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AN ANALYSIS OF FUEL PRICES AND
FUEL TAXATION IN SOUTH AFRICA
ABSTRACT
South African policy makers need to make forecasts regarding fuel prices in order to
predict future revenue generated by the general fuel levy. There has been extensive
research on the comparison between the use of VAT and the general fuel levy as a
means of taxing fuel. This paper shows that the general fuel levy is more appropriate in
South Africa given its progressive nature and in addition it gives policy makers greater
control. There has been a lack of literature regarding the estimation of the sensitivity of
fuel prices with respect to certain variables in South Africa. This paper provides useful
models which indicates that lagged oil price and lagged rand dollar exchange rate
variables are good predictors of fuel prices. This gives policy makers information to
make more precise estimates of future revenue. This paper will therefore show that the
general fuel levy is the more appropriate instrument for policy makers to use in South
Africa due to its progressive nature and predictive reliability.
1. INTRODUCTION
This paper investigates the fuel price in South Africa by looking at its various cost-‐per-‐
litre components, the taxation mechanisms imposed on it and the components which
effect its price significantly. With respect to the taxation mechanisms, the paper
investigates the use of the general fuel levy (hereon referred to as GFL) as a source of
revenue for the South African government. The topic is interesting because on average
South African consumers spent 17% of their monthly income on transport (Statistics
South Africa, 2012). Akinboade et al. (2008) estimated the long-‐term price and income
elasticity of demand for fuel in South Africa over the sample period 1978-‐2005 to be -‐
0,47 and 0,36 respectively. Given the inelastic nature of the demand for fuel, an increase
in the fuel price will still have considerable income effects on the consumer. This applies
to consumers ranging from those who own cars to those who use minibus taxis as a
primary means of transport. An increase in the price of fuel affects them all.
Fuel is also an extremely important input in production for almost all industries. An
increase in the price of fuel translates into an increase in costs for firms. It is likely that
a proportion of these higher fuel costs would be passed through to the consumer
(selling the product at a higher price) – reducing the total number of goods that the
consumer is able to buy. This places an additional financial burden upon the consumer
as it reduces the consumer’s real income.
The GFL is a significant source of revenue for government. The GFL revenue comprised
4.85% of total tax revenue in 2013/14 (National Treasury , 2015). This is small in
comparison to VAT which comprised 26.41% of total tax revenue. However, the amount
of revenue collected by the GFL is still substantial and significant (National Treasury ,
2015). The government analyzes the fuel price movements and regulates the GFL every
year in order reach its revenue target. Government has often shielded the consumer
from fuel price increases by keeping the GFL constant or by increasing the GFL by less
than the increase in the fuel price (Blecher, 2015). This is apparent in figure 1 below
where the GFL in real terms has remained fairly constant and stable over the period
2002/03 to 2014/15 compared to the upward trend of VAT. This paper will investigate
government’s mechanism of using the GFL as a source of revenue as opposed to using
VAT on the fuel price. This will be linked to a discussion regarding the general trends in
revenue.
Figure 1: Breakdown of fuel prices in South Africa 2002/03-‐2014/4
(Blecher, 2015)
As mentioned above, the volatility of fuel prices is a serious concern for policy makers
given its considerable effects on consumers. Hence there is a need for a model which
can explain variations in South African fuel prices. The model in this paper uses oil
prices, rand dollar exchange rates and the GFL to understand variations in these fuel
prices. In this paper, references to fuel will refer to both 93 octane petrol and 0.05%
sulphur diesel. The oil price and the rand dollar exchange rate in one month will be
shown to provide good predictions of the fuel price in the following month. This gives
policy makers a useful model to make decisions on how to regulate the fuel levy to
balance government’s interests in collecting more revenue as well as the consumer’s
interests of having a reduced financial burden.
Finally, this paper gives policy makers information and models which are useful in
predicting future fuel prices. This affords them the ability to adapt future fuel taxation
policy.
2. DATA
Reliable data of a time series nature was obtained as far back as January 1990. All the
fuel levy revenue data as well as the actual GFL levels for petrol and diesel were sourced
from the South African budget reviews as well as from the petrol price archives
available on the Department of Energy website. Petrol and diesel prices as well as the
values for the various components that make up these prices were obtained from
Engen’s publicly available fuel price reports (Engen, 2002-‐2015) and the Department of
Energy’s petrol price archives. Oil prices, rand dollar exchange rates and CPI data were
sourced from the South African Reserve Bank’s quarterly bulletins. Accurate 0.05%
sulphur wholesale diesel prices were obtained from June 1994; as a result there are 247
observations for wholesale diesel prices as opposed to 300 for 93 octane petrol pump
prices.
3. DECOMPOSITION OF THE FUEL PRICE
Analyzing the variation in the fuel price starts with understanding its composition.
While the fuel price as a whole might increase, some of its components may remain
constant. The price of fuel can be split into international and domestic influences
(SAPIA, 2014). This paper’s decomposition has a focus on the domestic influences. The
international influences are implicitly accounted for in the basic fuel price (BFP) where
the variables with the largest effects on the fuel price are the oil price and the rand
dollar exchange rate. This will be confirmed later in the paper using regression
analyses. It should also be noted that the paper distinguishes between the pump price of
petrol and the wholesale price of diesel. Both of these prices are taken from the coastal
region (ZONE 01A). The retail margin for petrol is regulated while it is not for diesel
(SAPIA, 2014). Any values used for the retail margin for diesel are estimates based on
the retail margin for petrol (SAPIA, 2014).
3.1 Basic fuel price
The BFP formula currently in effect acts as an import-‐parity mechanism. It represents
the approximate cost of importing a substantial amount of South Africa’s required liquid
fuel necessities from an international refinery and transporting it to South Africa
(SAPIA, 2014). The BFP is calculated using a formula which replaced the IBLC (in bond
landed cost) formula on 2 April 2003 (SAPIA, 2014). The BFP changes on the first
Wednesday of every month (Department of Energy, 2009). The new BFP formula takes
into account that the fuel requirements that would be imported from overseas
refineries must be of a similar quality to fuel available from domestic refineries
(Department of Energy, 2005). These overseas refineries must be able to supply South
Africa with a consistent supply of these fuel requirements on a sustainable basis
(Department of Energy, 2005).
