i

TRADE LIBERALIZATION, FINANCIAL DEVELOPMENT AND ECONOMIC

GROWTH IN NIGERIA

(1986-2013)

BY

ADEBIYI AYODEJI SOLA

ECN/2010/005

A LONG ESSAY SUBMITTED TO THE DEPARTMENT OF ECONOMICS, FACULTY

OF SOCIAL SCIENCES, OBAFEMI AWOLOWO UNIVERSITY,

ILE-IFE, OSUN STATE, NIGERIA.

IN PARTIAL FULFILMENT OF THE REQUIREMENT FOR THE AWARD OF

BACHELOR SCIENCE (B.sc) DEGREE IN ECONOMICS

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MAY, 2015

CERTIFICATION

This is to certify that the project titled; Trade Liberalization, Financial Development and Economic

Growth in Nigeria (1986-2013) was carried out by ADEBIYI AYODEJI SOLA (ECN/2010/005)

of the Department of Economics, Faculty of Social Sciences, Obafemi Awolowo University, Ile-

Ife.

.......................................... ……………………………

MR APANISILE T.O. DATE

(Supervisor)

........................................ ……………………………

PROF P.A OLOMOLA DATE

(Head of Department)

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DEDICATION

This research work is dedicated to the Almighty God, the author and finisher of my faith, who has

been the source of my inspiration and my very help in time of need; and to my mother, Shote T.I,

for her unquantifiable contribution towards my formation and her tenacious support.

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ACKNOWLEDGEMENTS

I hereby express my utmost gratitude to Almighty God for seeing me through the completion of

this research work. I am indebted to my Supervisor, Mr Apanisile T.O., who saw the potential in

me and gave me the go ahead to work on this study. Thank you for your guidance, support and

suggestion throughout the course of this research work. You are the best!

My sincere appreciation also goes to the most beautiful woman in my world currently, my mother,

Mrs Shote T.I. for her love, moral, financial and spiritual support and to my father, Mr Adebiyi

S.O., thanks for everything. May you both live long and enjoy the fruit of your labour. To my

siblings; Temitope, Fiyinfuoluwa and Inioluwa. You guys are the best. Likewise, I would like to

appreciate some specific set in the Apena’s clan. Rasheed and his wife, Amoke; Rasaq and Azeez

and his wife, Shakirah.

I must especially thank my heartthrob, Opeyemi, for her care and support. I hope there exist

cointegration in our relationship. If I were to launch into the list of myriad of friends I made on

campus, I will register the reader into the world of ennui. Yet, I want to specifically acknowledge

and thank all those who know they are deserving of praise and gratitude. Olamofe Olayemi,

Olupona Damilola, Oriade Adebayo, Adekunle Sunmbo, Adekunle Femi, Oyeola Kolapo,

Adeniran Adeyemi, Shodamola Daniel, Adeniran Abiodun, Oluwashetire Emmanuel, Bakare

Adedayo, Quoham Oladimeji, Bakare Timilehin, Afuape Oreoluwa, Nmerole Michael, Akpuenika

Tobechukwu, Oyedotun Opeyemi, Ade-Adeleye Paul and many more. My acknowledgement is

incomplete without acknowledging to all the lecturers, especially Dr Olayungbo and Dr Adedokun,

who duly impacted me throughout my stay on campus; the Class of Achievers; TMC, a family of

love; LQED 2013/2014 teammates; G-4 family; business associates, Adeniran Adeyemi, Ade-

Adeleye Paul, Aladeusi Ibikunle, Nmerole Michael; and my roommates in my finals, E-5.

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TABLE OF CONTENTS

CONTENTS PAGE

Certification………………………………………………………………………. i

Dedication…………………………………………………………………………. ii

Acknowledgement…………………………………………………………………. iii

Table of contents…………………………………………………………………… iv

Abstract…………………………………………………………………………….. vii

CHAPTER ONE

1.0 Background to the study…………………………………………………… 1

1.1 Statement of the problem…………………………………………………... 7

1.2 Research Questions………………………………………………………… 10

1.3 Objectives of the study…………………………………………………… 11

1.4 Hypothesis of the study……………………………………………………. 11

1.5 Justification of the study…………………………………………………… 12

1.6 Scope of the study…………………………………………………………. 12

1.7 Organization of the study………………………………............................... 13

CHAPTER TWO

2.0 Introduction………………………………………………………………… 14

2.1 Review of Theoretical Literature…………………………………………... 14

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2.2 Review of Empirical Literature…………………………………………..... 21

CHAPTER THREE

3.0 Introduction………………………………………………………………… 34

3.1 Theoretical Framework…………………………………………………….. 34

3.2 Model Specification………………………………………………………... 37

3.3 Measurement of variables………………………………………………….. 39

3.4 Analytical Technique………………………………………………………. 41

3.5 Sources of Data…………………………………………………………….. 43

CHAPTER FOUR

4.01 Introduction…………………………………………………………………. 44

4.02 Tabular Presentation of Data ……………………………………………… 44

4.03 Descriptive Statistics of Variables………………………………………… 47

4.04 Graphical Description of Variables……………………………………….. 50

4.05 Principal Component Analysis…………………………………………….. 53

4.06 Unit Root Test……………………………………………………………… 55

4.07 Cointegration Test…………………………………………………………. 57

4.08 Impulse Response………………………………………………………… 58

4.09 Variance Decomposition…………………………………………………… 64

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4.1 Overall Performance of the model………………………………………… 68

4.11 Granger Causality Test……………………………………………………. 70

4.12 Stability Test……………………………………………………………… 71

4.13 Residual Test……………………………………………………………… 73

CHAPTER FIVE

5.0 Summary…………………………………………………………………… 75

5.1 Conclusion…………………………………………………………………. 76

5.2 Recommendations………………………………………………………….. 77

REFERENCES…………………………………………………………………… 79

APPENDIX……………………………………………………………………….. 85

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ABSTRACT

This research work empirically investigates the impact of trade liberalization and financial

development on economic growth in Nigeria using annual observation over the period of 1986-

2013. Instead of using common proxies for the issues, principal components analysis is employed

to develop better measures (indexes) for trade liberalization, financial development and the joint

effects of both. Vector autoregressive (VAR) methodology and its derivatives, impulse response

function and variance decomposition, were employed that enable us to scrutinize the relationship

between trade liberalization, financial development and economic growth. The result using

pairwise Granger causality test further signified that only trade liberalization has a positive and a

significant effect on economic growth in Nigeria. However, financial development and economic

liberalization showed a positive, but insignificant contribution to economic growth. The

insignificant relationship could be as a result of the country’s overreliance on proceeds from crude

oil and so many negative factors bedevilling the Nigerian economy, for example corruption. The

private sector should have a greater control of the economy so as to enhance its contribution

towards the economic growth of Nigeria.

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CHAPTER ONE

TRADE LIBERALIZATION, FINANCIAL DEVELOPMENT AND ECONOMIC

GROWTH IN NIGERIA.

1.0 BACKGROUND TO THE STUDY

This research work empirically examines the impact of trade liberalization and financial

development on economic growth, using Nigeria as a case study. The relationship between trade

liberalization, financial development and economic growth have been a passionately debated

theoretical issue, predominantly after the emergence of the endogenous (new) growth theory

during the mid-1980. An endogenous growth theory implies policies which embrace openness,

competition, change and innovation will promote economic growth. Conversely, policies which

have the effect of restricting or slowing change by protecting or favouring particular existing

industry or firm will overtime slow growth to the detriment of the country. In view of this, most

developing countries that formerly followed restrictive economic policies started liberalizing their

trade and financial sectors in order to increase economic growth in the 1980s. The main argument

for this policy change was that both trade and financial liberalization policies reduce inefficiency

in the production process and positively influence economic growth. This argument is strengthened

by the fact that growth rates in countries with liberalized trade and financial services outperform

those with restrictive financial and trade policies (Darrat 1999, Levine 1997, Shaw 1973, World

Bank 1989).

Following this new line of reasoning, Nigeria, as a developing economy, has witnessed

unparalleled and staged reform attempt involving external (trade) and internal (financial)

liberalization, especially after the introduction of Structural Adjustment Programme (SAP) in

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1986. Economic liberalization process in Nigeria sped up in term of trade liberalization and

financial development after the SAP era and constituted an essential part of economic policies

since then. According to economic theories, the core importance of these reformed economic

policies and programmes is to provide efficiency in allocation of scarce resources.

The link between financial development and economic growth has long received significant

attention in literature ever since Joseph Schumpeter (1911), and more recently Ronald McKinnon

(1973) and Edward Shaw (1973) extensively compared the relationship between the two variables.

This attention is well-justified, since a better understanding of how the financial sector contributes

to economic growth has important regulatory implications. Within the finance-growth nexus

literature, some have argued that financial intermediaries mobilize, pool and channel domestic

savings into productive capital and contribute to economic growth. If this view is to be accepted,

then a competitive and well-developed banking sector must be an important contributor to

economic growth. In a competitive banking sector however, borrowing rates are higher and lending

rates are lower and thus the transformation of household savings into productive capital investment

is faster. On the other side of this debate is an argument that financial development is a

consequence, and not a cause, of economic growth. In this view, economic growth increases

demand for sophisticated financial instruments, which in turn leads to growth in the financial sector

(Ardic and Damar, 2006).Well-functioning financial institutions enhance overall economic

efficiency, create and expand liquidity, mobilize savings, promote capital accumulation, transfer

resources from traditional (non-growth) sectors to the more modern growth-inducing sectors, and

also encourage a competent entrepreneur response in these modern sectors of the economy.

The financial system of any society is the framework within which capital formation takes

place. According to Odife (1994), it is the framework within which the savings of some members

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of the society are made available to other members of the society. Put differently, it is the

arrangement or mechanism by which the savings surplus units of the economy transfer their

resources to the borrowing deficit units for the purpose of enhancing economic growth (Okereke

–Onyiuke, 2009). Financial development refers to factors, policies, and institutions that lead to

effective financial intermediation and markets, as well as deep and broad access to capital and

financial services. This definition thus spans the foundational supports of a financial system,

including the institutional and business environments; the financial intermediaries and markets

through which efficient risk diversification and capital allocation occur; and the results of this

financial intermediation process, which include the availability of, and access to capital [World

Economic Forum, 2012]. Economic theory suggests that financial markets and intermediaries exist

mainly because of two types of market frictions: information costs and transaction costs. The role

of financial markets and intermediaries is to assist in the trading, hedging, diversification, and

pooling of risk; provide insurance services; allocate savings and resources to the appropriate

investment projects; monitor managers and promote corporate control and governance; mobilize

savings efficiently; and facilitate the exchange of goods and services. Financial intermediation and

financial markets contributes directly to economic growth and aggregate economic welfare

through their effect on capital accumulation (the rate of investment) and on technological

innovation. First, greater financial development leads to greater mobilization of savings and its

allocation to the highest-return investment projects. This increased accumulation of capital

enhances economic growth. Second, by allocating capital to the right investment projects and

promoting sound corporate governance, financial development increases the rate of technological

innovation and productivity growth, further enhancing economic growth and welfare.

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The financial sector of any economy in the world plays a vital role in the growth and development

of the economy. The development of this sector determines how it will be able to effectively and

efficiently discharge its major role of mobilizing fund from the surplus sector to the deficit sector

of the economy. This sector has helped in facilitating the business transactions and economic

development. If a financial system is well developed, it will enhance investment by identifying

and funding good business opportunities, mobilizes savings, enables the trading, hedging and

diversification of risk and facilitates the exchange of goods and services. All these result in a more

efficient allocation of resources, rapid accumulation of physical and human capital, and faster

technological progress, which in turn results in economic growth. Prior to the introduction of the

Structural Adjustment Programme (SAP) in Nigeria in 1986, the Nigerian financial sector was

characterized by fixed and relatively low interest rates, mandatory sectorial allocation of bank

credit and quantitative ceilings on bank credit to the private sector, all of which engendered

distortions and inefficiencies [Akingunola, Badejo, Salami, Adekunle (2013)].

Influenced by the preponderance of such theoretical reasoning, along with repeated

recommendations of key world organizations like the World Bank and the International Monetary

Fund, the government of Nigeria has recently paid a great deal of attention to expanding the

breadth and depth of its financial market. Examples of such recent financial developments include

facilitating consolidation of the banking sector, continuous deregulation of bank lending and

deposit interest rates, rapid use of credit and debit cards, increasing use of payment technologies

like ATM machines and electronic transfer of deposits, expanding internet banking services, e-

banking, and mobile banking technology etc. Given the foregoing, Nigeria embarked on financial

sector liberalization in 1991. Consequently, interest rates were liberalized by switching from an

administered interest rate setting to a market-based interest rate determination; credit controls were

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also removed by eliminating directed and subsidized credit schemes. In fact, use of credit ceiling

was replaced with open market operation; prudential regulations were also put in place;

government owned-banks were also privatized just as entry and exit from the financial sector were

liberalized. Since the introduction of SAP into the Nigerian economy in July 1986, a great deal of

interest has been shown in the activities and developments in the money market and capital market

which encapsulates the financial system. A central component of the SAP reform was the

restructuring of the national financial system by relaxing some regulations considered inhibitive

to orderly growth and development within the system. Financial development deepens financial

markets and thereby promotes economic growth [McKinnon (1973) and Shaw (1973)].

Trade liberalization has been considered as an important determinant of economic growth and a

well debated issue in the recent growth literature. Initially, the developing nations of the world

followed restrictive trade policies but with passage of time and emergence of globalization, all the

nations realized the need to liberalize to their economies in terms of trade openness. Trade of a

country is a key determinant for the improvement of a country’s industrialization. Moreover,

development experienced by a country brings some changes in trade structure on the basis of

endowments and comparative advantage (Hultman, 1967).

