UNIVERSITY FOR DEVELOPMENT STUDIES
FACULTY OF MATHEMATICAL SCIENCES
ASSESSING THE EFFECT OF INEQUALITY IN INCOME DISTRIBUTIONS
ON THE FINANCIAL DEVELOPMENT IN GHANA
SULEMANA IZILDEEN
2022
UNIVERSITY FOR DEVELOPMENT STUDIES
ASSESSING THE EFFECT OF INEQUALITY IN INCOME DISTRIBUTIONS
ON THE FINANCIAL DEVELOPMENT IN GHANA
BY
SULEMANA IZILDEEN
(FMS/0193/18)
A Project Submitted to the Department of Mathematics, Faculty of Mathematical
Science, University for Development Studies in partial fulfilment of the
requirements for the award of Bachelor of science degree in Mathematics with
Economics
SEPTEMBER, 2022
DECLARATION
I hereby declare that this dissertation is the result of my own original work,
except for reference to the work of others which have been duly acknowledged;
and that no part of the work has been presented for another degree in this university
or elsewhere.
Candidate’s Name: Sulemana Izildeen
Candidate’s Signature: …………………………………………...…....…
Date: ……………………………………………………………………...
Supervisor’s Name: Mr. Luu Yin
Supervisor’s Signature: ……………………….…………………………
Date: ……………………………………………………………………...
Head of Department’s Name: Dr. Douglas Kwasi Boah
Head of Department’s Signature: …………………………...………...
Date: …………………………………………………………………...
i
ABSTRACT
This research assesses the impact of inequality in income distribution on financial
development in Ghana for the period 1987 to 2019. The study implores secondary
data which was obtained from Bank of Ghana (BoG) and World Bank Development
Indicators. A time series analysis is used to explore this relationship using this data.
An Unrestricted Error Correction Technique was adopted to analyze the long and
short-run effects of these variables. The goals of the academic work were to
uncover the effect of income inequality on financial development in Ghana. The
experimental results indicate that the outcome of financial development on income
inequality has a regression coefficient of 0.203, and which statistically
consequential in the long-run. The results also show that the financial system can
exclude the poor and send money to the rich and/or people who can provide
collateral or demonstrate the capacity to repay loans. As a result, even if the financial
sector develops, poor people cannot move to cities, invest in education or start new
businesses. The study, recommends that, an independent debt audit committee must
be setup by the government to analyze information released by all financiers on
how much money is owed to whom, under what conditions and terms and in what
manner the money released was destined to be used for.
ii
ACKNOWLEDGEMENT
I give thanks to the Almighty God for His unfounded favors in guiding me through
this degree program and research. Mr. Luu Yin, my supervisor, deserves my
heartfelt appreciation for all of his assistance and advice during this project. Also,
to my family, friends and respondents who shared their experiences to make this
work a success.
iii
DEDICATION
I dedicate this dissertation to Abukari Hudu, my role model and advisor as well as
family and friends.
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TABLE OF CONTENTS
DECLARATION ................................................................................................. i
ABSTRACT........................................................................................................ ii
ACKNOWLEDGEMENT ................................................................................. iii
DEDICATION ................................................................................................... iv
TABLE OF CONTENTS.................................................................................... v
LIST OF TABLES ........................................................................................... viii
LIST OF FIGURES ........................................................................................... ix
LIST OF ACRONYMS ...................................................................................... x
CHAPTER ONE ............................................................................................... 1
INTRODUCTION ............................................................................................ 1
1.1 Background of the study ......................................................................... 1
1.2 Problem statement of the study ............................................................... 2
1.3 Objectives of the study ........................................................................... 3
1.3.1 General objective of the study ............................................................ 3
1.3.2 Specific objectives of the study .......................................................... 3
1.4 Research questions of the study .............................................................. 4
1.5 Significance of the study......................................................................... 4
1.6 Scope of the study ................................................................................... 4
1.7 Organization of the study ........................................................................ 5
CHAPTER TWO .............................................................................................. 6
LITERATURE REVIEW ................................................................................ 6
2.1
Introductions ....................................................................................... 6
2.2
Theoretical review .............................................................................. 6
2.3
Empirical review............................................................................... 11
v
CHAPTER THREE ........................................................................................ 17
METHODOLOGY ......................................................................................... 17
3.1
Introduction ...................................................................................... 17
3.2
Design and approach of the research ................................................ 17
3.3
Data type and source......................................................................... 17
3.4
Co-integration ................................................................................... 18
3.5
Model specification .......................................................................... 20
3.5.1 Model 1 ............................................................................................. 20
3.5.2 Model 2 ............................................................................................. 21
3.5.3 Model 3 ............................................................................................. 21
3.5.4 Model 4 ............................................................................................. 22
3.6
Description of Variables ................................................................... 22
3.6.1 Dependent variable ........................................................................... 22
3.6.2 Independent variable ......................................................................... 23
3.6.3 Control Variables .............................................................................. 23
CHAPTER FOUR........................................................................................... 26
RESULTS AND DISCUSSIONS ................................................................... 26
4.1 Introduction ........................................................................................... 26
4.2 Effect of Income Inequality on Financial Development of Ghana
(Objective one) ................................................................................. 26
4.2.1 Unit root test ..................................................................................... 26
4.2.2 ARDL bound test for co-integration ................................................. 29
4.2.3 ARDL Bound Test for Co-integration results ................................... 30
4.2.4 Optimal lag selection ........................................................................ 30
4.2.5 Unrestricted error correction method (UECM)................................. 30
4.3 Inverted u-shaped hypothesis of income inequality on financial
vi
development in Ghana (Objective two) ............................................ 33
4.4 Granger causality test (Objective three) ............................................... 35
4.5 The prospect of reducing income inequality in Ghana (Objective four)
.......................................................................................................... 36
4.6 Diagnostic test ....................................................................................... 37
4.7 Normality test results ............................................................................ 37
Fig 4.1: Normality test results..................................................................... 37
4.8 Chapter conclusion ............................................................................... 38
CHAPTER FIVE ............................................................................................ 39
SUMMARY, CONCLUSIONS AND RECOMMENDATIONS ................ 39
5.0 Introduction ........................................................................................... 39
5.1 Summary ............................................................................................... 39
5.2 Conclusions ........................................................................................... 40
5.3 Recommendations ................................................................................. 42
REFERENCES ................................................................................................. 44
APPENDICES .................................................................................................. 52
vii
LIST OF TABLES
Table 4.1: Unit root/Stationarity (Augmented Dickey-Fuller) Test ..................... 27
Table 4.2: Unit root/Stationarity (Philips-Peron) Test .......................................... 27
Table 4.3 ARDL Bound Test for Cointegration results ........................................ 30
Table 4.6: Unrestricted error correction model (UECM) results ......................... 31
Table 4.8: Granger causality test results ............................................................... 35
Table 4.7: Serial Correlation and Heteroskedasticity results ................................ 37
viii
LIST OF FIGURES
Fig 4.1: Normality test results............................................................................... 37
ix
LIST OF ACRONYMS
OECD
Organization for Economic Co-operation and Development
GJ
Greenwood Jovanovich
MDG
Millennium Development Goal
GLSS
Ghana Living Standard Surveys
ARDL
Autoregressive Distributive Lag
AfDB
African Development Bank
GDP
Gross Domestic Product
EF
Economic Forum
ERP
Economic Reform Program
SEC
Securities and Exchange Commission
GSE
Ghana Stock Exchange
HFC
Housing Finance Company
NPRA
National Pensions Regulatory Authority
NIC
National Insurance Commission
GSS
Ghana Statistical Service
IMF
International Monetary Fund
SSA
Sub-Saharan Africa
FD
Financial Development
CV
Control Variable
ISIC
International Standard Industrial Classification
AIC
Akaike Information Criterion
SBC
Schwarz Bayesian Criterion
ECM
Error Corrected Model
UECM
Unrestricted Error Correction Method
x
GDPPC
Gross domestic product per capita
INF
Inflation
TR
Trade
INT
Interest rate
FDI
Foreign direct investment
xi
CHAPTER ONE
INTRODUCTION
1.1 Background of the study
The relationship between income distribution and the development process has
received a lot of attention in recent years. Much discussion has focused on Simon
Kuznets' hypothesis that long-term inequality behavior follows an inverted Ushaped pattern in which inequality first increases and then decreases with
development. This hypothesis raises serious questions about the welfare
implications of the development process and is firmly rooted in traditional
knowledge on the subject. Some argue that developing countries face the dark
potential of lowering absolute incomes and increasing relative inequality among
low-income people. It would be difficult to find a region or a country where income
and wealth are equally distributed among its inhabitants. Social and economic
inequality is not a new phenomenon. Income inequality has become a hot topic of
the 21st century due to the growing debate on development.
