1

“A COMPARATIVE ANALYSIS OF FDI BETWEEN INDIA AND CHINA”

Submitted to

Lovely Professional University

In partial fulfillment of the

Requirements for the award of Degree of

Master in Science and Economics (2014)

Supervisor: Submitted by:

Mr. Maninder Singh Kalyan Kumari Nirola (11211273)

Lecturer of Economics and Commerce

DEPARTMENT OF ECONOMICS

LOVELY PROFESSIONAL UNIVERSITY

JALANDHAR NEW DELHI GT ROAD

PHAGWARA

PUNJAB

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Acknowledgement

Primarily, I would like to express my sincere gratitude to my mentor Mr. Maninder Singh for the

continuous support of my dissertation, for his patience, motivation, enthusiasm, and immense

knowledge. His guidance helped me in all the time of research and writing of this dissertation. I

could not have imagined having a better advisor and mentor for my dissertation.

Besides my mentor, I would like to thank Dr.Surender Singla for this continuous guide and sup-

port. I would also like to thank Mr.Gurpreet Singh, Mr.Shounak Das, Mr.Arun and Mr. .Abshiek

Jha for their guidance throughout my research.

I thank my fellow classmates of MSc. economics: Mr.Kinga Delek, Mr.Dhan Bhadur,

Mr.Namgay Tshering, Mr.Dhan Kumar, Ms. Sonali and Ms. Rajbinder Kuar for the stimulating

discussions, for the sleepless nights we were working together before deadlines, and for all the

fun we have had in the last two years. I would also like to thank my roommate Miss.Uroosa Pra-

dhan from B.Sc Fashion technology for her continuous support during my research period.

Last but not the least; I would like to thank My Ministry, Ministry of Education and Govern-

ment of Bhutan for giving me this opportunity and lending me continuous support throughout my

service.

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CERTIFICATION APPROVAL BY FACULTY ADVISOR

TO WHOMSOEVER IT MAY CONCERN

This is to certify that the project report titled “A COMPARATIVE ANALYSIS OF FDI BE-

TWEEN INDIA AND CHINA” carried out by Ms. Kalyan Kumari Nirola has been accom-

plished under my guidance and supervision as a duly registered MSc. Economics student of the

Lovely Professional University, Phagwara. This dissertation is being submitted by her in

the partial fulfillment of the requirements for the award of the Master in Science and Econom-

ics from Lovely Professional University.

Their dissertation represent her original work and is worthy of consideration for the award of the

degree of Master in Science and Economics.

Mr. Maninder Singh

(Faculty Advisor)

Date:

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DECLARATION

I, Kalyan Kumari Nirola, hereby declare that the work presented herein is genuine work done

originally by me and has not been published or submitted elsewhere for the requirement of a de-

gree programme. Any literature, data or works done by others are cited within this Dissertation

and has been given due acknowledgement and listed in the reference section.

Kalyan Kumari Nirola

(11211273)

Date: 29 /05/2014

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Contents 1.Introduction ................................................................................................................................................ 6

1.1 The Scenario of China ....................................................................................................................... 9

1.2 The Scenario of India ........................................................................................................................ 9

2. Literature Review .................................................................................................................................... 12

3. Research objectives ................................................................................................................................. 22

3.1Data ................................................................................................................................................... 22

4. Research Methodology: .......................................................................................................................... 24

4.1 Model Building: ................................................................................................................................ 24

4.2 Hypothesis: ..................................................................................................................................... 26

5. Data Analysis: ......................................................................................................................................... 28

5.1 Model 1: ............................................................................................................................................ 28

5.2 Model 2 ............................................................................................................................................. 34

6. Conclusion and Recommendation .......................................................................................................... 41

6.1 Recommendations: ......................................................................................................................... 42

6.2 Conclusion: ..................................................................................... Error! Bookmark not defined.

7. References and Bibliography .................................................................................................................. 43

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Chapter 1

Introduction

FDI has been recognized as an important driver for economic growth and development. A rapid

development during the last two decades is the continuous growth of FDI in the global economic

landscape. This unprecedented growth of global FDI in 1990 around the world makes FDI an

important component of development strategy in both developed and developing nations and re-

quired policies are designed in order to stimulate inward flows (Dunning,2002). The home coun-

tries want to take the advantage of the vast markets opened by industrial growth. The hosts coun-

tries want to acquire technological, managerial skills accelerate domestic Savings, foreign ex-

change and overall economic growth.

FDI is an investment that a parent company makes in a foreign country (Glober man and Shapiro

2003). On the contrary, FII is an investment made by an investor in the markets of a foreign na-

tion (Glober man and Shapiro 2003). Both foreign direct investment (FDI) and Foreign Institu-

tional Investment (FII) are related to investment in a foreign country. The FII investment flows

only into the secondary market but FDI is considered more stable than FII. Foreign direct in-

vestment also helps in improving the health of the people by spending on preventive medicine

and potable water and in general, all expenditures directed towards increasing the well-being of

the citizens. Studies also have proved that Foreign direct investment have enhanced the technol-

ogy and economic growth of the country (Shujieet al., 2001)

India and China are the two major economies that have adopted market oriented economic poli-

cies designed to attract FDI inflows. India and China, these economies are now getting increas-

ingly integrated with the global economy as they open up their markets to international trade and

investment inflows. They are growing very fast due to foreign direct investment. Both the coun-

tries have been enjoying high positive average gross domestic product growth rate over the last

two decades although China substantially exceeds India.

There are very few analytical studies on the inter-state or inter-province differences in foreign

direct investment inflows (Yingqi et al., 2004). Several studies analyses inter-country differ-

ences in foreign direct investment emphasizing location advantages (Wei et al., 2000). These

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studies have identified various location advantages such as size of the market, income and its

growth rate, membership of a regional union, labor and skill content of the population, infra-

structure facilities like transport, electricity, and port facilities, other variable which is represent-

ing good governance such as legal dispute settlement the rule of law and spending on social sec-

tor to enhance the skills of the population

It is always essential for attracting the foreign investors, as it is the prime consideration prior to

invest their money into foreign countries. When we just talk about size of the market-the name of

two countries immediately came into our mind (Daisuke, 2008) .Those countries are certainly

India and China. India and China are the most populated and huge countries of the world and

they are always attracting many foreign investors due to many comparatives advantages like cost

of labour. This case has become even more intense in modern times as western companies feel

the pressure of market shrinking down in their home territory. Few research works has been done

in this field but it is always essential to have a closer look at the scenario of these two countries

as far as the advantages and disadvantages of investing in these two place are concern.Wal-Mart

the retail giant has made a lot of FDI in China while the large motor company from South Korea

–the Hyundai Motor Corporation has done the same in India. (Bandelji et al., 2002)

China and India adopted an ultra-import substitution strategy in the form of autarky or self-

reliance from early 1950s to late 1970s and to early 1990s in the case of China and India, respec-

tively. Under this strategy, trade was restricted and heavy reliance on import substitution and ex-

ports were merely carried out to pay for imports (Rajan, 2005). India took similar path of liberal-

ization since 1991(more than ten years after China's open door policy), by slowly liberalizing the

restrictions and allowing FDI through automatic route barring a few strategic industries of secu-

rity concern. FDI in India is freely allowed in all sectors, including the services sector, except

some beyond a ceiling. FDI for virtually all items or activities could be brought in through the

automatic route under the power vested with the Reserve Bank of India and for the remaining

items and activities through Government approvals.

