Pro poor growth microfinance some related evidence and a research agenda
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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

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

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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.


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


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


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?


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


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


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


“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


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


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


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


Identifying Info Asymmetries:Basic Intuition Behind the Design

Moral Hazard / Repayment Burden

Adverse Selection


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)


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


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


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


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)


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”)


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


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