DEPARTMENT OF ECONOMICS UNIVERSITY OF NIGERIA, … Anthony Ph.D (Economics... · this thesis has been approved by the department of economics, university of nigeria, nsukka for the

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Analysis of Financial Openness and Macroeconomic

Performance in Nigeria, 1986-2011

A Ph.D THESIS

BY

ORJI, ANTHONY

PG/PH.D/11/59630

DEPARTMENT OF ECONOMICS

UNIVERSITY OF NIGERIA, NSUKKA

Supervisor: Prof. C.C Agu.

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TITLE PAGE

Analysis of Financial Openness and Macroeconomic

Performance in Nigeria, 1986-2011

A Ph.D Thesis Report Presented to the Department of Economics,

University of Nigeria, Nsukka

In Partial Fulfillment for the award of Doctor of Philosophy (Ph.D)

in Economics

BY

Orji, Anthony

PG/Ph.D/11/59630

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APPROVAL PAGE

THIS THESIS HAS BEEN APPROVED BY THE DEPARTMENT OF

ECONOMICS, UNIVERSITY OF NIGERIA, NSUKKA FOR THE AWARD

OF Ph.D IN ECONOMICS

19/06/2014 19/06/2014

PROF. C. C. AGU DATE PROF. C. C. AGU DATE

SUPERVISOR HEAD OF DEPARTMENT

19/06/2014 19/06/2014

PROF. F. E. ONAH DATE PROF.AKPAN .H. EKPO DATE

INTERNAL EXAMINER EXTERNAL EXAMINER

19/06/2014

PROF. C. O. T. UGWU DATE

DEAN, FACULTY OF THE SOCIAL SCIENCES

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CERTIFICATION

This is to certify that the work embodied in this thesis is original and has not

been submitted in part or in full for any other degree of this university or any

other university.

19/06/2014 19/06/2014

PROF. C. C. AGU DATE PROF. C. C. AGU DATE

SUPERVISOR HEAD OF DEPARTMENT

19/06/2014 19/06/2014

PROF. F. E. ONAH DATE PROF.AKPAN .H. EKPO DATE

INTERNAL EXAMINER EXTERNAL EXAMINER

19/06/2014

PROF. C. O. T. UGWU DATE

DEAN, FACULTY OF THE SOCIAL SCIENCES

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DEDICATION

To My Father and My God,

On whose wings of Love, Grace, Mercy and Favour, I am soaring and smiling.

And to all who have dreams & visions and believe they can accomplish them by His Grace.

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ACKNOWLEDGEMENTS

I sincerely appreciate the Lord God Almighty who started this journey for me and has also completed it. His love, grace, favour, mercy and wisdom have made the difference in my life. To Him be all the glory. I am grateful to my supervisor, Professor C.C Agu for his invaluable guidance and contributions which made the completion of this thesis a rewarding experience. May God keep on keeping you sir. My gratitude equally goes to my erudite Professors, fellow lecturers, non-academic staff and all students of the Department of Economics, UNN who stood by me, encouraged me and supported me in the course of my research work. Prof. F. E. Onah, Prof. N. I. Ikpeze, Assoc. Prof.Onyukwu E. Onyukwu, Prof. (Mrs.) S. I. Madueme, Dr. (Mrs.) Gladys Aneke, Rev. Fr. (Prof). H. E. Ichoku, Dr. F. O. Asogwa, Dr. P.C Ekeocha, Prof. Fonta, Dr. Moses O. Oduh, Dr. U. M. Ozughalu, Dr. I. A. Ifelunni, Dr. N. E. Urama, Dr Ugbor Kalu and Dr. J.I Amuka are greatly appreciated. I am also indebted to Profs. Jean-Yves Duclos, Luca Tiberti and other resource persons in the Dept. of Economics, University of Laval, Quebec Canada who provided me with a conducive environment to work as a Visiting Scholar during this period.

I want to thank in a very special way Prof. Akpan Ekpo, Dr. Chukwuma Agu, Dr. Emmanuel Nwosu, Dr. Jude Chukwu and Dr Ezebuilo Ukwueze for their guidiance, encouragements and support. I am also very grateful other colleagues of mine for having stood with me throughout this period: Dr. Jonathan E.Ogbuabor, Ilori Ayobami, Godstime Eigbiremolen, Vivian Nnetu, Uchechi Anaduaka. Johnson Ugwu, Chisom Emecheta, Mrs Ifeoma C. Mba, , Nelson Nkalu, , C. E. Nnaji, and Moses Nnaji. Men and women of God like Daddy G.O, Pst. Abraham O.Ugwa, Pst.Abraham Okorie, Rev. Dr. Dan Ozoko, Pst.Innocent Eleke,Pst.&Pst(Mrs) Favour Ochi, Dr.Parker Joshua, Pst. Bernard Agidi, Pst&Pst (Mrs) Wale Johnson, Pst.(Mrs)Lucy Obajaja, Pst. Goodness Kuzayet, Rev,Eze Ogbonnaya&Rev.Uche Okoji are deeply appreciated for their love and prayers.

I am eternally grateful to my lovely wife, my Angel and my Queen, Onyinye Imelda Anthony-Orji (Mrs), and my godly, blessed, favoured, and wise children for their candid love and support. I could remember that this programme started in 2011 shortly after our wedding and my Angel was very encouraging and patient with me even when I had to spend more time with my books and computer than “necessary”. Similarly, I thank my mother, Madam Ngozi Nnenna Orji and my brothers (and their wives) and sisters (and their husbands); Mr. Robert Orji (Papa Ejima), Grace Okezie (Mrs), Ebere, Chinyere Chima (Mrs) and other members of Pa Orji Okorie (of blessed memory) family. My in-laws, Mummy and Daddy Jim Ezeibe and their entire family are also acknowledged for their love, support and prayers. Other friends of mine; Dr.Goodluck Ebere Jonathan (Mr. President), President Obama, Peter Mba, Arinze Onyia, Chief Emeka Ani , Samuel Okereke and a host of others I cannot mention for lack of space are also appreciated. May God Almighty bless all of you in Jesus name, Amen.

Orji, Anthony (PG/Ph.D/11/59630)

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ABSTRACT The greatest challenge facing the country today is how to develop the economy and reduce poverty. Meeting this challenge is particularly difficult, if Nigeria should rely solely on domestic resources, given the low rate of savings and the attendant savings-investment gap. Against this background, following the Mackinnon and Shaw hypothesis, it becomes crucial to liberalize the financial system and also attract foreign resources into the economy. This thesis therefore, focuses on financial openness and macroeconomic performance in Nigeria. The impact of financial openness on economic growth in Nigeria is also investigated using quarterly data from 1986-2011. It equally examines the direction of causality between financial openness and economic growth as well as the impact of financial openness on output volatility in Nigeria. For empirical analysis, it uses two measures of financial openness: de facto (total capital flow) variables following Aizenman and Noy (2009) and de jure (Chin-Ito Index) based on Chinn and Ito (2012). The study applies the Autoregressive Distributed Lag Model based on unrestricted error correction model (ARDL-UECM), Granger Causality Test and the Generalized Autoregressive Conditional Heteroscedasticity (GARCH) Model to address the three objectives of the work. The results show positive impact of financial openness on economic growth in Nigeria both in the short run and in the long run. Interestingly, the de facto and de jure measures of financial openness is found to have similar degrees of impact on Economic Growth in the short run and long run respectively. The results also reveal that credit to the private sector is negatively associated with growth, indicating that there are problems with credit allocation and utilization in the country which could be occasioned by weak regulation/supervision and non-adherence to prudential guidelines in the financial system. The study also find that real interest rate has a positive relationship with economic growth. The results support the McKinnon-Shaw hypothesis, that is, in the long run interest rate liberalisation will ultimately lead to increased economic growth. Again, the thesis find the institutional quality variable contributing negatively and positively to growth in the short run and long run respectively. The second regression result indicates the existence of bidirectional causality between financial openness and economic growth in Nigeria. The findings of the thesis further show that none of the two measures of financial openness contributed to output volatility in Nigeria, within the period under review. From this work, our knowledge of the various measurement issues associated with financial openness has been enhanced and we can conclude that both measures are potent and robust for the Nigerian economy. Thus, the study recommends that government should continue to reform the domestic financial system while removing barriers to capital account transactions. And this should be done with every sense of objectivity, economic management dexterity and in line with global best practices. Furthermore, the country’s institutional quality should be comprehensively reviewed and upgraded. Strong emphasis should be placed on deepening the country’s democracy, reforming the governance and electoral systems, and reorganizing the socio/political structures of the country. Respect for the rule of law should be given priority by the leaders and the led. This is because according to our finding, poor governance, which is exemplified by corruption and lack of respect for the rule of law are detrimental to growth. However, if these anomalies are corrected, then sound financial openness policies and improved institutional quality will impact positively on growth in the long run.

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List of Tables

Table 1: Volatility and Average of Selected Macroeconomic Variables for diff. Economies 6

Table 2.1: Major Financial Sector Reforms in Nigeria from 1986 21

Table 2.2: Summary of Some Related Works on Financial Openness & Growth 28

Table 4.1: Summary of ADF Unit root test results of the series 64

Table 4.2: Bounds test for the estimation with De facto Financial Openness Variable 65

Table 4.3: Bounds test for the estimation with De Jure Financial Openness Variable 66

Table 4.4: The ARDL Model for the De facto Financial Openness Equation 66

Table 4.5: Parsimonious Restricted ARDL-ECM for De facto Financial Openness 69

Table 4.6: The ARDL Model for the De jure Financial Openness Equation 70

Table 4.7: Parsimonious Restricted ARDL-ECM for De jure Financial Openness 72

Table 4.8: Results of diagnostic tests 73

Table 4.9: The Granger Causality Test Results 80

Table 4.10a: Summary of ADF Unit root test results of the GARCH series 81

Table 4.10b: Descriptive characteristics of the variables 81

Table 4:11: Testing for Autocorrelation 82

Table 4.12: ARCH-LM Heteroskedasticity test 83

Table 4.13: The GARCH Model Results 83

Table 4.14: Arch test 85

List of Figures

Figure 1: GDP Growth Rate in Nigeria (1986-2011) 104

Figure 2: Real Interest Rate in Nigeria (1986-2011) 105

Figure 3: Inflation Rate in Nigeria (1986-2011) 105

Figure 4: Liberalization in the Neoclassical Growth Model 19

Figure 5. Cumulative Sum of Recursive Residuals (CUSUM) Test 79

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TABLE OF CONTENTS PAGE

Title Page ii

Approval Page iii

Certification iv

Dedication v

Acknowledgements vi

Abstract vii

List of Tables viii

List of Figures viii

Table of Contents ix

Chapter One

Introduction 1

1.1 Background to the Study 1

1.2 Statement of the Problem 4

1.3 Research Questions 8

1.4 Research Objectives 8

1.5 Research Hypotheses 9

1.6 Justification of the Study 9

1.7 Scope of the Study 11

Chapter Two

Literature Review 12

2.1 Conceptual Literature and Measurement Issues 12

2.2 Theoretical Literature 16

2.2.1 The Orthodox Theory 16

2.2.2 The Dependency Theory 17

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2.2.3 The Neo-classical Counterrevolution Framework 18

2.2.4 Liberalization and the Neoclassical Growth Model 19

2.2.5 Major Financial Sector Reforms in Nigeria from 1986 21

2.2.6 Liberalization and Capital Flows in Nigeria 23

2.3 Empirical Literature 24

2.3.1 Financial Openness and Growth 24

2.3.2 Direction of Causality between Financial Openness and Growth 34

2.3.3 Financial Openness and Output Volatility 39

2.4 Shortcomings of Previous Studies 43

Chapter Three

Methodology 45

3.1 Theoretical Framework 45

3.2 Model Specification 47

3.3 Justification of the Models 54

3.4 Unit Root Tests 56

3.5 Cointegration Test and Estimation Procedure 56

3.5.1 The ARDL-UECM Procedure 56

3.5.2 The Granger Causality-ECM Procedure 58

3.6 The GARCH Model 59

3.6.3 Model Justification 61

3.6.4 Method of Estimation 62

3.7 Data Sources 63

3.8 Econometric Software 63

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Chapter Four

Presentation and Analysis of Results 64

4.1 Unit Root Tests 64

4.2 Bounds test 65

4.3 Estimation Results 66

4.4 Further interpretation and explanations of model parameters 74

4.5 Interpretation of the Parsimonious ARDL-ECM Models 75

4.6 ARDL- UECM and Short-run ARDL-ECM model diagnostic tests 78

4.7 Granger Causality Model Results 80

4.8 GARCH Model Results 80

4.9 Evaluation of Research Hypotheses 86

Chapter Five

Summary of Findings, Conclusions and Recommendations 87

5.1 Summary of Findings 87

5.2 Conclusion 88

5.3 Policy Implication of Findings and Policy Recommendations 90

5.4 Research Recommendations for Further Studies 93

References 94

Appendices 104

Appendix A: Graphs of Selected Macroeconomic Variables 104

Appendix B: The Normality Test 106

1

CHAPTER 1

INTRODUCTION

1.1 Background To The Study

Contemporary literature on economic development is replete with discussions on financial

openness and macroeconomic outcomes. This was sparked off by the seminal works of McKinnon

(1973) and Shaw (1973) which attributed financial repression as the cause of the unsatisfactory

growth performance of developing countries. Both McKinnon and Shaw advocated that financial

liberalization was needed to remedy the problems caused by the financial repressive policies of

developing countries. While this policy prescription initially generated some controversy, many

developing countries have adjusted their policies in the prescribed direction in recent years. In the

light of this, several countries, including developing and emerging economies have witnessed

some dramatic domestic financial/ capital account liberalization in the past three decades. The

opening of world economies and quest for greater integration also gave impetus for financial

liberalization and liberalization of the economies of both developing countries and emerging

economies. This is also in line with the “Washington Consensus”, which advocated for

liberalization of inflows, competitive exchange rate, interest rate liberalization, trade liberalization,

privatization, and deregulation of economic activities (Williamson, 1989; Lal, 2012).

Although, based on models of competitive and efficient markets, economic theory tells us that

financial openness should foster economic growth and development; empirical works so far have

not found indubitable evidence for the existence of such a link. While some countries have

benefited from financial liberalization, others have not enjoyed higher economic growth. Some

have even experienced some crises and recessions in the years following liberalization (Fratzscher

and Bussiere, 2004). Examples of this abound: Chile and Argentina in the early 1980s experienced

the negative effects of financial liberalisation. Mexico had their own negative experience between

1994 and 1995 and the Asian financial crisis equally affected many Asian Countries between 1997

and 1998, to name just a few. Also the global financial crisis of 2007–08 was triggered by, among

other things, insufficient financial market regulation (Bumann, et al, 2012). In their own view,

Andersen and Tarp (2003) equally argue that financial liberalisation in combination with a weak

regulatory structure may have strong adverse effects on growth.

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The counter -argument to those underlining the benefits of openness based on the efficient- market

has been to stress the presence of market distortions that may lead to welfare- reducing effects of

financial openness. Such market distortions can take various forms, such as asymmetric

information and hidden action (Stiglitz, 1998) or be related to political economy factors (Bhagwati,

1998).

The literature on financial openness and growth has also been riddled with the controversy

regarding the direction of causality in their relationship. Financial openness is frequently presented

as beneficial for the economy or vice versa. However, regarding the direction and magnitude, there

is a controversy on the relationship (Aslanoğlu and Deniz, 2012). In line with financial

liberalization, a high level of capital tends to flow in the economies that are attractive for the

investors. Especially in emerging economies where charming elements are relatively higher than

elsewhere, foreign investors may direct their funds to these countries. However, financial openness

may easily lead to some alterations in the domestic system especially when external conditions

and effects that flow with liberalization are not properly checked. According to Bacchetta (1992)

it is likely that after financial liberalization, first, capital inflows will be observed. Together with

capital stock increase, domestic investments will be less and less charming as marginal

productivity declines. This decline ends up with capital outflows. He explains that higher domestic

interest rate lets foreign capital in and leads to appreciation first, but this is followed by capital

outflow due to arbitration in foreign and domestic interest rates which further leads to depreciation

of domestic currency; which consequently affects the economy. So the magnitude and direction of

causality between financial openness and economic growth may not be easily determined except

by empirical evidence.

While most of the empirical results linking financial openness and growth have been mixed and

conflicting, another fundamental issue that has recently caught the attention of researchers is the

relationship between financial openness and macroeconomic volatility. Some authors have argued

that financial openness could be a source of greater macroeconomic volatility, exposing vulnerable

countries to sudden reversals of capital flows (Stiglitz, 1998, Kaminsky and Reinhart, 1999).

According to this school of thought which emerged after the financial crisis of the 1980’s and

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1990’s following capital account liberalization reforms by some countries, higher macroeconomic

volatility could be experienced by countries, either because they lack adequate financial

institutions to cope with large and sudden reversals of capital flows or because they lack policy

instruments to smooth cycles. In fact, Stiglitz (1998) suggested that the financial liberalization

thesis is “based on an ideological commitment to an idealized conception of markets that is

grounded neither in fact nor in economic theory”. On the other hand, some authors have argued

that opening the capital account can yield lower output volatility by promoting production base

diversification and enhance international capital flows (Razin and Rose, 1994, Bekaet, 2006).

From a welfare perspective there are two alternative ways to view the relationship between

financial openness and macroeconomic volatility. The first view is that financial openness should

help countries to untie consumption streams and output streams, allowing risk-averse agents to

smooth consumption and leaving output volatility inconsequential for welfare. Another way is to

consider that in addition to consumption volatility, output volatility is also detrimental to welfare.

In view of this, Ramey and Ramey (1995) are of the opinion that volatility has a detrimental impact

on output growth even after controlling for investment.

Following the global wind of liberalization, it is a common knowledge that Nigeria implemented

her Structural Adjustment Programme (SAP) in 1986. Before this period, interest rates in Nigeria

were generally fixed by the Central bank of Nigeria with periodic adjustments depending on the

government’s sectoral priorities (Agu, 1988; Uchendu, 1993). With the implementation of the

SAP, which focused on trade liberalization, the need for financial liberalization was also realized.

The steps that were taken in this regard were interest rate deregulation, introduction of an auction

market for treasury bills, identification of insolvent banks for restructuring, introduction of more

stringent prudential guidelines for banks, increase in banks’ minimum capital requirement and

upgrading and standardization of accounting procedures (Agu et al, 2014; Orji, et al 2014).

However, all of these measures were not implemented simultaneously. Interest rate deregulation

was the first step in 1986. Thereafter, the policy makers embarked on other major efforts of

financial liberalization. Legal reserve requirements were relaxed, credit controls were removed,

and the capital account was liberalized, among other measures.

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1.2 Statement of the Problem

Prior to the introduction of Structural Adjustment Programme (SAP) in Nigeria in 1986, financial

liberalization had become an emerging trend in both developed and developing countries. This

was the prescription of the World Bank and the IMF following the structural imbalance and severe

economic woes experienced by developing countries as a result of oil price shocks, rigid exchange

and interest rate controls and escalating real interest rate for external debt servicing of the 1970s

and 1980s ( Agu et al, 2014; Okpara, 2010). Furthermore, the basic thrust of the economic reform

embodied in the SAP was deregulation, particularly, financial deregulation. Some developing

countries like Nigeria, Cameroun, Ghana, Botswana, Malawi, Senegal, Kenya and Zambia adopted

the liberalization of interest rate as a prominent feature of their financial reforms. Also interest

rates were fully deregulated in Indonesia, Philippines and Srilanka in the early 1980. While Nepal

freed most key interest rates in 1986, Korea, Malaysia and Thailand relaxed control by more

frequent advertisement (Fry, 1997).

The proponents of liberalization suggest that it is ideal for an economy. Honohan (2000) argues

that the process of financial liberalization is expected to increase the variability of interest rates

with its associated distributional consequences. The overall effect is to induce competition within

the financial services industry and in the entire economy, however, the experience of several

countries in the 1980s and 1990s indicate otherwise. For example Chile experienced some banking

problems right after deregulating the financial sector. Caprio and Kliengebiel (1995) also argue

that many banking systems experienced different problems after liberalization. Bakeart et al (2005)

suggest that in developing countries, financial liberalization may not yield intended benefits

because of the strength of domestic institutions and other factors. Demirguc-kunt and Detragiache

(1998) conclude that the benefits of financial liberalization should be weighed against the

increased potential for fragility.

As beautiful as the ‘message’ of liberalization sounds, there is a serious debate in the literature as

per whether its purported benefits are as real as projected by its proponents. For example, the

economic performance of Sub- Saharan African (SSA) countries, which have opened up their

capital accounts, has attracted considerable attention in recent years. The low rates of economic

growth and development experienced in these countries have from 1980’s to date, been described

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as tragic. According to Babajide (2008), the average growth rate from 1961 to 2000 was 0.45%

for SSA, while it was 1.6% for Latin America and the Caribbean (LAC), 2.3 % for South Asia

(SA) and 4.9% for East Asia and the Pacific (EAP). For the Nigerian economy, the country has

experienced decades of slow development due to the unimpressive growth of her per capita income

and infrastructural deficits despite the liberalization of her financial system and capital account

(World Bank, 2012). Nigeria’s macroeconomic indicators have also been fluctuating since 1980.

For example, from early 1980s to the second half of the 1990s, annual inflation has averaged

around 30 percent. Subsequently, average inflation came down to one -digit rate. However, since

2001, inflation is back in the two-digit domain, with an average of about 12.50 %-22.17 % within

some years between 1986-2011 (NBS, 2012 and Akpan, 2013).

The causal relationships between Financial Openness and Growth have also created some concerns

among researchers in developed and developing economies like Nigeria. In developed countries,

several studies have tested for Granger causality between the two series using different samples

and estimation techniques but in Nigeria we have a dearth of such empirical findings, and where

it exists, it has been very inconclusive (Osinubi and Amaghionyeodiwe 2010, Oyatoye, et al 2011

and Ogbonna, et al, 2012).

Another key issue is the question of how financial openness impacts on output volatility. Some

empirical studies such as Greenaway, et al. (2002) and Serven and Schmidt-Hebbel (2002) have

also established that macroeconomic volatility can have adverse effect on growth and

development. The impact of financial openness on output volatility is very important for relatively

poor countries like Nigeria. Analyzing the performance of the Nigerian economy, World Bank

(2012) and CBN (2012) reveal that between 1986- 2012, broad macroeconomic aggregates in

Nigeria such as growth rate ( as shown in figure 1 in Appendix) , terms of trade , real interest rate

and inflation were among the most volatile in the developing world. For example the rate of growth

in Nigeria was 3% in 1986 when the liberalization process commenced but declined to -1% in

1987 and thereafter soared to 10% in 1988. It further declined to 7% in 1989 and rose again to 8%

in 1990. From 1990 to 1994, there was a continuous decline reaching as low as 1.3% in 1994.

There was a little improvement between 1995 and 1996 when the economy grew from 2% to 4%,

but it began to decline again from 3% in 1997 to 1% in 1999, the very year Nigeria embraced a

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new democratic leadership. Owing to some policy initiatives, the economy improved to 5% in

2000 but declined again to 3% and 2% in 2001 and 2002 respectively. However, when the new

government came on board in 2003, the economy grew again to 10% and 11% in 2004. The growth

was short-lived as the economy nosedived again to 5% in 2005 and increased minimally to 6%

between 2006 and 2008.The economy experienced another growth between 2009 and 2010 at 7%

and 8% respectively but declined again to 7% in 2011. These fluctuations and volatility in growth

rates of the economy have been attributed to many factors ranging from economic mismanagement

to erratic policy reversals. To buttress this point further, the table below shows the relative

performance of some macroeconomic variables in Nigeria when compared with other selected

emerging and developed economies.

Table 1: Volatility and Average of Selected Macro economic Variables for different Economies

Countries GDP Growth Rate Inflation Exchange Rate

Developed Economies Volatility Average Volatility Average Volatility Average

Canada 2.04 2.51 1.41 2.50 0.16 1.29

South Korea 4.01 6.40 2.14 4.40 476.46 631.33

Emerging Economies

Indonesia 4.08 4.99 10.47 10.52 3,757.88 5,780.65

South Africa 2.19 2.48 4.41 9.19 2.46 5.31

Egypt 1.74 4.46 7.03 11.02 1.76 3.69

Nigeria 6.23 4.61 18.58 22.17 57.32 62.90

Source: World Bank (2012) and Author’s Computation based on 5-year Averages (Means) and Standard Deviations of the selected macroeconomic variables for the various Economies (1986 – 2011)

Table 1 above shows the averages and volatilities of selected macroeconomic variables in some

selected economies. On average, the volatilities of Gross Domestic Product (GDP) growth,

inflation, and exchange rates are higher in emerging market economies than in developed

economies. Among emerging market economies, Nigeria exhibits the highest GDP volatility.

