2. LEGAL DISCLAIMER:
This document has been compiled for informational purposes. The
information herein is subject to updating, completion and amendment.
The contents of this Progress Report are strictly confidential. This document
is not to be distributed to any third party in whole or in part except with the
prior and express consent of IMPERIAL CYGNUS INVESTMENTS (Pty) Ltd
(ICI).
The information contained in the Report is selective and does not constitute
all the documentation related to the Project thus far. Such information will
be made available upon request.
3. ABSTRACT
This study considers the current state of electricity demand and supply within the
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
estimated the cost of electricity required between 2010 and 2030. Using growth
rates of 3% per year the study estimates that the country must invest about R27
billion into electricity generation. Using growth rates of 6% per year the study
estimates that the country must invest about R232 billion into electricity
generation. This suggests a massive investment requirement. The study also
considers the possible alternative that can be used to raise the funds including:
using ESKOM’s internal resources, government resources (through taxes),
increasing the price of electricity, and borrowing from multilateral organizations
such as the World Bank (probably with government guarantees). The different
options must however take into account ESKOM’S financial position as well as its
role as a developmental entity rather than a private firm.
By
Gareth FoulkesJones
4. TABLE OF CONTENTS
CHAPTER 1: INTRODUCTION
1.1 Background 1
1.2 Scope 7
1.3 Problem Statement 7
1.4 Outcome 9
CHAPTER 2: LITERATURE REVIEW
2.1 Introduction 13
2.2 Empirical Literature Review 14
2.3 Theoretical Framework 24
2.4 Conclusion 25
CHAPTER 3: RESEARCH METHODOLOGY
3.1 Introduction 26
3.2 Quantitative/Qualitative Research Approach 29
3.3 Data Sources 31
3.4 Conclusion 35
5. CHAPTER 4: RESULTS AND DISCUSSION OF RESEARCH FINDINGS
4.1 Introduction 36
4.2 Regression Results 37
4.3 Forecasting Electricity Demand or Consumption 41
4.4 Estimating Required Investment 48
4.5 Financing Investment 53
4.6 Competitor Analysis 53
4.7 Degree of Regulation/Deregulator 56
4.8 Product and Services Pricing Strategies 57
4.9 Barriers to Entry 63
4.10 Conclusion 69
CHAPTER 5: CONCLUSIONS AND RECOMMENDATIONS FOR FURTHER RESEARCH
5.1 Introduction 71
5.2 Conclusion and Policy Recommendation 71
5.3 Policy Recommendation 78
5.4 Limitations of Study 80
BIBLIOGRAPHY & REFERENCES 82
6. LIST OF TABLES
Table 1 South Africa Macro‐Economic Indicators 3
Table 2 South Africa’s Population by Province 4
Table 3.1 Data Sources 32
Table 3.2 Descriptive Statistics 33
Table 3.3 Correlation Matrix 34
Table 4.1 Demand for Electricity in South Africa 37
Table 4.2 Forecasted Electricity Consumption/Demand (2010 – 2030) 42
Table 4.3 Forecasted Electricity Consumption/Demand (Assuming 6% GDP Growth) 45
Table 4.4 Scenario 1: Required Investment Assuming 3% GDP Growth Rate 49
Table 4.5 Scenario 2: Required Investment Assuming 6% GDP Growth Rate 51
Table 4.6 The Tariff Design Process 60
Table 4.7 SWOT Analysis for ESKOM 64
Table 4.8 ESKOM Group Financial Performance 68
LIST OF FIGURES
Figure 4.1 Forecasted Electricity Demand (Assuming GDP Growth Rate equal to 3%) 44
Figure 4.2 Forecasted Electricity Demand (Assuming GDP Growth Rate equal to 6%) 46
Figure 4.3 Comparison of Scenario 1 and 2 Forecasted Electricity Consumption 47
Figure 4.4 A Natural Monopolists Demand and Cost Curves 56
8.
‐ 2 ‐
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
energy supply as observed by Howells et al (2005).
Furthermore, despite the evident level of technological sophistication within the South
African electricity sector, over 75% of South Africa’s rural households use fuel wood
energy to at least a limited extent in order to satisfy their energy needs. This level of use
as observed by Davis (1998) varies from a few times per month to daily, and depends
largely upon the individual needs and conditions of the respective households. Over and
above fuel wood, such rural households also make 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
expensive alternatives to that of fuel wood.
