1. Social Learning and Consumer Demand Markus Mobius (Harvard University and NBER) Paul Niehaus (Harvard University) Tanya Rosenblat (Wesleyan University and IAS) April 2006

2. Motivation We want to study social learning in the context of how consumer preferences form. How strong are social learning effects absolutely and relatively compared to informative advertising? How strong are social influence effects (on valuations) absolutely and relatively compared to persuasive advertising? Which agents are influential?

3. Learning Persuasion Strong Social Learning Agents communicate directly about the product, sharing factual information: “I didn’t buy it because it’s not Mac compatible” “I’ve heard Sony makes the most reliable ones” “They have a lot of vegetarian dishes on the menu”

4. Learning Persuasion Strong Social Learning Weak Social Learning Agents observe their friends’ consumption decisions and enjoyment of products and make inferences about the products’ attributes. “Greg got one for Christmas and I know he really liked it” These inferences should be sharper when friends know their friend’s preferences well.

5. Learning Persuasion Strong Social Learning Weak Social Learning Social Influence Agents observe their friends’ consumption decisions and.... Their private tastes are altered The status value of consuming the product is altered

6. Learning Persuasion Strong Social Learning Weak Social Learning Social Influence Persuasive Advertising Informative Advertising Agents observe advertising for the product. They may learn about objective features of the product or be persuaded to like it or be persuaded of its prestige value.

7. Methodology: basic paradigm Stage 1: Measure the network (Harvard Undergraduates) Stage 2:Distribute actual products and track social learning

8. Methodology Measuring the Social Network

9. Measuring the Network Rather than surveys, agents play in a trivia game Leveraged popularity of www.thefacebook.com www.thefacebook.com  Membership rate at Harvard College over 90% *  95% weekly return rate * * Data provided by the founders of thefacebook.com

10. Markus His Profile (Ad Space) His Friends

11. Trivia Game: Recruitment 1. On login, each Harvard undergraduate member of thefacebook.com saw an invitation to play in the trivia game. 2. Subjects agree to an informed consent form – now we can email them! 3. Subjects list 10 friends about whom they want to answer trivia questions. 4. This list of 10 people is what we’re interested in (not their performance in the trivia game)

13. Trivia Game: Trivia Questions 1. Subjects list 10 friends – this creates 10*N possible pairings. 2. Every night, new pairs are randomly selected by the computer Example: Suppose Markus listed Tanya as one of his 10 friends, and that this pairing gets picked.

14. Trivia Game Example a) Tanya (subject) gets an email asking her to log in and answer a question about herself b) Tanya logs in and answers, “which of the following kinds of music do you prefer?”

16. Trivia Game Example (cont.) c) Once Tanya has answered, Markus gets an email inviting him to log in and answer a question about one of his friends. d) After logging in, Markus has 20 seconds to answer “which of the following kinds of music does Tanya prefer?”

18. Trivia Game Example (cont.) e) If Markus’ answer is correct, he and Tanya are entered together into a nightly drawing to win a prize.

19. Trivia Game: Summary Subjects have incentives to list the 10 people they are most likely to be able to answer trivia questions about This is our (implicit) definition of a “friend” This definition is suited for measuring social learning about products. Answers to trivia questions are unimportant  ok if people game the answers as long as the people it’s easiest to game with are the same as those they know best.  Roommates were disallowed  20 second time limit to answer  On average subjects got 50% of 4/5 answer multiple choice questions right – and many were easy

20. Recruitment In addition to invitations on login,  Posters in all hallways  Workers in dining halls with laptops to step through signup  Personalized snail mail to all upper-class students  Article in The Crimson on first grand prize winner Average acquisition cost per subject ~= $2.50

21. Network Data 23,600 links from participants 12,782 links between participants 6,880 of these symmetric (3,440 coordinated friendships)  Similar to 2003 results Construct the network using “or” link definition  5576 out of 6389 undergraduates (87%) participated or were named One giant cluster Average path length between participants = 4.2 Cluster coefficient for participants = 17%  Lower than 2003 results – because many named friends are in different houses

22. Number of Roommate links, friend (N1), indirect friend (N2), and friends of distance 3 (N3) for an average subject (OR network on all participants of trivia game) Type of LinkNumber of Links Ratio Roommate.961 N17.688 N257.9160.32 N3347.14361.6

23. Methods in Comparison 2003 House Experiment in 2 undergraduate houses Email-data: Sacerdote and Marmaris (QJE 2006) Mutual-friend methods with facebook data? (Glaeser et al, QJE 2000)