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Besides, average inflation is higher in emerging market economies relative to developed

economies. Nigeria’s average inflation over this period is the highest in the emerging market

economies group, followed by Egypt and Indonesia. Inflation rate variability in Nigeria is also

high, although with slightly smaller volatility than that of Indonesia. Furthermore, exchange rate

variability in Nigeria is higher than Canada, South Africa, and Egypt but lower than South Korea

and Indonesia. On the other hand, Nigeria’s GDP growth showed some buoyancy among the

selected developed and emerging market economies, second only to South Korea and Indonesia,

which all grew on average around 5 to 6 percent, a year, within the period under review. Although,

Nigeria’s GDP showed some increase within the period, yet it is the most volatile among the

countries compared. This shows that Nigeria faces a macroeconomic environment that is indeed

more volatile than say Canada, South Korea, Indonesia, South Africa, and Egypt, at least in terms

of GDP Growth, inflation and exchange rates. This development further corroborates the finding

of Batini (2004) that “emerging market economies (like Nigeria) face more volatile

macroeconomic environment, and typically have weaker institutions that enjoy less credibility than

their developed economies counterparts”.

Over the last three decades, high macroeconomic volatility has become a key determinant as well

as consequence of poor economic management. This is in line with Kama (2006), which posits

that the ability of the financial sub-sector to play its role has been periodically punctuated by its

vulnerability to systemic distress and macroeconomic volatility. The Nigerian economy has also

been characterized by low growth trap as a result of low savings-investment equilibrium. With an

average annual investment rate of about 16% of GDP, Nigeria is still far behind the minimum

investment rate of about 30% of GDP required to minimize poverty and stimulate real growth

(CBN, 2010, World Bank, 2010).Real Interest Rate Movement and Inflation rate have also being

volatile (as depicted by figures 2 and 3 in Appendix). In addition to this, fiscal policy in Nigeria

has been characterized by highly volatile, inefficient and unsustainable public sector spending.

Another key issue here is the question of how to measure financial openness. Two broad

approaches can be found in the literature: one based on measuring de jure openness and the other

measuring de facto openness {(Raddatz, (2007); Fratcher and Bussierre, (2004); Lane and

Millessi-Ferreti 2005; Edison et al 2002b and Kray (1998)}. De jure openness is often proxied by

8

the removal of restrictions to capital account transactions as published in the IMF’s Annual Report

on Exchange Arrangements and Exchange Restrictions (AREAR). For the de facto openness

measures, different studies have used different capital flow variables. Each of these measurements

when adopted for cross-country regressions have their pitfalls respectively. Thus, these problems

call for country-specific regressions. Also despite the efforts to promote the ideals of domestic

financial market cum capital account openness in Nigeria through competitive market framework,

there is still the fundamental challenge of understanding its real impact on the economy. Thus,

macroeconomic outcomes resulting from financial openness in Nigeria is still largely unexplored.

Hence, further empirical investigation is needed to unravel the outcomes of financial openness

policies with respect to the growth and volatility of the Nigerian economy, using the de facto and

de jure approaches.

1.3. Research Questions

Based on the above discussions, this work intends to address the following research questions:

(1) What is the impact of financial openness on economic growth in Nigeria?

(2) Is there any evidence of the existence of a causal relationship between financial openness and

economic growth in Nigeria?

(3) Does financial openness contribute to output volatility in Nigeria?

1.4. Research Objectives

The overall objective of this study is to estimate and critically analyze the relationship between

financial openness policies and the performance of the Nigerian economy using the de facto and

de jure measurement approaches. Specifically, this work intends to achieve the following

objectives:

(1) To examine the impact of financial openness on economic growth in Nigeria.

(2) To investigate the existence of a causal relationship between financial openness and economic

growth in Nigeria.

(3) To determine the impact of financial openness on output volatility in Nigeria.

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1.5. Research Hypotheses

The hypotheses to guide this research work include:

Ho1: Financial Openness has no significant impact on Economic Growth in Nigeria

Ho2: There is no significant direction of causality between financial openness and economic

growth in Nigeria

Ho3: Financial Openness has made significant impact on output volatility in Nigeria

1.6. Justification of the Study

The Liberalization thesis has been one of the key issues at the fore front of development

macroeconomics and finance as the influence of the epoch-making studies of McKinnon (1973)

and Shaw (1973) has spread. It is pertinent to note that according to Fanelli and Medhora (1998),

the results of liberalization policies in different countries have been mixed:

(i) Although there is an increase in credit supply after liberalization, the result of financial

deepening is rather modest;

(ii) There have been open financial crises a few years after financial liberalization was adopted;

(iii) Some interventionist countries like South Korea have achieved impressive levels of financial

deepening and growth without significant liberalization.

Therefore, as the debate on the benefits of financial liberalization policy goes on in the literature,

this work will help policy makers to gain a deeper understanding of how this policy prescription

has contributed (or not contributed) to economic growth and output volatility in Nigeria. It may

look like a logical and inescapable step for an emerging economy like Nigeria, with ambition to

optimize its interaction and engagement with the international financial markets, but this study

will reveal some empirical truths that will guide the government and policy makers towards

implementing and maximizing the liberalization policies. Indeed the findings will guide policy

10

makers in designing and implementing macroeconomic and financial policies that will enhance

national development and improve the social welfare of the populace.

Also this work will enhance our knowledge of the various measurement issues associated with

financial openness and guide us in identifying the ones that are potent and/or the ones that are less

efficient. The first leads to the issue of whether, and how government should remove barriers to

capital account transactions. The second question requires policy-makers to decide, given that

legal barriers have been removed, how best to manage capital flows. Also, this research work will

analyse the role of both de facto and de jure openness measures and this will assist greatly in

unraveling the measurement technique that is most robust for the Nigerian Economy. Estimating

the causal relationship between financial openness and economic growth would also be

informative in predicting how economic growth will be affected if policymakers are to change

financial openness policies and vice versa.

In addition to this, as we pursue the Millennium Development Goals, Financial System Strategy

(FSS) 2020, and Vision 20:2020 which are basically aimed at poverty reduction, enhancement of

peoples’ welfare and quality of life, making Nigeria the safest and fastest financial hub of Africa,

engendering financial inclusion and stability and finally positioning Nigeria in the league of the

world’s top 20 economies by the year 2020, this study will elucidate on how financial openness

policies have impacted on the stability and volatility of the macro economy. This is because what

happens at the macro economy has direct and indirect impacts on the lives of the entire citizens.

Furthermore, we shall also utilize some macro models to address our specified objectives, bearing

in mind that Nigeria is still a developing country with several constraints. This is essential because

some methodologies that were adopted by other authors to study developed economies or

undertake cross-country studies may not adequately fit into our own domestic needs in Nigeria.

It will also be interesting to note that financial openness is a fascinating topic to study for

researchers of development macroeconomics and finance not only because of its compelling policy

relevance but also because of the enormous variations of approaches and experiences across

countries. Differences in speed and approach to liberalization have often been driven as much by

philosophy, political circumstances, and regional fads as by economic factors. Hence, a country-

11

specific study of the effects of financial openness can potentially reveal a wide array of natural

variation in experiences.

To the best of my knowledge, this thesis is among the first to examine in detail, the dynamic impact

of financial openness on overall economic growth and volatility in Nigeria using Capital flow

variables, the Chinn-Ito index, the ARDL – Bounds testing approach and GARCH Model.

Finally, this thesis will serve as a good reference material to other researchers who wish to explore

more empirical issues following our subject of discussion.

1.7. Scope of the Study

Our study spans through the period 1986- 2011 to account for the commencement of the

liberalization exercise in Nigeria. As we noted earlier, Nigeria implemented her Structural

Adjustment Programme (SAP) in 1986. Before this period, interest rates in Nigeria were generally

fixed by the Central bank of Nigeria with periodic adjustments depending on the government’s

sectoral priorities. With the implementation of the SAP, which started with trade liberalization, the

need for financial liberalization was also realized. Consequently, the capital account was

liberalized and opened up to give room for free flow of capital across our national borders. In this

study, our variables of interest for econometric analysis shall be de facto (capital flows) variables,

which are the sum of FDI, portfolio flows and other investments following Aizenman (2004 and

2008), and Aizenman and Noy (2009) and de jure (Chin-Ito Index) based on Chinn and Ito (2012).

12

CHAPTER 2

LITERATURE REVIEW

2.1 Conceptual Framework and Measurement Issues

A country’s balance of payment has two major accounts namely current and capital account. The

Current account details economic transactions which provide income for the recipient country.

These transactions include trade in goods (visibles), trade in services (invisibles), payment of

factor income (dividends, interests and migrant remittances from earnings abroad), and

international transfers (gifts).Capital account on the other hand represents a variety of financial

flows like foreign direct investment, portfolio/ equity flows (or portfolio/equity investments),

loans, acquisition of assets in one country by foreign residents, etc. Capital account openness

therefore refers to a process whereby there is a systematic reduction or removal of restrictions on

capital flows to a country. Also, it connotes a deliberate policy that allows domestic businesses to

borrow from foreign banks, and foreigners are allowed to purchase domestic debt instruments as

well as invest in the domestic stock market (Henry and Lombard, 2003).

Furthermore, following Agosin and Mayer (2000); Levine, et al (2002), Agenor (2003), and

Carnignani and Chowdhury (2005), financial liberalization may involve the process of liberalizing

domestic financial markets and lifting administrative or legal restrictions on capital movements;

and hence creating the necessary conditions for the integration of the domestic financial system

into the global market. In this study, however, we use capital account openness/financial openness

interchangeably. A key component of the argument here following Kose et al, (2008) is that it is

not just the capital inflows themselves, but what comes along with the capital inflows, that drives

the benefits of financial openness for a developing country like Nigeria. There is considerable

evidence that financial openness if properly managed, serves as a catalyst for a number of benefits.

Some of these benefits may be termed “collateral benefits” if they are not the primary motivations

for a country to undertake financial openness. These collateral benefits could include development

of the domestic financial sector, improvements in institutions (defined broadly to include

governance, rule of law, etc), better macroeconomic policies, etc. These benefits then result in

13

higher economic growth, usually, through gains in allocative efficiency. The diagram below

represents the two view points on how financial openness could interact with growth and volatility.

The Traditional View

GDP Growth

Financial Openness GDP Growth

Volatility

The traditional view focuses on the importance of channels through which financial openness and

capital flows could increase GDP Growth and reduce volatility.

An Alternative Perspective

Financial Openness GDP Growth

Volatility

More efficient international allocation

of capital

Capital deepening

International risk-sharing

Potential Collateral Benefits

Financial Market Development

Better governance

Institutional Development

Traditional Channels

Macroeconomic Discipline

14

This alternative perspective based on Kose et al (2008) acknowledges the relevance of the

traditional channels, but argues that the role of financial openness as a catalyst for certain

“collateral” benefits may be more important in increasing GDP growth and reducing volatility.

Another key conceptual issue in this study is the question of how to measure financial openness.

Two broad approaches can be found in the literature: one based on measuring de jure openness

and one measuring de facto openness. De jure openness is mostly proxied by the removal of

restrictions to capital account transactions as published in the IMF’s Annual Report on Exchange

Arrangements and Exchange Restrictions (AREAER). For de facto openness, Edison et al (2002b)

and Kraay (1998), for example, used seven variables- four based on FDI and portfolio flows

(combined FDI and portfolio net flows, combined FDI and portfolio inflows, FDI inflows,

portfolio inflows) - two proxies related to the size and composition of foreign debt (total foreign

debt and short-term foreign debt and trade openness –defined as the sum of exports and imports).

Moreover, they employed two proxies for stock variables (combined FDI and portfolio net stocks,

combined FDI and portfolio in-stocks). Net flows and stocks refer to the difference between the

asset and liabilities sides of the balance of payments (B.O.P) in a particular period. Aizenman and

Noy (2009), Ozdemir and Erbil (2008), and Kose et al (2008) equally adopted various capital flow

variables in the literature, to study de facto financial openness of different economies. The

AREAER measure has also been utilized in different forms in the literature. The usual way is to

simply define it as a discrete 0-1 variable, that is, indicating full ‘openness’ or ‘closedness’. Studies

using longer time periods, such as 5-year periods, generally use the share of the years in which a

country had an open capital account as the measure of openness. The advantage of these measures

is that they allow for a clear and easy identification of when a country had removed all barriers to

capital account transactions. However, a drawback is that countries may liberalize their capital

accounts by removing individual barriers gradually over time.

As an alternative, Quinn (1997) exploits the details of the description in the AREAER to construct

an openness measure which can take 9 different degrees of openness – from 0 to 4 in 0.5 point

increments. This allows for a much finer categorization of de jure openness and its changes.

However, a key drawback is that this openness measure has been created only for four years- 1958,

15

1973, 1982 and 1988- thus, not allowing the identification of those years in which a country

undertook those changes.

In this study, we adopt the capital account openness index developed by Chinn and Ito. The

KAOPEN index was used by Chinn and Ito (2002, 2006 and 2012) in their studies of the

determinants of financial development. The researchers found that the rate of financial

development, as measured by private credit creation and stock market activity, is linked to the

existence of capital controls, and that higher level of financial openness contributes to the

development of equity markets if a threshold level of institutions is attained, which is more

prevalent among emerging market countries. This index was also updated by Chinn and Ito (2012)

and it is available for use by researchers conducting empirical analysis in this area of research. The

construction of the KAOPEN index is based on the first principle component of four binary

variables in IMF’s Annual Report on Exchange Arrangements and Exchange Restrictions

(AREARER) and it takes higher values for more open financial regimes. These four variables are

defined as follows: K1 is the variable that indicates the presence of multiple exchange rates; K2 is

the variable that indicates restrictions on current account transactions; K3 is the variable that

indicates the restrictions on capital account transactions; and K4 is the variable that indicates

requirements of the surrender of export proceeds. One important merit of the index is its wide

coverage; it is available for more than 182 countries and for a long time period (1970 through

2011)

As mentioned by a number of authors (such as Edison et al., 2002), it is not easy to measure the

extent of openness in capital account transactions. By nature of its construction, the KAOPEN

index is considered to be a de jure measure of financial openness because it attempts to account

for regulatory restrictions on capital account transactions. Hence, this index is different from price-

based measures of financial openness, often referred to as de facto measures of financial openness.

One might argue that a de jure measure is a poor indicator of openness in the sense that releasing

controls do not necessarily lead to more cross-border transactions. The response to such concerns

is that the factors determining the magnitude of capital flows are many. The investment climate in

the country, as well as the culture might influence capital inflows. The policy tool that is most

directly related to the regulation of capital account transactions is capital account liberalization, i.e

16

eliminating the barriers to allow access. Whether a change in the rules helps increase the magnitude

of capital flows is another question.

As an alternative to these de jure measures, the literature has also analysed various de facto proxies

of openness. The rationale for looking at actual openness is that a country that is open de jure may

not necessarily experience such inflows. Since the question of interest is whether capital flows

benefit or hurt countries, one may argue that one should define openness in terms of both legal

restrictions (de jure) and actual capital flows (de facto). The literature has looked at various capital

flows related to FDI, portfolio flows and other investment flows (e.g. Aizenman 2008). Thus, in

this study we adopt Aizenman and Noy (2009) capital flow variables to measure our de facto

financial openness.

2.2 Theoretical Literature

Theoretically, financial openness is expected to engender flow of resources from capital- surplus

industrial countries to capital- deficit developing and emerging countries. However, there have

been some serious theoretical debates on the issues of liberalizing capital across national borders

with different schools having different views of international mobility of capital. These thoughts

are tailored mainly along the Orthodox, Dependency, and Neoclassical-counter-revolution

frameworks (Mailafiah, 2006).

2.2.1 The Orthodox Theory

Liberalization is seen by main stream economists as a means of solving global economic problems

definable in terms of wants, exchange, global resources, and growth. This model views capital

mobility as adding new resources, technology, management and competition to capital deficit

economies in a way that improves efficiency and stimulates change in a positive direction.

Currently the example of the Asia Tigers is used to drive home the growth-driven force of capital

mobility when FDI flows are encouraged with liberalization of capital account transactions. This

submission transcends the classical and neo-classical viewpoints.

Neo-classical theory suggests that free flows of external capital should equilibrate and smoothen

a country’s consumption or production path. In the real world, this theory has been disputed

because liberalization of the short term capital account has been associated with serious economic

17

and financial crises in Asia and Latin America in the 1990s which has necessitated the caution in

the 21st century to fully liberalize the capital account transactions. Long term flows are regarded

as much more stable and there is the suggestion that developing countries may wish to liberalize

only long –term flows while still controlling partially or wholly short-term flows. These

viewpoints have been contentious within the framework of globalization and pressure for full

integration of world financial markets.

Capital account openness has been identified as a necessary strategy to attract private capital flows

to substitute declining aids in developing countries {(Grill and Milesi-Ferrti, (1995), Quinn (1997),

and Demirguc-Kunt and Detragiache (1998)}. In these studies, it is imperative to note that capital

account liberalization correlated with growth as well as the deepening of the financial sector.

2.2.2 The Dependency Theory

This school of thought is tailored along the neo-Marxist analysis which developed from Marxism.

This view could be summarized as dependence on capital-surplus developed economies by the

capital deficit developing economies. Though unpopular as a result of the collapse of communism

in the 1980s and the subsequent acceptance of the market doctrine by the former Eastern bloc, it

helps historically to examine the diverging viewpoints of development economists.

According to this school which was popularized by Frank (1975), it is assumed that the dependence

worsens the conditions of the developing countries and engenders underdevelopment. Thus, the

penetration of capital from developed countries into developing nations through FDI flows and

short term capital cannot produce beneficial results in the host countries. The assumption is that

there exists a symbiotic relationship between the metropolis (developed) countries and the

underdevelopment of the satellite (developing) countries and that capital mobility to the

developing (satellite) nations is mainly to benefit the metropolis.

The structuralist import-substituting and capitalist industrialization strategy in Latin America in

which the “Foreign Monopoly Capital” was taking over the import substitution process was further

analyzed by Frank (1975). Frank noted that the strategy was unprogressive and that the peripheral

formations became more underdeveloped with their incorporation into the world capitalist system.

The theorists recommended the need to severe link with the exploitative international capitalism

18

as the recipe to developing the economies of the periphery. Revolutionary as this may sound; it is

unattainable in a world that is fast becoming a global village.

In his own view, Aremu (2005) posits that a modification of this thought has been formulated

drawing from the experiences of the newly industrializing economies (NIEs) of Latin America and

South East Asia. In these countries, foreign investors were attracted through the provision of

enabling environment, while other modalities for entry and operations were negotiated. Aremu

(2005) also suggest that the modification of this model presupposes that through a strategy of

autonomous and self-reliant macroeconomic policy objectives and implementation programmes,

developing nations can still use external stimuli, particularly FDI to achieve their development

aspirations and strategies.

2.2.3 The Neo-classical Counterrevolution Framework

This school of thought emerged following the questioning of the relevance of the dependency

argument at the end of the 1970s. The neoclassical counter revolution was therefore launched with

a re-affirmation of the dictates of the market and the importance of “getting the prices right”

(Mailafia, 1997). The counterrevolution, led by Ian Little, Bela Ballasa, Anne Krueger and Deepak

Lal, argued that the policy-induced distortions of developing countries largely responsible for their

poor development performance, and proposed that the problems of economic development can

only be solved by an economic system with freely operating markets and a minimalist government

(Ohiorhenuan, 2003). This formed the theoretical underpinnings of the structural adjustment

programmes of the 1980s.

The World Bank publication, “Accelerated Development in Sub-Saharan Africa: An Agenda for

Action” (World Bank, 1981) emphasized the importance of correct pricing policies and reduced

government intervention in economic activities as the two main keys to a revival in African growth

rates. Thus, the IMF conditions for access to her facilities included not only control of money

supply, but removal of price distortions including price controls , subsidies, foreign exchange,

tariffs, freeing of the markets from public sector intervention and elimination of restrictions against

foreign direct investment. An outcome of the protest against the harsh conditions of the IMF policy

prescriptions was the emergence of the “Washington Consensus” emanating from the IMF, World

19

Bank and the group of seven leading industrial countries, particularly United States of America.

The consensus advocated a focus on balanced budget, liberalization of trade and financial flows,

exchange rate correction, domestic market deregulation and privatization.

2.2.4 Liberalization and the Neoclassical Growth Model

This section illustrates the fundamental predictions of the neoclassical growth models about the

impact of capital account liberalization on a developing country. Following Diercks (2012) and

Henry (2006) who have also utilized this framework based on Solow, (1956) and Swan (1956), we

submit that understanding this framework can also be useful in the context of a developing country

like Nigeria, which is the focus of our study. The diagram below elucidates this point further.

However, our theoretical framework and model building in chapter three of this thesis follow the

Endogenous Growth Model.

Output per Unit of

Effective Labor (n + g +δ) k

sf (k)

A

ks.state k*s.state K

Figure 4: Liberalization in the Neoclassical Growth Model

Assume that output is produced using capital, labor, in a Cobb-Douglas Production

Function with labor-augmenting technological progress:

Y=F (K, AL) =Kα (AL) 1-α (2.1)

Let 푘 =be the amount of capital per unit of effective labour and 푦 =

the amount

20

of output per unit of effective labor. Using this notation and the homogeneity of the production

function we have:

y=f (k) =kα (2.2)

Let s denote the fraction of national income that is saved each period and assume that capital

depreciates at the rate δ; the labor force grows at the rate n, and total factor productivity grows at

the rate g. Savings each period builds up the national capital stock and helps to make capital more

abundant. Depreciation, a growing population, and rising total factor productivity, all work in the

other direction making capital less abundant. The following equation summarizes the net effect of

all these forces on the evolution of capital per unit of effective labor:

k (t) =sf (k (t)) - (n +g+δ) k (t) (2.3)

When k (t) = 0, the economy is in the steady state depicted by Point A in Figure 4 above. The ratio

of capital to effective labor (k) is constant at Point A. In contrast, the steady state level of capital

(K) is not constant, but growing at the rate n + g. Output per worker

grows at the rate g.

Finally, the steady state marginal product of capital equals the interest rate plus the depreciation

rate:

f'(ks.state)=r+δ (2.4)

Equation (2.4) gives a general expression of the equilibrium condition for investment. This

equation has important implications for the dynamics of a country’s investment and growth in the

aftermath of capital account liberalization, because the impact of liberalization works through the

cost of capital. Let r* denote the exogenously given world interest rate. The standard assumption

in the literature is that r* is less than r, because the rest of the world has more capital per unit of

effective labor than the developing countries. It is also standard to assume that the developing

country is small, which means that nothing it does affects world prices (Henry, 2006).

Under these assumptions, when the developing country liberalizes, capital surges in to exploit the

difference between the world interest rate and the country’s rate of return to capital. The absence

of any frictions in the model means that the country’s ratio of capital to effective labor jumps

immediately to its post-liberalization, steady state level. Figure 4 depicts this jump as a rightward

shift of the vertical line from ks.state to k*s.state.

21

In the post-liberalization steady state, the marginal product of capital is equal to the world interest

rate plus the rate of depreciation:

f'(k*s.state)=r*+δ (2.5).

According to Barro and Sala-i-Martin (1995), the instantaneous jump to a new steady state implies

that the country installs capital with speed. Although this may not be completely true, however,

the vital fact about the transition dynamics is that there must be a period of time during which the

capital stock grows faster than it does before or after the transition. To see why the growth rate of

the capital stock must increase temporarily, recall that in the pre-liberalization steady state the ratio

of capital to effective labor (ks.state) is constant, and the stock of capital (K) grows at the rate n + g.

In the post-liberalization steady state, the ratio of capital to effective labor (k*s.state) is also constant

and the capital stock once again grows at the rate n + g. However, because k*s.state > ks.state, it follows

that at some point during the transition, the growth rate of K must exceed n + g. (Henry, 2006 and

Diercks, 2012).

2.2.5 MAJOR FINANCIAL SECTOR REFORMS IN NIGERIA FROM 1986

Table 2.1 MAJOR REFORMS YEAR

INTEREST RATES LIBERALISATION

Two foreign exchange markets established.

Interest rate controls completely removed. Bank licensing liberalized. Foreign exchange market unified.

Foreign exchange bureaus established. Bank portfolio restrictions relaxed.

Banks permitted to pay interest on demand deposits. Auction markets for government securities introduced. Capital adequacy standards reviewed upwards. Extension of credit based on foreign exchange deposit banned.

1986

1987

1987

1988

1988

1989

1989

1989

1989

22

Risk-weighted capital standard introduced and banks’ required paid- Capital. Uniform accounting standards introduced for banks. Stabilization securities to mop up excess liquidity introduced. Bank licensing emerged. Central bank empowered to regulate and supervise all financial institutions Interest rates re-administered. Interest rate control removed once again. Privatisation of government owned banks begun again. Capital market deregulation commenced. Foreign exchange market reorganized. Credit control dismantled. Indirect monetary instruments introduced. Five banks taken over for restructuring. Interest and exchange rate controls re-imposed.

1990

1990

1990

1991

1991

1991

1992

1992

1992

1993

1993

1994

SECURITIES MARKET LIBERALISATION

Liberalization of capital/equity flows. Continuation of interest controls initiated fiscal reforms. Exchange controls relaxed. Autonomous foreign exchange market

introduced.

Liberalization of capital market continues. Retention of interest controls continuation of fiscal reforms. Official foreign exchange market operation by government transactions continued operation of the autonomous foreign exchange market.

1995

1995

1996

1996

1996

INCREASE IN BANK & CAPITAL MARKET RECQUIREMENTS

Recapitalization of banks and stock market. Five banks taken over by the CBN. Assets Management Company of Nigeria established.

2005 2009 2010

Source: Author’s compilation based on Ikhide and Alawole (2001), Agu et al (2014), Orji et al (2014) and various CBN publications.

