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“Constructed” preferences

“Constructed” preferences. Study effects relative to “complete, transitive” u(x) “Constructed” means expression of preference is like problem-solving: Will you vote for John Kerry? Answered by rapid intuition (tall, good hair) and deliberate logic (positions on issues)

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“Constructed” preferences

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  1. “Constructed” preferences • Study effects relative to “complete, transitive” u(x) • “Constructed” means expression of preference is like problem-solving: • Will you vote for John Kerry? • Answered by rapid intuition (tall, good hair) and deliberate logic (positions on issues) • Context-dependence (comparative) • Description-dependent “framing (descriptions guide attention) • Reference-dependence (changes, not levels; anchoring) • Some values “protected”/sacred (health, environment) • Is too much choice bad? • Open questions: • Are effects smaller with familiar choices? • Experts? • Markets? • New predictions (e.g. “big tip” labor supply experiment)

  2. 1/n heuristic & partition dependence in the lab (cf. “corporate socialism”

  3. Context-dependence (comparative) • Objects judged relative to others in a choice set • Asymmetric dominance • Compromise effects • Economic question: What is seller’s optimal choice set given context-dependent preferences?

  4. Description-dependent “framing” (descriptions guide attention) • Analogy to figure-ground in perception • Actual study with n=792 docs (Harvard Med, Brigham &Women’s, Hebrew U; McNeil et al JAMA ’80s) • treatment 1 yr 5 yrs choice • Surgery 10% 32% 66% 53% • Radiation 0% 23% 78% 47% • treatment 1 yr 5 yrs choice both frames • Surgery 90% 68% 34% 82% 60% • Radiation 100% 77% 22% 18% 40% • Asian disease problem • Pro-choice vs pro-life • Economic idea: Competitive framing; which frame “wins”? • Politics: “spin” (Lakoff) • e.g. aren’t we better off w/ Hussein gone? • Liberation vs. occupation

  5. Reference-dependence • Sensations depend on reference points r • E.g. put two hands in separate hot and cold water, then in one large warm bath • Hot hand feels colder and the cold hand feels hotter • Loss-aversion ≡ v’(x)|+ < v’(x) |- • a “kink” at 0; “first-order risk-aversion” aka focussing illusion? • Requires theory of “mental accounting” • What gains/losses are grouped together? • When are mental accounts closed/opened? • Conjecture: time and space matter, and cognitive boundaries • Example: Last-race-of-the-day effect (bets switch to longshots to “break even”, McGlothlin 1956)

  6. Reference-dependence modelling • Where does r come from? • Experiments: Usually status quo or pre-experiment condition • Koszegi-Rabin ’03: Reference point is based on (lagged) expectations • Solves problem of why r is not chosen to be superlow • Concept of “personal equilibrium” in which decision fulfills expectations (multiple equilibria, endowment effect, Giffen good effects…)

  7. Prospect theory value function: Note kink at zero and diminishing marginal sensitivity (concave for x>0, convex for x<0)

  8. Endowment effects (KKT JPE ’90)

  9. KKT “mugs” experiment (JPE ‘90)

  10. Plott-Zeiler review

  11. Data from young (PCC) and old (80 yr olds) using PZ instructions (Kovalchik et al JEBO in press 04)

  12. “Status quo bias” and defaults in organ donation (Johnson-Goldstein Sci 03)

  13. Loss-aversion in savings decisions (note few points with actual utility <0) from Chua & Camerer 03

  14. Disposition effects in housing (Genesove and Mayer, 2001) • Why is housing important? • It's big: Residential real estate $ value is close to stock market value. • It’s likely that limited rationality persists • most people buy houses rarely (don't learn from experience). • Very emotional ("I fell in love with that house"). • Like the "big, rare" decisions -- mating, fertility, education, jobhunting… • Advice market may not correct errors • buyer and seller agents typically paid a fixed % of $ price (Steve Levitt study shows agents sell their own houses more slowly and get more $). • Claim: • People hate selling their houses at a "loss" from nominal [not inflation-adjusted!] original purchase price.

  15. Boston condo slump in nominal prices

  16. G-M econometric model Model: Listing price L_ist depends on “hedonic terms” and m*Loss_ist (m=0 is no disposition effect) …but *measured* LOSS_ist excludes unobserved quality v_i …so the error term η_it contains true error and unobserved quality v_i …causes upward bias in measurement of m Intuitively: If a house has a great unobserved quality v_i, the purchase price P^0_is will be too high relative to the regression. The model will think that somebody who refused to cut their price is being loss-averse whereas they are really just pricing to capture the unobserved component of value.

