Angel Zawadi Marende

THE EFFECT OF MACROECONOMIC FACTORS ON FINANCIAL DEVELOPMENT OF COMMERCIAL BANKS IN KENYA

ANGEL ZAWADI MARENDE

UNITED STATES INTERNATIONAL UNIVERSITY

SUMMER 2017 THE EFFECT OF MACROECONOMIC FACTORS ON FINANCIAL DEVELOPMENT OF COMMERCIAL BANKS IN KENYA

ANGEL ZAWADI MARENDE

A Research Project Report Submitted to the Chandaria School of Business in Partial Fulfillment of the Requirement for the Degree of Masters in Business Administration (MBA)

UNITED STATES INTERNATIONAL UNIVERSITY

SUMMER 2017 STUDENT’S DECLARATION

I, the undersigned, declare that this is my original work, and has not been submitted to any other college, institution or university other than the United States International University- Africa in Nairobi for academic credit.

Signed: ______Date: ______Angel Zawadi Marende (ID 629386)

This project has been presented for examination, with my approval as the supervisor.

Signed: ______Date: ______Prof. Amos Njuguna

Signed: ______Date: ______Dean, Chandaria School of Business

All rights reserved. No part of this research project may be photocopied, recorded or otherwise produced, stored in a retrieval system or transmitted in any form or by any electronic or mechanical means without prior permission of the copyright owner.

iii

ABSTRACT The aim of this study was to determine the effect of macroeconomic factors on financial development of commercial banks in Kenya. The development measure of commercial banks used was the Liquid liabilities (LL), the ratio credit to the Private Sector (CPS), Commercial-Central Bank Assets (CCBA) and commercial bank Deposits (CBD) which was regressed against the macroeconomic variables including GDP growth rate, the exchange rate (US dollar), the money supply (M3). Inflation (CPI) and Lending Rate of the sampled commercial banks.

The period of the study was ten years from June 2006 to June 2016. The study employed quarterly secondary data which was obtained from the Central Bank of Kenya. Kenya National Bureau of Statistics and published quarterly financial statements from commercial banks selected in the sample. Analysis of the data made use of computer software 'e-views' version 7.0 to analyze the data. Given that the study model is a multivariate, the study used multiple regression technique in analyzing the relationship between the selected macro-economic factors and the financial development of commercial banks in Kenya.

The financial development of commercial banks as measured by the above 4 ratios was found to be positively correlated with GDP growth rate, money supply (M3), lending interest rate of individual commercial banks and inflation, and negatively correlated with exchange rate. The findings confirmed the researcher's priori expectation that these key development factors would be both positively and negatively correlated with the independent variables.

The rest of the paper is organized as follows: chapter one covers introduction to the study by addressing issues related to background of the study, statement of the problem, study objective and the significance of the study; chapter two focuses on literature review; chapter three is about the research methodology; chapter four covers data findings; and lastly chapter five addresses discussion of the findings, conclusions and recommendations.

The researcher concludes that there is a positive association between macro-economic factors and financial development of Commercial Banks in Kenya. From the results

obtained, the researcher recommended that that favorable macroeconomic environment seems to stimulate higher profits. Specifically, the macroeconomic environment (proxied by GDP growth, M3 and inflation) is observed to have a positive impact on bank development. Higher growth rate of GDP seem to have a strong positive impact on the development measure. The justification of this study was to provide more information to policy makers on the effects of on economic growth in Kenya, so as to make informed decisions. Lastly the researcher recommended that this study can be extended to include the whole of banking sector and not just commercial banks. The study may also be extended to cover other fields of development measurement such as effectiveness, economy, prudence and soundness of commercial banks in other countries, which can allow for generalization of the findings to the whole industry.

ACKNOWLEDGEMENT

I would like to thank the Almighty God for his Grace that has enabled me to complete this project. I too take this opportunity to thank the United States International University- Africa for their material support. I also acknowledge my fellow colleagues and family members for their continued support. I would like to express my profound gratitude to my supervisor Dr. Amos Njuguna for his invaluable support and positive criticism that gave me the motivation to achieve my academic objective.

I dedicate this project to my late parents, Mr. William and Mrs. Jemimmah Marende who always encouraged me to achieve my dreams. Finally yet importantly, I am grateful to my Aunt and Uncle Mr. & Mrs. Munyua for their encouragement, motivation and prayers. May God bless you abundantly.

TABLE OF CONTENTS STUDENT’S DECLARATION ...... ii COPYRIGHT PAGE ...... iii ABSTRACT ...... iv ACKNOWLEDGEMENT ...... vi TABLE OF CONTENTS ...... vii LIST OF TABLES ...... ix LIST OF FIGURES ...... x

CHAPTER ONE ...... 1 1.0 INTRODUCTION ...... 1 1.1 Background of the Problem ...... 1 1.2 Statement of the Problem ...... 7 1.3 General Objective ...... 9 1.4 Research Objectives ...... 9 1.5 Importance of the Study ...... 9 1.6 Scope of the Study ...... 10 1.7 Definition of Terms ...... 11 1.8 Chapter Summary ...... 12

CHAPTER TWO ...... 13 2.0 LITERATURE REVIEW ...... 13 2.1 Introduction ...... 13 2.2 Effect of Inflation on Financial Development of Commercial Banks ...... 13 2.3 Effect of Lending Interest Rates on Financial Development of Commercial Banks ...... 15 2.4 Effect of GDP Growth on Financial Development of Commercial Banks...... 17 2.5 Effect of Exchange Rates on Financial Development of Commercial Banks ...... 20 2.6 Effect of Money Supply on Financial Development of Commercial Banks ...... 22 2.7 Chapter Summary ...... 23

CHAPTER THREE ...... 25 3.0 RESEARCH METHODOLOGY ...... 25 3.1 Introduction ...... 25 3.2 Research Design ...... 25 3.3 Population and Sample ...... 25

vii

3.4 Data collection Methods ...... 26 3.5 Research Procedures ...... 27 3.6 Data Analysis ...... 28 3.7 Chapter Summary ...... 28

CHAPTER FOUR ...... 29 4.0 RESULTS AND ANALYSIS ...... 29 4.1 Introduction ...... 29 4.2 General Information ...... 29 4.3 Effect of Exchange rate on Commercial Bank Development in Kenya ...... 38 4.4 Effect of Inflation and Commercial Bank Development in Kenya ...... 39 4.5 Effect of GDP and Commercial Bank Development in Kenya ...... 40 4.6 Effect of Money Supply and Commercial Bank Development in Kenya...... 42 4.7 Effect of Lending Rate and Commercial Bank Development in Kenya ...... 44 4.8 Chapter Summary ...... 47

CHAPTER FIVE ...... 48 5.0 DISCUSSION CONCLUSION AND RECOMMENDATIONS ...... 48 5.1 Introduction ...... 48 5.2 Summary ...... 48 5.3 Discussion ...... 49 5.4 Conclusion ...... 59 5.5 Recommendations ...... 61 REFERENCES ...... 64 APPENDIX 1: LIST OF COMMERCIAL BANKS ...... 68 APPENDIX 2: COMMERCIAL BANKS SAMPLE ...... 70 APPENDIX 3: STUDY DATA ...... 71

viii

LIST OF TABLES Table 4.1: GDP Time series ...... 29 Table 4.2: Commercial Banks LL Time series data 2006-2016 ...... 31 Table 4.3: CPS Time series data from 2006-2016 ...... 33 Table 4.4: CCBA Time series data from 2006-2016 ...... 35 Table 4.5: CBD Time series Data from 2006-2016 ...... 37 Table 4.6: Effect of Inflation on financial deepening measures ...... 39 Table 4.7: Trace Test results of cointegration of GDP with individual independent variable ...... 40 Table 4.8: Summary of Cointegration between GDP and Financial deepening measures ...... 42 Table 4.9: Effect of Money supply on financial deepening measures ...... 43 Table 4.10: Effect of Lending rate on LL ...... 45 Table 4.11: Effect of Lending rate on CPS ...... 45 Table 4.12: Effect of Lending rate on CBA ...... 46 Table 4.13: Effect of Lending rate on CBD ...... 46

LIST OF FIGURES Figure 4.1: Kenya's GDP from 2006-2016 ...... 30 Figure 4.2: Time series data of Commercial Bank Liquid Liabilities ...... 32 Figure 4.3: Commercial Banks CPS Time series data from 2006-2016 ...... 34 Figure 4.4: CCBA Time series data from 2006-2016 ...... 36 Figure 4.5: CBD Time series data 2006-2016 ...... 38

LIST OF ABBREVIATIONS ADF Augmented Dickey Fuller ARDF Autoregressive Distributed Lag ATMS Automated Teller Machines CBD Commercial bank Deposits CBK Central Bank of Kenya CCBA Commercial-Central Bank Assets CPS Credit to Private sector CUSUM Cumulative Sum of Recursive results ECM Error Correction Model ECT Error Correction Term GDP Gross Domestic Product GNP Gross National Product KNBS Kenya National Bureau of Statistics LL Liquid Liabilities MRM Multiple Regression Model OLS Ordinary Least Squares

CHAPTER ONE 1.0 INTRODUCTION 1.1 Background of the Problem The banking sector plays a crucial and vital role in being a key financial intermediary in an economy because of the role it plays as ―a provider of liquidity in monetary services and as producers of information‖ Diamond and Dybvig (1983). The banking industry in Kenya has been a major accelerator to the growth of other industries and sectors through their intermediation role of regulating the demand and supply of credit. Banking industry in Kenya comprises of both banking and Non-Bank Financial (NBFIs) Institutions in most countries, banks provide essential financial services that play a key role in aiding economic growth. They lend money needed to start businesses, purchase homes, secure credit for the purchase of durable consumer goods, and furnish a safe place in which societies can store their wealth. In developing countries, improvements in the banking sector could have significant impact on the allocation of financial resources the most important source of financing for private investment firms. This is largely due to the underdevelopment of the financial markets. As a result of this vital role, the banking sector has been ―singled out for special protection and it is clear why such great emphasis is placed on regulation and supervision of the banking sector‖ Barth et al. (2006).

The banking environment in Kenya has in the past decade, undergone many regulatory and financial reforms. ―These reforms have brought about many structural changes in the sector and have also encouraged foreign banks to enter and expand their operations in the country‖ Kamau (2009). Kenya‘s financial sector is largely bank-based as ―the capital market is still considered narrow and shallow‖ Ngugi et al (2006). Banks dominate the financial sector in Kenya and as such the process of financial intermediation in the country depends heavily on commercial banks Kamau (2009). Infact Oloo (2009) describes the banking sector in Kenya as the bond that holds the country‘s economy together. Sectors such as the agricultural and manufacturing virtually depend on the banking sector for their very survival and growth.

The importance of the financial sector in the development of Kenya‘s overall economy cannot be underestimated because the banking sector dominates its economic development. This has been made possible by mobilizing the savings of general people and directing them towards investments thus promoting economic development and

growth. Therefore, the profitability of the banking sector has a direct impact on overall development and growth of the Kenyan economy. Profitability is one of the key indicators of industry development and is a signal on the prosperity of certain sectors in a country‘s economy. A sound and profitable banking sector will hold out negative shocks and act as a safeguard of financial stability Sologoub (2006).

Several macroeconomic variables affect the development of the banking sector in any country. An example of these macroeconomic variables may include: narrow export base, unplanned import, and small foreign exchange reserve, and huge population, tiny industrial base, high inflation emanated from a politics-influenced monetary policy, volatile exchange rate, and pandemic corruption among other macroeconomic variables. ―There are several important factors that are responsible for affecting banks profitability including: bank-specific, industry-specific and macroeconomic determinants.‖ Athanasoglou Brissimis, and Delis (2006).

Development can be defined as ―an approach to determining the extent to which set objectives or goals of an organization are achieved in a particular period. The objectives or goals can be in financial or non-financial terms; therefore, development can also be financial or non-financial. Bank development is defined as the capacity to generate sustainable profitability‖ European Central Bank (2010). Theoretical determinants of bank's performance stem from two broad sources: ―micro bank-specific factors and the macroeconomic environment. Micro bank specific factors include the individual risk exposure, operating strategies, and the degree of management expertise. Macro factors include GDP growth, inflation, unemployment, interest rate, exchange rate and the level of competition. Evidence from the 1994 Mexican financial crisis suggested, bank-specific variables explained the likelihood of bank failure, while macroeconomic factors determined financial development more generally and the timing of bank failures‖ Gonzalez-Hermosillo, Pazarbasioglu and Billings (1997).

Evaluating bank development is a complex process that involves assessing interaction between the environment, internal operations and external activities. The primary method of evaluating internal development and performance is analyzing accounting data. In general, a number of financial ratios are usually used to assess the performance of financial intermediaries.

―Financial ratios usually provide a broader understanding of the bank's financial condition since they are constructed from accounting data contained in the bank's balance sheet and financial statement. Another key management element that many studies have found to be a primary factor in assessing bank development is operating efficiency‖ (Bashir 2003).

In this study, the development of banks is measured using four main ratios: The ratio of liquid liabilities to nominal GDP provides a measure of the size of the financial intermediaries; The ratio credit to the Private Sector (CPS) relative to nominal GDP indicates the level of financial services and is employed to measure all private resources used to finance the private sector; Central Bank Assets (CCBA) is the ratio of commercial bank assets to commercial plus central bank assets; and the ratio of commercial banks deposits to nominal GDP that shows the liquidity of the banking sector. I choose GDP as the independent variable. Using the four ratios as dependent variable provides the convenience in comparing my results to other findings reported in the literature.

