Electricity conservation policies and sectorial output in Pakistan:An empirical analysis

Faisal Mehmood Mirza b,n, Olvar Bergland a, Naila Afzal b

a UMB School of Economics and Business, Norwegian University of Life Sciences, P.O. Box 5003, 1432 Ås, Norwayb Department of Economics, Hafiz Hayat Campus, Jalalpur Jattan road, University of Gujrat, Gujrat, Pakistan

H I G H L I G H T S

� We quantify impact of electricity conservation policies on sectorial value added.� Shocks to electricity consumption and technical efficiency positively affect the output.� Shocks to electricity price negatively affects value-added in the long run.� Policies inducing improvements in energy efficiency positively affect the output.

a r t i c l e i n f o

Article history:Received 7 March 2014Received in revised form13 May 2014Accepted 18 June 2014Available online 12 July 2014

Keywords:Electricity conservationSectorial outputImpulse responsePakistan

a b s t r a c t

Government of Pakistan has taken several measures in recent years for conserving electricity to reduceits electricity shortfall. In this context, this study attempts to quantify the impact of different electricityconservation policies on value-added of industrial and services sectors in Pakistan. Results indicate thatunanticipated shocks to electricity consumption and technical efficiency form a positive relationshipwhile electricity price has a negative relationship with value-added in industrial and services sectors inthe long run. Direct electricity conservation policies and policies that aim at increasing electricity priceswill have an adverse impact on value-added of both the sectors. Policies that induce improvements inenergy efficiency will have a positive impact on the sectorial output in the long run. Therefore, thegovernment should not pursue direct consumption curtailment polices to mitigate the electricity crisisrather steps should be taken to enhance energy efficiency in the economy. This can be done by settingtargets for reducing energy intensity in both the sectors. Moreover, energy efficiency should beincorporated in the mainstream of energy policy and specific laws should be enacted to establishinstitutions and develop methods to help in effective conservation and efficient consumption of limitedenergy resources.

& 2014 Elsevier Ltd. All rights reserved.

1. Introduction

Energy conservation is aimed at reducing energy demand throughthe use of energy efficient appliances/tools/equipments or by chan-ging the behavioral patterns of individuals' (Bhattacharyya, 2011).It is evident from the experience of many countries that energyconservation and efficiency measures are among the feasible optionsto address issues related to energy shortfall and climate change(Cheung and Kang, 2008; Geller et al., 2006). Today's wide spreadadoption of efficiency and conservation measures have their roots inthe oil price shocks of the 1970s where due to Yom Kippor War and

Iranian revolution, oil prices skyrocketed and economies around theglobe failed to meet their domestic energy demand (Geller et al.,2006; Kobayashi, 2012; Doshi and Zahur, 2013). Governments startedexploring policy options through which the overall energy demandcan be reduced and energy security can be maintained. Highlyindustrialized economies were the initiators in adopting energyefficiency and conservation measures.

Maintaining energy security was one of the major reasonsbehind widespread adoption of energy conservation and efficiencymeasures during 1970's and 1980's. But during the last twodecades, due to the mounting environmental concerns, most ofthe efficiency policies are being pursued to reduce CO2 emission.According to the International Energy Agency (IEA) projections,by 2035 energy-efficiency policies will be successful in reducing70 percent of the aggregate cumulative global CO2 emissions(IEA, 2012).

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Energy Policy

http://dx.doi.org/10.1016/j.enpol.2014.06.0160301-4215/& 2014 Elsevier Ltd. All rights reserved.

n Corresponding author. Tel.: þ92 3214130330; fax: þ92 52 3505156.E-mail addresses: faisal.mirza@uog.edu.pk (F. Mehmood Mirza),

olvar.bergland@umb.no (O. Bergland).

Energy Policy 73 (2014) 757–766

In developing countries like Pakistan, such energy conservationmeasures are pursued to overcome energy shortfalls. To this end,in 2005 a policy called “National Energy Conservation Policy” waspassed by the parliament which includes guidelines for all energyconsuming sectors to increase the end use energy efficiency inPakistan. However, during FY2008 severe electricity shortage wasthe major reason for slowdown in the economic activities. As aresult, to bridge the gap between electricity supply and demandthe government of Pakistan started to work on various electricityconservation and efficiency measures. For instance, a three day“Energy Summit” was conducted in 2010 under the chairmanshipof Prime Minister of Pakistan and all stakeholders decided ontaking necessary measures for electricity conservation. The mainelectricity saving measures were; two week holidays in govern-ment offices, closing down of all the markets by 8 pm, switchingoff of all the air conditioners till 11 am in government offices,implementation of daylight savings time (DST) policy and reduc-tion of electricity supply to Karachi electric supply company(KESC) from 650 MW to 300 MW. Along with these, measureswere also put into place to enhance the overall energy efficiency inthe economy with the collaboration of different organizations.1 Totackle the energy conservation issue through legislation, “EnergyEfficiency and Conservation Act, 2011” was prepared by theENERCON to establish necessary institutions and mechanisms forthe effective energy conservation.2

Pakistan has also signed and ratified the Kyoto Protocol Con-vention on Climate Change in 2005 and has developed its cleandevelopment mechanism (CDM) that focuses on measures toreduce the emissions of greenhouse gasses and ensure energyefficiency. By following global experience, Pakistan finds energyconservation and efficiency measures as a feasible option to meetthese commitments (GOP, 2005). Therefore, commitment of thegovernment with these obligations raises a question that howdifferent conservation policies can be implemented that not onlyPakistan conserves energy but also achieves the desired levels ofeconomic growth and CO2 emissions reduction as proposed byKyoto Protocol agreements.

