Dean Karlan

Yale UniversityInnovations for Poverty Action

M.I.T. Jameel Poverty Action LabFinancial Access Initiative

Analytical FrameworkThree questions

What is the market failure?Does a particular intervention help solve a

market failure?What is the welfare change?

Constraints to GrowthSubtitle: what are the market failures?

InformationMarket relationships

Trust? Knowledge of networks? RiskJudicial processesCredit (screening? enforcement?)Human capital Managerial capitalTransaction costs (i.e., price)

Impact Evaluation Design Themes1. Search for the resource constraint

Nobody has unlimited moneyNot everyone can get servicesTwo basic approaches:

If more eligible applicants than slots: Randomize which get treatment

If program can service all eligible applicants: Randomize information campaign to generate interest

Nice advantage: learn how to promote the program!

2. Answer questions for operations tooNot just impact of program versus nothingIdeally, deliver lessons for how to improve a program

Access to Finance1. Are there market failures (and what

is the underlying mechanism?) Does expanding supply more credit?

First stage of credit impact studies Does judicial reform lead to more contracts? Does introduction of insurance increase

investment?

What innovations solve market failures?

Microenterprise popular solution: Joint liability Improved screening, monitoring, enforcement? Training: Case study #1

Small/medium enterprise: Mentoring/consulting: Case study #2

Market linkages: Case study #3 (agriculture)

Price: Case study #4 (price)Credit scoring: Case study #5

Embedded in these: What is the welfare improvement?

OutlineFive specific projects to describe

Business Training for Microentrepreneurs Peru

Small/medium enterprise mentoring/consulting Mexico

Market linkages for horticultural crops Kenya

Price: expanding access to credit South Africa and Mexico

Credit scoring to measure impact of loans Philippines and South Africa

Peru Business Training:The InterventionWe add business training sessions to a

microfinance group banking program in urban and rural areas in Peru

We worked with FINCA-Peru, a self-sustainable MFI12 years of experience promoting village banks

for female microentrepreneurs in Ayacucho and Lima, Peru

273 village banks6,429 clients, 96 percent of which were womenTotal savings: $1,630,823Loan portfolio: $821,172

The TrainingTwo different set of materials for the two locations because

of differences in literacy and language Lima: Atinchik – spanish – written aids and homework Ayacucho: Freedom from Hunger – quechua – visual aids

Two modules Module 1: introduced attendees to what a business is, how a

business works, and the marketplace (identify their customers, competitors, and the position of the business in the marketplace and then learned about product, promotional strategies and commercial planning)

Module 2: how to separate business and home finances, differences between income, costs, and profit, calculate production costs, and product pricing.

Experimental DesignProgram had 300 village banks

Each village bank had about 20 womenEach village bank met weekly or monthly to

make loan paymentsAll credit officers trained in how to deliver

business trainingEach credit officer handled 12 village banks.

8 were assigned to treatment, randomly chosen4 were assigned to control, randomly chosen

ImpactsWeak evidence, but positive benefits for clientsStrongest result: business knowledge indices

increased, and revenues in bad months increased

For microfinance institution: lowered default and increased client retention

Implication: cost effective, given very low costBut much remains to be improved. Results weak compared to incoming studies on

mentoring/consulting

Mentoring/Consulting ProjectRun by Mexican state governmentFirms apply for grants for consultants/mentors

from consulting firms in PueblaLimited supply of grants for eligible firms

Thus randomization good for two reasons: 1) Helped solve political problem, by choosing

randomly no complaints of favoritism when allocating a scarce resource

2) Provides powerful impact evaluation by independent researchers to help strengthen future policy discussions

Fairly small sample, 433 firms

ResultsAfter one yearMonthly firm sales & profits up 78% and

110% respectivelyProductivity increased

Productivity defined as profits unexplained by assets

DN Program

• DrumNet is an NGO that encourages the production of an export oriented crop through a cashless micro-credit program by linking directly commercial banks, smallholder farmers, retail providers and exporters.• Solves trust problems found in contract

farming.• Information• Credit

DrumNet Program

• A farmer that wants to be a member of DrumNet has to:– Be a member of a registered SHG.– Express an interest export crop French beans.– Have irrigated land.

• Upon registration, DrumNet members – Receive a 4 week orientation on Good Agricultural

Practices and EUREPGAP requirements.– Open a personal savings account with local bank.– Make a cash contribution of USD 10 that will serve as

collateral for a line of credit of up to 4 times that amount to purchase inputs (seeds and fertilizer).

DrumNet Program• Farmers are organized into groups of 5 who are jointly liable

for the loans taken out. • At harvest time, DrumNet negotiates a price with the

exporter and arranges for the produce pick-up at pre-specified collection points.

