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This paper strives to determine the trend of causalitybetween energy consumption (EC) and EconomicProgression (GDP per capita) in the North AfricanCountry “Libya” using Annual data of year 1970-2009 byapplying techniques of Johnson Co integration. Thepaper look over how comprehensively are the twovariables interrelated. The results infer that there is apositive relationship between energy consumption andGDP Per capita and this relation causes advancement inGDP moreover energy consumed is used as additionalvariable to growth.. Thus an energy growth policy shouldbe adopted in such a way that it stimulates growth in theeconomy and thus expands employment opportunities.This paper will help in understanding the energy policyimplications.
Energy consumption is a vibrant component to economicprogression and it works as an incentive for growth. Thishas been recognized as the advancement of industrialnations in the nineteenth century can be appreciated asan upshot of a fourth major input “energy” other thanland, labor and capital. Energy consumption boosts thecompetency of factors of production and rushes livingstandards. It is widely documented that economicdevelopment and energy consumption areinterdependent.
To recognize the role of energy consumption at thenational level, it is necessary to understand therelationship between energy and GDP. The betteraccessibility of energy acts as a „key‟ incentive foreconomic development at various levels and anylimitations put on energy consumption to help decreaseemission shave an effect on development if causalityruns from energy to GDP. The second section reviews thepast studies on the topic. The third section explains thedescriptive information of the Libya energy sector. Thefourth section reports the data and methodology. The fifthsection reports result and the sixth section ends withconcluding remarks.
Per capita income is frequently used as a measure of the wealth ofthe population of a nation, particularly in comparison to other nations.In the following study relationship between per capita income andenergy is analyzed. Energy is wanted for man to live and to cultivate.Furthermore the fruition of the societies, the economic progressionand the way countries develop clue to an accumulative demand forenergy. Some developed countries have a much higher per-capitagross domestic product (GDP) than certain developing nations as theresearch conducted by Jaruwan Chontanawat, Lester C HuntRichard Pierse, June (2006)investigates for causality using areliabledata set and approach for 30 OECD and 78 non-OECDcountries. Their findings reveal that causality from EC to GDP isfound to be more established in the urbanized OECD countriesparalleled to the emerging non-OECD countries.
Energy Consumption is well thought-out as an incentivefor economic growth. The increased availability of energyservices acts as a key stimulus for economicdevelopment at different stages in the developmentprocess. Any constraints put on energy consumption tohelp reduce emissions will have an effect on growth anddevelopment if causality from energy to GDP exists.Energy grants access to encounter many elementaryhuman needs, predominantly heat, transport and light.Business, manufacturing, trade and public amenitiessuch as modern healthcare, learning and communicationare exceedinglyreliant onaccess to energy amenities.
Indeed, there is a through relationship between thedeficiencies of sufficient energy services and manypoverty pointers such as Unemployment, illiteracy, lifeprobability and total potency rate. Insufficient access toenergy also aggravates swift Urbanization in developingcountries, by driving people to pursue better livingsurroundings. Growing energy ingestion has long beenknotted openly to economic development andenhancement in human prosperity. Nonetheless it isuncertain whether increasing energy ingesting is a pre-condition for economic growth, or vice versa. Indeveloped countries there exists a robust directrelationship between EC and EG. This can be viewedwith the help of past studies.
Over the past few years the liaison betweeneconomic growth and energy has been broadlyexamined. Since the groundbreaking study of Kraftand Kraft (1978), the excessive research has beencarried out to find indication of bidirectional,unidirectional or no connection according to thecountry studied. In several countries, altered resultsarise for different time periods, leading to noconvinced inference.
With respect to numerous empirical influences, indicationof bidirectional bond is recognized in the findings ofJumbe (2004)and El-Sakka(2004) which inspect theCountries like Malawi and Canada correspondingly.Likewise the findings of Soytas and Sary (2003)suggestthe presence of bidirectional causality in Argentina, thework of Oh and Lee (2004) in Korea, Mahadeven andAsafu-Adjaye (2007) find bidirectional causality for anumber of countries. Wietze Lise & Kees Van Monfort(2005) also finds the(possibly bi-directional) causalityrelationship between the two variables.
On theother hand, the workings of Morimoto andHope (2004) and Wolde- Rufael (2004) in Sri-Lankaand Shanghaiexhibit the presence of unidirectionalcausality from energy consumption to economicgrowth. Similarly Al- Iriani (2006) finds aunidirectional causation among the subject variablesin six Gulf countries.
