关于农村金融发展对经济发展影响的外文翻译

浙江财经大学东方学院毕业论文 （或毕业设计） 外 文 文 献 翻 译 译文：农村金融发展不会促进经济发展吗？ 来自尼日利亚的实证 学生姓名 邵林敏 指导教师 彭武珍 分 院 信息分院 专业名称 统计学 班 级 10 统计 2 班 学 号 1020430231 2013年 12 月 10 日 农村金融发展不会促进经济发展吗？ 来自尼日利亚的实证 摘 要：强劲的经济发展是不可能没有金融深化的，尤其是在欠发达国家 （最不发达国家）大多数民众居住的农村社区。本文分析了农村金融发展对尼日 利亚经济增长的影响。本文选用了1980-2011年的时间序列数据，运用 Johansen 和 Juselius 的协整检验，以得出变量之间的长期关系。因此，用动态普通最小二 乘法（ DOLS ）方法揭示尼日利亚农村金融发展与 经济增长之间的关系。 协整检 验结果表明农村金融发展与尼日利亚经济增长之间存在长期关系。此外， DOLS 结果发现农村金融发展与尼日利亚经济增长之间存在显著的正相关关系。它在 这项研究中得到证实，农村金融作为全国经济增长的引擎。因此，可以得出 结论， 提高农村生产力的信贷可以减免弱势创业者的负担，从而使他们能够对尼日利 亚经济的发展做出最大的贡献。此外，本研究建议除其他事项外， 对于农村生产 的信贷分配的障碍应减少到最低限度。 关键词：农村发展；信贷分配；金融发展 1 引言 包容性增长的理念，促使第三世界的经济体发起，实现变化的政策和规划 旨在将瘫痪的经济代理人转变成积极的人员来提高他们的经济增长。尼日利亚 政府也不例外，政府促进包容性增长通过尼日利亚中央银行（CBN）运用双广 义目标金融包容策略。首先，要将无银行帐户的民众绝大多数在农村社区成为 金融体系的活跃成员。其次，它也强调在农民负担得起的成本上，提高农村居民 的信贷可得性。不幸的是，在使用金融包容性策略如村镇银行和农业信贷保证 计划等等，没有达到目标受益者。一方面，一些确定为负责非洲农村金融市场 的发展表现不佳的问题包括过度管制，监管不力和人才缺乏（Aliero，2009） 。 另一方面，该方案在那时间会受政治因素影响（Ibrahim 和 Aliero，2012） 。 尼日利亚历届政府都提出了结构调整计划的几个扶贫方案（SAP）通过国家 的经济增长和发展战略（需求）转换到议程。然而，这些方案都没有达到他们 的目标的目标（Ibrahim 和 Aliero ，2011） 。例如，从国家统计局的相关数 据显示，2011年全国的失业率为23.9 ，和2010 年的21.1、2009年的19.7 相比农村地区的 25.6 高于城镇的17.1% 。在尼日利亚贫困和失业是同一枚 硬币的两面，在其他方面，可能与农村缺乏足够的资金存取有关。 （Aliero，Ibrahim 和 Shuaibu ，2012） 。有人认为，金融发展具有降低失业率 的能力。正是沿着这条线 Dromel，Kolakez 和 Lehmann（ 2010）认为，私人信 贷（这是金融发展的一个指标）将持续显著降低失业率。这导致 Aliero 和 Ibrahim（ 2012）预测认为，提高获得正规金融服务，特别是农村群众的信贷， 不仅有降低失业率的能力，而且是发展中国家减少贫困的一种途径。 在金融发展突破中强调，欠发达的国家（最不发达国家）的经济发展可以 通过自下到上的干预主要是由人来实现。首先，最不发达国家的民众绝大多数 都居住在农村地区，小部分居住在城市。其次，历史表明，在最不发达国家选 择了城市化的项目（自上而下干预）之前，整个国家处于通过稳步发展为经济 繁荣的简单格式。据我所知，没有利用时间序列分析的技术对农村金融业发展 与经济增长之间的关系进行过研究。本研究通过揭示农村金融发展对尼日利亚 经济增长中的作用填补了空白。在实现这一目标的论文分为五个部分，包括引 言，第二部分为文献综述，第三部分包含了研究的方法，第四部分是实证结果， 而最后一部分总结全文。 2 研究方法 对于相关变量的时间序列二次数据来自 CBN 统计公报、国家统计局，国际 金融统计（IFS）和世界发展指标（WDI） 。数据涵盖了1980至2011年，并且变量 以自然对数表示。变量的对数变换是在计量经济学是非常流行，原因是：首先 许多经济时间序列数据表现出强烈的趋势，其次，采取了一系列的自然对数线 性化有效指数趋势（如果有的话）的时间序列数据，因为对数函数是一个指数 函数（Asteriou 和 Price，2007年）的倒数。第三，优点是它允许回归系数被 解释为弹性。在处理时间序列数据，选择记录变量可以防止建模和推论的麻烦 性（RAHAMAN 和 Salahuddin，2010） 。 一般所有系列都是 I(1)，然后用动态普通最小二乘法（DOLS）是估计协整向量 的单一特征变量之间的长期关系。在股票沃森动态 OLS 模型能有效地估算长期 运行参数，分析必须和整合的 I（1）变量中存在协整关系。因此，先检验单位 根的存在，然后测试该协整关系。幸运的是，检验平稳性的方法有很多，而 ADF 检验(1981）是以避免伪回归结果问题的最广泛应用的计量方法。一系列平 稳是被差分一次也就是一阶整合，并记为 I（1） （迪基和 Fuller ,1979） 。一般 来说一个系列，即被差分 n 次，n 阶整合后并记为 I（n） 。然而系列直接为平稳， 这就是 I（0） （ SHABBIR ，2012） 。 ADF 单位根检验是基于以下回归模型： 1.11t tjtkjjtYdaTY 其中，T 和 分别赋予一个时间序列，一个线性时间趋势和第一差分算子， 是一个常数，k 是对因变量滞后的最佳数量，并且 是随机误差项。零假设0 t 检验非平稳是：=0 意味着经济系列都是非平稳的。如果非平稳的假设是成立 的基本变量，它允许评估为共整合的关系。 在计量经济学中，两个或多个变量进行协整，如果它们有着共同的趋势， 即它们之间存在长期均衡关系。其中有很多方法检测变量之间的这些长期关系。 恩格尔和格兰杰的方法简单并且结果确定。然而，它不容许假设协整关系本身 的测试。相反，约翰森设置的确允许对变量之间的均衡关系的假说提供的所有 变量都集成的顺序相同的测试。Johansen 和 Juselius（以下简称 JJ）协整检 验是基于向量自回归（VAR）模型，涉及的协整向量，即数量两个测试数据-跟 踪（ ）和最大特征值（ ） 。在跟踪试验中，零假设是不同的协整向量tracemax 的数目小于或等于 ，其中 = 02 。在每一种情况下，零假设是对总体方案r 进行测试。最大特征值的测试是类似的，除了备择假设是显式的。零假设是， 协整向量的数量是 R 与 R + 1 的选择。JJ 的方法由涉及系列不受限制的 VAR 协 整限制。考虑下面 p 阶的 VAR 模型： 21 ttpttt BxYAY 其中 是一个 k-矢量非平稳 I(1)变量， 是一个 d 矢量确定性变量，和t tX 是创新的变量。具有全系列 I(1)，以及建立至少一个协整方程满足的股票沃t 森的应用前提（1993）动态 OLS 回归。因此，该模型是在下面指定的： 3.ln0 tjipgjjt uxdRGDP 其中 lnRGDP 是实际国内生产总值（RGDP）的自然对数，是协整向量， X 是 logRufindev （农村金融发展）向量， logFDI （外商直接投资）和 logInflation 作为解释变量。前人研究的重点是使用两种广义货币供应量作为 衡量经济增长的变量（ M2） ，或私人部门信贷率对经济增长（CPS）无关的经 济发展程度的扩展。没有将一个国家的经济发展程度作为标志的金融发展的研 究必然会产生不同的结果。金融部门的支持与合理的复杂的银行业的发达经济 体的经济增长发展是正常的。在另一方面，不仅银行经营产生的地方只有一个 月结束，而且巨型银行都不足以满足民众的金融需求最不发达国家。与此相关 联的，在三分之二的民众都居住在农村社区缺乏与金融机构，在这样的经济体 的金融业发展与经济增长之间的关系可能是非常弱的。因此，可以毫无疑问地 认为，正规信贷在农村的股票可以作为金融发展的最不发达国家的代表。在这 项研究中，评估了金融发展的常规措施和农村金融发展的稳健性，模型如下设 置它集中了两个相互竞争的措施一起： 4.lnlnttt fidevxRGDP 从模型表明，findev 是财务指标向量（M2和私人部门信贷增加与农村金融 发展） ，X 是控制变量的向量。