23

2.2.6 Liberalization and Capital Flows in Nigeria

The postulation of the McKinnon-Shaw hypothesis emphasizes that financial liberalization will

lead to an increase in savings, investment and subsequently, rapid economic growth. The Structural

Adjustment Programme (SAP) which was embraced by Nigeria in 1986 marked the beginning of

the liberalization of the Nigerian economy. Prior to the period, the economy was regulated, and

that affected the free movement of capital necessary for economic growth. One of the outcomes of

the financial liberalization as identified in the literature is the increase in capital flows and the

consequent augmentation of the country’s foreign exchange reserves (Owusu, 2012 and Agu et al,

2014). Furthermore, foreign direct investment (FDI) flow into the country increased from an

average of 1.3 % GDP (pre-liberalization) to an average of 2.6 % of GDP after liberalization. The

increase in FDI could be attributed to the increase of foreign investment in the oil and

communication sectors of the economy. At the commencement of the Structural Adjustment

Policy, the stock of total reserves (excluding gold) in Nigeria was $1.165 m in 1987. This figure

increased to $53.002m by the end of 2008. This increase in capital flows and reserves could also

be attributed to flexible exchange rate regime adopted after the adoption of Structural Adjustment

Programme (SAP) in 1986. Furthermore, the SAP heralded a lot of policy reforms that led to the

publication of an Industrial Policy for Nigeria in 1989. Critical policy reforms leading to the

changes in the investment climate in Nigeria for both domestic and foreign investors include, the

abrogation of the Exchange Control Act of 1962 as well as Nigerian Enterprises Promotion Decree

1989 and their subsequent replacements with the Nigerian Investment Promotion Council Decree

No 16 of 1995 and Foreign Exchange (Monitoring and Miscellaneous Provisions) Decree 17 of

1995. As it were, the country did not record any substantial Net Portfolio Investment (NPI) on her

Balance of Payment (BOP) until 1986. However, between July 1995 and July 1996, about US$6.0

million foreign portfolio investment (FPI) was made in the Nigerian capital market through the

Nigerian Stock Exchange (NSE) for the first time since 1962, while for the whole of 1996, foreign

investment through the Nigerian Stock Exchange totaled UD$32.99 million {(Onosode (1997) as

cited in Obiechina (2010)}.

24

2.3 Empirical Literature

2.3.1 Financial Openness and Growth

Despite intensive research devoted to this issue, the literature on the effects of financial openness

on growth has produced conflicting, and sometimes, contradictory results (Eichengreen, 2001,

Mishkin, 2007).

Building on the work of Schumpeter (1911), financial liberalization as advocated by McKinnon

(1973) and Shaw (1973) is a deliberate attempt by developing countries to move away from

financial repression. The models of McKinnon (1973) and Shaw (1973) introduce financial

development as a process and strategy to achieve faster economic growth. They find that

liberalization from restrictions such as interest rate ceilings, high reserve requirements, and

selective credit programme, facilitates economic development. In addition, they argue that positive

real interest rates lead to more efficient credit allocation which provides an additional impact on

growth.

Quinn (1997) was one of the first studies to identify a positive relationship between capital account

liberalization and growth. Quinn’s empirical estimates find that the change in his measure of

restrictions on capital account liberalization has a strongly significant effect on the growth in real

GDP per capita in his cross section of 58 countries over the period 1960- 1989.

Studies by French and Poterba ( 1991), Tesar and Werner (1995), Baxter and Jermann (1997) and

Lewis (1999), argue that financial account openness stimulates capital accumulation , productivity

growth and economic growth by relaxing financial constraints through greater access to external

capital, by promoting more disciplined macroeconomic policies under international pressure,

enhancing production specialization through risk-sharing and increasing the functioning of

domestic financial systems through the importation of financial services and intensification of

competition.

Klein and Olivei (1999) in a study of a cross section of 82 industrial and non-industrial nations

find a positive effect of capital account liberalization on growth among industrial countries, but

they do not find evidence that capital account liberalization promotes growth in non-industrial

countries. This significant result seems to be because of the presence of the OECD countries in the

25

sample. Klein and Olivei show that capital account liberalization significantly affects the change

in financial depth in a sample consisting of 20 OECD countries but not in a sample of the non-

OECD countries, nor in a narrower non-OECD sample of 18 Latin American countries known to

have had a relatively high incidence of capital account liberalizations. They also estimate a growth

model that includes the change in financial depth as regressor and find that financial development

is a significant determinant of growth per capita. They conclude that the beneficial effects of capital

account liberalization, at least with respect to promoting financial depth, are achieved only in an

environment where there is a constellation of other institutions that can usefully support the

changes brought about by the free flow of capital. Billiu (2000) also finds that capital account

liberalization spurs growth by promoting financial development.

Henry (2004) argues that if a developing country opens its stock market to foreign investors,

aggregate dividend yield falls by 240 basis points, growth rate of output increases by an average

of 1.1 percentage points per year, and the growth rate output per worker rises by 2.3 percentage

points per year. Also, Bekaert, et al (2005) show that foreign investors pressure local institutions

to adhere to international standards in order to improve local corporate governance and reduce the

division between internal and external finance.

Loyayza and Ranciere (2006) provide the summary of the relationship between financial

liberalization and economic growth. Their modern growth model reveals that the financial sector

impacts capital accumulation as well as the rate of technological development. Serven (2002) also

observes that financial openness grants markets dominant role in setting financial asset prices and

returns, allocating credit, and developing a wider array of financial instruments and intermediaries.

Fratzscher and Bussierre (2004) analyse the openness-growth nexus for a set of 45 developed

countries and emerging market economies: 11 OECD, 12 Asian, 8 Latin American, 9 European

Union (EU) countries, plus Bulgaria, Romania, Russia, South Africa and Turkey from 1980 to

2002.They conclude that the acceleration of growth immediately after liberalisation is found to be

often driven by an investment boom and a surge in portfolio and debt inflows. By contrast, the

quality of domestic institutions, the size of FDI inflows and the sequencing of the liberalisation

process are found to be important driving forces for growth in the medium to longer term.

26

Chinn and Itoh (2006) investigate whether financial openness leads to financial development over

the period 1980 to 2000 in 108 countries, including 30 Sub Saharan African (SSA) countries as

part of a broader set of developing or emerging countries. After controlling for legal institutions,

they find that a higher level of financial openness directly promotes the development of equity

markets and indirectly through its interaction with legal and institutional development; however,

the latter effect requires a certain threshold of institutional development. Their results are more

relevant to emerging economies than developing countries since they focused on equity markets.

Nonetheless, their study seems to support the argument that an argument legal and institutional

infrastructure is necessary for financial liberalization to be effective. They also find that trade

openness is a precondition for capital account liberalization and the development of the banking

system is required for equity market development.

Omoke (2010) in his own study concludes that in a period of financial liberalization, trade

openness and financial development have causal impact on economic growth. Others who find

similar results that financial development is important for economic growth are, Gallego and

Loayza (2002), Soukhakian (2005), and Okpara (2010).

O’Donnell (2001) and Chanda (2005) also consider the possibility of differing the effects of

capital account openness across countries. O’Donnell in this study examines the impact of capital

account openness using both IMF rule-based measures and quantitative-based measure of financial

openness. Using a standard set up, they find that the rule –based measure tends to be too coarse an

indicator of the degree of capital account liberalization, as it does not take into account the nature

of different types of controls. However, using the quantitative measure, he finds that capital

account openness does seem to speed up economic growth. However, like other researchers, he

finds that the benefits are not equal.

In another study, Klein and Olivei, (2001) analyse the impact of capital account liberalization on

growth and financial depth for a cross-section of countries over the period 1986-1995. They found

that countries with open capital accounts experienced a larger increase in financial depth than

countries with closed capital account, and through that channel, higher rates of economic growth

occurs. Also, Chinn and Ito (2005) do find a positive effect of financial openness on domestic

financial development if the institutional quality in the country is of a sufficiently high level.

27

On the other hand, some studies such as that of Eichengreen and Leblang, (2003) find a negative

relationship between financial openness and growth, while Grilli and Milesi-Ferretti, (1995), find

that financial openness does not affect growth. Using a cross section of countries, this study

considers average growth of per capita income for five non-overlapping five-year periods between

1966 and 1989. Their sample includes 61 countries, although, with 181 observations in one set of

regressions and 238 in another, not every country appears in each of the five sub periods. Their

results do not support the hypothesis that capital account liberalization promotes growth.

Following the financial crises in Asia, Russia and Latin America in the 1990s, some authors have

argued that capital account liberalization does not generate efficiency. Instead, liberalization

invites speculative hot money flows and increases the likelihood of financial crises with no

discernable positive effects on investments, output or any other real variable with non-trivial

welfare implications (Bhagwati, 1998; Stiglitz, 2002)

Rodrik (1998) questioned the effect of capital account liberalization on growth. In a sample that

includes almost 100 countries, developing as well as developed, he finds no significant effect of

capital account liberalization, as measured by Share, on the percentage change in real income per

capita over the period 1975 to 1989 in growth regressions that also include initial per capita

income, initial secondary-school enrollment rate, an index of the quality of governmental

institutions, and regional dummy variables. Likewise, he finds no relationship between capital

account liberalization and investment-to-income, or between capital account liberalization and

inflation.

Chanda (2001) suggests that the impact of capital account openness on economic growth may vary

with the level of ethnic and linguistic heterogeneity in the society, a proxy for the number of

interest groups. In particular he finds that capital controls lead to greater inefficiencies and lower

growth among countries with a high degree of ethnic and linguistic heterogeneity.

Edison, et al (2002) also finds little evidence of a relationship between capital account

liberalization and growth. Using a variety of econometric techniques and methodologies (i.e two

cross-sectional ones based on OLS and IV and one based on a dynamic panel data model using

GMM) and a new data set focusing on quantitative measures rather than rule-based measures, they

find that financial integration does not accelerate economic growth per se, even when controlling

28

for particular economic, financial, institutional, and policy characteristics. They do, however, find

that international financial integration is positively associated with real per capita GDP,

educational attainment, banking sector development, stock market development, the law-and-order

tradition of the country, and government integrity (low levels of government corruption).

Aizenman (2004) apply a two-step FGLS procedure for a panel of developing and OECD countries

for the years 1982-1998 using annual observations. He finds that de-facto financial openness

depends positively on lagged trade openness, and GDP/Capita. The budget surplus to GDP ratio

is occasionally significant and always negative for developing countries, but positive and

significant for the OECD countries. Including the corruption variable in his regressions also yields

negative and significant coefficients in almost all the iterations of the model he examined,

confirming Wei’s (2000) insight.

Klein and Olivei (2008) however, argue that the lack of a positive growth effect of financial

openness in developing countries is due to a missing effect of financial openness on financial

development for these countries.

Table 2.2: Summary of Some Related Empirical Works on Financial Openness and Growth

Study No. of

Countries

Liberalization

/Openness

Measure

Dependent Variable

and Estimation

Method

Results

Grilli &

Milesi-

Ferretti (1995)

61 Share Growth in income per

capita for five-year non-

overlapping

periods during 1971 –

1994 period. IV

estimation.

No Evidence of a significant effect

of Share on

growth of income per capita.

Quinn

(1997)

58 ΔQuinn,

between

1988 and

1958

Growth in income per

capita 1960 – 1989.Cross

Section, OLS.

ΔQuinn significantly raises

growth in income per

capita, though no regression

is presented with both

29

ΔCapital Controls and

ΔOpenness.

Kraay

(1998)

64, 94, or

117

Share;

Quinn;

or Volume

Growth in income

per capita over

1985 – 1997. Cross

Section.

OLS & IV. Samples

of 117 (Share); 94

(Volume); or 64

(Quinn).

No effect of Share or Quinn

on Growth. Coefficient

on Volume significant and

positive.

Rodrik

(1998)

About 100 Share Growth in income

per capita over

1975 – 1995. Cross

Section,

OLS.

No Evidence of a significant

effect of Share on

growth of income per capita

Klein &

Olivei

(2001)

67 Share Growth in income

per capita, 1976 –

1995. Cross

Section, IV.

Change in Financial

Depth (ΔFD ) as a

function of Share

and

then per capita

income growth as a

function of

instrumented

Significant effect of Share on

ΔFD, though results

Seem to be driven by OECD

countries in sample.

Significant effect of

instrumented values of ΔFD

And FD on growth.

30

Value of ΔFD (and

initial FD).

Arteta,

Eichengreen.

&

Wyplosz,

(2001)

51

to 59

Quinn in

Initial

Year; or

ΔQuinn

over

relevant

period

Growth in income

per capita 1973 –

81, 1982 – 87, 1988

– 92, or

Pooled for these 3

periods. Follows

Edwards (2001) but

with

OLS rather than

WLS and with

different

instruments.

Quinn significant for pooled

results but not for

Shorter subsamples. ΔQuinn

not significant.

Significant effect of

interaction of Quinn with

either

quality of law or openness

Bekaert,

Harvey &

Lundblad

(2001)

30

Emerging

markets

Official

Dates of

Stock

Market

Liberalizati

on

Growth rates in

income per capita

for various time

periods

between 1981 and

1997, resulting in

overlapping data.

Stock market liberalization

significantly contributes

to growth in income per

capita, with largest effects

shortly after liberalization

Chanda

(2001)

57

non-

OECD

Share Growth in income

per capita over

1975 – 1995. Share

interacted

with measure of

ethnic

heterogeneity.

Share significantly raises

growth in ethnically

heterogeneous countries and

significantly lowers it

in ethnically homogeneous

countries.

31

Edwards

(2001)

55 to 62

Quinn in

1988; or

ΔQuinn

1988 –

1973

Growth in income

per capita, 1980 –

1989. Cross

Section. WLS

(1985 GDP as

weight), IV. Also

uses interaction of

Quinn in

1988 and log (GDP

in 1980).

Quinn level significantly

raises GDP growth.

Interaction suggests that, at

low GDP, opening

Capital account may lower

GDP growth.

O’Donnell

(2001)

94 Share or

Volume

Growth in income

per capita over

1971 – 1994.

Regressions

include interaction

between FD and

Share, and Volume

and FD.

Neither Share nor interaction

of Share and FD

significant, but Volume

sometimes significant.

Edison,et al

(2002)

57 Share,CapSt

ocks, or

CapFlow

Growth in GDP per

capita over 1980–

2000,

Cross section (OLS

and IV); Panel

(GMM).

Regressions include

interaction

terms with financial

development, initial

Using a wide array of

measures, they find

no evidence that

international financial

integration accelerates

economic growth.

32

income, schooling,

institutional factors,

and macroeconomic

policies

Klein (2003) 84 or 52 Share or

Quinn

Growth in income

per capita 1976–96,

Cross section.

Specification

allows for

quadratic

interaction with

initial income.

Significant effect found for

middle-income

Countries but not for poor or

rich countries.

Hermes and

Lensink

(2003)

67

developing

Gross FDI

inflows to

GDP (1970-

1995)

Growth of real per

capita GDP

Positive significant

coefficient on interaction of

FDI with FD variables and

growth

Alfaro et al

(2004)

71 Net FDI

inflows to

GDP (1975-

1995)

Growth of real per

capita GDP

Robust to additional controls

and IV estimation and also

shows a positive significant

coefficient on interaction of

FDI with FD variables.

Bekaert et al

(2005)

95 De-jure

international

equity

market

liberalizatio

n (1980-

1997)

5-year average

growth rate of real

per capita GDP

Significant higher growth

gain post-liberalization for

countries with higher FD

33

Prasad et al

(2007)

83 Stock

liabilities,

gross and

net flow

liabilities to

GDP

Growth in real

sector value added

Countries with below median

FD showed negative

significant coefficient on

interaction of external finance

dependence of industry and

Financial Openness

Coricelli et

al (2008)

31 Euro-

Economies

and

Transition

Economies

Sum of

stock of

total foreign

assets and

liabilities as

a percentage

of GDP

Growth of real

GDP per capita

For financial development, the empirical analysis in Coricelli et al. (2008), using industry-level data for EU and transition countries, revealed that it indeed significantly contributed to growth and catching-up in transition economies. Similarly, financial integration seemed to play a comparably important role in the growth

performance of transition

economies.

Omoke

(2010)

Nigeria Direct

Credit,

Private

Credit and

Money

Supply

Financial Development Proxies

Domestic credit, Private

credit and broad money, as

percentages of GDP showed

no causal impact on growth.

NOTES: Share is proportion of years that IMF’s AREAR shows open capital accounts. Quinn is Quinn’s 0 – 4 measure of

capital account intensity.

ΔQuinn is change in value of Quinn 0 – 4 measures. Volume is measure of volume of capital flows. Cross Section refers to 1

observation per country. FD= Financial Development

Source: Author’s compilation based on Literature Reviewed and in line with Kose et al (2010) and Edison et al (2004)

34

2.3.2 Direction of Causality between Financial Openness and Economic Growth

Empirically, not many studies have been done on the actual direction of causality between financial

openness and economic growth. And for a developing economy like Nigeria, there seems to be a

dearth of such empirical studies. And where it exists, the results have been very inconclusive

(Osinubi and Amaghionyeodiwe 2010, Oyatoye, et al 2011 and Ogbonna,et al, 2012).

Our review on the direction of causality between financial openness and economic growth shall

therefore be based on disaggregated de facto financial openness and economic growth. This is

because different components of aggregate financial openness can be determined or caused by

different factors as argued by Ozdemir and Erbil (2008) and Aizenman and Noy (2009).

In this regard, Zhang (2001) looks at 11 countries on a country-by-country basis, dividing the

countries according to the time series properties of the data. Tests for long run causality based on

an error correction model, indicate a strong Granger-causal relationship between Openness to

capital flows (FDI) and GDP-growth. For six counties where there is no co integration relationship

between the log of FDI and growth, only one country exhibited Granger causality from Openness

to growth. Chowdhury and Mavrotas (2006) take a slightly different route by testing for Granger

causality using the Toda and Yamamoto (1995) specification, thereby overcoming possible pre-

testing problems in relation to test for co integration between series. Using data from 1969 to 2000,

they find that Financial Openness (proxied by FDI) does not Granger cause GDP in Chile, whereas

there is a bi-directional causality between GDP and Openness in Malaysia and Thailand.

De Mello (1999) looks at causation from Financial Openness (proxied by FDI) to growth in 32

countries of which 17 are non-OECD countries. First he focuses on the time series aspects of FDI

on growth, finding that the long run effect of FDI on growth is heterogeneous across countries.

Second, de Mello complements his time-series analysis by providing evidence from panel data

estimations. In the non-OECD sample he finds no causation from FDI to growth based on fixed

effects regressions with country specific intercepts, and a negative short run impact of FDI on GDP

using the mean group estimator.

Choe (2003) in another panel study, use method developed by Holtz-Eakin et al. (1988) in a data

set of 80 countries to test for causality between openness to capital flows and growth. His results

35

show a bi-directional causality between FDI flows and growth, but he finds the causal impact of

FDI on growth to be weak. In their own study, Basu et al. (2003) address the issue of the two-

way link between growth and FDI. They find a co-integrated relationship between FDI flows and

growth using a panel of 23 countries while allowing for country specific co- integrating vectors as

well as individual country and time fixed effects. They argue that trade openness is a crucial

determinant of the impact of FDI on growth, as they find that long run causality is unidirectional

from growth to FDI in relatively closed economies, while two-way causality between FDI and

growth is evident in open economies, both in the short and the long run.

Christopoulos and Tsionas (2004) address the high frequency factors influencing the finance

growth nexus by using panel cointegration analysis. They find that the long-run causality runs

from financial development to growth. Bekaert et al. (2001, 2005) show that financial liberalisation

spurs growth by improving resource allocation and increases the accumulation rate.

Quinn and Toyoda (2008) argue that in analyzing the causal relationship between capital account

liberalization and economic growth, differing time periods and collinearity among independent

variables result in measurement errors. In this line, they employ both time series and cross sectional

analysis and GMM analysis for a long time period of 1955-2004. They observe a positive

relationship between financial openness and economic growth independent of the country’s

economic development level.

Abu-Bader and Abu-Qarn (2008) examine the causal relationship between financial development

and economic growth in Egypt during the period 1960-2001 using a trivariate VAR framework.

The paper employs four different measures of financial development (ratio of money to GDP, ratio

of M2 minus currency to GDP, ratio of bank credit to the private sector to GDP, and the ratio of

credit issued to private sector to total domestic credit). The paper suggests that the causality is bi-

directional. Moreover, the paper shows that the impact of financial development on growth is

through both investment and efficiency.

Ahmed (2008) in another study, use financial openness as proxy for financial liberalization and

adopted the fully modified OLS (FMOLS) and Granger-causality test to the estimate long-run

financial development-growth and causality relationships. He finds that financial development

36

exerted a negative impact on economic growth when private credit was used as a proxy, while the

relationship was positive but insignificant when domestic saving was employed. However, the

financial liberation index exerted a positive and significant impact on economic growth. He

concludes that both in the short run and long run, the Granger-causality test does not find evidence

of any causality relationship between financial development and GDP per capita growth.

Aizenman and Noy (2009) study the causality and endogenous determination of financial openness

and trade openness. They construct a theoretical framework leading to two-way feedbacks between

financial and trade openness. Their results show that one standard deviation increase in

commercial openness is associated with a 9.5% increase in de facto financial openness (% of

GDP). Similarly, they find that an increase in de facto financial openness has powerful effects on

future trade openness. De jure restrictions on capital mobility have only a weak impact on de facto

financial openness, while de jure restrictions on the current account have a large adverse effect on

commercial openness. The authors further investigate the relative magnitudes of these directions

of causality using Geweke’s (1982) decomposition methodology. They conclude that in an era of

rapidly growing trade integration, countries cannot choose financial openness independently of

their degree of openness to trade. Dealing with greater exposure to turbulence by imposing

restrictions on financial flows is likely to be ineffectual.

Moudatsou and Kyrkilis (2009) study the causal-order between capital inflows (proxied by inward

FDI) and economic growth using a panel data set for two different Economic Associations that is

EU (European Union) and ASEAN (Association of South Eastern Asian Nations) over the period

1970-2003. They investigate three possible cases in their paper (1) Growth-driven FDI, is the case

when the growth of the host country attracts FDI (2) FDI-led growth , is the case when the FDI

improves the rate of growth of the host country and (3) the two way causal link between them.

From the heterogeneous panel analysis they find the following: Regarding the EU countries the

results support the hypothesis of GDP -FDI causality (growth driven FDI) in the panel. Regarding

the ASEAN there is evidence that there is a two ways causality between GDP per capita and FDI

in the cases of Indonesia and Thailand while in the cases of Singapore and the Philippines FDI is

host country GDP growth motivated.

37

Sridharan et al (2009) in a study of Brazil, Russia, India, China and South Africa (BRICS)

economies, report bidirectional causation between openness to capital flows and GDP for Brazil,

Russia and South Africa and unidirectional (FDI leads growth) for India and China. Similarly, in

a panel study of China, Japan, India, South Korea and Indonesia Agrawal and Khan (2011) analyse

the impact of openness on GDP Growth using data for 1993 to 2011. Their results show a

unidirectional impact where FDI promotes economic growth, and further provides an estimate that

one dollar of FDI adds about 7 dollars to the GDP of each of the five countries investigated.

However, the study by Geogantopoulos and Tsamis (2011) report a unidirectional link running

from GDP to FDI but found no bidirectional causation between FDI and GDP in Greece.

Imene and Schalck (2010) study the causality relationship between financial flows/ development

and economic growth in Tunisia. They focused on the link between finance and growth according

to the maturity of financial systems. Their empirical study aimed to determine the direction of

causality and impact of the development of the Tunisian financial system on economic growth

within B-VAR framework. They find a reciprocal relationships between the ratio of investment on

GDP and the loans granted to private and public sectors. They conclude that the Tunisian economy

knew a long period of financial repression before starting several phases of liberalization. Thus,

the economic role of government is highlighted, over the pre-reforms period as well as during the

recent time.

Hanh (2010) use the Pedroni co-integration technique and the GMM estimator to investigate the

possible causal connection between financial development, financial openness and trade openness

in twenty-nine Asian developing countries over the period 1994-2008. They find an evidence of

bidirectional causality between financial openness and trade openness as well as between financial

development and trade openness. Furthermore, when they interact the financial development and

financial openness terms, they find on one hand, that the openness indicator (Gross Private Capital)

is positively related to the ratio of credit to the private sector to GDP (PRIVO) indicator, but does

not influence the ratio of liquid liabilities to GDP (LLY) indicator. On the other hand, they find a

bidirectional causality between FDI and PRIVO indicators and a unidirectional causality running

from LLY to FDI. They therefore argue that trade openness is necessary for attracting financial

openness (foreign capital flows) promoting the development of financial system. In the same vein,

38

financial openness and financial development is seen as an important condition for trade openness

to take place in developing countries.

Aslanoğlu and Deniz (2012) analyze the causal relationship between stability in openness and

growth for emerging economies, namely as, China, Brazil, India, Indonesia, Korea, Mexico,

Russia, Turkey and South Africa while selecting a developed country, the UK, as a benchmark.

They test for causality between stability in financial openness and growth under different

frequencies and find that stability in openness has a positive impact on economic growth. They

conclude that the impetus on growth is conditional on economy’s ability attract foreign funds and

adopt exchange rate stability.

Zakari et al (2012) examines the causal role of openness on growth (as measured by foreign direct

investment (FDI) and GDP), making a comparison between selected countries of Africa and Asia.

