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9. Behavioural Economics and the Psychology of Cost-Benefit Analysis

Accounting for Human Decision-Making in Economic Appraisal

Taha Chaiechi

9.1. Introduction to Behavioural Cost-Benefit Analysis

🔍 What It Is

Behavioural Cost-Benefit Analysis (Behavioural CBA) integrates insights from psychology and behavioural economics into traditional economic appraisal. Unlike classical CBA, which assumes fully rational agents with stable preferences, Behavioural CBA recognises that real-world decisions are often influenced by cognitive biases, emotional responses, and social context. By adjusting valuation methods to account for tendencies like loss aversion, present bias, and framing effects, Behavioural CBA provides a more accurate and human-centred assessment of costs and benefits—enhancing both the realism and legitimacy of policy decisions.

Economic models are powerful tools—but they are only as realistic as the assumptions they rest on. For decades, Cost-Benefit Analysis (CBA) has served as a cornerstone of public investment appraisal, helping policymakers assess whether a project’s expected benefits outweigh its costs. Yet classical CBA has traditionally relied on a view of decision-makers—whether individuals, households, or institutions—as fully rational agents with stable preferences, perfect foresight, and access to complete information.

Real-world behaviour rarely follows this script. As demonstrated by a growing body of research in behavioural economics, people routinely deviate from rationality in predictable ways. They overestimate rare risks and underestimate common ones. They value losses more heavily than gains. They discount the future steeply. Their decisions are shaped by emotions, mental shortcuts, framing, and the social environment. These behavioural tendencies are not anomalies—they are central to how humans make decisions.

This chapter brings a behavioural lens to CBA. It explores how incorporating insights from psychology and behavioural economics can improve the accuracy, realism, and social legitimacy of cost-benefit evaluations. This includes addressing well-known divergences between Willingness to Pay (WTP) and Willingness to Accept (WTA), accounting for the role of framing in shaping valuation responses, and recognising the limits of purely market-based or revealed preference methods.

Building on the themes introduced in the previous chapter on Dynamic CBA, this chapter continues the shift away from idealised models of fixed values and perfect information. Instead, it argues for a more human-centred and context-sensitive approach to economic appraisal—one that acknowledges bounded rationality, decision biases, and social preferences. This is especially vital in public policy domains—such as health, infrastructure, energy, and environment—where stakeholder engagement, behaviour change, and perceived fairness play critical roles in implementation success.

The chapter proceeds by introducing foundational behavioural theories relevant to economic valuation, such as Prospect Theory and Dual-Process Thinking. It then applies these concepts to common CBA practices—focusing on how behavioural insights challenge traditional valuation methods, inform more nuanced interpretations of cost and benefit flows, and reveal why some high-benefit policies (e.g., energy efficiency or disaster preparedness) still suffer from low uptake.

The final sections offer practical guidance for analysts and policymakers: how to collect and interpret valuation data more effectively; how to incorporate behavioural adjustments into scenario modelling; and how to communicate results in ways that resonate with stakeholders’ mental models. The chapter closes with a call for a realist turn in CBA—one that not only calculates well, but connects deeply with how people think, feel, and decide.

9.2. Why Behavioural Economics Matters for CBA

Traditional economic appraisal methods assume that individuals make consistent, informed choices that reflect stable preferences over time. Within this framework, individuals are expected to maximise utility and respond predictably to incentives. This assumption underpins core valuation techniques such as willingness to pay (WTP) and cost-effectiveness analysis. However, a growing body of empirical evidence from behavioural economics has shown that actual decision-making frequently violates these assumptions.

Real-world decisions—whether about health behaviours, transport use, energy consumption, or investment in risk-reducing infrastructure—are often shaped by psychological shortcuts (heuristics), emotions, peer influence, and cognitive limitations. These behavioural factors lead to systematic biases such as:

  • Loss aversion – People feel losses more acutely than equivalent gains.

  • Present bias – Future benefits are heavily discounted, even when long-term payoffs are significant.

  • Status quo bias – People tend to stick with current arrangements, even when superior alternatives exist.

  • Framing effects – The way options are presented can change people’s preferences, even when underlying values remain the same.