包括在 DOLS 回归的滞后，使得其误差项独立的 随机回归所有过去的创新的目的。 Does Rural Financial Development Spur Economic Growth? Evidence from Nigeria Abstract：Robust economic development is not possible without financial deepening more especially in rural community where vast majority of the populace of Less Developed Countries (LDCs) resides. This paper analyses the impact of rural financial development on economic growth of Nigeria. The study uses time series data covering 1980 to 2011 periods paving the way for the application of Johansen and Juselius model of cointegration to detect the long-run relation among the variables in question. Accordingly, Dynamic Ordinary Least Square (DOLS) method was applied to unveil relationship between rural financial development and economic growth. The cointegration test result reveals the presence of long run relation between rural financial development and economic growth of Nigeria. Moreover, the DOLS results found a significant positive relationship between rural financial development and the growth of Nigerian economy. It has been confirmed in this study that rural finance serves as an engine of growth in the country. It could therefore be concluded that enhancing productive credit especially in rural areas could free the disadvantaged entrepreneur and thus enable them to contribute immensely toward the growth of Nigerian economy. The study therefore recommends among other things, barriers to the productive credit allocation in rural community should be reduced to the barest minimum. Keyword: Rural development；credit allocation；financial development 1 Introduction Inclusive growth notion compels the economies of third world to initiates and implements variants policies and programmes aimed at transforming the paralysed economic agent into active players towards enhancing the growth of their economy. Nigerian government is no exception, the government efforts of enhancing inclusive growth is well informed through the campaign of the Central Bank of Nigerias (CBN) financial inclusion strategies with the twin broad objectives; firstly, to incorporates the vast majority of the unbanked populace more especially in the rural community into active players of financial system. Secondly, it is also aimed at enhancing availability of credit to rural populace with the paramount emphasis on farmers at affordable cost. Unfortunately, the usufruct of the financial inclusion strategies such as Rural Banking and Agricultural Credit Guarantee Scheme (ACGSF) among others, does not reach the targeted beneficiaries. Some of the problems identified as responsible for poor performance in the development of Africas rural financial markets include excessive controls, ineffective supervision and dearth of qualified manpower on the one hand (Aliero, 2009). On the other hand, the programmes have at one time or the other been influenced by political considerations (Ibrahim and Aliero, 2012) Successive governments in Nigeria have introduced several poverty alleviation programmes from Structural Adjustment Programme (SAP) passed through National Economic Empowerment and Development Strategy(NEEDS) down to Transformation Agenda. However, such programmes have not achieved their targeted objectives (Ibrahim and Aliero, 2011). For instance, relevant data from NBS (2011) shows that the national unemployment rate stood at 23.9 percent in 2011 compared to 21.1% in 2010 and 19.7% in 2009 while the rate is higher in the rural area (25.6%) than in the urban area (17.1%). Poverty and unemployment in Nigeria are two sides of the same coin and could be linked to lack of adequate financial access particularly in the rural, among other things (Aliero, Ibrahim and Shuaibu, 2012). It has been argued that financial development has the capacity of reducing unemployment. It is along this line Dromel, Kolakez and Lehmann (2010) contend that development of private credit (which is a measure of financial development) would significantly lower unemployment persistence. This led Aliero and Ibrahim (2012) to predictably believed that enhancing access to formal financial services especially credit to the rural populace has not only have the capacity of reducing unemployment but also is a mean of reducing poverty in developing countries. Therecent breakthrough in the development finance emphasises that economic development of Less Developed Countries (LDCs) could be best achieved through bottom-top intervention principally due to duo reasons. Firstly, the vast majority of the populace of LDCs are dwelling in the rural areas while very small fractions are residing in the cities swimming within overwhelming quantity of national cake. Secondly, history shows that before LDCs opted for urbanisation programmes (top- bottom intervention), development is steadily trickling-down through the entire country paving the way for economic prosperity in simpler format. To my knowledge there is no any study ever conducted using time series techniques of data analysis on the relationship between rural financial sector development and economic growth. This study intends to fill-in the lacuna by unveiling the role of rural financial development on the growth of Nigerian economy. In achieving this objective the paper is divided into five sections including this introduction. Section two presents the empirical literature. Section three contains the methodology of the study. Section four is the empirical result while the last section concludes the paper. 2 Methodology Time series secondary data for the relevant variables were sourced from CBN statistical bulletin of various issues, National Bureau of Statistics, International Financial Statistics (IFS) and World Development Indicators (WDI). The data covers 1980-2011 period and variables were expressed in their natural logarithm. Logarithmic transformations of variables are very popular in econometrics for a number of reasons; firstly many economic time series data exhibit a strong trend, secondly, taking the natural logarithm of a series effectively linearizes the exponential trend (if any) in the time series data since the log function is the inverse of an exponential function (Asteriou and Price,2007). Thirdly, advantage is that it allows the regression coefficients to be interpreted as elasticity. In a study dealing with time series data, opting for log of the variables may prevent cumbersomeness in the modelling and inference (Rahaman and Salahuddin, 2010). Provided all series are I(1), then Dynamic Ordinary Least Square (DOLS) is robust to estimate the single cointegrating vector that characterizes the long-run relationship among the variables (Camacho-Gutierrez, 2010). The Stock-Watson DOLS model to be effective in estimating long-run parameters, the analysis must be in conformity with the existence a cointegration relation among sets of I(1) variables. Thus, it is pertinent to establish the presence of the unit root and then test the cointegrating