The previous article empirically examined the impact of **Foreign Direct Investment (FDI)** inflows in India on its GDP. The rate of growth of GDP mainly reflects the growth performance of the country. Since growth and development are related concepts in Economics, this article attempts to relate **FDI** to a development indicator. In particular, this article investigates the impact of **FDI** inflows in India on the reduction of poverty. It examines whether **FDI** has a positive relationship with per capita income.

## Per capita income as a proxy for poverty

**FDI** can be related to development in various ways. In respect of different types of advancing private sector investment, **FDI** enhances corporate administration. **FDI** can help enhance natural and labour living standards by generating taxes that support to reduce income inequality. Furthermore, **FDI** inflows help to bring advanced technologies and create employment opportunities. This, in turn, improves the standard of living by increasing per capita income (Islami, Mulolli, & Skenderi, 2016). This article contends that **FDI** is a key element for economic development.

The figure below shows the trend between per capita income and **FDI** for the period 1980 -2016. In recent years, per capita income has increased with an increase in **FDI** barring the global financial crisis period.

## Empirical analysis

#### Data and Study Period

The annual time series data for **FDI** and per capita income as a proxy of poverty is taken from World Bank for the period 1980 to 2016. For undertaking the empirical analysis, the World Bank data for the following variables are used.

(a) Foreign Direct Investment as a percentage of GDP

(b) Per capita income as a percentage of GDP

#### Tests applied

## Name of the Test |
## Objective |
## Variables |

Unit Root Test | To check stationarity in the data | FDI, per capita income |

Johansen Cointegration Test | To check the long-run relationship | FDI, per capita income |

Granger Causality Test | To determine the direction of causality | FDI, per capita income |

Time Series Regression | To determine the impact of FDI on per capita income |
FDI, per capita income |

Table 1: Tests applied for the Empirical Analysis

#### Variables and the model

For the regression analysis, per capita income as a proxy for poverty is the dependent variable and **FDI** is the independent variable. The basic model to find out the impact of **FDI** on poverty is

* PCI = f(FDI)*

We test the null hypothesis that **FDI** has no impact on per capita income.

## Unit root test for checking stationarit**y**

First, the stochastic property of the variables is checked. For this, the Augmented Dickey-Fuller unit root test is used (Dickey & Fuller, 1981). Table 2 presents the results.

## Series |
## (ADF) t statistic |
## ADF at 1% Level |
## ADF at 5% Level |

FDI | -1.4197 | -3.6267 | -2.9458 |

ΔFDI | -6.8668* | -3.6329* | -2.9484* |

PCI | -2.5150 | -3.6394 | -2.9511 |

ΔPCI | -5.7026 | -3.6463* | -2.9540* |

Table 2: Augmented Dickey-Fuller Unit Root Test Statistics for PCI and FDI

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 t statistic values confirm that both the variables are non-stationary at levels. After first order difference, they became stationary. So, we can investigate the long-run relationship between the variables.

## Co-integration test

There are many tests that were acknowledged in the literature for co-integration analysis such as the Co-integrating regression, Durbin-Watson test, Engle-Granger Co-integration test, and Johansen Co-integration test. But Johansen’s test for co-integration between the variables in the empirical model is used because it has an advantage over other mentioned tests. It takes into consideration the possibility of multiple co-integrating vectors (Johansen, 1988).

## Maximum Ranks |
## Trace Statistic |
## 5% Critical Value |
## Max Statistic |
## 5% Critical Value |

0 | 25.5350 | 15.49 | 24.5001 | 14.26 |

1 | 1.0349* | 3.84 | 1.0349 | 3.84 |

Table 3: Johansen Co-integration between **FDI** and per capita income

Note:Johansen Co-integration Test (Trace and Max Value stat) Results for per capita income andFDI

The above table presents the results based on two statistics. One is the Eigenvalue statistic and the second is a 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 lag 1 with the linear deterministic trend. The results of both trace and max statistic suggest that there exists a long run relationship between per capita income and **FDI**. Thus, this means both the variables are moving in the same direction in the long-run.

