Measuring trust in social networks
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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.

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Measuring trust in social networks

Measuring Trust in Social Networks

Tanya Rosenblat

(Wesleyan University, IQSS and IAS)

March 2, 2006


Motivation

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 varieswith social distance.


Trust social distance channels

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

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

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

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

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

House Experiment

Methodology


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 elicitation

Stage I: Network Elicitation

Goal: 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

Network Elicitation Game:

Tanya names Alain

Tanya

Alain


Network elicitation game1

Network Elicitation Game:

Tanya

Alain

Tanya and Alain get both 50 cents with 50% probability if they name each other.

Tanya

Alain


Network elicitation game2

Network Elicitation Game:

Tanya

Alain

Probability of receiving 50 cents increases to 75% if Tanya and Alain agree on attributes of friendship as well (time spent together).

Tanya

Alain


Network data

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

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

Symmetric Friendships


Symmetric friendships1

Symmetric Friendships

The agreement rate on time spent together (+/- 1 hour) is 80%


Network description

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

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 recipient

    • 2 tokens to the allocator and 2 to the recipient

    • 3 tokens to the allocator and 1 to the recipient


Stage ii game phase1

Stage II: Game Phase

  • Half the subjects have role of allocator and the other half are recipients.


Stage ii game phase2

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 phase3

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 phase4

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.


Measuring trust in social networks

Recipients

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

Share

staircase

Indirect

Friend

2 links

Indirect

Friend

3 links

Same

house


Measuring trust in social networks

Recipients

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

A possible pair

Share

staircase

Indirect

Friend

2 links

Indirect

Friend

3 links

Same

house


Stage ii recipients

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.


Measuring trust in social networks

Allocators

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.

Share

staircase

Indirect

Friend

2 links

Indirect

Friend

3 links

Same

house


Stage ii 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

Stage II: Sample Screen Shots

Allocator Screens


Stage ii sample screen shots1

Stage II: Sample Screen Shots

Recipient Screens


House experiment analysis

House Experiment: Analysis

Identify Types


Analysis am

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 am1

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.


Measuring trust in social networks

Recipients think that friends are about 20% less selfish under both treatments.


Measuring trust in social networks

Allocators are only weakly less selfish towards friends if the friends do NOT find out.


Measuring trust in social networks

Allocators are 15% less selfish towards friends if friends can find out.


House experiment summary

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

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

Types of Networks

  • Which types of networks matter for trust?

  • Survey work to identify

    • Social

    • Business

    • Religious

    • Kinship


Survey work in lima s north cone

Survey Work in Lima’s North Cone


Who is a sponsor

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”).


Measuring trust in social networks

Presenting Credit Program to Communities in Lima’s North Cone


Experimental design

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


Measuring trust in social networks

Random Variation 1

Sponsor-specific interest rate is randomized

Indirect

Friend

2 links

Indirect

Friend

3 links


Measuring trust in social networks

Random Variation 1

Sponsor-specific interest rate is randomized

Sponsor 2

r2 < r1

Indirect

Friend

2 links

Indirect

Friend

3 links


Measuring trust in social networks

Random Variation 1

Sponsor-specific interest rate is randomized

Should I try to get sponsored by Sponsor1 or Sponsor2?

Sponsor 2

r2 < r1

Indirect

Friend

2 links

Indirect

Friend

3 links

The easier it is to substitute sponsors, the higher is trust in the community.


Measuring trust in social networks

Random Variation 1

Sponsor-specific interest rate is randomized

Should I try to get sponsored by Sponsor1 or Sponsor2?

Sponsor 2

r2 < r1

Indirect

Friend

2 links

Indirect

Friend

3 links

Measure the extent to which agents substitutesocially close but expensive sponsors for more socially distant but cheaper sponsors.


Randomization of interest rates decrease in interest rate based on slope

Randomization of interest rates Decrease in interest rate based on slope:

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

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?).


Measuring trust in social networks

Random Variation 2

Sponsor’s liability for the cosigned loan is randomized (after borrower-sponsor pair is formed)

Sponsor’s liability might fall below 100%

Indirect

Friend

2 links

Indirect

Friend

3 links

Measure the extent to which sponsors can control ex-ante moral hazard.

(can separate type trust from enforcement trust by looking at repayment rates).


Measuring trust in social networks

Random Variation 3

Average interest rate at community level (to measure cronyism)

Community 2

High r

Community 1

Low r

Under cronyism, the share of sponsored loans to direct friends (insiders) increases as interest rate is reduced.


Setting

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

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

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

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

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

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

Social Distance of Actual Client-Sponsor by Slope


Social distance of actual client sponsor by slope1

Social Distance of Actual Client-Sponsor by Slope

Greater slope makes distant neighbors more attractive due to

lower interest. We see substitution away from expensive close

neighbors.


Social distance of actual client sponsor by slope2

Social Distance of Actual Client-Sponsor by Slope

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 interest

rates are essentially equal) – but not so different from each other.


Measuring trust in social networks

Logistic regressions confirm earlier graphs and quantify the size of the

social distance/interest rate tradeoff: a direct link to a sponsor is worth

about 4 interest rate points. A link to a neighbor at distance 2 is worth

about half that much.


Results using logistic regressions

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

Repayment rates of clients and sponsors


Repayment rates of clients and sponsors1

Repayment rates of clients and sponsors

Repayment rates after n months (n=1,2,..,12) are similar for sponsors

and non-sponsors in both communities.


Effect of second randomization

Effect of Second Randomization


Effect of second randomization1

Effect of Second Randomization

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 randomization2

Effect of Second Randomization

Evidence for enforcement trust!


Peru summary

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).


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