These patterns have direct implications for CBA. For instance, if WTP is elicited through surveys or experiments, the result may be highly sensitive to how questions are phrased or what reference points are used. Likewise, if people undervalue long-term benefits (such as climate mitigation or preventive health interventions), standard CBA may underestimate the true social value of these policies.

Behavioural Failures and Market Failures

Behavioural economics also blurs the line between market failure and individual decision failure. For example, low uptake of energy-efficient home upgrades is often attributed to a lack of information or financing, but behavioural factors like inertia, myopia, and complexity aversion play equally significant roles. Similarly, underinvestment in flood insurance or vaccinations cannot be explained solely by price signals or access barriers.

These “behavioural market failures” call for a recalibration of how we define public value and cost-effectiveness. They also suggest that some forms of government intervention—such as defaults, nudges, and behaviourally-informed communications—can be highly cost-effective even if they don’t alter underlying market prices.

Why CBA Needs Behavioural Inputs

Bringing behavioural insights into CBA helps analysts:

  • Improve valuation accuracy – by accounting for known biases in how people report preferences or respond to hypothetical scenarios.

  • Design better interventions – by identifying when poor outcomes are due to cognitive limitations, not lack of demand.

  • Anticipate implementation risks – by understanding how behavioural responses may differ from modelled expectations.

  • Enhance social legitimacy – by aligning appraisal with how real people perceive fairness, risk, and value.

In short, behavioural economics provides the analytical lens needed to make CBA not only more precise, but also more human. The next sections explore key behavioural frameworks and how they reshape core assumptions in valuation.

9.3. Key Behavioural Frameworks for Economic Valuation

Behavioural economics departs from classical economic theory by incorporating psychological insights into how individuals make decisions. Instead of assuming consistent, utility-maximising behaviour, behavioural economics recognises that people are influenced by cognitive limitations, emotional states, social context, and heuristics—mental shortcuts used to make complex decisions easier.

This has profound implications for Cost-Benefit Analysis (CBA), particularly in how we understand and measure preferences, value risk, and predict behavioural responses to policy. Several foundational theories and frameworks underpin this shift. Below are the key concepts and contributors relevant to integrating behavioural insights into CBA:

📖 Theory 1. Prospect Theory (Kahneman & Tversky, 1979)

One of the most influential theories in behavioural economics, Prospect Theory, posits that individuals evaluate outcomes relative to a reference point (often the status quo), rather than in absolute terms. Key concepts include:

  • Loss aversion: Losses loom larger than equivalent gains. For instance, people might demand more to give up a benefit than they are willing to pay to acquire it (WTA > WTP).

  • Diminishing sensitivity: The psychological impact of gains and losses decreases as their magnitude increases.

  • Probability weighting: People tend to overestimate small probabilities and underestimate large ones, distorting their response to risk.

Implication for CBA: Standard WTP or WTA methods may misrepresent true welfare effects, especially in risky or uncertain settings. CBA must consider framing effects and the role of reference points.

📖 Theory 2. Dual-Process Theory (Kahneman, 2011)

This framework suggests that human cognition operates through two systems:

  • System 1: Fast, intuitive, emotional, and automatic thinking.

  • System 2: Slow, analytical, rational, and deliberate thinking.

In many real-world decisions—especially under time pressure, stress, or cognitive overload—System 1 dominates. This leads to common heuristics and biases, including:

  • Anchoring: Relying too heavily on the first piece of information encountered.

  • Availability bias: Overestimating the importance of easily recalled events.

  • Overconfidence bias: Overestimating one’s knowledge or ability to predict outcomes.

Implication for CBA: Stakeholders may misjudge probabilities, underestimate costs, or fail to consider long-term effects. CBA frameworks must anticipate these behavioural patterns when projecting adoption rates, risk responses, or compliance behaviour.

📖 Theory 3. Time Inconsistency and Present Bias (Laibson, 1997; O’Donoghue & Rabin, 1999)

Traditional models assume exponential discounting (i.e., time preferences are consistent over time). Behavioural research suggests that people discount the near future much more heavily than the distant future—hyperbolic discounting. This is known as present bias.

Implication for CBA: Interventions with long-term benefits (e.g., preventive health, climate adaptation, education) may be undervalued in standard appraisals. Accounting for present bias can justify stronger upfront investments or incentives.