  17. Results: m is significant, smaller for investors (not owner-occupants; less “attachment”?)

  18. Cab driver “income targeting” (QJE 97)

  19. Cab driver instrumental variables (IV) showing experience effect

  20. Anchored valuation: Valuations for listening to poetry framed as labor (top) or leisure (bottom)(Ariely, Loewenstein, Prelec QJE 03 and working paperhttp://sds.hss.cmu.edu/faculty/Loewenstein/downloads/Sawyersubmitted.pdf

  21. “Arbitrary” valuations • Stock prices? • Wages (what are different jobs really worth?) • Depends on value to firm (hard to measure) • & “compensating differentials/disutility (hard to measure) • Exotic new products • Housing (SF Pittsburgh tend to buy “too much house”; Simonsohn and Loewenstein 03) • Exec comp'n (govt e.g. $150k for senator, vs CEO's, $38.5 million Britney Spears)

  22. What econ. would happen if valuations are arbitrary? • Perfect competition price=marginal cost…anchoring influences quantity, not price; expect large Q variations for similar products • Attempts to influence the anchor (QVC home shopping, etc., "for you just $59.95”). • Advertising!!! • If social comparison/imitation is an anchor, expect geographical, temporal, social clustering (see this in law & medical practice) • E.g., CEO pay linked to pay of Directors on Board's comp'n committee. Geographical differences in housing prices, London,Tokyo, NYC, SF. • Interindustry wage differentials for the same work (Stanford contracts out janitorial service so it doesn't have to pay as much; cf. airline security personnel??) • Sports salaries: $100k/yr Miami Dolphins 1972 vs $10million/yr modern football • Huge rise in CEO comp'n from 1990 (42 times worker wage) to 2000 (531 times); big differentials between US and Europe • Consumers who are most anchorable or influenceable will be most faddish -- children and toys!!? (McDonald's happy meal etc)

  23. Is too much choice bad? • Jams study (Iyengar-Lepper): • 6 jams 40% stopped, 30% purchased • 24 jams 60% stopped, 3% purchased • Assignment study: • Short list 74% did the extra credit assignment • Long list 60% did the extra credit assignment • Participation in 401(k) goes down 2% for every 10 extra funds • Shoe salesman: Never show more than 3 pairs of shoes… • Medical • 65% of nonpatients said they would want to be in charge of medical treatment…but only12% of ex-cancer patients said they would • Camerer conjecture: The curse of the composite • Paraphrased personals ad: “I want a man with the good looks of Brad Pitt, the compassion of Denzel Washington…” • Is there “too much” mate choice in big cities?

  24. Choice-aversion • How to model “too much choice”? • Anticipated regret from making a mistake • “grass is greener”/buyer’s remorse • Direct disutility for too-large choice set (e.g. too complex) • Policy question: • Markets are good at expanding choice…what is a good institution for limiting choice? • Example: Bottled water in supermarkets • Limit “useless” substitution? What is the right amount? • Pro-govt example: Swedish privatized social security • Offered hundreds of funds • Default fund is low-fee global index (not too popular) • Most popular fund is local tech, down 80% 1st yr

  25. Experimental markets & prob judgment • 1. Abstract stimuli vs natural events?? • pro: can precisely control information of individuals • can conpute a Bayesian prediction • con: maybe be fundamentally different mechanisms than for concrete events... • 2. Do markets eliminate biases? • Yes: specialization • Market is a dollar-weighted average opinion • Uninformed traders follow informed ones • Bankruptcy • No: Short-selling constraints • Confidence (and trade size) uncorrelated with information • Camerer (1987): Experience reduces pricing biases but *increases* allocation biases • Contingent claims markets: • Markets enforce correct prices..BUT probability judgment influences allocations and volume of trade (example: Iowa political markets)

  26. IIlusions of transparency • “Curse of knowledge” • Difficult to recover coarse partition from fine-grained one • Piaget example: New PhD’s teaching • EA Poe, “telltale heart” • Computer manuals • “ The tapper” study (tapping out songs with a pencil) • Hindsight bias • Recollection of P_t(X) at t+1 biased by whether X occurred • “I should have known!” • “You should have known” (“ignored warning signs”) • --> juries in legal cases (securities cases) • implications for principal-agent relations? • Spotlight effect (Tom Gilovich et al) • Eating/movies alone • Wearing a Barry Manilow t-shirt •  psychology: Shows how much we think others are attending when they’re not

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