institutional quality & stock dev. · institutional quality and stock market development in...
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MATTHEW, ENYINNAYA TIMOTHY
PG/M.SC/11/59833
INSTITUTIONAL QUALITY AND STOCK MARKET DEVELOPMENT IN NIGERIA - An Application of the ARDL
Approach
FACULTY OF SOCIAL SCIENCES
DEPARTMENT OF ECONOMICS
Paul Okeke
Digitally Signed by: Content manager’s Name
DN : CN = Webmaster’s name
O= University of Nigeria, Nsukka
OU = Innovation Centre
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INSTITUTIONAL QUALITY AND STOCK MARKET DEVELOPMENT IN NIGERIA - An Application of the ARDL Approach
By
MATTHEW, ENYINNAYA TIMOTHY
PG/M.SC/11/59833
AN M.SC DISSERTATION SUBMITTED TO THE
DEPARTMENT OF ECONOMICS
FACULTY OF SOCIAL SCIENCES
UNIVERSITY OF NIGERIA, NSUKKA
IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE AWARD OF MASTERS OF SCIENCE (M.SC) DEGREE IN ECONOM ICS
SUPERVISOR: PROF. N. I. IKPEZE
July, 2015
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TITLE PAGE
INSTITUTIONAL QUALITY AND STOCK MARKET DEVELOPMENT IN NIGERIA - An Application of the ARDL Approach
By
MATTHEW, ENYINNAYA TIMOTHY
PG/M.SC/11/59833
AN M.SC DISSERTATION SUBMITTED TO THE
DEPARTMENT OF ECONOMICS
FACULTY OF SOCIAL SCIENCES
UNIVERSITY OF NIGERIA, NSUKKA
IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE AWARD OF MASTERS OF SCIENCE (M.SC) DEGREE IN ECONOM ICS
SUPERVISOR: PROF. N. I. IKPEZE
July, 2015
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CERTIFICATION
This is to certify that Matthew, Enyinnaya Timothy, an M.Sc student of the University of Nigeria
Nsukka with registration number PG/M.Sc//11/59883 has successfully completed the research
required for the Award of Masters of Science Degree in Economics in the University of Nigeria
Nsukka.
Matthew, Enyinnaya Timothy Date
PG/M.Sc/11/59883
Prof. N. I. Ikpeze Date Supervisor
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APPROVAL
The research work titled: “Institutional Quality and Stock Market Development in Nigeria” has
followed due process and has been approved to have met the minimum requirement for the award
of the Masters of Science Degree in the Department of Economics - University of Nigeria, Nsukka.
Approved
Prof. N. I. Ikpeze Date Supervisor
Prof. C. C. Agu Date Head of Department
Prof. I. A. Madu Date Dean of Faculty
External Examiner Date
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DEDICATION
To the family of Late Matthew Ibekwe for their unwavering support spiritually and physically all
through the period of this program. My prayer is for God to bless each and every one of them and
also support them in all their endeavours in Jesus gracious name….Amen.
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ACKNOWLEDGEMENTS
My unreserved gratitude goes to God Almighty who has been my help in ages past and my hope
for the years to come. I say thank you Lord Jesus for the grace that saw me through this research
process despite all odds. I would also like to express my sincere thanks and appreciation to my able
supervisors, Professor N. I. Ikpeze, who played a fatherly role to make this research work a
success. His guidance, advice, suggestions and recommendations have been very invaluable during
the process of my dissertation, and to Dr. Orji, Anthony whose invaluable suggestions, advice, and
guidance has also made this dissertation a success. Another round of thanks goes to Dr. Manasseh,
Charles for his immense contribution and guidance all through this period. I wouldn’t fail to
acknowledge the support of my family members: Mrs Mercy N. Matthew, Mrs Okezue Judith; Mr.
Matthew Ndubuisi; Amarachi, Faith, Chimuanya and Abraham. Also, my Nephews and Nieces
cannot be forgotten; they are my source of joy and inspiration. I also acknowledge the support of
someone very close to my heart; Miss Chidinma Peace Mbanaso who is always there for me all
through this period. Finally, to my friends; Isiwu,Chidiebere Peter; Dick, Emmanuel; Ugwu,
Uchenna; Stanley, Noah Tams; Joseph, Oscar O; Chigbu, Bright; Nwokolo, Chukwudi and a host
of others who in one way or the other have made this work a success, but whose names are not
mentioned.
Matthew, Enyinnaya Timothy.
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ABSTRACT
This study was motivated by the growing concern on the impact of Institutional Quality on economic outcomes. The study focused specifically on the Nigerian Stock Market due to its critical role in the economy as a vehicle for efficient resource allocation. The Autoregressive Distributed Lag (ARDL) bounds testing procedure is employed using data from 1985 to 2012. The study used the ARDL model to ascertain the long-run impact of institutional quality on stock market development in Nigeria. The results from Empirical analysis of level of corruption, democratic accountability and bureaucratic quality exert significant impacts on stock market development as measured by market capitalisation ratio. Also, Banking sector development and stock market liquidity contribute significantly to stock market development. Moreover, a unidirectional causality runs from institutional quality to stock market development. The study therefore, recommends that the fight against corruption should be intensified while the market administrative and regulatory qualities should be enhanced for a sustainable stock market development in Nigeria.
Keywords: Institutional Quality, Stock market, development, Market capitalisation ratio, corruption, democratic accountability, bureaucratic quality.
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TABLE OF CONTENTS
Cover Page........................................................................................................................... i
Title page…………………………………………………………………………….… ii
Certification Page …………………………………………………………………….… iii
Approval Page …………………………….………………………………………….... iv
Dedication ……………………………………………………………………………. v
Acknowledgements ……………………………………………………………………. vi
Abstract………………………………………………………………………………… vii
Table of Contents ...…………………………………………………………………… viii
List of Tables ................................................................................................................... x
List of figures................................................................................................................... x
Appendices........................................................................................................................ xi
Chapter One:
1.1 Background to the Study............................................................................................ 1
1.2 Statement of the Problem........................................................................................... 4
1.3 Research Questions………………………………………………………………… 7
1.4 Objective of the Study…………………………………………………………… 7
1.5 Research Hypothesis................................................................................................. 7
1.6 Significance of the Study........................................................................................... 8
1.7 Scope of the Study.................................................................................................... 8
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Chapter Two:
2.1 Conceptual Framework............................................................................................. 9
2.2 Measuring Institutional Quality................................................................................ 11
2.3 Theoretical Literature................................................................................................ 12
2.4 Empirical Literature On Other Stock Markets........................................................... 16
2.5 Empirical Literature on Nigerian Stock Market....................................................... 21
2.6 Limitation of Previous Studies................................................................................. 22
Chapter Three:
3.1 Theoretical Framework.............................................................................................. 23
3.2 Model Specification................................................................................................. 25
3.3 Justification of Model and Choice of Variables....................................................... 28
3.4 Battery Test................................................................................................................ 31
3.5 Sources of Data.......................................................................................................... 31
Chapter Four:
4.1 Unit Root Tests.......................................................................................................... 33
4.2 Bounds Test For Cointegration................................................................................. 34
4.3 Impact Analysis......................................................................................................... 37
4.4 Diagnostic Test Discussions...................................................................................... 40
4.5 Analysis of Causality Test Based on Error-Correction Model................................ 42
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Chapter Five
5.1 Summary ................................................................................................................... 44
5.2 Recommendations..................................................................................................... 45
5.3 Conclusion................................................................................................................. 46
References.............................................................................................................................. 47
LIST OF TABLES
Table 1A: Summary of Unit Root Test Results – ADF.........…………………….............. 33
Table 1B: Summary of Unit Root Test Results – PP ......................................………......... 34
Table 2: Bounds Test for Cointegration................................................................................ 35
Table 3: Summary of Long-run and Short-run (ECM) Regression Result.......................... 36
Table 4: Long-run and Short-run Impact............................................................................. 38
Table 5: Granger Non-Causality Test................................................................................. 43
LIST OF FIGURES
Figure 1: Plots of Market Capitalisation (% of GDP)………….......................................... 2
Figure 2: The Nigerian Stock Exchange All Share Index (1995 to 2013)……………....... 5
Figure 3: Institutions – Stock Market Development Nexus.............….............................. 10
Figure 4: CUSUM and CUSUMSQ Graph........................................................................ 41
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APPENDICES
Appendix 1A: Unit Root Test - ADF.................................................................................. 55
Appendix 1B: Unit Root Test – PP. ............................................................................... 60
Appendix 2: UECM Result ................................................................................................ 65
Appendix 3A: Long-run Level Estimates ........................................................................ 66
Appendix 3B: Error Correction Model Result .................................................................. 67
Appendix 4A: ARCH Test ECM ................................................................................ 68
Appendix 4B: Normality Test.............................................................................................. 69
Appendix 4C: Serial Correlation Test for the ECM ......................................................... 70
Appendix 4D: Ramsey Reset Test .................................................................................. 71
Appendix 5: Granger Non-Causality Test .......................................................................... 72
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CHAPTER ONE
INTRODUCTION
1.1 Background to the Study: The role of institutional quality in sustainable development has received tremendous attention in
recent time and it has been a central issue in development policies of many nations to orchestrate
an insurmountable institution because of its critical position in the development of financial
system and stock market in particular. Institution plays a pivotal role in promoting the enactment
of rules and regulations, for proper surveillance of political, social and economic activities
globally. Furthermore, viable institutions support macroeconomic stability and promote social
cohesion, thus accelerating market efficiency and business development. It has been inferred that
countries with efficient working institutions advances strong legal framework for the
promotion of efficient mobilization and allocation of funds, thereby creating less risky business
environment. Consequently, the absence of adequate regulatory framework and supervision could
erode the investors’ confidence which will undermine the performance of the stock market (Law
and Azman-Saini, 2008).
The deepening and broadening of the stock market in Nigeria presents an important concern to
the policy makers (Manasseh et.al, 2014). This has brought to bear many institutional reforms
such as the establishment of the investment and securities tribunal (IST) for investors protection,
central securities clearing system (CSCS) for transparency, and prologue of other new practices
in the market like; the introduction of automated trading system (ATS), Desk for phone-in-
service, trade alert introduced by CSCS, a day transaction clearance (T+1) as against T + 14,
introduction of the capital trade point by investment securities Act (ISA), introduction of market
makers, and the establishment of Real Estate Investment Schemes (Manasseh et. al, 2012). Even
though the market is erratic in its performance over time, the introduction of these practices and
the newly established policy incorporating small and medium business enterprises in the
activities of the market have brought some remarkable improvement in the performance of
Nigeria stock market.
According to NSE (2013), the market performance shows that the number of securities listed on
the stock exchange have grown greatly. For example, in 1961, the number of securities listed was
8, but have grown to 190 on average between 1971 and 2010. It was also noted that the market
capitalization has soared from N6.6 billion in 1985 to about N12 trillion on averages between
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1995 and 2010. However, at the end of 2013, the impressive performance of the market climbs
to 47.2 percent return compared to 35.5 percent in 2012, and N13.226 trillion market
capitalisation compared to N8.97 trillion recorded at the end of 2012 respectively. While NSE
All Share Index which tracks the performance of the stock exchange blown to above 40,000
points compared to 28,078.81 points at the end of 2012, the portfolio of investors’ worth grew by
N4.25 trillion.
Even in the presence of the recorded remarkable improvement in recent time, as shown in
figure1 below, it is evident that the Nigeria stock market is the least in terms of market
capitalisation compared to other emerging markets like Kuala Lumpur stock market of
Malaysia; Singapore stock market and Johannesburg stock market of South Africa (World Bank,
2012).
Figure 1: Plots of Market Capitalisation (% of GDP)
350
300
250
200
150
100
50
0
MYS
NGA
SGP
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Note; NGA = Nigeria; MYS = Malaysia; SGP = Singapore and ZAF = South Africa. Source: Computed by Author.
Empirically, it has been opined that the poor performance of the Nigeria stock market is due to
its weak institutional framework, political upheavals and corruption among others (Igbatayo,
2011). To this, Gries and Meierrieks (2010) argued that weak institution, characterised by poor
legal framework, property right protection and high level of corruption is nonlinear in nature.
They further argued that weak institution makes savings and investment less attractive
particularly in the long run, thus discouraging additional demand for liquidity, financial services
like pooling of savings and monitoring of investment; and more sophisticated financial
instruments. Consequently, this affects the performance of the Nigerian stock market
development, emanating from lost incentive to boost investors’ confidence due to the market
insecurity and fear of the tendency of loosing their fund.
Furthermore, in the effort to promote the institutional environment and to give life line to the
Nigerian stock market, several reforms and measures to step up the level of confidence for the
resuscitation of the market was adopted by the authorities. Amongst the measures are the
establishment of anti-corruption agencies such as Independent corrupt practices and other related
offences commission (ICPC) and Economic and Financial Crimes Commission (EFCC). While
the ICPC is charged with the fight against corruption in all facets of the economy, the EFCC
specifically focused on combating economic and financial crimes like money laundering,
advance fee fraud, financial malpractices in banks and other financial institutions (Manasseh and
Aneke; 2013). Also, the enactment of the Freedom of Information Act (FIA) is a step in the right
direction to promote efficiency in information dissemination so as to mitigate the problem of
information asymmetry in the stock market (Aiyede, Nil). Hence, in 2010, Asset Management
Corporation of Nigeria (AMCON) was established, to also address the problem of non-
performing loans in the Nigerian banking industry, alongside Consumer and Financial
Protection Division so as to provide a platform through which consumers can seek redress and to
ensure transparency and accountability in the market. (Manasseh & Asogwa, 2013; and
ICPC, 2003).
In view of the above, considering the recent improvement in the institutional environment and
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substantial role of stock market development in mobilisation of savings, capital formation,
service provision, as well as provision of investment avenue, with the goal of accelerating
economic growth and development, it is therefore pertinent to assess the relationship between
institutional quality and stock market development, which has over time been neglected in
previous studies in Nigerian. This is because evidence has a strong hold that institutional quality
improves the enforcement of corruption control, which is often the source of insider-dealing and
constitutes a serious impediment to the development of the market (Gries & Meierrieksy,
(2010)). Such institutions reduce political risk, uncertainty in investment decision-making,
lowering transaction and agency costs, and thus encourage consistent inflow of external finance
into the market. This transforms to increase in the returns to shareholders, consolidation of
Investors’ confidence and stock market development in general, alongside greater integration
with world financial markets. However, an efficient and well developed stock market is partly
dependent on appropriate policies that would consolidate institutional framework for the creation
of favourable business environment and investors protection. Therefore, the understanding of
institutional factors that may significantly promote stock market development in Nigeria could
be of great importance for proper policy formulation that will spur the growth of the market and
economy in general.
1.2 Statement of Problem:
As the spine of financial market, stock market was purposely created to serve as one of the most
important source for companies to raise money. This allows equities to be publicly traded to aid
companies the avenue to raise additional financial capital for their expansion by selling shares of
ownership of the company in a public market. The liquidity that an exchange affords the investors
gives them the ability to quickly and easily sell securities, which serves as an attractive feature of
investing in stocks, compared to other less liquid investments (Cesari et.al, 2010 and Simkovic
2009). Empirically, it has been shown that the price of shares and other assets is an important part
of the dynamics of every economic activity, and can influence the disposition or attitude of the
many investors. An economy where the stock market is on the rise is considered to be the
substances of business activities. However, it is the primary indicator of a country's economic
strength and development (Singh, 2011). Thus, rising share prices boost business activities in the
market, affecting the wealth of households and consumption, with its significant effect on per-
capita income, reduction in poverty and unemployment as well promote economic growth.