In this 21st century, business and trade have become more competitive and diversified than ever

before. Local market is shrinking down in a faster pace, business is looking for options for ex-

pansion and international trade is being accelerated. As a result, FDI is being accelerated at a

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faster rate and different countries of the world are trying their best to attract more and more FDI

as it proves to be a great force for triggering the domestic economic development. (Daisuke et

al., 2008).

In this 21st century globalization makes this planet as a global village and people of different

countries are getting closer and closer (Dunning, 2002). Due to immense development of tech-

nologies, investors of different countries are looking forward to find business opportunities be-

yond the conventional territory and as a result, one of the most popular and highlighted terms in

modern business- , FDI is evolving at a greater pace than ever before (Alfaro et al., 2004). In this

era of globalization and intense competition, foreign direct investment has become a very com-

mon and immensely important phenomenon for consumers, producers and different governments

(Balasubramanyan et al., 1996).

While the large Multi-National Corporations of the West are getting advantages of market ex-

pansion from FDI, the host countries are also utilizing it as a major mechanism and source for

accelerating their domestic economic growth. Several research works are taking place over the

years in the field of FDI finding the suitability and attractiveness of various FDI destinations. If

any operators want to establish a large business sector across the globe, the first thing to consider

is the size of the market.

FDI inflows into the services sector declined by 3.5 percent to US$ 4.66 billion during the April

to January period of last fiscal, according to the Department of Industrial Policy and Promotion

(2002). Comparatively China is better in attracting FDI than India. One of the major determi-

nants of FDI inflows is the growth of GDP, China‟s total and per capita GDP are higher than In-

dia‟s, marking it more attractive for market seeking FDI. The investors find China as a better

destination of FDI than India because, China has higher literacy and education rates making it

more attractive to seeking investors. China is endowed with large natural resources. In addition,

china‟s physical infrastructure is more competitive, particularly in the coastal areas. (Mottaleb et

al., 2010). Nevertheless, India may have an advantage in technical work force, particularly in

information technology (Shapiro, 2002). Most of the population in India has better English lan-

guage skills than China. Inward FDI into China continues to surpass that of India. The

2011,World Investment Report, commissioned by the United Nations Conference on Trade and

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Development(UNCTAD,2009),outlines that China continues to be the largest recipient of FDI

flows amongst the developing countries and second overall after the U.S. In 2010, China re-

ceived US$106 billion, an increment from US$95 billion in 2009(. India, on the contrary, fell

from its impressive rating of 8th largest recipient of FDI flows in 2009 to 14th in the 2010. This

is equivalent to US$36 billion and US$25 billion FDI inflows received in 2009 and 2010, respec-

tively (World Bank report, 2011).

The Scenario of China

Ever since China reformed its economy, it has understood the immense importance of FDI and

urged for foreign capital participation in the economy. After the reform, China has received re-

markable amount of FDI .It has become the second largest recipient of FDI just behind the US

and definitely the largest among the developing countries (Liu et al., 2000). The FDI in China

becomes most popular since 1979 and it has received $306 billion in between the next 20 years

(China statistics, 2000). That is attributed to few major incidences in that span of 20 years in-

cluding the establishment of Special Economic Zones (SEZs). The government of China estab-

lished four SEZs in Guangdong and Fujian provinces and offered special incentive policies for

FDI in these SEZs. That make the movement of FDI in the country towards upward direction and

the trend has not been changed yet (Singh, 1996). China‟s overall economic reform process and

China‟s commitment to the open door policy and market-oriented economic reform, proved to be

a success in gaining the confidence of foreign investors in China (China Statistic, 2000). The es-

tablishment of new enterprises such as new foreign funded and joint venture companies has been

the main mode of absorbing FDI into China (Zhang et al., 2002).

The Scenario of India

India considered as one of the most suitable place for foreign investors despite problem areas

like bureaucratic hassle. The country presents a wide area of investment opportunities for the in-

vestors and increasingly promoting the country as the place to invest. Over the years, it has not

been able to attract foreign direct investment at the same pace of China, but the picture is im-

proving for India. The investors cannot ignore India anymore, which as the country has the po-

tentiality to become third largest economy of the world within short span of time. It is also the

second largest among emerging nations. India is also one of the few markets in the world, which

(Ahya et al., 1999).

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Recently the Government of India has liberalized their policies in certain sectors, like Increase in

the FDI limits in different sectors and also made the approval system far easier and accessible.

Unlike the historical tradition, today for investing in India government approval do not require in

the special cases of investing in various important sectors like energy, transportation, telecom-

munications etc. (Nagaraj et al., 2003).

FDI always brings certain benefits to national economies. It can contribute to Gross Domestic

Product, Gross Fixed Capital Formation and balance of payments. There have been empirical

studies indicating a positive link between higher GDP and FDI inflows (Kamath et al., 1994).

FDI can also contribute toward debt servicing repayments, stimulate export markets and produce

foreign exchange revenue. Foreign direct investment is increasingly being recognized as an im-

portant factor in the economic development of countries (Kamath et al., 1994).

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Figure 1: FDI in India and China from 2003 -2012

The graph above explains us clearly that India is lagging behind China in attracting foreign direct

investment inflows to the respective countries. There may be many reasons lagging behind, the

important one is that India has opened its trade in 1990 has were china has open much before.

There is a little fall of FDI inflow in China in the year 2009 due to the impact of world recession

but FDI inflows in India was constant.

1 2 3 4 5 6 7 8 9 10

Year 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012

India(Millions) 4234.34 5771.29 7269.4 20029.1 25227.7 43406.3 35581.4 27396.9 36498.7 23995.7

China(Millions) 56198.2 62108 104109 124082 156249 171535 131057 243703 280072 253475

0

50000

100000

150000

200000

250000

300000

Mill

ion

s FDI in India and China

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Chapter 2

Literature Review

By comparison to many literature and its in- depth study found out the emergence and

growth of inward FDI in China and India .The literature remains rather sparse in the area of Chi-

nese and Indian outward FDI and in need of further development. Some published research arti-

cles have found out the determinants of Chinese or Indian direct investments abroad, and a few

have attempted to explore the reasons behind it.Many researches has been carried to find out the

inward FDI flows to India and China rather the outward from these countries. Why firms engage

in FDI, Hymer (1959) was the first one to explore this phenomenon in his doctoral dissertation

and stated „FDI as a means of transferring tangible and intangible assets to organize international

production.‟

Graham et al., (2001) “Foreign direct investment in china”, even after the 1992 reforms the for-

eign direct investment in China remains very controlled by state policy and indeed significant

changes in this policy would seem to be indicated by China‟s admission to the WTO. Currently

the central government of China, as well as provincial governments do regulate entry of FDI

closely or at least attempt to do so. Entry of foreign firms is often conditioned on the achieve-

ment of industrial policy goals as laid out by the state. Foreign firms are most welcome when

these goals cannot be fulfilled by domestic firms. The entry of a foreign firm can be subject to

numerous conditions, for example, such performance requirements as having to use local suppli-

ers, often designated by the government, or locating in certain areas, or setting up the local oper-

ation as a joint venture. Notably that China seeks access to foreign capital and technology but

often still seeks to avoid or, barring complete avoidance, at least to regulate competition between

domestic enterprises and foreign invested ones. Thus, while the aversion to competition has sof-

tened in recent years, it has certainly not entirely gone away.

