Econ 219B
Psychology and Economics: Applications
(Lecture 3)
Stefano DellaVigna
January 31, 2018
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Outline
1 Investment Goods: Exercise II
2 Investment Goods: Job Search
3 Investment Goods: Work Effort
4 Investment Goods: Delay with Deadline
5 Leisure Goods: Credit Card Borrowing
6 Leisure Goods: Consumption and Savings
7 Leisure Goods: Commitment and Savings
8 Leisure Goods: Drinking
9 Methodology: Commitment Field Experiments
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Investment Goods: Exercise II
Section 1
Investment Goods: Exercise II
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Investment Goods: Exercise II
Explanation
Present-Biased preferences with naivete explains magnitudes,not just qualitative patterns
Related: Acland and Levy (MS 2015) field experimentPay a treatment group $100 to attend the gym for 4 weeksControl group not paid
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Investment Goods: Exercise II Acland and Levy 2015
Results
Moderate habit formation (as in Charness and Gneezy EMA)
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Investment Goods: Exercise II Acland and Levy 2015
Expectation of Future Attendance
Also: Elicit expectation of future attendance as WTP forp-coupons
How much are you willing to pay for coupon below?Also elicit unincentivized forecasts
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Investment Goods: Exercise II Acland and Levy 2015
Clear Evidence of Naivete
Evidence of naivete consistent with DVMAlso some evidence of projection bias (see later lectures)
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Investment Goods: Job Search
Section 2
Investment Goods: Job Search
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Investment Goods: Job Search DellaVigna and Paserman 2005
DellaVigna and Paserman (JOLE 2005)
Stylized facts:time devoted to job search by unemployed workers: 9hours/week
search effort predicts exit rates from unemployment better thanreservation wage choice
Model with costly search effort and reservation wage decision:
search effort — immediate cost, benefits in near future — drivenby βreservation wage — long-term payoffs — driven by δ
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Investment Goods: Job Search DellaVigna and Paserman 2005
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Investment Goods: Job Search DellaVigna and Paserman 2005
Correlations
Correlation between measures of impatience (smoking,impatience in interview, vocational clubs) and job searchoutcomes:
Impatience ↑ =⇒ search effort ↓Impatience ↑ =⇒ reservation wage ←→Impatience ↑ =⇒ exit rate from unemployment ↓
Impatience captures variation in βSophisticated or naive – does not matter
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Investment Goods: Job Search DellaVigna and Paserman 2005
Correlations
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Investment Goods: Job Search Paserman 2008
Paserman (EJ 2008)
Structural model estimated by max. likelihoodEstimation exploits non-stationarity of exit rate fromunemployment
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Investment Goods: Work Effort
Section 3
Investment Goods: Work Effort
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Investment Goods: Work Effort Kaur, Kremer, and Mullainathan (2015)
”Self-Control at Work”
Kaur, Kremer, and Mullainathan, “Self Control at Work.” JPE 2015
Setting: workers in India who are paid a piece rate w in a weeklypaycheck
Since effort at work is immediate and benefits delayed, effort atwork is an investment good(β, β, δ
)model, with δ = 1
Consider effort at work e, which costs −c (e) , with c ′ > 0,c ′′ > 0
Assume for special case c (e) = γe2/2
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Investment Goods: Work Effort Kaur, Kremer, and Mullainathan (2015)
Model
Two states:
high output yH with probability e pay wH
low output yL with probability 1− e pay wL
Notice: this is only local approximation, for e ∈ [0, 1]
Pay at t = 2
If working at t = 1, maximize
maxe1
β [e1wH + (1− e1)wL]− c (e1)
f.o.c.β [wH − wL]− c ′ (e∗1 ) = 0
Effort e∗1 increases in wH − wL and in β
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Investment Goods: Work Effort Kaur, Kremer, and Mullainathan (2015)
Model
Special case:
e∗1 =β [wH − wL]
γ
If working at t = 2 (same period as paydate), optimal effort e∗2 solves
maxe2
[e2wH + (1− e2)wL]− c (e2)
and thus (for the special case)
e∗2 =wH − wL
γ
Prediction 1. Effort is higher near payday for β < 1 (independent ofβ)
From t = 0 perspective, (perceived) utility V0 from working at t = 1is
V0 = e∗1wH + (1− e∗1 )wL − c (e∗1 )
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Investment Goods: Work Effort Kaur, Kremer, and Mullainathan (2015)
Model
Effect of altering wL on t = 0 (expected) welfare V0 is
dV0
dwL= (1− e∗1 ) +
de∗1dwL
[[wH − wL]− c ′ (e∗1 )] =
= (1− e∗1 ) +de∗1dwL
[(1− β
)[wH − wL]
]First term is direct effect on pay: lowering wL lowers pay andthus welfareThe second term is the effect on incentive, which is zero forβ = β = 1, by the envelope theorem – but envelope theoremdoes not apply for β < 1. Indeed, second term is negativeNotice that it is β which matters, since this is the valuefunction from the t = 0 perspective
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Investment Goods: Work Effort Kaur, Kremer, and Mullainathan (2015)
Model
Special case for β = β:
dV0
dwL= 1− β [wH − wL]
γ− β (1− β) [wH − wL]
γ
Second term becomes large as β goes below 1 and is highest atβ = 1/2If large enough, individual wants commitment device, prefers wL
low
Prediction 2. Individual with β < 1 may prefer commitmentdevice (low wL)
