外文翻译--FDI国内投资与经济增长：一个时间序列分析

中文3080字本科毕业论文外文翻译外文题目： Foreign Direct Investment, Domestic Investment and Economic Growth in China: A Time Series Analysis 出 处： The World Economy, 2008.10,1467-9701. 作 者： Sumei Tang, E. A. Selvanathan and S. Selvanathan. 外文原稿Foreign Direct Investment, Domestic Investment and Economic Growth in China: A Time Series AnalysisSumei Tang, E. A. Selvanathan and S. Selvanathan1. Introduction Despite a large amount of literature on the subject, the role of FDI in economic growth remains highly controversial. The proponents of FDI argue that it helps promote economic growth through technology diffusion and human capital development. This is particularly the case when MNEs in a host economy have vertical inter-firm linkages with domestic firms or have sub-national or sub-regional clusters of inter-related activities. Through formal and informal links and social contacts among employees, MNEs diffuse technology and management know-how to indigenous firms. Consequently, economic rents are created accruing to old technologies and traditional management styles. Also, FDI helps overcome capital shortage in host countries and complements domestic investment when FDI flows to high-risk areas or new industries where domestic investment is limited. When FDI occurs in resource industries, domestic investment in related industries may be stimulated. Moreover, FDI may result in an increased demand for exports from the host country, helping attract investment in the export industries. Empirical studies supporting these arguments include Sun (1998) and Shan (2002). Using the conventional regression model and panel data, Sun (1998) finds a high and significantly positive correlation between FDI and domestic investment in China. Shan (2002) uses a VAR model to examine the inter-relationships between FDI,industrial output growth and other variables in China. He concludes that FDI has a significantly beneficial impact on the Chinese economy when the ratio of FDI to industrial output rises. In contrast, opponents of FDI argue that FDI crowds out domestic investment, and has an adverse effect on growth . In particular, the industrial organisation theory stipulates that FDI is an aggressive global strategy by MNEs to advance monopoly power over and above indigenous firms of the host economy. The ownership-specific advantages of MNEs (e.g.advanced technologies, management know-how skills, transaction cost minimising and other intangible advantages)could be transformed into monopoly power. This monopoly power can be further reinforced by the other two advantages of MNEs: the market internalisation specific-advantage and the location-specific advantage (Dunning, 1981). In addition, FDI may disrupt backward linkages through substitution of imports for domestic commodities (Noorzoy, 1979).The present paper contributes to the existing literature by applying a multivariate VAR system with the error correction model (ECM) and time series techniques of co-integration and innovation accounting to explore the possible links between FDI, domestic investment and economic growth in China. Specifically, we use the impulse response function and variance decomposition plus the Granger causality