9.
‐ 3 ‐
Table 1: South Africa Macroeconomic Indicators
Year Growth Rate Unemployment Rate Savings (% of GDP)
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
2000 2007 Average 4.26 23.81 15.23
Source: Statistics South Africa (Various Years) and Statistics South Africa (2009)
10.
‐ 4 ‐
Table 2: South Africa's Population by Province
Province 1996 % of 1996
Total
Population
2001 % of 2001
Total
Population
2009 % of 2009
Total
Population
Eastern
Cape
6 302 525 15.53 6 436 763 14.06 6 648 600 13.5
Free State 2 633 504 6.49 2 706 775 6.04 2 902 400 5.9
Gauteng 7 348 423 18.11 8 837 178 19.72 10 531 300 21.4
KwaZulu
Natal
8 417 021 20.74 9 426 017 21.03 10 449 300 21.2
Limpopo 4 929 368 12.15 5 273 642 11.77 5 227 200 10.6
Mpumalang
a
2 800 711 6.90 3 122 990 6.97 3 606 800 7.3
Northern
Cape
840 321 2.07 822 727 1.84 1 147 600 2.3
North West 3 354 825 8.27 3 669 349 8.19 3 450 400 7.0
Western
Cape
3 956 875 9.75 4 524 335 10.09 5 356 900 10.9
South Africa 40 583 573 100.00 44 819 778 100.00 49 320 500 100.0
Source: Statistics South Africa (Various Years) and Statistics South Africa (2009)
11.
‐ 5 ‐
Having established a broad overview of the South African electricity sector, one may
now consider in greater detail the origins of its key player in the form of ESKOM. Its
origins were founded in the Electricity Supply Commission (ESCOM) in 1922. The South
African Government then proceeded to consolidate the nation’s electricity supply within
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
interconnected national transmission grid in the early 1970’s.
However, as a result of a commission of inquiry in 1983, ESCOM was renamed “ESKOM”.
Furthermore, the Electricity Act of 1987 was also implemented during this period. This
latter act resulted in ESKOM abandoning its core operating principle of “neither a profit
nor a loss” and thereby obliged the organization to supply electricity in a cost‐effective
manner, within the confines of its limited resources and in consideration of the national
interest.
These policies, coupled with conditions, which encouraged ESKOM to become more
operationally efficient, resulted in South Africa enjoying a well‐developed electricity
generation and distribution system by the early 1990s. However, the apartheid policies
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
12.
‐ 6 ‐
connections for urban black households. As a consequence, Ziramba (2008) observed
that by 1991, only a third of South Africa’s population had access to electricity.
Against this background, the modern ESKOM remains the sole supplier of electricity in
South Africa in real terms, and is statistically responsible for approximately 96% of
electricity generation (ESKOM Annual Report, 2007). The remaining 4% is split
between private generators accounting for 3.2%, and municipal authorities accounting
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 Africa which
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
energy supply, it is nonetheless still heavily dependent upon Coal as its principal energy
source. In terms of distribution, ESKOM also enjoys a dominant position. To this end, it
is responsible for nearly 60% of all direct sales to the 40% of electricity capacity
distributed by 177 amalgamated municipal authorities as according to Mabugu et al.
(2008).
Within South Africa, ESKOM continues to sell electricity to a varied set of clients, which
include industrial, mining, commercial, agricultural and residential customers. Over and
13.
‐ 7 ‐
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 approximately 26,000 kilometers throughout South Africa as well as several
other Southern African Development Countries (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 turn wholly state‐owned. It may also be further opined that
where one has a large number of distributors within a particular market, this may result
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 ESKOM and local governments into six regional electricity
distributors (REDs). The intention behind this was to promote greater competitiveness
in electricity generation, Furthermore, the aforementioned restructuring aims to create
an ESKOM owned subsidiary to retain 70% of the generation market share. The
remaining 30% would be shared between private independent power producers
constituting 20% and Black Economic Empowerment Groups making up the final 10%.
It is argued that such reforms to the EDI would 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
national electrification objectives.
14.