Prediction 3. If there are both types with β = 1 and β < 1,demand for commitment should be associated with a paydaycycle
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Investment Goods: Work Effort Kaur, Kremer, and Mullainathan (2015)
Experiment
Field experiment in India
Randomization of pay date (Tu, Th, Sa) to test proposition 1unconfounded with day-of-week effectsRandomization of availability of commitment device: get paidw/2 instead of w if miss production targetRandomization of whether choice is made evening before, ormorning of
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Investment Goods: Work Effort Kaur, Kremer, and Mullainathan (2015)
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Investment Goods: Work Effort Kaur, Kremer, and Mullainathan (2015)
Predictions
Prediction 1. Evidence of pay cycle in effort
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Investment Goods: Work Effort Kaur, Kremer, and Mullainathan (2015)
Predictions
Prediction 2. Quite significant take-up of commitment contract
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Investment Goods: Work Effort Kaur, Kremer, and Mullainathan (2015)
Results
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Investment Goods: Work Effort Kaur, Kremer, and Mullainathan (2015)
Predictions
Prediction 3. Correlation between payday effect and take-up ofcommitment, as well as with productivity effect
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Investment Goods: Work Effort Kaur, Kremer, and Mullainathan (2015)
Results
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Investment Goods: Work Effort Kaur, Kremer, and Mullainathan (2015)
Summary
Evidence very consistent with model of self-control problems and(at least partial) sophistication
Discount factor is not β − δ, but smoother decay (truehyperbolic)
Significant demand of commitment device – different than someof other settings, see later
Correlation with underlying measure of self-control
Great evidence in important setting
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Investment Goods: Delay with Deadline
Section 4
Investment Goods: Delay with Deadline
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Investment Goods: Delay with Deadline
Setting
Consider as individual that has to do an unpleasant task, with afixed deadline
Examples:
Paying a traffic fine (Heffetz, O’Donoghue, and Schneider,2016)Filing taxes (Martinez, Meier, and Sprenger, 2017; Benzarti,2016)Finding a job by the deadline (job search papers)
What can we infer from spike at deadline?
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Investment Goods: Delay with Deadline Heffetz, O’Donoghue, and Schneider (2016)
Paying a Traffic fine with multiple deadlines0
.01
.02
.03
.04
.05
Haza
rd ra
te
0 10 20 30 40 50 60 70 80 90 100 110 120 130Days since issue date
0.2
.4.6
.81
Cum
ulat
ive re
spon
se ra
te
0 10 20 30 40 50 60 70 80 90 100 110 120 130Days since issue date
OLD regime NEW regime
Figure 1: Hazard Rates and Cumulative Response Rates in OLD vs. NEWRegimes. Note: All tickets have a first deadline at day 30, second deadline at days 62-68, andthird deadline at days 101-107, indicated by the shaded areas (the latter two deadlines are a rangebecause they depend on ticket-issuance day of the week). First notification letter is received aroundday 40 (OLD) vs. day 20 (NEW). Based on 3,355,094 (OLD) and 3,020,357 (NEW) observations;see details in Section 3.
While the pattern in Figure 1 is consistent with forgetting, the specific way in which DOF
implemented the regime change resulted in issues of interpretation. To rule out alternative
explanations, we worked with DOF on a field experiment that was implemented over five
weeks, for tickets issued July 13, 2013 through August 16, 2013. The recipient of each ticket
was assigned to receive one of four versions of the first letter (at roughly day 20), and to
receive or not receive an additional letter at roughly day 48 (a 4 × 2 experimental design).
The four versions of the first letter vary in their informational content and presentation. The
results seem to confirm that the letters served primarily as reminders.
Figure 1 exhibits other important patterns. First, under both regimes, there is a dramatic
decline in hazard rates between the first and second deadlines. While this decline could be
due in part to forgetting, a similar decline in hazard rates over time is typically observed in
job search data, and has been attributed to heterogeneity in types (Salant (1977)). Second,
while there is a noticeable spike in hazard rates at the first deadline under both regimes
(and especially under the NEW regime), there are at most small spikes in hazard rates at
the second and third deadlines. This apparent limited response to later deadlines is also
3
Figure 1: Hazard Rates and Cumulative Response Rates in OLD vs. NEW Regimes. Note: All tickets have a first deadline atday 30, second deadline at days 62-68, and third deadline at days 101-107, indicated by the shaded areas (the latter twodeadlines are a range because they depend on ticket-issuance day of the week). First notification letter is received around day40 (OLD) vs. day 20 (NEW). Based on 3,355,094 (OLD) and 3,020,357 (NEW) observations; see details in Section 3.
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Investment Goods: Delay with Deadline Martinez, Meier, and Sprenger (2017)
Filing Taxes by April 15
500
1500
2500
500
1500
2500
01
23
01
23
0 20 40 60 80 0 20 40 60 80
2005 2006
2007 2008
Percentage of Filers Average Refund Value
Perc
enta
ge
Day of Season
Notes: 2005-2008 percentage of filers on each day of tax season (gray bars) and average refund value for filers on each
day (black line).
