Economics Education and Research Consortium
Working Paper Series
ISSN 1561-2422
No 06/06
Market power in oligopoly
The case of the Ukrainian cement industry
Olexiy Isayenko
Ivan Maryanchyk
This project (No. 05-061) was supported
by the Economics Education and Research Consortium
All opinions expressed here are those of the authors
and not those of the Economics Education and Research Consortium
Research dissemination by the EERC may include views on policy,
but the EERC itself takes no institutional policy positions
Research area: Enterprises and Product Markets
JEL Classification: L12 , L61, P23
ISAYENKO O.V., MARYANCHYK I.V. Market power in oligopoly: The case of the Ukrainian cement industry.
— Moscow: EERC, 2006.
The object under consideration is the Ukrainian cement industry, which has undergone a serious change in many dimensions, including ownership structure and market structure. We analyze the dynamics of the output market structure
and test the hypothesis of a possible collusive behavior introduced by a change in the ownership structure, especially by
the big international cement players entering the market. Empirical results point towards intensified competition and
reject the hypothesis of the collusion. Unconstrained capacities and dynamic property redistribution make tacit collusion
very unstable and demand further optimization of production process. Patterns of interregional trade, exporting behavior
and mergers' dynamics pose questions about the validity of the profit-maximizing behavior assumption.
Keywords. Ukraine, cement, collusion.
Acknowledgements. We thank Michael Alexeev and Russell Pittman for valuable comments and recommendations.
Olexiy Isayenko
Golden Gate Business
19-a, Kudryavska str., Kyiv, 04053 Ukraine
Tel.: +380 (44) 201 20 20
Fax: +380 (44) 201 20 20
E-mail: [email protected]
Ivan Maryanchyk
The University of Arizona
Department of Economics
1130 E. Helen St. Rm. 401, Tucson, AZ 85721, USA
Tel.: (520) 621 62 34
E-mail: [email protected]
© O.V. Isayenko, I.V. Maryanchyk 2006
CONTENTS
NON-TECHNICAL SUMMARY
4
1. INTRODUCTION
5
2. THE CEMENT INDUSTRY STORY AND ISSUES UNDER SCRUTINY
6
3. THEORETICAL MODEL
7
4. DATA
10
5. RESULTS
12
5.1. Estimation results — Market demand
5.2. Estimation results — Supply equation
12
13
6. CONCLUSIONS, DISCUSSION AND FURTHER QUESTIONS
14
APPENDICES
16
REFERENCES
22
Economics Education and Research Consortium: Russia and CIS
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NON-TECHNICAL SUMMARY
The disciplinary effect of competition leads to more efficient production, wider choice, and lower
prices. We study the development of the market structure of the cement industry in a postprivatization economy. Our research is aimed at defining whether Ukrainian cement industry, after
a short period of "wild" competition, came to the "peaceful" collusive outcome or a more efficient
competitive one. We use standard methodologies of market power identification to analyze market
structure evolution in the environment characterized by a booming economy with high demand for
construction materials, overcapacity of cement production facilities, changes in the ownership structure in the industry, and several periods of sharp price rises.
According to the results of our study, enterprises either acted competitively over the whole period
of interest or failed to succeed in utilizing alleged collusive agreements. However, their behavior
varied. Empirics show that one can separate out three comparatively homogenous periods in industry's life. Moreover, the reasons for different state of competition differ during these three stages.
During the first phase we observe serious instability in operation in the crises economic environment. Enterprises were privatized. The profit-optimizing behavior is very doubtful. Demand for
cement falls. The pricing behavior is erratic. Firms often have to use barter and tolling schemes in
order to survive. The demand elasticity is far from the point where collusion is possible.
The next phase is characterized by the property accumulation in hands of the so-called strategic
owners, mainly portfolio investors or temporary "asset optimizers". The profit-oriented behavior is
more evident here. In addition, demand elasticity moves into the zone of higher market power (the
only and the week evidence of collusion possibility). That is, we cannot reject cartel during this period, yet we also cannot reject the absence thereof. Meanwhile market stabilizes, sales grow. We
observe first international owners coming and acquiring production assets.
The third period is characterized by the booming construction industry and cement demand. Almost
all cement plans are purchased by either local industrial groups or international cement producers.
Demand elasticity shifts to the cartel-unfeasibility zone. In addition, data revealed that competition
improved during this phase, partly because of the international investors. Foreigners seem not to
hinder competition — the share of international owners in output was shown to be negatively related to the level of market power.
Several other important facts better characterize the incentive structure in the industry. Even in
2005, barely half of capacities were employed. In addition to turbulent property redistribution, it
makes the chances of a successful collusion very small. Also, the nature of the international trade,
the peculiarities of the interregional shipments, and possible non-profit-maximizing behavior in
pricing and output are possible areas of research, which would further clarify the nature of competition in the industry.
