an empirical analysis of the relationship between foreign direct investment and the agricultural...

HUAZHONG AGRICULTURAL UNIVERSITY

硕士学位论文

MASTER’S DEGREE DISSERTATION

外国直接投资对尼日利亚农业部门的影响

AN EMPIRICAL ANALYSIS OF THE RELATIONSHIP

BETWEEN FOREIGN DIRECT INVESTMENT AND THE

AGRICULTURAL SECTOR OF NIGERIA

研究生: 艾杜

CANDIDATE: IDOWU AYODEJI ADETUNJI

导师: 刘颖教授

SUPERVISOR: PROFESSOR LIUYING

专业: 国际贸易

MAJOR： INTERNATIONAL TRADE

研究方向:

FIELD：

经济学管理

中国武汉

WUHAN，CHINA

二○一三年六月

JUNE，2013

分类号密级

华中农业大学硕士学位论文


An Empirical Analysis of the Relationship between Foreign

Direct Investment and the Agricultural Sector of Nigeria

研究生 : 艾杜

学号： 20102060004

指导教师 : 刘颖教授

指导小组 : 陶建平教授

夏侯俊教授

朱再清教授

张红梅副教授

柳鹏程副教授

专业：国际贸易研究方向：经济学管理

获得学位名称：管理学硕士获得学位时间：2013 年 6 月

华中农业大学经济管理学院

二○一三年六月

An Empirical Analysis of the Relationship between

Foreign Direct Investment and the Agricultural Sector of

Nigeria

A THESIS SUBMITTED TO THE COLLEGE OF ECONOMICS AND

MANAGEMENT


WUHAN

CHINA

IN PARTIAL FUFILMENT OF THE REQUIREMENTS FOR THE DEGREE OF

MASTER OF SCIENCE IN INTERNATIONAL TRADE

BY

IDOWU AYODEJI ADETUNJI


JUNE, 2013

DEDICATION

My dedication goes to my primary source-the Almighty God who has made it

possible for me to undertake this study.


i

TABLE OF CONTENTS

摘要 ................................................................................................................................. I

ABSTRACT ...................................................................................................................... II

LIST OF ACRONYMS ................................................................................................... IV

1 INTRODUCTION ...................................................................................................... 1

1.1 Research Background ........................................................................................... 1

1.2 Problem Statement ................................................................................................ 1

1.3 Objective of the Study .......................................................................................... 3

1.4 Hypothesis Generation .......................................................................................... 3

1.5 Justification of the Study ...................................................................................... 3

1.6 Innovative Idea ..................................................................................................... 3

1.7 Thesis Structure .................................................................................................... 4

1.8 Limitation of the Study ......................................................................................... 4

2 LITERATURE REVIEW .......................................................................................... 5

3 NIGERIA AND ITS AGRICULTURAL SITUATION ......................................... 10

3.1 Geographical Location ........................................................................................ 10

3.2 Climatic and Agro Ecological Zones .................................................................. 11

3.3 Sectorial Classification of Nigeria’s Economy ................................................... 12

3.4 Importance of Agriculture to Nigeria’s Economy .............................................. 15

3.5 Overview of Nigeria’s Agricultural Sub-sectors ................................................. 18

3.5.1 Crop Sub-Sector .......................................................................................... 18

3.5.2 Livestock Sub-Sector .................................................................................. 19

3.5.3 Fisheries Sub-Sector .................................................................................... 20

3.5.4 Forest Sub-Sector ........................................................................................ 21

3.6 Factors Affecting Development of Agriculture in Nigeria ................................. 22

3.6.1 Absence of Political Will ............................................................................. 22

3.6.2 Inefficient Financing System ....................................................................... 22

3.6.3 Infrastructural Challenge ............................................................................. 23

3.6.4 Poor Farming Techniques and High Risk Aversion ..................................... 23

华中农业大学 2013 届硕士学位论文

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3.6.5 Underdeveloped Input and output markets.................................................. 24

3.7 Government Funding of the Agricultural Sector ................................................ 24

4 FOREIGN DIRECT INVESTMENT IN NIGERIA ............................................. 25

4.1 Determinants of FDI in Nigeria’s Economy ....................................................... 25

4.1.1 Market Size .................................................................................................. 25

4.1.2 Openness of the Economy ........................................................................... 26

4.1.3 Exchange Rate ............................................................................................. 26

4.1.4 Political Environment .................................................................................. 27

4.1.5 Human Capital ............................................................................................. 27

4.2 Sectorial Analysis of FDI in Nigeria’s Economy ............................................... 28

4.3 Effect of FDI on overall Economic Growth ....................................................... 29

4.3.1 Augmentation of domestic savings ............................................................. 29

4.3.2 Job creation .................................................................................................. 29

4.3.3 Enhancing efficiency ................................................................................... 30

4.4 Effort of Government at Attracting FDI ............................................................. 30

5 EMPIRICAL ANALYSIS OF ECONOMIC EFFECTS OF FDI IN THE

AGRICULTURAL SECTOR OF NIGERIA ................................................................ 32

5.1 Model Specification ............................................................................................ 32

5.2 Methodology ....................................................................................................... 33

5.2.1 Unit Root Test .............................................................................................. 33

5.2.2 Co-Integration Test ...................................................................................... 33

5.2.3 Lag length Selection .................................................................................... 34

5.2.4 Impulse Response Analysis ......................................................................... 34

5.2.5 Variance Decomposition .............................................................................. 35

5.3 Result Presentation and Analysis ........................................................................ 35

5.3.1 Descriptive statistics .................................................................................... 35

5.3.2 Stationary and Unit Root Tests Results ....................................................... 37

5.3.3 Co-integration Test Results ......................................................................... 38

5.3.4 Model Stability Diagnostic Check ............................................................... 39

5.3.5 Residual Test................................................................................................ 40

5.3.6 VAR Model Estimation Results................................................................... 40

5.3.7 Wald Test results .......................................................................................... 41


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5.3.8 Impulse response Function Results ............................................................. 41

5.3.9 Variance Decomposition .............................................................................. 43

6 SUMMARY AND CONCLUSION ......................................................................... 46

6.1 Conclusion of Research ...................................................................................... 46

6.2 Policy Recommendations ................................................................................... 47

6.3 Recommendations for Further Study .................................................................. 47

REFERENCES ................................................................................................................ 48

ACKNOWLEDGEMENT .............................................................................................. 54

APPENDIX A: VECTOR AUTO REGRESSION ESTIMATES ................................ 55

APPENDIX B: P-VALUES OF COEFFICIENTS IN VECTOR AUTO

REGRESSION MODEL ................................................................................................. 57

APPENDIX C: LAG LENGTH SELECTION CRITERIA ........................................ 60


iv

LIST OF FIGURES

Figure 3.1 Map of Nigeria ...................................................................................................11

Figure 3.2 Agro ecological Zones in Nigeria ..................................................................... 12

Figure 3.3 Map of Nigeria’s Economic Activity ................................................................ 14

Figure 3.4 Agricultural Products Import Quantity Indices ................................................. 16

Figure 3.5 Africa's Largest Agricultural producers 2010 ................................................... 18

Figure 3.6 Quantity of Fish Capture Prodcution in Metric Tonnes .................................... 20

Figure 3.7 Aquaculture Production in Nigeria ................................................................... 21

Figure 3.8 Top 10 Plantations in Nigeria ............................................................................ 22

Figure 3.9 Government expenditure on capital stock ......................................................... 23

Figure 3.10 Trend of Budgetary allocation to agriculture and output of agricultural

production ..................................................................................................... 24

Figure 4.1 Factors affecting exchange rate volatility in Nigeria ....................................... 27

Figure 4.2 Sectorial Analysis of Cumulative Foreign Direct Investment in Nigeria ......... 28

Figure 5.1 AR roots of characteristic polynomial .............................................................. 40

Figure 5.2 Response of log (FDI) to log (FDI) .................................................................. 42

Figure 5.3 Response of log (LABOR) to log (FDI) ........................................................... 42

Figure 5.4 Response of log (OUTPUT) to log (FDI) ......................................................... 43

Figure 5.5 percent log (FDI) variance due to log (FDI) ..................................................... 44

Figure 5.6 percent log (LABOR) variance due to log (FDI) ............................................. 44

Figure 5.7 percent log (OUTPUT) variance due to log (FDI) ........................................... 45


v

LIST OF TABLES

Table 3.1 Summary of Key Economic Facts ...................................................................... 15

Table 3.2 Agricultural Sector and sub-sectors contribution to real GDP in percentage ..... 16

Table 3.3 Top 20 crop commodities produced in Nigeria- Year 2011 (tonnes) .................. 19

Table 3.4 Stocks of live animals (Head) in Nigeria (2008-2011) ....................................... 20

Table 4.1 Nigeria’s GDP per Capita (2002-2011) .............................................................. 26

Table 5.1 Descriptive Statistics ......................................................................................... 36

Table 5.2 Unit root test of agricultural output for stationarity at first difference ............... 37

Table 5.3 Unit Root Test of Labor for Stationarity at First Difference .............................. 38

Table 5.4 Unit Root Test of FDI for Stationarity at First Difference.................................. 38

Table 5.5 Unrestricted Cointegration Rank Test (Trace) .................................................... 39

Table 5.6 Unrestricted Cointegration Rank Test (Maximum Eigenvalue) ......................... 39

Table 5.7 Residual Test Results .......................................................................................... 40

Table 5.8 Wald Test Results ............................................................................................... 41


I

摘要

当前尼日利亚正处于转型期，依照转型计划，政府正努力降低经济对石油出口

的过度依赖，呈现经济多元化发展，到 2020 年将尼日利亚建设成为世界二十大经

济体之一。农业发展成为促使该转型成功的主要驱动力。转型计划旨在通过鼓励对

农业领域的投资以及提升私人资本在农业领域参与度，促使农业继上世纪 70 年代

后重新成为尼日利亚经济的支柱。

然而，几届政府似乎均未成功地将农业建设成为经济支柱，分析其原因并不仅

仅是因为无作为或未制定出正确的激励政策与机制，亦非由于现行的政策执行力度

不够，更重要地是因为私人资本在该领域的参与度不够。尼日利亚的外国直接投资

（FDI）大部分流向提炼产业，例如石油、汽油产业等，以及服务业，例如通讯

业。正是由于这个原因，多数关于尼日利亚 FDI 的研究都是评估 FDI 对石油产业或

整个经济体的影响，只有少数研究对 FDI 而非农业 FDI 对农业领域的影响进行了评

估，针对性不强。

本文运用向量自回归模型对农业 FDI 与农业发展的关系进行了实证分析，评估

与预测了 1980-2007 年之间农业 FDI 对农业产出、劳动力等的影响。数据来源于尼

日利亚中央银行（CBN）统计资料（2009 年）。分析结果表明：1980-2007 年间的农

业 FDI 对农业产出无显著影响，但对农业部门劳动力有显著正影响；此外，预测结

果表明现有的农业 FDI 将不会显著地影响农业产出，但会对农业部门劳动力有显著

正影响。根据本文研究结果，政府和其他利益相关方应努力增加农业 FDI，且重点

增加农业科技领域的 FDI，提高农业生产的科技水平。

关键词：农业；FDI；尼日利亚；VAR


II

Abstract

Ongoing efforts of the current government of the Federal Republic of Nigeria to,

through its transformation agenda, diversify the economy and significantly reduce

Nigeria’s overreliance on oil and ultimately become one of the top twenty economies of

the world by the year 2020 places agriculture as the main driver to achieve this

transformation. The agenda proposes to return the agricultural sector, which prior to the

discovery of oil in the 70’s was the stronghold of Nigeria’s economy, to its place of

dominance as the leading sector in Nigeria by encouraging investments and increased

private sector participation.

Successive governments have seemingly failed in their attempts to revive the sector,

not only because of their lack of political will or inability to set the right policies and

machineries in motion nor the lack of implementation of the few existing right policies but

also, largely due to the low level of involvement of the private sector.

A large proportion of foreign direct investment (FDI) that Nigeria attracts goes into

the extractive (oil and gas) and services sectors (e.g. telecommunications) as against the

agricultural sector which is the bastion of its economy. This might suggest the reason why

many FDI studies in Nigeria have evaluated its impact on oil sector or the entire economy

as a whole. Only a few FDI studies that assessed the impact of FDI on the agricultural

sector of Nigeria exist, these studies however, also suffer a flaw; they examined FDI-

Agricultural sector relationship with FDI that flowed into the entire economy and not FDI

that was specifically obtained in the agricultural sector.

This study sought to investigate this prevalent gap in empirical analysis of FDI-

Agricultural sector relationship in Nigeria by using one of the most recent and advanced

econometric technique known as vector auto regression. This was achieved by evaluating

and forecasting the impact of FDI in the agricultural sector from 1980-2007, specifically

its impact on agricultural output and labor in a Vector Auto Regression (VAR)

environment.

Data used in this study was obtained from Central Bank of Nigeria (CBN)

statistical bulletin (2009). Results from the analysis revealed that FDI in the period

under review had no significant impact on agricultural output while it had a

significant positive influence on labor force (employment generation). In addition,

results of the forecast estimates show that the current volume of FDI would not

significantly affect agricultural output but will have significant positive impact on


III

labor. Recommendation from the conclusion of this research is for an increase in the

volume of FDI. Furthermore, the government and other stakeholders are implored to

seek FDI that will introduce improved technology into the agricultural sector even if

the opportunity cost of a reduction in labor may have to be paid.

