topic12: behavioralbiases0 5000 10000 15000 175 200 225 250 275 300 325 (dkr) 1999 income (dkr...
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Topic 12: Behavioral Biases
Nathaniel Hendren
Harvard
Spring, 2020
Nathaniel Hendren (Harvard) Behavioral Bias Spring, 2020 1 / 76
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General Model of Bias
I like rational models :-)They provide a starting point for understanding behavior and formingnormative opinions about policies
But, there is evidence in a wide range of settings that behavior is notwell-described by the canonical rational model
This lecture: consider implications of violations of rational/canonicalmodel
Then present evidence in several settings:Take Up of Benefits and EITCInertia in Health InsuranceUnemployment and Job SearchSavingsSlutsky symmetry and consideration sets
Nathaniel Hendren (Harvard) Behavioral Bias Spring, 2020 2 / 76
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1 General Model of Bias: Information versus Understanding
2 Imperfect Take Up of Benefits: The Case of EITC
3 Inertia in Health Insurance
4 Unemployment and Job Search
5 Savings
6 Unsolicited Thoughts
Nathaniel Hendren (Harvard) Behavioral Bias Spring, 2020 2 / 76
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General Model of BiasAt the outset, I think it’s important to discuss bias vs. imperfectinformation – what do we mean by “behavioral bias” or “rational”?
My view: it relates to if/how we can invoke the envelope theoremSuppose individuals make choices a ∈ Ω (p), where Ω (p) is somechoice set that depends on some vector of policies, p
a can be labor supply, savings, consumption, etc.p can be taxes, the ease-of-use of the Obamacare website, thefrequency and use of IRS EITC eligibility notices, 401K default optionsettings, etc.
Results in “experienced utility”, v (a)
Individuals make decisions to maximize potentially different utilityfunction, u (a)
U (p) = maxa∈Ω(p)
u (a) = u (a∗ (p))
where a∗ (p) is the set of choices the individual makes under policy p.Nathaniel Hendren (Harvard) Behavioral Bias Spring, 2020 3 / 76
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General Model of Bias
Consider marginal policy change, “dp”, that changes behavior, da∗dp .
Do we care?
Envelope theorem: Welfare impact only depends on how dp affectsconstraint set, Ω, weighted by marginal utilities, ua (formally:U ′ (p) = ∂PΩ∇au)
If increases budget by $1, then policy is valued at $1Irrespective of whether the policy causes a change in behavior, da∗
dp !
Nathaniel Hendren (Harvard) Behavioral Bias Spring, 2020 4 / 76
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“Violation” of the Envelope TheoremWhen people are not maximizing their experienced utility, behavioralresponses can have first order welfare impactsWrite experienced utility as
V (p) = v (a∗ (p)) = U (p) + v (a∗ (p))− u (a∗ (p))︸ ︷︷ ︸Behavioral Bias
so thatV ′ (p) = U ′ (p)︸ ︷︷ ︸
Std Welfare
+da∗dp [va − ua]︸ ︷︷ ︸
Improved Choices
Additional welfare impact if the policy causes people to make better(or worse) decisions
Increasing a increases welfare if people’s decisions under-value theirexperienced utility, va > uaAnd vice-versa if va < ua
Like an externality with marginal damage valued at va − ua:“Internality”
Nathaniel Hendren (Harvard) Behavioral Bias Spring, 2020 5 / 76
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Divergences of Decision and Experienced Utility
Why might experienced and decision utility diverge?
Inherent biasesPresent biasednessDifficulty with probability inference
Cognitive constraints
Lack of knowledge (Statistical decision theory analogue)
Lack of understanding of how actions today affect outcomes in future
Nathaniel Hendren (Harvard) Behavioral Bias Spring, 2020 6 / 76
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1 General Model of Bias: Information versus Understanding
2 Imperfect Take Up of Benefits: The Case of EITC
3 Inertia in Health Insurance
4 Unemployment and Job Search
5 Savings
6 Unsolicited Thoughts
Nathaniel Hendren (Harvard) Behavioral Bias Spring, 2020 6 / 76
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Imperfect Take-Up of Benefits
Large literature documenting how people do not take up benefits thatthey are seemingly eligible for
e.g. Deshpande paper for DIHere: focus on two studies analyzing the EITC
Information treatment: Bhargava and Manoli (2015, AER)Geographic variation in take-up: Chetty, Friedman, Saez (2013)
Nathaniel Hendren (Harvard) Behavioral Bias Spring, 2020 7 / 76
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Bhargava and Manoli (2015, AER)
Study imperfect take up of EITC benefitsRoughly 25% of benefits are unclaimedAverage of $1K per person (roughly 1 month of earnings...)
