ASTUDYOFTHEINVESTMENTANDFINANCINGOFELECTRICITY

GENERATIONINTHEFACEOFCHANGINGDEMANDIN

SOUTHAFRICA

BY

GARETHFOULKES­JONES

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prior and express consent of IMPERIAL CYGNUS INVESTMENTS (Pty) Ltd

(ICI).

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bemadeavailableuponrequest.

ABSTRACT

Thisstudyconsidersthecurrentstateofelectricitydemandandsupplywithinthe

South African market and how same may evolve until 2030. It estimates the

demand for electricity in South Africa and the corresponding required

investment. Assuming GDP growth trajectories of 3% and 6%, the study

estimatedthecostofelectricityrequiredbetween2010and2030.Usinggrowth

ratesof3%peryearthestudyestimatesthatthecountrymustinvestaboutR27

billion intoelectricitygeneration. Usinggrowthratesof6%peryear thestudy

estimates that the country must invest about R232 billion into electricity

generation. This suggests a massive investment requirement. The study also

considers thepossiblealternative that canbeused toraise the funds including:

using ESKOM’s internal resources, government resources (through taxes),

increasingthepriceofelectricity,andborrowingfrommultilateralorganizations

suchas theWorldBank(probablywithgovernmentguarantees). Thedifferent

optionsmusthowevertakeintoaccountESKOM’Sfinancialpositionaswellasits

roleasadevelopmentalentityratherthanaprivatefirm.

By

GarethFoulkes­Jones

TABLEOFCONTENTS

CHAPTER1:INTRODUCTION

1.1 Background 1

1.2 Scope 7

1.3 ProblemStatement 7

1.4 Outcome 9

CHAPTER2:LITERATUREREVIEW

2.1 Introduction 13

2.2 EmpiricalLiteratureReview 14

2.3 TheoreticalFramework 24

2.4 Conclusion 25

CHAPTER3:RESEARCHMETHODOLOGY

3.1 Introduction 26

3.2 Quantitative/QualitativeResearchApproach 29

3.3 DataSources 31

3.4 Conclusion 35

CHAPTER4:RESULTSANDDISCUSSIONOFRESEARCHFINDINGS

4.1 Introduction 36

4.2 RegressionResults 37

4.3 ForecastingElectricityDemandorConsumption 41

4.4 EstimatingRequiredInvestment 48

4.5 FinancingInvestment 53

4.6 CompetitorAnalysis 53

4.7 DegreeofRegulation/Deregulator 56

4.8 ProductandServicesPricingStrategies 57

4.9 BarrierstoEntry 63

4.10 Conclusion 69

CHAPTER5:CONCLUSIONSANDRECOMMENDATIONSFORFURTHERRESEARCH

5.1 Introduction 71

5.2 ConclusionandPolicyRecommendation 71

5.3 PolicyRecommendation 78

5.4 LimitationsofStudy 80

BIBLIOGRAPHY&REFERENCES 82

LISTOFTABLES

Table1 SouthAfricaMacro‐EconomicIndicators 3

Table2 SouthAfrica’sPopulationbyProvince 4

Table3.1 DataSources 32

Table3.2 DescriptiveStatistics 33

Table3.3 CorrelationMatrix 34

Table4.1 DemandforElectricityinSouthAfrica 37

Table4.2 ForecastedElectricityConsumption/Demand(2010–2030) 42

Table4.3 ForecastedElectricityConsumption/Demand(Assuming6%GDPGrowth) 45

Table4.4 Scenario1:RequiredInvestmentAssuming3%GDPGrowthRate 49

Table4.5 Scenario2:RequiredInvestmentAssuming6%GDPGrowthRate 51

Table4.6 TheTariffDesignProcess 60

Table4.7 SWOTAnalysisforESKOM 64

Table4.8 ESKOMGroupFinancialPerformance 68

LISTOFFIGURES

Figure4.1 ForecastedElectricityDemand(AssumingGDPGrowthRateequalto3%) 44

Figure4.2 ForecastedElectricityDemand(AssumingGDPGrowthRateequalto6%) 46

Figure4.3 ComparisonofScenario1and2ForecastedElectricityConsumption 47

Figure4.4 ANaturalMonopolistsDemandandCostCurves 56

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CHAPTER1

Introduction

1.1 Background

TheSouthAfricaneconomy isoneof themostdevelopedand industrialized inAfrica.

Furthermore, SouthAfrica is largelydominatedby secondaryandservice sectors, the

twoofwhichaccountedformorethan80%ofthecountry’sGDPin2008(Muradzikwa,

2009).Thenationconsistsofapopulationofsome50millioninhabitants,55%ofwhom

areprojectedtoliveinurbanareas.Between2000and2007theeconomygrewbyan

averageof4%perannum, inkeepingwith theSouthAfricanGovernment’s economic

policy. Please refer to Table 1 below, which displays the principal macro‐economic

indicatorsforthecountry.

Despite its marked degree of sophistication in certain respects, it is important to

observe that the South African energy sector consists of both first and third world

elements. Elaborating upon this, South Africa produces approximately 45% of the

electricityontheAfricancontinentandisthe12thhighestcarbonemitterintheworld.

Thislatterstatisticisattributabletotheextensiveuseofcoal‐firedpowerstations,with

a limited contribution from a combinationof hydro and nuclear power (DME, 2000).

Oneofthegivenreasonsforthepopularityofcoalinenergygenerationisonaccountof

itsrelativecheapnessandavailabilitycoupledwithSouthAfrica’s technologicalability

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to use low‐grade coal for effective electricity generation. It is also pertinent that

renewable forms of energy constitute no more than approximately 5% of the total

energysupplyasobservedbyHowellsetal(2005).

Furthermore,despitetheevidentleveloftechnologicalsophisticationwithintheSouth

African electricity sector, over 75%of SouthAfrica’s rural households use fuelwood

energytoatleastalimitedextentinordertosatisfytheirenergyneeds.Thislevelofuse

asobservedbyDavis(1998)variesfromafewtimespermonthtodaily,anddepends

largelyupontheindividualneedsandconditionsoftherespectivehouseholds.Overand

above fuelwood, such rural households alsomake extensive use of paraffin, candles,

batteries and reticulated electricity for a variety of applications. However, it was

observed by Davis (1998) that such alternatives are often found to be somewhat

expensivealternativestothatoffuelwood.

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Table1:SouthAfricaMacroeconomicIndicators

Year GrowthRate UnemploymentRate Savings(%ofGDP)

1999 2.4 15.10

2000 4.2 23.3 16.00

2001 2.7 26.2 15.60

2002 3.7 26.6 17.50

2003 3.1 24.8 16.20

2004 4.9 23.0 14.80

2005 5.0 23.5 13.60

2006 5.4 22.1 14.70

2007 5.1 21.0 13.40

2000­2007Average 4.26 23.81 15.23

Source:StatisticsSouthAfrica(VariousYears)andStatisticsSouthAfrica(2009)

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Table2:SouthAfrica'sPopulationbyProvince

Province 1996 %of1996

Total

Population

2001 %of2001

Total

Population

2009 %of2009

Total

Population

Eastern

Cape

6302525 15.53 6436763 14.06 6648600 13.5

FreeState 2633504 6.49 2706775 6.04 2902400 5.9

Gauteng 7348423 18.11 8837178 19.72 10531300 21.4

KwaZulu­

Natal

8417021 20.74 9426017 21.03 10449300 21.2

Limpopo 4929368 12.15 5273642 11.77 5227200 10.6

Mpumalang

a

2800711 6.90 3122990 6.97 3606800 7.3

Northern

Cape

840321 2.07 822727 1.84 1147600 2.3

NorthWest 3354825 8.27 3669349 8.19 3450400 7.0

Western

Cape

3956875 9.75 4524335 10.09 5356900 10.9

SouthAfrica 40583573 100.00 44819778 100.00 49320500 100.0

Source: Statistics South Africa (Various Years) and Statistics South Africa (2009)

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Having established a broad overviewof the SouthAfrican electricity sector, onemay

nowconsider ingreaterdetail theoriginsof itskeyplayer in the formofESKOM. Its

originswerefoundedintheElectricitySupplyCommission(ESCOM)in1922.TheSouth

AfricanGovernmentthenproceededtoconsolidatethenation’selectricitysupplywithin

this new entity. By 1948, ESCOM exercised a monopoly over the country’s power

stations and high voltage transmission lines. ESCOM proceeded upon an upward

trajectory over the following decades ultimately resulting in the completed

interconnectednationaltransmissiongridintheearly1970’s.

However,asaresultofacommissionofinquiryin1983,ESCOMwasrenamed“ESKOM”.

Furthermore,theElectricityActof1987wasalsoimplementedduringthisperiod.This

latteractresultedinESKOMabandoningitscoreoperatingprincipleof“neitheraprofit

noraloss”andtherebyobligedtheorganizationtosupplyelectricityinacost‐effective

manner,withintheconfinesofitslimitedresourcesandinconsiderationofthenational

interest.

These policies, coupled with conditions, which encouraged ESKOM to become more

operationally efficient, resulted in South Africa enjoying a well‐developed electricity

generationanddistributionsystembytheearly1990s.However,theapartheidpolicies

which had fostered such development had meant that the industrial sector and the

privileged white minority were given priority to electricity supply, whilst excluding

much of rural South Africa and resulting in enormous backlogs in the number of

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connections forurbanblackhouseholds.Asa consequence,Ziramba (2008)observed

thatby1991,onlyathirdofSouthAfrica’spopulationhadaccesstoelectricity.

Againstthisbackground,themodernESKOMremainsthesolesupplierofelectricityin

South Africa in real terms, and is statistically responsible for approximately 96% of

electricity generation (ESKOM Annual Report, 2007). The remaining 4% is split

betweenprivategeneratorsaccountingfor3.2%,andmunicipalauthoritiesaccounting

for the final 0.8% of supply. Furthermore, with the exception of the Motraco line,

ESKOM owns all transmission lines throughout South Africa. Presently, ESKOM is

responsible for generating approximately 45%of the electricity used in Africawhich

equates to roughly 38 000 MWe per annum (ESKOM, 2007). It is noteworthy that

approximately 88% of this output is derived from Coal, 2% by hydro‐electric

generation, 5% by nuclear power, 4% by pumped storage and 1% by oil‐fired gas

turbines (ESKOM, 2007). Therefore, whilst ESKOM has endeavoured to diversify its

energysupply,itisnonethelessstillheavilydependentuponCoalasitsprincipalenergy

source.Intermsofdistribution,ESKOMalsoenjoysadominantposition.Tothisend,it

is responsible for nearly 60% of all direct sales to the 40% of electricity capacity

distributed by 177 amalgamatedmunicipal authorities as according toMabugu et al.

(2008).

WithinSouthAfrica,ESKOMcontinuestosellelectricitytoavariedsetofclients,which

includeindustrial,mining,commercial,agriculturalandresidentialcustomers.Overand

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above such direct sales, it also sells to a number of redistributors. Furthermore,

according to ESKOM (Annual Reports, 2006 and 2007), ESKOM’s transmission lines

span the approximately26,000kilometers throughoutSouthAfricaaswell as several

otherSouthernAfricanDevelopmentCountries(SADC).

It is also noteworthy that in respect of ESKOM’s operations within the electricity

industry, legislation was passed in 2001, which converted ESKOM into a tax‐paying

public entity,which is in turnwholly state‐owned. Itmay alsobe furtheropined that

whereonehasalargenumberofdistributorswithinaparticularmarket,thismayresult

in a highly fragmented and inefficient Electricity Distribution Industry (EDI).

Consequently, the government effected such legislation in 2001 in order to help

rationalize the EDI. This policy resulted in a further consolidation of electricity

distribution assets held by ESKOMand local governments into six regional electricity

distributors(REDs).Theintentionbehindthiswastopromotegreatercompetitiveness

inelectricitygeneration,Furthermore,theaforementionedrestructuringaimstocreate

an ESKOM owned subsidiary to retain 70% of the generation market share. The

remaining 30% would be shared between private independent power producers

constituting20%andBlackEconomicEmpowermentGroupsmakingupthefinal10%.

It is argued that such reforms to the EDIwould result in a reliable and high quality

service being provided to all electricity consumers, and thereby help to promote the

Governments twin objectives of providing affordable electricity and meeting stated

nationalelectrificationobjectives.

