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AN ANALYSIS OF FOREIGN DIRECT INVESTMENT INFLOW AND ECONOMIC GROWTH IN NIGERIA

BY

EGBO OBIAMAKA P. PG/Ph.D/02/31901

DEPARTMENT OF BANKING AND FINANCE FACULTY OF BUSINESS ADMINISTRATION

UNIVERSITY OF NIGERIA

ENUGU CAMPUS

APRIL, 2010

ii

AN ANALYSIS OF FOREIGN DIRECT INVESTMENT INFLOW AND ECONOMIC GROWTH IN NIGERIA

BEING A THESIS PRESENTED TO THE DEPARTMENT OF BANKING AND FINANCE, FACULTY OF BUSINESS

ADMINISTRATION, UNIVERSITY OF NIGERIA, ENUGU CAMPUS

BY

EGBO OBIAMAKA P. PG/Ph.D/02/31901

IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE AWARD OF THE DOCTOR OF PHILOSOPHY DEGREE

IN FINANCE

SUPERVISOR: PROFESSOR C. U. UCHE

APRIL, 2010

iii

APPROVAL PAGE

This thesis has been approved for the Department of Banking and Finance, Faculty

of Business Administration, University of Nigeria, Enugu Campus, by

………………………………………….

PROFESSOR C. U. UCHE

(SUPERVISOR)

………………………………… ……………………………...

MRS. N. J. MODEBE PROFESSOR U. MODUM

(HEAD OF DEPARTMENT) (DEAN)

iv

CERTIFICATION

This is to certify that this thesis written by Egbo Obiamaka Priscilla with

Registration number PG/Ph.D/02/31901 presented to the Department of

Banking and Finance, University of Nigeria, Enugu Campus, is original and has not

been submitted for the award of any degree or diploma either in part or full in this

or any other institution of higher learning.

………………………………………….. …………………………. EGBO OBIAMAKA PRISCILLA DATE

This is to certify that this thesis written by Egbo Obiamaka Priscilla with

registration number PG/Ph.D/02/31901 presented to the Department of Banking

and Finance, University of Nigeria, Nsukka, Enugu Campus was supervised and

approved to have met the condition necessary for the award of the Doctor of

Philosophy Degree in Finance of the University.

………………………………………….. …………………………. PROF. C. U. UCHE DATE SUPERVISOR

………………………………………….. …………………………. MRS. N. J. MODEBE DATE HEAD OF DEPARTMENT

v

DEDICATION This work is dedicated to my husband David, who has been very supportive

towards my academics and above all to God Almighty who is able to do all things.

vi

ACKNOWLEDGMENTS

I would like to acknowledge the help of some individuals whom God has used as a

source of blessing and inspiration in the course of this work. I wish to particularly

appreciate my supervisor Prof. C.U. Uche, a man of great honour and

achievements. I am especially grateful to Dr. J.U.J Onwumere, with deep humility

on my part; I thank him for his special attention, commitment and trust in this

work, and all the time that he gave me during the absence of my supervisor. He has

always been there for me because without him, this work would not have ended

now.

I am also grateful to Prof. U. Modum, the Dean of Faculty of Business

Administration, UNEC for her support, Ven. Prof. Chinedu Nebo, Vice Chancellor

Emeritus of the University of Nigeria, Nsukka who by his fatherly advice

encouraged me a lot during my period of study. My thanks also go to Late Dr.

A.M.O. Anyafo of blessed memory, for directing this work and challenging me to

do an excellent research.

I am also indebted to my Head of Department Mrs. N.J Modebe for her support. I

would also like to appreciate Dr. Mrs. E. N. Ogamba for her encouraging words

support and all the efforts she put in making sure that this work comes to an end. I

also appreciate the support of Dr. B. E. Chikeleze, Dr. Mrs. J. Nnabuko, Dr. Mrs. R.

Okafor, Dr. I. C. Nwaizugbo, Mr. F.C. Alio, Mr. E. O. C. Onah, Mr. Nwude Chuke,

Mr. Asomugha and all the staff in the department of Banking and Finance, both

academic and administrative staff. I will ever remain grateful to Dr. (Mrs.) G. E.

Ugwuonah who since I met her, has been a source of inspiration to me. I wish to

also appreciate my friend Arc. Iyke. C. Ifeanacho for his advice, encouragement

and support, he has been so wonderful. I will not forget Dr. Vincent Onodugo for

his advice throughout the period of this study.

I offer special thanks to my parents, Mr. and Mrs. Alphonsus Eze, my mother-in-

law and all my in-laws, my brothers Dr. C. C. Eze of the Department of Geology

and Mining, Enugu State University of Science and Technology, Enugu, Dr. R. C.

Eze of the Department of Physics, St. John’s University, New York, Mr. Casmir Eze

and Mr. Francis Eze, and my sister, Mrs. Oz Idoko for their moral and financial

support.

vii

I would like to appreciate my darling husband, David and our kids Chioma,

Chisom, Chinkem and Chiezugo for their patience, support and encouragement all

through the duration of this study, especially for denying them my attention

during the course of this study.

I thank my distinguished friends – Victor Ufondu, Mrs. Blessing Okeke, Mrs. Ify

Nwankwo, Mrs. Funmi Awanye, , Emeka Ezeoke, Mr. Gbenga Awoniyi and whole

lots of others whose companionship and prayers formed a source of inspiration for

this work. To my Pastors Rev. Canon Emma Uzuegbunam and Ven. Chukwuma

Okafor who prayed for the success of this work. I would also like to appreciate my

parents in the Lord, His Lordship Bishop and Mrs. J. C. Ilonuba, the Bishop

Emeritus of Nsukka Dioceses of the Anglican Communion, for their prayers. I pray

that God will reward you accordingly.

Finally, to God Almighty, who by his infinite mercy, wisdom and help, made it

possible for me to complete this work. May His name be worshipped forever.

Egbo Obiamaka P.

PG/Ph.D./2002/31901

viii

ABSTRACT

This study ascertained the extent to which growth in Foreign Direct Investments

(FDIs) influences economic growth in Nigeria. While there is considerable

evidence on the link between FDIs and economic growth, the causality between

them remains a subject of investigation. This study used annual time series

variables computed from natural logarithms of gross domestic product (GDP) at

current price, net inflow of FDI, inflation rate and exchange rates, covering a

period of 27years, that is from 1981 to 2007. The study utilized data from

secondary sources. The study utilized the Ordinary Least Square, Unit root test to

test for stationarity of the time series, the Johansen Cointegration test to test for

the existence of long-run relationship among the variables and finally, Granger

causality test to establish the causal relationship between Foreign Direct

Investment and economic growth. The stationarity test (unit root) showed that the

included variables, gross domestic product (GDP), Foreign Direct Investment

(FDI), exchange rate (EXRATE) and inflation rate (INFRATE) were non-stationary

at their level and first difference with 2 lags. They were thus integrated of order

one 1(1). The Cointegration test using Johansen Cointegration test revealed that

the variables were cointegrated and had a stable relationship in the long-run. To

check for short-run relationship, the Granger causality test was adopted and it

showed that a causality relationship ran from FDIs to GDP and not from GDP to

FDIs. The findings showed that there is a positive relationship between FDI and

GDP which implies that FDI stimulates economic growth in Nigeria. This shows

that the growth which the country experienced during the period under review was

as a result of the inflow of FDI into the country. Thus, it was the FDIs that drove

growth which shows a one-way causality, which is from FDIs to GDP. As the result

suggests, it becomes beneficial for Nigeria to attract FDI in order to stimulate the

economic growth rate. There is, therefore, the need to improve the FDIs climate

and take advantage of the new global interest in the affairs of the country by

implementing sound macroeconomic policies, enforcing the rule of law, reducing

risks of policy reversals, and improving the provision of infrastructure. It is also

suggested that further studies should explore the possibility of using sector or

industry specific data in analyzing the relationship between FDIs and economic

growth in Nigeria. The major contribution of this study to knowledge is that it has

provided a new sturdy evidence on the analysis of FDI and economic growth in

Nigeria by the use of a modified version of Seabra and Flach (2005) model.

ix

TABLE OF CONTENTS

Approval Page III

Certification IV

Dedication v

Acknowledgments VI

Abstract VIII

List of Tables XIII

List of Figures ERROR! BOOKMARK NOT DEFINED.

Acronyms and their meaning XIV

List of Appendixes XV

Chapter One:Introduction 1

1.1 Background of the Study 1

1.2 Statement of Research Problem 10

1.3 Objectives of The Study 12

I.4 Research Questions 12

1.5 Research Hypotheses 12

1.6 Scope of the Study 12

1.7 Significance of the Study 13

1.8 Limitations of the Study 14

1.9 Definition of Terms 15

References 17

Chapter Two: Review of Related Literature 20

2.1 Overview 20

2.2 Definitions Of FDI 25

2.2.1 Different Types of FDI 26

2.3 Major Sources and Destinations of FDI 27

2.4 Factors that Influence FDI Decision Making 32

2.5 The Role of FDI: Positive and Negative Aspect 34

2.6 Theories of Economic Growth and FDI 38

(Interrelationship Between FDI and Economic Growth) 38

2.7 Some Other Theoritical and Empirical Evidience 44

2.8 Factors That Determines FDI Flow 49

2.9 Impact of FDI on Economic Growth in Nigeria 55

2.10 Growth Accounting Equation and the Solow Residual 59

x

2.10.1 The New Growth Theory 60

2.10.2 The Romer Growth Model 61

2.10.3 The Link Between Technology Creation and Growth 62

2.11 The Implications of FDI and Economic Growth in Nigeria (Recent Developments

in Economic Growth in Nigeria) 63

2.12 Some Facts About Global FDI Flow 68


References 87

Chapter Three: Research Methodology 104

3.1 Introduction 104

3.2 Research Design 104

3.3 Population and Sample Size 104

3.4 Nature and Sources of Data 105

3.5 Specification of Models 105

3.6 Ordinary Least Squares Method 105

3.6.1 Procedure of Ordinary Least Squares Method 106

3.6.1.1 Unit Root Test 107

3.6.1.2 Cointegration Test 108

3.6.1.3 Granger No-Causality Tests 111

3.6.3 The Model 114

3.7 The Technique of Analysis 116


References 118

Chapter Four:Data Presentation and Analysis 121

4.1 Unit Root Test 121

4.2 Summary of Augmented Dickey Fuller Test for Unit Root 122

4.3 Test for Cointegration with Johansen Cointegration Test 128

4.4 Granger Test 130

4.5 Test of Research Hypotheses 131


References 136

Chapter Five: Summary of Findings, Conclusion, and Recommendations 137

5.1 Summary of Research Findings 137

5.2 Policy Implication of the Findings 138

xi

5.3 Major Contribution of the Outcomes of the Study to Knowledge 140

5.4 Conclusion 141

5.5 Recommendations 142

Appendices 149

Bibliography 173

xii

LIST OF TABLES

Table 1.1: Nigeria Macroeconomic Indicators, 1997 – 2006 5

Table 2.1: Demand Composition (Percentage of GDP) 65

Table 2.2: FDI Inflows to the Top 10 Recipient African Economies, 1998 and 1999 (in

millions of U.S. Dollars)Figure 1.1: Nigeria: Real GDP 2003 - 2007 5

Table 2.3. Africa: Country Distribution of FDI Inflows, by Range, 2003

Table 2.4: The Top 7 Non-Financial TNCs from Developing Economies in Africa, Ranked

by Foreign Assets, 2002 (millions of dollars, Number of Employees) 72

Table 2.5: Selected Indicators of FDI and International Production, 1982-2003 (billions of

dollars and per cent) 73

Table 2.6: National Regulatory Changes, 1991-2003 74

Table 2.7: The 10 Largest Non-Financial TNCs from Africa, Ranked by Foreign Assets,

2004 (millions of dollars) 79

Table 2.8: FDI Inflows, by Host Region and Major Host Economy, 2004-2006 (billions of

dollars) 80

Table 2.9: FDI Inflows, by Host Region and Major Host Economy, 2006-2007 (billions of

dollars) 84

Test Result in Level Showing T-Statistics and Critical Values 123

Table 4.1 and Table 4.2 123




Table 4.9: Unit Root Test for the Variables in Levels with (2) Lags 125

Table 4.10: Test for Non-Stationarity By Calculating the Auto Correlation Function ACF

126

Table 4.11: Summary of Descriptive Statistics 128

Table 4.12: Result of Johansen Cointegration Test 128

Table 4.13 : Granger Causality Test 131

xiii

LIST OF FIGURE

Figure 2.1 Production Function with Constant Marginal Product of Capital 61

Figure 2.2-Real GDP Growth and Per Capita GDP ($Us at Constant 2000 Prices) 67

Figure 2.3-GDP By Sector in 2006 (Percentage) 67

Figure 2.4: Africa- FDI Inflows and their Share in Gross Fixed Capital Formation, 1985-

2003 71

Figure 2.5: The Top 10 Recipients of FDI Inflows in Africa, 2002 and 2003 (billions of

dollars) 72

Figure 2.6: The Top 20 Recipient of FDI Inflow, 2002 and 2003 (billions of dollars) 74

Figure 2.7: FDI Inflows to Africa, to 10 Recipient, 2003 – 2004 (billions of dollars) 75

Figure 2.8: Africa-FDI Inflows and their Share in Gross Fixed Capital Formation, 1995-

2005 77

Figure 2.9: Africa- FDI Inflows, Top 10 Economies, 2004-2005 (billions of dollars) 77

Figure 2.10: FDI Inflows, Global and by Group bf Economies, 1980-2006 (billions of

dollars) 81

Figure 2.11: Africa: FDI Inflows and theirs Share In GFCF, 1995-2006 82

Figure 2.12: Africa-FDI Inflows, Top 10 Economies, 2005-2006a (billions of dollars) 82

Figure 4.1: Statistical Description Of GDP Figure 4.2: Statistical Description of FDI

127

Figure 4.3: Statistical Description of Exrate Figure 4.4: Statistical Description of Nfrate

127

xiv

ACRONYMS AND THEIR MEANING

GDP Gross Domestic Product

FDI Foreign Direct Investment

GNP Gross National Product

GDI Gross Domestic Investment

UNCTAD United Nations Conference on Trade and Development

ODA Official Development Assistance

NEPAD New Partnership of African Development

TNC Trans-National Corporations

PPP Purchasing Power Parity

MNEs Multi-National Enterprises

TNCs Trans-National Corporations

R&D Research and Development

OLS Ordinary least Square

SMEs Small and Medium Enterprises

ADF Augmented Dickey Fuller

NEEDS National Economic Empowerment and Development Strategy

MDGs Millennium Development Goals

SIC Schwarz Information Criterion

AIC Akaike Information Criterion

IN_GDP Natural logarithm of Gross Domestic Product

IN_FDI Natural logarithm of Foreign Direct Investment

IN_INFRATE Natural logarithm of Inflation Rate

IN_EXRATE Natural logarithm of Exchange Rate

xv

LIST OF APPENDIXES

Net flow of FDI and GDP at current price from 1981 – 2007 186

Unit Root Test for the Variables with 2 year Lag 187

Summary of Johansen Cointegration Test 218

Ordinary Least Square Test 219

Wald Test 219

1

CHAPTER ONE

INTRODUCTION 1.1 BACKGROUND OF THE STUDY

Foreign Direct Investment (FDI) which is an investment made to acquire lasting

interest in enterprises operating outside of the economy of the investor, has long

been a subject of great interest in the field of international development. In an era

of volatile flows of global capital, the stability of FDI and its emergence as an

important source of foreign capital for developing economies has once again

renewed interest in its linkages with sustainable economic growth. FDI inflows

contributed to a strengthening of the balance of payments in several African

countries. In 2006, foreign reserves in the region as a whole grew by 30%, and by

even more in some major oil-exporting countries such as Nigeria and the Libyan

Arab Jamahiriya (World Investment Report, 2007). Indeed, for developing

countries taken as a group, net inflows of FDI have increased almost five fold from

an average of 0.44% of GNP in the period 1970-74 to 2.18% of GNP in the period

1993-97 (World Bank, 1999). FDI now forms a significant component of domestic

investment activity in developing countries accounting for more than 8% of Gross

Domestic Investment (GDI) in the mid-1990s up from 2% of GDI in the early

1970s. Finally, FDI is now the pre-eminent source of capital flows into developing

countries accounting for about 36% of total capital flows in the mid-1990s up from

approximately 18% of flows in the 1970-74 period (World Bank, 1999). Average

annual inflows of Foreign Direct Investment (FDI) into Africa doubled in the

1980s compared with the 1970s. It also increased significantly in the 1990s and in

the period 2000–2003. Comparisons with global flows and those of other regions

may be more useful, however. In the mid 1970s, Africa’s share of global FDI was

about 6%, a level that fell to the current 2–3%. Among developing countries,

Africa’s share of FDI in 1976 was about 28%; it is now less than 9% (United

Nations Conference on Trade and Development - UNCTAD, 2005). Also in

comparison with all other developing regions, Africa has remained aid dependent,

with FDI lagging behind Official Development Assistance (ODA). Between 1970

and 2003, FDI accounted for just one-fifth of all capital flows to Africa. It is well

known that FDI is one of the most dynamic international resource flows to

2

developing countries. FDI is particularly important because it is a package of

tangible and intangible assets and because firms deploying them are important

players in the global economy. There is considerable evidence that FDI can affect

growth and development by complementing domestic investment and by

facilitating trade and transfer of knowledge and technology (Holger and

Greenaway, 2004). The importance of FDI is envisioned in the New Partnership

for Africa’s Development (NEPAD), as it is perceived to be a key resource for the

translation of NEPAD’s vision of growth and development into reality. This is

because Africa, like many other developing regions of the world, needs a

substantial inflow of external resources in order to fill the saving and foreign

exchange gaps and leapfrog itself to sustainable growth levels in order to eliminate

its current pervasive poverty (Ajayi, 1999, 2000, 2003).

The literature on the FDI–growth relationship is vast for both developed and

developing countries. The basis for most of the empirical work focuses on

neoclassical and endogenous growth models. It is often claimed that FDI is an

important source of capital, that it complements domestic investment, creates new

jobs opportunities and is in most cases, related to the enhancement of technology

transfer, which of course boosts economic growth. While the positive FDI–growth

linkage is not unambiguously accepted, macroeconomic studies nevertheless

support a positive role for FDI especially in particular environments. Existing

literature identifies three main channels through which FDI can bring about

economic growth. The first is through the release it affords from the binding

constraint on domestic savings. In this case, Foreign Direct Investment augments

domestic savings in the process of capital accumulation. Second, FDI is the main

conduit through which technology transfer takes place. The transfer of technology

and technological spillover lead to an increase in factor productivity and efficiency

in the utilization of resources, which leads to growth. Third, FDI leads to increases

in exports as a result of increased capacity and competitiveness in domestic

production. Empirical analysis of the positive relationship is often said to depend

on another factor, called “absorptive capacity”, which includes the level of human

capital development, type of trade regimes and the degree of openness

(Borensztein et al., 1995, 1998).

3

One of the most salient features of today’s globalization drive is conscious

encouragement of cross-border investments, especially by trans-national

corporations and firms (TNCs). Many countries and continents (especially

developing) now see attracting FDI as an important element in their strategy for

economic development. This is most probably because FDI is seen as an

amalgamation of capital, technology, marketing and management. Sub-Saharan

Africa as a region now has to depend very much on FDI for so many reasons, some

of which are amplified by (Asiedu, 2001). The preference for FDI stems from its

acknowledged advantages (Sjoholm, 1999 and Obwona, 2001, 2004). The effort by

several African countries to improve their business climate stems from the desire

to attract FDI. In fact, one of the pillars on which the New Partnership for Africa’s

Development (NEPAD) was launched was to increase available capital to US$64

billion through a combination of reforms, resource mobilization and a conducive

environment for FDI (Funke and Nsouli, 2003). Unfortunately, the efforts of most

countries in Africa to attract FDI have been futile. This is in spite of the perceived

and obvious need for FDI in the continent. The development is disturbing, sending

very little hope of economic development and growth for these countries. Further,

the pattern of the FDI that does exist is often skewed towards extractive industries,

meaning that the differential rate of FDI inflow into sub-Saharan African countries

has been adduced to be due to natural resources, although the size of the local

market may also be a consideration (Morriset, 2000 and Asiedu, 2001).

Include Source Nigeria is turning out to be one of the most attractive countries in

terms of foreign investment inflows. Foreign Direct Investment increased from less

than US$ 1billion in 1990 to US$ 1.2billion in 2000, US$1.9 billion in 2004, US$

2.3billion in 2005 and US$ 4.5 billion in 2006. As percentage of GDP, Foreign

Direct Investment has increased substantially in recent years. The same pattern is

witnessed in portfolio investment, which grew from US$0.2 billion in 2003 to US$

2.9 billion in 2005 and US$ 0.92 billion in 2006. This is attributable to the

economic reforms and the resulting of macroeconomic stability, which have

instilled great credibility in the Nigerian economy. Home remittances are also

becoming an increasingly important catalyst to growth in Nigeria. In 2004, Nigeria

received an estimated US$ 2.26 billion in home remittances; this has continued to

increase remarkably with a recorded figure of over US$7 billion in 2006 (Bello,

4

2006). Nigeria’s economy has experienced strong growth in recent years. Real

GDP growth averaged 7.8 percent from 2004 to 2007, and growth of 6.4 percent in

2007 exceeded the low-income sub-Saharan (LI-SSA) median (4.0 percent), the LI

median (6.0 percent), and the rate in Indonesia (6.3 percent), although it was

lower than the rate in Kenya (7.0 percent) (see Figure 1.1). Oil accounts for nearly

40 percent of GDP, but from 2001 to 2006—except in 2003—real growth in other

sectors outpaced growth in the oil sector (IMF, 2008) Sectors that have

experienced particularly strong growth include telecommunications, which has

been liberalized and privatized over the past decade, and wholesale and retail

trade. Agriculture has also shown some growth, although it remains far from

fulfilling its potential (Economist Intelligent Unit, 2008).

Nigeria’s per capita GDP is high relative to GDP in other LI-SSA countries. In

purchasing power parity dollars, GDP per capita grew from $1,597.90 in 2003 to

$2,034.60 in 2007—an average annual growth rate of 5.6 percent. It is now far

higher than the LI-SSA’s median per capita GDP ($1,018.00) and Kenya’s

($1,359.00) but still much lower than Indonesia’s ($3,234.00). In 2007 Nigeria

had an estimated gross domestic product (GDP) of US$166.8 billion according to

the official exchange rate and US$292.7 billion according to Purchasing Power

Parity (PPP). GDP rose by 6.4 percent in real terms over the previous year. GDP

per capita was about US$1,200 using the official exchange rate and US$2,000

using the PPP method. About 60 percent of the population lives on less than US$1

per day. In 2007 the GDP was composed of the following sectors: agriculture, 17.6

percent; industry, 53.1 percent; and services, 29.3 percent. In 2006 Nigeria

received a net inflow of US$5.4 billion of Foreign Direct Investment (FDI), much

of which came from the United States. FDI constituted 74.8 percent of gross fixed

capital formation, reflecting low levels of domestic investment. Most FDI is

directed toward the energy sector. Between 2008 and 2020, Nigeria hopes to

attract US$600 billion of FDI to finance its Vision 2020 policy to transform the

country’s economy into one of the world’s 20 largest, see figure 1.1 below (Library

of Congress, 2008).

Table 1.1: Nigeria Macroeconomic Indicators, 1997

Indicators

1 Real GDP Growth Rate

2 Real Non-Oil GDP Growth Rate

3 Real Per Capita GDP Grth Rate

4 Inflation (%)

5 Investment Ratio (% of GDP)

6 Fiscal Balance (% of GDP)

7 Growth of Money Supply (%)

8 Export Growth, volume (%)

9 Import Growth, volume (%)

10 Terms of Trade (%)

11 Trade Balance (% of GDP)

12 Current Account ($billion)

13 Current Account (% of GDP)

14 Debt Service (% of Export)

15 Domestic Savings (% of GDP)

16 Reserves in months of imports

Source: ADB Statistics Division and IMF

Figure 1.1: Nigeria: Real

Source: IMF Article IV Nigeria, 2008 and IMF World Economic Outlook Database (April, 2008)

Table 1.1: Nigeria Macroeconomic Indicators, 1997 – 2006

1999-01 2000 2001 2002 2003 2004

2.7 5.4 3.1 1.5 10.9 6.1

3.9 2.6 3.7 8.0 4.6 7.4

-0.1 2.9 0.7 -1.2 7.7 3.2

10.2 6.9 18.9 13.7 14.0 15.0

23.1 20.3 24.1 26.2 23.9 22.4

-2.8 5.9 -4.9 -4.2 -1.3 7.7

29.3 48.1 27.0 21.6 24.1 14.0

2.4 19.4 -4.7 -11.8 33.2 3.6

8.4 -2.7 10.7 25.6 11.5 1.6

10.9 53.2 -10.4 -0.5 2.5 20.5

15.8 30.3 18.9 8.7 17.5 26.9

0.6 5.4 2.2 -5.4 -1.6 3.3

0.8 11.7 4.5 -11.7 -2.7 4.9

10.7 6.9 10.3 5.9 6.7 4.9

29.8 32.0 28.6 25.3 32.1 39.5

6.8 8.6 7.8 4.6 3.6 7.6

Source: ADB Statistics Division and IMF

Nigeria: Real GDP 2003 - 2007

Source: IMF Article IV Nigeria, 2008 and IMF World Economic Outlook

5

2004 2005 2006

6.1 6.9 6.2

7.4 8.2 7.0

3.2 4.3 3.6

15.0 17.9 9.4

22.4 20.9 21.1

7.7 9.9 17.5

14.0 16.5 17.0

3.6 -1.1 2.5

1.6 25.5 17.1

20.5 37.8 8.9

26.9 32.8 33.1

3.3 12.4 16.5

4.9 14.7 18.4

4.9 17.0 2.0

39.5 42.1 41.6

7.6 10.1 14.3

Source: IMF Article IV Nigeria, 2008 and IMF World Economic Outlook

6

Over the past two decades, many countries around the world have experienced

substantial growth in their economies, with even faster growth in international

transactions, especially in the form of Foreign Direct Investment (FDI). The share

of net FDI in world GDP has grown five-fold through the eighties and the nineties,

making the causes and consequences of FDI and economic growth a subject of

ever-growing interest. The concept of sustainable economic growth presents an

immense challenge for policy makers especially in developing countries. The issues

underlying the concept of economic growth have become even more distinct in the

prevailing era of globalisation where business processes and decisions have

become a “global” trait as opposed to the historical national traits. With

globalisation, there has been increased deregulation and liberation of international

markets that has led to increased trade and international investment across

boundaries of countries.

Up until the late 1980s, most of the developing countries relied on bilateral and

multilateral donor assistance (Overseas Development Assistance – ODA) as a

source of project development finance. The decade between 1990 and 2000

witnessed a remarkable and consistent decrease in development assistance to

developing countries that forced them to search for alternative and sustainable

sources of financing. Subsequently, by 1998, Foreign Direct Investment had

emerged as the largest source of capital for developing countries rising from

US$174 billion in 1992 to US$664 billion in 2001, (Towards Earth Summit, 2002).

To date, the growth in Foreign Direct Investment shows that sustainable growth

for several developing countries is progressively being influenced by Multinational

Enterprises (MNEs) through Foreign Direct Investment flows.

Thus, attracting Foreign Direct Investment has become very crucial for most

countries because of its perceived positive impact on economic growth and

development. Many countries have undertaken structural and regulatory reforms

such as privatisation of state enterprises, liberalisation of their foreign exchange

markets and establishment of fiscal incentives like tax holidays in order to attract

more Foreign Direct Investments. The quest by developing countries for increased

Foreign Direct Investment stems from the assumption that Foreign Direct

Investment leads to economic benefits within the host country, which assumptions

7

are based on economic theory. In addition, there is existing empirical research that

has further highlighted the benefits of Foreign Direct Investment. According to

World Bank, developing countries should endeavour to attract more Foreign Direct

Investment because it encourages production improvements, contributes to the

advancement in technology, boosts employment opportunities, bolsters business

sector competition and creates exports. In their article on Foreign Direct

Investment and Sustainable Growth, (Fortanier and Maher, 2001) indicated that

Foreign Direct Investment through multinational enterprises is an influential and

effective means to propagate technology from developed to developing countries.

Fortanier and Maher further indicate that Foreign Direct Investment is habitually

the only source of innovative and new technologies.

Empirical research studies also support the assertion that Foreign Direct

Investment positively contributes to the enhancement of the economies of host

countries. According to Mansfield and Romeo (1980), the technology that comes

with Foreign Direct Investment is newer compared to that sold through licensing.

Also Romer(1993) noted that Foreign Direct Investment is beneficial because it

narrows the “idea or knowledge gap” between the developed and host countries

and provides more growth opportunities. In addition, Foreign Direct Investment

inflows bring other tangible and intangible benefits which substantially impact on

economic growth and development. For example, Foreign Direct Investment

inflows through mergers and acquisitions can bring better managerial and

organisational skills. According to Fortanier and Maher (2001), corporate

governance is increasingly becoming a critical feature for cross border investment

decisions and that good corporate governance enhances the confidence of

investors.

Whereas empirical studies show that Foreign Direct Investments lead to economic

growth of host countries, there are other studies that have found contradictory

results. In some instances, it has been found that it is economic growth or its

prospect that leads to an increase in Foreign Direct Investment and not vice versa.

According to Gorg and Greenaway (2002), Foreign Direct Investment has negative

rather than positive spillovers in transition economies. The absence of positive

spillovers is attributed to the size of the economies. In his paper, Joze (2003)

8

indicates that the assertion that Foreign Direct Investment bolsters business

competition in host economies may either be true or false. He indicates that

sometimes multinational enterprises “crowd out” or force out domestic firms thus

reducing competition.

Most countries strive to attract Foreign Direct Investment (FDI) because of its

acknowledged advantages as a tool of economic development. Africa – and Nigeria

in particular – joined the rest of the world in seeking FDI as evidenced by the

formation of the New Partnership for Africa’s Development (NEPAD), which has

the attraction of foreign investment to Africa as a major component. FDI can also

be seen as an investment made to acquire a lasting management interest (normally

10% of voting stock) in a business enterprise operating in a country other than that

of the investor defined according to residency World Bank (1996). Such

investments may take the form of either “greenfield” investment (also called

“mortar and brick” investment) or merger and acquisition (M&A), which entails

the acquisition of existing interest rather than new investment. In corporate

governance, ownership of at least 10% of the ordinary shares or voting stock is the

criterion for the existence of a direct investment relationship. Ownership of less

than 10% is recorded as portfolio investment. FDI comprises not only merger and

acquisition and new investment, but also reinvested earnings and loans and

similar capital transfer between parent companies and their affiliates. Countries

could be both host to FDI projects in their own country and a participant in

investment projects in other countries. A country’s inward FDI position is made up

of the hosted FDI projects, while outward FDI comprises those investment projects

owned abroad.

The linkage between FDI and economic growth has been the subject of controversy

and considerable research for many decades. Interest in the area has been revived

in recent years largely due to the globalisation of the world economy and to the

recognition that multinational corporations play an increasingly important role in

trade, capital accumulation and economic growth in developing countries. Three

developments has added an additional twist to the literature on the FDI-led growth

study, particularly in the area of empirical studies. First, previous econometrics

studies based on the assumption that there is one way-causality from FDI to

9

economic growth has been noted and criticised in the study of (Kholdy, 1995). In

other words, not only FDI can cause economic growth (with either positive or

negative effects), but economic growth can also affect the inflow of FDI. Failure to

consider either direction of such causality can lead to an inefficient estimation of

the impacts of FDI/GDP on GDP/FDI and hence is subject to the problem of

simultaneity bias. Second, the so-called ‘new growth theory’, as propounded by

Paul Romer has resulted in some reappraisal of the determinants of growth in

modelling the role played by FDI in the growth process (Romer,1994). Third, new

developments in econometric theory, such as time series concepts of cointegration

and causality testing, have further expanded the debated on the FDI-growth

relationship.

