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Social Networks, Reputation and Commitment:Evidence from a Savings Monitors Experiment
Emily Breza† Arun G. Chandrasekhar‡
†Columbia Business School ‡Stanford
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Under-savings ubiquitous
I Evidence of large benefits of savings ... yet lowI e.g., Dupas and Robinson ‘13, Schaner ‘13, Beaman et al ‘14
I Access not necessarily the problem in IndiaI RBI-led expansion of rural branches, no-frills accountsI Low rates of account opening and use
I Psychological “frictions” make saving hard:I Can’t commit to save/procrastination? (e.g., Ashraf et al ‘06)I Can’t remember/inattention? (Karlan et al ‘12, Kast et al ‘13)
This paper: can we use social reputation to overcome such frictionsand encourage savings?
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Informal finance uses social reputation
Peer-driven financial institutions are thought to rely on this:
I RoSCAs, SHGs, VSLAs, Microfinance groups
In theories of MF/ROSCAs,“social reputation” often assumed
“the contributing member may admonish his partner forcausing him or her discomfort and material loss. He mightalso report this behavior to others in the village, thusaugmenting the admonishment felt. Such behavior is typicalof the close-knit communities in some LDCs.”
– Besley and Coate (1995)
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What we do
Encourage savings by assigning a unique monitor to each saver.
I Basic idea:I Make a bet with self about ability to save over 6 months.I Stakes: reputation gain/loss from progress in front of some other
member of village.
I Monitor assigned to a saver for the duration of experiment.I Informed about savings in target account.I Simply told about progress (bi-weekly).I Monitor need not do anything!
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Why should a saver care about the monitor?
“A person may save more if it is an important person knowingthey might get more benefits from this person later on.”
– Subject 1
“The monitor will feel that if in the future he or his friends givesher some job or tasks or responsibilities, the saver may not fulfillthem”
– 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
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What we do
Encourage savings by assigning a unique monitor to each saver.
I Basic idea:I Make a bet with self about ability to save over 6 months.I Stakes: reputation gain/loss from progress in front of some other
member of village.
I Monitor assigned to a saver for the duration of experiment.I Informed about savings in target account.I Simply told about progress (bi-weekly).I Monitor need not do anything!
I Not all monitors created equal...
I Central monitors?Can spread more info; more important in future interactions
I Proximate monitors?Info typically goes to people saver will run into.
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Setting
I 60 villages in rural Karnataka,India
I 1.5 to 3 hour’s drive fromBangalore
I Experimental participantsaged ∼18-45
I 1,300 savers who expresseddesire to save more
I 1,000 monitors
I Primary occupations:agriculture and sericulture
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Village network data0 1
8
9
10
20
2324
25
52
57
98
4
5
15
16
17
19
28
29
3031
46
47
56
9711
58
59
14
12
18
67
82
27
81
22
21
44
96
53
73
90
84
86
6
13
49
70
85
32
78
79
48
60
50
55
76
64
62
7488
71
51
75
68
63
72
2
3
92
80
7791
7
39
41
43
83
40
42
45
65
66
54
26
94
33
34
3536
37
61
95
38
69
89
8793
I ∼16,500 householdssurveyed across 75villages
I Relationships:relatives, friends,creditors, debtors,advisors and religiouscompany
I Undirected,unweighted ORnetwork
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A simple model social reputation flow
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Record savings
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Report to Monitor (Low Centrality)
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Only a few people hear gossip
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Report to Monitor (High Centrality)
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Many more people hear gossip
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Report to Monitor (Low Proximity)
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Only a few (distant) people hear gossip
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Report to Monitor (High Proximity)
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Only a few (close) people hear gossip
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Who would make a good monitor?
︸ ︷︷ ︸low centralityhigh proximity
>︸ ︷︷ ︸high centralityhigh proximity
> ︸ ︷︷ ︸low centralitylow proximity
I greater motivation to save if more people are likely to hear aboutyour good/bad deeds (centrality)
I more relevant if people informed of your good/bad deeds arethose you are likely going to meet in the future (proximity)
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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?
