industry, corporate-parent,
 and business-specific effects differs across sectors of
 the economy. Third, industry, corporate-parent, and
 business-specific effects are related in cross-section.
 For example, several of the studies show that indus-
 try and corporate-parent effects vary together. The
 choice of industry by diversifying corporate parents
 is related to industry performance, so that indus-
 try and corporate-parent effects are simultaneously
 determined. Fourth, industry, corporate-parent, and
 business-specific effects are related intertemporally.23
 Businesses that perform differently from the aver-
 age in one year are likely to perform differently in
 the subsequent year. Persistence in performance may
 arise not just because of business-specific factors, but
 also from year, industry, or corporate-parent factors.
 For example, year effects persist if unusual perfor-
 mance in one year tends to yield to similarly unusual
 performance in the subsequent year.
 Additional studies using similar approaches are
 less likely to generate important new insights because
 they are limited technically, by data and by method.
 23 The nature of the intertemporal link appears to vary by economic
 sector and by type of effect. Serial correlation in the idiosyncratic
 component of performance (i.e., the residual) is greater in manufac-
 turing than in other sectors. These results carry important implica-
 tions because they may reveal underlying patterns in the processes
 that generate the effects.
 The technical limitations originated at the incep-
 tion of the stream of research. The purpose of the
 studies was to identify the importance of indus-
 try, corporate-parent, and business influences with-
 out making claims about causality. The results not
 only confirm the importance of each of the effects,
 they also point to important relationships between the
 effects (i.e., to covariances between the sets of effects).
 Thus, in broad terms, the literature has also confirmed
 the limitations of the structure/conduct/performance
 models: There is evidence of feedback and coevo-
 lution between the industry, corporate-parent, and
 business-specific effects.
 Limitations of data arise from several sources. First,
 the SIC system includes very broad industry defi-
 nitions of at the four-digit level in some parts of
 the economy. As a result, the importance of indus-
 try is likely to be understated. Second, the Compu-
 stat data covers publicly traded firms and a large
 proportion of corporate revenue in the economy, but
 it does not include privately held firms. Any differ-
 ence in the performance of private firms would likely
 lead to increases in the portion of variance ascribed
 to industry and business-specific effects.24 Third, the
 studies use operating income as the measure of
 profit, and hence exclude extraordinary charges. In
 the early 1990s, companies under restructuring may
 have incurred significant extraordinary charges that
 are not reflected in the variance decomposition. If
 extraordinary charges reflect meaningful operational
 activity, then the importance of the various effects
 may be somewhat distorted.
 Limits on method also lead us to skepticism about
 the prospects for major new insights from research
 24This would occur for two reasons. First, privately held firms
 often operate in single segments, for which corporate effects are
 defined as zero. Thus, any variation in their overall profitability is
 attributed to business-specific effects. Second, an analysis of data
 from the Internal Revenue Service's statistics on Business and Cor-
 porate Activity suggests that privately held firms arise more fre-
 quently in the lodging, entertainment, services, wholesale trade,
 and retail trade sectors than the publicly held firms. McGahan and
 Porter (1997a) have shown that industry effects are more important
 in these sectors than in others. Thus, we surmise that the inclusion
 of privately held firms would likely increase the importance of the
 industry effects.
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 McGAHAN AND PORTER
 Variance in Accounting Profitability
 using this approach. First, estimates of the influ-
 ence of year, industry, corporate-parent, and business-
 specific effects are obtained without exogenous infor-
 mation. Moreover, the methods do not support causal
 inferences about the determinants of accounting prof-
 itability because no structural variables are employed
 in the models. The method is limited because it is only
 descriptive.
 Second, the objective of the decomposition-
 namely, the variance in profitability-is defined to
 amplify the importance of outliers rather than obser-
 vations close to the norm.25 The technique therefore
 bases conclusions about the importance of year, indus-
 try, corporate-parent, and business-specific effects on
 the performance of outliers to a greater degree than
 normal observations. The method cannot yield refined
 conclusions about the relative importance of industry
 to incumbents with normal and usual profitability, for
 example.
 Third, the simultaneity in least-squares techniques
 may not be appropriate if the underlying economic
 processes do justify the restrictive assumptions of
 nested ANOVA. For example, consider the possibility
 that corporate-parent effects are shaped by opportu-
 nities related to industry structure. In this case, the
 proper economic model should attribute the portion
 of variance that is jointly determined by industry and
 corporate-parent effects to industry. The methodology
 of this study allocates the jointly determined vari-
 ance between industry and corporate-parent effects.
 Similarly, the technique does not identify the struc-
 tural relationships that may exist among industry and
 business-specific effects, and among corporate-parent
 and business-specific effects.
 Fourth, the corporate-parent effect is defined by
 common tendencies in the performance of a corpo-
 ration's segments. This definition may lead to some
 counterintuitive implications. For example, if the var-
 ious businesses of a corporation have similarly high
 profits, then a high corporate-parent effect will be
 registered even if the high profits were not specifi-
 cally attributable to intervention by corporate head-
 quarters. Conversely, the method does not fully cap-
 ture the influence of a corporate headquartersthat
 25 This observation reflects Brush and Bromiley's (1997) criticism.
 intervenes to improve the performance of just a few
 member businesses. Defined in the particular manner
 adopted in this literature, then, the corporate-parent
 effect may have little relationship to the true economic
 influence of corporate headquarters.26
 Last, business-segment effects are estimated in the
 variance decomposition from the stable portions of
 the residuals in a regression rather than by a set
 of structural variables. Thus, the problems of data
 that lower estimates of industry and corporate-parent
 influence also inflate estimates of business-specific
 influence.27
 8. Where Do We Go from Here?
 Our analysis suggests several opportunities for
 extending the variance-decomposition literature. The
 limitations of the decomposition method are poten-
 tially mitigated by focusing on differences in the
 importance of effects within subpopulations. In
 McGahan and Porter (1997b, 1999), we show that the
 influence of industry and corporate-parent effects are
 substantially different for high and low performers,
 for example.