Foreign Direct Investment (FDI) and economic growth nexus has spurred volumes

of empirical studies on both developed and developing countries. This nexus has

been studied by explaining the determinants of both growth and FDI, the role of

Trans-National Companies (TNCs) in host countries, and the direction of causality

between the two variables. Empirical studies on the importance of inward FDI in

host countries suggest that the foreign capital inflow augment the supply of funds

for investment thus promoting capital formation in the host country. Inward FDI

can stimulate local investment by increasing domestic investment through links in

the production chain when foreign firms buy locally made inputs or when foreign

firms supply source intermediate inputs to local firms. Furthermore, inward FDI

can increase the host country’s export capacity causing the developing country to

increase its foreign exchange earning. FDI is also associated with new job

opportunities and enhancement of technology transfer, and boosts overall

economic growth in host countries. A number of firm-level studies, on the other

hand, however, do not lend support for the view that FDI necessarily promotes

economic growth, and this prompted the researcher to investigate into the subject

matter.

Nigeria has the potential to become Sub-Saharan Africa’s largest economy and a

major player in the global economy because of its rich human and material

resources. With its large reserves of human and natural resources, Nigeria has the

potential to build a prosperous economy, reduce poverty significantly, and provide

10

the health, education, and infrastructure services its population needs. However,

this has not been achieved because all major productive sectors have considerably

shrunk in size with the over dependence on oil. Nigeria as a country, given her

natural resource base and large market size, qualifies to be a major recipient of

FDI in Africa and indeed is one of the top three leading African countries that

consistently received FDI in the past decade. However, the level of FDI attracted

by Nigeria is mediocre (Asiedu, 2003) compared with the resource base and

potential need. Further, the empirical linkage between FDI and economic growth

in Nigeria is yet unclear, despite numerous studies that have examined the

influence of FDI on Nigeria’s economic growth with varying outcomes (Oseghale

and Amonkhienan, 1987; Odozi, 1995; Oyinlola, 1995; Adelegan, 2000; Akinlo,

2004). Most of the previous influential studies on FDI and growth in sub-Saharan

Africa are multi country studies. However, recent evidence affirms that the

relationship between FDI and growth may be country and period specific. Also

(Asiedu, 2001) submits that the determinants of FDI in one region may not be the

same for other regions. In his study on FDI and economic growth in Nigeria

Adeolu (2007), only investigated the empirical relationship between non-

extractive FDI and economic growth in Nigeria and examined the determinants of

FDI into the Nigerian economy and suggest that the determinants of FDI in

Nigeria are market size, infrastructure development and stable macroeconomic

policy and that although the overall effect of FDI on economic growth may not be

significant, the components of FDI do have a positive impact. In the same vein, the

determinants of FDI in countries within a region may be different from one

another and from one period to another.

1.2 STATEMENT OF RESEARCH PROBLEM

Despite the plethora of studies on FDI and economic growth in Nigeria, the

existing empirical evidence on the causal relationship between Foreign Direct

Investment and economic growth and the associated benefits is very inconclusive.

In spite of a seemingly positive association between FDI and economic growth, the

empirical literature has not reached a consensus on the direction of this impact

however suggesting that Foreign Direct Investment can be either beneficial or

harmful to economic growth. Moreover, in the framework of the developing

countries like ours, little research has yet been done on the topic. The principal

11

driving force for this work is that for developing economies, and for Nigeria in

particular, the issue of economic growth is an important one. These countries have

been stimulating growth with the help of various techniques, including policies

that would aim at foreign capital and technology transfer. It is thus, of interest to

investigate whether the start of growth can be attributed to an increased inflow of

FDI into the country over the period under review. It becomes natural therefore to

ask: if the growth which has been experienced in the economy for the past years

was as a result of the contribution of Foreign Direct Investment or if the country

has already attained this growth level before attracting Foreign Direct Investment?

The recent theoretical developments in the area of economic growth suggest that

successful developing countries were able to grow in large part due to the “catch

up” process in the level of technology Borenzstein et al (1998). One of the major

channels of the access to advanced technologies is Foreign Direct Investment.

Thus, an investigation of enhanced economic growth through the advanced in

technology can be closely associated with modelling the relationship between

growth and Foreign Direct Investment. Again, recent theoretical developments

allow researchers to model and evaluate not only the short-run, but also the long-

run impact of Foreign Direct Investment on growth. A closer examination of these

previous studies reveals that conscious effort was not made to take care of the fact

that more than 60% of the FDI inflows into Nigeria is made into the extractive (oil)

industry.

Hence, these studies actually modelled the influence of natural resources on

Nigeria’s economic growth. Most of the other empirical research that has been

undertaken in this area has used panel data for a number of countries to establish

the causal relationships. The results of studies carried out on the linkage between

FDI and economic growth in Nigeria are not unanimous in their submissions. Due

to this reason, it therefore becomes difficult to ascertain the direction of FDI and

economic growth relationship in Nigeria. There is therefore limited exhaustive

country specific research studies to establish the causal relationship and

interaction between Foreign Direct Investment and economic growth. Chowdhury

and Mavrotas (2005) proposed that individual country studies be carried out to

12

ascertain this causal relationship. This thus provides a major incentive for this

study.

1.3 OBJECTIVES OF THE STUDY

The objectives of this study include:

i. To ascertain the extent at which Foreign Direct Investment inflow

influences economic growth in Nigeria.

ii. To establish whether there is any kind of relationship between economic

growth and Foreign Direct Investments in Nigeria.

iii. To find out whether there is a bi-directional relationship between Foreign

Direct Investments and economic growth in Nigeria.

I.4 RESEARCH QUESTIONS

i. To what extent does the inflow in Foreign Direct Investment influence

economic growth?

ii. Is there a long-run causal relationship between Foreign Direct Investment

and economic growth?

iii. Is there a bi-directional relationship between Foreign Direct Investment

and economic growth?

1.5 RESEARCH HYPOTHESES

The following hypotheses are relevant for our study:

Ho1 Foreign Direct Investment inflow is not a major determinant of

economic growth in Nigeria.

Ho2 There is no long-run causal relationship between FDI and economic

growth in Nigeria.

Ho3 There is no bi-directional relationship between FDI and economic

growth in Nigeria.

1.6 SCOPE OF THE STUDY

The study covered the period of 1981 to 2007. This period was chosen because of

the researchers felt that it would be better to use a period of steady democratic

dispensation in Nigeria, which in essence means that investors must have taken a

critical look at the investment environment in the country before taking the

13

decision of doing business in Nigeria. Most studies on FDI and economic growth

seemed to have focused on the phenomenon among developing countries but this

study is based on the case of Nigeria. This study could not have come at a better

time than now. Due to the fact that Nigeria is experiencing a huge amount of

capital inflow, it would be worthwhile to examine the extent of this flow to the

growth of the economy.

1.7 SIGNIFICANCE OF THE STUDY

Based on the fact that there are no exhaustive empirical evidence on the causal

relationship between Foreign Direct Investment and economic growth in Nigeria,

the researcher deemed it necessary to undertake a country specific research study

to establish the causal relationship and interaction between Foreign Direct

Investment and economic growth. In another study done by (Chowdhury and

Mavrotas, 2005), they proposed that individual country studies be carried out to

ascertain this causal relationship. This thus provides a major incentive for this

study.

For Nigeria, this study will add to other studies on the subject matter and also fill

any gap that may exist in previous studies which has been undertaken to establish

whether Foreign Direct Investment leads to economic growth or vice versa.

Previous related studies such as (Adeolu, 2007), concentrated on investigating the

empirical relationship between non-extractive FDI and economic growth in

Nigeria and examined the determinants of FDI into the Nigerian economy but did

not go further to establish a link between them.

The findings of this study when added to the existing body of literature, will be a

valuable guide especially policy makers and a good source of reference for future

scholarly research. One advantage of academic research is that it investigates

matters which practitioners and policy makers find useful but have little time to

study. The study is very vital especially to policy makers and development partners

because it enables them to initiate, develop and manage long term economic

strategies based on empirical evidence.

14

This study contributes to the literature by examining the relationship between FDI

inflows and Nigeria’s economic growth and development, hence addressing the

country’s specific dimension to the FDI growth debate. The study is different from

previous studies in scope that is in terms of number of years covered.

This study will contribute significantly to knowledge by providing a new study

evidence on Foreign Direct Investment and economic growth relationship in

Nigeria. Conventional economic theory especially the endogenous growth theory

and a number of empirical studies support the notion that there is a causal

relationship between Foreign Direct Investments and economic growth and that

Foreign Direct Investment inflows enhance growth in host countries. For example

(Moran, 2002), indicates that Foreign Direct Investment is beneficial to host

countries because it avails a consolidated package of quality control practices,

management skills, human resource and marketing techniques and improved

production procedures all of which place the host country’s economy along the

frontiers of best practices.

To the body of academics, this study will serve as a guide for further researches in

area of FDI and economic growth which this study did not cover. Because of its

presumed benefits to the host country economies, proponents of Foreign Direct

Investments such as the World Bank and International Monetary Fund strongly

encourage countries to attract more Foreign Direct Investments as a way of

stimulating and increasing efficiency of resource allocation.

1.8 LIMITATIONS OF THE STUDY

The major limitation of this research was fund. A substantial amount was

committed to this work in terms of data gathering. In reviewing of the related

literature, the researcher faced some challenges of accessing journals with relevant

materials. Some internet sites were secured and could not be accessed, in some

cases, subscription were made in order to gain access to needed materials. The

researcher also faced a big challenge in acquiring the econometric software that

was used for the analysis. The timely aspect of the work was also impeded because

it took the researcher a good number of months to learn the software and apply it

to the work.

15

1.9 DEFINITION OF TERMS

Foreign Direct Investment: FDI is an investment made to acquire a lasting

management interest in a business enterprise operating in a country other than

that of the investor

Economic growth: It is the increase in the amount of the goods and services

produced by a country over time. It is normally measured as the percent rate of

increase in real gross domestic product (GDP).

Gross National Product (GNP): Is the monetary value the total annual flow

of goods and services in the economy of a nation. The GNP is normally measured

by totalling all personal spending, all government spending, and all investment

spending by a nation's industry both domestically and all over the world.

Gross Domestic Product (GDP): Is the total value of goods and services

produced in a country over a period of time. GDP may be calculated in three ways:

(1) by adding up the value of all goods and services produced, (2) by adding up the

expenditure on goods and services at the time of sale, or (3) by adding up

producers’ incomes from the sale of goods or services.

Absorptive capacity: Absorptive capacity is a limit to the rate or quantity of

scientific or technological information that a firm can absorb. If such limits exist

they provide one explanation for firms to develop internal R&D capacities.

Portfolio investment: The purchase of stocks, bonds, and money market

instruments by foreigners for the purpose of realizing a financial return, which

does not result in foreign management, ownership, or legal control. eg purchase of

shares in a foreign company, purchase of bonds issued by a foreign government,

acquisition of assets in a foreign country, and purchase of stocks in a foreign

company.

Purchasing power parity (PPP): Is a theory of long-term equilibrium

exchange rates based on relative price levels of two countries. In other words, PPP

16

is the amount of a certain basket of basic goods which can be bought in the given

country with the money it produces.

Capital formation: Capital formation is a statistical concept used in national

accounts statistics, econometrics and macroeconomics. It is sometimes also used

in corporate business accounts. It is a measure of the net additions to the

(physical) capital stock in an accounting period, or, a measure of the amount by

which the total physical capital stock increased during an accounting period;

though it may occasionally also refer to the total stock of capital formed, or to the

growth of this total stock.

Host country: A nation in which representatives or organizations of another

state are present because of government invitation and/or international

agreement.

Greenfield investment: A Greenfield Investment is the investment in a

manufacturing, office, or other physical company-related structure or group of

structures in an area where no previous facilities exist.

Mortar and Brick: This refers to a company that possesses a building or store

for operations or companies that have a physical presence that is, a physical store

and offer face-to-face consumer experiences.

Merger and acquisition: This refers to the aspect of corporate strategy,

corporate finance and management dealing with the buying, selling and combining

of different companies that can aid, finance, or help a growing company in a given

industry grow rapidly without having to create another business entity.

New Growth Theory: The endogenous growth theory or the New growth theory

holds that policy measures can have an impact on the long-run growth rate of an

economy. The new theories argue that, for an economy to innovate and thus grow,

some form of imperfect competition must be present. Its main focus is that

knowledge drives growth.

17

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Nigeria: A seemingly unrelated model”. African Review of Money, Finance

and Banking, Supplementary issue of “Savings and Development” 2000.

pp.5–25.

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Discussion Paper Series ESPD/DPS/99/2. United Nations Economic

Commission for Africa, Addis Ababa, Ethiopia.

Ajayi, S. Ibi. (2000), “What Africa needs to do to benefit from globalization”.

Finance and Development, 38(4): 6–8.

Ajayi, S. Ibi. (2003), “Globalization and Africa”. Journal of African Economies, 12

(Supp. 1): 120–50.

Akinlo, A.E. (2004), “Foreign Direct Investment and growth in Nigeria: An

empirical investigation”. Journal of Policy Modelling, 26: 627–39.

Asiedu, E. (2001), “On the determinants of Foreign Direct Investment to

developing countries: Is Africa different?” World Development, 30(1): 107–

191.

Asiedu, E. (2003), “Capital controls and Foreign Direct Investment”, World

Development, 32(3): 479–90.

Chowdhury, A. and Mavrotas, G. (2005), “FDI and Growth: A Causal

Relationship”, UNUWIDER Research Paper No. 2005/25, UNU-WIDER.

Economist Intelligence Unit (2008), Nigeria—Country Profile, 20-21.

Ekpo, A.H. (1995), “Foreign Direct Investment in Nigeria: Evidence from time

series data”. CBN Economic and Financial Review, 35(1): 59–78.

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Endozien, E.G. (1968), “Linkages, direct foreign investment and Nigeria’s

economic development”. The Nigerian Journal of Economic and Social

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Finance, Efficiency, Incentives, and Distortions”. The World Bank

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Holger, G. and Greenaway, D. (2004), “On Whether Domestic Firms benefit from

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Number 2 pp.171-197.

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Jerome, A. and Ogunkola J. (2004), “Foreign Direct Investment in Nigeria:

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Applied Economics, 27: 74-749

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Obinna, O.E. (1983). “Diversification of Nigeria’s external finances through

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Obwona, M. B. (2004). “Foreign Direct Investment in Africa”. In Financing Pro-

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20

CHAPTER TWO

REVIEW OF RELATED LITERATURE

2.1 OVERVIEW

An Analysis of the interrelation between FDI and economic growth is not new in

economic literature. In order to carefully examine this relationship in the setting of

developing economies, it is important to look at both theoretical and empirical

approaches undertaken to the investigation of this problem. Here, we are

interested in exploring the effect of FDI on economic growth, and also a possible

effect of economic growth on FDI, we pay close attention to studies which

investigate both directions of the relationship and other studies that will be of

interest to our study. Before we proceed, we will also look at the meaning of FDI

and economic growth and various types of FDI. It will be also necessary to review

the reasons for undertaking FDI and the reason why FDI may be believed to play a

significant role in influencing economic growth. Next, we will provide a review of

theoretical framework in which the validity of reasons is analysed and tested, and

finally, several examples of empirical studies in the area will be examined.

There is conflicting evidence in the literature regarding the question as to how, and

to what extent, FDI affects economic growth. FDI may affect economic growth

directly because it contributes to capital accumulation, and the transfer of new

technologies to the recipient country. In addition, FDI enhances economic growth

indirectly where the direct transfer of technology augments the stock of knowledge

in the recipient country through labour training and skill acquisition, new

management practices and organizational arrangements (De Mello, 1999).

Theoretically, however, in the context of either neo-classical or endogenous growth

models, the effects of FDI on the economic growth of the receiving country differ in

the recent growth models from their conventional counterparts. The conventional

economic growth theories are being augmented by discussing growth in the

context of an open rather than a closed economy, and the emergence of

externality-based growth models. Even with the inclusion of FDI in the model of

economic growth, traditional growth theories confine the possible impact of FDI to

21

the short-run level of income, when actually recent research has increasingly

uncovered an endogenous long-run role of FDI in economic growth determination

(De Mello, 1999). According to the neo-classical models, FDI can only affect

growth in the short run because of diminishing returns of capital in the long run.

In contrast with the conventional neo-classical model, which postulates that long

run growth can only happen from the both exogenous labour force growth and

technological progress, the rise of endogenous growth models (Barrow and Sala-i-

Martin, 1995) made it possible to model FDI as promoting economic growth even

in the long run through the permanent knowledge transfer that accompanies FDI.

As an externality, this knowledge transfer, with other externalities, will account for

the non-diminishing returns that result in long run growth (De Mello, 1997).

Hence, if growth determinants, including FDI, are made endogenous in the model,

long run effects of FDI will follow. Therefore, a particular channel whereby

technology spills over from advanced to lagging countries is the flow of FDI

(Bengoa and Sanchez-Robles, 2003).

Nevertheless, most studies generally indicate that the effect of FDI on growth

depends on other factors such as the degree of complementarity and substitution

between domestic investment and FDI, and other country-specific characteristics.

In their own findings, (Buckley et. al, 2002) argued that the extent to which FDI

contributes to growth depends on the economic and social conditions in the

recipient country. Countries with high rate of savings, open trade regime and high

technological levels would benefit from increase FDI to their economies. However,

FDI may have negative effect on the growth prospects of the recipient economy if

they result in a substantial reverse flows in the form of remittances of profits, and

dividends and/or if the multinational corporations (MNCs) obtain substantial or

other concessions from the host country. Also, (Bengoa and Sanchez-Robles,

2003) argued that in order to benefit from long-term capital flows, the host

country requires adequate human capital, sufficient infrastructure, economic

stability and liberalized markets. The view that FDI fosters economic growth in the

host country, provided that the host country is able to take advantage of its

spillovers is supported by empirical findings in (De Mello, 1999; Obwona, 2001).

Borensztein et al., (1998) go further to suggest that FDI is an important vehicle for

22

the transfer of technology, contributing relatively more to growth than domestic

investment. They use a model of endogenous growth, in which the rate of

technological progress is the main determinant of the long-term growth rate of

income.

The other theme of empirical research of FDI-growth relationship concentrated on

identifying determinants of FDI flow and analyzing the effects of these

determinants on the attractiveness of the host country to, and the volume and

type, of such flows. Two sets of factors are frequently cited. The first set includes

the size of the recipient market, relative factor prices, and balance of payments

constraints (Bhasin et al., 1994; Love and Lage-Hidalgo, 2000; Lipsey, 2000). The

second set includes institutional factors such as degree of openness and trade

policies, legislative environment and law enforcement (Lee and Mansfield, 1996),

and the degree of economic and political stability (Lipsey, 2000). Recognizing the

importance of FDI to their growth, many countries are using specific incentives to

attract FDI inflow. Tax breaks and rebates are examples of such incentives (Tung

and Cho, 2001). Nevertheless, the effectiveness of such incentives has been

questioned (Guisinger, 1992).

The FDI inflow differential and economic growth disparity among developing

countries have created much research interest among economists. There is a large

body of theoretical and empirical literature on the impact of FDI on economic

growth. The existing evidence, however, is mixed. In theory, FDI can be expected

to benefit the host country by transferring resources (the so-called resource

transfer effects), increasing employment opportunities (employment effects),

improving the balance of payments (balance of payments effects) and transferring

technology (technology effects). Researchers such as (Findlay, 1978; Lall, 1974;

Loungani and Razin, 2001 and Romer, 1990) among others, noted that FDI brings

much needed physical capital, new technology, managerial and marketing talents

and expertise, international best practices of doing business as well as increased

competition. These resources may have the potential to be diffused into indigenous

firms thereby creating more innovation and productivity growth. FDI contributes

more jobs to the local economy by directly adding new jobs and indirectly when

local spending increases due to purchases of goods and services by the new

23

increase in employees. All of these in turn are expected to have positive multiplier

effects for an economy. The benefits from the balance of payments effects include

improvement in the capital account due to the inflows of new capital into the host

country and improvements in the current account balance because of possible

decline in imports of goods and services which would otherwise have been

imported. The additional taxes from multinational corporations also have the

potential to improve the budget situation of the host country. Hymer, (1976)

suggested that the technological transfer benefits included, among other things,

the direct benefits from adopting the product, process and organizational

innovations initiated by the parent company which he named as “firm specific

assets”, and the indirect spillover effects on the rest of the economy.

Although economists agree regarding the direct benefits of technological transfer

on the host country firms, the measurement of indirect spillover effects is

shrouded with difficulties. As a result, the evidence is mixed. For example, an

extensive review by (Blomstorm, Globerman and Kokko, 2000) both at aggregate

and cases studies levels, found no strong consensus on the magnitude of spillover

effects. A study of UK owned 20 manufacturing industries by (Harris and

Robinson, 2004) concludes that “…inter-industry spillovers are just as likely to be

negative as positive…. and so there is clear evidence of an overall beneficial effects

on UK manufacturing industries resulting from supply side linkages associated

with FDI.” Using a World Bank survey of 1500 firms in five Chinese cities, (Hale

and Long, 2006) found evidence of positive spillover effects for more

technologically advanced firms but none or even negative spillover effects for

relatively small firms. From this, they concluded that a well functioning labour

market facilitates FDI spillover by creating network externalities among highly

skilled workers. Despite some of the evidence presented in recent studies, there are

several theoretical arguments why developing countries may not gain from FDI.

Krugman (1998) argues that the transfer of control from domestic to foreign firms

may not always be beneficial to the host countries because of the adverse selection

problem. FDI undertaken within a crisis situation under “Fire Sale” may transfer

ownership of firms from domestic to foreign firms that are less efficient. This

concern is particularly important to the developing countries including the SSA

countries, where, as part of privatization, state owned enterprises are sold to

24

foreign firms simply because foreign firms have more available funds than

domestic ones. As pointed out by (Salz, 1992; Agosin and Mayer, 2000), FDI may

also “crowd out” domestic firms through unfair competition. There is also a

concern that the enclave nature of many foreign owned firms and their minimal

linkage to the rest of the economy could reduce the potential spillover contribution

to the national economy. Moreover, the potential subsequent outflow of foreign

firms' subsidiary earnings to their parent companies could also cause deterioration

in the balance of payments. It is also argued that foreign corporations tend to

produce inappropriate goods that are tailored to satisfy the wealthy portion of the

host country’s consumers, thereby increasing inequality and engaging in transfer

pricing.

Empirical evidence on the link between FDI and economic growth is also

inconclusive. These authors, (Bosworth and Collins,1999; Blomstrom et al.,2000;

Borensztein et al. 1998; Zhang, 2001; De Mello, 1997; Balasubramanyam et al.,

1996 and Obwona, 2001) provide evidence on the positive effects of FDI on

economic growth. Growth enhancing effect of FDI is not, however, automatic, but

depends on various country specific factors. Also, (UNCTAD, 2005; Blomstrom et

al., 2000; and De Mello, 1997) indicate that the stronger the positive effect of FDI

is, the higher the level of development of a host country. Higher level of

development allows countries to reap the benefits of productivity fostered by

foreign investment. For similar reasons, (Bronsznestein et al., 1998) have found

that significant relations between FDI flows and economic growth depend on the

level of human capital. Host countries with better endowment of human capital are

believed to benefit more from FDI induced technology transfer as spillover-effects

than others with less human capital. More recently, (Balasubramanyam et al,. 1996

and UNCTAD, 2005) suggest that the positive effects of FDI also depend on

openness to trade. FDI can broaden access to export markets as transnational

corporations often serve as channels for the distribution of goods from one country

to other markets located in another country. Similarly, (Nair-Reichert and

Weinhold , 2000), using a mixed fixed and random panel data estimation method

to allow for cross country heterogeneity in the causal relationship, find some

evidence that there is efficacy of FDI in raising future growth rate, although

heterogeneous across countries, is higher for more open economies.

25

Alfaro et al. (2000) examined the role of financial market in FDI-growth nexus.

Their empirical evidence indicates that FDI plays an important role in contributing

to economic growth. However, the level of development of local financial markets

is crucial for the positive effects to be realized. In contrast, (Aitken and Harrison,

1999 and Carkovic and Levine, 2002) argue that there is no significant positive

relation between FDI and economic growth. Even when the relation is positive, the

effects tend to be weak. Rodrick (1999), for example argues that much of the

correlation between FDI and economic growth is driven by reverse causation. Few

studies such as (Salz, 1992) find a negative relationship between FDI and economic

growth. The majority of studies, however, conclude that FDI contributes to total

productivity and economic growth.

2.2 DEFINITIONS OF FDI

Foreign Direct Investment (FDI) is the process where people in one country obtain

ownership of assets for the purpose of gaining control over the production,

distribution and other activities of a firm in a foreign country (Moosa, 2002). The

OECD Benchmark Definition of Foreign Direct Investment (OECD, 1996) defines

FDI as “the objective of obtaining a lasting interest by a resident entity in one

economy (direct investor) in an entity resident in an economy other than that of

the investor (direct investment enterprise)”. The lasting interest reflects the

continuation of a long-term relationship between the direct investor and the

enterprise and a considerable level of influence on the management of the

enterprise. The terms “influence” or “control” and “long-term” are used to make a

distinction between FDI and portfolio investment because the latter is a short-term

investment where the investor does not seek to control the firm. The influence over

management decisions and productivity is also the part that differentiates FDI

from other types of international investments. This influence implies for instance,

that the investor has an ability to elect members on the board of directors of the

foreign firm or subsidiary (Moosa, 2002).

26

2.2.1 DIFFERENT TYPES OF FDI

The agents that engage in FDI are large multinational companies, also called

MNCs. From the perspective of the multinational company, or the investor, there

are two major types of FDI: horizontal FDI and vertical FDI.

Horizontal FDI is undertaken when the company wants to expand horizontally

to produce the same or comparable goods in the host country as in the home

country. Product differentiation is a central aspect for horizontal FDI to be

successful. There are two main motives for a company to engage in horizontal FDI.

The first one is that it is more profitable for the multinational company to be at the

foreign location, and the second motive is that the company can save a lot on low-

cost inputs, such as labour. In addition, horizontal FDI is often undertaken to

make substantial use of monopolistic or oligopolistic advantages, especially if there

are fewer restrictions in the host country.

Vertical FDI is undertaken when a company seeks to exploit raw materials, or

wants to be closer to the consumer by acquiring distribution outlets. The idea is to

make the production process more cost-efficient by reallocating some stages to

low-cost locations. By establishing their own network in the host country, it is

easier for the multinational companies to market their products (Brakman,

Garretsen and van Marrewijk, 2006). FDI can take the form of green field

investment, mergers and acquisitions (M&As) and joint ventures. Greenfield

investment is the process whereby the investing company establishes new

production and distribution facilities in a foreign country. Because this form

creates new employment opportunities and high value added output, the host

country is generally positive to greenfield investments. An acquisition of, or a

merger with an already existing company in a foreign country is another form of

FDI. M&As are cheaper than greenfield investments and makes it easier for the

investor to get quick market access. But M&As can be harmful to the host country

because they may only imply a transfer of ownership that is followed by layoffs and

closing of advantageous activities. Moreover, compared to greenfield investments,

the acquisition of companies in the host country is generally not as welcomed,

since the majority of countries prefer to maintain control over domestic

27

companies. Joint venture is the third form of FDI and can be seen as a partnership,

either with a company in the host country, a government institution or another

foreign company. Joint ventures are often formed to share the risk and expertise.

Usually, one partner provides the technical skills and access to financial means,

while the other partner offers its local knowledge concerning the market as well as

laws and regulations (Moosa, 2002). This is of course very valuable to the foreign

company and in particular if the investment takes place in a developing country.

2.3 MAJOR SOURCES AND DESTINATIONS OF FDI

Not surprisingly, the major sources of FDI are the high-income developed nations.

These countries accounted for over 90 percent of out flowing FDI in the years

1987-1992 and for more than 85 percent in the period 1993-1998. The main

recipients of FDI also turn out to be the advanced nations, which in the years

1988-1998 received over 70 percent of inflowing FDI. But even though it is clear

that the developed countries are the main destinations for FDI, an interesting fact

is that ten developing countries make up two-thirds of the total FDI inflow to all

developing countries. Among these, China received 30.6 percent. From 1988 to

1997, China experienced a fourfold increase of FDI during the years 1988-1992, the

country received 2.9 % of the total FDI in the world, which can be compared to

over 12 percent during the years 1993-1997 (Brakman et. al., 2006). The

development of the country has boomed and the growth rate is continuously

increasing at a rate that economic history has never seen before. This clearly

distinguishes China from other developing economies.

According to the study done by (Agrawal, 2000) on economic impact of Foreign

Direct Investment in South Asia by undertaking time-series, cross-section analysis

of panel data from five South Asian countries; India, Pakistan, Bangladesh, Sri

Lanka and Nepal, that there exist complementarily and linkage effects between

foreign and national investment. Further he argues that, the impact of FDI inflows

on GDP growth rate is negative prior to 1980, mildly positive for early eighties and

strongly positive over the late eighties and early nineties. Most South Asian

countries followed the import substitution policies and had high import tariffs in

the 1960s and 1970s. These policies gradually changed over the 1980s, and by the

28

early 1990s, most countries had largely abandoned the import substitution

strategy in favour of more open international trade and generally, market oriented

policies (Agrawal, 2000). Carkovic and Levine (2002) also concluded in their

econometric study on FDI and GDP growth that the exogenous component of FDI

does not exert a robust, independent influence on growth.

However, no consensus has yet been reached on the steady state as well as

dynamic effects of FDI on growth. While some studies argue that the impact of FDI

on growth is highly heterogeneous across countries with relatively open economies

showing statistically significant results, the other studies maintain that the

direction of causality between the two variables depends on the recipient country’s

trade regime. However, most studies don’t pay any serious attention to the

possibility of a bi-directional link between the two variables in reference.

Renewed research interest in FDI stems from the change of perspectives among

policy makers from “hostility” to “conscious encouragement”, especially among

developing countries. FDI had been seen as “parasitic” and retarding the

development of domestic industries for export promotion until recently. However,

(Bende-Nabende and Ford, 1998) submit that the wide externalities in respect of

technology transfer, the development of human capital and the opening up of the

economy to international forces, among other factors, have served to change the

former image. Caves (1996) observed that the rationale for increased efforts to

attract more FDI stems from the belief that FDI has several positive effects. Among

these are productivity gains, technology transfers, introduction of new processes,

managerial skills and know-how in the domestic market, employee training,

international production networks, and access to markets.

Borensztein et al. (1998) see FDI as an important vehicle for the transfer of

technology, contributing to growth in larger measure than domestic investment.

Findlay (1978) postulates that FDI increases the rate of technical progress in the

host country through a “contagion effect” from the more advanced technology,

management practices, etc., used by foreign firms. On the basis of these assertions

governments have often provided special incentives to foreign firms to set up

companies in their countries. Carkovic and Levine (2002) noted that the economic

29

rationale for offering special incentives to attract FDI frequently derives from the

belief that foreign investment produces externalities in the form of technology

transfers and spillovers. Curiously, the empirical evidence of these benefits both at

the firm level and at the national level remains ambiguous.

