This course focuses on the integration of psychological principles into economic models to understand better how individuals behave, especially in the context of poverty and decision making.
Behavioral Economics studies the joint influence of psychological and economic factors on individual behaviors. The aim is to augment traditional economic models, which often rely on rigid assumptions about human behavior.
Stable Preferences: Individuals have well-defined and stable preferences.
Rationality: Individuals behave rationally to maximize their utility function.
Self-Interest: Primarily motivated by self-interest.
Self-Control: Individuals possess perfect self-control to smooth consumption over time.
Discounting: Constant discount factor for future versus present consumption.
Risk Aversion: Individuals exhibit risk-averse behavior, characterized by a concave utility function.
Information Processing: Individuals update beliefs about information in a Bayesian manner.
Standard models often fail to predict actual behavior. Behavioral Economics seeks to identify and understand deviations from these models.
Behavioral Economics highlights real-world examples that contradict classical assumptions:
Information Bias: Individuals often hold beliefs that align with their interests (confirmation bias).
Changing Preferences: Preferences may not be stable over time or context.
Limited Attention: Individuals have constraints on processing information and may overlook important factors.
Emotional Influences: Emotions can skew decision-making processes.
Social Influences: Peer behavior can significantly impact individual choices.
Limited Self-Control: Individuals may not follow through on intentions (e.g., exercise).
Miscalibration of Information Processing: Many people fail to update their beliefs accurately based on new information.
Peer Effects: Social norms and peer behavior influence individual choices (e.g., purchasing behaviors).
One prominent example discussed is the laptop policy in class:
Externalities: Laptop usage can distract others.
Self-Control Issues: Students may intend to focus but can easily become distracted.
Policy Solutions: Ranging from laissez-faire to educational interventions or restrictions.
Introduction to Preferences: Examining how preferences change under different circumstances.
Beliefs and Information Processing: Understanding how beliefs influence decision-making.
Social Preferences and Behaviors: How individual preferences are shaped by social interactions and norms.
Specifying Paternalistic Policies: Discussing when and how intervention in choices can be beneficial (nudges).
Topics on Mental Health and Happiness: How mental health impacts economic decisions and behaviors.
Poverty and Economic Behavior: Exploring the implications of poverty on decision-making processes.
Behavioral Economics enriches the study of economics by incorporating psychological insights and recognizes that human behavior often deviates from the assumptions of classical models. This course aims to explore those deviations and their implications in policy and individual decision-making.
Behavioral economics integrates insights from psychology into economic theory, primarily focusing on the cognitive, emotional, and social factors that influence individuals’ decisions.
Surveys provide suggestive evidence about behavioral issues. Ethical considerations such as IRB approval depend on the anonymity and lack of personally identifiable information in surveys.
Economists view human behavior through three main lenses:
1. Constrained Optimization Individuals aim to maximize their utility subject to
constraints (e.g., budget).
2. Utility Function
Describes how different goods affect individual happiness.
Examples of goods include apples and bananas.
Higher quantities of consumption produce greater utility.
The general form of the utility function U is:
U = U(x1, x2, …, xn)
where xi are quantities of different goods
consumed.
3. Beliefs
Individuals form beliefs about the world based on prior experiences, leading to expectations about the
future (posterior beliefs).
1. Instantaneous Utility
Utility at a specific moment in time, e.g., U(t).
2. Aggregation Over Time
How individuals value current satisfaction versus future satisfaction leads to an evaluation of time preferences.
Individuals make decisions under uncertainty, e.g., entering a lottery.
Risk-averse individuals prefer outcomes with less uncertainty.
Social preferences reveal individuals’ willingness to consider the welfare of others in their utility function. This leads to altruism versus selfishness in decision-making.
Behavioral economics concerns itself with understanding why individuals may deviate from expected utility maximization. Influences on behavior include:
1. Framing Effects
How problems are presented influences choices.
2. Default Options and Nudges
Individuals often stick with default choices; carefully designed nudges can change behavior.
3. Heuristics
Cognitive shortcuts that simplify decision-making at the cost of accuracy.
Utility functions can be expanded to include social actors:
U = U(self, others)
This reflects that individuals
derive utility not only from personal consumption but also from the welfare of others.
Altruistic behaviors may arise from motives such as:
Warm Glow Effect: Individuals feel better about themselves when helping others.
Social Image Concern: Individuals care about how they are perceived by others.
Present bias refers to the tendency to give stronger weight to immediate rewards compared to future benefits.
The general representation can be shown as:
U = U0 + βU1
where β < 1 reflects present bias.
Individuals proactively engage in actions (like dieting bets) to enforce future good behaviors despite knowing their present biases.
Individuals need to update their beliefs based on new information; e.g., if a person receives an HIV test
result:
$$P(HIV | \text{Positive}) = \frac{P(\text{Positive|HIV}) \cdot
P(HIV)}{P(\text{Positive})}$$
Understanding the base rate is crucial for making accurate
assessments—neglecting it leads to base rate neglect phenomena.
Individuals frequently rely on anchors (initial pieces of information) which can significantly affect subsequent judgments and decisions, as demonstrated by:
Price Anchoring
Social Influence factors
The situation can alter behavior dramatically, as shown in studies like Darley & Batson’s on helping behavior in relation to time pressure and mindset. The results suggest that situational variables often overshadow stable personality traits.
Behavioral economics challenges the traditional utility maximization framework by incorporating preferences, beliefs, risk, and social influences.
Decisions are shaped not just by personal preferences but also by situational contexts, biases, and heuristics.
In this lecture and the next, we will discuss time preferences and the underlying theoretical models, focusing on the following:
Exponential Discounting as the Classical Economic Model
Limitations of Exponential Discounting
Introduction to Quasi-Hyperbolic Discounting
The Concepts of Sophistication and Naivete
We will explore various real-world situations like procrastination, credit card debt, and more that highlight the importance of understanding time preferences in economic decision-making.
Exponential discounting is the standard model used by economists to explain how individuals value present versus future benefits. The utility maximization can be represented mathematically as follows:
$$U = \sum_{t=0}^{\infty} \delta^t u_t$$
Where:
δ is the discount factor where 0 < δ < 1
ut is the utility received at time t
This functional form implies constant discounting over time.
While exponential discounting is tractable, it does not account for certain behaviors observed in individuals:
Inconsistencies over time (dynamic inconsistency)
Short-run impatience — tendency to favor immediate rewards over future gains.
To address the limitations of exponential discounting, the quasi-hyperbolic model introduces two parameters,
β and δ:
$$U = u_0 + \sum_{t=1}^{\infty} \beta \delta^t u_t$$
Where:
β reflects immediate discounting for future utilities, where 0 < β < 1
δ captures the long-term discounting factor.
This model allows for faster discounts of near-term utilities while treating future utilities under a longer-term perspective.
We can analyze decisions like whether to complete a problem set or pursue further education as follows:
Immediate costs vs. future benefits (e.g., higher income due to education)
Discounting future payoff leads to procrastination when immediate costs are perceived as larger.
Exercise — Immediate costs (effort and discomfort) vs. future health benefits.
Spending and Credit Cards — Immediate gratification of purchases vs. future debt incurred.
Investment Decisions — Decisions on savings today versus anticipated returns in the future.