According to Oliver (2000), macroeconomic factors are such factors that are pertinent to a broad economy at the regional or national level and affect a large population rather than a few select individuals. The government, businesses and consumers closely monitor macroeconomic factors such as inflation, gross domestic product, exchange rate and interest rates. ―Many studies show that bank financial development is influenced by the business cycle‖ Lowe and Rohling (1993). During boom times, firms and households commit larger proportions of their income flow to debt servicing with preference for leverage following a pro-cyclical pattern. Assuming all else constant, both the demand for leverage and banks' income will rise with the business cycle. More studies surveyed in Laker (1999) find that the variables most often found to be positively associated with strong bank income growth are GDP growth and changes in interest rates. Others also include the inflation rate, the long-term interest rate and/or the growth rate of money supply.

Inflation can be referred to as ―a sustained or persistent increase in the general prices of goods and services in the long run. This is primarily brought about by the increase in earning which is not proportionate with the increase in the production of goods and services‖ Calomiris et al (1997). Kaufman (1998) stated that this is due to the case of

more money chasing few goods general prices of goods and services are bound to increase leading to significant reduction in disposable income and the purchasing power of the low income earners bracket of population who comprise the majority and this ultimately leads to low level of savings and high rate of loan defaults. This ultimately affects the financial development of commercial banks.

The author further stated that interest rate is the cost usually expressed as a percentage of the amount borrowed (principal) charged by a lender to the borrower for lending money. To the lender (commercial bank) it is a return or a source of revenue while to the borrower it is a cost. The interest rate is usually charged per month or per annum and is determined by and directly proportion to the risk levels of the borrower. Amount borrowed should be invested in activities or use that generates more return than the lending rate to make economic sense.

Exchange rate is ―the amount of local or home currency required to purchase one unit of a foreign currency‖ Calomiris et al (1997). According to Schiller (2008), the interest rate is determined by the demand and supply of the foreign currency (BOP), trade balance, current account balance and capital account balance. GDP Domestic Gross Product (GDP) according to Wikipedia ―is the market value of all officially recognized final goods and services produced within a country in a given period of time. GDP per capita is often considered an indicator of a country's growth‖.

Schiller (2008) further explains that many developing countries have been able to withstand the external shock of the international financial meltdown, as a result of prudent financial management of their economies. All developing nations are being affected, directly or indirectly, by this international financial tsunami. A firm usually fails because of a combination of factors. The failure rates of corporations are determined by three factors i.e. firm risk which is dependent on the effectiveness of the management and adequacy of its capital; industry risk i.e. a shock to a specific industry such as its exposure to import reform, tariff reform etc.; and macroeconomic risk i.e. risk deriving from the macroeconomic or monetary factor.

―The banking environment in Kenya has, for the past decade, undergone many regulatory and financial reforms. These reforms have brought about many structural changes in the

sector and have also encouraged foreign banks to enter and expand their operations in the country‖ Kamau (2009). ―Kenya‘s financial sector is largely bank-based as the capital market is still considered narrow and shallow‖ Ngugi et al. (2006). ―Banks dominate the financial sector in Kenya and as such the process of financial intermediation in the country depends heavily on commercial banks‖ Kamau (2009). Infact Oloo (2009) describes the banking sector in Kenya as the bond that holds the country‘s economy together. Sectors such as the agricultural and manufacturing virtually depend on the banking sector for their very survival and growth. The performance and development of the banking industry in the Kenya has improved tremendously over the last ten years, as only two banks have been put under CBK statutory management during this period compared to bank-failures between 1986 and 1998 (Mwega, 2009).

According to the Central Bank of Kenya July 2016 Report, the Kenyan Banking Sector‘s total assets stood at Ksh. 3.6 trillion, with gross loans worth Ksh. 2.4 trillion, while the deposit base was Ksh. 2.6 trillion and profit before tax was Ksh. 38.4 billion as at 31st March 2016. Over the same period, the number of bank customer deposit accounts and loan accounts stood at 37,455,795 and 7,163,560 respectively. The number of commercial banks deposit accounts increased by 7.8 million (26.3%) from 29.7 million in March 2015 to 37.5 million in March 2016. Part of the increase in deposit accounts was driven by deposit accounts opened through the mobile phone platforms. However, the sector‘s gross loans and advances increased from Ksh. 2.0 trillion in March 2015 to Ksh. 2.2 trillion in March 2016, translating to an increase of 20%. The increase in the loan book was contributed by increased demand for credit from all the economic sectors.

Furthermore, the CBK 2016 report brought out that the value of gross non-performing loans (NPLs) increased by 47.5% from Ksh. 117.2 billion in March 2015 to Ksh. 172.9 billion in March 2016. This resulted in an increased ratio of gross NPLs to gross loans of 7.8% in March 2016 from 5.7% in March 2015. Consequently, the quality of assets, measured as a proportion of net non-performing loans to gross loans deteriorated from 2.6% in March 2015 to 4.3% in March 2016. The banking sector recorded a Ksh. 38.4 billion pre-tax profits in the quarter ended March 2016. This was an increase of 2.9% from Ksh. 37.3 billion registered in the quarter ending March 2015. This increase in profitability is attributed to a 15% (Ksh. 16.5 billion) increase in total income from Ksh. 110 billion in Q1 of 2015 to Ksh. 126.5 billion in Q1 of 2016. However, the increase in

income was neutralized by an increase of total expenses by 21.2% (Ksh. 15.4 billion) from Ksh. 72.7 billion in Q1 of 2015 to Ksh. 88.1 billion in Q1 of 2016.

M‘Amanja & Morrissey (2005) bring out that the effects of expansionary fiscal policy of the previous decade, which led to the establishment of highly protected but grossly inefficient private industries and state corporations, began to cause serious strain on the economy‘s scarce resources. He further states that budget deficits increased rapidly, exports and imports fell, and the economy performed poorly with average real GDP falling further to 4.2% over the period. The downward spiral continued in the fourth decade of independence. This was as a result of poor fiscal and monetary policy regime, external and internal shocks. This was further compacted by political events which resulted in the worst economic performance and development in the short history of the country. The average real GDP fell to a low of 2.2% between 1990 and 2002. The main unresolved question is, what went wrong, and what remedy, if any, is there for Kenya‘s economic rejuvenation?

Pazarbasioglu and Billings (1997) stated that this situation has propelled Kenya to focus on the following key objectives: to increase its rate of investment and capital formation, so as to accelerate the rate of economic growth; to increase the rate of savings and discourage actual and potential consumption; to diversify the flow of investments and spending from unproductive uses to socially most desirable channels; To check sectoral imbalances; To reduce widespread inequalities of income and wealth ; To improve the standard of living of the masses by providing social goods on a large scale.

The authors further explained that for the purpose of development, not only an expansionary budget but a deficit is desirable in Kenya. The government expenditure on developmental planning projects must be increased. It may be financed even by means of deficit financing. Deficit financing, here, refers to the creation of new money by printing additional notes by the government or by borrowing from the central bank which ultimately means creation of additional money supply. However, the government must use the technique of deficit financing cautiously. An excessive dose of deficit financing may lead to inflation which may endanger economic growth.

1.2 Statement of the Problem Although several studies have discussed the relationship between financial bank development and macroeconomic variables, none have looked at the effects on more than one variable. Those that have ventured into this area of study have considered only some selected macroeconomic variables. For instance, Wamucii (2010) examined the relationship between inflation and financial development of commercial banks in Kenya. He established that the development of commercial banks seemed to improve with the increase in inflation. Kipngetich (2011) did a study on the relationship between interest rates and financial development of commercial banks in Kenya and found that there is a positive relationship between interest rates and financial development of commercial banks.

Several studies by King & Levine (1993), Goldsmith (1969), Ndebbo (2004), Goldsmith (1973), McKinnon, (1973) and Shaw (1973) have confirmed that commercial bank development has a great positive impact on economic development. Schumpeter‘s (1911) groundbreaking work brought forth several theoretical and empirical literature over the years. They argued that governments that create strict sanctions, direct credit and keenly monitor interest rates thus requiring large reserves very easily deter the economic growth of a country. On the contrary, high interest rates entice one to have higher savings thus Commercial banks have a greater supply of funds to offer credit to the private sector to engage in investment. This simple action positively impacts the financial markets and ultimately the economy. Research on the relationship between financial development and economic growth thrives in both developed and developing countries. Several views on the connection between the two have been observed. ―From the findings, it is not clear whether financial development is the cause of economic growth or economic growth is cause of financial development‖ Levine (1997).

It is therefore clear that the area of macro-economic factors and the financial development of commercial banks in Kenya have not been fully explored together. Several studies have been undertaken however the results of these studies appear to be inconclusive. There is a research gap in Kenya as most of the studies done in the area are conducted in the USA, Europe and to some extent Asia. Empirical studies examining the relationship between a combination of macroeconomic factors and financial development have presented mixed findings as there has been no test on the combination of these factors.

Furthermore, there has been a gap in identifying the key measures of financial development.

Studies in Kenya have mostly focused on the direction of causality between financial deepening and economic growth and have produced mixed results. Moreover, most of the studies reviewed have focused on traditional indicators such as the ratio M2/GDP and credit to the private sector/GDP. The study provides further evidence to the existing ones by making use of complementary variables such as the ratio liquid liabilities to GDP which is a measure of extended broad money, the ratio of commercial bank assets to commercial plus central bank assets, the ratio credit to the private sector to GDP and the commercial bank deposits to GDP.

According to Carmen Lawrence (2011), the goal of economic growth is clearly the touchstone for judging major public policy decisions. Research in the last decade shows that countries with higher degree of financial development tend to post higher economic growth. Therefore, exploring the channels through which banking sector development affects economic growth, is critical for policy design in Kenya. Hence, the study would be valuable to Kenyan policymakers who have to prioritize among multiple policy reforms to help the society grow faster. The study will be important for future researchers and scholars especially in the area of finance- bank growth relationship.

This study aims to provide further evidence by examining the effects macroeconomic factors have on financial development of commercial banks in Kenya for the period 2006- 2016. Specifically, it extends the previous studies by widening the scope of financial development indicators to include in the study, the ratio commercial bank assets to commercial plus central bank assets, as a measure of the extent to which commercial banks allocate savings in the Kenyan economy against the central bank. The study goes further to use extended broad money measured as liquid liabilities, bank credit to the private sector and commercial bank deposits to measure activity and the size of the banking sector in Kenya, therefore filling the knowledge gap in the existing literature. This study therefore seeks to find out if macro-economic factors affect the financial development of commercial banks in Kenya which is captured by four alternative financial deepening indicators; and to establish whether they ultimately have a positive, negative or neutral effect on Kenya’s GDP.

1.3 General Objective The purpose of this study was to establish the impact of key macro-economic factors on the development of commercial banks in Kenya. This paper sets to clearly establish the relationship between each of the factors and financial development.

1.4 Research Objectives This Paper tackled the below objectives: 1.4.1 To determine the effects of Gross Domestic Product growth on the financial development of commercial banks in Kenya 1.4.2 To determine whether inflation has an impact on the financial development of commercial banks in Kenya 1.4.3 To determine whether lending rates affect the financial development of commercial banks in Kenya 1.4.4 To determine whether exchange rates affect the financial development of commercial banks in Kenya 1.4.5 To determine whether Money supply (M3) affect the financial development of commercial banks in Kenya

1.5 Importance of the Study This study is important in that it sheds light on how macro-economic factors affect the financial development of commercial banks in Kenya. This study will be of value to different stakeholders including:

1.5.1 Scholars and academicians This study will increase body of knowledge on the effect of macroeconomic variables on the development of commercial banks in Kenya. The study will extent the level of knowledge on the relationship between macroeconomic variables and development of commercial banks in Kenya. It will also suggest areas for further research so that future scholars can pick up these areas and study further.

1.5.2 Kenya Bankers Association The study will be important to the Kenya Bankers‘ Association and the Central Bank of Kenya in making banking policy decisions whose overall objectives is to influence the level of economic activity and ensure a stable banking sector.

1.5.3 The government through its relevant agencies and the policy makers in Kenya This study also contributes to the existing literature on economic reforms in Kenya. The results are useful in designing effective fiscal policy programs that can propel economic performance and development to achieve the desired level of development through financial intervention. The study provides an insight to the policy makers on the choice of reforms programs as well as providing guidelines on the implementation of such reforms to promote robust economic development. In addition, the study creates an understanding on the different category of fiscal variables and how they affect the overall welfare of different economic agents. This is especially important for individual investors.

This is all desirable for the budget making process since it can be used as a guiding principle when allocating national resources under different votes. The study contributes to the body of knowledge on the effectiveness of fiscal adjustment in achieving sustainable economic growth. It also provokes researchers to critically evaluate the effectiveness of different government policies in order to prescribe or suggest to the policy makers the best course of action for achieving economic goals.

1.6 Scope of the Study This research study was undertaken in Kenya, covering all the forty three banks licensed, supervised and regulated by CBK as at December 2016. The study covered a time frame of 40 quarters starting from January 2006 to December 2016. Quarterly time series data was collected from the various CBK statistical bulletins for financial deepening indicators while real GDP data was collected from various economic survey reports available online from Kenya National Bureau of Statistics (KNBS) website.

The study is limited to the period 2006 to 2016 for two reasons. First, the period is long enough to capture the effect of Inflation, interest rates, GDP, Money supply and Exchange rates on Kenya‘s commercial bank development. Secondly, the scope of 10

years allows for a critical in-depth exploratory study that will offer insight into the problem at hand.

