Credit Risk and Bank Interest Rate Spreads in Uganda Final

7/30/2019 Credit Risk and Bank Interest Rate Spreads in Uganda Final

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CREDIT RISK AND BANK INTEREST RATE SPREADS IN

UGANDA

by

MAKANGA BENARD


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DECLARATION

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

Signed..

MAKANGA.BENARD

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

research or private study, critical scholarly review or discourse with an acknowledgement, withoutwritten permission of the Directorate of Postgraduate Studies, on behalf of both the author andMakerere University.


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CERTIFICATION

The undersigned certify that they have read and hereby recommend for acceptance, a dissertation

entitled: CreditRisk and Interest Rate Spreads in Banking: A case of Uganda

DEDICATION

To My Family and Friends


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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 familymy 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.

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.


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TABLE OF CONTENT

DECLARATION................................................................................................................................. iiCERTIFICATION ............................................................................................................................. iiiDEDICATION.................................................................................................................................... iiiACKNOWLEDGEMENT ................................................................................................................. ivTABLE OF CONTENT ...................................................................................................................... vLIST OF TABLES AND FIGURES ................................................................................................ viiABSTRACT ...................................................................................................................................... viiiCHAPTER 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 ............................................................................................................................... 132.3.1 Credit risk trend in Uganda's banking system ....................................................................... 142.4 Interest rate spreads in Uganda. ................................................................................................ 162.5 Credit Risk and Interest rate Spreads ........................................................................................ 182.6 Client-Bank relationship and Interest rate spreads ................................................................... 212.7 Macroeconomic environment and interest rate spreads ............................................................ 22

METHODOLOGY ........................................................................................................................... 253.1 Introduction ............................................................................................................................... 253.2. Model Specification ................................................................................................................. 253.3 Variable Definitions, Measurement and Data Source ............................................................... 283.4 Data Estimation and Testing Procedures .................................................................................. 313.5Limitation .................................................................................................................................. 32

CHAPTER FOUR ............................................................................................................................. 33PRESENTATION, ANALYSIS AND INTERPRETATIONOF FINDINGS ............................. 33

4.1 Introduction ............................................................................................................................... 334.2 Objective 1: .............................................................................................................................. 334.3 Objective 2: .............................................................................................................................. 364.4: Objective 3,4&5. ...................................................................................................................... 38

4.4.1Time Series properties ........................................................................................................ 38


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4.4.2 Unit root tests..................................................................................................................... 404.4.3 Cointegration tests ............................................................................................................. 414.4.4 Estimation of the error correction model.......................................................................... 434.4.5 Empirical Results ............................................................................................................... 444.4.6 Diagnostic tests .................................................................................................................. 46

4.5 Key Findings ............................................................................................................................. 484.5.1 Interpretation of Empirical results in relation to: ............................................................... 484.5.2 Comparison of Empirical studies on Interest rate spreads with the current study ............. 51

CHAPTER FIVE .............................................................................................................................. 54CONCLUSION AND POLICY IMPLICATIONS ........................................................................ 54

5.1 Summary ................................................................................................................................... 545.2 Conclusions ............................................................................................................................... 555.3 Policy Recommendations.......................................................................................................... 555.4 Possible Areas for further research ........................................................................................... 57

References .......................................................................................................................................... 58APPENDIX 1 ..................................................................................................................................... 70


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LIST OF TABLES AND FIGURES

Figure1: Credit Risk trend in Uganda..34Figure 2: Interest Rate spreads trend in Ugandas banking system.36Table4.1:Descriptive Statistics39Table 4.2: Correlation Analysis..39Table 4.3: Results of Unit Root Tests for Variables in Levels40Table 4.4: Results of Unit Root Tests for Variables in First Difference.41Table 4.5: Johansen Cointegration Test42Table 4.6: The Long-Run IRS Function.43Table 4.7 General model results: Estimation of the IRS Equation.45Table 4.8: Preferred/specific Model: 46Table 4.9: Comparison of results of current study with those of others.52


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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


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of 18% to 34% 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 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= CTLInfCR ,,,, .. (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


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crises inform of higher spreads, mass defaults, bank bankruptcy, and currency crises mainly due to

the fragility of their 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


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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. 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.


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2.3. Credit Risk

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.


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However though, the benefit of education and training has been underestimated in most of the

studies on credit risk. Also, age and 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


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default probabilities of borrowers and hence contribute to financial stability and efficient

allocation of resources in the economy,

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


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risk averse reflected in a strong preference for liquid and low-risk assets as opposed to individual

lending.

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.


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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

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.


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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 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,


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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).

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


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preceding assertions are Panzar and Rosse (1987), and the IDB (2005) which disregard purported

relationship between bank concentration and spreads.

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


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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.

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


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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 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


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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.

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


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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 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.


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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.