Dynamic consistency refers to maintaining preference stability over time. The assumption in the exponential discounting model suggests that an individual’s preferences should remain unchanged:
If A ≻ B at time t ⇒ A ≻ B at
time t + 1
However, evidence suggests people do not often choose the same when faced with the same option in the future, displaying:
Preference reversals
Demand for commitment devices (e.g., gym contracts, freezing credit cards).
There are several ways individuals employ commitment devices, such as:
Ulysses tying himself to the mast of his ship to avoid temptation.
Freezing credit cards to create barriers to impulsive spending.
This lecture established the foundation for understanding time preferences and their implications on decision-making. The transition from exponential to quasi-hyperbolic discounting presents a more realistic view of human patience and impulse control.
We started with a discussion of the workhorse model of classical economics, specifically the Exponential Discounted Utility Model, highlighting its utility and limitations. We explored the assumptions of the model and identified inconsistencies with observed behaviors.
The key assumptions of the exponential discounting model include:
Constant Discount Factor (δ): The weight
placed on future utilities remains constant across time periods. For a time period t, the utility is discounted as:
Ut = U0 ⋅ δt
where U0 is the utility at the present time.
Dynamic Consistency: Preferences will not change over time. If you plan to make a choice today for the future, your future self will execute that plan unless affected by external factors.
No Demand for Commitment: Under traditional assumptions, individuals would not restrict their future choices since more options are perceived as better.
We discussed various observations that contradict the exponential discounting model:
Short-term impatience: Individuals often prefer immediate rewards over delayed ones even when the future reward is demonstrably larger.
Dynamic inconsistency: Individuals often fail to follow through with future plans, appearing to experience preference reversals.
Demand for commitment: People engage in strategies that restrict future choices as a means of controlling their impulses (e.g., deadlines, penalties).
To address the limitations of the exponential model, the quasi-hyperbolic discounting model introduces an additional parameter, β, to represent short-term bias:
$$U_t = \begin{cases}
1 & \text{if } t = 0 \\
\beta \cdot \delta^t & \text{if } t > 0
\end{cases}$$
β: Represents short-term bias, generally values are between 0 and 1 (e.g., β = 0.5).
δ: Represents long-term discounting, typically close to 1 (e.g., 0.95 or 0.99).
The quasi-hyperbolic model captures distinct behaviors in individuals:
Impatience in Short-Term Rewards: Individuals may choose smaller immediate rewards over larger delayed rewards.
Long-Term Patience: While short-term preference may lead to impulsive choices, individuals may consider future rewards when making long-term decisions.
We examined the difference between sophisticated and naive individuals, focusing on how self-control affects decision-making:
Sophisticated individuals fully understand their biases (β) and can predict their future decision-making, allowing them to make better current choices (e.g., they may avoid procrastination).
Naive individuals underestimate their future bias and fail to account for their preference reversals, often leading them to make suboptimal choices (e.g., procrastinating until the last moment).
We categorized different choices as investment goods (immediate costs, delayed benefits) or leisure goods (immediate rewards, delayed costs):
For investment goods (e.g., exercise), sophisticated individuals tend to commit early to avoid higher future costs.
For leisure goods (e.g., indulging in dessert), sophisticated individuals may discourage themselves from immediate consumption due to future regret, which could lead to self-sabotage because they are aware of their biases.
The introduction of quasi-hyperbolic discounting provides a more accurate representation of human behavior in terms of time preferences. Understanding the dynamics of sophistication and naivete is vital for creating models that can predict and improve decision-making, guiding individuals toward better long-term choices.
For more on these topics, refer to the paper by Ariely and Wertenbroch, which explores applications of these models in various real-world scenarios such as smoking cessation, drinking habits, and deadline effects.
Time preferences refer to the way individuals value rewards or consumption at different points in time. Key concepts discussed in the lecture include:
Exponential Discounting: A model in which individuals discount future utility at a constant rate. It leads to constant discounting, dynamic consistency, and no demand for commitment.
$$V_t = \frac{U(c_t)}{(1 + r)^t}$$
where Vt is the present value, U(ct) is the utility of consumption at time t, and r is the discount rate.
Quasi-Hyperbolic Discounting: An extension allowing for present bias, where individuals have different discount rates for the short run and the long run. The model introduces parameters β and δ:
Vt = U(ct) ⋅ βt ⋅ δt
where β < 1 captures present bias and δ ≈ 1 reflects future utility.
Individuals can be classified based on their ability to recognize their own present bias:
Full Naiveté: Believing they will behave differently in the future; they underestimate their future self’s present bias.
Full Sophistication: Perfect foresight; individuals understand their future behavior accurately.
Partial Naiveté: Individuals know they will be present-biased but underestimate the degree of it.
β < β̂ < 1
where β̂ reflects their belief about future behavior.
Commitment devices help individuals stick to their long-term goals by changing their future choices’ cost structure. They can include:
A commitment device is defined as an arrangement that restricts an agent’s future choice set.
Financial Penalties: Paying a fee if a certain goal (e.g., completing a paper by a deadline) is not met.
Restricting Access: Limiting access to temptation goods (e.g., unhealthy foods).
Public Commitment: Announcing intentions publicly to increase accountability.
Several studies illustrate the demand for commitment devices:
This study investigated deadlines set by MBA students at Sloan. Key findings include:
68% of students opted for early deadlines, indicating demand for commitment.
Students who chose evenly spaced deadlines performed better than those who set their own deadlines, indicating suboptimal choices.
This study with data entry workers in India provided insights into commitment devices:
36% of workers chose dominated contracts (lower pay until they reached a target).
Production increased by 2.3% when commitment contracts were offered, suggesting efficiency gains.
Payday effects were observed; individuals worked harder on paydays, further validating theories of self-control.
This study examined gym memberships:
Many individuals chose monthly contracts even when it was less cost-effective based on their actual usage.
Suggests that gym-goers may overestimate their future exercise frequency and employ the monthly fee as a commitment device.
This research emphasized the bundling of temptations, promoting healthier choices by linking them to enjoyable activities (e.g., audiobooks at the gym).
The exploration of time preferences, commitment devices, and their empirical applications reveals important implications for economic behavior. With insights from behavioral economics, firms and individuals can strategically design interventions to improve self-control and decision-making.
A commitment device is something chosen by an agent that helps them to commit to a future action they believe they would like to take.
Three types of agents can be considered:
Fully naive: They do not realize their future selves might behave differently.
Fully sophisticated: They accurately perceive their future behaviors.
Partially naive: They have beliefs (beta hat) about what their future selves will do, which may not match their actual behavior (beta).
To analyze the behavior of partially naive agents:
Start with backwards induction: Begin solving from the future to the present.
Use the agent’s beliefs (beta hat) to forecast future actions.
As you move backwards, utilize the actual choices made based on actual beta in its corresponding periods.
In economics, choices can be continuous (e.g., how much to consume) or discrete (e.g., whether to do an assignment or not). Continuous choice models involve optimization with constraints, similar to those discussed in introductory microeconomics courses.
This model describes how people evaluate present versus future rewards using the equation:
$$U = \sum_{t=0}^{\infty} \beta^t U(c_t)$$
where:
U(ct): Utility at time t.
β: Represents the degree of present-bias or how much the future is discounted.