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Theresultsofthisstudyareintendedtoleadtoabetterunderstandingofthedifferent

challengeswhichESKOMfaces,whichrangefromtheneedtoinvestefficientlytomeet

growing demand in the face of limited resourceswithwhich to do so, aswell as the

seeking of timely financing for such investments and selecting politically and

economicallyviablesourcesoffundstofinancesuchprojects.

1.2 Scope

The study utilized annual historical data for ESKOM for the period 1980 to 2009 in

order to forecast the demand for electricity and the requisite corresponding level of

investment(capitalexpenditure)requiredtomeetsuchdemand.

ThestudyalsoexaminesESKOM’sfinancialstatementsinordertoassessthesourcesof

fundswhichhavebeenusedinordertofinancetheinvestmentsinthepast.

Finally, themain objective of this study is therefore to better understand the role of

ESKOM in theSouthAfricanenergy sector, given thegrowingdemand forenergyand

the strategic goals of the company. More particularly, the aim is to forecast the

investmentrequiredtomeetSouthAfrica’sgrowingdemandforelectricity.Inorderto

achieve this objective, this research aims to forecast South Africa’s demand for

electricityoverthenext15yearsandthecorrespondingrequiredlevelsofinvestment.

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Thisobjectiveisimportantgiventherecentoutageswhichhaveadverselyaffectedthe

economy,onaccountofdemandregularlyexceedingsupply.

1.3 ProblemStatement

SouthAfricahasbeensubjectedtonumerousblackoutsinrecentyears.Inlightofthis,

the South African government has attempted to manage the demand for electricity.

Unfortunately,itiswidelybelievedthatsuchelectricitysupplychallengeswereinfact

predominantly the result of political indecision. In the early 2000’s therewere also

some debates onwhether ESKOM should be privatized or not. During this particular

period, the government as the sole shareholder underinvested in electricity capacity

building.Thisdearthofinvestmenteventuallyresultedintheelectricitycrisiswhichthe

countrynowfindsitselfcontendingwith.

TheimpactoftheaforementionedblackoutsapproximatedtoalossofR50Billionfor

the South African economy in that period (Inglesi, 2010). Furthermore, during this

period,thelevelofeconomicgrowthinthefirstquarterof2008fellto1.6%from5.4%

inthelastquarterof2007(Inglesi,2010).

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Giventheabovebackground,theauthorhassoughttoaddressthefollowingquestions

inthisdissertation:

1) What factors drive electricity consumption in South Africa and how is

consumptionlikelytochangeoverthenext10years?

2) Whatisthelevelofinvestmentrequiredtomeetsuchdemand/consumption?

3) Howshouldthislevelofdemandbefinanced?

Thereareanumberofreasonswhyastudyofthisnatureshouldbeconducted.Someof

thereasonsinthisinstancemaybestipulatedasfollows;

Itwill assist in highlighting the required investment necessary tomeet the country’s

electricitysupplyneeds;

Itwill informpolicymakers on the differentoptions available in respect of financing

electricitygenerationinthecountry;and

ThestudywillalsocontributetothediscussionssurroundingelectricitypricinginSouth

Africa.

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1.4 Outcome

Thisresearchwillhelppolicymakersinthreeprincipalways.Thesemaybestipulated

asfollows;

(i) ItwillcontributetothedebatearoundelectricitygenerationinAfricancountries

ingeneralandSouthAfrica inparticular.This isespecially importantgiventhe

suggestionbycommentatorsthatSouthAfricashouldinvestinhydro‐electricity

generation. The Inga Dam in the Democratic Republic of Congo by way of

examplehas suchvast electricitygenerationpotential, that itwouldbeable to

generatesufficientelectricitytosatisfytheneedsoftheentireAfricancontinent,

wereittobeproperlyharnessed.

(ii) Itwillalsoaidtheformulationofpolicy,giventhattherearepresentlyeffortsto

reform the South African energy sector in order to secure reliable and cost

effective supplyovertheensuingyearsanddecades.Consequently,anaccurate

estimation of the demand for electricity will assist policy makers as they

endeavour to secure the requisite supply of electricity for all sectors of the

economy.

(iii) ConsideringthemonopolisticnatureofESKOM,itisreasonabletoassertthatthe

national pricing policy is controlled by the government. Consequently, it is

critical to consider the appropriate funding structure for such a monopolistic

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parastatal.Thisaspectofthestudybearsrelevancetothequestionastowhether

ESKOMisinfactmoreofacommercialordevelopmentalentity.

With conclusion of the introduction, onemay now reviewwhat the ensuing Chapter

shall consider. Chapter Two shall provide a comprehensive review of existing and

pertinent literature pertaining to the subjectmatter. Following same, Chapter 3 shall

considerthemethodologyutilizedtoderivetheresultsinthepaper,aswellasexplain

thereasonswhysuchamethodologyisemployedinthisinstance.Chapter4willseekto

explaintheresultsderivedfromthemethodologyemployedinChapter3asappliedto

theempirical information inChapter2andadditionalsources.Finally,Chapter5shall

providedaconclusionofalltheaforementionedChapters,andwhererelevanttoreview

same.

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CHAPTER2

LiteratureReview

2.1 Introduction

Theaimofthissectionistoreviewrelevantliterature.Itishopedthatsuchaliterature

reviewwillhelpusbetterunderstandwhathasbeendoneandthusenableustoidentify

gapsintheliteraturethatneedtobefilled.Itwillalsohelpusrationalizethecontextof

the problem especially given the current debate on climate change and the financial

crisis. The literature review section is made up of two main components. The first

section considers the empirical literature.This is then followed by the section,which

considersthetheoreticalframeworkuponwhichthispaperisbased.

Theauthormusthastentocautionthatsomeoftheextantliteratureoncapitalstructure

is largely based on private‐owned firms rather than on parastatals such as ESKOM.

Consequently, the reader must bear in mind that conclusions drawn from such

literature,unlikeotherfirmswhichareprivatelyownedandarethusownedbyprivate

shareholderspursuingprofits,ESKOMisagovernmentownedentity,essentiallyowned

by taxpayers.Whilst its goals aredifferent,ESKOMoften raises finance in the capital

marketslikeanyprivatefirm.Ithoweverhasadditionalsourcesoffundsintheformof

government and multilateral organizations like the World Bank, Development Bank

Southern Africa and African Development bank among others. The government is

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believedtohaveguaranteedtherecentloanthatwasadvancedtoESKOMbytheWorld

Bank.

2.2 EmpiricalLiteratureReview

Generally there is adearthof literatureon theenergy sector inSouthAfrica, and the

electricity sector in particular (Inglesi. 2010). However, the 2007‐2008 energy crisis

whichplungedtheSouthAfricaneconomyintobothliteralandfigurativedarkness,and

thecurrentcampaignbyorganizationssuchastheWorldBankonthesustainableuseof

energyresourceshasseentheproliferationofstudiesonenergyandelectricityusage.

See for example studies by Inglesi (2010), Ziramba (2008), Odhiambo (2009), and

Bogetic and Fedderke (2005). According to Bogetic and Fedderke (2005) there are

three main reasons why forecasting infrastructure investment needs is important in

Sub‐Saharan Africa (SSA), in general, and South Africa, in particular. Firstly, there is

evidence of a strong relationship between infrastructure investment and economic

growth. Secondly, in South Africa, there have been various efforts to stimulate

infrastructure on account of its pivotal role in spurring economic growth within the

country’s Accelerated Shared Growth Initiative in South Africa (ASGISA) strategy.

Thirdly,thereisalsoalinkbetweeninfrastructureinvestmentorinfrastructurequality,

ontheonehand,andequityandpoverty,ontheother.Thisisespeciallygermaneforthe

SouthAfricaneconomygiven itshistoricalbackgroundwhereaccessto infrastructure,

amongotherthingswasthepreserveofaminoritygroup.

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ThestudiesonelectricitydemandcanbedividedintothosethatfocusedonSouthAfrica

andthosewhicharefocusedonothercountries.ThosethatfocusedonSouthAfricacan

further be subdivided into thosewhich attempted to estimate residential demand for

electricity and those that attempted to estimate the aggregate demand for electricity.

Thosepaperswhich concernedSouthAfricawere generallyaggregate innature, and

include: Bogetic and Fedderke (2005), Perkins, Fedderke and Luiz (2005), Odhiambo

(2009), Amusa et al (2009), Dergiades and Tsoulfidis (2008), Holtedahl and Joutz

(2004),Narayanetal(2007)andInglesi(2010).Thosefocusedonresidentialdemand

forelectricity include:Louwet al (2008),DonatosandMergos (1991),Hondroyiannis

(2004) and Walker (1979). In the following section we look at these studies and

criticallyanalysetheirfindings.

Usingdynamicheterogeneouspanelestimationtechniqueandapanelof52countries,

BogeticandFedderke(2005)estimateddemandfunctionsforelectricity.Theycovered

theperiod1980‐2002.They then forecasted thedemand forelectricity from2002 to

2010 and found that South Africa will need to invest about 0.2% of its GDP into

electricity generation (assuming a growth rate of 3.6 per annum). The figure would

double(to0.4%ofGDP) if theeconomyisassumedtogrowat6%perannum(asper

theASGISApolicyframework).

In a related paper Perkins, Fedderke and Luiz (2005) analyzed South Africa

infrastructure investment. The paper’s principle aim was to initiate some work on

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infrastructure and its role in development by developing a number of infrastructure

development indicators in South Africa for the period 1870 ‐2002. The paper also

attempted to analyze the link between economic growth and infrastructure

development. The paper produced three key findings. Firstly, they found that the

relationship between infrastructure and economic growth tends to be bi‐directional.

That is, investment in infrastructure tends to spur economic growth. But economic

growthalsotendstobeassociatedwithanincreaseininfrastructureneeds.Hencethe

causalityisnotunidirectional.Soeventhoughmoststudiestendtoestimateregression

equations with an endogenous economic growth and exogenous infrastructure, the

studybyPerkins,FedderkeandLuiz(2005)seemstosuggestthatinfrastructureisalso

endogenous. Secondly, Perkins, Fedderke and Luiz (2005) argue that South Africa’s

infrastructurehasdevelopedinphasesanditmaythusbeimportantforpolicymakers

to choose the right type of infrastructure and focus on it – rather than taking a

haphazardapproachtoinfrastructureinvestment.Mostimportantly,theprojectsmust

be chosen based on appropriate cost‐benefit analysis (Perkins, Fedderke and Luiz,

2005).

Using data from for the period 1971 – 2006, Odhiambo (2009) examined the

relationshipbetweenelectricityconsumptionandeconomicgrowthinSouthAfrica.He

foundabidirectionalcausalitybetweenelectricityconsumptionandeconomicgrowth,

corroborating the findings by Perkins, Fedderke and Luiz (2005). The policy

prescription emanating from the study is that investment in electricity infrastructure

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shouldbeintensifiedintandemwiththecountry’sdesiredgrowthtrajectory,aswellas

thecountry’sASGISApolicyframeworkandthecountry’sindustrializationpolicy.

Moststudiesondemandforelectricityaremacroinnature.Louwetal(2008)adopted

adifferenttackandusedamicroapproachtoinvestigatethedeterminantsofelectricity

consumption.TheyalsofocusedonthepoorhouseholdsinacommunityintheWestern

Cape. This is important given that electricity supply in South Africa, unlike in most

developing countries, is not an urban phenomenon. The government has, since 1994

embarkedonaprogrammetoensureaccesstoelectricitybytheruraldwellersaswell.

Poorhouseholdhavefreeaccesstothefirst50kWh/monththattheyconsume.Louwet

al(2008),usinghouseholdsurveydatacollectedin2001and2002,foundthatincome,

wood fuel usage and access to credit were the main factors affecting electricity

consumption.Duetodatalimitationstheirmodelhoweverdidnotcontrolfortheprice

of electricity and price of electricity substitutes. Consequently the model was

misspecified as it left out the main factors that should be included in any demand

function. Thus the impact of the price of electricity was not assessed nor did they

calculatethecross‐priceelasticities.

