Econ 219B Psychology and Economics: Applications (Lecture 11) - Stefano DellaVigna April 8, 2020 - Berkeley Economics
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Econ 219B
Psychology and Economics: Applications
(Lecture 11)
Stefano DellaVigna
April 8, 2020
Stefano DellaVigna Econ 219B: Applications (Lecture 11) April 8, 2020 1 / 92
Outline
1 Choice of Dominated Options
2 Memory
3 Mental Accounting
4 Persuasion
5 Emotions: Mood
6 Emotions: Arousal
7 Happiness
Stefano DellaVigna Econ 219B: Applications (Lecture 11) April 8, 2020 2 / 92
Choice of Dominated Options
Section 1
Choice of Dominated Options
Stefano DellaVigna Econ 219B: Applications (Lecture 11) April 8, 2020 3 / 92Choice of Dominated Options Bhargava, Loewenstein, and Sydnor (2017) Dominated Choice An especially strong case of non-standard decision making is the choice of a dominated option Bhargava, Loewenstein, and Sydnor (QJE 2017) Examine choice of health plans for employees of a large company Plans are such that the high-deductible plans tend to dominate the low-deductible plans Stefano DellaVigna Econ 219B: Applications (Lecture 11) April 8, 2020 4 / 92
Choice of Dominated Options Bhargava, Loewenstein, and Sydnor (2017)
Bhargava, Loewenstein,
CHOOSE TOSydnor
LOSE 1333
Stefano DellaVigna FIGURE
Econ 219B: I
Applications (Lecture 11) April 8, 2020 5 / 92Choice of Dominated Options Bhargava, Loewenstein, and Sydnor (2017) Bhargava, Loewenstein, Sydnor Large costs of picking the wrong plan 1340 QUARTERLY JOURNAL OF ECONOMICS Stefano DellaVigna Econ 219B: Applications (Lecture 11) April 8, 2020 6 / 92
Choice of Dominated Options Bhargava, Loewenstein, and Sydnor (2017)
Bhargava, Loewenstein, Sydnor
Incidence of errors is much
CHOOSE larger for low-income
TO LOSE 1347people
Stefano DellaVigna Econ 219B: Applications (Lecture 11) April 8, 2020 7 / 92Choice of Dominated Options Bhargava, Loewenstein, and Sydnor (2017) Bhargava, Loewenstein, Sydnor Large costs of picking the wrong plan for the poor Stefano DellaVigna Econ 219B: Applications (Lecture 11) April 8, 2020 8 / 92
Choice of Dominated Options Biases and Poverty
Other papers
Are behavioral biases disproportionately hurting the poor
(Mullainathan and Shafir)?
Key variables in determining implications of behavioral
economics for redistribution and inequality
Two forces:
Poor are likely less educated –> More bias
Poor have lower cost of time –> Can in principle search harder
In literature:
Bhargava et al. (2017): first force clearly dominates
Busse et al. (2013): also similar results for limited attention to
odometer, much smaller magnitude
Madrian and Shea (2001) – default effects larger for lower
income
Other papers?
Incidence of behavioral biases is key emerging theme
Stefano DellaVigna Econ 219B: Applications (Lecture 11) April 8, 2020 9 / 92Memory
Section 2
Memory
Stefano DellaVigna Econ 219B: Applications (Lecture 11) April 8, 2020 10 / 92Memory
Introduction
Limited memory likely to underlie several behavioral phenomena
Procrastination
Backward-looking reference points
Overinference and Experience effects
Start from Ericson (JEEA 2010) – Empirical evidence on
limited memory and naivete’
$
Individuals will receive 20 if they remember to send an email in
a particular week, months ahead
$
Or they can can get X unconditional
What do they prefer, and how how often do they remember?
Stefano DellaVigna Econ 219B: Applications (Lecture 11) April 8, 2020 11 / 92Memory Ericson (2010)
Ericson (2010)
48 Journal of the European Economic Association
TABLE 1. Excerpt from experiment instructions.
Downloaded from https://academic.oup.com/jeea/article-abstract/9/1/43/2298405 b
Please indicate your choices between the two rewards listed on each line by checking your
preferred box. Rewards in both columns will be mailed at the same time, on 11 January 2006.
Rewards in this column will be mailed to
Rewards in this column will be you if you email us with your contact
automatically mailed to you. information between Jan. 5 and Jan. 10,
2006. You must email us within this time
Row No further action is required on your part. period to receive your reward.
#0 ____ $20 ____ $20
#1 ____ $19.25 ____ $20
#2 ____ $18.50 ____ $20
... ... ...
#19 ____ $5.75 ____ $20
#20 ____ $5 ____ $20
0 to 20, subjects received the election they made in the row that was thus numbered.
What do
Theypeople
therefore hadchoose?
a 21% chance of receiving the election they had made in one of the
21 rows. With 60% probability (dice rolls from 21 to 80), subjects were assigned to
the $20 contingent payment condition, while with 19% probability (dice rolls from 81
to 99) they were assigned to receive an automatic payment of $20. These probabilities
were chosen to facilitate a simple description of the tasks to subjects. All subjects then
filled out a sheet giving their contact information. Subjects receiving the automatic
Stefano DellaVigna Econ 219B: Applications (Lecture 11) April 8, 2020 12 / 9254 Journal of the European Economic Association
m/jeea/article-abstract/9/1/43/2298405
Memory Ericson (2010)
0
TABLE 3. Relationship between beliefs and behavior: probit model and means.
0 .25 .5 .75 1
Subjective probability of claim implied by choices
Ericson (2010)
Downloaded from https://academic.oup.com/jeea/article-abstract/9/1/43/2298405
Dependent Variable: Remembered to Claim Payment
Coefficient Marginal Effect Coefficient Marginal Effect
F IGURE 2. Distribution of subjective probability of claim ( p̂). (Analysis Sample.)
Subjective Probability of Claim p̂ 0.873∗ 0.348∗ 1.009∗∗ 0.402∗∗
TABLE 2.(0.353)
Test for population
(0.140) overconfidence. 0.364 0.145)
In MBA Sample – – 0.584 0.228 ˆ
N Fraction Claimed Average Implied Claim t-statistic
Probability (0.113)
(0.303)
Male – – 0.137 0.055 ˆ
Analysis Sample 186 0.53 0.76(0.202) 5.61∗∗∗
(0.080)
by University of California Berkeley, Stefano DellaVignaby
Age in Years −0.0155
(0.04) −0.006
∗∗∗
. . .College Only 36 0.39 0.82(0.027) 4.76
(0.011)
N 186 (0.04) 186
. . .MBA
Pseudo R2 Only 150 0.56
0.025 0.75 0.045 4.01∗∗∗
(0.02)
∗ p < 0.05, ∗∗ p < 0.01
. . .Men Only 114 0.54 0.72 3.29∗∗
Analysis Sample. Standard errors in parentheses. Marginal effects are reported
(0.03)at the mean of the independent
. . .Women Only
variables. The symbol ˆ indicates
72 a dichotomous
0.50 variable, for which the effect
0.82of a discrete change from 0 ∗∗∗
4.99 to 1
is reported. (0.02)
∗∗ p < 0.01, ∗∗∗ p < 0.001.
