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How Increased Crude Oil Demand by China 

and India Affects the International Market 

By Amanda Niklausa and Julian Inchauspeb 

(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.

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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).

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

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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

2.2.1 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

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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).

2.2.2 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 2030 2012), 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.

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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

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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).

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 2008.

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

                                                            1The average between 1990 and 2010 was 3.2% p.a. (BP 2012).

Energy Consumption by Source (Million Tonnes Oil Equivalent)

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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/d2), India (3.5 Mb/d) and Middle-East countries (4 Mb/d) will account for

nearly all the net global increase by 2030. 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.                                                             2Millions of Barrels per day (MB/d).

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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.

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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 firm-

competitive 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

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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:

, . (3.5)

Or, written as an unconstrained optimisation problem:

(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)

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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 .

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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 quantity3.

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.

 

 

 

 

 

 

 

     

 

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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 1965-1993. 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

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“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 revenue-

maximising 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

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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 1973-1999 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 2007-2008 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 1973-

1975 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

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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 2020.

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 2008.

3.4 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.

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 2000-2008, 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 2000-2008. 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 NYMEX4 and compared it with the average weekly open interest positions, as

reported by the CFTC5. 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 -0.05

in the short run and -0.30 in the long-run. The price elasticity of supply is more difficult

                                                            4New York Mercantile Exchange is a commodity futures exchange owned and operated by Chicago Mercantile Exchange (CME) Group.

5The U.S. Commodity Futures Trading Commission is an independent agency of the United States government that regulates futures and option markets.

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 2004-2008,

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 data6 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.

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.

4.1.2 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.

4.1.3 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

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 effects7. 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 non-

stationary 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.

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).

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

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:

24  

(i) Preparation of variables (Taking logs, calculating ratios, calculating real crude

oil price).

(ii) Unit-root tests: These tests will tell us whether variables are stationary in

levels I(0), stationary in first difference I(1) or stationary in second difference

I(2).

(iii) Cointegration test (Johansen’s test). This test will give us information about

the number of cointegrating relationships that cannot be rejected.

(iv) Identifying weakly exogenous variables in the cointegrating relationship by

imposing restrictions on parameters.

(v) Assessment short-run Granger-causality among the variables.

(vi) Forecast variance decomposition will help obtain impulse-reaction functions.

To start, it is relevant to analyse the time series properties of the variables used before

estimating the VAR. The data has been collected from 1991Q1-2012Q2 with quarterly

frequencies. The total and combined consumption of oil by China and India is expressed

in natural logarithms of quantity consumed in thousands of barrels per day. Similarly, the

world total production and the production by OPEC are expressed in natural logarithms

of physical quantity. Both consumption and production data are sourced from the

International Energy Agency. As for the crude oil price, we use the Brent dated spot price

available from DataStream. We choose Brent prices as the benchmark for world crude oil

prices since it represents a large proportion of world crude trades and because the WTI

price (a long established benchmark) has recently been exposed to domestic US shocks

related to transportation capacity constraints. The Brent price is first deflated using the

US production price index (PPI) and expressed in 1990Q4 US dollars; in a second step

we take the lateral logarithm of this real price. When analysing the dynamics, it should be

taken into account that natural log differences are approximately equal to the percentage

growth rate. A summary of the variables is provided in Table 2.

25  

Variables Description Natural log of real price (base year 1990) based on Brent dated crude oil price.

Ratio of production of OPEC in total production,

Natural log of total world consumption of crude oil.

Natural log of combined crude oil consumption by China and India.

Quarter 2 seasonal dummy variable.

Quarter 3 seasonal dummy variable.

Quarter 4 seasonal dummy variable.

DEC Interventional dummy variable adjusting for Ecuador leaving OPEC in 1992.

DGAB Interventional dummy variable adjusting for Gabon leaving OPEC in 1994.

DANG Interventional dummy variable adjusting for Angola joining OPEC in 2007.

DIND Interventional dummy variable adjusting for Indonesia leaving OPEC in 2009.

Table 2- Variables.

