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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
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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)
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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
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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.
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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.
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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
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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
2.13 Definition of Terms 85
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
3.8 Definition of Terms 117
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
4.6 Definition of Terms 135
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
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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
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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 4.3 and Table 4.4 123
Table 4.5 and Table 4.6 124
Table 4.7 and Table 4.8 124
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
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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
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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
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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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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.
38
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
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
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 improvements in productivity. The outcome of this
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 capital flows between developing
and developed countries (Todaro & Smith, 2006).
THE ROMER GROWTH MODEL
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,
ssibility 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
61
Production function with constant marginal product of
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
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
vements in productivity. The outcome of this
term growth, which was prohibited by traditional growth theory.
Moreover, probably the most interesting part of endogenous growth models is
pital flows between developing
One of the main contributors to endogenous growth theory is Paul Romer. The
Romer growth model is particularly relevant for developing economies, because it
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 returns
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,
ssibility 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
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
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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
Source: UNCTAD, World Investment Report 2004
72
Figure 2.5: The top 10 recipients of FDI inflows in Africa, 2002 and 2003
(billions of dollars)
Source: UNCTAD, World Investment Report 2004
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
Source: UNCTAD, World Investment Report 2004
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)
Source: UNCTAD, World Investment Report 2004
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
Source: UNCTAD, World Investment Report 2006
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
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
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-
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 2005. Many
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
nced political instability or civil war in the recent past, which destroyed
2005 (billions of dollars)
Source: UNCTAD, World Investment Report 2006
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
Source: UNCTAD, World Investment Report 2006
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
ows 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 MFA in 2005. In Mauritius there was a
30% contraction in the volume of garments manufactured in 2005 following the
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
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
utflows 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 small number of countries. In 2004, nine of the top
78
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
ows 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
MFA in 2005. In Mauritius there was a
30% contraction in the volume of garments manufactured in 2005 following the
owned companies. In Lesotho, six textile TNCs
t the impact of
lived in Africa, where domestic capabilities
are inadequate for quickly absorbing and continuing production processes. It also
underscores the fact that Africa’s industrial progress requires competitive
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
utflows 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
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)
Source: UNCTAD, World Investment Report 2007
Figure 2.10: FDI inflows, global and by group of economies, 1980
dollars)
Source: UNCTAD, World Investment Report 2007
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
Source: UNCTAD, World Investment Report 2007
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
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 in 2005, with South African firms
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 2.12) received about 90%, or $32
billion, of the continent’s inflows. North African countries hosted record incoming
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
southern Africa because of large investments in oil and mining. Major investment
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
and increased profits and by a generally improved business climate, a UNCTAD
survey of investment trends reports. African FDI outflows also reached a record
n 2005, with South African firms
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
about 90%, or $32
billion, of the continent’s inflows. North African countries hosted record incoming
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
southern Africa because of large investments in oil and mining. Major investment
$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
Source: UNCTAD, World Investment Report 2007
Figure 2.12: Africa-FDI inflows, top 10 economies, 2005-2006a
(Billions of dollars)
Source: UNCTAD, World Investment Report 2007
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)
Source: UNCTAD, World Investment Report 2007
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
Source: UNCTAD, World Investment Report 2007
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 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
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,
nflows to these economies in
region in 2006 is used to extrapolate the 2007
85
2.13 DEFINITION OF TERMS
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.
105
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.
107
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 series may be stationary. If such a stationary linear combination exists,
stationary time series are said to be cointegrated. The stationary linear
combination is called the cointegrating equation and may be interpreted as a long
run equilibrium relationship among the variables. The purpose of the
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,
2000). In our case, the mission is to determine whether or not GDP, FDI, EXRATE
and INFRATE variables have a long-run relationship in a bivariate framework.
TESTING FOR COINTEGRATION USING JOHANSEN’S TEST
Johansen’s methodology takes its starting point in the vector auto regression
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-
stationary series may be stationary. If such a stationary linear combination exists,
. The stationary linear
and may be interpreted as a long-
run equilibrium relationship among the variables. The purpose of the
stationary series are
there exists a long run relationship
integrated relationship) (Miguel,
2000). In our case, the mission is to determine whether or not GDP, FDI, EXRATE
ariate framework.