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1. INTRODUCTION
The main line of inquiry is the structure of Ukrainian cement market. The long-lasting fashion of
accusing the international cement producers in tacit collusion created a solid series of empirical research on cement industries all over the world. We also go in this direction, recognizing though that
our data is unique in a sense that it allows following the natural experiment of ownership structure
change and market structure adjustment.
Specifically, we hypothesize that changes in ownership could lead to subsequent changes in market
structure. If the influx of foreign investors leads to the collusion in the output market, what are the
consequences for the market? What if indeed international owners advance competition?
According to theory, firms maximizing profits would set their marginal cost of production to the
marginal revenue. In perfect competition, marginal revenue would equal the market price, while in
the oligopolistic case firms will equalize it with the perceived marginal revenue, which in turn
would depend on the actions (sales) of other firms in the market.
Pioneering work aimed at empirical evaluation of the conjectural variation — the index of firms'
reaction to output alternation by competitors — was done by Iwata (1974). He proposes a methodology of conjectural variation estimation derived from oligopoly theory. According to Iwata, conjectural variation is the change in sales by firms in the market a definite firm believes would take
place if it changes its output. Another work in conjectural variation estimation is by Gollop and
Roberts (1979). They developed an econometric model allowing identification and evaluation of the
configuration of strategic interdependence between firms in the form of conjectural variation.
Another approach to evaluating market power is due to Bresnahan (1982). The idea is approximately the same — to estimate a simultaneous equation model consisting of the demand relation
and the first order condition of firm's profit maximization. Yet, instead of parameterization of conjectural variations, Bresnahan suggests to parameterize the degree of market power exercised by
firms in an industry. This parameter would show whether and how much marginal revenue perceived by producers differs from the price suggested by market demand given perfect competition.
Sullivan (1985) used data on the effect of taxes to research the level of competition in the US cigarette industry. The comparative static results relating demand elasticity to market structure were
used. Thus, evaluating cigarette demand elasticities he put lower bounds on the numbers equivalent
of firms in the industry. That is, he answered the questions about the absence of collusion. It is not
possible, however, to identify collusion using Sullivan's method.
Finally, the approach proposed by Porter (1983), Green and Porter (1984), Lee and Porter (1984),
and Ellison (1994) allows differentiating the features of various market behaviors using the switching regression technique. This literature shows that data may indicate which competition regime
players are involved in. In such a way, it is possible to define the periods with collusive behavior,
and those which are competitive.
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Recent research on the Portland cement industries includes McBride (1983). He analyses the nature
and sources of economies of scale in cement production. Jans and Rosenbaum (1996) use the US
cement market for a study of the effects of multimarket contracts on pricing. Das (1992) also focuses on the cement market to evaluate firms' strategic behavior. This paper suggests a discretechoice stochastic dynamic programming model of firms' decision-making as regards operating,
idling production, or staying in the market. Frrsund and Hjalmarsson (1983) used the short-run production function to evaluate long-run structural changes in the Swedish cement industry. Finally,
Azzeddine and Rosenbaum (2001) is the closest to my research. The authors analyzed the US Portland cement industry from the market power viewpoint. They studied the link between market price
and concentration, trying to delineate the role of market power and efficiency in this relationship. In
the paper, the marginal conditions approach is used to get the conjectural variation for the evaluation of market power. Their model indicates that market power effect on the link between price and
concentration is twice as large as the effect of efficiency.
2. THE CEMENT INDUSTRY STORY AND ISSUES UNDER SCRUTINY
The Portland cement industry produces a homogenous good. Due to high transportation costs, the
cement market is heavily concentrated around production units, which are scattered around raw
material deposits. Ukraine inherited her cement industry from the Soviet Union: it is characterized
by serious overcapacity. Moreover, due to regionalized markets and the tendency to "share" market stakes, the cement industry is recognized for its propensity for collusive behavior in the output
market.
In short, the production process includes grinding calcium carbonate and mixing it with lime and
sand. The mixture is then heated in kilns to produce clinker. Cement is a product of clinker combined with hydrated calcium sulphate and grounded. Despite high transportation costs Ukraine exports some of her cement to neighboring countries.
The key cost components are capital, labor, raw materials, natural gas, transportation, and electricity. Note that production is associated with low substitutability of material inputs. Demand is heavily related to the development of the building industry and the level of economic activity in the
country.
Ukraine has sixteen major cement producers (total output comprises 95% of the market) are united
in the association (former state concern) Ukrcement. Recent developments in the industry are characterized by the entry of big international producers. Firstly, CRH acquired Podolsky Cement, the
second largest cement plant in Ukraine, equipped with 6 kilns. Then, in 2001 HeidelbergCement
purchased Krivorozhsky Cement and Dniprocement, two big plants in the central part of Ukraine.
