Analysis of Cartel Duration: Evidence from EC
Prosecuted Cartels
By
Oindrila De
Centre for Competition Policy and University of East Anglia
Section 1: Introduction
In recent years theoretical and empirical literature increasingly focus on the duration of
cartels for various reasons. First, cartel duration is seen as a possible measure for cartel
success where as other measures of cartel success, e.g. overcharge (cartel’s price over and
above a competitive benchmark) is difficult to estimate. Second, it is a possible way to
determine the characteristics of a cartel or the distinctive features of the market under
which they operate by studying the duration of the agreement as a dependent variable
rather than the formation of such agreement since cartel population is unknown to us.
Third, information on cartel duration is used in some studies in recent years for
estimating cartel detection probabilities or for evaluating success of leniency
programmes. It may be worth mentioning here that in the theory, the condition for
sustainability of collusion is the same as their initial motivation to form such an
agreement, namely satisfying the incentive compatibility constraint (ICC). But satisfying
ICC may not be a sufficient condition to form a successful collusion in the presence of
multiple equilibria. Therefore, it is often argued that effective communication and
coordination among firms are important to reach an agreement. Besides, recent empirical
literature on cartel duration has started recognising the importance of coordination for
sustaining an agreement as well. But the coordination necessary for maintaining a cartel
seems different because of the changing nature of the market as well as the changes in the
policy environment under which the agreement has to operate.
1
This paper attempts to explore various issues related to cartel duration. First, it discusses
the difficulties associated with a clear definition of the cartel duration. Second, it explores
some interesting characteristics of the EU prosecuted cartel duration. Last but not the
least, this paper attempts to shed some lights on the market characteristics, policy
environment and the characteristics of the cartel itself that facilitate longer duration of an
agreement. The organization of this paper is as follows. Section 2 reviews the existing
literature on this subject and explains the hypotheses of this study. The section starts with
the discussion on the lack of clarity in the existing empirical literature about the concepts
of cartel formation, stability and durability and the analysis tries to extract a clear
definition of cartel duration. Next, it discusses the previous empirical studies on the cartel
duration and results obtained. Section 3 puts together some interesting observation on the
EC prosecuted cartels. Section 4 discusses the methodology adopted for this analysis.
Section 5 describes the variables chosen for this study and the results obtained. Section 6
concludes the chapter.
Section 2: Existing Literature
As mentioned in the introduction, the theory of collusion revolves around the question
when is collusion profitable for the firms? In the repeated game setting1 cooperation may
be a possible Nash equilibrium when punishment strategies are credible and subgame
perfect equilibrium at every stages of the game. In real world oligopoly, the market is
indeed characterized by repeated interactions which raise the possibility of credible
retaliation in response to a deviation by one firm in the previous period (trigger strategy).
This threat actually induces the firms to adhere to the price profile agreed upon by all the
firms in the first place since forgone profit of cooperation in next periods may outweigh
the short run gains from deviation in the presence of credible punishment. In formal
notation, Firm’s discounted future profit stream under collusion (C) is
∞
πi = ∑δ t πiC =
t =0
1
πiC .......(1)
1 −δ
Where δ is the discount rate. If the firm deviates, then his future profit stream comprises
of two components, immediate gain from deviation (D) and discounted future loss when
1
The modern economic theory of tacit coordination has drawn heavily from the game theory. Dynamic
oligopoly is widely represented by repeated game or super game models.
2
deviation trigger the punishment and all the firms go back to non-cooperative outcome
(NC).
∞
π i = π iD + ∑ δ t π iNC = π iD +
t =1
δ
π iNC .....(2)
1−δ
So, the collusive outcome will be supported when the profit stream defined in equation 2
is greater than profit stream defined in equation 1. That is,
1
δ
π iC ≥ π iD +
π iNC .............(3)
1− δ
1− δ
Equation (3) is known as equilibrium condition or incentive compatibility constraint
which makes cooperation profitable for the firms. Therefore, when the ICC is violated,
the agreement becomes unstable and the firms go back to their competitive equilibrium
forever. Friedman (1971) also showed that when firms are sufficiently patient (discount
factor close to one), tacit collusion is indeed an optimal outcome. But the problem
associated with the theory is the existence of multiple equilibria in repeated games which
may or may not involve cooperation. So the firms involved in tacit collusion may or may
not reach the joint profit maximizing outcome and practically it is really hard to choose
the right equilibrium without communication. But theoretical literature has not yet been
successful in analyzing how firms reach a particular agreement through coordination
among themselves, tacitly or explicitly. It is important to mention here that explicit
collusion definitely has an advantage over tacit collusion in this regard. Effective
communication and systematic coordination among firms can achieve the goal of joint
profit maximization which is difficult without direct communication.
