Determinants of the Kenyan Shilling Exchange Rate Movement Against the Chinese Yuan

DETERMINANTS OF THE KENYAN SHILLING EXCHANGE RATE MOVEMENT AGAINST THE CHINESE YUAN

YOGO CAROL OMONDI

UNITED STATES INTERNATIONAL UNIVERSITY-AFRICA

SUMMER 2019

DETERMINANTS OF THE KENYAN SHILLING EXCHANGE RATE MOVEMENT AGAINST THE CHINESE YUAN

YOGO CAROL OMONDI

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

UNITED STATES INTERNATIONAL UNIVERSITY-AFRICA

SUMMER 2019 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 for academic credit.

Signed ______Date______

Yogo Carol (ID NO: 653916)

This project has been submitted for examination with my approval as the university appointed supervisor.

Signed______Date______

Dr. Elizabeth N. Kalunda.

Signed______Date______Dean, Chandaria School of Business

All rights reserved; no part of this work should be reproduced, stored in a retrieval system or transmitted in any form or by any means, electronic, mechanical, photocopying, recording or otherwise without the express written authorization from the writer. © 2019 Yogo Carol.

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ABSTRACT

The general objective of this study was to analyze the determinants of exchange rate of the Kenyan shillings against Chinese Yuan. The research was guided by the following specific objectives: To determine the effect of interest rate on the exchange rate of the Kenyan Shilling against Chinese Yuan; To establish the effect of inflation on the exchange rate of the Kenyan Shilling against Chinese Yuan and to find out the effect of external public debt on the exchange rate of the Kenyan Shilling against Chinese Yuan.

The study used both descriptive and explanatory research design and applied regression analysis to establish the effects of interest rates, inflation rates and external debt on the exchange rates. The study variables were interest rates, inflation rates, external debt and exchange rates and census sampling was applied. Data was obtained from the Central Bank of Kenya for a period of 12 years from 2007-2018. Exchange rate was calculated as an annual average based on monthly averages, monthly central bank lending rate was the measure of interest rate, Inflation rate was measured by use of the percentage change in consumer price index and external debt was measured as a percent of the GDP.

The results of the first objective investigating the effect of interest rate on the exchange rate of the Kenyan Shilling against Chinese Yuan, revealed that the average annual Interest rates have been fluctuating throughout the study period, reaching highest annual average rate of 15.75% in 2012 and lowest annual average of 6.42% in 2010. The highest annual average increase of Interest rate was 7.35% which was recorded between 2011 to 2012. Correlation test results indicated a positive significant relationship between interest rate and the exchange rate at (p=0.006, r=0.229). The regression analysis further indicated that increasing interest rate by one unit; exchange rate will increase by 0.170.

The second objective investigating the effect of inflation rate on the exchange rate of the Kenyan Shilling against Chinese Yuan, showed that Inflation rates have been fluctuating with the highest annual average inflation rate was recorded in 2012 at14.27%, and lowest in 2007 at 4.765%. Correlation test results indicated a negative relationship between inflation rate and the exchange rate but was not significant at (p=0.098, r=-0.138). The regression analysis further indicated that increasing inflation rate by one unit, negatively affects exchange rate by 0.055 units though the effect is was not significant.

The third objective investigating the effect of external public debt on the exchange rate of the Kenyan Shilling against Chinese Yuan, revealed that the trend of external debt was

iv fluctuating with the highest annual average external debt recorded in 2018 at 55.9% of the GDP, and lowest in 2010 at 18.2% of the GDP. Correlation test results indicated a significant positive relationship between external debt and the exchange at (p=0.098, r=- 0.138).This was in agreement with regression analysis which supported that increasing external debt by one unit, exchange rate will increase by 0.086. Regression analysis further indicated that all the independent variables affects 27.6% of the exchange rate.

The study concluded that interest rate and external debt have a significant effect on exchange rate of Kenya shilling against Chinese Yuan. However inflation rate had no significant effect on exchange rate. The study recommends that the government to maintain lending interest rate in order to prevent the depreciation of the exchange rate, the government to implement suitable policies to control and manage inflation rates to prevent its negative effects on foreign exchange rate as indicated in other studies and finally the government to pursue policies that encourage increase in borrowing to prevent depreciation of exchange rate.

ACKNOWLEDGEMENT

I appreciate Dr. Elizabeth Kalunda for her timely and wise counsel during the preparation of this research. To my colleagues at the School of Graduate Studies, Research and Extension, I am grateful for your support and understanding during this project.

I would also like to acknowledge Prof Francis Wambalaba who advised me to join MBA and gave me a lot of support and hope in the beginning. Finally, I express my appreciation to my parents, siblings and friends for the encouragement and support in the course of my studies.

DEDICATION

I dedicate this research to the following: First, to the Almighty God for His grace and blessings which have propelled me this far. Secondly, my parents, Joshua Yogo аnd Cyprose Ayuka, who sacrificed and ensured that I got a good education background to reach this point. Lastly, my siblings for standing by me all the time.

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TABLE OF CONTENTS

STUDENT’S DECLARATION ...... ii

ABSTRACT ...... iv

ACKNOWLEDGEMENT ...... vi

DEDICATION...... vii

LIST OF TABLES ...... xi

LIST OF FIGURES ...... xii

ABBREVIATIONS AND ACRONYMS ...... xiii

CHAPTER ONE ...... 1

1.0 INTRODUCTION...... 1

1.1 Background of the Study ...... 1

1.2 Statement of the Problem ...... 4

1.3 General Objective ...... 5

1.4. Specific Objectives ...... 5

1.5 Significance of the Study ...... 5

1.6 Scope of the Study ...... 6

1.7 Definition of Terms...... 7

1.8 Chapter Summary ...... 7

CHAPTER TWO ...... 8

2.0 LITERATURE REVIEW ...... 8

2.1 Introduction ...... 8

2.2. Interest Rate and Exchange Rate...... 8

2.3. Effects of Inflation on Exchange Rate ...... 12

2.4. Effects of External Public Debt on Foreign Exchange ...... 16

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2.5. Chapter Summary ...... 19

CHAPTER THREE ...... 20

3.0 RESEARCH METHODOLOGY ...... 20

3.1 Introduction ...... 20

3.2 Research Design...... 20

3.3 Population and Sampling Design ...... 20

3.4 Data Collection Method ...... 21

3.5 Research Procedure...... 22

3.6 Data Analysis Methods ...... 22

3.7 Chapter Summary ...... 23

CHAPTER FOUR ...... 24

4.0 RESULTS AND FINDINGS ...... 24

4.1 Introduction ...... 24

4.2 Effects of Interest Rate on Exchange Rate ...... 24

4.3 Effects of Interest Rate on Exchange Rate ...... 25

4.4 Effects of External Debt on Exchange Rate ...... 26

4.5 Assumptions for Regression Analysis ...... 27

4.6 Multiple Linear Regression Analysis...... 29

4.7 Chapter Summary ...... 31

CHAPTER FIVE ...... 32

5.0 DISCUSSION, CONCLUSIONS AND RECOMMENDATIONS ...... 32

5.1 Introduction ...... 32

5.2 Summary...... 32

5.3 Discussion ...... 33

5.4 Conclusion ...... 39

5.5 Recommendations ...... 40

REFERENCES ...... 41

APENDIX 1: SECONDARY DATA COLLECTION SHEET...... 47

APENDIX 11: NACOSTI PERMIT...... 49

LIST OF TABLES

Table 4. 1:Correlation between Interest Rate and Exchange Rate...... 25 Table 4. 2:Correlation between Inflation Rate and Exchange Rate ...... 26 Table 4. 3:Correlation between External Debt and Exchange Rate ...... 27 Table 4. 4:Normality Test ...... 28 Table 4. 5:Multicollinearity test...... 28 Table 4. 6:Linearity Test...... 29 Table 4. 7:Test of Homogeneity of Variances ...... 29 Table 4. 8:Model Summary ...... 30 Table 4. 9:ANOVA ...... 30 Table 4. 10:Coefficients...... 30

LIST OF FIGURES

Figure 4. 1: Trend of Exchange Rate in Kenya from 2007-2018...... 24 Figure 4. 2: Trend of Central Bank’s interest rate from 2007 to 2018 ...... 25 Figure 4. 3: Trend of the average annual Inflation rate in Kenya from 2007 to 2018 ...... 26 Figure 4. 4: Trend of External Debt from 2007 to 2018...... 27

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ABBREVIATIONS AND ACRONYMS

CBK Central Bank of Kenya

CPI Consumer Price Index

CYN Chinese Yuan

GDP Gross Domestic Product

KES Kenya Shillings

OECD Organization for Economic Cooperation and Development

SPSS Statistical Package for Social Sciences

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CHAPTER ONE

1.0 INTRODUCTION

1.1 Background of the Study

Exchange rates are among the most studied and politically sensitive economic measures (Atif, Sauytbekova, & Macdonald, 2012). However, macroeconomists are still unable to reach any concrete agreement over long-term determinants of the exchange rates. Consensus is seen on the theoretical importance of exchange rate depreciation or appreciation as an instrument for stimulation of a country’s trade however the volatility in exchange rate leads to uncertainty in the global market. From the beginning of floating exchange rate regime, modeling the exchange rate has become a very important issue in economic studies. Along with interest rate and inflation, exchange rate is one of important indicators of a country's state of economy (Amato, Filardo, Galati, & Peter, 2005). Exchange rates significantly affect level of investment and trade in the economy, which are critical determinants for every country. For this reason, exchange rates are among the most observed, analyzed and manipulated economic variables.

Monetary approach developed in 1970's is one important tool to explain variation in the exchange rate (Saeed, Awan, Sial & Sher, 2012). Changes in exchange rate have pervasive effects, with consequences for prices, wages, interest rates, production levels, and employment opportunities. Fluctuations in the value of currencies of different economies have increased after the collapse of Bretton Woods System. Especially short-term variability has dramatically increased following the shift from fixed to flexible exchange rate in early 1970’s and thereafter. High volatility and sudden changes in exchange rate is one of the hurdles for the success of macroeconomic policy (Saeed et al. 2012).

Profitability of foreign exchange transactions is affected by the appreciation or loss of foreign currency Kilicarslan (2018). Exchange rate is associated with unpredictable movements in relative prices in the economy. For this reason, exchange rate stability is one of the main factors affecting foreign (direct and portfolio) investments, price stability and stable economic growth. The changes in the main economic factors make the exchange rates more volatile by causing unexpected changes in the exchange rate level. In addition, changes in these factors can lead to further growth of the volatility, by exceeding the target for the long-term equilibrium exchange rate in the short term.

