《Innovation, Diversity and Diffusion：A Self-Organisation Model》8 McGahan-1997-how much does industry matter, really

Strategic Management Journal, Vol 18, Summer 1997 Special Issue: Organizational and Competitive Interactions (Jul, 1997).15-30 Stable url: http://links.jstor.org/sici?sici=0143-2095%028199707902918%3c15%03ahmdimr%3e2.0.co%03b2-f trategic Management Journal is currently published by John Wiley Sons Your use of the jStOR archive indicates your

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Strategic Management Journal, Vol. 18(Summer Special Issue), 15-30(1997) HOW MUCH DOES INDUSTRY MATTER. REALLY? ANITA M. McGAHAN* and MICHAEL E. PORtER etts. School of Business Administration, Harvard University, Boston, Massa importance of year, industry, corporate-parent, and business- ategories. Our results indi fects account for 2 percent, 19 percent, 4 percent, and 32 percent, respectively, of the variance in profitabili substantially across broad economic sectors. Industry effects account for a smaller portion of profit variance in manufacturing but a larger portion in lodging/entertainment, services, wholesale/retail trade, and transportation, Across all sectors we find a negati between corporate-parent and industry effects. A detailed analysis suggests that industry. rporate-parent, and business-specific effects are related in complex ways, @1997 by John Wiley sons, Ltd Debate in strategy has long focused on the performance has received scant empirical study sources of performance differences among firms. reflecting both the unavailability of data and chal In the research growing out of the industrial- lenging statistical difficulties. Rumelt(1991)is organization tradition, industry structure is a cen- perhaps the most influential study. Rumelt's tral determinant of firm performance, and firm research followed methods introduced by Schma differences are considered against an industry lensee (1985)for disaggregating business-unit background. More recently, a line of thought profits into components associated with industry sometimes called the resource-based view argues effects, corporate-parent effects, and market-share that firm performance is most influenced by effects. Neither Rumelt(1991) nor Schmalensee unique organizational processes. Under this view, (1985) made claims about the economic or industry structure is less important than idiosyn- organizational processes underlying their results cratic historical factors giving rise to firm differ- both papers were descriptive rather than norma- ences tive. Nevertheless some have interpreted Rumelt's Despite the importance of these questions, the finding of low stable industry effects to support relative influence of firm and industry effects on the resource-based view In this paper, we revisit the influence of indus ry, business-specific, and corporate-parent influ Key words: profit components, firm performance, ences on profitability using comprehensive data ndustry effects and enhanced statistical methods We examine Conespondenne to: ian itH M, Md ane radsatd iech oileldfr the relative effects of these influences on prof Boston MA 02163, U.S.A itability the econe whole as Porter(1980) and Oster (1990)are in the industrial-organi. in broad economic sectors. Finally, we begin to (1991), Dierickx and Cool (1989a, 1989b), and Barney (1986,1989) For example, see Levinthal (1995: 20). CCC0143-2095/97/S10015-16$1750

16 A.M. McGahan and M. E. porter explore how the effects interact. Industry pr Our analysis differs from prior work in to have werful direct and indirect infl ays. First, we use recently compiled data from on profitabilit the Compustat Business Segment Reports for 1981 through 1994. This dataset covers activity in all sectors of the American economy(except ANTECEDENTS the financial sector ) whereas the prior studies cover only manufacturing. The breadth of cover- Schmalensee (1985)examined the accounting age provides not only a representative sample on profits of American manufacturing firms that were the economy but also allows examination of profit covered in the Federal Trade Commissions Line influences across sectors. The average time series of Business Report for a single year, 1975. He on each economic unit in our dataset is 5.7 years found that industry effects accounted for about which compares favorably with the 4-year series 20 percent of variation in business-unit profits on each business unit in Rumelt's data. Because (and nearly 100% of total variance explained), our dataset covers a 14-year period, our results and that corporate-parent effects(or 'firm effects, reflect several phases of the business cycle in his terminology) had no impact on variation. Second, we show how the results are affected Schmalensee's only measure of heterogeneity by a more robust statistical approach to inter among participants in the same industry was mar- temporal persistence. Rumelts specification ket share. He reported that share positively affec- allows for transient industry effects, but does not ted business-unit profits, but only by a negli- similarly allow for transient year, corporate-par ible amount ent, or business-unit effects. Our specification Rumelt (1991) extended Schmalensee's allows for transience in all effects, and we report approach by including data from the FTC Reports the effect of the difference in method on manufacturing firms for all available years Third, our unit of analysis differs. The Compu 1974 through 1977. With data on more than one stat Reports contain information on firm profit by year, Rumelt generalized Schmalensee's measure SIC code (i.e, by business segment), not by of intraindustry heterogeneity to all business-unit business unit. Schmalensee