Measuring Trust in Social Networks

1. Measuring Trust in Social Networks Dean Karlan (Yale University) Markus Mobius (Harvard University and NBER) Tanya Rosenblat (Wesleyan University, IQSS and IAS) November 2005

2. 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

3. 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.

4. 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?

5. 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

6. 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

7. Motivating Questions • Do social networks generate trust because they promote social learning or because they prevent moral hazard?

8. Motivating Questions • Do social networks allocate resources efficiently? Cronyism or efficient discrimination?

9. Policy Motivation • Motivating Policy Issue #1 • 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?

10. 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?

11. 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)

12. What is Trust? • “Confidence in or reliance on some quality or attribute of a person” (Oxford English Dictionary) Define trust as my belief that another player is willing to sacrifice her utility to improve my utility.

13. What is Trust? • “Confidence in or reliance on some quality or attribute of a person” (Oxford English Dictionary) 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.

14. Sources of Trust: 2. Cooperative: Enforcement Trust 1. Preference-Based: Type Trust

15. Sources of Trust: 2. Cooperative: Enforcement Trust 1. Preference-Based: Type Trust The other person is altruistic (or responsible, or kind) and takes my utility into account.

16. Sources of Trust: 2. Cooperative: Enforcement Trust 1. Preference-Based: Type Trust The other person is altruistic (or responsible, or kind) and takes my utility into account. Altruism can differ by social distance (have more information or feel differently towards friends, friends of friends, friends of friends of friends or strangers)

17. Sources of Trust: 2. Cooperative: Enforcement Trust 1. Preference-Based: Type Trust The other person is altruistic (or responsible, or kind) and takes my utility into account. The other person fears punishment in future interactions with me (or other players) if she does not take my utility into account. Altruism can differ by social distance (have more information or feel differently towards friends, friends of friends, friends of friends of friends or strangers)

18. Sources of Trust: 2. Cooperative: Enforcement Trust 1. Preference-Based: Type Trust The other person is altruistic (or responsible, or kind) and takes my utility into account. The other person fears punishment in future interactions with me (or other players) if she does not take my utility into account. Altruism can differ by social distance (have more information or feel differently towards friends, friends of friends, friends of friends of friends or strangers) Fear of punishment can differ by social distance (differently afraid of punishment from friends, friends of friends, friends of friends of friends or strangers)

19. Sources of Trust: 2. Cooperative: Enforcement Trust 1. Preference-Based: Type Trust The other person is altruistic (or responsible, or kind) and takes my utility into account. The other person fears punishment in future interactions with me (or other players) if she does not take my utility into account. Knowledge about other people’s types depends on network structure Punishment behavior depends on network structure

20. 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.

21. Types of Networks • Which types of networks matter for trust? • Survey work to identify • Social • Business • Religious • Kinship

22. 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”).

23. 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

24. Random Variation 1 Sponsor-specific interest rate is randomized Indirect Friend 2 links Indirect Friend 3 links

25. Random Variation 1 Sponsor-specific interest rate is randomized Sponsor 2 r2 < r1 Indirect Friend 2 links Indirect Friend 3 links

26. 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.

27. 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.

28. 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.

29. 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.

30. 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).

31. 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.

32. Field Work

33. 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.

34. Microlending Partner • Alternativa, a Peruvian NGO • Lending operation (both group and individual lending) • Also engaged in plethora of “community building”, “empowerment”, “information”, education, etc.

35. 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.

36. 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% 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.

37. 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

38. 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.

39. 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.

40. 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.

41. Presenting Credit Program to Communities in Lima’s North Cone

42. Survey Work in Lima’s North Cone

43. Sponsor Lottery Held in May as Incentive for Sponsor Participation

44. 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

45. Promotional Materials for Sponsors

47. Promotional Material for Clients