6. Challenges and Common Traps in Cost-Benefit Analysis

6.1. Introduction: Beyond the Numbers

Cost-Benefit Analysis (CBA) is often viewed as a gold standard for evidence-based policymaking, combining economic theory with structured decision-making tools. Its strength lies in its ability to convert diverse outcomes into a common monetary metric, guiding choices that promise the greatest net social benefit. But like any powerful tool, CBA must be applied with care and critical thinking. Behind the appearance of numerical precision lie numerous assumptions, judgments, and methodological choices that shape the final result. Recognising this complexity is crucial—not to diminish the value of CBA, but to ensure it is used wisely, ethically, and transparently.

In the preceding chapters, we established how Cost-Benefit Analysis (CBA) offers a rigorous framework for comparing alternative policy options based on their economic efficiency. It enables decision-makers to weigh costs against benefits, quantify trade-offs, and support more transparent, accountable public investment.

However, CBA is not infallible. While it is methodologically sound, its results are only as reliable as the assumptions, valuations, and inputs it relies on. Missteps—intentional or otherwise—can lead to flawed conclusions and unjust outcomes.

This chapter explores the common analytical traps and limitations of CBA, while grounding each in economic theory and practical examples. It also highlights how overlooking key issues such as distributional equity, non-market values, or dynamic change can compromise the integrity of a CBA. These insights are essential not only for practitioners but also for readers seeking a critical lens on how economic models influence real-world decision-making.

6.2. Overlooking Non-Monetary Values

Cost-Benefit Analysis (CBA) draws its strength from its ability to translate complex decisions into comparable monetary terms. However, one of the most enduring criticisms of traditional CBA is its systematic exclusion or undervaluation of non-market benefits—particularly those related to environmental services, cultural heritage, social cohesion, and psychological wellbeing. When these forms of value are omitted or inadequately captured, CBAs risk distorting the true worth of public goods and undermining investments that deliver significant societal returns.

Theories of Value in Economics

The issue lies not in the logic of CBA itself, but in the economic conception of value that underpins it. Early classical economists such as Adam Smith (1776), David Ricardo (1817), and Karl Marx debated the labour theory of value, which proposed that the value of a good was determined by the amount of labour required to produce it. However, this framework struggled to explain value in markets where labour input did not correspond with price, such as with rare collectibles or environmental goods.

In the late 19th century, the marginalist revolution led by economists like William Stanley Jevons (1871), Carl Menger (1871), and Léon Walras shifted the focus toward subjective value, giving rise to the modern utility-based theory. Under this framework, value is determined by the individual’s willingness to pay, based on the utility (satisfaction) derived from consuming a good or service. This approach continues to dominate neoclassical economics and serves as the foundation for modern CBA.

While utility theory is effective in analysing market transactions, it struggles to capture the value of goods that are not bought or sold, such as biodiversity, clean air, or cultural landscapes. This limitation becomes especially problematic in public sector analysis, where many high-impact benefits are non-market in nature.

Total Economic Value (TEV): A Broader Framework

To address this gap, environmental and welfare economists developed the Total Economic Value (TEV) framework. TEV recognises that value is not confined to market transactions but also includes non-use values that individuals hold for reasons beyond direct consumption. It breaks value down into the following components:

This broader conception of value is particularly relevant in public investments, environmental planning, and social policy, where market prices often underrepresent the societal significance of many outcomes.

Applied Research: Making Nature Count

In my own work, Making Nature’s Value Visible, I applied the TEV framework to evaluate the ecosystem services of a peri-urban natural reserve. By integrating replacement cost methods, willingness-to-pay, and benefit transfer techniques, we were able to quantify services including:

• Water purification provided by wetland systems

• Carbon sequestration from restored bushland

• Aesthetic and recreational benefits to local residents

• Educational and cultural values associated with indigenous land stewardship

The resulting valuation revealed that non-market benefits constituted over 70% of the total economic value of the landscape. Had we relied on market data alone, the project’s worth would have been drastically understated—possibly resulting in disinvestment or reallocation to lower-impact uses.

Why This Matters in CBA

Omitting non-market values doesn’t just underestimate the project’s worth—it can reverse decisions. Public goods, by their nature, deliver widespread but diffuse benefits that are often invisible to price-based analysis. This oversight leads to systematic bias against environmental, social, and cultural projects, even when they generate profound public value.