Since 2003 Dyckerhoff owns Kievcement, Volyn Cement, and Yugcement, spreading its activities
on the whole territory of the country. Lafarge owns Nikolaevcement in the west. Finally, in 2005
Eurocement acquired Kramatorsky and Balcem cement facilities, while HeidelbergCement purchased Dniprocement and Doncement.
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Nowadays, the market expands very fast. Production increases by about 20% yearly; the market has
virtually doubled in five years, since 2001. Capacity utilization is recovering very rapidly. In 1991
90.4% of cement producing equipment was exploited. Alas, this figure dropped down to 22.8% in
1996. Afterwards, capacity utilization grew almost every year and reached 57.5% in 2005. Another
sign of recovery is enhanced energy efficiency — almost all firms started saving on natural gas and
electric power after the state stopped subsidization. However, prices also revealed a persistent upward pattern. Another interesting tendency is ever-increasing productivity of labor. Yet, it is not
surprising — idle capacities need personnel, even if production is zero. Slowly, firms started disposing of burdensome labor, optimizing the structure of the labor force. However, revenues grow
faster than wages. Please, refer to Attachments for graphical exhibits of the recent tendencies in industry's development.
Industry analysts predict further expansion. For instance, now almost all neighboring countries
demonstrate much higher yearly cement consumption — 255, 280, and 380 kg per capita in Russia,
Poland, and Hungary respectively. Ukraine's figures most probably would catch-up.
Ukrainian cement producers were privatized using various approaches, including some stock scattering methods. Eventually property rights were accumulated in the hands of strategic investors. Finally, intermediary "strategists" have sold their cement plants to big internationals.
During recent decades, big international cement producers have managed to capture control over the
world's cement supplies. They penetrated to the Eastern-European markets as well. Cement companies were questioned by the EU and other antitrust authorities, and accused in price fixing and market sharing.
The booming Ukrainian economy is characterized by high demand for construction materials. At
the same time, the overcapacity of Ukrainian cement production facilities, the recent changes in the
ownership structure of the industry, and several periods of sharp price increases raise concerns
about the nature of competition in the market. An additional signal of the comparative ease of market power accumulation is the soviet-style association of cement producers, promoting members'
interests.
Another recent tendency is growth of input prices — natural gas, transport and electricity. Despite
this fact, profitability has increased as well. Firms try to decrease cost by investing in downstream
integration and minor cost saving technologies and adjustments. Finally, the majority of companies
plan to invest in the adoption of the "dry" production technology. As opposed to "wet;" the former
would allow producers to save about one half of energy consumption. Yet, this cannot be done earlier than in about three years.
3. THEORETICAL MODEL
In sum, the Ukrainian story is the following. There are three considerably homogenous periods in the
industry development. In 1995–1997, right after privatization, companies did not know how to oper-
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ate under free market conditions, the developments and movements were erratic, and the economy
was stagnating. In terms of strategic interactions, there were no such, presumably. We call this period
"learning under wild competition". The next period, 1998–2000, is characterized by the more or less
stable conduct, accumulation of property in the hands of domestic owners, and comparatively high
demand for cement. We assume, given the homogenous nature of the product and the excessive capacity that firms must have interacted within the classical Cournot model. Ultimately, in recent years
the majority of the producers were sold to international owners. Years 2001–2005 are also characterized by a boom in construction, leading to thriving demand. Yet, regardless of the magnitude of the
increase in cement demand, the increase in price was more noticeable. A couple of such stylized facts
could signify the high market power or, possibly, collusion. In the former socialist economies "free
market" or "western" practices are advertised (and are appealing) because of their superiority and
efficiency. Yet, in case our hypothesis about the import of counter-competitive practices is not rejected, we face the situation when "western investors" are "bad".
Following Bresnahan (1982) we assume that the market demand function has the following form
Q = D ( P, Y , α ) ,
(1)
Where Q is quantity, P — price, Y — demand shifters, and α are the parameters to be estimated.
The supply side of the model, namely the equality of the price and marginal cost for perfect competition, is as follows:
P = MC (q,W , β ) ,
(2)
Where W are production side exogenous variables and β are the parameters to be estimated. The key
idea is that if firms can influence price, their perceived marginal revenue would be equal to marginal cost (unlike equality of price and marginal above):
P = MC (q,W , β ) − λ h(Q, Y , α ) ,
(3)
where
P + λ h(Q, Y , α )
is firm's perceived marginal revenue which is equal to marginal cost. λ here is the parameter revealing market power. Following Jans and Rosenbaum (1996) we specify linear demand:
Q jt = α 0 + α1Pjt + α 2CE jt + α 3CE jt −1 + α 4 MPt + α 5GDPjt .
