Non-competitive market behaviour in the international coking coal market

Ž .Energy Economics 21 1999 195]212

Non-competitive market behaviour in theinternational coking coal market

Paul Graham, Sally Thorpe, Lindsay HoganU

Australian Bureau of Agricultural and Resource Economics, Minerals, Energy and ResourcesBranch, ABARE, GPO Box 1563, Canberra ACT 2601, Australia

Abstract

In this paper, a primal dual programming model of international coking coal trade isconstructed to test for non-competitive market behaviour. World trade in 1996 is simulatedunder perfect competition and various non-competitive market structures. Statistical testsare used to compare simulated trade flows with actual data. Assuming Cournot-Nashbehaviour, an all consumer oligopsony market structure is preferred to alternative models.Under an all consumer oligopsony world coking coal prices and trade are lower than underperfect competition. Under an oligopsonistic structure welfare gains from productivityincreases in Australian coal mines might largely accrue to coal buyers. Q 1999 Publishedby Elsevier Science

JEL classifications: D40; F10; Q40

Keywords: Market structure; Coal; Programming model

1. Introduction

The extent to which world coal trade and prices are distorted, if at all, byŽnon-competitive market behaviour has been an issue since the 1970s see, e.g.

. Ž .Smith, 1977 . Recently, Hogan et al. 1997a argued that, in an efficient marketwhere there is substantial scope to blend coals, arbitrage will result in prices of

U Corresponding author. Tel.: q612-6272-2034; fax: q612-6272-2328; e-mail: [email protected]

0140-9883r99r$ - see front matter Q 1999 Published by Elsevier ScienceŽ .PII: S 0 1 4 0 - 9 8 8 3 9 9 0 0 0 0 6 - 7

( )P. Graham et al. r Energy Economics 21 1999 195]212196

coal brands and blends with the same set of quality characteristics being equal in agiven period. They found that the pricing of Australia’s exports to Japan was notconsistent with an efficient market outcome.

Knowledge of the structure of the international coal market is important for allnations engaged in coal trade. The welfare implications of shifts in demand andsupply conditions due to government initiatives or external influences are likely todiffer with market structure. For example, under an oligopsonistic market structurewelfare benefits from reductions in the cost of coal export supply may largelyaccrue to coal buyers. This is of particular importance to the Australian coalindustry which has been the focus of two recent government inquiries into its

Ž .economic performance Taylor, 1994; Productivity Commission, 1998 .In this paper, a primal dual programming model is constructed to examine

various possible hypotheses about the structure of international coking coal trade.Using this primal dual model of international coal trade five non-competitivemarket structures based on Cournot]Nash behaviour are simulated in addition tothe perfectly competitive case. For each case, a range of price elasticities areassumed for both demand and supply functions. The resulting trade flows andimport and export prices are compared to the actual values in the base year, 1996.Statistical techniques are used to draw conclusions about the relative performanceof each market structure.

Following applied and theoretical research pioneered by Takayama and JudgeŽ .1964, 1971 , spatial equilibrium programming models have been widely used to

Žstudy international trade in commodities Kolstad and Burris, 1986; MacAulay,.1992 . Spatial equilibrium programming models of international coal trade have

tended to focus on analysing trade flows and market structures for thermal coalŽ .see, e.g. Abbey and Kolstad, 1983; Kolstad and Abbey, 1984 . Thermal coal islargely valued for its energy content in raising steam for electricity generation.Hence, thermal coal may be regarded as a homogeneous commodity in terms ofenergy content.

Coking coals receive price premia and discounts for various quality attributes inw Ž .the production of coke for blast furnace steel making see, e.g. Hogan et al. 1997bx Ž .for recent empirical estimates of premia and discounts . Sanders 1996 has argued

that such premia and discounts reflect the pure carbon contained in coking coals,Ž .while Roberts and Callcott 1984 found that net carbon content was a key

explanator of coking coal prices. In this paper, net carbon content is used torepresent heterogeneous coking coals as an approximately homogeneous commod-ity. A key contribution this paper seeks to make is to extend the analysis of marketstructure in thermal coal trade to coking coal trade where production and con-sumption are more concentrated.

Background to international coal trade is provided in Section 2. A primal dualmodel of world coking coal trade is presented in Section 3. A description of themodel simulations, assumptions and statistical tests is given in Section 4. Adiscussion of modelling results is presented in the Section 5 and conclusions areoffered in Section 6.

( )P. Graham et al. r Energy Economics 21 1999 195]212 197

2. International coal trade

The volume of international coal trade was around 479 million tonnes in 1996Ž .having grown strongly since the 1970s International Energy Agency, 1997a . World

production of coking and thermal coal is relatively large compared to trade, witharound 3730 million tonnes produced in 1996. The destination for coal exports hastended to shift away from Europe to Asia. There was a marked shift towards coalas an energy commodity following the oil shocks of the 1970s and the emergence oflarge coal importing markets in Asia. The rapid growth of coal import demand inAsia was matched by the development of key exporting capacity in countries suchas Australia and South Africa. The international market for coal has developedwith a reduction in intra-European trade and increases in seaborne trade toEurope and Asia.

