Proactive Detection of Collusive Practices: An Application in Spatial
Setting.
Paper Submitted for CRESSE 2015: Advances in the Analysis of
Competition Policy and Regulation
Sinan CORUS
ABSTRACT: In the last decade, collusion detection remained largely reactive, relying primarily on
leniency. Recently, this is criticised. Popularity of proactive detection techniques is increasing. Known
as screening, economic analysis of varying complexity is proposed to detect collusion in a proactive
manner. In this study, a data set formed by Turkish Competition Authority is used to flag some
suspicious patterns in a spatial market where no a priori knowledge of collusion exists.
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1 - INTRODUCTION
The conventional understanding within competition policy is ‘cartels are bad and should be
prosecuted’1. Although the estimates differ, on their upward effects on prices, some popular
ones are between 17-29% (Katsoulacos and Ulph 2013, p.558). Even though the harshness
differs across jurisdictions, cartel enforcement is a global phenomenon. Increased willingness
to fight cartels lead to exponential increase in the fines imposed, as a jump from 800 million
euros for 1990-1999, to 7.000 million euros for 2010-2013 is reported by EC2. With new
penalties such as: private litigations, criminal liabilities for the executives; the calculations of
cartel members gets more complicated.
Cartel investigations are traditionally initiated by: complaints of the competitors; consumers;
tips of former employees, or, more recently leniency applications with latter having a lion
share of 75% as of 2009 (EC 2009, p.82). Recently in antitrust policy an alternative is
proposed. Proactive detection, or more commonly known as screening, is a new tool in
fighting cartels. It is an exercise to decide whether to initiate an investigation. In pursuit of
collusive activities, market data; i.e. prices, quantities, market shares, costs are scanned to
assess the odds of an agreement within the industry (Harrington 2008, 15). Some suspicious
pattern i.e. sudden increase in prices, decrease in price volatility, atypical changes in sales
conditions, or odd bidding behaviour is sought (Schinkel 2008, 7-8). Recent discussions
show that stressing difference between using economics in “proactive detection phase” and
“phases of prosecution” is important. In detection; the aim is not proving collusion. It is not
estimating the damage. It is not generating evidence to establish guilt. Aim is providing some
“leads” for markets that may worth a closer look3.
In this paper, basing on a data set provided by Turkish Competition Authority (TCA), the aim
is to identify collusion in a market with no previous knowledge of collusion is present. This
study benefits from two strands of literature. First is related to detecting collusive practices.
This literature goes back to 1980’s. One particular feature is the evolution from complicated
empirical modelling to practical applications with better chance of actually being used in
policy. Second is related to spatial pricing in a price discrimination / collusion setting which
will constitute the theoretical framework behind the empirical analysis.
This work is a preliminary version and is going to be subject to revision. Nevertheless, initial
findings indicate that pricing behaviour in the market under consideration is consistent with
predictions of the theoretical framework; about a regime switch between collusion to
competition.
One special feature of this work is pursuing proactive detection when there is no prior
knowledge about presence or operations of a cartel. It is also important to see that a collusive
strategy of regional allocation can be picked up via analysis of price and distance relationship,
without any knowledge about allocation process. This suggests that detection may not
necessarily require inside knowledge about how a cartel operates. Finally, this study
In this study, cartel refers to hard core cartels, meaning an agreement among rivals not to compete.
See http://www.oecd.org/competition/cartels/
2 See http://ec.europa.eu/competition/cartels/statistics/statistics.pdf.
3 Schinkel 2013, p.260 & Abrantes-Metz 2013b, p.15, 19
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contributes to a handful4 of empirical studies in the area of price discrimination in spatial
setting.
Next section provides an overview of the literature. This section is followed by the description
of data. This is followed by the presentation of methodology and estimation.
2 – LITERATURE
2. 1. DETECTING COLLUSION
Efforts to identify collusive practices go back to early 1980`s. The early works primarily focus
on understanding the dynamics of collusive pricing. Therefore generally trails of prosecuted
cartels, activities of which are well-documented, are followed5. Methodology is comparing
either (i) behaviour of cartel members with the fringe firms, or (ii) behaviour in collusive
period with competitive period. The studies on bidding markets are more common due to
availability of data. Some examples are Porter (1983), Bresnahan (1987), Porter and Zona
(1993, 1999), Baldwin et.al (1997).
Porter (1983) tracks a well-documented cartel both in terms of data and activities, the Joint
Executive Committee – the railroad cartel of 1880-1886. The strategy is detecting periodic
regime switches between competition and collusion. Similarly, Bresnahan (1987) studies US
automobile industry to explain the drastic change in key indicators in the year 1955. After
comparing different models for three consecutive years, collusion is suggested as the most
plausible explanation. Porter and Zona (1993) study highway construction contracts in Long
Island in 1979-1985. Two models, one for competition and one for collusion are formed. They
show that competition model fails to explain the behaviour of colluding firms but the
collusion model does explain. Baldwin et.al. (1997) take on timber auctions – retrieving
timber from designated areas of forests – in US. With no insight from a preceding
prosecution, they basically ask whether the dispersion in bids during 1975-1981 is explained
by collusion. Different models with alternative interactions of supply shocks and market
regime are compared.
Porter and Zona (1999) focus on tenders for milk procurement in Ohio, following the trail of
a documented cartel. Cartel operates by customer allocation; each firm wins “own” tenders,
as “rivals” submit courtesy bids. The firms are grouped into two: ring members and the
control. The results indicate that; for ‘the decision to participate in a tender’, and for ‘the level
of the bid’, colluding firms bid dependently, while non-colluding firms bid independently.
Second generation works are more concerned with providing a framework that can be
applied to other settings. Most notable examples are Bajari and Ye (2003) and Marshall et.al
(2008).
Bajari and Ye (2003), study highway repair contracts in US in 1994-1998 without any
previous prosecution evidence. The idea is if the firms are not colluding, their bids should
first be conditionally independent. This means that, after controlling for all observable
As Miller and Osborne (2014) suggests “…the existing empirical literature empirical literature does
not address special discrimination despite the long and litigious history of discrimination in the
industry (p.222)”.
5 Porter (1983) for example studies operations of a legal cartel.
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factors, unexplained part across firms should be independent. Second, bids should be
exchangeable. The identity of the rivals should not matter for bidding. Firms should be
equally aggressive to all possible subset of competitors, not favouring one subset over
another. The methodology of looking for exchangeability and conditional independence in
pricing has been extended to other settings. Some other examples in this framework are
Padhi and Mohapatra (2011), Chotibhongs and Arditi (2012) and Aryal and Gabrielli
(2013).
Marshall et. al. (2008), examine price announcement behaviour in Vitamins cartel. Cartels;
not only need to agree on the level of prices, but also need to agree on the transition to this
level. Basing on a price leadership model, some expectations are formed under different
behavioural assumptions i.e. competition vs. collusion. These expectations centre on the
questions like,


If prices increase who is going lead the price increase? Or, will it be a joint increase?
Is it likely to see price announcements in advance?
The methodology is, observing the pattern over time and looking for some switches in
behaviour. Following table illustrates some results.
Figure 1 – Vitamin A Acetate 650 Feed Grade Price Announcement Patterns
(Source: Fig.1 in Marshall et.al 2008)
Before 1985 prices are announced by the market leader unilaterally. Advance announcement
is not observed. After 1985, price announcements become joint and in advance. This is
consistent with a change of behaviour from competition to collusion. Also it is noticeable that
the period for which colluders plead guilty is 1990-2000. On the other hand, change in the
market behaviour goes back to 1985. This can be interpreted as a sign of under enforcement.
Moving to more recent works, one notable pattern is they are getting less and less
complicated6. They have low data requirements. Publically available data is preferred.
This can at least partly be related to interplay between competition law and economics. Without legal
consent, it is not possible to initiate investigations proactively. Therefore simplification of the tools of
and the language in economic analysis is a necessary condition to convince the decision makers.
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Following the trails of prosecuted cartels is less popular. Based on enforcement experience
and predictions of the theory some “collusive markers” are put forward7.