The set of equations (4.1) can be model in a stable VAR if the variables are stationary or

share common trends. Hence, all the variables under study will be subject to unit root

test. We perform individual unit-root tests using augmented Dickey-Fuller (ADF) and

Phillips-Peron (PP) methods where the results are shown in Table 3. The null hypothesis

in the ADF and PP tests is that a series has a unit root, i.e. is non-stationary; this

hypothesis is rejected when the t-statistic is higher than the critical value. The symbols:

(***), (**) and (*) denote the rejection of the null hypothesis at the significant levels of

1%, 5% and 10%, respectively. The values in brackets denote the critical values at the

three significance levels. The lag length was selected by the Schwartz information

criterion.

Variable Level First difference

ADF PP ADF PP

1% Level 5% Level 10% Level

-0.729282 (-3.509281) (-2.895924) (-2.585172)

-0.688310 (-3.509281) (-2.895924) (-2.585172)

-8.622413*** (-3.510259) (-2.896346) (-2.585396)

-8.674653*** (-3.510259) (-2.896346) (-2.585396)

1% Level 5% Level 10% Level

-1.986949 (-3.509281) (-2.895924) (-2.585172)

-2.205716 (-3.509281) (-2.895924) (-2.585172)

-8.330004*** (-3.510259) (-2.896346) (-2.585396)

-8.330004*** (-3.510259) (-2.896346) (-2.585396)

1% Level 5% Level 10% Level

-0.830076 (-3.513344) (-2.897678) (-2.586103)

-0.697437 (-3.509281) (-2.895924) (-2.585172)

-3.713453*** (-3.513344) (-2.897678) (-2.586103)

-14.71165*** (-3.510259) (-2.896346) (-2.585396)

1% Level 5% Level 10% Level

-2.875127* (-3.511262) (-2.896779) (-2.585626)

-4.903481*** (-3.509281) (-2.895924) (-2.585172)

-11.49954*** (-3.510259) (-2.896346) (-2.585396)

-11.34485*** (-3.510259) (-2.896346) (-2.585396)

Table 3- Augmented Dickey-Fuller and Phillips-Perron Unit Root Tests.

26  

The results show that all variables are non-stationary in levels but become stationary in

first difference. The variable appears stationary under the ADF test and stationary

under the PP test, this is due to higher order autocorrelation not fully captured in the PP

test. The results indicate that we could use a VAR with all the variables in log deviations

from trend in first difference; however, omitting long-run cointegrating relationships if

they exist would be misleading. Thus, we need to perform contegration tests before

deciding how to model our data.

4.2.1 Identifying Long-Run Cointegrating Relationships

Based on the oil price theory, there exists a long run relationship between the crude oil

price and the quantity supplied by OPEC where the other variables may have a role to

play in this relationship as well. Cointegration techniques may be used for modelling this

long-run relationship between non-stationary variables. In the presence of cointegration,

the VAR model becomes a vector error correction model (VECM). The latter can capture

both the short-term and long-term dynamics.

In order to identify long run relationship between variables of interest Johansen’s VAR-

based cointegrating will be used, assuming that all the variables are first-order integrated.

Johansen’s test assumes a linear deterministic trend and the critical values assume no

exogenous series, it includes three seasonal dummy variables SD2, SD3, SD4 as well as

four dummy variables to adjust for the changes in the composition of OPEC, namely,

DANG, DEC, DGAB and DIND. The test uses two lags in first differences (three lags in

levels).

If there are k variables, there could be up to cointegrating relationships. In this

approach, the null hypothesis is tested using a trace statistic against the

alternative hypothesis . Rejecting implies that there is at least one

cointegrating vector. The next step is to test against , and so on.

