THE RELATIONSHIP BETWEEN FINANCIAL SECTOR DEVELOPMENT AND ECONOMIC
GROWTH IN SOUTH AFRICA
By
Amahle Gusha
202015934
Mini-Dissertation
Submitted in partial fulfilment of the requirements for the degree of
Bachelor of Commerce Honours
In
Economics
In the faculty of Management and Commerce
At the
University Of Fort Hare
East London Campus
2023
Supervisor: Dr S Tendengu
1|Page
Abstract
For decades, South African economy have embarked on financial sector reforms. However, the empirical
implications of these reforms have been divergent. This paper investigates the relationship between financial
development and economic growth using time series data in South Africa using data extracted from the World
Bank and SARB. This investigation will be carried out using three common indicators of financial development
broad money, GDP, and domestic credit to the private sector. The co-integration and presence of a long-term
relationship between the expansion of South Africa's financial sector and economic growth were determined
using the ARDL model. The study's findings suggest that there is a co-integration link. Both short- and long-term
research show that although certain conditions hinder economic growth, a big money supply promotes it. This
paper thus confirms the existence of a positive and long-term impact of all the indicators of financial development
on economic growth through bound test. It is therefore proposed that the financial reforms in South Africa should
be pushed forward to boost the development of the financial sector thus an increase in its role on economic
growth.
Keywords: Financial sector development, economic growth, ARDL cointegration, World Bank, SARB,
South Africa.
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Declarations
On originality of work
I, the undersigned, Amahle Gusha student number 202015934 hereby declare that this mini dissertation is my
own original work, and that it has not been submitted, and will not be presented at any other University for a
similar or any other degree award.
Signature:
Date: 02/12/2023
On plagiarism
I, Amahle Gusha, student number 202015934 hereby declare that I am fully aware of the University of Fort
Hare’s policy on plagiarism, and I have taken every precaution to comply with the regulations.
Signature:
Date: 02/12/2023
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Acknowledgements
First and foremost, I would like to express my sincere gratitude to the almighty God, His dear Son my Lord Jesus
Christ, and the Holy Spirit my ever-present helper, for His wonderful deeds which I’ve seen even throughout this
dissertation particularly, and in my academic journey. Likewise, I will forever be grateful and thankful to my
supervisor, Doctor S Tendengu. Without making any exceptions, I am super thankful to my dear mother who
had really been supportive to me in this academic journey, as well as my dear friends and family at large.
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Dedication
This mini dissertation is dedicated to my late aunty, Yanga Jacisa, in loving memory of your support and
encouragement. You were always there for me, and your words of wisdom and kindness have guided me through
many difficult moments. This dissertation is a testament to your lasting impact on my life.
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Table of Contents
Abstract ................................................................................................................................................................................. 2
Declarations ........................................................................................................................................................................... 3
Acknowledgements ............................................................................................................................................................... 4
Dedication ............................................................................................................................................................................. 5
Chapter one: Introduction and background of the study ..................................................................................................... 12
1.1 Introduction ................................................................................................................................................................... 12
1.2
Problem statement ................................................................................................................................................... 13
1.3
Research objectives ................................................................................................................................................. 13
1.3.1 Primary objectives .................................................................................................................................................. 13
1.4
Research hypothesis ................................................................................................................................................ 14
1.4.1 Null hypothesis ....................................................................................................................................................... 14
1.4.2 Alternative hypothesis ............................................................................................................................................ 14
1.5
Justification of the study .......................................................................................................................................... 14
1.6
Organization of the study ........................................................................................................................................ 14
1.7 Ethical considerations.................................................................................................................................................... 15
Chapter two: Overview of financial sector development and economic growth in South Africa ....................................... 16
2.1 Introduction ................................................................................................................................................................... 16
2.2 Overview of financial sector development in South Africa........................................................................................... 16
2.2.1 Financial sector reforms ......................................................................................................................................... 16
2.2.2 Comparison with BRICS countries (The BRICS perspective) ............................................................................... 20
2.2.3 Comparison with other Sub-Saharan countries (Kenya and Nigeria, respectively) ............................................... 21
2.3 Overview of the South African economic growth in relation with financial sector development ................................. 23
2.4 Chapter summary........................................................................................................................................................... 26
Chapter three: Theoretical framework and Empirical evidence .......................................................................................... 27
3.1 Introduction ................................................................................................................................................................... 27
3.2 Theoretical literature...................................................................................................................................................... 27
3.2.2 Introduction ............................................................................................................................................................ 27
3.2.3 The Endogenous Growth Theory............................................................................................................................ 27
3.2.4 Institutional Theory ................................................................................................................................................ 30
3.2.5 Resource-Dependent Theory .................................................................................................................................. 32
3.3 Empirical literature ........................................................................................................................................................ 33
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3.3.1 Empirical literature from developed countries ....................................................................................................... 33
3.3.2 Empirical literature from developing countries ...................................................................................................... 34
3.3.3 Empirical literature from South Africa ................................................................................................................... 36
3.4 Assessment of literature ................................................................................................................................................. 37
3.5 Chapter summary........................................................................................................................................................... 38
Chapter four: Research Methodology.................................................................................................................................. 39
4.1 Introduction ................................................................................................................................................................... 39
4.2 Model specification ....................................................................................................................................................... 39
4.3
Definitions of variables and prior expectations ....................................................................................................... 40
4.4
Estimation technique ............................................................................................................................................... 41
4.4.1 Unit root Tests ........................................................................................................................................................ 41
4.4.2 Augmented Dickey-Fuller Test and Phillips-Perron Test ....................................................................................... 41
4.4.3 Co-integration tests................................................................................................................................................. 42
4.4.5 Autoregressive distributed lag (ARDL) model ....................................................................................................... 42
4.4
Diagnostic Tests....................................................................................................................................................... 42
4.5.1 Residual Normality test .......................................................................................................................................... 42
4.5.2 Autocorrelation LM test ......................................................................................................................................... 43
4.5.3 The Heteroscedasticity test ..................................................................................................................................... 43
4.5.4 Impulse response .................................................................................................................................................... 43
4.5.5 Variance decomposition .......................................................................................................................................... 43
4.5
Data sources............................................................................................................................................................. 43
4.7 Chapter summary........................................................................................................................................................... 43
Chapter five: Estimation and interpretation of results ......................................................................................................... 44
5.1 Introduction ................................................................................................................................................................... 44
5.2 Descriptive statistics ...................................................................................................................................................... 44
5.4 Stationarity testing ......................................................................................................................................................... 45
5.5 Unit root test .................................................................................................................................................................. 47
5.6 Model selection and ARDL Co-integration Test ........................................................................................................... 48
5.6.1 Bound test ............................................................................................................................................................... 48
5.6.2 Long run model ...................................................................................................................................................... 49
5.6.3 ECM Model ............................................................................................................................................................ 50
5.7 Diagnostic tests.............................................................................................................................................................. 51
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Conclusion ........................................................................................................................................................................... 53
Chapter six: Conclusion and recommendations .................................................................................................................. 53
6.1 Introduction ................................................................................................................................................................... 53
6.2 Summary of the study and conclusions ......................................................................................................................... 54
6.3 Policy implications and recommendations .................................................................................................................... 55
6.4 Delimitation of study ..................................................................................................................................................... 56
Reference list ....................................................................................................................................................................... 56
Appendices .......................................................................................................................................................................... 61
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List of Figures
FIGURE 2.1. TREND IN BANKING SECTOR GROWTH IN SOUTH AFRICA (2000 - 2012) ........................................... 17
FIGURE 2.2 TRENDS IN STOCK MARKET CAPITALIZATION, TOTAL VALUE OF STOCKS TRADED AND TURNOVER
RATIO OF STOCKS TRADED IN SOUTH AFRICA (2000 – 2010) ........................................................................ 18
FIGURE 2.3 THE RATIO OF M1 TO DEPOSITS IN SOUTH AFRICA ............................................................................ 18
FIGURE 2.4 PRIVATE CREDIT TO GDP IN BRICS COUNTRIES FROM 2001 – 2020 .................................................... 20
FIGURE 2.5 STOCK MARKET CAPITALIZATION TO GDP FOR BRICS COUNTRIES FROM 2009 - 2020 ....................... 21
FIGURE 2.6 PRIVATE CREDIT BY DEPOSITS FOR THE SELECTED COUNTRIES FROM 2010 - 2020 ........................... 22
FIGURE 2.7 STOCK MARKET TURNOVER RATIO FOR THE SELECTED COUNTRIES FROM 2010 - 2020 .................... 23
FIGURE 2.8 VALUE ADDED TO GDP BY THE FINANCIAL INDUSTRY FROM 2015 – 2022 (IN MILLIONS ZAR) ......... 24
FIGURE 2.9 GDP-ANNUAL (%) FROM 2000 – 2024 ................................................................................................ 25
FIGURE 2.10 SA GDP BY SECTOR CONTRIBUTION 2023 ......................................................................................... 26
FIGURE 5.1 UNIT ROOT TESTS – GRAPHICAL PLOTS (LEVEL SERIES) .................................................................... 46
FIGURE 5.2 UNIT ROOT TESTS – GRAPHICAL PLOTS (1ST DIFFERENCE) ................................................................. 47
FIGURE 5.3 CUSUM TEST ....................................................................................................................................... 52
FIGURE 5.4 CUSUM OF SQUARES TEST .................................................................................................................. 52
List of Tables
TABLE 5.1 SUMMARY OF STATISTICS ..................................................................................................................... 44
TABLE 5.2 CORRELATION MATRIX ......................................................................................................................... 45
TABLE 5.3 UNIT ROOT TEST – (LEVEL SERIES)...................................................................................................... 48
TABLE 5.4 UNIT ROOT TEST – (DIFFERENCED) ...................................................................................................... 48
TABLE 5.5: BOUND TEST RESULTS ........................................................................................................................ 49
TABLE 5.6: LONG RUN COINTEGRATION RESULTS ................................................................................................. 49
TABLE 5.7: ECM MODEL RESULTS ........................................................................................................................ 50
TABLE 5.8: DIAGNOSTIC TESTS ............................................................................................................................. 51
TABLE 5.9 Q-STATIC ........................................................................................................................................... 53
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List of Appendices
APPENDIX 1: INTEGRATED AT I(0) .......................................................................................................................... 61
APPENDIX 2: INTEGRATED AT I(1) .......................................................................................................................... 62
APPENDIX 3: ARDL COINTEGRATION .................................................................................................................... 63
APPENDIX 4: LONG RUN COEFFICIENTS AND BOUND TEST ...................................................................................... 64
APPENDIX 5: ERROR CORRECTION REGRESSION ..................................................................................................... 65
APPENDIX 6: CUSUM TEST ..................................................................................................................................... 66
APPENDIX 7: CUSUM OF SQUARES ......................................................................................................................... 66
APPENDIX 8: Q-STATS ............................................................................................................................................ 67
APPENDIX 9: AUTOCORRELATION .......................................................................................................................... 68
APPENDIX 10: JARQUE-BERA TEST ........................................................................................................................ 69
APPENDIX 11: BREUSCH-GODFREY SERIAL CORRELATION LM TEST .................................................................... 69
APPENDIX 12: HETEROSKEDASTICITY TEST: ARCH .............................................................................................. 70
APPENDIX 13: RAMSET RESET TEST..................................................................................................................... 71
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List of acronyms and abbreviations
ADF
Augmented Dickey-Fuller
AIC
Akaike's Information Criterion
ARDL
Autoregressive distributed lag
DF
Dickey-Fuller test
GDE
Gross Domestic Expenditure
GDP
Gross Domestic Product
HQ
Hannan-Quinn information criterion
IMF
International Monetary Fund
JSE
Johannesburg Stock Exchange
LR
Likelihood Ratio test
PP
Phillips-Perron Test
SA
South Africa
SARB
South African Reserve Bank
SC
Schwarz information criterion
Stats SA
Statistics South Africa
VAR
Vector Autoregressive
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Chapter one: Introduction and background of the study
1.1 Introduction
Economic growth in South Africa is most efficiently driven by the financial sector. Over 70% of the gross
domestic product is generated by the financial sector, which can either stimulate or dampen economic growth
(Ncanywa & Mabusela, 2019). When the financial sector is efficient, financial intermediaries can operate
effectively by mobilizing financial resources efficiently, managing financial risks, and identifying profitable
investments, all of which contribute to economic growth and technological innovation (Jianguo & Qamruzzaman,
2018). In studies (Akinlo & Egbetunde, 2010; Balago, 2014; Ewubare & Ogbuagul, 2017; Tang & Abosedra,
2020; Valderrama, 2003), well-documented literature has shown how the development of the financial sector
contributes to long-term economic growth, but the results of these studies were different as others proposed that
a well-developed financial system promotes economic growth while others urged that it can be harmful to actual
aggregate economic development (Kapingura et al., 2022).
According to Qamruzzaman & Jianguo (2018), financial sector development is about adding value to the
economy and creating wealth for the agents of the financial system. By mobilizing depositors' savings, a financial
system selects investment opportunities for entrepreneurs through credit lines (Oriavwote & Eshenake, 2014a).
In developed countries, well-functioning systems promote growth by encouraging entrepreneurship,
technological advancement, and rapid human and physical capital accumulation (Oriavwote & Eshenake, 2014a).
Increasing transparency, monitoring, and reducing transaction costs are important functions performed by
financial intermediaries (Akinlo et al, 2010). Having less information asymmetry can be beneficial to banks and
stock markets in maximizing efficiency, limiting risk through portfolio diversification and hedge positions,
enhancing liquidity in productive assets, and encouraging trade and exchange (Ndlovu, 2013).
There has been extensive research into the link between the development of the financial sector and economic
growth, according to Akinlo and Egbetunde (2010), Balago (2014), Muhammad et al. (2014), and Le et al. (2014).
As an additional benefit, these studies suggest that reducing transaction costs in the financial sector may increase
investment and reduce risk. As an additional benefit, these studies suggest that reducing transaction costs in the
financial sector may increase investment and reduce risk (Ndlovu, 2013).