For CBA to remain a credible tool for public decision-making, analysts must integrate frameworks like TEV and apply robust valuation techniques for non-market goods. This includes contingent valuation, hedonic pricing, stated preference methods, and expert elicitation where appropriate.

6.2. Inaccurate Discount Rates

One of the most technically significant—and often misunderstood—challenges in CBA is the choice of discount rate. The discount rate is used to convert future costs and benefits into their present value, based on the principle that a dollar today is worth more than a dollar tomorrow. This is known in economics and finance as the Time Value of Money (TVM).

Time Value of Money in Economics

The concept of TVM is foundational in both microeconomics and investment analysis. It reflects the idea that resources have opportunity costs: money available now can be invested, consumed, or otherwise used in ways that produce returns. Therefore, future benefits must be “discounted” to reflect their lesser value today.

In the context of CBA, this principle is applied using a discount rate, which reflects society’s time preference for money, risk attitudes, and expected economic growth. Future costs and benefits are adjusted using the present value formula:

Economic Theory: The Ramsey Rule

In public sector economics, the choice of discount rate is often guided by the Ramsey Formula, developed by economist Frank P. Ramsey. The formula sets the social discount rate (r) as:

This formulation reflects ethical judgments about intergenerational equity, consumption smoothing, and risk aversion. If the pure time preference is set too high, it implies we value future generations less—an ethical stance that has been heavily criticised in the context of climate change and sustainability.

Why This Matters in CBA

Choosing a discount rate has a profound impact on whether a project appears worthwhile. A higher discount rate reduces the present value of long-term benefits, making future gains appear smaller and biasing against preventive or sustainability-focused investments. Conversely, a very low discount rate may overvalue long-term benefits and underplay near-term costs.

This becomes particularly critical in evaluating infrastructure, healthcare, education, and environmental projects, where costs are immediate but benefits accrue over decades.

An inaccurate or poorly justified discount rate can lead to misguided decisions, either by exaggerating near-term returns or dismissing long-term value. Public agencies must therefore choose discount rates transparently and ethically, guided by economic reasoning, social context, and intergenerational fairness.

Best practice involves:

• Following government appraisal guidelines (e.g. UK’s Green Book suggests 3.5%)

• Conducting sensitivity analysis using alternative discount rates (e.g. 3%, 5%, 7%)

• Justifying the chosen rate in terms of opportunity cost, growth expectations, and equity

6.3. Ignoring Distributional Effects

Traditional CBA is primarily designed to assess efficiency, not equity. It answers the question: “Does this project or policy deliver a net gain to society?” However, it often fails to answer a more socially important question: “Who benefits, and who bears the cost?” This oversight can result in policies that are efficient on paper but unjust or politically unviable in practice.

Economic Theory: Efficiency vs. Equity

At the core of this issue lies the tension between allocative efficiency and distributive equity. While efficiency refers to maximising total net benefits, equity focuses on how those benefits and costs are shared across different individuals or groups.

The classic Kaldor-Hicks compensation principle suggests that a project is desirable if winners gain enough to hypothetically compensate losers—even if no actual compensation takes place. This principle underpins most cost-benefit analysis today. However, critics argue that it allows policies that may disproportionately burden disadvantaged groups, so long as aggregate net benefits are positive.

John Rawls (1971), in A Theory of Justice, challenged this logic by arguing that fairness requires policies to be evaluated not by total gains, but by how they affect the least advantaged members of society. His difference principle proposes that inequalities are only justifiable if they benefit the most vulnerable.

Why This Matters in CBA

Ignoring distributional impacts can result in policies that exacerbate inequality, generate public resistance, or fail to meet broader social goals—even when they appear efficient in aggregate terms. For public sector CBAs, where projects are funded by taxes and aimed at improving collective wellbeing, equity considerations are not optional—they are essential.

Neglecting who benefits and who pays may also mislead decision-makers. A project with modest net benefits but widespread positive impact across vulnerable communities might be more desirable than a high-return project that benefits only a small, affluent population.

Best Practice Approaches

To improve distributional analysis in CBA, analysts can:

• Segment beneficiaries and losers by income group, geography, demographic group, etc.