According to (Raja and Naeem-Ullah, 2014), exchange rate is a great deal of significance in Pakistan owing to acceptance of balanced rate exchange prototype in addition to be acquainted with whether exchange rate consequence variables in macroeconomic variables of the country. Panel data analysis consisting of four countries was conducted. Raja and Naeem-Ullah (2014) examined the determinants of Foreign Exchange Markets in Pakistan where the study concluded with significant coefficients for relative interest rate, trade balance, terms of trade and net capital inflow. The results indicate that increase in interest rates and adverse trade balance has a negative significant impact on exchange rate while terms of trade and increase in net capital inflow results in favorable exchange rate for the home country.

Liu (2016) examined the potential determinants of renminbi on the US dollar exchange rate, yearly exchange rate inflation rates, interest rates, Gross Domestic Product of both China and the United State and foreign exchange reserve, as well as international balance of payments of China, was obtained to analyze potential determinants of RMB exchange rate. Multiple linear regression model was employed to analyze the relationships between the selected factors and the RMB exchange rate. The results indicated that inflation rates differential between China and US, the interest rates differential between the two countries, as well as the financial reform of China, in 2005, have an impact on the RMB exchange rate. On one hand, inflation rates differential between China and US are positively related to the RMB exchange rate. On the other hand, interest rates differential between China and US and the financial reform of China, in 2005, are negatively related to RMB exchange rate.

Razi, Shafiq, Ali, and Khan (2012) investigated the determinants of exchange rate and its impact on Pakistani economy by evaluating the GDP using data over period of 11 years (2001-2011). The study used multiple regression equation in order to explore the causal relationship between exchange rate with interest rate, inflation rate, current account and GDP. The result showed how these determinants fluctuates exchange rate, inflation differential, current account deficit, public debt and interest rate differential are most important determinants which have major impact on exchange rate and the result is statistically significant in the overall. Also, the studies by Razi et al. (2012) and Saeed et al. (2012) in Pakistan revealed that GDP, inflation, interest rate, current account balance, money stock, foreign reserve and total debt were the major factors influencing exchange rate instability in Pakistan.

The determination of appropriate and sustainable exchange rate in Nigeria has not been an easy task to both the government and policy makers. Ojo and Temitayo (2018), examined empirically the determinants of exchange rate in Nigeria using the ARDL Bounds test approach to co-integration for the period spanning 1986-2016. The result of the analysis showed that the gross domestic product (GDP), Interest rate (INT) and inflation rate (INF) had a positive effect on exchange rate in Nigeria while degree of openness (DOP) recorded a negative effect on exchange rate (EXR) in Nigeria. The study therefore concluded that gross domestic product, interest rate and inflation rate are the major determinant of exchange rate in Nigeria under the study period.

A strong Kenya Shilling reduces the competitiveness of our exports which could dampen economic growth. Kenyan exports become expensive abroad and imports become cheaper thereby discouraging domestic competitive industries as the share of foreign goods in our domestic market increases (CBK, 2019). There is a negative implication when the Kenya Shilling depreciates as this implies a higher cost in Shillings to finance imports. However, there is also a positive side to a weak Shilling as it means lower foreign prices for our exports; this increases the country’s competitiveness in the world market, which improves our balance of trade position. On a broader perspective with respect to this subject, a depreciation or appreciation of a currency is an adjustment process in response to the underlying fundamentals. Economists generally agree that a desirable exchange rate should be at a level that makes a country’s export of goods and services competitive in the world market (CBK, 2019).

The consequence of exchange rate fluctuations was felt in the entire economy because the weaker shilling raised the cost of imports including petroleum products and machinery, thus piling inflationary pressure (KNBS, 2017). It was also expected that the volatility of exchange rate would affect our foreign debt repayment. For instance, when Kenya government borrowed Eurobond the Shilling was trading at Ksh 88 to US dollar while it was at KES 106 in September 2015, thus increasing the burden of repaying of the debt and interest payments. These make exchange rate instability an important concern for economic growth in developing economies.

It is inevitable that Kenya has included the Chinese yuan as a reserve currency (CBK, 2016). The International Monetary Fund included the renminbi to its Special Drawing Right (SDR) basket alongside the U.S. dollar, euro, yen and the British pound. Currently, most of Kenya's foreign exchange reserves are in dollars. China's growing role in the global

3 economy has made the Chinese yuan one of the currencies of foreign trade. Bilateral trade and investment ties between Kenya and China have grown exponentially over the past decade. This has increased the demand for the Chinese Yuan by both Kenya and Chinese traders operating in Kenya (CBK, 2016).

1.2 Statement of the Problem

The importance of exchange rate stability in the attainment of macroeconomic policy objectives in both developed and developing economies cannot be over emphasized. Exchange rate is one of the determinants used in assessing the performance of an economy (Elfaki, 2018). Governments, particularly in developing economies over the years have adopted different exchange rate management policies with a view to achieve realistic and stable exchange rate.

Hacche (2015) investigated the variables affecting movements in the exchange rate for Sweden, the UK and Japan against the US dollar between January 1995 and December 2014. The variables were; money supply, industrial production, interest rate and inflation differential. The results showed that the variable interest rate differential constitutes a significant explanatory variable for exchange rate movements regarding all three countries included in the model. Saeed et al. (2012) investigated an econometric analysis of determinants of exchange rate for USD in terms of Pakistani Rupee within the framework of monetary approach. Monthly data from January 1982 to April 2010 for Pakistan relative to USA was used to examine the long run and short run behavior of PKR/USD exchange rate and relationship of exchange rate behavior with relative monetary variables. The results confirmed that stock of money, debt and foreign exchange reserve balance all in relative terms are significant determinants of exchange rate between Pakistani Rupee and US Dollar. Moreover, Political instability has a significant negative effect on the value of domestic currency.

Nwude (2012) investigated the factors that were assumed to be determinants of foreign exchange rate movement in Nigeria using 52 years annualized data from 1960-2011 and the least square method of analysis was employed. The factors investigated were gross domestic product, balance of payments external reserves, inflation rate, deposit rate and lending rate as independent variables while the foreign exchange movement is the dependent variable. The results of the study showed that there is no statistically significant relationship between the dependent and the independent variables. On the other hand,

Elfaki (2018) investigated the effects of growth rate of real GDP, real money supply, inflation rate, and trade openness on exchange rate stability in Sudan. Autoregressive distributed lag model approach was applied to estimate long run and short run relationship among exchange rate determinants, annual data covering period (1991-2016) was analyzed. The results revealed that, there is a long run relationship between exchange rate and its determinants and statistically significant. An increase in growth rate of real GDP leads to stability in exchange rate.

This study has evaluated the determinants of exchange rate because empirical test done in different countries in the world produce contradictory results, additionally most of the studies are on USD.

1.3 General Objective

The general objective of this study was to analyze the determinants of the Kenyan shilling exchange rate movement against the Chinese yuan

1.4. Specific Objectives

1.4.1 To determine the effect of interest rate on the exchange rate of the Kenyan Shilling against Chinese Yuan. 1.4.2 To establish the effect of inflation on the exchange rate of the Kenyan Shilling against Chinese Yuan. 1.4.3 To find out the effect of external public debt on the exchange rate of the Kenyan Shilling against Chinese Yuan.

1.5 Significance of the Study

1.5.1 Government

There is an increasing number in currency trading, appreciation or depreciation in foreign currency will always affect a country’s economic. In order to have better forecast on foreign exchange rate movement, researchers always try to identify and investigate the macroeconomic factors associated with foreign exchange rate. Once the determinants have been proven having a relationship with foreign exchange rate, governments can try to control these determinants in order to achieve desired foreign exchange rate. This study will assist government in spending right money at the right place, without wasting resources.

1.5.2 Policy Makers

The study will enable policy makers to have a clear view in formulating effective exchange policies and achieve economic growth. With appropriate understanding of factors influencing foreign exchange rate, it gives advantages to governments in controlling values of their currencies. In order to safeguard stability of a country’s economy, foreign exchange rate is needed to be supervised. This study would give a clear view to policy makers in order to implement efficient exchange policies after realizing the reason of falling or rising in currency values.

1.5.3 Investors

Foreign exchange rate will affect the real return of an investor's global investment portfolio. Investors prefer to invest in economy where foreign exchange rate is relatively stable, a country should become more attractive place for investment, where foreign exchange rate is playing the main role. In order to better forecast future foreign exchange rate, understand the determinants that force a move on foreign exchange rate is crucial. This study will of beneficial individuals’ investors both local and international and firms in improving their forecasting performance on foreign exchange rate by further understand and investigate the macroeconomic determinants of foreign exchange rate. A better forecast performance will help investors reduce investment risk and avoid losses when trading currencies.

1.5.4 Academicians and Researchers

This research can be a foundation to further research that has not been researched, also recommend other research on the related area of the topic.

1.6 Scope of the Study

The study was limited to the scope of determinants of exchange rate of the Kenyan shillings against Chinese Yuan. The analyzed the determinants of the exchange rate for a period of twelve years from the year 2007 to 2018 towards the Kenyan economy. The study used data on exchange rate and the variables; interest rate, inflation rate, and external public debt. The main source of data for the study was secondary data which was collected using the data collection sheet obtained from Kenya Central Bank. The data was analyzed using both descriptive and inferential statistics by the use statistical software and there were no challenges met during data collection.

1.7 Definition of Terms

1.7.1 External Debt

External debt is the portion of a country's debt that is borrowed from foreign lenders including commercial banks, governments or international financial institutions (Mirchandani, 2013). It was computed as the ratio of a country’s debt to gross domestic product (GDP).

1.7.2 Exchange Rate

An exchange rate is the value of a nation's currency in terms of the currency of another nation or economic zone (Raja & Naeem-Ullah, 2014). For this study it was KES /CYN.

1.7.3 Interest Rate

Interest rate is the amount charged, expressed as a percentage of principal, by a lender to a borrower for the use of assets. Interest rate is main tool of monetary policy and an important macroeconomic variable, which is positively linked with country’s economic growth (Ahmed, 2018).

1.7.4 Inflation

Inflation may be defined as ‘a sustained upward trend in the general level of prices’ and not the price of only one or two goods (Bashir, 2016).This was measured as the percentage change in the annual average consumer price index (CPI) of the corresponding months.