(4)
The demand specification reflects the fact that consumption of cement depends on construction expenditures (CE) and the economic development in the region j — demand for cement is positively
correlated with GDP. We also take into account that construction could last more that one year, thus
we include lagged construction variable. MP stands for metal price — we test the hypothesis
whether and to what extent ferrous metal is a complement to cement (e.g. in concrete structures).
For the purposes of market power identification discussed below, interaction between cement price
and the "demand rotator" will be included into the specification to see how slope of the demand
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function is affected by such variable:
Qt = α 0 + α1Pjt + α 2CE jt + α 3 Pjt × CE jt −1 + α 4 MEt + α 5GDPjt .
Here, we presume that last years' construction activities would change the slope of the demand
function. The related slope term would hence be equal to
(α1 + α3CE jt −1 ) .
However, several other variables are also tested as demand rotators.
Under the monopoly assumption, the expression for marginal revenue is as follows:
⎛ 1 ⎞
MRi = P + λ ⎜ ⎟ qi ,
⎝ α1 ⎠
(5)
where α1 is inverse market demand elasticity.1 λ equals Q/qi, (Q — total output, qi —output of we-th
firm). Yet, with the intensification of competition, λ converges to 1 in case of Cournot and to 0 in
case of Bertrand or perfect competition. Following Rosenbaum and Sukharomana (2001) we specify linear marginal cost function that resembles cost structure of Ukrainian cement producers.
MCit = β 0 + β1Git + β 2 Eit + β 3Tit + β 4Wit + β 5 Fit + β 6
qit
+ β 7TECi .
CAPi
(6)
Where G is natural gas cost aggregate, E — electricity cost aggregate, T — railway transportation cost,
W — average wage, q — cement produced, CAP — production capacity, TEC includes a number of
time-invariant technological characteristics of firms; they are availability of own raw resources, clinker
and grinding capacity, distance to quarry, type of transport in use, availability of "dry" technology. The
aggregate entries take into account the prices of inputs as well as the net consumption per ton of output.
Another, canonical, specification of the marginal cost is including both q and CAP variables linearly.
This approach will not allow identifying β6 directly and would require estimation of demand with the
rotator included (note that time-invariant capacity variable is now in the TEC vector):
MCit = β 0 + β1Git + β 2 Eit + β3Tit + β 4Wit + β5 Fit + β 6 qit + β 7TECi .
Equating marginal cost to marginal revenue we have the supply relation:
Pit = β 0 + β1Git + β 2 Eit + β 3Tit + β 4Wit + β 5 Fit + β 6
⎛ −1 ⎞
qit
+ β 7TECi + λ ⎜ ⎟ qit .
CAPi
⎝ α1 ⎠
(7)
In general, one may estimate the single parameter of the market power λ. Yet, there is also an option
to isolate the source of such market power specifying the linear function of market power to see
whether it depends on a number of factors:
λ = λ0 + λ1HHI t + λ2 IOt .
1
Here, the linear demand was assumed and the h-function has this simple form.
(8)
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Here we assume market power would naturally depend on concentration (Herfindahl–Hirschman
Index). Preliminary we suspect that monopolization would depend on the share of international
owners in the industry, IO. Hence, the extended version of the supply relation is as follows:
P = β 0 + β1Git + β 2 Eit + β3Tit + β 4Wit + β 5 Fit + β 6
⎛ −1 ⎞
+ ( λ0 + λ1HHI t + λ2 IOt ) ⎜ ⎟ qit .
⎝ α1 ⎠
qit
+ β 7TECi +
CAPi
(9)
The systems of two equations, the demand function and the supply relation are estimated using instrumental variables method. The possibility of "import" of the collusive behavior (or variation in
market power due to ownership structure change) can be studied in two ways. Let us consider three
time periods, discussed earlier. In 1995–1997 all firms were purely domestically owned, resembling
soviet era market structure. The only difference is that producers accepted market rules and started
to compete. The three following years are characterized by massive consolidation of producers under strategic investors' rule. Finally, the years 2001–2005 are recognized by a change of the rules —
big international players have acquired almost all available production units.
Measuring market power in three separated games/time periods, we can make judgments about
change in the rules, e.g. improvement or deterioration in competition. This can be done using the
time dummy's interaction with the variable of interest (within equation 7). A more direct way is to
construct the variable of international ownership, as discussed above (within equation 9).
4. DATA
The idea of the cement "market" is not trivial. Even though firms can ship all over Ukraine, the
market has more or less regional structure. On the one hand, it is cheaper to sell locally due to lower
transportation costs. On the other hand, historically, enterprises have a stable clientele and geographical markets. Exporting to competitors' market would be considered a "misdemeanor" and lead
to symmetric punishment. These considerations are implicit though; there are no explicit agreements on such strategies. Hence, instead of a potential 16 suppliers, a representative consumer has
to choose from, say, 5. It is not necessarily true for every region and every supplier, yet there is
such tendency. Additionally, it is possible to estimate the demand function on the national level. It
is much easier to obtain data for good demand shifters.
