THE RELATIONSHIP BETWEEN OIL PRICE VOLATILITY AND ECONOMIC GROWTH IN KENYA

Transcription

1 THE RELATIONSHIP BETWEEN OIL PRICE VOLATILITY AND ECONOMIC GROWTH IN KENYA PAUL NJANGIRU NDUNGU A RESEARCH PROJECT SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENT FOR THE MASTERS OF BUSINESS ADMINISTRATION OCTOBER 2013

2 DECLARATION I declare that this proposal is my original work and has not been presented for an award of a degree in any other University. Signature: Date STUDENT: PAUL NJANGIRU REG NO: D61/73134/2012 This research proposal report has been submitted for examination with my approval as the University Supervisor. Signature: Date:. SUPERVISOR: MR. ODIPO Senior Lecturer Department of Finance and Accounting ii

3 DEDICATION This project is dedicated to my Family members. iii

4 ACKNOWLEDGMENTS It has been an exciting and instructive study period in the University of Nairobi and I feel privileged to have had the opportunity to carry out this study as demonstration of knowledge gained during the period studying for my master s degree. With these acknowledgments, it would be impossible to remember those who in one way or another, directly or indirectly, have played a role in the realization of this research project. Let me, therefore, thank them all equally. First, I am indebted to the all-powerful God for all the blessings he showered on me and for being with me throughout the study. I am deeply obliged to my supervisor for his exemplary guidance and support without whose help; this project would not have been a success. Finally, yet importantly, I take this opportunity to express my deep gratitude to the lasting memory of my loving family, and friends who are a constant source of motivation and for their never ending support and encouragement during this project. iv

5 ABSTRACT More recently, the research emphasis has broadened to include not only the effects of changes in oil price level (mean price in a given period), but also the effects of price volatility (standard deviation in a given period) as well. The evidence confirms that volatility has a considerable influence on economic output. For example, recent US EIA estimates blamed oil price volatility between 1999 and 2001 for a loss of 0.7 percentage points of GDP growth in the U.S. economy. This translates to losses that potentially range in the tens and even hundreds of billions. This study therefore sought to establish the relationship between oil price volatility and economic growth in Kenya. This study chosen was a descriptive study since descriptive studies are more formalized and typically structured with clearly stated hypotheses or investigative questions. The study found that both economic performance and Oil Prices data were non-stationary; present observation is affected by previous observations. Partial Autocorrelation conducted by this study required that the non-stationarity data be differenced and through using a first order differencing, the time series to become stationary and eliminated misspecification in the Granger causality test, that is, all variables are stationary in their first difference forms but not in their levels. The study recommended that, since the time series become stationary and eliminated misspecification in the Granger causality test, it is highly viable that all variables in an economy be stationary in their first difference forms but not in their levels. The result of the study is a unidirectional casualty effect from OPV to GDP which contradicts with Hussain and Liew (2004) study that showed a feedback/bidirectional causal relationship between Oil Price and stock price in Malaysia; Granger, Huang and Yang (2000) found a bidirectional causal/granger relationship between the two in Asia till the Asian Financial Crisis, when the Singapore s Oil Prices lead the price of stock. v

6 ABBREVIATION i) ARIMA Autoregressive Integrated Moving Average ii) ERC Energy Regulatory Commission iii) GARCH Generalized Autoregressive Conditional Heteroscedasticity iv) GDPR Gross Domestic Product Growth Rate v) KNBS Kenya National Bureau of Statistics vi) NOPI Net Oil Price Increase vii) OECD Organization for Economic Co-operation and Development viii) OLS Ordinary Least Square ix) OPEC Organization of the Petroleum Exporting Countries x) PACF Partial Autocorrelation Function xi) RAC Refiner acquisition cost vi

7 TABLE OF CONTENT DEDICATION... iii ACKNOWLEDGMENTS... iv ABSTRACT... v CHAPTER ONE: INTRODUCTION Background of the study Oil Price Volatility Economic Growth Relationship between Oil Price Volatility and Economic Growth Statement of the Problem Objective of the Study Value of the Study... 8 CHAPTER TWO: LITERATURE REVIEW Introduction Review of Theories Empirical Review Exogenous Oil Price Shocks and Imperfect Competition Influence of oil price volatility CHAPTER THREE RESEARCH METHODOLGY Introduction Research Design Population of the Study Data Collection Data Analysis Model Specification vii

8 CHAPTER FOUR DATA FINDINGS ANDANALYSIS Introduction Data Findings Trend in Oil Price and Economic growth Performance Testing for Randomness using Partial Auto Correlation Functions (PACF) Conducting Auto Regressive Integrated Moving Average (ARIMA) on the Model Regression Analysis Summary and Interpretation of Findings Limitation of the study CHAPTER FIVE DISCUSSIONS, CONCLUSIONS AND RECOMMENDATIONS Introduction Discussions Conclusion Limitation of the study Suggestions for further research REFERENCES Appendix I: Crude Oil Prices Appendix II: Predictions and residuals Appendix IV: Macroeconomic Data viii