Therefore, what comes out of the above discussion is that the incentive compatibility
constraint may very well explain the stability of cartels where the stability can be defined
as the cartel’s ability to raise prices above the competitive level; but it can not fully
explain the formation of collusive agreement since there is a lack of systematic analysis
how the particular agreement is reached. Moreover, the theoretical and empirical
literature also sheds some lights on the punishment strategies of the firms. Other than
adopting grim strategy, colluding firms may prefer to go back to the collusive phase after
the punishment starts without the existence of renegotiation proof mechanism. If we
consider the Green and Porter (1984) model for instance, in the presence of imperfect
3
information, the firms will retaliate when the demand is low and will revert back to the
collusive equilibrium after certain periods of time. So price wars can be observed in the
equilibrium path which may actually sustain cartel agreements. In the empirical studies,
we do observe price wars as a reaction of cheating but that does not end the collusion
forever. Firms renegotiate through frequent meetings which may take them back to the
collusive phase (Genesove and Mullin 2001 for instance). Duration analysis of a cartel
provides us this whole picture of a cartel life which may or may not go through these
competitive phases and still sustained a long agreement through effective monitoring and
coordination mechanism. Therefore as a concept, cartel formation and duration seems to
be much closer to each other compared to the concept of cartel stability. Both formation
and duration require effective coordination among firms. But the objective of
coordination is different in both the cases. In the former case, firm’s objective is to reach
a successful agreement and here the coordination is static in nature where as duration of
collusion requires dynamic coordination since the objective is to monitor and punish the
deviators successfully and renegotiate the terms in the constantly changing market
conditions.
Very few empirical studies explicitly discuss the difficulties associated with the
definition of cartel duration and how it is different from stability and cartel formation.
(Connor 2005) mentioned about this lack of clarity in the literature about the possible
difference between cartel stability and durability. According to his definition stability
refers to the low variation in cartel discipline, where discipline may be the closeness
between cartels’ selling price and theoretical monopoly price which is exactly the same
as mentioned above. On the other hand, longevity or duration of a cartel measures the
lifespan of a cartel or, if it has more than one, the length of time of one episode. But there
is practical problem in defining duration as length of an episode, especially for the illegal
cartels. As Suslow (2005) points out, the Green and Porter (1984) type equilibrium
punishment price war is observationally very similar to the price war arising from actual
breakdown of a cartel. So, there is always a possibility of a misjudgement of the length of
each episode. That is probably why; the recent empirical studies on private international
cartels generally rely on the overall duration of an agreement rather than the length of
each episode.
4
Moreover, if we observe the empirical studies closely, this demarcation between cartel
stability and duration is not followed consistently. Boltova, Connor and Miller (2006)
defined stability as number of cartel episodes in a product market and used cartel duration
as an explanatory variable for the analysis. (Dick 1996) on the other hand measured
stability by length of each cartel episodes which is actually the measurement of cartel
duration according to Connor’s definition. The exceptions are case studies on cartel
stability. Though unavailability of sufficient data limit the studies within very few cartels,
but the analysis are quite rich in most of the cases Porter (1983), Ellison (1994) on JEC
cartel; Levenstein (1996, 1997) on the bromine cartel; Gupta (1997, 2001) on the tea
cartel; De Roos (2006) on lysine cartel etc .These studies unequivocally defined stability
as the departure from collusive outcome and tried to model and empirically test price
war.
This lack of clarity in the definition of cartel duration can be problematic in determining
the factors responsible for cartel duration and the interpretation of their possible effects.
Despite the fact the some studies looked at cartel duration though the primary objective
was to study formation or stability of a cartel (Hay and Kelly 1974, Dick 1996 for
instance), I will only review the literature where the primary objective is to study cartel
duration. (Jacquemin, Nambu at al 1981) first developed a model of cartel duration and
tested the model with the data from the Japanese legal export cartels between 1967 and
1972. (Marquez 1994) also tested the same model using international cartel data (active
over 1888-1984). Jacquemin et al used simple regression analysis where as Marquez
relied on Amemiya’s Maxumum Likelihood (ML) estimates.