and Rumelt examined effects rather than just market-share effects. He the business-unit returns given in the FtC data. eported that business-unit effects explain 44-46 We believe that the average business segment percent of variation(about 73%of the explained covers the activity of several business units. All variation), stable and transient industry effects else equal, the diversity of business-unit activity account for a total of 9-16 percent of variation, attributed to a single 4-digit SIC code may arti and corporate-parent effects explain 1-2 percent ficially reduce the measured influence of industr of variation. It is these results-the relatively low relative to Schmalensee and Rumelt. Moreover, proportion attributed to industry effects compared our need to rely on the SIC system for industry with business-unit effects-that have been inter- classification further diminishes the measured esti preted to support the resource-based perspective. 4 mates of industry influence because SIC industries err primarily in being overly broad. In our dis " Rumelt's report of low corporate-parent influence is not cussion, we suggest that the influence of industry consistent with a resource-based view of diversification, and might be even stronger if data of finer grail has stimulated additional research. In a study of diversified were available rt, Andrisani, and Phillips(1996) challenge RUr S, Ro Like Rumelt, our specification includes a num- on corporate-parent effects. The authors find that corporate- ber of potential sources of variation in accounting Rumelt(1991). The Roquebert et al. study is not directly industry factors, corporate-parent effects, and seg et al, exclude single-business firms from their analysis. This ment-specific effects. This last category, segment- our Compustat Business Segment data, singlesithied firms. In Specific effects, encompasses all business-segment ructed from the perf account for half of all assets. When we exclude single rporate-pare itially. Low estimates of industry tendency may compound IS ebert et al. approach may dist corporate-pare ises from the exclusion of ent influence because of negative all single ness nirms

How Much Does Industry Matter, Really? 17 differences, including diversity in market share, ventions may influence all four types of effects differentiation, heterogeneity in fixed assets, dif- on profitability (i.e, year, industry, corpora ferences in organizational processes, differences parent, and segment-specific).5 Because we have in organizational effectiveness, heterogeneity in no a priori hypothesis about the nature and direc activity configurations, anomalies in accounting tion of these biases, and because the Compustat practices, and differences in managerial com- Business Segment Reports are the best source of petence. Our objective is to understand the rela- available data on profitability, we proceed with tive significance of industry, corporate-parent, and the analysis but interpret the results with caution segment-specific differences in explaining profit Our specification differs from Schmalensee's variation when industries are defined by the (1985)in several ways. Because Schmalensee had only one year of data, his analysis excluded both the year effect(Y,) and the segment-specific effect( k). The segment-specific effect can only METHODS be identified when multiple years of data are available on each segment because only multiple Our analysis relies on the following model, which years identify when a segments performance dif- draws on the models used by Schmalensee and fers systematically from the mean given the Rumelt. simultaneity of year, industry, and corporate parent effects. Instead, Schmalensee included k=μ+y+α1+阝k+中k+∈;k,(1) measures of market share that had been developed by David Ravenscraft for an earlier study on the In this equation, rik, is the accounting profit in FTC data(Ravenscraft, 1983) year t of corporate-parent ks business in industry Our model also differs from Rumelt's(1991). Profit is measured as the ratio of operating which is reproduced as Equation 2 income to identifiable assets in percent. The first nnd-side term is H, which is the average rk,=μ+Y+α1+Bk+δn+dk+∈kn(2) profit over the entire period for all business seg- ments. The second term is Y, which represents Rumelt's model includes an additional term to the difference between u and the average profit represent industry-year interactions, 8ir. By of all business segments in year t. The next three including both a, and Sir, he distinguishes stable terms represent industry, corporate-parent, and industry effects from transient'industry effects segment-specific effects. The term a; is the Transient industry effects occur when all mem increment to profit associated with participation bers of an industry have high low profits in industry i; B is the increment to profit con- in year t ferred by membership in a diversified corporate- Rumelt proceeds by assuming that the error in parent k: and i k is the increment to profit Equation 2 is drawn independently. In making associated with the specific situation of business this assumption, he suppresses the possibility that segment i, k given the other effects. We assume a shock to the year, corporate-parent or business that a corporate-parent effect arises only if a specific effect at time t-l influences the year, business segment is a member of a diversified corporate parent or business-specific effect at time firm. The final term, ei k n, is the residual. Any of t. Suppose, for example, that a specific segment he increments to profit may be positive or nega- has an unusually good year at time t-1. Rumelt's tive. The model is estimated using dummy vari- specification does not account for the possibility ables to represent industry, corporate-parent, and segment-specific effec Our study is limited by shortcomings in 'For example, consider accounting measures of profit. Because account- which is relatively resear ing conventions exclude intangible assets fror ounting for research that similarly affect all members of the balance sheet, measured assets may be too low for some segments. The use of operating Powell(1996)uses executives'perceptions instead of income excludes the effects of differences in that indt accounting profit to assess the influence of industry. He finds accounts for about 20 percent of performance financing. Measurement error and accounting con- variation among the 54 single-business firms in his survey