They utilized data for 30 countries, 15 each from Africa and Asia for the period 1990 to 2009.

First, they analyzed the aggregate data and later disaggregated the data into Africa and Asia in

order to assess the regional impact of FDI on economic growth. Their empirical results reveal that

FDI has positive relationship with GDP growth for both Africa and Asia. However, they report

evidence of one-way causality for Africa and no such evidence for Asia. They conclude that FDI

promotes economic growth and recommend for more openness of the economies.

Abdelhafidh (2013) investigates the direction of causality between financial openness, finance and

growth in North African countries over the period 1970-2008. The study use Trivariate VAR

models to disentangle the direct and indirect impact of financial development on growth,

distinguishing between domestic saving and foreign inflows. They also disaggregated foreign

inflows into FDI, portfolio investment, grants, and loans. The result indicates that economic

growth Granger-causes domestic saving in some of the countries. However, in Tunisia and

Morocco particularly, grants Granger-cause growth and while growth Granger-causes loans. Also,

they find that in Egypt, FDI, grants, short-term loans, long-term loans, bank loans, bilateral loans

and multilateral loans, all Granger-cause growth with a reverse causality running form growth to

foreign inflows. Furthermore, they show that in Algeria, grants and multilateral foreign loans and

39

bonds Granger-cause growth. Thus, they conclude that these results reveal that policy initiatives

and directions should be tailored to each case given the peculiarity of each country that was

investigated.

Ayadi and Arbak (2013) using a sample of northern and southern Mediterranean countries for the

years 1985-2009 investigates the causal relationship between openness, financial sector

development and economic growth. Their empirical results reveal that bank deposits and credit to

the private sector are negatively associated with growth. They argue that this result confirms

deficiencies in credit allocation in the region and also suggests weak financial regulation and

supervision. On the stock market side, they find that stock market size and liquidity play a

significant role in growth, especially when accounting for the quality of an institution. They also

argue that investment, whether domestic or in the form of FDI (openness to capital inflows),

contributes significantly to economic growth while low inflation and stronger institutions are key

growth factors. They however conclude that initial GDP has a persistently and significantly

negative impact on growth, which implies that poorer countries are catching up with richer

countries in terms of economic growth.

2.3.3 Financial Openness and Output Volatility

There are plethora of literature on financial openness and its effect on macroeconomic volatility.

However, understanding the effect of financial openness on output volatility is very important for

relatively poor countries such as Nigeria which are exposed to exchange rate and terms of trade

shocks owing to their dependence on basic commodities such as oil. The empirical relationship

between financial openness and output volatility is undeniable, making volatility a fundamental

development concern. A number of studies have tried to explain the nature and causes of this

relationship but there is no consensus yet.

Backus, et al. (1992) argue that, if most shocks are country-specific and transitory, financial

opening should lower consumption volatility while raising investment volatility. However, the

empirical literature cannot provide statistically significant evidence on the relationship between

financial openness and macroeconomic volatility (Razin and Rose, 1994). Kaminsky and

40

Schmukler (2003) observe that, although equity markets stabilize in the long run (i.e. in five years

or longer) if financial liberalization persists, the amplitudes of booms and crashes substantially

increase immediately following financial liberalization.

Kose, et al (2003) in a detailed study , provide a comprehensive examination of changes in

macroeconomic volatility in a large group of industrial and developing economies over the period

of 1960 − 1999. They find that on average, the volatility of consumption growth relative to that of

income growth has increased for more financially integrated developing economies in the 1990s.

They also report a threshold effect, where the adverse effects increasing financial openness

diminish for more developed countries. In another study, Prasad, et al (2003) compare the volatility

experiences of a sample of 22 more financially integrated developing countries and 33 less

financially integrated developing countries. They find that over the 1990s, the most financially

integrated have experienced some increase in consumption volatility while the less financially

integrated group of developing countries and the industrialized countries both experienced

average declines in consumption volatility relative to the previous decade.

Buch, et al (2005), using a panel dataset for OECD countries, find that the implications of financial

openness for business cycle volatility depend on the nature of the shocks and the link between

macroeconomic policy, financial openness, and business cycle volatility varies over time. They

further argue that developing economies are more vulnerable to external shocks due to some

structure features, e.g., limited diversification of foreign trade, sudden reversal of capital flows,

the small country size. These factors hamper the unbiased empirical estimation of the relationship

between financial openness and macroeconomic volatility. This is in line with Kose (2002) which

shows in a dynamic small-open-economy model that terms of trade shocks can explain a sizeable

fraction of volatility.

Hagen and Zhang (2006) develop a model of a small open economy and show that financial

openness has non-monotonic relationship with macroeconomic volatility. After pooling the

empirical data of countries with different degrees of financial openness, they conclude that

domestic financial frictions may explain the lack of strong empirical evidences on the significant

linear relationship between financial openness and macroeconomic volatility. In another study,

41

Giovanni and Levchenko (2006) show that countries that are more open to trade tend to be more

volatile than others. They argue that this the outcome of counteracting forces. Two mechanisms

lead to a positive relationship: traded sectors are more volatile than nontraded ones, and trade leads

to specialization in fewer sectors. But traded sectors are less correlated with the rest of the economy

and so can act hedging activities.

Prati and Tressel (2006) find that foreign aid volatility increases trade balance volatility and

depresses exports through a Dutch-diseases mechanism. They argue that these effects could be

mitigated by actively managing the central bank’s net domestic assets. Levchenko and Mauro

(2007), concerned with the detrimental effects of sudden stops, reveal that countries with a more

diversified portfolio of foreign liabilities and a higher share of foreign direct investment tend to

fare better during capital-flow reversals.

Loayaz and Raddatz (2007) use a semi-structural vector autoregressions on a panel of countries to

study how financial openness, trade openness, factor-market flexibility, product-market flexibility

and domestic financial development influence the impact of terms of trace shocks on output. They

find that financial openness and labour market flexibility appear to reduce the impact of external

shocks. On the other hand, they find that trade openness increase the output consequences of terms-

of-trade shocks, especially when domestic markets are not well developed. These findings are

consistent with the finding of Bruner and Ventura (2006), who studied how trade integration can

lead to financial instability using a model of endogenously incomplete market. In the study, they

argued that trade integration can have different effects depending on domestic financial markets.

If those markets are thin, trade integration destroys risk-sharing and lowers welfare. If those

markets are deep, trade integration allows for better risk-sharing, thus raising welfare.

Furthermore, empirical analysis by Aguiar and Gopinath (2007) suggest that Emerging Market

Economies (EMEs) are vulnerable to sudden stops in capital inflows, and that these economies are

twice as volatile as that of industrial countries. Not surprisingly, if one looks at historical data, the

volatility of developing countries’ real GDP is at least about thirty percent higher than that of the

OECD countries. This aggregate volatility, in turn, has severe implications at the micro-level, and

particularly for the poor who are the least equipped to weather these aggregate shocks and are

42

therefore likely to face the brunt of its harmful impact. Another empirical study by Calderón and

Yeyati (2007) suggests that inequality increases with economic volatility—a doubling in aggregate

income volatility (measured as the standard deviation of per capita GDP) leads to a 2.7 percent

increase in the Gini coefficient, a 2.4 percent reduction in the income share of the poorest quintile,

and a 1.1 percent increase in the income share of the richest quintile.

Also, Kose, et al (2008), in another study, examined the risk-sharing implication of financial

integration by focusing on the cross-country correlations of output and consumption. They find

that there is no evidence that financial globalization fosters increased risk-sharing across all

countries, including the developing countries.

Ahmed and Suardy (2009), examine the effects of both financial and trade liberalization on real

output and consumption growth volatility in Sub-Saharan Africa. They find that trade liberalization

is associated with higher output and consumption volatility while financial liberalization is

observed to increase the efficacy of consumption smoothing and stabilize income and consumption

growth. They conclude that there is evidence that good institutions help reduce inflation levels and

volatility, which in turn promote lower growth volatility.

Udah (2010) argues that Macroeconomic uncertainty plays a key role in determining investment

behaviour in developing countries. Uncertainties arise from high and unstable inflation rate,

unstable fiscal deficits, overvaluation or depreciation and exchange rate misalignment.

Macroeconomic uncertainty or instability could also arise from political instability or poor

macroeconomic management. When the future is highly uncertain, investors take a ‘wait’ and ‘see’

attitude. At the microeconomic level firms may decide to limit their capacity in the face of

uncertainty in demand conditions, which leads to reduced investment capacity.

Mougani (2012) analyzes the impact of financial integration on economic activity and

macroeconomic volatility in Africa within the financial globalization contexts. The results of the

empirical analysis show that the impact of external capital flows on growth seems to depend

mainly on the initial conditions and policies implemented to stabilize foreign investment, increase

domestic investment, productivity and trade, develop the domestic financial system, expand trade

43

openness and other actions aimed at stimulating growth and reducing poverty. The analysis also

shows that financial instability was particularly severe as from the nineties. The instability was

more pronounced in the case of portfolio investments than in foreign direct investment because of

the longer-term relationship established by the latter.

Orji, et al (2013) in a recent study investigate the relative impacts of the uncertainty of

macroeconomic variables on investment using Generalized Autoregressive Conditional

Heteroscedasticity (GARCH) model. Their findings reveal the existence of long run relationship

between some of the macroeconomic variables and investment. And also, that the uncertainty of

most of the macroeconomic variables impact negatively on investment in Nigeria.

Conclusion

The arguments, simulations, and evidence in the foregoing papers seem not to agree on the exact

causal direction between financial openness and growth and also on the impact of financial

openness on growth and output volatility. Since the above issues that were raised in the literature

review still remains largely unsettled, this thesis has done more empirical investigations to unravel

the answers in the Nigerian context.

2.4 Shortcomings of Previous Studies

1. The issue of adopting an adequate measurement of financial openness remains unsettled in the

literature. Most of the papers used either de jure or de facto measures but in this study we are

adopting the two measures to see the financial openness measure that will be more robust and

significant for the Nigerian economy. This robustness check allows us to establish whether the

effect of openness on growth and volatility is equally strong for various measures of

liberalization.

2. The above studies did not investigate direction of causality between Financial Openness in

detail. And where efforts were made to do so, they were not based on the disaggregated de facto

financial openness measure in the Nigerian context as we have done in this study. (See for

44

example Ayadi and Arbak (2013), Choe, 2003, Osinubi and Amaghionyeodiwe 2010, Oyatoye,

et al 2011l and Ogbonna,et al, 2012).

3. Most of the papers did not address country specific issues. Most of the studies were based on

cross-country analysis (for example see Quinn and Toyoda, 2008; De Mello, 1999). But in this

study, we are using Nigeria as a case study. Thus, engaging in a detailed country-specific

analysis.

4. Previous authors that ran cross country regressions on the effects of financial openness on

growth and volatility got conflicting results because of the number of countries estimated,

measure of liberalization used, time frame and other factors.

5. Several related studies reviewed neglected the role of institutional factors in their regression

estimation and analysis. However, in this study we are incorporating the role of governance in

our model to account for the peculiar institutional cum socio/political environment upon which

this research is based.

45

CHAPTER 3

METHODOLOGY

3.1 Theoretical Framework

The endogenous growth theory was constructed from the shortcomings of the neoclassical model

of economic growth, with Arrow (1962), Romer (1986) and Lucas (1988) being the key

contributors. In neo-classical growth models, the long run rate of growth is exogenously

determined by either the savings rate (as in the Harrod-Domar model) or the rate of technical

progress (as in the Solow Model). However, the source of the savings rate and the rate of

technological progress cannot be explained (Ghatak and Siddiki, 1999). Endogenous growth

models attempt to explain a greater proportion of observed growth as well as why different

countries experience different growth rates. They generally use the neoclassical model but allow

the production function to exhibit increasing returns to scale, focus on externalities and assume

that technological change, although important, is not necessary to explain long-run growth.

In trying to resolve the contending issues surrounding the neo-classical model, the endogenous

growth theorists construct macro-economic models out of micro-economic foundations. Thus,

households are assumed to maximize their utilities, subject to some budget constraints, while firms

maximize profits. In this sense, the most important aspect is usually attributed to innovation (the

invention of new technologies) and the human capital. The engine for growth can be as simple as

a constant return to scale production function (the AK model) or more complicated set ups with

spill-over effects (spill-overs are positive externalities, benefits that are attributed to costs from

other firms). For instance, Pagano (1993) uses an endogenous growth model which incorporates

human capital (L) in his study of financial markets, liberalization and growth. This is because

financial liberalisation leads to increase in the quality of human capital by financing education to

less endowed households in the society as Gregorio (1996) explains.

The endogenous growth theory holds the view that human capital is one of the main sources of

economic growth and development. This is a very important argument in the developing countries

due to the abundance of labour. The model put forward by Pagano (1993) predicts that financial

46

liberalization and openness will lead to increase in: (a) saving and investment; (b) the proportion

of saving that goes to investment and (c) the efficiency of investment as a result of improvement

competition as well as availability of information regarding the investment projects.

Using an AK version of endogenous growth model Pagano (1993) postulates that the three factors

aforementioned in turn increase the rate of economic growth. The extended model predicts that

there is an additional efficiency gain caused by the accumulation of human capital as a result of

financial liberalisation. To explain the model, assume that aggregate output is a linear function of

aggregate capital stock.

Yt = AKt - - - - . -(3.1)

where Yt is aggregate output, Kt is the aggregate capital stock and t is time. This production

function represents a competitive economy with the presence of externality or spill-over effects

(Ghatak and Siddiki, 1999). Each firm faces constant returns to scale, but the economy as a whole

shows increasing returns to scale with respect to Kt.

Furthermore, suppose that the population is constant and the economy produces a single

commodity which can either be consumed or be invested. Also, assume that the rate of amortisation

of capital stock is zero and gross investment is:

It = Kt+1 - Kt

Kt+1 = It + Kt - - - - - - - (3.2)

This is assumed to be a closed economy with only one-sector and no government. If we assume

that financial intermediaries channel a proportion φ of saving, St, to investment, It (i.e. a proportion

(1 - φ) of saving is lost through the process of intermediation and does not go directly to

investments. On the basis of this, the capital / money market equilibrium condition can be

expressed as:

ψSt = It - - - - - - (3.3)

Using equations (3.1) and (3.2), the growth rate (g) at time t+1 can be written as:

47

gt+1 = (Yt+1 – Yt)/Yt = (AKt+1 – AKt)/AKt = Kt+1/Kt – 1

gt+1 = (It + Kt)/Kt -1 = It/Kt = AIt/AKt - - - - - (3.4)

where gt+1 is the growth rate of output at time t+1 and the steady state is defined as :

Kt = Kt+1 = K; Yt = Yt+1 = Y; gt = gt+1 = g. Substituting equation (3.3) into

equation (3.4) the steady state growth rate (g) can be written as follows:

g = A (I/Y) = Aψs - - - - - - (3.5)

where s is S/Y. Taking the logarithms of equation (3.5), it can be expressed as:

Ln g = Ln A + Ln ψ + Ln s - - - - - (3.6)

Equation (3.6) shows the growth rate as a linear function of its determinants and channels through

which financial liberalization policies affects growth (A, ψ, s.) Our empirical model is therefore

based on this relationship.

3.2 THE MODELS

3.2.1 MODELS FOR OBJECTIVE 1

1. Modeling the Impact of Financial Openness on Economic Growth

Here, following Ozdemir and Erbil (2008) we employ two different measures of financial

openness. The first category refers to the de facto measure of financial openness. This measure is

price-based. Following Aizenman (2004 and 2008) and Aizenman and Noy (2009), the de facto

measure of financial openness can be used as a variable to measure the actual observed outcomes

of the enforcement of existing regulations on financial flows.

The second category is the de jure measure of financial liberalization. De jure measures are quality

based measures which concentrate on events such as changing regulations and the response of the

monetary authorities to financial flows.

Growth Regression Model with De facto and De Jure Financial Openness Measures

Equation (3.6) distinguishes three channels: ψ, s and “A” (Improvement in financial

intermediation, Savings, efficiency of capital stock), through which financial liberalisation policies

48

could influence economic growth. Using endogenous growth theory, this study examines a

modified version of the growth model used by Ozdemir and Erbil (2008) where the growth rate of

real GDP per capita is regressed on other financial sector indicators and other variables. Others

who have used similar models include Fowowe (2002) and Owusu (2012). But we differ by

including the De jure (FODJ) variable using the Chinn-Ito Index and by adding institutional

(Governance) Index. Thus, accounting for the peculiar political/ institutional environment upon

which this research is based.

Expanding Equation (3.6) and adding other relevant variables of interest we have:

The De facto Financial Openness and Growth Equation

퐿푌푃퐶 = 훼 + 훼 퐿푃푆퐶 + 훼 푅퐼푁푇푅 + 훼 퐻푀퐿 + 훼 퐿푀퐾푇퐶퐴푃 + 훼 퐹푂퐷퐹 + 훼 푅퐸푋퐶푅

+ 훼 퐼푁푆푇 + 휇 … … … … … … … … … … … … … … … … … . … … … . (3.7푎)

The De jure Financial Openness and Growth Equation

퐿푌푃퐶 = 훼 + 훼 퐿푃푆퐶 + 훼 푅퐼푁푇푅 + 훼 퐻푀퐿 + 훼 퐿푀퐾푇퐶퐴푃 + 훼 퐹푂퐷퐽 + 훼 푅퐸푋퐶푅

+ 훼 퐼푁푆푇 + 휇 … … … … … … … … … … … … … … … … … . … … … . (3.7푏)

where:

훼 . … …훼 =Parameter estimates

L= Natural log operator

µ= Error term

The above growth equation also has the following variables:

LYPC= Real GDP per capita growth rate (proxy for economic growth) following Edison (2002),

Alfaro et al (2004), Bekaert et al (2005), Coricelli et al (2008), Odhiambo (2009) and Owusu

(2012).

PSC= Credit to the private sector

This captures the improvements in the banking sector. It is expected that improvements in financial

intermediation will affect economic growth positively (Levine 2008)

49

RINTR= Real Interest Rate

Liberalisation of interest rate, according to McKinnon-Shaw hypothesis, leads to increase in

savings then increase in investments and ultimately leading to increase in economic growth. Using

a simple aggregate production function framework, Montiel (1995) shows that interest rate

liberalisation can alter the economic growth rate through three main channels: (i) increase in

investment resulting from the increase in savings rate; (ii) improvement in the efficiency of capital

stock and (iii) improvement in the financial intermediation.

HML= Human Labour (Proxied by Secondary school enrolment rate)

To improve the efficiency of capital requires human effort and this has been captured by including

capital stock (K) and a labour factor (L) in equations (3.6 and 3.7a&b). This is because the

endogenous growth theory posits that human capital is one of the main sources of economic

growth, especially in the developing countries. Human Labour (HML) and especially trained

labour, is expected to enhance productivity by giving incentives for innovation (Owusu, 2012).

The measure for labour is proxied by the secondary enrolment rates, which is defined as the ratio

of the number of enrolment at secondary schools to the total population (Shabhaz et al., 2008).

MKTCAP= Market Capitalization

This represents the total market capitalization of All Shares traded on the floor of the Nigerian

Capital Market within the period under review. Capital Market Liberalization has been emphasized

in the literature as one of the core areas of financial liberalization. Thus, we expect a positive

relationship between capital market liberalization and growth. Beck et al. (2000) in their study

outline three key stock market indicators in measuring size, activity, and efficiency. The ratio of

stock market capitalization to GDP (MC) measures the size of the stock market because it

aggregates the value of all listed shares traded in the stock market. They emphasize that one can

assume that the size of the stock market is positively correlated with the ability to mobilise capital

and to diversify risk. To measure stock market liquidity/activity and efficiency, they also used the

value of stock traded to GDP variable (VT) and Turnover Ratio (TR) respectively.

FODJ= De jure Financial Openness measured by Chinn-Ito Index. We use this index because of

its wide acceptability and it is available for a long period (up to 1970-2011) for over 182 countries

50

of the world including Nigeria. As earlier stated, the construction of the Chinn-Ito index is based

on the first principle component of four binary variables in IMF’s Annual Report on Exchange

Arrangements and Exchange Restrictions (AREARER) and it takes higher values for more open

financial regimes. These four variables are defined as follows: K1 is the variable that indicates the

presence of multiple exchange rates; K2 is the variable that indicates restrictions on current account

transactions; K3 is the variable that indicates the restrictions on capital account transactions; and

K4 is the variable that indicates requirements of the surrender of export proceeds1.

FODF = Financial Openness de facto measures. Here we use total capital flow as a ratio of GDP

to capture our degree of de facto Financial Openness. The sum of FDI, portfolio investments and

other investments make up the capital flows, (Aizenman and Noy, 2009). According to the World

Bank, “Gross private capital flows are the sum of the absolute values of direct, portfolio, and other

investment inflows and outflows recorded in the balance of payments financial account, excluding

changes in the assets and liabilities of monetary authorities and general government”.

In line with the endogenous theory, we also expect a positive relationship since this variable also

captures capital stock/ effects of external investment inflows (Sanchez-Robles and Bengoa-Calvo,

2002).

REXCR=Real Exchange Rate. We expect a negative relationship with growth since a rise in

foreign currency against the local currency affects foreign exchange demand which equally affects

capital imports and exports, investments and growth ultimately. Ozdemir and Erbil (2008)

INST= Institutional Quality Index (Proxy for Governance)

This variable helps us to measure the socio-political environment in which this study is based. We

measured this Index based on the data collected by the World Bank and other relevant bodies like

Political Risk Group for different countries including Nigeria. We expect sound governance which

is exemplified by respect for the rule of law to contribute positively to economic growth.

1For more detailed discussion on the construction of the Chinn-Ito Index see Chinn and Ito (2012). We justify the use of this index owing to its wide acceptability and availability.

51

THE AUTO REGRESSIVE DISTRIBUTED LAG (ARDL) MODEL

The Auto Regressive Distributed Lag (ARDL) Model which uses a bounds test approach based on

unrestricted error correction model (UECM) was employed here to measure the impact of

Financial Openness on Economic Growth and to test for a long run relationship among the relevant

variables. This model was developed by Pesaran and Pesaran (1997) and used by Pesaran, et al

(2001); Masron (2009); Owusu (2012), among others. The main advantage of this approach lies in

the fact that it can be applied irrespective of whether the variables are I (0) or I (1). This approach

also allows for the model to take a sufficient number lags to capture the data generating process in

a general-to-specific modelling framework. Although, a dynamic error correction model (ECM)

can be derived from ARDL through a simple linear transformation, Banerjee et al., (1998) and

Pesaran et al., (2001), have introduced bound testing as an alternative to test for the existence of

cointegration among the variables. The bounds test procedure is merely based on an estimate of

unrestricted error correction model (UECM) using ordinary least squares estimator. Tang (2003)

argues that the UECM is a simple re-parameterization of a general ARDL model. Also following

Shrestha and Chowdhury (2007), to illustrate the ARDL modelling approach, the unrestricted error

correction model of equation (3.7a&b) respectively is:

∆퐿푌푃퐶 = 훼 + 훿 퐿푌푃퐶 + 훿 퐿푃푆퐶 + 훿 푅퐼푁푇푅 + 훿 퐻푀퐿 + 훿 퐿푀퐾푇퐶퐴푃 + 훿 퐹푂퐷퐹

+ 훿 퐸푋퐶푅 + 훿 퐼푁푆푇 + 훼 ∆퐿푌푃퐶 + 훽 ∆퐿푃푆퐶 + 훾 ∆푅퐼푁푇푅

+ 퓂 ∆퐻푀퐿 + 휉 Δ퐿푀퐾푇퐶퐴푃 + ℊ퓃 ∆퐿퐹푂퐷퐹 퓃 + 휙 Δ푅퐸푋퐶푅

+ Ω퓏 ∆퐼푁푆푇 퓏 + ὠ… . . (3.8푎)

52

∆퐿푌푃퐶 = 훼 + 훿 퐿푌푃퐶 + 훿 퐿푃푆퐶 + 훿 푅퐼푁푇푅 + 훿 퐻푀퐿 + 훿 퐿푀퐾푇퐶퐴푃 + 훿 퐹푂퐷퐽

+ 훿 퐸푋퐶푅 + 훿 퐼푁푆푇 + 훼 ∆퐿푌푃퐶 + 훽 ∆퐿푃푆퐶 + 훾 ∆푅퐼푁푇푅

+ 퓂 ∆퐻푀퐿 + 휉 Δ퐿푀퐾푇퐶퐴푃 + 휑퓂 ∆퐹푂퐷퐽 퓂 + 휙 Δ푅퐸푋퐶푅

+ Ω퓏 ∆퐼푁푆푇 퓏 +ὠ… . . (3.8푏)

The terms with the summation signs in equations (3.8a&b) represent the Error Correction Model

(ECM) dynamics and the coefficients 훿 are the long run multipliers corresponding to long run

relationship (Poon, 2010). 훼 푎푛푑ὠ represent the constant and the white noise respectively. Δ is

the first difference operator while 푝푎푛푑푞are the lag length for the UECM. We conduct an F-test

for a joint significance by using ordinary least square (OLS) technique. As stated earlier, the

ARDL-UECM process will indeed enable us test the existence of long run relationships for the

model above.

