112 Hamilton (2009) reports the same relationship for the USA.
0 1
0 200 400 600 800 1 000
1970 1973 1976 1979 1982 1985 1988 1991 1994 1997 2000 2003 2006 2009
2010 R/barrel
Oil Prices and Recessions in SA
Recession Oil price
0 1
0 1 2 3 4 5 6 7 8
1965 1968 1971 1974 1977 1980 1983 1986 1989 1992 1995 1998 2001 2004 2007 2010
% of GDP
Oil Expenditure and Recessions
Recession Oil expenditure/GDP
171 Historical shortages of fuel
As a result of the Arab Oil Embargo in 1973/4, shortages of fuel were experienced in South Africa, which led to long queues at filling stations and conservation responses by the government such as reduced road speed limits. In recent years, South Africa has experienced limited instances of physical fuel supply shortages, including the following episode:
“During December 2005, ahead of the introduction of cleaner fuels, South Africa experienced a series of interruptions to fuel supplies. There were stock-outs at many locations throughout the country, including shortages of jet fuel at Cape Town International Airport (CTIA). These supply interruptions negatively impacted many sectors of the economy with the severity of the hardship ranging from relative consumer inconvenience to loss of business and reputation damage.” (DME, 2007a: 17)
More recently, in July 2011 a strike in the chemical sector sparked panic buying by consumers and resulted in over 400 filling stations running dry in the country (Reuters, 2011). While not arising from shortages of imported crude oil, this episode once again demonstrated the serious impact of fuel shortages on economic activity and personal mobility.
4.1.3 Review of empirical models
A number of studies have investigated the impact of crude oil and/or refined petroleum fuel price increases on the South African economy, using different empirical methods. These are critically reviewed in the following paragraphs with a view to understanding the likely effects of declining world oil production and associated oil price shocks on South Africa.
One class of studies has employed time series econometric models. Based on the 1979/80 oil shock experience, Kantor and Barr (1986) estimated that a 10 percent increase in the price of petrol resulted in a 0.7 percentage point increase in consumer inflation (net of food prices) after seven months, although the simulated rate of inflation subsequently declined to below its starting rate.
Swanepoel (2006) used a vector autoregression (VAR) model to analyse the impact of three external shocks, including oil prices, on South African rates of import, producer and consumer price inflation.
Swanepoel (2006: 9-12) paradoxically found a negative response of non-oil import prices to an oil price shock. However, such a shock was found to have a (barely significant) positive effect on producer prices (but insignificant and negative after one or more lags), while the effect on consumer prices was statistically insignificant no matter what the time lag. These types of models are of limited usefulness for analysing the impact of global oil depletion, as they include too few variables and assume constant coefficients over time.
The Department of Transport (DoT, 2008) commissioned a study on the macroeconomic impact of rising fuel costs. The researchers employed a macro-econometric model of the economy using quarterly data and simulated the impact of a 25% per annum rise in the oil price, compared to the actual average oil price rise of 15%, for the period 1998 to 2008Q1. They found that on average annual terms: CPI inflation was 3% higher; average PPI inflation was 1.7% higher; real Gross Domestic Expenditure (GDE) growth was 1.5% lower; real GDP growth was 2% lower; total employment was 0.5% lower; the current account deficit (as a percentage of GDP) was 1.05% larger;
real import growth was 2.3% lower; and real export growth was 5% lower under the high oil price scenario.113 For every 1% increase in the oil price, the simulation results suggested that: CPI (PPI)
113 The simulations did not take into account likely changes in demand for South Africa’s exports resulting from the impact of the oil price rise on South Africa’s trading partners, and hence probably underestimated the negative impact of the oil price shock.
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inflation would have increased by 0.2% (0.17%); real GDE would have declined by 0.12%; real GDP would have fallen by 0.14%; total employment would have decreased by 0.04%; the current account balance would have deteriorated by 0.04% of GDP; real imports would have contracted by 0.17%;
and real exports would have fallen by 0.32%. At a sectoral level, the simulations found the greatest negative impact on the manufacturing sector, the electricity, gas and water sector, and construction.
