Having said this, some interesting findings came up, which we wish to share:
1. Financial Performance on Log of Intent and Outreach When looking at the relationship between financial indicators and the score obtained for intent and outreach, an expected trade-off with the OER is statistically significant (0.05). This result is consistent with the assumption that targeting the very poor, in rural area, increases operating costs. • The productivity of loan officers is positively correlated with the score obtained for intent and outreach (significant at 0.01 level). This synergy is contrary to the common belief that the productivity should be higher in urban areas. The explanation for this result may find its root in different lending methodologies (group in rural areas vs. individual in urban areas). •
2. Financial Performance on Log of Human Resources The regressions of financial performance on the log of Human Resource provided us with more statistically significant results. We also found differences between the types of institutions12. • The marginal changes for ROA tend to be stronger for Banks than for the other types of institutions. That means that ROA for Banks grows faster than for others. There are no significant differences between NBFI and NGO. • There seem to be no differences between types of MFIs with regard to the portfolio quality (PAR30). That probably results from the selection bias; the portfolio quality of an MFI is an important factor in selecting MFIs as clients. • Yet, we wanted to know if our model would be better if we removed all the variables interacting with legal status of MFIs (i.e. testing for joint significance). We found that they are jointly significant (0.05) which means that different types of institutions do not have the same regression function.
•
Now, holding the effects of the types of institution fixed, two synergies came up: Higher productivity is associated with a better score for Human Resources (significant at 0.1); higher Return on Asset (significant at 0.05) is also positively correlated with Human Resources.
3. Financial Performance on Measurement of Impact Two synergies were found in this regression: MFIs with good score for Measurement of impact tend to have a portfolio with lower financial risk, PAR30, (significant at 0.05) and a better productivity (significant at 0.01).
4. Financial Performance on log of Corporate Social Responsibility and Governance Corporate social responsibility and governance is correlated with the portfolio quality. A better score in this dimension is associated with a lower write-off ratio (significant at 0.1).
Next Steps There are many technical challenges to conducting an analysis of the relationship between financial and social performance. This exercise should be repeated with more observations, better data quality (SPIRIT 3.0) and refined models and/or using more advanced econometric methods. We will continue improving SPIRIT, our social performance scorecard, and its implementation across our clients. We will also continue seeking advise and insights from others stakeholders who are advancing the field of social performance assessment (in particular the Social Performance Task Force). We believe that more research is necessary in order to confirm that better social performance leads to higher Social Impact and to better understand the linkages between social and financial performance.
12. In order to allow for marginal differences between types of MFIs, we had to modify the model slightly logHR=0+1×PAR30+2×ROA+3×logBorLo+6×nborrow+7×GNICAP+1×Bank×ROA+2×NGO×ROA+3×Bank×PAR30+4×NGO×PAR30+u
14