IMPACT OF MACRO-ECONOMIC FACTORS ON EXCHANGE RATES BETWEEN KENYAN SHILLINGS AND EURO CURRENCY
BY
LEE BOMIN
UNITED STATES INTERNATIONAL UNIVERSITY – AFRICA
SUMMER 2019
IMPACT OF MACRO-ECONOMIC FACTORS ON EXCHANGE RATES BETWEEN KENYAN SHILLINGS AND EURO CURRENCY
BY
LEE BOMIN
A Project Report Submitted to Chandaria School of Business in Partial Fulfilment of the Requirements for the Degree of Masters in 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: ______
Lee Bomin (ID 653959)
This research project has been presented for examination with my approval as the appointed supervisor.
Signed: ______Date: ______
Dr. Francis M. Gatumo
Signed: ______Date: ______
Dean, Chandaria School of Business
ii COPYRIGHT All rights reserved. The copyright of this report vests in the author. No part of this research may be reproduced, recorded, photocopied, stored or published without permission of USIU-Africa or author. The research is to be sued for private study or non- commercial research purpose only.
Lee Bomin © 2019
iii ABSTRACT The purpose of this research was to investigate the impact of macro-economic factors on exchange rates between Kenyan Shillings and Euro currency. The research was guided by the following research questions: How do interest rates influence exchange rates between Kenyan Shillings and Euro currency? What is the impact of inflation rates on exchange rates between Kenyan Shillings and Euro currency? Does Gross Domestic Product growth rates affect the exchange rates between Kenyan Shillings and Euro currency?
The research used descriptive and explanatory research designs (also called casual research design) to investigate the cause and effect relationship between independent variables, which are interest rates, inflation rates and Gross Domestic Product growth rates, and dependent variable as exchange rates. Specifically, the research used correlation and regression analysis to establish the influence of interest rates, inflation rates, and Gross Domestic Product growth rates on exchange rates between Kenyan Shillings and Euro currency. The population included interest rates, inflation rates, GDP growth rates and exchange rates from the yearly average data period of 9 years (2010- 2018). In specific, population consisted of the actual data of exchange rates to interest rates, inflation rates, and GDP growth rates, thus the four variables had a total of 9 observations. The research used secondary data from Central Bank of Kenya, Kenya National Bureau of Statistics, and World Bank and data analysis proceed by IBM SPSS. A multiple linear regression model was used to analyze the relationship between three independent variables which are interest rates, inflation rates, and GDP growth rates and dependent variable as exchange rates between Kenyan Shillings and Euro currency. The major findings were presented in figures and tables.
On the influence of interest rates on exchange rates, the Pearson correlation results showed a negative relationship between interest rate and exchange rate as r = - 0.545, p = 0.129. This implies that exchange rates and interest rates are negatively correlated, however it is not statistically significant. According to regression analysis, the adjusted R square value was 0.197, which implies 19.7% of the variation in exchange rates was explained by variations in interest rates.
On the impact of inflation rates on exchange rates, the Pearson correlation results indicated a positive relationship between inflation rate and exchange rate as r = 0.491, p =
iv 0.180. This implies that exchange rates and inflation rates are positively correlated, however it is not statistically significant. A regression analysis showed that adjusted R square value was 0.132 which implies 13.2% of the variation in exchange rates was explained by variations in inflation rates.
On the effect of Gross Domestic Products growth rates on exchange rates, the Pearson correlation results presented a positive relationship between GDP growth rate and exchange rate as r = 0.129, p = 0.741. This explains that exchange rates and GDP growth rates are positively correlated, however it is not statistically significant. According to regression analysis, the adjusted R square value was zero which implies zero variation in exchange rates was explained by variations in GDP growth rate.
On the relationship between interest rates, inflation rates, GDP growth rates and exchange rates, the Pearson correlation results shows a positive relationship as r = 0.885, p = 0.041. This implies interest rates, inflation rates, GDP growth rates and exchange rates are positively correlated, and it is statistically significant. A multiple linear regression analysis resulted into the adjusted R square value of 0.653. This implied that 65.3% of the variation in exchange rate was explained by variations in interest rates, inflation rates, and GDP growth rates. Taking all factors into account interest rates, inflation rates and GDP growth rates increase by 33.631 all other factors held constant. Multiple linear regression results showed a positive relationship between exchange rates and inflation rates and a negative relationship between exchange rates and interest rates, GDP growth rates. P value was 0.041 at the 0.05 significant level. Thus, this relationship has statistically significant effect on exchange rates between Kenyan Shilling and Euro currency over the period 2010 to 2018.
The research concluded that interest rates has a negative impact on exchange rate. However, number of other studies found a positive relationship between interest rates and exchange rates and it explained by different economies and regulations. The research also concluded that inflation rate and GDP growth rate have a positive effect on exchange rates. The other studies found both positive and negative relationship between inflation rate and exchange rate and it explains country differences and level of economy. This research has a contrasting result with other researches for relationship between GDP growth rate and exchange rate.
v The research recommends that the Central Bank of Kenya need a clear and appropriate regulation for interest rates policies to reduce the level of fluctuation. Moreover, the research also recommends that the government should not interfere with interest rates because it causes fluctuation. The government needs to provide clear infrastructure and regulation that can improve the Kenyan economy. Moreover, Kenya should improve the productivity of domestic production, enhance manufacturing process and provide subsidy for export. This will enhance the balance of trade and balance of payment. Developing countries should eradicate barriers that limit trade and support favorable movement of goods and services between different countries to increase GDP. These actions will have a positive impact on exchange rate. Monetary stability should be enforced to reduce and moderate the rate of inflation in the country. This reduces the level of exchange rate volatility and increases the growth of the economy. The research recommends further research to capture any additional macro-economic variables such as balance of trade, external debt and so on. A further research can also focus on the other currency such as Japanese Yen, Chinese Yuan, or any other currency that trades with Kenya.
vi ACKNOWLEDGEMENT I appreciate to Dr. Francis M. Gatumo and lecturers for wise counsel and guiding me throughout this research. I also appreciate my family for their support, trust and encouragement of study abroad. I thank to my classmates at USIU-Africa who assist and advise me to develop myself and the research.
vii DEDICATION Dedicated to my family and friends for being there and supporting my MBA. This research would not be achieved without the assistance and support of the USIU-Africa lecturers, classmates, and friends. I will always remember your support and contribution for this research and my school life.
viii TABLE OF CONTENTS
STUDENT’S DECLARATION ...... ii COPYRIGHT ...... iii ABSTRACT ...... iv ACKNOWLEDGEMENT ...... vii DEDICATION ...... viii LIST OF TABLES ...... xi LIST OF FIGURES ...... xii ABBREVIATIONS AND ACRONYMS ...... xiii
CHAPTER ONE ...... 1 1.0 INTRODUCTION ...... 1 1.1Background of the Study ...... 1 1.2 Statement of the Problem ...... 6 1.3 Purpose of the Study ...... 7 1.4 Research Questions ...... 7 1.5 Significance of the Study ...... 7 1.6 Scope of the Study ...... 8 1.7 Definition of Terms ...... 9 1.8 Chapter Summary ...... 10
CHAPTER TWO ...... 11 2.0 LITERATURE REVIEW ...... 11 2.1 Introduction ...... 11 2.2 Influence of Interest Rates on Exchange Rates ...... 11 2.3 Impact of Inflation Rates on Exchange Rates ...... 16 2.4 Effect of Gross Domestic Product on Exchange Rates ...... 22 2.5 Chapter Summary ...... 27
CHAPTER THREE ...... 28 3.0 RESEARCH METHODOLOGY ...... 28 3.1 Introduction ...... 28 3.2 Research Design ...... 28 3.3 Population and Sampling Design ...... 29 3.4 Data Collection Method ...... 31 3.5 Research Procedure ...... 32
ix 3.6 Data Analysis Method ...... 32 3.7 Chapter Summary ...... 34
CHAPTER FOUR ...... 35 4.0 RESULTS AND FINDINGS ...... 35 4.1 Introduction ...... 35 4.2 General Information ...... 35 4.3 Influence of Interest Rates on Exchange Rates ...... 38 4.4 Impact of Inflation Rates on Exchange Rates ...... 41 4.5 Effect of Gross Domestic Product Growth Rates on Exchange Rates ...... 43 4.6 Test of Significance ...... 45 4.7 Chapter Summary ...... 48
CHAPTER FIVE ...... 49 5.0 DISCUSSION, CONCLUSIONS, AND RECOMMENDATIONS ...... 49 5.1 Introduction ...... 49 5.2 Summary ...... 49 5.3 Discussion ...... 51 5.4 Conclusions ...... 56 5.5 Recommendations ...... 57
REFERENCES ...... 59 APPENDIX I: DATA COLLECTION INSTRUMENT ...... 69 APPENDIX II: SECONDARY DATA COLLECTION ...... 70
x LIST OF TABLES Table 3.1 Source of Data ...... 30 Table 4.1 Overall Summary of Descriptive Statistics ...... 38 Table 4.2 Test of Normality ...... 38 Table 4.3 Model summary of Interest Rates and Exchange Rates Changes ...... 39 Table 4.4 ANOVA of Interest Rates and Exchange Rates Changes ...... 40 Table 4.5 Coefficients of Interest Rates and Exchange Rates Changes ...... 40 Table 4.6 Model Summary of Inflation Rates and Exchange Rates Changes ...... 41 Table 4.7 ANOVA of Inflation Rates and Exchange Rates Changes ...... 42 Table 4.8 Coefficients of Inflation Rates and Exchange Rates Changes ...... 42 Table 4.9 Model Summary of GDP Growth Rates and Exchange Rates Changes ...... 43 Table 4.10 ANOVA of GDP Growth Rates and Exchange Rates Changes ...... 44 Table 4.11 Coefficients of GDP Growth Rates and Exchange Rates Changes ...... 44 Table 4.12 Test of Multicollinearity ...... 45 Table 4.13 Model Summary ...... 46 Table 4.14 ANOVA ...... 46 Table 4.15 Coefficients of Correlation ...... 47
xi LIST OF FIGURES
Figure 4.1 Interest Rates in Kenya 2010-2018 ...... 35 Figure 4.2 Inflation Rates in Kenya 2010-2018 ...... 36 Figure 4.3 GDP Growth Rates in Kenya 2010-2018 ...... 36 Figure 4.4 Exchange Rates Changes between Kenyan Shilling and Euro in Kenya 2010- 2018 ...... 37 Figure 4.5 Interest Rates on Exchange Rates Changes in Kenya 2010-2018 ...... 39 Figure 4.6 Inflation Rates on Exchange Rates Changes in Kenya 2010-2018 ...... 41 Figure 4.7 GDP Growth Rates on Exchange Rate Changes in Kenya 2010-2018 ...... 43
xii ABBREVIATIONS AND ACRONYMS
ASEAN Association of Southeast Asian Nation CBK Central Bank of Kenya CPI Consumer Price Index EAC East Africa Community ECB European Central Bank EU European Union EMU European Monetary Union EMS European Monetary System EUR European Union Currency FOREX Foreign Exchange Market GARCH Generalized Auto Regressive Conditional Heteroskedasticity IBM International Business Machines IMF International Monetary Fund GBP Pound Sterling GDP Gross Domestic Product KES Kenyan Shilling KNBS Kenya National Bureau of Statistics OECD Organization for Economic Cooperation and Development PPP Purchasing Power Parity SPSS Statistical Package for the Social Sciences TL Turkish Lira USA United States of America USD United States Dollar ZAR South African Rand
xiii CHAPTER ONE
1.0 INTRODUCTION 1.1Background of the Study The financial world is expanding, and derive development and growth of the international financial market due to increased globalization (Kibiy & Tabitha, 2016). The economies are getting close with international trading, consequently the number of multinational firms that face the volatility of foreign exchange rate have increased (Abor, 2005). Taiwo and Adesola (2013) stated exchange rate has a significant role in the finance and economy of a country and one of the aspects was development and growth of the Foreign Exchange Markets.
Mankiw (2014) defined macro-economic as field that focuses on broad economic factors that affect large populations such as interest rates, inflation rates, number of unemployment, national income, growth of production, price level, government deficits, and balance of trade. Macro-economic may help to develop economic policies and also come up with fiscal and monetary policies to maintain or spur the development of the economy. According to Kibiy and Tabitha (2016) the government, analysts, investors, firms and other stakeholders are concerned with the volatility of exchange rate, because it is directly connected to the performance of economic factors such as uncertainty of employment, GDP, profits, cash flows, investment and trade. It can explain the connection between macro-economic policies and exchange rates.
Mohd, Abdoh and Ibrahim (2016) and Musyoki, Pokhariyal, and Pundo (2012) stated that exchange rate is applied in the purchase of goods and services from domestic currency to foreign currency hence it has been recognized as an important aspects in international macro-economics and finance. This is because exchange rate allows for the conversion of one national currency into another currency, thus it can accelerate international trade flow. Allayannis and Ofek (2001) investigated the movement of exchange rate affect the future cash flows, value of the firms, international trade and competitors of trade by one conversion of home currency value to foreign currency. It measures the value of the country’s currency compare to foreign currency. Currency volatility may be depreciation or appreciation of the currency and it is one of the essential issues in the world. Grube
1 and Subarna (2003) found that most of the Organization for Economic Cooperation and Development (OECD) countries have experienced volatility of exchange rate as well as current account deficits for most of the last two decades.
US Dollar is the most acceptable and adoptable currency and it is commonly used in international trade market. Lizardo and Mollick (2010) stated that the value of US Dollar was peaked but it has been continuously falling in 2001. US Dollar has fall 37% against the Canadian Dollar, 65% against the Euro, 41% against the British Pound and 15% against the Japanese Yen from 2001 to 2007. Economic factors may influence on depreciation of the US Dollar which include supply and demand of currency, economic growth, interest rates, inflation rates, level of import and export, and monetary policy,
One of the most important events in global financial market was introduction of Euro and it became one of the three major currencies in the world with US Dollar and Japanese Yen (Detken & Hartmann, 2000). Eleven nations (Austria, Belgium, Finland, France, Germany, Ireland, Italy, Luxembourg, The Netherlands, Portugal and Spain) pegged (fixed exchange) their currency as Euro in January 1999. Slovenia joined on January 2007 as twelfth country followed by Malta and Cyprus on January 2008, and Slovakia on January 2009. The European Central Bank (ECB) is the initial supranational central bank located in Frankfurt, Germany to provides the core of the European Union’s macro- economics and managing the Euro which is common currency for European Union members (Dutta, 2011). Moreover, Dutta (2011) found that European Monetary Union start coordinating with ECB about exchange rates among the Eurozone and the dual currencies circulation ended in February 2002. The ECB fixed exchange rates in Eurozone by defining the value of each currency as Euro and it is irrevocable. Indeed, Euro became one of the major currencies in the world and fluctuation of exchange rate of EUR/USD have become a topic for more research (Dutta, 2009).
When Euro was introduced on 1999, it experiences such a prolonged depreciation against the US Dollar, because there is no successful model to forecast the exchange rates fluctuations of EUR/USD in between 1999 to 2004 (Wenhao, 2004). The research by Wenhao (2004) investigated the factors are determine the EUR/USD exchange rate supported by Clostermann and Schnatz (2000) who constructing a synthetic EUR/USD
2 exchange rates and determine the factors which are inflation outcomes, growth performance, and foreign asset positions.
A number of economists designed the various models to analyze the determinants of the exchange rates of EUR/USD. The research done by Alquist and Chinn (2002) as well as Fernandez, Osbat, and Schnatz (2002) found that the interest rates, productivity, price of oil and fiscal stance have a significant influence on the EUR/USD exchange rate. Furthermore, the research found that each percentage point in US-Euro area productivity differential results in a 5 percent point appreciation of the US Dollar, thus it explains the depreciation of the Euro. Cellini and Cuccia (2014) also investigate on the EUR/USD exchange rate during 1999 to 2012 and the research found that there is a significant presence of day effects. Unfortunately, a few people can be successful to forecast exchange rate, because the exchange rate is influenced by different factors, and some factors are changing from time to time.
