Oil price volatility and its effects on economic growth

Faculty of Economics and Business

Department of Economics

Bachelor’s Thesis Economics

Oil price volatility and its effects on economic growth

Name: Quinn Veenstra Student number: 10564233 Date: February 1, 2016 Supervisor: R.E.F van Maurik

Statement of Originality

This document is written by Student Quinn Taro Rafferty Veenstra who declares to take full responsibility for the contents of this document. I declare that the text and the work presented in this document is original and that no sources other than those mentioned in the text and its references have been used in creating it. The Faculty of Economics and Business is responsible solely for the supervision of completion of

Table of contents

1. Introduction

page 4

2. Literature review

page 6

2.1 History of oil prices and oil price volatility

page 6

2.2 Previous research

page 8

3. Data and methodology

page 11

4. Results

page 14

5. Conclusion and recommendations

page 17

6. References

page 18

Appendix 1 – Test for stationarity

page 19

1. Introduction

In recent years, oil prices and oil price volatility have been an important topic of discussion under politicians, consumers and most importantly economists. There have been different opinions about what drives oil prices and what particular effect the changes in oil prices may have on an economy.

Crude oil is seen as one the most important driving forces of the global economy and the crude oil market is the largest commodity market in the world. Since the 1970s and 1980s, when several political incidents in the Middle East disturbed the certainty of the oil supply to rest of the world, it has been clear that the industrialized economies have become dependent on oil. Nowadays oil prices have been an important topic of discussion as well. After about five years of oil price stability, the price of a barrel of Brent crude oil in Europe fell from $100 per barrel in September 2014 to $46 per barrel in January 2015. Therefore, it is important to investigate what exact effects changes in oil prices have had on economic activity throughout the world. In addition, it is also important to distinguish the difference in effects on an emerging economy and a developed economy as the state of the economy of a country may have a big influence whether or not oil price fluctuations have a significant effect on the economic growth. Some previous research suggests that oil price changes have noticeable consequences on economic activity. However, this research has been mainly done for only the United States on its own or some other particular countries on their own and this research has been mainly done for the years following the oil crises in the 1970’s. Segal (2011) mentions that for the United States there exists a negative correlation between oil prices and economic activity. The still limited research that has been done on this topic suggest that oil price changes do have an effect on economic activity and that the particular effect depends on whether a country is an oil importing country or an oil exporting country. This paper will investigate more different countries throughout the world and will also keep in mind some important factors that may be of influence

concerning the effects of oil price changes on economic growth. A lot of previous papers have taken some factors in to account mot not all the factors at the same time, as this research will.

In this paper the following countries are going to be investigated: The United States, China, Japan, Germany, Russia, Brazil, India and France. Four of these countries are considered as developed economies and four countries are considered as emerging economies. The four developed economies, the United States, Japan, Germany and France, are the four largest developed economies in the world. The four emerging economies, also known as the BRIC countries, are Brazil, Russia, India and China. These four countries are considered as the four largest developing or emerging economies in the world and some economists say that these economies may overtake the G7 economies by 2027.

This brings up the following research question: What are the effects of oil price fluctuations on the economic growth of the investigated countries and what is the difference in effects between an emerging economy and a developed economy?

To answer this research question, panel data of the eight economies mentioned will be used from January 1991 to January 2014 on a year-to-year basis. The panel data will be used to make a linear

regression model that will show whether or not oil price fluctuations have a significant effect on economic growth and whether or not it matters if an economy is either emerging or developed.

This paper will proceed as follows. In section 2, a literature review regarding previous research on the effect of oil price fluctuations on economic growth will be brought up and discussed. Furthermore, relevant background information about oil price fluctuations for the investigated years and countries will be given. Section 3 describes the data sample being used to set up the regression and some additional

information about each country investigated will be given. In section 3 the method of research will also be thoroughly described and explained, which includes a description and an explanation of all the variables used in the regression. In section 4, the results of the regression analysis will be presented and explained. Section 5 will be the the conclusion of the results presented in section 4. Conclusions will be made about the effects of oil price fluctuations on economic growth and whether or not it matters if a country is a developed economy or an emerging economy. Moreover, section 5 will also include some

recommendations for any further research done on this particular subject.