There are several market characteristics that are considered important in de-termining market structure. They are the number and size of buyers and sellers,barriers and costs of entry and exit, product differentiation and vertical integrationŽ .see, e.g. Scherer and Ross, 1990; Carlton and Perloff, 1994 . Some of thesecharacteristics are discussed in relation to the international coal market below.

By the late 1980s, Australia had emerged as the leading exporter of coalaccounting for almost 30% of world exports in 1996. The US is the second largestexporter followed by South Africa with shares in world coal exports of approxi-

Ž .mately 17 and 12%, respectively International Energy Agency, 1997a .The coal market comprises two end-use markets } coking coal is used as a

reductant and energy source in the production of steel, while thermal coal is usedmainly in the production of electricity in coal fired power stations. The end-usemarkets are linked to the extent that premium coking coals may be used in all enduse applications. In addition, some thermal coals may be transformed into weakcoking coals by washing, while some thermal coals are used for pulverised coal

Ž .injection in blast furnace steel making Hogan et al., 1997b . As previously noted,in this study, attention is focused on coking coal trade. However, given marketdevelopments, an integrated analysis of both end use markets could be a promisingdirection for future research.

The case for non-competitive market behaviour in the coking coal market isthought to be far more convincing than that for thermal coal. There are relativelyless substitutes for coking coal in steel production compared to thermal coal forelectricity generation. There are limited opportunities for substitution away fromstrong coking coal in blast furnace steel production. However, the growing use ofpulverised fuel injection has resulted in some limited substitution of weaker cokingcoals. In addition, the main competitor to coking coals is the use of electric arc

Ž .furnace EAF technologies. These use electricity, possibly produced from coalfired power stations, to reduce scrap and some iron feed to produce steel.Currently around 30% of steel production is produced using the EAF route, andthe share could be expected to increase in the longer term. On balance, the relativecost competitiveness of integrated blast furnace steel production is expected tolimit the degree of competition other coals provide for coking coal.


Coking coal supply is concentrated. Three countries, Australia, Canada and theUS supply 81% of world coking coal imports, with Australia alone supplying 41%Ž . Ž .Fig. 1 International Energy Agency, 1997a . Domestic supplies of coking coalreduce the degree of market concentration for consumers. However, the importreliance of the major importers in Asia such as Japan and South Korea is over90%.

Pricing in the coking coal market has tended to be through long-term contractsŽ .greater than 5 years for a base rate of volume, with prices and volume changesdetermined through a negotiated process each year. Similar to thermal coal trade,steels mills in the importing countries of Asia tend to negotiate the purchase of

Ž .coking coals in groups Ellerman, 1995; Hogan et al., 1997a .

3. Primal dual model of international coking coal trade

In this section a spatial equilibrium programming model of coking coal trade isoutlined. The model is used to assess the applicability of various possible hypothe-ses about the actual structure of coking coal trade.

The model follows the structure of the primal dual programming models ofŽ .spatial equilibrium presented in Takayama and Judge 1971 and further illumi-

Ž . Ž . Ž .nated by Hashimoto 1984 , Kolstad and Burris 1986 and MacAulay 1992 , forexample. The model is structured to find the equilibrium prices and trade flowsbetween a given set of producers and consumers given assumptions about theirbehaviour and objectives.

The primal dual spatial equilibrium model typically maximises the sum of netrevenues from trade. The calculation of net revenues is dependent on assumptionsabout market structure and includes a transport cost recognising the spatialseparation of markets. The objective function is subject to a set of demand andsupply balance constraints and price constraints which reflect the market structureassumptions.

Fig. 1. World coking coal exports by country.


Although all of the models investigated can be solved in their primal forms, theprimal dual form was chosen because of its more open statement of the assump-tions of the model in price and quantity terms and, therefore, fewer problems inmodel construction. The primal dual form is also more useful because it can beapplied to models of imperfectly substitutable commodities involving cross-elastici-ties and those with restrictions in terms of both price and quantity which might be

Ž .applied to investigate policy issues MacAulay, 1992 .The model is formulated using a nested structure. As proposed by Kolstad and

Ž .Burris 1986 , many types of market structures can be captured by a term which isŽ .simply a producer’s consumer’s conjectures about the response of other produc-

Ž . Ž .ers consumers to a change in their production consumption level. This term isŽ .applied to the monopolist’s monopsonist’s margin in such a way as to reflect that

Ž .producer’s consumer’s degree of market power. In the case of a producer withmarket power, the monopolist’s margin is the extra revenue gained on eachproduct supplied as a result of selling that product at a price above marginal costs.

