COST OF CREDIT AND ECONOMIC GROWTH IN ESWATINI March 2024 MBONGENI WELCOME SHONGWE Supervisor: Professor Odongo Kodongo COST OF CREDIT AND ECONOMIC GROWTH IN ESWATINI By MBONGENI WELCOME SHONGWE STUDENT NUMBER: 2753295 A Thesis Submitted in Partial Fulfilment of the Requirements for the Degree of Master of Management in Finance and Investment of the University of the Witwatersrand, Johannesburg March 2024 i COPYRIGHT Permission has been granted to the Library of the University of the Witwatersrand, Johannesburg to lend copies of this Thesis report. The author reserves other publication rights and neither the Master ‘s Thesis nor extension extracts from it may be printed or reproduced without the author ‘s written permission. Copyright © Mbongeni Shongwe, 2023 ii DECLARATION I Mbongeni Welcome Shongwe declare that the thesis, which I hereby submit for the degree Master of Management in Finance and Investment at the University of the Witwatersrand, Johannesburg, is my work and has not previously been submitted by me for a degree at this or any other tertiary institution. SIGNATURE: M.W Shongwe Date: September 2023 iii COST OF CREDIT AND ECONOMIC GROWTH IN ESWATINI ABSTRACT The sluggish economy and low GDP growth in Eswatini have sparked concerns regarding the efficient allocation of high liquidity towards productive sectors. There is a pressing need to determine if the high cost of credit plays a role in exacerbating this issue. Despite the availability of ample liquidity, it remains unclear if it is effectively channeled into sectors that can fuel economic growth. Therefore, it is intriguing to investigate whether the high cost of credit is a contributing factor to this problem. This study examined the relationship between the cost of credit and economic growth in Eswatini, as well as the impact of banking sector liquidity on cost of credit and the role of excess liquidity in promoting economic growth. The study used the Autoregressive Distributed Lag model (ARDL) to analyse time series data from 1975 to 2021, the study found that factors such as domestic credit, GDP growth, liquid assets to liabilities, and trade significantly influence cost of credit in the short run. In the long run, variables like budget deficit, domestic credit, exchange rate, GDP growth, liquid assets to liabilities, and trade continue to significantly impact cost of credit. The study recommends that policymakers should increase credit availability, diversify credit risk and increase liquid assets relative to liabilities to lower cost of credit. Additionally, promoting financial inclusion and access to credit for SMEs can further stimulate economic growth. A thoughtful and measured approach by policymakers is crucial for creating a stable financial system that supports economic economic growth. Key words: Autoregressive Distributed Lag model,Cost of Credit , Budget Deficit, Economic Growth, Liquidity, Liabilities, Policymarkers, Risk , Time series data. iv vi DEDICATION First and foremost, I would like to dedicate this project to the Almighty God, who has bestowed upon me the grace and strength to embark on and complete this thesis. I am deeply grateful for the divine guidance and support throughout this entire journey. I would also like to extend my heartfelt appreciation to my friends and the entire Shongwe family. Your unwavering financial, spiritual, and emotional support has been instrumental in my educational career. I am profoundly grateful for your love, prayers, and encouragement. This journey has not been without its challenges, but your presence has made it easier to overcome them. A special dedication is reserved for my loving wife, Mrs. T. Shongwe, and my son, Awandze Shongwe. You have been my driving force, providing me with a compelling reason to strive for excellence even during my lowest moments. Your belief in me and your unwavering support have been the pillars of my strength. To all those mentioned above, I express my deepest gratitude for your contributions to my success. Your presence in my life has made this achievement all the more meaningful, and I am forever indebted to you. v ACKNOWLEDGEMENTS The author would like to express deep appreciation to all those who played a crucial role in the completion of this thesis. Firstly, heartfelt gratitude goes to Prof. Odongo Kodongo, the author's main supervisor, whose expertise and guidance were invaluable throughout the thesis writing process. The author is truly grateful for his mentorship and acknowledges that words cannot adequately express the extent of this gratitude. Furthermore, Prof. Kodongo's lectures on finance provided the author with the necessary skills to conduct the scientific tests required for this thesis, ensuring its adherence to the expected standards. Lastly, the author would like to extend sincere thanks to the research panel of the Master of Management in Finance and Investment. From the proposal stage to the completion of the thesis, their constructive comments and feedback were of utmost importance in enhancing the study. The author deeply appreciates their expertise, as it significantly contributed to the success of the research. All those mentioned above have played instrumental roles in the completion of this thesis, and their contributions are sincerely acknowledged and highly valued. vi TABLE OF CONTENTS COPYRIGHT...................................................................................................................... I DECLARATION ...............................................................................................................II ABSTRACT..................................................................................................................... III DEDICATION ................................................................................................................. IV ACKNOWLEDGEMENTS ............................................................................................... V TABLE OF CONTENTS .................................................................................................. VI LIST OF TABLES OF AND FIGURES ..........................................................................VIII ABBREVIATIONS .......................................................................................................... IX CHAPTER 1 INTRODUCTION ......................................................................................... 1 1.1. Background and Setting .................................................................................. 1 1.1.1. Eswatini Financial Sector. .............................................................................. 6 1.1.2. Trends in Bank Credit to the Private Sector ..................................................... 7 1.1.3. Economic Growth Developments..................................................................... 8 1.1.4. Cost of Credit ................................................................................................. 8 1.2. Problem Statement ......................................................................................... 9 1.3. Research Questions ...................................................................................... 10 1.4. Objectives of the Study ................................................................................. 10 1.5. Hypothesis ................................................................................................... 10 1.6. Significance of the Study .............................................................................. 11 1.7. Chapter Summary......................................................................................... 11 CHAPTER 2 LITERATURE REVIEW............................................................................. 12 2.1. Theoretical Literature ................................................................................... 12 2.1.1. Liquidity Theory Review ............................................................................... 12 2.1.2. Credit Rationing ........................................................................................... 13 2.1.3. Bank Credit .................................................................................................. 17 2.1.4. Cost of Credit ............................................................................................... 19 2.2. Empirical Literature...................................................................................... 21 2.2.1. Bank Credit .................................................................................................. 21 2.2.2. Cost of Credit ............................................................................................... 24 2.3. Summary of the Literature Review ................................................................ 26 vii CHAPTER 3 METHODOLOGY ...................................................................................... 28 3.1. Introduction.................................................................................................. 28 3.2. Estimation of Cost of Credit.......................................................................... 28 3.3. Emperical Model .......................................................................................... 29 3.4. Data Description........................................................................................... 30 3.4.1. Justification of Variable Selection ................................................................. 31 3.5. Data Analysis ............................................................................................... 31 3.5.1 Unit Root Test for Stationarity ........................................................................... 32 3.5.2 Bounds Testing ................................................................................................. 33 3.5.3 Lag Selection .................................................................................................... 33 3.5.4 Error Correction Model .................................................................................... 33 3.6. Methodology Chapter Summary.................................................................... 34 CHAPTER 4 DISCUSSION OF RESULTS ...................................................................... 35 4.1. Descriptive Statistics .................................................................................... 35 4.2. Correlation matrix ........................................................................................ 39 4.3. Trends of Variables ...................................................................................... 40 4.4. Stationary Test ............................................................................................. 43 4.5. Cointegration Test ........................................................................................ 45 4.6. Short-run ARDL Results: Dependent Variable Market Lending Rates ............ 45 4.7. Short-run ARDL Results: Dependent Variable Bank Spread .......................... 49 4.8. Long-run ARDL Results: Dependent Variable Market Lending Rates as Measure for Cost of Credit ............................................................................................ 51 4.9. Long-run ARDL Results: Dependent Variable Bank Spread as a Measure of Cost of Credit ............................................................................................................... 54 4.10. Granger causality test ................................................................................... 56 4.11. Simple Regression Model Dependent Variable Market Lending Rates as a Measure of Cost of Credit ............................................................................................. 57 4.12. Simple Regression Results, Economic Growth as a Dependent Variable ......... 58 4.13. Relationship between liquidity and cost of credit ........................................... 59 4.14. Theory behind the model .............................................................................. 