econmet final paper

8/21/2019 Econmet Final Paper

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A Revision on the Regression Analysis on the Factors

that Affect the Electric Power Consumption

per capita in the Philippines

In partial fulfillment of the course requirements in ECONMET

Submitted to:

Dr. Cesar Rufino

Submitted by:

Lean Marxelle Recoter

10933573

V24

December 14, 2012


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TABLE OF CONTENTS

Page

I. Introduction 3A. Background of the Study 3B. Statement of the Problem 4C. Objectives of the Study 4D. Significance of the Study 4E. Scope and Limitations of the Study 5

II. Review of Related Literature 5A. Electric Power Consumption and Age Dependency Ratio 5B. Electric Power Consumption and Gross Domestic Product 5C. Electric Power Consumption and Gross Domestic Savings 6

III. Operational Framework 6A. Presentation of Data 6B. Description of the Variables Used 8C. A-priori Expectations 11D. Introduction of Hypothesized Economic Model 13

IV. Methodology 13A. Data 13B. Estimation and Inference Procedures 15

V. Empirical Results and Interpretation 17


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A. OLS Regression 17B. Test for Multicollinearity 19C. Test for Heteroscedasticity 21D. Test for Autocorrelation 22

VI. Conclusion and Recommendation 23The Final Econometric Model 23

VII. Bibliography 25

I. Introduction

A. Background of the StudyWaking up in a comfortable and cozy bed, you realize that everything around you is

made with the help of electricity; the wooden bed frame, pillows, blanket, bed and your

clothing. As you walk to the rest room, you would turn on the light bulb using the switch. You

would brush your teeth and wash your face. As you go down for breakfast, the televisions

turned on, mothers cooking the breakfast in an electric stove, youre drying your hair in the

electric fan and the ovens toasting the bread. You dont need to go out of your house just to

experience electricity. Electricity can be found in every part of the urban section of our country.

Without electricity, urban settlers would find it difficult to live in their homes. With the high

technology, businesses, schools, departments, and other facilities that run through electricity

wont be able to do their part in contributing to our economy.


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Electricitycan be identified as afundamental form of energy observable in positive

and negative forms that occurs naturally (as in lightning) or is produced (as in a generator) and

that is expressed in terms of the movement and interaction of electrons.(Electricity,

Merriam-Webster Dictionary).

B. Statement of the ProblemThough we can think of a lot of other factors that can affect the electric power

consumption per capita, this analysis will try to prove whether the age dependency ratio, the

gross domestic product per capita and the gross domestic savings can explain what will happen

when we incorporate these factors with the electric power consumption per capita. Moreover,

we want to know whether there exists a significant relationship between the given exogenous

and endogenous variables.

C. Objectives of the StudyThe study is about electric power consumption per capita in the Philippines and how it

may be affected when there occur changes in the age dependency ratio, gross domestic

product per capita and gross domestic savings.

D.

Significance of the Study

With the factors affecting the electric power consumption per capita, we would learn

that Filipinos, especially the non-working and still dependent, may find it hard to live without

electricity. The increase in the gross domestic per capita would mean an increase in the


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consumption of electricity per capita, and there would be a decrease in its consumption when

there is an increase in the gross domestic savings.

E. Scope and Limitations of the StudySince there are a lot of studies about the electric power consumption and its

determinants in a lot of countries, this paper will study more on the Philippine setting. This is a

time-series data with only 39 years from 1971 to 2009 were all indicators data gathered were

from the World Bank. With other more important factors affecting consumption like prices,

inflation and taxes, The World Bank could not provide such data from 1971 onwards. The

sample size minimum cannot be accommodated if the adding of those data were insisted. The

sample size may not be enough to represent the entire population of each factor.