Given these concerns, the objective of the present study is toevaluate the impact of unanticipated shocks in electricity con-sumption, electricity price and technical efficiency on the value-added of industrial and services sectors in Pakistan. More pre-cisely, the analysis has been carried out to determine the likelyassociation between direct/indirect electricity conservation policesand sectorial value-added. The present study contributes to theexisting empirical literature on the issue as no study has analyzedthe impact of electricity conservation policies on sectorial outputin Pakistan.

Theoretically, the opinion over the impact of energy conserva-tion policies on economic performance splits into two differentschools of thought. First, the new classical view postulates thatenergy consumption is not an essential element in the productionprocess as other factors. Hence, conserving energy through con-servation policies will not hamper economic performance. Ozturkand Acaravci (2010) provided evidence for this theory in Turkeywhere GDP and energy consumption do not have a causal relation-ship in the short run. Jobert and Karanfil (2007) found that incomeand energy consumption are neutral to each other in the long runin the case of Turkey. The second school of thought claims thatenergy is a critical element in the production process so any

decrease in energy consumption through energy conservationpolicies will result in a slowdown in the overall economic activity.Several studies have empirically verified this theory in differentcountries. Asafu-Adjaye (2000) showed that in India and Indone-sia, unidirectional Granger causality from energy consumption toGDP exists both in the short run and the long run while inThailand and Philippines, bidirectional causality exists both inthe short and the long run. Hye and Riaz (2008) for the case ofPakistan found that bidirectional Granger causality betweenenergy consumption and economic growth exists in the shortrun whereas economic growth Granger causes energy consump-tion in the long run. Hence, energy plays a key role in GDP growthrates in these Asian countries both in the short run and the longrun. Soytas et al. (2001) found that energy is the vital element forGDP growth in Turkey both in the short run and the long run,therefore energy conservation polices may slowdown the eco-nomic performance of these countries. Hence these contrastingarguments make it vital to investigate the relationship betweenenergy conservation polices and economic performance.

Rest of the paper is organized as follows: Section 2 reviews theexisting literature, Section 3 contains a detailed discussion of theeconometric methodology employed and data sources, Section 4presents discussion of the empirical results while Section 5concluded the paper with important policy recommendations.

2. Trends in energy conservation policies a review of literature

Development and enactment of energy conservation lawsstarted right after the oil price shocks of 1970s (Geller et al.,2006; Doshi and Zahur, 2013). In United States, Energy Policy andConservation Act was enacted in 1975 as a direct policy responseto the energy crisis and since then a number of laws have been putin place (Geller et al., 2006). To save energy, United Kingdomstarted its campaign in 1973 while Japan passed its energyconservation law in 1979. Following the global experience, manyof the developing economies were successful in establishing policyframeworks for energy efficiency by the start of 1990 (Price et al.,2001; Balachandra et al., 2010).

Impact of these policies has been evaluated on different aspects ofthe economy since their inception. Specifically, impact has beenevaluated on energy savings, CO2 emissions reduction and onmacroeconomic variables like GDP, employment and sectorialvalue-added (REMI, 2004; Grover, 2007; NYERSDA, 2009; Seymoreet al., 2009; Erero, 2010; Ramer, 2011; Nyamdash and Denny, 2011).

Determination of the direction of Granger causality betweenenergy consumption and economic growth is necessary for opti-mal policy making regarding energy conservation. Considerablenumber of empirical studies has been carried out on this issue buteconomists have still not reached a consensus on the direction ofGranger causality (Table 1). Multi-country studies suggest thatGranger causality runs from energy consumption to economicgrowth; therefore energy conservation polices may hamper theeconomic growth rates (Asafu-Adjaye, 2000; Lee and Chang, 2007;Mehrara and Musai, 2012). However, for country specific studies,results vary to a great deal. For instance in Taiwan economy,direction of Granger causality runs from economic growth toenergy consumption (Cheng and Lai, 1997) and for the Turkisheconomy economic growth is Granger caused by energy consump-tion making energy conservation policies unfavorable for eco-nomic growth (Soytas et al., 2001). In case of Pakistan, aggregatelevel studies suggest that Granger causality runs from economicgrowth to energy consumption. Therefore energy conservationpolices can be implemented without hampering the economicgrowth rates (Hye and Riaz, 2008; Aqeel and Butt, 2001; Shahbazand Feridun, 2011). This generalization, however, does not hold

1 Gesellschaft fur Technische Zusammenarbeit GmbH (GTZ) signed an agree-ment with National Productivity Organization (NPO) to launch energy audits inindustrial sector mainly in textile subsectors of Pakistan. Under the supervision ofGerman experts energy audits were completed in 6 textile units (Retrieved fromthe NPO website: http://www.npo.gov.pk).

2 Retrieved from the Enercon website: http://www.enercon.gov.pk.

F. Mehmood Mirza et al. / Energy Policy 73 (2014) 757–766758

true at disaggregated level. Studies conducted at sectorial levelcome with varying findings possibly due to the different estima-tion techniques employed (Liew, Nathan and Wong, 2012; Qazi,Ahmed and Mudassar, 2012). Nwosa and Akinbobola (2012)analyze the relationship between energy consumption and sector-ial output in Nigeria. Estimating a bivariate Vector Autoregressivemodel for the data ranging from 1980 to 2010, they foundbi-directional causality between energy consumption and agricul-tural value added and a unidirectional causality running fromservices sector value-added to the energy consumption. Studyconcludes that energy conservation policies will have an adverseeffect on the sectorial value added in the Nigerian economy.

At the same time, quiet a number of studies have evaluated theexpected impact of energy conservation measures on the eco-nomic growth rates of various economies. Nyamdash and Denny(2011) with the help of demand side model quantified the impactof electricity conservation polices on industrial and servicessectors value-added in Ireland. Johansen cointegration test sup-ported the presence of long run relationship among all variables.Empirical results for impulse response functions across both thesectors were in line that direct electricity conservation policiesthat put a constraint on electricity consumption do not hamperthe sector specific value-added while polices through improvedenergy efficiencies have a negative effect on sectorial output.