De Gregorio (2003), while contributing to the debate on the importance of FDI,

notes that FDI may allow a country to bring in technologies and knowledge that

are not readily available to domestic investors, and in this way increases

productivity growth throughout the economy. FDI may also bring in expertise that

the country does not possess, and foreign investors may have access to global

markets. In fact, he found that increasing aggregate investment by 1 percentage

point of GDP increased economic growth of Latin American countries by 0.1% to

0.2% a year, but increasing FDI by the same amount increased growth by

approximately 0.6% a year during the period 1950–1985, thus indicating that FDI

is three times more efficient than domestic investment. A lot of research interest

has been shown on the relationship between FDI and economic growth, although

most of such work is not situated in Africa. The focus of the research work on FDI

and economic growth can be broadly classified into two. First, FDI is considered to

have direct impact on trade through which the growth process is assured

(Markussen and Vernables, 1998). Second, FDI is assumed to augment domestic

capital thereby stimulating the productivity of domestic investments (Borensztein

et al., 1998; Driffield, 2001). These two arguments are in conformity with

endogenous growth theories (Romer, 1990) and cross country models on

industrialization (Chenery et al., 1986) in which both the quantity and quality of

factors of production as well as the transformation of the production processes are

ingredients in developing a competitive advantage.

Moreover, FDI has empirically been found to stimulate economic growth by a

number of researchers (Borensztein et al., 1998; Glass and Saggi, 1998). Dees

(1998) submits that FDI has been important in explaining China’s economic

growth, while (De Mello, 1997) presents a positive correlation for selected Latin

American countries. Inflows of foreign capital are assumed to boost investment

levels. Blomstrom et al. (1994) report that FDI exerts a positive effect on economic

growth, but that there seems to be a threshold level of income above which FDI has

30

positive effect on economic growth and below which it does not. The explanation

was that only those countries that have reached a certain income level can absorb

new technologies and benefit from technology diffusion, and thus reap the extra

advantages that FDI can offer. Previous works suggest human capital as one of the

reasons for the differential response to FDI at different levels of income. This is

because it takes a well-educated population to understand and spread the benefits

of new innovations to the whole economy.

Borensztein et al. (1998) also found that the interaction of FDI and human capital

had important effect on economic growth, and suggest that the differences in the

technological absorptive ability may explain the variation in growth effects of FDI

across countries. They suggest further that countries may need a minimum

threshold stock of human capital in order to experience positive effects of FDI.

Balasubramanyan et al. (1996) report positive interaction between human capital

and FDI. They had earlier found significant results supporting the assumption that

FDI is more important for economic growth in export-promoting than import-

substituting countries. This implies that the impact of FDI varies across countries

and that trade policy can affect the role of FDI in economic growth. In summary,

(UNCTAD, 1999) submits that FDI has either a positive or negative impact on

output depending on the variables that are entered alongside it in the test

equation. These variables include the initial per capita GDP, education attainment,

domestic investment ratio, political instability, terms of trade, black market

exchange rate premiums, and the state of financial development.

Examining other variables that could explain the interaction between FDI and

growth, (Olofsdotter, 1998) submits that the beneficiary effects of FDI are stronger

in those countries with a higher level of institutional capability. He therefore

emphasized the importance of bureaucratic efficiency in enabling FDI effects. The

neoclassical economists argue that FDI influences economic growth by increasing

the amount of capital per person. However, because of diminishing returns to

capital, it does not influence long-run economic growth. Bengos and Sanchez-

Robles (2003) asserts that even though FDI is positively correlated with economic

growth, host countries require minimum human capital, economic stability and

liberalized markets in order to benefit from long-term FDI inflows.

31

Interestingly, (Bende-Nabende et al., 2002) found that direct long-term impact of

FDI on output is significant and positive for comparatively economically less

advanced Philippines and Thailand, but negative in the more economically

advanced Japan and Taiwan. Hence, the level of economic development may not

be the main enabling factor in FDI growth nexus. On the other hand, the

endogenous school of thought opines that FDI also influences long-run variables

such as research and development (R&D) and human capital (Romer, 1986; Lucas,

1988). FDI could be beneficial in the short term but not in the long term. Durham

(2004), for example, failed to establish a positive relationship between FDI and

growth, but instead suggests that the effects of FDI are contingent on the

“absorptive capability” of host countries. Obwona (2001) notes in his study of the

determinants of FDI and their impact on growth in Uganda that macroeconomic

and political stability and policy consistency are important parameters

determining the flow of FDI into Uganda and that FDI affects growth positively but

insignificantly. Ekpo (1995) reports that political regime, real income per capita,

rate of inflation, world interest rate, credit rating and debt service explain the

variance of FDI in Nigeria. For non-oil FDI, however, Nigeria’s credit rating is very

important in drawing the needed FDI into the country.

Furthermore, spillover effects could be observed in the labour markets through

learning and its impact on the productivity of domestic investment (Sjoholm,

1999). Sjoholm suggests that through technology transfer to their affiliates and

technological spillovers to unaffiliated firms in host economy, transnational

corporations (TNCs) can speed up development of new intermediate product

varieties, raise the quality of the product, facilitate international collaboration on

R&D, and introduce new forms of human capital. FDI also contributes to economic

growth via technology transfer. TNCs can transfer technology either directly

(internally) to their foreign owned enterprises (FOE) or indirectly (externally) to

domestically owned and controlled firms in the host country (Blomstrom et al.,

2000; UNCTAD, 2000). Spillovers of advanced technology from foreign owned

enterprises to domestically owned enterprises can take any of four ways: vertical

linkages between affiliates and domestic suppliers and consumers; horizontal

linkages between the affiliates and firms in the same industry in the host country

32

(Lim, 2001; Smarzynska, 2002); labour turnover from affiliates to domestic firms;

and internationalization of R&D (Hanson, 2001; Blomstrom and Kokko, 1998).

The pace of technological change in the economy as a whole will depend on the

innovative and social capabilities of the host country, together with the absorptive

capacity of other enterprises in the country (Carkovic and Levine, 2002).

Other than the capital augmenting element, some economists see FDI as having a

direct impact on trade in goods and services (Markussen and Vernables, 1998).

Trade theory expects FDI inflows to result in improved competitiveness of host

countries' exports (Blomstrom and Kokko, 1998). TNCs can have a negative impact

on the direct transfer of technology to the FOEs, however, and thereby reduce the

spillover from FDI in the host country in several ways. They can provide their

affiliate with too few or the wrong kind of technological capabilities, or even limit

access to the technology of the parent company. The transfer of technology can be

prevented if it is not consistent with the TNC’s profit maximizing objective and if

the cost of preventing the transfer is low. Consequently, the production of its

affiliates could be restricted to low- level activities and the scope for technical

change and technological learning within the affiliate reduced. This would be by

limiting downstream producers to low value intermediate products, and in some

cases “crowding out” local producers to eliminate competition. They may also limit

exports to competitors and confine production to the needs of the TNCs. These

may ultimately result in a decline in the overall growth rate of the “host country

and worsened balance of payment situation” (Blomstrom and Kokko, 1998).

2.4 FACTORS THAT INFLUENCE FDI DECISION MAKING

It is reasonable to suggest that the process of careful planning precedes the final

decision making about FDI activity on the top level of multinational corporations

(MNCs). According to economic theory and empirical evidence, financial flows

take place from the low-profit to higher-profit regions, making the future profit

anticipation (profit-seeking) one of the key motivation for undertaking investment

activity (Carbaugh, 2000). Although an important one, the expectation of high

future profits is not the only factor that is taken into account. Other factors that

influence the decision to invest into a foreign country may be conditionally divided

33

into two large groups of factors – “company-specific” and “country-specific”

factors.

Company-specific factors are the factors that differ among foreign companies of

the same or similar industry with regards to a specific country. These factors

include but are not limited to demand and cost factors.

By demand factors it is understood that a company may view FDI activity as a

means of its market expansion (de Mello, 1997). Whenever foreign demand for a

product of a particular firm is strong, and whenever it is more profitable to

produce goods in that country rather than export them, a company may undertake

Foreign Direct Investment into that country. Another demand reason for FDI is

eliminating foreign competition by acquiring a control package in a foreign firm,

the processes of globalisation makes firms expand their market and operate

overseas. Clearly, such factors will vary firms belonging to different sectors of

economy, but may be similar for firms in one particular field.

Cost factors are concerned with the firm’s struggle to increase profits by means of

decreasing costs. Whenever the costs of labour and costs of resources and final

goods transportation are comparatively low in a foreign country, the parent

company may shift a part or even the whole production to that country (Carbaugh,

2000). Other cost factors include economies of scale considerations, relative factor

prices, and the use of capital in a recipient country.

Country specific factors have a similar impact on decision-making of foreign

companies operating in any sector, with regard to a specific country. To start with,

such factors include institutional features of the recipient economy (De Mello,

1997). These are political stability, the development of democracy, a sound

legislation base regulating FDI and enforcing contracts, the status of intellectual

property rights, the degree of government intervention into economy, bureaucratic

procedures, the system of taxation and tax incentives. In addition, factors

associated with economic stability and economic performance of a country is

important, such as the degree of openness, availability of tax rebates, and import

and export regulations. De Mello (1999) also points to such scale factors as balance

34

of payment constraints, the size of the domestic market, all of which he refers to as

the absorptive capacity of the economy.

According to some studies of FDI in transitional economies, (Hirvensalo, 2001)

indicates that according to the national investment promotion agencies in these

economies, one of the main reason why investors undertake investment activity in

these economies is because of the prospect of economic growth itself. This is

followed by proximity to Western markets, favourable investment climate, political

stability, highly educated and productive workforce, well developed sectors of

telecommunication and infrastructure. In addition, moving ahead with market

oriented reforms, introducing inflation-stabilisation policies, and adopting sound

monetary and fiscal policies, are factors that are thought to reduce macroeconomic

risks and stimulate capital inflows in many Eastern European countries (Calvo et

al, 1996). Other factors that have been spurring FDI inflows into economies in

transition are the processes of privatisation, with immense opportunities for

foreign countries to acquire a controlling interest in newly privatised companies.

2.5 THE ROLE OF FDI: POSITIVE AND NEGATIVE ASPECT

In addition to the benefits that FDI brings to investors, the interest in studying FDI

lies in the area of the effects flowing from FDI. Although it seems to have become

publicly accepted wisdom that FDI is beneficial rather than harmful in enhancing

economic growth, empirical literature has not reached a consensus on whether FDI

has a positive impact on economic growth. Since FDI represents a composite

bundle of capital stock, technology, management, and know-how

(Balasubramanyan et al, 1996), it is believed to have multidimensional impact on

the recipient economy.

There are several ways in which FDI can stimulate economic growth. First, through

capital accumulation, FDI is expected to be growth enhancing in that more new

inputs are incorporated into production (Buckley, 2002). Economic growth may

additionally result from a wider range of intermediate goods in FDI-related

production (Feenstra and Markusen, 1994). Second, FDI is considered to be an

important source of technological change and human capital augmentation

35

(Buckley et al, 2002). Technological change occurs simultaneously through the

process of capital deepening, as new varieties of knowledge-based capital goods are

introduced, and through the human capital augmentation, as productivity-

increasing labour training, new skills acquisition, alternative advanced

management practices and organisational innovations take place. More

importantly, FDI leads to what is called “technology diffusion” – the transmission

of ideas and new technologies, productivity spillovers, sharing and implementation

of know-how, knowledge transfer (Borensztein et al, 1998), all of which are

important factors of economic development. Technological change occurs not only

within the FDI- recipient firm, but also in the overall economy, due to the spillover

effects such as positive externalities, are enhanced by FDI.

Furthermore, FDI is believed to improve efficiency of the locally owned firms.

Broadly speaking, the efficiency of firms in the host economy is supposed to be

increased in direct and indirect ways. Though by the direct effect it meant that FDI

will contribute to the productivity of the sector in which a foreign firm operates.

Some studies (Schoors et al, 2002) find that whenever firms in open sector are

owned domestically, productivity is not very high. They use cheap labour force as a

source of comparative advantage. This is in contrast to the foreign-owned firms in

the same sectors, which hire more expensive labour, but benefit from higher

productivity.

On the other hand, cross-sector, or indirect, effects are also present whenever

labour and knowledge are moving from sector to sector, technology diffusion

occurs. In addition, more productive foreign firms stimulate healthy competition

in the domestic market. In addition to the reasons mentioned above, FDI is

believed to be especially important for economies in transition because these

countries have much potential human capital, but lack the technology and capital

necessary for development and growth. FDI is seen as serving as a stimulus for

capital accumulation and technology transfer in these economies.

Moreover, as is widely known and understood, transitional economies lack capital

and financial means, and they have to rely on foreign assistance. During the

transition period, a country is faced with reorienting its production and

36

consumption structures and rebuilding its capital stock as a whole, since the

capital stock inherited from the past is old and inadequate for the new market

situation. Consequently, the speed of the transition may be related to the ability of

a country to stimulate capital inflows (Garibaldi et al, 2002).

The experience of transition economies, however, suggests that such sources of

external help as foreign aid and credits have proven themselves to not always be

beneficial for the recipient countries, since much of the aid is being stolen or used

ineffectively, whereas credits require interest payments. In this light, Foreign

Direct Investment plays an important role as an outward factor that can and does

represent a real working financial injection into transitional economies (Balatsky,

1999). Another reason why transition economies may be interested in attracting

FDI, in words of (Balatsky, 1999) is the ability of a foreign-owned sector to lead the

economy out of a temporary shock or a short-run recession, provided it is not very

deep in order not to affect domestic producers.

Furthermore, (Calvo et al., 1996) suggest that a large shift in capital flows to one or

more large (or more developed) countries in the region (such as Hungary, Czech

Republic, and Russia), may generate externalities for the neighbouring countries,

by means of making investors more familiar with the emerging markets and more

willing to invest into countries with similar economic prospects.

Finally, other important outcomes of FDI include increase in consumer choice,

enabling household to smooth consumption over time, provision of support for

pension funds and retirement accounts (Calvo et al., 1996), improving tax

collection on the local and state levels (Carbaugh, 2000), as well as possible

increase in domestic investment stemming from increased competition (de Mello,

1997). It is important to note, however, that not all researchers are so sanguine

with regard to the impact of FDI on the host economy. For example, with respect

to the spillover effects, some authors (Schoors et al, 2002; Blomstrom et al, 1998)

draw attention to the fact that the initial stages of the development and/or

transition to the market economies, FDI may have a negative impact on the

recipient economy. This fact is referred to as a “market stealing” effect, when

domestic firms are so unproductive compared to the foreign ones, that foreign-

owned firms drive domestic producers out of the market. Schoors et al., (2002),

37

however, find that the positive effect outweighs the negative one. They also find

that cross-section, or intersectional, spillover effects are more important than the

spillover effects diffused within the sector into which FDI was injected. This

happens because foreign-owned firms that operate on domestic markets usually

come into contact with firms of other sectors, suppliers and consumers of these

firm’s products. And, as suggested by (Blomstrom et al., 1998), since the foreign-

owned firms is producing a high-quality output, it requires its partners to comply

with this quality, driving up production standards of the firms from different

sectors of the economy. Nevertheless, it is not clear whether results obtained by

(Schoors et al., 2002) can be extended to other transitional economies, with which

domestic production is still at the initial stages of development. And it is therefore,

not unequivocal that FDI can be viewed as a remedy for unemployment since not

only workers may be hired by foreign-owned firms, but also workers may be fired

by domestically-owned firms that cannot compete. Similarly, it is not clear

whether FDI can strengthen domestic competition in the short-run.

Other ambiguous consequences of FDI inflows are pointed out by (Calvo et al.,

1996), who suggest that whenever capital inflows are large, they may have less

desirable macroeconomic effects, such as “… rapid monetary expansion,

inflationary pressures, real exchange rate appreciation and widening current

account deficits”. They also warn that FDI movements tend to possess some

cyclical components. In the case of developing countries, FDI may lead to “booms

and busts in capital inflows”, and, consequently, to economic upswings and

downswings in the host country. Therefore, they suggest that developing capital-

importing countries may be quite vulnerable to cyclically based FDI decisions, and

special policies should be implemented to reduce such vulnerability. Not

surprising, (de Mello, 1999), concludes that “whether FDI can be deemed to be a

catalyst for economic growth, capital accumulation, and technological progress

seems to be a less controversial hypothesis in theory than in practice” and

(Campos, 2002) points out that “a closer examination of the attendant empirical

evidence disappoints all but the most fervent believer”. Therefore, different

opinions presented in the literature, as well as evolving macroeconomic situation

in transitional economies stimulate further elaboration on the problem of FDI and

economic growth interrelation.

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2.6 THEORIES OF ECONOMIC GROWTH AND FDI

(INTERRELATIONSHIP BETWEEN FDI AND ECONOMIC

GROWTH)

According to the standard neoclassical theories, economic growth and

development are based on the utilization of land, labour and capital in production.

Since developing countries in general, have underutilized land and labour and

exhibit low savings rate, the marginal productivity of capital is likely to be greater

in these countries. Thus, the neo-liberal theories of development assume that

interdependence between the developed and the developing countries can benefit

the latter. This is because capital will flow from rich to poor areas where the

returns on capital investments will be highest, helping to bring about a

transformation of ‘backward’ economies. Furthermore, the standard neo-classical

theory predicts that poorer countries grow faster on average than richer countries

because of diminishing returns on capital. Poor countries were expected to

converge with the rich over time because of their higher capacity for absorbing

capital. The reality, however, is that over the years divergence has been the case,

the gap between the rich and poor economies has continued to increase. The

volume of capital flow to the poor economies relative to the rich has been low.

Arghiri (1972) “Unequal Exchange” brought the whole issue of the validity of

comparative advantage once again, into sharp focus. He accepts the law on its own

but tries to integrate international capital and commodity flow into the law. His

argument attempts to overthrow Ricardo’s most fundamental assumption-

international immobility of factors. He sets out to investigate how international

capital flows affect Ricardo’s law and endeavours to see the current form of the law

in a modern world. Arghiri shows that international capital flows negate gains for

all from trade. He reasons that since wages are low in LDCs, profits will be high. If

profits are re-invested, there will be rapid development and a narrowing of the gap

between the rich and the poor. Hence, trade would be mutually gainful. However,

with capital flows and foreign investment, this is not the case. Since foreigners face

low profits in their home countries, they are willing to accept much lower rates of

profit than local investors are. Hence, they invade local markets, drive down prices

39

and siphon profits back to their countries. In the advanced countries, therefore,

foreign investment leads to higher profits, higher prices and growth while in the

LDCs it creates economic imperialism and stagnation. Hence, Arghiri posits that

capital flows from the developed to the underdeveloped capitalist countries is

primarily to take advantage of the enormous difference in the cost of labour power.

According to this view, unequal exchange is predicated on the basis of the

dominant position enjoyed by the advanced industrial countries and the resultant

dependence of the poor countries on the rich. Other critics argue that FDI is often

associated with enclave investment, sweatshop employment, income inequality

and high external dependency (Durham, 2004). All these arguments regarding the

potential negative impact of FDI on growth point to the importance of certain

enabling conditions to ensure that the negative effects do not outweigh the positive

impacts. At present, the consensus seems to be that there is a positive association

between FDI inflow and economic growth, provided the enabling environment is

created. Given the fact that economic growth is strongly associated with increased

productivity, FDI inflow is particularly well suited to affect economic growth

positively. The main channels through which FDI affects economic growth has

been uncovered by the new growth theorists (Markusen, 1995; Lemi and Asefa,

2001; Barro and Sala-I-Martin, 1995; and Borensztein, et al, 1998). These authors

(Barro and Sala-I-Martin, 1995; and Borensztein, et al., 1998), in particular, have

developed a simple endogenous growth model which demonstrates the importance

of FDI in engendering growth through technological diffusion.

Typically, technological diffusion via knowledge transfer and adoption of best

practice across borders is arguably a key ingredient in rapid economic growth. And

this can take different forms. Imported capital goods may embody improved

technology. Technology licensing may allow countries to acquire innovations and

expatriates may transmit knowledge. Yet, it can be argued that FDI has greatest

potential as an effective means of transferring technical skills because it tends to

package and integrate elements from all of the above mechanisms. First, FDI can

encourage the adoption of new and improved technology in the production process

through capital spillovers. Second, FDI may stimulate knowledge transfers, both in

terms of manpower training and skill acquisition and by introduction of alternative

40

management practices and better organizational arrangements (Grossman

1991,1995; Lenisk et al 2001).

A number of empirical studies have been undertaken to establish robust results in

regard to the causal relationship of Foreign Direct Investment to economic growth,

its impact and determinants. The results of the studies showed varied evidence

with some indicating that Foreign Direct Investment causes economic growth,

others showing the reverse relationship and in some cases there is no reported

relationship. Research by the Federal Reserve Bank of Dallas indicates that

Foreign Direct Investment has the potential to boost technology, productivity,

investments and savings although academics, economists and policy makers are

yet to establish robust positive relations. Recent empirical studies show that the

impact of Foreign Direct Investment on economic growth is not straight forward as

previously envisaged but that it depends on country specific factors. Carkovic and

Levine (2006) found that a country’s capacity to benefit from Foreign Direct

Investment externalities is limited by local conditions, such as the development of

local financial markets or the educational level of the country’s population.

In the study conducted by (Basu and Guariglia, 2007), a sample of 119 developing

countries were used in the study for the period of 1970 – 1999 using the

Generalized Methods of Moments (GMM) and the study revealed that FDI

enhances both educational inequalities and economic growth in developing

countries. However, it reduces the share of agriculture sector in GDP. Johnson

(2006) also used a sample of 90 developed and developing countries in his study of

economic growth and FDI in the time period of 1980 – 2002 where he applied the

ordinary least square (OLS) method. He was able to ascertain that FDI inflows

accelerate economic growth in developing countries. But it is not valid for

developed countries. Also, (Hyun, 2006) used a sample of 59 developing countries

in his study for the period of 1984 – 1995, he also used ordinary least square (OLS)

method. He concluded that FDI has positive effect on economic growth but lagged

FDI values have no positive effects on current economic growth in these countries

for the period under study.

41

Carkovic and Levine (2002) using panel data from 72 developed and developing

countries performed both a cross section Ordinary Least Square and the

Generalised Method of Moments (GMM) analysis and found that there is no robust

link from foreign direct relationship to economic growth. De Mello (1999) using

both time series and panel data from a sample of 32 developed and developing

countries found weak indications of the causal relationship between Foreign Direct

Investment and economic growth.

Li & Liu (2005) used 21 developed countries and 63 developing countries to study

the impact of FDI on growth, using the time period of 1970 – 1999. He applied the

Unit Root Tests, Durbin – Wu –Hausman Test, and ordinary least square (OLS)

method and was able to ascertain that endogenous relationship between FDI and

economic growth has accelerated since the middle of 1980s. Also that relationship

between FDI, human capital and technological differences effect economic growth

in developing countries indirectly. Saha (2005) used 20 Latin America countries

and Caribbean countries during the period of 1990 – 2001. He used 3 Stage of

Least Squares and found out that FDI and economic growth are important

determinants of each other in Latin America and Caribbean and that there is an

endogenous relationship between FDI and economic growth. When conducting the

study of growth and FDI, (Durham, 2004) used 80 countries between the period of

1979 – 1998. He used the Extreme Bound Analysis (Sensitivity Analysis) for the

study. He concluded that there is no direct positive effect of current and lagged

values of FDI and portfolio investment on economic growth.

Hermes and Lensink (2003) in their own study using 67 less developed countries

during the time period of 1970 to 1995 with the ordinary least square (OLS)

method, found out that financial development level of a FDI attracting country is

an important pre-condition in order to provide positive effect of FDI on economic

growth. Bengoa and Sanchez –Robles (2003) used 18 Latin America countries for

the time period of 1970 to 1999. The Hausman Test and ordinary least square

(OLS) method was used in the study. They found out that economic freedom is an

important determinant of FDI inflows. Also FDI affects economic growth

positively.

42

Zhang and Ram (2002) used 85 countries for the period 1990 to 1997 by applying

the ordinary least square (OLS) method. They found out that there is a positive

relationship between FDI and economic growth in 1990s. Also, (Carkovic and

Levine, 2002) used 72 developed and developing countries for the time period

1960 to 1995. They applied Ordinary least square (OLS) and Generalized Methods

of Moments (GMM). From their findings, they established that FDI alone has no

statistically significant effect on economic growth. Alfaro, Chanda, Kalemli-Ozcan

and Sayek (2002) in their own study used two samples in their study. In Sample 1,

20 OECD countries and 51 non-OECD countries for the period 1975 to 1995 and in

sample 2, also 20 OECD countries and 29 non-OECD countries for a period of

sixteen years, that is, 1980 to 1995 with the use of Ordinary least square (OLS)

method. They concluded that FDI alone has an ambiguous effect on economic

growth. However, the countries which have developed financial markets can

benefit from FDI. Berthelemy & Demurger (2000) in their study of 24 Chinese

provinces for the period of 1985 to 1996 with the Generalized Methods of Moments

(GMM); they found out that FDI plays an important role in the economic growth of

Chinese provinces. In study conducted by (Nair–Reichert and Weinhold, 1999)

using 24 developing countries for the time period of 1971 to 1995, using MFR

model (mixed fixed and random model) and Causality Test; they found out that

although there is heterogeneity between countries, the effect of FDI on future

economic growth rates is more in more open countries.

Borensztein, Gregorio and Lee (1998) used 69 developing countries for the time

period of 1979 – 1989 using the SUR Method. From the study, they were able to

ascertain that FDI is an important tool for technology transfer. Also, it makes more

contributions to economic growth than domestic investment. In another study by

Balasubramanyam, Salisu and Sapsfort (1996), where they used 46 developing

countries for the time period of 1970 –to 1985 using OLS. They established that in

export promoting countries, effect of FDI on economic growth is more than import

– substituting countries. Also, Fry (1993) in his study used 16 developing countries

for the period of 1975 – 1991 (different time periods according to different

countries). The ordinary least square method was used and finally it was

ascertained that in 11 developing countries, FDI affects economic growth

negatively. But in Pacific Basin countries FDI affects economic growth positively.

43

The reason of these different evidences is that, in Pacific Basin countries economic

distortions are less. Bornschier, Chase-Dunn& Rubinson(1978) used 76 less

developed countries for the time period of 1960 – 197 by applying the OLS method.

It was found that FDI has negative impact on economic growth in developing

countries. Also, this impact increases as income level increases.

Choe (2003) used 80 countries in his study for the period of 1971 to 1995 by

applying Granger Causality Test. He concluded that FDI is Granger cause of

economic growth and economic growth is Granger cause of FDI. However,

economic growth affects FDI growth more. Basu, Chakraborty and Reagle (2003)

used 23 developing countries for the time period of 1978 to 1996 by the use of Unit

Root Tests and Panel Cointegration Test. They ascertained that there is a steady

state relationship between FDI and GDP growth in the long-run. Also Zhang

(2001) by using 11 East Asia and Latin America countries for the period of 1957 –

1997 (different time periods among these years) with the use Granger Causality

Test. Found that it is more possible for FDI to affect economic growth in export

promoting countries than import substituting countries.

Papanek (1973) used two samples: 1. Sample: 34 countries 1950s 2. Sample: 51

countries during the 1960s with OLS. Their findings revealed that savings and FDI

flows affect one third of economic growth; foreign aids have more impact than

other determinants on economic growth. There is no obvious relationship between

FDI and foreign aids. Also, economic growth is not correlated with export,

education, per capita income and country size. Hansen and Rand (2006) used 31

developing countries for the time period of 1970 to 2000 using Unit Root Tests,

Panel, Cointegration Test and VAR Analysis. They found a strong causality from

FDI through GDP growth.

Chowdhury and Mavrotas (2006) studied 3 countries for the period of 1969 to

2000 using Toda – Yamamoto Causality Test In Chile, GDP growth is the Granger

Cause of FDI but reverse is not true. In Malaysia and Thailand, FDI and economic

growth are Granger causes of each other. In their own study, (Hsiao and Hsiao,

2004) used 8 countries for a period of 1986 to 2004 using Granger Causality Test

and VAR Analysis, Unit Root Tests and GMM method. They found out that there is

44

one – way causality from FDI through GDP growth and exports. FDI and exports

make positive contribution to economic growth. According to the study of

(Mencinger, 2003) in 8 EU countries for the time period of 1994 to 2001 with

Granger Causality Test, he found out that FDI affects economic growth but

economic growth doesn’t affect FDI.

2.7 SOME OTHER THEORITICAL AND EMPIRICAL EVIDIENCE

Given the number of reasons for the importance of Foreign Direct Investment, it is

next essential to turn to the theoretical framework in which the studies of the FDI

effect on growth are undertaken. Most of the empirical work in the area is

grounded in models of endogenous and endogenous growth. Whenever the impact

of FDI on growth is analysed within the framework of Solow-type standard

neoclassical growth models, FDI is viewed as an addition to the capital stock of the

recipient economy. FDI is treated equally with domestic investment and the

impact of the former is viewed as being no different from the impact of the latter.

More importantly, in the words of (De Mello, 1997),

“…The basic shortcoming of conventional neo-classical growth

model, as far as FDI is concerned, is that long-run growth can

only result from technological progress and/or

population/labour force growth, which are both considered to be

exogenous. FDI would only affect output growth in the short-run

and, in the long-run, under the conventional assumption of

diminishing returns to capital inputs, the recipient economy

would converge to its steady state, as if FDI had never taken

place, leaving no permanent impact on economic growth”.

Additionally, as concluded by (Romer, 2001), capital accumulation cannot account

for a large part of either long-run growth or cross-country income differences in

the framework of Solow-type models.

Unsatisfied with a narrow and short-run impact interpretation of the role of FDI,

researchers have tried to incorporate other channels through which FDI influences

growth in both short and long run. They do so in the framework of endogenous

growth models. Whenever growth is endogenized, there are several channels

45

through which FDI affects growth permanently. As proposed by (Campos, 2002), it

is convenient to think about these various effects by specifying how FDI affects

each variable in the production function. FDI can affect growth first of all by

means of augmenting capital stock. Foreign and domestic capital may be viewed in

this respect as either substitutes or complements. If they are treated as

complements, the final impact of FDI on output is expected to be larger as a result

of externalities. Second, FDI can affect labour efficiency, being an important

source of human capital augmentation and technological change. Even if FDI does

not add to the capital stock significantly, it promotes knowledge transfers and

provides specific productivity-increasing skills, which are the most important

mechanism of promoting growth (de Mello, 1997). Furthermore, through

knowledge transfers and imitations by domestic firms, FDI also enhances

productivity of domestic research and development (R&D) activities. Finally, in

endogenous growth models, policy actions are also treated leading to permanent

increases in the rate of economic growth, and both success and failure of FDI-

promoting policies are therefore, long-lived (De Mello, 1999).

Further model specifications for empirical studies depends on the purpose of the

research, and may involve examining FDI-economic growth relation to the

framework of inter-temporal utility maximisation (Barro and Sala-i-Martin, 1995);

accounting for domestic absorption in the host country, analysing the relation

between FDI and convergence; investigating the degree of complementarily and

substitutability between foreign and domestic capital (Young, 1993); studying FDI

impact in specific industries; exploring the degree of impact of FDI with respect to

other macroeconomic factors, such as degree of openness and exports/imports

ratios.

An influential piece of work on the connection between growth and FDI is

provided by (Borensztein et al., 1998). They conduct a study for 69 countries

(OECD states, Latin American and several African countries) over two decades,

1970-79 and 1980-89. The authors extend the (Romer, 1990) model in which

technical progress is viewed in terms of an increased variety of capital goods

available as a result of “capital deepening”. The researchers suggest that FDI

should be treated differently from domestic capital by way of expanding the variety

46

of intermediate goods and capital equipments, and in such a way raising

productivity in the host country. Varying the model specification, they consistently

find that FDI has significant positive impact on growth in host countries. However,

the main conclusion of the research is that human capital and FDI display

complementary effects, and that there is a specific threshold level of human capital

in an economy in order for FDI to contribute to growth. The impact of FDI on host

economies, therefore, may even be negative in a country where this level is low.

