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8/10/2019 World Oil Prices, Energy Use, and Economic Growth Sectors: Relationships in Tunisia, by Hassen Guenichi

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THE JOURNAL OF ENERGY

AND DEVELOPMENT

Hassen Guenichi,

World Oil Prices, Energy Use,

and Economic Growth Sectors:

Relationships in Tunisia,

Volume 39, Number 1

Copyright 2014


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WORLD OIL PRICES, ENERGY USE, AND

ECONOMIC GROWTH SECTORS:

RELATIONSHIPS IN TUNISIA

Hassen Guenichi*

Introduction

The Tunisian economys energy dependency has deepened significantly since1994. Energy resource development that had contributed to the growth of theTunisian economy, particularly during the 1980s, is now being challenged by the

need to meet growing domestic demand. Indeed, 1994 marked a watershed year

for the country of Tunisia, which, for the first time, experienced a net energy

deficit. But, with the doubling of the pipeline capacity between Algeria and Italy

in 1995 and the commencement of operations a year later at the Miskar natural gas

fieldoutside of Tunisias traditional hydrocarbon-producing region of the Gulf

of Gabesa surplus energy balance was once again achieved. However, this

surplus was short lived and by 2001, the energy balance had slipped again into

a deficit. As is the case with many nations since the beginning of the 21st century,

*Hassen Guenichi is a permanent assistant in Quantitative Methods at the Higher Institute

of Computer Science and Management of Kairouan (Tunisia) and a research associate at the

Computational Mathematics Laboratory, Monastir University (Tunisia). He holds a masters degreein quantitative methods from the University of Sousse (Tunisia), a masters in mathematical

economics and econometrics from the University of Manar (Tunisia), and a Ph.D. in economic

sciences from the University of Sousse. His principal research areas are energy economics, economic

growth, energy consumption, oil prices, and econometric modeling of the relationships between

growth and energy in emerging countries. The authors most recent articles have been published in

such refereed and peer-reviewed journals as Energy Studies Review, International Journal of

Business and Management Research,Asian-African Journal of Econometrics, andThe International

Journal of Economic Issues.

The Journal of Energy and Development, Vol. 39, Nos. 1 and 2

Copyright 2014 by the International Research Center for Energy and Economic Development(ICEED). All rights reserved.

53


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energy consumption increasingly is outstripping energy production. Tunisias total

energy consumption has reached 9,445,000 tons of oil equivalent (toe). As of

2001, the energy consumption in the agricultural, industrial, and services sectors

were 317,000 toe, 1,900,000 toe, and 1,400,000 toe, respectively. Clearly, Tuni-

sias expanding energy consumption is a concern to policy and decision makers

alike who are interested in assessing the issue of causality between energy use and

economic growth. Formulating policies and strategies to reduce energy demand

without adversely affecting economic growth is essential not only in the Tunisian

case but for other nations as well.

Thus, it is not surprising that economists have spent a great deal of time and

effort in analyzing the issue of energy consumption and economic growth. The

ongoing debate among energy economists about the relationship between energy

use/consumption and economic output/growth has led to the emergence of two

opposite views. One suggests that energy is the prime source of value because

other factors of production, such as labor and capital, cannot be utilized without

energy. In this case, energy use is expected to be a limiting factor to economic

development. The other point of view suggests that energy is neutral to growth.

This is what has become known in the literature as the neutrality hypothesis. The

main reason for the neutral impact of energy on growth is that the cost of energy is

very small as a proportion of gross domestic product (GDP); thus, it is not likely to

have a significant impact on output growth. But, we find two other viewpoints in

the recent debate about the relationship between oil prices and economic growth.The first is that oil price shocks do not affect output and inflation in low-income

oil-exporting countries. However, oil price shocks do significantly influence the

real exchange rates. The implication is that a high real oil price may give rise to

wealth. The second view suggests that there is evidence supporting an asymmetric

long-run relationship between oil prices and GDP. An increase in oil prices seems

to retard aggregate economic activity by more than a fall in oil prices stimulates it.

Most of the empirical literature on the subject investigated the relationship

between energy use and output growth by testing for the existence and direction of

causality between the two variables in either a bivariate or a multivariate context.However, this literature produced conflicting results, and there is no consensus

judgment either on the existence or the direction of causality between energy use

and output growth.

There are cases where causality was found to be running from GDP to energy

use. These include the United States (J. Kraft and A. Kraft, A. Akarca and T. Long,

and E. Yu and B. Hwang), South Korea (E. Yu and J. Choi), some industrialized

countries (Y. Erol and E. Yu), Pakistan and Indonesia (A. Masih and R. Masih), and

Taiwan (B. Cheng and T. Lai).1

In some other instances, causality was found to be

running from energy use to GDP. These include the Philippines (E. Yu and J. Choi),India (A. Masih and R. Masih), and Indonesia (J. Asafu-Adjaye).2 Finally, there are

cases where causality was found to be running in both directions (bidirectional).