Keywords: Agriculture; FDI; Nigeria; VAR


IV

LIST OF ACRONYMS

ARDL: Auto-Regressive Distributed Lag

BIF: Business Investment Forum

CACS: Commercial Agriculture Credit Scheme

CBN: Central Bank of Nigeria

ECA: Excess Crude Account

ERV: Exchange Rate Volatility

FAO: Food and Agricultural Organization

FDI: Foreign Direct Investment

FMEN: Federal Ministry of Environment

FRN: Federal Republic of Nigeria

GLS: Generalized Least Squares

ITD: Inter-Tropical Discontinuity

MNE: Multi-National Enterprises

NAGPER: Nigerian Agriculture Public Expenditure Review

NEPAD: New Partnership for Africa’s Development

NEPD: Nigerian Enterprise Promotion Decree

NIPC: Nigerian Investment Promotion Commission

OSIC: One-stop Investment Centre

R & D: Research and Development

UNCTAD: United Nations Conference on Trade and Investment

USD: United States Dollar

VAR: Vector Auto-Regression

VECM: Vector Error Correction Model


1

1 INTRODUCTION

1.1 Research Background

The most spectacular manifestation of globalization, which occurred since 1990, is

the increase in foreign direct investment (FDI) (Furtan and Holzman, 2004). In the last

two decades, FDI flows have grown rapidly all over the world. This is because many

countries and especially developing countries see FDI as an important element in their

strategy for economic development (Ayanwale, 2007).

Nigeria has attracted huge volumes of foreign direct investment over the years with

the extractive industry getting the lion share. It is surprising to note that the agricultural

sector which has been the main stay of Nigeria’s economy receives the lowest volume of

foreign direct investment. The agricultural sector is a branch of Nigeria’s economy that

employs about 70 percent of the population. In 1960’s, agriculture contributed up to 64

percent to the total GDP but gradually declined in the 70’s to 48 percent, further declining

in 1980 to 20 percent and 19 percent in 1985, due to the oil glut of the 1980’s (Ukeje,

2003).

A host of research has been carried out to investigate the significance of foreign

direct investment on the economy of Nigeria; however, most of these researches are

concentrated on the sector where the large chunk of these investments goes to--the oil and

gas sector. The low level of foreign direct investment in the agricultural sector might be

one major reason why not much work has been done to analyze the impact of FDI in

Nigeria’s agricultural sector. The major gap this study points out and intends to address is

that, studies that have even attempted to empirically study the impact of FDI in the

agricultural sector of Nigeria use the FDI that is obtained in the entire economy rather than

use the FDI that flows specifically to the agricultural sector. The question that then comes

to mind is; what is the impact of foreign direct investment that flows specifically into the

agricultural sector on the output and productivity of the sector? This study, with the aid of

empirical models, intends to bridge this gap and examine the agriculture FDI, and

agricultural product output relationship in Nigeria.

1.2 Problem Statement

Nigeria’s economy of today is not one comprising of numerous activities, it can be

summarized as basically involved in exporting crude oil and importing almost every other


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commodity, the rest of the economy’s pie consists of the services sector which entail the

banking and insurance services, telecommunications and the capital market.

It is sad to note that Nigeria which, during the 60’s and 70’s, was a global

powerhouse in a sector like agriculture is today now, a major importer of agricultural

products. The country has experienced a humiliating decline in productivity in virtually all

sectors of the economy.

The oil and gas sector which the country hangs on to as its lifeline is also highly

susceptible to external shocks that emanate from the roller-coaster ride of world crude oil

prices. Though, the oil sector is performing well especially with the upsurge in crude oil

prices in recent years. But, it is evident the sector alone cannot address the numerous

economic challenges that the country is facing. Nigeria’s oil wealth and a rich natural-

resource base have not been converted into improved living standards and over 54.7

percent (2004) of the population continues to live below the national poverty line (World

Bank, 2012).

The resultant effect of external and internal imbalances consequently manifest in the

country’s weak balance of payment position, astronomical level of unemployment, high

levels of insecurity, high rate of risk aversion, low capacity utilization and waning

purchasing power of the ever increasing populace. Little wonder why breaking new

grounds and diversifying the economy is now the buzz concept across all strata of

Nigeria’s economy.

The contribution of agriculture to economic growth of Nigeria in present times is

very low as against what was obtainable in the past. Nigeria’s agriculture to a large extent

still possesses the characteristics of a peasant economy that was prominent in the pre-

independence era (Adewunmi and Omotesho, 2002). In spite of the presence of abundant

primary resources required to enhance growth in the sector, it is bedeviled by a host of

problems and challenges thereby making breakthroughs and successes almost

unachievable in the sector. Potentials of agriculture in Nigeria are enormous and can only

be harnessed if the problems and challenges it faces are promptly and adequately

addressed.

If the target of the current government of the Federal Republic of Nigeria is to make

Nigeria one of the top twenty economies by the year 2020, then a holistic overhaul of the

agricultural sector that will help address major problems of the sector like low level of

capital inflows and investment is a necessary step that will go a long way in achieving that

target.


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1.3 Objective of the Study

The purpose of this study is to empirically investigate the relationship between the

levels of productivity in the agricultural sector relative to the amount of foreign direct

investment that has been obtained in the sector. We use FDI as well as other economic

variables such as labor force in the agricultural sector to explain productivity in the

agricultural sector of Nigeria.

1.4 Hypothesis Generation

In this study, the following hypotheses are to be tested;

1. The level of agricultural sector output is significantly related to the level of FDI in the

agricultural sector.

2. The level of labor generation by the agricultural sector is significantly related to FDI in

the agricultural sector.

3. FDI into the agricultural sector and output of the agricultural sector have a

complementary long-run relationship

4. Foreign investment inflows to the agricultural sector have a complementary long run

relationship with the labor generation in the agricultural sector.

1.5 Justification of the Study

This study is important because understanding the linkage between FDI flows to the

agricultural sector and the levels of productivity in the sector may be key to uncovering

channels through which FDI stimulates the growth and development of Nigeria’s

agricultural sector and consequently, to identify the policy levers that may be engineered

to maximize both inflows and gains of FDI into the agricultural sector.

1.6 Innovative Idea

This study flashes the beam light to an ignored method of examining the level of

productivity in the agricultural sector with the foreign investment that directly accrues to

the sector.


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1.7 Thesis Structure

The structure of this thesis is organized as follows; chapter one contains the

introduction of this study while chapter two consists of the literature review. Chapter three

presents an overview of Nigeria and its agricultural situation while chapter four describes

foreign direct investment in Nigeria. Chapter five contains the empirical analysis of the

economic effects of foreign direct investment in the agricultural sector of Nigeria and

chapter six consists of summary and conclusion, and policy recommendations.

1.8 Limitation of the Study

This study unlike many other FDI studies in Nigeria is sector specific. It is limited to

modeling the relationship between foreign direct investment and the agricultural sector

rather than the FDI and entire economy relationship.


5

2 LITERATURE REVIEW

Theory and evidence shows that an agricultural economy is strategic to national

development, particularly for developing countries (Okorie and Eboh, 1990). A flourishing

agricultural economy is a sign of a healthy and wealthy economy. The agricultural sector

is an important stimulus market for industrial products and agricultural commodities are

sources of raw materials for manufacturing industries.

Nigeria’s agriculture enjoyed a boisterous era between the sixties and the seventies.

In 1960’s, agriculture contributed up to 64 percent to the total GDP but gradually declined

in the 70’s to 48 percent, further declining in 1980 to 20 percent and 19 percent in 1985,

due to the oil glut of the 1980’s (Ukeje, 2003). Most literatures support (Ukeje, 2003)

assertion that oil or the oil boom period of 1971-1977 is the reason for negligence and

failure of the agricultural sector in Nigeria. Nevertheless, the decline in the growth and

development of agriculture in Nigeria cannot be placed at the doorsteps of oil alone.

A few other studies have shown that the lack of political will for development of the

sector; policy somersaults of successive governments, unavailability of the right policies,

and poor implementation of good ones also aggregate as large contributors to the decline.

Among them is (Fasminrin and Braga, 2009), they established that the main reason for the

slow agricultural development in Nigeria despite the torrent of scientific information to

engender improvement is due to poor government involvement at the level of policy

formulation and implementation. This notion is also supported by (Ugwu and Kanu, 2011)

as they claim that agriculture contributed minimally, (in terms of output, foreign exchange

derivation, and capital formation), during the period of economic reforms in Nigeria due

to policy instability, poor coordination of policies, poor implementation and

mismanagement of policy instruments, and lack of transparency.

The capital investment, productivity and income recorded in today’s agricultural

sector of Nigeria are very low. Production is still dominated by small-scale farms

characterized by small, uneconomic and often fragmented holdings, use of simple

implements (hoes and cutlasses) and unimproved planting and storage materials. Okuneye

(1995) explained that agricultural production landscape in Nigeria which is dominated by

small-scale farmers who produce about 85 per cent of the total production still employ

rudimentary techniques.

The quantity and quality of government spending in the agricultural sector leaves

much to cheer. A review to assesses the quantity and quality of public spending in

agriculture and evaluate its degree of alignment with government policy goals conducted


6

by Nigeria Agriculture Public Expenditure Review (NAGPER) released the following

outcomes; (a) Low level of public spending, (b) discrepancies in the manner agricultural

funds are been spent, (c) pattern of public spending in agriculture raises doubts about the

quality of spending, (d) difficulty in analyzing public spending in agriculture due to the

preponderance of off-budget funds, (e) poor budget execution, (f) Poor data quality and

availability hinder policy analysis, program planning, and impact assessment (Mogues et

al, 2008).

Several studies have been conducted to show the significance of public financing and

investments in the agricultural sector in Nigeria. For example, (Lawal, 2011) employed

trend analysis and simple linear regression to examine the level of government spending in

the agricultural sector and the consequential effect on GDP; the result of the study shows

that public spending does not follow a regular pattern and the contribution of agricultural

sector to GDP is in direct consonance with government funding to the sector. The

conclusion of (Lawal, 2011) is interesting as it explains that the government cannot expect

high productivity from the agricultural sector when its investments in the sector are of low

quantity and poor quality. Similarly, (Udoh, 2012) employed bounds test and

Autoregressive distributed lag (ARDL) modeling approach to analyze both short- and

long-run impacts of public expenditure and foreign direct investment on agricultural

output growth in Nigeria. Their results indicate that an increase in public expenditure has a

positive influence on the growth of the agricultural output and that government spending

has a relatively higher elasticity than foreign direct investment.

The inability of government to adequately fund the agricultural sector shows the need

for alternative sources of funding from the private sector in the form of investments. These

investments could come through domestic or foreign sources. The foreign sources of

investment are of two major forms known as foreign direct investment and foreign

portfolio investment; others include credits, aids, and grants. Foreign direct Investment

(FDI) is a type of investment where a foreign investor or firm has an active and lasting

control in an enterprise of the host economy while foreign portfolio investment (FPI) is an

investment in which the investor has passive holdings in securities such as stocks and

bonds of the foreign nation.

Between the two, FDI is considered as a crucial component especially for developing

countries (Albuquerque, 2003). In addition, (Razin, 2002) opines that FDI’s contribution

to domestic investment and output growth dominates over the contributions of FPI flows.

Further research proves that there are two major reasons for the preference for FDI; first is

the job creation potentials, transfer of foreign technology and managerial expertise, and

larger increases in per capita GDP that FDI offers (Strazicich et al. 2001) and second is its


7

stability when compared to other forms of foreign investment (Bekaert and Harvey, 1998).

Foreign investors through their investments can help to reduce what (Romer, 1993)

referred to as “idea gaps” and “object gaps” between developed and developing countries

because they could offer new knowledge and investments in physical infrastructure like

roads and factories.

This makes FDI to be one of the major adoptions to bolster funds, investment, and

development into an economy especially the agricultural sector. From the nature of the

two forms of investment, especially based on investment stability, FDI is most adaptable

and suitable to the agricultural sector of Nigeria and therefore preferred to FPI.

The debate over the benefits of foreign direct investment to promotion of growth in

developing economies is legion. For instance, (Althukorala, 2003) opined that FDI

provides much needed resources to developing countries such as capital, technology,

managerial skills, entrepreneurial ability, brand and access to markets which are essential

for developing countries to industrialize, develop, create jobs and attack the poverty

situation in their countries. Whereas, (Alfaro et al. 2004) argues that the growth enhancing

effect of FDI is only possible in countries with developed financial systems.

Studies in Nigeria such as (Omisakin et al. 2009; Egbo and Onwumere, 2009)

amongst a host of others regard FDI as significant to Nigeria’s economy while studies like

(Akinlo, 2004; Omankhanlen, 2011) disagree with that perspective. According to

(Ayanwale, 2007), the relationship between FDI and economic growth in Nigeria is

unclear. This indication is buttressed by (Imodu, 2009) who explained that previous

studies carried out on the linkage between FDI and economic growth in Nigeria are not

unanimous in their submission.

This leaves the question of the significance and sustainability of FDI to Nigeria’s

economic growth yet unanswered; therefore this creates the need to examine this

relationship from a more streamlined perspective, i.e. conducting on a sector specific

study rather than an entire economy study.

The level of FDI in the agricultural sector of Nigeria is abysmally low; Ogbanje et al

(2010) revealed that the agricultural sector suffers the heaviest marginalization in terms of

inflows of foreign investment in spite of the sector’s significance to Nigeria as a major

provider of employment, foreign exchange and economic sustenance. The major reason

for this poor record could not be far from the high level of economic and political

instability that has battered the nation’s image.


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A few studies exist that have attempted to model the relationship between Nigeria’s

agricultural output and the inflow of foreign direct investment into the agricultural sector

whereas copious amount of investigations have been conducted to model the relationship

between FDI and economic growth in Nigeria. Studies like (Ogbanje et al. 2010) that have

attempted to focus on this path also suffer a major weakness by not examining the

productivity of the agricultural sector relative to FDI based on the inflows of FDI specific

to the sector but, rather, they conducted their research with inflow of FDI to the entire

economy.

The methodologies of empirically assessing impact of FDI in these economic growth

studies are quite similar. They mostly favor time series data over cross-sectional data and a

number of them prefer the autoregressive models developed by Christopher Simms over

the traditional simultaneous equation models of the Cowles foundation. This is because

these models are more robust at detecting the dynamic interactions involved within their

framework.

Within the framework of autoregression models, different approaches and techniques

can be employed to observe dynamic interactions and establish a relationship between FDI

and economic growth. For instance, (Tang et al. 2002; Shan, 2002) utilized impulse

response function and variance decomposition technique while (Dritsaki et al. 2004)

applied a cointegration and causality approach.