One line of literature is concerned with reduced price variation following collusion. This may
be due to standardization of pricing practices or production, elimination of award schemes
(i.e. rebates, discounts), umbrella pricing by fringe firms, or effective prevention of entry8.
Abrantes-Metz et.al (2006), Bolotova et.al (2008), and Blanckenburg and Geist (2009),
Bejger (2012) are some examples. Methodology is similar: looking for a subset of competitors
or a time period with high level of prices and low variation in prices.
Abrantes-Metz, Villas-Boas & Judge (2010) use compatibility with Benford`s Law to detect
Libor manipulation. Benford`s Law predicts first and second digits of a set of observations to
have a certain distribution. Deviation from this distribution is proposed as a test. In the
study, it is shown that, LIBOR is consistent with Benford`s Law second digit distribution for
20 years, but departs from it around manipulation time.
Hüschelrath and Veith (2013), follow the German cement cartel prosecution. The question
asked is: “Was it possible for the consumers to flag the collusion before the legal
proceedings? With only own procurement data available, how customers would have
identified the cartel?” A rich procurement data coming from 36 consumers is used. First a
candidate structural break point is identified. Second, a static panel data regression is run.
The findings indicate that it is possible, for at least large scale customers, to flag the collusion.
Abrantes-Metz (2013) focuses on aluminium market. Large purchasers are worried about
activities of some investment banks that took over some metal warehouses. They are
suspected to create bottlenecks in the market. Inventory accumulation increases aluminium
prices and profits. Following figure depicts the relationship between price, and inventory
accumulation.
See Harrington (2008, 2006a, 2006b) for collusive markers. In general; a steady period of high
prices followed by a decline, regime switches for price, a high correlation in pricing behaviour across
competitors, a change in relation of price and cost, reduction of variation in; price, product
characteristics or quality, elimination of discounts, price increases accompanied by declining imports,
stability in market shares are proposed as some potential markers for collusive behaviour.
8 See Connor (2005, p.335-336). For more formal frameworks linking price variation and collusion see
Harrington and Chen (2006) – possible buyer detection limiting collusive pricing – or Athey and
Bagwell (2008) – high level of patience in colluders leading to price rigidity under publically known
cost structure/privately known cost shocks.
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Figure 2 – Reversal of Relationship between Prices and Inventories
(Source: Fig.1 in Abrantes-Metz (2013))
Warehouse takeovers and reversal of the relation between prices and inventories coincide.
Similar breaks are also found between cost of warehousing & inventory accumulation and
cost of warehousing & prices. It is concluded that antitrust scrutiny or some regulatory action
might be in order.
A Brief Assessment of Proactive Detection: Why Is It Proposed? What are Potential Benefits?
At first, cartel investigations were initiated by complaints or tips from the competitors,
consumers, and former employees. Later leniency, immunity from penalties in the case of
self-reporting, adopted globally.
However, recent OECD roundtable showed that not everyone is convinced about the
“success” of the leniency programs. It is a fact that many cartels have been revealed via
leniency. But some question the “quality” of the revealed cartels.
“Out of the heterogeneous pool of different kinds of cartels undiscovered, what type of cartel
is typically brought up by a leniency application? There is reason to think that those are not
the most sophisticated cartels, but rather the less well-organized ones. Or old-and-dying
cartels that lost most of their profitability and so their stability. … sophisticated active
cartels, certainly those that formed with the leniency programs being a reality – say in the
last 20 years – can only exist because they found ways in which to avoid being destabilized
by the lure of leniency… when agency resources are limited, it is doubtful whether they
should be spend mostly on bringing these tail-end leniency cartels, since it means that other
cartels, in particular more sophisticated and profitable ones – that therefore typically also
are more damaging – cannot be pursued, that is, cannot be discovered, and in some cases
also cannot be investigated when a suspicion does exist. Indeed in practice resources are
likely spend primarily on leniency applications. (Schinkel 2013, 259)”
Deciding to collude is an assessment of relative pay-offs in collusion and competition9. The
most successful cartels (widest pay-off gap between competition and collusion) have the
Taking into account the cost of collusion (fines, damages, reputation loss) and probability of
detection on one side and profits from collusion on top of competition on the other.
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highest member loyalty10. Thus, it is harder to deter/desist them with leniency. Therefore
leniency is dependent on internal dynamics of the cartel (Abrantes-Metz 2013, 228).
Is it possible to assess whether this concern -leniency not attracting the most successful 0f
cartels- is justified? Experimental economics literature might be particularly relevant for this
question. One reason for this is, in real life only one antitrust regime is operational in a
jurisdiction at a time. The outcomes of the counterfactual are unknown11. Second, even if the
researcher tries to benefit from variations across jurisdictions, presence of many
unobservables limits the causal interpretations.
Past experimental work on leniency primarily focused on the effects of leniency on
deterrence. Two theoretical predictions have been tested12:
i.
ii.
The urge of being first to report (also referred in the literature as ‘race to the
courthouse’), is expected to destabilize the cartels, increase detection and make
collusion less likely.
Leniency increases cartel stability by contributing to retaliation and being open to
exploitation – parties might prefer to report the cartel taking turns to insure (at
least partly) against fines
In many past studies, collusion is primarily assessed on “quantity”. Number of cartels,
dynamics of cartel formation and collapse are at the centre of the analysis. Quality issues are
less of a concern.
Some informative questions to assess “quality” would be “what is the relative level of prices?”
or “what is the relative cartel stability?” in collusion periods, when i) leniency is possible and
when ii) leniency is not an option. Following table gives some hints from experimental works.
Table 1 – Some experimental works and their findings.
Work
Hinloopen and
Soetevent (2008)
Bigoni et.al
(2014)
Apesteguia et.al.
(2007)
Relevant Finding
The level of agreed prices show similar pattern in all cartels. If there is
leniency, actual collusive price is lower; survival probability of cartels
is lower.
Let us assume leniency causes overburdening in competition authority
and this reduces deterrence. Best way to overcome this problem is
increasing the fines. Investing in detection will not have the desired
effect
Within the cases of collusion, for the subset of cases in which selfreporting did not happen, market price is the maximum price. There is
With regards to difficulty to maintain collusion, the evidence provided by Clark and Houde (2013)
might be striking: Each price increase in gasoline cartel in Canada required on average 65 phone calls
between the conspirators.
11 The quality of cartels in the absence of leniency when the leniency is present, or the other way
around in a certain jurisdiction in a certain period.
12 See Hinloopen and Onderstal 2014, p.318.
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Bigoni et.al
(2012)
Dijkstra et.al.
(2012)
Hinloopen and
Onderstal (2014)
no cartel below the maximum price in that subset. However, in other
cartels –where reporting happens price is not necessarily the
maximum price.
“What does not kill us makes us stronger” effect: Average collusive
price is higher in treatment with self-reporting rather than treatments
without self-reporting. Also, when probability of detection and the
level of fines increase, under leniency, collusive prices increase even
further.
Cartels formed under leniency are able to increase prices higher.
Treatment with no leniency: cartels are; less stable and collusive prices
are lower13.
Treatment with leniency: negative effects for antitrust policy; more
stable cartels and higher collusive prices.
Most of the studies14 have findings that might justify quality issues in leniency. Nevertheless
this should be interpreted with some caution. There are two reasons for this. First, leniency
and antitrust policy are imperfectly modelled in these studies. Institutions like damages,
admission of guilt, leniency/amnesty plus are not included in any of these works. Fines are
either set to 10 % of turnover – maximum fine limit – or are fixed at a constant value.
Second, market structure is kept constant within an experiment15; only one market is tested
at a time. Thus, variation in outcome only results from subjects’ attitudes towards and/or
ability for colluding as a group. Relation of structural factors – like the nature of the market –
is less explored. This constrains drawing conclusions for the policy making.
Nevertheless, assuming quality issues do exist – leniency programs not delivering the most
successful of cartels, these problems might be addressed by proactive detection16. The more
successful a cartel is the greater is the divergence between competition and collusion. This
means that it is more likely for successful cartels to pop-up in the radar (Abrantes-Metz 2013,
228)17. This is the reasoning beyond the calls for proactive enforcement and its most
straightforward benefit: complementing leniency by addressing high quality cartels.