3.2.2 Objective 2- Modeling the Direction of Causality between Financial Openness and

Economic Growth

Aizenman and Noy (2009) study the causality and endogenous determination of financial openness

and trade openness. They construct a theoretical framework leading to two-way feedbacks between

financial openness and trade openness. But we differ from Aizenman (2004) and Aizenman and

Noy (2009) by using a disaggregated measure of de-facto financial openness. Aizeman (2004) uses

a composite measure of financial openness but we use a disaggregated de facto openness (Ratio of

FDI to GDP) because various components of financial openness might be determined by different

variables.

Testing for the Direction of Causality between Financial Openness and Economic Growth

The issue of causality relationship as proposed by Granger (1963) is useful in analyzing how an

economic time series can be used to forecast another. Thus, a variable 푋 is said to Granger-cause

another series푌 , if given the past of푌 , past values of 푋 can help forecast푌 . According to Gujarati

(2004) the Granger causality test assumes that the information relevant to the prediction of the

53

respective variables in a given model is contained solely in the time series data of these variables.

Generally, it is important to note that since the future cannot predict the past, if variable 푋

Granger- causes variable푌 , then changes in 푋 should precede changes in푌 .Therefore, in a

regression of 푌 on other variables (including its own past values), if we include past or lagged

values of 푋 and it significantly improves the prediction of푌 , then we can say that 푋 granger-

causes푌 . A similar definition applies if 푌 granger-causes푋 . Thus, the model for our second

objective involves the following pair of regressions:

푌푃퐶 = 훼 퐹푂퐷퐹 + 훽푗 푌푃퐶 + 휇 ...(3.90)

퐹푂퐷퐹 = 휆 퐹푂퐷퐹 + 훿푗 푌푃퐶 + 휇 ...(3.91)

where:

훼 ,훽푗, 휆 ,훿푗 = 퐶표푒푓푓푖푐푖푒푛푡푠

FODF = De facto Financial Openness for Nigeria. As noted above Aizenman (2004) uses a

composite measure of financial openness but we use a disaggregated de facto openness (Ratio of

FDI to GDP) because various components of financial openness might be determined by different

variables. See Aizenman (2004:28)

YPC= Per Capita Gross Domestic Product (this captures the level of economic growth in the

country). A country with high and sustained growth rate is more likely to attract more capital

inflows. Thus, a positive relationship is expected between growth rate and financial openness. A

stable economic growth rate signifies a good economic performance and therefore is more

attractive to foreign investors (Sahoo, 2006)

In equations (3.90) and (3.91) it is assumed that the disturbances 휇 and 휇 are uncorrelated. In

the case of the causality equations above, we can distinguish four cases:

54

1. Unidirectional Causality from FODF to YPC is indicated if the estimated coefficients on the

lagged FODF in (3.90) are statistically different from zero as a group (i.e.∑휇 ≠ 0) and the set

of estimated coefficients on the lagged YPC in (3.91) is not statistically different from zero(i.e

∑훿 = 0).

2. On the other hand, unidirectional causality from YPC to FODF exists if the set of lagged FODF

coefficients in (3.90) is not statistically different from zero (i.e ∑휇 = 0 ) and the set of the

lagged YPC coefficients in (3.91) is statistically different from zero (i.e ∑훿 ≠ 0).

3. Bilateral causality or Feedback is suggested when the sets of FODF and YPC coefficients are

statistically different from zero in both regressions.

4. Finally, independence is suggested when the sets of FODF and YPC coefficients are not

statistically significant in both regressions.

However, it is pertinent to note that the application of the standard Granger test requires that the

variables YPC and FODF be stationary. Further to this, we apply the Error Correction Model

(ECM) to examine the short-run and long-run equilibrium adjustment dynamics of the variables.

Thus, we specify our Granger causality model based on the ECM framework as follows:

∆푌푃퐶 = 훼 ∆퐹푂퐷퐹 + 훽푗 ∆푌푃퐶 + 휛 Ѱ + 휇 ...(3.92)

∆퐹푂퐷퐹 = 휆 ∆퐹푂퐷퐹 + 훿푗∆푌푃퐶 + 휛 휑 + 휇 ...(3.93)

Where ∆푌푃퐶 and ∆퐹푂퐷퐹 are established first-differenced stationary, co-integrated time series.

Ѱ푡−1 and 휑푡−1 are lagged values of the error term and must be stationary if the first-differenced

∆푌푃퐶 and ∆퐹푂퐷퐹 series are co-integrated. The inclusion of Ѱ푡−1 and 휑푡−1 differentiates the

error correction model from the usual Granger causality regression.

3.3 JUSTIFICATION OF THE MODELS

One of the reasons for adopting the Autoregressive Distributed Lag (ARDL) methodology is that

it has been favoured by many researchers in recent years owing to its wide applicability

irrespective of whether the underlying regressors are purely I(0), purely I(1) or mutually co-

55

integrated. Engle – Granger (1987) co-integration analysis and Johasen (1991) maximum

likelihood are the most commonly used co-integration methodologies but due to the high predictive

power of the ARDL –Bounds testing methodology, it has become the choice of many researchers

in co-integration analysis in recent times (Shrestha and Chowdhury, 2007)

Another reason for using the ARDL approach is that is has been found to be more robust and

performs better for finite samples than other co-integration techniques. Furthermore, according to

Banerjee et al (1993), a dynamic error correction model (ECM) can be established from ARDL

through a simple linear transformation. The ECM integrates the short run dynamics with the long

run equilibrium without losing long run information. Shrestha and Chowdhury (2007) have also

argued that using the ARDL-Bounds testing approach mitigates against problems resulting from

non-stationary time series. Narayan and Narayan (2005) have also shown that the ARDL –Bounds

testing methodology based on the unrestricted error correction model (UECM) has numerous

advantages over the traditional co-integration methods.

The Granger causality model which we specified to address our second objective has also been

widely used in economic literature. The application of the standard Granger test involving two

variables 푋푡and 푌푡 requires that the variables be stationary. However, following Gupta and Komen

(2008), since most economic variables are non-stationary in level forms, the standard Granger

causality test is conducted using regressions based on differenced stationary variables. This

differencing process may throw away some useful long-run information about causal relationships

among the variables. It is therefore advisable to apply the ECM framework to examine the direction

of causality among the variables (Gupta and Komen 2008).

This advanced framework as provided by Granger (1983, 1986) and Engle and Granger (1987,

1991) suggests a more elaborate test of causality which is applied within the co-integration and

error-correction model (ECM). Engle and Granger (1991) proved that if the variables are

integrated of order I (1) and co-integrated, the standard Granger causality test is not appropriate,

as it does not consider the error correction term, which corrects the disequilibrium in the short-run.

This framework allows for a causal linkage between two variables which emanates from a common

trend or long-run equilibrium relationship. As it were, such causality may not be detected by the

standard Granger test which analyses short-run information given by the past changes in a variable,

56

푋푡 which helps in explaining current changes in another variable 푌푡.Another usefulness of the ECM

framework lies in its ability to detect the possibility of reverse causality or bidirectional causality.

It is also important to note here that in the ECM framework, the possibility of finding no causality

in either direction (as obtainable in the standard granger test) is ruled out. This is because as long

as 푋푡 and 푌푡have common trend (co-integrated), causality must exist in one direction at least.

3.4 UNIT ROOT TESTS

It is important to check each time series variable for stationarity or unit root before conducting the

co-integration test on specified models. The unit root test has to be conducted first because without

it, if the regression analysis is conducted in the traditional way and time series variables are found

to be non-stationary, the result will be spurious. Here we use the Augmented Dickey Fuller (ADF)

for the unit root tests. The ADF is unit root test for time series. It is shown in the equation below:

∆푌 = 훽 + 훽 푡 + 훿푌 + 훼 ∆푌 + 휀 ... . .(3.94)

where 푌 is the variable in question, 휀 is white noise error term and ∆푌 = (푌 −푌 ),

∆푌 = (푌 −푌 ), etc.

These tests are used to determine whether the estimated δ is equal to zero or not. The number of

lagged difference terms to include is often determined empirically, the idea being to include

enough terms so that the error term in (3.94) is serially uncorrelated. Fuller (1976) has compiled

cumulative distribution of the ADF statistics by showing that if the value of the calculated ratio of

the coefficient is less than critical value from ADF statistics, then Y is said to be stationary. Note

that we shall also conduct this test for our GARCH models specified below in sub-section 3.6.2.

3.5 COINTEGRATION TEST AND ESTIMATION PROCEDURE

3.5.1 THE ARDL-UECM PROCEDURE

There are various estimation techniques that can be adopted to evaluate co-integration

relationships among macro-economic variables. For instance, Engle and Granger (1987) can be

used for univariate co-integration analysis as well as Philips and Hansen (1990) procedure while

57

Johansen (1988) and Johansen and Juselius (1990) technique can be used for multivariate co-

integration analysis. The full information for maximum likelihood co-integration approach has

also been compiled by Johansen (1995).

However, this study adopts the newly proposed autoregressive distributed lag (ARDL) approach

popularized by Pesaran and Shin (1995), Pesaran et al (1996) and Pesaran et al (2001). We use the

ARDL – Bounds testing methodology to estimate the specified models and empirically analyse

the long run relationship and the dynamic interactions between the relevant variables. First, a test

of stationarity will be conducted and completed, and then the ARDL – Bounds testing approach to

co-integration analysis follows.

The ARDL-Bounds testing approach based on unrestricted error correction model (UECM)

technique also involves two stages. The first stage is to estimate the ARDL model of interest by

ordinary least square (OLS) in order to test for the existence of a long run relationship among the

relevant variables. This is done by constructing an unrestricted error correction model (UECM)

and then testing whether the lagged levels of the variables in each of the equations are statistically

significant or not. In other words, whether the null hypothesis of no long term relationship is

rejected or accepted. To achieve this, a Wald test (f-statistics version for bound-testing

methodology) for the joint significance of the lagged levels of the variables, i.e. testing the null

hypothesis against the alternative is performed. If the F-statistics is above the upper critical value,

the null hypothesis of no long run can be rejected, irrespective of the orders of integration for the

time series.

Alternatively, if the statistics fall below the lower critical values, then the null hypothesis cannot

be rejected. However, if the F-statistics falls between the upper and lower critical values, then the

result is inconclusive. In this case the asymptotic distribution of the Wald test (F-statistics) is non-

standard under the null hypothesis of no co-integration between the variables of interest,

irrespective of whether the explanatory variables are purely I(0) or I(1).

The general UECM model is tested downwards sequentially by dropping the statistically non-

significant first differenced variables for each of the equations to arrive at a “goodness of fit”

equation using general-to-specific strategy. Once the long run relationship or co-integration has

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been established, the second stage of testing involves the estimation of the long run coefficients

(which represents the optimum order of the variables after selection by AIC or SBC) and then

deriving the associated error correction in order to calculate the adjustment coefficients of the error

correction term (Masih, et al, 2008). Therefore, the short run effects are captured by the

coefficients of the first differenced variables in the UECM model. Having done this, there is also

the need to perform a series of diagnostic tests on the stochastic properties established model. This

is because the existence of a long run relationship does not necessarily imply that the estimated

coefficients are stable (Bahmani-Oskooee and Brooks, 1999). This therefore, involves testing of

the residuals (i.e homoscedasticity, non- serial correlation, etc), as well as stability tests (i.e

Ramsey RESET and Cumulative Sum of Squares (CUSUM) tests) to ensure that the estimated

model is statistically robust.

It is also pertinent to note here that one of the arguments against the ARDL is that the estimators

will be inefficient and biased (or even inconsistent) in the presence of autocorrelation of the

disturbances (Feeny, 2005). However, Pesaran and Shin (1999) show that appropriately modifying

the orders of the ARDL model is adequate to simultaneously correct for residual serial correlation

and the problem of endogenous regressors, thus giving ARDL an advantage over other approaches

to cointegration. This is also justified by Harris and Sollis (2003) and Constant and Yue (2010).

In addition, Pesaran and Shin (1999), argue that endogeneity problems are addressed in this

technique by modeling the ARDL with the appropriate lags, thus correcting for both serial

correlation and endogeneity problems. Jalil et al (2008) in their study also show that endogeneity

is less of a problem if the estimated ARDL model is free of serial correlation. In this approach,

Khan el al, (2005) equally argue that where all the variables are assumed to be endogenous, the

long run and short run parameters of the model can be estimated simultaneously.

3.5.2 THE GRANGER CAUSALITY-ECM PROCEDURE

The steps involved in implementing the Standard Granger causality test based on our second

objective are as follows:

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1. We regress current YPC on all lagged YPC terms and other variables, if any, but we do not

include the lagged FODF variables in this regression. This is the restricted regression. From

this regression we obtain the restricted residual sum of squares, RSSR.

2. Now we run the regression including the lagged FODF terms. This is the unrestricted regression.

From this regression we obtain the unrestricted residual sum of squares, RSSUR.

3. The null hypothesis is H0:∑훼 = 0 that is, lagged FODF terms do not belong in the regression.

4. To test this hypothesis, we apply the F test given by Gujarati (2004) namely,

F = (RSSR − RSSUR)/m . . . . (3.95)

RSSUR/(n− k)

which follows the F distribution with m and (n− k) df. In the present case m is equal to the number

of lagged FODF terms and k is the number of parameters estimated in the unrestricted regression.

5. If the computed F value exceeds the critical F value at the chosen level of significance, we reject

the null hypothesis, in which case the lagged YPC terms belong in the regression. This is another

way of saying that FODF causes GDP.

6. We repeat Steps 1 to 5 to test model (3.93), that is, whether YPC causes FODF. In addition to

this we shall also estimate the Granger causality model based on the error correction mechanism

(ECM) as specified in models (3.92) and (3.93). This follows an important theorem, known as the

Granger representation theorem, which states that “if two variables Y and X are cointegrated, then

the relationship between the two can be expressed as ECM” (Gujarati 2004: 825)

3.6 THE GARCH MODEL FOR OBJECTIVE 3

3.6.1 Modeling the impact of Financial Openness on Output Volatility

Our methodological framework here draws from the seminal work of Engle (1982), in which he

introduced the Autoregressive Conditional Heteroscedasticity (ARCH) to capture the issues of

volatility in financial time series analysis. Bollerslev (1986) introduced the Generalised

Autoregressive Conditional Heteroskedasticity (GARCH) which is an extension of Engle’s

original work. For the analysis of the impact of financial openness on output volatility, we employ

the volatility of GDP growth rate as a measure of output volatility and also adopt the GARCH

Model. Among others, we explore the relationship between the two measures to capture the

response of output volatility to both de jure and de facto openness. Greater financial liberalization

60

and larger capital inflows are expected to increase output volatility (Neumann and Ron, 2008).

However, the relationship may be more complicated in that financial liberalization and capital

inflows may not move in tandem or step-for-step with one another. For example, a country may

have limited access to foreign capital even if is financially open. Conversely, a country that is not

deemed to be financially open to capital by de jure measures may, in fact, have large capital flows

due to the circumvention of these capital controls. Thus, we explore the impact of the de jure and

de facto measures within the Nigerian context.

Before specifying the GARCH model, it is necessary to begin briefly with a review of the ARCH

and GARCH models. Engle (1982) developed a new class of stochastic process; the

Autoregressive Conditional Heteroskedasticity (ARCH) model which is a process where the

conditional variance is a function of lagged squared residuals. As earlier stated also, Bollerslev

(1986) introduced the Generalised Autoregressive Conditional Heteroskedasticity (GARCH)

which is an extension of Engle’s original work. It allows the conditional variance to be a function

of the lagged variance; i.e. it allows for both autoregressive and moving average (ARMA)

components in the heteroskedasticity variance. He showed that the GARCH model allows a better

representation of the volatility process while being more parsimonious.

3.6.2 THE MODEL

Lensik (2002) argued that the principal directions in evaluating volatility or uncertainty are: (i)

standard deviations of the variables, (ii) dispersion of the unpredictable part of a stochastic process,

(iii) Generalized autoregressive conditional heteroscedasticity (GARCH) model of volatility. Also,

according to Ahmed (2009), and Popov (2011) output volatility can be calculated as the standard

deviation of change in real output or real GDP. However we follow Fang and Miller (2012), to

specify a GARCH (1, 1) model of output volatility. This is a multivariate modeling approach of

GARCH where other explanatory determinants of output volatility are included in the variance

equation with lags.

We therefore specify our fundamental GARCH volatility process as:

휎 = 훼 + 훼 휇 + 훼 휎 .......(3.96)

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which says that the conditional variance of 휇at time t depends not only on the squared error term

in the previous time period [as in ARCH(1)] but also on its conditional variance in the previous

time period (Gujarati, 2004).

where 휎 equals the conditional variance (squared variance), given information available at time t.

훼 is the constant, and the 훼 refers to a first order ARCH term (i.e., news about volatility from the

previous period) and 훼 a first order GARCH term (i.e., persistent coefficient). The conditions that

훼 ≥ 0,훼 ≥ 0,푎푛푑훼 + 훼 < 1ensure the positive and stable conditional variances of 휇 . The

sum 훼 + 훼 , measures the persistence of shocks to the conditional variances. To estimate output

variability, we take the conditional standard deviation of RGDP growth rate (Y) in GARCH (1, 1)

order as specified in equation (3.97)

To estimate the determinants of output volatility, we include the lagged values of our growth rate

and our de jure/ de facto measures of openness in the variance equation, while controlling for other

exogenous shocks. Thus, we specify our new conditional variance equation as:

휎 푌 = 훼 + 훼 휇 + 훼 휎 + 훿 Ž ....(3.97)

where “Ž” is a vector of explanatory variables that could determine or influence output volatility.

These explanatory variables included in the variance equation are: (1) FODJV which is the de jure

financial openness volatility variable from Chinn-Ito (2012) (2) FODFV which is the de facto

financial openness volatility variable measured from gross capital flows. (3) TOTV which stands

for Terms of trade volatility. This is used as a proxy for external risk premium. (4) EXRV is

Exchange Rate Volatility. (5) INFV which is inflation volatility. According to Aizenman (2008)

and Ahmed (2009) the values of these volatility variables can also be calculated from their standard

deviations.

3.6.3 MODEL JUSTIFICATION

The generalized autoregressive conditional heteroscedastic (GARCH) model has gained a lot of

attention in the literature since the introduction by Bollerslev (1986). Various works on testing for

or modeling of time-varying volatility of stock market returns and other economic variables have

been dominated by this model. The model includes past variances in the explanation of future

62

variances, which allows to capture the patterns usually exhibited by many financial time series

such as volatility clustering and large kurtosis.

GARCH model allows for both autoregressive and moving average components in the

Heteroskedastic variance. Conditional volatility measures such as GARCH is a better proxy for

measuring volatility than a measure of unconditional volatility like moving average standard

deviations or sample variability. GARCH models make use of future as well as past information

on its construction. Serven (2003) pointed out that sample variability does not amount to

uncertainty, except when events are unpredictable. He stressed that sample variability may

overstate uncertainty by including not only truly unpredictable innovations to the variables of

interest, but possibly (cyclical) movement partly predictable from their own past. Greene, (2005)

further stressed that another important usefulness of the GARCH specification is that it allows the

variance to evolve over time in a way that is much more general than the simple specification of

the ARCH model. Byrne and Davis (2002) also pointed out that conditional volatility measures

such as the ARCH or GARCH model highlights periods of concentrated volatility or volatility

clustering which might be expected to maximize uncertainty. Hsieh (1989) found that GARCH (1,

1) model worked well to capture most of the stochastic dependencies in the times series. Based on

tests of the standardized squared residuals, he found that the simple GARCH (1, 1) model did

better at describing data than previous models he used.

3.6.4 METHOD OF ESTIMATION

The GARCH (1, 1) is a generalization of the ARCH (q) model proposed by Engle (1982) as a way

to explain why large residuals tend to clump together, by regressing squared residual series on its

lag(s). However, empirical evidence shows that high ARCH order has to be selected in order to

catch the dynamics of the conditional variance. Bollerslev (1986) proposed the Generalized ARCH

(GARCH) model as a solution to the problem with the high ARCH orders. The GARCH reduces

the number of estimated parameters from an infinite number to just a few. According to Brook and

Burke (2003), the lag order (1, 1) is sufficient to capture all the volatility clustering that is present

in a data ceteris paribus.

The GARCH (1, 1) estimation process involves some steps. The first step is to examine the time

series properties of the data before applying other appropriate modeling procedures. Secondly we

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need to estimate the model to obtain the residuals from the regression with which to test for ARCH

and GARCH features. The third step involves regressing the squared residual series and

conditional variance on their lags and on the other explanatory variables in the model.

3.7 DATA SOURCES

The data used in the study covers annual time series data from 1986 to 2011. We used Eviews

interpolation technique to convert them to quarterly series2. The sources of the data include various

issues of the Central Bank of Nigeria (CBN) statistical bulletins/ financial reports and National

Bureau of Statistics (NBS) publications. Others include: World Bank Publications and data from

organizations like Political Risk Services (PRS) Group, etc.

3.8 ECONOMETRIC SOFTWARE

Models one and two were estimated with Stata 11&13, while model three was estimated with

Eviews 6.0 econometric software package.The suitability of Eviews and Stata is enhanced by their

interactive nature, which makes them user-friendly, and time efficient in terms of output and

robustness of statistics generated. They are suitable for different kinds of regression model

estimations and data analysis.

22 As obtainable in time series econometrics, we converted our data to quarterly series in order to have sufficient number of observations to run our models. Cointegration analysis requires large number of observations which our annual data may not provide sufficiently due to the number of years studied.

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CHAPTER FOUR

PRESENTATION AND ANALYSIS OF RESULTS

In this section, we present the empirical results and analysis based on the specified models. As

discussed earlier, before we go ahead with the ARDL bounds testing, we shall first of all test for

the stationarity of all the variables that are going to be used in the analysis to ensure their order of

integration. That is, whether they are of order I (0) or I (1) stationary.

4.1 UNIT ROOT TESTS

Unit root tests and the order of integration

Tables 4.1 presents the summary of the unit root test results for the series in levels and in first

differences. The ADF lag length was selected automatically by Akaike Information Criteria

(AIC).The result indicate that apart from Log(PSC), HML and REXCR which is integrated of

order zero, all other variables were non-stationary since their absolute value of ADF statistic

exceeded the critical value only at first difference. Furthermore, the results in Table 4.1 indicate

that most of the variables become stationary at first difference and this enabled the use of the error

correction model in the autoregressive framework.

Table 4.1 Summary of ADF Unit root test results of the series

Variable Mackinnon Critical Values

Level ADF Test Stat

1st Difference ADF Test Stat

Order of Integration

Log(YPC) -2.601 1.723 -3.286* I(1) YPC -3.600 -3.514 4.595* I(1) Log (FODF) -3.452 -3.220 -6.973* I(1) FODF -3.222 -2.084 4.223* I(1) FODJ -3.451 -2.352 -3.603* I(1) Log(PSC) -3.286 -3.505* I(0) Log(MKTCAP) -2.601 2.087 -3.946* I(1) HML -3.150 -3.465* I(0) RINTR -3.452 -3.192 -5.123* I(1) REXCR -2.891 -3.604* I(0) INST -2.600 0.544 -3.728* I(1)

NOTE: * indicates significant at 5%, probability levels Source: Computed by the Author

65

The results of the stationarity tests show that most of the variables are non-stationary at level.

These results are shown in Table 4.1 above. Having established the vector of variables of concern,

the order of integration and stationarity of all the series was conducted using the Augmented

Dickey-Fuller (ADF) principal of establishing unit root. The ADF test was conducted on variables

in order to determine their stationary nature and those found non stationary were differenced to get

rid of the stochastic trend, a phenomenon associated with time series data.

4.2 Bounds test

To select the appropriate lag length for the first differenced variables, we adopted a general-to-

specific approach using an Unrestricted Vector Autoregressive means of Schwarz Bayesian

Criterion (SBC). The results however, show a maximum of 2 lag lengths. As argued by Pesaran

and Pesaran (1997), variables ‘in first difference are of no direct interest’ to the bounds

cointegration test. Hence, any result that supports cointegration in at least one lag structure

provides evidence for the existence of a long-run relationships. The calculated F-statistic together

with the critical bounds values are also reported. The ARDL bounds test is based on the assumption

that the variables are I(0) or I(1) as shown above in the unit root table.

We chose a maximum lag order of 2 for the conditional ARDL vector error correction model by

using the Akaike Information Criteria (AIC). The calculated F-statistics are reported in Table 4.2

when each variable is considered as a dependent variable (normalized) in the ARDL regressions.

From these results, it is clear that there is a long run relationship amongst the variables when Log

(YPC) is the dependent variable because its F-statistic (4.60) is higher than the upper-bound critical

value (3.50) at the 5% level. This implies that the null hypothesis of no cointegration among the

variables is rejected.

Table 4.2 Bound test for the estimation with De facto Financial Openness Variable

Dependent Variable F- Statistics Decision Log(YPC) 4.60 Co-integration Log(FODF) 5.25 Co-integration Log(PSC) 4.97 Co-integration Log(MKTCAP) 6.85 Co-integration HML 3.06 No Co-integration RINTR 5.94 Co-integration REXCR 3.90 Co-integration INST 1.55 No Co-integration

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Table 4.3 Bound test for the estimation with De Jure Financial Openness Variable

Dependent Variable F- Statistics Decision Log(YPC) 4.55 Co-integration FODJ 3.99 Co-integration Log(PSC) 2.09 No co-integration Log(MKTCAP) 4.38 Co-integration HML 3.93 Co-integrated RINTR 8.63 Co-integration REXCR 3.91 Co-integration INST 1.29 No co-integration

Critical values;

The value of our F-statistic is 4.50 and 4.55, and we have (k + 1) = 8 variables (YPC, FODF/FODJ,

PSC, MKTCAP, HML, RINTR, REXCR, and INST) in our model. So, when we go to the Bounds

Test tables of critical values, we have k = 7.