‐ 8 ‐
The results of this study are intended to lead to a better understanding of the different
challenges which ESKOM faces, which range from the need to invest efficiently to meet
growing demand in the face of limited resources with which to do so, as well as the
seeking of timely financing for such investments and selecting politically and
economically viable sources of funds to finance such projects.
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 (capital expenditure) required to meet such demand.
The study also examines ESKOM’s financial statements in order to assess the sources of
funds which have been used in order to finance the investments in the past.
Finally, the main objective of this study is therefore to better understand the role of
ESKOM in the South African energy sector, given the growing demand for energy and
the strategic goals of the company. More particularly, the aim is to forecast the
investment required to meet South Africa’s growing demand for electricity. In order to
achieve this objective, this research aims to forecast South Africa’s demand for
electricity over the next 15 years and the corresponding required levels of investment.
15.
‐ 9 ‐
This objective is important given the recent outages which have adversely affected the
economy, on account of demand regularly exceeding supply.
1.3 Problem Statement
South Africa has been subjected to numerous blackouts in recent years. In light of this,
the South African government has attempted to manage the demand for electricity.
Unfortunately, it is widely believed that such electricity supply challenges were in fact
predominantly the result of political indecision. In the early 2000’s there were also
some debates on whether ESKOM should be privatized or not. During this particular
period, the government as the sole shareholder underinvested in electricity capacity
building. This dearth of investment eventually resulted in the electricity crisis which the
country now finds itself contending with.
The impact of the aforementioned blackouts approximated to a loss of R 50 Billion for
the South African economy in that period (Inglesi, 2010). Furthermore, during this
period, the level of economic growth in the first quarter of 2008 fell to 1.6% from 5.4%
in the last quarter of 2007 (Inglesi, 2010).
16.
‐ 10 ‐
Given the above background, the author has sought to address the following questions
in this dissertation:
1) What factors drive electricity consumption in South Africa and how is
consumption likely to change over the next 10 years?
2) What is the level of investment required to meet such demand/consumption?
3) How should this level of demand be financed?
There are a number of reasons why a study of this nature should be conducted. Some of
the reasons in this instance may be stipulated as follows;
It will assist in highlighting the required investment necessary to meet the country’s
electricity supply needs;
It will inform policy makers on the different options available in respect of financing
electricity generation in the country; and
The study will also contribute to the discussions surrounding electricity pricing in South
Africa.
18.
‐ 12 ‐
parastatal. This aspect of the study bears relevance to the question as to whether
ESKOM is in fact more of a commercial or developmental entity.
With conclusion of the introduction, one may now review what the ensuing Chapter
shall consider. Chapter Two shall provide a comprehensive review of existing and
pertinent literature pertaining to the subject matter. Following same, Chapter 3 shall
consider the methodology utilized to derive the results in the paper, as well as explain
the reasons why such a methodology is employed in this instance. Chapter 4 will seek to
explain the results derived from the methodology employed in Chapter 3 as applied to
the empirical information in Chapter 2 and additional sources. Finally, Chapter 5 shall
provided a conclusion of all the aforementioned Chapters, and where relevant to review
same.
19.
‐ 13 ‐
CHAPTER 2
Literature Review
2.1 Introduction
The aim of this section is to review relevant literature. It is hoped that such a literature
review will help us better understand what has been done and thus enable us to identify
gaps in the literature that need to be filled. It will also help us rationalize the context of
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
considers the theoretical framework upon which this paper is based.
The author must hasten to caution that some of the extant literature on capital structure
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, unlike other firms which are privately owned and are thus owned by private
shareholders pursuing profits, ESKOM is a government owned entity, essentially owned
by tax payers. Whilst its goals are different, ESKOM often raises finance in the capital
markets like any private firm. It however has additional sources of funds in the form of
government and multilateral organizations like the World Bank, Development Bank
Southern Africa and African Development bank among others. The government is
20.
‐ 14 ‐
believed to have guaranteed the recent loan that was advanced to ESKOM by the World
Bank.
2.2 Empirical Literature Review
Generally there is a dearth of literature on the energy sector in South Africa, and the
electricity sector in particular (Inglesi. 2010). However, the 2007‐2008 energy crisis
which plunged the South African economy into both literal and figurative darkness, and
the current campaign by organizations such as the World Bank on the sustainable use of
energy resources has seen the proliferation of studies on energy and electricity usage.
