Credit Risk and Bank Interest Rate Spreads in Uganda Final (1)

CREDIT RISK AND BANK INTEREST RATE SPREADS IN UGANDA

Hamis Mugendawala 2007/HD10/11256

A research Report submitted to the School of Graduate Studies in Partial Fulfillment of the Requirements for the Award of Master of Science (Accounting and Finance) Degree of Makerere University.

November, 2010

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DECLARATION

I, Hamis Mugendawala, declare that this dissertation is my own original work and that it has not been presented and will not be presented to any University for a similar or any other degree award.

Signed…………….…………………………….

MUGENDAWALA HAMIS (STUDENT)

Date……………………………

© Hamis, M. 2009 This dissertation is copyright material protected under the Berne Convention, the Copyright Act 1999 and other international and national enactments, in that behalf, on intellectual property. It may not be reproduced by any means, in full or in part, except for short extracts in fair dealing, for research or private study, critical scholarly review or discourse with an acknowledgement, without writi

ten permission of the Directorate of Postgraduate Studies, on behalf of both the author and Makerere University. CERTIFICATION The undersigned certify that they have read and hereby recommend for acceptance by Makerere University, a dissertation entitled: Credit Risk and Interest Rate Spreads in Banking: A case of Uganda, in partial fulfillment of the requirements for the degree of Master of Science (Accounting and Finance) of Makerere University

...................................................... Dr. Joseph Ntayi (SUPERVISOR) Date…………………………………………

..................................................... Thomas Bwire (SECOND SUPERVISOR)

Date: .................................................. ii

DEDICATION To my wife Namukose Zaujah and Daughter Namwase Sumayah.

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ACKNOWLEDGEMENT

This research has been a result of the many efforts, whose contribution is greatly acknowledged. I owe profound gratitude to my supervisors, Mr. Thomas Bwire and Dr. Joseph Ntayi for the many hours they devoted going through the entire manuscript with a fine-tooth comb and pointing out numerous ambiguities from the proposal stage to the final production. Without their dedication, this study would not have been possible. I also wish to extend my heartfelt gratitude to all academic and non-academic members of staff of Makerere University Business School, who in one way or the other helped me, realize my dreams while at the University.

I further wish to most sincerely thank the staff of the Bank of Uganda resource centre for giving me access to the data I was looking for.

Thanks also go to Hon. Mbagadhi Frederick Nkayi for all the material support towards the reality of this work. May the good Lord reward you abundantly.

Lastly, I thank my family—my wife Namukose Zaujah and daughter Namwase Sumayah for their encouragement. Above all, Glory is to the Almighty Allah for this wisdom. In HIM, all things are possible.

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All deficiencies that remain in the dissertation are entirely my own responsibility and should not be attributed to any of the acknowledged persons or institutions. TABLE OF CONTENT November, 2010.........................................................................................................................................1 DECLARATION ......................................................................................................................................i CERTIFICATION....................................................................................................................................ii DEDICATION.........................................................................................................................................iii ACKNOWLEDGEMENT......................................................................................................................iv TABLE OF CONTENT...........................................................................................................................v LIST OF TABLES AND FIGURES.....................................................................................................vii ABSTRACT...............................................................................................................................................x CHAPTER ONE..................................................................................................................................1 INTRODUCTION....................................................................................................................................1 1.1 Background to the Study..................................................................................................................1 1.2 Statement of the Problem..................................................................................................................3 1.3 Purpose of the Study.........................................................................................................................4 1.4 Objectives of the Study.....................................................................................................................4 1.5 Research Hypotheses........................................................................................................................5 1.6 Significance of the Study..................................................................................................................5 1.7 Scope of the Study............................................................................................................................5 1.8 Conceptual Framework.....................................................................................................................6 1.9 Organization of the Study.................................................................................................................7 CHAPTER TWO......................................................................................................................................8 LITERATURE REVIEW ......................................................................................................................8 2.1. Introduction......................................................................................................................................8 2.2 Financial Liberalization and interest spreads ..................................................................................9 2.3. Credit Risk ....................................................................................................................................12 2.3.1 Credit risk trend in Uganda's banking system.............................................................................14 2.4 Interest rate spreads in Uganda. .....................................................................................................16 2.5 Credit Risk and Interest rate Spreads.............................................................................................18 2.6 Client-Bank relationship and Interest rate spreads....................................................................21 2.7 Macroeconomic environment and interest rate spreads.............................................................22 METHODOLOGY.................................................................................................................................24 3.1 Introduction.....................................................................................................................................24 3.2 Research Design.............................................................................................................................25 3.3. Model Specification.......................................................................................................................25 3.4 Variable Definitions, Measurement and Data Source....................................................................27 3.5 Data Estimation and Testing Procedures.......................................................................................30 3.6 Limitation .......................................................................................................................................31 CHAPTER FOUR...................................................................................................................................32 v

PRESENTATION, ANALYSIS AND INTERPRETATIONOF FINDINGS.................................32 4.1 Introduction.....................................................................................................................................32 4.2 Objective 1: To analyze the trend of credit risk in Uganda’s banking system..............................32 Figure1: Credit Risk trend in Uganda........................................................................................33 4.3 Objective 2: To portray the state of interest rate spreads in the Ugandan banking system..........35 Figure 2: Interest Rate spreads trend in Uganda’s banking system..........................................35 4.4: Objective 3: To establish the relationship between Credit Risk and Interest rate spreads..........37 Objective 4: To establish the relationship between Macroeconomic factors (Inflation, Liquidity, Tbill rate) and interest rate spreads.........................................................................................................37 Objective 5: To establish the relationship between client-bank relationship and interest rate spread. ...............................................................................................................................................................37 4.4.1Time Series properties..............................................................................................................37 Table4.1: Descriptive Statistics..................................................................................................38 Table 4.2: Correlation Analysis.................................................................................................38 4.4.2 Unit root tests...........................................................................................................................39 Table 4.3: Results of Unit Root Tests for Variables in Levels..................................................39 Variable..............................................................................................................40 Table 4.4: Results of Unit Root Tests for Variables in First Difference...................................40 Variable..............................................................................................................40 Notes:..........................................................................................................................................40 (i) L is logarithm, D is the first difference and ADF is Augmented Dickey Fuller...40 4.4.3 Cointegration tests.......................................................................................................................41 Table 4.5: Johansen Cointegration Test.....................................................................................42 Table 4.6: The Long-Run IRS Function....................................................................................42 4.4.4 Estimation of the error correction model................................................................................43 4.4.5 Empirical Results.........................................................................................................................44 Table 4.7 General model results: Estimation of the IRS Equation......................................................44 Table 4.8: Preferred/specific Model: Estimation of the Interest Rate spread Model................45 4.4.6 Diagnostic tests........................................................................................................................45 4.5 Key Findings...................................................................................................................................47 4.5.1 Interpretation of Empirical results in relation to:....................................................................47 4.5.2 Comparison of Empirical studies on Interest rate spreads with the current study.....................51 Table 4.9: Comparison of results of current study with those of others...................................51 CHAPTER FIVE....................................................................................................................................53 CONCLUSION AND POLICY IMPLICATIONS.............................................................................53 5.1 Summary.........................................................................................................................................53 5.2 Conclusions.....................................................................................................................................54 5.3 Policy Recommendations...............................................................................................................54 5.4 Possible Areas for further research.................................................................................................56 References................................................................................................................................................57 APPENDIX 1...........................................................................................................................................68

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LIST OF TABLES AND FIGURES

November, 2010.........................................................................................................................................1 DECLARATION ......................................................................................................................................i CERTIFICATION....................................................................................................................................ii DEDICATION.........................................................................................................................................iii ACKNOWLEDGEMENT......................................................................................................................iv TABLE OF CONTENT...........................................................................................................................v LIST OF TABLES AND FIGURES.....................................................................................................vii ABSTRACT...............................................................................................................................................x CHAPTER ONE..................................................................................................................................1 INTRODUCTION....................................................................................................................................1 1.1 Background to the Study..................................................................................................................1 1.2 Statement of the Problem..................................................................................................................3 1.3 Purpose of the Study.........................................................................................................................4 1.4 Objectives of the Study.....................................................................................................................4 1.5 Research Hypotheses........................................................................................................................5 1.6 Significance of the Study..................................................................................................................5 vii

1.7 Scope of the Study............................................................................................................................5 1.8 Conceptual Framework.....................................................................................................................6 1.9 Organization of the Study.................................................................................................................7 CHAPTER TWO......................................................................................................................................8 LITERATURE REVIEW ......................................................................................................................8 2.1. Introduction......................................................................................................................................8 2.2 Financial Liberalization and interest spreads ..................................................................................9 2.3. Credit Risk ....................................................................................................................................12 2.3.1 Credit risk trend in Uganda's banking system.............................................................................14 2.4 Interest rate spreads in Uganda. .....................................................................................................16 2.5 Credit Risk and Interest rate Spreads.............................................................................................18 2.6 Client-Bank relationship and Interest rate spreads....................................................................21 2.7 Macroeconomic environment and interest rate spreads.............................................................22 METHODOLOGY.................................................................................................................................24 3.1 Introduction.....................................................................................................................................24 3.2 Research Design.............................................................................................................................25 3.3. Model Specification.......................................................................................................................25 3.4 Variable Definitions, Measurement and Data Source....................................................................27 3.5 Data Estimation and Testing Procedures.......................................................................................30 3.6 Limitation .......................................................................................................................................31 CHAPTER FOUR...................................................................................................................................32 PRESENTATION, ANALYSIS AND INTERPRETATIONOF FINDINGS.................................32 4.1 Introduction.....................................................................................................................................32 4.2 Objective 1: To analyze the trend of credit risk in Uganda’s banking system..............................32 Figure1: Credit Risk trend in Uganda........................................................................................33 4.3 Objective 2: To portray the state of interest rate spreads in the Ugandan banking system..........35 Figure 2: Interest Rate spreads trend in Uganda’s banking system..........................................35 4.4: Objective 3: To establish the relationship between Credit Risk and Interest rate spreads..........37 Objective 4: To establish the relationship between Macroeconomic factors (Inflation, Liquidity, Tbill rate) and interest rate spreads.........................................................................................................37 Objective 5: To establish the relationship between client-bank relationship and interest rate spread. ...............................................................................................................................................................37 4.4.1Time Series properties..............................................................................................................37 Table4.1: Descriptive Statistics..................................................................................................38 Table 4.2: Correlation Analysis.................................................................................................38 4.4.2 Unit root tests...........................................................................................................................39 Table 4.3: Results of Unit Root Tests for Variables in Levels..................................................39 Variable..............................................................................................................40 Table 4.4: Results of Unit Root Tests for Variables in First Difference...................................40 Variable..............................................................................................................40 Notes:..........................................................................................................................................40 (i) L is logarithm, D is the first difference and ADF is Augmented Dickey Fuller...40 4.4.3 Cointegration tests.......................................................................................................................41 Table 4.5: Johansen Cointegration Test.....................................................................................42 viii

Table 4.6: The Long-Run IRS Function....................................................................................42 4.4.4 Estimation of the error correction model................................................................................43 4.4.5 Empirical Results.........................................................................................................................44 Table 4.7 General model results: Estimation of the IRS Equation......................................................44 Table 4.8: Preferred/specific Model: Estimation of the Interest Rate spread Model................45 4.4.6 Diagnostic tests........................................................................................................................45 4.5 Key Findings...................................................................................................................................47 4.5.1 Interpretation of Empirical results in relation to:....................................................................47 4.5.2 Comparison of Empirical studies on Interest rate spreads with the current study.....................51 Table 4.9: Comparison of results of current study with those of others...................................51 CHAPTER FIVE....................................................................................................................................53 CONCLUSION AND POLICY IMPLICATIONS.............................................................................53 5.1 Summary.........................................................................................................................................53 5.2 Conclusions.....................................................................................................................................54 5.3 Policy Recommendations...............................................................................................................54 5.4 Possible Areas for further research.................................................................................................56 References................................................................................................................................................57 APPENDIX 1...........................................................................................................................................68

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ABSTRACT The study investigates the effect of credit risk on interest rate spreads in Uganda for the period 19812008, while controlling for macroeconomic factors (Inflation, Liquidity, T-bill rate) and client-bank relationship. This was accomplished using a modern econometric technique that was adopted and used on Ugandan macroeconomic data obtained from statistical publications of Bank of Uganda and IMF. E-views 3.0 statistical package was used in estimating the regression model.