South Africa is one of the largest economies in Africa and has undergone significant financial sector development
over the years. A major contributor to the country's economic development is the financial sector (Celestine
Sunday Ogonna et al., 2020). The sector has grown considerably, with its contribution to GDP increasing from
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12% in 1994 to 22% in 2019 (World Bank, 2021a). This growth has been accompanied by an expansion in the
range of financial services and products offered, including mobile banking, microfinance, and insurance
(Celestine Sunday Ogonna et al., 2020). Furthermore, financial sector development has contributed to improving
the financial inclusion in South Africa. According to the World Bank, 2021b, the number of bank accounts has
grown from 17 million in 2002 to 36.4 million in 2019.
1.2 Problem statement
Research and debate have been on-going in South Africa on the relationship between financial sector
development and economic growth. It is widely accepted that a well-developed financial sector can promote
economic growth, but it is unclear to what extent this relationship exists in South Africa (Mandiefe, 2015).
Despite having a relatively well-developed financial system compared to other African countries, South Africa
still lags developed countries in terms of financial market sophistication, access to financing and venture capital,
and credit availability (Mandiefe, 2015).
Several studies have suggested that the South African financial system, although advanced, is highly concentrated
and dominated by a few large players, leading to limited access to finance and high borrowing costs for smaller
businesses and individuals (Otchere et al., 2017). It may negatively affect the growth of the economy if jobs and
economic development are lost as a result. Furthermore, the COVID-19 pandemic has revealed weaknesses in
the South African financial sector, particularly in terms of loan disbursements and access to credit for struggling
firms, which may impede the country's economic recovery efforts (Otchere et al., 2017). Therefore, this study
will examine the relationship between financial sector development and economic growth in South Africa.
1.3 Research objectives
1.3.1 Primary objectives
A primary objective of this research is to investigate the relationship between the financial sector development
and economic growth in South Africa from 1992 to 2022. We will work on these specific objectives:
i.
To review the trends of financial development and economic growth in South Africa from 1992
to 2022
ii.
To check empirically the relationship between financial sector development and economic growth
in South Africa from 1992 to 2022.
iii.
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To make policy recommendation based on the findings.
1.4 Research hypothesis
1.4.1 Null hypothesis
H0: There is a positive relationship between of financial sector development and economic growth in South
Africa.
1.4.2 Alternative hypothesis
H1: There is a negative relationship between financial sector development and economic growth in South Africa.
1.5 Justification of the study
Financial sector development is believed to enhance resource allocation, increase productivity, and stimulate
savings and investment, leading to higher economic growth rates (Rajan and Zingales, 1998; Levine, 2005).
Various stakeholders, including the government and private sector, have identified financial sector growth as a
key driver of economic growth and development in South Africa (Acaravci, 2009). The country has made
significant progress in financial sector development over the years, including expanding the banking system,
promoting financial inclusion, and developing a vibrant capital market.
However, despite these efforts, South Africa continues to face various challenges in its financial sector, including
difficulty to access to finance for SMEs, limited financial literacy, and income inequality The difficulties these
problems provide call for a more thorough comprehension of the connections between the development of the
financial industry and the growth in the economy. As a result, research on the growth of the financial sector and
economic expansion in South Africa is essential for shedding light on the variables influencing financial
development and economic expansion. The study's conclusions can be used by decision-makers, financial
regulators, and other interested parties to create and carry out programmes and policies that support the expansion
of South Africa's financial industry and economic expansion.
1.6 Organization of the study
The research will examine methodology, variable analysis, and estimating methodologies to observe the link
between the development of the financial sector and economic growth in South Africa. The ARDL Cointegration
framework was provided as the estimation methodology for the investigation. The diagnostic tests were then
spoken about. The first segment is concluded with more study on variance decomposition analysis and impulse
response. The actual estimations of the research are based on the topics covered in this section. The research is
introduced in Chapter 1 with an explanation of the challenges, goals, and context of the study as well as its
importance. Trends on financial sector developments in South Africa are reviewed in Chapter 2. A thorough
review of the theoretical and empirical literature is given in Chapter 3. The study approach, which includes a
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description of the models and data employed, will be the main topic of Chapter 4. The study findings are
presented in Chapter 5. The research summary and conclusions are then presented in Chapter 6.
1.7 Ethical considerations
In this study, all ethical procedures were followed. The study used widely available secondary data to conduct
econometric models. The primary data is obtained from human participants as individuals, members of groups,
organization, or the institution through direct interaction or data collection. The secondary data is gathered from
reliable, publicly accessible sources including the World Bank and SARB.
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Chapter two: Overview of financial sector development and economic growth in South Africa
2.1 Introduction
This chapter gives a thorough analysis and review of the financial sector's evolution and the ways in which it has
influenced South Africa's economic expansion. The importance of the financial sector's development in
propelling South Africa's economic growth is covered in this chapter. Visual representations created from data
gathered over time from the South African Reserve Bank and the World Bank are used to achieve this. An
extensive overview of the development and expansion of the financial sector in South Africa is given in the first
section. The chapter also includes the expansion of the financial sector in relation to other African states and the
BRICS countries. The following section offers a thorough examination of the relationship between economic
expansion and financial sector growth.
2.2 Overview of financial sector development in South Africa
The largest, most advanced, and most intricate financial system in Africa is found in South Africa. According to
the (Bank of International Settlement, 2012b), it may be compared to the financial systems of developed nations.
Over the past few decades, South Africa has effectively developed a robust stock market and an advanced banking
system. Because of this, the Johannesburg Stock Exchange (2012) lists it as one of the top 20 stock exchanges in
the world. The only stock market in South Africa is the Johannesburg Stock market (JSE), which was founded in
1887 and has been a member of the Federation of International Stock Exchanges since 1963. However, in terms
of market value, it is one of the world's leading stock exchanges.
2.2.1 Financial sector reforms
To satisfy both international standards for modernity and the nation's developmental goals, South Africa started
a process of financial sector reform in the 1990s (Nyasha & Odhiambo, 2020). Both the stock market and the
banking industry were included in this change. The focus of changes in the banking sector has been on improving
the frameworks of law, regulation, justice, and supervision. Additionally, they have sought to lessen financial
repression, bring banks back to soundness, renovate the financial system, and launch programmes that encourage
new players in the market (BIS, 2012a). Modernising the trading environment and enhancing the industry's
judicial, regulatory, legal, and supervisory functions have been the main goals of stock market reforms.
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Figure 2.1. Trend in Banking Sector Growth in South Africa (2000 - 2012)
2012-09-01
2012-01-01
2011-05-01
2010-09-01
2010-01-01
2009-05-01
2008-09-01
2008-01-01
2007-05-01
2006-09-01
2006-01-01
2005-05-01
2004-09-01
2004-01-01
2003-05-01
2002-09-01
2002-01-01
2001-05-01
2000-09-01
90,0
80,0
70,0
60,0
50,0
40,0
30,0
20,0
10,0
0,0
2000-01-01
% GDP
Total Credit to Private Financial Sector in South
Africa
End of Quarter
Source: Bank for International Settlements data (2023).
These strict changes have eventually resulted in the development of a sophisticated and well-regulated financial
sector in South Africa. This is seen by the rise in loans to the private financial sector in the banking sector, which
increased from 57.9% of the nation's GDP in 2000 to 72.9% of GDP in 2010. Furthermore, from 22,920 in 2008
to 24,063 in 2010, there were more Automated Teller Machines (ATMs). The banking sector also had a low
number of loans that were not being repaid, and there were strong legal protections for banks. These findings
were reported by the Banking Association South Africa in 2010 and the World Bank in 2014.
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Figure 2.2 Trends in Stock Market Capitalization, Total Value of Stocks Traded and Turnover Ratio of Stocks
% GDP
Traded in South Africa (2000 – 2010)
300,00
250,00
200,00
150,00
100,00
50,00
0,00
End of Quarter
Stock Market Capitalization to GDP
Total Value Traded to GDP
Stock Market Turnover Ratio
Source: Bank for International Settlements data (2010)
Figure 2 illustrates the performance and growth of the South African stock market from 2000 to 2010. It focuses
on three key indicators: stock market capitalization, total value of stocks traded, and turnover ratio of stocks
exchanged. The changes implemented in the stock market resulted in a growth in the number of listed businesses
from 401 in 2006 to 417 in 2010. Additionally, there was a significant increase in stock market capitalization,
total value traded, and turnover ratio, as reported by JSE (2014) and the World Bank (2014).
Figure 2.3 The ratio of M1 to deposits in South Africa
9
8
7
6
%
5
4
3
2
1
YEARS
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2022
2021
2020
2019
2018
2017
2016
2015
2014
2013
2012
2011
2010
2009
2008
2007
2006
2005
2004
2003
2002
0
Source: IMF – IFS Statistic Compact Disc, 2023
The concept of enhanced financial development in South Africa is substantiated by the M1 to deposits ratio
depicted in Figure 3. M1 is utilised in this context as a representative for the utilisation of physical currency
(notes and coins) as well as for temporary deposits. The M1 to deposits ratio provides an indication of the
proportion of short-term deposits within the overall deposit pool. A reduction in this ratio signifies a higher
proportion of long-term deposits. Conversely, when the ratio of M1 to deposits grows over time, it indicates a
growing inclination among economic actors towards short-term deposits. Financial systems are primarily valued
in the economy for their ability to facilitate trade more effectively than they mobilise savings, and for their role
in allocating and channelling resources to economic actors that lack sufficient resources. The analysis of the M1to-deposits ratio in South Africa reveals a consistent decline from 2005 to 2022, in contrast to the levels observed
in 2002 and 2003.
The elevated M1-to-deposits ratio before to 2002 may be linked to the presence of financial repressive measures,
the rising inflation rate, and the unfavourable political climate that lacked sufficient safeguards for savings.
However, following 2003, the proportion of short-term or sight deposits in the overall deposits continued to
decline. In 2012, the value was 7.6, which is lower than the value of 7.8 in 2004. The decline in the proportion
of sight deposits is indicative of enhanced financial development and increasing confidence in the money market.
The IMF (2022) has found that the significant market concentration and high entry barriers in South Africa have
led to large costs for end-users. The rise in competition within the banking industry, facilitated by the emergence
of digital banks, appears to have led to a certain level of price convergence. However, transaction costs continue
to remain relatively elevated. Efforts to enhance competition and contestability through the promotion of
cooperatives and mutual banks, which are regulated by appropriate legal and regulatory frameworks, have proven
effective. However, the total effects of these initiatives have yet to be fully assessed.
According to the IMF (2022), open banking reforms and the implementation of a simpler supervisory framework
for limited banking firms are seen as measures to promote competition and eventually lower costs, while yet
maintaining required protections to preserve financial stability. It is recommended to continue the advancement
of the Electronic Trading Platform (ETP) for government bonds, as it has enhanced pricing transparency and the
process of determining prices. This will contribute to enhancing market efficiency. The ETP has created a
platform that facilitates market creation, leading to higher trading volumes and the development of a more
comprehensive yield curve. This, in turn, has contributed to the establishment of a dependable and low-risk price
reference for non-government bonds (IMF, 2022).
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2.2.2 Comparison with BRICS countries (The BRICS perspective)
Figure 2.4 Private credit to GDP in BRICS countries from 2001 – 2020
Private Credit to GDP
200,00
%
150,00
Russia
100,00
Brazil
50,00
South Africa
0,00
China
India
Years
Source: World Bank, 2023
The private credit to GDP ratio serves as a proxy measure for financial depth, representing the size of the financial
sector in relation to the overall economy. Financial depth refers to the aggregate size of banks, financial
institutions, and financial markets inside a country, which is then compared to a measure of economic output
(World Bank, 2012). The variable has exhibited a consistent upward trend since 2001, as seen by the data
presented in figure 4. This serves as a clear sign of the growth and progress in the banking sector of the BRICS
countries. The ratio of commercial bank assets to the combined assets of deposit money banks and central banks
has grown over those years, indicating a correlation with financial development and economic growth. The
financial system of China ranks in the top 25% in terms of this indicator, with a value of 182% in 2020. South
Africa follows with 120%, Brazil with 78%, and India and Russia have the lowest values at 55%.
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Figure 2.5 Stock Market Capitalization to GDP for BRICS countries from 2009 - 2020
%
Stock Market Capitalization to GDP
400,00
300,00
200,00
100,00
0,00
Years
Brazil
India
South Africa
China
Russia
Source: World Bank, 2023
Stock market capitalization is a useful indicator for comparing and evaluating the size and activity of stock
markets in different countries (World Bank, 2012). The market capitalization of every company listed on the
stock exchange is calculated by multiplying the share price by the total number of shares issued. Comparing
South Africa to other comparable emerging countries, the country's market capitalization as a proportion of GDP
is at least twice as high. Figure 5 offers more proof that most emerging economies had notable downturns in the
2008 global financial crisis. Compared to other similar developing economies, the graph shows that the stock
market in South Africa is more volatile.
The BRICS countries possess some of the world's most significant stock markets, as measured by stock market
capitalization. According to Figure 5, South Africa has the highest stock market capitalization at 348%, followed
by India at 98%, China at 82%, Brazil at 68%, and Russia at 46%.
2.2.3 Comparison with other Sub-Saharan countries (Kenya and Nigeria, respectively)
The bank-dominated financial sectors are at a developing stage in the majority of SSA countries. Over the past
ten years, the depth and scope of financial systems have steadily increased but are still at a low level thanks to
reform initiatives (Nikolaidou & Vogiazas, 2019). EIB (2013) emphasises that although access to financial
services is relatively limited, the region's level of financial intermediation is still much lower than that of other
emerging nations.
The 2008 global financial crisis did not have a significant impact on African banking institutions since these
systems typically have sufficient capitalization, little leverage, surplus liquidity, and little reliance on outside
funding. However, due to trade connections and exchange rate depreciations, the banking systems of the majority
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of SSA nations were indirectly impacted by the crisis. This increased the borrowers' financial difficulties and led
to a rise in non-performing loans (Nikolaidou & Vogiazas, 2019).
