Nudges and Public Policy

Discrimination

Discrimination occurs when members of a minority group (e.g., women, Blacks, Muslims, Dalits, immigrants) are treated less favourably than members of a majority group with otherwise identical characteristics and in similar circumstances.

Persistent gaps in outcomes – employment, leadership, income – across social groups cannot be fully explained by instrumental channels (education, experience, health). Discrimination contributes to the residual gap.

Constitutional & Legal Backdrop

• India, Article 15: State shall not discriminate on grounds of religion, race, caste, sex, place of birth. The First Amendment (1951) permits special provisions (affirmative action) for backward classes, SC/ST.
• US, 14th Amendment: Equal protection of laws. A 1971 Supreme Court ruling under the Civil Rights Act: practices that freeze the status quo of prior discriminatory employment are invalid, even if facially neutral.

Post Hoc Justification

People choose based on hidden preferences, then rationalise the choice with a different, publicly acceptable reason.

Magazine Choice Study (Zoe & Norton)

Participants chose between two magazines:

Result: 92% chose Magazine 1. When asked why, 64% claimed “coverage” was more important than feature articles.

Treatment: identical coverage and features; only special issue swapped (Magazine 1 → sports coverage, Magazine 2 → swimsuit). Result: only 46% chose Magazine 1; only 17% now claimed coverage was more important. The true preference was the special issue, not coverage. The earlier justification was post hoc.

Construction Hiring Study (Norton et al.)

Participants acted as hiring head for a stereotypically male construction job. Two key dimensions: industry experience vs engineering education.

• Control: CVs gender‑neutral (initials). 76% chose the more educated candidate; 48% ranked education higher.
• Treatment 1 (Male educated): the more educated candidate was given a male name. 75% chose the educated candidate; 50% ranked education higher.
• Treatment 2 (Female educated): the more educated candidate was given a female name. 43% chose the educated candidate; only 22% ranked education higher.

Interpretation: Participants decided whom to hire (man vs woman) first, then adjusted their justification of which attribute mattered. The change in justification reveals taste‑based decision‑making.

Taste‑Based Discrimination (Gary Becker, 1957)

Taste‑based discrimination arises when an employer has a personal distaste for hiring from a particular group, even when candidates are equally productive.

The distaste acts as a psychic cost $d$ (a fraction of the wage). The effective wage for a discriminated worker becomes $w^* (1 + d)$, where $w^*$ is the market wage. This shifts the quantity of labour demanded downward.

• $L^*$: labour hired from one’s own group at wage $w^*$.
• $L_b$: labour hired from the discriminated group.
• Because the demand curve slopes down, $L_b < L^*$ – this is the observed discrimination.

Exam tip: Taste‑based discrimination does not require productivity differences. The key is the employer’s psychic cost, which makes hiring from the minority group effectively more expensive and reduces the quantity hired.

Key takeaways

• Discrimination is differential treatment of identical individuals from different groups.
• Post hoc justification: people choose first, then invent a rational reason.
• The magazine and hiring studies show how hidden preferences (special issue, gender) drive choices, with justifications shifting to match the decision.
• Taste‑based discrimination (Becker) models an employer’s distaste as a psychic cost added to the wage, reducing employment of the disliked group.
• Gaps in outcomes (employment, leadership) persist beyond education and experience; discrimination is a residual cause.

Statistical Discrimination

Statistical discrimination explains unequal treatment as a rational response to incomplete information. When an agent lacks full information about an individual (e.g., productivity, honesty), she uses the group average of that individual’s social group as a proxy – a heuristic or rule of thumb. This arises from a signal‑extraction problem: the true quality is unobserved, so the agent infers it from the group mean. Proposed independently by Arrow and Phelps, it is also called Arrow‑Phelps statistical discrimination.

Mechanism

In the canonical model, an employer cannot directly observe a job applicant’s productivity. To decide whom to hire, the employer:

1. Notes the applicant’s group (e.g., race, caste).
2. Assigns the average productivity of that group to the applicant.
3. Hires the applicant with the highest inferred productivity.

If group averages differ – e.g., lower average education for Dalits or Blacks – the individual from the disadvantaged group is less likely to be hired, even if her actual productivity is high.

Examples

Both examples can also be explained by taste‑based discrimination (pure distaste). Disentangling the two is often difficult but policy‑critical.

Contrast with Taste‑Based Discrimination

Policy Implications

Why distinguishing the two matters: the optimal policy differs sharply.