 The most direct opportunities for further research
 reside in exploring new data. Reliable and compara-
 ble data on the accounting profits of firms in other
 parts of the world would yield insight on ques-
 tions about the relationships between the national
 economic environment and industrial performance.28
 Data on the profitability of privately held firms would
 provide results more representative of the entire econ-
 omy. Opportunities lie in exploring additional mea-
 sures of firm performance, including stock-market
 return and market share. Two studies (Wernerfelt and
 Montgomery 1988 and McGahan 1999a) decompose
 variance in Tobin's q and show that industry effects
 are as important as in the accounting-profit studies.
 26 Bowman and Helfat (2001) expand on this point.
 27 Technically, this would occur if the aggregate fixed effect on prof-
 itability were not affected (to the first order) by the diversity of
 four-digit activity reported for each segment.
 28 Two studies in this spirit, Furman (1998) and Khanna and Rivkin
 (2001), show that the relative importance of the effects may differ
 radically in non-U.S. settings.
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 McGAHAN AND PORTER
 Variance in Accounting Profitability
 Chang and Singh (2000) decompose variance in mar-
 ket share and show the critical importance of industry
 definition.
 While there are ways to continue to learn from
 this research, its limits suggest that the time has
 come to explore whole new approaches. What might
 some of the new empirical strategies look like? One
 would be to identify cross-sectional relationships
 between the industry, corporate-parent, and business-
 specific effects. The variation of business-specific
 effects within an industry may be related to the aver-
 age performance of members (i.e., the industry effect),
 for example (see McGahan 1999b). Industry character-
 istics may be related to diversity in the performance
 of incumbents (see Rivkin 1997). Newly diversified
 corporate parents may have businesses with varied
 performance, whereas seasoned diversifiers may have
 similarly performing businesses. The propensity of a
 diversified firm to enter high-performing industries
 also may be related to the number of member busi-
 nesses. Investigation of these relationships will shed
 light on how attractive industries emerge.
 Additional research is also needed on the intertem-
 poral relationships embodied in effects. What kinds
 of changes in competitive position follow from shocks
 to industry structure? How long does it take for
 the changes to occur? Serial correlation revealed in
 the research so far suggests underlying links in the
 processes that generate the effects. McGahan and
 Porter (1997b, 1999) address the broad characteristics
 of these intertemporal processes, but do not exam-
 ine cross-sectional relationships in rates of serial cor-
 relation. It is no doubt true that both industry and
 business-specific effects emerge through interaction in
 the strategies of rivals over time. Entry by diversified
 firms affects the evolution of the target industry. Sim-
 ilarly, diversifying firms may be attracted to particu-
 lar kinds of industries. Studies on the decomposition
 of variance cannot address these issues because mod-
 els would be overspecified if interaction terms were
 included for industry-year, corporate-parent-year, and
 business-specific-year effects (see Rumelt 1991 and
 McGahan and Porter 1997a). More research on the
 interaction of effects over time will reveal important
 insights into the competitive process.
 Finally, detailed case studies at the sectoral level
 are needed to support deeper understanding of the
 processes that generate favorable and unfavorable
 effects at the industry, corporate-parent, and business-
 specific levels. In the chemical sector, for exam-
 ple, Lieberman (1987) shows how rivalry between
 direct competitors influenced the evolution of indus-
 try structure. Additional field research is necessary
 to understand the mechanisms by which corporate
 parents influence business segments. Such research
 may generate important new hypotheses for linking
 diversification with the structure of the originating
 industry or the competitive position of the originat-
 ing business unit, for example. New hypotheses about
 the connections between effects promise to open up a
 whole new level of statistical inquiry.
 In sum, our results indicate that major differ-
 ences between studies in the research stream can
 be reconciled. The literature's findings are generally
 robust. Industry and corporate-parent influences on
 firm profitability are related in complex ways to one
 another in cross section and over time. The robust
 findings suggest that the research has successfully
 shown that industry, corporate-parent, and business-
 specific influences are all important. New approaches
 are needed to understand how industry, corporate-
 parent, and business-specific influences interact.
 Acknowledgments
 The authors are grateful to two anonymous referees, Rebecca Hen-
 derson, Jan Rivkin, Richard Rumelt, Richard Schmalensee, par-
 ticipants in the NBER Productivity group, and attendees at the
 Academy of Management meetings for comments and discussions
 related to this paper. Special thanks to Arthur Schleifer for exten-
 sive discussions about statistical methods. Thanks to Todd Eckler,
 Dan Elfenbein, Lucia Marshall, Michael Susanto, Geoff Verter, Sarah
 Woolverton, and especially Jan Rivkin for help in compiling the
 data. The Division of Research at the Harvard Graduate School of
 Business Administration provided financial support for this project.
 The first author thanks the SRC and BUILDE at Boston University
 for generous research support.
 References
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