18 A M. McGahan and M. E. Porter of a spillover effect on the segment in year t. parent, or segment-specific. The error, ik, /, for By including industry-year interaction dummies, which we assume independence, is the portion of however, his model does capture spillovers that the transient shock that is not influenced by the ffect all the members of an industry. Now sup- shock in the prior year. This specification accom- pose that the business cycle generates unusually modates both new shocks and spillovers from the high year effects in successive years. Rumelt's prior year, although it cannot capture differences industry-year interaction term may partly capture in the rate at which shocks resound across years the influence of the business cycle, which would (Rumelt's model can capture differences in the be attributed to persistence in the year effect in rate of persistence in industry shocks ) We a complete model. Rumelt justified his approach acknowledge this deficiency, but argue tha y reporting no autocorrelation in residuals from changes in the rate of persistence are important his estimation. Nonetheless, this justification does in the second order whereas the simple presence not address the possibility that industry-year of persistence in year, corporate-parent and seg interaction effects may proxy for persistence in ment-specific shocks is important in the first year, corporate-parent, and business-specific order. As a result of this difference in spec effects in his specification. This possibility is ficati salient given that Rumelt's data cover the period only with the stable effects in Rumelt's work immediately subsequent to the 1973 oil shock It is important to note that a;, Bk, and i k and to the removal of wage and price controls describe how a business under the Nixon administration all years by its industry, corporate parent, and Although we appreciate the benefits of model- segment-specific situation. The rate of persistence, ing transient industry effects, we exclude them p, reflects the influence of a shock in any single because the model would be overspecified if we year on the performance in just the subsequent equally represented transient year effects, transi- year. To isolate the portions of effects that are ent corporate-parent effects, and transient busi- stable, we subtract from (1) the rate of persist ness-specific effects. This point is important to ence, p, multiplied by the lagged value of rik the differences in our econometric model com pared with Rumelt's In Rumelt's view, an asym- rik,=pr k -1+(1-p)u+Y-pYi-I treatment of industry effects is justifie when the data cover a relatively short period +(1-px1+(1-p)4+(1-p)dk because corporate-parent and business-specific ();k effects will not change much (i.e, when shocks are small so that the persistence of shocks The left-hand side of this equation is the same between years is not important ) However, transi- as in (1): it is the profit to business segment i, k ence may arise at any level, and it is at least at time t in percent. The first term on the right plausible that industry effects will change slower hand side is the rate of persistence multiplied by than business-specific or corporate-parent effects. the profit to the same business segment at time Indeed, in another paper (McGahan and Porter, t-1. In calculating lagged variables, we lose data 1997)based on the same data set but somewhat for the first year for which we have information different methods, we show that shocks to busi- on each segment. The other terms on the right ness-specific and corporate-parent effects may be hand side include the year, industry, corporate larger than industry shocks parent, and segment-specific effects To deal with the possibility that a shock in We analyze this model in two ways, following year t-I might influence profits in year I, we Schmalensee and Rumelt. First, we conduct a allow for serial correlation on the errors in components-of-variance(COV) estimation under uation I according to the following process: the assumption that random processes generate each of the effects in Equation 1. Consider, for ∈;k=p∈,k-1+①.k (3) This assumption is completely separate from our prior dis- The parameter p captures the intertemporal per- the technical assumptions by which we estimate the model cussion of stable and transient effects. Here, we are describe sistence of effects regardless of source: in Equation 4 given that the model includes only stable effects, macroeconomic fluctuations, industry, corporate- year effects, and the error. At this point, we are interested