Trade liberalization involves the removal or reduction of restrictions or barriers on the free

exchange of goods between nations. This includes the removal or reduction of both tariff (duties

and surcharges) and non-tariff obstacles (like licensing rules, quotas and other requirements). The

easing or eradication of these restrictions is often referred to as promoting "free trade." An

increasing openness is expected to have positive impacts on economic growth [Jin (2000); Fry

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(1995, 1997); Darrat (1999); Levine (1997); McKinnon (1973); Shaw (1973) and World Bank

(1989)].

Trade liberalization measures, in particular, are believed to be a reaction to the failure of traditional

import substitution (MS) policies of the 1950s–1970s. The philosophy behind the reform

programmes was that the role of government in making decisions on resource allocation should be

minimized and the incentive structure should change in favour of exports through import

liberalization in order to follow an export promotion (EP) path instead of MS. It was argued that

private agents, guided by the operation of market forces, would better achieve the objectives of

growth and diversification of exports and output structure in favour of manufactured goods. Such

objectives would in turn be attained through the expansion of investment, better channeling of

resources and allocation of investment outlays to productive sectors. The change in the structure

of incentives would not only lead to growth and diversification but also to the upgrading of the

production structure, facilitated by imported technology and improved skills enhanced by trade

(S.M. Shafaeddin 2005). The IMF and World Bank required trade liberalization as a part of reform

packages when agreeing to loans (Foster, 2008).

In line with IMF and World Bank recommendations in the 1980s, Nigeria as well as other

developing countries adopted several policy reform in relation to her trade with other countries.

The policy reforms undertaken by the Nigerian government since the 1980s had the objectives of

making the entire economy more efficient, technologically up-to-date and competitive. This was

done with the expectation that efficiency improvement, technological upgrade and competitiveness

would ensure that the Nigerian economy will achieve rapid growth. In view of greater openness of

the Nigerian economy due to trade liberalization, private sector can build and expand capacity with

less regulation [Nwakama, Ibe (2014)]

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1.1 STATEMENT OF THE PROBLEM

The role of a developed financial system and liberalized trade in the development of any economy

cannot be over emphasized in view of its potentials and likely impact on the economy if well

harnessed. It is widely acknowledged that nations cannot develop without the needed long term

funds for development projects, and the more developed a financial system, the higher the potential

for sourcing long term fund for industrialization.

Over the years, the Nigerian financial system has experienced relative stability and has recorded

impressive growth. This growth has been most significant especially since the introduction of the

Structural Adjustment Programme (SAP) in 1986, which brought about the privatization,

commercialization and liberalization programmes, all of which has helped in boosting activities in

the financial system. However, the Nigeria’s financial system is not effectively providing its

development roles as such and is currently not in a position to fulfill its potential as a propeller of

economic growth and development. The formal financial system is relatively shallow and a

relatively low level of credit to the private sector. A parallel World Bank review of financing for

Rural Micro and Small-Scale Enterprises has also revealed that the absence of efficiently operating

rural financial markets in Nigeria has become a serious constraint on sustainable rural

development. In sum, both the formal and informal financial sectors in Nigeria are not currently

in a position to effectively support a strong expansion of the real sector and maximize their

contribution to economic growth and development. The market is yet to harness benefits that

accrues to dealings in the derivative market. This market is currently estimated as a $170trillion

market by Bank for International Settlement (BIS 2014). The Nigerian Stock Market is also still

relatively small in size and underdeveloped. For example, a comparison of the Nigerian Stock

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Market in terms of number of listed equities reveals that while Nigeria has only 198 equities listed

in 2013 (NSE report 20130), even though its stock exchange was established in 1960, Singapore

has 776 (established in 1973), Hong Kong 1421 (established in 1986). This thus indicates the

relative poor performance of the Nigerian Stock Market vis-à-vis those of other

countries. Moreover, Osazee (2007) pointed out that less than 21 percent of the 400,000 registered

companies in Nigeria are not currently quoted on the Nigerian Stock Exchange, a situation which

he attributes to the unattractiveness of the market as well as the lack of incentives for more

companies to go public.

Access by individuals and businesses to different forms of capital and financial services is a

limiting factor that also contributes to the bane of development of the Nigerian financial market.

Nigerian’s financial market is characterized by banks who sees the interest rate as a tool for making

profit, rather than being used as an efficient resource allocator variable. This has contributed to the

increasing poverty level in the country, as the greater part of the populace in the real sector are

denied access to capital. This is contrary to the aim of financial sector development.

Lack of a legal institutions that safeguard the interests of investors has also inhibited financial

development in the country.

Inadequate investor protection leads to a number of adverse effects, which can be detrimental to

external financing and ultimately to the development of well-functioning capital markets.

Literature warns of over-regulating investor protection. Specifically, a study of the impact of

investor protection regulation on corporate governance for a number of countries shows that

stringent investor protection regulation carries either a neutral or negative effect on company

performance. Nigeria’s financial system is still perceived by her investors to be overregulated. For

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example, in February 2014 the suspension of Lamido Sanusi, the then Governor of Central Bank

of Nigeria by President Goodluck Ebele Jonathan had negative implication on Nigeria’s economic

indices, the lowest level the country experienced in 15 years. Banking sectors stock went into

negative, prices of other stocks went down, even foreign exchange went down (Nigeria’s Premium

times 2014).

Theoretically, it is expected that trade liberalization will help countries to gain static and dynamic

gains from trade. Trade liberalization will help promote growth from the supply side by leading to

a more efficient use of resources, encourage competition, and increase the flow and knowledge

across national boundaries (Parikh, 2006). Consequently, it will increase the growth of output,

export and imports, and improve economic welfare. Advocates of Trade liberalization believes

that policy reforms so far has improved economic growth and performance significantly. However,

if the increase in the income elasticity of demand for imports due to liberalization process is too

great, it may constraint a country’s economic growth in the long run (Thirwall, 1979). Studies on

trade liberalization suggest that its impact on economic growth has not been similar across

countries (Foster, 2008; Kneller, et al., 2008). Critics argue that the total withdrawal of restrictions

on several matters have had negative effects on future growth and performance of the economy.

They are also of the view that Trade liberalization has worsened inequalities across and within the

countries, environmental degradation and vulnerability of the poor nations have increased and that

developed countries have established dominance over developing countries culminating in neo-

colonization.

The economic problem and difficulties of the early 1980s led to the implementation of the

structural adjustment programme in 1986 which aimed at among others to diversify the export

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base and promote non-oil exports so as to reduce the reliance on oil earnings, an objective that has

remained elusive till today. Hence, the Nigerian economy has remained susceptible to fluctuations

in the oil market. This is evident in the recent oil crisis which forced the country to revise her

budget following the dramatic fall in the price of oil. There is no doubt that various governments

in Nigeria recognize the strategic role of exports in achieving economic growth and development,

and have made conscious effort to create a strong export base for the Nigerian economy.

Lately, Nigeria is regarded to have the largest economy in sub-Saharan Africa after rebasing her

Gross domestic product. In the last four decades there has been little or no progress realized in

alleviating poverty despite the massive effort made and the many programmes established for that

purpose. Indeed, as in many other sub-Saharan Africa countries, both the number of poor and the

proportion of poor have been increasing in Nigeria. In particular, the 1998 United Nations human

development report declares that 48% of Nigeria’s population lives below the poverty line. This is

one of the potentials inherent in trade liberalization, as policy recommendation in this line suggests

that the household sector will be a beneficiary of a liberalized economy.

1.2 RESEARCH QUESTIONS

This research work shall be guided by the following research questions:

1. Has credit to private sector led to economic growth in Nigeria?

2. Has the growth of Nigerian stock market promote economic growth in Nigeria?

3. To identify the performance/growth drivers of the Nigerian financial market.

4. To identify the challenges facing the Nigerian financial market and examine various ways

of boosting its performance and growth.

5. Has Nigeria benefited from trade liberalization?

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1.3 OBJECTIVES OF THE STUDY

The broad objective of this research work is to examine the joint impact of trade liberalization and

financial development on economic growth. The specific objectives will include:

1. To examine the impact of financial development on economic growth.

2. To examine the impact of trade liberalization on economic growth

3. To determine the short run and long run effect of financial development and trade

liberalization on economic growth.

1.4 HYPOTHESES OF THE STUDY

A research hypothesis is a scientific statement expressing the relationship between two or

more variables which is meant to be tested. In the light of the primary objective of this

study, the following hypotheses have been formulated

Ho: Financial development and trade liberalization are not positively associated with

economic growth in Nigeria.

H1: Financial development and trade liberalization is positively associated with economic

growth in Nigeria.

1.5 JUSTIFICATION OF THE STUDY

In Nigeria’s empirical literature, the impact of trade liberalization and financial development on

economic growth have been separately examined. To the best of my knowledge, no work has been

done on the joint impact of trade liberalization and financial development on economic growth in

Nigeria.

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This paper empirically examines the impacts of trade liberalization and financial development on

economic growth in Nigeria by using a simple endogenous growth framework and by making use

of new developments in time series techniques for the period 1986-2013. This paper is

distinguished from earlier existing literature in two aspects. First of all, this paper tries to assess

the joint impact of trade liberalization and financial development on economic growth. Secondly,

there are different measures for trade liberalization and financial development in the literature and

the existing studies employ only one of these proxies in their analysis. However, the trade and

financial liberalization affect economic growth through different channels and each proxy captures

a single aspect of the issue. In order to overcome this problem, three composite indexes, for trade

liberalization, for financial development and for a narrow sense economic liberalization, are

constructed by applying principal components analysis.

1.6 SCOPE OF THE STUDY

This study tries to investigate the joint impact of trade liberalization and financial development on

the economic growth in Nigeria. The data that will be used as proxy to capture the impact of both

variables on economic growth will span from 1986-2013. The choice period is anchored on the

fact that the country’s trade was liberalized in the year 1986.

1.7 ORGANIZATION OF THE STUDY

This research study is divided into five chapters. Each dealing with specific aspect of the study.

Chapter one represents a general information of the study; it also includes the objective of the

study, scope of the study, justification of the study and plan of the study. Chapter two represents

literature review of past works and conceptual framework. Chapter three entails research

13

methodology, which will include the research design, method of data collection, data analysis

techniques and limitations, and problem of data collection. Chapter four will represent the analysis

of data, which will be the impact of trade liberalization and financial development on economic

growth in Nigeria. Finally, chapter five will summarize the findings from study, draw conclusion

and make relevant recommendations.

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CHAPTER TWO

LITERATURE REVIEW

2.0 INTRODUCTION

This chapter focuses on review of theoretical literature works as well as review of empirical

literature works associated or related to this study. To this effect, section 2.1 focuses on the review

of theoretical literature on the subject matter and section 2.2 focuses on the review of empirical

literature.

2.1 THEORETICAL REVIEW

In this section, a number of theories that relates to trade liberalization and financial development

shall be discussed.

2.1.1 CLASSICAL THEORY

The classical theory of trade is the oldest theory on trade. The classical theory of international

trade can be linked to the theory of Mercantilism (William Petty, Thomas Mun and Antoine de

Montchrétien model), the Absolute Advantage theory (Adam Smith model) and the Comparative

Advantage (David Ricardo model).

Mercantilism (William Petty, Thomas Mun and Antoine De Montchrétien Model)

Mercantilism is a philosophy from about 300 years ago. The base of this theory was the

“commercial revolution”, the transition from local economies to national economies, from

feudalism to capitalism, from a rudimentary trade to a larger international trade.

Mercantilism was the economic system of the major trading nations during the 16th, 17th, and

18th century, based on the premise that national wealth and power were best served by increasing

15

exports and collecting precious metals in return. It superseded the medieval feudal organization in

Western Europe, especially in Holland, France, United Kingdom, Belgium, Portugal and Spain.

The monarch controlled everything. Their policy was to export in the countries that they controlled

and not to import (to have a positive Balance of Trade). The theory was criticized by the newly

appeared class. More money was associated with less products and inflation. The standard of living

is weaker. Mercantilist ideas did not decline until the coming of the Industrial Revolution and of

laissez-faire.

Adam smith and absolute advantage

Adam Smith (1776) held that for two nations to trade with each other voluntarily, both nations

must gain. If one nation gained nothing or lost, it would refuse it. According to Smith, mutually

beneficial trade takes place based on absolute advantage. When one nation is more efficient than

(or has an absolute advantage over) the other nation is producing a second commodity, then both

nations gain by each specializing in the production of the commodity of its absolute advantage and

exchanging part of its output with the other nation for the commodity of its absolute disadvantage.

Smith thus argued that all nations would gain from free trade and strongly advocated a policy of

laissez-faire. Under free trade, world resources would be utilized mostly efficiently and world

welfare would be maximized.

The Ricardian trade theory

Although Smith’s ideas about absolute advantage were crucial for the early development of

classical thought for international trade, it is generally agreed that David Ricardo is the creator of

the classical theory of international trade, even though many concrete ideas about trade existed

before his Principles (Ricardo, 1817). Ricardo showed that the potential gains from trade are far

greater than Smith envisioned in the concept of absolute advantage.

16

In this theory the crucial variable used to explain international trade patterns is technology. The

theory holds that a difference in comparative costs of production is the necessary condition for the

existence of international trade. But this difference reflects a difference in techniques of

production. According to this theory, technological differences between countries determine

international division of labor and consumption and trade patterns. It holds that trade is beneficial

to all participating countries. This conclusion is against the viewpoint about trade held by the

doctrine of mercantilism. In mercantilism it is argued that the regulation and planning of economic

activity are efficient means of fostering the goals of nation.

2.1.2 HARROD DOMAR’S THEORY OF GROWTH

In economic growth literature, one of the earliest model for determining the financial development-

growth nexus was based on the pioneering works of the post-Keynesian growth models for closed

economies as designed by Harrod (1939) and Domar (1946). They tried to identify the pre-

conditions needed to enable an industrialized economy to reach steady-state equilibrium of growth.