In this respect, their findings is accorded with the research of (De Mello, 1997),

who also concludes that preconditions in recipient economies help convert new

capital effectively into higher levels of output in the recipient countries. In

particular, an increase in the investment productivity can only be achieved

provided there exist a sufficient level of human capital in an economy.

FDI also helps to increase local market competition, create modern job

opportunities and increase market access of the developed world (Noorbakhsh,

Paloni, Youssef, 2001) all of which should ultimately contribute to economic

growth in recipient countries. Hermes and Lensink (2000) interestingly

summarised different channels through which positive externalities associated

with FDI can occur namely: (i) competition channel where increased competition

is likely lead to increased productivity, efficiency and investment in human and/or

physical capital. Increased competition may lead to changes in the industrial

structure towards more competitiveness and more export-oriented activities; (ii)

training channel through increased training of labour and management; (iii)

linkages channel whereby foreign investment is often accompanied by technology

transfer; such transfers may take place through transactions with foreign firms and

(iv) domestic firms imitate the more advanced technologies used by foreign firms

commonly termed as the demonstration channel.

However, FDI may have negative effects on the growth prospects of the recipient

economy if they give rise to a substantial reverse flows in the form of remittances

of profits, and dividends and/or if the transnational corporations (TNCs) obtain

substantial or other concessions from the host country. FDI may not lead to

growth rate because MNCs tend to operate in imperfectly competitive sectors (with

47

high barriers to entry or a high degree of concentration). As a result, FDI may

crowd out domestic savings and investment. Moreover, FDI may have a negative

impact on the external balance because profit repatriation will tend to affect the

capital account negatively. It is also at times associated with enclave investment,

sweatshop employment, income inequality and high external dependency

(Ramirez, 2000). While the literature largely discussed the importance of FDI to

growth, one should also realise that economic growth could be an important factor

in attracting FDI flows as well.

The importance of economic growth to attracting FDI is closely linked to the fact

that FDI tends to be an important component of investing firm’s strategic

decisions. In fact (Brewer, 1993) suggests three hypotheses in explaining strategic

FDI projects namely, efficiency seeking hypothesis, resource seeking hypothesis

and market seeking or market size hypothesis. The importance of economic growth

in determining FDI flows can be explained by the market size hypothesis. As

(Pfefferman and Madarassy, 1992) stated, market size is one of the most

important considerations in making investment location decisions for three

reasons: larger potential for local sales, the greater profitability of local sales than

export sales and the relatively diverse resources which make local sourcing more

feasible. In other words, the market size hypothesis predicts that markets with

large populations and/or rapid economic growths (as measured by real GDP per

capita or its growth) tend to give multinational firms more opportunities to

generate greater sales and profits and thus become more attractive to their

investments. Empirical studies by (Schneider and Frey,1985; Bajo-Rubio and

Rivero, 1994 and Wang and Swain, 1995) all support this hypothesis.

In relation to these studies, the research conducted by (Campos, 2002) is

complementary. Campos used similar model specification and techniques to

analyse the FDI impact on growth in economies in transition. As opposed to

Borensztein et al, he finds that the effect of FDI on growth does not depend on the

existence or absence of any specific threshold level of human capital. Campos

explains this finding by the fact that in economies in transition the level of human

capital is so high that it is already above the minimum threshold level. Campos’s

principal hypothesis is that transitional economies have the necessary level of

48

physical and human capital, but are behind developed countries in terms of

technology. Thus, according to Campos, the transitional economy is an ideal

environment for testing whether FDI may really be viewed as contributing to

technological progress, and he finds that FDI has a positive impact on the

economic growth in these economies. Additionally, Campos is also concerned with

testing for double causality between FDI and growth, but does not find it. The test

serves as a pre-requisite for further econometric specification, as the use of

instrumental variables techniques is justified whenever FDI is found to be

endogenous to growth.

Another work in the field is conducted by (Buckley et al., 2002), who analyse FDI-

growth relation on regional level of provinces in China. This work is interesting

because the author also finds positive relationship between FDI and growth, but

this time on the level of marketization, and growth rate of provincial exports and

imports. Again, it is interesting to follow a scientific discourse of different

researches with regard to the dependence of FDI productivity increase on a

particular level of human capital. Similar to Campos, Buckley finds that there is no

evidence for an existence of a threshold level of human capital after FDI becomes

more effective.

In relation to the opposite direction of causality, it is instructive to consider the

result of empirical investigations of (Garibaldi et al., 2002). They provide an

extensive analysis of various factors that stimulate foreign direct and portfolio

investment, among them, the lagged values of real GDP growth. The authors find

that FDI constitute “… a large, relatively stable source of private financing in most

transitional economies”, and that the direct investment flows increase with good

macroeconomic performance. Macroeconomic stability, the extent of reforms,

trade liberalisation, natural resource endowments and the direct barriers to inward

investment all play a role in explaining the FDI pattern in economies in transition.

In relation to double causality, there are a number of studies that are primarily

concerned with finding whether the reversed causality really takes place. Many of

them (De Mello, 1996a; Kholdy, 1995) rely methodologically on the econometric

techniques of Granger causality. These studies are motivated by the fact that

49

growth prospect may play a role in investors decision making, and the increasing

market size, developing financial infrastructure, economic stabilisation. And other

factors considered in the previous sections, all can potentially attract FDI. In

particular, (Kholdy, 1995) examines the direction of causality between FDI and

technology spillovers in a number of high FDI inflow countries. The researcher

does not find the evidence of double causality, and explains this finding in terms of

price distortion and technology selection.

In their turn, the empirical findings of (De Mello, 1996a) are also not unequivocal

with respect to double causation. De Mello suggests that both direction of causality

depends on the trade regime of the host economy, which may range from import

substitution to export promotion. He also concludes that the direction of causality

to a large extent depends on the determinants of FDI: if these determinants have a

close association with growth, it is possible to find that FDI is caused by growth.

Whenever the evidence on double direction of causality is present, it additionally

reinforces the hypothesis of the existence of a particular threshold of factor

endowment, as an important determinant of the FDI efficiency in the host

economy.

2.8 FACTORS THAT DETERMINES FDI FLOW

There is no unanimously accepted single factor determining the flow of

investment. The literature is replete with information on the full range of factors

that are likely to induce the flow of Foreign Direct Investment anywhere. It is often

claimed that those factors that are favourable to domestic investments are also

likely to propel Foreign Direct Investment. These are the various factors that

propel the flow of FDI into a given geographical location, say a country or a region.

In making decisions to invest abroad, firms are influenced by a wide constellation

of economic, political, geographic, social and cultural issues. It is important to note

that while the list of factors is fairly long, not all determinants are equally

important to every investor in every location at all times. It is also true that some

determinants may be more important to a given investor at a given time than to

another investor.

50

While it is difficult to determine the exact quantity and quality of FDI

determinants that should be present in a location for it to attract a given level of

inflows, it is nevertheless clear that a critical minimum of these determinants must

be present before FDI inflows begin to occur (Ngowi, 2001). One would rationally

expect that investors would choose a location in accordance with the profitability

of that location. The profitability of investment is expected to be affected by

specific factors, however, including country characteristics as well as the types of

investment motives. As pointed out by Campos and Kinoshita (2002), market-

seeking investors, for example, will be attracted to a country that has a large but

fast growing market, while resource-seeking investors will search for a country

with abundant natural resources.

The factors influencing the flow of FDI thus range from the size of markets to the

quality of labour, infrastructure and institutions, to the availability of resources.

These and others are discussed below.

� A number of studies emphasize the importance of the size of the market and

growth in attracting FDI. Market size and growth have proved to be the

most prominent determinants of FDI, particularly for those FDI flows that

are market seeking. In countries with large markets, the stock of FDI is

expected to be large since market size is a measure of market demand in the

country. This is particularly true when the host country allows the

exploitation of economies of scale for import-substituting investment.

� The costs as well as the skills of labour are identified as the major

attractions for FDI. The cost of labour is important in location

considerations, especially when investment is export oriented (Wheeler and

Mody, 1992; Mody and Srinivasan, 1998). Lower labour cost reduces the

cost of production, all other factors remaining unchanged. Sometimes, the

availability of cheap labour justifies the relocation of a part of the

production process in foreign countries. Recent studies, however, have

shown that with FDI moving towards technologically intensive activities,

low cost unskilled labour is not in vogue. Rather, there is demand for

qualified human capital (Pigato, 2001). Thus, the investing firm is also

51

concerned about the quality of the labour force. It is generally believed that

highly educated personnel are able to learn and adopt new technology

faster, and the cost of retraining is also less. As a result of the need for high

quality labour, investors are most likely to target countries where the

government maintains a liberal policy on the employment of expatriate

staff. This is to enable investors to bring in foreigners to their operation in

order to bridge the gap in the skill of local personnel wherever it exists.

� That the availability of good infrastructure as crucial for attracting FDI is

well documented in the literature, regardless of the type of FDI. It is often

stated that good infrastructure increases the productivity of investment and

therefore stimulates FDI flows (Asiedu, 2002). A study by Wheeler and

Mody (1992) found infrastructure to be very important and dominant for

developing countries. In talking about infrastructure, it should be noted that

this is not limited to roads alone, but includes telecommunications.

Availability and efficiency of telephones, for example, is necessary to

facilitate communication between the host and home countries. In addition

to physical infrastructure, financial infrastructure is important for FDI

inflow. A well-developed financial market is known from available evidence

to enable a country to tap the full benefits of FDI. Alfaro et al. (2001), using

cross-section data, find that poorly developed financial infrastructure can

adversely affect an economy’s ability to take advantage of the potential

benefits of FDI. In a study by Bhinda, Griffth-Jones and Martin (1999), it

was found that problems related to funds mobilization were on the priority

list of the factors discouraging investors in Uganda, Tanzania and Zambia.

� Country risk is very important to FDI. Several studies have found FDI in

developing countries to be affected negatively by economic and political

uncertainty. There is abundant evidence to show the negative relationship

between FDI and political and economic stability. In a study on foreign

owned firms in Africa, Sachs and Sievers (1998) conclude that the greatest

concern is political and macroeconomic stability, while Lehman (1999) and

Jaspersen et al. (2000) find that countries that are less risky attract more

52

FDI. Perception of risk in Africa is still very high and continues to hinder

Foreign Direct Investment.

� Openness of an economy is also known to foster the inflows of FDI. The

more open an economy is, the more likely it is that it would follow

appropriate trade and exchange rate regimes and the more it would attract

FDI.

� The institutional environment is an important factor because it directly

affects business operations. In this category is a wide array of factors that

can promote or deter investment. The first of these is the existence of

corruption and bribery. Corruption deters the inflow of FDI because it is an

additional cost and because wherever it exists, it creates uncertainty, which

inhibits the flow of FDI. The second is the level of bureaucracy involved in

establishing a business in a country. Complex and time-consuming

procedures deter investment. The third institutional factor is the existence

of incentives in the form of fiscal and financial attractions. This last factor is

only useful to the extent that other favourable factors are already in place.

Fourth, there is also the institution of the judiciary, which is the key to

protecting property rights and law enforcement regulations. A frequent

measure of this is the rule of law, which is a composite of three indicators

(Campos and Kinoshita, 2003): sound political institutions and a strong

court system; fairness of the judicial system; and the substance of the law

itself. It is expected that countries with better legal infrastructure will be

able to attract more FDI. Related here is the enforceability of contracts: The

lack of enforceability in many African countries raises risk of capital loss

and hinders FDI.

� The availability of natural resources is a critical factor in attracting FDI.

This is particularly so in Africa where a large share of FDI has been in

countries with abundant natural resources. In some cases, the abundance of

natural resources has been combined with a large domestic market. African

countries that have been able to attract most FDI have been those with

natural and mineral resources as well as large domestic markets.

53

Traditionally about 60% of Africa’s FDI is allocated to oil and natural

resources (UNCTAD, 1999a/b). The Africa region possesses not only large

reserves of oil, gold, diamonds and copper, but also more than half of the

world’s cobalt and manganese, one-third of bauxite, and more than 80% of

chromium and platinum. A number of countries, including Angola, Nigeria,

Côte d’Ivoire, Botswana and Namibia, have been host to FDI because of this

advantage.

� Foreign investors may be attracted to countries with an existing

concentration of other foreign investors. In this case, the investment

decision by others is seen as a good signal of favourable conditions. The

term “agglomeration economies” is often applied to this situation (Campos

and Kinoshita, 2003). The clustering of investors leads to positive

externalities. Three types of such externalities have been identified in the

literature. The first is that technological spillovers can be shared among

foreign investors. Second, they can draw on a shared pool of skilled labour

and specialized input suppliers. Third, users and suppliers of inputs cluster

near each other because of the greater demand for a good and the supply of

inputs, which is provided by the large market.

� Return of investment is another major determinant of FDI flows. In general,

FDI will go to countries that pay a higher return on capital. For developing

countries, testing the rate of return on capital is difficult because most

developing countries do not have a well functioning capital market (Asiedu,

2002). What is often done is to use the inverse of real GDP per capita to

measure the return on capital. The implication of this is that all things being

equal, investments in countries with higher per capita income should yield

lower return and therefore real GDP per capita should be inversely related

to FDI (Asiedu,2002). The empirical result of the relationship between real

GDP per capita and FDI is mixed. In works by Edwards (1990) and

Jaspersen et al. (2000), using the inverse of income per capita as proxy for

the return on capital, they conclude that real GDP per capita and FDI/GDP

are negatively related. Results of studies by Schneider and Frey (1985) and

Tsai (1994) are different as they find a positive relationship between the two

54

variables. This is based on the argument that a higher GDP per capita

implies better prospects for FDI in the host country.

� Macroeconomic and other policies also play a role. Macroeconomic policy

errors resulting in exchange rate misalignment and the lack of convertible

currencies constrain FDI flows. In cases where policies are not sustainable,

FDI flows are hindered. A look at Africa reveals compelling evidence that

FDI may have been attracted by one or more of the following four categories

of considerations (Basu and Srinivasan, 2002).

� Investment that is intensive in natural resources: Given the abundance of

natural resources in Africa, a large share – almost 40% – has been in the

primary sector. For a number of countries, including Angola, Botswana,

Namibia and Nigeria, the oil and mineral sectors have been targeted.

� Investment driven by “specific” locational advantages: During the apartheid

era, a number of investors wishing to capture the large market in South

Africa located in Lesotho and Swaziland. These countries therefore at that

time benefited from inflows of FDI.

� Investment driven by host country policies that actively target foreign

investment: A few countries have tailored their policies to target Foreign

Direct Investment by ensuring political and economic stability. Such

policies provided specific tax incentives and created export-processing

zones. These countries include Mauritius and Seychelles.

� Investment in response to recent economic and structural reforms: A few

countries that were shunned by investors in the past are now the darlings of

investors in response to the far-reaching economic and structural reforms.

Uganda and Mozambique, whose economic reforms have been fairly

successful, have attracted FDI inflows.

55

2.9 IMPACT OF FDI ON ECONOMIC GROWTH IN NIGERIA

Most of the FDI in Nigeria go into the oil and extractive sectors and the economic

structure remains highly undiversified, with oil accounting for 95% of exports

(USAID, 2003). However, the Nigerian government has acted to stimulate non-oil

businesses through the promotion of Small and Medium Enterprise (SME). These

efforts and the momentum provided to the nation by the return of a democratic

government are reflected in the “Improvement and Optimism Indexes” compiled by

the World Economic Forum’s Africa Competitiveness Report (2000–2001), which

ranks Nigeria fourth among 12 African countries in terms of improvement and first,

in terms of “optimism” (AFDB/OECD 2003; Ariyo 2004).

However with the transition to democracy and intense competition for FDI by other

developing countries, the Nigerian administration now shows a welcoming attitude

to investors. The government has aimed its most generous incentives at the sectors

that present the greatest obstacles to economic development, particularly

infrastructure. Nigeria is becoming investor-friendly, with some laws allowing for

100% foreign ownership of businesses and unhindered repatriation of capital. In

addition, the government has put in place a range of incentives designed to lower

the cost of doing business to offset the higher-cost operating environment arising

from factors such as deficient infrastructure.

Various industries have been afforded ‘pioneer status’, giving start-ups a five-year

tax holiday. There are 69 industries benefiting from this incentive, including

mining, large-scale commercialised agriculture, food processing, manufacturing and

tourism. Manufacturers that add value to imported inputs are eligible for a five-year

10% local VAT concession. Manufacturers using a prescribed minimum level of local

raw materials, for instance, 70% for agro-allied industries and 60% for engineering

industries, are entitled to a five-year 20% tax concession.

Investors can take advantage of an infrastructure incentive that permits a 20% tax

deduction of the cost of providing infrastructure facilities that should have been

provided by the government. Such facilities include access roads, pipe-borne water

and electricity supply. There is a generous tax allowance on research and

56

development (R&D), with up to 120% of expenditure being tax deductible, provided

that such R&D activities are carried out in Nigeria and are related to the business

from which profits are derived. In the case of research into the use of local raw

materials, the tax-deductible allowance rises to 140%. The government is also

targeting investment into some economically disadvantaged areas, extending the tax

holiday available to ‘pioneer status’ industries to seven years and adding a 5%

capital depreciation allowance. Additional tax breaks are available for labour-

intensive modes of production (Financial Times, 2005).

According to the World Bank, Nigeria’s macroeconomic performance over the last

two years has been commendable. The economic reform efforts are showing positive

results including:

• In 2005, growth continued to be strong at 7% for the economy as a whole and 8%

for the non-oil sector. In the first quarter of 2006, the Nigerian economy grew by

8.3%.

• In January 2006, the country received its first credit rating (BB-) from Fitch and

Standard and Poor’s.

• Year-on-year inflation fell from 28% in August to 12% by December 2005.

• A Fiscal Responsibility Bill has passed critical second readings in both the Senate

and House.

• The National Assembly is discussing a Public Procurement Reform Bill.

• A bank consolidation program was implemented strengthening the financial

sector and enhancing its ability to provide credit to the private sector.

• The import tariff regime has been liberalized reducing the number of tariff bands

from 19 to 5 and lowering the average tariff from about 29% to 12%.

With the deregulation of the telecommunication sector, Nigeria’s

telecommunications sector is now in a rapid growth mode. According to the

Nigerian Communications Commission (NCC), there’s enormous growth potential

in the market, as demand for telecom service has been high because of market

liberalization and massive telecom investments. Over recent years, all branches of

the telecom industry have generated considerable growth and the telecom industry

has emerged as a main motor of the country’s economy. It is only the oil sector that

has seen more investment and telecom is now seen as the most lucrative branch for

57

investment in Nigeria’s economy. As a result, Nigeria presently boasts as Africa’s

largest and most promising telecom market. Even though Nigeria is trailing other

countries in terms of providing phone technology at an affordable price and doing so

reliably, the market has taken significant strides in its development (Ariyo, 2005).

There have been some studies on investment and growth in Nigeria with varying

results and submissions. For example, (Odozi, 1995) reports on the factors

affecting FDI flow into Nigeria in both the pre and post structural adjustment

programme (SAP) eras and found that the macroeconomic policies in place before

the SAP were discouraging foreign investors. This policy environment led to the

proliferation and growth of parallel markets and sustained capital flight. Ogiogio

(1995) reports negative contributions of public investment to GDP growth in

Nigeria for reasons of distortions. These authors, (Aluko, 1961; Brown, 1962 and

Obinna, 1983) report positive linkages between FDI and economic growth in

Nigeria. Endozien (1968) discusses the linkage effects of FDI on the Nigerian

economy and submits that these have not been considerable and that the broad

linkage effects were lower than the Chenery–Watanabe average (Chenery and

Watanabe, 1958). Also, (Oseghale and Amonkhienan, 1987) found that FDI is

positively associated with GDP, concluding that greater inflow of FDI will spell a

better economic performance for the country. Ariyo (1998) studied the investment

trend and its impact on Nigeria’s economic growth over the years. He found that

only private domestic investment consistently contributed to raising GDP growth

rates during the period considered (1970–1995). Furthermore, there is no reliable

evidence that all the investment variables included in his analysis have any

perceptible influence on economic growth. He therefore suggests the need for an

institutional rearrangement that recognizes and protects the interest of major

partners in the development of the economy.

Examining the contributions of foreign capital to the prosperity or poverty of

LDCs, (Oyinlola, 1995) conceptualized foreign capital to include foreign loans,

direct foreign investments and export earnings. Using Chenery and Stout’s two-

gap model (Chenery and Stout, 1966), he concluded that FDI has a negative effect

on economic development in Nigeria. Further, on the basis of time series data,

(Ekpo, 1995) reports that political regime, real income per capita, rate of inflation,

58

world interest rate, credit rating and debt service were the key factors explaining

the variability of FDI into Nigeria. Adelegan (2000) explored the seemingly

unrelated regression model to examine the impact of FDI on economic growth in

Nigeria and found out that FDI is pro-consumption and pro-import and negatively

related to gross domestic investment. Akinlo (2004) found that foreign capital has

a small and not statistically significant effect on economic growth in Nigeria.

However, these studies did not take into cognisance the fact that most of the FDI

was concentrated in the extractive industry. In other words, it could be put that

these works assessed the impact of investment in extractive industry (oil and

natural resources) on Nigeria’s economic growth. On firm level productivity

spillover, (Ayanwale and Bamire, 2001) assess the influence of FDI on firm level

productivity in Nigeria and report a positive spillover of foreign firms on domestic

firm’s productivity. Much of the other empirical work on FDI in Nigeria centred on

examination of its nature, determinants and potentials. For example, (Odozi, 1995)

notes that foreign investment in Nigeria was made up of mostly “greenfield”

investment, that is, it is mostly utilized for the establishment of new enterprises

and some through the existing enterprises. Aremu (1997) categorized the various

types of foreign investment in Nigeria into five: wholly foreign owned; joint

ventures; special contract arrangements; technology management and marketing

arrangements; and subcontract co-production and specialization.

In his study of the determinants of FDI in Nigeria, (Anyanwu, 1998) identified

change in domestic investment, change in domestic output or market size,

indigenization policy, and change in openness of the economy as major

determinants of FDI. He further noted that the abrogation of the indigenization

policy in 1995 encouraged FDI inflow into Nigeria and that effort must be made to

raise the nation’s economic growth so as to be able to attract more FDI. Jerome

and Ogunkola (2004) assessed the magnitude, direction and prospects of FDI in

Nigeria. They noted that while the FDI regime in Nigeria was generally improving,

some serious deficiencies remain. These deficiencies are mainly in the area of the

corporate environment (such as corporate law, bankruptcy, labour law, etc.) and

institutional uncertainty, as well as the rule of law. The establishment and the

activities of the Economic and Financial Crimes Commission, the Independent

59

Corrupt Practices Commission, and the Nigerian Investment Promotion

Commission are efforts to improve the corporate environment and uphold the rule

of law.

2.10 GROWTH ACCOUNTING EQUATION AND THE SOLOW

RESIDUAL

The theoretical framework will also take up growth theories that explain what

causes economic growth to increase, i.e. what factors are important in the growth

process of a country. The role of FDI as a growth enhancer through the

transferring of new technology from advanced economies to developing economies

will also be a focus of this section.

In growth accounting, a specific production function is used to show two sources of

growth. Output grows because of increases in inputs as well as increases in

productivity, as a result of improved technology and a highly skilled labour force.

Thus, the production function presents a quantitative connection between inputs

and outputs:

Y = AF ( K, N ), (2.1)

where Y is output, K is capital, N is labour and A is total factor productivity. The

higher A is, the more output produced (Dornbusch, Fischer and Startz, 2004). By

assuming constant returns to scale with respect to capital and labour, equation 2.1

above can be transformed into a specific link between input growth and output

growth that relates to Robert Solow’s growth accounting framework from 1957.

The growth accounting equation is written as:

∆Y/Y = [(1-� ) × ∆ N/N] + (� ×∆ K/K) + ∆A/A (2.2)

(Growth = labour share × labour growth + capital share × capital growth + total factor

productivity growth).

This growth accounting equation is central in economic growth theory. If the

proportional growth rates of output, the capital stock and the labour force in the

production function are known, the growth-accounting equation can be used to

calculate the growth rate of total factor productivity, A. In addition, it can be used

to break down the growth of Y into components, to see the contribution to output

from the increase in K, the increase in N, and the increase in A, separately. Thus,

60

the growth-accounting equation allows one to decompose growth into different

parts that in turn, can be ascribed to the apparent factors of the growth of the

capital stock and of the labour force and also, to a residual factor. That residual

factor is called the Solow residual and represents the part of growth that is not

accounted for by increases in the factors of production (Barro, 1999). The total

factor productivity growth is the same thing as the Solow residual and is not

observable in the same way as changes in inputs and outputs (Dornbusch et.al,

2004). The Solow residual is measured by turning equation 2.2 around:

∆ A/A = ∆ Y/ Y - [(1 - � ) × ∆ N/N] – (� × ∆ K/K) (2.3)

There are many reasons that changes in total factor productivity can occur. The

efficiency of government regulation, the degree of monopoly in the economy, the

degree of human capital in the economy and the educational level of the labour

force are only a few factors that affect total factor productivity. In China,

government regulations are rigorous and complex, which creates barriers for

domestic companies and especially for foreign companies entering the Chinese

market. Government regulations and laws have long constituted a main barrier for

multinationals by holding back efficient production processes. On the other hand,

foreign companies have taken the advantage of the cheap and skilled Chinese

labour force, which is one of main arguments for moving their businesses to China.

2.10.1 THE NEW GROWTH THEORY

The limits of neoclassical theories in explaining the sources of long-term economic

growth have resulted in a lot of dissatisfaction with traditional growth theory.

Despite the fact that the Solow Growth model identifies technological progress as

determinant of economic growth, the model leaves unexplained what determines

the technological advancement. The dissatisfaction with neoclassical growth theory

led to the development of the endogenous growth theory; also known as the new

growth theory, which I find is more related to the subject of this thesis. One way to

separate new growth theory from neoclassical growth theory, is by identifying that

many endogenous growth theories are expressed by the following simple equation,

Y = AK. This relationship is illustrated in figure below.

Figure 2.1 Production function with constant marginal product of

capital

Here, A represents factors that affect technology and K represents physical as well

as human capital. The production function assumes a constant marginal product of

capital, which is the most important theoretical difference from the neoclassical

assumption of diminishing marginal product of capital. The constant marginal

product of capital implies that investments in physical and human capital could

create external economies and impro

is continued long-term growth, which was prohibited by traditional growth theory.

Moreover, probably the most interesting part of endogenous growth models is

their contribution to explaining the international ca

and developed countries (Todaro & Smith, 2006).

2.10.2 THE ROMER GROWTH MOD

One of the main contributors to endogenous growth theory is Paul Romer. The

Romer growth model is particularly relevant for developing economies,

deals with technological spillovers that are often present in an industrialization

process. Romer’s model starts with the assumption that growth processes originate

from the level of the firm or industry. Because each industry has constant retu

to scale in production, the model does not violate the assumption of perfect

competition. What then distinguishes Romer is that he assumes that the economy

wide capital stock K has a positive effect on output at the industry level, hence,

there is a possibility of increasing returns to scale at the economy level. Knowledge

is included in each firm’s capital stock, and this knowledge part is seen as a public

good, a spillover to other firms in the economy. The model clarifies why growth

Production function with constant marginal product of



the most important theoretical difference from the neoclassical



create external economies and improvements in productivity. The outcome of this

term growth, which was prohibited by traditional growth theory.


their contribution to explaining the international capital flows between developing

and developed countries (Todaro & Smith, 2006).

THE ROMER GROWTH MODEL


Romer growth model is particularly relevant for developing economies,



from the level of the firm or industry. Because each industry has constant retu


competition. What then distinguishes Romer is that he assumes that the economy


ssibility of increasing returns to scale at the economy level. Knowledge



61

Production function with constant marginal product of



the most important theoretical difference from the neoclassical



vements in productivity. The outcome of this

term growth, which was prohibited by traditional growth theory.


pital flows between developing


Romer growth model is particularly relevant for developing economies, because it



from the level of the firm or industry. Because each industry has constant returns


competition. What then distinguishes Romer is that he assumes that the economy-


ssibility of increasing returns to scale at the economy level. Knowledge



62

can to some extent, depend on investment. If one focuses on the factors relating to

industrialization, the model has the following format:

Y = AK � L1-� K� (2.4)

For simplicity, symmetry across industries is assumed, implying that each industry

has the same level of capital and labour. Thus, the aggregate production function is

as follows:

Y = AK� +� L1-� (2.5)

After some manipulation of this equation, the per capita growth rate of income in

the economy can be shown to be:

g – n = � n / [1 – � - � ] (2.6)

with g representing the output growth rate and n representing the population

growth rate. In the Solow model, there are constant returns to scale and no

spillover effect (� = 0) hence the growth per capita would end up to be zero in the

long run. On the contrary, in the Romer model the factors that stimulate growth

are put together. Having � > 0 results in g – n > 0, so Y / L is increasing.

2.10.3 THE LINK BETWEEN TECHNOLOGY CREATION AND

GROWTH

The creation of new technologies requires investment, and the majority of

advanced economies devote enormous resources to R&D in their struggle to

generate new products and make the production processes more efficient. This

accumulation of superior technology generates a higher level of output. Even

though the accumulation of physical capital also leads to higher output, there is a

substantial difference between technology and other inputs to production.

Technology has a non-physical nature, which means that it is non-rival and that

more than one person can use it. Hence, the transferability of technology can be

very beneficial, especially if it is transferred from an advanced country to a

developing country. If a developing country is inferior due to the lack of

technologies, then technology can be transferred from another country without

making that country worse off (Weil, 2005).

Weil (2005) models the link between technology creation and growth and assumes

that the level of output per worker is higher with a high level of technological

63

progress. Hence, an increase in the fraction of the labour force involved in R&D

will increase the growth rate of output. At the same time, he notes that growth will

be higher if the cost of new inventions is low. His conclusion is that spending large

amounts on R&D will lower output in the short run, but increase output in the long

run. This finding of Weil is similar to Solow’s reasoning in his neoclassical growth

model, which states that increasing investment will cause a drop in consumption

in the short run, while in the long run investment will raise output and thus,

increase consumption. However, there is an important difference with physical

capital investment and R&D spending, according to Weil. The increase in the

growth rate of output due to an increase in R&D is permanent, while in the Solow

model, an increase in investment implies a higher steady-state level of output,

which means that the effect of this investment increase on the growth of output is

only temporary.

According to (Mansfield and Romeo, 1980), the cheapest means of transferring

technology is FDI. They base this on the fact that the firms involved in the FDI in

the recipient country have lower cost of products and processes, because they do

not have to spend money on acquiring new technology. It is already developed

somewhere else at a high cost. FDI is thus an important channel through which

new technology can be acquired by developing countries like China at the benefit

of a low cost. This technology will increase the output of the country through

increased efficiency in production and also create a spillover effect, which means

that other firms can take advantage of the technological advancement. The

conclusion here is that FDI is crucial in the growth of technological progress,

which in turn is the main determinant of output growth.