Figure 1: Filing Times and Refund Values
26
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Investment Goods: Delay with Deadline Martinez, Meier, and Sprenger (2017)
Inference
What can we learn from extent of last-minute completion?
If there is a lot of last-minute completion, does that indicateprocrastination?
Or could it indicate a particular distribution of the cost of doingthe action?
Heffetz, O’Donoghue, and Schneider, 2016:
Use multiple traffic finesThere are types that delay more on multiple traffic ticketinfractions
Martinez, Meier, and Sprenger (2017)
Structural estimation of time preference parameters using alsovariation in amount of tax refund and delay in refund
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Leisure Goods: Credit Card Borrowing
Section 5
Leisure Goods: Credit Card Borrowing
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Leisure Goods: Credit Card Borrowing Ausubel (1999)
Ausubel, “Adverse Selection in Credit Card
Market”
Joint-venture: company-researcher
Field Experiment: Randomized mailing of two millionsolicitations!
Follow borrowing behavior for 21 months
Variation of:
pre-teaser interest rate r0: 4.9% to 7.9%post-teaser interest rate r1: Standard - 4% to Standard +4%Duration of teaser period Ts (measured in years)
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Leisure Goods: Credit Card Borrowing Ausubel (1999)
Design
Part of the randomization – Incredible sample sizes. How muchwould this cost to run? Millions
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Leisure Goods: Credit Card Borrowing Ausubel (1999)
Design
Another set of experiments:
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Leisure Goods: Credit Card Borrowing Ausubel (1999)
Model
Setting:
Individual has initial credit card (r00 , r
01 ,T
0s ). Balances: b0
pre-teaser, b1 post-teaserCredit card offers: (r ′0, r
′1,T
′s)
Decision to take-up new credit card:
switching cost k > 0approx. saving in pre-teaser rates (Ts years): Ts
(r ′0 − r0
0
)b0
approx. saving in post-teaser rates (21/12− Ts years):(21/12− Ts) (r
′1 − r1)b1
Net benefit of switching:
NB ′ = −k + Ts
(r′
0 − r 00
)b0 + (21/12− Ts)
(r′
1 − r 01
)b1
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Leisure Goods: Credit Card Borrowing Ausubel (1999)
Model
Switch if NB + ε > 0
Take-up rate R is function of attractiveness NB :
R = R (NB) , R ′ > 0
Compare take-up rate of card i , R i , to take-up rate of StandardCard St, RSt
Standard Card (6.9% followed by 16%) (Card C above)
Assume R (approximately) linear in a neighborhood of NBSt ,that is,
R(NB i
)= R
(NBSt
)+ R ′NB
(NB i − NBSt
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Leisure Goods: Credit Card Borrowing Ausubel (1999)
Model
Compare cards Pre and St that differ only in interest rate r0(pre-teaser)
Assume bPre0 = bSt0 = b0 (Pre-teaser balance ) ≈ $2, 000
Difference in attractiveness:
R(NBPre
)− R
(NBSt
)= R ′NBTs
(rPre0 − rSt0
)b0
Pre-Teaser Offer (Card A): (4.9% followed by 16%)
NBPre − NBSt ≈ 6/12 ∗ 2% ∗ $2, 000 = $20R(NBPre
)− R
(NBSt
)= 386 out of 100,000
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Leisure Goods: Credit Card Borrowing Ausubel (1999)
Model
Compare cards Post and St that differ only in interest rate r1(post-teaser)
Assume bPost1 = bSt1 = b1 (Post-teaser balance) ≈ $1, 000
Difference in attractiveness:
R(NBPost)− R(NBSt) = R ′NB (21/12− Ts)(rPost1 − rSt1
)b1
Post-Teaser Offer (Card B in Exp. III): (6.9% followed by 14%)
NBPost − NBSt ≈ 15/12 ∗ 2% ∗ $1000 = $25R(NBPost)− R(NBSt) = 154 out of 100,000
Puzzle:
NBPost − NBSt > NBPre − NBSt
But R(NBPre)− R(NBSt) >> R(NBPost)− R(NBSt)
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Leisure Goods: Credit Card Borrowing Ausubel (1999)
Results
Plot NB and R(NB) for different offersCompare offers varying in r0 (flat line) and in r1 (steep line)
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Leisure Goods: Credit Card Borrowing Ausubel (1999)
Results
People underrespond to post-teaser interest rate.
Most likely explanation: Present Bias + Naivete
Naives overestimate switching to another card (procrastination) Underestimate post-teaser borrowing: b1 < b1 and b0 = b0
Compare cards:
NBPre − NBSt = Ts
(rPre0 − rSt0
)b0
and
NBPost − NB
St= (21/12− Ts)
(rPost1 − rSt1
)b1
Calibration: b1 ≈ (1/3) b1 Underestimation of borrowing by afactor of 3
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Leisure Goods: Consumption and Savings
Section 6
Leisure Goods: Consumption and Savings
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Leisure Goods: Consumption and Savings
Introduction
Laibson (1997) to Laibson, Repetto, and Tobacman (2007)
Leisure Good: Temptation to overconsume at present
Stylized facts:
Low liquid wealth accumulationExtensive credit card borrowing (SCF, Fed, Gross and Souleles2000)Consumption-income excess comovement (Hall and Mishkin,1982)Substantial illiquid wealth (housing+401(k)s)
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Leisure Goods: Consumption and Savings
Stefano DellaVigna Econ 219B : Applications (Lecture 3) January 31, 2018 45 / 100
Leisure Goods: Consumption and Savings
Reduced-form evidence here not sufficient
Life-cycle consumption model (Gourinchas and Parker, 2004)
Assume realistic features:
borrowing constraintsilliquid assetsbequests...
