IMPACT OF MONETARY POLICY ON ECONOMIC
GROWTH IN KENYA
Abstract……………………………………………………………………………2
CHAPTER ONE: Introduction…………………………………………………………….3
1.1 Background study………………………………………………………3
1.2 Overview of monetary policy………………………………………….4
1.3 GDP Trends in Kenya………………………………………………….5
1.4 Structural linkages of monetary policy…………………………………6
1.5 Statement of the problem………………………………………………7
1.6 Research Question…………………………………………………….7
1.7 Study objective…………………………………………………………8
1.8 Significance of the study………………………………………………8
CHAPTER TWO: Literature Review……………………………………………9
2.1. Introduction……………………………………………………………9
2.2 Theoretical Literature………………………………………………….9
2.2.1. Classical Quantity theory………………………………….10
2.2.2 Cambridge Quantity theory…………………………………12
2.2.3 Keynesian Theory………………………………………….12
2.2.4 Friedman Theory…………………………………………….14
2.3 Empirical Literature………………………………………………….16
CHAPTER 3: Methodology……………………………………………………19
3.1 Introduction……………………………………………………………19
3.2 Model specification……………………………………………………20
3.2.1 Theoretical Framework…………………………………………20
3.3.3 The empirical framework……………………………………….20
3.3 Data type, source and typology……………………………………….21
3.4 Estimation Procedures…………………………………………………23
3.5 Statistical tests…………………………………………………………24
3.5.1 Unit root and stationarity test…………………………………….24
3.5.2 Cointegrating test………………………………………………….24
3.5.3 Vector error correction model………………………………………24
CHAPTER FOUR: Data analysis and findings………………………………….27
4.1 Introduction………………………………………………………………27
4.2 Descriptive statistics………………………………………………………27
4.3 Statistical tests…………………………………………………………….28
4.3.1 Normality tests………………………………………………………28
4.3.2 Multicollinearlity……………………………………………………28
4.3.3 Stationarity………………………………………………………….29
4.3.4 ADF test results for variables……………………………………….30
4.3.5 Test for Cointegration……………………………………………….30
4.3.6 Vector auto-correction Model……………………………………….32
4.3.7 Granger causality……………………………………………………33
4.3.8 OLS Regression results…………………………………………….36
ABSTRACT
Monetary policy plays special roles in any developing country, and one of the important roles is to control the supply of money with the purpose of promoting economic growth and price stability. This study was undertaken to establish the impact of monetary policy on economic growth using an annual time series data and an OLS regression model obtain from World Bank, CBK and KNBS from the period 1980-2018. The paper found out that the variable rate of growth of exports was significant and had a positive relation to economic growth, whereas the ratio of investment was known to be positively related to economic growth but not significant. The policy implications of the study finding is that the Central bank of Kenya and the national government to address the challenges on variables such as inflation, interest rates and money supply so as to create a friendly macroeconomic environment.
Monetary policy is the macroeconomic policy laid down by the Central Bank’s actions in communication that involves management of money supply and interest rates. It can be described as the art of controlling the direction and movement of credit facility in the pursuance of stable price and economic growth.
The CBK formulates and conducts monetary policy with the aim of keeping overall inflation within the allowable margin (currently 2.5%) on either side of the target prescribed by the National Treasury after the annual Budget Policy Statement. The achievement and maintenance of a low and stable inflation is achieved and maintained results to the facilitation of high levels of domestic saving and private investments, which lead to improved economic growth, higher real incomes and increased employment opportunities.
Enhanced technology and capital formation are necessities in economic development. Various methods are embraced to occur include; price stabilization, mobilization of savings, full employment and equilibrium in balance of payments.
Implementation of monetary policy focuses on instruments, operating targets and policy goals. The Central Bank controls these instruments which includes; interest charges on reserves borrowed from the Central Bank, the reserve requirement ratio that determines the level of reserve bank must hold against their deposit liabilities and the composition of the Central Bank’s own balance sheet.
In Kenya, the general objectives of the monetary policy are inflation and output. These variables are influenced by the CBK by indirectly using interest rates and reserve money. The CBK uses various monetary tools such as; open market operations, Central Bank rate, overnight lending, required reserves, foreign market operations, licensing and supervision of commercial banks and communication of banks decisions.
The CBK uses contractionary monetary policy to reduce inflation. The CBK decreases the money supply by either increase in the discount rate, sale of government bonds, increase in the reserve ratio or restricting the amount of money banks can lend. Therefore, the banks end up charging a higher interest rate, making loans more expensive. However, contractionary monetary policy has some side effects, which results in an increase in the unemployment rate and a decrease in the growth rate of the GDP.
The CBK also uses expansionary monetary policy to increase money supply, lowering interest tares and to increase aggregate demand so as stimulate the economy. It increases liquidity by giving banks more money to lend. Therefore, banks lower interest rates, making loans cheaper. This increases demand and spurs economic growth.
In the short-run, effects of monetary policy are vast where academic counterparts view these effects as rather tame and insignificant. However, other business economies and economic policymakers believe that monetary authority can use its policy tools repetitively without fear of them losing effectiveness in the short-run.
In the long- run, according to (Rasche and Williams, 2005) documents suggest that most Central Banks face several challenges in implementing short- run stabilization policy. They include; inadequate and reliable information about the economy, inability to precisely forecast the future path of the economy and insufficient, credible information about how policy actions affect the economy. According to (Waliullah and Rabbi, 2011) discovered a reliable and stable long run relationship amongst money supply income and price level resulting to a strong correlation between monetary policy and real economic variables, unfortunately in the short-run monetary policy is relatively effective.
According to recent studies, it has been recorded that the goals of monetary policy have developed with the evolution of central banking and lead to changes in both behavior and performance in the economy.
Over the last 50 years, the GDP of Kenya grew substantially from 14.35 to 87.93 billion US dollars, rising at an increasing annual rate that reached a maximum of 23.74 % in 2007 and then decreased to 11.74 % in 2018, according to figure 1.3.1. However, a sharp rise of Gross Domestic Product was experienced from 1969 to 1972 which later fell significantly. There also have been instances where GDP has risen significantly (as in 2007 and 2011).
In previous studies, structural inter-linkages of the monetary policy framework captures the interaction between various sectors of the economy, which include; government sector, domestic demand and supply and external sector.
Domestic prices are determined by consumer prices (tradable and non-tradable), domestic interest rates and exchange rates. The interaction of money demand and money supply determines domestic interest rates, whereas excess money demand acts as one of the triggers of monetary policy actions.
A study done by, (Kamau, Sichei, Kiptui, & Were, 2013) states that the CBK follows the monetary targeting framework where it controls quantities to affect the prices in the economy. Reserve money is the operating target and is under the CBK’s control.
Broad money supply is the intermediate target and is related to reserve money through the money multiplier. Broad money supply is used because it is perceived to have all the instruments used to influence policy. Domestic interest rates are also affected by actions on the physical side. Money demand is determined by aggregate demand, price level, exchange rates and interest rates.