3.1. Notation

�In the study, index j denotes a major region which imports coal where j s Japan,.South Korea, Taiwan, India, Europe and Brazil . A rest of world importing region

is not included as most of its demand in the base year is supplied by the residualexporting region. Index i denotes a major region which exports coking coal, where

� 4i s Australia, Canada, US and others . Indexes jj and ii are used to denoteregions which are assumed to behave as oligopsonists or oligopolists, respectively,in particular simulations. Remaining importers or exporters belong to the competi-tive fringe respectively. The endogenous variables of the model are:

Ž .QD , quantity of coal imports into region j Mtnce ;jŽ .QS , quantity of coal exports from region i Mtnce ;i

Ž .Q , quantity of coal traded between regions i and j Mtnce ;i jŽ .PD , market price of coal in demand region j US$ per tnce ; andj

Ž .PS , market price of coal in supply region i US$ per tncei

where all variables must be non-negative. Mtnce is million tonnes of net coalequivalent where 1 t of coal contains 60% net carbon. In the analysis, it is assumedthat domestic demand for an exporter is fixed as is domestic supply for animporter. Other functional relationships and parameters are:

Ž .f QD , inverse demand function for region j under perfect competition orjŽ .average revenue function under oligopoly where f 9 QD is the deriva-j

tive with respect to QD ;jŽ .g QS , inverse supply function for region i under perfect competition ori

Ž .average cost function under oligopsony where g 9 QS is the derivativeiwith respect to QS ;i

RS , exporter ii’s aggregate conjecture for market j of the change in thei i jvolume of trade from all other suppliers given a change in its own trade


volume. For example, RS s y1 under perfect competition and RSi i j i i js 0 under Cournot]Nash oligopoly.

RD , importer jj’s aggregate conjecture for market i of the change in thei j jvolume of trade to all other consumers given a change in its own tradevolume. For example, RD s y1 under perfect competition and RDi j j i j js 0 under Cournot]Nash oligopsony.

Ž .A1 , scale term for an exponential version of function f QD ;j jŽ .B1 , scale term for an exponential version of function g QS ;i i

Ž .A2 , inverse price elasticity in exponential version of function f QD ;j jŽ .B2 , inverse price elasticity in exponential version of function g QS ; andi i

Ž .TC , unit transport cost between regions i and j US$ per tnce .i j

3.2. Model specification

The primal dual objective function and primal and dual constraints of the nestedmodel are as follows.

Ž . Ž .Adjusted net revenue s Ý f QD QD y Ý g QS QS y Ý Ý TC Qj j j i i i i j i j i j

Ž .Ž . 2q Ý Ý f 9 QD 1 q RS Qi i j j i i j i i j

Ž .Ž . 2 Ž .y Ý Ý g 9 QS 1 q RD Q 1i j j i i j j i j j

subject to:

Ž .QD F Ý Q 2j i i j

Ž .QS G Ý Q 3i j i j

Ž . Ž .f QD F Pd 4j j

Ž . Ž .PS F g QS 5i i

Ž .Ž . Ž .Ž . Ž .PD q Q f 9 QD 1 q RS F TC q PS q Q g 9 QS 1 q Rd 6j i i j j i i j i j i i j j i i j j

The first, second and third terms in the objective function represent coal importrevenues less total costs in coal production and transport costs in shipping coal toimport markets. Together these terms define net social revenue in the case ofperfectly competitive coking coal markets given the constraint set. The fourth andfifth terms subtract out non-competitive marketing rents on the supply or demandside of the markets, whichever market structure is assumed to prevail. If there areCournot]Nash oligopolists in coking coal trade, their rents must be deducted fromnet revenues to account for all pay-offs in the model, as given in the fourth term.Analogously, if there are Cournot]Nash oligopsonists in coking coal trade, theirmarket rents must be deducted from net revenues to ensure that the sum total ofadjusted net revenues in coal trade is zero. This is taken account of in the fifthterm.

Ž . Ž .Constraints 2 and 3 are the standard quantity restrictions associated with


Ž .spatial equilibrium trade models. Constraint 2 states that total import demand inregion j must not exceed the sum of trade from all i exporting regions to that

Ž .import region. Constraint 3 states that total supply from region i must be no lessthan the sum of trade from that region to all j importing regions.

The remaining constraints are the set of dual price conditions which flow fromthe first-order conditions for the given market structure. Under perfect competi-tion, individual producers and consumers take the prices faced as given. Producersurplus is maximised by choosing to supply those markets where the given importprice intersects the inverse supply function inclusive of given unit transport cost.Consumer surplus is maximised by choosing to buy from those export marketswhere the given export price inclusive of given unit transport cost intersects theinverse demand function. Hence, when trade is realised in the model under perfectcompetition, the export price differs from the import price by the given unit

w Ž .xtransport cost constraint 6 , the import price is read from the inverse demandw Ž .xfunction constraint 4 and the export price is read from the inverse supplyw Ž .xfunction constraint 5 . The inverse demand function and the inverse supply

functions are marginal functions to the competitive buyer or seller, respectively.Under a Cournot]Nash equilibrium in the model, an oligopolist is able to

Ž .influence by design in an upward direction the import market price which isw Ž .xinterpreted as average revenue constraint 4 . Profit is maximised by choosing the

volume of trade in each import market which equates the marginal revenueŽfunction the average revenue function is first adjusted to measure residual

.demand with marginal cost determined from the inverse supply function inclusivew Ž .xof the given unit transport cost constraint 6 . The import price exceeds the export

price inclusive of the given unit transport cost by the oligopolist’s margin.Ž .Similarly, an oligopsonist is able to influence by design in a downward direction

the export market price which is interpreted as average cost exclusive of the givenw Ž .xunit transport cost constraint 5 . Profit is maximised by choosing the volume of

wtrade in each export market which equates the inverse demand function constraintŽ .x Ž4 less the given unit transport cost with the marginal cost function the average

. w Ž .xcost function is first adjusted to measure residual supply constraint 6 . In thiscase, the import price exceeds the export price inclusive of unit transport cost bythe oligopsonist’s margin.