THE JOURNAL OF ENERGY AND DEVELOPMENT54


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Recently, the results of empirical studies examining the relationship between

economic growth and oil prices have offered up different results depending on the

country of study (low income, developing versus developed, oil exporting or

importing, etc.). F. Joutz et al. found an indirect relationship between the govern-

ment budget constraint and economic growth through investment in Venezuela.3

The latter is positively related to improvements in the countrys fiscal position. In

the work by S. Lardic and V. Mignon, the empirical analysis focuses on the

economies of the United States, the G7, Europe, and euro area.4

Results indicate that

while standard cointegration is rejected, there is evidence for asymmetric cointe-

gration between oil prices and GDP. In the case of low-income and oil-exporting

countries (Nigeria), the findings of P. Olomomla and A. Adejumo were contrary to

previous empirical results for other states; oil price shocks did not affect output and

inflation in Nigeria.5

However, oil price shocks do significantly influence the real

exchange rates. The implication is that a high real oil price may give rise to a wealth

effect that appreciates the real exchange rate.

A major limitation of the research on the relationship among output growth,

energy, and oil price is the failure to account for the time-series properties of the

variables involved and, thus, many of them may have produced spurious results. In

addition, the standard bivariate causality procedures used in this literature, such as

Grangers and Sims, may fail to detect additional channels of causality and also

may result in contradicting results.6

In particular, D. Stern argues that bivariate

tests may fail to detect a causal relationship because of the substitution effects thatmay occur between energy and other inputs.7

This means that changes in energy

consumption will be countered by opposite movements in the use of other factors.

Substitution may result in an insignificant effect of energy use on growth. The

latest advances in econometric theory of structural change and the lack of enough

studies examining the relationships among energy use, oil price, and economic

growth in developing countries are the main motivations for this paper.

The aim of this article is to divide the Tunisian economy into three principal

sectors (agriculture, industry, and services) to investigate empirically the relation-

ships among oil price, energy use, and output growth in the case of a developingcountryTunisia. We propose a framework based on the neo-classical one-sector

aggregate production technology where capital, labor, oil price, and energy are

treated as separate inputs. First, we estimate the date breaks, which allow us to

divide the sample into different regimes or subsamples. Second, in each regime we

use the time-series properties of the data and develop a vector error-correction

(VEC) model to test for multivariate cointegration and Granger causality.

This article is structured as follows. We briefly present the econometric frame-

work and explain the structural break estimations based on the Lumsdaine and Papell

(LP) approach, followed by the cointegration analysis in the presence of pre-determined structural breaks using the Saikkonen and Johansen cointegration tests

and, last, we provide vector error-correction model (VECM) estimations based

TUNISIA: OIL PRICES, ENERGY USE, & GROWTH 55


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upon Quintos and Johansen.8 The subsequent section is devoted to an empirical

overview of the link among oil prices, oil use, and economic growth sectors in the

long run in Tunisia; this is followed by the conclusion.

Methodology

Unit Root with Structural Breaks: It goes without saying that structural change is

of considerable importance in the analysis of macroeconomic time series. Structural

change occurs in many time series for any number of reasons, including economic

crises, changes in institutional arrangements, policy changes, regime shifts, and war.

An associated problem is the testing of the null hypothesis of structural stability

against the alternative of a one-time structural break. If such structural changes arepresent in the data-generating process, but not allowed for in the specification of an

econometric model, results may be biased towards the erroneous non-rejection of

the non-stationary hypothesis.9

Conventionally, dating of the potential break is assumed to be known a priori in

accordance with the underlying asymptotic distribution theory. Test statistics are

then constructed by adding dummy variables representing different intercepts and

slopes, thereby extending the standard Dickey-Fuller procedure.10

However, this

standard approach has been criticized, most notably by L. Christiano, who argued

that data-based procedures typically are used to determine the most likely locationof a break: evidence of an endogeneity or a sample selection problem.

11This

invalidates the distribution theory underlying conventional testing.

In response, a number of studies have developed different methodologies for

endogenizing dates, including E. Zivot and D. Andrews, P. Perron, R. Lumsdaine

and D. Papell, and C. Bai et al.12

Their research has shown that by endogenously

determining the time of structural breaks, bias in the usual unit root tests can be

reduced. P. Perrons 1992 work and his 1997 article coauthored with T. J. Vogelsang

propose a class of test statistics that allows for two different forms of a structural

break, namely, the Additive Outlier (AO) model, which is more relevant for

series exhibiting a sudden change in the mean (the crash model), and the In-

novational Outlier (IO) model, which captures changes in a more gradual manner

over time.13

With this in mind, Lumsdaine and Papell (LP) introduced a novel procedure to

capture two structural breaks in a series.14

They found that unit root tests ac-

counting for two structural breaks are more powerful than those that allow for

a single break. In support, D. Ben-David et al. argued that

just as failure to allow one break can cause non-rejection of the unit root null by the