The nominal exchange rate is determined in the foreign exchange market by the differential between domestic and international interest rates and prices. The real exchange rate has an impact on the exports and imports of goods and services in the external sector.
The purpose of this research paper is to assess the impact of the monetary policy on Kenya’s economic growth. Kenya`s monetary policy has progressed due to internal and macro-economic challenges encountered ultimately. The CBK takes awareness of structural economic interlinkages in the economy to effectively implement its task of formulating and conducting monetary policy.
Kenya’s vision 2030 shows the macroeconomic framework that was planned to move the economy up the value chain. To maintain long term economic and social success, the CBK aims at maintaining and ensuring low and stable inflation.
Demand for money has being recorded to be unstable through previous studies, which implies that the CBK doesn’t have control of the money supply process. However, the economy has been recording low economic growth and high rates of inflation despite the implementation of the monetary policy aimed at achieving stable prices and nurturing economic growth. It indicates the inadequate efficiency of the framework denotes that the public cannot make rational decisions on communications from CBK. Therefore, this study investigates how monetary policy influences economic growth.
The study sought answers to the following questions;
The general objective of this study is to provide a better understanding of the impacts of monetary policy on economic growth. Specifically, the study’s objectives will be to:
The significance of this study is to inform the researchers on how monetary policy can affect a country’s economic growth in the Kenyan market. It also suggests areas for further research so that the future scholars can pick up these areas and study further.
Banks and other financial institutions also gain from this study as it helps banks determine the likely impact of monetary policy committee decisions on market interest rate and improve the certainty of interest rate and also for the Central Bank to control inflation.
In Kenya`s economy, the monetary policy framework is an essential tool of macroeconomic management. It is important for policymakers because it enables them to identify the appropriate monetary policy instruments for any monetary action. Furthermore, it contributes to the debate on the relevance of monetary policy as presented in Kenya`s vision 2030.
In chapter one we have outlayed a brief background of how monetary policy has been over the past years then later demonstrated the GDP Trend from the year 1963-2018. Later, the objectives of the study was started, and there came the research question then finally significance of the study.
In chapter two, it elaborates and diverses on the literature review whereby it surveys scholarly articles, books and other sources that are relevant to this particular study. The chapter will also explain the theoretical and empirical literature on the impact of monetary policy on economic growth.
A literature review is a comprehensive summary of previous research on a topic. The literature review surveys scholarly articles, books and other sources relevant to a particular area of research. The review should enumerate, describe, summarize, objectively evaluate and clarify previous research. A literature review creates a landscape for the reader, giving him or her a full understanding of the developments in the field.
In this chapter, both theoretical and empirical literature on the impact of monetary policy on economic growth and are reviewed. The first section reviews the theory and exposes the theoretical foundations that underlie the effects of monetary policy on economic growth and prices. The theoretical representations of the models are described. The second section reviews studies carried out on the subject, and the final section deals with the critique of the literature.
The classical monetary is the first renowned theory of monetary policy and is enshrined in the Irving Fisher TQM, which lays the foundation for the link between monetary policy (money) and economic variables. In this theory, both velocity of money and output are assumed as constant, thus any increase in the quantity of money will only eventually increase prices proportionally in accordance with the quantity theory.
Theoretically, the conduct of monetary policy is well explained by the quantity theory of money. The theory lays down the foundation upon which the monetary policy is to be implemented as proposed by the classical economists. This study limits its focus on competing theories that explain the conduct of monetary policy.
In monetarist view it is unanticipated that change in money supply affects output and growth, suggesting that for CBK to accelerate growth in the economy, it is imperative that money supply is unpredictably increased.
In the Keynesian view, monetary policy is observed through the liquidity trap concept. The liquidity trap concept establishes that, if real interest rates fall to such a low level, an increase in money supply doesn’t hasten output and growth. Keynesian supporters established an indirect relationship between money supply and GDP.
Various studies have recorded monetary policy to have an impact on output in that: the increase in money supply leads to increased inflation while high inflation leads to increased interest rates. Unfortunately, high interest rates affect the investment function leading to a decline in investment activities, which furthermore leads to low capital formation, which results in low output.
Monetarist proponents agree with Keynesian that rising money supply will increase inflation. However, they recommend that the policy must accommodate the increase in real GDP without changing price level.
The theory was first developed by Jean Bodin in 1958 but refined later by Irving Fisher in 1911 (Jhingan, 1997). The classical economists treated money as a medium of exchange that is; people hold money only for transaction purposes. The quantity theory of money provides a very simple way to organize the thinking about the relation between money, prices and output. The classical quantity theory is the proposition that the price level is proportional to the money stock. All versions of the quantity theory of money demonstrates that there is a strong nexus between money and the price level. The theory seeks to establish an exact relationship between money and price ceteris paribus. Fisher’s identity was defined as;
MV = PT……………………………………………………………………………………………………. (2.1) Where ‘M’ represents the quantity of money in circulation, ‘V’ is the number of times a unit of money is used in transaction per unit of time, ‘P’ is the weighted average of all individual prices and p = MV, while ‘T’ is the sum of all transactions of T goods and services per unit of time.
Fisher’s quantity theory of money faced some criticism from Keynes. Some of the criticisms include; lack of theoretical value, constant velocity, truism, unrealistic assumption, neglect of the asset function and store of value function, multiplication of two non-compatible factors (M and V), lack of an explanation on how change in ‘M’ changes’ P’, and finally it is a static theory based on assumptions (Jhingan, 1997). The identity was later converted into a theory and specified as;
MV = PY …………………………………………….……………………………………… (2.2)
Where ‘Y’ is the physical output which is the same as real income. The income version of the quantity theory makes two significant changes to equation (1) as it converts it into a theory, and by substituting ‘T’ for ‘Y’, it brings real output in relation to money supply. In classical theory, ‘Y’ is the function of employment, thus the income version of the quantity theory integrates the classical theory of output and employment with the theory of money. Equation (2) implies that a rise in money ‘Y’ remaining constant results in a proportionate rise in prices. An increase in money expenditure while income is held constant implies aggregate demand increase while supply is fixed leading to rise in prices. Similarly, when money supply is reduced spending capacity of the people is reduced, leading to a proportionate fall in the prices (Dwivedi, 2010).
The Cambridge version or neoclassical or cash balance approach quantity theory of money was first developed by Alfred Marshall in 1917 (Dwivedi, 2010). According to neoclassical economists, individuals hold money for transaction purposes, though some are also held for security and for meeting the unexpected obligations (Dwivedi, 2010). Neoclassical economists hypothesized that income earners strike a balance between the convenience and security that money provides and the loss of income resulting from money holding (Dwivedi, 2010).
The hypothesis is stated as;
Md = kPQ ……………………………………………………………………………………………………………. (2.3)
Where ‘Md` is the demand for money, ‘P’ is the price, ‘Q’ is the real income and ‘k’ is the proportion of income held as currency and bank deposits. Unlike Fisher’s quantity theory of money, the neoclassical monetary theory links prices to the demand for money and not the supply of money since idle cash balance does not in reality create demand and affect prices. Several economists, including Don Patinkin and J.M. Keynes criticized the cash balance approach of neglecting the real balance effects that is integrating the commodity and money market, assumption of unity inelastic demand for money, neglect of speculative demand for money and neglect of interest rate (Jhingan, 1997).
