外文翻译---小额信贷机构的有效性研究

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 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. 译文 小额信贷机构的有效性研究 资料来源 : 科学与商业媒体 作者 : 马米扎， 哈克 迈克尔， 思科力 山姆 小额信贷机构 (即 MFIs) 为贫困家庭提供金融服务。 他们的数量在全球持续增长，并通过提供贷款给穷人、储蓄亲管、资金转移和保险设施造成许多积极影响。这有助于为许多发展中国家的家庭创造一个有利的社会经济环境。这些机构的性质完全不同于传统的金融机构 (如商业银行 ) 。小额信贷机构规模较小，服务仅限于向贫困家庭提供，经常提供小额免担保贷款。大多数的小额信贷机构依赖于捐助者的资金，不以营利为目的导向， 成员之间共享一个共同的纽带 他们的不同之处还在于他们的两个主要经营目标。首先，正如前面提到的，为贫困户扮演金融中介机构的角色。这就要求小额信贷机构能产生足够的收入来满足其营运和融资成本，可被称为“模范机构”。其次，他们有一个社会目标。这可以被定义为“社会福利主义”模式，其中包括致力于消除贫困和深度拓展财务上的可持续发展。 一个有效的小额信贷机构管理就是应该促进这两个目标的实现。正式的小额信贷机构 (银行小额信贷机构，非银行金融机构的小额信贷机构和小额信贷机构合作 )受到审慎监管并需要活动许可 ; 主要向他们的成员提供信贷 . 其中一些还吸收非 成员的储蓄。与此相反，半正式的小额信贷机构的机构，一般是非政府组织小额信贷机构 (即 NGO-MFIs) ，通常都不受管制，但要在某些社会立法机关登记。 最后， 非正规小额信贷机构包括放债人， 所有人和经纪人。不幸的是，他们的小规模和缺乏许可往往使他们难以参与进来，所以把他们从我们的研究中排除。问题是，是否可以证明那几类正式的小额信贷机构比其他的机构更有效。 资产规模最大的小额信贷机构出现在亚洲。而且由于大密度的人口和较低的工资， 亚洲还拥有最有效的小额信贷机构。其它的因素，如强烈的外展和保存的低营业费用也使得 亚洲小额信贷机 构变的有效率。然而，南亚的小额信贷机构相对东亚的金融机构更有效。这种差异可能是亚洲各种小额信贷机构的适用的贷款方法不同所造成的结果。许多印度小额信贷机构采用的就是例如贷款给自 助组织而不贷款给个人借款人，从而减少借贷的贷款方法。我们的研究结果显示， 在调节方式上银行小额信贷机构最有效，而在生产方式上非政府组织小额信贷机构是最有效的。我们的研究选择数据包络分析模型是有几个原因的。 首先， 这个模型能够更方便地将多个数据输入和输出。因此，由于它对多组数据分析的适用， 并能产生诸如减轻贫困和实现可 持续发展的多个结果的输出， 它特别适合小额信贷机构的效率分析。第二，参数的函数形式不需要指定生产函数。 第三， 这个方法不像参数方法的那样，它不需要任何价格信息设置双重成本函数。 第四， 它可以为监管人员提供在提高生产效率的组织的信息。最后，由于其它假设的规模收益比常数可以在一个分段线性实验的最佳方式边缘进行变通， 我们参照数据包络分析方法，提出了一种综合方法。 DEA 方法在传统上一直用于非盈利组织 (如医院 ) 的效率和银行效率的研究。文章接下来的部分就包括小额信贷机构对效率数据包络分析模型的简短评价。本节讨论 了用于分析小额信贷机构效率的方法， 部分数据分析给我们提供了一个结果， 最后得出了 有关跨区域多边金融机构的效率的结论。 南亚非政府组织小额信贷机构在纯技术角度而言的效率上占有一定优势。大型银行贷款机构，如印度尼西亚人民银行，阳光银行和乡村银行，之前在中介理论中都是高效的，但在现在的生产方式上效率很低。然而，银行小额贷款机构 (如卢哈纳， 三丰银行 ) 和非政府组织小额信贷机构 (如欧洲能力机构和沃格木瓦 ) 在生产方式上都是十分高效的。 而在输入导向工具这种方法中，菲格木是最低效的。 为了提高效率，这些小额信贷 机构应该像三丰银行和沃格木瓦那样，削减 98%的输入量。 同样，导向输出的方法显示，柬埔寨的阿克莱达银行是最低效的小额信贷机构。如数据中所示，非政府组织小额信贷机构，排在合作性小额信贷机构之后，具有最高的总平均效率。然而银行小额信贷机构，比非银行小额信贷机构要更好些。在银行小额信贷机构和非政府组织小额信贷机构中，在多边投资框架下至少有一个有效的小额信贷机构。银行小额信贷机构的效率评分具有很高的分散性。 就如数据显示，在合作性小额信贷机构中实行的所有措施中，非政府组织小额信贷机构的可生产性最强。而效率最低的 也是非银行小额信贷机构。 小额信贷机构有两种主要的类型。段后的数据显示的是他们各自的有效性评分和在包含了可控和不可控的投入模型中的排序。 我们的研究结果表明，模型 2 的效率值的幅度高于模型 1。我们无法控制的变量是农村居民人口占总人口的百分比。 这可能也正代表了每个地区的城市化速度。 由于这些机构大多不受管制或比其他小额信贷机构所受监管要少，政策制定者应该对非政府性小额信贷机构的监管予以更多关注，以使其有效性实现不受到阻碍。然而，一些银行小额信贷机构在提供小额信贷上的效率更高，特别是在非洲的三丰银行。随 着越来越多的银行小额信贷机构有权使用本地资本以及全球金融市场，他们被确立为最有竞争优势的金融中介机构。但是尽管银行小额信贷机构是目前最为有效的小型信贷机构模式，如果适当的调控和监管到位，非政府组织小额信贷机构很可能最终会成为更好的金融中介。