A large number of the empirical studies on FDI-economic growth relationship in

Nigeria employ the cointegraton and causality approach. For instance, (Egbo and

Onwumere, 2011) found out that a positive long-run relationship exists between FDI and

GDP while (Osinubi and Lloyd, 2010) analyzed the direction and significance of the effect

of foreign private investment alongside domestic investment, and net export on economic

growth in Nigeria. They found out that these variables were all positively related to

economic growth. However (Akinlo, 2004) used error correction models (ECM) to show

that private capital and lagged foreign capital have little or no statistically significant

effect on economic growth and likewise (Omankhanlen, 2011) used OLS and discovered

that FDI has not contributed significantly to the economic growth of Nigeria.

The place of agriculture in Nigeria’s economy cannot be overemphasized, agriculture

has over the years made tremendous impact on the country’s economy and still of great

relevance even with immense rivalry encountered from the oil sector after the oil boom. If

Nigeria’s agricultural sector is to return to its place of pride in Nigeria’s economy, then


9

issue of provision of funds and increased availability of capital needs to be addressed. As

defined by (Ogbanje et al. 2010), lack of capital is the major sustenance of the vicious

circle of poverty; this provokes the need for adequate funding since the agricultural sector

is important to alleviating poverty.


10

3 NIGERIA AND ITS AGRICULTURAL SITUATION

3.1 Geographical Location

The name Nigeria was taken from the “Niger River” running through the country.

Nigeria was colonized by the British in the late nineteenth and early twentieth century but

gained independence on 1st of October, 1960. According to the (Library of Congress, 2008)

country profile on Nigeria, Nigeria is the seventh most populous country in the world and

the most populous in Africa. Nigeria is a country situated in western part of Africa with

geographical coordinates of 10 degrees 00’N, 8 degrees 00’E. It shares border with

Republic of Benin in the west, Chad and Cameroun in the east, and Niger in the North. Its

coast in the south lies on the Gulf of Guinea at the Atlantic Ocean.

The comprehensive geography of Nigeria is divided into regions such as--- the south,

or Guinea coastlands; the central region, and the northern part of Nigeria. The country

consists of 36 states or provinces with Abuja as its current capital. The Federal Ministry of

Environment of Nigeria (FMEN) in 1993 estimated Nigeria’s irrigated land to be 9,570

km2; they state that arable land constitutes about 35 percent; pasture 15 percent; forest

reserve 10 percent; settlements 10 percent and the remaining 30 percent considered

uncultivable for one reason or the other (FMEN, 2001).

Nigeria is a country of marked ecological diversity and climatic contrasts, the lowest

point is the Atlantic Ocean at sea level while the highest point is the Chappal Waddi at

2,419m (Eroarome, 2009). The geology of Nigeria is dominated by igneous structures that

form most of the highlands and hills. The rocks of the basement complex, mainly of

igneous origin, are encountered in over 60 percent of the surface area. The landforms can

simply be classified into highlands, plateau, hills, plains and river valley systems. The

landforms are more deeply dissected in the south than in the northern parts (Udo, 1970).


11

Figure 3.1 Map of Nigeria

Source: (maps.com, 1993)

3.2 Climatic and Agro Ecological Zones

The climate of Nigeria varies greatly due to its close proximity to the Equator and the

Tropic of Cancer. The tropical region which falls towards the southern part has

temperatures of 90°F while the subtropical regions in the north experience a temperature

of 60°F to 100°F. Two main seasons are prevalent in Nigeria; rainy season and dry season

widely known as Harmattan.

The weather pattern of Nigeria is dictated largely by the seasonal northward and

southward oscillatory movement of the Inter-Tropical Discontinuity (ITD). The moist

southwesterly winds from the South Atlantic Ocean, which is the source of moisture

needed for rainfall and thunderstorms to occur, prevail over the country during the rainy

season (April – October). In reverse, northeasterly winds which raise and transport dust

particles from the Sahara Desert prevail all over the country during the Harmattan period

(November – March). The overall changes in temperature, rainfall and other

meteorological parameters determine the changes in climate in the country each year

(NIMET, 2010).

Rainfall in Nigeria is characterized by both latitudinal and longitudinal variations. In

the southern part of the country, the seasons could be classified as March, April, May

(MAM), April, May, June (AMJ) and June, July, August, September, (JJAS) while the

northern area has one rainfall season – June, July, August, September, (JJAS). The June,


12

July, August and September, (JJAS) season is common to both the southern and northern

parts of the country (Omogbai, 2010). Nigeria consists of nine agro ecological zones

ranging from (i) The mangrove forest and coastal vegetation, (ii) the freshwater swamp

communities, (iii) the tropical high forest zone, (iv) the derived Guinea savanna with relict

forest, (v) the Southern Guinea savanna zone, (vi) The northern Guinea savanna zone, (vii)

The Jos plateau, (viii) The Sudan savanna, and (ix) The Sahel savanna (Oyenuga, 1967).

The principal food crops are yam, cassava, and maize in the south; millet, sorghum, and

cowpea in the drier north. Cocoa, rubber, oil palm, groundnuts, and cotton are the main

cash crops.

Nigeria is a country blessed with rivers such as; Anambra, Cross River; Gongola,

Hadejia; Kaduna, Katsin-Ala; Ogun, Owena; Osse, Sokoto; Kamadugu, Yedseram;

Osun,Yobe, and Zamfara. Nigeria is also rich in varied natural resources; some of the

prominent minerals found in this region are natural gas, petroleum, tin, iron ore, coal,

limestone, lead and zinc. The most popular animals found in the jungles of Nigeria are---

elephants, buffalo, lions, leopards, antelope, monkeys, jackals and hyenas.

Figure 3.2 Agro ecological Zones in Nigeria

Source: (www.mapcruzin.com/free-world-landuse-maps.htm)

3.3 Sectorial Classification of Nigeria’s Economy

Nigeria’s economy can be observed and studied based on the type and degree of its

production activities. Some activities fall under the category of primary or secondary

production activities while others are under tertiary production activities. Agriculture,


13

mining, and quarrying including oil and gas make up primary production activities; they

have been the lifelines of the nation accounting for about 65 percent of the real gross

output and over 80 percent of government revenues. In addition, they account for over 90

percent of foreign exchange earnings and provide 75 percent of employment.

In contrast, secondary activities comprising manufacturing, building and construction

which traditionally have greater potential to broaden the productive base of an economy

and generate sustainable foreign exchange earnings and government revenues account for

a mere 4.14 percent and 2.0 percent of gross output respectively. The last category -

services which depend on wealth generated by the productive sectors for their operations

comprise about 30 percent of gross output. Significantly, the prominence of the tertiary

category on the economy has been on the rise in the last decade accounting for over 35

percent of the growth of the real gross domestic product (GDP). Thanks to growth and

development of the telecommunications sector; the sector’s share of GDP and contribution

to growth of GDP jumped from barely 1 percent and 3 percent respectively in 2005 to over

3 percent of GDP share and over 14 percent of GDP growth respectively, in 2010. This

represents an annual average growth rate of about 34 percent in the last five years.

Similarly, wholesale and retail trade sector accelerated by more than 10 percent per annum

in the last five years, accounting for over 32 percent of GDP growth and 16 percent of

GDP during 2006-2010. Manufacturing sector’s contribution to real GDP growth which

declined from over 5 percent in 2005 to about 3.96 percent in 2009, however edged up to

4.14 percent in 2010. The lackluster performance of the manufacturing sector reflects the

appalling state of infrastructure and a constellation of other growth-inhibiting constraints

as well (World Bank, 2012).

The Nigerian stock exchange in 2011 carried out market segmentation in a bid to

restructure its industry sectors; they classed the Nigerian economy into the following;

Agriculture, construction/real estate; consumer goods, financial services, health care;

Information communication technology (ICT), industrial goods; natural resources, oil and

gas; services, utilities, and conglomerates. The structure of Nigeria’s economy is largely

oil-based which explains why transformation agenda of the country’s economic team is

focused on diversifying the economy.


14

Figure 3.3 Map of Nigeria’s Economic Activity

Source: (Eroarome, 2009)

As with many other economies of the world, Nigeria is not an autarkic nation, it also

depends on other economies for goods in which it lacks comparative advantage or cannot

efficiently produce. The World Bank explains in its 2012 report on economic overview

and performance of global economies that over the last ten years, Nigeria has been

carrying out an ambitious reform agenda. The most far reaching of those was to base the

budget on a conservative reference price for oil that is a benchmark price, with excess

saved in a special, Excess Crude Account (ECA). The economy responded with strong

growth between 2003 and 2010 – averaging 7.6 percent.

Resources from the ECA proved invaluable to Nigeria during the global financial crisis

of 2008-2009, and financed a fiscal stimulus that maintained strong growth in domestic

demand and GDP throughout this period. GDP growth expanded from 6.0 percent in 2008 to

7.0 percent in 2009. The fiscal stimulus continued into 2010 which contributed to rapid

growth in domestic demand and GDP (8.4 percent), but could also be associated with the

continuation of double-digit inflation (13.8 percent), the depletion of remaining ECA

reserves, and a remaining balance of payments deficit despite the strengthening of oil prices.

Gross monetary foreign reserves declined from USD 42 to 32 billion during the year, the

draw-down of the ECA in 2010 despite the economic recovery and stronger oil prices

exposed weaknesses in the rules surrounding the management of the fund, motivating the

government to establish the Sovereign Wealth Fund in 2011 with the purpose of adopting

stronger rules for the responsible management of the country’s oil wealth.


15

Table 3.1 Summary of Key Economic Facts

Indicators Value Year Obtained

Budget Expenditures $.03 trillion 2011

Budget revenues $.01 trillion 2009

Debt - external $.01 trillion 2011

Distribution of Family Income -

GINI Index 43.70 percent 2003

Exports $101.10 billion 2011

GDP per capita, PPP $2,399.39 2010

GDP, PPP $.38 trillion 2010

Imports $67.36 billion 2011

Industrial production growth rate 1.80 percent 2011

Inflation rate (consumer prices) 10.80 percent 2011

Labor force 51.53 million 2011

Population 170.12 million 2012

Unemployment rate 4.90 percent 2011

Source: (globaledge.msu.edu and export.gov)

3.4 Importance of Agriculture to Nigeria’s Economy

Historical evidence reveals that agriculture, especially the crop production sub-sector,

from the pre and post-independence era was the cash cow of Nigeria. It was the major

source of foreign exchange earnings for Nigeria and the highest employer of labor. In

international trade markets, Nigeria’s agriculture dominated particularly with the export of

groundnut, palm oil, cocoa, and cotton.

Agriculture was once the mainstay of the economy (accounting for over 60 percent of

GDP and 90 percent of exports at the time of independence). However, it has been

neglected in favor of the oil sector (UNCTAD, 2009). Oil and gas is now governments’

main source of revenue as it contributes about 70 to 80 percent of revenue purse and

comprises over 90 percent of export earnings and 25 percent of GDP. The development of

the oil and gas sector led to a neglect of the agricultural sector and consequent decline of

the sector. Agriculture value added to the GDP declined from 48.6 percent in 2002 to 32

percent in 2006 (World Bank, 2012).

The country’s failure to benefit from its vast agricultural resources has turned it into a

net importer of agricultural commodities. The value of agricultural imports in 2010 was a


16

whooping USD 56,400 million, an increase from the 2009 value of USD 4,885 million

while the value of agricultural exports for 2010 was a paltry USD 1164 million, though a

rise from the 2009 figure of USD 991 thousand. Thus, agricultural sector of Nigeria has

not recorded much growth; average growth rate from 2005- 2011 is 0.96 percent (FAO,

2012). The declining competitiveness of Nigeria’s agriculture reflects the steep decline in

exports and rise in imports of agricultural commodities (Walkenhorst, 2007).

Figure 3.4 Agricultural Products Import Quantity Indices

Source: (FAO, 2012)

Poverty in Nigeria is high and concentrated in the rural areas. As depicted by (Eboh,

2010), agricultural sector performance and the poverty trend are somewhat associated;

when Nigeria recorded negative annual average agricultural growth from 1981-1985, the

poverty trend increased from 28 percent in 1980 to 43 percent in 1985. However, from

1986-1990 when the country recorded higher annual average agricultural growth (6.7

percent per annum); the poverty trend declined from 43 percent in 1985 to 34 percent in

1992. Again, a decline in annual average agricultural growth (2.4 percent per annum) from

1990-1996 was accompanied by subsequent increased poverty from 34 percent in 1990 to

65.6 percent in 1996.

Table 3.2 Agricultural Sector and sub-sectors contribution to real GDP in percentage

Activity Sector 2005 2006 2007 2008 2009 2010 2011

Crops 36.69% 37.20% 37.48% 37.56% 37.16% 36.40% 35.80%

Livestock 2.61% 2.63% 2.64% 2.66% 2.65% 2.61% 2.60%

Forestry 0.54% 0.54% 0.53% 0.53% 0.53% 0.52% 0.51%

Fishery 1.36% 1.37% 1.37% 1.38% 1.37% 1.34% 1.32%

Total for Agriculture 41.20% 41.74% 42.02% 42.13% 41.71% 40.87% 40.23%

Source: (CBN Statistical Bulletin, 2011)


17

The story of agriculture in Nigeria today is not as impressive as it was before; it has

been displaced by the oil and gas sector, also the services sector such as

telecommunication is pulling its weight against it. However, concerted efforts by

government to improve the sector can awake the sleeping giant and return it to its place of

pride. The major focus of the Nigerian government for agriculture is sufficiency in food

production and surplus for use as industrial raw materials for export. The priority areas

include:

All aspects of direct agricultural production, but in particular, rehabilitation of

groundnut, cotton, cocoa and oil palm production, fish production and forest

reserves.

Investment in processing of agricultural produce and storage facilities.

Investment in processing of agricultural input supply and distribution.

Agricultural mechanization e.g. Adoption and use of farm equipment (such as

bulldozers, tractors, etc.) including the provision of land clearing and land

preparation services.

