Determinants of Global Direct Investment in BRICS Economies

Due to Globalization and liberalization in the trade policy all the countries are coming together and they are investing in other countries in the form of foreign direct Investment, Foreign Portfolio Investment, foreign Institutional Investors and many different forms. In developing countries there has been remarkable shift in attitude towards attracting FDI .Capital flows in these countries from developed countries have become important source for the growth of the economy .More capital inflow will lead to more job creation in the country which will increase the employment rate in the country which in turn will increase the per-capita income of the country and this will lead to economic growth of the country. FDI has played a significant role in the growth and development of world economy particularly in developing countries like all the BRICS nation because it links the host economy with globalized markets and foster‘s economic growth (suvranshu pan, 2007).FDI is playing a supportive role to most of the recipients and host countries whether it is advanced, developing or a poor country. Capital flows in the form of FDI have been widely believed to be an important source of growth in recent years (suvranshu pan, 2007) .In the earlier approaches , the impact of FDI on growth was found to be limited in the short-run since long-run growth was largely considered to be contingent upon technological process (Helpman, 1991) . Empirical investigation have found that the positive impact of FDI is generally higher for recipient countries with higher level of development .Such findings shows that FDI has positive impact on the economic growth of the country (suvranshu pan, 2007).All the markets in open economy are closely related to each other, so change in any market activities will influence the activities of other activities. So, change in trade activities will influence the economic growth of the country. To find out the relationship between financial markets and goods market, national income in terms of savings and investment as

Where Y= national income, C= national consumption, I= national investment, G=Government expenditure (X-M) = net exports. Therefore, S – I = (X – M) means an economy‘s net exports must always equal the difference between savings and investment. Difference between national savings and national investment is known as Net Foreign investments. Net Foreign Investment and national income have direct relationship means increase in Net FDI will increase national income and vice-versa .Increase in exports will increase the inflow of money supply which in turn will increase the GDP of the country that will have positive impact on the economic growth. National Income account shows that net FDI always equals trade balance (suvranshu pan, 2007) . There may be several

growth.

Jayachandran and Seilan (2014) investigated the relationship between trade, Foreign Direct Investment (FDI) and economic growth of India over the period 1970-2007. The results of Granger causality test shows that there is a causal relationship between the examined variables. The direction of causality relationship is from FDIs to growth rate and there is no causality relationship from growth rates to FDIs. Most of empirical studies carried out in the past used multi regression model to study the impact of flow of FDI & FII.Sharma and Nishant (2014) paper attempts to examine the association between inflow of foreign direct investment and gross domestic product of BRICS countries viz., Brazil, Russia, India, China and South Africa for a period of 20 years from 1993 to 2012. Granger casualty test confirmed the results of co-integration and found that in Russia neither GDP Granger cause FDI nor FDI Granger cause GDP. For rest four nations unidirectional relationship was observed. In India and China GDP granger caused FDI whereas in Brazil and South Africa FDI Granger cause GDP.Ayyanar and Jayachandra (2014) paper provides an overview of FDI inflows and Trade in India and China. It shows that FDI and Trade is the fastest growing in India and China, contributing significantly to GDP, GDP growth, employment, trade and investment. In the result of regression co efficient of simple linear and semi log linear model implies that India and China had a possibility for positive relationship on Foreign Direct Investment and it plays a significant role in enhancing the level of economic growth. Import performance of China shows that regression co efficient in both the models are insignificant, so China had negative impact on their Import performance. Export performance of India shows the regression co efficient on both the models are insignificant. There is also a negative impact on India‘s Export performance. Sharma& Kaur (2013) paper examines the causal relationships between FDI and trade(Exports and Imports) in India and China. Granger causality test has been employed to examine the causal relation between FDI and trade by using the data over the period of 1976-2011.The results for China show unidirectional causality running from FDI to imports and FDI to exports, however, there exist bidirectional causality between imports and exports. India gives the results which are not similar to China where bidirectional causality between FDI and imports; FDI and exports; and exports and imports have been found.Zafar Ahmad Sultan (2013) examines the nature of relationship between export and FDI in India over the period 1980-2010.Using Johansen co-integration method, the paper finds a stable long run equilibrium relationship between FDI and export growth. The result of Granger causality based on however, neither export Granger cause FDI inflow nor FDI inflow Granger cause export from India.Ling Foo Sien (2013)examine empirically FDI and economic growth in the Brazil ,Russia, India, and China (BRIC) countries. By using annual time series data for the period 1980 to 2011, empirical analysis shows that FDI plays unambiguous role in contributing to economic growth in BRIC countries using VECM and Granger Causality on VECM method .

