behavioral economics and aging david laibson harvard university and nber july 8, 2009 rand
TRANSCRIPT
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Behavioral Economics and Aging
David LaibsonHarvard University and NBER
July 8, 2009RAND
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1. Motivating Experiments
A Thought Experiment
Would you like to haveA) 15 minute massage now
orB) 20 minute massage in an hour
Would you like to haveC) 15 minute massage in a week
orD) 20 minute massage in a week and an hour
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Read and van Leeuwen (1998)
TimeChoosing Today Eating Next Week
If you were deciding today,would you choosefruit or chocolatefor next week?
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Patient choices for the future:
TimeChoosing Today Eating Next Week
Today, subjectstypically choosefruit for next week.
74%choosefruit
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Impatient choices for today:
Time
Choosing and EatingSimultaneously
If you were deciding today,would you choosefruit or chocolatefor today?
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Time Inconsistent Preferences:
Time
Choosing and EatingSimultaneously
70%choose chocolate
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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.
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Extremely thirsty subjectsMcClure, 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.
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Outline
1. Motivating experimental evidence2. Theoretical framework 3. Field evidence4. Neuroscience foundations5. Neuroimaging evidence6. Policy discussion7. The age of reason
A copy of these slides will soon be available on my Harvard website.
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2. Theoretical Framework
• Classical functional form: exponential functions.
D(t) = t
D(t) = 1, Ut = ut + ut+1 ut+2ut+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.
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Exponential Discount Function
0
1
1 11 21 31 41 51
Week (time = t)
Dis
cou
nte
d v
alu
e o
f d
elay
ed r
ewar
d
Exponential Hyperbolic
Constant rate of decline
-D'(t)/D(t) = rate of decline of a discount function
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Discount Functions
0
1
1 11 21 31 41 51
Week
Exponential Hyperbolic
Rapid rateof decline in short run
Slow rate of decline in long run
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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? 100.00• 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
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An Alternative Functional Form
Quasi-hyperbolic discounting
(Phelps and Pollak 1968, Laibson 1997)
D(t) = 1, Ut = ut + ut+1 ut+2ut+3
Ut = ut + ut+1 ut+2ut+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)
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Building intuition
• To build intuition, assume that = ½ and = 1.• Discounted utility function becomes
Ut = ut + ½ut+1 ut+2ut+3
• Discounted utility from the perspective of time t+1.
Ut+1 = ut+1 + ½ut+2 ut+3
• Discount function reflects dynamic inconsistency: preferences held at date t do not agree with preferences held at date t+1.
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Application to massages = ½ and = 1
A 15 minutes nowB 20 minutes in 1 hour
C 15 minutes in 1 weekD 20 minutes in 1 week plus 1 hour
NPV in current minutes
15 minutes now10 minutes now
7.5 minutes now10 minutes now
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Application to massages = ½ and = 1
A 15 minutes nowB 20 minutes in 1 hour
C 15 minutes in 1 weekD 20 minutes in 1 week plus 1 hour
NPV in current minutes
15 minutes now10 minutes now
7.5 minutes now10 minutes now
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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.
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3. Field EvidenceDella 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
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Choi, Laibson, Madrian, Metrick (2002)Self-reports about undersaving.
SurveyMailed to 590 employees (random sample)
Matched to administrative data on actual savings behavior
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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
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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)
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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.
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LRT Results:
Ut = ut + ut+1 ut+2ut+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): 2.6 2.7
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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
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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
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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
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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)?
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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%
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Mesolimbic dopamine reward system
Frontalcortex
Parietalcortex
Affective vs. Analytic Cognition
mPFCmOFCvmPFC
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• 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
now t+1 t+2 t+3
PFC 1 1 1 1 …
Mesolimbic 1 0 0 0 …
Total 2 1 1 1 …
Total normed 1 1/2 1/2 1/2 …
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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
Ut = ut + ut+1 ut+2ut+3
(1/)Ut = (1/)ut + ut+1 ut+2ut+3
(1/)Ut =(1/)ut + [ut + ut+1 ut+2ut+3
limbic fronto-parietal cortex
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Hypothesis:
Limbic system discounts reward at a higher rate than does theprefrontal cortex.
time
disc
ount
val
ue prefrontal cortex
mesolimbic system
0.0
1.0
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5. Neuroimaging EvidenceMcClure, 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?