Augmented Dickey-Fuller test, failure to allow for two breaks, if they exist, can cause non-

rejection of the unit root null by the tests which only incorporate one break.15



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LP uses a modified version of the augmented Dickey-Fuller (ADF) test, which

specifies two endogenous breaks as follows:

DXt m bt uDU1t gDT1t vDU2t fDT2t aXt1

X

K

i1ciDXti et 1

where DU1t= 1 if t >TB1 and otherwise zero; DU2 t= 1 if t > TB2 and otherwise

zero; DT1t= t TB1 if t > TB1 and otherwise zero; and finally DT2t= t TB2 if

t > TB2 and otherwise zero. Two structural breaks are allowed for in both the time

trend and the intercept, which occur at TB1 and TB2. The breaks in the intercept are

shown in equation (1) by DU1tand DU2t, respectively, whereas the slope changes

(or shifts in the trend) are represented by DT1tand DT2t. The optimal lag length (k)

is based on the general-to-specific approach suggested by S. Ng and P. Perron.16

Cointegration Test in the Presence of Structural Breaks: As had been noted as far

back as 1989 by P. Perron, ignoring the issue of potential structural breaks can render

invalid the statistical results not only of unit root tests but of cointegration tests as well.

N. Kunitomo explains that in the presence of a structural change, traditional cointe-

gration tests, which do not allow for this, may produce spurious cointegration.17

In

the present research, therefore, considering the effects of potential structural breaks is

very important, especially because the world economy has been faced with structural

breaks like revolutions and wars in addition to significant policy changes.

P. Saikkonen and H. Lutkepohl (SL) and S. Johansen et al. have proposed a testfor cointegration analysis that allows for possible shifts in the mean of the data-

generating process.18

Because many standard types of data-generating processes

exhibit breaks caused by exogenous events that have occurred during the obser-

vation period, they suggest that it is necessary to take into account the level shift in

the series for proper inference regarding the cointegrating rank of the system.

P. Saikkonen and H. Lutkepohl argued that structural breaks can distort stan-

dard inference procedures substantially and, hence, it is necessary to make appro-

priate adjustment if structural shifts are known to have occurred or are suspected.19

The SL test investigates the consequences of structural breaks in a system context

based on the multiple equation frameworks of Johansen-Jeslius, while earlier ap-

proaches like A. Gregory et al. considered structural break in a single equation

framework and others did not consider the potential for structural breaks at all.20

According to P. Saikkonen and H. Lutkepohl, an observed n-dimensional time

series Xt = (x1t, . . . , xnt), Xt is the vector of observed variables (t = 1, . . . , T)

which are generated by the following process:21

Xt m0

m1

t g1

d1t g2

d2t g3

d3t dDT0t d1DU1t ut 2

where DT0tand DU1tare impulse and shift dummies, respectively, and account for

the existence of structural breaks. DT0tis equal to one, when t = T0, and equal to



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zero otherwise. Step (shift) dummy (DU1t) is equal to one when (t > T1), and is

equal to zero otherwise. The parametersg(i = 1, 2,"),m0,m1, andd are associatedwith the deterministic terms. The seasonal dummy variables d1t, d2t, and d3tare not

relevant to this research since our data are yearly. According to SL, the term u tis

an unobservable error process that is assumed to have a VAR (p) representation as

follows:22

ut A1ut1 . . . Aputp et t 1; 2 3

By subtracting ut1from both sides of equation (3) and rearranging the terms, the

usual error-correction form of equation (3) is given by:

Dut Put1Xp1

j1 GjDutj vt 4

This equation specifies the cointegration properties of the system. In this

equation, vt is a vector white noise process, while u t = Xt Dt and Dt are the

estimated deterministic trends. The rank ofP is the cointegrating rank of utand,hence, of Xt.

23The possible options in the SL procedure, as in Johansen, are three:

a constant, a linear trend term, or a linear trend orthogonal to the cointegration

relations. In this methodology, the critical values depend on the kind of the above-

mentioned deterministic trend that is included in the model. More interestingly, inSL the critical values remain valid even if dummy variables are included in the

model, while in the Johansen test the critical values are available only if there is no

shift dummy variable in the model. The SL approach can be adopted with any

number of (linearly independent) dummies in the model. It is also possible to

exclude the trend term from the model; that is, m = 0 may be assumed first. In this

methodology, as in Johansens, the model selection criteriaSchwarz Bayesian

criterion (SBC), Akaike information criterion (AIC), and HannanQuinn in-

formation criterion (HQ)are available for making the decision on the VAR

order. In the following section, we have applied SL tests for the cointegration rankof a system in the presence of structural breaks.