Statistical Techniques. Statistical tools used in this objective are Unit Root Test, Cointegration, VECM and Granger Causality.

Empirical Results - Global foreign direct investment (FDI) inflows fell by 16per cent in 2014 to $1.23 trillion, down from $1.47 trillion in 2013 (UNCTAD, 2015). FDI flows to developed countries dropped by 28 percent to $499 billion (UNCTAD, 2015).As per the UNCTAD Investment Report, 2015 , China become the largest recipient of FDI inflow in 2014. The graph in figure 1 shows that inflow of FDI in China is highest as compared to other BRICS economies . Inflow of FDI to other BRICS countries are also improving from the previous period . Inflow of FDI and increase in export will positively impact the economic growth whereas increase in import will have negative impact on the economic growth . There is almost 145% increase in the inflow of FDI in Brazil , Russia FDI inflow increase is about 77% but as a % of GDP increase in Brazil FDI inflow is about 272% , Russia it is 120% , India increase is about 133% , although there is sharp increase in the inflow of FDI in China but there is almost 36% decline in the inflow of FDI as a % of GDP. As per the UNCTAD Investment Report , 2015 among BRICS , China is as a proxy to measure the economic growth. It measures the economy total production in goods and services. It is the total income generation and spending of the government in the domestic boundary. Exports of goods and services will lead to income generation for the country .Thus it will have positive impact on the economic growth. Imports on other hand will lead to income generation for the foreign countries .So it will create negative impact on the economic growth .Increase in exports and FDI and decline in imports will increase the GDP which will and there will be more economic growth .The econometrics model used to know the causal relationship among exports, imports, FDI and GDP can be assessed by using granger causality test. Before going for the granger causality, stationarity of data will be checked by using Unit Root Test. To check the stationarity ADF (Augmented Dickey Fuller) Test and PP-test (Phillip‘s – Parron) test is used. The ADF test includes the extra lagged terms of the dependent variables in order to eliminate autocorrelation (Sridhar an 2009). The ADF test statistic has the same asymptotic distribution as the Dickey Fuller (DF) statistic, so same critical values can be used. (Meerza)

Source: Author Compilation

Cointegration is a regression of a Unit root test time series on another Unit root Time series. Cointegration test shows the long term relationship between the variables. Cointegration means that despite being individually non stationary, a linear combination between two or more time series can be stationary (Meerza).Dynamic relationship among the variables are measured using Cointegration analysis. The multivariate Cointegration test based on Johansen - Julius is used to determine the long run relationship (Miankhel, Thangavelu and Kalirajan 2009). The testing hypotheses are the null of non-cointegration against the alternative that is the existence of Cointegration by using the maximum likelihood procedure (Johansen and Juselius 1990). Cointegration is the property of data in time series where variables share common stochastic drift .Johansen Test relies on two types of tests, Eigenvalue and Trace statistics .The Cointegration analysis is multivariate analysis as there cannot be relation between one variable. Result of Cointegration in Table 2 is analyzed on the non-stationary data to find out the relationship among the variables. Trace test and Max eigenvalue probability at 5% significance level will help to determine whether variables are cointegrated or not . Reject the null hypothesis for all the countries by looking at trace statistics probability value at 5% significance level which shows that there is Cointegration among the variables. In case of Brazil two variables are co integrated as the probability

that there is long run relationship among GDP and exports of Brazil.

