Honours Thesis 2015 - An Analysis of Fuel Prices and Fuel Taxation in South Africa
1.
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.
2. 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
3. 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.
4. 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).
5.
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).
6. 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).
7. 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).
8. 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 43.58 486.03
February
2002 334.97 44.52 30.22 179.49 11.36 9.34 54.95 664.84
April
2008 740.45 50.54 59.85 163.45 11.84 9.01 76.83 1111.97
December
2008 441.84 55.08 57.34 156.60 62.52 11.71 82.98 868.06
August
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 22.35 393.02
February
2002 385.26 44.51 30.22 148.35 7.78 9.34 625.46
April
2008 915.87 50.53 59.85 142.86 11.84 9.01 1189.95
December
2008 672.79 55.07 57.34 136.87 62.40 11.71 996.18
August
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
9. 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 9 100
February
2002 50 7 5 27 2 1 8 100
April
2008 67 5 5 15 1 1 7 100
December
2008 51 6 7 18 7 1 10 100
August
2015 50 3 12 20 1 2 12 100
April
1995 40 10 4 38 8 100
February
2002 62 7 5 24 1 1 100
April
2008 77 4 5 12 1 1 100
December
2008 68 6 6 14 6 1 100
August
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%).
10. 0,00
20,00
40,00
60,00
80,00
100,00
120,00
140,00
160,00
180,00
Jan-‐90
Sep-‐90
May-‐91
Jan-‐92
Sep-‐92
May-‐93
Jan-‐94
Sep-‐94
May-‐95
Jan-‐96
Sep-‐96
May-‐97
Jan-‐98
Sep-‐98
May-‐99
Jan-‐00
Sep-‐00
May-‐01
Jan-‐02
Sep-‐02
May-‐03
Jan-‐04
Sep-‐04
May-‐05
Jan-‐06
Sep-‐06
May-‐07
Jan-‐08
Sep-‐08
May-‐09
Jan-‐10
Sep-‐10
May-‐11
Jan-‐12
Sep-‐12
May-‐13
Jan-‐14
Sep-‐14
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
11. 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.
12. 0,00
50,00
100,00
150,00
200,00
250,00
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
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.
14. 0,00
1,00
2,00
3,00
4,00
5,00
6,00
7,00
8,00
Jan-‐90
Oct-‐90
Jul-‐91
Apr-‐92
Jan-‐93
Oct-‐93
Jul-‐94
Apr-‐95
Jan-‐96
Oct-‐96
Jul-‐97
Apr-‐98
Jan-‐99
Oct-‐99
Jul-‐00
Apr-‐01
Jan-‐02
Oct-‐02
Jul-‐03
Apr-‐04
Jan-‐05
Oct-‐05
Jul-‐06
Apr-‐07
Jan-‐08
Oct-‐08
Jul-‐09
Apr-‐10
Jan-‐11
Oct-‐11
Jul-‐12
Apr-‐13
Jan-‐14
Oct-‐14
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
15. 0,00
200,00
400,00
600,00
800,00
1000,00
1200,00
1400,00
1600,00
Jan-‐90
Nov-‐90
Sep-‐91
Jul-‐92
May-‐93
Mar-‐94
Jan-‐95
Nov-‐95
Sep-‐96
Jul-‐97
May-‐98
Mar-‐99
Jan-‐00
Nov-‐00
Sep-‐01
Jul-‐02
May-‐03
Mar-‐04
Jan-‐05
Nov-‐05
Sep-‐06
Jul-‐07
May-‐08
Mar-‐09
Jan-‐10
Nov-‐10
Sep-‐11
Jul-‐12
May-‐13
Mar-‐14
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.
16. 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
17. 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
18. 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
19. 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.
20. 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).
21. Dependent
Variable
Regression
no.
Independent
variables
B1,
Coefficient
on
OilPrice
t-‐1
B2,
Coefficient
on
ExRate
t-‐1
B3,
Coefficient
on
PetrolGFL
t-‐1
R2
Adj
R2 N
Durbin
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.
22. 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
24. -‐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)
25. Independent
variables
Coefficient Std.
Error T-‐stat P-‐value
Δ
lnOilPrice
t-‐1
0.26 0.02 11.59 0.00
Δ
lnOilPrice
t-‐2 0.18 0.02 7.77 0.00
Δ
lnOilPrice
t-‐3 -‐0.06 0.02 -‐2.88 0.00
Δ
lnExRate
t-‐1
0.29 0.06 5.05 0.00
Δ
lnExRate
t-‐2 0.07 0.06 1.17 0.24
Δ
lnExRate
t-‐3
-‐0.10 0.06 -‐1.69 0.09
Δ
lnPetrolGFL
t-‐1
-‐0.06 0.07 -‐0.93 0.36
Δ
lnPetrolGFL
t-‐2
-‐0.07 0.07 -‐1.07 0.29
Δ
lnPetrolGFL
t-‐3
-‐0.13 0.07 -‐2.00 0.05
Intercept 0.00 0.00 0.71 0.05
R2
0.48
Adj
R2
0.46
N 296
DW
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.
26. 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 0.00
Δ
lnOilPrice
t-‐2 0.20 0.02 8.01 0.00
Δ
lnOilPrice
t-‐3 0.06 0.02 2.49 0.01
Δ
lnExRate
t-‐1
0.40 0.06 6.98 0.00
Δ
lnExRate
t-‐2 0.25 0.06 4.16 0.00
Δ
lnExRate
t-‐3
-‐0.12 0.06 -‐2.17 0.03
Δ
lnDieselGFL
t-‐1
-‐0.01 0.08 -‐0.13 0.89
Δ
lnDieselGFL
t-‐2
-‐0.05 0.08 -‐0.58 0.56
Δ
lnDieselGFL
t-‐3
-‐0.04 0.08 -‐0.47 0.64
Intercept 0.00 0.00 -‐0.04 0.97
R2
0.56
Adj
R2
0.54
N 246
DW
stat
(10,
246)
1.78
Dependent
Variable:
Δ
lnDt
27. 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 0.00
Δ
lnOilPrice
t-‐2 0.16 0.02 7.24 0.00
Δ
lnExRate
t-‐1
0.33 0.06 5.90 0.00
Intercept 0.00 0.00 0.71 0.60
R2
0.45
Adj
R2
0.45
N 297
DW
stat
(4,
297)
1.84
Dependent
Variable:
Δ
lnPt
28. 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 0.00
Δ
lnOilPrice
t-‐2 0.16 0.04 4.06 0.00
Δ
lnExRate
t-‐1
0.33 0.05 6.55 0.00
Intercept 0.00 0.00 0.46 0.65
R2
0.45
Adj
R2
-‐
N 297
DW
stat
(4,
297)
1.84
Dependent
Variable:
Δ
lnPt
29. 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 0.00
Δ
lnOilPrice
t-‐2 0.20 0.02 8.34 0.00
Δ
lnOilPrice
t-‐3 0.07 0.02 2.92 0.00
Δ
lnExRate
t-‐1
0.41 0.06 7.39 0.00
Δ
lnExRate
t-‐2 0.22 0.06 3.87 0.00
Intercept 0.00 0.00 -‐0.35 0.73
R2
0.55
Adj
R2
0.54
N 246
DW
stat
(6,
246)
1.76
Dependent
Variable:
Δ
lnDt
30. 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
31. 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
32. 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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