TEST 4: PRUEBA DE TIEMPO MÁXIMO DE OPERACIÓN

This part of the study carried out several tests using cross-country data of 103 microfinance institutions. The research sought to find factors associated with success in tapping commercial funds and also addressed the following concerns:

• Why do some MFIs access funding, while others do not?

• What are the requirements for success in connecting to the financial markets for funding? • What financing patterns are followed by MFI as they seek additional funding?

To help answer these questions, the influence of 33 variables on successful attraction of commercial finance was sought so as to determine the significant predictors of success with commercialisation. The quantitative assessment of the impact of the 33 variables on successful commercialisation of microfinance fulfilled most of the remaining objectives of the study while addressing above key research questions.

In summary, the findings identify key determinants of high degree of accuracy predictions as: • Information transparency;

• Repayment capacity;

• ROA;

• Fast growth; and, in some cases

• Inflation (Counts, 2008; Cull et al., 2008; Sengupta & Aubuchon, 2008; Lewis, 2008).

These results are resoundingly consistent and confirm some of the factors identified in Part I as true. That is, these factors are indicative of the importance of good financial returns and administrative efficiency (ROA, cash-flow adequacy and operating expense ratio), transparent reporting and information disclosure. And there are investor concerns for cost of funds (lending rates), as well as inflation levels in the recipient country of investment. Large MFIs with big loan sizes are much more likely to be attractive to financiers seeking high returns. The listing also underscores the importance of the risk profile; quality of asset (PAR), and ability to absorb new capital (level of indebtedness) for MFIs that would be successful in accessing commercial funding. Other key factors identified for enabling access to commercial funding include: regulatory status, as well as whether an institution is registered as a NGO. As expected, existence of growth opportunities was highlighted as an important factor. Incidentally, the results showed that it is irrelevant whether the main funding base is donations or not. This means MFIs can have multiple sources of funding, including donor funds and still be attractive to investors.

The research findings support previous studies that have looked at the funding evolution of microfinance institutions (Jansson, 2003). The results have important implications for investors, as well as MFIs seeking growth capital. Regulated MFIs pursuing commercialisation schemes in Africa need to show good financial performance metrics, a sizeable amount of assets (big balance sheet), and quality loan-book. Growth prospects and an enabling environment will also be more beneficial to commercial investors. Conversely, small, slow-growth and unprofitable MFIs offering small loan sizes do not appear to access significant amounts of capital from commercial sources. Such institutions are probably better off seeking donor development funds.

Besides exploring the information requirements for commercial investors in determining investment priorities, one of the major contentions in microfinance debates, the mission drift theory was tested in the sub-analysis (CGAP, 2000; Rhyne, 1998; Dunford, 2000; Beck et al., 2004). That is, commercialisation leads to the abandonment of the plight of the poor to serve the interest of the rich in search for more profits. By this argument it is suggested that commercialisation destroys the long-term social value of microfinance as a development strategy and poverty reduction tool. There was therefore the need to confirm or reject the fears of sceptics. The investigation of the effects of commercial microfinance on long-term social value of microfinance reveals that CEOs make financing decisions, not in the interest of the poor, but for institutional sustainability. It is plausible then to say that commercialisation motivates MFI CEOs to sacrifice long-term goals of the microfinance initiative. In that respect, commercialisation might not be good for the poor. That is, the poor are unnecessarily hurt by MFI actions.

Two measures of success were used as dependent variables: namely leverage multiplier added (LMA) and commercialisation index (CI). Besides using different model specifications for binary classification of successful and less successful institutions, the analysis sought to assess the strength of the two measures in the classification process. Of the two measures of commercialisation, the research found strong support to the hypothesis that the CI is a better measure of successful commercialisation than the LMA, given the variables used. However, this is in terms of correctly identifying MFIs likely to succeed in commercialisation in the future. Specifically, the influence of the legislation form of the MFI, regulatory status and growth variables was high among the models that fitted well for the LMA.

It would appear that the integration of various factors composing the index was useful in giving the index its sting. In all cases, the CI analysis outperformed the LMA using the same predictor variables and firms. Although this is the first attempt to model commercialisation, these results suggest the CI’s commercial rating rule has superior predictive abilities that could be explored to guide screening efforts for winners, investment decisions and other binary classification investigations. These results obviously imply that it is possible to develop a uniform commercial

success prediction rule for MFIs in Africa that would provide useful information to investors. The model will also be useful in guiding successful commercialisation schemes in Africa in that, it provides MFIs with a structured approach for achieving sustainable commercial microfinance. With regard to several estimations done to gauge robustness of fitted models, both logistic regression and random forests are able to correctly classify successful MFIs. The use of various techniques and sub-analysis helped in providing rigour and added improvements to the results in terms of accuracy in identifying key predictors of success by benchmarking the random forests data mining results, against those obtained by logistic and linear regression models. The best logistic model had a satisfactory goodness-of-fit (coefficient of concordance) and overall classification accuracy of 90% and 87% respectively.

Logistic model estimates for each MFI in the model were used to construct country classifications for success in commercialisation under the CI predictions. The highest classification accuracy of over 80% was found in Egypt, Ghana, Mali, South Africa and even Senegal. These results obviously imply that it is possible to develop a uniform commercial success prediction rule for MFIs in Africa that would provide useful information to investors. The model can also be useful in guiding successful commercialisation schemes in Africa because it provides MFIs with a structured approach for achieving sustainable commercial microfinance.

This study made a preliminary attempt at empirically testing the financing pattern of sample MFIs. The examination of the relative size of the estimated coefficients on various equity and debt variables in a regression model tried to explain how growth in assets had been financed. The pattern of coefficients was found to be consistent with the pecking order model predictions. The results established the MFI pecking order to be: firstly, own capital and donations (or quasi-equity), secondly, client savings and if MFIs need additional funds commercial debt would be raised (Helwege & Liang, 1996; Shyam-Sunders & Myers, 1999; Watson & Wilson, 2001; Zapalska et al., 2007; Pollinger et al., 2007; Cull et al., 2008).