Data for demand estimation consists of the regional price and shipments. The regions are created
taking into account natural geo-economic division of the country and shares of firms' supplies. Thus
the Cherkasy, Dnipropetrovsk, Kirovograd, Poltava, and Vinnytsia oblasts2 comprise together the
Central region; Chernivtsi, Hmelnytsky, Ivano-Frankivsk, Lviv, Rivne, Ternopil, Volyn, and Za-
2
Administrative region in Ukraine.
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karpattia are included in the Western region; Donetsk, Harkiv, and Lugansk are in the East; Chernigiv, Kiev, Zhytomyr, and Sumy are in the North; Herson, Krym, Mykolayiv, Odessa, and
Zaporizzhia are in the South.
Knowing shares of firms' shipments to different oblasts we can construct local sales figures as the
sum over all cement shipments to a given region. The weighted average regional price or (the simple average regional price) is deduced in a similar way using firms' yearly prices. Information for
the national demand estimation is obtained by aggregating available data. The key demand shifters
are national-level construction activities in real prices — nominal values adjusted for construction
materials and services producers' price index (1991 is the base year). The important shifter having
regional and time variation is regional value added.
Estimation of the supply relation, within which we identify market power, in general needs more
data. As it was stated, we use firm-level observations. The advantage of such a method is that it
stems directly from firm-level optimal behavior, while with aggregated data we assume a kind of
"on-average-behavior" of the industry, leading to the need for "as-if" interpretations of the market
power parameter. A common critique of the firm level model pertains to the fact that it is unnatural
to assume that firms face different input markets. Thus, the "more data" critique is just an illusion.
Yet, the nature of my data allows enjoyment of firm-level disaggregation.
Data on output consists of observations on firm-level production of cement in tons for the entire period of interest. We also obtained the data on installed capacity, which will give an idea of capacity
constraints.
The prices of material inputs in Ukrainian hryvnias (UAH) disaggregated by producers "are not observable to the econometrician". Yet, the following story must be taken into account. Cement enterprises do not control the markets of inputs. In general, the price for the key input — natural gas —
is regulated by state. The purchases are from private distributors which charge a price depending on
the conditions of supply, payment type, etc. That is, price is the price of Naftogaz (state monopoly)
plus a premium for uncertainty. That is why the variation in the price of this input over firms would
generally not identify differences in pricing behavior, since fluctuations in operation riskiness/uncertainty are homogenous in enterprises and vary over time only. While in the past barter
schemes and a lack of turnover capital would lead to comparatively higher/lower and unstable
prices due to prepayment/post-payment tariffs and higher-risk suppliers. Later, the gas price was
always quite predictable within the country. The only risk was and is related to the Russian factor
(the pricing behavior of the gas-extracting monopolist Gazprom). The same can be said about the
electric power supplied by the state generating companies and distributed by local distributors. The
system works as a British-style pool. The key transportation part of costs is a railway tariff, which is
unified over the country by the state-owned railway monopoly. In addition, heating oil is used for
technical purposes. While data on these input prices are on the industry level, that is, not firmspecific, data on average wages in the UAH is available for all enterprises in the sample.
The availability of company-specific wage data is one of several features of my database which allows us performing firm-based analysis. Another useful cost affecting information is on availability
of own raw resources. Some enterprises do not have such, which raises their costs considerably.
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In general, the rough decomposition of the unit cost by components is as follows: natural gas —
40%, electric power — 20%, labor cost — 20%, own raw materials — 5–7%, the rest 13–15%.
Thus, identifying cost differences in a couple of these categories would help considerably in understanding cost structures. Such information is available in the form of net per ton consumption of gas
and electricity. Multiplying gas price (the same for each firm) by the net consumption of gas per ton
of cement would reveal the real net cost of gas. The real net cost of electric power is constructed in
the same manner.
There is additional firm-specific information helping to understand cost structures. Clinker production capacities and grinding capacity would better explain capacity constraints. The distance to the
quarry represents internal transportation cost. The means of transportation — auto, rail, or hydro —
would influence cost since transportation by river is usually cheaper and automobile transport is
considered more expensive. Finally, two plants are able to use "dry" technology, which allows saving about one half of energy consumption.
In appendices, we report all variables used in this study. However, we report only the most sensible
regression results, so that some variables might seem not being used.
We have data on average yearly prices charged by firms in UAH per ton of cement. These are also
used to form proxies for average regional prices (enterprises mostly supply within the region). In
the end, it is necessary to specify what we mean by "firm". If a production unit is functioning independently, we treat it as a single firm. As soon as it is acquired by another producer, which is, presumably, already in possession of the production assets in Ukraine, they become a single firm. Their
indices are calculated as average (e.g., price or cost) or total (e.g. output). Further, we discuss instruments for capital, labor, employment in the inverse supply equation, etc. All variables are summarized in the Appendices, Table 1.
