1 中文 3080 字 本科畢業(yè)論文外文翻譯 外 文 題 目 ： Foreign Direct Investment, Domestic Investment and Economic Growth in China: A Time Series Analysis 出 處： 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 Analysis Sumei Tang, E. A. Selvanathan and S. Selvanathan 1. 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 2 (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 3 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: 19782003 In 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. 4 3. EMPIRICAL ANALYSIS AND FINDINGS a. Data and Unit Root Test Quarterly 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. qttqt G IOgG D P , q=1,.,4 t=1988,1989,.,2003 To minimise the effect of seasonal fluctuations when conducting co-integration analysis and model estimation, a variable of centred (orthogonalised) seasonal dummies is 5 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: tki itittyryy 11（ 1） Constant and no trend model: titki ittyryy 110（ 2） Constant and trend model: titki ittyryty 1120（ 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). 6 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 Model To 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 7 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 complementary 8 effects 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. 譯 文： 中國的 FDI,國內(nèi)投資與經(jīng)濟(jì)增長：一個時間序列分析 二、 引言 盡管 FDI 對投資影響的大量研究， FDI 在經(jīng)濟(jì)增長中扮演的角色也受到廣泛的爭議。 FDI 的支持者表示 FDI 可 以通過技術(shù)外溢與人力資本發(fā)展帶動經(jīng)濟(jì)增長。尤其是跨國公司在東道國與國內(nèi)企業(yè)間有垂直關(guān)系或與其他國家的子公司或區(qū)域部門內(nèi)部有垂直聯(lián)系。 FDI 流入高危領(lǐng)域或新興產(chǎn)業(yè)還克服了東道國資金短缺問題。當(dāng) FDI 進(jìn)入資源密集型產(chǎn)業(yè)時，相關(guān)產(chǎn)業(yè)的國內(nèi)投資也會受到刺激。 FDI 還有可能導(dǎo)致東道國出口需求增加，從而為出口產(chǎn)業(yè)吸引更多的投資。支持這一說法的實證要求包括 Sun（ 1998）和Shan（ 2002） 運(yùn)用 傳統(tǒng)的回歸模型和面板數(shù)據(jù)， Sun 發(fā)現(xiàn)中國的 FDI 與國內(nèi)投資對中國的經(jīng)濟(jì)增長存在高度積極關(guān)系。 Shan 運(yùn)用一個 VAR 模型去檢驗 中國的 FDI，產(chǎn)業(yè)產(chǎn)出增長和其他變量間的內(nèi)部聯(lián)系。他得出當(dāng) FDI 在產(chǎn)業(yè)產(chǎn)出的比率上升時， FDI 對中國經(jīng)濟(jì)有極大的收益性影響。 相反，反對 FDI 促進(jìn)增長論者認(rèn)為 FDI 擠出國內(nèi)投資，并對經(jīng)濟(jì)增長有負(fù)面影響。特別是，工業(yè)組織理論認(rèn)為 FDI 是跨國公司為了在東道國超過本土企業(yè)，提升其壟斷力量的極具侵略性的全球戰(zhàn)略?？鐕镜乃袡?quán)優(yōu)勢（例如先進(jìn)的技術(shù)，管理經(jīng)驗技巧，最小交易成本及其他無形優(yōu)勢）會轉(zhuǎn)為壟斷力。這種壟斷力量會進(jìn)一步地提升 9 跨國公司其他兩方面的優(yōu)勢：市場內(nèi)部一體優(yōu)勢和市場定位優(yōu)勢（鄧寧）。