David Laibson’s slides to follow
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3.1 Demographics
• Mortality, Retirement (PSID), Dependents (PSID),HS educational group
3.2 Income from transfers and wages
• Yt = after-tax labor and bequest income plus govttransfers (assumed exog., calibrated from PSID)
• yt ≡ ln(Yt). During working life:yt = f
W (t) + ut + νWt (3)
• During retirement:yt = f
R(t) + νRt (4)
3.3 Liquid assets and non-collateralized debt
• Xt + Yt represents liquid asset holdings at the
beginning of period t.
• Credit limit: Xt ≥ −λ · Yt
• λ = .30, so average credit limit is approximately
$8,000 (SCF).
3.4 Illiquid assets
• Zt represents illiquid asset holdings at age t.
• Z bounded below by zero.
• Z generates consumption flows each period of
γZ.
• Conceive of Z as having some of the properties
of home equity.
• Disallow withdrawals from Z; Z is perfectly
illiquid.
• Z stylized to preserve computational tractability.
3.5 Dynamics
• Let IXt and IZt represent net investment into as-
sets X and Z during period t
• Dynamic budget constraints:Xt+1 = RX · (Xt + IXt )Zt+1 = RZ · (Zt + IZt )Ct = Yt − IXt − IZt
• Interest rates:
RX =
(RCC if Xt + I
Xt < 0
R if Xt + IXt > 0
; RZ = 1
• Three assumptions forhRX, γ, RCC
i:
Benchmark: [1.0375, 0.05, 1.1175]Aggressive: [1.03, 0.06, 1.10]Very Aggressive: [1.02, 0.07, 1.09]
In full detail, self t has instantaneous payoff function
u(Ct, Zt, nt) = nt ·³Ct+γZtnt
´1−ρ − 11− ρ
and continuation payoffs given by:
βT+N−tXi=1
δi³Πi−1j=1st+j
´(st+i) · u(Ct+i, Zt+i, nt+i)...
+βT+N−tXi=1
δi³Πi−1j=1st+j
´(1− st+i) ·B(Xt+i, Zt+i)
• nt is effective household size: adults+(.4)(kids)
• γZt represents real after-tax net consumption flow
• st+1 is survival probability
• B(·) represents the payoff in the death state
3.7 Computation
• Dynamic problem:maxIXt ,I
Zt
u(Ct, Zt, nt) + βδEtVt,t+1(Λt+1)
s.t. Budget constraints
• Λt = (Xt + Yt, Zt, ut) (state variables)
• Functional Equation:Vt−1,t(Λt) =st[u(Ct, Zt, nt)+δEtVt,t+1(Λt+1)]+(1−st)EtB(Λt)
• Solve for eq strategies using backwards induction
• Simulate behavior
• Calculate descriptive moments of consumer be-havior
4 Estimation
Estimate parameter vector θ and evaluate models wrt data.
• me = N empirical moments, VCV matrix = Ω
• ms (θ) = analogous simulated moments
• q(θ) ≡ (ms (θ)−me)Ω−1 (ms (θ)−me)0, a scalar-valued loss function
• Minimize loss function: θ = argminθq(θ)
• θ is the MSM estimator.
• Pakes and Pollard (1989) prove asymptotic con-sistency and normality.