TESTING FOR COINTEGRATION USING JOHANSEN’S TEST
Johansen’s methodology takes its starting point in the vector auto regression
………………………………………………… (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
deterministic variables when present. Johansen proposes two different likelihood
ratio tests of the significance of these canonical correlations and thereby the
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
square distribution in general; asymptotic critical values can be found in
,1990) and are also given by most econometric software
packages. Since the critical values used for the maximum eigen value and trace test
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
Although Johansen’s methodology is typically used in a setting where all variables
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
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 cointegrating vector whose
space is spanned by the only stationary variable in the model. For instance, if the
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
deterministic variables when present. Johansen proposes two different likelihood
ratio tests of the significance of these canonical correlations and thereby the
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
square distribution in general; asymptotic critical values can be found in
conometric software
packages. Since the critical values used for the maximum eigen value and trace test
root assumption, they will no longer be correct
root processes. Thus, the real
sensitive Johansen’s procedures are to deviations from the pure-
Although Johansen’s methodology is typically used in a setting where all variables
system is theoretically not
1995) states that there is little need to pre-test the
variables in the system to establish their order of integration. If a single variable is
tegrating vector whose
space is spanned by the only stationary variable in the model. For instance, if the
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
3.8 DEFINITION OF TERMS
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
Source: Authors’ calculations based on domestic authorities’ data.
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
Variable ADF (0) ADF(1) ADF (2) Without Intercept and Trend
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
With Intercept and Trend
-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
Variable ADF (0) ADF(1) ADF (2) Without Intercept and Trend
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
With Intercept only -1.381865
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
With Intercept and Trend
-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
With Intercept only -2.393410
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
With Intercept and Trend
-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
Source: Authors’ calculations based on domestic authorities’ data.
Table 4.7 Table 4.8
Source: Authors’ calculations based on domestic authorities’ data.
Variable ADF (0) ADF(1) ADF (2) Without Intercept and Trend
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
With Intercept only -2.175865
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
With Intercept and Trend
-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
Variable ADF (0) ADF(1) ADF (2) Without Intercept and Trend
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
With Intercept only -2.322533
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
With Intercept and Trend
-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
With Intercept only -4.972901
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
With Intercept and Trend
-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
Variable ADF (0) ADF(1) ADF (2) Without Intercept and Trend
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
With Intercept only -1.128519
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
With Intercept and Trend
-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
Durbin-Watson statistics
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
criteria Schwarz criterion
Durbin-Watson statistics
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
criteria Schwarz criterion
Durbin-Watson statistics
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
Source: Authors’ calculations based on domestic authorities’ data.
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
Jarque-Bera 2.220161
Probability 0.329532
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
Jarque-Bera 2.089251
Probability 0.351824
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
Jarque-Bera 1.994433
Probability 0.368905
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)
2 Cointegrating Equation(s): Log likelihood 48.95968
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)
3 Cointegrating Equation(s): Log likelihood 49.76706
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
4.6 DEFINITION OF TERMS
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
10% Critical Value -1.6231
*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
Variable Coefficient Std. Error t-Statistic Prob.
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
ADF Test Statistic 0.003179 1% Critical Value* -3.7343
5% Critical Value -2.9907
10% Critical Value -2.6348
*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: 10:28
Sample(adjusted): 1984 2007
154
Included observations: 24 after adjusting endpoints
Variable Coefficient Std. Error t-Statistic Prob.
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
R-squared 0.050699 Mean dependent var 0.253461
Adjusted R-squared -0.091697 S.D. dependent var 0.190996
S.E. of regression 0.199561 Akaike info criterion -0.234382
Sum squared resid 0.796492 Schwarz criterion -0.038040
Log likelihood 6.812582 F-statistic 0.356041
Durbin-Watson stat 1.929477 Prob(F-statistic) 0.785299
Since the computed ADF test statistics (0.003179) is greater than the critical
values (-3.7343, -2.9907, and -2.6348 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 and Trend
ADF Test Statistic -2.012598 1% Critical Value* -4.3942
5% Critical Value -3.6118
10% Critical Value -3.2418
*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: 10:29
Sample(adjusted): 1984 2007
Included observations: 24 after adjusting endpoints
Variable Coefficient Std. Error t-Statistic Prob.
