Published on April 17, 2008
Does Microfinance Make $ense?Experimental Approaches: Does Microfinance Make $ense? Experimental Approaches IFC M&E Conference May 9, 2006 Jonathan Zinman Dartmouth College Plan for Talk: 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 Evaluating Impacts of Microcredit Access Using Randomized Credit Supply Decisions: 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 Measuring ImpactsUsing Derationing: Measuring Impacts Using Derationing Impact= the difference in an outcome of interest in 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 Measurement Strategy: 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 Derationing Implementations: Derationing Implementations Completed in South African consumer loan market Underway in Filipino microenterprise loan market Planning in Peruvian microenterprise loan market Market Settings: Market Settings Microenterprise credit market in Metro Manila For-profit lender Individual liability Partly secured Primarily small grocery/convenience stores No targeting Market Settings: 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 Implementation Details:Engineering Randomness: 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 What’s in it for the Lenders?: 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 Preliminary Results fromSouth African Implementation: Preliminary Results from South 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? Beyond Risk Assessment:Access Broadly Defined: 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 Access Broadly Defined: Access Broadly Defined Efficiency-Sustainability-Access nexus: Risk assessment (credit scoring) Enforcement & monitoring experiment in Peru (Karlan, Mullainathan,and Zinman) Access Broadly Defined: Savings: 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” Beyond Evaluation: Why?: 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 Beyond Evaluation: Why?: 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 Beyond Evaluation:Identifying Market Failures: 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 Why invest in the why of interventions?: 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 Experimentation &the Learning Organization:A Virtuous Cycle: Experimentation & the Learning Organization: A Virtuous Cycle
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