Their model starts with the incentive compatibility constraint of the firms. According to
the model, optimal durability depends on the growth rate of demand and the first period
profit relative to the initial cost of cartelization. The condition is that the growth of
demand should be less than the growth of cartelization cost and the initial profit level of
cartelization should be greater than the initial cost of forming a cartel. Initial cost may
depend upon the market concentration, Government encouragement, technical difficulties
such as areas of collusion, range of agreements etc and homogeneity of the product. The
result shows that the growth rate of the demand is inversely related to the time duration of
5
cartel. But authors did not found this result significant. The four-firm concentration ratio,
number of price/quantity agreements relative to the total number of agreements in the
sector (representing range of agreements) have negative significant effect on duration
where as homogeneous product and the number of cartels from mixed geographic areas
to total number of cartels (again representing range of agreements) has positive
significant effect on duration. Marquez found positive relationship between
concentration, market shares and duration but negative relationship between demand
growth and duration. Previous attempt to cartelize had no significant effect on the cartel
duration. These two papers reveal that cartel duration is very much dependent upon the
coordination mechanism and changes in the market conditions as discussed earlier. But it
is hard to interpret these variables in terms of illegal cartels. But the analysis of legal
cartels provides us useful information about the market conditions under which they
operate. It also indicates that the policy environment is very crucial for the working of a
cartel. Moreover, more sophisticated econometric methods are now available for the
duration analysis. Next, we will move to the studies those employ the survival analysis
for studying cartel duration.
In recent years, Suslow (2005), Zimmerman and Connor (2005), Levenstein and Suslow
(2006) used Cox proportional hazard regression (discussed later) for this purpose.
Brenner (2005) used weibull hazard model for evaluating the success of leniency
programme. The explanatory variables used in his study are number of firms, number of
countries, industry dummies and leniency and self reporting dummies and found no
significant result. One of the reasons may be it was too early to evaluate the effect of
leniency policy, especially the improvement that happened in 2002. Suslow (2005) used
the data of the inter-war international cartels where as Zimmerman and Connor (2005),
Levenstein and Suslow (2006) used recently prosecuted international cartels data. Since
the survival analysis is also used in this study, the detailed methodology for estimation
has been provided in the methodology section. It is interesting to discuss the results found
from the studies involved recent international cartels. Zimmerman and Connor found that
the seller concentration, large number of buyers, business downturn and antitrust
sanctions have positive, significant effect on the duration where as number of firms
(dummy for more than five firms), overcharges and cultural diversity (number of
countries per firm) have significant negative impact on the duration.
6
The most important recent paper is by Levenstein and Suslow. 33 cartels prosecuted in
EU, 20 cartels prosecuted in US and 19 cartels prosecuted in both jurisdictions are
included in their dataset. They highlighted on the cartel organization as the most
important factor for duration. The most interesting finding of their study is that firms’
patience (measured by the average annual interest rate of the US treasury bills with 3
months maturity) has positive effect on the duration. Discussing cartel organization, they
have developed an organization index by summing up 11 dummies representing different
aspects of cartel organization (bid rigging, market sharing, standardized product, terms
other than price, distribution chain, trade association, information exchange, retaliation,
compensation, exclusion, organizational hierarchy). They found organizational index has
a significant positive effect on cartel duration. But the problem is that the elements of this
index are so diverse that it is really difficult to interpret the result. The authors also found
that negative GDP trend gap (measured by the demand instability) has negative effect on
the duration. It is important to mention here that both the papers used aggregate level
GDP or GNI data for the demand variable which may not always represent industry
performance.
Section 3: EU Prosecuted Cartel Cases
The sample database consists of explicit collusion cases from 1990 to 2007. The number
of cases in this database is 94. The definition of explicit collusion used here is much
broader than what is defined in the law. The database includes Article 81 (1)
infringements, Article 81 (3) exemptions with conditions, negative clearance, readopted
cases. Moreover I have included the cartel cases under Article 65 of the European Coal
and Steel Treaty 1951. This database does not include other horizontal agreements like
joint ventures etc without any clear objective to collude or exemptions without any
conditions cases. The data are collected from the Commission’s decisions published in
the Official Journals, press releases, Court of First Instance and European Court of
Justice Judgments. For the purpose of this analysis, I have dropped one renewal and 4
radopted cases from my analysis. Moreover, the cartel members in the French beer case
(2004) negotiated an armistice agreement which never came into effect. I have dropped
7
this case from my duration analysis. Four understanding, we can divide the remaining 88
cases in five different groups on the basis of Commission’s decision type:
Table 1: Types of Decisions
Category
Number of Cartels
Exemption With Conditions/ Obligations
9
Infringement Art. 65 ECSC treaty
3
Infringement Art.81 with fine
63
Infringement Art.81 with fine and Infringement Art. 82
5
Infringement Art.81 without fine
8
Total
88
So, there are total 76 cases with Article 81(1) infringement 8 of them did not pay any fine
(ANSAC soda ash case in 1990, Quantel International, Scottish Salmon board and UK
agricultural tractors in 1992, CNSD in 1993, Trans-Atlantic Agreement in 1994 and
COAPI in 1995, EATA in 1999). There are two reasons behind this. Either the cartels
involve only associations of under takings (4 cases) or the firms applied for notification
but did not fulfil the requirements (4 cases). 5 out of 76 cartels are also charged with
infringement of Article 82 as one of the undertakings or association of undertakings
abused its dominant position in the market. There were 15 cases came before
Commission with notifications for negative clearance. Commission cleared 7 cases with
changes in the proposed agreements (termed as conditions/obligations) where as 8 cases
infringed Article 81.
