Social Learning and Consumer Demand

transcript

Markus Mobius (Harvard University and NBER)Paul Niehaus (Harvard University)Tanya Rosenblat (Wesleyan University and IAS)

CMPO, 2 June 2006

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?

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”

Learning Persuasion

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.

Learning Persuasion

Social Influence

Agents observe their friends’ consumption decisions and....

• Their private tastes are altered

• The status value of consuming the product is altered

Learning Persuasion

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.

Methodology: basic paradigm

Stage 1: Measure the network (Harvard Undergraduates)

Stage 2: Distribute actual products and track social learning

Methodology

Measuring the Social Network

Measuring the Network

Rather than surveys, agents play in a trivia game

Leveraged popularity of www.thefacebook.comMembership rate at Harvard College over

90% *95% weekly return rate *

* Data provided by the founders of thefacebook.com

home search global social net invite faq logout

quick search go

sponsor

• offensive? tell us.• announcesomething

My Profile [ edit ]My FriendsMy GroupsMy PartiesMy MessagesMy AccountMy Privacy

Work from bed!

(Or desk, or kitchen)

Write short abstractsand earn royalties

www.shvoong.com

Paul Niehaus' Profile (This is you) Har

Picture [ edit ]

Visualize My Friends

Edit My Profile

My Account Prefer ences

My Privacy Preferences

Connection

This is you.

Access

Paul is currently logged in from a non-residential location.

Friends at Harvard [ edit ]

Paul has 80 friends at Harvard.[ see all of them ]

RohitChopra

Anna ByrneRussellAnello

ShannonChristmas

Zach LazarDaniel

Morales

Other Schools [ edit ]

Information [ ed

Account Info:

Name: Paul Niehaus

Member Since: May 18, 2004

Last Update: June 6, 2005

Basic Info: [ ed

School: Harvard '04

Geography: Boston, MA

Status: Grad Student

Sex: Male

Concentration: Economics

Birthday: 03/11/1982

High School: St. John's Prep '00

Contact Info: [ ed

Email:

Screenname: pfn007

Mobile: 508.335.5242

Website: http:/ /www.people.fas.harvard.edu/~nieha

Personal Info: [ ed

Relationship Status: In a Relationship with

Lauren Young (Berkeley)

Interests: visiting / talking to / daydreaming aboutLauren Young

Clubs and Jobs: Americans for Being Awesome

Favorite Music: donkey kong count ry II soundtrack

Favorite Books: The Bible, Development as Freedom, LOTRThe Screwtape Letters , Moneyball, MWG!

Favorite Movies: Kindergarten Cop , Office Space, Friday,Good Will Hunting, Pumping Iron 20thanniversary edition, pretty much any othermovies with Ahnold except Junior, Dr.Strangelove, Kujo's happy bi rthday movie

Favorite Quote: good advice I have received from friends:

"it'll be snowy and cold tomorrow, so kee pwarm and avoid slipperiness."- Yi Qian

"you should have proposed toa heterosexwoman."- Michael Baldwin

"go to grad school. I went, an d I loved it."- Elhanan Helpman

Summer Plans [ ed

Job/Activity: hanging out with Lauren

Location: Cambridge, MA, 02140

Additional Info:also catc

• Markus

• His Profile

• (Ad Space)

• His Friends

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)

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.

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?”

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?”

Trivia Game Example (cont.)

e) If Markus’ answer is correct, he and Tanya are entered together into a nightly drawing to win a prize.

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

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

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

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 Link Number of

LinksRatio

Roommate .96 1

N1 7.68 8

N2 57.91 60.32

N3 347.14 361.6

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)

Methodology

Seeding Information

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

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

Product Samples

We want new products to maximize the potential for social learning.

Want to vary products byLikely demographic appealPotential for strong learning (need a manual?)Potential for weak learning and social

influence – the “buzz factor”

DurablesT-Mobile Sidekick II

Philips Key019 Digital Camcorder

Philips ShoqBox

Perishables Student Advantage Discount Card

Qdoba Meal Vouchers

Baptiste Studios Yoga Vouchers

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.

