IMPACT OF FOREIGN DIRECT INVESTMENT ON SELECTED MACRO ECONOMIC PARAMETERS OF INDIA AND CHINA

CHAPTER-7 IMPACT OF FOREIGN DIRECT INVESTMENT ON SELECTED MACRO ECONOMIC PARAMETERS OF INDIA AND CHINA In this era of globalized world economy, FDI is a particularly significant driving force behind the interdependence of national economies. Even though most of the FDI flows have always concentrated in the developed countries, its importance is undeniable for developing countries as well. Economic development is a wider concept. It centres on economic and social progress, but also entails many different aspects that are not easily quantified, such as political freedom, social justice, and environmental soundness. All these matters combine to contribute to an overall high standard of living. However, empirical evidence has amply demonstrated that all these varied elements of economic development correlate with economic growth. That is, as a general rule, countries with faster economic growth have more rapid improvement in health and education outcomes, progressively liberal political system, increasingly more equitable distribution of wealth, and enhanced capacity for environmental management (Sun, 2002). FDI involves the transfer of managerial resources to the host country. The endogenous growth theories explained the effect of FDI on economic growth through knowledge externalities and the existence of human capital in host developing countries. According to this theory, FDI contributes significantly to human capital such as managerial skills and research and development (R&D). MNC s can have a positive impact on human capital in host countries through the training courses they provide to their subsidiaries local workers. The training courses influence most levels of employees from those with simple skills to those who posses advanced technical and managerial skills. Research and development activities financed by MNCs also contribute to human capital in host countries and thus enable these economies to grow in the long term. (Lan, 2006) The purpose of Multilateral Agreement on Investment (MIA) is to establish a multilateral framework for investment. Its object is to protect imaginative rights of foreign investors to invest in all sectors of the host country s economy and to obtain 106

for them the same treatment as investors from the host nation. However, some concerns among developing countries are that the multinational firms might adversely affect the development of domestic (Agrawal, 2000). 7.1 Choice of Variables It has been observed after reviewing the literature available on the impact of FDI on various macroeconomic parameters in different economies that numerous studies found out the impact of FDI on growth in different regions. Some studies have evaluated positive ( Shan et al., 1998; Alarm, 2001; Hansen and Rand, 2003; etc.) while some others have found negative or no impact of FDI on growth (Lo, 2007; Mathiyazhagan, 2005). There arises a doubt on this relationship on account of lack of general consensus among the researchers about the nature of this impact. Therefore, GDP as a parameter of growth has been taken in the present study to verify the exact nature of impact. Very few studies investigated the impact of FDI on Foreign Exchange Reserves (RES). Moreover, FDI is supposed to resolve foreign exchange reserves constraints to development by its contribution to increased exports apart from bringing in net resource inflows on the capital account of the country. So, there is need to estimate the exact impact of FDI on RES. Gross Capital Formation (GCF) has been selected as a parameter for studying the impact because it has always remained a debatable issue that whether FDI has favourable or adverse impact on domestic capital. Mathiyazhagan, (2005) and Sharma, (2003) found insignificant impact of FDI on exports in India while Wen, (2004) found positive and significant impact of FDI on exports of China. So, a through investigation on account of disagreement regarding the impact of FDI on this variable is required. Generation of Employment (EMP) has b ecome the foremost agenda of India and China and a very important indicator of growth in the economy which advocates the logic for including this variable in the present study. Results may act as a guideline for Government of these countries to reshape their policies formulated for checking the unemployment. Moreover, genuine and extensive research work regarding this parameter in India and China is seriously lacking. All the evidences given above lay down the strong foundation for selecting Gross Domestic Product 107