A less pronounced benefit of proactive detection is better quality dawn raids. Guidance about
nature, length, type, coverage or parties of a cartel before inspections may help targeting the
dawn raids effectively (Bos 2009, p.110).
Actually higher winning prices. Since it is an auction setting, this should be translated as lower prices
in a regular market.
14 First two justify absence of quality issues.
15 There is only some limited number of market structures that have been tested. These are
homogenous Bertrand, Bertrand with product differentiation, English type oral auction and first price
sealed auction.
16 Note that Bigoni et.al 2014, suggest that it might not be possible to increase detection via increasing
the probability of detection. See Table 1.
17 Here the expectation is, cartels do not take strategic action and do not try to fly under the radar. Note
that even if cartels take precautions, this would mean cartel is not equally “successful”.
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A final point that should be considered is the growing interest on this line of research.
Awareness of economic consultancy firms is growing18. There is reason to hope that growing
interest will contribute to the prospects of proactive detection being a reality of antitrust
policy. OECD roundtable revealed that there have already been some successful attempts of
proactive detection (Mexico and Chile) already and there are some ongoing attempts (Italy
and Hungary).
2. 2. PRICE DISCRIMINATION AND COMPETITION IN SPATIAL SETTING
Theoretical framework for this study bases on a spatial setting. At the centre will be the
relation between distance and price under different behavioural assumptions.
The spatial competition first modelled in Hotelling (1929)19 is a linear one dimensional
market with two sellers, each producing one homogenous product. It is suggested that
optimally sellers will tend to cluster at the centre, being only marginally different 20.
Hoover (1937) is the first one to combine price discrimination and spatial setting. Hoover
offers three conditions for price discrimination to work in a spatial environment. First is the
absence of any returns for arbitrage21. Second is the ability to observe the location of the
seller22. Third is optimal scale to be large, resulting in concentration of production in certain
locations23. If this is not the case, production will spread and geographical advantages will
erode. “...material producing industries with standardized commodities” satisfy these
criteria. Some example industries are; iron, steel, oil, cement, furniture, lumber, ready mixed
concrete, plywood, fertilizer and sugar24. Hoover points that in these markets, competition
has only two dimensions; price and distance.
In this setting, as more distant consumers are served, the market power of the supplier falls.
Competitive pressure from rivals is stronger in the border regions. Therefore, prices are
expected to be lower in locations distant to the plant. Following Hobbs (1986) we can
formally express this as:
See
http://www.globaleconomicsgroup.com/antitrustcompetition-policy/aluminum-marketdislocation-evidence-incentives-and-reform/
http://www.oxera.com/Latest-Thinking/Agenda/2013/Hide-and-seek-the-effective-use-of-cartelscreens.aspx
19 Some models follow Salop (1979) framework of circular city.
20 d`Aspremont et.al.(1979) revisit this theorem and show that this result will not hold under
quadratic formulation of distance.
21 Arbitrage will not be possible if suppliers are discriminating only spatially - charging the same price
for everyone in a given location. A different case is “perfect spatial price discrimination” (see Braid
(2008)), in which consumers within a region are also discriminated according to their types. In this
case, favoured buyers might have resale opportunities.
22 See also Macleod et.al. (1988).
23 Capozza & van Order (1978) provide a similar reasoning. Large scale might have other implications
for pricing. Firms might try to avoid underutilization. In such a case, especially downturns in the
market might be associated with fierce competition and price wars (see McBride (1983)). This leads
some empirical studies to form cost structure dependent on capacity utilization (see Rosenbaum &
Sukharomana (2001)).
24 Hoover (1937), Vogel (2011), and Lederer & Hurter (1985)
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=
,
∗
+
The price at each point is the minimum of monopoly price and minimum price the least cost
firm can charge to this particular location25. Market is characterized by a Bertrand
competition at every location.
It is easy to see that in this framework, identical firm assumption is not very important for
our purposes – relation of distance and prices. Differences in costs across firms only change
the area served. Following figure from Vogel (2011) illustrates pricing in a three firm
oligopoly.
Figure 3 – Evolution of price in a spatial oligopoly
Source: Figure 1 in Vogel (2011)
Another extension of the literature is about taking the location of the firm as an endogenous
decision. Nevertheless, this does not seem to change the relation between prices, and
distance and/or the rival distance. For example Vogel (2011) divides the market process into
four consecutive stages: entry, location, pricing and consumption. The characteristics of
market equilibrium are refined by backward induction. It is easy to see that even when
pricing is contingent on entry and location, optimal pricing still involves a Bertrand
competition at every location. Lederer and Hurter (1985 & 1986), focus on a similar
extension of the Hoover’s (1937) setting in a two firm - four stage game of complete
information. Firms at the first stage chose locations; second stage is the revelation stage,
third is pricing and fourth is payoff. It is suggested that; optimal pricing will be determined
by the cost structure of the high cost firm, product being served by the low cost firm. Macleod
et.al (1988) study a homogenous product case with multiple plants. Firms first decide on
whether to enter into the market. If they do, they decide on the number of plants and where
to operate them. After choosing the locations, they decide on a price schedule. Equilibrium
price is characterised as a Bertrand outcome at each location with a series of price cuts. If
price goes below costs, firms stop price cutting. Naturally price cuts will go on until there is
only one firm, the low cost firm, willing to supply. The price at this level will be marginally
below the costs of the second low price firm26.
However Macleod et.al. (1988) make an interesting observation: it would be misleading to
always anticipate a negative relationship between distance and price. The results suggesting a
negative relation mostly follow one dimensional markets. In a multidimensional model
things may be slightly different. Consider the two firms (xi1 and xj2) operating on an irregular
plain as shown in the following figure:
tj is the unit transportation of the firm j and Distancej is the distance of firm j to the location.
Hamilton et.al. (1989) also arrive at similar conclusions. Hamilton and Thisse (1992) arrive at
similar conclusions where instead of a single price, a contract is offered to consumers.
25
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Figure 4 – Two firm two dimensional competition
Source: Figure 2a in Macleod et.al. (1988)
Recall that the argument for declining prices over the distance is conditional on the increased
competitive pressure from rivals. The assumption is that as distance to the consumer
increases, distance of the consumer with the rival decreases. But this is relevant only for the
consumers on the shaded area. For the rest of the plain this will not be the case.
Following figure introduces a third firm (Xk3).
Figure 5 – Three firm two dimensional competition
Source: Figure 2b in Macleod et.al. (1988)
The area where price declines with distance is now irregular. Therefore it is hard to suggest a
straightforward relationship. In some regions price declines by distance, while in some others
it does not. This interpretation in two dimensional world will be crucial in the empirical
modelling part.
However, let`s leave the complexity of two dimensional world aside for a while. Intuitive
setting provided in McBride (1983) may help to understand what a regime switch between
price discrimination and collusion would look like.
In this setting there are two identical firms (firm 1 and 2) located at point A and A`
respectively. They sell a homogenous product on a line with length AA`. Buyers at each point
on AA` are identical. The market operates under delivered pricing. Transportation cost is
linearly increasing with distance. Therefore c and c` are the delivered cost of product at each
point on AA`. Let p (0) and p`(0) represent monopolist pricing at distance zero, and ki
represent the distance from producers. Then p (k1) and p`(k2) will correspond to preferred
delivered price schedule at each point AA` for firm 1 and 2 respectively.
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Figure 6 – Three firm two dimensional competition
Source: Figure 3 in McBride (1988)
Firm 1 will enjoy monopoly power irrespective of behavioural assumption starting from point
A until point R`. Firm 2 will have no incentive to serve the area of AR` as doing so entails
loss. However, after R` behavioural assumptions matter. Firm 2 can serve R` at price equal
to c` without making any loss. Assuming a Bertrand competition at each point, firm 1 will be
cutting prices down to the delivery price of firm 2 after R`. As more distant customers are
served, price will fall linearly until point R is reached. After point R, firm 1 will be
outcompeted. Firm 2 will be serving RR` under competitive constraints from firm 1, and
`` ` as a monopoly. Therefore blue line | (0) ` ` (0)| will characterise pricing in the
absence of collusion.