Critical Values

Table CI (iii) on p.300 of Pesaran et al. (2001) is the relevant table for us to use here. We haven't

constrained the intercept of our model, and there is no linear trend term included in the ECM. The

lower and upper bounds for the F-test statistic at the 5% significance level is [2.32, 3.50], i.e. I (0)

= 2.32 and I (1) = 3.50. As the value of our F-statistic exceeds the upper bound at the 5%

significance level, we can conclude that there is evidence of a long-run relationship between the

two time-series (at this level of significance or greater).

4.3 Estimation Results

Table 4.4: The ARDL Model for the De facto Financial Openness (Long-run) Dependent Variable Log (YPC) Variables Coefficient Std. Error T-statistics Probability Constant -0.46292 0.1874 -2.47 0.016 Log(YPC(-1)) -0.11487 0.0256 -4.47 0.000 Log(FODF(-1)) .030031 0.0129 2.32 0.023 Log(PSC(-1)) -0.04457 0.0196 -2.27 0.026 Log(MKTCAP(-1)) 0.08475 0.0220 3.85 0.000 HML(-1) 0.00241 0.0024 0.98 0.331 RINTR(-1) 0.00081 0.00055 1.45 0.152 REXCR(-1) -0.00077 0.0002 -3.75 0.000 INST(-1) 0.00836 0.0099 0.84 0.402

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Short-run D(Log(YPC(-1)) 0.25674 0.1035 2.48 0.015 D(Log(YPC(-2)) 0.15798 0.0907 1.74 0.086 D(Log(FODF)) 0.022213 0.0217 1.02 0.312 D(Log(PSC)) -0.052936 0.1010 -5.24 0.000 D(Log(MKTCAP)) 0.08320 0.0363 2.29 0.025 D(HML) 0.02664 0.0096 2.76 0.007 D(RINTR(-1)) -0.00058 0.00078 -0.74 0.459 D(REXCR) -0.00092 0.00047 -1.93 0.057 D(INST(-1)) -0.04186 0.0377 -1.11 0.271 R-squared = 0.6453

Adj R-Squared = 0.5674 F-Statistics = 8.29 F-prob = 0.0000

Results of diagnostic tests X2 Statistics Probability Breusch-Godfrey LM test for autocorrelation

0.607 0.4357

White Heteroskedasticity 2.96 0.0851 Ramsey RESET Test

3.32 0.0739

The ARDL results above depicts the following process; Long-run (1, 1, 1, 1, 1, 1, 1, 1) and Short-

run (1, 2, 0, 0, 0, 0, 1, 0, 1). However, it is important to note that the long run elasticities or

coefficients can then be generated from the ARDL-UECM by using the estimated coefficients of

the one lagged independent variables, multiplied by a negative sign, and divided by the estimated

coefficient of the one lagged dependent variable (Bardsen, 1989 and Tang, 2003). The short run

coefficients are then derived from the estimated coefficient of the first differenced variable in

ARDL-UECM models (Poon, 2010). We applied these methods in calculating the long run and

short impact for the de facto and de jure estimated results. However, the ECM results showing the

short run dynamics for the parsimonious ARDL models are presented in tables 4.5 and 4.7

respectively.

Thus, from table 4.4, we see that the long-run multiplier between Log (YPC) and Log (FODF) is

- (0.030031/ -0.11487) = 0.26. In the long run, an increase of 1 percent in Log (FODF) will lead

to an increase of 0.26 percent in Log (YPC). In addition, the long-run multiplier between Log

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(YPC) and Log (PSC) is -(-0.04457/ -0.11487) = -0.38, implying that in the long run, an increase

of 1 percent in Log(PSC) will lead to a decrease of 0.38 percent in Log (YPC). The long-run

multiplier between Log (YPC) and Log (MKTCAP) is - (0.08475/ -0.11487) = 0.73. This means

that in the long run, an increase of 1 percent in Log (MKTCAP) will lead to an increase of 0.73

percent in Log (YPC). The long-run multiplier between Log (YPC) and RINTR is - (0.00081/ -

0.11487) = 0.007. This means that in the long run, an increase of 1 percent in RINTR will lead to

an increase of 0.007 percent in Log (YPC).And the long-run multiplier between Log (YPC) and

REXCR is - (-0.00077/ -0.11487) = -0.006.Thus, in the long run, an increase of 1 unit in REXCR

will lead to a decrease of 0.006 percent in Log (YPC). Also the long-run multiplier between Log

(YPC) and INST is - (0.00836/ -0.11487) = 0.07

The short run and long run results reported in Table 4.4 clearly show that the de facto financial

openness (FODF) has a positive short run and long run impact on the economic growth (YPC) in

Nigeria. The coefficient of de facto financial openness is positive, as expected, as well as

statistically insignificant and significant in the short run and long run respectively. This suggests

that 1% increase in de facto financial openness leads to an increase of 0.02% in economic growth

in the short run and 0.26% in economic growth in the long run. This supports previous studies such

as Fratzscher and Bussierre (2004), Coricelli et al (2008), Loyayza and Ranciere (2006) to mention

a few which found long run relationship between economic growth and de facto financial

openness. However, those who found contrasting results include; Rodrick (1998), Eichengreen and

Leblang, (2003), Klein and Olivei (2008) among others.

Other variables included in the model such as, Market Capitalization (MKTCAP) and Human

Labour (HML), are also statistically significant and positively related to Economic growth in

Nigeria. Real interest rate is also found to have a positive relationship with economic growth.

Although this is not very significant but the results support the McKinnon-Shaw hypothesis, i.e.

in the long run interest rate liberalisation will ultimately lead to rapid economic growth.

It is also observed that the coefficient of credit to the private sector (PSC) has a negative sign both

in the short run and long run. This is contrary to expectation. However, this corroborates Obamuyi

(2009) which finds a negative relationship between private sector credit and economic growth.

The study attributes this finding to the fact that private sector credits are mainly used by some

69

borrowers to buy and sell instead of investing it into productive activities. Again, it has also been

discovered that many bank managers simply issue loans to their cronies and family members who

use the funds for other purposes rather than investing them productively. The coefficient may

suggest that 1% increase in the volume of credit to the private sector leads to a reduction of 0.05%

and 0.38% in economic growth in the short run and long run respectively.

Our institutional quality variable which represents governance and rule of law also shows some

interesting results. In the short run it reveals a negative relationship with economic growth but in

the long run we see a positive relationship between the two variables. Thus, in the short run 1%

change in the quality of institution will lead to 0.04% reduction in economic growth. While in the

long run, 1% change in the quality of institutions will affect economic growth positively by 0.07%.

This result attests to the fact that the present style of governance among the leaders has serious

negative impact on the growth of the Nigeria. This is exemplified by the fact that the principle of

rule of law is not respected, corruption has been enthroned in several leadership quarters, and there

is no internal democracy even among the political parties. When the quality of governance and

institution is weak, it simply translates to corruption, embezzlement of state funds meant for

infrastructural development, and several other anti -socio/economic outcomes. This finding

supports the study of Gupta, et al (2001), Tanzi and Davoodi (1997) among others.

In order to get the parsimonious model, we estimated the model by OLS, constructed the

residuals series, and then fitted a regular (restricted) ECM:

Table 4.5 Parsimonious ARDL-ECM for de facto financial openness

Dependent Variable D(Log(YPC)) Variables Coefficient Std. Error T-statistics Probability Constant 0.01716 0.0072 2.37 0.020 D(Log(YPC(-1)) 1.03055 0.1470 7.01 0.000 D(Log(YPC(-2)) -0.17376 0.0992 -1.75 0.084 D(Log(FODF)) 0.03096 0.0188 1.64 0.105 D(Log(PSC)) -0.36393 0.0801 -4.54 0.000 D(Log(MKTCAP)) 0.10294 0.0311 3.30 0.001 D(HML) 0.02424 0.0074 3.27 0.002 ECMt-1 -0.90778 0.1766 -5.14 0.000

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R-Squared = 0.5617 Adj. R-squared = 0.5287 F-Statistics = 17.03 F-prob. = 0.0000

Results of diagnostic tests

X2 Statistics Probability Breusch-Godfrey LM test for autocorrelation

2.690 0.1010

White Heteroskedasticity 6.97 0.8083 Ramsey RESET Test

3.89 0.8083

From table 4.5, we notice that the coefficient of the error-correction term (ECMt-1) is negative and

very significant. This is what we would expect if there is co-integration and long run relationship

between economic growth (log (YPC)) and other regressors. The magnitude of this coefficient

implies that nearly 91% of any disequilibrium between log (YPC) and other variables is corrected

within one period (one quarter). The ECM results also show that a change in de facto financial

openness (FODF) is associated with a positive change in economic growth (Log (RGDP)). Also,

the coefficient of D (Log (MKTCAP)) shows that a change in the stock market capitalization is

positively associated with change in economic growth and it is statistically significant at 5% level.

Furthermore, the coefficient of the change in the Human Labour (D (HML)) is positive and

statistically significant at 5% level. However, the coefficient of D (Log (PSC)) is negative and

statistically significant. This coefficient may suggest that the bulk of the credit extended to the

private sector by the banks and other financial institutions goes into mostly buying and selling of

imported finished consumer goods rather than production for domestic consumption in the real

economy and export to the outside world.

Table 4.6: The ARDL model for the De jure Financial Openness (Long-run)

Dependent Variable Log(YPC) Variables Coefficient Std.Error T-statistics Probability Constant .0148587 0.0873 0.17 0.869 Log(YPC(-1)) -0.10509 0.0250 -4.19 0.000 FODJ(-1) 0.02805 0.0117 2.38 0.019

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Log(PSC(-1)) -0.03035 0.0199 -1.53 0.131 Log(MKTCAP(-1)) 0.08263 0.0216 3.82 0.000 HML(-1) 0.00069 0.0026 0.27 0.790 RINTR(-1) 0.00006 0.0004 0.15 0.884 REXCR(-1) -0.00049 0.0002 -2.31 0.024 INST(-1) 0.00357 0.0096 0.37 0.712

Short-run D(Log(YPC(-1)) 0.23126 0.0985 2.35 0.021 D(Log(YPC(-2)) 0.12609 0.0869 1.45 0.150 D(FODJ) 0.02641 0.0489 0.54 0.591 D(Log(PSC)) -0.056525 0.0971 -5.82 0.000 D(Log(MKTCAP)) 0.07409 0.0366 2.02 0.047 D(HML) 0.03196 0.0097 3.27 0.002 D(RINTR(-1)) -0.00029 0.0007 -0.39 0.701 D(REXCR) -0.00049 0.0005 -0.98 0.328 D(INST(-1)) -0.08324 0.0300 -2.77 0.007 R-squared = 0.6348

Adj R-Squared = 0.5600 F-Statistics = 8.49 F-prob. = 0.0000

Results of diagnostic tests X2 Statistics Probability Breusch-Godfrey LM test for autocorrelation

2.044 0.1528

White Heteroskedasticity 3.81 0.0509 Ramsey RESET Test 2.93 0.3084

The ARDL-UECM results above depicts the following process; Long-run (1, 1, 1, 1, 1, 1, 1, 1)

and Short-run (1, 2, 0, 0, 0, 1, 0, 0). Again, following our initial calculations for our de facto results

in table 4.4, we can also see from table 4.6, that the long-run multiplier between Log (YPC) and

(FODJ) is - (0.02805/ -0.1051) = 0.26. In the long run, an increase of 1unit in FODJ will lead to

an increase of 0.26 percent in Log (YPC). In addition, the long-run multiplier between Log(YPC)

and Log(PSC) is -( -0.03035/-0.1051) = -0.28, implying that in the long run, an increase of 1

percent in Log(PSC) will lead to a decrease of 0.28 percent in Log (YPC). Furthermore, the long-

run multiplier between Log (YPC) and Log (MKTCAP) is - (0.08263/ -0.1051) = 0.78. This means

that in the long run, an increase of 1 percent in Log (MKTCAP) will lead to an increase of 0.78

percent in Log (YPC). And in the long-run multiplier between Log (YPC) and REXCR is - (-

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0.0005/ -0.1051) = 0.004. In the long run, an increase of 1 unit in REXCR will lead to a decrease

of 0.004 percent in Log (YPC).

The short run and long run results reported in Table 4.6 equally show that the de jure financial

openness (FODJ) has a positive short run and long run impact on the economic growth (YPC) in

Nigeria. The coefficient of de jure financial openness is positive, as expected, as well as

statistically insignificant and significant in the short run and long run respectively. The result

equally suggests that 1% increase in de jure financial openness leads to an increase of 0.02% in

economic growth in the short run and 0.26% in economic growth in the long run. This supports

previous studies such as Quinn (1997), Bekaert et al (2005), Chinn and Ito (2005 and 2006) to

mention a few which found long run relationship between economic growth and de jure financial

openness. However, Ozdemir and Erbil (2008) found a negative impact of de jure financial

openness measure on growth.

Other variables included in the model such as, Market Capitalization (MKTCAP) and Human

Labour (HML), also have positive relation with Economic growth in Nigeria and statistically

significant in Nigeria.

However, it is also observed here that the coefficient of credit to the private sector (PSC) has a

negative sign both in the short run and long run. This is contrary to expectation. But this has

confirmed that high interest rate and excessive government borrowing are making private credit

inefficient and detrimental to growth; and that public expenditure is crowding out private sector

investment. This also reveals the problem of huge non-performing loans, and corporate

governance deficiencies of some lending banks, supporting the finding of Abubakar and Gani

(2013) and Nkoro and Uko (2013).

In order to get the parsimonious model, we estimated the model by OLS, constructed the

residuals series, and then fitted a regular (restricted) ECM:

Table 4.7: Parsimonious ARDL-ECM for de jure financial openness Dependent Variable D(Log(YPC)) Variables Coefficient Std. Error T-statistics Probability Constant 0.01924 0.0073 2.61 0.011 D(Log(YPC(-1)) 1.20877 0.2028 5.96 0.000 D(Log(YPC(-2)) -0.28793 0.1165 -2.47 0.015

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D(FODJ) 0.05619 0.0445 1.26 0.209 D(Log(PSC)) -0.40684 0.0797 -5.10 0.000 D(Log(MKTCAP)) 0.07527 0.0333 2.26 0.026 D(HML) 0.02057 0.0074 2.75 0.007 D(RINTR(-1)) 0.00028 0.0007 0.37 0.712 D(REXCR) -0.00025 0.0003 -0.68 0.496 D(INST) 0.06375 0.0292 2.18 0.032 ECMt-1 -0.8905 0.2109 -4.22 0.000 R-Squared = 0.5814 Adj. R-squared = 0.5349

F-Statistics = 12.50 F-prob. = 0.0000

From table 4.7, we also notice that the coefficient of the error-correction term (ECMt-1) is negative

and very significant. This is what we should expect if there is co-integration between log (YPC)

and other regressors. The magnitude of this coefficient implies that nearly 90% of any

disequilibrium between log (YPC) and other variables is corrected within one period (one quarter).

The ECM results also show that a change in de jure financial openness (FODJ) is associated with

a positive change in economic growth (Log (RGDP)). Also, the coefficient of D (Log (MKTCAP))

shows that a change in the stock market capitalization is positively associated with change in

economic growth and it is statistically significant at 5% level. Furthermore, the coefficient of the

change in the Human Labour (D (HML)) is positive and statistically significant at 5% level.

However, the coefficient of D (Log (PSC)) is negative and statistically significant. This coefficient

may suggest that the bulk of the credit extended to the private sector by the banks and other

financial institutions goes into mostly buying and selling of imported finished consumer goods

rather than production for domestic consumption in the real economy and export to the outside

world. Some of the credits also end up in the pockets of the cronies of the bank managers who

neither use the loans for productive purposes nor service the loans as at when due. Thus, we have

the perennial problem of bad loans that have become detrimental to the banking system’s ability

to purposefully finance private sector investments.

Table 4.8 Results of diagnostic tests

X2 Statistics Probability Breusch-Godfrey LM test for autocorrelation

2.501 0.1137

White Heteroskedasticity 4.24 0.0395 Ramsey RESET Test 5.58 0.1015

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4.4 Further Interpretation and Explanations of Model Parameters

4.4.1 The long run model for economic growth and de facto financial openness (ARDL-

UECM)

Using a log model, the effects of financial openness on economic growth was modeled and results

were presented in Table 4.4. Observations made from the table indicate that from the long run part

of the ARDL-UECM, private sector credit and exchange rate affect economic growth negatively

while de facto financial openness indicator (FODF), market capitalization, interest rate,

institutional quality and human labour impacted positively on economic growth.

Further analysis of results of Table 4.4 indicate that, the positive association of financial openness

and GDP with market capitalization imply that size of the stock market dominated by money

lending organisations like banks, finance companies, insurance companies, micro-finance

institutions and other money suppliers boost economic growth. The capital base of these financial

institutions is also a key determinant of financial openness. This also implies that the financial

openness policies put in place as directed by the IMF in 1980’s have been favourable in increasing

the level of economic activities at the stock market, hence leading to an increase in market

capitalization.

Also from the model, interest rate is found to have a positive relationship with economic growth,

but this effect was found to be statistically insignificant in the long run. This result is expected

since interest rate is supposed to have a positive relationship with savings which is a key driver of

economic growth. This finding however, agrees partly with Perera’s case of financial liberalisation

where he found that interest rate and real gross domestic product impacted positively on money

demand while financial liberalization had negatively impacted on both M1 and M2 in a study of

impact of financial liberalization on money demand and economic growth in Sri lank (Perera,

2005).

The results above further indicate that financial openness positively affects economic growth while

private sector credit negatively affects economic growth. This agrees with the findings of

Odhiambo (2009) in a similar study carried out on South Africa.

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4.4.2 The long run model for economic growth and de jure financial openness (the ARDL- UECM)

Also using a log model, the effects of de jure financial sector openness on economic growth was

modeled and results were presented in Table 4.6. Results from the long-run part of the model

indicate that de jure financial openness indicator (FODJ), market capitalization, real interest rate

and human labour impacted positively on economic growth, while private sector credit, real

exchange rate and institutional qualities affect economic growth negatively.

The results here clearly show that de jure financial openness, market capitalization and real interest

rate affect economic growth positively while private sector credit negatively affects economic

growth. The implications of these result outcomes have been discussed above. The model also

shows that about 64 percent variation in real gross domestic product is explained by the covariates

here considered. This is significant as indicated by its F-statistic of 8.49 and its probability of 0.00.

4.5 Interpretation of the Error Correction Models and Results for economic growth and de facto

financial openness

4.5.1 The short run dynamics and de facto financial openness

The short run dynamic model was estimated by the restricted ARDL ECM procedure. The levels

of the UECM ARDL model was estimated by OLS where the residuals series was constructed, we

then fitted a regular (restricted) ECM. The maximum lag was established by the minimum AIC

which minimizes the standard errors. The estimated OLS error correction terms measured the

transitory deviations from the steady state equilibrium value of each variable present in the long

run relationship. The coefficient of the error correction term in this case measures the speed of

adjustment from the short run to the long run equilibrium.

Parsimonious Restricted ARDL-ECM (1, 2, 0, 0, 0, 0, 0) Dependent Variable D(Log(YPC)) Variables Coefficient Std.Error T-statistics Probability Constant 0.01716 0.0072 2.37 0.020 D(Log(YPC(-1)) 1.03055 0.1470 7.01 0.000 D(Log(YPC(-2)) -0.17376 0.0992 -1.75 0.084 D(Log(FODF)) 0.03096 0.0188 1.64 0.105 D(Log(PSC)) -0.36393 0.0801 -4.54 0.000 D(Log(MKTCAP)) 0.10294 0.0311 3.30 0.001 D(HML) 0.02424 0.0074 3.27 0.002 ECMt-1 -0.90778 0.1766 -5.14 0.000

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R-Squared = 0.5617 Adj. R-squared = 0.5287 F-Statistics = 17.03 F-prob. = 0.0000

Results from the table above suggest that the current value of financial openness has a positive

impact on economic growth (although it is not significant).But the first lag of GDP per capita,

current value of private sector credit, market capitalization and human labour significantly affect

economic growth, although the second lag of GDP per capita is insignificant. All variables are here

considered significant at 5 percent level. The coefficient of ECMt-1 (-0.908) is significantly

different from zero and bears the right sign thus validating the existence of cointegration in the

system. Thus, it indicates that when an external shock disturbs the equilibrium condition of

economic growth, about 91 percent of it is absorbed within one period (i.e one quarter in this

study).

In view of the table above and as regards significance of the model, the F-statistic and its

probability justify that it is highly significant and thus reliable. The model explains about 56

percent of the overall variations in the dependent variable.

Moreover, as earlier stated, the current values of financial openness were found here to be rightly

signed. This result implies that the effect of financial openness on economic growth is positive.

Real interest rate was found to have had a positive impact on economic growth in the long run

though this diverts from the short run effect. The empirical evidence from the long run analysis

are therefore in line with the findings of the Shrestha and Chowdhury (2007), Ghatak (1997)

Odhiambo (2009b) and Odhiambo (2009c) which found the positive effects of interest rate

liberalisation and argue that interest rates liberalisation leads to more savings, which ultimately

leads to increase in investment and economic growth.

The Procession to Parsimonious Model

It is important to note that the table and result above represent that of the parsimonious model. The

reduction process eliminated most of the insignificant variables without losing valuable

information. The regression results show that the goodness of fit in both models is satisfactory.

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The F-statistics with its probability values of 0.000 indicate that, overall, the models are

significant. These results imply the rejection of the null hypotheses that all the right hand side

variables except the constant terms have zero parameter coefficients. The Breusch-Godfrey LM

test for autocorrelation of 0.10 does not point to any serious autocorrelation problems.

4.5.2 The short run dynamics of Economic Growth and de jure financial openness

The short run dynamics of economic growth show how the effects in the long run function of

economic growth adjusts period after period. The coefficient of the error correction term shows

the magnitude of this adjustment as presented in the table below.

Parsimonious Restricted ARDL-ECM (2, 1, 0, 0, 0, 1, 0, 0) Dependent Variable D(Log(YPC)) Variables Coefficient Std. Error T-statistics Probability Constant 0.01924 0.0073 2.61 0.011 D(Log(YPC(-1)) 1.20877 0.2028 5.96 0.000 D(Log(YPC(-2)) -0.28793 0.1165 -2.47 0.015 D(FODJ(-1)) 0.05619 0.0445 1.26 0.209 D(Log(PSC)) -0.40684 0.0797 -5.10 0.000 D(Log(MKTCAP)) 0.07527 0.0333 2.26 0.026 D(HML) 0.02057 0.0074 2.75 0.007 D(RINTR(-1)) 0.00028 0.0007 0.37 0.712 D(REXCR) -0.00025 0.0003 -0.68 0.496 D(INST) 0.06375 0.0292 2.18 0.032 ECMt-1 -0.8905 0.2109 -4.22 0.000 R-Squared = 0.5814 Adj. R-squared

= 0.5349 F-Statistics = 12.50 F-prob. = 0.0000

Source: Computed by the Author Analysis made with reference to the above table indicate that past values of real GDP affect current

values up to the second lag with significant values, while a one period lag of financial openness

affects current values of real GDP positively (though insignificant). Other covariates such as

private sector credit, market capitalization, human labour and institutional qualities affect

economic growth with significant values and varying magnitudes as can be seen from the table.

Of great importance is the coefficient of the error correction term here marked ECMt-1. As seen

from above two cases it bears the correct sign and it shows a very high adjustment towards

attainment of equilibrium condition. It validates the fact that cointegration exists between the

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variables in the model and more so that if there is an exogenous effect that disturbs the equilibrium

level of the economy, about 90% is attuned in the first period.

The model explains about 58 percent variations in the model and it is highly significant as indicated

by the F-statistic. The Breusch-Godfrey LM test for autocorrelation indicates no evidence of serial

correlation in the residuals.

Further analysis made from the table indicates that financial openness is positive both in the short

run and long run. In the long run financial openness is significantly related to economic growth

but is insignificant in the short run. These findings are in line with the findings of Bwire (2007)

that financial openness proxied by financial development (FD) had significant positive effect on

economic growth since the liberalisation of the financial sector, using data from Bank of Uganda.

Moreover, the findings are in line with the findings of Mckinnon’s (1973) model and the financial

deepening approach by Edward Shaw (1973), where financial liberalisation acts as a catalyst to

growth through investment in high yielding projects resulting in an increase in real income. With

both de facto and de jure financial openness variables being positive and significant in the long-

run based on their ARDL- UECM results, the results indicate that financial openness is a beneficial

policy. This could be attributed to its ability to increase the financial base of the economy and

increase productive capital inflows.

4.6 ARDL- UECM and Short-run ARDL-ECM model diagnostic tests

Here, the emphasis is on testing the presence or absence of serial correlation in the residuals

generated from the models, Ramsey model specification test, heteroskedasticity test and stability

test.