The study findings reveal that Credit risk, Liquidity, and the Treasury bill rate have a negative relationship with the interest rate spreads in Uganda, while inflation was found insignificant in explaining the high interest rate spreads. On the basis of these findings, it is recommended that while there is still need for more investment in ensuring macroeconomic stability, there is greater need for capacity building within the individual commercial banks’ human and technological resources for better credit risk assessment and management. Moreover, it is imperative that commercial banks reengineer their credit risk control processes by moving from their traditional mechanisms used to control credit risk to loan portfolio restructuring, loan sales and debt-equity swaps. Overall, the study recognizes the importance of a multidimensional approach to any policies directed at tackling the problem of the high interest rate spreads in the Uganda’s Banking system.

Finally, the fact that the variables under this study only explain 40% of the response variable is all but evidence for need for more research in this area. To this end therefore, this study could be complimented if more research is carried out on the quality of credit risk management systems and interest rate spreads in Uganda’s Banking system x

CHAPTER ONE INTRODUCTION 1.1 Background to the Study Banking systems in Uganda have been shown to exhibit significantly and persistently large interest rate spreads on average than those in other developing and developed countries (Nannyonjo, 2002; Beck and Hesse, 2006). The size of banking spreads serves as an indicator of efficiency in the financial sector because it reflects the costs of intermediation that banks incur (including normal profits). Some of these costs are imposed by the macroeconomic, regulatory and institutional environment in which banks operate while others are attributable to the internal characteristics of the banks themselves (Robinson, 2002).

High Interest rate Spreads are an impediment to financial intermediation, as they discourage potential savers with low returns on deposits and increase financing costs for borrowers, thus reducing investment and growth opportunities. This is of particular concern for developing and transition countries where financial systems are largely bank-based, as is the case in Uganda and tend to exhibit high and persistent spreads.

Interest rate spreads arise out of the core functions of financial institutions most especially the commercial banks which include lending and deposits taking. As banks lend, they charge interest and for attracting deposits, they offer interest on deposit as compensation for their clients’ thriftiness and the difference between the two rates forms the spread. 1

The function of extending credit continues to present with it considerable risk especially that of default (Credit Risk). For instance, financial defaulters/ credit risk nearly doubled in 2008 with an all-time single biggest defaulter by volume being Lehman Brothers who in September 2008 failed to pay $ 144 Billion of rated debt (Standard & Poor, 2009). Similarly, even financial institutions in Uganda continue to wriggle through a similar condition with many getting scathed. For example, in the late 90’s, Uganda’s financial system was grossly hit by mass credit default which culminated into insolvency and hence closure of four (4) local commercial banks—Greenland Bank, Cooperative Bank, International Credit Bank and Trust Bank. This created a banking crisis and the remaining local commercial banks experienced loss of customer confidence leading to poor financial performance (Bank of Uganda, 2002).

Though many blamed this scenario on the profligate lending, it is also patent that most of these banks, then faced with bigger portfolios of Non Performing Loans (Credit risk) supposedly were using wider Intermediation Spreads at the time (34% in some of them) as a coping mechanism which further interfered with the ability and willingness of borrowers to pay and so the spiral effect set in. Hitherto, some technocrats at Bank of Uganda and in commercial Banks allude to the fact that persistent credit risk /default risk, mainly buoyed by the blatant lack of accurate information on borrowers’ debt profile and repayment history; could be the causal factor for the current wider Interest rate Spreads.

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Between 1987—2000, Ugandan policy makers embarked on an ambitious and far reaching financial sector reform programme marked by the reforming of the legal and institutional frame work, restructuring of state-owned financial institutions, lifting of entry barriers to private sector operators in the financial sector, and the deregulation of interest rates from the government controls; with hope that intermediation spreads among other things would narrow (Bank of Uganda, 2005). Sequentially, the Credit Reference Bureau is another vehicle that was instituted by Bank of Uganda on the rationale that timely and accurate information on borrowers’ debt profile and repayment history would reduce information asymmetry between borrowers and lenders. This was expected to enable banks to among other things lower credit risk and Interest rate Spreads and hence contribute to financial deepening in the economy. 1.2 Statement of the Problem

Policy makers in Uganda have for some time been actively engaged in developing a panacea to the persistently wider interest rate spreads with hope that this would promote competitiveness, efficiency and stability in the domestic financial system and ultimately narrow the intermediation spreads (Bank of Uganda, 2005).

Unfortunately, interest rate spreads in Uganda have remained higher than in most transition Economies (Tumusiime, 2002; Beck and Hesse, 2006; Ministry of Finance Planning and Economic Development, 2008). Lending rates continue to ride high while lower rates are being offered on deposits. In 2005, for example, the average interest rate spread hit 20% with dispersions in the range of 18% to 34% while at 3

the same time, the net interest margins hit 13%, compared to 7.4% on average in the sub-Saharan African region, 6.3% on the average in low-income countries, and 5% in the world, and moreover, higher in comparison to neighbouring Kenya and Tanzania. Possibly, this could be a result that Uganda’s banking system is faced with unrelenting high probabilities of default (Credit risk).

It is hypothesized that when banks are faced with clients with a high probability of default (Credit risk), they hedge against the impending loss by increasing the lending rates and or lowering the deposit rates (Widening the spreads). Moreover, high and inflexible interest spreads are indicative of the existence of perceived market risks (Mugume and Ojwiya, 2009). This raises curiosity and hence the need to investigate whether the higher interest rate spreads in Uganda are due to Credit risk or it may as well be the case that, in addition to Credit risk, there are other structural factors which are important in explaining the spreads. 1.3 Purpose of the Study

This study investigates the impact of credit risk on the commercial bank interest rate spreads in Uganda. 1.4 Objectives of the Study i. To analyze the trend of credit risk in Uganda's banking system. ii.

To portray the interest rate spreads state in the Ugandan Banking system.

iii.

To establish the relationship between credit risk and interest rate spreads.

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iv.

To establish the relationship between macroeconomic factors (Inflation, Liquidity, T-bill rate) and interest rate spreads.

v.

To establish the relationship between client-bank relationship and interest rate spreads. 1.5 Research Hypotheses

i.

Credit risk, Liquidity, and T-bill rate have a positive relationship with interest rate spread in Uganda’s banking system.

ii.

Client—Bank relationship has a negative relationship with interest rate spreads in Uganda.

iii.

Inflation has a positive effect on interest rate spreads in Uganda. 1.6 Significance of the Study

The fact that the study attempts to analyze the determinants of Interest rate spreads in Uganda, with a view to identifying the role of credit risk in explaining the current state of interest rate spreads, is of great policy and empirical significance. This is because the monetary policy framework of Bank of Uganda and its implementation have been guided by a need to ensure, among others: i) realistic interest rate spreads that encourage financial deepening; and ii) a safe, sound, efficient and competitive banking system through discreet risk management. Moreover it is also a requirement for the award of an Msc Accounting and Finance Degree of Makerere University. 1.7 Scope of the Study This study covered credit risk as the principal independent variable and intermediation spread as the dependent variable. The study also covered the other determinants of intermediation spreads— 5

macroeconomic variables (Inflation, liquidity, Treasury bill rate) and the client-bank relationship. The study used time series data covering a period between 1981 and 2008. This period was chosen to cater for both the pre-reform and the reform periods in the analysis. 1.8 Conceptual Framework The conceptual model was inspired by the bank dealership model of Ho and Saunders (1981) with extensions from later studies incorporating different factors to explain the interest rate spreads (Angbanzo, 1997; Carbo and Rodriguez, 2007). The model bases on the hypothesis that credit risk is the cause of the persistently wider interest rate spreads in Uganda. Credit risk has been proxied by none performing loans to total loans advanced annually (Beck and Hesse, 2006; Calcagnini et al, 2009)

Barajas, Roberto et al, (1998) Bazibu (2005), Ho and Saunders (1981), Zarruck (1989) and Wong (1997); all argue that when Banks are faced with clients with high probability of default (credit risk), they hedge against the impending loss by increasing the lending rates and or lowering the deposit rates (widening the spreads). Therefore according to the conceptual model, it is expected that banks with high exposure to risky loans exhibit wider interest rate spreads. Moreover, scholars like Arano and Emily (2008) have also pointed at the other factors like the macro-economic variables and client-bank relationship as explanatory factors for the interest rate spreads. Therefore, the dependent variable represents the level of interest rate spread (IRS) while credit risk, macroeconomic factors and client -bank relationship represent the independent variable as illustrated in equation 1 below; 6

IRS= f ( CR, Inf , L, T , CB ) ………………………………………………………….. (1) Where: IRS- is interest rate spread over time, CR-is credit risk over time, Inf- is the inflation rate over time, L- is Liquidity in the market over time, T- is the 91day Treasury Bill rate over time, CB- is the Client-bank relationship proxied by average life time of loans dispensed to clients by banks over time. 1.9 Organization of the Study This research is divided into four subsequent chapters. Chapter 2 discusses the related literature while chapter 3 describes the model, methodology and data adopted and chapter 4 presents the results, while in chapter 5, the conclusions and policy recommendations arising from the findings are discussed.

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CHAPTER TWO LITERATURE REVIEW 2.1. Introduction Uganda’s financial system had for a long time been characterized by several distortions: statutory interest rate ceilings, directed credit, accommodation of government borrowing, exchange controls and informal modes of intermediation (Nannyonjo, 2002). The formal financial sector was also concentrated by two domestic commercial banks with excessively large branch networks and high overhead costs. In addition, securities, equities and inter-bank markets were either non-existent or operating inefficiently. Other constraints included deficiencies in the management, regulation and supervision of financial institutions and a low level of Central Bank autonomy. The last two decades have seen much of financial sector adjustments with intent to among others narrow the gap between lending rates and deposit rates (interest rate spread).

Reasons for the financial reforms have always been premised on the Financial Repression hypothesis of McKinnon (1973) and Shaw (1973) which contends that suppressive financial policies through measures such as interest rate controls, mandatory credit allocation to preferential sectors, greater reserve requirements and limitations to entry into the banking sector; among others, were responsible for low deposit interest rates resulting in low financial savings, high lending interest rates, monopoly power by banks, low financial intermediation, and concentration of credit in favoured sectors and firms, especially in developing countries (Tressel and Detragiache, 2008).

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Heeding the advice of McKinnon and Shaw, many countries, Uganda inclusive undertook to dismantle financial repressive policies during the last three decades, although to a different extent and at a different pace in the various regions of the world.

The financial liberalization process notwithstanding, one ubiquitous feature in the banking system of Uganda is the wide interest rate spread. Whereas there are various factors that have been associated to the wider interest rate spreads by prior empirical studies, this review of related literature will be limited to the factors in the theoretical framework. Moreover, given the fact that this study covers two series that is; the ex ante and ex post of the sector liberalization, the study begins by reviewing literature on financial liberalization and interest spreads to reflect on the effects of these policy changes on spreads. 2.2 Financial Liberalization and interest spreads

Typically, financial sector liberalization in Uganda has been associated with measures that were intended to make the central bank more sovereign. As a result, it would mitigate “financial repression” by freeing interest rates and allowing financial innovation, and trim down directed and subsidized credit, as well as allow greater freedom in terms of external flows of capital in various forms. This would increase the efficiency of financial intermediation proxied by narrow interest rate spreads in financial institutions.

