Transcript Behavioral economics field evidence (Della Vigna JEL in press)

Behavioral economics
field evidence (Della Vigna JEL in press)
• A boom in clever field studies
showing impact of psychology on
economic behavior
• Largely fueled by experimental data
 tells you what to look for
• Wide variety of topics & methods
• High impact (citations, buzz) and
easier to publish than experiments
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Categories
• Non-standard preferences
– Self control, reference-dependence, social
preferences
• Non-standard beliefs
– Overconfidence, non-Bayesian, projection
bias
• Non-standard decision making
– Limited attention, menu effects, social
pressure, persuasion
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Non-standard preferences
• Self-control
– Exercise: People overpay for annual health
club memberships
– Deadlines:
• Imposed equally-spaced deadlines for
proofreading improved performance (135 vs 70)
• Endogenous choice? Majority picked
deadlines…but bunched too close to end of the
term
– Credit and savings. Many examples
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Non-standard preferences
• Reference-dependence:
– Organ donation: Countries with “opt-out” of organ
donation have much higher rates than “opt-in” (US)
– Huge effect of default into savings (SMaRT plan + 06
Automatic Pension and Savings Protection Act
– SMaRT plan (Benartzi-Thaler 04 JPE) exploits two
elements of human nature
• Tendency to commit and not switch
• Dislike nominal take-home pay falling
•  commit workers to put 1/3 of future pay increase into
401(k)
• Raises savings strongly
– The major practical advance from behavioral
economics so far
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Reference-dependence in labor
supply
• Basic questions:
– Does supply rise with wage w?
• Participation (days worked) vs hours
• A: Very low + supply elasticities for males
• …but most data from fixed-hours
– Intertemporal substitution
• Do workers work long hours during temporary wage
increases (e.g. Alaska oil pipeline)? (Mulligan JPE 98?)
• “Participate” on high-wage days (Oettinger JPE baseball
stadium vendors)
– Alternative: Amateur “income targeting”
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Cab driver “income targeting” (Camerer et al QJE 97)
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Cab driver instrumental variables
(IV) showing experience effect
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Possibility of Poisoning Is Raised In the Death of a Haitian Colonel
... Is Raised In the Death of a Haitian Colonel ...
November 8, 1988 - AP (NYT) - International - News - 431 words
Critic's Notebook; Tokyo, City of the $12 Movie
... , City of the $12 Movie ...View free preview
November 8, 1988 - By VINCENT CANBY, Special to the New York Times (NYT) Movies - News - 1250 words
Save the Catskills Also
... Save the Catskills Also ...
November 8, 1988 - (NYT) - Editorials and Op-Ed - Letter - 121 words
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Interviews: Great idea!
• Q: From passage above, do you think
others did interviews too? Were they
more or less systematic than Farber’s?
(his are admittedly “not systematic”)
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Farber (JPE 04) hazard rate estimation: Do hrs
worked or accumulated income predict quitting?
• Note: If workers are
targetting, why isn’t
the income
distribution more
spiky?
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Do they quit because of hours or $?
Getting tired is a stronger regularity than targetting
• Note: Which has
more measurement
error, hours or $?
• Big tip experiment!
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Alan Krueger 6/26/03 NYTimes column
• Now their findings are being debated. First, Gerald S. Oettinger of
the University of Texas at Austin published a paper in the Journal of
Political Economy on the daily work decisions of food and beverage
vendors at a major-league baseball stadium. The vendors were
independent contractors, required to work until the seventh inning,
but they could choose which games to work. Vendors make more
when the number of fans is high and the number of other vendors is
low. Professor Oettinger found that vendors were more likely to go to
work when the expected payoff was higher -- for example, on days
when a larger crowd was expected because of a pivotal game or a
quality opponent. The decision of whether to work at all on a highpayoff day -- as opposed to how much to work -- was not considered
in the cabdriver study.
A: YES IT WAS. PERHAPS KRUEGER DID NOT READ OUR
PAPER.
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• And most recently, my Princeton colleague Henry S. Farber revisited
the question of cabdrivers, studying a different set of drivers. He
found that cabdrivers quit after they work a lot of hours and grow
weary. How much they have earned to that point has little or nothing
to do with their decision. Moreover, the amount the drivers earn
varies substantially from day to day, suggesting that their target
income levels, if they have them, fluctuate wildly. He suggests that
the earlier findings possibly resulted from reporting errors in the
data: because daily wages were derived by dividing total revenue by
hours worked, any mistake in reported hours would cause a mistake
in the opposite direction in the calculated wage, inducing a negative
correlation between wages and hours worked.
• A: REPORTING ERRORS ARE NOT ENOUGH BECAUSE WE
USED IV ESTIMATION. MUST BE REPORTING ERRORS *AND*
SPECIFIC-DATE SHOCKS TO LABOR SUPPLY.
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Big tip experiment
• Prediction of reference-dependent model:
– A large windfall will lead to lower labor supply
– Example: Give drivers a big surprising tip…
predict they will quit early (or street musicians etc)
– Tip must not be an indication of a shift in wages
– Do it? Only if it would convince true-believer labor
economists
– Thaler asked Kevin Murphy:
• “They might just go home to celebrate”
– Implication: Some labor economists will not commit to
reputational bets about whether theories are true
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Non-standard preferences
• Social preferences
– Charitable giving in the field
– Falk (04): Do people give more after small or large
“gift”? (postcards)
• Control 12.2%, small 14.4%, large 20.6%
– Does gift exchange for high wage “wear off”?
• Yes: Gneezy-List 07 (tiny n, absent in one study?)
• No: Kube+ 06, offer 15 Euro/hr
– Low group (paid 10) do 25% less, does not decline w/ time
– High group (paid 20) do 5% more, does not decline w/ time
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Field and lab experiments
• Does lab generalize to field?
