Demand for petrol/ diesel and macro- economic determinants

Presentation to SHELL3 May 2006

Dr. Nicola TheronLinette Ellis

Demand - theory

Demand generally a function of: Price of product Price of related products Income Population Tastes Other factors (N)

Price elasticity of demand Income elasticity of demand

...),,,,( NTYPyPxfD

Petrol/ diesel demand - literature

Drollas - early reviews of price and income elasticity in 1984 (published in ‘Energy Economics’).

US studies: “[w]hile a range of estimates are found in the literature, the consensus view is that the long run price elasticity of demand is around -0.8 while the long run income elasticity is slightly below unity”. (Graham & Glaister, 2002:2).

Some studies (Blum et al, 1988) found larger ranges for some European countries, with income elasticity varying between 0.86 and 1.90. Sterner (1990) used pooled data for OECD countries and found long run income elasticities of between 0.6 and 1.6. With another technique (time series data), he found the income elasticity to vary between 1.1 and 1.3.

Later work by Goodwin (1992) generally found that elasticity estimates had to be revised upwards from estimates calculated between the 1980s and 1990s.

International Income Elasticity for Fuel (Graham & Glaister, 2002:7 )

Country Income Elasticity

Short run Long run

Canada 0.12 0.53

US 0.18 1.00

Belgium 0.63 1.25

France 0.64 1.23

Italy 0.40 1.25

Portugal 0.37 1.93

Switzerland 0.85 1.54

Japan 0.15 0.77

Turkey 0.65 1.29

Summary data on price and income elasticity (Dahl & Roman (2004))

Price elasticity (short run)

Price elasticity (long run)

Income elasticity (short run)

Income elasticity (long run)

DIESEL

Mean -0.13 -0.67 0.55 1.13

Standard deviation 0.22 0.75 0.84 0.82

Minimum value -0.88 -2.63 -0.93 -0.19

Maximum value 0.31 0.22 3.32 3.00

Number of studies 27 27 26 26

PETROL

Mean -0.13 -0.61 0.25 0.69

Standard deviation 0.11 0.55 0.35 0.67

Minimum value -0.46 -2.47 -0.37 -1.46

Maximum value 0.25 0.88 2.03 2.68

Number of studies 70 112 71 114

SA and middle-income countries

India (Ramanathan, 1999). Short run income elasticity of 1.18 and a long run income elasticity of 2.68 – might be explained by low level of fuel consumption in India and the gradual increase in economic growth.

South Africa:Source Short term price

elasticityLong term price elasticity

S.A. Cloete & E.v.d.M.Smit (1988) -0.25 -0.37

S.D. Ngumeni (1994) -0.1 to -0.2

Bureau for Economic Policy Analysis (BEPA, 1989) -0.31

Bureau for Economic Research (2003) Petrol: Diesel:

-0.21 -0.51

-0.18 -0.06

Income elasticity (BER) (2003) Petrol: Diesel:

0.15 0.88

1.3

Philips curve (inflation vs. unemployment – US (1960s))

US – 1970-2000

US – Inflation (more persistent)

Expectations augmented Philips curve (US 1970-2000)

SA – Structural changes

Stable Macro economic environment GEAR ASGISA; Monetary policy – Inflation targeting regime Fiscal policy – Fiscal discipline Deficit as ratio of GDP: 2006/07 (-1,5%); 2007/08 (-1.4%); 2008/09 (-1.2%). More funds available for service delivery and infrastructure provision. Trade deficit 2006Q1 = R14,6 bn, 2005Q1 = R4,7bn – Is this a problem? Higher imports – driven by consumption expenditure (lower interest rates,

lower inflation, etc.). Current account deficit – dependence on capital inflows. JSE – R38bn in 3,5 months 2006 (R15bn last year same period). Risk factors? Commodity prices high – positive effect on exports. Oil – 14.2% of total imports in 2005. Commodities = 70% of SA exports- positive effect larger than negative oil

import effect.