How Much Does Industry Matter, really example, the industry effect. The random-effects expresses the variance of this portion of return assumption stipulates that each observed industry as a function of the rate of persistence, p; and effect is drawn randomly at some early date from of the population variances in year(ox), industry an underlying population of possible industry (oa), corporate-parent(oB), and segment-specific effects. Once the effect is established, it remains effects (o2); and of the population covariance fixed for the period under study. The population between industry and corporate-parent effects of possible industry effects cannot be observed, (2CaB). Note that oz=[(1-p2)o2]. We use this but is of hes expression to state our results in terms of o industry effects are randomly drawn from this estimating the following equation mposed o2 by ce in r rumelt de population(we expand on this point below ).8 σ2+aa+ob+G++2Ca+o2 (6) generate one type ect are not correlate with the processes that generate other types of In Equation 6, the term o represents the popu- effects, with one exception. Following Schma- lation variance lensee and Rumelt, we allow for covariance year interaction effects between corporate-parent and industry effects. a The Cov method is sufficiently unusual to positive covariance would arise if attractive indus- merit further discussion. o The main idea is that tries generated more opportunities for positive each effect is treated as though it were generated influence by corporate parents, or if corporate by an independent, random draw from an underly- parents skilled at exploiting relationships between ing population of the class of effects. Once drawn business units were also effective at selecting each effect is considered as fixed. The assumption tractive industries in which to compete. A nega- of random effects does not stipulate that the tive covariance would arise if the opportunities Compustat data represent a random sample of for positive influence by corporate parents were business segments in the economy. The assump particularly great in unattractive industries tion merely means that the represented effects are We then decompose the variance of business- generated by random processes segment returns using Equation 5 To estimate Equation 5, we exploit relation ships in the sample variation among year, indus oR=(1+p2)ox+(1-p)2(o2+o2+o3) try, segment-specific and corporate-parent effects +2(1-p)2Cg+02 (5) For example, the sample variation among industry effects is the sum of an unbiased estimate of industry variation plus a small portion of the The dependent variable in this equation, OR, Is underlying variation in the year effects plus a the variance of Ri k, which is defined by ri k. ri. -pri k -I as the portion of the return to busi- small portion of the underlying variation in the by the shock in the prior year. Equation 4 underlying. variation in the segment-specific ness segments. Searle(1971), chapter 9, provides a detailed +(1-p)Var(a;+Bx)+(1-p)os+ discussion and several helpful examples of components-of- Var(a;+B)=0a+oB+2Cov(a, B.). We therefore hav ariance analysis. Also see Chamberlain (1984 approach. Rumelt(1991)provides an excellent discussion of les te ation, Random effects occur when obs intuitive terms(pp. 172-173)and develops an example of the raws from an underlying v- COV approach(pp 174-176). Abowd et al.(1995)develop a ble probability distribution variance among persistence rates that accounts We obtain Equation 5 from Ri,=(1-p)u+ for the endogeneity of exit decisions Rumelt's approach on the grounds that the Ftc data do not +w,x 1. Using our assumptions about the independ represent a random sample of the population. We believe that random effects, we reduce the equation to oR=0, this criticism is based on a misconception

20 A.M. McGahan and M. E. Porter cally along with corresponding equations in which zero. The null model stipulates that profits are the dependent variable is the sample variation in entirely determined by shocks that persist at the each of year, corporate-parent, and segment-speci- rate, p, and the economic mean. The next step is fic effects, and the right-hand side is a linear to obtain year effects by regressing the residuals combination of the underlying population vari- from the null model on the year dummies. An ances of the effects. 2 The result is a system of F-test provides an assessment of the importance equations in unknowns that represent the unbiased of year effects by comparing the R implied by estimates of population variances, By estimating p, the economic mean, and the year effects with the parameters in each equation (i. e, the 'por- the R2 from the null model and by accounting tions'described above) and solving the system of for the number of year dummies that Intro equations, we estimate the portion of the variance duced to achieve the additional explanatory attributable to each type of effect power In our second approach to estimation, we ana- After we show that year effects are important lyze the variance of profits under the standard we obtain industry effects by regressing the assumptions of ordinary least squares. Dummy residuals from the model (of ffects with variables represent year, industry, corporate-par- the economic mean and accounting for persistence ent and segment-specific effects. Instead of exam- at the rate p) on the industry dummies. The ining each of the coefficients on the dummy industry effects are used to obtain an R in a ariables, we examine the percent of variance model that also includes the year effects, the explained by the models(R2 and adjusted R2) economic mean, and the persistence in shocks at and evaluate F-tests to assess the importance of rate p. We conduct an F-test to evaluate whether groups of effects. In theory, Equation 4 is esti- the industry dummies add significant explanatory mable through simultaneous analysis of variance power. The procedure is then repeated for corpo- (ANOVA)methods. In practice, however, compu- rate-parent and segment-specific effects. After we tational complexity prevented us from obtaining complete the procedure, we repeat it under a a simultaneous estimate of all types of effects. different ordering to verify that the significance Following Rumelt, we therefore estimate the of each group of effects is not sensitive to the model using nested ANOVA techniques. 