此外， FDI也可能通 過國內(nèi)商品的替代進(jìn)口破壞經(jīng)濟(jì)的后部聯(lián)系。 本文對現(xiàn)有文獻(xiàn)的貢獻(xiàn)主要是通過建立多變量的誤差修正模型和時間序列協(xié)整檢驗和創(chuàng)新技術(shù)會計去研究中國的 FDI，國內(nèi)投資和經(jīng)濟(jì)增長間可能的因果聯(lián)系。具體地說 ,我們使用了脈沖響應(yīng)函數(shù)和方差分解等方法 ,再加上格蘭杰因果關(guān)系測試過程 ,去分析下面的關(guān)系： 1.FDI 對中國國內(nèi)投資有互補(bǔ)或替代效應(yīng)； (2)FDI，國內(nèi)投資和經(jīng)濟(jì)增長三者間存在相互因果關(guān)系； (3)FDI 對中國的經(jīng)濟(jì)增長起到了重要的作用； (4)FDI對經(jīng)濟(jì)增長的貢獻(xiàn)超過國內(nèi)投資。 本文在許多方面和前期的一些研究存在不同。第 一，這是現(xiàn)有的研究中第一次直接去檢驗中國的 FDI 和國內(nèi)投資間的聯(lián)系。第二，本文采用的是純時間序列數(shù)據(jù)，與先前研究中使用可能引起數(shù)據(jù)可比性及不均勻性問題的橫向或面板數(shù)據(jù)不同。第三，早期的研究沒有檢驗 FDI,跟國內(nèi)投資和經(jīng)濟(jì)增長間的因果關(guān)系 。 沒有考慮到變量間可能的雙向因果關(guān)系會引發(fā)并發(fā)性問題。最后，我們的變量模型包括長期動態(tài)因素、企業(yè)內(nèi)容管理。忽略這些動態(tài)因素可能導(dǎo)致各種估計偏差。 本文的框架如下：第二部分講述中國的 FDI 流入，國內(nèi)投資和經(jīng)濟(jì)增長的概述；接下來的第三部分對此進(jìn)行分析；最后一部分得出結(jié)論并提出相 關(guān)政策。 二、 1978-2003 中國的 FDI 流入，國內(nèi)投資和經(jīng)濟(jì)增長概述 在 20 世紀(jì) 80 年代初期，向外國投資者提供稅收減免和經(jīng)濟(jì)特權(quán)的優(yōu)惠政策經(jīng)濟(jì)特區(qū)成立。在改革開放期間 , 為了吸引外商直接投資中國政府出臺了許多新的改善投資和經(jīng)營環(huán)境立法措施。表 1 表示 1978 年 -2003 年的 FDI/GDP,DI/GDP 和 FDI/DI 的比率變化。從表中可以看出， FDI/GDP 的比率到 1992 年為止都低于 1%，在 1994 年達(dá)到高峰 6.2%，然后又逐漸下降至 2003 年的 3.8%。 DI/GDP 的比率從 1978 年的 18.5%已快速增長到 2003 年的 47.6%。 FDI/DI 的比率從 1978 年 0.1%急劇增加到 1984 年的 1.7%，到 1994 年已達(dá)到了 18.1%的空前的高峰。然后 ,它逐漸下降到 2003 年的 8%。 10 三、實證研究及結(jié)論 （一）數(shù)據(jù)和單位根檢驗 本文采用的數(shù)據(jù)是用當(dāng)年人民幣價格計算的 FDI, DI 和 GDP 季度時間序列數(shù)據(jù)。數(shù)據(jù)來源于由中國國家統(tǒng)計局出版的中國統(tǒng)計月報（ 1987:1-2004:3）及新中國 50 年的綜合和統(tǒng)計數(shù)據(jù)材料和中國各種統(tǒng)計年鑒。由于缺乏季度和月度本地 GDP 統(tǒng)計數(shù)據(jù)，本季的 GDP是在每月工業(yè)總產(chǎn)值 (GIO)和每年的國內(nèi)生產(chǎn)總值的統(tǒng)計數(shù)據(jù)的基礎(chǔ)上得到的。 qttqt G IOgG D P , q=1,.,4 t=1988,1989,.,2003 為了在進(jìn)行協(xié)整分析和模型估計時減少季節(jié)性波動，在模型中引入了周期性虛擬假設(shè)變量。標(biāo)準(zhǔn)的 0-1 周期虛擬變量將影響變量模型的均值和趨勢，但主要的周期虛擬變量只影響平均值不影響趨勢。在 本文中運(yùn)用擴(kuò)展的 ADF檢驗來檢驗 FDI, DI 和 GDP這三個時間序列的平穩(wěn)性。 11 從圖 4（ a-c）可以看出 ,三個序列水平是不平穩(wěn)的。因此 ,我們通過單位根檢驗研究三個序列在一階差分下的平穩(wěn)性。 ADF 檢驗是在同一水平和一階差分下對以下三個模型的估計觀察進(jìn)行。 1.tki itittyryy 11（ 1） 2.titki ittyryy 110（ 2） 3.titki ittyryty 1120（ 3） ADF 檢驗結(jié)果見表 2。它們表明單位根的 0 假設(shè)是： （ a） 接受 FDI 在三個模型中的水平序列； （ b） 拒絕 DI 在模型（ 3）中的水平序列； （ c） 拒絕 GDP 在模型（ 1）的水平序列。結(jié)果表明，根據(jù)一階差分?jǐn)?shù)據(jù)，三個時間系列都是平穩(wěn)的。因此，我們得出結(jié)論這三個時間序列都成一階矩陣即 I（ 1）。 （二）變量的協(xié)整檢驗 現(xiàn)在進(jìn)行的協(xié)整