Exam tip: A one‑size‑fits‑all anti‑discrimination policy will fail if the underlying mechanism is misdiagnosed. Affirmative action may not cure statistical discrimination; improving information may not cure taste‑based discrimination.

Empirical Evidence

Distinguishing taste‑based from statistical discrimination in real‑world data requires clever research designs.

Blind Auditions (Goldin & Rouse)

Studied US symphony orchestras. Blind auditions (judges cannot see the player’s gender) vs. non‑blind auditions.

At first glance this seems like taste‑based discrimination (same violin quality, only gender information differs). But it could also be statistical if judges lack information on women’s willingness to travel or work weekends → they assign group average of lower travel availability.

Police Stop‑and‑Search (Knowles et al.)

Maryland police data: Black men were searched more often than whites.

• Taste‑based: police have distaste for African‑Americans.
• Statistical: police lack info on criminality → assign group average. If the average crime rate is higher for Blacks, searches will be more frequent, even without animus.

Correspondence Study (Bertrand & Mullainathan)

Classic field experiment to isolate discrimination on observable qualifications.

Design:

• Created 5,000 fictitious CVs with identical education and experience.
• Randomly assigned white‑sounding names (Emily, Greg) or black‑sounding names (Lakisha, Jamal).
• Submitted to ~13,000 job ads.

Results:

• The 3+ percentage‑point gap persisted across gender, city, and job type.
• Whites received call‑backs faster, and the gap widened with higher CV quality.

Because the CVs were identical in education and experience, the result is widely interpreted as taste‑based discrimination. However, the employer may still be using statistical discrimination on traits not captured in the CV – e.g., soft skills, self‑confidence, or language ability – if those traits correlate with race and are unobserved. The experiment cannot fully rule this out.

Key Takeaways

• Statistical discrimination arises from incomplete information and rational use of group averages; it is not based on animus.
• It relies on a signal‑extraction problem: the decision‑maker infers individual quality from the group mean.
• Distinguishing it from taste‑based discrimination is crucial because the two call for different policies: contact/affirmative action for taste; information provision for statistical.
• Empirical studies (Goldin & Rouse, Knowles et al., Bertrand & Mullainathan) suggest both types operate, but clean separation is difficult.
• Even when qualifications are identical, statistical discrimination on unobservable soft skills may persist, making it hard to claim pure taste‑based discrimination.
• Policy responses must match the root cause – a mismatch wastes resources and may fail to reduce inequality.

Stereotype Threat and Underperformance

Stereotype threat occurs when awareness of a negative stereotype about one's group impairs performance. The effect is situational: making a stereotype salient can lower test scores, while removing the threat eliminates the gap.

Exam tip: The Hoff & Pandey experiment is a clean test distinguishing stereotype threat from expectation of discrimination. The key is that adding a random‑chance component (treatment 2) eliminates the gap, which would not happen if pure stereotype threat were the mechanism.

Self‑Expectancy Effects and Self‑Fulfilling Prophecies

Two models show how initial beliefs can perpetuate skill differentials even when underlying ability is equal:

Model 1 – Worker's belief about employer updating Minority workers believe employers are slow to update beliefs about ability → less incentive to invest in skills → skill differential emerges → employers observe the differential and act accordingly → belief becomes self‑fulfilling.

Model 2 – Employer's incorrect belief about worker Employers incorrectly believe minority workers cannot meet the task → invest less in training those workers → skill gap emerges → original belief is confirmed → self‑fulfilling prophecy.

Pygmalion effect (Rosenthal & Jacobson, 1968): In a US public elementary school, teachers were told that a randomly selected 20% of their class were expected to develop much faster (as measured by IQ). By month 8, the “bloomers” gained 12 IQ points vs. 8 points for controls. Mechanisms: extra attention, encouragement, increased student motivation.

Diversity in Leadership Positions (Quotas)

Mandated reservation/quota for women on corporate boards, evaluation committees, etc., forces majority members to evaluate minority performance more often. However, results can be counter‑intuitive: Bagues et al. (Review of Economic Studies) found that women were less likely to succeed in the Spanish judiciary entry exam when the evaluation committee had more women (and men more likely to succeed with more men). This may arise because women on committees go to great lengths to avoid appearing biased.

