Frédéric Lantz

We generally consider that the price elasticity of the energy demand is quite small. But it appears that strong increases in gasoline price lead to modifications in consumer behaviours. The high volatility of petroleum prices and thus gasoline prices and the strong increases since the beginning of 2000 justify an analysis of price effects on gasoline consumption. Thus, in this paper, we estimate the effects of price variations on gasoline consumption, in the short and the long-term, in two countries: the United States and India. We use a co-integration modelling to test for long-run relationship between gasoline consumption, income, price and vehicle ownership in the two countries. We use an error correction model to test for short-run prices effects and more precisely for asymmetric effects on demand of increases and decreases in gasoline prices. The main conclusions are the following.

-The United States: (1) The long-term price elasticity is relatively high for an industrialised country because gasoline taxes are low (2) Households are more sensitive to a price increase than a price decrease.

-India: (1) Price elasticity in the long-run is quite high but is quite small in the shortrun. It is not surprising for an emergent country (2) It seems that there is no asymmetric effect of price variations on gasoline consumption.

I. Introduction

The transport sector still uses more than 95% of petroleum products, principally gasoline and diesel fuels. This consumption generates carbon dioxide emissions, which are partly responsible for climate change. Today, road transport, and especially passenger vehicles, emits approximately 60% of the transport sector' s carbon dioxide emissions (WBCSD 2004). Thus, the development of this kind of transport could be a real problem, in terms of fuel consumption and pollutant emissions, particularly in the developing countries. There are some policies to reduce CO 2 emissions: new standards, emission quotas and fiscal policy on the vehicle or/and on the fuel. The first two policies are especially directed towards industry, whether to reduce the emissions of the production process, or to stimulate the development of new technologies, despite there being a repercussion on the consumer. The third one, for its part, acts directly on households' behaviour, principally in the short run, changing their habits (in the long term, fiscal policy has also an impact on vehicle manufacturers). Therefore, we are particularly interested in policies directed towards consumers. So, in this way, it is important to analyse demand sensitivity to price variations, and more precisely its sensitivity to price increases and price decreases. In terms of economic policy implications, this can lead decision makers to choose between increasing the tax on pollutant fuels and decreasing the tax on more "green" fuels for example. To bring this analysis to a successful conclusion, we cannot generalize economic policies to every country in the world. It seems that these fuel price policies to reduce carbon emissions will be different in the industrialised world from emergent areas. In fact, economic and technical characteristics, as well as socio-cultural and transport behaviour, are really distinct depending on the country studied. Moreover, if it is important to reduce fuel consumption to decrease carbon dioxide emissions in the industrialised regions, in an emergent country, policies have to help the development of a sustainable transportation system, which can slow the progression of fuel consumption and pollutant emissions. In this way it is very interesting to compare two different countries: the first one industrialised, and the second emergent. We chose to analyse the United States and India. We can compare these two countries because they are great with large populations and big urban centers, so the need for motorised mobility is really important, even if the kind of mobility is different. The US has a very large fleet of personal vehicles: about 230 million cars in 2005, which is the biggest fleet in the world. Potentially, India, which has a large and increasing population plus strong economic growth, can reach in the future the US vehicle fleet level. Moreover, individual vehicles still use mainly gasoline fuel, while diesel is starting to be developed. In these two countries, there are some new standards, called CAFE 1 in the US and Bharat Stage in India, concerning fuel efficiency or conventional pollutants, which show that both countries' policies focus on the engine or fuel to reduce emissions rather than travellers' behaviour. About carbon dioxide emissions, there is not yet any clear environmental policy. However, despite these common points, it is clear that consumer behaviour is really different, because of the level of national income, the composition of the vehicle fleet (cars in the US, both cars and two-wheeled motor vehicles in India), the new technologies, the kind of motorisation and the quality of gasoline used (fuel specifications) Thus, our question concerns the reaction of consumption to a variation in price. Starting with the classical principle that fuel consumption in passenger motor vehicles depends on national 1 Corporate Average Fuel Economy income, fuel price and the stock of passenger cars, the aim of this paper is to estimate long and short run responses of energy demand to variations in its major determinants and particularly price, because this has important implications about reducing motor vehicles' carbon emissions. In this way, we think that it is very important to test the asymmetric effect of price variations. This means that households have not the same sensitivity to price rises or to similar falls. To do this analysis, we use an econometric model of fuel consumption based on a co-integration relationship. Some recent studies [START_REF] Eltony | Demand for gasoline in Kuwaït[END_REF]for Kuwaït Samimi 1995 for Australia, Ramanathan 1999 in India) have confirmed the existence of a cointegration relationship between gasoline demand and macroeconomic variables. They have shown that there is a long term relationship between gasoline consumption, gasoline price and income. In our analysis, we have added a variable of vehicle ownership (per adult aged 15 or more, as in Lescaroux, Rech 2008). Thus, we first estimate the long run price elasticity, in comparison to the long run income elasticity, and the importance of the stock of passenger vehicles. Then, through an Error Correction Model (ECM), we estimate especially short run price behaviour and we analyse whether variations in price have symmetric effects on fuel consumption in passenger vehicles. Results show that in an emergent country such as India income elasticity is high in the long run, but low in the short run, especially for two-wheelers. Moreover, the effect of price variations on fuel consumption seems to be symmetric in the short run. For the US, long run income elasticity appears to be higher than price elasticity. In the short run, households seem to be particularly sensitive to a strong price increase. This asymmetry is partly because of the irreversibility of technological progress. The outline of this paper is as follows. In Section II, we describe the bibliography and theoretical explanations for asymmetric effects. In Section III, we specify the long run demand equation, and the short run ECM. Thus, we analyse the different decompositions of price series borrowed from [START_REF] Wolffram | Positivistic measures of aggregate supply elasticities: some new approaches -some critical notes[END_REF][START_REF] Traill | Estimating irreversible supply functions[END_REF][START_REF] Gately | Imperfect price-reversibility of the US gasoline demand: asymmetric responses to price increases and declines[END_REF]. Section IV presents the econometric results for the US and India and section V summarizes and concludes with the policy implications of this analysis.

II.

A survey of literature and theoretical explanations for asymmetric effects

It is generally admitted that there is a form of asymmetry of the effect of price variations on energy demand. This discovery results from the attempt to anticipate energy demand after the oil price collapse of the mid 1980s. Energy demand previsions were over-estimated, because we thought that if price recovered its initial level, demand would also find once again the standard of the early 1970s. However, this was not the case and so it showed that there was a form of asymmetry in the reaction of demand to price increases and decreases. Moreover, households seem to be more sensitive to a price rise than a similar fall. The asymmetric effect of price variation was defined by [START_REF] Dargay | Have low oil price reversed the decline in energy demand? A case study of the UK[END_REF]: "the demand response is "asymmetric" if a given price increase results in a decrease in demand that is either larger or smaller than the increase resulting from an equivalent price fall". Theoretical explanations were given by [START_REF] Wirl | The asymmetrical energy demand pattern: some theoretical explanations[END_REF][START_REF] Walker | Irreversible price-induced efficiency improvements: theory and empirical application to road transportation[END_REF] in different economic sectors that use energy, and particularly in the transport sector. This means that the irreversibility of technological progress, price expectations and the existence of fixed costs are partly responsible for asymmetry. The first explanation is understood as the improvement of vehicle efficiency. Thus, when fuel prices increase, the industry triggers research and development efforts to make more fuel efficient equipment. Consumers move to another demand curve. Then, when the price falls and finds its initial level, the technology exists, vehicles are more efficient, and the energy demand is not restored to its initial level. Thus, the irreversibility of technological progress brings asymmetry. The second explanation is price expectations. If we consider that consumers are not myopic, so, when prices are low, households decide their consumption patterns anticipating that prices will increase. But, when prices are high, they react in accordance with this price. Thus, it seems that consumers react in function of high prices (real or anticipated), and not low prices, which brings asymmetry to consumers' behaviour. The third explanation is the existence of a fixed cost when households decide to change their equipment. Thus, if there are few variations in price, a small increase for example, consumers do not change immediately their equipment because the fixed cost is high. However, if the price variation is strong, it can be cost effective to change the equipment. This conclusion depends on whether a fixed cost constitutes a significant amount in the consumer' s calculation. This approach in terms of an asymmetric effect of price variations on energy consumption was developed in the 1990s. In this way, Dargay 1992a, 1992b[START_REF] Gately | Imperfect price-reversibility of the US gasoline demand: asymmetric responses to price increases and declines[END_REF][START_REF] Hogan | OECD oil demand dynamics: trends and asymmetries[END_REF] showed the evidence for an asymmetric effect of price variations on motor fuel demand in European countries such as France, Germany and the UK, in all OECD countries or in the US. Conclusions were that households are more sensitive to strong increases in price. Moreover, [START_REF] Gately | The imperfect price-reversibility of world oil demand[END_REF][START_REF] Gately | The asymmetric effects of changes in price and income on energy and oil demand[END_REF] enlarged this analysis to different groups of countries such as non-OECD countries. Conclusions were more heterogeneous, depending on the model used, or on the kind of OECD countries analysed (oil exporters, countries that experienced steady growth in per-capita income ...). In their different models, these studies pointed out the dynamic aspect of the relationship between energy consumption, price and income. Gasoline demand, for example, cannot react immediately after a price or income variation because of the existence of a vehicle. Thus, if gasoline prices increase, consumers will not change their equipment for a vehicle which uses another fuel because there is inertia in households' behaviour, because of the existence of an expensive equipment and because vehicle fleet turnover is long. It is necessary, in this context, to construct a model with a form of adjustment between the short and the long-term. These analyses have generally used a Koyck-lag model [START_REF] Hogan | OECD oil demand dynamics: trends and asymmetries[END_REF][START_REF] Gately | The asymmetric effects of changes in price and income on energy and oil demand[END_REF][START_REF] Griffin | Price asymmetry in energy demand models: a proxy for energy-saving technical change?[END_REF], a partial adjustment model [START_REF] Gately | Imperfect price-reversibility of the US gasoline demand: asymmetric responses to price increases and declines[END_REF][START_REF] Gately | The imperfect price-reversibility of world oil demand[END_REF], or an Error Correction Model (ECM) (Dargay 1992b(Dargay , 1993)). We have chosen to follow Dargay' s methodology in particular because our series are not stationary and a co-integration relationship does exist between them. Also every co-integration relationship can be written, in dynamic specification, with an ECM [START_REF] Engle | Co-integration and error correction. Representation, estimation and testing[END_REF] 2 . Today, it is really important to once more analyse this asymmetric effect. We have entered a period of dramatic increase in gasoline price. Moreover, because of this structural rise, fuel markets will be very tight in the future, which increases the price volatility. Thus, an analysis in terms of asymmetric price effect is justified.

