1

Xavier GineWorld Bank

Jessica GoldbergUniversity of

Maryland

Dean YangUniversity of

Michigan

Fingerprinting to Reduce Risky Borrowing: An RCT from Start to Finish…

to Start Again!

Raising Malawian agricultural productivity

• Government’s main approach to raising agricultural productivity has been large-scale fertilizer subsidies for smallholders– 11% of government budget in 2010/11– But not sustainable: requires continued donor support

• An open question: can improvements in rural financial services improve farmer input utilization without external subsidies?

• Emphasis on expansion of the supply of credit– Improved repayment rates can increase the supply of

credit and lower interest rates

2

Needs assessment

• Loan repayment rates for microfinance in Malawi are relatively low– Joint liability model is not strictly enforced

• Interest rates are high, and the supply of credit is constrained

• Many people who have defaulted are able to borrow again, and there are “ghost borrowers”

• Malawi does not have a national ID system, and most microfinance borrowers lack formal identity documents

3

Theory of Change

• The incentive to repay a loan is to preserve access to credit in the future (dynamic incentive)

• But to reward good borrowers and sanction defaulters, lenders need to be able to accurately track repayment

• Fingerprint technology could be a good substitute for identity documents or local knowledge– Borrowers who are fingerprinted may change their

own behavior– And those who default can be screened out in the

future

4

Potential channels of fingerprinting impact

5

Repay

Produce

Take-up

Offer credit contract

Screen

Monitor

Enforce

Apply

Potential channels of fingerprinting impact

6

Repay

Produce

Take-up

Offer credit contract

Screen

Monitor

Enforce

Apply

Adverse selection

Moral hazard (ex-ante)

Moral hazard (ex-post)

Fingerprinting occurs here, so effects can only be on actions after this point

Intervention and research questions

• Partner with a Malawian lender to randomize implementation of personal identification technology among loan applicants– Intervention: biometric (electronically scanned)

fingerprinting– Proof-of-concept, using USB fingerprint scanners and

custom-built software

• Key questions we ask: – What is the impact of fingerprinting on loan

repayment?– Is impact heterogeneous across borrower types?

• Prospect: may raise lending profitability and encourage lenders to expand rural credit provision

7

Relevant aspects of loans provided

• Malawi Rural Finance Company (MRFC) provides loans to paprika farmers in central Malawi– Dowa, Dedza, Mchinji, Kasungu– Reports low repayment rates and problems with “ghost

borrowers”

• Collaboration with private paprika buyer, Cheetah Paprika Ltd.– Designed input package– Identified farmer groups– Forwarded loan repayment to lender before paying farmer

• Mean loan amount ~MK 17,000 (~US$120) for paprika seeds, fertilizer and chemicals– Farmers specifies loan size by deciding on 1 vs. 2 bags of CAN

fertilizer– Inputs provided in kind, not in cash– 15% deposit

• Formally joint liability, but individual liability in practice8

Malawi Study Areas

N

Study design

• Randomization at the group level (214 groups)– Because loans are issued at the group level

• Control group:– Educational module emphasizing importance of credit history

administered• Defaulters can be excluded from future loans• Reliable borrowers can get more and larger loans in future

• Treatment group: – Educational module on credit history (identical to module given to

control group) administered, plus:– Biometric fingerprint collected from all farmers as part of loan

application– Use of fingerprints for unique identification explained– Fingerprint identification demonstrated within group

• Treatment stratified by locality and week of intervention visit

10

Balancing testsVariable: Full baseline sample Loan recipient sample

Mean in control group

Difference in treatment group

Mean in control group

Difference in treatment group

Male 0.81 -0.036 0.80 -0.066*(0.022) (0.037)

Married 0.92 -0.004 0.94 0.003(0.011) (0.016)

Age 39.50 0.019 39.96 -0.088(0.674) (1.171)

Years of education 5.27 -0.046 5.35 -0.124(0.175) (0.272)

Risk taker 0.57 -0.033 0.56 0.013(0.032) (0.051)

Days of hunger last year 6.41 -0.647 6.05 -0.292(0.832) (1.329)

Late paying previous loan 0.14 0.005 0.13 0.030(0.023) (0.032)

SD of past income 25110.62 1289.190 27568.34 -1158.511(1756.184) (2730.939)

Years of experience 2.10 0.096 2.22 0.299(0.142) (0.223)

Previous default 0.03 -0.002 0.02 0.008(0.010) (0.010)

No previous loan 0.74 -0.006 0.74 -0.020(0.027) (0.041)

11

Figure 1: Experimental Timeline

July 2007

August 2007

Sep. 30, 2008

Clubs organized

Baseline survey and fingerprinting begin

November 2007

Loans disbursed

Loans due

September 2007

Baseline survey and fingerprinting end

Follow-up survey

August2008

Fingerprinting

• Aug-Sep 2007

13

Demonstrating fingerprint identification

14

Analysis of heterogeneous effects

• Analyze impact separately based on underlying probability of repayment– I.e. expected repayment without any intervention– Each person (treatment and control) is sorted into

one of 5 quintiles of predicted repayment according to their baseline characteristics

• Prediction is that the intervention will have a bigger effect on the bottom quintiles, since these borrowers do not repay without the dynamic incentive

• We can’t randomize underlying characteristic of repayment – it’s a characteristic of an individual– But prediction of different impacts comes from a

theoretical model– So it is “kosher” to study the results this way

15

Repayment: % of balance paid on-time

16Worst 2nd quintile 3rd quintile 4th quintile Best0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

88%

79%

91% 93%

89%

26%

74%

92%

96%98%

FingerprintedControl

Repayment: % of balance paid (eventual)

