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1、毕业论文外文翻译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 propone
2、nts 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
3、. 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 cou
4、ntries 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 t
5、he 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 inves
6、tment 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, opponen
7、ts 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-s
8、pecific 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 speci
9、fic-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 cor
10、rection 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 pro
11、cedures 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 g
12、rowth 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-s
13、ectional 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 th
14、e 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 fo
15、llowed 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
16、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
17、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 i
18、n 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 ANALYSI
19、S 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 vari
20、ous 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.
21、 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 le
22、vel 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 s
23、eries 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 an
24、d 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
25、(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 c
26、o-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 i
27、s 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
28、 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 n
29、one 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 Accountin
30、g 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
31、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 sy
32、stem (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 p
33、er 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 influ
34、ence 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 causali
35、ty 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 G
36、DP 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.
37、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 in
38、vestment 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-w
39、ay 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 wi
40、th FDI. 4.CONCLUSIONS AND POLICY IMPLICATIONSBased 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 stimulate
41、d 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 app
42、ropriate FDI policies and regulations are required. 译 文：中国的FDI,国内投资与经济增长：一个时间序列分析一、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
43、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
44、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
45、。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
46、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%。
47、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
48、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） （2） （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