24. Methodology Seeding Information

25. 1. Elicit subjects’ initial valuations Center empirical estimates Decompose valuations (hedonics) 2. Randomized treatments Distribute product samples Information / instructions 3. Randomized advertising Print (Crimson) and online (thefacebook.com) Informative and persuasive 4. Elicit subjects’ final valuations

26. Example A hypothetical subject “Paul” might be exposed to the following treatments:  A friend of Paul’s of social distance 2 used a PDA  The friend was told about the PDA’s instant messenger capabilities  Paul saw an advertisement for the PDA in the newspaper that emphasized it’s hip-ness  Paul did not see online advertising for the PDA

27. Product Samples We want new products to maximize the potential for social learning. Want to vary products by  Likely demographic appeal  Potential for strong learning (need a manual?)  Potential for weak learning and social influence – the “buzz factor”

28. Durables T-Mobile Sidekick II Philips Key019 Digital Camcorder Philips ShoqBox

29. Perishables Student Advantage Discount Card Qdoba Meal Vouchers Baptiste Studios Yoga Vouchers

30. Step I: Elicit Valuations We want to elicit valuations for a product without telling subjects what the product is. Our solution: We treat a product as a vector of attributes which span a space containing the specific product. We can elicit valuations for each attribute without revealing product.

31. Step I: Configurators Familiar examples with posted menus of prices  many computer manufacturers (e.g. Dell)  some car manufacturers Here, subjects bid for features  Baseline bid for “featureless” product  Incremental bids for distinct features

32. Constructed Bids Subjects told that either this bid or their bid in the followup will be entered into a uniform-price auction with equal probability Construction: Incentives: bid as accurately as possible Extension: interactions between features

33. Feature descriptions Baseline bid Feature bids

34. ($20)($50)($35)($150) ($250)(Price)

35. Distributions of Imputed Bids Results from configurators look sensible  In each case, market prices lie between median bid and upper tail  T-Mobile and Philips confirmed that demand curves for their products are similar to results from more traditional analysis

36. Step 2: Randomized Product Trials Perishables  ½ year Student Advantage cards  5 yoga vouchers  5 meal vouchers Durables  Try out for approximately 4 weeks during end of term

37. Randomization Blocked by year of graduation, gender, and residential house Email invitations to come pick up samples Invitation times varied to vary strength of exposure (April 26 th – May 3 rd )

39. Info Treatments Varied information communicated verbally by workers doing distribution Information treatments correspond to product features in our configurators (5 or 6 features for each product). Reinforced this information treatment with reminder emails Each treatment given with 50% probability to each subject

41. “Buzz” Treatments Product-specific treatments without information content Intended to increase subject’s enjoyment of the product Examples  Subway tokens for yoga, Qdoba  5 free MP3s on ShoqBox  Extra pre-paid balance on Sidekicks  Special one-store subsidy on Student Advantage cards Given with 50% probability to each subject

42. Step 2: Advertising Delivered via thefacebook.com Mixed in with normal paid advertising 65% of subjects saw ads 232,736 impressions (approx. 300 per treated subject) 136 clicks (in line with averages) Online Advertising

43. Advertising Content Content from sponsor companies Tweaked to vary informational content in line with product features Also non-informative versions

44. Step 2: Advertising Inlets in The Crimson, Harvard’s student newspaper One of nation’s largest student papers, daily readership approx. 14,000 Delivered to undergrad students’ rooms Inlets allow randomization across residential houses Print Advertising

47. All ads for a product has the same style and differed only in the informational content.

48. Print advertising 4 inlets with two ads each. 3 ads emphasizing a single feature of a product. Residents in a house were exposed to either 2 or 3 impressions of the same print ad.

49. Step 4: Final Valuations Subjects receive full product descriptions and submit a second round of bids, which go into the auctions with 50% probability Subjects also…  Predict what the average bid will be  Predict what a sample of their friends will bid in the auction  Answer factual questions about each product  Indicate their confidence in these answers

52. Eliciting Confidence Levels Meet “Bob the Robot” and his clones Bob 1 – Bob 100 Subjects are randomly paired with an (unknown) Bob Subjects indicated a “cutoff Bob” at which they are indifferent about who should answer the question If assigned Bob is better than the cutoff, Bob answers the question; otherwise we use subject’s answer Incentive-compatible mechanism to elicit subject’s belief that he/she will get the question right

55. Analysis Measuring Learning

56. Analysis Stage I: Check whether info and ad treatments affected a subject’s knowledge. Stage II: Use info treatments as instruments to measure social learning.