(GDP), Gross Capital Formation (GCF), Exports (EXP), Employment (EMP) and Foreign Exchange Reserves (RES) as macro economic parameters to study the impact of FDI inflows in India and China. In this chapter an attempt has been made to assess the impact of FDI separately on the selected macro economic variables mentioned above in case of India and China. 7.2 Variables, Data Source and Period of the Study The sample period runs from year 1976 to 2008 for India and China. The data has been drawn from the World Development Indicators and World Development Reports published by the World Bank. The variables used in the study are Foreign Direct Investment (FDI), Gross Domestic Product (GDP), Gross Capital Formation (GCF), Export (EXP), Employment (EMP), and Foreign Exchange Reserves (RES) for India and China. In order to neutralise the impact of change in prices, all the variables except Employment has been deflated at 1993-94 prices by using Purchasing Power Parity Index (PPI). Description of Variables: Variables LNEMP LNEXP LNFDI LNGCF LNGDP LNRES Description Natural Log of Employment Natural Log of Exports Natural Log of Foreign Direct Investment Natural Log of Gross Capital Formation Inflation Natural Log of Gross domestic product Natural Log of Reserves 7.3 Expected Impact of FDI on Macro Economic Variables under Study The endogenous growth theories support strongly the role of FDI in promoting economic growth in host countries. In these theories, FDI is viewed as a way to transfer knowledge, promote learning by doing, bring in technology spillover, and human capital growth. Consequently, FDI stimulates economic growth in host countries (Lan, 2006). There are still inconclusive arguments for and against the role 108

of FDI inflows in enhancing economic growth in a country. Whether FDI inflows are beneficial or not to economic growth especially in host developing countries are still debated among economists. It is therefore very essential to analyse expected theoretical relationship between FDI and these macro economic variables before doing empirical investigation regarding their relationship. This has been discussed below: Gross Domestic Product: The most widespread belief among researchers and policy makers is that FDI boosts growth through different channels. It increases the capital stock, stimulates technological change through technological diffusion and generates technological spillovers for local firms. Foreign investment is expected to increase and improve the existing stock of knowledge in the recipient economy through labour training, skill acquisition and diffusion. It contributes by introducing new management practices and a more efficient organization of the production process. As a result, FDI improves the productivity of host countries and stimulates economic growth in terms of increase in GDP (Jallab, 2008). Investigating the impact of foreign capital on economic growth has important policy implications. Positive impact of FDI on economic growth weakens the arguments for restricting foreign investment in the host country. However the negative impact of FDI on growth would suggest a reconsideration of development policies adopted by countries for attracting FDI to enhance the level of their growth (Carkovic and Levine, 2000). Foreign Exchange Reserves: Foreign exchange reserves are the external assets that are readily available to and controlled by monetary authorities for direct financing of payments. It is the total of a country's gold holdings and convertible foreign currencies held in its banks, plus special drawing rights (SDR) and exchange reserve balances with the International Monetary Fund (IMF). Foreign investment causes certain advantages like technology transfer, marketing expertise, introduction of modern managerial techniques and thus creating immense possibilities of increased foreign exchange reserves in the country concerned. Foreign investors are generally considered to be better placed to tap international market than their local counterparts because of their massive assess to 109

the information and marketing networks of their parent enterprises which facilitates their efforts to increase foreign exchange reserves in the host country (Chopra, 2003). Gross Capital Formation: It is the creation of productive assets that expand an economy's capacity to produce goods and services. Private savings facilitate capital formation by allowing resources to be diverted to corporate investment rather than individual consumption (Scott, 2003). It is very crucial macro economic parameter which determines the growth of an economy. Since FDI establishes backward and forward linkages with local industries, it can also encourage domestic investment by creating an enabling investment environment through transferring technologies and better management techniques. Transnational corporations typically have access to a wide variety of financing options. The risk-adjusted cost of capital is usually lower for them than the domestic firms in developing countries. Foreign firms can undertake projects for which domestic investors do not have the capacities to carry out or which are considered too risky for host country firms. In such cases, FDI also serves to stimulate domestic investment by further boosting the total host country investment. FDI not only adds to external financial resources for host country development, it is also more stable than other forms of financing (Sun, 2002). Employment: FDI serves as a catalyst for rapid economic growth by enabling developing countries to catch up with advanced economies. FDI plays a major role in the larger development agenda of the host countries. There is one main social aspect of development such as employment. Increasing gainful and secure employment has always ranked high as a policy objective for developing countries. It is a principal means to achieve an equitable distribution of income and higher standard of welfare for the majority of the population. There are three basic mechanisms for FDI to generate employment in the recipient countries. First, foreign subsidiaries employ people in their domestic operations. Second, through backward and forward linkages, employment is created in enterprises that are suppliers, subcontractors, or service providers to them. Thirdly with the expansion of FDI in related industries, employment is also generated in different sectors of the economy. FDI often plays a unique role in employment creation and upgrading of the host countries because of 110