• A transaction agent is appointed in each collection point to serve as liaison between DrumNet and the farmers.

• At these collection points, farmers grade their produce and package it, although exporter has the final word on the grading.

• Once the produce is delivered to the exporter, the exporter pays DrumNet who in turn deducts any loan repayment and credits the rest to the member bank account.

Experimental DesignLocation• Gichugu division in the Kirinyaga district. It was chosen because of its agro-

climatic conditions (similar to original DN locations) and because the clustering of participants was feasible logistically.

Sample Framework• Original sample of 96 registered SHGs including disbanded groups. Run a

“filter” survey to find out the status.• Final sample of 36 SHG whose combined number of members reached the

target DrumNet capacity of 750 individuals (20-40 members in a group).

Randomization of SHGs • 12 got all services except for credit• 12 got all services including credit• 12 Control

– All analysis will cluster standard errors within SHG

Experimental Design

April 2004

June 2004 May 2005

Baseline Survey

36 SHG

DrumNet starts orientation of 24 SHG

September 2004

Orientations finish

Follow-up Survey

Demand ElasticitiesLarge-scale experiment in Mexico,

Compartamos BankRandomized over branches spread

throughout entire countryIncorporates general equilibrium effects

PriceHuge variance in rates around the worldHuge variance in depth of outreachSignificant debate, especially in high rate

countriesTwo sides to the calculation

CostsDemand

Most of the data/analysis/discussion is on costsFor years, World Bank (CGAP) and others

pushed to raise rates, to get to sustainability

Existing EvidenceFirst experimental study from South Africa58,168 direct mail letters to prior clientsMost received lower rates, some higherOne time offer, with about one month window

to apply

Direct Mail Example Letters

Add example of marketing letters here

Existing EvidenceDirect mail:

Limited exposure (i.e., probably low ‘gossip’ to spread the word)

To those paying attention to mail Note: high sensitivity to marketing on the same

letters Photo of a woman same effect on take-up as

dropping the interest rate by 4.3 percentage pointsNot long termUnlikely general equilbrium effects

Compartamos experimentKey differences

Randomization at branch levelData collected over 19 monthsTwo “take-up” sets of data

Specific face-to-face marketing Natural over time process, i.e., branch level

outcomes with normal marketing, growth, gossip and general equilibrium effects

Experimental Design “Crédito Mujer” village

banking loan product only for women

Bank branches grouped into 80 geographic clusters across Mexico Half assigned “high” rate Half assigned “low” rate

Branch Locations

Final sample included 132 branch offices in 80 geographic clusters across Mexico

“high” rate“low” rate

Terms of the Loan

Borrowers may already have a business or plan to start one

Loans from 1,000 to 20,000 pesos (≈ $75 -$1500 USD) .

Average loan size 7,365 pesos (≈ 550 USD).16 weekly payments Group meetings and group liability

Interest Rates Offered

"Low" Scenario "High" Scenario

Level % of Clients AnnualizedFlat

MonthlyAnnualized

Flat Monthly

Gold 22% 62.63% 3.0% 72.54% 3.5%Silver 27% 72.54% 3.5% 82.35% 4.0%

Bronze 51% 82.35% 4.0% 91.34% 4.5%Clients assigned to one of three rate levels based on their borrower profile “Low” scenario branches: approximately 10% lower annualized to clients with same profile

Data SourcesAdministrative Data: Compartamos’ client recordsTake up: Loan officers kept logs during direct loan

promotion activities to report outcome of their effortsCompetition: Branch managers of study branches were

asked to report rates of their top three competitors Credit Bureau: Data report from Mexico’s largest Credit

Bureau on municipalities served by study branches Government Loan Data: National Banking and Securities

Commission report on lending data from financial entities in municipalities served by study branches

Results: More Clients

Results: Larger Loan Portfolio

All together for CompartamosCompartamos is a for-profitImpact on society importantPromise to investors of course is that helping

society can be profitable.

Revenues increased by about 11%Costs did tooNet impact on profits: up about 3%

Statistically same as before

Costs, Income, & Profit (1)Dependent Variable:

Total Costs (1000s, Pesos)

Total Interest Income (1000s,

Pesos)Total Profits

(1000s, Pesos)Panel A: December 2008 Report      

Low Interest Rate 94.700** 167.094 32.070(46.349) (139.784) (102.520)

Quintile of Baseline Dependent Variable Yes Yes YesMean of dependent variable for high interest rate cluster 521.216 1474.239 953.022N 80 80 80

Pending issuesClearly one parameter not right for the whole

worldKey is to understand the heterogeneity

Competitive landscapeReturns to capital (discount rate)Financial literacyDisclosure policies and practices

