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外文翻译-小额信贷-其他专业.doc

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外文翻译-小额信贷-其他专业.doc

2010届毕业生毕业论文 外 文 翻 译 姓 名 院(系) 专业班级 学 号 指导老师 成 绩 小额信贷是否帮助穷人 孟加拉国旗舰计划所带来的新证据 摘要小额信贷运动使金融中介机构得到了创新,同样使贫困家庭减少了贷款的成本和风险。孟加拉国乡村银行的小额信贷机制已经在全世界得到推广。虽然小额贷款机制的目的是为客户带来社会和经济效益, 但是通过其获得一定量的利益的尝试已经开始实施了。本文借鉴一个新调查来研究小额信贷是是否真正的帮助穷人,该调查覆盖面近1800个家庭,其中部分家庭获得了孟加拉乡村银行的贷款,而另一部分则没有参与到小额贷款运动?#23567;?#26377;资格获得贷款的家庭,他们的消费水平低于平均消费水平,这种家庭中,绝大部分的孩子不可能?#31995;?#36215;学,男子也往往会有更多的工作压力,而女?#29992;?#26377;工作。更明显的,相对于对照组,符合贷款资格的家庭在消费?#31995;?#21464;化很小以及可以常年提供劳动力的特点。最重要的潜在影响不是贫穷本身,而是因而最重要是减少相关的家庭漏洞。似乎导致消费平滑主要原因是收入平滑,而不是借款和贷款。 评论家有大量的关于低收入国?#19994;?#20854;他方案的研究经验。虽然通常人们都是使用固定效力评估来控制与安置方案有关的不易观察的变量,但是使用固定效力评估会加剧偏见的影响,就如同本方案在较大的社区里特定人群的方案。 关键词小额信贷,项目评估,乡村银行,孟加拉 1. 介绍 小额信贷在很多人的?#38498;?#37324;是用来减少贫困。前提是操作简单。小额信贷提供小额贷款,?#28304;?#36827;小规模的创业活动,而不是向贫困家庭提供救济。这种信贷除非放债人收取非常高的利率往往收费高达每月10,否则不会发生。放债运作缺乏竞争,因为潜在的进入者很快发现,借款人通常不能提供任何?#38382;?#30340;抵押品,这就使贷款存在高成本和该风险。拉希德和汤森,1993。 然而,体制创新下的小额信贷运动似乎大大?#26723;?#20102;风险和提供金融服务和为贫困家庭提供服务的费用。创新包括借款合同、给予奖励、配出不良信用风险和连带借款人的活动,要求每周或每半周还款Morduch,1997。2005年该运动已经在世界银行,联合国领导人,以及其他已加入的国?#39318;?#32455;的推动下成为联系100万家庭的全球性的运动小额信贷首脑会议,1997。该运动在美国还得到相当多的支持包括钱第一夫人希拉里克林顿,现在该方案在美国有300个经营点经济学家,1997。纽?#38469;?#25253;1997还发表庆祝这个“继续的反贫穷方案的革命” 文章呼吁支持。 但是,小额贷款到底给贫困家庭带来了怎样的巨大的影响 虽然小额贷款确实做到了减少贫困,但只有极少数研究使?#23391;?#24403;大的样本和?#23454;?#30340;治疗/控制框架来研究这个问题。本研究调查了1800户家庭在19911992年间的孟加拉国格拉名银行的小额信贷项目,孟加拉国农村发展委员会BRAC,和孟加拉国农村发展委员会BRDB,本案例还包括了一组没有任何小额贷款项目服务地区的家庭。这里考虑的这三个贷款方案在孟加拉国一共超过了400万贫困客户,它们的作用是非常广泛的。格拉米银行的国际小额信贷旗舰运动,其模式已经被四大洲所复制,包括在美国的阿肯色州和内?#20405;?#21152;哥都取得明显成就。 从其带来的影响我们可以简单得出小额信贷所带来的成就。例如,如果享受乡村银行服务的家庭按照从小额信贷项目贷款的总数来安排,则前四分之一的家庭享有人均消费相较于在底层四分之一的家庭要高出十五个百分点。另外, 62的从乡村银行贷款的家庭的男孩可以上学,而34的上学的男孩的家庭没有贷款。而女孩的比例分别是55对40。 然而, 这些简单的比较,大部分是由于选择偏差造成的。一旦,对照组坐出了?#23454;?#30340;比较,不管是受教育的男孩还是受教育的女孩,有权使用小额贷款项目的家庭并没有明显提高人均消费水平。总之,人均消费水平低于对照组。这一结论是惊人的,关于小额贷款的反对声音也频繁的在国际响起。 然而,有权获?#23391;?#30446;资助确实使常年劳动力变得多元化。相应的,该方法也?#26723;?#24120;年各?#25351;?#26679;的消费,所以,尽管?#23391;?#30446;并没有提高平均消费水平,但他可以通过稳定收入的方法使这些家庭稳定消费水平。至于在其弱点?#31995;?#24433;响,结果突出了小额贷款的优势,这些优势很少被关于小额信贷的文献所关注(除皮特及科韩德科,1998b)。?#23391;?#30446;得到了一亿美金的援助,由此,我们?#37096;?#20197;看到它的优势。 这一结果同样证明,评估者很容易误导项目的成就,而且,他们拥?#34892;?#22810;相似评估经验,这些经验包括公众医疗及其他低收入国?#19994;?#31038;会项目。同这里一样,这些项目经常被限制在特殊的区域和特殊的目标人群,尤其是贫困家庭。不同于那些富有国家,收入为基础意味着测?#36816;?#20046;从未进行过。反而,例如,孟加拉国乡村小额贷款项目致力于“无地机能?#20445;?#36825;条规定要求贷款的家庭必须有超过半英亩的可耕种土地。 如果这条合理要求被强制实施,并且是建立在家庭外因的特殊之上,这条项目规定将是合理统计的基础。然后,我们就能从参与?#23391;?#30446;的家庭组及未参与?#23391;?#30446;的家庭组的比较中得到非常明朗的效果。这一方法是回归间断设计的一个?#38382;劍?#22350;贝尔,1969年),其见解提供了皮特和科韩德科工作(1998a和1998 b;在这里,他们用了相同的数据)的基础。 但是我们不能从这个例子里推出任何?#34892;?#32467;论,这个数据?#24471;?#20154;们经常违反规则。例如,30的乡村贷款人拥有?#23545;?#22810;于半英亩的土地,他们拥有土地所有权的面的有14英亩之大。那些记录在案有权借款的家庭或有权参与项目的家庭,其中一部分所拥有的土地大概是2英亩,相对的其他那部分要少一点。 下面的方法反而通过在乡村的比较,运用了测试组及对照组的数据。乡村中没有参与项目的组中,其采样严格遵循半英亩规定。然而,参与项目的村里,同组的?#27426;?#31216;性在这里同样出现了问题。采样战略在一开始就是一个解决办法。采样是设计好的,这样,对照组才可以同测试组作比较。强制要求测试组需要同对照组一样严格按照规定强制执行要求。 另外需要关注非随机安置方案的是,当考虑到地区固定影响水平或者他们的对等性?#20445;?#20363;如皮特和科韩德科,1998a)。当方案选择已经完成的好的地区的时候,出现向上偏差;?#27605;?#30446;倾向于?#29615;?#36798;地区?#20445;?#21017;出现向下偏差。 然后,柜员频繁声明这并不是解决非随机安置方案的万灵药。?#23548;?#19978;,?#27605;?#30446;安置被预测到针对目标人?#22909;?#26377;观察到影响?#20445;?#21253;括地区固定影响水平能使偏差增大。这个数据暗示,这是经常出现的状况。但是,带着减少变化和劳动力供应的期待,主要的定性结果对测试组及对照组的不易观察的乡村水平是健全的。 小额信贷在新兴优势突出的成果使得其很少考虑其脆弱性,这些好处应当判断有数百万美元支持这些方案。研究结果还表明如?#38395;?#21035;简单的误导性的指标,他们持有类似的在低收入国家其他社会项目评估如公共健康和低收入的经验教训。由于,这些计划往往局限于特定地区和特定目标群体即典型的贫困家庭,所以,以收入为基础的测试方法几乎在较富裕的国家从来没有使用过。 Does Microfinance Really Help the Poor New Evidence from Flagship Programs in Bangladesh Abstract The microfinance movement has built on innovations in financial intermediation that reduce the costs and risks of lending to poor households. Replications of the movement’s flagship, the Grameen Bank of Bangladesh, have now spread around the world. While programs aim to bring social and economic benefits to clients, few attempts have been made to quantify benefits rigorously. This paper draws on a new cross-sectional survey of nearly 1800 households, some of which are served by the Grameen Bank and two similar programs, and some of which have no access to programs. Households that are eligible to borrow and have access to the programs do not have