Transcript Why Beauty Matters An Experimental Investigation

Measuring Trust in
Social Networks
Dean Karlan (Yale University)
Markus Mobius (Harvard University and NBER)
Tanya Rosenblat (Wesleyan University, IQSS and IAS)
February 2006
Goals of the Field Experiment
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

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?
Motivating Questions

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
Motivating Questions

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
Motivating Questions

Do social networks generate trust because they
promote social learning or because they prevent
moral hazard?
Motivating Questions

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?

“Confidence in or reliance on some quality or attribute of a
person” (Oxford English Dictionary)
Define “trust” as willingness of agent to lend
money to another agent.
What is Trust?

“Confidence in or reliance on some quality or attribute of a
person” (Oxford English Dictionary)
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.
Sources of Trust:
1. Information-Based:
Type Trust
2. Cooperative:
Enforcement Trust
Sources of Trust:
1. Information-Based:
Type Trust
I know the other person’s type
(responsible/ irresponsible with
money).
2. Cooperative:
Enforcement Trust
Sources of Trust:
1. Information-Based:
Type Trust
I know the other person’s type
(responsible/ irresponsible with
money).
Information about other agents
decreases with social distance.
2. Cooperative:
Enforcement Trust
Sources of Trust:
1. Information-Based:
Type Trust
I know the other person’s type
(responsible/ irresponsible with
money).
Information about other agents
decreases with social distance.
2. Cooperative:
Enforcement Trust
The other person fears punishment
in future interactions with me (or other
players) if she does not repay me.
Sources of Trust:
1. Information-Based:
Type Trust
I know the other person’s type
(responsible/ irresponsible with
money).
2. Cooperative:
Enforcement Trust
The other person fears punishment
in future interactions with me (or other
players) if she does not repay me.
.
Information about other agents
decreases with social distance.
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

 Social
 Business
 Religious
 Kinship
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

Helps identify how trust varies with social distance
 Sponsor’s

liability for co-signed loan
Helps separate type trust from enforcement trust
 Interest

interest rate
rate at community level
Helps identify whether social networks are efficient
at allocating resources
Random Variation 1
Sponsor-specific interest rate is randomized
Direct
Friend
Direct
Friend
Sponsor 1
r1
Direct
Friend
Direct
Friend
Indirect
Friend
2 links
Indirect
Friend
3 links
Random Variation 1
Sponsor-specific interest rate is randomized
Direct
Friend
Direct
Friend
Sponsor 1
r1
Direct
Friend
Direct
Friend
Indirect
Friend
2 links
Indirect
Friend
3 links
Sponsor 2
r2 < r1
Random Variation 1
Sponsor-specific interest rate is randomized
Should I try
to get
sponsored by
Sponsor1 or
Sponsor2?
Direct
Friend
Direct
Friend
Sponsor 1
r1
Direct
Friend
Direct
Friend
Indirect
Friend
2 links
Indirect
Friend
Sponsor 2
r2 < r1
3 links
The easier it is to substitute sponsors,
the higher is trust in the community.
Random Variation 1
Sponsor-specific interest rate is randomized
Should I try
to get
sponsored by
Sponsor1 or
Sponsor2?
Direct
Friend
Direct
Friend
Sponsor 1
r1
Direct
Friend
Direct
Friend
Indirect
Friend
2 links
Indirect
Friend
Sponsor 2
r2 < r1
3 links
Measure the extent to which agents
substitute socially close but expensive
sponsors for more socially distant but
cheaper sponsors.
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.
Random Variation 2
Direct
Friend
Direct
Friend
Sponsor 1
r1
Direct
Friend
Sponsor’s liability for the cosigned loan is
randomized (after borrower-sponsor pair is formed)
Sponsor’s liability might fall below 100%
Direct
Friend
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).
Random Variation 3
Average interest rate at community level (to
measure cronyism)
Community 2
Community 1
High r
Low r
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…
1
1.5
2
All Communities
0
.5
mean of sd
Social Distance of Actual ClientSponsor by Slope
1
2
3
4
1
1.5
2
All Communities
Greater slope makes distant neighbors more attractive due to
lower interest. We see substitution away from expensive close
neighbors.
0
.5
mean of sd
Social Distance of Actual ClientSponsor by Slope
1
2
3
4
1
1.5
2
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 interest
rates are essentially equal) – but not so different from each other.
0
.5
mean of sd
Social Distance of Actual ClientSponsor by Slope
1
2
3
4
Social Distance of Actual ClientSponsor by Slope
.5
1
1.5
2
Community 1: 6dN
0
Slope=4 is an outlier in community 1.
1
2
3
4
0
.5
1
1.5
2
Community 1: Los Olivos
1
2
3
4
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:

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
80
60
40
0
20
0
20
40
60
mean of shareleft
80
100
Los Olivos
100
6dN
0
1
2
3
4
5
6
7
Non-sponsor loan
8
9 10 11 12
Sponsor loan
48 sponsor loans and 49 non-sponsor loans
0
1
2
3
4
5
6
7
Non-sponsor loan
8
9 10 11 12
Sponsor loan
55 sponsor loans and 89 non-sponsor loans
Repayment rates of clients and
sponsors
100
80
60
40
0
20
0
20
40
60
mean of shareleft
80
100
Repayment rates after n months (n=1,2,..,12) are similar for sponsors
6dN
Los Olivos
and non-sponsors
in both communities.
0
1
2
3
4
5
6
7
Non-sponsor loan
8
9 10 11 12
Sponsor loan
48 sponsor loans and 49 non-sponsor loans
0
1
2
3
4
5
6
7
Non-sponsor loan
8
9 10 11 12
Sponsor loan
55 sponsor loans and 89 non-sponsor loans
Effect of Second Randomization
100
0
20 40 60 80
mean of shareleft
Low quality clients
0
1
2
3
4
5
6
100 percent sponsor resp.
7
8
9
50 percent sponsor resp.
18 loans with 100 percent sponsors and 5 loans with 50 percent sponsors
100
0
20 40 60 80
mean of shareleft
High quality clients
0
1
2
3
4
5
100 percent sponsor resp.
6
7
9
50 percent sponsor resp.
19 loans with 100 percent sponsors and 7 loans with 50 percent sponsors
Note: This graph only includes loans which are 6 months and older.
8
Effect of Second Randomization
100
0
20 40 60 80
mean of shareleft
Low quality clients
0
1
2
3
4
5
6
100 percent sponsor resp.
7
8
9
50 percent sponsor resp.
18 loans with 100 percent sponsors and 5 loans with 50 percent sponsors
100
0
20 40 60 80
mean of shareleft
High quality clients
0
1
2
3
4
5
6
7
8
9
100 percent sponsor
resp.
50 percentrates
sponsor
Higher sponsor responsibility
increases
repayments
ofresp.
BAD clients
19 loans with 100 percent sponsors and 7 loans with 50 percent sponsors
(defined as
having paid back less than 50 percent after 6 months).
Note: This graph only includes loans which are 6 months and older.
No effect of repayment of high-quality clients.
Effect of Second Randomization
100
0
20 40 60 80
mean of shareleft
Low quality clients
0
1
2
3
4
5
6
100 percent sponsor resp.
7
8
9
50 percent sponsor resp.
18 loans with 100 percent sponsors and 5 loans with 50 percent sponsors
100
0
20 40 60 80
mean of shareleft
High quality clients
0
1
2
3
4
5
100 percent sponsor resp.
6
7
8
50 percent sponsor resp.
19 loans with 100 percent sponsors and 7 loans with 50 percent sponsors
Evidence for enforcement trust!
Note: This graph only includes loans which are 6 months and older.
9
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