Next, I will consider the duration of EC prosecuted cartels which is the centre of our
analysis. For the purpose of imposition of fine or granting exemption, it is very much
important for the Commission to define the duration of the infringement. On the basis of
this information, we have extracted data on the duration which is proven by the
Commission in the court2. It is to be noted that it is not always necessary that the cartel is
in operation when the Commission heard about the agreement. So my sample includes
the cases where the cartel died naturally but later detected by the authority. I have used
the natural life time of the cartel as duration, not the time till it was detected. For the
2
There are few changes in the duration of the individual cartel members ruled by CFI and ECJ. We have
not taken those into consideration since it did not affect the overall duration of the cartels.
8
negative clearance/ exemption cases, we have defined duration as from the starting of the
agreement till the end of exemption period. The Commission divides the active years of
cartels in three different categories for the purpose of fines- short duration (less than one
year), medium duration (one to five years) and long duration (more than 5 years). In the
figure below we have presented the duration of cartels in years. If a cartel existed for
more than 6 months, it is considered to be live for one year. Though the graph is showing
four cases with 1 year duration, in fact three of them lasted for less than one year which
can be considered as short duration according to the Commission’s definition. 30 cases
had medium duration (1-5 years) and majority of the cartel cases (55) have duration more
than five years. The average duration of the cartels is 8.93 years where the median life is
7.
Figure 1: Proven Cartel Duration of the DG Competition Cases
0
Number of Cases
5
10
15
EU Prosecuted Cartel Duration (Proven in the Court)
1
5
9
13
17
21
Duration (In Years)
25
29
33
37
Beside the number of years of duration proven in the court, some interesting information
is available about the actual duration of the cartels as it may not be the same. The
Commission has acknowledged the fact that many cartels have existed before the starting
dates finalized by them, but they could not produce hard evidences to prove them in the
court. The Commission in fact has reported the suspected starting year for the 18 cartels.
9
Moreover, the Commission has also indicated the existence of the cartels much prior to
the proven starting date for 9 cases but could not confirm the actual starting date. On the
basis of the information provided by the Commission, we can plot the suspected duration
vis a vis the duration proven in the court for these 18 cases.
Figure 2: Proven duration vis-à-vis Suspected Duration
Proven vis-a-vis Suspected Duration
100
90
Suspected Duration (in years)
80
70
60
50
40
30
20
10
0
0
20
40
60
80
100
Proven duration (in years)
As we can see from this scatter, suspected duration in many of these 18 cases are
significantly higher than the proven duration. In fact the suspected duration on an average
is 2.5 times more than the duration proved legally by the Commission.
Another interesting aspect of the EC cartel duration is that it is not symmetric across the
firms or across the agreements (market sharing, information exchange etc) or the subproduct/ geographic markets. Within one cartel, different firms entered into or exit from
the cartels at different points of time. Similarly, firms added or subtracted some
agreements within the overall agreement concerned. For example, they may start
information exchange agreement or other horizontal restrictions long after the main price
10
fixing agreement has started working. The cases may also involve different sub products
or sub-geographic markets. Duration can be different for these dimensions as well. The
table below reveals the number of cases with such special characteristics. There are five
cases where sub-agreements came into operation after the main agreements started
operating. They are Jahrhundertvertrag, Cement, Dutch Industrial Gases, Dutch Electrotechnical Equipments and Spanish Tobacco. The reason may be more agreements became
necessary other than only price fixing or market sharing agreements to enforce the cartels
successfully. The database also contains three cases where the cartel has different
duration for different sub-product markets (Vitamin, Haberdashery needle, Specialty
graphite) and one cases where there is different duration for different geographic markets
(industrial thread)
Table 2: Different Dimensions of Duration
Duration
Different members with different duration
Number of Cases
33
Different sub-agreements with different duration
Different sub-product markets with different duration
5
3
Different geographic markets with different duration
1
As mentioned before, 33 out of these 88 cases, some cartel members either entered into
the cartel late or exit from the cartel early or both. The table below shows that entry
occurred in 29 cases, exit in 24 cases and both simultaneously occurred in 21 cases. Late
entry or early exit or both can have a destabilizing effect on the collusion. So it will be
interesting to see whether frequent entry or exit had any affect on the overall duration of
the EC cartels.