Step I: Configurators

Familiar examples with posted menus of pricesmany computer manufacturers (e.g. Dell)some car manufacturers

Here, subjects bid for featuresBaseline bid for “featureless” product Incremental bids for distinct features

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

Potential additional featuresfor this product include:

Amtrak discounts: studentdiscounts on Amtrak trains.

Textbook discounts: ontextbook purchases atBarnes&Noble.com

Greyhound discounts:student discounts onGreyhound trains.

Online guides: websiteprovides a guide todiscounts by product typeand by city.

Clothing discounts: studentdiscounts at UrbanOutfitters.

Baseline bid for StudentDiscount Card

Textbook discounts

Clothing discounts

Greyhound discounts

Amtrak discounts

Online guides

Feature descriptions

Baseline bid

Feature bids

Card Yoga Food Camcorder ShoqBox Sidekick

Distributions of Imputed Bids

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

Distributions of Imputed Bids

Results from configurators look sensible In each case, market prices lie between

median bid and upper tailT-Mobile and Philips confirmed that demand

curves for their products are similar to results from more traditional analysis

Step 2: Randomized Product Trials

Perishables½ year Student Advantage cards5 yoga vouchers5 meal vouchers

DurablesTry out for approximately 4 weeks during end

of term

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 26th – May 3rd)

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

“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

home search global social net invite faq logout

quick search go

sponsor

• offensive? tell us.• announcesomething

My Profile [ edit ]My FriendsMy GroupsMy PartiesMy MessagesMy AccountMy Privacy

Work from bed!

(Or desk, or kitchen)

Write short abstractsand earn royalties

www.shvoong.com

Paul Niehaus' Profile (This is you) Har

Picture [ edit ]

Visualize My Friends

Edit My Profile

My Account Prefer ences

My Privacy Preferences

Connection

This is you.

Access

Paul is currently logged in from a non-residential location.

Friends at Harvard [ edit ]

Paul has 80 friends at Harvard.[ see all of them ]

RohitChopra

Anna ByrneRussellAnello

ShannonChristmas

Zach LazarDaniel

Morales

Other Schools [ edit ]

Information [ ed

Account Info:

Name: Paul Niehaus

Member Since: May 18, 2004

Last Update: June 6, 2005

Basic Info: [ ed

School: Harvard '04

Geography: Boston, MA

Status: Grad Student

Sex: Male

Concentration: Economics

Birthday: 03/11/1982

High School: St. John's Prep '00

Contact Info: [ ed

Email:

Screenname: pfn007

Mobile: 508.335.5242

Website: http:/ /www.people.fas.harvard.edu/~nieha

Personal Info: [ ed

Relationship Status: In a Relationship with

Lauren Young (Berkeley)

Interests: visiting / talking to / daydreaming aboutLauren Young

Clubs and Jobs: Americans for Being Awesome

Favorite Music: donkey kong count ry II soundtrack

Favorite Books: The Bible, Development as Freedom, LOTRThe Screwtape Letters, Moneyball, MWG!

Favorite Movies: Kindergarten Cop, Office Space, Friday,Good Will Hunting, Pumping Iron 20thanniversary edition, pretty much any othermovies with Ahnold except Junior, Dr.Strangelove, Kujo's happy bi rthday movie

Favorite Quote: good advice I have received from friends:

"it'll be snowy and cold tomorrow, so kee pwarm and avoid slipperiness."- Yi Qian

"you should have proposed toa heterosexwoman."- Michael Baldwin

"go to grad school. I went, an d I loved it."- Elhanan Helpman

Summer Plans [ ed

Job/Activity: hanging out with Lauren

Location: Cambridge, MA, 02140

Additional Info:also catc

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

Advertising Content Content from sponsor

companies Tweaked to vary

informational content in line with product features

Also non-informative versions

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

All ads for a product has the same styleand differed only in the informational content.

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.

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

Facebook Experiment

First Product

Personal Sound Systemwith MP3 players

Time left: 46

This product is a Personal Sound System,an MP3 player with two inbuilt speakers loudenough to fill a room. It is small enough to fitin your pocket and you can upload songsdirectly from your computer.