II. Review of Related Literature

A. Electric Power Consumption and Age Dependency RatioInstead of considering the urban population of the Philippines from the previous paper,

this paper will only consider the age structure of the Philippines population.To even out

consumption, people tend to save/dissave at different ages in their entire life. (Modigliani)

A research on the globalization and its implications on consumption, the Trends in

Global Consumption Patterns: Role of Neighborhood Interactions, found out that the age

dependency ratio has a negative effect on consumption levels. (Talukdar, 2011)


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Zhu (2011) proves that the increase of Chinas savings rate in recent years is due to the

fact that the total dependency ratio of China decreased. The high savings rate now is the price

paid for Chinas future demographic structure. (Zhu, 2011)

B. Electric Power Consumption and Gross Domestic ProductJaunky (2006) states that the income elasticity of electric power consumption is

established to be in great succession in the African Countries. Electricity consumption becomes

a need when there is recession and becomes a want when there is a boom. He also says that

electricity demand studies have useful applications. The estimation of consistent and stable

income elasticity can be of crucial information for the private investors and African government

planners considering any privatization program for electric utility sector. He also says that

greater access to electricity would lead to the reduction of the reliance on biomass which will in

turn lead to a more sustainable economic growth and a decline in environmental deterioration.

(Jaunky, 2006)

C. Electric Power Consumption and Gross Domestic SavingsUnfortunately, there are no studies about the relationship between the electric power

consumption and gross domestic savings. Comparing results would be impossible since it will be

a first for this study.

III.

Operational Framework

A. Presentation of DataYear Electric power Age GDP per Gross


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consumption

(kWh per

capita)

dependency

ratio (% of

working-age

population)

capita

(current

US$)

domestic

savings

(current

US$)

1971 238.125 94.8353 203.05 1.6E+09

1972 263.093 93.7391 213.538 1.7E+09

1973 325.157 92.7152 260.987 2.7E+09

1974 312.508 91.7519 346.702 3.4E+09

1975 317.562 90.8389 364.22 3.7E+09

1976 332.323 89.9848 406.463 4.6E+09

1977 331.119 89.1886 454.128 5.4E+09

1978 331.254 88.4214 510.282 6E+09

1979 348.03 87.6487 600.97 7.1E+09

1980 376.148 86.8505 689.496 7.8E+09

1981 339.531 86.0173 736.514 8.6E+09

1982 340.183 85.1623 746.277 8.2E+09

1983 364.493 84.3151 649.093 7.6E+09

1984 343.123 83.5122 597.161 6.1E+09

1985 351.878 82.7724 568.597 5.1E+09

1986 312.872 82.1022 537.809 5.7E+09

1987 320.861 81.481 581.911 5.9E+09


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1988 344.721 80.8693 646.818 7.6E+09

1989 362.317 80.2184 708.381 8.3E+09

1990 362.607 79.4996 719.009 8.1E+09

1991 355.885 78.7074 719.236 7.8E+09

1992 337.137 77.8608 819.316 8.7E+09

1993 337.611 76.9867 821.596 8.4E+09

1994 380.145 76.1197 946.553 1.1E+10

1995 401.86 75.2832 1070.24 1.1E+10

1996 431.998 74.4778 1169.65 1.3E+10

1997 466.124 73.696 1136.93 1.2E+10

1998 482.558 72.9403 975.232 1E+10

1999 471.497 72.2117 1096.81 1.2E+10

2000 503.751 71.5085 1048.07 1.3E+10

2001 523.489 70.8334 965.777 1.2E+10

2002 527.059 70.1802 1009.02 1.3E+10

2003 560.551 69.5289 1019.62 1.3E+10

2004 580.557 68.8547 1088.57 1.5E+10

2005 581.556 68.1417 1204.8 1.6E+10

2006 572.775 67.389 1402.85 2E+10

2007 586.593 66.6048 1684.78 2.6E+10

2008 589.322 65.7957 1925.21 2.9E+10


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2009 593.459 64.9721 1835.64 2.6E+10

B. Description of the Variables UsedFor the succeeding discussions to be crystal clear, the variables that will be used will be

explained in detail so as to not make the readers feel a foreigner when understanding the

analysis part. There will be two kinds of variables that will be used: the exogenous or

independent variable and the endogenous or the dependent variable. Independent variables

are variables which are not affected by other variables in the model. Dependent variables are

variables which can be affected by the independent variables in the model. The model

determines the dependent variables and the independent variables are determined outside the

model by other factors. In the table below, each variable will be dealt with in profundity.