GDP growth and business cycle upswings

Real GDP growth (1946-2004)

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GD

P

GDP growth and CPI

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1962

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GDP CPI

GDP and Interest Rates (Prime)

-4.0

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GDP Prime Rate

Growth and Exchange rates (R/$)

-4.0

-2.0

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GDP R/$

Forecast of Key Economic Variables – BER vs. Reuters Consensus (Mar 06)

8.69 (9.17)7.76 (7.94)7.918.01R/Euro Exchange Rate:

BER (eop)

6.92 (7.30)6.60 (6.75)

6.29 (6.35)6.28 (6.35)

6.366.45R/$ Exchange Rate:

BER (eop)Reuters Consensus

10.510.6

10.510.5

10.611.3Prime Interest Rate:

BERReuters Consensus

4.94.6

4.34.2

3.94.3CPIX Inflation:

BERReuters Consensus

4.24.7

4.54.5

4.94.5GDP Growth:

BERReuters Consensus

2007200620052004

8.69 (9.17)7.76 (7.94)7.918.01R/Euro Exchange Rate:

BER (eop)

6.92 (7.30)6.60 (6.75)

6.29 (6.35)6.28 (6.35)

6.366.45R/$ Exchange Rate:

BER (eop)Reuters Consensus

10.510.6

10.510.5

10.611.3Prime Interest Rate:

BERReuters Consensus

4.94.6

4.34.2

3.94.3CPIX Inflation:

BERReuters Consensus

4.24.7

4.54.5

4.94.5GDP Growth:

BERReuters Consensus

2007200620052004

Business confidence (RMB/ BER Business Confidence Index)

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Economic theory of demand - restated

Petrol - Income elasticity: If there is an increase in income, people earn more and they have more

disposable income, which will increase their demand for petrol, as they buy more or bigger cars, or go on more holidays or generally use their cars more. The data on long-term income elasticity suggest that this is approximately a 1:1 relationship. In other words, a 1% increase in income will lead to a 1% increase in the demand for fuel (the income elasticity seems to be higher for diesel than for petrol, but on average the income elasticity is around 1).

This is therefore a positive relationship. The relationship between the price of petrol (or diesel) and the demand

for petrol (or diesel) is a negative relationship (indicated by the minus sign on the price elasticities). The economic reason is simple: as the price of a product increases, the demand decreases.

UHAMBO EXAMPLES: Econometrix

Demand Elasticity (1999-2004) Petrol: -0.23 Diesel: -0.13

Income Elasticity Petrol: 0.38 (Used GDP) Diesel: 1.47

Same observations as above: Low price elasticity – Ed<1 = price inelastic Income elasticity higher (petrol – correct variable?) Increase in light diesel vehicle sales? Used three exogenous variables: $ price of Brent crude oil; Rand/$US exchange

rate; GDP growth rate.

Demand forecasts – OOC’s in Uhambo trial

CRA (for Sasol): Sasol’s current best estimate…demand for white fuels in the inland area will grow between 2005 and 2015 by:

Petrol: 1.4% Diesel: 3.4% Kerosene: 2.9%

Uhambo business plan: Petrol: 1% Diesel: 3.5% Kerosene: 0.9%

Engen (Business plan): Petrol: 0.2% Diesel: 4.7% Kerosene: 0.0%

Sasol/ Engen (Uhambo)

Source: Petrol Diesel Kerosene

Uhambo Business Plan 1.0% 3.5% 0.9%

CC Submission (28/4/2005) 0.6% 4.0% 2.5%

Sasol Oil (2006 budget) 1.4% 3.4% 2.9%

Engen (Business plan 2006) 0.2% 4.7% 0.0%

Demand forecasts – OOC’s in Uhambo trial

DME (Uhambo) Petrol: 0.6% Diesel: 0.4%

Petronet: Petrol: 0.5% Diesel: 2.5% Jet Fuel: 3.0% Revised later to 2.6% combined projected growth for petrol, diesel and jet

fuel. BP:

Petrol: 3% Diesel: 6% (CRA: “outliers”)

Caltex: Petrol: 2.7% up to 2006, and 3.3% beginning 2007; Diesel: 3.3% up to 2006, and 4.1% beginning in 2007.

