What is the primary purpose of sensitivity analysis in cost-benefit analysis?

The primary purpose of sensitivity analysis in cost-benefit analysis (CBA) is to assess the robustness of a project's conclusions – whether the project is worthwhile – when key input variables change. Cost-benefit analysis is a systematic approach to estimating the strengths and weaknesses of alternatives. It calculates the total costs and benefits of a project or policy to determine if it’s economically justifiable. A core output of CBA is a net present value (NPV), which represents the difference between the present value of benefits and the present value of costs. If the NPV is positive, the project is generally considered beneficial; if negative, it is not. However, CBA relies on estimates, and these estimates are often uncertain. Sensitivity analysis addresses this uncertainty.

Sensitivity analysis explores how changes in these uncertain variables affect the NPV and, consequently, the overall decision. It doesn't aim to find the 'right' answer, as that's often impossible with imperfect data; instead, it investigates how sensitive the decision is to changes in assumptions. This is achieved by systematically varying one or more input variables within a plausible range and observing the resulting impact on the NPV. For example, a CBA for a new highway might estimate the annual traffic volume. Sensitivity analysis would then examine how the NPV changes if the traffic volume is 10% higher or lower than the initial estimate. Other key variables often subjected to sensitivity analysis include discount rates (the rate used to calculate the present value of future costs and benefits), project costs, and benefit estimates (like increased productivity or reduced pollution).

There are several techniques used within sensitivity analysis. One-way sensitivity analysis changes one variable at a time, while multi-way sensitivity analysis considers the combined effect of multiple variables changing simultaneously. Scenario analysis, a related technique, examines the NPV under specific, predefined scenarios (e.g., a 'best-case,' 'worst-case,' and 'most likely' scenario). The results of sensitivity analysis are typically presented to show the range of possible NPVs under different conditions. If the NPV remains positive even when key variables are significantly altered, the project is considered more robust. Conversely, if small changes in variables lead to a negative NPV, the project is highly sensitive and potentially risky, requiring further investigation or reconsideration. Ultimately, sensitivity analysis helps decision-makers understand the potential risks and uncertainties associated with a project and make more informed decisions, even when faced with imperfect information.