5. RESULTS
5.1. Estimation results — Market demand
To estimate cement demand, we needed a proper variation in prices and quantities, as well as in shifters, over time or over space. The regionally-disaggregated demand equation could capture the characteristics of a regionalized cement market. In order to test the hypothesis about the regional geographic
market structure we collected processed the regional demand data. We failed to obtain tractable demand shifters and a rotator which lead to incorrect (positive) price elasticity parameters. Formally, we
are unable to reject the null hypothesis of a national market. Thus, we continue assuming the national
market for cement. In future, our hope is that we can amend existing data on regional cement demand
shifters and try to analyze the possibility of the demand estimation on the regional level.
Another issue is related to the definition of the national geographic market. In order to be on the
safe side with our Cournot assumption, we need to be sure that the national market is closed: there
are no imports or exports. As regards exports, only in recent years firms started shipping cement to
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the West. In the border regions, import of cement occasionally occurred. However, these instances
are rare. Once, the largest east-most producer was shutdown for reconstruction purposes and Russian producers supplemented cement supplies. However that case was short-lived. On top, Ukraine
introduced prohibitive import tariffs which made shipment to Ukraine loss-making.
Tables 2 and 3 in the Appendices contain summary statistics as well as estimation results. We instrumented the price and the interaction of price and constriction expenditures using the natural gas
price and the interaction. The model demonstrated a very good fit. Although the sample does not
entirely satisfy asymptotic properties of the used estimator, regression results allow performing
some analysis.
Assuming that we observe the equilibrium price and quantity and given the estimate of the demand
function slope we can project the trend in the price elasticity of the market demand. As is clear from
the Fig. 8 in the Appendices, the negative of elasticity is fluctuating considerably. Within the period
of 1995–1996, following Sullivan (1985) we can reject collusion and even Cournot, which goes in
line with my preliminary assumptions. That is, market operated at the inelastic portion of the demand curve. After 1998 the elasticity starts increasing, which is consistent with the rise in the market power. Even though, this type of analysis cannot prove the tacit collusion, one can disprove
such. In this way, the probability of a cartel is positive between 1998 and 2003 and virtually zero all
other years under question. The mid-period coincides with the most vigorous ownership redistribution though. It is highly improbable that producers were able to sustain collusion during those times.
In addition, this is also the period of serious increase in demand. In case the increase in supply was
not as fast, elasticity could have moved to the elastic portion of the demand curve.
5.2. Estimation results — Supply equation
We estimate the firm-level equations 7 and 9 using two different approaches. First, we assume that
marginal cost depends not on the output, but on the capacity utilization. In this manner, we can
identify the parameter of the market power. The second approach is the classic Bresnahan's demand
rotator estimation applied to the firm-level data. That is, marginal cost directly depends on the output, which also enters the supply relation with the terms comprising marginal revenue. To identify
separate parameters on these two output variables we assume that the price slope depends on another variable — rotator. Table 4 contains summary statistics of the sample. We tested all available
marginal cost shifters (as in Table 1). However, we report only those results that include economically crucial or statistically significant estimates.
We used RE technique since the Hausman test failed to reject. Table 5 reveals the results of the
specification without rotator. Table 6 displays results of the regressions with rotator. The specification with marginal cost depending linearly on output reveals comparable results to those of the former model. The instruments for output and output divided by elasticity as a function of the demand
rotator are demand shifter and the shifter divided by the elasticity. We used the total nation-wide
amount of construction contracts and subcontracts in monetary units. The latter approach demonstrates poorer performance, as evident from the lower precision of estimation. In many cases the
results are not statistically significant.
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The majority of the natural cost shifters are insignificant marginal cost determinants. However,
transportation and fuel prices appear to be significant in almost all specifications. This is quite reasonable taking into account the nature of cement as a good with high cost of transportation. Another
appealing qualitative result (not robust though) is that the marginal cost is increasing in output (in
capacity utilization for the first specification).
A comparatively poor fit is unfortunate but expected. To explain this, let us turn reader's attention to
the Fig. 5. The first half of the time span under examination is characterized by rather erratic pricing
by the majority of firms. While the market prices for key cost elements (gas, electricity, etc.) are
comparable for separate firms, the ways firms used to obtain inputs varied. Selling practices differed too. Tolling and barter schemes distorted the otherwise clear picture. Hence, we believe that
the linkage between input costs and cement prices was obscure.
The variables of interest are those embodying information on market power. In both specifications
of equation 7 the raw market power parameter shows that the industry operated as if it were between perfect competition and Cournot. In the parameterized version (equation 9), HHI generally
captures the market power effect. The positive drift in market power is paralleled by the change in
HHI. Accumulation of the international ownership negatively affects market power. In other words,
the international ownership introduces more competition. The final equation in Table 5 has a
dummy covering the period of the highest international ownership concentration. Its magnitude tells
us that the presence of international owners does not push the industry towards less competitive
market structure.