📖 Theory 4. Reference-Dependent Preferences (Kőszegi & Rabin, 2006)

Extending Prospect Theory, Kőszegi and Rabin developed a formal model of reference-dependent utility, where expectations shape utility. Disappointment or elation is not just about final outcomes, but deviations from expected outcomes.

Implication for CBA: People’s responses to policies may depend not just on actual benefits or costs, but on whether those outcomes meet, exceed, or fall short of expectations—relevant for satisfaction, trust, and legitimacy.

📖 Theory 5. Social Preferences and Fairness (Fehr & Schmidt, 1999; Bolton & Ockenfels, 2000)

Behavioural experiments reveal that individuals care about fairness, reciprocity, and inequality—not just outcomes, but their distribution. Social preferences influence behaviour in public goods provision, redistribution policies, and compliance.

Implication for CBA: Analyses that ignore equity considerations may underestimate public support or resistance. Incorporating distributive weights or participatory valuation can better reflect societal values.

📖 Theory 6. Nudge Theory and Choice Architecture (Thaler & Sunstein, 2008)

Thaler and Sunstein introduced the concept of nudging—structuring choices in a way that guides behaviour without restricting freedom. Default settings, framing, and simplification are powerful tools for influencing decisions.

Implication for CBA: Behavioural interventions (e.g., automatic enrolment, reminders, simplified forms) are often cost-effective ways to shift outcomes. Their low cost and high return make them important candidates for inclusion in economic evaluation.

📖 Theory 7. Emotional and Affective Influences (Loewenstein, 2000)

Emotions play a central role in decision-making, especially under uncertainty or when choices involve moral, health, or social dimensions. Fear, hope, and regret can influence risk perception and choice more than rational calculations.

Implication for CBA: Projects involving safety, health, or existential threats may require expanded valuation frameworks that incorporate emotional responses and perceived well-being, not just monetary metrics.

The idea that the future is unpredictable is undermined every day by the ease with which the past is explained.
— Kahneman, D. (2011). Thinking, Fast and Slow

By integrating these behavioural frameworks, CBA becomes more reflective of how people actually behave in the face of policy, uncertainty, and risk. The next section explores practical strategies for applying these insights to real-world valuation exercises.

9.3. The Role of Cognitive Bias in Project Evaluation

Understanding How Behavioural Biases Distort Valuation, Modelling, and Decision-Making in CBA

In classical economic appraisal, the assumption is that decision-makers, analysts, and stakeholders process information rationally, yielding consistent and unbiased valuations. However, a large body of research in behavioural economics demonstrates that cognitive biases can systematically distort both individual preferences and expert judgements. This is particularly problematic in cost-benefit analysis (CBA), where such biases can propagate through valuation estimates, demand forecasts, and risk assessments, ultimately leading to flawed investment decisions.

This section explores how three specific biases—anchoring, status quo bias, and overconfidence—influence project appraisal, and how they can be formalised and corrected in applied settings.

Willingness to Pay (WTP) and Willingness to Accept (WTA)

Willingness to Pay (WTP) and Willingness to Accept (WTA) are stated preference approaches designed to capture the value individuals place on non-market goods. WTP measures how much a person is prepared to pay for an improvement or benefit, while WTA gauges the amount of compensation they would demand to tolerate a loss or degradation. Commonly applied through contingent valuation surveys or choice modelling, these methods help uncover the perceived worth of goods like cleaner air, safer public spaces, reduced flood risks, or improved digital connectivity in underserved areas.

📌Example

 In flood-prone regions of the Netherlands, residents were surveyed on their WTP for enhanced flood defences. Results showed that intangible concerns like peace of mind and aesthetic preservation significantly influenced their valuations—factors not captured in standard infrastructure models.

However, WTP/WTA methods are sensitive to framing effects, income constraints, and present bias, making careful design and interpretation essential (Carson & Hanemann, 2005).

Anchoring: The Power of First Impressions in Valuation

Anchoring refers to a well-documented cognitive bias where individuals rely too heavily on an initial piece of information—known as the “anchor”—when forming judgments or making decisions. This heuristic shapes how people assess value, even when the anchor is arbitrary or unrelated to the true worth of the good or service in question.

