How Increased Crude Oil Demand by China and India Affects the International Market

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1 How Increased Crude Oil Demand by China and India Affects the International Market By Amanda Niklaus a and Julian Inchauspe b (a) Department of Economics, Curtin University, Perth, Australia. Presenting author. (b) Department of Economics, Curtin University, Perth, Australia. Corresponding author. Abstract The global crude oil market is characterised by complex interactions between demand and supply. The question that we address in this paper is how increased demand for crude oil by China and India affects the world crude oil market. More specifically, we study the implications for pricing, OPEC production and non-opec production in a VAR setting. An interesting hypothesis tested in this paper is whether or not oil demand by China and India is different to the oil demand by other countries. Theoretical aspects of the crude oil market are considered in the analysis. 1. Introduction This paper investigates the implications of increased crude oil demand from China and India for the world crude oil market. Before addressing this question, it is necessary to carefully study the structure of the international crude oil market. In particular, it is also necessary to understand the characteristics of supply and the interactions between OPEC and non-opec suppliers. All these considerations will be taken into account in the empirical model that is presented later. The balance of this paper is organised as follows. Section 2 presents a non-technical overview of trends in demand and supply. Section 3 performs a literature review using a theoretical model as a benchmark. Section 4 provides some empirical analysis based on considerations laid out in Sections 2 and 3. Conclusions are presented in Section 5. 1

2 2. An Overview of the Crude Oil Market The global crude oil market can be analysed by considering how quantity and price are affected by the complex interactions between demand and supply. Before emerging into a more detailed analysis, it is worth noting that the crude oil market can be described as a global market, as Smith (2009, 162) and other researchers in the area have pointed out. Figure 1 shows that the most important crude oil prices in the world move together (the price differences are due to different oil quality and specific shocks) WTI Crude Oil-WTI Spot Cushing US$/BBL Brent Crude Oil-Brent Dated FOB US$/BBL Dubai Crude Oil-Arab Gulf Dubai FOB US$/BBL Tapis Crude Oil-Malaysia Tapis FOB US$/BBL Urals Crude Oil-Urals FOB US$/BBL Bonny Crude Oil-Africa FOB Bonny Lght US$/BBL Figure 1- Crude Oil Prices. Source: DataStream. It is relevant to mention that Brent and Western Texas Intermediate (WTI) crude oil prices have been moving apart from late 2010 as can be seen in Figure 2.1. Typically, WTI from Cushing Oklahoma holds a higher price than Brent crude oil. This has been the case until recently as WTI is a lighter and sweeter type of oil, holding only about 0.24% of sulphur, making it easier to refine into gasoline. Whereas Brent crude contains about 0.37% of sulphur but is still considered as a sweet crude oil. It is interesting to compare them with heavier types of oil such as the heavy crude oil produced from Venezuela s Orinoco Belt which contains approximately 4.5% of sulphur (Energy & Capital 2012). Even OPEC supplying about 40% of the world s crude oil does not have such a sweet type of crude oil; hence this is why WTI has had higher prices over the years until recently. The concern is that WTI is losing its connection to the global 2

3 markets particularly the demand-supply issue. There were Enbridge s recent pipelines troubles where the company was forced to shut down one of its pipelines after a leak was discovered. The Midwest is oversupplied because of the import from Canada and due to the inadequate pipeline capacity to the Gulf Coast; the crude oil cannot reach this area (Tverberg 2011). On top of that, a year ago, Saudi Aramco decided to change their oil benchmark from WTI to Argus Sour Crude Index stating that Argus is closer to the heavier, more sour crude that the country exports. Next, some analysis of the most important trends in the global oil market is presented. 2.2 Trends in Oil Supply Historical Trends in Oil Supply: The Establishment of the International Market It is relevant to consider some historical facts affecting oil production. In the nineteenth century, the oil industry expanded fast in the US thanks to the law of capture. Effective since 1840, this law gives property rights to the owner of a well to extract unlimited amount of oil, even if it comes from someone else s property. This very competitive search for oil pushed the prices down in the 1860s (Dahl 2011). Later on, Rockefeller founded the Standard Oil Company in 1870 which dominated and revolutionized the oil industry by stabilizing the US market (Dahl 2011). Other important developments include the merge of Royal Dutch (which had been drilling in Indonesia since 1890) as well as Shell Transport and Trading (which started transporting Kerosene from Russia to the Far East in 1892) to form the Shell Group in 1907 (Dahl 2011). Standard Oil and Shell were the major producers by the end of the nineteenth century, but Standard Oil was broken up by antitrust laws in 1911 (Dahl 2011). In Britain, Churchill bought a controlling share in Anglo-Persian Oil Company, the first company to extract petroleum from the South Asian country of Iran, which later became British Petroleum (BP). BP played a fundamental role in supplying oil to the British fleet during the First World War. After the War, oil prices fell down. The major producers tried to increase prices but were prevented by the arrival of new entrants, namely Gulf, Texaco, Chevron and Mobil. These seven companies, referred pejoratively as the Seven Sisters, formed the Iran cartel and became the dominant firms in the oil industry between the mid-1940s to the 1970s. In parallel, during the 1950s, new rivals entered the oil market 3