Masana (ECONEX)

Petrol Diesel

2005 5.3% 5.9%

2006 3.3% 3.7%

2007 2.9% 3.5%

2008 3.1% 5.4%

2009 3.8% 5.8%

Shell (Uhambo)

RBB used Shell methodology: Demand for petrol = (GDP - 1.5%) Demand for diesel = (GDP + 1.4%)

2005 2006 2007 2008 2009 2010

GDP growth 3.5% 3.7% 4.4% 3.3% 2.9% 3.0%

Petrol Demand Growth 2.0% 2.2% 2.9% 1.8% 1.4% 1.5%

Diesel Demand Growth 4.9% 5.1% 5.8% 4.7% 4.3% 4.4%

Tribunal

“We have been presented by bald estimates by the participants in these hearings – many of whom appeared to rely on independent experts – but surprisingly few have attempted to explain the underlying basis for their estimates. A notable, if somewhat unfortunate, exception is Mr. Swart of Sasol who indicated that Sasol had used an observed correlation between the CPI and petrol demand to estimate demand growth. However, there is no discernible causal relationship between these variables…”

“In our view, common sense would suggest a high degree of correlation between income growth and rates of growth in fuel consumption. It may also reasonably be hypothesised that changes in the distribution of income would correlate with shifts in demand for fuel products.

Sasol - Petrol (Swart)

Sasol – Diesel (Swart)

Sasol

Battery of statistical tests (multicollinearity, heteroscedasticity; Durbin-Watson, etc) – all meaningless;

There is absolutely no point in running a battery of statistical tests on an equation that, (1) is not based on economic theory and therefore excludes the most important driver of demand, namely consumer income or economic growth and (2) includes an explanatory variable with the wrong sign, as was done with Mr. Swart’s petrol sales equation.

No income variable – positive growth expectations NO effect in their model; (Mr. Quinton Swart (who estimated the Sasol demand functions) said in his testimony that he has

not found GDP to be a good indicator of petrol demand, (although he has found it to be a good indicator of diesel demand);

Sasol – monthly data? Used levels not percentage change “CHAIRPERSON: What is the theory of the connection between inflation and petrol demand? MR SWART: I am myself not an economist, Chair. So it’s a difficult one for me to explain. It’s

things that I have tested and found to be relevant. I cannot in economic terms explain that.” (11 October 2005).

Sasol - testimony

(12 October 2005): “ADV FAGAN: Yes. I understand. And that would explain also why you couldn’t, in response to a question from the Chair yesterday, on an economic basis say why you had chosen CPI particularly.

MR SWART: Not being an economist, I thought about that last night and I would certainly think that inflation would have a direct impact on consumer expenditure and that consumer expenditure would directly influence petrol sales. I must also highlight that it’s much easier projecting. A model is just as good as the variables that you have to project when going into the future. If anybody can give a very good estimate of consumer expenditure growth in the future relative to what inflation will be, I would need to see that. I put it to you that inflation as well as GDP are two of the variables that’s mostly, together with the Rand/Dollar exchange rate, the most projected variables in the economy,”

Finally, on 17 October 2005: “ADV CILLIERS: Now the Chairman asked you whether you could think of a theoretical reason for such an influence of CPI on the demand for petrol. Can you?

MR SWART: I again submit that the reason I can think of is that inflation would have an impact on disposable income of the consumers and that would influence their purchasing power as well as patterns in terms of the sizes of vehicles they purchase, money available to them, etc.”

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PETROLVOL_NOM

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82 84 86 88 90 92 94 96 98 00 02 04

CPI

Correlation – Petrol and CPI (levels)

Shell – Petrol (forecast vs. real)

-8.0%

-6.0%

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0.0%

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6.0%

8.0%

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Vol_Petrol GDP minus 1.5 (Shell petrol)

Shell – Diesel (Forecast vs real)

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15.0%

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Vol_diesel GDP plus 1.4% (Shell diesel)

Price effect

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35.0%

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Vol_Petrol Petrol price (cent per liter %)

Regional effects - Regional Economic Growth (year on year % change)