testing procedures to investigate whether: (1) FDI has a complementary/substitution effect on domestic investment in China; (2) there exists any causal relationship between FDI, domestic investment and economic growth; (3) FDI has played an important role in Chinas economic growth; and (4) FDI contributes to growth more than domestic investment.This paper differs from earlier studies in a number of respects. Firstly, it represents the first attempt to directly test the relationship between FDI and domestic investment in China. Second, we use pure time-series data while previous studies use either cross-sectional or panel data, which are likely to suffer from problems of data comparability and heterogeneity. Third, earlier studies do not test for causality between FDI, domestic investment and economic growth. The failure to consider the possible two-way causality between the variables may lead to the simultaneity problem. Finally, our VAR model incorporates long-run dynamics or ECM. Neglecting these dynamics may produce various estimation biases.The organisation of the paper is as follows. Section 2 offers an overview of FDI inflows, domestic investment and economic growth in China. This is followed by econometric analysis in Section 3. The final section of the paper presents the conclusion and some policy implications.2. AN OVERVIEW OF FDI INFLOWS, DOMESTIC INVESTMENT AND ECONOMIC GROWTH IN CHINA: 19782003In the early 1980s, special economic zones were formed with preferential policies including tax concessions and special privileges for foreign investors. During the reform period, the Chinese government introduced various new legislative measures to improve investment conditions and the business environment in order to attract FDI.Table 1 presents the ratios of FDI to GDP, DI to GDP, and FDI to DI from 1978 to 2003. As can be seen, the proportions of FDI to GDP (column 2) were quite low and less than 1 per cent until 1990. It increased to a peak value of 6.2 per cent in 1994 and then steadily decreased to 3.8 per cent in 2003. The proportion of DI to GDP was 18.5 per cent in 1978 and increased steadily to 47.6 per cent in 2003. The proportion of FDI to DI increased dramatically from 0.1 per cent in 1978 to 1.7 per cent in 1984, and by 1994 it had reached an all-time high of 18.1 per cent. Since then, it gradually decreased to 8.0 per cent in 2003. 3. EMPIRICAL ANALYSIS AND FINDINGS a. Data and Unit Root TestQuarterly time series data for FDI, DI and GDP are available and all in current prices of the Chinese currency (yuan). They are compiled from China Monthly Statistics(1987:12004:3),Comprehensive Statistical Data and Materials for 50 Years of New Chinaand various issues of China Statistical Yearbook. GDP quarterly time series is constructed on the basis of the monthly gross industrial output (GIO) and the yearly GDP statistics due to lack of quarterly and monthly GDP statistics. It is found that the annual growth pattern of GDP is similar to that of GIO. q=1,.,4 t=1988,1989,.,2003To minimise the effect of seasonal fluctuations when conducting co-integration analysis and model estimation, a variable of centred (orthogonalised) seasonal dummies is incorporated. The standard 01 