DETERMINANTS OF FOREIGN DIRECT INVESTMENT...

________________________________________Determinants of Foreign Direct Investment in India

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CHAPTER- 6

DETERMINANTS OF FOREIGN DIRECT

INVESTMENT IN INDIA AND CHINA

FDI usually represents a long term commitment to host country and contribute

significantly to gross fixed capital formation in developing countries. FDI has several

advantages over other types of capital flows, in particular its greater stability and the

fact that it would not create obligations for the host country as has been observed in

the context of the Asian financial crisis of 1997-98 (Cho, 2003). The ongoing process

of integration of the world economy has led to a significant change in the attitudes of

the host countries with respect to inward foreign direct investment (FDI). FDI is no

longer regarded with suspicion by the developing countries and controls and

restrictions over the entry and operations of foreign firms are now being replaced by

selective policies aimed at FDI inflows, like incentives, both fiscal and in kind

(Banga, 2003).

Emerging issues in the areas of foreign direct investment are an essential part

of the core process of globalization. FDI can play a key role in improving the capacity

of the host country to respond to the opportunities offered by global economic

integration, a goal increasingly recognized as one of the key aims of any development

strategy. Virtually all countries are actively seeking to attract FDI, because of the

expected favourable effect on income generation from capital inflows, advanced

technology, management skills and market know-how (Cho, 2003).

The determinants of the FDI are numerous. Whether particular action of

investor or government is responsible for increase or decrease in the investment for a

given period is treated as determinant. There is not a single variable which would

influence investment to rise or fall but it is comprised of a set of variables. It would be

very valuable to review the key determinants and factors of FDI based on the theories

of international investment.

The FDI theories are categorized into two parts in order to know the

theoretical determinants of FDI. (a) Theories based on Perfect and (b) Theories based


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on Imperfect market. The perfect market assumes that there exists competition for

investment, equal opportunity, and there is equal return on investment across the

countries. Perfect competition within the industries implies that there are numerous

firms manufacturing same items of same quality and all industries have equal rate of

return and tax rate. According to imperfect theory, the financial markets are never

perfect. The information needed to take rational decision is rarely available. The risk

associated with different level of investment also differs. The investment schedule of

the investing firm depends upon rate of return in imperfect market. The industrial

organizations across the world are neither identical nor face same problems at a same

time.

A) Theories based on Perfect Markets:

Differential Rate of Return

Portfolio Diversification

Market Size

Resource Location

B) Theories Based on Imperfect Market:

Industrial Organization

Internationalization

Liquidity

Foreign Exchange Rate

Political Stability

Tax Policies

Government Regulations

Trade Policy (Gedam, 1996)


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Table 6.1: Hypotheses of FDI Theories and Their Evidence

Theory Concerning Hypothesis of the Theory Evidence Existing StudiesDifferential Rate ofReturn

FDI flows from low to high rate return region Evidence don’t support thehypothesis

Gedam (1996); Azam &Lukman, (2010)

Portfolio Diversification Expected risk and return determines the flow of FDI Weak support to validity of theory Agarwal, (2001)Market Size (GDP) GDP growth is proxy for potential market size for sales,

which in turn determines flow of FDIEvidence support the theory Coughlin and Segav, (2002);

Azam & Lukman, (2010)Resource Location FDI flows will be adversely affected if the natural

resources are highly protected.Evidence support the theory Zhang, (2001); UNCTAD,

(1998)Industrial Organization Structural imperfection determines the FDI flows Evidence support the theory Gedam, (1996)Internationalization FDI is result of firms replacing transaction cost with

internationalizationEvidence don’t support the theory Zhang (2001)

Liquidity Relationship between internal cash flows andreinvestment determines FDI flow, i.e. cost of internalfunds is lower than cost of external borrowings makesFDI to grow.

Evidence support the theory Chopra, (2003)

Foreign Exchange Rate Relative strength of currency determines the FDI flow Evidence support the theory Shan, (2002); Dees, (1998);Cheng & Ma (2008)

Political Stability Political, economic and social stability makes FDI tooccur and instability deter FDI

Mixed evidence or no evidence tosupport the hypothesis

Asiedu, (2002); Ali & Guo,(2005); Zhang, (2000)

Tax Policies Tax affects net return on investment therefore tax systemdetermines FDI.

Weak support to validity of theory Zhang, (2000)

Government Regulations Favourable regulations make the FDI to occur Evidence support the hypothesis Chopra, (2003)Openness More open economy to outside external trade can attract

more FDI.Evidence support the hypothesis Azam & Lukman, (2010);

Chopra, (2003)The Level of ExternalIndebtedness

More burden of repayment and debt servicing makingthe country less attractive for foreign investor

Evidence support the hypothesis Botric and Skuflic, (2005),Azam & Lukman, (2010);Chopra, (2003)

Foreign ExchangeReserves

More Reserves has positive impact on FDI Evidence support the hypothesis Cheng & Ma, (2008)


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6.1 Expected Theoretical Relationship between FDI and its

DeterminantsThere are so many determinants of FDI in the economy as suggested by

existing literature available on this issue. There is need to know the expected relation

between FDI and these determinants before doing empirical investigation regarding

relationship of FDI and some variables taken in this study so as to find main

determinants of FDI in India.

