Christian Benedict B. ARGA 11027614 AE-FIN A Study on the Effect of Inflation, Net Income, and Energy Use to the Fossil Fuel Consumption in the Philippines An Empirical Paper Presented to The Faculty of the School of Economics De La Salle University In Partial Fulfillment of the Requirements in ECONMET Submitted by: Christian Benedict B. Arga 11027614 Submitted to: Dr. Cesar Rufino December 14, 2012 1 Christian Benedict B. ARGA 11027614 AE-FIN Table of Contents Introduction I. II. III. IV. Background of the Study Statement of the Problem Objectives of the Study Scope and Limitations
Review of Related Literature I. II. III. Inflation Net Income Energy Use Operational Framework I. II. Variable Descriptions A-Priori Expectations Methodology I. II. Data Gathered Model Specifications Empirical Results and Interpretation I. II. Variable Analysis Critical Assumptions 2 Christian Benedict B. ARGA 11027614 AE-FIN 1. Multicollinearity 2. Homoscedasticity 3. Non-autocorrelation Remedial Measures and Adjusted Estimated Econometric Model I. II. Remedial Measures Adjusted Econometric Model Conclusions and Recommendations Bibliography Data Presentation 3 Christian Benedict B.
ARGA 11027614 AE-FIN Introduction I. Background of the Study In this empirical paper, the researcher aims to know the effects of net income, inflation and energy use on the consumption of fossil fuels in the country. This project will allow the student to use various econometric concepts and a variety of tests to determine the factors that will allow him a practical approach on the subject. Fossil fuel as defined by Encyclopedia Britannica is “any of a class of materials of biological origin occurring within the Earth’s crust that can be used as a source of energy. It is a hydrocarbon containing natural resource that is not acquired from plants or animals. Fossil fuel is a general term for buried combustible geologic deposits of organic materials, formed from decayed plants and animals that have been converted to crude oil, coal, natural gas, or heavy oils by exposure to heat and pressure in the earth’s crust over hundreds of millions of years. The depletion of fossil fuel has been an underlying problem in our economy. Unlike energy coming from hydroelectric power plants or windmills, the energy acquired from fossil fuel cannot be renewed and is gone forever. Christian Benedict B. ARGA 11027614 AE-FIN II. Statement of the Problem As said earlier, fossil fuel is a non-renewable energy source. All countries in the world are trying their best to conserve their respective resources. The problem in this empirical project is that whether various factors such as inflation, net income, and energy use of the country has an effect on its consumption of fossil fuels. III. Objectives of the Study There are various objectives to this study. First is to find out whether inflation, net income, and energy use has an effect on a country’s consumption on fossil fuels.
Second, is to educate the readers of this paper which of the independent variables affects the consumption of fossil fuel the most. And lastly, this paper aims to apply the various lessons learned in class to show the effects of the chosen variables on the fossil fuel consumption. 5 Christian Benedict B. ARGA 11027614 AE-FIN IV. Scope and Limitations The data gathered that was used in this project was limited and only allowed the researcher to gather up to 35 observations because some variables lack values for previous years. Because of this reason, the sample size is relatively small and cannot be compared to other countries for reference.
Review of Related Literature I. Inflation Inflation is defined by Investopedia, as the rate at which the general level of prices for goods and services is rising, and, subsequently, purchasing power is falling. Central banks attempt to stop severe inflation, along with severe deflation, in an attempt to keep the excessive growth of prices to a minimum. Inflation has affected the rate of many products at which they are consumed. For this project, we will find out if inflation has an effect on the consumption of fossil fuels. We want to find out if people would 6
Christian Benedict B. ARGA 11027614 AE-FIN consume more or less if the prices of fossil fuel has been affected by inflation. II. Net Income Net income, as learned in the student’s business subjects, is the money left after subtracting expenses and other deductibles like taxes and interest to the total revenue. We will observe if net income has an effect on fossil fuel consumption. Maybe, a higher net income may lead the consumer to consume more or perhaps, a lower net income may get the consumer to find other sources of energy which may be cheaper than fossil fuel. III. Energy Use
Fossil fuel burning powers our vehicles and industries, heats and cools our buildings, and runs appliances. It also produces electricity that we use for all sorts of purposes, such as lights and computers. This is quite obvious because as we consume more energy, the more fossil fuel we burn and vice versa. 7 Christian Benedict B. ARGA 11027614 AE-FIN Operational Framework I. Variable Description The model will contain the following components, the dependent variable and the independent variables. The independent variables are those that are exogenous in nature. It is not affected by any variable contained within the model.