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
R-squared 0.219570 Mean dependent var 0.253461
Adjusted R-squared 0.055269 S.D. dependent var 0.190996
S.E. of regression 0.185643 Akaike info criterion -0.346930
Sum squared resid 0.654804 Schwarz criterion -0.101502
Log likelihood 9.163162 F-statistic 1.336390
155
Durbin-Watson stat 1.951935 Prob(F-statistic) 0.292790
Since the computed ADF test statistics (-2.012598) is greater than the critical
values (-4.3942, -3.6118, and -3.2418 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.
UNIT ROOT TEST FOR IN_GDP (1ST DIFFERENCE) with (2) lag
Without Intercept and Trend
ADF Test Statistic -0.660122 1% Critical Value* -2.6700
5% Critical Value -1.9566
10% Critical Value -1.6235
*MacKinnon critical values for rejection of hypothesis of a unit root.
Augmented Dickey-Fuller Test Equation
Dependent Variable: D(IN_GDP,2)
Method: Least Squares
Date: 05/18/09 Time: 10:30
Sample(adjusted): 1985 2007
Included observations: 23 after adjusting endpoints
Variable Coefficient Std. Error t-Statistic Prob.
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
R-squared 0.320661 Mean dependent var 0.012474
Adjusted R-squared 0.252727 S.D. dependent var 0.260329
S.E. of regression 0.225041 Akaike info criterion -0.023957
Sum squared resid 1.012873 Schwarz criterion 0.124151
Log likelihood 3.275501 Durbin-Watson stat 2.149471
Since the computed ADF test statistics (-0.660122) is greater than the critical
values (-2.6700, -1.9566, and -1.6235 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
ADF Test Statistic -2.640987 1% Critical Value* -3.7497
5% Critical Value -2.9969
10% Critical Value -2.6381
*MacKinnon critical values for rejection of hypothesis of a unit root.
156
Augmented Dickey-Fuller Test Equation
Dependent Variable: D(IN_GDP,2)
Method: Least Squares
Date: 05/18/09 Time: 10:30
Sample(adjusted): 1985 2007
Included observations: 23 after adjusting endpoints
Variable Coefficient Std. Error t-Statistic Prob.
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
R-squared 0.495482 Mean dependent var 0.012474
Adjusted R-squared 0.415822 S.D. dependent var 0.260329
S.E. of regression 0.198974 Akaike info criterion -0.234518
Sum squared resid 0.752220 Schwarz criterion -0.037041
Log likelihood 6.696955 F-statistic 6.219914
Durbin-Watson stat 2.015476 Prob(F-statistic) 0.004004
Since the computed ADF test statistics (-2.640987) is greater than the critical
values (-3.7497, -2.9969, and -2.6381 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 and Trend
ADF Test Statistic -2.534371 1% Critical Value* -4.4167
5% Critical Value -3.6219
10% Critical Value -3.2474
*MacKinnon critical values for rejection of hypothesis of a unit root.
Augmented Dickey-Fuller Test Equation
Dependent Variable: D(IN_GDP,2)
Method: Least Squares
Date: 05/18/09 Time: 10:31
Sample(adjusted): 1985 2007
Included observations: 23 after adjusting endpoints
Variable Coefficient Std. Error t-Statistic Prob.
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
R-squared 0.496035 Mean dependent var 0.012474
157
Adjusted R-squared 0.384042 S.D. dependent var 0.260329
S.E. of regression 0.204314 Akaike info criterion -0.148656
Sum squared resid 0.751397 Schwarz criterion 0.098190
Log likelihood 6.709547 F-statistic 4.429184
Durbin-Watson stat 2.017834 Prob(F-statistic) 0.011459
Since the computed ADF test statistics (--2.534371) is greater than the critical
values (-4.4167, -3.6219, and -3.2474 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.