In the early 1960s, the Harrod-Domar approaches were adapted to open economies in the so-called

Third World nations. The models assumed that, there is an excess supply of labour and growth is

only constrained by the availability and productivity of capital. Harrod-Domar considered capital

accumulation as a key factor in the process of economic growth. Firstly, it creates income and

secondly, it augments the productive capacity of the economy by increasing its capital stock.

Hence, in their opinion, as long as net investment is taking place, real income and output will

continue to expand. However, to maintain full employment, the level of income and output should

expand at the same rate at which the productive capacity of the capital stock is expanding. Three

17

gaps were majorly identified by the model as constituting constraints to economic growth, and

these gaps were needed to be filled by foreign capital to enable investment. The three gaps are:

savings gap; trade balance gap (foreign exchange); and fiscal gap. Since developing countries are

characterized by low income which amount to low savings, the model specifies that the attainment

of the investment potentials requires foreign capital which is expected to augment the inadequacies

in savings.

2.1.3 NEO-CLASSICAL MODEL

The neoclassical growth theory of Robert Solow (1956) and Trevor Swan (1956) is generally

recognized as the modern beginning of fruitful theorizing about economic growth in market

economies. The neoclassical theory overcame the paradoxes of the Harrod-Domar model by

recognizing that substitution between labor and capital takes place in response to changes in their

relative prices. Profit-seeking firms will employ more machinery per worker if the wage rate rises

relative to the user cost of capital, and will employ more workers per machine if the user cost of

capital rises relative to the wage rate. This process insures that sustained increases in real income

per worker can be maintained consistently with long-run full employment of both labor and capital.

Solow’s model of long run growth

Solow builds his model of economic growth as an alternative to the Harrod-Domar model without

assuming its assumption of fixed proportions in production. Solow takes output as a whole and

postulates a continuous production function linking outputs to the inputs of capital and labour

which are substitutable. The Solow model is a major improvement over the Harrod-Domar model.

He is a pioneer in the construction of the basic neo-classical model, though he retained the main

features of the Harrod-Domar model like homogenous capital, proportional saving function and a

18

given growth rate in the labour force. The assumption of substitutability between labour and capital

gives the growth process adjustability and provides a touch of realism.

Unlike the Harrod-Domar model, he demonstrates steady-state growth paths. The long run rate of

growth is determined by an expanding labour force and technical progress. Solow proposed that

we begin the study of economic growth by assuming a standard neoclassical production function

with decreasing returns to capital. The neo-classical theory predicts that countries with higher

savings and lower population growth rates will grow at a faster pace. This theory highlights the

importance of technological progression in the process of economic growth. It postulates that

economic growth will cease without advances in technology. The Solow growth model assumes

that poorer countries should exhibit higher rates of return on both physical and human capital. It

also predicts convergence in per capita income across countries whereby poorer countries grow

faster and catch up with richer countries.

According to the neo-classical theory, apart from labour and capital, other factors account for the

differences in growth across countries. These factors are captured by the residual term or what is

termed as total factor productivity. Therefore growth is determined outside the system. The

augmented Solow model adds factors of production such as human capital that do not exhibit

constant returns to scale. An augmented Solow model includes accumulation of human as well as

physical capital. Therefore in this framework, foreign capital should affect the savings rate which

in turn affects economic growth.

The Heckscher-Ohlin theory (H-O model)

The H-O model is a general equilibrium mathematical model of international trade, developed by

Eli Heckscher and Bertil Ohlin at the Stockholm school of Economics. The model asserts that a

country’s trade is primarily determined by its endowments of factors. In formal terms, it assumes

19

a given set of goods and a given set of factors and that the output of each good is determined by a

production function whose arguments are the quantities of factors needed. The theory commonly

assumes that returns to scale are constant and that production functions are the same in all

countries, assumptions that can be taken literally, though their purpose is to see how much can be

explained or predicted by factor endowments alone. At any time, each country is endowed with

specific quantities of each factor and equilibrium occurs in free trade when consumers maximize

their welfare, given the prices of goods and their incomes from factor earnings, while competition

ensures that the distribution of production between countries and the allocation of factors within

them are such as to minimize cost. With suitable assumptions about the forms of consumer

preferences and production functions, the theory, stated in such formal terms, leads to the

conclusion that equilibrium is Pareto optimal.

2.1.4 SCHUMPETER’S THEORY

One of the popular theories that explain economic growth is the Schumpeter theory on economic

growth. According to the Schumpeterian view, finance affects the allocation of savings and

improves productivity growth and technological change. In this framework, financial markets

allocate savings, which may be partly from foreign capital flows, and finance innovations which

may be due to new technology introduced by foreign firms. Therefore financial development

improves capital accumulation and technological diffusion thus promoting economic growth.

Financial markets improve the liquidity and tradability of assets in an economy, provide

opportunities for economic agents to diversify risk, reduce information asymmetry by collecting

information on deficit units, promote savings mobilization and the attraction of foreign capital and

improve the corporate governance of firms. Thus by performing these functions, financial

20

intermediaries aid the process of economic growth. The channels through which finance is likely

to affect growth are through the improvement in savings, physical capital accumulation and total

factor productivity growth.

2.1.5 ENDOGENOUS GROWTH THEORY

Endogenous growth theory explains long-run growth as emanating from economic activities that

create new technological knowledge. This article sketches the outlines of the theory, especially the

‘Schumpeterian’ variety, and briefly describes how the theory has evolved in response to empirical

discoveries. Endogenous growth is long-run economic growth at a rate determined by forces that

are internal to the economic system, particularly those forces governing the opportunities and

incentives to create technological knowledge. In the long run the rate of economic growth, as

measured by the growth rate of output per person, depends on the growth rate of total factor

productivity (TFP), which is determined in turn by the rate of technological progress. The

neoclassical growth theory of Solow (1956) and Swan (1956) assumes the rate of technological

progress to be determined by a scientific process that is separate from, and independent of,

economic forces. Neoclassical theory thus implies that economists can take the long-run growth

rate as given exogenously from outside the economic system.

Endogenous growth theory challenges this neoclassical view by proposing channels through which

the rate of technological progress, and hence the long-run rate of economic growth, can be

influenced by economic factors. It starts from the observation that technological progress takes

place through innovations, in the form of new products, processes and markets, many of which are

the result of economic activities. For example, because firms learn from experience how to produce

21

more efficiently, a higher pace of economic activity can raise the pace of process innovation by

giving firms more production experience. Also, because many innovations result from R&D

expenditures undertaken by profit-seeking firms, economic policies with respect to trade,

competition, education, taxes and intellectual property can influence the rate of innovation by

affecting the private costs and benefits of doing R&D.

2.2 EMPIRICAL EVIDENCES

Limited works has been done on the joint impact of trade liberalization and financial development

on economic growth. This research work divides empirical evidences into three sections: empirical

findings from developed countries, empirical findings from developing countries and empirical

findings from Nigeria. As a result of the few works that has been done in this line (relationship

between the three variables), the author of this research work chooses to use findings from the

relationship between individual explanatory variable and economic growth as a proxy for

empirical evidences for the relationship between the three variables, that is the relationship

between financial development and economic growth, and relationship between trade

liberalization and economic growth.

2.2.1 TRADE LIBERALIZATION, FINANCIAL DEVELOPMENT AND ECONOMIC

GROWTH

Few works has been done on the joint impact of trade liberalization and financial development on

economic growth. Notable works that has been done in this line is being reviewed by the author

of this work. Paper reviewed in this line were works done in developing countries.

Muhammad and Qayyum (2007) empirically investigates the impact of trade and financial

liberalization on economic growth in Pakistan using annual observations over the period 1961-

22

2005. The analysis is based on the bound testing approach of cointegration advanced by Pesaran,

et al. (2001). The empirical findings suggest that both trade and financial policies play an important

role in enhancing economic growth in Pakistan in the long-run. However, the short-run responses

of the real deposit rate and trade policy variables are very low, suggesting further acceleration of

the reform process. The feedback coefficient suggests a very slow rate of adjustment towards long-

run equilibrium. The estimated equation remains stable over the period of study as indicated by

CUSUM and CUSUMQ stability tests. Muhsin, Peker and Kaplan (2007) empirically examined

the joint impacts of trade liberalization and financial development on economic growth for the

period 1960- 2004 in Turkey. The empirical results obtained from the Johansen co-integration

procedure showed that trade liberalization, financial development and the joint impacts of both

positively contributed to economic growth in Turkey for the period 1963-2005. Mohammad and

Sheidaei (2012) empirically analyzed the joint impact of trade liberalization and financial

development on economic growth in Iran, using endogenous growth theory during the period

1966-2010. In this article principal component analysis is applied to make better indexes for trade

liberalization, financial development and the joint effects of both. The empirical findings obtained

from Johansen co-integration procedure signify a positive relationship between trade

liberalization, financial development and the joint impact on economic growth in Iran.

2.2.2 FINANCIAL DEVELOPMENT AND ECONOMIC GROWTH

In the literature, there has been voluminous work, which shows how financial development

contributes to economic growth. The empirical work however, on the issue of causality between

financial development and economic growth, to this day remains sparse.

23

The general idea that economic growth is related to financial development and structure can be

traced as far back to at least the works of Walter Bagehot (1873). He argued that argue that it

played a critical role in igniting industrialization in England by facilitating the mobilization of

capital for “immense works.” Schumpeter (1911) emphasized the importance of the banking

system in economic growth and highlighted circumstances when financial institutions can actively

spur innovation and future growth by identifying and funding productive investments. Scholars in

support of the Schumpterian’s view could be regarded as proponents of Proponent of finance-led

growth or Supply - Leading Hypothesis.

We also have contrasting views recorded in literature. These proponents believes that economic

growth or development is followed by finance development and may be referred to as finance led

growth proponents or Demand - Following Hypothesis. Joan Robinson (1952) declares that “where

enterprise leads finance follows.” According to this view, economic development creates demands

for particular types of financial arrangements, and the financial system responds automatically to

these demands.

Moreover, some economists just do not believe that the finance-growth relationship is important.

Robert Lucas (1988) asserts that economists “badly over-stress” the role of financial factors in

economic growth, while development economists frequently express their skepticism about the

role of the financial system by ignoring it (Anand Chandavarkar 1992). For example, a collection

of essays by the “pioneers of development economics,” including three Nobel Laureates, does not

mention finance (Gerald Meir and Dudley Seers 1984). Furthermore, Nicholas Stern’s (1989)

24

review of development economics does not discuss the financial system, even in a section that lists

omitted topics.

Goldsmith (1969) was the first to document a positive correlation between growth and indicators

of financial development. He found out that a rough parallelism can be observed between

economic growth and financial development if periods of several decades are considered; and that

there are indications in the few countries for which the data are available that periods of more rapid

economic growth have been accompanied, though not without exception, by an above-average rate

of financial development.

Earlier works on finance-growth nexus focused more on cross-sectional regression approach.

Arestis and Demetriades (1996) argued that time series approach is more fruitful than cross-

sectional regression approach to the finance-growth nexus. More recent work includes Levine

(1998), King and Levine (1993, 1993a), Rousseau and Wachtel (1998), Rajan and Zingales (1998),

and Okedokun (1998). The following is empirical review of financial development and economic

growth from developed countries, developing countries and from Nigeria.

Empirical evidences from developed countries

Rousseau and Wachtel (1998) applied VAR approach to five industrialised countries over the

period 1870–1929 and found strong uni-directional link from finance to growth. They also

estimated a vector error-correction model (VECM) for each country and found evidence of an

economically important long-run relationship between the two sectors. Rousseau and Sylla (1999)

examined the historical role of finance in the U.S from 1790-1850 and found a strong support for

finance led growth. In addition, Rousseau (1999) investigates the Meiji era of Japan (1868-1884)

and shows that the financial sector was instrumental in boosting Japan’s explosive growth prior to

25

the First World War. Arestis, et al. (2001) examine the relative imp act of stock markets and banks

on long-term economic growth in Germany, the USA, Japan, the UK and France. They find that

both stock markets and banks have made important contributions to output growth in Germany,

France and Japan, with the stock market’s contribution ranging from about one-seventh to one-

third of the bank’s contribution. These results are consistent with the view that bank-based

financial system may be more able to promote long-term growth than stock market-based. The

authors further find that there is a weak relationship between financial development and economic

growth in the UK and the USA. The results also suggest that stock market volatility has negatively

affected economic growth in France and Japan. Liang (2007) examined banking sector

development and growth in China with reference to quality of legal institutions, employing a panel

data set covering 29 provinces over the period of 1990-2001 and concluded that without an

effective and well-developed legal system, banking sector development only partially contributed

to China’s economic growth. Contrarily, Boyreau-Debray (2003) work on the Chinese financial

development and growth revealed that credit extended by the banking sector at the state level has

a negative impact on provincial economic growth.