seasonal dummy variables will affect both the mean and the trend of the level series in a VAR system but the centred seasonal dummy variable only shifts the mean without contributing to the trend . In this paper, we employ the augmented DickeyFuller (ADF) test to test the stationarity of the three time series FDI, DI and GDP. As can be seen from Figures 4(ac), the three series appear to be non-stationary in level form. Therefore, we investigate the stationarity of the first difference of the three series by testing for unit roots. The ADF tests are performed on both the level and first differenced observations by estimating the following three models: No constant and no trend model: （1） Constant and no trend model: （2） Constant and trend model: （3） The results of the ADF test are shown in Table 2. They show that the null hypothesis of a unit root is: (a) accepted for the level series of FDI in all three models; (b) rejected for the level series of DI in model (3), and (c) rejected for the level series of GDP in model (1). Based on the first differenced data, the results indicate that all three series are stationary. Therefore, we conclude that the three time series are all integrated of order 1, I(1). b. Testing for Co-integration of Variables Now, the co-integration test is performed to investigate any long-term equilibrium relationships among the three variables of FDI, DI and GDP. After a careful search and trial, a model with six lags, constant and centred seasonal dummy variable was chosen. The result of the Johansen co-integration rank test is summarised in Table 3, which indicates the presence of two co-integrating vectors at 1 per cent and 5 per cent levels of significance, respectively (i.e. The null hypotheses of no co-integration is rejected for the rank of zero and less than or equal to 2). This means that there exists a long-term relationship among the three variables. c. The Error Correction ModelTo analyse the causal relationship between the three variables FDI, DI and GDP, we use an error correction model (ECM) of the following VAR system:When applied to the Chinese data, the VAR system performs quite well. As reported none of the diagnostic statistics are significant at the 95 per cent critical value. Therefore, there is nothing to suggest that the system model is incorrectly specified. Based on the Schwarzz (1978) and Akaike (1974) information criteria, the number of lags is chosen as six. d. Innovation Accounting and theGranger Causality Test The innovation accounting (variance decomposition and impulse response function) technique can be utilised to examine the relationships among economic variables . Using this technique, Kim and Seo (2003) explored the complementary or substitution relationship between FDI and domestic investment, and analysed the impact of FDI on economic growth in South Korea. On the other hand, the forecast error variance decomposition allows us to make inferences about the proportion of movements in a time series due to its own shocks versus shocks to other variables in the system (Enders, 1995, p. 311). These results suggest that the strength of the relationships between FDI, domestic investment and economic growth are different. FDI