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

**Foreign Direct Investment (FDI)** is a vital catalyst for economic growth. Over the years, **FDI** has developed impressively in its significance for Indian economy especially after liberalization. The previous article highlighted the importance of * Gross Domestic Product (GDP)* as one of the main indicators of economic performance of a country. The aim of this article is to empirically analyse and investigate the impact of

**FDI**inflows on

*in India after establishing long-run association and causality between these two variables.*

**GDP**## 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

*in both pre-reform (1980 – 1991) and post-reform (1991 onwards).*

**GDP**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

*is*

**GDP**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**andare examined by performing unit root test.**GDP** - 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 ADFt-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

*. The results are presented below.*

**GDP**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

*does not Granger Cause*

**GDP****FDI**can be rejected. It implies a bidirectional causality. The reverse causality holds in light of the fact that

**FDI**Granger causes

*and vice versa. The results indicate that if the*

**GDP****FDI**inflow increases, economic growth will enhance in the form of increased

*. On the other hand, the increase in*

**GDP***will foster more*

**GDP****FDI**inflow.

## Regression analysis

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

GDP |
Coef |
t-value |
P-value |
R^{2} |

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

*and validates*

**GDP****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

*Granger causality tests find reverse causality relationship. Regression results imply that*

**GDP.****FDI**has a positive and significant impact on

*. The fact that India’s limit as a host country in drawing*

**GDP****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.

## Discuss