Impact of FDI inflows on the exports of India

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.

Figure 1: FDI inflows as a % of GDP and exports from India (Source: World Bank)
Figure 1: FDI inflows as a % of GDP and exports from India (Source: World Bank)

Empirical analysis

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:

  1. FDI as a percentage of Gross Domestic Product (GDP).
  2. Exports of goods and services as a percentage of GDP.

Tests applied

Name of the Test



Unit Root TestTo check stationarity in the dataFDI, Exports
Johansen Cointegration TestTo check long-run relationshipFDI, Exports
Granger Causality TestTo determine the direction of causalityFDI, Exports
Time Series RegressionTo determine the impact of FDI on exportsFDI, 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 ADF unit root test have been given in the table below.

Series(ADF) t statisticADF at 1% LevelADF 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 unit root test in table 2 confirm that both the variables are non-stationary at level. The variables become stationary after first differencing. Therefore we can investigate longthe -run relationship between them.

Co-integration test

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.

Maximum Ranks

Trace Statistic

5% Critical Value

Max Statistic

5% Critical Value


Table 3: Johansen Co-integration Test (Trace and Max Value stat) results

Johansen test is based on maxithe mum likelihood method and is based on two statistics:

  1. Eigen value statistic and
  2. 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 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.

Null Hypothesis



FDI does not Granger cause  Exports9.87730.0005*
Exports does not Granger cause FDI4.79190.0156*

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.

Regression analysis

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.

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.
Umer Jeelanie Banday
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