How Much Does Industry Matter? 171 starting with Schmalensee's sample of 1775 systematic biases in reported returns, the esti- business-units from the 1975 file and appending mated variance components will reflect these data on the same business-units from the 1974, facts and, therefore, help in estimating their 1976, and 1977 files. After this expansion, one importance business-unit was judged to have unreliable asset measures(in 1976-77)and was dropped. Eight other observations were eliminated because assets A VARIANCE COMPONENTS MODEL yere reported as zero. Sample a then contained 6932 observations provided by 457 corporations In discussing the heterogeneity within industries on 1774 business-units operating in a total of 242 the term firm has an ambiguity that easily lead 4-digit FTC industries to confusion. In economics a 'firm'is usually an Sample B was constructed by adding to Sample autonomous competitive unit within an industry, A the 1070'small business-units which had failed but the term is also often used to indicate a legal Schmalensee's size criterion. After adjoining the entity: a ' company'or corporation,. Because units,34 were excluded due to (apparent) and because most large corporations are substan- measurement problems: negative or zero assets, tially diversified, legal or corporate 'firms'are sales-to-assets ratios over 30, and extreme year- at best, amalgams of individual theoretical to-year variations in assets that were unconnected competitive units. Confusion can arise if one o changes in sales. Sample B then contained author uses the term firm effects'to indicate 10, 866 observations provided by 463 corporations intra-industry dispersion among theoretical on 2810 business-units operating in a total of 242 firms', and another author uses the same term 4-digit FTC industries to denote differences among corporations which The rate of return was taken to be the ratio are not explained by their patterns of industry of profit before interest and taxes to total assets, activities expressed as a percentage. In sample a the To reduce the ambiguity in what follows I average return was 13.92 and the sample variance avoid the term 'firm. Instead, I use the term was 279.35. In sample B, the average and business-unit to denote that portion of a com sample variance of return were 13 17 and 410. 73 pany's operations which are wholly contained expective within a single industry. 12 I use the term The FtC defined operating income as total corporation to denote a legal company which evenues(including transfers from other units) owns and operates one or more business-units less cost of goods sold, less selling, advertising, Thus, both industries and corporations are and general and administrative expenses. Both considered to be sets of business-units expenses and assets were further divided into In this regard, note that Schmalensee(1985) traceable,and untraceable,components, the used the term firm-effects'to denote what I call traceable component being directly attributable corporate effects. Thus, his first proposition to the line of business and the untraceable firm effects do not exist'(p. 349)refers to what component being allocated by the reporting are here termed corporate effects. Consequently firm among lines of business using reasonable as he noted, finding insignificant corporate effects procedures. In 1975, 15.8 percent of the to does not rule out the presence of substantial expenses and 13.6 percent of total assets of the intra-industry effects. However, unless more than average business-unit were allocated one year of data are analyzed, intra-industry A number of scholars have advanced arguments effects pool with the error and cannot be detected that accounting rates of return are systematically Taking the unit of analysis to be the business biased measures of true internal rates of return II unit assume that each business -unit is observed Whatever the merits of this position, the purpose over time and is classified according to its industry of this study is to partition the variance in reported business-unit rates of return. If different 12 It is FTC LB researchers to refe industry practices or corporate policies do induce to a business-unit as an 'LB, I avoid this usage because Line of Businessrefers to an industry group rather than to In particular, see Fisher and McGowan(1983 an individual business-unit within a larger firm
172 R. P Rumelt membership and its corporate ownership. Let rikt nesses. Comparing all industry effects to market denote the rate of return reported in time period share effects may unfairly load the dice in favor t by the business-unit owned by corporation k of industry. Consequently, in this study I extend ind active in industry i. A particular business- Schmalensee's argument to the business-unit and unit is labeled ik, highlighting the fact that it is rather than give special attention to market simultaneously a member of an industry and a share, I measure the importance of all stable corporation. Working with this notation, I posit industry effects, and all stable business-uni the following descriptive model Were this a fixed-effects model, the r=队+α;+k+Y+8+中k+E;(2) assumption would be that the Eikt are disturbances, drawn independently from a distri- where the a are industry effects (i= 1 bution with mean zero and unknown variance la), the pk are corporate effects (k=1,.., IB), 02. In this model I make the additional assumption the y, are year effects(t Ly), the &it that all of the other effects, like the error term, are industry-year interaction effects (s distinct are realizations of random processes with zero it combinations), and the ik are business-unit means and constant, but unknown, variances effects(o distinct ik combinations). The eikt are 0,0B, ox, o, and oZ dom disturbances (one for each of the N Note that this random effects assumption does observations). Each corporation is only active in not mean that the various effects are inconstant a few industries, so lo lalg. Because a few Instead, for example, each business-unit effect ik dustries may not be observed over all years, Is is seen as having been independently generated by Laly. The model takes the assignment of a random process with variance o2, and, having business-units to corporations and industries as once been set, remaining fixed thereafter. given and is essentially descriptive, In particular, The random-effects assumption says nothing it offers no causal or structural explanation for about why effects differ from one an profitability differences across industries, years, effects may differ from one another in either corporations, or business-units--it simply posits fixed-effects or random effects models. The real the existence of differences in return associated substance of the random-effects assumption with these categories that the differences among effects, whatever their There are two key differences between this source, are,, not having been controlled model and Schmalensee's. First, the terms Y, and or contrived by the research design, and are Bit have been added to deal with year-to-year independent of other effects. That is, the effects variations in overall returns and year-to-year in the data represent a random sample of the variations in industry-specific returns. Second, effects in the population. Independence implies In this regard, it is useful to recall Schmalensee's example, is of no help in predicting the valuce the market-share term has been replaced by ik. that knowing the value of a particular persuasive reasons for turning to a nominal of other business-unit effects or the values of any measure of industry. He argued (1985: 343) industry, corporate, or year effects. An important that conventional market-level variables (e. g, exception to this assumption, involving an associ concentration) are very imperfect measures of ation between industry and corporate effects, is the theoretical constructs (perceived inter- discussed below dependence) they are supposed to represent. Readers familiar with fixed-effects regression Therefore, the fact that these variables perform models may be concerned that the effects posited poorly, relative to market-share, in cross-sectional in this model are not estimable. Such a concern regressions may not mean that industry,is is well placed-the individual effects cannot unimportant. Hence, Schmalensee sought to be estimated. Furthermore, regression methods measure the importance of all industry effects, cannot deliver unambiguous estimates of the using nominal industry categories, and compare relative importance of classes of effects. However it to the importance of market-share. But, just the statistical problem is not to estimate the as concentration is an imperfect measure of thousands of effects, but to estimate the six industry structure, so market-share is an imperfect variances. Despite the nesting in the model, the measure of resource heterogeneity among busi- variance components are estimable. Note that it