Measuring Trust in Social Networks Tanya Rosenblat (Wesleyan University, IQSS and IAS) March 2,...
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Transcript of Measuring Trust in Social Networks Tanya Rosenblat (Wesleyan University, IQSS and IAS) March 2,...
Measuring Trust in Social Networks
Tanya Rosenblat (Wesleyan University, IQSS and IAS)
March 2, 2006
Motivation
Trust game focuses on trust between strangers.
We are interested in trust between agents in a social network.
Specifically, we want to know how trust varies with social distance.
Trust & Social Distance: Channels
Preferences: We trust friends more because they like us more.
Beliefs: We trust friends more because we know their type (reliability for example).
Enforcement: We trust friends more because we interact more frequently with them and can punish them better.
Example 1 Andy consider lending money to Guillaume.
Preferences: Andy thinks Guillaume likes him and won’t inconvenience him by repaying late.
Beliefs: Andy knows that Guillaume is a reliable person – he is less sure of the reliability of people he knows less well.
Enforcement: Andy sees Guillaume every day and will hide Guillaume’s cigarettes or commit some other cruelty if he doesn’t repay in time.
Example 2 Muriel asks Tanya to look after her house and take care of financial
matters while she travels.
Preferences: Muriel thinks Tanya likes her and will exert some effort to avoid penalties (from unpaid paying utility penalties etc.).
Beliefs: Muriel thinks Tanya is more reliable than Guillaume who’ll set the house on fire.
Enforcement: Muriel sees Tanya often and can punish her if Tanya doesn’t keep her promise to look after the house.
First Experiment: Web-based Social networks in two student dorms
(N=569)
Preferences: use modified dictator games as in Andreoni-Miller (2002) to measure how altruistic we expect our friends to be and how altruistic they actually behave towards us (as compared to strangers).
Enforcment: Two within subject treatments to check for enforcement channel: (T1) recipient finds out and (T2) recipient does not find out.
Second Experiment: Field Two shantytowns in Lima, Peru (300
households each) Use a new microfinance experiment which
requires clients to find sponsors who cosign their loan. Our experiment simulates the situation: whom do I approach if I need money?
We randomize interest rates to measure how much easier it is to ask a friend for money than a socially more distant neighbor.
Clients’ choices reveal the sum of preferences/belief/enforcement channels.
House Experiment
Methodology
House Experiment: Methodology
Stage I: Network Elicitation Game
Choose two student dorms (N=802). About 50 percent of friends inside dorm.
569 subjects complete baseline survey.
Stage II: Modified Dictator Games
Half the subjects are allocators and play modified dictator games with 5 recipients of various social distance.
The other half of subjects are recipients and are asked about beliefs of how 5 randomly chosen allocators at various social distance allocate tokens.
Stage I: Network ElicitationGoal: high participation rate to get as complete network as possible
Web-based
Use a novel coordination game with monetary payoffs to induce subjects to reveal their social network.
Subjects name up to 10 friends and one attribute of their friendship (how much time they spend together during the week on average).
Earnings: participation fee plus experimental earnings
Network Elicitation Game:
Tanya Alain
Tanya names Alain
Network Elicitation Game:
Tanya Alain
Tanya Alain
Tanya and Alain get both 50 cents with 50% probability if they name each other.
Network Elicitation Game:
Tanya Alain
Tanya Alain
Probability of receiving 50 cents increases to 75% if Tanya and Alain agree on attributes of friendship as well (time spent together).