However, under the assumption of collusion, firms will not have to be constrained by
competitive pressures from each other. Assume the collusive strategy is market sharing. The
agreement is simple: Every customer is served by the nearest provider. Since competition is
relaxed prices will keep on rising linearly within ` ``. The price pattern under collusion will
be characterised by the red line | (0) ` (0)|.
The model suggests that, collusive pricing behaviour might be characterised by a stable
pattern in distance. On the other hand price discrimination framework implies firms will act
as monopolists in home markets and will adopt competitive pricing on the overlapping
market ` ``. Then price distance relation might go through a break. This break might be
picked up with a quadratic function relating prices with distance.
This suggests that in a spatial market it might be possible to identify collusion by looking at
the relation between prices and distance.
Why spatial models can be valuable for proactive detection?
Spatial framework suggest that if

The arbitrage is limited
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



Scale matters
Consumer locations are observable
Transportation is costly
Price and location are the only determinants of competitive power.
there might be a break within the relation of distance and prices if there is a regime switch
between collusion and competition. In the literature; iron, steel, oil, cement, furniture,
lumber, ready mixed concrete, plywood, fertilizer, sugar, airlines are pointed out as some
industries.
One thing to note that, these are the “usual suspects” of the antitrust enforcement. High
number of antitrust violations has been recorded in these industries. The reason for this is,
nature of the industry makes collusion very profitable27. Detecting collusive behaviour
proactively can have considerable benefits for social welfare.
Secondly spatial framework is not necessarily confined to geographical context, where the
distance is literal. It may refer to differentiation of products in terms of product
characteristics like sweetness & fruitiness of breakfast cereals or acceleration & gasoline
consumption of automobiles. In this case, all prices being equal, consumer would desire to
consume closest possible alternative to her desired combination. If other firms want to charm
the consumer, price should be cut in order to offset the deviation from desired bundle of
characteristics (Goddard et.al 2005, 432).
Then one can hope that spatial framework might provide a general framework for proactive
detection.
3. DATA
3.1. Introduction of the Data
Accessed data set: In this study a data set provided by TCA is used. We have no information
with regards to existence of a cartel, how it operates or what its coverage is. The data is
provided under confidentiality terms. Terms require masking; identities of the providers and
customers, the regions they makes sales to / operate in, date of the sales, costs, and details
One cautionary remark about returns to collusion is in order. Enforcement experience might
embody some selection bias. Our understanding of collusion is primarily driven by revealed cartels
(case law). We cannot rule out that cartels that are not caught are the ones with highest returns. Maybe
the cartels that are caught are the easiest ones to detect:
“Cartels are more likely to form when suppression of inter-firm rivalry has a higher expected yield in
terms of profitability for member firms then other profit enhancing activities. The ... common
folklore that cartels are more likely to form when competition is focused on price may arise because
it might be easier to detect cartels that focus primarily on price and more difficult to detect cartels
that focus on diminishing the unit value (Marshall and Marx 2012, p.86).”
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about the products concerned. Even though terms do not dictate concealment of the industry,
for now it will be concealed. Nevertheless, some information relevant for economic analysis
will be provided28.




Product is homogenous. The scope for branding is limited. Firms are not exclusively
producing this product but it has a big share in their revenues.
The sales are bulky in quantity. This increases the effect of transportation costs in
competitiveness. Competitiveness of a firm declines with the distance.
Scale of production is important. Marginal costs fall as production is expanded.
The product is an intermediate product. Thus, producers may choose to integrate
upwards or downwards. If we refer the product as X;
Product Y


Product X
Product Z
Facilities differ in complexities. Some operate like assembly plants, while others are more
complex.
In distribution vertical agreements are occasionally preferred but are not the norm.
The data set has two parts. First part is the reported figures from undertakings and cover 18
months. These figures involve







Providing facility (origin of production)
Type of the product
Destination county
Distance of the facility to the point of sale
Identity and type of the customer
Total revenue, quantity and (if any) transportation cost in each transaction
Monthly costs of some inputs
Distance: Distance is at the centre of this study. It is important to use a good measure. In
spatial applications, the distance data is generally retrieved from either web mapping or
geocoding applications. Two different approaches are present in calculating distance. One is
based on retrieving coordinates of competitors and/or centre for commercial activity (i.e.
city, town) and calculating the Euclidian distance between rivals and/or centres. This
approach is more common when distance covered is interpreted as a time loss for the
consumer29. Some examples in this fashion are; McManus (2007) analysing coffee
consumption around University of Virginia, Davis (2006) centring on spatial competition in
movie theatres, Dafny (2009) taking on the effects of hospital mergers, and Pinkse et.al.
(2002) focusing on spatial competition patterns in wholesale gasoline. Thomadsen (2005)
The factors outlined are standard factors to take into account in a competition assessment like;
assessing probability and effects of a collusive agreement or effects of a merger. See for competitive
assessment of markets in competition cases.
http://eur-lex.europa.eu/legal-content/EN/TXT/PDF/?uri=CELEX:52004XC0205(02)&from=EN
29 Clark and Houde (2013) and Lewis (2008) would be some exceptions where distance covered is
considerable, yet Euclidian distance is preferred.
28
14
studies fast-food consumption and uses a combination of both shortest route and Euclidian
Distance30.
Second measure of distance used in studies on spatial markets is the driving distance (the
length of the shortest road connecting two points). This is preferred when transportation
costs are actually borne and considerable, thus competitiveness declines over each extra mile.
Some examples are Bajari and Ye (2003) searching for collusion in construction projects in
US, Miller and Osbourne (2014) and Harrington et.al. (2014) studying cement markets, and
Houde (2013) centring on retail gasoline31.
In this study, distance calculations are based on the shortest driving distance. This is done as
follows: Data set includes “destination” information32 for each sale. The locations of the
facilities are also known. Basing on web mapping and satellite imagery application
maps.google.com and application programming interfaces (API`s) the distance of each
competitor to the county of sale is projected. Each sale is assumed to be delivered to the
centre of the county, as there is no information for the exact coordinates.
One problem is that web mapping provides only current distance data. This potentially can
cause deviations between distance measured now and distance by the time of sale.
One solution for that was retrieving distance data by the time of the sales via contacting
relevant public body. Unfortunately, authority reports no data to be available. However, same
public body publishes length of road network and composition of the roads: highway,
expressway, gravel road etc. It seems that public investment has been primarily concerned by
improving the quality of the existing roads. This makes it more likely for web mapping data
to be a fair representation of the driving distances by the time of sales. This can be seen by a
high level of correlation between two measures of distance33.
3.2. Descriptive Features of the Data
Below is the average trend in prices.
If the outlet is in a shopping mall Euclidian distance is used, if not shortest route is used.
Houde (2013) goes one step further and take traffic flows into consideration as well. One optimal
route minimizing time is defined for every consumer in a given traffic zone.
32 Mostly in county, in some cases district or neighbourhood level.
33 Correlation coefficient is 0.98.
30
31
15
Figure 7 – Evolution of Monthly Averages34
90
90
70
80
85
80
PRICE INDEX
95
100
100
105
MONTHLY AVERAGES
110
MONTHLY AVERAGES (WEIGHTED BY VOLUME)
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18
MONTH
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18
MONTH
MILL PRICES - ALL SALES
MILL PRICES - ALL SALES
DELIVERED PRICES - DELIVERED SALES
DELIVERED PRICES - DELIVERED SALES
The price seems to be stable until Month 7. Then it collapses without any sign of recovery. In
a period of year it suffers a decline of almost 30 %.
Turning back to distance, below is the evolution of average distance (weighted by the volume
of sale) over time.
95
DISTANCE INDEX
100 105 110 115
Figure 8 – Evolution of Distance35
1
2
3
4
5
6
7
8
9
10
MONTHCODE
11
12
REPORTED DISTANCE
13
14
15
16
17
18
PROJECTED DISTANCE
1
2
3
4
5
6
7
8
9
10
11
MONTH CODE
12
13
14
15
16
17
INDEX2
-130 -120 -110 -100 -90
DISTANCE INDEX
100
105
110
115
PROJECTED DISTANCE 2
18
PROJECTED DISTANCE
DISTANCE LEAD INDEX
Does not include sales within an undertaking or across different undertakings.