Please submit your bid for this product:______ Dollars

You can increase your earnings by 50 cents if youranswer to the following question is not more than10 percent off.

What is your best guess for the averagebid of all other participants?: ______ Dollars

Facebook Experiment

First Product

Personal Sound System with MP3 playersFor each of the following students please predict how they will bid in the auction. For each student if your answer is within10 percent of their true bid we will add10 cents to your earnings.

Danielle Sassoon (FR, Canaday) ______ Dollars Skyler Johnson (FR, Canaday) ______ Dollars

Rachel Thornton (FR, Canaday) ______ Dollars Danny Mou (FR, Canaday) ______ Dollars

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

Facebook Experiment

Second Product

T-Mobile Sidekick IITime left: 36

How confident are you that you can answer some YES/NOquestions about this product correctly?Your confidence: ______ percent

You can increase your earnings by 50 cents if your answer to the followingquestion is not more than 10 percent off.

Please estimate the average confidence of other participants in thisstudy to answer some YES/NO question about this product correctly?______ percent

Next Page >>

Facebook Experiment

Second Product

T-Mobile Sidekick IITime left: 84

Question 1Does the Sidekick include AOL messenger?

YES NO

Your confidence:______ percent

Question 2Does the Sidekick have a color screen?

YES NO

Question 3Does the Sidekick have 10 or more hours of batterylife?

YES NO

Question 4Does the Sidekick have a QWERTY keyboard?

YES NO

Question 5Does the Sidekick include a camera?

YES NO

Question 6Does the Sidekick use the Pocket PC OS?

YES NO

Analysis

Measuring Learning

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.

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

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

85.294.2

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

85.294.2

Subjects who received a product and were primed on a Feature are about 9% more likely to answer the question about the feature correctly.

Stage I: Info-TreatmentsFCONFIDENCE FCORRECTANSWER

(1) (2) (3) (4) (5) (6)

NUMTREATED .748*

(.373)

(.505)

(.007)

FTREATED 7.057*

(.825)

7.087*

(.825)

7.080*

(.825)

.082**

(.015)

.083**

(.014)

.085**

(.014)

Intercept 85.468*

(1.065)

85.361*

(1.065)

85.645*

(1.065)

.838**

(.019)

.837**

(.021)

.856**

(.010)

Fixed effects None RE FE None RE FE

N 1927 1927 1927 1930 1930 1930

R2 .054 .056 .058 .022 .023 .022

Significance Levels: *: 5% **: 1%

Stage I: Info-TreatmentsFCONFIDENCE FCORRECTANSWER

(1) (2) (3) (4) (5) (6)

NUMTREATED .748*

(.373)

(.505)

(.007)

FTREATED 7.057*

(.825)

7.087*

(.825)

7.080*

(.825)

.082**

(.015)

.083**

(.014)

.085**

(.014)

Intercept 85.468*

(1.065)

85.361*

(1.065)

87.645*

(1.065)

.838**

(.019)

.837**

(.021)

.856**

(.010)

N 1927 1927 1927 1930 1930 1930

R2 .054 .056 .058 .022 .023 .022

Both confidence and knowledge increases with info treatments.

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 %

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.

Stage I: Effect of Print Ad on Knowledge (NPG)

Effect of print ads on subjects who did not receive products or online ads.

64.8%71.3%

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.

Stage I: Ad-Treatments FCONFIDENCE FCORRECTANSWER

(1) (2) (3) (4) (5) (6)

PIMPRESSIONS 1.108

(.698)

(1.133)

-.022#

(.012)

(.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)

FCRIMSONNUMADS 1.883**

(.264)

1.659**

(.187)

1.614**

(.187)

.052**

(.005)

.051**

(.004)

.048**

(.004)

Intercept 63.496**

(0.249)

63.509**

(0.439)

63.144**

(0.138)

.650**

(.004)

.650**

(.005)

.640**

(.003)