III. A demand model of fuel consumption in passenger cars

To construct a demand model of fuel consumption in passenger cars, we have to point out the main determinants of this demand. We assume that fuel consumption depends on fuel prices, income and passenger vehicle fleets. To analyse the asymmetric effect of price variations on gasoline consumption, we have first to decompose price series, second, to look for the co-integration relationship between the different variables, and then, to build a short run ECM.

Price decomposition

Our starting point is the decomposition of the price series. We use, more precisely, three different specifications of price series. The first one was employed by Wolffram 1971, which distinguishes between a demand response to price rises and price falls. The second was used by [START_REF] Traill | Estimating irreversible supply functions[END_REF], who criticize Wolffram' s decomposition. They proposed to divide price series in two: one for the maximum historical price, and the other for all small variations. Gately 1992 took into account the first two forms (Wolffram, Traill et al.). He proposed to split up the price series into three different parts, one for the maximum historical price, the second for other positive variations in price and the third one for negative variations. Thus, we generate seven variables, P_max, P_rec, P_cut for Gately' s decomposition, P_w1, P_w2 for Wolffram' s and P_t1, P_t2 for Traill et al. They take the following form, in logarithms as in Hogan 1992 or Dargay, Gately 1995:

Decomposition 1: Gately (1992) ( ) ( ) ( ) { } ( ) ( ) { } - - - = - - - = = = - - = - - t i i i i i t t i i i i i t
_ 2 _ max _ 1 _
As an example of the price decomposition technique, Fig. 1 shows the real gasoline price in US Dollars in the US, in logarithm, with its different decompositions. So, we see two jumps in P_max (or P_t1). The first one is in 1973/1974, and the second one in 1978/1979, which represents the two oil shocks. The last peak in this period is reached in 1981. It confirms that P_max (or P_t1) is always positive and non-decreasing and it rises only when price increases above a previous maximum. P_rec, which is the cumulative series of positive variations in price, is also positive and non-decreasing, but the jumps are frequent and small because they include all small positive variations in the price. P_w1 is the sum of P_max and P_rec, and thus represents all price rises. P_cut (or P_w2) is the cumulative series of negative variations in price. The graph shows that it is negative and non-increasing. We can see a big cut in the 1980s, which represents the price collapse, and small cuts afterwards, up to 2005. Finally, P_t2 is the sum of P_cut and P_rec, and thus represents all small variations, both positive and negative.

A long-term relationship

We attempt to point out a long-term relationship between gasoline consumption, gasoline price, income and motorisation rate. Thus, we build an econometric model based on a cointegration relationship. We use this kind of model because our series are not stationary, so we look for a co-integration relationship between them to estimate the following equation:

t t t t t P Y V C ε η γ β α + ⋅ + ⋅ + ⋅ + = (1)
where C, V, Y and P are the logarithms of the long-run fuel consumption of passenger vehicles per adult, of the vehicle ownership per adult, of the real national income per adult, and of the real fuel price. The demand specification in logarithms has implications in terms of constant elasticities and especially for income elasticity because we use another specification for price elasticity. Y and P are generally used, and this is easy to understand, but V is never used in models with asymmetric effects of price variations. However, fuel is not demanded for itself, but to sustain a piece of equipment, thus the vehicle ownership is an important determinant of fuel consumption. So, if we miss out this variable, price and income elasticities would be overestimated. Moreover, using this variable adds robustness in the modelling.

Dynamic specification

Our concern is about equilibrium relationship. However, it is necessary to introduce a dynamic component into the modelling. In fact, the effect of price and income variations on fuel consumption is not instantaneous, because of present constraints, and notably those imposed by the existence of a stock of equipment. So, there is some rigidity and inertia. We can admit the existence of an adjustment process between the short and the long-run, which formalizes the passage from the present to the future. There are different forms of adjustment processes used in the Koyck lag model or the Error Correction Model, for example. Thus, our series are not stationary (C, V, Y and P), and a co-integration relationship exists, so the adjustment process from the short to the long-term can always be represented by an ECM. This can be written as:

t t i t t i i i t t i i i t t i i i t t i i t u P Y V C C + ⋅ + ∆ ⋅ + ∆ ⋅ + ∆ ⋅ + ∆ ⋅ + = ∆ - - = - = - = - = 1 0 0 0 1 ' ' ' ' ε λ η γ β δ α (2)
All variables are in logarithm form and in first differences, to make them stationary, (for example, C t = C t -C t-1 ), so that the change in gasoline consumption is specified as a function of current and lagged changes in price, income and vehicle ownership and lagged changes of gasoline consumption. The formulation 1 ˆt ε represents the error corrector term: all the short-run shocks will be rectified to return to the long run equilibrium. In fact, for cointegrated non-stationary series, a linear combination of the variables can be stationary, so it is exploited in the short-run, utilising the co-integration relation' s error term estimated, with one lag, as the error corrector term, because it is stationary. Thus, λ is always negative and can be interpreted as an adjustment coefficient. Another piece of information is that ' represents the short-run price elasticity and ', the short-run income elasticity. In this basic form, price elasticity is single for one year t, which specifies a symmetric effect of price variations. In our analysis, we also use the three decompositions presented, replacing

t P ∆ ⋅ ' η by: 1/ t t t cut P rec P P _ ' _ ' max _ ' 3 2 1 ∆ ⋅ + ∆ ⋅ + ∆ ⋅ η η η 2/ t t w P w P 2 _ 1 _ 2 ' ' 1 ' ' ∆ ⋅ + ∆ ⋅ η η 3/ t t t P t P 2 _ 1 _ 2 ' ' ' 1 ' ' ' ∆ ⋅ + ∆ ⋅ η η
. So, we have to test if variations in price have an asymmetric effect or not. In the asymmetric modelling, where we use the different price decompositions, we have to test if, in the Gately case, ' 1 = ' 2 = ' 3 and in the Wolffram or Traill et al. cases '' 1 = '' 2 and ''' 1 = ''' 2 . So, we use a Wald test where the null hypothesis H0 is the stability of the coefficients and the alternative hypothesis is the non-stability. If the probability of the test statistic is under the fixed threshold (5% maximum), we reject the null hypothesis and accept the asymmetric effect.

IV.

Empirical results

Data

Equations (1) and (2) were estimated for the US and India, using annual data covering the period 1978-2005.