2.11 THE IMPLICATIONS OF FDI AND ECONOMIC GROWTH IN

NIGERIA (RECENT DEVELOPMENTS IN ECONOMIC GROWTH IN

NIGERIA)

Macroeconomic developments in recent years have been encouraging, with GDP

growth averaging 6 per cent for 2000-05. After peaking at 10.2 per cent in 2003,

growth slowed to 6.1 per cent in 2004. Growth in 2005, estimated at 4.4 per cent, a

much lower rate than the government’s figure, was broadly based, with the oil,

64

agriculture, construction and telecommunications sectors performing particularly

well. High world oil prices have provided a big boost to the oil sector in recent

years. (African Economic Outlook, 2006)

In 2005, agricultural output increased by 7 per cent, up from 6.2 per cent in 2004,

reflecting both favourable weather conditions and government efforts to increase

farmers’ access to credit and fertilizers. Construction was estimated by the

government to grow by 10 per cent in 2005 as a result of booming real estate

development. Nigeria’s telecommunications sector grew by 12 per cent following

its accelerated liberalization and privatisation, which led to the introduction and

rapid spread of the global system for mobile communications (GSM) services. The

number of mobile phone lines increased from 230,000 in 2001 to 8.3 million in

2004 while fixed land lines increased by an average of 20 per cent annually, from

600,000 to 1.03 million during the same period (African Economic Outlook, 2006)

Growth in the manufacturing sector, at 8 per cent in 2005, is lower than the 10 per

cent recorded in 2004. Agriculture accounted for nearly one-third of GDP in 2004:

mining (primarily oil) accounted for about 36 per cent of GDP. Crude petroleum

production was estimated at 2.5 million barrels per day (mbd), about 2.05 mbd of

which is destined for exports. At an estimated average price of $55 per barrel in

2005, the price of Nigeria’s reference Bonny Light crude oil increased by about 11

per cent during the preceding year as a result of high world prices. Wholesale trade

represented about 15 per cent of GDP in 2004, whereas the manufacturing sector

accounted for only 5 per cent of GDP despite its recent strong growth (African

Economic Outlook, 2006).

The sectoral developments mentioned above reflected strong growth in private

consumption and private investment in both 2004 and 2005. In terms of the

composition of demand, the main development was a surge in net exports demand

to 18.8 per cent of GDP in 2005, compared with 8.2 per cent of GDP in 2003, and -

0.9 per cent in 2002, also reflecting the oil price increases of recent years.

Correspondingly, domestic consumption and investment shares declined in 2003

and 2004, reflecting the increase in the share of exports in total demand, this can

be seen from figure 2.1 ((African Economic Outlook, 2006).

65

Table 2.1: Demand Composition (percentage of GDP)

1997 2002 2003 2004 2005 2006 2007 Gross capital formation

17.1 26.2 23.9 22.4 22.5 23.8 25.6

Public 5.4 10.0 9.7 9.1 8.9 9.0 9.3 Private 11.7 16.2 14.2 13.2 13.5 14.7 16.3 Consumption 74.9 74.6 67.9 60.4 58.8 60.8 63.0 Public 7.1 24.2 23.7 22.1 22.0 22.1 22.1 Private 67.7 50.4 44.2 38.3 36.7 38.7 40.9 External sector 8.0 -0.9 8.2 17.2 18.8 15.3 11.4 Exports 47.4 40.8 49.7 54.6 53.9 51.3 48.3 Imports -39.3 -41.6 -41.5 -37.4 -35.8 -36.8 -36.9 Source: Domestic authorities and IMF data

The year 2007 was an eventful one in Nigeria, both politically and economically.

Growth slowed in the face of continued turmoil in the oil-producing Niger Delta,

but medium-term economic prospects are supported by high oil prices and

prudent macroeconomic policies. The National Economic Empowerment and

Development Strategy (NEEDS), which is targeted at accelerating economic

growth, reducing poverty, and achieving the Millennium Development Goals

(MDGs), remains the guiding framework for economic reforms. Oil revenues have

been managed carefully, with “excess” revenues saved under the oil price fiscal

rule. Nigeria successfully completed a two-year Policy Support Instrument (PSI)

with the IMF in mid-October 2007. Economic performance was mixed in 2007;

real GDP growth slowed to an estimated 3.2 per cent and inflation remained in

single digits at 6.7 per cent.

In addition, progress was registered in the financial sector, debt management,

foreign reserves management, exchange rate stability and the fight against

corruption. Fiscal prudence was institutionalised through enactment of the

National Procurement and the Fiscal Responsibility Acts. Nevertheless, the

Nigerian economy is still characterised by dismal infrastructure, widespread

insecurity, high levels of poverty, and simmering political and ethnic tensions,

notably in the oil-producing areas. (AfDB/OECD , 2008)

NEEDS is successfully spear-heading efforts to address structural and institutional

weaknesses of the economy, tackle corruption and overhaul public expenditure

management. Following the completion of the first phase (2004-07), an enhanced

66

programme with more ambitious targets is at the final stage of approval, having

undergone several reviews. Similarly, the government is continuing to improve

governance and transparency, notably through the Nigerian Extractive Industries

Transparency Initiative (NEITI) for the oil and gas industry. All these efforts are

intended to improve the investment climate.

In recent years Nigeria has made significant progress towards sustainable growth

and macroeconomic stability, taking advantage of high world prices of oil to

undertake bold economic reforms. Real GDP growth averaged 6.5 per cent during

the period 2003-07, but has slowed from a high of 10.7 per cent in 2003 to 7.2 per

cent in 2005, 5.6 per cent in 2006 and an estimated 3.2 per cent in 2007, largely

because of the disruptions in oil production in the Niger Delta. Real oil output

contracted by 4.5 per cent in 2006, after very weak growth of 0.5 per cent in

2005.Oil output is estimated to have contracted further by 5.6 per cent in 2007. On

the other hand, non-oil sector performance has been very encouraging, with

growth of 8.6 per cent in 2005, 9.4 per cent in 2006, and an estimated 9.8 per cent

in 2007. With the relative stability in the Niger Delta following negotiations

between the government and local militants, along with increased offshore

investments in the oil sector, oil production is projected to respond gradually in

the short term.

Consequently, real GDP is projected to grow by 6.2 per cent in 2008 and 6.1 per

cent in 2009. The leading non-oil sectors were telecommunications, general

commerce, manufacturing and agriculture. Agriculture, constituting 31.7 per cent

of GDP, grew by an estimated 7.7 per cent in 2007 compared to 7.4 per cent growth

in 2006. Manufacturing grew by 9.9 per cent in 2007, though it constitutes only

about 4 per cent of real GDP. The rapid growth of the communication sector

continued in 2007 with a growth rate of 32.9 per cent following 28.4 per cent and

34.5 per cent in 2005 and 2006 respectively. Total investment is estimated to have

increased by 15.2 per cent in 2007 with a projection of 12.2 per cent and 7.2 per

cent growth in 2008 and 2009 respectively. Private investment and private

consumption remain the key drivers of real GDP, contributing 3.2 per cent and 4.4

per cent to real GDP growth in 2007. The weak growth of the oil sector continued

67

to dampen the contribution of the external account to growth. These explanations

can be seen in the figure 2.2 and 2.3 below.

Figure 2.2-Real GDP growth and Per Capita GDP ($US at constant 2000 prices)

Source: African Economic Outlook, 2008

Figure 2.3-GDP by Sector in 2006 (percentage)

Source: African Economic Outlook, 2008

68

2.12 SOME FACTS ABOUT GLOBAL FDI FLOW

Foreign Direct Investment (FDI) flows to Africa in 1999 rose to $10 billion from $8

billion, in line with the faster growth rate generally experienced by the continent

during the 1990s, as numerous governments sought to create a more business-

friendly environment after the turbulent 1970s and 1980s. However, investments

by Transnational Corporations (TNCs) into Africa still represent only 1.2% of

global FDI flows and just 5% of total FDI into all developing countries, according

to the World Investment Report 2000.

About 70% of total 1999 FDI into Africa was concentrated in just five countries -

Angola, Egypt, Nigeria, South Africa and Morocco, Table 2.2. Investments in

natural resources continue to be the main focus of foreign investor interest in most

African countries, but there are also significant flows into manufacturing and

services. The great majority of the poorest African nations, however, remain

marginalized in terms of the absolute amount of foreign investment they receive.

However, when measured in terms of gross domestic capital formation, FDI to a

number of small African countries appears much more sizeable than the absolute

FDI figures would suggest. Angola, Equatorial Guinea, Lesotho and Zambia rank

first according to that yardstick.

Table 2.2: FDI inflows to the top 10 recipient African economies, 1998

and 1999 (in millions of U.S. dollars)

Economy 1998 1999

Africa 8,080 10,325

Angola 1,114 1,814

Egypt 1,077 1,500

Nigeria 1,051 1,400

South Africa 561 1,376

Morocco 329 847

Mozambique 213 384

Sudan 371 371

Tunisia 670 368

Côte d´Ivoire 315 279

Gabon 211 200

Source: UNCTAD, World Investment Report, 2000

69

In a broad sense, Foreign Direct Investment (FDI) is composed of a flow of capital,

expertise, and technology into the host country. Formally, it is defined as "an

investment made to acquire lasting interest in enterprises operating outside of the

economy of the investor" (IMF, 1993). Interested researchers, countries and

international organizations have increasingly recognized the importance of foreign

capital to growth. In our dynamic age of privatisation, liberalization, and

globalisation, FDI has emerged as an important form of international capital flow.

Recognizing the importance of investment with no borders, the World Bank has

devoted its 2005 issue of "World Development Report" to the issue of trade and

investment, discussing in detail the importance of foreign capital flow to the

economies of the host countries. According to the World Bank, "few countries have

grown without being open to trade”, (World Bank, 2005).

Generally, there is a wide agreement on the importance of openness that leads to

FDI flows. However, there is an ongoing debate about the merits of openness. The

debate has been motivated by the recent economic crisis in a number of countries

of Southeast Asia. Quick and massive movements of short-term portfolio

investment that took place in these countries were largely blamed for the crises.

Nonetheless, most observers agree to distinguish FDI from short-term portfolio

investment because FDI is a long-run investment and therefore is difficult to

reverse. Hence, recognizing the importance of openness to economic growth, an

increasing number of countries have adopted more liberal policies towards the

flow of foreign capital. As a result, FDI inflow to developing countries increased

from 0.1 percent of global GDP in 1970 to 3 percent in 2001 (World Bank, 2005).

On the global level, after a period of declining trends, global FDI inflow reached

$648 billion in 2004, increasing by 2% over its level in 2003, raising the stock of

FDI in 2004 to an estimated level of $9 trillion. Furthermore, there was a large

increase in the share of developing countries in FDI inflow. Inflows to developing

countries surged by 40%, to $233 billion, while those to the group of developed

countries declined by 14%. As a result, the share of developing countries in world

FDI inflows has increased to 36% of global FDI, the highest level since 1997

(UNCTAD, 2005). The observed uptrend in FDI was not evenly distributed among

different countries of the developing world. While FDI flow into Africa remained

70

stable at $18 billion between 2003 and 2004, Asia and Oceania witnessed a

significant upsurge during the same period.

Also, a similar significant uptrend in FDI inflow was recorded in Latin America

and Southeast Europe. Factors advanced to explain this increase in FDI flow into

the developing countries include intense competitive pressures in many industries

of the source countries, higher prices for many commodities, which stimulated FDI

to countries that are rich in natural resources, and higher expectations for

economic growth. UNCTAD (1996) identifies some of the most important factors

leading so such a surge in global FDI flows. They include the increasing trend in

privatisation and the resulting foreign firm's acquisition of domestic firms,

production globalisation, and global financial integration.

Among developing countries, the economies of Asia and Oceania were the largest

recipient as well as source of FDI. In 2004 FDI inflow to both regions amounted to

$148 billion, $46 billion more than in 2003. This marked the largest increase ever

to these regions, with China and India getting the lion share of the increase. China

continued to be the largest developing country recipient with $61 billion in FDI

inflows. Furthermore, a new destination of FDI has strongly emerged in West Asia

with inflows rising from $6.5 billion to $9.8 billion between 2003 and 2004.

Countries like Saudi Arabia, Syria and Turkey were identified as the major

recipients in that region, receiving more than half of the total inflow to that region.

In addition, Latin America and the Caribbean registered a significant upsurge of

FDI inflows in 2004, reaching $68 billion – 44% more than its level in 2003. FDI

inflows to South-East Europe and the CIS, a new group of economies under the

United Nations re-classification, grew at an all-time high rate of more than 40% in

2004, reaching $35 billion.

According to (UNCTAD, 2005), FDI further increases in FDI to developing

countries are expected in the near future due to expected favourable economic

growth wide spread consolidation, corporate restructuring, profit growth

persistence and the continuation of the pursuit of new markets by industries in the

source countries. Africa’s Foreign Direct Investment inflows grew by 28% to $15

billion in 2003, altogether, 36 African countries registered an increase in FDI

71

inflows, while 17 saw a decline. Inflows as a percentage of gross fixed capital

formation grew from 12% in 2002 to 14% in 2003, the second highest level in the

past decade (figure 2.4).

Figure 2.4: Africa- FDI inflows and their share in gross fixed capital formation, 1985-2003

Source: UNCTAD, World Investment Report 2004

The value of cross-border mergers and acquisitions (M&As) involving African

firms rose from $4.7 billion in 2002 to $6.4 billion in 2003. The resource-rich

countries were once again the main attraction for transnational corporations

(TNCs). Also in 2003 Morocco was Africa’s largest recipient of FDI inflows, which

climbed from $0.5 billion in 2002 to $2.3 billion in 2003, thanks to its

privatisation programme (figure 2.4).

Angola, Equatorial Guinea, Nigeria and Sudan - all resource rich - also performed

exceptionally well, each receiving inflows in excess of $1 billion (table 2.3).

Morocco and these four countries led the region’s list of the 10 largest recipient

countries of FDI inflows in 2003 (figure 2.5).

Table 2.3. Africa: Country distribution of FDI inflows, by range, 2003

Range Economy More than $2 billion Morocco $1 - 1.9 billion Angola, Equatorial Guinea, Nigeria, and Sudan $0.5 – 0.9 billion Algeria, Chad, Libyan Arab Jamahiriya, South Africa and Tunisia $0.1 – 0.4 billion Cameroun, Democratic Republic of Congo, Cote d’Ivoire, Egypt,

Ghana, Mali, Mauritania, Mozambique, Uganda, United Republic of Tanzania and Zambia

Less than $0.1 billion Benin, Botswana, Burkina Faso, Burundi, Cape Verde, Central African Republic, Comoros, Djibouti, Eritrea, Ethiopia, Gabon, Gambia, Guinea, Guinea-Bissau, Kenya, Lesotho, Liberia, Madagascar, Malawi, Mauritius, Namibia, Niger, Rwanda, Sao Tome and Principe, Senegal, Seychelles, Sierra Leone, Somalia, Swaziland, Togo and Zimbabwe


72

Figure 2.5: The top 10 recipients of FDI inflows in Africa, 2002 and 2003

(billions of dollars)


Several small African economies also shared in the growth of FDI inflows to the

continent, partly because of modest increases in inflows to the manufacturing and

services sectors. As a result, inflows were distributed more broadly than in any

year since 1999, with 22 countries receiving more than $0.1 billion, as compared to

16 countries in 2001 (table 2.3).

Table 2.4: The top 7 non-financial TNCs from developing economies in Africa,

ranked by foreign assets, 2002 (Millions of dollars, number of employees)

Rank Corporation Home economy Industry Foreign

TNI(%)

Assets Sales Employment

10 Sappi Limited South Africa Paper 3733 2941 9807 717 12 Sappi Limited South Africa Industrial

Chemicals 3623 3687 7107 38.4

18 MTN Group Limited South Africa Telecomm 2582 729 1970 52.1 19 Anglogold Limited South Africa Gold Ores 2301 831 30821 54.4 30 Naspers Limited South Africa Media 1655 412 1742 39.5 31 Barloworld Limited South Africa Diversified 1596 1984 9973 54.5

44 Nampak Limited South Africa Rubber and Plastic

782 328 109962 48.9


73

Outward FDI from African countries remains small, and only seven of the region’s

TNCs - all based in South Africa - are among UNCTAD’s top 50 TNCs based in

developing countries (table 2.4). Global Foreign Direct Investment (FDI) inflows

fell again in 2003, also in the same year, FDI inflows declined by 18% to $560

billion (table 2.5). This follows a massive fall of 41% in 2001 (from $1.4 trillion in

2000 to $818 billion) and another 17% in 2002 (to $679 billion). The value of

cross-border mergers and acquisitions (M&As) - the key driver of global FDI since

the late 1980s - was also down 20% last year. The drop in FDI inflows was

confined to the developed countries and Central and Eastern Europe (CEE).

Inflows to developing countries rose by 9%. Excluding Luxembourg, China was the

world’s largest host country in 2003 (figure 2.6). The structure of FDI has shifted

towards services, facilitated by the liberalization of FDI policies, UNCTAD finds

(table 2.5).

Table 2.5: Selected indicators of FDI and international production, 1982-2003 (billions of dollars and per cent) Item

Value at current price (Billions of dollars)

Annual growth rate (percent)

1982 1990 2003 2000 2001 2002 2003 FDI inflows 59 209 560 27,1 -41,1 -17,0 -17,6

FDI outflows 28 242 612 8,7 -39,2 -17.3 2,6

FDI inward stock 796 1950 8245 19,1 7,4 12,7 11,8

FDI Outward stock 590 1758 8197 18,5 5,9 13,8 13,7

Cross border M&As … 151 297 49,3 -48,1 37,7 -19,7

Sale of foreign affiliates 2717 5660 17580 16,7 -3,8 23,7 10,7

Gross product of foreign affiliates 636 1454 3706 15,1 -4,7 25,8 10,1

Total assts of foreign affiliates 2076 5883 30362 28,4 -5,3 19,6 12,5

Exports of foreign affiliates 717 1194 3077 11,4 -3,3 4,7 16,6

Employment of foreign affiliates (thousands)

19232 24197 54170 13,3 -3,2 12,3 8,3

GDO (in current price) 11737 22588 36153 2,7 -0,9 3,7 12,1

Ross fixed capital formation 2285 4815 7294 3,8 -3,6 -0,6 9,9

Royalties and license fees receipts 9 30 77 9,5 -2,5 6,7 …

Export of good and non-factor services

2246 4260 9228 11,4 -3,3 4,7 16,6

Source: UNCTAD, World Investment report 2004 Note: a. 2002

74

Table 2.6: National regulatory changes, 1991-2003

Item 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003

Number of countries that introduced changes

in their investment regimes

35 43 57 49 64 65 76 60 63 69 71 70 82

Number of regulatory changes of which are:

82 79 102 110 112 114 151 145 140 150 208 248 244

More favourable to FDI

80 79 101 108 106 98 135 136 131 147 194 236 220

Less favourable to FDI

2 - 1 2 6 16 16 9 9 3 14 12 24

Source: UNCTAD, World Investment report 2004 Note: a. Including liberalizing changes or changes aimed at strengthening market functioning, as well as increased incentives. b. Including changes aimed at both increasing control and reducing incentives.

Figure 2.6: The top 20 recipient of FDI inflow, 2002 and 2003 (billions of dollars)


Inflows of Foreign Direct Investment (FDI) to Africa in 2004 remained stable, at

US$ 18 billion. The levels were relatively high in historical terms but still a mere

3% of such investment globally. FDI in Africa’s natural resources was especially

pronounced in 2004, buoyed by high oil and mineral prices on world markets, the

report states. Investment inflows increased in 40 of the region’s 53 countries and

75

declined in 13. North Africa attracted about 30% of the total, or US$ 5.3 billion -

about the same as in 2003 - with the focus on natural resources. FDI flows to

Central Africa and East Africa were also relatively stable, but West Africa boasted

an increase of 14%, to US $3.6 billion. Southern Africa fell by 18%, to US$ 1 billion.

Egypt saw the biggest rise on the continent, as liberalization and privatization

attracted new foreign investment in a wide range of industries.

Nigeria, Angola, Equatorial Guinea and Sudan - all rich in natural resources -

joined Egypt as Africa’s top FDI recipients, all of them registering inflows of more

than US$ 1 billion. The five countries together accounted for almost half of African

FDI in 2004 (figure 2.6). FDI flows to many small African countries, by contrast,

especially those poor in natural resources and classified as having least developed

economies, were less than US$100 million each last year. Many of these nations,

especially the least developed countries (LDCs), have small domestic markets, lack

skilled workers and struggle with supply capacity problems. The report finds that

these difficulties have hampered some of the market-access initiatives put into

place at the international level to encourage investment in export-oriented

industries.

Figure 2.7: FDI inflows to Africa, to 10 recipient, 2003 – 2004 (billions of dollars)

S

Source: UNCTAD, World Investment Report 2005 Ranked on the basis of the magnitude of 2004 FDI inflows

Africa received record high Foreign Direct Investment (FDI) inflows in 2005 of

US$31 billion (figure 2.7), but this was mostly concentrated in a few countries and

76

industries, says (UNCTAD, 2006). A sharp rise in corporate profitability and high

commodity prices over the past two years helped produce a growth rate of 78% in

FDI inflows to the region. Prospects are good for another increase in 2006 given

high project commitments, large numbers of investors eager to gain access to

resources, and a generally favourable policy stance for FDI in the region. FDI

continued to be a major source of investment for Africa as its share in gross fixed

capital formation increased to 19% in 2005. However, the region’s share of global

FDI remained low at about 3% in 2005. In the manufacturing sector, a number of

Transnational Corporations (TNCs) in the textile industry pulled out of Africa

because quota advantages for African countries declined after the end of the Multi-

Fibre Arrangement (MFA) in 2005.

South Africa was the largest FDI recipient in the region in 2005, experiencing a

sharp jump in inflows to US$6.4 billion from only US$0.8 billion in 2004. South

Africa accounted for about 21% of the region’s total. This was mainly due to the

acquisition of Amalgamated Bank of South Africa by Barclays Bank (United

Kingdom) for US$5.5 billion. Africa’s top ten recipient countries - South Africa,

Egypt, Nigeria, Morocco, Sudan, Equatorial Guinea, the Democratic Republic of

Congo, Algeria, Tunisia and Chad, in that order - accounted for close to 86% of the

regional FDI total (figure 2.8). In eight of these countries, FDI inflows exceeded

US$1 billion (more than US$3 billion for Egypt, Nigeria and South Africa in

particular). Inflows to South Africa were also the most diversified: investment was

channelled into energy, machinery and mining, as well as into banking, which

received the largest share.

Figure 2.8: Africa-FDI inflows and their share in gross fixed capital formation, 19952005


At the other extreme, FDI inflows remained below US$100 million in 34 African

countries. These are mostly least developed countries (LDCs), including oil

producing Angola, which witnessed a drastic decline in FDI receipts in 2

of the low FDI recipients in the region have limited natural resources; lack the

capacity to engage in significant manufacturing, and, as a result, are among the

least integrated into the global production system. Some countries have also

experienced political instability or civil war in the recent past, which destroyed

much of their already limited production capacity.

Figure 2.9: Africa-FDI inflows, top 10 economies, a 2004

FDI inflows and their share in gross fixed capital formation, 1995

World Investment Report 2006


countries. These are mostly least developed countries (LDCs), including oil

producing Angola, which witnessed a drastic decline in FDI receipts in 2




nced political instability or civil war in the recent past, which destroyed

much of their already limited production capacity.

FDI inflows, top 10 economies, a 2004-2005 (billions of dollars)

77

FDI inflows and their share in gross fixed capital formation, 1995-


countries. These are mostly least developed countries (LDCs), including oil-

producing Angola, which witnessed a drastic decline in FDI receipts in 2005. Many




nced political instability or civil war in the recent past, which destroyed

2005 (billions of dollars)


FDI inflows to the region were concentrated in a few industries, such as oil, gas,

and mining. Six oil producing countries (Algeria, Chad, Egypt, Equatorial Guinea,

Nigeria and Sudan, in descending order of the value of FDI) accounted for about

48% of inflows to the region. Although countries such as Kenya, Mauritius,

Lesotho, Swaziland and Uganda had begun to receive FDI for their textile and

apparel industries due to the African Growth and Opportunity Act (AGOA), the

trend changed following the end of the

30% contraction in the volume of garments manufactured in 2005 following the

departure of Hong Kong (China)

closed, with a loss of 6,650 jobs. The setback demonstrates tha

trade-related initiatives can be short

are inadequate for quickly absorbing and continuing production processes. It also

underscores the fact that Africa’s industrial progress requires competitiv

production capacity, in addition to better market access and more welcoming

regulatory frameworks. The persistence of the critical capacity problem may

continue to hamper the region’s ability to attract and retain FDI in the

manufacturing sector.

FDI outflows from Africa in 2005 remained small and originated from a few

countries. Six home countries Egypt, Liberia, Libyan Arab Jamahiriya, Morocco,

Nigeria and South Africa accounted for over 80% of total outflows. The largest

African TNCs are also from a





ows to the region. Although countries such as Kenya, Mauritius,



trend changed following the end of the MFA in 2005. In Mauritius there was a


departure of Hong Kong (China)-owned companies. In Lesotho, six textile TNCs

closed, with a loss of 6,650 jobs. The setback demonstrates that the impact of

related initiatives can be short-lived in Africa, where domestic capabilities


underscores the fact that Africa’s industrial progress requires competitiv




utflows from Africa in 2005 remained small and originated from a few



African TNCs are also from a small number of countries. In 2004, nine of the top

78




ows to the region. Although countries such as Kenya, Mauritius,



MFA in 2005. In Mauritius there was a


owned companies. In Lesotho, six textile TNCs

t the impact of

lived in Africa, where domestic capabilities


underscores the fact that Africa’s industrial progress requires competitive




utflows from Africa in 2005 remained small and originated from a few



small number of countries. In 2004, nine of the top

79

10 non-financial African TNCs ranked by foreign assets (table 2.7) were South

African, although Orascom Construction (Egypt) also made it onto the list.

Table 2.7: The 10 largest non-financial TNCs from Africa, ranked by foreign assets, 2004 (Millions of dollars)

Corporation

Home economy

Industry

Assets Sales

Sasol Ltd South Africa Industrial Chemical

4902 12988 5541 10684

Sappi Ltd South Africa Paper 4187 6150 4351 4762 MTN Group Ltd South Africa Tele Comms 2819 5216 2068 5150 Steinhoff Inter Holdings

South Africa Household goods 2747 4345 1599 3395

Barlowworld Ltd South Africa Diversified 2170 4592 2935 6514 Naspers Lts South Africa Media 1707 2766 677 2479 Nampak Ltd South Africa Packaging 1626 1968 998 3107 Gold Fields Ltd South Africa Metal and metal

products 1183 4262 775 2068

Orascom Construction

South Africa Diversified 1067 2080 859 1396

Databec Ltd South Africa Computer and related activities

944 987 2552 2631

Sources: UNCTAD, World Investment Report 2006

In Africa, FDI inflows in 2006 exceeded their previous record level of 2005. High

prices and buoyant global demand for commodities were once again a key factor,

particularly in the oil industry, which attracted investment not only from

developed countries but also from some developing countries. Cross-border M&As

in the extraction and related service industries of Africa tripled in the first half of

2006, as compared to the same period in 2005. However, the regional FDI picture

is not uniformly bright across sectors, countries and sub-regions. Most of the

inflows are concentrated in the West, North and Central African sub-regions.

Inflows will continue to be small in low-income economies lacking natural

resources.

Also global Foreign Direct Investment (FDI) inflows grew in 2006 for the third

consecutive year to reach US$1.2 trillion, according to UNCTAD’s first estimate for

the year. The total is a 34% increase from 2005 (table 2.8), although still short of

the record of US$1.4 trillion set in 2000. Global Foreign Direct Investment (FDI)

inflows amounted to $1,306 billion in 2006, rising more than 38% over the

previous year and finishing close to the record level of 2000 (figure 2.9),

80

UNCTAD’s yearly review of investment trends reports. FDI inflows to developing

countries and economies in transition (the latter comprising South-East Europe

and the Commonwealth of Independent States (CIS)] rose by 10% and 56% (table

2.7), respectively, in 2006, and reached record levels for both groups of economies.

Table 2.8: FDI inflows, by host region and major host economy, 2004-2006 (Billions

of dollars)


Figure 2.10: FDI inflows, global and by group of economies, 1980

dollars)


Foreign Direct Investment

record US$36 billion (figure 2

and increased profits and by a generally improved business climate, a UNCTAD

survey of investment trends reports. African FDI outflows also reached a record

level in 2006 of $8 billion, up from $2 billion i

being the main investors from the region (see UNCTAD/ PRESS/PR /2007/037).

FDI inflows exceeded $1 billion in eight African countries and rose in 33 countries

in 2006. The top ten host African nations (figure

billion, of the continent’s inflows. North African countries hosted record incoming

FDI, partly from Asian TNCs. All countries in this sub

(where flows remained relatively large) received increased inflows in a vari

industries. In sub-Saharan Africa, FDI inflows climbed in all sub regions except

southern Africa because of large investments in oil and mining. Major investment

declines, however, were recorded for Angola (

billion). These were due to sales of foreign equity shares to the Government in the

former case and to local firms in the latter.

.10: FDI inflows, global and by group of economies, 1980-2006 (billions of


Foreign Direct Investment (FDI) into Africa doubled between 2004 and 2006 to

record US$36 billion (figure 2.10), spurred by the search for primary resources



level in 2006 of $8 billion, up from $2 billion in 2005, with South African firms



in 2006. The top ten host African nations (figure 2.12) received about 90%, or $32


FDI, partly from Asian TNCs. All countries in this sub-region except Morocco

(where flows remained relatively large) received increased inflows in a vari

Saharan Africa, FDI inflows climbed in all sub regions except


declines, however, were recorded for Angola (-$1.1 billion) and South Africa (

lion). These were due to sales of foreign equity shares to the Government in the

former case and to local firms in the latter.

81

2006 (billions of

(FDI) into Africa doubled between 2004 and 2006 to a

.10), spurred by the search for primary resources



n 2005, with South African firms



about 90%, or $32


region except Morocco

(where flows remained relatively large) received increased inflows in a variety of

Saharan Africa, FDI inflows climbed in all sub regions except


$1.1 billion) and South Africa (-$0.3

lion). These were due to sales of foreign equity shares to the Government in the

82

Figure 2.11: Africa: FDI inflows and theirs share in GFCF, 1995-2006


Figure 2.12: Africa-FDI inflows, top 10 economies, 2005-2006a

(Billions of dollars)


In 2007, Global Foreign Direct Investment (FDI) inflows grew to an estimated

US$1.5 trillion, surpassing the previous record set in the year 2000 (UNCTAD,

83

2007). In Africa, FDI inflows in 2007 remained relatively strong. The

unprecedented level of inflows (US$36 billion) was supported by a continuing

boom in global commodity markets. Cross-border M&As in the extraction and

related service industries of Africa remained a significant source of FDI, but new

inbound M&A deals also took place in the banking industry. Egypt, Morocco, and

South Africa were the main beneficiaries of FDI inflows. FDI flows to developed

countries in 2007 grew for the fourth consecutive year, reaching US$1 trillion.

Flows were particularly buoyant in the United Kingdom, France, and the

Netherlands. The United States maintained its position as the largest single FDI

recipient. The European Union (EU) as a whole continued to be the largest host

region, attracting almost 40% of total FDI inflows in 2007.

However, several risks to the world economy -most of them not new - may have

implications for FDI flows to and from developed countries in 2008, UNCTAD

said. High and volatile commodities prices may cause inflationary pressures, and a

tightening of financial market conditions cannot be excluded. The increasing

probability of a recession in the United States and uncertainties about global

repercussions if it occurs may lead to a more cautious attitude by investors. These

considerations underline the need for caution in assessing future FDI prospects for

developed countries.

FDI inflows to developing countries and economies in transition (the latter

comprising South-East Europe and CIS) rose by 16% and 41% (table 2.8),

respectively, and reached new record levels, UNCTAD economists said.