The BFP is a means of ensuring that domestic oil refineries can compete with
international ones. Domestic oil refineries are price takers because of the BFP as they
can only charge the listed BFP price (Department of Energy, 2005). This competitive
market and the fact that the domestic refineries are price takers ensures cost efficiency
(SAPIA, 2014). It also relaxes domestic inflationary pressures as individual firms cannot
affect the market BFP (Department of Energy, 2009). These refineries may not be able
to compete on price but they can reduce their costs by sourcing their inputs in
production carefully. Domestic refineries also have to take advantage of economies of
scale. Smaller refineries cannot do this. This means their margins for profit are too small
as a result of higher average costs. There is also little incentive for product
differentiation and innovation amongst local refineries as they are constrained to only
charge the BFP. The main drivers of the variation of the BFP come from oil price shocks,
rand dollar exchange rate shocks and the demand and supply of international fuel
products (Department of Energy, 2009).
The international influences which form the components of the BFP include: market
spot prices quoted every day for international petroleum products, the cost to transport
these products to South African ports, demurrage, insurance costs, ocean loss, cargo
dues, coastal storage and stock financing (Department of Energy, 2009).
3.2 Domestic influences on the fuel price
The domestic influences on the fuel price are particularly interesting. By looking at the
decomposition of the fuel price (with specific reference to the domestic influences) at
different points in time certain changes can be tracked. These changes result from
certain policy changes from the South African government as it has control over some of
the variables. The most important factors under its control include, the regulated
wholesale margin on fuel, the road accident fund levy, the general fuel levy, the
dealer margin on petrol, the slate levy and the service differential.
The wholesale margin is calculated using an annual oil industry profitability review in
accordance with a set of guidelines from the marketing-‐of-‐petroleum-‐activities-‐return
(M-‐PAR) mechanism (Department of Energy, 2005). This margin is a fixed maximum in
cents per litre (Department of Energy, 2009). The aim of this margin is to compensate
the marketers for the costs of marketing the petroleum (SAPIA, 2014). The target
margin level is 15% on the book value of depreciated assets before tax and interest
deductions (Department of Energy, 2009). If the industry average margin moves outside
the bounds of 10% or 20% the margin will be adjusted to 15%. The margin level must
be approved by the Minister of the Department of Minerals and Energy (Department of
Energy, 2005).
The road accident levy applies to petrol and diesel and is set by the Minister of Finance
(Department of Energy, 2009). It is a dedicated fund used to compensate third party
victims of accidents on the road (Department of Energy, 2009).
The dealer margin (retail margin) is only applicable to petrol. It is a fixed margin in
cents per litre which retail service stations are allowed to add onto the wholesale prices
charged by domestic oil companies (Department of Energy, 2005). The margin amount
is regulated annually and it is primarily based on the costs incurred by petrol retailers
in bringing the petrol from the domestic oil companies (the wholesalers) to the
market(Department of Energy, 2009).
The service differential compensates oil companies for the costs of moving the fuel
from its depot to the customer. The cost calculation is based on what the average cost
was for the previous calendar year. It is determined annually by the oil industry but has
to be confirmed by the Minister of the Department of Minerals and Energy. (Department
of Energy, 2005)
The slate levy effectively acts as a means of compensating the domestic oil refineries
for the time delay in the change of the BFP. The BFP only changes once a month while
the international prices of petroleum and some of the other factor prices that form part
of the BFP change daily. In reality, a daily BFP is calculated for petrol, diesel and paraffin
(Department of Energy, 2009). The daily BFP may be higher or lower than the actual
BFP that was quoted on the first Wednesday of the month (Department of Energy,
2009). If the daily BFP is higher than the actual BFP then consumers will effectively be
paying too little for their fuel on that particular day. This is referred to as an under
recovery situation. A unit under recovery is recorded on that day. The converse is true.
If the daily BFP is lower than the actual BFP a unit over recovery will be recorded on
that day (Department of Energy, 2009). This process is carried out every day over the
month. The monthly unit over or under recovery is multiplied by the quantity of fuel
sold domestically during the month. This value is recorded on the slate account. The
slate levy is used to fund the slate account when it has a negative balance (Department
of Energy, 2009).
Less important variables (form part of ‘Other’ in tables 1 and 2) under government
control include the customs and excise duty, petroleum pipelines levy, tracer dye
levy and the zone differential. These less important variables are classified as such as
they make up a very small proportion of the fuel price for both petrol and diesel.
The tracer dye levy is a very small component of the wholesale price of diesel. It is
used to fund the injection of a tracer dye into illuminating paraffin. This tracer dye is
used to reduce the unlawful mixing of diesel and illuminating paraffin (Department of
Energy, 2009).
Basic fuel price
Regulated wholesale margin
Road accident fund Levy
Fuel levy Other Service differential
Dealer margin Total
April 1995 156.82 39.27 25.14 172.91 22.07 26.26 43.58 486.03February 2002 334.97 44.52 30.22 179.49 11.36 9.34 54.95 664.84April 2008 740.45 50.54 59.85 163.45 11.84 9.01 76.83 1111.97December 2008 441.84 55.08 57.34 156.60 62.52 11.71 82.98 868.06August 2015 551.16 28.96 133.15 220.47 6.34 25.94 130.64 1096.32
April 1995 163.27 39.25 16.20 151.96 11.73 22.35 393.02February 2002 385.26 44.51 30.22 148.35 7.78 9.34 625.46April 2008 915.87 50.53 59.85 142.86 11.84 9.01 1189.95December 2008 672.79 55.07 57.34 136.87 62.40 11.71 996.18August 2015 489.91 55.94 133.15 207.50 6.00 25.94 918.44
Diesel
Petrol
The petroleum pipelines levy was enacted in terms of the Petroleum Pipelines Levies
Act, 2004 (Act No 28 of 2004). It is used to fund certain administrative costs of the
Petroleum Pipelines Regulator.
The zone differential reflects the cost of transporting fuel from the nearest coastal
harbor to the specific zone where it will be sold. Transport is carried out through rail (A
zones), roads (B zones) or pipeline (C zones). The fuel prices analyzed come from
Zone01A. This is a coastal zone and the ‘A’ indicates that the fuel is transported using
railways. The zone differential differs depending on the different zones. This reflects the
different costs in transporting fuel to different parts of the country. (SAPIA, 2014)
3.3 Changes in the decomposition of fuel prices over time
With a better understanding of the various components of the price of petrol and diesel
comparative conclusions can be made regarding the decomposition in different years.