Siddharthan (2004) „Regional differences in FDI inflows between India and China comparison‟,

this paper finds out that FDI mainly goes to those countries that are rich. China is technological-

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ly advanced, have high skilled workforce and endowed with good physical and institutional in-

frastructure .The main findings was that the determinants of regional distribution of FDI flows in

China and India are quite similar to the pattern influencing inter-country FDI flows .The devel-

oped regions and regions that are poor in physical, institutional and social infrastructure receive

very less FDI.FDI was less in the regions where they have low life expectancy ,low in human

development and socio-economic indicator, and poor in governance indicator.

Bajpai and Dasgupta (2004) China‟s record in attracting foreign direct investment is far superior

to that of India. In fact, India has been considered an underachiever in attracting FDI. However,

within this otherwise firm conviction about unmatched Chinese superiority in attracting FDI in-

flows vis-a-vis India, there has occasionally been some skepticism about what all China includes

while compiling its FDI figures and consequently about the actual intensity of the FDI gap be-

tween China and India as suggested by the official statistics of the respective countries. On the

other hand, it has been pointed out in the FDI literature that Indian FDI is hugely under-reported

because of non-conformity of India's method of measuring FDI to the international standards.

Lifeng (2005), after the comparative study of China and India in FDI tax laws, the findings of the

research are, i) although China and India have significant different in FDI tax laws, they can

learn a lot from each other. China should draw on some experiences of India in the process of

reform of tax laws. ii) Secondly, during the proceed of transiting to national treatment and trans-

forming traditional preferential duty as smoothly as possible.iii) to perfect the tax laws system is

the most important task facing China in the tax reform. China should recognize that reasonable

tax laws system, instead of relative lighter tax duty, is the most attractive magnet to FDI inflow

in the long term. iv) Finally China has transferred super national treatment to national treatment

in FDI tax laws; the attraction of China‟s tax laws to FDI will be limited in the future. In order to

absorb FDI successfully in both quantity and quality, China should pay more attention to perfect

the financial policy, the law system and the competitive environment etc.

Hattari (2006) has investigated trends, patterns and drivers of intra-ASEAN FDI flows using bi-

lateral FDI flows between ASEAN, China and India for the period 1990 to 2005. The data indi-

cate that intra-ASEAN FDI flows appears to have intensified during the period post-1997 finan-

cial crisis, with a large part of these flows concentrated between Singapore and its neighboring

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countries like Malaysia and Thailand. Augmented gravity model fits the data fairly well and is

able to capture around 65 percent of the variations in existing intra-ASEAN FDI flows. The find-

ing was larger host country size, higher the institution quality in the host country and greater fi-

nancial depth in the host country all appear to facilitate bilateral FDI flows within Asia.

The determinants of the outward foreign direct investment of China and India, (Tolentino, 2006)

the paper discussed about FDI outflow rather than inflow of India and China. All other research

does not discuss about FDI outflow but this paper has found out the reasons .This paper exam-

ines the relationships between several home country-specific macroeconomic factors and the

level of the outward FDI of China and India using multiple time-series data from 1982 to 2006

and from 1980 to 2006, respectively. With the use of a vector autoregressive model assessing the

causal relationships of the endogenous variables, the empirical research proves that Chinese na-

tional characteristics associated with income per capita, openness of the economy to international

trade, interest rate, human capital, technological capability, exchange rate and exchange rate vol-

atility do not cause the level of outward FDI of China and India. By contrast, the national tech-

nological capability of Granger causes their level of outward FDI. The level of outward FDI of

China does not Granger because any of the home country-specific macroeconomic factors con-

sidered, while the level of outward FDI of India Granger causes their national interest rate.

Sinha, (2007), The main findings was the reasons of India lagging behind China in attracting

FDI .China has grown rapidly and received $60 billion dollars in 2005 in FDI and India merely

got $6 billion in 2006(world bank report, 2005).India being the largest democracy in the world

lagged behind due to its focus on services and specialized skill based relatively small manufac-

turing based with respect to China. India‟s growth model is based on IT and skilled manufactur-

ing which is depended on human capital .Indian growth is related to human capital stock. The

reasons for attracting less FDI in India is mainly due to political instability, exchange rate volatil-

ity and extend of corruption .China liberalized it‟s economy in 1978 and grew rapidly to become

one of the largest economy in the world .china is attracting FDI due to its congenial business

climate, developing Special Economic Zone (SEZ) and flexible labor law etc.

Balek et al., (2008), the cointegration analysis and a vector error-correction (VEC) model are

applied to examine the short- and long-run relationships among FDI, economic growth, and the

15

environment in China and India. The results show that FDI inflow plays a pivotal role in deter-

mining the short- and long-run movement of economic growth through capital accumulation and

technical spillovers in the two countries. However, FDI inflow in both countries is found to have

a detrimental effect on environmental quality in both the short- and long-run, supporting pollu-

tion haven hypothesis. The result has found that in the short-run, there exists a unidirectional

causality from FDI inflow to economic growth and the environment in China and India ─ a

change in FDI inflow causes a consequence change in environmental quality and economic

growth, but the reverse does not hold. This research used Granger causality test to see the cause

and effect relationship of FDI and the economic growth.

According to Sweeney, „Foreign Direct Investment in India and China‟ (2008) FDI has been a

major contributor to both nations‟ growth brings in more than just investment capital. Both na-

tions still protect large economics sectors from investment, are shown to approve foreign acquisi-

tions of domestic firms and are characterized by excessive bureaucracy .However, continued

liberalization, when done strategically and carefully, may be an important source for maintain

prolonged economic growth.

In February 1998, during the middle of the last major global economic crisis, Chaun Leekpai, the

Thai Prime Minister, summarized the need to look inward:

If you are going to be part of this global market, you had better be able to defend yourself from

this market. . . . One of the lessons this crisis has taught us is that many of our structures and in-

stitutions were not ready for this new era. Now we have to adapt ourselves to meet international

standards. The whole of society expects it. They are looking for better government and transpar-

ent government.

A Comparative analysis of FDI in India and China (Himachalapathy, 2009) examines the status

of inward Foreign Direct Investment flow into India and china ever since Macro-economic struc-

tural changes initiated in 1991 in India and 1978 in china. The impact of ongoing process of Lib-

eralization, Privatization and Globalization and its implications in attracting inward FDI into two

giant economies has become focus of any study. The impact of FDI can be evaluated in terms of

Economic Indicators such as GDP, GDP growth rate, Import Trade, Export Trade and Trade

Openness. This paper was an in-depth study of FDI between India and China, there was finding

16

of FDI between inter-state and Inter-province of China, sectorial comparison of FDI between In-

dia and China. Chennai is in the third in the top five destination Cities in Asia-Pacific, 2008 in

attracting inward FDI flow in terms of the Capital of Investment that is 7 $ Billion US Dollar

Capital of Investment and 30535 new jobs while Shanghai and Beijing are in the first and se-

cond, ahead of Chennai in terms of the Capital of Investment and number of new Jobs created

(UNTCAD, 2009).

Maheshwari (2010), the objective of this research is to initiate discussions on standardizing the

method for measuring Foreign Direct Investment across countries. It is important to use con-

sistent method so that there is a faithful representation of a country's investment climate and the

information is relevant for the purpose of foreign investors. India and China measures Foreign

Direct Investment using two different methods. India measures FDI based on equity investments,

whereas China includes certain items, which do not strictly fall under the purview of

FDI.Inclusion of items other than equity increases the reported FDI in China. It is presumed that

overall higher reported FDI makes China appear more attractive than India. The findings suggest

that once adjustments for the definitions are made, difference between the FDI in China and In-

dia decreases substantially.