Variable Definition

Electric power consumption (kWhpc) An endogenous variable in our model A quantitative variable Measures the production of power

plants and combined heat and power

plants less transmission, distribution,

and transformation losses and own use

by heat and power plants.

Age dependency ratio(agedep) An exogenous variable in our model A quantitative variable Refers to ratio of dependents


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people younger than 15 or older than

64to the working-age population

those ages 15-64. The data are shown

as the proportion of dependents per

100 working-age population as defined

by World Bank.

GDP per capita (gdppc) An exogenous variable in our model A quantitative variable It is gross domestic product divided by

midyear population. GDP is the sum of

gross value added by all resident

producers in the economy plus any

product taxes and minus any subsidies

not included in the value of the

products. It is calculated without

making deductions for depreciation of

fabricated assets or for depletion and

degradation of natural resources. Data

are in current U.S. dollars.

Gross domestic savings (savings) An exogenous variable in our model A quantitative variable Calculated as GDP less final


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consumption expenditure (total

consumption). Data are in current U.S.

dollars.

Source: World Bank

C. A-priori ExpectationsThe variables agedep,gdppc and savings are presupposed to be the main factors

affecting electric power consumption (kWh per capita) in the Philippine setting which is why

they will be treated as statistically significant until disproven in this paper later. A-priori

expectations are thoughts or hypotheses that are said to be true. We will base our a-priori

expectations from the review of related literature that was given above. Although only the

algebraic sign of the direction of the relationship between the endogenous and exogenous

variables or the coefficients slope can be seen in a-priori expectation, not the magnitude of

their relationship. These are the a-priori expectations.

Variable Algebraic Sign A-priori Expectation

Agedep ( - ) negative Age dependency ratiohas a

negative relation to electric

power consumption (kWh per

capita) because as the age

dependency ratio increases,

electricity will decrease.

Gdppc ( + ) positive Gross domestic product per


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capita has also a positive

relation to electric power

consumption (kWh per capita)

according to experts, since as

the income per person

increases, the kWh per person

also increases. As what Jaunky

(2006) said, electricity

consumption becomes a

necessity when there is a

recession while it becomes a

luxury when there is a boom.

(Jaunky, 2006)

Savings ( -) negative Gross Domestic Savings has a

negative relation to electric

power consumption (kWh per

capita) because as you want

to save income, the more you

tend to decrease electric

consumption to decrease the

cost of paying for electricity.


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D.Introduction of Hypothesized Economic ModelThe combination of information of theories and concepts, researches and studies and a-

priori expectations proposed the model. The estimated model will be tested to look for the

right answer. By the regression model, we will truly know if the a-priori expectations, that is,

the age dependency ratio, gdp per capita and the savings were given the correct mathematical

signs and that these variables really affect the electric power consumption per capita.

The econometric model would be:

= 1 2 +3 4+

In this model, the lin-lin will be used to get the absolute change of the endogenous

variable on the independent variables. It just means that the absolute change in kWhpcis

estimated through the absolute changes in all the other factors: agedep, gdppc and savings.

IV. Methodology

A.DataThe dataset that will be used in this study was obtained by the researcher by

downloading the Philippines database from the World Bank database. A total of 39 years was

used since it is the widest and only available data given. The Electric power consumption (kWh

per capita) = kWhpc; Age dependency ratio = agedep; Gross Domestic Product (GDP) per capita