1.7 Definition of Terms 1.7.1 Inflation – ―A sustained or persistent increase in the general prices of goods and services in the long run‖ Adnan Noureen (2006)

1.7.2 Exchange rate – ―The price of a nation‘s currency in terms of another currency. An exchange rate thus has two components, the domestic currency and a foreign currency, and can be quoted either directly or indirectly. In a direct quotation, the price of a unit of foreign currency is expressed in terms of the domestic currency. In an indirect quotation, the price of a unit of domestic currency is expressed in terms of the foreign currency‖ Frankel Jeffrey (2011a)

1.7.3 Gross Domestic Product – ―A monetary measure of the market value of all final goods and services produced in a period (quarterly or yearly) or income.‖ Frankel Jeffrey (2011a)

1.7.4 Financial Development – ―Financial development can be defined as the policies, factors, and the institutions that lead to the efficient intermediation and effective financial markets or may be defined as the developments in the size, efficiency and stability of and access to the financial system‖ Adnan Noureen (2006)

1.7.5 Money Supply – ―is the total amount of monetary assets available in an economy at a specific time. Money supply data are recorded and published, usually by the government or the central bank of the country. Public and private sector analysts have long monitored changes in money supply because of the belief that it affects the price level, inflation, the exchange rate and the business cycle.‖ Johnson (2005)

1.7.6 Fiscal Policy – ―Fiscal policy is the means by which a government adjusts its spending levels and tax rates to monitor and influence a nation's economy. It is the sister strategy to monetary policy through which a central bank influences a nation's money supply‖. Frankel Jeffrey (2011a)

1.8 Chapter Summary This chapter clearly covered the statement of the problem, the purpose and the scope of the study in relation to macroeconomic factors relationship with bank development. It first started by giving a background of the study which brings out the problem that this paper sets out to tackle. The chapter also looked at the purpose of the study which is to show whether 5 main macro-economic factors can bring about development of commercial banks in Kenya. It further highlighted the possible research questions that will be tackled throughout the paper.

The chapter also further reviewed the importance of this study in that it sheds light on how macro-economic factors affect the financial development of commercial banks in Kenya. This study will be of value to different stakeholders including: scholars and academicians, managers of commercial banks, Kenya Bankers Association, Central Bank of Kenya, government through its relevant agencies and the policy makers in Kenya.

Lastly, it clearly highlighted the scope of the study which is limited to the period 2006 to 2016 for two reasons. First, the period is long enough to capture the effect of Inflation, interest rates, GDP, Money supply and Exchange rates on Kenya‘s commercial bank development. Secondly, the scope of 10 years allows for a critical in-depth exploratory study that will offer insight into the problem at hand.

Chapter two will proceeded to discuss the associated Literature; Chapter three will tackle the research methodology that will be used to investigate the macroeconomic factors; chapter four will clearly bring out the related findings and chapter five will fully discuss the findings and offer conclusions and recommendations based on these findings.

CHAPTER TWO 2.0 LITERATURE REVIEW 2.1 Introduction In this chapter, a critique of both theoretical and empirical literature on the relationship between Macro economic variables and the financial development of the banking sector. This chapter reviews the theories and exposes the theoretical foundations that elucidate on the effects of macroeconomic factors on financial development. It will further delve into the different theories that will present measurements of financial development.

2.2 Effect of Inflation on Financial Development of Commercial Banks This is a vital determinant of financial development of commercial banks in Kenya. This is because high rates of inflation are usually associated with high rates of borrowing which translates to high interest rates. This was further explained by Bashir (2003) who sated that the profitability of banks or its positive growth is brought about when inflation rates are anticipated. However, unanticipated inflation rates negatively affects the profitability of the commercial banks. This is because this enables banks to adjust their interest rates accordingly to suit the current financial climate. This also has positive effects on the traders as they are not suddenly jolted by the shocks in the financial markets which may greatly hinder their transactions thus ultimately affecting the profitability of the commercial banks.

This act of adequately adjusting the interest rates also caters to ensure that the bank can effectively increase its revenues rather than costs. Bourke (1989) also found a positive relation between bank profitability and inflation rates. He discovered that hired inflation rates led to higher loan rates and thus higher revenues were generated by the bank. However, he also noted that inflation has drawbacks if the rate of inflation is growing at a slower rate than other costs such as overheads.

This is further cemented by Perry (1992) assertion that the effect of inflation of banking development is directly related to whether the inflation is expected or unexpected. This is because when inflation is anticipated, the interested rates are also adjusted accordingly leading to a positive effect on the profitability. On the other hand, if inflation is not expected, then this tends to have a negative effect on the development of the bank due to cash flow constrictions for the borrowers. This eventually leads to untimely dissolution of

loan agreements which is detrimental to the bank‘s wellbeing as it leads to loan losses. It goes without saying that is banks are reluctant to adjust their interest rates it can lead to increase in costs rather than revenues which are essential for high bank development.

Bourke (1989) revealed a positive relationship between inflation and bank profitability. Higher inflation rate lead to higher loan rates, and hence higher revenues will be generated by the bank. Inflation has a negative effect on bank profitability if wages and other costs (overhead) are growing faster than the rate of inflation. In their work Demirguc-Kunt et al.(1998) performed the exhaustive analysis of variables which are not under the control of bank management and may have significant effect on bank development (i.e., external variables): inflation rate, GDP per capita, GDP per capita growth, taxation level, overall financial structure, various legal and institutional factors.

Mamatzakis and Remoundos (2003) examined the determinants of the performance and development of Greek commercial banks from 1989 to 2000. They measured the profitability of the commercial banks using the ratios of return on assets (ROA) and return on equity (ROE). They considered internal factors, like management policy decisions and external factors, like economic environment to explain the profitability of the banks. The results suggested that the variables related to management decisions assert a major impact on the profitability of Greek commercial banks.

Naceur (2003) investigates the impact of banks characteristics, final structure and macroeconomic indicators on banks net interest margin and profitability in Tunisian Banking Industry for the 1983-2000 period. High net interest margin and profitability tend to be associated with banks that hold relatively high amount of capital, and with large overheads. Naceur finds that inflation and growth rates have negative and stock market development has positive impact on profitability and net interest margin.

All in all, the research and findings one on the correlation between inflation and profitability are mixed. This is seen in the Empirical studies done by Guru et al (2002) for Malaysia and Jiang et al (2003 for Hong Kong. These studies corroborate that high inflation rates tend to lead to higher profitability and development for banks. This is however contradicted by Abreu and Mendes (2001) who bring out that there is negative correlation of inflation and development in European countries. This is further cemented

by findings from Demirguc-kunt and Huizinga (1999) who noticed that banks in developing countries were less profitable in environments that were affected by inflation coupled with a high capital ratio. This leads to an increase in costs for the bank rather than to increased revenue.

This study expects to have a positive and significant relationship between inflation and development which is characterized by profitability. This is because this theory is supported by several other theories like that of Anthalsogou et al. (2008), Kosmidou et al (2006) Pasiouras et al. (2007) and Demirguc-Kunt and Huizinga (1999). This is because high inflation rates are usually associated with high loan rates which ultimately leads to high incomes and thus increased profitability and development.

2.3 Effect of Lending Interest Rates on Financial Development of Commercial Banks This is the price one pays for borrowing money from any financial institution or lender. It can also be defined as ―the fee paid for on borrowed assets‖ Crowley (2007). The amount of ineptest paid can be seen as the ―rent money‖ for borrowing the asset. This is critical for the capitalist society and ―can be expressed as percentage rate over the period of one year.‖ It clearly gives an indication on the market information on the ground regarding ―expected changes in the purchasing power of money for future inflation‖ Ngugi (2001).

It is known that fluctuating process in the interest rates of the market bring about great changes in the performance and ultimate development of commercial banks. Samuelson (1945), states that under common conditions, increasing interest rates leads to the overall increase in the bank‘s profits. He argued that the entire banking system is immensely helped rather than hindered by an increase in interest rates. A more accurate measurement of how fluctuations on market interest rates affect banking firms largely depends on the sensitivity of bank's assets and liabilities (interest rates and volume) towards variations in open market rates.

Somoye and Bamidele (2009) studied the Impact of Macroeconomic instability on the banking sector lending behavior in Nigeria. The study concluded that banks in Nigeria took a long-run perspective of the macroeconomic instability in modifying their lending behavior. Also, they had both the short and long term view of the industry inherent characteristics in adjusting their lending behavior. The study recommended that banks

should monitors their deposit mobilization capacity and asset base because they had major implications on their lending behavior both in the short and long run; banks may only bother about the long-run implications of macroeconomic instability on their lending because it does not matter in the short-run; macroeconomic policy makers should adopt measures aimed at controlling inflation and sporadic increases in money supply because of their negative impact on bank lending and the consequential deleterious effect of loan curtailment on investment and economic growth in the long-run.

Okpara (2009) determined the major factors that influence the banking system in Nigeria. Using factor analysis techniques, the Akpara concluded that undue interference from board members, political crises, Undercapitalization, and fraudulent practices are considered the most critical factors that impact the development of banking system in Nigeria.

Njihia (2005) found that the loan component have a significant effect on quoted bank profit. If banks do not get enough deposits, capital adequacy level may be affected and extension of loans may not be done hence interest on deposits is an important consideration. Different degree of elasticity leads to non-proportional changes in the value of assets and liabilities as market interest rate change which then affect the value of the banking firm.

A positive relationship between interest rates and non-performing loans was discovered by Ogweso (2006). He found an indication that when interest rates increase, commercial banks should put in place systems to deal with non-performing loans to minimize the great effects on bank development. Furthermore, according to Matu (2001), the poor development of commercial banks puts pressure on them to retain high lending rates in an attempt to minimize the losses associated with these loans.

This is further solidified by Kipngetich (2011) who did a study on the relationship between interest rates and financial development of commercial banks in Kenya. He found that there is a positive correlation between interest rates and financial development of commercial banks. Banks should therefore carefully manage their interest rates to manage their performance and development. Interest rates on Borrowing are anticipated to have a positive relationship with the profitability of commercial banks.

2.4 Effect of GDP Growth on Financial Development of Commercial Banks This is the most common macro-economic indicator which is used to measure the total economic activity within an economy. The GDP Growth rate reflects the state of the economic cycle and is expected to have an impact on the demand for bank loans. The economic conditions and the specific market environment would affect the bank's mixture of assets and liabilities. Sufian and Habibullah (2010) point out that the GDP is expected to influence numerous factors related to the supply and demand for loans and deposits. Favourable economic conditions will affect the demand and supply of banking services positively. Bank's development, growth and profitability is limited by the growth rate of the economy. If the economy is growing at a substantial rate, a soundly managed bank would profit from loans and securities sales. Economic growth can enhance bank's profitability by increasing the demand for financial transactions, i.e., the household and business demand for loans. Strong economic conditions also characterized by the high demand for financial services, thereby increasing the bank's cash flows, profits and non interest earnings. Thus there is a positive relationship between the growth rates of Gross domestic product and the profitability of the bank.

Using country data for 80 countries over period 1988-1995, they found positive relationship between inflation and profitability, which may signify (1) about higher level of profits which bank could gain from float under the condition of inflationary environment; (2) that bank expenses caused by inflationary processes are lower than bank profits obtained due to the same reason. There was not observed any correlation between GDP per capita growth and bank profitability, while some evidence of positive relationship between GDP per capita index and profitability was noticed. The influence of structural and institutional factors on bank profitability was found to be more significant in developing countries than in developed.

In Colombian case, Barajas et al. (1999) examines the effects of financial liberalization on banks' interest margin. After liberalization, is found that loan quality increased and overall spread has not declined, the relevance of the different factors behind the bank spreads are affected by such measures. Guru et al. (2002) studies on a sample of seventeen commercial bank 1986-1995 time period in Malaysia. In this study, it is found that efficient expenses management is one of the most significant in explaining high bank

profitability, high interest ratio is associated with low bank profitability and inflation is found to have positive effect on bank development.

Demirguc-Kunt and Huizinga (2000) and Bikker and Hu (2002) attempted to identify possible cyclical movements in bank profitability - the extent to which bank profits are correlated with the business cycle. Their findings suggest that such correlation exists, although the variables used were not direct measures of the business cycle. Demirguc- Kunt and Huizinga (2000) used the annual growth rate of GDP and GNP per capita to identify such a relationship, while Bikker and Hu (2002) used a number of macroeconomic variables (such as GDP. unemployment rate and interest rate differential).

In this study profitability is expected to have a positive significant relationship with GDP growth. The positive impact of GDP growth supports the argument of the positive association between growth and financial sector development, and is also confirmed by Kosmidou (2006) and Hassan and Bashir (2003). GDP is expected to have impact on the demand for bank loans, whereby increase in bank loans would increase the bank profitability.

This is further supported by macroeconomic evidence for the period 1975-2003 in Chile which indicated that the tax reform explained an increase in private investment of three percentage points of GDP. The study indicated that tax reforms, which involved the reduction in corporate tax rates, resulted to impressive macroeconomic development by almost all standards. GDP growth averaged 7.6 per cent between 1985 and 1997, while unemployment and inflation dropped in a scenario of overall macroeconomic stability. Private investment showed an impressive development, climbing from 12 per cent of GDP in 1984-86 to 22.5 per cent of GDP in 1995-97.

2.4.1 Cointegration The cointegration test determines if the integrated variables are cointegrated. Cointegration regressions measure the long-term relationship between the dependent and the independent variables. The Johansen maximum likelihood procedure in a vector autoregressive framework introduced by Johansen (1988) is an essential tool in the estimation of models that involves time series data. The Johansen cointegration approach

is preferred in this study as it allows the researcher to estimate a dynamic error correction specification, which provides estimates of both the short and the long run dynamics. The approach has also been found to be the most reliable and appropriate for small sample properties. Johansen (1990) developed two likelihood ratio tests: the Trace Test and the Maximum Eigen value Test. The two procedures test for the presence of cointegrating vectors between financial development and economic growth. The long run equation estimated was expressed as follows:

∆(GDP)t = β0 + β1 +(GDP)t-1 + β2 (FD)t-1 + ∑ β3∆(GDP)t-i+ ∑ β4∆(GDP)t-I + εt

Where: ∆ expresses the difference operator. Furthermore, the first part of the equation with the coefficients β1 and β2 are long-run parameters. The short-run effects are captured by the coefficients of the first-differenced variables in equation represented by β3 and β4. In addition, p represents the maximum lag length which is determined by the user.