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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
Time
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Emotional system responds only to immediate rewards
y = 8mmx = -4mm z = -4mm0
7
T13
Earliest reward available todayEarliest reward available in 2 weeksEarliest reward available in 1 month
VStr MOFC MPFC PCC
Neu
ral
acti
vity
Seconds
McClure, Laibson, Loewenstein, and Cohen Science (2004)
0.4%
2s
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x = 44mm
x = 0mm
0 15T13
VCtx
0.4%
2s
RPar
DLPFC VLPFC LOFC
Analytic brain responds equally to all rewardsPMA
Earliest reward available in 2 weeksEarliest reward available in 1 month
Earliest reward available today
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0.0
-0.05
0.05
ChooseSmaller
ImmediateReward
ChooseLarger
DelayedReward
EmotionalSystem
Frontalsystem
Bra
in A
ctiv
ity
Brain Activity in the Frontal System and Emotional System Predict Behavior
(Data for choices with an immediate option.)
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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.
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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?
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Subjects water deprived for 3hr prior to experiment
(subject scheduled for 6:00)
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Free (10s max.) 2s Free (1.5s Max)Variable Duration
15s
(i) Decision Period (ii) Choice Made (iii) Pause (iv) Reward Delivery
15s 10s 5s
iv. Juice/Water squirt (1s )
…Time
i ii iii
A
B
Figure 1
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dd'-d (R,R')
{ This minute, 10 minutes, 20 minutes } { 1 minute, 5 minutes } {(1ml, 2ml), (1ml, 3ml), (2ml, 3ml)}
Experiment Design
d = This minuted'-d = 5 minutes(R,R') = (2ml, 3ml)
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Figure 5
x = 0mm x = -48mm
x = 0mm y = 8mm
Juiceonly
Amazononly
Both
Patient areas (p<0.001)
Impatient areas (p<0.001)
x = 0mm x = -48mm
x = -4mm y = 12mm
Patient areas (p<0.01)
Impatient areas (p<0.01)
Comparison with Amazon experiment:
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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.
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(D=0,D'=1)
(D=0,D'=5)
(D=10,D'=11)(D=10,D'=15)
(D=20,D'=21)(D=20,D'=25)
0.5
11.
52
Nor
med
Act
ivat
ion
0 5 10 15 20 25Time to later reward
Actual Predicted
Average Beta Area Activation, Actual and Predicted
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(D=0,D'=1) (D=0,D'=5)(D=10,D'=11)
(D=10,D'=15)
(D=20,D'=21) (D=20,D'=25)
0.5
11.
52
Nor
med
Act
ivat
ion
0 5 10 15 20 25Time to later reward
Actual Predicted
Average Delta Area Activation, Actual and Predicted
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Hare, Camerer, and Rangel (2009)
+
4sfood itempresentation
?-?s fixation
Rate Health
Rate Health
+
Rate Taste
Rate Taste
+
Decide
Decide
Health Session Taste Session Decision Session
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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”
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What is self-control?
• Rejecting a good tasting food that is not healthy• Accepting a bad tasting food that is healthy
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More activity in DLPFC in trials with successful self control than in trials with
unsuccessful self-control
L
p < .001 p < .005
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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.
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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
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6. PolicyDefaults 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
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Madrian and Shea (2001)Choi, Laibson, Madrian, Metrick (2004)
401(k) participation by tenure at firm
0%
20%
40%
60%
80%
100%
0 6 12 18 24 30 36 42 48
Tenure at company (months)
Automaticenrollment
Standard enrollment
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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.
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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
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401(k) participation by tenure
0%
20%
40%
60%
80%
100%
0 6 12 18 24 30 36 42 48 54
Tenure at company (months)
Frac
tion
of e
mpl
oyee
s ev
er
part
icip
ated
Active decision cohort Standard enrollment cohort
Active Decision Cohort
Standard enrollment cohort
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0%
10%
20%
30%
40%
50%
0 3 6 9 12 15 18 21 24 27 30 33
Time since baseline (months)
Frac
tion
Ever
Par
ticip
atin
g in
Pl
an 2003
2004
2005
Simplified enrollment raises participationBeshears, Choi, Laibson, Madrian (2006)
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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
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Cost saving at test company (preliminary estimates)
81
Annualized Savings
Plan $2,413,641
Members $1,872,263
Total Savings $4,285,904 0
50,000
100,000
150,000
200,000
250,000
300,000
350,000
Before SHD
After SHD
Rxs at Mail (annualized)
Now need to measure effects on health.
