Using 'Power Curves' to Assess Industry Dynamics

Major crises and downturns often produce shakeouts that redefine industry structures. However, these crises do not fundamentally change an underlying structural trend: the increasing inequality in the size and performance of large companies. Indeed, a financial crisis — for example, the one that erupted in 2008 — is likely to accelerate this intriguing long-term tendency.

The past decade has seen the rise of many "mega-institutions" — companies of unprecedented scale and scope — that have steadily pulled away from their smaller competitors. What has received less attention is the striking degree of inequality in the size and performance of even the mega-institutions themselves. Plotting the distribution of net income among the global top 150 corporations in 2005, for example, doesn't yield a common bell curve, which would imply a relatively even spread of values around a mean. The result instead is a "power curve," which, unlike normal distributions, implies that most companies are below average.

Such a curve is characterized by a short "head," comprising a small set of companies with extremely large incomes, and drops off quickly to a long "tail" of companies with a significantly smaller incomes. This pattern, similar to those illustrating the distribution of wealth among ultrarich individuals, is described by a mathematical relationship called a "power law." The relationship is simple: a variable (for example, net income) is a function of another variable (for example, rank by net income) with an exponent (for example, rank raised to a power).

"http://dw.com.com/redir?destUrl=http://www.mckinseyquarterly.com/links/31999&siteid=23&tag=related-mcKinsey&source=bnet">Exhibit 1 shows the top 30 US banks and savings institutions in June 1994, 2007, and 2008, measured by their domestic deposits (the 2008 shares of different institutions were adjusted to reflect the surge of banking M&A in the autumn of 2008). The exhibit shows that inequality has been increasing from 1994 (when the number-ten bank was roughly 30 percent of the size of the largest one) to 2008 (when it was only 10 percent as large as the first-ranked institution). It also shows how in 2008, the financial crisis accelerated the growth of the top five compared with the other banks in the top ten as the largest financial institutions took advantage of their relatively healthy balance sheets and absorbed banks in the next tier. Regulation could put a damper on this crisis-driven acceleration of inequality, but power curve dynamics suggest that it will not reverse the trend. Indeed, we found long-term patterns of increasing inequality in size and performance in a variety of industries and markets when we used metrics such as market value, revenues, income, and assets to plot the size of companies by rank.

Our analysis suggests that an industry's degree of openness and competitive intensity is an important determinant of its power curve dynamics. You would expect a bigger number of competitors and consumer choices to flatten the curve, but in fact the larger the system, the larger the gap between the number-one and the median spot. As "http://dw.com.com/redir?destUrl=http://www.mckinseyquarterly.com/links/31999&siteid=23&tag=related-mcKinsey&source=bnet">Exhibit 1 shows, after the liberalization of US interstate banking, in 1994, deposits grew significantly faster in the top-ranking banks than in the lower-ranking ones, creating a steeper power curve. Greater openness may create a more level playing field at first, but progressively greater differentiation and consolidation tend to occur over time, as they did when the United States liberalized its telecom market.

Power curves are also promoted by intangible assets — talent, networks, brands, and intellectual property — because they can drive increasing returns to scale, generate economies of scope, and help differentiate value propositions. "http://dw.com.com/redir?destUrl=http://www.mckinseyquarterly.com/links/31999&siteid=23&tag=related-mcKinsey&source=bnet">Exhibit 2 shows a significant degree of inequality, across the board, in the size and performance of companies in a number of sectors we researched. But the more labor- or capital-intensive sectors, such as chemicals and machinery, have flatter curves than intangible-rich ones, such as software and biotech.

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