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8/13/2019 “Electricity Consumption and Industrial Gross Domestic Product Nexus in Sfax: An ARDL Bounds Testing Approach,” by Rania Ben Hamida

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THE JOURNAL OF ENERGY

AND DEVELOPMENT

Rania Ben Hamida,

“ Electricity Consumption and Industrial

Gross Domestic Product Nexus in Sfax:

An ARDL Bounds Testing Approach ,”

Volume 38, Number 2

Copyright 2013



ELECTRICITY CONSUMPTION AND INDUSTRIALGROSS DOMESTIC PRODUCT NEXUS IN SFAX:

AN ARDL BOUNDS TESTING APPROACH

Rania Ben Hamida *

Introduction

T he study of the relationship between the level of development of a country and energy consumption is a debate that has piqued the interest of economists and

has been advanced and expanded upon over time. Since the 1970s, the number of surveys examining the causal relationship between economic development and energy consumption has increased for both developed and developing nations.However, the research on African countries and especially in North Africa islimited. In fact, empirical studies on nations like Tunisia—let alone that country’ssecond largest city of Sfax—are almost nonexistent. Prior academic findings haveshown that the causal relationship between economic growth and energy con-sumption differs from one country to another through time. The majority of previous studies have used the residual-based cointegration test of Engle and Granger and the maximum likelihood test based on S. Johansen and S. Johansenand K. Juselius. 1 However, these cointegration techniques may not be appropriatewhen the sample size is too small and when the variables do not have the sameorder of integration. 2

*Rania Ben Hamida, a Ph.D. candidate in economics at the Faculty of Economics and Management (MODEVI), University of Sfax, Tunisia, earned a master’s degree in economics fromthe same institution and a B.A. in accounting from the Business School of Sfax. This paper drawsfrom thesis research the author undertook at the Faculty of Economics and Management of Clermont-Ferrand (University of Auvergne, France) at the Center for Studies and Research onInternational Development (CERDI). Her research focuses on energy and the environment.

The Journal of Energy and Development , Vol. 38, Nos. 1 and 2Copyright 2013 by the International Research Center for Energy and Economic Development(ICEED). All rights reserved.

241



In addition to oil development, Sfax is benefiting from the commercialization of its natural gas resources. The most important of these is the Miskar gas field, con-sidered to be the first gas field discovered in Tunisia, which is owned by British GasCompany. Located offshore of Sfax in the Gulf of Gabes, Miskar began productionin 2006 and produces natural gas and condensates; its proven reserves are around 1.5trillion cubic feet with production expected to be around 200 million cubic feet/day.Its daily production represents over 45 percent of the nation’s gas production.

The second most prominent hydrocarbon field is that of Ashtart. This is an oilfield located about 76 kilometers off the coast of Sfax. 5 Similar to the Miskar field,this originally was discovered in the 1970s. Its daily production represents about10 percent of Tunisia’s oil production. The program of complementary de-velopment is in progress by the owners—the international oil and gas companyOMV—to improve its production capacity.

The deposit of Didon is the third most important hydrocarbon field; it is off-shore, located about 150 kilometers from Sfax. Its daily output is about 9 percentof national oil output. Discovered by Elf Aquitaine in 1975, this field is currentlyowned and operated by PA Resources.

Sfax’s success in attracting international energy companies has allowed it to become the nation’s largest gas provider—accounting for 70 percent of total production—reflecting its role as a center of the hydrocarbon sector in Tunisiawhile supplying needed energy to the local market. This trend is expected to

continue with exploration and production activities being undertaken by in-ternational oil and gas firms in the Gulf of Gabes offshore of Sfax that look promising, particularly for natural gas.

Thus, Sfax plays a crucial role in the Tunisian economy: being the number oneoil producer, the location of the first fishing port, the second largest industrialcenter in the country, and an important producer of oil and natural gas.

The development indicator chosen in this study is the industrial GDP specific toSfax. This indicator is calculated according to the sum of the value added by themanufacturing industry in millions of Tunisian dinars at current prices for the

period between 1980 and 2010.The manufacturing subsectors that are taken into consideration include: agri-cultural and food industries, building materials and glass industries, mechanicaland electrical industries, chemical industry, clothing and leather industries, and other manufacturing industries.