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= gdppc; and The Gross Domestic Savings = savings. The savings did not have a per capita data

so the total savings would be used instead. Heres the data:

kWhpc agedep gdppc savings Year

238.125 94.83533 203.05 1.6E+09 1971

263.093 93.73913 213.538 1.7E+09 1972

325.157 92.71521 260.988 2.7E+09 1973

312.508 91.75193 346.702 3.4E+09 1974

317.562 90.83888 364.22 3.7E+09 1975

332.323 89.9848 406.463 4.6E+09 1976

331.119 89.18862 454.129 5.4E+09 1977

331.254 88.42143 510.282 6E+09 1978

348.03 87.64865 600.97 7.1E+09 1979

376.148 86.85054 689.496 7.8E+09 1980

339.531 86.01734 736.514 8.6E+09 1981

340.184 85.16229 746.277 8.2E+09 1982

364.493 84.31507 649.093 7.6E+09 1983

343.123 83.51217 597.162 6.1E+09 1984

351.878 82.77241 568.597 5.1E+09 1985

312.872 82.10221 537.809 5.7E+09 1986

320.861 81.48103 581.911 5.9E+09 1987

344.721 80.86928 646.818 7.6E+09 1988

362.317 80.21838 708.381 8.3E+09 1989

362.607 79.49959 719.01 8.1E+09 1990


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355.885 78.7074 719.236 7.8E+09 1991

337.137 77.86081 819.316 8.7E+09 1992

337.611 76.98665 821.596 8.4E+09 1993

380.145 76.1197 946.553 1.1E+10 1994

401.86 75.28316 1070.24 1.1E+10 1995

431.998 74.47776 1169.65 1.3E+10 1996

466.124 73.69598 1136.93 1.2E+10 1997

482.558 72.94025 975.232 1E+10 1998

471.497 72.21166 1096.81 1.2E+10 1999

503.751 71.50848 1048.07 1.3E+10 2000

523.489 70.83336 965.777 1.2E+10 2001

527.059 70.18022 1009.02 1.3E+10 2002

560.551 69.52893 1019.62 1.3E+10 2003

580.557 68.85465 1088.57 1.5E+10 2004

581.556 68.14169 1204.8 1.6E+10 2005

572.775 67.389 1402.85 2E+10 2006

586.593 66.60477 1684.78 2.6E+10 2007

589.322 65.7957 1925.21 2.9E+10 2008

593.459 64.97207 1835.64 2.6E+10 2009

B. Estimation and Inference ProceduresBefore checking or testing for any violations to certain assumptions for the Classical

Linear Regression Model (CLRM), the inferences that all assumptions should be satisfied. These


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assumptions are as follows: (1) the stochastic random variable (u) should be normal, (2) there

should be no perfect multicollinearity among the exogenous variables, (3) there is equal

variance for the stochastic random variable or homoscedasticity, and (4) there is no

autocorrelation between the disturbances. The Ordinary Least Squares (OLS) regression will be

used. Since the belief is that there is no violation on the CLRM assumptions, the OLS regression

therefore, is the Best Linear Unbiased Estimator (BLUE). This will provide the model with the

best estimates for the chosen variables of electric power consumption per capita which will be

of help for the government and other concerned citizens. The finding of the minimum sum of

the squares of the errors and the mean errors or residuals will lead to the best and most

accurate estimates for the parameters of the model.

With the help of both Gretl and Stata/SE 12.0, the parameters were estimated

accurately and were presented in a tabular form together with the p-values, R-squared values,

standard errors, and other important significance indicators and statistics that would help

interpret the data. This study requires a 95% confidence interval (CI); since p-value lies

between1 , its value in each variable should be less than or equal to 0.05. A p-value that is

less than or equal to 0.05 would mean that the variable is significant and would indicate that

the variable should be kept in the model. The R-squared provides the explanatory power of the

model in determining the value of the endogenous variable. Its value should be a 100%; a 100%

R-squared value means that the model has the best explanatory power. (Gujarati & Porter,

2009)


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There would be tests that would help detect violations in the model in order for us to

get the correct interpretation of the model. These tests are: (1) Test for Multicollinearity, (2)

Test for Heteroscedasticity and (3) Test for Autocorrelation.

V. Empirical Results and Interpretation

A. OLS RegressionThe results for the regression of the model were obtained from Gretl, software for

regressing data:

The regression has provided actual values for the unknown parameters. This is the initial

model from primary OLS regression:

= 1206.1 9.81854 0.241055+ 1.803008+


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We will focus on the p-value and R-squared values since the p-value determines the

statistical significance of the parameter while the R-squared value speaks about the explanatory

power of the estimated model.