• Apply equity weights, giving greater importance to gains for disadvantaged groups

• Include a distributional matrix that shows how costs and benefits are spread

• Conduct stakeholder analysis or participatory engagement to capture hidden impacts

• Combine CBA with Social Impact Assessment (SIA) or Social Return on Investment (SROI) tools

Efficiency alone does not guarantee fairness. For public investments to be socially legitimate and ethically grounded, they must consider not only how much is gained or lost, but also who gains or loses. Ignoring distributional effects may lead to policies that entrench disadvantage, erode public trust, or fail to deliver inclusive development.

CBA practitioners must therefore embed distributional analysis into their evaluations—not as an afterthought, but as a core component of evidence-based policy.

6.4. Inaccurate Valuation of Intangibles

One of the most persistent challenges in Cost-Benefit Analysis is the valuation of intangible goods—impacts that are real and meaningful but not easily expressed in monetary terms. These include factors such as human life, mental health, social cohesion, environmental aesthetics, and biodiversity. When these are omitted or undervalued, CBAs risk underestimating a project’s true societal impact and biasing decisions against socially valuable investments.

Why Intangibles Matter in Public Decision-Making

In public policy, many of the most important outcomes are not traded in markets and therefore lack observable prices. Examples include:

• The peace of mind of knowing emergency services are accessible

• The psychological wellbeing associated with reduced disaster risk

• The existence value of natural habitats, endangered species, or cultural sites

• The intrinsic value of justice, dignity, or safety for vulnerable groups

These outcomes often matter more to communities than narrowly defined economic returns. Yet, when CBAs only include what can be monetised easily, they privilege physical infrastructure over social or environmental outcomes, leading to misallocation of public funds.

Economic Theory and Valuation Methods

Economists have developed several approaches to assign monetary values to non-market goods, drawing from both welfare economics and behavioural economics. Common methods include:

These methods are widely used in environmental economics, health economics, and transport planning, but they are also subject to numerous challenges: hypothetical bias, cognitive overload, framing effects, and ethical discomfort in monetising certain values (e.g., life or dignity).

Ethical and Practical Considerations

Some impacts, such as the value of human life, require particularly sensitive valuation. Governments often use a Value of a Statistical Life (VSL)—typically derived from labour market studies or stated preferences—but this can vary significantly between countries and contexts. Failing to include such values in CBA (or using them inconsistently) introduces ethical bias and reduces analytical integrity.

Similarly, trying to reduce values like cultural identity or ecosystem integrity to a single monetary figure can be intellectually and morally fraught. In such cases, analysts must complement monetised analysis with qualitative assessment, using a multi-criteria framework or narrative impact statements to ensure these values are recognised.

Just because something cannot be easily priced does not mean it lacks value. In fact, the most meaningful public goods—like health, dignity, and environmental integrity—are often the hardest to quantify. An overreliance on market prices or simplified proxies can lead to systematic underinvestment in projects that serve the public good. CBA must evolve to better account for these values, or risk becoming technically robust but socially irrelevant.

6.5. Assumption of Static Conditions

Many CBAs make the simplifying assumption that key variables—such as population size, demand patterns, economic conditions, or technology—remain constant over the project horizon. While this can make the analysis more manageable, it often leads to unrealistic projections and flawed investment decisions, especially for long-term public projects operating in rapidly changing environments.

Why Static Assumptions Are Problematic

Assuming fixed conditions over time ignores the dynamic nature of the real world. Infrastructure projects, disaster response systems, and environmental interventions unfold over years or decades, during which demographics shift, technologies evolve, economies fluctuate, and societal preferences change. Relying on fixed inputs can result in:

• Underestimating future demand (e.g., for health services or public transport)

• Missing emerging risks or opportunities (e.g., climate adaptation, digital transformation)

• Locking in suboptimal choices that may become obsolete (e.g., outdated communication systems)

Economic Theory: Dynamic Efficiency and Path Dependency

Traditional static models evaluate efficiency at a single point in time. However, in the context of long-term public investment, economists have introduced the concept of dynamic efficiency—a framework that seeks to maximise social welfare over time, accounting for how today’s decisions affect future possibilities.

Related to this is the idea of path dependency, from institutional economics and economic history, which suggests that early decisions can lock in future outcomes, making course correction difficult and costly. This is particularly relevant for infrastructure, technology platforms, and environmental management.

Economists such as Joseph Schumpeter (1942) and Kenneth Arrow (1962) emphasised the role of innovation, uncertainty, and irreversible investment in shaping economic outcomes over time—insights that are crucial for modern CBA, especially in contexts like climate resilience and smart cities.