Role of Minority Leaders and Attitude of the Majority

Taste bias against a group (e.g., women) can feed into statistical discrimination: if no minority leaders have ever been observed, majority members lack evidence of competence → they continue not to elect/select minority leaders → the cycle continues. Forcing exposure (e.g., through quotas or reservations) can break this cycle by providing positive examples, updating beliefs, and reducing bias.

Affirmative Action and Minority Role Models

Successful role models from a minority community can change aspirations and effort:

• Hoff & Pandey (revisited): Dalit children reduced effort when caste was made salient because they expected unfair evaluation. Seeing a successful person from one’s own group can counteract that belief.
• Cheryan et al.: Exposure to a non‑stereotypical computer science role model (e.g., someone who plays football, swims, loves cats) increased female subjects’ belief in their own success in CS – irrespective of the role model’s gender.

Intergroup Contact (Allport’s Hypothesis)

Intergroup contact reduces prejudice only under four conditions:

1. Equal status between groups in the situation.
2. Common goal.
3. Intergroup cooperation.
4. Support of authorities/law/custom.

Meta‑analytic evidence (Pettigrew & Tropp, 2006): Contact reduces prejudice in 94% of 515 studies, and generalises across racial, ethnic, age, mental‑illness, and LGBT contexts.

Random‑assignment experiments:

Exam tip: Allport’s four conditions are frequently tested. Remember the contrast between the taxi‑driver example (fails) and the office‑team example (passes). The meta‑analysis result (94%) is a powerful quantitative takeaway.

Key takeaways

• Stereotype threat impairs performance when a negative identity is made salient; the effect can be eliminated by reframing the task or removing human evaluation.
• Self‑fulfilling prophecies (Pygmalion effect) show that false beliefs about ability can become real through differential treatment and effort.
• Combatting discrimination requires structural interventions: quotas, role models, intergroup contact that meets Allport’s conditions.
• Contact reduces prejudice robustly across many group boundaries and generalises to broader attitudes (e.g., endorsement of affirmative action).

Gender Gaps

Gender gaps in labour-market outcomes — wages, employment, representation — persist across social groups. Even after controlling for education, experience, and other “human capital” factors, a residual unexplained gender wage gap remains (≈9% in the US). This suggests that behavioural or psychological differences between men and women may play a role.

The gender wage gap and representation

• US wage gap (raw): women earned 62.1% of men’s income in 1980, rising to 79.3% in 2010.
• Adjusted gap: after controlling for education, experience, age, etc., women still earn ≈9% less — the unexplained component.
• Top of the distribution: the pay gap is larger and the explained portion is very large.
• Representation (vertical segregation):
  • IIM Bangalore: 25–30% of MBA students are women → fewer women in top corporate positions later.
  • US: women hold only 2.5% of the five highest-paid executive positions per firm.
  • Fortune 500: 3.6% of CEOs, 14% of executive officers, 16% of board directors.
  • EU: fewer than 14% of board members are female.
  • Bertrand (2017): Fortune 500 → 19.9% board members women, 5.8% CEOs; EU → 23.3% board, 5.1% CEOs.
• India’s numbers are likely similar or worse; no specific India data is provided here.

What explains the gender gap? Two broad categories

1. Differences in traits: preferences for jobs, performance on different metrics, self-beliefs, confidence.
2. Differential response to labour‑market incentives: bargaining attitude (women less likely to negotiate raises), willingness to take roles that lead to promotion, competitiveness.

Because career progression is highly competitive, a key question is whether men and women differ in competitive performance or entry into competition:

• Intensive margin (performance in competitive environments): do women perform differently when they are already in a competition?
• Extensive margin (entry into competitive environments): are women less likely to choose to compete in the first place?

A lab experiment (Gneezy, Niederle & Rustichini, QJE) answers the intensive-margin question.

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Experiment: gender differences in competitive performance

Design: groups of 6 participants solve mazes for 15 minutes under different compensation schemes.

Treatments and results

Eliminating potential explanations

Four possible reasons for the larger gender gap in mixed‑gender tournament:

1. Women dislike effort when pay is uncertain. Tested by random‑pay treatment → women did not increase performance, so this is unlikely.
2. Women’s performance is maxed out — they cannot do better. Tested by single‑gender tournament → women performed much better when not competing against men; therefore not maxed out.
3. Women dislike competition in general. Single‑gender tournament shows women compete effectively → they do not dislike competition per se.
4. Women dislike competing against men specifically — likely due to a belief that they will lose.