Electricity in Tunisia is provided by the Tunisian Electricity and Gas Society(STEG). On August 3, 1962, the government decided to nationalize the production,transmission, and distribution of electricity and gas and assigned these activities tothe STEG. In 2010, electricity in Tunisia represented 6 percent of the GDP and the

country’s total consumption was 892 gigawatts (GW) per year. Tunisia’s electricityconsumption on a per capita basis is considered high when compared to other na-tions on the southern shore of the Mediterranean, despite the fact that Tunisia’s

SFAX: ELECTRICITY CONSUMPTION & GDP NEXUS 243



energy resources are relatively limited. Due to its electrification policy, STEG hassucceeded in the past 40 years in raising the electrification rate from 20 percent tonearly 100 percent in urban areas and in rural areas from 6 percent to 99 percent.

In this article, we are interested only in the district of Sfax City, which coversthe geographical area demarcated by El Ain Road to the north and the Gabes Road on the southeast. This district manages the following areas: la Sokra, Chaker,el Alya, Wed el Rmal, city of El Bahri, and El Houda. 6

For our research, the energy consumption of Sfax is expressed by the medium-voltage electricity consumption in the manufacturing industrial sector, which in-cludes the following industries: food, textiles and clothing, paper and publishing,chemicals, and petroleum. The medium-voltage electricity combined with high-voltage electricity are used to power manufacturing, services, tourism, transportationand communication, water and sanitation pumping activities, agricultural pumpingactivities, and various other industries.

Medium-voltage electricity sales ranked highest among the types of electricityconsumed with a percentage between 46 and 48 percent for the period 2000–2006. Low-voltage electricity sales ranked second at between 41 and 43 percent, with high-voltageelectricity consumption accounting for between 10 and 12 percent for the same period.Medium-voltage electricity consumption continues to increase over time (see figure 1).

Figure 1VARIATIONS IN MEDIUM-VOLTAGE ELECTRICITY CONSUMPTION

FOR SFAX (DISTRICT OF SFAX CITY), 1980–2010(in gigawatt hours)

Source: Constructed and expanded by the author based upon the annual data of the TunisianElectricity and Gas Society, South regional distribution and the Sfax City district.

THE JOURNAL OF ENERGY AND DEVELOPMENT244



This upward trend of electricity consumption is due to the increase in the number of subscribers from the development of various economic sectors in Sfax and, in particular, from the industrial sector, which has continued to evolve since the 1960s.

If we compile the trend of subscribers for medium-voltage electricity con-sumption with the variation of industrial firms over time, we can see some delay(figures 2 and 3). This can be explained by the fact that medium-voltage sales notonly power the manufacturing industry but also are used by other economic sectorsas has been mentioned earlier.

Literature Review

The relationship between energy consumption and economic growth has been

widely discussed. Though a consensus has been reached confirming the existenceof the relationship between these two variables, the direction of this relationship isstill under discussion.

At the empirical level, there exist four cases concerning the causal relationship between economic growth and energy consumption. The first case presents energyconsumption as a cause of the economic growth. This view was widely supported by A. Masih and R. Masih in the case of India, E. S. H. Yu and B. K. Hwang in thecase of the Philippines, B. Cheng for Brazil, C.-C. Lee concerning Canada and Belgium, G. Altinay and E. Karagol and U. Soytas and R. Sari regarding Turkey,

Figure 2TREND OF THE CUMULATIVE NUMBER OF

INDUSTRIAL FIRMS IN SFAX, 1980–2010

Source: Constructed and expanded by the author based upon data from the Promotion Agency for Industry (PAI) of Sfax.




J. Asafu-Adjaye in the cases of India and Indonesia, A. Shiu and P. Lam for China,Y. Wolde-Rufael for Gabon, Zambia, and Tunisia, J. Squali for Nigeria, and N. Odhiambo in the case of Tanzania. 7

The second group argues that economic growth guides the energy consumptionin many countries. The empirical work, which is consistent with this view, in-cludes the following studies: P. Narayan and R. Smyth in the case of Australia,J. Kraft and A. Kraft and S. Abosedra and H. Baghestani for the United States,A. M. M. Masih and R. Masih for Indonesia, B. Cheng and T. Lai regardingTaiwan, C.-C. Lee in the case of Japan, and U. Soytas and R. Sari for Italy, France,