UNIT ROOT TEST FOR IN_FDI (IN LEVEL) with (2) lag
Without Intercept and Trend
ADF Test Statistic 0.829500 1% Critical Value* -2.6968
5% Critical Value -1.9602
10% Critical Value -1.6251
*MacKinnon critical values for rejection of hypothesis of a unit root.
Augmented Dickey-Fuller Test Equation
Dependent Variable: D(IN_FDI)
Method: Least Squares
Date: 05/18/09 Time: 10:41
Sample(adjusted): 1984 2007
Included observations: 19
Excluded observations: 5 after adjusting endpoints
Variable Coefficient Std. Error t-Statistic Prob.
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
R-squared 0.544728 Mean dependent var 0.072421
Adjusted R-squared 0.487819 S.D. dependent var 1.432780
S.E. of regression 1.025395 Akaike info criterion 3.031971
Sum squared resid 16.82295 Schwarz criterion 3.181093
Log likelihood -25.80373 Durbin-Watson stat 1.828717
Since the computed ADF test statistics (0.829500) is greater than the critical
values (-2.6968, -1.9602, and -3.2474 at 1%, 5% and 10% significant level
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
ADF Test Statistic -0.228403 1% Critical Value* -3.8304
5% Critical Value -3.0294
10% Critical Value -2.6552
*MacKinnon critical values for rejection of hypothesis of a unit root.
Augmented Dickey-Fuller Test Equation
Dependent Variable: D(IN_FDI)
Method: Least Squares
Date: 05/18/09 Time: 10:41
Sample(adjusted): 1984 2007
Included observations: 19
Excluded observations: 5 after adjusting endpoints
Variable Coefficient Std. Error t-Statistic Prob.
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
R-squared 0.548507 Mean dependent var 0.072421
Adjusted R-squared 0.458208 S.D. dependent var 1.432780
S.E. of regression 1.054619 Akaike info criterion 3.128900
Sum squared resid 16.68332 Schwarz criterion 3.327729
Log likelihood -25.72455 F-statistic 6.074367
Durbin-Watson stat 1.816064 Prob(F-statistic) 0.006457
Since the computed ADF test statistics (-0.228403) is greater than the critical
values (-3.8304, -3.0294, and -2.6552 at 1%, 5% and 10% significant level
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.
With Intercept and Trend
ADF Test Statistic -1.829947 1% Critical Value* -4.5348
5% Critical Value -3.6746
10% Critical Value -3.2762
*MacKinnon critical values for rejection of hypothesis of a unit root.
Augmented Dickey-Fuller Test Equation
Dependent Variable: D(IN_FDI)
Method: Least Squares
Date: 05/18/09 Time: 10:42
Sample(adjusted): 1984 2007
159
Included observations: 19
Excluded observations: 5 after adjusting endpoints
Variable Coefficient Std. Error t-Statistic Prob.
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
R-squared 0.651159 Mean dependent var 0.072421
Adjusted R-squared 0.551491 S.D. dependent var 1.432780
S.E. of regression 0.959544 Akaike info criterion 2.976218
Sum squared resid 12.89016 Schwarz criterion 3.224755
Log likelihood -23.27407 F-statistic 6.533238
Durbin-Watson stat 1.988063 Prob(F-statistic) 0.003494
Since the computed ADF test statistics (-1.829947) is greater than the critical
values (-4.5348, -3.6746, and -3.2762 at 1%, 5% and 10% significant level
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.
UNIT ROOT TEST FOR IN_FDI (1ST DIFFERENCE) with (2) lag
Without Intercept and Trend
ADF Test Statistic -0.447221 1% Critical Value* -2.7158
5% Critical Value -1.9627
10% Critical Value -1.6262
*MacKinnon critical values for rejection of hypothesis of a unit root.
Augmented Dickey-Fuller Test Equation
Dependent Variable: D(IN_FDI,2)
Method: Least Squares
Date: 05/18/09 Time: 10:43
Sample(adjusted): 1985 2007
Included observations: 17
Excluded observations: 6 after adjusting endpoints
Variable Coefficient Std. Error t-Statistic Prob.