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, there is also a link between infrastructure investment or infrastructure quality,
on the one hand, and equity and poverty, on the other. This is especially germane for the
South African economy given its historical background where access to infrastructure,
among other things was the preserve of a minority group.
22.
‐ 16 ‐
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
growth also tends to be associated with an increase in infrastructure needs. Hence the
causality is not unidirectional. So even though most studies tend to estimate regression
equations with an endogenous economic growth and exogenous infrastructure, the
study by Perkins, Fedderke and Luiz (2005) seems to suggest that infrastructure is also
endogenous. Secondly, Perkins, Fedderke and Luiz (2005) argue that South Africa’s
infrastructure has developed in phases and it may thus be important for policy makers
to choose the right type of infrastructure and focus on it – rather than taking a
haphazard approach to infrastructure investment. Most importantly, the projects must
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
relationship between electricity consumption and economic growth in South Africa. He
found a bidirectional causality between electricity consumption and economic growth,
corroborating the findings by Perkins, Fedderke and Luiz (2005). The policy
prescription emanating from the study is that investment in electricity infrastructure
24.
‐ 18 ‐
by using more contemporary econometric approaches as well as more recent data,
calculated income and price elasticities of demand in South Africa. 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
autoregressive distributed lag (ARDL) model to estimate the elasticities. They also made
use of the error correction inherent in the ARDL framework to assess the short‐run and
long‐run impacts of the main drivers of electricity consumption in South Africa. 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
electricity is not a significant factor in the demand for electricity function then a policy
thrust that focuses on pricing may not be the optimal policy option.
In a recent study in the US, Dergiades and Tsoulfidis (2008) investigated the residential
demand for electricity for the period 1965‐2006. The explanatory variables which they
used include GDP per capita, price of electricity, price of oil for heating purposes (used
to proxy the price of a substitute), and weather conditions. Using the ARDL approach to
cointegration they found the coefficient of price to be significantly different from zero.
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
25.
‐ 19 ‐
number of electricity using appliances, this implies that the higher the number of
occupied houses the higher the usage of electricity.
In an attempt to capture the role of economic development in driving electricity
demand, Holtedahl and Joutz (2004) added an additional variable to the usual economic
variables normally included in an ordinary demand function that they estimated for the
Taiwanese economy. The variable added is the urbanization. Urbanization was
measured as the proportion of the population in cities of 100 000 or more (Holtedahl
and Joutz, 2004). The other economic variables included are population changes, price
of electricity and household disposable income. Price of electricity was found to be
negatively related to the demand for electricity. The price of the electricity coefficient
was also found to be significant; with their own price effect being found to be inelastic.
This implies that an increase in the price of electricity by 1% in Taiwan results in a
reduction in the quantity demanded by less than 1%. So using the price increases as a
strategy to curtail the demand for electricity as emphasized by the South African
government may not be the optimal strategy to embark on. This also points to the fact
that as an economy develops, electricity becomes a necessity that every household
cannot do without. So an increase in the price of electricity may result in households
moving some of the income from other sources into electricity rather than significantly
reducing the demand for electricity. The relationship between own‐price and electricity
demand was also found to be stable in both the short‐run and long‐run; implying that
28.
‐ 22 ‐
hence it may be necessary to introduce substitutes for electricity. The study also
suggests the alignment of electricity prices to those obtained in the European region.
The study also found that population increases tend to increase the demand for
electricity. Sales of electric appliances were found to be insignificant as was the price of
diesel in their relationship to the demand for electricity. The demand for electricity in
Greece was found to be fairly constant when comparing the demand of one region with
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
important when it comes to policy making, as the regions are more or less homogenous
in terms of electricity demand. A policy designed for one region can therefore, with
minimal modification, be easily applied to other regions.
According to Smith (1980), estimates of demand function for electricity are important
for policy making. For example, demand responsiveness to price changes has a bearing
on the demand forecasts; something that is critically important for investment planning
at national level as well as at firm level. Firms in any given economy are one of the
important sectors that consume a significant amount of electricity. Forecasting demand
is also important for regulatory reviews – especially given the current debate around
global warming and the need to efficiently and sustainably utilize energy (Smith, 1980).