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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)
I Account opening
I Goal elicitation (conducted at pre-screen home visit)
I Bi-weekly visits (reminders and weak monitoring)
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Treatments and Roll-Out
Village
I Sample villages selected (based on networks data)
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Treatments and Roll-Out
Pure Control
Potential Savers
Potential Monitors
I Potential savers & monitors visited, savings goals elicited
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Treatments and Roll-Out
Pure Control
Savers Attend
Meeting
Monitor Pool
Monitor Dropouts
Savers Not
Interested I Interested monitors and savers attend village meeting
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Treatments and Roll-Out
Pure Control
Monitored +BC Saver
Chosen Monitors
Monitor Dropouts
Savers Not
Interested
Excess Monitors
BC Saver
I Some savers randomly chosen to receive monitors
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Treatments and Roll-Out
Pure Control
Monitored +BC Saver
Endogenous
Chosen Monitors
Monitor Dropouts
Savers Not
Interested
Excess Monitors
BC Saver
Pure Control
Monitored +BC Saver Random
Chosen Monitors
Monitor Dropouts
Savers Not
Interested
Excess Monitors
BC Saver
Village A Village B
I Random vs. Endogenous Monitor assignment randomized atvillage level
I Random Matching (30 villages)I Savers randomly assigned to a monitor from pool
I Endogenous Matching (30 villages)I Savers choose monitor from pool in random order
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Timeline
Saving Period Begins:• Baseline Survey• Bi‐weekly visits start• Monitors start to get info
Village Meeting
Account Opening:• Bank or PO
Follow‐Up Survey
Saving Period Ends:• Endline Survey
6 Months ~15 Months
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Compensation
I Pure Control (no contact until end of 6 mos.)I No compensation
I Savers (takers only)I In Kind: Account opening servicesI Direct: Rs. 50 ($1) deposited into account
I MonitorsI Payment:
I Rs. 50 if saver reaches half of goal[helps in a robustness exercise]
I Rs. 150 if saver meets goalI Rs. 0 otherwise
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Results
1. Can we encourage savings using central/proximate monitors?
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Do randomly assigned monitors help?
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Results: Log Total (Form. + Inform.) Savings
7.4
7.5
7.6
7.7
7.8
7.9
8
8.1
BC, Random Monitor, Random
Mean log savings balances across all accounts
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Endline
0.2
.4.6
.81
Den
sity
-4 -2 0 2 4 6log(Total End Savings/Savings Goal)
Random Monitor No Monitor
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Does the network position of random monitors matter?
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7.4
7.5
7.6
7.7
7.8
7.9
8
8.1
8.2
BC Low Centrality Monitor High Centrality Monitor
Mean log savings balances across all accounts
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7.2
7.4
7.6
7.8
8
8.2
8.4
8.6
BC Far Saver‐Monitor Close Saver‐Monitor
Mean log savings balances across all accounts
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Monitor effectiveness & graph position
log (Form.+Inform. Sav.)iv = α+βCentmon(i) + γProxi,mon(i) + δ′Xiv + εiv
(1) (2) (3) (4) (5) (6)
Dependent VariableLog Total Savings
Log Total Savings
Log Total Savings
Log Total Savings
Log Total Savings
Log Total 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)
Observations 424 424 424 422 424 422R-squared 0.150 0.155 0.161 0.148 0.101 0.080Fixed Effects Village Village Village Village
ControlsSaver, Monitor
Saver, Monitor
Saver, Monitor
Saver, Monitor
Double-Post
LASSO
Double-Post
LASSO
(1) (2) (3) (4) (5) (6)
Dependent VariableLog Total Savings
Log Total Savings
Log Total Savings
Log Total Savings
Log Total Savings
Log Total 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)
Observations 424 424 424 422 424 422R-squared 0.150 0.155 0.161 0.148 0.101 0.080Fixed Effects Village Village Village Village
ControlsSaver, Monitor
Saver, Monitor
Saver, Monitor
Saver, Monitor
Double-Post
LASSO
Double-Post
LASSO
I Increasing monitor centrality by one standard deviation increases totalsavings by 14%
I Increasing social proximity by one standard deviation increases totalsavings by 16%
Regs. conditional on demographics (e.g., caste, wealth, age, geo.)
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Endline
0.2
.4.6
.81
Den
sity
-4 -2 0 2 4 6log(Total End Savings/Savings Goal)
R Monitor: High Centrality R Monitor: Low Centrality
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Results
1. Can we encourage savings using central/proximate monitors?I ↑ 1 · σ in centrality =⇒ > ↑ 14% total savingsI ↑ 1 · σ in proximity =⇒ > ↑ 16% total savingsI receiving a monitor =⇒ >↑ 35% total savings
2. Does information flow? Where is savings coming from?
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Results
1. Can we encourage savings using central/proximate monitors?I ↑ 1 · σ in centrality =⇒ > ↑ 14% total savingsI ↑ 1 · σ in proximity =⇒ > ↑ 16% total savingsI receiving a monitor =⇒ >↑ 35% total savings
2. Does information flow? Where is savings coming from?
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Did beliefs change?