Germany, Argentina, and Korea.8

A third view suggests that there is bi-directional causality between economicgrowth and energy consumption. This perspective largely has been supported bystudies such as D. I. Stern for the case of the United States, G. Hondroyiannis et al.with regard to Greece, C.-C. Lee for Sweden, and J. Asafu-Adjaye for the Phil-ippines and Thailand. 9

Finally, the last perspective indicates that there is no causality between energyconsumption and economic growth. This observation has been offered byU. Soytas and R. Sari in the cases of Brazil, Mexico, India, Indonesia, South

Africa, and Canada, C.-C. Lee concerning the United Kingdom and Germany,Y. Wolde-Rufael for Togo, Tunisia, and Zimbabwe, and A. Akinlo for the nationsof Cameroon, Cote d’Ivoire, Nigeria, Kenya, and Togo. 10

Figure 3VARIATIONS IN THE NUMBER OF MEDIUM-VOLTAGE SUBSCRIBERS

IN SFAX (DISTRICT OF SFAX CITY), 1990–2010

Source: Constructed and expanded by the author based upon the annual data of the TunisianElectricity and Gas Society, South regional distribution and the Sfax-City district.




Estimation Techniques and Empirical Analysis

Cointegration-ARDL Bounds Testing Procedure: In this study, we use the re-cently developed ARDL bounds testing approach to investigate the long-run

cointegration relationship between the two proxies of economic growth and en-ergy consumption. The ARDL modeling methodology originally was introduced by M. Pesaran and Y. Shin and later expanded upon by M. Pesaran et al. 11 TheARDL cointegration approach has many advantages in comparison to other cointegration methods. Relative to these other cointegration techniques, theARDL does not impose a restrictive assumption that all variables under study must be integrated of the same order; in other words, this approach can be applied whatever the variables are integrated of order one [I(1)], order zero [I(0)], or fractionally integrated. Unlike the other cointegration techniques, the ARDL testis suitable even through the size of the sample is small. The ARDL model used inthis study can be expressed as follows:

Model number 1: electricity consumption and economic growthCase number 1: lGDP is the endogenous variable

DlGDP t = b 0 + X n

i = 1 b 1i DlGDP t i + X n

i = 1 b 2i DlEC t i + b 3lGDP t 1

+ b 4lEC t 1 + mt ð1Þ

Case number 2: lEC is the endogenous variable

DlEC t = a 0 + X n

i = 1a 1i DlEC t i + X n

i = 1a 2i DlGDP t i + a 3lGDP t 1

+ a 4lEC t 1 + mt ð2Þ

where lGDP is the log of industrial GDP, lEC is the log of electricity con-sumption, mt is the white noise error term, and D is the first difference operator.The industrial GDP was calculated from the sum of manufacturing industry in

millions of Tunisian dinars at current prices for the period between 1980 and 2010. The manufacturing subsectors considered in this indicator are: agricul-tural and food industries, building materials and glass industries, mechanicaland electrical industries, chemical industries, clothing and leather industries,and other various manufacturing industries. The industrial GDP was collected from the Promotion Industry of Sfax and the National Institute of Statistics of Sfax.

The chosen variable of EC in this study is the medium-voltage electricitydestined for the manufacturing sector, which includes the following industries:

food, textiles and clothing, paper and publishing, chemical, and oil. This indicator was collected from the Tunisian Electricity and Gas Company of the Sfax Citydistrict for the same time period (1980–2010).




The null hypothesis of the bounds test procedure shows that there is no coin-tegration among the variables in equation (1), that is, H 0 : b3 = b4 = 0 against thealternative hypothesis of H 1 : b3 6¼ b4 6¼ 0. In equation (2) the null hypothesis of nocointegration is H 0 : a 3 = a 4 = 0 against the alternative hypothesis of H 1 : a 3 6¼a 4 6¼ 0. M. Pesaran and B. Pesaran and M. Pesaran et al. report two sets of criticalvalues for a given significance level. 12 One set of critical values assumes that allvariables included in the ARDL model are I(0) while the other is calculated on theassumption that the variables are I(1). If the computed test statistic exceeds theupper critical bounds value, then the H 0 hypothesis is rejected. If the F-statistic islower than the lower bounds value, then the null hypothesis of no cointegrationcannot be rejected. If the F-statistic falls into the bounds, then the cointegrationtest becomes inconclusive.