Table 4 reports results for testing the number of cointegration relations. Note that two

types of test statistics are reported; first, the trace statistics and second, the maximum

eigenvalue statistics. For each block, the first column is the number of cointegrating

relations under the null hypothesis, the second column is the ordered eigenvalues of the

matrix, the third column is the test statistic, and the last two columns are the 5% and 1%

critical values. The trace statistic, reported in the first block, tests the null hypothesis of

27  

the cointegrating relations against the alternative of cointegrating relations, where

is the number of endogenous variables, for . The alternative of

cointegrating relations corresponds to the case where none of the series have a unit root

and stationary VAR may be specified in terms of the levels of all of the series. The test

suggests the presence of one cointegrating vector at the 5% significance level. This

means that some variables have one common stochastic trend. The second panel in Table

4, reporting maximum eigenvalue statistics, tests the null hypothesis of the cointegrating

relations against the alternative of cointegrating relations and also shows one

cointegration at the 5% significance level.

Unrestricted Cointegration Rank Test (Trace)

Hypothesized Trace 0.05 No. of CE(s) Eigenvalue Statistic Critical Value Prob.**

None * 0.369889 64.64745 47.85613 0.0006 At most 1 0.147411 26.31310 29.79707 0.1196 At most 2 0.131277 13.07646 15.49471 0.1120 At most 3 0.016677 1.395834 3.841466 0.2374

Notes: Trace test indicates 1 cointegratingeqn(s) at the 0.05 level * denotes rejection of the hypothesis at the 0.05 level **MacKinnon-Haug-Michelis (1999) p-values Unrestricted Cointegration Rank Test (Maximum Eigenvalue)

Hypothesized Max-Eigen 0.05 No. of CE(s) Eigenvalue Statistic Critical Value Prob.**

None * 0.369889 38.33435 27.58434 0.0014 At most 1 0.147411 13.23664 21.13162 0.4307 At most 2 0.131277 11.68062 14.26460 0.1232 At most 3 0.016677 1.395834 3.841466 0.2374

Notes: Max-eigenvalue test indicates 1 cointegratingeqn(s) at the 0.05 level * denotes rejection of the hypothesis at the 0.05 level **MacKinnon-Haug-Michelis (1999) p-values

Table 4- Johansen Cointegration Test Results.

As cointergrating relationship exists among the variables, we will use the vector error

correction model (VECM) that will support the analysis of the cointegration structure.

4.2.2 Exogenous variables

Before presenting the results, it is convenient to explain that some coefficients in the

VECM will be set to zero if variables are weakly exogenous. A set of variables is said

28  

to be weakly exogenous for a parameter vector of interest for example , if estimating

within a conditional model does not lead to a loss of information relative to estimating

the vector in a full model that does not condition on . In addition, is said to be

strongly exogenous if it is weakly exogenous for the parameters of the conditional model

and forecasts of can be made conditional on without loss of forecast precision

(Lütkepohl and Krätzig 2007). Performing this kind of tests, we set some parameters to

zero, as is shown in Table 5. We estimated our VECM model twice, with and without

restrictions, and no significant changes were found in the parameter estimates, causality

tests and impulse-reaction analysis. The economic interpretation of these restrictions will

be analysed later.

4.2.3 The Full Model

To gain some clarity in our exposition, we will now proceed to write the full set of

equations in our final VECM specification:

(4.4)

(4.5)

(4.6)

Where (4.7)

29  

The lag order was selected by minimized AIC statistics for our dynamic VAR

specification. The parameter estimates and other relevant results are reported below in

Table 5. We are assuming normal distribution of each error term with mean zero and

constant variance , , .

The first thing to note from the results is that a long-run relationship has been

established. The error-correction term suggests that total demand, the demand from

China and India and the ratio of production by OPEC converge to a long-run equilibrium

relationship. The consumption from China and India are obviously related to total

demand as the latter includes the former. What is interesting is that the ratio of

production by OPEC is part of the long run relationship. This gives support to the idea

that the dominant firm-competitive fringe model is relevant in the long run. It also gives

evidence against the hypothesis that it does not matter where the production comes from.

It is also interesting that the price level plays no role in this long-run relationship which

is defined in terms of quantities (tests carried on the coefficient associated with P

suggests that the variable is weakly exogenous, so this coefficient has been set to zero,

which produces no major changes in the model). Essentially, what the results predict is

that the quantities converge to fixed proportions between demand from China and India,

demand from the rest of the world and production by OPEC and non-OPEC countries.