Acknowledging the paucity of research analyzing the demand for electricity in

developing countries in general and in SA in particular, Amusa et al (2009) uses

macroeconomicdatatoinvestigatethedeterminantsofaggregatedemandforelectricity

in South Africa. They cover the period 1960‐2007. They also used a bounds testing

approachtocointegration.TheirpaperwhichaimedtoimproveonPouris’(1987)study

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by using more contemporary econometric approaches as well as more recent data,

calculated income and price elasticities of demand in SouthAfrica. For example, they

argued that Pouris’s paper failed to test for data stationarity, suggesting that the

findings from Pouris’s the study may actually be spurious. Hence they adopted an

autoregressivedistributedlag(ARDL)modeltoestimatetheelasticities.Theyalsomade

useoftheerrorcorrectioninherentintheARDLframeworktoassesstheshort‐runand

long‐run impactsof themaindriversof electricity consumption inSouthAfrica.They

also tested parameter stability. To this end, they found that income, and not price of

electricity, is the main driver of electricity consumption in South Africa. This is an

important finding given the current debate around electricity pricing. If the price of

electricityisnotasignificantfactorinthedemandforelectricityfunctionthenapolicy

thrustthatfocusesonpricingmaynotbetheoptimalpolicyoption.

InarecentstudyintheUS,DergiadesandTsoulfidis(2008)investigatedtheresidential

demandforelectricityfortheperiod1965‐2006.Theexplanatoryvariableswhichthey

usedincludeGDPpercapita,priceofelectricity,priceofoilforheatingpurposes(used

toproxythepriceofasubstitute),andweatherconditions.UsingtheARDLapproachto

cointegrationtheyfoundthecoefficientofpricetobesignificantlydifferentfromzero.

They also found a stable relationship between the variables used. Furthermore, to

measure the intensity of electricity usage by households they used the number of

occupied stock of houses. Since occupied houses are most likely to have a higher

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number of electricity using appliances, this implies that the higher the number of

occupiedhousesthehighertheusageofelectricity.

In an attempt to capture the role of economic development in driving electricity

demand,HoltedahlandJoutz(2004)addedanadditionalvariabletotheusualeconomic

variablesnormallyincludedinanordinarydemandfunctionthattheyestimatedforthe

Taiwanese economy. The variable added is the urbanization. Urbanization was

measuredas theproportionof thepopulationincitiesof100000ormore(Holtedahl

andJoutz,2004).Theothereconomicvariablesincludedarepopulationchanges,price

of electricity and household disposable income. Price of electricity was found to be

negativelyrelatedtothedemandforelectricity.Thepriceof theelectricitycoefficient

wasalsofoundtobesignificant;withtheirownpriceeffectbeingfoundtobeinelastic.

This implies that an increase in the price of electricity by 1% in Taiwan results in a

reductioninthequantitydemandedbylessthan1%.Sousingthepriceincreasesasa

strategy to curtail the demand for electricity as emphasized by the South African

governmentmaynotbetheoptimalstrategytoembarkon.Thisalsopointstothefact

that as an economy develops, electricity becomes a necessity that every household

cannotdowithout. Soan increase in thepriceof electricitymay result inhouseholds

movingsomeoftheincomefromothersourcesintoelectricityratherthansignificantly

reducingthedemandforelectricity.Therelationshipbetweenown‐priceandelectricity

demandwasalso foundtobestable inboththeshort‐runand long‐run; implyingthat

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policiescanbeoptimallymadesincesucharelationshipexistsinboththeshort‐runand

long‐run.

Oneoftheimportantrecentstudiesconductedinthedevelopedeconomiesisthestudy

byNarayanetal,(2007).Narayanetal(2007)coveredtheG7countriesfortheperiod

1978‐2003andestimatedaresidentialdemandfunctionforG7countries.Theyuseda

panel cointegration approach. In this respect, they found that residential demand for

electricityintheG7countrieswasincomeelasticandpriceelasticinthelongrun.Sucha

resultisimportantforpolicymakersespeciallyasmostgovernmentsareendeavouring

to develop better demand management policies. In countries such as South Africa,

which is trying to restructure the entire energy sector as well as develop a more

sustainableenergypricingpolicy,theseresultsareofvitalimportance.

The high own‐price elasticity found by Narayan et al (2007) suggests that in the G7

countriesconsumersaresensitivetoelectricitypricechanges–apossiblereasonisthat

theyusegasesasasubstitute.Thisimpliesthatapricingpolicymaybemoreeffectivein

controllingelectricityusagethaninTaiwan,forexample.However,itisnoteworthythat

suchapricingpolicyappliesprovidedthatelectricitysubstitutesareavailable.Itmust

alsobenotedthatgroupingcountriesasaregionaswasdonebyNarayanetal(2007)

whilstusingmoredatapointsandthusprovidingmoredegreesoffreedom,andhence

enabling us to estimate more efficient parameters, may not give us an individual

countrypicture. Itwouldhave been useful if, in conjunction to a panel for thewhole

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region,individualresidentialelectricitydemandfunctionsforeachcountryintheregion

werealsoestimated.

Narayanetal(2007)alsoconcludedthatpricingpoliciesshouldbeusedtocontrolthe

residentialdemandforelectricityintheregion–especiallytakingcognizanceofthefact

thatresidentialdemandforelectricityispriceelastic.Thestudyalsoattemptedtolook

atthepolicyimplicationsoftheresultsespeciallyastheypertaintothesustainableuse

ofenergy,ingeneral,andelectricity,inparticularaswellasthereductionofgreenhouse

gasemissions.AccordingtoNarayanetal(2007)theG7countriesgeneratedabout40%

ofthetotalelectricitygeneratedinthewholeworld.Thispointstoasignificantemission

ofgreenhousegases;thuscontributingtoglobalwarming.

DonatosandMergos(1991)collecteddataontheGreekeconomyfortheperiod1961to

1986 and estimated a residential electricity demand function for that country. They

used several variablesasexplanatoryvariables including:householddisposal income,

priceof electricity, salesof electricityappliances,population changesand thepriceof

diesel.Thedependentvariableusedwas the per capita electricity consumption.They

consequently foundthatdemandforelectricity inGreece isprice inelasticand income

elastic.Tothisend,Hondroyiannis(2004)alsofoundcorroboratingresults.Thisimplies

that price has little impact on electricity demand. The policy implication emanating

from this is that trying tomanage demand using price changesmay not be effective

‐22‐

hence it may be necessary to introduce substitutes for electricity. The study also

suggeststhealignmentofelectricitypricestothoseobtainedintheEuropeanregion.

The study also found that population increases tend to increase the demand for

electricity.Salesofelectricapplianceswerefoundtobeinsignificantaswasthepriceof

diesel in theirrelationshipto thedemandforelectricity.Thedemandforelectricity in

Greecewasfoundtobefairlyconstantwhencomparingthedemandofoneregionwith

that of another. The implication of this finding is that regional differences appear to

have a minimal impact on the variation in electricity demand. This finding is also

importantwhenitcomestopolicymaking,astheregionsaremoreorlesshomogenous

in terms of electricity demand. A policy designed for one region can therefore, with

minimalmodification,beeasilyappliedtootherregions.

AccordingtoSmith(1980),estimatesofdemandfunction forelectricityare important

forpolicymaking.Forexample,demandresponsivenesstopricechangeshasabearing

onthedemandforecasts;somethingthatiscriticallyimportantforinvestmentplanning

at national level aswell as at firm level. Firms in any given economy are one of the

importantsectorsthatconsumeasignificantamountofelectricity.Forecastingdemand

is also important for regulatory reviews–especiallygiven the currentdebatearound

globalwarmingandtheneedtoefficientlyandsustainablyutilizeenergy(Smith,1980).

Walker (1979) estimated the residential electricity demand for a random sample of

householdsfortheUSeconomyduringtheperiod1972–1975. Householdelectricity

consumptionwasestimatedasafunctionofchangesinweather,realpriceofelectricity

‐23‐

andrealhouseholddisposableincome. TocapturetheimpactofanArabOilEmbargo

thatwasimposedintheearly1970’sWalker(1979)alsointroducedadummythattook

avalueof1aftertheembargoandzerobeforetheintroductionoftheembargo.Itwas

however found that the embargo aswell as the call by theUS government to reduce

electricityconsumptionduringtheembargodidnotresult inareduction inelectricity

consumption;thecoefficientontheembargodummywaspositiveandinsignificant.

Inglesi(2010)estimatedaggregatedemandforelectricityinSouthAfricausingdatafor

theperiod1980–2006.Inglesi(2010)usedanerrorcorrectionmodelsandtheEngle‐

Grangermethodologytoforecastelectricitydemand.Themainvariablesusedare:real

gross domestic product, real electricity consumption, average electricity price, real

disposable income and population changes. Itmust be noted that it is possible that

Inglesi’sresultsmaybeaffectedbydataproblems.Forexample,itispossiblethatreal

disposable incomeandrealGDParehighlycorrelated;hencethedatamaybeplagued

withproblemsofmulticollinearity.AlsorealGDPcanbeendogenousasitmaybedriven

by electricity generation or consumption. Inglesi found that the disposable income,

priceofelectricity,realGDPandpopulationtobesignificant.Thesevariablesalsohad

thehypothesisedsigns;withincomeelasticitybeing0.42(inelastic)andpriceelasticity

being‐0.55(inelastic).Thesefindingsareimportantforpolicy.Forexample,ifitistrue

that demand for electricity in SouthAfrica is price inelastic then a 1% change in the

priceofelectricityreducesdemandby less than1%; implyingthatdemandisnot that

responsive to price changes. The policymakers need such information if they are to

‐24‐

come up with effective demand management policies. More importantly, a long‐run

relationshipwasfoundbetweenelectricityconsumptionandpriceofelectricityaswell

asbetweenelectricityconsumptionandeconomicgrowth.Ashort‐runrelationshipwas

alsofoundbetweenpopulationgrowthandelectricityconsumption.

2.3 TheoreticalFramework

According to classical economics themain factorswhich affect the demandof a good

include; own price of the good, household real income (the relationship between

demandandincomedependingonwhetherthegoodisanormalorinferiorgood),the

priceofrelatedgoods(whetherthegoodsarecomplementsorsubstitutes),population

andexpectedfuturepricechanges.Inthecaseofresidentialdemandforelectricitythe

common substitutes are the natural gas, heating oil, fuelwood (inmost rural areas).

Normallytherelationshipbetweenthepriceofthegoodandthedemandforthegoodis

theparamountrelationship inanydemandfunction.Themaintheoriesexplainingthe

relationship are the cardinal utility theory, ordinal utility theory and the revealed

preference theory.The cardinalist approachassumes thatutility ismeasurable;hence

consumingoneadditionalutilityofagoodresultintotalsatisfactionderivedfromthat

changingbyacertainamount.Theordinalistapproacharguesthatthisisunrealisticas

measuringutility ispractically impossible.What is important,arguetheordinalists, is

thatonecancomparedifferentbundlesofgoods. Forexampleonecanstate thatone

‐25‐

derivesmoreutilityfromconsumingbundleAthanbundleB,butonemaynotknowthe

exactimpactsofachangeintotalutilityduetotheconsumptionofthetwobundles.

Mostmodels used to estimate the demand for electricity function have attempted to

estimate the price elasticity and income elasticity (Inglesi, 2010). According to

economic theory there is a negative relationship between disposable income and the

following variables: own price of the good, temperature (low temperature results in

moreelectricityconsumption)andpriceofsubstitutes.Itisalsoestimatedthatthereis

a positive relationship between electricity consumption and the following variables:

incomegrowth,populationgrowth,theamountofelectricalappliancesinahousehold,

householdsizeandpriceofcomplements.

2.4 Conclusion

This chapter reviewed the relevant literature. It started by considering the empirical

workconductedinSouthAfricanandothercountries.Itthenlookedatthetheoretical

framework or the theoretical models that explain the behavior of consumers. The

literature reviewhelpedonebetterunderstandwhathasbeendiscoveredhistorically

andthusenabledonetoidentifygapsintheliteraturethatneedtobeaddressed.Italso

helped rationalize the context of the problem especially given the current debate on

climatechangeandthefinancialcrisis.Thenextchapterconsidersthemethodologyto

beusedinformulatingtheanalysis.