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In the late 80s and most especially the 90s, Uganda embarked on reforming her financial sector. This was done in phases and it involved among others; the liberalization of the exchange rate which was concluded by the introduction of the Interbank Foreign Exchange Market (IFEM) late in 1993, strengthening of prudential regulations and bank supervision which led to the amendment of the Bank of Uganda statute, introduction of the interbank and capital markets, the abolition of the interest rate controls, Institutional reforms which led to an influx of new banks (both foreign and domestic); and the development of non-bank institutions such as insurance companies and credit institutions.

Though Cihak and Podpiera (2005), Tumusiime (2002), Nannyonjo (2002), Mugume and Ojwiya (2009), Hesse and Beck, (2006), Brownbridge and Harvey (1998) provide some detailed positive developments in the Ugandan financial sector accruing from the implementation of financial reforms, they all concede to the fact that interest rate spreads are still high in the country. To them, financial liberalization has always failed to nurture financial deepening proxied by among others, narrow interest rate spreads. They point out that the world over, and especially in economies where the market structure within which banks operate has remained concentrated, there are high non financial costs of operation, high reserve requirement or deposit insurance and, most banks hold higher capital ratios to cushion themselves against the high volumes of poor quality assets held. Moreover this contradiction has further been attested to by the works of Mlachila and Chirwa(2002), (2002),Jayati (2005),Noyer (2007), Pereira and Sundararajan (1990) and Aryeetey et al, (1997) who argue that financial liberalization especially in developing countries has been proceeded by financial crises inform of higher spreads, mass defaults, bank bankruptcy, and currency crises mainly due to the fragility of their 10

domestic financial systems coupled with the very weak institutions and policies that predated the liberalization process. Moreover the fact that most of the indigenous private sectors in developing countries like Uganda largely consist of households and small scale enterprises that operate outside the formal financial system (Aryeetey et al, 1997), makes the financial reforms out of touch and ineffective in lowering the spreads as a bigger populace remains unbanked and therefore remote.

Nonetheless, political economy theorists like Rajan and Zingales (1998), Chinn and Ito (2006) basically have difficulties with the foregoing arguments and indeed insist that financial liberalization helps in enhancing financial intermediation proxied by lower spreads as it dismantles the perfect rent seeking environments created by financial institutions that operate in repressed financial regimes. They further contend that opening up of the capital account helps attract foreign players in the domestic capital markets which is a prerequisite for augmentation of developing market. Moreover this is reinforced by Guiso et al, (2006) who in their study, find that financial liberalization in Italy was proceeded by easier access to finances and significant slowdown in the interest rate spreads.

Rather, Guiso et al, (2006) positive relationship between financial liberalization and narrow spreads in Italy could be due to the fact that this is a developed country with strong political and legal institutions that constrain expropriation while ensuring maximum contract enforcement and protection of creditors’ rights. For instance, Tressel and Detragiache (2008) found that financial liberalization policies do increase financial intermediation proxied by narrow interest spreads in the long run, but only in countries with well-developed political institutions that can limit the power of the executive. 11

They do not find any sustained effects of banking reforms in other countries. This proof implies that guaranteeing sufficient checks and balances on political power as a necessary step to improve the protection of property rights may be an indispensable condition for the banking system’s functioning to improve after liberalization. This is consistent with Acemoglu and Johnson (2005), who find that more stringent constraints on the executive has a significant positive effect on growth, investment, and financial development. The understanding here is that political checks and balances shield citizens from expropriation from politically influential elites, thereby conserving property rights which in turn, ensures that potentially all agents in the economy can access financial sector loans when they qualify culminating into lower risk and spreads.

In most of the empirical studies on financial liberalization and interest spreads underlies the fact that more controlled/repressed financial systems are neither the solution to narrowing spreads as this leads to opacity, corruption and crony capitalism all of which are wasteful and set the foundation for wider spreads (Jayati, 2005). This justifies the multisectoral approach adopted by countries like China, and the other Asian tigers which provides for self correction mechanisms that cater for better financing while protecting the economy during and after the reforms (Wyplosz, 2001).

The proceeding review attempts to explore the role of credit risk in keeping interest rate spreads higher in the Ugandan banking system. 2.3. Credit Risk 12

Credit risk is the risk of loss due to the inability or unwillingness of a counter-party to meet its contractual obligations (Bank of Uganda, 2007). Models proposed by Straka (2000) and Wheaton et al, (2001) have expressed default as the end result of some trigger event, which makes it no longer economically possible for a borrower to continue offsetting a credit obligation. Though there are various definitions of credit risk, one outstanding concept portrayed by almost every definition is the probability of loss due to default. However, a lot of divergences emerge on defining what default is, as this is mainly dependent on the philosophy and/or data available to each model builder. Liquidation, bankruptcy filing, loan loss (or charge off), nonperforming loans (NPLs) or loan delayed in payment obligation, are mainly used at banks as proxies of default risk. This research paper has proxied credit risk by the ratio of Nonperforming loans to total loans advanced (Beck and Hesse, 2006; Calcagnini et al, 2009; Maudos and Solis, 2009)

Other scholars like Bandyopadhyay (2007), Avery et al, (2004), Vigano (1993), Zorn and Lea (1989), and Quercia and Stegman (1992) have explained credit risk using the creditworthiness parameters like borrower’s quality, financial distress and collateral position. They contend that individual borrowers with characteristics such as divorced or separated, having several dependants, with unskilled manual occupation, uneducated, unemployed most of the year; are prone to defaulting on their credit obligations. This is supported by economic theories, most especially the human capital theory which regard education and training as an investment that can increase the scope of gainful employment and improve net productivity of an individual and hence their incomes. However though, the benefit of education and training has been underestimated in most of the studies on credit risk. Also, age and 13

collateral position as creditworthiness factors raise a lot of controversy as mixed arguments have been raised as to their impact on the credit risk (Bester, 1985; Chan and Kanatas, 1985; Besanko and Thakor, 1987; Chan and Thakor, 1987; Vigano, 1993; Rajan and Winton, 1995; Manove and Padilla, 2001; Vasanthi and Raja, 2006; Bandyopadhyay 2007; Arano and Emily, 2008) 2.3.1 Credit risk trend in Uganda's banking system

By far the biggest risk facing banks and financial intermediaries remains credit risk- the risk of customer or counterparty to default (Reserve Bank of Australia, 1997). In Uganda, the 1980s and 1990s saw the banking system coming under severe stress where many banks were riddled by high levels of non-performing assets (credit risk) with some banks going insolvent. By 1995 the non performing loans in the banking sector had accumulated to US$34million (Tumusiime, 2005). Moreover Mugume and Ojwiya (2009) indicate that credit risk peaked during the 1990s and early 2000. Mugume and Ojwiya blame this on the “adverse selection predicament” caused by information asymmetries that makes it hard to select good borrowers from a pool of loan applications. This underpins the recent establishment of the Credit Reference Bureau (CRB), on the rationale that;

•

timely and accurate information on borrower’s debt profile and repayment history would reduce information asymmetry between borrowers and lenders and that it would enable lenders to make informed decisions about allocation of credit which would finally lower default probabilities of borrowers and hence contribute to financial stability and efficient allocation of resources in the economy, 14

•

when financial institutions compete with each other for customers, multiple borrowing and over indebtedness would increase and loan default would rise unless the financial institutions had well developed credit information systems or access to databases that can capture relevant aspects of clients’ borrowing behavour,

•

information in credit registries would be vital for the development of a credit culture where borrowers seek to protect their reputation and collateral by meeting their obligations in a timely manner and that borrowers could also use their good repayment record as collateral for new credit,

•

Credit reference bureaus would provide the necessary infrastructure to ensure information integrity, security and up-to-date information on borrowers.

Relatedly, the Bank of Uganda instituted an internal programme to strengthen Banking Supervision with substantial resources being put into training and moving

towards

a risk-based approach to

banking supervision. Apparently, there have been reported improvements in the asset quality and profitability of the Commercial Banks (Tumusiime, 2005; Kasekende, 2008). This might be partly the reason for Uganda’s improvement in her risk profile to a 'B' plus in the recent Standard and Poor’s ratings. However, it should be noted that this improvement in asset quality may be as well be a result of lack of capacity for banks to ably capture and measure credit risk, banks becoming more risk averse reflected in a strong preference for liquid and low-risk assets as opposed to individual lending.

15

On average, much has been invested in credit risk management as a requirement by Bank of Uganda. This coupled with the creation of a Credit Reference Bureau, has to some extent improved the credit risk assessment in banking. However with the continued entry of new banks, good credit judgment might often be ignored due to competitive pressures as banks try to venture in nontraditional and unsecured products which may escalate credit risk going forward. Given that some scholars have linked credit risk with higher interest rate spreads, it might be a dream farfetched to have interest rate spreads lower in the country. 2.4 Interest rate spreads in Uganda. Crowley (2007), Barajas, Roberto et al. (1998) define interest rate spread as the difference between the weighted average lending rate (WALR) and the weighted average deposit rate (WADR). Wider spreads are always a proxy for an underdeveloped financial system characterized by inefficiency, lack of competition and higher concentration of the banking sector; among others and the reverse is also perceived to be true (Demirguc -Kunt and Huizinga, 1999; Mlachila and Chirwa, 2002; Mugume and Ojwiya, 2009). Banking systems in developing countries have been shown to exhibit significantly and persistently large intermediation spreads on average than those in developed countries. However the difference arises in the causal factors.

In Uganda, just like in any other developing countries, persistent high intermediation spreads have been of particular concern to the business fraternity and policy makers (Nannyonjo, 2002; Cihak and Podpiera, 2005; Tumusiime, 2005; Beck and Hesse, 2006; Ministry of Finance Planning and 16

Economic Development, 2008; Mugume and Ojwiya, 2009). Lending rates continue to ride high while lower rates are being offered on deposits. For instance in 2005, the average interest rate spread hit 20% with dispersions in the range of 18% to 34% (Bank of Uganda, 2007). While at the same time, the net interest margins hit 13%, compared to 7.4% on average in the sub-Saharan African region, 6.3% on the average in low-income countries, and 5% in the world, and moreover, higher in comparison to neighboring Kenya and Tanzania(Beck and Hesse, 2006).

Various views have been expressed as to why high interest spreads have persisted in Uganda. Beck and Hesse (2006) postulate that the small financial system, the high level of risk, the market structure and the instability of macroeconomic variables have played a bigger role in buoying the spreads in their current state. The bank of Uganda officials have on many occasions argued that lack of competition and the concentration of banks in urban areas is to blame for the current state of spreads. Mugume and Ojwiya (2009) postulate that high interest rate spreads in Uganda have been empirically explained by high operating costs faced by the banks, high liquidity in commercial banks, discount rates, inflation, volatile exchange rates and financial liberalization. Mlachila and Chirwa (2002) have found robust relationship between non financial costs, high reserve requirement, inflation, financial liberation and interest rate spreads in their study they did in Malawi.

While all the views contain merit, one may continue to question “why the interest spreads have remained high even when the country is experiencing relative macroeconomic stability, with more banks entering the sector, and with a stronger regulator in place?. Given that credit risk as a probable 17

cause has not been given the attention it deserves in partly explaining this state of affairs, this research undertook to establish the determinants of interest rate spreads in Uganda with a view to establish the extent to which credit risk can explain the current spreads state in the banking industry of Uganda. 2.5 Credit Risk and Interest rate Spreads The theoretical model of Ho and Saunders (1981) expanded by Angbazo (1997) and Maudos and Guevara (2004) indicate that there is a positive correlation between credit risk or loan quality and interest rate spreads. The model argues in part that when banks are faced by deterioration in loan quality (credit risk), they hedge against the impending loss by transferring a portion or all of it to their customers (either borrowers or depositors). This is done by increasing the lending rate and or lowering the deposit rate.