Reported Distance is reported by undertakings. Projected Distance is the estimates of web mapping.
Projected Distance 2 is similarly a web mapping estimate. Measures of distance are weighted by
volume of sale.
34
35
16
Interestingly, month seven marks the lowest average distance served. First panel in Figure 8
shows that after month 7, firms seem to serve regions further away. Within a couple of
months the average distance is increased 15 %. Assuming a two dimensional circular market
this would correspond to an increase in the region served around 35%. Second panel shows
distance lead of the provider. This refers to the difference in the distances between closest
competitor and the provider. A similar break is also present in this measure. Meaning that,
not only region served by the firms increase but also overlap between the firms increase.
Serving customers closer to rivals might require price reductions. First panel of Figure 9
depicts monthly correlation between prices and rival distance. Correlation between price and
rival distance intensifies after month 7 gradually. A break in similar fashion in the relation
between number of rivals and prices is shown in the second panel. Higher number of rivals
should translate as lower prices. As expected, number of rivals is negatively correlated with
prices. However, this relationship also follows a stable pattern until month 7, but intensifies
after that.
Figure 9 – Evolution of Monthly Correlation: Prices, Rival Distance & Number of Rivals36
MONTHLY CORRELATION
0 .1 .2 .3 .4 .5
MONTHLY CORRELATION: RIVAL DISTANCE AND PRICES
1
2
3
4
5
6
7
8
9
10
MONTHS
11
12
13
14
15
16
17
18
MONTHLY CORRELATION
-.5 -.4 -.3 -.2 -.1 0
MONTHLY CORRELATION: NUMBER OF RIVALS AND PRICES
1
2
3
4
5
6
7
8
9
10
MONTHS
11
12
13
14
15
16
17
18
4 - METHODOLOGY AND ESTIMATON
Oligopolistic models in spatial setting suggest that if





The arbitrage is limited
Scale matters
Consumer locations are observable
Transportation is costly
Price and location are the only determinants of competitiveness
Prices are net of transportation cost. Transportation cost is deducted if pricing schedule is delivered
pricing.
36
17
then, there might be a break within the relation of distance and prices if there is a regime
switch between collusion and competition.
The characteristics of the industry are in line with these assumptions. Descriptive analysis of
the data suggests that there is good reason to suspect the presence of such a break. In the
next section, an empirical estimation is conducted to see whether such a break might be
identified econometrically. Estimates provided are preliminary estimates and will be subject
to revision.
4 .1 – ESTIMATION
4.1.1 –ESTIMATION STRATEGY
The empirical objective in this study is deriving the relationship between distance and prices
in the guidance of theoretical predictions about a regime switch. The relation between
distance measure and prices is captured by two coefficients; these for Delta and Delta
squared. Via interacting with a two level factorial variable, the dummy for collusion, two
different equations that relate distance-based-competitiveness and prices are estimated by
OLS. In this estimation, dynamic effects (i.e. entry/exit, location choice or investment) are
ignored. This primarily stems from data structure. Observed time period is 18 months. This is
a relatively short time for dynamic considerations.
In this part, basing on the predictions of the theoretical framework, the relation between
prices and distance will be explored. A relation in following form is hypothesised.
, , ,
= (
,
,
,
,
, ,
)
where DELTA refers to distance measure used, i refers to each sale, k is provider, j is county
and t is month of sale. Data set extends to 18 months. In any month, multiple sales to same
customer can happen. These sales can be delivered to the same county or different counties
according to the will of the consumer.
Estimation will centre on the prediction of theoretical framework – the relation between
distance and prices goes through a break when there is a regime switch. This requires a
measure of distance compatible with this aim.
One option is using provider distance. Nevertheless, as Macleod (1988) et.al suggest, in two
dimensional world this might not be ideal. In one dimensional economy, producers are
located on a line. Therefore as the distance from provider increases, distance to rival
decreases. It means that competitive pressure from rival must necessarily increase.
Nevertheless, in two dimensional world, this is not the case. As Figure 4 and 5 depict,
distance to closest rival may increase/ stay constant. Therefore, even though provider firm is
serving more distant regions, market power may not be further restrained.
18
This issue has been addressed in the literature in two ways, first is controlling for distance to
rival(s). Some examples are; Thomadsen (2005), controlling for distance to closest fast food
restaurant in studying the effects of ownership structure (chain owned vs. franchise) on
pricing, Clark and Houde (2012), controlling for distance to different type of rivals (i.e.
closest rival, low cost rival) in studying collusive patterns in retail gasoline, Harrington et.al
(2014) controlling for distance to closest competitive fringe in analysing collusive stability in
Germen cement cartel.
Second is moving distance to the centre of the estimation. In the structural estimation
framework of Miller and Osbourne (2014), Houde (2012) and Davis (2006) the decision
maker (firm/consumer) is modelled as an optimizing agent facing a vector of distance to each
potential competitor/provider. Seeing the locations of potential competitors/providers,
firm/consumer decides on a price vector/consumption bundle. Thus, only reasonably close
firms exert competitive pressure to each other, alternatively, only reasonably close providers
are realistic alternatives to consumers.
In this study a simple transformation is used to collapse two dimensions (distance to
provider and distance of closest rival) into one37. A new variable is defined such that;
=
−
then, Delta measures the distance advantage relative to the closest rival. It is a distance based
competitiveness measure. For each sale;



Positive values mean that provider is the closest firm to the customer (home market)
Negative values mean that provider is serving to regions closer to its closest rival (home
market of the rival).
Values around zero mean that both firms are good alternatives for the consumer
(overlapping market)
Past empirical applications in spatial setting support focusing primarily on closest rival in
assessing competitive power. In an attempt to derive the patterns governing spatial
competition in wholesale gasoline, Pinske et.al. (2002), estimate reaction functions for
competitors. Results suggest that rivalry declines very fast with distance, indicating localized
competition. Furthermore, “being closest neighbour” is found to be the primary determinant
of competition.
Delta is the variable of interest, but some control variables are also used to adequately
capture important features of the market. In choosing control variables we primarily consider
the relation of the control with variable of interest, namely we are trying to control features
that might lead undertakings to expand/contract geography of their operations.
37
See Appendix for further information on how two measures of distance are computed.
19
One variable in this sort would be number of rivals close by. Delta provides a measure of
distance advantage relative to closest rival. However as Thomadsen (2005) puts it,
“… an outlet`s equilibrium prices will be different if there are two competitors
located half a mile away from the outlet but adjacent to each other than they will if
the two competitors are located a half mile away but on opposite sides of the outlet.”
The pricing behaviour when competing with i) a single rival ii) multiple rivals all close by,
might be different. In recognition of this, empirical studies control for effects of additional
rivals via number of rivals in a defined radius. An example for this would be Thomadsen
(2005), in which number of fast food restaurants within 2 miles is used. Similarly,
Harrington et.al. (2014), use number of cement providers within 150 km, while Dafny
(2009) and Houde (2013) uses number of competitors within 5 miles as an IV, for hospital
mergers and retail gasoline pricing respectively.
In this study as well, number of rivals in a defined radius is used as a control variable as well.
The radius chosen is the `average provider distance weighted by volume of sales`.
However, there is some other heterogeneity that are unobserved to the researcher but known
by customer and/or provider. This might cause bias in estimates. On the supply side,
difference in marginal costs is an example. Nature of the data makes it possible to associate
sales with production facilities. Therefore, any heterogeneity in cost structures that are
constant over time will be captured by fixed effects, yet time varying factors will not.
Regarding this study and supply side unobservables two things should be noted. First, supply
side unobservables might not be the biggest concern for the estimation. The reason is the
nature
of
variation
in
the
data.
Distances
and
prices
change
over
customer/location/provider/month basis, thus, has considerable variation within each
month. On the other hand, capacity utilization or cost realizations (i.e. wages, energy, other
inputs) vary provider/month basis and has similar trends across undertakings. For our
purposes the bias caused by any omitted factor will be limited with the covariance of the
omitted factor and distance. Differences in the nature of the variation between prices and
distance measure on one side and supply side features on the other limits the covariance.