plays an important role in Chinas economic growth but its influences are less than that of domestic investment (5.7 per cent versus 17.6 per cent). GDP shows stronger influences on Chinas domestic investment than FDI does (40.8 per cent versus 3.5 per cent). The influences of DI and GDP on FDI are relatively low (2.4 per cent and 1.9 per cent, respectively). But the relationship between GDP and DI is strong, with a 40.8 per cent influence from GDP to DI and 17.6 per cent in reverse. It is noted that each of the three variables explains the preponderance of its own past values (forecast error variances). This means that the current/past FDI, DI and GDP have strong influences on their own future/current trends. The Granger causality test results for the three variables. The results show that: (i) the effects of DI and GDP on FDI are not statistically significant; (ii) the effects of FDI and GDP on DI are statistically significant; (iii)the effects of FDI and DI on GDP are statistically significant. Thus, FDI affects DI and GDP but not the reverse, whereas the causal links between GDP and DI are bi-directional. These findings confirm the results of the variance decomposition analysis.We now use the impulse response function to reveal the dynamic causal relationships between FDI, domestic investment and economic growth. e. Empirical Findings Using a VAR system with ECM, we have found the following: 1. FDI plays an important role in complementing domestic investment in China, the larger the FDI the greater the domestic investment. Further, FDI has a significant effect on Chinas economic growth. 2. Chinas domestic investment and economic growth are positively correlated; great economic growth spurs large domestic investment, and vice versa. 3. Chinas domestic investment and GDP do not have much impact on FDI inflows in the long run. The causal link between GDP and DI is bi-directional, but there is only a one-way directional causality from FDI to DI and FDI to GDP. 4. Chinas domestic investment has a greater impact on growth than FDI does. These lend some support to the theoretical view that FDI has complementaryeffects on domestic investment, and that long-term economic growth is positively associated with FDI.一、 CONCLUSIONS AND POLICY IMPLICATIONS Based on the empirical analysis and findings, we conclude that rather than crowding out domestic investment, FDI has a complementary relationship with domestic investment. FDI has not only assisted in overcoming shortages of capital, it has also stimulated economic growth through complementing domestic investment in China. The findings of this study do have some important implications for policy makers in China and elsewhere. Since FDI complements domestic investment, less developed countries ought to encourage and promote FDI inflows, for which appropriate FDI policies and regulations are required. 译 文：中国的FDI,国内投资与经济增长：一个时间序列分析二、 引言尽管FDI对投资影响的大量研究，FDI在经济增长中扮演的角色也受到广泛的争议。FDI的支持者表示FDI可以通过技术外溢与人力资本发展带动经济增长。尤其是跨国公司在东道国与国内企业间有垂直关系或与其他国家的子公司或区域部门内部有垂直联系。FDI流入高危领域或新兴产业还克服了东道国资金短缺问题。当FDI进入资源密集型产业时，相关产业的国内投资也会受到刺激。FDI还有可能导致东道国出口需求增加，从而为出口产业吸引更多的投资。支持这一说法的实证要求包括Sun（1998）和Shan（2002）运用传统的回归模型和面板数据，Sun发现中国的FDI与国内投资对中国的经济增长存在高度积极关系。