Behavioral Economics and Aging David Laibson Harvard University and NBER July 8, 2009 RAND.

Slides:



Advertisements
Similar presentations
Instant Gratification, Multiple Selves, & Self-Control: How to Control Your Selves David Laibson Harvard University November 2010.
Advertisements

Decision Theory. Plan for today (ambitious) 1.Time inconsistency problem 2.Riskiness measures and gambling wealth  Riskiness measures – the idea and.
Copyright ©2004 Pearson Education, Inc. All rights reserved. Chapter 8 Personal Loans.
The Basics  Saving vs. Investing  The Time Value of Money  The Miracle of Compounding Interest The How 1. Make Automatic Transfers 2. Set Up Investment.
John Beshears James J. Choi Christopher Clayton Christopher Harris David Laibson Brigitte C. Madrian August 8, 2014.
Empirical evidence on quasi-hyperbolic discounting AEA, January 2010 David Laibson Harvard University and NBER or Laibson Lecture 1.
Behavioral Finance David Laibson Robert I. Goldman Professor of Economics Harvard College Professor Harvard University National Bureau of Economic Research.
Topic 4 Financing Strategies. Topic 4: Financing Strategies Learning Objectives – (a) Analyze the various sources of borrowing available to a client and.
Chapter 22: TAXATION AND SAVINGS – THEORY AND EVIDENCE
Can We Control Our Selves? David Laibson Robert I. Goldman Professor of Economics Harvard University March 27, 2013.
 How to Manage Your Cash › Daily Cash Needs  Lunch, movies, gas, or paying for other activities  Carry cash  Go to an ATM  Credit Card  Know pros.
Money, Banking, and the Federal Reserve System
CHAPTER TEN Liquidity And Reserve Management: Strategies And Policies
Expectations and our IS-LM model In this lecture we will examine how expectations about the future will impact investment and consumption today. We will.
Personal Finance Benchmark Demonstrate an understand that personal spending, saving, and credit decisions have significant implications for the.
Two Papers on Intertemporal Choice and Self Control SS200 - Meghana Bhatt.
Dr. Bruce J. West Chief Scientist Mathematical & Information Science Directorate Army Research Office UNCLASSIFIED.
Retirement Planning Miscellaneous Investing Basics Stocks and Bonds Mutual Funds Personal Finance Final Exam.
Basic Tools of Finance Finance is the field that studies how people make decisions regarding the allocation of resources over time and the handling of.
Bank & Insurance Ms. Cichon Rosholt High School. Financial Institutions Commercial Bank: Financial institution that offers a wide variety of banking services.
Behavioral Finance: Investment mistakes and solutions David Laibson Professor of Economics Harvard University National Bureau of Economic Research June,
David Laibson Robert I. Goldman Professor of Economics Harvard University Behavioral Economics and Behavior Change Second National Summit on Pension Reform.
Credit Card © Family Economics & Financial Education – Updated May 2011 – Credit Unit – Understanding a Credit Card – Slide 1 Funded by a grant from Take.
Fixed Rate Mortgage Loans
Chapter 9 Personal Loans. Copyright ©2014 Pearson Education, Inc. All rights reserved.9-2 Chapter Objectives Introduce personal loans Outline the types.
Your Retirement Your Retirement: Plan Today. Play Tomorrow About this presentation: This presentation includes the following plan: FedEx Kinko’s.
6-0 Week 3 Lecture 3 Ross, Westerfield and Jordan 7e Chapter 6 Discounted Cash Flow Valuation.
Consumers, Savers and Investors Chapter 6
The Age of Reason: Financial Decisions Over the Lifecycle Sumit Agarwal Federal Reserve Bank of Chicago John Driscoll Federal Reserve Board Xavier Gabaix.
Copyright © 2007 Pearson Addison-Wesley. All rights reserved. 9-1 Objectives Provide a background on personal loans Outline the types of interest rates.