Secondly, final two specifications control for some time varying supply features: one controls
the capacity utilization, while the other one controls for energy prices.
Turning to the demand side, two possible factors that might affect decision of a provider to
extend operations to a certain distance might (i) contractual obligations (vertical
agreements), (ii) size/quality of the consumer. Data set allows controlling these factors.
However, unobservable heterogeneity might still exist. Main source of heterogeneity
suggested in the literature is the brand affects. Nevo (2000a, 2000b, and 2001) suggests that
brand effects go a long way in controlling the factors (like quality) that are observable to the
consumer/producer but not observable to researcher. Similarly, Lewis (2008) favours a
high/low quality division in gasoline markets which captures brand recognition of the firm
and loyalty of customers. Studying bottled water, Dubious and Bonnet (2010) strikingly
20
suggest if only manufacturer identity is controlled estimations suggest that consumption of
spring water gives higher utility then mineral water. If both brand and manufacturer effects
are controlled38 the relationship is reversed, suggesting very big impact of advertisement and
brand.
In this study brand effects are negligible as homogeneity of products eliminates any return
for brand making. Inelastic feature of the demand has been handled in rather interesting
ways in the literature. Nevo (2000) points that when purchasing decisions of the consumers
are observable; strategy might involve explicit assumption of exogeneity of the observables.
In some studies this extends to quantity/price relation. Some suggest that if the analysis is
transaction/consumer level, then transaction level quantity and transaction level price might
not be necessarily endogenous. Thus, in explaining price movements across
transactions/consumers, volume of transaction can be used as a legitimate control. An
illustration of this would be Houde (2012) with the assumption of “consumer has inelastic
demand for gasoline. In this representation consumption is split between heterogeneous
consumption needs and a common fixed quantity (p.2156).” Another example would be
Harrington et.al. (2014), analysing the collusive patterns in cement. Building on a data set
provided by cement purchasers in which list prices, and discounts are separately observed,
empirical strategy does involve i) not controlling for demand shocks, which is justified by
inelasticity of demand, ii) controlling for volume of transaction in explaining prices. This
suggests an implicit assumption of exogeneity of sales volume similar to Houde (2012). In
this study, in some specifications; quantities are used as regressors, nevertheless, as Nevo
(2001), these estimates are approached with great caution. These specifications will not be
preferred.
Another control suggested in the literature is the regional features. Pinske et.al. (2002) point
that in spatial markets controlling for regional effects might remedy spatial correlation reasoning from unobserved factors being correlated within region. These might be thought as
features related specifically to a region, concentration of particular demographic/social
groups or industries in a micro geography. Such features might affect decisions of providers
to extend their operations to these areas.
Former empirical applications suggest that controlling for the effects of ownership of
multiple facilities nearby might also be relevant for the purpose of this study. One reason for
this might be efficiencies in distribution. Hortacsu and Syverson (2007), assess concerns for
vertical foreclosure in concrete and cement markets in US. Rejecting foreclosure, they find
possible productivity increases associated with vertical mergers. Findings indicate that it pays
off to operate multiple plants as such a strategy provides efficiencies in distribution of
products. The idea is “deliveries … typically ordered on very short notice by consumers at
different locations, can be made more efficiently, by having a dispatch office that substitutes
production and delivery among firm`s several local plants (p. 252).” Moreover, this might
be even more important when scale matters also in production. Firms might be more willing
to switch production from one plant to another if doing so means some rationalizing. In these
cases, observed price distance relation in the data set might be misleading for our purposes.
38
See Table 5.
21
A sale associated with a distant plant and the price in that transaction might be more related
to market power from a closer plant.
Final word of caution is on the potential measurement errors. First possible source of error is
related to prices. Nevo (2000a) suggest that a discrepancy between the data and the actual
market prices, such as, researcher observing list prices while favoured customers paying
discount prices, might be problematic. Since being a favourite customer is not random, but in
fact depends on many unobservable features, this might potentially create a bias. Leaving
some issues about the understanding the data set aside, the explanations in the data set
suggest that no further discounts are given from the observed prices. A second source of
measurement error might come from distance. As elaborated in the previous section, one
source of error would be mismatch between the distance covered by the time of transaction
and the distance measured now. Similarly, the mismatch between the actual and the
projected distances - the dispersion of consumers within a region while projection taking
centre of the region as the base – might be a second concern. Miller and Osbourne (2014) call
this as an “error in variable”. Fortunately, reported and estimated distance figures are very
highly correlated. This is potentially related to; first, the definition of locations being quite
narrow, and second, providers also using distance to the centre of the region in making
decisions.
4.1.1 – PRESENTATING VARIABLES
Prices: Prices of commercial sales. This excludes both intra-undertaking and interundertaking sales. In some cases, firms and/or customers prefer mill sales. To homogenize
the type of sales mill sales have been converted to delivered sales39.
Measure of Distance40:
In this study a simple transformation is used to collapse two dimensions (distance to
consumer and distance of closest rival to consumer) into one. If a new variable is defined
such that;
=
−
then, Delta measures the distance advantage/disadvantage of a particular provider relative to
the closest rival. Delta is a distance based competitiveness measure.
Delta Square: The square of Delta to capture nonlinearities.
Provider distance projections are used in this transformation, not the reported distance.
Transformation involves a linear interaction of distance and a “monthly transportation cost per unit of
distance”: Unit cost measure is multiplied by distance and added on mill price. Unit cost measure was
obtained from delivered pricing reports of one of the firms. Assumption made here is firms are equally
efficient in transportation.
40 For more detailed explanation of calculation of distance measures, see Appendix.
39
22
Number of rivals in a defined radius: The pricing behaviour when competing with i) a single
rival ii) multiple rivals all close by, might be different. To capture this feature, number of
rivals in a defined radius is used as a control variable. The radius chosen is the `average
provider distance weighted by volume of sales`.
Vertical relationship: In the data set for some customers, vertical relationship is reported. To
control for a preferential treatment towards vertically related customers, a dummy variable is
used.
Facility dummies: To control for heterogeneity in production facilities level, dummy variables
are assigned to each facility.
Energy Index: This index bases on the reports of undertakings about their monthly unit
energy price. The most commonly used form of energy is used to maximize the coherence
across reports.
Collusion: It is a dummy variable that captures effects of collusion. It takes value of 1 for the
first seven months.
Region: It refers to the region of the sale. One way to control for regions might be controlling
for provinces. This is not preferred for two purposes. First the provinces show great
differences in size, economic activity etc. Second, there are no economic or legislative reasons
for undertakings to be concerned with borders of the provinces. In light of that, geography of
interest has been divided to four different regions: Region 1 (26%), Region 2 (11%), Region 3
(43 %), and Region 4 (22 %).
Volume: For each transaction, the volume of purchase reported by undertakings.
Total Purchases: Data set includes detailed identity information for buyers. Basing on reports
from all undertakings, for each consumer, total volume of purchases has been calculated.
This includes summation of all purchases across 18 months for every customer from all
providers. Similar to the supply side of the market, the demand side is also analysed
according to the concept of “undertaking”. Thus, two consumers are identical if they are
controlled by the same group41.
Size of the buyer: This variable controls potential effects of buyer size. Top 5 % of the buyers
in terms of total volume of purchases42 are identified. If the sale is destined to one of these
buyers, this variable takes value one.
Understanding whether a firm is controlled by a group might be tricky even though one has detailed
identification on firm level. This has been done as thoroughly as possible, yet some error is still
probable.
42 This ranking excludes inter-undertaking, intra-undertaking or vertically related sales.
41
23
Own Facility Nearby: This variable controls for efficiencies and/or market power associated
with ownership of multiple facilities close to each other. It is a dummy variable taking value
one if there is an extra facility (or more) of the same undertaking close by.
4 .2 – ESTIMATION
Table 2 provides the OLS estimates. Estimations for seven different specifications are
reported. The estimation has been done by gradually adding some controls keeping the
structure of model unchanged i.e. the relation between distance measure and prices is
captured by two coefficients; these for Delta and Delta squared. Via interacting with a two
level factorial variable, the dummy for collusion, two different equations that relate distancebased-competitiveness have been estimated. All specifications control for (i) number of
rivals, (ii) production facility fixed effects, (iii) vertical relations.