Keynes (1936) studied both transaction and asset theories of money demand. Keynes extended Cambridge theory to include holding bonds and securities as an alternative to holding idle cash balance as an asset. Keynes theory links the demand for money to the variations in the interest thus introducing speculative demand for money that arises due to uncertainty about interest fluctuations. Thus, the money people hold to buy bonds in the future expecting bond prices to go down is speculative demand for money. Therefore, the theory distinguished three motives of holding money which are the transaction motive, precautionary motive and finally speculative motive (Dwivedi, 2010).
Keynes argued that, wages and prices may not be flexible, income rather than interest rate may determine savings and if the speculative demand for money (liquidity preference schedule) is infinitely elastic with respect to changes in interest rates (the liquidity trap), and then no extra investment would end up in unemployment equilibrium.
Since both transaction and precautionary demand for money are a function of the income, Keynes lumped them and expressed them as:
M I + M p = M T………………………………………………………………………………. (2.4)
Thus, total transaction demand for money can be expressed as;
M T = f (Y) = Ky……………………………………………………………………………… (2.5)
According to Keynes, the aggregate demand for money consists of two components;
Thus, aggregate demand for money (Md) is expressed as;
M d = M T + Msp ……………………………………………………………………………. (2.6)
Since MT = KY and Msp = f (i), given the income and interest rate, the Keynesian aggregate money demand function is expressed as
M d = kY + f ………………………………………………………………………………… (2.7)
Keynes criticized the assumption of constant velocity as proposed by the classical economists. Keynes view was that velocity is affected by behavioral economic variables and, most importantly, by the nominal interest rate. Keynes theory states that demand for money is negatively related to nominal interest, which is a significant departure from the classical quantity theory of money but, less departure from the Cambridge approach which did not rule out such a relationship (Dwivedi, 2010).
Friedman (1956), in his restatement of the quantity theory of money, did not specify any particular motives for holding money. Friedman viewed money as a monetary asset yielding a flow of nonobservable which enters as argument in aggregator functions such as utility and production functions. Friedman assumed that money competes with other assets such as bonds, stocks and physical goods for a place in individuals and business firms’ portfolios. He further states that the marginal utility of monetary services declines as quantity held increases. It’s defined as;
M d P= <l> V’p, Rh – Rm, Re – Rm, Tre – Rm……………………………………………………………….. (2.8)
Where ‘Y;’, is the real permanent income, ‘Rj,’ is the expected nominal rate of return on bonds; ‘Re’ is the expected nominal rate of return on equities, while ‘R m’ is the expected rate of return on money and; r’ is the expected inflation rate. By using the theory of portfolio choice, Friedman argues that the demand for money depends on permanent income and the incentives for holding other assets relative to money. In contrast to Keynes, Friedman states that demand for money is stable and insensitive to interest rates. The implication of this is that velocity is predictable, yielding the quantity theory conclusion that money is the primary. determinant of nominal aggregate spending (Dwivedi, 2010).
The New Keynesian Approach to monetary policy analysis has emerged in recent years as one of the most influential and prolific areas of research in macroeconomics (Gali, 2007). The approach has become the basis for the new generation of models being developed by central banks and increasingly used for simulation and forecasting purposes. Among the key defining features of the New Keynesian approach to monetary policy is that it adopts many of the tools originally associated with RBC theory, including the systematic use of dynamic stochastic general equilibrium (DSGE) models based on optimizing behavior by households and firms, rational expectations and market clearing (Gali, 2007). In this new approach, firms are modelled as monopolistic competitors while nominal rigidities are a key element of the model and a main source of monetary policy non neutrality. Emphasis is also given to the endogenous component of monetary policy (i.e., monetary policy rules) and the consequences of alternative specifications of that component, rather than to the effects of exogenous changes in a monetary policy instrument (Gali, 2007). An important characteristic of the New Keynesian framework generally, lies in its proven flexibility to accommodate a large number of extensions to the basic model, including those incorporating open economy features, imperfect information, unemployment and credit frictions (Gali, 2007).
Several studies about the relationship between economic growth and money supply, interest rates and inflation have been undertaken, with the results showing mixed outcomes. Monetary policies affect the growth of GDP to the extent that they affect the quantity of productivity of capital and labor. The studies below have recorded the empirical literature on how monetary policy has had an impact on the economic growth;
Ndung’u (1999) assessed whether the exchange rate is affected by monetary policy in Kenya. The study employed co-integration analysis on quarterly time series data covering the period 1970 to 1995. The study further investigated whether the monetary effects are permanent or transitory. The study established that excess money supply fed into the cyclical movements of the real exchange rate, implying that monetary shocks affect the real exchange rate. In addition, the study revealed that growth in money supply as end inflation depreciates nominal exchange rate. Further analysis established that the nominal exchange rate is determined by the real income growth, rate of inflation, money supply growth, cycles in the real exchange rate movements, the co-integrating factors and shocks.
Christensen (2004) employed a cross country survey of the role of money supply market in sub – Saharan African based on a new data set of 27 sub Saharan African countries during the 20 years period (1980-2000), the study findings reveals that money supply markets in these countries are generally small, highly short term and often have a narrow investor base. Also, in those countries‟ interest rate payment presents a significant burden to the budget, despite much smaller money supply than foreign indebtedness. Further still, it was revealed that the use of money supply is also found to have significant crowding-out effect on private investment.
Abbas and Christensen (2007), in their recent study analyzed optimal money supply levels in the low-income countries (including 40 sub-Saharan African Countries Kenya included) and emerging market between 1975 and 2004. They found out that moderate levels of marketable money supply as a percentage of GDP have significant positive effects on the gross domestic product. The study also provided evidence that the debt levels exceeding 35 percent of total bank deposits have a negative impact on the gross domestic product. However, the relevance of this conclusion to Kenya doubtful since a lot of development has overtime been witnessed in the 25 management of money supply. The country has witnessed an accelerated economic growth between 2005 and 2007, which was not captured in the study.
Kathanje et al. (2007) assessed the monetary policy function for Kenya during the period after liberalization (1997-2006). The study sought to investigate the interest rate-setting behavior of central banks using the Taylor rule that has not received much attention in developing countries. Variables used in the model include; monetary aggregates (Reserve money and M3), CPL, GDP growth, Kenya shilling to US dollar exchange rate and the interest rates (Repo, Interbank and Treasury bill rate) on monthly data. The study established that CBK has been successful in controlling inflation, at least for the greater period in the sample. It was further noted that CBK takes into account past inflation when implementing monetary policy.
Nogueira (2009) further sought to investigate the long-run neutrality of monetary policy in a sample of 14 developed and emerging economies. The study employed Auto Regressive Distributed Lag Model (ARDL) on short-term interest rates and real output using annual data covering the period 1948-1957. Using bounds testing approach to co-integration developed by Pesaran, Shin and Smith, the study established that co-integration does not exist between the variables of real output and monetary policy instrument (short-term nominal interest rates). In summary, the study supports the traditional economic theory that monetary policy does not affect output in the long run.