Conclusion on intensive margin: Women perform worse in mixed‑gender competition not because of lower ability or aversion to competition, but because they believe they cannot win against men, leading to lower effort. In single‑gender settings, their performance matches the piece‑rate level.

Exam tip: The elimination strategy — using iterative treatments to rule out confounds — is a classic experimental design. Know the logic: random pay rules out uncertainty aversion; single‑gender rules out ability cap and general competition aversion, isolating the “competing against males” belief effect.

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Key takeaways

• Gender gaps in wages and top‑level representation are large and partially unexplained by conventional human‑capital factors.
• Two behavioural questions: (1) Do women perform differently in competitive environments? (2) Do women choose to enter competitions less often?
• Lab experiment (maze solving) isolates gender differences in performance under competition.
• Piece‑rate baseline → small gender gap; mixed‑gender tournament → large gap; random pay → gap persists; single‑gender tournament → gap disappears.
• The driving mechanism is belief: women believe they cannot beat men, so they underperform in mixed‑gender competition.
• Single‑gender environments eliminate this performance gap, suggesting the difference is not about ability or general competition aversion.

Gender Differences in Competition Entry (Niederle & Vesterlund, 2007)

The classic question: do women shy away from competition and do men compete too much? The experiment tests whether gender predicts willingness to enter a competitive payment scheme.

Experiment design – groups of 4 participants solve simple two‑digit addition problems for 5 minutes. Three parts:

1. Piece rate – $0.50 per correct answer.
2. Tournament – $2 per correct answer for the group's top performer; others earn$0.
3. Choice – participants choose which payment scheme to apply to their performance in that round.

The key outcome is whether women are less likely than men to opt into the tournament in part 3, even after controlling for ability and confidence.

Performance differences? No – the distribution of scores in the piece‑rate and tournament parts is statistically similar for men and women. Ability on the simple math task is not the driver.

Results – tournament entry (Figure B in the paper):

The gap is large and persists even after controlling for self‑confidence (participants’ predicted rank). For rank‑1 performers adjusted for confidence, 80% of men still choose tournament vs. only 50–55% of women.

Exam tip: The key finding is that women under‑enter competition relative to men, and this is not fully explained by lower confidence. The gap exists even among equally confident high‑performers.

A replication at IIM Bangalore with top students shows the same pattern: a significant gender gap in tournament entry.

Key takeaways

• Men and women perform equally on the simple math task.
• Women are less likely to choose tournament payment, even when they are top performers.
• Lower self‑confidence among women explains part, but not all, of the gap.
• The preference to avoid competing against men appears to be a distinct driver.

Gender Differences in Volunteering for Low‑Promotability Tasks (Babcock et al.)

Promotability – the degree to which a task influences evaluation and promotion. Non‑promotable tasks are necessary but do not advance a career (e.g., organizing a picnic, receptionist duties, committee work). Women are overrepresented in such roles.

Experiment design – groups of 3 members. Each can choose to invest in a 2‑minute task (volunteer) or not. Payoffs:

This mimics a real office: everyone wants the picnic to happen, but no one wants to organize it because the payoff for volunteering is lower than free‑riding.

Result – mixed‑gender groups: Women are significantly more likely to volunteer than men – a gender gap of about 15 percentage points.

Is it preferences or beliefs? The experiment is repeated in single‑gender sessions (only men or only women). The gap disappears: men volunteer more (when only men are present) and women volunteer less (when only women are present). This shows the gap in mixed groups is driven by beliefs: women believe men will not volunteer, and men believe women will volunteer. It is not a pure preference for volunteering.

Key takeaways

• Non‑promotable tasks are essential but do not lead to promotion.
• Women volunteer for them more than men in mixed settings.
• The gap is caused by beliefs about others’ behaviour, not by an intrinsic preference.
• This contributes to the gender gap in top‑level representation and wages.

Policies to Reduce Gender Gaps (Banerjee & Mustafi)

Given the gender gap in volunteering for non‑promotable tasks, can social recognition close it? Paying more is not feasible (by definition, non‑promotable tasks cannot carry large monetary incentives).

Experimental treatments – baseline replicates Babcock et al.; then three variations:

Intuition:

• Positive recognition turns volunteering into a status contest that men enter.
• Negative recognition punishes free‑riding, but women already volunteer heavily – it pushes them even further, enlarging the gap.
• Combining both creates a balanced incentive that encourages both genders.

Exam tip: Social recognition is a low‑cost policy tool. The type matters: purely negative recognition can backfire and widen the gender gap.