the special features of foreign investments i.e its tendency to be larger in size, with greater technological sophistication, and ability to face more competitive pressures in their product markets as compared to domestic enterprises (Sun, 2002). Exports: FDI is expected to have strong positive relation with the exports volume. This is quite in tune with common knowledge that export performance is influenced by technology intensity and hence industry associated with high technology efforts has a tendency to export high proportion of their product. Country with an importer of foreign technology can be postulated to have better export performance. This is the reason to assume an improved export performance of a country where FDI stake is high. As exporting involves high degree of risk and uncertainty, so foreign firm with higher profitability due to greater assess to financial resources do better on export front. Export-oriented FDI is of higher quality than the domestic market-seeking FDI. This is because export-oriented companies tend to form micro-level linkages with domestic firms in the form of sourcing and partnerships. Their objective is to exploit the low cost infrastructure and cheap labour skill available locally for export purposes. In addition to knowledge spillovers to local suppliers, export-oriented foreign firms also cause information spillovers to purely domestic firms to enter into export market (Chopra, 2003). The government of India and China have accorded top priority to attract foreign direct investment in the recent years through planning process. In order to accelerate the process of the industrialization through mobilization of foreign direct investment, the government of India and China have changed investment policies and offered many incentives to foreign investors to invest in the countries. Theoretical association between FDI and the macro economic variables taken in the study has already been discussed. Practical implication regarding the effect of FDI on these variables has been outlined in the Table 7.1 which explains the 111

