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

7/31/2019 Credit Risk and Bank Interest Rate Spreads in Uganda Final (1)

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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 beenpresented and will not be presented to any University for a similar or any other degree award.

Signed..

MUGENDAWALA HAMIS

(STUDENT)

Date

Hamis, M. 2009This 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 re-search or private study, critical scholarly review or discourse with an acknowledgement, without writ-

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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: CreditRisk 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: ..................................................

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DEDICATIONTo 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 Ntayifor 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, re-

alize 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 familymy wife Namukose Zaujah and daughter Namwase Sumayah for their en-

couragement. Above all, Glory is to the Almighty Allah for this wisdom. In HIM, all things are possi-

ble.

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All deficiencies that remain in the dissertation are entirely my own responsibility and should not be at-

tributed 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 ..................................................................................................................................1INTRODUCTION ....................................................................................................................................1

1.1 Background to the Study ..................................................................................................................11.2 Statement of the Problem ..................................................................................................................31.3 Purpose of the Study .........................................................................................................................41.4 Objectives of the Study .....................................................................................................................41.5 Research Hypotheses ........................................................................................................................51.6 Significance of the Study ..................................................................................................................51.7 Scope of the Study ............................................................................................................................51.8 Conceptual Framework .....................................................................................................................61.9 Organization of the Study .................................................................................................................7

CHAPTER TWO ......................................................................................................................................8LITERATURE REVIEW ......................................................................................................................8

2.1. Introduction ......................................................................................................................................82.2 Financial Liberalization and interest spreads ..................................................................................92.3. Credit Risk ....................................................................................................................................122.3.1 Credit risk trend in Uganda's banking system .............................................................................142.4 Interest rate spreads in Uganda. .....................................................................................................162.5 Credit Risk and Interest rate Spreads .............................................................................................18

2.6 Client-Bank relationship and Interest rate spreads ....................................................................212.7 Macroeconomic environment and interest rate spreads .............................................................22

METHODOLOGY .................................................................................................................................24

3.1 Introduction .....................................................................................................................................243.2 Research Design .............................................................................................................................253.3. Model Specification .......................................................................................................................253.4 Variable Definitions, Measurement and Data Source ....................................................................273.5 Data Estimation and Testing Procedures .......................................................................................303.6 Limitation .......................................................................................................................................31

CHAPTER FOUR ...................................................................................................................................32

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PRESENTATION, ANALYSIS AND INTERPRETATIONOF FINDINGS .................................32

4.1 Introduction .....................................................................................................................................324.2 Objective 1: To analyze the trend of credit risk in Ugandas banking system ..............................32

Figure1: Credit Risk trend in Uganda ........................................................................................334.3 Objective 2: To portray the state of interest rate spreads in the Ugandan banking system ...... ... .35

Figure 2: Interest Rate spreads trend in Ugandas banking system ..........................................354.4: Objective 3: To establish the relationship between Credit Risk and Interest rate spreads ...... ... .37Objective 4: To establish the relationship between Macroeconomic factors (Inflation, Liquidity, T-bill rate) and interest rate spreads......................................................................................................... 37Objective 5: To establish the relationship between client-bank relationship and interest rate spread................................................................................................................................................................ 37

4.4.1Time Series properties ..............................................................................................................37Table4.1: Descriptive Statistics ..................................................................................................38Table 4.2: Correlation Analysis .................................................................................................38

4.4.2 Unit root tests ...........................................................................................................................39Table 4.3: Results of Unit Root Tests for Variables in Levels ..................................................39

Variable..............................................................................................................40Table 4.4: Results of Unit Root Tests for Variables in First Difference ...................................40

Variable..............................................................................................................40Notes: ..........................................................................................................................................40

(i) L is logarithm, D is the first difference and ADF is Augmented Dickey Fuller...404.4.3 Cointegration tests .......................................................................................................................41

Table 4.5: Johansen Cointegration Test .....................................................................................42Table 4.6: The Long-Run IRS Function ....................................................................................42

4.4.4 Estimation of the error correction model ................................................................................434.4.5 Empirical Results .........................................................................................................................44Table 4.7 General model results: Estimation of the IRS Equation ......................................................44

Table 4.8: Preferred/specific Model: Estimation of the Interest Rate spread Model ................454.4.6 Diagnostic tests ........................................................................................................................45

4.5 Key Findings ...................................................................................................................................474.5.1 Interpretation of Empirical results in relation to: ....................................................................47

4.5.2 Comparison of Empirical studies on Interest rate spreads with the current study .....................51Table 4.9: Comparison of results of current study with those of others ...................................51

CHAPTER FIVE ....................................................................................................................................53

CONCLUSION AND POLICY IMPLICATIONS .............................................................................53

5.1 Summary .........................................................................................................................................535.2 Conclusions .....................................................................................................................................545.3 Policy Recommendations ...............................................................................................................54

5.4 Possible Areas for further research .................................................................................................56References ................................................................................................................................................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 ..................................................................................................................11.2 Statement of the Problem ..................................................................................................................31.3 Purpose of the Study .........................................................................................................................41.4 Objectives of the Study .....................................................................................................................41.5 Research Hypotheses ........................................................................................................................51.6 Significance of the Study ..................................................................................................................5