C represents the logarithm of gasoline consumption of passenger vehicles per adult aged 15 or more. It comes from the Department of Energy (DOE) for the US (in gallons) and the International Energy Agency (IEA) for India (in litres). Population data comes from the World Bank database (WB). Y, the logarithm of the national income per adult originates from the WB both for India and the US, in constant 2000 USD. V, the logarithm of the vehicle ownership per adult is a result of the division between the passenger vehicle fleet (United Nations Organisation for cars and Motor Transport Statistics for two-wheeled motor vehicles) and the adult population, for India and the US. P represents the logarithm of the gasoline price. It comes from the DOE for the US and is expressed in constant 2000 USD per gallon; it comes from Enerdata for India and is in constant 2000 USD per litre. In order to illustrate the differences between these two countries, we present first the evolution of the personal vehicle fleet in India and in the US, and second the evolution of gasoline consumption and gasoline price in both countries. So this has some important implications for gasoline consumption. Concerning India, the vehicle fleet is not very high but in strong growth. This fleet is composed of two types of vehicles: cars at about 20% and two-wheeled motor vehicles around 80%. This increase in the vehicle fleet implies a rapid growth in gasoline consumption. shows that the growth in gasoline consumption in the US has been slowing since the beginning of the sample but more precisely since the 1990s. The strong increase in gasoline price leads to a decrease in gasoline consumption after 1978. Then, price is volatile but low, and consumption grows, but slowly. From the late 1990s, gasoline price is increasing strongly, and consumption continues to increase slowly, at the same rate as before. In 2005, in constant currency, price never increased beyond the historical maximum, reached in 1981. Consumption stagnation can be explained by the saturation of the automobile fleet and the increase in fuel efficiency since 1978, with the introduction of new standards named CAFE.

Concerning India, we can see that gasoline consumption is rising strongly after 1990. Concurrently, gasoline price is increasing all along the sample. Prices were controlled, so volatility is not so high. However the level of price is quite high. It is an Indian policy to keep gasoline price levels high. Since 2002, prices have been liberalised, so that there has been a rapid and strong rise since 2003.

It is important to remember that the quality of statistics in India is not the same as in the US.

There are some problems of reliability but also possibilities of fraud or subsidies (Indian companies maintain a certain level of prices, imposed by the government) etc.

Estimated results for the US

The variables C, V, Y and P (in logarithms) were found to be integrated for order one on the basis of augmented Dickey-Fuller 1979, Phillips-Perron 1988, and Kwiatkowski et al. 1992. They were all found to be stationary in first difference. Thus, to estimate our equation (1), we tested for co-integration. According to the Engle Granger two-step procedure, and to the Johanssen test, one co-integration relationship exists between the four variables. The long-run characteristics of the model are represented by the regression equation ( 1). The co-integration results appear in table 1. We estimated this relationship using the OLS4 estimator. All the coefficients are significant at 1%, except for the coefficient of car ownership. We added a dummy variable D86 which represents the effect of the mid 1980s price collapse on consumption. Its effect is negative, which means that the fall in gasoline price led to a rise in gasoline consumption. The long-run income elasticity is around 0.6, which is consistent with recent studies5 . The long-term price elasticity is about -0.3, which is relatively high for an industrialised country. This elasticity is consistent with recent studies (Small & Van Dender 2007). In the long-term, income elasticity is stronger than price elasticity (in absolute value). This means that households react stronger after an income variation than after a price variation.

For the dynamic asymmetric modelling, we estimate equation (2) thanks to an ECM. Results are presented in table 2. Regarding the dynamic asymmetric modelling, the Wald test seems to reject the null hypothesis of coefficient stability of P_max, P_rec and P_cut, of P_w1 and P_w2, and of P_t1 and P_t2. So there is a form of asymmetry of price variations on gasoline consumption; thus, we use a short run modelling with asymmetry. The coefficient for the error corrector term is significant and negative, which validates the ECM. Its value is around -0.25. AIC and BIC criterion seem to choose the first two modellings: Gately and Traill what are the same when we keep only the significant coefficient. Thus, we will explain these modelling. It seems that there is a break on the GDP series in 1985/1987, which represents the new situation in the USA with the Reagan government. Thus, after the 1982 recession, and up to 1989, GDP growth was around 3.8% per year, on average, The short run income elasticity is significant, at around 0.4. This coefficient is consistent with the literature [START_REF] Hughes | Evidence of a shift in the short-run price elasticity of gasoline demand[END_REF]. In fact, the estimated short run income elasticities in the US, according to different recent studies, range from 0.21 to 0.756 . Coefficients for the negative variations in price are always insignificant. This means that households react more after a rise in price than after a price fall. The coefficients for the maximum historical price are significant. Thus, households seem to be sensitive to price increases, and more precisely, to strong increases beyond the historical maximum, which is the interpretation of the P_max and P_t1 series. Their values are around -0.42. Following this analysis, there are two main conclusions. First, households are more sensitive to a price rise than a similar fall. The theoretical reasons for such asymmetry can be the irreversibility of technological progress because of the rapid technological development in the US and the insertion of more efficient vehicles.

The income elasticities (in the long and the short-run) correspond to what we find for industrialised countries. The US is on average a rich country, so the proportion of gasoline expenditure out of total household expenditure is low. Thus, the income elasticities are below 1. Price elasticities are quite high, but it can be logical for a country where taxes on the gasoline price are low because every variation is directly passed onto the consumer. However, only the coefficient of price increases is significant, and the coefficient for the maximum historical price is higher than the others. Even so, the elasticity of a high increase in price is very high. The US is a big country with a very low population density (32.35 inhabitants per km² compared to India: 368.15), which tends to increase the need for a personal vehicle. If price varies, the use of cars will not change. Thus, in the main a high increase in price can significantly reduce the use of private vehicles, and hence decrease gasoline consumption.

Estimated results for India

Unlike the US, we estimate two different equations instead of equation ( 1) and two more equations instead of equation (2). In fact, India' s fleet is composed of two kinds of vehicles and there is no reason that consumers react in the same way if they own a car or a twowheeled motor vehicle. Thus, we estimate the co-integration relationships as follows:

t t t t t P Y car V car C , 1 1 1 1 1 _ _ ε η γ β α + ⋅ + ⋅ + ⋅ + = (1.1) t t t t t P Y tw V tw C , 2 2 2 2 2 _ _ ε η γ β α + ⋅ + ⋅ + ⋅ + = (1.2)
Concerning the short run ECM, the two equations are:

t t i t t i i i t t i i i t t i i i t t i i t u P Y car V car C car C , 1 1 , 1 1 1 1 1 1 1 1 1 1 1 ' ' _ ' _ ' _ + ⋅ + ∆ ⋅ + ∆ ⋅ + ∆ ⋅ + ∆ ⋅ + = ∆ - - = - = - = - = ε λ η γ β δ α (2.1) t t i t t i i i t t i i i t t i i i t t i i t u P Y tw V tw C tw C , 2 1 , 2 2 1 2 1 2 1 2 1 2 2 ' ' _ ' _ ' _ + ⋅ + ∆ ⋅ + ∆ ⋅ + ∆ ⋅ + ∆ ⋅ + = ∆ - - = - = - = - = ε λ η γ β δ α (2.2)
Regarding the long run relationship, and in the same way as for the US, the different variables (in logarithms) were found to be integrated for order one and stationary in first difference. Engle and Granger' s two-step process and Johanssen' s test, seemed to conclude the existence of one co-integration relationship between these variables, and for the two equations, so we could estimate equation (1.1) and (1.2) as the co-integration relationships. The long-run characteristics of the models appear in tables 3 and 4 (ii) du03 is a dummy variable which equals 1 for 2003,2004,2005 All the coefficients are significantly different from zero at one or five percent. We added a dummy variable that equals one for the years 2003 to 2005. These years represent the progressive liberalisation of the price of gasoline. The coefficient is about -0.11 to -0.14, so it is negative showing that the liberalisation, which generally causes an increase in price, leads to a reduction in consumption. Concerning the consumption of the car fleet, price elasticity is about -0.6 and income elasticity 0.65. For the consumption of the two-wheeled motor vehicle fleet, price elasticity is around -0.35 and income elasticity 1.8. Thus, both for cars and two-wheeled vehicles, income elasticity is higher than price elasticity in the long-run. Our price elasticity is consistent with the literature, but we have income elasticities lower than other papers (see [START_REF] Ramanathan | Short and long-run elasticities of gasoline demand in India: an empirical analysis using co-integration techniques[END_REF]. We added a variable for vehicle ownership, which tends to reduce the coefficient for GDP. However, our income elasticities are quite high because we are in a period of "take off" of motorisation. Income elasticity of fuel consumption of two-wheeled vehicles is high and stronger than income elasticity of fuel consumption of cars. Thus, if households have a car, it means that they have more money than other Indians, so the fuel budget is smaller and an income variation does has an effect although not as strong as for an Indian who has a two-wheeled motor vehicle.