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
R-squared 0.852739 Mean dependent var 0.183584
Adjusted R-squared 0.831702 S.D. dependent var 2.665270
S.E. of regression 1.093404 Akaike info criterion 3.175253
160
Sum squared resid 16.73745 Schwarz criterion 3.322291
Log likelihood -23.98965 Durbin-Watson stat 1.930492
Since the computed ADF test statistics (-0.447221) is greater than the critical
values (-2.7158, -1.9627, and -1.6262 at 1%, 5% and 10% significant level
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.
With Intercept
ADF Test Statistic -2.546438 1% Critical Value* -3.8877
5% Critical Value -3.0521
10% Critical Value -2.6672
*MacKinnon critical values for rejection of hypothesis of a unit root.
Augmented Dickey-Fuller Test Equation
Dependent Variable: D(IN_FDI,2)
Method: Least Squares
Date: 05/18/09 Time: 10:43
Sample(adjusted): 1985 2007
Included observations: 17
Excluded observations: 6 after adjusting endpoints
Variable Coefficient Std. Error t-Statistic Prob.
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
R-squared 0.859947 Mean dependent var 0.183584
Adjusted R-squared 0.827627 S.D. dependent var 2.665270
S.E. of regression 1.106562 Akaike info criterion 3.242718
Sum squared resid 15.91824 Schwarz criterion 3.438768
Log likelihood -23.56310 F-statistic 26.60732
Durbin-Watson stat 1.949330 Prob(F-statistic) 0.00000
8
Since the computed ADF test statistics (-2.546438) is greater than the critical
values (-3.8877, -3.0521, and -2.6672 at 1%, 5% and 10% significant level
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.
161
With Intercept and Trend
ADF Test Statistic -2.542163 1% Critical Value* -4.6193
5% Critical Value -3.7119
10% Critical Value -3.2964
*MacKinnon critical values for rejection of hypothesis of a unit root.
Augmented Dickey-Fuller Test Equation
Dependent Variable: D(IN_FDI,2)
Method: Least Squares
Date: 05/18/09 Time: 10:44
Sample(adjusted): 1985 2007
Included observations: 17
Excluded observations: 6 after adjusting endpoints
Variable Coefficient Std. Error t-Statistic Prob.
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
R-squared 0.863719 Mean dependent var 0.183584
Adjusted R-squared 0.818292 S.D. dependent var 2.665270
S.E. of regression 1.136131 Akaike info criterion 3.333064
Sum squared resid 15.48954 Schwarz criterion 3.578126
Log likelihood -23.33104 F-statistic 19.01331
Durbin-Watson stat 1.938208 Prob(F-statistic) 0.000040
Since the computed ADF test statistics (-2.542163) is greater than the critical
values (-4.6193, --3.7119, and -3.2964 at 1%, 5% and 10% significant level
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.
UNIT ROOT TEST FOR IN_EXRATE (IN LEVEL) with (2) lag
Without Intercept and Trend
ADF Test Statistic 0.650382 1% Critical Value* -2.6649
5% Critical Value -1.9559
10% Critical Value -1.6231
*MacKinnon critical values for rejection of hypothesis of a unit root.
Augmented Dickey-Fuller Test Equation
Dependent Variable: D(IN_EXRATE)
Method: Least Squares
162
Date: 05/18/09 Time: 10:45
Sample(adjusted): 1984 2007
Included observations: 24 after adjusting endpoints
Variable Coefficient Std. Error t-Statistic Prob.
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
Adjusted R-squared -0.298365 S.D. dependent var 0.351643
S.E. of regression 0.400683 Akaike info criterion 1.125174
Sum squared resid 3.371478 Schwarz criterion 1.272431
Log likelihood -10.50209 Durbin-Watson stat 2.017030
Since the computed ADF test statistics (0.650382) 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_EXRATE series has a
unit root problem and the IN_EXRATE series is a non-stationary series.