Figure 2.6 Private credit by deposits for the selected countries from 2010 - 2020
%
Private Credit by Deposits
80,00
70,00
60,00
50,00
40,00
30,00
20,00
10,00
0,00
Years
KENYA
SOUTH AFRICA
NIGERIA
Source: World Bank, 2023
Compared to other African countries, South Africa possesses a very advanced financial industry (Acaravci &
Ozturk, 2020). A private credit to deposits ratio of 69.28% as seen in figure 6 signifies that a substantial
proportion of the deposits inside the banking system is allocated to private sector credit. This implies a resilient
financial system that can effectively facilitate economic endeavours. Kenya's financial industry has demonstrated
remarkable durability and ingenuity, particularly via the triumph of mobile banking, which has resulted in a
private credit by deposit ratio of 32.71%. Nigeria's financial industry needs to undergo changes to improve
stability, since it currently has a low private credit by deposits ratio of 11.23%.
An increase in the domestic credit extended by the banking sector or the domestic credit allocated to the private
sector will stimulate per capita economic development in sub-Saharan African nations. According to Acaravci
and Ozturk (2020), to promote economic development, governments should liberalise both their economy and
their banking sector due to the beneficial relationship between domestic credit provided by banks and economic
growth.
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Figure 2.7 Stock market turnover ratio for the selected countries from 2010 - 2020
%
Stock Market Turnover Ratio
45,00
40,00
35,00
30,00
25,00
20,00
15,00
10,00
5,00
0,00
Years
NIGERIA
SOUTH AFRICA
KENYA
Source: World Bank, 2023
Analysing financial development based on the stock market turnover ratio indicator entails evaluating the
effectiveness and degree of activity in the stock markets of South Africa, Kenya, and Nigeria. The stock market
turnover ratio is determined by dividing the aggregate value of shares exchanged over a specific time by the
mean market capitalization for that same period. South Africa boasts a well-regulated primary stock exchange,
known as the JSE, which is one of the major stock exchanges globally in terms of market capitalization. In 2020,
the JSE achieved a high turnover ratio of 27.94%, indicating robust investor engagement and liquidity within the
market. Kenya, with the Nairobi Securities market as its primary stock market, has a turnover ratio of 2.26%,
which is lower compared to the chosen countries. The turnover ratio of the Nigerian Stock Exchange was 4.39%,
indicating a superior performance compared to Kenya.
2.3 Overview of the South African economic growth in relation with financial sector development
Economic growth is the expansion of a country's production capacity over a period in an economy. Gross
domestic product (GDP) is the metric used to measure it. Economic growth is crucial as it directly influences the
level of life in an economy and has a significant role in generating employment opportunities (Pettinger, 2019).
The financial services industry is a crucial component of the South African economy and has a significant impact
on the lives of every citizen. Financial services facilitate individuals in conducting regular economic transactions,
accumulating, and safeguarding wealth for future goals and retirement, and mitigating the risks associated with
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personal calamities. The financial sector at the macroeconomic level facilitates economic expansion, the
generation of employment opportunities, the construction of essential infrastructure, and the promotion of
sustainable development for South Africa and its citizens.
Figure 2.8 Value added to GDP by the financial industry from 2015 – 2022 (in millions ZAR)
Source: Statista, 2023
In 2022, the financial industry in South Africa made a significant contribution of around 1.09 trillion rand to the
country's gross domestic product (GDP). The sector's contribution to the country's GDP in 2015 amounted to
almost 921 billion rand, which has now increased.
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Figure 2.9 GDP-annual (%) from 2000 – 2024
Gross Domestic Product (%)
8,00
6,00
4,00
2,00
0,00
-2,00
-4,00
-6,00
-8,00
Source: International Monetary Fund, 2023
The financial industry in South Africa has assets worth more than R6 trillion, which is equivalent to 10.5% of
the country's gross domestic product (GDP) each year. This sector employs 3.9% of the workforce and contributes
at least 15% of the corporate income tax. The entity has successfully weathered the crisis with minimal damage
and has maintained its robust performance from the previous ten years. The sector has seen a compound annual
growth rate of 9.1% since 2000, whereas the overall economic growth has been 3.6%. The financial industry in
South Africa has had significant employment growth, with a 24.5% rise in the number of persons working in the
sub-sector. This growth has positioned the financial sector as one of the fastest-growing employers in the country.
The sector's total assets have experienced substantial expansion, with a nominal compound average growth rate
of 12.3% from 2000 to 2010. The current value of financial sector assets is 252% of the gross domestic product.
Like other financial sectors, South Africa's financial industry has grown more interconnected and consolidated,
which exposes the country to substantial risks. Despite South Africa's strong macroeconomic foundations and
solid financial regulatory structure, it saw a relatively greater impact from the financial crisis compared to other
G-20 nations. This led to a significant loss of over 1 million jobs and a decrease in GDP, as seen in figure 9.
Growth has ultimately rebounded, but at a somewhat diminished level compared to 2007.
The financial industry saw a robust recovery from the effects of the COVID-19 pandemic, as evidenced by a
decrease in the nonperforming loans ratio from 4.5% in 2021 to 4.0% in 2022. Poverty continues to persist at
alarming levels, with around 30% of individuals living in conditions of extreme poverty in the year 2022. In
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2021, the Gini coefficient stood at 0.63, indicating a significant level of inequality. The unemployment rate stood
at around 32.7% in December 2022.
Figure 2.10 SA GDP by sector contribution 2023
South African GDP Contribution by Sector Q2 2023
3% 7%
17%
6%
12%
22%
14%
2%
3%
14%
Agric
Mining
Manufacturing
Electric
Const
Wholesale
Transport
Finance
Personal
Govt
According to Figure 10, the financial and business sector made the highest contribution to the GDP in the third
quarter of 2023, accounting for 22% of the GDP. This was followed by government expenditure, which
contributed 17%. The agricultural industry and the wholesale and retail commerce sector both contribute 14% to
the overall economy. The financial sector is undeniably crucial for economic growth.
2.4 Chapter summary
This section examined and offered data on the impact of the financial sector and advancements on the economic
expansion of South Africa. It analysed the contributions by sector and compared them with the BRICS nations
and other African countries. The developed financial sector has clearly had a beneficial impact on the growth of
South Africa by improving financial accessibility and enhancing the efficiency of financial goods in the economy.
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Chapter three: Theoretical framework and Empirical evidence
3.1 Introduction
The objective of this chapter is to provide the necessary contextual information for the research, which focuses
on examining the relationship between the development of the financial sector and economic growth in South
Africa, in a comprehensive manner. The relationship between the functioning of the financial system and
economic growth has been thoroughly examined within the field of development economics. In the realm of
theoretical discourse, this analysis offers insights into the interplay between the development of the financial
sector and economic growth, drawing upon established theories such as endogenous growth theory, institutional
theory, and resource-dependent theory. In contrast, the subsequent section of this chapter presents an examination
of empirical data, encompassing various studies conducted by scholars, pertaining to the correlation between the
development of the financial sector and economic growth. This analysis encompasses investigations conducted
in both developed and developing nations, as well as evidence specific to South Africa.
3.2 Theoretical literature
3.2.2 Introduction
A multitude of scholarly papers have been written with the objective of examining the influence of financial
development on several facets of an economy, such as domestic savings, capital accumulation, technological
innovation, and income growth. However, there is a lack of scholarly investigation undertaken on countries
within the Sub-Saharan African region, including South Africa. The Gross Domestic Product (GDP) of South
Africa exceeds the collective GDP of all other nations within the Southern African Development Community
(SADC) by a ratio of three. Therefore, it is important to analyse the influence of the development of the financial
sector on the economy of South Africa. The correlation between the development of the financial sector and
economic growth remains inconclusive since several theories have not yielded conclusive proof. This study
provides a comprehensive analysis of the extant scholarly works pertaining to growth theories in the field of
economics, with a special emphasis on the relationship between the growth of the financial sector and economic
growth.
3.2.3 The Endogenous Growth Theory
The field of finance plays a significant part in the endogenous growth hypothesis, as evidenced by its beneficial
influence on capital accumulation and savings levels (Romer, 1988). According to Barro and Sala-i-Martin
(2004), the endogenous growth hypothesis posits that economic growth is predominantly influenced by internal
variables rather than external influences. According to the theoretical framework, the proper use of technology
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and the presence of a proficient workforce have the potential to enhance productivity. The theory encompasses
a set of theoretical frameworks that aim to explain the process of economic growth by incorporating the role of
technical advancements and innovation. One of the key arguments put out by endogenous growth economists is
that enhancements in productivity are closely associated with accelerated innovation and increased expenditures
in human capital.
The Schumpeterian Growth Model
The relationship between endogenous growth theory and the concepts put out by Joseph Schumpeter may be
observed within the framework of their respective contributions to the comprehension of economic development
and innovation (Alcouffe & Kuhn, 2004). Schumpeter's work precedes the official establishment of endogenous
growth theory; however, it has exerted a significant effect on and made valuable contributions to the advancement
of this theoretical framework. Joseph Schumpeter is widely recognised for his seminal notion of "creative
destruction" and the pivotal role that innovation plays in propelling economic expansion. In his influential
publication "Capitalism, Socialism and Democracy" released in 1942, Schumpeter posited that the advancement
of the economy is propelled by the mechanism of innovation, entrepreneurship, and the implementation of novel
goods, procedures, and technology.
The speaker placed significant emphasis on the pivotal role played by entrepreneurs in the disruption of
established sectors and marketplaces, achieved via their inventive endeavours. One of the fundamental principles
elucidated by endogenous growth theory is that knowledge and innovation are not externally bestowed, but rather
internally generated through several methods including research and development (R&D), education, and
experiential learning. This statement is in accordance with Schumpeter's focus on the pivotal role of innovation
and entrepreneurship in driving economic progress.
AK Model
The relationship between financial development and economic growth is frequently explored through the
utilisation of a linear endogenous growth model, namely the AK model, which was initially suggested by Pagano
in 1993. The model illustrates a direct correlation between heightened financial development and higher levels
of economic growth. In the present investigation, we shall undertake an examination of a fundamental
endogenous model pertaining to the production function. The variable Y represents the output created at a specific
time t, and it is determined by a single element referred to as capital Kt:
𝑌�𝑡� = 𝑓�(𝐴�𝐾�𝑡�)
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The variable A represents the marginal productivity of capital or serves as an indicator of the overall efficiency
of the economy in utilising the capital stock K.
To enhance simplicity, the model operates on the assumption that the population remains constant throughout
time, and that the economy exclusively generates a single item that may be either consumed or invested. When
the single good is invested, it undergoes a depreciation process at a rate of 𝜎� each period. Therefore, the
calculation for gross investment may be expressed as:
𝐼�𝑡� = 𝐾�𝑡�+1 − (1 − 𝜎�) 𝐾�𝑡�
For a closed economy, capital market equilibrium requires saving, 𝑆�𝑡� to equals investment, 𝐼�𝑡�. Assuming the
proportion of saving, (1-φ), is lost through financial intermediation, then:
φ𝑆�𝑡� = 𝐼�𝑡�
From 𝑌�𝑡� = 𝑓�(𝐴�𝐾�𝑡�), the growth rate is obtained at time t+1 as
𝑔�𝑡�+1 = (𝑌�𝑡�+1⁄𝑌�𝑡� − 1) = (𝐾�𝑡�+1⁄𝐾�𝑡� − 1)
Therefore, using 𝐼�𝑡� = 𝐾�𝑡�+1 − (1 − 𝜎�) 𝐾�𝑡� and dropping the time element, the steady-state growth rate, Y (g)
will be obtained by:
𝑔�𝑡� = 𝐴� 𝐼�𝑡�/𝑌�𝑡�− 𝜎� = 𝐴�φ𝑆�𝑡� – 𝜎�
The equation 𝑔�𝑡� = 𝐴� 𝐼�𝑡�/𝑌�𝑡�− 𝜎� = 𝐴�φ𝑆�𝑡� – 𝜎� represents the relationship between the growth rate, denoted as
g, at a given time t, and many factors including the marginal productivity of capital (A), the savings rate (S), the
proportion of savings invested (φ), and depreciation (𝜎�). Therefore, it can be concluded that both the savings
rate and the productivity of capital have a favourable impact on long-term economic growth. Therefore, a higher
degree of development in the financial sector will result in increased economic growth if it leads to more savings.
(ii) This phenomenon leads to an increase in A. (iii) Additionally, it results in a decrease in the proportion of
savings that are squandered due to inefficient intermediation, represented by (1-φ).
The New Endogenous Growth Theory
The New Growth Theory posits that financial markets and institutions emerge because of internal factors in
reaction to market imperfections, therefore playing a role in fostering sustained economic growth. Financial
markets and institutions play a crucial role in alleviating the impact of transaction and information costs within
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the market. This, in turn, facilitates the allocation of resources towards sectors that have more potential for
stimulating economic growth. The New Endogenous Growth Theory aims to rectify the limitations of the
neoclassical growth theory, which posits that long-term economic growth is primarily driven by exogenously
determined technical advancements. Consequently, elucidating the disparities in technical advancements among
nations has proven to be challenging. The presence of technology disparities can elucidate the reasons for the
contrasting levels of economic prosperity observed across different nations.
The new growth theory postulates a theoretical framework in which the development of technology is internally
determined. The model incorporates the presence of a research and development sector that actively creates novel
ideas to predict technological advancements. Monopolistic competition entails the use of concepts to produce
capital commodities, hence enabling researchers to get financial gains from their endeavours. Factor inputs are
components utilised in the sector responsible to produce manufactured products (Romer, 1993).
The theory is confronted by three key assumptions, the first of which posits that technological innovation plays
a crucial role in facilitating long-term growth. Furthermore, advancements in technology are mostly propelled
by intentional actions taken by customers in response to market incentives. Thirdly, the economics pertaining to
ideas diverges in that the expenses associated with production are only borne once, but the ideas can be reused
without incurring further expenditures. The inclusion of monopolistic competition in the new endogenous growth
theory renders price-taking conduct irrelevant. The concepts of non-rivalry and non-excludability of
technological progress are evident in this observation. According to Romer (1993), the non-rivalrous nature of a
good, such as knowledge, implies that its use by one individual does not impede another individual's ability to
consume it.