In the context of Cost-Benefit Analysis (CBA), anchoring frequently arises in contingent valuation and stated preference surveys. For example, when respondents are asked how much they are willing to pay (WTP) for an environmental improvement or social benefit, the initial price point or suggested donation can significantly sway their answers. Even if that number is introduced randomly, (e.g., “Would you pay $50 to support this initiative?”), it subtly establishes a mental reference point around which respondents’ WTP estimates cluster.

This effect is not limited to direct price cues. Anchoring can also occur through related contextual information, such as the framing of a problem (e.g., highlighting losses vs. gains) or by referencing past costs, regional averages, or typical expenditures. Regardless of its source, the anchor skews valuation away from individuals’ intrinsic preferences and towards the suggested figure, potentially distorting the accuracy of stated preferences in CBA.

For CBA practitioners, failing to account for anchoring can lead to over- or underestimation of benefits, particularly in sectors like environmental protection, public health, or cultural heritage, where valuation relies heavily on contingent methods. This can skew investment decisions and lead to suboptimal allocation of public resources.

Best Practice Recommendations:

  • Pre-testing survey instruments to identify and mitigate anchoring effects.

  • Avoiding arbitrary starting points or explaining to respondents that suggested amounts are randomly assigned.

  • Applying statistical adjustments (e.g., regression models) to control for anchoring in the analysis phase.

  • Complementing stated preference methods with revealed preference data, where available, to cross-validate findings.

Modelling Anchoring in WTP Surveys

💡Formula

Let the WTP of an individual be modelled as:

WTPi=α+βXi+γAi+εi\text{WTP}_{i} = \alpha + \beta X_i + \gamma A_i + \varepsilon_i

Where:

  • Xi = observed characteristics of individual i (e.g. income, education)
  • Ai​ = anchoring variable (e.g. suggested bid or price prompt)
  • γ = marginal influence of the anchor
  • εi​ = unobserved error

Why It Matters in Behavioural CBA:
This model shows that a person’s stated value—such as willingness to pay (WTP)—is not solely determined by their own characteristics (e.g., income or education), but is also shaped by anchors they are presented with, such as suggested donations, price prompts, or default options. These anchors can systematically bias responses. Recognising and adjusting for the anchoring variable and its influence coefficient allows cost-benefit analysis (CBA) to more accurately reflect real-world decision-making and behavioural distortions.

Empirical studies (e.g. Green et al., 1998; Herriges & Shogren, 1996) consistently find γ>0, even when Ai​ is a randomised number with no substantive basis. This violates the standard assumption of stable preferences and implies that survey design—and not just true preferences—can shape valuation outcomes.

Practical Tip: To minimise anchoring, analysts should pilot test bid designs, randomise anchors across respondents, and use follow-up questions to assess internal consistency in WTP.

Status Quo Bias in Infrastructure and Environmental Projects

Status quo bias refers to the preference for the current state of affairs. When evaluating alternative investment projects—especially in infrastructure, climate adaptation, or transport planning—respondents and policymakers may irrationally favour the “do nothing” option, even when change yields a net benefit.

In discrete choice models used to simulate project uptake or stakeholder preferences, this can be represented by incorporating a status quo penalty into the utility function.

💡Formula: Utility Adjustment Model

Ualt=ValtδSQU_{alt} = V_{alt} – \delta_{SQ}

Where:

  • Valt= deterministic component of utility from the alternative option
  • δSQ = disutility from departing from the status quo
  • Ualt (or status quo) Ustatusquo​ for many respondents unless the project strongly dominates

When to Use It:

  • Behavioural CBA & Choice Experiments:
    When respondents irrationally favour the current state (status quo), even if an alternative offers higher objective value.
  • Low Take-Up of Interventions:
    In health, energy, or transport sectors, where superior options are rejected due to psychological or behavioural inertia.
  • Risk-averse contexts:
    When people perceive new alternatives as risky or unfamiliar and prefer “what they know.

This penalty term reduces the likelihood that respondents opt for change, even if the alternative has higher objective value. It has been used to explain slow uptake of energy-efficient technologies (Allcott & Greenstone, 2012), resistance to toll road pricing (Bain & Sullivan, 2024), and low adoption of urban greening initiatives.

Practical Tip: Behavioural survey techniques, like “consequential framing” and narrative-based scenarios, can reduce status quo bias by helping stakeholders imagine the future impacts of inaction.

