CAUSAL LINK BETWEEN FOREIGN DIRECT INVESTMENT, EXPORT AND ECONOMIC GROWTH IN INDIA: A COMPARISON OF TYDL AND GRANGER CAUSALITY TEST

Causal Asian-African Link between Journal Foreign of Economics Direct Investment, and Econometrics, Export and Vol. Economic 13, No. 2, Growth 2013: 133-143 in India 133 CAUSAL LINK BETWEEN FOREIGN DIRECT INVESTMENT, EXPORT AND ECONOMIC GROWTH IN INDIA: A COMPARISON OF TYDL AND GRANGER CAUSALITY TEST Debi Prasad Bal * and Seba Mohanty ** ABSTRACT This paper studies the causal link between FDI, export and economic growth in India. Here we compare the results of both Granger causality and TYDL no Granger causality test over the period of 1978-79 to 2011-12. The Granger causality test shows that there is unidirectional causality between economic growths to export; economic growth to FDI and export to FDI; but from TYDL method there is only unidirectional causality between economic growths to export in India which is supported by variance decomposition technique. Hence our result supports that TYDL is a better method to show the causal link among these three variables. From the policy perspective our result supports that India should focus on high level of economic growth instead of to follow either export-led growth of FDI-led growth. JEL Classification: G 20; F 10; F 43; C 51 Keywords: FDI; Export; Economic Growth; TYDL; Granger causality 1. INTRODUCTION Foreign Direct Investment and export of the country has played a crucial role for the sustainable growth and development in India. The Foreign Direct Investment (FDI) also has its own importance for the countries development. India needs FDI for the shortage of capital, technology, managerial skills and market access. FDI brings in capital and advanced technology that can enhance the technological capabilities of the host country firms. With this objective our study focuses the casual relationship among FDI, Export and Economic growth in the case of India. Hence, this study investigates whether the level of FDI, Export and economic growth of India are linked and if so then in which direction they are linked. The relationship between export, FDI and growth is well known. The contribution of FDI and Export towards economic growth increases rapidly after 1991.The percentage of FDI in terms of GDP was 0.03% in 1991 and it increases to 2.11% in 2006 and also further to 3.55% in 2008. After global recession the percentage of FDI in terms of GDP has been decreased to 1.52% in 2010 and in 2011 it is increased to 1.72%. Similarly in case of exports as a percentage of GDP * Research Scholar, Indian Institute of Technology, Hyderabad, India, E-mail: debiprasad.bal@gmail.com ** Research Scholar, Indian Institute of Technology, Kharagpur, India, E-mail: mohanty.seba@gmail.com

134 Debi Prasad Bal and Seba Mohanty increases tremendously from 1995 i.e. from 10.66% to 23.60% in 2008. At the time of recession the percentage of export in terms of GDP decreases and further it increase to 23.88 % in 2011. This rapid percentage increase of FDI and export in terms of GDP is exhibited in figure 1. Figure 1 We presented the trends of FDI and export as a percentage of GDP in the above figure 1. The globalization policy of 1991-92 had increased trend of both FDI and export of India; but this trend increase with slow rate is due to 1997 Asian financial crisis and it pretends up to the year 2000. The Asian financial crisis is recovered in 1999 and the increasing trend of both FDI and export again pick up from 2001 to 2007 which is due to reduction in the policy restrictions. Due to the global economic crisis the FDI and export of the country again move towards decline trend from 2007 to 2010. In empirical literature there is ample number of studies that discussed the causality between FDI, export and economic growth. However few prominent studies that has been paying attention to the linkage between FDI, export and economic growth. Amongst earlier studies Hasiao and Hsiao (2006) examines the Granger causality between GDP, exports and FDI among eight rapidly East and Southeast Asian economies from 1986 to 2004. They compared with time series data causality and panel data causality test and finally they indicate that; the panel data causality has superior results over the time series causality analysis. Dritsaki et al. (2004) investigate the relationship between Trade, Foreign Direct Investment (FDI) and economic growth in Greece over the period 1960-2002. By using cointegration analysis they suggest that there is a long-run equilibrium relationship between these variables. Finally, the Granger causality test infer that there is a bilateral causal relationship between exports and economic growth; while there is a unidirectional causal relationship between foreign direct investments and economic growth with direction from foreign direct investments to GDP and also a unidirectional causal relationship between foreign direct investments and exports. Naveed and Shabbir (2006) study the impact of FDI and trade openness on per capita GDP from 1971 to 2000 for 23 developed countries and also they analyzed the Granger causality between them. They came to conclude that trade openness is positively affect the per capita GDP growth while FDI is