Two models of low take up:1 Confusion and lack of understanding2 Stigma
In model 1, increasing take up improves welfare,“ua < va” as choosing to take up benefits increases utility
In model 2, increasing take up is pure social waste because ofenvelope theorem
ua = va as individuals were indifferent to taking up benefits because ofthe social stigma cost
Nathaniel Hendren (Harvard) Behavioral Bias Spring, 2020 8 / 76
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Bhargava and Manoli (2015, AER)
To distinguish these theories, paper conducts randomized experimentwith the IRS to increase knowledge of benefitsSend mailers to all CA taxpayers who failed to claim 2009 EITC creditdespite presumed eligibility given information on their return
Provided information about EITC and offered opportunity to re-fileInformed people of roughly $26M in unclaimed benefits
Roughly $4M was paid as a result of the experimentExperimental conditions included:
Simple and Complex NoticesVariation in potential benefit advertisingStigma: include wording saying that money is from the result of hardwork
Nathaniel Hendren (Harvard) Behavioral Bias Spring, 2020 9 / 76
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Simple and Complex Notices
Nathaniel Hendren (Harvard) Behavioral Bias Spring, 2020 10 / 76
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High and Low Benefit Treatments
Nathaniel Hendren (Harvard) Behavioral Bias Spring, 2020 11 / 76
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RCT Results
Nathaniel Hendren (Harvard) Behavioral Bias Spring, 2020 12 / 76
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RCT Results
Nathaniel Hendren (Harvard) Behavioral Bias Spring, 2020 13 / 76
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Bhargava and Manoli (2015, AER)
Results suggest:Imperfect information about benefits affects take upDisplaying potential benefits increases take upComplicated forms reduce take upIncreases take up at all eligible income levels
Does this suggest that increasing take up increases recipient welfare?
Nathaniel Hendren (Harvard) Behavioral Bias Spring, 2020 14 / 76
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Chetty, Friedman, Saez (2013)
Previous literature documents bunching of EITC recipients at therevenue-maximizing kink point (Saez 2010)
Chetty Friedman and Saez (2013) study bunching of EITC claimantsat the refund-maximizing kink point
Here: borrow slides discussing this paper from Chetty (2015, AER)
Ely Lecture: “Behavioral Economics and Public Policy: A PragmaticPerspective”
Nathaniel Hendren (Harvard) Behavioral Bias Spring, 2020 15 / 76
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Taxable Income Distribution for EITC Claimants in Texas P
erc
ent
of Tax F
ilers
2%
3%
4%
5%
1%
0%
$2,600 $12,600 $22,600 $32,600
Taxable Income
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Taxable Income Distribution for EITC Claimants in Texas P
erc
ent
of Tax F
ilers
2%
3%
4%
5%
1%
0%
Taxable Income
Sharp “bunching” at refund-maximizing point [Saez 2010]
$2,600 $12,600 $22,600 $32,600
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Taxable Income Distribution for EITC Claimants in Kansas P
erc
ent
of Tax F
ilers
2%
3%
4%
5%
1%
0%
Taxable Income
$2,600 $12,600 $22,600 $32,600
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Fraction of Tax Filers Who Report Income that Maximizes EITC Refund
in 1996
Note: Darker Color = More EITC Sharp Bunching
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Fraction of Tax Filers Who Report Income that Maximizes EITC Refund
in 1999
Note: Darker Color = More EITC Sharp Bunching
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Fraction of Tax Filers Who Report Income that Maximizes EITC Refund
in 2002
Note: Darker Color = More EITC Sharp Bunching
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Fraction of Tax Filers Who Report Income that Maximizes EITC Refund
in 2005
Note: Darker Color = More EITC Sharp Bunching
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Fraction of Tax Filers Who Report Income that Maximizes EITC Refund
in 2008
Note: Darker Color = More EITC Sharp Bunching
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Why does impact of EITC on income vary so much across areas?
Plausible behavioral model: differences in knowledge about EITC
To test this explanation, consider individuals who move
Knowledge model predicts asymmetric impact of moving:
Moving to a higher-bunching area should raise EITC refund
Moving to a lower-bunching area should not affect EITC refund
Differences in Knowledge about the EITC?
