Presentation on theme: "1 Behavioral economics field evidence (Della Vigna JEL in press) A boom in clever field studies showing impact of psychology on economic behavior Largely."— Presentation transcript:
1 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
2 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
3 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
4 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
5 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”
6 Cab driver “income targeting” (Camerer et al QJE 97)
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9 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”)
10 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?
11 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!
12 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 high-payoff 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.
13 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.
14 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
15 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
16 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
17 “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) 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)
18 John List JPE
19 Basic lab replication is weak (t=1.80,.05
20 Effect of quality on price: Lab (I) to field (III, IV-P) generalizes well
21 …but driven by “local (L)” dealers
22 Levitt-List (JEcPers 07) articulated concerns about stereotypical lab experiments 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
23 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
24 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)
25 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 –Hossain-Morgan 06 Ebay auctions with shipping costs included or excluded 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
26 Information vs “news”: Attention to NYTimes story about cancer drug moves a stock (Huberman-Regev 01 JFin)
27 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 (Cohen- Frazzini 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) (Barber- Odean 06)
28 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 (Goldman- Sachs 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?)
29 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
30 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)
31 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)
32 Conclusion Many areas remain: –Extension to political science –Other types of psychology Emotion, willpower, implicit discrimination, attention