Table 2.9: FDI inflows, by host

of dollars)


Note: a –preliminary estimates

World FDI inflows are projected on the basis of 105 economies for which date are available for

part of 2007, as of 19 December 2007. Data are estimated by annualizing their available data,

in most cases the first three quarters of 2007. The proportion of i

total inflows to their respective region or sub

data.

.9: FDI inflows, by host region and major host economy, 2006


preliminary estimates



in most cases the first three quarters of 2007. The proportion of inflows to these economies in

total inflows to their respective region or sub-region in 2006 is used to extrapolate the 2007

84

region and major host economy, 2006-2007 (billions



nflows to these economies in

region in 2006 is used to extrapolate the 2007

85


Balance of Payment: Is an accounting record of all monetary transactions

between a country and the rest of the world. These transactions include payments

for the country's exports and imports of goods, services, and financial capital, as

well as financial transfers.

Fire sale: A fire sale is the sale of goods at extremely discounted prices, typically

when the seller faces bankruptcy or other impending distress.

Causality: Causality is the relationship between an event (the cause) and a

second event (the effect), where the second event is a consequence of the first.

Financial market: Is a mechanism that allows people to buy and sell (trade)

financial securities (such as stocks and bonds), commodities (such as precious

metals or agricultural goods), and other fungible items of value at low transaction

costs and at prices that reflect the efficient-market hypothesis.

Terms of Trade: Is the relationship between the prices at which a country sells

its exports and the prices paid for its imports.

Exchange Rate Regime: This is the way a country manages its currency in

respect to foreign currencies and the foreign exchange market.

Capital Accumulation: This refers simply to the gathering or amassment of

objects of value; the increase in wealth; or the creation of wealth, it is often

equated with investment of profit income or savings, especially in real capital

goods.

Sensitivity Analysis: Is the study of how the variation (uncertainty) in the

output of a mathematical model can be apportioned, qualitatively or

quantitatively, to different sources of variation in the input of a model. Simply put,

it is a technique for systematically changing parameters in a model to determine

the effects of such changes.

86

Country Risk: This refers to the risk of investing in a country, dependent on

changes in the business environment that may adversely affect operating profits or

the value of assets in a specific country.

Return on in Investment: A performance measure used to evaluate the

efficiency of an investment or to compare the efficiency of a number of different

investments. It could be is the ratio of money gained or lost (whether realized or

unrealized) on an investment relative to the amount of money invested.

Privatisation: This is the incidence or process of transferring ownership of a

business, enterprise, agency or public service from the public sector (government)

to the private sector ("business").

Globalisation: Globalisation describes a process by which regional economies,

societies, and cultures have become integrated through a globe-spanning network

of communication and trade.

African Growth and Opportunity Act (AGOA): The purpose of this

legislation was to assist the economies of sub-Saharan Africa and to improve

economic relations between the United States and the region. AGOA provides

trade preferences for quota and duty-free entry into the United States for certain

goods.

87

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CHAPTER THREE

RESEARCH METHODOLOGY

3.1 INTRODUCTION

In this chapter, the researcher attempts to present the method of study analysis in

order to extend the empirical evidence in the case of Nigeria. The chapter defines

the variables of study and outlines the sources of data collection and analysis.

3.2 RESEARCH DESIGN

Research design according to (Onwumere, 2005) is a kind of blue-print that guides

the researcher in the investigation; a format which the researcher employs in order

to systematically apply the scientific method in the investigation of the problem.

Also, (Asika, 2006), describes research design as the structuring of investigation

aimed at identifying variables and their relationship to one another.

This research employed analytical research design because they are advantageous

for assessing large and small populations especially where a small population is to

be derived from a large one (Onwumere, 2005). It relied on past data which have a

common feature of an ex post-factor research. It aims at determining and

measuring the relationship between one variable and another or the impact of one

variable on another, in which the variables involved are not manipulated by the

research.

3.3 POPULATION AND SAMPLE SIZE

The population of the study is the entire Nigerian economy and the data will be

drawn for 1981 to 2007.

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3.4 NATURE AND SOURCES OF DATA

Secondary data was used for this study and they include the aggregate annual time

series at current prices for gross domestic product, GDP and total net inflows for

Foreign Direct Investment, FDI covering the period 1981-2007. This results in 27

pairs of observations. The unit of measurement for both variables is the Naira. The

data was extracted from the Central Bank of Nigeria and Federal Office of

Statistics. For this study, aggregate time series data were used because of its

stationarity characteristics. This implies that the mean and standard deviation do

not systematically differ over a period of time. In addition, aggregate data are

normally very useful in establishing long term econometric relationships between

variables.

3.5 SPECIFICATION OF MODELS

The study is largely quantitative and builds on existing research studies and

methodologies. In this study, the researcher used some methods to test the

hypothesis on the various relationships between Foreign Direct Investment and

economic growth. The statistical methods used are the Ordinary Least Squares

Method (OLS), Unit root test, the Cointegration test and the Granger causality test.

These methods are used in order to avoid a number of challenges and issues that

normally crop up when qualitative methods are used especially in econometric

studies. These include the issue of subjectivity and bias of responses and the

inability to incorporate such biases in econometric models.

3.6 ORDINARY LEAST SQUARES METHOD

The ordinary least squares method is one of the most popular and widely used

methods for regression analysis. The method was developed by Carl Friedrich

Gauss (1821) and has subsequently evolved to become the Classical Linear

Regression Model (CLRM). It is mainly used to establish whether one variable is

dependent on another or a combination of other variables. It entails establishing

106

the coefficient(s) of regression for a sample and then making inferences on the

population. The linear regression equation for this model is:

GDPi = α1 + β1FDIi + εi…….................................................................................... (1)

where; GDPi and FDIi represent the Gross Domestic Product and Foreign Direct

Investment at a particular time respectively while εi represents the “noise” or error

term; αi and βi represent the slope and coefficient of regression. The coefficient of

regression, βi indicates how a unit change in the independent variable (Foreign

Direct Investment – net inflow of FDI) affects the dependent variable (gross

domestic product). The error, εi, is incorporated in the equation to cater for other

factors that may influence GDP. The validity or strength of the Ordinary Least

Squares method depends on the accuracy of assumptions. In this study, the Gauss-

Markov assumptions are used and they include; that the dependent and

independent variables (GDP and FDI) are linearly co-related, the estimators (α, β)

are unbiased with an expected value of zero i.e. E (εi) = 0, which implies that on

average the errors cancel out each other.

3.6.1 PROCEDURE OF ORDINARY LEAST SQUARES METHOD

In order to estimate the regression model, E-views econometrics and statistical

package was used. The procedure involved specifying the dependent and

independent variables; in this case, GDP is the dependent variable while FDI is the

independent variable. The programs were run and from the output, the values of

the constant, α (slope), coefficient of regression, β and the error term, ε are

obtained. In addition, the output showed the t-statistic and p-values for the

coefficients which results in either rejecting or failure to reject the hypothesis at a

specified level of significance. The p-value is the probability of getting a result that

is at least as extreme as the critical value. The null hypothesis is rejected if the p-

value is less than or equal to the critical value. The output will show the coefficient

of determination (r2), which measures the proportion of the dependent variable

that is explained by the regression model.

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3.6.1.1 UNIT ROOT TEST

It is suggested that when dealing with time series data, a number of econometric

issues can influence the estimation of parameter using Ordinary Least Square

(OLS). Regressing a time series variable on another time series variable using

Ordinary Least Square (OLS) estimation can obtain a very high R2, although there

is no meaningful relationship between the variables. This situation reflects the

problem of spurious regression between totally unrelated variables generated by a

non-stationary process. Therefore, it is recommended that a stationarity (unit

root) test be carried out to test for the order of integration.

A stochastic process that is said to be stationary simply implies that the mean

[(E(Yt)] and the variance [Var(Yt)] of Y remain constant over time for all t, and the

covariance [covar(Yt, Ys)] and hence the correlation between any two values of Y

taken from different time periods depends on the difference apart in time between

the two values for all t≠s, Thomas(1993). Since standard regression analysis

requires that data series be stationary, it is obviously important that we first test

for this requirement to determine whether the series used in the regression process

is a difference stationary or a trend stationary. The Augmented Dickey-Fuller

(ADF) test is used. The ADF test simply runs a regression of the first-difference of

the series against a first-lagged value, constant, and a time trend as the following:

Without Intercept and Trend ∆Yt = δ Yt-1 + Ut ……………………………………… (2)

With Intercept ∆Yt = α + δ Yt-1 + Ut……………….……………….. (3)

With Intercept and Trend ∆Yt = α + βT + δ Yt-1 + Ut…………..……………. (4)

The hypothesis is

Ho: δ = 0 (Unit Root)

H1: δ ≠ 0

Decision rule:

Decision rule:

If t* > ADF critical value, ==> do not reject null hypothesis, i.e., unit root exists.

108

If t* < ADF critical value, ==> reject null hypothesis, i.e., unit root does not

exist.

The test for a unit root is a test on the coefficient of (Y t-1) in the regression. If the

ADF test-statistic (t-statistic) is less (in the absolute value) than the Mackinnon

critical t-values, the null hypothesis of a unit root cannot be rejected for the time

series and hence, one can conclude that the series is non-stationary at their levels.

The unit root test tests for the existence of a unit root in three cases: without

intercept and trend, with intercept only and with intercept and trend to take into

the account the impact of the trend on the series.

3.6.1.2 COINTEGRATION TEST

Cointegration methods have been very popular tools in applied economic work

since their introduction about twenty years ago. However, the strict unit-root

assumption that these methods typically rely upon is often not easy to justify on

economic or theoretical grounds. For instance, variables such as inflation, interest

rates, real exchange rates and unemployment rates all appear to be highly

persistent, and are frequently modelled as unit root processes. But, there is little a

priori reason to believe that these variables have an exact unit root, rather than a

root close to unity. In fact, these variables often show signs of mean reversion in

long enough samples. (Wallace and Warner (1993), Malley and Moutos (1996),

Cardoso (1998), Jonsson (2001), Khamis and Leone (2001) and Bagchi et al.

(2004). Since unit-root tests have very limited power to distinguish between a

unit-root and a close alternative, the pure unit-root assumption is typically based

on convenience rather than on strong theoretical or empirical facts. This has led

many economists and econometricians to believe near-integrated processes,

which explicitly allow for a small (unknown) deviation from the pure unit-root

assumption, to be a more appropriate way to describe many economic time series;

see, for example, Stock (1991), Cavanagh et al., (1995) and Elliott (1998).

The finding that many macro time series may contain a unit root has spurred the

development of the theory of non-stationary time series analysis. Engle and

Granger (1987) pointed

stationary series may be stationary. If such a stationary linear combination exists,

the non-stationary time series are said to be

combination is called the

run equilibrium relationship among the variables. The purpose of the

cointegration test is to determine whether a group of non

cointegrated or not.

In other words, to examine whether or not

between variables (stable and non

2000). In our case, the mission is to determine whether or not GDP, FDI, EXRATE

and INFRATE variables have a long

TESTING FOR COINTEGRATION USING JOHANSEN’S TEST

Johansen’s methodology takes its starting point in the vector auto regression

(VAR) of order p given by

where yt is an nx1 vector of variables that are integrated of order one

denoted I(1) – and εt is an

Where

If the coefficient matrix

and β each with rank r such that

cointegrating relationships, the elements of

Granger (1987) pointed out that a linear combination of two or more non


stationary time series are said to be cointegrated. The stationary linear

combination is called the cointegrating equation and may be interpreted as a long


cointegration test is to determine whether a group of non-stationary series are

In other words, to examine whether or not there exists a long run relationship

between variables (stable and non-spurious co-integrated relationship) (Miguel,


and INFRATE variables have a long-run relationship in a bivariate framework.



given by

………………………………………………… (5)

x1 vector of variables that are integrated of order one

is an nx1 vector of innovations. This VAR can be re

……………………………………………………... (6)

…………………………………………………….. (7)

rix Π has reduced rank r<n, then there exist

such that Π = αβ′ and β′yt is stationary. r is the number of

cointegrating relationships, the elements of α are known as the adjustment

109

out that a linear combination of two or more non-


. The stationary linear

and may be interpreted as a long-


stationary series are

there exists a long run relationship

integrated relationship) (Miguel,


ariate framework.



………………………………………………… (5)

x1 vector of variables that are integrated of order one – commonly

x1 vector of innovations. This VAR can be re-written as

……………………………………………………... (6)

…………………………………………………….. (7)

, then there exist nxr matrices α

is the number of

are known as the adjustment

parameters in the vector error correction model and each column of

cointegrating vector. It can be shown that for a given

estimator of β defines the combination of

correlations of ∆yt with

deterministic variables when present. Johansen proposes two different likelihood

ratio tests of the significance of these canonical correlations and thereby the

reduced rank of the Π matrix: the tr

in equations (4) and (5) respectively.

………………………………………………………………. (8)

..……………………………………………………………… (9)

Here T is the sample size and

test tests the null hypothesis of

hypothesis of n cointegrating vectors. The maximum eigen value test, on the other

hand, tests the null hypothesis of

hypothesis of r +1 cointegrating vectors. Neither of these test statistics follows a chi

square distribution in general; asymptotic critical values can be found in

(Johansen and Juselius,

packages. Since the critical values used for the maximum eigen value and trace test

statistics are based on a pure unit

when the variables in the system are near

question is how sensitive Johansen’s procedures are to deviations from the pure

unit root assumption.

Although Johansen’s methodology is typically used in a setting where all variables

in the system are I(1), having stationary variables in the

an issue and (Johansen

variables in the system to establish their order of integration. If a single variable is

I(0) instead of I(1), this will reveal itself through a coin

space is spanned by the only stationary variable in the model. For instance, if the

system in equation (2) describes a model in which y

parameters in the vector error correction model and each column of

cointegrating vector. It can be shown that for a given r, the maximum likelihood

defines the combination of yt −1 that yields the r largest canonical

with yt −1 after correcting for lagged differences and



matrix: the trace test and maximum eigen value test, shown

in equations (4) and (5) respectively.

………………………………………………………………. (8)

..……………………………………………………………… (9)

is the sample size and is the ith largest canonical correlation. The trace

test tests the null hypothesis of r cointegrating vectors against the alternative

cointegrating vectors. The maximum eigen value test, on the other

hand, tests the null hypothesis of r cointegrating vectors against the alternative

+1 cointegrating vectors. Neither of these test statistics follows a chi


,1990) and are also given by most econometric software


statistics are based on a pure unit-root assumption, they will no longer be correct

when the variables in the system are near- unit-root processes. Thus

sensitive Johansen’s procedures are to deviations from the pure


in the system are I(1), having stationary variables in the system is theoretically not

Johansen, 1995) states that there is little need to pre


I(0) instead of I(1), this will reveal itself through a cointegrating vector whose


system in equation (2) describes a model in which yt = (y1,t y2,t )′ where y

110

parameters in the vector error correction model and each column of β is a

, the maximum likelihood

largest canonical

after correcting for lagged differences and



ace test and maximum eigen value test, shown

………………………………………………………………. (8)

..……………………………………………………………… (9)

th largest canonical correlation. The trace

cointegrating vectors against the alternative

cointegrating vectors. The maximum eigen value test, on the other

ng vectors against the alternative

+1 cointegrating vectors. Neither of these test statistics follows a chi


conometric software


root assumption, they will no longer be correct

root processes. Thus, the real

sensitive Johansen’s procedures are to deviations from the pure-


system is theoretically not

1995) states that there is little need to pre-test the


tegrating vector whose


where y1,t is I(1)

111

and y2,t, is I(0), one should expect to find that there is one cointegrating vector in

the system which is given by β =(0 1)′ . In the case where Π has full rank, all n

variables in the system are stationary.

3.6.1.3 GRANGER NO-CAUSALITY TESTS

Correlation does not necessarily imply causation in any meaningful sense of that

word. The econometric graveyard is full of magnificent correlations, which are

simply spurious or meaningless. Interesting examples include a positive

correlation between teachers' salaries and the consumption of alcohol and a superb

positive correlation between the death rate in the UK and the proportion of

marriages solemnized in the Church of England. Economists debate correlations

which are less obviously meaningless.

The (Granger, 1969) approach to the question of whether x causes y is to see how

much of the current y can be explained by past values of y and then to see whether

adding lagged values of x can improve the explanation. Y is said to be Granger-

caused by x if x helps in the prediction of y, or equivalently if the coefficients on the

lagged x’s are statistically significant. Note that two-way causation is frequently the

case; x Granger causes y and y Granger causes x.

It is important to note that the statement “x Granger causes y” does not imply that

y is the effect or the result of x. Granger causality measures precedence and

information content but does not by itself indicate causality in the more common

use of the term.

It is better to use more rather than fewer lags, since the theory is couched in terms

of the relevance of all past information. Therefore, I picked a lag length of 3 for the

Granger test which I think corresponds to a reasonable time over which one of the

variables could help predict the other. The reported F-statistics are the Wald

statistics for the joint hypothesis that the coefficients on the lagged values of the

other variable are zero for each equation. The F-statistics is the Wald statistics for

the null hypothesis. If the F-statistics is greater than a certain critical value for an F

112

distribution, then we reject the null hypothesis that Y does not Granger-cause X,

which means that Y Granger-causes X.

If the coefficient of ∑ α�� , ∑ κ�� , ∑ σ�� and ∑ τ�� in equations 12a, 12b, 12c and 12d respectively is significantly different from zero, then we conclude that GDP

Granger causes FDI or FDI causes GDP and so on. Granger causality in both

directions is of course, a possibility.

The Granger no-causality test are used in time series analysis to examine the

direction of causality between two economic series has been one of the main

subjects of many econometrics studies for the past three decades. Recent studies

have shown that the conventional F-test for determining joint significance of

regression-derived parameters, used as a test of causality, is not valid if the

variables are non-stationary and the test statistics does not have a standard

distribution (Gujarati, 1995).

Generally, causality between two economic variables has been tested using

Granger and Sims causality test (Granger, 1969 and Sims, 1972). Within a bivariate

context, the Granger-type test states that “if a variable x causes variable y, the

mean square error (MSE) of a forecast of y based on the past values of both

variables is lower than that of a forecast that uses only past values of y”.

This Granger test is implemented by running the following regression:

p p ∆yt = α + Σ βi ∆yt-i + Σ γi ∆xt-i + εt ........................................................................(10) i=1 i=1

and testing the joint hypothesis H0:γ1 = γ2 = …γp = 0 against H1: γ1 ≠ γ2 ≠ … γp ≠ 0

Granger causality from the y variable to the coincident variable x is established if

the null hypothesis of the asymptotic chi-square (χ²) test is rejected. A significant

test statistic indicates that the x variable has predictive value for forecasting

movements in y over and above the information contained in the latter’s past.

Although the traditional pair-wise Granger causality tests is more revealing than

simple correlation coefficients, the Granger test abstracts from philosophical

issues of causality by merely insisting on temporal precedence and predictive

content as the necessary criteria for one variable to ‘Cause’ another. Another

113

shortcoming of the test is that critical values are only valid for stationary variables

that are not bound together in the long run by a cointegrating relationship

(Granger, 1988). This makes the causality test results somewhat weak and

conditional on the absence of cointegration between the relevant variables.

In cointegrated systems, such tests are more complex, since the existence of unit

roots gives various complications in statistical inference. For detailed exposition

see Toda and Phillips (1993), Toda and Yamamoto (1995), and Dufour and Renault

(1998). Thus there is a high risk of making wrong inferences about causality simply

due to the incorrect identification of the order of integration of the series or

number of cointegration vectors among the variables. Other alternative tests

proposed by Mosconi and Gianini (1992) and (Toda and Philips, 1993) in an

attempt to improve the size and power of the Granger no-causality test are

unwieldy and do not lend themselves to easy application.

We evade these complications by applying the more vigorous T-Y procedure

developed by Toda and Yamamoto (1995) and extended by Rambaldi and Doran

(1996) and Zapata and Rambaldi (1997) to test for the Granger no-causality in this

study. As stated by Giles and Mirza (1999), (Toda and Yamamoto, 1995), and

independently, Dolado and Lütkepohl, (1996), proposed method is simple and

gives an asymptotic chi-square (χ²) null distribution for the Wald Granger no-

Causality test statistic in a vector autoregressive (VAR) model, irrespective of the

system’s integration or cointegration properties. Zapata and Rambaldi (1997)

explained that the advantage of using the T-Y procedure is that in order to test

Granger causality in the VAR framework (as in this study), it is not necessary to

pre-test the variables for the integration and cointegration properties, provided the

maximal order of integration of the process does not exceed the true lag length of

the VAR model.

As said by (Toda and Yamamota, 1995), the T-Y procedure however does not

substitute the conventional unit roots and cointegration properties pre-testing in

time series analysis. They are considered as complementary to each other. The T-Y

procedure basically involves the estimation of an augmented VAR(k+dmax) model,

where k is the optimal lag length in the original VAR system, and dmax is the

114

maximal order of integration of the variables in the VAR system. The Granger no-

causality test utilises a modified Wald (MWald) test for zero restrictions on the

parameters of the original VAR(k) model. The remaining dmax autoregressive

parameters are regarded as zeros and ignored in the VAR(k)model. This test has an

asymptotic χ2 distribution when the augmented VAR (k + dmax) is estimated.

Rambaldi and Doran (1996) have shown that the MWald tests for testing Granger

no-causality experience efficiency improvement when Seemingly Unrelated

Regression (SUR) models are used in the estimation. Moreover, the MWald test

statistic is also easily computed in the SUR system.

3.6.3 THE MODEL

In their study of bivariate causality analysis between FDI inflow and economic

growth in Ghana, Frimpong and Oteng, (2008) followed Seabra and Flach, (2005)

method of the T-Y Granger no-causality test by estimating the following bivariate

VAR system using the SUR technique below:

�� = �� + � �� + � ��

��

��

��+��

…………………………………………………………………………………………………………..(11a)

�� = �� + � � � �� + � � � ��

��

��

��+� �

…………………………………………………………………………………………………………..(11b)

where lnGDP and lnFDI are, respectively, the natural logarithm of GDP growth

(proxy for economic growth) and of Foreign Direct Investment. k is the optimal lag

order, d is the maximal order of integration of the variables in the system and ε1

and ε2 are error terms that are assumed to be white noise.

Each variable is regressed on each other variable lagged from one (1) to the k+dmax

lags in the SUR system, and the restriction that the lagged variables of interest are

equal to zero is tested.

115

From equation (1a), “FDI does not Cause GDP” (i.e. FDI⇒ GDP ) if H0 :β1i = 0

against H1 :β1i ≠ 0, where i ≤ k . Similarly, from equation (1b), “GDP does not Cause

FDI”(i.e. GDP⇒ FDI ) if, H0 : β2i = 0 against H1 : β2i ≠ 0 where i ≤ k . Observe that

the extra (dmax) lags are not restricted in all cases. According to Toda and

Yamamoto (1995), this will ensure that the asymptotic critical values can be

applied when we test for causality between integrated variables.

Following the same procedure as mentioned above, the researchers also adapted

the (Seabra and Flach, 2005) model but in a modified version. They believe that in

as much as FDI contribute a lot to the growth of an economy, that there are other

factors that should be considered when looking at the link between FDI and

economic growth. Therefore, in this study, the researchers will also incorporate

other variable into the model to find out what their effect will be by estimating the

following techniques:

�� = �� + � �� + � ��

��

��

��

�� + � !�� "#$%&"��

��

��+ � '�� (�$%&"��

��

��+ ��

……………………………………………………………………………….……………………………..(12)

To test for causality among the variables, they were tested as related to past lagged

values of themselves and other variables, that is , the independent variables. They

are stated as follows:

�� = �� + � �� + � ��

��

��

��

�� + � !�� "#$%&"��

��

��+ � '�� (�$%&"��

��

��+ ��

……………………………………………………………………………….……………………………..(12a)

�� = )� + � *�� + � +��

��

��

��

�� + � ,�� "#$%&"��

��

��+ � -�� (�$%&"��

��

��+ � �

…………………………………………………….……………………………………………………….(12b)

�"#$%&" = .� + � /�� "#$%&"�� + � 0��

��

��

��

�� + � 1��

��

��+ � '�� (�$%&"��

��

��+ �2�

……………………………………………………….……………………………………………..……..(12c)

� (�$%&" = 3� + � 4�� (�$%&"�� + � 5��

��

��

��

�� + � 6��

��

��+ � 7�� ("#$%&"��

��

��+ �8�

………………………………………………………….………………………………………….……..(12d)

116

where lnGDP and lnFDI are, the natural logarithm of GDP growth (proxy for

economic growth) and of Foreign Direct Investment respectively, and lnINFL and

lnEXCO represent inflation rate and exchange rate that is macro economic

stability. k is the optimal lag order, d is the maximal order of integration of the

variables in the system and ε1, ε2, ε3 and ε4 are error terms that are assumed to be

white noise. Each variable is regressed on each other variable lagged from one (1)

to the k+dmax lags in the SUR system, and the restriction that the lagged variables

of interest are equal to zero is tested.

3.7 THE TECHNIQUE OF ANALYSIS

Before applying the T-Y no-causality test in the augmented VAR(k+dmax), we will

first establish the maximal integration order (dmax) of the variables by carrying

out an Augmented Dickey-Fuller (ADF) unit root tests on the GDP growth and FDI

series in their log-levels and log differenced forms.

Secondly, we will employ the AIC, SBC and Likelihood Ratio (LR) information

criteria to establish and select the optimum lag length of the VAR(k).

Thirdly, we conduct a cointegration test just to find out whether the two variables

are bound together in the long run, this will be established by Johansen Maximum

Likelihood (ML) cointegration test.

Using the established maximal order of integration (dmax=1) and the selected VAR

length (k=1), the following augmented VAR(2) model will be estimated using the

SUR technique:

Finally, we will conduct the T-Y Granger causality test using a modified Wald

(MWald) test to verify if the coefficients of the lagged variables are significantly

different from zero in the respective equations (11a) to (11d). The models to be

tested in this research work include models 1, 2, 3, 4, 5, 12a, 12b, 12c and 12d as

stated above.

117


Ordinary Least Square(OLS): This is a technique for estimating the unknown

parameters in a linear regression model. This method minimizes the sum of

squared distances between the observed responses in a set of data, and the fitted

responses from the regression model.

Cointegration: It is an econometric property of time series variables. If two or

more series are individually integrated (in the time series sense) but some linear

combination(s) of them has(/have) a lower order of integration then the series are

said to be cointegrated.

Granger causality test: Is a technique for determining whether one time series

is useful in forecasting another.

Coefficient of Determination: It is the proportion of variability in a data set

that is accounted for by the statistical model. It provides a measure of how well

future outcomes are likely to be predicted by the model.

Unit Root: A unit root is an attribute of a statistical model of a time series whose

autoregressive parameter is one.

Augumented Dickey Fuller (ADF) test: It is a test for a unit root in a time

series sample

118

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121

CHAPTER FOUR

DATA PRESENTATION AND ANALYSIS

4.1 UNIT ROOT TEST

It is suggested that when dealing with time series data, a number of econometric

issues can influence the estimation of parameter using OLS. Regressing a time

series variable on another time series variable using Ordinary Least Square (OLS)

estimation can obtain a very high R2, although there is no meaningful relationship

between the variables (Gujarati, 2007). This situation reflects the problem of

spurious regression between totally unrelated variables generated by a non-

stationary process. Therefore, it is recommended that a stationarity (unit root) test

be carried out to test for the order of integration. For this study, the unit root test

that was employed is the Augmented Dickey Fuller (ADF) test and it was

performed with EViews software.

The ADF tests allow you to specify how lagged difference terms are to be included

in the ADF test equation. In this case, we have chosen to estimate an ADF test that

includes a constant in the test regression and employs automatic lag length

selection using a Schwarz Information Criterion (SIC) and a maximum lag length

of 2years. Applying these settings to data on the Nigerian FDI and economic

growth figure for the period 1981 to July 2007, we can obtain the results as

described below. The first part of the unit root output provides information about

the form of the test (the type of test, the exogenous variables, and lag length used),

and contains the test output, associated critical values, and in this case, the p-

value.

122

4.2 SUMMARY OF AUGMENTED DICKEY FULLER TEST FOR UNIT

ROOT

From the result, if the statistic t α value is greater than the critical values, we do not

reject the null at conventional test sizes and vice versa. The second part of the

output shows the intermediate test equation is used to calculate the ADF statistic.

The analysis started by the test of the statistical properties of the data series used.

First, the order of integration in each of the GDP, FDI, EXRATE and INFRATE

series were tested. The stationarity test, that is the unit root showed that the

included variables were non-stationary at their level and first difference. The

exception is INFRATE, which is 1(0), but others are integrated of order one 1(1).

The lag lengths were chosen using Akaike Information Criteria (AIC). This means

that the null of a unit root for the individual series was not rejected for all of the

series tested. Given the short span of the individual series, we do not reject the unit

root null of unit roots for the 27 observations. On the other hand, some were

rejected in 0 and 1lag respectively. The results strongly support the conclusion that

the series are stationary only after being differenced once. Hence, it shows that the

series are integrated of order one, i.e., I(1) at the 1%, 5% and 10% significance

levels. In brief, the test results on the levels of GDP, FDI, EXRATE and INFRATE

indicate a failure to reject the null of non-stationarity.

Unit root test for the variable were in their levels and 1st difference forms. Note:

ADF (0), (1) and (2) are the lags that were used for the test, the C.V. is the critical

values at 1%, 5% and 10% significance levels respectively. This test is to find out if

the series are non-stationary or stationary. The summary of these tests can be seen

from tables 5.1 to 5.8 as shown below.

123

Test result in Level showing t-statistics and critical values

Table 4.1 Table 4.2

Source: Authors’ calculations based on domestic authorities’ data.

Table 4.3 Table 4.4


Variable ADF (0) ADF(1) ADF (2) Without Intercept and Trend

INGDP 6.345703 C. V. -2.6560 -1.9546 -1.6226

3.367182 C.V.

-2.6603 -1.9552 -1.6228

3.270008 C.V.

-2.6649 -1.9559 -1.6231

With Intercept only 0.427191

C. V. -3.7076 -2.9798 -2.6290

0.027238 C.V.

-3.7204 -2.9850 -2.6318

0.003179 C.V.

-3.7343 -2.9907 -2.6348

With Intercept and Trend

-2.347408 C. V. -3.7076 -2.9798 -2.6290

-2.458477 C.V.

-4.3738 -3.6027 -3.2367

-2.012598 C.V.

-4.3942 -3.6118 -3.2418


INFDI 0.347323 C.V. -2.6700 -1.9566 -1.6235

0.691032 C.V.

-2.6819 -1.9583 -1.6242

0.829500 C.V.

-2.6968 -1.9602 -1.6251

With Intercept only -2.623168

C.V. -3.7497 -2.9969 -2.6381

-0.710412 C.V.

-3.7856 -3.0114 -2.6457

-0.228403 C.V.

-3.8304 -3.0294 -2.6552


-2.546577 C.V. -4.4167 -3.6219 -3.2474

-2.102839 C.V.

-4.4691 -3.6454 -3.2602

-1.829947 C.V.

-4.5348 -3.6746 -3.2762


IN_ EXRATE

1.542149 C.V. -2.6560 -1.9546 -1.6226

0.891744 C.V. -2.6603 -1.9552 -1.6228

0.650382 C.V.

-2.6649 -1.9559 -1.6231


C.V. -3.7076 -2.9798 -2.6290

-1.521260 C.V. -3.7204 -2.9850 -2.6318

-1.756198 C.V.

-3.7343 -2.9907 -2.6348


-1.320506 C.V. -4.3552 -3.5943 -3.2321

-1.601235 C.V. -4.3738 -3.6027 -3.2367

-1.566450 C.V.