N 22,959 22,959 22,959 22,995 22,995 22,995

R2 .003 .003 .004 .006 .007 .008

Significance Levels: #:10% *: 5% **: 1%

Stage I: Ad-Treatments FCONFIDENCE FCORRECTANSWER

(1) (2) (3) (4) (5) (6)

PIMPRESSIONS 1.108

(.698)

(1.133)

-.022#

(.012)

(.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)

FCRIMSONNUMADS 1.883**

(.264)

1.659**

(.187)

1.614**

(.187)

.052**

(.005)

.051**

(.004)

.048**

(.004)

Intercept 63.496**

(0.249)

63.509**

(0.439)

63.144**

(0.138)

.650**

(.004)

.650**

(.005)

.640**

(.003)

N 22,959 22,959 22,959 22,995 22,995 22,995

R2 .003 .003 .004 .006 .007 .008

Both confidence and knowledge increases with ad treatments.

Stage I: Buzz-TreatmentsBID

All Products

Services Gadgets

BUZZ 8.504*

(4.206)

(1.561)

23.706*

(9.176)

NUMTREATED 3.780*

(1.886)

(.669)

5.837*

(4.526)

N 373 227 146

R2 .019 .01 .048

Stage I: Buzz-TreatmentsBID

All Products

Services Gadgets

BUZZ 8.504*

(4.206)

(1.561)

23.706*

(9.176)

NUMTREATED 3.780*

(1.886)

(.669)

5.837*

(4.526)

N 373 227 146

R2 .019 .01 .048

Buzz treatments raise valuations for gadgets.

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.

Confidence FCONFIDENCE

(1) (2)

PGFCONFIDENCE_R .064*

(.029)

(.031)

PGFCONFIDENCE_NW1 .040**

(.013)

(.014)

PGFCONFIDENCE_NW2 .005

(.005)

(.001)

.009**

(.001)

Control for # of Eligible NO YES

Intercept 59.628**

(.826)

67.870**

(1.197)

N 8,982 8,982

R2 0.018 0.045

FCONFIDENCE FCONFIDENCE

(1) (2)

(.029)

(.031)

(.013)

(.014)

(.005)

(.001)

.009**

(.001)

Intercept 59.628**

(.826)

67.870**

(1.197)

N 8,982 8,982

R2 0.018 0.045

FCONFIDENCE FCONFIDENCE

(1) (2)

(.029)

(.031)

(.013)

(.014)

(.005)

(.001)

.009**

(.001)

Intercept 59.628**

(.826)

67.870**

(1.197)

N 8,982 8,982

R2 0.018 0.045

Control for # of subjects who were eligible to receive products at distance R, NW1, NW2 and NW3.

FCORRECTANSWER FCORRECTANSWER

(1) (2)

PGFCORRECTANSWER_R .108**

(.026)

(.030)

PGFCORRECTANSWER_NW1 .041**

(.013)

(.014)

(.005)

.020**

(.005)

(.001)

.018**

(.002)

Intercept .567**

(.010)

0.696**

(0.014)

N 9,006 9,006

R2 0.033 0.064

(1) (2)

(.026)

(.030)

(.013)

(.014)

(.005)

.020**

(.005)

(.001)

.018**

(.002)

Intercept .567**

(.010)

0.696**

(0.014)

N 9,006 9,006

R2 0.033 0.064

(1) (2)

(.026)

(.030)

(.013)

(.014)

(.005)

.020**

(.005)

(.001)

.018**

(.002)

Intercept .567**

(.010)

0.696**

(0.014)

N 9,006 9,006

R2 0.033 0.064

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 severaltreated subjects on average.

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.

Effect of Info-Treated Friends on Knowledge (NPG)

Effect of PG-subject’s info-treatment on NPG-subject’s knowledge (only for subjects whoReported to have talked to specific PG subject)

Effect of PG-subject’s info-treatment on NPG-subject’s knowledge (only for subjects whoReported to have talked to specific PG subject and seen PG subject with product)

68.474.3

Effect of PG-subject’s info-treatment on NPG-subject’s knowledge (only for subjects whoReported to have talked to specific PG subject and seen PG subject with product)

68.474.3

Subjects who reported to have talked to a friend who had the productand 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.