In a study on credit card offers, various deals were presented to potential customers to analyze how quasi-hyperbolic discounting could affect decision-making. Relevant rates were examined:
6.9% for six months and 16% afterwards.
4.9% followed by 16% in a teaser deal.
6.9% followed by 4% as a post-teaser deal.
Findings suggest people often do not account for future borrowing costs when making present decisions.
Evidence indicates a strong correlation between the perceived future behaviors and the actual behavior based on initial choices:
Increase in response rates for better introductory offers by credit card companies.
The necessity of understanding the long-run effects ultimately to avoid negative outcomes for consumers.
In a study looking at a commitment savings account:
Participants could restrict access to their savings through various goal-based mechanisms.
Results indicated that providing limited options along with commitment features increased savings behavior significantly.
Research on alcohol consumption among low-income workers revealed:
High levels of alcohol consumption primarily serve as a self-medication for pain.
Potential commitment devices were explored to incentivize sobriety while minimizing consumption in such contexts.
Commitment devices should ideally lead to:
Sustained reductions in harmful behavior.
Increased awareness of future choices that might lead to long-run benefits.
The work of Esther Duflo and Michael Kremer:
Explores time-limited discounts as a commitment device to improve fertilizer usage.
The results demonstrate significant increases in fertilizer application when discounts were framed effectively.
In summary, commitment devices have shown potential for improving outcomes related to self-control problems. However, their effectiveness varies significantly across contexts. Future work should continue to examine how commitment devices can be designed and marketed to maximize their impact while minimizing failure rates.
In this lecture, we will explore the notion of risk preferences from the perspective of expected utility, focusing on how economics interprets choices involving risk.
Assumptions of economics regarding risk preferences
Measurement of risk preferences
Implications and limitations of risk preferences in economic models
Introduction to alternative models such as reference-dependent preferences
Risk Aversion: It refers to the tendency of individuals to prefer certainty over uncertain outcomes that could yield the same expected payoff. In other words, individuals are averse to taking risks.
Economics generally assumes that individuals will make choices that maximize expected utility when confronted with risk.
The expected utility model is a foundational concept in economics to describe how individuals evaluate risky
choices:
$$EU = \sum_{i=1}^{n} p_i \cdot u(x_i)$$
where EU is the expected utility, pi is the probability of outcome i, and u(xi) is the utility of outcome xi.
The expected monetary value (EMV) of a gamble involving two states (state 1 and state 2) is given by:
EMV = p ⋅ x + (1 − p) ⋅ y
where p is the probability of state 1 yielding outcome x, and 1 − p is the
probability of state 2 yielding outcome y.
Risk preferences can be evaluated using a utility function, which translates monetary outcomes to utility
values. A concave utility function indicates risk aversion:
$$\frac{d^2u}{dx^2} < 0$$
suggesting diminishing marginal utility
of wealth.
Economists typically measure risk aversion using two coefficients:
Absolute Risk Aversion (ARA):
$$r =
-\frac{u''(x)}{u'(x)}$$
Relative Risk Aversion (RRA):
γ = x ⋅ r
Several choices in life involve risk, such as:
Educational investments
Major purchases (e.g., housing)
Insurance decisions
Investment portfolios
Individuals often purchase insurance to mitigate risks, demonstrating risk aversion. The willingness to pay for insurance, despite its cost, indicates that people value reducing uncertainty.
The willingness to take on risk varies with context. For instance, people may engage in gambling or lottery tickets despite negative expected returns, which raises questions about individual risk tolerance and utility perceptions.
The model struggles to reconcile small-scale risk aversion with large-scale risk choices. For example, rejecting a 50% chance to gain $11 against a loss of $10 suggests extreme risk aversion, leading to implausible conclusions about larger risks.
Research by Matthew Rabin argues that consistent rejection of small-scale gambles implies unreasonable valuations of larger-scale gambles:
$u(w + x) \geq \frac{1}{2}u(w + y) + \frac{1}{2}u(w - 10)$ suggests diminishing marginal utility.
Small payouts lead to large wealth implications, hence failing to fit observed behaviors.
The expected utility model provides a framework for understanding risk preferences but fails to adequately explain discrepancies in small versus large-scale risk choices. Future discussions will delve into alternative models that account for these anomalies.
This lecture focuses on economic behavior under conditions of uncertainty and risk, particularly through the lens of the expected utility model.
The expected utility (EU) model is a powerful tool used by economists to evaluate choices made under uncertainty. It is defined as:
U(E) = ∑piU(xi)
Where:
U(E) is the utility of the expected value,
pi is the probability of outcome xi,
U(xi) is the utility associated with outcome xi.
Expected utility theory assists in understanding behaviors, such as investment decisions where higher risk typically demands higher expected returns.
Investment: Investments with increased volatility necessitate a higher return.
Crime: As the likelihood of getting caught increases, criminal behavior decreases.
Insurance Choices: Individuals may prefer lower deductibles despite higher premiums due to perceived risks.
Risk preferences are typically represented by a parameter γ known as the risk aversion coefficient. The model used frequently for estimating utility preferences is the Constant Relative Risk Aversion (CRRA) utility function:
$$U(W) = \frac{W^{1 - \gamma}}{1 - \gamma}$$
Where W is wealth, and γ represents the risk aversion level. Estimation of γ can be derived from individuals’ choices in risky situations through the concept of revealed preference.
Evidence showed that individuals exhibit varying degrees of risk aversion based on the scale of the gamble:
Small-scale gambles yield high γ (e.g., between 10-30).
Large-scale gambles yield moderate γ (e.g., between 0-2).
This contradiction poses a challenge: fitting a single γ to account for both small- and large-scale gambles is not feasible.
This theorem illustrates how declining small-scale gambles with positive expected value implies irrational choices in larger gambles.
Justin Sydnor’s research utilized data from 50,000 home insurance policies, focusing on deductible choices to ascertain risk preferences. Key points include:
A deductible is an out-of-pocket expense paid by a policyholder before reimbursement by the insurer:
Choosing a lower deductible means increased premium costs.
Policyholders often favor lower deductibles despite higher premiums.
The average yearly claim rate associated with chosen deductibles is under 5%, suggesting that:
People are willing to pay more for lower deductibles despite low likelihoods of claim.
This behavior indicates high risk aversion which is hard to reconcile with lower estimates in large-scale risks.
From Sydnor’s analysis, estimated γ values ranged significantly, often in the hundreds or thousands, challenging traditional notions of risk aversion.
Kahneman and Tversky proposed an alternative model, focusing on how people make decisions based on potential losses and gains, rather than final outcomes.
Reference Dependence: Utility is evaluated relative to a reference point.
Loss Aversion: Losses impact utility more than equivalent gains.
Diminishing Sensitivity: People are increasingly risk-averse with gains, but risk-seeking with losses.
$$\text{Utility} =
\begin{cases}
U(x) & \text{if } x \geq 0 \text{ (gains)} \\
-\lambda U(-x) & \text{if } x < 0 \text{ (losses)}
\end{cases}$$
Where λ > 1 reflects loss aversion.
Several experiments confirmed these concepts, including:
Choices revealing a preference for lower risk in the context of gains.
An observable endowment effect, where individuals value items more highly once owned.