The dependent variable, on the other hand, is endogenous in nature. It is affected by all the independent variables in the model. For this project, there will be three independent variables namely; inflation, netincome, and energy while the dependent variable will be fossil. Table 1 : Variable Description This is the independent variable. This is the fossil fossil fuel consumption of the Philippines from the years 1977 until 2011. It is expressed in percentage. 8 Christian Benedict B. ARGA 11027614 AE-FIN This is a dependent This is GDP variable. measured inflation by the deflator from 1977 up to 2011.
This variable is expressed in the annual percentage. This is a dependent is the variable. This yearly total income of our country from 1977 until netincome 2011. We can see that the data has negative values. This is due to the data being in BoP expressed in US$. This is a dependent is the variable. energy yearly This energy of from the 1977 consumption Philippines until 2011. This variable 9 Christian Benedict B. ARGA 11027614 AE-FIN is expressed in kilotonnes. II. A-Priori Expectations The A-Priori is a smart justification before actual testing and analysis is done with the data. Given that the fossil fuel onsumption is the dependent/endogenous variable, we will take a look at the relationship of this with the independent/exogenous variables. This will be presented in the table below: Table 2: A-Priori Expectations Endogenous Variable: fossil Exogenous Variable Relationship Reason As inflation goes up, the prices of fossil fuel goes up too therefore, the consumption of fossil fuel will be reduced. People will try to find cheaper sources of energy and maybe renewable ones are a good try. inflation negative 10 Christian Benedict B. ARGA 11027614 AE-FIN netincome positive energy positive
As netincome increases, there will be more money to spend therefore it may affect the consumption of fossil fuel positively. People will tend to buy more goods like food which requires electricity to cook. As energy consumption goes up, there will be more and more fossil fuel that will be consumed. Most of the world’s energy source comes from fossil fuels. Therefore, as people tend to consume more energy, more fossil fuel is going to be burned up. Methodology I. Data Gathered The data gathered has been acquired from the databank of the World Bank’s website. The data is from the Philippines dating back from 1977 until 2011.
There are a total of 35 observations. This is due to the reason that some of the variables lack data from 1976 and further back. So to keep the consistency of this project, only 35 observations per variable has been used. This is to ensure that the 11 Christian Benedict B. ARGA 11027614 AE-FIN data is unbiased and comparable to each other. Presented in the table below is the data summary from Stata12: II. Model Specifications The regression model to be formed will be based on intuition, economic theories, conducted studies and research materials related to the objectives of this paper.
The independent variables chosen will all be affecting the dependent variable, profits, proportionately; therefore, it is appropriate to use the lin-lin model or the linear-linear model. The estimated econometric model based on the A-priori expectations would look like this: fossil=? 1 +? 2 inflation+? 3 netincome+? 4 energy+U i 12 Christian Benedict B. ARGA 11027614 AE-FIN Empirical Results and Interpretation I. Estimated Econometric Model The summarization table earlier confirms that there are truly a total of thirty-five observations for each variable.