The estimation method is based on Johansen procedure estimation of the coin-

tegration relationships in the presence of two predetermined structural changes.24

Granger Causality Relationship:Having established the number of cointegrating

vectors, we performed Granger causality tests in order to verify the informational

relationships between the four variables.25

Granger causality from xk to xj means

that the conditional forecast for xj can be improved significantly by adding lagged xk

to the information set. The feasibility of the Granger causality tests depends on thestationary features of the system. If the series are stationary, the null hypothesis of

no Granger causality can be tested by using standard Wald tests.26



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In a cointegration model as in model (1), however, two sources of causation

come to light through the error-correction term (ECT), PYtk1 = ab9 Ytk1, ifa 6 0 or through the lagged dynamic terms

Pk1i1 GiDYtiif all Gi = 0.

27 The ECT

measures the long-run equilibrium relationship while the coefficients on lagged

difference terms indicate the short-run dynamics. Thus, in a cointegration model

(1), the proposition of xknot Granger causing xj in the long run is equivalent to

ajk= 0. In this context, xjis said to be weakly exogenous for the parameterb and xjdoes not react to the equilibrium errors. Additionally, in model (1) the proposition

of xknot Granger causing xjin the short run is equivalent to Gjk(L) = 0, where (L)

is the lag operator.

Empirical Analysis

Data Description:Data used in the analysis are quarterly time series on sector

production, capital, labor, energy use, and oil price for Tunisia during the period

19602005. The variables notations and definitions are as follows: Y = sector

production in U.S. dollars, K = sector capital stock in U.S. dollars, L = sector

employment number, EU = total energy use in the sector in thousands of tons of oil

equivalent, and OP = world oil price in U.S. dollars per barrel. All variables are

transformed into their natural logarithms so that their first differences approximate

their growth rates.

The Model: We estimate a five-dimensional macroeconometric model that

represents a vector of sector output (Y), world oil price (OP), labor (L), capital

stock (K), and energy use (EU), of a vector error-correction (VEC) representation

(see S. Johansen):28

DYt PYtk1Xk1

i1 GiDYti cDt et 5

where the reduced rank, r, of the 434 matrix ofP equals the number of cointe-

gration vectors in the system and n equals the number of (endogenous) series incointegration equation (5). Thus, Pcan be written as P ab0, where aandbareeach of the dimension r3 5 and rank r. The matrix b contains the cointegrating

vectors b, b bt;. . .;bt ; while the matrix of the adjustment coefficients adescribes the speed of adjustment of each of the four individual series in Yt to

deviations from the cointegration relationships.

Unit Root Test with Two Structural Breaks:We investigate the stationary status

of the variables using the Lumsdaine and Papell (LP) tests for unit roots in the

presence of two structural breaks.29

Table 1 provides the results of the LP test. Theprimary findings of the analysis are as follows: the unit root hypothesis is accepted

for all variables under investigation in each sector. The computed break dates are



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the most significant from this test, which corresponds closely with the expected

dates associated with the effects of the oil boom in 1973 and the first Gulf War in

1991.

Cointegration Test Results: As explained above, Johansen derived the likeli-

hood ratio (LR) test in order to determine the number of cointegrating relations in

a system of variables by allowing for the presence of potential structural breaks.We now apply a maximum likelihood approach for testing and determining the

long-run relationship in the model under investigation. As mentioned earlier, in

this procedure Johansen assumed that the break point is known a priori. In table 1,

we determined the time of the break endogenously by the LM procedure. The

empirical results based on this method showed two significant structural breaks in

the model under investigation, which are consistent with the timing of the 1973 oil

shock and the first Gulf War (1991). Therefore, at this stage we include two

dummy variables of regime change in order to take into account the two structural

breaks in the system. Following the Johansen procedure we consider three cases:an impulse dummy and shift with intercept included, an impulse dummy and shift

with trend and intercept included, and, finally, an impulse dummy and shift with

Table 1RESULTS OF THE LUMSDAINE AND PAPELL (LP) TEST

a

Sector Variables TB1 TB2 LM Unit Root Hypothesis

Agricultural

Sector

Y 1973 Q4 1994 Q2 4.96 Accepted

K 1973 Q4 1991 Q2 5.57 Accepted

L 1973 Q2 1999 Q1 6.55 Accepted

EU 1973 Q3 1991Q2 5.31 Accepted

OP 1982 Q2 1991 Q2 4.44 Accepted

Industrial

Sector

Y 1973 Q4 1991 Q1 5.78 Accepted

K 1976 Q3 1991 Q2 4.52 Accepted

L 1973 Q4 1991Q2 6.24 Accepted

EU 1973 Q3 1991 Q1 6.20 Accepted

OP 1982 Q2 1991 Q2 4.44 Accepted

Services

Sector

Y 1980 Q2 1991 Q2 4.95 Accepted

K 1973 Q4 1991 Q1 5.15 Accepted

L 1973 Q4 1991 Q2 5.85 Accepted

EU 1973 Q3 1991 Q3 5.78 Accepted

OP 1973 Q4 1991 Q2 4.44 Accepted

a Y = sector production in U.S. dollars, K = sector capital stock in U.S. dollars, L = sector

employment number, EU = total energy use in sector in thousand tons of oil equivalent, and OP = world

oil price in U.S. dollars per barrel; R. Lumsdaine and D. H. Papell, Multiple Trend Breaks and the Unit

Root Hypothesis,The Review of Economics and Statistics, vol. 79, no. 4 (1997), pp. 21218.



