Measuring Trust in Social Networks Dean Karlan (Princeton University and Yale University) Markus Mobius (Harvard University and NBER) Tanya Rosenblat (Wesleyan

Measuring Trust in Social Networks

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

May 2005

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

Motivating Policy Issue #1Individual lending risky (typically) for lenders, but

group lending often onerous for borrowersCan we strike a balance of the two? Use social

networks to overcome information asymmetries, but still provide individuals flexibility to have their own loans?

Policy Motivation

Policy Motivating Issue # 2 After quantifying the value of trust, we can

calibrate the potential magnitude of exercises which build social capital. Put another way, if we can create social capital, what impact on local economics development do we think we can possibly have? Problem: Is “created” trust/social network different than preexisting?

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 my belief that another player is willing to sacrifice her utility to improve my utility.Define trust as my belief that another player is willing to sacrifice her utility to improve my utility.

What is Trust?


Define trust as my belief that another player is willing to sacrifice her utility to improve my utility.Define trust as my belief that another player is willing to sacrifice her utility to improve my utility.

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

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

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

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

Sources of Trust:

The other person is altruistic (or responsible, or kind) and takes my utility into account.

2. Cooperative: Enforcement Trust2. Cooperative: Enforcement Trust


Sources of Trust:


Altruism can differ by social distance (have more information or feel differently towards friends, friends of friends, friends of friends of friends or strangers)



Sources of Trust:



The other person fears punishment in future interactions with me (or other players) if she does not take my utility into account.



Sources of Trust:




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



Sources of Trust:


Knowledge about other people’s types depends on network structure


Punishment behavior depends on network structure



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.

15-30 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?

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.

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

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.

The Lending Product

In a given community (~300 households) We identify 15-30 “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% oftheir 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.

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

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.

Pilot Launch of Credit Program The sponsor-based lending model was launched in

one community in late March. Since the launch, 40 members of this community

have received a loan sponsored by one of 25 “sponsors” chosen from their own community.

Of the 25 “sponsors” from the community, 64% (16 out of 25) have sponsored at least one loan.

“Sponsors” who have participated have sponsored between 1 and 7 community members.

The credit program has a portfolio of $21,000.

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%

15 out of 41 loans (37%) are with a sponsor who is a direct social contact (based on social network survey)

21 out of 41 loans (51%) are with a sponsor who is either socially close or geographically close (relative to other sponsors).

Social Distance of Borrower/Sponsors

Distance Frequency Percent

0* 6 14.6%

1 15 36.6%

2 6 14.6%

3 11 26.8%

4 3 7.3%

*A distance of 0 applies to borrowers who weren’t mentioned as part of any sponsors social network in the first Baseline Social Network Survey

Presenting Credit Program to Communities in Lima’s North Cone

Survey Work in Lima’s North Cone

Sponsor Lottery Held in May as Incentive for Sponsor Participation

Timeline:Full Launch of Credit Program July – September 2005: Generate a database of 60

communities in Lima’s North Cone, in which the credit program could be applied

July - August 2005: Evaluate results of Pilot Program, use results to revise survey instruments.

July – August 2005: Gather and train large team of surveyors

September 2005 - December 2005: Baseline Survey work in 30 communities.

January - April 2006: Staggered program launches in 30 communities

Promotional Materials for Sponsors

Promotional Material for Clients

Research Tools

Surveyor

Pocket PC Applications

Analysis Plan #1

First, note that we have social maps along many channels (trust, spending time, borrowed, etc.)

Simplest analysis, suppose 3 people on a line. Dep var = 1 if person went to the friend-of-a-friend

rather than the friend. Ind var = the discount received (randomized) for

extending to this f-of-a-f rather than the friend

Analysis Plan #2

Dep var: repayment rates Ind var: % of liability of sponsor

Set at 100% at time of contractEx-post, randomly reduced to somewhere between

50%-100%.

Documents

Measuring Trust in Social Networks Dean Karlan (Princeton University and Yale University) Markus Mobius (Harvard University and NBER) Tanya Rosenblat (Wesleyan