Taxes, Inflation, and Investment Strategy
Instantaneous Gratification: Behavior, Models, and Retirement Savings Policy David Laibson Harvard University and NBER July 16, 2008.
Lecture 1: Instant Gratification
The Economics and Neuroeconomics of Instant Gratification Canadian Economic Association “State of the Art Lecture” David Laibson Harvard University and.
It’s Your Money! Week 2: Annuities and Mutual Funds.
 How to Manage Your Cash › Daily Cash Needs  Lunch, movies, gas, or paying for other activities  Carry cash  Go to an ATM  Credit Card  Know pros.
Pay Yourself First.
Copyright © 2002 by The McGraw-Hill Companies, Inc. All rights reserved.
401(k)’s can partially solve two more public policy problems Brookings Institution September 18, 2015 John Beshears, Harvard Business School James Choi,
1 International Finance Chapter 15 Money, Interest Rates, and Exchange Rates.
Unit 4 Part 2: Credit Cards What You Need To KNOW.
Estimating pension discount rates David McCarthy.
Introduction to Saving. Saving Basics Savings is the portion of current income not spent on consumption. Recommended to have a minimum of 3-6 months salary.
Board of Governors of the Federal Reserve System Selected Findings from the Survey of Household Economics and Decisionmaking Dave Buchholz Federal Reserve.
Chapter 10 Choices Involving Time Copyright © 2014 McGraw-Hill Education. All rights reserved. No reproduction or distribution without the prior written.
$100 Dollar Bills on the Sidewalk: Sub Optimal Savings in 401(k) Plans (Choi et al., 2005) Presented By Sylvia Sirivar and Ruth Gbor.
McGraw-Hill/Irwin Copyright © 2011 by The McGraw-Hill Companies, Inc. All rights reserved. Chapter 19: Monetary Policy and the Federal Reserve 1.Describe.
David Laibson Harvard University Department of Economics Mannheim Lectures July 13, 2009 Lecture 2: Neuroeconomics and the multiple systems hypothesis.
The Economics of Instant Gratification NIH Behavior Change Conference David Laibson Harvard University and NBER June 15-16, 2009 Bethesda, Maryland.
Real Estate Finance, January XX, 2016 Review.  The interest rate can be thought of as the price of consumption now rather than later If you deposit $100.
Budgeting and Financial Planning Why should people make a plan for how to get and spend money? What strategies can be used to do this most effectively?
SAVINGS – Plan for Financial Security. Why Save?Savings is a trade off. You agree to save now in order to spend in the future.  Save for the Unexpected.
The Facts on Credit Those who are wise never pay interest… they earn it!
Ratio Analysis…. Types of ratios…  Performance Ratios: Return on capital employed. (Income Statement and Balance Sheet) Gross profit margin (Income Statement)
© Take Charge Today – August 2013 – Understanding Credit Cards – Slide 1 Funded by a grant from Take Charge America, Inc. to the Norton School of Family.
Consumer Economics Credit Credit Investing Investing.
Personal Finance Life Skills Preparing for a financially secure future.
Role of Financial Markets and Institutions
Building Your Financial House WELCOME !
Optimal illiquidity John Beshears James J. Choi Christopher Clayton
Revolutionizing Global Leadership
Procrastination and Impatience
Banking and the Management of Financial Institutions
Frames, Defaults, and Nudges with Applications to Household Finance
CHAPTER TEN Liquidity And Reserve Management: Strategies And Policies
David Laibson Harvard University January, 2010 AEA Mini-course
How are preferences revealed?
Introduction to Saving
Personal Finance Final Exam Review Game
CHAPTER TEN Liquidity And Reserve Management: Strategies And Policies
Presentation transcript:

Behavioral Economics and Aging David Laibson Harvard University and NBER July 8, 2009 RAND

1. Motivating Experiments A Thought Experiment Would you like to have A)15 minute massage now or B) 20 minute massage in an hour Would you like to have C) 15 minute massage in a week or D) 20 minute massage in a week and an hour

Read and van Leeuwen (1998) Time Choosing TodayEating Next Week If you were deciding today, would you choose fruit or chocolate for next week?

Patient choices for the future: Time Choosing TodayEating Next Week Today, subjects typically choose fruit for next week. 74% choose fruit

Impatient choices for today: Time Choosing and Eating Simultaneously If you were deciding today, would you choose fruit or chocolate for today?

Time Inconsistent Preferences: Time Choosing and Eating Simultaneously 70% choose chocolate

Read, Loewenstein & Kalyanaraman (1999) Choose among 24 movie videos Some are “low brow”: Four Weddings and a Funeral Some are “high brow”: Schindler’s List Picking for tonight: 66% of subjects choose low brow. Picking for next Wednesday: 37% choose low brow. Picking for second Wednesday: 29% choose low brow. Tonight I want to have fun… next week I want things that are good for me.

Extremely thirsty subjects McClure, Ericson, Laibson, Loewenstein and Cohen (2007) Choosing between, juice now or 2x juice in 5 minutes 60% of subjects choose first option. Choosing between juice in 20 minutes or 2x juice in 25 minutes 30% of subjects choose first option. We estimate that the 5-minute discount rate is 50% and the “long-run” discount rate is 0%. Ramsey (1930s), Strotz (1950s), & Herrnstein (1960s) were the first to understand that discount rates are higher in the short run than in the long run.

Conceptual Outline People are not internally consistent decision-makers Internal conflicts can be modeled and measured Early understanding of the neural foundations Scalable, inexpensive policies can transform behavior

Outline 1.Motivating experimental evidence 2.Theoretical framework 3.Field evidence 4.Neuroscience foundations 5.Neuroimaging evidence 6.Policy discussion 7. The age of reason A copy of these slides will soon be available on my Harvard website.

2. Theoretical Framework Classical functional form: exponential functions. D(t) =  t D(t) = 1,      U t = u t +  u t+1   u t+2   u t+3  But exponential function does not show instant gratification effect. Discount function declines at a constant rate. Discount function does not decline more quickly in the short-run than in the long-run.

Constant rate of decline -D'(t)/D(t) = rate of decline of a discount function

Rapid rate of decline in short run Slow rate of decline in long run

An exponential discounting paradox. Suppose people discount at least 1% between today and tomorrow. Suppose their discount functions were exponential. Then 100 utils in t years are worth 100*e (-0.01)*365*t utils today. What is 100 today worth today? What is 100 in a year worth today? 2.55 What is 100 in two years worth today? 0.07 What is 100 in three years worth today? 0.00

An Alternative Functional Form Quasi-hyperbolic discounting (Phelps and Pollak 1968, Laibson 1997) D(t) = 1,      U t = u t +  u t+1   u t+2   u t+3  U t = u t +  u t+1   u t+2   u t+3   uniformly discounts all future periods.  exponentially discounts all future periods. For continuous time: see Barro (2001), Luttmer and Marriotti (2003), and Harris and Laibson (2009)

Building intuition To build intuition, assume that  = ½ and  = 1. Discounted utility function becomes U t = u t + ½  u t+1  u t+2  u t+3  Discounted utility from the perspective of time t+1. U t+1 = u t+1 + ½  u t+2  u t+3  Discount function reflects dynamic inconsistency: preferences held at date t do not agree with preferences held at date t+1.

Application to massages  = ½ and  = 1 A 15 minutes now B 20 minutes in 1 hour C 15 minutes in 1 week D 20 minutes in 1 week plus 1 hour NPV in current minutes 15 minutes now 10 minutes now 7.5 minutes now 10 minutes now

Application to massages  = ½ and  = 1 A 15 minutes now B 20 minutes in 1 hour C 15 minutes in 1 week D 20 minutes in 1 week plus 1 hour NPV in current minutes 15 minutes now 10 minutes now 7.5 minutes now 10 minutes now

Exercise Assume that  = ½ and  = 1. Suppose exercise (current effort 6) generates delayed benefits (health improvement 8). Will you exercise? Exercise Today: -6 + ½ [8] = -2 Exercise Tomorrow: 0 + ½ [-6 + 8] = +1 Agent would like to relax today and exercise tomorrow. Agent won’t follow through without commitment.