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Respondents’ beliefs about saversI 560+ random respondents chosen 15 mo. after end of interventionI asked about 8 savers who had monitorsI asked if each saver was responsible (e.g., “good at meeting goals”)I is respondent more likely to say “Yes” when the saver truly did
meet her savings goal (or “No” when the saver didn’t) when therandom monitor is more central?
(1) (2) (3) (4) (5) (6)
Dependent Variable: Beliefs about SaverReached
GoalReached
GoalReached
GoalGood at
Meeting GoalsGood at
Meeting GoalsGood at
Meeting GoalsMonitor Centrality 0.0206 0.0157 0.0157 0.0389 0.0374 0.0353
(0.00937) (0.00804) (0.00854) (0.0144) (0.0140) (0.0148)Respondent-Monitor Proximity 0.00357 -0.00252 -0.00160 0.0476 0.0181 0.0360
(0.0194) (0.0196) (0.0239) (0.0422) (0.0366) (0.0342)
Observations 4,743 4,743 4,743 4,743 4,743 4,743R-squared 0.026 0.020 0.342 0.030 0.023 0.314Fixed Effects No Village Respondent No Village RespondentControls Saver Saver Saver Saver Saver Saver
(1) (2) (3) (4) (5) (6)
Dependent Variable: Beliefs about SaverReached
GoalReached
GoalReached
GoalGood at
Meeting GoalsGood at
Meeting GoalsGood at
Meeting GoalsMonitor Centrality 0.0206 0.0157 0.0157 0.0389 0.0374 0.0353
(0.00937) (0.00804) (0.00854) (0.0144) (0.0140) (0.0148)Respondent-Monitor Proximity 0.00357 -0.00252 -0.00160 0.0476 0.0181 0.0360
(0.0194) (0.0196) (0.0239) (0.0422) (0.0366) (0.0342)
Observations 4,743 4,743 4,743 4,743 4,743 4,743R-squared 0.026 0.020 0.342 0.030 0.023 0.314Fixed Effects No Village Respondent No Village RespondentControls Saver Saver Saver Saver Saver Saver
Central monitor causes beliefs to be updated in direction of actualgoal attainment (13.3%)
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Where did the savings come from?
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Retrospective
(1) (2) (3) (4) (5) (6)
Dependent Variable
Increased
Labor
Supply
Business
Profits
Cut
Unnecessary
Expenditures
Money from
Family and
Friends
Reduced
Transfers to
Others
Took a
Loan
Random Monitor 0.0712 0.0202 0.0787 -0.0227 0.0148 -0.0222
(0.0332) (0.0156) (0.0422) (0.0346) (0.0120) (0.0190)
Dep. Var. Mean 0.15 0.03 0.15 0.19 0.01 0.04
Observations 1,026 1,026 1,026 1,026 1,026 1,026
R-squared 0.055 0.026 0.020 0.056 0.016 0.014
Fixed Effects Village Village Village Village Village Village
Controls Saver Saver Saver Saver Saver Saver
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Results
1. Can we encourage savings using central/proximate monitors?I ↑ 1 · σ in centrality =⇒ > ↑ 14% total savingsI ↑ 1 · σ in proximity =⇒ > ↑ 16% total savingsI receiving a monitor =⇒ >↑ 35% total savings
2. Does information flow? Where is savings coming from?I Evidence of reputation updating 15 months laterI Save from labor/business, cut unnecessary expenditure
3. What happens in the medium term (15+ mo. later)?
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Results
1. Can we encourage savings using central/proximate monitors?I ↑ 1 · σ in centrality =⇒ > ↑ 14% total savingsI ↑ 1 · σ in proximity =⇒ > ↑ 16% total savingsI receiving a monitor =⇒ >↑ 35% total savings
2. Does information flow? Where is savings coming from?I Evidence of reputation updating 15 months laterI Save from labor/business, cut unnecessary expenditure
3. What happens in the medium term (15+ mo. later)?
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What happens 15 months out?
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↓ in (inability to respond to) shocks
(1) (2) (3) (4) (5) (6) (7) (8) (9) (10)
Dependent Variable: ShocksTotal
NumberTotal
NumberGreater than
MedianGreater than
Median Health HealthHH
ExpenditureHH
Expenditurelog(Tot. Sav.)
15 mos.log(Tot. Sav.)
