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

Dean S. KarlanPrinceton University,

M.I.T. Poverty Action Lab

Jonathan ZinmanFRBNY*

World BankApril 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:1. Outline research questions we need to

answer to help address I. and II.2. Outline related Karlan-Zinman field

experiments and findings

Research, Microfinance, andPro-Poor Growth

Some research findings we need to help answer the big questions:1. How do the poor make (financial) decisions?

– Do people make the “right” decisions?2. 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?

3. How large are marginal returns, broadly defined, to borrowing/investing?– Private returns– Social returns

4. 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

High Contract Rate Low Contract Rate

High Offer Rate

Low Offer Rate N/A

Moral Hazard / Repayment Burden

Adv

erse

Sel

ectio

n

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