(i) Market Size: Market size which is measured in terms of GDP is expected to

have positive relationship with FDI. Countries having more GDP growth rate

can attract more FDI inflows. Market oriented FDI aims to set up enterprises

to supply goods and services to the local market. This kind of FDI may be

undertaken to exploit new markets. The market size of host countries is very

important location factor for market oriented FDI. The general implication is

that host countries with larger market size, faster economic growth and higher

degree of economic development will provide more and better opportunities

for these industries to exploit their ownership advantages and therefore, will

attract more market-oriented FDI. Even for export-oriented FDI, the market

size of host countries is an important factor because larger economies can

provide larger economies of scale and spill-over effects (OECD, 2000).

(ii) Portfolio Diversification: The diversification of portfolio is also considered

to be another determinant. The approximate mix of bonds, securities, stock,

debenture, depository receipts, etc. refers to portfolio investment. The maturity

of these instruments may vary from few months to few years. The concern of

an investor is for these instruments at a time of risk perceptions. It implies that

the investors are able to invest in or take out their capital for diversification of

their portfolio assets due to perceived risk in a country. The higher is the

perceived country risk due to political, economic and financial changes in one

country, an investor would like to take out his capital out of the country

(Gedam, 1996).


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(iii) Resource Location: Location- specific determinants have a crucial influence

on a host country’s inflow of FDI. The relative importance of different

location-specific determinants depends on at least three aspects of investment:

(1) The motive for investment (e.g., resources, market or efficiency-seeking),

(2) The type of investment (e.g., services or manufacturing), and

(3) The size of the investors (small and medium MNEs or large MNEs)

Natural resources protected from international competition by

imposing high tariffs or quotas, still play an important role in attracting FDI by

a number of developing and developed countries. The theoretical analysis

concludes that policy related variables and economic determinants together

explain the variations in the FDI inflows in country. Empirical analysis

concludes that the variables considered for the study are more significant in

China as compared to India. In India, Long term debt is an important factor in

attracting FDI but in China Foreign exchange reserves and Sum of exports and

imports (Exim) have more influence on FDI. These flows will be adversely

affected if the natural resources are highly protected (UNCTAD 1998).

(iv) Differential Rate of Return: This theory explains mostly the held belief thatthe FDI flows to that country which has relatively higher return on theinvestment. No investor would like to invest if the rate of return on investmentis low. Therefore, the flow of capital will be in those countries which ensurethe highest possible rate of return (Gedam, 1996).

(v) Foreign Exchange Reserves: The high level of foreign exchange reserves interms of import cover reflects the strength of external payments position andhelp to improve the confidence of the prospective investors. Therefore, apositive relationship is postulated between the foreign exchange reserves andthe inflow of foreign direct investment (Chopra, 2003).

(vi) Internationalization: Internationalization refers to minimize or eliminate costof external transaction by increasing transaction within subsidiaries. Thistheory explains that FDI is an outcome of need to lower the cost oftransaction. In other words, need for internationalization of transaction costdetermines the FDI inflows. The internationalization of transaction cost isachieved through FDI investment in subsidiary to eliminate high cost of


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transaction or replace high cost transaction through low cost when it isimpossible to eliminate (Gedam, 1996).

(vii) Openness: Openness of a country is generally measured as the proportion ofexports and imports to the GDP (Trade/GDP). The more an emerging markettries to open its economy to outside external trade, the more this host countrycan attract FDI. Export oriented FDI depends upon liberal trade policiesreflected in openness of the country as the TNC is not interested in marketseeking behaviour initially and openness helps it in importing components,capital goods, and raw material (Zhang, 2001)

(viii) Government Regulations: This consists of rules and regulations governingthe entry and operations of foreign investors. FDI cannot take place unless it isallowed to enter in a country. Its potential relevance is evident when policychanges sharply in the direction of more or less openness. It should be noted,however that policy changes in the direction of openness differ in an importantway from those in the direction of restriction. Open policies are basicallyintended to induce FDI while restrictive policies such as sweepingnationalization of foreign affiliates, can effectively close the door to FDI(Chopra, 2003)

(ix) Political Stability: The reliability and political stability determines the FDIinflows. TNCs prefer stable government so that their investment is protected.Political instability may be in the form of negative attitude of the governmenttoward TNCs, non allowance of fund transfer, currency convertibility, war,bureaucracy and corruption. Political stability can also be measured bynumber of changes of democratically elected governments. Asiedu (2002)does not find any evidence relationship between FDI and political stability(Gedam, 1996).

(x) Tax Policies: Fiscal policies determine general tax levels, including corporateand personnel tax rates and thereby influence inward FDI. Other things beingequal a country with lower tax rates should stand a greater chance of attractingFDI project than a country with higher rates. It is difficult to ascertain howmuch influence it can have on the total inflows of FDI. (Chopra, 2003).