In Uganda, the uncertainty created by the existence of a weak legal regime especially in contract enforcement coupled with the inadequate borrower information has aggravated credit risk and probably the interest rate spreads. This is so because the inefficient legal systems and information inadequacies do not only cause interest rates to be high but also crowd out borrowers who would have obtained credit in an environment without information asymmetries. Moreover in such a case, lenders would require a risk premium in form of higher lending rates and or lower deposit rates to compensate for the likely event that some of its borrowers may default (Mugume and Ojwiya 2009).

18

Some empirical studies have found robust relationship between credit risk and interest rate spreads. Mugume and Ojwiya (2009) using data from Ugandan banks found a positive relationship between credit risk and interest rate spreads. Moreover this is reinforced by similar findings from studies by Mlachila and Chirwa (2002) in Malawi. Others include Randall (1998), Barajas, Roberto et al. (1998), Brock and Rojas-Suarez (2000), Gelos (2006), Crowley (2007), Arano and Emily (2008), and Calcagnini et al, (2009). This implies that banks use the spread between the deposit rate and lending rate as a buffer to any loss arising out of adverse selection. Nonetheless, some of these studies used data over quite a short time, moreover with different measures of credit risk from that of the current study.

On the contrary, Nannyonjo (2002), Samuel and Valderrama (2006) established a negative correlation between credit risk and interest rate spreads in Uganda and Barbados respectively. Similarly, the efficiency hypothesis supporters like Saunders and Schumacher (2000), Craigwell and Moore (2002) instead view wider spreads as a function of market structure and bank specific factors. To this end they postulate that size of a bank, its market power, and bank concentration have a higher explanatory power for intermediation spreads. Therefore they conclude by indicating that smaller banks, a market with a few banks but with a higher market power and hence with high concentration are likely to lead to wider interest rate spreads. Nonetheless, in contrast to some of the preceding assertions are Panzar and Rosse (1987), and the IDB (2005) which disregard purported relationship between bank concentration and spreads.

19

Institutional constraints related to financial regulations including liquidity requirements, statutory government securities holding requirement, capital controls, and tax have been found to have a positive correlation with Intermediation Spreads. In their studies, Barajas, Roberto et al. (1998), Saunders and Schumacher (2000), Gelos (2006), Nannyonjo (2002), Hesse and Beck (2006) came up with empirical evidence to the fact that financial regulation is costly to banks which makes them pass on all of the resultant costs to the customer by hiking the lending rates and or reducing deposit rates.

Reviewing literature on credit risk and interest rate spread reveals the following gaps: •

Though a lot has been researched on credit risk, intermediation ipreads; not much has been researched in detail on the relationship between the two

•

Most of the studies available relate to the Latin America, Asia, USA but not Africa and Uganda in particular and the few that relate to Uganda have examined data over a very short span.

•

A lot of emphasis has been placed on the other factors that cause higher Intermediation Spreads other than credit risk.

To this end therefore there is still valid reason for one to specifically investigate the direct relationship between credit risk and interest spreads especially in the Ugandan banking system. But as hinted by Arano and Emily (2008), Mugume and Ojwiya (2009), Mlachila and Chirwa (2002) and others, credit risk on its own may not suffice to explain intermediation spreads. Consequently, as an auxiliary intent for this study, macroeconomic factors and client-bank relationship have been studied to supplement the explanatory power of credit risk for the current state of interest rate spreads in Uganda. 20

2.6 Client-Bank relationship and Interest rate spreads

It has been well documented that the relationship between the bank and its client is an important aspect of obtaining favorable credit terms. The finding of more favorable rates provided by firms with stronger relationships reinforces the significant attention that banking institutions have accorded to relationship banking of recent. Moreover, relationship banking has never been important than during this error of economic slowdown partly blamed on weak client-bank relationships. According to Arano and Emily (2008), the greater the duration and scope of the relationship between the borrower and the lending institution, the more ‘soft’ information becomes available, and the more efficient the pricing of the loan due to a reduction in the asymmetric information problem which aggregates to lower credit risk and hence lower bank spreads. Degryse and Cayseele (2000) using ordinary least squares regression on a sample small business loans in Belgium found spreads decrease with the scope of the relationship. Further, this argument is reinforced by the findings from studies carried out by Diamond (1984), Ramakrishnan and Thakor (1984), Fama (1985), Sharpe (1990), Diamond (1991) and Boot (2000) who postulate that the greater the duration and scope of the relationship between the client and the financial institution, with this relationship providing both public and the more important private information, the more information becomes available, and the more efficient the pricing of the loans and deposits due to a reduction in the asymmetric information problem and hence lower spreads.

Nonetheless, the fact that very few banks especially in developing countries have built capacity to effectively capture and process soft information for informed decision making casts doubt on whether 21

spreads could be impacted by the relationship between the bank and its client. Petersen and Rajan (1994), Berger and Udell (1995) analyzed relationship lending on various loan types of the most recent approved loan, but were not able to find an association between the strength of the bank-client relationship and the interest rate charged on the loan. Instead, they were able to find an increase in the availability of credit based upon a stronger relationship between the bank and its client. Further, Harhoff and Korting (1998) using ordinary least squares regression on the actual rates charged on lines of credit against the premium paid obtained from a survey of small and medium-sized German firms find the interest rate spread not impacted by the relationship between client and the bank.

2.7 Macroeconomic environment and interest rate spreads The macroeconomic environment (Inflation, Liquidity, 91day T-bill rate) predominantly affects a country’s spreads through its impact on credit risk and therefore the quality of loans. An unstable and weak macroeconomic environment creates uncertainty about future economic growth and returns on investments, making defaults on loans more likely. In response to this increased credit risk, banks will raise the premium on loans thus increasing the Spreads (Central Bank of Solomon Islands, 2007; Mugume and Ojwiya, 2009). However, this has been contested by the findings of Seetanah et al, (2009). In their study, macroeconomic environment was not a significant variable in explaining interest spreads as the case was for the bank specific characteristics.

22

High and volatile inflation and the uncertainty this creates seems to lead to an increase in interest rate spreads. This is so because price swings always compromise borrowers’ ability to meet their loan obligations, and the quality of collateral is also likely to weaken which could increase the bank costs in loan recovery and default cases. Again, this will make banks hedge against the likelihood of default arising from the high and variable inflation by using higher spreads. MLachila and Chirwa (2002), Brock and Rojas-Suarez (2000), Demirguc-Kunt and Huizinga (1999), Mugume and Ojwiya (2009),Tennant and Folawewo (2009), Crowley (2007), Nannyonjo (2002) and Seetanah et al, (2009) all found a positive relationship between price instability represented by high and variable inflation and interest rate spreads. However, this is still contested by Samuel and Valderrama, (2006) whose study in the Barbados established a negative relationship between inflation and interest spreads. The possible explanation for the negative relationship would be that higher inflation indicates faster credit expansion at possibly lower lending rates and therefore lower spreads.

Liquidity also appears to be an influential factor in determining the Spreads. In countries where excess liquidity is very high (and banks have surplus funds), the marginal cost of deposit mobilization is high and the marginal benefits are likely to be very low. In this scenario, interest rates on deposits will be low, tending to increase the Spreads. Relatedly, it is believed that high liquidity in the banking system will exert upward pressure on inflation with all its effects on credit risk which will in turn lead to banks hedging against such effects by increasing the spreads. Conversely, Seetanah et al, (2009) have found that higher liquidity in the financial system can lead to low interest spreads in that whenever banks are liquid, their perceived liquidity exposure is low which translates into lower premiums on 23

both loans and deposits and hence narrow spreads. This is also consistent with the findings of Dermirguc-kunt et al, (2004).

The 91-day T-bill rate has also been found to influence interest rate spread. In most of the countries, banks use this as their reference rate for pricing their loans and deposits. Moreover this is reinforced by the findings from the studies of Samuel and Valderrama (2006), Nannyonjo (2002), Tennant and Folawewo (2009) that indicate a positive correlation between the T-bill rate and Interest rate spreads. Though the former two studies’ coefficients are significant, the latter manifested a weak linkage between the two. A positive relationship between the T-bill rate and interest rate spreads indicates that the higher the bill rate the higher the spreads and vice versa. This is so because the 91 days bill is used as the mirror for the risk return continuum of any financial system. To this end a higher bill rate would indicate the same risk profile for the sector which would make banks mark-up their credit facilities to compensate for perceived risk. However, this may not be always the case in undeveloped financial systems where information inadequacies constrain effective loan and deposit pricing.

CHAPTER THREE METHODOLOGY 3.1 Introduction This chapter provides the description on how the study was conducted to achieve its objectives and purpose. It brings out the model specification used, variable definitions, Variable Measurement and variable Data required, Data source, Data estimation and Testing procedures. 24

3.2 Research Design This was a quantitative research based on secondary time series data from the Central Bank and the IMF statistical year books. Further, it was a relationship study that aimed at establishing the association between interest rate spreads (response variable) and credit risk, macroeconomic variables and client bank relationship (explanatory variables) based on inferential statistics. 3.3. Model Specification The model used was inspired by the bank dealership model of Ho and Saunders (1981) with extensions from later studies incorporating different factors to explain the Interest rate spreads (Angbanzo, 1997; Maudos and Guevara, 2004; Carbo and Rodriguez, 2007).

The model bases on the hypothesis that Credit risk is the cause of the persistently wider intermediation spreads in Uganda. Credit risk has been proxied by Non Performing loans (NPLs) to total loans advanced (Beck and Hesse, 2006; Calcagnini et al, 2009). Moreover, the model incorporates the other determinants of interest rate spreads—Client-Bank relationship and the macroeconomic environment proxied by inflation, liquidity, and the 91-day T-bill rate.

Barajas et al, (1998), Bazibu (2005), Ho and Saunders (1981), Zarruck (1988), and Wong (1997) all argue that when Banks are faced with clients with high probability of default (Credit risk), they hedge against the impending loss by increasing the lending rates and or lowering the deposit rates (widening

25

the spreads). Therefore according to this model, it is expected that banks with high exposure to risky loans exhibit wider interest rate spreads.

However as highlighted by Arano and Emily (2008), Demirguc-Kunt and Huizinga (2000), Robinson (2002), Nannyonjo (2002), Beck and Hesse (2006) and Bandyopadhyay (2007) credit risk alone may not suffice to explain the intermediation spreads. To this end, it has been hinted that the relationship a bank has with a particular client and the macroeconomic environment in which financial institutions operate have the ability to affect the intermediation spreads.

The modified version of the model predicts that interest rate spreads are as a result of credit risk and; inflation, liquidity, T-bill rate, and client-bank relationship. The proposed methodology therefore analyses interest rate spreads by investigating the significance of credit risk, macroeconomic environment, and client-bank relationship variables in a spread determination function. Put symbolically, IRS= f ( CR, Inf , L, T , CB ) …………………………………………………………………………. (2) For estimation purposes, equation (2) will be transformed as below IRS t = β 0 + β1CRt + β 2 Inf t + β 3 Lt + β 4Tt + β 5 CBt + β 6 D + ε t …………………………………... (3) Where: IRS t - is interest rate spread over time, CR t -is credit risk over time, 26

Inf t - is the inflation rate over time, L t - is Liquidity in the market over time, T t - is the 91day Treasury Bill rate over time, CB t - is the Client-bank relationship proxied by average life time of loans dispensed to clients by banks at a given time, D - is a dummy variable that captures the impact of the financial reforms on the IRS; and

ε t ~i.i.d (0, σ 2 ), is a serially uncorrelated error term. From equation (3), it is hypothesized that variables-- β1 , β 2 , β 3 andβ 4 are positive while β 5 and β 6 are negative.

