Social Networks, Reputation and Commitment: Evidence from a Savings - PowerPoint PPT Presentation

Social Networks, Reputation and Commitment: Evidence from a Savings Monitors Experiment Emily Breza † Arun G. Chandrasekhar ‡ † Columbia Business School ‡ Stanford

Under-savings ubiquitous ◮ Evidence of large benefits of savings ... yet low ◮ e.g., Dupas and Robinson ‘13, Schaner ‘13, Beaman et al ‘14 ◮ Access not necessarily the problem in India ◮ RBI-led expansion of rural branches, no-frills accounts ◮ Low rates of account opening and use ◮ Psychological “frictions” make saving hard: ◮ Can’t commit to save/procrastination? (e.g., Ashraf et al ‘06) ◮ Can’t remember/inattention? (Karlan et al ‘12, Kast et al ‘13) This paper: can we use social reputation to overcome such frictions and encourage savings?

Informal finance uses social reputation Peer-driven financial institutions are thought to rely on this: ◮ RoSCAs, SHGs, VSLAs, Microfinance groups In theories of MF/ROSCAs,“social reputation” often assumed “the contributing member may admonish his partner for causing him or her discomfort and material loss. He might also report this behavior to others in the village, thus augmenting the admonishment felt. Such behavior is typical of the close-knit communities in some LDCs.” – Besley and Coate (1995)

What we do Encourage savings by assigning a unique monitor to each saver. ◮ Basic idea: ◮ Make a bet with self about ability to save over 6 months. ◮ Stakes: reputation gain/loss from progress in front of some other member of village. ◮ Monitor assigned to a saver for the duration of experiment. ◮ Informed about savings in target account . ◮ Simply told about progress (bi-weekly). ◮ Monitor need not do anything!

Why should a saver care about the monitor? “A person may save more if it is an important person knowing they might get more benefits from this person later on.” – Subject 1 “The monitor will feel that if in the future he or his friends gives her some job or tasks or responsibilities, the saver may not fulfill them” – Subject 2 “They would speak less to the saver and feel ‘cheated to trust’ [sic]. They may tell others...” – Subject 3 “People will only reach their goals if their monitors are family, friends, neighbors, or important people.” – Subject 4

What we do Encourage savings by assigning a unique monitor to each saver. ◮ Basic idea: ◮ Make a bet with self about ability to save over 6 months. ◮ Stakes: reputation gain/loss from progress in front of some other member of village. ◮ Monitor assigned to a saver for the duration of experiment. ◮ Informed about savings in target account . ◮ Simply told about progress (bi-weekly). ◮ Monitor need not do anything! ◮ Not all monitors created equal... ◮ Central monitors? Can spread more info; more important in future interactions ◮ Proximate monitors? Info typically goes to people saver will run into.

Setting ◮ 60 villages in rural Karnataka, India ◮ 1.5 to 3 hour’s drive from Bangalore ◮ Experimental participants aged ∼ 18-45 ◮ 1,300 savers who expressed desire to save more ◮ 1,000 monitors ◮ Primary occupations: agriculture and sericulture

0 26 87 93 Village network data 1 2 4 5 28 6 7 3 8 16 9 10 13 12 11 14 15 33 18 17 34 19 35 37 36 20 38 39 22 40 24 23 41 25 42 27 21 43 29 45 31 30 44 46 98 32 97 48 47 49 75 52 62 50 53 60 51 54 61 67 55 63 66 56 64 57 72 65 58 68 70 84 59 71 69 86 73 85 88 74 89 76 90 91 77 92 78 94 79 95 80 96 81 82 83 ◮ Undirected, ◮ Relationships: ◮ ∼ 16,500 households network unweighted OR company advisors and religious creditors, debtors, relatives, friends, villages surveyed across 75

A simple model social reputation flow

Record savings

Report to Monitor (Low Centrality)

Only a few people hear gossip

Report to Monitor (High Centrality)

Many more people hear gossip

Report to Monitor (Low Proximity)

Only a few (distant) people hear gossip

Report to Monitor (High Proximity)

Only a few (close) people hear gossip

Who would make a good monitor? > > � �� � � �� � � �� � low centrality high centrality low centrality high proximity high proximity low proximity ◮ greater motivation to save if more people are likely to hear about your good/bad deeds (centrality) ◮ more relevant if people informed of your good/bad deeds are those you are likely going to meet in the future (proximity)

Questions 1. Can we encourage savings using central/proximate monitors? 2. Does information flow? Where is savings coming from? 3. What happens in the medium term (15+ mo. later)? 4. When given choice of monitor, do individuals pick well or unwind?

