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)
Choosing Today
Eating Next Week
Time
If you were
deciding today,
would you choose
fruit or chocolate
for next week?
Patient choices for the future:
Choosing Today
Eating Next Week
Time
Today, subjects
typically choose
fruit for next week.
74%
choose
fruit
Impatient choices for today:
Choosing and Eating
Simultaneously
Time
If you were
deciding today,
would you choose
fruit or chocolate
for today?
Time Inconsistent Preferences:
Choosing and Eating
Simultaneously
Time
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.
Outline
1.
2.
3.
4.
5.
6.
7.
Motivating experimental evidence
Theoretical framework
Field evidence
Neuroscience foundations
Neuroimaging evidence
Policy discussion
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) = dt
D(t) = 1, d, d2, d3, ...
Ut = ut + d ut+1 + d2 ut+2 + d3 ut+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.
Discounted value of
delayed reward
Exponential Discount Function
1
Constant rate of decline
0
1
11
21
31
41
Week (time = t)
-D'(t)/D(t) = rate of decline of a discount function
Exponential
Hyperbolic
51
Discount Functions
Slow rate of decline
in long run
1
Rapid rate
of decline
in short run
0
1
11
21
31
41
Week
Exponential
Hyperbolic
51
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?
What is 100 in two years worth today?
What is 100 in three years worth today?
100.00
2.55
0.07
0.00
An Alternative Functional Form
Quasi-hyperbolic discounting
(Phelps and Pollak 1968, Laibson 1997)
D(t) = 1, bd, bd2, bd3, ...
Ut = ut + bdut+1 + bd2ut+2 + bd3ut+3 + ...
Ut = ut + b [dut+1 + d2ut+2 + d3ut+3 + ...]
b uniformly discounts all future periods.
d 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 b = ½ and d = 1.
• Discounted utility function becomes
Ut = ut + ½ [ut+1 + ut+2 + ut+3 + ...]
• Discounted utility from the perspective of time t+1.
Ut+1 =
ut+1 + ½ [ut+2 + ut+3 + ...]
• Discount function reflects dynamic inconsistency:
preferences held at date t do not agree with preferences
held at date t+1.
Application to massages
b = ½ and d = 1
NPV in
current minutes
A 15 minutes now
B 20 minutes in 1 hour
15 minutes now
10 minutes now
C 15 minutes in 1 week
D 20 minutes in 1 week plus 1 hour
7.5 minutes now
10 minutes now
Application to massages
b = ½ and d = 1
NPV in
current minutes
A 15 minutes now
B 20 minutes in 1 hour
15 minutes now
10 minutes now
C 15 minutes in 1 week
D 20 minutes in 1 week plus 1 hour
7.5 minutes now
10 minutes now
Exercise
• Assume that b = ½ and d = 1.
• Suppose exercise (current effort 6) generates delayed
benefits (health improvement 8).
• Will you exercise?
• Exercise Today:
• Exercise Tomorrow:
-6 + ½ [8] = -2
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
Typical breakdown among 100 employees
Out of
every 100
surveyed
employees
22
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:
Ut = ut + b [dut+1 + d2ut+2 + d3ut+3 + ...]




b = 0.70 (s.e. 0.11)
d = 0.96 (s.e. 0.01)
Null hypothesis of b = 1 rejected (t-stat of 3).
Specification test accepted.
Moments:
%Visa:
Visa/Y:
MPC:
f(W/Y):
Empirical
68%
13%
23%
2.6
Simulated (Hyperbolic)
63%
17%
31%
2.7
Kaur, Kremer, and Mullainathan (2009):
Compare two piece-rate contracts:
Linear piece-rate contract (“Control contract”)
1.
–
2.
Earn w per unit produced
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
Earnings
Never earn more under
commitment contract
May earn much less
T
Production
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
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%
Affective vs. Analytic Cognition
Frontal
cortex
mPFC
mOFC
vmPFC
Mesolimbic dopamine
reward system
Parietal
cortex
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
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
…
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 + b [dut+1 + d2ut+2 + d3ut+3 + ...]
(1/b)Ut =
(1/b)ut + dut+1 + d2ut+2 + d3ut+3 + ...
(1/b)Ut =(1/b-1)ut + [d0ut + d1ut+1 + d2ut+2 + d3ut+3 + ...]
limbic
fronto-parietal cortex
Hypothesis:
Limbic system discounts reward at a higher rate than does the
prefrontal cortex.