Granger Non-Causality Test: While the long-run relationships have beenidentified in the section on cointegration-ARDL bounds testing, this section de-tects the short-run and long-run Granger causality between the EC and GDP.Referring to Granger’s definition of causality, a time series X t causes another timeseries Y t if Y t can be predicted better (in a mean-squared-error sense) using pastvalues of X t than by not doing so; that is to say, if the past value of X t significantlycontributes to forecasting Y t , then the X t is considered to be the Granger cause of X t .

The Granger causality test is favorable to both large and small sample sizes. 13

The null hypothesis of the conventional Granger causality test supposes that X t does not cause Y t and vice versa by simply running the following two equations:

Y t = a0 + X n

i = 1 a1iY t i + X n

i = 1 b1iX t i + mt ð3Þ

X t = b0 + X n

i = 1 a2iY t i + X n

i = 1 b2iX t i + e t ð4Þ

where mt and e t are the white noise error processes and n denotes the number of

lagged variables.The null hypothesis that X t does not Granger cause Y t is rejected if b1i are jointlysignificant. 14 Nevertheless, the traditional causality test suffers from two method-ological limitations. 15 First, this standard test of causality does not examine the basictime series properties of the variables. 16 Second, this test runs the series stationaritymechanically by differencing the variables and, as a result, eliminates the long-runinformation that exists in the original variables. For this reason, in this study we usethe error-correction causality test, which involves the lagged error-correction termcoming from the cointegration equation. The Granger causality model used is:

Model number 1: causality between electricity consumption and economicgrowth, where ECM t-1 is the lagged correction term obtained from the long-runequilibrium:




DlGDP t = b 0 + X n

i = 1 b 1i DlGDP t i + X n

i = 1 b 2i DlEC t i + ECM t 1 + mt

ð5Þ

DlEC t = a 0 + Xn

i = 1 a 1i DlEC t i + Xn

i = 1 a 2i DlGDP t i + ECM t 1 + mt

ð6Þ

While the existence of a long-run relationship between GDP and EC suggeststhat there must be Granger causality in at least one direction, it does not indicatethe direction of temporal causality between the variables. The direction of thiscausality can only be detected by the F-statistic and the lagged error-correctionterm. The short-run causal effect is detected by the F-statistic on the explanatory

variables. 17

Empirical Analysis—The Test for Stationarity: Although the bounds test for cointegration does not require that all variables be integrated of order 1 [I(1)], it isimportant to elaborate upon the test for stationarity in order to check that thevariables are not integrated in order 2 [I(2)]. The critical values of the F-statisticscomputed by M. Pesaran et al. and P. Narayan and R. Smyth are based upon theassumption that the variables are I(0) or I(1). 18 The results of the tests for statio-narity on different variables based on the Phillips-Perron (PP) approach are pre-

sented in table 1. The results included in table 1 show that lEC is I(1) and lGDP isI(0). None of the variables in this study are I(2).

Empirical Analysis—Cointegration Test: In this section, the long-run relation-ship between lEC and lGDP is examined using the ARDL bounds testing

Table 1PHILLIPS-PERRON TEST RESULTS a

In level In first difference

lEC 2.54 –4.24***Critical value 1 percent –2.64 –3.67Critical value 5 percent –1.95 –2.96Critical value 10 percent –1.61 –2.62

In level

lGDP –6.63***Critical value 1 percent –3.67Critical value 5 percent –2.96

Critical value 10 percent –2.62a *** Denotes significance at the 1-percent level.




The empirical results reported in table 5 show that there is a unique uni-directional causal flow from electricity consumption to economic growth both inthe short and long run. The long-run causality from electricity consumption toeconomic growth is supported by the coefficient of the lagged error-correctionterm in the economic growth function, which is negative and statistically signif-icant. The short-run causality from electricity consumption to economic growth,on the other hand, is supported by the F-statistic in the economic growth function,which is statistically significant. Nevertheless, the reverse causality from eco-nomic growth to electricity consumption is rejected by the coefficient of the

lagged error-correction term and the F-statistic in the electricity function, whichare all statistically insignificant. A summary of the causality test between the twovariables is presented in table 6.