4.6.1 Tests for serial correlation of residuals

The serial correlation tests of the residuals were based on the Breusch-Godfrey LM test for

autocorrelation. All the estimated models have their second order tests below them. Results from

the second order tests indicate no evidence of serial correlation in all the models.

4.6.2 Ramsey reset test

All the estimated models indicate no evidence of omitted variable problem in all the results. Thus

they passed the model specification test.

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4.6.3 White Heteroskedasticity test

Also, most of our estimated models passed the white heteroskedasticity test in all the results.

4.6.4 Stability Tests

The stability of the long-run coefficient is tested by the short-run dynamics. Once the ECM models

of de jure and de facto financial openness equation have been estimated, the cumulative sum of

recursive residuals (CUSUM) tests are applied to assess the parameter stability (Pesaran and

Pesaran ,1997). The Graph below depicts the results for CUSUM tests. The results indicate the

absence of any instability of the coefficients because the plots of the CUSUM statistic fall inside

the critical bands of the 5% confidence interval of parameter stability. .

Figure 5. Cumulative Sum of Recursive Residuals (CUSUM) Test

4.7 GRANGER CAUSALITY MODEL RESULTS FOR OBJECTIVE 2

4.7.1 THE GRANGER CAUSALITY TEST

The Two variables were tested for their stationarity and found to be stationary at first difference.

The result of the granger causality test is presented below excluding the ECM values since our

main variables of interest here are financial openness (FODF) and economic growth (YPC). The

causality test results suggest a-bidirectional causation between Economic Growth (YPC) and the

de facto financial openness (FODF). Thus, we could represent this relationship as (FODF→YPC)

and (YPC→FODF). The probability of the F statistics is significant at 5 percent using a two-tailed

-30

-20

-10

0

10

20

30

90 92 94 96 98 00 02 04 06 08 10

CUSUM 5% Significance

80

test. This is a clear indication of the relative positive impact of financial openness on the economic

growth of Nigeria.

Table 4.9 Null Hypothesis Df F-Statistics Probability FODF does not Granger Cause YPC

2 11.384 0.003

YPC does not Granger Cause FODF

2 6.4938 0.039

This finding of bidirectional relationship between financial openness and economic growth

supports the results of Hanh (2010) and Chowdhury & Mavrotas (2006). The result of this

estimation (bidirectional causality) is also very informative in predicting how economic growth

will be affected if policymakers are to change financial openness policies and vice versa. Even

when we controlled for other macro variables, the model results showed no significant difference.

Thus, this evidence of bidirectional causality between financial openness and economic growth

suggests that financial openness is necessary for enhancing economic growth. In turn, economic

growth seems to be an important condition for financial openness to take place and also thrive in

Nigeria.

4.8 GARCH MODEL RESULTS FOR OBJECTIVE 3: Impact of Financial Openness on Output Volatility

4.8.1 THE GARCH MODEL

Unit root test

The result of the ADF test of stationarity is presented on Table 4.10 below. Here, Real exchange

rate was found to be stationary at level; while others, GDP growth rate (Y), terms of trade, de facto

financial openness, de jure financial openness and inflation were found to be stationary at first

difference at the 5% level for the ADF test.

81

Table 4.10a Summary of ADF Unit root test results of the GARCH series

Variable Mackinnon Critical Values

Level ADF Test Stat

1st Difference ADF Test Stat

Order of Integration

Y -3.600 -3.514 4.595* I(1) FODFV -3.222 -2.084 4.223* I(1) TOTV -2.5868 2.097 3.827* I(1) FODJV -3.451 -2.352 -3.603* I(1) REXRV -2.891 -3.604* I(0) INFV -3.455 -3.427 -4.286* I(1)

NOTE: * indicates significant at 5%, probability levels Source: Computed by the Author Table 4.10b Descriptive characteristics of the variables

Variables/ Statistics

Y FODFV TOTV REXRV INFV FODJV

Mean 2.199808 27.17365 104.4112 108.127 22.12626 2.030385 Median 2.3 27.07500 87.935 100.21 13.53 1.7 Maximum 8.89 36.73000 221.26 439.18 76.43 3.02 Minimum -4.21 16.35000 42.4 53.97 1.86 1.35 Std. Dev. 3.084427 4.728219 51.77508 50.61129 20.10329 0.599218 Skewness 0.098315 -0.047166 0.906833 4.036898 1.246872 0.582155 Kurtosis 2.370927 2.678203 2.467907 2.39166 3.144525 1.482444 Jarque-Bera 1.882386 0.487292 15.48087 2178.325 27.03845 15.85391 Probability 0.390162 0.783765 0.000435 0.00000 0.000001 0.000361

Computed by the author

As can be seen in Table 4.10b, four variables in the series are non-normally distributed. The null

hypothesis of normal distribution is rejected for TOTV, REXRV, FODJ and INFV at the 1% level,

and at the 5% level for the rest of the series. The mean and median of TOTV and REXRV are

positive and high above 100% respectively. This suggests that terms of trade and real exchange

rate especially at the beginning of each fiscal quarter were significantly positive and perhaps imply

that higher average values attract larger economic growth especially terms of trade.

The variables from the table also do not show evidence of fat tails, since the Kurtosis did not

exceed 3, which is the normal value. But there is little evidence of negative skewness, for de facto

financial openness volatility. These imply left and right fat tails, respectively. We can, therefore,

employ the GARCH model since there is no much kurtosis problem.

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Confirmation of data as AR (1)

Before the estimation of the GARCH model the data was confirmed to be suitable for AR (1). The

correlogram test for the series is shows a slow decay in AC and a spike in the PAC on first

observation and then drops to zero/under zero indicates the AR(1). The first order autocorrelation

is 0.914, and they gradually decline to .0030 after 15 lags. These autocorrelations are not large,

but they are very significant. Though some are positive and others negative, which is expected in

most economic time series and yet is an implication of the GARCH (1, 1) model.

Then the above is followed by an estimation of Y, AR (1) using OLS in order to test the hypothesis

whether there is autocorrelation (and hence no ARCH) or not.

Table 4.11

Dependent Variable: Y Method: ML – ARCH Coefficien

t Std. Error z-Statistic Prob.

C -11.42835 14.27232 -0.800736 0.4233 AR(1) 0.981731 0.014924 65.78130 0.0000 Variance Equation C 0.317251 0.058825 5.393102 0.0000 ARCH(1) 1.650808 0.317355 5.201769 0.0000

R-squared 0.830084 Mean dependent var 2.181845 Adjusted R-squared 0.824935 S.D. dependent var 3.094039 S.E. of regression 1.294568 Akaike info criterion 2.867000 Sum squared resid 165.9148 Schwarz criterion 2.969320 Log likelihood -143.6505 F-statistic 161.2139 Durbin-Watson stat 1.871836 Prob(F-statistic) 0.000000 Inverted AR Roots .98

Arch test In order to test the ARCH effect we performed the ARCH-LM Heteroskedasticity test. Often a

“Ljung box test” with 15 lagged autocorrelations is used.

83

Table 4.12 =================================================================== F-statistics 4.74130 Probability 0.000003 Obs *R-squared 43.72930 Probability 0.000121 ========================================================================

The test p-values shown in the last column are all zero, resoundingly rejecting the null hypothesis

which says that “there is no ARCH”. On top of the p-values, out of the result of the

Heteroskedasticity test we see that, the ARCH-LM statistic is 4.74130 significant at the 5% level,

thus we conclude that there is ARCH effects.

Table 4.13 The GARCH Model Results

Mean Equation Coefficient Z-Statistics Probability Constant (µ) 2.373219 3.388535 0.0007 AR(1) 0.737731 11.38609 0.0000 Variance Equation Constant(ώ) 5.607304 12.26144 0.0000 ARCH (1) (α) 0.225949 0.558670 0.5764 GARCH (1) (β) 0.424017 1.612945 0.1068 Y (-1) (λ) -0.166743 -1.427170 0.1535 FODFV (-1) (δ) -0.033537 -0.762094 0.4460 TOTV (-1) (ƞ) -0.010199 -1.270723 0.2038 FODJ (-1) (ζ) -0.0406922 -0.702016 0.4827 REXR (-1) (ν) -0.007939 -0.758922 0.4479 INFV (-1) (ρ) -0.022769 -1.096954 0.2727 R-squared Adjusted R-squared

0.808468 0.787649

F-statistics 38.83371 Prob(F-statistics) 0.00000

Inverted AR Roots .74 Computed by the author

Analysis of the GARCH Model

From the result in the table above, the ARCH and GARCH coefficients (0.2259 and 0.4240) are

not statistically significant. The sum of these coefficients is 0.650 which indicates that shocks to

volatility have a persistent effect on the conditional variance. These shocks will have a permanent

84

effect if the sum of the ARCH and GARCH coefficients equals unity (that is, the conditional

variance does not converge on a constant unconditional variance in the long run).

Interpretation of the output volatility determinants

The coefficients of lag of GDP growth rate, de facto and de jure financial openness volatility, terms

of trade, real exchange rate, inflation in the GARCH (1, 1) measure the predictive power of

previous values of the variables on economic growth in Nigerian economy. As can be seen from

Table 4.13 the coefficients are all negative implying that a change in either of the variables in the

previous period in Nigeria reduces conditional output volatility this quarter. However, the findings

infer that financial openness and other macroeconomic variables in the model have not made any

significant impact on output volatility in Nigeria.

This finding is in line with Ramey and Ramey (1995) who in a detailed study documented an

empirical relationship that show that growth and volatility are negatively correlated. This is an

important result since it suggests that policies and exogenous shocks that affect growth may not

necessarily influence volatility.

Furthermore, the findings from the above GARCH result agree with existing studies such as; Razin

and Rose (1994) ; Easterly, Islam, and Stiglitz (2001) ; Buch, Dopke, and Pierdzioch (2002),

O’Donnell (2001) and Kose et al (2008), that were unable to document a clear positive empirical

link between openness and macroeconomic volatility. For example, Razin and Rose (1994) study

the impact of trade and financial openness on the volatility of output, consumption, and investment

for a sample of 138 countries over the period 1950–88. They find no significant empirical link

between openness and macroeconomic volatility. Easterly, Islam, and Stiglitz (2001) explore the

sources of macroeconomic volatility using data for a sample of 74 countries over the period 1960–

97. They find that a higher level of development of the domestic financial sector is associated with

lower volatility.

On the other hand, an increase in the degree of trade openness leads to an increase in the volatility

of output, especially in developing countries. Their results indicate that neither financial openness

nor the volatility of capital flows has a significant impact on macroeconomic volatility.

85

Buch, Dopke, and Pierdzioch (2002) use data for 25 OECD countries to examine the link between

financial openness and business cycle volatility. They report that there is no consistent empirical

relationship between financial openness and the volatility of output.

O’Donnell (2001) examines the effect of financial integration on the volatility of output growth

over the period 1971–94 using data for 93 countries. He finds that a higher degree of financial

integration is associated with lower (higher) output volatility in OECD (non-OECD) countries. His

results also suggest that countries with more developed financial sectors are able to reduce output

volatility through financial integration.

Arch test

In order to test the ARCH effect of the GARCH estimation result above, we performed the ARCH-

LM Heteroskedasticity test. Often a “Ljung box test” with 15 lagged autocorrelations is used.

Table 4.14 ===================================================================== F-statistics 2.22306 Probability 0.012845 Obs *R-squared 27.85531 Probability 0.022492 =====================================================================

However, the GARCH model did not assume a symmetric response of volatility to past shocks.

The test p-values shown in the table above are close to zero, rejecting the null hypothesis which

says that “there is no ARCH” at 5 % level. Also, the Q statistics on all the lags in the specification

do reject the null thus they support the hypothesis that the standardized residuals not serially

correlated.

However, we have tested further even longer lag (36 default lag) for the square residual and we

have observed that the Q-stat from lag 1 to lag 7 did not reject the null. But it rejected the null

hypothesis from lags 8 to 16 and failed again to reject at lag 17 Choosing shorter lag might result

to one failing to capture the lag order however, the longer lag one chooses the lower the power

will be.

Note: If there is no serial correlation, the autocorrelations and partial autocorrelations at all lags

should be nearly zero, and all Q-statistics should be insignificant with large p-values.

86

4.9 EVALUATION OF RESEARCH HYPOTHESES

Hypothesis 1(Ho1): Financial Openness has no significant impact on Economic Growth in Nigeria.

Decision Rule: Reject Ho1 if tcal>ttab, accept H0 otherwise.

Conclusion: From the result discussed in section 4.3 (tables 4.4 and 4.6) both de facto and de jure

financial openness have been found to have long run significant positive impact on

economic growth in Nigeria. Therefore, we reject the null hypothesis that Financial

Openness has no significant impact on Economic Growth in Nigeria.

Hypothesis 2 (Ho2): There is no significant direction of causality between financial openness and

economic growth in Nigeria.

Decision Rule: Reject Ho1 if tcal>ttab, accept H0 otherwise.

Conclusion: Following the result shown in table 4.9, we find that there a significant bidirectional

relationship between financial openness and economic growth in Nigeria. Thus, we

reject the null hypothesis (H0) and conclude that the there is a clear significant

direction of causality between financial openness and economic growth in Nigeria.

Hypothesis 3 (Ho3): Financial Openness has made significant impact on output volatility in

Nigeria.

Decision Rule: Reject Ho1 if tcal>ttab, accept H0 otherwise.

Conclusion: From the GARCH model results in table 4.14, we have seen that both measures of

financial openness have not made any significant positive contribution to output

volatility in Nigeria. Therefore, we reject the null hypothesis (H0) and conclude that

Financial Openness has not made significant impact on output volatility in Nigeria.

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CHAPTER FIVE

SUMMARY OF THE FINDINGS, CONCLUSIONS AND RECOMENDATIONS

5.1 Summary of the findings

The study was set out to investigate the impact of financial openness on macroeconomic

performance in Nigeria. It established that the policies of financial openness and financial

liberalization introduced in Nigeria from late 1980s till present have made positive impact on

economic growth. However, our results suggest greater impact in the long run than in the short

run. As it were, one may deduce from the estimated models that de facto and de jure measurements

of financial openness have similar relationship with economic growth in the short run and long run

respectively.

Again, we also see from the results that financial openness in Nigeria have impacted positively

more on economic growth through market capitalization and negatively through private sector

credit, with the two being significant both in the short run and long run. Thus, there is significant

evidence that financial openness generally made positive contributions to economic growth within

the period under review. Nonetheless, an unexpected finding from the result is the negative sign

of private sector growth in both de jure and de facto financial openness growth results. This

supports the finding of Ayadi et al (2013) which indicates that credit to the private sector and bank

deposits are negatively associated with growth. This confirms deficiencies in credit allocation in

some countries and also suggests weak financial regulation and supervision. This result calls for

serious caution on the side of domestic financial sector managers to ensure that credit is targeted

at those sectors that are growth-enhancing. Generally, our finding agrees to the fact that capital

account openness, domestic financial market liberalization and stock market liberalization have

made different degrees of impact on the Nigerian Economy.

The results also support the findings of Ghatak (1997) in the Sri Lankan context as he explored

and found in a similar study, a positive impact of Financial Liberalization on economic growth of

Sri Lanka for the duration of 1950 to 1987. Furthermore, the findings of this study partly agree

with those of Odhiambo (2009) that financial development which results from interest rate reforms

did not cause investment and economic growth in South Africa in the short run but in the long run.

88

In our second model, we equally find that financial openness and economic growth in Nigeria have

a bidirectional causal relationship. This supports the finding of Hanh (2010). This evidence of

bidirectional causality between financial openness and economic growth suggests that financial

openness is necessary for enhancing economic growth. In turn, economic growth seems to be an

important condition for financial openness to take place and also thrive in Nigeria.

Finally, we find that past volatilities in financial openness and other macroeconomic variables in

our model did not lead to output volatility in Nigeria. This result supports the work of Kose et al

(2008).

5.2 Conclusion

This thesis focuses on the impact of financial openness on economic growth in Nigeria. The study

equally examines the direction of causality between financial openness and economic growth as

well as the impact of financial openness on output volatility in Nigeria. It uses two measures of

financial openness: de jure (Chin-Ito Index) based on Chinn and Ito (2012) and de facto capital

flows variables which are the sum of FDI, portfolio flows and other investments following

Aizenman (2004, 2008) and Aizenman and Noy (2009), for empirical analysis.

The thesis addresses three specified objectives namely; to examine the impact of financial

openness on economic growth in Nigeria; to empirically investigate the existence of a causal

relationship between financial openness and economic growth in Nigeria; and to determine the

impact of financial openness on output volatility in Nigeria. For the regression analysis, we use

bank and stock market data, international capital flow variables and institutional variables, among

others. These include Real GDP per capita, Credit to the private sector, Real Interest Rate, Human

Labour, Market Capitalization, Real Exchange Rate, and Institutional Quality Index. The study

applies the Autoregressive Distributed Lag Model based on unrestricted error correction model

(ARDL-UECM), Granger Causality Model and the Generalized Autoregressive Conditional

Heteroscedasticity (GARCH) Model to address the three objectives.

The results show positive impact of financial openness on economic growth in Nigeria both in the

short run and in the long run. Specifically we find that 1% increase in de facto financial openness

leads to an increase of 0.02% in economic growth in the short run and 0.26% in economic growth

89

in the long run. The results equally show that 1% increase in de jure financial openness leads to an

increase of 0.02% in economic growth in the short run and 0.26% in economic growth in the long

run. The results also reveal that credit to the private sector is negatively associated with growth,

indicating that there are problems with credit allocation and utilization in the country which could

be occasioned by weak regulation/supervision and non-adherence to prudential guidelines in the

financial system. We also find that real interest rate has a positive relationship with economic

growth. Although this is not very significant but the results support the McKinnon-Shaw

hypothesis, i.e. in the long run interest rate liberalisation will ultimately lead to rapid economic

growth. Human Labour (HML), is also statistically significant and positively related to Economic

growth in Nigeria. On the stock market side, the results show that market capitalization impacts

positively and significantly on economic growth. Our institutional quality variable which

represents governance and rule of law also shows some interesting results. In the short run it has a

negative relationship with economic growth but in the long run we see a positive relationship

between the two variables. This result attests to the fact that the present style of governance among

the leaders has serious negative impact on the growth of the Nigerian Economy. This is

exemplified by the fact that the principle of rule of law is not respected, corruption has been

enthroned in several leadership quarters, and there is no internal democracy even among the

political parties. When the quality of governance and institution is weak, it simply translates to

corruption, embezzlement of state funds meant for infrastructural development, and several other

anti -socio/economic outcomes.

The second regression result indicates the existence of bidirectional causality between financial

openness and economic growth in Nigeria. Even when we controlled for other macro variables,

the model results showed no significant difference.

The thesis further examines the impact of financial openness on output volatility in Nigeria. It

applies the Generalised Autoregressive Conditional Heteroskedasticity (GARCH) model. The

findings show that none of the two measures of financial openness contributed to output volatility

in Nigeria, within the period under review. Other control variables in the GARCH Model also

reveal a negative relationship with output volatility in Nigeria.

90

5.3 Policy implications of findings and Policy Recommendations

1. The first significant policy implication arising out of the empirical finding in the thesis is that

both the de facto and de jure measures of financial openness are equally robust and significantly

positively related to economic growth in Nigeria. Thus, policy makers or researchers who wish

to investigate long run or short run impact in the future could adopt either the de facto measure

of openness or the de jure measure of financial openness. This is because according to our model

results, the de facto and de jure financial openness measures showed similar impact in the long

run and short run respectively.

2. From this work, our knowledge of the various measurement issues associated with financial

openness has been enhanced and we have identified that both measures are potent and robust for

the Nigerian economy. Thus, we recommend that government should continue to remove barriers

to capital account transactions with every sense of objectivity, economic management dexterity

and in line with global best practices. Again, as earlier stated while justifying this study, an

important question requires policy-makers to decide, given that legal barriers have been

removed, how best to manage capital flows. Suffice it to note that countries such as China, India

and South Korea, are pointed out as poster child of success from openness. However, when

compared with some European countries, as well as many other developing countries like

Nigeria, these countries exhibit a much less “open” economy, measured either with trade and/or

financial openness indicators. Their high growth performance is more associated with a

“managed openness” policy, than a rush for “more openness” per se. In this light, having seen

the benefits of financial openness to the growth of the economy, the Nigerian Economic

managers should adopt the best economic management policies to guide international capital

flows and also ensure that the maximum benefits of such flows accrue to the country. In other

words, we recommend that the government and policy makers should adopt international best

practices and policies in guiding domestic financial system reforms and international capital

flows in order to ensure the maximum benefits of such policies to the economy.

3. Banks should be encouraged to extend more credit to the private sector. But there is a serious

need for discipline and discretion in credit allocation by the banks. Giving loans to friends and

91

cronies without serious certified profitable business ideas should be discouraged. Again the

government and financial sector players should educate the business community and other loan

seekers on the need to invest such credits in productive business ventures that will contribute to

rapid economic growth in the long run. To achieve this laudable goal, there is need to develop

and empower the relevant institutions. According to Prasad and Rajan (2008) “a successful

implementation of financial policy depends on the level of institutional and economic

development before the policy is implemented”. Ultimately, there is need to adopt value re-

orientation approach by the private sector towards banks’ borrowing and target investment in

productive activities of the economy in order to elicit economic growth (Orji, 2012).

4. As shown by our results, the liberalisation of interest rates is needed for generating higher

savings and investment in Nigeria. As it were, savings and investment can be facilitated by

maintaining higher real interest rates. Furthermore, monetary authorities and policy makers

should allow the market to determine the interest rates, but relevant policies must be put in place

to guard the market determined interest rates by setting objective margins for it. Again, sound

policies should be evolved to improve the efficiency of financial intermediaries while putting

inflationary pressures under control. This will ensure that lending and deposit rates put under

desirable levels. Depositors can be motivated to deposit more by increasing the deposit rates

while investors can be encouraged to use financial intermediaries by lowering the lending rates.

This is envisaged to improve the effect of financial sector development on economic growth

through real interest rate channel.

5. The government should enhance human capital development by developing the education sector.

Having seen from our empirical analysis that the quality of Human labour and capital contributes

to growth, policy makers should evolve sound education policies that will help in enhancing the

capacity of our teaming youths to contribute positively to the growth process of the nation.

Quality education with sufficient funding should be emphasized at all levels of government. This

is vital because management of financial openness and all international capital flows that will

contribute positively to the growth of the economy can only be accomplished by educated sound

92

minds. In this regard, we strongly advocate a sustainable progressive increase in budgetary

allocation to the education sector to 26% in Nigeria by the year 2020 and beyond.

6. Also, policy makers and monetary authorities should ensure that capital markets in Nigeria are

strategically developed and repositioned such that they are incorporated and integrated into the

financial system and the economy as a whole. The results indicate that the level of market

capitalization in Nigeria is positively related to growth. Thus, there is need to continue the drive

towards maximising the economic growth potentials of the Nigerian Stock Markets by

adequately ensuring that they keep providing funds to investors for long term investment,

business and development projects. As noted by Adjasi and Biekpe (2006), the efficiency and

productivity effects of stock market on economic growth are strong and positive when stock

markets are liquid and active. The recent political impasse between the Securities and Exchange

Commission (SEC) and the National Assembly in Nigeria whereby the SEC is being starved of

budgetary funds for their statutory operations should be highly discouraged.

7. The country’s institutional quality should be comprehensively reviewed and upgraded. Strong

emphasis should be made on deepening the country’s democracy, reforming the governance and

electoral systems and reorganizing the socio/political structures in the country. Respect for the

rule of law should be given priority by the leaders and the led. All avenues through which

corruption is encouraged in the system should be discontinued and if anybody is found guilty of

any corrupt practice, the person should be made to face the full wrath of the law. This is because

according to our finding, poor governance, which is exemplified by corruption and lack of

respect for the rule of law are detrimental to growth. However, if these anomalies are corrected,

improved institutional quality will impact positively on growth in the long run. To ensure

compliance and achieve maximum result, the judiciary and various anti-corruption agencies

should be properly funded and given full independence to function properly. This will enable

them to deal with cases of corruption and other governance issues decisively no matter whose

ox is gored.

93

8. This thesis supports a-bidirectional causality between financial openness and Economic growth

in Nigeria. We therefore recommend that, on one hand, the government and policy makers

should make suitable policies for attracting foreign capital flows along with deepening the

domestic financial system in order to enhance growth. On the other hand, the country’s growth

should be adequately managed in order to generate more gains not only in terms of domestic

financial development but also in terms of financial openness by attracting productive

international capital inflows. The recent rebasing of Nigeria’s GDP should not be an end in itself

but efforts should be geared towards formulating useful economic policies that will lead to

sustained economic growth in the Nigeria.

9. Emphasis should be placed on more robust domestic economic structural reforms such as

promoting a competitive and viable domestic banking system, with adequate regulatory and

supervisory framework. This should be complemented by other macroeconomic stability

policies. That is, fiscal deficits, rapidly depreciating exchange rate and high inflation should be

put in check. This is one of the ways to ensure that financial openness continues to contribute to

growth while lowering output volatility.

5.4 Research Recommendations for Further Studies

This study has considered the impact of financial openness on economic growth and output

volatility using the de jure and de facto measures. However, the following areas are

recommended for further research.

(i) Studying the impact of financial openness on other key macroeconomic variables like

Investment, Government Expenditure, Taxation, Imports and Exports using the Chinn-Ito

index.