The model was implemented using constant elasticity demand and supply sched-ules for the functions f and g. This specification and the marginal schedules f 9and g 9 relevant to specific Cournot]Nash market structures are as follows.

Ž . A 2 j Ž .f QD s A1 QD 7j j j

Ž . B2 i Ž .g QS s B1 QS 8i i i

Ž . A 2 jy1 Ž .f 9 QD s A2 A1 QD 9j j j j

Ž . B2 iy1 Ž .g 9 QS s B2 B1 QS 10i i i i


4. Simulation design

4.1. Market structure simulations

World coking coal trade is simulated under perfect competition and five non-competitive market structure assumptions } two based on market power in coalexporting countries and three based on market power in coal importing countries.The six market structure assumptions are:

v perfect competition;v Australia monopoly with competitive fringe;v Australia]US duopoly with competitive fringe;v Japan monopsony with competitive fringe;v Japan]Europe duopsony with competitive fringe; andv all consumer oligopsony.

Each market structure assumption is simulated using the primal dual model ofinternational coal trade presented in the previous section. In each case the model

Žis simulated using the GAMSrMINOS programming software Brooke et al.,.1988 .

In each of the non-competitive market structure cases, those with market powerare assumed to act according to Cournot]Nash behaviour so that suppliers, forexample, assume no change in the behaviour of others with respect to a change intheir supply. A Cournot]Nash oligopolist acts as a monopolist in terms of theresidual demand faced. This is the demand remaining after others are assumed tohave disposed of their output. A similar concept holds for the oligopsonist in termsof residual supply.

4.2. Price elasticities of supply and demand

The parameters of the demand and supply schedules in coking coal trade arecritical in determining the ability of producers and consumers to exercise marketpower. In particular, a profit maximising oligopolist will only operate where themarginal revenue function faced in the market is strictly positive. From the model

Žequations, this requires that the price elasticity for the residual demand average.revenue function exceeds the oligopolist’s share in the market. Subject to this

restriction, the less elastic is demand the greater are oligopoly profits from forcingup prices. Analogously, the less elastic is supply the greater are oligopsony profitsfrom forcing prices down. The use of a constant elasticity inverse supply functionrules out the problem that marginal cost is negative in the inelastic range of alinear inverse supply function.

The price elasticity of demand for metallurgical coal is derived from the priceelasticity of demand for steel, the substitution between imports in steel productionand the share of metallurgical coal in steel production. On this basis, low values forall three factors are expected to reinforce a relatively low responsiveness ofdemand to changes in the metallurgical coal price.


Ž .In general terms, the research of Ball and Loncar 1991 supports the use of alow price elasticity of demand for coking coal. For Western Europe and Japan,they model coking coal use as a function of pig iron use where pig iron use is afunction of several variables including the real price of coking coal. They estimatethat the price elasticity of coking coal demand ranges between y0.1 and y0.4.

ŽHowever, it is noted that in absolute terms, the total demand elasticity total supply. Želasticity with respect to price is an upper estimate of the import demand export

. Ž .supply elasticity due to the existence of domestic supply demand .Ž .On the supply side, the research of Beck et al. 1991 supports the use of an

elastic but far from infinite long run coal supply response to price. They use minelevel cash flow models for the Australian black coal industry to econometricallyestimate a log-linear long run supply function. They estimate that the priceelasticity of supply is around 3 for the Australian black coal industry. Long runprice elasticities of supply between 5 and 10 are implied in the world steel trade

Ž .model of Klijn and Nguyen 1993 . They also impose constant and common priceelasticities of demand of y0.2 for bars and y0.4 for flat steel in each region ofproduction.

It is clear that the price elasticity of demand for coking coal is relatively inelasticin the short run because non-capital inputs are relatively specific to the chosensteel making technology. The long run is defined as a period in which thetechnologies can be changed and new firms can enter or exit an industry. To theextent that market power may be exercised in the short run, some would debatewhether it can be exercised in the long run. Firms may develop alternativetechnologies which are far less reliant on inputs supplied by those who exercise

Ž .short run market power. Australian Coal Association and ACIL 1994 commenton Ball and Loncar’s research and argue along similar lines that a unitary elasticityof demand for coking coal may be more appropriate in the long run.

The development of a backstop technology is commonly seen as providing theultimate cap on the use of market power by suppliers. This reasoning may explainthe use of highly elastic export demand functions faced by coal suppliers in thelong run, as is the case in the ORANI general equilibrium model of the Australian

Ž .economy Dixon et al., 1982 where the export demand elasticity is set at y20. Ingeneral an export demand elasticity depends on the import demand elasticities andthe importer’s shares in a nation’s exports. If the import demand elasticities are thesame then the export demand elasticity will equal the common import demandelasticity. The implicit long run supply function for Australian coal used in ORANIis perfectly elastic ignoring general equilibrium effects.

Existing econometric and other research on the values of long run coking coaldemand and supply functions is far from definitive. The accuracy of existingeconometric estimates of the demand and supply parameters for commodities such

Ž .as coal and other energy fuels is difficult to assess. Bohi 1981 and Hartwick andŽ .Olewiler 1986 discuss some of the theoretical and practical issues involved.