Network Data
In addition to the network game Know who the roommates are Geographical network (where rooms are located in the
house) Data from the Registrar’s office Survey on lifestyle (clubs, sports) and socio-economic
status
Network Data: Statistics
House1 - 46% (259); House2 - 54% (310) Sophomores - 31%(174); Juniors - 30% (168); Seniors
- 40% (227) Female - 51% (290); Male - 49% (279)
5690 one-way relationships in the dataset; 4042 excluding people from other houses
2086 symmetric relationships (1043 coordinated friendships)
Symmetric Friendships
0 1 2 3 4 5 6 7 8 9 100
20
40
60
80
100
120
140
Symmetric Friendships
0 1 2 3 4 5 6 7 8 9 100
20
40
60
80
100
120
140
The agreement rate on time spent together (+/- 1 hour) is 80%
Network description Cluster coefficient (probability that a friend of my
friend is my friend) is 0.58
The average path length is 6.57
1 giant cluster and 34 singletons
If we ignore friends with less than 1 hr per week, many disjoint clusters (175).
Stage II: Game Phase
Use Andreoni-Miller (Econometrica, 2002) GARP framework to measure altruistic types
Modified dictator game in which the allocator divides tokens between herself and the recipient: tokens can have different values to the allocator and the recipient.
Subjects divide 50 tokens which are worth:1 token to the allocator and 3 to the recipient2 tokens to the allocator and 2 to the recipient3 tokens to the allocator and 1 to the recipient
Stage II: Game Phase Half the subjects have role of allocator and the other half are
recipients.
Stage II: Game Phase Half the subjects have role of allocator and the other half are
recipients.
Recipients are asked about their beliefs of how 7 possible allocators split tokens in all three dictator game.
Allocators are asked to allocate tokens between themselves and 5 possible recipients PLUS one anonymous recipient.
Stage II: Game Phase Half the subjects have role of allocator and the other half are
recipients.
Recipients are asked about their beliefs of how 7 possible allocators split tokens in all three dictator game.
Allocators are asked to allocate tokens between themselves and 5 possible recipients PLUS one anonymous recipient.
Two within treatments (all subjects): for each pair we ask about beliefs/allocations if the recipient (T1) does not find out who made the allocation and (T2) does find out.
Stage II: Game Phase Half the subjects have role of allocator and the other half are
recipients.
Recipients are asked about their beliefs of how 7 possible allocators split tokens in all three dictator game.
Allocators are asked to allocate tokens between themselves and 5 possible recipients PLUS one anonymous recipient.
Two within treatments (all subjects): for each pair we ask about beliefs/allocations if the recipient (T1) does not find out who made the allocation and (T2) does find out.
Recipients and allocators are paid for one pair and one decision only.
DirectFriend
DirectFriend
Direct Friend
DirectFriend
Recipient
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
IndirectFriend2 links
IndirectFriend3 links
Sharestaircase
Samehouse
Recipients
DirectFriend
DirectFriend
Direct Friend
DirectFriend
Recipient
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
IndirectFriend2 links
IndirectFriend3 links
Sharestaircase
Samehouse
Recipients
A possible pair
Stage II: Recipients Recipients make predictions about how much they will get from
an allocator in a given situation and how much an allocator will give to another recipient that they know in a given situation.
One decision is payoff-relevant:
=> The closer the estimate is to the actual number of tokens passed the higher are the earnings.
Incentive Compatible Mechanism to make good predictions
Get $15 if predict exactly the number of tokens that player 1 passed to player 2
For each mispredicted token $0.30 subtracted from $15. For example, if predict that player 1 passes 10 tokens and he actually passes 15 tokens then receive $15-5 x $0.30=$13.50.
DirectFriend
DirectFriend
Direct Friend
DirectFriend
Allocator
For Allocator choose 5 Recipients (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.
IndirectFriend2 links
IndirectFriend3 links
Sharestaircase
Samehouse
Allocators
Stage II: Allocators
We also ask allocator to allocate tokens to an anonymous recipient.
All together they make 6 times 3 allocation decisions in T1 treatment (recipient does not find out) and 6 times 3 allocation decisions in T2 treatment (recipient finds out).
Stage II: Sample Screen Shots
Allocator Screens
Stage II: Sample Screen Shots
Recipient Screens
House Experiment: Analysis
Identify Types
Analysis (AM)
Selfish types take all tokens under all payrates.