Table 3BOUNDS TEST RESULTS FOR LOG OF INDUSTRIAL GROSS DOMMESTIC PRODUCT

(LGDP) AND LOG OF ELECTRICITY CONSUMPTION (LEC) a

First Case: lGDP is the dependent variableVariable withTrend andConstant

Number of LagLength F-Statistic Probability Result

lGDP is thedependentvariable 1 4.935* 0.036 (5%) Cointegration

Asymptotic 1% 5% 10%critical values I(0) I(1) I(0) I(1) I(0) I(1)

30 7.593 8.350 5.377 5.963 4.427 4.95735 7.477 8.213 5.233 5.777 4.380 4.867

Second Case: lEC is the dependent variableVariablewithout Trendand Constant

Number of LagLength F-Statistic Probability Result

lEC is thedependentvariable 1 5.982** 0.022 (10%) Cointegration

Asymptotic 1% 5% 10%critical values I(0) I(1) I(0) I(1) I(0) I(1)

30 6.027 6.760 4.090 4.663 3.303 3.79735 5.763 6.480 3.957 4.530 3.223 3.757

a ** Denotes significance at the 5-percent level, * denotes significance at the 10-percent level.




Conclusion

This study examines the intertemporal causal relationship between energyconsumption and economic growth in Sfax—using one proxy of energy con-

sumption, namely, medium-voltage electricity consumption (EC) destined for use by the industrial sector and one proxy of economic growth, namely, gross do-mestic product (GDP) for the industrial manufacturing sector. Previous studiestackled the case of causality in Tunisia by using the Engle and Granger cointe-gration test and the maximum likelihood test based on S. Johansen and S. Johansenand K. Juselius. 20 This was the case in the research by N. Chouaibi and T. Abdessalem, who detected a uni-directional causality running from electricity

Table 4RELATIONSHIP ESTIMATION FOR LOG OF INDUSTRIAL GROSS DOMMESTIC

PRODUCT (LGDP) AND LOG OF ELECTRICITY CONSUMPTION (LEC) a

Coefficient Significance

First case: lGDP is the dependent variableConstant 2.788* 1%Trend 0.046** 5%lEC –0.248** 5%R

2= 0.9

Second case: lEC is the dependent variableConstant 0.012* 10%lGDP 0.085*** 1%R

2= 0.98

a *** Denotes significance at the 1-percent level,** denotes significance at the 5-percent level,and * denotes significance at the 10-percent level.

Table 5GRANGER NON-CAUSALITY TEST RESULTS FOR ELECTRICITY

CONSUMPTION (EC) AND ECONOMIC GROWTH (GDP) a

Dependent Variable Causal Flow F-Statistict-Test onthe ECM R

2

Economic growth (GDP)Electricity consumption (EC) /

economic growth (GDP)2.8067

(0.0482)** –2.7041**

(0.0123) 0.31

Electricity consumption (EC)

Economic growth (GDP) /

electricity consumption (EC)

0.7752

(0.5188)

0.0426

(0.9663) 0.41

a ** Denotes significance at the 5-percent level.




consumption to economic growth for Tunisia, and M. Belloumi, who identified a long-run bi-directional causal relationship between the two series and a short-rununi-directional causality from energy to GDP. 21 Using the ARDL bounds testing

procedure, the empirical results of this study found that there is a distinct uni-directional causal flow from electricity consumption to economic growth, both inthe short and long run. This study proves that electricity consumption promoteseconomic growth in Sfax. Consequently, policy makers must consider the im- portance of the electric power sector and must give priority to the different processstages, especially the quality and the capacity of its services as it has a significantimpact on economic growth of the government and can lead to more sustainabledevelopment for both Sfax and Tunisia as a whole.

NOTES

1 R. Engle and C. W. J. Granger, ‘‘Cointegration and Error Correction: Representation, Esti-mation and Testing,’’ Econometrica , vol. 55, no. 2 (1987), pp. 251–76; S. Johansen, ‘‘StatisticalAnalysis of Cointegrating Vectors,’’ Journal of Economic Dynamics and Control , vol. 12 (1988), pp. 231–54; and S. Johansen and K. Juselius, ‘‘Maximum Likelihood Estimation and Inference onCointegration with Applications to the Demand for Money,’’ Oxford Bulletin of Statistics , vol. 52,no. 2 (1999), pp. 169–210.