Empirical evidences from developing countries

Odedokun (1996) employed time series data for 71 developing countries and showed that financial

intermediation had promoted economic growth, in some 85% of the countries. While the empirical

works above focus on only banking sector development, they ignored the effect of stock market

development. Ghatak (1997) examines the impact of financial development on economic growth

in Sri Lanka over the period 1950–87. He concludes that interest rates and financial deepening

increase economic growth. Bell and Rousseau (2001) applied VAR approach for post-

independence India and found that financial intermediaries played a more emphatic role in

26

promoting investment than in increasing total factor productivity, and interpreted this as evidence

for the presence of a factor accumulation. Sinha and Macri (2001) have examined the relationship

between financial development and economic growth using time series date for eight Asian

countries including Pakistan over the period 1950-97. The regression results show a positive and

significant relationship between the income and financial variable for India, Malaysia, Pakistan,

and Sri Lanka. The multivariate causality tests show a two-way causality between income and

financial variables for India and Malaysia, one-way causality from financial variables for Japan

and Thailand, and reverse causality for Korea, Pakistan and Philippines. Thus, their results clearly

support the general view of a positive relationship between financial development and economic

growth. Odiambho (2004) investigates the role of financial development on economic growth in

South Africa. The study uses three proxies of financial development namely the ratio of M2 to

GDP, the ratio of currency to narrow money and the ratio of bank claims on the private sector to

GDP against economic growth proxy by real GDP per capita. He employed the Johansen-co-

integration approach and vector error correction model to empirically reveal overwhelming

demand-following response between financial development and economic growth. The study

totally rejects the supply leading hypothesis. Wadud (2005) examines the long-run causal

relationship between financial development and economic growth for three South Asian countries

namely India, Pakistan and Bangladesh. He disaggregated financial system into “bank-based” and

“capital market based” categories. The study employed a cointegration vector autoregressive

model to assess the long-run relationship between financial development and economic growth.

The empirical findings suggest that the results of error correction model indicate causality running

from financial development to economic growth.

27

Rousseau and Vuthipadadorn (2005) used time series approach to investigate whether the intensity

of financial intermediaries promoted investment and growth in 10 Asian economies including

Pakistan over the period 1950–2000. They used VAR and VECM to examine the nature of the

causality between measures of financial and real sector activity. They find strong uni-directional

causality from finance to investment in most cases, and weaker support for a causal link from

finance to the level of output. These findings are consistent with a factor accumulation channel as

the primary mechanism through which the financial sector influenced macroeconomic outcomes

in these countries.

Ardic and Damar (2006) analyzed the effects of financial sector deepening on economic growth

using a province-level data set for 1996-2001 on Turkey. The period covered was associated with

a weakly regulated and relatively unsupervised expansion of the banking sector which led to the

2001 financial crisis. The results indicate that a strong negative relationship between financial

deepening, both public and private, and economic growth exists. The study argues that it is possible

that financial development may not always contribute to economic growth, and the conditions

under which such a contribution takes place should be investigated further.

Guryay, et al., (2007) examine the relationship between financial development and economic

growth. The study employed ordinary least squares technique to show that there is insignificant

positive effect of financial development on economic growth for Northern Cyprus. They posit that

causality runs from growth to financial development without a feedback.

Akinlo and Egbetunde (2010) empirically examines the long run and causal relationship between

financial development and economic growth for ten countries in sub-Saharan Africa. Using the

vector error correction model (VECM), the study finds that financial development is cointegrated

with economic growth in the selected ten countries in sub-Saharan Africa. That is there is a long

28

run relationship between financial development and economic growth in the selected sub-Saharan

African countries. The results show that financial development Granger causes economic growth

in Central African Republic, Congo Republic, Gabon, and Nigeria while economic growth Granger

causes financial development in Zambia.

Empirical evidences from Nigeria

Empirical studies on Nigerian finance-growth dynamics are not only scanty in number but

restricted in scope in terms of the measure of financial development (Nkoro and Uko, 2013).

Ndebbio (2004), using an ordinary least square regression framework, finds that financial sector

development weakly affect per capita growth of output. He attributed the result to shallow finance

and the absence of well-functioning capital markets. Similarly, Nnanna (2004) using ordinary least

square regression technique, found that financial sector development did not significantly affect

per capita growth of output. In the same vein, Nzotta and Okereke (2009) examine financial

deepening and economic development in Nigeria between 1986 and 2007. The study made use of

time series data and two stages least squares analytical framework and found that four of the nine

variables; lending rates, financial savings ratio, cheques/GDP ratio and the deposit money

banks/GDP ratio had a significant relationship with financial deepening and concluded that the

financial system has not sustained an effective financial intermediation, especially credit allocation

and a high level of monetization of the economy. Agu and Chukwu (2008) employed the

augmented granger causality test approach to ascertain the direction of causality between “bank-

based” financial deepening variables and economic growth in Nigeria between 1970 and 2005.

Their co-integration results suggest that financial deepening and economic growth are positively

co-integrated. The study finds that the Nigerian evidence supports the demand-following

hypothesis for “bank based” financial deepening variables like private sector credit and broad

29

money; while it supports the supply-leading hypothesis for “bank-based” financial deepening

variables like loan deposit ratio and bank deposit liabilities. Thus, the study concludes that the

choice of bank-based financial deepening variable influences the causality outcome. However,

Olofin and Afangideh (2010) examine the financial structure and economic growth in Nigeria by

using annual data from 1970 to 2005. Small macro econometric model to capture the

interrelationships among aggregate bank credit activities, investment behaviour and economic

growth given the financial structure of the economy was developed. They adopted three stage least

square estimation techniques, while counter factual policy stimulations were conducted. The

results of these tests indicate that a developed financial system alleviates growth financing

constraints by increasing bank credit and investment activities with resultant rise in output. One

major outcome of this study is that financial structure has no independent effect on output growth

through bank credit and investment activities, but financial sector development merely allows

these activities to positively respond to growth in output. Odeniran and Udeaja (2010) examine

the relationship between financial sector development and economic growth in Nigeria. The study

employs granger causality tests in a VAR framework over the period 1960-2009. Four variables,

namely; ratios of broad money stock to GDP, growth in net domestic credit to GDP, growth in

private sector credit to GDP and growth in banks deposit liability to GDP were used to proxy

financial sector development. Ohwofasa and Aiyedogbon (2013) assessed the level of

development of financial deepening in the banking sector and the extent it has impacted on

economic growth over the last two decades. Vector autoregressive (VAR) methodology and its

derivatives, impulse response function and variance decomposition, were employed that enable us

to scrutinize the relationship between financial deepening and economic growth. The findings

show that the series are co-integrated and that long run relationship existed between the variables.

30

2.2.3 TRADE LIBERALIZATION AND ECONOMIC GROWTH

The relationship between trade and development remains controversial among researchers, in spite

of political pronouncements that take this nexus as given. Many empirical studies show a positive

association between trade liberalization and economic growth (Krueger, 1007; Grossman and

Helpman, 1991; Frankel and Romer, 1999; Wacziarg, 2001), some studies also show that the

relationship between the two variables is non-significant (UNCTAD, 1989; Rodriquez and Rodrik,

2001). Rodriguez and Rodrik raised some concerns about the robustness of these results as

conclusions remained sensitive to difficulties in measuring openness, statistically sensitive

specifications and collinearity of protectionist policies with other poorly executed policies in

developing economies. In recent research on trade liberalization, not much attention has been given

to the issue of imports, the balance of trade and current account of the balance of payments. It is

conceivable that trade liberalization may lead to faster growth of imports than exports if the

countries were highly protected in pre-liberalized period. The faster growth in imports in relation

to exports could have serious implications for balance of trade and this in itself could constrain

economic Some researchers postulates that policy shift towards trade openness has a tendency to

improve imports more than exports leading to trade deficits and consequently contributing to low

economic growth in future. On the import side, there is a strong positive impact of trade

liberalization on the growth of imports and this impact is through the sensitivity of price and

income changes (Melo and Vogt, 1984; Bertola and Faini, 1991). Some studies show that the

countries which went for liberalization programmes have improved their export performance

(Thomas et al, 1991; Weiss, 1992; Joshi and Little, 1996; Helleiner, 1994; Bleaney, 1999; and

Ahmed, 2000). Many of the works on trade liberalization and economic growth used cross country

data, example include World Bank (1990) and Mosley et al. (1991). Work has also been done on

31

panel data and time series, usually focusing on a single country (Papageorgiou et al., 1991;

Greenaway & Sapsford, 1994; Onafowora et al., 1996; Greenaway et al., 1997 and Narayan and

Smyth, 2005). Most of the cross-sectional and time series studies have found, at best, mixed

support for the hypothesis that trade liberalization promotes growth. Some set of studies has

applied panel data methods (Greenaway et al., 1998, 2002 and Parikh and Stribu, 2004). These

studies suggest, in contrast to much of the cross-sectional and time series literature, that

liberalization might have a positive effect on growth in real GDP. The following is empirical

review of trade liberalization and economic growth from developed countries, developing

countries and from Nigeria.

Empirical evidences from developed countries

Chow (1987) found a reciprocal causal relationship between export expansion and growth of

manufacturing industries in the four Asian NIEs- Hong Kong, Singapore, South Korea and

Taiwan. Kwan and Cotsomitis (1990) found a feedback relationship between exports and

economic growth in China for the period 1952-85. Jin (2003) analyzed the data before economic

crisis of 1997/98 in Korea. Findings of the paper show negative impact of trade liberalization on

growth due to crowding out of domestic investment. Inflation also negatively associated with

increased openness. Carmen and Pilar (2004) investigated the role played by manufacturing sector

imports on real GDP and employment for China. Using quarterly time series data set over the

period 1979-2002 and applying dynamic econometric technique of estimation study found a

positive and long run relation between economic growth index and index of trade openness.

Empirical evidences from developing countries

32

Khan et al. (1995) who showed that economic growth is enhanced through export promotion.

Another analysis conducted by Iqbal and Zahid (1998) concluded that Pakistan had gained walfare

effects through trade openness. Gilbert (2004) investigated trade openness policy, quality of

institutions and economic growth in the Sub-Saharan African countries. The results show that in

any of the countries where openness has no significant impact on economic growth it is as a result

of low institutional quality. Njikam (2009) tried to analyze the trade openness and development of

industrial performance in Cameroon, while trying to explore whether a relation exists between

infrastructure and industrial performance during the two time periods, before and after trade

openness this study utilized the annual values during the import-substitution era (1986-94) and

immediately after trade reform (1995-2003) for a sample of 29 industrial sectors. By means of

panel data techniques this study found that development in infrastructure leads to enhance the

productivity of industrial sector and in trade openness agenda better quality of infrastructure must

be given priority. Nazima, Hafiz, and Mehboob (2012) empirically investigates the relationship

among trade openness, industrial value added and economic growth of Pakistan. Annual time

series data set (1980 to 2009) was utilized to observe the connections amongst the indicators of

interest. Moreover, unit root test was applied to determine the time series properties while OLS

technique of estimation and Granger causality tests were employed to find out direction of

causality. The results inferred from the econometric model articulated that imports and exports

affect positively to economic growth till the industrial value added are taken into account.

Empirical evidences from Nigeria

Kingsley et al. (2004) examined the impact of openness on Nigeria’s long-run growth using the

cointegration approach. They reported that there is no significant relationship between openness

and economic growth, and that unbridled openness could have deleterious implications for growth

33

of local industries, the real sector and government revenue. Adebiyi (2006) investigated the

relationship between policies of trade openness and economic growth performance in Nigeria. This

study applied Vector Auto regression Techniques and used annual time series data set. Major

findings suggest that sustained economic growth in Nigeria can be achieved by implementing a

comprehensive trade openness programme. Atoyebi, Adekunjo, Edun, and Kadiri (2012) reported

that openness exerted negative impact on economic growth during the period of 1970 to 2010 in

Nigeria. However, Nduka (2013), Adelowokan and Maku (2013) reported that openness exerted

positive effect on economic growth in Nigeria during the period of 1970 to 2008 and 1960 to 2011

respectively. Eleanya and Onuzuruike empirically examines and compares the causal relationship

between trade openness and economic growth in Nigeria in the pre and post SAP (1970Q1-1985Q4

and 1986-2011) periods. Using Engle-Granger approach for cointegration test, the result confirms

that long-run relationship exist between economic growth and its determinants: trade openness,

investment, and government expenditure respectively.

34

CHAPTER THREE

RESEARCH METHODOLOGY

3.0 INTRODUCTION

Research methodology is the systematic and scientific process of gathering, recording and

analyzing data concerning problems and issues that the research is about.

3.1 THEORETICAL FRAMEWORK

With the emergence of the endogenous growth theories in 1980s, the relationship between

economic policy and growth became a highly debated issue. In the theoretical literature,

discussions are focused on different channels through which economic policy affects economic

growth. In this section, different models of growth will be discussed to provide a framework of

thought that helps to understand the impacts of each link between policy and growth. In other

words, each channel through which economic policy affects growth has different implications for

growth in different models. There are two competing theoretical frameworks in the growth

literature, namely neo-classical and endogenous growth theories. The main differences between

them are whether the policy change has a long term effect on the growth rate. On the one hand,

the neoclassical theory argues that a policy change has no effect on long-term economic growth

and, on the other hand, endogenous growth theory shows a policy change in economy does matter.

The endogenous growth theory is a reaction to the traditional Neo-Classical growth models,

represented by Solow (1956). This new approach to growth theory has sought to supply the missing

explanation of long term growth. Indeed, this approach provides a theory of technical progress,

one of the central missing elements of the neo-classical model. In other words, endogenous growth

theories seek to discover what lies behind the exogenous rate of technical progress and hence a

35

country’s growth rate. Endogenous growth theory recognizes that technological change occurs as

a result of the efforts of profit-maximizing firms to invent new blueprints, and technological

progress is an endogenous outcome of economic activity (Kar, Peker and Kaplan, 2008).

The crucial distinction between ‘old’ and ‘new’ growth theories is that the former utilizes the

assumption that returns to the capital stock is diminishing, while the latter argues that returns to

capital itself or, in a wider sense, to the stock of physical and human capital formation is constant

or increasing (Sala-I Martin, 1990a). This then implies that those variables that lead to non-

decreasing returns drive the growth rate. Numerous candidates have been recommended as the

source of non-decreasing returns: particularly, the stock of human capital Lucas (1988);

accumulated capital, Rebelo (1991); research and development, Romer (1986, 1990); or public

infrastructure investment (Barro, 1991).

