8
ISSN 1068364X, Coke and Chemistry, 2011, Vol. 54, No. 5, pp. 176–183. © Allerton Press, Inc., 2011. Original Russian Text © V.A. Brodskii, 2011, published in Koks i Khimiya, 2011, No. 5, pp. 41–48. 176 The construction of geographic and commodity boundaries is essential to the prevention of monopo lies in market systems. 1 Within each inquiry into monopolistic practices, the construction of such boundaries is central to the identification of economic actors with a dominant market position [1]. The standard method of constructing market boundaries is outlined in its latest form in [2]. How ever, experience shows that numerous principles of the current and previous editions are ambiguous and leave considerable scope for subjective interpretation. Con sequently, there are two mutually exclusive opinions regarding commodity boundaries in the market for cokingcoal concentrates. 2 One opinion (reflected in the website of the Russian Federal Antimonopoly Ser vice [3]) is that all concentrates are commodities within a single market. The other (reflected in the Antimonopoly Service’s ruling on case 110/51008 in letter AG/20956, August 21, 2008) is that coals of the coke group form a separate submarket. In the present work, we investigate the characteris tics of concentrates, which may be regarded as uni form commodities that are purchased for use with one another in mixtures, and we propose a corresponding algorithm for constructing market boundaries. Our basic idea is that, for such commodities, the commod ity boundaries of the market are expanded on account of the mixture properties. 1 The Russian law against monopolies and associated publications speak of determining the boundaries of consumer markets. We prefer to speak of the construction of geographic and commod ity boundaries, which more accurately reflects the process that leads to judgments regarding market boundaries. 2 We recognize the differences between cokingcoal concentrates and energycoal concentrates. In the present work, however, we only consider the former, and any reference to concentrates may be understood to mean cokingcoal concentrates. The proposed algorithm for constructing market boundaries is largely based on the results in [4]. 1. BASIC DEFINITIONS In the present work, we use the definition of a com modity market proposed in [4], as follows. A commodity market is a set of economic actors located in a certain territory (characterized by the geo graphic boundaries of the market) within a certain period and related as buyers and sellers of one or more commodities (characterized by the commodity boundaries of the market). This definition gives specific form to the standard definition in [1]. It has two main benefits: (1) it is con sistent with intuitive concepts regarding a commodity market as a set of buyers and sells of a particular group of commodities; (2) it is consistent with the market model used to construct most information bases, where data regarding the volume and prices in the sale and purchase of commodities are collected separately for each market participant. To identify markets where judgments regarding the possibility of monopolistic conditions may be required, we introduce the following concepts. The commodity of interest is the community for which we need to construct geographic and commod ity boundaries in a specific assessment of monopolistic conditions. The market participant of interest is the seller or pur chaser for whom geographic and commodity bound aries must be constructed in a specific assessment. The period of interest is the interval for which geo graphic and commodity boundaries must be con structed in a specific assessment. The commodity market of interest is the market whose geographic and commodity boundaries must be constructed in a specific assessment. ECONOMICS AND ORGANIZATION OF PRODUCTION Market Boundaries for CokingCoal Concentrates V. A. Brodskii OOO UK Mechel, Moscow, Russia email: [email protected] Received February 28, 2011 Abstract—The construction of geographic and commodity boundaries is considered in relation to the Rus sian market for cokingcoal concentrates. In this market, uniform commodities are purchased and used in mixtures with one another. Such commodities require special terminology and algorithms for the construc tion of the market boundaries. All Russian cokingcoal concentrates should be regarded as commodities within a single market. DOI: 10.3103/S1068364X11050024

Market boundaries for coking-coal concentrates

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ISSN 1068�364X, Coke and Chemistry, 2011, Vol. 54, No. 5, pp. 176–183. © Allerton Press, Inc., 2011.Original Russian Text © V.A. Brodskii, 2011, published in Koks i Khimiya, 2011, No. 5, pp. 41–48.