Table 3: Entry into and Exit from the Cartel
Entry/ Exit
Number of Cases
Entry into Cartels
29
Exit from the Cartel
24
Both Entry and Exit
20
Last but not the least, Table 4 summarizes the different causes of detection of these
cartels. I have reported the primary method of detection reported in the documents. As we
know, Commission mostly initiates investigation because of some unofficial complains,
from the consumer associations or competitors outside the cartel or press reports. These
primary sources are rarely mentioned in the documents. So the complain category in the
11
table strictly implies the formal complaint made by these organisations. Also, the firms
may reveal another secret cartel during the investigation (For example, information for
the Carbon and Graphite and Specialty Graphite cartel from Graphite Electrodes cartel
case, information for the Netherlands beer and the French beer cartels from the Belgium
Beer cartel investigation and information for the PVC cartel from a prior investigation in
another thermoplastic product market). Since Amnesty plus reward is not available in the
EU, it is not clear whether this reveals the success of leniency programme or the
efficiency of the Commission in the investigation procedure. I have categorized these
“follow up” cases in the Commission’s investigation category. The firms also notify their
agreements seeking negative clearance. The Commission investigates the anticompetitive effects of those notices (15 cases). It is important to mention here that it is
very difficult to distinguish between the natural causes of death of a cartel from the ones
detected by the authorities, especially under the leniency regime. Firms come forward to
bust a cartel implies cheating on the other cartel members. Similarly, a cartel can also be
termed as a failure if it becomes visible to its competitors or downstream consumers. This
is important because it is often argued that the duration analysis of the prosecuted cartels
mostly reveal the time till detection, not the actual life-time of a cartel. But it seems from
the above discussion, there is a thin line between the two concepts when it comes to the
privately enforced collusion.
Table 4: Cause of Cartel Detection
Causes of Detection
Number of Cases
Commission’s Investigation
US Investigation
Leniency
Notification
Complain
Total
29
9
20
15
15
88
Section 4: Methodology for Duration Analysis - Cox Proportional
Hazard and Competing Risk Model3
As mentioned earlier, Survival Analysis is the most popular method applied for studying
cartel duration. Survival model is known as event history model where the centre of
3
The definitions of the basic survival analysis and Cox Proportional Hazard Model are drawn heavily from
Kiefer (1988)
12
analysis is the occurrence of an event of interest (here the end of a collusive agreement)
and causes of such occurrence. Now, the question is why simple regression analysis is
inadequate for the analysis of cartel duration? Three important reasons are discussed in
the literature. First, the data are often censored (mostly right-censored) since the analysis
time may end without the occurrence of any event. It implies that for some observations
the true value exceeds the observed value. Though earlier literature on the survival
analysis put too much emphasis on the censoring as the special feature of the survival
data, but now censored normal regression analysis can be used to cope with this problem
successfully. Second, for the analysis of life-span data, it makes more sense to condition
on the history up to certain point. This is probably the most important reason for using
survival analysis instead of simple regression analysis. Third, lifetime data is clearly not
normally distributed. Duration data in general are positively skewed, only with positive
values and high variation which is clearly not a symmetric distribution like normal.
Moreover, occasionally the survival distribution is bimodal. The standard approach that
can be used for such data is to apply normal distribution after logarithmic transformation
of the duration variable but that often makes the original variable left skewed rather than
right skewed. Therefore, for survival data, normal regression may not provide robust
results. From the above analysis, it is clear why survival model has increasingly being
used to study cartel duration. There is no right censored survival time in our analysis. So,
the methodology presented here is based on the non-censored observations.
The dependent variable in this survival model is cartel life which is measured in years.
Suppose T is a random variable which depicts the length of the spell or the survival time
of a cartel. The cumulative distribution function of the survival time is given by
F(t) = Pr(T<t)
which is also known as failure function.4 The failure function specifies the probability
that the random variable T is less than some given value t. The probability density
function is given by f(t)= dF(t)/dt. But for duration analysis, we are more interested in the
survival function which is given by
∞
S (t ) = Pr ob(T ≥ t ) = ∫ f ( x)dx = 1 − F (t ) …….. (1)
t
4
In the discussion of duration data, F(t)= Pr(T<t) is used instead of Pr(T≤ t). See Kiefer 1988, Page 650.
