FINAL PROJECT OFBUSINESS RESEARCH METHODS:

“IMPACT OF ENERGY CONSUMPTION ON ECONOMIC PROGRESSION”

SUBMITTED TO:

PROF. DR.MUHAMMAD RAMZAN

SUBMITTED BY:

FATIMA PARVEEZ (MS-ECO)

SADIA ZUBAIR (MS-ECO)

ZAFAR IQBAL (MS-ECO)

AHSAN KHAN (MS-ECO)

 

ABSTRACTThis paper strives to determine the trend of causality between energy consumption (EC) and Economic Progression (GDP per capita) in the North African Country “Libya” using Annual data of year 1970-2009 by applying techniques of Johnson Co integration. The paper look over how comprehensively are the two variables interrelated. The results infer that there is a positive relationship between energy consumption and GDP Per capita and this relation causes advancement in GDP moreover energy consumed is used as additional variable to growth.. Thus an energy growth policy should be adopted in such a way that it stimulates growth in the economy and thus expands employment opportunities. This paper will help in understanding the energy policy implications.

INTRODUCTION (1)Energy consumption is a vibrant component to economic progression and it works as an incentive for growth. This has been recognized as the advancement of industrial nations in the nineteenth century can be appreciated as an upshot of a fourth major input “energy” other than land, labor and capital. Energy consumption boosts the competency of factors of production and rushes living standards. It is widely documented that economic development and energy consumption are interdependent.

INTRODUCTION (2)To recognize the role of energy consumption at the national level, it is necessary to understand the relationship between energy and GDP. The better accessibility of energy acts as a ‘key’ incentive for economic development at various levels and any limitations put on energy consumption to help decrease emission shave an effect on development if causality runs from energy to GDP. The second section reviews the past studies on the topic. The third section explains the descriptive information of the Libya energy sector. The fourth section reports the data and methodology. The fifth section reports result and the sixth section ends with concluding remarks.

LITERATURE REVIEW (1)Per capita income is frequently used as a measure of the wealth of the population of a nation, particularly in comparison to other nations. In the following study relationship between per capita income and energy is analyzed. Energy is wanted for man to live and to cultivate. Furthermore the fruition of the societies, the economic progression and the way countries develop clue to an accumulative demand for energy. Some developed countries have a much higher per-capita gross domestic product (GDP) than certain developing nations as the research conducted by Jaruwan Chontanawat, Lester C Hunt Richard Pierse, June (2006)investigates for causality using a

reliabledata set and approach for 30 OECD and 78 non-OECD countries. Their findings reveal that causality from EC to GDP is found to be more established in the urbanized OECD countries paralleled to the emerging non-OECD countries.

LITERATURE REVIEW (2)

Energy Consumption is well thought-out as an incentive for economic growth. The increased availability of energy services acts as a key stimulus for economic development at different stages in the development process. Any constraints put on energy consumption to help reduce emissions will have an effect on growth and development if causality from energy to GDP exists. Energy grants access to encounter many elementary human needs, predominantly heat, transport and light. Business, manufacturing, trade and public amenities such as modern healthcare, learning and communication are exceedinglyreliant onaccess to energy amenities.

LITERATURE REVIEW (3)Indeed, there is a through relationship between the deficiencies of sufficient energy services and many poverty pointers such as Unemployment, illiteracy, life probability and total potency rate. Insufficient access to energy also aggravates swift Urbanization in developing countries, by driving people to pursue better living surroundings. Growing energy ingestion has long been knotted openly to economic development and enhancement in human prosperity. Nonetheless it is uncertain whether increasing energy ingesting is a pre-condition for economic growth, or vice versa. In developed countries there exists a robust direct relationship between EC and EG. This can be viewed with the help of past studies.

LITERATURE REVIEW (4)

Over the past few years the liaison between economic growth and energy has been broadly examined. Since the groundbreaking study of Kraft and Kraft (1978), the excessive research has been carried out to find indication of bidirectional, unidirectional or no connection according to the country studied. In several countries, altered results arise for different time periods, leading to no convinced inference.

BIDIRECTIONAL RELATIONSHIP

 

With respect to numerous empirical influences, indication of bidirectional bond is recognized in the findings of Jumbe (2004)and El-Sakka(2004) which inspect the Countries like Malawi and Canada correspondingly. Likewise the findings of Soytas and Sary (2003)suggest the presence of bidirectional causality in Argentina, the work of Oh and Lee (2004) in Korea, Mahadeven and Asafu-Adjaye (2007) find bidirectional causality for a number of countries. Wietze Lise & Kees Van Monfort (2005) also finds the(possibly bi-directional) causality relationship between the two variables.

UNIDIRECTIONAL RELATIONSHIP

 

On theother hand, the workings of Morimoto and Hope (2004) and Wolde- Rufael (2004) in Sri-Lanka and Shanghaiexhibit the presence of unidirectional causality from energy consumption to economic growth. Similarly Al- Iriani (2006) finds a unidirectional causation among the subject variables in six Gulf countries.