Thus, endogenous growth models highlight sectors of the economy that influence the growth path

of an economy. This can be simply shown in a Robelo-type production function, known as the AK

model. Most of the endogenous growth models can be viewed as extensions or micro-foundations

of the AK model (Sala-I-Martin, 1990b). Rebelo (1991) formulated the simple form of the

endogenous growth model, which has since been widely used in empirical analysis. The AK model

takes its name from its production function. In its original form, the model setting involves

dynamic maximization. In this section, we will make the further assumption of a constant savings

rate. This assumption, however, does not change the main conclusions and intuitions of the model.

In the AK model, the production function takes the following form:

Yt = AKt (1)

36

Where Yt represents output, Kt is capital stock at time t and A is some positive constant. This

formulation of the production function means that there are constant returns to capital

accumulation. It is also important to note that A is equal to the return to investment in this model.

As will be explained in the next section, trade policy primarily affects the rate of return of capital

and hence growth. Therefore, A can be written as a function of trade policy (τ) as,

A = θ0 −θ1τ (2)

Equation (2) indicates that the rate of return of capital is a negative function of trade policy.

The accumulation of capital is formulated as:

Kt = It-1+ (1- ) Kt-1 and It = sYt ` (3)

Where s is the investment rate and δ is the depreciation rate. Both are assumed constants, and

investment at time t (It) is equal to the savings in the economy. The special formulation of the

production function in the AK model (equation 1) implies that the marginal product of each unit

of capital is always equal to A. It does not decline as the capital accumulates. This can be shown

easily: after substituting the value of investment into equation (3) and then dividing both sides by

Kt-1 and taking the logarithm of both sides, the resulting equation will be:

log(𝐾𝑡

𝐾𝑡−1) = log [sA+ (1- )] (4)

For small values of s, A, d and sA> d, equation (4) can be written as:

log Kt = sA− (5)

This equation says that the rate of growth of capital stock is constant if tariff rates are constant.

After taking the logarithm and derivative of the production function and substituting the value of

the equation of motion of the capital from equation (5) and the value of return to capital from the

equation (2), the long term rate of growth of output can be written as follows:

logYt = logKt = sθ0 – sθ1τ − (6)

37

From equation (6), it is obvious that the rate of growth of the economy is decreasing with tariff

rates and increasing with saving rates. Hence, any economic policy that increases the return to

investment will permanently increase the rate of growth of the economy. Almost all endogenous

growth literature has concentrated on the determinants of the return to investment, A, and how

policy change affects it (Sala-I Martin, 1990a).

3.2. MODEL SPECIFICATION

As discussed in the previous section, each proxy of trade liberalization and financial development

just shows one aspects of impact. Therefore, it is useful to develop a tool for description the

relationship among variables, so that include all different dimensions of the impact of trade

liberalization and financial development and represent a single measure for them. Principal

component analysis can be used to combine the information of such proxies. The main objective

of principal component analysis is to reduce the dimensions of data set which consists of a number

of interrelated variables, using the covariance among them, while retaining as much as possible of

the variation present in the data set (Jolliffe, 1986). This is achieved by the linear combination of

data which are Trade liberalization, financial development and economic growth in the long term:

orthogonal to each other. The principal component analysis can be applied using the original data

or their deviations from their means or standardized variables.

Since this method is sensitive to the unit of measurement, it is better to use of standardized

variables, when they are in different units. In addition, according to volatility of variables, the

principal components are estimated on the data matrix of the difference of the logs of the

standardized variables for the considered period. The variances of principal components are the

eigenvalues (λij) of the variance-covariance matrix (Σ) of the data. Eigenvector of the firs principal

38

component are the coefficients for linear combination of proxies. Therefore, one-dimensional

measure of trade liberalization or financial development can be found as follows:

OPt = ∑ λ𝑖𝑍𝑖𝑡5𝑖=1 (7)

Where OPt represents the one dimensional measure of trade liberalization (or financial

development) at time t, Zit is the standardized ith trade liberalization (or financial development)

proxy at time t, and λi is the eigenvector component that corresponds to a complementary measure

of ith proxy. For trade liberalization, three proxies, namely ratio of export to income (X/Y), ratio

of import to income (M/Y) and ratio of export plus imports to income (OPEN) are used to obtain

a trade liberalization index (TL).

TL = 𝛼𝐿 (𝐸𝑋

𝑌)+β𝐿 (

𝑀

𝑌)+γ𝐿(𝑂𝑃𝐸𝑁) + 𝜀 (8)

The index for financial development (FD) includes the monetary aggregates, namely

M1/Y, M2/Y, PSC/Y and SMC/Y.

FD = ηL(𝑀1

𝑌)+πL(

𝑀2

𝑌)+φL(

𝑃𝑆𝐶

𝑌)+ϕL(

𝑀𝐶

𝑌)+ε (9)

Due to the existing high correlation (r= 0.98) among trade liberalization and financial

development indexes, it may not be appropriate to include both at the same time in a regression.

Therefore, we have decided to construct another index that includes both proxies for trade

liberalization and financial development, namely X/Y, M/Y, OPEN/Y, M1/Y, M2/Y, PSC/Y and

SMC/Y. This new index (EL), therefore involves proxies for both external liberalization and

financial development. In a narrow sense, this index (EL) can be considered as an economic

liberalization index, which carries instruments from both aspects of the issue concerned here.

The EL index is as follows (Kar, Peker and Kaplan, 2008).

EL= 𝛼𝐿 (𝐸𝑋

𝑌)+β𝐿 (

𝑀

𝑌)+γ𝐿(𝑂𝑃𝐸𝑁) + η L (

𝑀1

𝑌) + πL (

𝑀2

𝑌) + φL (

𝑃𝑆𝐶

𝑌) + ϕL (

𝑀𝐶

𝑌) + ε (10)

39

L denotes the logarithm of the variables used as proxy for trade liberalization and financial

development; α, β, γ, η, π, φ, ϕ are co-efficient of the variables and ε represents residual term.

3.3 MEASUREMENT OF VARIABLES AND SOURCES

3.3.1 Measurement of variables used as a proxy for trade liberalization

Researchers in the recent empirical literature concentrate on finding reliable proxies of trade

liberalization. However, the share of export as a percentage of income, the share of the import as

a percentage of income and the share of export plus imports (trade volume) as a percentage of the

income constitute very common proxies for trade liberalization in the empirical literature. In this

article, the following proxies of trade liberalization are employed in the empirical analysis.

Export to G D P ratio (EXGDP): The first theoretical channel that links openness to economic

performance goes through the allocation of resources. According to this argument, opening up to

international trade brings about reallocation of resources according to comparative advantages

(Grossman and Helpman, 1992, Young, 1991). Since the direct effect of the allocation of resources

is observed on the level of exports, the share of exports in total production can be used to represent

this dimension of openness. In addition, the share of exports in production can be used as a proxy

of openness to capture the dimension of openness related to scale economies and the availability

of inputs.

Import to GDP ratio (MGDP): The import share in total production can be used as an openness

proxy characterizing the dimension of openness related to increased international competition. It

also represents the allocation effect of openness since the imports of those sectors that have

comparative disadvantages will increase following trade liberalization.

40

Foreign trade to GDP ratio (OPEN): The share of the total of exports and imports in total

production provide the proxy that represents the technology spillover dimension of openness.

Openness to trade facilitates access to the technological information in the world (Grossman and

Helpman 1992), which creates technological spillover through imports as well as exports.

3.3.2 Measurement of variables used as a proxy for financial development

One of the most difficult aspects of empirically investigating the relationship between financial

development and economic growth is the measurement of “financial development”. However, the

practitioners are forced to form a well-defined set of measures of financial development by the

availability of data at hand.

The proxies proposed for measuring the level of financial development are basically chosen from

the monetary and credit aggregates in an economy. The rationale for the inclusion of a wide range

of proxies is to maximize the information on financial development. In other words, diverse

aggregates should be able to catch different functions of the financial markets. In this article, the

following proxies for financial development are employed in the empirical analysis.

Broad Money Ratio (M2GDP): Monetary aggregates also provide an alternative set of variables

to measure the extent of financial development (D e Gregorio and Guidotti, 1995; Galetovic, 1996;

Lynch, 1 996). In the literature, the commonly used measure of financial development is a ratio of

some broad measure of the money stock, usually M2, to the level of nominal income (Gelb, 1989;

King and Levine, 1993a, 1993b; Murinde and Eng, 1994a, and 1994; Lyons and Murinde, 1994;

Demetriades and Hussein, 1996; A restis and Demetriades, 1997; Kwan et. al., 1998). This simple

indicator measures the degree of monetization in the economy. The monetization variable is

designed to show the real size of the financial sector of a growing economy.

41

Domestic credit to private sector by bank (CPSGDP): Domestic credit to private sector by

banks refers to financial resources provided to the private sector by other depository corporations

(deposit taking corporations except central banks), such as through loans, purchases of non-equity

securities, and trade credits and other accounts receivable, that establish a claim for repayment.

For some countries these claims include credit to public enterprises (World Bank).

Market Capitalization (MCGDP): this is the total value of listed shares of companies in

the stock market. The reason behind this measure is that the overall market size is positively

correlated with the ability of the market to mobilize capital and diversify risk on economy

wide basis (Levine and Zervos, 1996).

3.4 ANALYTICAL TECHNIQUE

The analysis to be made majorly dwells much on a pure quantitative econometric technique. In our

model some of the variables used are in rate form, while others are in gross volumes. As such,

estimating the relationship using the Ordinary Least Squares (OLS) for each model in the specified

system of equations could serve as major impediment to valid statistical estimation. In order avert

estimating spurious regressions and to ensure improving the Durbin-Watson statistic, those

variables in their gross estimates were logged. Conclusively, I adopt a non-restricted vector

autoregressive (VAR) model and the Johansen and Juselius [1992] approach to explore possible

nexus among the variables.

In order achieve the set objective of the study, the given below statistical instruments, among

others, will be used to estimate the built model.

42

PRINCIPAL COMPONENT ANALYSIS (PCA)

The PCA is statistical method used to transform a number of correlated variables into a smaller

number of uncorrelated variables called principal components, while retaining most of the original

variability in the data set.

Unit Root Test

Majorly, stationary data is what is made use in econometric estimation. So this statistical tool will

be used to test the stationary of variables this research work contains.

Cointegration Test

This is used to test whether there is long run association among the variables in a model.

Impulse Response

This will be used to estimate the dynamic relationships existing among the chosen variables.

Majorly, it will be used to show the response of a variable to the shock created in the economy by

the other variables and even the variables itself.

Variance decomposition

The variance decomposition shows the proportion of the forecast error variance of a variable which

can be attributed to its own shock and the innovations of other variables. It shows how many

unforeseen changes or variation in the model are explained by different shocks.

Coefficient of Determination (R2)

In adjudging how sound is the whole model to be built, coefficient of determination will be used

to test the goodness of fit of the model. That is, it will be used to measure how good is the

explanatory variables to explain the regressed each model contain.

43

Adjusted coefficient of determination (Adj R2)

This test will be carried out to test the effect of insignificant regressor that is adjusted for. It will

thus help to remove the effect of insignificant regressors.

F – Statistic

This test will be carried out to test the linearity assumption of the OLS. So also, it will be used to

test whether the independent variables jointly influence the dependent variables.

Durbin-Watson (DW) Statistic

This is also known as the d- statistic. It will be used this to test the existence of serial correlation

or autocorrelation in the residual of each model.

Granger causality test

This test is used for determining whether one time series is useful for forecasting another.

Stability test

This test is used is used for determining whether there exist structural break which could lead to

huge forecasting errors and unreliability of our model.

3.5 SOURCES OF DATA

Information used in estimating our model is basically secondary data and it was sourced from the

archives of Central Bank of Nigeria Annual Statistical Bulletin (2013) and World Bank indictors

(2014).

44

CHAPTER FOUR

DATA PRESENTATION, ANALYSIS AND INTERPRETATION

4.01 INTRODUCTION

This chapter focuses on clear presentation as well as analysis of the research data, with extensive

discussions of the findings for this research work. In a bid to ensure achieving the already stated

objectives contained in the chapter one of this research work, this chapter sets out to present the

data needed for the task, interpret the statistical result and analyses the result derived within the

framework this research objective contain.

4.0.2 TABULAR PRESENTATION OF DATA

The table below represents data used as proxy for trade liberalization and financial development.

Export as a percentage of gross domestic product is represented by (EXGDP), Import as a

percentage of gross domestic product is characterized by (MGDP), Trade as a percentage of gross

domestic product (OPEN), Broad money as percentage of gross domestic product (M2GDP),

Market capitalization as a percentage of gross domestic product (MCGDP), Credit to private sector

percentage of gross domestic product (CPSGDP), Gross fixed capital formation is used as proxy

for capital stock (GFC), Secondary school enrolment as a percentage of gross enrolment (SEC)

and Per capital real income (PRY).

45

TABLE 1: This shows the data trend of variables used for analysis

YEAR

EXGDP

MGDP

OPEN

M2GDP

MCGDP

CPSGDP

SEC

GFC

PRY

1986 13.31603 10.40073 23.71676 17.7 5.052 11.3 27.08 11351460000 196862.8

1987 26.94186 14.70481 41.64666 14.3 4.245845 10.9 27.07 15228580000 171136.3

1988 22.85462 12.45735 35.31198 14.6 3.798093 10.4 26.84 17562210000 179257.9

1989 43.98132 16.41044 60.39176 12.0 3.348506 8.0 24.13 26825510000 185922.6

1990 35.34425 17.68597 53.03022 11.2 3.448641 7.1 24.6 40121310000 204312.7

1991 41.70108 23.17552 64.8766 13.8 4.233328 7.6 24.74 45190230000 197941.2

1992 37.50938 23.5216 61.03097 12.7 3.564329 6.6 25.17 70809160000 193845.4

1993 33.82986 24.27999 58.10985 15.2 4.359078 11.7 25.6 96915510000 193000.2

1994 24.31023 17.99864 42.30887 16.5 4.736729 10.2 26.03 1.05575E+11 189953

1995 35.76149 24.00634 59.76783 9.9 6.204942 6.2 26.46 1.4192E+11 184702.1

1996 32.23857 25.45243 57.69099 8.6 7.087766 5.9 26.89 2.04048E+11 189143.4

1997 41.7746 35.08539 76.85999 9.9 6.729128 7.5 27.32 2.429E+11 189644.3

1998 29.69152 36.48173 66.17325 12.2 6.582361 8.8 27.75 2.42256E+11 189980.9

1999 33.86953 21.97686 55.84639 13.4 6.411339 9.2 23.42 2.31662E+11 186159.6

2000 51.73036 19.65017 71.38053 13.1 7.035005 7.9 24.5 3.31057E+11 191201.4

46

SOURCE: Central Bank of Nigeria Annual Statistical Bulletin (2013) and World Bank

indictors (2014).