Gupta and Sengupta (2012) estimated energy savings potentialand policy for energy conservation in selected Indian manufactur-ing industries using time series data from 1991–92 to 2008–09.To study the response of energy and non-energy inputs to thechange in their factor prices and the possibilities of pursuing energyconservation polices, they develop an econometric model allowingfor substitutability between energy and non-energy inputs usingtranslog cost function at the industry level. Study found that energyconsumption is significantly responsive to its own price increaseinsignificantly related to capital requirements for energy conserva-tion. Study proposed that increase in energy prices (by impositionof specific or ad valorem tax) can be the primary policy to bringenergy conservation in the manufacturing sector.

Ramer (2011) analyzed the economic impact of carbon tax as apolicy instrument to energy conservation in Switzerland. Theimpact was quantified both at aggregate and disaggregated level.Empirical findings within the framework of computable generalequilibrium model based on new growth theory highlighted thatthese policies have a moderate impact on welfare and consump-tion at aggregate level. On the other hand, at sectorial level, sectorswith high energy intensity are more adversely affected whereasthe output in sectors with low energy intensity was less affectedby such energy taxes.

Yuan et al. (2011) apply an integrated assessment model coupledwith fully forward looking general equilibrium model and thetechnology rich bottom-up model for the US economy to studyvarious policy options to mitigate the environmental concerns. Theresults suggest that CO2 emission tax is the most cost effective policytool to reduce carbon emissions while energy tax is the most efficientpolicy option to reduce the overall energy consumption.

REMI (2004) launched an assessment program to forecast theeconomic impacts of oil and natural gas conservation policies inthe US state of Connecticut. Under a model specifically developedfor policy analysis, impacts of oil and natural gas conservationpolicies separately as well as collectively were quantified. Fore-casted results confirmed that implementation of energy conserva-tion policies were expected to spur economic growth both atsectorial as well as on national level. Employment, real disposableincome and state revenue were forecasted to increase with naturalgas conservation policies playing the dominant role in the positivegrowth of all these macroeconomic variables.

NYSERDA (2009) quantified the macroeconomic impacts ofEnergy Smart Public Benefits Program (E$P) implemented in NewYork for energy conservation using REMI policy Insight Model. Theanalysis was done for a period of 1999–2008. Results of themacroeconomic analysis suggested that these initiatives were agood policy tool for New York with potential impacts on employ-ment, gross state product, personal income and total output.

Erero (2010) investigated the economic impact of increasingelectricity prices in South Africa as a tool for conserving energy.

Table 1Selected studies on the relationship between energy consumption and economic growth.

Study Technique Short run Long run

Studies at global level (aggregate level studies)(Asafu-Adjaye, 2000) Thailand, Philippines, India and Indonesia Johansen–Juselius cointegration EC2EG EC2EG

EC2EG EC2EGEC-EG EC-EGEC-EG EC-EG

(Cheng and Lai, 1997), Taiwan Cointegration and Hsiao version of Granger Causality EC’EG(Soytas et al., 2001) Turkey Johansen–Juselius cointegration EC-EG EC-EG(Kiran and Guris, 2009) Turkey Unit Root test and VAR model ElC2EG(Acaravci, 2010) Turkey Cointegration and VECMs ElC-EG(Mehrara and Musai, 2012) 11 oil exporting countries Panel Cointegration and Panel Causality test ElC’EG ElC’EG(Lee and Chang, 2007) 22 HICs and 18 LICs Panel data stationarity testing procedure and Panel VAR EC2EG

EC-EG

Studies conducted in Pakistan (aggregate level studies)Hye and Riaz (2008) ARDL and augmented Granger Causality test EC2EG EC’EGAqeel and Butt (2001) Cointegration and Hsiao's version of Granger Causality EC’EG EC’EGShahbaz and Feridun (2011) ARDL and Toda Yamamoto and Wald causality tests EC’EG EC’EG

Studies conducted in Pakistan (disaggregate level studies)Liewet al. (2012) Johansen–Juselius cointegration & Granger Causality test EC2AVA

EC’IVAEC’SVA

Qazi et al. (2012) Johansen maximum likelihood method OilC2EGELC-EGCOC’EGGC EC

’, -, 2, are used to indicate the direction of causality. Where AVA¼agricultural value-added, IVA¼ industrial value-added, SVA¼services value-addedELC¼electricity consumption, OilC¼oil consumption, COC¼coal consumption and GC¼gas consumption.

F. Mehmood Mirza et al. / Energy Policy 73 (2014) 757–766 759

By employing CGE model, the impact of a 35 percent increase inelectricity tariff on different macroeconomic variables was ana-lyzed. Findings illustrated that a 35 percent shock to electricitytariff in South African economy had a tendency to decreaseelectricity consumption, which in turn could decrease GDP andemployment by 1.53 percent and 2.99 percent respectively.

Barker et al. (2009) examine the macroeconomic reboundeffects arising from energy efficiency policies at the global level.The study finds that the long run rebound effects as a result ofenergy efficiency policies being pursued will reach up to 50percent on average across all the sectors of the economy by2030. To enhance the effectiveness of conservation policies, thestudy proposes complementary policies like imposition of energytaxes, behavioral changes and consumer education related toenergy efficiency. The policies should not only focus ensuringenergy saving only, rather should also focus on bring behavioralchanges to reduce greenhouse gas emissions with the improve-ment in the living standards.