Overconfidence in Forecasting and Risk Modelling

A persistent issue in economic appraisal is forecast optimism—a bias toward overestimating benefits and underestimating costs. This stems from overconfidence, particularly among expert analysts or project champions. Flyvbjerg (2003) found that in major infrastructure projects, actual costs exceeded forecasts in 90% of cases, and benefits were routinely overstated.

💡Formula

Expected Value of NPV (Stochastic NPV or Probabilistic NPV)

This has profound implications for standard Net Present Value (NPV) calculations:

NPV=t=0TBtCt(1+r)t\text{NPV} = \sum_{t=0}^{T} \frac{B_t – C_t}{(1 + r)^t}

If Bt is systematically biased upward and Ct downward, the project will appear falsely attractive.

To address this, probabilistic approaches can be applied:

E[NPV]=t=0TE[BtCt(1+r)t]\text{E[NPV]} = \sum_{t=0}^{T} \mathbb{E}\left[ \frac{B_t – C_t}{(1 + r)^t} \right]

Where:

  • E[Bt]and E[Ct] represent expected values over distributions (e.g., via Monte Carlo simulation)
  • Forecast variance and skewness are explicitly incorporated
  • This is also known as Stochastic NPV, Expected NPV, or Risk-Adjusted NPV.

When to Use It:

  • When costs and benefits are uncertain or variable over time

  • When forecasts are known to suffer from bias (e.g. strategic misrepresentation, optimism)

  • When using tools like Monte Carlo simulations to model a distribution of outcomes, rather than a single estimate

  • When risk management and scenario planning are central to the project (e.g. climate adaptation, infrastructure, tech R&D).

Practical Tip: Reference class forecasting (RCF) can mitigate overconfidence. This involves comparing new projects to a database of similar past projects and adjusting forecasts based on historical accuracy rates.

Summary Table: Biases and Their CBA Implications

Bias Type CBA Component Affected Analytical Adjustment Example Use Case
Anchoring WTP estimation Include anchor variables, randomised bids CV surveys for ecosystem services
Status quo bias Discrete choice modelling Include SQ utility penalty (

δSQ\delta_{SQ})

Transport upgrades or urban redevelopment
Overconfidence Forecasting and NPV calculation Reference class forecasting, probabilistic models Major capital investments, tech infrastructure

As behavioural distortions are rarely random, they must be actively identified, measured, and corrected in economic appraisal. Ignoring them doesn’t just violate theory—it leads to poorer investments and lower public trust.

9.4. Behaviourally-Informed Adjustments to Valuation and Discounting

Rethinking How We Value the Future, Uncertainty, and Human Preferences

Traditional Cost-Benefit Analysis (CBA) relies on assumptions of stable preferences and rational valuations of future costs and benefits. Behavioural economics disrupts these foundations by revealing that individuals often deviate from these expectations, particularly when dealing with uncertainty, future trade-offs, and emotionally charged outcomes.

This section presents key behavioural insights relevant to valuation and discounting in CBA and outlines practical methods to adjust standard approaches in light of these findings.

Willingness to Pay (WTP) vs. Willingness to Accept (WTA): A Persistent Disparity

Standard Assumption in CBA: WTP and WTA should be nearly identical when valuing small changes in goods or services, assuming well-formed and stable preferences (Hanemann, 1991).

Behavioural Insight: WTA tends to exceed WTP—often by a factor of 2 or more—due to loss aversion, a central insight from Prospect Theory (Kahneman & Tversky, 1979). Individuals value losses more heavily than equivalent gains, especially for public or non-market goods.

Implications for CBA:

Concept Traditional Assumption Behavioural Adjustment
WTP ≈ WTA Interchangeable in valuation Adjust for loss aversion (WTA > WTP)
Market prices reflect preferences Stakeholder biases can skew responses Use framing and deliberative methods to improve accuracy
Stated preference = true value Responses influenced by context and reference point Include sensitivity ranges for WTP/WTA ratios

Practical Tip:
If only WTP is available, analysts may apply a correction factor based on empirical WTA/WTP ratios (Horowitz & McConnell, 2002):

💡 Formula:  Behaviourally  Corrected Valuation

Adjusted Value=WTP×θ, where θ ∈ [1.5,2.5] is a behavioural adjustment formula often used to correct for loss aversion in WTP (Willingness to Pay) and WTA (Willingness to Accept) discrepancies.

      What Is It?