Agricultural support activities including research and funding of research activities.

Water resources development, especially for irrigation and flood control

infrastructures along river basins.

Development of earth dams and construction of wash bores and tube wells.

Development and fabrication of appropriate small-scale and mechanized

technologies for both on-farm processing (e.g. Threshing) and secondary

processing of agricultural produce for consumption or storage.

Provision of an enabling policy environment for private sector to take the lead in

production and value chain processes.

Promotion of inclusive policy implementation.

Increasing annual budgetary provision to 10 percent for agricultural development

in line with the Maputo declaration of 2003 as from 2011.

Review of commodity marketing policy.

Review of rural development sector strategy to raise the quality of life of rural

dwellers.

Liberalization of the markets for fertilizer and other inputs through the private

sector and government subsidy to be administered through innovative targeted

voucher scheme.


18

Promotion of private seed company involvement and community seed

development while quality control is assured by the national agricultural seed

council.

Increase in tractor density from 0.3 horse power per hectare by 2015 through

private sector participation.

Figure 3.5 Africa's Largest Agricultural producers 2010

Source: (FAOSTAT, 2012)

3.5 Overview of Nigeria’s Agricultural Sub-sectors

The agricultural sector of Nigeria is categorized into four sub-sectors namely; Crop

production, Livestock production, Fisheries production and Forestry Production.

3.5.1 Crop Sub-Sector

Food crops account for the bulk of the Nigeria’s agriculture. Major food crops in the

southwestern Nigeria include: plantain/banana (Musa spp), maize (Zea mays L.), rice

(Oryza sativa L.) and root crops such as cassava (Manihot esculenta Crantz), yam

(Dioscorea spp), sweet potato (Ipomoea batatas(L.) Lam), and cocoyam (Xanthosoma

spp). In the savannah zone; sorghum (Sorghum bicolor l.), maize, millet (Pennisetum

glaucum) and cowpea (Vigna unguiculata (L.) Walps) (Okigbo, 1980; Mudahar, 1986).

The contribution of crop production to the agricultural sector’s GDP has been on a

steady rise, the latest value for net crop production index (2004-2006 = 100) in Nigeria

was 99.3 as of 2011. Between year 2000 - 2011, the value for this indicator has fluctuated

between 79.44 in year 2000, 105.00 in 2006 and 98.67 in 2010 (FAOSTAT, 2013). The


19

crop production sub-sector ranks the highest amongst contributions from all the other sub-

sectors of the agricultural sector.

Table 3.3 Top 20 crop commodities produced in Nigeria- Year 2011 (tonnes)

Ranking Crop Commodity Value

1 Cassava 52,403,500

2 Yams 37,115,500

3 Maize 9,180,270

4 Oil palm fruit 8,500,000

5 Sorghum 6,897,060

6 Fresh Vegetables 6,000,000

7 Paddy Rice 4,567,320

8 Citrus fruit 3,500,000

9 Cocoyam 3,265,740

10 Groundnuts with shell 2,962,760

11 Sweet potatoes 2,725,000

12 Plantains 2,700,000

13 Cowpeas (dry) 1,860,800

14 Tomatoes 1,504,670

15 Sugar cane 1,450,000

16 Palm oil 1,350,000

17 Millet 1,271,100

18 Fresh Fruits 1,250,000

19 Onions 1,238,090

20 Okra 1,060,620


3.5.2 Livestock Sub-Sector

Nigeria is the largest livestock producer in Sub-Saharan Africa. However, Ethiopia

and Sudan respectively have the largest livestock population in the African continent

(Lamorde, 1998). The estimated domestic ruminant population in Nigeria has been put at

13.9 million cattle, forming 60 percent of the livestock population, 34.5 million goats, 22

million sheep (both accounting for 35.2 percent of the total population of the world’s small


20

ruminants) equine and camels account for 3.6 percent and 0.6 percent of the livestock

population respectively (RIMS, 1992).

Table 3.4 Stocks of live animals (Head) in Nigeria (2008-2011)

Livestock 2008 2009 2010 2011

Cattle 16,293,200 16,435,000 16,013,400 18,871,400

Goats 53,800,400 55,145,400 56,524,100 57,300,000

Pigs 6,908,030 7,184,360 7,471,730 7,700,000

Sheep 33,874,300 34,687,300 37,422,600 38,000,000


3.5.3 Fisheries Sub-Sector

Nigeria enjoys exclusive fishing rights over 256,000 Km2 of the adjoining Atlantic

Ocean (80 Km coastline x 320 Km) termed 'Exclusive Economic Zone' (E.E.Z.). Nigeria's

Fishing Industry is classified into Artisanal fishery and Industrial fishery. Artisanal fishery

is carried out in Costal and brackish waters as well as inland in lakes and rivers while

industrial fishery is carried out in deep coastal water as well as deep sea water and

includes shrimping (Oyatoye, 1982).

The fisheries subsector, though records the lowest contribution to agriculture GDP is

also an important subsector in the Nigerian economy. According to Central Bank of

Nigeria‘s published figures, a total of about 6 percent to 7 percent of Nigeria’s

Agricultural GDP has been realized from fisheries from 2005 - 2011.

Figure 3.6 Quantity of Fish Capture Prodcution in Metric Tonnes

Source: (FAO Country STAT Nigeria, 2013)


21

Figure 3.7 Aquaculture Production in Nigeria

Source: (FAO Fisheries and Aquaculture Department, 2013)

3.5.4 Forest Sub-Sector

The forestry sub-sector compared to the fisheries sub-sector performs a little better in

its contribution to agricultural GDP and overall development of the agricultural sector in

Nigeria. The sub-sector faces two main challenges to its growth which are; (i) A Low

proportion of rainforest suitable for trees to grow relative to the total land mass of the

country, only about 11 percent of the total land mass in Nigeria is earmarked as public

forest estate out of which 26 percent is in the high forest area (Aribisala, 1993; Aziakpono,

1994), and (ii) the gregarious exploitation of round logs for export until its ban in 1976

during the oil glut era (Ogunwusi, 2012).

This over exploitation of the wood resources has impacted negatively on the

development of the forest products industry. Historically, the forest products industry in

Nigeria was one of the most developed within the Nigerian economy in the 1960’s to the

early 1970’s. During this period, export of wood products and agricultural commodities

provided more than 70 percent of the country’s GDP. However, these challenges coupled

with several other factors such as aging of equipment resulted in the dwindling fortune of

the country’s forest industry (Ogunwusi, 2012).


22

Figure 3.8 Top 10 Plantations in Nigeria

Source: (FAO Country STAT Nigeria, 2013)

3.6 Factors Affecting Development of Agriculture in Nigeria

The agricultural sector has a huge potential to transform and diversify Nigeria’s

economy and make it less dependent on oil and gas. However, a host of constraints act as

stumbling blocks towards the attainment of this potential. Some of the factors that impede

the development of the agricultural sector are summarized into the following;

3.6.1 Absence of Political Will

This factor is the skeleton for the framework of other factors. Previous administration

and handlers of the agricultural sector have displayed a weak political impetus to drive

change in the agricultural sector. It is the job of the government to create an enabling

environment, develop infrastructure, safeguard investments, reduce official bottlenecks,

and motivate local and international investors to participate and increase investments in

the sector.

3.6.2 Inefficient Financing System

Low level of participation by the local and foreign investors, and insufficient credit

systems all contribute to unavailability of capital for the main stakeholders of the sector

predominantly the farmers. The complexity of some of the credit systems is a major

discouragement to these farmers. Although, the federal government through the Central

Bank of Nigeria set up Commercial Agricultural Credit Scheme (CACS) to give farmers

access to long-term funds at low interest rates. However, private commercial banks also


23

need to improve their participation and lower the interest rate for credits. The task of

financing is daunting and cannot be left for the government alone.

3.6.3 Infrastructural Challenge

Resources available to the sector is minimal and should therefore be efficiently

disbursed, enormous amount of funds have been leached away through subsidization of

fertilizer schemes which are usually hijacked by corrupt government officials and their

cohorts. These funds should instead be used to develop more rural and basic infrastructure,

access roads from farm to the markets should be built to facilitate the flow and sale of

commodities at good prices and most importantly to minimize post-harvest losses. Other

infrastructures such as irrigation and storage facilities should also be made more available.

Limited accessibility cuts small-scale farmers off from sources of inputs, equipment and

new technology and this keeps yields low.

Figure 3.9 Government expenditure on capital stock

Source: (FAO, 2012)

3.6.4 Poor Farming Techniques and High Risk Aversion

The lack of sophisticated and modern management procedures to address climatic

factors and incidence of pest and diseases pose adverse effects on yield. Also,

technological level is low and there is limited availability of quality seed varieties and

crop management packages


24

3.6.5 Underdeveloped Input and output markets

The subsistence nature of farms implying the minuteness in size and multiple

fragmentations is one of the major impediments to agricultural progress and efficiency.

Surveys in Nigeria show that 44 percent of male farmers and 72 percent of female farmers

across the country cultivate less than 1 hectare of land per household.

3.7 Government Funding of the Agricultural Sector

One of the most effective and majorly used instruments of financing the agricultural

sector in Nigeria is the Budget. Budget for agricultural sector at all levels of government is

channeled through two main frameworks which are “recurrent expenditure and capital

expenditure”. Mogues, et al. (2008) indicates that public spending in the agriculture sector

of Nigeria is “astronomically” low. Less than 2 percent of total federal expenditure was

allotted to agriculture during 2001 to 2005; far lower than spending in other key sectors

such as education, health, and water contrasting dramatically with the sector’s importance

in Nigeria’s economy and the policy emphasis on diversifying away from oil, an allotment

well below the 10 percent goal set by African leaders in the 2003 Maputo agreement.

However, (Adofu, et al. 2012) discovered that this minimal budgetary allocation to

agricultural sector still has a significant effect on agricultural production. Figure 3.10

overleaf shows the trend between budgetary allocation to the agricultural sector and output

of agricultural production, this trend displays the existence of a positive relationship.

Figure 3.10 Trend of Budgetary allocation to agriculture and output of agricultural

production



25

4 FOREIGN DIRECT INVESTMENT IN NIGERIA

4.1 Determinants of FDI in Nigeria’s Economy

A good number of theoretic and empirical studies have outlined various factors that

determine the inflow of FDI to Nigeria. Authors such as (Anyanwu, 2011) follow the

theoretic angle while from an empirical perspective, (Singh and Jun, 1995) highlight

export orientation as the strongest variable for explaining why a developing country

attracts FDI. Many other authors have carried out different empirical analysis to study the

determinants of FDI to Nigeria. Some of which are; (Dinda, 2010) and (Adefeso; Essien;

Eshenake; Nurudeen; and Okpara, 2012). Results from these studies interestingly differ.

For instance, (Nurudeen, 2012) reveals that Nigeria’s market size (GDP) has a significant

negative effect on FDI while (Anyanwu, 2011) negates that revelation.

The determinants of FDI are enormous, though 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; nevertheless, it is clear that a critical minimum of these

determinants must be present before FDI inflows begin to occur (Ngowi, 2001). Some of

the outstanding common factors that determine foreign investment in Nigeria include but

not limited to the following;

4.1.1 Market Size

Market size is significant because it is one of the first considerations of foreign

investors, traders, and potential immigrants about an economy. It is measured as GDP per

capita. With reforms in economic policy, investment laws and also improved financial

system, Nigeria’s market size is growing in terms of purchasing power along with a vast

population.

Historically, from 1960 until 2011, Nigeria GDP per capita averaged USD 371.67

reaching an all-time high of USD 561.90 in December of 2011 and a record low of USD

236.39 in December of 1968 (World Databank, 2013). FDI in Nigeria is resource-seeking

and therefore undermines the importance of market-size as a key determinant as

(Chakrabarti, 2001) asserts that market size should be the key determinant for market-

seeking FDI not for resource-seeking FDI. Most studies on FDI determinants in Nigeria


26

such as (Anyanwu, 1998; Ibrahim, 2008; Nurudeen, 2012) to mention a few all rate

market size as a significant factor in attracting FDI. However, (Dinda, 2010) does not

share this view.

Table 4.1 Nigeria’s GDP per Capita (2002-2011)

Indicator Name 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011

GDP per capita growth

(annual %)

-1 8 8 3 4 4 3 4 5 5

GDP per capita (current

US$)

455 508 644 803 1015 1129 1375 1091 1443 1502

GDP per capita

(constant 2000 US$)

371 399 431 443 459 476 492 514 541 566

Source: (World Databank, 2013)

4.1.2 Openness of the Economy

It is generally expected and accepted that a country’s degree of openness to

globalization and international trade should positively affect the inflows of FDI to the

country particularly inflows of resource-seeking or export-oriented FDI. It is usually

measured as (X + M)/GDP where X is import and M is export. Chakrabarti (2001) defines

it as trade intensity which refers to the ease with which capital can be moved in or out of a

country by investors. Most empirical studies on the determinants of FDI in Nigeria such as

(Anyanwu, 1998; Dinda, 2010; Adefeso, 2012) agree that openness of the economy is of

key essence to attracting FDI.

4.1.3 Exchange Rate

Exchange rate is keenly related to foreign investments as it is the rate at which a

foreign currency can be purchased or transacted locally. Nigeria in 1986 shifted from the

fixed to a flexible exchange rate system in a bid to achieve growth in exports, reduce high

rate of imports, attract more FDI, and improve the overall economy. When measured using

its nominal value, it serves as opportunity cost for potential investors who use the rate to

compare with what obtains in other parts of the world (Edun, 2011). Masayuki and

Ivohasina (2005) explain that if the exchange rate of a country depreciates, it attracts FDI

since foreign firms may merge with or acquire domestic industries.

However, (Ogunleye, 2009) argues that exchange rate volatility is being increasingly

recognized as a disincentive to the choice of the region as FDI destination because it adds


27

to the list of risks inherent in the region. In fact his study reveals endogeneity between

exchange rate volatility and FDI inflows in Nigeria. In addition, (Udoh and Egwaikhide,

2008) assert that exchange rate volatility exerts a negative effect on FDI. This implies that

stability of exchange rate is germane to the flow of FDI to Nigeria as against the recurring

phenomenon of volatility been experienced.