1.0
discount value
mesolimbic system
prefrontal cortex
0.0
time
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:
Time
delay
Reward
d>0
d’
R
R’
Hypothesis: fronto-parietal cortex.
Time
delay
d=0
d’
Reward R
R’
Hypothesis: fronto-parietal cortex and limbic.
McClure, Laibson, Loewenstein, and Cohen
Science (2004)
Emotional system responds only to immediate rewards
7
T13
0
Neural activity
x = -4mm
VStr
y = 8mm
MOFC
z = -4mm
MPFC
PCC
Seconds
Earliest reward available today
Earliest reward available in 2 weeks
Earliest reward available in 1 month
0.4%
2s
Analytic brain responds equally to all rewards
VCtx
PMA
RPar
DLPFC
VLPFC
LOFC
x = 44mm
0.4%
2s
x = 0mm
0
T13
15
Earliest reward available today
Earliest reward available in 2 weeks
Earliest reward available in 1 month
Brain Activity
Brain Activity in the Frontal System and
Emotional System Predict Behavior
(Data for choices with an immediate option.)
0.05
0.0
-0.05
Choose
Smaller
Immediate
Reward
Frontal
system
Emotional
System
Choose
Larger
Delayed
Reward
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
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?
 New experiment on primary rewards: Juice
McClure, Ericson, Laibson, Loewenstein, Cohen
(Journal of Neuroscience, 2007)
Subjects water deprived for 3hr prior to experiment
(subject scheduled for 6:00)
A
15s
i
ii
10s
5s
Time
…
iii
iv. Juice/Water squirt (1s
)
B
(i) Decision Period
Free (10s max.)
(ii) Choice Made
2s
15s
Figure 1
(iii) Pause
Variable Duration
(iv) Reward Delivery
Free (1.5s Max)
Experiment Design
d
d'-d
(R,R')
 { This minute, 10 minutes, 20 minutes }
 { 1 minute, 5 minutes }
 {(1ml, 2ml), (1ml, 3ml), (2ml, 3ml)}
d = This minute
d'-d = 5 minutes
(R,R') = (2ml, 3ml)
Comparison with Amazon experiment:
Impatient areas (p<0.001)
x = 0mm
y = 8mm
Patient areas (p<0.001)
x = 0mm
x = -48mm
Juice
only
Figure 5
Impatient areas (p<0.01)
x = -4mm
y = 12mm
Patient areas (p<0.01)
x = 0mm
Amazon
only
x = -48mm
Both
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.
1.5
2
Average Beta Area Activation, Actual and Predicted
(D=0,D'=1)
1
(D=0,D'=5)
(D=10,D'=11)
(D=10,D'=15)
(D=20,D'=25)
0
.5
(D=20,D'=21)
0
5
10
15
Time to later reward
Actual
Predicted
20
25
1
1.5
2
Average Delta Area Activation, Actual and Predicted
(D=0,D'=1)
(D=10,D'=15)
(D=0,D'=5)
(D=10,D'=11)
(D=20,D'=25)
0
.5
(D=20,D'=21)
0
5
10
15
Time to later reward
Actual
Predicted
20
25
Hare, Camerer, and Rangel (2009)
Health Session
4s
food item
presentation
Taste Session
Rate Health
+
Decision Session
Rate Taste
Decide
+
+
?-?s
fixation
Rate Health
Rate Taste
Decide
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
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 selfregulation (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.
2.
3.
4.
Experimental evidence for dynamic inconsistency.
Theoretical framework: quasi-hyperbolic discounting.
Field evidence: dynamic decisions.
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)
401(k) participation by tenure at firm
100%
Automatic
enrollment
80%
60%
Standard
enrollment
40%
20%
0%
0
6
12
18
24
30
36
Tenure at company (months)
42
48
Do people like a little paternalism?
Survey given to workers who were subject to automatic
enrollment:
“You are glad your company offers automatic
enrollment.”
Agree? Disagree?