(xi) Inflation: Low inflation rate is considered to be a sign of internal economic

stability in the host country. High inflation rate indicates incapability of the


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government to balance its budget and failure of the central bank to conduct

appropriate monetary policy. Changes in inflation rates of the domestic or

foreign country are anticipated to alter the net returns and optimal investment

decisions of the MNEs. It is expected to give negative impact on FDI (Banga,

2003).

(xii) Industrial Organization: Industrial organization theory states that firmspecific advantages, competition capabilities, managerial skills and practiceetc. are some of the crucial points for industrial organization to survive. Therelative advantages to TNCs in terms of these points make FDI to flow to acountry of their choice (Gedam, 1996).

(xiii) The Level of External Indebtedness: The level of external indebtednessmeans the net external assistance to India in the form of loans. It is expected tohave a negative impact on FDI inflows. The level of indebtedness shows theburden of repayment and debt servicing on the economy, thus making thecountry less attractive for foreign investors (Chopra, 2003).

(xiv) Foreign Exchange Rate: It is the rate at which one currency may be

converted into another. In other words it is the relative strength of the

domestic country in relation to the foreign country. High volatility of the

exchange rate of the currency in the host country discourages investment by

the foreign firms as it increases uncertainty regarding the future economic and

business prospects of the host country (Banga, 2003).

FDI arises mainly from the activities of TNCs that operate across the

countries. The literature on FDI determinants indicates that TNCs would allocate their

investments among countries in order to maximize their profits at low level of risk.

However the profit earned by TNCs depends on three factors:

Factors within the firm that enable it to grow and expand more successfully.

Factors in the host country that make the country as the best location to

produce across countries.

Factors associated with the firm’s trade-off between FDI and exports

(Gregorio and Lee, 2002).


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The relative importance of FDI has increased more since mid 1980s and it is

the largest single component of private capital flows to developing countries. The

global competition for FDI among developing economies is increasing. The two large

emerging economies in Asia wherein this competition is evident are India and China.

Both these economies are now getting increasingly integrated with the global

economy as they open up their markets to international trade and investment inflows.

China has been globalizing at a particularly rapid pace, accompanied by a many fold

increase in net inflows of FDI over 1980-2000 period. India began to liberalize its

economy about a decade later than China. However, India’s market-oriented

economic reforms undertaken in 1991 which were directed towards increased

liberalization, privatization and deregulation of the industrial sector, and to re-orient

the economy towards global competition by reducing trade barriers, and gradually

opening up its capital account, has led India to increasingly become a favourable

destination for foreign investors (Srivastava and Sen, 2004).

Despite good prospects for foreign direct investment, FDI inflows were

limited in India as compared to China. It is necessary to evaluate the policy

instruments that should be adopted by India to attract FDI and to recognize the

locational factors through which the country can influence the flow of FDI. So, some

major factors particularly affecting FDI inflows in India have been assessed in order

to estimate the determinants of FDI for India.

Zhang (2001) estimated the effect of location characteristics and government

policy on FDI. It was found that China’s huge market size, liberalized FDI regime and

improving infrastructure are attractive to multinationals. Dunning (1981) determined

the effect of three factors such as Ownership, Location and Internationalization on

FDI. Cheng and Kwan (2000) estimated the effects of the determinants of foreign

direct investment (FDI) in 29 Chinese regions from 1985 to 1995, and found that

large regional market, good infrastructure, and preferential policy had a positive effect

but wage cost had a negative effect on FDI. Taylor (2000) reports that the incentives

by the government of the country have positive affect on attracting FDI.


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Wilhelms (2004) explored the determinants of FDI in 67 emerging economies

and found that the factors like government fitness, market fitness, educational fitness,

and socio-cultural fitness are important determinants of FDI. Brewer (1993) discussed

the direct and indirect effects of government policies on FDI and concluded that same

government policy may have both positive and negative effects on FDI, therefore

empirical evidence on the impact of selective government policies on FDI inflows is

ambiguous.

Grubert and Mutti (1991) and Loree and Guisinger (1995) found that

investment incentives positively effect inward FDI flows and performance

requirements imposed by the host governments gives negative impact on FDI inflows.

Devereux and Griffith (1998) revealed that fiscal incentives plays primary role in

attracting export oriented FDI, while role played by other incentives was found to be

secondary.

Nunnenkamp (2002) argued that traditional market related determinants are

still dominant factors attracting FDI in spite of observing so many changes since

1980s. Gastanaga et. al (1998), Chakrabarti (2001) and Asiedu (2002) have explored

that the variables like openness and regional agreements in trade are very significant

factors in attracting FDI inflows. Blomstrom and Kokko (1997) analysed the direct

and indirect effects of regional trade agreements (RTA) on FDI inflows and

concluded that lowering interregional tariffs can lead to expanded markets which

results in increase in FDI inflows.