Consuming a multivariate causality test, Akinlo(2008) finds an inconsistent indication for elevenAfrican countries. Chiou-Wei (2008), carriesresearch for emerging industrial Asian countries andUSA usingNonlinear and linear Granger causality and reportsinconsistent results.Likewise, Huang etal. (2008)discovers noconnectedness between economic growth andenergy consumption in little-income groups while inintermediate-income and extraordinary-incomecountries they found that economic growth leadsenergy consumption.
Kashif Imran &Masood Mashkoor Siddiquiprobesthe causal interaction surrounded by a multivariateframework. Via modern panel unit roottechnique, residual based panel cointegration andpanel based error correction models the resultsabundantly support a cointegration relationship in thelong run. By the same tokenJames E. Payne(2009)study consumes U.S. yearly data from 1976 to 2006to observe the causal relationship among energyconsumption and employment in Illinois within amultivariate framework. The Toda-Yamamoto long-run causality tests expose unidirectional causalityfrom EC to EG.
Qiang Hou,(2009) workson the causality in Chinaeconomy and analyzes the positive relationship betweenthe subject variables.Ghosh (2002), Shiu and Lam(2004), Moritomo and Hope (2004),Jumbe(2004),Narayan and Smyth (2005), andYoo(2005), have focused on thecasual relationshipbetween electricity consumption and economic growth forseveraldeveloping countries. AnjumAqeel&MohammadSabihuddin Butt (2001) Qazi Muhammad Adnan Hye&Sana Riaz(2008)Chien-Chiang Lee,May (2005) bysmearing panel unit root, heterogeneous panelcointegration, and panel-based error correction modelsdeliver clear backing of a long-run cointegrationrelationship.
Located in the north of Africa Libya is the sixteenthlargest country in the world in terms of terrestrial mass.Almost six million and above occupants live in its capitalcity, Tripoli. Apart from petroleum, Libyas additionalnatural resources are natural gas and gypsum. Itseconomy depends predominantly on takings from the oilsector, which subsidies about 95% of export retributions.Libya‟s GDP per capita income is 14,884($).GDP growthrate is 10.6%.The value of total exports is 46.31 billionout of which 41.87 billion comprises of petroleum exports.The contribution of Industrial sector in GDP is 49.9%.In2009 Libya had the Develhighest Human opment Index inAfrica and the fourth utmost GDP per capita in Africa.
There is a multi-dimensional requisite for learning theenergy situation in Libya. First, Libya is an OPECmember since 1962. Second Libya has a premeditatedposition as a gas and oil transfer country. finally the 95%exports comprises of petroleum exports. It has largereserves of oil.Libya, a supporter of the Organization of PetroleumExporting Countries (OPEC), grasps the principal provenoil assets in Africa, trailed by Nigeria and Algeria. Libyahad global oil reserves of 46.4 billion barrels as perOiland Gas Journal (OGJ) which is projected as theprevalentassets of Africa. About 80 % of Libya‟sconfirmed oil reserves are positioned in the Sirte basin,which explains most of the country‟s oil productivity.
The data takes account of GDP and Energy consumptionfrom 1970-2009 and it is accumulated from WDI andUnited Nations Statistics. GDP is occupied in terms ofbillion dollars and energy consumption is taken in kilotonof oil equivalent.The first step involves the establishment of integrationorder of the variables by applying the Ng-Perron unit roottest. Panel unit root tests lead to statistics with a normaldistribution.The second step involves the valuation of the variableswhich have been tested for the order of integration andtheir testing predicts the same order.In the third step:Test for auto and functional form ismade.
The stationarity is frequently assumed in building andapproximating dynamic models in economics. Economic andfinancial time series usually reveal non stationarity in themean. For example asset prices, interchange rates and GDPetc. An essential econometric chore is to determine the mostapplicable form of the drift in the data.As the economic series exhibit trends over time and the meanvaries for each year the problem of stationarity arises in themodel and it does not leave the time series consistent overtime. Therefore in order to evade this difficulty and to includestationarity we de-trend the raw data through a process calleddifferencing. Stationarity is important because if the series arenon-stationary all theresults of ordinary least square are invalidand regression with such series leads to spurious regression.