3. Field Evidence Della Vigna and Malmendier (2004, 2006) Average cost of gym membership: $75 per month Average number of visits: 4 Average cost per vist: $19 Cost of “pay per visit”: $10

Choi, Laibson, Madrian, Metrick (2002) Self-reports about undersaving. Survey Mailed to 590 employees (random sample) Matched to administrative data on actual savings behavior

22 Typical breakdown among 100 employees Out of every 100 surveyed employees 68 self-report saving too little 24 plan to raise savings rate in next 2 months 3 actually follow through

Laibson, Repetto, and Tobacman (2007) Use MSM to estimate discounting parameters: –Substantial illiquid retirement wealth: W/Y = 3.9. –Extensive credit card borrowing: 68% didn’t pay their credit card in full last month Average credit card interest rate is 14% Credit card debt averages 13% of annual income –Consumption-income comovement: Marginal Propensity to Consume = 0.23 (i.e. consumption tracks income)

LRT Simulation Model Stochastic Income Lifecycle variation in labor supply (e.g. retirement) Social Security system Life-cycle variation in household dependents Bequests Illiquid asset Liquid asset Credit card debt Numerical solution (backwards induction) of 90 period lifecycle problem.

LRT Results: U t = u t +  u t+1   u t+2   u t+3    = 0.70 (s.e. 0.11)   = 0.96 (s.e. 0.01)  Null hypothesis of  = 1 rejected (t-stat of 3).  Specification test accepted. Moments: Empirical Simulated (Hyperbolic) %Visa: 68%63% Visa/Y: 13%17% MPC: 23%31% f(W/Y):

Kaur, Kremer, and Mullainathan (2009): Compare two piece-rate contracts: 1.Linear piece-rate contract (“Control contract”) –Earn w per unit produced 2.Linear piece-rate contract with penalty if worker does not achieve production target T (“Commitment contract”) –Earn w for each unit produced if production>=T, earn w/2 for each unit produced if production<T T Earnings Production Never earn more under commitment contract May earn much less

Kaur, Kremer, and Mullainathan (2009): Demand for Commitment (non-paydays) –Commitment contract (Target>0) chosen 39% of the time –Workers are 11 percentage points more likely to choose commitment contract the evening before Effect on Production (non-paydays) –Being offered contract choice increases average production by 5 percentage points relative to control –Implies 13 percentage point productivity increase for those that actually take up commitment contract –No effects on quality of output (accuracy) Payday Effects (behavior on paydays) –Workers 21 percentage points more likely to choose commitment (Target>0) morning of payday –Production is 5 percentage points higher on paydays

Some other field evidence Ashraf and Karlan (2004): commitment savings Della Vigna and Paserman (2005): job search Duflo (2009): immunization Duflo, Kremer, Robinson (2009): commitment fertilizer Karlan and Zinman (2009): commitment to stop smoking Milkman et al (2008): video rentals return sequencing Oster and Scott-Morton (2005): magazine marketing/sales Sapienza and Zingales (2008,2009): procrastination Thornton (2005): HIV testing Trope & Fischbach (2000): commitment to medical adherence Wertenbroch (1998): individual packaging

Small immediate rewards: Thornton (2005) Dollar reward for picking up results

Small immediate costs: Thornton (2005) Randomized distance (miles) to pick up info Fraction picking up info on HIV status

4. Neuroscience Foundations What is the underlying mechanism? Why are our preferences inconsistent? Is it adaptive? How should it be modeled? Does it arise from a single time preference mechanism (e.g., Herrnstein’s reward per unit time)? Or is it the resulting of multiple systems interacting (Shefrin and Thaler 1981, Bernheim and Rangel 2004, O’Donoghue and Loewenstein 2004, Fudenberg and Levine 2004)?