Moki (2012) investigated the relationship between national debt and the economic growth of African countries. The causal research design targeted all the 53 African countries for a 30 year period (1980 – 2010). Using multiple linear regression, the study results show that public debt had a significant positive relationship on the gross domestic product. Monetary policy had a negative relationship with GDP. Baum, Checherita-Westphal, and Rother (2012) sought to establish the relationship between economic growth and public debt using generalized method of moment least-squares regression. They applied this to 12 euro-area countries over the period 19902010, and found a positive correlation between debt and growth when the debt-to-GDP ratio is below 67 percent, no significant correlation when debt is between 67 and 95 percent of GDP, and a negative correlation when debt surpasses 95 percent of GDP. According to the authors, the negative correlation between debt and growth is related to the specificity of the 2008-2010 financial crisis.
Chiluwe and Olweny (2012) sought to establish the effect of monetary policy on private investment in Kenya by tracing the effects of monetary policy through transmission mechanism to explain how investment responded to changes in monetary policy. The study utilized quarterly macro-economic data covering the period 1996 to 2009. It employed unit roots and co-integration test using vector error correction model to explore the dynamic relationship of short-run and long-run effects of the variables due to exogenous shock. The study established that tightening of monetary policy has the effect of reducing investment while expansionary policy tends to increase investment.
Gichuki et al. (2012) sought to determine the optimal monetary instruments for Kenya, employing stochastic IS-LM model. The study sought to establish the optimal instrument between interest rates and reserve money in influencing the conduct of monetary policy in Kenya and further establish whether a combination policy mix of both instruments were a better policy than using either of them independently. Variables used in the model include gross domestic product, M3, and CBK overdraft interest rate. The study used quarterly data covering the period 1994 to 2010. The study established that the interest rate is a superior policy instrument over reserve money in meeting Kenya’s monetary policy objectives. The study further revealed that a combination policy mix performs better than the two instruments working independently.
Gichuki and Moyi (2013) assessed the monetary condition index for Kenya. The study employed a simple aggregate demand function for the computation of the monetary condition index. Variables utilized in the model include GDP, 91-day T-bill rate, credit to private sector, and real exchange rate. The study utilized quarterly time series data covering the period 2000 to 20II. Empirical results established presence of co-integration between real GDP and the exogenous variables, namely; the interest rates, exchange rates and claims on private sector implying that the three variables are the main channels of monetary transmission in Kenya. The study further established that monetary condition index is more responsive to interest rate and thus can be used to monitor the interest rate and exchange rate movements and their effect on aggregate demand and eventually inflation.
Asongu (2013) assessed the long-run and short-run effects of monetary policy on output and prices on annual data in a sample of 10 African countries experiencing high inflation rates. The study employed vector autoregressive, vector error correction and granger causality econometric techniques. Variables used in the model include; financial depth (M2/DP), credit efficiency (Credit/deposits) and size (deposits/total assets). The study established that permanent changes in financial depth, efficiency, credit and size affect prices in the long-run but in cases of disequilibrium; only financial depth and size adjust inflation to the co-integration relations. The study further established that monetary policy does not affect prices in the short run.
Baghebo and Stephen (2014) analyzed the monetary measures influencing growth-stabilizing circulation of money and favorable environmental basis to investment for economic development. Employing regression analysis ordinary least square (OLS) for data 1980-2011 the empirical estimation of the study. Findings of the study elaborated monetary policy promote the investment for the development in the economic growth.
From the reviewed literature, there seems to be no consensus over the monetary policy instrument to be employed in developing nations. In developed nations, monetary authorities target interest rates as their intermediate target, while in developing nations, most monetary authorities target monetary aggregates mostly due to inefficient financial market and uncompetitive banking sector Kathanje et aI., (2007). In addition to the mentioned challenges in the choice of monetary policy instrument, globalization of financial markets has drastically reduced the independence of monetary policy by significantly eroding the ability of small open economies to determine interest rates independently of world markets (Asongu, 2013).
There appears to be a difference in the choice of variables in testing the effects of monetary policy on economic growth. Kathanje et al (2007) used variables such as CPI, GDP, Monetary aggregates such as reserve money and M3 whereas Asongu used variables such as financial depth (M2/DP), credit efficiency (credit/deposits) and size (deposits/total assets)
Various tests were applied, such as the time series test (unit root tests and Cointegration tests) by Ndungu (1999) and Gichuki/Moyi (2013) since it captures an interrelationship between the macroeconomic variables.
CHAPTER THREE: METHODOLOGY
A research methodology involves specific techniques that are adopted in research processes to collect, assemble and evaluate data.
This chapter describes methods applied in the study. It covers the theoretical and empirical frameworks and also details the data analysis procedures that guided the study.
The link between monetary policy and economic growth can be derived from the quantity theory of money given as
???? =???? …………………………………………………………………………………………………… 3.1
Where ?? = represents the quantity of money in circulation at time t
??= is the number of times a unit of money is used in transaction per unit of time at time t
??= the weighted average of all individual prices at time t
??=he sums of all transactions of T goods and services per unit of time at to
Improving this theory is Jhingan (1997) later replaced T with Y as follows:
???? =???? …………………………………………………………………………………………………… 3.2
Where the rest of the terms are as defined above while ???s the physical output which is the same as real income
Making ?? the subject gives
………………………………………………………………………………………… 3.3
Taking logarithm both sides yields
?? = ??+?? – ?? ……………………………………………………………………………….. 3.4
From the theoretical framework, we extend equation 3 to yield the empirical model as shown
?? = ?0 + ?1 ?2?+ ?? + ?3 IY + ?1 EXPO + ?6 INTR + μ …………………………………… 3.5 Where;
?? =The rate of growth of Gross Domestic Product at time t
EXPO= The rate of growth of exports
?2= Growth of Money Supply
IY= The ratio of Investment to GDP
??= Inflation rate INTR= Interest rate β = parameters to be estimated μ = the error term
This study will be based on the annual time series data (1980-2018) of the macroeconomic variables GDP growth rate, Rate of growth of export, Money supply, Ratio of investment, Inflation Rate and Interest rate. It utilizes secondary data from World Bank Indicators (WDI), UNCTAD database and the Central Bank of Kenya (CBK).
Table 3.3.1 shows the type of data, source and typology of variables
| Variable proxy | Variable name | Description | Expected sign | References |
| ?? | Economic growth | Measure of national income, output and growth | Dependent variable | UNCTAD
(2014) |
| EXPO | Rate of growth of export | The value of all goods and other market services provided to the rest of the world. |
+ |
WDI
(2018) |
| ?2 | Money supply | It is the total
amount of money available to an economy at a specific time. |
+/- |
WDI
(2018) |
| IY | Ratio of investment | This refers to the share of investment in total production and is obtained by calculating gross capital formation as a percentage of GDP. |
+/- |
WDI
(2018) |
| ?? | Inflation Rate | Inflation is a wide spread persistent and appreciable increase in the general price level of goods and services in an economy. |
+/- |
WDI (2018) |
| INTR | Interest rate | It is the amount of interest due per period, as a proportion that is lent or borrowed. |
+/- |
WDI
(2018) |
The study estimated the influence of exogenous variables on endogenous variable using the ordinary least squares (OLS) method. The OLS has a unique advantage in this study as it uses observable sample whose regression equation can be estimated (Hayakwawa et al.,2008).