Table 7.1: FDI and Some Macro Economic Variables in India and China for the period 1976 to 2008 Years FDI (in US $ Million) GDP (in US $ Million) Reserves (in US $ Million) Exports (in US $ Million) GCF (in US $ Million) EMP (in Thousands) India China India China India China India China India China India China 1976 51-101196 151628 3070 1669 6,854 7,300 23,347 42,347 192881 383868 1977 36-117421 172347 5765 2878 7,602 8,090 25,611 49,106 196316 390222 1978 18-135833 214160 6793 2136 8,688 14,297 33,351 81,842 199826 396178 1979 49-150317 263190 7843 2750 10,268 22,255 37,866 95,106 203393 401934 1980 79 150 185402 306520 7354 3128 11488 32302 42,586 106761 262011.8 511,162 1981 92 380 197762 293852 5026 5556 11850 36088 51855 95639 268424.6 525,343 1982 72 410 201927 295370 4634 11806 12235 34479 50179 94275 274736 539,646 1983 6 640 220318 314637 5267 15497 13016 32768 51300 103205 281141.9 555,375 1984 19 1258 218222 317352 6141 17798 13952 35046 60683 108408 287766.1 571,099 1985 106 1661 227247 309083 6791 13285 12093 30524 52032 117736 294447.3 586,647 1986 118 1874 248982 304348 6825 11966 13124 34924 63699 114161 301344.3 602,038 1987 121 2314 275529 329851 6979 16952 15654 52565 68350 119879 308525.7 617,226 1988 91 3194 304809 413439 5409 19201 18628 68384 81958 153145 315672 631,925 1989 252 3392 301764 459782 4325 18485 21339 74988 80348 168194 322843.4 645,780 1990 162 3487 327930 404494 2050 30162 23225 74320 91353 141056 329759.8 658,576 1991 74 4366 290687 424117 4190 44297 24746 85084 72864 147801 336606.5 669,491 1992 277 11007 291925 499859 6326 21309 25982 78822 77443 182902 343260.1 678,648 1993 550 27515 284972 641069 10722 22938 28251 86556 66939 272784 349829.4 687,036 1994 973 33767 328472 582653 20303 53568 32386 118927 84357 236010 356674.7 695,164 1995 2144 37521 370522 756960 18631 76217 40316 147228 108645 304979 363303.4 703,066 1996 2821 41726 390520 892014 20848 107831 40881 171678 93960 346215 370192.3 711,018 1997 3577 44236 420040 985046 25304 143442 45494 207239 105795 361504 377025.5 718,710 1998 2462 43751 428750 1045199 27948 150024 47330 207429 100833 378238 384004.4 726,252 1999 2155 38753 454952 1098832 33159 158049 52885 220965 118966 398049 391142 733,880 2000 2339 38399 468970 1192836 38341 168480 61886 279558 113835 420883 398362.8 741,534 2001 3904 44241 483466 1311558 46497 216876 61619 299409 117506 480777 406655.2 749,214 2002 8574 49621 503954 1454040 68237 292148 73144 365398 127342 550504 415047.3 756,859 2003 4585 53510 592535 1647918 99806 410264 87543 484986 157705 676124 423616.5 764,237 2004 5474 60630 688803 1936502 126972 614355 130540 655827 196230 835690 430444.9 771,293 2005 6598 72406 808884 2282554 132567 822899 161471 836886 250521 970914 438765.7 778,171 2006 20336 72715 903226 2661265 170843 1066380 202354 1061463 307580 1130640 446882.7 784,523 2007 25127 83521 1141346 3400351 267582 1513107 241711 1341582 401231 1383120 455348.3 792,324 2008 41554 108312 1252403 4348303 306429 1895625 271645 1636095 430036 2040253 467231 799,004 Source: World Investments Reports for the years 1978 to 2009 112

relationship between FDI and these macro economic variables in India and China. FDI in China in the year 1980 was 150 US $ million which was nearly double to that of India (79 US $ million). Table shows that India s GDP (185402 US $ million) was found to be less than China s GDP (306520 US $ million) in the year 1976. Foreign exchange reserves in India (7354 U S $ million) were noticed to be higher than that of China (3128 US $ million) in the year 1980. GCF was also more in China (106761 US $ million) as compared to India (42811 US $ million) during that year. China s position was better than India as far as exports and employment are concerned in the year 1980. All the variables in India as well as China have shown increasing trends with the passage of time. FDI has also shown increasing trend in both the countries but wide fluctuations in India were noticed from the year 1980 to 2008. China s FDI (108312 US $ million) grew to become 2.5 times than that of India (41554 US $ million) in the year 2008. Moreover China showed growth in this variable in a more consistent manner. GDP of both the countries has grown with the course of time but China s GDP increased at a faster rate than India. During the year 2008 China s GDP (4348303 US $ million) was three time more than that of India (1252403 U S $ million). China registered high foreign exchange reserves (1895625 U S $ million) as compared to India (306429 US $ million) in the year 2008. Employments in both the countries have increased but the position of China regarding this variable remained consistent. China still hold better position by employing 811234 persons while India registered 467231 persons employed in the year 2008. Exports and GCF have grown in both India and China but China s performance regarding these variables was comparatively better than that of India with the due course of time. China s exports and GCF have increased from 32302 US $ million and 106761 US $ million in 1980 to 1636095 US $ million and 2040253 US $ million in the year 2008. Exports and GCF of India have also shown increasing trend i.e from 11488 US $ million and 42811 US $ million in 1980 to 271645 US $ million and 430036 US $ million respectively in 2008. 113