Ideally, since investment drives the long run price responsiveness of demand andsupply it is necessary to model the investment behaviour of coal buyers and sellersin a consistent intertemporal optimisation framework to accurately estimate long


run price elasticities. Dynamic duality theory has been developed to analyse suchŽdynamic responses under perfect competition see, e.g. McLaren and Cooper,

.1987 . However, no consistent dynamic framework exists which incorporatesw Ž .Cournot]Nash market behaviour see, e.g. Slade 1995 for a survey of existing

xempirical techniques . In general, static analysis is used to simultaneously estimatesupply, demand and market structure parameters.

The approach taken in this paper is to use a range of price elasticity estimates inthe simulation analysis. The elasticities are applied unilaterally to all regions. Priceelasticities of import demand are assumed to be y0.3, y1 or y3, while exportsupply elasticities are assumed to be 3 or 10. The elasticity assumptions that areconsidered most likely are the price elasticity of demand for coking coal in allimport markets of y0.3 and the price elasticity of supply in all export markets of 3.

4.3. Coking coal standardisation measure

The calorific value of thermal coal is the most important factor in determiningthe price received for different grades of thermal coal, although ash and sulphurcontents outside the agreed specification in thermal coal contracts typically incurprice penalties. Consequently, calorific value is often used in studies of the thermalcoal market to adjust coal flows to units of coal equivalent. More generally, energycontent is the standardisation measure used to compare production. consumptionand trade in coal, oil, natural gas and uranium.

In contrast, coking coal is valued on the basis of a range of its physical andchemical properties each of which influences its performance in steel makingŽ .Hogan et al., 1997b . In this paper, the net carbon content is chosen as a cokingcoal equivalence measure.

Ž .Net carbon content is defined by Roberts and Callcott 1984 as a measure of thechemical merit of a coking coal based on a conceptual thermochemical model ofthe blast furnace, the major technology used in steel making. It enables net carbonin coal to be calculated from standard proximate analysis of coals using theformula:

NCC s 93.22 y 0.728 Volatile matter y 1.438 Ash y 5.4 Sulphur

Ž .q 0.0422 Volatile matter ? Sulphur 11

Ž .where net carbon content NCC , volatile matter, sulphur and ash are in thestandard percentage terms.

In the important Australia]Japan coking coal trade, the net carbon contentseems to account reasonably well for the differences in benchmark prices for hard

Ž .and semisoft coking coal sold between Japanese fiscal years 1988 and 1996 Fig. 2 .To determine the average net carbon content to be applied to each exporter’s

coal in the simulation exercise, data on volatile matter, ash and sulphur is obtainedŽ .from Coal 96 Barlow Jonker, 1996 . The average net carbon of coal imports for

each country is then imputed from the volume weighted average of net carbon


Fig. 2. Australia]Japan benchmark prices in US$rt of net carbon.

imported in 1996. The resulting average net carbon of imports and exports for eachcountry is shown in Table 1.

4.4. Other data assumptions

Transport cost assumptions by country of origin and destination are given inTable 2. For the well known trading routes to Japan and Europe, transport costs

Ž .are based on estimates in International Energy Agency 1997a . Other transportcost estimates are determined by applying the formula used in the thermal coaltrade computer modelling package WOCTES, the description of which may be

Ž .found in Jolly et al. 1990 . Distances between trading regions, the main data

Table 1Ž .Average net carbon of coal imports and exports by country % in 1996

Ž .Country %

ImportersJapan 57.84Chinese Taipei 57.53South Korea 57.86India 57.13

Ž .EU 15 57.47Brazil 57.37

ExportersAustralia 57.14US 60.24Canada 57.16Others 56.85


Table 2Ž .Transport cost assumptions by country of origin and destination US$rt in 1996

Ž .Japan Chinese Taipei South Korea India EU 15 Brazil

Australia 7.15 8.25 9.42 10.12 9.60 15.73US 12.00 14.17 12.61 17.46 6.78 9.26Canada 7.60 10.74 8.56 17.75 11.45 11.05Others 6.40 20.00 20.00 20.00 5.29 17.28

requirement, are calculated as the average of distances between major ports forŽ .each region. Most port distances are sourced from Mannini 1989 . In the case of

the ‘Other’ or residual exporting region the distance to each importing region wascalculated as the average of known distances of the major suppliers who make upthe category for each importing region. Where trade between ‘Other’ and animporting region is nil, or very small, a transport cost in the upper range, asestimated by the formula, was assigned to rule out significant trade.

The constant term in each supply and demand schedule is determined from priceand quantity values for the base year 1996 for each region i and j given theassumed elasticity. Base demand and supply prices in 1996 are assumed to be the

Ž .average CIF import and FOB export unit values respectively see Table 3 .Ž . Ž .International Energy Agency, 1997a , Coal Manual TEX Report, 1997 and

Ž .Energy Prices and Taxes International Energy Agency, 1997b .