The significance of consumption by China and India gives some evidence to reject

Hypothesis II, whereas the significance of the OPEC ratio provides some evidence

against Hypothesis I.

30  

Cointegrating Vector (Eq. 7) (-1) (-1)

P(-1) (-1)

7.419946 -52.43008 0.00000* -8.168689 535.2608

*Cointegration Restrictions: 0, 0, 0; LR test for binding restrictions (rank = 1): Chi-square(3)12.37072 Probability0.00622

Short-Run Parameter Estimates ∆ (Eq. 3) ∆ (Eq. 4) ∆P(Eq. 5) ∆ (Eq. 6)

0.112374 [ 2.87559]

0.014068 [ 2.87845]

-0.020621 [-0.25159]

0.002945 [ 0.93846]

ECM -0.043111 [-4.80494]

0.000289 [ 0.25340]

0.00000* [ NA]

0.00000* [ NA]

∆ (-1) -0.224249 [-2.19741]

0.011190 [ 0.87671]

0.070074 [ 0.32738]

0.017123 [ 2.08977]

∆ (-2) -0.117246 [-1.25108]

0.010633 [ 0.90722]

-0.152064 [-0.77363]

0.002442 [ 0.32450]

∆ (-1) -1.776277 [-2.01091]

-0.172575 [-1.56214]

2.244623 [ 1.21155]

-0.074731 [-1.05370]

∆ (-2) -1.668465 [-1.93785]

-0.605208 [-5.62042]

0.082799 [ 0.04585]

0.027922 [ 0.40392]

∆P(-1) -0.098320 [-1.58065]

-0.004482 [-0.57611]

-0.055805 [-0.42775]

0.008025 [ 1.60691]

∆P(-2) 0.026743 [ 0.41782]

0.001335 [ 0.16683]

-0.032223 [-0.24003]

0.003611 [ 0.70276]

∆ (-1) 0.491068 [ 0.31232]

0.143364 [ 0.72906]

0.490503 [ 0.14874]

0.001617 [ 0.01281]

∆ (-2) -2.439893 [-1.66042]

-0.071256 [-0.38773]

1.098999 [ 0.35658]

-0.022492 [-0.19064]

SD2 0.007614 [ 0.21678]

-0.021944 [-4.99528]

0.171650 [ 2.32999]

-0.002312 [-0.81983]

SD3 -0.056199 [-1.12996]

-0.000833 [-0.13392]

0.209610 [ 2.00937]

0.000688 [ 0.17229]

SD4 0.023574 [ 0.51445]

-0.002522 [-0.44009]

0.042425 [ 0.44142]

-0.000170 [-0.04616]

DEC 0.083479 [ 1.81929]

0.012614 [ 2.19813]

0.141830 [ 1.47369]

0.007406 [ 2.01024]

DGAB -0.099456 [-2.29194]

-0.004508 [-0.83060]

-0.056574 [-0.62160]

-0.005662 [-1.62497]

DANG 0.029524 [ 0.81421]

0.004151 [ 0.91525]

-0.010817 [-0.14223]

-0.003684 [-1.26541]

DIND -0.127258 [-2.59589]

-0.016942 [-2.76336]

-0.190411 [-1.85187]

-0.005968 [-1.51625]

R-squared Adj. R-squared Sum sq. resids. S.E. equation F-statistic Log likelihood Akaike AIC Schwarz SC Mean dependent S.D. dependent

0.564579 0.459022 0.434893 0.081174 5.348589 100.1652 -2.003981 -1.508556 0.038809 0.110364

0.805760 0.758671 0.006802 0.010152 17.11157 272.7150 -6.161807 -5.666382 0.003517 0.020666

0.189806 -0.006605 1.913152 0.170256 0.966372 38.68678 -0.522573 -0.027148 0.017130 0.169697

0.237370 0.052490 0.002804 0.006517 1.283914 309.5005 -7.048204 -6.552779 0.000383 0.006696

Determinant resid. covariance (dof adj.) Determinant resid covariance Log likelihood Akaike information criterion Schwarz criterion

5.40E-13 2.16E-13 736.9036 -16.02177 -13.92350

Table 5- Vector Error Correction Estimates (t-statistics in [ ]).

