Behavioral Economics Guide

Conduct behavioral economics research using experimental methods, prospect theory, nudge frameworks, and key empirical tools for studying decision-making under bounded rationality.

Core Theoretical Frameworks

Prospect Theory (Kahneman & Tversky, 1979)

People evaluate outcomes relative to a reference point, with losses looming larger than equivalent gains:

Key features:
1. Reference dependence: Utility is defined over gains and losses, not absolute wealth
2. Loss aversion: lambda ≈ 2.25 (losses hurt ~2.25x more than equivalent gains)
3. Diminishing sensitivity: Marginal impact decreases as you move away from reference
4. Probability weighting: Overweight small probabilities, underweight large ones

Value function:
v(x) = x^alpha            if x >= 0  (alpha ≈ 0.88)
v(x) = -lambda * (-x)^beta if x < 0  (beta ≈ 0.88, lambda ≈ 2.25)

Probability weighting function (Prelec, 1998):
w(p) = exp(-(-ln(p))^alpha)   (alpha ≈ 0.65 for gains, 0.69 for losses)

Dual Process Theory (Kahneman, 2011)

System 1 (Fast)	System 2 (Slow)
Automatic, effortless	Deliberate, effortful
Intuitive, heuristic-based	Analytical, rule-based
Parallel processing	Serial processing
Emotional	Logical
Prone to biases	Can override biases
Default mode	Activated when needed

Nudge Theory (Thaler & Sunstein, 2008)

Nudges alter choice architecture to influence decisions without restricting options:

Nudge Type	Example	Mechanism
Default setting	Opt-out organ donation	Status quo bias
Salience	Calorie labels at point of sale	Attention focus
Social norms	"9 out of 10 neighbors recycle"	Conformity
Commitment device	Pre-commitment to savings plans	Present bias correction
Simplification	Pre-filled tax forms	Reduce cognitive load
Feedback	Real-time energy usage display	Information salience
Framing	"90% survival" vs "10% mortality"	Reference frame

Key Behavioral Biases and Experimental Tests

Bias	Definition	Classic Experiment
Anchoring	Over-reliance on first piece of information	Wheel of fortune + estimation task
Endowment effect	Overvaluing what you own	Mug trading experiment (Kahneman et al., 1990)
Status quo bias	Preference for current state	Default choice experiments
Present bias	Overweighting immediate outcomes	Discount rate elicitation
Sunk cost fallacy	Continuing due to past investment	Theater ticket scenario
Overconfidence	Overestimating own knowledge/ability	Calibration tasks
Availability heuristic	Judging probability by ease of recall	Frequency estimation tasks
Representativeness	Judging probability by similarity	Linda problem
Framing effect	Choices depend on how options are presented	Asian disease problem

Experimental Methods

Lab Experiments

# Example: Dictator Game implementation with oTree
# oTree is the standard platform for behavioral economics experiments

# models.py
class Player(BasePlayer):
    dictator_give = models.CurrencyField(
        min=0, max=100,
        label="How much do you want to give to the other participant?"
    )

# pages.py
class Decision(Page):
    form_model = 'player'
    form_fields = ['dictator_give']

    def vars_for_template(self):
        return {'endowment': 100}

class Results(Page):
    def vars_for_template(self):
        return {
            'kept': 100 - self.player.dictator_give,
            'given': self.player.dictator_give
        }

Field Experiments and RCTs

Design checklist for a behavioral field experiment:

1. RESEARCH QUESTION
   "Does changing the default retirement contribution rate from 3% to 6%
   increase average savings?"