13
It gives the probability that the random variable T will equal or exceed the value t.5As
mentioned earlier, the central concept of the duration analysis is not the unconditional
probability of an event taking place, but the conditional probability of the occurrence of
the event (Kiefer 1988). For example for this analysis, our interest is not in the
probability that a cartel lasted for 11 years but in the probability that the cartel breaks
down at the 11th year given that it survived 10 years. The event history data uses hazard
rate function instead of survivor function for the purpose of regression. This probability
that the cartel agreement ends at time t given that it has survived until that time is defined
by hazard function
h(t ) = Lim
∆t → 0
prob(t ≤ T ≤ t + ∆t | T ≥ t) f (t )
f (t )
=
=
……….. (2)
∆t
S (t ) 1 − F (t )
Hazard function in fact provides us the rate of instantaneous transition from one state to
another. An important characteristic of hazard function is that we can estimate duration
dependence by differentiating it. A positive duration dependence (dh(t)/dt>0 at t=t*)
implies that hazard rises with time and negative duration dependence means just the
opposite. The duration dependence can be observed from the estimation of the integrated
hazard or cumulative hazard which is given by
t
H (t ) = ∫ h(u )du A convex integrated hazard implies that the hazard itself is increasing
0
with time or positive duration dependence where as a concave integrated hazard implies
negative duration dependence.
Moreover, from equation (2), it is clear that the shape of the hazard function very much
depends on the shape of the underlying probability density function. But, one of the
advantages of the hazard model is that, it is possible to estimate the hazard rate without
assuming any specific probability distribution if the hazards are proportional.6 The
proportional hazard assumption states that changing an explanatory variable only has the
effect of multiplying the hazard rate by a constant. So, in the proportional hazard model,
the hazard function is given by
h(t , z ) = h0 (t ). exp( β z )
5
6
We are assuming no right censoring in the data.
Proposed by Cox (1972)
14
Where as h0 (τ) is the baseline hazard function and exp (βz) is a positive function,
independent of time which incorporates number of covariates which affects the hazard
ratio. Though other forms can also be used, exponential form is the most widely used
one. To obtain the estimate for L1 (β), we need a specific form for baseline hazard rate.
Cox (1972) proposed a partial maximum likelihood estimator without imposing
restriction on baseline rate. Cox’s estimator allows for right censoring and it maximizes
N
exp( β
z)
exp( β
Zr )
∑
L =∑
i=
1
r∈
R (ti )
Where R (t) is the set of cartel’s at risk just prior to t (cartel dissolution). So if there is no
information on baseline hazard, only the order of duration provides the information about
the unknown coefficients (Suslow 2005). It is important to mention here that the
dependent variable in survival model shows conditional probability of failure. So the
positive (negative) sign of the explanatory variables imply cartel’s dissolution
(durability). The z statistic provides the usual level of significance. As proportional
hazard model assumes that the covariates have a proportional effect on relative hazard
rate, it is also important to test whether the data satisfies the proportionality assumption
or not.
Section 5: Results
On the basis of theoretical predictions, previous empirical analysis and the unique
characteristics found in the EC prosecuted cartels, I have chosen the following variables
for the purpose of duration analysis. These variables range from cartel’s internal
characteristics (types of industry, number of firms, market shares), range of agreements
(number of agreements, type of agreements, choice of the geographic markets),
monitoring and enforcement (ringleader/price leader, involvement of association) and
external market environment (growth rate and fluctuation in demand). I have also
included the late entry into the cartel and early exit of the firms from the cartel to see
whether these changes in the cartel structure had any effect on the overall duration of the
cartels. It is important to mention here that I have also estimated the model with the total
duration (proven+ suspected). The results do not change much. So, I have not reported
the models here.
15
Duration of the cartel is calculated in years. NUM variable implies the maximum number
of firms involved in the particular cartel. The total number of observations is 73 instead
of 88 because 15 cases involved association of undertakings. MKTSHR is the total
market share covered by the cartel members. I have data for 70 cartel cases. I have
developed two dummy variables to indicate the number of agreements negotiated by the
cartel members. SINGLE implies either price fixing or market sharing agreement. MIX
implies more than one dimension of the agreements. LEADER is the dummy indicating
the cartels those involved a ringleader or price-leader for enforcing the agreement where
as TRADEASSO captures the effect of trade association support to the firms. I have used
number of entrants and exits per year of cartel duration (ENTPY and EXITPY) to capture
the effect of entry and exit. Another important variable that can affect the duration of
cartel is the market demand. According to the conventional wisdom, demand growth has
a positive effect on the cartel duration with high entry barrier. But a recent paper by
Capuano (2002) showed that high demand growth can not rule out the possibility of
collusion in the presence of Nash reversion punishment and entry is feasible and
sustainable in a growing market. Jacquemin et al and Marquez found non-significant
negative relationship between demand growth and duration where as Suslow found
significant negative relationship. Moreover fluctuating demand causes cartel breakdown
because of the uncertainty in the market. Unfortunately DG competition documents rarely
provide relevant data on the demand variables. I have extracted turnover data from
Eurostat at the two-digit NACE level. But I only have data starting from 1995-96 till
2005-2006 which does not represent the lifespan of many cartels in my database. But the
data do provide a rough picture of the growth/ stagnation in the markets concerned. I
have calculated linear demand growth (GROWTH) and the coefficient of variation (CV)
in demand (CVDEMAND) for the entire period. The expected sign for both
CVDEMAND and STDEVL variables are negative but the expected sign for GROWTH
is not known.