The evidence from Hilland and Devadoss (2013) shows Yuan has undervalued against US Dollar thus it has negative impact on the USA as decrease of USA export to China and increase of USA imports from China, result on USA trade deficit and USA Congress considered revalue of Yuan. Rodrik (2010) and Hilland and Devadoss (2013) reported that the Yuan has been undervalued by 12 to 30 percent from 2009 to 2010, and it declined the growth of China by 2.15 percent. As a result, an undervalued Yuan was interfering with USA comparative advantage in exporting commodities. Japan and EU are the one of the largest importers of Chinese agriculture products followed by USA. China has an advantage over the competitors in the USA market and it is also affected decline of domestic products and unemployment in USA.
International trade is an integral part of the modern world and it is affected by uncertainty of exchange rate as increase of transaction cost will occur decrease of the gains of international trade and vice versa (Sugut, Kiprop, & Kalrio, 2017). There are number of studies estimating the relation between volatility of exchange rate and the foreign trade. Grube and Subarna (2003) focus on exchange rate uncertainty on Mexican foreign trade and Mexico experienced huge volatility on exchange rate caused by internal economic problems, severe political disturbances. Levine and Carkovic (2001) discovered the effect of volatility of exchange rate with 73 countries that the volatility of exchange rate causes
3 a risk premium to be assessed on exchange rates, and it pushes up the interest rate. It reduces, however, tax revenues, economic growth, investment and the export trade flow. Researcher found that the most important factors to determine the exchange rate in Mexico was flows of capital and monetary and fiscal policies.
Pino, Tas, and Sharma (2016) discussed that East Asian (Indonesia, Malaysia, Republic of Korea, Singapore, Thailand, and The Philippines) economies had declined changing of structures and accomplished of economic growth with low inflation and macroeconomic stability, but it is important modification because of an economic crisis in 1997. The liberalization of the exchange rate is one of the main changes in East Asia thus the volatility of exchange rate become important concerns for policy makers. The research also found the relationship between volatility of exchange rate and international trade during 1974 to 2011. The evidence was that the volatility of exchange rate on trade flows effected by changes in world income and trade price. It has a negative relationship between volatility of exchange rate and international trade in long run, which implies that the trade flow will be increase while volatility of exchange rate decrease supported by Ouyang and Rajan 2016). The macro-economic factors influence the volatility of exchange rate for four ASEAN countries (Indonesia, Malaysia, Thailand, and Singapore), and the research indicated that the significant factor influence the volatility of exchange rate is from stock market and capital market (Lee & Boon, 2007). The research also stated that the Singapore Dollar is influenced by own innovation while Indonesian Rupiah effected by macro-economic variables rather than its own innovations.
More specifically, Dekle, Hsiao, and Wang (2002) determined the effect of high interest rate on exchange rates in three Asian countries which are South Korea, Malaysia and Thailand during the Asian financial crisis and its aftermath. The research discovered that domestic interest rates has an impact on exchange rates even though the impact is small. South Korea need to increase of interest rate by 300%, 150% for Malaysia, and 800% for Thailand, when the exchange rate depreciates by 40%.
During the Asian economy crisis, the South African currency affected and depreciated against US Dollar and ZAR/USD exchange rate has been more volatile than other currencies by adopting the free-floating exchange rate regime (Hsing, 2016). The researcher, Hsing (2016) found that a higher government bond yield of South Africa,
4 inflation rate of South Africa, GDP of USA, and stock price of USA will raise the ZAR/USD exchange rate while a higher government bond yield of USA, inflation rate of USA, GDP of South Africa, stock price of South Africa will decline the ZAR/USD exchange rate. As a result, the interest rate, GDP, stock price, inflation rate may need to be tested between two countries to find out the how these factors are affecting the volatility of exchange rate.
Taiwo and Adesola (2013) stated that currency of Nigeria as known as Naira remained unstable and investigated the relationship between the exchange rate and the performance of the banking industry during 1970-2005. The research discovered both positive and negative relationship between exchange rate and performance of the banking industry. Raji, Abdulkadir, and Badru (2018) investigated the relationship between Naira/US Dollar exchange rate and crude oil price period of 2001-2015. The research found a positive effect of crude oil price on Naira/US Dollar exchange rate that Naira appreciated relative to US Dollar when crude oil price increased and vice versa.
The East Africa Community countries also experiencing the fluctuation of exchange rate against the US Dollar since early 1980s, and it influence the international trade. The greater volatility of exchange rate causes uncertainty of trade thereby the trading activity risk increased and eventually it will depress trade. Therefore, Sugut, Kiprop, and Kalrio (2017) found that exchange rate affects East Africa Community trade negatively, the intended effect of the current trade liberalization policy being implemented in East Africa Community countries may be dammed thereby precipitating a balance of payment crisis. Research pointed that need for policy measure to maintain stability in the currency in addition to keeping it competitive.
Rutasitara (2004) investigated the effects of inflation rates on exchange rates regimes in Tanzania. Tanzania had fixed exchange rate system until 1985, and free-floating system from 1986 to current. The research found the influence of foreign prices on exchange rates during fixed exchange rate system, however volatility of exchange rate will transmit faster to domestic price under free-floating system.
Researchers have investigated the effect of currency conversion standard instability and related vulnerability on exchange, speculation, and monetary development in Kenya
5 (Oiro, 2015). Kenya has unpredictable fluctuations of the Kenyan Shilling continuously, and it affect the economic growth movement in Kenya by investment and international trade (Kibiy & Tabitha, 2016). Nicholas (2016) assert that Kenya settled the exchange rate administration from 1966 to 1992, when the multiparty framework and exchange rate became important factor. Kenya endured the double exchange rate framework until 1993, then official conversion standard was coordinated to the authority interbank rate and the KES was permitted to float against all other currency. Nicholas (2016) also conducted that Kenyan Shilling has fallen at KES 107 to US Dollar in October 2011 and this was the most minimal amount. Unfortunately, it has been failing again, nearing KES 106 to US Dollar on September 2015. The volatility of exchange rate has been created over the last two decades and it shows an uncertainty of economy, international trade flow, and risk of investment in Kenya (Otieno, 2013).
The exchange rate plays an important role in the Kenyan economy since it participates in the foreign exchange market, international trade and stock market. Kirui, Wawire, and Onono (2014) indicated that the fluctuations of exchange rate affect the stock market whether the currency is appreciates or depreciates and it supported by Musyoki, Pokhariyal, and Pundo (2012) who states the Kenyan economic growth and financial market has been affected by the exchange rate volatilities.
There are huge number of literatures on volatility of exchange rates, because it has a significant impact on macro-economic variables. Excess of volatility of exchange rates may reduce the economic growth, trade and level of international competitiveness. Moreover, it is also limit the international capital flow by reducing financial portfolio investment and investment in foreign operating facilities (Musyoki, Pokhariyal & Pundo, 2012).
1.2 Statement of the Problem According to Kibiy and Tabitha (2016), there has been a continuous trend of exchange rate fluctuations in Kenya and this has translated into a high degree of uncertainty for the monetary policy objectives that policymakers often seek to achieve; price stability, economic growth and trade. A significant number of studies have found both positive and negative effects of exchange rate variances on monetary development; however, a few number of study focus on Kenyan currency. Nicholas (2016) found that factors such
6 as balance of payment, GDP, interest rates and inflation affecting volatility of Kenyan Shillings against the US Dollar. Kibiy and Tabitha (2016) analyze the effect of money supply, external public debt, inflation, and interest rates on exchange rate volatility of the Kenyan Shillings against world major currencies which are US Dollar, Euro and Japanese Yen.
However, a few researches have focused on evaluating the strength of effects of interest rates, inflation rates, and GDP growth rates factors on volatility of exchange rates of the Kenyan Shilling against Euro currency. This research aimed to fill the gap by establishing the contribution and the extent to which each factor impacts on volatility of the exchange rates in Kenyan Shilling on Euro specifically.
1.3 Purpose of the Study The purpose of this research was to investigate the impact of macro-economic factors on exchange rates between Kenyan Shillings and Eurocurrency.
1.4 Research Questions 1.4.1 How do interest rates influence exchange rates between Kenyan Shillings and Euro currency? 1.4.2 what is the impact of inflation rates on exchange rates between Kenyan Shillings and Euro currency? 1.4.3 Does Gross Domestic Product growth rates affect exchange rates between Kenyan Shillings and Euro currency?
1.5 Significance of the Study 1.5.1 Policy Makers The research establishes the macro-economic factors on exchange rates between Kenyan Shillings and Euro currency. This research may offer solution to reduce the level of volatility between Kenyan Shilling and Euro currency. The government especially policy makers should strengthen the Kenyan Shillings and prevent the depreciation of domestic currency against the Euro currency. Moreover, such action will smoothen the international trade.
7
1.5.2 Investors This research provides the adequate information and knowledge on investment opportunities thus investors will have a better option for decision making. Investors can invest when the return is high, and supply of money is low. The portfolio as direct foreign investors will hedge this investment.
1.5.3 Importers and Exporters This research provides sufficient knowledge on exchange rates between Kenyan Shilling and Euro currency. Thus, importers and exporters may have information of exchange rate trends as the level of international trade increase. It will help to smoothen the international trade flow.
1.5.4 Financial Institution This research may help financial institutions in Kenya to understand factors affecting the volatility of exchange rates on Kenyan Shilling against Euro currency. Monetary policy, fiscal policy and debt management can utilize this research and the research can be a guideline for them to understand the importance of volatility of exchange rates.
1.5.5 Tourism The knowledge from this research can assist tourism and travel business to increase potential benefits from shifting currencies. It can also help to downside currency risks of selling foreign travel and appropriate period of time for tourism.
1.5.6 Scholars 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 scope of the research was to show the impact of selected macro-economic variables on exchange rates between Kenyan Shillings and Euro currency. The population of the research was whole of Kenyan economy indicators, specifically interest rates, inflation rates, and Gross Domestic Product growth rates. The research used the time series data on exchange rates and three macro-economic variables which were interest rates, inflation
8 rates and Gross Domestic Product growth rates. The source of data was published secondary data from Central Bank of Kenya, Kenya National Bureau of Statistics, and World Bank, and it covered the period of 9 years from 2010 to 2018 inclusive.
1.7 Definition of Terms 1.7.1 Exchange Rates Exchange rate is the number of units of domestic currency, the need to buy one unit of foreign currency. It can be quoted either directly or indirectly and it has been recognized as an important aspect in international macro economics and finance (Mohd et al., 2016). In this research, the exchange rate was the dependent variable.
1.7.2 Volatility of Exchange Rate Volatility of exchange rate is defined as the risk associated with unexpected movements in the exchange rate. Economic factors such as the interest rate, inflation rate, balance of payments, tax rate, trade flow, GDP and the money supply are the sources of volatility of exchange rate (Oiro, 2015).
1.7.3 Fixed Exchange Rate A fixed exchange rate is a regime applied by a country such as government or central bank fixed the official exchange rate from home currency to foreign currency promoted by Bretton Woods (Geza & Vasilescu, 2011).
1.7.4 Free Floating Exchange Rate A free-floating exchange rate also known as floating exchange rate is a regime by the forex market depend on demand and supply compare with foreign currency to set the currency price (Geza & Vasilescu, 2011).
1.7.3 Interest Rates Interest rate is the price, which brings into equilibrium the desire to hold wealth in cash with the supply of cash resources and the reward for parting with liquidity at the same time (Nicholas, 2016). Interest rate was an independent variable in this research.
1.7.4 Inflation Rates
9 The inflation rates refer to the change in the general level of prices in the economy over a given period of time. The change in the inflation rates would have a significant effect in the purchasing power of money and the cost of production in the manufacturing sector (Muchiri, 2017). In this research, inflation rate was an independent variable.
1.7.5 Gross Domestic Product Growth Rates The GDP is the monetary value of all the finished goods and services produced within a country’s borders in a specific time period. Though GDP is usually calculated on an annual basis, it can also be calculated on a quarterly basis (Hsing, 2016). GDP growth rate is a nation’s GDP changes/grows from one year to another year. GDP growth rate was treated as an independent variable in this research.
1.8 Chapter Summary This chapter presented the background of the study and identified the statement of the problem and justifies the claim for the study. The purpose of the study and research questions are presented in this chapter. The significance of the study also presented followed by the scope of the study based on the research. It also defined key terms used in the research. Chapter two presents the literature review based on the research questions. Chapter three determine the research methodology, and chapter four presents the major findings and results. Lastly, chapter five outlined the discussion, conclusions and recommendations.
10 CHAPTER TWO
2.0 LITERATURE REVIEW 2.1 Introduction The purpose of this research was to investigate the impact of macro-economic factors on exchange rates between Kenyan Shillings and Euro currency. This chapter investigated and evaluated the impact of selected macro-economic variables on exchange rates between Kenyan Shillings and Euro currency. The empirical literature review was based on the research questions which impact of interest rates were, inflation rates and Gross Domestic Product growth rates on exchange rates to review of literatures. The research questions were based on the variables causing change in the exchange rates.
2.2 Influence of Interest Rates on Exchange Rates 2.2.1 Interest Rates Economist defined interest as “a price paid for the use of credit or money”. Therefore it determined by “The interaction between the demand for investment capital and the supply of savings” (Patterson & Lygnerud, 1999). According to Devereux and Yetman (2010), an interest rate view as an amount of money where borrower pays for the use of money which he or she does not owned, and the borrower has to return to the lender who collect borrower’s consumption. In other word, interest rate is a percentage of money taken over during period. Interest rates are one of the macro-economic factors which may influence on the exchange rates.
In the beginning of 1999, the nominal interest rates in Euro area has been at low levels, especially in Italy. Italy has a huge fluctuation of interest rate because of the public debt, and depreciation of currency (Patterson & Lygnerud, 1999). Interest rates can be lay out by four stage which defined by financial sector policies in Kenya. The first stage covers before 1974, when interest rates remained unchanged. From 1974 to 1979 is the second stage and the interest rate ceiling are revised upward for the first time. Then, stage three start from 1980 to 1990, the interest rates became a significant role for monetary policy instrument and interest rates are liberalized during stage four in 1991 to 1999 (Ngugi, 2001). Interest rates has been increasing from 8.50% to 10.00% during 2013 to 2017 in Kenya (Central Bank of Kenya, 2018).
11 2.2.1.1 Interest Rate Parity Interest rate parity is one of the most important theory in global macro-economic models and international finance because it is the best way to explain how exchange rate values are determined and why exchange rates are fluctuating. This theory used to explain the value and movements of exchange rates and to analyze the relationship between the spot exchange rate and future (forward) exchange rate. In another vein, the theory suggests that transactions on a country’s financial account affect the value of the exchange rate on the FOREX market (Schmitz, 2010).
It plays a critical role in foreign exchange markets, connecting interest rates, spot exchange rates, and foreign exchange rates. For instance, the two countries will be exchanged at higher price than current spot price when both two countries offers a higher risk-free rate of return. The interest rate parity presents an idea that there is no arbitrage in the foreign exchange markets, means that investors cannot lock in the current exchange rate in one currency for a lower price and then purchase another currency from a country offering a higher interest rate (Levich, 2013).
The interest rate parity can be split by two, which is covered and uncovered interest rate parity. A covered interest rate parity is when the no-arbitrage condition could be satisfied through the use of forward contracts in an attempt to hedge against foreign exchange risk (Raja & Jaweed, 2014). On the other hand, an uncovered interest rate parity is when the no-arbitrage condition could be satisfied without the use of forward contracts to hedge against foreign exchange risk (Aliber, 1973). Interest rate parity can be sued to develop a model of exchange rate determination, it is also known as the asset approach. It refers to a condition of equality between the rates of return on comparable assets between two countries.
2.2.1.2 International Fisher Effect The international fisher effect is an exchange rate model developed by Irving Fisher in 1930, which states that any variation in short-term interest rates are the reflection of expected inflation movements (Ahmad, 2010). It uses present and future risk-free interest rates rather than inflation rates to describe why exchange rates change over time and it used to understand and predict of present and future spot currency price changes. The fisher effect assumed that spot currency prices will naturally achieve parity with perfect
12 ordering markets and monetary policy impact on the fisher effect because it define the nominal interest rate (Blaug, 2015).
Benazić (2013) indicated that the fisher effect describes the nominal interest rates reflect the real rate of return and expected rate of inflation, thus distinction between nominal and real interest rate is measured by expected inflation rates. However, the international fisher effect takes further step to assume the appreciation or depreciation of currency prices is proportionally related to differences in nominal interest rate.