Causal Link between Foreign Direct Investment, Export and Economic Growth in India 135 appeared as insignificant. Finally, there exists unidirectional causality between trade openness to per capita GDP growth. Nwosa (2011) examines the nature of causality and the relationship between foreign direct investment (FDI), export, and economic growth in Nigeria over the period of 1981-2010. They conclude that there is no causality between FDI and GDP as well as export and GDP; but they finds there is a positive relationship between FDI, export and economic growth. Tekin (2011) investigates the causality between real GDP, real export and inward FDI in least developed countries. He found multiplicity of causality relations among the variables but only two LDCs namely the Central African and Liberia have no causality in any direction. Regarding the studies in India, Dash and Sharma (2011) examines the relationship between FDI, trade and economic growth in India. The results suggest that there is uni-directional causality between FDI and economic growth. Gharana (2012) inspect the causal relationship of GDP, trade and FDI over the period from 1971 to 2008 in India. The result support the export led and FDI led growth in post liberalization period. Goswami and Saikia (2012) examine the relationship between FDI and export in North East region of India. They found there is bidirectional causality between FDI and exports. Ray (2012) analyze the effect of FDI on economic growth in India by using cointegration as well as Granger Causality approach form the period 1990-91 to 2010-11. He concludes that there exists unidirectional causality between economic growths to foreign direct investment. Durairaj (2010) examines the causal nexus among Export, Economic Growth and Foreign Direct Investment (FDI) in India over the period 1992 to 2008. The result suggests there is bidirectional relationship between Export and Economic Growth and a unidirectional causal relationship from Export to FDI. Dash and Parida (2012) explore the linkages between inward FDI, services trade (export and import) and economic output of India both at the aggregate and at the sectoral levels (manufacturing and services). The empirical findings shows; there is presence of bi-directional causal relationship between FDI and economic output as well as between services exports and economic output in aggregate level and in sector wise, they find unidirectional causality from FDI and services exports to both manufacturing and services output. Chakraborty and Nunnenkamp (2008) studied the industryspecific FDI and output data in post reform India. They apply a panel cointegration technique and concluded that there is long-run and short-run dynamics of the FDI growth relationship. In the final step; they conducted aggregate Granger causality and suggested that there is feedback effects between FDI and output in both short-run and the long-run. The present study differs from previous study in three ways. First, this study uses a new dataset covering the period of 1978-79 to 2011-12. Second, contrary to the previous studies that employed either standard Granger Causality test or Granger Causality test based on the Error Correction Model (ECM), it employs Granger no causality approach developed by Toda- Yamamoto-Dolado-Lutkephol (TYDL). Third, we compare both Granger causality and TYDL causality test which is very first kind of study in the case of India. 2. THEORETICAL FRAMEWORK Economists have described Exports has an engine of growth, but the hypothesis of export-led growth has remained empirically ambiguous particularly for developing countries. Export growth is important because of its effect on internal trade and economic stability of an economy.