3.2.4 Institutional Theory
Quality of institutions
In accordance with Filippidis and Katrakilidis (2014), institutions refer to the established regulations and
guidelines that a society establishes for itself. These institutions play a crucial role in shaping the incentives
individuals encounter, hence influencing the decisions they make in their interpersonal engagements. An
alternative perspective on institutions is examining their influence on the transaction costs associated with
enforcing contracts. According to Filippidis and Katrakilidis (2014), the presence of clearly stated rules and their
effective enforcement can significantly decrease the transaction costs encountered by economic agents, resulting
in enhanced efficiency of outcomes. The institution theory posits that variations in legal frameworks account for
the differences in institutions between nations. Conversely, the second hypothesis asserts that disparities in
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institutions arise from variations in the early functioning of the economic and political systems. Previous studies
have made attempts to examine this idea by conducting independent tests on legal origin or settler mortality
variables. However, recent advancements in methods and indexes have enabled researchers to get more
comprehensive measurements of legal institutions and government efficiency. Consequently, these improved
measures offer a more accurate understanding of the causal relationships between these factors.
Institutions have a pivotal role in driving financial growth. Social systems encompass a combination of informal
elements, such as restrictions, conventions, traditions, trust, and social capital, as well as formal components,
including rules, constitutions, laws, and property rights. Enhanced institutional quality, characterised by clearly
stated regulations and effective enforcement mechanisms, significantly decreases the transaction costs
encountered by economic actors, hence fostering more efficient results. The impact of institutions and the legal
framework on financial development is expected to be influenced by the financial sector's capacity to allocate
resources towards funding productive endeavours. According to Beck, Demirguc-Kunt, and Levine (2004), the
enhancement of creditor rights and the enforcement of contracts tend to facilitate the expansion of financial
markets.
The enhanced availability of borrower information contributes to the increased accessibility of loans and the
improved operational effectiveness of financial institutions. According to a study conducted by Filippidis and
Katrakilidis (2014), it was shown that the presence of creditor protection mechanisms, such as legislative
frameworks and information sharing institutions, is positively correlated with greater levels of private credit to
GDP ratios. Additionally, the study highlights that the legal roots of a country have a significant role in
determining the effectiveness of both creditor protection mechanisms and their impact on private credit to GDP
ratios. According to Beck, Demirguc-Kunt, and Levine (2004), the association between financial intermediary
development and initial endowments is more substantial compared to the association with legal beginnings.
Currently, economists continue to direct their attention towards the correlation between institutional factors and
the development of financial systems. This highlights the significance of institutions in elucidating a substantial
portion of the disparity in financial development among countries and over different periods of time (Kaidi et al.,
2019).
Law and finance Theory
The initial aspect of the law and finance theory posits that in nations whose legal systems uphold private property
rights, endorse private contractual agreements, and safeguard the legal entitlements of investors, individuals who
save money are more inclined to provide financial resources to enterprises, resulting in the thriving of financial
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markets (Beck & Levine, 2003). Legal structures that do not uphold private property rights or enable private
contracting to impede corporate financing and hinder financial development. The second part of the law and
finance theory highlights that the various legal traditions that originated in Europe in the past centuries and were
subsequently disseminated globally through conquest, colonisation, and imitation play a significant role in
understanding the variations in investor protection, the contractual environment, and financial development
across countries in the present day (Beck & Levine, 2003).
Efficient company finance and growth-enhancing financial development are facilitated by effective investor
protection. The law and finance theory highlights the significance of variations in contract, company, bankruptcy,
and securities laws across different countries. It also emphasises the importance of legal systems that prioritise
private property rights and have efficient enforcement mechanisms. These factors influence the extent of
expropriation and consequently impact the level of confidence individuals have when buying securities and
engaging in financial markets (Gazdar & Cherif, 2015). When considering the impact of legal institutions on
corporate finance and financial development, there are varying viewpoints on the extent to which the legal system
should primarily facilitate private contractual agreements and the extent to which it should establish specific laws
regarding shareholder and creditor rights (Gazdar & Cherif, 2015). Competent legal institutions enable
experienced financial market participants to create a wide range of intricate private contracts to address intricate
agency issues.
To ensure the successful implementation of this, it is crucial that courts impartially uphold private contracts and
possess the capacity and desire to comprehend elaborate contractual terms and validate technically difficult
sections that initiate specified activities (Gazdar & Cherif, 2015). Developing business, bankruptcy, and securities
laws might offer potential benefits in terms of organising financial transactions and safeguarding the interests of
minority owners and creditors, considering the challenges associated with enforcing intricate private contracts
(Rachdi & Mensi, 2012). Although standardisation can enhance efficiency by reducing transaction costs related
to various financial market agreements, excessively inflexible frameworks can limit customisation and impede
efficient contracting.
3.2.5 Resource-Dependent Theory
Based on the Resource-Dependent theory, the expansion of the financial sector aids in the quicker growth of
businesses that heavily rely on external funding. It also assists firms, particularly smaller and less transparent
ones, in overcoming limitations in obtaining funds (Beck et al., 2005). The beneficial impact of the development
of the financial sector on economic growth is mostly achieved through enhanced resource allocation and
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productivity growth, rather than simply increasing capital accumulation. The financial sector in a resourcedependent economy is moulded by the requirements of prominent enterprises in the mineral extraction sector,
since the structure of the financial sector is anticipated to mirror the production structure of the economy (Paun,
Musetescu, Danuletiu, & Topan, 2019). In resource-scarce nations, the local banking system has a reduced
significance, while the use of capital markets is increasingly prevalent.
Resource-dependent economies often have the same disadvantages that might hinder the growth of the financial
industry (Kurronen, 2012). The presence of abundant resources increases the likelihood of rent-seeking
behaviour and leads to ineffective governance. Resource-rich economies sometimes exhibit a tendency to
inadequately educate their population. Kurronen (2012) identifies four factors that contribute to the association
between developmental stages and limited natural resource endowment. 1) The poor majority has little patience
for taking advantage of the limited natural resources for profit; 2) Making the most of scarce resources and
investing in the development of valuable assets like human capital; 3) Countries that lack valuable resources have
less reason to restrict trade policies; 4) Lower-income countries start expanding into competitive manufacturing
earlier.
3.3 Empirical literature
3.3.1 Empirical literature from developed countries
According to the findings of Cecchetti and Kharroubi (2012), the adverse impacts of financial development on
productivity growth at the industry level may be attributed to the level of financial development. The result was
derived from an examination of industry-level data obtained from a sample of 50 advanced OECD nations
spanning the years 1980 to 2009. If a negative correlation exists between the expansion of the financial sector
and the extent of financial development, as well as a positive correlation between the magnitude of the financial
sector and the growth of industry productivity, individuals may erroneously attribute a detrimental impact to the
growth of the financial sector, when in fact it signifies the favourable influence of the level of financial
development.
The impacts of monetary policy were explored by Cecchetti and Kharroubi (2012). The anticipated trajectory of
the financial sector's expansion is projected to be impacted by the attitude of monetary policy and the prevailing
cost of capital. It is expected that the implementation of a more accommodative monetary policy and a reduction
in the cost of capital would lead to a more rapid growth of the financial industry. Since monetary policy tends to
be more accommodative during periods of low aggregate growth, it is plausible to argue that the findings suggest
a countercyclical operation of monetary policy.
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The experts have arrived at the determination that the magnitude and growth of a country's financial system might
hinder the emergence of productivity. During a certain phase, the proliferation of the financial system might
potentially impede tangible economic progress. Financial booms typically do not make significant contributions
to overall economic development due to the inherent competition between the financial sector and other sectors
of the economy for limited resources.
The study conducted by Shahbaz, Loganathan, Tiwari, and Jahromi (2015) aimed to analyse the correlation
between financial development and income inequality in Iran. The researchers utilised the ARDL limits testing
technique to assess the long-term association, considering any structural breaks in the data series. Their findings
indicated that to achieve a more equitable distribution of income, it is necessary to enhance the banking sector in
Iran. To narrow the socioeconomic disparity, it is imperative to facilitate access to financial services for
entrepreneurs. This measure will not only alleviate income inequality but also foster economic development
inside the nation.
The authors Shahbaz, Loganathan, Tiwari, and Jahromi (2015) suggest that the expansion of the capital market
might serve as a potential solution for Iran's economic growth. Shahbaz et al. (2017) conducted a study that
analysed the variables influencing the economic growth of China and India using data spanning from 1970 to
2013. Their research findings indicate that the growth of their economies was notably shaped by the advancement
of their industrial sectors.
The study conducted by Asteriou and Spanos (2019) analysed data from 26 European Union nations to investigate
the correlation between financial development and economic growth during the Global Financial Crisis in 2008.
The findings indicate that excluding the crisis era, financial development has a positive impact on economic
growth. However, during the crisis periods, it has a detrimental influence on economic activity.
During the period between 2008 and 2009, the research indicates that the proportion of commercial bank assets
had a crucial role in preventing an economic collapse. This implies that enough capital held by banks contributed
to maintaining the stability of the financial system. The data from the subprime crisis period indicate that liquid
liabilities impeded economic development. The level of foreign trade openness in a country's economy was the
main factor that drove growth during both crisis periods. Our findings suggest the need for more research into
the unorthodox financial development strategies that contribute to economic growth following a crisis.
3.3.2 Empirical literature from developing countries
A study conducted by Nguyen, Brown, and Skully (2019) investigated the influence of a country's degree of
economic development on the link between finance growth and rising economies. The study analysed data from
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two samples: a full sample covering the global crisis era from 1980 to 2011, and a pre-crisis sample from 1980
to 2006. Their assessment of financial progress relied on four key elements: banking, stock markets, bond
markets, and insurance. This enabled them to compare the outcomes for each. Banking had a detrimental
correlation with growth across all levels, indicating that a significant portion of bank loans may be allocated
towards non-growth endeavours, such as personal expenditures.
Stock markets had a beneficial impact on economic development for middle income nations. However, for high
income countries, this positive effect was only observed in the pre-crisis period. The bond market performance
showed improvement when comparing the whole sample to the pre-crisis sample. Lower income outcomes
continued to have a detrimental impact on bond markets, indicating the existence of a certain threshold that must
be met to stimulate growth. Insurance provided the most effective support for economic growth across all stages
of development. The measure was derived from the actual amount of insurance per person, and as a result, we
suggest that the policy should focus on supporting this sector to stimulate more expansion.
Gebrehiwot and Mekonnen (2020) conducted a 28-year research (1990-2017) to investigate the relationship
between financial growth and economic expansion in 34 African countries. The authors' focus was to use
empirical research to verify if financial development promotes economic success in Africa. The authors assert
that the positive and significant influence of financial expansion on African economic growth is mostly
manifested through its impact on investment and innovation. The study suggests that financial development may
play a more crucial role in promoting economic growth in underdeveloped African nations, considering their less
advanced financial systems.
The research undertaken by Qamarzumman (2017) investigated the relationship between financial growth and
economic expansion in the context of Bangladesh. The study included yearly data spanning from 1980 to 2016.
The study conducted a comprehensive investigation of the enduring relationship between autoregressive
distributed lag (ARDL) models and identified a robust link between financial development and economic growth.
The country of Bangladesh has exhibited a robust and persistent correlation between the advancement of financial
systems and the expansion of its economy, as evidenced via rigorous study.
The study revealed that financial advancement had a substantial impact on investments and the efficiency of
resource allocation, leading to a major jump in Bangladesh's economic growth. The research revealed compelling
evidence of reciprocal interaction between the financial and real sectors, suggesting that financial expansion not
only stimulates but also reflects changes in the real sector. The results were consistent across several indicators
of financial development and economic growth.
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Ducanes and Bautista (2019) examined the impact of financing on the growth of two sectors in a sample of 77
developing nations. The two parts were connected through a nonlinear relationship, and financial advantages
were only realised upon surpassing a specified threshold. According to Ducanes and Bautista (2019), a certain
degree of financial development is required to get substantial advantages because of the first substantial and
uneven investment in these areas.
Ducanes and Bautista (2019) highlight the significance of enhancing the domestic financial system in decreasing
the reliance of enterprises on cash flows for investment decisions and alleviating external financial limitations,
particularly for small firms. These, in turn, have consequences for growth that is more comprehensive and stable.
In this context, it has been discovered that remittance-receiving countries, despite being considered financially
underdeveloped based on traditional measures, may not be as financially underdeveloped as they appear. This is
because remittances can help alleviate financing constraints.
3.3.3 Empirical literature from South Africa
Kapingura (2013) conducted a study that goes beyond previous research by examining the degree to which
financial markets and financial intermediaries complement or replace each other in South Africa, rather than only
focusing on the relationship between economic growth and financial development. The author discovered data
indicating a reciprocal relationship between the development of the stock market and economic growth. The
relationship between the stock market and economic growth is greater, with a causality level of 5%, compared to
the banking sector which has a causality level of 10%. The stock market offers a range of financial services that
can contribute to economic growth, surpassing the banking industry in this regard. Regarding the bond market,
there is evidence of a one-way causation from the bond market to GDP. This implies the existence of the supplyleading theory.
The empirical findings indicate that the gradual movement of services through financial markets, specifically the
stock market and the bond market, play a crucial role in providing funds for research and development investment
in Africa, hence contributing to economic growth (Kapingura, 2013). The author's conclusion is that, due to the
underdeveloped and illiquid nature of financial markets in Africa, it is necessary for authorities to promote the
development of these markets. This can be achieved by implementing a combination of taxes, legal measures,
and regulatory policies that will eliminate obstacles to financial market operations and ultimately improve their
efficiency.
The study conducted by Akinboade and Kinfack (2014) utilised econometrics approach to investigate the
correlation between four Millennium Development Goals (MDGs) goals, four indices of financial sector
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development, and economic growth in South Africa. Typically, an increase in per capita income leads to an
increase in per capita spending on education in the near term. The reduction in the ratio of total domestic credit
to GDP leads to a decline in expenditure on education. There exist extremely significant long-term correlations
among the variables. According to a study conducted by Akinboade and Kinfack (2014), there is a positive
correlation between enhancing access to credit from the private sector and rising per capita earnings, and the
improvement of health outcomes in South Africa. There is no correlation, either in the short term or the long
term, between household expenditure on clothing, economic growth, and the expansion of the financial sector.
Manyedi and Ndlovu (2018) employed time series data spanning from 1990 to 2016 to examine the impact of
financial sector development on economic growth in South Africa. Their research indicated that the expansion
of the finance industry had a significant positive impact on the economic growth of South Africa. Additionally,
they found data indicating that the level of financial depth had a significant role in this correlation.