INCONSISTENT RELATIONSHIPConsuming a multivariate causality test, Akinlo (2008) finds an inconsistent indication for eleven African countries. Chiou-Wei (2008), carries research for emerging industrial Asian countries and USA using

Nonlinear and linear Granger causality and reports inconsistent results.

Likewise, Huang etal. (2008)discovers no connectedness between economic growth and energy consumption in little-income groups while in intermediate-income and extraordinary-income countries they found that economic growth leads energy consumption.

MULTI FRAMEWORK APPROACH (1)

Kashif Imran &Masood Mashkoor Siddiquiprobes the causal interaction surrounded by a multivariate framework. Via modern panel unit root technique, residual based panel cointegration and panel based error correction models the results abundantly support a cointegration relationship in the long run. By the same tokenJames E. Payne(2009) study consumes U.S. yearly data from 1976 to 2006 to observe the causal relationship among energy consumption and employment in Illinois within a multivariate framework. The Toda-Yamamoto long-run causality tests expose unidirectional causality from EC to EG.

MULTI FRAMEWORK APPROACH (2)Qiang Hou,(2009) workson the causality in China economy and analyzes the positive relationship between the subject variables.Ghosh (2002), Shiu and Lam (2004), Moritomo and Hope (2004), Jumbe(2004),Narayan and Smyth (2005), and Yoo(2005), have focused on thecasual relationship between electricity consumption and economic growth for severaldeveloping countries. AnjumAqeel&Mohammad Sabihuddin Butt (2001) Qazi Muhammad Adnan Hye& Sana Riaz(2008)Chien-Chiang Lee,May (2005) by smearing panel unit root, heterogeneous panel cointegration, and panel-based error correction models deliver clear backing of a long-run cointegration relationship.

DESCRIPTIVE INFORMATION OF LIBYA (1)

Located in the north of Africa Libya is the sixteenth largest country in the world in terms of terrestrial mass. Almost six million and above occupants live in its capital city, Tripoli. Apart from petroleum, Libya's additional natural resources are natural gas and gypsum. Its economy depends predominantly on takings from the oil sector, which subsidies about 95% of export retributions. Libya’s GDP per capita income is 14,884($).GDP growth rate is 10.6%.The value of total exports is 46.31 billion out of which 41.87 billion comprises of petroleum exports. The contribution of Industrial sector in GDP is 49.9%.In 2009 Libya had the Develhighest Human opment Index in Africa and the fourth utmost GDP per capita in Africa.

DESCRIPTIVE INFORMATION OF LIBYA (2) 

There is a multi-dimensional requisite for learning the energy situation in Libya. First, Libya is an OPEC member since 1962. Second Libya has a premeditated position as a gas and oil transfer country. finally the 95% exports comprises of petroleum exports. It has large reserves of oil.

 

Libya, a supporter of the Organization of Petroleum Exporting Countries (OPEC), grasps the principal proven oil assets in Africa, trailed by Nigeria and Algeria. Libya had global oil reserves of 46.4 billion barrels as perOil and Gas Journal (OGJ) which is projected as the prevalentassets of Africa. About 80 % of Libya’s confirmed oil reserves are positioned in the Sirte basin, which explains most of the country’s oil productivity.

DATA AND METHODOLOGY 

The data takes account of GDP and Energy consumption from 1970-2009 and it is accumulated from WDI and United Nations Statistics. GDP is occupied in terms of billion dollars and energy consumption is taken in kiloton of oil equivalent.

The first step involves the establishment of integration order of the variables by applying the Ng-Perron unit root test. Panel unit root tests lead to statistics with a normal distribution.

The second step involves the valuation of the variables which have been tested for the order of integration and their testing predicts the same order.

In the third step:Test for auto and functional form is made.

CHECKING FOR STATIONARY (1) 

The stationarity is frequently assumed in building and approximating dynamic models in economics. Economic and financial time series usually reveal non stationarity in the mean. For example asset prices, interchange rates and GDP etc. An essential econometric chore is to determine the most applicable form of the drift in the data.

 

As the economic series exhibit trends over time and the mean varies for each year the problem of stationarity arises in the model and it does not leave the time series consistent over time. Therefore in order to evade this difficulty and to include stationarity we de-trend the raw data through a process called differencing. Stationarity is important because if the series are non-stationary all theresults of ordinary least square are invalid and regression with such series leads to spurious regression.

CHECKING FOR STATIONARY (2)A time series is said to be stationary when it has the following three characteristics:

E(Yt) = constant for all (t)

Var (Yt) = constant for all (t)

Cov (Yt,Yt-1) =constant for all (t)

 

All conditions require expected value of mean, variance and covariance constant over time.

 

We can create the non-stationary series to a stationary series by various ways for example by taking logs, by ratios, by first difference, by second difference, by higher order difference.

 

Time period taken for research may include any vigorous year which is characterized by fluctuations. This fluctuated year must be excluded from the data as its estimation leads to inaccurate results. While checking the relationship abnormal and extra ordinary years must be omitted from the data to minimize the fluctuations and variations.

