Projects and policies often involve significant investments of time, resources, and money for donors, governments, beneficiaries, or others. To ensure that they are using their limited resources most effectively, organizations increasingly conduct value-for-money and cost analyses to assess the expected benefits and costs of different activities. This blog post discusses how such analyses can be extended to help organizations understand the risks and uncertainty inherent in their projects and policies.
Economic, political, or environmental shocks can significantly affect the expected benefits and costs associated with a project or make a project impossible to implement. Similarly, cost overruns, delays, or shifts in beneficiary needs, market prices, and exchange rates can turn a once-promising project into one that is no longer cost-effective.
In environments with risks and uncertainty, it is not sufficient to simply consider the “expected” or “most-likely” outcome associated with a project or policy. Rather, it is important to understand and plan for the range of things that could happen.
A better understanding of risks and uncertainty can benefit an organization in several ways:
While many textbooks discuss the technical approach for risk analysis (such as sensitivity analysis and Monte Carlo simulations), there were no practical guidelines on how these methods can come together for the integration of risk and uncertainty into the analysis. USAID decided to address this gap in 2022. Limestone worked with USAID to develop guidelines for incorporating risk and uncertainty when estimating measures of value for money and cost analysis using tools such as cost-benefit and cost-effectiveness analysis. Below, we summarize some of the key insights from our contribution to USAID’s practitioners’ guide to Handling Risk and Uncertainty in Cost-Benefit Analysis.
Experience and existing literature on risk analysis can help practitioners identify the key sources of risk (technical, financial, political, environmental, etc.). However, it is important to divide the related critical assumptions into two buckets:
This categorization helps separate the critical assumptions that can inform probabilistic approaches to risk analysis (such as expected value analysis or Monte Carlo simulations) from those that require different methods such as scenario analysis. Furthermore, the guide introduces decision parameters as a third category of critical assumptions:
Decision parameters can help in designing alternative scenarios. Separately labeling decision parameters also helps ensure they don’t get mixed up with risk and uncertain variables.
The second most important contribution of the report is a step-by-step guide on how to integrate risk and uncertainty in analytical models.
Risk analysis is a vital component of successful project planning. By systematically identifying, assessing, and managing risks and uncertainty, organizations can make more informed decisions, allocate resources more efficiently, and design projects that are more resilient to potential challenges. Moreover, risk analysis fosters transparency and trust among stakeholders, leading to improved collaboration and accountability. Ultimately, incorporating risk analysis into the planning, implementation, and evaluation of projects and policies can substantially enhance their likelihood of success and contribute to lasting, positive change for the communities they serve.
Bahman Kashi is the President of Limestone Analytics and an Adjunct Professor of Economics at Queen’s University. Christopher Cotton is Research Officer at Limestone Analytics and the Jarislowsky-Deutsch Chair in Economic & Financial Policy at Queen’s University.
