dean karlan yale university innovations for poverty action m.i.t. jameel poverty action lab...
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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!
[email protected]://karlan.yale.edu
http://www.poverty-action.orghttp://www.povertyactionlab.org