57. Analysis Stage I: Check whether info and ad treatments affected a subject’s knowledge.  Product Group (PG) – Likelihood of answering a question about a feature correctly if primed about that feature at distribution  Non-Product Group (NPG) – Likelihood of answering a question about a feature correctly if exposed to informative advertising about that feature

58. Stage I: Effect of Info Treatments on Knowledge (PG)

59. 85.2 94.2

60. Stage I: Effect of Info Treatments on Knowledge (PG) 85.2 94.2 Subjects who received a product and were primed on a Feature are about 9% more likely to answer the question about the feature correctly.

61. Stage I: Info-Treatments FCONFIDENCEFCORRECTANSWER (1)(2)(3)(4)(5)(6) NUMTREATED.748* (.373).766 (.505).007 (.007).007 (.007) FTREATED7.057* (.825) 7.087* (.825) 7.080* (.825).082** (.015).083** (.014).085** (.014) Intercept85.468* (1.065) 85.361* (1.065) 85.645* (1.065).838** (.019).837** (.021).856** (.010) Fixed effectsNoneREFENoneREFE N1927 1930 R2R2.054.056.058.022.023.022 Significance Levels: *: 5% **: 1%

62. Stage I: Info-Treatments FCONFIDENCEFCORRECTANSWER (1)(2)(3)(4)(5)(6) NUMTREATED.748* (.373).766 (.505).007 (.007).007 (.007) FTREATED7.057* (.825) 7.087* (.825) 7.080* (.825).082** (.015).083** (.014).085** (.014) Intercept85.468* (1.065) 85.361* (1.065) 87.645* (1.065).838** (.019).837** (.021).856** (.010) Fixed effectsNoneREFENoneREFE N1927 1930 R2R2.054.056.058.022.023.022 Significance Levels: *: 5% **: 1% Both confidence and knowledge increases with info treatments.

63. Stage I: Effect of Online Ad on Knowledge (NPG) Effect of online ads on subjects who did not receive products or print ads.

64. Stage I: Effect of Online Ad on Knowledge (NPG) Effect of online ads on subjects who did not receive products or print ads. 64.7 % 73.5 % 71.0 %

65. Stage I: Effect of Online Ad on Knowledge (NPG) Effect of online ads on subjects who did not receive products or print ads. 64.7 % 73.5 % 71.0 % Subjects who received online ads are about 5-8% more likely to answer the question about the feature correctly.

66. Stage I: Effect of Print Ad on Knowledge (NPG) Effect of print ads on subjects who did not receive products or online ads.

67. Stage I: Effect of Print Ad on Knowledge (NPG) 64.8% 71.3% 79.8% Effect of print ads on subjects who did not receive products or online ads.

68. Stage I: Effect of Print Ad on Knowledge (NPG) 64.8% 71.3% 79.8% Effect of print ads on subjects who did not receive products or online ads. Subjects who received print ads are about 8-15% more likely to answer the question about the feature correctly. The effect is increasing in intensity of exposure.

69. Stage I: Ad-Treatments FCONFIDENCE FCORRECTANSWER (1)(2)(3)(4)(5)(6) PIMPRESSIONS 1.108 (.698) 1.142 (1.133) -.022 # (.012) -.022 (.014) FIMPRESSIONS 2.278 (1.525) 2.198* (1.075) 2.182* (1.075).121** (.026).121** (.026).120** (.025) PCRIMSONNUMADS -.520** (.146) -.496* (.243) -.008** (.003) -.008** (.003) FCRIMSONNUMADS1.883** (.264) 1.659** (.187) 1.614** (.187).052** (.005).051** (.004).048** (.004) Intercept63.496** (0.249) 63.509** (0.439) 63.144** (0.138).650** (.004).650** (.005).640** (.003) Fixed effectsNoneREFENoneREFE N22,959 22,995 R2R2.003.004.006.007.008 Significance Levels: #:10% *: 5% **: 1%

70. Stage I: Ad-Treatments FCONFIDENCE FCORRECTANSWER (1)(2)(3)(4)(5)(6) PIMPRESSIONS 1.108 (.698) 1.142 (1.133) -.022 # (.012) -.022 (.014) FIMPRESSIONS 2.278 (1.525) 2.198* (1.075) 2.182* (1.075).121** (.026).121** (.026).120** (.025) PCRIMSONNUMADS -.520** (.146) -.496* (.243) -.008** (.003) -.008** (.003) FCRIMSONNUMADS1.883** (.264) 1.659** (.187) 1.614** (.187).052** (.005).051** (.004).048** (.004) Intercept63.496** (0.249) 63.509** (0.439) 63.144** (0.138).650** (.004).650** (.005).640** (.003) Fixed effectsNoneREFENoneREFE N22,959 22,995 R2R2.003.004.006.007.008 Significance Levels: #:10% *: 5% **: 1% Both confidence and knowledge increases with ad treatments.