4.5. Statistical tests of forecasting accuracy

In order to assess the performance of alternative models, trade flows Q , or thei jresulting shares as a fraction of total trade, are compared to the actual base yearvalues. Export and import prices, PD and PS , are also similarly assessed. Thej i

Table 3Prices in US$rt in the base year 1996

Country US$rt

( )Import unit ¨alues CIFJapan 56.39Chinese Taipei 59.40South Korea 56.35India 58.32

Ž .EU 15 59.96Brazil 61.00

( )Export unit ¨alues FOBAustralia 47.56US 50.05Canada 51.52Others 48.00


accuracy of price forecasts is also important in model performance, particularly inthe context of different market structures.

Ž . Ž .Following Kolstad and Abbey 1984 and Kolstad and Burris 1986 , the Spear-man correlation and Theil inequality coefficients are used to compare the tradeshares under different market structures with the base year outcomes and, thereby,assess the performance of each alternative model.

If the model perfectly predicts each trade share, actual and simulated valueswould lie on a 458 line when plotted against each other. Therefore, to determinesimulation performance, one could regress the actual values against the predictedvalues and test if the coefficient on the predicted values is one, and on theintercept term zero using an F-test but this requires an assumption that the errorterm is normally distributed, amongst other things. An alternative test, which isdistribution free is to find if the actual values and the error between predicted andactual values has no association, using a non-parametric test. This is done using the

Ž .Spearman’s rank correlation coefficient Newbold, 1988 . The test statistic iscompared to the relevant critical value at the 95% level of confidence and the nullhypothesis rejected if the test statistic lies above the critical value. The nullhypothesis is that the model predicts trade, or, equivalently, that there is noassociation between actual values and the error between predicted and actualvalues.

The Theil inequality coefficient is the root-mean-square error scaled so that theŽ .value will always lie between zero and one Pindyck and Rubinfeld, 1991 . If the

value equals zero then the simulated shares of trade exactly equal the actualshares. Thus a lower value is considered an indicator of a good forecasting modelin relative terms. However, no hypothesis may be tested because the Theil analysisdoes not specify a distribution.

5. Results

In each model solution, the objective function was zero and marginal revenueand marginal cost non-negative. These conditions are necessary for a solution to bea local optimum. The Spearman correlation and Theil inequality coefficients arereported for each market structure assumption and elasticity scenario in Table 4.From the Spearman correlation test, the perfect competition assumption is rejectedat the 95% level of confidence in all elasticity scenarios. Of the remaining fivenon-competitive market structures, the Australia monopoly with competitive fringeand Japan monopsony with competitive fringe can be rejected in all elasticityscenarios if the confidence level is lowered to 90%.

The null hypothesis that the Australia]US duopoly predicts trade in 1996 isaccepted at the 95% confidence level using the Spearman correlation test in five ofthe six elasticity scenarios. This market structure is rejected where the priceelasticity of supply is assumed to be 3 and the price elasticity of demand is y0.3.However, the Australia]US duopoly performs relatively poorly in terms of theTheil coefficient. In particular, for the elasticity scenarios where the price elasticity


Table 4aComparison of actual and simulated trade flows in the base year 1996 under six elasticity scenarios

Test Market structurestatistics Perfect Australia Australia Japan Japan and All

competition monopoly and US monopsony Europe consumerduopoly duopsony oligopsony

1. Price elasticity of demand y0.3Supply 3

r y0.558 y0.504 y0.458 y0.575 y0.424 y0.285sTheil 0.342 0.401 0.453 0.219 0.206 0.125

Supply 10r y0.557 y0.504 y0.361 y0.421 y0.458 y0.431sTheil 0.390 0.447 0.523 0.317 0.267 0.215

2. Price elasticity of demand y1Supply 3

r y0.647 y0.462 y0.344 y0.566 y0.423 y0.313sTheil 0.344 0.294 0.319 0.230 0.216 0.129


3. Price elasticity of demand y3Supply 3

r y0.646 y0.482 y0.203 y0.555 y0.380 y0.193sTheil 0.340 0.197 0.213 0.273 0.247 0.145


a r is the Spearman rank correlation coefficient and Theil is the Theil inequality coefficient. Boldscase indicates the lowest Theil statistic or that the null hypothesis for the Spearman correlation test

Ž .cannot be rejected at the 95% level critical value s 0.409 . The null hypothesis is that the modelpredicts trade in the base year 1996. The critical value for the 90% confidence level is 0.343.

of demand is y0.3, this market structure has the highest Theil coefficient of all themarket structures simulated.

The all consumer oligopsony assumption has the lowest Theil coefficient in allelasticity scenarios. Based on the Spearman correlation test at the 95% level ofconfidence, the simulated coal trade flows under the all consumer oligopsonyassumption cannot be rejected as a predictor of actual trade flows where theelasticity of supply is assumed to be 3. However, if the elasticity of supply isassumed to be 10 the all consumer oligopsony assumption is rejected in two of thethree remaining elasticity scenarios. The all consumer oligopsony market structureconsistently outperforms the Japan]Europe duopsony market structure.