176

The construction of geographic and commodityboundaries is essential to the prevention of monopo�

lies in market systems.1 Within each inquiry into

monopolistic practices, the construction of suchboundaries is central to the identification of economicactors with a dominant market position [1].

The standard method of constructing marketboundaries is outlined in its latest form in [2]. How�ever, experience shows that numerous principles of thecurrent and previous editions are ambiguous and leaveconsiderable scope for subjective interpretation. Con�sequently, there are two mutually exclusive opinionsregarding commodity boundaries in the market for

coking�coal concentrates.2 One opinion (reflected in

the website of the Russian Federal Antimonopoly Ser�vice [3]) is that all concentrates are commoditieswithin a single market. The other (reflected in theAntimonopoly Service’s ruling on case 110/51�008 inletter AG/20956, August 21, 2008) is that coals of thecoke group form a separate submarket.

In the present work, we investigate the characteris�tics of concentrates, which may be regarded as uni�form commodities that are purchased for use with oneanother in mixtures, and we propose a correspondingalgorithm for constructing market boundaries. Ourbasic idea is that, for such commodities, the commod�ity boundaries of the market are expanded on accountof the mixture properties.

1 The Russian law against monopolies and associated publicationsspeak of determining the boundaries of consumer markets. Weprefer to speak of the construction of geographic and commod�ity boundaries, which more accurately reflects the process thatleads to judgments regarding market boundaries.

2 We recognize the differences between coking�coal concentratesand energy�coal concentrates. In the present work, however, weonly consider the former, and any reference to concentrates maybe understood to mean coking�coal concentrates.

The proposed algorithm for constructing marketboundaries is largely based on the results in [4].

1. BASIC DEFINITIONS

In the present work, we use the definition of a com�modity market proposed in [4], as follows.

A commodity market is a set of economic actorslocated in a certain territory (characterized by the geo�graphic boundaries of the market) within a certainperiod and related as buyers and sellers of one or morecommodities (characterized by the commodityboundaries of the market).

This definition gives specific form to the standarddefinition in [1]. It has two main benefits: (1) it is con�sistent with intuitive concepts regarding a commoditymarket as a set of buyers and sells of a particular groupof commodities; (2) it is consistent with the marketmodel used to construct most information bases,where data regarding the volume and prices in the saleand purchase of commodities are collected separatelyfor each market participant.

To identify markets where judgments regarding thepossibility of monopolistic conditions may berequired, we introduce the following concepts.

The commodity of interest is the community forwhich we need to construct geographic and commod�ity boundaries in a specific assessment of monopolisticconditions.

The market participant of interest is the seller or pur�chaser for whom geographic and commodity bound�aries must be constructed in a specific assessment.

The period of interest is the interval for which geo�graphic and commodity boundaries must be con�structed in a specific assessment.

The commodity market of interest is the marketwhose geographic and commodity boundaries must beconstructed in a specific assessment.

ECONOMICS AND ORGANIZATION OF PRODUCTION

Market Boundaries for Coking�Coal ConcentratesV. A. Brodskii

OOO UK Mechel, Moscow, Russiae�mail: [email protected]

Received February 28, 2011

Abstract—The construction of geographic and commodity boundaries is considered in relation to the Rus�sian market for coking�coal concentrates. In this market, uniform commodities are purchased and used inmixtures with one another. Such commodities require special terminology and algorithms for the construc�tion of the market boundaries. All Russian coking�coal concentrates should be regarded as commoditieswithin a single market.

DOI: 10.3103/S1068364X11050024

COKE AND CHEMISTRY Vol. 54 No. 5 2011

MARKET BOUNDARIES FOR COKING�COAL CONCENTRATES 177

2. COMMODITIES PURCHASED FOR USE IN MIXTURES

The distinguishing feature of Russian concentratescurrently produced is that no single commodity per�mits the production of high�quality coke. Therefore,at Russian coke plants, the production of quality cokedepends on the coking of coal batch (coal blends) con�sisting of 5–10 purchased concentrates in specific pro�portions. Each batch is characterized by a particularstructure—that is, a list of constituent concentratesand the proportions of each. For each coke plant, theproduct quality and the production economics dependon stability of the coal batch. However, for various rea�sons, there may be considerable variation in the coalbatch at specific coke plants over a period such as anaccounting month or quarter.