7.4 Statistical Diagnostic Initially, the study used the regression analysis for finding the dependence of GDP on FDI then the dependence of other variables like RES, GCF, EXP and EMP on FDI has been determined individually. Stationarity of all the variables was tested by applying ADF test then simple regression technique was used. The results of the analysis have been reported in Table no. 7.2 for India and Table no. 7.3 for China. Value of coefficient indicates the change in dependent variable due to unit change in independent variable. Value of adjusted R 2 shows the percentage explained variation in dependent variable caused by the independent variable. X coefficients as shown in Table no. 7.2 for India and Table no. 7.3 for China show positive impact of FDI on all the variables of the study. It means that any increase in FDI will lead to raise the dependent variables. Table 7.2: Regression Results (FDI as Independent Variable) (India) Variable Coefficients Standard Error t- Statistics Prob. Adjusted F-statistic D.W R 2 EMP 0.084 0.008 10.43 0.000 0.77 108.95* 0.42 EXP 0.475 0.026 17.64 0.000 0.90 311.40* 0.97 GCF 0.342 0.024 13.77 0.000 0.85 189.66* 0.70 GDP 0.315 0.021 14.91 0.000 0.87 222.55* 0.75 RES 0.579 0.040 14.23 0.000 0.86 202.66* 0.80 ** denotes significance at the level 1%. Table 7.3: Regression Results (FDI as Independent Variable) (China) Variable Coefficients Standard t- Prob Adjusted F-statistic D.W Error Statistics. R 2 EMP 0.060 0.000 48.81 0.000 0.98 2383.0* 0.47 EXP 0.401 0.035 11.14 0.000 0.79 124.1* 0.25 GCF 0.281 0.028 9.83 0.000 0.74 96.7* 0.26 GDP 0.259 0.026 9.814 0.000 0.74 96.3* 0.23 RES 0.540 0.049 10.94 0.000 0.78 119.8* 0.33 * denotes significance at the level 1%. 114

All the variables have significant F value in both the countries showing the significance of the model. The value of adjusted R 2 ranges between 0.77% to 0.90% in India and 0.74% to 0.98% in China, indicating the presence of multicollinearity between the variables taken in the study. The analysis further reveals that the value of Durbin-Watson statistics is very low for India as well for China which signifies the existence of autocorrelation. To overcome this problem, cointegration test has been applied to examine the impact of FDI on macro economic parameters selected in the study which is free from all these types of errors. 7.5 Econometric Methodology The technique of cointegration has been used to assess the impact of FDI on various macroeconomic parameters taken for this objective. The first step of the estimation process is to examine the time series properties of the data series i.e. presence of drift and trend in the data and test for stationarity and the order of integration. In fact most economic variables are non-stationary (integrated) in their level form. These non stationary time series may result to spurious regressions. Although a simple least squares regression of integrated variables may be spurious, one or more linear combinations of the series may exist that result in a stationary residual. There is need to check for the stationary of each series before applying aforesaid technique. All the six variables namely LNGDP, LNEMP, LNEXP, LNGCF, LNFDI, and LNRES have been tested for stationarity by applying the Augmented Dickey-Fuller test (ADF) before estimating any relationships between FDI and various macroeconomic parameters. 7.6 Results and Discussion The analysis of these variables in this chapter indicates the dependence of growth of gross domestic product and other macro economic constituents of the economy on FDI based on empirical investigation of five variables. The ADF test results for the six variables involved in the analysis have been presented in Table 7.4 for India and in Table 7.5 for China. It has been observed that the null hypothesis of presence of Unit Root has been rejected for all the first difference variable specified. This shows that all variables are integrated of order one and hence, observed stationary at first difference. This suggests that Cointegration analysis can be applied to examine existence of long run relationship among the variables under study. 115