136 Debi Prasad Bal and Seba Mohanty Moreover, the rate of economic growth and the distribution of income and wealth in a country are closely related to export growth. According to neo classical point of view economic growth can be achieved through export promotion policies. But India has been described as an import substituting country per excellence [Rodrik (1996:15)]. So the focus of the export led economic growth debate is on whether India s trade policies should be export promotion or to import substitution. As compared to other countries; china is the most dominating country in export promotion policies. China s experience since the 1980s tends to support the argument that trade openness is a mechanism for achieving more rapid and efficient growth and better distribution of domestic resources (Findlay and Watson, 1996, p. 4). Krueger (1995), among others, identifies trade policy as the crucial element of economic policy. Export-led growth has been celebrated as the rational and efficient alternative to import-substitution, industrialization and inwardoriented strategies of economic development. The export-led growth strategy hypothesis stress that exports are the key factor in promoting productivity growth. The main explanations put forward for this relationship are:- (i) an expansion of exports may promote specialization in the production of export products and in turn it may boost the skills and productivity level, which lead to an increase in the level of national output, (ii) an increase in exports may relax the foreign exchange constraint (Chenery and Strout, 1966) and makes it easier to import inputs to meet domestic demand, which enable the output expansion, (iii) export promotion may also eliminate controls that result in an overvaluation of the domestic currency and (iv) export development of certain goods based upon a country s comparative advantage may allow the exploitation of economies of scale, which may lead to increased growth. FDI on the other hand is traditionally conceived of as an addition to the capital stock of the host economy (Brems 1970). The common beliefs among policy makers and academician is that FDI brings productivity gains, technology transfers, introduction of new processes, managerial skills and know-how in the domestic market, employee training, international production networks, etc. The main arguments in favour of FDI promoting economic growth are: (i) Multinational corporation (MNCS) is the principal vehicles for the international transfer of technology and the knowledge know-how. Besides, there is a strong complementary between FDI and Human Capital that contributes to economic growth (Borenztein, et al., 1998), (ii) The superiority of MNCS over local firms by means of output per worker leads to enhance the productivity level, and (iii) Emergence of a network type of organization expands the scope of interactions between MNCs and enterprises from host countries which will create international economic integration. And, (iv) Learning of improved management practices. Therefore, higher level of economic growth will be attained through efficient use of resources which would creates a market for the output produced, thus in turn attract higher levels of FDI. Though both export and FDI play an important role for countries economic growth but still in India their multichannel causal link is not explored mostly. 3. DATA SOURCES AND METHODOLOGY Annual time series for 1978-79 to 2011-12 data for Per capita GDP (as a proxy for economic growth), is collected from the Handbook of Statistics on Indian Economy published by Reserve Bank of India. The data of FDI has collected from World Development Indicator published by

Causal Link between Foreign Direct Investment, Export and Economic Growth in India 137 World Bank and the Export has collected from International Financial Statistics published by international Monetary Fund. We employed both Granger Causality and Toda and Yamamoto (1995), Dolado and Lutkephol (1996) and Rambaldi and Doran (1996), causality test for our study to show the causal relationship between FDI and Export with economic growth of India. Before estimating TYDL test, it is essential to know the stationary condition of the variables. Hence, the Dicky-Fuller and Augmented Dicky-Fuller test is explained below. We use the Granger causality test in our study. This is a well-known test for bivariate causality, which involves estimating a linear reduced-form vector autoregression (VAR). To check the Granger Causality for estimate the following equation: Y t = 0 + 1 Y t-1 + + p Y t-p + 1 X t-1 +..+ q X t-q + t (1) X t = 0 + 1 X t-1 +...+ p X t-p + 1 Y t-1 + + q Y t-q + µ t (2) We can use TYDL technique even the variables are stationary and cointegarted (Toda and Yamamoto; 1995: 227). The only condition of this method is that the order of integration should not exceed the true lag length of the model. TYDL Granger Causality test is a simple procedure which is estimation of an augmented, or over fitted VAR. it uses a modified WALD (MWALD) test to test for restrictions on the parameters of the VAR(p) model. Four steps are involved for constructing this procedure. The first step is to determine the maximal order of integration of the series (denoted as d max ). The determination of true lag length (k) of the VAR system by using AIC, SC, HQ, FPE criteria is the second step. The third step is estimating the unrestricted VARL (k + d max ) by using Seemingly Unrelated regression technique (SURE). The last step is to conduct standard WALD tests to the first k VAR coefficient matrix. To draw the inferences on Granger Causality while the coefficients matrix of last d max lagged vectors in the model are ignored. As shown by Toda and Yamamoto (1995), Dolado and Lutkephol (1996) and rambaldi and Doran (1996) it is enough to add extra and redundant lags for satisfy the standard asymptotic properties of the WALD statistics which follows 2 distribution. Therefore the TYDL enables the proposed MWALD statistics to test linear or nonlinear restrictions on this k coefficients matrix using the standard asymptotic theory (Fugarolas et al. 2007). By following Fugarolas et al. (2007) the specification tested is defined by the following five variables. The augmented VARL (k + d max ) system can be shown in the following matrix notation. LnYt 10 11, i12, i13, i LnY k ti k dmax LnD LnD LnX 11, j 12, j 13, j 21, j 22, j 23, j tt 20 21, i 22, i 23, i ti i1 jk1 31, j 32, j 33, j t 30 31, i32, i33, i LnXt i 31, j 32, j 33, j LnY t j e yt LnDt j ed t LnX e t j xt (3)