60 4.15. Cost of credit and its impact on economic growth .......................................... 61 4.16. Diagnostics tests and Model Stability Tests ................................................... 62 4.17. Residual Plot ................................................................................................ 63 CHAPTER 5 SUMMARY, CONCLUSION AND RECOMMENDATIONS...................... 64 5.1. Summary of findings .................................................................................... 64 viii 5.2. Conclusion ................................................................................................... 65 5.3. Recommendations ........................................................................................ 66 5.4. Recommendations for further study............................................................... 67 REFERENCES................................................................................................................. 68 APPENDIX...................................................................................................................... 78 LIST OF TABLES OF AND FIGURES Figure 1: Bank Liquidity Outlook of Eswatini. .......................................................................... 4 Figure 2: Prime Rates of Countries with the same Liquidity Ratios as Eswatini for 2022.................. 5 Figure 3: Private Sector Credit of Eswatini for the Period 2000-2021 ............................................ 7 Figure 4: Trend Variables used in the Study_1 .........................................................................42 Figure 5: Trend of Variables in the Study_2 .............................................................................43 Figure 6: Cumulative Residuals Plot .......................................................................................63 Figure 7: Cumulative Residuals Plot .......................................................................................63 List of Tables Table 1:Descriptive Statistics.................................................................................................39 Table 2: Correlation matrix ...................................................................................................40 Table 3:Stationarity Test Results: Augmented Dickey-Fuller ......................................................44 Table 4: Stationarity Test Results: Philips Peron Test ................................................................44 Table 5: Cointegration Test Results ........................................................................................45 Table 6: Model Results: Short-Run Error Correction Model Coefficients Results, Market Lending Rates Dependant Variables....................................................................................................48 Table 7: Model Results: Short-Run Error Correction Model Coefficients Results. Bank Spread .......51 Table 8: Model Results:Long-run ARDL Results: Dependent Variable Market Lending Rates as a Measure of Cost of Credit .....................................................................................................53 Table 9: Model Results: Long-run ARDL Results: Dependent Variable Bank Spread as a Measure of Cost of Credit......................................................................................................................55 Table 10: Granger causality test Results ..................................................................................57 Table 11: Model Results: Simple Regression Results, Market Lending Rates ................................58 Table 12: Model Results: Simple Regression Results, GDP Growth Rates ....................................59 ix ABBREVIATIONS ARDL Auto Regressive Distributed Lag CBE Central Bank of Eswatini ECM Error Correction Model FDI Foreign direct investment GDP Gross Domestic Product IMF International Monetary Fund SADC Southern African Development Community SME Small and Medium-sized Enterprise RGDP Real Gross Domestic Product USA United States of America 1 CHAPTER 1 INTRODUCTION 1.1. Background and Setting The relationship between the cost of credit, financial development, and economic growth has been a subject of ongoing debate. Monetization and the development of the financial sector are widely recognized as important factors driving economic growth. However, the precise nature of the relationship between the cost of credit and growth remains a topic of contention, with differing perspectives among economists. This issue is characterized by two main categories of theories: supply-side and demand-side theories. Understanding this relationship is crucial for policymakers aiming to foster sustainable economic growth (Tesso, 2015). Baoko and Ibrahim (2017) emphasize the significance of financial banking institutions in funding economic activities within a nation. In the African context, banking systems exhibit certain characteristics such as adequate capitalization, low leverage, limited reliance on external financing, and high levels of liquidity. These features enhance the ability of banks to issue loans and provide depositors with accessible cash reserves (Gungoraydinoglu et al., 2017). This study focuses on the bank lending channel, which operates based on two fundamental principles: the central bank's ability to regulate the cost of credit in the banking sector and banks' responsibility to protect borrowers. These assertions indicate that banking systems play a crucial role in driving economic growth and require effective regulatory measures to ensure their stability and functionality. The supply-leading theory proposes that the availability of credit is essential in promoting economic growth and job opportunities. This theory emphasizes the crucial role of credit as a catalyst for providing funding to businesses and entrepreneurs, ultimately leading to a boost in the production of goods and services. Jerome et al. (2022) argue that credit is an essential engine of growth; without it, the economy cannot thrive, and investment opportunities and expansion will be diminished. Schumpeter (1912) also contends that a robust and dynamic banking sector is critical to economic expansion. Patrick (1966) adds that a well-developed financial system, besides promoting finance for economic growth, also facilitates financial transactions and mobilizes savings. In contrast, the demand-side theory suggests that economic growth stimulates the demand for financial services. Robinson (1952) supports this view, stating that finance follows where enterprise leads. This presents a finance-led growth relationship that contrasts with the supply-side theory. The theories present two distinct 2 perspectives on the relationship between credit availability and economic growth, highlighting the importance of a well-functioning financial system. According to Demir and Danisman (2021) financial institutions play a vital role in the economy by enabling the smooth transfer of funds from surplus agents to deficit agents across diverse economic sectors. In Eswatini's economy, commercial banks represent the most dependable sources of credit, with four commercial banks acting as providers of credit and facing potential risks of loan defaults by consumers 1. Deposits constitute the primary source of funding for the banking industry, accounting for 77.0 percent of all assets and they increased by 10.5 percent in 2021 increase, reaching E16.9 billion ($ 880 Million). The value of time deposits increased by 13.3 percent, while demand deposits and savings deposits rose by 6.3 and 9.6 percent, respectively. Total shareholders' equity, constituting the second-largest source of finance, amounted to E3.2 billion ($160 Million) in 2020 and they represented 14.9 percent of all assets 2. According to Bordo, Duca, and Koch (2016), banks play a crucial role in transferring funds from surplus units to deficit units, enabling the financing of illiquid projects with liquid cash that depositors can access at any time. However, this process also carries risks, as banks use liquid assets to finance illiquid projects. This is one of the reasons why banks need to be regulated. While this process is vital for the economy, it necessitates the regulation of banks. The minimum capital requirements for banks were set at 20 percent for 2021 and 2022. However, despite these liquidity levels, commercial banks in Eswatini continue to have excess liquidity. Three of the banks have liquidity that is twice the amount of the minimum capital required, indicating an excess liquidity of 20 percent.This suggests that efforts to increase capital holdings by enforcing capital requirements after the commencement of a crisis can often worsen a liquidity constraint and lower asset quality for banks3. Several authors, including Nyasha et.al (2015), Akani (2018), Demir and Danisman, (2021), have extensively discussed the benefits of the bank lending channel. According to this theory, multiple variables influence the availability of loanable funds for commercial banks, some of which can be directly influenced by the central bank's monetary policy stance. As a result, changes in the cost of extending credit are observed. To comprehensively capture the impact of fluctuations in loanable funds on credit extension and the bank lending channel, it becomes essential to consider demand- side characteristics as control variables within the supply-demand framework. The bank lending channel has been extensively studied by Deminar and Ozturk (2021), Amidu (2014) and Ananzeh (2016). This theory posits that banks, leveraging their position and ability to 1 Central Bank of Eswatini Research Bulleting, Issue 6 2021 2 Recent Economic Developments Report, Central Bank of Eswatini March 2021 3 Central Bank of Eswatini, Financial Stability Report, Issue 5 June 2021 3 manage asymmetric information in credit markets, can attract consumers. In light of this information asymmetry, commercial banks, influenced by the central bank's discount rate, become the primary source of funding. When monetary policy takes an expansionary stance, boosting bank liquidity, loanable funds increase. As a result, banks extend credit in the form of cash to maximize profits through interest charges. This implies that banks rely on liquidity to provide credit to both individuals and businesses. The basic assumption of the bank lending channel theory is that the economy's cost of credit will decrease as liquidity increases. This information highlights the importance of bank liquidity in the credit market and its role in the monetary transmission mechanism, (Mishkin ,1996). Samad (2004) highlights the importance of liquidity for commercial banks, as it impacts their ability to meet their financial obligations in a timely and efficient manner. In this regard, retail and wholesale distribution networks play a crucial role, with money obtained from retail channels considered more dependable and less sensitive to interest rates. To assess liquidity, financial ratios can be used, such as the current ratio, quick ratio, and cash ratio. This emphasises the significance of liquidity within the banking sector and its function in guaranteeing the bank's capability to fulfill its monetary responsibilities. In Eswatini's banking sector, liquidity requirements are measured through the ratio of total liquid assets to total liabilities, with a minimum ratio of 20 percent expected for banks. Any amount exceeding this is considered excess liquidity.4 To calculate excess liquidity, the total liquid assets ratio to total liabilities is subtracted from the minimum liquidity requirement set by the central bank (Gatev, 2004), which was 20 percent for the year 2021. The central bank adjusts liquidity requirements according to the economic environment, emphasizing the need to maintain adequate liquidity in the banking sector to ensure the bank's ability to meet financial obligations and respond to changes