Figure 4.1 Factors affecting exchange rate volatility in Nigeria

Source: (Shehu, 2012)

4.1.4 Political Environment

The importance of a good and stable political environment as one of the key

determinants of the inflows of foreign direct investment into the host country cannot be

underscored. A stable political environment is simply a booster of investor confidence and

it is identified by the probability of change of government and the frequency of occurrence

of political violence. Nigeria still has some hurdles to cross in order to attain political

stability.

4.1.5 Human Capital

This refers to the availability of people or workforce that has requisite knowledge,

capabilities, skills, and experience to cope with the pace of emerging and dynamic

technologies. Human capital can be measured using the demography of the work force,

rate of recruitment and security of jobs, level of training and the performance level or

output.

Liquidity

Surfeit

Huge

Service Debt

Payments

High

Arbitrage

Premium

High Import

Dependency

Macroecono

mic

Instability

Exchange

Rate

Volatility


28

The nexus of wage rate and level of skill of workforce greatly influence the flow of

FDI to the host country. Countries with lower wage rate have the advantage of attracting

more FDI for instance the call centers of major telecommunication companies in U.S.A

operates from India because the labor in India is relatively cheaper compared to U.S.A.

However, FDI flows more into countries with a highly skilled labor force. Although wage

rate in Nigeria is relatively low, it is unbalanced by an equally minimally skilled labor

force.

4.2 Sectorial Analysis of FDI in Nigeria’s Economy

A sector wise analysis of FDI in Nigeria would be inchoate without a brief on the

source of foreign direct investment to Nigeria. USA is Nigeria’s’ greatest source of FDI

attaining USD3.4 billion in 2008. The UK is another key FDI contributor as it accounts for

about 20 percent of Nigeria’s total foreign investment. China is also following the trail to

be one of Nigeria’s most important sources of FDI; in Africa, Nigeria its second largest

trading partner. From USD3 billion in 2003, China’s direct investment in Nigeria is

reported to be now worth around USD6 billion. Other significant sources include Italy,

Brazil, Netherlands, France, and South Africa.

However the lion share of foreign direct investment (FDI) into Nigeria is in the oil

and gas sector, for instance receives 75 percent of China’s FDI into Nigeria goes into oil

and gas. Figure 4.2 below, depicts the distribution of FDI in various sectors of Nigeria’s

economy. The agricultural sector is one of the least attractive sectors for FDI in Nigeria.

Through 1970 to 2001 the sector comprised only 1.7 percent of the total FDI (FAO, 2012).

Figure 4.2 Sectorial Analysis of Cumulative Foreign Direct Investment in Nigeria



29

4.3 Effect of FDI on overall Economic Growth

FDI is seen to complement scarce domestic financial resources. It is also expected to

help modernize production by transferring know-how and technology while increasing

domestic productivity and competition, and improving international competitiveness

(Ernst, 2005). Several other literatures provide insights to how FDI can impact the

economy of a developing economy. For instance, (Adams, 2009) used the theory of

development and world systems to convey his points on angles by which FDI can impact

the economy of the host country.

4.3.1 Augmentation of domestic savings

Domestic savings in Nigeria is abysmally low; this is due to the low production

coupled with over-dependence on primary commodities. With the accumulation of foreign

capital inflows, the domestic resources of any economy are augmented thereby enhancing

economic development. Multinational enterprises (MNE’s) by the virtue of their large size

and financial strength have access to financial resources not available to many host

countries domestic firms, the funds may be gotten from the capital market or sourced

internally from the company because of their size and reputation (Edun, 2011).

For capital-scarce developing countries like Nigeria, such offshore capital inflows

are desirable as they help to stimulate investment, employment and growth. A high inflow

of foreign private investment would lead to rise in gross domestic investment, which will

in turn lead to growth (Anthony, 2011). According to a World Bank report released in 2011,

Foreign capital inflow, which comprises Foreign Direct Investment, FDI, (investment in

real assets) and Foreign Portfolio Investment (investment in financial assets) in Nigeria for

2010 stands at N7.7 billion (Afego, 2012).

4.3.2 Job creation

One of the reasons why policy makers of countries strive to attract foreign direct

investment (FDI) is to create new jobs in their economies. The impact of FDI’s on

employment can be direct when, for instance, a foreign company employs a number of the

host country’s citizens while it could be indirect when jobs are created for local suppliers

and people who are not directly connected to the company as a result of increased

spending by either the company or its employees. Specific to Nigeria, more investments in

manufacturing and other extractive sectors would lead to increase in the number of jobs


30

4.3.3 Enhancing efficiency

Borensztein et al. (1997) in their study suggests that FDI is an important vehicle for

the transfer of technology. When multinational enterprises invest in a foreign country, they

often transfer significant technology. Adams (2009) corroborates this submission in his

study; he asserts that in the context of developing countries, FDI contributes to the

economic development of the host country by enhancing its efficiency through the transfer

of new technology, marketing and managerial skills, innovations and best practices.

4.4 Effort of Government at Attracting FDI

It is believed that the private sector is the primary actor in trade and investment

while the major role of government especially in developing countries like Nigeria, should

to create an environment conducive for private economic activities. The effort of various

governments to attract FDI into Nigeria has been to repeal laws that are inimical to foreign

participation in Nigeria’s business environment. This they did by tweaking the first and

second indigenization policy of 1972 and 1977 respectively then under the Nigerian

Enterprises Promotion Decree (NEPD); they tightened or loosened different aspects of the

policy till a complete release of restrictions of foreign participation was achieved in 1999

when Nigerian Investment Promotion Commission (NIPC) was set up in a bid to create an

investment friendly environment for accelerated inflow of foreign investment into the

national economy.

At the continental level, the Nigerian Government aligns with the rest of Africa

under the New Partnership for Africa’s Development (NEPAD) while at the national level;

it pursues the agenda of attracting FDI through (NIPC). NIPC was created to serve as the

first point of contact for investors who intend to set up projects in any sector of the

Nigerian economy. It is charged with the responsibility of attracting, promoting and

coordinating investment promotion activities in the Nigerian economy. The commission

since its commencement of operations has organized Business and Investment Forums

(BIFs) across the world, mainly to showcase the investment opportunities in Nigeria, the

incentives available to investors, and various reforms being undertaken to improve the

investment climate. Innovatively, the commission set up a One-Stop Investment Centre

(OSIC) where investors can interface with all relevant government agencies at a single

location where it also networks, and match-make both local and foreign investors.


31

These efforts significantly impacted the inflow of FDI mostly in non-oil sectors;

from USD1.0billion in 1999 to USD20.0billion in 2008, and slight decline to

USD12.0billion in 2009 due to the global meltdown/economic crises (UNCTAD Yearly

Reports). (Zakari et al. 2010) employed an independent t-test to empirically evaluate the

role of NIPC in attracting FDI to Nigeria and revealed that within his observed time frame

the commission succeeded at improving Nigeria’s lot with regards to inflow of FDI. The

government has also explored other means to attract foreign investments such as;

establishment of Free Trade zones which has been particularly successful in Nigeria,

developing investment incentives such as terminal tax holidays, exemption duties and but

not limited to setting up specialized committees such as Doing Business and

Competitiveness Committee’ and `Investor-Care Committee to specifically handle the

interests of foreign investors.


32

5 EMPIRICAL ANALYSIS OF ECONOMIC EFFECTS OF

FDI IN THE AGRICULTURAL SECTOR OF NIGERIA

The core objective of this empirical study is to evaluate and forecast the impact of

foreign direct investment in the agricultural sector of Nigeria from 1980-2007 by

employing a vector auto regression (VAR) model to assess its impact specifically on

agricultural output and labor.

5.1 Model Specification

The model employed in this study is a vector auto regression system of three time

series variables; FDI, output and labor consisting of three equations. Each of the variables

serve as the dependent variable in each of the equations while the regressors in all the

equations are lagged values of all the variables.

An unrestricted VAR with lag length p can be expressed as:

1 1 ... .....................................................................................(5.1)t t p t p tY C Y Y

Where Yt denotes a vector of variables (agricultural output, labor and FDI), C

represents a vector of corresponding constant terms; Φ1,…,Φp are matrices of coefficients

and Ψt is an unobservable zero-mean independent white noise process. This model is often

referred to as a VAR(p) process because the number of lags are the same “p”.

Given three endogenous variables, the basic VAR model can be mathematically expressed

with the following estimation equations:

1 1 1 1 1

1 1 1

1 1 2 3 ..................................................(5.2.1)k k k

t j t j j t j j t j t

j j j

Y Y Y Y

2 1 1 1 2

1 1 1

2 1 2 3 .................................................(5.2.2)k k k


j j j

Y Y Y Y

3 1 1 1 3

1 1 1

3 1 2 3 ..................................................(5.2.3)k k k


j j j

Y Y Y Y

Where the 's re the stochastic error terms called impulses or innovations or shocks,

while Y1, Y2 and Y3 are the variables and K is the maximum lag length. Lutkepohl (2007)


33

explains that given a sample y1,…,yt and pre-sample values of y-p+1,…,y0, the K equations of

a an unrestricted VAR as we employed in this study may be estimated separately by least

squares (LS) without losing efficiency relative to generalized LS (GLS) approaches. In

fact, in this case LS is identical to GLS. Under standard assumptions, the LS estimator is

consistent and asymptotically normally distributed.

5.2 Methodology

5.2.1 Unit Root Test

Sharp (2010) defines unit root theory as the cornerstone to the methodology used for

testing the stationarity or non-stationarity of a time series. Differencing of variables in a

VAR model to the first or second order helps to eliminate their stochastic trend and unit

roots. To carry out unit root test in this study, we will employ the Augmented Dickey-

Fuller tests using three test equations. These equations are mathematically expressed as;

1 1

1

.................................................................................(5.3.1)k

t t t i t t

i

y y y y

1 1

1

...................................................................................(5.3.2)k

t t i t t

i

y y y

1 1

1

............................................................................(5.3.3)k

t t t i t t

i

y y y y

Where 1t t ty y y is the first difference of the series; ρ, ∝ and are parameters

to be estimated while μ is a stochastic disturbance term.

5.2.2 Co-Integration Test

Co-integration indicates the presence of a causal relationship between the variables

but it fails to show the direction of this relationship. If a variable does not have unit root at

first difference, then the variable is said to be integrated at “first difference” (I1), if the

variable has unit root at first difference, then we move on to check at second difference, if

it does not have unit root at this point, then the variable is said to be integrated at “second

difference” (I2). The implication here is that; only variables that have the same integration

order can be employed in a model.

1

ˆln 1 .................................................................................................(5.4.1)n

trace i

i r

T


34

max 1ˆln 1 ....................................................................................................(5.4.2)rT

Where trace is the trace statistic, max is the eigen-max statistic, 1̂ denotes the

smallest Eigen-values, and T is the sample size. The null hypothesis tested in trace is no

co-integration.

5.2.3 Lag length Selection

Lagging can be explained as a system of creating new variables from or within the

same variable. A VAR model that employs same lag length for each of the variables is

referred to as VAR(p) model. Determining the optimal lag length is important as Lutkepohl

(2005) asserts that choosing lags unnecessarily (over-lagging) will reduce the forecast

precision of the corresponding estimated VAR(p) model whereas under-lagging a model

also could result in loss of potentially useful information from the distant past.

This study employed Schwarz (1978) info criterion to determine lag length. This criterion

alongside Bayesian information criterion are the most applied in the empirical analysis of

both univariate and multivariate time series.

( ) 2

1

1 lnln ( ) ...........................................................................................(5.5)

Tp

t

t

TSC m

T T

Where µt(p)

are the estimated residuals of the VAR(p) process, while m is the number

of estimated parameters and T is the sample size. We used this information criterion to

help automatically select the optimal lag length. This decision is supported by the

assertion of (Ivanov and Kilian, 2005) that Schwarz Information Criterion (SIC) is more

accurate for finite sample size.

5.2.4 Impulse Response Analysis

The most unambiguous way to analyze causality between variables in a VAR

framework is to trace out the effects of shocks in those variables by computing and

observing their interactions through an impulse response function; also with this system,

individual coefficients in the estimated framework can be easily interpreted. With impulse

response function, we can determine the response of one variable to an impulse or

innovations or shocks in the error term of another variable in a system that involves a

number of other variables as well. Lutkepohl (2007) provides a mathematical definition

explaining that if the process yt is I(0), it has Wold’s moving average (MA) representation.


35

0 1 1 2 2 ...,........................................................................................(5.6)t t t ty u u u

Where represents coefficients of this equation, they may be interpreted as

reflecting the responses hitting the system.

5.2.5 Variance Decomposition

As against the objective of impulse response functions, the aim of variance

decomposition is to obtain accurate information about forecast ability even though both

computations are useful in assessing how shocks to economic variables reverberate

through a system. (Kirchgasner and Wolters, 2007) formulate an equation to show that

variance can be decomposed into those parts that are generated by the impact of the

individual innovations m , 1,...m k on the variable j when a forecast over periods is

performed.

1

0

12

1 0

( )

, 1,... , 1,2,..............................................................................(5.7)

( )

i

jm

ijm k

i

js

s i

m k

With an increasing time horizon, i.e. for , it is not only the variance of the

forecast error but also the variance of the variable itself that can be decomposed into those

fractions that are generated by the different innovations m . As these fractions are by

construction, orthogonal to each other, they add up to one. Thus analysis of the forecast

error leads to a decomposition of the variances of the variables in the system. It is also

important to consider the ordering of the variables when conducting these tests, as in

practise the error terms of the equations in the VAR will be correlated, so the result will be

dependent on the order in which the equations are estimated in the model.

5.3 Result Presentation and Analysis

5.3.1 Descriptive statistics

Table 5.1 below presents descriptive statistics of the macroeconomic data in natural

log form; it shows the minimum and maximum values of data with as well as their

standard deviation.


