| Abstract: |
Based on a dataset of manufacturing sectors from five major European economies
(France, Germany, Italy, Spain and the United Kingdom) between 2000 and 2011,
we identify a number of key sector-level features that, according to
established economic research, have a positive impact on the likelihood of
collusion. Each feature is proxied by an â??Antitrust Risk Indicatorâ??
(ARI).
We rank the sectors according to their ARI scores. At 2-digit level,
sectors that appears more exposed to collusion risk are those that tend to
score high in most of the ARIs: Tobacco, Pharmaceuticals, Beverages,
Chemicals. The 4-digit analysis suggests higher anticompetitive risk in
Tobacco products, Spirits, Sugar, Railway Locomotives and Aircraft (high
concentration and fixed costs), Coating of Metals and Printing (low import
penetration), Tobacco products, Meat products, Footwear and Clothing (high
market stability), Plastic products and Spinning/Weaving of textiles (high
symmetry of market leaders).
We then rank sectors according to the
distribution of antitrust intervention by the European Commission between 2000
and 2013, in terms of merger control and anti-cartel enforcement. Tobacco,
Paper and paper products, Pharmaceuticals and Food products are the sectors
for which a notified merger has a greater likelihood of being deemed
problematic by the Commission. There has been a greater incidence of
anti-cartel action in Chemicals, Tobacco, Beverages, Electric equipment and
Rubber and plastic.
Antitrust investigations are based on the identification
of narrow product markets. The characteristics of these markets are not
necessarily well represented by average measures at sector level.
Nevertheless, a simple comparison exercise shows that the European
Commissionâ??s interventions have been largely consistent with sector rankings
based on market concentration
Introduction
The object of this paper is
twofold: to provide a broad descriptive analysis of the risk of collusive
behaviour throughout Europe in the manufacturing sector; and to identify those
manufacturing sectors in which the European Commission has been more active in
the past in its capacity of antitrust authority.
This paper is close in
spirit to industry and market studies, although our target is wider and
encompasses the whole manufacturing sector in Europe, as explained further
below. Our methodology resembles Ilzkovitz et al (2008), in which the authors
couple a variety of product market indicators to measures of antitrust
enforcement to determine whether an economic sector is characterised by weak
competition. In the manufacturing sector they identify Basic metals and Motor
vehicles as the sectors in which competition issues are more likely to arise.
Symeonidis (2003) asks in which United Kingdom manufacturing industries
collusion is more likely, finding no clear link with industry concentration
(industries where collusion had a higher incidence were Basic metals, Building
materials and Electrical engineering). Yet Symeonidis's (2003) analysis is
based on observed collusive agreements that were considered lawful during the
period of observation1. Our aim instead is to investigate potential
infringements of competition law that could be pursued by an antitrust
authority. During our observation period, collusion is illegal and therefore
participating to a cartel is risky: the inability to coordinate in an explicit
and transparent manner between market players and the threat of antitrust
intervention make collusion instable. We are looking after market
characteristics that help counter-balancing those effects and make collusion
more likely in this context.
The exercise that we propose in this paper,
ranking economic sectors according to their predisposition to collusion, has
an intrinsic limitation. The antitrust definition of a market (our theoretical
subject of study â?? referred to in this paper as 'antitrust market') is
conventionally based on tests, such as the SSNIP test2, that identify the
boundaries of a market by measuring the degree of competition that different
products exert on each other. If two products are very good substitutes â??
such that a significant proportion of demand and/or of supply would shift to
one product if the price of the other is changed - then the products are
considered to belong to the same market. This often leads to markets the
boundaries of which are much narrower than those captured by product
classification at sector level.
However, macroscopic analysis such as the one
proposed in this paper, is necessarily based on sector data: that is, data
that aggregate information from multiple markets that are grouped together for
statistical purposes. In fact, we are only able to capture an imperfect link
between antitrust markets and the observable average performance of the
sectors they belong to. Previous research has been confronted with the same
challenge (see, for example, Griffith et al, 2010, on the effect of the EU
Single Market Programme on mark-ups and productivity).
To partially mitigate
that problem, we focus on market characteristics that we presume could be
shared by the majority of products within the same statistical sector. This
would be the case if, for example, antitrust product markets within a certain
sector share regulatory features (eg similar barriers to entry), production
features (eg similar levels of economies of scale) or demand characteristics
(eg a customer base which is largely the same).