9 CHAPTER ONE: INTRODUCTION 1.1Background of the study To the extent they correlate negatively with economic indicators, future oil (and natural gas) price streams represent a highly risky obligation for energy consumers. Every time oil (and natural gas) prices rise, economic activity e.g. people's income and the value of their assets declines by some measure. Based on capital market theory (and common sense), any future cost stream that rises at a time when economic activity and asset values are in decline is highly risky. This adds an important dimension to fossil price risk and the idea of energy security and diversity, (Awerbuch, 1993). More recently, the research emphasis has broadened to include not only the effects of changes in oil price level (mean price in a given period), but also the effects of price volatility (standard deviation in a given period) as well. The evidence confirms that volatility has a considerable influence on economic output. For example, recent US EIA estimates blamed oil price volatility between 1999 and 2001 for a loss of 0.7 percentage points of GDP growth in the U.S. economy. This translates to losses that potentially range in the tens and even hundreds of billions, (Bolinger, 2002) Oil Price Volatility The idea that rising oil prices and price volatility serve to stifle economic activity and reduce asset values has by now become widely accepted in the literature and seems virtually axiomatic. For example, Yang, Hwang and Huang (2002) flatly state that "Higher oil prices yield subsequent recessions in oil consuming nations, as oil prices are 1

10 negatively correlated to economic activities. The increased volatility of oil prices has drawn considerable interest in the finance literature, as evidenced by, for example, Yaron (2009). Our investigation is therefore interesting and relevant for a number of reasons. First, our results provide evidence on whether fluctuations in observed industry output can be explained by real options models. Second, our results provide evidence on whether a mechanism such as the option value of waiting may be one reason to cause the response of production to oil shocks to be asymmetric, which is an issue of considerable recent interest (cf. Hamilton (2003)). Third, applying this empirical model to the G-7 provides a test of robustness of Elder and Serletis (2009), who find that oil price uncertainty adversely affects investment and production in the US. Fourth, the cross section of G-7 countries offers a diverse pattern of oil consumption, oil exports and economic conditions in which to analyze aggregate effects of the option value of waiting. For example, oil expenditures as a share of GDP for the US were 4.8% in 2003 and as high as 8% in the early 1980 s considerably larger than for the remaining G-7 countries. Two countries in our sample were net oil exporters over at least part of our sample: Canada (since the mid 1980 s) and the UK (prior to about 2005). Finally, applying this empirical model to the G-7 also provides additional insight into whether the apparent asymmetry in the response of US output to oil prices is actually due to domestic, or international, factors, ( Glick,2007). 2

11 1.1.2 Economic Growth The existence of a negative relationship between oil prices and macroeconomic activity has become widely accepted since Hamilton s 1983 work indicating that oil prices increases reduced US output growth between 1948 and Hamilton's results have been confirmed and extended by a number of other researchers. More recently, the research emphasis has broadened to include not only the effects of changes in oil price level (mean price in a given period), but also the effects of price volatility (standard deviation in a given period) as well. The evidence confirms that volatility has a considerable influence on economic output. For example, recent US EIA estimates blamed oil price volatility between 1999 and 2001 for a loss of 0.7 percentage points of GDP growth in the U.S. economy. This translates to losses that potentially range in the tens and even hundreds of billions (Golove, 2002) Relationship between Oil Price Volatility and Economic Growth The negative relationship between oil prices and asset values suggests that the financial risk of oil price fluctuations should be observable. Beta, a standard finance risk indicator, measures the covariance between fluctuations in an asset's value and fluctuations in the value of a widely diversified asset portfolio. A number of researchers, using different data and different estimation procedures, find that the estimated Beta for oil (and natural gas) is negative, which implies a strong negative covariance risk with a widely diversified asset portfolio. This has several implications (Mork, 2008). 3

12 First, it implies that traditional electricity generating cost estimates significantly understate the cost of fossil-based generation. Further, a negative beta for fossil fuels clearly suggests that fossil fuel price spikes have a double whammy effect for consumers. They not only drive up the cost of everything from driving to switching on the lights, but they also produce measurable declines in consumers' wealth higher energy prices eventually lower their incomes and the value of their homes and other assets. It is therefore essential that policy makers fully understand the impact of fossil fuel price movements on their national economies. Moreover, it is essential that policy makers understand that one of the principal implications of the negative relationship between fossil (Goodwin, 1986). Hamilton (1983) stated that the correlation between oil price evolution and economic output was not of a historical coincidence for the periods. An increasing oil price was followed 3-4 quarters later by slower output growth with a recovery beginning after 6-7 quarters. These results also apply to the period The negative effect is more distinct in inflationary times. It wouldn t have been possible to anticipate these reductions in real GNP growth on the basis of the previous situation of output, prices, or money supply, (Gisser, 2009). In general, Hamilton s results have been confirmed by several subsequent studies. In 1986, Gisser and Goodwin indicated for the analyzed period from 1961 to 1982 that the oil price hadn t lost its potential to predict GNP growth. Moreover, they presented two interesting results concerning the relationship between oil price changes and 4