1.8 Chapter Summary

This study is divided into five chapters in which chapter one gives an overview of the research topic which includes background to determinations of exchange rate of the Kenyan shillings against Chinese Yuan, statement of the problem, general objective, specific objectives, significance of this study, scope of the study, definition of terms and chapter summary. In chapter two there is a review of literature on determinants of foreign exchange rate. Further in chapter three there is a focus on research methodology which was used to carry out the research. It described the research design, the method of data collections, sampling design, research instrument, and data analysis methods. Chapter four explained the result obtained from processed data. Detailed analyses were discussed by aid of graphs and tables for a clearer view of results. Chapter five is the last chapter in this study. It summarized the major findings of this research, discussion of the research, conclusions, and recommendation for the study.

CHAPTER TWO

2.0 LITERATURE REVIEW

2.1 Introduction

This chapter is going to provide a thorough literature review that will be based on the research objectives. There will be a discussion on three key issues that relate to the main objective of the study. Firstly, there will be a discussion on the effect of interest rate on the exchange rate of the Kenyan Shilling against Chinese Yuan from the year 2014 to 2018. Secondly, focus will be on the effect of inflation on the exchange rate of the Kenyan Shilling against Chinese Yuan from the year 2007 to 2018. Thirdly, there will be another discussion on the effect of external public debt on the exchange rate of the Kenyan Shilling against Chinese Yuan from the year 2007 to 2018.

2.2. Interest Rate and Exchange Rate.

2.2.1. Effects of Interest rate on exchange rate.

There are different policy variables critical to understanding interest rate and exchange rate movements. Identifying a more suitable structural model explaining the above phenomenon is not simple. Baxter and Stockman (2011) explored the connection between interest differentials and exchange rates for a defined floating rate period. His information was drawn from the Minneapolis Federal Reserve Bank Board of Governors database. The research used multivariate and univariate methods in data analysis in his research; he demonstrated a weak relationship between the two and found that there was a powerful link between the two factors that links further to the business cycle frequencies. However, in the two rates that affected movements, policy variables were not discovered. The study also found that the interest rate and exchange rate movements and the policy factors that explain the movements remain an empirical issue.

Understanding the connection between interest rate and exchange rate needs a more appropriate model or method of assessment. Karagol (2012) performed a long-term connection between interest rate and exchange rate research in Asian nations. He used the panel cointegration method, which they argued was the secret behind a clear long-term connection between interest rate and exchange rate. In their research, they used panel root testing to test their hypothesis and then tested the long-run consequences of the cointegration of the two rates.

The research used panel cointegration experiments and Johansen-cointegration to evaluate the entire sample period for ten South East Asian and South Asian nations including Pakistan, Thailand, Singapore, Sri Lanka, Philippines, Malaysia, Korea, Indonesia, India, and Bangladesh. For nine out of ten nations, Johansen-cointegration statistics showed powerful proof of the two rates (interest and exchange rates) being co-integrated. In addition, the research results showed that the interest rate differential and exchange rate connection between Asian nations changes with a shift in nominal system. The study found that correlations are negative in times of freely falling regimes and positive in times of pegged regimes.

Khalid (2017) investigated the economic effects of interest rates and exchange rates on stock market capitalization by considering annual data for Pakistan covering the 1990-2017 periods. The main intention of the study was to analyze the short-run together with the long- run interconnections between the aggregate market capitalization and macroeconomic variables by employing the econometric tools of Johansen approach, Error Correction Model (ECM) and then inspection of Variance Decomposition. By applying the Johansen Vesalius approach, it is detected that the whole series of data are co-integrated showing the long-term relationships among the examined variables. The long-term coefficient shows that a 1% increase in interest rate and in exchange rate contributes 0.23% decrease and 3.17% increase in market capitalization, respectively.

Sene (2014) performed a survey in Ghana on the assessment of the causality of exchange rate and interest rate volatility. The research used the Vector Autoregressive (VAR) model and the Granger causality test in order to achieve its findings. The study stipulated that the volatility of interest rates directly affects the exchange rate and market attractiveness. The study also found that stable exchange rates boost foreign direct investment inflows and high foreign direct investment inflows boost the stability of the country's exchange rate.

Ndungu (2011) conducted a study on the liberalization and short-term capital flow of Kenya's foreign exchange market. He mentioned in his study that the exchange rate was affected by different variables and policy actions. One of these factors is the short-term movement of assets. He found that interest rate differentials drive exchange rate adjustments that generate capital flows in turn. The relationship between exchange rates and interest rates plays a major role in both empirical modeling. With regard to empirical

9 methods, in an attempt to identify monetary and risk premium shocks in a credible manner, recent vector auto regressions have allowed simultaneous interaction between exchange rates and interest rates.

2.2.2. Emerging Economies (EMEs)

In both empirical and theoretical modelling, the relationship between exchange rates and interest rates plays a key role. With regard to empirical methods, recent vector auto regressions have permitted simultaneous interaction between exchange rates and interest rates in an attempt to identify monetary and risk premium shocks in a credible manner. Calvo and Reinhart (2012) conducted a study between interest rates and foreign exchange rates in Singapore, insisting that there are significant differences between advanced economies and rising economic power.

These distinctions include credibility problems, the existence of liability dollarization, a large degree of exchange rate pass-through and no stationarities in the inflationary process. They also discovered that the specificities of increasing financial power in these countries are accountable for a comparatively tiny degree of exchange-rate flexibility, known as' fear of floating. Eichengreen (2005) models the absence of exchange-rate flexibility by looking at interest-rate responses to offset foreign-exchange market fluctuations.

Cecchetti, Mohanty and Zampolli (2011) also conducted the same study in Eastern and Central Europe and Latin America. They argue that the effects of the balance sheet raising the domestic currency find that not all rising economic power display exchange rate degrees above those seen in advanced economies. They found that while in Eastern and Central Europe and Latin America nation’s pass-through tends to be large, it is comparatively small in many Asian economies. This means despite the recently proclaimed switch to floating exchange rates, the evidence seems to suggest a reversion to some degree of exchange rate management, albeit one which seems to be less tight than before the crisis.

Amaoko-Adu (2012) conducted a risk premium shock study, covariance between exchange rates and interest rates in Ghana, and found that risk premium shock, exchange rate covariance, and interest rate covariance are negative for expansionary depreciation and positive for contractionary depreciation. The exact timing of such interest rate and response to the exchange rate depends on the nature of the aggregate demand response to the value of the domestic currency. Overall, interest rates are found to react differently to shocks depending on whether depreciation is expansive or contractionary. Exchange rate

10 smoothing by means of interest rates which falls under the category of "fear of floating" is shown to originate in optimal policy under flotation (Amaoko-Adu, 2012).

According to Ndung'u (2011), periods of weak exchange rates in his research in Kenya could lead to extensive bankruptcies. He concentrated on the role of dollarization of production liability and its effect on the risk premium in Kenya. He discovered that it is unlikely that a recession will lead to weaker exchange rates. He further found that the covariance of exchange rates and interest rates on the Kenyan market is negative for expansionary depreciations and positive for contractionary shocks, subject to adverse risk premium shock.

2.2.3. Challenges facing use of Interest Rates in Stabilizing Exchange Rates

Shabir (2013) evaluated the connection between nominal interest rates and nominal exchange rates following currency crises, with specific focus on the Asian crisis, discovered no proof of the weakening effect of greater interest rate exchange rates. Using a big panel data set, they examined the utility of greater interest rates through speculative attacks. They did not find a very powerful beneficial or negative link between increasing interest rates and the outcome of the speculative attack.

Reinhart and Rogoff (2015) empirically evaluated the exchange-rate crisis connection in Korea, the Philippines and Thailand in 2009-2012, proposing a model that defines a monetary policy rule and discovered that exogenous interest-rate rises caused exchange- rate gratitude in Korea and the Philippines, but depreciation in Thailand, but mixed outcomes were obtained. Using a straightforward linear expectation model for the same nations, Kim and Ratti (2016) given proof that sharp rises in interest rates lead to company mistakes that deepen the currency crisis further. More technically, one standard deviation shocks in the interest rate is associated with statistically significant response (depreciation) in the exchange rate in Thailand, Korea and the Philippines.

According to Taylor (2012), there are countless hurdles in using interest rates to stabilize exchange rates, these hurdles often emanate from the unexpected trend of interest rates that could jeopardize the investment and capital inflow favorability of the country. Taylor (2012), does not take into account the rise in interest rates used by Nigerian commercial banks. He emphasizes the absence of rigorous rules to avoid business banks from increasing their own interest rates. While central banks provide the rules for setting interest rates, most commercial banks have their own processes for setting interest rates. Therefore, using

11 interest rates to stabilize exchange rates could be ineffective since the establishment of interest rates is not a uniform process.

As Sylla and Homer (2011) said, a number of researches were performed to determine the impact of interest rates on exchange rates. Most of these researches concentrated on how the interest rate system plays a main role in stabilizing the exchange rate and the economy as a whole, comparing two or more nations. The exchange rate regime on the IMF's exchange rate model in Kenya, Uganda and the remainder of East Africa was one of the most significant researches. There were not only concerns about the correctness of IMF- type plans in the holder of East African countries. Geithner (2011) states that there are also a number of economists who disputed the rise in the pace of concern aimed at calming the trade charge, for example by increasing the ambiguity and the likelihood of evasion due to liquidation, a rise in the interest rate can truly lead to an extra fall in the trade rate.

2.3. Effects of Inflation on Exchange Rate

2.3.1. Effects of inflation rate Inflation is one of the main variables influencing the exchange rate. A low inflation rate situation will, in principle, indicate a growing currency rate as the purchasing power of the currency increases relative to other currencies (Baxter & Stockman, 2011). Miller & Foster (2012) tried to create the connection between exchange prices and inflation in Latin America. The study discovered that the rate of inflation is usually used to assess the price stability of the economy. The research concentrated on Real Exchange Rate (RER) volatility and misalignment in global trade and investment. The research discovered that during the study period from 1993 to 2003, RER volatility had a adverse and significant effect on trade and investment.

Quang and Sayim (2016) examined the impact of macro-economic factors such as economic outputs, unemployment rate, and inflation on the foreign exchange rates between USA and four big emerging countries: India, Mexico, Brazil and China for the period of 2005 to 2014. The study used stepwise multiple regression methods to investigate the impact of market fundamental on the exchange rates. The empirical findings reveal that the macro-economic factors significantly predict and influence the exchange rates between (US dollar/Chinese yuan (USD/CNY), USD/INR (US dollar/Indian rupee), USD/BRL (USdollar / Brazilian real), and USD/MNX (US dollar/Mexican pesos).