3.4 Variable Definitions, Measurement and Data Source

Interest rate spread (IRS) Interest rate spread is the difference between the weighted average lending rate (WALR) and the weighted average deposit Rate (WADR) (Barajas et al, 1998; Beck and Hesse, 2006; Central Bank of Solomon Islands, 2007; Crowley, 2007; Vera and Andreas, 2007). In the current study, the interest rate spread was captured over two sub-periods; the pre-sector reform and reform periods. The financial sector reforms adopted towards the end of the 80s (1987) were aimed at among other things causing 27

financial intermediation efficiency proxied by narrow interest rate spreads. Data on interest rate spreads included the WALR and WADR from the research department of the central bank.

Credit Risk (CR) Guided by the previous empirical studies by Calcagnini et al, (2009), Fungáčová and Poghosyan (2008), Beck and Hesse (2006), credit risk was proxied by the ratio of Nonperforming Loans (NPLs) to the total loans advanced by the banks in the same period. In banking, NPLs loss provisions arise out of probable defaults that banks envisage of borrowers that turn risky which makes it the closest measure of credit risk. This study pre-supposes that banks with higher NPLs (Credit risk) exhibit wider interest rate spreads and vice versa. Data on the non performing loans was sought from the financial statements of all the commercial banks that are published in the Bank of Uganda annual supervision reports and IMF statistical year books.

Inflation (Inf) This is the rate of change in the general price levels of consumer goods and services captured annually within the country. Inflation was measured by the annual changes in the consumer price index (CPI). High and volatile inflation and the uncertainty it creates seem to lead to an increase in interest rate spreads. Similarly, in a weak macroeconomic environment, and in developing countries in particular, the quality of collateral is likely to be weak which increases the costs to banks in their effort to recover loans. Moreover, this will tend to increase the amount of Non Performing loans provisioning and lead

28

to higher spreads. Data on inflation rates was sought from the CPI office at the Uganda Bureau of Statistics. Liquidity in the market (L) Liquidity in the market was taken as liquid assets that are held by banks over time. Excess liquidity also appears to be an influential factor in determining the spreads. In countries where excess liquidity is very high (and banks have surplus funds), the marginal cost of deposit mobilization is high and the marginal benefits are likely to be very low. In this scenario, interest rates on deposits will be low, tending to increase the Spreads. Data on market liquidity was sought from the financial statements that commercial banks submit to the central bank and published in the annual supervision reports. Treasury bill rate (T) This is interest rate on the 91-day government debt instrument. The 91-day Bill rate in most of the countries is taken as the benchmark for any credit pricing (Nannyonjo, 2002; Samuel and Valderrama, 2006; Tennant and Folawewo, 2009). In Uganda, the bank rate, lending rate and deposit rate are in most cases referenced to this rate. This study presupposes that any increase in the 91-day T-bill rate leads to wider spreads as it will raise the cost of finance and of doing business which finally interfere with the borrower’s ability to pay. Data on Treasury bill rate was accessed from the financial markets time series of annualized T-bill yields at the Central bank.

Client-Bank Relationship (CB) This was taken as the average time a customer has banked with a particular financial institution. This was proxied by the average loan life Time of the loans dispensed by the banks at a given time 29

(Calcagnini et al, 2009) which was estimated from the simple interest model; Time=

Interest Pr incipalxRate

where Time is the average loan life time, Principal is the total amount of loans expended by banks at a given time, while Rate is the weighted average lending rate at a given time. This study hypothesizes that the greater the duration and scope of the relationship between the borrower and the lending institution, the more ‘soft’ information becomes available, and the more efficient the pricing of the loan due to a reduction in the asymmetric information problem which aggregates to lower credit risk and hence lower bank spreads (Diamond, 1984; Ramakrishnan and Thakor, 1984; Fama, 1985; Sharpe, 1990; Boot and Thakor, 1994; Berger and Udell, 1995; Scott, 1999; Boot, 2000; Degryse and Cayseele, 2000; Arano and Emily, 2008). Data for Client-Bank relationship was sought from the financial statements submitted to the central bank at the end of each financial year. 3.5 Data Estimation and Testing Procedures Quarterly time series data on commercial bank financials and Ugandan macroeconomic variables for the period 1981: I-2008: IV was used. The data was taken from the Publications of Bank of Uganda, IMF Statistical year books, Uganda Bureau of Statistics and Ministry of Finance, Planning and Economic Development of the republic of Uganda.

Ordinary Least squares (OLS) estimation was used in the estimation of equation (3). This choice was premised on the fact that OLS is best linear unbiased estimator (BLUE). Moreover, the greater part of the preceding empirical studies used this popular technique. However, the express use of this technique when analyzing economic relationships using time series data has some limitations (Phillips, 1986) 30

that derive from the fact that macroeconomic time series data is non-stationary. This implies that, the variables may have a mean, variance, and co-variance not equal to zero. Working with such variables in their levels will present a high likelihood of spurious regression results. To this end, the researcher performed stationarity tests using the Augmented Dickey Fuller (ADF) unit root testing procedure (Dickey and Fuller, 1979) for each of the variables in equation (3) which indicated variables to be I (1). But Valid estimates and inferences of time series data are, however, possible so long as a set of non-stationary variables are cointegrated, that is, if there exists a set of linear combination of variables that are stationary (Engle and Granger, 1987). Accordingly, the cointegration technique developed in Johansen (1988) and applied in Johansen and Juselius (1990) was employed in this study and two cointegrating equations were established. We normalized for the interest rate spreads and thereafter proceeded to estimate a long run Interest rate spread model. It should be noted that if sets of nonstationary variables co integrate, then a corresponding error correction model (ECM), which attempts to restore the lost long term properties due to differencing of variables, can be specified and is consistent with long run equilibrium behavior (Engle and Granger, 1987).

3.6

Limitation

Results of this research should be taken with caution as some of the time series were not readily available on a quarterly basis. This made the researcher to transform the existing macroeconomic data into quarterly data (see Appendix I) using the computer method of direct linear interpolation which imposes a linear trend on the data. This may imply that part of the findings are based on interpolated 31

data which could lead to the findings herein to differ in some way from those of the prior empirical studies. Nonetheless, the author made sure that this limitation is counteracted by the rigorous model and residual assumption tests.

CHAPTER FOUR PRESENTATION, ANALYSIS AND INTERPRETATIONOF FINDINGS 4.1 Introduction

This chapter presents findings in orientation to the conceptualizations from the annual time series data. The bondage between the variables in the study was estimated by the Ordinary Least Squares (OLS) method of analysis. The findings abridged from secondary data, are interpreted in relation to the research objectives. 4.2 Objective 1: To analyze the trend of credit risk in Uganda’s banking system

32

Between 1987—2000, Ugandan policy makers embarked on an ambitious and far reaching Financial sector reform programme marked by the reforming of the legal and institutional frame work, restructuring of state-owned financial institutions, lifting of entry barriers to private sector operators in the financial sector, and the deregulation of interest rates from the government controls; with hope that interest rate spreads among other things would narrow (Bank of Uganda, 2005). Sequentially, the Credit Reference Bureau is another vehicle that was instituted by Bank of Uganda on the rationale that timely and accurate information on borrowers’ debt profiles and repayment history would reduce information asymmetry between borrowers and lenders. This was expected to enable banks to among other things lower credit risk and possibly interest rate spreads and hence contribute to financial stability in the economy.

Figure1: Credit Risk trend in Uganda

33

Source: Authors computation using data from Bank of Uganda and IMF statistical year books As seen from figure 1, prior to the 1987 Economic Sector Adjustment Programme (ESAP), Credit risk proxied by the ratio of Non Performing Loans (NPLs) to Total loans advanced was on a rising trend mainly on account of economic and political distortions that engulfed the nation between 1981 and 1986 thereby causing a lot of uncertainty in the financial sector. The year 1987 was marked by currency reform in Uganda in a bid to revive confidence in the financial sector and this caused a transitory reduction in credit risk from 33.3% to 30% in 1986 and 1988 respectively. Credit risk took a significant nosedive in the early 90s on account of the implementation of the Industrial Development Agency’s funded Economic Recovery Programme (ERP) and the passage of the Financial Institutions Statute of 1993 which raised the minimum capital requirements for commercial banks from less than a Billion shillings to now four Billion shillings and increased on site inspection. However, after 1993/94, Credit risk significantly edged up to the highest ever rate of 61 percent in 1999 mainly on account of the deteriorating asset quality in the gigantic Uganda Commercial Bank that was then bloated with 80% of her total assets as non performing. Also, this trend was escalated by the insolvency of the four local banks—Greenland Bank, Cooperative Bank, International Credit Bank and Trust Bank.

Available theory can also be used to explain this credit risk trend. Nannyonjo (2002), Diaz-Alejandro (1985), Burkett and Dutt (1991), Gibson and Tsakalotos(1994), Arestis and Demetriades (1997), Chang and Velasco (1998),Demirguc-Kunt and Huizinga (1999) in their studies indicate that financial sector liberalization in particular has been at the root of many recent cases of financial and banking crises, even though this contradicts the ever revered Mckinnon (1973) and Shaw (1973) financial 34

repression hypothesis which contends otherwise. In this line therefore one can conclude that the significant surge in credit risk that proceeded 1994 was sparked by the sector adjustments that the country was undertaking.

Since the year 2000, credit risk has been on a declining trend though punctuated by some upsurges. This indicates that the Bank of Uganda’s strengthening of banking supervision and its move towards a risk based approach of banking supervision have yielded positive results. However though, these results may also be indicative of the deficiencies in assessing credit risk in banks or of the fact that banks have become more risk averse as reflected in the surging demand for government securities that has crowded-out private sector credit. Moreover this trend may also be as a result of the closure of the insolvent banks and the transfer of the Non Performing Loans of the UCB to the Non Performing Assets Recovery Trust (NPART) coupled with a reduction in its branch network. 4.3 Objective 2: To portray the state of interest rate spreads in the Ugandan banking system Uganda, just like any other developing country, persistent high interest rate spreads have been of particular concern to the business fraternity and policy makers (Nannyonjo, 2002; Cihak and Podpiera, 2005; Tumusiime, 2005; Beck and Hesse, 2006; Ministry of Finance Planning and Economic Development, 2008). Lending rates continue to ride high while lower rates are being offered on deposits. Figure 2: Interest Rate spreads trend in Uganda’s banking system

35

45 40 35 30 25 20 15 10 5

Interest rate spread(%)

WADR

WALR

2008

2007

2006

2005

2004

2003

2002

2001

2000

1999

1998

1997

1996

1995

1994

1993

1992

1991

1990

1989

1988

1987

1986

1985

1984

1983

1982

1981

0

Linear (Interest rate spread(%))

WADR—Weighted Average Deposit Rate; WALR—Weighted Average Lending Rate Source: Authors computation using data from Bank of Uganda and IMF statistical year books From Figure 2, the line of best fit (Linear trend) indicates a steadily rising trend for interest rate spreads though at different rates of change. Going by the graph (curvature), the early 80’s were marked by low interest rate spreads in the region of 3 and 8 percent. This is on account of the higher deposit rates that reigned by then that narrowed the gap between the lending rate. Spreads ebbed to their lowest in 1992 after which they significantly edged up to their highest in the recent history at 26% in 1993 at a time when Commercial banks, for the first time, were formally allowed by Bank of Uganda (BoU) to set their own interest rates based on their own analysis of market conditions in a bid to create more competition in the sector. Currently, Uganda’s spreads range between 14 and 17 percent which is 36

still significantly high compared to 7.4% on average in the sub-Saharan African region, 6.3% on the average in low-income countries, and 5% in the world, and moreover, higher in comparison to neighboring Kenya and Tanzania (see Beck and Hesse, 2006).