13 macroeconomic variables. First, they showed that monetary and fiscal policy measures alone cannot explain the effects of oil price shocks on macro economy after oil market disruptions. Thus, oil shocks also have an impact on economic output by other means than inflationary cost-push effects. Second, oil price effects on the U.S. economy did not change after 1973 when the OPEC period began, (Huang, 2002). Hooker (1996) confirmed Hamilton s results and demonstrated for the period that the oil price level and its changes do exert influence on GDP growth. This is shown by an increase of 10% in oil prices that led to a GDP growth roughly 0.6 % lower in the third and fourth quarters after the shock. According to Hamilton's (2000) calculations with data from 1949:2 to 1980:4, a 10% increase in oil prices will result four quarters later in a level of GDP growth that is 1.4% lower than it actually would be. Regarding Hamilton s analysis, more data was added by further investigations until 1988 including the oil price collapse in The real price of oil was considered in the analysis (Mork 1989), as well. Instead of using the producer price index (PPI) for crude oil, which merely reflects controlled prices of domestically produced oil, Mork operated with the refiner acquisition cost (RAC) for (domestic and imported) crude oil since The study verified Hamilton s result as to a negative correlation between output growth and oil price increases. The correlation is even stronger than expected. A supposed linear relationship between oil price changes and economic growth would imply a stimulation of economic growth by an oil price decline. However, in the 1980s, economic output 5

14 growth was slowed down by oil price changes although oil price declines occurred as well. Thus, Mork examined possible asymmetric effects of oil market disruptions, (Yang, 2002). 1.2 Statement of the Problem Oil represents one of the most important macroeconomic factors in the world economy and the crude oil market is the largest commodity market in the world. As a difference from other commodities oil is probably one of the few or the only production input that can affect both positively and negatively economic growth, to an extent that it might even lead to a recession (González & Nabiyev, 2009). Since the 1970s, the international crude oil price has been going up and down. González and Nabiyev (2009) point out that oil prices aren't just rising, but the volatility is also worsening-fluctuations are more pronounced than they were in the 1990s, creating unpredictable consequences. On the other hand, according to Li and Zhao (2011), crude oil price fluctuation from 1970s to 2011 has been increasingly erratic with the volatility being more erratic since Oil price volatility dampens growth through different channels, from an increase in production cost to inflation expectations. Besides, oil price increases can translate into higher transportation, production, and heating costs, which can put a drag on corporate earnings. It can also affect price stability, firm profitability and a country s financial system stability (Li & Zhao, 2011). However, issues on oil price volatility and how it impacts on economic growth have continued to generate controversies among economic researchers and policy makers. Moradi, Salehi and Keivanfar (2010) argue that fluctuations of oil have had differing 6

15 effects on great economic variables. Adding credence to such assertion, Babatunde, Adenikinju and Adenikinju (2013) aver that the characteristics of the economy in each country matter for the kind of association that is found between oil prices and stock markets. While some Akpan (2009), Aliyu (2009), Olomola (2006) argue that it can promote growth or has the potential of doing so others (Darby, 1982 and Cerralo, 2005) are of the view that it can inhibit growth. Moradi, Salehi and Keivanfar (2010) states that the literature on oil price volatility and its attendant consequence on economic growth are quite broad and continue to expand, and it is the channel through which the impact is transmitted and nature/severity of the impact that has been argued by researchers. Oriakhi and Iyoha (2013) examined the effect oil price volatility on the growth of Nigerian. They established that oil price volatility impacted directly on real government expenditure, real exchange rate and real import, while impacting on real GDP, real money supply and inflation through other variables, notably real government expenditure. Akide (2007) investigated the impact of oil price volatility on economic growth and found that within the period of study oil price shocks did not affect output and inflation, but significantly influenced real exchange rate. Jimenez and Sanchez (2005) assessed the effect of oil price volatility on the real economic activity and established evidence of nonlinear impact of oil price volatility on real GDP. Importantly, oil price increases had an impact on GDP growth of a larger magnitude than that of oil price declines. Adeniyi, Oyinlola and Omisakin, (2011) studied the relationship between oil price shocks and economic growth. They established that oil price shocks do not account for a significant proportion of observed movements in macroeconomic aggregates. Additionally, the 7

16 relationship between oil price volatility and economic growth has been complicated by the fact that most governments and supra national organizations advocates green energy sources. As a result, corporate entities have resorted to green (renewable) energy sources when oil prices go up which might result into change in the relationship between oil price volatility and economic growth (Armstrong, 2011). From the foregoing, how reliable oil price is as an economic variable predicting fluctuations in GDP growth remains controversial. This notwithstanding, developing countries are most at risk and the poorest people stand to suffer more as food prices increase with rising oil price volatility. The potential for instability has led policymakers to seek to understand how oil-price dynamics affect economic growth. Kenya relies heavily on fossils fuel including for electricity generation increasing both the cost of doing business and living cost (inflation) (Ombok, 2011). However, despite a number of studies that have been done on how oil price volatility affects economic growth in developed and developing countries alike, no study has been done in Kenya, to the best knowledge of the researcher. 1.3 Objective of the Study The study seeks to investigate the relationship between oil price volatility and economic growth in Kenya. 1.4 Value of the Study A study of oil price volatility is an area that was beneficial to the following group of persons: 8