in the economic environment. The regulation of liquidity in Eswatini's banking sector is crucial for ensuring financial stability. Over the 20-year period, commercial banks in Eswatini have maintained liquidity above the minimum requirement, with excess liquidity rising consistently. However, in 2009, there was a sharp increase in excess liquidity by 11 percentage points after a drop of 4 percent due to the world financial crisis, leading to credit rationing as banks were hesitant to lend. From 2017 to 2021, excess liquidity has been on an upward trend, with the excess liquidity in 2020 and 2021 recorded at 20.1 percentage points despite the minimum liquidity requirement being set at 20 percent for both years 5. This indicates that banks had twice the required liquidity, highlighting a liquidity glut in Eswatini where banks held liquidity instead of extending credit to households and corporations. These emphasise the need for finding the right balance between maintaining adequate liquidity levels in 4 Central Bank Data Quarterly Review Tables, March 2023 5 Central Bank of Eswatini Integrated Annual Report 2021 4 commercial banks and providing credit to support economic growth in Eswatini. Ultimately, this will lead to a more stable banking sector and support increased economic growth. Figure 1: Bank Liquidity Outlook of Eswatini. Source: Author’s Computations (Central Bank of Eswatini Database) Tsiang (1980) argued that Keynes' liquidity preference theory was vulnerable to criticism from those who claimed that financing was necessary to cover unpaid investment expenses. Conversely, Ohlin, (1937) presented the interpretation of neoclassical theory, which examined the relationship between the supply of credit from planned savings and the demand for funding, largely driven by planned investments. However, Keynes believed that banks should generate credit or cash balances to finance households and firms. Keynes contended that the interest rate was determined by the demand for liquidity and the supply of liquidity, generated either by banks or savings. Throughout this discussion, Timsina and Pradhan (2016). presented unique monetary policy perspectives, but Keynes' liquidity theories are still relevant to the present economic climate. The excess liquidity in the banking sector of Eswatini does not seem to have expected benefits as the country has one of the highest lending rates compared to countries with similar liquidity ratios. According to a report, Eswatini was ranked the 16th highest country out of 20 in terms of high lending rates with similar levels of excess liquidity. Several other countries such as Chile, Paraguay, and Tanzania had higher lending rates or prime rates than Eswatini yet they have similar liquidity levels 6. 6 World Bank Database, assessed at https://data.worldbank.org/indicator/FD.RES.LIQU.AS.ZS on the 5th of June 2023 0 5 10 15 20 25 0 10 20 30 40 50 2 0 0 0 2 0 0 1 2 0 0 2 2 0 0 3 2 0 0 4 2 0 0 5 2 0 0 6 2 0 0 7 2 0 0 8 2 0 0 9 2 0 1 0 2 0 1 1 2 0 1 2 2 0 1 3 2 0 1 4 2 0 1 5 2 0 1 6 2 0 1 7 2 0 1 8 2 0 1 9 2 0 2 0 2 0 2 1 Liquidity Ratio Minimum Liquidity Requirement Excess Liquidity https://data.worldbank.org/indicator/FD.RES.LIQU.AS.ZS 5 Figure 2: Prime Rates of Countries with the same Liquidity Ratios as Eswatini for 2022 Source: Author’s Computations (World Bank Database) According to Akey & Lewellen (2017) policymakers need to explore different ways to address the high lending rates, including measures to enhance financial stability by managing credit and liquidity risks. Additionally, there is the need to attract foreign investment as it could provide alternative funding sources, reducing the country's reliance on local deposits. Tran (2021) added that promotion of SMEs could also lead to greater liquidity, reducing borrowing costs, particularly for this crucial sector of the economy. This implies that policymakers must strategically address these issues to ensure sustainable economic growth and financial stability in Eswatini. According to Khaw (2019), economic policy is a critical tool for regulating economic performance. Creel , Hubert & Labondance (2022). argue that poor economic performance can raise debt costs by causing policy uncertainty, which results in an increase in the risk premium, hence pushing up the costs of credit. Akey and Lewellen (2017) suggest that economic performance can influence debt financing costs in two ways. Firstly, significant economic policy uncertainty increases information asymmetry between enterprises and their creditors, leading creditors to raise interest rates to compensate for this information disadvantage. Secondly, according to the real options theory, enterprises that face uncertainty are more likely to postpone their investment until the uncertainty is resolved. This means that maintaining a stable economic environment is important as it can help to reduce the cost of credit, making it easier for businesses to access financing and invest in future growth. According to Guangli Zhang ( 2015), Gungoraydinogluet.al., (2017) if there are low levels of economic growth in a jurisdiction, cashflows of firms become more volatile resulting in higher risks of default. This also leads to a rise in corporate finance costs. Bordo, Duca, and Koch (2016) found there is a negative relationship between policy uncertainty brought about by a deteriorating 16.68 16.00 12.51 11.25 10.98 10.75 9.34 9.00 9.00 8.92 8.10 8.02 8.00 8.00 7.81 7.22 7.15 7.03 7.00 5.75 - 2.00 4.00 6.00 8.00 10.00 12.00 14.00 16.00 18.00 Tanzania Rwanda Paraguay Chile Peru Eswatini Colombia Lesotho Belarus Indonesia Comoros Bolivia Namibia Algeria Vietnam Mauritius Philippines Jordan Panama Botswana 6 economy and access to bank credit. They used the global financial crisis as an exogenous shock to study the relationship between policy uncertainty and costs of credit. They found that during a financial crisis banks had liquidity challenges since capital financial markets do not operate fully. The liquidity channels are disturbed which then results to high costs of credit and hence results to fluctuations in cash flows of firms (Bajaj et.al.,, 2021). Literature has different views on the level or the extent to which banks through credit extension promote economic growth. Bernanke (1983) found that during periods of credit contraction, credit rationing can lead to a decline in investment and a subsequent slowdown in economic growth. Shimeles and Gugsa (2006) found that the impact of credit on growth is positive but weak, indicating that the effectiveness of credit in promoting growth varies by region. On the other hand, Mlachila et al. (2010) found that credit provided by banks supports economic growth, but its effectiveness varies across countries, depending on the level of financial development and institutional quality. This may appear surprising considering the numerous theoretical reasons claiming that access to well-functioning global finance markets enhances growth in a variety of ways. According to Alquist & Chabot (2014) globally linked banks sever the relationship between a country's amount of savings and its ability to invest by distributing finances across investment projects until the marginal rates of return are equalised. As a result, globally interconnected capital markets are rendered important in equitable wealth distribution. 1.1.1.Eswatini Financial Sector. Eswatini has a small developing financial sector, generally dominated by the banking sector. The banking sector, composed of 4 commercial banks with 74 branches across the country. It remains a well-capitalized and profitable financial system, with South African banks accounting for the larger share of the market. The banking sector of Eswatini is regulated by the Central Bank of Eswatini (CBE). The fourth lending institution in Eswatini is the locally owned Eswatini Development and Savings Bank (SBS). However, the fifth financial institution, the Eswatini Building Society, is not considered in this context. This is primarily due to the fact that it does not maintain reserves with the Central Bank and does not rely on borrowing from the Central Bank7. Eswatini's financial markets are characterized by their underdeveloped nature and limited integration with international financial markets. The Non-banking Financial Institutions such as the pension funds, money market funds, insurance companies, cooperatives and micro lenders are regulated by the Financial Services Authority8. According to Samouel & Aram (2016) the country is dominated by excessively liquid banking sector and there is no is functional stock markets. Bank lending is therefore the reliable form of raising capital for firms and they act as a dependable conduit of monetary policy transmission. 7 Central Bank of Eswatini, Financial Stability Report June 2019 Issue 5 8 Financial Services Regulatory Authority Quarterly Report March 2022 7 The crucial role of the financial sector in driving economic growth has long been recognized, with early economists such as Schumpeter (1911) advocating for finance-led growth. Through the efficient allocation of savings, the financial sector plays a vital role in directing funds towards productive investments, particularly in the formal sectors of the economy. Among the various components of the financial sector, the banking sector stands out as a significant channel for financial intermediation within the economy. Eswatini’s discount rate tracks South African Reserve Bank’s (SARB) repo rate, like all Common Monetary Area member states. For instance, the discount rate of Eswatini was reduced from 11.5 percent in 2008 to a low of 5 percent in 2013, after South Africa reduced their repo rate to counter the impact of the 2008 Global financial crisis 9. In 2021 Eswatini’s discount rate was recorded to be 3.75 percent to counter the effects of the COVID 19 pandemic, in 2022 it was increased to 6.75 percent as monetary policy tightening was happening around the globe to curb inflation. Treasury bills (TB) and bonds, on the other hand, are valued higher than the South African TB rate and bond prices, but the local market for these assets remains limited. The government is the primary issuer of TBs and bonds, while the primary holders are pension funds and commercial banks10. 