Replication sitesRural peru, urban Manila, urban Ghana, rural

Philippines

Expanding Microenterprise Credit Access:Using Randomized Supply Decisions to

Estimate the Impacts in Manila

Dean S. KarlanYale UniversityInnovations for Poverty

ActionM.I.T. Jameel Poverty Action

Lab

Jonathan ZinmanDartmouth College

Innovations for Poverty Action

M.I.T. Jameel Poverty Action Lab

MotivationMicrocredit certainly a “big” idea in development policyOne key premise underlies the movement:

Credit market failures existSpecifically, “microcredit”, by lowing transaction costs or

removing information asymmetries, removes credit constraints for the poor

Countless key implications argued: As credit constraints relaxed, impacts spread through all

facets of business, consumption, health, education, etc.Even on to informal institutions, trust, political participation,

household bargaining, the list of “theories” goes on….Of course some argue this could be too much debt

Consumer disclosure issues In USA…. debt is now the culprit, not the savior…

Experiments to measure impactImpact studies of microcredit have been done

and done and done and done…Questions linger regarding identification

Basic selection problems:Who chooses to borrow?

Entrepreneurial spirit? Resourceful individuals?Who do MFI’s agree to lend to?Program placement: MFI’s target growing

areas

Microcredit Randomized TrialsNow we are beginning to see a wave of them

South Africa: urban/peri-urban, for-profit, individual lending, formal sector employed individuals (Karlan and Zinman, 2008)

Philippines: urban Manila, for-profit, individual lending (Karlan and Zinman, 2009)

India: Spandana, urban Hyderabad, for-profit, group lending (Banerjee, Duflo, Glennerster and Kinnan, 2009)

Peru: Arariwa, rural, village banking, non-profit (Karlan and Zinman, 2010?)

Mexico: Compartamos, peri-urban/urban, village banking, for-profit (Angelucci, Conley, Karlan and Zinman, 2011?)

Morocco: Al-Amana, rural, village banking, non-profit (Crepon, Duflo and Pariente, 2010?)

Philippines: FICO and First Valley (getting under way, 2012?) Bosnia: for-profit, individual liability, urban and rural

(Augsburg, de Haas, Hamgart and Meghir, 2011?)

Simple but critical testOne theory: markets are actually complete. High prices for informal credit

Incomplete credit markets?Or higher price for better service?

Will increased loans higher debt or change in debt composition?

If so, prima facie evidence for credit constraints

Basic Methodology

1. Lender randomizes marginal loan applicants:

100-point credit scorecard based on applicant’s:

1 – 30

Auto reject

60 – 100

Auto approve

31 – 45

Randomly

approve 60%

46 – 59

Randomly

approve 85%

• Business capacity

• Personal financial resources

• Outside financial resources

• Personal and business stability

• Demographic characteristics

Basic Methodology

2. Follow-up with household survey that measures:

• Borrowing, broadly defined

• Business income, expenses, and profits

• Investments, broadly defined

• Psychological and political outlook

Surveyors completed 1,114 of 1,601 in sample for70% response rate.

Number of days between treatment and follow-up:

Mean Median Standard Dev.

411 378 76

Why did the bank do this?

Weekly credit committees: slow, costly, subject to inconsistencies of subjective scoring.

Computerized credit scoring fast (loan decision in 1 hour!) and quantitatively-based.

Future scorecard revision requires data points on “below the line” applicants.

Market Settings

Small-business Microloan market in Philippines:

• For-profit rural bank

• Regulated but no viable credit bureau

• Unsecured

• Individual liability

• High-risk

• Short-term (13 weeks), fixed repayments

• Expensive by many international standards (63% APR)

ResultsLoan use increases: 9.6% more likely to have

a loanEvidence of market failure being solved

ImpactsA lot of non-resultsTo address multiple outcome issues, we compute

indices, and several are consistent and statistically significant.

Key results:Profits go up for men, but not women (consistent with

de Mel et al capital drop experiment in Sri Lanka)Cut back in firm investments and cutback in formal

insuranceIncreased education for families of male borrowersIncreased access to informal creditIncreased trust in communityIncreased stress (consistent finding in South Africa)

South AfricaSimilar studyConsumer lendingFound increase in employment after getting a

loanNet impact: 8 percentage point reduction in

poverty headcount ratio

Further researchWhat are patterns of impact?When should credit be increased?Who should lenders (and their funders) target?

Replication needed to further answer these questions. No one study can satisfy the policy questions being posed.Need varying competitive settingsNeed varying cultural and economic conditions.

Thank you!

dean.karlan@yale.eduhttp://karlan.yale.edu

http://www.poverty-action.orghttp://www.povertyactionlab.org