Shan运用一个VAR模型去检验中国的FDI，产业产出增长和其他变量间的内部联系。他得出当FDI在产业产出的比率上升时，FDI对中国经济有极大的收益性影响。相反，反对FDI促进增长论者认为FDI挤出国内投资，并对经济增长有负面影响。特别是，工业组织理论认为FDI是跨国公司为了在东道国超过本土企业，提升其垄断力量的极具侵略性的全球战略。跨国公司的所有权优势（例如先进的技术，管理经验技巧，最小交易成本及其他无形优势）会转为垄断力。这种垄断力量会进一步地提升跨国公司其他两方面的优势：市场内部一体优势和市场定位优势（邓宁）。此外，FDI也可能通过国内商品的替代进口破坏经济的后部联系。本文对现有文献的贡献主要是通过建立多变量的误差修正模型和时间序列协整检验和创新技术会计去研究中国的FDI，国内投资和经济增长间可能的因果联系。具体地说,我们使用了脉冲响应函数和方差分解等方法,再加上格兰杰因果关系测试过程,去分析下面的关系：1.FDI对中国国内投资有互补或替代效应；(2)FDI，国内投资和经济增长三者间存在相互因果关系；(3)FDI对中国的经济增长起到了重要的作用；(4)FDI对经济增长的贡献超过国内投资。本文在许多方面和前期的一些研究存在不同。第一，这是现有的研究中第一次直接去检验中国的FDI和国内投资间的联系。第二，本文采用的是纯时间序列数据，与先前研究中使用可能引起数据可比性及不均匀性问题的横向或面板数据不同。第三，早期的研究没有检验FDI,跟国内投资和经济增长间的因果关系。没有考虑到变量间可能的双向因果关系会引发并发性问题。最后，我们的变量模型包括长期动态因素、企业内容管理。忽略这些动态因素可能导致各种估计偏差。 本文的框架如下：第二部分讲述中国的FDI流入，国内投资和经济增长的概述；接下来的第三部分对此进行分析；最后一部分得出结论并提出相关政策。二、1978-2003中国的FDI流入，国内投资和经济增长概述在20世纪80年代初期，向外国投资者提供税收减免和经济特权的优惠政策经济特区成立。在改革开放期间, 为了吸引外商直接投资中国政府出台了许多新的改善投资和经营环境立法措施。表1表示1978年-2003年的FDI/GDP,DI/GDP和FDI/DI的比率变化。从表中可以看出，FDI/GDP的比率到1992年为止都低于1%，在1994年达到高峰6.2%，然后又逐渐下降至2003年的3.8%。DI/GDP的比率从1978年的18.5%已快速增长到2003年的47.6%。FDI/DI的比率从1978年0.1%急剧增加到1984年的1.7%，到1994年已达到了18.1%的空前的高峰。然后,它逐渐下降到2003年的8%。三、实证研究及结论（一）数据和单位根检验本文采用的数据是用当年人民币价格计算的FDI, DI 和 GDP季度时间序列数据。数据来源于由中国国家统计局出版的中国统计月报（1987:1-2004:3）及新中国50年的综合和统计数据材料和中国各种统计年鉴。由于缺乏季度和月度本地GDP统计数据，本季的GDP是在每月工业总产值(GIO)和每年的国内生产总值的统计数据的基础上得到的。 q=1,.,4 t=1988,1989,.,2003为了在进行协整分析和模型估计时减少季节性波动，在模型中引入了周期性虚拟假设变量。标准的0-1周期虚拟变量将影响变量模型的均值和趋势，但主要的周期虚拟变量只影响平均值不影响趋势。在本文中运用扩展的ADF检验来检验FDI, DI 和 GDP这三个时间序列的平稳性。从图4（a-c）可以看出,三个序列水平是不平稳的。因此,我们通过单位根检验研究三个序列在一阶差分下的平稳性。ADF检验是在同一水平和一阶差分下对以下三个模型的估计观察进行。1. （1）2. （2）3. （3）ADF检验结果见表2。它们表明单位根的0假设是：（a）接受FDI在三个模型中的水平序列；（b）拒绝DI在模型（3）中的水平序列；（c）拒绝GDP在模型（1）的水平序列。结果表明，根据一阶差分数据，三个时间系列都是平稳的。因此，我们得出结论这三个时间序列都成一阶矩阵即I（1）。（二）变量的协整检验现在进行的协整检验时检验FDI, DI 和 GDP这三个变量在任何长期均衡关系的协整。在仔细的选择和检验后，选折一个由滞后长度为6、稳定的、周期性虚拟变量组成的模型。Johansen的协整等级检验结果如下表3。其表明存在两个分别在1%和5%水平意义的协整向量（矩阵为零或其秩小于等于2而拒绝零假设：无协整）。这意味着三个变量FDI, DI 和 GDP之间存在长期的均衡关系。（三）误差修正模型为了分析FDI, DI 和 GDP这三个变量间的因果联系，选用VAR系统下的误差修正变量模型（ECM）：本文中的数据在这个变量系统中运用得非常好。由结果可知，没有一个检验统计量在95%的临界水平下是有意义的。因此，没有任何迹象证明该系统模型是不正确的。根据Schwarzz（1978）和Akaike（1974）的信息准则，滞后的长度被选为6。（四）会计创新和格兰杰因果检验创新会计（方差分解和脉冲响应功能）技术可以用来研究经济变量之间的关系。运用这个检验方法，Kim and Seo (2003)使用这种技术研究了韩国外商直接投资与国内投资的互补或替代关系，并分析了外国直接投资对韩国经济增长的影响。另一方面，预测误差方差分解使得我们作出有关时间序列的比例变动，这个比例变动是由于系统中变量的自身冲击受到其他变量的冲击(Enders, 1995, p. 311)。这结果表明，FDI，国内投资和经济增长之间的关系强度是不同的。FDI 在中国的作用经济增长中起着重要，但国内投资的作用较小（分别为5.7%和17.6%）。GDP的增长对FDI的影响比对中国的国内投资的影响要较强（分别为40.8%和3.5%）。国内投资和GDP对FDI的影响较低（分别为2.4%和1.9%）。但DI与GDP之间的联系是比较强劲的，DI/GDP为40.8%，GDP/DI 为17.6%。这表明，这三个变量解释了各自过去的优势值（预测误差方差）。这意味着，目前/过去的FDI, DI 和 GDP对其以后/当前的趋势有重要影响。Granger因果检验的结果表明：（1）DI和GDP对FDI没有显著的统计性影响；（2）FDI和GDP对DI的影响是显著的；（3） FDI和DI对GDP的影响是显著的。因此，FDI影响DI和GDP，反向作用不明显，GDP和DI间因果联系是双向的。这些研究结果证实了方差分解的结果分析。接着进行脉冲响应函数的动态因果关系分析，揭示FDI, DI 和 GDP关系。（五）实证结果本文选用误差修正模型，得出了以下结论：1外国直接投资在对国内投资的补充作用明显，外国直接投资越多，国内投资越大。另外，FDI对中国的经济增长有重大影响。 2中国的国内投资和经济增长呈积极正相关关系。巨大的经济增长刺激国内投资的增长，反之亦然。3在长期，中国的国内投资和国内生产总值对外国直接投资的流入没有太大影响。国内生产总值和国内投资之间存在双向因果关系，同时外国直接投资对国内投资和外国直接投资对国内生产总值都只是单向因果关系。 4中国的国内投资对经济增长的影响比对外国直接投资产生的影响更大。 这些结论这在某种程度上支持了外国直接投资对国内投资有补充效用而长期的经济增长与外国直接投资呈积极正相关的理论。四、结论及相关政策在实证分析和调查结果的基础上，我们得出这样的结论，外国直接投资对国内投资不具有排挤作用，反而补充带动国内投资。外国直接投资不仅帮助克服资金短缺， 它还通过补充国内投资刺激中国经济增长。这项研究的结果对中国和其他地方的政策制定者也有一些重要的政策含义。由于外国直接投资对国内投资具有补充作用，那些欠发达的国家应该通过适当的外国直接投资政策和法规鼓励和促进外国直接投资流入。13