The data to be used in the analysis of this research is a macroeconomic time series which, from a theoretical perspective suffers from non-stationarity (Nelson and Plosser, 1982). It will be vital to run a stationarity test first before using it since running a regression on a non stationary data may lead to invalid empirical result, and therefore the study will test stationarity using Augmented Dickey-Fuller (1979, 1981).
When two or more macroeconomic variables have a long run relationship, we conclude that those variables are co-integration. Suppose the economic variables in this study have unit root, then the study will proceed to test for co-integration tests. To test for co-integration, the study employed Johansen (1982) test. According to Johansen (1982) test, if the residuals are stationary, then it means that the variables are co-integrated
It determines whether the error-correcting term has a long run causality effect. It is a special model in that it ensures that the economic variables in the model are stationary after first differencing. For its development, the economic variables must have co-integrating vectors which will be done first in ******above. This model is vital in checking whether an individual lagged economic variable has any significant effect on the dependent variable. This will be carried in this study through all the lagged variables and GDP. The sign of the coefficient of ECT will guide in the conclusion of the direction of causality.
This will be to test for the existence of the short-run causality between macroeconomic variables under investigation. The test checks whether one time series data could be used to predict another time series data and therefore will be used in this study to check whether monetary policy could be used to forecast the GDP of Kenya in the future. The study will therefore conduct a check on whether the lagged variables combined have any significant influence on the dependent variable. It will also be used to determine the type of causality between the GDP and monetary policy.
This study will utilize the Shapiro-Wilk test to conduct a normality test for the error term. It will involve computation of the W, V, Z and P-value. We use the p-value to make an inference of normality. If our calculated p-value exceeds the critical value, then the variable will be statistically significant or normal in our case. If the calculated p-value is smaller than the critical value, then a variable is not significant or not normal. The credibility of the OLS parameters will be test through testing for the degree of multicollinearity and heteroscedasticity.
This chapter presents analysis and findings of the study as set out in the research objectives and methodology. The study findings are presented on the effect of monetary policy on economic growth in Kenya. This chapter looks at the data to be analyzed, the regression analysis and interpretation.
This paper uses annual data from 1980 to 2018 to examine the impacts of monetary policy on economic growth in Kenya. The summary statistic of the variables used in our regression in Table: 4.2.1.
| Variable | Obs | Mean | Std. Dev. | Min | Max |
| Yt | 39 | 3.938462 | 2.308065 | -.8 | 8.4 |
| m2 | 39 | 35.48974 | 4.720214 | 26.7 | 43.2 |
| Pt | 39 | 11.97692 | 8.568408 | 1.6 | 46 |
| Intr | 39 | 8.148718 | 4.37456 | 2.3 | 20.5 |
| Expo | 39 | 3.920513 | 7.811266 | -10.6 | 31.5 |
| Iy | 39 | 20.27205 | 4.070558 | 13.52 | 28.55 |
From the descriptive statistics in Table 4.2.1, the economic growth rate of Kenya under the period of study was found to have an average growth rate of 3.94% with a standard deviation of 2.31%. The result also indicates that the minimum GDP growth rate was -0.85% while the maximum was 8.4%. The descriptive statistic further indicates that inflation is relatively higher compared to interest rate, money supply and exports. Further, money supply has a higher average mean of
35.48974 compared to the rest of the variables which are relatively lower.
4.3 Correlation statistics.
Correlations measure the strength and direction of the linear relationship between two variables. They range from -1 to +1, with -1 indicating a perfect negative correlation, +1 indicating a perfect positive correlation
(obs=39)
| yt m2 pt intr expo iy | |
| yt |
1.0000 |
| m2 | 0.1442 1.0000 |
| pt | -0.5024 -0.1751 1.0000 |
| intr | -0.1239 0.5751 -0.1505 1.0000 |
| expo | 0.1143 -0.0488 0.3838 -0.1879 1.0000 |
| iy | 0.2330 -0.6554 0.0905 -0.6901 0.1918 1.0000 |
In this data set, we have no missing values, so all correlations are based on all 39 observations. From the table, Inflation (pt) and Interest rate (INTR) are negatively correlated with economic growth, whereas Money supply (M2), Rate of growth of exports and Ratio of investment are positively related to economic growth.
This study used the Shapiro-Wilk test to determine normality of variables. A variable is normal if the mean, median and mode are equal (that is normally skewed). The Shapiro-Wilk test gives four options, a W, V, Z and P-value. We use the p-value to make an inference of normality. If our calculated p-value exceeds the critical value, then our conclusion is that the variable is normal. But if the calculated p-value is smaller than the critical value, then a variable will be non-normal.
| Variable | Obs | W | V | Z | Prob>z
|
Normality |
| Yt | 39 | 0.95938 | 1.575 | 0.954 | 0.17001 | Normal |
| M2 | 39 | 0.93482 | 2.527 | 1.948 | 0.02572 | Non Normal |
| Pt | 39 | 0.81726 | 7.084 | 4.114 | 0.00002 | Non Normal |
| Intr | 39 | 0.93792 | 2.407 | 1.845 | 0.03249 | Non Normal |
| EXPO | 39 | 0.91585 | 3.262 | 2.484 | 0.00649 | Non Normal |
| IY | 39 | 0.94477 | 2.141 | 1.600 | 0.05482 | Normal |
Results from Table 4.3.1 show that only GDP growth rate and ratio of investment are normal at a 5% level of significance while the rest of the variables are not normal.
This problem arises when two or more independent variables are strongly related. According to Gujarati (2012), a correlation of 0.8 and above indicates the possibility of collinearity between two variables. This study used the Vector Integrating Factor (VIF) and Tolerance (1/VIF) to test for multicollinearity. The VIF test directs that one first runs a regression followed by a VIF command in Stata. Then an inference is made based on the magnitude of the VIF value. If the VIF value is less than 10, then a variable has no multicollinearity. Conversely, if the VIF is greater than 10, then multicollinearity exists.
| Variable | VIF | 1/VIF | Multicollinearlity |
| M2 | 1.43 | 0.701410 | No multicollinearlity |
| Pt | 1.42 | 0.704367 | No multicollinearlity |
| INTR | 1.13 | 0.884842 | No multicollinearlity |
| EXPO | 1.10 | 0.906988 | No multicollinearity |
| IY | 1.05 | 0.950265 | No multicollinearity |
| Mean VIF | 1.23 |
Results from Table 4.3.2 shows that multicollinearity is not a serious problem since all the VIF was less than 10
4.4.3 Stationarity (unit root test)
The study employs the ADF test to test for stationarity in the individual variables. According to
ADF test, a variable is declared stationary when it’s Test Statistic is smaller than the t-critical.