17 By testing the relationship between oil price volatility and economic growth, the study would be an eye opener to the current and would be investors in Kenya as they seek to know how sensitive the economic variables such as commodity prices and foreign exchange market are to oil price changes. This is a key decision factor especially in foreign direct investment. As already established oil price volatility might affect inflation through commodity prices and other macroeconomic aggregates hence overall performance of the economy, thus the study findings would be invaluable to the government. The government, through its monetary policymakers and regulatory agency such as Energy Regulatory Commission (ERC), by knowing the effect of oil price volatility and economic growth will thus make proper policies that guide oil market and its pertinent substitutes (hydroelectricity and renewable energy sources), to mitigate poor performance of the economy. This study was of benefit to corporate management as they would learn how oil price volatility affects economic growth and their performance, by extension. This would be integral in their decision making on the source of energy to use or hedging techniques as may be appropriate. Additionally, the study was beneficial to the students and academicians in Kenya by narrowing the knowledge gap on the relationship between oil price volatility and economic growth. The study formed a good base upon which further research was based since it was a source of empirical study and secondary material. 9

18 CHAPTER TWO: LITERATURE REVIEW 2.1 Introduction This chapter reviews literature on the relationship between oil price volatility and economic growth in Kenya. The chapter, thus, presents relevant theories, literature and empirical review on this relationship. 2.2 Review of Theories The standard growth theories focus on primary inputs such as; capital, labour & land, while failing to recognize the role of primary energy inputs such as; oil price. However, efforts have been made at evolving some theories which capture the role of oil price volatility on economic growth, thus incorporating the linkage between energy resources; its availability and volatility and economic growth. Just as Moradi, Salehi and Keivanfar (2010), the theories reviewed are primarily reduced-form models, rather than a single theory. The study reviews the following theories: theory of economic growth, linear/symmetric relationship theory of growth, asymmetry-in-effects theory of economic growth and renaissance growth theory. Mainstream theory of economic growth postulates that production is the most important determinant of growth of any economy, and production which is the transformation of matter in some way, requires energy. This theory categorizes capital, labour and land as primary factors of production; these exist at the beginning of the production period and are not directly used up in production (though they can be degraded or added to). While energy resources (such as; oil and gas, fuels, coal) are categorized as intermediate inputs, these are created during the production period and are entirely used up during the 10

19 production process. In determining the marginal product of oil as an energy resource useful in determining economic growth, this theory considers in one part its capacity to do work, cleanliness, amenability to storage, flexibility of use, safety, cost of conversion and so on, it also considers other attributes such as; what form of capital, labour or materials it is used in conjunction with. The theory estimates the ideal price to be paid for crude oil as one that should be proportional to its marginal product. Linear/Symmetric relationship theory of growth which has as its proponents, Hamilton (1983), Gisser (1985), Goodwin (1985), Hooker (1986) and Laser (1987) postulated that volatility in GNP growth is driven by oil price volatility. They hinged their theory on the happenings in the oil market between 1948 and 1972 and its impact on the economies of oil-exporting and importing countries respectively. Hooker (2002), after rigorous empirical studies demonstrated that between 1948 and 1972 oil price level and its changes exerted influence on GDP growth significantly. Laser (1987), who was a late entrant into the symmetric school of thought, confirms the symmetric relationship between oil price volatility and economic growth. After an empirical study of her own, she submitted that an increase in oil prices necessitates a decrease in GDP, while the effect of an oil price decrease on GDP is ambiguous, because its effects varied in different countries. Asymmetry-in-effects theory of economic growth posits that the correlation between crude oil price decreases and economic activities in an economy is significantly different and perhaps zero. Mark et al. (1994), members of this school in a study of some African countries, confirmed the asymmetry in effect of oil price volatility on economic growth. 11

20 Ferderer (1996) another member of this school explained the asymmetric mechanism between the influence of oil price volatility and economic growth by focusing on three possible ways: Counter-inflationary monetary policy, sectoral shocks and uncertainty. He finds a significant relationship between oil price increases and counter-inflationary policy responses. Balke (1996) supports Federer s position/submission. He posited that monetary policy alone cannot sufficiently explain real effects of oil price volatility on real GDP. Renaissance growth theory/model was an off-shoot of the symmetric and asymmetry in effect schools. Lee (1998) who was a leading proponent of this school focused her theoretical work on attempting to distinguish between oil price changes and oil price volatility. Lee (1998) defined volatility as the standard deviation in a given period. She submitted that both have negative impacts on economic growth, but in different ways: Volatility has a negative and significant impact on economic growth immediately, while the impact of oil price changes delays until after a year. She concludes by stating that it is volatility/change in crude oil prices rather than oil price level that has a significant influence on economic growth. 2.3 Empirical Review Various empirical reviews have shown that oil price increases have a clear negative impact on economic growth while oil price declines don t affect economic activity significantly as Mork (1989) examined that the asymmetric response to oil changes by decomposing oil price changes in real price increases and decreases. The analysis showed 12