31  

The dynamic adjustment towards the long-run equilibrium is defined by the estimates

vector . The speed of adjustment in equation (4.3) is which

suggests that each quarter the consumption by China and India make an adjustment of

4.311% towards the equilibrium relationship (recall that the variables are expressed in

natural logarithms). The speed of adjustment in equation (4.4) is much slower: total

demand adjusts towards the long-run equilibrium at the average rate of 0.0289% per

quarter. The speeds of adjustment for equations (4.5) and (4.6) explaining and

, respectively were negligible, meaning that the adjustment towards the long-run

equilibrium is not driven by these two variables. These results suggest that the

consumption by China and India have to adjust quickly to the market conditions that

change at a much slower pace over time. This preliminary result will be enriched and

better analysed with the aim of short-run responses in the sections to come.

The R-squared statistics for and are high and hence indicate the success of the

model in predicting the values of these dependent variables within the sample. However,

for the two last variables it seems to be indicating a poorly fitting of the model as one of

the values is even negative. On the other hand, the coefficients of correlation for

and are not high, meaning that these two variables are subject to large shocks

which cannot be fully explained by the model. This is expected, for instance, short-run

price shocks associated with political events in the Middle-East have not been modelled

through dummy variables, but assessing their impact is not the main purpose of the

model. We choose to keep all the variables in the VAR to provide a dynamic analysis of

the relationships between the variables. Further analysis of the shocks is carried through

impulse-reaction functions later.

The F-statistic reported in Table 5 tests the hypothesis that all of the slope coefficients

(excluding the constant, or intercept) are equal to zero at the same time in a given

equation of the VAR model. The p-values reported in Table 5 are essentially zero, so we

reject the null hypothesis that all of the regression coefficients are zero.

4.2.4 Granger Causality: Block Exogeneity Wald Test in VAR

The Granger causality tests are an important vehicle for understanding the dynamics

behind the short-run coefficient estimates in Table 5. In each equation (4.3)-(4.6), the

32  

null hypothesis of this test states that all the coefficients associated with a particular

variable are equal to zero; rejection of this hypothesis implies causality. With this test, it

is possible to find well-defined unidirectional causality, bidirectional causality or no

causality.

The results are summarized in Table 6 Unfortunately, the results indicate no clear

causality explaining , which was of interest in our research. According to

economic theory, in the long-run, we would expect the ratio of OPEC’s production to

affect total consumption as well as the prices of oil but we could not find such a causal

link. The results only suggest causality from total consumption and consumption by

China and India. This means that past values of total consumption will improve the

accuracy of the forecast of the combined consumption of China and India. When

interpreting these results, it is important to consider the limitations of short-run Granger

causality tests. For instance, a variable not included in the model (such as income by

different group of countries) may be Granger-causing total consumption, consumption by

China and India and the ratio of production by OPEC. The absence of Granger-causality

tests carried in this model does not imply that there is no causality at all between the

variables. Extensive literature has covered causality between income and different types

of energy consumption, but this question was not pursued here. If we had found

significant Granger-causality in this section, we could have extracted some information

from it, but its absence suggests that further research needs to be done to investigate

casual links.

LOG( ) LOG( ) LOG(P) DLOG( )

LOG( ) - No No No

LOG( ) Yes - No No

LOG(P) No No - No

DLOG( ) No No No -

Table 6- Summary of Causality Test Results. (‘Yes’ indicates a statistically significant causation running from

a row variable to a column variable at 5% significance level.)