Grover (2007) examined the Oregon Business Energy Tax Credit(BETC) and Residential Energy Tax Credit (RETC) conservationprograms for their proposed short term impacts on employment,output, wages, tax revenues and energy savings. Under theseprograms a personal income tax credit is offered to those indivi-duals who invest in energy efficiency and conservation improve-ments. Impact analysis for Planning (IMPLAN) technique wasemployed to carry out the analysis. As RETC and BETC energyconservation programs forces the participants to invest more inenergy efficient activities, hence the results showed that due toincreased energy savings, disposable income of businesses andhousehold's increases while cost of production decreases. All thisin turn has increased the output, tax revenue, wages and employ-ment in Oregon.

Seymore et al. (2009) used Global Trade Analysis Project (GTAP)model to analyze the impact of two cent/KWh electricity genera-tion tax in the South Africa and the other member countries ofSouthern African Development Community (SADC) and SouthernAfrican Customs Union (SACU). Estimation results showed thatSouth Africa was successful in reducing the CO2 emission butmacroeconomic variables such as GDP, investment level, publicprivate consumption, employment and wages of skilled labordecreased due to the implementation of electricity generationtax. SACU and SADC member countries benefitted from the policybecause domestic prices have increased in South Africa due togeneration tax, making it less competitive in international market.

3. Data and methodology

3.1. Data

The primary objective of this paper is to analyze the impact ofelectricity conservation policies on the value-added of industrialand services sectors in Pakistan. To this end, annual time seriesdata from 1971 to 2009 is used. Sector specific electricity con-sumption, electricity price and technical efficiency are used asthree measures of electricity conservation and efficiency polices.The sector specific value-added data in constant local currencyunit (LCU) is collected from World Development Indicators (WDI).The data for nominal electricity prices (weighted average of pricesdetermined by Pakistan Electric Power Company (PEPCO) andKarachi Electric Supply Company (KESC) (Paisa/kWh)) for boththe sectors is obtained from National Transmission and DispatchCompany of Pakistan's (NTDC) annual report. Information onsector specific electricity consumption in GWh is collected fromdifferent Energy Year books published by Hydrocarbon Develop-ment Institute of Pakistan. Technical efficiency is proxied by the

ratio between sector specific value-added and electricity con-sumption as no clear quantitative measure and exact data ontechnical efficiency exists (Nanduri, 1998).3 Summary statistics ofdata are presented in Table 2.

3.2. Stationarity diagnostics

For the determination of long run relationship between vari-ables, cointegration test requires the series under consideration tobe non-stationary at same order of integration. Further, causalitytests are very sensitive towards stationarity properties of theseries (Granger and Newbold, 1974). For the identification of orderof integration of each of the series we applied Augmented DickeyFuller and Phillips and Perron (1988) unit root tests as specified bythe following model:

ΔYt ¼ β1þδYt�1þ ∑m

i ¼ 1αi ΔYt� iþεt ð1Þ

Where Yt is the series of interest. Δ is the first difference operatorand εt is assumed to be the white noise process with zero meanand constant variance. Philips–Perron test was also conducted instationarity diagnostics because it makes non-parametric adjust-ments to the test to make it robust against heteroskedasticity andautocorrelation. The null hypothesis in Eq. (1) involves testingδ¼ 0, i.e. Yt has a unit root, against one-tailed alternative hypoth-esis that δo0. Dickey and Fuller have proved that inferences aboutthe estimated coefficient of Yt�1 cannot be based upon the usual tstatistic rather the t (tau) statistics (Dickey and Fuller, 1979;Mackinnon, 1991).

3.3. Johansen Juselius cointegration test

In order to investigate the response of the sectorial value addedto unanticipated shock in electricity consumption, electricity priceand technical efficiency, following Hall et al. (2001), Stern (2004)and Nyamdash and Denny (2011), a demand side model has beenemployed and impulse response functions have been estimated.Estimation of the impulse response functions requires that theunderlying variable form a stable long-run relationship with eachother and should not drift too far away from each other. Two seriesXt and Yt which are individually non-stationary; their linearcombination illustrated as Zt ¼ Xt�βYt can be stationary showingthat these series are co-integrated.

Several methods to test for cointegrating vectors have beenproposed in the literature. Among all, ordinary least squareapproach by Engle and Granger (1987) and maximum likelihoodmethod in a fully specified error correction model by Johansen andJuselius (1990) are frequently applied (Gonzalo, 1992).

For estimation of long run relationship in multivariate setting,Johansen–Juselius cointegration approach is preferred over theEngle and Granger two step cointegration method because in thelater approach, if we regress Y on X, the results will be differentfrom regressing X on Y. Moreover, Engle–Granger approach is atwo-step process and a mistake occurring in the first step willcarry implications to the second step as well.

The maximum likelihood methodology suggested by Johansenand Juselius (1990) is based on the following VAR model:

Xt ¼ A1Xt�1þA2Xt�2þ…þApXt�pþεt ð2ÞWhere Xt is a (n�1) vector of economic time series.4 A1,A2, …, Ap

represent the (n�n) coefficient matrices and εt is an (n�1) vector

3 TAi ¼ VAiECi

. Where TAi¼sector specific technical efficiency, VAi¼sector specificvalue added, and ECi¼sector specific electricity consumption.

4 In this study economic time series are; VAi¼sector specific value added,ECi¼sector specific electricity consumption, TAi¼sector specific technical efficiencyand Pi¼sector specific electricity price.

F. Mehmood Mirza et al. / Energy Policy 73 (2014) 757–766760

of error terms with zero mean and constant variance. The Eq. (3)can be written in a VECM formulation as follows:

ΔXt ¼ ∑p�1

i ¼ 1ΓiΔXt�p�1þΠXt�1þεt ð3Þ

Where Γi ¼ ðI�A1�A2�…�ApÞ (i¼1, 2,…, p�1) and Π ¼�ðI�A1�A2�…�ApÞ. The rank of the matrix Π contains theinformation on long run relationship among variables. Its rank isdenoted by r and equals the number of independent cointegratingvectors. If r¼ 0, then there is no evidence for long run relationshipamong series. Instead, if the rank of Π matrix is n and r ¼ n, thenthe elements of X are stationary. ΠXt�1 is the error-correctionfactor if r¼ 1. For other cases, 1 oron implies that there aremultiple cointegrating vectors. The number of distinct cointegrat-ing vectors can be obtained by checking the significance ofcharacteristic roots of Π matrix.