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a trend statistically independent (orthogonal) to a cointegration relation included.

The cointegration results in these three cases are presented in table 2.

The optimal number of lags is determined by AIC and SC, which is more appro-

priate for the short span of the data. The hypothesis of the long-run relationship among

non-stationary variables is tested and the result is reported in table 2. These findings

indicate that the hypothesis of no cointegration r = 0 is rejected at the 10-percent,

5-percent, and 1-percent significance level. The existence of one cointegration vector

is not rejected in any of the three cases mentioned above for the manufacturing

sector and rejected in the remaining sectors. The assumption of existence of two

cointegration vectors is accepted for the industrial, agricultural, and services sectors.

Estimation Results: In the aforementioned results, we find that two structural

breaks occur in each variable in each sector and that long-run relationships exist in

our predefined model. In the following work, we divide our samples into threesubsamples limited by the break dates and estimate the long-run and the short-run

relationships between the variables. Finally, we try to interpret the causal re-

lationships between variables and determine how each endogenous variable re-

sponds over time to a shock to other variables.

Table 2RESULTS OF THE COINTEGRATION TEST USING THE SAIKKONEN

AND LUTKEPOHL (SL) APPROACH

Intercept Included (C) Intercept and Trend Included (C/T)

G0 LR Pval 90% 95% 99% G0 LR Pval 90% 95% 99%

Agricultural Sector Agricultural Sector

0 230.04 0.000 94.4 98.2 105.8 0 232.24 0.000 107.8 112.9 122.7

1 127.69 0.000 68.5 71.9 78.5 1 126.47 0.000 79.3 83.6 92.3

2 54.06 0.013 46.5 49.4 55.2 2 54.90 0.100 54.7 58.4 65.8

3 17.33 0.738 28.4 30.8 35.7 3 18.73 0.844 33.8 36.8 42.9

4 8.22 0.477 14.1 16.1 20.4 4 8.08 0.705 16.5 16.4 23.0

Industrial Sector Industrial Sector

0 139.76 0.000 94.4 98.2 105.8 0 138.19 0.000 107.8 112.9 112.7

1 73.78 0.035 68.5 71.9 78.5 1 92.89 0.001 79.3 83.6 92.3

2 30.70 0.853 46.5 49.4 55.2 2 35.64 0.800 54.7 58.4 65.8

3 14.24 0.930 28.4 30.8 35.7 3 18.72 0.824 33.8 36.8 42.9

4 3.75 0.948 14.1 16.1 20.4 4 5.80 0.887 16.5 16.4 23.0

Services Sector Services Sector

0 126.13 0.000 94.4 98.2 105.8 0 125.40 0.006 107.8 112.9 122.7

1 72.94 0.048 68.5 71.9 78.5 1 84.07 0.049 79.3 83.6 92.3

2 33.76 0.690 46.5 49.4 55.2 2 44.92 0.418 54.7 58.4 65.83 14.28 0.910 28.4 30.8 35.7 3 20.05 0.781 33.8 36.8 42.9

4 4.93 0.844 14.1 16.1 20.4 4 4.23 0.876 16.5 16.4 23.0



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VECM Estimation Results in the First RegimeFirst Quarter (Q1) 1960 throughFourth Quarter (Q4) 1973: As highlighted in table 3, the empirical results of the

Tunisian agricultural sector show that in the long-run relationship in the first re-

gime, physical capital has the most significant effect on agricultural production

Table 3VECTOR ERROR-CORRECTION MODEL (VECM) ESTIMATION

RESULTS IN THE FIRST REGIMEa

Y(21) K(21) L(21) EU(21) OP(21) C

Agricultural Sector

b1 1 2.34 2.04 1.68 0.81 3.03

[5.16] [2.06] [4.20] [4.79]b2 0.77 1 5.50 0.14 0.50 4.26

[3.44] [5.15] [0.32] [2.71]

a1 0.001 0.01 0.002 0.01 0.51

[0.17] [1.17] [2.76] [0.68] [4.65]

a2 0.03 0.01 0.002 0.002 0.17[3.96] [1.66] [4.47] [0.18] [2.24]