3 The order of introduction nested ANOVA allows us to evaluate whether There is controversy in the literature about the each group of effects (i.e, year, industry, seg- suitability of the COv and ANOVA approaches ment-specific or corporate-parent) is significant for this type of model. Schmalensee suggests that y introducing them in order. Under the approach, ANOVA results establish whether each set of we first evaluate the full model in Equation 4 to effects is significant, and that COV results are obtain an estimate of p. We then obtain a null preferable for evaluating the relative importance model by restricting all of the year, industry, of each type of effect. He seems to prefer the corporate-parent, and segment-specific effects to COV over the ANOVA results(perhaps because he finds the random-effects assumption more natural than the fixed-effects assumption ). The For example, the equation that represents the rclationship Cov approach does not generate any test of between the expected variance in the observed industry effects, significance, however, whereas the AnovA approach generates F-statistics. The COV Es=+a,0+aG+a+a3+a(2Cma)+a,appoachalsoincorporatesacontroversial assumption of independence in the random proc- tuition is based on the idea that the observed indust of independence does not allow for the endogen effects are calculated from data that include noise associated eity of relationships between the levels of effects with the draws of year effects, corporate-parent effects, seg- and subsequent entry or exit, for example. These amount of the noise' from each source is related to the issues apparently motivated Schmalensee to number of draws from each distribution Rumelt(1991: 174- include the ANOVA estimates along with his 176)provides a detailed example and shows how each of Cov analys See Searle (1987: chapter 3), for a detailed discussion of Rumelt(1991)makes a different assessment nested ANOVA of the two approaches. He argues that an anova

How Much Does Industry Matter, Really? 21 test for significance is not a prerequisite to COv drop 2743 records that do not contain a primary estimation. By Rumelt's logic, a COV analysis SIC designation. A total of 22,041 segments are is a simple statistical description of the data, and excluded because they are in SICs identified as he offers it as his flagship approach. Rumelt 'not elsewhere classified, ' nonclassifiable estab includes an ANOVA estimation because it has lishments, or ' government, excluding finance independent merit as a method for estimating the We also drop segments designated as'depository importance of effects. He suggests that further institutions(15,689 financial business segments sumptions would be warranted if with SICs e 6000s) because returns are not the two methods generate results that are very dif. comparable with those in other industries. We exclude 2529 segments that are the only organi We subscribe to Rumelt,s logic, but add two zations covered by Compustat in their primary additional words of caution. First, the nested siC classifications in specific years(analogous to ANOVA analysis is inherently imprecise because monopolies) because we cannot distinguish their it largely attributes covariance between types of industry effects from their segment-specific effects to the first effect introduced. in contrast. effects. Another 1433 observations are excluded the Cov approach is based on the assumption because they are associated with segments that that effects are independently generated; for are in Compustat for only one year. We then example, an incidence of exit by a segment is exclude 29, 077 very small segments with sales assumed to be unrelated to the industry effect for less than $10 million and an additional 5675 the segment. Although the COV approach is lim- segments with assets less than $10 million ited for this reason, we have no a priori hypoth- Single-year appearances and small segments are esis that the Cov analysis is biased, and hence often anomalous because they are created for the we also offer it as our flagship approach. We also disposition of assets prior to exit, for example concur with Rumelt's suggestion that qualitatively The exclusion of small segments is comparable important differences in results should motivate with Schmalensee's(1985) exclusion of units that further research on the appropriateness of the account for less than I percent market share in assumptions in each model his Ftc Line of business data set Our screened data set includes 72.742 obser- of 5196 business DATA per year. This figure is substantially larger than in previous studies. Schmalensee's dataset included The Compustat Business Segment Reports include 1775 observations. Rumelt ran his analysis on information on companies with equity that is two datasets, which he labeled Sample A and publicly traded in American markets. For each Sample B. Sample A excluded the small business corporate parent, the Compustat Reports identify units that Schmalensee had excluded, up to 10 lines of business because SEC guidelines Sample B did not. Rumelt had 6932 observations require the reporting of information on segments in Sample A and 10,866 observations in that comprise 10 percent or more of the parents Sample B. Because his results for the two data business. Each line is identified by a segment sets were similar, we focus our comparison with