The lecture emphasized understanding economic decision-making under risk through expected utility theory, considerations of risk preferences, and the implications of prospect theory. Future discussions will delve deeper into applications of these concepts in real-world scenarios and consider social preferences.
In their 1979 article, Kahneman and Tversky introduced Prospect Theory based on empirical evidence. Key concepts include:
Reference-Dependent Utility: Utility is derived from changes relative to a reference
point (denoted r), not absolute levels of consumption
(denoted c).
U(c) = U(c − r)
Loss Aversion: Losses have a greater impact on utility than equivalent gains. For instance, losing $100 feels worse than gaining $100 feels good. This is visually represented in the value function, which is flatter in the gain domain and steeper in the loss domain.
Diminishing Sensitivity: The value function exhibits diminishing sensitivity. The
perceived change in utility decreases as the value moves further from the reference point.
U″(x) < 0 in gains,
U″(x) > 0 in losses
The reference point (r) can be the status quo or the expected outcome. It plays a crucial role in determining whether a change is perceived as a gain or a loss.
Endowment Effect: Individuals tend to value items they own more than equivalent items they do not own.
Loss Aversion in Labor Supply: Workers may choose not to work during high wage days if they feel they have already reached their income target.
Consider a worker with wage variations:
Workers may decide to work fewer hours on high-wage days after reaching their income goals.
Loss aversion affects the decision-making process regarding how many hours to work based on reference points.
Sellers often price their homes relative to their purchase price, leading to higher listing prices when the sale is at a loss.
Empirical studies show that sellers are less likely to sell their homes at a loss.
Behavioral finance illustrates how reference-dependent preferences operate in trading scenarios:
Investors are more likely to sell winning stocks (realized gains) than losing stocks (realized losses), demonstrating the disposition effect.
Firms can leverage psychological principles to maximize profit:
High-Price Strategy: Start with a high price to create perceived gains when lowering prices.
Temporary Discounts: Use short-term promotions to play into loss aversion without permanently lowering prices.
Free Trials: Encourage users to feel ownership, leading to increased likelihood of purchase.
Prospect Theory provides significant insights into decision-making behaviors influenced by loss aversion and reference dependence. Understanding these concepts is crucial not only for academic pursuits but also for practical applications in economics and business settings.
This document outlines key concepts and findings from a series of experimental games designed to measure social preferences among individuals. These games are conducted to better understand altruism, fairness, and decision-making processes in economic contexts. Three primary games are discussed:
The Dictator Game
The Ultimatum Game
The Trust Game
The Dictator Game is utilized to assess an individual’s willingness to share resources with another participant. This game gives insight into altruistic behavior and selfishness in decision-making.
One participant is designated as the Dictator and is endowed with a certain amount of money, which we denote as M.
The Dictator can choose how much to give x to another
participant, called the Receiver:
x ∈ [0, M]
The Receiver receives M − x.
If the Dictator decides to give more to the Receiver, it may indicate higher levels of altruism.
A common criticism is whether real stakes influence behavior in hypothetical scenarios.
The Ultimatum Game assesses how individuals negotiate resource division with others while considering fairness and retaliation.
There are two players: the Proposer and the Responder.
The Proposer suggests a division of a sum S (e.g., 10
candies):
S = x + y
where
x is offered to the Responder and y is kept by the Proposer.
The Responder can choose to accept the offer or reject it.
If rejected, both players receive nothing.
Proposers are incentivized to offer a fair amount to ensure acceptance (typically half or more).
Responders are likely to reject offers they perceive as unfair, leading to both players receiving nothing, demonstrating preferences for fairness over personal gain.
The Trust Game evaluates levels of trust and reciprocity between individuals.
One player acts as the Investor, while the other is the Responder.
The Investor chooses an amount T to send to the Responder,
which is then multiplied by a factor k (typically k = 2 or k = 3).
Amount Received by Responder = k × T
The Responder then decides how much of this multiplied amount R to send back to the Investor:
R ≤ k × T
Investors may send larger amounts in hopes of receiving more in return, reflecting trust.
Responders who receive trust may reciprocate, leading to a mutually beneficial situation, while betrayal (not sending back) characterizes distrust and risk.
Altruism vs. Self-Interest: The Dictator Game assesses pure altruism, while the Ultimatum Game and Trust Game introduce reciprocity.
Communication Effects: Allowing communication may significantly influence outcomes in Ultimatum and Trust Games, as it can create an emotional bond and trust between players.
Public vs. Private Decisions: Decisions perceived in public scenarios (e.g., classroom context) tend to be more conservative concerning the amounts given or offered.
These experimental games illustrate how individuals navigate social preferences influenced by perceived fairness, trust, and potential outcomes. Further research may focus on refining these games to enhance predictive power regarding real-world economic and social behaviors.
Social preferences refer to how individuals consider the well-being of others in their decision-making processes.
The assumption of self-interest is prevalent in traditional economic models, where individuals primarily care about their own outcomes.
Key questions:
What are social preferences?
How do we measure social preferences?
Are individuals genuinely altruistic, or do they behave nicely primarily for social image?
Can policies change social preferences towards pro-social behavior?
Three commonly used games in behavioral economics:
Dictator Game
One player (the dictator) decides how to divide a certain amount of money between themselves and another player.
Classic measure of generosity and social concern.
Ultimatum Game
One player proposes a split of a sum of money, and the second player can accept or reject the offer.
If rejected, both players receive nothing.
This game assesses fairness and the impact of perceived disrespect on decision-making.
Trust Game
The first player sends a certain amount of money to the second player, which is typically tripled.
The second player then decides how much money to send back.
This game measures trust and reciprocity in relationships.
Typical outcomes indicate that players tend to offer around 40-50% in ultimatum games.
High offers lead to high acceptance rates, while low offers (below 20%) are often rejected due to fairness concerns.
Implication: Decisions reflect not only self-interest but also concerns about fairness and perceptions of the proposer’s intentions.
Distributional Preferences
Concern for the distribution of outcomes between individuals.
Can be interested (considering personal outcomes) or disinterested (concerned about societal outcomes).
Example utility functions:
$$\begin{aligned}
U_1 = \rho x_1 + (1 - \rho) x_2 & \quad (\text{if } x_2 \geq x_1) \\
U_1 = \sigma x_1 + (1 - \sigma) x_2 & \quad (\text{if } x_2 < x_1)
\end{aligned}$$
Face-Saving Concerns
Individuals care about maintaining a positive social image.
Behavior is influenced by how actions are perceived by others.
Intentions-Based Preferences
Preferences based on how outcomes are generated, including concerns for reciprocity and procedural justice.
Rho (ρ): Weight put on others’ outcomes when ahead.
Sigma (σ): Weight put on others’ outcomes when behind.
Generally, people are more generous (higher ρ) when they are ahead, and less concerned with inequality (lower σ) when behind.
Initial results indicate that individuals exhibit pro-social behavior in dictator and ultimatum games.
Additional considerations:
People may give money or resources not only based on generosity but also to foster a favorable image.
The context of giving (e.g., public vs. private) can significantly impact generosity.
Individuals may behave differently when they anticipate repeated interactions with the same individuals.
Future discussions will explore the implications of social recognition in pro-social behavior and the potential for policy interventions to enhance social preferences.