Industrial Sector

b1 1 0 3.26 0.53 0.03 2.18

[2.08] [0.61] [0.16]

b2 0 1 30.98 11.72 5.38 3.22[3.67] [2.52] [5.44]

a1 0.07 0.17 0.004 0.04 0.22

[2.64] [2.70] [1.83] [2.75] [1.13]

a2 0.009 0.005 0.0002 0.003 0.17

[2.05] [0.41] [0.51] [1.22] [4.47]

Services Sector

b1 1 0 0.83 0.83 0.68 2.37[0.75] [1.08] [4.23]

b2 0 1 3.68 2.50 0.46 3.57

[2.20] [2.15] [1.89]

a1 0.007 0.03 0.002 0.002 0.51[0.83] [2.91] [3.24] [0.60] [4.37]

a2 0.01 0.004 0.002 0.002 0.15

[2.15] [0.71] [5.34] [1.16] [2.35]


employment number, EU = total energy use in sector in thousand tons of oil equivalent, OP = world

oil price in U.S. dollars per barrel, and C = drift.



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(a 1-percent increase in K leads to a 2.34-percent increase in agricultural growth).

A 1-percent increase in total energy use (EU) leads to a 1.68-percent increase in

agricultural production. A 1-percent increase in human capital (L) leads to a 2.04-

percent increase in production. Lastly, a 1-percent increase in oil prices (OP) leads

to a 0.81-percent decrease in agricultural growth It is apparent from these findings

that energy use and oil price have the most important effects on the Tunisian

agriculture sector in this sub-period. The VECM estimation results of the Tunisian

industrial sector suggest only a long-run relationship between energy use and

industrial production growth. Indeed, a 1-percent increase in energy use (EU) leads

to a 0.52-percent increase in industrial production, but the oil price (OP) coefficient

is not significant.

Moreover, the results indicate that, unlike the industrial sector, the oil price has

a great effect on production in the services sector in the long run and the energy

use coefficient is not significant. The results suggest the dependence of the Tunisian

economy on energy and its price, thus contradicting the neoclassical results and

other studies on developed countries because in Tunisia energy is not a limiting

factor for production growth.

Table 4 reports the outcomes from the Granger causality tests. These tests are

conducted using a joint F-statistic for the exclusion of one variable from one

equation as previously illustrated. The results of these tests indicate that for the

agricultural sector Granger causality is running in both directions between output

growth and energy use and between output growth and oil price. Thus, in contrastwith the neoclassical argument that energy is neutral to growth, our results for

Table 4GRANGER CAUSALITY TEST RESULTS IN THE FIRST REGIME

Null Hypothesis F-Statistic P-Value

Agricultural Sector

Energy does not Granger cause output growth 6.04746 0.00345

Output growth does not Granger cause energy 10.28020 9.6E-05Oil price does not Granger cause output growth 3.05791 0.01376

Output growth does not Granger cause oil price 5.41766 0.00602

Industrial Sector


Output growth does not Granger cause energy 0.09686 0.15150

Oil price does not Granger cause output growth 0.25438 0.08900


Services Sector


Output growth does not Granger cause energy 0.86683 0.42615Oil price does not Granger cause output growth 4.67759 0.02802




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Tunisia are consistent with the view that energy and oil price have a causal impact

on output growth. Our results are also in line with findings by D. Stern, H. Yang,

and S. Lardic and V. Mignon who obtained similar results for other countries.30

Unlike the agricultural sector, the results for the industrial sector tests indicate

that Granger causality is not running between all variables. These results for this

sector are consistent with the neoclassical view that energy is neutral to growth. Our

results are consistent with the findings by U. Erol and E. Yu, E. Yu and J. Choi,

E. Yu and B. K. Hwang, B. S. Cheng, E. Yu et al., and E. Yu and J. C. Jin, which

offered evidence in favor of the neutrality-of-energy hypothesis.31

In the case of the

services sector, the Granger causality is running only in one directionbetween oil

price and output growth (oil price causes output)but is absent between energy use

and services production. These conclusions are consistent with the neoclassical view

that energy use is neutral to growth and are in line with the results of U. Erol and

E. Yu.32

The outcomes are consistent with the research of S. Lardic and V. Mignon,

which suggested that oil prices cause output growth and uncovered an asymmetric

long-run relationship between oil prices and GDP.33

VECM Estimation Results in the Second RegimeFirst Quarter (Q1) 1974 throughThird Quarter (Q3) 1991: In this time period, the oil price coefficients are still significant

for growth in both the agricultural and industrial sectors. One can explain this by the

fact that during this time span Tunisia exported oil and used the revenues for in-

vestments projects, which in turn fostered growth. But, the energy use coefficients are

still not significant in terms of the long-run relationships; the energy consumption wasnot a limiting factor for the agricultural and industrial sectors during this time frame.