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But the continuous and unimpressive performance of the market indicated by the NSE all share
index in figure 2 below, which overtime has been as a result of fall in share prices of the listed
companies have incapacitated the ability of the market in performing its functions. This has left
many investors and market analysts in a state of chaos, with its ugly consequences on the reduction
in investor’s confidence and the business activities in the market.
Figure 2: The Nigerian Stock Exchange All Share Index (1995 to 2013):
Source: Authors Computation.
As a result of the uninspiring performance of the market, many researches have been in their quest
to investigate the likely causes or why the market has been struggling to regain itself since the
amazing record in 2007 that championed Nigerian stock exchange the world best performing stock
market with a return of 74.9 percent. Eventually, in 2008, it turned to be the world worst-
performing equity gauge in dollar terms with a whopping drop of over 30 per cent as shown by
Renaissance Capital January end report 2009 (Ighomwenghian, 2009). In the absence of
macroeconomic instability and financial crisis that rocked the global economies in 2008, evidence
has shown strong concern over institutional quality as a forerunner to financial development of
which stock market is integral part of (Thorsten & Levine, 2005; LLSV, 1997, 1998 and 2000a;
Manasseh et.al, 2014).
Therefore, the degradation of institutional environment such as high rate of frauds like insider
dealings, market manipulations, false trading, market rigging and false representations have been a
common practice in the Nigerian stock market. Issues of business integrity and respect for
legitimate laws and regulations have lost its whim and caprices, leaving investor’s confidence in
a shuffle. Consequently, after the boom in 2007, the Nigerian Stock Exchange market has not
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really done well in terms of its recorded performance indicators. The All-Share Index, which is the
barometer to measure the market performance witnessed a sharp decline (see figure 2 above) with
its value dropping from 57,990.2 and 24,085.8 basis points in 2007 and 2005, to 31,450.78 and
20,827.17 points in 2008 and 2009 respectively. Also in 2009, it drops further from 20,827.17
points to 20,730.63 basis points in 2011. Be that as it may, in 2014, the market recorded 5.2% drop
in all share index at the end of first quarter in March 27th from 41.329.14 points it recorded during
the beginning of trading in January 2014 to close at 39,186.93 points at the end of March. This has
seriously influence negatively, the contribution of the market to economic growth. For instance,
from 1988 to 1997, the market contributed about 7.28% to growth on average. Then, from 1998 to
2006, it accounted for 11.86% on average. In 2007, it makes a remarkable impact, contributing
about 57.88% to growth. Hence, after the impressive contribution in 2007, from 2008 to 2012, the
market only contributed just 7.93% on average to economic growth.
Notwithstanding, efforts has been made by the market authorities to strengthened the investors’
confidence through various forms of reforms. For example, Independent Corrupt Practices and
Other Related Crimes Commission (ICPC) were established in 2000 to investigate reports of
corruption and inappropriate cases to prosecute the offenders. In 2003, Economic and Financial
Crimes Commission (EFCC) came into place to investigate financial crimes and money laundering.
In the same vein, following the high level of corruption in the banking industry, Asset Management
Corporation of Nigeria (AMCON) was established in 2010 to addressing the problem of non-
performing loans in the Nigerian banking industry, alongside the Consumer and Financial
Protection Division, to provide a platform through which consumers can seek redress (NEED, 2004
and ICPC, 2003). Recently, NSE created a market Surveillance Department to ensure that the
Exchange is meeting its oversight responsibilities by conducting daily and other routine
surveillance of trading activity on the facilities of the Exchange. Furthermore, Surveillance
investigators also conduct regular review of trading activity within the Exchange’s Matching
System for various forms of manipulative or deceptive conduct (NSE, 2013).
The Surveillance Department also reviews trading activity on the Exchange for potential violations
of insider trading prohibitions. Indications of potential violations may be referred to the appropriate
arms for necessary action. To increase the level of compliance and integrity in the market, the NSE
launched the first-ever electronic issuers‟ portal, X-Issuer, in the Nigerian stock market. Issuers
submit financial and other material information to the market quickly and efficiently from the
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comfort of their offices (NSE, 2013). Hence, given the above, it is therefore pertinent to examine
the relationship between institutional quality and stock market development in Nigeria. This is
paramount because many studies in Nigeria such as Ezeoha et al, (2009); Osamnwunyi and Osagie
(2012); Adefoso et al, (2013) emphasized more on the relationship between macroeconomic
determinant and stock market development without much regard to institutional factors. However,
among the work reviewed in Nigeria, only the work of Manasseh et al (2012) in a study on stock
market development, financial sector reforms and economic growth, discussed the impact of
institutional framework on financial sector development. Hence, in view of the above, there is the
need to address the influence of institutional quality on stock market development in Nigeria by
answering the research questions that follows.
1.3 Research Questions:
The following research question will be addressed in this study;
1. Is there any long-run relationship between institutional quality and stock market
development in Nigeria?
2. Does institutional quality significantly impact on stock market development in Nigeria?
3. What is the direction of causality between institutional quality and stock market development in Nigeria?
1.4 Objective of the study.
The broad objective of this study is to empirically establish the impacts of institutional quality on stock market development in Nigeria. Under this, the following specific objectives were derived:
1. To investigate if there is a long-run relationship between Institutional quality and stock market development in Nigeria.
2. To examine the impact of institutional quality on stock market development in Nigeria. 3. To determine the direction of causality between institutional quality and stock market
development in Nigeria
1.5 Research Hypothesis:
The study will be guided by the following hypothesis: Ho1: Institutional quality does not impact on stock market development in Nigeria. Ho2: There is no long-run relationship between Institutional quality and stock market
development in Nigeria.
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Ho3: There is no causality between Institutional quality and stock market development in Nigeria.
1.6 Significance of the study:
The essence of this study is to explore the relevance or otherwise of the institutional quality in
Nigeria with respect to stock market development.
The increasing depth of the stock market in Nigeria represents an important challenge to policies
and institutions, hence the need to assess how the institutional environment affects the performance
of the stock market.
Also, the current stock market trend in the country suggests that the market has a high development
potential, the outcome of this study could provide the much needed policy measures necessary to
help the market increase its development-pace.
Additionally, it will add to the existing literature on the determinants of stock market development
thus, providing relevant information that could guide future researchers in this area of study. The
study will be of benefit to policy makers (especially market regulators), financial analysts and
investors generally.
1.7 Scope of the study:
The study will focus on the Nigerian economy and the stock market in particular and will cover the
period of 1985 to 2012. The scope of this study is seriously limited by data availability as data on
institutional quality by the Political Risk Service (PRS) group was initially published in 1984. This
study will make use of stock market capitalization as a percentage of GDP as the dependent
variable, institutional quality measured using the composite political risk index from the
International Country Risk Guide (ICRG), a publication of the PRS Group is to be used as the
independent variable while controlling for other macroeconomic variables that may affect stock
market development. Special focus will be on some institutional components including corruption
control, law and order, bureaucratic quality and democratic accountability.
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CHAPTER TWO
LITERATURE REVIEW
2.1 Conceptual Framework.
The concepts related to this study are used globally as many people are interested in stock market
and its development, however, the definition as it concerns this work may be arbitrary. Therefore,
we will state some of these concepts as they are used in this study. More so, in order to understand
how institutional qualities affect stock market development, it is important to understand the
impact or pathways through which institutional quality affects stock market development. Some of
the concepts in this study are:
Stock Market Development: Stock market development (like economic development) is a multi-
faceted concept which lacks a common definition (Demirguc-Kunt and Levine, 1993). However, in
this study, stock market development is seen as the deepening of the stock market by increasing
market capitalisation. This therefore rests upon the ability of the market to offer high-quality,
reliable, tailored service to the needs and demands of the market participants; also required is high
level transparency, fair pricing and easy entry/exit on the market. This process of stock market
deepening is seen to be affected by both economic and institutional factors.
An important indicator of stock market development is the capitalization ratio (market
capitalization as a proportion of GDP). Market capitalization represents the aggregate value of a
stock. It is obtained by multiplying the number of shares outstanding by their current price per
share. Market capitalization ratio therefore, equals the value of listed shares divided by GDP and
analysts frequently use the ratio as a measure of stock market size. In terms of economic
significance, the assumption behind market capitalization is that market size is positively correlated
with the ability to mobilize capital, diversify risk, enhance company growth, facilitate wealth
distribution, promote and improve resource allocation, enhance capital productivity and improve
corporate governance. Beck et al.(1999).
Institutional Quality : In this study, institutions are taken to mean political (and judicial) rules,
economic rules and regulations within which business activities are conducted and determined. The
quality of institutions in this study is measured by the existence of rule of law, quality of
bureaucracy, control of corruption, and government effectiveness and accountability which are
mutually reinforcing aspects of growth-enhancing institutions. In Nigeria, the capital market
operates within the confines of the institutional framework. Figure 3 shows how the institutional
mechanism affects stock market development through its effect on certain indicators that affects
market performance.
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Figure 3: INSTITUTIONS – STOCK MARKET DEVELOPMENT N EXUS
─ +
+ +
Stock Market Development
Economic
Development
Institutions
- Law and Order - Control of Corruption - Government Effectiveness
Capital Market Governance
- Shareholders right
- Investors protection
- Market Capitalization - Market Liquidity - All Share Index
Investors Confidence - Transaction cost - Expropriation risk
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In the figure, efficient institutional framework leads to efficient and effective market regulation
which will ensure investor-protection. This will also lead to reduction in transaction and agency
costs which will cause improved operating and investment performance. Another outcome of good-
working institutions on the stock market is low risk of expropriation. With high level investor-
confidence and assurance, investors will be willing to increase their participation in the market in
the form of more investment or pay higher share prices in the hope that more of the firm’s profits
would come back to them as interest or dividends. The result of positive institutions on the stock is
a better performance of market indicators like market capitalization, ASI and market liquidity and
these positive changes ultimately leads to greater stock market development and economic growth.
The causal effect could run in the opposite direction because more stock market development will
necessitate the need for better reforms and policies or more effective institutions for broader
economic development. This is depicted by the broken line running from stock market
development to Institutions in figure 3.
2.2 Measuring Institutional Quality.
The importance of institutional quality on economic performance has generated increasing interest
to empirically investigate the impact of "institutions" on various economic outcomes. Thus, a
number of empirical works (such as; Knack and Keefer, 1995; Kaufmann et al., 1999) have made
efforts to establish the link between "institutions" and growth/development. Such studies used
institutional quality variables as explanatory variables while controlling for other variables that
may affect the dependent variable of interest. The two most discussed issues are the measurement
of governance/institutional quality and the direction of causality between institutional development
and economic performance. (Juzhong et al.,2010). Leading the publication of a large number of
governance/institutional indicators are:
I . Worldwide Governance Indicators (WGI) - published annually by the WorldBank and also
known as Kaufmann et al. (1999). The WGIs is popular and has been widely used to study the
influence of institutions and governance on various outcomes. It consists of governance indicators
such as: (a) voice and accountability, (b) political stability and absence of violence, (c) government
effectiveness, (d) regulatory quality, (e) rule of law, and (f) control of corruption. Studies that
employed it in studying institutions and economic outcomes include: Asongu (2012); Okeahalam
(2005)
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II. The International Country Risk Guide (ICRG) index published by the Political Risk Service
(PRS) Group. It has been reliably used by a number of studies due to its broad coverage both across
countries and over time. It covers about 130 countries and has been published since 1984. Studies
that have used the ICRG index include Hall and Jones, 1999; Knack and Anderson, 1999; Chong
and Calderon, 2000; and Yartey, 2008. The index contains variables such as: corruption in
Government, the Rule of Law, Expropriation Risk, Repudiation of contracts by Government and
Quality of the Bureaucracy.
III. Global Competitiveness Index (GCI) produced by the World Economic Forum. First
introduced in 2004, this index measures national competitiveness, taking into account macro and
micro foundations of national competitiveness. A total of 113 variables are aggregated into a
weighted average of 12 pillars, including institutions, infrastructure, macroeconomy, health and
primary education, higher education and training, goods market efficiency, labour market
efficiency, financial market sophistication, technological readiness, market size, business
sophistication, and innovation.
IV. Transparency International's Corruption Perceptions Index. It is an aggregate of corruption
ratings produced by experts (such as ICRG) and from surveys. The index was first published in
1995, for 41 countries with data aggregated from 7 surveys. The 1999 index covered 99 countries,
with information from 14 sources.
V. The Freedom House's Freedom in the World Country Ratings, measuring the degree of political
rights and civil liberty in about 193 countries. It is considered as the first publication used in
measuring institutional quality. Country scores by experts are transformed into indexes of political
rights (electoral process, political pluralism and participation, functioning of government) and civil
liberties (freedom of expression and belief, associational organizational rights, rule of law, personal
autonomy and individual rights), which are then averaged to show an overall freedom rating.
Depending on the ratings, nations are classified as “Free”, “Partly Free”, or “Not Free”.
2.3 Theoretical Literature
The increasing concern in economic literature over the role of institutions in economic outcomes is
part of a concerted effort towards unearthing the deep determinants of economic growth and
development. This arises as some of the neoclassical growth models (e.g. Solow and Swan, 1956;
Becker, 1962) could not convincingly answer some causal question. For example, if capital
25
accumulation and technological innovation are truly the determinants of output levels across
countries, why have some countries failed to take necessary actions to accumulate and acquire the
capital and technology required to maintain a balanced growth. To answer the above question, the
New Institutional Economics (NIE), drawing primarily from the work of Douglas North (North and
Thomas, 1973; North, 1981, 1990, and 2005), incorporated the influence of institutions in their
analysis and emphasized the importance of institutions as a major determinant of development and
long-term economic performance. They defined institutions as the humanly devised constraints that
shape human interactions (North, 1990). The broad definition offered by North sees institutions as
formal and informal rules of the game, and their enforcement characteristics. Therefore drawing
from the definition by North, institutions are all rules or forms of conduct, set up with the set
objective to ease uncertainties (which arises as a result of imperfect information and limited
rationality), control the environment/game and lower transaction cost (Menard and Shirey, 2005).
Also, Ostrom (1990), in a clear and vital contribution defined institutions as “the set of working
rules that are used to determine who is eligible to make decisions in some arena, what actions are
allowed or constrained, what aggregate rules will be used, what procedures must be followed, what
information must or must not be provided, and what payoffs will be assigned to individuals
dependent on their actions”. This definition is analogous to that of North as institutions are seen to
be vital instruments of cohesion in the economic space.
The NIE in their view see political and institutional factors as playing an important role in
facilitating the development of markets. This new train of thought shows that the financial market
does not operate in a vacuum, but is affected by some factors including a set of institutions. The
major role of these institutions is to monitor the level of transparency in the market, in the activity
of the government and in the country's competitiveness. In the same vein, institutional quality does
not only affect the afore-mentioned factors, but also the level of foreign direct investment. Thus,
the improvement of the governmental institutions will have a beneficial two-fold impact. First, it
reduces the transaction costs, and the cost of enforcing contracts, thereby facilitating transactions
that improves the business environment (Coase, 1937, and Williamson, 1985). Then, it produces
stable rules, which are key factors for the success of new investment and projects (North, 1990).
The contribution of Williamson suggests that institutional quality are vital in mitigating the effects
of market frictions such as liquidity costs, transaction cost and information asymmetry. In the
presence of information asymmetries for instance, different problems might arise, and the exchange
of resources can become costly, sometimes to such an extent as to prevent capital markets from
26
function effectively. For example, problems of adverse selection might arise when firms have the
possibility of hiding their expected profits or their level of efficiency. Problems of moral hazard,
instead, might arise because of the incentive of firms to misreport the actual return on their
investment. These informational problems generate agency costs, and the financial contract is the
result of agents’ attempts to reduce these costs.