Various views have been expressed as to why high interest spreads have persisted in Uganda. Beck and Hesse (2006) postulate that the small financial system, the high level of risk, the market structure and the instability of macroeconomic variables have played a bigger role in buoying the spreads in their current state. The bank of Uganda officials have on many occasions argued that lack of competition and the concentration in banking is to blame for the current state of spreads. 4.4: Objective 3: To establish the relationship between Credit Risk and Interest rate spreads Objective 4: To establish the relationship between Macroeconomic factors (Inflation, Liquidity, Tbill rate) and interest rate spreads. Objective 5: To establish the relationship between client-bank relationship and interest rate spread.

4.4.1Time Series properties To fulfill the fundamental statistical requirements for the empirical model, data transformation was carried out to establish the normality and stationarity of the data prior to empirical estimation of the model in investigating the determinants of Interest rate spread (IRS) in Uganda (1981-2008). Descriptive statistics for the data were undertaken for variables in levels to describe the basic features of data used in the study. Table 4.1 summarizes the descriptive statistics for the series in levels. The results illustrate that most of the variables satisfy the normality test. The low Jarque-Bera probability 37

values for some of the series can be ascribed partly to structural change in the data and partly to the weaknesses of the direct linear interpolation method used in the generation of quarterly data. The method imposes a linear trend on the data. Accordingly, undertaking descriptive statistics for variables in the two sub periods (Pre-ESAP and ESAP) separately and use of annual data could probably generate better Jarque-Bera probability values.

Table4.1: Descriptive Statistics Mean Median Maximum Minimum Std. Dev. Skewness Kurtosis

CB 0.797302 0.815000 0.998199 0.394077 0.164037 -0.495100 2.320728

CR 20.26979 18.50000 60.59226 0.180080 16.40371 0.495100 2.320728

INF 42.33221 9.029167 699.7500 2.307804 108.0276 4.331423 22.16449

IRS 11.86737 13.13798 26.00000 3.500000 4.525424 0.312090 3.105313

L 590.9880 274.7500 2258.000 1.155000 659.1260 0.791376 2.073714

T 17.91817 11.76000 43.00000 5.850000 11.33447 0.905351 2.282759

Jarque-Bera Probability

6.548663 0.37842

6.548663 0.37842

20.08882 0.000000

1.819804 0.402564

15.27412 0.0482

17.22689 0.0182

Observations

109

109

109

109

109

109

Source: Author’s computations using financial statements of all the commercial banks that are published in the Bank of Uganda annual supervision reports and IMF statistical year books for years 1981—2008. Table 4.2: Correlation Analysis CB

CR

INF

IRS

L

T

38

CB CR INF IRS L T

1 -1 -0.0636107 0.11376282 0.52377026 -0.128460

-1 1 0.0636107 -0.1137628 -0.5237702 0.1284604

-0.0636107 0.0636107 1 -0.296578 -0.267028 0.145386

0.11376 -0.11376 -0.296578 1 0.445018 -0.49664

0.523770 -0.523770 -0.267028 0.445018 1 -0.590898

-0.128460 0.1284604 0.145386 -0.49664 -0.590898 1

Source: Author’s computations using financial statements of all the commercial banks that are published in the Bank of Uganda annual supervision reports and IMF statistical year books for years 1981—2008. During the preliminary analysis, it was discovered that variables CB and CR were perfectly correlated (negatively) and that exclusion of one led to virtually no statistical difference in the results obtained. Moreover this was reinforced by the stepwise regression analysis which also proved the same. Table 4.2 justifies why variable CB had to be dropped from the model being estimated after which the researcher proceeded to test for stationarity of data. 4.4.2 Unit root tests By means of conventional testing procedures of Augmented Dickey-Fuller (ADF) the order of integration of the variables (and the degree of differencing required in order to induce stationarity) was determined. Integrated variables have a mean that changes over time and a non-constant variance. This implies that working with such variables in their levels gives a high likelihood of spurious regression results which makes deduction untenable since the standard statistical tests like the “F” distribution and the students “t” distribution are invalid. The unit root test results are presented in table 4.3 and 4.4. Unit root test results for the variables in levels indicate that all the variables are non-stationary at all levels of significance (see Tables 4.3) Table 4.3: Results of Unit Root Tests for Variables in Levels

39

Variable LCR LINF LRS LL LT Notes:

(i) (ii) (iii) (iv)

ADF -2.740780 -3.460659* -2.303598 -2.691016 -2.557701

Order of Integration I(1) I(1) I(1) I(1) I(1)

L is logarithm and ADF is Augmented Dickey Fuller. Asterisk *, ** and *** indicate significance at the 1%, 5% and 10% significance levels respectively. MacKinnon (1980) critical values are used for rejection of hypothesis of a unit root. Critical values for ADF statistics are -4.0485, -3.4531, and -3.1519 at 1%, 5% and 10% respectively.

Source: Author’s computations using EVIEWS 3.0 based on the information from financial statements of all the commercial banks that are published in the Bank of Uganda annual supervision reports and IMF statistical year books for years 1981—2008. Using the ADF unit root testing procedure, the first differences of the log of the non-stationary series were subjected to the unit root tests which confirmed the results in table 4.3 above and reveal that the series are integrated of order zero in their first differences. . The summary of the results are presented in table 4.4 Table 4.4: Results of Unit Root Tests for Variables in First Difference Variable LCR LINF LRS LL LT

ADF -5.957670 -5.433128 -5.365922 -5.115730 -4.766369

Order of Integration I(0) I(0) I(0) I(0) I(0)

Notes: (ii) (iii) (iv)

(i) L is logarithm, D is the first difference and ADF is Augmented Dickey Fuller. Asterisk *, ** and *** indicate significance at the 1%, 5% and 10% significance levels respectively. Mackinnon (1980) critical values are used for rejection of hypothesis of a unit root. Critical values for ADF Statistics are -4.0468, -3.4523, and -3.1514 at 1%, 5% and 10% respectively.

40

Source: Author’s computations using EVIEWS 3.0

4.4.3 Cointegration tests As pointed out by Engle and Granger (1987), even though individual time series are nonstationary (with trend), their linear combinations can be, since equilibrium forces tend to keep such series together in the long run. When this happens, the variables are said to be cointegrated and errorcorrection terms exist to account for short-term deviations from the long-run equilibrium relationship implied by the cointegration. Moreover, over differencing of nonstationary variables to achieve stationarity leads to loss of long run properties which can be restored by the error correction term. To test for cointegration among these five non-stationary variables, a procedure developed in Johansen (1988) and applied in Johansen and Juselius (1990) is applied.

To apply the Johansen procedure (see Johansen, 1988; and Johansen and Juselius, 1990) for cointegration analysis, the maximum likelihood procedure developed in Johansen (1988) and applied in Johansen and Juselius (1990) is adopted. Results from the cointegration test are presented in table 4.5 in which the maximal eigenvalue statistics are reported. The cumulative form of the eigenvalue statistic and/or the trace statistic is not reported. This was because of the advantage of the econometric package (Eviews 3.0) used in the analysis, which computes the trace statistic automatically and only reports the number of cointegrating equations. The eigenvalue statistics reject the null hypothesis that there are zero cointegrating vectors or five common trends. The test suggests that there are two long-run relationships (see Table 4.5) among the 41

five variables (CR, INF, IRS, L, and T). However, as shown in table 4.6 only one long run IRS function has been specified. The normalization process was guided by economic theory, according to which, IRS is the regressand. Table 4.5: Johansen Cointegration Test Eigenvalue 0.492541 0.320620 0.167607 0.114408 0.034115

Likelihood Ratio 146.0758 75.52846 35.32471 16.24583 3.609929

5 Percent Critical Value 87.31 62.99 42.44 25.32 12.25

1 Percent Critical Value 96.58 70.05 48.45 30.45 16.26

Hypothesized No. of CE(s) None ** At most 1 ** At most 2 At most 3 At most 4

*(**) denotes rejection of the hypothesis at 5%(1%) significance level L.R. test indicates 2 cointegrating equation(s) at 5% significance level Source: Author’s computations using EVIEWS 3.0 Table 4.6: The Long-Run IRS Function Normalized Cointegrating Coefficients: 1 Cointegrating Equation(s) LIRS

LCR

LINF

LL

LT

@TREND(81:2) C

1.000000

0.132279

0.579640

0.059386

0.365932

0.010196

(0.03724)

(0.07741)

(0.04449)

(0.12000)

(0.00290)

-6.100510

Log likelihood 199.2698

In parentheses are t-statistic values and before the parentheses are parameter coefficients. Source: Author’s computations using EVIEWS 3.0

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Following the results in table 4.5 cointegration is accepted and therefore the residual generated from the long run IRS function tabulated in table 4.6 if lagged once (ECT_1) can be used as an error correction term in the dynamic model. 4.4.4 Estimation of the error correction model Following Engle-Granger (1987) representation theorem, the third step involved an estimation of the error correction of the relationship and testing the adequacy of the estimated equation. At this stage, an error correction specifications of the form k

k

i =0

i =1

∆LIRS t = δ 0 + ∑ δ i ∆LZ t −i + ∑ δ i ∆LIRS t −i +λ1 ECT− 1 + ε t , was formulated. Where Z t , a vector of cointegrated variables as is defined before and ECT_1 is the error correction term lagged one period with λ1 as a measure of the adjustment mechanism. The equation above represents the initial overparametized error correction model. At this stage, the overparametization of the model makes it difficult to be interpreted in any meaningful way. Accordingly, using Hendry’s (1985) general-to-specific approach, one proceeds through a simplification process to make the model more interpretable and a certainly more prudent classification of the data. The simplification process, guided by statistical rather than economic reasons, proceeds principally by setting certain parameters starting with those with ‘‘t’’ values between less than one and zero in absolute terms to zero.

The overall validity of the reduction sequence is the need to maximize the goodness of fit of the model with the minimum number of variables. The model is also to be assessed in terms of the diagnostic 43

tests such as residual autocorrelation, normality and heteroskedasticity, in addition to information criterion. The object purpose is to ensure data admissibility and then consider whether the dynamic responses of the variables conform to theory. 4.4.5 Empirical Results This section reports the econometric results on the determinants of Interest Rate Spreads in Uganda during the study period (1981-2008). Using the general-to-specific modeling procedure (as presented in the preceding section), the analysis began with two lags for each variable, the dummy variable and the error correction term, ECT_1 (see Table 4.7). The optimal lag length of two (2) was one at which increasing the order of the model by one lag could not be rejected using Akaike Information criterion test. The results for the overparametized model are presented in table 4.8. Table 4.7 General model results: Estimation of the IRS Equation Dependent Variable: DLIRS Method: Least Squares Sample(adjusted): 1981:4 2008:1 Included observations: 106 after adjusting endpoints Variable Coefficient Std. Error t-Statistic C -0.002397 0.022711 -0.105550 DLIRS_1 0.561228 0.102004 5.502023 DLIRS_2 0.098750 0.110545 0.893294 DLCR -0.035460 0.020701 -1.712936 DLCR_1 0.027274 0.021628 1.261054 DLCR_2 0.015596 0.020788 0.750235 DLINF 0.021927 0.034580 0.634096 DLINF_1 -0.012914 0.036635 -0.352499 DLINF_2 0.030387 0.032998 0.920897 DLL -0.105499 0.038515 -2.739156 DLL_1 0.027159 0.044506 0.610239 DLL_2 -0.038474 0.041928 -0.917628 DLT -0.365279 0.132543 -2.755926 DLT_1 0.171079 0.162488 1.052875 DLT_2 -0.007956 0.137302 -0.057944 D87 0.013707 0.026663 0.514090 ECT_1 -0.161170 0.051019 -3.159023 R-squared 0.469687 Akaike info criterion -1.739156

44

Adjusted R-squared S.E. of regression F-statistic

0.374350 0.094279 4.926586

Prob(F-statistic) 0.000000 Durbin-Watson stat 2.044888

Hendry’s (1985) general-to-specific approach was then used to eliminate lags with insignificant parameter estimates. Accordingly, the overparametized model was reduced until a parsimonious one was obtained. The estimation results of the parsimonious model are presented in table 4.8.

