The previous article empirically examined the relationship between Foreign Direct Investment (FDI) inflows and the rate of inflation in the Indian economy. The aim of this article is to investigate the impact of FDI inflows on the exports of India. The relationship between FDI and exports has been of great interest to researchers especially after the economic liberalisation in 1991. The FDI boost is crucial in order to grow capital, innovation, administrative know-how, marketing skill with access to global markets (UNCTAD, 2003). The figure below shows the trend of FDI inflows and export growth in India since 1980. The trend indicates that an increase in FDI is associated with the increase in exports post-reform until 2002. There is volatility between the years 2004 to 2013 in FDI inflows. exports also exhibit ups and downs.
Data and study period
World Bank website is the source for the annual time series data for FDI inflows and exports, for the period 1980 to 2016. Following are the variables:
- FDI as a percentage of Gross Domestic Product (GDP).
- Exports of goods and services as a percentage of GDP.
Name of the Test
|Unit Root Test||To check stationarity in the data||FDI, Exports|
|Johansen Cointegration Test||To check the long-run relationship||FDI, Exports|
|Granger Causality Test||To determine the direction of causality||FDI, Exports|
|Time Series Regression||To determine the impact of FDI on exports||FDI, Exports|
Table 1: Tests applied for the Empirical Analysis
For the regression analysis, export is the dependent variable and FDI is the independent variable. Moreover, the basic model to find out the impact of FDI inflows on exports.
Exports = f(FDI)
The aim is to test the null hypothesis that FDI has no impact on exports.
Unit root test
Before estimating the equation it is important to check the stochastic properties of the variables. For this Augmented Dickey-Fuller unit root test is useful. After checking the stationarity, Johansen co-integration helps to estimate the long-run relationship. The results of the ADF unit root test have been given in the table below.
|Series||(ADF) t statistic||ADF at 1% Level||ADF at 5% Level|
Table 2: Augmented Dickey-Fuller Unit Root Test Statistics
Note: A variable is stationary when the ADF t-statistics is greater than the critical values and non-stationary when t-statistics is less than the critical value.
The results of the unit root test in table 2 confirm that both the variables are non-stationary at the level. The variables become stationary after first differencing. Therefore we can investigate long the -run relationship between them.
Johansen co-integration test is used between the variables in the empirical model because it takes into consideration the possibility of multiple co-integrating vectors.
5% Critical Value
5% Critical Value
Table 3: Johansen Co-integration Test (Trace and Max Value stat) results
Johansen test is based on maxi the mum likelihood method and is based on two statistics:
- Eigenvalue statistic and
- max statistic.
When the rank is zero, it means there is no co-integration relationship and if the rank is one, it means there is one co-integration equation and so on. The above results of Johansen co-integration are based on lags 2 and the trend is constant. The results of both trace and max statistic suggest that there exists a long-run association between export and FDI, meaning both the variables are moving in the same direction in the long-run.
Granger causality test
This test is to estimate the causality between FDI inflows and exports. The results are presented below.
|FDI does not Granger cause Exports||9.8773||0.0005*|
|Exports do not Granger cause FDI||4.7919||0.0156*|
Table 4: Granger Causality between FDI and exports
Based on the p-values, both the null hypotheses are rejected at 5% level of significance. It implies bidirectional causality. The reverse causality holds in light of the fact that FDI Granger causes exports and vice versa. The results indicate that if FDI inflow increases, exports will increase and on the other hand it will increase the FDI inflows.
The linear regression model examines the impact of FDI inflows.
Table 5: Regression Coefficient of FDI
Note: Superscripts “*” denote 1% and 5% significance
The table above gives the regression results between FDI and exports. The results reveal that an increase in FDI will increase and validate FDI-led exports growth hypothesis. The coefficients show that a 1 percent increase in FDI will cause an increase of 6.558 percent in exports. Furthermore, the p-value is statistically significant at 1% level of significance. Therefore the null hypothesis of no impact of FDI on exports can be rejected.
The positive impact of FDI inflows on exports
FDI is instrumental in literature for its potential for creating export-led economic growth. The results of cointegration analysis in this article found that there exists a long-run relationship between FDI inflows and exports. Granger causality tests showed reverse causality relationship between the variables. Further, regression results implied that FDI has a positive and statistically significant impact on exports. FDI enhances the profitability development through different means such as empowering the selection of remote innovations, use of economies of scale and developing stable macroeconomic conditions through expanding employment, work efficiency, capital gains and foreign exchange reserves. To sum up, the government ought to empower more capital oriented FDI inflows to directly affect growth.
- UNCTAD (2003) ‘World Investment Report 2002: Transnational Corporations and Export Competitiveness’, United Nations. New York and Geneva, pp. 1–350. doi: 10.1016/S0969-5931(03)00022-2.