Foreign Direct Investment

FDI in news recently The UNCTAD summit shed light on the fact that discussing the

commitment of the G20 countries towards open trade and investment regimes, expanding markets and resisting protectionism in all its forms.

Following the UNCTAD summit in June 2012, a big set of reforms in FDI was announced by the Indian govt. in September 2012. As per the reforms, 51% FDI in multi brand retail and 100% retail in single brand retail was announced. Also 49% foreign investment in aviation sector was announced. The cap for FDI was raised from 49% to 74% in broadcasting too.

These developments led us to research as to why a country wants to induce FDI and what are the factors that induce FDI in any economy.

Theoretical background of Foreign Direct Investment

Foreign direct investment are the net inflows of investment to acquire a lasting management interest (10 percent or more of voting stock) in an enterprise operating in an economy other than that of the investor.

It is the sum of equity capital, reinvestment of earnings, other long-term capital, and short-term capital as shown in the balance of payments.

There is ample evidence that FDI is a key ingredient to

sustainable economic growth. Going far beyond simple financing, FDI is instrumental in the rapid and efficient cross-border transfer and adoption of best practice

FDI and Economic development

FDI has an important impact on country’s trade balance, increasing labour standards and skills, transfer of technology, skills and the general business climate.

FDI also provides an opportunity for technological transfer and up gradation, access to global managerial skills and practices ,optimal utilization of human capabilities and natural resources, making industry internationally competitive,opening up export markets,access to international quality goods and services and augmenting employment opportunities.

Literature Review

A lot of research and analysis of the factors affecting the FDI have been done already. Not only has the research been done on the developed nations but also on the developing countries.

The main literature studied by us includes Analysis of Factors Affecting Foreign Direct Investment in Developing Countries by Bushra Yasmin, Aamrah Hussain and Muhammad Ali Chaudhary and Cheng and Kwan (1999)

As per the FDI confidence index of A.T. Kearney India has been ranked 2nd showing the strong confidence of overseas investors in India.

FDI in India A recent UNCTAD survey projected India as the second most

important FDI destination (after China) for transnational corporations during 2010–2012.

The sectors which attracted higher inflows were services, telecommunication, construction activities and computer software and hardware.

Mauritius, Singapore, the US and the UK were among the leading sources of FDI.

FDI in India-The facts

In 2008-09, FDI stood at $27.3 billion.

FDI in 2009-10 was $24.2 billion

In 2010-11, FDI into India declined to $19.43 billion, a significant decrease from both 2008 and 2009

Foreign direct investment (FDI) in India may cross $35 billion in 2011-2012 as against $19.4 billion in the last financial year

Objective of the study

The objective of this study being conducted is:

To study the trends and patterns of flow of FDI.

To assess the determinants of FDI inflows.

To evaluate the impact of FDI on the Indian Economy.

RESEARCH METHODOLOGY

The study is based on secondary data.

The required data has been collected from various sources i.e. Asian Development Bank’s Reports, various Bulletins of Reserve Bank of India, publications from Ministry of Commerce, Govt. of India, Economic and Social Survey of Asia and from websites of World Bank, IMF, WTO, RBI, UNCTAD etc.

Hypothesis

The study has been taken up with the following hypothesis:

Flow of FDI shows a positive trend over the period 1990-2011. FDI has had a positive impact on economic growth of the country. The f actors that are effective for the study of trends in FDI are:

• Real GDP per capita

• Trade openness

• Extent of urbanization

• Investment on infrastructure

• Inflation

FACTORS AFFECTING FDI

1.Real GDP per capita: FDI is attracted in a country where there is a huge market potential which is the product of no. of buyers and their purchasing power which in turn is measured by the real gdp per capita.

2.Trade openness: Countries with more open economic setup are believed to be hot property among foreign investors. It is measured by Taking the sum of imports and exports of a country.

3.Extent of urbanization: Higher the urbanization higher the attractiveness of a market.

FACTORS AFFECTING FDI (CONT)

4. Investment on infrastructure: FDI is mostly attracted in a country with decent basic infrastructure as it saves a lot of time. Expense and energies than those who will have to develop infrastructure from scratch.