The international fisher effect implies that sum of the nominal interest rate, real interest rate, and expected inflation would not be affected by the changes in nominal interest rate. Ahmad (2010) covered the possess of fisher effect demonstrated that money is neutral, and a fully anticipated inflation does not have any effect on the real interest rate which is determined by the marginal productivity of the capital. Fisher effect might not hold fully during the fluctuation especially in the short run.
2.2.2 Relationship between Interest Rates and Exchange Rates Recently, there has been special relationship between interest rate and exchange rate globally, therefore it gives an important role to determine the economy which are the movement of the inflation, trade flows, external debt and output (Okoth, 2013). A relationship between an interest rate and exchange rate became a debatable issue both theoretical and empirical among economists.
Mwangi and Ochieng (2017) found that the change of an interest rate can have both positive and negative influence on the world economy by influencing stock and bond market interest rate, business spending, inflation, and recessions. According to Mundell- Fleming model also known as IS-LM-BoP model, an increase in interest rate is necessary to stabilize the exchange rate depreciation and to curb the inflationary pressure and thereby helps to avoid many adverse economic consequences (Dash, 2014).
Sanchez (2008) analyze the relationship between interest rates and exchange rates using a simple model that include the role of exchange rate pass-through into domestic prices and distinguishes between cases of expansionary and contractionary depreciations. This study
13 found that there is correlation between interest rates and exchange rates, which is positive effect for contractionary ones and negative effect for expansionary depreciations.
Patra (2004) researched about the theoretical relationship between interest rates and exchange rates differential in macro-economic using by cointegration approach between US and India during 1993–2003. The research employed both Engle-Granger and Johansen tests for existence of cointegration and the research develop that it should have a systematic relationship between interest rates and exchange rates because of some good reasons. Nevertheless, the researcher could not discover empirical representation which could not supported by the data thus empirical support in favors of the above relationship. The outcome was robust for different measures of interest rate differentials.
A few research examines the long run relationship between interest rates and exchange rates over the decades using recently developed panel cointegration methods supported by Macdonald and Nagayasu (2000). Belke, Geisslreither, and Gros (2004) provide the view on the interaction of interest rate and volatility of exchange rate in the Mercosur countries. The research argued several models which can explain systematic correlations between interest rate and volatility of exchange rate. The research discusses that both variables are largely driven either by the credibility of a country or by politics in general and thus should move in the same direction. Eventually, the hypothesis of a positive correlation between two variables empirical. Furthermore, the research had control for the third variables which are exchange rate misalignment, financial stress, and volatility of monetary. As a result, there was a significant co-movement between interest rate and exchange rate in Mercosur countries.
Narayan and Smyth (2006) explored the short-run and long-run relationship between China’s exchange rate, foreign exchange reserves and the interest rate differential between China and US using monthly data from 1980 to 2002 using cointegration approach. The research discovered the interest rate differential has positive relationship, but it is insignificant statistically in long-run. In the short-run, the research found that the relationship between the interest rate differential, exchange rate, and foreign exchange reserves is non-monotonic.
14 Beng and Ying (2000) tested for the systematic empirical relationship between interest rates and exchange rates in case of the Malaysian Ringgit/US Dollar using Johansen maximum likelihood cointegration procedure. The research indicated the existence of fairly robust long-run relationship between the interest rates and exchange rates, and it is also indicated the existence of stable adjustment dynamics when either interest rates of exchange rates differential deviates from their long-run equilibrium position.
Bautista (2006) extended research of Beng and Ying (2000) to investigate the relationship between interest rate differential and exchange rate in six East Asia countries which are Indonesia, South Korea, Malaysia, Philippines, Singapore and Thailand period of 1986 – 2004 and noting the shifts in the nominal exchange rate regime. In the early 1970s, East Asia countries continued to peg their currencies to the US dollar as the former opted to float while developed countries has been analyzed the relationship extensively. Most of the East Asian countries provides an opportunity to analyze the relationship use of the dynamic conditional correlation multivariate Generalized Auto Regressive Conditional Heteroskedasticity (GARCH) model from 1997 crisis. The GARCH model evaluated to determine the correlation of interest rate and exchange rate over time. The research discovered that it has a positive correlation during pegged regimes and negative correlation during freely falling regimes supported by Alam, Butt, Iqbal, and Bhatti (2002).
Kibiy and Tabitha (2016) investigated the factors determining exchange rate volatility of the Kenya Shillings against world major currencies which are US Dollar, European Euro, and Japanese Yen using linear regression in SPSS statistical software. The research had three independent variables which are interest rate, inflation rate, and external public debt. The research found that although not all the independent variables were statistically significant in determining each of the currency volatility, overall, all of them contributed for exchange rate volatility period between 2006 to 2015. Nicholas (2016) also analyze the factors affecting Kenya Shillings volatility against the US Dollar used by multiple linear regression model to define the relationship between exchange rate and other factors which includes interest rates, inflation rates, and GDP rates. As a result, it has a negative relationship between interest rates and Kenya Shilling volatility with coefficient of -0.193 period of 2010-2014.
15 Benita and Lauterbach (2004) analyzed policy and macro-economic variables influence the daily volatility of exchange rate for panel data versus a specific country analysis. The study used daily exchange rate volatility between U.S. Dollar and 43 different currencies period of 1990 to 2001. The study found a positive correlation between exchange rates, real interest rates, and the increase of Central Bank intervention. The study, however, revealed that these positive correlations are misleading, and it reflect a cross-country difference. This means that countries with higher volatility of exchange rate can maintain higher real interest rates and employ more intervention of Central Bank. In addition, the study found that the flexibility of the exchange rate regime, intervention of Central Bank and uncertainty of the domestic economy may affect the volatility of exchange rates.
Central banks typically raise short-term interest rates to defend against currency depreciations. Many researcher has empirical literature in this area, but it was not able to mark a clear systematic relationship between interest rates and exchange rate. Hnatkovska, Lahiri, and Vegh (2007) discovered the relationship between interest rate and exchange rate has non-monotonic relationship using optimizing model of a small open economy. A small open economy model where higher interest rates have three effects which are the rise of fiscal burden on the government, it reduces output due to higher working capital costs and raise the demand for domestic currency assets. The first two effects lead to currency depreciation and the last effect leads currency appreciation. Furthermore, the research used the quantitative model to determine the constant increase in the policy- controlled interest rate has a non-monotonic effect on the steady state exchange rate. It means that a small increase of interest rate tends to appreciation of exchange rate, however a large increase of interest rate tends to depreciation of exchange rate. Consequently, the research explained the incapability of non-structural empirical models to find a systematic relationship. Therefore, this research may contribute the knowledge between interest rates and exchange rates in Kenya.
2.3 Impact of Inflation Rates on Exchange Rates 2.3.1 Inflation Rates Inflation can be defined as a sustained or continuous rise in the general price level or sustained or continuous fall in the value of money (Vdovichenko, Voronina, Dynnikova, & Subbotin, 2002). Notice from the definition, “Inflation refers to the movement in the general level of prices and it does not refer to changes in one price relative to other prices”
16 support by Baxter and Stockman (1989). These changes are common even when the overall level of prices is stable.
The other notice is that the prices are those of goods and services, it is not assets. Therefore, the rise in the price level must be somewhat substantial and continue over a period longer than a day, week or month. Gali and Gertler (1999) defined an inflation as a process of continually rising prices or equivalently (synonym) of continually falling value of money. Ryan and Milne (1994) mentioned that inflation may be affected by the deregulation of controlled sectors of the economy, together with the usual determinants of inflation in developing economies. This research also discovered that the lower income group inflation rate has negative effect on product price changes while the upper income group inflation rate is not.
For instant, a higher inflation rate in the United Kingdom compare to other Euro area countries will tend to reduce the value of pound (Pettinger, 2017). This is because, the meaning of high inflation rates in UK is that the price goods and services in UK will increase quicker than other Euro area countries. Therefore, the goods and services in UK will be less competitive and other Euro area countries. Furthermore, UK exports will decrease and there will be less demand for Pound Sterling while UK imports increase. Consequently, changes of inflation rates have effect on exchange rates.
2.3.1.1 Exchange Rate Pass-Through Takhtamanova (2010) defined the pass-through as the impact of the changes on exchange rate on the domestic inflation, thus fluctuations of exchange rate will affect the inflation movement and it is called exchange rate pass-through. Jašová, Moessner, and Takáts (2016) mentioned that exchange rate pass through is the one of major subject in economic policy and central bank, consequently it is important to be aware the effects of inflation on movement of exchange rates especially when inflation remains below than central bank targets. Exchange rate pass through display the relationship between exchange rate and inflation it is of a special interest in a period of low inflation, given that both the size of this pass-through and its speed of transmission are essential for a proper assessment and management of the monetary policy and to improve inflation forecasting (Comunale & Kunovac, 2017).
17 Campa and González (2006) research on pass-through of exchange rate changes into the import product prices from outside of the Euro area to Euro area which are 12 countries. The research used time series data on import unit values for 13 different product categories for each country, it was authorized to account for different rates of pass- through for the different product categories. Comunale and Kunovac (2017) developed the evidence on exchange rate pass-through on inflation in the four main Euro area; Germany, France, Italy and Spain, used Bayesian Structured Time Series with identification based on a combination of zero and sign restrictions, period of 1992–2016. The research found that a huge point but volatile pass-through to import prices and overall very small pass-through to consumer inflation. The study found the relationship in exchange rate pass-through and inflation, however it was difficult to measure in Euro area because of identification of the sources.
Jašová et al. (2016) presented a general concept of exchange rate pass-through to evolved advanced and emerging market economies. The research discovered decrease of emerging economies on average after the financial crisis, hence it declined in pass-through is linked to drop the inflation. Conversely, inflation has leaned to be consistently low and exchange rate pass-through has also continuously low in advanced economies. Nevertheless, pass- through estimates are still lower in advanced than in emerging economies. The economists supposed to find a relationship between volatility of exchange rates and national price levels (Devereux & Yetman, 2002). From the assumption of purchasing power parity, economists sensed that restriction of domestic inflation may turn more complex in an environment of volatility of exchange rate.
Historically, exchange rate had a huge effect on countries experiencing currency crisis. Borensztein and Gregorio (1999) contrived the influence of a large devaluations on inflation and the research used the sample of currency crisis in 41 countries. This reveal explained that evolution of inflation has extremely low pass-through after the currency crisis excluding the EMS crisis in 1992. Taylor (2000) claimed that low pass-through has been diminishing the level and volatility of advisable inflation. As a result, the low inflation can reduce the pass-through level and it would be to maintain a low inflation rate.
18 2.3.1.2 Purchasing Power Parity Purchasing power parity (PPP) is one of the economic theories that has a lot of observation all over the world. Taylor (2003) states that exchange rates between two currencies are in equilibrium when their purchasing power is the same in each of the two countries. This means that the exchange rate between two countries should equal the ratio of the two countries’ price level of a fixed basket of goods and services. When a country’s domestic price level is increases that country’s exchange rate must depreciated in order to return to purchasing power parity. Jiang, Bahmani-Oskooee, and Chang (2015) explained the purchasing power parity as “due to arbitrage activities in the international commodities market, the real exchange rates that combine the movements of relative prices with nominal exchange rates are expected to return to a constant equilibrium value in the long run.” Purchasing power parity hypothesis became important in open economy, to construction of exchange rates and the long-run relationship of purchasing power parity has been investigated by Taylor (1995), Taylor and Taylor (2004), Chang and Tzeng (2013), Bahmani-Oskooee, Chang, and Wu (2014) provided the theoretical and empirical features on purchasing power parity and exchange rate.
Telatar and Hasanov (2009) resolved the purchasing power parity within Central and Eastern European countries through linear and nonlinear tests, consequently the purchasing power parity hypothesis supported the Central and Easter European countries when nonlinearities are accounted. Chowdhury (2007) measured the hypothesis of long- run purchasing power parity in Bangladesh with four trading countries which are US, Euro area, Japan and India period of 1994 – 2002 using nonlinear econometric techniques.
2.3.2 Relationship between Inflation Rates and Exchange Rates Exchange rate effected by inflation and it is one of the major factors. Duarte and Stockman (2002) indicated that the low inflation rate will rise of exchange rate, as the purchasing power of the currency will increase as compared to the other currencies. In general, the inflation rate is used to measure the price stability in the economy and inflation can be two sides which are demand side inflation and supply side inflation.
Machlup (1960) analyze the demand side inflation is demand-pull inflation which occurs when aggregate demand and output is growing at an unsustainable rate leading to increased pressure on scarce resources and a positive output gap. When there is excess
19 demand in the economy, producers are able to raise prices and achieve bigger profit margins because they know that demand is running ahead of supply. Typically, demand- pull inflation becomes a threat when an economy has experienced a strong boom with GDP rising faster than the long run trend growth of potential GDP. Demand-pull inflation may cause a depreciation of the exchange rate which makes exports more competitive in overseas markets leading to an injection of fresh demand into the circular flow and a rise in national and demand for factor resources – there may also be a positive multiplier effect on the level of demand and output arising from the initial boost to export sales. On the other hands, the cost-push inflation occurs when businesses respond to rising costs, by increasing their prices to protect profit margins. A fall in the exchange rate cause the cost- push inflation because it normally leads to an increase in the prices of imported products (Machlup, 1960).
For the last year, most critical topics was the foreign exchange rate in Turkey. Abdurehman and Hacilar (2016) investigated on relationship between exchange rate and inflation in Turkey, because Turkey faced the depreciation of Turkish currency (Turkish Lira) against the US Dollar, Euro, British Pound, and Japanese Yen. The research believed that inflation rates has most important factor for Turkish economy among various factors, because Turkey has been suffered high inflation rates for a long time. After the 2001 crisis, Turkey has succeeded to decrease of the inflation rate to one digit, but it still remains one of the highest in the world. The research used OLS model to estimate the relationship between inflation rates and exchange rates of Turkish Lira and British Pound, however the result shows that there is no relationship between inflation rates and TL/GBP. Therefore, the inflation differential between Turkey and the United Kingdome does not explain the TL/GBP exchange rate and the PPP is not supported in Turkey. The research also used both ARCH and GARCH model to determine the effect for the PPP deviations and this research found evidence of both significant on ARCH and GARCH effects in the long run.
Iran has experienced constant and high inflation and fluctuation of exchange rate in last decades. According to Monfared and Akın (2017) the most critical issues was crises of oil after Iran-Iraq war which incurred high inflation in 1979. It attracted a quite number of economists and this research explored the relationship between exchange rate and inflation using Vector Autoregression model and Hendry General to Specific Modelling
20 method based on the time series data between 1976 to 2012. The research covered the result of direct relationship between exchange rate and inflation under the Hendry General model and the inflation increased when exchange rate increased. VAR model take the money supply as one of variable and it shows the positive direction between exchange rate and inflation.
Koku, Caushi, Fetai, and Fetai (2016) examines empirically the relationships between inflation and exchange rates in Western Balkan countries includes Albania, Serbia, and Macedonia. The main purpose of this research was to determine whether fixed exchange rates play a significant role in inflationary performance or whether flexible exchange rates perform as a better shock-absorbing instrument in the Western Balkan countries. As a result, the research reveals that an exchange rate is still the main source of inflationary pressures in Western Balkan countries. Therefore, policy makers must weigh the relative costs and benefits associated with introducing a flexible exchange rate in small open economies because such regime is likely to incur more costs than benefits. Kataranova (2010) also found similar results, the relationship between inflation and exchange rate has been observed in the last 10 to 15 years in almost all countries.
Twarowska and Kąkol (2014) analysed the factors affecting fluctuations in the exchange rate of Polish Zloty against Euro used by linear regression analysis period of 2000-2013. The research used four main factors which are economic growth rate, inflation rate, interest rate, government deficit, balance of payment, and the level of forex market development and speculative capital flows. The research established that inflation rate and financial account balance are most of the major factors to determine level of exchange rates. The research found a negative relationship between inflation rates and exchange rate between EUR/PLN exchange rates. The evidence from research, the fiscal and monetary policies are play a significant role in volatility of exchange rates.
Hamid, Shahzad, Saqib, and Maqbool (2016) examined the impact of inflation rate on exchange rate in Pakistan used by SPSS programme to utilize for regression analysis period of 1999 to 2015. The study found that inflation rate and exchange rate have a negative relationship in Pakistan. The t-estimation of inflation rate is discovered -1.356 which depicts that inflation rate has huge effect on conversion scale of Pakistan. Moreover, coefficient of inflation rate is demonstrating negative effect on swapping scale.