Leontieff (fair) types divide the surplus equally under all payrates.
Social Maximizers keep everything if and only if a token is worth more to them.
Analysis (AM)
About 50% of agents have pure types, the rest have weak types.
Force weak types into selfish/fair/SM categories by looking at minimum Euclidean distance of actual decision vector from type’s decision.
0.330.40
0.27
0.59
0.28
0.14
0.2
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Direct Friend Unknown Recipient
Recipient does not find out
Type 1: Selfish Type 2: FairType 3: Social Maximizer
0.15
0.460.39 0.37 0.40
0.23
0.1
.2.3
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Direct Friend Unknown Recipient
Recipient does find out
Type 1: Selfish Type 2: FairType 3: Social Maximizer
Recipient states beliefs; AND-Network
0.330.40
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0.59
0.28
0.14
0.2
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Direct Friend Unknown Recipient
Recipient does not find out
Type 1: Selfish Type 2: FairType 3: Social Maximizer
0.15
0.460.39 0.37 0.40
0.23
0.1
.2.3
.4.5
mea
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Direct Friend Unknown Recipient
Recipient does find out
Type 1: Selfish Type 2: FairType 3: Social Maximizer
Recipient states beliefs; AND-Network
Recipients think that friends are about 20% less selfish under both treatments.
0.49
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0.54
0.200.26
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Direct Friend Unknown Recipient
Recipient does not find out
Type 1: Selfish Type 2: FairType 3: Social Maximizer
0.29
0.37 0.34
0.44
0.260.30
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Direct Friend Unknown Recipient
Recipient does find out
Type 1: Selfish Type 2: FairType 3: Social Maximizer
Allocator makes decisions; AND-Network
0.49
0.230.28
0.54
0.200.26
0.2
.4.6
mea
n of
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Direct Friend Unknown Recipient
Recipient does not find out
Type 1: Selfish Type 2: FairType 3: Social Maximizer
0.29
0.37 0.34
0.44
0.260.30
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Direct Friend Unknown Recipient
Recipient does find out
Type 1: Selfish Type 2: FairType 3: Social Maximizer
Allocator makes decisions; AND-Network
Allocators are only weakly less selfish towards friends if the friends do NOT find out.
0.49
0.230.28
0.54
0.200.26
0.2
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mea
n of
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Direct Friend Unknown Recipient
Recipient does not find out
Type 1: Selfish Type 2: FairType 3: Social Maximizer
0.29
0.37 0.34
0.44
0.260.30
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Direct Friend Unknown Recipient
Recipient does find out
Type 1: Selfish Type 2: FairType 3: Social Maximizer
Allocator makes decisions; AND-Network
Allocators are 15% less selfish towards friends if friends can find out.
House Experiment: Summary Preferences: some directed altruism – but altruists
tend to be altruistic to everybody and not just their friends.
Enforcement: strong evidence that enforcement makes people treat their friend a lot better.
Recipients seem to find it difficult to distinguish the preference channel from enforcement channel: they always expect friends to treat them more nicely than everybody else.
Field Experiment Location – Urban
shantytowns of Lima, Peru
Trust Measurement Tool - a new microfinance program where borrowers can obtain loans at low interest by finding a “sponsor” from a predetermined group of people in the community who are willing to cosign the loan.
Types of Networks
Which types of networks matter for trust? Survey work to identify
SocialBusinessReligiousKinship
Survey Work in Lima’s North Cone
Who is a “sponsor”?
From surveys, select people who either have income or assets to serve as guarantors on other people’s loans.
25-30 for each community If join the program, allowed to take out
personal loans (up to 30% of sponsor “capacity”).
Presenting Credit Program to Communities in Lima’s North Cone
Experimental Design
Three random variations:
Sponsor-specific interest rate Helps identify how trust varies with social distance (all channels)
Sponsor’s liability for co-signed loan Helps separate out enforcement channel.