3.4 Assessment of literature
After conducting a thorough analysis of the available literature, the connection between the development of the
financial sector and economic growth is intricate and diverse. This link is impacted by several factors and can
differ among nations and regions. Theoretical literature examines many frameworks that aim to comprehend the
correlation between the development of the financial sector and economic growth. The Endogenous rise Theory
emphasises the significance of finance in the accumulation of capital and the rise of productivity.
The text underscores the significance of innovation and entrepreneurship in propelling economic advancement,
in accordance with Joseph Schumpeter's concepts of creative destruction. The AK Model places significant
emphasis on the influence of financial development on both savings and productivity. The New Endogenous
Growth Theory emphasises the significance of financial markets in promoting innovation and technological
advancement. Institutional Theory emphasises the importance of well-defined regulations, ownership rights, and
the legal structure in fostering the growth of the financial sector. Resource-Dependent Theory posits that the
advancement of financial systems may be advantageous for companies that heavily rely on external funding,
particularly in economies abundant in natural resources.
Empirical research has looked at the relationship between the rise of the financial industry and economic
expansion in both industrialised and developing nations. The results of research on the connection between
economic growth and financial development have been conflicting. While some studies find that financial
development has a positive impact on growth, others find that it has dependent or adverse impacts. Studies carried
out in developed countries indicate that there may be a complex relationship between the growth of the financial
37 | P a g e
industry and economic productivity. The outcomes might be impacted by variables including the level of financial
development and the influence of monetary policy. Several studies suggest that an overly large financial sector
might be detrimental to economic expansion.
Because different financial industry sectors—such as banking, stock markets, bond markets, and insurance—
have differing effects, there is inconsistent evidence of a link between financial development and economic
growth in emerging economies. Insurance has shown promise in promoting economic growth across a range of
socioeconomic levels. Studies carried out only in South Africa underscore the importance of financial markets,
encompassing the stock and bond markets, in promoting economic growth. According to the research, these
markets have the capacity to provide a variety of financial services that are important for promoting economic
growth. There appears to be less of a relationship between the banking industry and economic expansion.
3.5 Chapter summary
The main objective of this chapter was to present and analyse theoretical literature and empirical research that
elucidate the connection between the development of the financial sector and economic growth. The discussed
economic growth theories include the endogenous growth theory, which proposes that financial development has
a positive impact on economic growth by increasing capital accumulation and productivity through technological
advancements. The institutional theory explores how institutions influence the efficiency and effectiveness of
financial markets. Lastly, the resource-dependent theory suggests that financial development can facilitate faster
growth, improved resource allocation, and enhanced productivity for industries that rely on external finance. The
empirical information presented in this chapter is derived from prior research conducted by several experts, who
have reached diverse conclusions in their own investigations.
38 | P a g e
Chapter four: Research Methodology
4.1 Introduction
This chapter outlines the methodologies that will be employed to examine the relationship that exists between
the development of the financial sector and the economic growth in South Africa. This chapter delineates the
methodology employed and provides an account of how the estimation of said methodology was implemented.
The chapter provides an elaborate explanation of the variables and different tests employed in the study's model,
which encompasses the data period and data sources.
4.2 Model specification
This paper introduces an econometric model that examines the correlation between the financial sector and
economic growth in South Africa. The purpose of this study was to assess financial sector growth using three
indicators: the ratio of broad money stock to GDP, the bank deposit to GDP, and the domestic credit to private
sector. The dependent variable utilised was GDP per capita.
This study aims to analyse the empirical relationship between financial development and economic growth. To
do this, a model developed by Cecchetti and Kharroubi (2012) will be employed. The typical quadratic
relationship between economic growth and financial development is:
GDP = (MCYt + BDCYt + DCCYt + RRATEt + INFLt) ……………………………...(1)
Where:
GDP is the gross domestic product per capita.
MCYt is the ratio of broad money stock to GDP.
BDCYt is the bank deposits to GDP.
DCCYt is the domestic credit to the private sector.
The RRATEt real interest rate
INFLt is inflation.
Logarithms are essential for enhancing the performance of distributions, reducing the influence of outliers, and
reducing the extremes of data. Using logarithm in the model is essential since the exponential trend in time series
data is linear, and Brooks (2008) supports with this statement. There will be an introduction of logarithm to the
39 | P a g e
model assuming a constant, β; coefficients of explanatory variables, β1 to β5 and error term εt then the model
becomes:
LogGDPt = β + Log β1MCYt +Log β2BDCYt +Log β3DCCYt +Log β4RRATEt +Log β5INFLt + εt (2)
4.3 Definitions of variables and prior expectations
Economic growth
As a measure of economic progress employed in the study, real GDP per capita acts as the dependent variable.
According to(Aziakpono, 2011) claims that if GDP growth outpaced population growth, average family income
would increase and there would be more resources available for investment and development. Increasing
investment and lending by financial institutions is likely to result in a better financial sector that promotes
economic growth in a healthy and expanding economy (Aziakpono, 2011).
Indicators of financial sector development
One way to evaluate the monetization of the economy is to look at the GDP to broad money (M3) ratio. Liberto
(2020) defines broad money (M3) as follows: currency, deposits with a two-year predetermined maturity,
redeemable deposits with a three-month notice period, repurchase agreements, money market fund shares/units,
and debt instruments with a two-year maturity. A greater ratio results in more money being available for
investments as well as a greater selection of financial services and goods, including loans and insurance (Liberto,
2020).
The ability of the financial sector to sustain the economy is shown by the ratio of bank deposit to GDP, which
measures the economy's capacity to fund itself (Cavusoglu et al., 2019). A higher ratio indicates a more developed
financial sector since it signifies that a bigger amount of savings is going into the banking system. According to
(Cavusoglu et al., 2019) this may result in improved access to financing and investment possibilities, the potential
creation of jobs, and overall economic growth.
Domestic credit to the private sector is a frequently used indication of the financial industry's allocated efficiency,
and allocated efficiency is expected to rise as the financial system matures. It stands for financing for the private
sector (Oriavwote & Eshenake, 2014). Additionally, compared to public sector credit, private sector credit
produces better production (Cavusoglu et al., 2019). Increasing domestic credit to the private sector can
encourage business investment and give small and medium-sized businesses and entrepreneurs access to
financing.
40 | P a g e
According to (Cecchetti & Kharroubi, 2012) real interest rates and inflation are two of the control variables in
the model that are often used in literature. Real interest rates and inflation are frequently used to gauge an
economy's overall health. Real interest rates show the cost of borrowing when inflation is considered, whereas
inflation represents the overall level of prices for goods and services. High inflation and real interest rates can
discourage investment and limit economic growth, while low inflation and real interest rates can boost economic
activity, according to the World Bank Global.
4.4 Estimation technique
4.4.1 Unit root Tests
The Augmented Dickey Fuller (ADF) (Dickey & Fuller, 1979) and the (Philips & Perron, 1988) unit root tests
will be employed in this study. The ADF and Phillips-Perron tests have been challenged for their lack of power
when variables are stationary, but their root is near to a non-stationary border (Khobai, Mugano, & Le Roux,
2017). This test will to be used to determine whether the data presented is stationary. Unit root tests can be used
to determine whether data is significant at the level of first difference. Using the three degrees of significance,
1% is strongly significant, 5% is semi-significant, and 10% is badly significant. Under two criteria, Augmented
Dickey Fuller and Philip Perron, unit root testing can be applied at the constant, intercept, and none levels
(Shrestha & Bhatta, 2018)
4.4.2 Augmented Dickey-Fuller Test and Phillips-Perron Test
A unit root test was performed in a time series by Dickey and Fuller (Dickey & Fuller, 1979). In terms of DickeyFuller test, this test is generally known to be used in econometrics for determining whether a certain type of time
series data possesses an autoregressive unit root. This test, Dickey-Fuller test, is said to be the simple approach
in testing for a unit root. However, most of time series data are complex, thus simple autoregressive model can
be in adequate to be used in such economic time series, hence the use of Augmented Dickey-Fuller (ADF) test
maybe necessary in such cases also.
ADF test is simply an extraction from Dickey- Fuller test, it is an expansion form where Dickey-Fuller test is the
original of it (Brooks, 2008). Also, according to (Brooks, 2008), (Philips & Perron, 1988) formed their theory
of non-stationarity pertaining to the unit root. The Phillips-Perron test is a comparable test to the ADF test. PhilipPerron test is simply made use of in the unit root testing for the testing of the null hypothesis that order 1
integrated a time series is found to be. It is also worth to mention that the Pillips-Perron test is an extended
modification of the ADF test.
41 | P a g e
Hamilton (1994) states that the ADF removes all effects that are found to be structural in time series data,
producing results that are more robust. In contrast, Phillips Perron acknowledges automatic amendment, allowing
auto-correlated residuals. The PP unit root test typically has a stronger presence of serial correlation and
heteroscedasticity than the ADF. Unit root techniques are often restricted due to the lack of covariance stationary
unit roots, which require special handling. Additionally, when unit roots are present, they might provide statistical
challenges due to OLS's bias.
4.4.3 Co-integration tests
Co-integration tests are for a cause of analysing the time series processes that are not stationary meaning their
variances and means tends to vary over time. This simply asserts that the method is applicable in econometrics
even for the estimation of parameters that are of a long-run or equilibrium in systems that possesses variables of
unit root (Brooks, 2008). Co-integration experiments are applied after stationary variables have been determined
so that they can be checked if there is any significant impact between them. The “aim of using co-integration in
this analysis is to look at the long-run relationship between financial sector development and economic growth.
4.4.5 Autoregressive distributed lag (ARDL) model
The ARDL method is adopted in this work for several reasons, including its efficiency in predicting long-run
connections and its lack of residual correlation, which lessens the problem of endogeneity. ARDL co-integration
is a technique in which is mostly preferred in cases where variables are different in terms of how they are
integrated this marks out different order such as I(0), I(1) or even when the two coexist, and robust becomes
inevitable if the variables of the study have a long-run relationship that is even single in a sample size that is
found to be small. ARDL makes the estimated standard error unbiased and eliminates the autocorrelation
problem. It is applicable in this study because of its reliability and effectiveness in estimating smaller samples.
The first stage in the ARDL co-integration methodology is to assess if there is a long-run link between the variable
or not, which is done using the Bound F-statistic. When the F-value is higher than (I1) bound values at all
significant levels (1%, 5% & 10%) that simply says that there is co-integration between variables, short-run and
long-run tests are required. After finding the long-run relationship, the specified ARDL model coefficients will
be computed.
4.4 Diagnostic Tests
4.5.1 Residual Normality test
We'll utilise the Jarque-Bera test to determine normality. It makes use of the random variable's distribution
feature. The initial two moments of the variance and the mean determine the whole distribution. Under the
42 | P a g e
assumption that the series distribution is symmetric, the test statistic asymptotically fits into an X2. If the model's
residuals are highly skewed or leptokurtic, the null hypothesis of normality is rejected (Gujarati, 2004).
4.5.2 Autocorrelation LM test
The autocorrelation test will be performed to see whether there is any connection between the error term in one
time and the error term in any other period. First-order autocorrelation occurs when the error term in one period
is associated with the error term in the preceding period. The Durbin-Watson test specifically examines if there
is a link between an error and its immediately preceding value, known as first-order autocorrelation. If there is
proof of a connection between the consecutive residuals, the null hypothesis, which assumes that the error
components are independent, is rejected (Gujarati, 2004).
4.5.3 The Heteroscedasticity test
4.5.4 Impulse response
The impulse response function (IRF) describes how any dynamic system responds to an internal or external
change. This can provide the dependent variables' time path in a VAR to all explanatory factors' shocks (Gujarati,
2004).
4.5.5 Variance decomposition
According to (Brooks, 2008), variance decomposition analysis displays the percentage of movements in the
dependent variables that are caused by the shocks themselves as opposed to shocks to other variables. variation
decomposition technique therefore assesses the extent to which innovations to each explanatory variable account
for the variation of the s-step forecast error variance of a given variable.
4.5
Data sources
Time series analysis and yearly data covering the years 1992–2022 are used in this study. The sample period that
was chosen considers modifications to the economic structures and policies within the economy of South Africa.
The model is evaluated using GDP and financial sector development data. For the given time, data is sourced
from the World Bank and the South African Reserve Bank. EViews software was used to investigate the included
variables, including the dependent and independent variables.
4.7 Chapter summary
In conclusion, this chapter discussed the methodology and techniques that are to be utilised in the study. The
chapter estimates the model technique and further discusses the variables that are going to be utilised in the study
and their apriori expectations. Conclusively, the study utilises the ARDL model co-integration test to establish
the relationship between financial sector development and economic growth in South Africa.
43 | P a g e
Chapter five: Estimation and interpretation of results
5.1 Introduction
To ascertain the link between the development of the financial sector and economic growth in South Africa, the
main objective of this chapter is to present, assess, and interpret the findings of the models that are estimated in
the previous chapter. The following is an outline of the chapter: The co-integration tests are given and examined
after the formal and informal unit roots testing. The variance decomposition test is conducted to wrap up the
chapter after the VECM tests are given and examined.
5.2 Descriptive statistics
Table 5.1 Summary of statistics
Mean
Median
Maximum
Minimum
Std. Dev.
Skewness
Kurtosis
LGDP
10.61182
10.77455
11.54231
9.335651
0.704551
-0.315748
1.707664
LMCY
13.80382
14.14935
15.11517
12.05649
0.978363
-0.385395
1.782856
LBDCY
14.07197
14.43699
15.40781
12.25489
1.016472
-0.394369
1.750101
LDCCY
11.14949
11.48194
12.95522
8.763584
1.253674
-0.442377
1.999305
LINFL
1.771670
1.744783
2.630066
0.724087
0.398892
-0.284453
3.410380
RRATE
5.164682
4.647315
12.69103
0.774484
2.902218
1.108438
3.531101
Jarque-Bera
Probability
2.499946
0.286513
2.507964
0.285366
2.639429
0.267212
2.155887
0.340295
0.594578
0.742829
6.279232
0.043299
Sum
Sum Sq. Dev.