• Specification tests: q(θ) ∼ χ2(N−#parameters)
Leisure Goods: Consumption and Savings
Two steps of estimation: of MSM (Method of SimulatedMoments)
1 Estimate (‘calibrate’) auxiliary parameters
Interest rateMortalityIncome shocks
2 Estimate main parameters (β, δ) using Method of SimulatedMoments
Simulate model (cannot solve analytically)Choose parameters (β, δ) that minimize distance of simulatedmoments to estimated momentsTake into account uncertainty in estimates of 1st stage
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Leisure Goods: Commitment and Savings
Section 7
Leisure Goods: Commitment and Savings
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Leisure Goods: Commitment and Savings Ashraf, Karlan, and Yin (2005)
Ashraf, Karlan, and Yin (2005), QJE
Different Methodology: Commitment Device Field Experiment
Different Setting: Philippines
Three treatments:
SEED Treatment (N=842): Encourage to save, Offercommitment device (account with savings goal)Marketing Treatment (N=466): Encourage to save, Offer nocommitmentControl Treatment (N=469)
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Leisure Goods: Commitment and Savings Ashraf, Karlan, and Yin (2005)
Results
Result 1. Take-up of commitment device (in SEEDTreatment):
Out of 842 treated people, 202 take up SEED Take up of24%167 also got lock-up box (did not observe savings there)
Result 2. Effect of Availability of Commitment on TotalSavings (including funds in non-committed account)
Compare SEED to Marketing (Include all 842 people,Intent-to-Treat)Share of people with increased Balances: 5.6 percentage(33.3 percent in SEED and 27.7 in Marketing)Share of people with increased Balances by at least 20 percent:6.4 percentage pointsTotal Balances: 287 Pesos after 6 months (not significant)
To compute Treatment-on-The-Treated, divide by 202/842
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Leisure Goods: Commitment and Savings Ashraf, Karlan, and Yin (2005)
Results
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Leisure Goods: Commitment and Savings Ashraf, Karlan, and Yin (2005)
Results
Survey response to hyperbolic-discounting-type question:
Preference between 200 Pesos now and in 1 monthPreference between 200 Pesos in 6 months and in 7 monthsOn average, evidence of hyperbolic-discounting-type preferences
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Leisure Goods: Commitment and Savings Ashraf, Karlan, and Yin (2005)
Results: Selection
Result 3. Who takes up the Commitment device?Correlate survey response with commitment take-up (see alsoFehr-Goette paper)
Evidence of correlation for women, not for men
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Leisure Goods: Drinking
Section 8
Leisure Goods: Drinking
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Leisure Goods: Drinking Schilbach (2015)
Schilbach (2015)
Consider population with high levels of drinking while working
Offer incentives to not drink during work hours
Examine impact on
Drinking during work hoursDrinking after work hoursEarningsSavings with (and without) savings commitment
Frank’s slides follow
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Introduction Background Design Alcohol Impact Commitment Outlook
Heavily concentrated alcohol consumption in India
0.7
5.0
1.4
2.8
2.3
4.9
01
23
45
Sta
ndard
drinks p
er
day
India USA Russia
Source: WHO (2014), WHO (2001), own calculation.
Per capita (age 15+) Male drinkers only
6 / 53
Introduction Background Design Alcohol Impact Commitment Outlook
Study sample
• Cycle-rickshaw peddlers in Chennai• 35 years old, 5 years of education• 80% are married, 2 children• Average daily labor incomes of about Rs. 300 ($5)
• Alcohol consumption• Individuals drink (almost) every day, usually alone.• A third of labor incomes spent on hard liquor (>80 proof)• Individuals drink over 5 standard drinks per day.• High levels of intoxication, often during the day• 80% say they would be better off if all liquor stores closed.
SELECTION TABLE
12 / 53
Introduction Background Design Alcohol Impact Commitment Outlook
Experimental design
• 229 individuals paid to visit study office for 20 days• Daily visits any time between 6 pm and 10 pm• Measure blood-alcohol content (BAC) using breathalyzer test• Short survey
• Labor market outcomes• Alcohol consumption• Expenditure patterns
• Opportunity to save money at study office
13 / 53
Introduction Background Design Alcohol Impact Commitment Outlook
Financial incentives for sobriety: three treatment groups
(I) Control Group: unconditional payments• Paid Rs. 90 regardless of BAC
(II) Incentive Group: monetary incentives to show up sober• Paid Rs. 60 if BAC > 0• Paid Rs. 120 if BAC = 0
(III) Choice Group• Choice between incentives and unconditional payments
BALANCE TABLES
14 / 53
Introduction Background Design Alcohol Impact Commitment Outlook
Experimental design
-Day 1 Day 4 Day 7 Day 13 Day 20?
BaselineConsentScreening
?
Incentivesassigned
?
Choice 1
?
Choice 2
?
Choice 3Endline
Control
AAAAU
Incentives
(2/3)
Control
(1/3)
Incentives(1/3)J
JJJJ
Choice(1/3)
Control(1/3)
-
-
Choice-
CCCCW
-
Choice
15 / 53
Introduction Background Design Alcohol Impact Commitment Outlook
Financial incentives significantly increased daytime sobriety.
← Alcohol treatment assigned
30
40
50
60
Fra
ction s
ober
(%)
0 5 10 15 20Day in Study
Incentives Choice Control
Sobriety at the Study Office
ATTENDANCE IN THE INCENTIVE GROUP IS LOWER.
18 / 53
Introduction Background Design Alcohol Impact Commitment Outlook
...but reported overall drinking did not fall by much.
↑
drinking before study office visit
overall drinking
↓
01
23
45
67
Num
ber
of sta
ndard
drinks
0 5 10 15 20Day in Study
Incentives and Choice Groups pooled Control Group
# Standard Drinks Before Study Office Visit and Overall
20 / 53
Appendix
Intertemporal substitution: time of first drink
0.2
.4.6
.81
cdf
6 8 10 12 14 16 18 20 22 24
Time of first drink
Incentives Choice Control
BACK TO SUMMARY OF EFFECTS ON DRINKING
69 / 53
Introduction Background Design Alcohol Impact Commitment Outlook
No significant effects on earnings
← Alcohol treatment assigned
250
270
290
310
330
Daily
earn
ings (
Rs.)
0 5 10 15 20Day in Study
Incentives and Choice Groups pooled Control Group
Earnings (Rs/day)
25 / 53
Introduction Background Design Alcohol Impact Commitment Outlook
Measuring the impact of increased sobriety on savings
• All subjects got personalized savings box at study office.• Could save up to Rs. 200 per day.• Paid out entire amount plus matching contribution on day 20.