33  

4.2.5 Restricted Variance Decomposition

The variance decomposition analyses the impact of an exogenous shock to one of the

variables by analysing how the n-period ahead forecast innovations are explained. When

the elements of the covariance matrix off the main diagonal are zero, the dynamic

response to the shock is simply driven by the autoregressive parameters. The variance

decomposition in this sections analyses the possibility of contemporaneous shocks (for

example, if there is a shock to oil demand, it would seem reasonable to assume a

contemporaneous shock to oil production will accompany it). In Table 7, we report the

percentage contributions of the four identified shocks to the forecast error variance of the

four variables at various horizons in the variance decomposition based on the estimated

VAR(2).

The Cholesky decomposition is applied to the reduced form residuals with the following

variable ordering , , and . The forecast error for is determined by

shocks to at the next period but not to the other variables, that is only

shocks today affect the forecast error. According to section I of Table 7, at two quarters

forecast, about 2.52% and 1.69% of the forecast error variance of the changes in

can be accounted for by and shocks, respectively. This does not increase by more

than that over the twenty quarters horizon.

Section II of Table 7 analyses shocks that are contemporaneous to and ,

following the order of the Cholesky variance decomposition. At one quarter forecast,

about 7.74% of the forecast error variance in can be accounted for by shock.

This increases to 9.28% for a two quarters horizon and to 15.67% at twenty quarters

horizon. Interestingly, explains 9.18% of the forecast error variance of the changes in

at five quarters horizon and this increase to 46.06% at twenty quarters horizon.

Whereas does not expain more than 3% over the twenty quarters horizon and the rest

of the changes is explained by itself. We conclude that demand shocks coming from

China and India provoke a later reaction in OPEC production and have little influence on

oil price.

With a similar analysis technique, we arrive at the conclusion that Section III of Table 7

indicates that is an important factor for ’s fluctuations.

34  

Finally, section IV of Table 7 indicates that shocks to oil price are on average more

correlated to shocks in demand that lead to increased production than changes in the

production of OPEC relative to total consumption.

I. Variance Decomposition of ∆ :

Period S.E. ∆ ∆ ∆ ∆

1 0.081174 0.000000 0.000000 0.000000 100.0000

2 0.089832 2.517385 0.194015 1.689863 95.59874

3 0.093816 2.797492 0.174290 2.753945 94.27427

4 0.101388 2.687978 0.382809 3.362629 93.56658

5 0.108992 2.633144 0.426195 3.683207 93.25745

… … … … … …

20 0.199152 1.816226 1.132068 4.808487 92.24322

II. Variance Decomposition of ∆ :

Period S.E. ∆ ∆ ∆ ∆

1 0.010152 92.25559 0.000000 0.000000 7.744405

2 0.013433 87.76563 0.018882 2.940034 9.275454

3 0.013777 88.53064 0.179766 2.713430 8.576159

4 0.014508 84.52785 4.654393 2.514393 8.303366

5 0.016593 79.22335 9.178521 2.487652 9.110478

… … … … … …

20 0.028634 37.45038 46.05621 0.824107 15.66930

III. Variance Decomposition of ∆ :

Period S.E. ∆ ∆ ∆ ∆

1 0.170256 10.53841 82.74272 0.000000 6.718869

2 0.241891 12.68436 77.23046 0.273208 9.811965

3 0.289757 14.20036 75.30217 0.324262 10.17321

4 0.332419 13.98999 76.18584 0.292668 9.531497

5 0.370522 12.95406 77.89136 0.244847 8.909738

… … … … … …

20 0.734792 13.15491 77.27902 0.212168 9.353907

IV. Variance Decomposition of ∆ :

Period S.E. ∆ ∆ ∆ ∆

1 0.006517 10.64313 3.736481 84.66783 0.952560

2 0.009671 12.56162 6.437379 79.34009 1.660915

3 0.011997 11.64703 7.018212 79.07894 2.255811

4 0.013894 11.57433 6.333815 79.78799 2.303868

5 0.015609 11.48792 6.302312 79.77993 2.429840

… … … … … …

20 0.032160 11.49308 6.202312 79.48522 2.819393

Cholesky Ordering: , , ,

Table 7- Forecast Error Variance Decomposition for the Four Variables.