Walker (1979) estimated the residential electricity demand for a random sample of
households for the US economy during the period 1972 – 1975. Household electricity
consumption was estimated as a function of changes in weather, real price of electricity
30.
‐ 24 ‐
come up with effective demand management policies. More importantly, a long‐run
relationship was found between electricity consumption and price of electricity as well
as between electricity consumption and economic growth. A short‐run relationship was
also found between population growth and electricity consumption.
2.3 Theoretical Framework
According to classical economics the main factors which affect the demand of a good
include; own price of the good, household real income (the relationship between
demand and income depending on whether the good is a normal or inferior good), the
price of related goods (whether the goods are complements or substitutes), population
and expected future price changes. In the case of residential demand for electricity the
common substitutes are the natural gas, heating oil, fuel wood (in most rural areas).
Normally the relationship between the price of the good and the demand for the good is
the paramount relationship in any demand function. The main theories explaining the
relationship are the cardinal utility theory, ordinal utility theory and the revealed
preference theory. The cardinalist approach assumes that utility is measurable; hence
consuming one additional utility of a good result in total satisfaction derived from that
changing by a certain amount. The ordinalist approach argues that this is unrealistic as
measuring utility is practically impossible. What is important, argue the ordinalists, is
that one can compare different bundles of goods. For example one can state that one
31.
‐ 25 ‐
derives more utility from consuming bundle A than bundle B, but one may not know the
exact impacts of a change in total utility due to the consumption of the two bundles.
Most models 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
more electricity consumption) and price of substitutes. It is also estimated that there is
a positive relationship between electricity consumption and the following variables:
income growth, population growth, the amount of electrical appliances in a household,
household size and price of complements.
2.4 Conclusion
This chapter reviewed the relevant literature. It started by considering the empirical
work conducted in South African and other countries. It then looked at the theoretical
framework or the theoretical models that explain the behavior of consumers. The
literature review helped one better understand what has been discovered historically
and thus enabled one to identify gaps in the literature that need to be addressed. It also
helped rationalize the context of the problem especially given the current debate on
climate change and the financial crisis. The next chapter considers the methodology to
be used in formulating the analysis.
32.
‐ 26 ‐
CHAPTER 3
Research Methodology
3.1 Introduction
The major theory that guides the research is the neoclassical demand theory. It provides
the author with the theoretical model that informs the econometric model to be
estimated. It also assists with the important variables to be included in the econometric
model. The expected signs, or the hypothesis, of the models are actually gleaned or
informed by the theoretical model. That is, variables shall not be dropped or included in
the model simply because they are significant or simply because they make the model
significant. The variables are included because theory provides that they must be.
Despite its strengths and benefits the demand theory can also fail to explain some
special cases. For example while the price of a good is expected to be negatively related
to the quantity demanded of the good, there are special cases where the demand may
actually be positively related to the price of a good. That is, an increase in the price of
good X may actually result in an increase in demand for good X. One example is that of a
good associated with status: with people demanding more of the good even as the price
goes up. The need to join the Jones’s (or the so‐called band wagon effect) and also
explain why an increase in the price of a good can result in more being demanded. It is
however expected that in this case electricity is a normal good whose price tends to go
33.
‐ 27 ‐
down as price increases and as such we do not expect the demand model to be affected
by the special cases mentioned.
The aim of this chapter is therefore to explain the methodology utilized in order to
estimate the demand for electricity function. Like the demand for any commodity, the
demand for electricity is a function of income, population and price of electricity, among
other factors. The author closely follows Bogetic and Fedderke (2006) in this respect.
The model by Bogetic and Fedderke (2006) is a reduced form equation for the demand
for infrastructure. It expresses the demand for infrastructure as a function of per capita
income, sectoral shares in GDP (with the individual shares of the following sectors
included as separate variables: agriculture, manufacturing and services).
Generally most demand models to be estimated take similar forms: the dependent
variable is expressed as a function of several variables. The difference may be in the
functional form that the actual estimated equation takes as well as the variables
included. For example, Lakhani and Bumb (1978) estimated the following model:
ttttt GDPaPAaPEaaDE !++++= 3210
Where:
DE – is the demand for electricity at time t.