• Cross-randomized matching contribution to benchmark effects• 10% vs. 20% of amount saved
• Cross-randomized commitment savings feature• Allowed to withdraw any day between 6 pm and 10 pm• Not allowed to withdraw until day 20
29 / 53
Introduction Background Design Alcohol Impact Commitment Outlook
Incentives for sobriety increased savings.
← Alcohol treatment assigned
0200
400
600
Cum
ula
tive s
avin
gs a
t stu
dy o
ffic
e (
Rs)
0 5 10 15 20Day in Study
Incentives and Choice Groups pooled Control Group
Cumulative Savings by Treatment Group
DEPOSITS WITHDRAWALS
31 / 53
Introduction Background Design Alcohol Impact Commitment Outlook
Incentives for sobriety increased savings.
(1) (2) (3)VARIABLES Rs/day Rs/day Rs/day
Pooled alcohol treatment 12.45** 13.41*** 11.55**(6.262) (5.018) (4.792)
High matching contribution 9.29 10.11** 11.65**(6.532) (4.873) (4.619)
Commitment savings 7.59 2.88 2.86(6.539) (5.074) (4.820)
Daily study payment (Rs) 0.35***(0.050)
Observations 3,435 3,435 3,435R-squared 0.006 0.113 0.129Baseline survey controls NO YES YESPhase 1 controls NO YES YESControl mean 20.42 20.42 20.42
Standard errors in parentheses, clustered by individual.32 / 53
Introduction Background Design Alcohol Impact Commitment Outlook
Interaction between sobriety and commitment savings
0100
200
300
400
500
600
700
Cum
ula
tive s
avin
gs (
Rs)
0 5 10 15 20Day in Study
Pooled alcohol treatment, commitment savings
Pooled alcohol treatment, no commitment savings
No alcohol treatment, commitment savings
No alcohol treatment, no commitment savings
Sobriety Incentives vs. Commitment Savings
WITHDRAWALS DEPOSITS
39 / 53
Appendix
Sobriety incentives vs. commitment savings: deposits
0200
400
600
800
Cum
ula
tive d
epsits (
Rs)
0 5 10 15 20Day in Study
Sobriety incentives, commitment savings
Sobriety incentives, no commitment savings
No sobriety incentives, commitment savings
No sobriety incentives, no commitment savings
Sobriety vs. Commitment Savings: Cumulative Deposits
BACK TO SAVINGS SECTION
65 / 53
Appendix
Sobriety incentives vs. commitment savings: withdrawals
050
100
150
200
Cum
ula
tive w
ithdra
wals
(R
s)
0 5 10 15 20Day in Study
Sobriety incentives, commitment savings
Sobriety incentives, no commitment savings
No sobriety incentives, commitment savings
No sobriety incentives, no commitment savings
Sobriety vs. Commitment Savings: Cumulative Withdrawals
BACK TO SAVINGS SECTION
66 / 53
Introduction Background Design Alcohol Impact Commitment Outlook
Eliciting willingness to pay for incentives
• Choice Group chooses between:• Incentives for sobriety• Unconditional payments
• Choice sessions on days 7, 13, 20, each for subsequent week• Elicit preferences for set of 3 choices• Then randomly select one choice to be implemented (RLIS)
41 / 53
Introduction Background Design Alcohol Impact Commitment Outlook
Demand for incentives
• Option A: incentives for sobriety• Same payment structure as Incentive Group• Rs. 60 if BAC > 0, Rs. 120 if BAC = 0
• Option B: payment of Rs. Y regardless of BAC
Option A Option BBAC > 0 BAC = 0 regardless of BAC
(1) Rs. 60 Rs. 120 Rs. 90(2) Rs. 60 Rs. 120 Rs. 120(3) Rs. 60 Rs. 120 Rs. 150
42 / 53
Introduction Background Design Alcohol Impact Commitment Outlook
Demand for commitment persists over time.
0.2
.4.6
.8F
raction o
f C
hoic
e G
roup w
ho c
hose incentives
1 2 3 1 2 3 1 2 3Week
Choice 1: unconditional payment = Rs 90
Choice 2: unconditional payment = Rs 120
Choice 3: unconditional payment = Rs 150
Demand for Incentives over Time
46 / 53
Introduction Background Design Alcohol Impact Commitment Outlook
Exposure to incentives increases demand for incentives.
0.2
.4.6
.8F
raction o
f in
div
iduals
who c
hose incentives
Choice 1 (Rs 90) Choice 2 (Rs 120) Choice 3 (Rs 150)
Incentive Group Choice Group Control Group
Demand for Incentive across Treatment Groups
50 / 53
Leisure Goods: Drinking Schilbach (2015)
Unique Features
Unique feature 1: Effect of commitment device on drinking onanother patience-related activity: savings
How do we interpret the effect?
Effect on withdrawing – mechanical given drunkennessEffect on deposits – sophistication?