2.4.2 Error correction Model Granger (1987), showed that if two variables are cointegrated, then they have an error correction representation. The Error Correction Model (ECM) provides information about the long run, short run relationship as well as the speed of adjustment between the variables in incorporating to the estimated equation, the error correction term (ECT). Therefore, the following error correction model was estimated: ∆(GDP)t = β0 + ∑ β1∆(GDP)t-i+ ∑ β2∆(FD) t-i t-I + β3ECTt-i + εt

Where β3 is the coefficient of the ECT.

The ECM enables to distinguish between the short-run and the long-run and its results indicate the speed of adjustment back to long run equilibrium after a short run shock. The estimated equation is used to obtain the ECT (ECTt-1) which is later used in the ECM.

2.4.3 Residual Diagnostics Post estimation tests are conducted in order to confirm the adequacy of the model as well as to ascertain the validity of inferences made from the estimated result, based on an examination of the structure of the residuals. The diagnostic tests of the estimated model

suggest that the model passes the residuals normality test, the residuals serial correlation LM test, and the residuals heteroskedasticity test. The plots of the cumulative sum of recursive residuals (CUSUM) is as well included to test if the estimated model is stable over the study period.

2.5 Effect of Exchange Rates on Financial Development of Commercial Banks This is a measure of competitiveness amongst countries all around the world. It is commonly known as the real exchange rate. Furthermore, it is the index of competitiveness of currency of currency of any country and an inverse relationship between this index and competitiveness exists. Lower the value of this index in any country, higher the competitiveness of currency of that country have. It is commonly thought that the inconsistency and volatility of the exchange rate greatly affects expected cash flows. This has an adverse effect on the profitability experienced by banks and therefore affects their performance and development as a result of changes in the home currency denominated revenues and costs. It also affects the terms of competition for firms with international activities. Amihud and Levich (1994) and Opati (2009) did a study on causal relationship between inflation and exchange rates in Kenya where it was recognized that an increase in inflation leads to the depreciation of the local currency. Furthermore, Ndungu (2000) declares that Kenya‘s exchange rate policy has undergone various shifts which are mostly driven to a large extent, by the economic events especially balance of payment crisis.

Koima (2011) did a study on the relationship between financial performance for multinational corporations in Kenya and exchange rates volatility and found that Sterling Pound. United States Dollar, Euro exchange rate and Japanese Yen exchange rate influence the financial performance of Multinational Corporation.

2.5.1 Model Specification This study followed the steps of Jalil, Wahid and Shahbaz (2010) and Waiyaki (2013) and adopted the endogenous growth model. Proponents of the endogenous growth models such as Pagano, (1993), hold that capital accumulation can increase the long run trend rate of economic growth. However, to permit capital accumulation it is necessary to increase the savings ratios. Thus, a well-functioning financial system encourages investment, promotes technological innovation that ultimately leads to economic growth

through savings. To capture the potential effects of financial deepening on economic growth, we considered the simplest endogenous growth model: the ‗AK‘ model, where aggregate output is a linear function of the aggregate capital stock Yt = AKt ------(1) Where, Yt is output at time t, A is total factor productivity and Kt is the measure of real capital stock.

The AK model can be derived assuming that the population is stationary and that only capital stock is subject to constant return to scale. Conventionally to estimate the capital stock, Kt is measured as the previous period amount of capital (Kt-1) corrected for depreciation (δ) plus gross investment in current period (It). Thus, with capital depreciating at the rate, the gross investment becomes: It=Kt+1 - (1- δ)Kt ------(2)

In a closed economy with no government, capital market equilibrium requires that Savings equals investment. However, Pagano (1993) assumes that a proportion of 1-θ is lost during the process of financial intermediation and thus the fraction δ of total savings can be used to finance investment. Therefore, the savings-investment relationship can be written as: θSt=It ------(3)

From equation (1), we introduce the growth rate at time t+l which is gt+l=Yt+l/Yt – l=Kt+l /Kt-l. Using eq. (2) and dropping the time indices, the steady-state growth rate can be written as, the steady state growth rate of output becomes: gy=A I/Y– δ = A θ S - δ ------(4)

The capital market equilibrium condition (3) has been used and denoted the gross saving rate s or St /Yt. Thus, s=St /Yt = St / AKt

Equation 4 expresses that economic growth depends on the total factor productivity (A), the efficiency of financial intermediation (θ), and the rate of savings(S). Financial deepening is assumed to affect development through the amount of savings put in investment. Wurgler (2000) as quoted by Ngugi, Amanja and Maana (2012) shows that even if financial deepening does not lead to higher levels of investment, it allocates

existing investment better and therefore promotes development. Importantly, when the rate of depreciation is assumed to be constant, economic growth depends on financial deepening. From the above Yt can be expressed as follows: Yt =β0 + β1 St/Yit + ε------(5)

Whereby Yt is the natural logarithm of real GDP of Kenya and St/Yit the natural logarithm of savings to nominal GDP that proxies financial development (St/Yit = FD). B0 is the intercept, β1 is the coefficient that gives the effects of financial development on economic growth while ε is the error term.The generic model estimated was given as: GDP= β0 + β1FDit + εt ------(6)

In equation (6), FD was estimated using each alternative measure namely: liquid liabilities to GDP (LL), credit to the private sector to GDP (CPS), commercial bank deposits to GDP (CBD).(CCBA) on the other hand is the commercial bank assets to commercial bank and central bank assets

For the GDP growth, the study took into consideration the GDP growth figures by the computed by the KNBS. The Exchange rate between Kenya Shillings and US dollar and Money supply (M3) was obtained from the already compiled figures by the Central Bank of Kenya. The error term represents the effect of factors other than selected macroeconomic factors on the financial development of the banking sector in Kenya.

Engineering techniques to manage risk associated with business cycle forecasting. Generally, higher economic growth encourages bank to lend more and permits them to Charge higher margins, as well as improving the quality of their assets. Neely and Wheelock (1997) use per capita income and suggest that this variable exerts a strong positive effect on bank earnings. The depreciation of the Kenya shilling against United States Dollar is expected to decrease bank profitability and development.

2.6 Effect of Money Supply on Financial Development of Commercial Banks This is the ―sum of currency outside banks and deposit liabilities of commercial banks‖ CBK (2012). Deposit liabilities are more expressly defined as follows: narrow money (Ml); broad money (M2); and extended broad money (M3). These aggregates are further defined as follows:

Ml = Currency outside banking system + demand deposits. M2 =M1 + time and savings deposits + certificates of deposits + deposit Liabilities of Non-Bank Financial Institutions (NBFIs). M3= M2 + residents' foreign currency deposits.

The Central Bank of Kenya (CBK) has been targeting monetary aggregate (broad money M3) in its policy decisions. Rotich (2007) implied that at times of high inflation, or positive output, the CBK responded by reducing money supply. Money supply (M3) is expected to have positive effect on profitability and development of commercial banks.

2.7 Chapter Summary This chapter covered literature on macroeconomic factors on bank development. It first started by reviewing the theories on which the study will be build including Structure- Conduct-Performance theory which argues that concentration level of the market through "conduct" link determines the profitability of firm. The study also looked at portfolio theory which looks at both risk and return. It further reviewed the market power hypothesis which states that market power is the main variable that causes profitability of firms to change.

The study further reviewed the empirical studies: for instance. Perry (1992) states that the extent to which inflation affects bank profitability depends on whether inflation expectations are fully anticipated. Other empirical studies. Bourke (1989) and Molyneux and Thornton (1992)) have shown a positive relationship between either inflation or long- term interest rate and profitability. Athanasoglou, Delis and Stakouras (2006) revealed that bank profitability and inflation has a strong effect on profitability while banks‘ profits are not significantly affected by real GDP per capita fluctuations.

Naceur (2003) finds that inflation and growth rates have negative and stock market development has positive impact on profitability and net interest margin. Wamucii (2010) did a study in Kenya on the relationship between inflation and financial performance of commercial banks in Kenya. Kipngetich (2011) did a study on the relationship between interest rates and financial performance of commercial banks in Kenya and found that there is a positive relationship between interest rates and financial performance of commercial banks.

The review of literature clearly found a research gap in Kenya as most of the studies done in the area are conducted in the USA, Europe and to some extent Asia. The empirical studies also indicated that the researchers in Kenya only considered one selected macroeconomic variable in a single study. The current study therefore seeks to contribute towards this research gap by establishing the relationship between the macroeconomic variable and financial development of commercial banks in Kenya.

CHAPTER THREE

3.0 RESEARCH METHODOLOGY 3.1 Introduction This chapter describes the procedures and methodologies that were undertaken in conducting the study to arrive at conclusions regarding the relationship between macroeconomic factors and the development of the banking sector in Kenya. Specifically, the chapter covers: research design, population, study sample; data collection, data analysis and model specification.

3.2 Research Design The study employed descriptive as well as correlation research designs. The researcher used time series empirical data on the variables to examine the relationship between selected macroeconomic variables i.e. GDP growth, inflation rate, lending interest rate, exchange rate and money supply (M3) by establishing correlation coefficients between the variables and the financial performance and development of commercial banks as measured by ROA.

3.3 Population and Sample The population of this study comprised of all licensed commercial banks in Kenya between the period of June 2006 and June 2016.

3.3.1 Population As at 30 June 2016, there were 44 registered commercial banks comprising of 43 commercial banks and 1 Mortgage Company.

3.3.2 Sample The sample was made up of the 10 commercial banks selected on the basis of size and market share. According to Parkinson (2011) stratified random sampling is a modification of random sampling in which one divides the population into two or more relevant and significant groups based on one or more attributes. This sampling technique was used because it barred the introduction of biasness. Simple random sampling was applied within each stratum. This ensured that all elements of the study had an equal chance of

being selected i.e. Banks from each strata had an equal chance of selection. Therefore the sample will include 3 large banks, 3 medium banks and 4 small banks. 3.4 Data Collection Methods The data is public data as it is published in the websites of the relevant government agencies including CBK and KNBS. Quarterly time series data was collected from the various CBK statistical bulletins for financial deepening indicators while real GDP data was collected from various economic survey reports available online from Kenya National Bureau of Statistics (KNBS) website. Lending interest rate is obtained by dividing the net advances by interest on advances. The period of study for which data was obtained focused on a ten year period between June 2006 and June 2016.

This study used four key variables to determine different aspects of the below macroeconomic factors:  Consumer Price Index for inflation.  Gross Domestic Product (GDP) growth.  Lending interest rate.  Exchange rate (Kenya Shilling and US Dollar)  Money supply (M3) - The data on inflation (CPI) and GDP growth was obtained from KNBS while data on Money Supply (M3) and exchange rate (USD and Kenya Shilling) was obtained from the CBK. Since all measures capture different information on the role of the financial intermediaries on economic growth.

3.4.1 Liquid liabilities (LL) The ratio of liquid liabilities to nominal GDP provides a measure of the size of the financial intermediaries. The size of financial sector is a measure of its depth (Goldsmith 1969, Levine 1993, and Rousseau and Wachtel, 2000). This study will thus consider Liquid liabilities that mainly consist M3, which is an extended monetization measure including foreign reserves and other large demand deposits and monetary deposits.

3.4.2 The ratio credit to the Private Sector (CPS) This is relative to nominal GDP indicates the level of financial services and is employed to measure all private resources used to finance the private sector. It is the most important

measure of financial intermediary development, (Levine and Zervos, 1998 and Yartey, 2007) as it captures the channeling of funds from savers to investors in the private sector (Ang, 2007). This indicator excludes credit to government, government agencies and public enterprises as well as credit issued by the Central Bank (Levine, et al 2000).

3.4.3 Commercial-Central Bank Assets (CCBA) This is the ratio of commercial bank assets to commercial plus central bank assets .King and Levine (1993) took account of the central banks along with the commercial banks in the measurement of financial sector indicators and assessed the extent to which commercial banks channel savings into investment, monitor firms, influence corporate governance and undertake risk management, relative to the central bank (Huang, 2005). Commercial banks are expected to be more efficient and effective in allocating the savings in productive and profitable projects compared to central banks.

3.4.4 Commercial Bank Deposits (CBD) This is the ratio of commercial banks deposits to nominal GDP that shows the liquidity of the banking sector (Levine and Zervos, 1998) as quoted by Waiyaki (2013). Commercial bank deposits equal demand deposits plus time and saving deposits. The indicator provides an alternative measure to a broad money ratio, especially for developing countries, where a large component of the broad money stock is held outside the banking system (Kar and Pentecost, 2000). This study makes use of real GDP as the dependent variable to measure economic growth. This choice is in line with the works of Ujah (2010).

3.5 Research Procedures This refers to means by which the researcher used to gather the required data. The researcher sought the secondary data from public published websites of the relevant government agencies including CBK and KNBS. Quarterly time series data was collected from the various CBK statistical bulletins for financial deepening indicators while real GDP data was collected from various economic survey reports available online from Kenya National Bureau of Statistics (KNBS) website. Lending interest rate is obtained by dividing the net advances by interest on advances. The period of study for which data was obtained focused on a ten year period between June 2006 and June 2016.

3.6 Data Analysis The quantitative data in this research was analyzed by descriptive statistics including mean, frequency, standard deviation and percentages to profile sample characteristics and major patterns emerging from the data. Data was presented in tables after the use of computer software 'e-views' version 7.0 to further analyze the data. Given that the study model is a multivariate, the study used multiple regression technique in analyzing the relationship between the selected macro-economic factors and the financial development of commercial banks in Kenya. In addition a multivariate regression model was applied to determine the relative importance of each of the four variables with respect to factors influencing customer relationship management. Multiple regression is a flexible method of data analysis that may be appropriate whenever quantitative variables (the dependent variables) is to examined in relationship to any other factors (expressed as independent or predictor variable). The analysis further entailed the computation of the various coefficients of correlation denoted as 'P' in the model to determine the relationship between macro-economic factors and the financial performance of commercial banks in Kenya.