Shiv and Fedorikhin (1999) Cognitive burden/load is manipulated by having subjects keep a 2-digit or 7-digit number in mind as they walk from one room to another On the way, subjects are given a choice between a piece of cake or a fruit-salad Processing burden% choosing cake Low (remember only 2 digits)41% High (remember 7 digits)63%

Mesolimbic dopamine reward system Frontal cortex Parietal cortex Affective vs. Analytic Cognition mPFC mOFC vmPFC

Hypothesize that the fronto-parietal system is patient Hypothesize that mesolimbic system is impatient. Then integrated preferences are quasi-hyperbolic Relationship to quasi-hyperbolic model nowt+1t+2t+3 PFC1111… Mesolimbic1000… Total2111… Total normed11/2 …

Relationship to quasi-hyperbolic model Hypothesize that the fronto-parietal system is patient Hypothesize that mesolimbic system is impatient. Then integrated preferences are quasi-hyperbolic U t = u t +  u t+1   u t+2   u t+3  (1/  )U t = (1/  )u t +  u t+1   u t+2   u t+3  (1/  )U t =(1/  )u t + [   u t +   u t+1   u t+2   u t+3   limbic fronto-parietal cortex

Hypothesis: Limbic system discounts reward at a higher rate than does the prefrontal cortex. time discount value prefrontal cortex mesolimbic system

5. Neuroimaging Evidence McClure, Laibson, Loewenstein, and Cohen Science (2004) Do agents think differently about immediate rewards and delayed rewards? Does immediacy have a special emotional drive/reward component? Does emotional (mesolimbic) brain discount delayed rewards more rapidly than the analytic (fronto-parietal cortex) brain?

Choices involving Amazon gift certificates: delay d>0 d’ Reward R R’ Hypothesis: fronto-parietal cortex. delay d=0 d’ Reward R R’ Hypothesis: fronto-parietal cortex and limbic. Time

Emotional system responds only to immediate rewards y = 8mmx = -4mmz = -4mm 0 7 T 13 Earliest reward available today Earliest reward available in 2 weeks Earliest reward available in 1 month VStr MOFCMPFC PCC Neural activity Seconds McClure, Laibson, Loewenstein, and Cohen Science (2004) 0.4% 2s

x = 44mm x = 0mm 0 15 T 13 VCtx 0.4% 2s RPar DLPFCVLPFCLOFC Analytic brain responds equally to all rewards PMA Earliest reward available in 2 weeks Earliest reward available in 1 month Earliest reward available today

Choose Smaller Immediate Reward Choose Larger Delayed Reward Emotional System Frontal system Brain Activity Brain Activity in the Frontal System and Emotional System Predict Behavior (Data for choices with an immediate option.)

Conclusions of Amazon study Time discounting results from the combined influence of two neural systems: Mesolimbic dopamine system is impatient. Fronto-parietal system is patient. These two systems are separately implicated in ‘emotional’ and ‘analytic’ brain processes. When subjects select delayed rewards over immediately available alternatives, analytic cortical areas show enhanced changes in activity.

Open questions  New experiment on primary rewards: Juice McClure, Ericson, Laibson, Loewenstein, Cohen (Journal of Neuroscience, 2007) 1.What is now and what is later? Our “immediate” option (Amazon gift certificate) did not generate immediate “consumption.” Also, we did not control the time of consumption. 2.How does the limbic signal decay as rewards are delayed? 3.Would our results replicate with a different reward domain? 4.Would our results replicate over a different time horizon?

Subjects water deprived for 3hr prior to experiment (subject scheduled for 6:00)

Free (10s max.)2sFree (1.5s Max) Variable Duration 15s (i) Decision Period(ii) Choice Made(iii) Pause(iv) Reward Delivery 15s10s5s iv. Juice/Water squirt (1s ) … Time iiiiii A B Figure 1

d d'-d (R,R')  { This minute, 10 minutes, 20 minutes }  { 1 minute, 5 minutes }  {(1ml, 2ml), (1ml, 3ml), (2ml, 3ml)} Experiment Design d = This minute d'-d = 5 minutes (R,R') = (2ml, 3ml)

This minute 10 minutes 20 Minutes P(choose early) Delay to early reward (d) Behavioral evidence for non-exponential discounting