With Intercept
ADF Test Statistic -1.756198 1% Critical Value* -3.7343
5% Critical Value -2.9907
10% Critical Value -2.6348
*MacKinnon critical values for rejection of hypothesis of a unit root.
Augmented Dickey-Fuller Test Equation
Dependent Variable: D(IN_EXRATE)
Method: Least Squares
Date: 05/18/09 Time: 10:46
Sample(adjusted): 1984 2007
Included observations: 24 after adjusting endpoints
Variable Coefficient Std. Error t-Statistic Prob.
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
R-squared 0.142307 Mean dependent var 0.214908
Adjusted R-squared 0.013653 S.D. dependent var 0.351643
S.E. of regression 0.349234 Akaike info criterion 0.884865
Sum squared resid 2.439294 Schwarz criterion 1.081207
Log likelihood -6.618380 F-statistic 1.106118
Durbin-Watson stat 2.065805 Prob(F-statistic) 0.369895
163
Since the computed ADF test statistics (-1.756198) is greater than the critical
values (-3.7343, -2.9907, and -2.6348 at 1%, 5% and 10% significant level
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 and Trend
ADF Test Statistic -1.566450 1% Critical Value* -4.3942
5% Critical Value -3.6118
10% Critical Value -3.2418
*MacKinnon critical values for rejection of hypothesis of a unit root.
Augmented Dickey-Fuller Test Equation
Dependent Variable: D(IN_EXRATE)
Method: Least Squares
Date: 05/18/09 Time: 10:47
Sample(adjusted): 1984 2007
Included observations: 24 after adjusting endpoints
Variable Coefficient Std. Error t-Statistic Prob.
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
R-squared 0.207077 Mean dependent var 0.214908
Adjusted R-squared 0.040146 S.D. dependent var 0.351643
S.E. of regression 0.344512 Akaike info criterion 0.889678
Sum squared resid 2.255085 Schwarz criterion 1.135106
Log likelihood -5.676132 F-statistic 1.240494
Durbin-Watson stat 2.042848 Prob(F-statistic) 0.327375
Since the computed ADF test statistics (-1.566450) is greater than the critical
values (-4.3942, -3.6118, and -3.2418 at 1%, 5% and 10% significant level
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.
164
UNIT ROOT TEST FOR IN_EXRATE (1ST DIFFERENCE) with (2) lag
Without Intercept and Trend
ADF Test Statistic -1.001336 1% Critical Value* -2.6700
5% Critical Value -1.9566
10% Critical Value -1.6235
*MacKinnon critical values for rejection of hypothesis of a unit root.
Augmented Dickey-Fuller Test Equation
Dependent Variable: D(IN_EXRATE,2)
Method: Least Squares
Date: 05/18/09 Time: 10:47
Sample(adjusted): 1985 2007
Included observations: 23 after adjusting endpoints
Variable Coefficient Std. Error t-Statistic Prob.
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
Adjusted R-squared 0.301946 S.D. dependent var 0.489752
S.E. of regression 0.409186 Akaike info criterion 1.171813
Sum squared resid 3.348662 Schwarz criterion 1.319921
Log likelihood -10.47585 Durbin-Watson stat 2.010247
Since the computed ADF test statistics (-1.001336) is greater than the critical
values (-2.6700, -1.9566, and -1.6235 at 1%, 5% and 10% significant level
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
ADF Test Statistic -2.281272 1% Critical Value* -3.7497
5% Critical Value -2.9969
10% Critical Value -2.6381
*MacKinnon critical values for rejection of hypothesis of a unit root.
Augmented Dickey-Fuller Test Equation
Dependent Variable: D(IN_EXRATE,2)
Method: Least Squares
Date: 05/18/09 Time: 10:48
Sample(adjusted): 1985 2007
Included observations: 23 after adjusting endpoints
165
Variable Coefficient Std. Error t-Statistic Prob.