We could estimate equations (2.1) and (2.2) for the dynamic specifications, making series stationary. It is a restricted ECM, where (ii) D00 is a dummy variable which equals 1 in 2000, 0 otherwise. (iii) There is no significant lag Wald' s test seemed to conclude the symmetric effect of price variations on gasoline consumption. Thus, we did not choose the asymmetric modelling with price decomposition, and we used the price series P. The coefficient for the error corrector term is significant and negative for equations 2.1 and 2.2, which validates the use of an ECM. Estimating the ECM thanks to the OLS estimator, we obtained the short-run elasticities. For both of the models (gasoline consumption on cars and two-wheelers), there is a multicollinearity phenomenon between the vehicle ownership rate and the GDP. In fact, vehicle ownership is very dependent on national income. Consequently, we build a restricted ECM by summing the two variables. Regarding equation 2.1 first (the gasoline consumption of cars) short-run price elasticity is about -0.3, which is quite small. In the same way, short-term income elasticity is also very low, around 0.31, and is lower than for the US.

t t t Y car V Z ∆ + ∆ = _ and t t t Y tw V Z ∆ + ∆ = _ '
Secondly, for equation 2.2 (the gasoline consumption of two-wheeled motor vehicles) results are quite similar: short-run price elasticity is about -0.21 and short-term income elasticity is around 0.28. This modelling induces a price elasticity which is consistent with the literature (about -0.28 for Ramanathan in 1999), but our income elasticity seems to be very small compared to other studies [START_REF] Ramanathan | Short and long-run elasticities of gasoline demand in India: an empirical analysis using co-integration techniques[END_REF][START_REF] Mcrae | Gasoline demand in developing countries[END_REF]. It can be explained by the introduction of a vehicle ownership variable, which can reduce the income effect.

The conclusions for the Indian model are three-fold. First, it seems that there is no asymmetric effect of price variations on gasoline consumption, both for the fuel consumption of cars and two-wheeled vehicles. Thus, households have the same reaction but in the opposite direction, if the price of gasoline rises or if it falls in a similar proportion. The reason is that, on average, households are very poor, so they do not have any choice in buying a more efficient vehicle or taking other irreversible decisions. Taking the bus or travelling on foot or by bicycle when gasoline prices are high, and using a private vehicle when price falls is an example of reversible behaviour. In Delhi, fifty percent of the transport consists of buses while 81% of households have a vehicle: two-wheeled motor vehicles or cars7 . Second, it seems that price elasticity is quite small in the short-run. Its low level is not surprising in an emergent country. First, in this kind of country with high economic growth, the transport sector needs to develop. Thus, even if the price varies, the necessity to travel is very high. Moreover, households who own a private vehicle (more precisely a car) use it, even when there are price increases. This can be a desire to be distinguishable from others because it is a sign of wealth. Generally, cars are imported in India, and so driving a foreign car is synonymous with wealth. In the long-run, price elasticity is higher than for an industrialised country which is very interesting for a policy related to gasoline prices. Third, it seems that price elasticity in the long-run is quite high. It can be interesting in terms of substitution in the future. We expect that in a context of high gasoline prices (due to the market or to fiscal policy) consumption tends to slow down.

IV. Conclusion

This paper used a co-integration technique to estimate the price elasticity of gasoline consumption on passenger vehicles. The model explained consumption per capita by its different determinants: gasoline price, national income per capita and vehicle ownership. Moreover, it tried to find out if price variation has a symmetric effect on gasoline consumption, thanks to short-term modelling, such as an ECM. We pointed out the fact that there is a form of asymmetry of price variations on gasoline consumption in an industrialised country like the US. This is because of the importance of automobile transport and the irreversibility of technological progress. The model also showed that there is no asymmetry of price variations on consumption in an emergent area such as India. Household behaviour, therefore, seemed to be reversible. In terms of price and income elasticities of fuel consumption, it seems that they are smaller in the short-run than in the long-run. For India, they are especially low in the short-run and high in the long-term (more precisely concerning the income elasticity of fuel consumption of twowheeled vehicles). For the US, elasticities are consistent with the literature and not so different in the short and long-run. Thus, the principal aspect of this paper is the comparison between India and the US. It seems that consumers' reactions are very different in these two kinds of countries. This difference implies that if we want to reduce gasoline consumption, or to prevent too strong growth, economic policies will be distinct. We can say that in the US, there is a trend to reduce gasoline consumption when prices rise sharply. Thus, in a context where gasoline prices increase tremendously, we can let this natural trend happen. Yet, the government can accompany this movement by introducing new standards of fuel efficiency, for example. Concerning India, price elasticities are low in the short-run and higher in the long-run. Thus, a policy related to gasoline prices could be introduced in the long-run. However, the level of elasticity is not very high. It would be interesting to develop other economic policies such as improved standards or the promotion of public transport.

Fig. 1 :

 1 Fig. 1: The different decompositions of gasoline real price in the US

Fig

  Fig. 2: Vehicle ownership per adult in India and in the US 3

Fig. 3 :

 3 Fig. 3: Evolution of gasoline consumption and gasoline price (in logarithms) in the US and in India

Decomposition 3: Traill et al. (1978)

	P	_	max	t		max	O P	,...,	t P
	P	_	rec				0 max	, 0	P	_	max	1	P	1	P	_	max	P
	P	_	cut				0 min	, 0	P	_	max	1	P	1	P	_	max	P
	Decomposition 2: Wolffram (1971)
	P	_	t 1 w	=	P	_	max	t	+	P	_	t rec
	P	_	w	2	t	=	P	_	cut	t
	P		t	t	=	P				t
	P		t		t	=	P	cut	t	+	P	_	t rec

Table 1 : Results of the co-integration relationship

 1

	Estimation of coefficients for the following variables: co-integration relationship
	Y	0.60***
	P	-0.28***
	D86	-0.16***
	Notes:
	(i) *, **, *** indicate significance levels at 10%, 5%, and 1% respectively.
	(ii) D86 is a dummy variable which equals 1 in 1986, 0 otherwise.

Table 2 : Results of the ECM

 2

	Estimation of coefficients	ECM asymmetry	ECM asymmetry	ECM asymmetry
	for	the	following	Gately	Traill	Wolffram
	variables:
	Y.Du87 1 ˆ-t ε		0.4** -0.25***	0.4* -0.25***	0.92*** -0.31**
	D87			0.25***	0.25***	0.25***
	P
	P_max		-0.42***
	P_w1				-0.42***
	P_t1					-0.25***
	R²			0.94	0.94	0.91
	AIC			-5.30	-5.30	-5.01
	BIC			-5.11	-5.11	-4.82
	Durbin-Watson		2.42	2.42	1.74
	Jarque-Bera		0.06	0.06	0.42
	White -F stat		1.09	1.09	1.89
	Wald test		Rejection of the	Rejection of the	Rejection of the
				symmetry	symmetry	symmetry
	Notes:
	(i) *, **, *** indicate significance levels at 10%, 5%, and 1% respectively.
	(ii) D87 is a dummy variable which equals 1 in 1987, 0 otherwise.
	(iii) There is no significant lag

Table 3 : Results of the co-integration relationship for the consumption equation of cars

 3

	Estimation of coefficients for the following variables:	co-integration relationship
	V_car	0.43***
	Y	0.65***
	P	-0.58***
	du03	-0.11***
	Notes:
	(i) *, **, *** indicate significance levels at 10%, 5%, and 1% respectively
	(ii) du03 is a dummy variable which equals 1 for 2003, 2004, 2005

Table 4 : Results of the co-integration relationship for the consumption equation of two- wheelers

 4

	Estimation of coefficients for the following variables:	co-integration relationship
	constant	-10.88***
	V_tw	0.10*
	Y	1.89***
	P	-0.35**
	du03	-0.14***
	Notes:
	(i) *, **, *** indicate significance levels at 10%, 5%, and 1% respectively.

Table 5 : Results of the ECM for car fuel consumption

 5

	Estimation of coefficients for the following variables:	ECM Symmetry
	C t-1	0.29**
	Z ˆ-t ε	1	0.31*** -0.51***
	D03	-0.08***
	P		-0.30***
	R²		0.60
	AIC	-4.48
	BIC	-4.24
	Durbin-Watson	2.29
	Jarque-Bera	0.71
	White F-stat	1.33
	Notes:
	(i) *, **, *** indicate significance levels at 10%, 5%, and 1% respectively.
	(ii) D03 is a dummy variable which equals 1 in 2003, 0 otherwise.
	(iii) There is no significant lag

Table 6 : Results of the ECM for two-wheeler fuel consumption

 6

	Estimation of coefficients for the following variables:	ECM Symmetry
	C t-1	0.32**
	Z' ˆ-t ε	1	0.28*** -0.33**
	D00	0.08**
	P		-0.21***
	R²		0.61
	AIC	-4.24
	BIC	-4.00
	Durbin-Watson	2.20
	Jarque-Bera	1.45
	White F-stat	0.69
	Notes:
	(i) *, **, *** indicate significance levels at 10%, 5%, and 1% respectively.