This minute 10 minutes 20 Minutes P(choose early) d’-d = 5 min d’-d = 1 min Delay to early reward (d) Behavioral evidence for non-exponential discounting This minute 10 minutes 20 minutes Delay to early reward (d)

Discount functions fit to behavioral data LimbicCortical β = 0.53 (se = 0.041) δ = 0.98 (se = 0.014)  = 0.47 (se = 0.101)  = 1.02 (se = 0.018) Evidence for two-system model Can reject restriction to a single exponential: t-stat > 5 Double exponential generalization fits data best

Figure 4 x = -12mmx = -2mmx = -8mm z = -10mm NAcc MOFC/SGC ACCPCu PCC NAcc ACC SGC PCu x = 0mm x = 40mmx = -48mm PCC SMA/PMA Vis Ctx PPar BA10 Ant Ins BA9/44 BA T Areas that respond primarily to immediate rewards Areas that show little discounting Neuroimaging data

Figure 5 x = 0mmx = -48mm x = 0mmy = 8mm Juice only Amazon only Both Patient areas (p<0.001) Impatient areas (p<0.001) x = 0mmx = -48mmx = -4mmy = 12mm Patient areas (p<0.01) Impatient areas (p<0.01) Comparison with Amazon experiment:

Measuring discount functions using neuroimaging data Impatient voxels are in the emotional (mesolimbic) reward system Patient voxels are in the analytic (prefrontal and parietal) cortex Average (exponential) discount rate in the impatient regions is 4% per minute. Average (exponential) discount rate in the patient regions is 1% per minute.

Hare, Camerer, and Rangel (2009) + 4s food item presentation ?-?s fixation Rate Health + Rate Taste + Decide Health SessionTaste SessionDecision Session

Rating Details Taste and health ratings made on five point scale: -2,-1,0,1,2 Decisions also reported on a five point scale: SN,N,0,Y,SY “strong no” to “strong yes”

What is self-control? Rejecting a good tasting food that is not healthy Accepting a bad tasting food that is healthy

Subjects SC (self-control) group = 19 dieting subjects who showed self-control during the decision phase NSC (no self-control) group = 18 comparison subjects who did not exhibit self-control during the decision phase

Who is classified as a self- controller: SC? (must meet all criteria below) 1)Use self-control on > %50 of trials in which self-control is required (decline Liked- Unhealthy items or choose Disliked-Healthy ones) 2)Decision =  1 HR +  2 LR +   1 >  2 3)R 2 for HR > R 2 for LR

Examples of individual behavioral fits Self-controller Non- self-controller

Disliked Healthy Disliked Unhealthy Liked Unhealthy Liked Healthy Percent Yes Result: NSC group chose based on taste

** Disliked Healthy Disliked Unhealthy Liked Unhealthy Liked Healthy Percent Yes Result: SC group chose based on taste and health

** Disliked Healthy Disliked Unhealthy Liked Unhealthy Liked Healthy Percent Yes SC group versus NSC group

Question: Is there evidence for a single valuation system? Neuroimaging Results

Activity in vmPFC is correlated with a behavioral measure of decision value (regardless of SC) L  p <.001  p <.005

* * * Taste RatingHealth Rating Beta vmPFC BOLD signal reflects both taste and health ratings

BOLD Health Rating Beta Decision Health Rating Beta The effect of Health Rating in the vmPFC is correlated with its effect on behavior Robust reg Coef =.847

Neuroimaging Results Question: Does self-control involve DLPFC modulation of the vmPFC valuation network?

More activity in DLPFC in trials with successful self control than in trials with unsuccessful self-control L  p <.001  p <.005

Summary of neuroimaging evidence One system associated with midbrain dopamine neurons (mesolimbic dopamine system) discounts at a high rate. Second system associated with lateral prefrontal and posterior parietal cortex responsible for self- regulation (and shows relatively little discounting) Combined function of these two systems accounts for decision making across choice domains, including non-exponential discounting regularities.