21 for the U.S. economy that the correlation with price decreases is significantly different and perhaps zero. Mork, Olsen and Mysen (1994) confirmed the asymmetry in effects for other OECD countries. In comparison with the other countries, oil price increases seem to slow down economic growth in the U.S. to a great extent, even if this country is less dependent on imported oil than countries like Germany, France, and Japan. Lee et al. (1995) also revealed the stability of asymmetric effects in the period before and after 1985 and whether or not it depended on other variables. In order to explain the socalled asymmetry puzzle, the asymmetric mechanism between the influence of oil price changes and economic activity, Ferderer (1996) focused on three possible ways: counter inflationary monetary policy, sectoral shocks, and uncertainty and established that that oil price falls increase oil price volatility, generating a positive effect on returns of stock markets, that offsets the negative effects generated by oil price surges. Originally, three other models were also supposed to have the potential to explain the oil price macro economy relationship, but could be excluded due to the fact that they presume a symmetric relationship between oil price changes and output growth. These are: the model of real balances (supposes that oil price increases lead to inflation which lowers the quantity of real balances in the systems), the income transfer model (describing income transfer between oil exporting and oil importing countries) and finally 13

22 the potential output model (suggesting that oil and capital are complements, so that an increasing oil price decreases the economy s productive capacity). Ferderer finds a significant relationship between oil price increases and counter inflationary policy responses. Nevertheless, the analysis shows that oil price increases help predict output growth irrespective of monetary policy variables. In addition, Ferderer showed that monetary policy measures in response to decreases in real oil prices closely resemble those for oil price increases. Therefore, haphazard regulatory responses can lead to uncertainty in oil price-output relationship. Ferderer suggests that sectoral shocks and uncertainty channels could account for part of the asymmetry effects but this would need additional research. Balke et al. (2002) give a similar explanation of the asymmetric effects oil price shocks have on macroeconomic activity. Monetary policy cannot alone explain real effects of oil price shocks on real GDP. They also conclude that interest rates seem to be an important mechanism through which oil prices affect economic output. Possible explanations are anticipation of asymmetric real effects and Ferderer s suggestion of investment uncertainty. 2.4 Exogenous Oil Price Shocks and Imperfect Competition Rotemberg and Woodford (1996) asked in a considerably different approach how come that oil prices have such an impact on the economy when the factor of production in question, oil, represents only a small part of the total marginal cost of production. In their analysis, Rotemberg and Woodford focus on private added value subtracting government 14

23 added value because their theories of pricing and production decisions do not apply to governmental demand. In order to explain the apparent contradiction, they included the effects on real wages and the imperfectly competitive product market model in their analysis. Using data from (1948) to (1980), they observed that private output does indeed decline following a positive innovation in oil prices. A 1 percent increase in oil prices results in a reduction in output of about percent after five-seven quarters. Interestingly, this decline in output is higher in the second year following the oil price change than in the first. Finally, Rotemberg and Woodford estimated that after an oil price increase of 10%, real wages would fall by 1% after five or six quarters. Moreover, Chaudhuri (2000) showed another important relationship between oil prices and real prices of primary commodities. The analysis showed that the non-stationary behavior of real commodity prices is due to the non-stationary behavior of real oil prices. Of course, this impact varies depending on the commodities. This is also the case even if oil is not being used directly in the production of commodities. A change in the oil price may affect the prices of primary commodities through the impact of the oil price changes on real exchange rates. 2.5 Influence of oil price volatility There are large price increases and decreases reflecting a substantial rise in the volatility of the real oil price. Volatility is defined as the standard deviation in a given period. Recent experience has shown the magnitude of oil price volatility: in the first quarter of 1997, the world oil price expressed in nominal dollars per barrel was at $21.02 and fell to 15

24 a low of $10.86 in the first quarter of Then, in the second quarter of 1999, the world oil price began to rise dramatically to a high of $29.11 in the third quarter of Thus, Hooker (1996) argued that the relationship oil price-u.s. economy growth had changed and could not be described neither by a linear relation between oil prices and output nor by the asymmetric relation presented by Mork (1989) after 1986 and increasing oil price volatility. Hooker s analysis could not confirm that only oil price increases have a negative effect on economic growth, while oil price decreases don t affect macroeconomy. These results are completed by subsequent studies. Hamilton (1996) agreed with Hooker and found out that the majority of increases in oil prices since 1986 have been followed immediately by even larger decreases. Therefore, he proposed to compare the current price of oil with the price level of the previous year rather than only compare it with the price level of the previous quarter; he introduced the net oil price increase (NOPI). Applied on the data after 1986, it showed that individual price increases were simple corrections to earlier declines except for the time during the Gulf War, which was followed by the first recession in the US. In contrast to Hooker, Hamilton demonstrated that the relation between GDP growth and NOPI remains statistically significant when the full sample from 1948:1 to 1994:2 is used. Thus, Hamilton concluded that even if oil price increases seemed to have had a smaller macroeconomic effect after 1973, oil supply disruptions have a major effect on macroeconomy as the Gulf War showed. 16