21
Measurement of price is most critical part of economic, thus calculating the price index is always one of the most significant economic activity (Chen & Hu, 2018). Inflation can be measured by the change in a price index such as Consumer Price Index (CPI), Wholesale Price Index, and Implicit Price Index (Semuel & Nurina, 2015). Mukras, Ochieng and Gideon (2016) explained that the Consumer Price Index is the most common way to measuring the inflation which explain the rise in the price level reduces the purchasing power of the currency in an economic unit. It is important reference for governments, firms and residents for decision-making.
Diewert (2001) defined Consumer Price Index as “A measure that examines the weighted average of prices of a basket of consumer goods and services, such as transportation, food and medical care.” It is calculated by taking price changes for each item in the predetermined basket of goods and averaging them. Changes in the CPI are used to assess price changes associated with the cost of living. The CPI is important for federal government where determines the economic policies. In addition, CPI can adjust prices in gross domestic product and the government uses it to improve benefit levels for recipients of social security and other government programs (Officer, 2007).
The CPI true value is considered to be the cost of living index which is a unique concept of individual and preferences for goods and services of each individual which the person is willing to pay for it (Wynne & Sigalla, 1994). It is also giving you the different cost of living between one country to the other country. Therefore, it is helpful to compare countries because it compensates for exchange rate differences.
2.4 Effect of Gross Domestic Product on Exchange Rates 2.4.1 Gross Domestic Product Leamer (2009) defined Gross Domestic Product as “The market value of goods and services produced within a selected geographic area (usually a country) in a selected interval in time (often a year).” A formal and simple definition of GDP is the total value of final goods and services made in a year (MacKenzie, 2015). In 1944, GDP became the main tool for measure of the size and health of a country’s economy and expressed as a comparison to the previous time period. The main reason to estimate GDP is to track country’s economic progress over time.
22
Samuelson and Nordhaus (2009) indicated the importance of GDP, that is GDP gives an overall picture of the state of the economy to that of a satellite in space that can survey the weather across an entire continent. Moreover, GDP allows policy makers and central banks to judge the economy size, situation, opportunity, threat or inflation looms on the horizon. Bista and Tomasik (2018) examined the impact of different time zone on trade flows and extended relevance of country’s economic size on different time zone. This research found that a low-income country has a negative effect on different time zone and exports than a high-income country.
MacKenzie (2015) indicated that GDP statistics are useful when data is adjusted for price of final goods and services, inflation and size of country’s total population. Measurements of GDP has three approaches which are expenditure approach, income approach and production approach (Lee, 2012). The expenditure approach is summation of all final expenditures within an economy, while the income approach is summation of all factor incomes within an economy which can described the incomes directly generated by productive activity. Lastly, the production approach is summation of all production activity within an economy.
2.4.1.1 Factors Influencing Gross Domestic Product There are several factors which affect the economic growth, and economic growth is measured by GDP (Boldeanu & Constantinescu, 2015). The factors include natural resources, human resources, technology and capital goods and it has direct impact on the value of goods and services which have been supplied. However, the growth rate of GDP has different factors to determine such as exchange rates, employment rates, investments, public expenditure and so on. However, researchers value the variables in differently.
Public expenditure on economic growth view as differently from many researchers, Ghosh and Gregoriou (2008) proved the public spending had a significant and positive impact on economic growth measured by 15 developing countries. On the other hand, Benos (2009) examined the human capital and infrastructure had a significant impact on long-run economic growth defined by 14 EU members. Critically, both researches have used same methodology called the generalized method of moments.
23 The government debt or government burden is one of the major factor which influencing the economic growth and it affect economic growth either positive or negative. Loayza and Soto (2002) stated the government can play an advantageous role in the economy, but it could be a heavy burden when the government imposes high taxes. It occurs when the government desire to use the revenue to maintain ineffective public programs and an inflated bureaucracy, misrepresents of market incentives, and interferes negatively in the economy by assuming roles most appropriate for the private sector. The government debt is generally captured through the ratio of government consumption to GDP. Barro (1996) founds the economic growth is improved by lower government consumption, lower fertility, maintenance of the law, lower inflation, and development of international trade through the investigation of 100 countries over the period of 1960 to 1990. Moreover, based on the Catch-up effect theory also known as convergence that the per capita incomes in poorer countries will tend to grow faster than richer countries. As poorer countries develop quickly, and richer countries develop slowly, the poorer countries catch up to the richer and incomes converge (Anyanwu, 2014).
International trade also affect the economy growth in both importing and exporting countries and it has positive relationship between tow variables. Harrison (1996) and Sachs and Warner (1995) explored the performance of international trading and GDP growth rates and a large proportion of countries depended on agriculture and vulnerable to shocks. Upreti (2015) expressed the higher economic growth rates were strongly correlated with higher export growth rates and the research discovered the positive correlation on economic growth and exports in low- and middle-income countries.
The main focus of policies is to maintain sustainable and strong economic growth in developing countries and it has been investigated specific determinants of economic growth since generating sustained economic growth in Africa remains one of the most pressing challenges to develop (Anyanwu, 2014). A number of researches, Bleaney and Nishiyama (2002), Abou-Ali and Kheir-El-Din (2009) and Fowowe (2008) identified the determinant of economic growth in both theoretical and empirical, however the result was not conclusive.
The developing countries were not able to grow because of economic traps which includes wars of conflicts, natural resources, dependence on neighboring country, lack of
24 rule, regulation and law, technology, and infrastructure (Collier, 2007). The developing countries had low levels of socio-economic conditions, even though developing countries GDP per capita was higher than the world average. Upreti (2015) confirmed that Catch- up effect theory was true from the evidence of low level of GDP per capita on developing countries.
2.4.2 Relationship between Gross Domestic Product and Exchange Rates Many theoretical and empirical research discovered that the more open a country is the more benefit on growth (Tharakan, 1999). The relationship between exchange rate and economic growth has received considerable attention, however still contentious. Barguellil, Ben-Salha and Zmami (2018) investigated the influence of volatility of exchange rate on economic growth based on 45 emerging and developing countries between 1985 to 2015. The volatility of exchange rate has a negative effect on economic growth and the volatility of exchange rate rely on the exchange rate regimes and financial openness. Edwards and Levy (2005) confirmed exchange rate and economic growth has positive relationship on the adjustment process to shocks. Reversely, Doğanlar (2002), Servén (2003) and Demir (2010) discovered negative impact on volatility of exchange rate and macro-economic aggregates which may influence economic growth which included employment, investment and international trade.
Hamid et al. (2016) investigated the impact of GDP on the exchange rate volatility in Pakistan used by SPSS programme to utilize for regression analysis period of 1999 to 2015. The study found that GDP has a positive effect on exchange rates and generally noteworthy. The t-estimation of GDP is discovered 11.561 which express the critical effect of GDP on conversion standard unpredictability of Pakistan.
Semuel and Nurina (2015) analyzed the effect of exchange rates on GDP in Indonesia using quantitative methods. The study used Partial Least Square (PLS) to test the hypothesis which is useful to determine the structural relationship between the independent variables and the dependent variable. The study covered period from June 2005 to December 2013. The study discovered positive relationship between exchange rate and GDP with a path coefficient of 0.214 with a t-statistic of 3.477 which is greater than the value of constant 1.96. This implies that exchange rates and GDP have a
25 statistically positive significant. This means that increase of GDP leads to increase of exchange rates. Dollar (1992) explored the relationship between volatility of exchange rate and economic growth with 95 developing countries over the period of 1976 – 1985 and come up with a negative relationship between the two variables. Schnabl (2009) focused the negative impact on volatility of exchange rate on economic growth on some of European and Asian countries specifically. Recently, Vieira and MacDonald (2016) proved the existence of negative impact of volatility of exchange rate on long-run economic growth by analyzing 82 countries between 1970 to 2009. Bleaney and Greenaway (2001) indicated the influence of volatility of exchange rate on economic growth in 14 Sub- Saharan African countries over the period of 1980 – 1995. The research found the negative impact on investment not on economic growth.
Tyers, Golley, Yongxiang and Bain (2008) focused on how shocks that enhance the rate of GDP growth influence on bilateral exchange rate between China, Hong Kong, Taiwan and Canada and Mexico which represented the North America. More recently, Barbosa, Jayme and Missio (2018) developed the research and examined the effects of macroeconomic policies on the long-run exchange rate sample of 14 developing countries during 30 years from 1980 to 2010 used by econometric techniques. The research compared the result between two group of countries such as Asia and Latin American nations, consequently it required different types of approaches to get competitive exchange rate depending on the region.
On the other way, Vita and Kyaw (2011) questioned whether the choice of exchange rate regime has an impact on economic growth in 70 developing countries over the period of 1981 to 2004. The result of the research shows that the absence of any robust relation between the choice of exchange rate regime and economic growth, furthermore the choice of exchange rate policy has no direct impact on the long-run growth on the developing countries. Sosvilla-Rivero and Ramos-Herrera (2014) extended to empirical examination of the relationship on exchange rate regimes and economic growth and they discovered the intermediate exchange rate regimes has the best growth of performance, while the flexible exchange regime has a smallest growth on the performance. Nevertheless, some countries have a higher economic growth under intermediate exchange rate regimes when countries analyzed by income level.
26
There is also indirect relationship between volatility of exchange rate and economic growth. Katusiime, Agbola, and Shamsuddin (2016) argued that the volatility of exchange rate may negatively effects on economic growth such as international trade and investment. The volatility of exchange rate might prevent or postpone the investments, especially when investment decisions are irreparable or cost of adaption to volatility of exchange rate is high (Goldberg & Koistad, 1994). Arratibel, Furceri, Martin, and Zdzienicka (2011) also discovered the empirical evidence on negative effects of volatility of exchange rate on investment. The number of researches on volatility of exchange rate and economic growth is scanty and unclear in Africa. According to Adewuyi and Akpokodje (2013) the volatility of exchange rate has a significant positive effect on economic growth.
2.5 Chapter Summary This chapter presented the literature review on the impact of interest rates, inflation rates and GDP on exchange rates and relationship of independent variables – interest rates, inflation rates, Gross Domestic Product growth rates – and dependent variable. The empirical literature review was conducted guided by the research questions on the study. This reviewed based on the research question and various resources from journal articles, working papers, and books which was critically reviewed. The chapter three presents the research methodology which is applicable for this research.
27 CHAPTER THREE
3.0 RESEARCH METHODOLOGY 3.1 Introduction This chapter presents the research design and appropriate methodology for this research. It describes the research design, population and sampling design, data collection methods, research procedure, and data analysis method. Instruments are reliability relevance, validity, ethical issues, and the research method covered this research.
3.2 Research Design Toledo-Pereyra (2012) defined the research design as a blueprint of the research process plan when research hypothesis or research idea have been clearly figured. It is important to establish the most suitable research design to obtain the most precise results possible. Saundres, Lewis, and Thornhill (2016) explained that research design is the overall scheme of “How you will go about answering your research questions.” It proves that research questions should be clear and intelligible, and it is important because it will select the type of research design. Moreover, research design provides a framework for the data collection and analysis. A 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 used descriptive and explanatory research design to investigate the research questions.
Specifically, this research expressed the impact of macro-economic factors on exchange rates between Kenyan Shillings and Euro currency. This research used the descriptive and explanatory research design and described the three major variables, interest rates, inflation rates, and GDP growth rates, related with volatility of exchange rates in Kenya. Thus, descriptive and explanatory research designs (also called casual research design) are the most suitable for this research which seeks to understand and analyze the cause and effect relationship between independent and dependent variables.
The main purpose of descriptive research is to gain an accurate profile of events, persons or situations and descriptive research is systematically to describe the problem and support the information on an occurrence (Saundres et al., 2016). Descriptive research outline utilized as a part of this research. Moreover, an exploration outline helps
28 specialists to lay out the examination questions approach, usage systems, and information gathering and investigation for the lead of an examination extend. According to Kothari (2004) it has three sorts of research outline which includes quantitative plan, subjective plan, and blended strategies plan. This research utilized the quantitative research outline which incorporates the expressive research plan.
In addition, descriptive research is a precursor to explanatory research (Saundres et al., 2016). An explanatory research design is to establish the causal relationship between variables, and it measured by correlation. Thus, an explanatory research also suitable for this research. This research describes the relationship between interest rates, inflation rates, and GDP growth rates on exchange rates between Kenyan Shillings and Eurocurrency.
3.3 Population and Sampling Design 3.3.1 Population Population is the full set of cases or elements from which a sample is taken (Saundres et al., 2016). Cooper and Schindler (2008) defined a population as a collection of people, items or events about which you want to make inferences of apply your results. Zikmund, Babin, Carr, and Griffin (2013) mentioned population have a common feature about which the researcher desired to make a creation.
The population of this research include the interest rates, inflation rates, GDP growth rates and exchange rate from Central Bank of Kenya, Kenya National Bureau of Statistics, and World Bank. The data was attained for the period of 9 year from 2010 to 2018. This research used three independent variables which included interest rates, inflation rates, GDP growth rates, and dependent variable as exchange rates and data collected from 2010 to 2018. The data used annual figures for each variable, since the population is large. The data collected enabled research questions.
Institution Frequency Central Bank of Kenya 2 Kenya National Bureau of Statistics 1
29 World Bank 1 Total 4 Table 3.1 Source of Data
3.3.2 Sampling Design 3.3.2.1 Sampling Frame Saundres, Lewis, and Thornhill (2016) explained sampling frame as a list of items or elements in the population from which a sample is taken. In other words, sampling frame is the set of source materials from which the sample is selected (Zikmund et al., 2013). The main purpose of sampling frames, which is to provide a means for choosing the particular members of the target population that are to be interviewed in the survey. A good sample frame should be complete, accurate and up-to-date. This research used 9- year data during 2010 to 2018, thus it is possible to relate interest rates, inflation rates, and GDP growth rates on exchange rates between Kenyan Shillings and Euro currency.
3.3.2.2 Sampling Technique A sampling technique is the name or other identification of the specific process by which the entities of the sample have been selected (Kothari, 2004). A sampling technique can divide into two types which are probability or representative sampling and non- probability sampling. A probability sampling uses randomization to make sure that every element of the population gets an equal chance to be part of the selected sample. The types included simple random sampling, stratified sampling, systematic sampling, cluster sampling and multi stage sampling. On the other hand, a non-probability sampling is not relying on randomization, it is more reliant on the researcher’s ability to select elements for a sample. The types included convenience sampling, purposive sampling, quota sampling and referral/snowball sampling (Saundres et al., 2016).
This research used purposive sampling since the research has specific elements and period of time to investigate and analyze. Purposive sampling select sample based on characteristics of a population and the objective of the research and it is also known as judgmental, selective or subjective sampling (Guarte & Barrios, 2006). Purposive sampling is one of the most cost-effective and time-effective sampling methods since the
30 researcher knows what to collect and where to collect. However, it can occur the errors in judgment by researcher (Black, 2010).
3.3.2.3 Sample Size Sample size is a smaller set of the larger population, even though large samples give more dependable results than small samples defined by (Hancock & Algozzine, 2017). It is not mandatory to use sample as entire target population, however this research used entire population as sample size period covered from 2010 to 2018. The reason why this period being selected is that it is enough to give demonstration to the influence of variables on the volatility of exchange rate on Kenyan Shillings against Euro currency. Moreover, it was propagated of new constitution in 2010, and it can indicate the trend of each year since the data collected yearly bases.
3.4 Data Collection Method Data is raw facts that are collected together for reference or analyze to produce a reliable and valuable information (Zikmund et al., 2013). Hancock and Algozzine (2017) defined data collection as a process of collecting information from all the relevant sources to find answers to the research problem, test the hypothesis and evaluate the outcomes. Data collection methods can be divided into two categories as primary methods and secondary methods (Hox & Boeije, 2005).
A primary data is the original data that has been collected for special purpose, means that someone collected the data from first hand sources using by questionnaires, interviews, survey or experiments. Reversely, a secondary data is a data which has already collected by someone else and it published in online portals, journals, magazines or books. A research will gather data from other studies, researches, surveys or experiments to use for another research which can indicate a new result and conclusion. This existing data provides a practical option for other researchers who may have a time limited, lack of resources, or limited of data accessible.