6.2 Choice of Variables

From the through review of existing literature and some of the studies

discussed above, it has been observed that a lot of work has been accomplished on

finding the effect of reforms, change in economic policies, socio-political

environment and cost of capital on FDI inflows in India. There is a strong consensus

in the literature that multinational corporations invest in specific locations mainly

because of strong economic fundamentals in the host countries like availability of

infrastructure, stable macro economic environment and favourable policies.


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The studies conducted on empirical investigation of the some important

factors like foreign exchange reserve (RES), Openness (OP i.e sum of Exports and

Imports as a percentage of GDP), Long Term Debt (LTD) are really inadequate. The

variable openness is suppose to give positive influence on FDI inflows as every

country adopting more liberalised policy regime is able to attract more FDI inflows

but Tolentino (2009) found no relationship between FDI and Openness in China. So,

it becomes imperative to study this variable on account of this conflicting view. The

emerging economic giants, the BRIC (Brazil, Russian Federation, India, and China)

countries hold the largest foreign exchange reserves globally. India and China are

amongst the top 10 nations in the world in terms of foreign exchange reserves. These

countries have considerable position among the ten largest gold holding countries in

the World (Economic Survey 2009-10). The growing importance of this variable in

these countries requires to estimate the influence of foreign exchange reserve on FDI.

The variable Long Term Debt shows the dependence of the country on external

sources which may cause doubt regarding the financial credibility of the country that

influences negatively the inflows of FDI in that country. But now a day’s developing

country may use this external debt for the development of infrastructure and

mobilisation and efficient use of physical and financial resources of country which in

turn may attract foreign investors. So, it is important to study the effect of the variable

to have more clarity regarding this variable in India and China. Very few studies have

evaluated the impact of inflation but there is serious need to evaluate extensively the

impact of this ever changing essential variable with the updated time period in India.

Pan, (2003) has found positive impact of exchange rate in case of China while

negative impact of this variable has been reported by Ali and Guo (2005) in China.

Moreover Tolentino (2007) has investigated no relationship between FDI and

exchange rate in china. It requires the verification of its true impact on FDI inflows in

India and China due to lack of general consensus. Market size is a very important

factor required to be checked in the current time period. So, it has been reflected

through selection of the variable GDP as a determinant of FDI inflows in India.

All the facts and logics given above reveal that FDI is not only influenced by

the regulatory framework but also by many other economic factors. Keeping in view


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the findings of existing literature, an attempt has been made in this chapter to trace the

effect of some selected important economic variables on FDI inflows in India and

China.

6.3 Variables, Data Source and Period of the Study

This chapter explores the determinants of foreign direct investment which

influence FDI inflows in India and China. Quarterly data for the period 1990-91 to 2008-

09 has been used in India while annual data covering the period from 1976-2008 has been

used in case of China. The data has been taken from the Handbook of Statistics of

Indian Economy published by Reserve Bank of India and from World Development

Indicators and World Development Reports published by the World Bank for India

and China respectively. The variables examined for the study are Gross domestic

product (GDP), Foreign exchange reserves (RES), Openness (OP i.e sum of Exports

and Imports as a percentage of GDP), Long Term Debt (LTD), Exchange rate

(EXCH) and Inflation (INF).

Description of Variables:

Variables Description

LNFDI Natural Log of Foreign Direct Investment

LNEXCH Natural Log of Exchange rate

LNGDP Natural Log of Gross domestic product

LNINF Natural Log of Inflation

LNLTD Natural Log of External indebtedness

LNOP Natural Log of Openness (sum of Exports and Imports as a percentageof GDP)

LNRES Natural Log of Foreign exchange reserves


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6.4 Statistical Diagnostic (India)

As we know that economic time series move together therefore if we include all

the explanatory variable in the regression equation there may be the problem of

multicollinearity. Before proceeding to further analysis, the existence of

multicollinearity among the independent variable had also been examined. For this

purpose, Pearson’s correlation matrix has been formed that signalled high correlation

among various independent variables i.e. causing the problem of multicollinearity.

Using Ordinary Least Square linear equation, the explanatory variables are

regressed to test the significance of these variables. The multiple regression analysis

has been used and the regression results have been reported in Table 6.2. In the

analysis a combinations of variables like GDP INF OP EXCH have been found to be

statistically significant in India, while coefficients of LTD and RES do not have

significant t-value. The value of F is found to be significant in all the equations which

show the significance of the model. The value of adjusted R2 is found to be 0.89

which indicates the percentage variation in FDI due to the combination of variables

taken in the study.

Table 6.2 Regression Results (FDI as Dependent Variable)(India)

Variables Coefficients Std. Error t-Statistics Prob. VIF

C -4.164178 5.410556 -0.769640 0.4441 -

GDP -1.650801 0.721537 -2.287893 0.0252 17.801

INF 8.263051 1.408933 5.864760 0.0000 57.807

LTD 0.341063 0.209984 1.624231 0.1089 4.700

OP -1.165718 0.567235 -2.055089 0.0437 8.786

RES -0.178360 0.346542 -0.514687 0.6084 35.484

EXCH -1.578592 0.835465 -1.889477 0.0630 6.150

Adjusted R2 0.89

F-statistics 109.61

Prob. (F-statistic) 0.000

Durbin-Watson 1.000


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Further the analysis also reveals that the value of Durbin-Watson statistics is

very low which reveals the presence of autocorrelation and the value of VIF for all

variables is very high which points out the interrelationship between these

explanatory variables indicating the existence of multicollinearity. But Adjusted R2 is

very high indicating the spurious regression due to above mentioned problems.