It is the Granger' s representation theorem

The left Y-axis is for Indian consumption, while the right Y-axis is for the US

Ordinary Least Squares

[START_REF] Hughes | Evidence of a shift in the short-run price elasticity of gasoline demand[END_REF]

[START_REF] Hughes | Evidence of a shift in the short-run price elasticity of gasoline demand[END_REF]

[START_REF] Bose | Transportation in developing countries: greenhouse gas scenarios for Delhi, India[END_REF]

for excellent research assistance, to Jean-Pierre Orfeuil


Vincent Bremond
email: vincent.bremond@ifpen.fremmanuel.hache@ifpen.frvalerie.mignon@u-paris10.fr

Emmanuel Hache
email: emmanuel.hache@ifpenergiesnouvelles.fr.

Valérie Mignon
email: valerie.mignon@u-paris10.fr.

Does OPEC still exist as a cartel? An empirical investigation

Keywords: JEL Classification: C22, C23, L11, Q40 Oil prices, oil production, OPEC, cartel, cointegration, causality

La collection "Les cahiers de l'économie" a pour objectif de présenter des travaux réalisés à IFP Energies nouvelles et à IFP School, travaux de recherche ou notes de synthèse en économie, finance et gestion. La forme peut être encore provisoire, afin de susciter des échanges de points de vue sur les sujets abordés. Les opinions émises dans les textes publiés dans cette collection doivent être considérées comme propres à leurs auteurs et ne reflètent pas nécessairement le point de vue d' IFP Energies nouvelles ou d' IFP School.

Pour toute information sur le contenu, prière de contacter directement l' auteur. Pour toute information complémentaire, prière de contacter

Introduction

Since the creation of the Organization of the Petroleum Exporting Countries1 (OPEC) at the beginning of the 1960s, many authors have focused on its role in the oil market and, notably, its capability to influence oil prices in both the short and long run (see [START_REF] Dahl | Dynamic Modeling and testing of OPEC Behavior[END_REF] among others). At the same time, the oil pricing system witnessed major transformations in the last 50 years, which can be summarized by a shift from an administered pricing system to a market related price since the middle of the 1980s. The posted price period, with the different pricing systems in the physical market Gulf price (Single Basis System), was implemented by the International Oil Companies (IOC) at the beginning of the 20 th Century.

According to Yergin (1991) and [START_REF] Fattouh | The origins and evolution of the current international oil pricing system: A critical assessment[END_REF], the aim of this pricing regime was to lower the tax paid by the IOC to the host countries, leading to a very low and stable official price whatever the market conditions. The entry into the market of new large scale players, such as the Soviet Union at the end of the 1950s, triggered a major change with a huge surplus of production. In reaction, the so-called seven sisters cartel decided to cut by 10 per cent the posted prices in the market to safeguard their market share. This factor can be considered as the key element which triggered the creation of the OPEC in 1960. Nevertheless, it took 13 years for the Organization to claim its "market power" in the oil sector. Since 1973, the oil market as well as the oil pricing regime have thus experienced a period of continuous change.

Within this context, our aim is to investigate the dynamics of the production behavior of countries belonging to the OPEC, as well as non-member countries that are considered as major key players in the oil market. More specifically, our aim is to determine if OPEC acts as a cartel by testing whether the production decisions of the different countries are coordinated and if they have an influence on oil prices.

A related question has been previously investigated by [START_REF] Dahl | Dynamic Modeling and testing of OPEC Behavior[END_REF] who test numerous theories about the OPEC behavior. They show that OPEC is not a cartel, and that some countries behave in a non-competitive way or with a target revenue goal. Considering various sub-periods, [START_REF] Loderer | A Test of the OPEC Cartel Hypothesis: 1974-1983[END_REF] shows that while the announcements of OPEC decisions do not affect prices in the 1974-1980 period, the alternative hypothesis that OPEC could act as a cartel is not rejected during the beginning of the 1980s. Using cointegration and Granger causality tests, [START_REF] Gülen | Is OPEC a Cartel? Evidence from Cointegration and Causality Tests[END_REF] obtains similar findings: OPEC production Granger-causes oil prices during the 1982-1993 period, whereas none causality is found for the previous periods investigated. Testing different models (namely cartel models, competitive models, target revenue models and property rights models), [START_REF] Griffin | OPEC Behavior: A Test of Alternative Hypotheses[END_REF] puts forward that the partial market sharing is the best fitted model for the OPEC countries, while non-OPEC countries are better represented by the competitive model. Other studies consider that OPEC is a divided cartel. [START_REF] Hnyilicza | Pricing Policies for a Two-part Exhaustible Resource Cartel, the Case of OPEC[END_REF] split OPEC in two groups, namely "saver" and "spender" countries, the spender countries being composed of members with an immediate need for cash and rate of discount lower than the savers, and focuses on the bargaining power between these two groups. Following the same typology, [START_REF] Aperjis | The Oil Market in the 1980s, OPEC Oil Policy and Economic Development[END_REF] concludes that a conflict can exist between OPEC members regarding their production decisions. There also exists a set of studies concerned with target behavior models, such as [START_REF] Teece | OPEC Behaviour: An alternative view[END_REF] and [START_REF] Adelman | OPEC as a Cartel[END_REF] who modeled OPEC behavior according to a target revenue model. Alhajji and Huetnner (2000) conclude as well that OPEC does not act as a cartel, and that the target revenue model is not rejected for Algeria, Libya and Nigeria. Finally, one can mention the works by [START_REF] Johany | The Myth of the OPEC Cartel, the Role of Saudi Arabia[END_REF] focusing on the impact of the uncertainty about property rights, and [START_REF] Macavoy | Crude Oil Prices as Determined by OPEC and Market Fundamentals[END_REF] showing that political events and market fundamentals (a growing demand and speculation) have played a key role in explaining the price dynamics during the 1970s, more than the OPEC behavior itself. This brief survey of the literature shows that no consensus exists regarding the OPEC production behavior. Our aim is to contribute to this literature by testing if OPEC acts as a cartel. To this end, we rely on time series and panel (i) cointegration techniques to investigate the existence of a long-term relationship between the production of each member and that of the OPEC, and (ii) Granger causality tests to apprehend the influence of OPEC production decisions on oil prices.

The rest of the paper is organized as follows. In Section 2, we briefly describe the major changes observed in the oil pricing regime since the first oil shock, which also define the subperiods of our empirical study. Section 3 describes the data, and Section 4 provides the results of cointegration and causality tests. Finally, Section 5 concludes the article.

Main changes in the oil pricing regime

In 1973, the oil market experienced a new way of pricing with the introduction of the Government Selling Price (GSP) or Official Selling Price (OSP). This price can be considered as the historical counter-part of the Posted price implemented by the IOC few decades earlier except that the prices were now determined by OPEC, with the Arabian Light (34°, API) as the crude marker. OPEC was at that time the dominant player in the market (with more than 50 per cent of the market share).

Nevertheless, if this period was marked by the two world oil shocks (1973-1974 and 1979-1980), with a sharp increase of the prices on the market-from 3.65 to 11.65 US dollars per barrel between October and December 1973 in nominal terms, and from around 14.00 US dollars in 1978 to 40.85 in November 1980-it also had a significant positive impact on the level of production from countries outside the Organization. Thus, during the 1973-1982 period, the non-OPEC countries' production registered an increase about 44.5 per cent, with Mexico (+ 2.5 millions barrel per day), the United Kingdom (+ 2.0 millions) and Norway (+ 0.5 million) representing around 45 per cent of this increase. It helps to develop the spot markets and can explain the emergence of a dual system in terms of pricing with the coexistence of an OPEC reference price (administered price) and a market price. This leads to the second sub-period of our study. The 1982The -1986 period is characterized by an important decline of oil prices, given rise to the oil counter-shock in 1986 with oil prices at less than 10 US dollars per barrel. It has also conducted to the implementation of the quotas policy by the Organization in March 1982. From 1982to 1986, the oil pricing system has experienced a transition period with the administered OPEC price which lasts until 1985, a growing influence of the price in the spot markets and the introduction of the netback pricing system in 1986. This latter has been abandoned a few months after due to the implementation of a new market share policy from Saudi Arabia. The year 1986 can be considered as a milestone in the oil markets with the introduction and the widespread of a new system: the market related regime. It represents the first construction phase of a complex structure on the market with the introduction of a pricing system based on three reference prices: West Texas Intermediate (WTI) for North America, Brent for Europe and South American countries, and Oman-Dubai for crude oil sent to the East Asia, by the national Mexican company PEMEX. It also represents a sort of "golden youth" on the oil market including the import of the classical tools of modern finance (swap, options) created by the financial revolution, the so-called "financial big bang" of the early 1980s. During 1986-1993, we observed a sharp increase of the liquidity (in terms of financial contracts such as Light Sweet Crude Oil) in the financial market, especially on the New York Mercantile Exchange (NYMEX) and on the International Petroleum Exchange2 (IPE). This was followed by a gradual financialization on the market during 1993-2001. For example, the number of futures contract in the NYMEX increased by more than 40 per cent during this period and at the same time the oil-pricing regime experienced some controversy: squeeze, decrease of the liquidity due to a marked decrease of the Brent and WTI production which have introduced some doubts regarding the efficiency of the price formation on the market. This brief description of the main changes in the oil pricing system leads us to consider five sub-periods in our empirical analysis: January 1973 to February 1982, March 1982to April 1986, May 1986to February 1993, March 1993 to December 2000, and the period starting in January 2001.