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


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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 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= CTLInfCR ,,,, . (2)


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

IRS t ttttt CTLInfCR 6543210 ... (3)

Where:

IRS t - is interest rate spread over time,

CRt -is credit risk over time,

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.


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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 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.


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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 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


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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

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

Intere

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.


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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) 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


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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 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.


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CHAPTER FOUR

PRESENTATION, ANALYSIS AND INTERPRETATIONOF FINDINGS

4.1 I ntroduction

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

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.


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Figure1: Credit Risk trend in Uganda

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


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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 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


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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 por tray the state of interest rate spreads in the Ugandan bank ing 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 Ugandas banking system

WADRWeighted Average Deposit Rate; WALRWeighted Average Lending Rate

0

5

10

15

20

25

30

35

40

45

1981

1982

1983

1984

1985

1986

1987

1988

1989

1990

1991

1992

1993

1994

1995

1996

1997

1998

1999

2000

2001

2002

2003

2004

2005

2006

2007

2008

Interes t rate s pread(% ) WAD R WAL R L inear (Interes t rate s pread(% ))


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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 80s 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, Ugandas spreads range between 14 and 17

percent which is 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.


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4.4: Objective 3: To establ ish the relationship between Credit Ri sk and I nterest rate spreads

Objective 4: To establish the relationship between M acroeconomic factors (I nf lati on, Liqui dity, T-

bil l r ate) and interest rate spreads.

Objective 5: To establish the relationship between client-bank r elati onship and interest r ate

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 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.


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Table4.1: Descri ptive Statistics

CB CR INF IRS L T

Mean 0.797302 20.26979 42.33221 11.86737 590.9880 17.91817

Median 0.815000 18.50000 9.029167 13.13798 274.7500 11.76000

Maximum 0.998199 60.59226 699.7500 26.00000 2258.000 43.00000

Minimum 0.394077 0.180080 2.307804 3.500000 1.155000 5.850000

Std. Dev. 0.164037 16.40371 108.0276 4.525424 659.1260 11.33447

Skewness -0.495100 0.495100 4.331423 0.312090 0.791376 0.905351

Kurtosis 2.320728 2.320728 22.16449 3.105313 2.073714 2.282759

Jarque-Bera 6.548663 6.548663 20.08882 1.819804 15.27412 17.22689

Probability 0.37842 0.37842 0.000000 0.402564 0.0482 0.0182

Observations 109 109 109 109 109 109Source: Authors 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 19812008.

Table 4.2: Correlation Analysis

CB CR INF IRS L T

CB 1 -1 -0.0636107 0.11376 0.523770 -0.128460

CR -1 1 0.0636107 -0.11376 -0.523770 0.1284604

INF -0.0636107 0.0636107 1 -0.296578 -0.267028 0.145386

IRS 0.11376282 -0.1137628 -0.296578 1 0.445018 -0.49664

L 0.52377026 -0.5237702 -0.267028 0.445018 1 -0.590898T -0.128460 0.1284604 0.145386 -0.49664 -0.590898 1

Source: Authors computations using financial statements of all the commercial banks that arepublished in the Bank of Uganda annual supervision reports and IMF statistical year books for

years 19812008.

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.


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4.4.2 Un it 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

Variable ADF Order of Integration

LCR -2.740780 I(1)

LINF -3.460659* I(1)

LRS -2.303598 I(1)

LL -2.691016 I(1)

LT -2.557701 I(1)

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

Source: Authors computations using EVIEWS 3.0 based on the information from financialstatements of all the commercial banks that are published in the Bank of Uganda annual supervision

reports and IMF statistical year books for years 19812008.


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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 ADF Order of Integration

LCR -5.957670 I(0)

LINF -5.433128 I(0)

LRS -5.365922 I(0)

LL -5.115730 I(0)

LT -4.766369 I(0)

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

Source:Authors computations usingEVIEWS 3.0

4.4.3 Coin tegration 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 error-

correction 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


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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

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 LikelihoodRatio

5 PercentCritical Value

1 PercentCritical Value

HypothesizedNo. of CE(s)

0.492541 146.0758 87.31 96.58 None **

0.320620 75.52846 62.99 70.05 At most 1 **

0.167607 35.32471 42.44 48.45 At most 20.114408 16.24583 25.32 30.45 At most 3

0.034115 3.609929 12.25 16.26 At most 4

*(**) denotes rejection of the hypothesis at 5%(1%) significance level


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L.R. test indicates 2 cointegrating equation(s) at 5% significance level


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 -6.100510

(0.03724) (0.07741) (0.04449) (0.12000) (0.00290)

Log likelihood 199.2698

In parentheses are t-statistic values and before the parentheses are parameter coefficients.


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

i

k

i

itiitit LILZLIRS

10 1

0 ,


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was formulated. Where tZ , a vector of cointegrated variables as is defined before and ECT_1 is the

error correction term lagged one period with 1 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 dif

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