1 assessing the impact of microfinance in india: experiences from the field maren duvendack visiting...
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Assessing the Impact of Microfinance in India:
Experiences from the Field
Maren Duvendack
Visiting PhD Researcher
GIDR Seminar
29 November 2008
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Agenda Introduction to Microfinance India’s Rural Credit Market Recent Microfinance Developments
Commercialisation Private Vs. Public Microfinance
Introduction to Impact Assessments Methodological Challenges: Biases
Selection Bias – Solution? Propensity Score Matching Drawbacks Attrition Bias – Solution?
Methodology – Research Design & Sampling Experiences from the Field Conclusion
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Introduction to Microfinance What is microfinance?
Provision of financial (e.g. loans, savings, insurances, remittances) and non-financial services (e.g. consultancy services, financial literacy training) to low-income households
Microfinance is a response to market failure
It relies on social mechanisms (e.g. peer monitoring) to enforce contracts and to reduce the impacts of capital market imperfections and asymmetric information
Microfinance important strategy in the fight against poverty
Importance of microfinance recognised by United Nations and Nobel Prize Committee
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India’s Rural Credit Market
Financial exclusion of India’s poor recurring problem for more than 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 (e.g. the IRDP is a prominent example)
Emergence of microfinance in India mainly due to lack of effective government policies
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Recent Microfinance Developments -Commercialisation Commercialisation defined as the transformation from being a subsidised, donor dependent operation to becoming a regulated financial intermediary
The trend presents itself in 2 different ways:
1. Transformation of not-for-profit organisations into NBFCs
2. Entry of commercial banks through downscaling, e.g. ICICI bank’s approach with the partnership model
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Recent Microfinance Developments -Private Vs. Public Microfinance Direct competition between private and public
microfinance initiatives This led to the first microfinance crisis in India: Andhra
Pradesh, 2006 Government officials shut down offices of SPANDANA
and SHARE because they allegedly maintained abusive lending practices
Crisis had adverse effects on repayment behaviour and public confidence in MFI practices
The crisis might not have been a one-off event Peaceful co-existence of private vs. public run
microfinance initiatives needed
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Introduction to Impact Assessments No clear empirical evidence yet that microfinance has
positive impacts
Impact assessments crucial for donors and microfinance institutions
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
impact assessments
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Introduction to Impact Assessments in India
Only 9 comprehensive 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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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: refers to clients exiting a microfinance programme
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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• 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”
• Matching on entire vector X of observable characteristics
• BUT: not feasible since X expected to be extremely large
• Rosenbaum and Rubin (1983) propose matching based on propensity score:
• Assumption: Participation independent of outcomes given X. No bias P(X) when no bias given X
)1Pr()( iii XDXP
Selection Bias – Solution?
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PSM Drawbacks
Basis for matching: observable characteristics Underlying assumption: no selection bias due to
unobservables Unobservables, e.g. entrepreneurial abilities, persistence
to seek goals, organizational skills, risk attitudes and access to social networks are crucial in microfinance
Combine PSM with difference-in-difference, picks up on unobservables but baseline data set required
Availability of cross-sectional data set only, qualitative tools might help to illuminate role of unobservables
PSM results good approximation to those obtained under experimental approach
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Attrition Bias – Solution? Attrition bias in the context of programme evaluations refers
to clients dropping out of microfinance programmes 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 Study builds upon SEWA Bank impact assessment conducted
by USAID in 1998 and 2000 Existing SEWA Bank panel has not yet been subjected
advanced statistical techniques, thus much can be learnt by re-analysing it
In addition, new cross-section was collected with the aim to illuminate the role of the unobservables by adding social capital
section to questionnaire to get a clearer picture on short-term versus long-term impacts
Original USAID questionnaire adjusted, pre-tested and then administered to 220 households
8 case study interviews with clients and non-clients to further help illuminate the role of the unobservables
Sampling of drop-outs to account for attrition bias
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Methodology – Sampling Sample: 220 households, criterion: women above 18 and economically
active
70 borrowers as of FY 2007, 70 savers as of FY 2007, 70 non-clients as a control group and 10 drop-outs
Sample determined by following a 3-step process:
Selection of geographical area: 10 wards in the old city of Ahmedabad
Selection of the 2 client samples and drop-outs: proportionate random sample was drawn from FY 2007 client list covering those 10 wards, oversampling done, replacements accounted for
Selection of the non-client sample: mini-census conducted to identify matching non-clients, enumerators were given checklist with matching criteria
8 case studies, random sample of 4 matching pairs consisting of clients and non-clients. Aim to illuminate role of the unobservables by detailing credit/work histories.
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Experiences from the Field (1)
Client sample: Difficulties in finding addresses, hiding of
respondents Busy respondents, no time for interviews Suspicion and dishonesty Request for payments, i.e. sitting fees Corruption
Non-client sample: Mostly talkative, helpful and cooperative
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Experiences from the Field (2) Drop-out sample:
Major challenge. SEWA Bank has no records on drop-outs, virtual denial of drop-out reality
More attention needed for future studies Case study sample:
Suspicion Presence of husband or other family members led to biased
answers of female respondents Obliged to use SEWA Bank staff as a translator which led to
biased translations General remarks:
Social capital type questions led to noisy data Gender issues SEWA Bank database incomplete
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Conclusion No miracle cure for controlling biases exists
However, accounting for biases should be prerequisites for future impact studies
This study is trying to contribute to the impact evaluation literature as follows: New insights by re-analysing the existing SEWA Bank panel
Collection of new cross-section to compare it with the panel (short-term vs long-term benefits of microfinance) and to illuminate the role of the unobservables by adding a social capital section to the questionnaire
Case studies of clients and non-clients with the aim support the quantitative results and to further illuminate the role of the unobservables
