نهاد های تأمین منابع مالی کوچک و بهره وری

Microfinance Institutions (MFIs) are special financial institutions. They have both a social nature and a for-profit nature. Their performance has been traditionally measured by means of financial ratios. The paper goes beyond simple financial ratios using a data envelopment analysis (DEA) approach to measure the efficiency of MFIs. Special care is taken in the specification of the DEA model. We take a methodological approach based on multivariate analysis. We rank DEA efficiencies under different models and specifications; e.g. particular sets of inputs and outputs. This serves to explore what is behind a DEA score. The results show that we can explain MFIs efficiency by means of four principal components of efficiency, and this way we are able to understand differences between DEA scores. It is shown that there are country effects on efficiency; and effects that depend on non-governmental organization (NGO)/non-NGO status of the MFI.

Microcredit is the provision of small loans to very poor people for self-employment projects that generate income. It is a new approach to fight poverty. In its heart are new financial institutions, often non-profit organisations, whose aim is to serve those people who would not have access to a loan from a traditional trading bank. The fact that Microfinance Institutions (MFIs) tend not to operate in the same way as traditional banks does not mean that they are not interested in profitability and efficiency issues. However, existing tools to assess the performance of traditional banking institutions may not be appropriate within this new context. How can we assess if a MFI is efficient? How should we compare MFIs? How far is existing knowledge on traditional financial institutions appropriate in order to understand the behaviour of MFIs? These are the issues that are addressed in the current paper. The paper starts with a discussion of microcredit and its role in the fight against financial exclusion. Existing tools for the assessment of performance in MFIs are next reviewed and some lessons are drawn from this review. It is suggested that data envelopment analysis (DEA) is an appropriate tool for the assessment of MFI performance. There is, however, an issue to be resolved: how should the DEA model be specified? Which inputs and which outputs should it contain? A methodological approach based on multivariate analysis is applied in order to select appropriate model specifications, understand the way in which the relative efficiency of a MFI is determined by the choice of model, and to produce a ranking of MFIs in terms of efficiency. The methodology is applied to the analysis of 30 Latin American microcredit institutions. The paper ends with a concluding section that lists and discusses the findings.

DEA has long been applied to the measurement of financial institutions efficiency. Here we have used it to assess efficiency of MFIs, which have a banking side and a social side. We have suggested a methodological approach that goes behind a DEA measure and explains the scores obtained under different choices of models and specifications. We have obtained DEA efficiencies for every combination of inputs and outputs of 30 Latin American MFIs. This way, we can see that the level of efficiency achieved by a MFI depends on the specification chosen. So the choice of a particular model or specification is relevant for efficiency assessment. We have then followed a multivariate approach on efficiencies obtained through DEA: we have combined principal component analysis with property fitting. We have obtained four principal components of efficiency, each one related to a different issue: overall efficiency, NGO status, input choice and output choice. This way we can understand why a MFI achieves a level of efficiency under a given specification, or which are the paths to efficiency followed by a group of MFIs. Finally, there is no reason why we should be fanatic believers in a DEA efficiency world, but the converse is also true. Efficiency and productivity ratios that have emerged from the deliberations of a committee need not be associated with efficiency nor with productivity. We have shown that our approach to efficiency analysis not only produces an overall ranking of MFIs in terms of the use they make of inputs and outputs, but also reveals features that distinguish NGOs from non-NGO institutions, that we can explain the reasons why some MFIs are or are not efficient, and that there are country effects in the data. We finish by encouraging analysts, rating agencies, and users to go beyond ratio analysis in MFIs and incorporate measures of efficiency based on Data Envelopment Analysis.