Understanding social preferences is critical for predicting behavior in economic contexts.
The interplay between self-interest and concern for others can lead to significant differences in economic outcomes, highlighting the importance of developing frameworks that account for social motivations.
Review of methods used to elicit social preferences:
Dictator Games
Ultimatum Games
Trust Games
Observations:
Apparent generosity in Dictator Games (about 20-25% shared).
Ambiguity in motivations for giving: altruism vs. social image concerns.
Two primary motivations:
Self-image concerns: wanting to feel good about oneself.
Social image concerns: worrying about others’ perceptions.
Importance of understanding these motivations for policy implications.
Next lecture will include real-world examples and evidence.
Experimental design with two treatments:
Standard Dictator Game (split 10 euros).
Exit option: either participate or receive a fixed payout (costless exit).
Findings:
Standard dictator game: average share = 0.87 euros.
Costless exit option: average share = 0.58 euros.
Implication:
Presence of costless exit reduced generosity; self-image concern prevails.
Introduction of a computer’s role in decision-making.
Two scenarios:
Dictator chooses; receives feedback on choices.
Computer makes choice with probability p.
Outcomes:
Responses changed based on the external perception of the decision.
Higher likelihood of selfish choices when able to hide behind computer decisions.
Conclusions:
Indicates that self-image concerns can drive behavior.
Discussion on the importance of intention, not just outcome.
Example: A boy keeping a larger apple but not being viewed negatively if intentions are clear.
Proposed utility function:
U(x, x′) = v(x) + ρv(x′)
where x is self-payoff and x′ is other-person’s payoff.
Societal norms often determine how decisions are made:
Recognition of social image can lead to avoiding selfish decisions.
Existence of moral wiggle room allows individuals to prioritize self-image over altruism.
Brief discussion of upcoming studies and experiments.
Expected focus on the malleability of social preferences and potential for policy design.
Understanding the complexities of social preferences is crucial for designing effective policies.
Future lectures will build on today’s findings with field evidence.
This lecture is focused on social preferences, providing a more uplifting examination compared to previous lectures. The discussion will address the following topics:
Social preferences in the workplace
The impact of pay inequality on productivity
The influence of policies on pro-sociality
The underestimation of the benefits of pro-sociality
Bandiera et al. conducted a study on fruit farms in the UK, focusing on the effects of relative pay on productivity. The study distinguished between:
Piece rates (payment per unit of output)
Relative pay (payment based on productivity compared to that of others)
The introduction of relative pay led to unexpected productivity outcomes:
Workers displayed a strong increase in productivity when compensated with piece rates, showing a better than 50% increase compared to relative pay schemes.
Under relative pay schemes, productivity stagnated at about 5 kilograms per hour.
The study highlighted that reduced effort among workers might stem from social preferences; when workers anticipate that high effort may disadvantage their peers, they may reduce their output voluntarily.
Breza et al. examined low-skill manufacturing workers in rural India to explore how perceived pay inequality affects morale and productivity:
Workers expressed reduced cooperation and performance when they perceived unfair pay disparities.
Even high-wage workers exhibited lower productivity when surrounded by lower-wage colleagues compared to a compressed wage group.
This hypothesis posits that exposing individuals to diverse groups can reduce prejudice and increase pro-social behavior. This was illustrated through:
Gautam Rao’s study involving rich and poor children in Delhi, which found increased generosity among rich students exposed to their poorer peers.
Evidence from sports leagues in India, which increased integration across castes.
Corno et al. explored the impact of interracial interactions among college roommates in South Africa, leading to:
Reduced stereotypes and improved friendships among races.
Enhanced academic performance and lower dropout rates among disadvantaged groups.
Kumar and Epley’s research highlighted how people might underestimate the positive impact of being generous:
When predicting others’ reactions to acts of kindness (e.g., gratitude letters), individuals often assume negative feedback where none exists.
Key results indicated:
Participants generally underestimated the positive reaction receivers would have towards their gratitude letters.
The research showed that the acts of kindness led to greater happiness in recipients than anticipated.
These findings suggest significant implications for understanding social preferences:
Social preferences are deeply rooted in individual interactions and can be manipulated through exposure to diverse groups.
Relative pay dynamics and perceptions of fairness are crucial in designing effective workplace incentive schemes.
Understanding and correcting biases regarding the impact of pro-social behavior can enhance individual and collective well-being.
In this lecture, we explore the concept of attention in the context of decision-making within the framework of behavioral economics. We start with a review of preferences discussed in previous lectures, including:
Time preferences
Risk preferences
Social preferences
In particular, we focus on how individuals make decisions based on their environment, information, and learning mechanisms.
Attention plays a fundamental role in how people gather information and make choices. Critical questions include:
How do we decide what to attend to?
What factors influence our learning from new information?
We introduce the concept of change blindness, where individuals fail to notice significant changes in their environment.
Example: Participants often miss a change in a bank teller when distracted.
The gorilla experiment illustrates how focusing on specific tasks (e.g., counting passes) can lead to missing obvious stimuli (e.g., a gorilla walking through the scene).
The study of dichotic listening experiments from 1958 demonstrates that:
Individuals can effectively focus on one auditory channel but often fail to recall information from the unattended channel.
Attention is limited, which impacts memory retention.
Several factors can affect an individual’s attention:
Physical environment (noise, temperature).
Distractions including social media and outside worries.
Social status, which can influence logics of attention and response.
Sleep deprivation and general stress levels.
Across many contexts, rational inattention models suggest that individuals may neglect important information due to limited attention resources. However:
If the costs of inattention exceed the benefits, individuals will adjust their focus accordingly.
There exists a conceptual paradox where critical information is often overlooked, leading to potential welfare losses.
The research by Raj Chetty and colleagues investigates consumer inattention regarding sales taxes:
Sales taxes are perceived through price tags at points of purchase, which may not register until checkout.
Data from grocery stores shows significant disparities in demand when making taxes salient versus non-salient.
The perceived value of a good, V, can be decomposed into:
V = v + o
where v is the visible/salient component and o is the opaque/invisible component.
For inattentive consumers, the perceived value, V̂, is given by:
V̂ = v + (1 − θ)o
where
θ measures the degree of attention to the opaque component.
Chetty et al. analyzed how demand for goods responds when sales tax information is rendered evident. Their
findings support that:
$$\Delta \log D = \frac{\theta \cdot
t_p}{\eta}$$
where D is demand, tp is the sales tax, and η is price elasticity. The estimated degree of inattention (θ) is significant as it quantifies how consumers respond to the
introduction of sales taxes.
To demonstrate the impact of inattention, Chetty et al. utilized a controlled experiment:
By highlighting the sales tax on price tags, they gauged the consumer response.
The difference-in-differences estimator was used to compare treated groups with control groups.
The findings indicated that consumers are considerably inattentive regarding sales taxes compared to visible price changes.
Building on the concept of rational inattention, Schwartzstein’s model illustrates how beliefs affect attention:
If consumers lack a relevant theory about a product, they may not pay attention to important information.
Case Study: Seaweed farmers in Indonesia failed to recognize pod size as a factor affecting yield, ultimately leading to suboptimal farming practices.
The importance of emphasizing experimental learning and providing explicit data to inform better decision-making.