This research used secondary data collection methods and data collected means of a checklist. The data will be collected from Central Bank of Kenya, Kenya National Bureau of Statistics, and World Bank for the period of 9 years between 2010 to 2018. Data collected equivalented to the yearly average figures for three dependent variables which
31 are interest rates, inflation rates, and GDP growth rates and exchange rates as dependent variable. Data collection involved the time series data for exchange rates on Kenyan Shillings against Euro currency from 2010 to 2018 from Central Bank of Kenya, Kenya National Bureau of Statistics, and World Bank. The main purpose of data collection was to investigate the three research questions of this research. This research used a checklist for data collection instrument. 3.5 Research Procedure Research procedures are described as identifying and developing a research topic to investigate the research problem and research questions (Abbott & McKinney, 2013). The researcher should consider several factors include research design and data collection instruments. Saundres et al. (2016) indicated that researchers must check the quality of data before data collection whether the data is validated, reliable, suitable, and adequacy for the research.
This research used secondary data which collected from government censuses, specifically Central Bank of Kenya and Kenya National Bureau of Statistics and international organizations as World Bank. The biggest advantage of secondary data is that is easily available and hence it required less time and cost than primary data. However, data might not be specific to the what researcher’s needs and the conclusion might be incomplete.
The collected data information is regard to the impact of interest rates, inflation rates, and GDP growth rates on volatility of exchange rates between Kenyan Shillings and Euro currency. The collected data entered into IBM SPSS statistics data spreadsheet and analyzed by descriptive and inferential statistics. Data entered by order as follow, dependent variables which is exchange rates changes (percentage) and independent variables which are interest rates, inflation rates and GDP growth rates (percentage). The results provide the creditable conclusion. In this research the researcher collected requisite data from Central Bank of Kenya, Kenya National Bureau of Statistics, and World Bank.
3.6 Data Analysis Method This research used quantitative method to analyze the data using descriptive statistics and the result presented by using a variety of statistical instrument such as graphs, charts,
32 tables and percentages. Grimes and Schulz (2002) defined descriptive research as “Concerned with and designed only to describe the existing distribution of variables, without regard to causal or other hypotheses.” Descriptive research should be clear, specific, and measurable definition of the research problem and questions (Bryman & Bell, 2015).
Rugg (2007) defined descriptive statistics as basic features of the quantitative data in a research which provide simple summaries about the sample and the measures. Descriptive statistics divided by two categories which are measure of central tendency – mean, average, median, and mode – and measures of variability (spread) – standard deviation, mean absolute deviation, variance, range, percentile, quartiles, skewness, kurtosis, and correlation (Naghshpour, 2012).
The data collected from Central Bank of Kenya, Kenya National Bureau of Statistics, and World Bank and it requires the review of quality controls to secure necessary data for the research. Data was verified collected as code before entering to software. Data verification proves that the collected data was clean without errors, and any other ambiguities. Statistical analysis software such as IBM SPSS statistics assisted to analyze, calculate and drawing a chart.
This research used multiple linear regression formula which is statistical tool to analyze the relationship between three dependent variables – interest rates, inflation rates, and GDP growth rates – and independent variable which is exchange rates between Kenyan Shillings and Euro currency. A linear regression assisted in discovering the effects of independent variable on dependent variable.
The relationship between volatility of exchange rates on Kenyan Shillings against Euro currency and interest rates, inflation rates and GDP growth rates can be represented in the following model:
푌 = 훽0 + 훽1푋1 + 훽2푋2 + 훽3푋3
Where by, Y = Exchange rates changes (%) - Dependent variable
푋1= Interest rates (%) - Independent variable
33 푋2= Inflation rates (%) - Independent variable
푋3= GDP growth rates (%) - Independent variable
훽0= Constants to be estimated by the model
훽1, 훽2 and 훽3 are Coefficients
According to Kothari (2004) the results should be measurable significant within 0.05 level. The Pearson Product Moment Correlation Coefficient tested the relationship between independent and dependent variables it shows the direction of two variables at 95% confidence level. This research used the ANOVA, t-tests, F-tests, z-tests, and the chi-square at 95% confidence level.
3.7 Chapter Summary This chapter presented and discussed the research methodology used to conduct this research. It has defined on research design, population and sampling design, and data collection methods include data collection techniques. The research procedure has been introduced as well as data analysis method and ethical issues have been considered. The next chapter presents the major results and findings of this research.
34 CHAPTER FOUR
4.0 RESULTS AND FINDINGS 4.1 Introduction This chapter presents the research findings on impact of interest rates, inflation rates and Gross Domestic Product growth rates on exchange rates between Kenyan Shillings and Euro currency. The research conducted on a 9 years period from 2010 to 2018.
4.2 General Information 4.2.1 Interest Rates in Kenya 2010-2018 Interest rates are fluctuating in Kenya and averaged 9.77% from 2010 until 2018, reaching highest rate of 15.75% in 2012 and lowest of 6.42% in 2010 as indicated in figure 4.1. Interest rate has been increased by 7.35% between 2011 to 2012 and it is a largest increase within the period of 2010-2018.
Interest Rates in Kenya 2010-2018 18.00 15.75 16.00
14.00
12.00 10.63 10.13 10.00 9.27 10.00 8.40 8.83 8.50
8.00 6.42
Percentage (%) Percentage 6.00
4.00
2.00
- 2010 2011 2012 2013 2014 2015 2016 2017 2018 Year
Figure 4.1 Interest Rates in Kenya 2010-2018
4.2.2 Inflation Rates in Kenya 2010-2018 Inflation rates has not been stable in Kenya especially from 2010 to 2013. It has a highest rate in 2011 as 14.02%, and lowest rate in 2010 as 3.96% which has a huge difference in a year with 10.06%. The average inflation rate is 7.32% between 2010 and 2018 (Figure 4.2).
35 Inflation Rates in Kenya 2010-2018 16.00 14.02 14.00
12.00
9.38 10.00 7.99 8.00 6.88 6.58 6.30 5.72
6.00 5.04 Percentage (%) Percentage 3.96 4.00
2.00
- 2010 2011 2012 2013 2014 2015 2016 2017 2018 Year
Figure 4.2 Inflation Rates in Kenya 2010-2018
4.2.3 Gross Domestic Products Growth Rates in Kenya 2010-2018 Actual value of GDP had increased over the year, however the GDP Growth rates has abrupt shift between 2010-2012 which proved growth of Kenyan economic is slow. The average of GDP growth rate is 5.86% with highest value of 8.41% in 2010 and lowest value of 4.56% in 2012 as per Figure 4.3.
GDP Growth Rates in Kenya 2010-2018 9.008.41
8.00
7.00 6.11 5.97 5.88 5.72 5.87
6.00 5.36 4.87 5.00 4.56
4.00
Percentage (%) Percentage 3.00
2.00
1.00
0.00 2010 2011 2012 2013 2014 2015 2016 2017 2018 Year
Figure 4.3 GDP Growth Rates in Kenya 2010-2018
36 4.2.4 Exchange Rates between Kenyan Shilling and Euro in Kenya 2010-2018 Referred on figure 4.4, Kenyan exchange rates are fluctuating over the year, especially highest percentage with 30.2% from 2011 to 2012. The average of exchange rates changes is 1.47% with highest value of 18.19% in 2011 and -12.01% in 2012. The value of Kenyan Shilling has been weakened through 2013 to 2015, however the value got stronger from 2015 to 2017.
Exchange Rates changes in Kenya 2010-2018 20.00 18.19
15.00
10.00
5.45 5.09 5.00 2.92 1.07 1.36
- (2.46)
Percentage (%) Percentage 2010 2011 2012 2013 2014 2015 2016 2017 2018 (6.37) (5.00)
(10.00) (12.01)
(15.00) Years
Figure 4.4 Exchange Rates Changes between Kenyan Shilling and Euro in Kenya 2010-2018
4.2.5 Descriptive Statistics Descriptive statistics presents the overall summary of the variables which includes average (mean), minimum, maximum and standard deviation. It provides graphical and numerical method to understand the analysis.
Exchange Interest Inflation GDP Growth Rates Rates (%) Rates (%) Rates (%) Changes (%) N Valid 9 9 9 9 Missing 0 0 0 0 Mean 9.7700 7.3189 5.8611 1.4711 Std. Deviation 2.56243 2.96673 1.09072 8.43652 Minimum 6.42 3.96 4.56 -12.01 Maximum 15.75 14.02 8.41 18.19
37 Table 4.1 Overall Summary of Descriptive Statistics 4.2.6 Test of Normality A test was done to establish the normality of the interest rate, inflation rate, GDP growth rate, and exchange rate. The Shapiro-Wilk (SW) values were indicated as per table 4.2. The Shapiro-Wilk (SW) values for interest rate, inflation rate, and exchange rate were not statistically significant (p > 0.05), this implies that the variables were not normally distributed. However, value of GDP growth rate is statistically significant (p < 0.05).
Shapiro-Wilk Statistic df Sig. Exchange Rates Changes (%) .952 9 .712 Interest Rates (%) .842 9 .060 Inflation Rates (%) .872 9 .128 GDP Growth Rates (%) .826 9 .041 Table 4.2 Test of Normality
*. This is a lower bound of the true significance. a. Lilliefors Significance Correction
4.3 Influence of Interest Rates on Exchange Rates A figure 4.5 indicated the trends of interest rates and exchange rates change in Kenya revealed that when interest rate is high, exchange rate is low and vice versa. However, it is also showing that exchange rate is high when interest rate is high during 2010-2011, and 2015-2016 as shown in figure 4.5.
38 Interest Rates on Exhcnage Rates Changes in Kenya 2010-2018 20.00 18.19 15.75 15.00 10.13 10.63 10.00 8.83 8.50 9.27 10.00 8.40
6.42 5.45 5.09 5.00 2.92 1.07 1.36 - (2.46)
Percentage (%) Percentage 2010 2011 2012 2013 2014 2015 2016 2017 2018 (6.37) (5.00)
(10.00) (12.01)
(15.00) Year Exchange Rates Changes (%) Interest Rates (%)
Figure 4.5 Interest Rates on Exchange Rates Changes in Kenya 2010-2018 4.3.1 Simple Linear Regression of Interest Rates and Exchange Rates Changes The effects of interest rate on exchange rate has been analysed by Pearson correlation. The result showed that it is negatively correlated between interest rate and exchange rate (r = -0.545, p = 0.129). This explains that interest rate and exchange rate are negatively related, however there is not statistically significant correlation since P value is greater than 0.05 as shown in table 4.3. The R value represents the simple correlation which is - 0.545 and R Square represents the total variation in the dependent variable (exchange rate), can be explained by the independent variable (interest rate). From the analysis, Adjusted R Square value was 0.197 which implies 19.7% of the variation in exchange rate was caused by variations in interest rate as shown in table 4.3. However, the significance had a P value of 0.129 > 0.05, thus the relationship is not statistically significant.
Change Statistics Model R R Adjusted Std. Error R F df1 df2 Sig. F Square R Square of the Square Change Change Estimate Change 1 -.545a .297 .197 7.56126 .297 2.959 1 7 .129 Table 4.3 Model summary of Interest Rates and Exchange Rates Changes a. Predictors: (Constant), Interest Rates (%)
39
An ANOVA table reports how well the regression equation fits the data. An analysis done at 95% of confidence level, the F critical is 2.959 and the P value is 0.129. This analysis confirmed that interest rates were not in relationship with exchange rates as shown in table 4.4.
Model Sum of Squares df Mean Square F Sig. 1 Regression 169.190 1 169.190 2.959 .129b Residual 400.209 7 57.173 Total 569.399 8 Table 4.4 ANOVA of Interest Rates and Exchange Rates Changes a. Dependent Variable: Exchange Rates Changes (%) b. Predictors: (Constant), Interest Rates (%)
The coefficients table provides to predict dependent variable (exchange rate) from independent variable (interest rate), as well as determine whether interest rate contributes statistically significantly to the model. The “B” column represents the constant figure to regression equation. According to Table 4.5, the regression equation will be
푌 = 19.005 − 1.795푋1 , where Y is the dependent variable (Exchange Rates) and X1 is independent variable (Interest Rates). It means that interest rates are taking the constant exchange rates by 19.005 and an increase in interest rate result into 1.795 decrease in exchange rate. Nevertheless, there were not statistically significant as per Table 4.5 (p > 0.05).
95.0% Unstandardized Standardized Confidence Coefficients Coefficients Interval for B Lower Upper Model B Std. Error Beta t Sig. Bound Bound 1 (Constant) 19.005 10.500 1.810 .113 -5.823 43.833 Interest Rates(%) -1.795 1.043 -.545 -1.720 .129 -4.262 .672 Table 4.5 Coefficients of Interest Rates and Exchange Rates Changes a. Dependent Variable: Exchange Rates Changes (%)
40 4.4 Impact of Inflation Rates on Exchange Rates Figure 4.6 presents that during period of 2010 to 2013 there has been a wide fluctuation in exchange rates and inflation rates has also fluctuated from 3.96% to 5.72% with highest being in 2011. The result present most of time the exchange rates are high when the inflation rates are high. Through the year, inflation rates were higher than exchange rates except 2011.
Inflation Rates on Exchange Rates Changes in Kenya 2010-2018 20.00 18.19 14.02 15.00 9.38 10.00 7.99 6.88 6.58 6.30 5.72 5.09 5.04 3.96 5.00 2.92 5.45 1.07 1.36 - (2.46)
percentage (%) percentage 2010 2011 2012 2013 2014 2015 2016 2017 2018 (6.37) (5.00)
(10.00) (12.01)
(15.00) Year Exchange Rates Changes (%) Inflation Rates (%)
Figure 4.6 Inflation Rates on Exchange Rates Changes in Kenya 2010-2018
4.4.1 Simple Linear Regression of Inflation Rates and Exchange Rates Changes The effects of inflation rate on exchange rate has been analysed by Pearson correlation. The result showed that it is positively correlated of inflation rate on exchange rate (r = 0.491, p = 0.180). This proves that inflation rate and exchange rate are positively related, however there is no statistically significant correlation between two variables since P value is greater than 0.05 as shown in table 4.6. The R value represents the simple correlation which is 0.491 and R Square represents the total variation in the dependent variable (Exchange Rate), can be explained by the independent variable (Inflation Rate). From the analysis, Adjusted R Square value is 0.132 which implies 13.2% of the variation in exchange rate was caused by variations in inflation rate as shown in table 4.6. However, the significance had a P value of 0.180 > 0.05, thus the relationship is not statistically significant.
Table 4.6 Model Summary of Inflation Rates and Exchange Rates Changes
41 Change Statistics Model R R Adjusted Std. Error R F df1 df2 Sig. F Square R Square of the Square Change Change Estimate Change 1 .491a .241 .132 7.85819 .241 2.221 1 7 .180 a. Predictors: (Constant), Inflation Rates (%)
An analysis has been done at 95% of confidence level, the F critical is 2.221 and the P value is 0.180. This analysis confirmed that inflation rates and exchange rates were not statistically significant as shown in table 4.7.
Table 4.7 ANOVA of Inflation Rates and Exchange Rates Changes
Model Sum of Squares df Mean Square F Sig. 1 Regression 137.141 1 137.141 2.221 .180b Residual 432.258 7 61.751 Total 569.399 8 a. Dependent Variable: Exchange Rates Changes (%) b. Predictors: (Constant), Inflation Rates (%)
The coefficients table determine whether inflation rate contributes statistically significantly to the model. From the table below (Table 4.8) the regression equation will be
푌 = −8.743 + 1.396푋2 , where Y is the dependent variable (Exchange Rates) and X2 is independent variable (Inflation Rates). It means that inflation rates are taking the constant exchange rates by -8.743 and increase in inflation rate by 1.396 will increase exchange rate. Nevertheless, the relationship between inflation rates and exchange rates were not statistically significant as per Table 4.8 (p > 0.05).
Table 4.8 Coefficients of Inflation Rates and Exchange Rates Changes
95.0% Unstandardized Standardized Confidence Coefficients Coefficients Interval for B Std. Lower Upper Model B Beta t Sig. Error Bound Bound 1 (Constant) -8.743 7.337 -1.192 .272 -26.09 8.607 Inflation Rates (%) 1.396 .936 .491 1.490 .180 -.819 3.610 a. Dependent Variable: Exchange Rates Changes (%)
42
4.5 Effect of Gross Domestic Product Growth Rates on Exchange Rates According to results of the GDP Growth rates and Exchange rates in Kenya revealed that when exchange rates increase when GDP Growth rate is decrease as exhibited in 2010- 2011, and 2015-2016. However, some instances exchange rates decrease when GDP Growth rates increase as exhibited in 2014-2015, and 2017-2018 as figure below (Figure 4.7). This implies an inverse association between GDP Growth rates and exchange rates.