4 such as Getty and Occidental oil producing in North Africa. Taxes for the companies increased to substantial levels over major producing countries following the initiative of Venezuela (Dahl 2011). Oil companies paying taxes did not immediately cut their prices, but falling demand from European recession and increased competition would later push prices down. This led to reduce taxes for producing countries giving incentive to Venezuela, Iran, Iraq, Kuwait, and Saudi Arabia to form a cartel in 1960 which was named Organisation of the Petroleum Exporting Countries (OPEC). They were later joined by Qatar, Libya, Indonesia, United Arab Emirates, Algeria and Nigeria (Dahl 2011). Up to the oil crisis of 1973, the Seven Sisters controlled the majority of the world s petroleum resources. From 1973, the Seven Sisters have become less influential, but overall OPEC and state-owned oil companies in emerging-market economies have become more dominant (Dahl 2011) Current Supply Trends: Facts and Forecasts Global oil supply has increased by 2.2% in 2010, this gain in production has been shared almost equally between OPEC and non-opec producers (BP 2011). Indeed, non-opec countries accounted for 58.2% of global oil production in 2010 which has not changed much since 2000 (BP 2011). This process was led by China which recorded its biggest increase in production ever, and by the US and Russia; in fact, non-opec production grew by 1.8% which is the largest increase since 2002 (BP 2011). Meanwhile, Norway and the UK have seen a decline in their oil production. OPEC countries have seen their production amplify by 2.5% in 2010, where the largest increase in production came from Nigeria and Qatar (BP 2011). Additional capacity in 2010 came mainly from non-oecd countries making almost 90% of the global total (BP 2011). Therefore, installed refining capacity is now greater in non-oecd countries than in OECD (BP 2011). An interesting report with projections for energy consumption and production until 2030 is provided by BP (BP Energy Outlook ), which is one of the most respectable sources of data, analysis and projections for energy. This report is based on a consensus on the evolution of the world economy, policy, and technology. According to this report, OPEC will continue to be the leading supplier with major contributions from Iraq and Saudi Arabia. Concurrently, non-opec supply is also expected to increase. 4

5 Even though Iraq has great uncertainty regarding its capacity expansion, due to limited project development capacity, infrastructure constraints, security challenges as well as political instability, BP expect Iraq to account for 20% of global oil supply for the next 20 years (BP 2012). This is important as the ability and willingness of OPEC members to expand capacity and production is one of the main factors determining the future path of oil market. It is important to note that shale oil has been developing quickly in the last decade. Shale oil has long been set aside because of high extraction costs. It is only recently that producers from the US and Canada have shown that the extraction of shale gas can be facilitated by new technology that combines horizontal drilling with hydraulic fracturing which made it economically viable. The same technology is being applied to the extraction of shale oil in some countries, although shale oil resources are not as developed as shale gas. Most of the development of shale oil resources occurs in western United States around the Green River Formation, which is estimated to contain about 1.5 trillion barrels of shale oil (USGS 2006). Due to different quality of shale oil found in various countries, not all of it is extractable with today s technology. The total resources of shale oil deposits of a selected group of 33 countries are estimated to be about 2.8 trillion U.S. barrels of shale oil according to USGS (2006). More recently, China National Petroleum Corp (CNPC) started to cooperate with foreign companies such as Shell and Hess corp. to explore shale oil in the country s Santanghu Basin (Bai and Aizhu 2012). If these projects go ahead in the future, it is likely to bring further advancement in technology in the extraction and production of shale oil. This again shows the constant interest and importance of China in the energy market. In summary, the global oil supply has increased, an increase coming from both OPEC and non-opec countries. The main increase in supply comes notably from China, the US, Russia, Nigeria and Qatar. Accordingly, we will now analyse the demand side of the oil market. 2.3 Trends in Demand The demand for all types of energy has grown substantially due to the fact that GDP growth in non-oecd has been above the world average, while to the mature energy 5

6 consumption by OECD countries has been steady. Non-OECD oil consumption growth rate reached 5.5% in 2010, which contrasts with the 0.9% steady growth from OECD (BP 2011). Indeed, among the large increase in the consumption of all types of energy, oil remains the world s leading fuel satisfying 33.6% of global energy consumption, even though it has been losing market share since 2000 (Figure 2). Energy Consumption by Source (Million Tonnes Oil Equivalent) Figure 2- Trends in Oil Consumption. Source: BP (2011), Statistical Review of World Energy. The increase in oil consumption in 2010 (Figure 2) has not been matched by the global production of oil, leading to a consequent decrease in inventories. Global oil consumption grew by 3.1% while production increased by only 2.2% (BP 2011). This could be attributed to the OPEC production interruptions implemented since late As other energy sources may be substitutes for oil, it is important to consider trends in oil demand compared to global trends for combined energy sources. To analyse this, we consider an energy demand forecast provided by BP (2012), which is based on consensual assumptions on key variables. Population and income will remain the key determinants of energy demand. Assuming a population growth of 1.4 billion until 2030 and a global GDP growth of 3.7% p.a. 1, overall growth of primary energy consumption is 1 The average between 1990 and 2010 was 3.2% p.a. (BP 2012). 6