Number of co-integrated vectors is determined by estimatingΠmatrix. Under Johansen–Juselius approach, we find two teststatistics for the determination of cointegrating vectors i.e. like-lihood ratio or trace statistics and maximum eigenvalue statisticswhich are as follow:

λtraceðrÞ ¼ �T ∑n

i ¼ rþ1ln ð1�λ^rÞ

λmaxðr; rþ1Þ ¼ �T ln ð1�λ^rþ1ÞTrace statistic is employed to test the null hypothesis that number ofcointegrating vectors is less or equal to r while max statistic tests thenull hypothesis that cointegrating vectors are equal to r. Both thesetests are based on characteristic roots and if characteristic roots areclose to zero, both λtrace and λmax statistics will be small and there willbe less evidence for long run relationship among series.

3.4. Vector error correction model

One of the implications of Granger representation theorem is thatif the series have a cointegrating relationship, then Granger causalityexists at least in one direction; which shows whether a particularvariable improves the forecast of the dependent variable whenincluded in the model (Engle and Granger, 1987). Under VAR modelGranger causality is examined by the joint significance of laggedindependent terms. However, when the series are co-integrated thecausal relationship is determined under a VECM which distinguishesbetween short and long run causalities. Significant error correctionterms point towards long run Granger causality while joint signifi-cance of lagged independent variables is an indication for short runGranger causality. We use the following VECM to determine thedirection of causality and assess the impact of unanticipated shocksin value-added, electricity consumption, electricity price and techni-cal efficiency of industrial and services sectors on each other.

ΔVAit ¼ β0þ ∑

k

j ¼ 1β1ΔVAi

t� jþ ∑k

j ¼ 1β2ΔECi

t� jþ ∑k

j ¼ 1β3ΔPi

t� j

þ ∑k

j ¼ 1β4 ΔTAi

t� jþαvaECTit�1þμ1t ð4Þ

ΔECit ¼ β0þ ∑

k

j ¼ 1β1ΔVAi

t� jþ ∑k

j ¼ 1β2ΔECi

t� jþ ∑k

j ¼ 1β3ΔPi

t� j

þ ∑k

j ¼ 1β4ΔTAi

t� jþαecECTit�1þ μ2t ð5Þ

ΔPit ¼ β0þ ∑

k

j ¼ 1β1 ΔVAi

t� jþ ∑k

j ¼ 1β2ΔECi

t� jþ ∑k

j ¼ 1β3ΔPi

t� j

þ ∑k

j ¼ 1β4ΔTAi

t� jþαpECTit�1þμ3t ð6Þ

ΔTAit ¼ β0þ ∑

k

j ¼ 1β1 ΔVAi

t� jþ ∑k

j ¼ 1β2ΔECi

t� jþ ∑k

j ¼ 1β3ΔPi

t� j

þ ∑k

j ¼ 1β4ΔTAi

t� jþαtaECTit�1þμ4t ð7Þ

Where VAit is the value-added of industrial and services sectors at

time t, ECit is the sector specific electricity consumption at time t, Pi

t isthe sector specific electricity price at time t and TAi

t is sector specifictechnical efficiency at time t. ECTi

t�1 is error correction term and α isthe adjustment coefficient.

In a VAR system, the individual regression coefficients areusually difficult to interpret. Hence these are interpreted throughtechniques associated with VAR models i.e. Granger causality test,impulse response functions and variance decomposition methods.We evaluate the impact of electricity conservation policies onindustrial and services sectors value-added through impulseresponse functions, which trace out the response of current andfuture values of each of the variables to unanticipated shock in thecurrent value of one of the VECM errors. Impulse responsefunctions have been extensively used for policy evolution in theliterature. Studies pertaining to policy assessment in energy sectorinclude Nyamdash and Denny (2011), Azgun (2011) and Tiwari(2011) etc.

4. Empirical results and discussion

4.1. Unit root properties of the data

Order of integration of the series was determined by Augmen-ted Dickey Fuller (ADF) and Phillips–Perron (PP) unit root tests.Both tests confirmed that at levels all the series in the regressionmodel failed to reject Ho i.e. the series are non-stationary. Serieswere found to be stationary at 1st difference for all the sectors atdifferent levels of significance. Optimal lag length for all the serieswas determined through SBIC criteria. Results for stationarity atlevels and first difference are summarized in Table 3.

4.2. Johansen–Juselius cointegration test

Since unit root test revealed that all the series are I(1), we canapply cointegration test to test for their long run relationship. Numberof cointegrating relationships among variables was determined byJohansen–Juselius multivariate cointegration methodology. Results for

Table 2Descriptive statistics

Units Industrial sector Services sector

Mean S.D. Min Max Mean S.D. Min Max

VA Rs. Billion 604.527 376.424 152.293 1387.117 1293.424 743.822 344.946 2895.016EC GWh 10634.92 5974.999 2855 20408.01 2171.077 1514.831 378 5605P Paisa/kwh 368.358 97.022 200.936 546.244 558.566 153.658 292.319 767.805TA Ratio 0.056 0.005 0.049 0.071 0.652 0.116 0.468 0.913

F. Mehmood Mirza et al. / Energy Policy 73 (2014) 757–766 761

Johansen Trace and Max statistics are indicated in Table 4. The modelspecification under a VAR framework has a drift but no deterministictime trend. As Johansen cointegration test is sensitive to orderspecification and lag length; hence series are ordered as VA EC TA Pwith two lags for both the sectors.