notably higher consumption levels than control households, and, for the most part, their children are no more likely to be in school. Men also tend to work harder, and women less. More favorably, relative to controls, households eligible for programs have substantially and significantly lower variation in consumption and labor supply across seasons. The most important potential impacts are thus associated with the reduction of vulnerability, not of poverty per se. The consumption-smoothing appears to be driven largely by income-smoothing, not by borrowing and lending. The evaluation holds lessons for studies of other programs in low-income countries. While it is common to use fixed effects estimators to control for unobservable variables correlated with the placement of programs, using fixed effects estimators can exacerbate biases when, as here, programs target their programs to specific populations within larger communities. Key words microfinance, project evaluation, Grameen Bank, Bangladesh 1. Introduction Microfinance has captured the imaginations of many people working to reduce poverty. The premise is simple. Rather than giving handouts to poor households, microfinance programs offer small loans to foster small-scale entrepreneurial activities. Such credit would otherwise not be available -- or would be only available at the very high interest rates charged by moneylenders who often charge as much as 10 per month. Moneylenders operate with little competition since potential entrants quickly find that costs and risks are high -- and borrowers are usually unable to offer standard forms of collateral, if any at all Rashid and Townsend, 1993. However, the emerging microfinance movement demonstrates institutional innovations that appear to greatly reduce the risk and cost of providing financial services to poor households. Innovations include contracts that give borrowers incentives to exclude bad credit risks and monitor other borrowers’ activities, schedules of loans that increase over time conditional on successful performance, and weekly or semi-weekly loan repayment requirements Morduch, 1997. The movement is now global, and leaders at the World Bank, United Nations, and other international organizations have joined in pushing to reach 100 million households around the world by the year 2005 Microfinance Summit, 1997. The movement has also generated considerable support in the U.S. including the high-profile support of Hillary Rodham Clinton; Buntin, 1997, and small-scale programs now operate in 300 U.S. sites Economist, 1997. The New York Times 1997 has celebrated this “much-needed revolution in anti-poverty programs” and called for enhanced support. But how great is the ultimate impact on poor households While strong claims are made for the ability of microfinance to reduce poverty, only a handful of studies use sizeable samples and appropriate treatment/control frameworks to answer the question. The present study investigates a 1991-92 cross-sectional survey of nearly 1800 households in Bangladesh served by microfinance programs of the Grameen Bank, the Bangladesh Rural Advancement Committee BRAC, and the Bangladesh Rural Development Board BRDB. The sample also includes a control group of households in areas not served by any microfinance programs. The three lending programs considered here together serve over four million poor clients in Bangladesh, but their role is much broader. The Grameen Bank is the flagship of the international microfinance movement, and its model has now been replicated on four continents, including sites in the United States as varied as rural Arkansas and inner-city Chicago. Simple estimates of impacts show clear achievements. For example, if households served by the Grameen Bank are ordered by the amounts they have borrowed from the program, the top quarter enjoys 15 higher consumption per capita than households in the bottom quarter. In addition, 62 of the school-age sons of Grameen Bank borrowers are enrolled in school versus 34 of the sons of eligible households that do not borrow. For daughters, the Grameen advantage is 55 versus 40. These simple comparisons appear to be driven entirely by selection biases, however. Once