17Worst 2nd quintile 3rd quintile 4th quintile Best

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

92%

83%

93% 94%92%

67%

77%

93%96%

99%

FingerprintedControl

Repayment: balance, eventual (MK)

18Worst 2nd quintile 3rd quintile 4th quintile Best

0

1,000

2,000

3,000

4,000

5,000

6,000

7,000

8,000

1,506

2,975

1,133 1,024

1,737

7,609

3,888

1,486

572

197

FingerprintedControl

Fraction of land allocated to paprika

19

Worst 2nd quintile 3rd quintile 4th quintile Best0%

5%

10%

15%

20%

25%

19%

15%

21% 22%

23%

11%

16%

19%

21%

23%

FingerprintedControl

Market inputs used on paprika (MK)

20Worst 2nd quintile 3rd quintile 4th quintile Best0

2,000

4,000

6,000

8,000

10,000

12,000

14,000

9,600

8,381

9,858

8,088

8,874

2,503

4,911

11,803

11,262

12,378

FingerprintedControl

Benefit-cost calculation

• Under conservative assumptions, benefit-cost ratio for lender is an attractive 2.34– MK 491 benefit vs. MK 209 cost per individual

fingerprinted

• Could be even more attractive with:– Passage of time, as threat becomes more credible– More cost-effective equipment package– Larger volume lower cost per fingerprint checked by

overseas vendor• E.g., if in context of credit bureau with other lenders

• Does not consider benefits to households from possibly higher income for fingerprinted households – Our estimates too imprecise to say whether income

definitely increased due to fingerprinting

21

The next step

• Expand study in context of new national credit bureaus by incorporating fingerprints– Study supply side behavior

• Lenders may increase the supply of loans, change interest rates, and adjust monitoring or other lending practices

– Larger sample size• With enhanced power, may find effects on crop

output, household well-being– Longer time-frame

• Effects on borrowers may be magnified, as credibility of system is demonstrated

• Defaulters will be screened out of the system

• Intervene with fingerprinting at different points to more cleanly separate moral hazard from adverse selection effects

22

Timeline

• Early 2014 – Recruitment of MFIs (letters of invitation, preliminary meetings.)

• May 2014 – Contract with Technobrain for fingerprinting solution• June-October 2014 – Information gathering and technology

development• September-November 2014 – Baseline Survey• November 2014-March 2015 – Training of Credit Officers and

roll out of technology • Ongoing - Monitoring by credit officers• Ongoing - Repayment data – Received from MFIs every 6 months

during the course of the study• July 2015 - Midline survey• July 2016 - Follow up survey• Early 2017 - Results

23

Partnership Roles

Active MFIs MAMN Credit Bureaus Passive MFIs

• Fingerprint borrowers under treatment loan officers

• Utilize technology to verify new and existing borrowers at the time of loan application

• Incorporate Biometric ID in loan tracking processes

• Provide credit history information to national credit bureaus incorporating biometric ID

• Facilitate the relationship between IPA and partners

• House and maintain the central servers

• Resolve duplicate registrations with the assistance of MFIs

• Work to provide a sustainable solution for the system at the close of the project

• Incorporate the biometric ID in the credit reporting and tracking process

• Allow MFIs to

request credit history or score based on the biometric ID

• Provide information on the size and location of their borrowing portfolio

• Provide information as to their own progress with technological innovation to encourage future collaboration

24

Scaled up study design

• Randomization: at the credit officer level

• Variation in timing of fingerprinting– Borrowers and lenders can take different actions at

different points in the loan cycle– Which actions are affected by fingerprinting?

• Within-region variation: test spillovers– Is there sorting of customers to MFIs in their area

that are/are not collecting fingerprints?– Do lenders respond strategically to customer sorting

and informational advantages?

25

Distribution of customers by region

• In June 2014 Credit Officers provided information on the distribution of their borrowers across the country

• Mapped is the proportion of borrowers from participating MFIs that will be fingerprinted– In white areas no borrowers will be

fingerprinted– In medium areas a fraction (25-

75%) of borrowers will be fingerprinted, allowing for the greatest movement between groups (spillovers)

– In dark areas almost all borrowers will be fingerprinted

26

Schematic of experimental design

27

Credit Officer Distribution

MFI Treatment Control Total

CUMO 41 42 83

MEDF 42 41 83

Microloan 34 34 68

Total 117 117 234

Intervention Roll-Out

MFI Phase 1 Phase 2 Phase 3

CUMO November 2014 January 2015 February 2015

MEDF November 2014 December 2015 March 2015

Microloan November 2014 January 2015 February 2015

Fingerprint identification system

28

• Securetab: Custom built Android-Platform to capture borrowers’ biographical information, contact details and fingerprints

• Each treatment credit officer will receive a tablet

• Fingerprint and loan repayment data can be shared with both national credit bureaus and used to screen future applicants

Fingerprint identification system

29

Preliminary statistics from baseline survey

• The baseline study is ongoing, targeted to include 5000 customers across 27 of Malawi’s 28 districts.

30

Characteristics of the Average Borrower Household

Borrower Age 38 years

Borrower Gender 74 % Female

Borrower Education 6 years

Borrower Position in Household 42% are the household head

Number of Members in the Household 6 persons

Agricultural Income (past 12 months) 22,000 MWK (median)

Non-Agricultural Income (past 12 months) 135,900 MWK (median)

Preliminary statistics from baseline survey

• The baseline study is ongoing, targeted to include 5000 customers across 27 of Malawi’s 28 districts.

31

Borrowing Habits

Loans within the past year from institutions other than the participating MFI

Proportion with loans 28%

Average number of Loans 1.15 loans

Amount Borrowed 32,300 (median: 10,000)

Proportion with outstanding loans 41%