Income inequality is a broader concept than poverty. Unlike poverty, income
inequality is determined by the population as a whole and not only affects the poor.
This only applies to the degree of income inequality in a given population. As
international attention has shifted to poverty, the role of income distribution and
growth in poverty reduction has become more important (Fosu, 2010; Ali and
Torbeck, 2000; Ravalion, 2001; Khan, 2009; Nashold, 2002). But research shows
that economic growth does not reduce poverty, regardless of the pace of economic
growth.
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Furthermore, there is a growing consensus that high income inequality can hinder
growth (Birdsall, 2005). In recent decades, income inequality has increased in many
countries (global and otherwise), increasing inequality in access to basic services
such as health care and education. Concern for these trends has been at the heart of
Goal 10 of the United Nations Sustainable Development Goals, which aims to
"reduce inequalities nationally and internationally". The COVID-19 pandemic,
exacerbated by the specter of inequality, amplifies this goal. From an ethical point
of view, the pursuit of this goal is clearly justified. It also shows the negative impact
of inequality on various social, economic and political outcomes. The World
Development Report (2006) highlights the long-term consequences of growing
inequality (World Bank, 2006). Indeed, economists in particular have long been
interested in the relationship between equity and efficiency. Interestingly, contrary
to the 2006 report, the old classic view is based on a conflict between equality and
development.
1.2 Problem statement of the study
Many researchers have tried to explore the endless explanations behind the rising
wealth inequality from several perspectives. However, not enough attention has
been paid to examining the speculative and pilot effects of trust companies on
financial inequality (Zhang and Chen, 2015). Moreover, although studies on the
relationship between the level of multilateralism and the level of disparity between
countries and monetary development have been extensively conducted, African
countries are not included (Tita and Asiakpono, 2016). There is no evidence of
testing in Africa. This study is the first of its kind and fills this gap by limiting the
2
evaluation to Ghana.
In addition, research studies using direct imputation to fill in the concentration of
missing data due to missing information in each expedition are also unclear.
Furthermore, no study has used log lag to examine whether there is a U-shaped
anti-hypothesis between event variability and wage inequality.
The reason for conducting research on this topic is that the majority (70%) of the
population still lives in rural areas that do not have the poverty line and are
dependent on agriculture.
1.3 Objectives of the study
1.3.1 General objective of the study
The ultimate aim of this study is to determine the effect of inequality in income
distribution on the financial development of Ghana.
1.3.2 Specific objectives of the study
The research aims to accomplish the following objectives:
1. To discover the U-shaped hypothesis of income inequality on financial
development in Ghana.
2. To discover the direction between income Inequality and financial
development in Ghana.
3. Evaluate the prospect of reducing the rising income inequalities in Ghana.
3
1.4 Research questions of the study
The objective will be achieved by answering the following research questions:
1. Income inequality and financial development: are there links?
2. Other macroeconomics variables under study and financial development:
What do the facts tell us?
3. What major factors are causing the rising income inequalities?
1.5 Significance of the study
The relationship between financial development and income inequality has recently
attracted the attention of development practitioners, financial experts and policy
makers. This study will help policy makers to develop important policies to address
the issue of inequality. This study provides new insights that can help policymakers
develop strategies to reduce income inequality. The results of this study will also
be useful for future research on the relationship between financial development and
income inequality. This study will help regulators and policy analysts to effectively
and efficiently allocate resources to key areas of the economy. Finally, it lays the
groundwork for further research in other countries in the sub-Saharan Africa, where
income inequality is rising rapidly.
1.6 Scope of the study
An experimental study of the impact of income inequality on national development
was approved. However, this study is conducted on a country-by-country basis, and
this activity is limited to the Ghanaian economy. To obtain annual statistics for the
4
period of analysis (2016), the World Bank's World Development Indicators
database was used. Two caveats should be clarified in the following areas. These
limitations may limit researchers' ability to generalize their findings. One limitation
is that it relies heavily on data from the World Bank's World Development
Indicators catalog. This means that the validity of the results is limited by the
validity of the World Bank facts and the investigation is limited to Ghana.
1.7 Organization of the study
This thesis consists of 5 chapters and is organized as follows. The first chapter of
the study introduces the topic and focuses on areas such as the background of the
research, description of the problem, purpose of the research, research question,
relevance of the paper and shortcomings and scope of the study. The second is the
literature review. Chapter 3 describes the research methodology. Chapter 4 contains
data information, results and discussion. The final chapter summarizes the findings,
conclusions and recommendations.
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CHAPTER TWO
LITERATURE REVIEW
2.1 Introductions
The idea that events in the financial sector have a positive effect on income
inequality is supported by numerous experimental studies. This chapter discusses
the theoretical and the empirical of income inequality and financial development.
2.2 Theoretical review
Written hypothetical research on the impact of unequal income distribution on the
development of monetary structure is still ongoing. Expanding the monetary sector
provides easier access to finance for the most vulnerable municipalities, allowing
them to expand their businesses and benefit from improved physical and human
resources reduce wage inequality (Aghion and Bolton, 1997; Banerjee and
Newman, 1993; Galor and Zeira, 1993; Mukherjee and Ray, 2003, 2010; Shahbaz
and Islam, 2011; Bourguignon and Verdier, 2000, Kuguugu, 2008) and
(Mookherjee and Ray, 2003, 2010).
The income inequality hypothesis suggests that financial growth leads to decrease
in income inequality. Although we have not explored the possibility of this effect,
there are three points to disprove the so-called inverted U-shaped hypothesis (also
known as the GJ hypothesis) of income inequality on financial development
introduced in 1990.
Greenwood and Jovanovich (1990) developed a model that predicts the inverted Ushaped hypothesis. As the financial sector expands, the income gap reduces, and
6
the economy grows. They hypothesized an inverted U-curve linking a growing
financial development with an unequal distribution of income. According to this
hypothesis, mediation would be impossible because of the widening gap between
rich and poor in the early stages of money circulation.
As monetary activity enters an intermediate stage of development, the field of forex
trading begins to develop. There is increasing interest in cash instruments as
advance payments move toward medium-term cash flows. Further improvements
in the areas that require these developments will raise the threshold and lower the
exchange rate. If the financial sector is highly developed, it will be easier to get
credit. During this period, the poor will take advantage of the momentum in the
monetary sector to reduce the unequal distribution of income. These declines
suspect a realigned U-shape, where rises in the early stages of the financial
development and falls as the market moves to the next stage.
Also, in their model, Greenwood and Jovanovich (1990) suggest that both methods
may be cheaper but riskier if arbitrage traders can diversify their bets by financing
currency management. Similarly, the fixed costs associated with these
organizations, such as departmental costs, prevent low-income groups from joining.
This is because the income gap between the highest and the lowest is widening,
while the poor save less and gradually accumulate wealth.
However, since transport costs are fixed, in the long run all intermediaries become
private parties to these arrangements, giving them vertical legitimacy. Greenwood
and Jovanovic's (1990) model predicted an inverse U-shaped relationship between
7
wage inequality and local currency development, with income inequality first rising
and then falling. In terms of long-term stability, many people join currency unions.
This hypothesis is called the inverted U-shaped theory of financial sector growth.
If the revised U-shaped hypothesis is correct, further downward development will
initially increase income inequality, but will continue to do so as countries naturally
achieve some degree of monetary expansion.