UNIT ROOT TEST 

Unit root tests are valuable toconclude the order of integration of the variables and it provides the time-series properties of data. An Augmented Dickey-Fuller(ADF) test is engaged in order to device a demanding test which corroborates the existence of unit root in the analysis. This test characterizes a wider description of the customary Dickey-Fuller test (1979).

 

The unit root test authenticates for stationarity following the 3 categories below:

Difference level

First difference

Second difference

METHODOLOGY (1)Data is tested for stationarity at difference level. Hypothesis is given as follows:

 

H0= Series are non-stationary.

Ha= Series are stationary.

 

Difference level unit root

At the difference level GDP has a unit root test with the following results: 

Augmented Dickey Fuller test

t-Statistic

Probability values

Intercept

0.2866

0.973

Trend and intercept

-1.4293

0.83

 

METHODOLOGY (2) 

At the difference level Energy Consumption has a unit root test with the following results:

 

Augmented Dickey Fuller test

t-Statistic

Probability values

Intercept

-1.6682

0.4388

Trend and intercept

-0.703

0.965

 

 

Conditions for acceptance/rejection:

 If Probability is less than 0.05 we reject H0.

 If Probability is greater than 0.05 we fail to reject H0

 

Conclusion:

As Probability is greater than 0.05 we fail to reject H0.This states that series are non-stationary so we test it again at First difference.

FIRST DIFFERENCE UNIT ROOT TEST (1)Data is tested for stationarity at First difference. Hypothesis is given as follows:

  H0 = Series are non-stationary.

  Ha= Series are stationary.

 

At the first difference level GDP has a unit root test with the following results:

 Augmented Dickey Fuller test

t-Statistic

Probability values

intercept

-3.901

0.005

Trend and intercept

-3.983

0.019

 

FIRST DIFFERENCE UNIT ROOT TEST (2) 

At the first difference level Energy consumption has a unit root test with the following results:

 

Augmented Dickey Fuller test

t-Statistic

Probability values

intercept

-10.077

0.000

Trend and intercept

-10.398

0.000

 

Conclusion:

As Probability is less than 0.05 we reject H0.This states that series are stationary are first difference.

FIRST DIFFERENCE UNIT ROOT TEST (3)The second step involves the Johenson Co-Integration technique. It shows long run relationship and effect of independent variable on the dependent variable. Cointegration can be well-thought out to be implemented as series is cointegrated of I(1).

 

The lag order criterion is selected as “3”.Johenson co-integration technique comprises of 2 tests:

Trace Test

Maximum Eigen value Test

 

Co-integration test is applied with the following hypothesis:

H0=Variables are not co-integrated

Ha =Variables are co-integrated

FIRST DIFFERENCE UNIT ROOT TEST (4)The result for co-integration Trace test indicates only 1 co-integrating equation at the significance level of 0.05%.It also yields the long run equilibrium. Thus we reject H0.

The result for co-integration Eigen value Test also indicated 1 co-integrating equation at the significance level of 0.05%.Thus we reject H0.

Regression Equation is found as follows:

LGDP=b0+ b1(EC)+et

1.0000 =b0+ 0.9253(EC) +et

The regression equation predicts the positive relationship between GDP growth and Energy Consumption. The equation shows if independent variable (Energy consumption) goes by 1 unit, dependent variable (GDP) goes by 0.925 units.

DIAGNOSTIC TESTS: The following diagnostic tests are performed:AutocorrelationNormality test AutocorrelationIn order to check presence and absence of Autocorrelation,Serial correlation LM test is performed with the following hypothesis and results.Hypothesis:H0 = Absence of Autocorrelation.

Ha = Presence of Autocorrelation

Results: R-squareDurbin-Watson Test0.8421.576 Since value of R-square is less than Durbin Watson statistic it implies the absence of auto in the model

NORMALITY TESTNormality test is performed in order to check the functional form of the model. The following hypothesis and results are observed:

 

H0 = functional form of model is wrong

Ha = functional form of model is not wrong

 

Jarque Bera Probability is 0.0000 which is less than 0.05 thus we reject H0 and accept Ha which implies that functional form is not wrong.

SUMMARY AND CONCLUSIONS:The study scrutinizes the fundamental relationship between economic growth and energy consumption in Libya using the results of

Johenson Co-integration causality which foretellsthe evidence of causality running from energy consumption to economic growth. A unidirectional causality running from energy consumption to economic

growth is found between the variables.It also implies the positive relationship between the variables and that the independent variable(EC) will affect the dependent variable(GDP) in the long run sodropping energy consumption could escort to a drop in economic growth.

 

When any energy preservationtrials are commenced, considerable care should be taken which will not unfavorably affect the economic growth. In the light of above argument it is shimmering that energy obliges as an engine of economicgrowth and economic commotion will be affected in the upshot of changes in EC. The unceasing energy use does crop a continuous upsurge in output. Thusenergy is indispensable for economic and communal development of a state or a country.

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