Baxter and Stockman (2011) did research in Ghana on the effects of inflation on the exchange rate. The research discovered that theoretically a low inflation rate situation shows a growing currency rate as the currency's purchasing power rises compared to other currencies. They found that the rate of inflation is usually used to assess the price stability of the economy. Conceptually, inflation can be split into two sides: demand-side inflation, which is inflation driven by demand, and supply-side inflation, which is inflation driven by cost. They recommended that for open-economy countries, inflation should come from domestic factors that entail internal pressure and also overseas factors that entail external pressure.

Bamidele and Joseph (2013), using monthly time series data for the period August 2011 to November 2014, used the granger-causality strategy to assess the connection between exchange rate and inflation in South Sudan. The research shows that, without feedback from exchange rate to CPI, unidirectional causality exists. This implies that the depreciation of South Sudanese currency is harmful to the economy of South Sudan. Although CPI has not caused changes in the exchange rate, there is no way to conclude with higher confidence that the findings are accurate. The effect of the pressure of an increase in price level on exchange rate could have been from the response of monetary authorities in bridging the gap between the price level and the purchasing power of people in the economy. Sanam and Monfared, (2017)analyzed the relationship between exchange rate and inflation based on time series data, using Hendry General to Specific Modeling method and Vector Auto regression (VAR) model. To this end, they used annual data for the period 1976-2012.

The findings showed that there is a direct relationship between exchange rate and inflation. An increase in foreign exchange rates makes the inflation goes up. According to the results, both the money supply and the exchange rate affect the inflation in the positive direction. Calvo and Reinhart (2012) in their study requested proof to support the presence of impact of chosen macroeconomic factors: difficult currency foreign exchange rate, interest rate and inflation rate on share price changes based on weighted average monthly information for businesses listed on the Nairobi Securities Exchange in Kenya from January 2008 to December 2012.

On the other side, Ndung'u (2011) has undertaken studies to determine the effect of chosen macroeconomic factors on foreign direct investment in Kenya. For a 10-year period starting in 2005, the research examined information from 271 participants and primary data were

13 gathered. Analysis of linear regression disclosed a detrimental and negative effect of inflation on exchange rate.

2.3.2. Factors Influencing Inflation. Kibui (2014) assessed the primary inflation determinants of Kuwait. The investigator used three autonomous variables, namely cash supply, actual gross national product and imported inflation. The research was estimated using the Granger causality test that showed that national inflation was mainly affected by the growth of national liquidity that overshadowed the theoretically anticipated impact of imported inflation. According to the scientist, these outcomes could be caused by two primary variables; the first is the financial and political developments during the era of the research, and the second is the distinction in each inflation measure building.

Arize (2011) examines inflation determinants in Azerbaijan during 2000-2009 through the application of modeling of co-integration. The model concept aimed at capturing external and internal inflation factors such as large money (M3), oil GDP and non-oil GDP, nominal effective exchange rates, credit and deposit rates. The empirical results show that domestic currency appreciation has multiple inflation effects. Furthermore, non-oil GDP elasticity is higher than oil GDP. As an oil exporter, Azerbaijan extracts an economic boom in foreign asset inflow that affects the exchange rate while domestic liquidity expansion reinforces the process of inflation. The spillover effect of the external factor, expressed as higher prices in trading partners and depreciation of exchange rates, the absence of independent monetary policy with a combination of fixed exchange rates, contribute to the long-term inflation process. The shock of short-term inflation, exacerbated by supply side bottleneck, manifested in Azerbaijan as the production of long-term inflation determinants.

Bain and Howells (2012) explored the relationship between inflation, output gap and real- money gap for a sample of Sub-Saharan countries including Botswana, Niger, Cameroon, Nigeria, Ivory Coast, Rwanda, Ethiopia, Senegal, Ghana, Sierra Leone, Kenya, Swaziland, Madagascar, Malawi Zambia, Mali, Zimbabwe and Mozambique. Using panel cointegration estimation techniques, the researchers used annual data on these countries covering a maximum period of time from 1960 to 2003 to estimate the structural gaps. The results of this study showed that both economically and statistically significant is the estimated output gap and money gaps in accounting for SSA inflation. In addition, the structural output and real money gaps have a significant predictive power on inflation. 14

Ndung'u (2011) used quarterly data from 1983:Q1 to 2009:Q3 to investigate sources of inflation in Kenya. This study's analysis was based on a one-equation model. Results showed that Kenya's main inflation determinants are politics, foreign prices, currency movements, and public spending. For six quarters, the effects of shocks in politics and changes in foreign prices on inflation persist. Changes in public spending and nominal exchange rates, respectively, affect inflation over three and four quarters.

2.3.3. Pass-Through The sources of internal variables are world commodity prices rising or fluctuating. The impact of the exchange rate on inflation itself relies on the exchange rate system being chosen by the country. The exchange rate scheme plays a significant role in decreasing or minimizing the danger of changes in the exchange rate that will affect the economy. Any shift in exchange rates will greatly affect the economy (Eichengreen, 2014). Depending on the floating exchange rate scheme, exchange rate changes may have a powerful effect on price levels through aggregate demand and aggregate supply. On the aggregate supply, depreciation of domestic currency can affect the price level directly through imported goods that domestic consumers pay. However, this condition occurs if the country is the recipient countries of international prices or an international price taker (Engle, 2012).

A pass-through empirical analysis concentrating on inflation and the actual exchange rate would consider how national prices of non-tradable and tradable goods are affected by modifications in nominal exchange rates. However, in most nations, there are important information constraints. For very few nations, non-tradable prices are accessible. Data constraints are more serious in developing nations. In many demand curve types, this is the case. There are rarely importable national prices accessible (Rais & Anwar, 2012). In applying inflation targeting in emerging countries, Chile has been a pioneer. This is very interesting because for centuries Chile has had elevated and chronic inflation. Repeated efforts to quench it proved unsuccessful until 2000. Engle (2012) referred to Chile as the leading case of an economy in which inflation tended to perpetuate itself in his celebrated work on inflationary inertia.

If the inflationary effects of exchange rate changes are large, authorities will have to implement monetary and fiscal policies that compensate for the inflationary effects of exchange rate changes. Historically, pass-through has tended to be big in nations 15 experiencing a currency crisis. Borensztein and De Gregorio (2009) used a sample of 41 nations and discovered that after one year, 30 percent of nominal devaluation had been transferred to inflation. The pass-through after two years was on average a very elevated 60 percent. They also discovered that the degree of pass-through was considerably smaller in developed nations.

Taylor (2000) asserted that this reduced pass-through resulted in a decrease in inflation and volatility. He recognized that the important decrease in the extent of the pass-through and the growth of a virtuous circle of kinds is one of the beneficial effects of a powerful dedication to price stability. Lower inflation decreases the pass-through, helping to keep a low inflation rate. Taylor's proposal was tested by Campa and Goldberg (2009) using domestic import prices information for OECD countries. Their findings indicate that currency conditions are linked only slightly to the pass-through degree. More recently, Rais & Anwar (2012) have used a sample of advanced nations to analyze this issue and have concluded that the decline in the pass-through has been related to changes in monetary policy procedures and in particular the adoption of inflation targeting.

2.4. Effects of External Public Debt on Foreign Exchange

2.4.1. Debt Ratios Also correlated with the percentage of economic growth, interests, and foreign exchange rates was the amount of public debt. Foreign debt is one of the major factors that trigger both short-term and long-term volatility of the exchange rate (Bamidele & Joseph, 2013).

The effect of currency depreciation on foreign debt in Croatia was evaluated by Baldacci, Gupta and Granados (2009). They evaluated monthly gross foreign debt and nominal exchange rate information empirically using Johansen's cointegration strategy to test cointegration presence among factors. The findings indicated that the nominal exchange rate had a statistically significant beneficial impact on foreign debt. Therefore, it is unfavorable to raise Croatia's external debt, given that foreign debt is mostly denominated in foreign currency.

Public debt and personal property, explaining the flight of capital from sub-Saharan African districts; claim that external borrowing is important and linked to capital flight (Ali & Mustafa, 2012). They studied other variables; difference in interest rates, inflation, appreciation of exchange rates, fiscal policy indicators, economic growth, and political

16 climate and governance indicators. The findings showed that the most significant determinant of capital flight was internal borrowing. For each dollar that flowed around 80 cents in the region during the period 1970-1996 as overseas loans flowed out in the same year as capital flight, proposing debt-fueled capital flight; thus calling for reforms on both creditors and debtors to support responsible lending and an accountable debt management.

According to Saheed, Sani, and Idakwoji (2015), examined the impact of public external debt on exchange rate in Nigeria. Using the Ordinary Least Square, on the secondary data sourced from the CBN and DMO among other sources, findings reveals that all the dependent variables, that is, external debt, debt service payment and foreign reserve proved to be statistically significant in explaining exchange rate fluctuation in Nigeria within the period of observation, with debt service payment having the strongest effect.

In his research on the impact of external debt servicing and receipts on exchange rates in Nigeria, Amaoko-Adu (2012) used the following variables: Naira exchange rates, internal government debt receipts and public debt maintenance as proxy for external debt servicing. They used normal least square (OLS) and co-integration testing analytical methods. Results showed that external debt receipts have a positive and significant impact on naira's value and exchange rate, while debt maintenance has a negative impact on the value and exchange rate of naira.

A Sene research (2004) examined the connection between external debt and the real exchange rate of equilibrium for developing nations. The investigator used the Reinhart- Rog off model and found that overhanging debt in the long run leads to true recognition of the exchange rate. Interest rates, inflation, internal public debt and money supply are determinants of Kenya's exchange rate volatility shilling against significant world currencies, according to Checherita and Rother (2010). Foreign debt is a significant variable in Kenya. Previously, the country has relied heavily on concessionary loans and budget deficit support from development partners abroad. According to data from central Bank of Kenya, foreign debt stands at 51% of total debt as at April 2018 statistics.

Bamidele and Joseph (2013) examined the mechanism through which the dynamics of the real exchange rate are affected by public infrastructure expenditure. Using a two-sector based open economy model with cross-sectoral adjustment expenses, they have shown that government spending produces a non-monotonic U-shaped adjustment route with sharp inter temporal tradeoffs for the real exchange rate. The effect of government spending on

17 the real exchange rate depends critically on, the composition of public spending, the underlying financing policy, the intensity of private capital in production, and the relative productivity of public infrastructure (Campa &Linda, 2009).