Max Eigenvalue probability also shows two Cointegration equations by rejecting the null hypothesis. In case of Russia more than two variables are cointegrated as trace stats p -value and Max Eigenvalue for At most 2 Cointegration equation are less than 0.05 .So, null hypothesis will be rejected at 5% level of significance which shows that there is long run equilibrium relationship among export , FDI and GDP . Eigenvalue result shows the positive relationship among FDI, export and GDP. In case of India only export and FDI are positively correlated with the probability value of trace statistics 0.0392 which reject null hypothesis at 5% level of significance. In China also only FDI and exports are positively correlated In South Africa export and GDP are positively correlated. The correlation among the variables shows the long term relationship among these variables. In all the countries there is at least one variable which is correlated. Result of Trace statistics and max eigenvalue of Cointegration test based on Johansen co integration reject the null hypothesis that there exists no co integration at 95% confidence interval. This shows the long run relationship among the non-stationary variables and so Vector error correction model (VECM) model should be used

The study confirms the existence of correlation among the variables which shows the long run relationship among the variables. So VECM model will be applied for the evaluation of short run properties of the cointegrated variable. In case of no cointegrated equation, it shows the short run relationship between variables and no need for VECM analysis and to directly proceed for Granger – Causality. Cointegration rank in VECM shows the number of factors between the independent variables and the dependent variable will give rise to a stable long run relationship between the variables. (Fadli Fizari Abu Hassan Asari, 2011). VECM helps us to know that the past value of independent variable have impact on the present value of dependent variable.

Denote significance level *** at 1%, ** at 5% and * at 10%

Denote significance level *** at 1%, ** at 5% and * at 10%

Denote significance level *** at 1%, ** at 5% and * at 10%

Result of Vector Error Correction Model in Table.4. is done using intercept and no trend in linear trend deterministic trend specification. Co-efficient of the variable in the result shows that whether today‘s value is affected by the past value of that variable. Cointegration result shows that in all countries at least one variable is cointegrated, so VECM is applied with one or two cointegrating factor and two lags in each country equation has been estimated. Selection of lag length criteria is based on AIC and SICS value. From the result 3 it can be said that for Brazil lag 1 of expo is significant at 1%, lag1 of differenced FDI and lag1of differenced import is significant at 5% and 1% respectively. The coefficient value of expo is positive with the convergence speed to its equilibrium is 3.25%. It means that in short run exports are adjusted by 4.5% of deviation from its equilibrium from the past quarter. It shows that increase in export in last quarter by 1% will increase the GDP by 3.25% in present quarter. The negative coefficient value of lag difference of FDI which is significant at 5% level of significance shows long term relationship with GDP .The negative coefficient and significant variables shows that there is causal relationship among the variables in the long run. Joint F-test of coefficient of independent variable shows the short run relationship among the dependent and independent variable. Result of Table.4. shows that lag of GDP of past two quarter is significant. The coefficient value of GDP is negative with the convergence speed to its equilibrium is -0.48 %. It means that in short run GDP are adjusted by 4.8% of deviation from its equilibrium from the past two quarter. There is short run relationship among change in GDP itself. Table 5 shows the VECM result of India which also shows that short run GDP are adjusted by 3.5% of deviation from its equilibrium from the past two quarter. There is short run relationship among change in GDP itself. Table6 shows the result of China where the variables which have short run relationship between lag 1 and 2 of export.

The Granger causality test assumes that the information relevant to the prediction of the respective variables is contained solely in the time series data on these variables (Gujarati 1998). Although Cointegration shows the long run relationship among the variables but direction of causal relation is not specified in Cointegration. So, to know the direction of causal relationship or long term movement, granger causality test is used. Granger causality uses chi-square statistics and probability value to know the direction in which the variables cause each other. Hypothesis is checked using F-statistics value. Chi-Square statistics and probability values constructed under the null hypothesis of non-causality show that