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40
60
80
100
120
Change in ZIP-3 Sharp Bunching Rate Among Prior Residents
Change in E
ITC
Refu
nd f
or
Movers
($)
-1% -0.5% 0% 0.5% 1%
p-value for diff. in slopes: p < 0.0001
β = 59.7
(5.7)
β = 6.0
(6.2)
Effects of Moving to Higher vs. Lower Bunching Areas on EITC Refund Amounts
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Chetty, Friedman, and Saez (2013): Clear Bunching
Paper documents clear evidence of heterogeneous bunching acrossareas
Driven mainly by self-employed (Saez 2010)
Easy to manipulate income
Paper goes on to exploit bunching variation to ask a much deeper(more difficult) question:
How does EITC affect real labor supply?
Nathaniel Hendren (Harvard) Behavioral Bias Spring, 2020 26 / 76
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0
.5
1
1.5
2
2.5
3
3.5
Perc
ent
of W
age-E
arn
ers
1k
2k
3k
4k
EIT
C A
mount ($
)
0k
Income Distribution For Single Wage Earners with One Child
W-2 Wage Earnings
Is the EITC having
an effect on this
distribution?
$0 $10K $20K $30K
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Lowest Information Decile Highest Information Decile
W-2 Wage Earnings
0
.5
1
1.5
2
2.5
3
3.5
1k
2k
3k
4k
0k
$0 $10K $20K $30K $25K $35K $15K $5K
Income Distribution For Single Wage Earners with One Child
High vs. Low Sharp Bunching Areas P
erc
ent
of W
age-E
arn
ers
EIT
C A
mount ($
)
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Comparisons across areas could be biased by omitted variables
Study changes in earnings around childbirth to address this concern
Individuals without children are essentially ineligible for the EITC
Birth of a child generates sharp variation in marginal incentives
Child Birth Research Design
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Earnings Distribution in the Year Before First Child Birth for Wage Earners
2%
4%
0%
6%
$0 $30K $40K $10K $20K
Lowest Information Decile Highest Information Decile
W-2 Wage Earnings
Perc
ent
of In
div
iduals
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Earnings Distribution in the Year of First Child Birth for Wage Earners P
erc
ent
of In
div
iduals
2%
4%
0%
6%
$0 $30K $40K $10K $20K
Lowest Information Decile Highest Information Decile
W-2 Wage Earnings
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Summary
Paper goes on to document that EITC primarily increases earnings inthe phase-in region as opposed to reductions in phase-out region
Suggests EITC increases labor supply and real earningsWelfare implications?
Depends on whether we think it is good to increase labor supply...Externalities?Or does the envelope theorem apply?
Nathaniel Hendren (Harvard) Behavioral Bias Spring, 2020 32 / 76
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1 General Model of Bias: Information versus Understanding
2 Imperfect Take Up of Benefits: The Case of EITC
3 Inertia in Health Insurance
4 Unemployment and Job Search
5 Savings
6 Unsolicited Thoughts
Nathaniel Hendren (Harvard) Behavioral Bias Spring, 2020 32 / 76
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Health Insurance: Dominated Plan Choices
Evidence people also make “sub-optimal” choices in health insurancecontexts
Plans are often difficult to understandBut, not clear privately inefficient choices lead to socially inefficientoutcomesHandel (2013, AER): “Adverse Selection and Inertia in HealthInsurance Markets: When Nudging Hurts”Studies choice of two PPO contractsIn year 0, tradeoff between greater coverage and price
PPO500 is better if have high expensesIn year 1, PPO500 completely dominates PPO250
Nathaniel Hendren (Harvard) Behavioral Bias Spring, 2020 33 / 76
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Inertia: Handel (2013)
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Inertia: Handel (2013)
Nathaniel Hendren (Harvard) Behavioral Bias Spring, 2020 35 / 76
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How Many People Switched?
Nathaniel Hendren (Harvard) Behavioral Bias Spring, 2020 36 / 76
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Health Insurance: Dominated Plan Choices
Everyone has the option to switch to PPO250But, only 11% of those who chose PPO500 in year 0 switch toPPO25089% remain in dominated plan!Leave at least $374 per family on the tableThose who switched would have left more money on the table ($453)
Some evidence of rationalityIs this inertia bad?