PE – the price of electricity at time t.
PA – the price of a substitute at time t.
34.
‐ 28 ‐
GDP – gross domestic product at time t.
Inglesi (2010) estimated the following:
t220 !+++= ttt PEaIncomeaaED
Where:
ED – is the demand for electricity at time t.
PE – the price of electricity at time t.
Income – gross domestic product at time t (GDP)
In Odhiambo (2009) the role of income or GDP is also emphasized. Odhiambo (2009)
was however looking at the direction of causality between electricity consumption and
economic growth. Ziramba (2008) uses a model similar to Inglesi (2010) but he
included a time variable.
As explained in the following section the Author adopted a model similar to the above
models. The only exception is that the Author included more variables. In addition to
income and price of electricity the author has also included a variable to measure
population.
35.
‐ 29 ‐
3.2 Quantitative/Qualitative Research Approach
The author adopted a quantitative research approach. To better understand the
determinants of demand for electricity and thus be able to forecast additional
investment required for the generation of additional electricity one will need to address
three main points. First, one needs to adopt a theoretical model of consumer behavior
(demand theory). Having established the theoretical underpinnings, the Author then
adopted an econometric model to estimate the necessary parameters. Once the
parameters have been estimated one then need to utilize them in order to forecast the
future demand for electricity and the concomitant funding required to finance such
investments.
Consequently, the theory allowed the author to develop the variables to be included in
the model. These variables are electricity consumption, income, price of electricity,
population and GDP. The author then collected secondary data for the different
variables. Finally one then utilized the data to run the regressions and thus render the
quantitative analysis.
The Author seeks to estimate the demand for electricity using the following model:
ttt
ttt
tot ePbPopb
GDP
Services
b
GDP
Manu
b
GDP
Agric
bGDPbbE +++!
"
#
$
%
&
+!
"
#
$
%
&
+!
"
#
$
%
&
++= 654321
Where:
38.
‐ 32 ‐
Table 3.1: Data Sources
Variable Explanation Source
GDP Gross domestic product South African Reserve Bank
Price of Electricity Average price of electricity (c/kWh) Statistics South Africa
Electricity
consumption
Electricity consumption or
demand (GWh)
Department of Minerals and
Energy, South Africa
Government
Population Total population in South Africa Statistics South Africa
It is noteworthy, that all data used was thoroughly reviewed and tested for stationarity
prior to the running of any regressions for this paper. However, whilst the GDP could be
calculated quarterly, the other variables were only available as annual figures. This
unfortunately resulted in a reduction of the sample size to a total of 33 observations,
thus impacting upon the degrees of freedom, which would be afforded in the
interpretation of the paper’s results. The following table shows the descriptive statistics
of the data. The average price during the period was R20.75, with a standard deviation
of 3.8. The minimum and maximum prices were R16.25 and R26.3 respectively. The
other variables are as stated in the table. GDP and electricity demand were seasonally
adjusted and the values are based on 2005 prices.
39.
‐ 33 ‐
Table 3.2: Descriptive Statistics
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: Statistics South Africa Database, Reserve Bank of South Africa database.
The table below shows the correlation matrix for the data with the asterisks indicating
the level of significance of the relationship. For example the price of electricity and
electricity demand were highly correlated at 1% level of significance.
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Table 3.3: Correlation Matrix
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: Statistics South Africa Database, Reserve Bank of South Africa database.
The data were also tested for stationarity and the price of electricity variable was found
to be stationary. The other variables were integrated in order. It is interesting to note
that they became stationary after differencing them once.
Validity of the Research
The research is valid since relevant questions were answered. The research is also not
based on survey data where this issue would be more important.
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Reliability of the Research
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.
Ethics and Confidentiality
The research is based on secondary data. ESKOM, the unit of study, is a government
owned utility. Information concerning the entity is publicly available. So there are no
confidentiality concerns on account of the information used exists and is freely available
within the public domain.
3.3 Conclusion
It is common for researchers to argue that a dissertation, after asking relevant
questions, must explain how the question are to addressed, must conduct the research
as the per the methodology, and finally must explain the results. The aim of this section
is to explain the methodology to be used to address the research questions raised in the
previous chapters. This chapter looked at the models used in the literature and
explained the different variables used. After considering all the models the author then
choose a model that includes the generally accepted variables. The next chapter utilizes
the model in order to estimate the equations as well as to forecast the demand for
electricity in future.