Key takeaways

• Monetary incentives are not appropriate for non‑promotable tasks.
• Positive social recognition reduces the gender gap by attracting men.
• Negative social recognition increases overall volunteering but expands the gap.
• A combination of positive and negative recognition eliminates the gap.
• Policy interventions should be designed with awareness of belief‑driven gender differences.

Nudges: Definition and Key Concepts

A nudge is any aspect of the choice architecture that alters people’s behavior in a predictable way without forbidding any options or significantly changing economic incentives. To count as a nudge, an incentive must be easy and cheap to avoid.

Definition (Thaler & Sunstein): A nudge is any aspect of the choice architecture that alters behaviour predictably, while preserving freedom of choice and leaving economic incentives largely unchanged.

Libertarian Paternalism

Nudges are often called libertarian paternalism:

• Libertarian because individuals retain the same set of options and can freely choose.
• Paternalistic because the choice architecture is designed according to values or objectives set by someone else (e.g., policymakers).

Classic Examples

Exam tip: The core of a nudge is that no option is removed and no monetary incentive is altered. If a policy bans fries or taxes sugary drinks, it is not a nudge.

Key takeaways

• A nudge alters behaviour predictably while preserving free choice and not changing economic incentives.
• Termed libertarian paternalism: free choice (libertarian) + design by others (paternalistic).
• Examples: smart lunchrooms, escalator markings, urinal fly.
• Banning or taxing is not a nudge.

1. Automatic Enrollment in Retirement Savings (Madrian & Shea)

Problem: People save too little. Traditional policy (interest rate changes) assumes rational intertemporal choice, but real behaviour is inertia-ridden.

Intervention: A large US corporation changed the default for new employees from “opt in” to “opt out” at a 3% contribution rate into a money market fund.

Results:

• Participation jumped from ~49-57% to 86%.
• The default contribution rate (3%) became the modal choice.
• Investment allocation shifted heavily toward the default (money market, not stocks).

Exam tip: The default effect is one of the most powerful nudges. People stick with the status quo. Changing the default from “opt in” to “opt out” dramatically increases participation without restricting freedom.

2. Save More Tomorrow Program (Thaler & Benartzi)

Problem: Employees agree with financial advisors that they should save more, but say they “cannot afford it now” (present bias).

Intervention: Ask employees to commit now to increase their savings rate next year (i.e., “save more tomorrow”). Nothing changes immediately; only future contributions rise.

Result: The treatment group showed a substantial increase in savings compared to the control group. The program was a great success because it leverages present bias: people are willing to commit to future sacrifice, but not immediate.

3. Behavioural Insights Team (BIT) and Tax Compliance

Context: The UK’s Behavioural Insights Team (BIT, or “Nudge Unit”) – established in 2010 under the Cameron government – ran a field experiment on tax debtors.

Treatments (randomised letters to 100,000 debtors):

1. Local norm: “Most people in your local area have paid on time.”
2. Debt norm: “Most people with a debt like yours have already paid.”
3. Both norms: Combined local and debt norm.

Results: Relative to a control letter (no norm information), all norm letters increased payment. The largest effect came from the combined local + debt norm letter.

Exam tip: This is a classic example of social norms nudges – extremely cheap (just extra ink) yet yields “medium-sized gains with nano-sized investment”. Nudges are not silver bullets but cost-effective.

Key takeaways

• Default nudges (automatic enrolment) boost savings participation from ~50% to >80%.
• Commitment devices (Save More Tomorrow) overcome present bias by shifting sacrifice to the future.
• Social norm messages in tax letters significantly improve compliance at negligible cost.
• Nudges are cheap and produce medium-sized effects – a favourable cost-benefit ratio.

Designing Choice Architecture: When Are Nudges Suitable?

Nudges are most effective when people face specific decision-making problems:

The Indian Context (Economic Survey 2019)

A table from the Economic Survey lists several policies (e.g., Give up LPG, Aadhaar, Jan Dhan Yojana, Swachh Bharat, ban on alcohol) as potential nudges. Critical note: Not all qualify as nudges. For example:

• Aadhaar: Public services contingent on Aadhaar may involve forbidding options (if you lack Aadhaar, you lose access) – does it change economic incentives significantly? Possibly not a nudge.
• Swachh Bharat: Toilets provided? Check definition.
• Ban on alcohol: Forbids an option → clearly not a nudge.