| Variable | Test statistic | 1% critical value | 5% critical value | 10% critical value | Nature |
| Yt | -3.444 | -3.662 | -2.964 | -2.614 | Not stationary |
| M2 | -1.712 | -3.662 | -2.964 | -2.614 | Not Stationary |
| Pt | -3.414 | -3.662 | -2.964 | -2.614 | Not stationary |
| INTR | -2.152 | -3.662 | -2.964 | -2.614 | Not Stationary |
| EXPO | -6.033 | -3.662 | -2.964 | -2.614 | Stationary |
| IY | -2.225 | -3.662 | -2.964 | -2.614 | Not stationary |
Results from Table 4.3.3 indicate that; export growth rate is stationary because it test statistic is less than the critical value. On the contrary, economic growth, rate of money supply, inflation, interest rates and ratio of investments are non-stationary. These non-stationary variables require additional attention to determine whether they are co-integrated. Therefore, taking the first difference gives the results in the table below.
| Variable
|
Test Statistic | 1% critical value
|
5% critical value
|
10% critical value
|
Nature |
| Yt | -6.784 | -3.668 | -2.966 | -2.616 | Stationary |
| M2 | -7.045 | -3.668 | -2.966 | -2.616 | Stationary |
| Pt | -6.956 | -3.668 | -2.966 | -2.616 | Stationary |
| INTR | -7.580 | -3.668 | -2.966 | -2.616 | Stationary |
| IY | -8.450 | -3.668 | -2.966 | -2.616 | Stationary |
Table 4.3.2 shows all variables that were non stationary at order zero being stationary at order one.
When variables have a long run equilibrium relationship, we say they are co-integrated. Most of the time when economic variables are individually non-stationary; it is likely that co-integration may occur. Co-integration test is normally a pre-test for a time series data which tries to eliminate spurious regression situations of non-stationary data. Thus co-integration relationship existence implies that the regression of non-stationary series in their levels yield meaningful and not spurious results. To test for co-integration, the study employed the Johansen test (1982). According to the Johansen test (1982), we reject at the 5% level and also reject the null hypothesis if the trace and max statistics > 5% critical value otherwise fail to reject the null hypothesis.
Johansen tests for cointegration
| Trend: | Constant | Number of obs | = | 37 |
| Sample: | 1982-2018 | Lags | = | 2 |
| Maximum
Rank |
Parms | LL | Eigenvalue | Trace Statistic | 5% critical value | |||||||||||
| 0 | 30 | -442.83983 | . | 76.0549 | 68.52 | |||||||||||
| 1 | 39 | -423.54674 | 0.64756 | 37.4687* | 47.21 | |||||||||||
| 2 | 46 | -413.82728 | 0.40867 | 18.0298 | 29.68 | |||||||||||
| 3 | 51 | -408.50604 | 0.24996 | 7.3873 | 15.41 | |||||||||||
| 4 | 54 | -406.15744 | 0.11922 | 2.6901 | 3.76 | |||||||||||
| 5 | 55 | -404.81238 | 0.07013 | |||||||||||||
|
Maximum Rank |
Parms | LL | Eigenvalue | Max Statistic | 5% critical value | |||||||||||
| 0 | 30 | -442.83983 | . | 38.5862 | 33.46 | |||||||||||
| 1 | 39 | -423.54674 | 0.64756 | 19.4389 | 27.07 | |||||||||||
| 2 | 46 | -413.82728 | 0.40867 | 10.6425 | 20.07 | |||||||||||
| 3 | 51 | -408.50604 | 0.24996 | 4.6972 | 14.07 | |||||||||||
| 4 | 54 | -406.15744 | 0.11922 | 2.6901 | 3.76 | |||||||||||
| 5 | 55 | -404.81238 | 0.07013 | |||||||||||||
?0 : No co-integrating vector
?1 : There is at least one co-integrating vector
If the trace statistic > critical value, we reject the ?0 at Rank 0. According to the data shown in table 4.3.5 the trace statistic is greater than the critical value (75.0549 > 68.52), therefore we reject ?0 . and conclude that there is at least one cointegrating vector.
?0 : There is one co-integrating vector
?1 : There is more than one co-integrating vector
If the trace statistic > critical value, we reject the ?0 at Rank 1. In table 4.1 the trace statistic < critical value (37.4687<47.21), therefore we fail to reject the ?0 and conclude that there is one cointegrating vector.
| Coef. | Std. Err. | z | P>|z| | [95% Conf. | Interval] | ||
| D_yt | _ce1 L1. | -.2382309 | .1281114 | -1.86 | 0.063 | -.4893247 | -.4893247 |
| D_m2 | _ce1 L1. | -.1081442 | .1178003 | -0.92 | 0.359 | -.3390286 | .1227401 |
| D_pt | _ce1 L1. | 1.274138 | .3440009 | 3.70 | 0.000 | .5999089 | 1.948368 |
| D_intr | _ce1 L1. | -.4612675 | .1154188 | -4.00 | 0.000 | -.6874841 | -.2350509 |
| D_expo | _ce1 L1. | .7012833 | .5207573 | 1.35 | 0.178 | -.3193823 | 1.721949 |
| D_iy | _ce1 L1. | .3174992 | .1255032 | 2.53 | 0.011 | .0715175 | .5634809 |
The variables pt and iy, have both positive coefficient and the prob values are less than 0.05 (meaning its significant) thus we conclude that there isn’t Long Run Causality.
The intr variable has a positive coefficient and the prob-value is less than 0.05 (meaning it’s significant) we conclude that there is Long Run Causality.
The yt and m2 variables have both negative coefficients and the prob-values are less than 0.05 (meaning it isn’t significant); therefore, there is no Long Run Causality.
The expo variable has a positive coefficient and the prob-value is less than 0.05 (meaning it’s not significant); therefore, there is no Long Run Causality.
To test for the existence of causality between macroeconomic variables used in the model, we ?0 : No Granger Causality.
?1 : Null hypothesis is not true.