Data and unit root tests

We consider a sample of 15 countries including (i) 11 countries belonging to the OPEC (Algeria, Indonesia, Iran, Iraq, Kuwait, Libya, Nigeria, Qatar, Saudi Arabia, the United Arab Emirates, and Venezuela), and (ii) 4 other non-OPEC countries (namely Mexico, Norway, the United Kingdom, and Russia). Production and price series are extracted from Datastream.

We use monthly data from January 1973 to July 2009. Note that for some countries, production data are not available on the whole period. More specifically, we consider Indonesian production until May 2008, given that Indonesia leaves OPEC after that date. For Mexico, Norway and UK, the analysis begins in 1982-due to the establishment of a larger oil production policy than previously-while the Russian analysis starts after its creation in 1991. Furthermore, we exclude Ecuador and Gabon from the analysis because of their coming and going into the Organization.

Recall that our aim is to test for a cooperative behavior between OPEC members by investigating the link between the production of one member country and the global production of the other OPEC members. For each country i, we define the production of the other member countries-which we call "rest of the cartel production"-as the difference between the total OPEC production and the individual production of country i. 4The crude oil price is the UK Brent in US dollars. It is expressed in real terms using the US CPI (extracted from Datastream) as the deflator. Finally, note that production and price series are expressed in logarithmic terms.

The first step is to determine the integration order of our series. Given that our sample period is characterized by various oil pricing regimes (see Section 2), we consider a test robust to structural breaks. We rely on the [START_REF] Zivot | Further evidence of great crash, the oil price shock and unit root hypothesis[END_REF] test. Under the null hypothesis, there is a unit root without any exogenous structural break, whereas the alternative hypothesis is the stationarity with a break date determined endogenously. The conclusions of the test are reported in Table A1 in the appendix and show that the majority of the considered series are I(1). Table 1 summarizes the results and lists the countries for which both the individual production and the rest of the cartel production are I(1).

Cointegration and Granger causality tests

Time series analysis

In order to test for the existence of a long-run relationship between the production of exporting countries and that of the OPEC, we rely on the [START_REF] Engle | Cointegration and Error Correction: Representation, Estimation and Testing[END_REF] test based on the null hypothesis of no cointegration. Results displayed in Table 2 5 show that, for the first three periods, none of the large producing countries is cointegrated with the rest of the Organization production. This result indicates that the individual shares of production are not constant over time. Countries for which cointegration is obtained do not account for more than 30 percent of the OPEC production.

Contrary to the first three periods, the 1993.03-2000.12 sample is characterized by the presence of massive producers (particularly Saudi Arabia) accounting for nearly 50 per cent of the OPEC market share. Moreover, the absence of cointegration is also rejected for two 5 See Table A2 in the appendix for the detailed results.

countries belonging to our non-OPEC group, leading to the question of a possible larger agreement between producer countries. This sub-period could also be considered as some kind of "Golden Age" in terms of coordination within the Organization. Finally, in the last period (2001.01-2009.07), cointegration is observed for countries representing about 65 to 70 percent of the OPEC production, but without any non-OPEC producers. As for the previous period, Saudi Arabia belongs to these cointegrated countries.

It is worth noting that during the first three periods characterized by the absence of cointegration for the major oil producers, the OPEC market share in world oil production was highly volatile and unstable (see Figure 1). On the contrary, this market share is quite stable since 1993. These findings may lead us to the conclusion that OPEC also fits its production policy according to the non-OPEC supply and demand. To investigate the direction of the link between production and price series, we implement the Granger causality test. The first main conclusion that emerges from Table 3 6 is that, except for Libya, we can not reject the null hypothesis of no causality for each OPEC member during the first controversial 1973.01-1982.02 sub-period (including two oil shocks). As a consequence, there is no evidence that production policies are linked to price movements, a result which is relevant with the fact that the price was fixed during this period. The second sub-period is mainly characterized by the rejection of the null hypothesis of no causality from price to production for the OPEC as a whole, illustrating the fact that OPEC is price taker. Whereas the links between price and production are relatively weak in this period, the null of no causality from production to price is rejected for major producers in the third period, with a feedback effect observed for Saudi Arabia. The sub-period following the counter shock is thus the only one for which the OPEC production policy seems to have had an effect on price. It reflects the abandonment of the "netback" pricing system from Saudi Arabia and other OPEC countries which experienced the former system. For the fourth period, the main finding is the existence of a relation running from price to production for the OPEC. The last period is probably one of the most interesting, because several producers display a link from price to production in a context of increasing prices. Once again, OPEC is price taker. These results lead us to test if there is a split between countries inside OPEC itself. We classify the member countries into two sub-groups:

• "saver" group: Iran, Libya, Kuwait, Qatar, Saudi Arabia, U.A.E. and Venezuela;

• "spender" group: Algeria, Indonesia, Iraq and Nigeria.

This separation aims at investigating if some countries have more incentive to cheat than others. To explain this behavior, suppose that cheating is induced by the short-run wish to earn money. Various reasons may justify this behavior. The first one relies on demographic facts: a country with a large population and/or an increasing growth rate will have to "feed" its population. Second, the type of crude oil may play an important role. It seems reasonable to think that good quality crude (i.e. with the lightest and lowest sulfur content) will be easily sold (and at a higher price), whatever the price. Thus, the owner has a relative less incentive to let oil in the ground, which involves faster reserves depletion. Finally, the type of political regime matters. A democratic regime supposes regular election dates and so, a short or midterm view. Given that the population opinion obviously plays a crucial role in winning votes and elections, accounting for it may justify the "spending oil" behavior of countries to earn money.

Table 4 reports some descriptive characteristics for our two groups of countries. The spender group is characterized by large population countries (from 28 to 240 millions), with a very valuable crude oil (but with small reserves), and a (relatively) flexible political regime. Note that the presence of Iraq is due to three major wars, which involves that political regimes have to rebuild the country. The saver group-characterized by a smaller discount rate than the spenders-is composed by small population countries (between 830 000 and 66 millions), with large R/P ratios, and relatively heavier crude oil. These countries are less democratic than the other ones; the two exceptions being Iran and Venezuela due to their specific political regimes. Furthermore, despite a low R/P ratio, Qatar is included too because of its high gas reserves, making oil business only as a complement into the Qatari GDP. Let us now investigate the specific properties of these two groups of countries through a panel data analysis.

Panel data analysis

To complement our time series analysis, we now implement panel cointegration tests by searching for a long-term relationship between the production of the various countries and the total OPEC production. We consider the seven tests proposed by [START_REF] Pedroni | Critical values for cointegration tests in heterogeneous panels with multiple regressors[END_REF][START_REF] Pedroni | Panel cointegration. Asymptotic and finite sample properties of pooled time series tests with an application to the PPP hypothesis[END_REF].7

These tests are based on the null hypothesis of no cointegration. Among the 7 Pedroni's tests, 4 are based on the within dimension (panel cointegration tests) and 3 on the between dimension (group-mean panel cointegration tests). Group-mean panel cointegration statistics are more general in the sense that they allow for heterogeneous coefficients under the alternative hypothesis: under the alternative hypothesis, there exists a cointegration relationship, and this relationship is not necessarily the same for each country.