25 Lee et al. (1995) showed that it s not sufficient any more to explore the issue of causality of real oil price to the macro economy through 1992 with the classical instrument - the oil price level variable - but that volatility has to be taken into account. Using the same instruments as Mork (1989), they showed that in the period after which has not been considered by Mork the sole consideration of changes in the real oil price had lost its power to predict real GNP. 17

26 CHAPTER THREE RESEARCH METHODOLGY 3.1 Introduction This section discusses the research design, target population, research instruments, data collection and data analysis procedures that were used in this study. It discusses the methodology used to analyze the relationship between oil price volatility and economic growth in Kenya. 3.2 Research Design This study was a descriptive study. According to Schindler and Coopers (2004) descriptive studies are more formalized and typically structured with clearly stated hypotheses or investigative questions. It serves a variety of research objectives such as descriptions of phenomenon or characteristics associated with a subject population, estimates of proportions of a population that have these characteristics and discovery of associations among different variables. 3.3 Population of the Study The study utilized data from oil industry in Kenya over a period of 10 years ( ). 3.4 Data Collection Census was used in carrying out the study. That is, the whole population was covered hence no sampling done. Secondary data was used in this study. The data to be collected 18

27 was crude oil prices within the test period and the country s (Kenya) official oil cap prices as regulated by ERC on quarterly basis. Economic growth, as measured by gross domestic product (GDP) growth rate, was collected on quarterly basis. These was obtained from CBK, Kenya National Bureau of Statistics (KNBS) and Energy Regulatory Commission (ERC). Other macroeconomic variables affecting economic growth and its resultant relationship with oil prices such as inflation, government expenditure and exchange rate (US Dollar to Kenyan Shilling) collected. The data was collected on quarterly basis. 3.5 Data Analysis The research is both quantitative and qualitative in nature. This implies that both descriptive statistics and inferential statistics was employed. Once the data is collected it was checked for completeness ready for analysis. The data from the field was first coded according to the themes researched on the study. Analysis was done with aid of the statistical package for social sciences (SPSS) package. Descriptive statistics generated such as percentages, mean scores and proportions was presented in tables and figures. Qualitative data from open questions was presented in prose Model Specification Granger causality test model (1973) was utilized to measure the linear causation between the relationship between oil price volatility and economic growth in Kenya. 19

28 Oil price volatility was measured as the variance in the returns in quarterly oil prices, thus: Price Return, R t (OPV t ) = (P t - P t-1 )/P t-1 * 100 (1) Whereby P t and P t-1 denotes the quarterly price of crude oil of the current and previous month respectively The study measured the bidirectional effect or Granger causality relationship between oil price volatility and economic growth using Autoregressive Moving Average (ARIMA) and Generalized Autoregressive Conditional Heteroscedasticity (GARCH). ARIMA test was of the form: GDPR t = a 0 OPV t-1 + b 0 GDPR t-1 + ε it (2) OPV t = a 1 GDPR t-1 + b 1 OPV t-1 + ε it (3) Where: GDPR = quarterly GDP growth rate; OPV = quarterly oil price volatility; t, t-1 = current quarter and previous quarter data on a time series ε = error term in the models; and, a and b = are the coefficients of the variables GDPR and OPV variables. Test for Unit Root between oil price volatility and GDP growth rate was done using the Partial Autocorrelation Function (PACF). This also help eliminate auto regression in the data owing to its time series nature which might lead to non-randomness. Randomness assumption was quintessential in the subsequent bivariate and multivariate analysis. 20

29 Further, multiple linear regressions using Ordinary Least Square (OLS) was used to test the relationship between economic growth and oil price volatility after eliminating autoregressive element in the variable. The bivariate regression model below formed the basis of multivariate analysis: GDPR = β 0 + β 1 OPV + ε Where GDPR is GDP growth rate, β 0 is regression constant, β 1 regression coefficient, OPV is oil price volatility and ε is error term. However, the relationship between GDP and oil prices is moderated by other macroeconomic such as inflation, exchange rate and government expenditure. Factoring in these factors after removing their autoregressive elements, a moderated multiple regression analysis was: GDPR 0 1OPV 2IN 3EXR 4GOV Where GDPR = Gross Domestic Product measured as the Quarterly GDP growth rate; OPV = Quarterly oil price volatility in the Kenya market as reported by Energy Regulatory Commission; IN = Quarterly inflation rate; EXR = Quarterly average exchange rate; GOV = Quarterly government expenditure; 21