Table 4.8: Preferred/specific Model: Estimation of the Interest Rate spread Model Dependent Variable: DLIRS Method: Least Squares Date: 08/12/09 Time: 15:29 Sample(adjusted): 1981:3 2008:1 Included observations: 107 after adjusting endpoints Variable Coefficient Std. Error t-Statistic C 0.000211 0.020732 0.010179 DLIRS_1 0.585723 0.086474 6.773377* DLCR -0.033503 0.019149 -1.749547*** DLCR_1 0.026409 0.019653 1.343764 DLL -0.090172 0.030185 -2.987291** DLT -0.364842 0.126418 -2.886004** DLT_1 0.161197 0.128585 1.253622 D87 0.08382 0.024005 2.349191*** ECT_1 -0.144140 0.037302 -3.864158* R-squared 0.448796 Akaike info criterion -1.86235 Adjusted R-squared 0.403800 Prob(F-statistic) 0.000000 S.E. of regression 0.091603 Durbin-Watson stat 2.114522 F-statistic 9.974067

*, **, *** indicates significance at 1%, 5% and 10% respectively 4.4.6 Diagnostic tests Evaluating the general and the preferred model (see Tables4.7 and 4.8 respectively) results, one can see that the reduction process has eliminated most of the insignificant variables without losing valuable 45

information. The whole information criterion shows improvement of the results of the preferred model over the general model. Specifically, the Akaike information criterion (AIC) declined from -1.739 in the general model to –1.862 in the preferred model (see Tables 4.7 and 4.8 respectively). Furthermore, as the results in tables 4.7 and 4.8 show, the standard error of the model declined from 0.094 in the general model to 0.092 in the reduced model.

Regression results in table 4.8 show that the goodness of fit (Adj. R-squared) is 0.40, implying that the regressors in the model explain about 40 percent of the variations in IRS during the 1981-2008 period. Thus, about 60 percent of IRS remains unexplained. The F-statistic of 9.974, with probability value of 0.0000 indicates that the overall model is highly significant. This implies a rejection of the null hypothesis that all the right hand variables except the constant have zero parameter coefficients.

The Durbin-Watson statistics (DW) does not point to autocorrelation problem. The Jarque-Bera statistic for testing for normality of the residual for the estimated model is 127.96, with probability value of 0.016. Therefore, the normality assumption is not rejected. The Auto Regressive Conditional Heteroskedasticity (ARCH) for stability of the residuals yields an F-statistic of 0.140694, with a probability value of 0.708. This is quite satisfactory in terms of explaining the coefficient stability of the model. Besides, the residuals are white noise as per the correlogram plot (not reported herein) of the residuals.

46

In addition, the Ramsey RESET test for specification error yields F-statistic of 0.000106, with a probability value of 0.9918. This suggests that the model is not misspecified. Also, the test for serial correlation among variables in the model using Breusch-Godfrey Serial Correlation LM test was carried out. The result was an F-statistic of 0.5116, with a probability value of 0.601. This reveals that there is no serial correlation among variables.

The results of the model evaluation reveal that no weakness has been found. The fundamental statistical requirements have been adequately met, thus it can be inferred that the empirical results of the model are indeed reliable. The next section discusses the economic interpretation of the empirical results. 4.5 Key Findings 4.5.1 Interpretation of Empirical results in relation to: Objective 3: To establish the relationship between credit risk and interest rate spreads Objective 4: To establish the relationship between credit risk, Macroeconomic factors (Inflation, Liquidity, and T-bill rate), and Interest rate spread Objective 5: To establish the relationship between Client-Bank relationship and interest rate spreads

At 1 percent level of significance, the coefficient of the first lags of Interest Rate Spreads (IRS) and the Error Correction Term (ECT_1) are significantly different from zero. The coefficients of the first differences of liquidity (L) and T-Bill rate (T) are significantly different from zero at the 5 percent level. However, coefficients of the first differences of liquidity (L) and the T-bill rate (T) are not 47

correctly signed as expected. The coefficient of the first difference of Credit Risk (CR) is wrongly signed but significant at 10 percent level while the coefficients of the first lags for Credit Risk (CR_1), and t-bill rate (T_1) are insignificant though correctly signed as expected. The coefficient of the Dummy (D87) is wrongly signed from the earlier hypothesized and significant at 10 percent level.

The positive influence of the first lag of the Interest Rate Spreads implies that Banks rely mainly on the past Interest rates (lending and deposit) to price the current spread and that the higher the lagged spreads, the higher the current Interest Rate Spreads. In the Ugandan context this could be explained by the fact that in pricing their loans and deposits, Ugandan commercial banks use the magnitude of the previous spread as a determinant of the current spread probably with little or no consideration of the fundamentals that determine the spread. One can therefore conclude that in Uganda, current interest rate spreads (IRS) depend mainly on the magnitude of the previous spreads.

The Error Correction Term (ECT_1) in the model is correctly signed and is significant at 1 percent level. This confirms the earlier results presented in Table 4.5 that Interest rate Spreads (IRS), Credit Risk (CR), Inflation (INF), Liquidity (L) and the T-bill rate (T) are cointegrated.

The ECT_1

coefficient of -0.144 implies that in each period, the level of Interest Rate Spreads (IRS) adjust by about 14.4 percent of the gap between the current level and the long run equilibrium level.

The coefficient of the first difference of the variable capturing Liquidity (L) carries a negative sign and is significant at 5 percent level. This is an indication of the short term negative correlation between 48

liquidity and Interest rate spreads. This therefore means that where there is low liquidity in the banks, the interest rate spreads will be higher and vice versa. This is in contradiction with the earlier hypothesis that assumed a positive relationship between liquidity and interest rate spreads. In the Ugandan context, this implies that whenever commercial banks are faced with low liquidity levels, they tend to hike the lending rates for the little liquid assets available. Similarly, where banks have higher liquidity, ceteris paribus, they are likely to lower the spread due to downward pressure from the supply side. However, the coefficient of the first lag although insignificant is correctly signed as predicted. This is an indication that in the long run, liquidity in banks is positively correlated with interest rates spreads. Moreover, some theory points to the fact that high liquidity increases the marginal cost of deposit mobilization which lowers the likely marginal benefits which finally makes banks to offer very low deposit rates, tending to increase the spreads (Central Bank of Solomon Islands, 2007).

The coefficient of the first difference of the T-bill rate (T) has a significant but negative impact on the interest rate spreads (IRS) at 5percent level. The negative correlation between the T-bill rate (T) could probably indicate that the deposit rate is more reactive to the T-bill rate than the lending rate such that the lower the T-bill rate the higher will be the spread and the reverse could also be true. It could also imply that whenever T-bill rates are lower, Banks compensate for the loss of income from government papers by exerting an upward pressure on lending rates. However, the first lag of the same variable (T_1), though correctly signed (positive) is insignificant. This therefore is implicit of the fact that

49

though in the long run, T-bill Rate has a positive relationship, its insignificance rules out its predictive power of current interest rate spreads.

The coefficient of the first difference of the variable capturing Credit Risk (CR) is significant at the 10 percent level and negatively signed. The negative relationship between Credit Risk and Interest rate spreads rejects the earlier hypothesis that credit risk explains the current interest rates spreads in Uganda and therefore suggests that banks did not attach high enough premium to the lending rates. In addition, this may be an effect created by smaller banks in Uganda that make riskier or more diversified loans but also face more competition for deposits which exerts pressure on deposit rates hence narrowing the deposit and lending spreads. Even in the long run (see CR_1) though correctly signed, the coefficient is completely insignificant. This indicates that in the long run, Credit risk could probably have an indirect relationship with the interest rate spreads may be via increased cost of doing business in banks as they spend more on borrower screening and loan recovery efforts or for being risk averse.

The dummy (D87), a variable representing the impact of financial liberalization on Interest rate Spreads (IRS) has a significant coefficient though wrongly signed at 10 percent level of confidence. This suggests that the Economic Sector Adjustment Programme (ESAP) instead led to an increase in the spreads contrary to the earlier hypothesis. Moreover this finding is reinforced by theory as presented by Nannyonjo(2002), Diaz-Alejandro(1985),Burkett and Dutt(1991), Gibson and Tsakalotos(1994), Arestis and Demetriades(1997), Chang (1998),Demirguc-Kunt and Huizinga (1999) in which Financial 50

sector liberalization in particular has been at the root of many recent cases of high interest rate spreads, bankruptcy of financial institutions and lack of monetary control. However this contradicts the ever revered McKinnon (1973) and Shaw (1973) financial repression hypothesis which contend otherwise. 4.5.2 Comparison of Empirical studies on Interest rate spreads with the current study

The current study confirms and at the same time differs with some earlier studies. This is majorly as a result of inclusion of variables never modeled together before and the differences in the methodology and specifications used in the various preceding empirical studies. For comparison purposes, table 4.9 provides a summary of empirical findings relating to the study variables from selected studies both in Uganda and other developing countries. Table 4.9: Comparison of results of current study with those of others Variable

Lagged IRS Credit Risk Inflation Liquidity T-bill rate

Current study

+ve -ve +ve♯ -ve -ve

Other studies Nannyonjo 2002 (Uganda)

Samuel and Valderrama 2006 (Barbados)

N.A -ve +ve -ve +ve

N.A -ve -ve N.A +ve

Crowley 2007(English speaking African Countries including Uganda) N.A N.A +ve♯ N.A N.A

Tennant &Folawewo 2009(Low &middle income countries including Uganda) N.A N.A +ve N.A +ve♯

DablaNorris& Floer Kemeier 2007 (Armenia ) N.A N.A +ve♯ -ve N.A

Mlachila & Chirwa 2002 (Malawi).

Mugume & Ajwiya 2009 (Uganda)

N/A +ve +ve N/A N/A

N/A +ve♯ +ve N/A +ve

Notes: ♯ implies “not significant” and N.A implies “not included in the model” Source: Own compilation From table 4.9, the current study coefficient of the variable capturing credit risk bears consistency with results obtained by Nannyonjo (2002) and Samuel and Valderrama (2006) on Ugandan and Barbados’ 51

data respectively. Moreover, as indicated by the positive coefficients of inflation variable by studies of Nannyonjo (2002),Crowley (2007), Tennant and Folawewo (2009), Dabla-Norris and Floerkemeier (2007), Mlachila and Chirwa (2002), Mugume and Ajwiya (2009) so is the finding of the current study. Similarly, the coefficient for liquidity is negatively signed which is reinforced by the empirical results from Nannyonjo’s (2002), and Dabla-Norris and Kemeier(2007).

The current study however, differs a bit from earlier studies in that it includes other variables not modeled in other selected studies. For comparative purposes, table 4.9 provides a summary of empirical findings for selected studies both in Uganda and other developing countries. First, whereas the econometric results of the T-bill rate bear positive influence on Interest Rate Spread in most of the selected empirical studies, the variable bears negative coefficient in the current study. This could be attributed to the utilization of interpolated data by quarters and relatively long sample period. Additionally, data in the current study is not inflation adjusted as it is the case with some empirical studies.