1.1.2.Trends in Bank Credit to the Private Sector The private sector was resilient in 2021, which was aided by increased economic activity resulting from the relaxation of COVID-19 restrictions and supportive monetary policy. As a result, credit extension to the private sector increased by 3.3 percent year on year to E15.8 billion ($839 million) at the end of 2022, up from 9.3 percent the previous year. This further indicates that credit for private households has grown rapidly due to competition within the industry, resulting in a variety of credit products, including personal unsecured loans. The extension of credit to the private sector started to pick up again in 2013-2018 as more digital credit products were introduced due to the banks automating their systems during this period.11. Figure 3: Private Sector Credit of Eswatini for the Period 2000-2021 Source: Author’s Computations (Central Bank of Eswatini Database) 9 Central Bank of Eswatini Research Bulletin 2018, Issue 4 10 Central Bank of Eswatini Research Bulletin 2019, Issue 5 11 Eswatini Financial Stability Report, Issue 4 June 2021 -60% -50% -40% -30% -20% -10% 0% 10% - 2,000 4,000 6,000 8,000 10,000 12,000 14,000 16,000 18,000 G ro w th R a te s in p e rc e n ta g e V a lu e i n S Z L 'M il li o n s Private Sector Credit Private Sector Credit Growth (RHS) 8 1.1.3.Economic Growth Developments Eswatini’s economic activity as measured by real GDP, was estimated to have grown by 7.9 percent in 2021 rebounding from a revised 1.6 percent contraction in 202012. The stronger-than-expected recovery was mainly driven by activities in the secondary sector and due to base effects. In the advent of COVID-19, weak external demand as well as stiff lockdown restrictions on non-food processing (mainly manufacturing of beverages) weighed more negatively on export-oriented manufacturing. The easing of lockdown restrictions locally and internationally, in 2021, despite emergence of new COVID-19 variants, supported a strong rebound in the affected subsectors within the secondary sector13. In 2022 the economy was forecasted to have expanded by 3.6 percent due to base effects depicting a decline of 4.3 percentage points compared to 7.9 percent growth that was recorded in 202114. On another note, Eswatini’s GDP growth averaged 6.1 percent between 1980 and 1989, with a high of more than 10 percent in 1987. This was during a period of political upheaval among the country's neighbors, which made the country an ideal investment destination. Eswatini had rapid economic expansion as South Africa confronted the issues of political turmoil and investors used Eswatini as a conduit to conduct business. However, that picture reversed in the 1990s as such unrests faded in South Africa, resulting in average growth rates of 3.8 percent between 1990 and 199915 . The GDP of Eswatini grew by an average of 2.9 percent between 2010 and 2016, but the trend has largely declined from the peak in roughly 2006. The banking industry was crucial to Eswatini's economic growth under such benevolent economic circumstances. Eswatini was not immune to the effects of the global financial crisis-induced economic recession, which changed the outlook for an already weakening economy dramatically in late 2007 (Banu, 2013). The Eswatini economy was negatively impacted by slower economic development in important export markets, reduced commodity prices, and a decrease in capital flows to developing nations which resulted to a deteriorating in profitability of Eswatini’s banking sector 16. 1.1.4.Cost of Credit The cost of credit refers to the interest rate charged on loans and other forms of credit. Higher interest rates can make borrowing more expensive, which may reduce the amount of credit demanded by consumers and businesses. This can potentially slow down economic growth because credit is often used to finance investment and consumption, both of which contribute to economic activity, (Markauskas & Saboniene, 2020). Depending on the degree of risk to the lender, a few variables may raise the cost of credit. These include a lengthier payback term since the time value 12 Central Statistics Office of Eswatini, Annual GDP Figures 2021 Report 13 Central Bank of Eswatini Annual Integrated Report 2021/2022 14 International Monetary Fund, Kingdom of Eswatini 2023 Article IV Consultation 15Central Bank of Eswatini Quarterly Review, December 2016 16 Ministry of Economic Planning and Development, Economic Outlook Report 2013 9 of money and opportunity expenses increase the longer a debt is outstanding. According to Arena, Reinhart & Vazquez (2016) the economic environment also determines the risk of default, since in poor economic environment the risk of default tends to be high hence increasing the cost of credit . The economic outlook, recent macroeconomic and financial developments in Eswatini have highlighted an increase in the cost of credit, rising from 7.25 percent in 2021 to 10.50 percent in 2022. This change could potentially impact economic growth in the country. The report states that Real GDP growth fell to 3.6 percent in 2022 from 7.9 percent in the previous year , which could be partly attributed to factors such as fiscal constraints and an increase in the cost of credit17. In addition, it was noted that private sector credit growth in Eswatini declined slightly from 4.8 percent in 2021 to 4.4 percent in 2022. This suggests that the availability of credit may be decreasing in the country, which could have negative implications for economic growth18. The relationship between the cost of credit and economic growth is complex and context-specific, but in general, higher borrowing costs can make it more expensive to finance investment and consumption, which can reduce demand and slow down economic growth. Furthermore, higher costs of credit can also increase the risks associated with borrowing, such as default risk, which can discourage lending and borrowing thus resulting to a decrease in economic growth, (Markauskas & Saboniene, 2020). 1.2. Problem Statement Eswatini is facing a sluggish economy, with the country's GDP growth remaining low, averaging between 1 to 3 percent in real terms over the 2019-2022 period. In 2021 the economy grew by 7.9 percent due to base effects brought about by the COVID 19 pandemic. The manufacturing sector plays a significant role in Eswatini’s economy, contributing approximately 27 percent to the country’s GDP. Additionally, the trade sector also makes a notable contribution, accounting for around 15 percent of the GDP. These productive sectors are crucial in driving economic activity and growth in the country19. In the years 2020, 2021, and 2022, there was a consistent excess liquidity of 20.2 percent, 20.1 percent, and 21.1 percent, respectively, which is twice the minimum liquidity required 20. The high liquidity is not being effectively channeled into productive sectors to drive economic growth as suggested by Lay (2020). It is interesting to establish whether the high cost of credit is a contributing factor to this problem. Furthermore, private sector credit has been fluctuating over the years, and this has hindered the private sector's ability to serve its purpose of boosting economic growth in the country 21. The ineffective use of excess liquidity to promote economic growth and the unclear reasons why the liquidity is not being channeled to productive sectors indicate significant barriers to the country's economic growth (Creel , Hubert & Labondance, 2022). 17 African Development Bank Group's eSwatini Economic Report 2023 18 IMF 2023 Article IV Consultation-Press Release 19 Central Bank of Eswatini, Annual Economic Review Report 2021 20 Central Bank of Eswatini , Financial Stability Report, Issue 6 21 Central Bank of Eswatini, Research Bulleting Issue 5 2020 10 In order to promote sustainable economic growth in Eswatini, it is crucial to identify and address any existing barriers to the effective utilization of liquidity and explore solutions for strategic financing. This requires a comprehensive examination of the relationship between aggregate liquidity and the cost of credit, as well as the interplay between the cost of credit and economic growth. Additionally, it is essential to understand the reasons behind the inefficiencies in channeling excess liquidity into productive sectors and the implications of fluctuations in private sector credit for driving economic growth. However, the existing literature on Eswatini's economic challenges and the utilization of excess liquidity has certain limitations and gaps that need to be addressed. While there is recognition of the high levels of liquidity in the country, there is a lack of in-depth analysis regarding the specific factors that contribute to the ineffective allocation of this liquidity towards productive sectors that can drive economic growth. Furthermore, the literature fails to provide a clear understanding of the underlying causes of fluctuations in private sector credit and how it affects the country's ability to stimulate economic growth. These gaps in the existing literature impede a comprehensive understanding of the barriers to efficient liquidity utilization and hinder the development of targeted solutions for strategic financing (Creel, Hubert & Labondance, 2022 1.3. Research Questions The study aims to address the following research questions: 1. What is the relationship between banking sector liquidity and the cost of credit in Eswatini? 2. How does the cost of credit impact economic growth in Eswatini? 3. What role does excess liquidity play in promoting economic growth in Eswatini's economy? 1.4. Objectives of the Study The main objective of the study is to examine the nature of the relationship that exist between cost of credit and economic growth of Eswatini in the short run and in the long run. The specific objectives of the study are to: 1. Establish relationship between banking sector liquidity and cost of credit in Eswatini. 2. Ascertain the relationship between cost of credit and economic growth in Eswatini. 3. Establish the possible role of excess liquidity in promoting economic growth in Eswatini’s economy. 1.5. Hypothesis H1: High liquidity lowers the cost of credit. H1: High cost of credit leads to low economic growth. 11 1.6. Significance of the Study The proposed study that aims to deepen the understanding of the interaction between the cost of credit and economic growth in Eswatini and assist local monetary policy authorities in making better-informed policy decisions regarding the cost of credit. The study intends to fill a gap in knowledge as there are no existing studies on the relationship between the cost of credit and economic growth in Eswatini. The study will provide empirical findings on how the cost of credit affects the economy in Eswatini, which will contribute to the body of knowledge and may suggest possible strategies for distributing the liquidity of Eswatini's banking sector to promote economic growth. Overall, the proposed study appears to be a potentially valuable contribution to understanding the Eswatini economy and improving related policy decisions. 1.7. Chapter Summary In this chapter an overview of cost of credit and economic growth was made. First, the chapter presented some background information with necessary statistics to give a clear picture of where Eswatini stands. The last sections included the problem statement, objectives, research hypotheses, justification of the study. The next chapter will discuss the literature review. 12 CHAPTER 2 LITERATURE REVIEW This chapter provides a review of literature related to cost of credit and economic growth. The chapter also describes the theoretical approaches, empirical studies and methodological review on the macroeconomic variables that are linked to cost of credit and economic growth both directly and indirectly. 2.1. Theoretical Literature 2.1.1.Liquidity Theory Review During periods of uncertainty, banks tend to reduce their liquidity creation as they prioritize holding more liquid assets and limit lending activities. This cautious approach is driven by concerns over potential liquidity shortages and funding difficulties, as indicated by the work of Diamond and Rajan (2011).. External funding costs also increase, limiting access to liquid funds, and uncertainty reduces credit demand (Bloom et al., 2013). This decrease in liquidity creation also applies to loan commitments, as banks cannot provide more guarantees due to limited loanable funds. Effective policy interventions are necessary, as researchers have studied methodologies to understand bank liquidity creation and credit availability (Diamond & Rajan, 2011; Bloom et al., 2013). The bank lending channel theory offers a solution, allowing banks to increase credit availability in credit markets even when funding is tight. Policymakers can adopt this theory to maintain financial stability, ensure credit availability, foster economic growth and job creation (Diamond and Rajan, 2011 and Bloom et al., 2013). However according to targets Dell’Ariccia et al. (2014), an increase in uncertainty can have competing impacts as well. According to the "search for yield" hypothesis, banks may take on more risk to offset their eroded profits due to uncertainty. This behavior could result in an increase in risky loans, but it may also lead to a decrease in off-balance sheet activities. As customers gain more credit in the spot market, their demand for credit commitments may decrease. On the other hand, uncertainty can also lead to an increase in the deposit supply from the public into banks, where they serve as safe havens due to the availability of deposit insurance (Gatev and Strahan, 2006). Existing literature has investigated the