1、中文 2060 字 外文翻译原文 Efficiency of Microfinance Institutions Material Source: Springer Science+ Business Media, LLC. Author: Mamiza, Haq Michael, Skully Shams Microfinance institutions (MFIs) provide a range of financial services to poor households. Their worldwide growth in numbers has had a positive i
2、mpact by providing the poor with loans, savings pro ducts, fund transfers and insurance facilities. This has helped create an encouraging socio-economic environment for many of these developing countries households. The nature of these institutions is quite different from traditional financial insti
3、tutions (such as commercial banks). MFIs are significantly smaller in size, limit their services towards poor households and often provide small collateral-free group loans. Most MFIs depend on donor funds and are not-for-profit oriented organizations that share a common bond among the members. They
4、 also differ in their two main operational objectives. First, as mentioned they act as financial intermediaries to poor households. This is known as the institution is paradigm which affirms that MFIs should generate enough revenue to meet their operating and financing costs. Second, they have a soc
5、ial goal. This can be defined as the welfarists paradigm which includes a focus on poverty alleviation and depth of outreach along with achieving financial sustainability. An efficient MFI management should promote these two objectives. The formal MFI institutions (bank MFIs, non bank financial inst
6、itution MFIs and cooperative MFIs) are subject to prudential regulation and their activities licensed； mainly delivering credit facilities to their members. Some of these also mobilize savings from non-members. In contrast, semiformal MFI institutions, typically non-government organization MFIs (NGO
7、-MFIs), are usually unregulated but registered under some society legislation. Table shows the range of products and funding sources each type entail. Finally, the informal MFI institutions include money lenders, shop keepers and pawn brokers. Unfortunately, their small size and often lack of licens
8、ing make them difficult to identify and so they are excluded from our study. The question, though, among the remaining four MFI types is whether one category may prove more efficient than the others. MFIs with the largest asset size are found in Asia. Asia also has the most efficient MFIs due to lar
9、ge population densities and lower wages. Other factors such as strong outreach and preservation of low operating expenses have also helped Asian MFIs to be efficient. However, South Asian MFIs are relatively more efficient than their counterparts in East Asian MFIs. This differences in efficiency ma
10、y be the result of various lending methodology applied by the Asian MFIs. Many Indian MFIs, for example, reduce their staffing costs by lending to self-help groups rather than to the individual borrower. Our findings show that bank-MFIs are the most efficient under intermediation approach while NGO-
11、MFIs are the most efficient under production approach. Our study chose to apply the DEA model for several reasons. First, the DEA model is able to incorporate multiple inputs and outputs easily. Thus, DEA is particularly well-suited for efficiency analysis of MFIs as it consider smultiple inputs and
12、 produces multiple outputs such as alleviating poverty and achieving sustainability. Second a parametric functional form does not have to be specified for the production function. Third, DEA does not require any price information for dual cost function as is required for parametric approaches. Fourt
13、h, DEA has the potential to provide information to the supervisors in improving the productive efficiency of the organization. Finally, DEA presents a generalization approach because other assumptions than constant return to scale can be accommodated within a convex piecewise linear best practice fr
14、ontier. DEA has traditionally been used for the study of non-profit organization (such as hospital) efficiency and bank efficiency. The rest of the paper is structured as follows. The next section covers a brief literature on efficiency measurement of MFIs. Section discusses the methodology used to
15、analyze MFI efficiency. Section presents the results. Section provides a summary and finally draws the conclusion on the MFI efficiency across the regions. The pure technical efficient frontier is dominated by South Asian NGO-MFIs. Large bank-MFIs like BRI, Banco Solidario and Grameen Bank, which we
16、re efficient under intermediation approach, are now inefficient under production approach. Yet, bank-MFIs such as Ruhuna, DECSI, and NGO-MFIs such as CEP and Wilgamuwa are all efficient under production approach. Under the input oriented VRS measure, FINCOMUN is the least efficient. In order to be e
17、fficient, these MFIs should reduce their inputs by 98% as done by DECSI and Wilgamuwa. Similarly, the output-oriented measure shows that ACLEDA in Cambodia is the least efficient MFI. As shown on the data, the NGO-MFIs have the highest overall mean efficiency score followed by the cooperative-MFIs.
18、The bank-MFIs, however, are better than the NBFI-MFIs. Among the bank-MFIs and NGO-MFIs, there is at least one efficient MFI under both CRS and VRS measures. There is highest dispersion in the bank-MFIs efficiency score. Data also shows that NGO-MFIs are the most productive under all measures follow
19、ed by the cooperative-MFIs. The least efficient are the non-bank MFIs. There are two main types of Microfinance institutions. Data behind this paragraph present efficiency scores and their rank ordering from the model, in which both controllable and uncontrollable inputs are incorporated. Our findin
20、gs show that the magnitudes of the efficiency scores are higher in Model 2 compared to Model 1. Our uncontrollable variable is the percentage of rural population to total population. This may also represent the urbanization rate for each region. Data 1 presents the result of the efficiency scores un
21、der intermediation approach. The mean score of technical efficiency under constant return to scale is approximately 50%, however since we consider that MFIs do not operate in optimal level so we also report the variable return to scale results for the pure technical efficiency and scale efficiency u
22、nder both output and input oriented and the mean score ranges between 0.65 and 0.86. The rank orderings are quite similar to those based on residual values in Model 1. The Pearson correlation coefficient is 84%, indicates that the two rank ordering are positively correlated at 1%significance level. Comparing individual rankings between model 1 and model 2 we find remarkable difference which is the change in ranking for ASA, BRI, Grameen Bank, and CMAC. These are now ranked as 1 or the most efficient. However, the peer summary reflects that these DMUs are