Theoretically, the NIE shows that effective institutions can make a difference in the success of the
market reforms and even assumes that the institutions represent the crucial factors for long-term
economic growth (Yahyoui, 2009). Chtouro (2004) held that institutional quality impacts on the
economic performance of long-term spread through the containment of transaction costs, the
reduction of risks and the disappearance of inflexibilities which distort the functioning of the
markets. While experts in institutional economics focus generally on the improvement of
institutions, specialists in financial economics focus only on the financial markets to analyse the
problem of institutional development and reforms in the financial markets especially the
transitional economies. (Yahyoui, 2009). However, the work of Levine (1997) clearly brought to
light a relationship between the financial markets and the institutions. He thus, defined institutions
as "third type" factors, implying that the institutions are very important structures which if not
present, the improvements that could take place in the financial system will be distorted. In
analyzing transitional economies, Pistor et al. (2000), shows that the structure of the institutions in
these economies is very crucial for development of their financial markets.
Furthermore, international financial agencies are also emphasizing the importance of quality
institutional framework in attaining higher economic performance especially in the developing
economies. The World Bank for example, in her 1997 report, maintained that government in
developing countries must strengthen their institutional structures to ensure the proper functioning
of their financial markets. The report shows that "the market and the government are
complementary, since it is the latter's job to establish the institutional basis for the functioning of
the former". The rational expectation of economic agents is that such reforms would improve the
flow of information in the financial market and that the establishment of good governance leads to
the broadening of the social standards that will guarantee property and contract rights ((Yahyoui,
2009).
Differences in perception on the definition of institutions and financial markets development,
possibly accounted for the weakness of the studies to establish a link between financial markets
27
development and institutional quality. It is clearly admitted because institutional economics and the
financial and monetary theories are separated in the areas of research. According to Kaminsky and
Schmukle (2002), interest in this area was renewed by the end of the 1990s, focusing on explaining
financial globalization and the causes of financial crisis of this period and the costs and benefits of
financial liberalization. Martell and Stulz (2003), opined that functional and effective institutional
structure is required if the financial market must tap into the benefits of Liberalization. Similarly,
La Porta, Lopez de-Silanes, Shleifer and Vishny (1997 and 1998) argue that legal origin determines
the level of financial development. Their contention was that common law-based systems,
originating from English law, are better suited for development of financial markets than civil law
systems, arguing that common law has been instrumental in protecting private property than civil
law, which aims at addressing corruption in the judiciary and improving the power of the state.
Applying the propositions of La Porta et al. (1997 and 1998), Pistor et al. (2000) deduced that the
effectiveness of legal institutions has a stronger impact on equity and credit market development.
Beck et al. (2003) assessed "why" legal origin matter in financial development and concluded that
the possible channels are the political and adaptability channels. The political mechanism works
through the importance that legal traditions attach to protecting the rights of private investors vis-a-
vis those of the state knowing that private property rights protection make up the foundation for
financial development. The adaptability mechanism tries to understand the ability of a legal system
to adjust to changing and evolving circumstances and that when a country's legal system adapts
only timidly to changing circumstances (especially economic), large gaps will open between the
financial needs of an economy and the ability of the legal system to support and fulfil those needs.
Law and Azman-Saini (2008) held that well-developed legal and institutional systems interact with
financial opening to ensure the development of financial markets. They however, noted that this
may not be the case for countries with emerging markets which lacks good institutional framework.
They suggests that a high level bureaucratic quality, well developed legal system and low levels of
corruption can increase the effect of financial openness on the development of the financial
markets. They also emphasized the importance of the development of a sound banking sector as a
necessity for the development of the financial markets but maintained that the development of
financial intermediaries and markets, changes according to a healthy institutional framework and
therefore concludes that if the institutional framework is strengthened and the beneficial aspects of
the institutions are identified, these could ease the development of the financial markets.
28
The position of Law and Azman-Saini (2008) is in tandem with that of La Porta et al. (1998) and
Lombardo and Pagano (2000) concerning the positive effect of high institutional quality on
financial market development. For La Porta et al. (1998), the legal institutions play a crucial role in
the functioning of the financial markets especially in transition economies. In the same vein, the
work of Lombardo and Pagano (2000) on the link between the quality of the institutional
framework and return on equity noted that the adjusted return risk on equity has a positive
relationship with the quality of legal institutions measured by the origin of the legal system,
compliance with the law and the absence of corruption.
Acemoglu and Johnson (2005) highlight the importance of property right institutions. They
contend that property right institutions have a crucial power in determining long-run growth,
investment and financial development, whereas contractual institutions shape financial
intermediation and slightly influence growth and financial development. Roe and Siegel (2009)
emphasized the role of political sector development. They held that current political instability
explains the level of financial development more than historical legal origin as postulated by La
Porta. Their position was close to that of Rajan and Zingales (2003) in which they linked political
stability to economic growth and financial development while exploring political economy as
determinants of financial development. Rajan and Zingales (2003) also argue that simultaneous
opening of both trade and capital accounts hold the key to successful financial development.
Opening trade and capital account not only fosters competition and reduces inefficiencies but might
also give incumbents new opportunities that will bring them even higher profits to cover any
negative effect from higher competition.
2.4 Empirical Literature On Other Stock Markets.
Much of the studies on the determinants of stock market development have focused more on
macroeconomic determinants other than institutional determinants. Worthy of mention include the
work by Demirguc-Kunt and Levine (1996) that examined the relationship between stock market
development and financial intermediary in developing countries and it discovered that most stock
market indicators are highly correlated with financial intermediary development.
Levine and Zervos (1998) presented empirical evidence supporting a finding that various measures
of real economic growth and stock market development across countries are positively related, and
that the association is particularly strong for developing countries. Their results also show that after
controlling for initial conditions and economic and political factors, the measures of banking and
29
stock market development are robustly correlated with current and future rates of economic growth
and productivity improvement. The result show that stock market liquidity is positively and
significantly related to economic variables as a one-standard-deviation increase in initial stock
market liquidity (0.3) would increase per capita growth by 0.8 percentage points per year.
Garcia and Liu (1999) investigated empirically the macroeconomic determinants of stock market
development. Using pooled data from 15 industrial and developing countries from 1980 to 1995,
they found the real income level and stock market liquidity to be important predictors of market
capitalization, while macroeconomic stability does not have any explaining power. They also
concluded that banks and markets are complementary instead of being substitutes. A basic
requirement for financial market development is macroeconomic stability.
Using a cross sectional regressions and a dynamic panel generalized-method of moments (GMM)
estimator for a sample of 65 countries over 1960 to 1995 period, Boyd et al. (2001), provided
evidence that there is a significant and economically important negative relationship between
inflation and financial development.
Naceur et al. (2005), building on the work of Garcia and Liu (1999), investigated macroeconomic
factors as determinants of stock market development in the MENA region. Employing an
unbalanced panel data from 11 MENA countries over 1979 to 1999 and using fixed effects
specification, they found that credit to private sector, inflation change, savings rate and the ratio of
value traded to GDP are the important determinants of stock market development. They also
provided evidence that financial intermediaries and stock market are complements rather than
substitutes in the growth process and this finding is analogous to that of Garcia and Liu (1999).
Maysami et al (2005) identified short and long-term interest rates, industrial production, price
levels, exchange rate and money supply using cointegration for Singapore stock market as
fundamental factors that influence activities in the stock market. Chen et al (1986) investigated the
impact of some macroeconomic variables on stock market returns. They included short and long
term interest rates, expected and unexpected inflation, industrial production and the spread between
high and low grade bonds. Their data span through the period 1953 to 1972 and applied 12 cross
sectional regression and concluded that some of these macroeconomic variables have a significant
effect on stock returns such as industrial production and changes in risk premium.
30
Pal and Mittal (2011), examined the long run relationship between two Indian markets (BSE
Sensex and S&P CNX Nifty markets) and some macroeconomic variables such as interest rates,
inflation, exchange rate and gross domestic savings. They used the quarterly data from
January1995 to December 2008 and with the use of unit root test, cointegration and error correction
mechanism, they found out that the inflation rate has a significant impact on the capital markets
whereas interest rate and foreign exchange rate have impact on one capital market (i.e. the S&P
CNX Nifty and the BSE Sensex respectively). Gross domestic saving played insignificant role in
the analysis. Naceur, Ghazouani and Omran (2005) using unbalanced panel data from twelve
MENA regional countries in estimating a fixed and random effects specification, observed that
financial intermediary-development and stock market liquidity were significant determinants of
stock market development in the region. Yartey (2008) evaluated the influence of both
macroeconomic and institutional factors on stock market development in 42 emerging economies
with a focus on South Africa. He used a panel data and adopted a Generalized Methods of Moment
(GMM), in which he found that macroeconomic factors such as income level, gross domestic
investment, banking (or financial) sector development, private capital flows and stock market
liquidity are important correlates of stock market development in emerging economies.
Tweneboah and Adam (2008) used a quarterly data from 1991 to 2007 to estimate the impact of
macroeconomic variables on stock prices in Ghana. They used inward foreign investments, the
Treasury bill rate (as a measure of interest rate), the consumer price index (as a measure of
inflation), average international crude oil price and the exchange rate as macroeconomic
independent variables while Databank Stock Index (DSI) represent the Ghanian stock market.
Using Cointegration test and vector error correction model (VECM), they found that there is
cointegration between macroeconomic variables and stock prices in Ghana which suggests a long-
run relationship. The analysis of the VECM indicates that the lagged values of interest rate and
inflation have a significant influence on the stock market. However, the inward foreign direct
investment (FDI), oil price and the exchange rate demonstrated weak influence on price changes.
Kandir (2008) examined the role of macroeconomic factors in explaining Turkish stock returns. He
considered macroeconomic variable including growth rate of industrial production index, change in
consumer price index, growth rate of narrowly defined money supply, change in exchange rate,
interest rate, growth rate of international crude oil prices and return on the MSCI world Equity
index between July 1997 and June 2005. He observed that exchange rate, interest rate and world
market return seem to affect all of the portfolio returns, while inflation rate is significant for only
31
three of the twelve portfolios. Also, industrial production, money supply and oil prices do not
appear to have significant effect on stock returns in turkey.
Given that this study may not review all the literature that linked macroeconomic factors to stock
market development, we will not loose sight of the primary objective of this study which focuses
on institutional factors as determinants of stock market development in Nigeria; we therefore
review some literatures on the above subject matter.
Demirguc-Kunt and Maksimovic (1998), in their investigation of how differences in legal and
financial systems affect firms' use of external financing to fund growth, asserts that countries with
high legal systems score high on an efficiency index as a greater proportion of firms in such
countries use long-term external financing. They reported that growth as a result of external
financing is associated with active stock market and a large banking sector. They observed that
increased reliance on external financing occurs in part because established firms in countries with
well-functioning institutions have lower profit rates. Lombardo and Pagano (2000), in a cross-
country study held that stock market returns are positively correlated with overall measures of the
quality of institutions. Their result shows that dividend yields and earning-price ratios are
positively correlated with judicial efficiency and rule of law, controlling for risk and expected
earnings growth. Their interpretation was that the positive relationship that exists between the
overall quality of the legal system and stock market returns is the consequence of the curtailment of
insiders' private benefits and the increase of firms' profitability associated with better institutions.
Using a sample of 129 countries over 25 years, Djankov, Macliesh and Shliefer (2007) report that
contract rights and enforcement institutions play a big role in the development of financial markets.
Their results also showed that improvements in credit rights or the introduction of credit registries
(or information sharing institutions) leads to an increase in the private credit to GDP ratio. Also,
they reported that legal origins are an important determinant of both creditor rights and information
sharing institution.
Anghel (2005) in a cross-sectional analysis observed that different aspects of the quality of
institutions from a country (corruption, protection of property rights, policies related to opening a
business and maintaining it, etc) are very significant in explaining the level of foreign direct
investment attracted to a country at each point in time. He concluded the ability of a country to
obtain benefits from financial globalization and its vulnerability to financial crisis can be
32
significantly affected by the quality of its domestic institutions and of its macroeconomic
framework.
In their contribution to this literature, Girma and Shortland (2008) appraised the impact of the
political system and legal origin in financial development. Using panel data on developed and
developing countries from 1975 - 2000, their results showed that the degree of democracy and
political stability are significant explanatory factors in determining the speed of financial
development. In a study to examine the determinants of financial development, Law and Habibulla
(2009) used data from 27 economies during 1980 - 2001. Their dynamic panel data analysis results
demonstrated that institutional quality is a statistically significant determinant of banking sector
development and capital market development.
Chinn and Ito (2006), focusing on the links between capital account liberalization, legal and
institutional development, and financial development, employed a panel data analysis
encompassing 108 countries from 1980 to 2000, find that a higher level of financial openness
contributes to the development of equity markets only if a threshold level of general legal systems
and institutions is attained. They noted that among emerging market countries, a higher level of
bureaucratic quality and law and order, as well as the lower levels of corruption, increases the
effect of financial opening in fostering the development of equity markets. Hooper et al. (2009)
applied the international asset pricing models to investigate the link between the quality of
institutions and performance of global stock markets. Their result shows a positive and significant
relationship between stock market performance (measured by the average monthly stock index
excess returns) and institutional quality. They also find a negative association between stock
market total risk factors and the institutional environment. Their study thus, suggests that countries
with better-developed institutional structure have stock markets with higher returns on equity and
lower levels of risk and therefore concluded that a precondition for financial market development is
the improvement of the institutions which govern the process of exchange.
Cherif and Gazdarb (2010) examining institutional and macroeconomic determinants of stock
market development in MENA region, found that the institutional environment as captured by a
composite policy risk index does not appear to be a driving force for the stock market capitalization
in the region. However, in contrast to this finding, Yartey (2008) using a panel data of 42 emerging
markets found that political risk, law and order, and bureaucratic quality are important
33
determinants of stock market development because they enhance the viability of external finance.
His finding also applied specifically to South Africa.
Asongu (2012) examined government quality as determinants of stock market performance in
African countries. Adopting the Instrumental Variable technique, he noted that government quality
dynamics of corruption-control, government -effectiveness, political-stability or no violence, voice
and accountability, regulation quality and rule of law, significantly account for stock market
performance measured by market capitalization, value traded, turnover ratios and number of listed
companies. His findings suggests that countries with better developed government institutions,
would favour stock markets with higher market capitalization, better turnover ratios, higher value
in shares traded and greater number of listed companies.
2.5 Empirical Literature on Nigerian Stock Market.
While financial literature agrees that the stock market is an effective channel for mobilization and
allocation of investment funds which enables businesses harness their human, physical and
organisational resources for optimal output and economic growth, the Nigerian stock market is
seen as playing this important role in the Nigerian economy as various studies to determine the
relationship between various stock market development indicators and economic growth have
proven.
However, the focus of this study is to examine the factors that promote stock market development
while giving primary attention to institutional factors. Some studies that have shown the
importance of macroeconomic factors in stock market development include that of Osisanwo; and
Atanda (2012). They adopted the OLS technique and conclude that interest rate, previous stock
returns level, money supply and exchange rate are very important determinants of stock market
returns which in turn, stimulate the interest of investors and thus, market development.
Arguments in favour of the above finding to answer the question of "what actually determines
stock market development in Nigeria is therefore on the increase. From previous studies supported
by financial literatures and theories, the determinants of stock market development can be
narrowed down to two major factors; economic and institutional determinants.