Exam tip: Always test a policy against the two criteria: 1) No option forbidden, 2) No significant change in economic incentives. The Economic Survey’s classification is debatable – be ready to critique.

Key takeaways

• Nudges are suited for self-control problems (virtues/vices), rare, difficult, no-feedback, and unknown-preference decisions.
• Not every “behavioural” policy is a nudge – ban or large financial penalty disqualifies it.
• Governments worldwide (UK, US, India, World Bank) have institutionalised nudge units.

Choice Architecture and Nudges

Choice architecture is the design of environments in which people make decisions. A well-designed choice architecture aligns visual cues with functionality, reduces friction, respects human cognitive limitations, and guides behaviour without coercion. The key is to understand how the automatic system (System 1) and reflective system (System 2) interact.

Choice Architecture Principles

Consistency between cues and function

A good choice architecture ensures that visual cues match the required action. Conflicting cues create inefficiencies, errors, and frustration.

• Door example: Two rings on a door suggest pulling; if the door must be pushed, users waste time and become confused.
• Stop sign example: A green background with the word “STOP” conflicts with the learned association (red = stop, green = go). This increases reaction time and risk.
• Stroop-like colour-word conflict: The automatic system reads the word faster than it perceives colour. When the word “red” is printed in blue, the brain experiences a conflict between word-reading and colour-processing areas.

Exam tip: The Stroop test illustrates the dominance of automatic processing. Nudges should not rely on conflicting cues—always align the automatic response with the desired behaviour.

Defaults – the path of least resistance

Defaults are options that take effect when an individual makes no active choice. They are powerful because of the status quo bias – people tend to stick with what is already set.

Defaults are often inevitable in choice architecture. However, their interpretation can be controversial.

Controversy – No Child Left Behind policy: In the US, a policy required schools to provide student contact details to military recruiters unless parents opted out. Some districts interpreted this as opt-in (parents had to actively consent), while the Department of Defense interpreted it as opt-out (parents had to actively refuse). This subjective ambiguity generated legal and ethical disputes.

Exam tip: Defaults are not neutral – they steer behaviour. Always identify the default in a policy and who benefits from it.

Active (mandated) choice

To avoid the biases of defaults, some architectures force a decision – the user cannot proceed without making a choice.

• Example: School admissions requiring parents to check “yes” or “no” before continuing.
• Downside: Cognitive burden – most people click “yes” to terms & conditions without reading. For complex processes, required choices can become a nuisance.

Trade-off: Defaults are effortless but can be manipulative; active choices respect autonomy but increase friction.

Post-Completion Errors

Post-completion errors occur when the main task is finished, but a subsidiary step is forgotten. The designer should force the user to resolve the subsidiary step before completing the main task.

• ATM: Users withdraw cash but forget the card. Better design: card must be retrieved first, then cash is released.
• Medicine packaging: Pills designed with calendar blisters reduce forgetting.
• Gmail attachment reminder: If the email body contains “attached” but no attachment is added, a prompt appears.

Design principle: Anticipate common human errors and build safeguards that prevent the error from having consequences.

Mapping Choices to Welfare

Mapping refers to how well a person can connect a choice to its outcome. When mapping is difficult (e.g., medical treatments, investment options), choice architecture should simplify information.

• Example: Camera companies used megapixels as a simple metric for image clarity, making the mapping easier for consumers.
• Medical decisions: A doctor might present survival rates, side-effects, and quality-of-life scores in a comprehensible format.

Goal: Help people translate complex choices into personally relevant, measurable units.

Half a Dozen Nudges

Exam tip: A nudge must be cheap and avoid altering economic incentives. Rewards like “vitality bucks” or cash payments shift the cost-benefit calculation and are therefore not pure nudges.

Civility check detail: The automatic system (System 1) reacts emotionally; the reflective system (System 2) later regrets the response. A 24-hour pause reduces hot-headed mistakes – analogous to cooling-off periods for crimes of passion.

Key takeaways

• Design visual cues that match intended function to avoid automatic-system conflict.
• Defaults are powerful because of status quo bias; they are inevitable but can be contested.
• Active (mandated) choice respects autonomy but increases cognitive load.
• Post-completion errors (e.g., forgetting ATM card) can be prevented by reordering steps.
• Simplify mapping between choices and welfare using comprehensible metrics.
• Evaluate whether a policy is a true nudge by checking if it changes economic incentives – if so, it is not a pure nudge.