The elasticity assumptions that are most consistent with expectations about priceelasticities in the coking coal market are the price elasticity of supply of 3 and priceelasticity of demand of y0.3. Under these assumptions, the all oligopsony marketstructure is selected as the most likely of those tested that explain trade flows. This


Table 5Ž .Actual and simulated coking coal trade shares and prices US$rtnce in 1996

Ž .Japan Chinese South India EU 15 Brazil ExportTaipei Korea prices

( )i Actual shares of total tradeAustralia 20.5 2.6 5.5 6.9 7.5 2.1 49.94US 3.8 1.6 1.8 11.5 3.6 52.54Canada 10.9 0.7 2.8 3.2 0.8 51.31Others 7.8 0.3 1.2 0.2 3.3 1.5 50.66

Total tradeImport prices 58.49 61.95 58.44 61.25 62.59 63.79 146.3 Mtnce

( )ii All consumer oligopsony with low price elasticities of demand and supplySimulated shares of total trade

Australia 21.8 5.0 6.5 7.1 8.4 50.93US 4.6 0.4 8.5 5.5 49.46Canada 8.4 0.2 4.3 2.2 2.6 50.46Others 7.6 6.8 50.42


( )ii Perfect competition with low price elasticities of demand and supplySimulated shares of total trade

Australia 35.6 5.1 7.0 51.02US 11.3 8.1 50.26Canada 6.7 11.1 50.95Others 0.4 14.7 51.79


simulation has the lowest Theil coefficient of all simulations undertaken. The tradeshares and import and export prices for this market structure are given in Table 5together with the actual values in the base year and the perfect competition case inthe same elasticity scenario.

From the trade shares, the all consumer oligopsony and perfectly competitivemarket structures have less variation in the number of exporters supplying eachimporting region than the actual trade shares. Trade diversification is reducedmost under perfect competition. Notably the Australia]Japan trade is simulated tohave represented 36% of world trade in 1996, well above the actual share of 21%.

Ž .However, South Korea imports all its coking coal from Canada and the EU 15imports from the US and others. In practice, diversification policies may bepursued by some importers to reduce the risks of adjustment costs from disruptionto the supply of raw materials in steel production by importing from a number ofregions. Similarly, diversification policies may be pursued by some exporters toreduce the risks of adjustment costs when mines operate below capacity due tounexpected adjustments in consumer demand. Such diversification policies may

Žalso be pursued in practice to reduce the potential for any given country buyer or.seller to influence prices in the market.


The inability of the models to fully reflect diversification of actual trade flowsmay also reflect aggregation error. In particular, the ‘Other’ export region could bedisaggregated. This would provide a more accurate assessment of supply andtransport conditions in particular export regions of the rest of the world outsideAustralia, Canada and the US.

6. Conclusion

It has often been hypothesised that, compared with perfect competition, non-competitive market structures may be more appropriate in terms of price andquantity outcomes in coking coal trade due to the relatively few coal exportingcountries, scarcity of substitutes and group approach to purchases by some coalimporters. In this paper world coking coal trade in 1996 was simulated underperfect competition and five non-competitive market structures using a spatialequilibrium model. Each market structure was simulated under a range of assump-

Ž . Ž .tions for the price elasticity of supply 3, 10 and demand y0.3, y1, y3 . Worldcoking coal trade was standardised by adjusting coals according to their average netcarbon content. Simulated trade flows were compared with actual trade flow datausing the Spearman rank correlation test and Theil’s inequality coefficient.

Based on the simulation study, world coking coal trade in 1996 is judged to bemost accurately represented by an all consumer oligopsony market structure.However, the trade flows simulated under an Australia]US duopoly cannot berejected as a predictor of actual trade for five of the six elasticity scenarios. Theperfect competition model performs consistently poorly across all price elasticityassumptions. Under an all consumer oligopsony world coking coal prices and tradeare lower than under perfect competition. The economic performance of theAustralian coal industry has been the focus of two recent government inquiries.Notably, under an oligopsonistic structure welfare gains from productivity increasesin Australian coal mines might largely accrue to coal buyers.

A model which includes the possibility of counteracting sources of market powermay be an appropriate avenue for further study. A multilateral market powermodel may also go some way in explaining the variability observed in the simulatedprices under the various market structure assumptions. There are a number ofother market structures outside of the nested approach taken here which may alsoprove useful. For example the existence of annual negotiations of prices andquantities in the Asian region suggests the Stackelberg model may provide someinsights.

It may also be useful to include data on the average quality of coals traded byexporter and by destination since some countries may export a significantlydifferent quality of coking coal to different markets. Alternatively, the division ofcoking coals into two categories } hard and other coking coals } may be useful,particularly if different degrees of market power were thought to exist for eachcoking coal type.


Acknowledgements

An earlier version of this paper was presented at the 42nd Annual Conference ofthe Australian Agricultural and Resource Economics Society held at the Universityof New England in Armidale on 19]21 January 1998. The authors would like tothank Nico Klijn and Jane Melanie of the Australian Bureau of Agricultural andResource Economics for their helpful comments on an earlier draft.

References

Abbey, D.S., Kolstad, C.D., 1983. The structure of international steam coal markets. Nat. Resour. J. 23,859]891.

Australian Coal Association and ACIL, 1994. Winning Coal Part III: The Market for Coal, CoalMarketing, Price Formation and Market Access, Canberra.

Ball, K., Loncar, T., 1991. Factors Influencing the Demand for Australian Coal, ABARE TechnicalPaper 91.4. Australian Government Publishing Service, Canberra.