Tables 1 and 2 show the decomposition of fuel in 1995, 2002, 2008 and 2015. The BFP
makes up the largest proportion of the pump price. It is expected that the largest
proportion of the pump price composes of the direct cost of fuel and not all the other
indirect costs like taxes and levies. This was not apparent in 1995 as the BFP only
formed 32% for petrol and 40% for diesel. In August 2015, the BFP composed of
approximately half of the fuel price for petrol and diesel. Over the twenty year period
the BFP relative share of the fuel price increased.
Table 1: Decomposition of petrol and diesel in real terms
Basic fuel price
Regulated wholesale margin
Road accident fund levy
Fuel levy Other Service differential
Dealer margin Total
April 1995 32 8 5 36 10 9 100February 2002 50 7 5 27 2 1 8 100April 2008 67 5 5 15 1 1 7 100December 2008 51 6 7 18 7 1 10 100August 2015 50 3 12 20 1 2 12 100
April 1995 40 10 4 38 8 100February 2002 62 7 5 24 1 1 100April 2008 77 4 5 12 1 1 100December 2008 68 6 6 14 6 1 100August 2015 53 6 14 23 1 3 100
Petrol
Diesel
Table 2: Decomposition of petrol and diesel in percentages
In the wake of the global 2007/08 financial crisis, prices were extremely volatile and
there was considerable instability in the financial sector. The real price per barrel of
brent crude oil in April 2008 was $139.94 while the rand dollar exchange rate was
relatively stable at R7.78. At this point in time the oil price was on a gradual upward
trend and the price continued to increase up until June 2008, illustrated by figure 2,
where it reached a maximum real price of $166.02 dollars. Table 2 shows the high BFP
proportions. This follows from the high oil price at the time. Oil is the most important
factor input in producing fuel. When its price goes up it will result in an increase of the
BFP. Most of the components which make up the composition of the fuel price are
regulated and/or change annually. Therefore, if there is an increase (decrease) in the
fuel price the relative share of these components can only decrease (increase). As a
result, the high oil price in April 2008 ensured a high nominal fuel price for petrol (864
c/l) and diesel (924,5 c/l) with a considerable proportion of the price attributed to the
BFP for both petrol (67%) and diesel (77%).
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Figure 2: Real price per barrel of brent crude oil (US dollars)
Figure 2 illustrates the massive crash in the oil price which started in July 2008. In
November 2008 the approximate percentage change in the real oil price was -‐27%.
This was the largest absolute percentage change in 18 years. Given this crash it is
expected that the fuel price would be substantially lower and that the BFP proportion
would also have declined significantly. Table 2 confirms this hypothesis. The relative
share of BFP is down from 67% and 77% in April 2008 for petrol and diesel
respectively to 51% and 68% in December 2008. The pump price for petrol decreased
from 864 c/l in April 2008 to 704 c/l in December 2008. The wholesale price of diesel
decreased from 924,5 c/l in April 2008 to 807,9 c/l in December 2008. This provides
evidence to the fact that the fuel price is highly responsive to the oil price.
The rand experienced a severe depreciation against the dollar between April 2008 and
December 2008. A weaker depreciated rand will increase the BFP as more rands will be
needed to purchase the same amount of US dollars to acquire the oil. The depreciation
did not lead to an increase in the BFP over this period as the depreciation of the rand
was offset by a much larger crash in the oil price resulting in a decrease in the BFP. As a
result of the price decrease in fuel, the proportions for the other variables, including the
fuel levy and the RAF levy, increased for both petrol and diesel.
3.4 The general fuel levy and its changes over time
The tax on fuel used as a source of income for the South African government is the GFL.
This levy is an indirect specific tax on consumption levied on each litre of fuel
consumed. It is not earmarked. The Minister of Finance announces the change in the
GFL effective from April each year (SAPIA, 2014)
The fuel levy proportion dropped substantially between 1995 and 2015 from 36% and
38% to 20% and 23% for petrol and diesel respectively. While the fuel levy proportion
for fuel in 2015 is higher than previous years, it is still lower than the values quoted in
2002 and substantially lower than those in 1995.
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Real GFL for 0.05% Sulphur Diesel (c/l) Real GFL for 93 Octane Petrol (c/l)
Figure 3: Real general fuel levy for 93 octane petrol (c/l) and 0.05% sulphur
diesel (c/l)
Figure 1 and 3 confirm that the fuel levy has remained relatively constant over a long
period.
3.5 General fuel levy revenue
The low price elasticity of demand for fuel makes the taxation of fuel a suitable
mechanism for generating consistent and sustainable revenue for the government. A
moderate increase in the fuel price caused by a higher tax rate will not reduce
consumption of fuel significantly.
Given that GFL revenue is not earmarked, distribution of this revenue is subject to the
discretion of the Minister of Finance who publicly announces the proposed distribution
of revenue in the annual budget speech.
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Figure 4: Real general fuel levy revenue (R billion)
Figure 5: Percentage of total revenue attributed to GFL
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Ln real petrol pump price (c/l) Ln real diesel wholesale price (c/l)
Figure 4 shows that the real revenue generated by the GFL has been following an
upward trend since 1990. This is largely due to the fact that consumption of fuel in
South Africa has increased over the period 1990 to 2014 because the real GFL per litre
has remained fairly constant over this period as depicted by figure 3.
Figure 5 plots the GFL revenue as a percentage of the government’s total revenue. A
downward trend is evident. The percentage of total revenue attributed to the GFL was
7.44% and 6.01% in 1995 and 2002. This shows government has shifted its focus from
the GFL to other tax mechanisms given that there has been an upward trend in the GFL
revenue between 1990 and 2014. Government has clearly limited increases in the GFL.
4. THE PRICE OF FUEL OVER TIME
Figure 6: The logged real price of petrol and diesel over time
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Real petrol price Real diesel price Linear (Real petrol price)
Figure 7: Real petrol and diesel prices over time
Figure 6 shows the relatively constant growth rate of fuel prices over the period 1990 to
2014 while figure 7 shows the clear upward trend in real fuel prices. It is evident from
Figure 7 that increases in the real fuel price are persistent over long periods. This is
noticeable over the period between 2003 and halfway through 2008 and the period
between 2009 and 2014. Over these periods increases in the real fuel price were
substantial and fairly consistent. This poses a problem for policy makers. These long
term upward trends have negative effects on the consumer. Therefore, the taxation
mechanism on fuel needs to be flexible in order to give policy makers control. Policy
makers will be able to adjust the rate of taxation on fuel to protect the consumer during
these long-‐term increasing fuel prices. Given these persistent increases in fuel prices
over these long periods, a progressive taxation system is needed to protect low income
households. Comparing the use of VAT versus the GFL as an instrument of fuel taxation
has been widely debated.
