How local government can avoid unwise capital investments
Sisyphus' profligacy and dishonesty about money carried an epic punishment. Image by Gerard Van der Leun, CC BY-NC-ND 2.0
Santa Clara County's misguided decision to purchase a collection of failing hospitals is a classic example of capital mismanagement, prompting the need for the widely-panned Measure A sales tax hike. Harvard Business Review explains the mindset, processes, and protocols good leaders exhibit (county Supes take note) when protecting, growing, and making the most of capital assets.
Of all the decisions that business executives must make, none is more challenging—and none has received more attention—than choosing among alternative capital investment opportunities. What makes this kind of decision so demanding, of course, is not the problem of projecting return on investment under any given set of assumptions. The difficulty is in the assumptions and in their impact. Each assumption involves its own degree—often a high degree—of uncertainty; and, taken together, these combined uncertainties can multiply into a total uncertainty of critical proportions. This is where the element of risk enters, and it is in the evaluation of risk that the executive has been able to get little help from currently available tools and techniques.
There is a way to help the executive sharpen key capital investment decisions by providing him or her with a realistic measurement of the risks involved. Armed with this gauge, which evaluates the risk at each possible level of return, he or she is then in a position to measure more knowledgeably alternative courses of action against corporate objectives.
Need for New Concept
The evaluation of a capital investment project starts with the principle that the productivity of capital is measured by the rate of return we expect to receive over some future period. A dollar received next year is worth less to us than a dollar in hand today. Expenditures three years hence are less costly than expenditures of equal magnitude two years from now. For this reason we cannot calculate the rate of return realistically unless we take into account (a) when the sums involved in an investment are spent and (b) when the returns are received.
Comparing alternative investments is thus complicated by the fact that they usually differ not only in size but also in the length of time over which expenditures will have to be made and benefits returned.
These facts of investment life long ago made apparent the shortcomings of approaches that simply aver-aged expenditures and benefits, or lumped them, as in the number-of-years-to-pay-out method. These shortcomings stimulated students of decision making to explore more precise methods for determining whether one investment would leave a company better off in the long run than would another course of action.
It is not surprising, then, that much effort has been applied to the development of ways to improve our ability to discriminate among investment alternatives. The focus of all of these investigations has been to sharpen the definition of the value of capital investments to the company. The controversy and furor that once came out in the business press over the most appropriate way of calculating these values have largely been resolved in favor of the discounted cash flow method as a reasonable means of measuring the rate of return that can be expected in the future from an investment made today.
Thus we have methods which are more or less elaborate mathematical formulas for comparing the outcomes of various investments and the combinations of the variables that will affect the investments. As these techniques have progressed, the mathematics involved has become more and more precise, so that we can now calculate discounted returns to a fraction of a percent.
But sophisticated executives know that behind these precise calculations are data which are not that precise. At best, the rate-of-return information they are provided with is based on an average of different opinions with varying reliabilities and different ranges of probability. When the expected returns on two investments are close, executives are likely to be influenced by intangibles—a precarious pursuit at best. Even when the figures for two investments are quite far apart, and the choice seems clear, there lurk memories of the Edsel and other ill-fated ventures.
In short, the decision makers realize that there is something more they ought to know, something in addition to the expected rate of return. What is missing has to do with the nature of the data on which the expected rate of return is calculated and with the way those data are processed. It involves uncertainty, with possibilities and probabilities extending across a wide range of rewards and risks.
The expected rate of return represents only a few points on a continuous cure of possible combinations of future happenings. It is a bit like trying to predict the outcome in a dice game by saying that the most likely outcome is a 7. The description is incomplete because it does not tell us about all the other things that could happen. In Exhibit I, for instance, we see the odds on throws of only two dice having 6 sides. Now suppose that each of eight dice has 100 sides. This is a situation more comparable to business investment, where the company’s market share might become any 1 of 100 different sizes and where there are eight factors (pricing, promotion, and so on) that can affect the outcome.
Describing uncertainty—a throw of the dice
Our willingness to bet on a roll of the dice depends not only on the odds but also on the stakes. Since the probability of rolling a 7 is 1 in 6, we might be quite willing to risk a few dollars on that outcome at suitable odds. But would we be equally willing to wager $10,000 or $100,000 at those same odds, or even at better odds? In short, risk is influenced both by the odds on various events occurring and by the magnitude of the rewards or penalties that are involved when they do occur.
To illustrate again, suppose that a company is considering an investment of $1 million. The best estimate of the probable return is $200,000 a year. It could well be that this estimate is the average of three possible returns—a 1-in-3 chance of getting no return at all, a 1-in-3 chance of getting $200,000 per year, a 1-in-3 chance of getting $400,000 per year. Suppose that getting no return at all would put the company out of business. Then, by accepting this proposal, management is taking a 1-in-3 chance of going bankrupt.
If only the best-estimate analysis is used, however, management might go ahead, unaware that it is taking a big chance. If all of the available information were examined, management might prefer an alternative proposal with a smaller, but more certain (that is, less variable) expectation.
Read the whole thing here.
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