The next group of explanatory variables considered represents the industry classification.
The maximum number of cases comes from the chemical sector (23). During this period
of study, many firms in the chemical sectors were part of more than one cartels (ADM,
Ajinamoto, Akzo Nobel, Atofina, Aventis, BASF, Cheil, Degussa, Desang, Solvay etc)
16
and some of them are also repeat offenders7 (ADM, BASF, Akzo Nobel, Aventis,
Degussa etc). Apart from the four Soda ash cartels in 1990 (including one re-adoption
case in 2000), all 19 chemical cartels were detected between 2000 and 2007 after the
famous discovery of Lysine cartel in 20008. The service group comprises of wide range
of services- communication, real estate and renting, community, social and personal
services, electricity, gas and water supply service etc.
Out of twelve cases in the transport sector, nine belong to water transport, more
specifically maritime transport. This sector enjoyed block exemption from Article 81(1)
for certain characteristics of the agreements including permission for uniform rates till
quite recently. Most of the cases under the heading of metal come from steel industry. In
the EU, coal and steel sector was part of European Coal and Steel Community (ECSC)
treaty for quite a long time (1951-2001). Three out of this eight metal sector cases are
part of the ECSC treaty. The Raw material sector includes plastic and glass products,
non-metallic mineral products, paper and paper board and textile products. For the
survival analysis, I have used sector dummies for chemical (CHEM), services
(SERVICE), Transport (TRANS), raw materials (RAWMATER), metal (MET), food,
beverages and tobacco (FBT), Machinery (MACH) and finance (FINANCE). I have used
CHEM as the reference group. Every other dummy should be compared around it.
Table 5 summarizes the variables used for the study and the expected sign.
Table 5: The Variables Used for Survival Analysis
______________________________________________________________________________________
Variable
Description
Expected Sign
(duration)
______________________________________________________________________________________
DURY
Duration (in years)
TOTALDURY
Proven plus suspected duration (in years)
NUM (73*)
Number of firms
-
7
Repeated offenders are those individual or firm who has committed the crime twice (but not
simultaneously)
8
ADM’s junior manager informed FBI about the price fixing cartel when FBI was investigating into a
possible corporate spying by the Japanese competitor against ADM in 1992.
17
MKTSHR (70*)
Total Market share of the cartel
+
SINGLE
Single agreements
-
MIX
More than one agreements
+
LEADER
Ringleader/ Price leader
+
ASSO
Association involved
+
TRADEASSO
Trade Association Support
+
ENTPY
Number of Entrants per year
-
EXITPY
Number of Exit firms per year
-
DGROWTH
Demand Growth (%)
?
CVDEMAND
Coefficient of Variation in Demand
-
PROPLENY
Proportional of cartel life spent under new policy regime
-
CHEM
Chemical
FBT
Food, Beverage and tobacco
FINANCE
Financial services
MACH
Machinery
MET
Metals
RAWMATER
Raw Material
SERVICE
Services
TRANS
Land, Water and Air Transport
_______________________________________________________________________
_
* Number of observations
As revealed from the histogram in section 2, the duration data is bimodal (5, 28). The
normality test rejects the null hypothesis. Under this circumstance, Cox proportional
hazard function seems a preferred model since it does not assume any underlying
distribution. I have also tried censored normal regression taking into account the leftcensored variables (right-censored in the normal regression because the true value is
higher than the observed value). The dependent variable is the log of duration because
duration can not be negative. The result changes significantly. So, for this purpose, I have
relied on the Cox model since normal regression may not provide robust result in the
presence of bimodality. Table 6 summarizes the result of the Cox proportional hazard
model. The results are given in hazard ratio. Model 1 is estimated with the full set of
observations (88). Model 2 includes number of firms (so the number of observations has
been reduced to73) and Model 3 is with market share of the cartels (70 observations). Z
statistics are provided in the parentheses. The significant variables are markets with
asterisk.
