Management, these days, is full of corporate metrics that managers are urged to follow, to maximize or to minimize, in any case to outperform: EVA, CFROI, shareholder value, etc... to name a few.
The theory underlying these catch-all metrics boils down to the idea that following them is good for the corporation as a whole. As a result corporate decisions should always be benchmarked against them. In a nutshell, what is good for shareholders is also good for the firm as a whole. Managers who do well with respect to the metrics shall then be handsomely rewarded.
Surprisingly enough, the discussion around the true relevance of these benchmarks is rather tenuous. Yes, market imperfections are discussed and principal-agent models, game-theoretic-models have been crafted to discuss situations when incentives of different stakeholders are not properly aligned.
However, it is rather puzzling that a principle known under the name of "Goodhart's law" is hardly mentioned in business books. The law is named after Professor Charles A. E. Goodhart, Norman Sosnow Professor of Banking and Finance at the London School of Economics, who crafted it with central banking in mind. According to the 99th edition of Pears Cyclopaedia (1990--1, pp. G 27, G31), the law states that:
"As soon as the government attempts to regulate any particular set of financial assets, these become unreliable as indicators of economic trends."
"financial institutions can... easily devise new types of financial assets."
Professor Charles Goodhart was Chief Adviser to the Bank of England. He gave his own statement of the law in his Monetary Theory and Practice textbook, page 96:
"Any observed statistical regularity will tend to collapse once pressure is placed upon it for control purposes."
In other words, when a measure becomes an objective, it is no longer a good measure. Too much emphasis on a measure as a target kills the measure that may have been relevant in the first place. Why? Because it is all to obvious that people who are subject to a given metric (say, outperform some profitability index) will do whatever it takes to beat it. They will game it as much as they can. For instance, in the case of an asset profitability ratio, they will minimize the denominator (assets) instead of maximizing the numerator (profit). This will last as long as the asset accounting trick is not discovered. And, this can last long: It was recently discovered that Toshiba manipulated its profitability seven years in a row! The more distant history is also quite rich in such stories, chief among them that of former USSR and its obsession for production targets (yielding the results that we all know about).
Now, even if metrics parameters are not manipulated, metric based management and compensation is not a good idea at all as it tends to reward (convex exposure to) luck rather than skill. Take the example of stock-options. A lot of managers became wealthy upon their exercise. Was it because they were remarkably skilled or just because it turned out that interest rates were going south lifting the stock price? How do you distinguish between what is due to chance and what is due to skill? Is not it unfortunate that metrics end up paying well people that were simply in the right place at the right moment?
Worse, in economies like our today digital economies which are highly non-linear (knowledge-based economies, complex systems, winner-take-all etc...), it is not even clear that any such measure does exist. More often than not, obliquity is at work. Metrics are very poor at capturing the richness and complexity in which we live and operate. They simply don't work.
So, why is that most business books authors and professors forget about Goodhart's law as if they were petrified telling students that, indeed, the oblique world outside is not made for metrics?