appropriate comparisons with control groups are made, access to the three microfinance programs does not yield meaningful increases in per capita consumption, the education of sons, nor the education of daughters. If anything, the levels are slightly lower than for control groups. The results are surprising and contradict frequent claims made about the programs in international discussions of microfinance. Access to the programs does, however, appear to aid the diversification of labor supply across seasons. In turn, access is associated with a reduction in the variability of consumption across seasons. Thus, while the programs may not increase consumption on average, they may offer households ways to smooth consumption through smoothing income. In pointing to impacts on vulnerability, the results highlight an advantage that is seldom considered in the emerging microfinance literature an exception is Pitt and Khandker, 1998a. These benefits should be judged against the tens of millions of dollars that have supported the programs. The results also demonstrate how misleading simple performance indicators can be, and they hold lessons for evaluations of similar public health and other social programs in low-income countries.1 As here, such programs are often limited to particular regions and particular target groups, typically poor households. Unlike in wealthier countries, income-based means tests are almost never used. Instead, for example, the microfinance programs in rural Bangladesh focus on the “functionally landless” -- implemented as a rule barring lending to households owning over a half acre of cultivable land. The program rule can be the basis of a plausible econometric strategy if the eligibility requirement is strictly enforced and built around a feature that is exogenous to the household.Then, clean impacts can be gauged by comparing the status of households clustered just below the arbitrary dividing line to households clustered just above. This approach is a form of regression discontinuity design Campbell, 1969, and the insights provide the basis of Pitt and Khandker’s.1Simple evaluations are subject to multiple selection biases self-selection into the programs by the most able, non-random program placement, and endogenous determination of the intensity of participation e.g., the size of loans in microfinance. The typical problem stems from the near impossibility of finding good instrumental variables work 1998a and 1998b; they use the same data as used here. But the idea can not be implemented reliably in this sample. The data demonstrate frequent violations of the rules. For example, 30 of Grameen borrowers own more land than the half-acre cut-off, with landholdings as large as fourteen acres. Among households labeled in the survey as “eligible” to borrow and with access to programs, the fraction of borrowers is nearly twice as high for those holding over half an acre versus those below 63 versus 34 for the three programs combined; The first two rows of Table 1 give figures disaggregated by program. Counter to historical observations suggesting an absence of land markets in South Asia, there is also substantial evidence of land sales. The data show that nearly one eighth of borrowers purchased substantial amounts of land in the six years prior to the survey. The approach below instead exploits the treatment/control aspect of the data through comparisons across villages. The groups in villages not served by programs were sampled with strict adherence to the half acre rule, however, and the asymmetry with groups in program villages creates problems here as well. A solution is to turn the sampling strategy on its head. While the sample was designed so that the control groups are comparable to the “treated” groups, the rule violations require that the treatment groups be redefined in order to bring them into conformity with the controls. An additional concern is given by non-random program placement. Upward biases arise when programs choose regions that are already doing well, and downward biases arise when programs favor disadvantaged areas. The typical response to the problem is to estimate impacts .

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