As an example, Table 1 presents nine compositionsof the coal batch actually employed at a single Russiancoke plant between January and September 2010.

In Table 1, each concentrate is denoted by a set oftwo identifiers: the supplier’s name and the rank ofconcentrate. This is necessary because concentrates ofthe same rank from different enrichment facilities willbe different in most cases.

Thus, concentrates purchased as components ofcoal batch may be described as commodities pur�chased for use in mixtures.

3. UNIFORM COMMODITIES PURCHASED FOR USE IN MIXTURES

Commodities purchased for use in mixtures may beuniform or nonuniform. The concentrates in Table 1are uniform commodities. The coke, iron ore, andlimestone in blast�furnace batch are nonuniformcommodities purchased for use in mixtures. We con�fine our attention to uniform commodities, defined ascommodities whose properties are described by thesame set of characteristics.

To identify uniform commodities within specificassessments of monopolistic conditions, we use themethod in Section 3.6 of [2].

4. ESTABLISHING THE INTERCHANGEABILITY OF UNIFORM

COMMODITIES BY COMPARING DIFFERENT MIXTURE COMPOSITIONS

Because concentrates are uniform commodities pur�chased for use in mixtures, their interchangeability can�not readily be established by the standard method [2].

That method [2] is based on the assumption thatpairwise comparison can reveal the interchangeabilityof several commodities, as shown in [4]. In the case ofuniform commodities purchased for use in mixtures,that assumption means that their interchangeabilitycan only be established if conversion from one mixturecomposition to another entails the replacement of

Table 1. Example of coal�batch compositions employed at a specific Russian coke plant

Supplier Rank of concentrates

Actual content in batch composition, %

1 2 3 4 5 6 7 8 9

Enrichment facility 1 GZhO 3.0 0 3.0 3.0 4.2 3.0 6.0 3.0 0

Enrichment facility 1 KS 5.0 8.0 5.0 5.0 1.9 3.0 3.0 0 0

Enrichment facility 2 GZh 10.0 8.0 8.0 8.0 0 0 0 0 0

Enrichment facility 3 GZh 0 0 0 0 0 0 0 5.0 10.0

Enrichment facility 4 GZh + Zh, Zh 0 0 0 0 1.8 0 0 3.0 3.0

Enrichment facility 5 2Zh 20.0 26.0 24.0 18.0 28.1 36.0 26.0 30.0 25.0

Enrichment facility 6 GZh 0 5.0 5.0 11.0 1.3 0 0 0 0

Enrichment facility 7 Zh 5.0 0 0 0 0 0 0 0 0

Enrichment facility 7 OS + KS 24.0 32.0 32.0 32.0 35.7 36.0 38.0 34.0 31.0

Enrichment facility 8 K 10.0 5.0 5.0 0 0 0 0 0 0

KO + KS,

Enrichment facility 9 KO + OS + KS, 23.0 16.0 18.0 23.0 27.0 22.0 27.0 25.0 31.0

Total KO + OS, KS 100 100 100 100 100 100 100 100 100

178

COKE AND CHEMISTRY Vol. 54 No. 5 2011

BRODSKII

only one concentrate by another, while all the othersremain the same.

However, it is clear from Table 1 that the designationand/or proportion of two or more components changeson passing from one mixture composition to another.For example, mixture 2 differs from mixture 1 in termsof nine components; mixture 3 from mixture 2 in termsof four; mixture 4 from mixture 3 in terms of three; mix�ture 5 from mixture 4 in terms of seven; mixture 6 frommixture 5 in terms of six; and so on.