Table 7.4: Augmented Dickey Fuller Test Results Unit Root Tests at Logarithmic levels (India) Sr. No. Variables Without Drift and Time Trend With Drift With Drift and Time Trend 1 LNEMP 3.0499-2.4283-2.3502 2 LNEXP 7.4194 2.1400-0.1733 3 LNFDI 1.5434-0.0066-3.3197 4 LNGCF 4.4694 1. 0824-0.1724 5 LNGDP 6.2119 0.7299-0.8886 6 LNRES 3.1628 0.8559-1.0069 Unit Root Tests at First Differences Sr. No. Variables Without Drift and Time Trend With Drift With Drift and Time Trend 1 LNEMP -4.0257** -5.5312** -5.9421** 2 LNEXP -0.0497-4.9221** -5.6423** 3 LNFDI -5.1482** -5.6036** -5.6041** 4 LNGCF 0.0315-6.8629** -6.9850** 5 LNGDP -0.1278-5.0601** -5.1616** 6 LNRES -4.0198** -4.6284** -5.0965** ** denotes significance at the level 1%. Critical values obtained from Mackinnon (1991) are -2.85, -3.41, and -1.94 at 5% level and -3.43, -3.96, and -2.56 at 1% for first second and third model respectively. 116

Table 7.5: Augmented Dickey Fuller Test Results Unit Root Tests at Logarithmic levels (China) Sr. No. Variables Without Drift and Time Trend With Drift With Drift and Time Trend 1 LNEMP 2.0866-8.8025** -4.5680** 2 LNEXP 5.9997-0.0612-1.9431 3 LNFDI 0.8769-3.8241** -2.8888 4 LNGCF 4.7323 0.6619-1.4879 5 LNGDP 5.3285 1.0739-1.1801 6 LNRES 3.5011 0.0724-2.4192 Unit Root Tests at First Differences Sr. No. Variables Without Drift and Time Trend With Drift With Drift and Time Trend 1 LNEMP -2.5657** -5.1688** -6.0565** 2 LNEXP -2.5004** -4.3318** -4.2493** 3 LNFDI -3.9107** -3.8874** -4.9390** 4 LNGCF -2.9003** -4.3939** -4.4379** 5 LNGDP -2.9477** -4.7277** -4.9089** 6 LNRES -1.6967-3.8171** -5.4153** ** denotes significance at the level 1%. Critical values obtained from Mackinnon (1991) are -2.85, -3.41, and -1.94 at 5% level and -3.43, -3.96, and -2.56 at 1% for first second and third model respectively. 117

Further implication is that there is a possibility to have a co-integrating vector whose coefficient can directly be interpreted as long-term impact coefficients. Therefore, as next step, Johansen Trace test has to be used to check whether we have a cointegration test where each form differs in the assumed deterministic component(s) in the series: After selecting the order of integrating, next step is to test the cointegration rank. Here, a Vector Autoregressive Regression (VAR) system is to be formed. This step involves testing for the appropriate lag length of the system. In this model lag length has been taken as one for VAR and Zero for cointegration window due to small sample size and less number of observations. Third model for cointegrating window has been selected which is the most realistic one i.e intercept in Cointegrating equation and VAR. The impact of FDI on these macroeconomic variables has been determined by taking five cointegrating relationships i.e GDP with FDI, GCF with FDI, RES with FDI, EXP with FDI and EMP with FDI. This cointegrating relationship represents the foundation of a complete Dynamic Error Correction Model. Vector error correction model (VECM) of the endogenous variables has been specified which provides a generalization of the partial adjustment model and permits the estimation of short-run and long-run elasticities. This long-run association shows the elasticities of GDP, GCF, RES, EXP and EXP with respect to FDI. Here X coefficient (elasticities) shows the percentage change in macro economic parameters due to one % change in FDI. Results are presented in Table 7.6 and 7.7 for India and China respectively. Table 7.6: Estimated Cointegrating Relationship (India) Equations Variables Constant Impact of FDI 1 LNGDP 10.824 0.274** (8.64) 2 LNGCF 9.375 0.290** (7.92) 3 LNEXP 7.543 0.426** (11.55) 4 LNEMP 12.307 0.066** (4.66) 5 LNRES 5.715 0.6242** (9.20) ** denotes significance at the level 1%. Figures in Parentheses are t values. 118