138 Debi Prasad Bal and Seba Mohanty Where, Ln Y t = Logarithm of per capita GDP as a proxy for Economic Growth Ln D t = Logarithm of Foreign Direct Investment, Ln X t = Logarithm of Export, The Granger Non causality hypotheses can be tested using MWALD on the following set of restrictions: (i) H 0 : 12,i = YD,i = 0 for all i k FDI does not Granger cause Economic Growth. (ii) H 0 : 13,i = YX,i = 0 for all i k Export does not Granger cause on Economic Growth. (iii) H 0 = 23,i = DX,i = 0 for all i k Export does not Granger cause FDI. 4. ESTIMATION AND RESULTS The first step is to know the stationary or non-stationary of the variables in order to avoid the spurious regression in time series analysis. Hence, the ADF (1979) tests are used and the results are present in table 1. Table 1 Results for the Unit Root Tests for the Variable under Study using ADF Test Variables Level 1 st Order Difference Inference on Integration LY -2.53 (0.30) -3.98 (0.019) I(1) LFDI -1.23 (0.64) -4.41 (0.001) I(1) LX -2.28 (0.43) -5.52 (0.000) I(1) Source: Author s Calculation; Note: the parenthesis shows the p-value. The Augmented Dicky- Fuller (ADF) test for the null of a unit root (Dicky and Fuller, 1979) shows that the GDP which is the proxy for economic growth (LY), Foreign Direct Investment (LFDI) and export (LX) are I(1) which means that the data of all the variables are stationary at first order difference which is presented on Table 1. In order to examine the direction among them; we examine the Granger causality test among the variables and the results is presented in Table 2. Table 2 Results of Granger Causal Relation Null Hypothesis F-Statistics P-Value LY does not Granger Cause LX. 5.83 0.007 LX does not Granger Cause LY. 2.12 0.13 LY does not Granger Cause LFDI. 2.68 0.06 LFDI does not Granger Cause LY. 1.37 0.27 LFDI does not Granger Cause LX. 2.29 0.12 LX does not Granger Cause LFDI. 3.23 0.05 Sources:Author s own Calculation.

Causal Link between Foreign Direct Investment, Export and Economic Growth in India 139 From the above table 2, it shows that there is unidirectional causal relationship between economic growth (LY) and export (LX), economic growth (LY) and Foreign Direct Investment (LFDI) and export (LX) and Foreign Direct Investment (LFDI). We are rejecting the null hypothesis of LY and LX, LY and LFDI and LX and LFDI at 5% level of significance. The optimal lag length is important to identify the true dynamics of the model. VAR system is used to determine the optimal lag length. We employed all the popular criteria to choose the optimal lag order and the results shown in the table 3. Table 3 Optimal Lag Order in the VAR System Lag LogL LR FPE AIC SC HQ 0-50.57630 NA 0.007142 3.571753 3.711873 3.616579 1 71.32375 211.2934* 3.86e-06* -3.954917* -3.394438* -3.775615* 2 78.45667 10.93714 4.47e-06-3.830445-2.849606-3.516666 3 87.13116 11.56599 4.82e-06-3.808744-2.407547-3.360489 4 97.63304 11.90213 4.84e-06-3.908869-2.087313-3.326138 * indicates lag order selected by the criterion LR: sequential modified LR test statistic (each test at 5% level) FPE: Final prediction error AIC: Akaike information criterion SC: Schwarz information criterion HQ: Hannan-Quinn information criterion From the above Table 2 it is clear that the lag order 1 is the optimal lag (k = 1) according to sequential modified LR test statistics, FPE, AIC and HQ information criterion. The LogL is the only exception which is not showing the significant results. Hence, we accept the judgment of the four criteria which indicates optimal lag order of 1. Thus, our augmented VARL is of order k + d max = 1 + 1 = 2. 4.1. Estimation of the Augmented VARL (2) System and the Results of Hypotheses Test We estimate the following VARL (2) systems by using Seeming Unrelated Regression (SUR) technique for the sample period of 1980-81 to 2011-12 and the coefficients are in matrix notation are in below: LnYt 10 yy,1 yd,1 yx,1 LnY,2,2,2 1 ti yy yd yx 2 LnDt 20 dy,1 dd,1 dx,1 LnDt i dy,2 dd,2 dx,2 i1 j2 LnX t 30 LnX xy,1 xd,1 xx,1 t i xy,2 xd,2 xx,2 LnY t j e yt LnDt j ed t LnXt j ex t (4)