direct impact of income diversification on bank liquidity creation and found that dispersed resources may have drawbacks that negatively affect liquidity creation (Dang, 2020). However, other studies suggest that various factors, such as income structure and diversification, could indirectly influence how bank liquidity creation reacts to uncertainty. Diversifying income sources can allow banks to take advantage of scope economies, like cross- selling opportunities, which may mitigate the negative effects of adverse shocks on lending (Gertler 13 and Kiyotaki, 2015). Additionally, the "loss leader" hypothesis suggests that banks may adopt lower interest margins or lending rates to attract customers and build long-term relationships after acquiring non-interest income, which could help maintain bank liquidity creation during uncertain times (Petersen & Rajan, 1995). Overall, it appears that a flexibly adjusted interest rate framework and a diversified income structure may both have positive impacts on bank liquidity creation during uncertainty shocks. According to Houcine (2020), the interbank market allows banks to hoard liquidity from each other through reduced federal funds sold or other interbank loans on the asset side of the balance sheet , causing liquidity shocks to spread to other banks. On the liability side of the balance sheet, banks may raise more liquid deposits and other liquid liabilities as a response to more uncertainty, hoarding liquid funds that may otherwise be intermediated through other banks or nonbank financial institutions and markets. According to (Demoussis et al., 2017). increasing liquid liabilities is more consistent with standard liquidity management practices, as it is easier to reverse compared to changes in illiquid liabilities such as 10-year bonds. Quarterly changes in Economic Policy Uncertainty (EPU) may result in banks raising illiquid liabilities to fund more liquid assets, but increasing liquid liabilities is more common. Merton and Thakor (2019) suggest that high leverage in banks compared to non-financial firms can be attributed to factors such as deposit insurance, tax benefits of debt, and reduced bankruptcy costs. These factors can distort the attitudes of stakeholders towards risk. Due to the diversity of banks' stakeholders, including insured and uninsured depositors, regulators, other creditors, investors, and borrowers, banks' management faces different pressures. Barinov et.al (2020) also emphasised the importance of the priority structure of debt, specifically in terms of the contracting costs that arise from conflicts of interest between bondholders and shareholders. Merton and Thakor (2019) suggest that insurance can facilitate more effective contracting because depositors function as both customers and investors. Depositors provide funding but are unwilling to bear credit risk. Though insurance cannot eliminate risk entirely, it can prevent depositor runs, thereby significantly reducing the probability of default. This creates an incentive for banks to take on as much debt as necessary to outweigh taxable earnings up to 100 percent, less the minimum required amount of equity. According to Mourouzidou-Damtsa (2019) theory, when the costs of financial distress are disregarded, utilizing debt helps minimize the cost of credit and maximises the value of a firm without any constraints. However, it should be noted that there is no evidence suggesting that losses are lower in the event of default. 2.1.2.Credit Rationing The topic of credit rationing by lending institutions has witnessed substantial research efforts in recent decades, although theoretical justification for how different forms of credit rationing appear in markets is limited. According to Müller (2022) regulators, comprehending the emergence of such 14 rationing methods is essential to maintain economic and financial stability during crises and pandemics and for effective decision-making concerning monetary policy. Even though there are empirical indications regarding credit rationing, further research is required to better understand this phenomenon and determine the potential causes behind the problem. Demoussis et al. (2017) documents that in the product market, a price below the equilibrium price generates excess demand over supply and results in a form of rationing. However, in a credit market, this is not the case as rationing can persist despite a lack of regulatory price control. This implies that the credit market behaves differently from the typical product market and exhibits an anomaly in this regard. Stiglitz and Weiss (1981) define credit rationing as the limitation of lenders of the supply of additional credit to borrowers who demand funds at a set quoted rate by the financial institution. This situation can occur when lenders and borrowers cannot overcome the inherent informational asymmetries that give rise to adverse selection and moral hazard. They divided the concept of credit rationing into two concepts namely; Type 1 rationing where a borrower receives a loan below the requested amount, and Type 2 rationing where credit is entirely denied. On another note, according to Evans and Jovanovic, et .al (1989) Credit rationing is an important banking market outcome as it can adversely affect the performance and even survival prospects of businesses by constraining investment and employment choices. Drakosa & Giannakopoulos (2018) posits that rationed firms were a subset of loan applying firms at the early stages of the literature, and unless a company explicitly revealed its demand for credit, there was no possibility of being classified as rationed. The phenomena of credit rationing can be challenging to manage and present a significant problem for regulators responsible for monetary policy. Such regulators play a vital role in maintaining economic and financial stability, especially during pandemics and crises. Credit rationing leads to suboptimal decision-making as businesses face constraints in accessing credit, resulting in reduced employment and investment opportunities. This adversely affects businesses' performance and has detrimental effects on their survival prospects. Accessing affordable financing poses a significant challenge for small and medium-sized enterprises (SMEs) worldwide, as highlighted by Cheng et al. (2014). This challenge, often referred to as the "Macmillan gap," is particularly prevalent in developing countries and transitioning economies. The gap arises due to information asymmetry between lenders and borrowers, with financial institutions perceiving SMEs as high-risk due to factors such as weak collateral quality, limited guarantee capacities, small loan sizes, frequent loan requests, and complex loan procedures. Consequently, financial institutions prefer to lend to larger enterprises with more substantial funds, resulting in reduced productivity and misallocation of resources. 15 Kaplan and Zingales (1997) argued that financially constrained firms exhibit a higher sensitivity of physical investments to cash flow shocks. Whited and Wu (2006) estimated the Euler equation for investment with financial constraints by incorporating the long-term debt-to-asset ratio and cash- flow-to-asset ratio. Nahla and Kutan (2016) discovered a non-linear relationship between the debt- to-asset ratio and firm labor productivity. Additionally, credit scores are commonly assessed based on liquidity conditions, indebtedness, and payment behaviors (Wagner, 2014). Moreover, Hadlock and Pierce (2010) identified firm scale and age as useful predictors of financial constraint levels. Ferrando and Ruggieri (2018) developed a synthetic indicator of financial constraints using a classification based on specific firm characteristics and financial pressures. Credit rationing is an important topic in economics that has been extensively researched. Jaffee and Stiglitz (1989) have highlighted that credit markets differ from standard markets in that they involve promises for future repayment, leading to moral hazard and adverse selection issues. To reduce firms' credit constraints and overcome information asymmetry and incentive problems, banks can adequately screen and monitor borrowers and make appropriate use of information (Diamond, 1984). The literature on bank lending highlights two ways in which SMEs are financed, based on the type of information exchanged between the borrower and the bank, as noted by( Berger and Udell, 2006). The first approach, transaction lending technology, involves information exchange based on quantitative and transferable 'hard' information. In contrast, relationship lending relies on qualitative information obtained through long-term interaction. The success of banks in providing appropriate incentives for borrowers and overcoming information asymmetry depends on their lending technology. Another area of research has investigated whether discouraged borrowers have correctly or incorrectly assessed the likelihood of their rejection, as studied by Diagne (1999) and Vochozka (2017). In their study, Calabrese et al. (2021) examined the concept of credit rationing, which manifests in various forms for firms. This includes being partially satisfied with a loan, being completely denied a loan, or facing high costs that make it impossible to obtain a loan. These instances represent credit constraints from the supply side. Despite banks implementing more stringent screening processes and evaluation techniques for loan applicants, many small and medium-sized enterprises (SMEs) still face financial constraints (Artola & Genre, 2011). It is important to note that credit rationing resulting from imperfect information only applies to those who actively apply for loans. However, to achieve unbiased estimates of the relationship between borrowers and lenders, it is crucial to model not just loan applicants, but also those who require external bank lending but choose not to apply. These firms belong to a group known as discouraged 16 potential bank borrowers. By considering both loan applicants and discouraged borrowers, a more comprehensive understanding of the borrower-lender relationship can be obtained. Research on the constraints faced by small and medium-sized enterprises (SMEs) in accessing finance has expanded beyond the traditional definition of credit rationing proposed by Stiglitz and Weiss (1981). Discouraged borrowers refer to firms that would have applied for credit but chose not to because they anticipated rejection (Cavalluzzo et al., 2002; Kon & Storey, 2003). This form of rationing represents constraints on the demand side of credit. Calabrese et al. (2021) argue that the level of discouragement among borrowers is influenced by factors such as application costs, screening errors, and the interest rate difference between banks and other lenders. Previous empirical studies support the prevalence of discouragement among potential borrowers (Freel et al., 2012; Qi & Nguyen, 2021). Therefore, it is crucial to include discouraged borrowers when measuring credit rationing, as their exclusion would underestimate the phenomenon (Freel et al., 2012). Additionally, another line of research has examined whether discouraged borrowers accurately perceive the likelihood of loan rejection (Diagne, 1999; Vochozka, 2017). The traditional focus of research on finance for SMEs, as highlighted by Stiglitz and Weiss (1981), has primarily centered on firms that actively apply for funding, with an emphasis on credit rationing. The analysis of credit markets based on the theory of imperfect information has a long history, with Jappelli (1990) explaining how differences in borrower quality that go unnoticed can lead to credit rationing. Furthermore, empirical evidence has shown that many firms are unable to secure bank loans for reasons unrelated to their quality. Stiglitz and Weiss (1981) highlighted the significance of informational asymmetries in justifying a form of credit rationing where the market withholds funds to protect