The following assumption is the one that limits the discrepancy. This hypothesis
was developed by Banerjee and Newman (1993) and Galor and Zeira (1993). This
assumption is due to the immaturity of the market. With the development of the
money market, access to credit became wider and easier. As financial
intermediaries evolve, they could help vulnerable people overcome credit
constraints and reduce income inequality. A similar prediction can be seen in the
Banerjee and Newman (1993) model. This hypothesis expects a direct negative
relationship between monetary expansion and wage inequality. The emergence of
the corporate sector, contracts and brokers reduces short-term financial market
failures and allows taxpayers to develop human resources to increase reserves.
Galor and Zeira (1993) and Banerjee and Newman (1993) expect money flow to
reduce income inequality regardless of the stage of financial development.
Previous negotiations assumed that the rich could be more helpful in developing
the foreign exchange sector than the poor, but this is generally not the case. Families
who have recently lost access to assets may benefit as the market grows and
reserves become more accessible.
8
Galor and Zeira (1993) model two domains of intergenerational inheritance. This
model allows intermediaries providing non-distributable funds for education to
operate in the financial sector. Meanwhile, those whose assets do not exceed their
quota due to the immaturity of the capital sector are eligible for these loans.
As a result, there is an imbalance in income passed on to the next partner through
inheritance. In their model, capital sector currency weakness, follow these
imbalances and perpetuate the equilibrium economy with temporary wage
transfers.
Similarly, Banerjee and Newman (1993) developed a three-domain model that
requires the integrated participation of two technical members. Due to the impure
financial sector, only wealthy brokers can afford enough money to handle these
amazingly large distribution benefits. The base income also affects the distribution
and improvement of incomes in industries with limited working capital. So, despite
the progress made in the financial sector and the income gap, normal comparisons
should be negative.
This hypothesis is known as the inequality hypothesis that money limits
development. If the inequality-reduction hypothesis is correct, additional access to
money will help reduce inequality and narrow the income gap in both poor and rich
countries.
The third is the unbalanced variance hypothesis proposed by Clark et al (2006). The
idea of balanced growth suggests that the development of the financial sector
benefits the rich when the financial institution is weak. This hypothesis suggests
9
that the rich can go further and pay better than the poor (Rajan and Zingales, 2003).
This prevents equal transfer of rewards and creates a positive relationship between
financial development and income inequality. While most financial experts do not
believe that the long-term development of the financial industry will widen the
income gap, some Marxist predictions and writings often portray customers as
greedy agents who only care about the rich and famous. Of course, this view is so
general that most papers supporting a deregulated economic base have been
confirmed by two leading market analysts.
Rajan and Zingales (2003) said one of the reasons why the development of the
financial area is beneficial for the wealthy is that the financial industry can provide
insurance and send money to the wealthiest partners who can process loans. As the
financial industry develops, they can continue to give money to the rich, but reject
the poor who do not have security.
In short, despite the potential development of the financial industry, opponents
cannot devote their resources to private businesses. This trend could only be
increased if the wealthy could block new foundations from accessing funds and
limit opportunities for marginalized people to thrive outside the mainstream market
sector. In fact, despite exchange rate fluctuations and income differences, the
relationship is surprisingly positive. This hypothesis of financial development is
known as the imbalanced growth hypothesis. If this hypothesis is correct, some
countries will have stable ecosystems, which could be increased by financial
development.
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2.3 Empirical review
Initial review of multiple studies. Clark et al. (2006) investigated the relationship
between financial development and income inequality in 83 countries from 1960 to
1995. In their studies, they mainly used the least squares method and the current
cumulative method. Their results support the predictions of Galor and Zeira (1993),
who found a negative relationship between income inequality and financial
development, but found weak support for the rearranged U-shaped hypothesis.
Westley (2001) also investigated the impact of financial development on
remittances in Latin American countries and found that microfinance regulation
could reduce income inequality through easier access to monetary assets. Dollar
and Kraay (2002) found that major stock markets support income dispersion and
that high inflation, government spending, and currency fluctuations cause
significant income disparities.
Batou et al. (2010) evaluated the exploratory improvement of the Greenwood
Jovanovic (1990) hypothesis using data on African countries and used a finegrained summary methodology. They found that financial development inevitably
affected remittances but did not favor income inequality and a U-shaped
rebalancing of income inequality.
Ling-Zheng and Xia-Hai (2012) applied Hansen's (1999) threshold model to
analyze the relationship between financial development and income inequality
using country information. Their observational data shows that financial
development causes income inequality and supports a U-shaped relationship
11
between financial development and income inequality.
Sebastian and V. Sebastian (2011) used an efficiency model to examine the
relationship between financial sector development and the income gap. Their study
found that financial development exacerbated income inequality, but found no
support for the inverted U-shaped hypothesis.
Tan and Law (2012) examined the factors influencing financial development and
income inequality using data from 35 countries. Their empirical evidence showed
that there is a U-shaped relationship between the development of the financial
sector and the income distribution.
Nikoloski (2013) used a multivariate power regression model to fit the direct and
indirect relationship between financial development and the income inequality
between 1962 and 2006. Research evidence supports the idea of an inverted U
shape in financial development and the income inequality.
Rehman et al. (2008), disaggregated data from 51 regions in different stages of the
developing world to understand why this affects income disparities between
countries. They split the data into four different payoff groups to evaluate Kuznets'
hypotheses. Their composition demonstrates that public spending, opportunity, and
globalization are important determinants of income disparities in low-income,
middle-income and high-income countries. They also found that monetary
development exacerbated income inequality regardless of the stage of monetary
development and provided evidence in favor of the modified U-shaped hypothesis.
12
Bittencourt (2006) investigated the relationship between monetary development in
Brazil and the wage gap from 1980 to 1990. Combining the generalized least
squares method with a cascade time series exposure model was found to have a
potential impact on market access monetary and credit.
Baliamone-Lut and Lutz (2005) investigated the effects of financial development,
exchange and uncertain capital on income disparities in rural and urban Africa.
They found that financial development and uncertain capital had little effect on
reducing the income gap between urban and rural areas, while trade had a strong
effect on reducing the wage gap.
Jaumotte, Lall and Papa Georgiou (2013) studied the income gap with a focus on
globalization and currency development. In a sample of 51 countries from 1981 to
2003, the results show truly significant positive coefficients of regionally
developed currencies and income differences.
Similar results were obtained in Dabla-Norris, Kochhar Rika, Sufafifat and Tsuunta
(2015). They analyzed financial expansion and income inequalities in 97 countries
from 1980 to 2012. Their results showed that currency movements exacerbate the
income gap.
Selim and Liu (2010) investigated the relationship between financial area
development and poverty using a vector decomposition model with fixed effects.
They say currency development has helped reduce poverty, but they hate nontraditional currency barriers.
13
Hamori and Hashiguchi (2012) increased income inequality by using annual board
information from 126 countries from 1963 to 2002 and adding a fixed effects panel
and tiny additions to the financial sector.
Naceur and Zhang (2016) used budget reductions in 143 countries from 1961 to
2011 for specific areas of designed financial development, such as affordability,
capacity, scalability, stability, and independence from rat competition. They found
that wage inequality and poverty can decrease as funding patterns shrink.
Kim and Lin (2011) found that both developed and developing countries use the
instrumental variable reduction of management information to explain the indirect
effect of financial development on wage inequality. This means that financial
expansion benefits the rich and further promotes income inequality immediately
after the country enters the inflationary phase. The improvement in financial
conditions during this period had a negative effect on vulnerability and increased
income inequality.
Rettu and Tan (2009) used ARDL to examine the effect of financial expansion on
income inequality in Malaysia between 1980 and 2000. Their results showed that
monetary expansion has nothing to do with the narrowing of income inequality in
neighboring regions.
Sehrawat and Giri (2015) conducted a study on income trends and differentials
between Indian currency blocks from 1982 to 2012. The report points out that
monetary, financial and copper costs create an imbalance between short-term and
long-term fuel income. Their results also do not support the rearranged U-shaped
14
hypothesis that income inequality widened in the early stages of the financial sector
but actually increased as financial development entered the progressive phase.