In Nigeria, majority of the studies on determinants of stock market development have focused more
on macroeconomic determinants or factors such as income level, inflation and interest rates,
exchange rate, savings ratio, investment ratio, stock market liquidity and the gross domestic
product (GDP) while variables used as proxy for stock market development include market
capitalization ratio, turnover ratio and total value of shares traded ratio.
34
Some of such studies includes; Antai et al (2012), who using a cointegration and error correction
mechanism (ECM) found that savings rate, inflation, real economic growth and financial
intermediary progress were determinants of stock market development measured by market
capitalization - GDP ratio. Maku and Atanda (2010), employed the Augmented Engle-Granger
Cointegration test for the period 1984 - 2007 and discovered that stock market performance in
Nigeria is mainly determined by macroeconomic forces in the long-run. The study revealed that the
NSE all share index is more responsive to changes in exchange rate, inflation rate, money supply,
and real output. All the macroeconomic variables were found to have simultaneous and significant
impact on the performance of the Nigerian stock market. Okpara (2010), observed that a positive
relationship exist between stock market development indicators and the GDP which is a
macroeconomic variable. Osamwonyi and Osagie (2012) in their analysis using a VECM model
found that macroeconomic variables (including; interest rates, inflation rates, exchange rates, fiscal
deficit, GDP and money supply) have a significant influence on stock market index (All Share
Index).
2.6 Limitation of Previous Studies:
While a number of studies have focused on macroeconomic factors as determinants of stock market
development in Nigeria, much has not been done to investigate the influence of institutional quality
on stock market development in Nigeria. Also, some of the studies that attempt to empirically
analyse the relationship between stock market development and institutional quality are mostly
cross country studies. For example, Yartey (2008) is based on stock markets in "emerging"
economies while Cherif and Gazdar (2010) focused on countries in the MENA region.
To the best of the knowledge of this researcher, no specific study on institutional quality and stock
market development in Nigeria has been done except the work of Manasseh et al. (2012) which
using corruption index and the Freedom House country rating indices to capture corruption and
institutional/legal framework suggests that institutional quality affects development in the financial
sector. This work however, differs from Manasseh et al (2012), in that it focuses specifically on the
stock market as a part of the financial sector and will be making use of the International Country
Risk Guide (ICRG) index to capture institutional quality. Also, while Manasseh et al (2012) used
quarterly data, this study will make use of annual data. Having observed that there is a dearth of
literature on this topic, the researcher therefore, wishes to contribute to the pool of existing
literature but specifically on the Nigerian stock market.
35
CHAPTER THREE
RESEARCH METHODOLOGY
3.1: Theoretical Framework.
The theoretical foundation of this study is based on the work of Williamson, 1985; North, 1990; La
Porta et al., 1997; and Levine, 1997. While Williamson (1985) argued that good institutions
reduces transaction costs and guard against default in investment rules which makes the market
more attractive for investment purposes, North (1990) maintained that institutions produce stable
rules which are key factors for the success of new investments and also creates a growth-enhancing
business environment. Formal institutions according to North are largely laws, rules and
regulations to which people unambiguously subscribe while Williamson measured institutional
quality as the degree of property rights, the bureaucratic quality, governance structures and the
polity. Following the work of Williamson, Nee and Opper (2009) opined that bureaucracy which
has the essential feature as a rule-governed hierarchy, is structured to ensure that authority and
obligations are specified and limited by formal rules meaning that a bureaucrat’s actions are
weighed based on its legitimacy when conducted within the framework of the formal rules
governing the bureaucracy – the bureaucratic quality therefore measures the degree of compliance
by economic actors to the formal rules governing the bureaucracy – in this case the stock market –
and it facilitates the smooth operation of the market by promoting administrative objectivity while
limiting arbitrary and impulsive actions.
La Porta et al. (1997) is of the view that quality institutions are necessary for sustainable financial
development as the effectiveness of legal institutions has a stronger impact on equity and credit
market development (Pistor et al., 2000). The Law and finance Theory (developed by La Porta et
al., 1997) suggests that the legal origin of a financial system affects its development because the
degree of protection of private ownership rights is influenced by the type of legal institutions
existing in such systems. Their observation is that poorly developed equity markets are associated
with financial systems with low level of shareholder rights. Levine, (1997), described institutions
as “third type” factors crucial for efficient working stock markets meaning that where good
institutions are not present, improvement in stock market operations will be distorted. He examined
the relationship between the legal system and banking system development using long-term GDP
rate, capital accumulation and productivity improvement. His observation is that banks develop
more in countries where the legal system guarantee creditors’ rights and also incorporate such
36
rights into investment contracts than in countries where laws are not enforceable and neglect
creditors thus supporting the argument that institutions will not only provide the basics but must be
capable of controlling and regulating all activities in the financial sector to attain a desirable level
of development.
The aim of good institutional quality therefore, is to provide cohesion and guard the stock market
against unethical practices that will undermine the efficient operation of the market. It will rather
provide a suitable environment for investment and other forms of economic interactions in the
market. It is expected that good institutional quality will enhance the capacity of the stock market
to mobilize savings and allocate resources efficiently leading to high investment returns. Good
institutional quality should also promote healthy competition among the players in the market and
reduce the presence of instability in the market (Manasseh and Asogwa, 2013). High quality
institutions are seen as necessary to abate the effect of financial crisis in the system. Crisis in the
stock market have received special attention due to its crucial role in the financial system. The
issue of corruption has been adjudged to be a catalyst of financial crisis with far reaching
consequences (Igbatayo, 20011). This supports North’s assertion that where the “game” is not
played according to the rules, it confers undue advantage to certain players leading to instability in
the system. This position is supported by the work of Yartey (2008) that corruption has a negative
effect on the stock market; making it less attractive to long-term investors and very risky to invest
in.
Furthermore, the role of the administrative capability of the state in providing and guaranteeing the
institutional foundations for financial sector development has been examined. Nee and Opper
(2009) observed that while reliable legal framework is crucial for long-term financing, bureaucratic
effectiveness is also a necessary factor. This lends credence to the theory by Williamson that
bureaucratic quality is an important determinant of economic outcomes. Good bureaucratic quality
reduces transaction costs and uncertainties. It also stimulates equal and fair treatment of all actors
without regard of the person. One can therefore aver that high bureaucratic quality strengthens the
regulatory structure of the stock market through proper enforcement of rules (Hart, 1995)
Empirically, different approaches have been employed by many studies to measure the influence
of institutional quality on stock market development. Beck et al., (2002) using a panel data analysis
observed that legal systems are important determinants of stock market development and private
property rights protection. Houssem and Sami (2012) applying the GMM system approach to
37
Institutional theories conclude that institutional variables such as law and order, corruption,
investment profile, external conflicts, and democratic accountability have a positive but mostly not-
significant relationship with various measures of financial development and economic growth
(except for credit to the private sector) in the Middle East and North Africa (MENA) countries.
However, Gries and Meierrieks (2010) using dynamic panel models deduce that institutional
variables (measured by rule of law, democratic accountability, protection of property rights,
political stability and bureaucratic quality) are important determinants of financial development
(measured by ratio of Private Credit by deposit money banks to GDP) in Sub-Saharan Africa
countries and suggest that poor institutional quality have constrained financial development in Sub-
Saharan Africa.
Therefore, to measure the relationship between stock market development and the quality of
institutions in Nigeria, this study will apply the Autoregressive distributed lag (ARD) model to
different measures of institutional quality like bureaucratic quality, control of corruption and
democratic accountability following Yartey (2008). The study used annual time series data
covering the period of 1985 to 2012.
3.2: Model Specification.
To test the theories on the effect of institutional quality on stock market development, this study
employs the (ARDL) modelling approach. The Auto-Regressive Distributed Lag method (ARDL)
“Bounds test” approach is based on the ordinary least square (OLS) estimation of a conditional
unrestricted error correction model (UECM) for cointegration as developed by Pesaran et al.
(2001). The advantages of the ARDL approach are as enumerated in the model justification of this
study. According to Pesaran et al., (2001), a conditional ARDL(p,q) error correction model (ECM)
is specified as:
p 1
t 1 yy t 1 y i t i t t
i 1
t 1 t 1 2 t 2 s t s t
y co c t y xt -1+ z x .......................................................(1)
t 1,2..........
x p x p x .......p x
π π ψ ω µ
ε
−
− −
=
− − −
∆ = + + + ∆ + ∆ +
=∆ = ∆ + ∆ + ∆ +
∑x.x
Where tx is the K-dimensional I(1) variable that are not Cointegrated among themselves, tµ and tε are
serially uncorrelated disturbances with zero means and constant variance – covariances, and pi are KxK
coefficient matrices such that the vector autoregressive process in tx∆ is stable, Pesaran and Shin, (1995).
38
According to Pesaran et al. (2001), equation (1) can be delineated according to how the deterministic
components are specified. This study adopts the third case of unrestricted intercepts and no trends and is
therefore specified as:
p 1
t o yy t 1 y t - 1 i t i t t
i 1
y c y x + X X ....................................(2)π π ψ ω µ−
− −
=
∆ = + + ∆ + ∆ +∑x.x
The reduced form of equation (2) is given as:
.1
11 ............................................(3)οµ λ π ε−
=−∆ = + + ∆ +∑ t
tt i tt
pZ Z Z
Where; λ and π are vector matrices that contain the long-run multipliers and short-run dynamic
coefficients of the VECM respectively. ∆ is the first-difference operator while οµ is a (K+1)
vector of intercepts, tZ is a vector of Xt and Yt variables where Yt is an I(1) regressand and Xt is a
vector matrix of a set of regressors which can either be I(0) or I(1).
Model 1: Institutional Quality and Stock Market Development.
To investigate the impact of institutional quality on stock market development in Nigeria with
emphasis on the long-run relationship between institutional quality and stock market development
in Nigeria, we state the ARDL of order P, var(P), following Pesaran et al. (2001) and adopted by
Choong et al. (2005); Odhiambo (2009); and Josphat and Daniel (2012) as:
t 0 1 t 1 2 t 1 3 t 1 4 t 1 5 t 1
p p p p p p
1 t i 2 t i 3 t i 4 t i 5 t i 6 t
i 1 i 0 i 0 i 0 i 0 i 0
.............................................
MKTC MKTC INST GDPPC BSD SML 6TO
MKTC INST GDPPC BSD SML TO
β β β β β β β
δ δ δ δ δ δ µ
− − − − −
− − − − −
= = = = = =
∆ = + + + + + +
∆ + ∆ + ∆ + ∆ + ∆ + ∆ +∑ ∑ ∑ ∑ ∑ ∑
......................................................................................................................................................(4)
where ∆ is the first-difference operator, 1tµ and 2tµ is the error term which is white noise with
standard deviation 1tσ and 2tσ , the sβ and sα measures the intercept and coefficient of the
variables, MKTCt is stock market capitalization ratio; one lag of the dependent variable (MKTCt-1)
is included because the study believe that stock market development is a dynamic concept; INSTit
is Political Risk Rating as a measure of institutional quality at time t, some of the components
include law and order, bureaucratic quality, corruption control and democratic accountability; Non-
Institutional Control variables which can affect stock market development are included and they
are macroeconomic variables including GDP Per Capita (GDPPC), Banking Sector development
(BSD), trade openness and stock market liquidity (SML), To evaluate empirically institutional
39
quality and stock market development in Nigeria with particular emphasis on establishing if a long-
run relationship exists between them, this study adopted apply the Autoregressive Distributed Lag
(ARDL) approach, alternatively called the bounds testing approach as proposed by Pesaran et al.
(2001). The variables are converted to their natural logarithmic form.
Adopting the autoregressive distributed lag (ARDL) bounds testing approach to test for the
existence of a cointegrating long-run relationship is based on the Wald test (F-statistics) for the
joint significance of the lagged levels of the variables. The null hypothesis of no long-run
relationship between the variables in equation (3) is stated as:
0 1 2 3 4 5 6H : 0β β β β β β== = = = = against the alternative hypothesis of cointegration
1 1 2 3 4 5H : 6 0β β β β β β≠ ≠ ≠ ≠ ≠ ≠ . The computed F-statistic will then be compared to the non-
standard critical bounds values as reported in Pesaran et al. (2001). Given the medium size of the
study samples, the optimal lag length for estimating Eq.(3) is selected using the Akaike
Information Criteria (AIC) and the Schwarz Bayes Criterion (SBC) which is known for its
consistency in selecting models.
The lower and upper bounds critical values assumes that the regressors are purely I(0),and purely
I(1), respectively. If the computed F-statistic lies below the lower critical values, the null
hypothesis of no cointegration is not rejected but if the computed F-statistic lies above the upper
critical values, the null hypothesis of no cointegration will not be accepted. The test is inconclusive
if the computed F-statistic lies in between the lower and upper critical values. With this test, the
first objective of this study is achieved.
Once the bounds test establishes a long-run relationship between the variables, an estimate of the
long-run equilibrium relationship between the variables is stated as: p p p p p p
t 0 1 t i 2 t i 3 t i 4 t i 5 t i 6 t..........................
i 1 i 0 i 0 i 0 i 0 i o
MKTC MKTC INST GDPPC BSD SML TO (5)β β β β β β β ε− − − − −
= = = = = =
= + + + + + + +∑ ∑ ∑ ∑ ∑ ∑
The final step involves the estimation of an error correction model (ECM) which measures the
speed of adjustment to restore equilibrium in the dynamic model (Manasseh et al., 2012) as: p p p p p p
t 0 1 t i 2 t i 3 t i 4 t i 5 t i 6 t i t 1 t...............
i 1 i 0 i 0 i 0 i 0 i 0
MKTC MKTC INST GDPPC BSD SML TO ECT (6)δ δ δ δ δ δ δ ϖ ε− − − − − − −= = = = = =
∆ = + ∆ + ∆ + ∆ + ∆ + ∆ + ∆ + +∑ ∑ ∑ ∑ ∑ ∑
With equation (4) and (5), objective one of this study is confirmed while the second objective of
this study is achieved using equation (4) as the coefficients will measure the long-run and short-run
impact of institutional quality on stock market development in Nigeria.
40
Model 2: Causality Testing:
After investigating the long-run relationship institutional quality and stock market development in
Nigeria, this study employ the Granger causality test to determine the direction of causality
between the variables. According to Granger (1969), X causes Y by explaining how much of the
current Y can be explained by past values of Y and then to see whether adding lagged values of X
can improve the explanation. Y is said to be Granger-Caused by X if X helps in the prediction of Y,
or equivalently if the coefficients of the lagged Xs are statistically significant. (Isu & Okpara
(2013)). However, Granger (1988) noted that if a set of variables are cointegrated, there must be
short-run and long-run causality which cannot be captured by the standard first difference VAR
model. Therefore, to overcome this limitation, the Granger causality test has to be conducted within
the framework of the error correction model (ECM). Therefore, if there is no cointegration, this test
for Granger causality will not hold.
To examine the short-run and long-run Granger-causality between the proxies of institutional
quality and stock development in Nigeria, assuming cointegration in Eq. (4), the following model
to test objective three of this study is stated as:
m n
t 0 k t k t t i t 1 t
k 1 i 0
y y x ECT .........................................................................(7)β β ϖ µ∆ − − −= =
= + + ∆ + +∑ ∑�
n m
t 0 k t l k t k t 1 t
k 1 k 0
x x y ECT v .........................................................................(8)γ ϕ δ− − −= =
∆ = + ∆ + ∆ + +∑ ∑
Where yt and xt are stock market development and Institutional Variables respectively. Ut and Vt
are mutually uncorrelated error terms that capture all variations in yt and xt not in the lagged values.
Stock market development is represented by the log of market capitalisation ratio (LMKTCR)
while Institutional quality is represented by the log of Political risk rating index (LPRR).
