Does Microfinance Make $ense? Experimental Approaches

1. Does Microfinance Make $ense?Experimental Approaches IFC M&E Conference May 9, 2006 Jonathan Zinman Dartmouth College

2. Plan for Talk • Evaluating Impacts of Microcredit Access Using Randomized Credit Supply Decisions • Design • Implementations • Some Results • Beyond Risk Assessment: Evaluating “Access” Interventions Broadly Defined • Other efficiency and strategy interventions • Enforcement • Pricing • Other contract terms: (maturity, loan size) • Savings takeup • Product presentation (marketing, mental accounting) • Product development (reminders) • Distribution Channels (“impulse savings”) • Beyond Impact Evaluation: Experimentation and Innovation • The importance of measuring why interventions (don’t) work • The possibility of transforming organizations into learning laboratories

3. Evaluating Impacts of Microcredit Access Using Randomized Credit Supply Decisions Ongoing work with Dean Karlan (Yale) Our Methodology: • Lender randomizes credit supply decisions: • Randomized-control design: social science gold standard • Subject pool of marginal applicants (“grey area”) • Some in grey area randomly treated (“derationed”) • Remaining in grey area control group (“rationed”) • We follow-up with household and/or business surveys: • Measure investments, broadly defined • Measure impacts, broadly defined • On borrowing/credit access • On various measures of well-being

4. Measuring ImpactsUsing Derationing • Impact= the difference in an outcome of interestin derationed and rationed groups: • Examples of outcomes: • lender’s profits • applicant borrowing (do rationed get credit elsewhere?) • applicant revenues • applicant consumption smoothness • NOT needed to measure impacts using this method: • No baseline survey needed • No perfect compliance with treatment assignment needed: workable if some derationed borrowers get loans, or vice versa • Can use statistical technique called “Intent to Treat” to measure impacts based on remaining random variation

5. Measurement Strategy Formally: (1) Yi = a + bderationedi + driski + fmonthi + ei • Y is an outcome from admin or survey data • derationed is randomly assigned by Lender • risk conditions the randomization (“reversal”) probability on the Lender’s assessment of how close to creditworthy • month partials out aggregate shocks in the time series

6. Derationing Implementations • Completed in South African consumer loan market • Underway in Filipino microenterprise loan market • Planning in Peruvian microenterprise loan market

7. Market Settings • Microenterprise credit market in Metro Manila • For-profit lender • Individual liability • Partly secured • Primarily small grocery/convenience stores • No targeting

8. Market Settings • Consumer loan market in South Africa • For-profit lender regulated by Microfinance Regulatory Council • Unsecured • Individual liability • High-risk • Short-term (4 months), fixed repayments • Expensive (11.75% monthly, simple) • Untargeted, “working poor” clientele

9. Implementation Details:Engineering Randomness • South Africa: derationing by random reversal (or not) of rejections in grey area • Metro Manila: derationing via implementation of new credit scoring model with random component in grey area

10. What’s in it for the Lenders? • Improve profitability by careful identification of the profitability frontier • What does the marginal profitable/break-even applicant look like • “Pilot approach” • Systematic and gradual changes • Improve efficiency by process innovation • Introduction of credit scoring • Experimentation and the learning organization • Democratization of approach used by sophisticated firms • ICIC, Green Bank

11. Preliminary Results fromSouth African Implementation • Derationing does increase borrowing over the 6-12 months following the experiment • Some positive impacts 6-12 months out: • Derationed households have less hunger • Derationed households more likely to maintain formal employment • No negative impacts on households • But power issues: small sample, so imprecise estimation of null effects • Derationed loans did have substantially worse repayment. • Profitability?

12. Beyond Risk Assessment:Access Broadly Defined Several other aspects of financial product delivery affect access: • Loan pricing: targeted groups may have different takeup elasticities • Dehijia et al vs. Karlan-Zinman • Maturity & loan amount elasticities may dwarf price elasticities for constrained borrowers • Karlan-Zinman; Attanasio et al

13. Access Broadly Defined • Efficiency-Sustainability-Access nexus: • Risk assessment (credit scoring) • Enforcement & monitoring experiment in Peru (Karlan, Mullainathan,and Zinman)

14. Access Broadly Defined: Savings • Do consumers have difficulty saving? • Self-control; Household control • Other motivation and follow-through problems • Then savings takeup decision critical: what drives it? • Product presentation: • Mental accounting (KMZ puzzles experiment) • Marketing and framing a la BKMSZ on loans • Product features (reminders, SMART, SEED) • Distribution channels: “Impulse Savings”

15. Beyond Evaluation: Why? • Interventions: how do we know what to try in the first place? • Intuition • Theory • Anecdata • Past Evaluations • Presence or absence underlying market failures interventions are designed to solve

16. Beyond Evaluation: Why? • Scientific evidence on empirical relevance of specific market failures also rare • Important to build into evaluations, experimentation • Example: measuring adverse selection and moral hazard • Most important theoretical motivations for microcredit • Little clean evidence on importance of either friction

17. Beyond Evaluation:Identifying Market Failures • Karlan-Zinman pricing experiment in South Africa (2005a, 2005b) • Derive profit-maximizing interest rate by randomizing interest rates • This requires one dimension of interest rate variation • Also measure why optimal interest rate is where it is • Demand elasticities • Repayment elasticities due to separate effects of adverse selection and moral hazard • Requires three dimensions of interest rate variation

18. Why invest in the why of interventions? • Policy • E.g.: adverse selection and moral hazard have different remedies • Practice: • Investments in screening? • Investments in enforcement? • Design of future interventions • Ongoing experimentation as process innovation

19. Experiment Evaluate Innovate Experimentation &the Learning Organization:A Virtuous Cycle