Baligh and Piraee (2013) studied the development of financial regions and income
inequality in Iran between 1973 and 2010. Their results supported Galor and Zeira's
(1993) hypothesis that there is a direct negative relationship between financial
development and income.
In addition, Hafeez, Khan and Ahmed (2008) analyzed the difference between
financial region development, emerging economies and incomes and found that
regardless of the phase of the financial sector, financial development increased
income inequality. This confirms Galor and Zeira's (1993) prediction that there is
a direct negative relationship between monetary expansion and wage inequality.
Bittencourt (2006) investigated the impact of financial zone development on
income inequality in Brazil from 1980 to 1990 and differences in credit approvals
and payments to vulnerable groups when financial zones emerged.
Kai and Hamori (2009) used fixed and abnormal effects models to investigate the
relationship between financial area development, income disparity and
globalization in 29 sub-Saharan countries from 1980 to 2002. They note that
currency changes widen the wage gap, while globalization benefits the rich and
oppresses the vulnerable.
Batou et al (2010) studied the development of the financial sector and income
inequalities in 22 African countries from 1990 to 2004 using dynamic management
15
estimation techniques. The development of the financial sector increases income
inequality. They found support for the inverted U-shaped hypothesis of
compensatory evolution.
16
CHAPTER THREE
METHODOLOGY
3.1 Introduction
This section provides an overview of the built-in navigation methods. This section
provides an overview of study and approach of the research, data sources, model
details, and variables used.
3.2 Design and approach of the research
As Malhorta and Brix (2007) suggest, study design is an important design for
conducting analytical activities. Describes exploratory activities in which the
analyst expects to obtain data to help solve a research problem. With this in mind,
it recognizes the necessary elements of research, organizes and organizes all
collected data, presents findings, research and predictions, and deploys strategic
experts to achieve research objectives. You will be ready. The research methods
are quantitative. Quantitative methodology has been described by Burns and Grove
(1993) as a cautious strategy focused on causality between understanding, testing
and relational factors. According to Cassel and Simon (1994), the idea of a
quantitative methodology is to provide reliable, realistic and cumulative
assessments to predict conditions and possible outcomes between factors.
3.3 Data type and source
Data for this study was a secondary data and was taken from two sources. Thus,
WorldBank Development Indicator Database (2016) and central bank of Ghana.
Financial development indicators are taken from the World Bank World
17
Development Indicators Database (2016). Domestic credit to GDP was used to
measure the exchange rate. Gini coefficients were obtained by inserting directly
from the World Bank, World Development Indicators Database (2016) and
manufacturer calculations. Control factors include GDP per capita, trade, inflation
and foreign direct investment as reported in the World Bank World Development
Indicators Database (2016) whiles interest rate was taken from bank of Ghana
3.4 Co-integration
Econometric studies of time series information are increasingly focused on the topic
of co-integration to address transitivity concerns and earlier suspicions of faulty
sample design. Inductive co-integration is a successful method for recognizing the
occurrence of coherent state consistency in a cluster. If you reject co-integration
between the series, the regression is not fundamental and the results are not
significant. Co-integration occurs despite convergence of factors. We need to solve
for a series with constant mean, constant variance, and constant autocovariance for
each dummy variable.
Temporal time series are stochastic or covariance-based interactions with
fundamental changes. A major change event indicates that the considered time
series is not stationary, but a stationary time series is not required. Then, estimating
an econometric model by recalling these nonstationary time series, we estimate the
fit of the model by approximating a general least squares method. It has been found
to be highly inaccurate and erroneous in estimations and strategies, including the
reliability coefficient (R2), Fisher coefficient (F-statistics), Durbin-Watson (DW
measure) and T-statistics.
18
In the long run, the time series specified in the regulation can be co-integrated for
complete data storage. The Autoregressive Distribution Lag (ARDL) method
proposed by Pesaran, et al., (2001) uses an unconstrained error recovery model to
investigate the long-term dynamic relationship between groups.
Laurenceson and Chai (2003) tried to avoid problems with unstable time series data
by using the distributed autoregressive method of applause. As now noted, the
factors considered in this review are to some extent relevant to cointegration testing.
There is usually at least one difference. Therefore, the co-integration testing
strategy of Johansen (1991) and Johansen-Juselius (1990) is incorrect and
inappropriate.
As Engle and Granger (1986) and Hassler and Wolters (2006) have focused, the
recognition of cyclic autoregressive tapping methods arises from the fact that
combinations of vibration parameters are relative and autoregressive in the gaff
healing process. The rotated blank model is reparametrized with error correction.
Again, the ideal delay was carefully chosen with three delay selection strategies.
Minimize the latency of large-scale tests to measure common sense given a paired
t-test system and standard information rules (e.g., Akaike's information measure
model, expected final error, Bazian information standard, and Hannankin
information measure). It is important to note that goodness-of-fit tests are shorthand
for unit root tests. Therefore, the self-regressive relief winding method is actually
included in the regulation chain control in a different order.
On the other hand, long-range correlations between small sample sizes and average
19
intervals are just as orderly and reliable. Extended tensile testing of Dickey-Fuller
devices is used to continuously monitor the condition of the series. The unit root
test must determine how many times a factor must be divided to achieve stability,
but other units depend on the usefulness of the Dickey-Fuller derivative unit root
test.
Using the autoregressive periodic decay method to find the most ideal trace length
for each series is to obtain a common standard error term that is not affected by
series integration, anomalies, and non-uniform variances. It is important to change
the facts. Models with the smallest Akaike-based size, small Schwarz Bayesian size
or standard distance, and high confidence are fit. Pesaran and Sheen (1998) argue
that “a corresponding change in demand in the ARDL model is sufficient to address
the relevance and endogeneity of the residual series”.
3.5 Model specification
Following Shahbaz and Islam (2011), the next specification empirically analyzes
the effect of financial expansion on income inequality and examines whether the
inverted U-shaped hypothesis is supported in Ghana.
3.5.1 Model 1
This model represents the basic specifications and the following models
have been developed based on this model.
FD  f (GINIt , CVt ).................................................................(3.1)
Where Financial Development (𝐹𝐷) is a function (𝑓) of Gini Coefficient
(𝐺𝑖𝑛𝑖) and the Control Variables (𝐶𝑉) which include: Inflation, Foreign
20
Direct Investment, Gross Domestic Product per Capita, Interest rate and
Trade Openness.
3.5.2 Model 2
Here model contains a simple linear function specification for the model.
FDt   0  1GINI t   2CVt   t .........................................................(3.2)
𝐹𝐷 is the currency, period and coefficient vectors, 𝐺𝑖𝑛𝑖 is the Gini coefficient and
𝐶𝑉 is the control variable including GDP per capita, inflation, interest rates, foreign
direct financing, and trade. 𝜀 is the error term and subscript t, is the time period.
3.5.3 Model 3
When testing the inverted U-shaped theory, a non-linear specification of Gini index
is added to the model. Clark et al. (2003, 2006), the researchers present an inverse
U-shaped theoretical analysis using a non-linear specification.
FDt   0  1GINIt   2GINIt 2   t .......................................................(3.3)
Here, 𝐹𝐷 is the financial development used as a proxy for domestic credit for the
private sector, 𝛼0 is regular repayment, 𝛼1, 𝛼2, is the parameters for the Gini
coefficient and Gini coefficient square. Gini square refers to the nonlinearity term
used to test for U-shaped feedback. When the derivative of monetary development
is convex relative to the Gini, the model predicts an inverted U-shape. A typical Ushaped relationship occurs when the derivative of financial history to the Gini is
concave.
21
3.5.4 Model 4
This long-term and short-term effects on this sequence are analyzed using
unconstrained error correction techniques.
p
p
p
p
i 1
i 1
FDt   0  1T    i FDt i    i GINI t i   i GDPt i   i INFt i 
i 1
p
i 1
p
p
i FDI t i   i TRt i    i INTt i 1FDt 1  2GINI t 1 

i 1
i 1
i 1
3GDPt 1  4 INFt 1  5 FDI t 1  6TRt 1  7 INTt 1   ...................(3.4)
where 𝐹𝐷 is the financial development, 𝐺𝐼𝑁𝐼 is the Gini coefficient, 𝐺𝐷𝑃 is the
GDP per capita, INF is the inflation rate, FDI is the foreign direct investment, INT
is the interest rate and 𝑇𝑅 is the trade openness. 𝑇 is the passage of time. The first
segment of the sum symbol ∑ of the model is short-term, and the rest are the longterm parameters 𝜆. The maximum delay is ᴘ. 𝛥 represents the first-order difference
operator. 𝛼0 is the drift component. 𝜇 is the normal residual of the white noise. The
subscript t and t-i indicate current and deferred values.