Design Treatments: 1300+ savers, 1000+ monitors, 60 villages 1. No Monitor (BC): in all 60 villages 2. Researchers Choose Monitor at Random (R): 30 villages 3. Savers Choose Monitor Endogenously (E): 30 villages All received bundle of services (resembles business correspondent ) ◮ Account opening ◮ Goal elicitation (conducted at pre-screen home visit) ◮ Bi-weekly visits (reminders and weak monitoring)

Treatments and Roll-Out Village ◮ Sample villages selected (based on networks data)

Treatments and Roll-Out Pure Potential Potential Control Savers Monitors ◮ Potential savers & monitors visited, savings goals elicited

Treatments and Roll-Out Savers Monitor Attend Pool Pure Meeting Control Savers Monitor Not Dropouts Interested ◮ Interested monitors and savers attend village meeting

Treatments and Roll-Out Chosen BC Monitored Monitors Saver +BC Saver Pure Excess Control Monitors Savers Monitor Not Dropouts Interested ◮ Some savers randomly chosen to receive monitors

Treatments and Roll-Out Village A Village B Chosen Chosen BC Saver BC Saver Monitored Monitors Monitored Monitors +BC Saver +BC Saver Random Endogenous Pure Pure Excess Excess Control Control Monitors Monitors Savers Savers Not Not Monitor Monitor Interested Interested Dropouts Dropouts ◮ Random vs. Endogenous Monitor assignment randomized at village level ◮ Random Matching (30 villages) ◮ Savers randomly assigned to a monitor from pool ◮ Endogenous Matching (30 villages) ◮ Savers choose monitor from pool in random order

Timeline Account Opening: 6 Months ~15 Months • Bank or PO Follow ‐ Up Village Saving Period Begins: Saving Period Ends: Survey Meeting • Baseline Survey • Endline Survey • Bi ‐ weekly visits start • Monitors start to get info

Compensation ◮ Pure Control (no contact until end of 6 mos.) ◮ No compensation ◮ Savers (takers only) ◮ In Kind: Account opening services ◮ Direct: Rs. 50 ($1) deposited into account ◮ Monitors ◮ Payment: ◮ Rs. 50 if saver reaches half of goal [helps in a robustness exercise] ◮ Rs. 150 if saver meets goal ◮ Rs. 0 otherwise

Results 1. Can we encourage savings using central/proximate monitors?

Do randomly assigned monitors help?

Results: Log Total (Form. + Inform.) Savings Mean log savings balances across all accounts 8.1 8 7.9 7.8 7.7 7.6 7.5 7.4 BC, Random Monitor, Random

Endline 1 .8 .6 Density .4 .2 0 -4 -2 0 2 4 6 log(Total End Savings/Savings Goal) Random Monitor No Monitor

Does the network position of random monitors matter?

Mean log savings balances across all accounts 8.2 8.1 8 7.9 7.8 7.7 7.6 7.5 7.4 BC Low Centrality Monitor High Centrality Monitor

Mean log savings balances across all accounts 8.6 8.4 8.2 8 7.8 7.6 7.4 7.2 BC Far Saver ‐ Monitor Close Saver ‐ Monitor

Monitor effectiveness & graph position log (Form.+Inform. Sav.) iv = α + βCent mon ( i ) + γProx i,mon ( i ) + δ ′ X iv + ǫ iv (1) (2) (3) (4) (5) (6) Log Total Log Total Log Total Log Total Log Total Log Total Dependent Variable Savings Savings Savings Savings Savings Savings Monitor Centrality 0.178** 0.134* 0.153** (0.0736) (0.0729) (0.0675) Saver-Monitor Proximity 1.032*** 0.865** 1.108*** (0.352) (0.334) (0.294) Model-Based Regressor 1.450** 1.819*** (0.693) (0.632) R-squared 0.150 0.155 0.161 0.148 0.101 0.080 Fixed Effects Village Village Village Village Double- Double- Saver, Saver, Saver, Saver, Post Post Controls Monitor Monitor Monitor Monitor LASSO LASSO ◮ Increasing monitor centrality by one standard deviation increases total savings by 14% ◮ Increasing social proximity by one standard deviation increases total savings by 16% Regs. conditional on demographics (e.g., caste, wealth, age, geo.)

Endline 1 .8 .6 Density .4 .2 0 -4 -2 0 2 4 6 log(Total End Savings/Savings Goal) R Monitor: High Centrality R Monitor: Low Centrality

Results 1. Can we encourage savings using central/proximate monitors? ◮ ↑ 1 · σ in centrality = ⇒ > ↑ 14% total savings ◮ ↑ 1 · σ in proximity = ⇒ > ↑ 16% total savings ◮ receiving a monitor = ⇒ > ↑ 35% total savings