5. COMPARISON OF VAT AND THE GFL AS A MEANS OF FUEL TAXATION
South Africa has a highly unequal income distribution amongst its households. The
World Bank estimated the Gini coefficient to be 65.0. Government’s policy makers are
fully aware of this unequal society in South Africa. That is why South Africa has a more
progressive taxation system (Inchauste, Lustig, Maboshe, Purfield, & Woolard, 2015).
South African policy makers have two main requirements when evaluating how to tax
fuel: progressivity of the taxation mechanism and regulatory control.
5.1 VAT on fuel
VAT may be considered to be regressive in nature but because of a wide range of zero-‐
rated items (which form a large part of a poorer household’s consumption) it is not
(Inchauste, Lustig, Maboshe, Purfield, & Woolard, 2015). Some of these items include
basic foodstuffs like brown bread, maize rice and milk. Charging VAT on these items
would significantly reduce the real wealth of these poorer households given that poorer
households tend on average to consume relatively more of their income than richer
households. VAT is also only progressive because many goods purchased by poorer
households are purchased in rural markets where it is hard to enforce VAT collection.
Akazili et al. (2011) referred to these goods as escaping the VAT ‘net’. Go et al., (2005)
highlighted the usefulness of VAT as it removes the arbitrary taxation of intermediate
inputs and taxes the final product, thus eliminating distortions in input choices. Go et al.,
(2005) did however report that VAT was mildly regressive despite its zero-‐rated items.
Thus, there is ambiguity amongst scholars regarding whether VAT is regressive or
progressive. VAT levied on fuel will be regressive.
If VAT is levied on fuel consumers will end up being arbitrarily taxed. Firms which use
fuel as an input in production will have to pay VAT. These firms would pass on some of
this extra cost to the consumer by increasing the price of its goods. As a result of the
increase in the price of goods, the VAT amount will also increase as VAT is an increasing
function of the pretax price of the product. Thus, consumers will pay the VAT on the
fuel, higher prices for goods and more VAT on these goods. Essentially, the consumers
are paying VAT more than once. If policy makers decided to institute VAT on fuel, it
would be wise to give firms VAT rebates if they use the fuel in the process of
manufacturing goods. This solution is viable but it is costly to administer and enforce.
There would be cases where firms report fuel which has been used for personal use
under company use. The complications in using VAT for fuel are clear.
Johnson et al. (2012) discusses and investigates motoring taxation in the United
Kingdom (UK). Considering only households which run at least one car, the motoring
taxation becomes regressive (Johnson, Leicester, & Stoye, 2012). This indication of
regressivity on fuel taxation in the United Kingdom is a warning sign for implementing a
similar taxation system in South Africa.
5.2 Effects of progressive and regressive taxation on fuel
Given the concern for poorer households in South Africa, policy makers will not
deliberately employ a regressive taxation policy on fuel. This is because fuel prices have
significant effects on the consumers – with an emphasis on poorer households. Policy
makers have to be very careful in setting a tax rate on fuel as changes in fuel prices have
other significant effects on the economy. Changes in the GFL also have substantial
knock-‐on effects on the fuel price as the GFL makes up the second largest relative of the
fuel price after the BFP share.
An increase in the fuel levy will increase the pump price of fuel. It will also have other
indirect effects which increases the prices of other consumer goods because of the
increase in the fuel input for firms (Mabugu, Chitiga, & Amusa, 2009). Mabugu et al.
(2009) investigated a fuel levy reform in South Africa. The investigation showed that
petroleum expenditure is concentrated at the top end of the household income
distribution – amongst the rich households. This would indicate that large fuel taxes on
fuel would be unambiguously progressive in nature but as indicated above it does not
consider the indirect effects of fuel price increases. If the indirect petroleum
consumption is included then the distribution of total (direct and indirect) expenditure
amongst households is far more even (Mabugu, Chitiga, & Amusa, 2009). This indicates
that a tax on fuel won’t be as progressive as expected when taking the indirect effects of
an increase on poorer households into account. Mabugu et al. (2009) also show the
effects of a 10% increase in the fuel levy enforced in all nine provinces simultaneously –
illustrated by Figure 8.
Figure 8: The effects of a 10% increase in the general fuel levy in South Africa
Percentage Change
Gross domestic product -‐0.31
Total revenue -‐0.06
Fuel levy revenue 37.73
Imports -‐0.11
(Mabugu, Chitiga, & Amusa, 2009)
Figure 8 effectively shows the negative indirect effects of a GFL increase of this kind.
GDP drops as a result of a leftward shift in aggregate demand caused by the tax increase.
Although fuel levy revenue increased substantially, total revenue declined marginally.
This is due to a reduction in economic activity which caused other revenue streams to
decline. VAT revenue would have decreased because of lower consumption induced by
the lower output. Figure 8 further emphasizes the caution required when setting the tax
rate for fuel in South Africa. (Mabugu, Chitiga, & Amusa, 2009)
As stated earlier, the need for a flexible taxation mechanism on fuel is required. That is
why the GFL is used and not VAT. The VAT rate has not changed from 14% since 1993.
If VAT was used to tax fuel, it would not give policy makers much control or flexibility in
reacting to oil and exchange rate shocks. Thus, if there were a surge in the petrol price,
this surge would be magnified by the 14% associated with VAT. This would be a double
blow for consumers. Policy makers would not simply reduce the VAT rate to offset the
increase in the fuel price because this would have significant knock on effects for
revenue streams attributed to VAT on consumption goods.
Using the GFL affords policy makers more control. If there is a surge in the petrol price
the Minister of Finance can protect consumers by offsetting this price increase by
reducing the GFL the following April. The same reasoning applies to a situation where
the fuel price decreases substantially. This situation presents an opportunity to the
Minister of Finance to increase the GFL to offset the loss of revenue during periods
described in the first situation where the GFL was reduced to protect consumers.
5.3 The progressivity of the GFL
The progressivity of a GFL has been widely debated. Akazili et al. (2011) investigates
the mechanisms for financing health care in Ghana. These authors computed a Kakwani
index value of -‐0.041 for the fuel levy.1 This reveals the regressive nature of the fuel levy
in Ghana. It must be noted that the fuel levy in Ghana is composed of the levies on
petrol, diesel, engine oil and kerosene. The inclusion of taxation on kerosene makes this
fuel levy regressive because kerosene is primarily consumed by poorer households
(Akazili, Gyapong, & McIntyre, 2011).