Following Odhiambo (2009), where a long-run relationship exist between X and Y, Granger-
causality also exist in atleast one direction but does not indicate the direction of temporal causality
between the variables. In this case, the direction of causality is determined using the F-statistic and
the lagged error-correction term. The t-statistic on the coefficient of the lagged error-correction
term represents the long-run causal relationship while the F-statistic on the explanatory variable
represents the short-run causal relationship.
3.3 Justification of Model and Choice of Variables:
The preference for the bounds testing approach over traditional bivariate cointegration techniques
(Engle & Granger, 1987; Johansen, 1988; Johansen & Juselius, 1990) as observed by Choong et al.
41
(2005), and Rahman & Salahudin (2012) in their study of the stock market is predicated on four
fundamental reasons. First, the ARDL model can be estimated by ordinary least square (OLS) once
the model lag order is identified. Second, the long-run and short-run parameters of the model can
be estimated simultaneously, thus capturing the long-run and short-run impacts of the variables.
Third, pre-testing for unit roots is not required as the bounds test can be applied irrespective of the
order of integration of the regressors, be it purely I(0), purely I(1) or fractionally integrated,
however, in this work, test for unit root will be carried out to ensure that none of the variables is
I(2) which will collapse the model. Fourth, the efficiency of the test is further enhanced particularly
with small sample sizes. Furthermore, the error correction model (ECM) can be derived from
ARDL through simple linear transformation, which integrates short-run adjustments with long-run
equilibrium without losing long-run information (Pesaran & Shin, 1999).
The logic behind the inclusion of relevant variables is discussed below:
Dependent Variable: Stock Market Development. In measuring stock market development, we
use market capitalization as a share of GDP. This measure equals the value of listed shares divided
by GDP. The assumption behind this measure is that overall market size is positively correlated
with the ability to mobilize capital and diversify risk on an economy-wide basis. (Yartey, 2008).
Other indicators that have been used as proxy for stock market development in the financial
literature include the number of listed companies, changes in the stock market index. The reason
behind the use of market capitalization - GDP ratio is because it is less arbitrary than other
indicators. Also, Demirguc-Kunt and Levine (1996) have shown that different measures of stock
market development are highly correlated.
Independent Variables: Institutional Quality: Recent research works have focused on the impact
of institutional factors on stock market development and this is related to literatures that examine
the relationship between legal institutional framework and corporate finance. One measure of
institutional quality is political risk as low political risk indicates the existence of good quality
institutions. Political risk has also been considered as a major determinant of foreign capital flows
especially in emerging economies.
Another reason to look at the impact of institutional quality on stock market development is
because it is widely believed that the strengthening of institutions in a country could broaden
appeal and confidence in stock market investment. (Cherif & Gazdar, 2010).
To evaluate the influence of institutional factors on stock market development in Nigeria, this study
will use the political risk index, a composite index from the International Country Risk Guide
42
(ICRG). The ICRG composite political risk index is the sum of 12 indices which include
government stability, socioeconomic conditions, investment profile, internal conflict, corruption,
military in politics, religion in politics, law and order, ethnic tensions, democratic accountability,
and bureaucracy quality. A numerical value is assigned to the components according to a preset
weighted scale for each country covered by the system including Nigeria and all countries are
assessed on the same basis to allow for comparability (Yartey, 2008). A low risk is assigned a high
value while a high risk attracts a low rating.
One advantage that the ICRG indicators enjoy is that it gives a clear indication of the aspect of the
institutions that influences the object of study more and has been widely used in literature. (For
example; Knack & Keifer, 1995; Yartey, 2008; Cherif & Gazdar, 2010). This index is rated from
zero (0) to one hundred (100). The lowest risk attracts overall highest rating (i.e. 100) while the
highest risk attracts overall lowest rating (0).
A weakness of this index according to Yartey (2008) is that it does not give a clear direction on
which aspects of institutions that policy should target. Therefore to overcome this challenge, the
study will investigate the impact of four components or sub-indicators on stock market
development in Nigeria; law and order, bureaucratic quality, democratic accountability and
corruption.
- Law and Order: It measures the strength and impartiality of the legal system including
adherence to rules and regulations. Its strength also determines the extent property rights and
investments are protected.
- Bureaucratic Quality: It assesses the institutional strength and expertise to minimise reversions of
policy or interruptions in government services. High bureaucratic quality is positive to stock
market development.
- Corruption: It refers to the level of corruption in the political system. High level of corruption
attracts a low value of the corruption and is negatively related to economic development.
- Democratic accountability: It measures how responsive the government is to its people, on the
basis that the less responsive it is, the more likely it is that the government will fail.
Control Variables: These include the following:
GDP Per Capita: This has been found to be highly correlated with the size of the stock market.
We use the log of GDP per Capita in US Dollars to measure income level. It is expected to have a
positive impact on stock market development. This is because economic expansion will lead to
increase in demand for financial services which will lead to the establishment of larger and more
43
efficient financial institutions to meet such demand. This agrees with the demand driven hypothesis
in economics. (Adenuga, 2010).
Banking Sector Development: Credit to the private sector relative to GDP will be the proxy to
measure banking sector development in this study. Credit to the private sector represents banks'
contribution to private sector activity through intermediation. It captures the amount of resources
channelled through the banking sector to private firms. It is expected that credit to the private
sector will be positively correlated with stock market capitalization. In the literature, the
relationship between financial intermediaries and stock markets - whether they are strategic
complements or substitutes is still an open question.
Stock Market Liquidity (SML): The study measures stock market liquidity using value traded as
a percentage of GPD. With a more liquid stock market, the amount traded and the saving invested
increase. A liquid market facilitates the easy buying and selling of securities and therefore ease
investment in the long-run. Therefore, a more liquid stock market is expected to boost stock market
development.
3.4 Battery Test:
3.4.1 Unit Root Test: The bound test is based on the assumption that the regressors are I(0) 0r
I(1) while the dependent variable is I(1) and that the procedure will crash in the presence of I(2)
series. To meet this condition, a unite root test (using the Augmented Dickey Fuller (ADF) test)
will be carried out to avoid the case of a spurious regression.
3.4.2: Normality Test: Pesaran et al. (2001) assumed that t2iidN(0, )ε σ� , that is, the error term
has a normal distribution. The Jarque-Bera (JB) test of normality will be employed in this study to
ensure this is the case.
3.4.3: Others: Given that the ARDL bound test approach allows the cointegration relationship to
be estimated by OLS once the lag order of the model is identified, the researcher will carry out the
following test in order to ensure that the OLS assumptions are not violated: (i) Multicollinearity
Test (ii) Heteroscedasticity Test (iii) Serial Correlation test using the Autoregressive LM/Durbin-
Watson test.
3.5 Sources of Data:
This study will make use of secondary data (time series) from various sources which include; the
Central Bank of Nigeria (CBN) Statistical Bulletin and Annual report (various issues), Securities
44
and Exchange Commission(SEC's) Nigerian Capital Market Statistical Bulletin (various issues),
and the International Country Risk Guide(ICRG) by the Political Risk Service(PRS) Group. The
study will cover the period 1985 to 2012. The scope of the study is limited by the availability of
data but significantly covered the Structural Adjustment Program (SAP) era, known by its
transformation and liberalization policies which also affected the financial sector. The study is a
time series analysis.
45
CHAPTER FOUR
DATA ANALYSIS AND PRESENTATION OF RESULT
4.1: Unit Root Tests.
Before applying the ARDL bounds test in this study, the order of integration of the variables is
examined using the unit root test. The rationale behind this test of stationarity is to ensure that the
ARDL assumption of the order of integration of the variables is met. According to Pesaran et al.,
(2001), the dependent variable must be integrated of order one, I(1), while the independent
variable(s) may be integrated of order zero, I(0) or I(1). If any variable is integrated of order two,
I(2), the ARDL testing approach collapses. This means that the ARDL approach is based on the
assumption that the variables are I(0) or I(1) series. Therefore, pre-testing for stationarity is of
critical importance in the ARDL analysis.
This study applied the Augumented Dickey-Fuller (ADF) test for this purpose while the Phillips-
Perron (PP) test was applied for confirmatory analysis with the results summarised in Table 1A&B
(see Appendix 1A&B for full result).
Table 1A: Summary of Unit Root Test Results - ADF
Variables Order of ADF Test Critical ADF
Integration Statistics Statistics
LMKTCR I(1) -5.0029 -3.5950 @5%
LPCI I(0) -5.6923 -3.5875 @5%
LCC I(1) -4.8756 -3.5950 @5%
LCPSR (BSD) I(1) -4.8152 -3.6032 @5%
LDACCNT I(1) -4.0615 -3.7597 @5%
LSML I(1) -4.6527 -3.5950 @5%
LBQ I(1) -5.1158 -3.6328 @5%
46
Table 1B: Summary of Unit Root Test Results - PP
Variables Order of PP Test Critical PP
Integration Statistics Statistics
LMKTCR I(1) -4.9998 -3.5950 @5%
LPCI I(0) -5.6922 -3.5875 @5%
LCC I(1) -4.8756 -3.5950 @5%
LCPSR (BSD) I(1) -8.3937 -3.5950 @5%
LDACCNT I(1) -6.0839 -3.6220 @5%
LSML I(1) -4.6460 -3.5950 @5%
LBQ I(1) -5.1356 -3.6328 @5%
The results showed that the logs of market capitalization ratio (LMKTCR), control of corruption
(LCC), credit to the private sector from banks (LCPSR or BSD), democratic accountability
(LDACCNT), stock market liquidity (liquidity (LSML) and bureaucratic quality(LBQ) are
integrated of order one, I(1), after first differencing while the log of per capita income (LPCI or
GDPPC) is integrated of order zero, I(0) meaning that it is stationary at levels. No variable is
integrated of order two, I(2), thus, indicating the suitability of the variables for the ARDL bounds
testing approach.
4.2: Bounds Test for Cointegration.
Having established the order of integration of the variables, the next step in the ARDL approach is
to test for cointegration among the variables. This test is applied to Equation (4) using the Wald
test to test 0 1 2 3 4 5 6H : 0β β β β β β= = = = = = against the 1 1 2 3 4 5H : 6 0β β β β β β≠ ≠ ≠ ≠ ≠ ≠ , having
obtained the coefficient of the long run parameters using the OLS technique. The Schwarz Bayesian
Criterion (SBC) and the Akaike Information Criterion (AIC) were used to select the appropriate lag length
as the result is presented below:
47
VAR Lag Order Selection Criteria Endogenous variables: LMKTCR LCC LDACCNT LBQ LSML LCPSR LPCI Exogenous variables: C Date: 10/22/14 Time: 00:32 Sample: 1985 2012 Included observations: 26
Lag LogL LR FPE AIC SC HQ 0 -87.15813 NA 3.30e-06 7.242933 7.581651 7.340471 1 75.90478 225.7794 5.89e-10 -3.820028* 1.178610* -2.356949* 2 154.6604 66.63934* 1.61e-10* -1.531137 1.260746 -0.750828 * indicates lag order selected by the criterion
LR: sequential modified LR test statistic (each test at 5% level) FPE: Final prediction error AIC: Akaike information criterion SC: Schwarz information criterion HQ: Hannan-Quinn information criterion Pesaran and Pesaran (1997) suggestes that any result that supports cointegration in at least one lag structure
provides evidence for the existence of a long-run relationship. The computed F-statistics together with the
critical bounds values are presented in Table 2.
Table 2: Bounds Test for Cointegration.
Case v: With Unrestricted Intercept and Trend.
Flmktcr(LMKTCR/LCC, LDACCNT, LBQ, LSML, LCPSR, LPCI) Computed Fstatistics: 6.98**
Critical Bound Values: 1% 5% 10%
.................. ....................... ..................................
I(0) I(1) I(0) I(1) I(0) I(1)
3.60 4.90 2.87 4.00 2.53 3.59
Asymptotic critical bounds values are obtained from Table CI(v) Case v: Unrestricted intercept and Unrestricted trend
for K = 6 from Pesaran et al. (2001); ** denotes significant at 5% level of significance. The number of regressors is 6.
The result of the ARDL bound test for cointegration shows that the computed F-statistics (6.98) is
significant at 5% level because the computed F statistics exceeds the upper bound critical value
(4.00) and therefore provides evidence to reject the null hypothesis of no cointegration at 5%
48
significance level meaning that a long-run cointegration relationship does exist. Since the bounds
test shows that there is cointegration, the next step is to estimate the long-run levels model,
(Equation 5) by OLS, and construct the residuals series (Error Correction Term (ECT)) and then fit
a regular restricted error correction model (ECM) using equation (6) to obtain the coefficient of
one-lagged level of the ECT. The result is presented in table 3.
Table 3: Summary of Long-run and Short-run (ECM) Regression Result
Dependent Variable = LMKTCR
Long-Run Results
Variable Coefficient T-Statistic
C 3.6200* 1.7817
@Trend 0.0513 1.0657
LCC -0.7356*** 3.0587
LDACCNT 0.3660** 2.5071
LBQ 0.0219 0.0681
LSML 0.2695*** 4.7477
LCPSR -0.0323 -0.1859
LPCI -0.0408 -0.1964
Short-Run Results (ECM)
Dependent Variable: ∆LMKTCR
Variable Coefficient T-Statistic
Constant -0.2067 -1.2773
49
@Trend 0.0083 1.2778
∆LCC -0.4032 -0.5812
∆LDACCNT 0.1192 0.8826
∆LBQ -0.1874 -0.5069
∆LSML 0-2214** -2.1291
∆LCPSR -0.2370 -1.0532
∆LPCI 0.3836 0.9929
ECT(-1) -0.31517** 4.6166
Note: ***, **, *: denotes significance at 1, 5, and 10% levels respectively.
The ECT is -0.315 and also has the expected negative sign, suggesting that the speed of adjustment
of stock market capitalization to changes in institutional quality and other determining variables is
about 31.5% within the first year to ensure full convergence to its equilibrium level. The ECT is
statistically significant at 5% level, giving a confirmation to the bounds test for cointegration
meaning that a long-run equilibrium relationship exists between the variables. Based on this result,
the study concludes that a long-run relationship exists between stock market development and
institutional quality in Nigeria.
4.3: Impact Analysis:
Having established a long-run relationship, long-run elasticities are estimated to determine the
long-run impact of institutional quality on stock market development. This is done by dividing the
coefficient of the one-lagged level independent variable (see appendix 2) by the estimated
coefficient of the one-lagged level dependent variable and multiplying it with a negative sign
(Pesaran et al. 2001). The result is as presented below:
50
Table 4: Long-run and Short-run Impact.
Variable UECM Coefficient Long-run Elasticity
LMKTCR(-1) -1.3725
LCC(-1) -1.0078 -(-1.0078/-1.3725) = -0.73*
LDACCNT(-1) 1.5035 -(1.5035/-1.3725) = 1.10***
LBQ(-1) 1.1439 -(1.1439/-1.3725) = 0.83
LSML(-1) 0.2767 -(0.2767/-1.3725) = 0.20
LCPSR(-1) 1.4562 -(1.4562/-1.3725) = 1.06**
LPCI(-1) 1.1601 -(1.1601/-1.3725) = 0.85
Short-Run Elasticities:
Variable Coefficient t-Statistic
D(LCC) -1.1464 2.3956**
D(LDACCNT) 0.6886 3.4995***
D(LBQ) 1.5421 2.4456**
D(LSML) 0.3107 2.7133**
Note: ***, **, and * denotes significant at 1, 5 and 10% levels respectively.