itself from asymmetric information, thereby excluding borrowers with characteristics similar to those who receive loans. They also noted that banks are concerned about the interest rates they receive from granting loans and the associated risks. However, the interest rates charged by banks can also influence the risk profile of the loan pool, either by adversely selecting prospective borrowers (adverse selection effect) or by impacting borrowers' behavior (incentive effect or moral hazard), as suggested by Bloom et al. (2007). Regulators must undertake research to identify the various forms of credit rationing and study their impact on businesses and economies to develop policies that address credit rationing. Understanding the phenomenon of credit rationing is crucial for policymakers to maintain economic and financial stability, especially during pandemics and crises. The negative repercussions of credit rationing on businesses highlight the need for regulators to have an accurate understanding of the implications of credit rationing. Further, it is imperative to develop policies that can overcome the information asymmetries responsible for such market failures 17 2.1.3. Bank Credit Haq et.al (2018) have presented a theoretical evaluation of the transmission mechanism linking credit and economic growth. They use the neoclassical growth model as a foundation to demonstrate how credit interventions can promote economic growth by increasing savings, which subsequently lead to capital accumulation and gross domestic investment. By increasing the liquidity of deposit- taking institutions or banks, savings create an opportunity for them to offer credit to borrowers who require liquidity. Based on a distinct group of theoretical models, Amoo, Eboreime, and Adamu (2017) suggest that financial credit can impact growth by reducing inequality through the equitable distribution of liquid assets from those with excess to those in need of them. This process results in investment opportunities that generate employment and income for those in need, leading to a reduction in social inequalities (Kirikkalel & Athari, 2020). According to Amidu (2014) bank lending is a vital facilitator of economic expansion since credit plays a crucial role in the movement of money by financing capital formation, consumption, and production, all of which impact economic activity. However, for the monetary policy transmission mechanism to function effectively in support of the policy objectives, the financial sector must be properly managed and regulated. To achieve maximum potential for growth and productivity, credit must be provided to the private sector within a controlled banking environment. This helps to foster economic growth, promotes employment opportunities, and enhances the competitiveness of the economy, as Qi and Nguyen (2020) suggest based on their own class of theoretical models. Rajwani and Azaaviele (2018).) assert that the demand-side processes of economic policy have a significant impact on the investment decisions of economic players. Therefore, it is crucial to have policies that foster a positive economic outlook. Clear and transparent economic policies are necessary to enable firms to make well-informed investment decisions. On the other hand, Cheong, et.al (2014) argue that if the decision-making process is unclear and unpredictable, it can negatively affect the credit needs of corporations and their investment activities. This might even result in the postponement or cancellation of investment plans. Qi and Nguyen (2020) suggest that firms tend to avoid taking credit from financial institutions during uncertain times, such as during war threats, terrorist attacks, military tensions, conflicts, and economic meltdowns. During such uncertain times, household demand for credit is also negatively impacted. Firms become more likely to default, which leads to an increase in the unemployment rate and a reduction in household savings, reducing the likelihood of households applying for financing for their consumption and investment needs. Economic uncertainties make households less confident about the future economic outlook of their jurisdiction. In terms of supply-side processes, Deminar and Ozturk (2021) point out that in an uncertain economy, banks are less willing to fund the investments of both businesses and individuals, as default risks for borrowers increase. The future prospects of investment ventures become less certain. As economic uncertainty rises, 18 banking institutions tend to charge higher interest rates for loans, resulting in a decline in credit levels, as an extra risk premium is added to loan pricing. Saunders (2022) state that private sector lending is a crucial indicator of financial progress. Therefore, the study of bank loans and growth is of significance in examining the relationship between finance and economic development. Through their research, they provide evidence that the financial sector, as measured by the ratio of bank credit extended to the private sector to GDP, plays a significant role in enhancing investment productivity by improving capital allocation and facilitating greater levels of investment. By modifying the composition of deposits, the financial system can positively impact the actual economic performance by providing valuable information. According to Timsina (2014), Şenay & Deniz (2019) and other similar studies, the financial sector is crucial in channeling funds towards profitable investments, especially in the formal sectors of the economy. They highlight that the banking industry has a significant role to play in financial intermediation in the economy. These studies also suggest that financial development can promote economic growth by improving the efficiency of loanable funds allocation, increasing savings, and encouraging capital accumulation. They argue that well-developed financial markets are indispensable to the overall economic progress of developing and less developed countries. Ketteni and Kottaridi (2019) contend that banks perform an important social function by extending credit, which enhances output, encourages capital investment, and ultimately improves the standard of living. Steward, Chowdury, and Arjoon (2021) contend that the availability of credit plays a crucial role in actual economic activity, as changes in loan growth, both in terms of volume and credit standards used for enterprise loans, have a significant positive impact on GDP. Ketteni and Kottaridi (2019) draw a similar parallel to the role of financial services in raising the income of the underprivileged by increasing the supply of financial services they can access, which directly contributes to poverty decline. Private sector lending serves a crucial function as a means of monetary policy transmission, with its influence on the credit of the banking sector having a potential impact on inflation and actual economic activity that cannot be disregarded. Shabir et.al (2022) argue that increasing rates of accumulation of physical and human capital is necessary for effective utilization of resultant productive assets and ensuring universal access to them. This can only be accomplished by having access to bank finance. Financial intermediation, which involves collecting funds from the surplus sector as deposits and lending them to various economic sectors, promotes economic growth. According to Bordo, Duca, and Koch (2016), credit extension is one of the primary responsibilities of financial institutions. 19 Wu, Yao, Chen, and Jeon (2021) propose that according to the neo-classical growth hypothesis, labour and capital are the primary determinants of production. In other words, production can be represented as 𝑌 = 𝑓(𝐾, 𝐿) where Y represents total production, K represents total capital stock, and L represents the labor force. When technology and human capital are combined, this equation can be expressed as 𝑌𝑖,𝑡 = 𝐴𝐾(𝐿ℎ). Obtaining bank loans allows for the acquisition of more capital under this production function. In the presence of new technology, adjustments to labor and capital are necessary to maintain growth equilibrium. Alternatively, other theoretical models argue that increasing the accumulation of human capital may also promote growth. However, Galor and Zeira's (1993) model suggests that growth may be hindered by income inequality and credit market frictions, preventing some individuals from investing in education. According to Khawa et.al (2021), the role of financing offered by banks would be of great assistance in acquiring new technologies and so increasing total factor productivity. Credit provided by the private sector encourages growth by boosting investment and productivity. In particular, the capital accumulation channel is mostly relevant for advanced countries. In standard neoclassical theories investment-savings is the engine of growth, (Ketteni & Kottaridi, 2019) 2.1.4.Cost of Credit The theoretical literature on the impact of competition on credit access has been fueled by the studies of Petersen and Rajan (1995) and Dell'Ariccia and Marquez (2006). Abbassi et al. (2016) present two contrasting hypotheses: the market power hypothesis and the information hypothesis. The market power hypothesis suggests that an increase in competition within the banking sector would lead to a decrease in lending rates and an easing of financing constraints. This is based on the idea that increased competition would result in lower prices, following the general economic theory that competition leads to price reduction. On the other hand, the information hypothesis argues against this notion. It posits that increased competition among banks actually worsens financing impediments, leading to higher lending rates. According to this hypothesis, reduced competition would incentivize banks to focus on building relationships with borrowers to gather "soft" information. This, in turn, would reduce information asymmetries and facilitate credit access. Essentially, the increase in bank competition may lead to a decrease in investments in relationships, which could have a negative impact on credit access. This suggests that both bank competition and relationship building are crucial in determining the cost of credit or credit access. Boissay et al. (2017) further support the idea that both bank competition and relationship building play vital roles in shaping the dynamics of credit access. Beck et al. (2013) and Ryan et al. (2014) highlight the ongoing debate over the implications of bank competition for economic welfare and growth. While competition can be advantageous in several sectors, it can pose problems for the banking industry due to its unique characteristics and the importance of information. The negative consequences of increased competition in the banking 20 sector can manifest in various ways, which include weakening financial stability, and influencing the relationship between bank competition and the cost of credit due to information asymmetries. Keynes (1937) defined finance as the demand for cash in exchange for a postponement of payment and the supply of bonds and planned investments. Finance and saving behavior were closely related, where the cost of credit is an essential aspect. According to Robertson and Ohlin (1940), saving behavior can significantly impact the economy, while Keynes maintained that the cost of credit is crucial in balancing the attractiveness of capital assets with cash, which is independent of current saving. Keynes believed that current savings equal current income minus consumption, and the supply of savings can never be depleted until full employment is reached, irrespective of how much investment occurs. Keynes also argued that a shortage of savings that clogs the investment market cannot exist, and new investment would lead to appropriate levels of income (Dalziel, 1996). Therefore, the cost of credit, which plays a vital role in influencing investment levels, should be taken into account when analysing finance and