A time series is said to be stationary when it has the following threecharacteristics: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 covarianceconstant over time.We can create the non-stationary series to a stationary series by variousways for example by taking logs, by ratios, by first difference, by seconddifference, by higher order difference.Time period taken for research may include any vigorous year which ischaracterized by fluctuations. This fluctuated year must be excluded from thedata as its estimation leads to inaccurate results. While checking therelationship abnormal and extra ordinary years must be omitted from thedata to minimize the fluctuations and variations.
Unit root tests are valuable toconclude the order ofintegration of the variables and it provides the time-seriesproperties of data. An Augmented Dickey-Fuller(ADF) testis engaged in order to device a demanding test whichcorroborates the existence of unit root in the analysis.This test characterizes a wider description of thecustomary Dickey-Fuller test (1979).The unit root test authenticates for stationarity followingthe 3 categories below:Difference levelFirst differenceSecond difference
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 rootAt the difference level GDP has a unit root test with the following results:Augmented Dickey Fuller testt-StatisticProbability valuesIntercept0.28660.973Trend and intercept-1.42930.83
At the difference level Energy Consumption has a unit root test with the following results:Augmented Dickey Fuller testt-StatisticProbability valuesIntercept-1.66820.4388Trend and intercept-0.7030.965Conditions for acceptance/rejection:If Probability is less than 0.05 we reject H0.If Probability is greater than 0.05 we fail to reject H0Conclusion:As Probability is greater than 0.05 we fail to reject H0.This states that series are non-stationary sowe test it again at First difference.
Data is tested for stationarity at First difference. Hypothesis is given asfollows: 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 testt-StatisticProbability valuesintercept-3.9010.005Trend and intercept-3.9830.019
At the first difference level Energy consumption has a unit root test with thefollowing results:Augmented Dickey Fuller testt-StatisticProbability valuesintercept-10.0770.000Trend and intercept-10.3980.000Conclusion:As Probability is less than 0.05 we reject H0.This states that series arestationary are first difference.
The second step involves the Johenson Co-Integrationtechnique. It shows long run relationship and effect ofindependent variable on the dependent variable. Cointegrationcan be well-thought out to be implemented as series iscointegrated of I(1).The lag order criterion is selected as “3”.Johenson co-integration technique comprises of 2 tests:Trace TestMaximum Eigen value TestCo-integration test is applied with the following hypothesis:H0=Variables are not co-integratedHa =Variables are co-integrated
The result for co-integration Trace test indicates only 1co-integrating equation at the significance level of0.05%.It also yields the long run equilibrium. Thus wereject H0.The result for co-integration Eigen value Test alsoindicated 1 co-integrating equation at the significancelevel of 0.05%.Thus we reject H0.Regression Equation is found as follows:LGDP=b0+ b1(EC)+et1.0000 =b0+ 0.9253(EC) +etThe regression equation predicts the positive relationshipbetween GDP growth and Energy Consumption. Theequation shows if independent variable (Energyconsumption) goes by 1 unit, dependent variable (GDP)goes by 0.925 units.
The following diagnostic tests are performed:AutocorrelationNormality testAutocorrelationIn order to check presence and absence of Autocorrelation,Serial correlationLM test is performed with the following hypothesis and results.Hypothesis:H0 = Absence of Autocorrelation.Ha = Presence of AutocorrelationResults: R-squareDurbin-Watson Test0.8421.576Since value of R-square is less than Durbin Watson statistic it implies theabsence of auto in the model
Normality test is performed in order to check thefunctional form of the model. The followinghypothesis and results are observed:H0 = functional form of model is wrongHa = functional form of model is not wrongJarque Bera Probability is 0.0000 which is less than0.05 thus we reject H0 and accept Ha which impliesthat functional form is not wrong.
The study scrutinizes the fundamental relationship between economicgrowth and energy consumption in Libya using the results ofJohenson Co-integration causality which foretellsthe evidence of causalityrunning from energy consumption to economic growth. A unidirectionalcausality running from energy consumption to economicgrowth is found between the variables.It also implies the positive relationshipbetween the variables and that the independent variable(EC) will affect thedependent variable(GDP) in the long run sodropping energy consumptioncould escort to a drop in economic growth.When any energy preservationtrials are commenced, considerable careshould be taken which will not unfavorably affect the economic growth. In thelight of above argument it is shimmering that energy obliges as an engine ofeconomicgrowth and economic commotion will be affected in the upshot ofchanges in EC. The unceasing energy use does crop a continuous upsurgein output. Thusenergy is indispensable for economic and communaldevelopment of a state or a country.
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