30 CHAPTER FOUR DATA FINDINGS ANDANALYSIS 4.1 Introduction This chapter presents the data findings on the economic growth and GDP growth Index performance and analysis aimed at determining the granger causality between them. The quarterly GDP growth data was collected from CBK offices while the data on Oil Prices (converted to Ksh from Dollar) were obtained from the ERC offices. Using statistical Package for Social Sciences (SPSS version 11.5), the study began by conducting a unit root test by testing for autocorrelation (ACF) and partial autocorrelation (PACF) in the individual variables and in so doing check for non-stationarity. 4.2 Data Findings The study conducted a descriptive analysis on the data found from the secondary sources which is presented in appendix 1. The study found out that the average quarterly Oil Price value that Kenyan Shilling to US dollar has ever attained from 2005 to 2012 is 77.9 while the maximum GDP growth as economic growth indicator for the afore-mentioned five years is The minimum value for the Oil Price and GDP growth is 66.5 and respectively. The study also found that half of the values for the 96 quarters within the 8 year period lied either below or above for the Oil Prices and for the economic growth; this is shown by the median. 4.3 Trend in Oil Price and Economic growth Performance Figure 4.1 below presents the trend in Oil Price over time (2005 to 2012). The trend shows that the Ksh taken at US dollar base has been taking occasional dip and peeks pointing to randomness of the variable. 22

31 Figure 4.1: Trend in Ksh/US Dollar Oil Price 23

32 Figure 4.2 below shows the economic growth performance for the study period. The figure illustrates that the performance has had an up and down trend with no aggregate consistent upward sloping graph. This points to randomness hence absence of nonstationarity. Looking at the period after the timeline (2005) all the way to beginning of 2012, the performance had been on the decline point to the negative effect of global financial crisis on the economic growth performance. However, looking at the shilling performance against the US Dollar in the same period (figure 4.1), the performance was random; decline and rise with the rise in Dollar value being evidently on the increase between May of 2008 and beginning of 2012 showing a tinge of non-randomness. This leads to a preliminary deduction that there may be no causality between the two variables and that Oil Price may be non-random. 24

33 Figure 4.2: ECONOMIC GROWTH 20-Share Index ( ) 25

34 Partial ACF 4.4 Testing for Randomness using Partial Auto Correlation Functions (PACF) The study further tested for the unit root/non-stationary and the nature of the nonstationarity using autocorrelation with number of lags (prior periods) being 6. The results are presented in figure 4.3 and 4.4. On both figures, number 1,2,3..6 imply the partial correlation between the value of the variable today (that is at time "T") with the value at time "T-1," "T-2," "T-6" respectively. This horizontal line (and its mirror image on the negative side) defines the critical limits (95% confidence interval). If a bar goes beyond the horizontal line, then significant autocorrelation between the present and lagged values of the variable is indicated and partial correlation coefficient is statistically significant. Figure 4.3: Oil Price Partial Autocorrelation Function Graph 1.0 Exchange Rate Confidence Limits Coef f icient Lag Number Figure 4.3 above illustrates that Oil Price variable exhibit non-stationarity as at least one of the vertical bars (first lag) is higher than the horizontal line(s) that indicate the cut-off points for statistical significance. This hints at a first order differencing to take care of the non-stationarity. In figure 4.4 the first-lag partial auto-correlation is also above the critical limit indicating the presence of non-stationarity and suggests first-order differencing as the remedy. 26

35 Partial ACF Partial ACF Figure 4.4: GDP growth Index Partial Autocorrelation Function Graph NSE 20 Share Index Confidence Limits Coefficient Lag Number Removal of Non-Stationarity The data was again tested for non-stationarity after an attempt to remove the same using differencing order 1 as suggested in PACF test shown in Figure 4.3 and 4.4. The results were presented in Figure 4.5 and 4.6 above. Figure 4.5: GDP GROWTH Partial Autocorrelation Graph after First-Order Differencing 1.0 NSE 20 Share Index Confidence Limits Coefficient Lag Number Transforms: difference (1) Figure 4.5 shows that when an attempt to remove non-stationarity is made by using first order differencing, none of the partial-autocorrelation coefficients become above the critical limit. This indicates the absence of non-stationarity and strongly indicates the use of first-order differenced transformations of this variable in any regression analysis. 27

36 Partial ACF Partial ACF Figure 4.6: Oil Price Partial Autocorrelation Graph after First-Order Differencing Exchange Rate Confidence Limits Coefficient Lag Number Transforms: difference (1) According to figure 4.6, when first order differencing is used in an attempt to remove the non-stationarity, the non-stationarity is substantially done away with but the partial autocorrelation coefficient as shown by the first lag still rise above the horizontal line a little. However, when a second order differencing is done, the non-stationarity problem becomes even more (see figure 4.6). This point to first order differencing as the only remedy on removing the non-stationarity. Figure 4.7: Oil Price Partial Autocorrelation Graph after Second-Order Differencing 1.0 Exchange Rate Confidence Limits Coefficient Lag Number Transforms: difference (2) 28