Table 7.7: Estimated Cointegrating Relationship (CHINA) Equations Variables Constant Coefficients 1 LNGDP 9.703 0.418** (9.05) 2 LNGCF 8.442 0.452** (9.34) 3 LNEXP 6.411 0.608** (9.573) 4 LNEMP 12.86 0.062** (23.29) 5 LNRES 4.193 0.784** (9.89) ** denotes significance at the level 1%. Figures in Parentheses are t values The results of the analysis given in these Tables can also be depicted in the form of the following regression equations to have more lucid explanation. Equation no. 7.1 to 7.5 indicates the impact of independent variable FDI on various macro economic parameters taken in the study for India while Equation no. 7.6 to 7.10 expresses this relationship for China. For India GDP = 10.824 + 0.274 FDI +u 1i (8.64) (7.1) GCF = 9.375 + 0.290 FDI + u 2i (7.92) (7.2) EXP = 7.543 + 0.426 FDI + u 3i (11.55) (7.3) EMP = 12.307 + 0.066 FDI + u 4i (4.66) (7.4) RES = 5.715 + 0.624 FDI + u 5i (9.20) (7.5) 119

For China GDP = 9.703 + 0.418 FDI + u 1c (9.05) (7.6) GCF = 8.442 +0.452 FDI + u 2c (9.34) (7.7) EXP = 6.411 + 0.608 FDI + u 3c (9.573) (7.8) EMP = 12.860 + 0.062 FDI + u 4c (23.29) (7.9) RES = 4.193 + 0.784 FDI + u 5ic (9.89) (7.10) In India, X coefficient is 0.274 in case of GDP which shows positive and significant impact of FDI on GDP. It means one % increase in FDI results in 0.274 percentage increase in GDP. Similarly in China, X coefficient in this case is 0.418 which shows the positive impact of FDI on GDP indicating 0.418% increase in GDP due to 1% change in FDI. Positive and significant impact of FDI on GDP has been found in India as well as in China. This is due the reason that FDI enhances economic growth by promoting technological developments and increasing capital stock, level of production, income and export potential of a country. The results further show that the GDP of China is more influenced by FDI as compared to India. This is because of more growth in manufacturing sectors on account of existence as well as optimum utilisation of resources, existence of favourable marketing structure and suitable reforms in defective policy framework of China. X coefficients of 0.290 and 0.452 have been calculated in India and China respectively so far as the gross capital formation (GCF) is concerned showing 1 % increase in FDI would cause the gross capital formation to rise by 0.290% and 0.452% for India and China respectively. This also indicates the positive impact of FDI on GCF of both the countries. Since FDI establishes backward and forward linkages with local industries, it can also encourage domestic investment by creating investment environment and by transferring technologies. However, the relationship between FDI and domestic investment depends on government policies, the quality of FDI and domestic regulatory environment (Alfaro, 2003). There is comparatively less 120