The data can therefore be regressed and is comparable for there are equal numbers of observations per variable included in the estimated model. However, in order to determine the individual contribution of each variable, the values should be in terms of the same unit of measurement. The model has been transformed into the Log-Log model so that the data is comparable to each other. Stata12 generated the missing values. Using the Ordinary Least Squares regression functionality of Stata12, the following table has been generated which will lead us to acquire our estimated econometric model: 13 Christian Benedict B. ARGA 11027614
AE-FIN The estimated model is now: fossil=-2. 324895. 0168192inflation+. 0182493netincome+. 5711335energy+U i From doing the Ordinary Least Squares regression, we are actually looking at the p-values and the r-squared values. The pvalue will tell us the significance of the variable while the r-squared will tell us the explanatory ability. The significant parameters in the data are the netincome, energy, and the constant. We could say that these parameters are significant because when the p-value generated by Stata12 is generated by two, the p value falls below the required value of 0. 05. The r-squared of the data is at 0. 349 or 93. 49%. This tells us that this data explains that the data is a good fit for the real world because it explains 93. 49% of the real world model. II. Variable Analysis Given the independent variable, fossil, and the independent variables, it is given that for every 1 unit increase in inflation, fossil fuel consumption would go down by 0. 0168912%. This gives inflation a negative relationship with fossil fuel consumption and 14 Christian Benedict B. ARGA 11027614 AE-FIN thus matches with our A-priori expectation. Next, for every 1 unit increase in net income, fossil fuel consumption will go up by 0. 182493%. This satisfies our A-priori expectation that net income has a positive relationship with fossil fuel consumption. Energy is also proven to give a positive relationship with fossil fuel consumption. This is given by its coefficient of 0. 5711335. So this means that for every 1 unit increase in energy use, fossil fuel consumption goes up by 0. 5711335% which also satisfies our Apriori expectation. Now for the constant, which has a coefficient of 2. 324895, tells us that if nothing happens with inflation, net income, and energy use, fossil fuel consumption still goes down by 2. 324895%.
Although it is a fantasy that there will nothing happen with inflation, net income, and energy, there may be missing factors which were not included in this project. III. Critical Assumptions Classical linear regression models (CLRM) have three critical assumptions ? non-multicollinearity, homoscedasticity and nonautocorrelation. Violations of these will each give up an unfavorable, unreliable and inaccurate outcome for the estimated model. It is for these reasons that the above interpretation of the 15 Christian Benedict B. ARGA 11027614 AE-FIN model could not be used yet to conclude anything regarding the consumption of fossil fuel. . Multicollinearity, as defined by Penn State University, is an event whenever two or more of the predictors in a regression model are moderately or highly correlated. To test for multicollinearity, the Variable-Inflating Factor will be used. Shown below is the result generated by Stata12: There is multicollinearity within the model if the Mean Variance-Inflating Factor is greater than 10. From the result generated by Stata12, the Mean VIF is 1. 08. This value is very far from 10 therefore, the model does not possess the problem for multicollinearity. 2.
Homoscedasticity Homoscedasticity means that the variance around the regression line is constant for all X values. Heteroscedasticity are most commonly present in cross-section data. When 16 Christian Benedict B. ARGA 11027614 AE-FIN heteroscedasticity is present, OLS is no longer the best linear unbiased estimate; therefore, violation of this assumption is a far graver problem than the violation of non-multicollinearity. The researcher used Stata12 to test for homoscedasticity. Using the variables above for the test, Stata12 gave the result as follows: Given that our H o : P-Value (one-tailed) > 0. 5: constant variance and H A : P-Value (one-tailed) < 0. 05: non-constant variance, Stata12 yielded a p-value of 0. 3693. Because the pvalue that was acquired is greater than 0. 05, we accept the null hypothesis which states that our OLS has a constant variance and thus, is not suffering from heteroscedasticity. 3. Non-autocorrelation Another critical assumption of the CLRM is non- autocorrelation. Autocorrelation is the correlation between the past and present value of the data. This violation is commonly 17 Christian Benedict B. ARGA 11027614 AE-FIN present in time series data because of the sluggishness of economic variables.
The presence of autocorrelation will result to OLS not being the best linear unbiased estimate, although it is still unbiased, constant, asymptotically normal and sufficient. R–squared are overestimated, t-values F-values and x 2 are all wrong, all leading to counter-intuitive signs. The root cause of all these errors will be coming from the standard errors being underestimated. This will result to wrong policy implementation and misleading inferences. For this, the researcher used the Breusch-Godfrey LM test for autocorrelation. The result can be seen below: Given that our H o : P-Value (one-tailed) > 0. 5: no serial correlation and H A : P-Value (one-tailed) < 0. 05: with serial correlation, Stata12 yielded a p-value of 0. 1119 which is greater than 0. 05 thus, we accept the null hypothesis that there is no serial correlation in the model. Although the data has been tested through the BreuschGodfrey LM test for autocorrelation, we still need to test it 18 Christian Benedict B. ARGA 11027614 AE-FIN furthermore but now through the Durbin-Watson d statistic test. The Breusch-Godfrey test has been done first because it is preferred by statisticians for auto correlation testing.