According to Tang.H (2011), “FDI Policies in China and India”, this paper has examine empiri-

cally how the different FDI policy approaches in China and India shape foreign firms‟ perception

about the business environment. Using World Bank survey data, the paper tries to find that For-

eign-Investment enterprises in the host country‟s India perceive more obstacles to business oper-

ations and growth than domestic firms, especially for issues related to government regulations

and the legal environment. The findings are consistent with the fact that India‟s FDI liberaliza-

tion has lagged domestic liberalization. Opposite patterns are observed in the data for China.

Compared to domestic firms, FIEs in China generally find government officials more helpful in

promoting business development. In the area of tax exemption, FIEs perceive far fewer obsta-

cles. There is no difference in perception about the legal and financial institutions between FIEs

and domestic firms.This paper is the first attempt to provide a quantitative assessment of the im-

plications of FDI policies in the two largest developing nations. The findings suggest different

policy priorities for the two countries. For India, the priority in their next stage of reforms should

17

be on reducing obstacles for FIEs, whereas for China the priority should be on domestic liberali-

zation.

Duan (2010), FDI in BRICs a sector level analysis, it compares the overall trends and industrial

patterns of inward foreign direct investment in the BRICs and explains their determinants. The

overall trend of the inward FDI in the BRICs is increasing. Nevertheless, the industrial patterns

of inward FDI are different from each other. In Brazil, Russia and India, the tertiary sector re-

ceives the most inward FDI on average over the past decade, while the primary sector receives

the least and the secondary sector is in the middle. But China has a special industrial patterns of

inward FDI, that is, the secondary sector dominant the majority of the inward FDI and the prima-

ry and tertiary sectors receive only a bit. In addition, there are three main factors that determinant

the industrial patterns of inward foreign direct investment in the BRICs,they are develop courses,

resources and the business environment. This research fills the gap of study on the industrial pat-

terns of inward foreign direct investment in the BRICs, which can give help to the further study.

However, the study of the determinants of the industrial patterns of inward foreign direct invest-

ment in the BRICs still belongs to theoretical analysis.

Asghar et al., (2011), this study examines empirically the relationship between FDI and econom-

ic growth using heterogeneous panel for the period 1983-2008. The empirical findings of Lars-

son panel co-integration show that FDI and economic growth are cointegrated. The results of

panel homogeneous causality hypothesis show the existence of bi-directional causality between

FDI and economic growth while the results of panel homogeneous non-causality hypothesis con-

firm the existence of unidirectional causality running from FDI to economic growth in selected

panel. The results of heterogeneous causality hypothesis show the existence of bi-directional

causality between FDI and economic growth only in case of Malaysia. The existence of uni-

directional causality running from FDI to economic growth is observed in cases of Nepal, Singa-

pore, Japan and Thailand whereas the uni-directional causality is also found running from eco-

nomic growth to FDI for Pakistan, Bangladesh and Sri Lanka. However, no causality in any di-

rection is found in cases of India, Maldives, Indonesia, China, Philippines, Korea Dem and Sin-

gapore.

18

According to Agrawal (2011), a Comparative Study of China and India, he tries to find out the

effect of FDI on economic growth of China and India .The study takes care of the issue of struc-

tural change in the economy by choosing the appropriate time period and method. First of all the

model was built on modified growth model from basic growth model. The factors included in the

model were GDP ,Human capital ,labor force,FDI and gross Capital formation among which

GDP was dependent variable while rest four were independent variable. After running ordinary

least Square(OLS) method of regression ,the study confirms that FDI promotes economic growth

and further and further provides an estimate that 1% increase in FDI would result in 0.07% in-

crease in GDP of China and 0.02% increase in GDP of India ,were China‟s growth exceeds to

India‟s by 0.05% due to 1 % increase in FDI.The findings of the research states that ,majority of

the foreign investor prefer China over India due to bigger market size, offers market ,easy acces-

sibility to export ,incentives, cost effectiveness, developed infrastructure and macro-economic

climate. The benefits of FDI are not known to all developing countries, as most of them compete

with each other to attract FDI by liberalizing their trade policies and offering various incentive

packages, such as tax rebates, trade liberalization measures, establishment of special economic

zones and incentive packages to foreign investors.

Kalirajan et al., (2012),With a few exceptions, however, many developing countries are not very

successful in inviting FDI.The findings in this paper states that, how China is more successful in

attracting FDI consistently than India. The reports on FDI Favorable destinations given by dif-

ferent agencies indicates different findings, mostly they indicate china as the number one favora-

ble destination .Some report argued that China‟s potential FDI has shown a declining trend late-

ly and India‟s domestic policy had been major „behind the border‟ constraint to attract FDI con-

tinuously. This paper uses panel data of 2000 to 2009 over 20 top export destinations for China

and India examine whether china and India are able to achieve their potential FDI, the trend of

FDI.The analysis in this studies shows that china‟s potential FDI has shown a declining trend

over 2008 and 2009from 81% to 78% while India has shown a declining trend from 67% to 72.

According to Bose (2012), “Advantages and Disadvantages of FDI in China and India”, he has

studied the positive and negative sides for the foreign investors while they go for direct invest-

ment in India and China. A descriptive and explorative research study has been carried out for

investigating the current proposition of the concerned case of FDI in those two countries. Ad-

19

vantages of investing in India includes-huge market size and a fast developing economy, availa-

bility of diversified resources and cheap labour force, increasing improvement of infrastructure,

public private partnerships, IT revolution and English literacy, openness towards FDI, regulatory

framework, and investment protection, whereas few drawbacks likes huge section of poor and

middle class, bureaucracy, power shortage and ethnic diversified are also available in the coun-

try. As far as the case of China is concern positives areas are the immense size and growth of the

Chinese economy and very bright prospects, resource availability and low cost of labour force,

immense development in relevant infrastructure, openness to international trade and easy access

to international markets, development and alteration of the regulatory framework, investment

protection and promotion. There are also few drawbacks as well like the regulator burden, hin-

drances in free flow of information, lack of English literacy and so on. However, factors like ab-

sence of market economy in China and hugely diversified culture in India make life bit difficult

for the operators, but the benefits are overwhelming in compare to drawbacks. That is the prime

reason why these two destinations will keep attracting foreign investors and will remain as the

most attractive paces to put the money and earn future dividend (Chen et al., 1995).

Ray (2012) “Impact of Foreign Direct Investment on Economic Growth in India”, the role of FDI

in the growth process is very important for all the developing countries including India. The pur-

pose of the paper is to analyze the causal relationship between Foreign Direct Investment and

economic growth in India and tries to analyze and empirically estimate the effect of FDI on eco-

nomic growth in India, using the cointegration approach for the period, 1990-91 to 2010-11. The

empirical analysis is based on ordinary Least Square Method .This method suggests that there is

positive relationship between foreign direct investment and GDP and vice versa. The data‟s is

checked for stationary using Augmented dickey Fuller test .if the data seems Non stationary then

it will be converted to stationary by differencing. The unit root test clarified that both economic

growth and foreign direct investment were found to be integrated of order one using the Kwiat-

kowski, Phillips, Schmidt and Shinn (KPSS) test for unit root only. The cointegration test con-

firmed an existence of long run equilibrium relationship between the two as confirmed by the

Johansen cointegration test results. The Granger causality test finally confirmed the presence of

causality, which runs from economic growth to foreign direct investment. The Ganger test has

four steps; the test should satisfy all the steps to show long run relationship between FDI and

20

GDP growth. The error correction estimates gave evidence that the error-correction term is statis-

tically significant and has a negative sign, which confirms that there is not any problem in the

long-run equilibrium relation between the independent and dependent variables. For FDI to be a

noteworthy provider to economic growth, India would do better by focusing on improving infra-

structure, human resources, developing local entrepreneurship, creating a stable macroeconomic

framework and conditions favorable for productive investments to augment the process of devel-

opment.