-4.3942 -3.6118 -3.2418

Variable ADF (0) ADF(1) ADF (2)

Without Intercept and Trend IN_IN FRATE

-0.982273 C.V. -2.6560 -1.9546 -1.6226

-0.735267 C.V. -2.6603 -1.9552 -1.6228

-0.769417 C.V. -2.6649 -1.9559 -1.6231


C.V. -3.7076 -2.9798 -2.6290

-3.355455 C.V. -3.7204 -2.9850 -2.6318

-1.943292 C.V. -3.7343 -2.9907 -2.6348


-2.812207 C.V. -4.3552 -3.5943 -3.2321

-3.163094 C.V. -4.3738 -3.6027 -3.2367

-1.983848 C.V. -4.3942 -3.6118 -3.2418

124

All the series shows that there is a unit root problem which indicates a non-

stationarity of the variables, Foreign Direct Investment (FDI), gross domestic

product (GDP) which is used as a proxy for economic growth, Exchange rate

(EXRATE) and Inflation rate (INFRATE).

Test result in 1st difference showing t-statistics and critical values

Table 4.5 Table 4.6


Table 4.7 Table 4.8



INGDP -1.827215 C.V. -3.7076 -2.9798 -2.6290

-1.370285 C.V. -2.6649 -1.9559 -1.6231

-0.660122 C.V. -2.6700 -1.9566 -1.6235


C.V. -3.7204 -2.9850 -2.6318

-3.812882 C.V. -3.7343 -2.9907 -2.6348

-2.640987 C.V. -3.7497 -2.9969 -2.6381


-4.104322 C.V. -4.3738 -3.6027 -3.2367

-3.725041 C.V. -4.3942 -3.6118 -3.2418

-2.534371 C.V. -4.4167 -3.6219 -3.2474


INFDI -1.382488 C.V. -2.6819 -1.9583 -1.6242

-1.290604 C.V. -2.6968 -1.9602 -1.6251

-2.447221 C.V. -2.7158 -1.9627 -1.6262


C.V. -3.7856 -3.0114 -2.6457

-2.277376 C.V. -3.8304 -3.0294 -2.6552

-2.546438 C.V. -3.8877 -3.0521 -2.6672


-9.103098 C.V. -4.4691 -3.6454 -3.2602

-2.242128 C.V. -4.5348 -3.6746 -3.2762

-2.542163 C.V. -4.6193 -3.7119 -3.2964

Variable

ADF (0) ADF(1) ADF (2)

Without Intercept and Trend IN_INFRATE

-5.080180 C.V. -2.6603 -1.9552 -1.6228

-5.772577 C.V. -2.6649 -1.9559 -1.6231

-1.509130 C.V. -2.6700 -1.9566 -1.6235


C.V. -3.7204 -2.9850 -2.6318

-5.665484 C.V. -3.7343 -2.9907 -2.6348

-3.451973 C.V. -3.7497 -2.9969 -2.6381


-4.952879 C.V. -4.3738 -3.6027 -3.2367

-5.545532 C.V. -4.3942 -3.6118 -3.2418

-3.375882 C.V. -4.4167 -3.6219 -3.2474


IN_ EXRATE

-1.267570 C.V. -2.6603 -1.9552 -1.6228

-1.343666 C.V. -2.6649 -1.9559 -1.6231

-1.701336 C.V. -2.6700 -1.9566 -1.6235


C.V. -3.7204 -2.9850 -2.6318

-2.324431 C.V. -3.7343 -2.9907 -2.6348

-2.681272 C.V. -3.7497 -2.9969 -2.6381


-2.612916 C.V. -4.3738 -3.6027 -3.2367

-2.668975 C.V. -4.3942 -3.6118 -3.2418

-3.267211 C.V. -4.4167 -3.6219 -3.2474

125

In table 4.9, we have the summary of the unit root test for some of the variables

that were used in the analysis. The R-squared statistics measure the regression in

predicting the values of the dependent variables within the sample. The R-squared

statistics is the fraction of the simple mean of the dependent variable. The R-

squared becomes negative if the regression does not have an intercept or constant

or if the two-squared least squares is used. The log likelihood is the value of the

function evaluated at the estimated values of the coefficients. The figures were

arrived at by looking at the log likelihood of the equation. The AIC or the Akaike

Information Criterion is a guide to the selection of the number of terms in an

equation. This is normally based on the sum of squared residuals but places a

penalty on extra coefficients. The Schwarz criterion is basically the same with AIC

but the only difference is that it places larger penalty for extra coefficients. The

Durbin-Watson statistics is a test for serial correlation. If it is less than 2 or close

to 2, there is an evidence of positive serial correlation.

Table 4.9: Unit root test for the variables in levels with (2) lags

R-squared Log-Likelihood Akaike info criteria

Schwarz criterion

Durbin-Watson statistics

IN_GDP in levels Without Intercept and Trend 0.015845 6.379904 -0.28165 0.134402 1.927139 With Intercept 0.050699 6.812582 -0.234382 -0.038040 1.929477 With Intercept and Trend 0.219570 9.163162 -0.346930 -0.101502 1.951935 IN_GDP in difference Without Intercept and Trend 0.320661 3.275501 -0.023957 0.124151 2.149471 With Intercept 0.495482 6.696955 -0.234518 -0.037041 2.015476 With Intercept and Trend 0.496035 6.709547 -0.148656 0.098190 2.017834 R-Squared Log-Likelihood Akaike info

criteria Schwarz criterion


IN_FDI in levels Without Intercept and Trend 0.544728 -25.80373 3.031971 3.181093 1.828717 With Intercept 0.548507 -25.72455 3.128900 3.327729 1.816064 With Intercept and Trend 0.651159 -23.27407 2.976218 3.224755 1.988063

IN_FDI in difference

126

Without Intercept and Trend 0.852739 -23.98965 3.175253 3.322291 1.930492 With Intercept 0.859947 -23.56310 3.242718 3.438768 1.949330 With Intercept and Trend 0.863719 -23.33104 3.333064 3.578126 1.938208 R-Squared Log-Likelihood Akaike info



IN_EXRATE in levels Without Intercept and Trend -0.185464 -10.50209 1.125174 1.272431 2.017030 With Intercept 0.142307 -6.618380 0.884865 1.081207 2.065805 With Intercept and Trend 0.207077 -5.676132 0.889678 1.135106 2.042848 IN_EXRATE in difference Without Intercept and Trend 0.365406 -10.47585 1.171813 1.319921 2.010247

With Intercept 0.473150 -8.336048 1.072700 1.270177 2.003364 With Intercept and Trend 0.553528 -6.432356 0.994118 1.240964 2.138722 R-Squared Log-Likelihood Akaike info



IN_INFRATE in levels Without Intercept and Trend 0.284424 -24.88413 2.323678 2.470934 2.031279 With Intercept 0.386407 -23.03905 2.253254 2.449596 1.916733 With Intercept and Trend 0.398214 -22.80589 2.317158 2.562585 1.903975 IN_INFRATE in difference Without Intercept and Trend 0.621380 -24.65166 2.404492 2.552600 1.812523 With Intercept 0.624877 -24.54496 2.482170 2.679647 1.812859 With Intercept and Trend 0.626281 -24.50182 2.565376 2.812222 1.817765 Source: Authors’ calculations based on domestic authorities’ data.

Table 4.10: Test for non-stationarity by calculating the auto correlation

function ACF

Date: 05/07/09 Time: 10:37 Sample: 1981 2007 Included observations: 27

Autocorrelation Partial Correlation AC PAC Q-Stat Prob

. |*******| . |*******| 1 0.902 0.902 24.498 0.000 . |****** | . *| . | 2 0.803 -0.059 44.670 0.000 . |***** | . *| . | 3 0.695 -0.098 60.441 0.000 . |**** | . *| . | 4 0.585 -0.079 72.092 0.000 . |**** | . | . | 5 0.480 -0.037 80.30

6 0.000

127

. |*** | . *| . | 6 0.372 -0.091 85.467 0.000 . |**. | . | . | 7 0.270 -0.045 88.326 0.000 . |* . | . *| . | 8 0.166 -0.091 89.467 0.000 . |* . | . | . | 9 0.074 -0.029 89.702 0.000 . | . | . *| . | 10 -0.017 -0.077 89.716 0.000 . *| . | . *| . | 11 -0.113 -0.120 90.335 0.000 .**| . | . *| . | 12 -0.201 -0.073 92.438 0.000


From table 4.10 above, it can be seen that the AC’s are significantly positive and

that AC(k) dies off geometrically with increasing lags k, it is a sign that the series

obeys a low-order autoregressive (AR) process. In addition, since the partial

autocorrelation (PAC) is significantly positive at lag 1 and close to zero thereafter,

the pattern of autocorrelation can be captured by an auto regression of order one,

that is, AR(1).

Figure 4.1: Statistical description of GDP Figure 4.2: Statistical description of FDI

Figure 4.3: Statistical description of EXRATE Figure 4.4: Statistical description of NFRATE

0

1

2

3

4

5

5 6 7 8 9 10 11

Series: IN_FDI

Sample 1981 2007

Observations 25

Mean 8.348624

Median 8.124743

Maximum 10.89751

Minimum 4.922168

Std. Dev. 1.672277

Skewness -0.137877

Kurtosis 2.113908

Jarque-Bera 0.897082

Probability 0.638559

0

1

2

3

4

5

11 12 13 14 15 16 17

Series: IN_GDP

Sample 1981 2007

Observations 27

Mean 13.66757

Median 13.71000

Maximum 16.96314

Minimum 10.77100

Std. Dev. 2.059819

Skewness -0.044467

Kurtosis 1.598013



0

2

4

6

8

10

0 1 2 3 4 5

Series: IN_EXRATE

Sample 1981 2007

Observations 27

Mean 2.695052

Median 3.085852

Maximum 4.894104

Minimum -0.494296

Std. Dev. 1.898441

Skewness -0.386694

Kurtosis 1.877955



0

1

2

3

4

5

1.5 2.0 2.5 3.0 3.5 4.0 4.5

Series: IN_INFRATE

Sample 1981 2007

Observat ions 27

Mean 2.773496

Median 2.639057

Maximum 4.287716

Minimum 1.686399

Std. Dev. 0.811959

Skewness 0.328700

Kurtosis 1.842134



128

Table 4.11: Summary of Descriptive Statistics

IN_GDP IN_FDI IN_EXRATE IN_INFRATE Mean 13.66757 8.348624 2.695052 2.773496 Median 13.71000 8.124743 3.085852 2.639057 Maximum 16.96314 10.89751 4.894104 4.287716 Minimum 10.77100 4.922168 -0.494296 1.686399 Std. Dev. 2.059819 1.672277 1.898441 0.811959 Skewness -0.044467 -0.137877 -0.386694 0.328700 Kurtosis 1.598013 2.113908 1.877955 1.842134

Jarque-Bera 2.220161 0.897082 2.089251 1.994433 Probability 0.329532 0.638559 0.351824 0.368905

Observations 27 25 27 27 Source: Authors’ calculations based on domestic authorities’ data.

4.3 TEST FOR COINTEGRATION WITH JOHANSEN

COINTEGRATION TEST

Having established that the various series are integrated of the first order, the

second step in testing the relationship between FDI, GDP, EXRATE and INFRATE

is to test for the cointegration relationship between the variables, in order to

determine if there is a long-run relationship between the two variables. The test for

the long-run relationship between both variables was done using Johansen

cointegration test.

Table 4.12: Result of Johansen cointegration test

Date: 05/11/09 Time: 07:57 Sample(adjusted): 1984 2007 Included observations: 19 Excluded observations: 5 after adjusting endpoints Trend assumption: Linear deterministic trend Series: IN_GDP IN_FDI IN_EXRATE IN_INFRATE Lags interval (in first differences): 1 to 2

Unrestricted Cointegration Rank Test

Hypothesized Trace 5 Percent 1 Percent No. of CE(s) Eigenvalue Statistic Critical Value Critical Value

None ** 0.988106 124.4197 47.21 54.46

At most 1 ** 0.865360 40.21760 29.68 35.65 At most 2 0.081476 2.119745 15.41 20.04 At most 3 0.026228 0.504985 3.76 6.65

*(**) denotes rejection of the hypothesis at the 5%(1%) level

129

Trace test indicates 2 cointegrating equation(s) at both 5% and 1% levels

Hypothesized Max-Eigen 5 Percent 1 Percent No. of CE(s) Eigenvalue Statistic Critical Value Critical Value

None ** 0.988106 84.20215 27.07 32.24

At most 1 ** 0.865360 38.09786 20.97 25.52 At most 2 0.081476 1.614761 14.07 18.63 At most 3 0.026228 0.504985 3.76 6.65

*(**) denotes rejection of the hypothesis at the 5%(1%) level Max-eigenvalue test indicates 2 cointegrating equation(s) at both 5% and 1% levels

Unrestricted Cointegrating Coefficients (normalized by b'*S11*b=I):

IN_GDP IN_FDI IN_EXRATE IN_INFRATE 5.320715 -2.014583 -4.296426 1.985489 -2.427806 0.284648 1.942140 -3.348116 1.114231 -2.497838 0.232472 3.343278 2.767251 -2.167853 -0.911008 2.765554

Unrestricted Adjustment Coefficients (alpha):

D(IN_GDP) -0.146835 -0.048866 -0.012257 -0.007700 D(IN_FDI) -0.533662 0.552181 0.007598 0.025069

D(IN_EXRATE) 0.102663 -0.048821 0.045735 -0.026874 D(IN_INFRATE) -0.252865 0.121526 0.021275 0.045121

1 Cointegrating Equation(s): Log likelihood 29.91075

Normalized cointegrating coefficients (std.err. in parentheses) IN_GDP IN_FDI IN_EXRATE IN_INFRATE 1.000000 -0.378630 -0.807490 0.373162

(0.01629) (0.01078) (0.02478)

Adjustment coefficients (std.err. in parentheses) D(IN_GDP) -0.781267

(0.14969) D(IN_FDI) -2.839464

(1.09394) D(IN_EXRATE) 0.546242

(0.42004) D(IN_INFRATE) -1.345422

(0.56369)


Normalized cointegrating coefficients (std.err. in parentheses) IN_GDP IN_FDI IN_EXRATE IN_INFRATE 1.000000 0.000000 -0.796578 1.830272

(0.05173) (0.22433) 0.000000 1.000000 0.028822 3.848374

(0.13762) (0.59676)

Adjustment coefficients (std.err. in parentheses)

130

D(IN_GDP) -0.662629 0.281902 (0.13415) (0.04667)

D(IN_FDI) -4.180053 1.232284 (0.53580) (0.18640)

D(IN_EXRATE) 0.664769 -0.220720 (0.45178) (0.15717)

D(IN_INFRATE) -1.640464 0.544009 (0.57251) (0.19917)


Normalized cointegrating coefficients (std.err. in parentheses) IN_GDP IN_FDI IN_EXRATE IN_INFRATE 1.000000 0.000000 0.000000 9.125249

(2.08564) 0.000000 1.000000 0.000000 3.584425

(0.50074) 0.000000 0.000000 1.000000 9.157898

(2.51938)

Adjustment coefficients (std.err. in parentheses) D(IN_GDP) -0.676287 0.312519 0.533111

(0.13438) (0.07272) (0.10655) D(IN_FDI) -4.171586 1.213304 3.367019

(0.54523) (0.29503) (0.43232) D(IN_EXRATE) 0.715728 -0.334958 -0.525270

(0.45087) (0.24397) (0.35750) D(IN_INFRATE) -1.616758 0.490867 1.327382

(0.58128) (0.31454) (0.46090)

Table 4.12 reports the cointegration test results. It can be seen from the test results

in the table that there are two cointegrating equations at both 1% and 5%

significance level. This implies a long run relationship among the variables. That

is, there is a long-run steady-state relationship between FDI, GDP, EXRATE and

INFRATE for Nigeria. Once we have established a cointegration relationship

between the variables, then we may conclude that there exists a long-run

relationship between them, even if they are individually non-stationary. If the trace

statistics or the Likelihood ratio is greater than the critical value, then there is a

cointegration.

4.4 GRANGER TEST

From the granger causality test as applied in this work, the following results were

obtained.

131

Table 4.13 : Granger Causality test

Pairwise Granger Causality Tests Date: 05/11/09 Time: 07:57 Sample: 1981 2007 Lags: 3 Null Hypothesis: Obs F-Statistic Probability IN_FDI does not Granger Cause IN_GDP 19 2.87981 0.08004 IN_GDP does not Granger Cause IN_FDI 0.94361 0.45015 IN_EXRATE does not Granger Cause IN_GDP 24 2.89669 0.06539 IN_GDP does not Granger Cause IN_EXRATE 1.22121 0.33243 IN_INFRATE does not Granger Cause IN_GDP 24 1.02492 0.40631 IN_GDP does not Granger Cause IN_INFRATE 0.88101 0.47055 IN_EXRATE does not Granger Cause IN_FDI 19 2.05260 0.16021 IN_FDI does not Granger Cause IN_EXRATE 0.70506 0.56713 IN_INFRATE does not Granger Cause IN_FDI 19 0.71594 0.56121 IN_FDI does not Granger Cause IN_INFRATE 0.09807 0.95958 IN_INFRATE does not Granger Cause IN_EXRATE 24 1.83817 0.17851 IN_EXRATE does not Granger Cause IN_INFRATE 2.91176 0.06451

4.5 TEST OF RESEARCH HYPOTHESES

Hypothesis 1

To test hypothesis One, we restate it in null and alternate forms as -

Ho: Growth in Foreign Direct Investment is not a major determinant of

economic growth in Nigeria.

HA: Growth in Foreign Direct Investment is a major determinant of economic

growth in Nigeria.

Results

From our findings, we were able to ascertain that Foreign Direct Investment inflow

into Nigeria for the period under review is a major determinant of economic

growth in the country.

Table 4.14: OLS Regression

Dependent Variable: GDP Method: Least Squares Date: 05/11/09 Time: 19:28 Sample: 1981 2007 Included observations: 27

132

Variable Coefficient Std. Error t-Statistic Prob.

C 898800.1 1002023. 0.896986 0.3783 FDI 258.1693 51.68672 4.994887 0.0000

R-squared 0.499488 Mean dependent var 3825390. Adjusted R-squared 0.479468 S.D. dependent var 5854329. S.E. of regression 4223775. Akaike info criterion 33.42154 Sum squared resid 4.46E+14 Schwarz criterion 33.51753 Log likelihood -449.1908 F-statistic 24.94890 Durbin-Watson stat 1.066784 Prob(F-statistic) 0.000038

Estimation Command: ===================== LS GDP C FDI Estimation Equation: ===================== GDP = C(1) + C(2)*FDI Substituted Coefficients: ===================== GDP = 898800.0969 + 258.1693386*FDI

Looking at table 4.14 below, we can see that the probability value 0.0000 is lower

than 0.5 which suggest the rejection of the null hypothesis for a two tailed test at

5% significance level. It can also be seen that the calculated t-value of 4.994 for

FDI is equally significant at the 5% level of significance. By this, the null

hypothesis that growth in Foreign Direct Investment is not a major determinant of

economic growth in Nigeria is rejected, thereby accepting the alternate hypothesis

that the growth in Foreign Direct Investment is a major determinant of economic

growth in Nigeria. This implies that it is Foreign Direct Investment that drives

economic growth in Nigeria, showing that economic growth which has been

experienced in Nigeria for the period under review has a lot to do with the inflow

of Foreign Direct Investment into the country.

Hypothesis 2

To test hypothesis Two, we restate it in null and alternate forms as -

Ho: There is no long-run relationship between FDI and economic growth in

Nigeria.

133

HA: There is a long-run relationship between FDI and economic growth in

Nigeria.

Results

In checking for long-run relationship between the said variables, that is GDP, FDI,

EXRATE and INFRATE, the Johansen cointegration test was employed in our

modified model. From table 4.12 above, the trace statistics which tests the null

hypothesis of cointegrating relations against the alternative hypothesis (124.4197

and 40.2176 at none and at most 1 respectively) is greater than the critical value of

47.21/54.46 and 29.68/35.65 at 5% and 1% levels respectively. This denotes the

rejection of the null hypothesis at 5% and 1% level of significance, showing that

there is a cointegrating relationship between the variables GDP, FDI, EXRATR and

INFRATE. This indicates that there is a long-run relationship between GDP which

was used as a proxy for economic growth and other variables. The result also

shows that despite being individually non-stationary, linear combinations of the

variables are cointegrated. From these findings, we reject the null hypothesis

which states that there is no long-run relationship between FDI and economic

growth in Nigeria and therefore accept the alternate hypothesis that there is a

long-run relationship between FDI and economic growth in Nigeria.

Hypothesis 3

To test hypothesis Three, we restate it in null and alternate forms as -

Ho: There is bi-directional relationship between FDI and economic growth in

Nigeria.

HA: There is a unidirectional relationship between FDI and economic growth in

Nigeria

Results

From our Granger causality test for short-term relationship between Foreign

Direct Investment and economic growth in Nigeria, we found that GDP granger

causes FDI, this implies that it is the growth in Foreign Direct Investment that

causes economic growth in Nigeria throughout the period under review. Let’s take

a look at the table below:

134

Table 4.15: Causality between the Variables

Pairwise Granger Causality Tests Date: 05/11/09 Time: 07:57 Sample: 1981 2007 Lags: 3

Null Hypothesis: Obs F-Statistic Probability Causality

IN_FDI does not Granger Cause IN_GDP 19 2.87981 0.04004 Yes IN_GDP does not Granger Cause IN_FDI 0.94361 0.45015 No

IN_EXRATE does not Granger Cause IN_GDP 24 2.89669 0.06539 Yes IN_GDP does not Granger Cause IN_EXRATE 1.22121 0.33243 No

IN_INFRATE does not Granger Cause IN_GDP 24 1.02492 0.40631 No IN_GDP does not Granger Cause IN_INFRATE 0.88101 0.47055 No

IN_EXRATE does not Granger Cause IN_FDI 19 2.05260 0.16021 Yes IN_FDI does not Granger Cause IN_EXRATE 0.70506 0.56713 No

IN_INFRATE does not Granger Cause IN_FDI 19 0.71594 0.56121 No IN_FDI does not Granger Cause IN_INFRATE 0.09807 0.95958 No

IN_INFRATE does not Granger Cause IN_EXRATE 24 1.83817 0.17851 No IN_EXRATE does not Granger Cause IN_INFRATE 2.91176 0.06451 Yes

From table 4.15 above, the F-statistic and the probability values indicate if the null

hypothesis should be accepted or rejected. In the second row where we have the

null hypothesis IN_FDI does not Granger cause IN_GDP, we have the F-statistic

as 2.87981 with a probability value of 0.04004 which indicates a causality. On the

other hand, the null hypothesis that IN_GDP does not Granger IN_FDI has

0.94361 as the F-statistic with a probability value of 0.45015 indicating that there

is no causality. From the above observation, the null hypothesis that FDI does not

Granger cause GDP is rejected. This shows that the null hypothesis that there is a

bi-directional relationship between FDI and economic growth in Nigeria is rejected

thereby accepting the alternate hypothesis that there is a unidirectional

relationship between FDI and economic growth in Nigeria. The result show that

there is a causality between Foreign Direct Investment and economic growth in

Nigeria for the period under review and the causality runs for FDI to GDP and not

from GDP to FDI indicating a unidirectional relationship. There is also causality

between EXRATE/GDP, EXRATE/FDI and EXRATE/INFRATE. The findings also

revealed that there is no causality relationship between GDP/EXRATE,

INFRATE/GDP, GDP/ INFRATE, FDI/EXRATE, INFRATE/FDI, FDI/INFRATE

and EXRATE/INFRATE.

135


Schwarz Information Criterion (SIC): This is a criterion for model selection

among a class of parametric models with different numbers of parameters.

Akaike Information Criteria (AIC): It is a measure of the goodness of fit of an

estimated statistical model.

IN_GDP: Natural logarithm of Gross Domestic Product

IN_FDI: Natural logarithm of Foreign Direct Investment

IN_INFRATE: Natural logarithm of Inflation Rate

IN_EXRATE: Natural logarithm of Exchange Rate

136

REFERENCES

Granger, C. W. .J. (1969) “Investigating Causal Relations by Econometric Models

and Cross-Spectral Methods,” Econometrica, 37, 424–438.

Gujarati, D. (2007), Basic Econometrics. 4th Edition, McGraw-Hill, New York pp.

825.

137

CHAPTER FIVE

SUMMARY OF FINDINGS, CONCLUSION, AND RECOMMENDATIONS

This chapter summarizes the various research findings that were made from the

study and supports them with the objectives of this research. Conclusions and

recommendations were therefore made based on these findings.

5.1 SUMMARY OF RESEARCH FINDINGS

The summary of our findings are as follows:

i. The analysis showed that the inflow of Foreign Direct Investment is a major

determinant of economic growth and development in Nigeria for the period

under review.

ii The study revealed that the variables (GDP, FDI, EXRATE and INFRATE)

that were used for the study were cointegrated and have a stable

relationship in the long-run. The presence of cointegration between GDP,

FDI, EXRATE and INFRATE based on Johansen cointegration test, allowed

the use of Granger Causality test to determine the causal direction between

the variables.

iii. From the result of the Granger causality test, it was ascertained that the

causality runs form FDI to GDP and not from GDP to FDI. The positive

relationship implies that Foreign Direct Investment stimulates economic

growth in Nigeria. The result can be put forward as a guide for policy

makers to take the advantage of Foreign Direct Investment spillover effects.

The positive relationship also indicates that Foreign Direct Investment has

really contributed to the growth of the Nigerian economy for the period

under review.

iv. Strong evidence emerging from this study shows that economic growth as

measured by GDP in Nigeria is Granger caused by FDI, which shows that

138

Nigeria’s capacity to progress on economic development will depend on the

country’s performance in attracting Foreign Direct Investment. This study

supports the impact of FDI on GDP growth in Nigeria. These findings

confirm the relevance of the economic reform programmes in Nigeria to

reduce macro-economic instability, remove economic distortions, promote

exports and restore sustainable domestic investment for economic growth.

v. The study also showed that there is no significant positive spillover from

Foreign Direct Investment and exchange rate (FDI - EXRATE) and Foreign

Direct Investment and inflation rate (FDI - INFRATE). This implies that

they do not have a direct effect on each other, no causality exist between

them.

v. Finally from the findings of this study, the conservative views that the

direction of causality runs from FDI to economic growth was confirmed in

the case of Nigeria. This supports the validity of policy guidelines which

stipulates the importance of Foreign Direct Investment for the growth and

stability of developing countries under the assumption of FDI led growth.

5.2 POLICY IMPLICATION OF THE FINDINGS

This result confirms previous evidence obtained by a number of writers for other

countries, and is in accordance with the endogenous growth hypothesis. The same

results also confirm the effect of their high GDP growth experienced during most

of the period studied on the pace of FDI flow into these countries. In contrast to

other developing countries, Nigeria has abundant resources and domestic

investment that could finance their development. However, influx of FDI has great

potential to yield higher growth through higher efficiency in physical and human

capital and through positive externalities such as facilitating transition and

diffusing technology as well as introduction of alternative management practices,

organizational arrangement, and improved entrepreneurial skills. Nevertheless,

FDI externalities may have trivial effects if the links with local business were weak.

Thus, policies should be adopted to strengthen the relationship between FDI and

domestic investments and such relationship has to be complementary rather than

competitive.

139

It is also important to adopt policy measures to deepen the domestic capital

markets by increasing savings and developing a strong domestic institutional

investor base and strengthening the prudential supervision of financial markets.

Privatization is being used with great success in many developing countries, as a

vehicle to deepen capital markets and encourage Foreign Direct Investment. While

most countries in Africa started the process of privatizing state-owned enterprises

and opening up private investment opportunity in telecommunications, air-lines,

tourism, and some industries such as petrochemicals, cement, and utilities, more

effort should be put to expedite the process toward decreasing the role of the

government in the market and providing better incentives and institutional

requirements for private investment.

Empirical studies suggest that capital inflows are more beneficial and creates less

problem if they are long-term, and in the form of direct investment, induced by

growth prospects of the economy, invested in physical assets than consumed and

domestically induced. As opposed to short-term portfolio investment, long-term

FDI has positive spill over effect on the economy (Baharumshah et al, 2006).

Short-term investment and portfolio investments are often associated with

increase in consumption and cause fragility in the financial systems.

Thus, it is important for the country to improve the quality of FDI that it can

attract. The country should also be selective in attracting FDI. Theory also suggests

that uncertain capital flows and a more volatile profile of FDI inflows are growth

retarding. Accordingly, a key policy option is to maintain a steady stream of foreign

capital flows and to minimize the fluctuations in these inflows (Lensink and

Morrissey, 2001).

The new wave of globalization sweeping through the world has intensified the

competition for FDI among developing countries. Thus, concentrated efforts are

needed at both national and regional level in order to attract significant FDI flows

to the country and improve prospects for sustained growth and development.

Nigeria and other African countries should work together to design and formulate

adequate policies to attract stable investment flows. They must take policy

140

measures that would substantially enlarge and diversify their economic base,

policies that would improve local skills and build up a stock of human capital

recourses capabilities, enhance economic stability and liberalize their market in

order to benefit from long-term FDI inflows.

The recent pattern of FDI flows to some countries, Nigeria inclusive has been

toward the oil sector. Attracting FDI to the extractive sector, that is, oil sector,

proved not to be growth enhancing as much as other productive sectors (Akinlo,

2004). Oil sector is often an enclave sector with little backward and inward

linkages with other sectors. The country could benefit from increased FDI into the

oil sector if the sector is liberalized and integrated into the economy.

Growth enhancing policies coupled with sound macroeconomic policies foster a

healthy rate of returns to investment and hence attract FDI. To maximize the

benefit of FDI Nigerian leaders should establish investment agencies, improve the

local regulatory environment, develop the local financial market, and enhance

transparency in macroeconomic policies. A sound and transparent legal system

governing financial transaction should be put in place. A central body or

institution should be established to promote and market investment opportunity

and attract genuine FDI.

Finally, these findings may provide useful information for the formulation of a

general strategy that consider Nigeria and other African countries as block when

negotiating business deals and attract Foreign Direct Investment. It is very difficult

for a small country, with limited domestic market to establish a viable capital

market and attract large-scale investment. Accordingly, monetary cooperation is

required and regional capital market should be supported and investment

opportunity should be promoted at the country and regional level.

5.3 MAJOR CONTRIBUTION OF THE OUTCOMES OF THE STUDY

TO KNOWLEDGE

From the result of this study, one can easily have a better view of the impact of

Foreign Direct Investment in Nigeria’s economic growth and development. From

141

our result, one can clearly conclude that what drives the growth of Nigerian

economy is partly the inflow of Foreign Direct Investment especially for the period

under review.

The study did not just look at the bivariate relationship between Foreign Direct

Investment and economic growth, it analysed the multivariate aspect of FDI led

growth by incorporating exchange rate and inflation variable as a means of

checking their effect on the economic growth and development in Nigeria.

The study provided proof that for a country like ours to really attain a level of

growth that is needed for its development, it is pertinent that the leaders will do all

it takes to improve the investment environment in the country to pave way for

smooth inflow of investment into the country.

This study has also provided new study evidence on the analysis of FDI and growth

in Nigeria through a model which was used by other authors but in a modified

form. The inclusion of foreign exchange and inflation rate in the model helped us

to ascertain their influence in determining the extent of growth and Foreign Direct

Investment in Nigeria.