2. Literature review

In this literature review, first a brief description of background information and history about international oil prices in general will be given. After that, some recent research regarding the subject will be discussed. Oil is a bulk commodity, which is linked with the daily life of a consumer and closely linked with the national economic development, which takes up almost 40% of the total consumption of global energy consumption (Yan, 2012, p.39). Yan (2012) further mentions that oil has a very uncertain supply with big price fluctuations. As a result, oil has become the most strategic fossil energy in the worldwide economy. As mentioned in the introduction, after the Oil shocks in the 1970s and the several global

recessions that followed, interest has risen into the relation between oil price changes and economic activity and economic growth. Now again since entering in the 21st _{century, oil prices have been through major ups}

and downs through the years causing dramatic changes to economic security in several countries as for example China (Yan, 2012, p. 39).

2.1 History of oil prices and oil price volatility

In this section a brief description of oil prices and oil price changes will be given and discussed. The time lapse will begin in the years after 1945, as the oil consumption and technological level before the Second War were of a much lower level than after 1945. First of all, the decades that will be discussed before the 1990s are important to understand what time period a lot of previous research has focused on, which will be the main focus of section 2.2. For the research done in this paper, the last two decades are of particular importance as the history of oil price volatility can give a better look at why and how oil price volatility may have an impact on economic growth of big economies throughout the world.

After the Second World War, the biggest consumption economies entered into the full construction period, which caused extraordinary worldwide interest in oil demand. By 1967, the total consumption of oil surpassed the consumption of coal in the total world energy consumption and reached a total share of 40%, becoming the biggest energy source in the world. During the 1960s, the Organization of the Petroleum Exporting Countries (OPEC) began to settle as an important intergovernmental organization for oil exporting countries, which consisted of mostly countries from the Middle East. The organization established a good foundation for the oil exporting countries to cooperate with each other and was initially founded to counter the cartel that the western oil companies has formed in the late 1920s. However, the Organization of the Petroleum Exporting Countries did not succeed in increasing the oil prices, which were mainly shaped by the western transnational oil companies. As a result, the OPEC did not have significant

power to influence to crude oil price and thus the oil price remained relatively stable for the period 1945-1965. The oil price stability, however began to change when the oil exporting companies began to nationalize the operating petroleum companies. Consequently, the OPEC began to have a much bigger influence on the crude oil market and the crude oil price and resulted in a decrease in influence of the western companies (Yan, 2012). Due to the outbreak of the fourth Middle East war in 1973, which is considered as the First Oil Shock, the long period of oil price stability really came to an end. The first Oil Shock was followed by the Second Oil Shock in 1979-1980, due to the Iranian Revolution. These two events are considered to be the cause of the rise of crude oil prices in the 1980s (Wirl, 2008, p.1041). The rise of the oil prices is generally seen as the main reason for the economic recessions throughout the world in the early 1980s. However, starting in 1985 the oil prices started to become stable again even though the oil prices did go up because of the Russian Revolution in 1990-1991 and the First Gulf War. This relative oil price stability lasted until the beginning of the 21st _{century. In the early 2000s the so-called ‘Oil Bubble’}

appeared, which was a period in which oil price increased rapidly. Based on this ‘Oil Bubble’, economists concluded that oil prices were very unpredictable. In recent years, following the recession in 2008, oil prices have been very volatile making crude oil prices even less predictable (Yan, 2012, p. 41). The figure below shows the path of crude oil prices through the years starting in 1861, however the oil prices became rather important after the Second World War.