Significant evidence that PPO 250 had much higher cost enrolleesThis was why they increased the price...
Inertia kept many healthy people enrolled in the more generous 250deductible plan
Lowers prices of the more generous policy
Nathaniel Hendren (Harvard) Behavioral Bias Spring, 2020 37 / 76
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Costs Went Up for PPO250
Nathaniel Hendren (Harvard) Behavioral Bias Spring, 2020 38 / 76
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Handel (2013): Nudging versus Adverse Selection
Develops model with inertia (switching costs) to explain why only11% switched
Uses model to study impact of reducing inertia
Results suggest adverse selection would increase
Would overall reduce welfare despite improving individual choices
Nathaniel Hendren (Harvard) Behavioral Bias Spring, 2020 39 / 76
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1 General Model of Bias: Information versus Understanding
2 Imperfect Take Up of Benefits: The Case of EITC
3 Inertia in Health Insurance
4 Unemployment and Job Search
5 Savings
6 Unsolicited Thoughts
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Behavioral Bias and Unemployment
Large literature documenting behavioral anomalies in job search andunemployment contexts
Discuss two papers here:
1 Spinnewijn (2015): “Unemployed but Optimistic: Optimal InsuranceDesign with Biased Beliefs”
2 Della Vigna et al. (2016): “Reference-Dependent Job Search:Evidence from Hungary”
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Spinnewijn (2015): Unemployment Duration Expectations
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Spinnewijn (2015)
On average, beliefs are 6.8 weeks less than actual experience
Implications of biased beliefs:People may under-search?Under-save?Deplete savings too quickly during unemployment?
Explain why consumption drops at benefit exhaustion in Ganong andNoel (2016)?
Optimal policy implications:Increase benefits during unemployment? Why?
Nathaniel Hendren (Harvard) Behavioral Bias Spring, 2020 42 / 76
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Della Vigna et al. (2016)
Provide evidence of reference-dependent job search
Follow model of Koszegi and Rabin (2006) with loss aversion:
u (c |r ) = v (c)+ ηgain1 c ≥ r [v (c)− v (r )]+ ηloss1 c < r [v (c)− v (r )]−ψ (e)
where e is search effort and
r =1N
t−1
∑k=t−N
yk
is the average income in the past N periods
Nathaniel Hendren (Harvard) Behavioral Bias Spring, 2020 43 / 76
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Della Vigna et al. (2016)
Model predicts:Upon unemployment onset, search hard because consumption fallsbelow reference pointBut, effort declines throughout the spell as the reference point adjustsSearch effort rises in anticipation of a future benefit cut or exhaustion
Exploit data from HungaryChange in benefit formulaCompare groups who entered just before vs. after the reform
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Policy change
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Reference Dependent Model Prediction
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Results: Empirical Hazard Rates
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Reference Dependent Model Fits Spikes
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UI Summary
Evidence that people are over-optimistic about unemploymentduration
Stated vs. true beliefs?
Evidence of spike in job search around drops in benefits
Consistent with reference dependent preferences
Implications for optimal UI?
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1 General Model of Bias: Information versus Understanding
2 Imperfect Take Up of Benefits: The Case of EITC
3 Inertia in Health Insurance
4 Unemployment and Job Search
5 Savings
6 Unsolicited Thoughts
Nathaniel Hendren (Harvard) Behavioral Bias Spring, 2020 49 / 76
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Savings
Large debate about whether people are saving “enough” for retirement
Scholz (2006, JPE): “Are Americans Savings “Optimally” forRetirement?”