relationship. Fortunately, there are variant ways of checking stationarity of series however Augmented Dickey Fuller (ADF) (1981) is the most widely applied econometric method for testing unit root in order to avoid problems of the spurious regression results. A series which is stationary after being differenced once is said to be integrated of order 1 and was denoted by I (1) (Dickey and Fuller, 1979). In general a series, that is stationary after being differenced times is integrated of n order , denoted by I ( ) while a series that appears stationary without differencing, nn is said to be I (0) (Shabbir, 2012). ADF (1981) unit root test for stationarity test is based on the following regression model: 1.110t tjtkjjtYdaTY Where , T and respectively confers a time series, a linear time trend and first t difference operator, is a constant, k is respecting the optimum number of lags on 0 the dependent variable, and is random error term. The null hypothesis for testing t non-stationarity is : = 0 meaning economic series are non-stationary. If the 0H hypothesis of non-stationary is established for the underlying variables, it permits the assessments for co-integration relations. In econometrics two or more variables are said to be co-integrated if they share common trends i.e. they have long-run equilibrium relationships between them (Aqeel and Butt, 2001; Shahbaz, 2009). There are various methods of detecting these long- run relations between variables. Engle and Grangers (1987) approach for co- integration is simple and popular for its certain agreeable attributes. However, it did not permit the testing of hypotheses on the cointegrating relationships themselves. Contrarily, the Johansen setup does permit the testing of hypotheses about the equilibrium relationships between the variables all provided the variables have same order of integration (Brooks, 2008). Johansen and Juselius (henceforth JJ) (1990) cointegration technique is based on the Vector Autoregressive (VAR) models which involved two test statistics for the number of cointegrating vectors, namely - the trace ( ) and the maximum value statistics ( ). In the trace test, the null hypothesis tracemax is that the number of distinct cointegrating vectors is less than or equal to , where r = 0 to 2. In each case the null hypothesis is tested against the general alternatives. The maximum eigenvalue test is similar, except that the alternative hypothesis is explicit. The null hypothesis is that the number of cointegrating vectors is against the r alternative of cointegrating vectors. JJs method rests the restrictions imposed 1r by cointegration on the unrestricted VAR involving the series. Consider a VAR of order p below: 21 ttpttt BxYAY Where is a K-vector of non-stationary I(1) variables, is a d-vector of t tX deterministic variables, and is the vector of innovations. Having all series I(1), as t well as establishing at least one cointegration equation satisfies the preconditions for the application of the Stock-Watson (1993) DOLS regression. Thus, the model is specified below: 3.ln0 tjipgjjt uxdRGDP Where lnRGDP is the natural log of Real Gross Domestic Product (RGDP), is the cointegrating vector, X is vector of log of logRufindev (Rural Financial Development), logFDI (Foreign Direct Investment) and logInflation as explanatory variables. The emphases of the previous studies was measuring financial development using either broad money supply as a ratio to economic growth (M2) or Credit to the Private Sector