‐26‐

CHAPTER3

ResearchMethodology

3.1 Introduction

Themajortheorythatguidestheresearchistheneoclassicaldemandtheory.Itprovides

the author with the theoretical model that informs the econometric model to be

estimated.Italsoassistswiththeimportantvariablestobeincludedintheeconometric

model. The expected signs, or the hypothesis, of the models are actually gleaned or

informedbythetheoreticalmodel.Thatis,variablesshallnotbedroppedorincludedin

themodelsimplybecausetheyaresignificantorsimplybecausetheymakethemodel

significant.Thevariablesareincludedbecausetheoryprovidesthattheymustbe.

Despite its strengths and benefits the demand theory can also fail to explain some

specialcases.Forexamplewhilethepriceofagoodisexpectedtobenegativelyrelated

tothequantitydemandedof thegood, therearespecialcaseswherethedemandmay

actuallybepositivelyrelatedtothepriceofagood.Thatis,anincreaseinthepriceof

goodXmayactuallyresultinanincreaseindemandforgoodX.Oneexampleisthatofa

goodassociatedwithstatus:withpeopledemandingmoreofthegoodevenastheprice

goes up. The need to join the Jones’s (or the so‐called band wagon effect) and also

explainwhyanincreaseinthepriceofagoodcanresultinmorebeingdemanded.Itis

howeverexpectedthatinthiscaseelectricityisanormalgoodwhosepricetendstogo

‐27‐

downaspriceincreasesandassuchwedonotexpectthedemandmodeltobeaffected

bythespecialcasesmentioned.

The aim of this chapter is therefore to explain the methodology utilized in order to

estimatethedemandforelectricity function.Likethedemandforanycommodity, the

demandforelectricityisafunctionofincome,populationandpriceofelectricity,among

other factors.Theauthor closely followsBogetic andFedderke (2006) in this respect.

ThemodelbyBogeticandFedderke(2006)isareducedformequationforthedemand

forinfrastructure.Itexpressesthedemandforinfrastructureasafunctionofpercapita

income, sectoral shares in GDP (with the individual shares of the following sectors

includedasseparatevariables:agriculture,manufacturingandservices).

Generally most demand models to be estimated take similar forms: the dependent

variable is expressed as a function of several variables. The differencemay be in the

functional form that the actual estimated equation takes as well as the variables

included.Forexample,LakhaniandBumb(1978)estimatedthefollowingmodel:

tttttGDPaPAaPEaaDE !++++=3210

Where:

DE–isthedemandforelectricityattimet.

PE–thepriceofelectricityattimet.

PA–thepriceofasubstituteattimet.

‐28‐

GDP–grossdomesticproductattimet.

Inglesi(2010)estimatedthefollowing:

t220!+++=

tttPEaIncomeaaED

Where:

ED–isthedemandforelectricityattimet.

PE–thepriceofelectricityattimet.

Income–grossdomesticproductattimet(GDP)

InOdhiambo (2009)the roleof incomeorGDP is alsoemphasized.Odhiambo (2009)

washoweverlookingatthedirectionofcausalitybetweenelectricityconsumptionand

economic growth. Ziramba (2008) uses a model similar to Inglesi (2010) but he

includedatimevariable.

AsexplainedinthefollowingsectiontheAuthoradoptedamodelsimilartotheabove

models.Theonlyexception is that theAuthor includedmorevariables. Inaddition to

income and price of electricity the author has also included a variable to measure

population.

‐29‐

3.2 Quantitative/QualitativeResearchApproach

The author adopted a quantitative research approach. To better understand the

determinants of demand for electricity and thus be able to forecast additional

investmentrequiredforthegenerationofadditionalelectricityonewillneedtoaddress

threemainpoints.First,oneneedstoadopta theoreticalmodelofconsumerbehavior

(demand theory). Having established the theoretical underpinnings, the Author then

adopted an econometric model to estimate the necessary parameters. Once the

parametershavebeenestimatedonethenneedtoutilizetheminordertoforecastthe

future demand for electricity and the concomitant funding required to finance such

investments.

Consequently,thetheoryallowedtheauthortodevelopthevariablestobeincludedin

the model. These variables are electricity consumption, income, price of electricity,

population and GDP. The author then collected secondary data for the different

variables.Finallyonethenutilizedthedatatoruntheregressionsandthusrenderthe

quantitativeanalysis.

TheAuthorseekstoestimatethedemandforelectricityusingthefollowingmodel:

ttt

ttt

tot ePbPopbGDP

Servicesb

GDP

Manub

GDP

AgricbGDPbbE +++!

"

#$%

&+!

"

#$%

&+!

"

#$%

&++=

654321

Where:

‐30‐

Et=demandforelectricityattimet

GDPt=GDPinSouthAfricaattimet

Popt=populationinthecountryattimet.

Pt=priceofelectricityattimet.

et=independentlyidentifiednormallydistributederrorterm.

tGDP

Agric!"

#$%

& =shareofagriculturalsectorinrealGDP

t

GDP

Manu!"

#$%

& =shareofmanufacturingsectorinrealGDP

tGDP

Services!"

#$%

& =shareofservicessectorinrealGDP

Once one has estimated the above using historical data, the authorwill then forecast

electricity demand or consumption for the next ten years and from the estimated

demand,therequiredinvestmentshallbeestablished.Theforecastswillalsobebased

on the currentpolicies being pursued by theSouthAfrican government. For example

onepolicyframework(ASGISA)targetsaretoincreasetheGDPgrowthrateto6%per

annumby2014.Consequently,onescenarioistoforecastGDPwiththegrowthrateof

6%inmind.Theotheroptionistousehistoricalgrowthratestoforecastfuturegrowth

rates (say 3% per annum). The author therefore expects to have two scenarios

providingdifferingresults.

‐31‐

Once the required investmenthasbeenestimated inmonetary terms theauthor shall

then consider the different sources of raising funds tomeet the required investment.

Thusthesectiononfinancingwillconsiderthedifferentmeansofraisingtherequisite

funds.Moreover, it isperhapspertinent tonote that fundsmaybe raised from loans,

alternativelyfrominternalcoffers/financialresourcesaswellasfromanincreaseinthe

priceofelectricity.Finally,thesectiononfundingalsoconsidersboththedisadvantages

andadvantagesofusingthesedifferentsourcesoffunding,anddiscussesthepotential

ramificationsofeach.

3.3 DataSources

Aconsiderableamountof thedatagatheredfor thisstudywassourcedfromStatistics

South Africa (Stats SA). Stats SA is the main statistical agency in the country. Its

responsibility is to collect both primary and secondary data by conducting various

surveys;whichvaryfromhouseholdsurveystomeasureinflationtofirm‐levelsurveys

whichmeasureeconomicactivitywithinthecountry.Consequently,thedatagenerated

bythisorganizationisconsideredbothaccurateandreliable.Theauthoralsocollected

additional data from the Reserve Bank of South Africa. The Reserve Bank is South

Africa’s central bank, and extensively collects and collates macroeconomic data for

publicconsumption.Thefollowingtableshowsthesourcesofdatausedintheanalysis.

‐32‐

Table3.1:DataSources

Variable Explanation Source

GDP Grossdomesticproduct SouthAfricanReserveBank

PriceofElectricity Averagepriceofelectricity(c/kWh) StatisticsSouthAfrica

Electricity

consumption

Electricityconsumptionor

demand(GWh)

DepartmentofMineralsand

Energy, South Africa

Government

Population TotalpopulationinSouthAfrica StatisticsSouthAfrica

Itisnoteworthy,thatalldatausedwasthoroughlyreviewedandtestedforstationarity

priortotherunningofanyregressionsforthispaper.However,whilsttheGDPcouldbe

calculated quarterly, the other variables were only available as annual figures. This

unfortunately resulted ina reductionof the sample size toa totalof33observations,

thus impacting upon the degrees of freedom, which would be afforded in the

interpretationofthepaper’sresults.Thefollowingtableshowsthedescriptivestatistics

ofthedata.TheaveragepriceduringtheperiodwasR20.75,withastandarddeviation

of 3.8. Theminimum andmaximum priceswere R16.25 and R26.3 respectively. The

othervariablesareasstated inthetable.GDPandelectricitydemandwereseasonally

adjustedandthevaluesarebasedon2005prices.

‐33‐

Table3.2:DescriptiveStatistics

Variable Obs Mean Std.Dev Min Max

Electricity

Demand

33 127956.3 61344.11 10340 204979

Price

(ZAR)

33 20.75 3.8 16.25 26.3

GDP 33 4928913 1055338 3742469 7258084

Population 33 40303.1 6651.75 29075 50110

Agriculture 33 127004.8 19550.81 80872 162360

Services 33 2766634 756706.2 1838225 4422452

Source:Source:StatisticsSouthAfricaDatabase,ReserveBankofSouthAfricadatabase.

Thetablebelowshowsthecorrelationmatrixforthedatawiththeasterisksindicating

the level of significance of the relationship. For example the price of electricity and

electricitydemandwerehighlycorrelatedat1%levelofsignificance.

‐34‐

Table3.3:CorrelationMatrix

Electricity

Demand logPrice GDP Population Agriculture Services

Electricity

Demand 1.00

logPrice ‐0.97*** 1.00

GDP 0.55** ‐0.57** 1.00

Population ‐0.67** 0.62**

‐

0.74 1.00

Agriculture 0.70*** ‐0.72*** 0.45 ‐0.58 1.00

Services 0.86*** ‐0.89* 0.49 ‐0.81 0.81 1.00

Source:Source:StatisticsSouthAfricaDatabase,ReserveBankofSouthAfricadatabase.

Thedatawerealsotestedforstationarityandthepriceofelectricityvariablewasfound

tobestationary.Theothervariableswere integrated inorder. It is interestingtonote

thattheybecamestationaryafterdifferencingthemonce.

ValidityoftheResearch

Theresearchisvalidsincerelevantquestionswereanswered.Theresearchisalsonot

basedonsurveydatawherethisissuewouldbemoreimportant.

‐35‐

ReliabilityoftheResearch

Reliability of the research usually revolves around the reproducibility of the same

results by other researchers. The model estimated is a normal model and is

reproducible.

EthicsandConfidentiality

The research is based on secondary data. ESKOM, the unit of study, is a government

ownedutility. Informationconcerning theentity ispubliclyavailable. So there areno

confidentialityconcernsonaccountoftheinformationusedexistsandisfreelyavailable

withinthepublicdomain.

3.3 Conclusion

It is common for researchers to argue that a dissertation, after asking relevant

questions,mustexplainhowthequestionaretoaddressed,mustconducttheresearch

astheperthemethodology,andfinallymustexplaintheresults.Theaimofthissection

istoexplainthemethodologytobeusedtoaddresstheresearchquestionsraisedinthe

previous chapters. This chapter looked at the models used in the literature and

explainedthedifferentvariablesused.Afterconsideringallthemodelstheauthorthen

chooseamodelthatincludesthegenerallyacceptedvariables.Thenextchapterutilizes

the model in order to estimate the equations as well as to forecast the demand for

electricityinfuture.

‐36‐

CHAPTER4

AnalysisofResults

4.1 Introduction

Theaimof this section is toanalyze the results that emanated from themethodology

that was explained in chapter 3. It commences with estimating the demand for

electricity inSouthAfrica.Theregressionresultssection is thenfollowedbyasection

which basically estimates the forecasted demand for electricity. It concludes with a

costing exercise intended to derive the requisite investment needed to meet the

forecasteddemand.

‐37‐

4.2 RegressionResults

Table4.1belowshowstheregressionresultsfortheequationstatedinchapter3.

Table4.1:DemandforElectricityinSouthAfrica

Explanatory

Variable

Regression

One

Regression

Two

Regression

Three

RegressionFour

Constant 1091655 973091 955955.5 910709

(21.32)*** (19.60)*** (18.66)*** (6.88)***

logPrice ‐326042 ‐289681.1 ‐300071.9 ‐289140

(‐18.96)*** (‐17.86)*** (‐19.91)*** (‐9.78)***

GDP 0.081 0.121 0.11

(3.78)*** (3.60)*** (2.59)**

Population 59.13 61.45

(2.17)** (2.12)**

Agriculture ‐575614

(‐0.81)

Services 0.004

‐0.30

F(5,28) 359.54*** 272.45*** 305.14*** 241.88***

Prob>F 0.0000 0.0000 0.0000 0.000

‐38‐

Number of

Observations

33 33 33 33

R­squared 0.9456 0.9625 0.9688 0.9700

Notes: GDP is the gross domestic product in 2005 prices, prices in 2005 prices,

agriculture divided by GDP in 2005 prices, services sector divided by GDP in 2005

prices.