Johansen test statistics λtrace and λmax both are in line that atleast 1 cointegrating relationship exists among value-added,electricity consumption, electricity price and technical efficiencyfor industrial and services sectors. Jamil and Ahmad (2010, 2011)also confirm for the existence of long run relationship betweensectorial electricity consumption, electricity price and the sectorialvalue added. Confirmation for the existence of long run relation-ship among series suggests that further estimation should beprocessed under a VECM framework.

4.3. Long run relationship

Since Johansen cointegration test has confirmed the existenceof long run equilibrium relationship among series, so the long runcoefficients have been estimated for both the sectors. Table 5indicates that electricity consumption is positively and signifi-cantly associated with value-added in both the sectors. Specifi-cally, a one unit increase in electricity consumption tends toincrease value-added by 0.096 units in industrial sector while in

services sector, value-added increases by 1.842 units. The sign ofthe technical efficiency coefficient is also positive and statisticallysignificant in both the sectors indicating that a one unit increase intechnical efficiency increases value-added by 6950.481 units inindustrial sector and by 7437.64 units in services sector. Incontrast, the sign of the electricity price coefficient is negative inthe value-added equation for both the sectors, however, it is onlystatistically significant in industrial sector indicating that indus-trial value-added decreases by 1.736 units with a one unit increasein electricity price.

4.4. Short run dynamics

When the series are co-integrated then at least one or all of theerror correction terms (ECTs) should be statistically significant show-ing the response of dependent variables towards the long runequilibrium. The coefficients of the ECTs are expected to be negativeshowing the convergence of variables towards long run equilibrium.Table 6 represents the short and long run Granger causality resultscorresponding to Eqs. (4)–(7). Joint F-test of the lagged explanatoryvariables illustrates that in all of the equations, none of the laggedindependent variables are jointly statistically significant. This impliesthat no short run Granger causality exists among all the series in boththe sectors. However, results for long run Granger causality demon-strate that coefficient of the ECT is negative and statistically significantin value-added, consumption and price equation in industrial sectorwhereas it is significant only in value-added equation in the servicessector. These results show the existence of Granger causality betweenvalue-added and electricity consumption in industrial sector and theservices sector in the long run.

4.5. Impulse response functions (IRFs)

IRF traces the response of dependent variable to an unantici-pated shock or innovation in the vector error correction model.In this study we have estimated IRFs for sector specific value-added, electricity consumption, electricity price and technicalefficiency. Results of IRFs for industrial and services sectorscorresponding to Eqs. (4)–(7) are presented in Figs. 1 and 2.

Figs. 1a and 2a illustrate that one standard deviation shock inelectricity consumption, technical efficiency and electricity pricehas a permanent impact on value-added of both the sectors.Electricity consumption forms a positive relationship with value-added in both the sectors. Positive association between electricityconsumption and value-added points towards high energy intensityin the economy. ADB (2009) suggests that energy intensity inPakistan is 15% more than India and 25% more than Philippines.Similarly, Masuduzzaman (2012) showed that economic growth in

Table 3Results for unit root test

ADF-test p-value PP-test p-value ADF-test p-value PP-value p-value

At levels At 1st differenceIndustrial sector Industrial Sector

VAt 1.135 0.996 2.589 0.999 �2.782 0.061 �3.534 0.007Pt �1.745 0.408 �1.653 0.455 �3.842 0.003 �5.228 0.000ECt 0.488 0.985 0.852 0.992 �3.691 0.004 �3.9 0.002TAt �0.692 0.849 -0.756 0.832 �3.442 0.010 �6.703 0.000

Services sector Services sector

VAt 1.711 0.998 5.632 1.000 �2.538 0.1065 �2.442 0.1302Pt �1.522 0.523 �1.456 0.555 �4.26 0.000 �7.136 0.000ECt 1.285 0.997 1.926 0.999 �3.196 0.020 �3.861 0.002TAt �1.888 0.338 �2.181 0.213 �4.721 0.000 �6.914 0.000

Table 4Cointegration test based on trace test and max test.

Ho Industrial sector Services sector

Trace test Max test Trace test Max test

r¼0 58.241a 34.844a 54.718a 29.104a

r¼1 23.398 17.826a 25.613a 19.147a

r¼2 5.572 3.126 6.4664 5.315r¼3 2.446 2.446 1.152 1.152

a Shows the rejection of Ho at 5% level.

Table 5Long run relationship.

Variables Industrial sector Services sector

Coefficient t-value Coefficient t-value

VA 1 1EC �0.096 �12.995 �1.842 �2.190TA �6950.481 �6.230 �7437.64 �4.015P 1.736 5.279 2.572 0.894

F. Mehmood Mirza et al. / Energy Policy 73 (2014) 757–766762

Bangladesh is highly responsive to changes in electricity consump-tion. IRF of technical efficiency and value-added in both sectorspostulates that impulse to technical efficiency establishes a positiverelationship with value-added. Since improvements in technicalefficiency are associated with decreased electricity consumption inthe production processes therefore, total cost of productiondecreases and value-added increases. The above results assert thathuge potential for energy savings exists in different sectors ofPakistan economy.5 The response of value-added to shock inelectricity price is in contrast to the shock in electricity consump-tion and technical efficiency. Electricity price forms a negativerelationship with value-added in both the sectors. Increased elec-tricity price causes the consumption to decrease and hence thevalue-added. This implies that in Pakistan, electricity demand ishighly responsive to electricity price changes. Khan and Qayyum(2009) estimated that price elasticity of electricity demand in caseof Pakistan is greater than one. Garen et al. (2011) estimated thatincreased electricity prices have an adverse impact on industrialand commercial sector output in the US economy.