To rank sectors according to
their predisposition to collusion we follow the common wisdom in economic
literature concerning the role of marketâ??s structural features (see, for an
exhaustive overview: Ivaldi et al, 2003, or Motta, 2004). The general
intuition is that the more concentrated, stable and transparent markets are,
the easier is for players to coordinate on a collusive price and stick to it
without yielding to the temptation of undercutting the rivals and break the
cartel agreement.
On the basis of the available data (see Section 2 below),
we are able to measure proxies and account for the following factors: (1)
market concentration; (2) likelihood of entry; (3) stability of demand and
supply; (4) market symmetry3. The treatment and measurement of each factor is
described in the next Section.
In the second part of our analysis we look at
antitrust intervention by the European Commission. We look specifically at
merger investigations and cartel infringement decisions. Both types of
competition policy interventions give insights about the treatment of
collusion likelihood by a competition authority. Regarding merger control, a
merger has a higher chance to be considered 'problematic' from a competition
policy perspective if it occurs in an already malfunctioning market where
concentration levels are high, likelihood of entry is low, and supply and
demand are relatively inelastic. A crucial determinant of a merger decision
is, moreover, whether a merger has 'coordinated effects' ie whether the merger
will make future collusion more likely.
Finally we propose and discuss a
simple comparison exercise: the European Commissionâ??s antitrust action is
matched with the ranking of manufacturing sectors according to their collusion
risk. Gual and Mas (2011) have an approach broadly similar to ours. They focus
on Commission antitrust investigations only (ie they do not look at merger
decisions), between 1999 and 2004 and check whether the probability of
dropping the investigation is lower when industry characteristics suggest a
lower likelihood of antitrust infringement. They find positive and weakly
significant links consistent with theoretical prediction. For example, higher
industry concentration rates are positively correlated with the probability of
antitrust sanctioning.
It is important to stress that this exercise suffers
from the fundamental limitation described above: that sector data does not
necessarily convey information for antitrust product markets. Therefore, while
the exercise can provide for an interesting consistency check between
antitrust action and status of competition at sector level and deliver
suggestions for follow-up inquiries, it should not in itself be used in a
normative fashion to judge the quality of antitrust intervention. An ad-hoc
case-by-case ex-post analysis should instead be performed for that purpose
(see Neven and Zenger, 2008, for a good overview of the literature).
The
paper is organised as follows. We first provide an illustration of the
Antitrust Risk Indicators. We then describe our data sample in Section 2.
Section 3 reports the sectorsâ?? rankings and discusses the results. Section 4
concludes.
1. The Antitrust Risk Indicators
Below we report and explain the
construction of the Antitrust Risk Indicators (ARIs) used to rank sectorsâ??
predisposition to collusion. A good summary of the underlying economic theory
can be found in Motta (2004). Note that the indicators are computed at
European wide level (ie they are cross-country averages) and on a 10
years-wide time period (with two exceptions described below). We are in fact
interested in capturing the probability of potential cartels with boundaries
that are wider than national, to identify true 'European' issues4. Moreover
the time period of observation has to be sufficiently long as anti-competitive
behaviours are usually put in place for years (for example: the average
duration of an international cartel is between 6 and 14 years â?? See
Mariniello, 2013). We note that market structures are generally stable over
time; in other words, to give an example: the average market performance
within the tobacco sector during the period 2000 and 2011 is a good proxy of
the performance of the tobacco sector at any point of time during that period.
Again, this is the case if, despite changes prompted by regulatory
intervention, sectors tend to preserve their key structural features over
time, at least in relative terms if compared with other sectors of the
economy. The literature reports consistent findings5.
(1) Market
concentration
A higher degree of market concentration is associated with
higher likelihood of collusion. It is easier to coordinate and reach a
collusive agreement within a smaller group of players. Also, if concentration
is high, deviation from a collusive equilibrium is less profitable: the
remaining slice of the market a player would grab by undercutting rivals is
smaller if compared to a market where many players are active. This means that
cartels are generally more stable when markets are more concentrated.
We use
three measures to proxy the average level of market concentration within a
sector: the average price-cost margin for the period 2000 â?? 2011, the
industry concentration ratio for 2010 and the Herfindal-Hirschman Index (HHI)
for 2010.
Price-cost margins have been widely used in the literature to proxy
the degree of market concentration (See Griffith et al, 2010), as the
companiesâ?? ability to extract rents and increase the gap between marginal
costs and prices is decreasing in the level of competition in the market. They
are, however, imperfect indicators: margins may be high, for example, because
companies are more efficient or because they benefit from economies of scale,
but calculating exact firm-level marginal cost is an extremely difficult
exercise affected by other limitations (see Altomonte et al, 2010, for an
example of such an exercise). We resort to use sector-wide production value
and average variable costs as proxy of marginal costs; that is: we use the sum
of the costs of labour, capital and all intermediate inputs as in Griffith et
al (2010)6.