• Enrolled employees:
• Non-enrolled employees:
• All employees:
98% agree
79% agree
97% agree
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
401(k) participation by tenure
Fraction of employees ever
participated
100%
Active Decision Cohort
80%
60%
Standard enrollment cohort
40%
20%
0%
0
6
12 18 24 30 36 42
Tenure at company (months)
Active decision cohort
48
Standard enrollment cohort
54
Simplified enrollment raises participation
Fraction Ever Participating in
Plan
Beshears, Choi, Laibson, Madrian (2006)
50%
2005
2004
40%
30%
2003
20%
10%
0%
0
3
6
9 12 15 18 21 24 27 30 33
Time since baseline (months)
Extensions to health domain
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%
Cost saving at test company
(preliminary estimates)
Rxs at Mail (annualized)
350,000
300,000
Annualized Savings
250,000
200,000
50,000
$2,413,641
Members
$1,872,263
Total Savings
$4,285,904
Before SHD
150,000
100,000
Plan
After SHD
0
Now need to measure effects on health.
81
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
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
84
Data from Harvard Staff
$581
$516
Control Treatment
$518
$451
$385
$320
$255
85
3% of Harvard staff
in Control Treatment
put all $$$
in low-cost fund
Fees salient
$494
Fees from
random
allocation
$431
Data from Harvard Staff
$581
$516
Control Treatment
$518
$451
Fees salient
$494
$385
$320
$255
86
3% of Harvard staff
in Control Treatment
put all $$$
in low-cost fund
9% of Harvard staff
in Fee Treatment
put all $$$
in low-cost fund
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-tovalue (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
Home Equity Loan APR by Borrower Age
6.50
6.00
5.75
5.50
5.25
Borrower Age (Years)
80
77
74
71
68
65
62
59
56
53
50
47
44
41
38
35
32
29
26
23
5.00
20
APR (Percent)
6.25
Home Equity Credit Line APR by Borrower Age
5.50
5.00
4.75
4.50
4.25
Borrower Age (Years)
80
77
74
71
68
65
62
59
56
53
50
47
44
41
38
35
32
29
26
23
4.00
20
APR (Percent)
5.25
(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-tonine-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
Propensity of Ever Experiencing a "Eureka" Moment by
Borrower Age
90%
85%
80%
70%
65%
60%
55%
50%
45%
Borrower Age (Years)
80
76
72
68
64
60
56
52
48
44
40
36
32
28
24
40%
20
Percent
75%
Fraction of Borrowers in Each Age Group
Experiencing a Eureka Moment, by Month
Month One
Month Three
Month Five
No Eureka
Percent of Borrowers
60%
50%
Month Two
Month Four
Month Six
40%
30%
20%
10%
0%
18 to 24
25 to 34
35 to 44
45 to 64
Borrower Age Category
Over 65
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
Frequency of Fee Payment by Borrower Age
0.33
0.31
0.29
0.27
0.25
Late Fee
0.23
Over Limit Fee
0.21
Cash Advance Fee
0.19
0.17
0.15
20
23
26
29
32
35
38
41
44
47
50
53
56
59
62
65
68
71
74
77
80
Fee Frequency (per month)
0.35
Borrower Age (Years)
Auto Loan APR by Borrower Age
9.50
9.00
8.75
8.50
8.25
8.00
20
23
26
29
32
35
38
41
44
47
50
53
56
59
62
65
68
71
74
77
80
APR (Percent)
9.25
Borrower Age (Years)
Credit Card APR by Borrower Age
18.50
18.00
17.75
17.50
17.25
17.00
20
23
26
29
32
35
38
41
44
47
50
53
56
59
62
65
68
71
74
77
80
APR (Percent)
18.25
Borrower Age (Years)
Mortgage APR by Borrower Age
13.00
12.50
12.25
12.00
11.75
11.50
20
23
26
29
32
35
38
41
44
47
50
53
56
59
62
65
68
71
74
77
80
APR (Percent)
12.75
Borrower Age (Years)
Small Business Credit Card APR by Borrower Age
16.00
15.50
15.25
15.00
14.75
14.50
20
23
26
29
32
35
38
41
44
47
50
53
56
59
62
65
68
71
74
77
80
APR (Percent)
15.75
Borrower Age (Years)
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
1.0
84
0.5
69
0.0
50
-0.5
31
-1.0
16
Word Recall (N = 2,230)
Matrix Reasoning (N = 2,440)
Spatial Relations (N = 1,618)
Pattern Comparison (N = 6,547)
-1.5
Percentile
Z-Score
1.5
7
-2.0
20
30
40
50
60
70
80
90
Chronological Age
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.
2.
3.
4.
5.
6.
7.
Motivating experimental evidence
Theoretical framework
Field evidence
Neuroscience foundations
Neuroimaging evidence
Policy applications
The age of reason
A copy of these slides will soon be available on my
Harvard website.