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Transcript of 1 Xavier Gine World Bank Jessica Goldberg University of Maryland Dean Yang University of Michigan...
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
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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
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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)
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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
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Demonstrating fingerprint identification
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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
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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
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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
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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
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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
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Schematic of experimental design
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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
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• 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
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Preliminary statistics from baseline survey
• The baseline study is ongoing, targeted to include 5000 customers across 27 of Malawi’s 28 districts.
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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%