71. Stage I: Buzz-Treatments BID All Products ServicesGadgets BUZZ8.504* (4.206) 1.516 (1.561) 23.706* (9.176) NUMTREATED3.780* (1.886).822 (.669) 5.837* (4.526) N373227146 R2R2.019.01.048 Significance Levels: *: 5% **: 1%

72. Stage I: Buzz-Treatments BID All Products ServicesGadgets BUZZ8.504* (4.206) 1.516 (1.561) 23.706* (9.176) NUMTREATED3.780* (1.886).822 (.669) 5.837* (4.526) N373227146 R2R2.019.01.048 Significance Levels: *: 5% **: 1% Buzz treatments raise valuations for gadgets.

73. Analysis: stage II Use successful first stage as instruments for measuring the effects of social learning. Regress confidence or correct answers of every NPG member on sum friends’ knowledge (PG) at various social distance using sum of info treatments as instruments.

74. Confidence FCONFIDENCE (1) (2) PGFCONFIDENCE_R.064* (.029).057 # (.031) PGFCONFIDENCE_NW1.040** (.013).034* (.014) PGFCONFIDENCE_NW2.005 (.005).008 # (.005) PGFCONFIDENCE_NW3.003** (.001).009** (.001) Control for # of EligibleNOYES Intercept59.628** (.826) 67.870** (1.197) N8,982 R2R2 0.0180.045 Significance Levels: #:10% *: 5% **: 1%

75. FCONFIDENCE (1) (2) PGFCONFIDENCE_R.064* (.029).057 # (.031) PGFCONFIDENCE_NW1.040** (.013).034* (.014) PGFCONFIDENCE_NW2.005 (.005).008 # (.005) PGFCONFIDENCE_NW3.003** (.001).009** (.001) Control for # of EligibleNOYES Intercept59.628** (.826) 67.870** (1.197) N8,982 R2R2 0.0180.045 Significance Levels: #:10% *: 5% **: 1%

76. FCONFIDENCE (1) (2) PGFCONFIDENCE_R.064* (.029).057 # (.031) PGFCONFIDENCE_NW1.040** (.013).034* (.014) PGFCONFIDENCE_NW2.005 (.005).008 # (.005) PGFCONFIDENCE_NW3.003** (.001).009** (.001) Control for # of EligibleNOYES Intercept59.628** (.826) 67.870** (1.197) N8,982 R2R2 0.0180.045 Significance Levels: #:10% *: 5% **: 1% Control for # of subjects who were eligible to receive products at distance R, NW1, NW2 and NW3.

77. FCORRECTANSWER (1) (2) PGFCORRECTANSWER_R.108** (.026).070 * (.030) PGFCORRECTANSWER_NW1.041** (.013).018 (.014) PGFCORRECTANSWER_NW2.019** (.005).020** (.005) PGFCORRECTANSWER_NW3.007** (.001).018** (.002) Control for # of EligibleNOYES Intercept.567** (.010) 0.696** (0.014) N9,006 R2R2 0.0330.064 Significance Levels: #:10% *: 5% **: 1%

78. FCORRECTANSWER (1) (2) PGFCORRECTANSWER_R.108** (.026).070 * (.030) PGFCORRECTANSWER_NW1.041** (.013).018 (.014) PGFCORRECTANSWER_NW2.019** (.005).020** (.005) PGFCORRECTANSWER_NW3.007** (.001).018** (.002) Control for # of EligibleNOYES Intercept.567** (.010) 0.696** (0.014) N9,006 R2R2 0.0330.064 Significance Levels: #:10% *: 5% **: 1%

79. FCORRECTANSWER (1) (2) PGFCORRECTANSWER_R.108** (.026).070 * (.030) PGFCORRECTANSWER_NW1.041** (.013).018 (.014) PGFCORRECTANSWER_NW2.019** (.005).020** (.005) PGFCORRECTANSWER_NW3.007** (.001).018** (.002) Control for # of EligibleNOYES Intercept.567** (.010) 0.696** (0.014) N9,006 R2R2 0.0330.064 Significance Levels: #:10% *: 5% **: 1% One standard deviation increase in each friend’s knowledge (about 30%) raises my knowledge by 1% to 2%. The total effect is about 9% because subjects are influenced by several treated subjects on average.

