2. SUBMITTED TO:
PROF. DR.MUHAMMAD RAMZAN
SUBMITTED BY:
FATIMA PARVEEZ (MS-ECO)
SADIA ZUBAIR (MS-ECO)
ZAFAR IQBAL (MS-ECO)
AHSAN KHAN (MS-ECO)

3. This 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.

4. 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.

5. 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.

6. 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.

7. 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.

8. 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.

9. 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.

10. 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.

11. 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.

12. Consuming 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.

13. 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.

14. 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.

15. 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.

16. 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.

17. 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.

18. 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.

19. 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.

20. 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

21. 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

22. 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.

23. 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

24. 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.

25. 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

26. 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.

27. The following diagnostic tests are performed:
Autocorrelation
Normality test
Autocorrelation
In 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-square
Durbin-Watson Test
0.842
1.576
Since value of R-square is less than Durbin Watson statistic it implies the
absence of auto in the model

28. Normality 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.

29. 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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