中文 2060字 外文翻譯原文 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 impact 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 institutions (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 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 social 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 institution 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-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 licensing 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 large 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 may 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-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 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. Fourth, 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 frontier. 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 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 were 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 efficient, 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. 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 followed 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 findings 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 under 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 under 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 efficient by default which means that each DMU uses a unique combination of inputs and outputs such that it is compared only to itself when the efficiency score is calculated. Thus we cannot consider them to be the role model for the inefficient DMUs. Data 2 presents the result of the efficiency scores under production approach. The mean score of technical efficiency under constant return to scale is approximately 64%, however the mean efficiency score for pure technical efficiency and scale efficiency under variable return to scale, both output and input oriented; ranges between 0.73 and 0.88. The rank orderings are quite similar to those based on residual values in Model 1. The Pearson correlation coefficient is 76%, indicates that the two rank ordering are positively correlated at 1%significance level. Comparing individual rankings between Model 1 and Model 2, there appears to be some differences. Bank Rakyat Indonesia, CMAC, CMF and COAC have improved in their efficiency rankings. They are now the most efficient with a score of 1. However, based on the peer analysis we find that these MFIs cannot be identified as the role model for the inefficient MFIs as they utilize a unique combination of input and output such that it is only compared to itself. This study investigated the cost efficiency of MFIs (bank-MFIs, NBFI-MFIs, cooperative-MFIs and NGO-MFIs) in Africa, Asia, and the Latin America using the data envelopment analysis (DEA). The MFIs were compared using the intermediation and production approaches to identify which MFI type is the most efficient in minimizing costs and providing financial services to poor households. Our findings show that under the intermediation approach four out of thirteen bank-MFIs are both input and output oriented, pure technical efficient and scale efficient. Under production approach, six out of twelve NGO- MFIs are found to be the most efficient. We find more MFIs show VRS pure technical efficiency than either CRS technical efficiency or VRS pure technical efficiency under both the intermediation and production approach. Five out of twelve NGO-MFIs are pure technical efficient under production approach while three out of thirteen bank-MFIs are pure technical efficient under intermediation approach. Only one bank-MFI and one cooperative-MFI under intermediation approach and two bank-MFIs and one NGO-MFI under production approach are CRS technical efficient and VRS pure technical efficient. The results discussed above may suggest that high level of cost efficiency may have decreased due to the amount of non-performing loans specifically for bank-MFIs under the intermediation approach. In other words, cost efficient managers are better managing their loan customers and properly monitoring MFIs operating costs. Furthermore, the levels of efficiency have much more to do with efficient utilization of resources rather than scale of production. In conclusion, we can suggest NGO-MFIs may be promoted in developing regions as these MFIs are found to be the most efficient under production approach. This result is not surprising given the NGO-MFIs dual objectives of alleviating poverty through increased out reach and simultaneously achieving financial sustainability. Over the years NGO-MFIs have learnt to develop staff productivity, to increase branching and distribution system, to build outstanding portfolio quality and to extend relationship banking culture to the poor. As these institutions are mostly either unregulated or less regulated than other MFIs, policymakers should approach further NGO-MFI regulation with care so that this efficiency is not hampered. Nevertheless, some bank-MFIs are quite efficient in providing microfinance particularly DECSI in Africa. As more bank-MFIs are established they may have the competitive advantage as financial intermediaries in areas like access to local capital as well as the global financial markets. So while bank-MFIs are the most efficient type of MFI at present under intermediation approach, NGO-MFIs may eventually perform better as an intermediary in the longrun if proper regulation and supervision are in place. 譯文 小額信貸機構的有效性研究 資料來源 : 科學與商業媒體 作者 : 馬米扎， 哈克 邁克爾， 思科力 山姆 小額信貸機構 (即 MFIs) 為貧困家庭提供金融服務。 他們的數量在全球持續增長，并通過提供貸款給窮人、儲蓄親管、資金轉移和保險設施造成許多積極影響。這有助于為許多發展中國家的家庭創造一個有利的社會經濟環境。這些機構的性質完全不同于傳統的金融機構 (如商業銀行 ) 。小額信貸機構規模較小，服務僅限于向貧困家庭提供，經常提供小額免擔保貸款。大多數的小額信貸機構依賴于捐助者的資金，不以營利為目的導向， 成員之間共享一個共同的紐帶 他們的不同之處還在于他們的兩個主要經營目標。首先，正如前面提到的，為貧困戶扮演金融中介機構的角色。這就要求小額信貸機構能產生足夠的收入來滿足其營運和融資成本，可被稱為“模范機構”。其次，他們有一個社會目標。這可以被定義為“社會福利主義”模式，其中包括致力于消除貧困和深度拓展財務上的可持續發展。 一個有效的小額信貸機構管理就是應該促進這兩個目標的實現。正式的小額信貸機構 (銀行小額信貸機構，非銀行金融機構的小額信貸機構和小額信貸機構合作 )受到審慎監管并需要活動許可 ; 主要向他們的成員提供信貸 . 其中一些還吸收非 成員的儲蓄。與此相反，半正式的小額信貸機構的機構，一般是非政府組織小額信貸機構 (即 NGO-MFIs) ，通常都不受管制，但要在某些社會立法機關登記。 最后， 非正規小額信貸機構包括放債人， 所有人和經紀人。不幸的是，他們的小規模和缺乏許可往往使他們難以參與進來，所以把他們從我們的研究中排除。問題是，是否可以證明那幾類正式的小額信貸機構比其他的機構更有效。 資產規模最大的小額信貸機構出現在亞洲。而且由于大密度的人口和較低的工資， 亞洲還擁有最有效的小額信貸機構。其它的因素，如強烈的外展和保存的低營業費用也使得 亞洲小額信貸機 構變的有效率。然而，南亞的小額信貸機構相對東亞的金融機構更有效。這種差異可能是亞洲各種小額信貸機構的適用的貸款方法不同所造成的結果。許多印度小額信貸機構采用的就