Baseline model includes an additional control variable: a dummy variable that takes value 1 if
the sale is destined to a consumer at top 5 %. Second specification adds region, third
specification adds the effects of own facility close by. Third specification is the preferred
specification in this estimation.
When first three models are compared, the coefficients for Delta and Delta squared seem to
be robust to addition of control variables especially under competition. Results indicate that,
under competition the maximum and minimum point estimates for Delta are 5 % apart from
each other43, while estimates for Delta squared are 3% apart. In the collusion period, the
differences are still within a considerable margin but somewhat larger; 11 % for both Delta
and Delta squared. This is comforting for the empirical strategy. It suggests that the break in
the relation between prices and Delta might be exogenous, and surely a regime switch is a
good candidate for an exogenous break44. A second feature is weakness of nonlinear effects in
collusive periods. Estimates are still significant45 but confidence intervals border zero. In
competitive period, nonlinear effects increase significance and are more precise46. Comparing
point estimates, nonlinear effect coefficient for competitive period is four to five times of that
is estimated for collusive period.
Recall that according to the transformed measure of distance, higher positive values
correspond to sales in home market, more negative values refer to sales destined to rival
home market. Values around zero refer to overlapping markets.
Maximum-minimum point estimate difference divided by maximum. Absolute values are concerned.
Nevertheless it is not possible to say whether it is the only candidate.
45 Significant at 5 %, insignificant at 1% level.
46 This increased precision effect is to a extend must be related to higher number of observations in
competitive period.
43
44
24
Table 2 – OLS Estimates – Alternative Specifications
DELTA
SPECIFICATION (1)
SPECIFICATION (2)
SPECIFICATION (3)
SPECIFICATION (4)
SPECIFICATION (5)
SPECIFICATION (6)
SPECIFICATION (7)
BASELINE
(1) + REGION
(2) + OWN FACILITY
NEARBY
(3) - BUYER SIZE + TOTAL
PURCHASE
(3) - BUYER SIZE +
TRANSACTION VOLUME
(3) + CAPACITY
UTILIZATION
(3) + ENERGY INDEX 1
Coefficient
R. St. Err
Coefficient
R. St. Err
Coefficient
R. St. Err
Coefficient
R. St. Err
Coefficient
R. St. Err
Coefficient
R. St. Err
Coefficient
R. St. Err
Competition
0.051526
0.003946
0.050592
0.004027
0.053944
0.003968
0.052630
0.003960
0.055259
0.003962
0.054446
0.000409
0.056777
0.004094
Collusion47
0.018026
0.004270
0.017567
0.004259
0.019746
0.004205
0.018328
0.004203
0.021858
0.004171
0.018455
0.004360
0.017133
0.004431
Competition
0.000221
0.000019
0.000223
0.000018
0.000227
0.000018
0.000231
0.000019
0.000237
0.000019
0.000230
0.000019
0.000240
0.000019
Collusion
-0.000051
0. 0000204
-0.000046
0.000021
-0.000045
0.000208
-0.000038
0.000021
-0.000034
0.000021
-0.000466
0.000021
-0.000051
0.000021
Competition
-2.360509
0. 1974772
-2.576519
0.219055
1.026496
0.216677
-2.494459
0.218669
-2.517706
0.218931
-2.557352
0.222000
-2.597989
0.222678
Collusion
-0.616229
0. 1946025
-0.827672
0.212551
-0.820775
0.212432
-0.839393
0.214125
-0.865380
0.212752
-0.908854
0.219676
-0.871240
0.220212
DELTA SQ
NBR RIVALS
FACILITY FIXED
EFFECTS
LARGE BUYER
VERTICAL
RELATIONS48
COLLUSION
REGION
NEARBY OWN
FACILITY
TOTAL PURCH.
(18 MONTHS)
TRANSACTION
VOLUME
CAPUT49
ENERGY
INDEX1
R2
YES
YES
YES
-3.719947
0.405335
-3.862273
0.409468
-4.061949
0.410083
-0.830908
0.447891
-0.881230
0.448290
-1.026496
0.446724
YES
YES
NO
NO
-1.017219
0.449714
-0.633699
YES
0.447660
YES
-4.179048
0.421233
-4.197918
0.419570
-0.686074
0.482012
-0.727163
0.4950489
YES
YES
YES
YES
YES
YES
YES
NO
YES
YES
YES
YES
YES
YES
NO
NO
NO
NO
NO
NO
NO
NO
NO
-0.000353
NO
NO
NO
NO
NO
NO
NO
NO
NO
NO
0.464600
0.465400
0.468500
0.478500
4.163798
0.742119
3.596265
0.727659
-0.000022
0.000001
3.275960
0.775670
2.998724
NO
0.000048
0.935107
0.928800
NO
NO
NO
NO
-1.354874
0.912373
NO
0.464000
3.003940
0.472800
NO
0.071234
0.016597
0.472800
Insignificant at 1 % in all, insignificant at 5% at fourth specification.
Insignificant at 5% at first, second, and insignificant at 10 % in sixth and seventh specifications.
49 Insignificant at 10 % at the sixth specification.
47
48
25
First thing to note is the size of the predicted gap between competition and collusion around
the neighbourhood of zero - overlapping markets. Holding other factors constant price
difference is around 10%. This gap decreases to 5-7% when there are no firms nearby and
increases to 12-15% when there are multiple rivals nearby. Secondly, for seven months, the
relation between price and distance-based-competitiveness does not exhibit any nonlinear
patterns. Rather, the observation is a quite stable and linear relation. Thus quadratic term do
not capture anything. Nevertheless, this pattern is disrupted later. Following competitive
periods are characterised by nonlinear patterns. It seems that penetrating into rival territory
requires some serious price cuts. Price cuts reach a limit – the limit being potentially unit
production cost. The sales to rival territory are compensated50 primarily by monopoly pricing
in home market. Prices increase quadratically as market power increases. This pricing
behaviour is captured by the quadratic term in the equation.
Figure 9 – Estimated Relationship between Price & Delta
95
100
Linear Prediction
105
110
115
Predicted Effects of Collusion on Prices at Different Delta Values
-150
0
DELTA1
150
Competition
Collusion
Predicted Effects of Collusion (No Rivals, Many Rivals)
Predicted Effects of Collusion on Delta (Small Buyer, Large Buyer)
No Rivals
110
Linear Prediction
90
100
110
100
90
Linear Prediction
Three Rivals
120
LARGEBUYER
120
SMALL BUYER
-150
0
150 -150
0
150
-150
0
150 -150
0
150
Note that the urge to expand production to reduce costs might also be pushing firms to expand into
rival territory further in competitive periods.
50
26
Fourth, fifth, sixth, and seventh specifications extends the third specification each by adding
a different control variable. Fourth and fifth are demand side controls. Fourth specification
controls for buyer size via using total volume of purchases for 18 months. Fifth specification
controls via using transaction quantity. Sixth and seven are supply side controls. Sixth
specification controls for capacity utilization51 while seventh controls for monthly energy unit
costs52. One noticeable feature is that inclusion of additional control variables reinforces
existing patterns – weak nonlinear effects in collusive period & strong nonlinear effects in
competitive periods. Nevertheless, this might be misleading.
First, with in an established buyer/seller relation, it is very plausible for buyers to ask
discounts for occasional large shipments. At the same time it is also plausible for providers to
be motivated to extend their operations some extra miles. Such a motivation might be
present especially with a production technology in which expanding production means
spreading costs. Second, recall that our estimation does not control for buyer heterogeneity
beyond two accounts: size, and vertical relations. Nonetheless, customers have other
unobservable qualities and display considerable heterogeneity. Undertakings might be
willing to cover more distance to serve good quality buyers. Buyers might be convinced to
transfer their purchases from alternative suppliers to new supplier as long as better terms are
provided. Third, dynamic considerations might be in play. Since capacity utilizations matters
for cost undertakings might be more willing to supply to buyers with whom they foresee a
long term and predictable relationship. This might lead undertakings to cover some extra
distance to secure some utilization. In short, as well as price, it is possible to think of
“quantity” as a dependent variable (potentially related to Delta as well). Thus it is a bad
control “bad control”. As opposed to a good control variable, it is not fixed, but takes values at
the same time or after the variable of interest53. Following this, if controlling for quantity in
each sale is problematic, controlling summation of these sales (total volume of purchases of
each customer), summation of these sales across customers (capacity utilization) also may
cause some problems54.