Figure 1: Crude oil prices since 1861 ($ per barrel) Source: http://www.wikiwand.com/en/Price_of_oil

2.2 Previous Research

In this section, the main findings of previous research concerning the effects of oil price fluctuations on economic growth will be discussed. The first researcher who made an in-depth paper about the

relationship between oil price changes and economic activity after the two Oil Shocks in the 1970s is Hamilton. Hamilton (1983, p. 246) concludes in his paper, which is an investigation on what had caused several US recessions since the Second World War, that seven out of the eight recessions that the United States had gone through after the Second World War were preceded and caused by dramatic increases in crude oil prices. Thus, Hamilton states that there is a simple symmetric relationship between oil prices and economic activity.

However, the statements made in Hamilton’s paper are countered by a number of authors. The main critic of Hamilton’s report is Hooker (1996), who also did a research on the effect of oil prices on the macro economy. Hooker came to the conclusion that oil shocks before and during the Oil Shock in 1973 had a significant impact on the United States economy. Hooker (1996, p. 211) further mentions that oil prices became less significant after the Second Oil Shock in 1979 as he could not find a significant relationship between that oil shock and the big recession that followed in the US in the early 1980’s. He concludes that after the early 1980’s the oil price-macroeconomy relationship changed from a simple symmetric relationship, wherein an oil price increase or decrease results into a linear movement in the economic activity of a country (the United States in particular), to a more complex asymmetric relationship. Blanchard and Gali (2007) partially agree with the statements Hooker made in his paper. Blanchard and Gali (2007, p. 1) also seem to question the exact significant effect of oil price changes on economic fluctuations. The reason behind this doubt, is that since the 1990s the global economy has experienced at least two major oil shocks which were almost identical to the two oil shocks in the 1970s. Nonetheless, the effect of the two shocks in the 1990s did not effect the GDP growth in the industrialized countries compared to the 1970s. Blanchard and Gali (2007, p. 65) state that the major oil price changes may have coincided with some large shocks of a different nature. So it may have seemed to many

economists that oil prices were the main cause of the economic downturns in the 1970s and 1980s. However, Blanchard and Gali state that this may have been the cause of the increase of other commodity prices in the same period. Though they do mention in their conclusions that their findings should be

interpreted with caution as the model they used to investigate the problem at hand is still too primitive. For my own research it is important to keep their paper in the back of my mind as a limitation of a research on the significance of oil price changes on economic activity. This is because of the fact that a simultaneous

effect may make it seem like oil prices have an effect on economic growth while in reality another factor may have caused the change in economic growth while the oil price volatility may be insignificant.

The problem with the previous mentioned papers, which discuss the relationship between oil price fluctuations and economic activity, is that they mostly only consider the economy of the United States and do not take a look at the other economies in the world, which is an important aspect of this paper. Even though the papers discussed do give a particular view on the relationship investigated, they do not give an entire view on the relationship between oil prices and economic activity for more countries in the world. The following papers do take other countries in to consideration, just as the investigation in this paper will do as well. Jiminez-Rodriquez and Sanchez (2005) researched the effect of oil prices on economic growth for some OECD countries. They came to the conclusion that real GDP growth is negatively correlated with oil prices for oil importing countries. However, for oil exporting countries results are mixed. For example, the GDP growth of Norway is positively correlated with oil prices, while for the United Kingdom a rise in oil prices is found to have a significant negative impact on GDP growth. In this paper, it is interesting to investigate what exact effect oil price fluctuations have on GDP growth for the countries investigated and if the effects are similar to the findings of Jiminez-Rodriquez and Sanchez (2005) for the OECD countries. Mork, Olsen and Mysen (1994, p. 33) investigate in their paper the effects of oil price increases and decreases on the macroeconomy for seven individual OECD countries. They came to the conclusion that for all individual countries, so in their research no panel data was used, there was a negative correlation between the coefficients of oil price volatility and economic growth. For most countries these relations were significant as well. Mork, Olsen and Mysen’s (1994, p. 33) overall conclusion is that oil price

fluctuations should always be a force to be reckoned with concerning the effect on shaping business cycles of leading market economies. This force should always be taken into consideration as long as oil remains an important energy source for the large economies throughout the world.