15 mos.Monitor Treatment: Random Assignment -0.199 -0.249 -0.0757 -0.0944 -0.0752 -0.103 -0.0521 -0.0721 0.324 0.290
(0.128) (0.131) (0.0416) (0.0441) (0.0615) (0.0670) (0.0384) (0.0419) (0.196) (0.190)
Observations 1,153 1,153 1,153 1,153 1,153 1,153 1,153 1,153 1,152 1,152R-squared 0.021 0.021 0.019 0.016 0.020 0.020 0.010 0.011 0.074 0.083Mean of Dep. Var (Control) 1.769 1.769 0.577 0.577 0.862 0.862 0.500 0.500 3.779 4.264Fixed Effects Village No Village No Village No Village No No NoControls Saver Saver Saver Saver Saver Saver Saver Saver Saver Saver
Asked about not having enough money to cover necessary expenses inresponse to:
I Health shock, livestock health shock, other urgent consumptionneed etc.
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15 Months Later
0.2
.4.6
.81
Den
sity
-10 -5 0 5log(Total EL2 Savings/Savings Goal)
Random Monitor No Monitor
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15 Months Later
0.2
.4.6
.81
Den
sity
-10 -5 0 5log(Total EL2 Savings/Savings Goal)
R Monitor: High Centrality R Monitor: Low Centrality
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Results
1. Can we encourage savings using central/proximate monitors?I ↑ 1 · σ in centrality =⇒ > ↑ 14% total savingsI ↑ 1 · σ in proximity =⇒ > ↑ 16% total savingsI receiving a monitor =⇒ >↑ 35% total savings
2. Does information flow? Where is savings coming from?I Evidence of reputation updating 15 months laterI Where does savings come from?
3. What happens in the medium term (15+ mo. later)?I 10%-20% decline in being unable to respond to shocksI Monitor benefits persist: 34% increase in total savings
4. When given choice of monitor, do individuals pick well or unwind?
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Results
1. Can we encourage savings using central/proximate monitors?I ↑ 1 · σ in centrality =⇒ > ↑ 14% total savingsI ↑ 1 · σ in proximity =⇒ > ↑ 16% total savingsI receiving a monitor =⇒ >↑ 35% total savings
2. Does information flow? Where is savings coming from?I Evidence of reputation updating 15 months laterI Where does savings come from?
3. What happens in the medium term (15+ mo. later)?I 10%-20% decline in being unable to respond to shocksI Monitor benefits persist: 34% increase in total savings
4. When given choice of monitor, do individuals pick well or unwind?
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Can savers get there on their own in endogenous villages?
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Endogenous treatment
Goal: Benchmarking exercise
I Policy-relevant alternative, naturalistic implementationI recall MF and ROSCAs often have endogenous group formationI Stickk.com has individuals pick a “referee” to verify their progress
toward their goals and to provide motivationI an MFI has actually approached us to try to implement
something similar in an urban customer population
I Experimental design allows for this measurement
What should we expect? Lots of possible outcomes:
I savers could pick enablers, unwind any benefits of a monitor
I savers could pick savings-maximizing allocation of monitors
I anything in between
Note: Experiment not designed to unpack choice
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7.4
7.5
7.6
7.7
7.8
7.9
8
8.1
8.2
8.3
BC, Random Monitor, Random BC, Endogenous Monitor, Endogenous
Mean log savings balances across all accounts
7.4
7.5
7.6
7.7
7.8
7.9
8
8.1
8.2
8.3
BC, Random Monitor, Random BC, Endogenous Monitor, Endogenous
Mean log savings balances across all accounts
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7.4
7.5
7.6
7.7
7.8
7.9
8
8.1
8.2
8.3
BC, Random Monitor, Random BC, Endogenous Monitor, Endogenous
Mean log savings balances across all accounts
7.4
7.5
7.6
7.7
7.8
7.9
8
8.1
8.2
8.3
BC, Random Monitor, Random BC, Endogenous Monitor, Endogenous
Mean log savings balances across all accounts
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Results
1. Can we encourage savings using central/proximate monitors?I ↑ 1 · σ in centrality =⇒ > ↑ 14% total savingsI ↑ 1 · σ in proximity =⇒ > ↑ 16% total savingsI receiving a monitor =⇒ >↑ 35% total savings
2. Does information flow? Where is savings coming from?I Evidence of reputation updating 15 months laterI Where does savings come from?
3. What happens in the medium term (15+ mo. later)?I 10% Lower incidence of being unable to respond to shocksI Monitor benefits persist: 34% increase in total savings
4. When given choice of monitor, pick well enough.
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Conclusions
I Leveraging reputational force can encourage savingsI Particularly powerful if “right” partner is chosen
I Community doesn’t unwind it; chosen monitors can be used
I Reputational channel may be an important driver of behavior inRoSCAs, SHGs, MFI groups, etc.