According to Iqbal et al.,(2013), examines the FDI flows in China and India as china is receiving

more FDI comparing to India the main reasons were China opened its door to FDI in 1979 and

liberalized its trade .China adopted proactive approaches like providing low labor costs, potential

foreign market ,favorable investment incentives which plays important role for inviting FDI.The

main purpose of conducting the research is to find out the patterns and flow of Foreign Direct

Investment and economic growth in Chinese and Indian economies. An exploratory qualitative

research is to find out the flow, patterns and directions of FDI and economic growth in these

economies. The research examined which country is in better position for attracting more FDI

and enhancing their economic growth. By examining researches and articles of different schol-

ars and other data of FDI published by World Banks, IMF and central banks and other facts and

figures relating to FDI and economic growth of these countries. The paper found that China is in

far better position in infrastructure, economic activities and China has favorable business envi-

ronment as compare to India. However India compete China in better legal and political system.

The main findings of the foreign direct investments have positive impact on economic growth of

Indian and Chinese economies. FDI positively influences GDP growth rate of these countries

that lead to increase in their per capita income.

Santra (2014), trade among India and China is growing steadily still there is scope of further im-

provement, as shown by current study. There are many commodities in which India is having

comparative advantage as compare to China like Chemicals, Food and live animals, Mineral

fuels, lubricants and related material, crude materials. Improvement in export of these commodi-

ties from India to China is possible. There are also some commodities in which China is having

comparative advantage over India like machinery and transport equipment. In some commodi-

ties, China and India both are having comparative advantage so they are basically competing

21

with each other in the world market in these commodities. In some commodity group, both the

countries do not have comparative advantage like Animal and vegetable oil fats, commodity and

transactions, beverages and tobacco. For most of the commodities Revealed Comparative Ad-

vantage has been stable throughout the period of study, there are very few commodities in which

there was large variation within the time period.

Zelaya et al., (2014) “Foreign direct investment decisions into china and India”, they have inves-

tigated the foreign direct investment decisions of the multinational companies into China and

India between 2003 and 2008. The results indicate that large market size, high GDP growth and

low wages are the major determinants of their foreign direct investment decision. Profitable

companies chose to invest into India and China because of labour availability and flexible regu-

lations. The characteristics that affect the size of the FDI drastically differ when an MNE invests

into India, as oppose to China. Furthermore, the result shows that a 10% increase in the size of

the firm (measured through total assets) is associated with about a 2.1% increase in the size of

the FDI into China and India. However, independently those firms investing solely into China

drive these results, whereas the results are not statistically significant for firms investing only

into India. First, larger firms may have prior experience with foreign investment, which provides

an easier transition for these multinationals to relocate into China. It may be the case that larger

multinationals that are more profitable are able to better service such large emerging countries,

where during unfavorable time periods profitable firms may be able to keep their investment

afloat.

22

Chapter 3

Research objectives

Economies are rapidly changing the phase from agriculture to manufacturing to services sector.

Almost all the developed nations like USA, Japan, Germany, and UK have been following the

same path. Among the developing countires,Brazil ,Russia and China have followed the same

route but India seem to have skipped the manufacturing sectors and focus more on services sec-

tor whether half of the economics growth is contributed. Current internet and telecommunica-

tions seems to be a rational opportunity but supplementary with manufacturing activity is desira-

ble. If India continues with the same phase of economies then India might miss that phase and as

China grows, consumption might increase, resources might become scares, and efficiencies seek-

ing manufacturing might shift to other country. There is not much developed manufacturing ac-

tivity in India and the country is flooded with the Chinese products. The skill intensive and ser-

vices sector growth has resulted in jobless growth. Due to this, India is experiencing rapid

growth and other part of the country is unaffected by the growth. The pattern of growth might

create pressure on the inadequate infrastructure and put burden the urban areas. Continue growth

in services sector might lead to migration of significantly larger labor force to urban areas and

might create inequalities and unbalanced growth. The objectives of this research is,

1) To study the Impact of FDI on Economic growth of India and China.

2) To highlight the determinants of Foreign Direct Investment in India and China.

Period of Study

The period of study consists of ten calendar years commencing from 2003 to 2012.

Data

The basis of the research is on secondary data from the various sources such as World Bank re-

port and United on conference on Trade and Development .Secondary data are edited primary

sources, second-hand version. It is simply the analysis of pre-existing data in a different way or

to answer a different question than originally intended. Secondary data analysis utilizes the data

23

that was collected by someone else in order to further a study of interest (Sachdeva, 2009, Busi-

ness research methodology). The data set has been collected from the databank of World Bank

and has been matched up against the data available on the site of UNCTAD (United Nations

Conference on Trade and Development). Above two data, sources have been chosen because

they are the most reliable sources of data and are used by almost every researcher.

24

Chapter 4

Research Methodology

Methodology is always the most important of any study. It provides the necessary base and struc-

ture of every article. Therefore, a sound methodology is always the most prioritized concern of

every research. Proper and adequate importance has been given for preparing the methodology

and afterwards it has been decided that a qualitative and descriptive research methods are appro-

priate for this study. The entire research has been carried out and directed towards achieving the

above-mentioned research objectives. The research questions consisted of evaluating the impact

of FDI on economic growth and determinant of FDI inflows in China and India. In order to an-

swering the research questions, efficiently thorough review of the existing literature has been

done and descriptive data has been gathered which is essentially relevant for this study.

The research is to find out the flow, patterns and directions of FDI and Economic Growth in In-

dia and China. It is to examine which country is in better position for attracting more FDI and

enhancing their economic growth. It is also to examine which country‟s economy is performing

well as compare to other in term of FDI and Economic Growth. Secondary data of World Bank,

IMF, UNCTAD investments reports and other financial institutions are used to achieve the ob-

jectives of this paper.

Model Building

The purpose of the research is to study the impact of foreign direct investment on economic

growth. Two models were framed and fitted. Let the model be economic growth model and for-

eign direct investment model. The first model, economic growth model depicts the contribution

of foreign direct investment to economic growth in India and China. The second model, foreign

direct investment model shows the factors contributing the foreign direct investment in India and

China. The two model equations are expressed below:

1) FDIG= f (GDPG)

2) FDI = f [RD GDP, TTGDP, FN.Position, EXR, FOREX GDP, Inflation].

FDI=β0+β1RD GDP+β2TTGDP+β3FN.Position+β4EXE+β5FOREX GDP+β6Inflation

25

Where,

GDP = Gross Domestic Product

FDI = Foreign Direct Investment

TT GDP= Total Trade as percentage of GDP.

Inflation = Inflation rate calculated as percentage change in Consumer Price Index (CPI percent).

FOREX GDP = Foreign Exchange Reserves as percentage of GDP.

FN. Position = Financial Position

FN. Position = Ratio of external debts to exports

RD GDP = Research and development expenditure as percentage of GDP

EXR = Exchange rate

FDIG = Foreign Direct Investment Growth.