2001 45.44807 36.36478 81.81285 18.4 9.608133 11.1 26.86 3.72136E+11 194679.2

2002 35.96569 27.41795 63.38364 19.3 9.811744 11.9 29.42 4.99682E+11 197013.1

2003 39.7879 35.431 75.2189 19.7 13.71158 11.1 32.53 8.65876E+11 211957.6

2004 30.16075 18.28738 48.44813 18.7 18.51272 12.5 34.75 8.63073E+11 276274.2

2005 31.65697 19.09139 50.74836 18.1 19.84891 12.6 34.7 8.04401E+11 278447.6

2006 43.11133 21.49798 64.60931 20.5 27.58423 12.3 34.19 1.54653E+12 293470.8

2007 33.72852 30.73439 64.46291 24.8 63.81128 17.8 31.61 1.93696E+12 305261.8

2008 39.88313 25.08984 64.97297 33.0 39.35985 28.5 35.09 2.05301E+12 315776.4

2009 30.76862 31.03424 61.80285 38.0 28.35659 36.7 38.9 3.05058E+12 328605.1

2010 24.46866 17.89759 42.36625 20.4 18.29764 18.7 43.8 8.42107E+12 344766.4

2011 31.19384 21.36716 52.56099 19.2 16.24333 16.9 44.01 8.97008E+12 350953.6

2012 31.66873 13.03588 44.70461 19.5 20.79171 20.6 44.03 9.25889E+12 364328.5

2013 26.65813 9.790534 36.44866 18.9 23.78072 19.7 44.08 1.03368E+13 380183.8

47

4.0.3 DESCRIPTIVE STATISTICS OF VARIABLES

TABLE 2: Descriptive Statistics of trade liberalization index

EXGDP MGDP OPEN

Mean 33.90554 22.51172 56.41725

Median 33.77919 21.73742 58.93884

Maximum 51.73036 36.48173 81.81285

Minimum 13.31603 9.790534 23.71676

Std. Dev. 8.034738 7.701293 13.51269

Skewness -0.16566 0.332413 -0.37724

Kurtosis 3.347812 2.307703 2.821602

Jarque-Bera 0.269206 1.074813 0.701248

Probability 0.874063 0.584262 0.704249

Sum 949.355 630.3281 1579.683

Sum Sq. Dev. 1743.04 1601.368 4930.004

Observations 28 28 28

Source: Researcher’s computation using E-views.

48

TABLE 3: Descriptive Statistics of financial development index

CPSGDP M2GDP MCGDP

Mean 12.84625 17.26497 13.80556

Median 11.07757 17.06965 7.061386

Maximum 36.74587 37.99238 63.81128

Minimum 5.917133 8.577088 3.348506

Std. Dev. 6.998034 6.493888 13.58144

Skewness 1.858025 1.521131 2.085554

Kurtosis 6.469637 5.710898 7.758584

Jarque-Bera 30.15531 19.37172 46.71597

Probability 0 0.000062 0

Sum 359.6949 483.4192 386.5555

Sum Sq. Dev. 1322.257 1138.606 4980.299

Observations 28 28 28

Source: Researcher’s computation using E-views.

In an attempt to investigate the impact of trade liberalization, financial development on economic

growth in Nigeria. This section focuses on the descriptive statistics of data employed in the study.

The descriptive statistics gives information about the mean, median and standard deviation of the

data series. It also shows the maximum and minimum values of variables, as well as the skewness

and kurtosis of data. Table 2 and Table 3 above shows that all the data series display a high level

49

of consistency as their mean and median values are perpetually within the maximum and minimum

values of those series. Moreover, the skewness and kurtosis statistics provide useful information

about the symmetry of the probability distribution of various data series as well as the thickness

of the tails of these distributions respectively. These two statistics are particularly of great

importance since they are used in the computation of Jarque-Bera statistics which is used in testing

for the normality or asymptotic property of a particular series.

50

4.0.4A GRAPHICAL DESCRIPTION OF FINANCIAL DEVELOPMENT INDICATORS

In order to measure the improvement in the financial sector following the financial reforms process

since the adoption of Structural Adjustment Program, the author of this research decided to present

a graphical illustration of the trends of the standard indicators used in this study. Also, in a bit to

effectively analyze the development of this indices, we looked at how the various index perform

averagely since the introduction of SAP.

M2GDP which represent the ratio of broad money relative to GDP is a measure of the level of

financial deepening in a country. Between the periods of 1986-1995, the average monetary asset

of the country stood around 13.8 percent of the GDP, this figure increased marginally to 14.8

within 1996-2004, while amid 2005-2013, the index records its highest feat (23.6%) since the

0.0

10.0

20.0

30.0

40.0

50.0

60.0

70.0

19

86

19

87

19

88

19

89

19

90

19

91

19

92

19

93

19

94

19

95

19

96

19

97

19

98

19

99

20

00

20

01

20

02

20

03

20

04

20

05

20

06

20

07

20

08

20

09

20

10

20

11

20

12

20

13

Trend of financial development indicators relative to GDP

M2 MC CPS

51

introduction of SAP. It should be noted that a large ratio of M2GDP represents a more developed

and efficient financial sector. We can postulate given the above analysis that M2GDP shows that

the financial sector of the country is growing, even though its figures are not impressive.

Market Capitalization relative to GDP (MCGDP) shows the overall market size relative to

GDP. It shows how effective the market can mobilize capital and diversify risk. On the

average this index has been on the rise since the introduction of SAP. Between the periods

of 1986-1995, the average market capitalization of the country stood around 4.29 percent of the

GDP, this figure upsurged to 9.5 within 1996-2004, while amid 2005-2013, the index records its

utmost exploit (28.67%) since the introduction of SAP. It recorded its highest figure in 2007

before the global financial meltdown and since 2011, it has been on the rise. It can, hence,

be concluded that the global economic meltdown has affected the performance of the

Nigerian Stock market and, in turn, Nigeria’s economic growth.

Credit to private sector relative to GDP (CPSGDP). Between the periods of 1986-1995,

the average credit lent to private sector was around 9.0 percent of the GDP, this figure upsurged

marginally to 9.6 within 1996-2004, while amid 2005-2013, the index records its highest figure

(20.4%). It should be noted that the highest figure in this trend was in the year 2009 after the

financial meltdown which rock so many nations. In order to bailout the firms caught up in the

mess, several loans was given out.

52

4.04B GRAPHICAL DESCRIPTION OF TRADE LIBERALIZATION INDICATORS

In order to measure the improvement in the trade liberalization following the trade reforms

process since the adoption of Structural Adjustment Program, the author of this research decided

to present a graphical illustration of the trends of the standard indicators used in this study.

In the above diagram, EXGDP represents the ratio of export relative to GDP, while MGDP

represents the ratio of import relative to GDP and the ratio of total trade relative to GDP. Since

the introduction of SAP, this figures have experienced a remarkably turn around. Hence, we can

postulate that Nigeria has benefitted from liberalizing her trade.

0

10

20

30

40

50

60

70

80

90

19

86

19

87

19

88

19

89

19

90

19

91

19

92

19

93

19

94

19

95

19

96

19

97

19

98

19

99

20

00

20

01

20

02

20

03

20

04

20

05

20

06

20

07

20

08

20

09

20

10

20

11

20

12

20

13

Trend of trade liberalization indicators relative to GDP

EXPORT IMPORT OPEN

53

4.05 PRINCIPAL COMPONENT ANALYSIS (PCA)

The PCA is statistical method used to transform a number of correlated variables into a smaller

number of uncorrelated variables called principal components, while retaining most of the

original variability in the data set. As a run-up to the use of PCA, the correlation for the variables

is examined. Table 2 presents the correlation matrix for the selected economic liberalization

(trade liberalization and financial development) variables.

Table 4: Correlation Matrix (Pearson (n)):

Variables EXGDP MGDP OPEN M2GDP MCGDP CPSGDP

EXGDP 1 0.475 0.865 -0.096 0.022 -0.190

MGDP 0.475 1 0.852 0.133 0.161 0.015

OPEN 0.865 0.852 1 0.019 0.105 -0.104

M2GDP -0.096 0.133 0.019 1 0.718 0.936

MCGDP 0.022 0.161 0.105 0.718 1 0.659

CPSGDP -0.190 0.015 -0.104 0.936 0.659 1

Values in bold are different from 0 with a significance level alpha=0.05

The correlation coefficients among the variables are relatively high especially between CPSGDP

and M2GDP and between OPEN and EXGDP. If all the variables are used simultaneously in the

models, there is a high probability of multicollinearity, which may lead to incorrect inferences.

In order to overcome this problem, the principal components for the selected economic

liberalization variables are estimated. Table 3 which translates to model 1 represents trade

liberalization indicators, while table 4 which translates to model 2 represent components of

financial development indicators, equally table 5 represents economic liberalization indicators.

The computed results in our analysis shows that the first components explains the largest

54

variance. Hence, the first components of each model are being used to generate the results. Also

the composite index (TL, FD & EL) has been formulated by adding the multiplication of actual

values of each of the variables that makes up the models and their corresponding eigenvectors

obtained from first components.

Table 5: Results of Principle component analysis for model 1

PC

Variables

Eigen Values

Proportion of

variance

Eigen Vectors

1 EXGDP 2.474 0.8248 0.548

2 MGDP 0.526 0.1752 0.544

3 OPEN 7.36E-16 - 0.636

Source: Author’s computation using e-view

TL = 0.548 L (EXGDP) + 0.544 L (MGDP) + 0.636 L (OPEN) ------------ (1)

Table 6: Results of Principle component analysis for model 2

PC

Variables

Eigen Values

Proportion of

variance

Eigen Vectors

1 M2GDP 2.549 0.8496 0.605

2 CPSGDP 0.391 0.1304 0.592

3 MCGDP 0.060 0.0200 0.532

Source: Author’s computation using e-view

FD = 0.605 L (M2GDP) + 0.592 L (CPSGDP) + 0.532 L (MCGDP) -------- (2)

55

Table 7: Results of Principle component analysis for model 3

PC

Variables

Eigen Values

Proportion of

variance

Eigen Vectors

1 EXGDP 2.578 0.4296 0.001

2 MGDP 2.498 0.4163 0.160

3 OPEN 0.505 0.0842 0.092

4 M2GDP 0.365 0.0608 0.596

5 CPSGDP 0.054 0.0090 0.533

6 MCGDP 8.33E-17 0.0000 0.572

Source: Author’s computation using e-view

EL = 0.001 L (EXGDP) + 0.160 L (MGDP) + 0.092 L (OPEN) + 0.596 L (M2GDP) + 0.533 L

(CPSGDP) + 0.572 L (MCGDP) ……………………………………(3)

4.0.6 UNIT ROOT TEST

Macroeconomic data are usually time series data and they are habitually not stationary at level i.e.

they have unit roots. To make them stationary, they have to be differentiated once or twice. Non

stationary variables have stochastic trend affecting the behavior of the variables. To determine the

level of stationarity, unit root test is performed using different methods. In this work, we use

Augmented Dickey-Fuller and Phillip Perrons test.

56

TABLE 8: UNIT ROOT TEST FOR STATIONARY

Variables

Augmented Dickey - Fuller

Phillips – Perron

Levels

1st difference

Remarks

Levels

1st difference

Remarks

LPRY

-1.785108

-5.222815*

I(1)

-1.816125

-5.202433*

I(1)

LGFC

-2.579477

-5.563896*

I(1)

-2.610548 -8.814390*

I(1)

SEC

-2.370193

-4.817219**

I(1)

-1.720153

-3.801731**

I(1)

TL

-3.276341

-3.728671**

I(1)

-3.276341

-13.92834*

I(1)

FD

-2.288979

-4.507487*

I(1)

-2.412770

-4.507487*

I(1)

EL

-2.297385

-4.810774*

I(1)

-2.427821

-4.810774*

I(1)

*/** represents stationary at 1 and 5 percent level respectively.

The null hypothesis states that the variables has unit root i.e. non stationary. The decision rule is

to reject the null hypothesis when the probability values are less than 5% at 5% level of

significance. The result shows that all variable are integrated at order one, that is, they became

stationary after first differencing. The Phillip-Perrons unit root test results, as reported in the

table, confirmed results from ADF test.

57

4.0.7 COINTEGRATION TEST

It is also important to determine whether long run relationship exist between our variables. If there

is long run relationship between our variables, then the variables are said to be cointegrated. The

existence of cointegration makes it necessary to determine the number of cointegrating equations

so as to determine the short run and long run behaviour of variables. For this purpose, we employ

Johansen Cointegration test. The null hypothesis states that there is no cointegration. The decision

is to reject the null hypothesis when the trace statistics and Max-Eigen statistics probability value

are less than 5% at 5% level of significance.