Standard errors in parentheses.
1
result being driven by a misunderstanding of the instructions. Choosing a contingent
payment over an automatic payment of the same amount is a weakly dominated action
.8
that should only be chosen if p̂ = 1 and costs of claiming and trying to claim are zero.
If the 16 subjects in the Analysis Sample (8.6%) who make this weakly dominated
Fraction claimed
.6
choice are excluded, the results are virtually unchanged: average p̂ is 0.74, the fraction
onUniversity
claimed is 0.52, t = 4.91. Finally, this result is not driven by subjects attempting to
08 April 2020
claim the payment outside the specified claim window. Only five subjects attempted to
.4
claim payment after the claim window had passed. The analysis thus far counts them
as having failed to complete the memory task. However, they may not have found
of California Berkeley, Stefano D
credible the experimenters’ claim that payment would be denied if they did not fulfill
.2
45° line
0
0 .2 .4 .6 .8 1
Subjective probability of claim implied by choices
F IGURE 3. Average claim behavior by subjective probability of claim ( p̂). (Analysis Sample. Marker size
Stefano DellaVigna
indicates the number of subjects in each p̂ value group.)
Econ 219B: Applications (Lecture 11) April 8, 2020 13 / 92Memory Haushofer (2015)
More Evidence on Memory
Haushofer (2015) presents more evidence on limited memory
Experiment in Kenya via SMS messages
$
Transfer of 3 through mobile system MPesa
$
Subject can get extra 13 by sending message on exact date
$13 would be sent 5 weeks later (to keep time discounting out
of picture)
Can estimate extent of memory decay over time (not naivete)
Stefano DellaVigna Econ 219B: Applications (Lecture 11) April 8, 2020 14 / 92Memory Haushofer (2015)
More Evidence on Memory
Option 1:
Get $3 Send request Get $13
Immediately Today Tomorrow 1 week 2 weeks 3 weeks 4 weeks 5 weeks
Call 1
Call 2
Option 2:
Get $3 No request No $13
Immediately Today Tomorrow 1 week 2 weeks 3 weeks 4 weeks 5 weeks
Call 1
Call 2
1
.9
.94
.92
.3 .4 .5 .6 .7 .8
Proportion remembering
.75 .75 .74
.65
.57
y = .86^t (t in weeks)
.48
.2
.1
N = 50 at each time horizon
0
Immediately Today Tomorrow 1 week 2 weeks 3 weeks 4 weeks 5 weeks
Stefano DellaVigna Delay
Econ 219B: Applications (Lecture 11) April 8, 2020 15 / 92Memory Zimmermann (2020) Motivated Memory Above we discussed natural limitations to memory But memory can also work selectively Important part of several behavioral models, e.g., Benabou-Tirole Stefano DellaVigna Econ 219B: Applications (Lecture 11) April 8, 2020 16 / 92
Memory Zimmermann (2020)
Motivated Memory
Zimmermann (AER 2020) experiment on selective recall
Two experimental sessions, one month apart
Dictator game for 10 euro with German Red Cross (filler)
IQ test (10 Raven matrices) with earnings of 4 euro
Belief elicitation about performance, compared to 9 people in
group – Are you in upper half?
(Noisy) feedback about the ranking: select 3 out of 9 member
and say how you did relative to each of them
In ConfidenceDirect, second belief elicitation, with quadratic
scoring rule
In Confidence1Month, second belief elicitation is done 1 month
later
Stefano DellaVigna Econ 219B: Applications (Lecture 11) April 8, 2020 17 / 92Memory Zimmermann (2020)
Motivated Memory 346 THE AMERICAN ECONOMIC REVIEW FEBRUARY 2020
Panel A. ConfidenceDirect Panel B. Confidence1month
Positive Negative Positive Negative
0.4 0.4
0.3 0.3
Fraction
Fraction
0.2 0.2
0.1 0.1
0 0
50
50
50
50
50
0
50
0
50
0
0
50
−
−
−
−
Belief adjustment Belief adjustment
Figure 1
Notes: Histograms of belief adjustments (posterior − prior) for treatments ConfidenceDirect (panel A) and
Confidence1month (panel B), separately for positive and negative feedback. Belief adjustments are censored at
+/− 50.
Panel A. ConfidenceDirect Panel B. Confidence1month
90 90
80 80
Pr(upperhalf )
70 Pr(upperhalf ) 70
60 60
50 50
40 40
30 30
)
)
)
)
)
)
)
)
37
34
48
59
15
23
33
37
=
=
=
=
=
=
=
=
(N
(N
(N
(N
(N
(N
(N
(N
5
5
6
6
5
5
6
6
<
>
<
>
Test performance Test performance
Prior Posterior positive feedback
Posterior negative feedback
Figure 2
Stefano DellaVigna Econ 219B: Applications (Lecture 11) April 8, 2020 18 / 92Memory Zimmermann (2020)
Motivated Memory
Additional treatments: Two Recall treatments
First recall treatment: one month later, ask how many
comparisons to other 3 subjects were positive
350 THE AMERICAN ECONOMIC REVIEW FEBRUARY 2020
RecallHigh: Same, but 50 euros (!) to recall feedback
1
Average recall accuracy
0.8
0.6
0.4
0.2
Recall RecallHigh
0
0 1 2 3
Positive comparisons
Stefano DellaVigna Econ 219B: Applications (Lecture 11) April 8, 2020 19 / 92Memory Zimmermann (2020)
Motivated Memory
Additional treatments: Announcement treatment
Announce at end of session 1 that will ask about recall in
session 2
358 THE AMERICAN ECONOMIC REVIEW FEBRUARY 2020
90
80
70
Pr(upperhalf )
60
50
40 Prior Posterior positive feedback
Posterior negative feedback
30
6 (N = 49)
Test performance
Figure 4. Announcement
Stefano DellaVigna Econ 219B: Applications (Lecture 11) April 8, 2020 20 / 92Mental Accounting
Section 3
Mental Accounting
Stefano DellaVigna Econ 219B: Applications (Lecture 11) April 8, 2020 21 / 92Mental Accounting
Introduction
Thaler (1981): Mental Accounting is tendency of individuals to
form special accounts for different expenditures, and keep
inflows and outflow separated across accounts
$
Example: 200/wk food budget and 100/wk entertaiment $
budget
Deviates from standard model with just one budget
Why use mental accounting?
Self control problems
Simplicity
What is the evidence for this?
