Analysis to find the impact of FDI inflows on the GDP of India

By Umer Jeelanie Banday & Saptarshi Basu Roy Choudhury on July 30, 2018

Foreign Direct Investment (FDI) is a vital catalyst for economic growth. Over the years, FDI has developed impressively in its significance for the Indian economy, especially after liberalization. The previous article highlighted the importance of Gross Domestic Product (GDP) as one of the main indicators of the economic performance of a country. This article aims to empirically analyse and investigate the impact of FDI inflows on GDP in India after establishing a long-run association and causality between these two variables.

FDI inflows started increasing significantly since 2000 and reached its peak in the year 2008-2009

Empirical analysis

The source for annual time series data for GDP and FDI inflows for the period 1980- 2016 data is the World Bank. Therefore the chosen time period allows us to take into consideration the impact of FDI inflows on GDP in both pre-reform (1980 – 1991) and post-reform (1991 onwards).

For the regression analysis, GDP is the dependent variable whereas FDI is the independent variable. The basic model to find out the impact of FDI on GDP is

 GDP =  f(FDI)

Testing the null hypothesis that FDI has no impact on GDP.

Name of the Test	Objective
Unit Root Test	To check stationarity in the data
Johansen Cointegration Test	To check the long-run relationship
Granger Causality Test	To determine the direction of causality
Time Series Regression	To determine the impact of FDI on GDP

Table 1: Tests applied for the Empirical analysis

First, time series properties of FDI and GDP are examined by performing unit root test.
Second, the Johansen cointegration test helps to check the existence of the long run relationship between them.
Third, the Granger causality test helps to determine the direction of causality.
Fourth, linear time series regression will determine the impact of FDI inflows on GDP.

While the first three tests are done using EVIEWS software, the linear regression is done using STATA.

Checking stationarity in the data

As the first step of the empirical analysis, it is essential to check the stationarity in the data of the variables before examining the impact. The null hypothesis for the test is that there is a unit root and the time series is non-stationary. On the other hand, the alternative hypothesis is that the series is stationary. The results of Philips-Perron (PP) and Augmented Dickey-Fuller (ADF) unit root tests have been presented in the table below.

Series	(PP ) t statistic	PP at 1% Level	PP at 5% Level	(ADF) t statistic	ADF at 1% Level	ADF at 5% Level
GDP	19.3563	-3.6267	-2.9458	-13.7263	-3.6267	-2.9458
ΔGDP	-10.2849	-3.6394*	-2.9511*	-5.5716*	-3.6537*	-2.9571*
FDI	-1.6337	-3.6267	-2.9458	-3.6267	-3.6267	-2.9458
ΔFDI	-5.9220	-3.6329*	-2.9484*	-3.1971*	-3.6463*	-2.9540*

Table 2: Augmented Dickey-Fuller and Phillips–Perron Unit Root Test Statistics.

NOTE

A variable is stationary when the Phillips–Perron (PP) and 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 the table above confirm that both the variables are non-stationary at level. Therefore this means the null hypothesis can’t be rejected. The variables become stationary after first differencing to investigate the long-run relationship among them. The null hypothesis of the existence of unit root or non-stationarity in the data can be rejected at the first difference.

Co-integration test

Johansen co-integration test shows the long run association between the variables. When the variables are co-integrated, they have a long run equilibrium relationship. When the dependent and independent variables are non-stationary at the level, it means that the variables are co-integrated. The results are shown in table 3 below.

Maximum Ranks	Trace Statistic	5% Critical Value	P-Value	Max Statistic	5% Critical Value	P-Value
0	28.7537	15.4947	0.0003*	25.8236*	14.2646	0.0005*
1	2.93008	3.84146	0.0869*	2.93008*	3.84146	0.0869*

Table 3: Johansen Co-integration Test (Trace and Max Value stat). Results for GDP and FDI

Johansen test relies on the maximum likelihood method and on two statistics: Eigenvalue statistic and the maximum 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 the Johansen co-integration test imply that there is co-integration between the two variables. The results of both trace and max statistic suggest that there is a long run association between FDI and GDP.

Granger causality test

Next, attempt to estimate the causality from FDI to GDP and vice versa. Applying Granger causality to check the robustness of the results and detect the nature of the causal relationship between FDI and GDP. The results are presented below.

Equation	Chi2	Prob
GDP to FDI	2.85864	0.0403*
FDI to GDP	3.04565	0.0320*

Table 4: Granger Causality between FDI and GDP

The above table presents the results of the Granger causality test. Based on the p-values, both the null hypotheses that FDI does not Granger Cause GDP and GDP does not Granger Cause FDI can be rejected. It implies a bidirectional causality. The reverse causality holds in light of the fact that FDI Granger causes GDP and vice versa. The results indicate that if the FDI inflow increases, economic growth will enhance in the form of increased GDP. On the other hand, the increase in GDP will foster more FDI inflow.

Regression analysis

Developing the linear regression model to study the impact of FDI on GDP.

GDP	Coef	t-value	P-value	R²
FDI	3437.45*	5.37	0.000	0.4514
Cons	3.32476*	7.34	0.000

Table 5: Regression Coefficient of FDI

NOTE

Superscripts “*” denote 1% and 5% significance

The table above gives the regression results between GDP and FDI. The results reveal that an increase in FDI will increase GDP and validates FDI led-growth hypothesis. The coefficients show that for a, 1% increase in FDI there will be a statistically significant increase in GDP.

The null hypothesis stating that FDI has no impact on GDP can be rejected at 1% and 5% level of significance.

A positive relationship between FDI and GDP

FDI inflows have assumed a huge role in the development and advancement of an economy, especially in India. GDP of India has been growing four-crease since 1991. The results of cointegration analysis in this article reveal that there is a long-run relationship between FDI inflows and GDP. Granger causality tests find reverse causality relationship. Regression results imply that FDI has a positive and significant impact on GDP. The fact that India’s limit as a host country in drawing FDI took off in the post-reform period supports the findings. However, the quantum of FDI inflows in respect to its size has been low when compared with other developing nations.

Fundamental explanations behind these low FDI inflows have been identified with the venture atmosphere, poor foundation, remote conversion scale variance and business help. Be that as it may, amid pre-reform period FDI expanded at CAGR of 19.05% while amid post-reform period it has developed at 24.28%. The results of the empirical analysis in this article reveal that FDI has a significant and positive relationship with GDP. It can be inferred that FDI is important for socio-economic development for India. The next article will empirically examine the impact of FDI on Indian rate of inflation.

References

Dickey, D. A., & Fuller, W. A. (1981). Likelihood Ratio Statistics for Autoregressive Time Series with a Unit Root. Econometrica, 49(4), 1057. https://doi.org/10.2307/1912517.
Gujarati, D. (2004). Basic Econometrics, 3rd Edition. New York: McGraw-Hill,2004. New York. https://doi.org/10.1126/science.1186874.
Phillips, P., & Perron, P. (1988). Testing for a unit root in time series regression. Biometrika, 335–346. https://doi.org/10.1093/biomet/75.2.335.