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Assessing the Impact of Microfinance:
A Methodological Study Using Evidence from India
Maren DuvendackProcedural Paper Presentation
23 May 2008
Supervisors: Arjan Verschoor & Nitya Rao
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Introduction to Microfinance
What is microfinance? Provision of financial and non-financial services to low-income
households
Microfinance important strategy in the fight against poverty
Importance of microfinance recognised by United Nations and Nobel Prize Committee
No clear empirical evidence yet that microfinance has positive impacts
Impact assessments crucial for donors and microfinance institutions
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Introduction of Research Project Challenge of every impact assessment:
Measurement of counterfactual Elimination of biases (i.e. selection & attrition bias)
Limited number of rigorous impact studies exist
Study intends to focus on methodological challenges of microfinance impact assessment studies
Suggest solutions to bias problem
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Research Questions
What is the impact of microfinance on the households’ economic and social well-being?
How are microfinance assessment studies measuring the impact of microfinance?
What are the methodological challenges of microfinance impact assessments?
How can a rigorous treatment of biases, in particular drop-outs, improve the accuracy of impact assessment studies?
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Research Context
Financial exclusion of India’s poor recurring problem for almost 100 years
Access to finance poverty reduction, thus Indian government launched various policy initiatives aimed at financial inclusion
BUT: Most government-run subsidised credit programmes had negative effects
Emergence of microfinance in India mainly due to lack of effective government policies
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Research Context
Emergence of microfinance in India in the 1990s
Tremendous growth of Indian microfinance in terms of outreach and loan disbursements
BUT: Only 8 impact assessment studies conducted in India
Studies vary significantly in terms of scope and approach
They investigate one or more of the following impacts: Poverty reduction Financial services Women’s empowerment
Studies provide conflicting results, impact of microfinance unclear
Thus, more systematic approach to impact assessments needed
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Conceptual Approaches Core elements of conceptual frameworks in impact assessments:
The impact chain model The units of assessment The impact type
Agent
Agent
Behaviours and practices over a period of time
Modified behaviours and practices over a period of time
Outcomes for the agent and/or other
agents
Program Intervention
Modified outcomes for the agent and/or
other agents
The difference between outcomes is the impact
Mediating Processes
Mediating Processes
Impact
Source: Hulme, 2000.
The impact chain model:
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Conceptual Approaches
Units of assessment: Individual, enterprise, household, community and
institutional level Majority of studies examine impact at multiple levels
Identification of impact type: Economic, social or socio-political impacts Early impact studies mainly investigated economic impacts,
using indicators such as income, assets and expenditure In the 1980s, focus on social impacts, using indicators such
as education, health, housing and sanitation More recently, shift towards socio-political indicators such
as women’s empowerment
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Paradigms of Impact Assessments Attribution additional challenge of impact
assessments
Two main paradigms can be extracted commonly used to demonstrate attribution:
Scientific Method Humanities Tradition
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Scientific Method Typically attempts to attribute effects of an intervention to its
causes by utilising either…
Design Pros Cons…an experimental design
Free from biases Delivers robust results
Extensive cooperation from MFI needed Time & cost intensive Raises ethical questions
…a quasi-experimental design
Attempts to mimic experimental design Most popular design among MF impact assessors
Identification of identical control group difficult without introducing biases
…a non-experimental design
Less time & cost intensive than other two designs
Not particularly practitioner-friendly due to application of econometrical techniques
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Humanities Tradition
Humanities tradition seeks to explain & interpret the underlying processes of an intervention
Dual function:
Triangulation to crosscheck quantitative data
Provides understanding of changes in social relationships
Difficulties in demonstrating attribution due to lack of control group approach
Causality inferred by collecting data on causal chain by interviewing programme participants, then comparison to data from areas which did not have access to programme
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Methodological Challenges: Biases Biases common occurrence in impact evaluations
adversely effect impact results, thus solution crucial Typically the following biases occur in the context of
microfinance: Selection bias: self-selection & non-random programme
placement Attrition bias
Only handful of rigorous impact studies exist that control for biases: Hulme and Mosley (1996) Coleman (1999) Pitt and Khandker (1998) Alexander and Karlan (2007)
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Selection Bias – Hulme & Mosley, ColemanHulme and Mosely (1996) study of microfinance programmes in seven
different countries Controlled for self-selection bias but not non-random programme
placement bias Novelty: sampling of prospective clients as a control group Mixed results, depending on programme design and country context
Coleman (1999) study on Thailand, uses village-level fixed-effects to control for non-random programme placement bias
Also, he uses Hulme & Mosley’s (1996) approach of sampling prospective clients as a control group
Difference-in-difference approach employed Little impact found, more importantly microfinance led to vicious
circle of bad debts
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Selection Bias – Pitt & Khandker Until today, most rigorous attempt at controlling for selection bias
Quasi-experiment & eligibility requirements used to measure programme impact Primary eligibility criterion: landownership
“Treatment” Village “Control” Village
Overall findings: microcredit has positive impacts BUT: accuracy of results disputed due to lax enforcement of eligibility criteria Econometric debate between Pitt & Khandker and Morduch, not resolved until today
Eligible but do not participate
Participants
Not eligible
Would be eligible
Would not be eligible
Source: Armendáriz de Aghion and Morduch, 2005.