Barlow Jonker, 1996, Coal 1996. Barlow Jonker, Sydney.Beck, T., Jolly, L., Loncar, T., 1991. Supply Response in the Australian Black Coal Industry, ABARE

Technical Paper 91.1. Australian Government Publishing Service, Canberra.Bohi, D., 1981. Analysing Demand Behaviour, A Study of Energy Elasticities. Johns Hopkins University

Press for Resources for the Future, Baltimore.Brooke, A., Kendrick, D., Meeraus, A., 1988. Release 2.25 GAMS, A User’s Guide. Scientific Press, San

Francisco.Carlton, D.W., Perloff, J.M., 1994. Modern Industrial Organisation, 2nd ed. Harper Collins College

Publishers, New York.Dixon, P.B., Parmenter, B.R., Sutton, J., Vincent, D.W., 1982, ORANI: A Multisectoral Model of the

Australian Economy. North-Holland, Amsterdam.Ellerman, A.D., 1995. The world price of coal. Energy Policy 23, 499]506.Hartwick, J.M., Olewiler, N.D., 1986. The Economics of Natural Resource Use. Harper and Row

Publishers, New York.Ž .Hashimoto, H., 1984. A spatial Nash equilibrium model. In: Beckman, M., Kelle, W. Eds. , Spatial

Price Equilibrium: Advances in Theory, Computation and Application. Springer-Verlag, Berlin, pp.20]40.

Hogan, L., Thorpe, S., Graham, P., Middleton, S., 1997a. Coal price-quality relationships and theoutlook for coal. In: Outlook 97, Proceedings of the National Agricultural and Resource OutlookConference, 4]6 February, Minerals and Energy, vol. 3. ABARE, Canberra. pp. 249]268.

Hogan, L., Thorpe, S., Middleton, S., 1997b. Quality Adjusted Prices for Australia’s Black Coal Exports,ABARE report to the Department of Primary Industries and Energy. ABARE, Canberra.

International Energy Agency, 1997a. Coal Information 1996. IEArOECD, Paris.International Energy Agency, 1997b. Energy Prices and Taxes: First Quarter 1997. IEArOECD, Paris.Jolly, L., Beck, T., Savage, E., 1990. Reform of International Coal Protection: Implications for Australia

and World Trade, ABARE Discussion Paper 90.1. Australian Government Publishing Service,Canberra.

Klijn, N., Nguyen, D., 1993. A Policy Simulation Model of World Markets for Steel and SteelmakingMaterials, ABARE paper presented at the 22nd Conference of Economists, Murdoch University andCurtin University, Perth, 27 September to 1 October.

Kolstad, C.D., Abbey, D.S., 1984. The effect of market conduct on international steam coal trade. Eur.Econ. Rev. 24, 39]59.

Kolstad, C.D., Burris, A.E., 1986. Imperfectly competitive equilibria in international commodity mar-kets. Am. J. Agric. Econ. 68, 27]36.


MacAulay, T.G., 1992. Alternative spatial equilibrium formulations: a synthesis. In: Griffiths, W.,Ž .Lutkepohl, H., Bock, M.E. Eds. , Readings in Econometric Theory and Practice. North-Holland,

Amsterdam, pp. 297]324.Mannini, A., 1989. World Coal Ports, IEACR 15. IEA Coal Research, London.McLaren, K.R., Cooper, R.J., 1987. Functional forms in intertemporal duality. In: King, M.L., Giles,

Ž .D.E.A. Eds. , Specification Analysis in the Linear Model. Routledge and Kegan Paul, London.Newbold, P., 1988. Statistics for Business and Economics, 2nd ed. Prentice-Hall International, New

Jersey.Pindyck, R.S., Rubinfeld, D.L., 1991. Econometric Models and Economic Forecasts, 3rd ed. McGraw-

Hill, New York.Productivity Commission, 1998. The Australian Black Coal Industry, Inquiry Report, Auslnfo, Canberra.Roberts, O.C., Callcott, T.G., 1984, Net Carbon in Coking Coal, Report 84-17. ACIRL, North Ryde.Sanders, D., 1996. Coal Characterisation in Marketing } an Elementary Approach, Paper Presented at

a Workshop, Coal Characterisation } for Existing and Emerging Utilisation Technologies, CRC forBlack Coal Utilisation Advanced Technology Centre, University of Newcastle, New South Wales,14]15 February.

Scherer, F.M., Ross, D., 1990. Industrial Market Structure and Economic Performance, 3rd ed.Houghton Mifflin Company, Boston.

Slade, M.E., 1995. Empirical games: the oligopoly case. Can. J. Econ. 28, 369]402.Smith, B., 1977. Bilateral monopoly and export price bargaining in the resource goods trade. Econ. Rec.

53, 30]50.Takayama, T., Judge, J.J., 1964. Equilibrium among spatially separated markets: a reformulation.

Econometrica 32, 510]529.Takayama, T., Judge, J.J., 1971. Spatial and Temporal Price and Allocation Models. North-Holland,

Amsterdam.Taylor, R., 1994. Study of the Queensland and New South Wales Black Coal Industry: A Report to the

Australian Coal Industry Council, Canberra.TEX Report, 1997, 1997 Coal Manual. TEX Report, Tokyo.

Non-competitive market behaviour in the international coking coal market

Documents

Transcript of Non-competitive market behaviour in the international coking coal market