7 forecasted to grow by 1.6% over this period, primarily pushed by fast-growing non- OECD countries (BP 2012). Non-OECD countries are expected to increase their consumption by 69% in 2030 (above the 2010 level), which contrasts OECD energy consumption forecasted to be just 4% higher than in 2010 (BP 2012). The economic development of non-oecd countries creates an appetite for energy that can only be met by expanded consumption of all types of fuel. Gas and non-fossil fuels will gain share at the expense of coal and oil. The fastest growing fuels are renewable about 8.2% p.a., whereas oil will be the slowest at 0.7% p.a., according to BP (2012). These projections are explained by an expected shift from oil in transportation to gas and renewables by 2030, and by a combination of relative fuel prices, technological innovation and policy interventions. Most of the growth in oil demand will be attributable to China and India, both of which are expected to increase their net oil imports. According to BP (2012), China and India will become the world s largest and third largest economies and energy consumers, respectively by 2030, accounting for much of the consumption increase in liquid fuels (Figure 3). The increase in global liquids (oil, biofuels and other liquids) demand by China (8 Mb/d 2 ), India (3.5 Mb/d) and Middle-East countries (4 Mb/d) will account for nearly all the net global increase by Furthermore, China and India will account for 35% of the global population and are likely to represent 94% of the net oil demand growth (BP 2012). Figure 3- Liquid Fuels Demand Growth. Source: BP (2012), Energy Outlook. 2 Millions of Barrels per day (MB/d). 7

8 Chinese energy consumption grew by 11.2% in 2010 giving China the world largest share of global energy consumption at 20.3% (BP 2011). In fact, more than half of global liquids demand growth is in China, and its refinery expansion plans will affect product balances globally. 3. Theoretical Considerations and Literature Review There is an extensive literature on the behaviour of OPEC, the structure of the world crude oil market and price decisions. This Section examines some of this literature, with particular focus on the interactions among the OPEC members as well as the increased demand from China and India. Section 3.1 discusses a popular baseline model used to analyse the global crude oil market. Section 3.2 addresses literature that deals with the deviations from this baseline model, and in doing so, it addresses the imperative question: Is there a necessity for a reassessment of the market structure? Section 3.3 layouts the studies that concentrate on the structure of the crude oil demand. Section 3.4 comments on alternative theories such as the speculative behaviour in the crude oil market. 3.1 The Baseline Model and Related Empirical Studies Since the formation of OPEC in the 1970s, many theoretical models have been developed to study its behaviour. The consensus economic model that has been used as a baseline to study the global oil market is described in Dahl (2011), and is attributable to many authors that have worked on modelling the global oil market. According to this model, the key feature of the global oil market is its dominant firm-competitive fringe structure. The dominant firm in that model represents OPEC, which behaves as a cartel and restricts its output in order to maximize its profit subject to the supply by non-opec countries. The competitive fringe represents the non-opec countries that satisfy the residual demand of the global market, i.e. the demand that is not satisfied by OPEC. Due to the natural endowments of oil and other economic restrictions, OPEC countries satisfy a great part of the global demand. This gives enough market power to OPEC to influence the price and obtain economic profits by restricting output, while firms from the competitive fringe act as price-takers. 8

9 There is no agreement on a specific model to describe the oil market behaviour and it seems that different strategies have been used over different periods of time. However, there has been a consensus model that has been used as a baseline to study the global oil market. We will introduce this baseline model as presented in Dahl (2011); this model is attributable to many authors that have worked on modelling the global oil market. According to this model, the key feature of the global oil market is its dominant firmcompetitive fringe structure. The conception of this model has been dominated by historical facts. The oil world market behaviour seems to be constantly changing. Dahl (2011) observed that oil is a market where historically monopolies (such as Rockefeller s) have risen and then faded away, and that OPEC has been subject to cartel instability. In fact, monopolies have emerged but have not been sustained. Not surprisingly, there is a variety of models that can be found in the literature. According to Adelman (1982, quoted in Griffin 1985, 955), OPEC s actual behaviour has fluctuated between the dominant firm and market-sharing models depending on market conditions. OPEC has often been studied as an individual market and repeatedly referred to as a cartel, a monopoly and sometimes an oligopoly, but this view has been greatly challenged. This led Griffin (1985) to study an alternative hypothesis for explaining OPEC countries oil production. Similarly, Jones (1990) conducted a study on OPEC and its behaviour under falling prices and in the same way concluded that OPEC s production behaviour could be best explained by a partial market-sharing cartel model. Although this idea has been partially rejected by Dahl and Yücel (1991), who found no formal evidence of coordination across OPEC producers to support a strict market-sharing cartel, it seems that in terms of the ability of the various models to explain production, the partial-sharing cartel model dominates for OPEC producers. Assumptions- There is a dominant firm representing OPEC which supplies the amount of crude oil. OPEC can be represented as a single firm in this model because it is assumed that it behaves as a cartel (we discuss deviations from this assumption later on). There is a competitive fringe which represents the non-opec countries that satisfy the residual demand of the global market, i.e. the demand that is not satisfied by OPEC. Conversely, we can say that OPEC satisfies the residual demand which is not satisfied by the competitive fringe, i.e.. It is assumed that the competitive firm is formed by a large number of small firms, so each firm in the competitive fringe is 9