Outline 1.Experimental evidence for dynamic inconsistency. 2.Theoretical framework: quasi-hyperbolic discounting. 3.Field evidence: dynamic decisions. 4.Neuroscience: –Mesolimbic Dopamine System (emotional, impatient) –Fronto-Parietal Cortex (analytic, patient) 5.Neuroimaging evidence –Study 1: Amazon gift certificates –Study 2: juice squirts –Study 3: choice of snack foods 6. Policy

6. Policy Defaults in the savings domain Welcome to the company If you don’t do anything – You are automatically enrolled in the 401(k) – You save 2% of your pay – Your contributions go into a default fund Call this phone number to opt out of enrollment or change your investment allocations

Madrian and Shea (2001) Choi, Laibson, Madrian, Metrick (2004) Automatic enrollment Standard enrollment

Employees enrolled under automatic enrollment cluster at default contribution rate. Fraction of Participants at different contribution rates: Default contribution rate under automatic enrollment

Participants stay at the automatic enrollment defaults for a long time. Fraction of Participants Hired Under Automatic Enrollment who are still at both Default Contribution Rate and Asset Allocation Company B Company C Company D Fraction of Participants Tenure at Company (Months)

Survey given to workers who were subject to automatic enrollment: “You are glad your company offers automatic enrollment.” Agree? Disagree? Enrolled employees: 98% agree Non-enrolled employees:79% agree All employees:97% agree Do people like a little paternalism? Source: Harris Interactive Inc.

The power of deadlines: Active decisions Carroll, Choi, Laibson, Madrian, Metrick (2004) Active decision mechanisms require employees to make an active choice about 401(k) participation. Welcome to the company You are required to submit this form within 30 days of hire, regardless of your 401(k) participation choice If you don’t want to participate, indicate that decision If you want to participate, indicate your contribution rate and asset allocation Being passive is not an option

Active Decision Cohort Standard enrollment cohort

Simplified enrollment raises participation Beshears, Choi, Laibson, Madrian (2006)

Use automaticity and deadlines to nudge people to make better health decisions One early example: Home delivery of chronic meds (e.g. maintenance drugs for diabetes and CVD) Pharmaceutical adherence is about 50% One problem: need to pick up your meds Idea: use active decision intervention to encourage workers on chronic meds to consider home delivery Early results: HD take up rises from 14% to 38% Extensions to health domain

Cost saving at test company (preliminary estimates) 81 Annualized Savings Plan $2,413,641 Members $1,872,263 Total Savings $4,285,904 Rxs at Mail (annualized) Now need to measure effects on health.

Policy Debates Pension Protection Act (2006) Federal Thrift Savings Plan adopts autoenrollment (2009) Auto-IRA mandate (2009?) Consumer Financial Protection Agency (2009?) –Default/privileged plain vanilla financial products –Disclosure –Simplicity –Transparency –Education

$100 bills on the sidewalk Choi, Laibson, Madrian (2004) Employer 401(k) match is an instantaneous, riskless return Particularly appealing if you are over 59½ years old – Can withdraw money from 401(k) without penalty On average, half of employees over 59½ years old are not fully exploiting their employer match Educational intervention has no effect

84 Education and Disclosure Choi, Laibson, Madrian (2007) Experimental study with 400 subjects Subjects are Harvard staff members Subjects read prospectuses of four S&P 500 index funds Subjects allocate $10,000 across the four index funds Subjects get to keep their gains net of fees

85 Data from Harvard Staff Control Treatment Fees salient 3% of Harvard staff in Control Treatment put all $$$ in low-cost fund $494 $518 Fees from random allocation $431

86 Data from Harvard Staff Control Treatment Fees salient 3% of Harvard staff in Control Treatment put all $$$ in low-cost fund 9% of Harvard staff in Fee Treatment put all $$$ in low-cost fund $494 $518 Fees from random allocation $431

7. The Age of Reason Agarwal, Driscoll, Gabaix, Laibson (2008)

(1,2) Home Equity Loans and Home Equity Credit Lines Proprietary data from large financial institutions 75,000 contracts for home equity loans and lines of credit, from March-December 2002 (all prime borrowers) We observe: –Contract terms: APR and loan amount –Borrower demographic information: age, employment status, years on the job, home tenure, home state location –Borrower financial information: income, debt-to-income ratio –Borrower risk characteristics: FICO (credit) score, loan-to- value (LTV) ratio