GDPG = level of Economic Growth.

Karl Pearson Correlation Coefficient is used to show the impact of FDI flows on economic

growth in India and China for the first model. It helps to show the relationship between FDI in-

flows and GDP growth.

For the second model multiple regression is been used for finding out the major determinants of

Foreign direct investment in these two Countries. Multiple regressions (the term was first used

by Pearson, 1908) are to learn more about the relationship between several independent or pre-

dictor variables and a dependent or criterion variable. To make multiple regression clear let us

take an example that how much a person enjoy his/her job and which factor influences the most.

There are many variables, which effect the job satisfaction like salary of the person, experience,

working environment, sex, age and reputation of the organization. If the collected data on all of

these variables, perhaps by surveying a few hundred members of the public, the findings will be

to see how many and which of these variables gave rise to the most accurate prediction of job

satisfaction. The finding may for the job satisfaction of the individual, may be that some variable

26

may be strongly influencing and some will not be influencing the job satisfaction. The variable

like Salary and working environment will be strong factors contributing to job satisfaction than

others because the coefficient for salary and working environment will be much higher than oth-

er variables. When using multiple regressions in psychology, many researchers use the term “in-

dependent variables” to identify those variables that they think will influence some other “de-

pendent variable”. The term “predictor variables” for those variables that may be useful in pre-

dicting the scores on another variable that we call the “criterion variable”. As pointed out before,

human behavior is inherently noisy and therefore it is not possible to produce totally accurate

predictions, but multiple regressions allow us to identify a set of predictor variables which to-

gether provide a useful estimate of a participant‟s likely score on a criterion variable. In linear

multiple regression, the model specification is that the dependent variable, yi is a linear combina-

tion of the parameters (but need not be linear in the independent variables). For example, in line-

ar multiple regression for modeling data points there is p independent variable and p parameters,

β0,β1....... βp: (Gujarati, 2003)

Yi = β1xi1 + β2xi2 + ...+ βpxip+ εi

Where xij is the ith observation on the jth independent variable, and where the first independent

variable takes the value 1 for all i (so β1 is the regression intercept).

There are several econometric tests such as Durbin – Watson [D-W] statistic, coefficient of de-

termination- R2, T and F- ratio, Standard error of coefficients are carried out to find out the rele-

vancy of the model.

Hypothesis

The research objectives are translated into the hypothesis, which are then, tested using statistical

analysis:

For Model 1:

Ho: There is significant impact of FDI on economic growth of India and China.

H1: There is no significant impact of FDI on economic growth of India and China.

27

For Model 2.

Ho: Major determinants of FDI are GDP growth, Inflation rate, exchange rate, FOREX

reserve, and financial position.

H1: Major determinants of FDI are not GDP growth, Inflation rate, exchange rate,

FOREX reserve, and financial position.

28

Chapter 5

Data Analysis

The data from World Bank and UNTCAD has been collected for last ten years to analyze the

Pattern of FDI follow between the two giant nations (India and China).The objectives is to find

out the impact of FDI inflows to GDP growth and the major determinant of FDI inflows. Two

models are design to explain the objectives of the research. Karl Pearson correlation is used to

explain the impact of FDI on GDP growth and multiple regressions to determine the major de-

terminant of FDI inflows. The data has been collected only for ten years only because it can also

show the impact of global recession on FDI inflows between India and China.

Model 1:

FDIG = f [GDPG]

A correlation coefficient is a statistical measure of the degree to which, changes to the value of

one variable predict change to the value of another. In positively correlated variables, the value

increases or decreases simultaneously in negatively correlated variables, the value of one in-

creases and other decreases (Guajarati, 2002).

The coefficients of correlation are expressed as values between +1 and -1. Correlation coefficient

of +1 indicates a perfect positive correlation. A change in the value of one variable will predict a

change in the same direction in the second variable. Correlation coefficient of -1 indicates a per-

fect negative correlation. A change in the value of one variable predicts a change in the opposite

direction in the second variable. When there is no correlation, the coefficient will be zero (Guaja-

rati, 2002).

29

Table 1: Karl Pearson Correlation between GDP growth and FDI inflows for India.

Correlations

FDIIndia GDPIndia

FDIIndia Pearson Correlation 1 .726*

Sig. (2-tailed) .017

N 10 10

GDPIndia Pearson Correlation .726* 1

Sig. (2-tailed) .017

N 10 10

*. Correlation is significant at the 0.05 level (2-tailed).

This means that as one variable increases in value, the second variable also increase in value.

Similarly, as one variable decreases in value, the second variable also decreases in value. This is

called a positive correlation. Pearson‟s r-value of 0.726 was positive. Pearson‟s r is positive, we

can conclude that when the amount of GDP growth increases (our first variable), the participant

FDI inflows (our second variable) also increases.

Here in this research we have used Karl Pearson correlation coefficient to explain the relation-

ship between gross domestic product growth with foreign direct investment inflows in India and

China, we have also used scatter plot diagram to make it more clear about the relationship. The

purpose of the research is to find out the impact of FDI on GDP growth of India and China. With

data for ten years, we could find out that GDP growth of India is positively correlated to FDI in-

flows in India. The Karl Pearson Correlation coefficient between GDP growth and FDI inflows

of India is 0.726, which indicates positive correlation .The Increase in FDI inflows has positive

impact on Indian economy.

If the Sig (2-Tailed) value is less than or equal to .05 then there is a statistically significant corre-

lations between your two variables. That means, increases or decreases in one variable do signif-

icantly relate to increases or decreases in your second variable.

30

The Sig. (2-Tailed) value in the table above is 0.017. This value is less than .05. Because of this,

we can conclude that there is a statistically significant correlation between amount GDP growth

in India and FDI inflows in India.

Figure 2: Impact of FDI on Indian GDP growth.

The scatterplot can explain about the relationship between variables, just like Pearson‟s r. With

it, determine the strength and direction of the relationship between variables. The scatter plot

above clearly explains that the positive correlation between FDI inflows and GDP growth. The

scatterplot above shows the positive relationship between GDP growth in India and FDI inflows

in India.

31

Table 2: Karl Pearson Correlation between GDP growth and FDI inflows for China.

Correlations

FDIChina GDPChina

FDIChina Pearson Correlation 1 .943**

Sig. (2-tailed) .000

N 10 10

GDPChina Pearson Correlation .943** 1

Sig. (2-tailed) .000

N 10 10

**. Correlation is significant at the 0.01 level (2-tailed).

This means that as one variable increases in value, the second variable also increase in value.

Similarly, as one variable decreases in value, the second variable also decreases in value. This is

called a positive correlation. When Pearson‟s r is positive, that means that when the amount of

GPD growth in China increases (our first variable), the FDI inflows in China (our second varia-

ble) also increases.

Using Karl Pearson Correlation coefficient we could conclude that GDP growth of China de-

pends positively on FDI flows. Higher the FDI inflow higher will be the growth rate of GDP of

China. As we can see from the table above that FDI inflows is positively related to GDP growth

of any country. The GDP growth rate of China depends highly positively on FDI inflows, as the

coefficient is 0.943, which is almost equal to 1.If FDI inflows increases the GDP growth increase

showing high positive correlation. The main determinant of GDP growth of China is mostly ex-

plained by FDI inflows.