Undoubtedly, we may find in a particular case thatone type of concentrate is replaced by another in thetwo compositions being compared, without change inthe other mixture components. In general, however—for example, in Table 1—we cannot say which type ofconcentrate is replaced by which other type in com�paring pairs of batches.

In other words, the example in Table 1 clearlyshows that, in the case of uniform commodities pur�chased for use in mixtures, simultaneous change inthree or more components of the uniform mixtureprevents the use of the standard method of pairwisecomparison.

5. ADJACENT MIXTURE COMPOSITIONS

For uniform commodities, we may define a mixturecomposition as the result of mixing several uniformcommodities in specific proportions. The purchaserformulates these compositions for production use in aspecified period, under the actual or potentially possi�

ble conditions.3

For uniform commodities, all mixture composi�tions that contain the same components (commodi�ties) but differ in their proportions will be regarded asa single mixture composition. For example, this ruleapplies to mixtures 6 and 7 in Table 1. Such aggregatedmixture compositions are possible because the con�struction of commodity boundaries in markets is basedonly the list of commodities in the mixture composi�tion and not on the proportions of the individual com�ponents. Such aggregation offers at least two benefits:(1) the number of mixture compositions that must beconsidered is reduced; (2) the initial informationrequested from the purchasers by the antimonopolyservice is reduced and simplified.

In what follows, we only consider mixture compo�sitions of uniform commodities. To identify the rela�tion between different mixture compositions, weintroduce the concept of adjacent mixture composi�tions, defined as two mixture compositions that shareat least one component (commodity).

3 The potentially possible conditions are the result of measuresthat may be taken so as to replace one mixture composition byanother, according to the standard method in [2]. The durationof these measures must be no more than a year, and their costmust be no more than 10% of the commodity price.

Note that this definition applies when both adja�cent mixture compositions are adopted by a singlepurchaser and when each is employed by a differentpurchaser.

In Table 1, mixtures 1 and 2 are adjacent mixturecompositions.

6. INITIAL MATRIX OF ADJACENT MIXTURE COMPOSITIONS FOR SEVERAL BUYERS

We introduce the following definition in order toemploy the algorithm for constructing market bound�aries in [4].

The initial matrix of adjacent mixture compositionsfor several economic actors regarded as partial buyers inthe commodity market of interest is a quadratic matrixformulated on the following principles.

(1) Each column and each row of the matrix corre�sponds to a particular mixture composition for theconditions of the particular economic actors.

(2) The matrix element at the intersection of row iand column j is 1 (YES) if the mixture compositioncorresponding to row i and the mixture compositioncorresponding to column j are adjacent mixture com�positions; otherwise, this matrix element is 0 (NO).

Then, for the sake of brevity, we may speak simply ofthe initial matrix of adjacent compositions (Table 2).

In Table 2, we show 12 mixture compositions, eachof which is assigned a code corresponding to the codeof one of the coke plants (Table 3). Accordingly, inTable 2, we may make the simplifying assumption that,in the period of interest, each coke plant employs onlyone mixture composition, whose components consistof all the concentrates purchased by the plant in thatperiod. That simplifying assumption is made becausegenerally available sources lack information regardingthe structure of the mixtures actually employed at dif�ferent Russian coke plants in the period of interest.

The elements of the initial matrix of adjacent com�positions in Table 2 are determined in accordance withthe rules already outlined, on the basis of the data inTable 4.

In Table 2, for each mixture composition, we showits weight (%), which is equal to the coke plant’s mar�ket share, calculated from the initial data in Table 4.

7. INITIAL DATA

We assume that the initial data required for formu�late the initial matrix of adjacent compositions may beobtained from each final purchaser (consumer) andexpressed in a summary table (Table 4).

Table 4 presents the matrix of concentrate suppliesto the Russian market from January to September2010. For the sake of clarity, we have adopted the sim�plifying assumption that each supplier provides a sin�

COKE AND CHEMISTRY Vol. 54 No. 5 2011

MARKET BOUNDARIES FOR COKING�COAL CONCENTRATES 179

gle commodity, which is identified by the supplier’sname.