Source:Source:StatisticsSouthAfricaDatabase,ReserveBankofSouthAfricadatabase.

TobetterunderstandthedemandforelectricityinSouthAfricatheauthorcommenced

byrunningaregressionofelectricitydemandbasedupononanumberoffactors.The

authorutilized fivemainexplanatoryvariables:priceofelectricity,GDP,population in

South Africa, agriculture sector, and the services sector. To better understand the

importance of each variable the author implemented a stepwise regression. More

specifically,byusingonevariablethattheauthorthoughtshouldplayapivotalrolein

drivingelectricitydemand,andthensequentiallyaddingonevariabletotheregression.

Theresultsareasshowninthetableabove.

RegressionOneshowstheresultsfortheregressionofelectricitydemandontheprice

of electricity. As per the author’s hypothesis and theoretical predictions, there is an

‐39‐

inverse relationship between electricity demand and its price. Furthermore, the

coefficientforthepricevariableisnegativeandsignificantatthe1%levelsignificance;

suggesting that an increase in the priceof electricity tends to reduce the demand for

electricity. Thewholemodel is also significant at the1% level of significance as seen

fromanFvalueof359.54andaProb>F=0.0000.Themodel’sR‐squared,at94.56%,is

alsoverylarge.Thisimpliesthatabout94.56%ofthevariabilityinelectricitydemandis

duetovariabilityinthepriceofelectricity.

However, one important variable which should drive electricity consumption is the

incomeleveloftheSouthAfricancitizens.TheauthorusedGDPtomeasuresuchincome

level.Ahigherlevelofincomeincreasesthepurchasingpowerofthegeneralpopulation

resulting in higher demand for goods and services, including electricity. The author

therefore expected a positive relationship between changes in GDP and electricity

demand.ChangesinGDPcanalsobeusedtomeasurethegrowthrateoftheeconomy.A

growingeconomymustbesupportedbyanincreasedsupplyofelectricity.Lowerlevels

ofelectricitysupplymaypushtheeconomyawayfromitsoptimalgrowthpath.Itmay

also result in lower investment and ultimately cause untold suffering to the general

population due to decreased GDP and lower income levels, once the Keynesian

multiplier (in reverse gear) kicks in. To this end, Regression Two shows the results

whenelectricitydemandisregressedonpriceandGDPalone. Thepricecoefficient is

stillnegativeandsignificantat1%level.TheGDPparameterispositiveandsignificant

at 1% level, implying that an increase inGDP increases the demand for electricity in

‐40‐

South Africa. The model for Regression Two is also significant at the 1% level.

Compared to Regression One the R‐squared for Regression Two also marginally

increasedto96.25%from94.56%;anincreaseofabout1.79%.

InRegressionThree theauthor thenadded thepopulationvariable.The results show

that, as per the author’s expectation, the variable is positive and significant at a 5%

level, implyingthatanincreaseinpopulationincreasesthedemandforelectricity.The

parameters for the price and GDP variable are still significant and have the correct

signs.Thenegativesignforthepriceofelectricityparametercorroboratesfindingsby

Inglesi (2010), Amusa et al. (2009) andPouris, (1987), but is contrary to findings by

Ziramba(2008)whofoundthatthepriceofelectricitywasinsignificant.Thet‐statistic

for theGDP variable actuallymarginally increasedwhen the population variablewas

added. The whole model is also significant. R‐squared increased from 96.25% to

96.88% (an increase of about 0.65%). Also, the positive sign of the GDP parameter

supportsthefindingsbyZiramba(2008).

InRegression4theauthorthenranthefullmodel.Theresultsareasshownincolumn

five of the above table. The results show that the agriculture and services sector

variable are insignificant. The parameters for the price of electricity, GDP and

populationvariablesarestillsignificantandhavethecorrectsigns.Itmustbenotedthat

addingtheagricultureandservices sectorshassomewhataffectedtheresults asseen

fromadecline in the t‐statistics for thepricevariable from‐19.91 to‐9.78. TheGDP

‐41‐

parameter’st‐statisticsalsodecreasedfrom3.60to2.59.TheGDPparameter’slevelof

significance also declined from 1% level to 5% level when the author added the

agriculture and services sectors. The parameter for the population variable also

declinedto2.12from2.17.Moreover,theR‐squaredincreasedto97%whentheauthor

addedtheagricultureandservicesvariables;amarginalincreaseofonly0.12%.

Itisquiteclearfromtheresultsthatthepriceofelectricity,GDPandpopulationplaya

very important role in driving the demand for electricity. It is also clear that these

variablesarenotequallyimportantindrivingthedemandforelectricity.Thestepwise

regressionsuggeststhatthemostimportantvariableisthepriceofelectricity.Giventhe

insignificanceoftheagricultureandservicessectorintheregressionresultswesuggest

that the most appropriate model of the four is regression three. The author shall

thereforeusethismodelinthesubsequentdiscussions.Finally,theauthorshallalsouse

regressionthreeforforecastingthedemandforelectricity.

4.3 ForecastingElectricityDemandorConsumption

Scenario1:Assuming3%GDPGrowthRate

Inordertoforecastelectricitydemandtheauthorshallcommencebymakingaseriesof

assumptions.Firstly,theauthorshallassumethatGDPwillgrowby3%peryearinthe

next15years.PopulationforecastswereobtainedfromStatsSA.Also,thepricevariable

‐42‐

wasforecastedusingmovingaveragemethod.Themovingaveragewasbasedonafour

yearperiod.

Table4.2:ForecastedElectricityConsumption/Demand(2010–2030)

Year ForecastedElectricitydemand

(3%GDPgrowth)inGWh

2010 162307

2011 162185

2012 159409

2013 157529

2014 156769

2015 157557

2016 158200

2017 158961

2018 160067

2019 161356

2020 162670

2021 164386

2022 165935

2023 167165

‐43‐

2024 168213

2025 168909

2026 169619

2027 170453

2028 171342

2029 172143

2030 173043

Source:StatisticsSouthAfricaDatabase

The following figure indicates the forecasted electricity demand for the period 2010‐

2030.Itshowsagradualincreaseinelectricityconsumptionduringtheperiod.

‐44‐

Figure 4.1: Forecasted Electricity Demand (Assuming GDP growth Rate equal to

3%)

Source:OwnCalculationsfromStatisticsSouthAfricaDatabase

Scenario2:GDPassumedtogrowat6%perYear

In scenario two the author shall assume that GDP grows by 6% in line with the

government’sASGISApolicy.PopulationforecastsarethoseobtainedfromStatsSA.The

pricewasforecastusingmovingaveragemethod(asinScenario1).

‐45‐

Table4.3:ForecastElectricityDemandAssuming6%GDPGrowth

Year ForecastedElectricitydemand(6%GDPgrowth) in

GWh

2010 188184

2011 190392

2012 190408

2013 190893

2014 192984

2015 196819

2016 200717

2017 204957

2018 209777

2019 215032

2020 220580

2021 226814

2022 233183

2023 239554

2024 246086

2025 252629

2026 259572

‐46‐

2027 267049

2028 275016

2029 283360

2030 292295

Source:StatisticsSouthAfricaDatabase

Figure 4.2: Forecast Electricity Demand (Assuming GDP growth Rate equal to

6%)

‐47‐

Figure4.3:AcomparisonofScenario1andScenario2ForecastElectricityConsumption

Source:OwncalculationsfromStatisticsSouthAfricaDatabase

‐48‐

4.4 EstimatingRequiredInvestment

Toestimatetherequired investmenttheauthorneededthecostofgeneratingagiven

unitofelectricity.AccordingtotheESKOMannualReport(2010)theoperatingcostof

generatingakWhofelectricityisR0.282(or28.2cents).Giventhattheauthor’sfigures

are inGWh,one thenmultiplied the costby1000000 inorder toderive the costper

GWh.TheauthorfoundthatitcostsR282000topresentlygenerateaGWhofelectricity

inSouthAfrica.Pleasenotethattheauthorisusing2010pricestocalculatethecostof

generatingelectricityinSouthAfrica.Accordingtothescenario1forecasts(scenario1

assumes an annual GDP growth rate of 3%) the country must have invested a

cumulative amount of about R27 billion into electricity generation if it is not to face

cripplingshortages(seeTable4.4below).Scenario2assumeshighergrowthrateand

thushigherelectricityconsumption.Scenario2assumesanannualGDPgrowthrateof

6%.Accordingtotheresultsfromthefollowingtablesanaccumulatedamountequalto

R232billionmusthavebeeninvestedintoelectricitygenerationby2030ifthecountry

istoavoidelectricityshortageslikethosewitnessedin2008.

‐49‐

Table4.4:Scenario1:RequiredInvestmentAssuming3%GDPGrowth

Year Forecast

Electricity

demand

(3%GDP

growth)

Price to

generate

electricity

per

GigaWatthou

r

(GWh)

[South

African

Rands in

Millions]

Costof

generating

Electricity

(2010

Prices)

ZARMillions

Additional

Investment

Required to

meet

electricity

demand

[South

African

Rands in

Millions]

Annual

Increasein

Additional

Investment(%)

2010 162307 0.280 45770 ‐

2011 162185 0.282 45736 34

2012 159409 0.282 44953 817 19.42

2013 157529 0.280 44108 1347 64.87

2014 156769 0.282 44209 1562 15.89

2015 157557 0.282 44431 1339 ‐14.23

2016 158200 0.280 44296 1158 ‐13.52

2017 158961 0.282 44827 943 ‐18.54

2018 160067 0.282 45139 632 ‐33.04

‐50‐

2019 161356 0.280 45180 268 ‐57.54

2020 162670 0.282 45873 1026 ‐61.73

2021 164386 0.282 46357 587 47.65

2022 165935 0.282 46794 1023 74.47

2023 167165 0.282 47141 1370 33.87

2024 168213 0.280 47100 1666 21.58

2025 168909 0.282 47632 1862 11.78

2026 169619 0.282 47833 2062 10.75

2027 170453 0.280 47727 2297 11.40

2028 171342 0.282 48318 2548 10.90

2029 172143 0.282 48544 2774 8.86

2030 173043 0.282 48798 3028 9.15

Source:StatisticsSouthAfricaDatabase

‐51‐

Table4.5:RequiredInvestmentAssuming6%GDPGrowth

Year Forecast

Electricity

Demand

(6%GDP

growth)

Priceper

GigaWatthour

[South African

Rands in

Millions]

Costof

Electricity

(Assuming

constant

cost)

[South

African

Rands in

Millions]

Additional

Required

Investment

(Using

2010prices)

[South African

RandsinMillions]

Annual

increasein

additional

investment

(%)

2010 188184.96 0.280 53068 ‐

2011 190392.99 0.282 53691 623

2012 190108.23 0.282 53611 542 ‐13

2013 190893.74 0.280 53832 764 41

2014 192984.44 0.282 54422 1353 77

2015 196819.55 0.282 55503 2435 80

2016 200717.81 0.280 56602 3534 45

2017 204957.38 0.282 57798 4730 34

2018 209777.51 0.282 59157 6089 29

2019 215032.27 0.280 60639 7571 24

2020 220580.58 0.282 62204 9136 21

‐52‐

2021 226814.57 0.282 63962 10894 19

2022 233183.79 0.282 65758 12690 16

2023 239554.92 0.282 67554 14486 14

2024 246086.85 0.280 69396 16328 13

2025 252629.72 0.282 71242 18173 11

2026 259572.33 0.282 73199 20131 11

2027 267049.58 0.280 75308 22240 10

2028 275016.97 0.282 77555 24487 10

2029 283360.56 0.282 79908 26840 10

2030 292295.35 0.282 82427 29359 9

Source:StatisticsSouthAfricaDatabase

The last columns in Table 4.4 andTable 4.5 show the growth rates of the additional

investment. It shows that the average growth rate in investment required during the

periodisapproximately7%perannum(usingscenario1)and24%usingscenario2.It

mustbenotedthattheauthor’sestimatedresultsshowthatinsomeyeartherequired

investmentmaybe lowerthanthepreviousperiod.This in turnprovidesthenegative

growthratesasseeninthelastcolumnsinTable4.4andTable4.5.