Inchauste et al. (2011) investigated the distributional impact of fiscal policy in South
Africa and this paper obtained a Kakwani index value of 0.025 for the South African GFL.
This paper declares that both VAT and the GFL are progressive (Inchauste, Lustig,
Maboshe, Purfield, & Woolard, 2015). This progressive nature of the GFL shown in this
paper provides reason to use the GFL as the fuel tax instrument.
There are doubts regarding the progressivity of VAT and the limited control it gives
policy makers in South Africa. Therefore the GFL is a more suitable tax instrument given
the research regarding its progressivity.
There is room for further research concerning a more appropriate means of taxing fuel
other than the current GFL or VAT. One option may be to change the GFL from annual to
monthly adjustment. This would give policy makers even more control. However, it
would create serious implications for the predictability of revenue associated with the
tax.
1 The kakwani index in the current setting is a measure of the progressivity of a particular tax (Inchauste et al., 2015). The index is equal to the difference between the concentration index of a tax and the gini coefficient for incomes (Inchauste et al., 2015). The theoretical range of the index is between -‐1 and 1. The higher the index value the more progress the tax is.
6. South African fuel prices – Empirical analysis and regression results
This section estimates the sensitivity of the 93 octane coastal petrol pump price and the
0.05% sulphur coastal wholesale diesel price in relation to certain components. The
components expected to affect these fuel prices most significantly are the oil price and
the rand dollar exchange rate. This has been evident throughout the paper so far. All the
regression models have been estimated using OLS and will be in real terms. The
variables have all been logged transformed which allows for an elasticity interpretation
of the coefficients. The independent variables are all lagged by either 1,2 or 3 periods
(months).
6.1 The basic finite distributed lag model
ln Pt = B0 + B1 ln OilPrice t-‐1 + B2 ln OilPrice t-‐2 + B3 ln OilPrice t-‐3 + B4 ln ExRate t-‐1 +
B5 ln ExRatet-‐2 + B6 ln ExRatet-‐3 + B7 ln PetrolGFL t-‐1 + B8 ln PetrolGFL t-‐2 + B9 ln PetrolGFLt-‐3
+ ut (1)
ln Dt = B0 + B1 ln OilPrice t-‐1 + B2 ln OilPrice t-‐2 + B3 ln OilPrice t-‐3 + B4 ln ExRate t-‐1 +
B5 ln ExRatet-‐2 + B6 ln ExRatet-‐3 + B7 ln DieselGFL t-‐1 + B8 ln DieselGFL t-‐2 + B9 ln DieselGFLt-‐3
+ ut (2)
lnPt represents the logged current petrol price and lnDt the logged current diesel price.
Regression models (1) and (2) contain the exhaustive list of the independent variables
for the model. Regressions have been run, using these two models above, where either
one, two or three of the possible independent variables are included. The exhaustive list
of independent variables is: Logged oil price in dollars (lnOilPrice), logged rand dollar
exchange rate (lnExRate), logged general fuel levy on petrol in cents per litre
(lnPetrolGFL) and the logged general fuel levy on diesel in cents per litre (lnDieselGFL).
Dependent Variable
Regression no.
Independent variablesB1, Coefficient on
OilPrice t-‐1B2, Coefficient on
ExRate t-‐1B3, Coefficient on PetrolGFL t-‐1
R2 Adj R2 NDurbin
Watson d-‐statistic
1 lnOilPrice t-‐1 0.55 0.55 0.55 299 0.04
2 lnExRate t-‐1 0.54 0.53 0.53 299 0.04
3 lnPetrolGFL t-‐1 0.66 0.03 0.03 299 0.02
4 lnOilPrice t-‐1 & lnExRate t-‐1 0.49 0.47 0.96 0.96 299 0.33
5 lnOilPrice t-‐1 & lnExRate t-‐1 & lnPetrolGFL t-‐1 0.5 0.45 0.42 0.97 0.97 299 0.5
6 lnOilPrice t-‐1 0.75 0.82 0.81 247 0.12
7 lnExRate t-‐1 0.84 0.49 0.49 247 0.03
8 lnDieselGFL t-‐1 1.47 0.15 0.15 247 0.03
9 lnOilPrice t-‐1 & lnExRate t-‐1 0.62 0.51 0.97 0.97 247 0.45
10 lnOilPrice t-‐1 & lnExRate t-‐1 & lnDieselGFL t-‐1 0.61 0.5 0.2 0.97 0.97 247 0.49
Notes: All coefficients are statistically significant at the 1% significance level.
Diesel
Petrol
Figure 9: Regression results from the basic finite distributed lag model
One of the general observations in this paper has been how significantly the oil price
and the rand dollar exchange rate affect the domestic fuel price. This is confirmed in
figure 9. Figure 9 gives certain values associated with different regressions in the form
of models (1) and (2). Regressions 1,2,6 and 7 show how strong the effects of the oil
price and exchange rate in the previous month are on the current fuel price exhibited in
the high R2. The the oil price lag effect on the price of diesel is high (regression 6) -‐ R2 is
equal to 0.82. The low R2 values from regressions 3 and 8 suggest that using the lagged
GFL value is not a good predictor of the current fuel price. The final regressions (5&10)
have extremely high R2 values of 0.97 for both regressions. The coefficients on the
independent variables are interpreted as an elasticity. For example, looking at
regression 1, the coefficient on lnOilPricet-‐1 is 0.55 which means a 1% increase in the
real oil price in the previous month will result in a 0.55% increase in the current real
price of petrol.
These regressions have been shown for the purposes of supporting the earlier claims of
this paper – the importance of oil prices and the exchange rate.
6.2 Evaluating the basic model
These regressions are not useful as a final model because of the presence of auto
correlation in the residuals which violates one of the Gauss Markov assumptions for
time series (Woolridge, 2014). The Durbin Watson test is traditionally used to test for
autocorrelation of this kind. The very low Durbin-‐Watson test statistics (figure 9) are
signs of autocorrelation in the residuals. Using a table of Durbin-‐Watson critical values
it is evident that all of these regressions exhibit serial auto correlation in the errors at
the 1% significance level. With Corr (ut , us | X) ≠ 0 , t ≠s OLS estimation will still be
unbiased and consistent but no longer efficient (Woolridge, 2014). Thus, it will no
longer produce the best linear unbiased estimators (Woolridge, 2014).