Response of electricity consumption to shocks in sectorial value-added, technical efficiency and electricity price for both the sectors areillustrated in Figs. 1b and 2b. In the industrial sector, in response to onestandard deviation shock in value-added, electricity consumptionincreases in the short run and then establishes a negative relationshipwith value-added in the long run. In the services sector, unanticipatedshock to value-added creates a positive and permanent relationshipwith electricity consumption in the long run. Jamil and Ahmed (2010)pointed that electricity consumption tends to increase with anincrease in sectorial output in the long run for the case of Pakistan.Shock to technical efficiency has a positive and permanent impact onservices sector electricity consumption. However; in the industrialsector, response of electricity consumption to technical efficiencyshock is negligible in the short run but it forms a positive relationshipwith technical efficiency in the long run. The underline phenomenonis called rebound effect. Under rebound effect when due to increased

energy efficiencies, energy consumption decreases it forces the pricesto decrease and with decreased prices people increase their consump-tion in the long run (Jin, 2007; Frondel et al., 2008; Gillingham et al.,2013). Electricity consumption in both sectors responds in the sameway to one standard deviation shock in electricity price. However, inthe services sector electricity consumption decreases permanentlywhile in the industrial sector it increases for a short period but has apermanent decreasing trend in the long run. Alter and Syed (2011)showed that electricity is a necessity in the short run but a luxury inthe long run. Garen et al., (2011) examined that in US, electricity pricehas a negative impact on electricity consumption both in short and thelong run. However, people are more responsive towards long run pricechanges.

Response of industrial and services sectors electricity price toshocks in sector specific value-added, electricity consumption andtechnical efficiency are presented in Figs. 1c and 2c. Electricity pricedecreases in response to one standard deviation shock in sectorspecific value-added in both the sectors. However, it has an increas-ing trend in services sector for a very short period. Response ofelectricity price to shock in electricity consumption is the same forboth sectors i.e. it increases for short time duration and afterward itforms a permanent decreasing trend because in the long runincreased prices attract new investments in the power generation.Therefore; additional electricity supply creates a surplus whichdecreases prices in the long run. 1994 Pakistan power policy, whichwas passed by the political government to curb the gap betweenelectricity demand and supply by bringing in the independent powerproducers (IPPs), created the same situation in later periods. During1998, Pakistan had electricity surplus which decreased electricityprices and IPPs were forced to bear losses (Fraser, 2005). Efficiencyhas a negative impact on services sector electricity price.

Finally, response of industrial and services sectors technical effi-ciency to a shock in sector specific value-added, electricity consump-tion and electricity price is presented in Figs. 1d and 2d respectively.The impact of one standard deviation shock in sector specific value-added on technical efficiency is negligible however, it respondsnegatively to the shock in electricity consumption in both the sectors.Electricity price shock has a negative permanent impact on technicalefficiency in industrial sector and positive effect in the services sector.

Table 6Short run dynamics.

Variable Industrial sector Services sector

ΔVA ΔEC ΔP ΔTA ΔVA ΔEC ΔP ΔTA

Long runECTt�1 �0.187n

(0.056)�1.904n

(0.838)�0.166n

(0.067)�8.85E-06(6.0E-06)

�0.007n

(0.002)�0.017(0.019)

0.004(0.006)

9.42E-06(6.6E-06)

Short runΔVAt�1 – 4.135

(3.688)�0.222(0.295)

4.61E-06(2.7E-05)

– 1.694(1.853)

1.476n

(0.623)0.001(0.001)

ΔVAt�2 – �3.473(3.518)

0.076(0.281)

�3.58E-05(2.5E-05)

– �1.283(1.992)

�0.508(0.671)

4.64E-05(0.001)

ΔECt�1 0.008(0.020)

– �0.010(0.023)

�2.43E-06(2.1E-06)

0.111n

(0.033)– �0.103

(0.085)�0.0001(8.9E-05)

ΔECt�2 0.020(0.017)

– �0.021(0.021)

3.22E-06(1.9E-06)

0.118n

(0.058)– �0.216

(0.149)3.46E-05(0.0002)

ΔPt�1 �0.081(0.152)

5.559n

(2.257)– �1.99E-05

(1.6E-05)0.010(0.073)

�0.179(0.555)

– 0.0002(0.0002)

ΔPt�2 0.157(0.169)

2.091(2.506)

– 1.73E-05(1.8E-05)

0.022(0.075)

�1.928n

(0.568)– 0.0003

(0.00020)ΔTAt�1 �2213.567

(3125.48)�4848.525(46403.8)

�2743.433(3710.21)

– 184.939(113.165)

997.292(857.887)

�506.906(288.726)

–

ΔTAt�2 324.888(2760.10)

�49006.88(40979.0)

�5183.730(3276.47)

– 200.458(147.056)

�292.343(1114.82)

�303.679(375.197)

–

Note: standard errors are presented in parenthesis.n shows significant error correction coefficient.

5 On average 25% energy saving potential is present in different sectors of theeconomy (Retrieved from the Enercon website: http://www.enercon.gov.pk).

F. Mehmood Mirza et al. / Energy Policy 73 (2014) 757–766 763

5. Conclusions and policy implications

Severe electricity shortages in Pakistan have forced the govern-ment to focus not only on new power generation plants but also onalternative ways to curb the shortages. In the present study, wequantify the impact of these electricity conservation policies on thevalue-added of industrial and services sectors. We use annual timeseries data from 1971 to 2009 for carrying out the empiricalinvestigation. Augmented Dickey Fuller and Phillips–Perron unitroot tests highlight that all the series are non-stationary at levels.The Johansen multivariate cointegration method confirms that atleast one cointegrating relationship among value-added, electricityconsumption, electricity price and technical efficiency exists for

both the sectors. Vector error correction model was applied to studythe short run dynamics of the model. Since Granger causality testdoes not describe the underlying mechanism through which thesevariables are linked, the response of depended variable to unanti-cipated shocks in one of independent variables was capturedthrough the impulse response functions (IRFs).