5.Inflation: FDI is affected by the price level changes in the economy though there is no direct relationship between the two but the purchasing power of buyers is affected by inflation thus there purchase preferences

Mathematical Model

To analyse factors that affect the FDI inflows in a country, we have used “Linear multiple regression model” for testing the hypothesis based on time series analysis

H0: β =0

H1: β ≠0

Based on the factors stated in the previous section, we formulate the following equation:

FDI= α + ß₁CGDP + ß₂OPEN + ß₃URB + ß₄INV + ß₅GDPD

With Time series 1980-2010

Key to equation…Where,

FDI: Foreign Direct Investment including equity capital, reinvestment of earnings, other long-term capital, and short-term capital as shown in the balance of payments (measured in current US$)

CGDP: Real Gross Domestic Product per Capita (constant 2000 US$)

OPEN: Trade openness constructed imports plus exports as percentage of GDP

URB: Extent of urbanization measured by urban population as percentage of total population

INV: Investment done for infrastructural development (expressed as % of GDP)

GDPD: Inflation represented by GDP deflator (annual %)

Stages for testing hypothesis

For precise results hypothesis is been tested under 4 stages for the following parameters:

1. Testing the hypothesis about an individual partial regression coefficient.

2. Testing the overall significance of the estimated multiple regression model, i.e. finding out if all the slope coefficients are simultaneously equal to 0.

3. Testing the model for the best fit.

STAGE 1: testing the hypothesis for India based on data from 1990-2010

We tested the hypothesis for the above said parameters and the results were obtained as follows:

1. Real per capita GDP, trade openness and inflation are positively related with the FDI inflows for the period

1990-2010.

2. Rate of urbanization and investment showed negative association with FDI inflows for the same period.

Observations

1. Results for urbanization and investment seem to be incorrect logically and theoretically since they are positively associated with stock of FDI.

2. Inflation is believed to have an inverse impact on FDI

but the results demonstrate the reverse.

3. Except urbanization, none of the factors are statistically significant. This makes our model very weak.

Overall significance of model The hypothesis tested in this case is:

H₀: All slope coefficients are simultaneously 0.

H₁: Not all slope coefficients are simultaneously 0.

We test the overall significance of the model using the F-test significance. The F-test significance value for this model is 3.724E-06, which is way less than the value of (1-α) i.e. 0.05. Thus we reject the null hypothesis and can say that our model is overall significant.

Goodness of fit is measured by the adjusted R². The value of adjusted R² is 0.826 or 82% which makes this a model of good fit.

Comments…As we can see that though the model is overall significant, there is no significant relationship of individual factors with the dependent variable. We tried to analyse the reason for such behaviour by finding the extent of multi-collinearity amongst the variables. Following results were obtained:

high level of correlation amongst the factors All of them move on almost one-on-one basis making the model dubious and

irrelevant But this problem is bound to happen on account of less number of degrees of

freedom and also because we were not using panel data.

Stage 2: Testing the hypothesis for India based on data from year 1980-2010

We tested the hypothesis for the above said parameters and the results were obtained as follows:

1. Real per capita GDP, trade openness and inflation are positively related with the FDI inflows for the period 1980-

2010.

2. Rate of urbanization and investment showed negative association with FDI inflows for the same period

Observations

1. Results for urbanization and investment continue to be incorrect logically and theoretically.

2. Inflation again demonstrates positive relationship with FDI stocks.

3. Except real per capita GDP and urbanization, none of the factors are statistically significant. Thus our model remains weak.

Overall significance of model

The hypothesis tested in this case is:

H₀: All slope coefficients are simultaneously 0

H₁: Not all slope coefficients are simultaneously 0

We test the overall significance of the model using the F-test significance. The F-test significance value for this model is 1.41256E-09, which is way less than the value of (1-α) i.e. 0.05. Thus we reject the null hypothesis and can say that our model continues to be overall significant.

Goodness of fit is measured by the adjusted R². The value of adjusted R² is 0.821 or 82% which makes this a model of good fit. In other words, 82% variation in FDI stocks level is explained by variation in the independent variables.

Com ments…As we can see that though the model is overall significant, there is no significant relationship of individual factors with the dependent variable. We tried to analyze the reason for such behaviour by finding the extent of multi-collinearity amongst the variables. Following results were obtained:

Even after increasing the degrees of freedom, the problem of multi collinearity persists. In order to get more accurate we now shifted from absolute figures to percentage figures. Though most of our variables are already in percentage terms, we would alter our model a little bit to get more accurate results.