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
R-squared 0.473150 Mean dependent var -0.003346
Adjusted R-squared 0.389963 S.D. dependent var 0.489752
S.E. of regression 0.382520 Akaike info criterion 1.072700
Sum squared resid 2.780111 Schwarz criterion 1.270177
Log likelihood -8.336048 F-statistic 5.687796
Durbin-Watson stat 2.003364 Prob(F-statistic) 0.005928
Since the computed ADF test statistics (-2.281272) is greater than the critical
values (-3.7497, -2.9969, and -2.6381 at 1%, 5% and 10% significant level
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 and Trend
ADF Test Statistic -3.067211 1% Critical Value* -4.4167
5% Critical Value -3.6219
10% Critical Value -3.2474
*MacKinnon critical values for rejection of hypothesis of a unit root.
Augmented Dickey-Fuller Test Equation
Dependent Variable: D(IN_EXRATE,2)
Method: Least Squares
Date: 05/18/09 Time: 10:49
Sample(adjusted): 1985 2007
Included observations: 23 after adjusting endpoints
Variable Coefficient Std. Error t-Statistic Prob.
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
R-squared 0.553528 Mean dependent var -0.003346
Adjusted R-squared 0.454311 S.D. dependent var 0.489752
S.E. of regression 0.361783 Akaike info criterion 0.994118
Sum squared resid 2.355970 Schwarz criterion 1.240964
Log likelihood -6.432356 F-statistic 5.579010
Durbin-Watson stat 2.138722 Prob(F-statistic) 0.004217
166
Since the computed ADF test statistics (-3.067211) is greater than the critical
values (-4.4167, -3.6219, and -3.2474 at 1%, 5% and 10% significant level
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
UNIT ROOT TEST FOR IN_INFRATE (IN LEVEL) with (2) lag
Without Intercept and Trend
ADF Test Statistic -0.769417 1% Critical Value* -2.6649
5% Critical Value -1.9559
10% Critical Value -1.6231
*MacKinnon critical values for rejection of hypothesis of a unit root.
Augmented Dickey-Fuller Test Equation
Dependent Variable: D(IN_INFRATE)
Method: Least Squares
Date: 05/18/09 Time: 10:50
Sample(adjusted): 1984 2007
Included observations: 24 after adjusting endpoints
Variable Coefficient Std. Error t-Statistic Prob.
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
R-squared 0.284424 Mean dependent var -0.060740
Adjusted R-squared 0.216273 S.D. dependent var 0.824081
S.E. of regression 0.729545 Akaike info criterion 2.323678
Sum squared resid 11.17696 Schwarz criterion 2.470934
Log likelihood -24.88413 Durbin-Watson stat 2.031279
Since the computed ADF test statistics (-0.769417) 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_INFRATE series has a
unit root problem and the IN_INFRATE series is a non-stationary series
With Intercept
ADF Test Statistic -1.943292 1% Critical Value* -3.7343
5% Critical Value -2.9907
10% Critical Value -2.6348
*MacKinnon critical values for rejection of hypothesis of a unit root.
Augmented Dickey-Fuller Test Equation
167
Dependent Variable: D(IN_INFRATE)
Method: Least Squares
Date: 05/18/09 Time: 10:50
Sample(adjusted): 1984 2007
Included observations: 24 after adjusting endpoints
Variable Coefficient Std. Error t-Statistic Prob.
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
R-squared 0.386407 Mean dependent var -0.060740
Adjusted R-squared 0.294368 S.D. dependent var 0.824081
S.E. of regression 0.692243 Akaike info criterion 2.253254
Sum squared resid 9.584021 Schwarz criterion 2.449596
Log likelihood -23.03905 F-statistic 4.198305
Durbin-Watson stat 1.916733 Prob(F-statistic) 0.018585
Since the computed ADF test statistics (-1.943292) is greater than the critical
values (-3.7343, -2.9907, and -2.6348 at 1%, 5% and 10% significant level
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 and Trend
ADF Test Statistic -1.983848 1% Critical Value* -4.3942
5% Critical Value -3.6118
10% Critical Value -3.2418
*MacKinnon critical values for rejection of hypothesis of a unit root.
Augmented Dickey-Fuller Test Equation
Dependent Variable: D(IN_INFRATE)
Method: Least Squares
Date: 05/18/09 Time: 10:51
Sample(adjusted): 1984 2007
Included observations: 24 after adjusting endpoints
Variable Coefficient Std. Error t-Statistic Prob.