2.4.2. Government Spending An increase in government spending puts pressure on the national currency to appreciate, leading to a deterioration of the current account and possibly a twin deficit and a decline in consumption through a global situation of risk-sharing. This mechanism encompasses a broad variety of models, including both new and neoclassical Keynesian models. However, empirical evidence has not been established for such a system. For instance, Cecchetti, Mohanty, and Zampolli (2011) figured out that the trade balance improves in the U.S. information after a shock from government spending.

For their portion, Boboye and Ojo (2012) used the linear regression model to empirically evaluate the effect of government spending and the actual exchange rate. The findings show that Nigerian government spending has an important connection with the real exchange rate. Furthermore, the outcome indicates that changes in the exchange rate affect external debt shock, the payment of external debt service and the economic growth of the nation.

The study did not consider other macro-economic variables such as interest rates using the per capita income approach, Ahmed (2018) research used Jordanian empirical information. The research was anchored on the theory of economics, which maintains that sensible rates of indebtedness lead to increased economic growth, particularly for a developing economy. The theory attributes this connection to restricted capital stocks in order to undertake investment possibilities with greater yield rates than well-developed nations. Such investment opportunities are linked to favorable economic growth that allows an economy to repay the sophisticated debt in a timely manner.The variables in this study included: per capita growth for economic growth, rates of investment, the proportion of public debt to

FDB, inflation rate, rate of fiscal balance to GDP, debt service payments and trade openness. The findings showed that economic growth in Jordan was affected by terms of trade, investment rates and rate of fiscal balance.

In Ghana, Owusu-Nantwi and Erickson (2016) explored the response of Foreign Direct Investment (FDI) following modifications in the country's public debt. The study focused on the long-term and causal relationship between the two factors, informed by the willingness to pursue prudent macroeconomic policies directed at enhancing Ghanaian citizens ' social economic welfare. The factors in this research included aggregate production to measure FDB, personal capital, government capital, government debt, inflation, and government consumption, expenditure on investment, openness, population growth, jobs, and labor. The results showed that there was a favorable and substantial connection between the actual FDI development rate and the amount of government debt. This was interpreted as meaning that over the research period, government debt contributed favorably to Ghana's financial development.

Study Ajayi and Oke (2012) used a linear regression model to evaluate Kenyan information from the 1993/1994 to 2014/2015 financial years, with FDB growth rate as a function of government debt, inflation rate, and unemployment taken as control variables. The results showed that public debt had a positive impact on FDB development. The findings revealed obviously that greater public debt resulted in lower economic development. The regression model also disclosed that there is no important connection between the independent variable that was government debt and Kenyan economic growth.

2.5. Chapter Summary

This chapter given a review of literature associated with the aim of the study. The section presents past studies explaining the micro-economic factor affecting the exchange rate in different areas over the last five years. The chapter principally cantered on reviewing literature associated with the three research queries of the study. The next chapter deals with the research methodology applicable to this study.

CHAPTER THREE

3.0 RESEARCH METHODOLOGY

3.1 Introduction

This chapter explains the research methodology that was used in this study. The chapter presents the research design, population and sampling design, data collection method, research procedures and data analysis methods.

3.2 Research Design

Research design is the overall scheme of how to answer the research questions (Saundres, Lewis & Thornhill, 2016). The research design is defined by Toledo-Pereyra (2012) as a blueprint for collection, measurement and analysis of data when the research objective has been clearly figured. Therefore the most suitable research design obtains the most precise results possible and the choice of research design reflects decision about the priority being given to a range of dimensions of the research process (Bryman & Bell, 2015).

This research explained how microeconomic elements namely interest rate, inflation and external public debt determines the exchange rate of the Kenya shilling against Chinese Yuan. The study therefore adopted descriptive and explanatory research design.

Explanatory research design focuses on understanding, explaining, predicting and controlling relationships between variables (Saunders & Lewis, 2012) and therefore in this study it helps to explain the relationship between microeconomic elements and the exchange rate where Exchange rate was the dependent variable depending on interest rate, inflation rate and the public external debt.

The descriptive design on the other hand was used to describe the trend of the exchange rate, interest rates, inflation rate and public external debt for the 12 years from 2007 to 2018.

3.3 Population and Sampling Design

3.3.1 Population

Population is a collection of people, items or events about which one can use to make conclusions or apply your results (Cooper & Schindler, 2008). Saundres, Lewis and Thornhill (2016) defined population as a full set of cases or elements from which a sample is taken. Target population is that accessible population having the experience and

20 knowledge of the phenomenon under study which the researcher can draw a study population from (Holloway & Wheeler, 2010) .

The population the study was 12 years data on interest rates, inflation rates, public external debt and exchange rate for the years 2007 to 2018 obtained from Central Bank of Kenya website.

3.3.2 Sampling Design

3.3.2.1 Sampling Frame

The sampling frame is defined as a complete list of all the cases in the population, from which a probability sample is taken (Saundres, Lewis, & Thornhill, 2016) . Bryman and Bell (2015) defined the sampling frame as a list of elements from which the sample is collected and found closely associated to the population. The study sampling frame was the national data on the study variables. This research used 12 year data during 2007 to 2018, thus it is possible to relate interest rates, inflation rates, and external public debt on exchange rates of Kenyan Shillings against Chinese Yuan.

3.3.2.2 Sampling Technique

According to Kothari (2004) sampling technique is the identification of the specific process by which the entities of the sample are being selected. This research used census as the sampling technique.

3.3.2.3 Sample Size

Hancock and Algozzine (2017) defined sample size as a smaller set of the total population and noted that large samples give more dependable results than small samples. This research used entire population as sample size period covered from 2007 to 2018. A period of 12 years was selected because it is broad enough to demonstrate the trend and influence of variables on the exchange rate of Kenyan Shillings against Chinese Yuan.

3.4 Data Collection Method

The study relied on secondary data which was collected from the website of the Central Bank of Kenya, for the period of 12 years between 2007 and 2018. The data that was collected contained monthly figures for the interest rate, inflation rate, external debt as a percent of GDP and the exchange rate of the Kenya shillings against Chinese Yuan as per the central Bank of Kenya.

The secondary data was collected by use of a checklist. According to Bryman & Cramer (2012) checklist provides a guide to reviewers about the type of relevant information that could be obtained from secondary sources. The checklist for this study comprised of 4 columns as per the variables of the study, which were foreign exchange rate, interest rates, inflation, and external public debt. The datasets downloaded from the website of the Central Bank of Kenya was validated through a thorough review and cleaning to ensure that only the necessary data was collected and none was missing.

3.5 Research Procedure.

Research procedure is defined as a detailed description of the steps taken to conduct a research (Abbott & McKinney, 2013). In this research a secondary data collection sheet was designed, tested and validated. The validation of the checklist was done through a thorough review by the expert supervising the project then tested using data from the website of the Central Bank of Kenya. The permission to conduct the research was then obtained from NACOSTI and finally the data was collected from the website of Central Bank of Kenya.

3.6 Data Analysis Methods

Data collected was coded into SPSS and then the descriptive and inferential analysis was done. In the descriptive statistics, the line graphs showing the trends of the interest rates, inflation rate, external debt and exchange rate from 2007 to 2018 were used. In the inferential statistics correlation analysis was done to investigate the significant relationship between exchange rate and the independent variables. Assumptions for the regression analysis were tested and then a Multiple Linear regression analysis was conducted to investigate the extent to which the three independent variables significantly affect the dependent variable. The regression model showing the relationship between exchange rate of Kenyan Shillings against Chinese Yuan and interest rates, inflation rates and external debt were presented in the following model:

Y = β0 + β1X1 + β2X2 + β3X3.

Where; Y = Exchange rate

X1 = interest rates

X2 = Inflation rate

X3= external debt

β0 = Constants to be estimated by the model

β1, β2 and β3= Coefficients of the independent variables.

Exchange rate refers to the exchange rate determined by national authorities or to the rate determined in the legally sanctioned exchange market (Wel, 2016). It was calculated as an annual average based on monthly averages.

Interest rate is a percentage of principal paid a certain number of times per period for all periods during the total term of the loan or credit (Chowa & Chenb, 2012). Interest rates are normally expressed as a percentage of the principal for a period of one year; sometimes they are expressed for different periods like for a month or a day. This study made use of central bank lending rate.

Inflation rate is the rate at which the general level of prices for goods and services is rising and, consequently, the purchasing power of currency is falling (Bentzen, 2015). This was measured by use of the percentage change in consumer price index. The consumer price index measures the changes in the cost of a basket of consumer goods and services.

External Public debt is the entire stock of direct government fixed-term contractual obligations to others outstanding on a particular date. It includes domestic and foreign liabilities such as currency and money deposits, securities other than shares, and loans (Baksay, Karvalits & Kuti, 2013). It is the gross amount of government liabilities reduced by the amount of equity and financial derivatives held by the government. Public debt was measured as a percent of the GDP.

3.7 Chapter Summary

This chapter presented the research design, population and sample of the study, data collection method, research procedures and data analysis methods. Ethical issues to be considered have also been discussed. The next chapter presents the results and finding.

CHAPTER FOUR

4.0 RESULTS AND FINDINGS

4.1 Introduction

This chapter presents the findings on the effects of interest rates, inflation rates and External debt on exchange rates between Kenyan Shillings and Chinese Yuan. The research conducted on a 12 years period from 2007 to 2018.

4.2 Effects of Interest Rate on Exchange Rate

4.2.1 Trend of Exchange Rate

The exchange rate of Kenya shillings against Chinese Yuan has been steadily increasing from 2007 to 2015 then slightly went down in 2016. The highest average annual exchange rate was recorded in 2015 at15.67 and dropped slightly to an average of 15.29 in 2016 as shown in the figure 4.1 below.

15.6179 15.288 15.46915.337 14.008 14.272 13.751 13.399 11.213 11.7074 9.9588

8.8295 Average Exchange rate Exchange Average

2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 Year Year Year Year Year Year Year Year Year Year Year Year

Figure 4. 1: Trend of Exchange Rate in Kenya from 2007-2018. 4.2.2. Trend of Interest Rate

The findings shows that the average annual Interest rates have been fluctuating from 2007 to 2018 in Kenya, reaching highest annual average rate of 15.75% in 2012 and lowest annual average of 6.42% in 2010. The highest annual average increase of Interest rate was 7.35% which was recorded between 2011 to 2012 as indicated in figure 4.2.