36

Table 5.1 Descriptive Statistics

LOG (FDI) LOG (LABOR) LOG (OUTPUT)

Mean 6.163550 9.437671 11.17186

Median 7.097342 9.440102 11.42612

Maximum 7.192859 9.448491 11.84764

Minimum 4.764735 9.418330 9.975622

Std. Dev. 1.076626 0.007886 0.593251

Skewness -0.342763 -1.045413 -0.809189

Kurtosis 1.247110 3.199716 2.309315

Jarque-Bera 4.132996 5.146683 3.612223

Probability 0.126628 0.076280 0.164292

Sum 172.5794 264.2548 312.8119

Sum Sq. Dev. 31.29634 0.001679 9.502557

Observations 28 28 28

Source: (Author’s estimation, 2012)

A skewness statistics is a measure of where the data lies i.e. (if the data is balanced

around the mean or if it is weighty either to the left or right). We can express the range of

skewness mathematically as; -1 < x <1 where x is the skewness value. Table (5.1) above

shows that the skewness values of FDI and output are higher than -1 but not equal to 1

which means they are not skewed but the skewness value for Labor is close to -1 which

implies it is negatively skewed. The next statistic kurtosis is a measure of flatness or

peaking of the variables relative to their normality. Table (5.1) also indicates that kurtosis

values of labor and output are close to three which is the standard value while the kurtosis

value for FDI is a distant half of three. Having positive values of kurtosis across all the

coefficients implies that the entire distribution are peaked not flat meaning the data set is

normal and reliable for analysis. The Jarque-Bera statistic uses the skewness and kutosis to

determine if the data set has a normal distribution. It can be mathematically expressed as;

22 1

3 .........................................................................................................(5.8)6 4

Ts k

The JB statistics tests the null hypothesis that each variable in the model is

distributed normally by testing the joint hypothesis that kurtosis and skewness are near


37

three and zero respectively. It is measured against the chi squared distribution. Table (5.1)

shows that the p-value of the kurtosis for the three variables as greater than 0.05 which

implies that each of these series are evenly or normally distributed.

5.3.2 Stationary and Unit Root Tests Results

We subjected the variables in the model to a stationarity test as part of the necessary

diagnostic check and to ensure that our model is specified correctly. If the variables are not

stationary that is, having a unit root, ordinary least squares (OLS) cannot estimate the

coefficients in the model efficiently. It is only when economic variables are either

stationary or corrected and made stationary that they can be suitable for economic analysis,

forecasting and making policy decisions. This test was carried out in line with the

procedure of Dickey and Fuller (1981) in which test of test of null hypothesis of unit roots

are carried out via three test equations models namely; (1) no intercept and no trend (2)

intercept (3) intercept and trend. Accepting or failing to accept the null hypothesis is

hinged on the result of the t-statistics and p-values of the Augmented Dickey Fuller (ADF)

statistics; if the p-value is significant i.e. less than 0.05 and the t-statistics is greater than

the critical value (in absolute terms), we fail to accept the null and vice versa.

Another key reason for carrying out these tests is to determine the integration order

of the variables in the model. It is pertinent to note that integration order is determined by

taking the first or second difference of the variables and also the autoregressive function in

the statistical software (eviews 5.1) does the differencing and not the ADF test. The

variables all have an integration order of I1.

Table 5.2 Unit root test of agricultural output for stationarity at first difference

Variable Statistic Model (1)

ADF None

Model (2)

ADF

Intercept

Model (3)

ADF Trend and

Intercept

Log (output)

5 percent sig level -1.9654 -2.9810 -3.5950

ADFα -2.1368 -5.8028 -6.3071

Probability 0.0340 0.0001 0.0001

Ho: D log (output) has a unit root

Results in Table (5.2) shows that agricultural output is made stationary after first

differencing, we choose model (2) because its p-value is more significant than that of

model (1) even though they both meet the condition of t-statistics been less than the

critical value. Due to results obtained we therefore reject the null hypothesis.


38

Table 5.3 Unit Root Test of Labor for Stationarity at First Difference


ADF None

Model (2)

ADF Intercept

Model (3)

ADF Trend and Intercept

Log (labor)


ADFα -2.4680 -0.9926 -14.752

Probability 0.0158 0.7373 0.0000

H0: D log (labor) has a unit root

Results in Table (5.3) shows that labor is made stationary after first differencing, here

we choose model (1) as it meets the conditions required to reject the null hypothesis.

Therefore, we the null hypothesis of unit root is rejected.

Table 5.4 Unit Root Test of FDI for Stationarity at First Difference


ADF None

Model (2)

ADF Intercept

Model (3)

ADF Trend and Intercept

Log (FDI)


ADFα -4.6297 -5.0351 -4.9915

Probability 0.0001 0.0004 0.0024

Ho: D log (FDI) has a unit root

Results in Table (5.4) shows that FDI is made stationary after first differencing, here

we also choose model (1) as it meets the conditions required to reject the null hypothesis.

Therefore, we the null hypothesis of unit root is rejected. In the three cases, we reject the

null hypothesis (Ho). (Mackinnon, 1994) critical value for rejection of hypothesis of unit

root applied. ADFα is the critical value and D means differencing.

Source: Author’s estimation using Eviews 5.1

5.3.3 Co-integration Test Results

Since all the variables have the same order of integration, the next step will be to

obtain the number of co-integrating vector(s) and determine if our model is or is not a co-

integrated model. To do this, we will employ Johansen-Juselius maximum likelihood

method of co-integration. If our model is co-integrated, then VECM, a restricted form of

VARs will have to be used but if not, we continue with the unrestricted model. The

implication of the variables if found to be co-integrated means that they all share a

common stochastic trend and will grow proportionally, in order words, a long run

relationship exist amongst them. The JJ maximum likelihood test will be done on the

variables in their non-stationary form.


39

Table 5.5 Unrestricted Cointegration Rank Test (Trace)

Hypothesized No. of

CE(s) Eigenvalue Trace Statistic 0.05 Critical Value Prob.**

None * 0.768647 51.24535 29.79707 0.0001

At most 1 0.441895 14.65010 15.49471 0.0667

At most 2 0.002792 0.069893 3.841466 0.7915

Trace test indicates 1 cointegrating equation(s) at the 0.05 level

denotes rejection of the hypothesis at the 0.05 level

**MacKinnon-Haug-Michelis (1999) p-values

For unrestricted co-integration rank test (Trace): We reject its Ho on “No CE” while

we fail to reject the Ho’s on “At most I CE” and “At most 2 CEs”. The Trace test indicates

one co-integrating equation (CE) at 0.05level.

Table 5.6 Unrestricted Cointegration Rank Test (Maximum Eigenvalue)

Hypothesized No.

of CE(s) Eigenvalue Trace Statistic 0.05 Critical Value Prob.**

None * 0.768647 36.59525 21.13162 0.0002

At most 1 * 0.441895 14.58020 14.26460 0.0446

At most 2 0.002792 0.069893 3.841466 0.7915

Max-eigenvalue test indicates 2 cointegrating equation(s) at the 0.05 level

denotes rejection of the hypothesis at the 0.05 level

**MacKinnon-Haug-Michelis (1999) p-values

For unrestricted co-integration rank test (Maximum Eigenvalue): We reject its Ho on

“No CE” and “At most 1 CE” but fail to reject Ho for “At most 2 CEs”. The Maximum

Eigenvalue test indicates two CEs at 0.05level.

Other ways to conclude on this test would be to follow the number of CEs determined

or to identify the number of rejections (*) from both tests. Equal number of CEs or equal

number of rejections supports for VECM while unequal number of CEs or rejections

supports VAR. We can deduce that agricultural output, labor and FDI do not have a

stochastic trend justifying our use of a VAR model.

5.3.4 Model Stability Diagnostic Check

Statistically, there is a strong linkage between model stability, forecasting, and policy

analysis. It is imperative to diagnose the residuals of an autoregressive model through its

roots to verify the absence of serial correlation and normality of distribution.


40

Figure 5.1 AR roots of characteristic polynomial

Figure 5.1 shows the graphical representation of the AR roots using a complex

coordinate system. It explains that the VAR model does not have a root outside the unit

circle implying that our model satisfies the stability condition.

5.3.5 Residual Test

Table 5.7 Residual Test Results

Lags LM-Stat Prob

1 11.64723 0.2339

2 14.11512 0.1183

probs from chi-square with 9 df

Ho: no serial correlation at lag order h

Table 5.7 above shows results of residual test, we fail to reject Ho to further confirm

there is no serial correlation of the residuals.

Source: Author’s estimation from E-views 5.1

5.3.6 VAR Model Estimation Results

Every VAR environment has an equation for each of the variables; our main interest

was the equation where agricultural output is the dependent variable and lags of all the

variables as independent variables. The VAR estimates do not present the p-values for

testing the corresponding parameters. However, based on each value of the t-statistics, it is

easy to conclude whether or not a lagged variable has a significant adjusted effect on the

corresponding dependent variable, by using a critical point of t0 = 2 or 1.96. For example,

if |t0| > 2, or 1.96, then it can be concluded that the corresponding independent variable has

a significant adjusted (partial) effect. Based on the t-statistics values, OLS estimates reveal


41

that only the first lag of output is significant to explain variability in output while the other

independent variables are not significant.

However, the model has an R2 of 95.94 percent indicating that it is nicely fitted, a DW

value of 2.27 showing that the residuals in the model are not serially or auto correlated and

an adjusted R2 of 94.66 percent meaning that about 5.33 percent of the variability in

agricultural output coming from other factors were not observed in this model. For the

sake of brevity the results of the vector autoregression estimates is presented at as

appendix (A), p-values of Coefficients presented as appendix (B) and Lag length selection

criteria results presented as appendix (C).

5.3.7 Wald Test results

Wald test is an econometric property of time series variables used to test joint

significance of several independent variable coefficients on the dependent variable.

Table 5.8 Wald Test Results

Wald Test:

System: Untitled

Test Statistic Value df Probability

Chi-square 0.721276 2 0.6972

Null Hypothesis Summary:

Normalized Restriction (= 0) Value Std. Err.

C(3) 0.013480 0.109125

C(4) -0.053250 0.092126

Restrictions are linear in coefficients.

Table 5.8 shows results of Wald test of joint significance on both lags of FDI. The null

hypothesis of this test is that the combination of coefficients is not significant to explain

variability in the dependent variable. In this case, we accept the null as the p-value of chi-

square is greater than 0.05.

5.3.8 Impulse response Function Results

The figures below are the graphical representation of the impulse response function; the

ordinates indicate the fluctuations caused by impacts of the units, while the abscissa shows

the duration of fluctuations. The solid line represents the response function curve or forecast

estimates while the two dotted lines define the 95 percent confidence interval. IRF helps to


42

determine in what manner or for how long each of these variables affect each other if a

shock is applied to the innovations or residual. The shock is applied to the residuals by

giving them One Standard Deviation “±2S.E”. Ordering of variables is very important when

using IRF and therefore “Cholesky dof adjusted” was used to carry out ordering.

Figure 5.2 Response of log (FDI) to log (FDI)

Figure 5.2 explains that given one standard deviation of FDI after positive impact, it

responds by trending downwards. In the first phase the response value is 23.50 percent, in

the fourth phase; response is zero and afterwards goes negative. This means that if FDI

increases over time, due to influence of certain conditions, its contribution would weaken

and after a certain period the influence would become counterproductive hindering its own

growth. It shows that FDI inflows in the agricultural sector are not smooth and easily

affected by other conditions.

Figure 5.3 Response of log (LABOR) to log (FDI)

In figure 5.3, at the beginning, the response value of labor on the shock or impact of

FDI is zero, and slides to the minimum value -0.20 percent in the seventh session as its


43

greatest response to the shock. Here the IRF is negative indicating that if the current FDI

increases due to impact of certain conditions the agricultural sector will reduce

unemployment for the next seven years or lags.

Figure 5.4 Response of log (OUTPUT) to log (FDI)

Figure 5.4 shows the response of output to a shock of FDI. Its highest fluctuation is at

the second session while the lowest is at the third session where a sharp negative trend is

observed before it again heads towards the center. The response value of output is close to

zero, that is, if the current FDI increases due to impact of certain conditions, it does not

result in any change in output, either current or during the subsequent lag.

5.3.9 Variance Decomposition

Variance decomposition literarily means breaking the variance of the error of forecast

for each variable into several components. It is a structure that helps to analyze

contribution rate of the impact of each structural change on the endogenous variable

(usually measured by variance).


44

Figure 5.5 percent log (FDI) variance due to log (FDI)

Figure 5.5 shows that in the first five periods, the change of FDI is mostly due to its

own contribution with volatility of variance between 53 -100 percent. This indicates that

pre-FDI investment in the agricultural sector has a decisive impact on the latter part of the

changes. Starting from the sixth period, the change of FDI depends on other factors, that is,

other factors play a decisive role on the change of FDI. Without considering the

contribution of FDI on its own, other factors (including labor, output, etc.) in the tenth

period contributes the change in quantity of the food reserves up to 64.92 percent, with

output’s contribution up to the maximum 53.5 percent, while the labor contribution of FDI

is very small.

Figure 5.6 percent log (LABOR) variance due to log (FDI)

Figure 5.6 explains that FDI’s contribution on the variance of the labor shows an

increasing trend, up to 9.5 percent in the first period, with increase of lag phases, FDI has

a greater impact on the labor movements, and in the tenth period, its contribution is up to

the rate of 41.14 percent.


45

Figure 5.7 percent log (OUTPUT) variance due to log (FDI)

Figure 5.7 explains that FDI’s contribution on the variance of output is close to zero,

indicating that FDI has an weak impact on the output of agricultural sector, fluctuating

between 0.26 percent -0.39 percent, virtually negligible. The effect of FDI on output is

consistent all through the lag phases.