3.7 Chapter Summary A descriptive research design was used to carry out the study. The study population consisted of 43 commercial banks and the sample size consisted of 10 commercial banks. The study relied on secondary data which was collected from annual financial reports. Chapter Four shall clearly document the results and offer analysis on the findings.

CHAPTER FOUR 4.0 RESULTS AND ANALYSIS 4.1 Introduction This chapter analyses the findings, interprets and presents data in line with the objectives of the study. The data obtained is presented in tabular form and percentages. The chapter is further sub divided into several sections that are pertinent to the subjects under study.

4.2 General Information 4.2.1 Gross Domestic Product (GDP) Table 4.1: GDP Time series

YEAR GDP - Dollar (Billion)

2006 25.83

2007 31.96

2008 35.90

2009 37.02

2010 40.00

2011 12.99

2012 50.41

2013 55.10

2014 61.45

2015 63.77

2016 70.53 Table 4.1 shows the time series data for Kenya‘s GDP during the period 2006 to 2016

Figure 4.1: Kenya's GDP from 2006-2016

The GDP exhibited time series changes that had a maximum recorded value of 70.53 and a minimum value of 12.99 which showed a steady GDP growth rate in the period. The GDP however dropped in the year 2011 from 40.00 billion to 12.99 billion dollars.

4.2.2 Liquid Liabilities (LL) Table 4.2: Commercial Banks LL Time series data 2006-2016 YEAR CBA ConsolidatedBarclays KCB Standard CharteredFidelity I&M Victoria Giro Prime 2006 1.0467 1.0207 1.0182 1.0157 1.0132 1.0107 1.0082 1.0057 1.0032 1.0007 2007 1.084 1.004 1.0015 0.999 0.9965 0.994 0.9915 0.989 0.9865 0.984 2008 1.0973 1.0083 1.0058 1.0033 1.0008 0.9983 0.9958 0.9933 0.9908 0.9883 2009 1.04896 1.00566 1.00316 1.00066 0.99816 0.99566 0.99316 0.99066 0.98816 0.98566 2010 1.0267 1.07867 1.07617 1.07367 1.07117 1.06867 1.06617 1.06367 1.06117 1.05867 2011 1.01342 1.01342 1.01092 1.00842 1.00592 1.00342 1.00092 0.99842 0.99592 0.99342 2012 1.0354 1.0084 1.0059 1.0034 1.0009 0.9984 0.9959 0.9934 0.9909 0.9884 2013 1.0445 1.07645 1.07395 1.07145 1.06895 1.06645 1.06395 1.06145 1.05895 1.05645 2014 1.00243 1.00243 0.99993 0.99743 0.99493 0.99243 0.98993 0.98743 0.98493 0.98243 2015 1.0254 1.0094 1.0069 1.0044 1.0019 0.9994 0.9969 0.9944 0.9919 0.9894 2016 1.075 1.07875 1.07625 1.07375 1.07125 1.06875 1.06625 1.06375 1.06125 1.05875

Mean 1.03625 Median 1.032693 Maximum 1.048641 Minimum 1.02577 Std. Dev 0.007104 Skewness 0.647585

Kurtosis 1.886145

Figure 4.2: Time series data of Commercial Bank Liquid Liabilities

Figure 2 shows how CBA, Barclays, KCB and Consolidated banks have recorded higher rates of LL than the other sampled banks. Table 4.2 displays the time series LL data for the 10 sampled banks. The LL had a mean of 1.03625 during the period 2006-2016. On the other hand LL has a skewness coefficient of 0.6475 which is greater than zero. This implies that it may not be symmetrical around the mean thus deviating from a normal distribution curve. The maximum recorded LL bank value during this period was of 1.04864 and a minimum value of 1.025770 which correlates with the skewness coefficient.

4.2.3 Credit to the Private sector (CPS) Table 4.3: CPS Time series data from 2006-2016 YEAR CBA ConsolidatedBarclays KCB Standard CharteredFidelity I&M Victoria Giro Prime 2006 1.0267 1.0767 1.1267 1.1767 1.2267 1.2767 1.3267 1.3767 1.4267 1.4767 2007 1.054 1.104 1.154 1.204 1.254 1.304 1.354 1.404 1.454 1.504 2008 1.05473 1.10473 1.15473 1.20473 1.25473 1.30473 1.35473 1.40473 1.45473 1.50473 2009 1.09896 1.14896 1.19896 1.24896 1.29896 1.34896 1.39896 1.44896 1.49896 1.54896 2010 1.2367 1.2867 1.3367 1.3867 1.4367 1.4867 1.5367 1.5867 1.6367 1.6867 2011 1.00542 1.05542 1.10542 1.15542 1.20542 1.25542 1.30542 1.35542 1.40542 1.45542 2012 1.08954 1.13954 1.18954 1.23954 1.28954 1.33954 1.38954 1.43954 1.48954 1.53954 2013 1.06455 1.11455 1.16455 1.21455 1.26455 1.31455 1.36455 1.41455 1.46455 1.51455 2014 1.001653 1.051653 1.101653 1.151653 1.201653 1.251653 1.301653 1.351653 1.401653 1.451653 2015 1.8054 1.8554 1.9054 1.9554 2.0054 2.0554 2.1054 2.1554 2.2054 2.2554 2016 1.9005 1.9505 2.0005 2.0505 2.1005 2.1505 2.2005 2.2505 2.3005 2.3505

Mean 1.034387 Median 1.031162 Maximum 1.051025 Minimum 1.02439 Std. Dev 0.00859 Skewness 0.821455 Kurtosis 2.114475

The CPS ratio had a mean of 1.034387 and a median of 1.031162 during the period 2006- 2016. Furthermore, CPS has a skewness coefficient of 0.821455 implying that it may not be symmetrical around the mean thus deviating from a normal distribution curve. The recorded CPS bank range during this period was 1.024359 to 1.051025 with a Kurtosis of 2.114475.

Figure 4.3: Commercial Banks CPS Time series data from 2006-2016

Figure 4.3 clearly shows the Credit to Private sector information of the Commercial banks in Kenya during the period 2006-2016.

4.2.4 Commercial – Central Bank Assets (CCBA) Table 4.4: CCBA Time series data from 2006-2016 YEAR CBA ConsolidatedBarclays KCB Standard CharteredFidelity I&M Victoria Giro Prime 2006 1.0167 0.9667 0.9167 0.8667 0.8167 0.7667 0.7167 0.6667 0.6167 0.5667 2007 1.0154 0.9654 0.9154 0.8654 0.8154 0.7654 0.7154 0.6654 0.6154 0.5654 2008 1.03473 0.98473 0.93473 0.88473 0.83473 0.78473 0.73473 0.68473 0.63473 0.58473 2009 1.0456 0.9956 0.9456 0.8956 0.8456 0.7956 0.7456 0.6956 0.6456 0.5956 2010 1.06767 1.01767 0.96767 0.91767 0.86767 0.81767 0.76767 0.71767 0.66767 0.61767 2011 1.00542 0.95542 0.90542 0.85542 0.80542 0.75542 0.70542 0.65542 0.60542 0.55542 2012 1.067954 1.017954 0.967954 0.917954 0.867954 0.817954 0.767954 0.717954 0.667954 0.617954 2013 1.023455 0.973455 0.923455 0.873455 0.823455 0.773455 0.723455 0.673455 0.623455 0.573455 2014 1.065653 1.015653 0.965653 0.915653 0.865653 0.815653 0.765653 0.715653 0.665653 0.615653 2015 1.4554 1.4054 1.3554 1.3054 1.2554 1.2054 1.1554 1.1054 1.0554 1.0054 2016 1.6705 1.6205 1.5705 1.5205 1.4705 1.4205 1.3705 1.3205 1.2705 1.2205

Mean 0.980636 Median 0.980538 Maximum 0.983615 Minimum 0.977779 Std. Dev 0.001553 Skewness -0.37706 Kurtosis 1.911027

Table 4.4 above displays the time series data for the 10 selected commercial banks. The CCBA of the sampled 10 commercial banks exhibited a mean of 0.980636 during the period 2006-2016. This shows a symmetrical distribution of the mean bringing about normal distribution. There was a maximum recorded value of 0.983615 and a minimum value of 0.977779. The standard deviation of CCBA data was 0.001553 with a skewness of -0.037706. The CCBA coefficient of Kurtosis is 1.91027 and a probability of 0.249041. This information is presented in a simplified form in Figure 4.4

Figure 4.4: CCBA Time series data from 2006-2016

4.2.5 Commercial Bank Deposits (CBD) Table 4.5: CBD Time series Data from 2006-2016

YEAR CBA ConsolidatedBarclays KCB Standard CharteredFidelity I&M Victoria Giro Prime 2006 1.0267 0.9767 0.9267 0.8767 0.8267 0.7767 0.7267 0.6767 0.6267 0.5767 2007 1.0344 0.9844 0.9344 0.8844 0.8344 0.7844 0.7344 0.6844 0.6344 0.5844 2008 1.04773 0.99773 0.94773 0.89773 0.84773 0.79773 0.74773 0.69773 0.64773 0.59773 2009 1.0786 1.0286 0.9786 0.9286 0.8786 0.8286 0.7786 0.7286 0.6786 0.6286 2010 1.05667 1.00667 0.95667 0.90667 0.85667 0.80667 0.75667 0.70667 0.65667 0.60667 2011 1.05642 1.00642 0.95642 0.90642 0.85642 0.80642 0.75642 0.70642 0.65642 0.60642 2012 1.0654 1.0154 0.9654 0.9154 0.8654 0.8154 0.7654 0.7154 0.6654 0.6154 2013 1.0455 0.9955 0.9455 0.8955 0.8455 0.7955 0.7455 0.6955 0.6455 0.5955 2014 1.0653 1.0153 0.9653 0.9153 0.8653 0.8153 0.7653 0.7153 0.6653 0.6153 2015 1.054 1.004 0.954 0.904 0.854 0.804 0.754 0.704 0.654 0.604 2016 1.051 1.001 0.951 0.901 0.851 0.801 0.751 0.701 0.651 0.601

Mean 0.980636 Median 0.980538 Maximum 0.983615 Minimum 0.977779 Std. Dev 0.001553 Skewness -0.37706 Kurtosis 1.911027

Table 4.5 above shows that CBD has an overall a mean of 1.009771 during the period 2006-2016. There was a maximum recorded value of 1.027069 and a minimum value of 0.995476. The Data had a standard deviation of 0.0.010158 and skewness of 0.524185. The CBD coefficient of Kurtosis is 1.801626 and a probability of 0.0.051938. This is further shown in Figure 4.5 below

Figure 4.5: CBD Time series data 2006-2016

4.3 Effect of Exchange rate on Commercial Bank Development in Kenya Table 4.1 to 4.5 represent the results of the descriptive statistics of all the variables. Respectively (0.079316), (0.647585), (0.821455), -0.037706) and (0.524185) during the period 2006 to 2016. This period was characterized by high fluctuating exchange rates indicating that the distribution of GDP and CCBA are symmetrical around the mean and thus close to normal distribution. On the other hand, LL, CPS and CBD have skewness coefficients far greater than zero (0.647585), (0.821455) and (0.524185) respectively) implying that they may not be symmetrical around the mean and thus deviating from normal distribution. The negative skewness coefficient of CCBA indicates that its distribution is slightly left skewed while the other variables are right skewed.

GDP LL CPS CCBA and CBD‟ s coefficients of kurtosis are respectively (1.831024.) (1.886145) (2.114475) (1.911027) and (1.801626) and therefore less than 3. This means that they are flatter than a normal distribution with a wider peak. Therefore, based on the Kurtosis, none of the variables exhibit a normal distribution.

The normality test using the J-B probability is (0.197185), (0.033224), (0.017183), (0.249041) and (0.051938) for GDP, LL, CPS, CCBA and CBD respectively. This implies that GDP CCBA and CBD are normally distributed since their probability value is above 5percent significance level. On the other hand, LL and CPS with probability values less than 5 percent are not normally distributed. From these tests, all the variables do not exhibit a normal distribution.

4.4 Effect of Inflation and Commercial Bank Development in Kenya Correlation ranges between -1 and +1 and quantifies the direction and strength of the linear association between the two variables. The sign of the correlation coefficient indicates the direction of the association. The magnitude of the correlation coefficient indicates the strength of the association. A correlation above 0.8 between explanatory variables signifies high correlation of the variables. Table 4.2 presents the correlation matrix showing the relationship between financial deepening measures among themselves and GDP.

Table 4.6: Effect of Inflation on financial deepening measures

Correlation of GDP and Financial Deeping measures Table 4.6 shows that GDP and the four financial deepening indicators are positively correlated with high values as 0.89 percent for GDP with LL and 0.88 percent for GDP and CBD. The correlation between GDP and CPS; and GDP and CCBA is moderate with respective coefficients of 0.65 percent 0.75 percent. On the other hand, the four financial deepening measures are both moderately and highly positively correlated with 0.87 percent for LL and CPS; 0.78 percent for LL and CCBA, 0.93 percent for LL and CBD, 0.73 percent for CPS and CCBA; 0.76 percent for CPS and CBD; and 0.75 percent for CCBA and CBD. The high correlation among financial deepening indicators suggests that

the indicators can be used as substitutes. The decision criterion in the PP unit root test involves comparing the computed PP t-statistics values with the critical values at 1 percent, 5 percent and 10 percent. If the computed PP t-statistics is greater in absolute terms compared to the critical values, then the null hypothesis of non-stationarity in time series variables is rejected and vice versa. Table 4.3 indicates the order of integration of each of the variables.