     This is not a classical economic formula, but one rooted in behavioural economics, specifically based on findings from:

  • Prospect Theory (Kahneman & Tversky, 1979)

  • Empirical studies like Horowitz & McConnell (2002)

This can serve as a conservative estimate for projects with high emotional salience or public opposition (e.g., siting infrastructure, climate relocation).

Time Preference and Discounting: Beyond the Constant Rate

Traditional CBAs use a constant exponential discount rate to convert future costs and benefits into present values:

💡 Formula: Basic Present Value (PV)

PV=Bt(1+r)tPV = \frac{B_t}{(1 + r)^t}

Where:

  • PV = present value
  • Bt= benefit or cost at time t
  • r= discount rate
  • t = number of years into the future

This implies a time-consistent valuation of the future, meaning preferences do not change over time.

Behavioural Challenge: People discount the future in a time-inconsistent manner—exhibiting present bias, which leads them to place disproportionately high value on immediate outcomes compared to future ones. This behaviour aligns more closely with hyperbolic or quasi-hyperbolic discounting models.

💡 Formula: Hyperbolic Discounting

Hyperbolic Discounting Formula is widely cited in behavioural economics and intertemporal choice theory.

PV=Bt1+ktPV = \frac{B_t}{1 + k t}

Here,  is the hyperbolic discount rate (not constant), causing the discount factor to decline over time less steeply than exponential models, allowing future benefits to retain more value in the present.

What It Means:

Unlike the exponential discounting model which uses

(1+r)t(1 + r)^tHyperbolic discounting assumes that people discount the near future more heavily than the distant future, but with declining impatience over time. This is more consistent with observed human behaviour.

Implications for CBA:

  • Projects with long-term benefits (e.g., climate adaptation, public health prevention) may be undervalued under standard discounting.

  • Time inconsistency can justify declining discount rates (Weitzman, 2001), where rates fall over time to reflect growing concern for future generations.

Example Application: In evaluating a childhood vaccination program, using a declining discount rate better reflects societal preferences to invest in long-term population health, even when immediate payoffs are minimal.

Behavioural Discounting in Public Contexts

Ethical and Social Considerations

Public policy analysts increasingly question whether individual time preferences—especially when influenced by myopia or present bias—should guide societal decisions. Instead, CBA may incorporate social discounting frameworks based on:

  • Intergenerational equity (Stern Review, 2007)

  • Catastrophic risk adjustment (climate finance)

  • Distributional justice (future vs. current welfare)

📈Policy Tip:

Use multiple discount rates in sensitivity testing:

  • 3.5% for general public projects (UK Treasury)

  • 1–2% for projects with long-term social/environmental implications

  • 0% or negative for existential or irreversible impacts (e.g., biodiversity, sea-level rise)

Summary Box: Integrating Behavioural Valuation into CBA

 

Area Behavioural Bias Recommended Adjustment
WTP vs. WTA Loss aversion Use higher WTA or correction factors
Future valuation Present bias Use hyperbolic or declining discount rates
Risk perception Framing effects Test alternative decision frames in analysis
Emotional salience Endowment effect Complement stated preferences with deliberative methods

9.5 Strategies to Mitigate Behavioural Bias in CBA

Behavioural distortions such as loss aversion, anchoring, present bias, and overconfidence present a serious challenge to robust economic appraisal, particularly in public policy domains where stakeholder perceptions and long-term planning intersect. Traditional CBA methods do not account for these non-rational tendencies, leading to systematic errors in valuation, forecasting, and decision-making. This section outlines key strategies that policy analysts and project evaluators can use to mitigate such biases and enhance the behavioural validity of CBA outcomes.

Reframing Techniques to Align with Decision Heuristics

One of the most powerful tools available to practitioners is reframing—structuring options in ways that are psychologically congruent with how people naturally process decisions. Research consistently shows that individuals respond more strongly to loss-framed messaging than to equivalent gain-framed messages (Kahneman & Tversky, 1979). This insight can be used to increase stakeholder acceptance of projects by framing benefits as losses avoided.

📌 Example

In climate resilience projects, instead of stating “this investment will generate $10 million in long-term benefits”, framing it as “this project will prevent $10 million in damages” can significantly increase public willingness to support.