46

6 SUMMARY AND CONCLUSION

6.1 Conclusion of Research

The relationship between agriculture FDI and agricultural sector production in

Nigeria is a new area of study. We find support for the view that there is a very low level

of FDI that flows into the agricultural sector of Nigeria. For each of the hypotheses posed

in this study, we establish the following findings; first, that FDI inflow to the agricultural

sector does not significantly affect the output of the agricultural sector while it has a

positive significant relationship on labor generation. Second, that FDI inflow to the

agricultural sector does not have a complimentary long-run relationship with output of the

agricultural sector while a complimentary long-run relationship exits with labor generation.

Therefore in respective order, we reject the first null hypothesis while we accept the

second null hypothesis and also we reject the third null hypothesis while we accept the

fourth null hypothesis. The reason for this non-significant relationship between FDI

inflows into the agricultural sector and the sector’s output could be a combination of two

factors. First, because of the low level of FDI in the agricultural sector and second, the

type of FDI that flows into the sector is not technology-oriented, i.e. the kind of FDI that

the sector receives focuses more on enhancing the sector’s capacity and capability of

providing jobs for the unemployed (irrespective of how crude or meager these jobs might

be) and focuses less on the providing the necessary level of technology required to

improve output in the sector.

Thus, we conclude that if Nigeria wants to increase the level of production and

holistically develop its agricultural sector, open policies towards FDI are important.

Nigeria does have a preponderance of human resources and natural resources, such as

water, and land which enhances the ability to produce primary agricultural products.

However, the expansion of agriculture production, reduction in reliance of import, and

attainment of food security requires capital, energy, technology, and international business

connections. It is the second list that Nigeria is lacking. FDI can serve as a ready supply of

such inputs.


47

6.2 Policy Recommendations

Based on the review of literature and empirical analysis, and findings of this research

study, the followings policies are recommended to reduce the barriers to FDI significance

in the agricultural sector of Nigeria.

1. Government should seek for more FDI into the agricultural sector since the success

of sector is essential to the attainment of a truly diversified economy in Nigeria.

Factors such as foreign ownership restrictions and multiple corporate taxes that

scare the investor from investing should be reviewed and addressed.

2. Government should not just focus on attracting FDI to the sector but attract the

type of FDI that seeks to enhance domestic capacity or domestic investment.

3. Government must further target specific types of FDI that are able to generate

spillover effects in the entire value chain of the agricultural sector and by extension

the overall economy.

4. Government should provide more funding and support for universities, colleges

and other research and development (R & D) institutions so that new innovations

can be created. The growth and competitiveness of a sector thrives on its

innovations. Where innovation is constantly occurring, there FDI will be attracted.

Overall, good corporate governance and the rule of law must be allowed to prevail so

as to not only attract FDI but to ensure that the agenda, aims and objectives of all

stakeholders are met.

6.3 Recommendations for Further Study

Diversification of the economy is the burning issue in Nigeria. This study is not an

attempt to provide all the answers to why FDI is not significant in the agricultural sector

of Nigeria. However, a more detailed research into the various sub-sectors i.e. crop,

livestock, fishery and forestry should be conducted to ascertain the sub-sector to which

FDI can best be channeled in order to maximize output.


48

REFERENCES

1. Abba, A.S. (2012). “A Research of Economic Effects of Exchange Rate Volatility in Nigeria.” PhD

Dissertation, Wuhan University of Technology, China.

2. Adams, S. (2009). “Can Foreign Direct Investment (FDI) help to promote growth in Africa?”

African Journal of Business Management, vol. 3 (5), pp. 178-183, May 2009. Available online at:

http://www.academicjournals.org/AJBM. ISSN 1993-8233 © 2009 Academic Journals.

3. Adefeso, H.A. and A.A. Agboola. (2012). “Determinants of Foreign Direct Investment Inflows in

Nigeria: An Empirical Investigation.” International Business Management, vol. 6: pp. 83-89.

4. Adewumi, M.O., and O.A. Omotesho. (2002). “An Analysis of Production Objectives of Small-

Scale Rural Farming Households in Kwara State, Nigeria.” Journal of Rural Development, vol. 25:

pp. 201-211.

5. Albuquerque, R. (2003). “The Composition of International Capital Flows: Risk Sharing Through

Foreign Direct Investment.” Journal of International Economics, vol.61, pp. 353−383.

6. Alfaro L., C. Areendam, S. Kalemli-Ozcan, and S. Sayek. (2004). “FDI and Economic Growth:

The Role of the Financial Markets.” Journal of International Economics.

7. Anyanwu, J.C. (1998). ‘‘An Econometric Investigation of the Determinants of Foreign Direct

Investment in Nigeria.’’ Annual Conference, Nigeria Economic Society.

8. Anyanwu, J.C. (2011). Determinants of Foreign Direct Investment Inflows to Africa, 1980-2007,

Working Paper Series N° 136, African Development Bank, Tunis, Tunisia.

9. Aribisala, O.A. (1993). “Raw Materials Revolution and Impact on Industrialization in Nigeria.

Mednet Publications Ltd.” ISBN 978 – 024 – 000 – 4.

10. Athukorala P.P.A.W. (2003). “The Impact of Foreign Direct Investment on Economic Growth in

Sri Lanka.” Proceedings of the Paradeniya University Research Sessions, Vol. 8, No.4. pp. 40-57.

11. Ayanwale, A. B. (2007). “FDI and Economic Growth: Evidence from Nigeria”. African Economic

Research Consortium, Nairobi. April 2007. AERC Research Paper 165. ISBN 9966-778-09-8.

12. Aziakpono, M.J. (1994). “Interest Rate Deregulation and Nigerian Agricultural Financing by

Commercial Banks”. Unpublished thesis, Nigeria: University of Benin.

13. Bekaert, G., and C.R. Harvey. (1998). “Capital Flows and the Behavior of Emerging Market

Equity Returns.” NBER Working Paper Series, 6669.

14. Borensztein, E., D.J Gregorio, and J.W Lee. (1998) “How does foreign direct investment affect

economic growth?” Journal of International Economics, vol. 45, pp. 115-135.

15. Chakrabarti, A. (2001). ‘‘The Determinants of Foreign Direct Investment pp. Sensitivity Analysis

of Cross-country Regressions.’’ kyklos, 54(1), pp. 89-114.

16. Dinda, .S. (2010). “Factors Determining FDI to Nigeria: An Empirical Investigation.” Munich

Personal RePEc Archive (MPRA). Available Online at: http://mpra.ub.uni-muenchen.de/28097/


49

MPRA Paper No. 28097, posted 12. January 2011 / 13:17. JEL Classification Number: C13, C22,

F18, Q32, O13, O55.

17. Dritsaki, M., C. Dritsaki, and A. Adamopoulos. (2004). “A Causal Relationship between Trade,

Foreign Direct Investment and Economic Growth for Greece.” American Journal of Applied

Sciences, vol.1 No.3, pp. 230-5.

18. Eboh, E.C. (2010) “Agricultural Economics of Nigeria: Paradoxes and Crossroads of Multimodal

Nature”. University of Nigeria Inaugural Lecture.

19. Edun, A.O. (2011). “A study of interactive effect of FDI on infrastructural development: Cement

contribution in Nigeria.” PhD Dissertation, Wuhan University of Technology, China.

20. Egwaikhide C.I. (2012). “The Impact of Foreign Direct Investment on Nigeria’s Economic

Growth; 1980-2009: Evidence from the Johansen’s Cointegration Approach.” International Journal

of Business and Social Science, Vol. 3 No. 6; 64(1): 89–112. [Special Issue -March 2012]

21. Ernst, C. (2005). “The FDI – employment link in a globalizing world: The case of Argentina,

Brazil and Mexico.” Employment Strategy Papers. Available online at:

www.ilo.org/public/english/employment/strat/researchpap.htm.

22. Eroarome, M.A. (2009) “Country Pasture/Forage Resource Profiles” © FAO.

23. Eshenake, J. S., and V.E. Oriavwote. (2012). “Foreign Direct Investment in Nigeria: The Roles of

Real Effective Exchange Rate, Market Size, Openness and Inflation.” European Journal of

Economics, Finance and Administrative Sciences ISSN 1450-2275. Issue 53 September, 2012 ©

EuroJournals, Inc. 2012. Available online at: http://www.eurojournals.com/EJEFAS.htm.

24. FAO (2012). “Foreign Agricultural Investment Country Profiles.” © FAO 2012. Available online

at: http://www.fao.org/tc/policy-support/investment-policy/databases-and-country-profiles.

25. FAOSTAT Data. © 2004-2013. Fishery and Aquaculture Country profiles. Nigeria. Fishery and

Aquaculture Country Profiles. In: FAO Fisheries and Aquaculture Department [online]. Rome.

Updated 5 August 2004. [Cited 13 February 2013]. Available online at:

http://www.fao.org/fishery/countrysector/FI-CP_NG/3/en.

26. Fasinmirin, J. T., and F. Braga. (2009). “Agriculture for Sustainable Food, Energy and Industrial

Development in the Sub-Saharan Africa: The Case of Nigeria”. African Journal of Food Science,

Vol. 3 (13) pp. 429-433. December 2009. Available Online at: http://www.acadjourn.org/ajfs.

ISSN 1996-0794.

27. FMEN (Federal Ministry of Environment of Nigeria) (2001). National Action Programmme to

combat desertification. Available online at: http://www.uncd.int/action

programmes/Africa/national/2001/Nigeria-eng.pdf.

28. Furtan, W. H., and J. J. Holzman. (2004). “The Effect of FDI on Agriculture and Food Trade: An

Empirical Analysis”. Statistics Canada Agriculture Division. Agriculture and Rural Working Paper

Series Working Paper No. 68. Catalogue no. 21-601-MIE — No. 068. ISSN: 1707-0368. ISBN: 0-

662-37434-7.


50

29. Gideon .S. (1978) “Estimating the Dimensions of a Model.” Annals of Statistics, vol. 6, Issue 2.

30. Ibrahim, W.O. and S.O. Onokosi-Alliyu. (2008). “Determinants of Foreign Direct Investment in

Nigeria: Political Factor Effect Revisited.” Available online at SSRN:

http://ssrn.com/abstract=1136083 or http://dx.doi.org/10.2139/ssrn.1136083.

31. Ivanov, V. and L. Kilian. (2005) “A Practitioner’s Guide to Lag Order Selection for VAR Impulse

Response Analysis.”

32. Lamorde, A.G. (1998). “Scenario Building for the Nigerian Livestock Industry in the 21st

Century.” A paper presented at the Silver Anniversary Conference of the Nigerian Society for

Animal Production-Gateway Hotel, Abeokuta, Nigeria. March 21-26 1998.

33. Lawal, W.A (2011). “An Analysis of Government Spending on Agricultural Sector and its

Contribution to GDP in Nigeria.” International Journal of Business and Social Science. Vol 2, No.

20; November 2011. Available online at www.ijbssnet.com.

34. Library of Congress – Federal Research Division. (July 2008). “Country profile: Nigeria.” pp. 9.

Retrieved 28 December 2011.

35. Lutkepohl, H. (2005). “New Introduction to Multiple Time Series Analysis.” Berlin: Springer-

Verlag Berlin Heideiberg Germany.

36. Masayuki, H. and F. Ivohasina (2005). ‘‘The Determinants of Foreign Direct Investment into

Japan.’’ Kobe University Economic Review 51.

37. Mogues, T., M. Morris, L. Freinkman, A. Adubi, S. Ehuiet, C. Nwoko, O. Taiwo, C. Nege, P.

Okonji, and L. Chete. (2008). “Agricultural Public Spending in Nigeria.” IFPRI Discussion Paper

00789. Development Strategy and Governance Division. International Food Policy Research

Institute.

38. Mudahar, M.S. (1986). “Fertilizer problems and policies in sub-Sahran Africa. p. 1-32.” In: A.U.

Mokwunye and P.L.G. Vlek (ed.) Management of nitrogen and phosphorus fertilizers in sub-

Saharan Africa. Proceedings of a Symposium, Lome, Togo, March 25- 28, 1985. Martinus Nijhoff

Publishers.

39. Ngowi, H.P. (2001). “Can Africa Increase its Global Share of Foreign Direct Investment?” West

African Review, vol. 2(2): pp. 1-9.

40. Nigerian Meteorological Agency NIMET (2010). “Nigeria Climate Review Bulletin 2010”

Available online at: www.nimetng.org.

41. Nurudeen, Abu, Wafure, Obida Gobna and Auta, Elisha Menson, “Determinants of Foreign Direct

Investment: The Case of Nigeria.” (July 2, 2012). The IUP Journal of Monetary Economics, Vol.

IX, No. 3, pp. 50-67, August 2011. Available online at SSRN: http://ssrn.com/abstract=2097497.

42. Obiamaka P. E. and J.U.J. Onwumere. (2011) “Analyzing the Impact of Foreign Direct Investment

on Nigeria’s Economic Growth: A Cointegration Approach.” International Journal of Research in

management ISSN 2249-5908. Issue 1, Vol. 3 (November-2011).


51

43. Ogbanje, E. C., O. J. Okwu, and S. F. Saror. (2010). “An Analysis of Foreign Direct Investment in

Nigeria: The Fate of Nigeria’s Agricultural Sector.” Journal of Production Agriculture and

Technology (PAT) Nassarawa State University, Keffi. Available Online at:

www.patnsukjournal.net/currentissue. PAT December 2010; 6 (2): pp. 15-25. ISSN: 0794-5213.

44. Ogunleye, E.K. (2009). “Exchange Rate Volatility and Foreign Direct Investment in Sub-Saharan

Africa: Evidence from Nigeria and South Africa.” Excerpt from a PhD Dissertation “Exchange

Rate Volatility and Foreign Direct Investment Inflows in Selected Sub-Sahara African Countries,

1970-2005”

45. Ogunwusi, A.A. (2012). “The Forest Products Industry in Nigeria.” African Research Review. An

International Multidisciplinary Journal, Ethiopia, vol. 6 (4), Serial No. 27, pp.191-205. October,

2012. ISSN: 1994-9057 (Print), ISSN 2070--0083 (Online).

46. Okigbo, B.N. (1980). “A review of cropping systems in relation to residue management in the

humid tropics of Africa.” In: Organic Recycling in Africa. FAO Soil Bulletin 53, FAO, Rome, pp.