– Gary Becker (2002 interview): “Economists have a theory of
behavior in markets, not in labs, and the relevant theories can
be very different.” (italics mine)
•
Labs and markets are not very different in principle
(stereotyping error to think they are)
•  For every market M1, can find another market M2
which is more different than a lab experiment L1
• Focus should be on generalizing from empirical findings
to a particular domain of interest, not from lab to field
– Example: Interested in Las Vegas slot machine gambling?
– Lab experiment generalizes well
– Field data from betting on cockfighting in Phillipines may not
generalize well.
• External validity is a misleading phrase because the
“external” world varies
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“Behavioralist meets
the market” (List JPE 06 )
• “Gift exchange”
– Offer price W, supply quality q
– Dealers W – c(q) buyers v(q)-p
q
c(q)
v(q)
1
4
6
2
5
8
3
8
15
4
15
30
5
50
80
• Twist:
– Use sports card dealers in lab and field
– Field: Approach, offer $20 or $65, request high quality
(PSA 9) or top quality (PSA 10)
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John List JPE
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Basic lab replication is weak
(t=1.80, .05<p<.10 2-tailed)
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Effect of quality on price:
Lab (I) to field (III, IV-P) generalizes well
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…but driven by “local (L)” dealers
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articulated concerns
about stereotypical lab experiments
Levitt-List (JEcPers 07)
• 1. Scrutiny
• 2. Context in which decision is embedded
• 3. Self-selection and experience
– 1. is a very minor problem in most experiments
• Largest in dictator games, a “weak” situation
• Scrutiny is no greater in lab (web) than field (& is a
consequence of IRB informed consent)
– 2-3 are strong arguments for lab experiments
because of extraordinary control over subtle fx
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Levitt-List conclusion re: List 04
• Problems:
– The “could not have card graded” condition is not present in the lab
experiments
– The “local/nonlocal” comparison is not reported for lab experiments
•  There is no clean comparison between lab and field
• In fact, Levitt-List have zero examples of an unconfounded lab-field
failure of generalization
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Non-standard beliefs
• Overconfidence
– Malmendier-Tate 04,05:
Overconfident CEOs hold options to
expiration
• 55% more likely to do merger, also invest more heavily in response
to free cash flow
– Investors trading (Barber-Odean)
• Trade too much (esp. men)
• Non-Bayesian
– Winning lottery numbers are underbet in later weeks (law of
small numbers)
– Hot hand biases betting on NBA teams
• Projection bias (overforecast persistence of states)
– Catalog returns for cold-weather clothes higher if ordering day is
cold (as if buyers expect cold weather to persist)
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Non-standard decision making
• Limited attention
– Attention is scarce!
• Value of a good is v+i (i is invisible)
• Choices reveal v+(1-θ)i
• θ =0 full attention; θ > 0 degree of limited attention
Ebay auctions with shipping costs
included or excluded
– Hossain-Morgan 06
• Estimate θ = .45, .18
– Chetty+ 07:
Do consumers include sales tax?
• Field experiment with sales tax included or excluded
• Estimate θ=.75
– Ranking sensitivity
• Hospital, college rankings are continuous (0-100) but often
presented as ranks (e.g. ranks 3-4 might be 93, 94)
• Pope 07 shows sensitivity to ranks, not (reported) continous
variable
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Information vs “news”: Attention to NYTimes
story about cancer drug moves a stock
(Huberman-Regev 01 JFin)
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Limited attention in finance
• M-Th announcements of earnings “surprises” estimate
θ=.42; Friday announcements θ=.59 (Della Vigna-Pollet 06 )
• Attention to announcements lower on days when more
announcements take place (Hirshleifer+ 07)
• Stocks of supplier companies decline 1-3 mos later after
bad earnings news from companies they supply (CohenFrazzini 06)
• Markets forecast effects of demographics (e.g. baby
boom  school busses) only 5yrs ahead (Della Vigna-Pollet in
press)
• Investors buy 20% more often for stocks in highest
decile on volume or price change (gets attention) (BarberOdean 06)
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Non-standard decision making
• Menu effects
– 1/n heuristic (partition-dependence)
• Choices of retirement plans (next slide)
• In lab exps, field exps, field data too (GoldmanSachs derivative markets)
– Choice “overdose” & paralysis
• Jams: More sample w/ 24 jams, but fewer buy
(Iyengar-Lepper 00)
• Firms w/ 2 funds available, 75% participate; >40
funds, only 65% (Iyengar+ 04)
• Equilibrium implications? (How do people
ever choose in the face of complexity?)
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Horse and rabbit stew
(Benartzi-Thaler 01)
• Fund allocations 
stock/bond mixes
• Equal-spreading
heuristic can lead to
too much stock
• Stock-balanced fund
 heavy stock weight
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Non-standard decision making
• Social pressure
– Soccer refs add more “extra-time” when it
benefits home team ( 4 mins when -1 goal, 2
mins when +1 goal; Garicano+ 05)
– Envelope stuffers: Pairs stuff more (221 vs
190) and within-pair variance falls (Falk-Ichino 06)
– Supermarket cashiers: More productive ones
influence less productive (1% .23%) if they
can see slow ones (Mas-Moretti 06)
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Examples in political economy
• Menu effects on ballots (top is chosen more
often in local elections)
• Attention: High news “distraction” (e.g.
Olympics) lowers USAID foreign disaster relief (30%) (Eisensee-Stromberg 07)
• Role of media in supplying information: E.g. Fox
News on local cable  more Republican votes
(Della Vigna-Kaplan 07)
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Conclusion
• Many areas remain:
– Extension to political science
– Other types of psychology
• Emotion, willpower, implicit discrimination,
attention
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