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1 Xavier Gine World Bank Jessica Goldberg University of Maryland Dean Yang University of Michigan Fingerprinting to Reduce Risky Borrowing: An RCT from.

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Presentation on theme: "1 Xavier Gine World Bank Jessica Goldberg University of Maryland Dean Yang University of Michigan Fingerprinting to Reduce Risky Borrowing: An RCT from."— Presentation transcript:

1 1 Xavier Gine World Bank Jessica Goldberg University of Maryland Dean Yang University of Michigan Fingerprinting to Reduce Risky Borrowing: An RCT from Start to Finish…to Start Again!

2 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

3 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

4 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

5 Potential channels of fingerprinting impact 5 Repay Produce Take-up Offer credit contract Screen Monitor Enforce Apply

6 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

7 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

8 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 practice 8

9 Malawi Study Areas N

10 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

11 Balancing tests Variable: Full baseline sampleLoan recipient sample Mean in control group Difference in treatment group Mean in control group Difference in treatment group Male0.81-0.0360.80-0.066* (0.022)(0.037) Married0.92-0.0040.940.003 (0.011)(0.016) Age39.500.01939.96-0.088 (0.674)(1.171) Years of education5.27-0.0465.35-0.124 (0.175)(0.272) Risk taker0.57-0.0330.560.013 (0.032)(0.051) Days of hunger last year6.41-0.6476.05-0.292 (0.832)(1.329) Late paying previous loan0.140.0050.130.030 (0.023)(0.032) SD of past income25110.621289.19027568.34-1158.511 (1756.184)(2730.939) Years of experience2.100.0962.220.299 (0.142)(0.223) Previous default0.03-0.0020.020.008 (0.010) No previous loan0.74-0.0060.74-0.020 (0.027)(0.041) 11

12 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 August 2008

13 Fingerprinting Aug-Sep 2007 13

14 Demonstrating fingerprint identification 14

15 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

16 Repayment: % of balance paid on-time 16

17 Repayment: % of balance paid (eventual) 17

18 Repayment: balance, eventual (MK) 18

19 Fraction of land allocated to paprika 19

20 Market inputs used on paprika (MK) 20

21 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

22 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

23 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

24 Partnership Roles Active MFIsMAMN 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

25 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

26 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

27 Schematic of experimental design 27 Credit Officer Distribution MFITreatmentControlTotal CUMO414283 MEDF424183 Microloan34 68 Total117 234 Intervention Roll-Out MFIPhase 1Phase 2Phase 3 CUMO November 2014 January 2015February 2015 MEDF November 2014December 2015 March 2015 Microloan November 2014 January 2015February 2015

28 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

29 Fingerprint identification system 29

30 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 Age38 years Borrower Gender74 % Female Borrower Education6 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)

31 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 loans28% Average number of Loans1.15 loans Amount Borrowed 32,300 (median: 10,000) Proportion with outstanding loans41%

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