4.5 3.0 3.7 2.7 4.2 GDPRSA at market

prices

2.7 2.7 4.2 6.8 0.2 Limpopo

4.2 2.7 2.5 1.3 3.1 Mpumalanga

4.4 2.9 5.1 2.3 5.9 Gauteng

4.9 4.5 1.6 0.9 1.5 North West

4.9 2.8 2.6 4.4 4.7 KwaZulu-Natal

3.9 2.0 3.9 -1.1 2.1 Free State

3.0 3.6 1.5 -1.7 2.0 Northern Cape

4.6 2.5 1.6 2.7 4.3 Eastern Cape

5.3 3.5 4.4 3.7 4.2 Western Cape

20042003200220012000Province

4.5 3.0 3.7 2.7 4.2 GDPRSA at market

prices

2.7 2.7 4.2 6.8 0.2 Limpopo

4.2 2.7 2.5 1.3 3.1 Mpumalanga

4.4 2.9 5.1 2.3 5.9 Gauteng

4.9 4.5 1.6 0.9 1.5 North West

4.9 2.8 2.6 4.4 4.7 KwaZulu-Natal

3.9 2.0 3.9 -1.1 2.1 Free State

3.0 3.6 1.5 -1.7 2.0 Northern Cape

4.6 2.5 1.6 2.7 4.3 Eastern Cape

5.3 3.5 4.4 3.7 4.2 Western Cape

20042003200220012000Province

Other possible regional factors (Intergovernmental Fiscal Review)

Western Cape South Africa Western Cape as % of SA

Area (square km) 129 370 1 219 207 10.6%

Paved km 7 172 56 431 12.7%

Gravel km 24 991 199 936 12.5%

Access km 7 822 92 160 8.5%

Total km 39 985 348 527 11.5%

Road network density (m/sq km) 309 282 109.6%

No of registered vehicles 1 188 6 949 17.1%

Vehicles per km of provincial road

29.7 20.2 147.0%

Provincial spending on roads (2003/04)

391 5 089 7.7%

Other factors

NAAMSA: Local Sales: Passenger Cars - Number

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Other factors

South Africa is a developing country, but has a low persons-per-car ratio among the affluent, similar to the US at around 2:1. Yet this ratio is as high as 80:1 among poorer people. The mobilization of poorer people via mini-bus taxi’s from 1984 led to petrol growth rates of almost 10%, while traditional bus companies saw 40% declines in passengers. The impending move of petrol mini-bus taxi’s to diesel midi buses will accentuate the swing to diesel-powered cars, started by the advent of diesel 4X4’s and the move to road-freight trucks as railway efficiency declined. These structural shifts will have an effect in the medium run.

Estimation of demand equations

Linette Ellis

BER

Estimating demand equations for petrol and diesel sales volumes

Choice of explanatory variables Explanatory variables must make economic sense Both real price and demand variables important determinants (e.g. gdp,

income, car sales/stock)

Positive relationship between real GDP growth and growth in petrol sales volumes…

-8%

-4%

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4%

8%

12%

16%

83 85 87 89 91 93 95 97 99 01 03 05

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Petrol sales volumes Real GDP

But correlation only 17% between 1983 and 2005,

even lower after 1995

Stronger relationship between growth in real disposable income of households and growth in petrol sales volumes…

-8%

-4%

0%

4%

8%

12%

16%

20%

83 85 87 89 91 93 95 97 99 01 03 05

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Petrol sales volumes Real Disposable Income

Correlation 38% between 1983 and 2005, 26%

after 1995

Strong negative relationship between petrol sales and real (CPI deflated) price of petrol

-10%

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5%

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83 85 87 89 91 93 95 97 99 01 03 05

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Petrol sales volumes Real petrol price

-73% Correlation between 1983 and 2005, -

64% after 1995

Incorrect specification of equation can lead to non-sensical results, e.g. positive relationship between sales and price

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Petro

l price (cen

ts per litre)

Petrol sales volumes Petrol price

79% Positive correlation between petrol sales and price if level data is used

Strong positive relationship between real GDP growth and growth in diesel sales volumes…

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15%

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Diesel sales volumes Real GDP

67% between 1983 and 2005, 38% after 1995

Negative relationship between diesel sales and real (PPI deflated) price of diesel, but weaker than seen in petrol

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Diesel sales volumes Real diesel price

-19% Correlation between 1983 and 2005, -

13% after 1995

Estimating demand equations for petrol and diesel sales volumes

Choice of explanatory variables Explanatory variables must make economic sense Both real price and demand variables important determinants

Equation specification Levels vs. % change Logs Cointegration techniques

Interpretation of statistical results Coefficient sign Magnitude of coefficient and t-statistic Goodness of fit and R-squared Autocorrelation and Durbin-Watson

Forecasting