An important part of the research in this paper is the variable oil price volatility. Narayan and Narayan (2007, p. 6553) did a research based solely on oil price volatility and its shocks. So, they looked at how the variable oil price volatility moved over a period of time. In their research they used the period 1991-2006. In their conclusion, they mention that across the various samples they used, there is evidence that proves that there exists asymmetry in the shocks of oil prices, which means that an increase of oil prices may take a longer time than a decrease in oil prices or the other way around. Their second conclusion in their paper regarding the modelling of oil price volatility was that over their full sample period certain shocks have permanent effects on volatility. According to Narayan and Narayan this implies that the

behavior of oil prices tends to change over small periods of times. This results into a difficult task in predicting what may happen to oil prices in the short term future.

Another factor in this paper is the dummy variable for a developing economy. Yoshino and Taghizadeh Hesary (2014) did an investigation in their paper on the effects of crude oil prices on economic growth for two developed economies (the United States and Japan) and one emerging economy (China). Yoshino and Taghizadeh Hesary (2014, p. 15) came to the conclusion that the impact of oil price

fluctuations on GDP growth are much milder on the developed economies in comparison to the emerging economy. In this paper, it is important to look if this conclusion also holds for a larger sample of countries and a longer time span. As the sample of countries, as mentioned in the introduction, will be eight countries of which four countries will be emerging economies and the other four countries will be developed economies. The time span in this paper will also be different in comparison to that of Yoshino and Taghizadeh Hesary (2014) as they investigated a very recent time span from 2007-2013, while in this paper the larger period 1991-2014 will be considered. So the differences that are being implemented into this research can show if the research of Yoshino and Taghizadeh Hesary (2014) holds for more countries in a larger time span. The reason this paper will investigate the period from 1991 till 2014 is because of the fact that there has been a lot of research and papers done on the Oil Shocks in the 1970s while there is still not much known about the specific effects of oil price fluctuations in het last couple of decades. Therefore, the research in this paper will take a closer look at this specific time period.

3. Data and methodology

This part of the paper covers the data that is used in the empirical model. Firstly, the method of research will be explained and discussed in further detail. After that, the dependent and independent variables will be closely looked at and discussed. Finally, some summary statistics of the panel data variables discussed will be shown in a table. First of all, I will begin with the explanation of the model that is going to be used to investigate the research question. To determine whether or not oil price volatility has a

significant effect on GDP growth, a linear regression model will be used. This will be done with the use of panel data for the eight countries that were mentioned in the introduction: The United States, France, Germany, Japan, Brazil, India, Russia and China. So for each country data is collected for each variable for the years 1991 till 2014. With all of the data from all of the countries combined a linear regression model will be made in STATA, which will eventually show whether or not oil price volatility has a significant effect on GDP growth for the combination of the investigated countries

Firstly, all of the variables in the regression have to be stationary. All the variables in a regression have to be stationary as we are dealing with panel data, also known as cross-sectional times series data. A stationary variable in a time series has statistical properties such as a mean and a variance which are constant over time. If the variable does not have a mean or a variance that is constant overtime than the variable is non-stationary. So stationarity requires the future to be like the past, at least in a probabilistic way (Stock & Watson, 2012, p.578). This leads to liable statistical forecasting methods as variables can be relatively easy to predict if they are stationary. Non-stationary variables can be transformed into approximate stationary variables through the use of mathematical transformations. Furthermore, variables of which we do not know for sure if they are stationary or non-stationary can be tested statistically through the use of a unit root test. Stationarity will be discussed in further detail when all the individual variables are discussed.