3.6 Description of Variables
3.6.1 Dependent variable
Financial development is utilized as a substitute for domestic credit to the private
sector as a percentage of Gross Domestic Product (GDP). A few specialists use cash
from a wide perspective as a level of GDP to quantify financial development.
Nonetheless, Broad Money has neglected to evaluate the main role of monetary
delegates. Homegrown reliability has additionally been viewed as one of the most
amazing indicators of monetary shakiness (Kaminsky and Reinhart, 1999). The
wellspring of this determined information that scientists buy into is from the World
22
Bank's World Development Indicators Database (2016). The quantity of
perceptions is 33.
3.6.2 Independent variable
The Gini coefficient appraises the inconsistent appropriation of pay. The Gini
coefficient appraises the level of inconsistent appropriation of pay between
families. A file of 0 shows wonderful equality and a coefficient of 1 demonstrates
amazing disparity. The wellspring of the computational information bought into by
the scientists was gotten from the World Bank's Development Research Group
(2016). There were missing datapoints and for the investigator to get balanced data,
the researcher applied linear interpolation to estimate the unobserved data points.
Interpolation is a theory that can be applied to time-series data to control omitted
values, Fung (2006). The efficacy of this technique practically, as the
approximation of time series is concerned, demands on the soundness of the
regression model used which will make the regression model a good approximation
to reality (Chow & Lin, 1971). The interpolation was used in Microsoft Excel to
estimate the missing values. The quantity of perceptions is 33.
3.6.3 Control Variables

Controlling factors include inflation favorable to shoppers' spending
reports. Inflation is an increase in the cost of support during a critical period.
According to Easterly and Fischer (2001), expansionary changes can lead
to monetary instability if steps are not taken to reduce them. The poor will
be hit harder than the rich because the rich can prepare for dramatic changes.
A buyer's cost list shows the annual rate change for a typical customer
23
supplying a basket of items over a period of time, whether fixed or
fluctuating annually. The source of this expert calculation information is the
World Bank's World Development Indicators (2016) database. The number
of observations is 33.

Foreign direct investment represents a speculative flow of value into the
economy. Value of equity, retained earnings and other capital. Direct
speculation is a category of cross-border companies in which residents of
one country participate in, significantly influence or control the
management of companies operating in another economy. The source of
this expert's information comes from the World Bank's World Development
Indicator database (2016). The number of observations is 33.

GDP per capita is measured as the ratio of population to gross domestic
product and depends on the country's lifestyle. Gross domestic product is
the total value of all residents of the economy minus detailed expenditures
and short-term futures. It is determined without fear of damage or
consumption of produced resources or destruction of normal goods. The
source of this expert's information is the World Bank's World Development
Indicators database (2016). The number of observations is 33.

It is the number of imports of goods, labor and goods measured in gross
domestic product (GDP). It assesses the suitability or compatibility of a
country from an economic point of view. With the advent of globalization,
many countries began to gradually raise their exchange rates, which
24
obviously had a significant impact on the improvement of the foreign
exchange industry. This indicator provides a complete picture of the total
burden of all tradable economies, that is, the dependence of domestic
producers on unknown industries. The source of this expertly processed
information is the World Bank's World Development Indicators Database
(2016). The number of observations is 33.

This is considered an excellent indicator. A prime rate is an annual lending
fee charged by a national bank to borrow from a bank to cover a temporary
shortfall in assets. High interest rates limit the bank's credit stock because
banks cannot borrow large amounts of cash to lend secret territories to
expand control over monetary intermediaries. On the other hand, lower
borrowing costs help improve money intermediation, which helps improve
the forex industry. This marker was used because of the importance of the
excess unit and the cost of capital and lack of income. The source of this
scholarly written information is the central bank of Ghana. The number of
observations is 33.
25
CHAPTER FOUR
RESULTS AND DISCUSSIONS
4.1 Introduction
The chapter begins with a review of the datasets utilized in the research. This is
followed by a detailed explanation of the findings, with a focus on meeting the
study's numerous aims. The motivation behind this research is to find out how
income inequality can disturb financial development in Ghana using an ARDL
approach and if there is a U-shaped inverted hypothesis. The results from the unit
root/stationarity (ADF & PP) test, the summary statistics of the series, the
correlation matrix, the Unrestricted Error Correction Method and finally the
diagnostic test are presented and discussed in this chapter.
4.2 Effect of Income Inequality on Financial Development of Ghana (Objective
one)
4.2.1 Unit root test
The ADF and PP methods were used to evaluate the variables' stationarity, and the
results are shown in Tables 4.2 and 4.3 respectively. The variables were evaluated
at raw values and not log values due to the nature of the data. The unit roots of the
variables were accepted by the ADF and PP tests. The unit root test helps in
determining the appropriate model to evaluate the long run and short run effect of
income inequality on financial development.
26
4.2.1.1 Unit root/Stationarity (Augmented Dickey-Fuller) Test
Table 4.1: Unit root/Stationarity (Augmented Dickey-Fuller) Test
t-statistic
Critical
value 1 %
Critical
value 5 %
Critical
P-values
value 10 %
-3.702
-3.702
-3.702
-3.702
-3.702
-3.702
-3.702
-2.980
-2.980
-2.980
-2.980
-2.980
-2.980
-2.980
-2.622
-2.622
-2.622
-2.622
-2.622
-2.622
-2.622
0.4459
0.3218
0.0340
0.5194
0.9682
0.2885
0.6450
-3.709
-3.709
-3.709
-3.709
-3.709
-3.709
-2.983
-2.983
-2.983
-2.983
-2.983
-2.983
-2.623
-2.623
-2.623
-2.623
-2.623
-2.623
0.0000
0.0000
0.0001
0.0000
0.0000
0.0000
LEVELS
FD
-1.671
GINI
-1.922
INF
-3.010
FDI
-1.528
GDPPC
0.132
TR
-1.996
INT
-1.265
FIRST DIFFERENCING
FD
-7.949
GINI
-5.269
FDI
-4.675
GDPPC
-6.365
TR
-5.716
INT
-5.596
4.2.1.2 Unit root/Stationarity (Philips-Peron) Test
Table 4.2: Unit root/Stationarity (Philips-Peron) Test
t-statistic
Critical
value 1 %
Critical
value 5 %
Critical
P-values
value 10 %
-2.980
-2.980
-2.980
-2.980
-2.980
-2.980
-2.980
-2.622
-2.622
-2.622
-2.622
-2.622
-2.622
-2.622
0.4720
0.2332
0.0383
0.4972
0.9835
0.3259
0.5632
-2.983
-2.983
-2.983
-2.983
-2.983
-2.983
-2.623
-2.623
-2.623
-2.623
-2.623
-2.623
0.0000
0.0000
0.0001
0.0000
0.0000
0.0000
LEVELS
FD
-1.621
-3.702
GINI
-2.128
-3.702
INF
-2.965
-3.702
FDI
-1.573
-3.702
GDPPC
0.456
-3.702
TR
-1.913
-3.702
INT
-1.439
-3.702
FIRST DIFFERENCING
FD
-7.988
-3.709
GINI
-5.266
-3.709
FDI
-4.610
-3.709
GDPPC
-6.367
-3.709
TR
-5.805
-3.709
INT
-5.606
-3.709
Source: computation by researcher
The check for unit root is to guarantee that not a bit of the sequence is integrated at
27
I (2) or higher. As a measure of robustness, both unit root test (i.e., ADF and PP)
computed for the variables at level fail to reject the null hypothesis of unit roots for
financial development (FD), Gini index (GINI), foreign direct investment (FDI),
GDP per Capita (GDPPC), trade openness (TR) and interest rate (INT). Inflation
(INF) is stationary at levels at a 5% significance level using both ADF and PP test.