Best Practice Approaches

To address this challenge, analysts should incorporate scenarios, sensitivity testing, and adaptive modelling into their CBAs. Practical steps include:

• Scenario Planning: Develop alternative futures (e.g., high growth, climate risk, tech disruption) to test project resilience

• Demand Forecasting Models: Use demographic and economic trend analysis to predict future usage

• Monte Carlo Simulations: Model uncertainty across multiple variables simultaneously

• Staged Investments: Design projects in modular or phased formats to allow flexibility over time

• Real Options Analysis: Apply finance-based tools that account for decision flexibility in the face of uncertainty

Assuming static conditions may simplify analysis, but often comes at the cost of realism and strategic foresight. In a world defined by rapid change and complexity, CBA must evolve to consider not just “what is” but “what could be.” Incorporating dynamic analysis helps ensure that investments are future-proof, adaptable, and socially valuable over time.

6.6. Simplistic Monetisation

At the core of Cost-Benefit Analysis lies the principle of monetising costs and benefits to enable comparison. However, this strength is also a frequent source of criticism. In some cases, efforts to reduce all project outcomes to monetary terms can oversimplify, misrepresent, or even distort the true value of a policy or intervention, especially when dealing with complex social, ethical, or environmental outcomes.

Theoretical Context: Beyond Price as Value

The practice of assigning monetary values to all forms of benefit is rooted in utilitarian economic theory, where value is defined by individual preferences, usually expressed as willingness to pay (WTP). This approach assumes that people make rational trade-offs and that their preferences are sufficient to guide policy.

However, several important critiques have emerged:

• Amartya Sen’s “Capability Approach” argues that wellbeing should not be measured solely by income or market choices but by what people are able to do or be (e.g. live a healthy life, participate in community, be secure). Many essential aspects of human welfare are not reflected in markets and cannot be meaningfully priced.

• Behavioural economics, notably the work of Daniel Kahneman (1979) and Richard Thaler (2008), has shown that individuals often do not make consistent or rational choices, especially when faced with complex, long-term, or ethical decisions. For example, people may be unwilling to assign a monetary value to clean air or their child’s safety, not because it lacks value, but because the very act of monetisation feels morally inappropriate.

• Even within traditional welfare economics, marginal utility of income means that WTP is strongly influenced by wealth. The preferences of richer individuals carry more weight, skewing CBA outcomes unless corrective measures (e.g., equity weights) are introduced.

Why This Matters

Attempting to assign a single monetary value to complex social outcomes can result in loss of nuance and policy distortion. For instance:

• Mental health improvements may be reduced to productivity gains

• Cultural heritage may be valued only by its tourism revenue

• First responder training may be assessed purely by wage uplift, ignoring public safety benefits.

This approach not only underrepresents true societal value, but also discourages investment in social or environmental projects whose benefits are difficult to quantify—even if they are deeply valued by communities.

Improving Practice: What Can Be Done?

To address the limitations of simplistic monetisation, analysts and policymakers can adopt more pluralistic and transparent approaches:

1. Use Multi-Criteria Analysis (MCA): Combine monetary metrics with non-monetised scores or qualitative descriptors to compare options on multiple dimensions.

2. Provide Narrative Impact Statements: Accompany monetised results with rich, contextual descriptions of impacts that resist quantification.

3. Make the Invisible Visible: Clearly indicate which impacts were excluded or only partially valued, so decision-makers understand the limits of the numbers presented.

4. Engage Stakeholders in Valuation: Involve affected communities in identifying and prioritising what matters to them, particularly for culturally sensitive or ethically complex issues.

5. Embrace Partial Monetisation When Appropriate: Not everything needs a price tag. Sometimes it’s better to present key impacts side-by-side with quantified ones than to risk false precision.

While monetisation is a core strength of CBA, not everything that counts can be counted. When complex human, cultural, or environmental values are compressed into simplistic financial terms, important nuances are lost. A more balanced and honest approach recognises that some values defy pricing, and that good public decisions must be informed by evidence, ethics, and engagement, not just economics.

6.7. Bias in Data Selection

Even the most technically sound Cost-Benefit Analysis can produce misleading results if it is built on biased or selectively chosen data. Whether intentional or unconscious, the selection of inputs that favour a predetermined conclusion compromises the objectivity, credibility, and usefulness of CBA. In public policy settings—where decisions affect wide populations—this kind of analytical bias can have serious social, economic, and ethical consequences.

