Honours Thesis 2015 - An Analysis of Fuel Prices and Fuel Taxation in South Africa

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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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

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0,50

Feb-‐90

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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

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Feb-‐90

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Feb-‐11

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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.



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


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


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)



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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Honours Thesis 2015 - An Analysis of Fuel Prices and Fuel Taxation in South Africa

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