1. Uri Simonsohn The Wharton School 1

2. The paper in one slide:  Jan 4 th 2007: Consumer Reports on carseats  Jan 18 th : Retraction  Unique opportunity: Do consumers continue using Jan 4 th info?  Test on 6,000+ eBay auctions for carseats  Main finding: Full return to baseline  My interpretation: voluntarily ignored info.  Alt explanations Information ‘depreciates’ Post-retraction buyers didn’t know Kind-of alternative: Sellers’ behavior 2

3. Outline  Background  New information: release and retraction  Auction data  Main results  Alternative specifications  Conclusions 3

4. Can people voluntarily ignore information they possess? Existing evidence: Debriefing paradigm Hindsight bias Anchoring Mock juries and inadmissible evidence 4

5. Debriefing Paradigm Ross, Lepper & Colleageus (JPSP 1975;1980)  Critique of false feedback in Psych Paradigm:  Give false feedback on personality test  Debrief: “feedback was false”  Ask their beliefs  …still influenced by retracted feedback 5

6. Anchoring  Subjects asked to make numerical estimate Length of Mississippi river WTP for keyboard.  Asked first: is the amount larger or smaller than anchor.  Final estimate is correlated with anchor.  Even when anchor is roulette or SS# 6

7. OPIM 690  Write down the last 2 digits of your SS#:__  Would you be willing to pay that amount for yearly access to NYTimes.com?  What is the most you would pay? _____ 7

8. Hindsight Bias  People told some outcome  Asked to estimate what those without information would predict.  Finding: estimates are biased towards the to-be-ignored outcome.  Next: results from Fischhoff (1975) 8

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10. Mock Juries & inadmissible evidence  Dozens of studies  Random assignment across “jurors”  Control: baseline evidence  T1: control + extra evidence  T2: T1 + extra evidence is inadmissible.  Decisions by T2 fall between control and T1. 10

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12. Outline  Background  New information: release and retraction  Auction data  Main results  Alternative specifications  Conclusions 12

13. January 4 th, 2007 13 Corr( rank 2007,rank 2005) = -.08

14. Retraction and empirical strategy  Jan 18 th : Oops!  Outsourced, 30 vs 38 vs 70 MPH  Unique opportunity to study: 1) Causal effects of expert advice Contributions: ○ Individual level measures of WTP ○ Simple identification strategy (wrong info) Compared to -Discontinuities around discrete scores -Differences across sites -Timing 2) Ability of consumers to ignore retracted information. 14

15. How would people learn of a new Consumer Rerports carseat rating? Important because:  1) Face validity of quick market reactions  2) Post-retraction awareness. 15

16. From CR to consumers.  CR in print Subscribers: slow ○ Library got it 01/11 ○ They claim: letter for retraction ○ Otherwise, not till May Newstands: slower ○ No retraction till May  cr.org Comscore 100k users 15% of carseat buyers visit within 30 5% same day  Not a direct source of info 16

17. How about the media? 17

18. Number of stories about “Consumer Reports” and “Carseats” sources: newsbank+lexisnexis 18 50+ Newspapers 600+ Stories

19. Internet coverage  Can’t do same search for web-coverage  Can use web.archive.org to check specific sites.  All major sites covered it 19

20. In Sum  CR info indirectly received via media  Fast  Retracted information remained available following retraction I’d argue: Post-retraction buyers probably read stories before being retracted. 20

21. Outline  Background  New information: release and retraction  Auction data  Main results  Alternative specifications  Conclusions 21

22. Why auctions  Retailers don’t change prices often  Few decision makers behind them  Auctions: 1000s of DMs interacting Prices change continuously  Aside: Unexploited side to eBay data: pulse on demand shocks. 22

23. Auctions Data  6 months: 3 before & 3 after Many analyses focus on: ○ Before: 3 weeks ○ During: 2 weeks ○ After: 3 weeks  Auctions: 6k  Bids: 35k 23

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25. Descriptive statistics 25

26. Annoyance:  Shipping is only observed for sold items.  Estimate OLS for sold items (w/shipping)  Estimate Tobit for all (w.o./shipping) 26

27. Outline or regression specifications  Y: (tot.price i /Avg.Price i,k ) i:auction, k:carseat model  Time variables (dummies): Primarily: before, during, after. Also: biweekly dummies (next slide) Also: 3-day-dummies  Key predictor Primarily: ΔRanking Also: carseat-model-dummies e.g. Y=OLS(during*ΔRanking, after* ΔRanking, controls) 27

28. First: bird’s eye view  Estimate Y=OLS(biweekly*ΔRanking) 1 observation every 14 days.  Plot point estimates 28 3.98 SD

29. Next: more fine grained look Time: before, during, after 29

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31. Plotting time*dranking betas 31

32. So far: just before-after  How quick are the reactions?  Y=OLS(3-day-dummies* Δranking) 32

33. price=OLS(3-day dummies * Δrank) omitted cat.: two previous weeks 33

34. How about non-winning bids?  Camerer et al (1989) “Curse of Knowledge” Market forces reduce it Rational agents trade more  Same here? Are non winning bidders ‘cursed’?  Unit of observation: auction  bid  Quantile Regression 34

35. Specification  Bids are unit of observation.  If more than one bid by same bidder, take highest only.  Estimate quantile regressions of: bid $ = f(Time*ΔRanking)  With quantiles at 20%,40%,60%,80%. 35

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37. Plotting the betas 37

38. 38 Dividing point estimates by average bid % at quantile

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40. From ΔRanking to model-dummies  Previous analyses:  Impose Δ%price=b* ΔRanking  Don’t allow for heterogeneity in effect  Next: estimates by model.  Plot avg(OLS,Tobit) 40

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43. Price=f(demand AND supply) 43

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45. Starting Price Number of paid features 45

46. # of items for sale % New 46

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48. Summary of evidence  Biweekly: biggest price drop in 6 months  During vs. After: Market responded to information Ceased to once retracted  3-day: Market respond virtually immediately  Quantile regressions Bidders across the full spectrum do so.  Carseat dummies Every carseat (6/6) exhibits the pattern  Supply: No evidence of changes in supply 48

49. Interpretation  Consumers successfully ignored information they possessed once it was retracted. 49

50. Alternative Explanations 1) Knowledge depreciates …& coincides w/retraction But: 3-day graphs 2) Buyers never knew Retracted information still available online - Evenflo 50

51. Why cursed in the lab but not here?  Field, but not lab: credible instruction to ignore. Mock juries & substantive instructions Debriefing paradigm & credible instruction Should you really ignore info in ○ Hindsight Bias ○ Knowledge curse  Field, but not lab: DM control information Dilution effect goes away when you can scratch irrelevant info Hindsight and anchoring attenuate when explicitly consider alternatives. 51

52. Future research  Run lab experiments explicitly manipulating variables that differ in vs. outside the lab. 52