140 Debi Prasad Bal and Seba Mohanty The next step is to test whether we will accept or reject the null hypothesis by using MWALD test. The results of MWALD test are shown in Table 4. From the above results in Table 4, shows that except economic growth does not Granger causes export others are not able to reject the null hypothesis. Table 4 TYDL Granger Non-Causality Test Null Hypothesis MWALD (df=1) Inference Economic Growth does not Granger Cause Export. 8.01 * (0.004) Rejected at 1% Export does not Granger Cause Economic Growth. 2.39 (0.12) Economic Growth does not Granger cause FDI. 0.07 (0.77) FDI does not Granger Cause Economic Growth. 1.51 (0.21) FDI does not Granger Cause Export. 1.50 (0.21) Export does not Granger Cause FDI 0.04 (0.82) Not RejectedNot RejectedNot RejectedNot RejectedNot Rejected Note: The p-values are shown inside parentheses. * shows significance at 1% level. 4.2. Comparing the Results of Granger Causality Test and TYDL Causality Test In order to compare the robustness of the Granger and TYDL causality test we check the longrun relationship among them with the help of Johensen cointegration test and the results are presented in Table 5. Unrestricted Cointegration Rank Test (Trace) Table 5 Johansen Cointegration Test Hypothesized Trace 0.05 No. of CE(s) Eigenvalue Statistic Critical Value Prob.** None 0.386019 23.90511 29.79707 0.2045 At most 1 0.225537 8.295767 15.49471 0.4343 At most 2 0.003651 0.117034 3.841466 0.7323 Trace test indicates no cointegration at the 0.05 level * denotes rejection of the hypothesis at the 0.05 level **MacKinnon-Haug-Michelis (1999) p-values Unrestricted Cointegration Rank Test (Maximum Eigenvalue) Hypothesized Max-Eigen 0.05 No. of CE(s) Eigenvalue Statistic Critical Value Prob.** None 0.386019 15.60934 21.13162 0.2485 At most 1 0.225537 8.178733 14.26460 0.3608 At most 2 0.003651 0.117034 3.841466 0.7323 Max-eigenvalue test indicates no cointegration at the 0.05 level * denotes rejection of the hypothesis at the 0.05 level **MacKinnon-Haug-Michelis (1999) p-values Notes: The choice of lag length (p) is very crucial, since the results are highly sensitive to alternative lag specifications. We chose an optimal lag length of 1 as suggested by Final Prediction Error, Hannan Quinn information criteria and Sims (1980) Likelihood Ratio test; Source: Author s calculation.