The R-squared value of the model is 0.887840 or 88.7840% which is nearer to 1 or 100%.

It means that the estimated model is very powerful in explaining the effects of the regressors

on the regressand. It can increase or decrease when a revision of work will be done,

removing/adding variables that are/arent needed. The adjusted R-squared is 0.878226 or

87.8226% which is relatively somewhat lower than the original R-squared value.

The p-value indicates how significant or dependable an estimate is with respect to the

actual population. This is a reverse of the r-squared value, the smaller its value, the better the

estimation result will be. This is due the fact that the level of doubt in estimating is parallel with

the p-value. Therefore, the higher will be the confidence level. With the p-values given, every

variable is very important in the model.

The age dependency ratioprovided 3 stars, which means that the coefficient is

significant at the 1% confidence with a coefficient of9.81854. This means that for every unit

decrease in the age dependency ratio, the consumption of electric power per capita will

increase by 9.81854kWh.

The savingsalso provided 3 stars, but is lesser than the age dependency ratio but still

good enough for its p-value is lesser than 0.01 which means that it is also significant to the

model at 1% confidence level. With a coefficient of1.803008, it would mean that for every

US$ saved (since the data was in US$), consumption of electricity per capita will increase by

1.803008kWh which is counter-intuitive because the a-priori expectation is that savings has a


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negative relationship with the consumption of electricity. But nonetheless, the increase in the

consumption of electricity is not that significant.

Although the p-value of Gross Domestic Product per capita is not as significant as

compared to the two variables above, it still passed the 98% confidence interval and is more

than our requirement, the 95% confidence interval. With a coefficient of0.241055, for every

unit increase in gdp, the consumption of electricity per capita would decrease by0.241055. It

defied Jaunkys research and our a-priori expectations with a 98.23% confidence interval. This

means that there should be a recheck on the theories we use, or it is either some factors are

not included in the regression.

It is very heartwarming to know that the variables were almost in line with the a-priori

expectations. In the first part, it was assumed that the CLRM does not violate any of its

assumptions. There will be a test to verify that CLRM is not violated, and if it is, then corrective

measures should be done.

B. Test for Multicollinearity


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Multicollinearity means that the exogenous variables have a relationship and thus, will

produce a suspect inference as discussed in the lecture. Since the collinearity is not tolerable

and is very high with gdppc and savings, there are corrective measures that were discussed in

the class: drop the culprit; it is the one with the highest VIF, which is gdppc. Dropping the

gdppc from the model, the final OLS will be:

And the model will be:

= 888.8356.865056 6.317769+

This means that the interpretations will change. The R-squared value of the model will

be 0.867983 or 86.7983% which is less near to 100% but still a great percentage. The adjusted

R-squared is 0.860649 or 86.0649% which is relatively somewhat lower than the original

adjusted R-squared value.


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Surprisingly, the age dependency ratiostill provided 3 stars and still the lowest for its p-

value which means that it is still as essential as ever. With the new coefficient of 6.86505 it

would mean that for every increase in the age dependency ratio, the consumption of electric

power per capita will decrease by 6.86505kWh which is a lesser decrease in the consumption

of electricity than the previous model.

The savingsstill provided 3 stars, still significant at 1% confidence level. With the new

coefficient of6.317769, it would mean that for every US$ saved, the consumption of electric

power per capita will increase by 6.317769kWh which is a lesser increase in the consumption

of electricity than the previous model.

C. Test for Heteroscedasticity

Heteroscedasticity is the absence of homoscedasticity, as discussed in class. It will

create biased inference which is more dangerous than multicollinearity because the OLS will

not be BLUE anymore. With the Breusch-Pagan test, the null hypothesis states that there is no

heteroscedasticity. True enough, we should accept it since the p-value is greater than 0.05. This

means that there is homoscedasticity.