Until recently, quite weak. Rare component in Thaler agenda
without too much support
Stefano DellaVigna Econ 219B: Applications (Lecture 11) April 8, 2020 22 / 92Mental Accounting Hastings and Shapiro (2013)
Gas Prices
Hastings and Shapiro (QJE 2013)
Assume a mental account for gasoline
Choice at the pump for regular gas, or premium (usually 10c
more expensive)
Mental accounting: Price of gasoline goes up –> switch to
regular gasoline (from premium) to try to stay more in account
Notice: Proportional thinking makes opposite prediction
Standard model:
Makes same prediction based on income effect, but much
smaller impact
Can also look at 2009 when price of gasoline went down
Stefano DellaVigna Econ 219B: Applications (Lecture 11) April 8, 2020 23 / 92Mental Accounting Hastings and Shapiro (2013)
Gas Prices, Data
FIGURE II: Regular share and the price of regular gasoline (retailer data)
.82
4
Price of regular gasoline (dollars)
.81
3.5
Regular share
3
.8
2.5
.79
2
.78
1.5
2006w1 2007w1 2008w1 2009w1
Week
Regular share Price of regular
Notes: Data are from the retailer. The plot shows the weekly share of transactions that go to regular gasoline and the
Gas price and purchase of regular gasoline clearly move together
weekly average transaction price of regular gasoline (in current US dollars).
Notice: Also true in 2009 when income effects go the other way
Stefano DellaVigna Econ 219B: Applications (Lecture 11) April 8, 2020 24 / 92Mental Accounting Hastings and Shapiro (2013)
Gas Prices, Model Fit
FIGURE VII: Fit of psychological models of decision-making
Baseline Category budgeting
.78 .79 .8 .81 .82
.78 .79 .8 .81 .82
Regular share
Regular share
2006w1 2007w1 2008w1 2009w1 2006w1 2007w1 2008w1 2009w1
Week Week
Salience Loss aversion
.78 .79 .8 .81 .82
.78 .79 .8 .81 .82
Regular share
Regular share
2006w1 2007w1 2008w1 2009w1 2006w1 2007w1 2008w1 2009w1
Week Week
Observed Predicted
Notes: Data are from the retailer. The line labeled “observed” shows the weekly share of transactions that go to
Simple mental accounting model does good job of fit
regular gasoline. The line labeled “predicted” shows the average predicted probability of buying regular gasoline from
estimates of the model in equation (8) with different specifications of Gi jt . In the baseline model we set Gi jt = 08i, j,t.
Stefano DellaVigna Econ 219B: Applications (Lecture 11) April 8, 2020 25 / 92Mental Accounting Hastings and Shapiro (2018)
Food Stamps
Hastings and Shapiro (AER 2018)
What happens when food stamps come in?
Large majority of individuals spend more on food than food
stamp amount
Standard model: Increase in food expenditure should equal the
marginal propensity to consume on food from income shocks
(about 0.1)
Mental accounting: MPCF from food stamps will be high, since
same account
Use data from a retailer where can observe is spend with food
stamps
Three empirical strategies:
1 Individuals enter food stamp program
2 Exit from program most likely after 6, 12, 18... months
3 Legislative changes in food stamp magnitude
Stefano DellaVigna Econ 219B: Applications (Lecture 11) April 8, 2020 26 / 92.
Share of household
Mental Accounting Hastings and Shapiro (2018)
.2
Food Stamps
0
−12 0 12
Months relative to SNAP adoption
Figure 5: Monthly expenditure before and after SNAP adoption
Panel B: SNAP benefits Panel A: SNAP-eligible spending
500
200
Monthly SNAP benefit (dollars)
Monthly expenditure (dollars)
150
450
100
400
50
350
0
−12 0 12 −12 0 12
Months relative to SNAP adoption Months relative to SNAP adoption
Panel B: SNAP-ineligible spending
Strategy 1: Identify entry into SNAP as 6 months of SNAP
ample is the set of SNAP adopters. Panel A plots the share of households with positive SNAP
each of the 12 months before and after the household’s first SNAP adoption. Panel B plots the
200
spending, after 6 months of no SNAP
AP benefit in each of the 12 months before and after the first SNAP adoption.
MPC of about 0.5/0.6
hly expenditure (dollars)
150
Stefano DellaVigna Econ 219B: Applications (Lecture 11) April 8, 2020 27 / 92
00Mental Accounting Hastings and Shapiro (2018)
Food Stamps
Figure 6: Participation, benefits, and spending over the six-month SNAP clock
Panel A: SNAP use
−.02
Change in share of households using SNAP
−.08 −.06−.1 −.04
7,13,.. 8,14,.. 9,15,.. 10,16,.. 11,17,.. 12,18,..
Months following SNAP adoption
Panel B: SNAP benefits Panel C: SNAP-eligible spending
−5
−2
Change in monthly SNAP−eligible spending ($)
58
Change in monthly SNAP benefits ($)
−4
−10
−8 −6
−15
−10
−20
−12
7,13,.. 8,14,.. 9,15,.. 10,16,.. 11,17,.. 12,18,.. 7,13,.. 8,14,.. 9,15,.. 10,16,.. 11,17,.. 12,18,..
Months following SNAP adoption Months following SNAP adoption
Notes: Each figure plots coefficients from a regression of the dependent variable on a vector of indicators for the position of the current month in a monthly clock
that begins in the most recent adoption month and resets every six months or at the next SNAP adoption, whichever comes first. So, for example, the first month
Strategy 2: Identify exit from SNAP every 6 months
of the clock corresponds to months 7, 13, 19, etc. following SNAP adoption. The unit of observation for each regression is the household-month. The sample is
the set of SNAP adopters. Error bars are ±2 coefficient standard errors. Standard errors are clustered by household. Each regression includes calendar month fixed
effects. The omitted category consists of the first six months (inclusive of the adoption month) after the household’s most recent SNAP adoption, all months after
MPC of about 0.5/0.6
the first 24 months (inclusive of the adoption month) following the household’s most recent adoption, and all months for which there is no preceding adoption. In
panel A, the dependent variable is the change in an indicator for whether the household-month is a SNAP month. In panel B, the dependent variable is the change
Stefano
in monthlyDellaVigna
SNAP benefits. In Panel C, the dependentEcon
variable219B: Applications
is the change (Lecturespending.