GDP Growth Rats on Exchange Ratese changes in Kenya 2010-2018 20.00 18.19
15.00
10.00 8.41
6.11 5.88 5.72 5.87 5.97 4.56 5.36 5.09 5.00 2.92 1.36 5.45 1.07 4.87 - (2.46)
Percentage (%) Percentage 2010 2011 2012 2013 2014 2015 2016 2017 2018 (6.37) (5.00)
(10.00) (12.01)
(15.00) Year
Exchange Rates Changes (%) GDP Growth Rates (%)
Figure 4.7 GDP Growth Rates on Exchange Rate Changes in Kenya 2010-2018
4.5.1 Simple Linear Regression of GDP Growth Rates and Exchange Rates Changes The result showed that it is positively correlated of GDP Growth rate on exchange rate (r = 0.129, p = 0.741). This implies that GDP Growth rate and exchange rate are positively related, however there is no significant correlation since P value is greater than 0.05 as per table 4.9. The R value represents the simple correlation which is 0.129 and R Square represents the total variation in the dependent variable (Exchange Rate), can be explained by the independent variable (GDP Growth rate). According to analysis, Adjusted R Square value is zero which implies zero the variation in exchange rate was caused by variations in GDP Growth rate as shown in table 4.9. However, the significance had a P value of 0.741 > 0.05, therefore the relationship is not statistically significant.
Table 4.9 Model Summary of GDP Growth Rates and Exchange Rates Changes Change Statistics
43 Model R R Adjusted Std. Error R F df1 df2 Sig. F Square R Square of the Square Change Change Estimate Change 1 .129a .017 0 8.94395 .017 .118 1 7 .741 a. Predictors: (Constant), GDP Growth Rates (%)
An analysis has been done at 95% of confidence level, the F critical is 0.118 and the P value is 0.741. This analysis confirmed again there is not statistically significant as per table 4.10.
Table 4.10 ANOVA of GDP Growth Rates and Exchange Rates Changes
Model Sum of Squares df Mean Square F Sig. 1 Regression 9.439 1 9.439 .118 .741b Residual 559.959 7 79.994 Total 569.399 8 a. Dependent Variable: Exchange Rates Changes (%) b. Predictors: (Constant), GDP Growth Rates (%)
Table 4.11 the impact of GDP growth rates on the exchange rate by regression equation will be 푌 = −4.366 + 0.996푋3 , where Y is the dependent variable (Exchange Rates) and X3 is independent variable (GDP Growth rates). It means that GDP Growth rates are taking the constant exchange rates by -4.366 and increase in GDP Growth rates by 0.996 will increase exchange rate. However, the relationship between GDP growth rates and exchange rates were not statistically significant as per Table 4.11 (p > 0.05).
Table 4.11 Coefficients of GDP Growth Rates and Exchange Rates Changes
95.0% Unstandardized Standardized Confidence Coefficients Coefficients Interval for B Lower Upper Model B Std. Error Beta t Sig. Bound Bound 1 (Constant) -4.366 17.252 -.253 .807 -45.16 36.428 GDP Growth Rates (%) .996 2.899 .129 .344 .741 -5.860 7.851 a. Dependent Variable: Exchange Rates Changes (%)
44 4.6 Test of Significance Kothari (2004) indicated that the results are measurably significant inside the 0.05 level, which implies that the noteworthiness esteem must be less than 0.05. The t-value controlled the importance, which shows what number of standard blunders implies the example wanders from the tried esteem. In addition, the Pearson Product Moment Correlation Coefficient tested the direction and magnitude of the relationship between the dependent and independent variables at 95% confidence level. The model significance testing is through the ANOVA, t-tests, F-tests, z-tests, and the chi-square at confidence of 95%.
4.6.1 Test of Multicollinearity A Pearson correlation was done between the interest rates, inflation rates, GDP growth rates and exchange rates as shown in table 4.12. The result revealed that there was a positive correlation between exchange rates and inflation rates (r = 0.491, p = 0.129 > 0.05); GDP growth rates (r = 0.129, p = 0.741 > 0.05). However, there was a negative correlation between exchange rate and interest rates (r = -0.545, p = 0.129 > 0.05). This result indicates that with increase of inflation rate and GDP Growth rate effect an increase of exchange rate. On the other hand, a decline of interest rate effect an increase of exchange rate. However, the relationship between interest rates, inflation rates and GDP growth rates on exchange rates were not statistically significant since the p value is greater than 0.05.
Table 4.12 Test of Multicollinearity Exchange GDP Interest Inflation Rates Growth Rates (%) Rates (%) Changes (%) Rates (%) Exchange Person Correlation 1 Rates Changes (%) Sig. (2-tailed) Interest Person Correlation -.545 1 Rates (%) Sig. (2-tailed) .129
45 Inflation Person Correlation .491 .273 1 Rates (%) Sig. (2-tailed) .180 .477 GDP Person Correlation .129 -.732* -.394 1 Growth Rates (%) Sig. (2-tailed) .741 .025 .294
4.6.2 Multiple Linear Regression A multiple linear regression analysis done between independent variables (interest rate, inflation rate and GDP Growth rate) and dependent variable (exchange rate). The results showed that it is positively correlated between independent variables and dependent variable (r = 0.885, p = 0.041). This implies that independent variables and dependent variable are positively correlated and there is significant correlation since P value is less than 0.05 as per table 4.13. An adjusted R Square value is 0.653 which implies 65.3% of the variation in exchange rate was caused by variations in interest rate, inflation rate and GDP Growth rate as shown in table 4.13. The significance had a P value of 0.041 < 0.05, therefore the relationship is statistically significant.
Table 4.13 Model Summary
Change Statistics Model R R Adjusted Std. Error R F df1 df2 Sig. F Square R Square of the Square Change Change Estimate Change 1 .885a .783 .653 4.97180 .783 6.012 3 5 .041 a. Predictors: (Constant), Interest Rates, Inflation Rates, GDP Growth Rates
An analysis has been done at 95% level of confidence, the F critical is 6.012 and the P value is 0.041. This analysis confirmed there is significant relationship between predictors and dependent variable as per table 4.14.
Table 4.14 ANOVA
Model Sum of Squares df Mean Square F Sig. 1 Regression 445.805 3 148.602 6.012 .041b Residual 123.594 5 24.719 Total 569.399 8
46 a. Dependent Variable: Exchange Rates Changes b. Predictors: (Constant), Interest Rates, Inflation Rates, GDP Growth Rates
The regression equation illustrated in Table 4.15 has established that taking all factors into account (interest rate, inflation rate and GDP growth rate) all other factors held constant exchange rate increase by 33.631. The findings presented that with all other variables held at zero, a unit change in interest rate would lead to a decline of 3.130 in exchange rate (Beta -0.951, p < 0.05), and a unit change in inflation rate would lead to positive 1.774 in exchange rate (Beta 0.624, p < 0.05), and a unit change in GDP Growth rate would result in 2.485 decline in exchange rate (Beta -0.321, p > 0.05).
Table 4.15 Coefficients of Correlation
95.0% Unstandardized Standardized Confidence Coefficients Coefficients Interval for B Lower Upper Model B Std. Error Beta t Sig. Bound Bound 1 (Constant) 33.631 24.027 1.400 .220 -28.13 95.394 Interest Rates -3.130 1.007 -.951 -3.107 .027 -5.719 -5.41 Inflation Rates 1.774 .645 .624 2.751 .040 .116 3.432 GDP Growth Rates -2.485 2.477 -.321 -1.003 .362 -8.852 3.882 a. Dependent Variable: Exchange Rates Changes
As per Table 4.15, the equation
푌 = 훽0 + 훽1푋1 + 훽2푋2 + 훽3푋3
Becomes, 푌 = 33.631 − 3.130푋1 + 1.774푋2 − 2.485푋3
Where by, Y = Exchange rates (%) - Dependent variable
푋1= Interest rates (%) - Independent variable
푋2= Inflation rates (%) - Independent variable
푋3= GDP growth rates (%) - Independent variable It has a statistically significant between interest rates, inflation rates and exchange rates (p < 0.05), but relationship between GDP growth rate and exchange rate is not statistically significant (p > 0.05) as per table 4.15.
47
4.7 Chapter Summary The chapter presented the results of major finding of the research based on three research questions on the impact of interest rates, inflation rates and GDP growth rates on exchange rates between Kenyan Shillings and Euro currency. The next chapter presents discussion, conclusions and recommendations.
48 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 based on the three research questions. Recommendations for policy practice on the relationship between interest rates, inflation rates, and GDP growth rates on exchange rates between Kenyan Shillings and Euro currency.
5.2 Summary The purpose of this research was to investigate the impact of macro-economic factors on exchange rates between Kenyan Shillings and Euro currency. The research was guided by the following research questions: How do interest rates influence on exchange rates between Kenyan shillings and Euro currency? What is the impact of inflation rates on exchange rates between Kenyan Shillings and Euro currency? Does Gross Domestic Product growth rates effect on exchange rates between Kenyan Shilling and Euro currency? This research used descriptive and explanatory research designs to investigate the cause and effect relationship between independent variables which are interest rates, inflation rates and Gross Domestic Product growth rates, and dependent variable as exchange rates. Specifically, the research used correlation and regression analysis to establish the influence of interest rates, inflation rates, and Gross Domestic Product growth rates on exchange rates between Kenyan Shilling and Euro currency.
The population included interest rates, inflation rates, GDP growth rates and exchange rates of the yearly average data period of 9 years from 2010 to 2018. In specific, population consisted of the actual data of exchange rates to interest rates, inflation rates, and GDP growth rates, thus the four variables had a total of 9 observations. The research used secondary data from Central Bank of Kenya, Kenya National Bureau of Statistics, and World Bank and data analysis proceed by IBM SPSS. A multiple linear regression model was used to analyze the relationship between three independent variables which are interest rates, inflation rates, and GDP growth rates and dependent variable as exchange rates between Kenyan Shillings and Euro currency. The major findings were presented in figures and tables.
49 On the influence of interest rates on exchange rates, the Pearson correlation results showed a negative relationship as r = -0.545, p = 0.129. This implies that exchange rates and interest rates are negatively correlated, however it is not statistically significant since the P value is greater than 0.05. This presents that exchange rates and interest rates has an inverse relationship with the exchange rates and vice versa. According to regression analysis, the adjusted R square value was 0.197 which implies 19.7% of the variation in exchange rates was caused by variations in interest rates. An ANOVA analysis and coefficient done at 95% confidence level and it has confirmed it is not statistically significant since the F critical is 2.959 and P value is 0.129.
On the impact of inflation rates on exchange rates, the Pearson correlation results indicated a positive relationship as r = 0.491, p = 0.180. This implies that exchange rates and inflation rates are positively correlated, however it is not statistically significant since the P value is greater than 0.05. This presents that exchange rates increases with every increase of inflation rate. A regression analysis showed that adjusted R square value was 0.132 which implies 13.2% of the variation in exchange rates was caused by variations in inflation rates. An ANOVA analysis and coefficient done by 95% confidence level and the F critical was 2.221 and the P value was 0.180, confirming that relationship between inflation rate and exchange rate is not statistically significant.
On the effect of Gross Domestic Products growth rates on exchange rates, the Pearson correlation results presented a positive relationship as r = 0.129, p = 0.741. This explains that exchange rates and GDP growth rates are positively correlated, however it is not statistically significant since the P value is greater than 0.05. According to regression analysis, the adjusted R square value was zero which implies zero variation in exchange rates was caused by variations in GDP growth rate. An ANOVA analysis and coefficient done by 95% confidence level and the F critical was 0.118 and the P value was 0.741, confirming that relationship between GDP growth rate and exchange rate is not statistically significant.
A multiple linear regression analysis done between independent variables (interest rate, inflation rate and GDP Growth rate) and dependent variable (exchange rate). The results showed that it is positively correlated between independent variables and dependent variable (r = 0.885, p = 0.041). This implies that independent variables and dependent
50 variable are positively correlated and there is significant correlation since P value is less than 0.05. The adjusted R square value of 0.653. This implied that 65.3% of the variation in exchange rate was caused by variations in interest rates, inflation rates, and GDP growth rates. Taking all factors into account interest rates, inflation rates and GDP growth rates reduced by 33.631 all other factors held constant. Multiple linear regression results showed a positive relationship between exchange rates and inflation rates and a negative relationship between exchange rates and interest rates, GDP growth rates. P value was 0.041 at the 0.05 significant level. Therefore, this relationship has significant effect of interest rates, inflation rates and GDP growth rates on exchange rates between Kenyan Shillings and Euro currency the period of 2010 to 2018.
5.3 Discussion 5.3.1 Influence of Interest Rates on Exchange Rates This research presents that interest rates has a negative effect on exchange rates (r = - 0.545, p = 0.129). This result implies that interest rates and exchange rates are negatively correlated however it is not statistically significant. Recently, there has been a special link between interest rates and exchange rates in both developed countries and developing countries. Mwangi and Ochieng (2017) found that the change of an interest rate can have both positive and negative influence on the world economy by influencing stock and bond market interest rate, business spending, inflation, and recessions. Sanchez (2008) analyze the relationship between interest rates and exchange rates using a simple model and the study found that there is correlation between interest rates and exchange rates, which is positive effect for contractionary ones and negative effect for expansionary depreciations. However, number of studies, Narayan and Smyth (2006), Bautista (2006) and Beng and Ying (2000) found a positive relationship between interest rates and exchange rates.
Kibiy and Tabitha (2016) investigated the factors determining exchange rates volatility of the Kenyan Shilling against world major currencies which are US Dollar, European Euro, and Japanese Yen. The research found a positive relationship between interest rate and all currencies as KES/USD, KES/EUR, and KES/JPY exchange rates. However, US Dollar and Euro exchange rates are not significantly correlated. This may due to an overlap with the other independent variables in the regression model. This findings confirmed the research of Benita and Lauterbach (2004) which indicated that there are positive correlations between interest rates and exchange rates. The research discovered that
51 countries with high exchange rates are maintain higher interest rates. Indeed, the research found a negative relationship between interest rates and exchange rates in Israel. The research concluded increasing of interest rates is efficient in restraining volatility of exchange rates. In addition, the research discovered that volatility of exchange rates and intervention of central banks are negatively correlated, which implies that intervention of central banks tends to moderate exchange rate fluctuation.
Ngugi (2001) examined the interest rates spread in Kenya and research found that increase of interest rate spread causes of high intermediation costs which includes information costs, transaction costs and operational costs. Interest rates has been increasing from 8.50% to 10.00% during 2013 to 2017 in Kenya. A higher interest rate may cause by inflation like the lender will demand a higher interest rate on what is borrowed, so as to make up for the lost value. Thus, inflation pushes interest rates higher.
A credit risk can also lead to higher interest rates that it refers to the risk that a borrower may not repay a loan. Most banks classify borrower as risky and most likely to default payments. Therefore, many banks increased their interest rate so that they can insure themselves from the effects of unpaid loans. In addition, operating costs are generally high in Kenya. This cost includes employee remuneration such as security costs and legal costs. Thus, the banks pass on this operational expense to the customer and hence the higher interest rates.
According to regression analysis exchange rates decreases with every increase in interest rates. This indicated that a unite change in interest rates would leads to a decrease in exchange rates. The relationship between interest rates and exchange rates has been a debatable issue in both theoretically and empirically. An interest rate policy is significant because it provides the appropriate information about exchange rate movement which indicate expectation of market and economy of the country. Therefore, stabilized of interest rates can reduce the exchange rates fluctuation and it will attract both local and foreign investors.
5.3.2 Impact of Inflation Rates on Exchange Rates This research presents that inflation rates has a positive effect on exchange rates (r = 0.491, p = 0.180). This result implies that inflation rates and exchange rates are positively
52 correlated however it is not statistically significant. Duarte and Stockman (2002) indicated that the low inflation rate will rise of exchange rate, as the purchasing power of the currency will increase as compared to the other currencies. In general, the inflation rate is used to measure the price stability in the economy. Theoretically, lower inflation rates tend to rise exchange rates, as the purchasing power of the currency will increase. Generally, higher inflation is higher interest rates in the economy hence, inflation may affect both interest rates and exchange rates. Abdurehman and Hacilar (2016) supported that inflation rates have most important factor for Turkish economy among various factors. However, the research shows that there is no relationship between inflation rates and TL/GBP exchange rates.