The time series for petrol prices and diesel prices are highly persistent and non-‐
stationary. 2Thus these time series violate weak dependence and therefore it is hard to
justify the use of lagged independent variables as opposed to only contemporaneous
ones (Woolridge, 2014). In this model, transitory shocks will permit far into the future.
The weak dependence assumption is important as it justifies the use of OLS. It also
implies that the law of large numbers and the central limit theorem hold (Woolridge,
2014). Thus, there is need for a better model to predict fuel prices.
By taking the first differences of all the variables it is expected that the resulting model
will be stationary and weakly dependent. This first differenced transformation causes
one monthly observation be to be lost in the beginning of the sample for every variable.
The benefits of first differencing in this case are that the process becomes stationary
and weakly dependent, approximate growth rate interpretations can be made from the
regression and any linear trend will be removed (Woolridge, 2014). It is also expected
that the differencing will solve the problem of the auto correlation in the residuals
exhibited in the basic model.
2 Corr(Pt , Pt-‐1) =0.99 Corr(Dt , Dt-‐1) =0.99
-‐0,40
-‐0,30
-‐0,20
-‐0,10
0,00
0,10
0,20
0,30
0,40
0,50
Feb-‐90
Nov-‐90
Aug-‐91
May-‐92
Feb-‐93
Nov-‐93
Aug-‐94
May-‐95
Feb-‐96
Nov-‐96
Aug-‐97
May-‐98
Feb-‐99
Nov-‐99
Aug-‐00
May-‐01
Feb-‐02
Nov-‐02
Aug-‐03
May-‐04
Feb-‐05
Nov-‐05
Aug-‐06
May-‐07
Feb-‐08
Nov-‐08
Aug-‐09
May-‐10
Feb-‐11
Nov-‐11
Aug-‐12
May-‐13
Feb-‐14
Nov-‐14
-‐0,15
-‐0,10
-‐0,05
0,00
0,05
0,10
0,15
0,20
0,25
Feb-‐90
Nov-‐90
Aug-‐91
May-‐92
Feb-‐93
Nov-‐93
Aug-‐94
May-‐95
Feb-‐96
Nov-‐96
Aug-‐97
May-‐98
Feb-‐99
Nov-‐99
Aug-‐00
May-‐01
Feb-‐02
Nov-‐02
Aug-‐03
May-‐04
Feb-‐05
Nov-‐05
Aug-‐06
May-‐07
Feb-‐08
Nov-‐08
Aug-‐09
May-‐10
Feb-‐11
Nov-‐11
Aug-‐12
May-‐13
Feb-‐14
Nov-‐14
Figure 10: First difference of log real price of Brent crude oil (US dollars)
Figure 11: First difference of logged rand dollar exchange rate
-‐0,25
-‐0,20
-‐0,15
-‐0,10
-‐0,05
0,00
0,05
0,10
0,15
0,20
0,25
0,30
Feb-‐90
Nov-‐90
Aug-‐91
May-‐92
Feb-‐93
Nov-‐93
Aug-‐94
May-‐95
Feb-‐96
Nov-‐96
Aug-‐97
May-‐98
Feb-‐99
Nov-‐99
Aug-‐00
May-‐01
Feb-‐02
Nov-‐02
Aug-‐03
May-‐04
Feb-‐05
Nov-‐05
Aug-‐06
May-‐07
Feb-‐08
Nov-‐08
Aug-‐09
May-‐10
Feb-‐11
Nov-‐11
Aug-‐12
May-‐13
Feb-‐14
Nov-‐14
Figure 12: First difference of logged petrol price
First differencing of the variables has, as expected, created stationary processes. This is
illustrated by figures 10, 11 and 12. The first differenced variables have an approximate
constant mean and variance. There is no evidence of seasonality or any sort of cyclical
trend in the first differenced variables.
6.3 The complete first differenced model
Δ lnPt = B0 + B1 Δ lnOilPrice t-‐1 + B2 Δ lnOilPrice t-‐2 + B3 Δ lnOilPrice t-‐3 + B4 Δ lnExRate t-‐1 +
B5 Δ lnExRatet-‐2 + B6 Δ lnExRatet-‐3 + B7 Δ lnPetrolGFL t-‐1 + B8 Δ lnPetrolGFL t-‐2 +
B9 Δ lnPetrolGFLt-‐3 + ut
(3)
Δ lnDt = B0 + B1 Δ lnOilPrice t-‐1 + B2 Δ lnOilPrice t-‐2 + B3 Δ lnOilPrice t-‐3 + B4 Δ lnExRate t-‐1 +
B5 Δ lnExRatet-‐2 + B6 Δ lnExRatet-‐3 + B7 Δ lnDieselGFL t-‐1 + B8 Δ lnDieselGFL t-‐2 +
B9 Δ lnDieselGFLt-‐3 + ut (4)
Independent variables
Coefficient Std. Error T-‐stat P-‐value
Δ lnOilPrice t-‐1 0.26 0.02 11.59 0.00Δ lnOilPrice t-‐2 0.18 0.02 7.77 0.00Δ lnOilPrice t-‐3 -‐0.06 0.02 -‐2.88 0.00Δ lnExRate t-‐1 0.29 0.06 5.05 0.00Δ lnExRate t-‐2 0.07 0.06 1.17 0.24Δ lnExRate t-‐3 -‐0.10 0.06 -‐1.69 0.09Δ lnPetrolGFL t-‐1 -‐0.06 0.07 -‐0.93 0.36Δ lnPetrolGFL t-‐2 -‐0.07 0.07 -‐1.07 0.29Δ lnPetrolGFL t-‐3 -‐0.13 0.07 -‐2.00 0.05
Intercept 0.00 0.00 0.71 0.05
R2 0.48Adj R2 0.46N 296DW stat (10, 296) 1.86
Dependent Variable: Δ lnPt
By running a regression using this complete model it can be determined which variables
are statistically and economically significant.
Figure 13: Complete first differenced model for petrol
Figure 13 represents regression model (3). Variables ΔlnExRate t-‐2 , ΔlnExRate t-‐3 ,
ΔlnPetrolGFL t-‐1, ΔlnPetrolGFL t-‐2 and ΔlnPetrolGFL t-‐3 should be excluded from the
regression because they are not statistically significant at the 5% significance level.