307.7429
13.89899
400.3107
26.80146
408.0870
28.93004
323.3352
44.00753
51.37844
4.455224
149.7758
235.8403
Observations
30
30
30
30
30
30
The bank deposit to GDP ratio (LBDCY) exhibits a mean that is 14.07% higher than the mean of all other
variables. This implies that LBDCY has a significant role in the overall economic output of South Africa, since
it is the primary contributor to the country's Gross Domestic Product (GDP). The p-value for the LGDP is
0.286513, which above the 10% significance level. Consequently, we may conclude that the null hypothesis,
which states that the residuals are normally distributed, is accepted. The p-value for the LMCY is calculated to
be 0.285366, indicating that it above the predetermined significance level of 10%. Consequently, we accept the
null hypothesis, which posits that the residuals conform to a normal distribution. The p-value for the LBDCY
variable is determined to be 0.267212, which above the predetermined significance criterion of 10%.
Consequently, we may conclude that the null hypothesis, which posits that the residuals follow a normal
distribution, is accepted. The p-value for the LDCCY variable is calculated to be 0.340295, which above the
predetermined significance level of 10%. Consequently, we may conclude that there is insufficient evidence to
reject the null hypothesis, indicating that the residuals follow a normal distribution. The p-value associated with
44 | P a g e
the LINFL variable is 0.742829, which exceeds the predetermined significance limit of 10%. As a result, we fail
to reject the null hypothesis, indicating that the residuals conform to a normal distribution. The p-value associated
with the RRATE variable is 0.043299, which above the predetermined significance level of 5%. Consequently,
we may reject the null hypothesis that the residuals follow a normal distribution.
Table 5.2 Correlation matrix
Covariance Analysis:
OrdinaryDate: 10/15/23
Time: 02:31 Sample:
1992 2022
Included observations: 30
Balanced sample (list missing value deletion)
Correlation
Probability
LGDP
LGDP
1.000000
LMCY
LBDCY
LDCCY
LINFL
LMCY
0.997891
0.0000
1.000000
-----
LBDCY
0.997780
0.0000
0.999663
0.0000
1.000000
-----
LDCCY
0.983890
0.0000
0.986916
0.0000
0.985628
0.0000
1.000000
-----
LINFL
-0.570259
0.0012
-0.583241
0.0009
-0.578397
0.0010
-0.643040
0.0002
1.000000
-----
RRATE
-0.482344
0.0081
-0.469331
0.0102
-0.476793
0.0089
-0.453987
0.0134
0.170127
0.3776
RRATE
1.000000
-----
The correlation between LGDP and LMCY is 0.997891, it is stronger. The correlation between LGDP and
LBDCY is 0.997780, it is stronger. The correlation between LGDP and LDCCY is 0.983890, it is stronger. The
correlation between LGDP and LINFL is -0.570259, it is non-existing. The correlation between LGDP and
RRATE is -0.482344, it is also non-existing.
5.4 Stationarity testing
As it has been mentioned before that the stationarity test is a necessity to avoid spurious or bogus regressions
and as it has been foretold that stationary testing help in determining the estimation techniques that is going to
be used in the study. Thus, this study made an employment o an informal and formal stationarity tests, where the
informal stationarity test entails graph and the formal stationarity test pertains to the Augmented Dickey-Fuller
(ADF) and Phillips Perron (PP).
45 | P a g e
In terms of the graphical plots of the data, this shows the state at which the data is at in terms of stationarity,
moreover, this shows how the data trends in the circumference of its mean at level series as well as first difference.
For a variable to be considered as one that has high chance of being stationary the graph must cross an average
value many times. However, if this condition does not hold or situation occur it will show that the data is more
trending persistently and thus, the graphical analysis of the stationarity of the variables of the study are presented
in Figure 5.1.
Figure 5.1 Unit root tests – Graphical plots (level series)
LGDP
LMCY
11.6
16
11.2
15
10.8
10.4
14
10.0
13
9.6
9.2
12
1995
2000
2005
2010
2015
2020
1995
2000
LBDCY
2005
2010
2015
2020
2010
2015
2020
2010
2015
2020
LDCCY
16
13
12
15
11
14
10
13
9
12
8
1995
2000
2005
2010
2015
2020
1995
2000
LINFL
2005
rrate
3.0
14
12
2.5
10
2.0
8
1.5
6
4
1.0
0.5
2
0
1995
2000
2005
2010
2015
2020
1995
2000
2005
LGDP, LMCY, LBDCY & LDCCY are trending meaning that they are controlled by the time, therefore they are
non-stationary. While LINFL and RRATE are stationary.
46 | P a g e
Figure 5.2 Unit root tests – Graphical plots (1st difference)
Differenced LGDP
Differenced LMCY
.16
.20
.12
.16
.08
.12
.04
.08
.00
.04
-.04
1995
2000
2005
2010
2015
2020
.00
1995
2000
.25
1.2
.20
0.8
.15
0.4
.10
0.0
.05
-0.4
1995
2000
2005
2010
2015
2020
-0.8
1995
2000
2015
2020
2005
2010
2015
2020
2015
2020
Differenced rrate
Differenced LINFL
.8
6
.6
4
.4
2
.2
0
.0
-2
-.2
-4
-.4
-.6
2010
Differenced LDCCY
Differenced LBDCY
.00
2005
-6
1995
2000
2005
2010
2015
2020
1995
2000
2005
2010
According to the findings above on Figure 5.2 it is a graphical presentation of the variables of the study, and this
shows that every variable is stationary at first differenced level. Simply because all fluctuate around the mean or
average value, hence they are also considered to be integrated as first difference. Meaning they can now be
estimated at least, even though it is not enough to conclude based on a graphical presentation only because they
are an informal way of testing stationarity, it does not provide full explicit results. Below the formal tests is
conducted through the unit root tests using the ADF and PP testing techniques.
5.5 Unit root test
The process of assessing stationarity by means of Unit Root testing involves the utilisation of two specific types
of unit root tests, namely the Augmented Dickey-Fuller (ADF) test and the Phillips-Perron (PP) test. Two types
of unit root tests are commonly employed to assess stationarity before to conducting co-integration tests, which
47 | P a g e
serves as the initial stage in the analysis. Each test is run at three different conditions: none, intercept, and trend
plus intercept. The outcomes of the unit root tests may be observed in Tables 5.2 and 5.3, which present the
findings for both the level series and the first difference.
Table 5.3 Unit root test – (level series)
Variable
LGDP
LMCY
LBDCY
LDCCY
LINFL
RRATE
Augmented Dickey fuller
Trend &
Intercept
intercept
0.246457
-4.522534***
-1.301342
-3.039087**
-1.150644
-2.682107*
-2.731636
-2.100086
-5.151122***
-4.270928***
-2.938790
-2.088551
none
1.000638
2.519294
1.866758
3.658790
-0.792057
-2.447362**
Philips Perron
Trend &
intercept
0.246457
-0.436280
-0.087576
-2.553550
-7.222519***
-2.593372
Intercept
none
-4.240093***
-2.840364*
-2.878804*
-2.080372
-4.233365***
-2.071004
6.892273
6.547553
5.876891
3.553920
-0.791053
-1.037830
***Significant at 1%, **significant at 5%, *significant at 10%
The results are not stationary and therefore we can conclude that they are not credible results.
Table 5.4 Unit root test – (differenced)
Variable
∆LGDP
∆LMCY
∆LBDCY
∆LDCCY
∆LINFL
∆RRATE
Augmented Dickey fuller
Trend
& Intercept
intercept
-4.639030*** -1.569995
-4.917749*** -3.319923**
-3.635451**
-2.518189
-6.158344*** -8.035775***
-1.780098
-1.894481
-6.268223*** -6.120601***
none
-1.277577
-1.233545
-1.094204
-5.522866***
-4.598829***
-6.232045***
Philips Perron
Trend
&
intercept
-4.644852***
-4.904098***
-3.573692*
-10.21375***
-5.050089***
-6.222633***
Intercept
none
-3.199194
-3.524104**
-2.587267
-8.217003***
-5.228447***
-6.089812***
-0.945854
-1.053741
-0.867487
-5.633854***
-5.382380***
-6.196075***
***Significant at 1%, **significant at 5%, *significant at 10%
As per to the table above, Table 5.3, the results are showing that the data at first difference series has now found
stationarity when it came to the two, ADF and PP tests where it mounted to a significance of 1%. Thus, it is
now acceptable to conclude that the data is now good and significant to be used in the Autoregressive distributed
lag model to test for cointegration. This takes place in the following section.
5.6 Model selection and ARDL Co-integration Test
This study will use an ARDL model as some of the variables are stationary at 1st difference and some are
stationary at the level which is one of the conditions for using ARDL model. The number of lags chosen for this
study is two, as determined by the various criteria that were considered (AIC, SC, GQ, LR and FPE).
5.6.1 Bound test
The bound F-test is a statistical tool that may be employed to ascertain the presence of co-integration. The
evaluation of the estimated F-statistic in relation to the critical values (CV) of the lower and upper bounds serves
48 | P a g e
as the foundation for either confirming or refuting the hypothesis. The critical values are assessed at significance
levels of 1%, 5%, and 10%. If the F-statistic is found to be lower than the crucial values, it may be concluded
that there is an absence of a long-term link. However, if the F-statistic surpasses at least one of the I (1) bound
values, it indicates the presence of co-integration. The F-value observed in this model, which is 4.074243,
exceeds the critical values for the I (1) limit at both the 5% and 10% levels of significance. Consequently, the
null hypothesis, positing the absence of a sustained association between the variables, is refuted. Consequently,
it has been established that co-integration is present in the model, indicating a long-term relationship between
the dependent and independent variables.
Table 5.5: Bound test results
F-Bounds Test
Null Hypothesis: No levels relationship
Test Statistic
Value
F-statistic
k
4.074243
5
Signif.
I(0)
I(1)
10%
5%
2.5%
1%
Asymptotic:
n=1000
2.08
2.39
2.7
3.06
3
3.38
3.73
4.15
5.6.2 Long run model
A dependent variable is LGDP.
Table 5.6: Long run cointegration results
Levels Equation
Case 2: Restricted Constant and No Trend
Variable
Coefficient
Std. Error
t-Statistic
Prob.
LMCY
LBDCY
LDCCY
LINFL
RRATE
C
1.249338
-0.583493
-0.020593
-0.032992
-0.016227
2.201809
1.140739
1.028186
0.105830
0.063143
0.010691
0.876111
1.095200
-0.567497
-0.194582
-0.522493
-1.517853
2.513162
0.2991
0.5829
0.8496
0.6127
0.1600
0.0307
EC = LGDP - (1.2493*LMCY -0.5835*LBDCY -0.0206*LDCCY -0.0330*LINFL
-0.0162*RRATE + 2.2018)
The positive coefficient (1.249338) and statistical significance at the 5% level suggest that there is a positive
association between LGDP and LMCY, according to the empirical data. Accordingly, the LGDP will rise by
1.249338% for every 1% increase in LMCY. The statistically not significant negative coefficient (-0.5835)
suggests that there is a negative link between LBDCY and LGDP. This implies that the GDP will fall by 0.5835%
for every 1% rise in bank deposit liabilities. The coefficient, which is negative (-0.0206) and statistically not
49 | P a g e
significant, suggests that there is a negative association between LGDP and LDCCY. This implies that the GDP
will fall by 0.0206% for every 1% increase in domestic credit to the private sector.
The coefficient is negative (-0.0330) and statistically not significant, suggesting that there is a negative link
between LGDP and LINFL. This implies that the LGDP will fall by 0.0330% for every 1% increase in inflation.
The results are conceivable because inflation has an inverse influence on LGDP, even though an increase in
inflation would encourage individuals to spend more money since they know it will be worth less later. Finally,
the coefficient is negative (-0.0162) and statistically not significant, suggesting that there is a negative link
between LGDP and RRATE.
This means that a 1% increase in real interest rates will result in a decrease in LGDP of 0.0206%. The result is
possible because when interest rates increase that makes borrowing expensive meaning that individuals will have
less money to spend and lead to decrease in LGDP.
5.6.3 ECM Model
Both the long-run and the long-run information are incorporated in the Error Correction Model. To determine if
the variables drift away or return in the short run, the ECM model is utilized.
Table 5.7: ECM Model results
ARDL Error Correction Regression
Dependent Variable: D(LGDP)
Selected Model: ARDL(2, 2, 2, 1,
1, 2)
Case 2: Restricted Constant and No
Trend Date: 10/15/23 Time: 12:45
Sample: 1992 2022
Included observations: 30
ECM Regression
Case 2: Restricted Constant and No Trend
Variable
Coefficient
Std. Error
t-Statistic
Prob.
D(LGDP(-1))
D(LMCY)
D(LMCY(-1))
D(LBDCY)
D(LBDCY(-1))
D(LDCCY)
D(LINFL)
D(RRATE)
D(RRATE(-1))
CointEq(-1)*
-0.028501
0.095512
-0.019670
-0.226085
-0.090359
-0.013434
0.031663
0.000739
0.002740
-0.421628
0.148104
0.109558
0.130520
0.117127
0.124234
0.017192
0.012981
0.001401
0.001973
0.062416
-0.192436
0.871797
-0.150706
-1.930244
-0.727326
-0.781366
2.439251
0.527667
1.388447
-6.755110
0.8513
0.4038
0.8832
0.0824
0.4837
0.4527
0.0349
0.6092
0.1951
0.0001
R-squared
Adjusted R-squared
S.E. of regression
Sum squared resid
Log likelihood
Durbin-Watson stat
0.879542
0.811784
0.013887
0.003086
80.61549
2.536693
50 | P a g e
Mean dependent var
S.D. dependent var
Akaike info criterion
Schwarz criterion
Hannan-Quinn criter.
0.073190
0.032010
-5.431961
-4.948077
-5.292620
The error correction coefficient, by definition, must be negative and significant, as indicated by the tstatistic and its p-value. The cointEq is the speed of adjustment parameter, and it represents how much
of the previous period's disequilibrium is rectified in the current period. It is consideredsignificant when
the cointEq is negative and the t-ratio and p-value are both significant. The errorcorrection coefficient
in this model is negative (-0.42162800). Through the pace of adjustment this demonstrates that there
are short run impacts of the model. This means that the model correctsat disequilibrium of the previous
period at a rate of 42.16%. Since the error correction coefficientis negative, it is concluded that if the
two variables move out of equilibrium, they will come backin the long run equilibrium. The R-squared
(0.879542) and adjusted R-squared (0.811784) both have high values indicating that this model is
normal and has a strong predictability.