The first independent variables in the regression model are inflation rate, government deficit, exports and foreign direct investment. The other independent variables are oil price volatility, and a dummy variable for a developed economy (so 1 if a country is a developed economy and 0 if a country is an emerging economy). To begin, all of the data for almost all the variables used are from the databank of the World Bank, except for the data found for the oil price volatility (data found on oil price volatility are from investing.com). Inflation rate will be measured as the yearly growth rate of the GDP implicit deflator. The GDP implicit deflator shows the rate of the price change for the entire economy. The GDP implicit deflator is different in comparison to the CPI inflation, as CPI inflation reflects the goods and services bought by consumers, while the GDP implicit deflator reflects the prices of all goods and services produced in the

country itself. Government deficit, exports and foreign direct investment are all percentages of GDP. Oil price volatility is measured by the change of the crude oil price compared to the year before. The particular crude oil price used is a mix of several leading crude oils in the world.

Now to come back at the stationarity of the variables. Government deficit, exports, foreign direct investment in this research are known to be stationary as they are all a percentage of the gross domestic product, which in this research is the dependent variable. As for oil price volatility and inflation this is not known. The variable oil price volatility is not known for always being stationary. In the case of the inflation rate, a lot of research has been done on the topic of stationarity. There have been a lot of mixed results concerning inflation rate. Consequently, a unit root test was done in STATA for both the variable oil price volatility and the variable inflation rate to test whether or not the variables are stationary or non-stationary variables. The results of the unit root test for both variables can be found in Appendix 1. The first table shows the results of the unit root rest for the oil price volatility (OPV). The null hypothesis, panels contain unit root tests, are rejected with a p-value of 0.0000. Therefore, it can be concluded that the variable oil price volatility in the panel data used is stationary at a 1% significance level. Now to continue with the result for the variable inflation rate. For the variable inflation we can also conclude that the variable is stationary. The table, that can be found in Appendix 1, shows that the null hypothesis, the panels contain unit roots, can be rejected at a 1% significance level as the p-value is 0.0000. Now that for each variable an explanation of why the variable does not contain any unit roots has been given, an explanation of the method of research can be given.

The model that will be used to answer the research question that was mentioned in the

introduction will be a linear regression model. Two linear regressions will be made to test both parts of the research question. The first regression will test whether or not oil price volatility on its own has a significant effect on GDP growth. In the second regression, an interaction term will be included. The interaction term will consist of a multiplication of the dummy variable, developed economy, and of the variable oil price volatility. Adding this interaction term, gives the possibility to test whether the effect of oil price volatility on GDP growth is significantly bigger or smaller for a developed economy in comparison to an emerging economy. This second regression is needed to answer the second part of the research question that needs to know the difference in effects of oil price volatility between an emerging economy and a developed

economy.

Now finally to continue with some summary statistics of the panel data imported into STATA will be given. The summary statistics can be found in table 1 in Appendix 2. The only thing that is out of the

ordinary in table 1, which shows the summary statistics, is the maximum of the inflation in the summary statistics. This may lead to unreliable results concerning the coefficient of the variable inflation in both linear regression models.

4. Results

In this section, the results of the regressions done will be shown. Two regressions will be done in total. The first linear regression model is mainly focused on the effect of oil price volatility on economic growth. For this regression, the heteroscedastic option in STATA is used (so the option robust). The heteroscedastic option keeps in mind that the error terms do not have constant variances, while if the homoscedastic option would have been used, it would consider the error terms to have constant error terms. Table 2 down below shows the coefficients, so the particular effects of all the variables on economic growth in the first model. The model itself looks as followed:

GDPgrowth = β0 + β1*INFLATION

+

β6*OPV

Table 2: Linear regression model

GDP COEFFICIENT ROBUST STD. ERROR T-VALUE P>|T| 95% CONFIDENCE INTERVAL INFLATION -0.00305 0.00264 -1.16 0.250 -0.00827; 0.00216 DEVELOPED -2.8198 0.5795 -4.87 0.000 -3.9631; -1.6764 EXPORTS -0.00339 0.00428 -0.79 0.430 -0.01184; 0.0051 DEFICIT 0.01087 0.0066 1.65 0.101 -0.00213; 0.02387 FDI 0.02306 0.00564 4.08 0.000 0.0119; 0.03419 OPV 0.0202 0.00856 2.36 0.019 0.0033; 0.03709 CONSTANT 2.0241 1.10255 1.84 0.068 -0.1511; 4.1993

Table 2 above shows that the coefficient of oil price volatility is 0.0202, with a p-value of 0.019. Therefore, it can be said that at a 5% level oil price volatility has a significant effect on the GDP growth, while at a 1% significance level this can not be concluded. Now to comment on the variable ‘Developed’ in this regression model, it can be concluded that being a developed economy has a negative effect of

-2.8198% on GDP growth, while for an emerging economy this would be 0%. In addition, the coefficient of the variable ‘Developed’ is highly significant with a p-value of 0.000. This however, does not give any

useful information concerning the causality between a developed or emerging economy and oil price volatility and its effects on economic growth. This topic will be investigated in further detail in the second regression model in table 3.

Now to continue with the second linear regression model, which includes the interaction term that was discussed in section 4. The following figure shows the effects on economic growth. This second model, just like the first regression model, keeps the heteroscedasticity of the error terms in mind. The second model looks as followed:

GDPgrowth = β0 + β1*INFLATION

+

β6*OPV + β7*OPV*DEVELOPED

Table 3: Linear regression model including interaction term

GDP COEFFICIENT ROBUST STD. ERROR T-VALUE P>|T| 95% CONFIDENCE INTERVAL INFLATION -0.00301 0.00267 -1.13 0.260 -0.00827; 0.00225 DEVELOPED -2.704364 0.06585 -4.11 0.000 -4.0036; -1.40515 EXPORTS -0.00332 0.00428 -0.78 0.439 -0.01176; 0.00513 DEFICIT 0.010798 0.00666 1.62 0.107 -0.00234; 0.02393 FDI 0.02303 0.00565 4.08 0.000 0.0119; 0.03417 OPV 0.02584 0.01484 1.74 0.083 -0.00344; 0.05511 OPV*DEVELOPED -0.01124 0.01703 -0.66 0.510 -0.04484; 0.0224 CONSTANT 1.9649 1.12628 1.74 0.083 -0.2572; 4.1869

In table 3, we can see that oil price volatility now has a coefficient of 0.02584 with a p-value of 0.083. So the coefficient of oil price volatility has actually become less significant as compared to the regression done in table 2. This is the case as OPV, in table 3, is not even significant at a 5% significance level anymore. Now continuing with the interaction term, in which oil price volatility is multiplied with the dummy

variable for a developed economy, we can see that the coefficient is estimated at -0.01124 and it has a p-value of 0.510. This p-p-value is extremely high, which implies that the coefficient of the interaction term is highly insignificant and thus for this regression model we can not make any conclusions about the

5. Conclusion

Considering the results from the linear regression models in STATA that were presented in section 4 and keeping previous research on the effects of oil price fluctuations in mind, some conclusions can be made. Firstly, a conclusion will be made based on the first linear regression model, discussed in section 3 and section 4. After that, a conclusion will be made based on the second linear regression model.

To begin with the first regression model, which is only focused on the effect of oil price volatility on economic growth. We can conclude that oil price volatility does have an effect on economic growth, however this effect is not highly significant as at a 1% significance level the coefficient is considered insignificant. Moving on to the second regression model, this model does not add any relevant information to the answer the research question as the coefficient for oil price volatility becomes less significant

compared to the first regression model and the interaction term between the variables ‘developed’ and ‘oil price volatility’ is highly insignificant. Therefore, we can come to the conclusion that in this research there may be some kind of positive effect of oil price volatility on economic growth for the panel investigated. However, we can not conclude that there is a difference in the effects of oil price volatility between a developed economy and an emerging economy. Furthermore, it is difficult to base the conclusions of a regression made with data from different countries on one particular country as countries differ so much on the basis of being an oil exporting or importing economy and the other characteristics of determinants of GDP.