Reframing is particularly valuable when dealing with intangible or long-horizon outcomes (e.g., biodiversity, public health, or intergenerational equity).

Behaviourally-Informed Valuation Instruments

Valuation tools such as contingent valuation or choice modelling must be designed with an understanding of reference dependence and sensitivity to anchors. When surveys are conducted to elicit willingness to pay (WTP) or willingness to accept (WTA), seemingly neutral details—such as the order of questions or the presence of a numerical reference—can bias the result.

Strategies include:

  • Avoiding starting-point bias by randomising anchor values across respondents.

  • Providing clear reference points in survey framing to anchor preferences consistently.

  • Testing for range insensitivity by varying the scope of the good being valued.

Tip: Use pre-testing or experimental survey designs to identify anchoring effects in WTP responses and recalibrate accordingly.

Debiasing Forecasts through Reference Class Forecasting

Forecasting errors are among the most persistent problems in applied CBA. Behavioural economics reveals that overconfidence and optimism bias—especially among project proponents—often lead to inflated benefit estimates and underestimation of costs. To address this, Kahneman and Lovallo (1993) proposed reference class forecasting (RCF), a method that grounds forecasts in the statistical distribution of outcomes from similar past projects.

The basic steps in RCF are:

  1. Identify a reference class of similar completed projects.
  2. Establish a probability distribution of actual outcomes (e.g., cost overruns, benefit realisation).
  3. Position the current project within that distribution to estimate likely outcomes.

📌Example

Flyvbjerg (2007) used RCF to reveal that major transportation infrastructure projects systematically underestimate costs by 20–45%.

RCF provides a powerful antidote to strategic misrepresentation, especially in politically sensitive or capital-intensive projects.

Participatory and Deliberative Processes to Surface Bias

Behavioural tendencies such as status quo bias or present bias often operate below the level of conscious reasoning. Including diverse stakeholder perspectives in the appraisal process can help surface and challenge these blind spots. Tools such as citizen juries, expert panels, or deliberative valuation can elicit richer understandings of how individuals conceptualise trade-offs over time.

Applied Insight: In transport projects, involving affected communities in multi-criteria assessments can shift appraisal emphasis from narrow cost-per-km metrics to broader concerns such as accessibility and long-term liveability.

Participatory methods also build procedural legitimacy, increasing the likelihood that CBA findings are accepted and used.

Embedding Flexibility through Adaptive Tools

When behavioural uncertainty is high, it is prudent to design appraisal methods that accommodate future learning. Real Options Analysis (ROA) provides a toolset for valuing flexibility in investment decisions, treating them not as one-off choices but as a sequence of decisions contingent on future states.

Dynamic models incorporating options logic can mitigate the risks of:

  • Committing prematurely to large investments when behavioural responses are unknown.

  • Misallocating resources due to path dependency or irreversibility.

In parallel, scenario planning—in which several plausible futures are analysed based on behavioural as well as structural uncertainty—allows decision-makers to consider how framing effects, cultural shifts, or technological preferences might evolve.

Behavioural Bias and Mitigation Strategies in CBA

Behavioural Bias Practical Implications in CBA Mitigation Strategy
Loss Aversion Undervaluing gains; resistance to change Frame benefits as avoided losses
Anchoring Bias Biased valuation based on arbitrary cues Randomise reference points in surveys
Overconfidence Underestimated costs or overestimated benefits Reference class forecasting
Present Bias Discounting future gains too heavily Use hyperbolic discount models; reframe time horizons
Status Quo Bias Resistance to transformational or innovative projects Stakeholder deliberation; scenario planning

📝Key Takeaways

As we have explored throughout this chapter, behavioural economics offers critical insights into the real-world application of cost-benefit analysis, especially in contexts where human judgment, uncertainty, and risk perception are central. By challenging the assumption of full rationality and perfect foresight, behavioural perspectives encourage more nuanced approaches to valuation, forecasting, and stakeholder engagement. Tools such as reference-dependent preferences, loss aversion, and behavioural discounting are not merely academic concepts—they provide practical strategies to improve the robustness, realism, and legitimacy of CBAs in complex public decision-making. Ultimately, acknowledging and incorporating cognitive biases, emotional framing, and bounded rationality is not a weakness in economic appraisal—it is a necessary evolution. As public investment decisions become increasingly transformational and high-stakes, behavioural insights will be essential for ensuring that CBA remains a tool for inclusive, ethical, and empirically grounded policy.