Therefore to overcome these problems, Cointegration technique has been applied to

find out the factors influencing FDI,

6.5 Econometric Methodology

Before estimating any relationships between FDI and its explanatory variables,

there is a need to check the stationarity of each series. As non stationary time series

will necessarily contain permanent component. Therefore mean and variance of this

time series will depend on time. So, it may give spurious regression results. As per

description given above about the variables in study, these have been taken in

logarithmic form to make them stationary at lesser order of integration. Further the

coefficients of log linear model provide elasticities which can be interpreted in the

form of percentages and thus free from quantification of variables under evaluation.

The stationarity of these seven variables LNFDI, LNGDP, LNEXCH, LNINF, LNOP,

LNLTD, and LNRES has been tested by applying formal Unit root procedure i.e. the

Augmented Dickey-Fuller (ADF) test. Cointegration analysis has been applied to

study the long run relationship among these variables for estimating impact of these

variables on FDI in India.

6.6 Results and Discussion

The ADF test results for the seven variables involved in the analysis have been

presented in Table 6.2 and in equation form for lucid explanation. It has been

observed that the null hypothesis of presence of unit root has been rejected for all the

first difference variables specified. This shows that all variables exhibit integrated

order one. This means that the series are non-stationary in level but stationary in first-

differences.


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Table 6.3: Augmented Dickey Fuller Test ResultsUnit Root Tests at Logarithmic levels

(India)

Sr. No. Variables Without Drift

and Time Trend

With Drift With Drift and

Time Trend

1 LNEXCH 1.6742 -1.1933 -1.6029

2 LNFDI 1.4655 -1.5516 -2.4106

3 LNGDP 4.2041 0.4483 -2.4804

4 LNINF 2.0729 -2.3701 -3.0247

5 LNLTD 1.3970 -1.0846 -3.4890

6 LNOP 2.4279 -0.2746 -1.9671

7 LNRES 3.9227 -1.7300 -2.9310

Unit Root Tests at First Differences


and Time Trend


Time Trend

1 LNEXCH -7.3690** -7.5214** -7.4697**

2 LNFDI -9.6118** -9.9363** -9.9070**

3 LNGDP -1.9955* -5.2942* -5.3080**

4 LNINF -1.8027 -3.3916** -3.9629**

5 LNLTD -9.2524** -9.4327** -9.3718**

6 LNOP -3.6648** -4.5342** -4.5110**

7 LNRES -4.0914** -5.5667** -8.1919**

* denotes significance at the level 5% and ** denotes significance at the level 1%. Critical values obtained from Mackinnon(1991) are -2.85, -3.41, and -1.94 at 5% level and -3.43, -3.96, and -2.56 at 1% for first second and third model respectively.


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Table 6.4: Cointegration Test Results (Trace)(India)

Hypothesized

No. of CE(s)

Eigenvalue Trace Statistic P values

None * 0.700581 218.9815 0.0000

At most 1 * 0.455655 130.9501 0.0000

At most 2 * 0.439117 86.55355 0.0013

At most 3 0.264717 44.34179 0.1030

At most 4 0.191001 21.89427 0.3044

At most 5 0.081650 6.421340 0.6457

At most 6 0.002783 0.203478 0.6519

Trace test indicates 3 cointegrating equations at the 0.05 level. *denotes rejection of the hypothesis at the 0.05 level

The implication is that there is a possibility to have a co-integrating vector

whose coefficient can directly be interpreted as long-term equilibrium. So,

Cointegration analysis has been used to study the long run relationship among the

variables under study. Firstly Cointegration Trace Test and Maximum Eigenvalue test

have been applied to check the cointegration relationship. Results of these tests have

been reported in Table 6.3 and in Table 6.4, which shows three cointegrating vectors.

This cointegrating relationship represents the foundation of a complete Vector Error

Correction Model (VECM).

Three alternative cointegrating equations representing the relationships among

the variables under study have been obtained after executing cointegration test.

