COMPARING ALTERNATIVE METHODS TO ESTIMATE CORPORATE AND INDUSTRY EFFECTS Introduction In recent years, a controversy has arisen over the relative importance of corporate, business unit or industry effects on business unit profitability (Schmalensee, 1985; Rumelt, 1991 Powell, 1996; Roquebert, Phillips Westfall, 1996; McGahan Porter, 1997a). The underlying issues center on differing approaches to explaining performance. Strategic management scholars have emphasized corporate portfolio and corporate management along with organizational capabilities at the business unit level( Chandler, 1991, 1992; Prahalad& Hamel, 1990). In contrast, work drawing most heavily from industrial organization economics and the structure-conduct-performance paradigm emphasizes the importance of industry positioning as a determinant of performance(Bain, 1951, 1956; Porter, 1980) Efforts to assess the relative importance of corporate, industry, and business effects have relied on three statistical techniques -analysis of variance(ANOVA), variance components analysis(VCA), and simultaneous equations systems. Although controlling for sample haracteristics reduces the differences in findings(Bowman Helfat, 1998), some remaining differences in findings appear to depend on the researchers choice of statistical technique (McGahan and Porter, 1997a; McGahan and Porter, 1997b; Rocquebert, et al, 1996). We use a Monte Carlo simulation to compare the three approaches and to determine which approach(es) provide the best estimates of relative importance anova and VCa have been most widely used in empirical studies assessing effect size Both techniques have problems when interpreted as measuring the relative importance of the effects of industry, corporate and business units. Brush and bromiley(1997) question the metric