‐53‐

4.5 FinancingInvestment

This section considers a number of factors thatmay influence ESKOM’s capability to

raisetherequisite funds for investment.Eventhough it isamonopoly,ESKOMcannot

freely determine the price of electricity. The price is controlled by the government

throughtheNationalEnergyRegulatorofSouthAfrica(NERSA).Tobetterunderstand

how it prices its product and the environment in which it operates the author

formulated a SWOT analysis in order to consider other factors, which may affect

ESKOM’soperations.

4.6 CompetitorandMarketAnalysis

Presently, there are a few private players (also known as independent power

producers)inSouthAfrica.Theindependentpowerproducerscontributeabout5%to

the South African energy market (ESKOM, 2010). Essentially, ESKOM has no major

competitor in the generation and distribution of electricity (Foulkes‐Jones, 2010;

ESKOM, 2010). The parastatal is thus amonopolist; facing almost the entiremarket

demand. ESKOM can also be considered to be a natural monopoly. According to

Foulkes‐Jones (2010) and Muradzikwa et al (2006) a natural monopoly is a market

structure inwhichonlyone firmcansolelysupplythewholemarketatrelatively low

costs. The natural monopolist’s long run average cost curve (LRAC) is downward

sloping over a large range of output (Foulkes‐Jones, 2010); with “the monopolist

‐54‐

actually singlehandedly catering for the entire market in that range of the LRAC”

(Parkinetal,2008;McConnellandBrue,2005).Thisisofsuchsignificancethatshould

other smaller firms try to enter themarket the costsmay be pushed up and thereby

ultimatelyharmingtheendconsumers.Tothisend,Figure4.4hereinbelowshowsthat

if a profit‐maximizing natural monopolist were to produce as a profit‐maximizer it

wouldproduce1000units(this iswherethe MarginalRevenue=MarginalCosts1). It

would charge a price equal to $25. Producing at this point however is suboptimal.

Firstly,thereisexcesscapacitysincethefirmisnotproducingatthepointwhereLRAC

is at its minimum (Parkin et al, 2008; McConnell and Brue, 2005). Moreover, the

consumer is charged a higher price than would be necessary were the firm were to

produce at theminimum point of the LRAC. Thus the naturalmonopolistmaymake

profitatthispoint.Thereishowevernoguaranteethatsuchprofitswillbereinvestedto

enhance futuregenerationofelectricity. It ispossible that if thenaturalmonopolist is

private the profit may be shared among the shareholders or even consumed as

perquisiteconsumptionbythemanagement(Foulkes‐Jones,2010).Itmaybenecessary

forthegovernmenttointervenesothattheprivatemonopolistisforcedtoproduceata

sociallyacceptablepoint,suchaspoint“F”.Also,atpoint“F”moreisproducedatlower

costs. This is beneficial to the consumer as the goods are likely to be sold at lower

1MarginalRevenue(MR)istheincreaseintotalrevenueduetoaunitincreaseinoutputsold.MarginalCosts(MC)isthe

increaseintotalcostsduetoaunitincreaseinoutputproduced.

‐55‐

prices; increasing the consumer surplus. Another alternative would be for the

government to nationalize the monopolist and ensure that it produces a socially

desirableoutputlevel.Thedrawbackisthatmostparastatalsinemergingordeveloping

countriesarenotasproductiveasprivatelyowned firms.Also, anumberof themare

corruptorcaneasilybeabusedbypoliticiansor thosecloselyconnectedtotheruling

elite.

AccordingtoFoulkes‐Jones(2010)andParkinetal(2008)thegovernmentcan,through

regulating bodies, force the monopolist to produce and charge the price that

corresponds to the point where the long run marginal cost curve (LRMC) cuts the

demandcurve.Itmustbenotedhoweverthatatthatpointthemonopolistwillnotbe

abletocoveritsproductioncosts.Thusintheabsenceofgovernmentsupportitmaybe

forcedtoshutdownintheshortrunifitcannotcoveritsaveragecost,orexitinthelong

runifsuchlossespersists.

‐56‐

Figure4.4:ANaturalMonopolist’sDemandandCostsCurve

Source:AdoptedfromFoulkes‐Jones(2010);Muradzikwaetal(2006),andParkinetal

2008)

4.7 DegreeofRegulationandDeregulation:TheCaseofESKOM

AsalreadyexplainedinSection4.6,ESKOMisaparastatal.Assuchitfacesanumberof

challenges. One important challenge is that it does not have the freedom to single‐

handedlydictatethepriceofelectricity(Foulkes‐Jones,2010;ESKOM,2010).IfESKOM

needs to change the price of electricity itmust attain approval from the government.

Thegovernment, through theNationalEnergyRegulatorof SouthAfrica (NERSA)will

M R D

L R A C L R M C

E

D

20

25

0 2000 4000

F

Q u a n t i t y

Price and Costs

‐57‐

have to hold public hearings where members of the public and various other

stakeholders are allowed to air their objection to such decisions. As a result of such

hearings, ESKOM may occasionally be requested not to increase the price. One

significantaspecthoweveristhatanincreaseinthepriceofelectricitymayhaveserious

consequencesfortheentireeconomy,thisisespeciallyattributabletotheimportanceof

electricity in production input. Ithas been argued by some commentators that in the

past ESKOM was not allowed to charge a viable price that would have ensured a

sustained supply of electricity (Foulkes‐Jones, 2010). Consequently the South African

Economy experienced serious power outages which serious affected the country’s

growth rate as well as its position as a destination for foreign direct investment

(Foulkes‐Jones,2010).

4.8 ProductandServicesPricingStrategies

AsstatedearlierESKOMisessentiallyanaturalmonopoly. It isaparastatalownedby

government and regulated by government through NERSA (ESKOM, 2010). The

regulations are in terms of how it must produce and the price that it must charge.

ESKOM(2009)statesthatitsstrategicpricingobjectivesare:

• Economicefficiencyandsustainability

• Revenuerecovery

• Fairnessandequity

‐58‐

Theauthorbrieflyconsiderseachoneofthesepoints,asfollows;

• Economic efficiencyand sustainability: tariffswill contain cost reflective signals

thatpromoteeconomicefficiencyandsustainability (Foulkes‐Jones,2010).The

marketstructureinwhichESKOMfindsitselfinmustbeconsidered.Theefficient

productionofelectricitymustalsotakeintoaccounttheenvironmentalimpactof

suchproduction.Thepossibilityofotherplayersmustalsobetakenintoaccount.

More importantly, theuseofother sourcesof electricity, rather thancoal as is

currently thecaseneedstobeconsidered.Otherpossiblesources includewind

powerandsolarenergy.

• Revenue recovery: tariff structures will not expose ESKOM to unacceptable

revenue risk and provide themeans for adequate revenue recovery to ensure

reliability of supply (Foulkes‐Jones, 2010). This may be compromised by the

politicalpressurefromotherstakeholderswhomaynotasfarsightedasESKOM

or otherswhomay fail to understand the rational of high salaries for ESKOM

management.Especiallywhensuchsalariesarefargreaterthanthemanagement

of private sector management let alone that of the management of other

governmentdepartments.Thismustbeviewed in lightof the fact thatCEOsor

themanagement generally have their own labourmarket inwhich themarket

conditionsestablishthesalariesandbenefitsofsuchemployees.

• Fairness and equity: tariffs will be designed in such a way that they as non‐

discriminatoryaspossible.Thismustbedonebytakingintoaccounttheneedsof

all customers on a fair and equitable basis (Foulkes‐Jones, 2010). Thismeans

‐59‐

accesstoelectricityisnotfortherichonlybutforSouthAfricans,includingthe

poor(mostofwhomwerepreviouslydisadvantagedbytheapartheidsystem).At

the same time sustainability is only possible if funds are made available for

investment into the futuregenerationof electricity forall SouthAfricans. Such

finance can come from price increases. Other sources such as government

support or loans frommultilateral institutions in the formof theWorldBank,

Development Southern Africa and African Development Bank should also be

considered.

According to ESKOM (2009) the design of the electricity tariffs by ESKOM involves 7

principlestepsasshowninthefollowingtable.ThetablewasadoptedfromtheESKOM

TariffDesignPaper(2009)andFoulkes‐Jones(2010).

‐60‐

Table4.6:TheTariffDesignProcess

Step Process

Step1

The approved revenue requirement and volumes: The approved revenue

requirementincludingthereturnforeachESKOMdivisionisusedasthetotal

costof thebusiness tobe recovered through the tariffs,basedon submitted

volumes. The above costs are allocated in four main areas, energy,

transmission network, distribution network and retail (The approved

revenuesareinturnapprovedbyNERSA).

Step2

Allocate energy cost: Energy costs are allocated to customers based on the

Generationcosts,consumptionprofilesandapprovedkWhvolumes

Step3

Allocate Transmission costs. Transmission costs are allocated based on the

capacity in each transmission zone of each customer based on the

Transmissiontariff.

Step4

AllocateDistributioncosts:Distributioncostsaresplitbetweennetworkand

retail (customer service and administration) costs. The network costs are

allocatedbasedoncapacity,thevoltageofthesupplyandwhetherasupplyis

onaruralorurbannetwork.Theretailcostsareallocatedonthesizeof the

supply.

Step5

Convert costs into retail tariffs: The costs that have been allocated are then

convertedintothetariffstructurerequired,atcostreflectiverates.Thetariff

structureusedwilldependonthecustomertype.

‐61‐

Step6

Rateimpactanalysisandscalingtoincludesubsidies:Thecostreflectiverates

are evaluated against the current tariff and adjusted where required to

includeallowablesubsidies

Step7

Test against revenue neutrality: The sum of all the rates and volumes

calculated are tested against the approved revenue requirement to ensure

revenueneutrality.

AdoptedfromtheESKOMTariffDesign(2009)andFoulkes‐Jones(2010)

Therehavebeenattemptsbythegovernmenttoensureaccesstoelectricityforall.For

example, in 2002 the government established a stepped tariff structure for the poor.

The first 50kWh/month was to be supplied for free, between 50 kWh and 400

kWh/month the rate should be 30cents/kWh, any consumption above 400kWh is

chargedat50cents/kWh(ESKOM,2010;Foulkes‐Jones,2010).Toensurethatthatcosts

arecorrectlyandfairlyleviedESKOMdifferentiatescostsintoruralandurbannetworks

(Foulkes‐Jones,2010).

In 2007 and 2008 the countrywitnessed cripplingpower outageswhich significantly

affectedtheresilienteconomy.Naturally,commentatorsprofferedanumberofreasons

whytheentityfailedtosupplyadequateelectricityin2007and2008.Thesesuggestions

range from inability to correctly forecast the demand of electricity to political

indifferencebythethenMbeki‐ledgovernment.The2008/2009financialcrisishasseen

‐62‐

reduced demand for electricity, giving ESKOM some time in which to address its

shortfalls in supply. The entity has had time to strategize and ensure that what

happenedin2008/09willnotberepeatedagain.Short‐runstrategiesincludedemand

management toensure thatwhat is available is efficientlyutilized.Long‐runplanning

requirestheentitytobuildnewpowerstations.TothisendESKOMmanagedtosecure

some funding from theWorldBank. It ishoweverhoped thatonce thevariouspower

stationsunderconstructionbyESKOMareoperationaltheriskofpoweroutageswillbe

significantlyreduced,ifnoteliminated.

Theoptionofraisingfundsforinvestmentbyincreasesinpriceisnotpoliticallyviable

presently.Economicallyitmayalsobeunpalatable.Anincreaseinthepriceofelectricity

can have a ripple effect through the economy. This may cause macroeconomic

instability in an economy which is already contending with more than 20%

unemployment.Themacro‐instabilitycanalsopushtheinflationrateoutofthecentral

bank’s target of between 3% and 6%. The implications of this include a substantial

reduction in investor confidence and possibly a commensurate drop in foreign direct

investment.Ultimatelyunemploymentmayincrease,causingpoliticalinstabilityakinto

thecurrentEgyptiandebacle.