Results from Pedroni's tests are reported in Table 5. As it is frequently the case, these results are somewhat mixed. They can be summarized as follows. Considering first the group-mean panel cointegration tests, the null hypothesis of no cointegration is always rejected, meaning that a long-term relationship exists between the production series, whatever the panel and the sub-period considered. Turning now to the tests based on the within dimension, the results are less clear-cut. Indeed, if the homogeneity assumption of the cointegrating relationship between countries is retained, our findings show that the null of no cointegration is not rejected in the following cases: (i) the 1986.05-1993.02 sub-period for the three groups of countries, and (ii) the 1982. 05-1986.04, 1986.05-1993.02 and 1993.02-2000.12 periods for the group of spenders. To sum up, these findings tend to show that OPEC acts as a cartel especially with the group of savers since there exists a long-term relationship between the production of countries belonging to this group and the total OPEC production. The influence of the OPEC seems to be weakened on the 1982.05-1993.02 sub-period since no cointegrating relationship exists on this sub-period, including the case of the savers' countries. This result is not surprising and illustrates the decreasing influence of the OPEC following the oil countershock. In order to investigate the direction of the link between production and price series, we now proceed to Granger-type causality tests. Results are reported in Table 6. When a causal link exists, it generally runs from price to production. Indeed, a causality running from production to price is observed only in the following cases: (i) on the 1986.05-1993.02 sub-period for the complete panel and the group of savers, (ii) on the 1982.03-1986.04 sub-period for the group of spenders, and (iii) on the whole period for the savers' countries. These results tend to confirm the findings of the cointegration tests since they put forward a higher influence of the OPEC on the group of savers. They also highlight the growing role of the OPEC during the 1982.03-1986.04 sub-period-with a causality running from production to price in the group of spenders-corresponding to the introduction of quotas and the development of the spot market. Price-prod: null of no causality from price to production, prod-price: null of no causality from production to price.

Conclusion

In this paper, we analyze the evolution of the production behavior of countries belonging to the OPEC, as well as four non-member countries that are considered as key players in the oil market (Mexico, Norway, Russia, and the UK). More specifically, we aim at determining if OPEC acts as a cartel. To this end, we rely on time series and panel cointegration and causality tests to investigate whether production decisions of the different countries are coordinated and if they have an influence on oil prices.

Our findings shows that the influence of OPEC has evolved through time, following the changes in the oil pricing system registered on the market. In particular, investigating the OPEC behavior on various sub-periods, we find that, while OPEC's influence was strong in the period that just follows the oil counter-shock, it acts as a price taker for the majority of the considered sub-periods since 1973. Finally, by splitting OPEC into two groups, the savers and spenders, we show that OPEC may be viewed as a divided organization in the sense that it acts as a cartel mainly with a subgroup of its members. 1973.01-1982.02 1982.02-1986.04 1986.05-1993.02 1993.03-2000.12 2001.01-2009.07 Algeria I(1) I( 1 1973.01-1982.02 1982.03-1986.04 1986.05-1993.01 1993.02-2000.12 2001.01-2009 Price-prod: null of no causality from price to production, prod-price: null of no causality from production to price.

-: different orders of integration. °: data unavailable.

1973. 01-1982.02 1982.03-1986.04 1986.05-1993.02 1993.03-2000.12 2001.01-2009

  After the introduction at the end of December 2000 of the law modernizing raw materials markets-the Commodity Futures Modernization Act 3 (CFMA)-two major changes have been registered. From January 2001 to January 2009, we observed a sharp rise in transaction volumes in the financial markets, and a context of high volatility on the oil market. Between January and July 2008, oil prices increased to almost 147 US dollars per barrel and collapsed a few weeks later to under 35 US dollars per barrel. This context has left many analysts and researchers puzzled by the underlying explanations for determination of prices and the influence of the non-commercial players in the market. The interaction between a physical price based on geographical reference prices and a financial one based on futures contracts with the underlying assumption of a growing speculation factor seems to have changed drastically the market conditions.

Figure 1 .

 1 Figure 1. OPEC market share in world oil production.

  resp. I(1), I(2)) : series are integrated of order 0 (resp. 1, 2).

Table 1 . Individual and rest of the cartel productions integrated of order 1.

 1

	1973.01-1982.02	1982.03-1986.04	1986.05-1993.02	1993.03-2000.12	2001.01-2009.07
	Algeria, Indonesia,	Algeria, Indonesia,	Algeria, Indonesia,	Algeria, Indonesia,	Indonesia, Iran,
	Iran, Kuwait,	Iraq, Kuwait,	Libya, Nigeria,	Iraq, Libya,	Iraq, Kuwait,
	Libya, Nigeria,	Qatar, Venezuela,	Qatar, U.K.	Nigeria, Qatar,	Libya, Nigeria,
	Qatar, Saudi	Mexico, U.K.		Saudi Arabia,	Qatar, Saudi
	Arabia, Venezuela.			U.A.E., Mexico,	Arabia, U.A.E.,
				Norway, U.K.,	Venezuela,
				Russia	Mexico, Norway,
					U.K., Russia

Table 2 . Results of the Engle-Granger cointegration test.

 2

	Sub-periods	Cointegrated countries
	1973.01-1982.02	Algeria, Kuwait, Nigeria, Qatar
	1982.02-1986.04	Algeria, Indonesia, Mexico
	1986.05-1993.02	Indonesia, Libya, Nigeria, Qatar
	1993.03-2000.12	Nigeria, Qatar, U.A.E., Saudi Arabia, Mexico, Norway
	2001.01-2009.07	Iran, Nigeria, Qatar, Saudi Arabia*, U.A.E.*

*: rejection of the null hypothesis of no cointegration at the 10% significance level. Otherwise, the significance level is 5%.

Table 3 . Results of the Granger causality test.

 3 : rejection of the null hypothesis of no causality at the 10 % significance level. Otherwise, the significance level is 5 %. Price-prod: null of no causality from price to production, prod-price: null of no causality from production to price.

*

Table 4 .

 4 Descriptive facts.

	Countries	Population (in	Pop. Growth Rate	Rate of	R/P (BP	(R/P)/pop	Current Balance	Type of Crude (Heaviness, Sulfur	Type of political
		millions) (CIA	(CIA Factbook)	Unemployment	Stats)		(2008 & 2009) (in	Content) (Energy Intelligence	regime (CIA
		Factbook)		(CIA Factbook)			$ billion)	Group)	Factbook)
	Algeria	34.18	1.196	10.2%	16.7	0.48	(34.45 & -4.26) Light, Low Sulfur Content	Republic
	Indonesia	240.27	1.136	7.7%	10.2	0.004	(0.125 & 10.58) Light, Low Sulfur Content	Republic
	Iraq	28.95	2.506	15.2%	(a)	> 3.45*	(12.2 & -19.9)	Medium, Low to medium sulfur	Republic
								Content	(instable)
	Nigeria	149.23	2	5%	45.6	0.31	(39.36 & 10.01) Medium, Low Sulfur Content	Federal
									Republic
	Iran	66.43	0.883	11.8%	86.9	1.30	(23.99 & 2.653) Medium, Medium to high sulfur	Islamic
								content	Republic
	Kuwait	2.69	3.549 (b)	2.2%	99.6	37.03	(64.78 & 32.01) Medium, High Sulfur Content	Constitutional
									Emirate
	Libya	6.32	2.172	30%	64.6	10.2	(35.7 & 8.257) Medium, Low to medium Sulfur	Authoritarian
								Content	state, de facto
	Qatar	0.83	0.957	0.5%	54.1	65.18	(14.23 & 4.619) Low to High, Medium Sulfur	Emirate
								Content
	Saudi Arabia	28.69	1.848	11.7%	66.5	2.318	(133.5 & 26.5) Medium, Medium sulfur Content	Monarchy
	U.A.E.	4.80	3.689	2.4%	89.7	18.68	(22.31 & -4.04) Medium, Low to Medium Sulfur	Federation of
								Content	Emirates
	Venezuela	26.81	1.508	7.9%	(a)	> 3.72*	(37.39 & 8.561) High to oil sands, Low to extra	Federal
								high Sulfur Content	Republic

*Assumption based on a R/P ratio equal to 100 years. (a): more than 100 years, (b): reflects a return to pre-Gulf crisis immigration of expatriates.

Table 5 . Panel cointegration tests (p-values).