Table 4.6 further shows the results of the PP unit root test indicate that at their levels with intercept, all the variables have a unit root meaning that they are not stationary. However, after first difference as presented in Table 4.3, there is no unit root and therefore, we reject the null hypothesis of non-stationarity. This implies that all the variables have become stationary at first difference level or I (1).With the establishment of the order of integration, we proceeded to testing for cointegration between the dependent and the independent variables to find out if there exists a long-run relationship.

4.5 Effect of GDP and Commercial Bank Development in Kenya Table 4.7: Trace Test results of cointegration of GDP with individual independent variable

Note: the 1percent, 5 percent and 10 percent level of significance is denoted by *, ** and *** respectively

Table 4.7 shows the cointegration of the variables and in relation to GDP. When variables are integrated of the same order as shown in Table 4.7 above, the Johansen cointegration approach is appropriate in order to detect the existence of a long- run cointegrating relationship among the variables. This approach uses the Trace and the Max-Eigen Tests. The criterion decision for both tests involves comparing the computed trace statistic as well as Max-Eigen statistic values with the critical values. If the computed trace statistic and Max-Eigen statistic values are less than the critical values, the null hypothesis of number of cointegrating equations is not rejected. Tables 4.6 and 4.7 present the results of the cointegration tests between GDP and each individual independent variable. The Trace and the Max-Eigen Tests are specified with an intercept and a trend.

The Trace statistics reported in Table 4.5 reject the null hypotheses of r = 0 (i.e. no cointegrating equation) between GDP and all the financial deepening measures, in favor of the general alternative hypothesis r≥1 (i.e. at least one cointegrating equation) The results indicate that at 5 percent level of significance, there is at most one cointegrating equation between the GDP and LL; GDP and CPS; and GDP and CCBA(r≤1).There is also evidence of the existence of two cointegrating equations between GDP and CBD implying that we do reject the null hypothesis of no cointegration of at most one cointegrating equation.

Table 4.8: Summary of Cointegration between GDP and Financial deepening measures

Note: r stands for the number of cointegrating equations

Table 4.8 The Max-Eigen Test reported in Table 4.6 confirms the results of the Trace Test by revealing the existence of one cointegrating equation between the GDP and LL; GDP and CPS; and GDP and CCBA at 5 percent level of significance. The results confirm also the existence of two cointegrating equations between GDP and CBD. Thus, from the Johansen cointegration test using both the Trace and the Max-Eigen tests, there is evidence of the existence of a long run association between economic growth and financial deepening measures representing the macroeconomic factors.

4.6 Effect of Money Supply and Commercial Bank Development in Kenya The estimation process starts with finding the appropriate value of p, the number of lags in the unrestricted VAR. The optimal number of lags is determined based on the Schwarz criterion was 5. The long and short run parameters of the cointegrating equations were estimated using of the over-parameter and the parsimonious approaches. Table 4.9 represents the summarized results of the money supply and GDP.

Table 4.9: Effect of Money supply on financial deepening measures

The results presented in Table 4.9 show that the R-squared (R2) that measures the proportion of variations in the dependent variable attributed to the independent variables are (0.881153), (0.905739), (0.883904) and (0.868564) for GDP and LL model, GDP and CPS model, GDP and CCBA model and GDP and CBD model respectively. This implies that LL, CPS, CCBA and CBD individually could explain about 88, 90, 88 and 86 percent of the variations in GDP in their respective models. The remaining variations are the error terms and can be attributed to other factors not included in the models.

The Durbin Watson (DW) statistics seeks to establish whether there exists autocorrelation in the estimated model. A good model is one that has DW statistic close to and above 2 as it may be assumed to have no first-order autocorrelation (Gujarati, 2004). The DW of the four models estimated indicate the following statistics: (2.015903), (1.956684), (1.992697) and (1.984945). These values are very close and above 2, implying that there is no autocorrelation in the estimated models. The probability (F-stat) that for the four models suggest that the models are statistically significant since their values are all (0.000000).

4.7 Effect of Lending Rate and Commercial Bank Development in Kenya We start the estimation of the short run by generating the error correction term of each model, then we use the parsimonious ECM approach (which involved here five lags of each independent variable). The parsimonious ECM result is gotten by deleting insignificant variables from the over parameterize ECM results. Tables 4.11 and 4.12 present the short run results of individual regression models where D(GDP) is the dependent variable

Table 4.10: Effect of Lending rate on LL

Table 4.10 above represents the Short run regression results of LL against GDP

Table 4.11: Effect of Lending rate on CPS

Table 4.11 above represents the Short run regression results of CPS against GDP

Table 4.12: Effect of Lending rate on CBA

Table 4.12 above represents the Short run regression results of CCBA against GDP

Table 4.13: Effect of Lending rate on CBD

Table 4.13 above represents the Short run regression results of CBD against GDP

4.8 Chapter Summary The diagnostic tests for the long run individual model equations included the J-B normality test used to test whether the residuals are normally distributed, the Breusch- Godfrey Serial correlation LM test to test for residual serial correlation, and the Breusch- Pagan-Godfrey test whether the residual is homo skedastic or hetero skedastic. The Cumulative Sum of Recursive Residuals (CUSUM) to test residual stability.

CHAPTER FIVE

5.0 DISCUSSION CONCLUSION AND RECOMMENDATIONS 5.1 Introduction This chapter summarizes the research problem and discusses the broader implications of the findings for theory, practice, policy and further research in the field of macroeconomic factors on the development on commercial banks in Kenya. The structure of the chapter is guided by the research objectives. The chapter attempts to explain why the findings are the way they are and to what extent they are consistent with or contrary to past empirical findings and theoretical arguments.

5.2 Summary The purpose of this study is to critically examine how do macro-economic factors affect the financial development of commercial banks in Kenya over the period 2006-2016. The specific objectives were: to determine how inflation, GDP, Exchange rate Money Supply and Lending rates affect commercial banks in Kenya.

A descriptive research design was used to carry out the study. The study population consisted of 43 Kenyan banks and the sample size consisted of 10 banks. The study relied on secondary data which was collected from annual financial reports. After data collection, data analysis was done. This process is important as it makes data sensible. Data analysis tool used is dependent on the type of data to be analyzed depending on whether the data is qualitative or quantitative. The quantitative data in this research was analyzed by descriptive statistics including mean, frequency, standard deviation and percentages to profile sample characteristics and major patterns emerging from the data. Data was presented in tables.

An error correction model was adopted to estimate the effects of each alternative measure of financial deepening namely liquid liabilities, credit to the private sector, commercial and central bank assets and commercial bank deposits on GDP. Correlation analysis showed high positive correlation among some financial deepening indicators, implying the regression of GDP with each individual alternative measure separately. Stationarity tests showed that all the variables were integrated at 1 suggesting a test of cointegration to

determine the long run relationship between the variables. The Johansen cointegration test gave evidence of the existence of cointegrating equations between GDP and each of the financial deepening alternative measures. The long run regression results revealed that liquid liabilities, credit to the private sector, commercial-central bank assets as well as commercial bank deposits had positive and significant statistical parameters in explaining how macroeconomic variables (represented through the measures of financial deepening) lead to financial development of commercial banks in Kenya within the period of study. Evidence from the study shows that the models used were stable. The error correction terms of all the models appeared with negative signs and are statistically significant at 5 percent level, thus confirming the cointegrating relationship between GDP and financial deepening.

5.3 Discussion 5.3.1 Effect of Inflation on Commercial Bank Development in Kenya The study found a positive correlation between the financial deepening measures representing the financial development and inflation as shown in table 4.6. This therefore implies that during the period under study the levels of inflation were anticipated by the Kenyan banks. This gave the banks the opportunity to adjust the interest rates accordingly and consequently to earn higher profits. This finding is consistent with the research findings by Bashir (2003), Bourke (1989), and Perry (1992)

Naceur (2003) and Kosmidou el al. (2006) stated that it is integral for banks to constantly monitor the changes in inflation rates as the relationship between inflation and economic output (GDP) plays out like a very delicate dance. ―High levels of inflations affect the levels of investment made by potential investors‖ Pasiouras el al. (2007). Keeping a close eye on inflation is most important for fixed-income investors, as future income streams must be discounted by inflation to determine how much value today's money will have in the future. For stock investors, inflation, whether real or anticipated, is what motivates them to take on the increased risk of investing in the stock market, in the hope of generating the highest real rates of return. Real returns are the returns on investment that are left standing after commissions, taxes, inflation and all other frictional costs are taken into account. As long as inflation is moderate, the stock market provides the best chances for this compared to fixed income and cash.

There are times when it is most helpful to simply take the inflation and GDP numbers at face value and move on. However, the study brings out that there are many things that demand investors‘ attention and ultimately affect the development of commercial banks. The study confirmed an inverse correlation between inflation and financial development of commercial banks exists as brought out by Naceur (2003). From the cointegration approach, we report that inflation reduces the efficiency of commercial banks in the long- and-short runs. Financial sector improves its performance through its previous policies and developments. Economic growth also promotes financial development through the causal channels. Social spending enhances the performance of financial sector in the long run and ultimately leads to the overall development. Policy making authorities should formulate appropriate steps to curb inflation in the country to obtain the fruits from financial sector's development.

Furthermore, the study brought out positive correlations between interest rate and inflation as supported by Makin (2003). The positive correlation between interest rate and a high inflation rate does not necessary mean a higher interest rate causes a greater inflation. On the contrary it could mean that the central bank is responding to greater inflation by raising the interest rate, when the inflation starts to increase, the price of goods and services sky rockets, the central bank is forced to increase their lending rate to reduce demand, consequently commercial banks also increase their lending rate intendment with central banks rates.

Gale and Orszag (2005) note that in cases of extremely rapid inflation lenders will want to have a high interest rate as a necessity to protect their investment. If the interest rate is not kept high the lenders will lose their money and borrowers will be the beneficiaries from it. These are the constant problems that face commercial banks in Kenya in trying to ensure the levels of inflation do not affect their lending rates to their customers. This is because a great tip in the lending and saving scales will have an adverse effect on the development on commercial banks.

This is seen in Table 4.6 which shows that GDP and the four financial deepening indicators are positively correlated with high values as 0.89 percent for GDP with LL and 0.88 percent for GDP and CBD. The correlation between GDP and CPS; and GDP and CCBA is moderate with respective coefficients of 0.65 percent 0.75 percent. On the other

hand, the four financial deepening measures are both moderately and highly positively correlated with 0.87 percent for LL and CPS; 0.78 percent for LL and CCBA, 0.93 percent for LL and CBD, 0.73 percent for CPS and CCBA; 0.76 percent for CPS and CBD; and 0.75 percent for CCBA and CBD.

High levels of borrowing during periods of inflation create instability due to higher probability rates of defaulting. This is cemented by Gale and Orszag (2005) who clearly document this problem and bring out that this is a factor that ultimately impedes the development of commercial banks. The balance of savings and loans unaffected by great fluctuations in inflation are necessary for the long term development goals of commercial banks in Kenya.

5.3.2 Effect of Exchange rate on Commercial Bank Development in Kenya The study found a negative correlation between the financial deepening measures representing financial development and the exchange rate as shown in table 4.1 to 4.5. This means that Foreign exchange rate fluctuations affect banks both directly and indirectly. The direct effect comes from banks‘ holdings of assets (or liabilities) with net payment streams denominated in a foreign currency. Foreign exchange rate fluctuations alter the domestic currency values of such assets. This explicit source of foreign exchange risk is the easiest to identify, and it is the most easily hedged. This is supported by Ndungu (2000).

The indirect sources of risk are more subtle but just as important. A bank without foreign assets or liabilities can be exposed to currency risk because the exchange rate can affect the profitability of its domestic banking operations. From the study, commercial banks in Kenya are exposed to foreign exchange risk. A stronger dollar decreases its profitability and ultimately its development. In essence, the bank is ―short‖ dollars against foreign currency. Any time the value of the exchange rate is linked to foreign competition, to the demand for loans, or to other aspects of banking conditions, it will affect the banks. This is supported by Koima (2011) study on changes of International exchange rates.

The Lower the value of exchange index in any country, higher the competitiveness of currency of that country have. It is commonly thought that the inconsistency and volatility of the exchange rate greatly affects expected cash flows. This has an adverse effect on the

profitability experienced by banks and therefore affects their performance and development as a result of changes in the home currency denominated revenues and costs. During the period of study, Kenya‘s exchange rate index and levels of inflation continued to increase which affected the terms of competition for firms with international activities. This is supported by studies done by Amihud and Levich (1994) and Opati (2009) on causal relationship between inflation and exchange rates in Kenya where it was recognized that an increase in inflation leads to the depreciation of the local currency. Furthermore, Ndungu (2000) declares that Kenya‘s exchange rate policy has undergone various shifts which are mostly driven to a large extent, by the economic events especially balance of payment crisis. Koima (2011) study on the relationship between financial performance for multinational corporations in Kenya and exchange rates volatility and found that Sterling Pound. United States Dollar, Euro exchange rate and Japanese Yen exchange rate influence the financial performance of Multinational Corporation.

Foreign exchange risk also may be linked to other types of market risk, such as interest rate risk as discussed earlier. Interest rates and exchange rates often move simultaneously. So, a bank‘s interest rate position indirectly affects its overall foreign exchange exposure. The foreign exchange rate sensitivity of a bank with an open interest rate position typically will differ from that of a bank with no interest rate exposure, even if the two banks have the same actual holdings of assets denominated in foreign currencies. Again, the vulnerability of the bank as a whole to foreign exchange fluctuations depends on more than just its holdings of foreign exchange.

The study revealed that exchange rate movement also affects the stock market performance greatly especially through its spiral effects. The study concluded that there is a weak relationship between foreign exchange rate fluctuations and the performance of commercial banks in Kenya in the study period. This is supported by Opati (2009).