However, in these equations FDI appears on the right hand side as an independent

variable. As the objective of this study was to detect the determinants of FDI so there

was need to impose following restrictions on cointegration relationship. (See

annexure I)


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β11 = 1, β12 = 0, β13 = 0

β21 = 1, β23 = 0, β24 = 0

β31 = 1, β34 = 0, β35 = 0

With these restrictions three alternative equilibrium relationship defining the

determinants of FDI have been obtained in order to investigate impact of all the

variables on the FDI. (See Harris and Sollis, 2006 for details on imposing restrictions)

Table 6.5: Cointegration Test Results (Maximum Eigenvalue)(India)

Hypothesized

No. of CE(s)

Eigenvalue Max-EigenStatistic

P values

None * 0.700581 88.03142 0.0000

At most 1 * 0.455655 44.39657 0.0153

At most 2 * 0.439117 42.21176 0.0040

At most 3 0.264717 22.44753 0.1983

At most 4 0.191001 15.47293 0.2571

At most 5 0.081650 6.217861 0.5854

At most 6 0.002783 0.203478 0.6519

Max-eigenvalue test indicates 3 cointegrating equations at the 0.05 level * denotes rejection of the hypothesis at the 0.05 level

Table 6.6: Estimated Cointegrating Relationship(India)

Equations Independent Variables Impact on FDI1 LNGDP 5.035**

(4.70)2 LNEXCH -5.351**

-(9.92)3 LNOP 19.832**

(5.86)4 LNRES 10.842**

(10.82)5 LNLTD 6.576**

(6.51)6 LNINF -13.574**

-(10.58)** denotes significance at the level 1%. Figures in Parentheses are t values


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VECM has been applied to obtain the final results of the analysis which are

reported in the Table 6.5. The results of this analysis have also been represented in

equation 6.1 given as below:

FDI = 13.816+5.035GDP-5.351EXCH+19.832OP+10.842RES+6.576LTD-13.574INF + ut ------- (6.1)(4.702) - (9.92) (5.86) (10.82) (6.51) - (10.58)

The analysis shows that combinations of variables like GDP, RES, OP, INF,

EXCH, and LTD are found to be statistically significant in India. Here X coefficients

(elasticities) show the percentage change in FDI due to one % change in other

variables taken in the study.

X coefficient of GDP is estimated to be 5.035 found to give positive and

statistically significant impact indicating 1% Change in GDP will raise FDI by

5.035%. Larger market size (GDP), faster economic growth and higher degree of

economic development provide more and better opportunities for the foreign

investors to expand and exploit developed resources of the country for taking all the

profitable economic advantages

Exchange rate is found to be significant variable having negative impact on FDI

as the coefficient of this variable is determined as -5.351. It shows that the 1% change

in this variable will tend to decrease 5.351% in FDI. Volatility of exchange rate and

frequent change in the value of currency create the uncertainty among the foreign

investors about the price stability and monetary regulatory mechanism of the country

concerned. It discourages the foreign investors to invest in a country with exchange

rate instability.

Inflation is also estimated to be statistically significant variable affecting FDI as

it explains that the 13.57% variation in FDI due to 1% change in inflation. The value

of X coefficient is estimated to be -13.574 depicts the negative impact of inflation on

FDI. This is because of the reason that high level of price in the country results in

rising cost of production on account of increase in input prices like wages, cost of raw

material, land prices and cost of capital. High price of the product also adversely

affects domestic as well as international demand of product. All these factors

ultimately lead to reduction in profitability in business thus discourage foreign

investment in the countries with high inflation rate.


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X coefficient of 6.576 has been calculated so far as the LTD as a determinant of

FDI is concerned showing 1% increase in LTD would bring 6.57% variation in FDI. It

also indicates the positive impact of this variable on FDI in case of India that is due to

the optimum and extensive utilisation of these funds for different growth oriented

economic activities like expansion of means of transport and communication,

generation of power, development of banking and financial sector in the economy.

This in turn made India to visualise a tremendous growth rate in manufacturing sector

in past years that resulted in more FDI inflows in the country.

Openness is found to be important variable having positive and significant

impact on FDI as the coefficient of this variable is registered as 19.832 which shows

the that 1 % change in this variable has tendency to bring 19.83% increase in FDI. It

shows that the more an emerging market tries to open its economy to outside external

trade, the more it can attract FDI. Export oriented FDI depends upon liberal trade

policies reflected in openness of the country as the transnational corporations are not

interested in market seeking behaviour initially.

The relationship between reserves and FDI is found to be positive and

statistically significant. The coefficient of this variable is determined as 10.842

indicating that 1% increase in reserves would cause the FDI to rise by 10.84%. As

high level of foreign exchange reserves reflects the strength of external payments

position and helps to improve the confidence of the prospective investors.

The variable openness, inflation and Reserve have been found to be the major

contributor in explaining 19.83%, 13.57% and 10.84% variation respectively in FDI

when there is 1% change in these variables. Variables openness, Reserve, GDP, and

LTD have estimated to give positive impact to FDI while negative impact of Inflation

and Exchange rate has been noticed on FDI.