80. Alternative approach: Regressing knowledge on friends’ knowledge only measures average amount of social learning. We can instead measure social learning conditional on two subjects having reported to have talked to each other (collected during follow-up – 350 NPG subjects listed specific PG subjects whom they had talked to). We exploit the fact that we both randomly distributed products and randomized information for each subject who received a product. We assume that a NPG-subject’s pre-information is uncorrelated with the info treatment received by the PG-subject whom he or she talks to about the product. This excludes the following situation: If I know that a Sidekick has AOL messenger I will specifically seek out subjects who received a product and whom we told about the AOL messenger capability of the Sidekick.

81. Effect of Info-Treated Friends on Knowledge (NPG) Effect of PG-subject’s info-treatment on NPG-subject’s knowledge (only for subjects who Reported to have talked to specific PG subject)

82. Effect of Info-Treated Friends on Knowledge (NPG) Effect of PG-subject’s info-treatment on NPG-subject’s knowledge (only for subjects who Reported to have talked to specific PG subject and seen PG subject with product) 68.4 74.3

83. Effect of Info-Treated Friends on Knowledge (NPG) Effect of PG-subject’s info-treatment on NPG-subject’s knowledge (only for subjects who Reported to have talked to specific PG subject and seen PG subject with product) 68.4 74.3 Subjects who reported to have talked to a friend who had the product and whom they have seen use the product are 6% more likely to correctly answer a question about the feature if their friend had received an info treatment.

84. IV-Regression – confidence in answer FCONFIDENCE Talked about OR seen (all) Talked about OR seen (services) Talked about OR seen (gadget) Talked about AND seen FR_FCONFIDENCE.142** (.054).124 (.100).151* (.064).184* (.074) Intercept61.617** (5.626) 67.697** (10.124) 59.495** (6.795) 57.503** (7.790) N1,9124001,5111,207 Significance Levels: #:10% *: 5% **: 1%

85. IV-Regression – confidence in answer FCONFIDENCE Talked about OR seen (all) Talked about OR seen (services) Talked about OR seen (gadget) Talked about AND seen FR_FCONFIDENCE.142** (.054).124 (.100).151* (.064).184* (.074) Intercept61.617** (5.626) 67.697** (10.124) 59.495** (6.795) 57.503** (7.790) N1,9124001,5111,207 Significance Levels: #:10% *: 5% **: 1%

86. IV-Regression - knowledge FCORRECTANSWER Talked about OR seen (all) Talked about OR seen (services) Talked about OR seen (gadget) Talked about AND seen FR_FCORRECTANSWER.180** (.067).011 (.106).246** (.077).325** (.112) Intercept.567** (.068).890** (.107).461** (.077).400** (.109) N1,9194001,5191,209 Significance Levels: #:10% *: 5% **: 1%

87. IV-Regression - knowledge FCORRECTANSWER Talked about OR seen (all) Talked about OR seen (services) Talked about OR seen (gadget) Talked about AND seen FR_FCORRECTANSWER.180** (.067).011 (.106).246** (.077).325** (.112) Intercept.567** (.068).890** (.107).461** (.077).400** (.109) N1,9194001,5191,209 Significance Levels: #:10% *: 5% **: 1% Info-treatment of friend is used as instrument. Estimated social-learning effects are about 3-15 times greater than the average effects estimated across all subjects.

88. Observations Conditional on having communicated about the product social learning seems strongest for gadgets rather than services. This might indicate that visual observation is important for social learning. It is also possible that our feature set for gadgets provides a more natural decomposition of real-world communication than our feature set for services.

89. Summary Three methodological contributions  Application–specific measure of social connectedness  Hedonic analysis using configurators  Measure of confidence using the Bobs Advertising increases information. Social learning is as important as effects of advertising. Future work:  Disentangle weak and strong social learning channels  Measure social influence.

91. Numbers 2360 social network 1200 baseline survey 600 people eligible in each category 100 people chosen 1100 people follow-up survey

92. Product Tester F3 F1 F2 F3 F1 F2 Eligible Nontester Product Tester vs Eligible NonTester

93. Recipient Direct Friend Direct Friend Direct Friend Direct Friend Recipients are asked to make predictions in 7 situations (in random order): 1 direct friend; 1 indirect friend of social distance 2; 1 indirect friend of social distance 3; 1 person from the same staircase; 1 person from the same house; 2 pairs chosen among direct and indirect friends Indirect Friend 2 links Indirect Friend 3 links Share staircase Same house Recipients A possible pair