Causal Link between Foreign Direct Investment, Export and Economic Growth in India 141 Table 5 presents the results of cointegration. The results reveal that the trace statistics and maximum eigenvalue confirm the absence of cointegrating vector between Economic Growth, FDI and Export. Since there is no indication of co-integrating vector in Johansen approach, which clearly shows there is no long-run relationship among economic growth, FDI and export. Although there is no long run Granger causality because of no cointegrating vector, but there may exist short run causality in a reduced VAR framework. Forecast error variance decomposition is important for examining the shocks in variable movements. We are interested in discussing the impact of shocks such as FDI and Exports on Economic Growth. Table 6 illustrates the decomposition of forecast error variance. The significance level of the results is indicated by standard errors. The results indicate that around 100 per cent variation in economic growth is explained by own shock, whereas, no variation is explained by either foreign direct investment or export. But the results are somewhat different in case of FDI and export. Around 99 per cent variation in FDI is explained by its own shock at 1-step ahead horizon, whereas, 16 per cent is explained by economic growth and no variation is explained by export. Similarly, at 10-step ahead horizon, 8.39 percent variation in FDI is explained by economic growth, 84.73 per cent by its own shock and 6.87 per cent variation is explained by export. Table 6 Forecast Error Variance (in percentage) By Innovations in Variables Explained Year Standard Error Y FDI X Economic Growth (Y) 1 0.03 100 0.00 0.00 3 0.06 90.93 4.74 4.31 5 0.09 85.51 5.69 8.78 7 0.12 82.08 5.82 12.08 10 0.15 78.44 6.50 15.05 FDI 1 0.59 0.16 99.83 0.00 3 0.80 0.75 97.70 1.54 5 0.85 3.99 90.44 5.55 7 0.87 6.32 87.33 6.34 10 0.90 8.39 84.73 6.87 Export (X) 1 0.08 12.66 1.15 86.18 3 0.14 45.22 1.70 53.04 5 0.18 51.18 8.53 40.28 7 0.22 54.55 9.65 35.78 10 0.25 58.64 8.62 32.72 Source: Author s calculation Similarly, 12.66 per cent variation in export is explained by economic growth and 86.18 per cent is explained by its own shock at 1-step ahead horizon. In 10 th step it increases from 12.66 to 58.64 per cent variation in export is explained by economic growth which indicates there exit a strong short run relationship among economic growth to export. The variation of export is least explained by FDI from 1-step to 10-step ahead. Similarly, the variation of FDI is least explained by economic growth and export from 1-step to 10-step.

142 Debi Prasad Bal and Seba Mohanty Thus, from the above error variance table 6 it shows there is strong relationship between economic growths to export and there is negligible relationship among other variables. The present study also finds the same kind of results from TYDL Causality test, i.e., there is unidirectional relation exit between economic growths to export and there is no relation among other variables. But at the same time we can t able to reject the null hypothesis of the unidirectional relationship between economic growth to FDI and export to FDI. Therefore our result strongly supports TYDL Causality test and this method is better method over Granger Causality test. 5. CONCLUSION This paper examines the causal relation between FDI, export and economic growth in India over the period of 1978-79 to 2010-11. We show the relationship among them by using both Granger causality and TYDL causality test and then we compare both the causality test by using the cointegration and variance decomposition technique. The Johensen cointegration test suggest that there is no long run relation among them but the variance decomposition results clearly reveal that there exist unidirectional causality between economic growth and export in India. Although the Granger causality test shows there is causal relation between economic growth to export but also it shows there exist unidirectional causality between economic growth to FDI and export to FDI too. Further, we examine with the same data period and implemented the TYDL Granger non causality test and concluded that there only exist unidirectional causal relation among economic growth and export in India. In the final step; we compare both Granger causality test and TYDL no Granger causality test with the results from variance decomposition method. Hence, from the results of variance decomposition method we conclude that, only export is more explained by economic growth and others are least variation among them. Hence, our results from TYDL Granger no causality test corroborate with the results from variance decomposition methods. Therefore, our results concluded that TYDL method is better technique to shows the casual relationship among them. The aforementioned findings have important implications for the formulation of long term growth path in India. As India is a developing economy so India needs more to invest in both primary, secondary as well as service sectors. As these sectors grow which will be path to grow in export of the country. Therefore from policy point of view our study supports that India should give more priority to developed overall economic growth which will simultaneously improve the export of the country. Although the TYDL causality test does not support there is no relationship between FDI as well as export and economic growth but variance decomposition method explained there is least variation among them. References Ahmad, N., Hayat, M.F., Luman, M. and Ullah, S. (2012), The Causal Links between Foreign Direct Investment And Economic Growth in Pakistan, European Journal of Business and Economics, Vol. 6. Borensztein, E., De Gregorio, J. and Lee, J. W. (1998), How Does Foreign Direct Investment Affect Economic Growth? Journal of International Economics, Vol. 45 (1), pp. 115-135.

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