Average Interest rate at community level Helps identify whether social networks are efficient at allocating
resources
DirectFriend
DirectFriend
Direct Friend
DirectFriend
Sponsor 1r1
Sponsor-specific interest rate is randomized
IndirectFriend2 links
IndirectFriend3 links
Random Variation 1
DirectFriend
DirectFriend
Direct Friend
DirectFriend
Sponsor 1r1
Sponsor-specific interest rate is randomized
IndirectFriend2 links
IndirectFriend3 links
Sponsor 2r2 < r1
Random Variation 1
DirectFriend
DirectFriend
Direct Friend
DirectFriend
Sponsor 1r1
Sponsor-specific interest rate is randomized
IndirectFriend2 links
IndirectFriend3 links
Random Variation 1
Sponsor 2r2 < r1
The easier it is to substitute sponsors, the higher is trust in the community.
Should I try to get
sponsored by Sponsor1 or Sponsor2?
DirectFriend
DirectFriend
Direct Friend
DirectFriend
Sponsor 1r1
Sponsor-specific interest rate is randomized
IndirectFriend2 links
IndirectFriend3 links
Random Variation 1
Sponsor 2r2 < r1
Measure the extent to which agents substitute socially close but expensive sponsors for more socially distant but cheaper sponsors.
Should I try to get
sponsored by Sponsor1 or Sponsor2?
Randomization of interest rates Decrease in interest rate based on slope:
SD1 SD2 SD3 SD4Slope 1 0.125 0.250 0.375 0.500Slope 2 0.250 0.500 0.750 1.000Slope 3 0.500 1.000 1.500 2.000Slope 4 0.750 1.500 2.250 3.000
Each client is randomly assigned a slope (1,2,3,4):
Close friends generally provide the highest interest rate and distant acquaintances the lowest, but the decrease depends on SLOPE
Demand Effects
The interest rate on the previous slide for 75% of the sample and 0.5 percent higher for 25% of the sample to check for demand effects (people borrow more and for a different reason when interest rates are lower?).
DirectFriend
DirectFriend
Direct Friend
DirectFriend
Sponsor 1r1
Sponsor’s liability for the cosigned loan is randomized (after borrower-sponsor pair is formed)
IndirectFriend2 links
IndirectFriend3 links
Random Variation 2
Measure the extent to which sponsors can control ex-ante moral hazard.(can separate type trust from enforcement trust by looking at repayment rates).
Sponsor’s liability might fall below 100%
Community 1
Low r
Community 2
High r
Random Variation 3 Average interest rate at community level (to measure cronyism)
Under cronyism, the share of sponsored loans to direct friends (insiders) increases as interest rate is reduced.
Setting Urban Shantytowns in Lima’s North Cone.
Some MFIs operate there, offering both individual and group lending, with varying levels of penetration but never very high.
Work has been conducted in 2 communities in Lima’s North Cone.
Survey Work Household census
Establish basic information on household assets and composition.
Provides us with household roster for Social Mapping Provides us with starting point to identify potential
sponsors
Identify and sign-up sponsors through series of community meetings
Offer lending product to community as a whole
Microlending Partner
Alternativa, a Peruvian NGO
Lending operation (both group and individual lending)
Also engaged in plethora of “community building”, “empowerment”, “information”, education, etc.
Lending Product Community ~300 households
We identify 25-30 “sponsors” who have assets and/or stable income, sufficient to act as a guarantor on other people’s loans.
A sponsor is given a “capacity”, the maximum amount of credit they can guarantee.
A sponsor can borrow 30% of their capacity for themselves.
Individuals in the community are each given a “sponsor card” which lists the sponsors in their community and their interest rate if they borrow from each sponsor.
Status So far work has been conducted in 2
communities in Lima’s North Cone.
The first community has 240 households and the second community has 371 households.
Characteristics of Sponsored Loans The average size of a sponsored loan is
317 Dollars or 1,040 soles.
The average interest rate for sponsored loans is 4.08%
Social Distance of Actual Client-Sponsor by Slope
0.5
11.