Yes, argues structural model + savings suggests they are (or have been)But, very sensitive to structural assumptions
General concern: growing switch from pensions to 401KsRequire individuals to save on their ownGrowing use of tax dollars: $100B per year on subsidies for 401Ks andIRAs (JCT, 2012)
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401K and Tax-advantaged Retirement Savings
Significant evidence that default options in 401K plans affect savingsbehavior
Choi et al (2002, 2004)Significant evidence that providing tax incentives for 401Kcontributions increases investments in those assets
Poterba, Venti, Wise (AER, 1994; JEP 1996)Given behavioral biases, are tax incentives the best way to increasesavings?Chetty, Friedman, Leth-Petersen, Nielsen, Olsen (2014)
Use administrative wealth data for all Danish householdsBegin by studying policy that changed retirement savings subsidy
Note: Subsequent slides re-produced from Chetty (2015, AEA ElyLecture)
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Impact of 1999 Pension Subsidy Reduction On Pension Contributions
0
5000
10000
15000
175 200 225 250 275 300 325
Pensio
n C
ontr
ibution (
DK
r)
1999
Income (DKr 1000s)
Treatment Group
Retirement subsidy reduced
by 12 cents per dollar in 1999
Control Group
Subsidy unchanged
1996
Note: $1 6 DKr
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0
5000
10000
15000
1996 1997
1999
175 200 225 250 275 300 325
Pensio
n C
ontr
ibution (
DK
r)
Income (DKr 1000s)
Impact of 1999 Pension Subsidy Reduction On Pension Contributions
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0
5000
10000
15000
1996 1997 1998
1999
175 200 225 250 275 300 325
Pensio
n C
ontr
ibution (
DK
r)
Income (DKr 1000s)
Impact of 1999 Pension Subsidy Reduction On Pension Contributions
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0
5000
10000
15000
1996 1997 1998
1999
175 200 225 250 275 300 325
Pensio
n C
ontr
ibution (
DK
r)
Income (DKr 1000s)
Impact of 1999 Pension Subsidy Reduction On Pension Contributions
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0
5000
10000
15000
1996 1997 1998
1999 2000
175 200 225 250 275 300 325
Pensio
n C
ontr
ibution (
DK
r)
Income (DKr 1000s)
Impact of 1999 Pension Subsidy Reduction On Pension Contributions
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0
5000
10000
15000
1996 1997 1998
1999 2000 2001
175 200 225 250 275 300 325
Pensio
n C
ontr
ibution (
DK
r)
Income (DKr 1000s)
Impact of 1999 Pension Subsidy Reduction On Pension Contributions
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020
4060
-100 -50 0 50 100Percent Change in Capital Pension Contributions
1997 to 1998
Per
cent
of I
ndiv
idua
lsImpact of 1999 Capital Pension Subsidy Reduction on Distribution of
Capital Pension Contributions for Prior Contributors
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020
4060
-100 -50 0 50 100Percent Change in Capital Pension Contributions
1997 to 1998 1998 to 1999
Per
cent
of I
ndiv
idua
lsImpact of 1999 Capital Pension Subsidy Reduction on Distribution of
Capital Pension Contributions for Prior Contributors
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-.02
-.01
0.0
1.0
2
1996 1997 1998 1999 2000 2001
Diff
eren
ce in
MP
S A
bove
vs.
Bel
ow T
op T
ax C
utof
f
YearRetirement Accounts Taxable Savings Accounts
Change in Marginal Propensity to Save in Retirement vs. Non-Retirement Accounts at Top Tax Cutoff by Year
Crowd-out: 𝜙𝐿= 120%(59%)
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Effect of Tax Subsidies
Aggregate reduction is driven by 19% of treated households whoentirely stop contributing to pensions
Remaining 81% do not change retirement contributions at allConsistent with inattention model (Carroll et al. (2009, QJE))
90% of the reduction in retirement contributions is offset by moresaving in non-retirement accounts
Crowd out -> smaller impact on total savings$1 of tax subsidy generates 1 cent increase in total savings
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Defaults
Compare to impact of change in defaultsMadrian and Shea (2001); Choi et al (2004)
Chetty et al (2013) study people switching firms with an opt-in versusan opt-out retirement savings program in the Danish data
Key question: do defaults increase total savings or just a shift inassets?
Track savings around job changes, exploiting variation in employers’retirement plansIf you move to a firm where employers contribute more to retirementsavings, do you offset this with decreased savings?
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Year Relative to Firm Switch
Contr
ibution o
r Taxable
Savin
g R
ate
(%
of in
com
e)
Employer Pensions
Event Study around Switches to Firm with >3% Increase in Employer Pension Rate
Individuals with Positive Pension Contributions or Savings Prior to Switch
Δ Employer Pensions = 5.64
0
4
8
12
-4 -2 0 2 4
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Year Relative to Firm Switch
Contr
ibution o
r Taxable
Savin
g R
ate
(%
of in
com
e)
Employer Pensions
Event Study around Switches to Firm with >3% Increase in Employer Pension Rate
Individuals with Positive Pension Contributions or Savings Prior to Switch
Δ Employer Pensions = 5.64
0
4
8
12
-4 -2 0 2 4
Δ Individual Pensions = -0.56
Individual Pensions
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Year Relative to Firm Switch
Contr
ibution o
r Taxable
Savin
g R
ate
(%
of in
com
e)
Employer Pensions
Event Study around Switches to Firm with >3% Increase in Employer Pension Rate
Individuals with Positive Pension Contributions or Savings Prior to Switch
Δ Employer Pensions = 5.64
0
4
8
12
-4 -2 0 2 4
Δ Taxable Savings = 0.02
Taxable Saving
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Summary of Nudge Effect
Approximately 85% of individuals respond passively to changes inemployer contributions
They simply increase their savingsSavings increase is permanent and leads to increased wealth atretirement
Suggests default policies can significantly increase savings rates forlarger share of the population
And potentially cost less too...