13-37.

47. Okorie, A. and E. C. Eboh. (1990). “Fundamentality of Agriculture in National Development.” In

Ikeme, A. I. (ed.). Challenges of agriculture in national development. Enugu: Optimal Computer

Press Ltd.

48. Okpara, G.C., F.N. Ajuka, and W. C. Nwaoha. (2012). “An Error Correction Model Analysis of the

Determinant of Foreign Direct Investment: Evidence from Nigeria.” Munich Personal RePEc

Archive (MPRA). Available online at: http://mpra.ub.uni-muenchen.de/36676/ MPRA Paper No.

36676, posted 14. February 2012 / 19:47.

49. Okuneye, P. A. (1995). “Nigerian Agriculture on the Run. Refuses to move”. University of

Agriculture, Abeokuta. Inaugural Lecture Series No. 2 36p.

50. Omankhanlen, A. E. (2011). “Foreign Direct Investment and its Effect on the Nigerian Economy”

Business Intelligence Journal - July, 2011, vol.4 No.2. pp. 253-261.

51. Omisakin, O., O. Adeniyi, and A. Omojolaibi. (2009) “FDI, Trade Openness and Growth in

Nigeria.”

52. Omogbai, B.E. (2010) “An Empirical Prediction of Seasonal Rainfall in Nigeria” J Hum Ecol,

32(1): 23-27 (2010).

53. Orji, A. and P.N Mba. (2011) “Foreign Private Investment, Capital Formation and Economic

Growth in Nigeria: a two stage least square approach.” Journal of Economics and Sustainable

Development. Available online at: www.iiste.org ISSN 2222-1700 (Paper) ISSN 2222-2855

(Online).

54. Osinubi, T.S. and A. A. Lloyd. (2010) “Foreign Private Investment and Economic Growth in

Nigeria.” Vol. 3, Issue 1, pp. 105-127, June 2010 ISSN-1843-763X.

55. Oyatoye, E. T. O. (1982) “Developing a viable fish farming industry in Nigeria: an alternative

strategy to the strategy in the Green Revolution Programme.” Proceedings of the 2nd Annual


52

Conference of the Fisheries Society of Nigeria(FISON), Calabar, Cross Rivers State, Nigeria,

25th-27th January 1982, pp.99-109.

56. Oyenuga, V.A. (1967). “Agriculture in Nigeria.” Food and Agriculture Organization of the United

Nations. FAO, Rome, Italy. pp. 308.

57. Razin, A. (2002). “FDI Contribution to Capital Flows and Investment in Capacity.” NBER

Working Paper, 9204.

58. RIM. (1992): “Nigerian Livestock resources.” Four volume report to the Federal Government of

Nigeria. Submitted by Resource Inventory Management Ltd. 1. Executive Summary and Atlas. 2.

National synthesis. 3. State Reports. 4. Urban Reports and commercially managed livestock

survey report.

59. Romer, M. (1993). “Idea Gaps and Object Gaps in Economic development. Journal of Monetary

Economics. 32(3): pp. 543-573.

60. Shan, J. (2002). “A VAR Approach to the Economics of FDI in China.” Journal of Applied

Economics 2002, vol. 34, pp. 885-893.ISSN 0003-6846 (print). ISSN 1466-4283 (online). © 2002

Taylor and Francis Ltd. Available online at: http://www.tandf.co.uk/journals. DOI:

10.1080/0003684011005894 1.

61. Sharp, G.D. (2010). “Lag Length Selection for Vector Error Correction Models.”

62. Singh, H. and W. J. Kwang. (1995). “Some New Evidence on Determinants of Foreign Direct

Investment in Developing Countries.” Policy Research Working Paper 1531. The World Bank

International Economics Department International Finance Division November 1995. Available

online at: [email protected]. November 1995. (41 pages).

63. Strazicich, M.C., Co, C.Y. and J. Lee, (2001). “Are Shocks to Foreign Investment in Developing

Countries Permanent or Temporary? Evidence from Panel Unit Root Test.” Economic Letters, vol.

70, pp. 405-421.

64. Tang, S., E.A. Selvanathan and S. Selvanathan. (2008). “Foreign Direct Investment, Domestic

Investment and Economic Growth in China: A Time Series Analysis.” © 2008 United Nation

University. Journal Compilation © 2008 Blackwell Publishing Ltd. The World Economy (2008).

DOI: 10.1111/j.1467-9701.2008.00129.x

65. Udo, R.K. (1970). “Geographical Regions of Nigeria.” Morrison and Gibb: London. pp. 212.

66. Udoh, E. and F.O. Egwaikhide. (2008) “Exchange Rate Volatility, Inflation Uncertainty and

Foreign Direct Investment in Nigeria. Botswana Journal of Economics, vol. 5. No. 7, October,

2008.

67. Udoh, E., E.A. Udeaja, and F.S. Ebong. (2012) “Public Expenditure, Foreign Direct Investment

and Agricultural Sector Growth in Developing Countries: The Case of Nigeria.” Middle Eastern

Finance and Economics ISSN: 1450-2889. Issue 17 (2012) © EuroJournals Publishing, Inc. 2012.

Available online at: http://www.middleeasterneconomicsandfinance.com.

68. Ugwu and Kanu (2011) “Effects of agricultural reforms on the agricultural sector in Nigeria.”


53

69. Ukeje, R. O. (2003). “Macroeconomics: An Introduction.” Port Harcourt. Davidson Publications.

70. UNCTAD, (2009). “Investment Policy Review (2009).” Available online at:

http://www.unctad.org/en/docs/diaepcb20081_en.pdf.

71. Walkenhorst, P. (2007). “Distortions to agricultural incentives: Nigeria. In Poverty Alleviation

Through Reducing Distortions to Agricultural Incentives. Washington, D.C.: World Bank.

72. Wolters, J., and G. Kirchgassner. (2007) “Introduction to Multiple Time Series Analysis.” Berlin:

Springer-Verlag Berlin Heideiberg Germany.

73. World Bank. (2012). “Nigeria: Economic Overview and Country Performance.” World Bank

report. Available online at: http://www.worldbank.org/en/country/nigeria/overview. World Bank

data, Nigeria: http://data.worldbank.org/country/nigeria

74. Zakari, A., H.M. Aliero, and A.B. Abdul-Quadir. (2010). “The Role of Nigerian Investment

Promotion Commission (NIPC) in Attracting Foreign Direct Investment in Nigeria.” European

Scientific Journal April edition vol. 8, No.7 ISSN: 1857 – 7881 (Print) e - ISSN 1857- 7431 149

UDC: 339.727.22(662.6).


54

ACKNOWLEDGEMENT

All glory to God Almighty, the One who has blessed me with this privilege and given

me the enablement to see this programme to its completion.

My sincere appreciation goes to the government and people of the People’s Republic of

China and most especially the Chinese Scholarship Council for affording me this

opportunity.

I wish to appreciate my erudite supervisor in person of Professor Liu Ying for

showing so much interest in the smooth running of my studies and my overall wellbeing in

China. I also wish to acknowledge all staff of the department of international cooperation

& exchange for their coordination and timely assistance whenever sought.

I graciously thank my family for all their support and encouragement-my parents (Mr.

Mrs. M.A Idowu), my uncle & aunt (Cmdre. & Mrs. S.R Shekoni), Uncle Jide Idowu, my

siblings, and my heartthrob Mrs. Oluwafifunmike Ayomideji Idowu. I love you all.

I also wish to express humble thanks to my colleagues and friends on campus, top on

the list is Wang Yi Zhou, Izuchukwu, Rebecca Agboola, Alhaja Modinat Adekoya, Aishat

Biu, Rosina Heita, He Ya Qing who translated the abstract of this dissertation into Chinese,

Huang Zhou Qin, Xiang Ai, Chen wen Qiong, Xiang Yong and a host of others.

Idowu Ayodeji Adetunji

06 June, 2013


55

APPENDIX A: VECTOR AUTO REGRESSION ESTIMATES

Vector Autoregression Estimates

Date: 11/07/12 Time: 16:24

Sample (adjusted): 1982 2007

Included observations: 26 after adjustments

Standard errors in ( ) & t-statistics in [ ]

LNOUTPUT LNFDI LNLABOUR

LNOUTPUT(-1) 0.638533 0.334022 -0.000251

(0.22321) (0.43059) (0.00253)

[ 2.86064] [ 0.77573] [-0.09904]

LNOUTPUT(-2) 0.324468 0.562524 0.002344

(0.24657) (0.47565) (0.00280)

[ 1.31590] [ 1.18263] [ 0.83794]

LNFDI(-1) 0.013480 0.490306 -0.000505

(0.10913) (0.21051) (0.00124)

[ 0.12353] [ 2.32915] [-0.40814]

LNFDI(-2) -0.053250 0.108833 -0.000831

(0.09213) (0.17772) (0.00105)

[-0.57801] [ 0.61240] [-0.79522]

LNLABOUR(-1) -11.15661 43.69148 1.560900

(13.6502) (26.3320) (0.15485)

[-0.81732] [ 1.65926] [ 10.0803]


56

LNLABOUR(-2) 11.76359 -25.42161 -0.563604

(14.7649) (28.4822) (0.16749)

[ 0.79673] [-0.89254] [-3.36499]

C -4.992999 -179.8332 0.009863

(51.9517) (100.217) (0.58933)

[-0.09611] [-1.79443] [ 0.01674]

R-squared 0.959430 0.961488 0.977742

Adj. R-squared 0.946619 0.949326 0.970713

Sum sq. resids 0.281941 1.049165 3.63E-05

S.E. equation 0.121815 0.234988 0.001382

F-statistic 74.88818 79.05879 139.1033

Log likelihood 21.92161 4.838918 138.3777

Akaike AIC -1.147816 0.166237 -10.10598

Schwarz SC -0.809098 0.504955 -9.767260

Mean dependent 11.25560 6.269081 9.437347

S.D. dependent 0.527239 1.043888 0.008075

Determinant resid covariance (dof

adj.) 1.19E-09

Determinant resid covariance 4.63E-10

Log likelihood 168.7285

Akaike information criterion -11.36373

Schwarz criterion -10.34758


57

APPENDIX B: P-VALUES OF COEFFICIENTS IN VECTOR

AUTO REGRESSION MODEL

System: UNTITLED

Estimation Method: Least Squares

Date: 11/08/12 Time: 18:32

Sample: 1982 2007

Included observations: 26

Total system (balanced) observations 78

Coefficient Std. Error t-Statistic Prob.

C(1) 0.638533 0.223214 2.860638 0.0059

C(2) 0.324468 0.246575 1.315902 0.1935

C(3) 0.013480 0.109125 0.123526 0.9021

C(4) -0.053250 0.092126 -0.578006 0.5655

C(5) -11.15661 13.65025 -0.817319 0.4171

C(6) 11.76359 14.76488 0.796728 0.4289

C(7) -4.992999 51.95169 -0.096109 0.9238

C(8) 0.334022 0.430590 0.775732 0.4411

C(9) 0.562524 0.475654 1.182632 0.2419

C(10) 0.490306 0.210508 2.329154 0.0234

C(11) 0.108833 0.177716 0.612398 0.5427

C(12) 43.69148 26.33198 1.659255 0.1026

C(13) -25.42161 28.48217 -0.892545 0.3759

C(14) -179.8332 100.2173 -1.794433 0.0780

C(15) -0.000251 0.002532 -0.099039 0.9215

C(16) 0.002344 0.002797 0.837942 0.4056

C(17) -0.000505 0.001238 -0.408145 0.6847

C(18) -0.000831 0.001045 -0.795220 0.4298

C(19) 1.560900 0.154846 10.08033 0.0000

C(20) -0.563604 0.167490 -3.364995 0.0014

C(21) 0.009863 0.589331 0.016736 0.9867


58

Determinant residual covariance 4.63E-10

Equation: LNOUTPUT = C(1)*LNOUTPUT(-1) +

C(2)*LNOUTPUT(-2) + C(3)

*LNFDI(-1) + C(4)*LNFDI(-2) + C(5)*LNLABOUR(-1) +

C(6)

*LNLABOUR(-2) + C(7)

Observations: 26

R-squared 0.959430 Mean dependent var 11.25560

Adjusted R-

squared 0.946619 S.D. dependent var 0.527239

S.E. of regression 0.121815 Sum squared resid 0.281941

Durbin-Watson stat 2.265008

Equation: LNFDI = C(8)*LNOUTPUT(-1) + C(9)*LNOUTPUT(-

2) + C(10)

*LNFDI(-1) + C(11)*LNFDI(-2) + C(12)*LNLABOUR(-1) +

C(13)


Observations: 26


Adjusted R-


S.E. of regression 0.234988 Sum squared resid 1.049165


Equation: LNLABOUR = C(15)*LNOUTPUT(-1) +

C(16)*LNOUTPUT(-2) +

C(17)*LNFDI(-1) + C(18)*LNFDI(-2) +

C(19)*LNLABOUR(-1) + C(20)


Observations: 26


59


Adjusted R-


S.E. of regression 0.001382 Sum squared resid 3.63E-05



60

APPENDIX C: LAG LENGTH SELECTION CRITERIA

VAR Lag Order Selection Criteria

Endogenous variables: LNFDI LNLABOUR

LNOUTPUT

Exogenous variables: C

Date: 11/11/12 Time: 05:24

Sample: 1980 2007

Included observations: 26

Lag Log L LR FPE AIC SC HQ

0 62.35055 NA 2.09e-06 -4.565427 -4.420262 -4.523624

1 154.3934 155.7648 3.54e-09 -10.95334 -10.37268* -10.78613

2 168.7285 20.95137* 2.43e-09* -11.36373* -10.34758 -11.07112*

* indicates lag order selected by the criterion

LR: sequential modified LR test statistic (each test at 5%

level)

FPE: Final prediction error

AIC: Akaike information criterion

SC: Schwarz information criterion

HQ: Hannan-Quinn information criterion

an empirical analysis of the relationship between foreign direct investment and the agricultural...

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