While providing somewhat useful estimates which generally conform to theoretical expectations, the DoT model and results have several limitations. First, the model does not account for the indirect channel of oil shock impacts, as it keeps the “exogenous variables” (such as demand for South Africa’s exports and capital flows) constant. Second, the model assumes a constant government deficit-to-GDP ratio, which historically has not been the case in the wake of oil price shocks. This assumption results in counter-intuitive estimates for the impact on government revenues, expenditure and debt levels. Third, the model assumes that a 1% rise in the oil price translates into a 0.81% rise in the petrol price, whereas the crude oil component of the petrol price did not exceed 52% in the period under consideration. Fourth, it is counter-intuitive that the agriculture and transport sectors are amongst the least affected, since these sectors are most heavily dependent on petroleum fuels as a proportion of their total energy use (see Chapter 3). Finally, this model shares the limitation of other time series methods in that it assumes linear relationships among the variables and that the coefficients remain constant over time, which might not be an accurate reflection of economic responses to oil price shocks.
A second category of studies have analysed quantitatively the impact of exogenous oil price shocks on the South African economy at a point in time, using input-output and/or computable general equilibrium (CGE) models linked to household survey data sets. The results of three such studies are summarised in Table 4-2. In the subsequent paragraphs, the major results of these studies are presented according to macroeconomic impacts on the balance of payments and exchange rate, consumer prices, real output (GDP), employment and wages, and household-level impacts on poverty and inequality.
McDonald and van Schoor (2005) employed a CGE model linked to a household survey data set to estimate the economy-wide and sectoral impacts of a 20% rise in the crude oil price. In their most realistic scenario, which allows for increases in the prices of other energy sources and energy-intensive commodities (such as coal, gold, and iron ore), they report: a 2.9% appreciation in the rand exchange rate; a 0.2% (0.3%) fall in GDP in the short term (long term); declining wages for both skilled (-0.9%) and unskilled (-0.6%) workers; rising import expenditures (0.8%) and export values (0.9%); and a fall in government dissaving as a percentage of GDP by 2.1%. McDonald & van Schoor (2005) find that the prices of energy intensive goods and services, including plastics, chemicals and transport increase, but by smaller percentages (under 2%) than might have been expected. This is attributed to the relatively small share of petroleum in total costs for most industries. In terms of sectoral winners and losers, they found that the petroleum industry suffered the most, given that crude oil accounted for half of its total costs. The greatest limitation of McDonald & van Schoor’s (2005) model is that the overall CPI is held constant by assumption, as it acts as the numeraire for the model. This means that there is no monetary policy response to rising inflation, which historically has had a major impact on the economy following oil shocks.
Essama-Nssah et al. (2007) employed a disaggregated CGE model together with micro-simulation analysis of household survey data to analyse the macroeconomic and distributional impacts of a 125% oil price shock on the South African economy.114 The oil price shock drives a real depreciation of the currency, which in turn serves to boost exports (including several manufacturing industries) and dampen imports and non-traded sectors (especially services).The major findings are that: GDP
114 The results summarised here are from a variation of the experiment which includes spill-over effects of oil prices to the prices of imported chemical products and other commodities.
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contracts by approximately 2%; household consumption declines by approximately 7%; there are relatively small increases in food and transportation prices, while the prices of a range of other goods and services actually decline as a result of reductions in demand driven by falling wages; and low- and semi-skilled employment declines, reflecting output changes, with labour shifting from (largely non-traded) services into agriculture and industry. High-skilled workers are found to gain, as they are not as susceptible to job losses and their consumption baskets are not skewed towards basic goods whose prices rise the most (food, transport and fuel). The poverty rate increases slightly (by 0.5 percentage points), as does the degree of inequality (the Gini coefficient rises by 1%).
Table 4-2: Review of empirical estimates of the impact of oil price shocks on South Africa
Authors McDonald & van
Schoor (2005)
Essama-Nssah et al.