2. TREATMENT ARMS
   - Control: Default contribution = 3% (status quo)
   - Treatment 1: Default contribution = 6% (higher default)
   - Treatment 2: Default contribution = 6% + active choice prompt

3. RANDOMIZATION
   - Unit: Individual employees
   - Method: Stratified randomization by age, salary, tenure
   - Balance checks: t-tests on observables across treatment arms

4. SAMPLE SIZE
   - Power calculation: N = 1,200 per arm (power=0.80, MDE=2pp,
     alpha=0.05, ICC adjusted for clustering by department)

5. OUTCOME MEASURES
   - Primary: Contribution rate at 6 months
   - Secondary: Total savings at 12 months, opt-out rate
   - Administrative data (no survey needed)

6. PRE-REGISTRATION
   - Register on AEA RCT Registry before treatment assignment

Survey Experiments

# Example: Willingness-to-Pay (WTP) elicitation using BDM mechanism
# Becker-DeGroot-Marschak procedure

import numpy as np

def bdm_auction(stated_wtp, item_cost_range=(0, 20)):
    """
    Becker-DeGroot-Marschak incentive-compatible mechanism.
    Random price drawn; participant buys if WTP >= price.
    """
    random_price = np.random.uniform(*item_cost_range)
    buys = stated_wtp >= random_price
    payment = random_price if buys else 0
    return {
        "stated_wtp": stated_wtp,
        "random_price": round(random_price, 2),
        "purchased": buys,
        "payment": round(payment, 2)
    }

# This is incentive-compatible: truthfully reporting WTP is optimal
# because the price is determined independently of the stated WTP

Time Preferences and Discounting

# Estimating discount factors from multiple price list (MPL) choices

def estimate_discount_factor(choices, amounts, delays):
    """
    Estimate quasi-hyperbolic discounting parameters (beta, delta)
    from a series of smaller-sooner vs. larger-later choices.

    beta: present bias (< 1 means present-biased)
    delta: long-run discount factor (per period)
    """
    from scipy.optimize import minimize

    def neg_log_likelihood(params):
        beta, delta = params
        ll = 0
        for choice, (ss, ll_amt), (t_ss, t_ll) in zip(choices, amounts, delays):
            # Discounted utility of each option
            if t_ss == 0:
                u_ss = ss  # No discounting for immediate
                u_ll = beta * (delta ** t_ll) * ll_amt
            else:
                u_ss = beta * (delta ** t_ss) * ss
                u_ll = beta * (delta ** t_ll) * ll_amt

            p_ll = 1 / (1 + np.exp(-(u_ll - u_ss)))  # Logit
            ll += choice * np.log(p_ll + 1e-10) + (1-choice) * np.log(1-p_ll + 1e-10)
        return -ll

    result = minimize(neg_log_likelihood, [0.9, 0.95],
                      bounds=[(0.01, 1.5), (0.8, 1.0)])
    return {"beta": result.x[0], "delta": result.x[1]}

Data Analysis in Behavioral Economics

Common Estimation Methods

Method	Use Case	Software
OLS / Logit	Treatment effects, survey experiments	Stata, R, Python
IV / 2SLS	Endogeneity in field settings	Stata (ivregress), R (ivreg)
Difference-in-differences	Policy evaluation	Stata, R (did package)
Structural estimation	Utility function parameters	Stata, MATLAB, Python
Random utility models	Discrete choice experiments	R (mlogit), Python (pylogit)
Clustering corrections	Within-group correlation	Stata vce(cluster), R sandwich

Key Resources

Resource	Type	Description
oTree	Software	Open-source platform for behavioral experiments
Gorilla	Platform	Online experiment builder (psychology/economics)
LIONESS Lab	Platform	Real-time interactive online experiments
AEA RCT Registry	Registry	Pre-registration for economics experiments
J-PAL	Organization	Poverty Action Lab, methodological resources
NBER Behavioral Finance	Working papers	Latest research in behavioral finance

Top Journals and Venues

Journal	Focus
American Economic Review	Top 5, publishes major behavioral papers
Quarterly Journal of Economics	Top 5, strong behavioral presence
Journal of Political Economy	Top 5
Econometrica	Top 5, theory + experiments
Journal of the European Economic Association	Top field journal
Management Science	Behavioral operations, decision-making
Experimental Economics	Dedicated experiments journal
Journal of Behavioral and Experimental Economics	Broader behavioral
Journal of Economic Behavior & Organization	Interdisciplinary behavioral