saving behavior in macroeconomic models (Dalziel, 2001). According to Durkin (1997) the cost of credit is an important factor to consider when discussing the intermediation of loanable funds (ILF) banking model in macroeconomic models. The model assumes that bank loans act as intermediaries for savings between non-bank savers and borrowers. Nilsen (2002) posits that, in reality, banks create new monetary purchasing power through loans, and the primary function of banks is to create funding through deposits when lending, without any intermediation. The cost of credit should be taken into consideration when borrowers or depositors seek assurance that others will accept their new deposits as payment for goods, services, or assets. Understanding the difference between resources (saving) and debt-based money (financing) is crucial in defining respective macroeconomic roles and the cost of credit (Bianchi et al., 2014. According to Baltensperger (1978), the creation of new monetary purchasing power through bank loans has implications for the cost of credit in the economy. Due to the greater supply of credit, the increased availability of credit can lead to lower interest rates, which can incentivize more borrowing and investment in the economy. Furthermore, the cost of credit can be influenced by various factors such as inflation, central bank policies, and market conditions, which can affect the demand for credit and the willingness of lenders to supply it. Therefore, it can be argued that the cost of credit plays a critical role in driving investment and fostering economic growth (Clerc et.al, 2015). This is because lower interest rates can incentivize borrowing and investment, while higher rates can restrict borrowing and slow down economic activity (Manaresi, et.al, 2019). In analysing the cost of credit and its impact on the economy as a whole, it is essential to consider the role of banks and the creation of new purchasing power. 21 Bibow (1995) argued that the cost of credit plays a critical role in driving investment and fostering economic growth, particularly during boom periods of financial cycles when banks decide to lend more. While banks have no technical limits to increasing the stocks of loans and deposits instantaneously and discontinuously, they must also consider other limitations, such as the implications of new lending for their profitability and solvency. Additionally, when creating deposits too fast, they face increased credit risk and liquidity risk. The deposit multiplier model of banking is fundamentally mistaken as it disregards the fact that modern central banks target interest rates and supply as many reserves and cash as banks demand, making the quantity of reserves a consequence and not a cause of lending and money creation (Arestis & Howells, 1999). Avery (1981) introduced a credit screening model under imperfect information that expands on the Phelps approach to include a wider range of cases, and the cost of credit plays a crucial role in this model. In Avery's model, the creditor gathers information on the applicant's financial and economic attributes (known as "economic variables" in Avery's model, or "test scores" in Phelps' approach) and summarizes this data as a probability distribution of rates of return, considering the costs that may arise due to default. The creditor also takes into account the group membership alongside the economic factors as this provides insights into the probability distribution of returns. However, the creditor's decision rule, according to Chick (1993), remains the same, only granting credit if the expected return exceeds the expected costs, including a provision for potential default costs that the cost of credit captures. During an economic boom, banks tend to be more willing to lend money and compete for credit, which can result in a lower cost of credit for borrowers. As Decker and Goodhart (2018) note, the cost of credit is subject to various factors, such as demand for loans, the central bank's interest rates, and expected return on investment. However, during an economic downturn or recession, the cost of credit can be higher, limiting access to loans and investments. This is an issue raised by Deleidi (2019), who suggests that the cost of credit can increase during a recession since banks are trying to mimimise the risk of loses. 2.2. Empirical Literature 2.2.1.Bank Credit Timsina (2014) looked at the supply side effects of commercial bank loans to the private sector on economic growth in Nepal. With the use of time series data covering the years 1975 through 2013, the study employed the Johansen co-integration technique and the Error Correction Model. The empirical findings demonstrated that bank loan to the private sector only contributes to Nepal's long- term economic growth. The findings show that a 1 percentage point increase in real private sector lending over the long term results in a 0.40 percentage point rise in real GDP. The empirical findings suggest that policymakers should concentrate on long-term strategies to encourage economic 22 growth, including the establishment of a modern banking sector, an effective financial market, and infrastructure so as to expand private sector lending, which is essential for fostering long-term economic growth. Ananzeh (2016) investigated the association between bank loans and economic development in Jordan across a variety of industries from 1993 to 2014. Granger Causality Test and the Vector Error Correction Model (VECM) were applied. The study found a long term relationship between bank credit, credit for the agricultural sector, credit for the industrial sector, credit for the construction sector, and credit for the tourism industry. On another note, Granger causality tests for the Jordanian economy's construction and agricultural sectors showed a causal link between economic development and bank loans. A bidirectional causal relationship between economic growth and bank loan to the construction sector, one of the most significant economic sectors, as reported by the results. Furthermore, the findings demonstrated that the effectiveness of bank credit facilities in stimulating key sectors of the Jordanian economy. This then resulted in highlighting the need to strengthen the role of the financial sector for various economic sectors by adopting more suitable macroeconomic policies. Dey & Flaherty (2005), analysed the impact of bank credit and stock market liquidity on GDP growth using a two-stage regression model . Their results indicated that bank credit and stock market liquidity were not consistent determinants of GDP growth. However, Dey & Flaherty's findings were contradicted by Cappiello et al (2010) in their study of the European Area. They found that, contrary to recent findings in the US, the supply of credit had a significant impact on economic activity, both in terms of credit volumes and credit standards applied to loans to enterprises. Specifically, a change in loan growth had a positive and statistically significant effect on GDP. Ho & Saadaoui (2022) looked at whether there were any threshold effects in the connection between bank loans and economic development. Simple probability smampling was used to select ASEA nations between 1993 and 2019. In a dynamic panel with the possibility of an endogenous set of explanatory factors, the Kremer et al. (2013) was employed to estimate threshold effects. A threshold for the credit-to-GDP ratio of 96.5 percent (significant at the 5 percent level) was found. The short-term beneficial impact of bank credit expansion on economic growth was roughly 0.08 (significant at the 1 percent level) for observations below or equal to the threshold. For data over the threshold, however, the impact of increased bank lending on economic growth is just marginally favorable (around 0.01) and not statistically significant. Exporting companies are extremely important in ASEAN countries since their economies are more export-oriented than those in other regions of the world. The findings indicated that the loan recipient (firms against households), the structural factors (export-led development), and the geographical heterogeneity needed to be taken into consideration in empirical investigations of threshold effects. 23 Akpansung & Babalola (2011) studied the relationship between banking sector credit and economic growth in Nigeria between 1970 and 2008. Granger causality test and a Two-Stage Least Squares (TSLS) estimation technique were used to establish the causal links between the pairs of variables of interest and for the regression models . The Granger causality test results showed evidence of a unidirectional causal relationship from GDP to private sector credit (PSC) and from industrial production index (IND) to GDP. The regression models' estimated results indicated that private sector credit has a positive impact on economic growth during the study period. However, lending (interest) rate impedes economic growth. Banu (2013) discovered a link between credit and economic development using Ordinary Least Squares. Since one of the variables that contributed to the global financial crisis was credit, the aim was to demonstrate in this article if there is a link between credit and economic development given that the economy cannot expand without credit. The study attempted to establish the alleged existence of a relationship between the GDP, credits supplied to public administration, and credits offered to families using statistical software. The research findings indicated that home credit offerings have a larger impact on the GDP creation than do public administration credit offerings. Wu, Yao, Chen, Jeon (2021) examined the the influence of economic growth on four aspects of banking soundness—loan growth rates, interest rate spreads, capitalization, and risk. The study used panel data from roughly 500 commercial banks in seven emerging Asian economies, analysed through the Autoregressive Distributed Lag Model. They found consistent evidence that a decline in economic growth slows down banks' loan growth, reduces their interest rate spreads, and increases their risk, but encourages banks to increase their capital holdings by using bank-level. Tinoco-Zermeno, Hugo, Torres-Preciado & Venegas-Martınez ( 2014) investigated the long-term effects of inflation on the dynamics of private sector bank credit and economic growth in Mexico between 1969 and 2011 using an ARDL-type model. The study's results indicated that the availability of private sector bank credit had a positive impact on real GDP. Furthermore, inflation rates had a negative contribution to the increase in private credit, liquid liabilities, and financial development. The study found that a one percent increase in inflation was linked to a 0.07 percent decrease in the long-run real rate of output through its effect on bank credit to the private sector. Additionally, the study found that financial liberalization policies had stimulated economic growth. The study's outcomes identified inflation rates as detrimental to long-run financial development and economic growth, thus reinforcing the literature on finance and growth. Abubakar & Kassim (2016) examined the effects of bank credit on economic output in five major economic sectors in Malaysia, including agriculture, manufacturing, mining and quarrying, construction, and services, using quarterly data from 1997Q1 to 2014Q4 and the ARDL and ECM approaches .The study found varying impacts of bank credit on economic sectors, with significant effects, especially in the short run, on the mining and quarrying and manufacturing sectors, but no 24 effect on the agriculture sector. In contrast, bank credit has a larger long-term impact on the output of the construction and services sectors. These findings suggested that it is essential to account for sector-specific characteristics when extending bank financing to effectively support sectoral growth. For the agriculture sector, concessional financing is necessary since it is not sufficiently being served by the banking system. Kirikkalelia & Alireza (2020) studied the causal link between bank credit supply and economic growth in Turkey for the banks with the different ownership structures between 1993Q4 and 2017Q3. Wavelet coherence test was used to determine the casual relationship between bank credit and economic growth. The findings from wavelet coherence were that economic growth leads credit supply in the short to medium term, but in the long run causality between credit supply and economic growth for only public and private banks. The findings also suggested that bank ownership affects the strength of the linkage between credit supply and economic growth in Turkey especially in the short and medium terms. 