measuring trust in social networks dean karlan (yale university) markus mobius (harvard university...

Measuring Trust in Social Networks

Dean Karlan (Yale University)Markus Mobius (Harvard University and NBER)Tanya Rosenblat (Wesleyan University, IQSS and IAS)

February 2006

Measure economic value of trust: how does trust decline with social distance

Identify separately sources of trust: “type” trust versus “enforcement” trust

Develop a new microfinance lending system that uses social networks to overcome information asymmetry issues without resorting to full group lending

Goals of the Field Experiment

Motivating Questions How does social distance (geodesic distance, degree

of structural equivalence, compadrazgo) affect trust?

The less distance matters the more trust the social network embeds.

‘Social distance’ can be measured in different ways: simple geodesic distance between agents degree of structural equivalence (number of friends shared

by two agents) fictive kinship – compadrazgo Some poor households in

Latin America accumulate over 100 co-parents.

Motivating Questions

What type of agents are effective trust intermediaries?

For example, if I have a friend B who is trusted by C will I have the same cost of lending from C as B?


How much risk sharing within a community can be explained by trust?

Assume, a fixed distribution of rates of return across households which is determined by investment opportunities in the wider economy. We expect that trust enables efficient risk-sharing by facilitating the transfer of resources from low-return to high-return households


Can observed differences in levels of trust across communities be explained by differences in network density?

a community can exhibit low trust because there are few links between households which limits social learning and the ability to control moral hazard


Do social networks generate trust because they promote social learning or because they prevent moral hazard?


Do social networks allocate resources efficiently?

Cronyism or efficient discrimination?

Policy Motivation

Individual lending risky (typically) for lenders, but group lending often onerous for borrowers

Can we strike a balance of the two? Use social networks to overcome information asymmetries, but still provide individuals flexibility to have their own loans?

What is Trust? – some common definitions

“Firm reliance on the integrity, ability, or character of a person” (The American Heritage Dictionary)

“Assured resting of the mind on the integrity, veracity, justice, friendship, or other sound principle, of another person; confidence; reliance;” (Webster’s Dictionary)

“Confidence in or reliance on some quality or attribute of a person” (Oxford English Dictionary)

What is Trust?


Define “trust” as willingness of agent to lend money to another agent. Define “trust” as willingness of agent to lend money to another agent.

What is Trust?


Define “trust” as willingness of agent to lend money to another agent.Define “trust” as willingness of agent to lend money to another agent.

Trust will arise naturally in repeated interactions. Research Strategy – look at social networks.Trust will arise naturally in repeated interactions. Research Strategy – look at social networks.

Sources of Trust:2. Cooperative: Enforcement Trust2. Cooperative: Enforcement Trust

1. Information-Based:Type Trust1. Information-Based:Type Trust

Sources of Trust:

I know the other person’s type (responsible/ irresponsible with money).

2. Cooperative: Enforcement Trust2. Cooperative: Enforcement Trust


Sources of Trust:


Information about other agents decreases with social distance.



Sources of Trust:



The other person fears punishment in future interactions with me (or other players) if she does not repay me.



Sources of Trust:


.


The other person fears punishment in future interactions with me (or other players) if she does not repay me.

Fear of punishment can differ by social distance (differently afraid of punishment from friends, friends of friends, friends of friends of friends or strangers)



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

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”).

Experimental Design

3 random variations:Sponsor-specific interest rate

Helps identify how trust varies with social distance

Sponsor’s liability for co-signed loan Helps separate type trust from enforcement trust

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


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

All interest rates are between 3 and 5 percent per month

Every client is randomly assigned one of 4 `slopes': slope 1 decreases the interest rate by 0.125 percent

per month for 1-step increase in social distance. Slopes 2 to 4 imply 0.25, 0.5 and 0.75 decrements.

Therefore, close friends generally provide the highest interest rate and distant acquaintances the lowest but thedecrease depends on SLOPE.

Demand Effects

The interest rate offset for close friends is either 4.5 percent with 75 percent probability (DEMAND=0) or 5 percent (DEMAND=1) with 25 percent probability and DEMAND is a i.i.d. draw across clients.

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.

Field Work

The setting: Urban Shantytowns in Lima’s North Cone Many have land titles (de Soto program from late

90s) Some MFIs operate there, offering both individual

and group lending, with varying levels of penetration but never very high.

Pilot work has been conducted in 2 communities in Lima’s North Cone.

Experimental Process

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

Conduct Social Mapping survey on (a) all sponsors and (b) all people mentioned by the sponsor as in their social networks

Offer lending product to community as a whole Conduct Social Mapping survey on anyone who borrows but was

not included in initial Social Mapping surveys

Microlending Partner

Alternativa, a Peruvian NGO Lending operation (both group and individual

lending) Also engaged in plethora of “community building”,

“empowerment”, “information”, education, etc.

The 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.

The Lending Product

We have Y sponsors and Z borrowers. Each (Y,Z) pairing is randomly chosen from a set of

interest rates (3% to 5% per month, for instance) The sponsor is initially 100% liable for the loan, but

with a certain probability, after the contract is signed, the sponsor’s liability is reduced (between 50-70%). This allows us to separately identify the willingness of a sponsor to trust an individual because they know they are a safe “type” versus because they know they can successfully enforce the loan.

Baseline Survey Work

Pilot 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.

Baseline census was applied to 153 households in the first community and 224 households in the second community.

Social network survey has been applied to 185 individuals in the first community and 165 individuals in the second community. Social network survey work is ongoing.

Credit Program so far… 26 sponsors in community 1 and 25

sponsors in community 2 (Since March/July 2005).

26 client-sponsor loans with unique clients in community 1 and 50 loans in community 2.

Characteristics of Sponsored Loans

The average size of a sponsored loan is $317 or 1040 soles.

The average interest rate for sponsored loans is 4.08%

65 of the 76 loans are between unrelated parties and 11 loans involve a relative.

Presenting Credit Program to Communities in Lima’s North Cone

Survey Work in Lima’s North Cone

Timeline:Full Launch of Credit Program April 2005-November 2005: pilot program in 2

communities January - April 2006: Identifying 30 launch

communities April 2006 -> staggered rollout of program in 30 new

communities

Promotional Materials for Sponsors

Promotional Material for Clients

Research Tools

Surveyor

Pocket PC Applications

Results so far…

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.


0.5

11.5

2m

ea

n o

f sd

1 2 3 4

Community 1: 6dN

Slope=4 is an outlier in community 1.

0.5

11.5

2m

ea

n o

f sd

1 2 3 4

Community 1: Los Olivos

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: 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 social distance is worth about as much as a one percent drop in interest rate

Demand Effects

Loan demand is weakly sensitive to interest rates.

Results: 25 percent of clients have a 0.5 percent interest rate

offset

Some evidence that higher rates reduce bowering – but not significant

Consistent with hypothesis that clients in our program are severely credit constrained.

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

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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

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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

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801

00

mean

of share

left

0 1 2 3 4 5 6 7 8 9


Low quality clients


020

40

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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!

Conclusion: We develop a new microfinance program to measure

trust within a social network. Preliminary evidence suggest 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) (type trust)

Evidence that sponsors can enforce repayment for a chosen client (enforcement trust).

Future work:

More communities Decompose trust by link type Distinguish type and enforcement trust

AND: Cronyism

measuring trust in social networks dean karlan (yale university) markus mobius (harvard university...

Documents

low trust

levels of trust

type trustsources of

effective trust intermediaries

social learning

social distanceidentify

willingness of agent

simple geodesic distance