5.7 Diagnostic tests
Table 5.8: Diagnostic tests
Tests
Null hypothesis
T-statistic
P-value
Conclusion
Normality Jarque-
Residuals are
JBstatic =
=0.919998
Residuals are
bera test
normally
0.166767
normally
distributed
distributed
Breusch-Godfrey
There is no
Obs*R-squared
Series correlation
autocorrelation
=
autocorrelation
LM Test
in the residuals
5.301706
in the residuals.
= 0.4016
Heteroscedasticity Residuals are
Obs*R-squared
WHITE TEST
=
homoscedasticity
18.67163
in the residuals.
homoscedastic
Ramsey RESET
No
Test
misspecification
= 1.956987
= 0.2004
There is no
= 0.0820
There is
There is no
misspecification
in the residuals.
51 | P a g e
Figure 5.3 CUSUM Test
10.0
7.5
5.0
2.5
0.0
-2.5
-5.0
-7.5
-10.0
12
13
14
15
16
17
CUSUM
18
19
20
21
5% Significance
Figure 5.4 CUSUM of squares test
1.6
1.2
0.8
0.4
0.0
-0.4
12
13
14
15
16
17
18
19
20
21
The cusum test and the cusum of squares test are used to assess the stability of the model. The
cumulative total did not cross the crucial lines at the 5% level of significance, showing that the
model is stable. It is evident that this model satisfies each assumption.
52 | P a g e
Table 5.9 Q-STATIC
Date: 05/15/23 Time:
13:41Sample: 1992
2022 Included
observations: 30
Q-statistic probabilities adjusted for 2 dynamic regressors
Autocorrelation
Partial Correlation
AC
PAC
1 -0.253 -0.253
2 0.056 -0.009
3 -0.085 -0.078
4 0.069 0.031
5 -0.203 -0.189
6 -0.017 -0.132
7 -0.144 -0.198
8 0.012 -0.127
9 0.020 -0.027
10 -0.057 -0.147
11 0.098 -0.004
12 0.117 0.075
Q-Stat Prob*
1.8663
1.9608
2.1911
2.3474
3.7732
3.7842
4.5774
4.5832
4.6008
4.7467
5.2099
5.9205
0.172
0.375
0.534
0.672
0.583
0.706
0.711
0.801
0.868
0.907
0.921
0.920
*Probabilities may not be valid for this equation specification.
Conclusion
The aim of this research was to determine the correlation between the advancement of South
Africa's financial industry and the country's economic expansion from 1992 to 2022. Unit root
tests were run to evaluate the stationarity of each variable prior to the use of any approaches.
It was found that some variables could only become stationary by differencing, as they were
not in a stationary condition. The co-integration and presence of a long-lasting relationship
between the expansion of South Africa's financial sector and economic growth were determined
using the ARDL model. The study's findings suggest that there is a co-integration link. Both
short- and long-term research show that although certain conditions hinder economic growth,
a big money supply promotes it. The analysis proved that financial services development
contributes positively to economic expansion. Financial sector development has been observed
to enhance economic growth by bolstering competitiveness and fostering innovation. The paper
suggests that the government should give priority to factors that increase the broad money
supply to stimulate economic growth. It is necessary to have comprehensive financial
investment plans and strategies.
Chapter six: Conclusion and recommendations
6.1 Introduction
This chapter provides a conclusive summary of all the discoveries that were previously covered
in chapter five. The primary aim of the study was to ascertain the relationship between the
53 | P a g e
development of the financial sector and the economic growth in South Africa from 1992 to
2022. The previous discussion encompassed the overview, theoretical framework, and
empirical data pertaining to both foreign investment inflows and economic growth.
6.2 Summary of the study and conclusions
The ARDL cointegration technique was employed in the study to determine the economic
relationship between the development of the financial sector and economic growth in South
Africa. The study looked at the connection between South Africa's economic expansion and
the development of the financial industry. In contrast to other sectors within South Africa as
well as in comparison to the BRICS countries and other African countries, it offered an
examination of the financial sector's contribution to the nation's economic growth. By
increasing financial accessibility and the effectiveness of financial commodities in the
economy, the developed financial sector has undoubtedly aided in South Africa's progress.
The study examined theoretical literature that investigated several frameworks for
comprehending the correlation between the development of the financial sector and economic
growth. The Endogenous rise Theory emphasises the significance of finance in both the
accumulation of capital and the rise of productivity. The text underscores the significance of
innovation and entrepreneurship in propelling economic advancement, in accordance with
Joseph Schumpeter's concepts of creative destruction. The AK Model highlights the influence
of financial development on both savings and productivity. The New Endogenous Growth
Theory emphasises the significance of financial markets in promoting innovation and
technological advancement.
Institutional Theory emphasises the importance of well-defined regulations, ownership rights,
and the legal structure in fostering the growth of the financial sector. Resource-Dependent
Theory posits that the advancement of financial systems may be advantageous for companies
that heavily rely on external funding, particularly in economies abundant in resources.
Empirical research has looked at the relationship between the rise of the financial industry and
economic expansion in both industrialised and developing nations. The outcomes of the
research have been conflicting; although some studies find that financial development has a
positive impact on economic growth, others find that it has negative or dependent effects.
Studies conducted in developed countries indicate that there can be a complex relationship
between the growth of the financial industry and economic output. The degree of financial
development and the influence of monetary policy are two examples of variables that might
54 | P a g e
affect the outcome. Several studies suggest that an overly large financial sector might be
detrimental to economic expansion.
The relationship between financial development and economic growth is not the same in
emerging economies because different parts of the financial sector—banking, stock markets,
bond markets, and insurance—have different effects. Insurance has shown promise in
promoting economic growth in a range of social categories. Studies carried out only in South
Africa underscore the importance of financial markets, encompassing the stock and bond
markets, in promoting economic expansion. According to the research, these markets have the
capacity to provide a variety of financial services that are important for promoting economic
growth. There appears to be less of a relationship between the banking industry and economic
expansion.
The study's conclusions align with the empirical research presented in chapter three, which
examined and determined the correlation between financial sector expansion and economic
growth.
6.3 Policy implications and recommendations
The financial services operations in South Africa operates within a globalized context, where
a crisis in one economy can swiftly and severely affect others. Enhancing the integration of
South Africa's financial sector with the global economy is crucial for promoting job creation
and sustaining economic growth through increased international trade. However, this may
potentially lead to heightened financial stability concerns and necessitate more oversight of the
financial industry. The crisis has emphasised the necessity for improved coordination between
financial regulation, monetary policy, fiscal policy, and other economic policies. It has also
emphasised the importance of considering systemic risks in financial regulation.
There is a need to further enhance pro-growth policies to stimulate investment and promote the
development of the financial sector. South Africa should prioritise the development of financial
institutions to enhance their financial services and products, particularly in rural regions where
there is a limited presence of banks and microfinance organizations. This will enhance the
availability of liquid assets in the economy and facilitate the acquisition of funds by small-scale
business enterprises.
The financial industry in South Africa is distinguished by exorbitant and non-transparent
charges, and in certain instances, the unjust treatment of clients. For those who save money,
55 | P a g e
especially those who are poor and vulnerable, the options for saving are restricted, costly, and
unsuitable. Obtaining finance may be challenging for borrowers, especially for small and
medium enterprises. Consistent grievances lodged with the appropriate ombudsmen and a
multitude of impartial investigations substantiate the issue of fees and charges. An investigation
conducted by the Competition Commission in the banking sector concluded that bank charges
are excessively high.
To tackle this difficulty, it is necessary to establish a regulatory body inside the Financial
Services Board that specifically focuses on overseeing the market behaviour of retail banking
services. The primary focus of this new regulatory body will be on matters pertaining to market
structure and bank expenses. It will collaborate closely with the National Credit Regulator,
which plays a complementary function in overseeing the provision of credit. The
implementation of the complete Treating Customers Fairly effort involves the establishment of
defined standards for market conduct, which are supported by specific laws. This project will
gradually establish uniform standards of consumer protection throughout the sector.
6.4 Delimitation of study
This study's focus is on South Africa's growing economy and the development of its financial
industry. This study considers six variables: real GDP, inflation, the M3 broad money ratio, the
ratio of bank deposits to GDP, domestic lending to the private sector, and the real interest rate.
This research covers the period from 1992 to 2022, or a total of thirty years.
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Appendices
Appendix 1: Integrated at I(0)
61 | P a g e
LGDP
LMCY
11.6
16
11.2
15
10.8
10.4
14
10.0
13
9.6
9.2
12
1995
2000
2005
2010
2015
2020
1995
2000
LBDCY
2005
2010
2015
2020
2010
2015
2020
2010
2015
2020
LDCCY
16
13
12
15
11
14
10
13
9
12
8
1995
2000
2005
2010
2015
2020
1995
2000
LINFL
2005
rrate
3.0
14
12
2.5
10
2.0
8
1.5
6
4
1.0
0.5
2
0
1995
2000
2005
Appendix 2: Integrated at I(1)
62 | P a g e
2010
2015
2020
1995
2000
2005
Differenced LGDP
Differenced LMCY
.16
.20
.12
.16
.08
.12
.04
.08
.00
.04
-.04
1995
2000
2005
2010
2015
2020
.00
1995
2000
Differenced LBDCY
1.2
.20
0.8
.15
0.4
.10
0.0
.05
-0.4
1995
2000
2005
2010
2015
2020
-0.8
1995
2000
Differenced LINFL
6
.6
4
.4
2020
2005
2010
2015
2020
2015
2020
2
.2
0
.0
-2
-.2
-4
-.4
-6
1995
2000
2005
Appendix 3: ARDL cointegration
63 | P a g e
2015
Differenced rrate
.8
-.6
2010
Differenced LDCCY
.25
.00
2005
2010
2015
2020
1995
2000
2005
2010
Dependent Variable: LGDP
Method: ARDL
Date: 11/29/23 Time: 12:11
Sample: 1994 2022
Included observations: 26
Dependent lags: 2 (Automatic)
Automatic-lag linear regressors (2 max. lags): LDCCY LBDCY LMCY LINFL
LRRATE
Deterministics: Unrestricted constant and unrestricted trend (Case 5)
Model selection method: Akaike info criterion (AIC)
Number of models evaluated: 486
Selected model: ARDL(2,2,2,2,2,2)
Variable
Coefficient
Std. Error
t-Statistic
Prob.*
LGDP(-1)
LGDP(-2)
LDCCY
LDCCY(-1)
LDCCY(-2)
LBDCY
LBDCY(-1)
LBDCY(-2)
LMCY
LMCY(-1)
LMCY(-2)
LINFL
LINFL(-1)
LINFL(-2)
LRRATE
LRRATE(-1)
LRRATE(-2)
C
@TREND
1.641009
-0.661789
0.000309
7.35E-05
-0.000219
0.000201
-0.000266
-0.001217
-0.000297
7.28E-05
0.000860
9.08E-05
6.49E-05
4.71E-05
-1.43E-05
-1.88E-05
-1.17E-05
0.224200
0.000389
0.009821
0.008738
8.93E-05
8.67E-05
0.000114
0.000172
0.000226
0.000225
0.000212
0.000256
0.000226
3.07E-05
2.26E-05
1.75E-05
1.18E-05
1.18E-05
1.26E-05
0.011282
2.83E-05
167.0838
-75.73439
3.463314
0.847796
-1.919480
1.162996
-1.177255
-5.403291
-1.397764
0.284663
3.801459
2.960199
2.871026
2.691957
-1.216944
-1.589043
-0.930459
19.87244
13.77557
0.0000
0.0000
0.0105
0.4246
0.0964
0.2829
0.2776
0.0010
0.2049
0.7841
0.0067
0.0211
0.0240
0.0310
0.2631
0.1561
0.3831
0.0000
0.0000
R-squared
Adjusted R-squared
S.E. of regression
Sum squared resid
Log likelihood
F-statistic
1.000000
1.000000
1.31E-05
1.20E-09
272.5322
1.38E+09
Mean dependent var
S.D. dependent var
Akaike info criterion
Schwarz criterion
Hannan-Quinn criter.
Prob(F-statistic)
10.36735
0.411972
-19.50248
-18.58310
-19.23773
0.000000
*Note: p-values and any subsequent test results do not account for model
selection.
Appendix 4: Long run coefficients and bound test
F-Bounds Test
Test Statistic
F-statistic
k
64 | P a g e
Null Hypothesis: No levels relationship
Value
4.074243
5
Signif.
I(0)
I(1)
10%
5%
2.5%
1%
Asymptotic:
n=1000
2.08
2.39
2.7
3.06
3
3.38
3.73
4.15
Levels Equation
Case 2: Restricted Constant and No Trend
Variable
Coefficient
Std. Error
t-Statistic
Prob.
LMCY
LBDCY
LDCCY
LINFL
RRATE
C
1.249338
-0.583493
-0.020593
-0.032992
-0.016227
2.201809
1.140739
1.028186
0.105830
0.063143
0.010691
0.876111
1.095200
-0.567497
-0.194582
-0.522493
-1.517853
2.513162
0.2991
0.5829
0.8496
0.6127
0.1600
0.0307
EC = LGDP - (1.2493*LMCY -0.5835*LBDCY -0.0206*LDCCY -0.0330*LINFL
-0.0162*RRATE + 2.2018)
Appendix 5: Error correction regression
ARDL Error Correction Regression
Dependent Variable: D(LGDP)
Selected Model: ARDL(2, 2, 2, 1,
1, 2)
Case 2: Restricted Constant and No
TrendDate: 10/15/23 Time: 12:45
Sample: 1992 2022
Included observations: 30
ECM Regression
Case 2: Restricted Constant and No Trend
Variable
Coefficient
Std. Error
t-Statistic
Prob.