2.2.2.Cost of Credit Ağca & Igan (2019) conducted a study on the relationship between fiscal consolidation and the movement of credit spreads or sovereign risks. They found that the impact of fiscal consolidation on the cost of credit is not solely dependent on the transmission channel of sovereign risk premium. The study accounted for contemporaneous GDP growth and sovereign risk indicators, and concluded that their results were not affected by crises, change in expected future growth, or policy uncertainties. Ağca & Igan (2019) suggested that the credibility channel is also a factor, and even after adjusting for crisis episodes or including forward-looking indicators of growth and policy uncertainty, there was no significant change in the coefficient on fiscal consolidation. Therefore, the cost of credit is seen to be affected by multiple factors, one of which is fiscal consolidation. Ağca & Igan (2019) utilized loan-level data from 15 advanced economies from 1990-2014 to demonstrate that fiscal consolidations lead to an increase in the cost of credit. The study utilized a formula that includes loan spread, fiscal indicator, country, loan, and firm as controls, as well as fixed effects for each country, industry, and year. Loan spread measures the difference between the rate charged for a firm's loan and the benchmark rate (proxy for the cost of credit) , while fiscal represents the percentage of GDP allocated toward fiscal consolidations. Country, Loan, and Firm are sets of controls for the same level. According to Ağca & Igan (2019) an increase is observed when fiscal consolidation measures involve both tax hikes and spending cuts , but is somewhat less pronounced when the consolidation is substantial. The cost of credit increases with tax hikes affecting specific sectors, whereas spending cuts directed at certain sectors do not lead to a noticeable increase in credit costs. Firms that face higher credit costs typically tend to be small, domestically-based operations with high levels of leverage, government dependence, and financial constraints. Thus, there are potential costs 25 associated with fiscal consolidations that extend beyond the impact on aggregate demand and primarily affect firms operating in sectors directly affected by the consolidation measures and those with limited access to alternative financing sources and international markets Agostino, Coin, and Marinelli ( 2017) sutidied the impact of bank competition on economic welfare and growth has been debated extensively, few single-country studies have focused on the influence of bank competition on the cost of credit. This study aimed to contribute to the literature on this topic by examining the effects of bank competition on the cost of credit across 20 European countries over the period 2001-2011, using a panel of firms. The findings suggest that bank competition leads to an increase in the cost of credit, with the positive impact of competition being stronger for smaller companies. These results are in line with the information hypothesis, which suggests that a lack of competition incentivizes banks to invest in soft information, leading to higher credit costs. However, the study also highlights the impact of the institutional and economic framework, as well as the crisis, on the relationship between bank competition and credit costs. Tran (2021) conducted a study to explore the relationship between economic policy uncertainty and the cost of credit in different countries. Using OLS (ordinary least squares) analysis, Tran argued that economic policy uncertainty influences the cost of credit through two main mechanisms: information asymmetry and default risk. Economic policy uncertainty is typically associated with low levels of economic growth. Tran's study analysed a sample of 163,243 firm-years from 17 countries, spanning the period from 2003 to 2016. The findings shed light on the significant impact of economic policy uncertainty on the cost of credit. In a separate study, Saunders (2022) focused on examining prices in the secondary corporate loan market to develop a novel credit spread metric. This metric demonstrated strong predictive capabilities for economic activity across various macroeconomic outcomes in Europe and the US. Importantly, Saunders' credit spread metric contained exclusive information that was not available through public market credit spreads. This highlights the potential for using alternative sources of data to gain valuable insights into economic trends and market dynamics. Together, the studies by Tran (2021) and Saunders (2022) contribute to our understanding of the relationship between economic policy uncertainty, the cost of credit, and their implications for economic activity. The cost of credit was the dependent variable and economic policy uncertainty as an exploratory variable to examine how economic policy uncertainty impacts the cost of corporate debt financing. To control the 2008–2009 crisis, a dummy variable was utelised, and additional controls are both at the company and national levels. Lagged operational cash flow values, Tobin's Q, financial leverage, asset tangibility, and company size were examples of firm-level variables. These lagged numbers were utilised to remove any potential endogeneity that might result from the causality issue between loan cost and business characteristics. The anti-self-dealing index, the creditor protection 26 index, the four cultural dimensions identified by Hofstede, private credit, market capitalization, GDP growth, inflation, and real interest rate are among the factors at the national level. It was found that that economic policy uncertainty positively affects cost of credit and this effect is stronger during the global financial crisis from 2008 to 2009. Moreover, the research findings show that large firms’ debt financing cost is less affected by economic policy uncertainty. Carvalho, Gao & Ma (2023) presented evidence indicating that changes in lender optimism can cause excessive fluctuations in credit spreads during the credit cycle. By analysing data on the real estate properties of loan officers originating large corporate loans, the study reveals that credit spreads overreact to sophisticated lenders' recent local economic experiences, specifically local housing price growth. These effects are most evident when borrowers possess real estate assets and during times of greater uncertainty about real estate values, such as during boom-and-bust cycles in housing prices. The analysis suggested that recent personal experiences shape sophisticated lenders' beliefs about real estate values, which in turn impacts their pricing decisions. Blanco and García (2021) conducted a comprehensive study to investigate the role of trust in lender- borrower relationships and its impact on the interest rate spread. Their research utilized a sample of 20,699 loans from 47 countries, covering the period from 2003 to 2018. In addition to examining the role of trust, the study also explored the moderating effects of the country's legal enforcement and level of economic development. The researchers aimed to understand how these factors influence the relationship between trust and the interest rate spread. The findings of the study revealed that trust alone does not have a significant impact on loan spreads. However, an interesting observation emerged when considering the strength of the country's formal institutions. In cases where the formal institutions were weak, trust was found to have the potential to reduce loan spreads, consequently lowering the cost of credit. This suggests that trust and formal institutions can act as substitutes in mitigating interest rates. Furthermore, the study highlighted that both trust and legal enforcement exerted a more substantial influence on the interest rate spread in countries with lower levels of economic development. This implies that the interplay between trust, legal enforcement, and economic development is complex and warrants further examination. Overall, the research conducted by Blanco and García sheds light on the multifaceted nature of trust in lender-borrower relationships and its implications for interest rates. By considering the moderating effects of legal enforcement and economic development, the study provides valuable insights into the mechanisms at play in different contexts. 2.3. Summary of the Literature Review According to the existing literature on the subject, there is a negative correlation between the cost of credit, which is measured through loan spread and lending rates, and the growth of the economy. This indicates that an increase in the cost of credit could be detrimental to economic growth. 27 Therefore, it is essential for lending entities and policymakers to strike a balance between profit- making motives and the need to promote a thriving and healthy economy. By doing so, they can ensure a sustainable and inclusive environment for both borrowers and lenders, thereby contributing positively to the overall economic growth of the country. Based on the reviewed literature, there are several theories concerning credit channels, including loan-to-funds ratio, credit rationing, and liquidity theory, which offer informed ways to approach the concept of credit and its cost. Information asymmetry and risk were also found to be contributors to the cost of credit. From literature the predictor variables for the cost of credit were found to be; GDP growth rates, private sector credit, exchange rates, budget deficits, Foreign direct investment, banks liquidity ratio and trade to GDP ratio. The autoregressive model was the best for developing countries with a few data points like Eswatini. This is because of its ability to capture short and long-term dynamics using the Error Correction Model, the model also works with I (0) and I (1) variables which is normally the case with economic variables. The model is robust enough to regress the variables even if they are off different order of cointegration. The existing literature has overlooked the topic of the cost of credit and its influence on economic growth, particularly in Eswatini. Understanding the relationship between aggregate liquidity, cost of credit, and economic growth is essential for grasping the broader economic climate. Countries with high liquidity levels may struggle to effectively allocate funds to productive sectors, which can impede economic growth. The impact of the cost of credit, loan spreads, and lending rates on economic growth varies across different jurisdictions, with both positive and negative effects observed. This knowledge can assist policymakers and stakeholders in making informed decisions regarding macroeconomic policies that foster sustainable economic growth. While there is considerable research on bank credit and economic growth in specific countries, there is a lack of focus on the specific impact of the cost of credit on economic growth. This study aims to fill this gap in the field of finance and banking and contribute to the existing body of knowledge. 28 CHAPTER 3 METHODOLOGY 3.1. Introduction The goal of this study is to examine the impact of the cost of credit on economic growth in Eswatini. To achieve this, the researchers have chosen to a