(2007) Fofana et al. (2008)
Oil price shock (%) 20% 125% 50%
$/barrel increase 4 40 10
Domestic fuel prices 10.4% 68% 25%
Imports 0.8% -10.3% -4.6%
Exports 0.9% 9.1% 0.6%
Exchange rate (R/$) -2.9% --
Real exchange rate (R/FCU)a 22.4% decrease
CPI -- 2.7% -2.1%
Real GDP -0.2% -2.5% -2.2%
Household consumption -8.8%
Employment -2.7%
Unemployment (% points) 10-35%
Wages - skilled -0.9% -15% -15%
Wages - unskilled -0.6% 0%
Government dissaving -2.1% -22%
Sectors benefitting exporters exporters mining
mining transport equipment synfuels
electricity leather, wood electricity
Sectors suffering agriculture food agriculture
petroleum basic chemicals manufacturing
services services private services
Income inequality (Gini) 1% 0.7%
Poverty (headcount) 0.5% 1.2%
Equivalent oil price shockb 100% 100% 100%
Imports 4.0% -8.2% -9.2%
Exports 4.5% 7.3% 1.2%
CPI -- 2.2% -4.2%
Real GDP -1.0% -2.0% -4.4%
Income inequality (Gini) 0.8% 1.4%
Poverty (headcount) 0.4% 2.4%
Source: McDonald and van Schoor (2005), Essama-Nssah et al. (2007), Fofana et al. (2008) Notes:
a. FCU = foreign currency unit
b. In the lower part of the table, the impacts are standardised for a 100% oil price rise.
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Fofana et al. (2008) utilised a CGE model with an explicit energy component, combined with a micro-simulation household model, to analyse the macro and micro economic impacts of an oil price shock.
Following a 50% increase in crude oil prices, they find that: real GDP contracts by 2.2%; consumer prices fall by 2.1%; imports decline by 4.6% while exports rise by 0.6%; the real exchange rate depreciates; skilled wages fall by about 30% in rural areas but by less than 4% in urban areas; the unemployment rate rises, especially for unskilled workers and females (between 20 and 35 percentage points). Inequality rises in the country as a whole (Gini coefficient rises by 0.7), but declines in rural areas. Poverty increases by both the headcount and poverty gap measures and the poorest suffer the most, especially in rural areas. Fofana et al. (2008) find that substitutes for crude oil, such as synthetic fuels and electricity, benefit from higher oil prices, while mining receives a boost from exchange rate depreciation. However, a fall in aggregate demand has a negative effect on all other sectors, and especially on agriculture, food, light manufacturing and private services.
Comparing the results of the three CGE-based studies, it is clear that they are mostly in broad agreement with one another. For example, they identify essentially the same set of sectors that suffer the most and benefit the most from an oil price shock. All three studies estimate an increase in exports, although McDonald and van Schoor (2005) find that imports rise, whereas the other studies find that imports contract. Essama-Nssah et al. (2007) find that CPI increases, while Fofana et al. (2008) estimate the opposite. Both of these studies find that income inequality and headcount poverty rise, although the magnitudes are more than twice as large in Fofana et al. (2008) for an equivalent oil price shock. All three studies find a negative impact on GDP, but the magnitudes are quite different for a 100% oil price increase. These differences could be explained by different assumptions made by the modellers, as well as the fact that the Essama-Nssah et al. (2007) study used data from 2003 whereas the other studies were based on 2000 data.
These CGE models make an important contribution to understanding the macroeconomic and distributive impacts of oil price shocks by incorporating economy-wide interactions and adjustments.
A particular strength of the linked CGE-household models is that they allow analysis on three level, i.e. macro, meso (sectoral) and micro. Nonetheless, these models also have certain limitations. First, at a theoretical level CGE models assume competitive conditions exist in markets, allowing prices to adjust to balance demand and supply. In practice, the South African economy is characterised by a high degree of concentration, which is likely to impact on the way markets respond to shocks. In addition, a high degree of factor substitutability is often assumed, but may not be feasible in practice. Second, certain marked differences in the modelling results obtained for example by Fofana et al. (2008) and Essama-Nssah et al. (2007) indicate the sensitivity of CGE type models to some key assumptions, and also underscore the uncertainty involved in predicting the impacts of oil price shocks. Third, the models do not include the lagged, indirect effect of an oil price shock on the demand for South Africa’s exports via its effect on the global economy; historical oil shocks have been associated with international recessions and reduced trade flows. Fourth, most of the CGE models account for short run effects but not longer term impacts. Fifth, none of the models addresses the impact of physical oil shortages on economic activity. Sixth, the models are mostly based on data from the year 2000, which was before the steep rise in oil prices that resulted in oil comprising a significantly greater share of domestic expenditure; thus the models likely underestimate the impacts of future oil shocks. Finally, there are some counter-intuitive results that are not fully explained, such as Fofana et al.’s (2008) finding of slightly larger increases in poverty and inequality in a scenario in which the government subsidises petroleum products.
