bidding for the particular project that has been won. Thus they do not explic- itly reflect costs of other unsuccessful bids, which have to be recovered at least in part through the equity return. Second, under the 25–30 year contracts of the PFI, bidders may price their target returns over a fixed rate such as the swap rate rather than the gilt rate, as assumed in the conventional CAPM. The use of swap rates rather than the gilt rate as the risk-free rate in the cost of equity would increase the average cost of capital from 5.3 per cent to between 5.75 per cent and 6.25 per cent.
Consequently, of the ‘spread’ of 2.4 per cent per annum, 1.7 per cent is thought to be accounted for by two factors:
• unrecovered bid costs on other projects (about 1 per cent);
• the higher cost of underlying rates for private sector borrowing compared with public sector borrowing, primarily caused by the cost of swaps compared with gilts (about 0.7 per cent).
After taking account of these two factors, the excess projected return to project investors is therefore estimated as being about 0.7 per cent. These excess returns are attributed to ‘structural issues’ that in the past have limited compe- tition in the PFI market. For instance, the length of PFI procurements and the level of bid costs incurred by the private sector are thought still to create bar- riers to entry to the market.
We do not disagree with the PwC report as to the significance of a compet- itive PFI market – market capacity and competitive pressures in the bidding process are essential ingredients in generating value for money. But we are also aware that there is a missing piece of the jigsaw, and that is ‘true’ uncer- tainty, and we now consider the possible implications of this distinction for PPP projects.
On what basis, for instance, would one have assigned a probability distribu- tion to September 11 and its impact on world tourism?
From the viewpoint of the preceding analysis, the relevant questions would seem to be, first, do firms take account of uncertainty and second, how do firms allow for uncertainty in their decision-making?
The answer to the first question is straightforward. Firms ignore uncertainty at their peril. Shackle (1955) argues that ‘true’ uncertainty involves not only unique events but also typically crucial ones.
Crucialness is the real and important source of uniqueness in any occasion of choos- ing: and far from being unusual, it is all-pervasive. (p. 63)
By a crucial experiment I mean one where the person concerned cannot exclude from his mind the possibility that the very act of performing the experiment may destroy for ever the circumstances in which it was performed. (p. 6)
For a business man, the possibility of a crippling loss of capital implies a large-scale operation, and perhaps we can say, typically either the founding of a new enterprise or a large extension of plant for an existing one. It is, indeed, in the field of invest- ment-decisions (in the economist’s sense of the word ‘investment’), that personal uniqueness of the occasion mainly occurs. (p. 86)
The large sunk costs would suggest that infrastructure is a crucial investment, while the network effects, externalities and other characteristics identified in Chapter 2 would indicate that there is a degree of uniqueness to every venture.
The UK Green Book urges public sector project appraisers to ‘consider how future uncertainties can affect the choice between options’ (HM Treasury, 2003a, p. 32), and recommends sensitivity analysis, Monte Carlo analysis and scenario planning (creating detailed models of future states of the world).
Sensitivity analysis and Monte Carlo analysis are considered in the next chap- ter, and both are standard tools for dealing with risk (rather than ‘true’ uncer- tainty). It is interesting to note that the old US Green Book (United States Government, 1958) suggested three ways of dealing with uncertainty. One was conservatism in estimating costs and benefits; a second was a conservative estimate of the economic life of projects; and the third was an addition of a premium to the discount rate that varied directly with the lack of confidence in benefit and cost estimates. Marglin (1967, pp. 97–8) criticized this last approach in what is essentially an early statement of the ‘old’ view owing to Arrow and Lind (1970) that we discussed earlier based on the government’s diversification capacity.
The fact that the failure or below par performance of some projects may be balanced by an unexpected degree of success of others allows a government to concentrate more on expected values, and to worry less about the dispersion of outcomes, of individual projects than private investors can afford to do.
However, Marglin points out that ‘conservatism is an appropriate counter- measure for the invariably optimistic bias of the technicians who estimate benefits and costs’ (p. 73). Obviously, ‘optimism bias’ is not a new problem in public sector procurement.
The private sector firm engaged in infrastructure investment faces a very different scenario, and cannot overlook what Marglin describes as the
‘nonactuarial nature of uncertainty’ (p. 73). As Blatt (1983) puts it:
the threat of disaster, i.e. bankruptcy, is a very important factor. The future is too uncertain to predict at all well, but a businessman wants to have a reasonable chance of avoiding disaster, not merely for the next project, but for his entire expected time before retirement. He must, therefore, choose between projects available right now in a sufficiently conservative fashion so that he retains a fair chance of not sitting in the poorhouse or the debtors’ prison at retirement age . . . The precise meaning of ‘disaster’ can vary considerably: to a businessman, it is a loss so great that it drives him bankrupt; to a manager, employed by a firm, a much smaller loss than that can result in his being dismissed, without much of a chance of finding alternative employment as a manager. (pp. 279–80)
How then is uncertainty likely to be taken into account? There are many, as we noted earlier, who consider that probability analysis cannot help. In Shackle’s words:
we might say without further ado that frequency-ratio probability has nothing to do with true uncertainty. (p. 26)
To [the mathematical probability theorist] we simply multiply the frequency- ratio of each contingent profit by the amount of that profit and by one thousand, and add together all the answers. The result is what is called the mathematical expectation. This impressive name may mislead us into thinking that the mathe- matician has performed a miracle, has got something out of his calculating machine that he did not put into it, and has changed the enterpriser’s situation from essential uncertainty to certainty. But he has not . . . the frequency-ratios tell us nothing about the individual throws of the dice, or the individual and particu- lar business ventures. (p. 84)
It is because in the text-book examples of drawing from an urn, and in the games of chance, factors whose existence and nature are unknown are excluded by the rules of the game, that these examples and games are irrelevant to reality.