in monthly SNAP-eligible 11) April 8, 2020 28 / 92Mental Accounting Hastings and Shapiro (2018)
Food Stamps
Figure 7: Monthly SNAP benefits and SNAP-eligible spending around benefit changes
Panel A: Administrative data for retailer states
Farm Bill ARRA
Average monthly SNAP benefit per household (dollars)
300
280
260
240
220
Jan−2008 Oct−2008 Apr−2009 Dec−2009
Panel B: Retailer data
Farm Bill ARRA
380
550
Monthly SNAP−eligible spending (dollars)
Monthly SNAP benefits (dollars)
350
520
320
490
290
460
260
430
Jan−2008 Oct−2008 Apr−2009 Dec−2009
SNAP−eligible spending SNAP benefits
Notes: Panel A plots the average monthly SNAP benefit per household between January 2008 and December 2009
Strategy 3: Identify from legislative changes in levels of benefits
from administrative data. The series was obtained by weighing the average monthly SNAP benefit per household
for each state (according to data from the United States Department of Agriculture Food and Nutrition Service via
as of May 2017) by
Stefano DellaVigna Econ
the number of retailer households with 219B: Applications
at least one SNAP month.(Lecture 11) coefficients from a regression
Panel B plots April
of 8, 2020 29 / 92Mental Accounting Hastings and Shapiro (2018)
Food Stamps
Table 1: Estimated marginal propensities to consume
(1) (2) (3) (4)
SNAP-eligible SNAP-eligible SNAP-eligible SNAP-ineligible
spending spending spending spending
MPC out of
SNAP benefits 0.5891 0.5495 0.5884 0.0230
(0.0074) (0.0360) (0.0073) (0.0043)
cash -0.0019 -0.0013 -0.0020 0.0421
(0.0494) (0.0494) (0.0494) (0.0688)
p-value for equality of MPCs 0.0000 0.0000 0.0000 0.7764
Instruments:
Change in price of regular gasoline Yes Yes Yes Yes
⇥(Household average gallons per month)
SNAP adoption Yes No Yes Yes
First month of SNAP clock No Yes Yes Yes
Number of household-months 2005392 2005392 2005392 2005392
Number of households 24456 24456 24456 24456
tes: The sample is the set of SNAP adopters. The unit of observation is the household-month. Each column reports coefficient estimates from a 2SLS regressi
h standard errors in parentheses clustered by household and calendar month using the method in Thompson (2011). All models are estimated in first differen
Estimated MPCF stable across the three strategies around 0.6
d include calendar month fixed effects. Endogenous regressors are SNAP benefits and the additive inverse of fuel spending; coefficients on these regress
reported as marginal propensities to consume. The “price of regular gasoline” is the quantity-weighted average spending per gallon on regular grade gaso
Estimated MPCF from other income skocks (gas prices) much
ong all households before any discounts or coupons. “Household average gallons per month” is the average monthly number of gallons of gasoline purchased
iven household during the panel. “SNAP adoption” is an indicator for whether the month is an adoption month as defined in section 3.5. “First month of SN
smaller
ck” is an indicator equal to one in the first month of a six-month clock that begins in the most recent adoption month. The indicator is set to zero in the first
nths (inclusive of the adoption month) following the most recent adoption, in any month after the first 24 months (inclusive of the adoption month) follow
most recent adoption,
Stefanoand in any month for which there Econ
DellaVigna is no preceding adoption. (Lecture 11)
219B: Applications April 8, 2020 30 / 92Persuasion
Section 4
Persuasion
Stefano DellaVigna Econ 219B: Applications (Lecture 11) April 8, 2020 31 / 92Persuasion
Introduction
Persuasion: Change in opinion/action beyond prediction of
Bayesian model
Persuasion: Sender attempts to convince Receiver with
words/images to take an action
Rational persuasion through Bayesian updating
Non-rational persuasion, i.e.: neglect of incentives of person
presenting information
Effect of persuasion directly on utility function
(advertising/emotions)
Compare to Social Pressure: Presence of Sender exerts pressure
to take an action
Stefano DellaVigna Econ 219B: Applications (Lecture 11) April 8, 2020 32 / 92Persuasion DellaVigna and Gentzkow (2010)
Overview on Persuasion
DellaVigna and Gentzkow (Annual Review Econ, 2010):
Persuading consumers: Marketing
Persuading voters: Political Communication
Persuading donors: Fund-raising
Persuading investors: Financial releases
First problem: How to measure when persuasion occurs?
Treatment group T, control group C, Persuasion Rate is
yT − yC 1
f = 100 ∗ ,
eT − eC 1 − y 0
ei is the share of group i receiving the message,
yi is the share of group i adopting the behavior of interest,
y0 is the share that would adopt if there were no message
Stefano DellaVigna Econ 219B: Applications (Lecture 11) April 8, 2020 33 / 92Persuasion DellaVigna and Gentzkow (2010)
TWLLE w8 PWRT W
PERSUWSION RWTES- SUMMWRY OF STU:IES
Paper Treatment Iontrol Variable t Time Treatment Iontrol Exposure Persuasion
Horizon group t T group t C rate e T -e C rate f
%w* %f* %5* %G* %D* %wM* %ww* %wf*
Persuading Ionsumers
Simester et al, %fMMG* %NE* wG clothing catalogs sent wf catalogs Share Purchasing w year AF,Gy AA,Dy wMMy> 5,fy
Yk w item FD,wy FF,Ry wMMy> F,Dy
Lertrand8 Karlan8 Mullainathan8 Mailer with female photo Mailer no photo Wpplied for loan w month D,wy R,Zy wMMy> M,Gy
Shafir8 and Zinman %fMwM* %FE* Mailer with 5,Zy interest rate Mailer F,Zy i,r, D,wy R,Zy wMMy> M,Gy
Persuading Voters
Gosnell %wDfF* Iard reminding of registration No card Registration Few days 5f,My AA,My wMM,My wA,5y
Gerber and Green %fMMM* %FE* :oorBtoB:oor GOTV Ianvassing No GOTV Turnout Few days 5G,fy 55,Ry fG,Dy wZ,Fy
GOTV Mailing of wBA Iards No GOTV 5f,Ry 5f,fy wMMy> w,My
Green8 Gerber8 :oorBtoB:oor Ianvassing No GOTV Turnout Few days Aw,My fR,Fy fD,Ay ww,Zy
and Nickerson %fMMA* %FE*
Green and Gerber %fMMw* %FE* Phone Ialls Ly Youth Vote No GOTV Turnout Few days Gw,wy FF,My GA,Gy fM,5y
Phone Ialls wRBAM YearBOlds No GOTV Turnout 5w,Fy 5M,Zy 5w,5y 5,Zy
:ellaVigna and Kaplan %fMMG* %NE* Wvailab, of Fox News Via Iable No F,N, via cable Rep, Vote Share MB5 years ZF,5y ZF,My A,Gy ww,Fy =
Enikolopov8 Petrova8 and Wvailability of independent antiB Vote Share of
Zhuravskaya %fMwM* %NE* Putin TV station %NTV* No NTV antiBPutin parties A months wG,My wM,Gy 5G,My G,Gy=
Knight and Ihiang %fMwM* %NE* Unsurprising :em, Endors, %NYT* No endors, Support for Gore Few GZ,Zy GZ,My wMM,My f,My
Surprising :em, Endors, %:enver* No endors, weeks ZZ,wy Zf,My wMM,My F,Zy
