This expert guide explains the fundamentals and practical application of cost benefit analysis (CBA) for organizational decision-making.
The article outlines a five-step process for conducting CBA, including establishing a framework, identifying and categorizing costs and benefits, quantifying and comparing them, analyzing results, and making recommendations.
Real-world case studies from Seattle's monorail extension and California's solid waste program are included to illustrate CBA in action.
Key risks and limitations of CBA, especially regarding intangibles and the influence of data accuracy and human biases, are discussed.
Action Items
None specified in the source material.
Overview of Cost Benefit Analysis (CBA)
CBA is a systematic process for estimating the strengths and weaknesses of alternatives to inform decisions, involving subtracting estimated costs from anticipated benefits.
Utilized in various fields (business, government, nonprofits) for decisions such as project feasibility, resource allocation, and policy advisability.
Originated with French engineer Jules Dupuit in the mid-1800s; popularized by economist Alfred Marshall.
Typical Scenarios for Application
Used to benchmark and compare projects, assess policy desirability, evaluate hiring, weigh investments, and quantify stakeholder impact.
Five-Step Cost Benefit Analysis Process
Establish a Framework: Clearly define the program or change to be analyzed, including its context and the risks of the status quo.
Identify and Categorize Costs/Benefits: Classify as direct/indirect, tangible/intangible, and real; consider all foreseeable costs and benefits.
Quantify Costs and Benefits: Project costs/benefits over the lifetime of the initiative, converting future amounts to present value.
Compare Aggregates: Calculate net present value (NPV) and return on investment (ROI); consider legal/social feasibility, using with/without comparisons rather than before/after.
Analyze Results and Recommend: Perform sensitivity analysis (“what-if”), consider discount rates (social, hurdle, annual effective) to test financial viability, and make a data-driven recommendation.
Risks, Uncertainties, and Controversies in CBA
Human bias can influence data selection and estimates; prior project data may be misapplied.
Intangible factors (e.g., human life, brand loyalty, environment) are difficult to monetize, potentially leading to controversy.
Results are sensitive to data accuracy, future uncertainty, and the use of heuristics.
Risk management can involve probability theory and explicit sensitivity analysis.
Real-World Case Studies
Seattle Monorail Extension
Total estimated cost: $1.68 billion (2002 USD).
Benefits projected over 23 years included travel time savings, reduced parking and auto costs, and fewer accidents.
Net present value (NPV): $390 million; benefit-cost ratio: 1.23; nominal rate of return: 7.95%.
Sensitivity analysis revealed a 60% chance of staying at or under budget; expected returns ranged from 5.2% (high cost/low benefit) to 9.9% (low cost/high benefit).
California Solid Waste Reduction Program
Annual program cost: $16,440; annual benefit: $1,308,865; resulting in net savings of $1,292,425 per year.
Benefits arose from significant disposal cost reductions after program implementation.
Decisions
Recommendation to use cost benefit analysis for data-driven decision-making — rationale: provides a clear framework, considers all costs/benefits, and enables informed choices despite uncertainties.
Open Questions / Follow-Ups
How to address quantification of highly intangible or subjective benefits and costs in specific CBA implementations?
What best practices can be applied to minimize human bias and ensure data accuracy during the CBA process?