Would be great to know if sobriety incentives increases or lowersdemand for savings commitment device (not in design)
Unique feature 2: Exceptional demand for commitment device byfor drinking
1/3 population even when very expensiveOther existing studies – Demand typically goes to near zero
Stefano DellaVigna Econ 219B : Applications (Lecture 3) January 31, 2018 85 / 100
Methodology: Commitment Field Experiments
Section 9
Methodology: Commitment Field Experiments
Stefano DellaVigna Econ 219B : Applications (Lecture 3) January 31, 2018 86 / 100
Methodology: Commitment Field Experiments
Growing literature on field experiments offering
commitment devices
Recipe for typical device:
Random assignment into Treatment (T) and Control (C)Group T: Offered commitment option (action that imposesconstraints)Group C: No optionObserve take-up of commitment in TObserve outcome (e.g., saving, smoking, eating) in C and T
Stefano DellaVigna Econ 219B : Applications (Lecture 3) January 31, 2018 87 / 100
Methodology: Commitment Field Experiments
Three sets of results
1 Take-up. What share in T uses commitment device?Standard agent would not choose additional constraints Smoking gun for time inconsistencyTime inconsistency can be from present bias+ sophisticationOR from hot/cold states or intra-family bargaining
2 Effect on outcome. Compare outcomes in T and CNotice: Compare everybody in T to everybody in CCannot focus on those that took up the commitment in T, sincedo not know who they compare to in CTreatment on Treated: rescale by dividing by take-up(assumption of no effect on non-takers)
3 Who Takes Up? Document who in T takes up commitmentCorrelation with measured time preferences, previous behavior,etc.This is not causal evidence, but still interesting
Stefano DellaVigna Econ 219B : Applications (Lecture 3) January 31, 2018 88 / 100
Methodology: Commitment Field Experiments
Three sets of results
1 Take-up. What share in T uses commitment device?Standard agent would not choose additional constraints Smoking gun for time inconsistencyTime inconsistency can be from present bias+ sophisticationOR from hot/cold states or intra-family bargaining
2 Effect on outcome. Compare outcomes in T and CNotice: Compare everybody in T to everybody in CCannot focus on those that took up the commitment in T, sincedo not know who they compare to in CTreatment on Treated: rescale by dividing by take-up(assumption of no effect on non-takers)
3 Who Takes Up? Document who in T takes up commitmentCorrelation with measured time preferences, previous behavior,etc.This is not causal evidence, but still interesting
Stefano DellaVigna Econ 219B : Applications (Lecture 3) January 31, 2018 88 / 100
Methodology: Commitment Field Experiments
Three sets of results
1 Take-up. What share in T uses commitment device?Standard agent would not choose additional constraints Smoking gun for time inconsistencyTime inconsistency can be from present bias+ sophisticationOR from hot/cold states or intra-family bargaining
2 Effect on outcome. Compare outcomes in T and CNotice: Compare everybody in T to everybody in CCannot focus on those that took up the commitment in T, sincedo not know who they compare to in CTreatment on Treated: rescale by dividing by take-up(assumption of no effect on non-takers)
3 Who Takes Up? Document who in T takes up commitmentCorrelation with measured time preferences, previous behavior,etc.This is not causal evidence, but still interesting
Stefano DellaVigna Econ 219B : Applications (Lecture 3) January 31, 2018 88 / 100
Methodology: Commitment Field Experiments
Representative studies: Investment Goods
Homework Completion (Ariely-Wertenbroch PS)
Deadlines are penalties for delivering homework lateResult 1. Very large take-up rate (65 percent)Result 2. Large effect on quality of homework and delay (in exp.2)
Stefano DellaVigna Econ 219B : Applications (Lecture 3) January 31, 2018 89 / 100
Methodology: Commitment Field Experiments
Representative studies: Investment Goods
Health-club attendance (Royer, Stehr, and Sydnor, AEJ Applied2014)
First pay a treatment group to go to the gymThen offer half of this treated group commitment device tokeep goingCommitment device is money deposited into an account.Money forfeited if do not attend at least once every 14 days for4 monthsResult 1: Low demand for commitment: 13% take-up, withaverage sum of $63Result 2: Some effect on attendance
Stefano DellaVigna Econ 219B : Applications (Lecture 3) January 31, 2018 90 / 100
Methodology: Commitment Field Experiments
Health-club attendance0
.51
1.5
22.5
3
-20 -10 0 10 20Week Relative to Start of Incentives
Control IncentiveIncentive+Commit
Average Weekly Visits Overall
Stefano DellaVigna Econ 219B : Applications (Lecture 3) January 31, 2018 91 / 100
Methodology: Commitment Field Experiments
Representative studies: Leisure Goods
Consumption/Savings (Ashraf-Karlan-Yin)
Result 1. Commitment device take-up 24%Result 2. Significant effect on overall savings
Consumption/Savings (Beshears, Choi, Laibson, Madrian,Mekong, 2011)
RAND panel respondents, 495 subjects, given $50, $100, or$500Choice between
Liquid account (r=22% yearly)Commitment account (set a goal) with r of 21%, 22%, or 23%Penalty for early withdrawal(Notice: only group with r=21% is a commitment device design)Can choose share into each account