The lecture integrates the evidence supporting the systematic existence of inattention in decision-making, with a focus on taxes, consumer behavior, and learning failures. Understanding inattention can provide insights into:
Consumer welfare implications.
The role of public policy in promoting more informed decision-making.
In this lecture, we discuss the concept of utility derived from beliefs, also addressing the implications of attention and systematic deviations from optimal belief formation and information acquisition.
Utility from Beliefs: People derive direct utility from their beliefs about themselves, the world, and future events.
Anticipatory Utility: Utility can stem from looking forward to future positive events or outcomes.
Ego Utility: Individuals derive utility from thinking positively about themselves (e.g., intelligence or appearance).
Attention is limited; individuals may miss critical information due to inattention or beliefs about what is important.
Example: Many people underestimate taxes in purchases because they do not see total costs clearly.
People may:
Misinterpret the importance of certain information.
Suffer from cognitive biases leading to incorrect beliefs.
Economists typically define utility functions over tangible outcomes (money, health, etc.). However, utility can also come from beliefs about these outcomes:
U(x, b) = f(x) + g(b)
Where:
U is the overall utility,
x represents outcomes,
b represents beliefs,
f(x) describes utility derived from outcomes,
g(b) describes utility derived from beliefs.
Anticipatory utility refers to the utility gained from anticipating future events, both positive and negative:
Individuals derive utility not only from outcomes but from the anticipation and emotions tied to future experiences.
1. Timing of Experiences:
Individuals may prefer to delay positive experiences to maximize anticipatory utility.
2. Information Gathering:
Anticipatory utility can lead individuals to avoid acquiring negative information (e.g., health screening).
Participants were asked about their willingness to pay for experiences (e.g., receiving a kiss from a movie star or avoiding an electric shock) scheduled for varying times in the future.
Willingness to pay for a kiss peaked at a certain delay, highlighting anticipatory utility effects.
Willingness to pay to avoid an electric shock was highest when it was immediate, indicating a desire to remove negative anticipation.
The implications can be summed up as:
E[U] = p ⋅ U(xA) + (1 − p) ⋅ U(xB)
Where:
E[U] is the expected utility,
p is the probability of the positive event,
U(xA) is the utility from the event occurring,
U(xB) is the utility from the event not occurring.
Low rates of genetic testing for Huntington’s Disease were observed, indicating that many individuals avoided seeking information.
Participants tended to overestimate their health status, displaying anticipatory utility behavior.
Testing rates remain low despite high potential benefits of obtaining certain information.
Subjective probability of having the disease was generally lower than the actual probability, revealing an optimism bias.
The concept of utility from beliefs plays a critical role in understanding how people make decisions regarding future outcomes and the information they choose to acquire. Individuals may intentionally avoid information that could lead to negative anticipatory utility, impacting their health and happiness over time.
In this lecture, we discussed anticipatory utility, which is the utility derived from beliefs about future events and how these beliefs influence consumer behavior, particularly in terms of consumption timing and information acquisition.
Anticipatory utility affects how individuals choose to engage in activities based on their expectations of future outcomes, providing a motivation to pursue positively anticipated events.
When individuals derive utility from beliefs, it affects their motivation to seek or avoid information, particularly negative information. For example, individuals at risk of Huntington’s disease may avoid testing due to the fear of confirming a negative diagnosis, thereby maintaining a facade of health.
We discussed a simple model where individuals have a constraint to maintain accurate beliefs about information received. The key conditions for individuals wanting to gather information are summarized in the following utility function:
f(p)
Where:
f(p) represents the utility derived from beliefs.
If f is concave, individuals are information-averse.
If f is convex, individuals are information-loving.
The decision to gather information relies on the character of f(p).
If individuals can manipulate beliefs, they might favor believing they have a higher probability (p = 1) of being healthy, especially if no consequences exist for self-deception.
Overoptimism about future events may lead to higher momentary utility but may distort decision-making adversely in the long run.
Research shows that individuals often believe they are less likely to face negative outcomes (e.g., health issues, divorce) compared to average populations. Examples include:
99% of drivers consider themselves better than average drivers.
Small business owners overestimate their chances of success.
Smokers acknowledge health risks but remain unconvinced it applies to them.
Kahneman and Tversky’s work has established that individuals employ heuristics, or shortcuts, to simplify complex decision-making processes. This leads to systematic biases:
1. Representativeness Heuristic: Individuals tend to judge probabilities by how much they resemble existing prototypes.
Example: Participants believed it more probable that Linda is a bank teller and a feminist (Statement 8) rather than just a bank teller (Statement 6), despite the logic suggesting otherwise.
2. Availability Heuristic: The probability judgment of an event is influenced by how easily instances can be recalled from memory.
Example: People overestimate the frequency of homicide compared to suicide based on recent media exposure.
3. Base Rate Neglect: Individuals often ignore the base rate information when making
probability judgments, focusing instead on specific information such as test accuracy or event descriptions.
$$P(H | T) = \frac{P(T | H) P(H)}{P(T)}$$
Where:
P(H|T) is the probability of having a condition given a positive test result.
P(T|H) is the probability of a positive test result when conditioned on actually having the disease.
P(H) is the base rate of the disease within the population.
Understanding these biases and heuristics can inform better decision-making strategies in various fields by emphasizing the importance of accurate information dissemination and how to counteract biases effectively.
This lecture will cover:
State-dependent preferences
Preference changes
Projection and attribution bias
We will focus mainly on how people’s preferences can change in predictable and sometimes unpredictable ways over time.
People’s preferences can change based on their physiological or psychological states, for example:
Hunger
Pain
Mood
These state-dependent preferences can cause significant deviations in decision-making.
1. Short-term Temporary Fluctuations
Example: When hungry, people desire different foods than when satiated.
2. Long-term Systematic Changes
Caused by personal choices (e.g., addiction) or natural aging.
3. Adaptation
Gradual return to baseline preferences after significant changes (e.g., lottery winnings).
Individuals who were starved exhibited changes in behavior and preferences, showing a heightened interest in food.
Hunger caused preference reversals, e.g., sudden interest in food-related purchases.
Nisbett and Kanouse (1969) showed evidence that shopping while hungry leads to higher consumption of food, especially junk food.
Preferenceshungry > Preferencessatiated
Lack of sleep impairs self-control and can lead to unhealthy choices.
Projection bias refers to people’s tendency to project their current preferences onto their future preferences, underestimating future changes.
People understand the direction of change but not its magnitude.
Let true utility at time t depend on consumption ct and state st:
u(ct, st)
The predicted future utility:
û(cτ, sτ) = (1 − α)u(cτ, sτ) + αu(cτ, st)
where α is the degree of projection bias.
Addiction: Individuals underestimate the future craving for substances like nicotine.
Depression: Depressed individuals project their current state onto their past and future experiences, leading to a lack of hope.
Attribution bias is a backward-looking bias where past states overly influence evaluations.
The tendency to rate a restaurant favorably if the meal was experienced while hungry.
Recommending movies viewed when tired results in negative reviews.
State-dependent preferences can substantially affect decision-making and choices in various scenarios.
Projection bias demonstrates systematic errors in predicting future preferences based on current emotional or physical states.
Attribution bias influences how past experiences mold current evaluations.
Overview of gender discrimination, identity, and the implications of the gender gap.