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Abaluck and Adams (2017)
Often, consumers don’t consider all relevant options when makingdecisions
E.g. Handel evidence on switching costs. Is this a true “cost” ofswitching or just an “inattention” to the price of the other good?
How can we identify what people consider? And their willingness topay conditional on considering?Abaluck and Adams: think about Slutsky symmetrySlutsky: Compensated $100 increase in price of good 1 should beequivalent to compensated $100 decrease in price of all rival goodsbesides good 1Abaluck and Adams: Not true if people didn’t consider good 1.
Nathaniel Hendren (Harvard) Behavioral Bias Spring, 2020 67 / 76
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Abaluck and Adams (2017)
To illustrate, consider an insurance plan choice between 0 and 1Suppose 0 is the default option and suppose individuals choosedefault unless it becomes sufficiently unattractive that it motivatesattention on other goods. I.e. the attention on other goods is afunction of the price of good 0.This implies that consumers only care about the price of good 0, notthe price difference between good 0and good 1Paper shows one can identify the probability of considering good 1conditional on price of good 0 separately from violations of slutskysymmetry.
Nathaniel Hendren (Harvard) Behavioral Bias Spring, 2020 68 / 76
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1 General Model of Bias: Information versus Understanding
2 Imperfect Take Up of Benefits: The Case of EITC
3 Inertia in Health Insurance
4 Unemployment and Job Search
5 Savings
6 Unsolicited Thoughts
Nathaniel Hendren (Harvard) Behavioral Bias Spring, 2020 68 / 76
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Thoughts on Research Proposals
Some common themesInterests in fiscal externalities
Interest in COVIDInterest in long-run impacts of policiesI learned some things about new tax policies
Many of you are grappling with: Is this question worth pursuing?
Every research project takes yearsWhen should you pursue the project?
Nathaniel Hendren (Harvard) Behavioral Bias Spring, 2020 69 / 76
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Thoughts on “Is it worth pursuing”?Some things you’ll hear that limit upside of knowledge generation:
One-sided projectsSame variation of previous paperProgram not large enough to be of ’general interest’
My take:Start with a puzzle (and sometimes you have to find your puzzle)Don’t require massive data acquisition before first-analysis (unless it’s atwo-sided question and you care)If you’re genuinely interest in a project – there’s no substitute for this!One-sided projects can still be ok
As you go, your project idea always evolves...let it! Iterate betweenempirics, theory, and ideas
Idea <–> Theory <–> EmpiricsPapers never follow a linear path (e.g. ask me about my JMP /Movers paper w Raj / etc).
The “scientific method” is not about testing hypotheses in data, butrather a series of learning opportunities as you explore data.
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Thoughts on “Is it worth pursuing”?Some things you’ll hear that limit upside of knowledge generation:
One-sided projectsSame variation of previous paperProgram not large enough to be of ’general interest’
My take:Start with a puzzle (and sometimes you have to find your puzzle)Don’t require massive data acquisition before first-analysis (unless it’s atwo-sided question and you care)If you’re genuinely interest in a project – there’s no substitute for this!One-sided projects can still be ok
As you go, your project idea always evolves...let it! Iterate betweenempirics, theory, and ideas
Idea <–> Theory <–> EmpiricsPapers never follow a linear path (e.g. ask me about my JMP /Movers paper w Raj / etc).
The “scientific method” is not about testing hypotheses in data, butrather a series of learning opportunities as you explore data.