18 16 15.75 14 12 10 10.13 10.63 10 9.2292 8.85 8.833 9.333 8 7.875 8.396 8.5 6 6.417 4 2 0 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 Year Year Year Year Year Year Year Year Year Year Year Year

Figure 4. 2: Trend of Central Bank’s interest rate from 2007 to 2018

4.2.3 Correlation between Interest Rate and Exchange Rate.

A correlation test was done to investigate the significance relationship between interest rate and the exchange rate. The results indicated that there was a positive significant relationship between interest rate and the exchange rate at (p=0.006, r=0.229) as shown in the table 4.1 below.

Table 4. 1: Correlation between Interest Rate and Exchange Rate.

Exchange Rate Interest Rate

Pearson Correlation 1 .229** Exchange Rate Sig. (2-tailed) .006 N 144 144 **. Correlation is significant at the 0.01 level (2-tailed).

4.3 Effects of Interest Rate on Exchange Rate

4.2.3 Trend of Inflation Rate

Average Inflation rates have been fluctuating in Kenya and the findings shows that the highest annual average inflation rate was recorded in 2012 at14.27%, and lowest in 2007 at 4.765%. The highest annual average increase in inflation rate was recorded between 2007 and 2008 at about 7% increase and the highest decrease was recorded between 2009 and 2010 at about 9% decrease as shown in the figure 4.3.

16 14 14.11 14.278 12 10 10.3 8 7.9917 7.6708 6.81 6.538 6.576 6 5.614 5.563 5.5875 4 4.765 2 0 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 Year Year Year Year Year Year Year Year Year Year Year Year

Figure 4. 3: Trend of the average annual Inflation rate in Kenya from 2007 to 2018

4.3.2. Correlation between Inflation Rate and Exchange Rate

A correlation test was done to investigate the significance relationship between inflation rate and the exchange rate. The results indicated that there was a negative relationship between inflation rate and the exchange rate but was not significant at (p=0.098, r=-0.138) as shown in the table 4.2 below.

Table 4. 2:Correlation between Inflation Rate and Exchange Rate

Exchange Rate Inflation rate

Pearson Correlation 1 -.138 Exchange Rate Sig. (2-tailed) .098 N 144 144

4.4 Effects of External Debt on Exchange Rate

4.4.1External Debt

Average external debt have been fluctuating in Kenya and the findings shows that the highest annual average external debt was recorded in 2018 at 55.9% of the GDP, and lowest in 2010 at 18.2% of the GDP. The highest annual average increase in external was recorded between 2016 and 2017 at about 8% increase and the highest decrease was recorded between 2008 and 2009 at about 14% decrease as shown in the figure 4.4.

60.0 55.9 49.5 50.0 41.0 40.0 34.3 30.9 32.4 27.7 30.0 21.7 21.8 23.6 18.4 18.2 20.0

10.0

0.0 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018

Figure 4. 4: Trend of External Debt from 2007 to 2018.

4.4.2. Correlation between External Debt and Exchange Rate

A correlation test was done to investigate the significance relationship between external debt and the exchange rate. The results indicated that there was a significant positive relationship between external debt and the exchange at (p=0.098, r=-0.138) as shown in the table 4.3 below.

Table 4. 3:Correlation between External Debt and Exchange Rate

Exchange External Debt Rate Pearson Correlation 1 .486** Exchange Rate Sig. (2-tailed) .000 N 144 144 **. Correlation is significant at the 0.01 level (2-tailed).

4.5 Assumptions for Regression Analysis

Assumptions for regression analysis helps to investigate whether the data fits for regression analysis or not. The assumptions include normality, linearity, multi-collinearity and Homogeinity.

4.5.1 Normality Test.

Normality test was done to investigate the regression assumption that the data is normally distributed. The results showed that the skewness values are between -0.5 and +1 which are

27 closer to 0 and the kurtosis values were between -3 and +3. This is an indication that the data is normally distributed as required by the regression assumptions as shown in the table 4.4 below.

Table 4. 4: Normality Test

N Skewness Kurtosis Exchange Rate 144 -.555 -.855 Interest Rate(Central Bank Lending rate) 144 1.645 3.116 Inflation rate (Consumer Price Index) 144 1.263 .577 External Debt (Debt as percentage of GDP) 144 .799 -.452

4.5.2 Multicollinearity Test.

Multicollinearity test was done using the Variance Inflation factor (VIF). Multicollinеаrity is а state of very high intеrcorrеlаtions or intеr-аssociаtions among the indеpеndеnt vаriаblеs. A VIF value of less than 10 is an indication that the multicolliniearity is not significant.

The results showed that all the VIF values were below 10, meaning that the multicollinearity was not significant as required by the regression assumption as shown in the table 4.5.

Table 4. 5: Multicollinearity Test

Collinearity Statistics

Tolerance VIF

Interest Rate(Central Bank Lending rate) 0.922 1.084

Inflation rate (Consumer Price Index) 0.910 1.099

External Debt (Debt as percentage of GDP) 0.945 1.058

4.5.3. Linearity Test

Linearity test was done to investigate if Exchange rate has a significant linear relationship with the interest rate, inflation rate and external debt. A significant linear relationship is an assumption for the regression analysis.

The results showed that foreign exchange rate has a significant linear relationship with interest rate at (p=0.000, F=11.101), inflation rate at ((p=0.007, F=3.188), External Debt at (p=0.000, F=131.592) as shown in the table 4.6 below

Table 4. 6: Linearity Test

Sum of Mean df F Sig. Squares Square Foreign Exchange Rate * Interest Linearity 39.930 1 39.930 11.101 0.001 Rate Foreign Exchange Rate * Inflation Linearity 14.585 1 14.585 3.188 0.007 rate Linearity 179.465 1 179.465 131.592 0.000 Foreign Exchange Rate * External Debt

4.5.4 Homoscedasticity/Homogeneity Test.

Homoscedasticity test was done to investigate whether the data was homogeneous or not. Levene statistics test was done to test the homogeneity. A significant Levene statistics value indicates that the data was not homogeneous. The results showed that the Levene statistics value (1.557) was not significant at p=0.86. This is an indication that the data was homogeneous and meets the assumption of regression analysis as shown in the table 4.7 below. Table 4. 7: Test of Homogeneity of Variances

Levene Statistic df1 df2 Sig. 1.557 17 125 .086

4.6 Multiple Linear Regression Analysis.

A regression analysis was run to investigate the extent to which Interest rate, inflation rate and external public debt affects the exchange rate of Kenya shillings against Chinese Yuan. The results from the model summary table shows that R-square=0.276 indicating that variables affects 27.6% of the exchange rate as shown in the table 4.8 below.

Table 4. 8:Model Summary

Model R R Square Adjusted R Square Std. Error of the Estimate 1 .526a .276 .261 1.983 a. Predictors: (Constant), External Debt (Debt as percentage of GDP), Interest Rate(Central Bank Lending rate), Inflation rate (Consumer Price Index)

The ANOVA table in this study shows whether the multiple linear regression model of

Y = β0 + β1X1 + β2X2 + β3X3 is significantly linear or not. The results indicates that p=0.000 meaning that the regression model is significantly linear.

Table 4. 9: ANOVA

Model Sum of df Mean Square F Sig. Squares Regression 210.146 3 70.049 17.821 .000b Residual 550.291 140 3.931 Total 760.437 143

In this case Y = Exchange rate, X1 = interest rates, X2 = Inflation rate, X3= external debt,

β0 = Constants to be estimated by the model, β1, β2 and β3= Coefficients of the independent variables.

Table 4. 10: Coefficients

Model Standardized Unstandardized Coefficients Coefficients t Sig. B Std. Error Beta (Constant) 9.421 .744 12.662 .000 Interest Rate .170 .064 .200 2.676 .008

Inflation rate -.055 .038 -.110 -1.454 .148 External Debt .086 .014 .443 5.992 .000 a. Dependent Variable: Foreign Exchange Rate (Kenya Shilling/Chinese Yuan)

The coefficients tables indicates that the linear regression model Y = β0 + β1X1 + β2X2 +

β3X3is Y=9.421+0.170X1-0.055X2+0.086X3.

Where; Y = Exchange rate, X1 = interest rates, X2 = Inflation rate, X3= external debt, β0 =

Constants to be estimated by the model, β1, β2 and β3= Coefficients of the independent variables.

The model indicates that increasing interest rate by one unit; exchange rate will increase by 0.170.

The model also indicates that increasing external debt by one unit, exchange rate will increase by 0.086. This effect was significant with p value of less than 0.05

The model reveals that increasing inflation rate by one unit, exchange rate will be negatively affected by 0.055 units though the effect is not significant as shown in the coefficients table.

4.7 Chapter Summary

The chapter presented the finding of the research based on three research objectives on the effects of interest rates, inflation rates and external public debt on exchange rates of Kenyan Shillings against Chinese Yuan. The next chapter presents discussion, conclusions and recommendations.

CHAPTER FIVE

5.0 DISCUSSION, CONCLUSIONS, AND RECOMMENDATIONS

5.1 Introduction

This chapter presents the summary of major findings, and discussion, conclusions of the results in line with the three main research objectives. Recommendations on the relationship between interest rates, inflation rates, and external public debt on exchange rates of Kenya shillings against Chinese Yuan.

5.2 Summary.

The purpose of this research was to investigate the impact of macro-economic factors on exchange rate of Kenyan Shillings against Chinese Yuan. The research objectives were to determine the effect of interest rate on the exchange rate of the Kenyan Shilling against Chinese Yuan, to establish the effect of inflation on the exchange rate of the Kenyan Shilling against Chinese Yuan and to find out the effect of external public debt on the exchange rate of the Kenyan Shilling against Chinese Yuan.

Explanatory research design was used to explain the relationship between independent variables which are interest rates, inflation rates and External public debt and dependent variable which is the exchange rate. The research used census as the sampling technique and selected data between 2007 and 2018.

The data that was collected for 12 years contained monthly figures for the interest rate, inflation rate, external debt as a percent of GDP and the exchange rate of the Kenya shillings against Chinese Yuan as per the central Bank of Kenya. Descriptive and trend analysis were done using table and graphs and multiple linear regression analysis was done to demonstrate the relationship between independent and dependent variables using a linear model.