35  

4.2.6 Impulse Responses

In this section, we introduce impulse-response analysis to provide a more graphical

interpretation of the variance decompositions. Impulse responses trace out the

responsiveness of the dependent variables in the VAR to shocks to each of the variables

as represented in Figure 5. The shocks do not converge to zero as the restrictions

imposed imply that the shocks are permanent.

Figure 5- Impulse Response Analysis.

response to a shock in and is just below 0.02 in the first period, this shows

that OPEC is quick in responding to demand or price shocks. However, it only responds

to a shock in in the second period. This makes sense as shocks in world consumption

are likely to be more significant, hence OPEC is expected to take some time to increase

oil production.

Not surprisingly, does not absorb price shocks as negative values are obtained for all

the periods observed, this can be explained by the fact that the price elasticity of demand

of China and India is more elastic than total demand: large changes in oil prices have

more influence on the combined consumption by China and India. The same pattern can

be observed from the response of to a shock in .

 

 

 

 

Res

pons

e of

:

 

To a shock in:

     

-.002

.000

.002

.004

.006

.008

2 4 6 8 10 12 14 16 18 20

Response of R_P_OPEC to L_TC_P_CHINA_P_INDIA

-.002

.000

.002

.004

.006

.008

2 4 6 8 10 12 14 16 18 20

Response of R_P_OPEC to L_T_C

-.002

.000

.002

.004

.006

.008

2 4 6 8 10 12 14 16 18 20

Response of R_P_OPEC to L_R_PRICE

-.002

.000

.002

.004

.006

.008

2 4 6 8 10 12 14 16 18 20

Response of R_P_OPEC to R_P_OPEC

-.02

.00

.02

.04

.06

.08

2 4 6 8 10 12 14 16 18 20

Response of L_TC_P_CHINA_P_INDIA to L_TC_P_CHINA_P_INDIA

-.02

.00

.02

.04

.06

.08

2 4 6 8 10 12 14 16 18 20

Response of L_TC_P_CHINA_P_INDIA to L_T_C

-.02

.00

.02

.04

.06

.08

2 4 6 8 10 12 14 16 18 20

Response of L_TC_P_CHINA_P_INDIA to L_R_PRICE

-.02

.00

.02

.04

.06

.08

2 4 6 8 10 12 14 16 18 20

Response of L_TC_P_CHINA_P_INDIA to R_P_OPEC

-.002

.000

.002

.004

.006

.008

.010

2 4 6 8 10 12 14 16 18 20

Response of L_T_C to L_TC_P_CHINA_P_INDIA

-.002

.000

.002

.004

.006

.008

.010

2 4 6 8 10 12 14 16 18 20

Response of L_T_C to L_T_C

-.002

.000

.002

.004

.006

.008

.010

2 4 6 8 10 12 14 16 18 20

Response of L_T_C to L_R_PRICE

-.002

.000

.002

.004

.006

.008

.010

2 4 6 8 10 12 14 16 18 20

Response of L_T_C to R_P_OPEC

.00

.04

.08

.12

.16

2 4 6 8 10 12 14 16 18 20

Response of L_R_PRICE to L_TC_P_CHINA_P_INDIA

.00

.04

.08

.12

.16

2 4 6 8 10 12 14 16 18 20

Response of L_R_PRICE to L_T_C

.00

.04

.08

.12

.16

2 4 6 8 10 12 14 16 18 20

Response of L_R_PRICE to L_R_PRICE

.00

.04

.08

.12

.16

2 4 6 8 10 12 14 16 18 20

Response of L_R_PRICE to R_P_OPEC

Response to Cholesky One S.D. Innovations

36  

It can also be seen from the estimations that responds with no lags to a shock in

showing the significant impact of OPEC’s production decisions on price.

Some information can also be drawn from the contemporaneous correlation of the error

term as represented in Table 8.