Gerber8 Karlan8 and Lergan %fMMD* Free wMBweek subscription to :em, Vote Share
%FE* Washington Post No Subscr, %stated in survey* f months FG,fy ZF,My D5,My wD,Zy=
Gentzkow %fMMF* %NE* Exposure to Television No Television Turnout wM years Z5,Zy ZF,Zy RM,My 5,5y
Gentzkow and Shapiro %fMMD* %NE* Read Local Newspaper No local paper Turnout MB5 years GM,My FD,My fZ,My wf,Dy
Stefano DellaVigna Econ 219B: Applications (Lecture 11) April 8, 2020 34 / 92Persuasion DellaVigna and Gentzkow (2010)
TABLE M. PART B
PERSUASION RATESB SUMMARY OF STUDIES
Paper Treatment Control Variable t Time Treatment Control Exposure Persuasion
Horizon group t T group t C rate e T -e C rate f
hMN h8N hDN hUN hAN hMvN hMMN hM8N
Persuading Donors
List and Lucking%Reiley Fund%raiser mailer with low seed No mailer Share M%, weeks ,*Uy vy MvvyS ,*Uy
h8vv8N hFEN Fund%raiser mailer with high seed No mailer Giving Money O*8y vy MvvyS O*8y
Landry. Lange. List. Price. Door%To%Door Fund%raising No visit Share immediate Mv*Oy vy ,f*,y 8A*Uy
and Rupp h8vvfN hFEN Campaign for University Center Giving Money
DellaVigna. List. and Malmendier Door%To%Door Fund%raising No visit Share immediate D*fy vy DM*Uy MM*vy
h8vvAN hFEN Campaign for Out%of%State Charity Giving Money
Falk h8vvUN hFEN Fund%raiser mailer with no gift No mailer Share M%, weeks M8*8y vy MvvyS M8*8y
Mailer with gift hD post%cardsN No mailer Giving Money 8v*fy vy MvvyS 8v*fy
Persuading Investors
Engelberg and Parsons h8vvAN hNEN Coverage of Earnings News No coverage Trading of Shares , days v*v8,y v*vMUy fv*vy v*vMvy
in Local Paper of Stock in News
Notes: Calculations of persuasion rates by the authors* The list of papers indicates whether the study is a natural experiment hkNEkN or a field experiment hkFEkN* Columns hAN and hMvN report the value of the behavior studied hColumn hDNN for the
Treatment and Control group* Column hMMN reports the Exposure Rate. that is. the difference between the Treatment and the Control group in the share of people exposed to the Treatment* Column hM8N computes the estimated persuasion rate f as
MvvShtT%tCNGhheT%eCNShM%tCNN* The persuasion rate denotes the share of the audience that was not previously convinced and that is convinced by the message* The studies where the exposure rate hColumn hMMN is denoted by kMvvySk are cases in
which the data on the differential exposure rate between treatment and control is not available* In these case. we assume eT%eCYMvvy. which implies that the persuasion rate is a lower bound for the actual persuasion rate* In the studies on
kPersuading Donorsk. even in cases in which an explicit control group with no mailer or no visit was not run. we assume that such a control would have yielded tCYvy. since these behaviors are very rare in absence of a fund%raiser* For studies
Persuasion rate helps reconcile seemingly very different results,
e.g. persuading voters
Stefano DellaVigna Econ 219B: Applications (Lecture 11) April 8, 2020 35 / 92Persuasion DellaVigna and Kaplan (2007)
More in Detail
More in detail: DellaVigna-Kaplan (QJE, 2007), Fox News
natural experiment
1 Fast expansion of Fox News in cable markets
October 1996: Launch of 24-hour cable channel
June 2000: 17 percent of US population listens regularly to Fox
News (Scarborough Research, 2000)
2 Geographical differentiation in expansion
Cable markets: Town-level variation in exposure to Fox News
9,256 towns with variation even within a county
3 Conservative content
Unique right-wing TV channel (Groseclose and Milyo, 2004)
Stefano DellaVigna Econ 219B: Applications (Lecture 11) April 8, 2020 36 / 92Persuasion DellaVigna and Kaplan (2007)
Empirical Results
Selection. In which towns does Fox News select? (Table 3):
FOX R, Pres R
dk,2000 = α + βvk,1996 + βContrk,1996 + Γ2000 Xk,2000 +
Γ00−90 Xk,00−90 + ΓC Ck,2000 + εk .
Controls X
Cable controls (Number of channels and potential subscribers)
US House district or county fixed effects
Conditional on X , Fox News availability is orthogonal to
political variables
demographic variables
Stefano DellaVigna Econ 219B: Applications (Lecture 11) April 8, 2020 37 / 92Persuasion DellaVigna and Kaplan (2007) Fox News Availability Stefano DellaVigna Econ 219B: Applications (Lecture 11) April 8, 2020 38 / 92
Persuasion DellaVigna and Kaplan (2007)
Baseline effect – Presidential races
Effect on Presidential Republican vote share (Table 4):
R, Pres R, Pres FOX
vk,2000 − vk,1996 = α + βF dk,2000 + Γ2000 Xk,2000 +
Γ00−90 Xk,00−90 + ΓC Ck,2000 + εk .
Results:
Significant effect of Fox News with district (Column 3) and
county fixed effects (Column 4)
.4-.7 percentage point effect on Republican vote share in Pres.
elections
Similar effect on Senate elections
Effect is on ideology, not
person-specific
Effect on turnout
Stefano DellaVigna Econ 219B: Applications (Lecture 11) April 8, 2020 39 / 92Persuasion DellaVigna and Kaplan (2007) Presidential Vote Share Stefano DellaVigna Econ 219B: Applications (Lecture 11) April 8, 2020 40 / 92
Persuasion DellaVigna and Kaplan (2007)
Generalizing the Effect
Magnitude of effect: How do we generalize beyond Fox News?
Estimate audience of Fox News in towns that have Fox News via
cable (First stage)
Use Scarborough micro data on audience with Zip code of
respondent
Fox News exposure via cable increases regular audience by 6 to
10 percentage points
How many people did Fox News convince?
Heuristic answer: Divide effect on voting (.4-.6 percentage
point) by audience measure (.6 to .10)
Result: Fox News convinced 3 to 8 percent of audience (Recall
measure) or 11 to 28 percent (Diary measure)
Stefano DellaVigna Econ 219B: Applications (Lecture 11) April 8, 2020 41 / 92Persuasion DellaVigna and Kaplan (2007)
Interpretation
How do we interpret the results?
Benchmark model:
1 New media source with unknown bias β, with β ∼ N β0 , γ1β
2 Media observes
(differential) quality of Republican politician,
1
θt ∼ N 0, γθ , i.i.d., in periods 1, 2, . . . , T
3 Media broadcast: ψt = θt + β. Positive β implies
pro-Republican media bias
4 Voting in period T . Voters vote Republican if θbT + α > 0,
with α ideological preference
Stefano DellaVigna Econ 219B: Applications (Lecture 11) April 8, 2020 42 / 92Persuasion DellaVigna and Kaplan (2007)
Signal extraction problem. New media (Fox News) says
Republican politician (George W. Bush) is great
Is Bush great?
Or is Fox News pro-Republican?
A bit of both, the audience thinks. Updated media bias after T
periods:
γβ β0 + T γθ ψ̄T
β̂T = .