Twarowska and Kąkol (2014) analysed the factors affecting fluctuations in the exchange rate of Polish Zloty against Euro and established that inflation rates and financial account balance are most of the major factors to determine level of exchange rates. The research found a negative relationship between inflation rates and exchange rate between EUR/PLN exchange rates. The evidence from research, the fiscal and monetary policies are play a significant role in volatility of exchange rates. Hamid et al. (2016) investigate the impact of inflation rates on volatility of exchange rates in Pakistan using multiple linear regression model. The research found that there is a negative relationship between inflation rates and exchange rates.
On the other hands, Monfared and Akın (2017) examined a positive relationship between inflation rates and exchange rates as an increase of inflation leads increase of exchange rates in Iran. This implies inflation rates has a direct effect on exchange rates thus, a unit increase in inflation increase of exchange rates. Kibiy and Tabitha (2016) also found a positive relationship between inflations rates and KES/USD and KES/EUR exchange rates however, it is not to be statistically significant. Nevertheless, KES/JPY exchange rates has a significant relationship with inflation rates thus, increase in inflation would lead to an increase in volatility while a reduction in inflation in the country would lead to a fall in the Japanese Yen volatility.
A country with a lower inflation rate exhibits a rising currency value, as its purchasing power increase relative to other currencies. Contrarily, a country with a higher inflation
53 leads depreciation in their currency that usually accompanied by higher interest rates. Inflation can be measured by the change in a price index such as consumer price index, wholesale price index, and implicit price index (Semuel & Nurina, 2015). Consumer price index is important for federal government where determines the economic policies and it is able to compare cost of living between countries which reflects exchange rate differences.
The results of regression analysis exchange rates increase with every increase in inflation rates. This implies that a unite change in inflation rates would leads to an increase in exchange rates. The relationship between inflation rates and exchange rates has been a debatable issue in both theoretically and empirically. The policy is key to play, and it is significant because it provides the appropriate information about exchange rate movement which indicate expectation of market and economy of the country. Therefore, stabilized of inflation can reduce the exchange rates fluctuation and it will attract both local and foreign investors.
5.3.3 Effect of Gross Domestic Product Growth Rates on Exchange Rates This research presents that GDP growth rates has a positive effect on exchange rates (r = 0.129, p = 0.741). This result implies that GPD growth rates and exchange rates are positively correlated however it is not statistically significant. One of factor affecting exchange rates is a GDP. An increase of GDP will increase the supply of foreign countries, and it causes the depreciation of domestic currency. The relationship between exchange rate and economic growth has received considerable attention, however still contentious.
Hamid et al. (2016) investigated the impact of GDP on the exchange rate volatility in Pakistan and it shows GDP has a positive effect on exchange rates and generally noteworthy. Semuel and Nurina (2015) analyzed the effect of exchange rates on GDP in Indonesia and the research indicated that exchange rates and GDP have a statistically positive significant. This means that increase of GDP leads to increase of exchange rates.
However, Dollar (1992) explored the relationship between volatility of exchange rate and economic growth with 95 developing countries over the period of 1976 – 1985 and come
54 up with a negative relationship between the two variables. Schnabl (2009) focused the negative impact on volatility of exchange rate on economic growth on some of European and Asian countries specifically. Recently, Vieira and MacDonald (2016) proved the existence of negative impact of volatility of exchange rate on long-run economic growth by analyzing 82 countries between 1970 to 2009. Bleaney and Greenaway (2001) indicated the influence of volatility of exchange rate on economic growth in 14 Sub- Saharan African countries over the period of 1980 – 1995. The research found the negative impact on investment not on economic growth.
GDP can affect by natural resources, human resources, technology and capital goods and it has direct impact on the value of goods and services which have been supplied. GDP growth rate also determine exchange rates, employment rates, investments, public expenditure and so on. However, it has been challenged to measure GDP from unclear information and other political issues and policies are important for measure of GDP. Anyanwu (2014) mentioned that the main focus of policies is to maintain sustainable and strong economic growth in developing countries, and it has been investigated specific determinants of economic growth since generating sustained economic growth in Africa remains one of the most pressing challenges to develop.
Collier (2007) indicated that it has challenges to grow in developing countries, because of economic traps which includes conflicts between tribes, lack of natural resources, lack of rules, regulation and law, corruptions, and lack of infrastructure. This is why it is difficult to improve economics in developing countries, and it incur a high level of volatility of exchange rates. This also implies that the number of investors and international trades will decrease because of uncertainty returns. Upreti (2015) expressed the higher economic growth rates were strongly correlated with higher export growth rates and the research discovered the positive correlation on economic growth and exports in low- and middle-income countries.
The results of regression analysis exchange rates increase with every increase in GDP growth rates. This implies that a unite change in GDP growth rates would leads to an increase in exchange rates. The relationship between GDP growth rates and exchange rates has been a debatable issue in both theoretically and empirically. All measurement and information of GDP is significant because it provides the appropriate information
55 about exchange rate movement which indicate expectation of market and economy of the country. Therefore, stabilized of economy can reduce the exchange rates fluctuation and it will attract both local and foreign investors and increase of international trade flow.
5.4 Conclusions 5.4.1 Influence of Interest Rates on Exchange Rates Results from the research established that exchange rates were negatively influenced by interest rates. Specifically, interest rates and exchange rates have an inverse relationship as a higher interest rate leads decreases the level of exchange rate. However, number of studies found a positive relationship between interest rates and exchange rates. It can explain by different economies and regulations. Managing inflation, credit risk, operational cost and central bank intervention can stabilize the interest rates. Therefore, level of volatility of exchange rates will decline and it attract both local and foreign investors to develop Kenyan economy.
5.4.2 Impact of Inflation Rates on Exchange Rates Inflation occurs when an economy grows due to increased spending. Prices rise and the currency within the economy is worth less than it was before. This implies that the currency essentially won’t buy as much as it would before, and exchange rate weakens compared to other currencies. According to results, exchange rates were positively affected by inflation rates. Other studies found both positive and negative relationship between inflation rate and exchange rate. Moreover, a few studies found no relationship between two variables. It is possible to have a different result, because of country differences and level of economy. However, the majority of studies found a similar result as this research.
5.4.3 Effect of Gross Domestic Product Growth Rates on Exchange Rates The research identified that GDP growth rates has positive impact on exchange rates. This result is contrasting with other researches which has a negative relationship between GDP and exchange rates. This can occur, because of country differences such as productivity of factors of production, manufacturing, and other variations. Especially, developing countries should have an appropriate infrastructure, regulation and law to develop an economy. This may lead stabilized exchange rates and it may also attract both local and foreign investors and increase of international trade flow. The countries should
56 eliminate barriers and challenges which gives limitation of trade and economic growth. Moreover, the government and authorities should provide a clear and manageable regulation to support Kenyan economy.
5.5 Recommendations 5.5.1 Recommendations for Improvement 5.5.1.1 Influence of Interest Rates on Exchange Rates The research recommends the Central Bank of Kenya and authorities need to have a clear and appropriate regulation for interest rates policies to reduce the level of fluctuation. Moreover, the research also recommends that the government should seek to minimize some variables which incur interest rates fluctuation. A lower Central Bank Rate should be adopted in order to reduce the volatility of exchange rates and hence it improves stability of exchange rates.
5.5.1.2 Impact of Inflation Rates on Exchange Rates Inflation should also be controlled by use of sound and effective monetary policies. The research recommends that to harmonize monetary and fiscal policies to other economic polices such as investment or trade policy, can reduce inflation and boost up economic growth of the country. The increase of money supply and production of goods and services can also rise up value of the economy. In addition, the Central Bank of Kenya and the government should implement suitable policies to control and managing inflation rates and consumer prices. A monetary policy is a key to define inflation rates and level of exchange rate volatility.
5.5.1.3 Effect of Gross Domestic Product Growth Rates on Exchange Rates GDP reflects whole economic of the country thus, it is having a significant effect on exchange rates extend to international trade and investment. The government need to provide a clear infrastructure and regulation which can improve Kenyan economic. The regulation should be appropriate in Kenyan economic and it should be able to implement in real life. Moreover, Kenya should improve the productivity of domestic production and increase level of manufacturing process and subsidy for export. This will help to grow in term of balance of international trade and provide higher revenue.
57
5.5.2 Recommendations for Further Studies This research focused on the relationship between selected macro-economic variables – interest rates, inflation rates, GDP growth rates – and exchange rates between Kenyan Shillings and Euro currency. The scope was limited to three dependent variables, thus further research can capture any additional macro-economic variables such as balance of trade, external debt and so on. A further research can also focus on the other currency such as Japanese Yen, Chinese Yuan, or any other currency transacted with Kenyan Shilling.
58 REFERENCES Abbott, M. L., & McKinney, J. (2013). Understanding and Applying Research Design (1st Edition). New Jersey: John Wiley & Sons, Incorporated. Abdurehman, A. A., & Hacilar, S. (2016). The relationship between exchange rate and inflation: The case of Western Balkans Countries. International Journal of Economics and Financial Issues, 6(4), 1454–1459. Abor, J. (2005). Managing foreign exchange risk among Ghanaian firms. Journal of Risk Finance, 6(4), 306–318. Abou-Ali, H., & Kheir-El-Din, H. (2009). Inflation and Growth in Egypt: Is There a Threshold Effect? Middle East Development Journal, 1(1), 59–78. Adewuyi, A. O., & Akpokodje, G. (2013). Exchange Rate Volatility and Economic Activities of Africa’s Sub-Groups. The International Trade Journal, 27(4), 349–384. Ahmad, S. (2010). The long-run Fisher effect in developing economies. Studies in Economics and Finance, 27(4), 268–275. Alam, S., Butt, M. S., Iqbal, A., & Bhatti, R. H. (2002). The Long-run Relationship between Real Exchange Rate and Real Interest Rate in Asian Countries. Pakistan Institute of Development Economics, Islamabad The, 40(4), 577–602. Aliber, R. Z. (1973). The Interest Rate Parity Theorem : A Reinterpretation. Journal of Political Economy, 81(6), 1451–1459. Allayannis, G., & Ofek, E. (2001). Exchange rate exposure, hedging, and the use of foreign currency derivatives. Journal of International Money and Finance, 20(2), 273–296. Alquist, R., & Chinn, M. D. (2002). Productivity and the Euro-Dollar Exchange Rate puzzle. National Bureau of Economic Research (NBER) Working paper No.8824 Anyanwu, J. C. (2014). Factors Affecting Economic Growth in Africa : Are There any Lessons from China? African Development Review, 26(3), 468–493. Arratibel, O., Furceri, D., Martin, R., & Zdzienicka, A. (2011). The effect of nominal exchange rate volatility on real macroeconomic performance in the CEE countries. Economic Systems, 35(2), 261–277. Bahmani-Oskooee, M., Chang, T., & Wu, T. (2014). Purchasing power parity in emerging markets: A panel stationary test with both sharp and smooth breaks. Applied Financial Economics, 24(22), 1429–1438. Barbosa, L. O. S., Jayme, F. G., & Missio, F. J. (2018). Managing real exchange rate for economic growth: Empirical evidences from developing countries. Journal of Post
59 Keynesian Economics, 1–22. Barguellil, A., Ben-Salha, O., & Zmami, M. (2018). Exchange Rate Volatility and Economic Growth. Journal of Economic Integration, 33(2), 1302–1336. Barro, R. J. (1996). Determinants of economic growth: A cross-country empirical study (No. 5698). NBER Working paper series. Bautista, C. C. (2006). The exchange rate-interest differential relationship in six East Asian countries. Economics Letters, 92(1), 137–142. Baxter, M., & Stockman, A. C. (1989). Business cycles and the exchange-rate regime. Some international evidence. Journal of Monetary Economics, 23(3), 377–400. Belke, A., Geisslreither, K., & Gros, D. (2004). On the relationship between exchange rates and interest rates: Evidence from the Southern Cone. Cuadernos de Economía, 41, 35–64. Benazić, M. (2013). Testing the fisher effect in Croatia: An empirical investigation. Economic Research-Ekonomska Istrazivanja, 26(1), 83–102. Beng, G. W., & Ying, S. L. (2000). Exchange rate and interest rate differential: The case of the Malaysian Ringgit/US Dollar. Applied Economics Letters, 7(2), 95–97. Benita, G., & Lauterbach, B. (2004). Policy Factors And Exchange Rate Volatility: Panel Data Versus A Specific Country Analysis. International Research Journal of Finance and Economics, 1–32. Benos, N. (2009). Fiscal policy and economic growth: empirical evidence from EU countries. MPRA Paper No. 19174, University of Ioannina Bista, R., & Tomasik, R. (2018). Time zones, GDP & exports. Applied Economics Letters, 1–5. Black, K. (2010). Business Statistics: Contemporary Decision Making (6th Edition). John Wiley & Sons. Blaug, M. (2015). Barber’s The works of Irving Fisher. A Research Annual Barber’s, 5(33), 193–200. Bleaney, M., & Greenaway, D. (2001). The impact of terms of trade and real exchange rate volatility on investment and growth in sub-Saharan Africa. Journal of Development Economics, 65(2), 491–500. Bleaney, M., & Nishiyama, A. (2002). Explaining growth: A contest between models. Journal of Economic Growth, 7(1), 43–56. Boldeanu, F. T., & Constantinescu, L. (2015). The main determinants affecting economic growth. Bulletin of the Transilvania University of Braşov (Vol. 8).
60 Borensztein, E., & Gregorio, J. D. (1999). Devaluation and Inflation after Currency crises. Unpulished manuscript. Bryman, A., & Bell, E. (2015). Business Research Method (4th Edition). Oxford University Press. Campa, J. M., & González, M. J. M. (2006). Differences in exchange rate pass-through in the euro area. European Economic Review, 50(1), 121–145. Cellini, R., & Cuccia, T. (2014). Seasonal processes in the Euro-US Dollar daily exchange rate. Applied Financial Economics, 24(3), 161–174. Chang, T., & Tzeng, H. W. (2013). Purchasing power parity in nine transition countries: Panel surkss test. International Journal of Finance & Economics Economics, 18(2), 74–81. Chen, M., & Hu, X. (2018). Linkage between consumer price index and purchasing power parity: Theoretic and empirical study. Journal of International Trade and Economic Development, 27(7), 729–760. Chowdhury, I. (2007). Purchasing power parity and the real exchange rate in Bangladesh: A nonlinear analysis. Journal of the Asia Pacific Economy, 12(1), 61–75. Clostermann, J., & Schnatz, B. (2000). The determinants of the euro-dollar exchange rate. Deutsche Bundesbank. Frankfurt am Main. Collier, P. (2007). The Bottom Billion et altry. Newsletter university of Nottingham. Comunale, M., & Kunovac, D. (2017). Exchange rate pass-through in the euro area Task. European Central Bank Wokring Paper Series No. 2003. Cooper, D. R., & Schindler, P. S. (2008). Business Research Methods (10th Edition). MA: McGraw-Hill. Dash, P. (2014). The Relationship between Interest Rate and Exchange Rate in India. Journal of Emerging Issues in Economics, Finance and Banking (JEIEFB), 3(1), 1– 28. Dekle, R., Hsiao, C., & Wang, S. (2002). High interest rates and exchange rate stabilization in Korea, Malaysia, and Thailand: An empirical investigation of the traditional and revisionist views. Review of International Economics, 10(1), 64–78. Demir, F. (2010). Exchange Rate Volatility and Employment Growth in Developing Countries: Evidence from Turkey. World Development, 38(8), 1127–1140. Detken, C., & Hartmann, P. (2000). The Euro and International Capital Markets. International Finance, 3(1), 53–94. Devereux, M. B., & Yetman, J. (2002). Price-Setting and Exchange Rate Pass-Through:
61 Theory and Evidence. Hong Kong Institute for Monetary Research. Devereux, M. B., & Yetman, J. (2010). Price adjustment and exchange rate pass-through. Journal of International Money and Finance, 29(1), 181–200. Diewert, W. E. (2001). The Consumer Price Index and index number purpose. Journal of Economic and Social Measurement, 27, 167–248. Doğanlar, M. (2002). Estimating the impact of exchange rate volatility on exports: Evidence from Asian countries. Applied Economics Letters, 9(13), 859–863. Dollar, D. (1992). Outward-oriented developing countries really do grow more rapidly. Evidence from 95 LDC, 1975-1985. Economic Development and Cultural Change, 40(3), 523–544. Duarte, M., & Stockman, A. C. (2002). Exchange Rate Pass-Through, Exchange Rate Volatility, and Exchange Rate Disconnect. Journal of Monetary Economics, 49(604), 941–946. Dutta, M. (2009). Chapter 3 Optimum Currency Areas: U.S. Dollar, Euro, and Asian Money. The Asian Economy and Asian Moeny (Vol. 287). Elsevier. Dutta, M. (2011). Chapter 5 The Euro and the European Central Bank (ECB): Theory of Optimum Currency Area Revisited. The United States of Europe: European Union and the Euro revolution, Revised Edition (Vol. 292). Emerald Group Publishing Ltd. Edwards, S., & Levy, Y. E. (2005). Flexible exchange rates as shock absorbers. European Economic Review, 49(8), 2079–2105. Fernandez, F. M., Osbat, C., & Schnatz, B. (2002). Determinants of the Euro Real Effective Exchange Rate: a Beer/Peer Approach. European Central Bank Working Paper No. 85. Fowowe, B. (2008). Financial liberalization policies and economic growth: Panel data evidence from Sub-Saharan Africa. African Development Review, 20(3), 549–574. Gali, J., & Gertler, M. (1999). Infation dynamics: A structural econometric analysis. Journal of Monetary Economics, 44, 195–222. Geza, P., & Vasilescu, L. G. (2011). Bretton Woods Fixed Exchange Rate System versus Floating Exchange Rate System. MPRA Paper No. 29932. Ghosh, S., & Gregoriou, A. (2008). The composition of government spending and growth: Is current or capital spending better? Oxford Economic Papers, 60(3), 484–516. Goldberg, L. S., & Koistad, C. D. (1994). Foreign direct investment, exchange rate variability and demand uncertainty (No. 4815). NBER Working paper series. Grimes, D. A., & Schulz, K. F. (2002). Descriptive studies: what they can and cannot do.