8. FINAL INITIAL MATRIX OF ADJACENT MIXTURE COMPOSITIONS FOR SEVERAL

PURCHASERS

We introduce the following definition in accor�dance with [4]. The final matrix of adjacent mixturecompositions for several purchasers is a submatrix ofthe initial matrix of adjacent compositions, with thefollowing three properties.

(1) All the mixture compositions whose codes arein the names of the submatrix columns are related.

(2) Eliminating one or more mixture compositionswith negligible weight from the submatrix does not dis�rupt the relations within the submatrix.

(3) At least one mixture composition within thesubmatrix contains the commodity of interest.

In what follows, for the sake of brevity, we will refersimply to the final matrix of adjacent compositions.

The meaning of these three properties was consid�ered in [4]. A method of verification was also pro�posed—that is, an algorithm for conversion from theinitial matrix of adjacent compositions to the finalmatrix of adjacent compositions.

It is simple to establish that the initial matrix ofadjacent compositions (Table 2) satisfies all threerequirements imposed on the final matrix of adjacentcompositions. These requirements are satisfied for anycommodity in Table 4 if it is regarded as the commod�ity of interest.

9. REFINEMENT OF THE STANDARD CONCEPTS

In view of the characteristics of commodities pur�chased for use in mixtures, we adopt the following def�inition: the commodity boundary of a market is the setof commodities forming the mixtures in the finalmatrix of adjacent compositions.

This definition offers several benefits with respectto that in the standard method [2]. First, it does notrequire pairwise establishment of the interchangeabil�ity of commodities. Second, it permits the use ofobjective data regarding the actual mixture composi�tions that are interchangeable for each purchaser.

Table 2. Initial matrix of adjacent mixture compositions for coking�coal concentrates

Mixture compositions

CP�1 CP�2 CP�3 CP�4 CP�5 CP�6 CP�7 CP�8 CP�9 CP�10 CP�11 CP�12

CP�1 1 1 1 1 1 1 1 1 1 1 1 1

Mix

ture

com

posi

tion

s

CP�2 1 1 1 1 1 1 1 1 1 1 1 1

CP�3 1 1 1 1 1 1 1 1 1 1 1 1

CP�4 1 1 1 1 1 1 1 1 1 1 1 1

CP�5 1 1 1 1 1 1 1 1 1 1 1 1

CP�6 1 1 1 1 1 1 1 1 1 1 1 1

CP�7 1 1 1 1 1 1 1 1 1 1 1 1

CP�8 1 1 1 1 1 1 1 1 1 1 1 1

CP�9 1 1 1 1 1 1 1 1 1 1 1 1

CP�10 1 1 1 1 1 1 1 1 1 1 1 1

CP�11 1 1 1 1 1 1 1 1 1 1 1 1

CP�12 1 1 1 1 1 1 1 1 1 1 1 1

Weight of mixture, % 11.3 1.2 13.6 9.6 7.6 4.1 17.5 1.8 7.9 6.4 13.7 5.2

Table 3. Customer codes for coking�coal concentrates

Customer Code

OAO Altai�Koks CP�1

OAO Gubakhinskii Koks CP�2

OAO ZSMK CP�3

OAO Koks CP�4

OOO Mechel�Koks CP�5

OOO Moskoks CP�6

OAO MMK CP�7

OAO NKMK CP�8

OAO NLMK CP�9

OAO NTMK CP�10

OAO Severstal’ CP�11

OOO Ural’skaya Stal’ CP�12

180

COKE AND CHEMISTRY Vol. 54 No. 5 2011

BRODSKII

Tabl

e 4.