Moving to the discussion on the estimates for other variables, results suggest that the effect of
number of rivals on prices in first seven months is significant, and negative55. This implies
presence of more rivals is associated with lower prices even in collusion. Nevertheless, point
estimate almost triples in the competition period. The effect of number of rivals can be seen
from lower right panel of Figure 9. The effect of collusion on predicted prices at alternative
values of delta is depicted for different number of rivals. When the competition from
immediate rivals disappear (no rival being present in a defined radius), the divergence
between competition and collusion diminishes considerably. However it is not eliminated as
number of rival variable controls for rivals close to customer. This does not mean distant
firms are not serving to this region. When number of rivals increase to three, prices in
Utilization values are not available for 5 % of the observations, thus this estimation has slightly less
observations then the first five specifications.
52 Compared to specification six, energy index value is not available for an additional 1.5% of the
observations.
53 See Angrist and Pischke (2009, p.64) for bad controls.
54 For ideas about more proper control for demand see extensions.
55 Basing on specification three from now on.
51
27
collusion and competition diverges considerably. Collusion really pays off as all firms have
some considerable competitive power.
It is notable that having a facility close by also has significant effect on prices. Even though
the estimate is not very precise, considerable gains from operating multiple plants are
present. It is not clear whether this effect is related to “efficiencies in distribution” as
Hortacsu and Syverson (2007) claim or fortification of market power. Nevertheless the effect
gradually fades out in other specifications, suggesting that it might be capturing other
features related to the supply56.
Size of the buyer follows an expected pattern. Being a large buyer translates as lower prices.
Point estimates suggest that buyer size might offset the additional facility effect.
Nevertheless, in alternative specifications buyer size retains its significance and precision as
opposed to the effects associated with ownership of multiple facilities.
Finally, vertical relations do not seem to matter a lot. The point estimate is not large and is
largely imprecise, as confidence intervals border zero. This is not a surprise as using
distribution channels is not the established norm in the industry. Thus, firms do not seem to
have a preferential treatment for vertically related buyers57.
Extensions:
As mentioned in the introduction this study is a work in progress and the results presented
are preliminary. The intention is to extend the analysis further before finalizing it.
One possible extension is to make sure of the robustness of findings to omission of important
features. To this aim two additional factors might be controlled. First is adding multimarket
contact to the analysis within the context of Piloff (1999), or Jans and Rosenbaum (1997).
This might provide opportunity to study the differences in collusive/competitive pricing
when parties are in contact in different markets. Second is controlling demand for the
industry in a different way. In many studies, demand is not controlled directly i.e. controlling
the goods/services purchased in the industry, but rather indirectly i.e. production in
downstream market, regional income/industrial production etc. The nature of the data set,
(variation is driven across geographies and providers rather then time), makes it harder to
come up with a measure of this sort. However this might be a good practise if some
satisfactory data is found.
It is lowest and most imprecise when capacity utilization is used. This might suggest that return of
owning additional plant might be considerably related to lessening of capacity constraints.
57 This might be at least partially related to measurement error. During refinement of data the
impression was, categorization of customers was not precise.
56
28
5 – CONCLUSION
Leniency has been dominating cartel enforcement as the main tool of detection for some
time. Nevertheless, some concerns have been voiced in antitrust community about the merits
of a reactionary cartel enforcement program. Proposals include benefiting from economic
analysis proactively at detection phase. This not only addresses some shortcomings of
leniency programs (like being dependent to internal dynamics of the cartel), but also
potentially contributes to quality of the inspections.
This study contributes to the literature of proactive detection. It bases on a spatial
framework. Theoretical predictions suggest that if; the arbitrage is limited, scale matters,
consumer locations are observable, transportation is costly and price and location are the
only determinants of competitiveness, then there might be a break in the relation of distance
and prices if there is a regime switch between collusion and competition.
These predictions have been tested on a data set provided by TCA. Results suggest that there
is a break in the price distance relationship. Firms seem to price in a pattern consistent with
collusion in the first seven month. On the other hand, later pricing behaviour is more
consistent with competitive pricing.
One special feature of this work is pursuing proactive detection when there is no prior
knowledge about presence or operations of a cartel. It is also important to see that a collusive
agreement operating via regional allocation can be picked up via analysis of price and
distance relationship, without any reference to allocation process.
Spatial framework might serve well to the aim of proactive detection for two reasons: i )
spatial industries are “usual suspects” of antitrust enforcement ii) spatial framework is not
necessarily confined to geographical space and can be generalized to product characteristics
space. Therefore there is good reason to hope that spatial framework may provide a general
framework for proactive detection.
29
Appendix A – More on Data Set and Strategy
The sales investigated are commercial sales. They do not include sales within an undertaking
and sales between undertakings.
The price is found by dividing the revenue to total quantity. Two types of sales are present:
Mill pricing and delivered pricing. To assure conformity between two types of sales, all sales
have been converted into delivered sales. This has been preferred instead of the other way
around as; first, delivered sales are more in numbers, even though the difference is small (8
%). Second, the assumption done here is any consumer transporting the product is equally
efficient with the producing firm. This is more reasonable then the assumption that the
transportation costs reported by all firms are as they actually realize -not diverging from
actual cost, say as a marketing strategy.
Every sale is assumed to be destined to centre of a county58. Some of the sales have no county
information but some province information. These sales are assumed to be destined to the
centre of the province. Number of observations in this sort is very small. In some cases,
county names are reported as province names. In others they are reported with incorrect
province matches. These cases have been corrected with the assumption that the most
specific information (county) is accurate.
Two measures of distance are used in this study. One is the reported driving distance and is
provided by undertakings. For some observations, it is missing59. Some of these missing cases
are easy to recover, as some other delivery to the same county has been done within 18
months. Reported distance is used in descriptive part. The second measure of distance is the
projected distance. Basing on web mapping and satellite imagery application
maps.google.com and application programming interfaces (API`s) the distance of each
competitor to the delivery point is projected. Projected distance calculations also are based
on shortest driving distance. In estimation part projection distance calculations are used for
conformity with rival distance estimation.
Recall that methodology requires an assessment of distance in relative terms. This requires
an assessment of closeness of rivals to each county. Rival distance is projected according to
the notion of undertaking.
In some cases more specific information i.e. district, neighbourhood is present. In these cases sales
are assumed to be delivered to the centre of that district / neighbourhood.
59 In some cases it is missing but can be retrieved. Undertakings skip one customer but report another
customer in the same county. In that case reported figure is used in both cases. Nevertheless, for 3 % of
the cases it is not possible to retrieve distance reports.
58
30
Figure A.1 – Undertaking based interpretation of distance
Following a hypothetical four-facility-seven undertaking case, only four distances are
calculated. This can be formalized as
= min(
,
= min(
,
=
,
=
,
,
,
,
,
)
)
while, the distance of the closest rival to a county j would be
= min(
,
,
,
)
The distances are not only projected for providers in the data set. It is simple to locate other
production facilities on the map basing on addresses. Distance between each facility and
county is projected for them as well. To simplify the data retrieving process, the distance data
is retrieved for firms only within 500% of the average distance to any county in the data set.
Recall that each observation in our data set corresponds to a sale. Building on the distance of
closest rival to the county of sale, and the distance of the provider to the same county we
compute a relative competitiveness measure,
Where
= (
for any producer k, providing sale i.
−
)
=
31
To reflect the dynamics of competition more accurately, the effect of multiple rivals operating
close to a customer is also modelled. Following diagram illustrates how this works. For a
customer located in county j there are four facilities in an exogenously defined radius i.e. A2,
A3, B1, B2. This means that if the observed sale is done by undertaking A or B there is only
one rival (with two different facilities). On the other hand if the sale is originated from D1 or
C1, number of rivals in this radius will be two (two different undertakings, two facilities
each).
Figure A.2 – Number of Rivals Calculation
32
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