6. CONCLUSIONS, DISCUSSION AND FURTHER QUESTIONS
Beginning this research we had an intuition on the possibility of the tacit collusion in the Ukrainian
cement market. We applied several methods to test our speculation. However, none of them proved
that we were right. On the contrary, data revealed that competition even improved during the period
of interest; partly because of the international investors. This does not establish the absence of the
very attempts of tacit collusion. Such attempts may have happened. However, a number of factors
might not guarantee the stability of an alleged cartel.
One of the factors that would make collusion unstable is the absence of capacity constraints. Fig. 3
shows that production facilities were underused during most of the time. Even in 2005, barely half
of capacities were employed. Without capacities' constraints it is hard to expect that producers
would stick to the collusive agreement and abstain from undercutting.
Another preliminary hypothesis, about the detrimental role of international owners, is also rejected.
Foreigners seem not to hinder competition. It was shown in two ways — with the variable of internationals' share in output and with the analysis of competition in time. On the one hand, the share of
international owners in output was shown to be negatively related to the level of market power. On
the other hand, competition was the strongest in time periods with the high concentration of foreign
owners.
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There are several things that might give deeper insight into the processes in the cement industry and
shed light on the results of the empirical study. Those are the nature of the international trade, the
peculiarities of the interregional shipments, and possible non-profit-maximizing behavior in pricing
and output.
One of the noteworthy facts is an increasing international trade in cement. Consider Fig. 6. Export
of cement was virtually zero up until 2000–2003. Import from the West was not possible due to
more costly technologies and inputs of the Western neighbors. Imports from Russia were impeded
by high tariffs. It is much easier to agree on collusion in such isolated environment. However, it was
positively difficult to sustain a collusive arrangement in times of the considerable property right redistribution.
However, what happened after that? Did collusion become more questionable due to the intensification in international trade? What if in fact export was one of the tools in the attempt to sustain collusion? The latter surmise is clearer. On the one hand firms do not want to deviate, on the other hand
— idle production capacities. To cope with the tradeoff companies ship abroad. The former guess is
not as clear-cut, yet could be substantiated. Consider a situation when several firms do not produce
for a significant time period due to the protracted property redistribution phase. After the comeback,
they need to launch production, which is not cheap. Hence, the newcomers would need to sell locally, fighting for the market, and if possible, abroad. The "misbehavior" of such companies would
add to the probability of ruining the cartel. From this perspective, growing exports on behalf of such
firms would be an implicit manifestation of cartel deterioration.
Besides exporting and local markets contest the mentioned firms very often sold cement below
market price and, supposedly, below the production cost. In addition, cement was transported very
long distances. Finally, we observed that sometimes firms would ship cement from the very East to
reach the foreign market on the West. Meanwhile, another international producer had huge production capacities on both sides of the border. Moreover, this producer did not attempt to ship its cement over the frontier. We tried to look for the reasons of such behavior and discovered that a couple of acquisitions went off recently. As a matter of fact, those "misbehaving" producers were purchased as well. What if the mentioned firms did not profit-maximize? What if the most sought-after
goal was to make big cement players acquire the production facility? The company that has big
sales figures and large market would be able to elicit higher acquisition price. What if the increased
sales were merely a signaling tool? These are among several interesting questions for the further
research.
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APPENDICES
Thousand ton
14000
12000
10000
8000
6000
4000
2000
0
1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005
Fig. 1. Cement sales
Thousand sq. m.
9000
8500
8000
7500
7000
6500
6000
5500
5000
4500
4000
1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005
Fig. 2. Apartments construction
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100%
90%
80%
70%
60%
50%
40%
30%
20%
10%
2005
2004
2003
2002
2001
2000
1999
1998
1997
1996
1995
1994
1993
1992
1991
0
Fig. 3. Capacity utilization
UAH
250
200
150
100
50
0
1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005
Fig. 4. Cement price
UAH
300
250
200
150
100
50
0
1994
1996
1998
2000
2002
Fig. 5. Prices by firm
2004
2006
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0.12
0.1
0.08
0.06
0.04
0.02
0
1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005
Fig. 6. Export share in the total output
1
0.9
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0
1999
2000
2001
2002
2003
2004
2005
Fig. 7. Share in output by international owners
Elasticity
1.8
1.6
1.4
1.2
1.0
0.8
0.6
0.4
0.2
0
1995
1997
1999
2001
2003
Fig. 8. Cement demand elasticity dynamics
2005
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Table 1. Variables and data sources
Name
3
Description
Measurement
Source
RP
Cement prices in regions
UAH per ton
Ukrcement
Q
Sales/demand in regions
Thousand ton
Ukrcement
GDP
Gross regional product value added
in regions is used
UAH
Derzhkomstat
CE
Construction of apartments
Thousand sq. m.