Therefore, the decision criteria will be to reject the null hypothesis if the prob-value of the ?ℎ?2statistic is ≤ 0.05 and to accept the null hypothesis if the p-value of the ?ℎ?2statistic is ≤ 0.05.
| Equation | Excluded | chi2 | df | Prob > chi2 | |||
| D_yt | D_m2 | 2.3202 | 2 | 0.313 | |||
| D_yt | D_pt | 1.9382 | 2 | 0.379 | |||
| D_yt | D_intr | .27566 | 2 | 0.871 | |||
| D_yt | Expo | 2.9456 | 2 | 0.229 | |||
| D_yt | D_iy | 1.4393 | 2 | 0.487 | |||
| D_yt | All | 7.1959 | 10 | 0.707 | |||
|
D_m2 |
D_yt |
6.0372 |
2 |
0.049 |
|||
| D_m2 | D_pt | .19862 | 2 | 0.905 | |||
| D_m2 | D_intr | 12.14 | 2 | 0.002 | |||
| D_m2 | Expo | .38976 | 2 | 0.823 | |||
| D_m2 | D_iy | 5.3783 | 2 | 0.068 | |||
| D_m2 | All | 30.606 | 10 | 0.001 | |||
|
D_pt |
D_yt |
.2529 |
2 |
0.881 |
|||
| D_pt | D_m2 | 11.902 | 2 | 0.003 | |||
| D_pt | D_intr | 16.711 | 2 | 0.000 | |||
| D_pt | Expo | 1.4832 | 2 | 0.476 | |||
| D_pt | D_iy | 3.922 | 2 | 0.141 | |||
| D_pt | All | 36.439 | 10 | 0.000 | |||
|
D_intr |
D_yt |
.28935 |
2 |
0.865 |
|||
| D_intr | D-m2 | 3.2087 | 2 | 0.201 | |||
| D_intr | D_pt | 3.6623 | 2 | 0.160 | |||
| D_intr | Expo | 15.366 | 2 | 0.000 | |||
| D_intr | D_iy | .25912 | 2 | 0.878 | |||
| D_intr | All | 42.491 | 10 | 0.000 | |||
| expo |
D_yt |
4.4817 |
2 |
0.106 |
|||
| expo | D-m2 | 6.1338 | 2 | 0.047 | |||
| expo | D_pt | 4.5604 | 2 | 0.102 | |||
| expo | D_intr | .89759 | 2 | 0.638 | |||
| expo | D_iy | 2.1688 | 2 | 0.338 | |||
| expo | All | 16.473 | 10 | 0.087 | |||
| D_iy | D_yt | 1.696 | 2 | 0.428 | |||
| D_iy | D-m2 | 3.0873 | 2 | 0.214 | |||
| D_iy | D_pt | .75495 | 2 | 0.686 | |||
| D_iy | D_intr | 4.3411 | 2 | 0.114 | |||
| D_iy | Expo | .15323 | 2 | 0.926 | |||
| D_iy | All | 12.97 | 10 | 0.225 | |||
For the Yt equation, since the variables m2, pt, intr, expo and iy are greater than 0.05, we fail to reject the null hypothesis and therefore, the variables do not granger cause Yt. After combining all variables, the p-value of the ?ℎ?2 is greater than 0.05. Therefore, they do not granger cause Yt.
For the m2 equation, variables pt, expo and iy are greater than 0.05; therefore, we fail to reject the null hypothesis, and they do not granger cause m2. However, for variables Yt and intr we reject the null hypothesis since the p-value is less than 0.05 and they granger cause m2. Combining all variables, the p-value is less than 0.05. Therefore, it means they granger cause m2.
For the pt equation, variables yt, expo and iy are greater than 0.05; therefore, we fail to reject the null hypothesis; hence they do not granger cause pt. Variables m2 and intr are less than 0.05. Therefore, we reject the null hypothesis and they granger cause pt. Combining all the variables, the p-value is less than 0.05 hence they granger cause m2.
For the intr equation, the yt, m2, pt and iy variables are greater than 0.05 hence we fail to reject the null hypothesis, and they do not granger cause intr. Expo is less than 0.05. Therefore, we reject the null hypothesis and expo granger causes intr. Combining all the variables, the p-value is less than 0.05; therefore, they granger cause intr.
For the expo equation, the yt, pt intr and iy variables are greater than 0.05; hence they do not granger cause expo. However, m2 is less than 0.05 therefore, m2 granger causes expo. Combining all the variables, the p-value is greater than 0.05 hence they do not granger cause expo.
Lastly for the iy equation, variables yt, m2, pt, intr and expo are greater than 0.05; therefore, they do not granger cause it. Combining all the variables, the p-value is greater than 0.05 hence they do not granger cause it.
The table below shows regression results that have been obtained after analysis our data in Stata:
| Source | SS | Df | MS | |
| Model | 88.2341675 | 5 | 17.6468335 | |
| Residual | 112.462928 | 32 | 3.5144665 | |
| Total | 200.697096 | 37 | 5.42424583 |
Number of obs = 38
F(5, 32) = 5.02
Prob > F = 0.0017
R-squared = 0.4396
| Adj R-squared | = | 0.3521 |
| Root MSE | = | 1.8747 |
| D_Yt | Coef. | Std. Err. | t | P>tttttttt | [95% Conf
–.494353 |
. Interval] .0887991 |
| D_M2 | –.202777 | .1431446 | -1.42 | 0.166 | ||
| D_Pt | –.1966037 | .0424786 | -4.63 | 0.000 | -.2831299 | –.1100776 |
| D_Intr | –.0086193 | .1064434 | -0.08 | 0.936 | –.2254375 | .2081989 |
| Expo | .1284278 | .0464117 | 2.77 | 0.009 | .0338903 | .2229653 |
| D_IY | .1068863 | .1272076 | 0.84 | 0.407 | –.1522272 | .3659998 |
| _cons | –.4598129 | .3600341 | -1.28 | 0.211 | -1.193178 | .2735525 |
In order to determine the effect of monetary policy on economic growth and the six variables, the researcher conducted a regression analysis. As per STATA generated table 4.4.8 above, the equation ?? = ?0 + ?1 ?2?+ ?2?? + ?3 INTR + ?4 EXPO + ?5 IY + μ becomes
??=-0.4598+-0.2028?2?+-0.1967??+-0.0086INTR+0.1284EXPO+0.1069IY+ μ
Where ?? is the dependent variable
An R-squared of 0.4396 indicates that 43.96% of the variation in the Economic growth is explained by the explanatory variables in the model. The regression further indicates that the rate of growth of exports and ratio of investment are positively related to the economic growth of Kenya under the period of study. However, the result indicate that only export growth rate was statistically significant in influencing GDP growth rate of Kenya.
Particularly, the results indicate that one unit increase in the rate of exports increases the rate of economic growth (Yt) level in the country by 0.1284278 units ceteris paribus despite the effects being close to zero. Similarly, a one unit increase in the ratio of investments (IY) increases the rate of economic growth by 0.1068863 units ceteris paribus despite the effects being close to zero.
The regression results further indicate that money supply, inflation and interest rates have a reducing effect in the economy. Concerning the rate of interest variable, a one percent increase in interest rate reduced the rate of economic growth by -0.008 units ceteris paribus. The implication of this finding is that increase in the level of interest rates increases the cost of borrowing in the economy, discouraging people to invest and boost the economy. Similarly, according to money supply variable, a one percent increase in money supply has reduced the rate of economic growth by 0.203 units ceteris paribus. This implies that there will be less production since there is less money to save and consume. Finally, according to the inflation rate variable, a one percent increase in inflation will lead to a decrease in economic growth by 0.197 units ceteris paribus. This implies that people will spend more to consume since the prices of various commodities have increased.
This chapter presents a general overview of the study, summary of the study findings, conclusion, policy implication and areas of further study.