The outcomes of the Augmented Dickey-Fuller unit root test that was established by
Dickey and Fuller (1981) are reported in Table 4.1. The results at level show that
inflation is stationary at constant, but financial development (FD), GDP per capita
(GDP), Gini index (Gini),foreign direct investment (FDI), trade openness (TR) and
interest rate (INT) contain unit root at level.
In particular, the ADF strongly accepts the null hypothesis of unit root at 5% and
10% just as PP. Thus, most of the variables are not stationary in level, implying the
variables are not integrated of order zero [i.e. I(0)]. When variables are not
stationary in level, they are normally differenced till stationarity is established in
which instance a co-integration test is required to examine if a long run relationship
could be established. Thus, the variables were tested in their first difference to see
whether they contain unit roots, using the same ADF and PP procedures. The results
of the first differences, both ADF and PP tests are also presented Tables 4.1 and
Table 4.2 respectively. The results show that financial development, Gini index,
foreign direct investment, GDP per capita, trade openness and interest do no longer
contain unit roots at their first differences.
This means that these variables are stationary in their first differences [i.e. I(1)].
28
This conclusion has one main implications for estimating the empirical model of
this study. It can be established from the two results that variables of the model
include stationarity at levels and stationarity at first difference. Therefore, to
proceed for co-integration test, it must be bound test of cointegration that makes
room for integration of different orders. However, all variables were first
differenced stationary, I (1).
4.2.2 ARDL bound test for co-integration
As mentioned above, the unit root process is not recommended in empirical
analysis. The relationship between financial development and the explanatory
variables cannot be directly estimated without the use of dummy or
uncorrelated regression problems, which further suggests that testing for the
underlying existence of co-integration is both necessary and useful. The cointegration limit test is used to investigate the long-run relationship in the first
equation because the variables are integrals of different orders, especially the
I(0) and I(1) variables. In this case, it is best to use co-integration constraints
to determine co-integration. The test results are shown in Table 4.3.
If the F-statistic is between the limits, the test is inconclusive. If it exceeds
the upper bound, the null hypothesis of no level effect is rejected. If it is below
the lower bound, the null hypothesis of no level effects cannot be rejected.
The survey results are summarized in Table 4.3. The calculated F-statistic is
greater than the upper bound at 1%, 5%, and 10%. Therefore, the null
hypothesis of no co-integration no longer holds. In the long run, this suggests
that the variables are co-integrated. Therefore, there is a reasonable long-term
29
relationship between income inequality and financial development.
Therefore, the Gini index can be used, among other things, as a long-run
variable to explain financial development. This means that financial
development and the Gini index, as well as other regression variables, have
long-run deterministic trends.
4.2.3 ARDL Bound Test for Co-integration results
Table 4.3 ARDL Bound Test for Co-integration results
Test Statistic
Value
Significant
I(0)
I(1)
F-Statistic
24.161
10%
2.12
3.23
K
6
5%
2.45
3.61
2.5%
2.75
3.99
1%
3.15
4.43
4.2.4 Optimal lag selection
Table 4C shows the optimal choice of lag to analyze the most important and
most important variables of the study. Asterisks (*) indicate the optimal and
optimal delay to be selected for different information criteria. The
unrestricted error correction model (UECM) combines long-term with shortterm without losing long-term data. The choice of the optimal lag length has
important implications for the evaluation of ARDL models. The test chose
lag 3 as the optimal lag. The lag length is carefully chosen by the Akaike
information criteria (AIC).
4.2.5 Unrestricted error correction method (UECM)
Given that the variables are co-integrated, the next step is to estimate the short-run
dynamics in the error-corrected model (ECM) to capture the rate of adjustment to
30
equilibrium at any shock to the independent variables. Using a general-to-specific
framework, a parsimonious error correction model is estimated, and the results are
shown in Tables 4.9.
4.2.5.1 Unrestricted error correction model (UECM) results
Table 4.6: Unrestricted error correction model (UECM) results
Model selection method: Akaike info criterion (AIC)
Dynamic regressors (3 lags, automatic): FD, GINI, INF, FDI, GDPPC, TRD,
INTR
Selected Model: ARDL (1, 3, 3, 3, 3, 3, 3)
Variable
Coefficient
Std. Error
t-Statistic
Prob.*
FD (-1)
-0.2381985
0.1146999
-2.08
0.106
GINI
0.2036068
0.023872
8.53
0.001
GINI (-1)
0.0951547
0.0236218
4.03
0.016
GINI (-2)
0.0121971
0.0248729
0.49
0.650
GINI (-3)
0.1932435
0.03252278
5.94
0.004
INF
-0.0802676
0.0172768
-4.65
0.010
INF (-1)
-0.1084938
0.0225451
-4.81
0.009
INF (-2)
0.0774479
0.0171246
4.52
0.011
INF (-3)
0.1294095
0.0161686
8.00
0.001
FDI
-1.963977
0.2703283
-7.27
0.002
FDI (-1)
2.324167
0.2850506
8.15
0.001
FDI (-2)
-0.1894521
0.1421164
-1.33
0.253
FDI (-3)
-0.3699953
0.1134979
-3.26
0.031
GDPPC
-0.0041867
0.0007742
-5.41
0.006
GDPPC (-1)
-0.0035208
0.0008882
-3.96
0.017
GDPPC (-2)
-0.0007915
0.0010093
-0.78
0.477
GDPPC (-3)
0.0090656
0.0012115
7.48
0.002
TR
0.1990414
0.0223027
8.92
0.001
TR (-1)
-0.1627062
0.0306851
-5.30
0.006
TR (-2)
0.1635025
0.0183089
8.93
0.001
TR (-3)
0.1935892
0.0338966
5.71
0.005
INT
0.1958411
0.0547465
3.58
0.023
INT (-1)
-0.140241
0.0395509
-3.55
0.024
INT (-2)
-0.5797426
0.0829021
-6.99
0.002
INT (-3)
0.2300392
0.0667328
3.45
0.026
ECT
-0.6329689
0.1918596
-3.30
0.003
The long-term and short-term results of the series are discussed accordingly.
31
The coefficient of determination R2 revealed from Table 4.9 indicates that 99.49%
of the variation observed in the dependent variable FD can be explained by the
variation in the independent variable. This shows that 99.91% of the variation in
FD growth can be explained by explanatory variables. This is high and reveals the
reality that about 99.91% of financial development can be explained by explanatory
variables. The goodness-of-fit test of the ECM model represented by R-square (R2)
was correctly adjusted with an adjusted R2 of 99.33%.
Furthermore, the error correction term is -0.63. This means that the error-corrected
model adjusts to the previous imbalance of the system at a rate of 63% per year. It
also means that the ECM term actually corrects the imbalance in the system. The
Error Correction Model (ECM) suggests that if the financial sector is out of balance,
it will correct 63% of the imbalance per year. The imbalance correction rate is 63%
per annum. This adjustment is necessary to maintain long-term balance and reduce
imbalances over time. Moreover, the sign is negative and significant, indicating a
long-run equilibrium relationship between the variables. Therefore, the imbalance
of 63% is reviewed at t-1 and adjusted annually for changes in FD. This means that
the ECM model is robust and suitable for policy recommendations.
In addition, the effects of GINI, TR and INT on FD were found to be positive and
significant. Also, only GINI's second delay is negligible. As can be seen from the
results, the previous value of FD has no effect on its current value as it can be seen
that the first lag is not statistically significant, resulting in a decrease of 0.24% in
the current growth of the financial sector. This implies that, financial institutions
develops when Gini coefficient, interest rate and trade increases.
32
According to the results, the overall negative effect of inflation on financial growth
is 0.08%, which is statistically significant even at the 99% confidence level. The
first lag has a negative impact of 0.108%, while the second and third lags have a
positive impact of 0.077% and 0.129%, respectively. FDI is considered statically
significant at 99% confidence level, with an effect on financial development of 1.964%, the first lag effect is 2.324%, which is statistically significant, and the
second and third lag coefficients are -0.189 and -0.370, i.e., the second lag is not
statistically significant and the third lag is 5% significant. GDPPC is statistically
significant at 99%, but stratum 2 is insignificant. The impact of GDPPC on financial
development is negative except for the third delay. Typically, a unit change in
financial growth will result in a 0.0042% decrease in GDPPC.