6.7 Statistical Diagnostic for China

The stepwise regression analysis has been applied to find out the impact of

variables like GDP, INF, OP, EXCH, LTD and RES on FDI in China. Using Ordinary

Least Square, these explanatory variables are regressed to find out determinants of

FDI. The regression results of the above said analysis have been reported in Table 6.6

which shows that a combination of variables like LTD, RES and EXCH have been


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found to be statistically significant in China, while the coefficients of other variables

like GDP, INF and OP have shown insignificant t-value. The value of F is found to be

significant indicating the significance of the model. The value of adjusted R2 has been

estimated to be 0.921 which indicates the 92 % variation in FDI is explained by the

combination of these three variables i.e LTD, RES and EXCH in case of China, but a

very low value i.e. 1.194 of Durbin-Watson statistics and such a high value of

Adjusted R2 reveals the existence of spurious regression due to problem of

autocorrelation. Moreover, the values of VIF for all the variables have found to be

greater than 1 which indicates the interdependence among the explanatory variables

suggesting the problem of multicollinearity. There arises a need to verify the

reliability and robustness of these regression results by applying the cointegration

technique for finding the variables influencing FDI.

Table: 6.7 Regression Results (FDI as Dependent Variable)(China)

Variables Coefficients Std. Error t-Statistics Prob. VIF

C -5.310 1.213 -4.378 0.000 -

LTD 0.580 0.096 6.041 0.000 2.595

RES 0.533 0.167 3.184 0.003 3.568

EXCH 1.253 0.431 2.909 0.007 3.387

Adjusted R2 0.921

F-statistics 1.016

Prob. (F-statistic) 0.000

Durbin-Watson 1.194

6.8 Econometric Methodology

There is a need to check the stationarity of each data series before estimating any

relationships between FDI and its explanatory variables. All the variables in this

analysis have been taken in logarithmic form to make them stationary at lesser order

of integration. The stationarity of these variables has been tested by applying formal


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Table 6.8: Augmented Dickey Fuller Test Results

Unit Root Tests at Logarithmic levels

(China)


and Time Trend


Time Trend

1 LNFDI 0.6840 -4.1272* -2.1760

2 LNLTD -0.7906 -4.7332* -4.0304*

3 LNEXCH 0.2382 -1.1759 0.0992

4 LNRES 3.7463 -0.6648 -3.3241

Unit Root Tests at First Differences


and Time Trend


Time Trend

1 LNFDI -4.1029 -3.7463* -4.9632*

2 LNLTD -4.4278* -2.6660* -5.9029**

3 LNEXCH -4.0281* -4.0419** -3.4967*

4 LNRES -1.8907* -6.0382* -5.9065*

* denotes significance at the level 5% and ** denotes significance at the level 1%. Critical values obtained from Mackinnon(1991) are -2.85, -3.41, and -1.94 at 5% level and -3.43, -3.96, and -2.56 at 1% for first second and third model respectively.

Table 6.9: Cointegration Test Results (Trace)(China)

Hypothesized

No. of CE(s)

Eigenvalue Trace Statistic P values

None * 0.990704 212.2737 0.0000

At most 1 * 0.804975 67.25049 0.0000

At most 2 * 0.410514 16.57697 0.0342

At most 3 0.006217 0.193329 0.6602

Trace test indicates 2cointegrating equations at the 0.05 level. *denotes rejection of the hypothesis at the 0.05 level


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unit root procedure i.e. the Augmented Dickey-Fuller (ADF) test. Cointegration

analysis has been applied to study the long run relationship among these variables for

estimating impact of these variables on FDI in India. As the quarterly data of these

variables was not available for the period under study in case of China, therefore it

was not feasible to find the influence of all the six variables on FDI by applying

cointegration technique due to lesser number of observations and more number of

variables causing the loss of degree of freedom in the analysis. Only three explanatory

variables can be examined under cointegration analysis on account of the above

mentioned limitation, so there arises the problem of choice among all the independent

variables taken in the study. It was decided to choose these three variables i.e

LNEXCH, LNLTD, and LNRES on the basis of their significance obtained in the

regression results as mentioned in the Table 6.6.

6.9 Results and Discussion

The ADF test results for four variables such as LNFDI, LNEXCH, LNLTD,

and LNRES have been reported in Table 6.7 which shows that the null hypothesis of

presence of unit root has been rejected for all the first difference variables specified.

This means that the series are non-stationary at level but stationary at first-differences.

Cointegration analysis has been applied to estimate the long run relationship among

these variables. Firstly Cointegration Trace Test and Maximum Eigenvalue test have

been used to find out the existence of cointegration relationship. Table 6.8 and in

Table 6.9, exhibit these results showing the presence of three cointegrating vectors

which represents the foundation of a complete Vector Error Correction Model

(VECM). Results of which have been reported in the Table 6.10.

Table 6.10: Cointegration Test Results (Maximum Eigenvalue)

(China)

Hypothesized

No. of CE(s)

Eigenvalue Max-Eigen Statistic P values

None * 0.990704 145.0232 0.0000

At most 1 * 0.804975 50.67352 0.0000

At most 2 * 0.410514 16.38365 0.0228

At most 3 0.006217 0.193329 0.6602Max-eigenvalue test indicates 2cointegrating equations at the 0.05 level * denotes rejection of the hypothesis at the 0.05 level


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There have been obtained three alternative cointegrating equations after

applying cointegration test representing the relationships among the variables under

study. However, in these equations FDI appears on the right hand side as an

independent variable but the objective of this analysis was to identify the determinants

of FDI in China. Therefore some restrictions on cointegration relationship are

required to be imposed. (See annexure I) which are given as under.