Now for the results of the Durbin-Watson: The result for the Durbin-Watson yielded a value of 0. 6512995. As learned through the course, autocorrelation exists if the result is close to 0 or 4. The model possesses positive autocorrelation if the yielded d is close to 0. On the other hand, if d is closer to 4, the model experiences a negative autocorrelation. For the model to be autocorrelation free, the yielded d must be closer to 2 than 0 or 4. In this case, Stata12 returned a result which is closer to 0 which tells us that autocorrelation exists in the model.
Because the assumption of no autocorrelation was violated, we must correct this by implementing remedial measures. 19 Christian Benedict B. ARGA 11027614 AE-FIN Remedial Measures and Adjusted Estimated Econometric Model I. Remedial Measure Because the model was tested with positive autocorrelation above, a remedial measure is needed to be implemented to correct this. For this problem, it will be corrected through the use of the Prais-Winsten estimation. It is a procedure meant to take care of the serial correlation of type AR(1) in a linear model.
It is a modification of Cochrane–Orcutt estimation in the sense that it does not lose the first observation and leads to more efficiency as a result. Stata12 could also apply this method whenever autocorrelation is detected through the Durbin-Watson testing. The results are as follows: 20 Christian Benedict B. ARGA 11027614 AE-FIN After the application of the Prais-Winsten estimation, we acquired a transformed Durbin-Watson statistic of 1. 206692. This value is closer to 2 compared to the original DW statistic of 0. 651299. Having this result, we can now safely say that the model now is autocorrelation free.
II. Adjusted Econometric Model With the execution of the Prais-Winsten, the r-squared and the adjusted r-squared has changed together with the coefficients of the variables. With the transformed r-squared of 0. 9994, it has improved from the previous r-squared of 0. 9349. This means that the model now explains 99. 94% of the real world variance. Because of the Prais-Winsten transformation, we acquired new coefficients and thus we have our adjusted econometric model: fossil=-2. 956624. 0344159inflation+. 0145774netincome+. 6412211energy+U i The same concept still applies with our previous econometric model.
The relationships between the dependent variable and the independent variables haven’t changed so that means that it still meets our A-priori expectation. 21 Christian Benedict B. ARGA 11027614 AE-FIN Conclusion and Recommendations Now that the model with fossil fuel consumption as its dependent variable; inflation, net income, and energy use as its independent variables, has now been empirically tested and that the concepts and skills that have been learned in class have been applied, it is now time to supply in the conclusions and recommendations about the subject matter.
Fossil fuel as said earlier is a non-renewable source of energy. Once that it has been burned up, it can never be acquired again. Unlike hydroelectric and wind energy sources, these are easily renewable through the resources provided by Mother Nature. Also, the burning of fossil fuel damages the ozone layer and increases the amount of greenhouse gases. It has been proven earlier that the A-priori expectations have been met. First I’d like to discuss the relationship of inflation and fossil fuel consumption. It is said above that fossil fuel consumption and inflation have a negative relationship.
This means that whenever inflation goes up, the consumption of fossil fuel goes down. If the global economy wants to reduce the consumption of this non-renewable energy source and encourage people to turn to other sources of energy other than this, taking advantage of the inflation may be a good idea. If fossil fuels’ prices like oil and coal have gone up due to the inflation, people may find it practical to switch to other sources of energy like solar, wind, or geothermal heat. 22 Christian Benedict B. ARGA 11027614 AE-FIN Fossil fuel consumption and net income has been proven to have a positive relationship.
This is because whenever the net income of a household increases, they can afford more goods such as food and electric dependent devices which consumes more fossil fuel. If the net income on the other hand goes down, the consumption of fossil fuel also goes down. This is due to the fact that people tend to consume less when their income is lower. They buy less food and other goods which consume fossil fuel to be utilized. Energy use also has a positive relationship with the consumption of fossil fuel. Today, very few have switched to the use of renewable energy.
There are very few houses that have solar panels to power up their household. Some industrial organizations have windmills and large solar panels that power up their buildings and machineries. But even though these alternative sources are more eco-friendly, it comes with a very large price tag. It is very expensive to switch to renewable energy sources than just plainly using fossil fuel for power. So this means that the more a household or organization use energy, more and more fossil fuel is burned and consumed. Fossil fuel is the most reliable source of energy today.