(ii) Analyzing the impact of financial openness on regional economic blocks like ECOWAS,

WAEMU, WAMZ, among others, using the de jure and de facto measures of financial

openness.

(iii) Investigating the effect of financial openness policies on the economy through various

other sectors of the economy using the Chinn-Ito Index or the de facto measure.

94

REFERENCES

Agenor, P. (2003), “Benefits and Costs of International Financial Integration: Theory and Facts” The World Economy (26) 8: 1089-1118

Agenor, P. and Aizenman, J. (1999), “Volatility and the Welfare Costs of Financial Market

Integration”, in the Asian Financial Crisis; Causes, Contagion and Consequences, ed. By Pierre-Richard Agenor, Marcus Miller, David Vines and Axel Weber, Cambridge: Cambridge University Press.

Agu, C.C, Orji, A. And Eigbiremolen G. (2014) “Financial Liberalization, Interest Rate Structure

and Savings Mobilization: The Nigerian Experience” International Journal of Current Research 6 (1), January, 2014

Agu, C.C. (1988), “Interest Rate Policy in Nigeria: An Attendant Distortion” Savings and

Investment Journal, 12 (1). Aguiar, M. and Gopinath, G. (2007), “Emerging Market Business Cycles: The Cycle is the Trend”

NBER Working Paper 10734 Ahmed, M. and Suardi, S (2009), “Do Financial and Trade Liberalization Cause Macroeconomic

Volatility in Sub-Saharan Africa?” Centre for Strategic Economic Studies, Working Paper No. 44, Victoria University, Melbourne

Aizenman, J. and Noy, I. (2009), “Endogenous Financial and Trade Openness”. Review of

Development Economics, 13: 175-189 Aizenman, J. and Noy, I. (2004), “Endogenous Financial Openness: Efficiency and Political

Economy Considerations”, NBER Working Paper No. W10144. Aizenman, J., (2008). "On the hidden links between financial and trade opening," Journal of

International Money and Finance, Elsevier, 27(3): 372-386 Akpan, H. E (2013) “Growth without Development in Nigeria: Confronting the Paradox”, Paper

presented at the 43rd Annual ICAN Conference held at the International Conference Centre, Abuja, 8 – 10, October, 2013

Alfaro, L., Chanda, A., Kalemli-Ozcan, A., and Sayek, S. (2004), “FDI and Economic Growth:

the Role of Local Financial Markets,” Journal of International Economics 64: 89-112 Aremu, J.A (2005), “Attracting and Negotiating Foreign Direct Investment with Transnational

Corporations in Nigeria.” CBN Papers

95

Babajide, F. (2008), “Financial Liberalization Policies and Economic Growth: Panel Data Evidence from Sub-Saharan Africa” African Development Review (20) 3: 549-574 Backus, D.K., Kehoe, P., Kydland, F., (1992) “International Real Business Cycles”, Journal of

Political Economy 100 (4): 745-775. Bahmani-Oskooee, M and and Brooks, T.J (1999), “Bilatera J-curve between U.S and Her Trading

Partners”, Weltwirtschaftliches Archiv, (135): 156-165 Banerjee A. (1993), “Co-integration, Error Correction, and the Econometric Analysis of Non-

Stationary Data”. Oxford, Oxford University Press. Bardsen, G. (1989). Estimation of Long-Run Coefficients in Error Correction Models, Oxford

Bulletin of Economics and Statistics, 51: 345-350. Barro, R.J. and Xavie S. (1995) “Economic Growth”, New York: McGraw Hill. Batini N. (2004) “Achieving and Maintaining Price Stability in Nigeria” IMF Working Paper

WP/04/97 Baxter, M and Jermann, U.J (1997), “The International Diversification Puzzle is worse than you

think.” The American Economic Review, 87(1): 170-180.

Bekaert G., Harvey, C.R. and Lundblad, C. (2006). “Growth Volatility and financial liberalization,” Journal of International Money and Finance, 25: 370-403.

Bekaert G., Harvey, C.R. and Lundblad, C. (2005). “Does Financial Liberalization spur Growth”, Journal of Financial Economics 77(1):3-55.

Bhagwati, J. (1998), “The Capital Myth: The difference between Trade in Widgets and Trade in

Dollars” Foreign Affairs, (77): 7-12. Bollerslev, T. (1986), “Generalized Autoregressive Conditional Heteroskedasticity”, Journal of

Econometrics, 31, 307-327. Buch C.M., Jorg, D and Christian P. (2005). “Financial Openness and Business Cycle Volatility”:

Kiel Institute for World Economics. Working paper, Germany. Buch C.M., and Yener, S. (2005). “Consumption volatility and financial openness” Kiel working

Paper 1260 Braun, M. and Raddatz, C. (2007), “Trade Liberalization, Capital Account Liberalization and the

Real Effects of Financial Development,” Journal of International Money and Finance, 26(5): 730-761

96

Calderon, C. and Yeyati, E.L (2007), “Financial and the Poor: What matters and What Doesn’t?’ The World Bank, manuscript

Campos, N.F and Coricelli, F. (2012), “Financial Liberalization and Reversals; Political and

Economic Determinants” Economic Policy fifty-fourth Panel Meeting Hosted by the National Bank of Portland Warsaw, 27-28 October 2011

Caprio, G. and Klingebiel, F (1995), “Bank Insolvencies: Cross Country Experience”, World Bank

Working Papers No. 1620 Carmignani, F and Chowdhury, A (2005) “The Impact of Financial Openness on Economic

Integration: Evidence from the Europe and the Cis” Department Of Economics, University Of Milan – Bicocca Working Paper Series

Central Bank of Nigeria (2012) Statistical Bulletin Central Bank of Nigeria (2010) Statistical Bulletin Central Bank of Nigeria (2005) Statistical Bulletin Central Bank of Nigeria (2004) Statistical Bulletin Chanda, A. (2005), “The Influence of Capital Controls on Long Run Growth: Where and How

Much?” Journal of Development, 77 (2): 441-466 Chanda, A. (2001). “The Influence of Capital Controls on Long Run Growth: Where and How

Much?” Brown University, Mimeo Chinn, M.D. and Ito, H. (2012), “Notes on the Chinn-Ito Financial Openness Index 2010 Update”

NBER Working Papers Chinn, M.D and Ito, H. (2006), “What Matters for Financial Development? Capital Controls,

Institutions, and Interactions,” Journal of Development Economics 81 (1); 163-192. Chinn, M.D and H. (2002) capital account liberalization, institutions and financial development:

Cross country evidence.” NBER Working Paper No. 8967 Chowdhury, A., and Mavrotas, G. (2006), FDI and Growth: What Causes What? World economy,

29(1), 9-19. Coricelli, F., Jazbec, B., Masten, I., (2008), “Sources and Obstacles for Growth in Transition

Economies; the Role of Credit”, festschrift in Honour of G.Calvo, MIT Press Dermirguc-Kunt, A. and Detragiache, E. (1998), “Financial Liberalization and Financial Fragility”

IMF Working Paper WP/98/83 Diercks, A. (2012), “Capital Account Liberalization: A New look” NBER Working Papers

97

Easterly, W., Islam, R., and Stiglitz, J., (2001). Volatility and Macroeconomic Paradigms for Rich and Poor Countries. In: Dreze, J. (Ed.), Advances in Macroeconomic Theory. Palgrave, New York.

Edison, H.J; Levine, R. l and Slok, T. (2002), “International Financial Integration and Economic

Growth,” Journal of International Money and Finance, 21 (60):749-776 Edwards, S. (2001), “Capital Mobility and Economic Performance: Are Emerging Economies

Different?” NBER Working Paper No.8076 Eichengreen, B. and Leblang, D. (2003), “Capital Account Liberalization and Growth: Was Mr.

Mahathir right”? International Journal of Finance and Economics (8):20 -224 Engle, R. F. (1982), “Autoregressive Conditional Heteroscedasticity with Estimates of the

Variance of United Kingdom Inflation”, Econometrica, 50, 987-1007 Fratzscher, M and Bussiere, M (2004), “Financial Openness and Growth: Short- Run Gain, Long-

Run Pain? European Central Bank Working Paper Series No. 348 French, K.R and Poterba J.M (1991), “Investor Diversification and International Equity Markets”

NBER Working Paper no 3609 Fry, M.J. (1997), “In Favour of Financial Liberalization”, Economic Journal, 107: 442-754 Gallego, F and Loayza (2002) “The Golden Period for Growth in Chile- Explainations and

Forecast.” Journal of Econonomics Chilena, 5:37-67 Ghatak, S. (1997). “Financial Liberalisation: The Case of Sri Lanka”, Empirical Economics,

22:117-129. Giovanni, J.D and Levchenko, A.A (2006), “Trade Openness and Volatility” IMF Development

Studies Working Papers No.219 Greenaway, D, Morgan, W and Wright, P. (2002), “Trade Liberalization and Growth in

Developing Countries” Journal of Development Economics, 67; 229-244 Grilli, V. and Millessi-Ferretti, G.M (1995), “Economic Effects and Structural Determinants of

Capital Controls”, IMF Staff Papers, (42) 3: 517-550 Gupta, R. and Komen, K. (2008) “Time Aggregation and the Contradictions with Causal

Relationships: Can Economic Theory Come to the Rescue?” University of Pretoria Department of Economics Working Paper Series 2008-02 February 2008

Hagen, J.V and Zhang, H (2006), “Financial Liberalization in a Small Open Economy” CESifo

Working Paper Series 1771, CESifo Group Munich

98

Henry, P.B and Lorentzen, P.L (2003), “Domestic Capital Market Reform and Access to Global Finance: Making Markets Work,” Research Papers 1820, Standford University, Graduate, Graduate School of Business.

Henry, P.B (2006), “Capital Account Liberalization: Theory, Evidence and Speculation” NBER

Working Paper 12698 Hermes, N. and Lensik, R. (2005), “Does financial liberalization influence saving, investment and

economic growth? Evidence from 25 emerging market economies; 1973-1996” UNU-WIDER Research Paper No.2005/69, Finland: United Nations University.

Honohan, P. (2000), “How Interest Rates Changed Under Financial Liberalization- a Cross

Country Review,” Policy Research Working Series 2313 Johansen, S. (1995), “Likelihood-Based Inference in Co-integrated Vector Autoregressive

Models”. Oxford, Oxford University Press. Johansen, S. (1991), “Estimation and Hypothesis Testing of Co-integration Vectors in Gaussian

Vector Autoregressive Models,” Econometrica 59:1551-1580 Johansen, S.and Juselius, K. (1990), “Maximum Likelihood Estimation and Inference on Co-

integration- with Applications to the Demand for money”. Oxford Bulletin of Economics and Statistics 52: 169-210

Johansen, S. (1988) “Statistical Analysis of Co-integration Vectors”, Journal of economic

Dynamics and Control, 12: 231-254 Kama, U (2006), “Recent Reforms in the Nigerian Banking Industry: Issues and Challenges” CBN

Bullion, 30:3 Kaminsky, G.L and Schmukler, S.L (2003), “Short-Run Pain, Long-Run Gain: The Effects of

Financial Liberalization” IMF Working Paper WP/03/34 Kaminsky, G.L and Reinhart, C.M (1999), “The Twin Crises: The Causes of Banking and Balance

of Payments Problems,” American Economic Review 89 (3): 473-500 Klien, M.W and Olivei, G.P (2008), “Capital Account Liberalization, Financial Depth and

Economic Growth” Journal of International Money and Finance, 27(6): 861-875. Klien, M.W (2003), “Capital Account Openness and the Variety of Growth Experience” NBER

Working Papers 9500 Klien, M.W and Olivei, G (2001), “Capital Account Liberalization, Financial Depth and Economic

Growth” Mimeo, Tufts University

99

Klien, M.W and Olivei, G (1999), “Capital Account Openness and the Variety of Growth Experience” NBER Working Paper 7384

Kose, A., Ayhan, M., Prasad, E, Rogoff, K. Shang-Jin, W., (2008), “Financial Globalization: A

Reappraisal”, IMF Staff Papers Kose, A. Prasad, E., Terrones, M.E (2003), “Financial Integration and Macroeconomic Volatility”

IMF Staff Papers 50: 119-142 Kose, A (2002), “Explaining Business Cycles in Small Open Economies: How much do World

Prices Matter”? Journal of International Economies 56:299-327 Kraay, A (1998), “In Search of the Macroeconomic Effects of Capital Account Liberalization”

Manuscript, the World Bank Lal, D (2012) “Is the Washington Consensus Dead?” Cato Journal, 32 (3): 493-512 Lane, P.R., and Milesi- Ferretti, G.M (2007), “The External Wealth of Nations Mark II: Revised

and Extended Estimates of Foreign Assets and Liabilities, 1970-2004” Journal of International Economics 73 (2): 223-250

Lane, P. R and Milesi-Ferretti, G.M (2005), “A Global Perspective on External Positions,” NBER

Working 11589 Law (2006), “The Determinants of Financial Development from Openness and Institutional

Quality Perspectives” Working Papers, Department of Economics, Universiti Putra Malaysia

Levine, R. (2001), “International Financial Liberalization and Economic Growth”. Review of

International Economics 9(4): 688-702 Lewis, K.K (1999), “Trying to Explain Home Bias in Equities and Consumption” Journal of

Economic Literature, 3792): 571-608 Loayza, N.V and Raddatz C. (2007), “The Structural Determinants of External Vulnerability” The

World Bank Economic Review, 21 (3): 359-387 Loayza, N.V and Ranciere, R. (2006), “Financial Development, Financial Fragility and Growth”

Journal of Money, Credit and Banking, 38(4): 1051-1076 Luintel, K.B (2008), “Financial Structure and Economic Growth”, Journal of Development

Economics, 86 (1), 181-200 Mailafia, O. (2006), “Capital Account Liberalization: Reflections on Theory and Policy” CBN

Economic and Financial Review 44 (4)

100

Maniragaba,V.N (2011), "Effects of Financial Liberalisation on Money Demand Economic Growth: Uganda’s Experience (1978-2008)". Makerere University, Uganda.

Masron, T.A (2009), “Macroeconomic Synchronization and Foreign Direct Investment Inflow into ASEAN: Evidence from Malaysia’s Experiences” International Business Research, 20 (3): 11-46

Mckinnon, R.I (1973), “Money and Capital in Economic Development”. Washington D.C, The

Brookings Institution Mishkin, F.S (2006), “The Next Great Globalization: How Disadvantaged Nations can Harness

their Financial System to Get Rich” Princeton, NJ: Princeton University Press Montiel, P.J (1995), “Financial Policies and Economic Growth: Theory, Evidence and Country-

Experience from Sub-Saharan Africa”, AERC Special Paper no. 18, Nairobi. Narayan, P.K and Narayan, S. (2005), "Estimating Income and Price Elasticities of Imports for

Fiji in a co-integration framework” Economic Modelling 22: 423-438 Neumann, R. and Ron P. (2008) "Volatile capital flows: Interactions between de jure and de facto

financial liberalization." Economics Bulletin, 6 (2): 1-10 Nigeria Bureau of Statistics Report 2012 Obiechina, M.E (2010) “Capital Flows and Financial Crises: Policy Issues and. Challenges for

Nigeria” Economic and Financial Review 48 (1): 193-112. Odhiambo, N. M (2009), "Interest Rate Liberalisation, Financial Deepening and Economic

Growth in South Africa": Ninth Annual IBER & TLC Conference Proceedings 2009 Odhiambo, N.M. (2009b). "Interest Rate Reforms, Financial Deepening And Economic

Development In Kenya: An Empirical Investigation", The Journal of Developing Areas, 43 (1): 295-313

Odhiambo, NM. (2009c)." Interest rate liberalization and Economic Growth in Zambia: A

Dynamic linkage", African Development Review, 21(3): 541 - 557. O’Donnell, B. (2001), “Financial Openness and Economic Performance”, (unpublished cross-

country research work, Dublin, Ireland: Trinity College). Ogbonna, I.C, Uwajumogu, N.R, Nwokoye E, Nzeribe G (2012) “Economic Growth and Foreign

Direct Investment in Nigeria: An Empirical Investigation Journal of Economics and Sustainable Development 3(13): 34-40

Ohiorhenuam, J.F.E. (2003) “Development Thought, Policy Advice and Economic Development

in Africa in the 20th Century”: Evolution of Development Thought: 1-38

101

Ojo, M. O (2006), “Capital Account Liberalization: The ECOWAS Experience”, CBN Economic and Financial Review 44:4

Okpara, G.C (2010), “The Effect of Financial Liberalization on Selected Macroeconomic

variables: Lessons from Nigeria”. The International Journal of Applied Economics and Finance 4 (2): 53-61

Omoke (2010) “The Causal Relationship among Financial Development, Trade Openness and

Economic Growth in Nigeria”. International Journal of Economics and Finance, 2(2):1-15

Onosode, G.O. (1997) Foreign Investment Not a Panacea for Resource Mobilization: Daily

Times: March 5. Orji, A, Eigbiremolen, G and Ogbuabor, J.E. (2014) “Financial Liberalization and Private

Investment: Further Evidence from Nigerian Data”, Review of Economics & Finance 4 (2): 77-86

Orji, A., Onyia A.M and Ani, E.G (2013) “Calming the Tide: Private Investment and

Macroeconomic Uncertainty in Nigeria” Asian Journal of Empirical Research, 2013, 3(5):633-653

Orji, A. (2012) “Bank Savings and Bank Credits in Nigeria: Determinants and Impact on

Economic Growth”. International Journal of Economics and Financial Issues, 2 (3): 357-372

Osinubi,T.S and Amaghionyeodiwe L.A (2010) “Foreign Private Investment And Economic

Growth In Nigeria” Review Of Economic And Business Studies 3(1):105-127 Owusu, E.l (2012), “Financial Liberalization and Economic Growth in ECOWAS Countries”: An

Unpublished PHD Research Work. Oyatoye, E. O., Arogundade, K. K, Adebisi, S. O and Oluwakayode, E. F. (2011) “Foreign Direct

Investment, Export and Economic Growth in Nigeria” European Journal of Humanities and Social Sciences 2 (1):67-86

Özdemir, D and Erbil, C. (2008), “Does Financial Liberalization Trigger long-run Economic

Growth? Evidence from Turkey and Other Recent EU Members” EcoMod International Conference on Policy Modeling, Berlin, Germany, July 2-4, 2008

Pagano. M. (1993), “Financial Market and Growth: An Overview’, European Economic Review,

37 (1993): 613 – 622. Pesaran, M.H, Shin, Y and Smith, R.J (2001), “Bounds Testing Approaches to the Analysis of

Level Relationships”, Journal of Applied Econometrics 16 (3): 289-326

102

Pesaran, M.H and Pesaran, B. (1997), Working with Microfit 4.0: Interactive Econometric Analysis. Oxford, Oxford University Press.

Pesaran, M.H, Shin, Y and Smith, R.J (1996), “Testing the Existence of Long Run Relationship”

DAE Working Paper Series No.9622, Department of Economics, University of Cambridge.

Pesaran, M.H and Shin, Y (1995), “Autoregressive Distributed Lag Modelling Approach to Co-

integration Analysis”. DAE Working Paper Series No.9514, Department of Economics, University of Cambridge

Poon, W.C (2010), “A Monetary Policy Rule: The Augmented Monetary Conditions Index for

Philipines Using UECM and Bounds Tests”, Discussion Paper 04/10, Monash University, Department of Economics.

Portes, R. and Rey, H (2003), “The Determinants of Cross Border Equity Flows” Manuscript

Princeton University. Prasad, E., Rajan, R. and Subramanian, A. (2007), “Foreign Capital and Economic Growth”

Brooking Papers on Economic Activity, 2007 (1): 153-209 Prasad, E., Rogoff, K., Wei, S.J and Kose, M.A. (2003), “The Effects of Financial Globalization

on Developing Countries: Some Empirical Evidence. IMF Occasional Paper No.220. Washington, D.C

Prati, A and Tressel, T (2006), “Aid Volatility and Dutch Disease: Is there a Role for

Macroeconomic Policies”? IMF Working Paper WP/06/145 Quinn, D.P and Toyoda, A.M (2007), “Ideology and Voter Sentiment as Determinants of Financial

Globalization.” American Journal of Political Science 51: 344-363 Quinn, D. (1997), “The Correlates of Change in International Financial Regulation”, American

Political Science Review 91(3):531-551. Quinn, D. and Inclan, C. (1997), “The Origins of Financial Openness: A Study of Current and

Capital Account Liberalization”, American Journal of Political Science 41(3): 771-813. Ramey, G and Ramey, V.A (1994), “Cross-Country Evidence on the Link between Volatility and

Growth,” NBER Working Papers 4959 Raddatz, C., (2007). “Liquidity needs and vulnerability to Financial Underdevelopment”, Journal

of Financial Economics 80 (3): 677-722 Razin, A. and Rose, A. (1994), “Business Cycle Volatility and Openness: An Exploratory Cross-

Section Analysis.” NBER Working Papers 4208

103

Rodrick D. (1998), “Who Needs Capital Account Convertibility?” in Stanley Fisher, et al., Should the IMF Pursue Capital Account Convertibility? Essays in International Finance, No 207, International Finance Section, Department of Economics, Princeton University, Princeton N.J.

Serven, L. and Schmidt-Hebbel, K. (2002), “Financial Liberalization, Saving and Growth” Paper

Presented at the Banco de Mexico Conference on Macroeconomic Stability, Financial and Economic Development”, Mexico, DF, Nov.12-13

Shaw, E.S, (1973), “Financial Deepening in Economic Growth” New York: Oxford University

Press Shrestha, M.B and Chowdury, K (2007), “Testing Financial Liberalization Hypothesis with ARDL

modelling approach”, Applied Financial Economics 17(18): 1529-1540 Solow, R. M (1956), “A Contribution to the Theory of Economic Growth” Quarterly Journal of

Economics 70 (1): 65-94 Stiglitz, J.E (2002). Globalization’s Discontents. New York: W.W Norton and Company Stiglitz, J.E (1998), “Towards a New International Economic Architecture: Principles and

Policies.” Presented at the European Investment Bank, Fiesole Italy, October, 15. Tang, T. C. (2003). “Japanese aggregate import demand function: Reassessment from the ‘bound’ testing approach. Japan and the World Economy. 15 (4): 419-436. Tesar, L.L and Werner, I. M (1995), “Home Bias and High Turnover” Journal of International

Money and Finance. 14 (4): 467-492 Toda, H. Y. and Yamamoto, T. (1995), “Statistical Inference in Vector Autoregressions with

Possibly Integrated Processes” Journal of Econometrics, 66, 225-250. Uchendu, O.A. (1993), “Interest Rate Policy in Savings and Investment in Nigeria” CBN Economic

and Financial Review 31:13-25 Udah, E.B (2010), “Macroeconomic Reforms, Government Size and Investment Behaviour in

Nigeria: An Empirical Investigation” Journal of Sustainable Development in Africa. 12 (1):258-280

Williamson, J. (1989), “What Washington Means by Policy Reform” In J.Williamson (ed.) Latin

American Re-adjustment: How Much Has Happened? Washington: Institute for International Economics.

World Bank Development Indicators 2012 Zhang, K.H.L., (2001). “Does Foreign Direct Investment Promote Economic Growth? Evidence

from East Asia and Latin America”. Contemporary Economic Policy 19: 175-185.

104

APPENDIX

A. Selected Macroeconomic Variables

-8.754-10.752

7.5436.467

12.766

-0.6180.4342.090.91-0.307

4.9942.8022.716

0.474

5.3188.164

21.177

10.33510.585

5.3936.2116.9725.9846.968.724

7.4 7.1

-15

-10

-5

0

5

10

15

20

25

Source: World Bank and CBN (2012)

Fig. 1 NIGERIA GDP GROWTH RATE

105

11.63228745

-24.06710072

-3.92085612

-16.57844108

16.92732415

-0.110733084

-32.05731051

-13.74923058

-5.702380374

-22.91094097

-12.46367043

16.2132807

25.13000989

7.127715426

-12.22751594

11.46917384

-5.098434484

8.560264468

-1.281265538-1.513291538-2.223439769

11.57313977

4.052791229

23.82479618

-7.252417692

13.36478135

-40

-30

-20

-10

0

10

20

3019

86

1987

1988

1989

1990

1991

1992

1993

1994

1995

1996

1997

1998

1999

2000

2001

2002

2003

2004

2005

2006

2007

2008

2009

2010

2011

2012

Source : World Bank and CBN (2012)

Fig.2 Nigeria:Real Interest Rate

6.2511.765

34.211

49.02

7.89512.195

44.565

57.14357.416

72.729

29.292

10.6737.8626.6186.938

18.869

12.88314.03315.00117.856

8.2185.413

11.58112.54313.7210.812.1

0

10

20

30

40

50

60

70

80

1986

1987

1988

1989

1990

1991

1992

1993

1994

1995

1996

1997

1998

1999

2000

2001

2002

2003

2004

2005

2006

2007

2008

2009

2010

2011

2012

Source : World Bank and CBN (2012)

Fig. 3 Nigeria: INFLATION RATE

106

B: The Normality Test

0

5

10

15

20

25

-1 0 1 2

S eries: S tandardized ResidualsS ample 1986:2 2011:4Observations 103

Mean 0.061664Median -0.065751Maximum 2.560122Minimum -1.642017S td. Dev. 0.825837S kewness 0.469314K urtosis 3.531363

Jarque-B era 4.992796P robability 0.082381