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
R-squared 0.398214 Mean dependent var -0.060740
Adjusted R-squared 0.271522 S.D. dependent var 0.824081
168
S.E. of regression 0.703361 Akaike info criterion 2.317158
Sum squared resid 9.399604 Schwarz criterion 2.562585
Log likelihood -22.80589 F-statistic 3.143173
Durbin-Watson stat 1.903975 Prob(F-statistic) 0.038406
Since the computed ADF test statistics (-1.983848) is greater than the critical
values (-4.3942, -3.6118, and -3.2418 at 1%, 5% and 10% significant level
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
UNIT ROOT TEST FOR IN_INFRATE (1ST DIFFERENCE) with (2) lag
Without Intercept and Trend
ADF Test Statistic -3.509130 1% Critical Value* -2.6700
5% Critical Value -1.9566
10% Critical Value -1.6235
*MacKinnon critical values for rejection of hypothesis of a unit root.
Augmented Dickey-Fuller Test Equation
Dependent Variable: D(IN_INFRATE,2)
Method: Least Squares
Date: 05/18/09 Time: 10:52
Sample(adjusted): 1985 2007
Included observations: 23 after adjusting endpoints
Variable Coefficient Std. Error t-Statistic Prob.
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
R-squared 0.621380 Mean dependent var -0.041409
Adjusted R-squared 0.583518 S.D. dependent var 1.174344
S.E. of regression 0.757868 Akaike info criterion 2.404492
Sum squared resid 11.48728 Schwarz criterion 2.552600
Log likelihood -24.65166 Durbin-Watson stat 1.812523
Since the computed ADF test statistics (-3.509130) is smaller than the critical
values (-2.6700, -1.9566, and -1.6235 at 1%, 5% and 10% significant level
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
ADF Test Statistic -2.451973 1% Critical Value* -3.7497
5% Critical Value -2.9969
169
10% Critical Value -2.6381
*MacKinnon critical values for rejection of hypothesis of a unit root.
Augmented Dickey-Fuller Test Equation
Dependent Variable: D(IN_INFRATE,2)
Method: Least Squares
Date: 05/18/09 Time: 10:53
Sample(adjusted): 1985 2007
Included observations: 23 after adjusting endpoints
Variable Coefficient Std. Error t-Statistic Prob.
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
R-squared 0.624877 Mean dependent var -0.041409
Adjusted R-squared 0.565647 S.D. dependent var 1.174344
S.E. of regression 0.773957 Akaike info criterion 2.482170
Sum squared resid 11.38118 Schwarz criterion 2.679647
Log likelihood -24.54496 F-statistic 10.55002
Durbin-Watson stat 1.812859 Prob(F-statistic) 0.000264
Since the computed ADF test statistics (-2.451973) is greater than the critical
values (-3.7497, -2.9969, and -2.6381 at 1%, 5% and 10% significant level
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 and Trend
ADF Test Statistic -3.075882 1% Critical Value* -4.4167
5% Critical Value -3.6219
10% Critical Value -3.2474
*MacKinnon critical values for rejection of hypothesis of a unit root.
Augmented Dickey-Fuller Test Equation
Dependent Variable: D(IN_INFRATE,2)
Method: Least Squares
Date: 05/18/09 Time: 10:54
Sample(adjusted): 1985 2007
Included observations: 23 after adjusting endpoints
Variable Coefficient Std. Error t-Statistic Prob.
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
R-squared 0.626281 Mean dependent var -0.041409
Adjusted R-squared 0.543233 S.D. dependent var 1.174344
S.E. of regression 0.793675 Akaike info criterion 2.565376
Sum squared resid 11.33857 Schwarz criterion 2.812222
Log likelihood -24.50182 F-statistic 7.541144
Durbin-Watson stat 1.817765 Prob(F-statistic) 0.000944
Since the computed ADF test statistics (-3.075882) is greater than the critical
values (-4.4167, -3.6219, and -3.2474 at 1%, 5% and 10% significant level
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
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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