If for example, a certain country would want to reduce its fossil fuel consumption, they can implement laws like to increase the taxes on fossil fuel so that people would be forced to switch to other energy resources. The use of fossil 23 Christian Benedict B. ARGA 11027614 AE-FIN fuel hasn’t really been a win for our planet. Burning fossil fuels release greenhouse gases which lead to the depletion of the ozone layer. Aside from this, fossil fuel may someday be burnt out completely. Embracing other sources of energy which may be safe for the environment may be a good thing because fossil fuel will not last for a very, very long time. 4 Christian Benedict B. ARGA 11027614 AE-FIN Bibliography Gujarati, D. , &Porter, D. (2009). Basic econometrics. Singapore: McGrawHill/Irwin. Damassa, T. (n. d. ). Fossil Fuel Consumption and its Implications | World Resources Institute. World Resources Institute | Global Warming, Climate Change, Ecosystems, Sustainable Markets, Good 2, Governance 2012, & the from Environment. Retrieved December http://www. wri. org/stories/2006/11/fossil-fuel-consumption-and-itsimplications Data | The World Bank. (n. d. ). Data | The World Bank. Retrieved November 30, 2012, from http://data. worldbank. rg/ Fossil Fuels and Energy Use. (n. d. ). B. C. Air Quality – Home. Retrieved December 9, 2012, from http://www. bcairquality. ca/101/fossil-fuels. html Fossil | Department of Energy. (n. d. ). Energy. gov | Department of Energy. Retrieved December 5, 2012, from http://energy. gov/science- innovation/energy-sources/fossil 25 Christian Benedict B. ARGA 11027614 AE-FIN Data Presentation Year 1977 1978 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 Fossil fuel energy consumption (% of total) 55. 31347732 54. 8931033 53. 78592188 49. 29623659 45. 48943701 43. 37568802 44. 38229089 35. 31123038 35. 76117547 36. 35431158 40. 26445038 41. 63311248 42. 63808088 43. 43920259 44. 68749999 48. 64714575 49. 75059198 52. 18039963 55. 84399353 56. 88793876 58. 26781808 56. 4225028 53. 69666356 53. 57196521 54. 87855853 56. 41898661 57. 72775356 56. 78790726 58. 16262043 56. 49811715 56. 71572428 57. 1050697 Inflation, GDP deflator (annual %) 8. 272875998 9. 331739787 14. 83954359 14. 24994909 11. 70313893 8. 701224356 14. 22188106 53. 3359576 17. 63285991 2. 952878059 7. 4981921 9. 647022877 9. 033063566 12. 97128127 16. 268799 7. 932658326 6. 832158129 9. 991314599 7. 550870238 7. 661037838 6. 224392022 22. 38172301 6. 585053049 5. 709799946 5. 54947782 4. 162229818 3. 201335984 5. 516871 5. 828020679 4. 949030524 3. 090323839 7. 549059634 Net income (BoP, current US$) -125000000 -118000000 -206000000 -420000000 -503000000 -1021000000 -836000000 -1449000000 -1300000000 -1301000000 -1190000000 -1185000000 -1349000000 -872000000 -500000000 445000000 924000000 1850000000 3662000000 3282000000 4681000000 3769000000 -1062000000 -30000000 -57000000 -430000000 -287000000 -74000000 -298000000 -1261000000 -899000000 105000000 26
Energy use (kT) 20161. 985 20500. 008 21698. 546 22748. 27 22856. 792 23462. 731 25453. 089 22902. 456 24008. 288 24091. 214 25209. 039 26593. 306 27975. 579 28891. 831 28878. 767 30215. 561 30233. 992 32369. 158 33981. 925 35217. 379 37071. 776 38075. 904 39044. 627 40423. 602 38777. 981 39300. 931 39385. 191 39152. 811 39178. 35 38848. 861 38142. 364 39605. 368 Christian Benedict B. ARGA 2009 2010 2011 57. 04278229 57. 04278229 57. 04278229 2. 773246711 4. 222388865 4. 246089114 11027614 -193000000 505000000 1293000000 38842. 497 38842. 497 38842. 497 AE-FIN 27