Making Microfinance Investment Responsible

1. Making Microfinance Investment Responsible Client protection and financial performance, win win or trade-off?

3. Background • The relationship between social aspects of business and financial performance has been studied in depth for many industries but not for microfinance, despite its multiple ambitions • Previous research in microfinance scarce: • Industry studies (e.g. by Cerise, Incofin, Microfinanza Rating) • Academic Studies on Outreach using average loan size as proxy (e.g. Cull et al., 2007; Hermes et al., 2011) • One work in progress on non-linear relationship between external and internal social responsibility and financial performance using data from Microfinanza Rating (Hoepner, Liu & Wilson, 2012) • In this context, ten organizations joined forces to organize a large scale analysis of the relationship between social performance (especially client protection principles) and financial performance in microfinance • This presentation engages with the background and process of this research project

4. Previous research on social and financial performance 1/3 • Financial results - Social performance matters, St Andrews University - MicroFinanza Rating, May 2012 • Methodology: Pooled regression a la Cull et al. (2007), linear and non-linear relationships tested. • Strength: data reliability from microfinance and social ratings (review of data and systems - including1-2 weeks on site visit to the MFI) • Challenge: small database, research to be repeated with larger dataset • Dataset: 114 observations, financialindicators and scores of social performance indicators, from financial and social ratings (2007-2012)

5. Previous research on social and financial performance 2/3 Client protection implementation: pays off • U shaped relationship: • client protection • financial performance • But the MFI’s efforts to implement client protection will pay off once the MFI has reached the client protection “minimum critical mass” necessary to build the clients’ loyalty and the trust of the government and investors. • Building client protection systems from scratch can be costly;

6. Previous research on social and financial performance 3/3 • Example of the Intuition of the econometrics • Take financial performance (i.e. ROE and FSS) • Regress on economic controls (Real Gross Portfolio Yield, Operating Expense Ratio, Risk Coverage Ratio) and structural/demographic controls (ln[Age], Region) to identify performance drivers (i.e. RGPY strongly positive, OER strongly negative, African MFIs somewhat weaker ROE) • Regress in addition on social indicator and distinguish between non-profit and non-for- profit institutions (i.e. no significant result) • Allow for nonlinear shapes in regression (i.e. for-profit MFIs experience clear U-shaped relationship between client protection and financial performance)

7. CLIENT PROTECTION: Data Providers

8. Context of study • Aim of Analysis: • Explain the relationship between financial performance measures and client protection ratings while controlling for MFI characteristics • Expected Conceptual Insights from Analysis: • Understanding of the contemporaneous relationships between financial performance and client protection principles (e.g. Are MFIs associated with good financial outcomes, on average, also associated with good aspects of client protection?) • Impossibilities: (i) Explaining causality over time; (ii) Studying non-linearities in pooled dataset

9. Dataset Construction: step by step • Pooling the datasets of the eight providers: • Signing of (restrictive) Non-Disclosure Agreements between Research Team of Uni St. Andrews and the providers • Reception of original datasets • Analysis of original datasets according to data availability/missing data • Research Team independently suggests a matching of different data providers criteria on common client protection themes • Common Themes identified were: Price Transparency, Complaints Procedure, Privacy of Client Data and Ethical Staff Behavior, Avoidance of Over-Indebtedness, Appropriate Debt Collections Practices. • 30min or longer interviews with each provider to (i) confirm or revise the matching, (ii) understand of additional data points are available and (iii) receive general feedback • Reception of additional data points from providers (last one received Nov 9th 2012) • Aligning of the providers’ datasets • Rescaling the assessment schemes to a common range with Min 0 and Max 1 • Measuring the variability across data providers • Identifying duplicates (same year, same MFI rated by two or more providers) and choosing the one which is not MiX or otherwise averaging the assessment • Outcome: Up to 2,907 MFI-Year observations between 2004-2011 from 95 countries

10. Key Variables • Dependent Variables (“to be explained”): • Operating Expense Ratio • ROE • ROA • PAR30 • Key Independent Variables (“to explain”): • Price Transparency • Complaints Procedure • Privacy of Client Data • Ethical Staff Behavior • Avoidance of Over-Indebtedness • Appropriate Debt Collections Practices.

11. Control Variables • So far: • Country • Region • Age • Size • Year of Assessment • Assessing Institution • Variability of Assessing Institution • Further planned: • MFI’s legal status, Real Gross Portfolio Yield, Operating Expense Ratio, Risk Coverage Ratio, Data Collection Reliability (i.e. Self reported, Self Reported and Verified, Rated), Country Ratings on Institutional/Regulatory Aspects • Any further suggestion very welcome …?

12. Technical Details • Pooled/Very unbalanced panel regression • Robust standard errors clustered at regional or country level • Regions classified according to Worldbank • Automatic multicollinearity adjustments • Use of strict significance levels 95%, 99%, 99.9% • Robustness tests so far • without Mix data • weighting observations by 1/provider sample and 1/variability • Using only 2010-2011 as sample • Using only 2011 as sample • Combinations of the above • Individual provider analyses performed

13. Progress so far • Precise matching of datasets on 6 client protection criteria • Two iteration of in-depth data cleaning involving 30min interviews and, if suitable, an update data supply from each provider • ~1,500 regressions (Main regressions and many robustness tests)

14. Results • What we find? • Results to emerge through statistical analysis and joint interpretation … • What we did not find (so far at least): • In main regression and all robustness test so far (i.e. without MiX, weighting observations by accuracy and provider, 2010-2011 sample, 2011 sample etc.) … • … the relationship between Price Transparency, Complaints Procedure, Privacy of Client Data and Ethical Staff Behavior has never been statistically significantly ‘linearly negative’ at 10%, 5% or 1% level.

15. Lessons learned so far • Client Protection ratings matter for financial outcomes though in varying ways. Hence, the information itself is clearly valuable • ‘Indirectly financial aspects’ of client protection such as transparency or privacy appear to be non-negatively related with financial return measures, which might be explainable by a ‘quality of management’ narrative • However, when it comes to directly financial aspects such as preventing over-indebtedness or appropriately collecting debts the relationship seems quite mixed. • Similarly, the relationships between any aspect of client protection and risk measures appear quite mixed • Further research will go deeper into contextual aspects to enhance the robustness of these results • Any further suggestions? • Feedback very welcome …

16. Next steps of research • Phase 1 : MIR Action Group Brief client protection→financial performance • Phase 2 : e-MFP AG European Dialogue or Handbook social performance → ( = client protection + outreach + quality + SPM + social responsibility) ↓ financial performance Investigating the complete set of social performance indicatorswillclarify the global picture of the social and financialrelationship

17. Investor perspectives • Christophe Bochatay, Triple Jump • Robin Gravesteijn, Oikocredit • Maxime Bouan, BlueOrchard