ΔlnExRatet-‐3 is also not economically feasible because of its negative coefficient. A
depreciation in the rand (a positive ΔlnExRate t-‐3) ceteris paribus is expected to increase
the petrol price – not decrease it as suggested by a negative coefficient. ΔlnOilPrice t-‐3
may be statistically significant but it is not economically feasible. A negative coefficient
on ΔlnOilPrice t-‐3 does not make sense as an increase in the oil price is expected to ceteris
paribus increase the petrol price. Thus, all of these variables including ΔlnOilPrice t-‐3
should be excluded with confidence. Figure 15 presents the reduced regression model
for the petrol price.
Figure 14: Complete first differenced model for diesel
Figure 14 represents regression model (4). It is easy to see that ΔlnDieselGFL t-‐1 ,
ΔlnDieselGFL t-‐2 and ΔlnDieselGFL t-‐3 are far from statistically significant – as shown by
the high p-‐values. ΔlnExRate t-‐3 may statistically significant at the 5% significance level
but it is not economically feasible because of its negative coefficient. Thus, ΔlnExRate t-‐3
should also be excluded from the regression. Figure 16 presents the reduced regression
model for the diesel price.
From the regressions displayed in figures 13 and 14 the lack of significance of the
general fuel levy effect on fuel prices is evident. This may be attributed to fact that the
GFL only changes annually. It is also clear that no independent variables lagged by three
months are significant apart from ΔlnOilPrice t-‐3 with respect to Δln Dt.3 This means that
the long term effect of a transitory shock drops off after the second lag. Independent
variables lagged by more than three periods are not expected to have any significant
effect on the dependent variables. 3 From the regression displayed in figure 14.
Independent variables
Coefficient Std. Error T-‐stat P-‐value
Δ lnOilPrice t-‐1 0.29 0.02 12.15 0.00Δ lnOilPrice t-‐2 0.20 0.02 8.01 0.00Δ lnOilPrice t-‐3 0.06 0.02 2.49 0.01Δ lnExRate t-‐1 0.40 0.06 6.98 0.00Δ lnExRate t-‐2 0.25 0.06 4.16 0.00Δ lnExRate t-‐3 -‐0.12 0.06 -‐2.17 0.03Δ lnDieselGFL t-‐1 -‐0.01 0.08 -‐0.13 0.89Δ lnDieselGFL t-‐2 -‐0.05 0.08 -‐0.58 0.56Δ lnDieselGFL t-‐3 -‐0.04 0.08 -‐0.47 0.64Intercept 0.00 0.00 -‐0.04 0.97
R2 0.56Adj R2 0.54N 246DW stat (10, 246) 1.78
Dependent Variable: Δ lnDt
Figure 15: Reduced first differenced model for petrol
Δ lnPt = B0 + B1 Δ lnOilPrice t-‐1 + B2 Δ lnOilPrice t-‐2 + B3 Δ lnExRate t-‐1 + ut
(5)
Diagnostics:
Corr (Δ Pt , Δ Pt-‐1 ) = 0.24
The results obtained from highly persistent time series (which are not weakly
dependent) can be misleading if any of the classical linear model assumptions are
violated (Woolridge, 2014). As mentioned above, if a process does not exhibit weak
dependence, it is hard to justify the use of OLS estimation. The first differenced
regression for petrol, like expected, is not highly persistent in the dependent variable Δ
Pt. The violation of weakly dependence is no longer a concern.
DW = 1.84 > dU = 1.75
We fail to reject the null hypothesis of no serial correlation in errors at the 1%
significance level.4 First differencing has resolved the problem of serial correlation in
the errors, which was exhibited in the basic finite distributed lag model.
4 H0: Corr(ut , us | X) = 0 , t ≠s alternatively H0: ρ=0
Independent variables Coefficient Std. Error T-‐stat P-‐value
Δ lnOilPrice t-‐1 0.25 0.02 11.40 0.00Δ lnOilPrice t-‐2 0.16 0.02 7.24 0.00Δ lnExRate t-‐1 0.33 0.06 5.90 0.00Intercept 0.00 0.00 0.71 0.60
R2 0.45Adj R2 0.45N 297DW stat (4, 297) 1.84
Dependent Variable: Δ lnPt
A concern regarding this regression is the heteroskedasticity in the errors – a violation
of one of the Gauss-‐Markov assumptions.5 Testing for heteroskedasticity is possible
using the Breusch-‐Pagan test. A chi-‐squared test statistic of 4.38 with a p-‐value of 0.04
is obtained. Thus, the null hypothesis of constant variance of the residuals is rejected at
the 5% significance level. The presence of heteroskedasticity causes OLS estimators to
be inefficient but not biased and inconsistent. Robust standard errors can be computed
to account for the presence of heterosckedasticity (Woolridge, 2014). Figure 16 shows
these new robust standard errors and t-‐distribution statistics.
It is not likely that endogeneity will be a serious problem. As shown in the basic model,
the oil price and the rand dollar exchange rate are very good predictors of the fuel price
exhibited by the high R-‐squared. In the basic model the error accounted for
approximately 4% of the variation in the petrol price and 3% for the diesel price. Given
that these two variables are good predictors of the fuel price, any correlation with these
variables and the error will not seriously affect the results of the regression. There is no
concern for violations of the other Gauss-‐Markov assumptions.
Figure 16: Reduced first differenced model for petrol with robust standard errors
5 Var(ut | X) = Var (ut) = σ2
Independent variables Coefficient
Robust Std. Errors
T-‐stat P-‐value
Δ lnOilPrice t-‐1 0.25 0.04 7.25 0.00Δ lnOilPrice t-‐2 0.16 0.04 4.06 0.00Δ lnExRate t-‐1 0.33 0.05 6.55 0.00Intercept 0.00 0.00 0.46 0.65
R2 0.45Adj R2 -‐N 297DW stat (4, 297) 1.84
Dependent Variable: Δ lnPt
The robust standard errors have not changed effects of the independent variables on
the dependent variable.
