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[email protected] www.e-mfp.eu
Making Microfinance Investment Responsible
Client protection and financial performance, win win or trade-off?
[email protected] www.e-mfp.eu
[email protected] www.e-mfp.eu
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
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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 - including 1-2 weeks on site visit to the MFI)
Challenge: small database, research to be repeated with larger dataset
Dataset: 114 observations, financial indicators and scores of social performance indicators, from financial and social ratings (2007-2012)
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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;
Previous research on social and financial performance 2/3
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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)
Previous research on social and financial performance 3/3
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CLIENT PROTECTION: Data Providers
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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
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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
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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.
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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 …?
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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
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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)
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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.
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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 …
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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 indicators will clarify the global picture of the social and financial relationship
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Investor perspectives
Christophe Bochatay, Triple Jump Robin Gravesteijn, Oikocredit Maxime Bouan, BlueOrchard