Table 4 shows that the level of corruption is negatively related to stock market development in the
long-run and is significant at 10% level. This result meets the a priori expectation, meaning that a
1% increase in the level of corruption in Nigeria is expected to result in a 0.73% fall in stock
market capitalisation meaning that a 10% increase in corruption level will lead to a 7.30% decrease
in stock market development. This is because corruption in the stock market is seen as a catalyst
for poor corporate governance and weak market regulation leading to several unethical practices
such as share-price manipulation, insider information abuse, and outright fraud in the market. This
conforms to the findings of Igbatayo (2011) that corrupt practices in the capital market were major
contributors to the crisis experienced in the market between 2008 and 2010 beside the global
51
financial crisis. Also, corruption discourages foreign capital inflow which is seen as having a
positive relationship with financial development (Anghel, 2005) by creating a less transparent
business environment and weak regulation, thus, it creates unfair advantages to certain parties
against others leading to erosion of investors’ confidence in the efficient working of the market.
The coefficient of the log of democratic accountability (LDACCNT) indicates that democratic
accountability has a significant and positive effect on stock market development. The effect
indicates that a 1% rise in LDACCNT enhances greater stock market capitalisation by 1.10%,
ceteris paribus. This finding supports the view of Asongu (2012) that greater government
responsiveness to the yearning of its citizens especially stock market investors, boosts their
confidence and desire to participate actively in the market which deepens market operations. The
impact of bureaucratic quality (LBQ) is positive but not significant. The result shows that a 1%
increase in regulatory capacity and administrative mechanism in the market will lead to a 0.83%
increase in stock market development. This finding corresponds with the view of Yartey (2010).
Also, Tadesse (2005), provides evidence that high quality regulation enthrones transparency,
fairness and effectiveness in the stock market and thus increased market liquidity. This is because
the market serves as a channel for adequate monitoring and controlling of management decisions
by providing the required information to outsiders (investors, shareholders, underwriting
investment bankers) which build their confidence to invest leading to increase in market liquidity.
Furthermore, the result shows that the long-rum impact of credit to the private sector on stock
market development is positive and considerably significant at 5% level. The result reveals that
1.06% increase in stock market development is accounted for a 1% increase in credit to the private
sector. This means that development of the banking sector and the stock market in Nigeria is
complementary and not substitutes. This relationship can also explained the huge fluctuations
recorded in stock market capitalisation as a result of the global financial crisis which led to a slump
in share price of most banks which is seen as the most active sector in the Nigerian stock Market.
Stock market liquidity (LSML) is positively related to stock market development as a 1% increase
in LSML increases market capitalisation by 0.20%. This however, is not significant. The variables
without exception have the expected signs.
The short-run elasticities are computed as the estimated coefficients of the first differenced
variables. The short-run results as reported in Table 4 shows that institutional quality exerts
significant impact on stock market development in the short-run. The level of corruption is
negatively related to stock market development as expected. It is found that a 1% increase in the
52
level of corruption reduces stock market capitalization by 1.14% and is significant at 5% level.
Democratic accountability and Bureaucratic quality are positively associated with stock market
development as expected and are significant at 1 and 5% levels respectively. This means that
increased regulatory capacity enhances stock market operation even in the short-run (Asongu, 2012
and Yartey, 2008). Stock market liquidity which is a macroeconomic variable is also significant at
5%, and corresponds with the expectation. A 1% rise in SML will increase market capitalization
by 0.31% in the short-run. The positive effect is because a liquid stock market makes investment
less risky and more attractive as they allow savers to acquire and/or quickly sell their assets
(equity) when the need arises, either to access their savings or to alter their portfolios. Also, as
stock market liquidity accelerates longer-term, more profitable investments, it increases capital
allocation efficiency and therefore boost economic growth prospects in the long-run (Levine &
Zervous, 1998).
4.4: Diagnostic Test Discussions:
The Autoregressive Conditional Heteroskedasticity (ARCH) test for testing heteroskedasticity in
the error process in the model has an F-statistic of 0.3921(χ 2 = 0.5143) which is not statistically
significant. This indicates that the problem of heteroskedasticity is none existent in the model. The
Breush – Godfrey serial correlation lagrange multiplier (LM) test for higher order – serial
correlation has an F-statistic of 0.7651 (χ 2 = 0.2455) which is also not statistically significant
meaning that we cannot reject the null hypothesis of no serial correlation in the residuals. The
Jacque – Bera χ 2 – Statistic of 0.4252 indicates that the error process is normally distributed as the
short-run ARDL model showes a goodness-of-fit of 60% suggesting that the model is correctly
fitted. Therefore, the residuals are expected to be distributed as white noise and the coefficient
valid for policy discussion.
ECM Diagnostic Tests.
Test F-Statistic Prob. Value
χ 2 ARCH 0.3927 0.5143
χ 2 Normal 1.7102 0.4252
53
Reset 0.2490 0.6268
χ 2 Serial 0.7651 0.2455
R2 = 0.60
Finally, the Cummulative Sum of recursive residuals (CUSUM) and CUSUM of squares
(CUSUMSQ) test proposed by Brown et al. (1975) is applied to confirm that the model satisfied
the stability test. Figure 4 below present plots of both CUSUM and CUSUMSQ test statistics that
fall inside the critical bounds of 5% significance. This implies that the estimated parameters are
stable over the period 1985 – 2012.
Figure 4 : CUSUM and CUSUMSQ Graph.
-8
-6
-4
-2
0
2
4
6
8
2007 2008 2009 2010 2011 2012
CUSUM 5% Significance
54
-0.4
0.0
0.4
0.8
1.2
1.6
2007 2008 2009 2010 2011 2012
CUSUM of Squares 5% Significance
The above stability test is further confirmed by the Ramsey Reset test with an F-stat. of 0.2490 (P-
value = 0.6268) meaning that the model is dynamically stable.
4.5. Analysis of Causality Test Based on Error-Correction Model.
Having established long-run relationship, this study will test for causality between
institutional quality and stock market development to capture objective three of this study. This is
done within the error-correction model as Granger (1988) noted that if a set of variables are
cointegrated, there must be short-run and long-run causality which cannot be captured by the
standard first difference VAR model. Therefore the causality in this case is established through the
significance of the coefficient of the lagged error-correction term and joint significance of the
lagged differences of the explanatory variables using the wald test, Odhiambo (2009). The
causality test is reported as:
55
Table 5: Granger Non-Causality Test.
Stock Market Development and Institutional Quality
Dependent Variable
Causal Flow F-statistic t-Test
on ECM
R2
Stock Market Development
(y)
Institutional Quality →
Stock Market Development
0.5391(0.5919)
-2.9520**
0.33
Institutional Quality ( x)
Stock Market Development → Institutional
Quality
3.1278(0.0669)
0.27
Note: ** denotes significant at 5% level and ( ) is p-value.
The Granger causality test results reported in table 5 shows that a long-run unidirectional causal
relationship that runs from institutional quality to stock market development exists. This result is
supported by the coefficient of the lagged error-correction term in equation (7). The summary of
the results indicates that the coefficient is -1.6007 (see appendix 4) while the t-statistic is -2.9520
which is significant at 5% level. The application of the wald test to the coefficient of the variables
in equation (7) and (8) indicates that the null hypothesis of no causal relationship cannot be
rejected meaning that the causal relationship in the short-run is not significant.
Summary of Causality Test.
Variables. Causality General Conclusion
Stock Market Development (∆LMKTCR) and Institutional Quality (∆LPRR)
There is a long-run unidirectional causal flow from Institutional quality to stock market development
Institutional quality Granger – Causes stock market development especially in the long-run.
56
CHAPTER FIVE
SUMMARY, RECOMMENDATIONS AND CONCLUSION.
5.1 SUMMARY
The literature on institutional quality – stock market development nexus centres on the importance
of institutions for a sustainable development of the stock market. While the stock market is seen as
the spine of the financial market considering its strategic role as a vehicle for investment financing
and efficient resource mobilization and allocation, the existence of the right- kind of institutions is
central for the stock market to perform optimally. This is because viable institutions support
macroeconomic stability, promotes social cohesion, creates less-risky business environment and
therefore accelerate market efficiency. This study therefore resorted to establish the nature of the
relationship that exists between institutional quality and stock market development in Nigeria with
the objective to: First, investigate if a long-run relationship exists between institutional quality and
stock market development in Nigeria. Second, examine the impact of institutional quality on stock
market development in Nigeria. And third, determine the direction of causality between
institutional quality and stock market development in Nigeria.
After employing the Auto-Regressive Distributed Lag (ARDL) model, the study discovered that a
long-run relationship exists between institutional quality and stock market development in Nigeria.
Also, the study shows that the quality of institutions in Nigeria impacts significantly on stock
market development. The result reveals that the level of corruption, democratic accountability and
bureaucratic quality which are institutional factors are important determinants of stock market
development. This suggests that if corrupt practices are reduced to their barest minimum, the stock
market will experience accelerated development. Moreover, if there is accountability in governance
and efficient administrative and regulatory system, the stock market will also perform better.
Efficient institutions reduce political risk which is an important factor in investment decisions. This
result is generally in agreement with theoretical and empirical literature on the relationship between
institutional quality and stock market development. The result further indicates that Banking sector
development (using credit to the private sector) and stock market development are positively
related. This suggests that banking sector development is a compliment to the stock market in
financing investment. Stock market liquidity is also an important determinant of stock market
development.
57
Furthermore, the long-run unidirectional causality from institutional quality to stock market
development supports the literature and also resolves the third objective of this study. The findings
of this study presents its critical policy relevance to the government or market regulators in that it
brought to the fore the dimension of institutional quality that needs urgent attention for a
sustainable development of the stock market.
5.2 RECOMMENDATIONS
The findings of this study having identified institutions as very critical for stock market
development in Nigeria, based on these findings, this study makes the following recommendations:
� First, corrupt practices within the stock market should be checked so as to boost investors’
confidence and stock market liquidity. This means that the fight against corruption both at
the macro and micro levels should be intensified by strengthening the operations of anti-
corruption agencies like the EFCC and the ICPC. Also, legal proceedings against suspects
(individuals and entities), investigated for acts of corruption in the market should be fast-
tracked and adequate sanctions imposed if found guilty to serve as deterrents to others. The
researcher is of the view that the Investments and Securities Tribunal, if properly
empowered by broadening its areas of jurisdiction can be more effective to carry out this
task than the regular courts known for its slow judicial processes.
� Second, the market regulators should raise an effective standard of market regulation,
supervision and oversight. Such measures include ensuring that listed firms maintain
adequate records, comply with the regulatory financial resources requirements, have a
compliance manual and compliance monitoring programs.
� Third, there should be established structures for timely access to reliable information –
listed companies should be mandated to release performance results and profit forecasts to
the public immediately this information receives the board approval. This will enforce
transparency and accountability. Furthermore, an enforcement framework should be
developed to prevent market manipulation – robust policing and enforcement framework
should be put in place to take strict disciplinary measures against any form of market
manipulation or insider abuse. Such standards should be made publicly available to serve as
a deterrent to operators and market participants.
58
Also of critical importance is the need to enhance liquidity in the market. One way of doing this, is
to ensure that unfunded pension liabilities are funded by the government and companies and that
such funds are invested in the stock market. Finally, the corporate governance code issued by SEC
(the Securities and Exchange Commission) should be maximally enforced.
5.3 CONCLUSION
The role of institutions in promoting sustainable development has drawn significant attention and is
seen as central in development policies of modern economies. Theoretical and empirical findings
are in support of good quality institutions as fundamental for effective and efficient stock market
operations and development. This study therefore explored the correctness of this assertion with
respect to the Nigeria stock market by investigating the nature of the relationship between
institutional quality and stock market development in Nigeria with a set of data covering the period
(1985 – 2012). By adopting the Autoregressive Distributed Lag (ARDL) approach, the researcher
was able to investigate the nature of the relationship as well as estimate the impact of institutional
quality on the Nigerian stock market.
This study has made some significant findings that should attract the attention of market regulators
and therefore concludes that efforts should be geared toward strengthening the institutions in
Nigeria so that the stock market can perform its critical role in the economy optimally. Institutional
aspects that require urgent attention include control of corruption which is inimical to stock market
development. Also, regulatory policies that increase market transparency and effectiveness and
ultimately leads to stock market development should be employed. While this study focused only
on the impact of formal institutions, the researcher recommend that further studies in this area
should inculcate the influence of informal institutions.
59
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Appendix 1A: Unit Root Test - ADF LMKTCR (Log of Market Capitalization Ratio) Null Hypothesis: D(LMKTCR) has a unit root Exogenous: Constant, Linear Trend Lag Length: 0 (Automatic - based on SIC, maxlag=6)
t-Statistic Prob.* Augmented Dickey-Fuller test statistic -5.002853 0.0023
Test critical values: 1% level -4.356068 5% level -3.595026 10% level -3.233456 *MacKinnon (1996) one-sided p-values.
Augmented Dickey-Fuller Test Equation Dependent Variable: D(LMKTCR,2) Method: Least Squares Date: 10/23/14 Time: 14:29 Sample (adjusted): 1987 2012 Included observations: 26 after adjustments
Variable Coefficient Std. Error t-Statistic Prob. D(LMKTCR(-1)) -1.060824 0.212044 -5.002853 0.0000
C -0.047664 0.107342 -0.444035 0.6612 @TREND(1985) 0.006924 0.006629 1.044457 0.3071
R-squared 0.522463 Mean dependent var 0.012971
Adjusted R-squared 0.480939 S.D. dependent var 0.348630 S.E. of regression 0.251174 Akaike info criterion 0.182825 Sum squared resid 1.451032 Schwarz criterion 0.327990 Log likelihood 0.623275 Hannan-Quinn criter. 0.224627 F-statistic 12.58193 Durbin-Watson stat 1.947781 Prob(F-statistic) 0.000204
LPCI(Log of Per Capita Income -Proxy for GDP Per Ca pita – GDPPC). Null Hypothesis: LPCI has a unit root Exogenous: Constant, Linear Trend Lag Length: 0 (Automatic - based on SIC, maxlag=6)
t-Statistic Prob.* Augmented Dickey-Fuller test statistic -5.692295 0.0435
Test critical values: 1% level -4.339330 5% level -3.587527 10% level -3.229230 *MacKinnon (1996) one-sided p-values.
Augmented Dickey-Fuller Test Equation Dependent Variable: D(LPCI) Method: Least Squares Date: 10/24/14 Time: 15:58
68
Sample (adjusted): 1986 2012 Included observations: 27 after adjustments
Variable Coefficient Std. Error t-Statistic Prob. LPCI(-1) -0.561470 0.098637 -5.692295 0.0435
C 0.775210 0.672971 1.151922 0.2607 @TREND(1985) 0.007949 0.022362 0.355476 0.7253
R-squared 0.145118 Mean dependent var 0.209068
Adjusted R-squared 0.073877 S.D. dependent var 0.161938 S.E. of regression 0.155841 Akaike info criterion -0.775518 Sum squared resid 0.582876 Schwarz criterion -0.631536 Log likelihood 13.46949 Hannan-Quinn criter. -0.732704 F-statistic 2.037019 Durbin-Watson stat 1.683046 Prob(F-statistic) 0.152362
LCC(Log of Corruption Control)
Null Hypothesis: D(LCC) has a unit root Exogenous: Constant, Linear Trend Lag Length: 0 (Automatic - based on SIC, maxlag=6)
t-Statistic Prob.* Augmented Dickey-Fuller test statistic -4.875664 0.0031
Test critical values: 1% level -4.356068 5% level -3.595026 10% level -3.233456 *MacKinnon (1996) one-sided p-values.