On the first objective investigating the effect of interest rate on the exchange rate of the Kenyan Shilling against Chinese Yuan, the findings showed that the average annual Interest rates have been fluctuating from 2007 to 2018 in Kenya, reaching highest annual average rate of 15.75% in 2012 and lowest annual average of 6.42% in 2010. The highest annual average increase of Interest rate was 7.35% which was recorded between 2011 to 2012. A correlation test results indicated that there was a positive significant relationship between

32 interest rate and the exchange rate at (p=0.006, r=0.229). The regression analysis further indicated that increasing interest rate by one unit; exchange rate will increase by 0.170.

On the second objective investigating the effect of inflation rate on the exchange rate of the Kenyan Shilling against Chinese Yuan, the findings showed that Inflation rates have been fluctuating in Kenya and that the highest annual average inflation rate was recorded in 2012 at14.27%, and lowest in 2007 at 4.765%. A correlation test results indicated that there was a negative relationship between inflation rate and the exchange rate but was not significant at (p=0.098, r=-0.138). The regression analysis further confirmed that increasing inflation rate by one unit, negatively affects exchange rate by 0.055 units though the effect is was not significant.

On the third objective investigating the effect of external public debt on the exchange rate of the Kenyan Shilling against Chinese Yuan, the finding showed that the trend of external debt have been fluctuating in Kenya and that the highest annual average external debt was recorded in 2018 at 55.9% of the GDP, and lowest in 2010 at 18.2% of the GDP .A correlation test results indicated that there was a significant positive relationship between external debt and the exchange at (p=0.098, r=-0.138).This was confirmed by regression analysis which confirmed that increasing external debt by one unit, exchange rate will increase by 0.086.. Analysis Further indicated that all the independent variables affects 27.6% of the exchange rate.

5.3 Discussion

5.3.1 Effect of Interest Rate on the Exchange Rate

This research through correlation and regression analysis indicates that interest rate significantly affects exchange rate positively and that increasing interest rate by 1%, increases exchange rate by 17%. This finding is in line with Khalid (2017) who investigated the economic effects of interest rates on exchange rates by considering annual data for Pakistan covering the 1990-2017 periods. By applying the Johansen Vesalius approach, the long-term coefficient showed that a 1% increase in interest rate contributes 0.23% increase in exchange rate.

The positive correlation between interest rate and the exchange rate found in this study is in line with the findings of Sene (2014) who performed a survey in Ghana on the assessment of the causality of exchange rate and interest rate volatility. The research used the Vector Autoregressive (VAR) model and the Granger causality test in order to achieve

33 its findings .The study stipulated that the volatility of interest rates has a positive correlation the exchange rate.

There are different policy variables critical to understanding interest rate and exchange rate movements. Baxter and Stockman (2011) explored the connection between interest differentials and exchange rates for a defined floating rate period. His information was drawn from the Minneapolis Federal Reserve Bank Board of Governors database. The research used multivariate and univariate methods in data analysis in his research; he demonstrated a weak relationship between the two and found that there was a powerful link between the two factors that links further to the business cycle frequencies. However, in the two rates that affected movements, Baxter did not discover the policy variables. He also found that the interest rate and exchange rate movements and the policy factors that explain the movements remain an empirical issue.

Understanding the connection between interest rate and exchange rate needs a more appropriate model or method of assessment. Karagol (2012) performed a long-term connection between interest rate and exchange rate research in Asian nations. The research used panel cointegration experiments and Johansen-cointegration to evaluate the entire sample period for ten South East Asian and South Asian nations. For nine out of ten nations, Johansen-cointegration statistics showed powerful proof of the interest and exchange rates being co-integrated.

In addition, the research results showed that the effect of interest rate on the exchange rate between Asian nations changes with a shift in nominal system. The study found that correlation between interest rate and exchange rate is negative in times of freely falling regimes and positive in times of pegged regimes.

Negative for expansionary depreciation and positive for contractionary depreciation. The exact timing of such interest rate and response to the exchange rate depends on the nature of the aggregate demand response to the value of the domestic currency. Overall, interest rates are found to react differently to shocks depending on whether depreciation is

34 expansive or contractionary. Exchange rate smoothing by means of interest rates which falls under the category of "fear of floating" is shown to originate in optimal policy under flotation (Amaoko-Adu 2012).

5.3.2 Effects of Inflation on Exchange Rate The correlation and the regression analysis in this study showed that there is no significant relationship between inflation rate and foreign exchange rate. This was in line with the findings by Bamidele and Joseph (2013), which used monthly time series data for the period August 2011 to November 2014 to assess the connection between exchange rate and inflation in South Sudan. The research shows that, there was no significant effect of inflation on exchange rate. This contradicts the findings by Sanam and Monfared, (2017) who analyzed the relationship between exchange rate and inflation based on time series data, using Hendry General to Specific Modeling method and Vector Auto regression (VAR) model. To this end, they used annual data for the period 1976-2012. The findings showed that there is a direct relationship between exchange rate and inflation. An increase in foreign exchange rates makes the inflation goes up. According to the results, both the money supply and the exchange rate affect the inflation in the positive direction.

The findings of this study on the inflation against exchange rate also contradicts the findings by Miller & Foster (2012) who tried to create the connection between exchange prices and

35 inflation in Latin America. The study discovered that the rate of inflation has a significant relationship with the price stability of the economy. Quang and Sayim(2016) examined the impact of inflation on the foreign exchange rates between USA and four big emerging countries: India, Mexico, Brazil and China for the period of 2005 to 2014. The study used stepwise multiple regression methods to investigate the impact of market fundamental on the exchange rates. The empirical findings reveal that the macro-economic factors including inflation rate significantly predict and influence the exchange rates between (US dollar/Chinese yuan (USD/CNY), USD/INR (US dollar/Indian rupee), USD/BRL (USdollar / Brazilian real), and USD/MNX (US dollar/Mexican pesos).

As one of the main variables influencing Ghana's exchange rate, Baxter and Stockman (2011) researched inflation. The research discovered that theoretically a low inflation rate situation shows a growing currency rate as the currency's purchasing power rises compared to other currencies (Baxter & Stockman, 2011). They found that the rate of inflation is usually used to assess the price stability of the economy. Conceptually, inflation can be split into two sides: demand-side inflation, which is inflation driven by demand, and supply-side inflation, which is inflation driven by cost. They recommended that for open- economy countries, inflation should come from domestic factors that entail internal pressure and also overseas factors that entail external pressure. Sanam and Monfared, (2017)analyzed the relationship between exchange rate and inflation based on time series data, using Hendry General to Specific Modeling method and Vector Auto regression (VAR) model. To this end, they used annual data for the period 1976-2012. The findings showed that there is a direct relationship between exchange rate and inflation. An increase in foreign exchange rates makes the inflation goes up. According to the results, both the money supply and the exchange rate affect the inflation in the positive direction. Calvo and Reinhart (2012) in their study requested proof to support the presence of impact of chosen macroeconomic factors: difficult currency foreign exchange rate, interest rate and inflation rate on share price changes based on weighted average monthly information for businesses listed on the Nairobi Securities Exchange in Kenya from January 2008 to December 2012.

36 gathered. Analysis of linear regression disclosed a detrimental and negative effect of inflation on exchange rate.

Kibui (2014) assessed the primary inflation determinants of Kuwait. The investigator used three autonomous variables, namely cash supply, actual gross national product and imported inflation. The research was estimated using the Granger causality test that showed that national inflation was mainly affected by the growth of national liquidity that overshadowed the theoretically anticipated impact of imported inflation. According to the scientist, these outcomes could be caused by two primary variables; the first is the financial and political developments during the era of the research, and the second is the distinction in each inflation measure building.

The sources of internal variables are world commodity prices rising or fluctuating. The impact of the exchange rate on inflation itself relies on the exchange rate system being chosen by the country. The exchange rate scheme plays an significant role in decreasing or minimizing the danger of changes in the exchange rate that will affect the economy. Any shift in exchange rates will greatly affect the economy (Eichengreen, 2014). Depending on the floating exchange rate scheme, exchange rate changes may have a powerful effect on price levels through aggregate demand and aggregate supply. On the aggregate supply, depreciation of domestic currency can affect the price level directly through imported goods that domestic consumers pay. However, this condition occurs if the country is the recipient countries of international prices or an international price taker (Engle, 2012). If the inflationary effects of exchange rate changes are large, authorities will have to implement monetary and fiscal policies that compensate for the inflationary effects of exchange rate changes. Historically, pass-through has tended to be big in nations experiencing a currency crisis. Borensztein and De Gregorio (2009) used a sample of 41 nations and discovered that after one year, 30 percent of nominal devaluation had been transferred to inflation.

5.3.3 Effects of External Public Debt on Foreign Exchange Rate The research findings, showed a positive significant relationship between external debt and the exchange rate. This is in line with the research findings by Baldacci, Gupta and Granados (2009) on the effect of currency depreciation on foreign debt in Croatia. They evaluated monthly gross foreign debt and nominal exchange rate information empirically

37 using Johansen's cointegration strategy to test cointegration presence among factors. The findings indicated that the nominal exchange rate had a statistically significant correlation with the foreign debt.

The findings by Saheed, Sani, and Idakwoji, (2015), who examined the impact of public external debt on exchange rate in Nigeria and used the Ordinary Least Square, on the secondary data sourced from the CBN and DMO among other sources revealed that all the dependent variables, that is, external debt, debt service payment and foreign reserve proved to be statistically significant in explaining exchange rate fluctuation in Nigeria within the period of observation, with debt service payment having the strongest effect.

Achieng (2015) looked at government spending and currency changes in Kenya's commercial banks. A descriptive research survey was used by the study. As of December 2014, the target population included all 43 commercial banks operating in Kenya. The secondary data was gathered from the consolidated financial statements of the banks as well as from the headquarters of the Kenya Central Bank. The research discovered a beneficial connection between changes in foreign exchange rates and banks ' economic performance as measured by the return on assets ratio. The study did not consider other macro-economic variables such as interest rate using the per capita income approach, Ahmed (2018) research used Jordanian empirical information. The research was anchored on the theory of economics, which maintains that sensible rates of indebtedness lead to

38 increased economic growth, particularly for a developing economy. The theory attributes this connection to restricted capital stocks in order to undertake investment possibilities with greater yield rates than well-developed nations. Such investment opportunities are linked to favorable economic growth that allows an economy to repay the sophisticated debt in a timely manner.The variables in this study included: per capita growth for economic growth, rates of investment, the proportion of public debt to FDB, inflation rate, rate of fiscal balance to GDP, debt service payments and trade openness. The findings showed that economic growth in Jordan was affected by terms of trade, investment rates and rate of fiscal balance.

