1 Assessing the Impact of Microfinance in India: Experiences from the Field Maren Duvendack Visiting PhD Researcher GIDR Seminar 29 November 2008

2 Agenda Introduction to Microfinance Indias 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

3 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

4 Indias Rural Credit Market Financial exclusion of Indias 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

5 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 banks approach with the partnership model

6 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

7 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

8 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 Womens empowerment Studies provide conflicting results, impact of microfinance unclear Thus, more systematic approach to impact assessments needed

9 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)

10 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 Selection Bias – Solution?

11 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

12 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

13 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

14 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.

15 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

16 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

17 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

18 Q & A Session For further questions or comments please