10 a price-taker. This market structure has correspondence with the actual structure. Countries with large endowments of crude oil created OPEC; nowadays these large players satisfy about 40% of global demand. The non-opec suppliers typically lack enough market power to individually affect the global price of crude oil; hence they act as price-taker when making economic decisions. In this structure, OPEC has enough market power to influence the price of oil to obtain economic (i.e. abnormal) profits. The dominant firm restricts its output in order to maximize its profit subject to the supply by the competitive fringe. More specifically, the dominant firm acts as a monopolist on the residual demand that cannot be satisfied by the competitive fringe. This problem is represented by the following set of equations: Global demand for crude oil:. (3.1) Competitive fringe supply:. (3.2) Demand facing OPEC:. (3.3) OPEC cost function:. (3.4) Where are constants and OPEC s profit maximization problem: Or, written as an unconstrained optimisation problem:,. (3.5) (3.6) Equilibrium- To obtain the equilibrium we assume that the dominant firm maximizes its profit after making the correct predictions about the quantity to be supplied by the competitive fringe, i.e.. In the real world, it is possible to make error predictions. However, by trial-and-error the dominant firm should find the level of output that provides the maximum profit.. (3.7) 10

11 The first-order necessary condition for OPEC profit maximization is:. (3.8) Solution (OPEC profit-maximising level of output):. (3.9) Equation (3.8) states that the marginal revenue has to be equal to the marginal cost at the optimum. From this condition, we can estimate and then the equilibrium values of the rest of the variables in the model. We have represented our solution in Figure 4 (as in Dahl, 2011). The competitive fringe supply curve, as it is competitive, is equal to its marginal cost curve:. The world demand is also represented in Figure 4, and OPEC s demand is determined by the difference between the world demand and the production of the fringe. As a result OPEC faces a kinked demand curve (Dahl 2011). The marginal revenue from OPEC is determined by the marginal revenue of the flatter part of the demand curve to the left of the kink, that is, the difference between the world demand and the supply of the competitive fringe (Dahl 2011). The marginal revenue of the steeper part of the demand on the right of the kink is determined by the total world demand,. This gives the non-linear marginal revenue for OPEC, as depicted in Figure 4. The optimum quantity for OPEC, is found where the marginal cost of OPEC, is equal to its marginal revenue, and the price, is the one on the demand for OPEC, the red kinked demand. When the price is below, the fringe will not supply any oil and OPEC faces the entire demand. When the price is above, the producers outside OPEC are able to supply the whole demand and OPEC faces none. The fringe would therefore produce so that. 11

12 Figure 4- Dominant Firm-Competitive Fringe Model. In the next section we will consider deviations from this baseline model. Before presenting these special cases, it is convenient to summarize the results of our model as follows: It is worth remarking that similar results would arise if the control variable for OPEC was the price level, or a combination of price and quantity 3. Various studies have analysed the empirical relevance of the dominant firm-competitive fringe model. Griffin (1985) used multi-step simultaneous-equation OLS regression techniques to compare four different hypotheses for explaining oil production in OPEC countries: cartel, competitive, target revenue and property rights. Griffin (1985) concluded that the hypothesis of the partial market-sharing cartel for OPEC and the competitive fringe hypothesis for non-opec countries could not be rejected, respectively. A similar study based on more recent data is provided in Alhajji and Huettner (2000a), who used a multi-equation econometric model to test the hypotheses of dominant firm, Cournot s equilibrium and competition for the world crude oil market. The authors concluded that the dominant firm-competitive fringe model is valid, however the dominant firm is not OPEC but Saudi Arabia alone, and the competitive fringe is formed by countries other than Saudi Arabia. The authors explain that this result is natural as there is no mechanism for punishing OPEC members from cheating within an implicit 3 Price-setting by the dominant firm (which is possible in this model) should not be confused with price competition between the dominant firm and the competitive fringe. 12