The overall objective of this study was to determine the impact of monetary policy variables on economic growth using annual time series data for the year 1980-2018. The study used a linear regression model with GDP (yt) as the dependent variable and 5 other independent variables namely; Money supply (M2), Rate of growth of exports, Ratio of investment, interest rate and inflation. Prior to running the regression analysis, the variables were tested for unit root test using the ADF method and all the variables except Rate of growth of exports were non-stationary. I further performed the differencing of the variables to obtain stationarity. Based on the unit root test, I performed, we performed the Johansen test for cointegrating test by examining trace statistic and the critical value, and therefore, we reached at rank 1 since the trace statistic started to be less than the 5% critical value.
After performing the cointegration test, I then proceeded to vector autocorrelation model where I determined whether there was long-run causality. Here we concluded that if there is significance between the variables, then there exists a long run causality effect but when there is no significance, there is no long run causality effect. After the VECM model, there was granger causality test whereby null hypothesis was rejected since the p-value of the chi-square was greater than 0.05 and therefore they did not granger cause
Finally, OLS regression results was ran and they further indicated that rate of growth of exports and ratio of investment are positively related to economic growth but rate of growth of export became statistically significant. The remaining variables which were interest rates, money supply and inflation were negatively related to economic growth.
The data analysis was run in Stata software, and the conclusion deduced that the variables; money supply, inflation, interest rates had negative effects on economic growth whereas rate of growth of exports and ratio of investments had positive effects on economic growth. This study therefore concludes that the variable; rate of growth of exports has a significant and a positive impact on economic growth, and thus policymakers should understand its impact and how it can influence economic growth prospects in the country.
Given the results of the study, as outlined here above, the study advances the following policy recommendations in the use of monetary policy in controlling the prevailing economic growth
The Central Bank of Kenya and the government to place more emphasis on quantity based nominal anchors such as money supply since it has significant effects on real GDP. These will ensure that the economy is not adversely affected.
To address challenges of inflation in the country, the Central Bank of Kenya and the government should address the structural rigidity in the country as the problem of inflation in Kenya is not a monetary phenomenon as revealed by the study findings. Since Kenya is operating far below full-employment equilibrium, the government should ensure increase in GDP translates to improved purchasing power and reduced poverty index.
The study findings further suggest that high interest rate affects economic growth. To address this challenge, the CBK should aim at achieving a stable microeconomic environment in the economy, and this will ensure more borrowing leading to more investments, thus improving the economy.
This study was limited to monetary policy instruments namely: GDP, Money supply, inflation, interest rates, rate of growth of exports and ratio of investment. This limitation creates a need for further research for the inclusion of other monetary policy instruments that will impact on economic growth positively. The study further noted that monetary policy implementation in developing countries like Kenya face additional challenges that are not present in developed economies; such as fiscal dominance. Therefore, to better understand the impact of monetary policy shocks on real GDP, there is a need for a further study that would include fiscal policy variables as dependent variables in the analysis.
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Appendix 1: Table showing data used in the study
| YEAR | Yt | M2 | Pt | INTR | EXPO | IY |
| 1980 | 5.6 | 29.9 | 13.9 | 4.8 | 5.4 | 26.53 |
| 1981 | 3.8 | 29.5 | 11.6 | 3.6 | -4.2 | 26.99 |
| 1982 | 1.5 | 30.4 | 20.7 | 2.3 | 3.2 | 24.42 |
| 1983 | 1.3 | 28.2 | 11.4 | 2.6 | -2.2 | 23.97 |
| 1984 | 1.8 | 28.3 | 10.3 | 2.6 | 0.9 | 22.43 |
| 1985 | 4.3 | 26.7 | 13 | 2.8 | 6.7 | 28.55 |
| 1986 | 7.2 | 30.4 | 2.5 | 2.8 | 9.8 | 26.03 |
| 1987 | 5.9 | 30.2 | 8.6 | 3.7 | 0.3 | 26.81 |
| 1988 | 6.2 | 28.9 | 12.3 | 4.7 | 4.6 | 27.32 |
| 1989 | 4.7 | 28.4 | 13.8 | 5.3 | 9.4 | 20.88 |
| 1990 | 4.2 | 29.6 | 17.8 | 5.1 | 22.5 | 26.3 |
| 1991 | 1.4 | 31 | 20.1 | 4.5 | -1.2 | 23.39 |
| 1992 | -0.8 | 36.5 | 27.3 | 4.5 | -0.8 | 17.4 |
| 1993 | 0.4 | 37.1 | 46 | 4.7 | 31.5 | 18.8 |
| 1994 | 2.6 | 38 | 28.8 | 20.5 | -1.2 | 17.44 |
| 1995 | 4.4 | 42.2 | 1.6 | 15.2 | -7.7 | 17.58 |
| 1996 | 4.1 | 35.8 | 8.9 | 16.2 | 4.6 | 13.52 |
| 1997 | 0.5 | 38.4 | 11.4 | 13.5 | -10.6 | 14.27 |
| 1998 | 3.3 | 35.8 | 6.7 | 11.1 | -4.9 | 13.81 |
| 1999 | 2.3 | 35.8 | 5.7 | 12.8 | 9.3 | 16.32 |
| 2000 | 0.6 | 35.2 | 10 | 14.2 | 1.1 | 18.05 |
| 2001 | 3.8 | 35.2 | 5.7 | 13 | 3.6 | 18.4 |
| 2002 | 0.5 | 38.2 | 2 | 13 | 7.1 | 16.44 |
| 2003 | 2.9 | 39 | 9.8 | 12.4 | 7.2 | 16.88 |
| 2004 | 5.1 | 39.3 | 11.6 | 10.1 | 12.6 | 17.52 |
| 2005 | 5.9 | 38.9 | 10.3 | 7.8 | 9.5 | 18.22 |
| 2006 | 6.5 | 34.6 | 14.5 | 8.5 | 3.5 | 18.63 |
| 2007 | 6.9 | 36.1 | 9.8 | 8.2 | 6.2 | 20.46 |
| 2008 | 0.2 | 36.1 | 26.2 | 8.7 | 2.4 | 19.61 |
| 2009 | 3.3 | 36.5 | 9.2 | 8.8 | -5.2 | 19.33 |
| 2010 | 8.4 | 40.3 | 4 | 9.8 | 8.7 | 20.74 |
| 2011 | 6.1 | 40.9 | 14 | 9.4 | 9.2 | 21.7 |
| 2012 | 4.6 | 40.9 | 9.4 | 8.2 | 6.8 | 21.48 |
| 2013 | 5.9 | 42.3 | 5.7 | 8.7 | -2.2 | 20.11 |
| 2014 | 5.4 | 43.2 | 6.9 | 8.1 | 5.8 | 22.43 |
| 2015 | 5.7 | 42.4 | 6.6 | 6.9 | 6.2 | 18.09 |
| 2016 | 5.9 | 39.4 | 6.3 | 7.9 | -2.2 | 16.2 |
| 2017 | 4.9 | 37 | 8 | 6 | -6.8 | 16.74 |
| 2018 | 6.3 | 37.5 | 4.7 | 4.8 | 4 | 16.82 |
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