Behavioural Economics 7A: BEE for policy Flashcards
(35 cards)
Why might standard economic policy tools fail, and how can behavioural economics help?
Standard theory assumes rational agents who maximise expected utility, so policy tools focus on:
Taxes and subsidies
Information provision
Regulation
Challenges:
Prices don’t always work as expected
E.g. Gneezy & Rustichini (2000): Fining parents for lateness led to more lateness — parents treated the fine as a price.
📝 User note: £15 for 30 minutes late was seen as good value.
Inertia: People stick to habits and avoid change.
Information avoidance and excessive discounting reduce the effectiveness of health campaigns (e.g. cervical screening).
People may fail to act in their own best interest (e.g. not switching energy tariffs).
Opportunities:
People care about others → leverage social preferences.
Use behavioural regularities:
Default effects
Social norms
Salience
Heuristics
Nudges can improve outcomes without heavy-handed regulation.
What is the role of experiments in behavioural public policy?
Experiments provide the evidence base for behavioural interventions:
Predict responses: A/B testing, RCTs, field experiments.
Evaluate policies: Ex-post analysis.
Measure preferences: For input into policy design.
They help policymakers understand what works, why, and for whom.
Field experiments: purpose and design
Field experiments aim to estimate causal effects by constructing a counterfactual using randomisation.
Treatment effect = difference in outcomes between treated and control groups.
Randomisation ensures unbiased estimates (List, Sadoff, Wagner, 2010).
Conducted in natural settings (context-rich vs. lab).
Examples:
A/B testing by firms.
Medical drug trials.
Economic policy evaluations.
📝 User note: Online platforms use tricks to increase clicks (salience, defaults).
Ethical concerns in field experiments
Control groups may miss out on beneficial interventions.
E.g. educational programs only given to some pupils.
Justification: Need a baseline to prove effectiveness and argue for wider adoption.
COVID-19 vaccine trials raised similar debates.
What did Esther Duflo’s research in India reveal about gender and policy?
Study: Chattopadhyay & Duflo (2004)
Research question: Do female leaders influence policy?
Design: 1/3 of village councils randomly reserved for women.
Findings: Reserved councils prioritised issues raised by women.
📝 Example of a natural experiment used for ex-post policy evaluation.
What did Duflo et al. (2015) find about microfinance in India?
Study: Banerjee et al. (2015)
Design: Randomised neighbourhoods into treatment (microfinance branch) and control.
Findings:
No increase in consumption or women’s empowerment.
Some shift in spending from temptation goods to durables.
📝 Example of ex-ante policy evaluation using an RCT.
What is Slovic’s “psychic numbing” and how does it affect charitable giving?
People are more emotionally moved by one identifiable victim than by statistics.
Psychophysical model: Diminishing sensitivity to large numbers.
Affect heuristic: Emotions drive action more than reason.
Identifiable victim effect: Named individuals elicit more compassion (Small et al., 2007).
📝 Mother Teresa quote: “If I look at the mass I will never act. If I look at the one, I will.”
What motivates charitable giving according to behavioural economics?
Standard theory: Giving only for tax incentives or reciprocity.
Behavioural insights:
Warm glow (Andreoni, 1989, 1990)
Self-image and social image
Moral wiggle room: Avoiding the ask to avoid guilt (Andreoni et al., 2017)
📝 People may avoid donation requests to avoid the emotional cost of saying no.
How do field and lab experiments contribute to understanding charity?
Landry et al. (2006): Door-to-door giving influenced by:
Donation type (lottery vs. voluntary)
Solicitor attractiveness
Charness & Holder (2019): Lab experiment shows group identity and competition increase giving.
Exley (2020): Performance metrics can be used as excuses not to give (moral wiggle room).
What behavioural barriers hinder climate change action?
Time preferences:
Costs now, benefits later → excessive discounting.
Uncertainty & loss aversion:
Costs are certain, benefits are uncertain.
Framing mitigation as a loss reduces motivation.
Social norms:
Weak norms around eco-behaviour (e.g. flying, meat).
Public goods problem:
Climate mitigation is non-excludable → free-riding.
📝 Tree-planting and offsetting may offer moral wiggle room to pollute.
How do time preferences affect climate change mitigation?
Costs of mitigation are immediate, while benefits are delayed — often accruing to future generations.
This leads to:
Present bias: overemphasis on current costs.
Excessive discounting: future benefits are undervalued.
Raises the issue of intergenerational discounting:
How much should we value future lives and welfare?
Are future gains worth anything to today’s society?
How do uncertainty and loss aversion affect climate action?
Climate mitigation involves certain costs now in exchange for uncertain future benefits.
Behavioural insights:
Loss aversion: losses loom larger than gains → people resist incurring costs.
Risk aversion over gains → people undervalue uncertain benefits.
Risk seeking over losses → people may gamble on avoiding climate catastrophe.
Examples:
Cost of electric vehicles.
Sacrificing meat for sustainability.
Reframing gains as avoided losses may help, but can also backfire.
What role do social norms play in climate-related behaviour?
Many environmentally harmful behaviours are not yet seen as socially inappropriate.