LOG( ) LOG( ) LOG( )

1 0.6668733 0.5746587 0.5100631

LOG( )

0.6668733 1 0.8398973 0.7267639

LOG( )

0.5746587 0.8398973 1 0.9488617

LOG( )

0.5100631 0.7267639 0.9488617 1

Table 8- Correlation Matrix.

It reflects the degree to which new information producing an abnormal change in one

variable is shared by other variables in the same quarter. The world consumption and the

combined consumption of China and India exhibit the highest correlation. Whereas the

correlation for OPEC’s production and the world consumption as well as the combined

consumption of China and India is relatively lower. This pattern of contemporaneous

correlations is consistent with what we expect from the structure between pairs of

variables. In line with the general observations made above, the contemporaneous

correlations of the real price and the combined consumption of China and India are quite

strong. However, the correlation between the real price and the total consumption is

higher. This is not quite consistent with what we have observed in the impulse responses

as the price responded less strongly to a shock in total consumption than to a shock in the

combined consumption of China and India. The strong correlation between the world

consumption and the combined consumption of China and India is probably due to the

fact that the consumption of China and India is included in the total world consumption.

37  

5. Conclusions

Our research questions focused on the role of OPEC and non-OPEC production and

implications of increased demand in China and India. The research was conducted in

several steps. We proposed a reduced set of equations to test two specific hypotheses (for

which we expected rejection). Hypothesis I stated that OPEC producers are not different

to non-OPEC producers. Hypothesis II stated that demand from China and India is not

different than demand from the rest of the world, i.e. it does not matter where demand

comes from. Both hypotheses were rejected by the estimation results. This gives support

to the following ideas: (i) the (modified) dominant firm-competitive fringe model is

relevant in the short- and long-run, and (ii) the combined demand from China and India

is found to exhibit different characteristics than total demand. The most significant

contribution in our VAR model can be found in the long-term cointegrating relationship.

Through the estimation of a vector error-correction term, the data suggest that the

consumption by China and India shares a long-term relationship with total demand and

the ratio of OPEC production to total production. Interestingly, prices do not play a role

in this long-term relationship between proportional quantities of supply and demand. We

would naturally expect a long-run relationship between total demand and demand from

China and India as the former includes the latter (in the similar way as macroeconomists

include GDP and consumption in an error-correction term). The finding that the ratio or

production by OPEC to total production is a contributor to this long-term relationship is

also an interesting result.

In our empirical VAR model, we concentrated our efforts in trying to improve the setup

in Mu and Ye (2011). Despite using a different setup, our findings on Granger causality

are consistent with Mu and Ye (2011): no economically significant causality was found

between the variables (the absence of Granger-Causality tests does not suggest that there

is no causality between the variables, and further research needs to be done in order to

investigate potential causal links). In the impulse-reaction analysis conducted later we

have found some interesting results, although these results may be subject to the setting

of our Cholesky variance decomposition. The combined demand from China and India

was found to be more price elastic than the total demand. In other words, crude oil

demand by developed or low-growth countries are more price-inelastic. This result sets

the basis for further future research.

38  

This result was, somehow, surprising. However, a careful re-consideration of the issue

leads us to think that this finding can be explained, although there are confronting

factors. China and India’s infrastructure constraints could deprive them from fast

adaption to pacey economic growth, which would suggest that the demand in these

countries should be relatively inelastic. On the other hand, microeconomic theory

suggests that lower income economies such as China and India would be more sensitive

to changes in the prices of the goods they consume compared to higher income countries.

The overall econometric results suggest that the second argument prevails. Figure 5 also

suggests that China and India have high price-elasticity in the short-run but relatively

inelastic in the medium-run. We also found that an increase in OPEC production relative

to the total production produces a significant increase in consumption in China and India

during the first two quarters (increased OPEC production decreases the oil price which in

turn leads to higher consumption). As later pointed out, the main contribution of was not

measuring elasticities but identifying a long-run equilibrium structure. The identification

of the error-correction term was independent from the variance decomposition that was

applied later.

Overall, this paper has contributed by, at least, partially answering some of the most

important questions for today’s global crude oil market.

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