13 cartel agreement. This follows from an old belief by Moran (1981), a political scientist, who argued that Saudi Arabia has taken decisions based on its own market power. Overall, Griffin (1985) and Alhajji and Huettner (2000a) found some evidence in favour of the dominant firm-competitive fringe model, but their results are far from perfect. First, important factors are ignored, meaning that alternative modelling approaches could lead to different results. Second, these models might be subject to econometric disadvantages that were not clearly identified at the time. For instance, Griffin (1985) used non-stationary data meaning that his results may be subject to spurious regression biasedness. Third, even if we concede some validity for their results, their datasets do not include observations for the last decade. Consequently, an interesting research question is whether or not the dominant firm-competitive fringe model is relevant to describe the current oil market, after all the important factors are taken into account. 3.2 Extensions and Deviations from the Baseline Model There are many critical studies that can be seen as extensions or deviations of the dominant firm-competitive fringe model. These studies are classified in three groups in this section. First, some authors have questioned whether OPEC is actually a cartel. For instance, Gülen (1996) used cointegration analysis and causality testing to determine whether OPEC is a cartel with members coordinating their output and cutting production to increase the oil prices for the time period Only three members were found to be moving together in setting production according to the cartels hypothesis. This study repeated the first test conducted by Dahl and Yücel (1991) but for a longer time span. Similarly, Alhaji and Huettner (2000a) found no proof that some OPEC countries have cut production voluntarily in 1999 after an OPEC s meeting, apart from Saudi Arabia. More recently Brémond, Hache and Mignon (2012), tested if OPEC s production decisions of the different members were coordinated and if they had any influence on the price. Their results indicated that OPEC acts mainly as a price taker, and that by further dividing OPEC between savers and spenders; it acts as a cartel principally with a subgroup of its members. OPEC countries face quotas, that is, restrictions on the amount of oil that they can produce over some period. Game theory suggests that in a collusive agreement such as OPEC cartel, individual countries may have incentive to 13

14 cheat, i.e. to produce in excess of the agreed quota. There is evidence showing that the production quotas have been often violated. At some point in the early 1980s, the difference between actual production and quotas widened significantly. Analysts in the 1980s thought OPEC was moving from a cartel (where all firms agree to collude) to competition resulting from each country cheating on the initial agreement. Aguiar- Conraria and Wen (2012) explain the decline in economic volatility in the mid-1980s in oil importing countries, when OPEC changed its market strategies from setting price to setting quantities in an interesting way. By combining their finding with the fact that OPEC changed its market strategy in the 1980s, the authors found an alternative to the Great Moderation in that it could be explained by the US economy moving from a state of equilibrium indeterminacy to a state of equilibrium determinacy. Indeed, they concluded that the stronger the dependence on foreign oil, the larger the likelihood of indeterminacy provided that oil exporters act as a cartel fixing the price of oil. On the other hand, if oil exporters fix the quantity then the theory of indeterminacy becomes unlikely. Later evidence suggests that in the following two decades the gap between actual and quota production closed down again. The OPEC may be far from being a perfect cartel, but the evidences suggest that overall there is room for collusive behaviour. Second, some models have focused on the political issues that provoke interruptions of supply in the Middle East. For instance, Barsky and Lutz (2004) found that there is a link between political events in the Middle East and the changes in the price of oil. However, according to the authors, this is one of many factors driving oil prices. In another paper, Matthies (2003) explains the increase in oil prices a few days before the US led military attacks against Iraq actually began, by the expectations of shorter oil supplies due to the war in the Middle East. Third, some authors have suggested that some OPEC countries follow a revenuemaximising strategy as opposed to the profit-sharing maximisation strategy that is described in the dominant firm-competitive fringe model. Alhajji and Huettner (2000b) found evidence supporting the target revenue hypothesis for non-opec countries in which governments own and control oil production; these countries include Mexico, China, Egypt, former USSR and India. The authors also found that the behaviour of Iran, Libya and Nigeria have similarities with the target revenue model. Non-OPEC countries, where the oil is privately owned and produced such as the US and Canada or publicly owned and privately produced such as the UK, Norway and Australia, are suspected to be 14

15 price-takers and behave competitively. This result is in conflict with other findings. For instance, Dahl and Yücel (1991) rejected the idea that non-opec producers dynamically optimize and follow the target revenue model for their production decisions; they also found no evidence of any behaviour in a competitive fringe or any coordination of their output with OPEC or any other free-world production. One crucial difference in all the studies reviewed above is that they consider data for different time samples, which suggests that there is a need of a re-assessment of the current oil market situation. 3.3 Studies Focusing on the Structure of Crude Oil Demand From the demand side, interesting studies have recently contributed to explaining the current oil market situation. Kriechene (2002) examines the world market for crude oil by estimating the elasticities. It was found that the demand elasticity was highly price inelastic in the short-run and this was explained by a structural change in with high taxation on oil consumption in oil-importing countries. According to the author, this contributed to the decrease in the demand elasticity, through energy saving and substitution, by compressing long-run demand for oil to a non-elastic region. An interesting question for today s oil market is how the high growth in China and India is affecting the price-elasticity of crude oil demand. Some recent literature has focused on the issues related to the demand changes driven by the rapid economic growth of China and India. Kilian (2009) argues that the recent oil price run-up until mid-2008 is primarily due to a strong global demand driven by a booming world economy and an increase in precautionary demand. After reviewing several strands of theories about oil prices, Hamilton (2009a) concludes that the scarcity rent may have started to become an important factor in the price of crude oil owing to the strong demand growth from China and other emerging countries. Similarly and in another paper, Hamilton (2009b) finds that the causes of price shocks in were due to a strong demand growth and stagnating production. In a similar way, Smith (2009) analyses the global demand shift, non-opec and OPEC supply shifts relative to levels and concludes that a main part of the oil price rise since 2004 is due to a combination of unexpected demand growth from China and other developing nations as well as a negative shift in oil supply. An interesting study by Skeer and Wang (2007) analyses different scenarios for China s oil demand through 2020 and to find that new 15