Importance of studying gender differences:
Equality, fairness, and justice issues.
Efficiency argument: Discrimination limits productivity.
Understanding the formation of preferences and personality through gender studies.
Women earn less money than men for equal work.
Bald-summary based on Claudia Goldin’s work.
$$\text{Gender Gap} =
\frac{\text{Average Female Earnings} - \text{Average Male Earnings}}{\text{Average Male
Earnings}}$$
Observations from 2010:
Mean annual earnings ratio = 0.72
Median annual earnings ratio = 0.77
Key factors for improvement:
Reduction in gender gap in education.
Technological innovations aiding women’s workforce participation.
Labor demand shifts towards services.
Increased competition lowering discrimination.
Examining Goldin and Rouse’s paper on blind auditions in orchestras.
Impact of blind auditions on improving gender ratios.
Blind auditions led to more female musicians being recruited.
Yet, disparities remain in representation among conductors and directors.
Discussing gender representation in economics and the lack of progress for female faculty.
Lundberg and Stearns (2019) observed trends in gender in economics academia.
Examining beliefs and skills in the workplace.
Bertrand and Mullainathan’s (2004) study on name-based discrimination using job applications.
Statistical vs. case-based discrimination:
Case-based: preference for certain types of applicants.
Statistical: belief that one group will perform better than another based on past outcomes.
Akerlof and Kranton (2000): identity considerations in economic models.
Gender identity norms and their implications on earnings.
Men prefer to earn more than their wives.
Consequential social dynamics leading to divorce or underreporting of women’s ambition.
Exploring Kleven et al. on the impact of motherhood on earnings.
$$\text{Child Penalty} = \frac{\text{Female Earnings
Post-Childbirth}}{\text{Baseline Earnings}}$$
Vesterlund et al.’s study on how men and women accept tasks.
Findings revealed women often say yes to non-promotable tasks more frequently than men.
Gender wage gaps persist despite improvements.
Biases in beliefs and identity norms contribute to wage and promotion disparities.
Importance of addressing biases in hiring and promotion practices.
Lecture 19: Frames, defaults, nudges, and mental accounting (Madrian and Shea, 2001).
Lecture 20: Malleability and inaccessibility of preferences (Ariely, 2003).
This lecture covers:
Gender discrimination and identity
The child penalty for women in the workforce
Policies to address gender disparities
The concept of nudges, defaults, and decision-making frameworks
Study by Bertrand et al.: The authors argue there exists a norm where men perceive that they should earn more than their spouses, leading to:
Formation of fewer couples
Increased instability and divorce rates in couples
Reduced working hours for women and a phenomenon termed "the second shift"
Study by Kleven et al.: The study highlights a significant "child penalty" for women, characterized by:
A persistent earnings gap approximately 20% post-childbirth.
A reduction in hours worked by approximately 10%.
The remaining 10% attributed to lower wages and productivity, due to reduced job market engagement.
Earnings
Gap ≈ (Earningsmen−Earningswomen) ÷ Earningsmen ≈ 20%
Child penalties appear to be transmitted through generations, suggesting that:
Daughters whose mothers experience child penalties are likely to encounter similar penalties later in life.
This highlights how gender identity norms and family structures shape labor market outcomes.
Proposals include:
Gender-neutral tenure clock stopping policies aimed at supporting childcare responsibilities for both parents.
However, studies indicate that such policies can inadvertently reinforce gender inequalities.
The study investigates whether women avoid career-enhancing actions due to potential negative perceptions in the dating market. Key findings include:
Unmarried female MBA students perform similar to their married counterparts when performance is unobserved.
However, their participation rates are lower, especially in visible settings.
This indicates that women may be holding back in certain environments due to perceived social penalties.
Vesterlund Study: This study analyzes the distribution of tasks assigned to men and women, highlighting:
Women often take on more non-promotable tasks, hindering career advancement.
Women are more likely to agree to tasks when asked, entrenching this phenomenon.
Participants in threshold public goods games have shown that:
Women are significantly more likely to volunteer for tasks than men.
Social norms and expectations regarding gender roles influence participation levels.
Standard economics recommendations:
Standard tools to increase savings: matching contributions, improving financial education, or enhancing investment choices.
However, such strategies often fail. Research indicates default effects can significantly impact savings behavior.
This study demonstrated:
Participation rates in 401(k) plans increased dramatically with automatic enrollment.
Active choice scenarios differ significantly from when employees are simply enrolled by default.
Overall participation rates increased from 37% to 86%, particularly benefiting lower-income workers.
Given 401(k) defaults, findings show:
Many participants are prone to accept whatever the defaults entail, often leading to suboptimal investment outcomes.
It accentuates the importance of carefully selected default options.
Cronqvist and Thaler’s Study: Analysis of Sweden’s privatization of social security shows:
Unsurprisingly, uneducated choices resulted in underperformance compared to defaults.
Highlights dangers of encouraging active choice without proper financial literacy.
Key Takeaways:
Gender identity norms and child penalties play critical roles in labor market outcomes.
Default options significantly influence consumer behavior, sometimes resulting in less-than-ideal financial decisions.
Active choice strategies need to be coupled with education and awareness to yield optimal outcomes.
This lecture discusses the concepts of malleability and inaccessibility of preferences. We begin with a review of the impact of defaults, nudges, and framing effects on decision making.
Definition: A default option is the choice that is automatically selected if no alternative is chosen by the
individual.
Examples:
Retirement Savings: Setting a default savings rate in retirement accounts can greatly influence savings behavior as many individuals opt for the default rather than actively choosing an alternative.
Organ Donation: Countries with presumed consent (opt-out) for organ donation have significantly higher donation rates compared to countries that require explicit consent (opt-in).
Nudge Theory: According to Thaler and Sunstein, a nudge can steer individuals’ choices without
removing their freedom of choice.
Key Components of Nudges:
Simplification of choices
Information disclosure
Use of social norms (e.g., feedback about energy consumption relative to neighbors)
Active Choice: Individuals are encouraged to make a decision explicitly.
Implications: Active choice is most effective when preferences are diverse, while default choices can be beneficial when individuals lack knowledge, allowing them to make better decisions without coercion.
Design: Two treatment groups received encouragement to make a plan for receiving a flu shot (date plan and time plan).
Results: Increased uptake rates from control (33%) to treatment (37%).
Research by Bettinger et al. showed that providing assistance in completing FAFSA applications significantly increased college enrollment.
Psychological research indicates that individuals often lack awareness of the origins of their preferences.
Nisbett and Wilson’s Findings: People often make up reasons for their preferences post hoc, failing to recognize the influence of external factors.
1. Two-String Problem:
Subjects fail to solve the problem until an external cue (motion of the cords) is introduced, leading to the epiphany of how to use them.
2. Bystander Effect:
Individuals are less likely to help others in an emergency when others are present, misattributing their inaction to personal beliefs about the situation.
3. Position Effect:
Individuals select items based on their position in a lineup, but fail to acknowledge that this positioning influenced their choice.
People construct preferences and values dynamically, based on cues and context.
Anchoring: Initial values can disproportionately influence subsequent estimates of willingness to pay (WTP).
Purpose: Elicit true preferences for goods without the influence of bargaining strategies.