Str

uctu

re o

f th

e m

arke

t fo

r co

kin

g�co

al c

once

ntr

ates

from

Jan

uary

to

Sep

tem

ber

2010

(10

3 t)

Sup

plie

r

Pur

chas

ers

of c

okin

g�co

al c

once

ntr

ates

in t

he

Rus

sian

mar

ket

CP

�1C

P�2

CP

�3C

P�4

CP

�5C

P�6

CP

�7C

P�8

CP

�9C

P�1

0C

P�1

1C

P�1

2To

tal

Mar

ket

shar

e, %

Run

nin

g to

tal

of m

arke

t sh

are,

%

Evr

az G

roup

–15

3743

–39

520

–13

241

7–

262

4984

17.6

17.6

Mec

hel

––

–54

1357

762

148

–53

262

822

835

8312

.630

.2

Sib

ugle

met

1466

––

203

––

167

_80

428

222

374

3264

11.5

41.8

Ras

pads

kaya

6890

110

860

213

2552

419

740

059

944

102

3232

11.4

53.2

Sev

erst

al’

–20

––

168

318

58–

97–

2548

2232

3111

.464

.6

Bel

on57

258

––

14–

1729

–25

410

7–

–27

349.

774

.2

Pro

mm

et H

oldi

ng

18–

–11

74–

––

24–

57–

395

1668

5.9

80.1

UG

MK

295

55–

––

3330

014

441

2543

843

1644

5.8

85.9

Sv.

Sok

ol L

MZ

––

––

––

761

143

–21

––

925

3.3

89.2

Pro

kop’

evsk

2520

––

––

854

––

21–

–92

03.

292

.4

SD

S10

1–

––

––

546

––

––

4469

12.

494

.9

Str

oise

rvis

303

27–

––

––

–17

0–

–8

508

1.8

96.7

Topp

rom

32–

_41

05

––

––

––

1045

71.

698

.3

Arc

elor

Mit

tal

232

36–

12–

––

––

––

–28

01.

099

.3

SU

EK

71–

––

––

9–

76–

––

156

0.6

99.8

Kol

mar

33–

––

––

––

––

––

330.

199

.9

Zar

ech

nay

a–

15–

––

––

––

––

–15

0.1

100.

0

Tota

l32

1633

638

5327

1321

5211

5849

6251

822

4218

0738

8014

8828

325

100.

0–

Mar

ket

shar

e, %

11.3

51.

1913

.60

9.58

7.60

4.09

17.5

1.83

7.92

6.38

13.7

05.

2510

0.0

––

Not

e:T

hes

e da

ta a

re t

aken

from

Rus

sian

Mar

ket

for

Cok

ing

Coa

l Pro

duct

ion

an

d C

onsu

mpt

ion

: In

form

atio

n B

ulle

tin

of O

OO

Ras

Min

an

d E

aste

rn C

oal�

Ch

emis

try

Inst

itut

e, J

an�

uary

–O

ctob

er 2

010.

COKE AND CHEMISTRY Vol. 54 No. 5 2011

MARKET BOUNDARIES FOR COKING�COAL CONCENTRATES 181

Third, the resulting model of a market of uniformcommodities purchased for use in mixtures adequatelyreflects the market relations of the participants.

The following two�part definition of the geo�graphic boundaries of a market was offered in [4]:

⎯either one or more administrative regions(within the territory of the Russian Federation) thatinclude all Russian producers (sellers) and consumers(buyers) of the commodities characterized by the mar�ket’s commodity boundaries, without exception;

⎯or one or more administrative regions (withinthe territory of the Russian Federation) that are spec�ified a priori for the purposes of a specific assessmentof monopolistic conditions and that include some por�tion of the Russian producers (sellers) and consumers(buyers) of the commodities characterized by the mar�ket’s commodity boundaries.

These definitions are then used in formulating thealgorithm.

10. ALGORITHM FOR CONSTRUCTING THE COMMODITY AND GEOGRAPHIC

BOUNDARIES OF A MARKET IN UNIFORM COMMODITIES PURCHASED

FOR USE IN MIXTURES

Table 5 presents the algorithm for constructing thecommodity and geographic boundaries of a market inuniform commodities purchased for use in mixtures.The algorithm includes three stages, for each of whichthe implementation agency and the basic proceduresare specified.