IV-Regression – confidence in answer FCONFIDENCE

Talked about OR

Talked about OR seen

(services)

(gadget)

Talked about AND

FR_FCONFIDENCE .142**

(.054)

(.100)

(.064)

(.074)

Intercept 61.617**

(5.626)

67.697**

(10.124)

59.495**

(6.795)

57.503**

(7.790)

N 1,912 400 1,511 1,207

IV-Regression – confidence in answer FCONFIDENCE

Talked about OR

(services)

(gadget)

Talked about AND

FR_FCONFIDENCE .142**

(.054)

(.100)

(.064)

(.074)

Intercept 61.617**

(5.626)

67.697**

(10.124)

59.495**

(6.795)

57.503**

(7.790)

N 1,912 400 1,511 1,207

IV-Regression - knowledge FCORRECTANSWER

Talked about OR

(services)

(gadget)

Talked about AND

FR_FCORRECTANSWER .180**

(.067)

(.106)

.246**

(.077)

.325**

(.112)

Intercept .567**

(.068)

.890**

(.107)

.461**

(.077)

.400**

(.109)

N 1,919 400 1,519 1,209

IV-Regression - knowledge FCORRECTANSWER

Talked about OR

(services)

(gadget)

Talked about AND

FR_FCORRECTANSWER .180**

(.067)

(.106)

.246**

(.077)

.325**

(.112)

Intercept .567**

(.068)

.890**

(.107)

.461**

(.077)

.400**

(.109)

N 1,919 400 1,519 1,209

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.

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.

Analysis

Alternative Model

An untreated (uninformed) subject has a probability p of interacting with some treated (informed) subject.

The interaction probability p depends on the social distance between uninformed and informed subject.

We distinguish three types of social distances: room mates (M), direct friends (NW1) and indirect friends (NW2).

We define knowledge as the subjective or objective probability of answering a question about the product correctly.

If an informed and uninformed subject interact the knowledge of the informed subject is transferred to the uninformed subject (informed = treated with a product).

We define knowledge as the subjective or objective probability of answering a question about the product correctly.

If an informed and uninformed subject interact the knowledge of the informed subject is transferred to the uninformed subject (informed = treated with a product).

After interacting the uninformed subject has the same probability of answering a question correctly as the informed subject.

Model Assume that the knowledge of an informed subject is and the

knowledge of an uninformed subject is .

Assume that the uninformed’s probability of interacting with some informed subject is X. Then we can express the final expected knowledge of the uninformed agent as:

UniformedInformedFinal FXFXF )1(

InfFUnifF

What is X?Assume that the uninformed agent has room mates who were

offered a product, direct friends and indirect friends. Then we can express X as:

21 )1()1()1(1 21NWNWR n

R pppX

Rn1NWn 2NWn

What is X?Assume that the uninformed agent has room mates who were

offered a product, direct friends and indirect friends. Then we can express X as:

21 )1()1()1(1 21NWNWR n

R pppX

Rn1NWn 2NWn

The probability of interacting with some informed subject is 1 minus theprobability of interacting with none of them.

Model We obtain:

21 )1()1()1)(( 21NWNWR n

RUniformedInformedFinalInformed pppFFFF

• We observe and in the followup survey.InfF FinalF

Model We obtain:

21 )1()1()1)(( 21NWNWR n

• We observe and in the followup survey.

• We do not observe because we cannot do a baseline quiz without revealing the product.

InfF FinalF

UniformedF

Model We obtain expression (*):

21 )1()1()1)(( 21NWNWR n

• We observe and in the followup survey.

• However, we do not observe because we cannot do a baseline quiz without revealing the product.

• Moreover, we expect the information of uninformed agents to vary with the number of eligible neighbors (and hence the number of neighbors who were offered a treatment) due to selection.