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1.7 Scope of the Study ............................................................................................................................51.8 Conceptual Framework .....................................................................................................................61.9 Organization of the Study .................................................................................................................7

CHAPTER TWO ......................................................................................................................................8

LITERATURE REVIEW ......................................................................................................................8

2.1. Introduction ......................................................................................................................................82.2 Financial Liberalization and interest spreads ..................................................................................92.3. Credit Risk ....................................................................................................................................122.3.1 Credit risk trend in Uganda's banking system .............................................................................142.4 Interest rate spreads in Uganda. .....................................................................................................162.5 Credit Risk and Interest rate Spreads .............................................................................................18

2.6 Client-Bank relationship and Interest rate spreads ....................................................................212.7 Macroeconomic environment and interest rate spreads .............................................................22

METHODOLOGY .................................................................................................................................24

3.1 Introduction .....................................................................................................................................243.2 Research Design .............................................................................................................................25

3.3. Model Specification .......................................................................................................................253.4 Variable Definitions, Measurement and Data Source ....................................................................273.5 Data Estimation and Testing Procedures .......................................................................................303.6 Limitation .......................................................................................................................................31

CHAPTER FOUR ...................................................................................................................................32

PRESENTATION, ANALYSIS AND INTERPRETATIONOF FINDINGS .................................32

4.1 Introduction .....................................................................................................................................324.2 Objective 1: To analyze the trend of credit risk in Ugandas banking system ..............................32

Figure1: Credit Risk trend in Uganda ........................................................................................334.3 Objective 2: To portray the state of interest rate spreads in the Ugandan banking system ...... ... .35

Figure 2: Interest Rate spreads trend in Ugandas banking system ..........................................35

4.4: Objective 3: To establish the relationship between Credit Risk and Interest rate spreads ...... ... .37Objective 4: To establish the relationship between Macroeconomic factors (Inflation, Liquidity, T-bill rate) and interest rate spreads......................................................................................................... 37Objective 5: To establish the relationship between client-bank relationship and interest rate spread................................................................................................................................................................ 37

4.4.1Time Series properties ..............................................................................................................37Table4.1: Descriptive Statistics ..................................................................................................38Table 4.2: Correlation Analysis .................................................................................................38

4.4.2 Unit root tests ...........................................................................................................................39Table 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 ...................................40Variable..............................................................................................................40

Notes: ..........................................................................................................................................40(i) L is logarithm, D is the first difference and ADF is Augmented Dickey Fuller...40

4.4.3 Cointegration tests .......................................................................................................................41Table 4.5: Johansen Cointegration Test .....................................................................................42

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Table 4.6: The Long-Run IRS Function ....................................................................................424.4.4 Estimation of the error correction model ................................................................................43

4.4.5 Empirical Results .........................................................................................................................44Table 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 ........................................................................................................................454.5 Key Findings ...................................................................................................................................47

4.5.1 Interpretation of Empirical results in relation to: ....................................................................474.5.2 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 ...................................51CHAPTER FIVE ....................................................................................................................................53

CONCLUSION AND POLICY IMPLICATIONS .............................................................................53

5.1 Summary .........................................................................................................................................535.2 Conclusions .....................................................................................................................................545.3 Policy Recommendations ...............................................................................................................545.4 Possible Areas for further research .................................................................................................56

References ................................................................................................................................................57APPENDIX 1 ...........................................................................................................................................68

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ABSTRACT

The study investigates the effect of credit risk on interest rate spreads in Uganda for the period 1981-

2008, 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 Ugandas 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 Ugandas Banking system

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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.

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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

90s, Ugandas financial system was grossly hit by mass credit default which culminated into

insolvency and hence closure of four (4) local commercial banksGreenland 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 19872000, 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

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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

Ugandas 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

Ugandas banking system.

ii. ClientBank 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

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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;

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IRS= ( )CBTLInfCRf ,,,, .. (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

Ugandas 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

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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.

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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 systems 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

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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

borrowers 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

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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 borrowers 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,

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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 Ugandas improvement in her risk profile to a 'B' plus in the recent Standard and Poors

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.

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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

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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

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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).

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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.

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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.

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2.6 Client-Bank relationship and Interest rate spreads

Ithas 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

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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

countrys 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.

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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

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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.

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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 spreadsClient-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

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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= ( )CBTLInfCRf ,,,, . (2)

For estimation purposes, equation (2) will be transformed as below

IRS t tttttt DCBTLInfCR +++++++= 6543210 ... (3)

Where:

IRS t - is interest rate spread over time,

CRt -is credit risk over time,

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Inft - 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

t ~i.i.d(0,2 ), is a serially uncorrelated error term.

From equation (3), it is hypothesized that variables-- 4321 ,, and are positive while 5 and 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

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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), Fungov 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

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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 borrowers 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

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

Interest

Pr

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)

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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 non-

stationary 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

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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 Ugandas banking system

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Between 19872000, 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

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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

Agencys 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 banksGreenland 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

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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 Ugandas 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

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

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