52

CHAPTER FIVE CONCLUSION AND POLICY IMPLICATIONS 5.1 Summary This study investigated the determinants of interest rate spreads in Uganda with a view to identifying the role of credit risk in explaining the current state of interest rate spreads. The variables used for the study were: Credit Risk (CR), Inflation (INF), Liquidity (L), T-bill rate (T) and client Bank relationship (CB). A dummy variable (D87) was included as one of the regressors in order to test the view that financial liberalization policies help to narrow the interest rate spreads. Time series data was employed and its properties explored. Stationarity tests were carried out using the ADF unit root testing procedure followed by cointegration analysis developed in Johansen and Juselius (1990). The study employed econometric analysis in which the Error Correction Term method of modeling was adopted and applied to restore the lost long term data properties due to differencing while creating a link between the long run variables and short-run disequilibrium. This was followed by the findings. The findings bear consistency with the previous studies in Uganda and other country and cross-country studies in developing countries. However, some of the findings of this study reflect deviations from the previous empirical studies which could be attributed to the use of different variable measurements. The

53

next chapter provides the conclusion of this study and policy implications drawn from the empirical findings

5.2 Conclusions The econometric results reveal that 40 percent of the interest rate spreads can be explained by the variables under the study. Further, the results indicate that the major determinants of interest rate spreads in Uganda include; the 91-days Treasury bill rate, Liquidity, and the lagged interest rate spreads.

Credit risk (CR) was found to be weak in explaining the currently wider interest rate spreads in Uganda. This is illustrated by the small value of its coefficient with its significance level at 10%. Moreover financial sector liberalization was found to have a slightly significant effect on the interest rate spreads but with a wrongly signed coefficient. This was accomplished by the use of a dummy variable (D87) to capture the impact of the Economic Sector Adjustment Programme with effect from 1987. Besides, Inflation rate, and Client Bank relationship were seen to be insignificant in explaining the interest rate spreads in Uganda. 5.3 Policy Recommendations The trend of credit risk in Uganda’s banking system The 90’s were marked by the worst experience in the country’s banking history when Non Performing Loans (NPLs) reached unbearable levels. Since then, credit risk as proxied by the NPLs to total loans advanced has been on a declining trend though punctuated by some upsurges. This indicates that the 54

Bank of Uganda’s strengthening of banking supervisory and its move towards a risk based approach of banking supervision have yielded positive results. However though, these results may also be indicative of the deficiencies in assessing credit risk in banks or of the fact that banks have become more risk averse as reflected in the surging demand for government securities that has crowded-out private sector credit.

It is therefore recommended that commercial banks move from their traditional mechanisms used to control credit risk, to loan portfolio restructuring. Other options that could be tried for dealing with credit risk include loan sales and debt-equity swaps but all of which require developed capital markets. The state of intermediation spreads in Uganda. In the last 30 years or so, interest rate spreads in Uganda have more than doubled. This is a worrying fact given the adversity of high interest rate spreads on growth and macroeconomic equilibrium. The five variables under study account for 40 percent of the current Interest rate spread state. This implies that more investment in policy research on the other likely explanatory variables to the current state of intermediation spreads is required to supplement the current study.

The relationship between credit risk and Interest rate spread. Empirical results reveal a negative relationship between credit risk and interest rate spreads at 10 percent level of significance. This implies that credit risk is not form the basis for banks’ decision to charge higher spreads. Nevertheless, this may reflect deficiencies in assessing of credit risk due to lack

55

of capacity in the local banks. This therefore implies the need for capacity building within the individual bank’s human and technology resources for better credit risk assessment and management. The relationship between credit risk, Macroeconomic factors (Inflation, Liquidity, and T-bill rate), Client-Bank relationship; and interest rate spreads. The empirical results reveal the fact that many of the factors commonly believed to be salient determinants of interest rate spreads may not be as vital as earlier perceived. Those that have been revealed to have a significant impact at the different levels of significance are wrongly signed from the expected.

It is therefore recommended that a multidimensional approach to policy directed to

narrowing interest rate spreads (IRS) be adopted. 5.4 Possible Areas for further research In examining the factors that determine the interest rate spreads in Uganda from 1981 to 2008, the study limited itself to five variables; that is, Credit risk, Inflation, Liquidity, T-bill rate and Client-bank relationship. The fact that these variables explain 40 percent of the current status of the interest rate spreads in Uganda suggests that there are variables that can complement this study in explaining IRS that deserve a scholarly investigation. To this end therefore, this study could be complimented if more research is carried out on the quality of credit risk management systems and interest rate spreads in Uganda’s Banking system.

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67

APPENDIX 1 Quarterly data used in the analysis. Years 1981 I II III IV 1982 I II III IV 1983 I II III IV

IRS 7 7.125 7.25 7.375 7.5 7.0075 6.515 6.0225 5.53 5.0225 4.515 4.0075

CR 14.5 15.5 16.5 17.5 18.5 18.05 17.6 17.15 16.7 18.025 19.35 20.675

INF 56.76667 65.3375 73.90833 82.47917 91.05 74.3125 57.575 40.8375 24.1 27.4 30.7 34

L

T

13.624 12.663 11.702 10.741 9.78 23.0225 36.265 49.5075 62.75 80.4525 98.155 115.8575

7 8 9 10 11 11.5 12 12.5 13 14.5825 16.165 17.7475

CB 0.855 0.845 0.835 0.825 0.815 0.8195 0.824 0.8285 0.833 0.81975 0.8065 0.79325

D87 0 0 0 0 0 0 0 0 0 0 0 0

68

1984 I II III IV 1985 I II III IV 1986 I II III IV 1987 I II III IV 1988 I II III IV 1989 I II III IV 1990 I II III IV 1991 I II III IV 1992 I II III IV 1993 I II III IV 1994 I II III IV 1995 I

3.5 4.7 5.9 7.1 8.3 9.375 10.45 11.525 12.6 11.7625 10.925 10.0875 9.25 8.6575 8.065 7.4725 6.88 6.66 6.44 6.22 6 6 6 6 6 7.0625 8.125 9.1875 10.25 8.9425 7.635 6.3275 5.02 10.265 15.51 20.755 26 24.3225 22.645 20.9675 19.29 18.1693 17.0485 15.9278 14.8071

22 22.5 23 23.5 24 26.325 28.65 30.975 33.3 32.975 32.65 32.325 32 31.5 31 30.5 30 32.5 35 37.5 40 39.5 39 38.5 38 28.645 19.28999 9.934991 0.579988 0.574422 0.568856 0.56329 0.557724 0.463313 0.368902 0.274491 0.18008 6.83256 13.48504 20.13752 26.79 29.99349 33.19698 36.40047 39.60396

37.3 202.9125 368.525 534.1375 699.75 527.7932 355.8364 183.8795 11.92273 11.19934 10.47595 9.752557 9.029167 9.300625 9.572083 9.843542 10.115 19.24425 28.37351 37.50276 46.63201 39.93293 33.23385 26.53478 19.8357 22.03394 24.23217 26.43041 28.62865 31.18374 33.73883 36.29392 38.84901 30.18856 21.5281 12.86765 4.20719 4.867588 5.527985 6.188383 6.848781 7.474618 8.100455 8.726293 9.35213

133.56 117.0094 100.4588 83.90813 67.3575 50.80688 34.25625 17.70563 1.155 1.254 1.353 1.452 1.551 2.19425 2.8375 3.48075 4.124 4.163 4.202 4.241 4.28 9.315 14.35 19.385 24.42 30.915 37.41 43.905 50.4 78.67 106.94 135.21 163.48 172.76 182.04 191.32 200.6 243 285.4 327.8 370.2 337.9 305.6 273.3 241

19.33 19.9975 20.665 21.3325 22 24.175 26.35 28.525 30.7 31.025 31.35 31.675 32 32.25 32.5 32.75 33 35.5 38 40.5 43 42.5 42 41.5 41 39.25 37.5 35.75 34 35 36 37 38 33.75 29.5 25.25 21 18.875 16.75 14.625 12.5 11.575 10.65 9.725 8.8

0.78 0.775 0.77 0.765 0.76 0.73675 0.7135 0.69025 0.667 0.67025 0.6735 0.67675 0.68 0.685 0.69 0.695 0.7 0.675 0.65 0.625 0.6 0.605 0.61 0.615 0.62 0.71355 0.8071 0.90065 0.9942 0.994256 0.994311 0.994367 0.994423 0.995367 0.996311 0.997255 0.998199 0.931674 0.86515 0.798625 0.7321 0.700065 0.66803 0.635995 0.60396

0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1

69

II III IV 1996 I II III IV 1997 II III IV 1998 I II III IV 1999 I II III IV 2000 I II III IV 2001 I II III IV 2002 I II III IV 2003 I II III IV 2004 I II III IV 2005 I II III IV 2006 I II

14.2636 13.72 13.1765 12.633 12.9504 13.2679 13.5854 13.9029 13.8605 13.8181 13.7757 13.7333 14.5202 15.307 16.0939 16.8808 16.4036 15.9264 15.4492 14.9721 15.1399 15.3078 15.4757 15.6435 15.2535 14.8634 14.4733 14.0832 13.6182 13.1532 12.6882 12.2231 13.1658 14.1085 15.0512 15.9939 15.1848 14.3757 13.5667 12.7576 12.8527 12.9478 13.0429 13.138 13.2509

39.26369 38.92343 38.58316 38.24289 35.19813 32.15336 29.1086 26.06383 33.71266 41.36149 49.01031 56.65914 57.64242 58.6257 59.60898 60.59226 47.92038 35.24851 22.57664 9.904762 9.060049 8.215337 7.370624 6.525912 5.650863 4.775815 3.900767 3.025719 4.069761 5.113804 6.157846 7.201889 5.938776 4.675663 3.41255 2.149437 2.188389 2.227342 2.266294 2.305246 2.462934 2.620621 2.778308 2.935995 3.225456

8.246653 7.141175 6.035698 4.930221 6.058824 7.187426 8.316029 9.444631 7.660425 5.876218 4.092011 2.307804 3.777756 5.247708 6.717659 8.187611 7.21004 6.232469 5.254899 4.277328 4.332523 4.387718 4.442913 4.498108 4.779358 5.060609 5.341859 5.62311 5.665472 5.707833 5.750195 5.792556 6.296407 6.800258 7.304108 7.807959 6.784517 5.761076 4.737634 3.714192 5.381338 7.048484 8.71563 10.38278 9.061124

252.25 263.5 274.75 286 296.75 307.5 318.25 329 344.75 360.5 376.25 392 489.75 587.5 685.25 783 863.75 944.5 1025.25 1106 1152.5 1199 1245.5 1292 1360 1428 1496 1564 1501.5 1439 1376.5 1314 1370 1426 1482 1538 1543 1548 1553 1558 1543.5 1529 1514.5 1500 1547

9.525 10.25 10.975 11.7 11.425 11.15 10.875 10.6 9.8375 9.075 8.3125 7.55 7.5225 7.495 7.4675 7.44 8.88 10.32 11.76 13.2 12.65 12.1 11.55 11 9.7125 8.425 7.1375 5.85 8.6125 11.375 14.1375 16.9 14.925 12.95 10.975 9 8.875 8.75 8.625 8.5 8.4 8.3 8.2 8.1 8.35

0.607363 0.610766 0.614168 0.617571 0.648019 0.678466 0.708914 0.739362 0.662873 0.586385 0.509897 0.433409 0.423576 0.413743 0.40391 0.394077 0.520796 0.647515 0.774234 0.900952 0.9094 0.917847 0.926294 0.934741 0.943491 0.952242 0.960992 0.969743 0.959302 0.948862 0.938422 0.927981 0.940612 0.953243 0.965874 0.978506 0.978116 0.977727 0.977337 0.976948 0.975371 0.973794 0.972217 0.97064 0.967745

1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1

70

III IV 2007 I II III IV 2008 I

13.3639 13.4768 13.5898 13.6054 13.621 13.6366 13.6522

3.514916 3.804376 4.093836 3.621038 3.14824 2.675441 2.202643

7.739471 6.417819 5.096167 7.178436 9.260704 11.34297 13.42524

1594 1641 1688 1830.5 1973 2115.5 2258

8.6 8.85 9.1 9.075 9.05 9.025 9

0.964851 0.961956 0.959062 0.96379 0.968518 0.973246 0.977974

1 1 1 1 1 1 1

71