influence of FDI on GCF in India than China due to inadequate, inefficient and poor quality infrastructural facilities, defective marketing structure and weak regulatory system in India as compared to China. Exports is also found to be statistically significant variable affected by FDI in both the countries as the coefficient of this variable are determined as 0.426 and 0.608 in India and China respectively. It shows the 0.426 % variation in this variable is due to 1% change in FDI in India while 0.608% variation in this variable has been explained by 1% variation in FDI in case of China. FDI is also expected to have strong positive relation with the exports volume. This is because of the reason that the country with an importer of foreign technology can be postulated to have better export performance (Chopra, 2003). However exports are more affected by FDI in China than India as a result of reservation of many items for small scale industries, heavy taxes, instability of exchange rate and high rate of inflation in India. FDI is found to be significant factor that causes the 0.066 % and 0.062 % variations in employment in India and China respectively. As the value of X coefficients depict the positive impact of FDI on this variable in both the countries. FDI helps to start new industries and also to expand existing industrial capacity, which in turn tends to increase output and thus employment in a country. The coefficient of Reserves are estimated to be 0.624 and 0.784 indicating that a 1% increase in FDI would cause the Reserves to rise by 0.624 % and 0.784 % for India and China respectively. It has been found out that Reserves are positively related to FDI in both India and China but relatively more variation in Reserves is found to be in China than in India. This is because of the reason that FDI has resolved foreign exchange reserves constraints in China through its contribution toward expansion of export. Chinese economy has made commendable improvement in the export performance of the country by giving special emphasis on involvement of foreign investment in the export oriented manufacturing sector in the country, which led to expansion of foreign exchange reserves faster in China than India. 121

Table 7.8 Error Correction Term of VECM (India) Variables Error Correction Term LNGDP 1.053** (3.18) LNRES 0.362** (3.19) LNEMP 0.289* (2.16) * denotes significance at the level 5%. ** denotes significance at the level 1%. Figures in Parentheses are t values. Table 7.9 Error Correction Term of VECM (China) Variables Error Correction Term LNEXP 0.649** (2.75) LNRES 0.378* (2.26) LNFDI 0.566* (1.98) * denotes significance at the level 5%. ** denotes significance at the level 1%. Figures in Parentheses are t values. The results in Table 7.8 and 7.9 provide short run dynamics pertaining to the adjustment of disequilibrium in the short period for India and China respectively. In this Table error correction term represent the speed of adjustment and serves as a measure of disequilibrium representing the stochastic shocks in the dependent variable. This shows the proportion by which the long run disequilibrium in the dependent variable is corrected in each short period. It has been observed that any disequilibrium in the growth of GDP and RES would be corrected in the 0.94 years (1/1.053 =0.94), and 2.76 years (1/0.362= 2.76) respectively in India while EMP will converge toward equilibrium in 3.46 years (1/0.289= 3.46). Coefficients for all other variables are found to be statistically insignificant. In case of China any disequilibrium in the growth of FDI will converge toward equilibrium in 1.76 years 122

(1/0.566=1.76) while disequilibrium in the growth of EXP and RES would be corrected in the 1.54 years (1/0.649=1.54) and 2.64 years (1/0.378=2.64) respectively in China. Coefficients for all other variables are found to be statistically insignificant. In nutshell it can be said that X coefficients in both the countries show positive and significant impact of FDI on GDP, GCF, Exports, EMP and Reserves. The value of X coefficient (elasticities) shows a trend from 0.066 to 0.624 in India. The minimum variation due to FDI is found out in case of EMP (0.066%) and maximum in Reserves (0.624%) in India. The value of X coefficient (elasticities) shows a trend from 0.062 to 0.784 in China. The minimum variation due to FDI is also found out in case of EMP (0.062 %) and maximum in Reserves (0.784%) in China. FDI influences all these macro economic variables in both the countries, but the influence of FDI on GDP, GCF, Exports, EMP and Reserves is more in China as compared to India. All the equations have significant t value in both the countries showing the significance of these variables. The results of our analysis corroborates with earlier studies in which impact of FDI on various macro economic parameters in different regions of the countries has been analysed. Alarm (2001) found that FDI stimulated GDP, domestic investments, agriculture, industry, imports and exports in India and Bangladesh. Athukorala and Sen, (1995) found that FDI has positive impact on GDP, Exports and Employment generation in case of Malaysia in the past two decades. Panayides at al., (2003) concluded that the economic growth as measured by GDP in China was significantly caused by the FDI. Lan, (2006) found the positive and statistically significant impact of FDI on economic growth, exports, domestic investment, government expenditure, growth of labour and exchange rate in Vietnam. Ma, (2009) found significant and positive effect of FDI on GDP in China. 123