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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: Statistics South Africa Database, Reserve Bank of South Africa database.
To better understand the demand for electricity in South Africa the author commenced
by running a regression of electricity demand based upon on a number of factors. The
author utilized five main explanatory variables: price of electricity, 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, by using one variable that the author thought should play a pivotal role in
driving electricity demand, and then sequentially adding one variable to the regression.
The results are as shown in the table above.
Regression One shows the results for the regression of electricity demand on the price
of electricity. As per the author’s hypothesis and theoretical predictions, there is an
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inverse relationship between electricity demand and its price. Furthermore, the
coefficient for the price variable is negative and significant at the 1% level significance;
suggesting that an increase in the price of electricity tends to reduce the demand for
electricity. The whole model is also significant at the 1% level of significance as seen
from an F value of 359.54 and a Prob>F = 0.0000. The model’s R‐squared, at 94.56%, is
also very large. This implies that about 94.56% of the variability in electricity demand is
due to variability in the price of electricity.
However, one important variable which should drive electricity consumption is the
income level of the South African citizens. The author used GDP to measure such income
level. A higher level of income increases the purchasing power of the general population
resulting in higher demand for goods and services, including electricity. The author
therefore expected a positive relationship between changes in GDP and electricity
demand. Changes in GDP can also be used to measure the growth rate of the economy. A
growing economy must be supported by an increased supply of electricity. Lower levels
of electricity supply may push the economy away from its optimal growth path. It may
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
when electricity demand is regressed on price and GDP alone. The price coefficient is
still negative and significant at 1% level. The GDP parameter is positive and significant
at 1% level, implying that an increase in GDP increases the demand for electricity in
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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
increased to 96.25% from 94.56%; an increase of about 1.79%.
In Regression Three the author then added the population variable. The results show
that, as per the author’s expectation, the variable is positive and significant at a 5%
level, implying that an increase in population increases the demand for electricity. The
parameters for the price and GDP variable are still significant and have the correct
signs. The negative sign for the price of electricity parameter corroborates findings by
Inglesi (2010), Amusa et al. (2009) and Pouris, (1987), but is contrary to findings by
Ziramba (2008) who found that the price of electricity was insignificant. The t‐statistic
for the GDP variable actually marginally increased when the population variable was
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
supports the findings by Ziramba (2008).
In Regression 4 the author then ran the full model. The results are as shown in column
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
population variables are still significant and have the correct signs. It must be noted that
adding the agriculture and services sectors has somewhat affected the results as seen
from a decline in the t‐statistics for the price variable from ‐19.91 to ‐9.78. The GDP
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parameter’s t‐statistics also decreased from 3.60 to 2.59. The GDP parameter’s level of
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
declined to 2.12 from 2.17. Moreover, the R‐squared increased to 97% when the author
added the agriculture and services variables; a marginal increase of only 0.12%.
It is quite clear from the results that the price of electricity, GDP and population play a
very important role in driving the demand for electricity. It is also clear that these
variables are not equally important in driving the demand for electricity. The stepwise
regression suggests that the most important variable is the price of electricity. Given the
insignificance of the agriculture and services sector in the regression results we suggest
that the most appropriate model of the four is regression three. The author shall
therefore use this model in the subsequent discussions. Finally, the author shall also use
regression three for forecasting the demand for electricity.
4.3 Forecasting Electricity Demand or Consumption
Scenario 1: Assuming 3% GDP Growth Rate
In order to forecast electricity demand the author shall commence by making a series of
assumptions. Firstly, the author shall assume that GDP will grow by 3% per year in the
next 15 years. Population forecasts were obtained from Stats SA. Also, the price variable
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2024 168 213
2025 168 909
2026 169 619
2027 170 453
2028 171 342
2029 172 143
2030 173 043
Source: Statistics South Africa Database
The following figure indicates the forecasted electricity demand for the period 2010‐
2030. It shows a gradual increase in electricity consumption during the period.
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Figure 4.1: Forecasted Electricity Demand (Assuming GDP growth Rate equal to
3%)
Source: Own Calculations from Statistics South Africa Database
Scenario 2: GDP assumed to grow at 6% per Year
In scenario two the author shall assume that GDP grows by 6% in line with the
government’s ASGISA policy. Population forecasts are those obtained from Stats SA. The
price was forecast using moving average method (as in Scenario 1).