γβ + T γθ
Estimated quality of Republican politician:
h i h i
γθ ∗ 0 + W ψT − β̂T W ψT − β̂T
θ̂T = =
γθ + W γθ + W
Stefano DellaVigna Econ 219B: Applications (Lecture 11) April 8, 2020 43 / 92Persuasion DellaVigna and Kaplan (2007)
Persuasion. Voter with persuasion λ (0 ≤ λ ≤ 1) does not take
into account enough media bias:
W λ [ψT − (1 − λ) β̂T ]
θ̂Tλ =
γθ + W λ
Vote share for Republican candidate.
P(α + θbTλ ≥ 0) = 1 − F (−θbTλ )
Proposition 1. Three results:
1 Short-Run I: Republican media bias increases Republican vote
λ )]/∂β > 0.
share: ∂[1 − F (−θbT
2 Short-Run II: Media bias effect higher if persuasion (λ > 0).
3 Long-run (T → ∞). Media bias effect ⇐⇒ persuasion λ > 0.
Stefano DellaVigna Econ 219B: Applications (Lecture 11) April 8, 2020 44 / 92Persuasion Cain, Loewenstein, and Moore (2005)
Evidence for Persuasion Bias
Cain-Loewenstein-Moore (JLegalStudies, 2005).
Psychology Experiment
Pay subjects for precision of estimates of number of coins in a
jar
Have to rely on the advice of second group of subjects: advisors
(Advisors inspect jar from close)
Two experimental treatments:
Aligned incentives. Advisors paid for closeness of subjects’ guess
Mis-Aligned incentives, Common knowledge. Advisors paid for
how high the subjects’ guess is. Incentive common-knowledge
(Mis-Aligned incentives, Not Common knowledge.)
Stefano DellaVigna Econ 219B: Applications (Lecture 11) April 8, 2020 45 / 92Persuasion Cain, Loewenstein, and Moore (2005) Payoffs Stefano DellaVigna Econ 219B: Applications (Lecture 11) April 8, 2020 46 / 92
Persuasion Cain, Loewenstein, and Moore (2005) Result 1 Advisors increase estimate in Mis-Aligned incentives treatment — Even more so when common knowledge Stefano DellaVigna Econ 219B: Applications (Lecture 11) April 8, 2020 47 / 92
Persuasion Cain, Loewenstein, and Moore (2005) Result 2 Estimate of subjects is higher in Treatment with Mis-Aligned incentives Subjects do not take sufficiently into account incentives of information provider Effect even stronger when incentives are known Advisors feel free(er) to increase estimate Applications to many settings Stefano DellaVigna Econ 219B: Applications (Lecture 11) April 8, 2020 48 / 92
Persuasion Malmendier and Shanthikumar (2007)
Application: Small Investors
Application 1: Malmendier-Shanthikumar (JFE, 2007).
Field evidence that small investors suffer from similar bias
Examine recommendations by analysts to investors
Substantial upward distortion in recommendations (Buy=Sell,
Hold=Sell, etc)
Higher distortion for analysis working in Inv. Bank affiliated with
company they cover (through IPO/SEO)
Stefano DellaVigna Econ 219B: Applications (Lecture 11) April 8, 2020 49 / 92Persuasion Malmendier and Shanthikumar (2007)
Question
Question: Do investors discount this bias?
Analyze Trade Imbalance (essentially, whether trade is initiated
by Buyer)
Assume that
large investors do large trades
small investors do small trades
See how small and large investors respond to recommendations
Examine separately for affiliated and unaffiliated analysts
Stefano DellaVigna Econ 219B: Applications (Lecture 11) April 8, 2020 50 / 92Persuasion Malmendier and Shanthikumar (2007) Analyst Recommendations Stefano DellaVigna Econ 219B: Applications (Lecture 11) April 8, 2020 51 / 92
Persuasion Malmendier and Shanthikumar (2007)
Results
Results:
Small investor takes analyst recommendations literally (buy
Buys, sell Sells)
Large investors discount for bias (hold Buys, sell Holds)
Difference is particularly large for affiliated analysts
Small investors do not respond to affiliation information
Strong evidence of distortion induced by incentives
Stefano DellaVigna Econ 219B: Applications (Lecture 11) April 8, 2020 52 / 92Emotions: Mood
Section 5
Emotions: Mood
Stefano DellaVigna Econ 219B: Applications (Lecture 11) April 8, 2020 53 / 92Emotions: Mood
Emotions Matter
Emotions play a role in several of the phenomena considered so
far:
Self-control problems Temptation
Projection bias in food consumption Hunger
Social preferences in giving
Empathy
Gneezy-List (2006) transient effect of gift Hot-Cold
gift-exchange
Psychology: Large literature on emotions (Loewenstein and
Lerner, 2003)
Message 1: Emotions are very important
Message 1: Different emotions operate very differently: anger 6=
mood 6= joy
Stefano DellaVigna Econ 219B: Applications (Lecture 11) April 8, 2020 54 / 92Emotions: Mood
Consider two examples of emotions:
Mood
Arousal
Psychology: even minor mood manipulations have a substantial
impact on behavior and emotions
On sunnier days, subjects tip more at restaurants (Rind, 1996)
On sunnier days, subjects express higher levels of overall
happiness (Schwarz and Clore, 1983)
Should this impact economic decisions?