4.3 Inverted u-shaped hypothesis of income inequality on financial
development in Ghana (Objective two)
Clark et al. (2003) If the Gini square is significant, nonlinear theory follows.
However, if the Gini square is not significant and the Gini square is significant
without the square, then linear theory follows. However, both Genie Square and
Genie are significant at the 1% level. Therefore, the researchers used the derivation
proposed by Greenwood and Jovanovich (1990) to determine whether an outcome
threatened the inverse U-shaped predictions. To understand the overall effect of
variables with significant quadratic terms, the researchers derived the FD for Gini.
Using Gini and Gini-squared,
33
FD  1Gini   2Gini 2
where; 1 and  2 are estimated parameters which are -0.3492769 and 0.0238334
The first-order derivative gives us,
FD
 1  2  2Gini substituting
Gini
the
estimated coefficient of Gini and Gini-squared which are -0.3492769 and
0.0238334 respectively in
FD
, the researcher had -0.3016101.
Gini
The second derivative are,
 2 FD
 2 FD
substituting
into
, the researcher

2

2
Gini 2
Gini 2
0.0476668.
This did not support the inverted U-shaped theory as it confirms a positive
correlation between income inequality and financial development and supports the
inequality theory developed by Clark et al. (2006). This suggests that regardless of
financial development, financial expansion tends to benefit the rich and wellconnected more than the vulnerable. This is because the wealthy can provide
collateral and are more likely to repay loans than the vulnerable, which creates an
unstable atmosphere even when the market is stable, as it is difficult for the
vulnerable to raise funds. To educate the underprivileged or to start a new business.
Similar results were reported by Batuo et al. (2010), J. Sebastian and W. Sebastian
(2011), Liang (2008), Ling-Chheng and Xia-Hai (2012), Shahbaz and Islam (2011)
and Tot Slot Tan and Law (2012). Clark et al. (2006), Kim and Lin (2011),
Nikoloski (2013), Rehman et al. (2008) and Rotheli (2011) find evidence
supporting the inverted U-shaped theory that income inequality worsens in the early
34
stages of financial growth but improves as financial development matures.
4.4 Granger causality test (Objective three)
The Granger test was used to achieve aim three, which sought to assess the direction
of causation between financial development and Gini index. According to
Granger's (1989) theory, the presence of a link between two variables does not
imply that they may be used to forecast one another, and a long-standing association
does not show causation.
Table 4.8: Granger causality test results
Null Hypothesis:
Obs.
F-Statistic
Prob.
Gini does not Granger Cause FD
33
1.5647
0.457
4.975
0.796
FD does not Granger Cause Gini
33
5% significant level
Given that the probability value is greater than the 5% level of significance, it is
clear from the aforementioned test result that the income inequality granger does
not influences financial development. As a result, we cannot reject the null
hypothesis and come to the conclusion that income inequality is not caused by
financial development. Therefore, we find that financial development does not
directly cause income inequality and reject the alternative hypothesis while
accepting the null hypothesis. This suggests that financial development has little
bearing on how Ghana's income inequality will behave over the long term. This
means that, the expansion of the financial sector maybe as a result of other factors
and not income inequality.
35
4.5 The prospect of reducing income inequality in Ghana (Objective four)
With majority of Ghanaians now concerned about wealth and income inequality
in the country, it is not possible to completely eliminate income inequality in the
country. However, measures can be taken to reduce income inequality. They
include;
Make state tax systems less regressive. State tax systems tend to ask the most
from those with the least because they rely heavily on sales taxes and user fees,
which hit low-income households especially hard. States can move their tax
systems in a more progressive direction by strengthening their income taxes,
adopting state earned income tax credits (or other low-incomes tax credits) to
boost after tax incomes at the bottom, and rejecting tax cuts incomes that
disproportionately benefits higher-income families and profitable corporations
Raise the minimum wage and index it to inflation. The country can raise wages
for workers at the bottom of the pay scale by implementing a higher country
minimum wage and indexing it so that it keeps up with rising living cost. The
minimum wage in Ghana is currently 13.53 cedis per day.
36
4.6 Diagnostic test
The results of the diagnostic analysis are shown in the table and figure below
4.6.1 Serial Correlation and Heteroskedasticity results
Table 4.7: Serial Correlation and Heteroskedasticity results
f-statistics
Breusch-Godfrey Serial Correlation LM 1.185
Test
Breusch-Pagan-Godfrey Heteroskedasticity 2.00
Test
Jarque-Bera test normality test
4.222
p-value
0.2764
0.1576
0.7617
4.7 Normality test results
0
.1
Density
.2
.3
Fig 4.1: Normality test results
-4
-2
0
Residuals
2
4
The table above shows the results of the diagnostic tests. Diagnostic tests are used
to demonstrate the arithmetic capabilities of the model. If the probability value is
37
less than the 0.05 level of significance, the null hypothesis is rejected. A p-value
less than 0.05 indicates that you can reject the null hypothesis. On the other hand,
larger p values indicate that these predictor adjustments were not associated with
changes in response. Tests for serial correlation, heteroscedasticity, and normality
of residuals are diagnostic. All tests show that the model is well specified. The pvalue of the LM test for serial correlation is 0.2764, which means there is no serial
correlation, so the error terms are independent of each other. The p-value of
heteroscedasticity (0.1576) means that there is no heteroscedasticity, so the null
hypothesis of homoscedasticity cannot be rejected, and therefore the variance of
the error term is constant.
The Jarque Bera statistic measures the normality of the residual using the null
hypothesis of a normal distribution. Since the p-value of the test statistic is 0.7617
(𝑝 > 0.05), it means that the residuals are normally distributed.
The figure above shows the bell-shaped distribution of the residuals. The x-axis
shows the residuals and the y-axis represents the density of the data set. Therefore,
the histogram confirms the results of the Jarque Bera normality test.
4.8 Chapter conclusion
The empirical results show that, income inequality contributes to financial
development, but its role is negligible. The technique used to estimate the
long-term and short-term dynamics between series is the lag of the
autoregressive distribution. The ARDL method is also suitable when the
series are integrated in levels or first differences.
38
CHAPTER FIVE
SUMMARY, CONCLUSIONS AND RECOMMENDATIONS
5.0 Introduction
The basis of this section is the conclusion, conclusion, praise and future direction
of the research paper. The abstract describes the problem, the purpose, the method
used and the results. The conclusion also discusses the overall outcome of the
outcome and confirms that the objective has been achieved. The proposal also
includes specific solutions implemented by unilateral authorities and finally future
research directions.
5.1 Summary
While financial development limits growth, income inequality increases poverty.
A study of Ghana's standard of living in 1991/1992, 1998/1999 and 2005/2006
shows an increasing trend in the Gini index between 1992 and 2013, from 0.37 to
0.42. A worrying aspect of this growth is that it reduces the impact of the growing
economy on reducing poverty and improving the equitable distribution of resources
in Ghana. See also Cook et al. (2016) report on poverty and inequality in Ghana.
According to the Ghana Statistical Service, the Gini index increased from 37% in
1992 to 42.3% in 2013. This increase resulted in a situation where the rich and the
wealthy were in conflict. Revenue sharing is available. The poor live in extreme
poverty. When resources are unevenly distributed, these practices can lead to
instability and dysfunction. This threatens the political stability of the country.
Using the ARDL approach, this study aims to examine the effect of income
39
inequality on Ghana's financial development. Unconstrained error correction
methods were used to analyse the long-term and short-term effects of these
variables. Clark et al. methodology (2003, 2006) introduced the second term of the
variable finance evolution and used it to test the existence of an inverted U-shaped
theory.
