identification strategy: a field experiment on dynamic incentives in rural credit markets
DESCRIPTION
Xavier Gine's (World Bank) presentation on Identification Strategy: A Field Experiment on Dynamic Incentives in Rural Credit Markets. Presented at the Microfinance Impact & Innovation conference 2010.TRANSCRIPT
1
Xavier GineWorld Bank
Jessica GoldbergU. Michigan
Dean YangU. Michigan
Identification Strategy:A Field Experiment on Dynamic
Incentives in Rural Credit Markets
Motivation
• Lending in low-income countries is difficult– Clients typically lack adequate collateral – Lenders have limited information about
creditworthiness of clients
• Problem is worse in agriculture as lenders cannot use microfinance mechanisms
• One key problem is lack of national ID system– Loan defaulters can avoid sanctions by using
different identities– Easier when multiple lenders operate in same area– Lenders respond by limiting the supply of credit
2
What we do
• Fingerprinting helps in future identification, in absence of a national ID system– Helps lenders identify past defaulters (within own
institution and potentially across banks)– Allows lenders to use dynamic incentives
• In this project, we ask: – What is the impact of fingerprinting on loan
repayment?– Is impact heterogeneous across borrower types?– What asymmetric information problems are being
reduced?
• Prospect: may raise lending profitability and encourage lenders to expand rural credit provision
3
Relevant aspects of loans provided
• Malawi Rural Finance Company (MRFC) provides loans to paprika farmers in central Malawi– Dowa, Dedza, Mchinji, Kasungu
• 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 practice
4
Treatment and control groups
• 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
5
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
7
Demonstrating fingerprint identification
8
Theory: How do dynamic incentive vary by borrower type?
• Borrowers differ in the probability that production is successful (adverse selection)
• Borrowers can divert the loan amount instead of investing it in production (moral hazard)
• Lender offers a loan amount that can take on two values (depending on the number of fertilizer bags borrowed).
• Assumptions– Limited liability: lender can only seize value of cash crop
produced• No scope for strategic or opportunistic default• When inputs are diverted loan recovery is not possible
– Loan can always be repaid when lower loan amount is taken.
9
Theory: How do dynamic incentive vary by borrower type?
• Without biometric identification, borrowers can obtain a fresh loan even if they have defaulted in the past by simply using a different identity.
– Lenders are forced to offer the same one season contract every period
• When biometric technology is available, the lender has the ability to use dynamic incentives by denying credit to past defaulters.
– Borrowers face a tradeoff between diverting inputs (and jeopardizing chances of a loan in the future) versus ensuring repayment of the current loan (and securing a loan in the future)
10
Summary of theoretical predictions
• Repayment + 0
• Loan size - 0(adverse selection)
• Input diversion - 0(moral hazard)
11
Impact of dynamic incentive, by borrower type
“Worst” “Best”(low p) (high p)
Simple treatment vs. control comparison
12
Treatment Control Difference P-value of difference
Total borrowed (MK) 16,590 17,279 -688.37 0.23
Repayment by Sep. 30
Balance outstanding (MK) 2,262 3,652 -1389.70 0.11
% paid 88% 80% 8% 0.11
Eventual repayment
Balance outstanding (MK) 1,726 2,484 -758.79 0.33
% paid 90% 88% 3% 0.52
Measuring borrower type
• Theory predicts that impact of dynamic incentives will be heterogeneous according to borrower type (probability of success, p)– All effects are smaller the larger the borrower’s probability of
success
• Empirical implementation:– In model, loan repayment rate is monotonic in probability of
success– Take predicted loan repayment rate as proxy for probability of
success
• We create an index of how likely someone is to repay the loan (essentially a credit score):– Run regression of repayment rate on baseline observables, for
control group only– Then predict repayment rate for all borrowers– Determinants of repayment: Locality, age, gender, risk
indicators, performance on previous loans, income volatility, years of experience growing paprika
13
1%3%
6%
8%
3%
6%
9%
7%
18%
39%
0%
5%
10%
15%
20%
25%
30%
35%
40%
45%
less than 10%
10% to 20%
20% to 30%
30% to 40%
40% to 50%
50% to 60%
60% to 70%
70% to 80%
80% to 90%
more than 90%
Predicted Repayment for Loan Recipients
Predicted percentage repaid
Results: Loan Approval and Take-up
• Fingerprinting has no affect on:
– Probability that loans will be approved by credit officers
– Probability of taking a loan
• However, fingerprint does affect loan size.
– “Worst” clients take out smaller loans by MK 2,722 (roughly US$19) (p=0.13)
15
No impact on loan officer knowledge or behavior
16
Repayment: % of balance paid on-time
17
88%
79%
91% 93%
89%
26%
74%
92%
96%98%
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
Worst 2nd quintile 3rd quintile 4th quintile Best
Fingerprinted
Control
Repayment: % of balance paid (eventual)
18
92%
83%
93% 94%92%
67%
77%
93%96%
99%
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
Worst 2nd quintile 3rd quintile 4th quintile Best
Fingerprinted
Control
Repayment: balance, eventual (MK)
19
1,506
2,975
1,133 1,024
1,737
7,609
3,888
1,486
572
197
0
1,000
2,000
3,000
4,000
5,000
6,000
7,000
8,000
Worst 2nd quintile 3rd quintile 4th quintile Best
Fingerprinted
Control
Fraction of land allocated to paprika
20
19%
15%
21% 22%
23%
11%
16%
19%
21%
23%
0%
5%
10%
15%
20%
25%
Worst 2nd quintile 3rd quintile 4th quintile Best
Fingerprinted
Control
Market inputs used on paprika (MK)
21
9,600
8,381
9,858
8,088
8,874
2,503
4,911
11,803
11,262
12,378
0
2,000
4,000
6,000
8,000
10,000
12,000
14,000
Worst 2nd quintile 3rd quintile 4th quintile Best
Fingerprinted
Control
Summary of results
• An increase in the credibility of a lender’s dynamic incentive raises on-time and eventual loan repayment– As predicted by theory, effect is larger the worse the borrower’s
“type”– Effect for “worst” borrowers (lowest quintile of predicted
repayment) is dramatic: 32 pp increase in eventual repayment
• Evidence of reduction in asymmetric information problems for these “worst” borrowers– Less adverse selection (smaller loan sizes)– Less ex-ante moral hazard (greater input use in paprika farming)
– No strong evidence of reduction in ex-post moral hazard (no higher repayment conditional on loan size, income)
22
Cost-Benefit analysis
• Under conservative assumptions, benefit-cost ratio for lender is an attractive 2.27– MK 476 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
23
Conclusions / Points for Discussion
• Results suggests benefits of establishing cross-lender credit bureau, using fingerprints as unique identifier.– Common platform should be used
• Scale-up may face several potential challenges:– Not everyone can be enrolled (UK Passport Service Trial)– Accuracy– Individuals may have a negative attitude towards technology
– Biometric technology is not infallible
24