16 demand from China s transport sector would raise world oil prices by 1-3% in reference scenarios or by 3-10% if oil supply investment is constrained in Adding to the above studies, a recent econometric study by Li and Lin (2011) supports the idea that increased oil imports by China and India are a major driving force for the oil prices. The authors use an error correction model to analyse the impact of the quantity of crude oil imported by China and India on the oil pricing system, also incorporating the strategic production decisions by OPEC members. Through their empirical work, using monthly data from 2002 to 2010, they find evidence to support the hypotheses that increased oil imports by China and India act as a demand shock, driving world oil prices upwards. A study by Mu and Ye (2011) looks at the impact of China and India high economic growth on the oil market from a different angle. They analyse China s net import from 1997 to 2010 and its impact on the crude oil prices. Mu and Ye (2011) base their analysis on a vector autoregression (VAR) analysis employing monthly data on China s net oil import. Contrasting with Li and Lin (2011), the paper from Mu and Ye (2011) finds no significance between growth of China s net import and the monthly oil price changes, with no Granger causality between the two variables. However, in a second part of their analysis, calculating the price changes implied by increases in China s oil demand from a longer-term supply and demand shift perspective, they find that about 17% of the historical price changes between 2002 and 2010 are due to increased demand for imported oil from China, which the authors found minor. This paper casts a doubt on the popular belief that the predominant demand growth from China has a significant impact on oil price changes between 2002 and Speculation in the Oil Market Finally, it is worth making some remarks in regards to the role of speculation in oil markets, a topic that has been largely discussed in the media and the literature. The popular belief that financial speculators play a significant role in driving oil prices is wide-spread, but this belief has been discarded by studies conducted by specialists in the area. We refer to the work by Ripple (2008, 2009) and Smith (2009) to explain why the role of financial speculators will not be considered here. 16

17 Ripple (2008, 2009) has explained that the data for futures contracts is often misunderstood. Futures contracts provide valuable price discovery and are frequently used as the basis for analysing energy price volatility, but might be misinterpreted. Based on the price series for WTI over the period , the price volatility seems to be increasing and this is typically attributed to speculators. By using the correct data definitions, Ripple (2009) has shown that the data indicating a general increase in price volatility and the swings in crude-oil prices from 2000 to 2008 are not attributable to a rising role of outside speculators in the oil market. It has been demonstrated in this work that the volatility on daily returns on futures prices (what really matters to speculators) indicated no particular positive or negative slope over the period Ripple (2009) emphasizes his point with another equivalent method of evaluating the volatility: plotting a rolling measure of the coefficient of variation over the same period. He found a clear downward sloping trend line and the volatility of the coefficient of variation appears to decline over the period. Ripple (2009) also found that the price volatility is likely to have attracted the non-commercials rather than the other way around and the market may have been even more volatile without them. This is concluded after an analysis of the share of open interest held by non-commercial traders, along with an analysis of the relations between trading volumes and open interest. Finally, Ripple (2009) concluded his analysis into the role of traders, by examining the relations between trading activity and open interest. Indeed, he used the trading volumes for crude oil on the NYMEX 4 and compared it with the average weekly open interest positions, as reported by the CFTC 5. The common beliefs state that if non-commercials were operating like the bad version of speculators is expected to, we would see an increase in the amount of trading volume for a given level of open interest. Contrastingly, the author found little evidence of either increased price volatility or an increase in the relative role of non-commercial traders. On the other hand, Smith (2009) suggests that rapid changes and much of volatility in crude oil prices are attributable to the inelasticity of demand and supply in the short run. Indeed, empirical estimates of price elasticity of demand for crude oil average are in the short run and in the long-run. The price elasticity of supply is more difficult 4 New York Mercantile Exchange is a commodity futures exchange owned and operated by Chicago Mercantile Exchange (CME) Group. 5 The U.S. Commodity Futures Trading Commission is an independent agency of the United States government that regulates futures and option markets. 17