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2021 226814.57 0.282 63 962 10 894 19
2022 233183.79 0.282 65 758 12 690 16
2023 239554.92 0.282 67 554 14 486 14
2024 246086.85 0.280 69 396 16 328 13
2025 252629.72 0.282 71 242 18 173 11
2026 259572.33 0.282 73 199 20 131 11
2027 267049.58 0.280 75 308 22 240 10
2028 275016.97 0.282 77 555 24 487 10
2029 283360.56 0.282 79 908 26 840 10
2030 292295.35 0.282 82 427 29 359 9
Source: Statistics South Africa Database
The last columns in Table 4.4 and Table 4.5 show the growth rates of the additional
investment. It shows that the average growth rate in investment required during the
period is approximately 7% per annum (using scenario 1) and 24% using scenario 2. It
must be noted that the author’s estimated results show that in some year the required
investment may be lower than the previous period. This in turn provides the negative
growth rates as seen in the last columns in Table 4.4 and Table 4.5.
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4.5 Financing Investment
This section considers a number of factors that may influence ESKOM’s capability to
raise the requisite funds for investment. Even though it is a monopoly, ESKOM cannot
freely determine the price of electricity. The price is controlled by the government
through the National Energy Regulator of South Africa (NERSA). To better understand
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’s operations.
4.6 Competitor and Market Analysis
Presently, there are a few private players (also known as independent power
producers) in South Africa. The independent power producers contribute about 5% 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 a monopolist; facing almost the entire market
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 in which only one firm can solely supply the whole market at relatively 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
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actually singlehandedly catering for the entire market in that range of the LRAC”
(Parkin et al, 2008; McConnell and Brue, 2005). This is of such significance that should
other smaller firms try to enter the market the costs may be pushed up and thereby
ultimately harming the end consumers. To this end, Figure 4.4 herein below shows that
if a profit‐maximizing natural monopolist were to produce as a profit‐maximizer it
would produce 1000 units (this is where the Marginal Revenue = Marginal Costs1). It
would charge a price equal to $25. Producing at this point however is suboptimal.
Firstly, there is excess capacity since the firm is not producing at the point where LRAC
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 the minimum point of the LRAC. Thus the natural monopolist may make
profit at this point. There is however no guarantee that such profits will be reinvested to
enhance future generation of electricity. It is possible that if the natural monopolist is
private the profit may be shared among the shareholders or even consumed as
perquisite consumption by the management (Foulkes‐Jones, 2010). It may be necessary
for the government to intervene so that the private monopolist is forced to produce at a
socially acceptable point, such as point “F”. Also, at point “F” more is produced at lower
costs. This is beneficial to the consumer as the goods are likely to be sold at lower
1
Marginal Revenue (MR) is the increase in total revenue due to a unit increase in output sold. Marginal Costs (MC) is the
increase in total costs due to a unit increase in output produced.
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prices; increasing the consumer surplus. Another alternative would be for the
government to nationalize the monopolist and ensure that it produces a socially
desirable output level. The drawback is that most parastatals in emerging or developing
countries are not as productive as privately owned firms. Also, a number of them are
corrupt or can easily be abused by politicians or those closely connected to the ruling
elite.
According to Foulkes‐Jones (2010) and Parkin et al (2008) the government can, 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
demand curve. It must be noted however that at that point the monopolist will not be
able to cover its production costs. Thus in the absence of government support it may be
forced to shut down in the short run if it cannot cover its average cost, or exit in the long
run if such losses persists.
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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
significant aspect however is that an increase in the price of electricity may have serious
consequences for the entire economy, this is especially attributable to the importance of
electricity in production input. It has 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 Product and Services Pricing Strategies
As stated earlier ESKOM is essentially a natural monopoly. It is a parastatal owned by
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) states that its strategic pricing objectives are:
• Economic efficiency and sustainability
• Revenue recovery
• Fairness and equity
