measuring trust in social networks

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 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:

Tanya Alain

Tanya names Alain


Tanya Alain

Tanya Alain

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


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.


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.


recipients.



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.



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


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



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.



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

.4.6

mean o

f sh

are

Direct Friend Unknown Recipient

Recipient does not find out

Type 1: Selfish Type 2: Fair

Type 3: Social Maximizer

0.15

0.460.39 0.37 0.40

0.23

0.1

.2.3

.4.5

mean o

f sh

are


Recipient does find out



Recipient states beliefs; AND-Network

0.330.40

0.27

0.59

0.28

0.14

0.2

.4.6

mean o

f sh

are





0.15

0.460.39 0.37 0.40

0.23

0.1

.2.3

.4.5

mean o

f sh

are





Recipient states beliefs; AND-Network

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

0.49

0.230.28

0.54

0.200.26

0.2

.4.6

mean o

f sh

are





0.29

0.37 0.34

0.44

0.260.30

0.1

.2.3

.4m

ean o

f sh

are





Allocator makes decisions; AND-Network

0.49

0.230.28

0.54

0.200.26

0.2

.4.6

mean o

f sh

are





0.29

0.37 0.34

0.44

0.260.30

0.1

.2.3

.4m

ean o

f sh

are






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

.4.6

mean o

f sh

are





0.29

0.37 0.34

0.44

0.260.30

0.1

.2.3

.4m

ean o

f sh

are






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



Random Variation 1

DirectFriend

DirectFriend

Direct Friend

DirectFriend

Sponsor 1r1




Sponsor 2r2 < r1

Random Variation 1

DirectFriend

DirectFriend

Direct Friend

DirectFriend

Sponsor 1r1




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




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 SD4

Slope 1 0.125 0.250 0.375 0.500

Slope 2 0.250 0.500 0.750 1.000

Slope 3 0.500 1.000 1.500 2.000

Slope 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)



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

11.5

2m

ea

n o

f sd

1 2 3 4

All Communities


0.5

11.5

2m

ea

n o

f sd

1 2 3 4

All Communities

Greater slope makes distant neighbors more attractive due tolower interest. We see substitution away from expensive closeneighbors.


0.5

11.5

2m

ea

n o

f 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

020

40

60

80

10

0m

ean

of share

left

0 1 2 3 4 5 6 7 8 9 10 11 12

48 sponsor loans and 49 non-sponsor loans

6dN

Non-sponsor loan Sponsor loan

020

40

60

80

10

0m

ean

of share

left

0 1 2 3 4 5 6 7 8 9 10 11 12


Los Olivos


Repayment rates of clients and sponsors

020

40

60

80

10

0m

ean

of share

left

0 1 2 3 4 5 6 7 8 9 10 11 12


6dN


020

40

60

80

10

0m

ean

of share

left

0 1 2 3 4 5 6 7 8 9 10 11 12


Los Olivos


Repayment rates after n months (n=1,2,..,12) are similar for sponsorsand non-sponsors in both communities.

Effect of Second Randomization0

20

40

60

801

00

mean

of share

left

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

40

60

801

00

mean

of share

left

0 1 2 3 4 5 6 7 8 9


High quality clients


Note: This graph only includes loans which are 6 months and older.


20

40

60

801

00

mean

of share

left

0 1 2 3 4 5 6 7 8 9


Low quality clients


020

40

60

801

00

mean

of share

left

0 1 2 3 4 5 6 7 8 9




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.


20

40

60

801

00

mean

of share

left

0 1 2 3 4 5 6 7 8 9


Low quality clients


020

40

60

801

00

mean

of share

left

0 1 2 3 4 5 6 7 8 9




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).

measuring trust in social networks

Documents