TABLE 9: summary of cointegration results

H0: rank = r Max Eigen 5% Trace 5%

Model 1

r = 0 23.71160 27.58434 47.48807 47.85613

r ≤ 1 16.34420 21.13162 23.77647 29.79707

r ≤ 2 7.163857 14.26460 15.49471 0.5279

r ≤ 3 0.268411 3.841466 3.841466 0.6044

Model 2

r = 0 29.35373 27.58434 48.85208 47.85613

r ≤ 1 12.22709 21.13162 19.49835 29.79707

r ≤ 2 7.085140 14.26460 7.271269 15.49471

r ≤ 3 0.186129 3.841466 0.186129 3.841466

Model 3

r = 0 28.66921 27.58434 47.28861 47.85613

58

r ≤ 1 11.38457 21.13162 18.61941 29.79707

r ≤ 2 7.173475 14.26460 7.234840 15.49471

r ≤ 3 0.061365 3.841466 0.061365 3.841466

The above table shows a summary of the cointegration test performed using E-views econometric

software programme. In the process of testing for long run association or cointegration among the

three models, there is need for selecting the optimum lag. Using the lag order selection criteria,

lag one was selected for each of the model. The maximum eigenvalue and trace statistics developed

by Johansen are applied to test whether there is a long term relationship among the variables.

In model one, both trace and maximum eigenvalue statistics agree that there is no cointegration

among the variables at five-percent level. Hence, there is no long run relationship among the

variables that makes up the model. In model two, trace statistics and maximum eigenvalue

indicates three cointegrating vectors. Trace statistics indicates no cointegration among the

variables in model three, while maximum eigenvalue statistics indicate three cointegation vectors.

4.0.8 IMPUSE RESPONSE

To identify orthogonalized impulse in each of the variables, variance-covariance matrix of the

VAR model was factorized using the Cholesky impulse method suggested by Doan (1992). An

impulse response function traces the effect of a one-time shock to one of the innovations on

current and future values of the endogenous variables. If the innovations εt are

contemporaneously uncorrelated, we will use unrestricted VAR to generate our result, if

otherwise, we will generate our results using restricted VAR (VECM). Hence, our

aforementioned model one and three will be estimated using restricted VAR (VECM), while

model two will be estimated using unrestricted VAR. In this analysis we have chosen the first

59

five periods (1-5) to be our short run, while the next five period (6-10) is our long run. In order to

display the response function clearer, the author present the charts that are useful for our analysis

i.e. the relative impact of trade liberalization (TL), financial development (FD) and economic

liberalization (representing the joint impact of TL & FD) on economic growth. Also, in this

analysis, the author chooses not to look at the effect of own shocks. The multiple graphs below

represents the various relationships in our models.

Figure 3: Responses of per capital real income to Capital stock, human capital development

and trade liberalization using unrestricted VAR.

Source: Researcher’s computation using E-views

-.02

-.01

.00

.01

.02

.03

.04

1 2 3 4 5 6 7 8 9 10

Response of LPRY to LPRY

-.02

-.01

.00

.01

.02

.03

.04

1 2 3 4 5 6 7 8 9 10

Response of LPRY to LGFC

-.02

-.01

.00

.01

.02

.03

.04

1 2 3 4 5 6 7 8 9 10

Response of LPRY to SEC

-.02

-.01

.00

.01

.02

.03

.04

1 2 3 4 5 6 7 8 9 10

Response of LPRY to TL

Response to Cholesky One S.D. Innovations ± 2 S.E.

60

INTERPRETATION OF THE ABOVE GRAPH

The graph labelled responses of LPRY to LGFC shows how per capita real income will responds

to one unit shock or one standard deviation (1 S.D) shock in Capital stock (LGFC) in the short run

and long run. In the short run, per capita real income is expected to react a bit faster on the average

than the long run. The expected result is in line with economic theory. Theory suggests that

investment and economic growth are positively related. The graph titled responses of LPRY to

SEC shows how per capita real income is expected to react 1 S.D shock in human capital

development (SEC) in the short run and long run. The response of economic growth (LPRY)

throughout the whole period is positive. However, in the short run, it is expected that after the

second year, fall of the SEC is evident. The result given is contradictory to what theory suggests.

The last graph shows the future response of per capita real income to 1 positive unit shock in trade

liberalization (TL) in the short run and long run. The reaction throughout the short run and long

run is positive. This shock causes economic growth of Nigeria to peak in year two, then begins to

decrease. In the sixth period it is expected to become relatively stable.

61

Figure 4: Response of per capital real income to Capital stock, human capital development

and financial development using restricted VAR (VECM).

Source: Researcher’s computation using E-views

.000

.004

.008

.012

.016

.020

.024

.028

1 2 3 4 5 6 7 8 9 10

Response of LPRY to LPRY

.000

.004

.008

.012

.016

.020

.024

.028

1 2 3 4 5 6 7 8 9 10

Response of LPRY to LGFC

.000

.004

.008

.012

.016

.020

.024

.028

1 2 3 4 5 6 7 8 9 10

Response of LPRY to SEC

.000

.004

.008

.012

.016

.020

.024

.028

1 2 3 4 5 6 7 8 9 10

Response of LPRY to FD

Response to Cholesky One S.D. Innovations

62

INTERPRETATION OF THE ABOVE GRAPH

The graph labelled responses of LPRY to LGFC shows how per capita real income will responds

to one unit shock or one standard deviation (1 S.D) shock in Capital stock (LGFC) in the short run

and long run. In the short run, third period to be precise, per capita real income is expected to peak,

it becomes relatively stable in the long run without a significant improvement. The graph titled

responses of LPRY to SEC shows how per capita real income is expected to react 1 S.D shock in

human capital development (SEC) in the short run and long run. The response of economic growth

(LPRY) throughout the whole period is positive. However, in the short run, the response was sharp

and it reached its maximum in the fourth period. After the fourth period, LPRY is expected to fall

till the seventh period and then rise after that. We can conclude the fourth period is the optimum

capacity utilization of SEC. The result given is contradictory to what theory suggests. The last

graph shows the future response of per capita real income to 1 positive unit shock in financial

development (FD) in the short run and long run. The reaction throughout the short run and long

run is positive. This shock causes economic growth of Nigeria to peak in third year, then becomes

stable till the fourth year. After this a slight fall is expected before LPRY picks up.

63

Figure 5: Responses of per capital real income to Capital stock, human capital development

and economic liberalization using unrestricted VAR.

-.02

-.01

.00

.01

.02

.03

.04

1 2 3 4 5 6 7 8 9 10

Response of LPRY to LPRY

-.02

-.01

.00

.01

.02

.03

.04

1 2 3 4 5 6 7 8 9 10

Response of LPRY to LGFC

-.02

-.01

.00

.01

.02

.03

.04

1 2 3 4 5 6 7 8 9 10

Response of LPRY to SEC

-.02

-.01

.00

.01

.02

.03

.04

1 2 3 4 5 6 7 8 9 10

Response of LPRY to EL

Response to Cholesky One S.D. Innovations ± 2 S.E.

64

INTERPRETATION OF THE ABOVE GRAPH

The graph labelled responses of LPRY to LGFC shows how per capita real income will responds

to one unit shock or one standard deviation (1 S.D) shock in Capital stock (LGFC) in the short run

and long run. The reaction throughout the short run and long run is positive and relatively stable,

as the response of LPRY increases at a constant rate. The graph titled responses of LPRY to SEC

shows how per capita real income is expected to react 1 S.D shock in human capital development

(SEC) in the short run and long run. In the short run, per capita real income response is positive,

while in the long run its response is negative. The result given is contradictory to what theory

suggests. The last graph shows the future response of per capita real income to 1 positive unit

shock in economic liberalization (EL) in the short run and long run. Throughout the whole period

(short run and long run), per capita real income is expected to react positively. Per capita real

income is expected to reach its climax in the short run and then fall.

4.0.9 VARIANCE DECOMPOSITION

The impulse response functions illustrate the qualitative response of per capita real income in the

system to shocks to other variables. To indicate the relative importance of these shocks requires

variance decomposition. The variance decomposition shows the proportion of the forecast error

variance of a variable which can be attributed to its own shocks and the innovations of other

variables. It shows us how many unforeseen changes or variations of the variables in the model

are explained by different shocks. Just has we have done in the case of the impulse-response, we

will use unrestricted VAR to generate our result if the innovations εt are contemporaneously

uncorrelated, if otherwise, we will generate our results using restricted VAR (VECM). Hence, our

aforementioned model one and three will be estimated using restricted VAR (VECM), while model

two will be estimated using unrestricted VAR. In this analysis we have chosen the first five periods

65

(1-5) to be our short run, while the next five period (6-10) is our long run. Impulse or innovation

or shock will be used interchangeably as they mean the same thing. The results of the

decomposition are presented in the table below.

TABLE 10: Variance decomposition result for log (pry) using VAR – Model 1

Period S.E. LPRY LGFC SEC TL

1 0.024220 100.0000 0.000000 0.000000 0.000000

2 0.034768 85.09501 2.695105 3.516721 8.693163

3 0.041192 81.11817 5.004486 4.214703 9.662637

4 0.046202 77.92552 7.567756 4.484237 10.02249

5 0.050380 75.23600 10.21349 4.481832 10.06868

6 0.054052 72.83189 12.85151 4.333883 9.982721

7 0.057389 70.64060 15.41577 4.111872 9.831749

8 0.060493 68.62548 17.86525 3.859650 9.649618

9 0.063429 66.76574 20.17688 3.602936 9.454436

10 0.066237 65.04728 22.34030 3.355906 9.256507

Cholesky Ordering: LPRY LGFC SEC TL

Source: Researcher’s computation using E-views

INTERPRETATION OF THE ABOVE TABLE

In period five which depicts short run, impulse to per capita real income (LPRY) accounts for

75.23% variation of the total fluctuation in per capita real income. This can be referred to as own

shock. However, it is expected to account for 65.04% in the long run i.e. period ten. This shows

that own shock contribution or significance is expected to drop. Impulse or shock to Capital stock

(LGFC) in period five is expected to account for 10.21% of total variation in per capita real income,

while its significance is expected to increase in the long run to 22.34%. This significant increase

in the contribution Capital stock supports economic theory which emphasis that investment is

expected to have a long term effect on economic growth. Shock or innovation to human capital

development (SEC) in the short run is expected to account for only 4.48% variation of the total

fluctuation in per capita real income, nevertheless its envisaged increase in the long run is expected

66

to decrease slightly to 3.36%. Impulse or shock to trade liberalization (TL) in period five is

expected to account for 10.06% of total variation in per capita real income, while its significance

is expected to slightly decrease in the long run to 9.26%. This show that 1 S.D shock to TL has

significant effect on LPRY in both the short run and long run, however, its impact is expected to

decrease marginally in the long run.

From the foregoing analysis, we can deduce that trade liberalization has significant effect in the

variation of economic growth.

TABLE 11: Variance decomposition result for log (pry) using VECM – Model 2

Period S.E. LPRY LGFC SEC FD

1 0.022005 100.0000 0.000000 0.000000 0.000000

2 0.035473 91.04525 1.246170 0.003047 7.705530

3 0.045218 83.01767 1.897766 2.303639 12.78092

4 0.053815 78.32467 2.040497 4.896001 14.73883

5 0.061391 77.44480 2.168503 5.284756 15.10194

6 0.067964 78.05675 2.331841 4.715661 14.89575

7 0.073701 78.69878 2.470787 4.156719 14.67371

8 0.078849 79.00005 2.560191 3.808318 14.63144

9 0.083680 78.98808 2.608934 3.676163 14.72683

10 0.088333 78.85134 2.637445 3.662918 14.84830

Cholesky Ordering: LPRY LGFC SEC TL

Source: Researcher’s computation using E-views

INTERPRETATION OF THE ABOVE TABLE

In period five which depicts short run, impulse to per capita real income (LPRY) accounts for

77.44% variation of the total fluctuation in per capita real income. This can be referred to as own

shock. However, it is expected to account for 78.86% in the long run i.e. period ten. This shows

that own shock contribution or significance is expected to slightly increase. Impulse or shock to

Capital stock (LGFC) in period five is expected to account for only 2.17% of total variation in per

67

capita real income, while its significance is expected to slightly increase in the long run to 2.64%.

Shock or innovation to human capital development (SEC) in the short run is expected to account

for 5.28% variation of the total fluctuation in per capita real income, nevertheless its significant is

expected to slightly decrease to 3.66% in the long run. Impulse or shock financial development

(FD) in period five is expected to account for 15.1% of total variation in per capita real income,

while its significance is expected to slightly decrease in the long run to 14.84%. This show that 1

S.D shock to FD has significant effect on LPRY in both the short run and long run, however, its

impact is expected to decrease marginally in the long run.

From the foregoing analysis, we can deduce that financial development has significant effect in

the variation of economic growth.

TABLE 12: Variance decomposition result for log (pry) using VAR – Model 3

Period S.E. LPRY LGFC SEC EL

1 0.025187 100.0000 0.000000 0.000000 0.000000

2 0.031354 93.90118 0.329373 1.323150 4.446302

3 0.035943 85.28813 0.822688 1.671179 12.21800

4 0.040062 77.93959 1.418904 1.366294 19.27521

5 0.043858 72.58499 2.161478 1.323494 23.93003

6 0.047357 68.77180 3.090420 1.770160 26.36762

7 0.050574 65.92654 4.207269 2.519686 27.34650

8 0.053530 63.63903 5.477358 3.348831 27.53478

9 0.056258 61.66947 6.845891 4.128979 27.35566

10 0.058797 59.89460 8.254914 4.816285 27.03420

Cholesky Ordering: LPRY LGFC SEC TL

Source: Researcher’s computation using E-views

68

INTERPRETATION OF THE ABOVE TABLE

In period five which depicts short run, impulse to per capita real income (LPRY) accounts for

72.59% variation of the total fluctuation in per capita real income. This can be referred to as own

shock. However, it is expected to account for 59.89% in the long run i.e. period ten. This shows

that own shock contribution or significance is expected to decrease. Impulse or shock to Capital

stock (LGFC) in period five is expected to account for only 2.16% of total variation in per capita

real income, while its significance is expected to increase in the long run to 8.25%. Shock or

innovation to human capital development (SEC) in the short run is expected to account for 1.32%

variation of the total fluctuation in per capita real income, nevertheless it is expected to increase

to 4.82% in the long run. Impulse or shock financial development (FD) in period five is expected

to account for 23.93% of total variation in per capita real income, while its significance is expected

to still increase in the long run to 27.03%. This show that 1 S.D shock to EL has significant effect

on LPRY in both the short run and long run. From the foregoing analysis, we can deduce that

economic liberalization has significant effect in the variation of economic growth.