If the Sig (2-Tailed) value is less than or equal to 0.05 then there is a statistically significant cor-

relations between the two variables. That means, increases or decreases in one variable do signif-

icantly relate to increases or decreases in your second variable.

The Sig. (2-Tailed) value in the above table is 0.000. This value is less than 0.05. This can be

concluded that there is a statistically significant correlation between amount GDP growth in Chi-

na and FDI inflows in China.

32

Figure 3: Impact of FDI on Chinese GDP growth.

The scatterplot explains about the relationship between variables, just like Pearson‟s r. With it,

determine the strength and direction of the relationship between variables. The scatterplot above

shows us the strong correlation between GDP growth in China and FDI inflows.

Now the comparison between India and China concerning FDI inflow and GDP growth, the Karl

Pearson correlation coefficient for china is much higher than for India, which clearly shows us

that China attracts higher FDI than India. The FDI has higher impact on GDP growth of China

than India.

Table 3: Comparisons of FDI and GDP growth in India and China.

r India China

Correlation coefficient 0.726 0.943

33

The Karl Pearson correlation coefficient of India shows the relationship between GDP growth

and FDI inflows. The coefficient of China is much higher than India, which shows China‟s FDI

inflows, is highly positively collated with GDP growth than India.

The null hypothesis is accepted, as there is significant impact on GDP growth due to FDI inflow.

Therefore, we conclude stating that the FDI inflow determines positively the GDP growth of the

country.

Figure 4: Comparison of FDI inflows and GDP growth rate.

1 2 3 4 5 6 7 8 9 10

GDP in India 2012451223236425178842835714319628533855193700871414070244892984715640

FDI Inflows 4234.34 5771.29 7269.4 20029.1 25227.7 43406.3 35581.4 27396.9 36498.7 23995.7

0

500000

1000000

1500000

2000000

2500000

3000000

3500000

4000000

4500000

5000000

Mill

ion

s

FDI and GDP in India

34

The graph clearly states that GDP growth of India depends positively on foreign direct invest-

ment. The FDI Inflow was US $ 4234 million increases continuously and in 2012, the FDI an

inflow was US $ 23,996.the increase in FDI contributes to rise in GDP of India. There was not

much impact on FDI inflows in India due to global recession as there is continuous increase in

FDI flows.

Figure 5: Comparison of FDI inflows and GDP growth of China.

The line graph above is to explain the relationship between GDP growth and FDI inflows. We

can see from the above graph that GDP continuously grows with the increase in FDI inflows.

The FDI inflow of China in the year 2003 was US $ 56,198 and increases to US $ 253474.9443.

Model 2

The second model called as foreign direct investment model, which is used to explain the major

determinant of FDI inflows. We take only six major variables which impact the FDI inflows in

the two giant nations. To find out the impact of this variable on FDI inflows will be explained

with the help of multiple regressions, where FDI inflows will be dependent variable and other

(RD GDP, TTGDP, FN.Position, EXR, FOREX GDP, inflation).We will see the impact of each

variable with FDI .

FDI = f [RD GDP, TTGDP, FN.Position, EXR, FOREX GDP, Inflation].

1 2 3 4 5 6 7 8 9 10

GDP 4E+06 5E+06 5E+06 6E+06 7E+06 8E+06 9E+06 1E+07 1E+07 1E+07

FDI China 5619862108 1E+05 1E+05 2E+05 2E+05 1E+05 2E+05 3E+05 3E+05

0

5000000

10000000

15000000

Mill

ion

s

FDI and GDP of China

35

Table 4: India’s FDI inflow and various economic indicators in between 2003 to 2012.

Year

FDI In-

flows GDP at PPP

Total trade FOREX Ex-

change

rate

Finan-

cial po-

sition

National ex-

penditure on

R$D

Infla-

tion

2003 4234.34 2012451.32 22384.21 128763.54 46.58 89.02 1512243.87 3.21

2004 5771.29 2232364.12 28234.22 131631.14 45.32 102.02 1651949.45 3.77

2005 7269.40 2517884.10 27276.33 137824.83 44.1 78.40 1863234.23 4.25

2006 20029.11 2835713.55 31769.96 178049.78 45.31 82.52 2098428.03 6.15

2007 25227.74 3196284.69 38703.71 276578.10 41.35 84.99 2365250.67 6.37

2008 43406.27 3385519.06 74360.04 257422.72 43.51 74.43 2505284.11 8.35

2009 35581.37 3700871.26 67410.01 284682.88 48.41 98.26 2738644.73 10.88

2010 27396.88 4140702.10 91023.66 300480.14 45.73 83.79 3064119.55 11.99

2011 36498.65 4489298.02 106686.78 298739.48 46.67 75.46 3322080.53 8.86

2012 23995.68 4715640.32 136063.39 300425.51 53.44 85.41 3489573.84 9.31

Source: World Bank report, World Bank indicators.

36

Table 5: China’s FDI inflows and various economic indicators in between 2003 to 2012.

Year FDI

Inflows

GDP at PPP Total trade FOREX Ex-

chan

ge

rate

Finan-

cial

posi-

tion

National ex-

penditure on

R$D

Infla

fla-

tion

2003 56198.21 3778152.41 7632403.62 512364.21 8.28 62.45 4478915.02 3.21

2004 62108.04 4669851.85 1080755.68 622948.55 8.28 86.99 5743917.77 3.88

2005 104108.69 5364250.89 124626.79 831409.62 8.19 36.48 6598028.60 1.82

2006 124082.03 6231357.48 208918.92 1080755.68 7.97 32.95 7664569.70 1.46

2007 156249.33 7305066.02 308036.02 1546364.66 7.61 29.61 8985231.20 4.75

2008 171534.65 8162755 348832.53 1966037.43 6.95 25.33 10040188.65 5.86

2009 131057.05 8982336.12 220130.40 2452899.05 6.83 35.38 11048273.44 0.7

2010 243703.43 10036535.19 223023.87 2913711.65 6.77 33.97 12344938.29 3.31

2011 280072.21 11185373.66 181903.73 3254674.12 6.46 35.67 13758009.6 5.41

2012 253474.94 12268638.11 231844.87 3387512.97 6.31 34.79 15090424.88 2.65

Source: World Bank report, World Bank indicators

37

Table 6: The Output of SPSS for Multiple regressions.

Variable Co-efficient standard error t-statistic

India China India China India China

constant 39360.712 1084000 105554.54 586579.100 .370 1.848

Total trade .192 .042 0.458 .111 .417 .380

FOREX .274 .228 0.261 .080 1.036 1.227

Exchange

rate

991.593 745.04

3446.259

71409.368 .282 1.716

FP 634.342 .098 740.098 1733.733 .830 .132

National

exe on R

and D

-.039 -.014

.045

.018 .838 .800

Inflation 1883.646 1403.36 3065.546 5300.675 .609 2.648

Variable India China

R2 .800 .983

Adjusted R2 .201 .933

D-W Statistic 3.28 3.56

F-ratio 1.34 19.55

Note: Significant at 0.05 levels

The output of SPSS for multiple regressions shows the result as

FDI India=39360.7-0.039RD+0.19TT+634.3FP+991.5ER+0.274FRX +1883.6Inflation.