Augmented Dickey-Fuller Test Equation Dependent Variable: D(LCC,2) Method: Least Squares Date: 10/23/14 Time: 14:41 Sample (adjusted): 1987 2012 Included observations: 26 after adjustments
Variable Coefficient Std. Error t-Statistic Prob. D(LCC(-1)) -1.015533 0.208286 -4.875664 0.0001
C -0.042176 0.068207 -0.618345 0.5424 @TREND(1985) 0.002161 0.004168 0.518387 0.6091
R-squared 0.508276 Mean dependent var -8.70E-18
Adjusted R-squared 0.465517 S.D. dependent var 0.217015 S.E. of regression 0.158656 Akaike info criterion -0.735988 Sum squared resid 0.578951 Schwarz criterion -0.590823 Log likelihood 12.56784 Hannan-Quinn criter. -0.694186 F-statistic 11.88710 Durbin-Watson stat 2.002765 Prob(F-statistic) 0.000285
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LCPSR [ Used to to represent Banking Sector Develo pment (BSD)] Null Hypothesis: D(LCPSR) has a unit root Exogenous: Constant, Linear Trend Lag Length: 1 (Automatic - based on SIC, maxlag=6)
t-Statistic Prob.* Augmented Dickey-Fuller test statistic -4.815227 0.0038
Test critical values: 1% level -4.374307 5% level -3.603202 10% level -3.238054 *MacKinnon (1996) one-sided p-values.
Augmented Dickey-Fuller Test Equation Dependent Variable: D(LCPSR,2) Method: Least Squares Date: 10/23/14 Time: 14:34 Sample (adjusted): 1988 2012 Included observations: 25 after adjustments
Variable Coefficient Std. Error t-Statistic Prob. D(LCPSR(-1)) -1.389410 0.288545 -4.815227 0.0001
D(LCPSR(-1),2) 0.361557 0.202210 1.788031 0.0882 C -0.179592 0.116140 -1.546331 0.1370
@TREND(1985) 0.013986 0.007147 1.956800 0.0638 R-squared 0.575378 Mean dependent var 0.013437
Adjusted R-squared 0.514718 S.D. dependent var 0.343614 S.E. of regression 0.239369 Akaike info criterion 0.124026 Sum squared resid 1.203248 Schwarz criterion 0.319046 Log likelihood 2.449679 Hannan-Quinn criter. 0.178116 F-statistic 9.485261 Durbin-Watson stat 2.194681 Prob(F-statistic) 0.000368
LTO (Log of Trade Openness) Null Hypothesis: LTO has a unit root Exogenous: Constant, Linear Trend Lag Length: 0 (Automatic - based on SIC, maxlag=6)
t-Statistic Prob.* Augmented Dickey-Fuller test statistic -3.476673 0.0624
Test critical values: 1% level -4.339330 5% level -3.587527 10% level -3.229230 *MacKinnon (1996) one-sided p-values.
Augmented Dickey-Fuller Test Equation Dependent Variable: D(LTO) Method: Least Squares Date: 10/24/14 Time: 16:08
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Sample (adjusted): 1986 2012 Included observations: 27 after adjustments
Variable Coefficient Std. Error t-Statistic Prob. LTO(-1) -0.607572 0.174757 -3.476673 0.0020
C 2.367252 0.658464 3.595110 0.0015 @TREND(1985) 0.006751 0.006664 1.012932 0.3212
R-squared 0.349276 Mean dependent var 0.028847
Adjusted R-squared 0.295049 S.D. dependent var 0.276011 S.E. of regression 0.231743 Akaike info criterion 0.018061 Sum squared resid 1.288912 Schwarz criterion 0.162043 Log likelihood 2.756181 Hannan-Quinn criter. 0.060874 F-statistic 6.441000 Durbin-Watson stat 2.293133 Prob(F-statistic) 0.005764
LDACCNT( Log of Democratic Accountability) Null Hypothesis: D(LDACCNT) has a unit root Exogenous: Constant, Linear Trend Lag Length: 4 (Automatic - based on SIC, maxlag=6)
t-Statistic Prob.* Augmented Dickey-Fuller test statistic -4.061514 0.0304
Test critical values: 1% level -4.728363 5% level -3.759743 10% level -3.324976 *MacKinnon (1996) one-sided p-values.
Warning: Probabilities and critical values calculated for 20 observations and may not be accurate for a sample size of 15
Augmented Dickey-Fuller Test Equation Dependent Variable: D(LDACCNT,2) Method: Least Squares Date: 10/24/14 Time: 16:13 Sample (adjusted): 1991 2012 Included observations: 15 after adjustments
Variable Coefficient Std. Error t-Statistic Prob. D(LDACCNT(-1)) -2.787845 0.686405 -4.061514 0.0036
D(LDACCNT(-1),2) 1.196722 0.601321 1.990157 0.0818 D(LDACCNT(-2),2) 1.047020 0.451194 2.320555 0.0489 D(LDACCNT(-3),2) 0.512480 0.360886 1.420060 0.1934 D(LDACCNT(-4),2) 0.227828 0.350984 0.649113 0.5345
C 0.142213 0.215721 0.659247 0.5283 @TREND(1985) -0.004683 0.010777 -0.434579 0.6754
R-squared 0.907225 Mean dependent var 0.010239
Adjusted R-squared 0.837643 S.D. dependent var 0.613197 S.E. of regression 0.247079 Akaike info criterion 0.346505 Sum squared resid 0.488383 Schwarz criterion 0.676928 Log likelihood 4.401212 Hannan-Quinn criter. 0.342985 F-statistic 13.03830 Durbin-Watson stat 3.112421 Prob(F-statistic) 0.000953
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LSML(Log of Stock Market Liquidity). Null Hypothesis: D(LSML) has a unit root Exogenous: Constant, Linear Trend Lag Length: 0 (Automatic - based on SIC, maxlag=6)
t-Statistic Prob.* Augmented Dickey-Fuller test statistic -4.652764 0.0051
Test critical values: 1% level -4.356068 5% level -3.595026 10% level -3.233456 *MacKinnon (1996) one-sided p-values.
Augmented Dickey-Fuller Test Equation Dependent Variable: D(LSML,2) Method: Least Squares Date: 10/24/14 Time: 16:19 Sample (adjusted): 1987 2012 Included observations: 26 after adjustments
Variable Coefficient Std. Error t-Statistic Prob. D(LSML(-1)) -0.956218 0.205516 -4.652764 0.0001
C -0.157584 0.282113 -0.558586 0.5818 @TREND(1985) 0.013447 0.017356 0.774811 0.4463
R-squared 0.485600 Mean dependent var -0.009804
Adjusted R-squared 0.440869 S.D. dependent var 0.881992 S.E. of regression 0.659510 Akaike info criterion 2.113528 Sum squared resid 10.00393 Schwarz criterion 2.258693 Log likelihood -24.47586 Hannan-Quinn criter. 2.155330 F-statistic 10.85613 Durbin-Watson stat 1.864282 Prob(F-statistic) 0.000479
LBQ( Log of Bureaucratic Quality). Null Hypothesis: D(LBQ) has a unit root Exogenous: Constant, Linear Trend Lag Length: 0 (Automatic - based on SIC, maxlag=1)
t-Statistic Prob.* Augmented Dickey-Fuller test statistic -5.115975 0.0025
Test critical values: 1% level -4.440739 5% level -3.632896 10% level -3.254671 *MacKinnon (1996) one-sided p-values.
Augmented Dickey-Fuller Test Equation Dependent Variable: D(LBQ,2) Method: Least Squares Date: 10/24/14 Time: 16:21 Sample (adjusted): 1987 2012 Included observations: 22 after adjustments
72
Variable Coefficient Std. Error t-Statistic Prob. D(LBQ(-1)) -1.149939 0.224774 -5.115975 0.0001
C 0.129281 0.068505 1.887185 0.0745 @TREND(1985) -0.006338 0.003994 -1.586964 0.1290
R-squared 0.579541 Mean dependent var -5.73E-18
Adjusted R-squared 0.535283 S.D. dependent var 0.213910 S.E. of regression 0.145823 Akaike info criterion -0.886726 Sum squared resid 0.404021 Schwarz criterion -0.737948 Log likelihood 12.75399 Hannan-Quinn criter. -0.851678 F-statistic 13.09438 Durbin-Watson stat 2.176119 Prob(F-statistic) 0.000266
Appendix 1B: Unit Root Test – PP.
Null Hypothesis: D(LMKTCR) has a unit root Exogenous: Constant, Linear Trend Bandwidth: 2 (Newey-West automatic) using Bartlett kernel
Adj. t-Stat Prob.* Phillips-Perron test statistic -4.999829 0.0023
Test critical values: 1% level -4.356068 5% level -3.595026 10% level -3.233456 *MacKinnon (1996) one-sided p-values. Residual variance (no correction) 0.055809
HAC corrected variance (Bartlett kernel) 0.050747
Phillips-Perron Test Equation Dependent Variable: D(LMKTCR,2) Method: Least Squares Date: 10/24/14 Time: 17:35 Sample (adjusted): 1987 2012 Included observations: 26 after adjustments
Variable Coefficient Std. Error t-Statistic Prob. D(LMKTCR(-1)) -1.060824 0.212044 -5.002853 0.0000
C -0.047664 0.107342 -0.444035 0.6612 @TREND(1985) 0.006924 0.006629 1.044457 0.3071
R-squared 0.522463 Mean dependent var 0.012971
Adjusted R-squared 0.480939 S.D. dependent var 0.348630 S.E. of regression 0.251174 Akaike info criterion 0.182825 Sum squared resid 1.451032 Schwarz criterion 0.327990 Log likelihood 0.623275 Hannan-Quinn criter. 0.224627 F-statistic 12.58193 Durbin-Watson stat 1.947781 Prob(F-statistic) 0.000204
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Null Hypothesis: D(LCC) has a unit root Exogenous: Constant, Linear Trend Bandwidth: 0 (Newey-West automatic) using Bartlett kernel
Adj. t-Stat Prob.* Phillips-Perron test statistic -4.875664 0.0031
Test critical values: 1% level -4.356068 5% level -3.595026 10% level -3.233456 *MacKinnon (1996) one-sided p-values. Residual variance (no correction) 0.022267
HAC corrected variance (Bartlett kernel) 0.022267
Phillips-Perron Test Equation Dependent Variable: D(LCC,2) Method: Least Squares Date: 10/24/14 Time: 17:40 Sample (adjusted): 1987 2012 Included observations: 26 after adjustments
Variable Coefficient Std. Error t-Statistic Prob. D(LCC(-1)) -1.015533 0.208286 -4.875664 0.0001
C -0.042176 0.068207 -0.618345 0.5424 @TREND(1985) 0.002161 0.004168 0.518387 0.6091
R-squared 0.508276 Mean dependent var -8.70E-18
Adjusted R-squared 0.465517 S.D. dependent var 0.217015 S.E. of regression 0.158656 Akaike info criterion -0.735988 Sum squared resid 0.578951 Schwarz criterion -0.590823 Log likelihood 12.56784 Hannan-Quinn criter. -0.694186 F-statistic 11.88710 Durbin-Watson stat 2.002765 Prob(F-statistic) 0.000285
Null Hypothesis: LPCI has a unit root Exogenous: Constant, Linear Trend Bandwidth: 1 (Newey-West automatic) using Bartlett kernel
Adj. t-Stat Prob.* Phillips-Perron test statistic -5.692295 0.0392
Test critical values: 1% level -4.339330 5% level -3.587527 10% level -3.229230 *MacKinnon (1996) one-sided p-values.