Foreign debt is a significant variable in Kenya. Previously, the country has relied heavily on concessionary loans and budget deficit support from development partners abroad. According to data from central Bank of Kenya, foreign debt stands at 51% of total debt as at April 2018 statistics.

5.4 Conclusion

5.4.1 Interest Rate on Exchange Rate

The findings of this study indicate that there is a weak positive significant correlation between interest rate and exchange rate. The regression analysis also indicated that increasing interest rate by one percent, increases exchange rate by 17%. The study therefore concludes that interest rate significantly affects exchange rate of the Kenya shillings against the Chinese Yuan.

5.4.2 Inflation Rate on Exchange Rate.

The findings of this study indicate that there is no significant correlation between inflation rate and exchange rate. The regression analysis also indicated that there is no significant effect of inflation rate on the exchange rate. The study therefore concludes that inflation rate does not significantly affects the exchange rate of the Kenya shillings against the Chinese Yuan.

5.4.2 External Debt on Exchange Rate.

The findings of this study indicate that there is a weak positive significant correlation between external debt and exchange rate. The regression analysis also indicated that increasing external debt by one percent increases exchange rate by 8.6%. The study

39 therefore concludes that external debt significantly affects exchange rate of the Kenya shillings against the Chinese Yuan.

5.5 Recommendations

5.5.1 Recommendations for Improvement

5.5.1.1 Effects of Interest Rate on Exchange Rate.

The study findings showed that there is a positive significant relationship between interest rate and exchange rate meaning that an increase in interest rate leads to an increase in exchange rate of Kenya shilling against Chinese Yuan. The study therefore recommends that the government to maintain lending interest rate to prevent the depreciation of the exchange rate of Kenya shilling against Chinese Yuan.

5.5.1.2 Effects of Inflation Rate on Exchange Rate.

The study findings showed that there is no significant effect of inflation on exchange rate. However, several findings in other studies indicated that there is a significant effect of inflation on exchange rate both positively and negatively. The study therefore recommends that that the government to implement suitable policies to control and manage inflation rates to prevent its negative effects on foreign exchange rate as indicated in other studies. A monetary policy is a key to define inflation rates and level of exchange rate volatility.

5.5.1.1 Effects of Public External Debt on Exchange Rate.

The study findings showed that there is a positive significant relationship between external public debt and exchange rate meaning that an increase in public external debt leads to an increase in exchange rate of Kenya shilling against Chinese Yuan. The study therefore recommends that, the government to pursue policies that encourage increase in borrowing and decrease in lending. This will prevent depreciation of exchange rate.

5.5.2 Recommendations for Further Research.

The study focused on the effects of interest rate, inflation rate and external debt on the exchange rate of Kenya shilling against Chinese Yuan. The findings have shown that these factors can only affect 27.6% of the exchange rate. Therefore the study recommends that further research to be done on other micro-economic factors to investigate extent to which they affect the exchange rate.

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APENDIX 1: SECONDARY DATA COLLECTION SHEET.

Foreign Exchange Interest Inflation External Debt Rate (Kenya Rate(Central rate (Debt as Shilling/Chinese Bank Lending (Consumer percentage of Year Month Yuan) rate) Price Index) GDP) 2007 January 8.879 10 4.63 31.23 2007 February 8.94 10 3.02 31.16 2007 March 8.954 10 2.19 30.6 2007 April 8.876 10 1.85 31.44 2007 May 8.742 10 1.96 31.4 2007 June 8.715 8.5 4.07 29.66 2007 July 8.838 8.5 5.48 30.71 2007 August 8.824 8.75 5.3 30.6 2007 September 8.876 8.75 5.53 30.57 2007 October 8.912 8.75 5.38 31.4 2007 November 8.825 8.75 6.08 31.73 2007 December 8.573 8.75 5.7 30.44 2008 January 9.403 8.75 9.4 31.25 2008 February 9.836 8.75 10.58 31.38 2008 March 9.161 8.75 11.9 31.32 2008 April 8.876 8.75 16.12 31.39 2008 May 8.879 8.75 18.61 31.66 2008 June 9.242 9 17.87 32.42 2008 July 9.745 9 17.12 31.89 2008 August 9.879 9 18.33 31.95 2008 September 10.445 9 18.73 31.9 2008 October 11.211 9 18.74 32.01 2008 November 11.447 9 19.54 33.17 2008 December 11.382 8.5 17.83 38.07 2009 January 11.555 8.5 13.22 17.9 2009 February 11.635 8.5 14.69 17.97 2009 March 11.74 8.25 14.6 17.94 2009 April 11.656 8.25 12.42 17.88 2009 May 11.409 8 9.61 18.09 2009 June 11.391 8 8.6 18.69 2009 July 9.903 7.75 8.44 18.58 2009 August 11.178 7.75 7.36 18.51 2009 September 11.071 7.75 6.74 18.33 2009 October 11.021 7.75 6.62 18.29 2009 November 10.947 7 5 18.2 2009 December 11.047 7 5.32 20.57 2010 January 11.101 7 5.95 16.93 2010 February 11.235 7 5.18 16.92 2010 March 11.271 6.75 3.97 17.34

2010 April 11.318 6.75 3.66 17.31 2010 May 11.503 6.75 3.88 17.46 2010 June 11.88 6.75 3.49 18.22 2010 July 12.016 6 3.57 18.13 2010 August 11.852 6 3.22 18.24 2010 September 12 6 3.21 19.14 2010 October 12.103 6 3.18 19.27 2010 November 12.089 6 3.84 19.3 2010 December 12.121 6 4.51 19.33 2011 January 12.279 5.75 5.42 18.69 2011 February 12.39 5.75 6.54 19.14 2011 March 12.823 6 9.19 19.52 2011 April 12.847 6 12.05 19.81 2011 May 13.152 6 12.95 20.52 2011 June 13.752 6.25 14.48 21.95 2011 July 13.919 6.25 15.53 22.6 2011 August 14.483 6.25 16.67 23.33 2011 September 15.081 7 17.32 24.28 2011 October 15.9 11 18.91 24.59 2011 November 14.739 16.5 19.72 22.12 2011 December 13.646 18 18.93 20.81 2012 January 13.667 18 18.31 19.94 2012 February 13.201 18 16.69 19.25 2012 March 13.131 18 15.61 19.64 2012 April 13.193 18 13.06 20.35 2012 May 13.338 18 12.22 20.93 2012 June 13.322 18 10.05 22.49 2012 July 13.207 16.5 7.74 22.28 2012 August 13.218 16.5 6.09 22.41 2012 September 13.384 13 5.32 23.3 2012 October 13.586 13 4.14 23.58 2012 November 13.738 11 3.25 23.94 2012 December 13.799 11 3.2 23.86 2013 January 13.968 9.5 3.67 22.86 2013 February 14.029 9.5 4.45 22.66 2013 March 13.807 9.5 4.11 22.29 2013 April 13.612 9.5 4.14 22.4 2013 May 13.704 8.5 4.05 22.82 2013 June 13.934 8.5 4.91 23.13 2013 July 14.159 8.5 6.03 24 2013 August 14.292 8.5 6.67 24.34 2013 September 14.284 8.5 8.29 24.39 2013 October 13.976 8.5 7.76 24.35 2013 November 14.131 8.5 7.36 25.01 2013 December 14.205 8.5 7.15 25.29 48

2014 January 14.248 8.5 7.21 39.76 2014 February 14.191 8.5 6.86 23.96 2014 March 14.014 8.5 6.27 24.4 2014 April 13.932 8.5 6.41 24.48 2014 May 14.011 8.5 7.3 24.75 2014 June 14.061 8.5 7.39 24.93 2014 July 14.157 8.5 7.67 28.26 2014 August 14.313 8.5 8.36 28.36 2014 September 14.47 8.5 6.6 28.39 2014 October 14.565 8.5 6.43 28.31 2014 November 14.688 8.5 6.09 28.34 2014 December 14.613 8.5 6.02 28.34 2015 January 14.689 8.5 5.53 30.47 2015 February 14.633 8.5 5.61 28.64 2015 March 14.702 8.5 6.31 31.92 2015 April 15.066 8.5 7.08 31.47 2015 May 15.537 8.5 6.87 32.67 2015 June 15.742 10 7.03 34 2015 July 16.3 11.5 6.62 34.68 2015 August 16.147 11.5 5.84 36.27 2015 September 16.53 11.5 5.97 37.68 2015 October 16.183 11.5 6.72 38.17 2015 November 16.042 11.5 7.32 36.7 2015 December 15.844 11.5 8.01 38.47 2016 January 15.578 11.5 7.78 38.48 2016 February 15.561 11.5 6.84 38.29 2016 March 15.593 11.5 6.45 38.73 2016 April 15.629 11.5 5.27 39.19 2016 May 15.428 10.5 5 39.08 2016 June 15.347 10.5 5.8 41.93 2016 July 15.174 10.5 6.4 41.8 2016 August 15.255 10 6.26 41.93 2016 September 15.18 10 6.34 43 2016 October 15.067 10 6.47 42.89 2016 November 14.88 10 6.68 42.67 2016 December 14.76 10 6.35 44.1 2017 January 15.041 10 6.99 44.18 2017 February 15.078 10 9.04 44.19 2017 March 14.913 10 10.28 47.87 2017 April 16.995 10 11.48 48.05 2017 May 14.989 10 11.7 48.49 2017 June 15.202 10 9.21 50.88 2017 July 15.335 10 7.47 51.12 2017 August 15.518 10 8.04 51.21 2017 September 15.7 10 7.06 51.22 49

2017 October 15.605 10 5.72 52.17 2017 November 15.638 10 4.73 52.26 2017 December 15.618 10 4.5 52.09 2018 January 15.994 10 4.83 51.36 2018 February 16.045 10 4.46 55.36 2018 March 15.992 9.5 4.18 54.27 2018 April 15.977 9.5 3.73 55.34 2018 May 15.793 9.5 3.95 55.58 2018 June 15.629 9.5 4.28 55.3 2018 July 15.006 9 4.35 56.18 2018 August 14.691 9 4.04 56.41 2018 September 14.708 9 5.7 56.28 2018 October 14.614 9 5.53 57.34 2018 November 14.744 9 5.58 58.52 2018 December 14.849 9 5.71 58.83

APENDIX 11: NACOSTI PERMIT.