‐63‐

4.9 Barrierstoentry

The energy sector, especially the electricity generation segment of the value chain, is

verydifficulttopenetratepresently.Consequently,thereisnothreatofpotentialentry

by competitors, thereby ensuring that ESKOM’s monopoly is likely to persist for the

foreseeable future. The principal challenge in this instance is attributable to the

prohibitivelyhighinitialset‐upcosts.Whilstcompetitionmaybeacatalystforadrivein

efficiency,itmustbenotedthatinacasewherethemonopolyisnaturalitmayactually

betoconsumers’advantagethatESKOMremainsamonopoly.

The price of electricity is also considered to be very low by international standards.

AccordingtoKowalikandCoetzee(2007)theaveragepriceofelectricityinSouthAfrica

was 25cents per kilowatt in 2007.However, it is noteworthy that the domestic tariff

varies from onemunicipality to the other. In response to this discrepancy, there are

plans afoot to establish a common price across allmunicipalitieswithin the country.

Therefore,itisreasonabletoassertthatthelowelectricitypricesmakeitverydifficult

foranypotentialnewentrantstorecouptheirinvestedfunds.Thepaybackperiodmay

therefore prove very long. The government is however encouraging the entrance of

independentproducersofrenewableenergy.

‐64‐

ThefollowingtablewhichwasadoptedfromFoulkes‐Jones(2010)showssomeof the

weaknessesandstrengthsoftheutility.

Table4.7:SWOTAnalysisforESKOM

STRENGTHS WEAKNESSES

• Coal is the main source of energy. Coal is

abundantinSouthAfrica

• Barrierstoentry

• High solar radiation and high rate of solar

radiation

• Strong wind capacity in a majority of

provinces

• Highrateofinnovationinrenewableenergy

sources and skilled labour in nuclear

technology

• Increasedusageofgas‐poweredstations

• HighCO2emissions

• Lowlevelsofnaturalgasfields

• Highdemandforenergy

• Low skills development and

highrateofskillgaps

• Lowrateofcommercialization

oftheinnovations

OPPORTUNITIES THREATS

‐65‐

• KyotoProtocol

• Contractstoserviceoilrigsandrefineries

• PotentialforSolarPVEnergyFarms

• BedModularReactorhasbeen identifiedby

ESKOM and governmental heads as an

opportunity

• Developmentofwindfarms

• Discovery of new natural gas fields off the

Westcoast

• The manufacture of renewable energy

technologyinSouthAfrica

• Wavetechnology

• Developedoilandfuelrefineries

• Environmentalconcerns

• Runningoutofcapacity

• Increasedillegalconnections

• KyotoProtocol

• Low cost of electricity

diminishes the potential of

alternativesourcesofenergy

• Highlabourandinputcosts

• Low economies of scale for

renewable energy –

technologytooexpensive

• Cheaper to import products

suchasgascylindersandsolar

panel material from China

rathermanufacturethem

AdoptedfromFoulkes‐Jones(2010)

AsstatedinthetableaboveESKOMhasanumberofstrengths.Itsmainingredientfor

electricitygenerationiscoal.CoalisabundantinSouthAfrica.SouthAfricaisoneofthe

biggestproducersofcoalintheworld.Furthermore,therearebarriersofentrywhich

makeitdifficultfortheotherplayerstoenterthesector.Theserestrictionsrangefrom

regulatory to initial set up costs. Moreover, government to date has done little to

‐66‐

actively encourage private players to enter the sector. However, were other private

players to enter themarket, itwill nonetheless take time for them to really compete

withESKOM.

AnotherpotentialstrengthisthatSouthAfricapreviouslyproducedelectricitythrough

themediumofnuclearpower.Consequently, thecountryenjoysaskilled labour force

that could facilitate in thegenerationof nuclearpower.There is also apossibilityof

usingalternative sourcesofpower in the formofwindandsolar.ESKOMmay inany

event have to diversify and use these sources as well. It can, with the support of

governmentandinterestedprivateplayers,developsolarfarmswhichcanalsoproduce

electricity.

According toFoulkes‐Jones (2010),ESKOMhasan advantage in thatwhilst itmaybe

difficulttounilaterallyincreasethetariffdomestically,itisfarmoreatlibertytodoso

with its customers based in other countries. Consequently, this is an opportunity for

ESKOMtogenerateadditionalrevenuetofuelitsfutureexpansion.

ItshouldbenotedthatwhilstESKOMsuffersthepotentialthreatwhichisalwaysposed

bynewmarketentrants, itwillnonetheless takea considerableamountof time fora

seriouscompetitortobecomeestablished.

‐67‐

GiventheaboveenvironmentalfactorswhichinfluencethecapabilityofESKOMtoraise

capital, the author now wishes to consider ESKOM’s financial position and assess

whether the firm will be able to raise the funds required for investment. The table

belowshowsESKOM’sfinancialperformanceindicatorsfortheperiod2006–2009.The

parastatal’s EBIT has been declining during the period in question. In 2006 it was

R7030millionbutby2009thefigurehaddeclinedtoanegativeR2115million.Thenet

profitfortheyearalsodeclinedfrom4447millionin2006toanegativeof168million

in 2008. By 2009 the net profit had declined to a negative 9668million. In spite of

deterioratingfinancialpositionasshownbythedecliningEBITandnetprofitthefirm

witnessedagradualincreaseinitsassetbaseandtotalequityduringtheperiod2006–

2009.

The debt service ratio shows the ability of the firm to honour itsdebt obligations. In

2006thedebtserviceratiowas0.56butby2009theratiohaddeclinedtoanegative

0.55. This could be attributable to the high debt level in ESKOM’s balance sheet. For

example in 2006 the entity’s debt‐equity ratio was 0.19 but by 2009 the ratio has

increaseto1.22;implyingthattheentity’scapitalstructureisdominatedwithdebt.

‐68‐

Table4.8:ESKOMGroupFinancialPerformance

Financial

Performance

Indicator

2006 2007 2008 2009

EBIT

(ZAR)Millions

7032 6452 3215 ‐2115

Net profit for

theYear

(ZAR)Millions

4447 7220 ‐168 ‐9668

TotalAssets

(ZAR)Millions

125716 139838 166170 199302

TotalEquity

(ZAR)Millions

48670 55890 61129 59578

Debt/Equity

Ratio

(ZAR)Millions

0.19 ‐0.21 0.40 1.22

Debt Service

CoverRatio

(ZAR)Millions

0.56 0.44 ‐0.17 ‐0.55

InterestCover

(ZAR)Millions

6.8 9.11 2.5 ‐0.80

‐69‐

Source:ESKOMIntegratedReport(2010)

Giventheabove,itwouldappearthatESKOMshouldincreasethepriceofelectricityin

ordertoraisethefundsforexpansion.However,thismayonlyconstituteapartofthe

possiblesolution,asESKOMcanalsobepartiallyfundedthroughtaxes.Forexample,an

emergencyfundcanbeestablishedtofinancetheinfrastructurerequired. ESKOMcan

also secure loans from multilateral organizations such as the World Bank and the

African Development Bank. The other alternative is to open the sector to private

participantstherebymitigatingtheresponsibilityofESKOMsomewhatandreducingthe

need forESKOMto carry suchprohibitive levelsofdebt. Furthermore, soas toavoid

duplication of investment in infrastructure, the private players should be allowed to

feedintothenationalgrid.Theycaninturn“sell”thepowertheygeneratetoESKOMin

theformof“PowerPurchaseAgreements”atapredeterminedtariff,therebyensuringa

securealternativesourceofelectricityataprofitableratetoprivateindustry.Theprice

of electricity can continue to be controlled by the government as is currently being

done,andtherebyensureastrongregulatoryregime.

4.10 Conclusion

This chapter estimated the demand for electricity in South Africa. The price of

electricity,GDPandpopulationwerefoundtobeimportantvariablesthatinfluencethe

demandforelectricity.Thissuggeststhatastheeconomygrowsmoreelectricityneeds

‐70‐

tobegenerated.Populationisthereforeanimportantfactorindeterminingthedemand

for electricity. The government must therefore take population growth into account

whencraftingenergypolicies.

Thechapteralsoforecastthedemandforelectricityandfoundthatasignificantamount

of money will need to be invested into electricity generation in future. The author

utilized two scenarios; Scenario 1 assumed an annual GDP growth rate of 3% per

annum,whilstScenario2assumedagrowthrateof6%perannum.AssumingScenario

1, a total of some R27 billionmust have been invested into electricity generation by

2030toavoidpoweroutagesaswitnessed in2008. If on theotherhandonewere to

assumeagrowthrateof6%,thetotalamountincreasestoR232billion.Thissuggests

that doubling the GDP growth rate significantly increases the amount of required

investment. Furthermore, it is estimated that South Africa’s demand for electricity is

growingat15%peryear(ESKOM,2007).

The chapter also looks at ESKOM’s financial position. This is important given the

author’sneedtoestablishwhetherESKOMcanaffordtoraisetherequiredinvestments

fromitsinternalcoffers.ThefinancialstatementsshowsthatifESKOMcontinuesonthe

sametrajectoryasithasinthepast5yearsitismostlikelytofailtoraisetherequisite

fundsfrominternalcoffers.Itmadelossesin2008and2009.Otherfinancialindicators

suchastheinterestcoverageratioanddebtservicecoverratiodonotappearpromising

either.

‐71‐

CHAPTER5

ConclusionandDiscussionofResearchFindings

5.1 Introduction

Thischapteraimstoconcludethedissertation.Itstartsbymakinggeneralcommentson

the main results especially given the research questions raised in Chapter 1. More

importantly, it also considers thepolicy implicationsemanating from the results. It is

composedof twosections.Section5.1 looksat theconclusionandsection5.2looksat

thepolicyrecommendations.

5.2 ConclusionandPolicyRecommendations

Thestudysoughttoanswerthreemainquestions.Firstly,itconsideredthemainfactors

which drive electricity consumption in South Africa. The study also proceeded to

forecast electricity consumption or demand in South Africa given the factors

investigated earlier. Given such factors and the electricity consumption levels the

questiononinvestmentrequiredtomeetsuchdemandwasthenaddressed.Finally,the

thirdissuethatthestudyaddressedwastheissueoffinancingtheinvestmentrequired

togenerateincreasingelectricitydemandinSouthAfrica.

‐72‐

The author’s results suggest that the priceof electricity, income (asmeasured by the

gross domestic product) and population are themost important drivers of electricity

consumptioninSouthAfrica.Thepriceofelectricitywasfoundtobeinverselyrelatedto

electricity consumption or demand. This is consistent with economic theory which

postulatesthatthehigherthepriceofacommoditythemoreexpensiveitbecomesand

thusthelowerthedemandforthatproduct.Theimplicationisthatonepotentialtoolto

usetoreduceelectricityconsumptionistoincreasethepriceofelectricity.Usingprice

changes as a tool tomanage electricity consumption could be appliedwith a view to

punishing those firms and individuals that may be wasting electricity. However, the

poorwouldbeunabletoaffordsuchpriceincreases,andmustthereforebeprotected.

Whetheritispoliticallyfeasiblehowever,isanotherquestiontobediscussedinanother

forum. Furthermore, using the price as a tool is especially useful and efficient in the

short run. Moreover, the government or ESKOM can formulate a short run demand

management policy which involves educating consumers on electricity usage and on

how not to waste electricity as well as the commensurate costs incurred with the

wastefuluseofelectricity.

A higher price of electricitymaymake electricity generationmore lucrative and thus

attractmoreplayers intothesector. InSouthAfricathere isadeliberatepolicybythe

governmenttoattractmoreindependentprivateproducersofelectricity.Itisexpected

thatinfutureabout30%oftheelectricitygeneratedinSouthAfricashouldbeproduced

‐73‐

byprivateplayers.ThistrendrunsinstarkcontrasttoESKOMpresentrole,wherebyit

generatesinexcessof90%ofthenation’selectricityrequirements.