2 P. K. Narayan and R. Smyth, ‘‘Electricity Consumption, Employment and Real Income inAustralia: Evidence from Multivariate Granger Causality Tests,’’ Energy Policy , vol. 33, no. 9

(2005), pp. 1109–116.3 National Institute of Statistics in Sfax, Sfax, Tunisia, 2010.

4 Chamber of Commerce and Industry in Sfax, available at http://www.ccis.org.tn/english/sfax.php .

5 ‘‘Guide to Exports of Sfax in 2010,’’ available at http://www.sfaxexport.com/fr/ presentation_sfax.php .

6 This is according to the mapping provided by the district of Sfax City.

7 A. M. M. Masih and R. Masih, ‘‘Energy Consumption, Real Income and Temporal Causality:Results from Multi-Country Study Based on Cointegration and Error-Correction Modeling Tech-niques,’’ Energy Economics , vol. 18, no. 3 (1996), pp. 165–83; E. S. H. Yu and B. K. Hwang, ‘‘The

Table 6SUMMARY OF CAUSALITY TEST RESULTS

Variables Causality General conclusion

Economic growth ( DlGDP)and electricity consumption(DlEC)

There is a distinct uni-directionalcausal flow from electricityconsumption to economic growth

Electricity consumptionGranger-causes economicgrowth


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Frisch Centennial Symposium , ed. S. Storm, (Cambridge: Cambridge University Press, 1999)chapter 11, and M. H. Pesaran, Y. Shin, and R. J. Smith, ‘‘Bound Testing Approaches to theAnalysis of Level Relationship,’’ Journal of Applied Econometrics , vol. 16, no. 3 (2001), pp. 289– 326.

12 M. H. Pesaran and B. Pesaran, Microfit 4.0 (Window Version) (New York: Oxford UniversityPress, 1997), and M. H. Pesaran et al., op. cit.

13 D. K. Guilkey and M. Salemi, ‘‘Small Sample Properties of Three Tests for Granger-CausalOrdering in a Bivariate Stochastic System,’’ Review of Economics and Statistics , vol. 64, no. 4(1982), pp. 668–81, and J. Geweke, R. Meese, and W. Dent, ‘‘Comparing Alternative Tests of Causality in Temporal Systems: Analytical Results and Experimental Evidence,’’ Journal of Econometrics , vol. 21, no. 2 (1983), pp.161–94.

14 C. W. J. Granger, ‘‘Investigating Causal Relations by Econometric Models and Cross-spectral

Methods,’’ Econometrica , vol. 37, no. 3 (1969), pp. 424–38.15 N. M. Odhiambo, ‘‘Is Financial Development Still a Spur to Economic Growth? A Causal

Evidence from South Africa,’’ Savings and Development , vol. 28, no. 1 (2004), pp. 47–62.

16 C. W. J. Granger, ‘‘Some Recent Developments in a Concept of Causality,’’ Journal of Econometrics , vol. 39, nos. 1–2 (1988), pp. 199–211.

17 P. K. Narayan and R. Smyth, ‘‘Higher Education, Real Income and Real Investment in China:Evidence from Granger Causality Tests,’’ Education Economics , vol. 14, no. 1 (2006), pp. 107–25,and N. M. Odhiambo, ‘‘Energy Consumption and Economic Growth Nexus in Tanzania: An ARDL

Bounds Testing Approach.’’18 M. Pesaran et al., op. cit., and P. K. Narayan and R. Smyth, ‘‘Electricity Consumption,

Employment and Real Income in Australia: Evidence from Multivariate Granger Causality Tests.’’

19 P. K. Narayan and R. Smyth, ‘‘Electricity Consumption, Employment and Real Income inAustralia: Evidence from Multivariate Granger Causality Tests.’’

20 R. Engle and C. W. J. Granger, op. cit.; S. Johansen, op. cit.; and S. Johansen and K. Juselius,op. cit.

21

N. Chouaibi and T. Abdessalem, ‘‘Causality between Electricity Consumption and EconomicGrowth in Tunisia: Policy Implications,’’ International Journal of Economic Policy in Emerging Economics , vol. 4, no. 4 (2011), pp. 211–26, and M. Belloumi, ‘‘Energy Consumption and GDP inTunisia: Cointegration and Causality Analysis,’’ Energy Policy , vol. 37 (2009), pp. 2745–753.


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