18 to determine but according to OECD reports, it is about 0.04 in the short-run and 0.35 in the long-run. Smith (2009) justifies part of the sharp increase in oil price in , to shifts in demand and supply curves that are highly inelastic in the short-run. Demand is inelastic due to the time it takes to change the stock of fuel-consuming equipment and supply is inelastic in the short-run owing to the time it takes to increase production capacity of oil fields. Price volatility encourages producers to hold inventories but those are costly. Hence, they might not be sufficient to offset the inelasticity of demand and supply and this could explain that shocks to demand or supply lead to high levels of volatility in oil prices. Furthermore, to understand why the price of oil kept increasing between January and July 2008, even though a high demand should have been predicted, Smith (2009) suggests that, when demand and supply are both highly rigid, low elasticities combine to create a large multiplier and each physical shock could trigger a short-run price adjustment about ten times as large. This way, Smith s (2009) research provides solid foundations to explain how small shocks in production or consumption lead to large changes in the world oil price. However, while those above mentioned theories are interesting approaches, the analysis of high frequency data 6 and the role of speculation go beyond the scope of this paper. 4. Empirical Analysis: The Role of China and India 4.1 Methodology The proposed methodology adopts a general-to-specific approach. We start by proposing research questions followed by an analysis on how these questions can be accessed in a general model, given the data limitations and the econometric tools available for the purposes of our research. Naturally, from all the questions that economic theory may suggest, only few can be tested against data and, more often than not, these tests are imperfect. The general approach will be narrowed later to address specific research questions. 6 Among the variables included in our methodology, only oil (spot and futures) prices are available on high-frequency (daily). Quantities traded are available on quarterly or annual data. 18

19 4.1.1 Research Questions There are two main research questions. First, we want to analyse the implications of increased demand from China and India for the global crude oil market. In particular, we want to see if changes in demand from China and India have implications for the crude oil market share of OPEC and non-opec economies. Second, we would like to know the dynamic relationships between the increases in crude demand due to China and India, the crude price as well as the market share of OPEC and non-opec countries Data Sources To approach and isolate our research questions, a set of relevant variables have been selected. In addition, during the research process we will have to control the econometric working environment for exogenous effects on the oil market, or at least the most important of them. Table 1 summarizes the sources of the variables that are relevant and available to address our research questions. Variables Frequency Source Brent Crude Oil Price (in US$/barrel) Available for all main markets. Crude Oil Production/Supply (number of barrels) Available for each OECD country, main non-oecd countries (including China) and for each OPEC country. Crude Oil Demand (number of barrels) Source 1: Monthly demand for OECD countries and quarterly for non- OECD countries. Quarterly Quarterly Quarterly DataStream International Energy Agency (IEA), Monthly Oil Data Services. International Energy Agency (IEA), Monthly Oil Data Services. Table 1- Variables and Sources Econometric Modelling The structure of our econometric framework is underpinned by our theoretical considerations in Sections 2 and 3 on the market structure. In addition, we will propose improvements on Mu and Ye (2011) approach to address similar questions. These considerations motivate a set of various time series of interest. The econometric methodology is based on vector autoregressive (VAR) analysis. Keeping the theoretical structure in mind, the initial step in our empirical research will be to define a relevant set of variables to address our questions. There are several aspects 19

20 that need to be considered. First and as was explained earlier, the data should be grouped in a convenient way that will be consistent with theoretical hypotheses. Second, some variables may be used in natural logarithm whereas some variables may need to be differenced. Time series that are trended are typically used in their logarithmic form; unit root and cointegration tests need to be used to decide whether the variables in a VAR should appear in levels or first difference. A unit root test tells us whether a time series is stationary or non-stationary (trended). Econometric models that use non-stationary time series may be subject to spurious-regression effects 7. Variables that shared a common trend are said to be cointegrated and may be modelled in an error correction term. The second step specifies and identifies a VAR structure that will allow us to confront our hypotheses against data. Our VAR will be used to capture the linear interdependencies among multiple time series and can be restricted to form a set of specific equations that correspond with economic theory. Within the VAR structure, we consider specifying an error correction term, in which case the VAR becomes a VECM (Vector Error Correction Model). In this setup, cointegration means that some nonstationary variables may share a linear relationship that is stationary and usually interpreted as a long-run equilibrium relationship. The previous study by Mu and Ye (2011) is used as a baseline for shaping our VAR model. The latter study employs VAR methodology to analyse the role of China in the global crude oil market, but their results suffer from several disadvantages. In particular, their three-variable VAR, using monthly data from 1997 to 2010, is sensitive to the definitions of the variables. First, the log of real oil price is transformed into a stationary variable by subtracting a linear trend which does not seem to be consistent with the transformations made to the other two variables. For instance, the authors convert the log of China s net imports, a stationary process by calculating the month-over-month change, i.e. the difference between its value in a given month and its value in the same month the previous year. Second, the log of oil production is transformed into a stationary variable by taking the first difference with respect to the value in the previous month. At the very least, Mu and Ye (2011) results are difficult to interpret due to these inconsistent variable definitions. More precise variable definitions would have aided in the data analysis which motivated the proposed research in this document. Furthermore, the ordering in the 7 Spurious regressions yield biased estimators, a high coefficient of determination and highly significant t- values. 20