Stefano DellaVigna Econ 219B: Applications (Lecture 11) April 8, 2020 55 / 92Emotions: Mood Hirshleifer and Shumway (2003)
Field Evidence
Field: Impact of mood fluctuations on stock returns:
Daily weather and Sport matches
No effect on fundamentals
However: If good mood leads to more optimistic expectations
Increase in stock prices
Evidence:
Saunders (1993): Days with higher cloud cover in New York
are associated with lower aggregate US stock returns
Hirshleifer and Shumway (2003) extend to 26 countries
between 1982 and 1997
Use weather of the city where the stock market is located
Negative relationship between cloud cover (de-trended from
seasonal averages) and aggregate stock returns in 18 of the 26
cities
Stefano DellaVigna Econ 219B: Applications (Lecture 11) April 8, 2020 56 / 92Emotions: Mood Hirshleifer and Shumway (2003) Weather and Stock Returns Stefano DellaVigna Econ 219B: Applications (Lecture 11) April 8, 2020 57 / 92
Emotions: Mood Hirshleifer and Shumway (2003)
Weather and Stock Returns
Magnitude:
Days with completely covered skies have daily stock returns .11
percent lower than days with sunny skies
Five percent of a standard deviation
Small magnitude, but not negligible
After controlling for cloud cover, other weather variables such as
rain and snow are unrelated to returns
Edmans-Garcia-Norli, 2007: Evidence from international
soccer matches (39 countries, 1973-2004)
Interpretations:
Mood impacts risk aversion or perception of volatility
Mood is projected to economic fundamentals
Stefano DellaVigna Econ 219B: Applications (Lecture 11) April 8, 2020 58 / 92Emotions: Mood Simonsohn (2007)
College Enrollment
Simonsohn (2007): Subtle role of mood
Weather on the day of campus visit to a prestigious university
(CMU)
Students visiting on days with more cloud cover are significantly
more likely to enroll
Higher cloud cover induces the students to focus more on
academic attributes versus social attributes of the school
Support from laboratory experiment
Stefano DellaVigna Econ 219B: Applications (Lecture 11) April 8, 2020 59 / 92Emotions: Arousal
Section 6
Emotions: Arousal
Stefano DellaVigna Econ 219B: Applications (Lecture 11) April 8, 2020 60 / 92Emotions: Arousal
Separate impact of emotions: Arousal
Josephson (1987): Arousal due to violent content
Control group exposed to non-violent clip
Treatment group exposed to violent clip
Treatment group more likely to display more aggressive
behavior, such as aggressive play during a hockey game
Impact not due to imitation (violent movie did not involve sport
scenes)
Consistent finding from large set of experiments (Table 11)
Dahl-DellaVigna (2009): Field evidence — Exploit timing of
release of blockbuster violent movies
Stefano DellaVigna Econ 219B: Applications (Lecture 11) April 8, 2020 61 / 92Emotions: Arousal Dahl and DellaVigna (2009)
Model
Consumer chooses between strongly violent movie av , mildly
violent movie am , non-violent movie an , or alternative social
activity as
Utility depends on quality of movies Demand functions P(a ) j
Heterogeneity:
High taste for violence (Young): Ny consumers
Low taste for violence (Old): No consumers
Aggregate demand for group i: Ni P(aij )
Production function of violence V (not part of utility fct.)
depends on av , am , an , and as :
X X j
ln V = [ αi Ni P(aij )+σi Ni (1−P(aiv )−P(aim )−P(ain ))]
i=y ,o j=v ,m,n
Stefano DellaVigna Econ 219B: Applications (Lecture 11) April 8, 2020 62 / 92Emotions: Arousal Dahl and DellaVigna (2009)
Estimate (Aj is total attendance to movie of type j)
ln V = β0 + β v Av + β m Am + β n An + ε
Estimated impact of exposure to violent movies β v :
β v = x v (αyv − σy ) + (1 − x v )(αov − σo )
First point — Estimate of net effect
Direct effect: Increase in violent movie exposure α v
Indirect effect: Decrease in Social Activity σi i
Second point — Estimate on self-selected population:
Estimate parameters for group actually attending movies
Young over-represented: x v > N y / (N y + N o )
Stefano DellaVigna Econ 219B: Applications (Lecture 11) April 8, 2020 63 / 92Emotions: Arousal Dahl and DellaVigna (2009)
Comparison with Psychology experiments
Natural Experiment. Estimated impact of exposure to violent
movies β v :
β v = x v (αyv − σy ) + (1 − x v )(αov − σo )
Psychology Experiments. Manipulate a directly, holding
constant as out of equilibrium
v Ny Ny
βlab = αyv + (1 − )αv
Ny + No Ny + No o
Two differences:
‘Shut down’ alternative activity, and hence σi does not appear
Weights representative of (student) population, not of
population that selects into violent movies
Stefano DellaVigna Econ 219B: Applications (Lecture 11) April 8, 2020 64 / 92Emotions: Arousal Dahl and DellaVigna (2009)
Data
Movie data
Revenue data: Weekend (top 50) and Day (top 10) from The
Numbers
Violence Ratings from 0 to 10 from Kids In Mind (Appendix
Table 1)
Strong Violence Measure Avt : Audience with violence 8-10
(Figure 1a)
Mild Violence Measure Amt : Audience with violence 5-7 (Figure
1b)
Assault data
Source: National Incident-Based Reporting System (NIBRS)
All incidents of aggravated assault, simple assault, and
intimidation from 1995 to 2004
Sample: Agencies with no missing data on crime for > 7 days
Sample: 1995-2004, days in weekend (Friday, Saturday, Sunday)
Stefano DellaVigna Econ 219B: Applications (Lecture 11) April 8, 2020 65 / 92Emotions: Arousal Dahl and DellaVigna (2009) Movie Attendance Stefano DellaVigna Econ 219B: Applications (Lecture 11) April 8, 2020 66 / 92
Emotions: Arousal Dahl and DellaVigna (2009) Log Assault Residuals Stefano DellaVigna Econ 219B: Applications (Lecture 11) April 8, 2020 67 / 92
Emotions: Arousal Dahl and DellaVigna (2009)
Regression and Results
Regression Specification. (Table 3)
log Vt = β v Avt + β m Am n n
t + β At + ΓXt + εt
Coefficient β v is percent increase in assault for one million
people watching strongly violent movies day t (Avt ) (Similarly
β m and β n )
Cluster standard errors by week
Results.
No effect of movie exposure in morning or afternoon (Columns
1-2)
Negative effect in the evening (Column 3)
Stronger negative effect the night after (Column 4)
Stefano DellaVigna Econ 219B: Applications (Lecture 11) April 8, 2020 68 / 92Emotions: Arousal Dahl and DellaVigna (2009) Stefano DellaVigna Econ 219B: Applications (Lecture 11) April 8, 2020 69 / 92
Emotions: Arousal Dahl and DellaVigna (2009)
Summary of Findings
1 Violent movies lower same-day violent crime in the evening
(incapacitation)
2 Violent movies lower violent crime in the night after exposure
(less consumption of alcohol in bars)
No lagged effect of exposure in weeks following movie
3
attendance No intertemporal substitution
4 Strongly violent movies have slightly smaller impact compared to
mildly violent movies in the night after exposure
Interpret Finding 4 in light of Lab-Field debate
Stefano DellaVigna Econ 219B: Applications (Lecture 11) April 8, 2020 70 / 92Emotions: Arousal Dahl and DellaVigna (2009)
Interpretation
Finding 4. Non-monotonicity in Violent Content
Night hours: β̂ v = −0.0192 versus β̂ m = −0.0205
Odd if more violent movies attract more potential criminals
Model above Can estimate direct effect of violent movies if
can control for selection
xv − xn
v v n
α −α=β − β + m (βm − βn )
x − xn
Do not observe selection of criminals x j , but observe selection
of correlated demographics (young males)
Stefano DellaVigna Econ 219B: Applications (Lecture 11) April 8, 2020 71 / 92Emotions: Arousal Dahl and DellaVigna (2009)
IMDB ratings data — Share of young males among raters
increases with movie violence (Figure 2) Use as estimate of
xj
Compute α\ v − α = .011 (p = .08), about one third of total
effect
Pattern consistent with arousal induced by strongly violent
movies (αv > αm )
Bottom-line 1: Can reconcile with laboratory estimates
Bottom-line 2: Can provide benchmark for size of arousal effect
Stefano DellaVigna Econ 219B: Applications (Lecture 11) April 8, 2020 72 / 92Emotions: Arousal Dahl and DellaVigna (2009) Share of Young Males vs. Movie Violence Stefano DellaVigna Econ 219B: Applications (Lecture 11) April 8, 2020 73 / 92
Emotions: Arousal Dahl and DellaVigna (2009) The Arousal Effect Stefano DellaVigna Econ 219B: Applications (Lecture 11) April 8, 2020 74 / 92
Emotions: Arousal Dahl and DellaVigna (2009)
Lab vs. Field
Differences from laboratory evidence (Levitt-List, 2007):
Exposure to violent movies is
Less dangerous than alternative activity (αv < σ)
(Natural Experiment)
More dangerous than non-violent movies (αv > αn )
(Laboratory Experiments and indirect evidence above)
Both types of evidence are valid for different policy evaluations
Laboratory: Banning exposure to unexpected violence
Field: Banning temporarily violent movies
Stefano DellaVigna Econ 219B: Applications (Lecture 11) April 8, 2020 75 / 92Happiness
Section 7
Happiness
Stefano DellaVigna Econ 219B: Applications (Lecture 11) April 8, 2020 76 / 92Happiness
Measuring Utility Through Happiness
Is there a more direct way to measure utility?
