making microfinance investment responsible

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Making Microfinance Investment Responsible

Client protection and financial performance, win win or trade-off?


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


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)


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;



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)



CLIENT PROTECTION: Data Providers


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


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


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.


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 …?


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


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)


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.


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 …


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


Investor perspectives

Christophe Bochatay, Triple Jump Robin Gravesteijn, Oikocredit Maxime Bouan, BlueOrchard

making microfinance investment responsible

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