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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
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$100 bills on the sidewalkChoi, 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
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84
Education and DisclosureChoi, 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
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85
$255
$320
$385
$451
$516
$581Data from Harvard Staff
Control TreatmentFees salient
3% of Harvard staffin Control Treatment
put all $$$in low-cost fund
$494$518
Fees from random allocation$431
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86
$255
$320
$385
$451
$516
$581Data from Harvard Staff
Control TreatmentFees salient
3% of Harvard staffin Control Treatment
put all $$$in low-cost fund
9% of Harvard staffin Fee Treatment
put all $$$in low-cost fund
$494$518
Fees from random allocation$431
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7. The Age of ReasonAgarwal, Driscoll, Gabaix, Laibson (2008)
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(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
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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
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Home Equity Loan APR by Borrower Age
5.00
5.25
5.50
5.75
6.00
6.25
6.5020 23 26 29 32 35 38 41 44 47 50 53 56 59 62 65 68 71 74 77 80
Borrower Age (Years)
AP
R (
Pe
rce
nt)
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Home Equity Credit Line APR by Borrower Age
4.00
4.25
4.50
4.75
5.00
5.25
5.5020 23 26 29 32 35 38 41 44 47 50 53 56 59 62 65 68 71 74 77 80
Borrower Age (Years)
AP
R (
Pe
rce
nt)
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(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
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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
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Propensity of Ever Experiencing a "Eureka" Moment by Borrower Age
40%
45%
50%
55%
60%
65%
70%
75%
80%
85%
90%
20
24
28
32
36
40
44
48
52
56
60
64
68
72
76
80
Borrower Age (Years)
Pe
rce
nt
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Fraction of Borrowers in Each Age Group Experiencing a Eureka Moment, by Month
0%
10%
20%
30%
40%
50%
60%
18 to 24 25 to 34 35 to 44 45 to 64 Over 65Borrower Age Category
Per
cen
t o
f B
orr
ow
ers
Month One Month TwoMonth Three Month FourMonth Five Month SixNo Eureka
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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
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Frequency of Fee Payment by Borrower Age
0.15
0.17
0.19
0.21
0.23
0.25
0.27
0.29
0.31
0.33
0.352
02
32
62
93
23
53
84
14
44
75
05
35
65
96
26
56
87
17
47
78
0
Borrower Age (Years)
Fe
e F
req
ue
nc
y (
pe
r m
on
th)
Late Fee
Over Limit Fee
Cash Advance Fee
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Auto Loan APR by Borrower Age
8.00
8.25
8.50
8.75
9.00
9.25
9.50
Borrower Age (Years)
AP
R (
Pe
rce
nt)
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Credit Card APR by Borrower Age
17.00
17.25
17.50
17.75
18.00
18.25
18.502
02
32
62
93
23
53
84
14
44
75
05
35
65
96
26
56
87
17
47
78
0
Borrower Age (Years)
AP
R (
Pe
rce
nt)
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Mortgage APR by Borrower Age
11.50
11.75
12.00
12.25
12.50
12.75
13.002
02
32
62
93
23
53
84
14
44
75
05
35
65
96
26
56
87
17
47
78
0
Borrower Age (Years)
AP
R (
Pe
rce
nt)
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Small Business Credit Card APR by Borrower Age
14.50
14.75
15.00
15.25
15.50
15.75
16.00
20
23
26
29
32
35
38
41
44
47
50
53
56
59
62
65
68
71
74
77
80
Borrower Age (Years)
AP
R (
Pe
rce
nt)
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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
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Chronological Age
20 30 40 50 60 70 80 90
Z-S
core
-2.0
-1.5
-1.0
-0.5
0.0
0.5
1.0
1.5
Word Recall (N = 2,230)Matrix Reasoning (N = 2,440)Spatial Relations (N = 1,618)Pattern Comparison (N = 6,547)
84
69
50
31
16
7
Pe
rce
ntil
e
Salthouse Studies – Memory and Analytic Tasks
Source: Salthouse (forth.)
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DementiaFerri 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%
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Cognitive Impairment w/o Dementia(Plassman et al 2008)
Prevalence:
71–79: 16.0%
80–89: 29.2%
90+: 39.0%
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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).
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Outline
1. Motivating experimental evidence2. Theoretical framework 3. Field evidence4. Neuroscience foundations5. Neuroimaging evidence6. Policy applications7. The age of reason
A copy of these slides will soon be available on my Harvard website.