For any further research, I recommend to take a larger sample of countries with high similarities concerning the structure of their GDP. Only then a good regression concerning the effect of oil price changes on GDP growth can be made. In addition, any outliers in the sample of the variables have to be left out as this may cause the variables to be insignificant in the regression models. In this research, only two linear models were used. However, it may be more useful to use non-linear models as the relation between oil prices and GDP changes have such a difficult relationship. Further research should also keep in mind that when getting a significant coefficient for the variable oil price, it does not mean that oil price volatility has an actual effect on economic growth. This is because of the fact that another variable may have caused the change in GDP at the same moment. Thus, further research should think about which variables to add and which variables they should drop in their model.

References

Blanchard, O. and Gali, J. (2007) The Macroeconomic Effects of Oil Shocks: Why Are the 2000s so Different From the 1970s? NBER Working Paper Series. Working paper 13368.

Hamilton, J. D. (1983) Oil and the Macro economy since World War II. Journal of Political Economy

91 (2). 228-248.

Jimenez-Rodriguez, R. (2008). The Impact of Oil Price Shocks: Evidence From the Industries of Six OECD Countries. Energy Economics. 30, 3095-3108.

Jimenez-Rodriguez, R., & Sanchez , M. (2005) Oil price shocks and real GDP growth: empirical evidence for some OECD countries. Journal of Applied Economics. 37, 201-228.

Mork, K. A., Olsen, O., Mysen, H. T. (1994) Macroeconomic Responses to Oil Price Increases and Decreases in Seven OECD Countries. Energy Economics, 15(4), 19-35.

Narayan, P. K., & Narayan, S. (2007) Modelling oil price volatility. Energy Policy. 35(12) 6549-6559.

Segal, P. (2011) Oil price shocks and the macro economy. Oxford Review of Economic Policy, 27 (1), 169-185.

Stock, J., & Watson, M. (2012) Introduction to Econometrics: Third Edition. Essex: Edinburgh Gate.

Taghizadeh Hesary, F., & Yoshino, N. (2013) Which Side of the Economy is Affected More by Oil Prices: Supply or Demand? United States Association for Energy Economics (USAEE) Research Paper No, 13-139

Taghizadeh Hesary, F., & Yoshino, N. (2014) Economic impacts of oil price fluctuations in developed and emerging economies. IEEJ Energy Journal

Appendix 1 – Test for stationarity

Levin-Lin-Chu unit-root test for OPV

--- Ho: Panels contain unit roots Number of panels = 8 Ha: Panels are stationary Number of periods = 24 --- Statistic p-value --- Unadjusted t -11.5298 Adjusted t* -5.6954 0.0000 ---

Levin-Lin-Chu unit-root test for inflation

--- Ho: Panels contain unit roots Number of panels = 8 Ha: Panels are stationary Number of periods = 24 --- Statistic p-value --- Unadjusted t -36.8998 Adjusted t* -37.6482 0.0000 ---

Appendix 2 – Summary statistics

Table 1: Summary statistics panel data

VARIABLE OBSERVATIONS MEAN STD. DEV. MIN MAX

GDP 192 3.3704 4.3287 -14.5311 14.27646 INFLATION 192 50.3191 261.8681 -2.164421 2302.841 DEVELOPED 192 0.5 0.5013 0 1 OPV 192 10.0453 32.5455 -54.5722 74.88 EXPORTS 192 95.5052 55.5608 1 191 DEFICIT 192 74.1406 32.4033 1 111 FDI 192 96.5 55.5698 1 192