The estimation results for the sector of services suggest that, for the period

19731991, the energy use and oil price were not significant factors for growth

(see table 5). Indeed, for growth only the capital and labor coefficients are sig-

nificant for a long-run relationship.

For the second period of our sample, the Granger causality tests are reported in

table 6. The results indicate that, like the first regime, for the agricultural sector the

Granger causality runs in both directions, between energy use and agricultural output

growth and is unidirectional between oil price and output growth (oil price causesoutput). These results are aligned with findings by K. H. Ghali and M. I. T. El-Sakka

that energy use is not neutral to growth.34

But with the unidirectional relationship

between oil price and agricultural output, we find that oil price has a negative effect

on output growth. The increase in oil price delayed the output growth.

In the case of the industrial sector, the Granger causality is bidirectional between

energy use and output growth and unidirectional between oil price and industrial output.

These results are consistence with the view of non-neutrality of energy to growth. But,

in Tunisia the industrial sector growth is dependent upon fluctuations in world oil prices.

For this period, the services sector output growth is independent of energy and

its price. Indeed, the Granger causality test and VECM estimation fail to find



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a relationship between energy use and output and between oil price and output

growth.

VECM Estimation Results in the Third Regime Third Quarter (Q3) 1991 throughFourth Quarter (Q4) 2005: The empirical results indicate that in the agricultural

Table 5VECTOR ERROR-CORRECTION MODEL (VECM) ESTIMATION RESULTS IN THE

SECOND REGIMEa

Y(2

1) K(2

1) L(2

1) EU(2

1) OP(2

1) C

Agricultural Sector

b1 1 2.01 0.19 0.27 0.32 1.30

[6.41] [0.41] [1.04] [2.62]

b2 0.11 1 1.84 2.35 3.52 9.99

[3.11] [5.50] [2.17] [6.75]

a1 1.22 1.38 0.004 2.43 6.64[1.57] [2.28] [0.22] [3.09] [3.20]

a2 0.28 0.38 0.0007 0.52 1.42[1.65] [2.80] [0.17] [1.06] [3.07]

Industrial Sector

b1 1 4.56 0.21 1.05 0.41 3.64

[5.65] [0.47] [2.27] [3.10]

b2 0.72 1 1.65 0.999 0.57 1.94[2.28] [3.94] [2.20] [4.40]

a1 0.36 0.21 0.003 0.14 0.77

[2.46] [4.03] [3.28] [2.77] [2.49]

a2 0.06 0.05 0.004 0.06 0.31[2.83] [1.61] [5.13] [3.00] [1.16]

Services Sector

b1 1 0.50 1.39 1.54 0.001 6.79

[3.01] [2.69] [2.47] [0.02]

b2 2.09 1 7.96 6.01 0.396 2.69[0.09] [5.32] [3.34] [2.00]

a1 0.016 0.13 0.009 0.093 2.27

[0.09] [0.38] [1.38] [0.67] [1.60]

a2 0.042 0.044 0.003 0.064 0.575

[0.68] [0.37] [1.46] [1.32] [1.16]






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sector the total energy use (EU) and oil price (OP) coefficients are not statistically

significant. This result shows that these two factors are not a limiting factor on

economic growth in Tunisia. This is due to the governments intervention by sub-sidizing this sector. In the third long-run relationship, the industrial sector pro-

duction depends on the total energy use and oil price fluctuations. Indeed, the EU

and OP coefficients are statistically significant (a 1-percent increase in EU and OP

leads, respectively, to an increase of 1.91 percent and a decrease of 0.84 percent in

production). Table 7 provides the VECM estimation results for the third regime.

For the services sector, the VECM estimation results show the dependence of

this sector on energy use, which is the limiting factor to its production growth.

However, the coefficient of oil price in the first cointegration relationship is not

significant. This result can be explained by the fact that the Tunisian servicessector is quite developed and contains many sub-sectors, such as tourism and

communications, which are not dependent on oil price fluctuations.

Table 8 reports the results of the Granger causality tests indicating that for the

agricultural sector Granger causality runs in one direction and only between output

growth and energy use (energy use causes output). Thus, in contrast with the

neoclassical argument that energy is neutral to growth, our results for Tunisia are

consistent with the view that energy has a causal impact on output growth. The

absence of relationships between world oil price and output growth in the case of

the agricultural sector can be explained by the use of government subsidies.35

The results in table 8 indicate that for the industrial sector Granger causality is

bidirectional running between energy use and output growth and is unidirectional

Table 6GRANGER CAUSALITY TEST RESULTS IN THE SECOND REGIME


Agricultural Sector





Industrial Sector





Services Sector







16/21

between oil price and output. These results for this sector and period contradict the

neoclassical view that energy is neutral to growth. For the services sector, how-ever, the Granger causality is running only in one direction between energy use

and output growth (energy use causes output), but it is absent between energy oil