Above tables shows the granger causality pair-wise analysis result. Probability value at 1% and 5% level of significance denotes the rejection of null hypothesis. In Table 3.8 in case of Brazil it is found that FDI granger cause export and export also granger cause FDI which shows bi-directional relationship among them. GDP granger cause expo but expo does not cause GDP which shows uni-directional relationship. There is also bi-directional relationship between import and export and import and FDI as both causes each other. There is uni – directional relationship among GDP and import as only GDP cause import. There is no relationship between GDP and FDI which shows that change in FDI in short period does not have any effect on GDP of Brazil. In case of Russia there is bi-directional relationship between GDP and expo , Import and expo, import and FDI and between import and GDP. There is uni-directional relationship between GDP and FDI in Russia and no-directional relationship between FDI and expo which shows that change in expo performance in short run will not affect the FDI inflow in Russia. In India analysis result there is no bi-directional relationship. There is uni-directional relationship FDI and expo, GDP and expo and FDI and import which shows that all the variables have short run relationship with FDI which shows that change in FDI in short period will cause change in import, export as well as GDP. Result also shows that there is no relationship among GDP and export, Import and export and GDP and import. It shows that India‘s GDP is not affected by short run change in import or export. In case of China there exist bi-directional relationship among import and export only. There is uni-directional relationship exist also among one variable only that is between export and GDP and the other variable does not cause any variable. It shows that there is short term association between export and import and export and GDP which shows that in China change in export for a short period will cause variation in their GDP causes export, export causes import, import causes FDI and GDP causes import. Rest of the variable does not cause each other which shows that short run changes among them does not impact each other.

• In Brazil it is found that FDI granger causes export and export also grangers cause FDI which shows bi-directional relationship among them. GDP granger cause expo but expo does not cause GDP which shows uni-directional relationship. There is also bi-directional relationship between import and export and import and FDI as both causes each other. There is uni – directional relationship among GDP and import as only GDP cause import. • In Russia there is bi-directional relationship between GDP and export, Import and export, import and FDI and between import and GDP. There is uni-directional relationship between GDP and FDI in Russia and no-directional relationship between FDI and expo which shows that change in expo performance in short run will not affect the FDI inflow. • India result shows there is no bi-directional relationship. There is uni-directional relationship FDI and export, GDP and expo and FDI and import which show that all the variables have short run relationship with FDI which shows that change in FDI in short period will cause change in import, export as well as GDP. Result also shows that there is no relationship among GDP and export, Import and export and GDP and import. • China there exist bi-directional relationship among import and export only. There is uni-directional relationship exist also among one variable only that is between export and GDP and the other variable does not cause any variable. There is short term association between export and import and export and GDP which shows that in China change in export for a short period will cause variation in their GDP.

Growth, Import, Export and FDI of BRICS economy quarterly data is used from 2000 to 2015. The study is done individually for all the BRICS countries individually. In Brazil it is found that there is bi-directional relationship among FDI and Export and Import and Export which shows that short run changes among these variables impact each other. In Russia there is bi-directional relationship between GDP and Export , Import and Export, Import and FDI and between import and GDP. In India it shows that there is no variables which impact each other .There is only uni-directional relationship FDI and Export, GDP and Export and FDI and Import which shows that these variables have short run impact. China there exist bi-directional relationship among Import and Export only which shows that there is short run relationship among Export and Import of China. In South Africa it shows that there is no variable which impact each other .There is only uni-directional relationship among some variables that is GDP causes Export, Export causes Import, Import causes FDI and GDP causes Import.

Fadli Fizari Abu Hassan Asari, N. S. (2011). A Vector Error Correction Model (VECM)Approach in Explaining the Relationship Between InterestRate and Inflation Towards Exchange Rate Volatility in Malaysia. World Applied Sciences Journal, pp. 49-56. Meerza, S. I. Causal links between trade, foreign direct investment and economic growth for Bangladesh. Mun, H. W. (2008). FDI and Economic Growth Relationship: An Empirical Study on Malaysia. International Business Research, 1, pp. 11-18. P, S. (2009). Causal Relationship between Foreign Direct Investment and Growth: Evidence from BRICS Countries. International Business Research, 2(4), pp. 198-203.

Research Scholar