social networks, reputation and commitment: evidence from a...

Social Networks, Reputation and Commitment:Evidence from a Savings Monitors Experiment

Emily Breza† Arun G. Chandrasekhar‡

†Columbia Business School ‡Stanford

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?

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)

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!

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

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.

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

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

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 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)

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)

I Account opening

I Goal elicitation (conducted at pre-screen home visit)

I Bi-weekly visits (reminders and weak monitoring)

Treatments and Roll-Out

Village

I Sample villages selected (based on networks data)


Pure Control

Potential Savers

Potential Monitors

I Potential savers & monitors visited, savings goals elicited


Pure Control

Savers Attend

Meeting

Monitor Pool

Monitor Dropouts

Savers Not

Interested I Interested monitors and savers attend village meeting


Pure Control

Monitored +BC Saver

Chosen Monitors

Monitor Dropouts

Savers Not

Interested

Excess Monitors

BC Saver

I Some savers randomly chosen to receive monitors


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

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

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

Results

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

Do randomly assigned monitors help?

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

Endline

0.2

.4.6

.81

Den

sity

-4 -2 0 2 4 6log(Total End Savings/Savings Goal)

Random Monitor No Monitor

Does the network position of random monitors matter?

7.4

7.5

7.6

7.7

7.8

7.9

8

8.1

8.2

BC Low Centrality Monitor High Centrality Monitor


7.2

7.4

7.6

7.8

8

8.2

8.4

8.6

BC Far Saver‐Monitor Close Saver‐Monitor


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.)

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

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?

Did beliefs change?

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 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 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%)

Where did the savings come from?

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

Results


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)?

What happens 15 months out?

↓ 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.

15 Months Later

0.2

.4.6

.81

Den

sity

-10 -5 0 5log(Total EL2 Savings/Savings Goal)

Random Monitor No Monitor

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

Results


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?

Can savers get there on their own in endogenous villages?

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

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


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


Results


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.

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.

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