Stage 3: Testing the hypothesis based on percentage based model

As increasing degrees of freedom hasn’t worked, we altered our model a bit.

We have now expressed the stock of FDI flows as a percentage of annual GDP.

The factors selected for the model remain same.

The time span for the research has been kept as 1980-2010.

ResultsWe tested the hypothesis for the above said parameters and the results were obtained as follows:

1. trade openness and inflation are positively related with the FDI inflows for the period 1980-2010.

2. Rate of urbanization ,investment and Real per capita GDP, showed negative association with FDI inflows for the same

period

Observations

1. When we revise the model in the above manner, only trade openness emerges as significant factor in explaining changes in FDI stocks.

2. The reverse behaviour of urbanization, investment and inflation continues and is unexplainable.

3. No factor is individually is capable of explaining the variations on its own.

Overall significance of the model

The hypothesis tested in this case is:

H₀: All slope coefficients are simultaneously 0.

H₁: Not all slope coefficients are simultaneously 0.

We test the overall significance of the model using the F-test significance. The F-test significance value for this model is 5.86E-09, which is again quite small as compared to the value of (1-α) i.e. 0.10. Thus we reject the null hypothesis and can say that our model continues to be overall significant.

Goodness of fit is measured by the adjusted R². The value of adjusted R² is 0.79 or 79% which makes this a model of good fit. In other words, 79% variation in FDI stocks level is collectively explained by variation in the independent variables

Comments:

Even on repetitive experiments we have been unable to track the significant factors that affect the inflows of FDI in a country. We made one last attempt by converting the model to log-linear model. The modified model is given in the next section.

Stage 4: Testing the hypothesis for log-linear model

We tried various ways of finding significant factors affecting FDI inflows in a country but couldn’t succeed so far.

Final attempt by converting observations to a log-linear model

Variables and methodology remains the same.

Results…We tested the hypothesis for the above said

parameters and the results were obtained as follows:

Trade openness and inflation are positively related with the FDI inflows for the period 1980-2010.

Rate of urbanization ,investment and Real per capita GDP, showed negative association with FDI inflows for the same period

Overall significance of the modelThe hypothesis tested in this case is:

H₀: All slope coefficients are simultaneously 0

H₁: Not all slope coefficients are simultaneously 0

We test the overall significance of the model using the F-test significance. The F-test significance value for this model is 4.11687E-12, which is again quite small as compared to the value of (1-α) i.e. 0.10. Thus we reject the null hypothesis and can say that our model continues to be overall significant.

Goodness of fit is measured by the adjusted R². The value of adjusted R² is 0.88 or 88% which makes this a model of good fit. In other words, 79% variation in FDI stocks level is collectively explained by variation in the independent variables.

We are now in a position to draw final conclusions about our research.

Final conclusions

1. Repetitive experiments revealed that though the overall model for estimating the factors responsible for inducing FDI stocks was significant; no factor could individually explain the changes in dependent variables. In other words, there are some additional factors at play which we may have omitted.

2. Subsequent hypothesis testing also revealed that real per capita GDP, trade openness and urbanization had strong influence on the model.

3. Urbanization and investment level are believed to induce FDI i.e. they have positive relationship with FDI. But in our hypothesis, they repeatedly showed inverse relation with FDI.

4. It maybe because we used a substitute variable in case of investment, but the behaviour of urbanization was unexplainable.

5. Inflation is purely insignificant variable in explaining variations in FDI stocks.

Limitations

The data suffered from the problem of multi-collinearity. This confirms the fact that such research should be done extensively with panel data. That would increase degrees of freedom and would help reduce multi-collinearity.

Also a lot of material variables were excluded from the research in the wake of unavailability of data. More complete and accurate sources of data need to be found out to perform this research.

Positives and further scope

Overall significance of model convinces one that the selected variables do have an impact on the FDI flows in the country. With more accurate and detailed data, this research could be used to predict the value of foreign capital flows into the country, and that too with a good level of precision.

The question as to whether a country can increase FDI flows by improving on the dependent variable is also answered by the research. The goodness of fit of the model shows that most of changes in the independent are duly explained by dependent variables.

Priyank Khemkha

Sachin Jain

Karan Mago

Puneet Bansal

Prateek Jain

Ashish Jain

THANK YOU