📚 References

Allcott, H., & Greenstone, M. (2012). Is there an energy efficiency gap? Journal of Economic Perspectives, 26(1), 3–28. https://doi.org/10.1257/jep.26.1.3

Bain, R., & Sullivan, R. (2024). The traffic impact of road pricing: A meta-analysis of 76 case studies of toll activation and deactivation. CSRB Group.  https://www.robbain.com/The%20Traffic%20Impact%20of%20Road%20Pricing_CSRB%20Group_2024.pdf

Bolton, G. E., & Ockenfels, A. (2000). ERC: A theory of equity, reciprocity, and competition. The American Economic Review, 90(1), 166–93. http://www.jstor.org/stable/117286

Carson, R. T., & Hanemann, W. M. (2005). Contingent valuation. In K.-G. Mäler & J. R. Vincent (Eds.), Handbook of Environmental Economics (pp. 821–936). Elsevier. https://doi.org/10.1016/S1574-0099(05)02017-6

Fehr, E., & Schmidt, K. M. (1999). A theory of fairness, competition, and cooperation. The Quarterly Journal of Economics, 114(3), 817–68. http://www.jstor.org/stable/2586885

Flyvbjerg, B., Bruzelius, N., & Rothengatter, W. (2003). Megaprojects and risk: An anatomy of ambition. Cambridge University Press.

Green, D., Jacowitz, K. E., Kahneman, D., & McFadden, D. (1998). Referendum contingent valuation, anchoring, and willingness to pay for public goods. Resource and Energy Economics, 20(1), 85–116.

Herriges, J. A., & Shogren, J. F. (1996). Starting point bias in dichotomous choice valuation with follow-up questioning. Journal of Environmental Economics and Management, 30(1), 112–131.

Horowitz, J. K., & McConnell, K. E. (2002). A review of WTA/WTP studies. Journal of Environmental Economics and Management, 44(3), 426–447. https://doi.org/10.1006/jeem.2001.1215

Kahneman, D. (2011). Thinking, fast and slow. Farrar, Straus and Giroux.

Kahneman, D., & Lovallo, D. (1993). Timid choices and bold forecasts: A cognitive perspective on risk taking. Management Science, 39(1), 17–31.

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

Kőszegi, B., & Rabin, M. (2006). A model of reference-dependent preferences. The Quarterly Journal of Economics, 121(4), 1133–1165. https://doi.org/10.1093/qje/121.4.1133

Laibson, D. (1997). Golden eggs and hyperbolic discounting. The Quarterly Journal of Economics, 112(2), 443–477. http://www.jstor.org/stable/2951242

Loewenstein, G. (2000). Emotions in economic theory and economic behavior. The American Economic Review, 90(2), 426–432. https://doi.org/10.1257/aer.90.2.426

O’Donoghue, T., & Rabin, M. (1999). Doing it now or later. The American Economic Review, 89(1), 103–124. http://www.jstor.org/stable/116981

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.

Weitzman, M. L. (2001). Gamma discounting. American Economic Review, 91(1), 260–271. https://doi.org/10.1257/aer.91.1.260

📚Further Reading

Derbyshire, S. W. G. (2005). Priceless: On knowing the price of everything and the value of nothing. BMJ, 330(7499), 1091. https://doi.org/10.1136/bmj.330.7499.1091

Sugden, R. (2005). Anomalies and stated preference techniques: A framework for a discussion of coping strategies. Environmental and Resource Economics, 32, 1–12. https://doi.org/10.1007/s10640-005-6025-3

Sunstein, C. R. (2021). Sludge: What stops us from getting things done and what to do about it. MIT Press.

Thaler, R. (1980). Toward a positive theory of consumer choice. Journal of Economic Behavior & Organization, 1(1), 39–60. https://doi.org/10.1016/0167-2681(80)90051-7

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

Tversky, A., & Kahneman, D. (1991). Loss aversion in riskless choice: A reference-dependent model. The Quarterly Journal of Economics, 106(4), 1039–1061. https://doi.org/10.2307/2937956

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Cost-Benefit Analysis: A Practical Guide for Decision-Making Copyright © 2025 by Taha Chaiechi is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License, except where otherwise noted.

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