Home Equity Regressions We regress APRs for home equity loans and credit lines on: –Risk controls: FICO score and Loan to Value (LTV) –Financial controls: Income and debt-to-income ratio –Demographic controls: state dummies, home tenure, employment status –Age spline: piecewise linear function of borrower age with knots at age 30, 40, 50, 60 and 70. Next slide plots fitted values on age splines

What is the Channel for the Age Effect? Banks offer different APRs when the loan-to- value (LTV) ratio is: –less than 80 percent –between 80 and 90 percent –over 90 percent Borrowers estimate their LTV by estimating their house value Banks form their own LTV estimates “Rate-Changing Mistake”: when borrower and bank LTVs straddle two of these categories –E.g., borrower LTV 80.

Rate Changing Mistakes generate two sources of disadvantage for the customer: –If I underestimate my LTV (Loan-to-Value ratio), the bank can penalize me by deviating from its normal offer sheet. –If I overestimate my LTV (i.e., underestimate the value of my house), the bank will penalize me by not correcting my mistake and allowing me to borrow at too high a rate.

A Rate-Changing Mistake costs 125 to 150 basis points. Next slides plot: –Rate-Changing Mistakes by age –APRs for borrowers who do NOT make a Rate-Changing Mistake

For consumers who don’t make a Rate- Changing Mistake, age effect is small All the action is due to consumers who make a Rate-Changing Mistake –That is, consumers who over- or under-estimate their house values (relative to bank model) The propensity to make the mistake is U- shaped with age Hence, the final APR is U-shaped with age

Two channels by which RCM raise interest payments Direct channel: old and young borrowers may have a higher ex-ante likelihood of making a RCM Indirect channel: old and young borrowers may have a higher ex-poste likelihood of accepting the high interest rates they receive after they make a RCM (instead of shopping around)

(3) “Eureka”: Learning to Avoid Interest Charges on Balance Transfer Offers Balance transfer offers: borrowers pay lower APRs on balances transferred from other cards for a six-to- nine-month period New purchases on card have higher APRs Payments go towards balance transferred first, then towards new purchases Optimal strategy: make no new purchases on card to which balance has been transferred

Eureka: Predictions Borrowers may not initially understand / be informed about card terms Borrowers may learn about terms by observing interest charges on purchases, or talking to friends –We should see “eureka” moments: new purchases on balance-transfer cards should drop to zero (in the month after borrowers “figure out” the card terms) Study: 14,798 accounts which accepted such offers over the period January 2000 to December 2002

Seven other examples Three kinds of credit card fees: –Late payment –Over limit –Cash advance Credit card APRs Mortgage APRs Auto loan APRs Small business credit card APRs

U-shape for prices paid in 10 examples –Home equity loans –Home equity lines of credit –Eureka moments for balance transfers –Late payment fees –Over credit limit fees –Cash advance fees –Auto loans –Credit cards –Small business credit cards –Mortgages

Salthouse Studies – Memory and Analytic Tasks Source: Salthouse (forth.)

Dementia Ferri et al 2005 Prevalence of dementia: 60-64:0.8% 65-69:1.7% 70-74:3.3% 75-79:6.5% 80-84: 12.8% 85+: 30.1%

Cognitive Impairment w/o Dementia (Plassman et al 2008) Prevalence: 71–79: 16.0% 80–89: 29.2% 90+: 39.0%

Regulation? Regulator creates a very broad safe harbor for financial services (e.g., caps on mutual fund fees, plain vanilla credit cards, mortgages without prepayment penalties, etc…). An investor may conduct a financial transaction that is outside the safe harbor if the investor is advised by a fiduciary (with legal liability).

Outline 1.Motivating experimental evidence 2.Theoretical framework 3.Field evidence 4.Neuroscience foundations 5.Neuroimaging evidence 6.Policy applications 7. The age of reason A copy of these slides will soon be available on my Harvard website.