On the basis of IRFs, the following conclusions can be drawn.Response of the value-added to direct consumption curtailmentpolicies indicates that value-added gets adversely affected due tosuch policies in the long run for both the sectors.

Response of the value-added to conservation polices implementedthrough increased electricity price show that increased electricity pricetends to decrease electricity consumption which in turn decreases

-80

-40

0

40

80

120

1 2 3 4 5 6 7 8 9 10

Response of IVA to IC

-80

-40

0

40

80

120

1 2 3 4 5 6 7 8 9 10

Response of IVA to IEF

-80

-40

0

40

80

120

1 2 3 4 5 6 7 8 9 10

Response of IVA to IPResponse to Cholesky One S.D. Innovations

-600

-400

-200

0

200

400

1 2 3 4 5 6 7 8 9 10

Response of IC to IVA

-600

-400

-200

0

200

400

1 2 3 4 5 6 7 8 9 10

Response of IC to IEF

-600

-400

-200

0

200

400

1 2 3 4 5 6 7 8 9 10

Response of IC to IP

-30

-20

-10

0

10

1 2 3 4 5 6 7 8 9 10

Response of IP to IVA

-30

-20

-10

0

10

1 2 3 4 5 6 7 8 9 10

Response of IP to IC

-30

-20

-10

0

10

1 2 3 4 5 6 7 8 9 10

Response of IP to IEF

-0.002

-0.001

0.000

0.001

0.002

1 2 3 4 5 6 7 8 9 10

Response of IEF to IVA

-0.002

-0.001

0.000

0.001

0.002

1 2 3 4 5 6 7 8 9 10

Response of IEF to IC

-0.002

-0.001

0.000

0.001

0.002

1 2 3 4 5 6 7 8 9 10

Response of IEF to IP

Fig. 1. Impulse responses of industrial sector.

F. Mehmood Mirza et al. / Energy Policy 73 (2014) 757–766764

industrial value-added in the long run. On the other hand, in the shortrun services sector's value added is neutral to these policies and in thelong run it has a decreasing trend. Therefore, electricity conservationpolicies implemented through increased prices will have an adverseimpact on sectorial value-added.

Response of the industrial value added to energy efficiencypolices propagates that in the short run value added is neutral tothese polices while in the long run efficiency policies have afavorable impact on industrial value added. However, servicesvalue added shows increasing trend towards energy efficiencypolices both in short and the long run.

To sum up, electricity conservation policies which directlyreduce electricity consumption have adverse impact on sectorialoutput.6 The same is true for those conservation policies which areimplemented through increased electricity price (Garen et al., 2011).These results confirm that electricity is the main component for the

-40

0

40

80

1 2 3 4 5 6 7 8 9 10

Response of SVA to SC

-40

0

40

80

1 2 3 4 5 6 7 8 9 10

-40

0

40

80

1 2 3 4 5 6 7 8 9 10

Response of SVA to SPResponse of SVA to SEFResponse to Cholesky One S.D. Innovations

-300

-200

-100

0

100

200

300

1 2 3 4 5 6 7 8 9 10

Response of SC to SVA

-300

-200

-100

0

100

200

300

1 2 3 4 5 6 7 8 9 10

Response of SC to SEF

-300

-200

-100

0

100

200

300

1 2 3 4 5 6 7 8 9 10

Response of SC to SP

-20

-15

-10

-5

0

5

10

1 2 3 4 5 6 7 8 9 10

Response of SP to SVA

-20

-15

-10

-5

0

5

10

1 2 3 4 5 6 7 8 9 10

Response of SP to SC

-20

-15

-10

-5

0

5

10

1 2 3 4 5 6 7 8 9 10

Response of SP to SEF

-0.06

-0.04

-0.02

0.00

0.02

0.04

1 2 3 4 5 6 7 8 9 10

Response of SEF to SVA

-0.06

-0.04

-0.02

0.00

0.02

0.04

1 2 3 4 5 6 7 8 9 10

Response of SEF to SC

-0.06

-0.04

-0.02

0.00

0.02

0.04

1 2 3 4 5 6 7 8 9 10

Response of SEF to SP

Fig. 2. Impulse responses of services sector.

6 These conclusions are also substantiated by the findings of Hye and Riaz(2008), Aqeel and Butt (2001) and Shahbaz and Feridun (2011), who found abidirectional causality between electricity consumption and economic growth inPakistan in the long run. Similar results were also presented by Alam et al. (2012)for the Bangladesh economy.

F. Mehmood Mirza et al. / Energy Policy 73 (2014) 757–766 765

steady growth in industrial and services sector in the economy andat the same time Pakistan claims high energy intensity in the region(ADB, 2009). Under this situation, demand side rationing programslike two holidays in a week and closing of markets before 8 pm forconserving the energy will have adverse impact on sector specificvalue-added. Results for IRFs confirm that electricity conservationpolices implemented through increased efficiency will not affectsector specific value-added adversely.

On the basis of the empirical findings, we propose the follow-ing recommendations.

(1) The government of Pakistan should not pursue direct con-sumption curtailment polices in order to mitigate the electri-city crisis. Similarly, the policy of increasing electricity pricesto reduce consumption should not be followed due to itsadverse impact on sector specific value-added in the long run.

(2) The government should concentrate on enhancing energyefficiency in the economy due to its favorable impact onsectorial value-added. This can be done, for example by settingtargets for reducing energy intensity.

(3) Energy efficiency should be incorporated in the mainstreamof energy policy and specific laws should be enacted to establishinstitutions and develop methods to help in effective conserva-tion and efficient consumption of limited energy resources.

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