Assessing the Impact of Microfinance in India: Experiences from the Field

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 • 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

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. 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

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: • Transformation of not-for-profit organisations into NBFCs • Entry of commercial banks through downscaling, e.g. ICICI bank’s 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 • Women’s 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. 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” • 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

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 email: m.duvendack@uea.ac.uk