## Granger causality test

In this section, it is estimated the causality from **FDI** to per capita income and vice versa. Granger causality is applied to check the causal relationship between FDI and PCI. The results are presented below.

## Null Hypothesis |
## F-Stat |
## Prob |

FDI does not Granger cause PCI | 4.2250 | 0.0242* |

PCI does not Granger cause FDI | 6.9463 | 0.0033* |

Table 4: Granger Causality between **FDI** and per capita income

The result of the Granger Causality test confirms that there is bidirectional causality between the two variables. Based on the p-values, both the null hypotheses in table 4 are rejected at 5% level of significance. The reverse causality holds in light of the fact that **FDI** Granger causes per capita income and vice versa. Moreover, the results indicate that if the **FDI** inflow increases, per capita income, will increase. This will enhance the standard of living and reduce poverty. On the other hand, the increase in per capita income will increase **FDI** inflows. It means both the variables have a strong causal effect and dependency on each other.

## Regression analysis

We use a simple linear regression model to study the impact of **FDI** on per capita income.

## PCI |
## Coef |
## t-value |
## P-value |

FDI | 1.0543* | 2.78 | 0.009 |

Cons | 3.5356* | 7.42 | 0.000 |

**Note: ***Superscripts “*” denote 1% level of significance*

Table 5: Regression Coefficient of **FDI**

The results reveal that an increase in **FDI** will increase per capita income and validates **FDI** led income growth hypothesis. The coefficient shows that a 1% increase in **FDI** will cause an increase of 1.0543% in per capita income. The p-value is statistically significant at 1% level of significance. This helped to reject the null hypothesis.

## A positive relationship between **FDI** and the per capita income

Foreign direct investment is a strategy that has numerous potential advantages for poverty reduction. This article investigated the relationship between per capita income and **FDI** inflows in India over the period of 1980-2016 by employing cointegration and Granger causality tests. Furthermore, the result of cointegration analysis shows that there exists a long-run relationship between **FDI** and per capita income. Furthermore, Granger causality tests resulted in bidirectional causality between them. Moreover, regression results imply that **FDI** has a positive and statistically significant impact on per capita income. The results are consistent with each other. Therefor, this proves that **FDI** inflows in India have had a significant impact on poverty reduction by increasing per capita income. This is because of the creation of skilled labour, infrastructure, investment opportunities and more jobs in multinational companies. However, there are various other indices used in Economic literature to measure poverty and there is disagreement among experts as to which are the most appropriate measures. In this respect, the relation between **FDI** and other poverty indices such as the ones based on purchasing power parity (PPP) and consumption patterns of essential goods can also be investigated.** **

#### 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 - Islami, X., Mulolli, E., & Skenderi, N. (2016). Relationship in Between FDI Inflow and Economic Growth in Kosovo.
*European Journal of Economics and Business Studies*,*2*(1), 50–57. Retrieved from http://journals.euser.org/files/articles/ejes_jan_apr_16/Xhavit.pdf - Johansen, S. (1988). Statistical analysis of cointegration vectors.
*Journal of Economic Dynamics and Control*,*12*(2–3), 231–254. https://doi.org/10.1016/0165-1889(88)90041-3 - World Bank. (2018). World Bank Open Data. Retrieved January 22, 2019, from https://data.worldbank.org/

### Umer Jeelanie Banday

Mr. Banday has been a recipient of “Young Scholar Grant’ to attend the 40th National Bureau of Economic Research (NBER) summer Institute in Massachusetts USA, selected by Indian Council for Research on International Economic Relations (ICRIER). He has to his credit various research papers in national and international journals. He also serves as a reviewer to various Journals of Taylor & Francis Group, Emerald, Sage and Member of editorial board Macrothink Institute, United States.

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