E.g. flying, driving, eating meat.
Social norms are powerful behavioural drivers, but:
They are not yet fully established for climate-conscious behaviour.
Examples of norm-building:
Reusable coffee cups
Hotel towel reuse campaigns
Until norms are widespread, we miss out on a key channel for behaviour change.
Why is climate change a public goods problem?
Climate mitigation is a global public goods game:
Non-excludable: everyone benefits from others’ efforts.
Incentive to free-ride: individuals benefit without contributing.
Without enforcement, the Nash Equilibrium is no one contributes.
Example:
100 people, each with 20 effort units.
Each unit contributes 2 units of benefit, shared equally.
Private return per unit = 0.02 → not worth it individually.
But if all contribute, total welfare doubles (from 2000 to 4000).
Socially optimal ≠ privately optimal.
Can behavioural nudges solve climate change?
Nudges and behavioural tools can help, but they are not sufficient.
Climate change requires:
Regulation
Taxation
Global coordination
Behavioural interventions are complements, not substitutes.
Also raises equity concerns:
Across generations (intergenerational justice).
Across nations (developed vs. developing countries).
What is contingent valuation and how is it used in policy?
Contingent Valuation (CV) is a method to estimate the value individuals place on a good, often non-market goods.
It creates a hypothetical market to elicit:
Willingness to Pay (WTP) for a gain.
Willingness to Accept (WTA) compensation for a loss.
Applications:
Environmental valuation: Used in cost-benefit analyses (CBAs).
The Environmental Valuation Reference Inventory includes over 4,000 studies.
Health: Helps determine the QALY threshold for cost-effectiveness.
Safety: Used in Value of Statistical Life (VSL) estimates in UK policy.
What is the difference between WTP and WTA in contingent valuation?
What methods are used to elicit values in contingent valuation?
Open-ended: “What is the maximum amount you would pay?”
Dichotomous choice: “Would you pay £50?”
Dichotomous choice with follow-up: Adjusts based on initial response.
Iterative bidding: Sequential yes/no questions with increasing or decreasing amounts.
Payment card: Respondents sort values into “would pay,” “would not pay,” and “unsure.”
Source:
Carson, R. T. (2000) – Contingent Valuation: A User’s Guide, Environmental Science & Technology, 34, 1413–1418.
What are the main controversies around contingent valuation?
Moral concerns:
Is it ethical to put a price on life, endangered species, or natural environments?
Methodological concerns:
Responses may be influenced by:
Anchoring
Framing
Embedding
Loss aversion
Policy dilemma:
If we don’t assign value, policy assumes zero benefit.
But if values are unreliable, resource allocation may be flawed.
What are the arguments for and against valuing forests?
✅ Arguments for:
If forests are not valued, their implicit value is zero in policy.
Without monetary values, Cost-Benefit Analysis (CBA) will:
Include costs (e.g., of protection)
But exclude benefits (e.g., biodiversity, carbon storage)
Monetary valuation is necessary to:
Compare forests to other priorities (e.g., education, health).
Ensure democratic decision-making by reflecting public preferences.
Quote from lecture:
“Forests are great, but so is education and so is health. Would it be fair to force people to pay for something without taking into account how much they value it?”
❌ Arguments against (User notes):
Attempts to value forests have been scientifically difficult.
Example: REDD+ (Reducing Emissions from Deforestation and Forest Degradation):
Pays governments to preserve forests.
But some take the money and deforest anyway.
Problems with tree-based carbon offsetting:
Trees can exacerbate fires.
When trees die, they release stored carbon.
These are short-term fixes, not long-term solutions.
Danae’s point:
“Offsetting is a moral wiggle room — a cop-out to keep polluting.”
Why do we need to assign a monetary value to life in policymaking?
Policies that save lives often involve costs:
Direct monetary costs.
Opportunity costs (e.g., foregone benefits in other domains).
In the absence of a market, we need to estimate the value of reducing fatality risk.
Jones-Lee (1982): Monetising benefits allows:
Comparison with costs.
Optimal allocation of scarce societal resources.
The goal is not to price a life, but to value small changes in risk.
What is the Value of a Statistical Life (VSL)?
VSL is the aggregate willingness to pay (WTP) for a small reduction in fatality risk across a population.
It is not the value of an individual life, but the value of risk reduction.
Example:
Population: 100,000 people.
Risk reduction: 1 in 100,000.
If each person is WTP £20, total = £2,000,000.
This is the VSL.
Mishan (1971) and Schelling (1968): The good being valued is risk reduction, not life itself.
How is VSL derived from individual preferences?
How is VSL estimated in UK policy?
Study:
Carthy et al. (1998) – Journal of Risk and Uncertainty.
Method:
Contingent Valuation / Standard Gamble (CV/SG) chained approach.
Sample: 167 respondents, quota-selected by gender, age, and social class.
Findings:
Recommended VSL range: £1.0M – £1.6M (1997 prices).
UK government adopted £902,500 in 1997 → now ~£2 million.
Other methods:
Revealed Preference (RP) approaches (e.g., wage-risk trade-offs).
UK data insufficient for RP, so stated preference methods are used.