Stefano DellaVigna Econ 219B : Applications (Lecture 3) January 31, 2018 92 / 100
Methodology: Commitment Field Experiments
Representative studies: Leisure Goods
Consumption/Savings (Ashraf-Karlan-Yin)
Result 1. Commitment device take-up 24%Result 2. Significant effect on overall savings
Consumption/Savings (Beshears, Choi, Laibson, Madrian,Mekong, 2011)
RAND panel respondents, 495 subjects, given $50, $100, or$500Choice between
Liquid account (r=22% yearly)Commitment account (set a goal) with r of 21%, 22%, or 23%Penalty for early withdrawal(Notice: only group with r=21% is a commitment device design)Can choose share into each account
Stefano DellaVigna Econ 219B : Applications (Lecture 3) January 31, 2018 92 / 100
Methodology: Commitment Field Experiments
Beshears et al., 2011
Result 1. Commitment device take-up quite high – up to 56%
Stefano DellaVigna Econ 219B : Applications (Lecture 3) January 31, 2018 93 / 100
Methodology: Commitment Field Experiments
Representative studies: Leisure Goods
Retirement Savings (SMRT plan, Thaler and Benartzi, 2007 –last lecture)
Result 1. Take-up rate 80% when offered in personResult 2. Huge effects on 401(k) contribution rates
Online gaming (Chow, 2010 and Acland and Chow, 2010)
Offer online interface that one can use to limit play of onlinegamesResult 1. Take-up rate relatively high initially, but declines to5-10%Result 2. Suggestive effects on time spent playing
Stefano DellaVigna Econ 219B : Applications (Lecture 3) January 31, 2018 94 / 100
Methodology: Commitment Field Experiments
Representative studies: Leisure Goods
Retirement Savings (SMRT plan, Thaler and Benartzi, 2007 –last lecture)
Result 1. Take-up rate 80% when offered in personResult 2. Huge effects on 401(k) contribution rates
Online gaming (Chow, 2010 and Acland and Chow, 2010)
Offer online interface that one can use to limit play of onlinegamesResult 1. Take-up rate relatively high initially, but declines to5-10%Result 2. Suggestive effects on time spent playing
Stefano DellaVigna Econ 219B : Applications (Lecture 3) January 31, 2018 94 / 100
Methodology: Commitment Field Experiments
Representative studies: Leisure Goods
Smoking (Gine, Karlan, and Zinman, 2010)
Offer urine test for smoking in 6 monthsCan deposit money into account – forfeited if fail test at month6Result 1. Low take-up: 11% of 781 offered productResult 1. Conditional on take-up, average deposit of 57 pesos(4 weeks worth of cigarettes)Result 2: At 6 months, increase of 4-5 percentage point inchance of making urine test
Stefano DellaVigna Econ 219B : Applications (Lecture 3) January 31, 2018 95 / 100
Methodology: Commitment Field Experiments
Representative studies: Leisure Goods
Smoking (Gine, Karlan, and Zinman, 2010), continued
Result 2: At 12 months, similar increase at surprise test
Stefano DellaVigna Econ 219B : Applications (Lecture 3) January 31, 2018 96 / 100
Methodology: Commitment Field Experiments
Representative studies: Leisure Goods
Why often low-take up? At least 3 possibilities:
Self-control not prevalentSelf-control prevalent, but naivete’ is strongDemand for commitment outweighed by costs of commitmentin terms of loss of flexibility
Important to have designs to separate explanations
Stefano DellaVigna Econ 219B : Applications (Lecture 3) January 31, 2018 97 / 100
Methodology: Commitment Field Experiments
Representative studies: Leisure Goods
Alternative design of the commitment device field experiments:2*2 Design (Chow, 2010)
Offer everyone the commitment deviceThen randomly assign whether commitment device is actuallyofferedTherefore groups are 2 (wanted comm./did not) * (gotcomm./did not)
Advantage of this design
More power on demand for commitment since everybody (notjust 1/2 of subjects) is askedCan estimate effect of commitment both on the subjects thatdemand it, and the ones who do not (but who may end upusing it)See also Chassang, Padro-i-Miguel, Snowberg, (AER 2012)
Stefano DellaVigna Econ 219B : Applications (Lecture 3) January 31, 2018 98 / 100
Methodology: Commitment Field Experiments
Representative studies: Leisure Goods
Alternative design of the commitment device field experiments:2*2 Design (Chow, 2010)
Offer everyone the commitment deviceThen randomly assign whether commitment device is actuallyofferedTherefore groups are 2 (wanted comm./did not) * (gotcomm./did not)
Advantage of this design
More power on demand for commitment since everybody (notjust 1/2 of subjects) is askedCan estimate effect of commitment both on the subjects thatdemand it, and the ones who do not (but who may end upusing it)See also Chassang, Padro-i-Miguel, Snowberg, (AER 2012)
Stefano DellaVigna Econ 219B : Applications (Lecture 3) January 31, 2018 98 / 100
Next Lecture
Section 10
Next Lecture
Stefano DellaVigna Econ 219B : Applications (Lecture 3) January 31, 2018 99 / 100
Next Lecture
Next Lecture
Present Bias
Laboratory ExperimentsErrors in Applying Present Bias
Then Reference-Dependent Preferences
HousingBunching-based Evidence
Stefano DellaVigna Econ 219B : Applications (Lecture 3) January 31, 2018 100 / 100
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