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D. Test for AutocorrelationKendall and Buckland (1971) said that, autocorrelation is the correlation between

members of series of observations ordered in time or space. It can be present because of

inertia or slowness of economic time series, and specification bias due to the omitted variables

of the model (which in our case, the gdppc). (Gujarati & Porter, 2009)

With the help of Breusch-Godfrey test in Stata 12, we reject the null hypothesis since

the p-value is less than 0.05 which means that there is autocorrelation in the model. With this,

we correct the autocorrelation with the help of Prais-Winsten AR (1) regression:


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With this, the Durbin-Watson statistic before, this was 0.347650 and transformed to

1.846266 which is close to 2 and would mean that there is no more autocorrelation.

VI. Conclusion and Recommendations

The Final Econometric Model will be:

= 1253.37310.75765+ 1.069+

With this, the study has revealed to us that at first, gdppcis important and does not

violate any assumptions in the OLS regression. But as we go on, it violated an assumption which

is essential in our research. Although it contradicts to one of the related literatures, I stick to

savings since I did not find any research on that.

It is therefore correct to say that the a-priori expectations were met, that an increase in

the age dependency ratiowould mean a decrease in the consumption of electric power per

capitaof 10.75765kWh and an increase in savingswould result to an increase in kWh per capita

consumptionwhich is counter-intuitive but is still carefully correct to say that it did not defy our

a-priori expectation since the increase in consumption of electricity is still minimal, 1.069kWh.

It just means that there is a negative linear relation with the age dependency ratio of the

Philippines with the electricity consumption. Dependent people, may it be young or old, to the

working people would prefer to use electricity more moderately as the age dependency ratio

increases. It is essential for them since they live in high-technology, electricity requiring


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environment, that having a brownout for just 10mins would be very inconvenient for the rest of

them. Also, if there is a constraint in the budget, there should be a decrease in the kWhper

capita consumption.You cant live luxurious when there is a recession, unless youre very rich

that is. But still, when you save, you try to decrease any unnecessary consumption of goods

that you can live without in order to save more.

Although, with all the cure and correction that were done, the explanatory power of this

model went to trash. R-squared and Adjusted R-squared became 30+% which makes the model

suck.

For the recommendation, the next researcher, should there be one, must find the per

capita of savings in order to know the difference with the savings in total. Also, the researcher

must find the data for prices, inflation and taxes that would help widen the idea on kWhper

capita consumption.Also, if there would be data in peso, kindly do so, so that readers will not

have a hard time converting the US$ into peso anymore.


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VII. BibliographyElectricity. (n.d.). Retrieved December 14, 2012, from Merriam-Webster: http://www.merriam-

webster.com/dictionary/electricity

Gujarati, D., & Porter, D. (2009). Basic Econometrics Fifth Edition.Singapore: McGrawHill/Irwin.

Jaunky, V. (2006, December). Income Elasticities of Electric Power Consumption: Evidence from African

Countries. Mauritius.

Kendall, M., & Buckland, W. (1971). Dictionary of Statistical Terms.New York: Hafner Pub. Co.

Modigliani, F. (n.d.). The Life Cycle Hypothesis of Saving, the Demand for Wealth and the Supply of

Capital.Retrieved September 6, 2012, from Alda:

http://www.alda.name/texty/Franco%20Modigliani%20-

%20The%20Life%20Cycle%20Hypothesis%20of%20Savings,%20the%20Demand%20for%20Wealth%20a

nd%20the%20Supply%20of%20Capital%20-%201966.pdf

Talukdar, D. (2011, July 28-30). 2011 China India Consumer Insights Conference: Day 2 Agenda.

Retrieved September 6, 2012, from Yale School of Management:

http://ciip.som.yale.edu/cci/sites/cci.som.yale.edu/files/1ATalukdar.pdf

The World Bank.(n.d.). Retrieved December 14, 2012, from Philippines:

http://databank.worldbank.org/ddp/editReport?REQUEST_SOURCE=search&CNO=2&country=PHL&seri

es=&period=

Zhu, C. (2011, November). Modern Economy.Retrieved December 14, 2012, from Scientific Research

Open Access: http://www.scirp.org/journal/PaperInformation.aspx?PaperID=8699&JournalID=163

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