The algorithm is based on the following simplifyingassumptions, which determine its applicability.

(1) As a rule, in markets with imperfect competi�tion—or, equivalently, markets subjected to theassessment of monopolistic conditions—a few majorparticipants (buyers and sellers) set the conditions ofcompetition.

(2) As a rule, specialists in the assessment ofmonopolistic conditions, with practical experience inspecific markets, will possess a priori informationregarding the potential participants in the commoditymarket of interest and also regarding the compositionof the commodities that must be regarded as poten�tially uniform with the commodity of interest.

11. BOUNDARIES OF THE MARKET FOR COKING�COAL CONCENTRATES

Table 4 presents the initial information required toconstruct the commodity and geographic boundariesof the market for coking�coal concentrates using thealgorithm in Table 5.

The initial matrix of adjacent compositions for thecoking�coal concentrates is shown in Table 2. Thismatrix corresponds to all the requirements imposed onthe final matrix; in other words, it is also the finalmatrix of adjacent compositions. According to thedefinition in Sec. 9, this means that all the coking�coalconcentrates in the mixture corresponding to the finalmatrix of adjacent compositions (Table 2) must beregarded as commodities exchanged in the same mar�ket—in other words, as the required commodity andgeographic boundaries of the market.

Table 5. Algorithm for constructing the commodity and geographic boundaries of the market

Stage Agent Brief description

1 Antimonopoly agency 1.1. Defining the commodity of interest. Formulating a list of uniform commodities. Formulating a list of buyers and sellers of the uniform commodities

1.2. Developing forms for data collection regarding mixtures of uniform commodities (Table 1) and supplies of uniform commodities during the period of interest (Table 4)

1.3. Formulating queries for the buyers and sellers of the uniform commodities during the period of interest

2 Buyers and sellers of the uniform commodities

2.1. Buyers: supply of initial data in response to inquiries by the antimonopoly agency (Tables 1 and 4)

2.2. Sellers: Buyers: supply of initial data in response to inquiries by the antimonop�oly agency (Table 4)

3 Antimonopoly agency 3.1. Formulation of a supply matrix (Table 4) and the initial matrix of adjacent com�positions (Table 2) on the basis of information from the buyers and sellers of the rel�evant uniform commoditie

Construction of the final matrix of adjacent compositions in accordance with the def�inition in Sec. 8

3.3. Construction of the commodity and geographic boundaries of the commodity market of interest in accordance with the definitions in Sec. 9

182

COKE AND CHEMISTRY Vol. 54 No. 5 2011

BRODSKII

12. DISCUSSION

(1) Algorithms for the construction of commodityboundaries for markets on the assumption that theinterchangeability of the commodities of interest maybe established by pairwise comparison were proposedin [2, 4]. However, these methods only permit theidentification of some of the commodities forming thecommodity boundaries of the market of interest. Theother commodities forming the boundaries are notstrictly interchangeable according to the standard def�inition but are blended to form mixtures of therequired quality. The method of constructing com�modity boundaries proposed in the present workapplies to commodities purchased for use in mixtures.

(2) The algorithm in Table 5 is based on informationobtained from buyers and sellers of the commodities.This is consistent with the standard method [2].According to Sec. 3.2 of the standard, “the commodityboundaries of a commodity market of interest are basedon the opinions of buyers (both physical and legal enti�ties) regarding the interchangeability of the commodi�ties that form a single commodity group” [2].

As a rule, however, analysis of the boundaries ofcommodity markets by the Russian Federal Antimo�nopoly Service is based either on staff research or onthe judgment of outside experts. This is probably dueto concerns that the initial data prepared by marketparticipants may be distorted so as to reflect partisaninterests.

Nevertheless, it goes without saying that the spe�cialists at each specific buyer of a particular commod�ity for production use have access to the most com�plete and reliable information regarding the inter�changeability of commodities for that buyer’s ownpurposes. Therefore, without denying the risks of dis�tortion, we simply note that various methods may beused to minimize those risks, without discarding theinformation supplied by buyers. An analogy with min�imizing the distortion of information for tax collectionmay be useful here. As we know, various measures areavailable to tax and law�enforcement agencies in rela�tion to the risks of misrepresentations in tax data.