There are also a number of factors which may make it impossible for ESKOM to

effectively use price increases as an effective tool to manage electricity demand or

consumption. Firstly, ESKOM is largely considered to a developmental entity. It is

thereforenotaprivate firmmotivatedbyprofit.Also, it isownedbygovernmentand

cannot freely increase the price of electricity to achieve desired objectives. Any price

increasemust also be approved by government throughNERSA.NERSAmust in turn

consultthepublic,throughpublichearingsandfollowdueprocess.Naturallythepublic

tends to resists any such price increases. Moreover, Organizations such as COSATU

(workerorganizations)tendtoplaceconsiderablepressureonESKOMnottoincrease

thepriceofelectricity.Despitesuchpressures,ESKOMisinvariablyrequiredtocharge

lower prices to poor households. Notwithstanding this cheaper rate, there are

nonetheless a large number of illegal connections to the grid, which constitute a

considerable draw on the electricity supply, without the commensurate financial

contributionforsame.

AnothersignificantfactorwhichdriveselectricityconsumptioninSouthAfricaisthatof

income. To this end, the author used the gross domestic product (GDP) tomeasure

income.Accordingtoeconomictheoryanincreaseinincomeincreasesthedemandfora

normalgood.Foraninferiorgoodanincreaseinpricereducesquantitydemanded.The

‐74‐

author’s results suggest that there is a positive relationship between income and

quantity demanded of electricity. This is in line with the author’s hypothesis and

economictheory.This implies thatan increase inGDPincreasesquantityofelectricity

demanded. This in turn implies that as the economy develops, more electricity is

required to support such growth. It also goeswithout saying that electricity in South

Africa,likeinanyothercountry,isanormalandnotinferiorgood.Itmustbenotedthat

eventhoughincomemayhavebeenincreasinginSouthAfrica,forthemajorityofSouth

Africa’spoorthishashadlittlebenefit.Thisinturnleaveslittleincometobespenton

suchresourcesaselectricitybytheimpoverishedsectorsofthesociety.

Shiftsinpopulationalsoaffectelectricityconsumption.Thegreaterthepopulationthe

higher the amount of electricity consumed. The author hypothesized that population

and electricity consumed would be positively related. The results confirm this

hypothesis. The population coefficient is positive and significant at 1% level. The

implication is that population control can be used as a tool to reduce electricity

consumption in the country. It may however be socially and politically difficult to

implementsuchastrategyboth in theshortrunand longrun.Presently,eventhough

the country conducts population census once very ten years, South Africa does not

appeartohaveaclearpopulationpolicy.

Another important issue that the paper addressed concerns the various potential

sources of finance available to ESKOM. From the field of corporate finance and to a

‐75‐

certain extent financial economics ingeneral, a firm’smain sources of finance can be

grouped into external and internal finance. Internal finance is generated by the firm

throughretainedearnings.Fromafirm’spointofviewitisthecheapestsourceoffunds.

Furthermore,themajordifferencebetweendebtandequityisthatdebtholdershavea

contractspecifyingthattheirclaimsmustbepaidinfullbeforethefirmcanmakeany

payments to theequityholders.A seconddistinctionbetweendebtand equity is that

paymentstodebt‐holdersaregenerallyviewedasatax‐deductibleexpenseofthefirm.

Incontrastthedividendsonanequityinstrumentareviewedasapayoutofprofitsand

theyarenotatax‐deductibleexpense.Externalfinanceislargelydebt.Also,debtmaybe

categorizedintoprivatedebtandpublicdebt.Privatedebtwouldconstitutedebtfrom,

forexample,banksorinsurancefirms,whilepublicdebtisdebttypicallyacquiredfrom

bondholders,forexample.Morespecifically,thisconstitutesdebtthatmaybetradedon

bondexchanges.

Inthefollowingtheauthorconsidersthedifferenttheoriesofcapitalstructure.Itmust

howeverbenotedthatmostof thetheories in theextant literature focusonprivately

owned firms and not developmental or government owned entities such as ESKOM.

However,ESKOMalsoraisesfundsinthecapitalmarketsjustlikeotherfirms,therefore

thesamefactors thataffectprivate firmsmust, toacertaindegree,alsoaffectESKOM.

We must however caution that such theories of capital are especially germane to

privatelyownedfirmsandmaynotbesuitableforentitieslikeESKOM.

‐76‐

From literature concerning capital structure, the main theories on how firms raise

capitalare:trade‐offtheoryandpeckingordertheory.Accordingtothetrade‐offtheory,

value‐maximizing firms choose the level of debt by balancing tax benefits of debt

against the costs associatedwithdebt suchasbankruptcyandagency costs.The firm

choosesthelevelofdebtthatmaximizesitsvalue.Theoptimalcapitalstructureoccurs

wherethemarginalbenefitofincreasingdebtbyanadditionalunitisequaltomarginal

cost of increasing debt by the same unit. It is important to note that as the firm

increases its debt level the probability of bankruptcy and financial distress also

increases.

Inperfectcapitalmarkets,howafirmraisescapitalisirrelevanttothevalueofthefirm.

Howeverthepossibilityofbankruptcycostsandfinancialdistressandtheinclusionof

taxesimplythatModiglianiandMiller’sirrelevancepropositionsdonotapplyinreality.

Thatis,anincreaseindebtforexample,increasestheprobabilitythatthefirmwillfail

to honour its debt obligations. This will tend to affect the value of the firm. Also,

potential providers of finance tend to require a firm to also contribute something to

business rather than requiring100%of the funds to financeabusiness to come from

outside(e.g.as100%).

Inafollow‐uptotheir1958article,ModiglianiandMiller(1963)introducedcorporate

taxesandtheirconclusionwasthat tomaximize itsvalue,a firmshouldactuallyhave

100%debt.ThecornersolutionimpliedbytheModiglianiandMillermodelis,however,

‐77‐

at oddswith the empirical behaviourof firms. Since then the important question has

beenwhy are firms not using 100%debt to finance their activities?Research efforts

havefocusedontryingtoidentifycostsassociatedwithdebtfinancing,whichfirmstend

to trade‐off against the taxbenefits.But this ishowevernotvery relevant forESKOM

which is government owned and can also raise funds frommultilateral organizations

suchastheWorldBank.Thegovernmentalsotendstoguaranteesuchloans.Suchthat

the theoretical capital structuremodelsmaynotbe sufficiently relevant inexplaining

thebehaviorofESKOMwhenraisingcapital.

TheauthoralsoconsideredESKOM’sfinancialpositionandfoundthattheentitymade

lossesinthepasttwofinancialyearsthatis2008and2009.SeeTable4.8.Fromsucha

financialpositionitdoesnotappearthatESKOMisinapositiontoraisefinanceforthe

essentialinvestmentsforthegenerationofextraelectricity.Furthermore,ESKOMlacks

the massive retained earnings that could be used to finance such future investment.

Consequently, the financialpositionappearstosuggest that itmayhavetodependon

external funds such as loans. The loans can be from multilateral developmental

institutionssuchastheWorldBank,theAfricanDevelopmentBankortheDevelopment

Bank Southern Africa. It can also depend on government funding. It is possible that

ESKOM can borrow by issuing long term bonds. Unfortunately, given its present

financial position, itmay be considered a risky investment bymany investors. Credit

ratingagenciesmayalso lowlyrate it. Investorsmaythuschargehigher interestrates

foranysuchloans.

‐78‐

5.3 PolicyRecommendations

There is an urgent need for government intervention to ensure sufficient electricity

generation in South Africa. South Africa as an economy cannot afford regular power

outages such as those witnessed in 2008. From the thesis the following suggestions

shallbemade;

GovernmentmustallowmoreprivateplayerstoentertheSouthAfricanenergymarket.

Theseindependentpowerproducerscouldgenerateelectricityandfeeditintonational

grid. The advantage is that ESKOM has already established a massive infrastructure

throughoutthecountry,therebyintroducingprivateplayerstofeedintothegridwould

notposeamajorchallenge.Presently,accordingtotheDepartmentofEnergy,thepolicy

isthatindependentprivateplayersarerequiredtocontribute/generate30%ofthetotal

electricity consumed. Currently the contribution by private players is very minimal;

with ESKOM still supply more than 95% of the country’s electricity. More steps are

beingtakenbythegovernmenttoencourageprivatesectorparticipation.

However, according toMr. Godsell the former ESKOM chief executive officer, private

players are not necessarily the panacea to South Africa’s electricity problems (Helen

SuzmanFoundation,2010).Privatesectororganizationsareusuallyprofit‐oriented.Itis

thuspossible that theprivateplayers canactuallyproduceelectricityathigherprices

thanESKOMespecially givenESKOM’smonopolyon power and the possibility that it

‐79‐

mayhugelybenefitfromeconomiesofscale.Itishowevertheauthors’consideredview

that introducing more players in electricity sector can bring about competition and

ultimatelytheconsumermaybenefitfromsuchcompetition.

Related to the private sector participation in the generation of electricity is the

ownershipof thenationalgrid.Somecommentatorssuggests that it isnotoptimal for

ESKOM (also a generator of electricity) to own the national grid. Some suggests that

perhapsthenationalgridmustbeownedbyan independentorganizationwhichthen

allowsallelectricitygeneratorsaccessontothesystem.Thislatterproposalisintended

to promoted equity and reasonable access to all. However, and perhaps most

pertinently, the Minister of Energy observed that the grid must be owned by a

government‐ownedentityandmustnotbeinprivatehands.

Theindependentpowerproducersmustbeencouragedtogoandexpandbeyondfossil‐

based fuel sources. These includewind, solar and nuclear. It should also be borne in

mind that there is movement towards the production of clean energy. In 2003 the

governmentcameupwithaRenewableEnergyWhitePaperwhichaimedatintroducing

renewable energy and ensuring that about 4% of electricity comes from renewable

energyby2013(HelenSuzmanFoundation,2010).

GovernmentorESKOMshouldalsobeallowedtoraisethepriceofelectricityinorderto

more effectively manage electricity demand in the short run. This may however not

‐80‐

prove a feasible political strategy given that a significant portionof the population is

poor.Thismayalsonotbeacceptabletoworkers’unionssuchastheCongressofSouth

AfricaTradeUnions.Incidentally,thegovernmentwillrequireCOSATU’ssupportifitis

tosuccessfullyimplementcrediblepoliciesofthisnature.

Government should enact a clear population policy over and above conducting

censuses.Suchapolicycanhelpgovernmentsufficientlysupplyelectricitytotheentire

country.

5.4 Limitationsofthestudy

Despitethestudyachievingresultsnotdissimilartothosefoundinotherstudiesorin

accordancewith fundamental economic theory, it nonetheless has certain limitations.

Firstly, due to data limitations, the sample size was smaller than initially expected.

Originally thestudywassupposedtobebasedonquarterlydata for theperiod1985‐

2010.Thatis,morethan100datapoints.However,duetothefactthatdataonpriceof

electricity, electricity consumption and population was only available on an annual

basis,thestudyhadtouseannualratherthanquarterlydata.Thissubstantiallyreduced

thesamplesize.Whilstanysampleabove30isstillusable(Gujarati,2004)theauthor

stillcautionsthattheresultsmust,onaccountofthis,beinterpretedwiththiscaveatin

mind.

‐81‐

Itisalsopossiblethatsuchareductioninthesamplesizeaffectstheprecisenessofthe

estimatedparameters.Usuallyparameterefficiencyimproveswithlargersamples.This

observationrunsintandemwiththenotionofmostestimators,includingtheOrdinary

Least Squares estimator, are being asymptotically efficient. The efficiency of the

parametersmaythusbeaffectedbythereductioninsamplesize.

The secondweakness of the study is its use of historical data for the period 1993 to

2010 to forecast electricity consumption up to the year 2030. According to Gujarati

(2004) the further one forecasts into the future the less accurate the forecast. The

author’s forecasts were however based on a realistic set of assumptions, based on

expectedgrowthratesinGDPandpopulationgrowthrates.Theauthorformulatedtwo

scenarios.Scenario1assumedtheGDPgrowthrateforthedurationoftheperiodwould

be3%and theother scenarioassumedagrowth rateof6%. It ispossible that actual

growthrateduringtheperiodinquestionwillfallwithinthisrange.Inthatinstance,the

forecastedelectricityconsumptionandthecorrespondingrequiredinvestmentwillalso

fallbetweentheamountsestimatedusingthetwogrowthrates.IntheirstudyFedderke

andBogetic(2006)alsomadethesameassumptions.

‐82‐

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