What about happiness questions?
‘Taken all together, how would you say things are these days,
would you say that you are very happy, pretty happy, or not too
happy?’
or ‘How satisfied are you with your life as a whole?’
Response on 1 to 7 or 0 to 10 scale
Could average response measure utility?
Stefano DellaVigna Econ 219B: Applications (Lecture 11) April 8, 2020 77 / 92Happiness
There are a number of issues:
1 (Noise I) Is the measure of happiness just noise?
2 (Noise II) Even if valid, there are no incentives, how affected is
it by irrelevant cues?
3 (Scale) Happiness is measured on discrete intervals, with ceiling
and floor effect
4 (Content) What exactly does the measure capture?
Instantaneous utility? Discounted utility?
Revealed preference approach remains heavily favored by
economists (myself included)
Still, significant progress in last 10-15 years on taking some role
in economics
Stefano DellaVigna Econ 219B: Applications (Lecture 11) April 8, 2020 78 / 92Happiness Oreopoulos (2006)
Issue 1: Noise I
Issue 1 (Noise I). To address,
Take happiness measure h
Does it responds to well-identified, important shifters X which
affect important economic outcomes?
Oreopoulos (AER 2006). Exploit binding compulsory
schooling laws to study returns to education
Stefano DellaVigna Econ 219B: Applications (Lecture 11) April 8, 2020 79 / 92Happiness Oreopoulos (2006)
UK: 1947 increase in minimum schooling from 14 to 15
Stefano DellaVigna Econ 219B: Applications (Lecture 11) April 8, 2020 80 / 92Happiness Oreopoulos (2006)
Northern Ireland: 1957 increase from 14 to 15
Stefano DellaVigna Econ 219B: Applications (Lecture 11) April 8, 2020 81 / 92Happiness Oreopoulos (2006)
Clear impact on earnings: compare earnings for adults aged 32-64 as
a function of year of birth
Stefano DellaVigna Econ 219B: Applications (Lecture 11) April 8, 2020 82 / 92Happiness Oreopoulos (2006)
Implied returns to compulsory education: 0.148 (0.046)
Stefano DellaVigna Econ 219B: Applications (Lecture 11) April 8, 2020 83 / 92Happiness Oreopoulos (2007)
Did this affect happiness measures?
Oreopoulos (JPubE 2007)
Eurobarometer Surveys in UK and N. Ireland, 1973-1998
Question on 1-4 scale
Stefano DellaVigna Econ 219B: Applications (Lecture 11) April 8, 2020 84 / 92Happiness Oreopoulos (2007) Results One year of additional (compulsory) education increases happiness somewhere between 2 and 8 percent In addition, large effects on health and wealth Reinforces puzzle: Why don’t people stay in school longer? Happiness response captures real information Happiness answer also responds to cues (Issue 2), has scale effects (Issue 3), but valid enough to use in combination with other measures However, Issue 4: How would we use happiness measure as part of economic research? Stefano DellaVigna Econ 219B: Applications (Lecture 11) April 8, 2020 85 / 92
Happiness Benjamin et al. (various)
Happiness in Economic Research
Research agenda by Dan Benjamin, Ori Heffetz, Miles Kimball,
Alex Rees-Jones
Study Econ101a-type simple issues with happiness measures
Critical to know how to correctly interpret these measures
Paper 1. Benjamin, Heffetz, Kimball, and Rees-Jones
(AER 2012)
How does happiness (subjective well-being) relate to choice?
Compare forecasted happiness with choice in several
hypothetical scenarios
Forecasts of happiness predict choice quite well, but other
factors also play a role
Stefano DellaVigna Econ 219B: Applications (Lecture 11) April 8, 2020 86 / 92Happiness Benjamin et al. (various)
Paper 2. Benjamin et al. (AER 2014)
Medical students choosing match for residency
Survey to elicit ranking of medical schools for residency + Ask
anticipated happiness
How well does happiness predict choice relative to other factors?
Stefano DellaVigna Econ 219B: Applications (Lecture 11) April 8, 2020 87 / 92Happiness Benjamin et al. (various) Some evidence that one can also elicit intertemporal happiness Stefano DellaVigna Econ 219B: Applications (Lecture 11) April 8, 2020 88 / 92
Happiness Uses of Happiness Data
Uses of Happiness Data
Di Tella, MacCulloch, and Oswald (AER 2001):
How much do people dislike inflation versus unemployment?
Life satisfaction from Euro-Barometer: “On the whole, are you
very satisfied, fairly satisfied, not very satisfied, or not at all
satisfied with the life you lead?”
Aggregate to country-year and estimate panel regression
Stefano DellaVigna Econ 219B: Applications (Lecture 11) April 8, 2020 89 / 92Happiness Other Work
Other Important Work on Happiness
Luttmer (QJE 2005): Documents relative aspect of happiness:
An increase in income of neighbors (appropriately instrumented)
lower life satisfaction
Stevenson and Wolfers (Brookings 2008):
Debunks Easterlin paradox (income growth over time does not
increase happiness)
Clear link over time between log income and happiness
Finkelstein, Luttmer, Notowidigdo (JEEA 2014):
How does marginal utility of consumption vary with health?
Needed for optimal policies
Observe changes in happiness for varying health
Stefano DellaVigna Econ 219B: Applications (Lecture 11) April 8, 2020 90 / 92Next Lecture
Section 8
Next Lecture
Stefano DellaVigna Econ 219B: Applications (Lecture 11) April 8, 2020 91 / 92Next Lecture
Next Lecture
Market Response to Biases
Employees: Behavioral Labor
Investors: Behavioral Finance
Voters: Behavioral Political Economy
Stefano DellaVigna Econ 219B: Applications (Lecture 11) April 8, 2020 92 / 92You can also read