62 The Lancet, 359(9301), 145–149. Grube, B. T., & Subarna K. S. (2003). Effects of Exchange Rate Uncertainty on Mexican Foreign Trade. Multinational Business Review, 11(2), 3–16. Guarte, J. M., & Barrios, E. B. (2006). Estimation under purposive sampling. Communications in Statistics: Simulation and Computation, 35(2), 277–284. Hamid, M., Shahzad, A., Saqib, M. H., & Maqbool, B. (2016). Impact of inflation, interest rate and GDP on the exchange rate volatility in Pakistan. International Journal of Research in Management and Business, 2(4), 65–72. Hancock, D. R., & Algozzine, B. (2017). Doing Case Study Research: A Practical Guide for Beginning Researchers (3rd Edition). New York: Teachers College Press. Harrison, A. (1996). Openness and growth: A time-series, cross-country analysis for developing countries. Journal of Development Economics, 48(2), 419–447. Hilland, A., & Devadoss, S. (2013). Implications of Yuan/Dollar exchange rate for trade. Journal of International Trade Law and Policy, 12(3), 243–257. Hnatkovska, V., Lahiri, A., & Vegh, C. A. (2007). The Non-Monotonic Relationship between Interest Rates and Exchange Rates. University of Maryland, UCLA and NBER. Hox, J. J., & Boeije, H. R. (2005). Data Collection, Primary Versus Secondary. Utrecht, The Netherlands: Utrecht University. Hsing, Y. (2016). Determinants of the ZAR/USD exchange rate and policy implications: A simultaneous-equation model. Cogent Economics and Finance, 4(1), 1–7. Jašová, M., Moessner, R., & Takáts, E. (2016). Exchange rate pass-through: What has changed since the crisis. BIS Working Paper No. 583. Jiang, C., Bahmani-Oskooee, M., & Chang, T. (2015). Revisiting Purchasing Power Parity in OECD. Applied Economics, 47(40), 4323–4334. Kataranova, M. (2010). The Relationship Between the Exchange Rate and Inflation in Russia. Problems of Economic Transition, 53(3), 45–68. Katusiime, L., Agbola, F. W., & Shamsuddin, A. (2016). Exchange rate volatility– economic growth nexus in Uganda. Applied Economics, 48(26), 2428–2442. Kibiy, J., & Tabitha, N. (2016). Determinants of Exchange Rate Volatility on the Kenyan Shilling against world major currencies. International Journal of Social Sciences and Information Tehcnology, II(IX), 1181–1202. Kirui, E., Wawire, N. H. W., & Onono, P. O. (2014). Macroeconomic Variables, Volatility and Stock Market Returns: A Case of Nairobi Securities Exchange, Kenya.
63 International Journal of Economics and Finance, 6(8), 214–228. Koku, P. S., Caushi, A., Fetai, A., & Fetai, B. (2016). The relationship between exchange rate and inflation: The case of Western Balkans Countries. Nomics and Finance - JBEF (2016), Vol.5(4) Journal of Business, Economics and Finance, 5(4), 360–364. Kothari, C. R. (2004). Research Methodology: Methods and Techniques. London: New Age International. Leamer, E. E. (2009). Gross Domestic Product. In Macroeconomic Patternts and Stories (1st Edition, pp. 19–38). Springer-Verlag Berlin Heidelberg. Lee, C., & Boon, T. H. (2007). Macroeconomic factors of exchange rate volatility: Evidence from four neighbouring ASEAN economics. Studies in Economics and Finance, 24(4), 266–285. Lee, P. (2012). Balancing the three approaches to measuring Gross Domestic Product. London. Retrieved from http://www.ons.gov.uk/ons/dcp171766_273489.pdf Levich, R. M. (2013). Interest Rate Parity. The Evidence and Impact of Financial Globalization. Elsevier Inc. Levine, R., & Carkovic, M. (2001). How Much Bang for the Buck ? Mexico and Dollarization. Journal of Money, Credit and Banking, 33(2), 339–363. Lizardo, R. A., & Mollick, A. V. (2010). Oil price fluctuations and U.S. dollar exchange rates. Energy Economics, 32, 399–408. Loayza, N., & Soto, R. (2002). The Sources of Economic Growth: An Overview. Central Banking Analysis and Economic policies Book Series, 6(1), 1-40 Macdonald, R., & Nagayasu, J. (2000). The Long-Rung Relationship Between Real Exchange Rates and Real Interest Rate Differentials: A Panel Study. Palgrave Macmillan Journals on Behalf of the International Monetary Fund, 47(1), 116–128. Machlup, F. (1960). Another View of Cost-Push and Demand-Pull Inflation. The Review of Economics and Statistics, 42(2), 125–139. MacKenzie, D. W. (2015). Gross Domestic Product. (S. C. L. Cooper, Ed.) (Vol. 1). Wiley Encyclopedia of Management. Mankiw, N. G. (2014). Principles of Macroeconomics (7th Edition). Texas: Cengage Learning. Mohd, W., Abdoh, Y. M., Hafizha, N., Yusuf, M., Azreen, S., Zulkifli, M., … Ibrahim, J. (2016). Macroeconomic Factors That Influence Exchange Rate Fluctuation in ASEAN Countries. International Academic Research Journal of Social Science, 2(1), 89–94.
64 Monfared, S. S., & Akın, F. (2017). the Relationship Between Exchage Rates and Inflation: the Case of Iran. European Journal of Sustainable Development, 6(4), 329–340. Muchiri, M. (2017). Effect of Inflation and Interest Rates on Foreign Exchange Rates in Kenya. University of Nairobi. Mukras, M. S., Ochieng, O., & Gideon, M. (2016). The determinants of inflation in the Kenyan economy. International Journal of Economics, 1(1), 46–60. Musyoki, D., Pokhariyal, G. P., & Pundo, M. (2012). The impact of real exchange rate volatility on economic growth: Kenyan evidence. Research Journal of Finance and Accounting, 5(8), 110–121. Mwangi, P. M., & Ochieng, D. E. (2017). The effect of selected Macro-economic variables on Exchange rates in Kenya. African Development Finance Journal, 1(2), 162–177. Naghshpour, S. (2012). Statistics for Economics. New York: Business Expert Press. Narayan, P. K., & Smyth, R. (2006). The dynamic relationship between real exchange rates, real interest rates and foreign exchange reserves: Empirical evidence from China. Applied Financial Economics, 16(9), 639–651. Ngugi, R. W. (2001). An empirical analysis of interest rate spread in Kenya. African Economic Research Consortium. University of Nairobi. Nicholas, N. K. (2016). Factors affecting Kenya Shillings volatility against the United Stateds Dollar. University of Nairobi. Officer, L. H. (2007). An Improved Long-Run Consumer Price Index for the United States. Historical Methods: A Journal of Quantitative and Interdisciplinary History, 40(3), 135–148. Oiro, M. O. (2015). Real Exchange Rate Volatility and Exports in Kenya: 2005-2012. Journal of World Economic Research, 4(5), 115–131. Okoth, M. N. (2013). The effect of interest rate and inflation rate on exchange rates in Kenya. University of Nairobi. Otieno, B. A. (2013). Factor Influencing Real Exchange Rate and Export Sector Performance in Kenya. International Journal of Sciences: Basic and Applied Research. Ouyang, A. Y., & Rajan, R. S. (2016). Does Inflation Targeting in Asia Reduce Exchange Rate Volatility International Economic Journal, 30(2), 294–311. Patra, N. (2004). Long run relationship between real exchange rates and real interest rate
65 differentials: The cointegration approach. Dept. of Economics, Delhi School of Economics, University of Delhi, 2–39. Patterson, B., & Lygnerud, K. (1999). The Determination of Interest Rates (Economic Affairs Series No. ECON 116 EN). Pettinger, T. (2017). Inflation and Exchange Rates. Retrieved from https://www.economicshelp.org/blog/1605/economics/higher-inflation-and- exchange-rates/ Pino, G., Tas, D., & Sharma, S. C. (2016). An investigation of the effects of exchange rate volatility on exports in East Asia. Applied Economics, 48(26), 2397–2411. Raja, M. H., & Jaweed, K. (2014). Covered Interest Rate Parity. University Collage of Oslo and Akershus. Raji, J. O., Abdulkadir, R. I., & Badru, B. O. (2018). Dynamic relationship between Nigeria-US exchange rate and crude oil price. African Journal of Economic and Management Studies, 9(2), 213–230. Rodrik, D. (2010). Making Room for China in the World Economy. The American Economic Review, 100(2), 89–93. Rugg, G. (2007). Using Statistics: A Gentle Introduction. England: McGraw-Hill Education. Rutasitara, L. (2004). Exchange rate regimes and inflation in Tanzania. African Economic Research Consortium. Ryan, T. C. I., & Milne, W. J. (1994). Analysing Inflation in Developing Countries: An Econometric Study with Application to Kenya. The Journal of Development Studies, 31(1), 134–156. Sachs, J. D., & Warner, A. M. (1995). Natural resource abundance and economic growth. NBER Working paper series (Vol. 30). Samuelson, P. A., & Nordhaus, W. D. (2009). Economics (19th Edition). New York: McGraw-Hill/Irwin. Sanchez, M. (2008). The link between interest rates and exchange rates: Do contractionary depreciations make a difference International Economic Journal, 22(1), 43–61. Saundres, M., Lewis, P., & Thornhill, A. (2016). Research Methods for Business Students (7th Edition). England: Pearson Education Limited. Schmitz, A. (2010). Interest Rate Parity. In International Economics: Theory and Policy (pp. 821–850). 2012 Book Archive.
66 Schnabl, G. (2009). Exchange rate volatility and growth in emerging Europe and East Asia. Open Economies Review, 20(4), 565–587. Semuel, H., & Nurina, S. (2015). Analysis of the Effect of Inflation, Interest Rates, and Exchange Rates on Gross Domestic Product (GDP) in Indonesia. Proceedings of the International Conference on Global Business, Economics, Finance and Social Sciences, (February), 20–22. Servén, L. (2003). Real exchange rate uncertainty and private investment in LDCs. Review of Economics and Statistics, 85(1), 212–218. Sosvilla-Rivero, S., & Ramos-Herrera, M. C. (2014). Exchange-rate regimes and economic growth: An empirical evaluation. Applied Economics Letters, 21(12), 870–873. Sugut, H. C., Kiprop, S., & Kalrio, A. M. (2017). The effect of volatility on trade in the east african community countries (1995 – 2015). European Journal of Business and Social Sciences, 6(6), 145–159. Taiwo, O., & Adesola, O. A. (2013). Exchange rate volatility and bank performance in Nigeria. Asian Economic and Financial Review, 3(2), 178–185. Takhtamanova, Y. F. (2010). Understanding changes in exchange rate pass-through. Journal of Macroeconomics, 32(4), 1118–1130. Taylor, A. M., & Taylor, M. P. (2004). The Purchasing Power Parity Debate. Journal of Economic Perspectives, 18(4), 135–158. Taylor, J. B. (2000). Low inflation, pass-through, and the pricing power of firms. European Economic Review, 44(7), 1389–1408. Taylor, M. P. (1995). The Economics of Exchange Rates. American Economic Association The, 33(1), 13–47. Taylor, M. P. (2003). Purchasing power parit. Review of International Economics, 11(3), 436–452. Telatar, E., & Hasanov, M. (2009). Purchasing power parity in Central and Eastern European countries. Eastern European Economics ISSN:, 47(5), 25–41. Tharakan, J. (1999). Economic growth and exchange rate uncertainty. Applied Economics, 31(3), 347–358. Toledo-Pereyra, L. H. (2012). Research design. Journal of Investigative Surgery, 25(5), 279–280. Twarowska, K., & Kąkol, M. (2014). Analysis of Factors Affecting Fluctuations in the Exchange Rate of Polish Zloty Against Euro. In Human Capital without borders:
67 Knowledge and learning for quality of life (pp. 889–898). Tyers, R., Golley, J., Yongxiang, B., & Bain, I. (2008). China’s economic growth and its real exchange rate. China Economic Journal, 1(2), 123–145. Upreti, P. (2015). Factors Affecting Economic Growth in Developing Countries. Major Themes in Economics, 17(5), 37–54. Vdovichenko, A., Voronina, V., Dynnikova, O., & Subbotin, V. (2002). Inflation and exchange rate policy. Problems of Economic Transition, 47(5), 6–31. Vieira, F. V., & MacDonald, R. (2016). Exchange rate volatility and exports: A panel data analysis. Journal of Economic Studies Exchange, 43(2), 203–221. Vita, G. D., & Kyaw, K. S. (2011). Does the choice of exchange rate regime affect the economic growth of developing countries. The Journal of Developing Areas, 45(1), 135–153. Wenhao, L. (2004). Currency competition between Euro and US dollar. Working Paper of the Institute of Management Berlin at the Berlin School of Economics and Law (HWR Berlin) No. 18. Wynne, M. A., & Sigalla, F. D. (1994). The Consumer Price Index. Federal Reserve Bank of Dallas Economic Review, (June), 1–22. Zikmund, W. G., Babin, B. J., Carr, J. C., & Griffin, M. (2013). Business Research Methods. (9th Edition). South-western Cengage Learning.
68 APPENDIX I: DATA COLLECTION INSTRUMENT
Variables CBK KNBS World Bank
Exchange Rates [ o ] [ ] [ ]
Interest Rates [ o ] [ ] [ ]
Inflation Rates [ ] [ o ] [ ]
Gross Domestic [ ] [ ] [ o ] Product Growth Rates
Total 2 1 1
69 APPENDIX II: SECONDARY DATA COLLECTION
Exchange Rates Exchange Rates Interest Inflation GDP Growth Year (KES/EUR) Changes (%) Rates (%) Rates (%) Rates (%)
2009 107.56 - - - -
2010 104.91 (2.46) 6.42 3.96 8.41
2011 124.00 18.19 8.40 14.02 6.11
2012 109.11 (12.01) 15.75 9.38 4.56
2013 115.05 5.45 8.83 5.72 5.88
2014 116.28 1.07 8.50 6.88 5.36
2015 108.87 (6.37) 10.13 6.58 5.72
2016 112.06 2.92 10.63 6.30 5.87
2017 117.76 5.09 10.00 7.99 4.87
2018 119.35 1.36 9.27 5.04 5.97
70