4.1 Overall performance of the models

Table 17: Coefficient of Determination (R2), Adjusted R2, F-statistics and Durbin-Watson tests

result

Models

R2

Adjusted R2

Prob(F-statistics)

Durbin-Watson

Model 1 0.9633 0.9567 0.0000 1.5597

Model 2 0.3666 0.2082 0.0820 2.3505

Model 3 0.964 0.9532 0.0000 1.4999

For model 1, based on the regression equation estimation result after adjustment, the overall

performance of the model was good. Both the R-squared (96.33 percent) and the adjusted R-

69

squared (95.67 percent) were satisfactory. The coefficient of determination R² measures the

percentage of the variation of the dependent variable (LPRY) that is explained by the variation of

the independent variables. The Durbin-Watson statistics (1.5597) was a little lower than the

traditional benchmark of 2.0 in the model, though it fell between the acceptable region of 1.5-2.5

and the F-stat (F-stat 144.65, p=0.0000) of the model was also significant at five percent indicating

that the model has a good fit.

For model 2, based on the regression equation estimation result after adjustment, the overall

performance of the model was not good. Both the R-squared (36.67 percent) and the adjusted R-

squared (20.82 percent) were not satisfactory. The Durbin-Watson statistics (2.3505) was a little

higher than the traditional benchmark of 2.0 in the model, though it fell between the acceptable

region of 1.5-2.5 and the F-stat (F-stat 2.315, p=0.0820) of the model was not significant at five

percent indicating that the model does not have a good fit.

For model 3, based on the regression equation estimation result after adjustment, the overall

performance of the model was good. Both the R-squared (96.03 percent) and the adjusted R-

squared (95.31 percent) were satisfactory. The Durbin-Watson statistics (1.4999) was a little lower

than the traditional benchmark of 2.0 in the model, though it fell between the acceptable region of

1.5-2.5 and the F-stat (F-stat 133.33, p=0.0000) of the model was also significant at five percent

indicating that the model has a good fit.

Also the coefficients of the individual variables were examined to determine the nature of the

relationship between per capita real income (LPRY) which is used as a proxy for economic growth

and other exogenous variables that make up each of the models. In model 1, the coefficients of

gross fixed capital formation (LGFC, -0.005 p= 0.798) was found to be negative and insignificant

contrary to a priori expectation. However, the coefficient of Human capital development was found

70

to be positive and significant (SEC, 0.0053, p=0.042). Also the coefficient of trade liberalization

(TL, 0.001) was positive and significant (p=0.042). This shows that trade liberalization and human

capital development has contributed positively to economic growth. In model 2, the coefficients

of gross fixed capital formation (LGFC, 0.087 p= 0.084) was found to be positive and insignificant.

However, the coefficient of Human capital development was found to be negative and insignificant

contrary to a priori expectation (SEC, -0.0024, p=0.423). Also the coefficient of financial

development (FD, 0.0009) was positive and insignificant (p=0.1687). This shows that financial

development is positively related to economic growth, but has insignificance contribution to

economic growth. In model 3, the coefficients of gross fixed capital formation (LGFC, 0.019 p=

0.145) was found to be positive and insignificant. The coefficient of Human capital development

was found to be positive and insignificant (SEC, 0.004, p=0.113). The coefficient of economic

liberalization (TL, 0.001) was positive and insignificant (p=0.117). This shows that economic

liberalization is related positively to economic growth, but has insignificance contribution to

economic growth.

4.11 Granger Causality Test:

Pairwise Granger Causality Tests

Sample: 1968 – 2013

Lags: 1

Table 13: Granger Causality test result

Null hypothesis: Obs F-statistics Probability

TL does not Granger Cause LPRY

LPRY does not Granger Cause TL

27 5.58121

2.00782

0.0266

0.1693

FD does not Granger Cause LPRY

LPRY does not Granger Cause FD

27 0.58383

2.54607

0.4523

0.1237

EL does not Granger Cause LPRY

LPRY does not Granger Cause EL

27 1.42224

1.70237

0.2447

0.2044

71

The above causality test is used to test individual pairwise causality test among these variables

under the consideration. The first row in the table above represents the result of our aforementioned

model 1, while the second row embodies our aforesaid model 2, the third row equally represents

our model 3. The result above shows that, at 5%level of significance, causality test only run from

trade liberalization (TL) to per capita real income or economic growth (LPRY). No causal relation

exists among the other pairwise variables.

4.12 STABILITY TEST

A series of data can often contain a structural break, due to a change in policy or sudden shock to

the economy, for example the financial meltdown in 2008. In order to test for a structural break,

we will be using the chow-break point test. The year2008 will be selected as the breakpoint date

for our model 2 & 3 due to the financial meltdown that occurred in that year and also a view at the

FD and EL graph shows that after 2008 a sharp fall was eminent. Likewise, we will be selecting

the midpoint period as the breakpoint period for our model 1, because a view at the TL graph

shows that there exist fluctuation throughout the scope of our study.

72

4.12.1 CHOW TEST

This is a test on the constancy of the model’s period over the entire sample.

Table 14: Chow test result

Model 1 Chow Breakpoint Test: 2000

Null Hypothesis: No breaks at specified breakpoints

Equation Sample: 1987 2013

F-statistic 3.098512 Prob. F(5,17)

0.0362

Log likelihood ratio 17.49054

Prob. Chi-Square(5)

0.0037

Wald Statistic 15.49256 Prob. Chi-Square(5)

0.0085

Model 2 Chow Breakpoint Test: 2008

Null Hypothesis: No breaks at specified breakpoints

Equation Sample: 1988 2013

F-statistic 0.435548 Prob. F(6,14)

0.8432

Log likelihood ratio 4.449784

Prob. Chi-Square(6)

0.616

Model3 Chow Breakpoint Test: 2008

Null Hypothesis: No breaks at specified breakpoints

Equation Sample: 1987 2013

F-statistic 0.330344 Prob. F(5,17)

0.8876

Log likelihood ratio 2.503575

Prob. Chi-Square(5)

0.776

Wald Statistic 1.65172 Prob. Chi-Square(5)

0.8949

As seen from the table above for model 1, P value of the log likelihood ratio is less than 5%. Thus

we reject the null hypothesis. Hence we accept the alternative hypothesis that there exist breaks at

specified period. For model 2, P value of the log likelihood ratio is greater than 5%. Thus we accept

the null hypothesis that there exist no breaks at specified period. Likewise for model 3, P value of

the log likelihood ratio is greater than 5%. Thus we accept the null hypothesis that there exist no

breaks at specified period.

73

4.13 Residual Test

This is usually carried out to verify the viability of the error term which is in line with the

assumption of restricted and unrestricted VAR technique.

4.13.1 SERIAL CORRELATION TEST:

The significance of this test is to express the statistical significance of the Residual error term of

the model.

Table 15: Breusch-Godfrey serial correlation LM test result

Breusch-Godfrey Serial Correlation LM Test:

Model1 F-statistic 0.946572 Prob. F(1,21) 0.3417

Obs*R-squared 1.16453 Prob. Chi-Square(1) 0.2805

Model 2 F-statistic 1.676535 Prob. F(1,19) 0.2109

Obs*R-squared 2.108183 Prob. Chi-Square(1) 0.1465

Model 3 F-statistic 1.102716 Prob. F(1,21) 0.3056

Obs*R-squared 1.347045 Prob. Chi-Square(1) 0.2458

As seen from the table above, P value of the Observed R2 is greater than 5% in the three models.

Thus, we cannot reject the null hypothesis. Hence we accept the null hypothesis that there is no

serial correlation problem as regard the error term in our three models.

74

4.13.2 HETEROSKEDACITY TEST

Table 16: Breusch-Pagan-Godfrey heteroskedasticity test result

Heteroskedasticity Test: Breusch-Pagan-Godfrey

Model 1 F-statistic 2.536179 Prob. F(4,22) 0.069

Obs*R-squared 8.521069 Prob. Chi-Square(4) 0.0743

Scaled explained SS 9.575576 Prob. Chi-Square(4) 0.0482

Model 2 F-statistic 0.742749 Prob. F(8,17) 0.6545

Obs*R-squared 6.734021 Prob. Chi-Square(8) 0.5656

Scaled explained SS 8.893405 Prob. Chi-Square(8) 0.3514

Model 3 F-statistic 0.935815 Prob. F(4,22) 0.4616

Obs*R-squared 3.926 Prob. Chi-Square(4) 0.4161

Scaled explained SS 5.881178 Prob. Chi-Square(4) 0.2082

As seen from the table above, P value of the observed R2 is more than 5% in the three models.

Hence, we accept the null hypothesis that there is no heteroskedasticity problem as regard the error

term.

75

CHAPTER FIVE

SUMMARY, CONCLUSION AND POLICY RECOMMENDATION

5.0 SUMMARY

The Main objective of this work which is to examine the joint impact of trade liberalization and

financial development on economic growth in Nigeria was descriptively and empirically carried

out in the preceding chapter. Using descriptive statistics, it was observed that trade liberalization

and financial indicators witnessed a consistent increase over the years except a few years when

they were dips. The empirical analysis were carried out using empirical tests like principal

component analysis, unit root tests, cointegration tests and lag length criteria with the aid of

electronic statistical tools like E-views. The major econometric technique used for this work to see

how to see how changes in trade liberalization and financial development indicators have so far

affected economic growth is the restricted and unrestricted Vector Autoregression (VAR) model.

With the reliability test that we first performed, we discovered that the variables in this study were

non stationary at level, but stationary at level and we were able to use VAR to study the reaction

of economic growth to impulses or shocks trade liberalization, financial development and

economic liberalization. The response of economic growth to shocks to trade liberalization,

financial development and economic liberalization in the short run and long run.

The granger causality test carried out in chapter four constitutes our findings and conclusion of the

relationship between trade liberalization, financial development, economic liberalization and

economic growth. For the broad objective, the test results shows that the Nigeria economic growth

is not explained by the joint influence of trade liberalization and financial development. In other

words, there is no causal relationship between economic liberalization and economic growth.

76

Furthermore, our first specific objectives shows that economic growth is not explained or caused

by trade liberalization. Also, our second specific objectives shows that economic growth is not

explained or caused by financial development.

5.1 CONCLUSION

This study has empirically investigated whether trade liberalization and financial development and

their joint impact have had any significant impact on economic growth in Nigeria, which as a

developing economy has witnessed several reforms since the adoption of SAP in 1968. The

theoretical framework of this research is based on endogenous growth model and Mushnin Kar’s

study (2008).

In order to survey such relationship, three alternative measures were developed by using principal

component analysis namely, trade liberalization, financial development and economic

liberalization proxies. The empirical results obtained by employing the methods of time series

econometric method for the period 1968-2013 indicated that trade liberalization was positively

related with economic growth and its contribution to economic growth was found to be significant.

Also financial development was found to be positively related with economic growth, however its

contribution to economic growth was insignificant. Furthermore, the joint impact of trade

liberalization and financial development in terms of economic liberalization was also found to be

positively related with economic growth, interestingly the joint impacts contribution to economic

growth was found to be insignificant.

The insignificant relationship between these variables and economic growth could be as a result

of so many factors bedeviling the Nigerian economy. Among them are too much dependence on

oil; dominance of fiscal measures especially government expenditure in stimulating the economy;

77

security and corruption. Corruption has had a corrosive effect on The Nigerian growth prospects.

It scares off potential investment by undermining the credibility and legitimacy of the government

and creating an uncertain business environment. It also results in misallocation of capital and other

factors of production as resources are moved based on personal relationship rather than return on

investment. It has quashed entrepreneurship and harmed the poor at the same time.

5.2 POLICY RECOMMENDATION

Trade liberalization and financial development has played a significant role in the economic

development of many nations of the world, although not in isolation. Nigeria can also benefit from

economic liberalization if trade and financial reforms are accompanied with appropriate measures

or policies. Also in this section recommendation is divided into two phases.

Financial development recommendations:

Policy makers should design policies that will promote the financial and capital markets,

remove the obstacles that impede their growth and strengthen the competitiveness of the

banking system and the capital market.

The monetary authority of the country should ensure efficiency in its regulation and

supervision of all financial institutions in allowing more private banks and non-bank

financial institutions to broaden their financial market to accelerate financial development

and improve the financial structure that leads to increase economic growth of Nigeria.

The development of the micro finance sector is also very necessary so as to make credit

accessible to micro entrepreneurs who are often left out in the formal credit markets. This

will boost private sector development and investments which is the engine of growth and

development.

78

Trade liberalization recommendations:

Essential trade oriented policy should be adopted to enhance economic growth in Nigeria

via high exports flows in order to accumulate more foreign proceeds to boost output growth

rate in the country.

The government should encourage exportation and revamps the deteriorating exports

oriented manufacturing sectors in order to enhance the level of economy integration and

increase the national output

Other measures include sustenance of political stability that country currently enjoys;

encouragement of inflows of foreign direct investment; and sustenance of the war on corruption.

79

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