Table 7VECTOR ERROR-CORRECTION MODEL (VECM) ESTIMATION RESULTS

IN THE THIRD REGIMEa

Y(2

1) K(2

1) L(2

1) EU(2

1) OP(2

1) C

Agricultural Sector

b1 1 1.44 0.66 1.25 0.30 2.83

[4.88] [3.88] [4.36] [1.56]

b2 0.12 1 0.03 1.66 0.04 3.30

[1.63] [0.44] [15.59] [1.40]

a1 0.003 0.01 0.006 0.15 0.18[0.11] [0.29] [3.98] [2.85] [0.81]

a2 0.19 0.15 0.009 0.53 0.15[2.59] [1.54] [2.10] [3.95] [0.26]

Industrial Sector

b1 1 0 1.62 1.91 0.08 3.71

[5.76] [2.35] [2.36]

b2 0 1 0.95 0.02 0.19 7.08[2.11] [0.04] [3.25]

a1 0.09 0.13 0.01 0.11 0.34

[3.59] [3.02] [3.38] [5.38] [4.77]

a2 0.086 0.13 0.003 0.05 0.09[3.50] [3.23] [1.21] [2.95] [0.23]

Services Sector

b1 1 0.19 2.65 2.58 0.08 2.540

[3.96] [9.17] [8.01] [0.38]

b2 1.31 1 1.34 0.02 0.097 1.768[2.97] [21.9] [0.31] [13.5]

a1 0.004 0.079 0.028 0.08 0.43

[0.096] [2.11] [4.70] [3.05] [0.50]

a2 0.25 0.65 0.064 0.176 1.50

[1.17] [3.80] [2.34] [1.34] [0.37]






17/21

price and services production. The results show that the services sector growth

depends on energy consumption but not on the world oil price.

Conclusion

The latest advances in econometric theory of structural change and a lack of the

studies examining the relationships between energy and economic growth in de-

veloping countries are the main motivations behind this research. In order to

examine the relationships between world oil price and the economic growth

sectors in a developing country (in this case Tunisia), we use the technique of

cointegration in the presence of structural breaks. The predetermined dates ofstructural changes divide the sample into regimes that allow us to better evaluate

and understand the relationships among world oil price, energy use, and economic

growth.

The results show that the agricultural and the services sectors are not influ-

enced much by a surge in oil prices. But it is very clear that our results contradict

neoclassical theory that energy is neutral to growth. Indeed, we find that energy

use always has had a great impact on the production of all economic sectors. We

can explain this as the agricultural sector still is supported by the government via

subsidies. In the last decade, the services sector has witnessed significant developedin part due to the rapid growth in the communications industry as mobile phone

ownership has become widespread among the Tunisian population. Additionally,

Table 8GRANGER CAUSALITY TEST RESULTS IN THE THIRD REGIME


Agricultural Sector





Industrial Sector





Services Sector







18/21

the tourism subsector has become a major driver of the Tunisian economy, which is

made even more attractive by the cost differential for the tourists coming from high

cost-of-living developed nations and enjoying the relatively lower costs in Tunisia,

where oil prices are maintained by the government.

The industrial sector is influenced by energy use and world oil prices fluctuations,

especially in the last two regimes (first quarter 1974 through third quarter 1991 and

third quarter 1991 through fourth quarter 2005). This is due to the importance of this

sector to the overall Tunisian economy. Indeed, in the first period (first quarter 1960

through fourth quarter 1973), Tunisia was in the first stages of development after

gaining its independence. The industrial sector was nascent and the country had

excess oil production capacity, which is why we find that the production of this sector

was greatly influenced by the use of energy and not by the price of oil. In the last two

time periods, the industrial sector became one of the bases of the Tunisian economy.

Then, particularly in the last regime, Tunisias energy supply and demand balance

became onerousa situation with major implications for the industrial sector as it is

a significant energy consumer. Therefore, it makes sense that a surge in oil prices has

a notable impact on the growth of the industrial sector.

In general, the results reject the neoclassical assumption of the neutrality of

energy to economic growth. Nonetheless, world oil prices do have a significant

effect on the Tunisian industrial sector, although locally the oil prices are main-

tained by the government. The results of this study are in line with some findings,

but contradict others. In particular, our results are consistent with those of S. Lardicand V. Mignon, who suggest an asymmetric relationship between oil price and

output growth: the increase in the oil price delayed or had a negative influence on

economic growth.36

Finally, we can better see the effects of world oil prices on the

global Tunisian economy if we add the variable of public expenditure in the model

because many products and sectors are subsidized.

NOTES

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29R. Lumsdaine and D. H. Papell, op. cit.

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Cointegration Analysis, Energy Economics, vol. 26, no. 2 (2004), pp. 22538.35These include quantities of fuel for farmers, new energy-saving materials, encouraging

farmers by a sum of money or a tax exemption.

36S. Lardic and V. Mignon, op. cit.


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