β11 = 1, β13 = 0, β14 = 0

β21 = 1, β22 = 0, β24 = 0

β31 = 1, β32= 0, β33 = 0

With these restrictions three alternative equilibrium relationships defining the

determinants of FDI have been obtained in order to investigate impact of all the

variables on the FDI.

VECM has been executed to obtain the results of the analysis which are

shown in the Table 6.10. These results have also been expressed with the help of

equation 6.2 given as under:

FDI = -5.6502 +1.874 EXCH + 1.502LTD + 6.836RES ------- (6.2)(11.10) (12.79) (6.06)

Table 6.11: Estimated Cointegrating Relationship (China)

Equations Independent Variables Impact on FDI

1 LNEXCH 1.874**

(11.10)

2 LNLTD 1.502**

(12.79)

3 LNRES 6.836**

(6.06)** denotes significance at the level 1%. Figures in Parentheses are t values

The results of the analysis reveal that all the variables such as, EXCH, LTD and

RES have found to be statistically significant in China. X coefficients (elasticities) of


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these variables depict the percentage change in FDI due to one % change in these

variables taken in the study.

X coefficient of exchange rate is estimated to be 1.874 indicating positive and

statistically significant impact of this variable on FDI. 1% Change in EXCH has been

found to change FDI by 1.874%. The relationship between relative exchange rate and

FDI can be attributed to the fact that the appreciation of the source country currency

relative to that of the host country currency will reduce the relative cost of capital and

enable MNCs to invest more in that country as compared to countries with

depreciated currency (Liu, 2010).

Long term debt is determined to be a significant variable causing positive

impact on FDI as the coefficient of this variable is evaluated as 1.502 which shows

that the 1% increase in this variable will tend to increase FDI by 1.502%. Effective

utilisation of these funds for different growth oriented economic activities like

expansion of means of transport and communication, generation of power,

development of banking and financial sector in the economy resulted in more FDI

inflows in the country

The variable Reserves is found to be important variable having positive and

significant impact on FDI as the coefficient of this variable has been calculated as

6.836 which shows the that 1 % change in this variable has tendency to bring 6.836 %

increase in FDI. As high level of foreign exchange reserves reflects the strength of

external payments position and helps to improve the confidence of the prospective

investors.

All the variables like Exchange rate, Long term debt and Foreign exchange

reserves have been examined to be give positive impact to FDI and Foreign exchange

reserves has found to be the most important variable causing more variation in FDI as

compared to other explanatory variables analysed in the study.

In brief it can be said that X coefficients have shown positive and significant

impact of Exchange rate, Long term debt and Foreign exchange reserves in case of

China. In India, the variable Gross domestic product (GDP), Foreign exchange

reserves (RES), Openness (OP i.e sum of Exports and Imports as a percentage of

GDP) and Long Term Debt (LTD) has shown positive influence on FDI. However the


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Exchange rate (EXCH) and Inflation (INF) has negative impact on FDI. Conflicting

results of the exchange rate in both the countries can be attributed to the fact that If

FDI aim at producing for re-exports, it is complementary to the international trade.

Thus an appreciation of the local currency is supposed to reduce the FDI inflows since

it raises the local labour costs. A decrease in the relative labour cost, either through a

fall in its relative wages or real exchange rate deprecation, will increase the foreign

investment. On the other hand, if FDI aim at serving the local market, FDI and trade

are substitutes of each other. An appreciation of the local currency increase FDI

inflows due to higher purchasing power of the local consumers. The depreciation in

the real exchange rate of the FDI recipient countries will increase the FDI inflow

since it reduced cost of capital investment (Ren and Pentecost, 2008).

The results of this analysis substantiate the findings of some previous studies.

Cheng & Ma, (2008) observed positive and significant impact of real GDP, real per

capita GDP, foreign reserves and currency appreciation on the FDI flows. Shan

(2002) found that Labour supply, low Labour wage, Exports and Exchange rate has

significant and positive influence on FDI inflows. Dees (1998) concluded that the

variables Market size, low labour wage and Exchange rate have positive impact on

FDI. Botric and Skuflic (2005) and Casi and Resmini (2010) have found positive

influence of GDP and openness on FDI inflows in South East European countries and

some EU regions. Venkataramany (2002) found that the variables exports, GDP,

Terms of Trade have positive and Inflation has negative but significant impact on

FDI. Banga (2003) estimated the positive impact of GDP, Education and external debt

on FDI while exchange rate has been found to influence it negatively. Helldin (2007)

observed the positive and significant impact of GDP and domestic investment and

negative impact of Exchange rate on FDI.

However, the determinants of FDI differ from country to country depending

upon other incentives available in the country. The economic parameters have to be in

order as well, so that Indian economy can compete with China with more competitive

strengths to increase its share in global foreign direct investments. The substantial

amount of foreign direct investment from all over the world has played an important

role in the growth of the economy.

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