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Selection Bias – Solution? Propensity score matching (PSM) popular method used to
eliminate selection bias Works by matching participants to non-participants based
on predicted probability of programme participation or the “propensity score”
Basis for matching: observable characteristics drawback Underlying assumption: no selection bias due to
unobservables Combine PSM with difference-in-difference, picks up on
unobservables but baseline data set required PSM results good approximation to those obtained under
experimental approach
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Attrition Bias
Drop-out rates estimated to be between 3.5% to 60% in microfinance programmes worldwide
Two different types of clients exiting: Graduates
Drop-outs
Attrition bias neglected by majority of studies, Alexander and Karlan (2007) one of the few recognising its importance
Solution to attrition bias: Better sampling
Systematic interviews with drop-outs
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Methodology – Research Design
Mainly a quantitative study with selected qualitative elements Questionnaire survey of 500 households Semi-structured interviews with selected key borrowers, in
particular drop-outs
Study proposes to employ propensity score matching (PSM) as a means to control for selection bias as well as attrition bias novelty in the context of microfinance Requirement: sampling of participants and non-
participants as well as drop-outs
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Methodology – Overview (1)
Research Questions Steps Methods Challenge
1) What is the impact of microfinance on the households’ economic and social well-being?
Household survey, n=500, data collection on economic & social indicators, e.g. income, assets, education, health, etc.
Administration of questionnaires, max. 1.5 hours and as pre-coded as possible
Collection of appropriate income data
2) How are microfinance assessment studies measuring the impact of microfinance?
Control group needed to identify counterfactual – what would have happened had the programme not existed - requires sampling of “treated” (i.e. part.) and “non-treated” (i.e. non-part.)
PSM helps to create control group which is very similar to treatment group, only difference: control group did not participate
Identification of counterfactual
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Methodology – Overview (2)
Research Questions Steps Methods Challenge
3) What are the methodological challenges of microfinance impact assessments?
Simple comparison of impact indicators between part. and non-part. leads to distorted results due to differences in observable & unobservable characteristics
PSM, eliminates selection bias due to observables Possibly conduct semi-structured interviews with selected part. and non-part. to understand role of unobservable characteristics
Correction for selection bias
4) How can a rigorous treatment of biases, in particular drop-outs, improve the accuracy of impact assessment studies?
Sampling of drop-outs in addition to part. and non-part.
PSM Semi-structured interviews with selected key drop-outs to understand reasons for attrition
Tracing drop-outs
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Methodology – Sampling Procedure
Study proposes to employ multistage cluster sampling, as illustrated by figure
Sampling in stages: first, identify large areas, then narrow them down by selecting smaller areas within those larger ones
Research location: Andhra Pradesh
Sample selection criteria:
Mature microfinance programmes preferred, at least 5 years of operation
Participants: 4-5 loan cycles required
Region:Telangana
District:Khammam
Mandal:tbd
Village:tbd
State:Andhra Pradesh
Household:tbd
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Methodology – Ethics
Oral and/or written consent of research participants shall be obtained before embarking on data collection
Data collected shall be kept confidential and will be anonymised
Reliance on research assistant and translators is expected, they shall be treated with the utmost respect and their expenses shall be covered by the researcher
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Timeline
Preparation Fieldwork
Preparation Procedural Paper
Procedural Paper Presentation
Fieldwork Recce
23 May
Fieldwork in India
Data Analysis
Writing-Up
2008 2009 2010Jan Mar May Jul Sep Nov Jan Mar May Jul Sep Nov Jan Mar May Jul Sep Nov
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Q & A Session