Do CEOs Matter? Absolutely.

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Harris Collingwood has an article in the current edition of The Atlantic that raises the question "Do CEOs Matter?"  His answer to this question, as far as I can tell, is some mix of "maybe", "not much" and "sometimes."  I have a simpler answer to his question: "Yes."


Collingwood begins with a long anecdote about Steve Jobs, the gist of which is that Apple's stockholders care a lot about Steve Jobs' health.  This seems pretty understandable to me.  I've been a CEO, and been a member of Boards that have hired and fired CEOs; the hiring and firing of the CEO is generally seen to be the single most important duty of the Board of any company.  As we nationalize various financial institutions and automobile companies, the entire political system seems to think that picking the right CEO for these institutions is a pretty important decision.  Very successful private equity funds typically install new CEOs as part of investments predicated on improving corporate performance.  So are most institutional shareholders, Boards, the federal government and private equity funds suckers?

To be sure, none of these entities believe that any CEO ever completely determines firm performance.  The rational standard, it seems to me, that ought to be applied to determine whether "the cult of the CEO has gone too far" is whether the formal and informal compensation provided to CEOs is justified by their contribution to value.  More concretely: would firms be worth more money if they paid CEOs less, reduced the prestige of the position or otherwise took CEOs down a peg?  Shareholders, Boards and private equity funds, when voting with their own money, say no. Conventional wisdom is sometimes wrong, however, and maybe it is in this case.  What evidence does Collingwood present to challenge the very widely-held belief that CEOs matter?

Speaking of the CEO's ability to drive company performance, he starts with this:

But how strong is this power--or any executive power? In their groundbreaking "Leadership and Organizational Performance: A Study of Large Corporations," first published in 1972 in American Sociological Review, Stanley Lieberson and James O'Connor argued that it's weak indeed. Perhaps reflecting the anti-authoritarian spirit of the times, the authors asserted that the CEO's influence was seldom decisive in a company's performance. They had the numbers to back up this view. Working with a database of 167 companies, they teased out the effects that various factors had on corporate profitability, from the competitive state of the industry to the size and structure of an individual company to the CEO's managerial decisions. "Industry effects," such as the amount of available capital and the stability of the market, accounted for almost 30 percent of the variance in corporate profits. "Company effects," such as the firm's historical place in the corporate pecking order, explained about 23 percent. "CEO effects" explained just 14.5 percent. And even this impact should be viewed skeptically: it unavoidably bundles CEO actions that were genuinely smart and skillful with those that were merely lucky.

Other scholars have attempted to replicate and extend Lieberson and O'Connor's findings, and many have likewise concluded that external forces influence corporate performance far more than CEOs do. Indeed, more-recent studies have tended to find a smaller CEO effect than Lieberson and O'Connor did--ranging from 4.5 percent to 12.8 percent of profit variance. (The scholar Alison Mackey, at Ohio State University, is a prominent dissenter. In a recent paper, she criticizes the number-crunching methods of Lieberson and O'Connor and, using a different methodology, concludes that CEOs have a dominant influence on performance that may well justify their high pay.)

Let's start with the observation that even if we assume that the choice of CEO drives on the order of 10% of the variation in corporate performance (as per Collingwood's interpretation of these studies), that is a very big number in absolute dollars.  If we apply the rational standard of whether owners would be better or worse off by paying CEOs less and treating them less well, this creates a pretty big umbrella for CEO comp and pomp.  Collingwood doesn't present the basic numbers that would be required to evaluate this question, especially how big is "variance in performance", so we could take a tenth of that, assign it to the CEO, and decide what the person is worth economically.

Here's some simple illustrative math.  I picked the median company on the most recent Fortune 500 (i.e., number 250), Smith International.  It has about $11 billion in sales and $1.6 billion in operating income.  A 1% swing in $1.6 billion is $16 million.  As context the median Fortune 500 CEO recently had total annual comp of about $6 million.  So as a shareholder of Smith International going into the market to hire a CEO, the question I would ask myself if presented with the choice of paying $6 million per year or, say, doubling this to $12 million per year, is not "Will the CEO I get for $12 million fundamentally transform my business?" or whatever; instead, I'd rationally ask myself, "Can the $12 million dollar CEO drive about 0.6% more operating profit than the person I would hire at $6 million?".

Even more fundamentally, Collingwood's interpretation of the quantitative analysis of what impact CEOs have on performance is extremely naïve.  Start with his lead analysis, Lieberson and O'Connor.  Notice that, according to Collingwood, what happened when other scholars attempted to replicate Lieberson and O'Connor's findings: "many have likewise concluded that external forces influence corporate performance far more than CEOs do."  Collingwood does not address what was probably the most influential attempt to replicate their findings, by Weiner and Mahoney, that showed that these results are highly dependent on unverifiable assumptions.

To understand why this is so, we need to consider, at least is rough terms, how you do this analysis.  Imagine by analogy that you have a list of 500 election precincts from the 2008 U.S. presidential election, and you know what percentages of votes were cast in each precinct for Obama, McCain and Other.  In addition, you have a list of 100 facts that describe each precinct, i.e., average income, average age, population density, and so on.  You want to measure the "impact" of average income on likelihood of voting for Obama.  You might start by asking which descriptor is most correlated with voting for Obama.  Let's say you discover that it's population density.  You could then observe the mathematical relationship that "for every additional hundred people per square mile in a precinct, Obama's vote percentage is about 0.1% higher".  You could then use this relationship to "adjust" the result for each of your 500 precincts to get a population density-adjusted vote percentage for Obama.  You might then observe that average age is correlated with the population density-adjusted Obama vote percentage, and in a similar fashion now further adjust the Obama percentage by precinct to get a population density and age-adjusted Obama vote percentage, and so on.  Once you reach some stopping condition, you could then measure the correlation between income and the adjusted-for-all-other-relevant-variables Obama vote percentage, and call this the estimated impact of income on likelihood to vote for Obama after adjusting for all relevant factors.

The problem should be obvious.  If income is correlated with population density, age and the other factors for which you adjusted, then how do you know whether you should "adjust" for any of these factors first?  That is, how do you know that it wasn't the income difference that was driving the difference in voting behavior, and therefore when you "adjusted" by these other factors, you were really under-estimating the causal impact of income on voting?  What order you choose to put the factors into the model can have a huge impact on the estimate for the impact of each factor.  This is most easily seen in a limit case.  Suppose across my 500 precincts age and income are perfectly correlated (e.g., the precinct with average age 18 has an average income of $18,000, the precinct with an average age of 19 has an average income of $19,000, all the way up to the precinct with an average age of 60 that has an average income of $60,000). Further suppose that age (and by extension, for this example, income) is correlated with voting behavior and no other descriptors are correlated at all with voting behavior.  In this case if I "adjust" for age first, I will estimate that income has no effect on voting.  If I don't, I will estimate that income has an enormous effect on voting behavior.  Which is correct?  There is no way to determine the answer with this data set.

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Jim Manzi

Jim Manzi is Founder and Chairman of Applied Predictive Technologies (APT), an applied artificial intelligence software company. He is In also a Senior Fellow at the Manhattan Institute and a Contributing Editor of National Review, where he writes frequently for both the print and online editions on topics related to science, technology, business and economics.
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