37 4.5 Conducting Auto Regressive Integrated Moving Average (ARIMA) on the Model The study chose to conduct ARIMA instead of autocorelation analysis in SPSS due to flexibility of the former in enabling the choice of period lag, differencing and moving average. The ARIMA (p,d,q) regression is conducted by co-integrating the two variables (Oil Price and GDP growth index) by their first order differencing. The study used ARIMA (1,1,0); autoregressive parameter at lag 1, first order differencing and no moving average parameter. The results of the analysis were presented in figure 4.8 and 4.9. Since the study sought to establish the granger cause effect between Oil Price and economic performance, ARIMA (1,1,0) brought out the relationship in these autoregressive models: GDP = a 0 OPV t-1 + b 0 GDP t-1 + e it and OPV = a 1 GDP t-1 + b 1 OPV t-1 + e it The study further sought to establish if the Oil Price granger causes the economic performance as stipulated in the first model. According to the results, the final outcome were obtained just after 2 iterations and obtained a standard error/white noise ε it of value The study found that the coefficient of first order autocorrelation (1 lag) in economic performance indicator was.0326 while the coefficient between the same and 1 lag of first order differenced Oil Price was GDP = GDP t OPV t-1 The result of the causality test means that for economic growth performance (AR1) auto correlates with itself such that for every 1 unit increase in the change of economic growth performance between two and 1 periods back (that is, economic growth performance in 29

38 2005) the effect on the change in itself between the last period and the current period (2005) is If the difference in economic growth performance between two periods back/prior increases, then the difference between this quarter and last quarter increased by the same coefficient (.0325). However, is the significant is low as it is prone to errors, that is, there is 81.4% likely hood that this parameter might be wrong. The results further indicates that for every 1 unit increase in the change of Oil Price between the last and current periods, the effect on the change in economic growth performance between the last period and the current period is decrease. This is quite significant as the parameter estimate might be 7.72% incorrect as shown by the p-value. This shows that Oil Price significantly granger causes decrease in economic performance. This depicts causality from Oil Price to economic growth performance (OPV GDP) as the first alternative hypothesis (H A: a 0 = 0) developed in the previous chapter is rejected and null hypothesis (H 0 : a 0 0) accepted since a 0 > 0. The nature of the relationship is however negative. From the results, the study found that the standard error/ε it (marked in yellow) for the residual was The study found when the today s Oil Price (in the first regression model) is auto regressed with itself/ar1 (taking yesterday s rate/lag 1) as shown the second model, the Beta value/β (coefficient) become that is: OPV = OPV t GDP t-1 This means that for every 1 unit increases in the change of Oil Price between two and 1 periods back (that is, Oil Price of 2005) the effect on the change in Oil Price between the last period and the current period (that is, Oil Price for second quarter of 2005) is

39 That is the difference in Oil Price within the first quarter of 2005 increases, then the difference within the second quarter 2005 increases, too. This is significant as the p-value is below.05 critical for 95% confidence level. The figure also showed that when the for every 1 unit increase in the change of GDP growth between the last and current periods (that is, GDP GROWTH indicator first quarter of 2005), the effect on the change in Oil Price between the last period and the current period (that is, Oil Price for Feb 2005 Jan 2005) is If the difference in GDP growth indicator between second 2005 and first quarter of 2005 increases, then the difference between Oil Price for second 2005 and first quarter of 2005 decreases. However this is very insignificant as the p-value exceed the.05 critical limit (α) for 95% confidence limit. The study concludes that economic performance insignificantly or to a very small extent granger causes Oil Price. The second results rejected the second alternative hypothesis (H A : a 1 = 0) thereby accepting the null hypothesis (H 0 : a 1 0) since a 1 > 0. Although this shows causality from GDP to OPV (GDP OPV), the p-value is too erratic for any statistic significant conclusions to be drawn. The results of the above results, therefore, points to a unidirectional casualty from OPV to GDP (denoted OPV GDP) as H A(1) is rejected but H A(2) is rejected but with low significance level as we might probably 48% reject an alternative hypothesis that should be accepted. 31

40 4.3 Regression Analysis In the endeavor, the study sought to establish the relationship between oil price and economic growth, the relationship was moderated by the exchange rate, government expenditure and inflation and multiple linear regression analysis used to establish this. The coefficient of determination between the overall independent variables and economic growth was used to measure the strength of the relationship. Table 4.1 illustrates that the strength of the relationship between economic growth and independent variables. From the determination coefficients, it can be noted that there is a strong relationship between dependent and independent variables given an R 2 values of and adjusted to This shows that the independent variables (oil price volatility, inflation rate, average exchange rate, government expenditure) accounts for 80.1% of the variations in economic growth as measured by GDP growth rate. The study also used Durbin Watson (DW) test to check that the residuals of the models were not autocorrelated since independence of the residuals is one of the basic hypotheses of regression analysis. Being that the DW statistic were close to the prescribed value of 2.0 (2.006) for residual independence, it can be concluded that there was no autocorrelation. Table 4.1: Model Goodness of Fit R (Correlation) R Square (Coefficient of Determination) Adjusted R Square 32 Std. Error of the Estimate Durbin- Watson.925 a a. Dependent Variable: GDP Growth Rate b. Predictors: (Constant), Oil Price Volatility, Inflation Rate, Average Exchange Rate, Government Expenditure