Additionally, Amihud and Levich (1994) brought out that the Kenyan shilling exchange rates against the United States Dollar was observed to be really high during the study period. The normality test using the J-B probability is (0.197185), (0.033224), (0.017183), (0.249041) and (0.051938) for GDP, LL, CPS, CCBA and CBD respectively. This implies that GDP CCBA and CBD are normally distributed since their probability

value is above 5percent significance level. On the other hand, LL and CPS with probability values less than 5 percent are not normally distributed. From these tests, all the variables do not exhibit a normal distribution.

The study recommended relevant authorities for instance. The Central Bank of Kenya should adequately put measures to safeguard the value of the domestic currency. This would ensure that the value on the same does not fluctuate much day in day out. From the research findings, it is clear that the Kenyan currency has been depreciating in values against the dollar over the recent years and this depreciation has had a negative impact on the returns of commercial banks in Kenya. The researcher also concludes that total assets owned by commercial banks and the inflation rates were increasing over the years.

This study concludes that the government should deploy adequate measures to safeguard the domestic currency. It should promote foreign direct investments so as to spur economic growth and consequently cause the local currency to appreciate. This would translate to a more stable currency against international currencies. This would consequently lower borrowing costs thus making loans even more affordable.

5.3.3 Effect of Money Supply on Commercial Bank Development in Kenya According to table 4.7, the study established that the Liquid Liabilities and money supply as measured by broad money (M3) are positively correlated with a correlation coefficient of 0.8259 billion. This implies that when the CBK increases money supply, households get more money at their disposal and are therefore looking for investment opportunities. Although money supply is basically determined by the central bank's policy, it could also affect the behavior of banks. Kosmidou (2008) and Mamatzakis and Remoundos (2003) used money supply as a measure of market size and found that the variable significantly affects bank profitability.

Central Bank is the most important institution and source of money supply because it has got the monopoly of issuing notes. The Central Bank can bring about variations in money supply by changing bank rate, by open market operations, by changing cash reserve ratios of commercial banks. This ultimately means that the central bank easily controls the performance and ultimately the level of development of commercial banks in Kenya.

In addition, the effects of commercial banks‘ liquid liabilities on commercial bank development findings suggested that liquid liabilities that measure the size of the financial sector had a positive and significant effect on GDP. Based on the period of the study, this implies, that an expansion of the size of the banking sector measured by its level of monetization significantly helped the economy to grow.

Table 4.6 also brings out that when there is a budget surplus showing an increase in the government deposits, there is a corresponding decline in the money supply. The government may hold this surplus (idle balances) continuously or it may use these balances to repay the outstanding debts. If the balances are held indefinitely, there will be net decrease in the money supply which may induce further secondary declines in the supply of money hindering commercial bank development.

It is also seen that the assumption that banks lend as much as they can while meeting the central bank‘s reserve requirement does not always hold. When business conditions are depressed, bank lending may be limited by the demand for credit by firms that banks consider good credit risks. In that case, the ―money multiplier‖ — the overall addition to the money supply resulting from a Ksh1 increase in lending — will be less than the maximum allowed under the central bank‘s rules.

This is seen in the results presented in Table 4.8 show that the R-squared (R2) that measures the proportion of variations in the dependent variable attributed to the independent variables are (0.881153), (0.905739), (0.883904) and (0.868564) for GDP and LL model, GDP and CPS model, GDP and CCBA model and GDP and CBD model respectively. This implies that LL, CPS, CCBA and CBD individually could explain about 88, 90, 88 and 86 percent of the variations in GDP in their respective models. The remaining variations are the error terms and can be attributed to other factors not included in the models.

Similarly, the effects of-money supply will depend on whether the government pays back debts held by the central bank or debts held by the general public. Again, in case of a deficit the effect on money supply will depend on whether it is financed by a resort to the printing press or by borrowing from public or from the central bank. Resorting to printing more money in times of financial needs. Such a method of bringing about changes in

money supply is highly inflationary and should be resorted to sparingly. This is supported by Rotich (2007) Secondly, the results also bring out that when the government may choose to borrow from the Central Bank (out of its idle reserves) or from commercial banks (out of their time deposits). Except in the second case of borrowing from public, financing the deficit will make a net addition to the money supply and stimulate commercial bank development through increased loans. This is supported by Rotich (2007) who implied that at times of high inflation, or positive output, the CBK responded by reducing money supply. Money supply (M3) is expected to have positive effect on profitability and development of commercial banks.

5.3.4 Effect of GDP on Commercial Bank Development in Kenya From table 4.4 above, there is a positive correlation between the financial deepening measures and GDP growth. The results about the impact of GDP growth on the financial deepening measures is consistent with the results of Sufian and Habibullah (2010). Pasiouras and Kosmidou (2007), and Kosmidou (2008) and provides support to the argument of positive association between economic growth and financial sector performance. This therefore means that growth in the economy will lead to growth in the demand and supply of funds from banks which in turn lead to higher profitability.

For banks the annual growth in the GDP is vital. If overall economic output is declining or merely holding steady, most companies will not be able to increase their profits, which is the primary driver of stock performance. However, too much GDP growth is also dangerous, as it will most likely come with an increase in inflation, which erodes stock market gains by making our money (and future corporate profits) less valuable.

This is further brought out by Barajas et al. (1999) the impact of commercial bank deposits on GDP which show that the liquidity of the banking sector had positively and significantly influenced economic growth during the period of the study which was affected by high exchange rates. This implies therefore that an increase in exchange rates will further push banks to lend for development and stimulate economic growth in the country.

Furthermore, the effects of commercial-central bank assets on GDP was found to have positive and statistically significant effect on economic growth. The implication is that as commercial banks increase in assets, efficient allocation of assets is done relative to the central bank and this positively contributed to the growth of the economy.

CCBA has a positive effect on GDP since our estimate in the model suggests that CCBA has a positive coefficient and is statistically significant in relation to GDP (p= 0.0214<0.05). This may be interpreted as one percent increase in CCBA will lead to an increase in GDP by (2.303083) on average. Commercial and central bank assets are hence an important factor in explaining changes in GPD for the period covered by the study. This supported by the study done by Guru et al (2002)

This finding is in line with Demiriguc-Kunt and Huizinga (2000) who found that in countries with very low levels of financial development, the effect of Commercial and central Banks is statistically significant, positive, and economically large. A larger share of the country‘s savings being allocated by commercial banks is presumed to be more efficiently allocated relative to the central bank.

Using correlation, CBD was found to be significantly and positively affect GDP. The sign of the coefficient is positive with a probability value less than 0.05 percent (0.0107). This implies that CBD has positive and significant effects on GDP. A plausible interpretation of this result could be that a percent increase in commercial bank deposits could improve GDP by (0.667067). Bank deposits in the economy show the ease of transfers across counters as affected by interest rate controls. .As such, capital investments would be expected to flow into the economy easily from the domestic savings, large part of which are generated from deposits. Waiyaki (2013). This finding corroborates the findings of Waiyaki (2013) and Audu and Okumoko (2013) in Kenya and Nigeria respectively.

The short run estimation results of the individual equation model are also consistent with theoretical expectations and highlight the significance of the macroeconomic factors of the banking sector on economic growth in Kenya through LL, CPS, CCBA and CBD. The error correction terms of each of the four models (ect1 ect2, ect3 and ect4) which measured the speed of adjustment to equilibrium were respectively (-0.395115,-0.326607, 0.507210 and -0.475146) implying that about 39 percent, 32 percent 50 percent and 47

percent of the previous quarter`s deviations from equilibrium is corrected within the next quarter. These ECTs appear with negative sign and are statistically significant at 5 percent level, confirming the cointegration between GDP and financial development indicators. This is supported by Hassan and Bashir (2003).

These results are supported by Sufian and Habibullah (2010) who point out that the GDP is expected to influence numerous factors related to the supply and demand for loans and deposits. Favourable economic conditions will affect the demand and supply of banking services positively. Bank's development, growth and profitability is limited by the growth rate of the economy. If the economy is growing at a substantial rate, a soundly managed bank would profit from loans and securities sales. Economic growth can enhance bank's profitability by increasing the demand for financial transactions, i.e., the household and business demand for loans. Strong economic conditions also characterized by the high demand for financial services, thereby increasing the bank's cash flows, profits and non interest earnings. Thus there is a positive relationship between the growth rates of Gross domestic product and the profitability of the bank.

5.3.5 Effect of Lending Interest Rate on Commercial Bank Development in Kenya The study found a positive correlation between the financial deepening measures and the lending interest rates of individual banks as shown in table 4.8. This means that as lending interest rates increase by 1% the deepening measures increase by 1.45%. This empirical finding is consistent with the findings of Kipngetich (2011) and Samuelson (1945). It is important to note that other bank specific factors also affect the development of banks.

This is supported by the studies done by Ogweso (2006) who brought out the positive relationship between interest rates and non-performing loans. He found an indication that when interest rates increase, commercial banks should put in place systems to deal with non-performing loans to minimize the great effects on bank development. Furthermore, according to Matu (2001), the poor development of commercial banks puts pressure on them to retain high lending rates in an attempt to minimize the losses asassociated with these loans.

This is further solidified by Kipngetich (2011) who did a study on the relationship between lending interest rates and financial development of commercial banks in Kenya. He found that there is a positive correlation between lending interest rates and financial development of commercial banks. Banks should therefore carefully manage their interest rates to manage their performance and development. Interest rates on Borrowing are anticipated to have a positive relationship with the profitability of commercial banks.

Lending rate affect the non-performing assets in banks as it increases the cost of loans charged on the borrowers. Mode or type of interest rate charged (whether fixed or float) for they all have different dynamics that might affect the borrower‘s ability to repay credit loaned. Goldstein and Turner (1996) also concluded that accumulation of nonperforming assets is attributable to high cost of loans.

Regulations on lending rates have far reaching effects on loan non-performance for such regulations determine the lending rate in banks and also help mitigate moral hazards incidental to NPAs. When the regulations are lax or ineffective, the level of non- performing assets increases. In Kenya, banks specific policies and regulations are the responsibility of board of directors, managing directors and credit risk management committees. This concurs with Demirguc-Kunt and Huizinga (1997) finding that stringent regulations enforced by central banks lower realized interest margins (spread) and subsequently loan non-performance.

Credit risk management technique remotely affects the value of a bank‘s lending rates spread as lending rates are benchmarked against the associated non-performing assets. Credit risk assessment and management ensures that loan are channeled to intended purposes, loans are allocated to only those who qualify/can repay, loan security/collateral is enough to cover loan, assessment of the character of the loan candidate and there is sufficient margin to cover loan. Credit risk management, therefore, directly influences the level of asset nonperformance in commercial banks

The impact of credit to the private sector on GDP growth in Kenya indicated that credit to the private sector that measures the activity of the financial sector through the channeling of funds from savers to investors in the private sector, had positively and significantly influenced the growth of the economy during the period of the study. The implication of

this finding is that an increase in credit released to the private sector enhanced the growth of the economy.

In linear regression, the size of the coefficient for each independent variable gives the size of the effect that variable is having on the dependent variable, and the sign on the coefficient (positive or negative) gives the direction of the effect. The coefficient tells how much the dependent variable is expected to increase when the independent variable increases by one, holding all the other independent variables constant. The interpretation of the result sat 5 percent level of significance is provided both for the single equation model and the individual equation models.

The empirical results of long-run parameters suggest that LL as expected, has a positive coefficient. This could be interpreted as a percent increase in liquid liabilities leads to an increase in GDP by (1.075595) on average. In addition, the p-value is significant at 5 percent since 0.0217<0.05 implying that LL has a positive and significant effect on GDP. Moreover, the finding match the findings of Waiyaki (2013) who found a positive and significant relationship between M3 and economic growth in Kenya for the 1997 to 2012 period

The coefficient of CPS has a positive sign. This could be interpreted as a one percent increase in CPS increases GDP by (0.404581) on average. This means that CPS has positive influence on GDP. The probability value is significant (0.0193). This result is consistent with the finding of Onuonga (2014).

5.4 Conclusion The study set the objective of determining the effects of financial macroeconomic factors on financial development of commercial banks in Kenya using quarterly data collected from various CBK statistical bulletins and economic survey reports. The study covered the period running from 2006 to 2016 and data were analyzed using "eviews' version 7. The macroeconomic variables which included exchange rate, M3, inflation, and lending interest rates were the independent variables whereas the GDP was the dependent variable. The main objective was broken down into the following specific objectives.

5.4.1 To determine the effect of Gross Domestic Product growth on the financial development of commercial banks in Kenya The study found a positive correlation between Gross Domestic Product Growth and financial development of commercial banks in Kenya. Based on the period of the study, there was high annual GDP growth which led to high rates of development in banks which is vital for the Kenyan economy. If overall economic output is declining or merely holding steady, most companies will not be able to increase their profits, which is the primary driver of commercial bank development.

5.4.2 To determine whether inflation has an impact on the financial development of commercial banks in Kenya The study found a positive correlation between inflation and financial development of commercial banks in Kenya. Based on the period of the study, this implies, that high rates of inflation led to the led to higher loan rates and thus higher revenues were generated by the bank. This development of the banking sector measured by its level of monetization significantly helped the economy to grow.

5.4.3 To determine whether lending rates affect the financial development of commercial banks in Kenya The study found a positive correlation between inflation and financial development of commercial banks in Kenya. Based on the period of the study, this implies, that Interest rates on Borrowing are seen to have a positive relationship with the profitability and development of commercial banks.

5.4.4 To determine whether exchange rates affect the financial development of commercial banks in Kenya The study found a negative correlation between exchange rates and financial development of commercial banks in Kenya. Based on the period of the study, this implies that Foreign exchange rate fluctuations affect banks both directly and indirectly. The direct effect comes from banks‘ holdings of assets (or liabilities) with net payment streams denominated in a foreign currency. Foreign exchange rate fluctuations alter the domestic currency values of such assets.