(p. 39)
It is the uniqueness and crucialness that creates the problem: ‘Napoleon could not repeat the battle of Waterloo a hundred times in the hope that, in a certain proportion of cases, the Prussians would arrive too late’ (p. 25).
From the viewpoint of infrastructure, whether probability analysis will suffice turns on two questions. Is there a sufficiently large number of projects that are similar, statistically speaking, to each other and to those in the past to enable reliable probability distributions to be formed? Or, are the
infrastructure ventures sufficiently different (unique, crucial) that we are dealing with projects that cannot be treated as if off a production line? Some routine PPP projects involving schools and accommodation may be able to be replicated in adequate numbers, in the appropriate sense, but for many other projects this would not be so, especially if PPPs are seen as particu- larly valuable for major, complex, innovative and largely one-off projects.
If, then, standard probability analysis based on known probabilities cannot deal with true uncertainty, what can? At a theoretical level, the main approach to the analysis of uncertainty is in terms of state preference theory which represents the random events facing an individual in terms of a set of mutually exclusive and exhaustive states of nature or states of the world.13 At an empirical level, a not uncommon approach is to recognize the distinc- tion between risk and uncertainty, and then treat both as risk. For example, Lessard and Miller (2001) do this, but at the same time emphasize that risks are multi-dimensional, and can combine and interact to create turbulence so that projects become ‘ungovernable’ (p. 8). They also argue the importance of creating options for subsequent choices within a real options framework.
Real options theory (Dixit and Pindyck, 1995) presumes that decision- making is sequential and that decision-makers may benefit from choosing options that may seem suboptimal today but which increase flexibility at later times, leading to better decision-making when more is known about the project. There is some affinity in this accumulation of information with Bayesian decision theory, which is the third approach to uncertainty that we would mention.
Bayesian theory starts with incomplete knowledge of the prior distribu- tion and is based on the formation of subjective probabilities derived from preferences over risky options that are updated in the light of new informa- tion.14 Many argue that the Bayesian approach to learning by experiment provides a logical framework for quantifying partial belief that brings uncer- tainty within the fold of probability analysis and decision-making under risk, rendering redundant the distinction that Knight made between risk and uncertainty (LeRoy and Singell, 1987; Hirschleifer and Riley, 1992).
However, not everyone agrees that the distinction between risk and ‘true’
uncertainty can be discarded (Kelsey and Quiggin, 1992; Runde, 1995).
Implementation of the Bayesian decision rule is far from simple (Kirman and Salmon, 1995): it is computationally complex, may be unable to cope with ‘radical’ uncertainty (Casson, 2000b, Chapter 4), and relies on a process of trial and error that the decision-maker in infrastructure projects cannot pursue.
Among the authors cited earlier there is consensus that ‘ineradicable uncertainty’ cannot be handled by frequency distributions and calculations of mathematical expectation, and must be tackled by a different line of
thought, but there is less agreement thereafter. Firms may be forced to fall back on simple expediencies such as allowing a margin for error to be on the safe side (Shackle, 1955, p. 83), or employ a simple decision rule like the pay- back limit rule; that is, whether the cumulative net revenue from a project will cover the original investment within a specified period of time (Blatt, 1983, p.
286). Firms may take into account both the ‘best possible’ and the ‘worst possible’ outcome of each course of action (the former the highest hope from the project, the latter the worst fear), and make these pairs of outcomes the basis of the decision, having in mind an upper limit for the loss that can be contemplated and whether the worst-case scenario could be survived (Shackle, 1955, p. 89). Blatt argues for the adoption of a risk-adjusted, time-dependent cost of capital, not just one with a constant risk margin, for which business- men (or businesswomen) would set their maximum allowable risk differently and have different subjective estimates of the horizon of uncertainty. This time-dependency, he notes (1983, p. 285), explains why there is so much controversy over the determination of the appropriate value of the risk- adjusted discount rate. In his view, no appropriate value exists!
Despite these different ideas about coping with uncertainty, there is agreement at least among the latter writers that, in the face of true uncer- tainty, firms will adopt a conservative ‘safety-first’ approach in which the objective seems clear.
The business management has to see each project it starts to its conclusion, and its policy must be such that it retains a reasonable chance of surviving in business for longer than just the very next project. (Blatt, 1983, p. 261)
The same can be said of specialist equity investors and others drawn into the venture. Any sensible bidder for a PPP project would want to allow some measure for uncertainty. How exactly this is done is not clear but it would seem apparent that the ‘ineluctable, irreducible uncertainties that every- where confront us in this life’ (Shackle, 1955, p. 16) will not go away by assuming them away, and that firms with an instinct for survival will allow for true uncertainty in one way or another.
If we are correct, then there are obvious implications for the ‘missing’ 0.7 per cent in the PricewaterhouseCoopers rate of return study, which they attribute to structural weaknesses in the PPP market but which we would see in some measure as linked with uncertainty. But uncertainty also has impli- cations for value-for-money tests. Earlier we referred to the analysis of Grout (1997) who argued that the riskiness of PPP cashflows is higher than that for PSC cashflows, warranting a higher rate for the PPP discounting than for the PSC. The fact that uncertainty exists and will be priced into the PPP cashflows reinforces this point.