Demand for petroleum products
A third set of empirical investigations relates to the price and income elasticity of demand for petroleum fuels. According to conventional economic theory, demand for fuel is hypothesized to have a positive income elasticity (i.e., when income rises, people buy more fuel, ceteris paribus) and a negative price elasticity (i.e. when the price of fuel rises, demand falls, ceteris paribus).
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Furthermore, the level of new vehicle sales is hypothesized to influence the demand for fuel with a positive elasticity. It is useful to separate petrol and diesel demand, as the former is mostly used for passenger transport and thus depends on the disposable income of households, while the latter is used mainly for freight transport and productive activities (e.g. agriculture, mining and construction), and hence depends mainly on the overall level of economic activity (GDP). Theron (2008) provides a review of empirical fuel demand studies and also provides her own elasticity estimates. Theron (2008: 273) states that “[t]he evidence from the literature as well as the SA data is that the income variable is the most important determinant of fuel demand; the GDP growth rate in the case of diesel and real disposable income in the case of petrol. There is a negative relationship between the real prices and volumes sold, but this relationship is weaker than the income relationship.” I applied the autoregressive distributed lag (ARDL) modelling approach to estimate price and income elasticities of demand for petrol and diesel for the period 1994 to 2008Q3.115 The results conformed to theoretical expectations for the demand functions and were in line with previous estimates, but used a superior time series methodology. Key figures from the literature are summarised in Table 4-3. As mentioned earlier, however, the assumptions underlying these estimates (linearity and parameter constancy) might not hold in the longer term in the post oil peak era, as behavioural patterns are likely to undergo fundamental shifts.
Table 4-3: Price and income elasticity of demand for petrol and diesel in South Africa
Author(s) Elasticity
Short run Long run
Petrol Diesel Petrol Diesel
Wakeford Price -0.17 -0.14 -0.52 -0.14
1994-2008 Income 0.16 0.94 0.51 1.41
BER (2005) Price –0.19 –0.62 –0.10
1984-2004 Income 0.10 1.00 1.36
Econometrix (2005)* Price -0.24 -0.14
1999-2004 Income 0.38 1.47
BER (2003) Price –0.21 –0.18 –0.51 –0.06
Source: Theron (2008) and author’s estimates
Note: * It was not stipulated whether these were long run or short run estimates, but they are closer to other long run estimates.
Between 1995 and 2009, the average annual growth rates of real GDP and petroleum consumption were 3.3% and 2.1%, respectively. The growth rates were fairly closely coupled except between 1999 and 2001, and to a lesser extent between 2005 and 2006, periods during which the rand price of oil rose substantially (see Figure 4-13). The steep drop in petroleum consumption in 2008 preceded the recession, and again can be explained by the spike in the oil price.
Impact of fuel shortages
Physical shortages of petroleum fuels might have somewhat different economic impacts to those of price shocks, but there is little empirical research on this issue. According to the DME (2007: 8),
“[t]he cost of fuel shortages on the economy has been conservatively estimated at 273 c/l [cents per litre].” Furthermore, the DME found that a total disruption to economy-wide fuel supplies was estimated to result in losses to the economy of R1,340 million per day (in 2010 prices), which
115 This period was determined by the availability of petroleum sales data from SAPIA (2009). SAPIA stopped publishing these data after September 2008; the Department of Energy was supposed to assume responsibility for publication of these data, but as of July 2011 had not done so. The details of the estimation methodology and results are contained in Appendix D.
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represents 18% of GDP on an annualised basis. In accordance with the biophysical economics perspective, a lack of fuel means that there is less energy available to do useful work, and economic activity will be inhibited. Clearly, the sectors depending most heavily on road transport will be affected to a greater degree.
Figure 4-13: Growth in real GDP and petroleum consumption, 1995-2009
Source: Author’s calculations based on SAPIA (2009) and SARB (2011)
Figure 4-14 below shows that the statistical correlation between growth in real GDP and growth in petroleum consumption has been rather weak, with an R-squared of just 0.26. This partly reflects the absence of other variables such as fuel prices.
Figure 4-14: Correlation between real GDP and petroleum consumption
Source: Based on SAPIA (2009) and SARB (2011) -4
-2 0 2 4 6 8
1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009
Annual % change