52
mea
n of
sd
1 2 3 4
All Communities
Social Distance of Actual Client-Sponsor by Slope
0.5
11.
52
mea
n of
sd
1 2 3 4
All Communities
Greater slope makes distant neighbors more attractive due tolower interest. We see substitution away from expensive closeneighbors.
Social Distance of Actual Client-Sponsor by Slope
0.5
11.
52
mea
n of
sd
1 2 3 4
All Communities
Effect is mainly driven by clients substituting SD=1 for SD=2 sponsors.There is less substitution of SD=2 sponsors for SD=3,4 sponsors.Therefore, slope 2,3,4 look different from slope 1 (where all interestrates are essentially equal) – but not so different from each other.
Logistic regressions confirm earlier graphs and quantify the size of thesocial distance/interest rate tradeoff: a direct link to a sponsor is worthabout 4 interest rate points. A link to a neighbor at distance 2 is worthabout half that much.
Results using logistic regressions: Direct social neighbor has the same effect as a 3-4
percent decrease in interest rate
Even acquaintance at social distance 3 is worth about as much as one percent decrease in interest rate
Independent effect of geographic distance: one standard deviation decrease in geographic distance is worth about as much as a one percent drop in interest rate
Repayment rates of clients and sponsors
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48 sponsor loans and 49 non-sponsor loans
6dN
Non-sponsor loan Sponsor loan
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55 sponsor loans and 89 non-sponsor loans
Los Olivos
Non-sponsor loan Sponsor loan
Repayment rates of clients and sponsors
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48 sponsor loans and 49 non-sponsor loans
6dN
Non-sponsor loan Sponsor loan
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55 sponsor loans and 89 non-sponsor loans
Los Olivos
Non-sponsor loan Sponsor loan
Repayment rates after n months (n=1,2,..,12) are similar for sponsorsand non-sponsors in both communities.
Effect of Second Randomization0
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18 loans with 100 percent sponsors and 5 loans with 50 percent sponsors
Low quality clients
100 percent sponsor resp. 50 percent sponsor resp.
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19 loans with 100 percent sponsors and 7 loans with 50 percent sponsors
High quality clients
100 percent sponsor resp. 50 percent sponsor resp.
Note: This graph only includes loans which are 6 months and older.
Effect of Second Randomization0
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6080
100
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0 1 2 3 4 5 6 7 8 9
18 loans with 100 percent sponsors and 5 loans with 50 percent sponsors
Low quality clients
100 percent sponsor resp. 50 percent sponsor resp.
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19 loans with 100 percent sponsors and 7 loans with 50 percent sponsors
High quality clients
100 percent sponsor resp. 50 percent sponsor resp.
Note: This graph only includes loans which are 6 months and older.
Higher sponsor responsibility increases repayments rates of BAD clients(defined as having paid back less than 50 percent after 6 months).No effect of repayment of high-quality clients.
Effect of Second Randomization0
2040
6080
100
mea
n of
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0 1 2 3 4 5 6 7 8 9
18 loans with 100 percent sponsors and 5 loans with 50 percent sponsors
Low quality clients
100 percent sponsor resp. 50 percent sponsor resp.
020
4060
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0 1 2 3 4 5 6 7 8 9
19 loans with 100 percent sponsors and 7 loans with 50 percent sponsors
High quality clients
100 percent sponsor resp. 50 percent sponsor resp.
Note: This graph only includes loans which are 6 months and older.Evidence for enforcement trust!
Peru: Summary We develop a new microfinance program to measure trust within a
social network.
Preliminary evidence suggests that social networks can greatly reduce borrowing costs (measured in terms of interest rate on loan).
Evidence that sponsors pick clients who are as likely to repay as they are (micro-finance organization is no better) (belief/type channel)
Evidence that sponsors can enforce repayment for a chosen client (enforcement trust).