Risk Analysis of Economic Analysis

The approach also goes beyond sensitivity analysis and switching values for assessing risk. The former estimates how sensitive project outcomes are to changes in the values of critical variables. In a transportation project, for example, sensitivity analysis would indicate to analysts what the effect of a 10 percent decline in traffic on the net present value of the project would be. Switching value analysis identifies the value that a critical variable must assume for the project to become unacceptable. In the transportation example, it would identify the volume of traffic at which the project’s net present value would go to zero. While both techniques are useful in project design, they do not take probabilities or correlations into account, which limits their usefulness. Switching value analysis may tell us that a project will fail if a given variable departs by more than 25 percent from its posited value, but if we do not know the likelihood of this event, the information is of limited use. The major shortcoming of both types of analyses, however, is the disregard for correlation. When one thing goes wrong, something else is likely to go wrong: correlations can be devastating. For example, if projected traffic along a given corridor falters because the expected economic growth failed to materialize, fiscal receipts may also fall short. Consequently, counterpart funds may also be in short supply. Analyzing the impact of one variable at a time may mislead us into believing that risky projects are in fact robust.

Monte Carlo analysis takes into account probabilities and correlations and identifies the likely impact of each variable on project outcomes. It can also take into account delays and other events that may impinge on project outcomes. More important, it helps assess the expected net present value of the project, the probability distribution of the outcome, and the probability of project failure. By ranking the variables in terms of their impact on project outcomes and probability of occurrence, Monte Carlo simulation helps analysts design better projects and identify the variables worth tracking during project performance. Until recently, Monte Carlo simulations were time-consuming, expensive, and difficult. With the advent of personal computers and readily available risk analysis programs, Monte Carlo techniques are as convenient to use as spreadsheets.