Method: Present a random price; if the bid exceeds this price, the individual buys the item.
Ensures incentive compatibility.
Two groups were presented with the same poetry reading but framed differently: as a paid activity vs. a cost to endure. Outcomes showed significant differences in WTP based on framing.
Preferences are not stable; they are influenced by external factors, which can be strategically manipulated.
Understanding how preferences are constructed can inform policy design and intervention strategies aimed at improving decision-making in various domains.
The next lectures will focus on poverty, happiness, and mental health.
This lecture is focused on understanding poverty through the lens of psychology. It discusses the implications of scarcity and the psychological impacts of living in poverty, drawing on various studies, particularly the work of Mani et al.
Observations of suboptimal behaviors among the poor:
Lack of investment in high-return opportunities (e.g., fertilizer, machinery).
Low levels of savings, particularly precautionary savings for future shocks.
High engagement in high-interest loans.
Poor parenting practices and associated outcomes.
Lower productivity and punctuality.
Poor health outcomes and medical adherence.
Inadequate consumption of nutritious food and increased substance abuse.
Potential explanations include:
Education: Lower education may lead to poor decision-making in financial investments and parenting.
Environmental Conditions: Lack of transportation can affect job punctuality and productivity.
Institutional Structures: Defaults for saving or health insurance can promote positive behaviors among the rich but not the poor.
Treatment Effects of Poverty: Living in poverty impacts cognitive function and decision-making.
Scarcity is defined as not having enough of something, leading to limited cognitive capacity. This can
manifest in:
Cognitive Capacity = Total Cognitive Resources − Cognitive
Load from Scarcity
When thoughts about money dominate, they leave less cognitive bandwidth
for other tasks.
Two main studies were highlighted:
Mall Study:
Participants were divided into rich and poor, tested on cognitive tasks after answering hypothetical financial questions.
Results showed that poor participants performed significantly worse on tasks after hard financial questions compared to rich participants.
Sugarcane Farmer Study:
Examined pre-harvest and post-harvest cognitive performance.
Results indicated improved cognitive functioning post-harvest when farmers had cash in hand.
Sleep Deprivation and Health: Poor sleep quality is prevalent among the poor and is linked to decreased cognitive function and productivity.
Mental Health: There is a bidirectional relationship between poverty and mental health conditions, such as anxiety and depression. Poverty affects depression.
Cash transfers and psychological interventions can alleviate stress and improve cognitive performance.
Providing tools like financial literacy can help the poor make better choices.
Understanding the challenges posed by environmental factors (e.g., noise, pollution) is crucial for developing supportive policies.
The psychological impacts of poverty are profound, affecting cognitive function, decision-making, and overall well-being. Addressing these issues requires multifaceted approaches, including financial support, education, and psycho-social interventions.
This lecture will cover:
Concept of happiness and subjective well-being
Rationality and revealed preferences
Measuring happiness and utility
Mental health considerations
Happiness and subjective well-being refer to how individuals experience the quality of their lives and evaluates their satisfaction.
Subjective Well-Being = Happiness + Life Satisfaction
Rationality in economics involves the idea that beliefs, preferences, and actions are consistent. An
individual’s choices can reveal their underlying preferences.
Key Definition: If a person behaves in a certain way, it must be that their preferences can
rationalize that behavior.
Utility can be thought of as the satisfaction or benefit derived from consuming goods or services. In classical economics, it is assumed that individuals seek to maximize their utility.
Measuring happiness can be complex due to the differing ways in which it can be experienced over time.
Decision Utility: The utility that a person thinks they will achieve from a choice. It aligns with their expressed preferences.
Experience Utility: The actual utility experienced during and after the outcome, often measured through emotional responses.
Decision Utility ≠ Experience Utility
Common methods for measuring well-being include:
Self-reports (surveys and interviews)
Observational measures (facial expressions and physiological data)
Brain activity scans (to evaluate emotional responses)
Several factors can influence an individual’s happiness:
Economic status (income level)
Social connections (relationships)
Psychological support (therapy and coaching)
Life satisfaction (measured by feedback on life events)
Cultural Differences: Responses to happiness surveys can vary significantly across cultures, impacting how happiness is measured.
Income: Broadly, richer individuals report higher happiness; however, baseline sufficiency seems to settle around an income level.
Social Relationships: Maintaining strong social connections is one of the most consistent predictors of happiness.
Adaptation: People often adapt to life events (both positive and negative), leading to a return to baseline happiness over time.
As we consider interventions or policies to enhance well-being:
Understanding human biases and limitations in decision-making is essential.
Policymakers should consider implementing programs that improve access to mental health resources.
Incorporating measures of subjective well-being can inform more holistic policy-oriented approaches.
In future lectures, discussions on actual implementations of policies aimed at improving mental health will take place.
Consider engaging with your own happiness practices: reflect on the social ties you nurture, the balance between work and life satisfaction, and the active choices you make towards your well-being.
This lecture focuses on paternalism, examining its definitions, motivations, and implications when applied to governmental policies that influence behavioral agents.
Paternalism is defined as “An attempt to influence or control people’s conduct for their own good.” It assumes individuals may not be fully optimizing their decisions, particularly impacting themselves.
Internalities refer to the effects individuals impose on their future selves, often leading to decisions that are not in their long-term best interest.
The reasons for government involvement in economics can be categorized as follows:
Externalities:
Positive and negative impacts of one’s actions on others that are not accounted for (e.g., pollution from factories).
Desire for Equity:
Government programs aim to redistribute wealth (e.g., Social Security, unemployment insurance).
Addressing Market Failures:
Under-provision of public goods and information asymmetry.
Macroeconomic Stability:
Use of fiscal and monetary policies for economic stabilization.
Internalities:
Interventions that account for decisions affecting one’s future self (the focus of this lecture).
Involves significant interventions such as:
Outlawing certain choices (e.g., prohibiting smoking).
Imposing high taxes on unhealthy goods.
Defined as “Libertarian Paternalism,” emphasizes:
Preserving individual choice while nudging towards better decisions (e.g., default enrollment in retirement plans).
Policies aimed at assisting those who make mistakes without harming individuals who are already making optimal choices.
Informational Issues: Government officials may lack the necessary information.
Corruption: Elected officials may misuse paternalistic powers for self-gain or benefit special interests.
Freedom of Choice: The emphasis on individual freedom clashes with paternalism.
Unpopularity: People may resist intervention.
Many individuals lack the knowledge or capacity to make optimal choices.
Interventions such as nudges can lead to improved decisions without imposing restrictions (e.g., reminders).
Soft paternalism can enhance welfare without restricting freedom.
Employees agree to increase retirement contributions after receiving pay raises.
Leveraged principles of present bias and loss aversion.
The market sometimes fails to address self-control problems.
Consumers may be targeted by firms exploiting their lack of awareness (e.g., credit card debt).
Regulatory measures (e.g., Consumer Financial Protection Bureau) aim to protect consumers.
Nudges help individuals make better choices (e.g., simplified energy bills).
Sludges are obstacles or manipulations that make decision-making difficult (e.g., complex rebate forms).
Incorporating psychological insights into policy design can influence individual behaviors positively. While paternalism, especially in its softer forms, can yield benefits, careful implementation and respect for individual preferences remain crucial.