5.4.5 To determine whether Money supply (M3) affect the financial development of commercial banks in Kenya The study found a positive correlation between Money Supply (M3) rates and financial development of commercial banks in Kenya. Based on the period of the study it is clear that Central Bank is the most important institution and source of money supply in Kenya because it has got the monopoly of issuing notes. The Central Bank can bring about variations in money supply by changing bank rate, by open market operations, by changing cash reserve ratios of commercial banks. This ultimately means that the central bank easily controls the performance and ultimately the level of development of commercial banks in Kenya.

All in all, the study revealed that macroeconomic variables jointly influenced the financial development of banks as measured by the financial deepening factors. The study also found that the financial deepening factors were correlated with the individual macroeconomic variables as they were negatively correlated with the exchange rate and positively correlated with M3, GDP growth and Inflation. The objective of the study, which was to establish the relationship between macroeconomic variables and the financial development of the commercial banks, was therefore met.

5.5 Recommendations From the research findings, the researcher made the following recommendations with regards to policy and practice to safeguard the commercial banks against dire shocks presented by the macroeconomic factors:

5.5.1 Suggestions for Improvement 5.5.1.1 To determine the effect of Gross Domestic Product growth on the financial development of commercial banks in Kenya The researcher recommends the intensification of fiscal policies affected by the macroeconomic factors that affect the country‘s GDP. Financial services should include members of the public that excluded from the formal banking system. This is because financial inclusion policies broaden the scope of activity of the financial sector, increases financial assets of formal financial institutions and gives more opportunities to economic agents to save and invest thus promoting the development of the commercial banks.

5.5.1.2 To determine whether inflation has an impact on the financial development of commercial banks in Kenya Kenyan government should streamline the immediate economic environment whereby all commercial banks operate in this country paying close attention to the factors that bring about increased inflation rates. This measure would curb variances in the inflation rates and bring about Market stabilization of the banking sector. This would further regulate lending and deposit rates thus ensuring that the rates are almost uniform across all banks.

5.5.1.3 To determine whether lending rates affect the financial development of commercial banks in Kenya Increasing the interests paid to depositors on their deposits will be an incentive to encourage people to save more with commercial banks. This will encourage savings and borrowing for investment purposes. Implicit taxes should be kept at minimal levels by maintaining low reserve- and cash requirement ratios. This will ensure that lending rates are kept down as banks endeavor to maintain their profit and development margins. 5.5.1.4 To determine whether exchange rates affect the financial development of commercial banks in Kenya The relevant authorities for instance The Central Bank of Kenya should adequately put measures to safeguard the value of the domestic currency. This would ensure that the value on the same does not fluctuate much day in day out.

5.5.1.5 To determine whether Money supply (M3) affect the financial development of commercial banks in Kenya The Central Bank of Kenya ought to implement efficient monetary and fiscal policies so as to help curb significant deficits in balance of payments. The government at large should deploy measures that are aimed at increasing the national income of the country based on investments funded locally.

5.5.2 Recommended areas for further research The objective of this study was to examine the relationship between macroeconomic variables and financial development of commercial banks in Kenya. This research could be replicated by increasing the sample of analysis and establish whether the results would be different from the current study.

This study can be extended to include the whole of banking sector and not just commercial banks. This would be very beneficial for Managers of commercial banks, the central bank of Kenya and Kenya Bankers Association.

The study may also be extended to cover other fields of development measurement such as effectiveness, economy, prudence and soundness of commercial banks in other countries. This will greatly benefit the government through relevant agencies and policy makers in Kenya.

Alternatively, Scholars, academicians and future researchers could replicate the study but consider other methods of analysis such as GARCH model, ARCH model, VAR model, and Cointegration analysis among other models and try to establish if the results would be different.

Another study could be done but add some more variables to establish their lagged effect on the development of the commercial bank.

REFERENCES Athanasoglou. P., Brissimis, S., Delis, M. (2005). ―Bank-Specific, Industry-Specific and Macroeconomics Determents of Bank Profitability.‖ Journal of Bank of Greece, No. 25.

Alesina, A., Filipe C., & Guido T. (2008). ―Why is Fiscal Policy Often Procyclical?‖ Journal of the European Economic Association.

Alesina, A., & Roberto P. (1996). ―Fiscal Discipline and the Budget Process.” American Economic Review.

Bain, J.S. (1951). ―The Relation of Profit Rate to Industry Concentration: America Manufacturing, 1936-1940.‖ Quarterly Journal of Economics 65(3).

Barajas, A., Steiner, R. & Salazar, N. (1999). ―Interest Spreads in Banking in Colombia 1974-96.” Journal of the Colombian Banking

Barth, J.R., Caprio, G.J., Levine, R. (2006). ―Rethinking Bank Regulation: Till Angels Govern.‖ Cambridge University Press, New York.

Calderón, C., & Klaus S. (2008). “Business Cycles and Fiscal Policies: The Role of Institutions and Financial Markets‖, Working Paper 481, Central Bank of Chile.

Central Bank of Kenya (2016) ―Performance and Developments in the Kenyan Banking Sector for the Quarter Ended 31st March 2016”

Diamond, D.W., Dybvig, P.H. (1983). “Bank runs, deposit insurance, and liquidity.‖ Journal of Political Economy.

Didier, T., Constantino H., & Sergio S. (2011). ―How Resilient Were Emerging Economies to the Global Crisis?” World Bank Policy Research

European Central Bank (2010). ―Beyond ROE - How to measure Bank Performance.” Appendix to the Report on E. U Banking Structures. Retrieved from http://www.worldbank.org/en/about/annual-report

Frankel, J. (2011). ―A Solution to Fiscal Procyclicality: The Structural Budget Institutions Pioneered by Chile.” NBER Working Paper

Frankel, J. (2011). ―A Lesson from the South for Fiscal Policy in the US and Other Advanced Countries forthcoming.” Comparative Economic Studies.‖ Journal of Economics and fiscal Policies

Frankel, J., Carlos, V., & Guillermo, V. (2011). ―On Graduation from Procyclicality.‖ University of Maryland

Gavin, M. & Roberto P. (1997). ―Fiscal Policy in Latin America.‖ NBER Macroeconomics Annual repot

Gonzalez, H., Pazarbasioglu, B. & Billing, R. (1997). ―Determinants of Banking System fragility: A case study of Mexico.” IMF staff papers 44(3), pp 295-314.

Ilzetski, E., & Carlos V. (2008). ―Procyclical Fiscal Policy in Developing Countries: Truth or Fiction?” NBER Working Paper 14191.

Johnson, P. (2005).‖ Money stock.‖ A Glossary of Political Economy Terms

Kamau, A.W. (2009). ―Efficiency in the Banking Sector: An Empirical Investigation of Commercial Banks in Kenya. A thesis submitted in partial fulfillment of the Requirements of Nairobi University for the Degree of Doctor of Philosophy.‖ Nairobi: University of Nairobi

Kaminsky, G., Carmen R., & Carlos, V. (2005). ―When It Rains, It Pours: Procyclical Capital Flows and Macroeconomic Policies.” NBER Macroeconomics Annual 2004, 19:11-82.

Kipngetich, K. M. (2011). ―The relationship between interest rates and financial performance of commercial banks in Kenya‖. Unpublished MBA project, University of - Nairobi, 2011.

Laker, J. (1999). ―Monitoring financial system stability.‖ Reserve Bank of Australia Bulletin; October; pp 1 -3.

Lowe, P., & Rohling, T. (1993). ―Agency costs, balance sheets and the business cycle.‖ Reserve Bank of Australia Discussion Paper no. 9931.

Mendoza, E., & Marcelo O. (2006). ―Fiscal Policy and Macroeconomic Uncertainty in Developing Countries: The Tale of the Tormented Insurer.” NBER Working Paper 12586, October.

Ngugi. N., Amanja, D., & Maana, L. (2006). ―Capital Market. Financial Deepening and Economic growth in Kenya.‖

Noureen, A. (2006). ―The role of the financial sector in economic growth.” New York, NY: Macmillan

Oliver, B. (2000). ―Macroeconomics”. 2nd edition Practice Hall New York.

Oloo, O. (2009). ―The Banking Survey 2009.‖ Think Business. Nairobi

Sufian, F., & Chong, R., (2008). ―Determinants of Bank Profitability in a Developing Economy: Empirical Evidence from Philippines.” Asian Academy of Management Journal of Accounting and Finance. 4(2) p.91-112. Retrieved from: /?///?.// w\vw.usm.mv/journal/aamiaf/voI%204-2-2008/4-2-5.pdf.

Persson, T., & Guido, T. (2004). “Constitutional Rules and Fiscal Policy Outcomes.” American Economic Review, 94(1):25-45.

Poterba, J., & Jürgen, H. (1999). “Fiscal Institutions and Fiscal Performance.” University of Chicago Press.

Sologoub. D. (2006). ―The determinants of Bank Interest Margins and Profitability: Case of Ukraine.” Retrieved from www.bof.fi/bofit/seminar/bofcefP6/sologub.pdf Talvi, E., & Carlos, V. (2005). ―Tax Base Variability and Procyclicality of Fiscal Policy.‖ Journal of Development Economics

Tornell, A., & Philip L. (1999). ―The Voracity Effect.‖ American Economic Review

Végh, C. & Guillermo V. (2011). ―Cyclicality of Tax Rate Policy.‖ University of Maryland and Colby College

Von Hagen, J., & Harden, I. (1995). ―Budget Processes and Commitment to Fiscal Discipline.” European Economic Review, p39(3-4):771-779.

Wamucii. J. C., (2010). “The relationship between inflation and financial performance of commercial banks in Kenya.” Unpublished MBA Project, University of - Nairobi, 2010.

APPENDIX 1: LIST OF COMMERCIAL BANKS Licensed Commercial Banks in Kenya 1. ABC Bank (Kenya) 2. Bank of Africa 3. Bank of Baroda 4. Bank of India 5. Barclays Bank 6. CFC Stanbic Bank 7. Chase Bank (Kenya) 8. Charter House Bank (under statutory management) 9. Citibank 10. Commercial Bank of Africa 11. Consolidated Bank of Kenya 12. Cooperative Bank of Kenya 13. Credit Bank 14. Development Bank of Kenya 15. Diamond Trust Bank 16. Dubai Bank Kenya 17. Ecobank 18. Equatorial Commercial Bank 19. Equity Bank 20. Family Bank 21. Fidelity Commercial Bank Limited 22. Fina Bank 23. First Community Bank 24. Giro Commercial Bank 25. Guardian Bank 26. Gulf African Bank 27. Habib Bank 28. Habib Bank AG Zurich 29. I&M Bank 30. Imperial Bank Kenya 31. Jamii Bora Bank 32. Kenya Commercial Bank

33. K-Rep Bank 34. Middle East Bank Kenya 35. National Bank of Kenya 36. NIC Bank 37. Oriental Commercial Bank 38. Paramount Universal Bank 39. Prime Bank (Kenya) 40. Standard Chartered Kenya 41. Trans National Bank Kenya 42. United Bank for Africa 43. Victoria Commercial Bank Representative offices 1. HDFC Bank Limited 2. Nedbank 3. Hong Kong and Shanghai Banking Corporation 4. FirstRand Bank 5. Bank of China

APPENDIX 2: COMMERCIAL BANKS SAMPLE

No BANK 1 Commercial Bank of Africa 2 Consolidated Bank Kenya Limited 3 Barclays Bank of Kenya 4 Kenya Commercial Bank 5 Standard Chartered Kenya 6 Fidelity Commercial Bank 7 I&M Bank 8 Victoria Commercial Bank 9 Giro Bank 10 Prime Bank Kenya

APPENDIX 3: STUDY DATA EXCHANGE M3 (Ksh. INFLATION PERIOD GDP RATE (USD) 'Billion) (CPI) 2006 Q2 0.24% 78.66 378.13 53.3 Q3 -2.46% 78.52 388.24 54.6 Q4 0.47% 79.53 404.3 54.97 2007 Q1 6.53% 69.2 407.1 61.4 Q2 7.45% 69.63 411.5 63.2 Q3 6.55% 69.5 412.3 67.8 Q4 5.18% 71.41 423.3 70.32 2008 Q1 3.21% 79.27 444.5 69.3 Q2 1.78% 80.2 415.3 71.46 Q3 4.30% 80.72 511.57 72.65 Q4 2.00% 74.8 517.97 74.8 2009 Q1 7.33% 76.6 523.98 83.22 Q2 4.33% 77.5 554.62 84.33 Q3 2.20% 78.9 578.71 94.21 Q4 3.40% 79.45 611.23 96.45 2010 Q1 0.70% 73.72 634.89 101.91 Q2 0.33% 72.1 666.45 102.56 Q3 1.40% 72.8 681.67 104.8 Q4 2.33% 78.04 752.45 110.5 2011 Q1 2.10% 78.3 789.45 114.7 Q2 3.65% 79.3 821.34 118.2 Q3 4.13% 80.26 881.23 118.7 Q4 2.02% 77.85 912.13 120.9 2012 Q1 4.10% 75.6 915.56 123.7 Q2 5.23% 76.4 956.89 128.5 Q3 5.70% 73.1 1127.56 129.7 Q4 4.90% 74.7 1156.9 131.4 2013 Q1 3.33% 76.95 1270.1 133.75 Q2 7.33% 77.78 1245.3 133.9

Q3 6.93% 79.95 1317.45 134.9 Q4 7.10% 75.43 1356.71 136.56 2014 Q1 6.50% 76.54 1456.67 136.3 Q2 4.53% 79.2 1576.67 139.2 Q3 4.24% 80.45 1598.1 140.1 Q4 3.65% 81.6 1623.65 140.6 2015 Q1 4.13% 90.54 1645.3 142.4 Q2 2.02% 95.5 1698.01 143.5 Q3 3.33% 82.96 1734.12 144.7 Q4 7.33% 100.5 1756.1 145.6 2016 Q1 6.93% 101.1 1789.6 145.9 Q2 7.10% 98.7 1801.78 156.1