D(LGDP(-1))
D(LMCY)
D(LMCY(-1))
D(LBDCY)
D(LBDCY(-1))
D(LDCCY)
D(LINFL)
D(RRATE)
D(RRATE(-1))
CointEq(-1)*
-0.028501
0.095512
-0.019670
-0.226085
-0.090359
-0.013434
0.031663
0.000739
0.002740
-0.421628
0.148104
0.109558
0.130520
0.117127
0.124234
0.017192
0.012981
0.001401
0.001973
0.062416
-0.192436
0.871797
-0.150706
-1.930244
-0.727326
-0.781366
2.439251
0.527667
1.388447
-6.755110
0.8513
0.4038
0.8832
0.0824
0.4837
0.4527
0.0349
0.6092
0.1951
0.0001
R-squared
Adjusted R-squared
S.E. of regression
Sum squared resid
Log likelihood
Durbin-Watson stat
0.879542
0.811784
0.013887
0.003086
80.61549
2.536693
65 | P a g e
Mean dependent var
S.D. dependent var
Akaike info criterion
Schwarz criterion
Hannan-Quinn criter.
0.073190
0.032010
-5.431961
-4.948077
-5.292620
Appendix 6: Cusum test
10.0
7.5
5.0
2.5
0.0
-2.5
-5.0
-7.5
-10.0
12
13
14
15
16
17
CUSUM
18
19
20
21
5% Significance
Appendix 7: Cusum of Squares
1.6
1.2
0.8
0.4
0.0
-0.4
12
66 | P a g e
13
14
15
16
17
18
19
20
21
Appendix 8: Q-stats
Date: 05/15/23 Time:
13:41Sample: 1992
2022 Included
observations: 30
Q-statistic probabilities adjusted for 2 dynamic regressors
Autocorrelation
Partial Correlation
AC
PAC
1 -0.253 -0.253
2 0.056 -0.009
3 -0.085 -0.078
4 0.069 0.031
5 -0.203 -0.189
6 -0.017 -0.132
7 -0.144 -0.198
8 0.012 -0.127
9 0.020 -0.027
10 -0.057 -0.147
11 0.098 -0.004
12 0.117 0.075
*Probabilities may not be valid for this equation specification.
67 | P a g e
Q-Stat Prob*
1.8663
1.9608
2.1911
2.3474
3.7732
3.7842
4.5774
4.5832
4.6008
4.7467
5.2099
5.9205
0.172
0.375
0.534
0.672
0.583
0.706
0.711
0.801
0.868
0.907
0.921
0.920
Appendix 9: Autocorrelation
Level 1
Date: 10/15/23 Time:
02:36Sample: 1992
2022 Included
observations: 30
Autocorrelation
Partial Correlation
AC
PAC
Q-Stat
Prob
1 0.903 0.903
2 0.812 -0.023
3 0.716 -0.072
4 0.622 -0.051
5 0.528 -0.055
6 0.434 -0.060
7 0.340 -0.070
8 0.245 -0.072
9 0.154 -0.054
10 0.066 -0.062
11 -0.016 -0.048
12 -0.094 -0.062
13 -0.166 -0.053
14 -0.232 -0.057
15 -0.290 -0.039
16 -0.333 -0.012
27.022
49.631
67.881
82.163
92.868
100.41
105.22
107.84
108.92
109.13
109.14
109.62
111.17
114.41
119.78
127.39
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
PAC
Q-Stat
Prob
1 0.439 0.439
2 0.399 0.256
3 0.249 0.008
4 0.174 -0.015
5 0.227 0.144
6 0.198 0.064
7 0.107 -0.094
8 0.073 -0.037
9 -0.004 -0.049
10 -0.045 -0.067
11 0.042 0.093
12 -0.157 -0.224
6.1974
11.504
13.642
14.732
16.668
18.206
18.673
18.901
18.902
18.996
19.083
20.386
0.013
0.003
0.003
0.005
0.005
0.006
0.009
0.015
0.026
0.040
0.060
0.060
1st difference
Date: 10/15/23 Time:
02:40Sample: 1992
2022 Included
observations: 29
Autocorrelation
68 | P a g e
Partial Correlation
AC
Appendix 10: Jarque-Bera test
7
Seri es : Res i dua l s
Sa mpl e 1994 2022
Obs erva ti ons 26
6
5
4
3
2
1
0
-2.0e-05
-1.0e-05
2.5e-11
1.0e-05
Appendix 11: Breusch-Godfrey Serial Correlation LM Test
Breus ch-Godfrey Serial Correlation LM Tes t:
Null hypothes is : No s erial correlation at up to 2 lags
F-s tatis tic
Obs *R-s quared
0.905205
6.911577
Prob. F(2,5)
Prob. Chi-Square(2)
0.4618
0.0316
Tes t Equation:
Dependent Variable: RESID
Method: ARDL
Date: 11/29/23 Tim e: 12:14
Sam ple (adjus ted): 1994 2022
Included obs ervations : 26 after adjus tm ents
Pres am ple and interior m is s ing value lagged res iduals s et to zero.
Variable
Coefficient
Std. Error
t-Statis tic
Prob.
LGDP(-1)
LGDP(-2)
LDCCY
LDCCY(-1)
LDCCY(-2)
LBDCY
LBDCY(-1)
LBDCY(-2)
LMCY
LMCY(-1)
LMCY(-2)
LINFL
LINFL(-1)
LINFL(-2)
LRRATE
LRRATE(-1)
LRRATE(-2)
C
@TREND
RESID(-1)
RESID(-2)
0.003997
-0.003502
-4.36E-05
5.20E-05
-7.80E-05
-5.34E-05
0.000204
-0.000148
-2.20E-05
-0.000109
0.000101
-9.91E-06
2.08E-05
-1.17E-05
1.31E-05
-4.66E-06
5.31E-06
-0.004647
-1.27E-05
-0.344846
-0.935149
0.011892
0.010586
0.000129
0.000117
0.000129
0.000201
0.000377
0.000254
0.000217
0.000306
0.000244
5.02E-05
3.00E-05
2.63E-05
2.10E-05
1.26E-05
1.37E-05
0.013522
3.40E-05
0.523067
0.880061
0.336083
-0.330851
-0.338548
0.445522
-0.602850
-0.265452
0.539465
-0.582028
-0.101652
-0.357316
0.411684
-0.197416
0.693155
-0.444141
0.621305
-0.370524
0.386685
-0.343637
-0.373328
-0.659277
-1.062596
0.7505
0.7542
0.7487
0.6746
0.5729
0.8013
0.6127
0.5858
0.9230
0.7354
0.6976
0.8513
0.5191
0.6755
0.5616
0.7262
0.7149
0.7451
0.7242
0.5389
0.3366
R-s quared
Adjus ted R-s quared
S.E. of regres s ion
Sum s quared res id
Log likelihood
F-s tatis tic
Prob(F-s tatis tic)
69 | P a g e
0.265830
-2.670850
1.33E-05
8.78E-10
276.5494
0.090521
0.999967
Mean dependent var
S.D. dependent var
Akaike info criterion
Schwarz criterion
Hannan-Quinn criter.
Durbin-Wats on s tat
-8.21E-16
6.92E-06
-19.65765
-18.64149
-19.36503
2.629707
Mea n
Media n
Ma ximum
Minimum
Std. Dev.
Skewnes s
Kurtos is
-8.21e-16
5.01e-07
1.23e-05
-1.92e-05
6.92e-06
-0.697415
3.984635
Ja rque-Bera
Proba bility
3.157979
0.206183
Appendix 12: Heteroskedasticity Test: ARCH
Heteroskedasticity Test: ARCH
F-statistic
Obs*R-squared
0.006867
0.007489
Prob. F(1,22)
Prob. Chi-Square(1)
0.9347
0.9310
Test Equation:
Dependent Variable: RESID^2
Method: Least Squares
Date: 11/29/23 Time: 12:17
Sample (adjusted): 1995 2022
Included observations: 24 after adjustments
Variable
Coefficient
Std. Error
t-Statistic
Prob.
C
RESID^2(-1)
4.87E-11
0.017688
2.04E-11
0.213440
2.385921
0.082869
0.0261
0.9347
R-squared
Adjusted R-squared
S.E. of regression
Sum squared resid
Log likelihood
F-statistic
Prob(F-statistic)
70 | P a g e
0.000312
-0.045128
8.54E-11
1.60E-19
523.4036
0.006867
0.934705
Mean dependent var
S.D. dependent var
Akaike info criterion
Schwarz criterion
Hannan-Quinn criter.
Durbin-Watson stat
4.96E-11
8.35E-11
-43.45030
-43.35213
-43.42426
2.065547
Appendix 13: Ramset RESET test
Ram s ey RESET Tes t
Equation: UNTITLED
Om itted Variables : Squares of fitted values
Specification: LGDP LGDP(-1) LGDP(-2) LDCCY LDCCY(-1) LDCCY(-2)
LBDCY LBDCY(-1) LBDCY(-2) LMCY LMCY(-1) LMCY(-2) LINFL
LINFL(-1) LINFL(-2) LRRATE LRRATE(-1) LRRATE(-2) C @TREND
t-s tatis tic
F-s tatis tic
Likelihood ratio
Value
19.72572
389.1040
108.8721
df
6
(1, 6)
1
Probability
0.0000
0.0000
0.0000
Sum of Sq.
1.18E-09
1.20E-09
1.82E-11
df
1
7
6
Mean Squares
1.18E-09
1.71E-10
3.03E-12
F-tes t s um m ary:
Tes t SSR
Res tricted SSR
Unres tricted SSR
LR tes t s um m ary:
Res tricted LogL
Unres tricted LogL
Value
272.5322
326.9683
Unres tricted Tes t Equation:
Dependent Variable: LGDP
Method: Leas t Squares
Date: 11/29/23 Tim e: 12:17
Sam ple (adjus ted): 1994 2022
Included obs ervations : 26 after adjus tm ents
Variable
Coefficient
Std. Error
t-Statis tic
Prob.
LGDP(-1)
LGDP(-2)
LDCCY
LDCCY(-1)
LDCCY(-2)
LBDCY
LBDCY(-1)
LBDCY(-2)
LMCY
LMCY(-1)
LMCY(-2)
LINFL
LINFL(-1)
LINFL(-2)
LRRATE
LRRATE(-1)
LRRATE(-2)
C
@TREND
FITTED^2
1.055638
-0.347817
-7.14E-05
-2.14E-05
6.14E-05
-7.81E-05
1.77E-05
0.000297
0.000108
3.76E-05
-0.000187
-2.42E-05
-1.68E-05
-1.52E-05
2.67E-07
1.40E-06
-1.55E-06
1.546247
-0.000597
0.014017
0.029707
0.015961
2.27E-05
1.25E-05
2.08E-05
2.70E-05
3.34E-05
8.24E-05
3.49E-05
3.41E-05
6.10E-05
7.12E-06
5.12E-06
3.93E-06
1.73E-06
1.88E-06
1.76E-06
0.067043
5.01E-05
0.000711
35.53556
-21.79237
-3.149047
-1.710799
2.954082
-2.897140
0.530326
3.601375
3.083319
1.101602
-3.062996
-3.400919
-3.277317
-3.870173
0.154199
0.745772
-0.883323
23.06345
-11.90478
19.72429
0.0000
0.0000
0.0198
0.1380
0.0255
0.0274
0.6149
0.0113
0.0216
0.3129
0.0221
0.0145
0.0169
0.0083
0.8825
0.4840
0.4111
0.0000
0.0000
0.0000
R-s quared
Adjus ted R-s quared
S.E. of regres s ion
Sum s quared res id
Log likelihood
F-s tatis tic
Prob(F-s tatis tic)
71 | P a g e
1.000000
1.000000
1.74E-06
1.82E-11
326.9683
7.37E+10
0.000000
Mean dependent var
S.D. dependent var
Akaike info criterion
Schwarz criterion
Hannan-Quinn criter.
Durbin-Wats on s tat
10.36735
0.411972
-23.61295
-22.64518
-23.33426
2.356635
DATA
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
2019
2020
2021
2022
72 | P a g e
LGDP
9.335651
9.452423
9.556975
9.651623
9.738082
9.817657
9.891364
9.960010
10.02424
10.08460
10.14152
10.19537
10.24647
10.29509
10.34145
10.38576
10.42819
10.46889
10.50799
10.54563
10.58190
10.61690
10.65072
10.68343
10.71511
10.74581
10.77560
10.80452
10.83264
10.85998
10.88660
LDCCY
4.510674
4.562558
4.615701
4.654028
4.659127
4.628549
4.641685
4.772162
4.762989
4.796367
4.577978
4.632471
4.725802
4.806339
4.934527
4.958795
4.835932
4.796572
4.821039
4.778877
4.829655
4.844418
4.858559
4.845786
4.823185
4.844426
4.776630
4.765812
4.692901
4.537119
4.523076
LBDCY
3.825779
3.757611
3.807505
3.828529
3.844151
3.909940
3.963212
3.970231
3.915630
3.972928
3.891148
3.943727
3.915964
3.995733
4.076306
4.127965
4.156622
4.100144
4.079015
4.117757
4.103484
4.083474
4.092602
4.126896
4.086834
4.083538
4.086624
4.094217
4.257521
4.219508
4.276666
LMCY
3.802601
3.726092
3.769181
3.784440
3.798645
3.858675
3.904005
3.917193
3.857870
3.940019
3.953500
3.987755
4.008121
4.090352
4.180894
4.263998
4.294495
4.244372
4.221989
4.217001
4.197958
4.174091
4.178044
4.209188
4.194371
4.191846
4.194449
4.205390
4.305641
4.252875
4.266199
LINFL
2.630067
2.273923
2.190373
2.161071
1.995262
2.151503
1.928699
1.645093
1.675030
1.740800
2.250735
1.736849
NA
0.724089
1.176778
1.820963
2.310015
1.976206
1.408479
1.609291
1.744783
1.755177
1.813168
1.513068
1.882726
1.645625
1.507885
1.415913
1.166282
1.528591
1.951569
LRRATE
1.295860
0.902065
1.659269
1.851190
2.365072
2.395155
2.540896
2.341331
1.583214
1.711695
1.048787
2.085405
1.599465
1.560560
1.570760
1.514258
1.908824
1.030515
1.250177
1.187630
1.356575
0.919982
1.271733
1.299576
1.187310
1.536290
1.809724
1.657029
0.645611
-0.255558
1.269076