74
Residual variance (no correction) 0.021588
HAC corrected variance (Bartlett kernel) 0.022752
Phillips-Perron Test Equation Dependent Variable: D(LPCI) Method: Least Squares Date: 10/24/14 Time: 17:42 Sample (adjusted): 1986 2012 Included observations: 27 after adjustments
Variable Coefficient Std. Error t-Statistic Prob. LPCI(-1) -0.561461 0.098637 -5.692295 0.0454
C 0.775210 0.672971 1.151922 0.2607 @TREND(1985) 0.007949 0.022362 0.355476 0.7253
R-squared 0.145118 Mean dependent var 0.209068
Adjusted R-squared 0.073877 S.D. dependent var 0.161938 S.E. of regression 0.155841 Akaike info criterion -0.775518 Sum squared resid 0.582876 Schwarz criterion -0.631536 Log likelihood 13.46949 Hannan-Quinn criter. -0.732704 F-statistic 2.037019 Durbin-Watson stat 1.683046 Prob(F-statistic) 0.152362
Null Hypothesis: D(LSML) has a unit root Exogenous: Constant, Linear Trend Bandwidth: 2 (Newey-West automatic) using Bartlett kernel
Adj. t-Stat Prob.* Phillips-Perron test statistic -4.646063 0.0052
Test critical values: 1% level -4.356068 5% level -3.595026 10% level -3.233456 *MacKinnon (1996) one-sided p-values. Residual variance (no correction) 0.384767
HAC corrected variance (Bartlett kernel) 0.369224
Phillips-Perron Test Equation Dependent Variable: D(LSML,2) Method: Least Squares Date: 10/24/14 Time: 17:55 Sample (adjusted): 1987 2012 Included observations: 26 after adjustments
Variable Coefficient Std. Error t-Statistic Prob. D(LSML(-1)) -0.956218 0.205516 -4.652764 0.0001
C -0.157584 0.282113 -0.558586 0.5818
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@TREND(1985) 0.013447 0.017356 0.774811 0.4463 R-squared 0.485600 Mean dependent var -0.009804
Adjusted R-squared 0.440869 S.D. dependent var 0.881992 S.E. of regression 0.659510 Akaike info criterion 2.113528 Sum squared resid 10.00393 Schwarz criterion 2.258693 Log likelihood -24.47586 Hannan-Quinn criter. 2.155330 F-statistic 10.85613 Durbin-Watson stat 1.864282 Prob(F-statistic) 0.000479
Null Hypothesis: D(LDACCNT) has a unit root Exogenous: Constant, Linear Trend Bandwidth: 0 (Newey-West automatic) using Bartlett kernel
Adj. t-Stat Prob.* Phillips-Perron test statistic -6.083912 0.0003
Test critical values: 1% level -4.416345 5% level -3.622033 10% level -3.248592 *MacKinnon (1996) one-sided p-values. Residual variance (no correction) 0.117611
HAC corrected variance (Bartlett kernel) 0.117611
Phillips-Perron Test Equation Dependent Variable: D(LDACCNT,2) Method: Least Squares Date: 10/24/14 Time: 17:56 Sample (adjusted): 1987 2012 Included observations: 23 after adjustments
Variable Coefficient Std. Error t-Statistic Prob. D(LDACCNT(-1)) -1.298054 0.213358 -6.083912 0.0000
C -0.094413 0.161903 -0.583145 0.5663 @TREND(1985) 0.005419 0.009687 0.559380 0.5821
R-squared 0.649211 Mean dependent var -1.29E-17
Adjusted R-squared 0.614132 S.D. dependent var 0.592042 S.E. of regression 0.367767 Akaike info criterion 0.958371 Sum squared resid 2.705044 Schwarz criterion 1.106479 Log likelihood -8.021262 Hannan-Quinn criter. 0.995619 F-statistic 18.50717 Durbin-Watson stat 2.021259 Prob(F-statistic) 0.000028
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Null Hypothesis: D(LCPSR) has a unit root Exogenous: Constant, Linear Trend Bandwidth: 13 (Newey-West automatic) using Bartlett kernel
Adj. t-Stat Prob.* Phillips-Perron test statistic -8.393796 0.0000
Test critical values: 1% level -4.356068 5% level -3.595026 10% level -3.233456 *MacKinnon (1996) one-sided p-values. Residual variance (no correction) 0.053341
HAC corrected variance (Bartlett kernel) 0.005632
Phillips-Perron Test Equation Dependent Variable: D(LCPSR,2) Method: Least Squares Date: 10/24/14 Time: 17:58 Sample (adjusted): 1987 2012 Included observations: 26 after adjustments
Variable Coefficient Std. Error t-Statistic Prob. D(LCPSR(-1)) -1.020958 0.205310 -4.972772 0.0000
C -0.133164 0.106480 -1.250595 0.2237 @TREND(1985) 0.010513 0.006594 1.594190 0.1245
R-squared 0.520362 Mean dependent var 0.004005
Adjusted R-squared 0.478655 S.D. dependent var 0.340089 S.E. of regression 0.245559 Akaike info criterion 0.137606 Sum squared resid 1.386879 Schwarz criterion 0.282771 Log likelihood 1.211127 Hannan-Quinn criter. 0.179408 F-statistic 12.47644 Durbin-Watson stat 2.016519 Prob(F-statistic) 0.000214
Null Hypothesis: D(LBQ) has a unit root Exogenous: Constant, Linear Trend Bandwidth: 1 (Newey-West automatic) using Bartlett kernel
Adj. t-Stat Prob.* Phillips-Perron test statistic -5.135601 0.0024
Test critical values: 1% level -4.440739 5% level -3.632896 10% level -3.254671 *MacKinnon (1996) one-sided p-values. Residual variance (no correction) 0.018365
HAC corrected variance (Bartlett kernel) 0.017209
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Phillips-Perron Test Equation Dependent Variable: D(LBQ,2) Method: Least Squares Date: 10/24/14 Time: 17:59 Sample (adjusted): 1987 2012 Included observations: 22 after adjustments
Variable Coefficient Std. Error t-Statistic Prob. D(LBQ(-1)) -1.149939 0.224774 -5.115975 0.0001
C 0.129281 0.068505 1.887185 0.0745 @TREND(1985) -0.006338 0.003994 -1.586964 0.1290
R-squared 0.579541 Mean dependent var -5.73E-18
Adjusted R-squared 0.535283 S.D. dependent var 0.213910 S.E. of regression 0.145823 Akaike info criterion -0.886726 Sum squared resid 0.404021 Schwarz criterion -0.737948 Log likelihood 12.75399 Hannan-Quinn criter. -0.851678 F-statistic 13.09438 Durbin-Watson stat 2.176119 Prob(F-statistic) 0.000266
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Appendix 2: UECM Result. UECM Reg. Dependent Variable: D(LMKTCR) Method: Least Squares Date: 10/23/14 Time: 11:02 Sample (adjusted): 1987 2012 Included observations: 23 after adjustments
Variable Coefficient Std. Error t-Statistic Prob. C 18.44869 3.945692 4.675654 0.0023
@TREND 0.342037 0.081457 4.198971 0.0040 LMKTCR(-1) -1.372561 0.428289 -3.204752 0.0150
LCC(-1) -1.007864 0.470859 -2.140480 0.0696 LDACCNT(-1) 1.503509 0.390627 3.848961 0.0063
LBQ(-1) 1.143397 0.686662 1.665153 0.1398 LSML(-1) 0.276770 0.160236 1.727266 0.1278
LCPSR(-1) 1.456247 0.295599 4.926434 0.0017 LPCI(-1) 1.160121 0.367156 3.159752 0.0159
D(LMKTCR(-1)) 0.400196 0.320739 1.247733 0.2522 D(LCC(-1)) -1.146418 0.478548 -2.395616 0.0478
D(LDACCNT(-1)) 0.688605 0.196767 3.499591 0.0100 D(LSML(-1)) 0.310758 0.114531 2.713320 0.0301
D(LCPSR(-1)) 0.349690 0.187743 1.862595 0.1048 D(LPCI(-1)) -0.329567 0.307287 -1.072507 0.3191 D(LBQ(-1)) 1.542136 0.630568 2.445630 0.0444
R-squared 0.924401 Mean dependent var 0.055971
Adjusted R-squared 0.762405 S.D. dependent var 0.261484 S.E. of regression 0.127457 Akaike info criterion -1.080353 Sum squared resid 0.113717 Schwarz criterion -0.290444 Log likelihood 28.42406 Hannan-Quinn criter. -0.881694 F-statistic 5.706292 Durbin-Watson stat 2.127520 Prob(F-statistic) 0.013407
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Appendix 3A: Long-run Level Estimates Dependent Variable: LMKTCR Method: Least Squares Date: 10/27/14 Time: 12:16 Sample: 1985 2012 Included observations: 26
Variable Coefficient Std. Error t-Statistic Prob. C 3.620008 2.031727 1.781740 0.0917
@TREND 0.051370 0.048200 1.065766 0.3006 LCC -0.735672 0.240511 -3.058786 0.0068
LDACCNT 0.366033 0.145996 2.507149 0.0482 LBQ 0.021921 0.321762 0.068128 0.9464
LSML 0.269523 0.056769 4.747744 0.0002 LCPSR -0.032308 0.173750 -0.185944 0.8546 LPCI -0.040881 0.208146 -0.196405 0.8465
R-squared 0.954934 Mean dependent var 2.590849
Adjusted R-squared 0.937408 S.D. dependent var 0.665346 S.E. of regression 0.166459 Akaike info criterion -0.500480 Sum squared resid 0.498753 Schwarz criterion -0.113373 Log likelihood 14.50624 Hannan-Quinn criter. -0.389007 F-statistic 54.48759 Durbin-Watson stat 2.559528 Prob(F-statistic) 0.000000
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Appendix 3B: Error Correction Model Result ECM Model Dependent Variable: D(LMKTCR) Method: Least Squares Date: 10/24/14 Time: 12:53 Sample (adjusted): 1987 2012 Included observations: 23 after adjustments
Variable Coefficient Std. Error t-Statistic Prob. C -0.206787 0.161892 -1.277316 0.2238
@TREND 0.008334 0.006522 1.277806 0.2237 D(LMKTCR(-1)) 1.058303 0.422226 2.506484 0.0263
D(LCC(-1)) -0.403257 0.693766 -0.581258 0.5710 D(LDACCNT(-1)) 0.119246 0.135107 0.882601 0.3935
D(LSML(-1)) -0.221496 0.104031 -2.129138 0.0529 D(LCPSR(-1)) -0.237018 0.225044 -1.053208 0.3114
D(LPCI(-1)) 0.383663 0.386389 0.992947 0.3389 D(LBQ(-1)) -0.187491 0.369819 -0.506982 0.6207
ECT(-1) -0.315175 0.682789 -4.616609 0.0213 R-squared 0.604345 Mean dependent var 0.055971
Adjusted R-squared 0.330431 S.D. dependent var 0.261484 S.E. of regression 0.213965 Akaike info criterion 0.053012 Sum squared resid 0.595153 Schwarz criterion 0.546705 Log likelihood 9.390362 Hannan-Quinn criter. 0.177174 F-statistic 2.206327 Durbin-Watson stat 2.528256 Prob(F-statistic) 0.094614
Wald Test: To test if the Short -run relationship is Significant. Equation: Untitled
Test Statistic Value df Probability F-statistic 1.553224 (6, 13) 0.2373
Chi-square 9.319345 6 0.1564
Null Hypothesis: C(4)=C(5)=C(6)=C(7)= C(8)=C(9)=0 Null Hypothesis Summary:
Normalized Restriction (= 0) Value Std. Err. C(4) -0.403257 0.693766
C(5) 0.119246 0.135107 C(6) -0.221496 0.104031 C(7) -0.237018 0.225044 C(8) 0.383663 0.386389 C(9) -0.187491 0.369819
Restrictions are linear in coefficients. The result shows that the short run relationship is not significant because the p-value is above 5%.
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Appendix 4A: ARCH Test - ECM Heteroskedasticity Test: ARCH
F-statistic 0.392751 Prob. F(1,19) 0.5383
Obs*R-squared 0.425302 Prob. Chi-Square(1) 0.5143
Test Equation: Dependent Variable: RESID^2 Method: Least Squares Date: 10/27/14 Time: 19:01 Sample (adjusted): 1988 2012 Included observations: 21 after adjustments
Variable Coefficient Std. Error t-Statistic Prob. C 0.031352 0.014119 2.220579 0.0387
RESID^2(-1) -0.142106 0.226754 -0.626699 0.5383 R-squared 0.020252 Mean dependent var 0.027463
Adjusted R-squared -0.031313 S.D. dependent var 0.057227 S.E. of regression 0.058116 Akaike info criterion -2.762343 Sum squared resid 0.064173 Schwarz criterion -2.662864 Log likelihood 31.00460 Hannan-Quinn criter. -2.740753 F-statistic 0.392751 Durbin-Watson stat 2.132616 Prob(F-statistic) 0.538315
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Appendix 4B : Normality Test
The Jarque- Bera Normality Test.
0
2
4
6
8
10
-0.10 -0.05 0.00 0.05 0.10 0.15
Series: ResidualsSample 1987 2012Observations 23
Mean 5.18e-15Median 0.002996Maximum 0.174538Minimum -0.108072Std. Dev. 0.071896Skewness 0.665349Kurtosis 3.117806
Jarque-Bera 1.710277Probability 0.425224
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Appendix 4C: Serial Correlation Test for the ECM. Breusch-Godfrey Serial Correlation LM Test:
F-statistic 0.765158 Prob. F(2,11) 0.4885
Obs*R-squared 2.808968 Prob. Chi-Square(2) 0.2455
Test Equation: Dependent Variable: RESID Method: Least Squares Date: 10/24/14 Time: 13:02 Sample: 1987 2012 Included observations: 23 Presample and interior missing value lagged residuals set to zero.
Variable Coefficient Std. Error t-Statistic Prob. C 0.022496 0.165964 0.135549 0.8946
@TREND -0.002074 0.007043 -0.294453 0.7739 D(LMKTCR(-1)) 0.098130 0.497068 0.197417 0.8471
D(LCC(-1)) 0.167447 0.732403 0.228628 0.8234 D(LDACCNT(-1)) 0.004110 0.138248 0.029728 0.9768
D(LSML(-1)) 0.031800 0.125622 0.253141 0.8048 D(LCPSR(-1)) 0.002828 0.229246 0.012335 0.9904
D(LPCI(-1)) 0.009952 0.393662 0.025281 0.9803 D(LBQ(-1)) -0.081001 0.382444 -0.211797 0.8361
ECT(-1) 0.137695 0.808950 0.170215 0.8679 RESID(-1) -0.482715 0.424262 -1.137776 0.2794 RESID(-2) 0.063263 0.475481 0.133050 0.8966
R-squared 0.122129 Mean dependent var 7.60E-17
Adjusted R-squared -0.755742 S.D. dependent var 0.164476 S.E. of regression 0.217938 Akaike info criterion 0.096669 Sum squared resid 0.522468 Schwarz criterion 0.689101 Log likelihood 10.88830 Hannan-Quinn criter. 0.245664 F-statistic 0.139120 Durbin-Watson stat 2.273204 Prob(F-statistic) 0.998597
It shows there is no serial correlation in the ECM.
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Appendix 4D: Ramsey RESET Test Ramsey RESET Test Equation: UNTITLED Specification: D(LMKTCR) C @TREND D(LMKTCR(-1)) D(LCC(-1)) D(LDACCNT(-1)) D(LSML(-1)) D(LCPSR(-1)) D(LPCI(-1)) D(LBQ(-1)) ECT(-1) Omitted Variables: Squares of fitted values
Value df Probability
t-statistic 0.499025 12 0.6268 F-statistic 0.249026 (1, 12) 0.6268 Likelihood ratio 0.472414 1 0.4919
F-test summary:
Sum of Sq. df Mean
Squares Test SSR 0.012100 1 0.012100 Restricted SSR 0.595153 13 0.045781 Unrestricted SSR 0.583054 12 0.048588 Unrestricted SSR 0.583054 12 0.048588
LR test summary: Value df
Restricted LogL 9.390362 13 Unrestricted LogL 9.626569 12
Unrestricted Test Equation: Dependent Variable: D(LMKTCR) Method: Least Squares Date: 10/27/14 Time: 20:26 Sample: 1987 2012 Included observations: 23
Variable Coefficient Std. Error t-Statistic Prob. C -0.178254 0.176309 -1.011035 0.3320
@TREND 0.009177 0.006928 1.324550 0.2100 D(LMKTCR(-1)) 1.038825 0.436724 2.378675 0.0348
D(LCC(-1)) -0.276183 0.758726 -0.364009 0.7222 D(LDACCNT(-1)) 0.128487 0.140414 0.915062 0.3782
D(LSML(-1)) -0.218883 0.107300 -2.039903 0.0640 D(LCPSR(-1)) -0.242150 0.232068 -1.043446 0.3173
D(LPCI(-1)) 0.332123 0.411238 0.807616 0.4350 D(LBQ(-1)) -0.174175 0.381920 -0.456050 0.6565
ECT(-1) -1.774525 0.703824 -2.521262 0.0268 FITTED^2 -0.700146 1.403029 -0.499025 0.6268
R-squared 0.612389 Mean dependent var 0.055971
Adjusted R-squared 0.289380 S.D. dependent var 0.261484 S.E. of regression 0.220426 Akaike info criterion 0.119429 Sum squared resid 0.583054 Schwarz criterion 0.662491 Log likelihood 9.626569 Hannan-Quinn criter. 0.256007 F-statistic 1.895889 Durbin-Watson stat 2.523824 Prob(F-statistic) 0.146274
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Appendix 5: Granger Non-Causality Test.
Dependent Variable: D(LMKTCR) Method: Least Squares Date: 11/07/14 Time: 12:14 Sample (adjusted): 1987 2012 Included observations: 24 after adjustments
Variable Coefficient Std. Error t-Statistic Prob. C 0.016353 0.050392 0.324522 0.7491
D(LMKTCR(-1)) 0.641119 0.316342 2.026663 0.0570 D(LPRR) 0.558487 0.720134 0.775532 0.4476
D(LPRR(-1)) -0.416626 0.718030 -0.580234 0.5686 ECT(-1) -1.600746 0.542250 -2.952042 0.0082
R-squared 0.332398 Mean dependent var 0.057533
Adjusted R-squared 0.191850 S.D. dependent var 0.255851 S.E. of regression 0.230003 Akaike info criterion 0.081600 Sum squared resid 1.005124 Schwarz criterion 0.327028 Log likelihood 4.020796 Hannan-Quinn criter. 0.146712 F-statistic 2.365014 Durbin-Watson stat 2.218072 Prob(F-statistic) 0.089491
Dependent Variable: D(LPRR) Method: Least Squares Date: 11/07/14 Time: 12:22 Sample (adjusted): 1987 2012 Included observations: 24 after adjustments
Variable Coefficient Std. Error t-Statistic Prob. C 0.009421 0.015701 0.600022 0.5556
D(LPRR(-1)) -0.106817 0.225870 -0.472913 0.6417 D(LMKTCR) 0.054941 0.070843 0.775532 0.4476
D(LMKTCR(-1)) -0.236697 0.094995 -2.491665 0.0221 ECT(-1) 0.414581 0.182062 2.277136 0.0345
R-squared 0.272184 Mean dependent var -0.002568
Adjusted R-squared 0.118959 S.D. dependent var 0.076856 S.E. of regression 0.072140 Akaike info criterion -2.237367 Sum squared resid 0.098879 Schwarz criterion -1.991939 Log likelihood 31.84841 Hannan-Quinn criter. -2.172255 F-statistic 1.776371 Durbin-Watson stat 1.423540 Prob(F-statistic) 0.175302