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Table 6: Cox Proportional Hazard Model
Variable
Model1
Num
Model2 (With Firm
Model3 (With
Number)
.9830 (-1.07)
Market Share)
Mktshr
1.006 (0.92)
Leader
.7437 (-1.23)
.8486 (-0.68)
.4693 (-2.47*)
Single
1.9148 (2.29)*
1.6194 (1.57)
2.2581 (2.51*)
Entpy
4.6802 (4.25*)
4.9783 (3.57*)
5.1236 (4.27*)
Expy
.2318 (-2.15*)
.2540 (-2.03*)
.1265 (-2.23*)
Propleny
3.8558 (3.68*)
3.9183 (3.06*)
4.6987 (2.91*)
Dgrowth
3.5900 (2.76*)
6.820 (2.04*)
3.0804 (3.28*)
Cvdemand
.0003 (-1.93)
.0001 (-1.28)
.0000 (-1.23)
Fbt
4.6632 (2.87*)
3.2883 (2.05*)
4.2746 (2.36*)
Finance
1.8321(1.15)
2.40004 (0.99)
1.7687 (0.80)
Mach
.8181 (-0.62)
.8268 (-0.52)
.8146 (-0.54)
Met
.7369 (-0.80)
.8798 (-0.35)
.5402 (-1.29)
19
Rawmater
1.0095 (0.02)
1.0225 (0.05)
.7281 (-0.63)
Service
.3181 (-2.25*)
.5794 (-0.99)
.1359 (-2.70*)
Trans
3.4926 (1.89)
4.8149 (2.22*)
11.9609 (2.87*)
The important result of this analysis is that it reveals that the cartel external shocks do
have a disruptive effect on cartels where as structure of the cartelized market do not have
a significant effect on cartel duration. Growing demand has a higher hazard. Stricter
policy regime increases the hazard in the market. Number of firms and market share does
not have any significant effect on cartel duration. But the changes in the number of firms
over the lifetime of the cartel do affect cartel life. The late entry has a disruptive effect
where as early exit of some cartel members actually decreases hazard. If the cartels have
a price leader or ringleader, the hazard decreases and this result is significant in the last
model. The cartels with single agreement as opposed to the mixture of agreements (price
fixing, market sharing etc) have shorter duration. Before I conclude, it is important to
mention that I had included many other variables in the analysis (number of agreements
in a cartel, price fixing, market sharing dummies, association and trade association,
geographic market such as global, national or regional cartel and various interactive
terms). But the variables were insignificant so I dropped them from the analysis
considering low degrees of freedom. The analysis also revealed that the industry specific
characteristics also play a major role. I have used these set of dummies as control variable
and the variables become insignificant if I drop them from the analysis. Moreover, food,
beverage, tobacco cartels and cartels in transport sector reveal significantly higher hazard
compared to chemical sector where as service cartels have significantly lower hazard.
Section 6: Conclusion
This study provides an analysis of the cartel duration of the EC prosecuted cartels. To my
knowledge, this is the first study which focuses extensively on the European cartels apart
from Brenner (2005) which mainly focussed on the evaluation of the leniency
programme.
20
This research tried to explore the duration of the EC prosecuted cartels and identifies
certain characteristics that may have affected the duration of these cartels. The hazard
model analysis reveals that the changing nature of the market, i.e. demand growth and
changes in the policy regime (leniency specially) had significant negative effect on the
cartel duration. Moreover cartel’s monitoring mechanism like market leadership or ring
leadership improves duration where as single agreement as opposed to multi-dimensional
agreements have increased hazard. Another interesting aspect is that the structure of the
market (number of firms, market share, nature of agreements) etc does not have any
significant effect on the cartel duration. In fact the duration increases with the number of
the firms within this sample thought the result is not significant. But it is clear tat the
changes in the market structure over the cartel life time do have an effect on cartel
duration. Where as late entry of the firms into the cartel have significantly higher hazard,
the early exit actually increases the duration of the cartel.
Secondly, in recent years, major changes have occurred in the antitrust regime in the
Europe, especially within the time period of my research. Leniency policy was introduced
in 1996 and amended in 2002 and 2006. Guidelines for fines were introduced in 1998 and
modified in 2002 and 2006. Almost no studies have systematically analyzed the impact of
new antitrust regime on EC cartel duration. (Brenner 2005) only used time dummy for
the period before or after leniency and found no significance on duration. (Harrington and
Change 2007) developed a theoretical model which evaluates effect of antitrust policy on
cartel duration. The model provides a ground to infer about the unobserved population of
cartel from observing the duration of discovered cartels. The result shows more
aggressive the detection and conviction policy is, duration of discovered cartels increase
in the short run. The preliminary analysis in the present analysis also shows that if higher
proportion of cartel life is spent in the stricter policy regime, it has a negative effect on
cartel duration. I want to extend this study to incorporate correlated competing risk model
for the causes of cartel detection. I want to especially focus on the cartels detected by the
leniency application.
21
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