6.4 Interpretation of the reduced first differenced model for petrol
The coefficients in the first differenced regression have an elasticity interpretation. The
coefficient on Δ lnOilPrice t-‐1 is 0.25 and is interpreted as follows: a 10% increase in the
the real price of oil in the current month will result in a 2.5% increase in the real price
of petrol in the next month. Thus, a relatively inelastic relationship between the oil price
and the petrol price is evident. The long-‐run propensity effect of oil price in this model
is equal to 0.41. The coefficient for Δ lnOilPrice t-‐2 is 0.16 which is smaller than the
coefficient for Δ lnOilPrice t-‐1 which is 0.25. This shows how oil prices further into the
past have less of an effect on fuel current prices. This accords with general logic. No
investor or policy maker will assign too much weight to oil prices three or four months
ago. The price will have changed since then and current data is readily available. The
exchange rate has a greater effect on the fuel price than the oil price, exhibited by the
higher coefficient of 0.33.
Figure 17: Reduced first differenced model for diesel
Δ lnDt = B0 + B1 Δ lnOilPrice t-‐1 + B2 Δ lnOilPrice t-‐2 + B3 Δ lnOilPrice t-‐3 + B4 Δ lnExRate t-‐1 +
B5 Δ lnExRatet-‐2 + + ut (6)
Independent variables
Coefficient Std. Error T-‐stat P-‐value
Δ lnOilPrice t-‐1 0.29 0.02 12.22 0.00Δ lnOilPrice t-‐2 0.20 0.02 8.34 0.00Δ lnOilPrice t-‐3 0.07 0.02 2.92 0.00Δ lnExRate t-‐1 0.41 0.06 7.39 0.00Δ lnExRate t-‐2 0.22 0.06 3.87 0.00Intercept 0.00 0.00 -‐0.35 0.73
R2 0.55Adj R2 0.54N 246DW stat (6, 246) 1.76
Dependent Variable: Δ lnDt
Diagnostics:
Corr (Δ Dt , Δ Dt-‐1) =0.32
Violation of weak dependence is no longer a concern.
DW = 1.76 > dU = 1.75
We fail to reject the null hypothesis of no serial correlation in errors at the 1%
significance level. Serial correlation in the errors is no longer a concern.
Breusch-‐Pagan test: A Chi-‐squared test statistic of 3.05 with a p-‐value of 0.08 is
obtained. We fail to reject the null hypothesis of constant variance at the 5%
significance level. Heteroskedasticity of the errors is not a concern.
Endogeneity is not a concern as per the reasoning for the first differenced petrol model.
6.5 Interpretation of the reduced first differenced model for diesel
Regression model (6) has two extra explanatory variables (ΔlnOilPrice t-‐3 and
Δ lnExRatet-‐2) compared to (5). The long-‐run propensity effect for oil prices is higher at
0.56 and 0.63 for the exchange rate. Therefore, changes in both these variables persist
further into the future compared to (5). Δ lnExRatet-‐1 has the largest coefficient with a
value of 0.41 which is also higher than the coefficient for that variable in (5). This shows
a more elastic relationship between the exchange rate and diesel prices compared to the
exchange rate and petrol prices.
6.6 Implications on policy
(5) and (6) are the final regression models that have been of particular interest for this
paper. The log-‐levels basic models (1) and (2) delivered valuable insights regarding the
significant effects of lagged oil prices and lagged rand dollar exchange rates on fuel
prices. Models (1) and (2) were flawed given the serial correlation in the errors across
time. (5) and (6) accounted for the serial correlation, however, a significant amount of
R-‐squared was sacrificed to account for this. (5) and (6) should be used in conjuction
with (1) and (2) to determine the effects of these independent variables on the fuel
price. Predicting future diesel price changes is easier than for petrol. This is because of
the higher R-‐squared (0.55 compared to 0.45) and the inclusion of the Δ lnOilPrice t-‐3
variable. This enables policy makers to look further into the future when estimating
future diesel prices compared to the model for petrol.
These models are useful in giving policy makers insight into future fuel prices. It also
gives them insight into future revenue collections through the GFL. As mentioned
earlier in the paper, the GFL is anually adjusted to shield the consumer from fuel price
increases or to meet revenue targets. Therefore, these models help predict the way in
which policy makers will adjust the GFL in the future to achieve these goals.
7. Conclusion
This paper used data from January 1990 to December 2014 to examine the components
of the fuel price, the different possible taxation mechanisms imposed on fuel and the
variables which affect its price significantly. An analysis of the decomposition of the fuel
price was undertaken to clarify the components and their weighting in determining the
ultimate pump price. Specifically, the changes of the GFL over time were considered. It
is evident from the real values of the GFL that government has purposely limited
increases in the GFL over the last two decades (Blecher, 2015). If the GFL had increased
in line with VAT it would be 411 cents per litre in 2014/15 as opposed to 224.5 cents
per litre (Blecher, 2015). Government has been moving away from the GFL as an
overriding source of revenue and is increasingly drawing from other revenue streams.
This is shown in the decreasing trend in the percentage of total revenue attributed to
the GFL.
Given the upward trend in fuel prices, policy makers need a progressive taxation
mechanism that affords them more control. Control is necessary so that policy makers
can adjust taxation policy, given changing fuel prices, in order to meet revenue targets
or to shield the consumer from fuel price hikes. Progressivity of the tax is required
given the high level of poverty in South Africa. A fine balance has to be achieved
between generation of revenue and support of financially pressuarised consumers in
the interests of South Africa’s long term growth prospects and economic stability. Policy
makers in South Africa would not wish to institute a taxation policy that is
unambiguously regressive. This paper discusses how VAT on fuel would result in
consumers being arbitrarily taxed. Control is also limited with respect to VAT as the
VAT rate changes infrequently. The last time it changed was in 1993. The GFL was
shown to be flexible and progressive and is therefore a better means of fuel taxation as
opposed to VAT.
A model was needed to provide useful forecasts on future fuel prices so that policy
makers could more accurately assess the future revenue to be collected through the
GFL. The models in this paper show that lagged oil price and lagged rand dollar
exchange rate variables are significant in explaining variations in fuel prices. It was
clear that the GFL values do not significantly predict fuel prices. The first differenced
models used in conjunction with the basic model in levels can provide useful insights
into fuel price variation. These models are important as the prediction of fuel prices
gives policy makers information needed to plan for and adjust future taxation policy.
There is room for further research in investigating a more appropriate means of taxing
fuel. Perhaps one which is regulated more frequently than the GFL. An investigation into
the effects of other independent variables on the fuel price in South Africa would be
useful. The models in this paper present the most important variables.
Ultimately, this paper provides useful models and insights that enable policy makers to
estimate more predictable revenues from fuel, given that the GFL is the chosen
instrument of taxation.
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