21 Cholesky variance decomposition used in Mu and Ye (2011) is somehow ad hoc, so improving it is another one of the motivations for this paper. For setting up our VAR model, we will also use some econometric tools. To choose between competing models and identify lag structures, we will use information criterion tools. Increasing the number of lags in a VAR model leads to a trade-off between a better log-likelihood value and increased number of parameter estimates affecting the statistical efficiency of the model. For selecting a parsimonious model, the literature has proposed different information criterion indicators. First, we will consider the Akaike criterion, which accounts for both the goodness of fit and the numbers of parameters that have to be estimated to achieve this particular degree of fit, by imposing a penalty for increasing the number of parameters. A second tool for model selection is the Hannan-Quinn information criterion, which considers not only the value of the log-likelihood objective function but also the sum of square residuals and the number of observations. Lastly, Schwartz criterion works in a similar way as the above mentioned criterions, but punishes more severely for the number of parameters in the model than the other criteria. In our VAR setup, we use Cholesky and other variance decompositions to obtain impulse reactions of interest. Impulse-reaction functions allow for evaluating the response in each variable in a system to a shock to one of the variables, provided that a structure for the relations among the variables shocks can be identified. Adding to the above VAR-oriented tools, the Granger causality test can provide information on whether one variable x can Granger-cause another variable y. To carry out this test, we would perform a statistical test using the null hypothesis that all the lagged values of x in the equation for y are equal to zero at the same time; if the null cannot be rejected, we conclude that x Granger-causes y. Causality of tests of this type may face certain short-comings. For instance, one could find causality from x to y and also from y to x; in this case, it may be of interest to know which of the two effects is stronger. In addition, if there is a third variable z Granger-causing x and y, the results obtained from a model including x and y only may be misleading. This is meant to emphasize, once again, that theory should provide the background for the relationships that can be tested for causality (Lütkepohl and Krätzig 2007). 21

22 4.2 The Model and the Hypotheses We start by introducing some previous theoretical considerations, which are reflected in the set of equations (4.1). In a second step we will explain how these theoretical identities could be re-expressed in a reduced form. The first equation in set (4.1) states that the optimal crude oil production by OPEC, depends on the quantity demanded at a particular price level and the amount of demand that is satisfied by non-opec producers. The second equation describes the decisions by the competitive fringe formed by non-opec countries: their long run oil output level depends on the global crude oil demand and the production by OPEC. The third equation simply states that the equilibrium price in the global crude oil market is a function of demand and supply (by OPEC and non-opec economies). Finally, the last equation in the system simply disaggregates demand to distinguish between the demand from China and India and the demand from the rest of the world, which is of interest for our research. (4.1) Further assumptions need to be made to obtain a reduced form of system (4.1). As it is, equations set (4.1) cannot be introduced in a VAR system, for two reasons. First, is an accounting definition, so it does not make sense to introduce and in a same VAR model. This means that at least one of the variables will have to be dropped and that we should device an alternative mechanism for measuring OPEC crude production relative to non-opec production. To circumvent this problem, we propose using the variables and the share of OPEC production to total production, i.e., instead of and. The second problem is that 22 should, again, hold by definition, so one of the variables is redundant. To solve this issue, we will consider (which we already decided to include in the model) and only. These two considerations leave us with two concrete testable hypotheses:

23 Hypothesis I- For the determination of a global crude oil market equilibrium ( ), it does not matter whether the crude oil is supplied by OPEC or non-opec producers. In other words, the ratio of OPEC crude oil production to total crude oil production does not have any significant impact on the other variables of the VAR system. Hypothesis II- For the determination of a global crude oil market equilibrium ( ), it does not matter whether the demand for crude oil comes from China and India or some other part of the world. All the variation in price and oil production (and possibly the distribution of market shares between OPEC and non-opec countries) in the VAR system should be explained solely by the world demand, and should not have any additional impact on the other VAR variables. Of course, we are interested in testing whether these two hypotheses are violated in the real world and, if they are, we would like to know what would be the implications for the other variables in the VAR model. A similar methodology was employed by Mu and Ye (2011) to assess Hypothesis II, although the authors did not state it this way. Mu and Ye (2011) used price, total crude oil world demand and net imports by China and India. They wanted to assess if the net imports from China and India have a significant effect of equilibrium price and quantity (which already included consumption by China and India). In our model we use total consumption by China and India instead of net imports because we think that they are more relevant in a model that uses data for total consumption and total production. In addition, we added the ratio variable, which we think could be relevant for our analysis. Hopefully, our analysis carried out using methodology ad absurdum (by contradiction), will shed some light on the implications of OPEC production and the increased demand from China and India for the global crude market. To analyse the dynamic relationship between OPEC s production, the total world demand as well as the impact on oil prices of the increased demand from China and India, we estimate a four variable vector autoregression (VAR) model over the entire sample period as follows:, (4.2) Where is a vector of stationary endogenous variables, and includes seasonal and interventional dummy variables. In order to identify a more specific structure, we proceed as follows: 23

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