Nathaniel Hendren (Harvard) Behavioral Bias Spring, 2020 70 / 76
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Thoughts on “Is it worth pursuing”?Some things you’ll hear that limit upside of knowledge generation:
One-sided projectsSame variation of previous paperProgram not large enough to be of ’general interest’
My take:Start with a puzzle (and sometimes you have to find your puzzle)Don’t require massive data acquisition before first-analysis (unless it’s atwo-sided question and you care)If you’re genuinely interest in a project – there’s no substitute for this!One-sided projects can still be ok
As you go, your project idea always evolves...let it! Iterate betweenempirics, theory, and ideas
Idea <–> Theory <–> EmpiricsPapers never follow a linear path (e.g. ask me about my JMP /Movers paper w Raj / etc).
The “scientific method” is not about testing hypotheses in data, butrather a series of learning opportunities as you explore data.
Nathaniel Hendren (Harvard) Behavioral Bias Spring, 2020 70 / 76
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General Advice for Grad School
Stay curiousProfessors always joke that undergrads come up with better ideas thangrad studentsDon’t be afraid to be creative – crazy questions are okDon’t get caught up in the literature / what’s been done
But once you have a “good” idea, read deeply in that literature andfigure out what has been done, then iterate with your idea
Choose topics that you are passionate aboutResearching those topics isn’t work!Will be easier to convince others its interesting if you think it is
Nathaniel Hendren (Harvard) Behavioral Bias Spring, 2020 71 / 76
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Execution of Research
After classes end in 2nd year, fewer opportunities for “discipline” –here’s how I wish I’d applied mine:
Write good code and document your exploratory results in commentsin your codeWhen getting a dataset, first thing to do is open it up and look at it
Spend an hour to make sure the data looks reasonableIt’s always worth writing out a model to explain your patterns / deriveyour regression equations
Not always clear it goes in the paper but still useful regardless
Nathaniel Hendren (Harvard) Behavioral Bias Spring, 2020 72 / 76
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Data AdviceMost common question I am asked: How can I get access to US Tax/ Census data?
My response:Can your question be asked without tax data? e.g. can you useless-restricted census data / FSRDCDo you have power? If you’re using cross-state variation, you’re ruiningmost of the value of population dataCan you do preliminary analysis using public data to have a sense ofwhether your pattern is there?
If you have a project worth pushing for census / tax data, here are thepaths:
If you can only use Census data, submit an FSRDC applicationSubmit to the SOI call for proposalsCollaborate with a researcher at the Office of Tax Analysis at Treasuryor the Joint Committee for Taxation (both of whom have access to thedata).
Ask for advice from folks with access, but remember many (like me)may be prevented from working on your project idea because it requiresformal approval
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Other Data Partners
Other countries’ admin data is often less restrictive:Norway, Denmark, Sweden, Germany, Italy, France...
Firms have an enormous amount of informationGenerally under-explored in research:
Transactions / sales informationHR informationSearch / website info
Other good sources for merging to gain new outcomes:Voterfiles (contains race/demographics)credit reports / court records
Nathaniel Hendren (Harvard) Behavioral Bias Spring, 2020 74 / 76
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Presentations
Graduate school has far too few opportunities to presentTake each presentation seriously, not just as feedback on your work butas an opportunity to improve your skills at presentingBut don’t let the stress overwhelm you – everyone gets stressed inpresentations (including me) but the hope is you can translate it intoproductive energy
Practice your presentations (I have never given a seminar that I havenot practiced at least 10 times through)
Think through how you want to make your arguments to the listenerPractice transitions between slidesKnow your slides and the detailsPut some effort into slide construction – often one graph can “make” apaperMore practice ex-ante can also reduce stress
Appreciate feedbackYou are not your paper
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Some Topics I Find Interesting1 Desirability of place-based versus national policy2 Endogeneity of public policies (i.e. political economy) – what are we
missing by not thinking about political economy constraints?3 Why don’t people take up social benefits? (and should we incentivize
them to?)4 What other markets are missing because of private information and
what are the welfare implications? (Credit? Reclassification risk?Income insurance?)
5 Career trajectories within the firm6 Competition in insurance markets – what’s the equilibrium? [Note:
I’ve given up trying to think this can be solved...]7 Government versus markets - should the govt, e.g., provide schooling
directly or fund charter schools?8 Endogenous preferences and impact on PF / role of policy (MVPF of
being a jerk? Altruism? Endogenous altruism? Endogenousreductions in gender bias or racism?
9 The economic incidence of COVIDNathaniel Hendren (Harvard) Behavioral Bias Spring, 2020 76 / 76