number, which allows tracking of performance his Sample a to accommodate a simultaneous between years even if the name or primary SIC comparison with Schmalensee of the segment changed. For each business seg- The raw Compustat Business Segment data the data set contains a primary 4-digit SIc (after screening only for missing observations and operating income, sales, and identifiable for financial firms)account for about two-thirds We used Compustat's conventions for of the corporate sales and 45 percent of the dealing with the SIC revisions in 1981, 1987, corporate assets reported to the Internal Revenue and 1992 Service for nonfinancial sectors from 1985 to We screened the Compustat data base in sev- 1992, the last year for which data are available ral ways. Before screening, the data set con- After the application of our screens, the data tained 151, 929 records, each of which described cover slightly more than half of corporate sales a single business segment in a particular year and slightly more than a quarter of corporate between 1981 and 1994. From this dataset, we assets in nonfinancial sectors. Schmalensee

22 A.M. McGahan and M. e. Porter reports that the Ftc data in his study accounted tries, corporate parents, and business units in for about half of manufacturing sales and two- varying degrees. Our results are less vulnerable thirds of manufacturing assets in 1975. Thus, to anomalies because the entire period of our our analysis covers a comparable percentage of study is longer competitive activity but over a much broader a third advantage of the compustat data is of economic sect that it captures a large portion of activity in In our analyses, we require that each obser- all sectors of the American economy. Whereas ation include information on lagged perform- Schmalensee's and Rumelt's studies focused on ance. Thus, the first observation on each business the manufacturing sector, our study covers the segment is excluded. Our COv and nested retail sector, wholesale services, mining and ANOVA results are therefore based on 58, 132 agriculture, food and textiles production, chemical remaining observations. The screened dataset rep- businesses, transportation services, lodging and resents the activities of 12. 296 distinct business other services entertainment. and all other indus. segments in a total of 628 different industries, tries except those in the financial and government which are represented by their 4-digit SIC codes. sectors. This difference also means that we have The average business segment posts 5.7 years of many more observations than previous author data(including lagged information for the first In our final report of results, we exploit the observation). Each industry includes the activities variety in the Compustat data to show how results of 7.7 business segments in the average year and differ by economic sector 21.3 business segments on average over the entire There is evidence that the business segments in period. Our analysis covers 7003 corporations, of our data set are considerably larger than operating which 1791 participate in more than one industry business units. The average segment in our in at least one year for which we have data. screened data base has assets of $903 million. Slightly less than half our observations are and diversified corporate parents post information associated with diversified corporate parents. on 2.6 segments on average. Montgomery Table I describes the business segments in the (1994: 164)indicates that the Fortune 500 partici screened data base by year and by economic pated in 10.65, 10.85, and 10.90 different SICs sector. The mean profit is 9.3 percent with a on average in 1985, 1989, and 1992, respectively ariance of 248 Thus, it is likely that a typical There are several potent advantages to the ment, which is characterized by a 4-digit SIC Compustat data. First, the 5.7-year time series on code, actually reflects operating activity in several each segment allows us to identify those effects related 4-digit SIC codes. As a consequence, that are stable over a somewhat longer period the operations posted to each SIC in the Compu he Rumelt study. Any measure of stability stat data are probably more diverse than the actua is integrally related to the number of years of operations in each SIC data on each economic unit. A longer time series There are also some disadvantages to the Com inherently leads to lower estimates of stability in pustat data. The broadening of industry definition effects. We believe that the 5.7-year average pro- beyond the actual 4-digit level probably tends to vides a useful benchmark dampen industry and corporate-parent effects, and A second advantage of the Compustat data is potentially to distort segment-specific effects that it covers a longer period of time: 14 years These problems are exacerbated because the SIC vS.4 years for the FTC dataset. The longer period system does not map closely to strategically dis- allows us to measure the influence of various tinct industries in some cases. as a result of effects over several phases of the business cycle. 4 these limitations, our results must be interpreted Although Rumelt's year effects surely capture with caution. A finding of high segment-specific part of the impact of these macroeconomic con- effects with low industry and low corporate-par ditions, he does not have the latitude to examine ent effects may reflect aggregation in Compustat whether the unusual conditions influenced indus 15 Seven out of our 628 industries are 2-digit, and 50 are 3- The Ftc dataset covers an unusual period in nd in the dataset, and the 3-digit industries account for another

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