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