Measuring Trust in Social Networks

1. Measuring Trust in Social Networks Tanya Rosenblat (Wesleyan University, IQSS and IAS) March 2, 2006

2. 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 varieswith social distance.

3. 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.

4. 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.

5. 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.

6. 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.

7. 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.

8. House Experiment Methodology

9. 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.

10. 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

12. Network Elicitation Game: Tanya names Alain Tanya Alain

13. Network Elicitation Game: Tanya Alain Tanya and Alain get both 50 cents with 50% probability if they name each other. Tanya Alain

14. Network Elicitation Game: Tanya Alain Probability of receiving 50 cents increases to 75% if Tanya and Alain agree on attributes of friendship as well (time spent together). Tanya Alain

17. 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

18. 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)

19. Symmetric Friendships

20. Symmetric Friendships The agreement rate on time spent together (+/- 1 hour) is 80%

21. 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).

25. 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 recipient • 2 tokens to the allocator and 2 to the recipient • 3 tokens to the allocator and 1 to the recipient

26. Stage II: Game Phase • Half the subjects have role of allocator and the other half are recipients.

27. Stage II: Game Phase • Half the subjects have role of allocator and the other half are 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.

28. Stage II: Game Phase • Half the subjects have role of allocator and the other half are 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. • 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.

29. Stage II: Game Phase • Half the subjects have role of allocator and the other half are 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. • 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.

30. Recipients 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 Share staircase Indirect Friend 2 links Indirect Friend 3 links Same house

31. Recipients 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 A possible pair Share staircase Indirect Friend 2 links Indirect Friend 3 links Same house

32. 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.

33. Allocators 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. Share staircase Indirect Friend 2 links Indirect Friend 3 links Same house

34. 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).

35. Stage II: Sample Screen Shots Allocator Screens

37. Stage II: Sample Screen Shots Recipient Screens

40. House Experiment: Analysis Identify Types

41. 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.

42. 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.

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

46. Allocators are only weakly less selfish towards friends if the friends do NOT find out.

47. Allocators are 15% less selfish towards friends if friends can find out.

48. 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.

49. 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.

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