Derzhkomstat
SC
Subcontracts by construction
organizations
Thousand UAH
Derzhkomstat
MP
Ferrous metal (complement) price
Price index
Derzhkomstat
q
Firm's Output
Thousand ton
Industry experts, Cement
producers
P
Firms' cement prices
UAH per ton
Industry experts, Cement
producers
W
Average wage
UAH
Industry experts, Cement
producers
E
Electricity cost aggregate — product
of year-average price and net use
per ton
Price index
Ukrcement, Industry experts
G
Gas cost aggregate — product of
year-average price and net use per ton
Price index
Ukrcement, Industry experts
F
Fuel (heating oil) prices
Price index
Derzhkomstat
T
Railways tariff
UAH per 100 km
Derzhkomstat
CAP
Installed capacity
Thousand ton
Ukrcement, Industry experts
RES
Own resources
1 if yes
Ukrcement
CCAP
Clinker capacity
Thousand ton
Ukrcement
GCAP
Grinding capacity
Thousand ton
Ukrcement
QD
Distance to quarry3
Km
Ukrcement
TT
Transport type in production process
0 — the most efficient;
4 — the least;
= (–1) if no quarry.
Industry experts, Cement
producers
D
Availability of dry technology
1 if yes
Industry experts
The distance to the closest, if several available. 500km — if no own query.
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Table 2. Summary statistics — sample for the cement demand estimation
Variable
Mean
Std. Dev.
Min
Max
Price
145.27
50.10
57.00
222.00
Quantity
7166.27
2424.78
5036.00
12137.00
Construction
6651.46
965.13
5558.00
8663.00
51.18
21.82
25.00
88.00
Gas price
Time period
1995–2005
Table 3. The cement demand estimation (Dependent variable — Quantity)
Estimate
SE
Price
–52.72531
16.6886
Price×Construction
0.012065
0.002122
Subcontracts
0.017584
0.032658
_CONS
3209.053
887.8931
Instrumented
Price
Price×Construction
Instruments
Natural Gas Price
Natural Gas Price×Construction
Table 4. Summary statistics — sample for the cement supply estimation
Variable
Mean
Std. Dev.
Min
Max
P
171.11
44.69
0.00
316.92
Q
653.93
627.25
0.00
3070.00
CAP
1789.49
1620.98
10.00
7106.00
T
373.99
74.41
279.00
548.00
F
188.87
37.07
138.70
256.30
D
0.10
0.30
0.00
1.00
W
508.06
311.56
135.40
1803.00
IO
0.51
0.20
0.22
0.89
E
29688.11
5770.58
15956.57
45529.07
G
13143.25
6116.10
0.00
35662.50
0.13
0.02
0.11
0.18
HHI
Time period
Number of firms
1999–2005
15
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Table 5. Cement supply estimation without rotator (Dependent variable — Price)
Equation 7
Equation 9
Equation with dummy
Estimate
SE
Estimate
SE
Estimate
SE
Const
–0.0055
16.4436
12.3503
16.8574
2.7253
16.8838
G
0.0003
0.0007
0.0006
0.0007
0.0005
0.0006
E
–0.0003
0.0004
–0.0002
0.0004
–0.0002
0.0004
T
0.1962
0.0708
0.3440
0.0954
0.1977
0.0750
W
–0.0186
0.0137
–0.0160
0.0129
–0.0139
0.0140
F
0.5407
0.1625
0.1423
0.2180
0.4999
0.1668
q/CAP
10.3828
6.2262
9.6702
5.9185
9.2083
5.7429
Dry
–19.6457
9.8054
–17.8264
9.5782
–17.2303
8.0817
λ, λ0
0.4576
0.2272
–3.9834
1.2835
0.3307
0.4333
HHI
57.7525
16.7850
IO
–7.5138
2.2193
0.0487
0.3780
λ0 in 2002–2004
Table 6. Cement supply estimation with rotator (Dependent variable — Price)
Equation 7
Equation 9
Estimate
SE
Estimate
SE
Const
–6.8170
39.3895
70.7338
180.3420
G
0.0024
0.0026
–0.0027
0.0123
E
–0.0003
0.0008
–0.0002
0.0031
T
0.1925
0.1709
1.4388
5.1983
W
–0.0735
0.1405
0.1714
0.3929
F
0.4212
0.3873
–2.1030
10.2101
q
0.0535
0.1205
–0.0631
0.5383
Dry
–50.2221
57.2526
74.4428
183.2330
λ, λ0
0.4239
0.8638
–12.6463
45.3937
HHI
183.9691
569.5989
IO
–18.1188
40.5777
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