(3) It has been suggested that the Russian marketfor coking�coal concentrates consists of two submar�kets: (1) concentrates of the coke group; (2) concen�trates of the clinkering group. This opinion is incor�rect, for the following reasons.

(A) The identification of submarkets is based onthe well�known division of coking�coal ranks intothree groups, in accordance with their role in coking(Table 6).

This classification was developed in relation to theformulation of coal batch at each individual coke plant.It focuses not on concentrates but on the supposedlypure coal ranks that are components of the concen�trates. Clearly, these ranks cannot be regarded as com�modities. For example, all or some of the products ofenrichment facilities (EF) 4, 7, and 9 in Table 1 are mix�tures of two or three ranks: GZh + Zh (EF 4); OS + KS

Table 6. Groups of pure coal ranks with different roles in coking

Group of coking coalsLimits

of parameter values

Parameters

Vdaf, % y, mm R, % Vt, % St

1. Coke group (K, KO, OS min max

19.0 24.0

8.014.0

1.3 1.6

43.0 95.0

4.0 7.0

2. Clinkering group (GZh, Zh) minmax

30.5 38.5

13.530.0

0.7 1.0

71.090.0

5.0 9.0

3. Lean additives (KS, KSN, GZhO) min max

16.5 28.0

5.514.0

0.9 1.5

33.0 50.0

0.5 5.0

Similarity of parameter values in differ�ent groups*, %:

groups 1 and 2 0 2.3 0 36.5 40.0

groups 1 and 3 43.5 70.6 33.3 11.3 15.4

groups 2 and 3 11.4 2.0 8.2 36.8 0

Note: The data are taken from a draft of the method proposed by the Eastern Coal�Chemistry Institute for determining the value of Rus�sian coal used in coke production (Appendix to letter TsA/34236 of the Russian Federal Antimonopoly Service, December 18,2008).

* The similarity is calculated as the ratio of the range of common values for the two groups to the difference between the maximumand minimum values. It will vary from 0 for completely different parameter values in the two groups to 100% for identical param�eter values.

COKE AND CHEMISTRY Vol. 54 No. 5 2011

MARKET BOUNDARIES FOR COKING�COAL CONCENTRATES 183

(EF 7), KO + KS, KO + OS + KS, KO + OS, and KS(EF 9). In many cases, a particular concentrate con�tains coal from different groups in Table 4. Hence, theuse of a classification developed for pure coal ranks can�not be regarded as acceptable.

(B) The division of coking coals into groups thatcorrespond to their function in coking (Table 6) is notdefined in existing classifications of coal products or inother standard materials.

(C) The boundaries between different coal groupsin Table 6 is very vague, as indicated by the similarityof the parameters for groups 1 and 3 and also by the useof different groups of coking coals in different editionsof the proposed method. Thus, the December 2008edition includes the three groups in Table 4, whereasthe February 2009 edition includes only two groups:(1) coal of the coke group and lean additives; (2) coalof the clinkering group.

(D) The identification of submarkets in the cokegroup is based on the assumption that the interchange�ability of the commodities may be established by pair�wise comparison of different concentrates. As we haveshown, however, this is not confirmed by analysis ofactual mixture compositions.

REFERENCES

1. Federal Law 135�F3 on the Protection of Competition,2006.

2. Procedure for the Analysis of Competition in a Com�modity Market, Appendix to Order 220 of the RussianFederal Antimonopoly Service, 2010.

3. Analysis of the Competition in Coking Coals,http://www.fas.gov.ru/competition/goods/analisys/a_4851.shtml.

4. Brodskii, V.A., Key Economic Concepts of Antimo�nopoly Law, Ekon. Polit., 2010, no. 6, pp. 124–143.