Sources of Bias: Human and Institutional

Bias can arise in many ways throughout the analytical process, including:

• Selective use of evidence: Emphasising data that supports a preferred outcome while ignoring contradictory findings

• Overly optimistic assumptions: Inflated benefits or underestimated costs

• Strategic framing: Presenting results to appeal to political or financial stakeholders

• Narrow stakeholder engagement: Relying only on data from vested interests or ignoring marginalised voices

These practices may not always be malicious—they are often the result of institutional incentives, time constraints, or cognitive bias. However, they can severely distort CBA results, particularly in cases where analysts are under pressure to justify funding, approve a project, or deliver a quick policy solution.

Economic Theory: Public Choice and Cognitive Bias

This challenge links closely to Public Choice Theory, which explores how political and bureaucratic incentives influence supposedly neutral economic processes. They argue that analysts and institutions may be motivated by self-interest—such as securing funding, pleasing superiors, or avoiding controversy—which can shape how evidence is gathered and presented.

From the lens of behavioural economics, researchers like Daniel Kahneman (1979) have shown how confirmation bias, anchoring, and availability heuristics can influence data interpretation, even among trained professionals. Analysts may subconsciously seek or weigh evidence that aligns with their expectations or organisational goals.

Why This Matters

When data selection is biased—whether by design or by omission—the resulting analysis loses its evidentiary power. Decisions made on that basis can lead to resource misallocation, political backlash, and erosion of public trust. Moreover, biased CBAs can crowd out better alternatives, especially if those alternatives are less politically attractive or have longer payoff timelines.

Best Practice Recommendations

To reduce the risk of data selection bias in CBA:

1. Document Assumptions Transparently: Clearly state data sources, exclusion criteria, and the rationale for each input.

2. Use Sensitivity Analysis to Challenge Assumptions: Explore how different data sets or parameter changes affect outcomes.

3. Diversify Stakeholder Input: Include perspectives from a wide range of users, communities, and experts, especially those who may be impacted differently.

4. Independent Review: Where feasible, submit CBAs to peer review or external audit before being used for major investment decisions.

5. Avoid “Analysis for Hire” Models: Ensure analysts are institutionally separate from project sponsors or political offices to preserve objectivity.

Bias in data selection is often subtle but always corrosive. It undermines the foundational principle that CBA should serve as a neutral decision-support tool. To preserve the legitimacy and usefulness of CBA in public policy, analysts must commit to intellectual honesty, methodological transparency, and a willingness to challenge their own assumptions. In doing so, CBA can remain not only economically rigorous but also socially trustworthy.

📚References

Arrow, K. J. (1962). The economic implications of learning by doing. The Review of Economic Studies, 29(3), 155–173.

Jevons, W. S. (1871). The theory of political economy. Macmillan.

Kahneman, D., & Tversky, A. (1979). Prospect theory: An analysis of decision under risk. Econometrica, 47(2), 263–291. https://doi.org/10.2307/1914185

Menger, C. (1871). Principles of economics (J. Dingwall & B. F. Hoselitz, Trans.). Ludwig von Mises Institute, 2007.

Rawls, J. (1971). A theory of justice. Harvard University Press. https://www.jstor.org/stable/j.ctvkjb25m

Ricardo, D. (1817). On the principles of political economy and taxation. John Murray.

Schumpeter, J. A. (1942). Capitalism, socialism and democracy. Harper & Brothers.

Smith, A. (1776). An inquiry into the nature and causes of the wealth of nations. W. Strahan and T. Cadell.

Stern, N. (2007). The economics of climate change: The Stern Review. Cambridge University Press.

Thaler, R. H., & Sunstein, C. R. (2008). Nudge: Improving decisions about health, wealth, and happiness. Yale University Press.

📚Further Reading

Brookshire, D. S., & Neill, H. R. (1992). Benefit transfers: Conceptual and empirical issues. Water Resources Research, 28(3), 651–655. https://doi.org/10.1029/91WR02592

Desvousges, W. H., Johnson, F. R., Dunford, R. W., Boyle, K. J., Hudson, S. P., & Wilson, K. N. (1998). Environmental policy analysis with limited information: Principles and applications of the transfer method. Edward Elgar.

Sen, A. (1999). Development as freedom. Oxford University Press.