All statistical evaluations were performed using a statistical econometrics package
(Stata package) and the study followed a quantitative approach. Dickey-Fuller
results for interest rate (INT), financial development (FD), GDP per capita (GDP),
Gini index (GINI), inflation rate (INF), foreign direct investment (FDI) and trade
(TR level) include unit root. Since no serial correlation was found in the LM test of
serial correlation, you cannot reject the null hypothesis. Since there is no
heteroscedasticity, we cannot reject the null hypothesis of equal variances.
Jarke Bera's statistics also show that waste is normally distributed. The results also
did not support the inverted U-shaped hypothesis.
5.2 Conclusions
This study aims to determine the impact of income inequality on Ghana's financial
development over the period 1987-2019 using an autoregressive distributional lag
approach. The impact of unequal distribution on growth in the financial sector has
generated great interest among financial experts and policy makers. This study used
a time series of econometric procedures also used a nonlinear specification to test
for the U-shaped inverted theory. An unconstrained error correction method was
used to analyze the short- and long-term effects of these variables. The purpose of
40
this study was to determine the impact of income inequality on the growth of the
financial sector in Ghana. The results show that;
i.
the financial system can send money to wealthy and connected people who
can provide guarantees and repay loans. The poor were excluded. As a
result, despite the development of the financial sector, poor people cannot
move to cities, invest in education or start new businesses.
ii.
The results of the non-linear specification show that income inequality has a
positive effect on Ghana's financial development, which is consistent with
Clarke et al. (2006) The Theory of the Expansion of Inequality. Because
financiers are greedy brokers who only serve the interests of the rich and
vulnerable. The results also show that the financial system can exclude the
poor and send money to rich and connected people who can provide
collateral and repay loans. As a result, even if the financial sector develops,
poor people cannot move to cities, invest in education or start new
businesses.
iii.
Empirical results show that income inequality cannot reduce financial
development in the long run. This is evidence that Korea's financial system
is not developed enough to address income inequality.
iv.
The development of the financial sector exacerbates income inequality in
the short term. Scholars such as Harris (2012) and Andersen, Sam and Trapp
(2012) have gone a long way towards fully applying financial contributions
to the proposed growth of financial inequality theory in an African context.
41
They argue that existing empirical and theoretical frameworks are too
disproportionate to draw strong conclusions. This suggests that much
remains to be discovered before we can say with confidence that fiscal
expansion improves the distribution of income. This means that the goal set
by the researcher has been achieved.
5.3 Recommendations
Based on these results, additional recommendations were made.
i.
This study recommends the implementation of policies to increase financial
access. It consists of technologies aimed at dramatically reducing
intermediation costs. This can be achieved through strong competitiveness
in the banking sector, foreign access to the financial sector and flexible ways
to introduce creative financial products. Currently, the evidence comes
mainly from mobile banking. Technological advances play an important
role in reducing intermediation costs in the use of financial services. Poor
households can be attracted by increasing access to credit, either directly or
through special credit lines.
ii.
The government should make the rich pay more taxes than the poor. A
continuous tax system redistributes income between the haves and the havenots. In addition, Ghana's tax system is divided into deductions,
contributions and exemptions, making it difficult to get the income you
need. Therefore, it is necessary to ensure that taxes are levied on the rich
and wealthy, such as large corporations.
42
iii.
The governments can resolve monetary inconsistencies to address financial
instability in the banking sector. A currency mismatch exposes the economy
to systemic risks. A currency mismatch occurs when a country's legal debt
is denominated in a foreign currency, while its cash flows are primarily
denominated in its domestic currency. Combating the currency mismatch
can be achieved by setting up an autonomous payment monitoring
committee that examines the facts under what terms the government and all
creditors typically owe.
43
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51
APPENDICES
Table 4A: Raw data for the research
YEAR
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
2016
2017
2018
FD
3.15
3.14
5.84
4.93
3.66
4.94
4.84
5.25
5.07
6.01
8.20
9.36
12.56
13.97
11.88
12.15
12.49
13.17
15.54
11.09
14.49
12.61
17.74
16.66
17.20
18.07
15.29
16.50
17.93
17.60
16.25
13.84
GINI
35.3
36
36
37.2
38.4
38.1
38.43
38.77
39.10
39.43
39.77
40.1
38.27
3.57
6.84
9.83
12.57
15.09
42.8
42.8
42.77
42.75
42.76
42.80
42.85
42.4
42.6
42.86
43.17
43.5
43.49
43.48
INF
39.82
31.36
25.22
37.26
18.03
10.06
24.96
24.87
59.46
46.56
27.89
14.62
12.41
25.19
32.91
14.82
26.67
12.62
15.12
10.92
10.73
16.52
19.25
10.71
8.73
7.13
11.67
15.49
17.15
17.45
11.7
0.41
FDI
0.09
0.10
0.29
0.25
0.30
0.35
2.10
4.28
1.65
1.73
1.19
2.24
3.16
3.33
1.68
0.96
1.79
1.57
1.35
3.12
5.59
9.52
9.13
7.86
8.21
7.86
5.10
6.27
6.49
6.34
5.52
4.56
52
GDPPC
374
373
366
399
434
410
370
329
380
397
385
408
410
258
269
305
368
418
493
913
1081
1217
1078
1299
1549
1588
2361
1900
1706
1913
2021
2194
TRADE
45.85
42.25
41.09
42.73
42.49
45.99
56.67
62.02
57.42
72.2
85.4
80.6
81.71
116.05
110.05
97.49
97.29
99.67
98.17
65.92
65.35
69.51
71.59
75.38
86.3
93.17
61.28
67.62
77.28
70.01
73.84
71.94
INT
23.5
26
26
26.7
31.83
22.58
34.17
34.17
41
45
45
43.17
28.25
27
27
24.92
25.71
19.13
16.83
14.3
12.6
15.79
15.02
13.68
12.92
14.46
15.67
18.67
23
25.92
22.25
17.25
Table 4B: Optimal lag selection results
Lag LL
LR
df
p
0
1
2
3
4
-84.72
-59.45
-57.75
-57.63
-57.46
50.544*
3.3902
0.2584
0.32683
1
1
1
1
0.000
0.066
0.611
0.568
0
1
2
3
4
-113.91
-100.92
-100.56
-100.26
-99.80
25.985*
.72001
.59533
.91562
1
1
1
1
0.000
0.396
0.440
0.339
0
1
2
3
4
-764.46
-643.09
-592.17
-445.92
5542.71
242.74
101.84
292.49
11977*
49
49
49
49
0.000
0.000
0.000
0.000
FPE
FD
21.6352
4.05756
3.86966*
4.1134
4.36516
GINI
161.986
70.8554*
74.0916
77.8486
80.9529
INF
3.0e+14
2.3e+12
3.9e+12
5.8e+10*
AIC
SC
HQ
5.91217
4.23824
4.1903*
4.25036
4.30805
5.95932
4.33254
4.33175*
4.43895
4.54379
5.92694
4.26777
4.2346*
4.30942
4.38188
7.92536
7.0983*
7.14244
7.19087
7.22827
7.9725
7.1926*
7.28388
7.37947
7.46401
7.94012
7.12783*
7.18674
7.24994
7.3021
53.2047
48.2135
48.081
41.3744*
-368.256
53.3081
49.0404
49.6314
43.6484*
-365.258
53.5347
50.8538
53.0315
48.6352*
-358.684
Table 4C: Regression analysis for the u-shaped hypothesis
. tsset YEAR
time variable:
delta:
YEAR, 1987 to 2019
1 unit
. reg FINDEVT GINI GINI2
Source
SS
df
MS
Model
Residual
558.489961
262.777151
2
30
279.244981
8.75923835
Total
821.267112
32
25.6645972
FINDEVT
Coef.
Std. Err.
t
P>|t|
[95% Conf. Interval]
GINI
GINI2
_cons
-.3492769
.0238334
-14.02361
.0655721
.0029971
3.484464
-5.33
7.95
-4.02
0.000
0.000
0.000
-.4831931
.0177125
-21.13984
53
Number of obs
F(2, 30)
Prob > F
R-squared
Adj R-squared
Root MSE
=
=
=
=
=
=
33
31.88
0.0000
0.6800
0.6587
2.9596
-.2153608
.0299543
-6.907387