FDIChina=1084000-0.014RD+0.042TT+0.098FP+745.04ER+.228FRX+1403.3Inflation

The output of SPSS shows that without the major determinants (RD GDP, TTGDP, FN.Position,

EXR, FOREX GDP, Inflation) of FDI there will be 39360.71 million of FDI inflows in India and

1084000 million in China. It means the determinant likes Exchange rate, inflation, FOREX and

others does not impact much in China than India.Every1 unit change in total trade in India leads

to 0.92 unit change in FDI in flows of India and 0.042 unit in China. Every 1 unit change in

38

FOREX leads to 0.274 unit change in FDI inflow of India and 0.228 units in China. Every 1 unit

change in exchange rate leads to 991.593 unit change in FDI inflows of India and 745.04 units in

China. Inflation has also have direct impact on FDI inflows of the economy,1 unit change in In-

flation leads to 1883.646 unit change in FDI flows of India and 1403.36 unit in China. But na-

tional expenditure on research and development has shown negative sign, which means every 1

unit change in national expenditure on Research and development leads to decrease in FDI flow

in India by 0.039 units and 0.041 units in China. R-Square is the proportion of variance in the

dependent variable (FDI inflows of India and china) which can be explained by the independent

variables (RD GDP, TTGDP, FN.Position, EXR, FOREX GDP, and Inflation). R-Square, also

known as the Coefficient of determination is a commonly used statistic to evaluate model fit. R-

square is 1 minus the ratio of residual variability (Guajarati, 2002). When the variability of the

residual values around the regression line relative to the overall variability is small, the predic-

tions from the regression equation are good. This is an overall measure of the strength of associa-

tion and does not reflect the extent to which any particular independent variable is associated

with the dependent variable. Adjusted R-square - the addition of extraneous predictors to the

model. Adjusted R-squared is computed using the formula 1 - ((1 – R-Square) ((N - 1) /( N - k -

1)) where k is the number of predictors,(Guarajati,2002).

Std. Error of the Estimate - is also referred to as the root mean squared error. It is the standard

deviation of the error term and the square root of the Mean Square for the Residuals. T statistics-

and their associated 2-tailed p-values used in testing whether a given coefficient is significantly

different from zero. Using an alpha value of 0.05.

39

Figure 6: Effect of major determinants of FDI inflows.

Another useful technique for screening the data is a scatterplot matrix. While this is probably

more relevant as a diagnostic tool searching for non-linearity‟s and outliers in the data, but it can

also be a useful data screening tool, possibly revealing information in the joint distributions of

the variables that would not be apparent from examining univariate distributions.

The Scatter plot matrix shows that the determinant like GDP, Inflation Financial position, Ex-

penditure on research and development and exchange rate determines the foreign direct invest-

ment heavily. The conclusion from the scatter plot is that GDP, Financial position, inflation, ex-

penditure on research and development and Inflation are the major determinates of FDI inflows

in India and China.

The second Model, Foreign Direct Investment Model found that all variables are statistically sig-

nificant. The results of Foreign Direct Investment Model shows that RD GDP (reserve GDP),

TTGDP, FN.Position, EXR (exchange rate), FOREX GDP, Inflation are the major determinants

of FDI inflows in India and China. The Multiple regression results of above shows that TTGDP,

FN.Position, EXR (exchange rate), FOREX GDP, Inflation and TTGDP are the positive factors

for FDI inflows in India and China whereas RD GDP acts as negative force in attracting FDI

flows in two country. The result shows that RD GDP and EXR (exchange rate) does not show

the predicted signs, RD GDP shows negative sign and exchange rate shows unexpected positive

sign instead of negative sign .In the foreign direct investment model portrays their respective

signs except exchange rate and research and development GDP.The main reason behind the de-

viation is due to the currency of China and India in international market and low expenditure on

Research and development activities in these countries. Positive sign with exchange rate could be

attributed to the appreciation of currency in international market which helped the foreign firms

to acquire the firm specific assets at cheap rates and gain higher profits. Research and Develop-

40

ment shows negative sign, which indicate low FDI inflows in R and D sectors. The null hypothe-

sis is accepted since all the determinants are statically significant and reject alternate hypothesis.

41

Chapter 5

Conclusion and Recommendation

Conclusion

Global economies are suffering with financial crisis and economic hurdles. However, the two

giant economies (India and China) are growing very fast despite so many economic crises still

stands as a global investment destination. It is observed from the results of above analysis that

TTGDP, FOREX, Exchange rate, FN Position, RDGDP, inflation are the main determinants of

FDI inflows in India and China. These macroeconomic variables have a profound impact on the

inflows of FDI in India and China. The results of foreign Direct Investment Model reveal that

TTGDP, FOREX, and Financial Position variables exhibit a positive relationship with FDI while

RDGDP and Exchange Rate variables exhibit a negative relationship with FDI inflows. Hence,

TTGDP, FOREX, and Position variables are the pull factors for FDI inflows to the country and

RDGDP and Exchange rate are deterrent forces for FDI inflows into the country. Thus, it is con-

cluded that the above analysis is successful in identifying those variables, which are important in

attracting FDI inflows to the country. The study also reveals that FDI is a significant factor influ-

encing the level of economic growth in India. The results of Economic Growth Model and For-

eign Direct Investment Model show that FDI plays a crucial role in enhancing the level of eco-

nomic growth in the country. It helps in increasing the trade in the international market. The ma-

jor determinants explained above are more influential in china than in India as the R_ square is

much higher in China than in India.

This study examines the status of inward Foreign Direct Investment flow into India and China.

Ever since Macro Economic structural changes initiated in 1991 in India and 1978 in China, the

impact of ongoing process of Liberalization, Privatization and Globalization and its implications

in attracting inward FDI into these countries has become focal point of this study, at a time When

Economy of India and China experience a slowdown in the backdrop of global financial crisis

and Economic recession. Globalization process and its implication on inward FDI can be evalu-

ated in terms of Economic Indicators such as GDP, GDP growth rate, exchange rate, inflation,

financial position (ratio of export and external debt) and total trade. The main aim of this paper is

42

to find out the impact of FDI inflows to GDP growth of these respective countries and to find the

major factors contributing to FDI inflows. The paper consist of two models, economic growth

model is to show the impact of FDI inflows to GDP growth of India and China. It is explained

with Karl Pearson‟s Correlation Coefficient. Second Model, Foreign investment model is to

show the major determinants of FDI inflows in these two Countries. Foreign direct model is ex-

plained with multiple regressions. Using SPSS software the output of first model shows that

GDP growth of China is influenced more by FDI inflows than India. The output of second model

shows us that major determinant of FDI inflows are Inflation, foreign exchange reserves, finan-

cial position, Total trade and exchange rate.

Recommendations

Though the research has been able to accomplish significant results, there are some issues that

need to be addressed in future research and are limitations of this study. First of all, it is very dif-

ficult to obtain entire data on China and India on different parameter. India and China have

grown at different time periods and India faces a lag of thirteen years, comparing different time

dimensions can be misleading as their might be macroeconomic global factors such as Asian Cri-

sis, recession in Japan and Gulf war that might have influence on the flows into these countries

differently. Also, this study does not statistically test all the factors that determine foreign direct

investment in emerging markets because it is very difficult to include the entire variable due to

unavailability of data, although most of the relevant determinants have been included. Third, this

study only discusses China and India and does not include other emerging markets such as Brazil

and Russia (BRIC countries). A study of FDI determinants for BRIC economies over last twen-

ty-five years can add to the findings of this study. Also, sector wise analysis can be done to pin-

point the exact sectors that led to the Chinese growth and their relationship with FDI flows over

time.

Fourth, the study takes account of only FDI inflows in this countries neglecting outward FDI.

43

Chapter 6

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