5.4 CONCLUSION

As one of the empirical studies on the analysis of Foreign Direct Investment and

economic growth in Nigeria, this study has made an attempt to understanding the

relationship and interaction between them. The proxy for economic growth used in

this study was gross domestic product. It focused on the period 1981 to 2007 and

used time series data obtained from the CBN, World Bank and Federal Office of

Statistics. Some statistical methods; Ordinary Least Squares, Unit root,

Cointegration and the Granger causality test were used to test for correlation and

direction of causality. The result arising from this study shows that there is a long

run relationship between the variable and that the direction of flow is from FDI to

growth, which implies that the growth which has been experienced in the country

for the past years has been partly due to the inflow of Foreign Direct Investment

into the country.

142

Undoubtedly, the findings of this report go a long way in bridging the existing

information gap and also enabling policy makers to plan and formulate both short

and long term policies from an informed perspective. For a third world and a

country like Nigeria, attracting FDI is of paramount importance if the country

needs to grow, given its positive benefits. However, countries ought to be aware of

the risks such as destabilisation of exchange rates and other macroeconomic

fundamentals associated with accumulating too much Foreign Direct Investment

beyond their absorptive capacity.

The Nigerian economy was reformed and became more outward looking with the

structural adjustment program launched in the 1980’s. The main objectives of this

program can be summarized as: i) minimizing state intervention; ii) establishing a

free market economy iii) integrating the economy with the global economic

system. This liberalization process through liberalized import regime, new foreign

investment and export promotion policies have enabled Nigeria to take its place in

the global economy.

5.5 RECOMMENDATIONS

Based on the foregoing findings and conclusions emanating from this study, the

following recommendations were made:

There is a need for domestic actions which involve actions to be taken by policy

makers in the country. These include image building (re-branding Nigeria),

domestic regulatory reforms, and marketing of investment opportunities.

Image building: Improving the currently bad image of the country is the key to

reversing the dismal FDI trend of the country and Africa at large. This requires an

increase in Political stability, Macroeconomic stability and the protection of

property rights as well as the rule of law.

Supporting existing investors: Improving the investment climate for existing

domestic and foreign investors through infrastructure development; provision of

143

services and changes in the regulatory framework by relaxing laws on profit

repatriation etc, will encourage them to increase their investments and also attract

new investors. In the case of domestic investors, an improvement in the

investment climate will also encourage them to keep their wealth in the region and

reduce capital flight.

Marketing investment opportunities: Creating awareness of investment

opportunities through the use of existing investors and information

communication technologies such as the internet. Experience has shown that over-

reliance on IPAs for investment promotion has not been very effective in the

African region, so there is the need for a shift of emphasis from IPAs to existing

investors. This is also relevant because studies have shown that existing investors

play a very important role in attracting new investors to new investment locations.

For example, in a recent study of foreign direct investor perceptions conducted by

the United Nations Industrial Development Organisation (UNIDO) in four African

countries, namely Ethiopia, Uganda, Nigeria, and Tanzania, existing investors

were found to be responsible for roughly 50% of foreign investor awareness of

domestic investment opportunities (UNIDO, 2002). There is also the need for the

country to adopt a more targeted investment promotion strategy. In other words,

she should identify sectors where they have comparative and competitive

advantages and then promote FDI into those sectors. This would make investment

promotion less costly and more effective.

Diversification of the economy: Several African countries rely on the export of

a few primary commodities for foreign exchange earnings. This exposes them to

significant terms of trade shocks. Diversification of the economy will enable them

to cushion the effects of these shocks and reduce country risk. The reduction in

country risk will increase the attractiveness of the economy to FDI in the

secondary and tertiary sectors.

Trade liberalization: Openness to trade will signal commitment to outward-

looking, market-oriented policies and enhance trading opportunities thereby

attracting foreign investors intent on taking advantage of the new trading

opportunities.

144

Privatization: The privatization of inefficient state-owned enterprises will boost

foreign investment. African countries and Nigeria in particular have now

recognized that the privatization of public corporations is necessary to reduce

government fiscal deficits and several countries have instituted privatization

programmes. However, progress in the privatization of enterprises has been slow

in several countries because of domestic political pressure by powerful interest

groups that are against the process.

Also to spread and sustain growth in Nigeria, the evidence here points to three key

objectives: avoiding collapses in growth, accelerating productivity growth, and

increasing private investment. This can be accomplished by increasing the number

and variety of firms and farms that can compete in the global economy. This

implies pushing for more exports, increasing connectivity to regional and global

markets through deeper regional integration. These in turn require adopting the

four sets of policies proposed in Challenges of African Growth (Ndulu et al, 2007),

published by the World Bank’s Africa Region. These include:

Improving the investment climate: This requires reducing indirect costs to

firms, with energy and transportation topping the list of major impediments. It

also requires reducing and mitigating risks, particularly those relating to crime,

property security, political instability, and macroeconomic instability. Although

individual countries are the focal point of action, their efforts could be pooled to

coordinate policy, promote investment, improve security, and increase

connectivity.

Improving infrastructure: This is essential to reducing the transaction costs in

producing goods and services. Transportation and energy make up the largest part

of indirect costs for businesses, weighing heavily on the competitiveness of firms in

most African countries. The focus would be on reducing the high costs associated

with the remoteness of landlocked countries to facilitate their trade with

neighbours and the rest of the world. Again, there will be a clear need to look

beyond country borders and adopt a regional approach to coordinating cross

border infrastructure investment, maintenance, management, and use to lower

costs.

145

Spurring innovation: This will require investment in information technology

and skill formation (higher education) to enhance productivity and

competitiveness. The potential comparative advantage of low wages in Africa is too

often nullified by low productivity. Surveys of investors show that labour is not

cheap where productivity is low. Information and communication technologies can

be the main driver of productivity growth. And there is strong empirical evidence

showing that investment in information and communication technologies and in

higher education boosts competitiveness, making both key parts of the growth

agenda. African countries can make a huge leap forward over antiquated

technology by exploiting the technological advantages of information and

communication technologies as late starters.

Building institutional capacity: The World Bank’s Investment Climate

Assessment surveys and analysis for World Development Report 2005 (World

Bank, 2004) spotlight costs associated with contract enforcement difficulties,

crime, corruption, and regulation as among those weighing most heavily on the

profitability of enterprises. The main focus here would be to strengthen the

capacity of relevant public institutions for protecting property rights and the

scrutiny of, and accountability for, public action. Action on these four fronts can

accelerate growth in Africa and Nigeria in particular and help countries break out

of the boom-bust-stagnate cycles. The patterns described in this essay provide a

guide for public policy, not a formula for success. Each country faces its own

challenges and opportunities, and each country has to work within its own

historical and geographical resources and constraints. Sustained faster growth in

Africa is possible, if Africa’s economies can meet the challenges of avoiding growth

collapses, raising productivity, and boosting private investment.

Nigerian leaders should make sure that the principles enshrined in the New

Partnership for Africa’s Development (NEPAD) documents are taken seriously and

implemented in the country, because there is the distinct possibility that this may

change the quality of economic policy-making in the country and improve the

investment climate.

146

In addition to natural resources, we believe that the sectors that present the best

long-term opportunities for foreign investment in the region are utilities and

infrastructure. At the moment, the public sector provides most of these services,

but there is growing recognition of the fact that they can be better and more

efficiently provided by the private sector. If the current wave of privatization

continues unabated, there will be an increase in the number of public utilities

marked for privatization in the country, as can be seen in the communication

sector.

FDI can play an important role in Nigerian’s economic growth and development as

can be deduced from our study. From statistics and our research, we know that the

country has been attracting a reasonable amount of FDI inflow into the country

but not very significant when compared with other countries in the developed

world. This has been largely due to the combined effects of political and

macroeconomic instability, weak infrastructure, poor governance, inhospitable

regulatory environments, intensification of competition for FDI flows due to

globalization, and poor marketing strategies. There is therefore the need to make

the country very attractive for investment. They require a new and more effective

approach to investment promotion. An enabling environment has to be created

first before marketing investment opportunities to foreign entrepreneurs could be

done effectively. The maintenance of a sustained political and macroeconomic

policy environment would place the country in a better position to attaining this

objective.

A robust and efficient mechanism of monitoring and recording Foreign Direct

Investment flows should be established. This will enable policy makers, academics

and stakeholders make accurate decisions, forecasts and also undertake studies.

Furthermore, the realization of Nigeria’s FDI potentials will also depend on the

ability of her leaders to improve the FDI climate and take advantage of the new

global interest in the affairs of the country by implementing sound macroeconomic

policies, enforcing the rule of law, reducing risks of policy reversals, and improving

the provision of infrastructure.

147

There is need to consciously improve the business environment to enable

manufacturing to contribute positively to growth. One way to improve the business

environment is by conscious provision of necessary infrastructure, which will

lower the costs of doing business in Nigeria. There is a need for the improvement

of power supply by upgrading to a higher mega watt. We also think that further

liberalization of the power sector should be done by encouraging independent

power supply providers. These should be encouraged to complement the efforts of

the Power Holding Company, whose inability is apparent in constant power

failures and attendant high costs of providing electricity, and therefore affecting

business profitability in the country, which we believe is one of the major aims of

investing in businesses. This will go long way in enabling the inflow of FDI in the

manufacturing sector to contribute significantly to economic growth.

The leaders should improve on its effort to curb corruption which they are already

doing with the help of agencies established to fight corruption such as the

Economic and Financial Crimes Commission (EFCC) and Independent Corrupt

Practices Commission (ICPC). These agencies should be seen to do their job to

convince both foreigners and nationals that Nigeria is a safe place to invest in.

Greater policy sensitivity towards the openness of the economy is needed so that

the traded commodities will be beneficial to the economy as a whole. There is need

for guided training and integration of the human resources of the country to

enable them to contribute positively to economic growth wherever they find

themselves employed either with foreign or with indigenous firms and whichever

sector they are in. The need for training high quality personnel in the country

cannot be overemphasized.

148

REFERENCES

Akinlo, A. (2004), "Foreign Direct Investment and growth in Nigeria: An empirical

investigation," Journal of Policy Modelling 26, 627–639

Bahraumahah, A.Z and M.A. Thanoon (2006), "Foreign Capital flow and economic

growth in East Asian Countries" China Economic Review,17 (2006) 70-83.

Lensink, R., and O. Morrissey (2001), “Foreign Direct Investment: Flows,

Volatility and Growth in Developing Countries.” University of Nottingham,

CREDIT Research Paper.

Ndulu, B.J., L. Chakraborti, L. Lijane, V. Ramachandran, and J. Wolgin. (2007),

Challenges of African Growth: Opportunities, Constraints, and Strategic

Directions. Washington, D.C.: World Bank.

UNIDO (2002), Foreign Direct Investor Perceptions in Sub-Saharan Africa.

Vienna: United Nations Industrial Development Organisation.

World Bank. (2004), World Development Report 2005: A Better Investment

Climate for Everyone. Washington, D.C.

149

APPENDICES APPENDIX I GRAPHICAL REPRESENTATION OF FDI AND GDP DATA

Net Flow of FDI and GDP at Current Price from 1981 to 2007

Net Flow of FDI in Nigeria from 1981 to 2007

-10000

0

10000

20000

30000

40000

50000

60000

0

5000000

10000000

15000000

20000000

25000000

1 2 3 4 5 6 7 8 9 101112131415161718192021222324252627

FD

I

GD

P

YEAR

Year CGDP NFDI

-10000

0

10000

20000

30000

40000

50000

60000

19

81

19

82

19

83

19

84

19

85

19

86

19

87

19

88

19

89

19

90

19

91

19

92

19

93

19

94

19

95

19

96

19

97

19

98

19

99

20

00

20

01

20

02

20

03

20

04

20

05

20

06

20

07

Ne

t F

low

of

FD

I in

mil

lio

ns

Year

Net Flow of FDI

150

GDP trend in Nigeria from 1981 to 2007

0

5,000,000

10,000,000

15,000,000

20,000,000

25,000,000

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27

GD

P

YEAR

Year

151

VARIOUS DATA GENERATED FOR THE STATISTICAL ANALYSIS

S/N Year Net Flow of FDI

GDP Current

Price Exchange Rate Inflation Rate

1 1981 137.3 47,619.70 0.61 20.9

2 1982 1,624.90 49,069.30 0.6729 7.7

3 1983 556.7 53,107.40 0.7241 23.2

4 1984 534.8 59,622.50 0.7649 39.6

5 1985 329.7 67,908.60 0.8938 5.5

6 1986 2,499.60 69,147.00 2.0206 5.4

7 1987 680 105,222.90 4.0179 10.2

8 1988 1,345.60 139,085.00 4.5367 38.3

9 1989 -439.4 216,707.50 7.3916 40.9

10 1990 -464.30 267,550.00 8.0378 7.5

11 1991 1,808.00 312,139.80 9.9095 13.0

12 1992 8,269.20 532,613.80 17.2984 44.5

13 1993 32,994.40 683,869.20 22.0511 57.2

14 1994 3,907.20 899,863.20 21.8861 57.0

15 1995 48,677.00 1,933,211.60 21.8861 72.8

16 1996 2,731.00 2,702,719.10 21.8861 29.3

17 1997 5,730.90 2,801,972.60 21.8861 8.5

18 1998 24,078.80 2,708,430.90 21.8861 10.0

19 1999 1,779.10 3,194,023.60 92.6934 6.6

20 2000 3,347.00 4,537,637.20 102.1952 6.9

21 2001 3,377.00 4,685,912.20 111.9433 18.9

22 2002 8,205.50 5,403,006.80 120.9702 12.9

23 2003 13,056.50 6,947,819.90 129.3565 14.0

24 2004 19,909.10 11,411,066.90 133.5004 15.0

25 2005 25,881.80 14,610,881.50 132.147 17.9

26 2006 41,470.80 15,564,594.70 128.6516 8.2

27 2007 54,041.90 23,280,715.00 125.8331 5.4

Source: Central Bank of Nigeria Statistical Bulletin

152

STATISTICAL DATA IN THEIR LOG FORM

S/N Year IN_GDP IN_FDI IN_INFRATE IN_EXRATE

1 1981 10.7710018201 4.92216831277 3.03974915897 -0.494296321815

2 1982 10.8009888636 7.39320155441 2.04122032886 -0.396158548788

3 1983 10.8800715572 6.32202649519 3.14415227867 -0.322825774587

4 1984 10.9957882986 6.28189284523 3.67882911826 -0.268010172654

5 1985 11.1259179624 5.79818315008 1.70474809224 -0.112273242483

6 1986 11.1439899522 7.82388599805 1.68639895357 0.703394497011

7 1987 11.5638362362 6.52209279817 2.32238772029 1.39075937808

8 1988 11.842840536 7.20459528922 3.64544989619 1.51219987551

9 1989 12.286303797 NA 3.71113006305 2.00034422032

10 1990 12.4970617437 NA 2.01490302054 2.08415541391

11 1991 12.6512064434 7.49997654095 2.56494935746 2.29349389298

12 1992 13.1855518627 9.02029304815 3.79548918917 2.85061401168

13 1993 13.4355219502 10.4040931291 4.04655389839 3.09336248727

14 1994 13.7099980308 8.27057628392 4.04305126783 3.08585173212

15 1995 14.4746932193 10.7929619183 4.2877159552 3.08585173212

16 1996 14.8097688983 7.91242312147 3.37758751602 3.08585173212

17 1997 14.8458342271 8.65362786544 2.1400661635 3.08585173212

18 1998 14.8118800215 10.0890870643 2.30258509299 3.08585173212

19 1999 14.9767919966 7.48386289744 1.88706964903 4.52929727263

20 2000 15.327916994 8.11581970121 1.9315214116 4.62688470993

21 2001 15.3600711612 8.12474302039 2.93916192207 4.71799249311

22 2002 15.5024661714 9.01255994012 2.55722731137 4.79554423427

23 2003 15.7539384849 9.47704137304 2.63905732962 4.86257215863

24 2004 16.2500942232 9.89893219262 2.7080502011 4.89410447409

25 2005 16.4972771173 10.161295298 2.88480071285 4.88391493932

26 2006 16.5605093223 10.6327448441 2.10413415427 4.85710797549

27 2007 16.9631358934 10.8975149506 1.68639895357 4.83495642571

Source: Authors’ calculations based on data generated.

153

APPENDIX II

Unit Root Test for in_GDP (in Level) with (2) Lag

Without Intercept and Trend

ADF Test Statistic 3.270008 1% Critical Value* -2.6649

5% Critical Value -1.9559


*MacKinnon critical values for rejection of hypothesis of a unit root.

Augmented Dickey-Fuller Test Equation

Dependent Variable: D(IN_GDP)

Method: Least Squares

Date: 05/18/09 Time: 10:27

Sample(adjusted): 1984 2007

Included observations: 24 after adjusting endpoints


IN_GDP(-1) 0.018735 0.005729 3.270008 0.0037

D(IN_GDP(-1)) 0.170531 0.218583 0.780166 0.4440

D(IN_GDP(-2)) -0.209854 0.222714 -0.942256 0.3568

R-squared 0.015845 Mean dependent var 0.253461

Adjusted R-squared -0.077884 S.D. dependent var 0.190996

S.E. of regression 0.198294 Akaike info criterion -0.281659

Sum squared resid 0.825735 Schwarz criterion -0.134402

Log likelihood 6.379904 Durbin-Watson stat 1.927139

Since the computed ADF test statistics (3.270008) is greater than the critical

values (-2.6649, -1.9559, and -1.6231 at 1%, 5% and 10% significant level

respectively), we cannot reject Ho. That means that the IN_GDP series has a unit

root problem and the IN_GDP series is a non-stationary series.

With Intercept








Date: 05/18/09 Time: 10:28


154



IN_GDP(-1) 7.16E-05 0.022530 0.003179 0.9975

D(IN_GDP(-1)) 0.149593 0.221332 0.675874 0.5069

D(IN_GDP(-2)) -0.196324 0.224692 -0.873744 0.3926

C 0.263417 0.307405 0.856906 0.4016





Log likelihood 6.812582 F-statistic 0.356041

Durbin-Watson stat 1.929477 Prob(F-statistic) 0.785299






ADF Test Statistic -2.012598 1% Critical Value* -4.3942







Date: 05/18/09 Time: 10:29




IN_GDP(-1) -0.351884 0.174840 -2.012598 0.0585

D(IN_GDP(-1)) 0.288792 0.217040 1.330598 0.1991

D(IN_GDP(-2)) -0.043725 0.222158 -0.196819 0.8461

C 3.677454 1.707870 2.153240 0.0444

@TREND(1981) 0.093980 0.046350 2.027628 0.0569


Adjusted R-squared 0.055269 S.D. dependent var 0.190996




155


Since the computed ADF test statistics (-2.012598) is greater than the critical




UNIT ROOT TEST FOR IN_GDP (1ST DIFFERENCE) with (2) lag







Dependent Variable: D(IN_GDP,2)


Date: 05/18/09 Time: 10:30




D(IN_GDP(-1)) -0.115197 0.174509 -0.660122 0.5167

D(IN_GDP(-1),2) -0.392644 0.226446 -1.733940 0.0983

D(IN_GDP(-2),2) -0.455725 0.214302 -2.126560 0.0461




Sum squared resid 1.012873 Schwarz criterion 0.124151

Log likelihood 3.275501 Durbin-Watson stat 2.149471





With Intercept





156




Date: 05/18/09 Time: 10:30




D(IN_GDP(-1)) -0.964118 0.365060 -2.640987 0.0161

D(IN_GDP(-1),2) 0.119189 0.282625 0.421721 0.6780

D(IN_GDP(-2),2) -0.148545 0.224130 -0.662763 0.5154

C 0.252073 0.098240 2.565878 0.0189



















Date: 05/18/09 Time: 10:31




D(IN_GDP(-1)) -0.957503 0.377807 -2.534371 0.0208

D(IN_GDP(-1),2) 0.111768 0.294984 0.378894 0.7092

D(IN_GDP(-2),2) -0.153608 0.232952 -0.659396 0.5180

C 0.264267 0.133102 1.985449 0.0625

@TREND(1981) -0.000919 0.006544 -0.140432 0.8899


157






Since the computed ADF test statistics (--2.534371) is greater than the critical




UNIT ROOT TEST FOR IN_FDI (IN LEVEL) with (2) lag







Dependent Variable: D(IN_FDI)


Date: 05/18/09 Time: 10:41


Included observations: 19

Excluded observations: 5 after adjusting endpoints


IN_FDI(-1) 0.023724 0.028601 0.829500 0.4190

D(IN_FDI(-1)) -0.896391 0.210249 -4.263479 0.0006

D(IN_FDI(-2)) -0.365897 0.195707 -1.869614 0.0800



S.E. of regression 1.025395 Akaike info criterion 3.031971


Log likelihood -25.80373 Durbin-Watson stat 1.828717



respectively), we cannot reject Ho. That means that the IN_FDI series has a unit

root problem and the IN_FDI series is a non-stationary series.

158

With Intercept








Date: 05/18/09 Time: 10:41





IN_FDI(-1) -0.044471 0.194704 -0.228403 0.8224

D(IN_FDI(-1)) -0.846669 0.257786 -3.284393 0.0050

D(IN_FDI(-2)) -0.350632 0.205844 -1.703390 0.1091

C 0.586771 1.656060 0.354317 0.7280





Log likelihood -25.72455 F-statistic 6.074367














Date: 05/18/09 Time: 10:42


159




IN_FDI(-1) -0.586219 0.320348 -1.829947 0.0886

D(IN_FDI(-1)) -0.493671 0.291990 -1.690711 0.1130

D(IN_FDI(-2)) -0.181661 0.204955 -0.886345 0.3904

C 3.383304 2.041729 1.657077 0.1197

@TREND(1981) 0.111769 0.055066 2.029717 0.0618











UNIT ROOT TEST FOR IN_FDI (1ST DIFFERENCE) with (2) lag







Dependent Variable: D(IN_FDI,2)


Date: 05/18/09 Time: 10:43





D(IN_FDI(-1)) -1.706297 0.697239 -2.447221 0.0282

D(IN_FDI(-1),2) -0.016217 0.494555 -0.032791 0.9743

D(IN_FDI(-2),2) -0.138867 0.225541 -0.615705 0.5480




160







With Intercept








Date: 05/18/09 Time: 10:43





D(IN_FDI(-1)) -1.857401 0.729412 -2.546438 0.0244

D(IN_FDI(-1),2) 0.095653 0.518858 0.184353 0.8566

D(IN_FDI(-2),2) -0.091155 0.235591 -0.386922 0.7051

C 0.227723 0.278411 0.817938 0.4281







8





161









Date: 05/18/09 Time: 10:44





D(IN_FDI(-1)) -1.932252 0.760082 -2.542163 0.0258

D(IN_FDI(-1),2) 0.138992 0.538004 0.258348 0.8005

D(IN_FDI(-2),2) -0.081198 0.242503 -0.334833 0.7435

C -0.151245 0.717028 -0.210933 0.8365

@TREND(1981) 0.023195 0.040248 0.576302 0.5751








values (-4.6193, --3.7119, and -3.2964 at 1%, 5% and 10% significant level



UNIT ROOT TEST FOR IN_EXRATE (IN LEVEL) with (2) lag







Dependent Variable: D(IN_EXRATE)


162

Date: 05/18/09 Time: 10:45




IN_EXRATE(-1) 0.019049 0.029288 0.650382 0.5225

D(IN_EXRATE(-1)) 0.236827 0.225518 1.050144 0.3056

D(IN_EXRATE(-2)) 0.089271 0.230691 0.386972 0.7027

R-squared -0.185464 Mean dependent var 0.214908







respectively), we cannot reject Ho. That means that the IN_EXRATE series has a

unit root problem and the IN_EXRATE series is a non-stationary series.

With Intercept








Date: 05/18/09 Time: 10:46




IN_EXRATE(-1) -0.074378 0.042352 -1.756198 0.0944

D(IN_EXRATE(-1)) 0.034735 0.209714 0.165629 0.8701

D(IN_EXRATE(-2)) -0.082919 0.210495 -0.393925 0.6978

C 0.439172 0.158855 2.764610 0.0120







163













Date: 05/18/09 Time: 10:47




IN_EXRATE(-1) -0.338549 0.216125 -1.566450 0.1337

D(IN_EXRATE(-1)) 0.203411 0.247246 0.822708 0.4209

D(IN_EXRATE(-2)) 0.079775 0.245301 0.325211 0.7486

C 0.140144 0.286653 0.488899 0.6305

@TREND(1981) 0.067805 0.054427 1.245804 0.2280











164

UNIT ROOT TEST FOR IN_EXRATE (1ST DIFFERENCE) with (2) lag







Dependent Variable: D(IN_EXRATE,2)


Date: 05/18/09 Time: 10:47




D(IN_EXRATE(-1)) -0.478803 0.281428 -1.701336 0.1044

D(IN_EXRATE(-1),2) -0.261428 0.269016 -0.971795 0.3428

D(IN_EXRATE(-2),2) -0.161851 0.220344 -0.734541 0.4711

R-squared 0.365406 Mean dependent var -0.003346








unit root problem and the IN_EXRATE series is a non-stationary series

With Intercept








Date: 05/18/09 Time: 10:48



165


D(IN_EXRATE(-1)) -1.080946 0.403147 -2.681272 0.0148

D(IN_EXRATE(-1),2) 0.137911 0.322933 0.427058 0.6741

D(IN_EXRATE(-2),2) 0.056262 0.233822 0.240618 0.8124

C 0.240964 0.122242 1.971199 0.0634



















Date: 05/18/09 Time: 10:49




D(IN_EXRATE(-1)) -1.317914 0.403376 -3.267211 0.0043

D(IN_EXRATE(-1),2) 0.260414 0.312916 0.832216 0.4162

D(IN_EXRATE(-2),2) 0.108476 0.223041 0.486353 0.6326

C 0.622178 0.241274 2.578722 0.0189

@TREND(1981) -0.021804 0.012113 -1.800141 0.0886







166





UNIT ROOT TEST FOR IN_INFRATE (IN LEVEL) with (2) lag







Dependent Variable: D(IN_INFRATE)


Date: 05/18/09 Time: 10:50




IN_INFRATE(-1) -0.039625 0.051500 -0.769417 0.4502

D(IN_INFRATE(-1)) 0.056939 0.180371 0.315680 0.7554

D(IN_INFRATE(-2)) -0.475394 0.177892 -2.672379 0.0143








respectively), we cannot reject Ho. That means that the IN_INFRATE series has a

unit root problem and the IN_INFRATE series is a non-stationary series

With Intercept






167



Date: 05/18/09 Time: 10:50




IN_INFRATE(-1) -0.504356 0.259537 -1.943292 0.0662

D(IN_INFRATE(-1)) 0.295166 0.215324 1.370799 0.1856

D(IN_INFRATE(-2)) -0.250366 0.209106 -1.197315 0.2452

C 1.368339 0.750504 1.823226 0.0833



















Date: 05/18/09 Time: 10:51




IN_INFRATE(-1) -0.529483 0.266897 -1.983848 0.0619

D(IN_INFRATE(-1)) 0.293953 0.218791 1.343533 0.1949

D(IN_INFRATE(-2)) -0.236438 0.213685 -1.106480 0.2823

C 1.627261 0.872545 1.864958 0.0777

@TREND(1981) -0.012932 0.021181 -0.610551 0.5487



168









UNIT ROOT TEST FOR IN_INFRATE (1ST DIFFERENCE) with (2) lag







Dependent Variable: D(IN_INFRATE,2)


Date: 05/18/09 Time: 10:52




D(IN_INFRATE(-1)) -1.487480 0.423888 -3.509130 0.0022

D(IN_INFRATE(-1),2) 0.513030 0.286367 1.791513 0.0884

D(IN_INFRATE(-2),2) 0.019478 0.213856 0.091078 0.9283






Since the computed ADF test statistics (-3.509130) is smaller than the critical


respectively), it means we can reject Ho. This implies that the IN_INFRATE series

does not have a unit root problem and the IN_INFRATE series is a stationary

series at 1%, 10% and 5% significant level.

With Intercept



169






Date: 05/18/09 Time: 10:53




D(IN_INFRATE(-1)) -1.495917 0.433351 -3.451973 0.0027

D(IN_INFRATE(-1),2) 0.514161 0.292458 1.758067 0.0948

D(IN_INFRATE(-2),2) 0.024300 0.218696 0.111113 0.9127

C -0.068186 0.162018 -0.420854 0.6786



















Date: 05/18/09 Time: 10:54




D(IN_INFRATE(-1)) -1.503149 0.445261 -3.375882 0.0034

D(IN_INFRATE(-1),2) 0.518783 0.300435 1.726770 0.1013

170

D(IN_INFRATE(-2),2) 0.025377 0.224306 0.113134 0.9112

C 0.029470 0.410601 0.071773 0.9436

@TREND(1981) -0.006511 0.025033 -0.260080 0.7978











171

APPENDIX III Summary of Johansen cointegration test Date: 05/25/09 Time: 14:21 Sample: 1981 2007 Included observations: 19 Series: IN_GDP IN_FDI IN_EXRATE IN_INFRATE Lags interval: 1 to 2

Data Trend: None None Linear Linear Quadratic

Rank or No

Intercept Intercept Intercept Intercept Intercept

No. of CEs No Trend No Trend No Trend Trend Trend

Selected (5% level) Number of Cointegrating Relations by Model (columns)

Trace 2 2 2 2 3

Max-Eig 2 2 2 3 3

Log Likelihood by Rank (rows) and Model (columns) 0 -22.77338 -22.77338 -12.19032 -12.19032 -11.10771 1 -

0.406020 21.34886 29.91075 44.72130 45.15773

2 9.778851 41.98690 48.95968 69.83534 70.26830 3 11.49508 49.22767 49.76706 80.87380 81.30314 4 12.22629 50.01955 50.01955 81.44800 81.44800

Akaike Information Criteria by Rank (rows) and Model (columns) 0 5.765619 5.765619 5.072665 5.072665 5.379759 1 4.253265 2.068541 1.483079 0.029336 0.299186 2 4.023279 0.843484 0.320034 -1.666878 -1.501926 3 4.684728 1.028666 1.077151 -

1.881452* -1.821383

4 5.449864 1.892679 1.892679 -0.994526 -0.994526

Schwarz Criteria by Rank (rows) and Model (columns) 0 7.356253 7.356253 6.862129 6.862129 7.368052 1 6.241558 4.106541 3.670201 2.266166 2.685138 2 6.409230 3.328850 2.904814 1.017317* 1.281684 3 7.468338 3.961398 4.059590 1.250109 1.359885 4 8.631132 5.272776 5.272776 2.584400 2.584400

172

APPENDIX IV Ordinary Least Square regression result OLS Regression Dependent Variable: GDP Method: Least Squares Date: 06/11/09 Time: 19:28 Sample: 1981 2007 Included observations: 27

Variable Coefficient Std. Error t-Statistic Prob. C 898800.1 1002023. 0.896986 0.3783 FDI 258.1693 51.68672 4.994887 0.0000

R-squared 0.499488 Mean dependent var 3825390. Adjusted R-squared

0.479468 S.D. dependent var 5854329.

S.E. of regression 4223775. Akaike info criterion 33.42154 Sum squared resid

4.46E+14 Schwarz criterion 33.51753

Log likelihood -449.1908 F-statistic 24.94890 Durbin-Watson stat

1.066784 Prob(F-statistic) 0.000038

Estimation Command: ===================== LS GDP C FDI Estimation Equation: ===================== GDP = C(1) + C(2)*FDI Substituted Coefficients: ===================== GDP = 898800.0969 + 258.1693386*FDI Wald Test Wald Test: Equation: Untitled Null Hypothesis:

GDP = C(1) + C(2)*FDI

F-statistic 0.789436 Probability 0.382740 Chi-square 0.789436 Probability 0.374271

173

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