InfF FinalF

UniformedF

We instead compare agents in similar “cells”:

NW2 friends: Eligible, Treated

Roommate (M) friends: Eligible , Treated

Subject without product

We instead compare untreated agents in similar “cells”:

We say the green subject lives in a (1,4+,4) cell to indicate that she has onetreated room-mate, and four treated NW1 and NW2 friends AND she has at least one more eligible (but non-treated) NW1 friend (indicated by plus sign).

For example, compare a (1,4+,4) cell with a (1,5,4) cell:

The green agent on the right faces the same neighborhood as the agent on the leftbut the randomization turned one eligible, untreated agent into a treated agent.

By dividing expression (*) for all agents in cell (1,5,4) by expression (*) for all agents in cell (1,4+,4) we obtain the marginal impact of treating one more NW1 neighbor:

1)4,4,1()4,4,1(

)4,5,1()4,5,1(

1 NWFinalInformed

FinalInformed pFF

1)4,4,1()4,4,1(

)4,5,1()4,5,1(

1 NWFinalInformed

FinalInformed pFF

Since we only have finitely many observations per cell we get an estimate forp. For each marginal comparison between two neighboring cells we get a newestimate. From this we can construct an estimate for p and a confidence interval.

1)4,4,1()4,4,1(

)4,5,1()4,5,1(

1 NWFinalInformed

FinalInformed pFF

By comparing neighboring cells we are essentially differing out the unobserved knowledge of the uninformed agent.

Analysis

Results

We are estimating the interaction probabilities separately for each product.

We use both subjective knowledge (“What is the probability that you can answer a Yes/No question correctly?”) and objective knowledge (“Actual share of correctly answered questions in the quiz”).

Results - Card

0.14 0.13

raction P

robability

M NW1 NW2

Objective Knowledge Subjective Knowledge

Results - Card

0.14 0.13

raction P

robability

M NW1 NW2

SE (0.16)* (0.21)* (0.02)* (0.04)* (0.09) (0.03)

Results - Yoga

raction P

robabili

M NW1 NW2

SE (0.19)* (0.23)* (0.04)* (0.03)* (0.03) (0.05)*

Results – Restaurant

0.120.10

0.01 0.01

raction P

robability

M NW1 NW2

SE (0.03)* (0.08)* (0.03)* (0.04)* (0.02) (0.01)

Results – Camcorder

0.12 0.13

0.04 0.05

ction P

robability

M NW1 NW2

camcorder

SE (0.02)* (0.02)* (0.02)* (0.03)* (0.02)* (0.02)*

Results – MP3

0.120.08

0.04 0.04

raction P

robability

M NW1 NW2

SE (0.06)* (0.07)* (0.03)* (0.04)* (0.02)* (0.01)*

Results – PDA

0.120.16

0.06 0.05

raction P

robability

M NW1 NW2

SE (0.04)* (0.07)* (0.03)* (0.04)* (0.02) (0.02)

Results

For “private products” the interaction probability for NW2 neighbors is usually insignificant.

For “public products” the NW2 effect is small but significant.

NW2 neighborhoods are also 7-times as large as NW1 neighborhoods! Therefore, the expected number of influenced NW2 agents can be large.

Who is influenced the most by social learning (close or distant neighbors)?(expected number of interactions taking Nhood size into account; subjective knowledge and significant probabilities only)

M NW1 NW2 TOTAL

CARD 0.50 1.12 1.62

YOGA 0.60 1.60 1.20

FOOD 0.24 0.80 1.04

CAM. 0.65 1.12 2.85 4.62

SOUND 0.50 0.64 2.28 3.42

PDA 0.45 1.44 2.85 4.74

Who is influenced the most by social learning (close or distant neighbors)?(expected number of interactions taking Nhood size into account; subjective knowledge and significant probabilities only)

M NW1 NW2 TOTAL

CARD 0.50 1.12 1.62

YOGA 0.60 1.60 1.20

FOOD 0.24 0.80 1.04

CAM. 0.65 1.12 2.85 4.62

SOUND 0.50 0.64 2.28 3.42

PDA 0.45 1.44 2.85 4.74

Although there is a greater probability to interact with close agents the expected number of interactions increases with distance.

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.

Social Learning and Consumer Demand

Documents