Pro-Poor Growth & Microfinance: Some Related Evidence, and a Research Agenda

1. Pro-Poor Growth & Microfinance:Some Related Evidence,and a Research Agenda Jonathan Zinman FRBNY* Dean S. Karlan Princeton University, M.I.T. Poverty Action Lab World Bank April 21st, 2005 * Views expressed are those of the authors and do not necessarily represent those of the Federal Reserve System or the Federal Reserve Bank of New York.

2. Some Key Questions, &Overview of Talk I. Can microfinance be used to promote pro-poor growth? II. If it can, how? Talk today: • Outline research questions we need to answer to help address I. and II. • Outline related Karlan-Zinman field experiments and findings

3. Research, Microfinance, andPro-Poor Growth Some research findings we need to help answer the big questions: • How do the poor make (financial) decisions? • Do people make the “right” decisions? • How do financial markets work, and not work, in terms of bringing together capital and productive opportunities (broadly defined)? • If there are financial constraints, what underlying frictions cause them? • What is the nature of financial constraints? • How large are marginal returns, broadly defined, to borrowing/investing? • Private returns • Social returns • If 1-3 motivate interventions, which ones are most effective? • Optimal design ex-ante • Evaluation ex-post

4. Set of Research Questions #1:How do the Poor Make Decisions? • Response to incentives • Response to intertemporal tradeoffs • Importance, or lack thereof, of “behavioral”/“psychological” factors, of bounded rationality • Do folks make the “right” decision?

5. Set of Research Questions #2: How do financial markets work, or not? • Lots of theory (e.g., on adverse selection and moral hazard) • Lots of practice • Little clean evidence on specific failures • Even best work on the finance-growth nexus is very reduced-form, looks at symptoms of financial frictions rather than diagnosing specific problems • Particularly true of information asymmetries • Chiappori and Salanie (2000 survey article) • Nobel Committee citation for 2001 Prize

6. Set of Research Questions #3:What are the marginal borrower/investor’s returns? • The trillion-dollar “impact” question • Has microfinance delivered on its promise? • Again, theory and practice far ahead of evidence • Keys to getting better answers here: • Defining and measuring impacts broadly • Measuring impacts cleanly (methodology) • Benchmarking any impacts against alternative (social) investments • I.e., can’t ignore opportunity cost of allocating resources to microfinance

7. Set of Research Questions #4:Interventions • If basic research (the “R” in “R&D”) produces evidence that favors intervention in microfinance markets, what next? • The “D”, and the “E” • “D”evelop and “D”esign Interventions • “E”valuate

8. “Market Field Experiments” • Answering Questions #1-#4 is difficult • Identifying causality • Identifying deep economic parameters of interest • What we’ve been doing: • Designing “market field experiments” meant to identify deep parameters • Finding financial institutions willing to implement randomized-control designs as part of their day-to-day operations • Working with institutions to implement experimental protocols subject to operational constraints • This type of partnership between academics and firms is novel, especially in a market setting

9. Interplay Between Field Experiments & Other Methodologies • Field Experiments not a panacea, but complement to other methodologies: • Strengths: • Clean evidence derived from “gold standard” methodology of behavioral sciences • Large stakes • Natural setting • Weaknesses: • Expensive • Less control than, e.g., lab • External Validity

10. New Evidence on Questions #1-#4 from Karlan-Zinman Field Experiments • Experiment #1: Randomize interest rates and marketing strategies offered by South African consumer lender • Quick background: • “Cash loan” market providing term loans (modal 4 months) at 12% per month • Targets working poor • Market sprung up to replace moneylenders following usury deregulation • Dominated by for-profit lenders

11. Experiment #1: Design Overview • Randomize marketing strategies • Randomize interest rates along 3 different dimensions: • Single dimension sufficient for deriving demand curves for consumer credit • Multiple dimensions needed to identify and disentangle whether adverse selection and moral hazard needed in this market • “Offer rate” advertised on direct mailers sent to 60,000 former clients • Offer rate is generally =< Lender’s standard rate • “Contract rate” revealed to clients only after the come in to apply, hence revealing demand to borrow at their offer rate • Contract rate always =< offer rate • “Dynamic repayment incentive” • All randomizations conditional on observable risk

12. Identifying Info Asymmetries:Basic Intuition Behind the Design Moral Hazard / Repayment Burden Adverse Selection

13. What Have we Learned from Interest Rate Randomizations? Re: Question #1 (Decision-Making) • Intertemporal tradeoffs: these borrowers are price-elastic on average, but: • Demand curves are relatively flat (contra recent evidence from US showing price elasticities > |1| • Elasticity is decreasing in income • Female borrowers are more elastic than males • They are more elastic with respect term (a la Attanasio, Goldberg & Kyriadzidou 2004) • See KZ 2005 on Demand Curves and Credit Constraints (new draft soon)

14. What Have we Learned from Interest Rate Randomizations? Re: Question 2. How financial markets work: • Evidence that both adverse selection and moral hazard matter: • But surprising pattern by gender: only female borrowers exhibit adverse selection, only male borrowers moral hazard • Not necessarily gender per se • Effects are large where present • 20% of defaults • Effects are consistent with “relationships” mitigating information problems • But: functional form (power) issues

15. Project #1:Marketing Randomizations Evidence on Question #1 (Decision-Making) • See Bertrand, Karlan, Mullainathan, Shafir, and Zinman (2005) • Direct mailers included randomly assigned marketing “treatments” motivated by (lab) findings from psychology • Treatments manipulated how loan offer was “cued” and “framed” • Examples: • Deadlines • More v. less information • Photos • Suggestions • Predictions: • Psych/Behavioral Economics: These treatments will affect demand. (But how much?) • Neoclassical Economics: treatments irrelevant

16. Marketing Randomizations:Novelty • What’s unique here compared to lab findings, and similar marketing field experiments • Real stakes • Commodity (i.e., not a branded product) • Consumers familiar with product (borrowed before) • Marketing effects “priced”/scaled vis a vis interest rate elasticity

17. Marketing Experiment:Findings and Lessons • Many treatments do matter • But was hard to predict ex-ante (from lab, theory) which would work in our setting • Are psychologists right that context matters much, and consequently that it’s difficult to create general theories of consumer choice (and human behavior more generally)? • Consider framing effects when designing and marketing programs (Question #4)

18. Project #2: A new experiment Re: Question #3. Marginal returns, and the billion-dollar impacts question. Design: • Work with lenders to randomly assign loans to marginal applicants who would normally be rejected • South Africa, Philippines • Consumer loans, commercial loans • Follow up 6-months later with household surveys to measure impacts • On households (wide range of proxies for well-being) • On micro-businesses • Then compare outcomes (and inputs) of those who randomly got loans (the “derationed”) and those who stayed rejected (the “rationed”)

19. Take-Aways • Microfinance’s role, if any, in promoting pro-poor growth depends on answers to several questions on which we still lack convincing evidence • Market field experiments can help answer these questions • Field experimentation can then feed back into other, complementary methodologies