 5

			Panel cointegration tests		Group-mean panel cointegration tests
		Panel v	Panel rho Panel PP Panel ADF	Group-	Group-	Group-
						mean rho	mean PP	mean ADF
	All OPEC
	1973.01-	0.1086	0***	0.0001***	0***	0***	0***	0***
	1982.02
	1982.03-	0.1720	0.002***	0***	0***	0.0002***	0***	0***
	1986.04
	1986.05-	0.2974	0.2554	0.2700	0.3439	0***	0***	0***
	1993.02
	1993.03-	0.0275** 0.0135**	0.0745*	0.1166	0***	0***	0***
	2000.12
	2001.01-	0***	0***	0***	0***	0***	0***	0***
	2009.07
	Savers
	1973.01-	0.3058	0***	0.0017*** 0.0012***	0***	0***	0***
	1982.02
	1982.03-	0.1923	0.0105**	0***	0***	0.0066*** 0.0002*** 0.0002***
	1986.04
	1986.05-	0.2400	0.2045	0.2464	0.3342	0.0008*** 0.0017*** 0.0002***
	1993.02
	1993.03-	0.1025	0***	0.0010*** 0.0012***	0***	0***	0.0001***
	2000.12
	2001.01-	0***	0***	0.0002***	0***	0.0347**	0.0958*	0.0022***
	2009.07
	Spenders
	1973.01-	0***	0***	0.0053*** 0.0089*** 0.0020*** 0.0043*** 0.0049***
	1982.02
	1982.03-	0.3919	0.1489	0.1329	0.0046*** 0.0083*** 0.0069*** 0.0003***
	1986.04
	1986.05-	0.3942	0.3983	0.3981	0.3980	0.0051*** 0.0057*** 0.0041***
	1993.02
	1993.03-	0.1338	0.3505	0.3976	0.3915	0.0146**	0.0933*	0.1084
	2000.12
	2001.01-	0***	0***	0***	0***	0.0001*** 0.0007*** 0.0009***
	2009.07

*** (resp.**, *): rejection of the null hypothesis of no cointegration at the 1% (resp. 5%, 10%) significance level.

Table 6 . Granger causality tests (p-values).

 6 ** (resp.**, *): rejection of the null hypothesis of no causality at the 1% (resp. 5%, 10%) significance level.

		1973.01-	1982.03-	1986.05-	1993.03-	2001.07-
		1982.02	1986.04	1993.02	2000.12	2009.07
	All OPEC
	Price-Prod	0.1869	0.0040***	0.0083***	0.0242**	0.0875*
	Prod-Price	0.5534	0.1149	0.0157**	0.6285	0.8423
	Savers
	Price-Prod	0.2169	0.0006***	0.1157	0.0418**	0.0001***
	Prod-Price	0.2189	0.1959	0.0966*	0.5502	0.1057
	Spenders
	Price-Prod	0.4736	0.1130	0.0332**	0.0822*	0.1979
	Prod-Price	0.9175	0.0409**	0.2196	0.7902	0.6738

*

Table A1 . Results of the Zivot and Andrews test.

 A1

Table A2 . Results of the Engle-Granger cointegration test.

 A2

Table A3 . Results of the Granger causality test.

 A3

	*** (resp.**, *): rejection of the null hypothesis of no causality at the 1% (resp. 5%, 10%) significance level.

Table A4 . Panel unit root tests (p-values).

 A4 .07 table reports the p-values of the following panel unit root tests: Im, Pesaran and Shin (IPS, 2003),Maddala and Wu (1999, MW) and[START_REF] Hadri | Testing for unit roots in heterogeneous panel data[END_REF]. The IPS and MW tests consider the unit root as the null hypothesis, while the[START_REF] Hadri | Testing for unit roots in heterogeneous panel data[END_REF] test is based on the null of no unit root. ***: rejection of the null hypothesis at the 1% significance level.

	# 25. J.P. CUEILLE, J. MASSERON*	# 40. A. PIERRU, D. BABUSIAUX
		( E								)			'	( E	)	)
															<	'	!
		-			D									<	)
	Price-Prod All OPEC Savers Spenders # 26. J.P. CUEILLE, E. JOURDAIN Prod-Price Price-Prod .) , ) ' ! 8 ) 8 ) ) ! -0 # 27. J.P. CUEILLE, E. DOS SANTOS This IPS Prod-Price 0.3064 0.3238 0.1321 ) ) ) )	*	Price-Prod )	Prod-Price Price-Prod MW 0.4652 0.3880 0.5115 + % # 41. D. BABUSIAUX / ) ( 7% Prod-Price Price-Prod Prod-Price Hadri 0*** 0*** 0*** , ) :) % # 42. D. BABUSIAUX 4 ) 8 ) ,
	Algeria						*	)		*	0.1394	0.0169**	0.0057*** 0.6418 :) %	0.1698	-	-
		# )			0							# 13. K. FAID
	Indonesia # 28. C. BAUDOUIN, J.P. FAVENNEC 0.9449 0.4685	0.5656	0.1425	0.9644 # 43. P. COPINSCHI 0.5177 :) % )	0.5177	*	0.1397 )	*	0.7732
		3												J	@ ) K
	Iran					0								-	-	-	-/ ) # 14. S. NACHET -%	-	) 0.0005*** 0.2494
	# 29. P. COUSSY, S. FURLAN, E. JOURDAIN, G.	< # 44. V. LEPEZ
	Iraq		-LANDRIEU, J.V. SPADARO, A. RABL -0.1902 8 ) ) E , 0.0630*	-	-3 ) 3 8	= *	)	0.8693 / +	#	0.4157 C 6
	Kuwait	) A )		0.5418 !		)			0.6882 '	)	0.9961	-	-# 15. J.-L. KARNIK, R. BAKER, D. PERRUCHET --0.0006*** 0.4112 -%
		# )			1							/	)	* ' 0 = 2	=
															*
	Libya # 30. J.P. INDJEHAGOPIAN, F. LANTZ, V. SIMON 0.0066*** --	0.9712	0.0083*** -	=	0.3839	0***	0.9666
	Nigeria : 7		! !	1	;	,			-)	-	0.7185	0.0008*** 0.3887 # 16. N. ALBA-SAUNAL 0.5623 ; ) )	0.8383	0.8253
															<	> )	<
	Qatar # 31. A. PIERRU, A. MAURO 0.7250 ' -	0.0143** 0.4219 ! <	0.3938	0.0052*** # 17. E. DELAFOSSE =	0.9589	0.5803
	Saudi Arabia 0.1260 # 32. V. LEPEZ, G. MANDONNET	-	-	0.0003*** 0.0659* $	0.4183 )	0.6188 < * ? , 5 0*** ,	'	0.8805
		$	*									<		)	7	=
	U.A.E. 3				-)			-			-	-	-	-# 18. J.P. FAVENNEC, D. BABUSIAUX* 0.0013* / 8 @ 0.0014*** 0.0001*** 0.6882
	# 33. J. P. FAVENNEC, P. COPINSCHI	;	** '	)
		/ 8			)					!	8 7	)	,	7	=
	Venezuela 7			0.5976					0.0004*** 0.4293	-	-# 19. S. FURLAN	-	-	0.0676*
	# 34. D. BABUSIAUX					/ 8	)	)	! A	)
	OPEC	3	)	0.2359	*			0.0789* * )	0.6855 ) ! '	0.2506	0.0797* 8 , -	&	0.0092* )	0.1525	0***	0.6339
		) +												** # 20. M. CADREN
															)	) !	8 )	)
	Mexico # 35. D. RILEY	°			°		0.0054*** 0.9346	-	-	0.0001* )	'	0.5911	)	0.4669	0.7461
		# )	;	%									+	#	**	&
	Norway # 36. D. BABUSIAUX, A. PIERRU * * * ° ° * ( ) ? 8 '	-	-	-	-# 21. J.L. KARNIK, J. MASSERON* 0.7146 0.2452 0.6690 / 8 * ! 8 -B	0.1007 )
	U.K.		)	%		° ! F			°	%	0.6079	0.4237	0.4410	0.7902 # 22. J.P. FAVENNEC, D. BABUSIAUX 0.3488 0.3488 0.7344 / 8 8	0.4619
	Russia # 37. P. ALBA, O. RECH °	°			°	°	°	-	°	0.7868 B	0.6458	0.0052*** 0.4406
	# 11. E. DELAFOSSE $ 2 ) 3 %	)	)	) !	C	# 23. D. BABUSIAUX, S. YAFIL* . ,	)	,
	3 -# 38. J.P. FAVENNEC, D. BABUSIAUX ) 5 2; ! ' ) G , C % %	3 # 24. D. BABUSIAUX, J. JAYLET* B
	# 12. F. LANTZ, C. IOANNIDIS # -% # 39. S. JUAN, F. LANTZ / H ) ) ) ! '	!		I A 8	( -	D	)	)	C
		+			%

OPEC was formed by five countries (Iran, Iraq, Kuwait, Saudi Arabia, and Venezuela) in September 1960 in Baghdad.

The IPE became the InterContinental Exchange (ICE) in 2001.

For more information, see the CFTC website at http://www .cftc.gov/lawandregulation/index.htm.

Note that in the appendix, the rest of cartel prod is denoted as "country2".

Panel unit root tests have been applied (see TableA4in the appendix) and show that all production series are integrated of order 1, whether one considers the complete panel or the two sub-samples. This confirms the findings obtained in the time series framework.