In order to accommodate for the limitations of price-cost margins
measures, we complement that indicator with industry concentration ratios and
HHI indexes, calculated respectively as the simple sum of companiesâ?? market
shares and the sum of the square of companiesâ?? market shares. These are also
widely used measures of concentration (see Ilzkovitz et al, 2007), even if
they are possibly even more subject to the fundamental limitation that affect
macro-analysis as described above: market shares at sector level are not
necessarily a good proxy of market shares at market level. In our case,
moreover, market shares are available only for the biggest 4 companies in the
sector and only for year 2010. We construct the indicators accordingly: C4 is
the sum of the market shares of the four biggest companies in the sector in
2010; HHI4 is the sum of the square of the market shares of the four biggest
companies in the sector in 2010.
(2) Entry
Entry has a disruptive effect on
collusive behaviour. The mere threat of entry makes collusion less
sustainable: when effective entry is likely, incumbent players may find it
difficult to maintain high prices in the market without risking sudden loss of
customers. Moreover, a high firmsâ?? turnover implies that coordination is
less likely: instability in the identity and in the number of counterparts
make collusive agreements more difficult to reach. Sectors where entry is more
likely should therefore ceteris paribus be associated with lower probability
of collusion.
Our dataset does not contain information that can directly help
measuring the likelihood of entry; likewise, it does not contain information
on the pattern of actual entries by new companies that occurred in the period
of observation. The data report just the change in number of companies and do
not disentangle entry from exit. Low growth rates may therefore mean low entry
rates or high entry rates accompanied by equally high exit rates. The change
in the number of companies cannot therefore be used to proxy entry. We
nevertheless can exploit the information available in our dataset to measure
proxies that provides indications on the degree of a sectorâ??s openness to
outside competitive pressure.
To do so, we build 2 indicators: (a) firmsâ??
size and (b) import penetration. Firmsâ?? size is computed as the average size
of companies within the sector during the period of observation (2000-2011).
Relatively bigger sizes imply the existence of economies of scale, possibly
due to higher fixed costs and barriers to entry. Bigger average size should
therefore imply lower likelihood of entry7.
Import penetration is the yearly
average of sector imports divided by sector production. This indicator is
again computed over the period 2000-2011. A high ratio of imports over total
production suggests that the sector tends to have relatively lower barriers to
entry to foreign competitors. Moreover, it is reasonable to assume that
reaching a collusive agreement with exporters is comparatively more difficult:
exporters, for example, tend to be exposed to different costs shocks.
Therefore it would be more difficult for local producers to explain price
changes by exporters and detect potential deviation from collusive outcomes
that may not be justified by change in production costs.
(3) Market
stability
Stable markets are more predisposed to collusion. Collusive
agreements crucially rely on playersâ?? ability to capture other playersâ??
deviation from the agreed price. When markets are subject to frequent and
unpredictable demand or supply shocks, attributing a change in price to a
deviation is more difficult, therefore collusion is less stable.
We compute
two indicators to capture marketsâ?? stability: (a) variance in market size
and (b) variance in import penetration. Variance in market size is computed as
the variance of the yearly growth rate of production values in nominal terms.
Variance in import penetration is the variance of the yearly growth rate of
the ratio of imports over total production. The two variables are calculated
over the full period of observation 2000-2011. High variance levels are
presumed to indicate lower market predictability and lower likelihood of
collusion.
(4) Market symmetry
The last dimension of analysis is market
symmetry. Symmetric markets where players hold similar market shares tend to
be more predisposed to collusion. Symmetry aligns playersâ?? incentive to
stick to a cartel agreement. Conversely, if a company is much smaller than the
others, it may have a relatively higher incentive to deviate, undercut its
rivals and enjoy all marketâ??s profits. To test for symmetry we compute an
Asymmetry Indicator based on Giniâ??s coefficient8. In our case we employ it
on the distribution of the production shares of the top four companies in each
sector for year 2010. If the asymmetry indicator is 0, that indicates that the
four observed companies have identical production shares ie the market is
perfectly symmetric. When the indicator instead approaches 100 that meansthat
there exists a huge gap between the market share held by the biggest company
and the one held by the smaller ones9.
2. The Dataset
Our dataset contains a
number of widely-used data for European manufacturing sectors from 2000 to
2011 for 5 European countries: France, Germany, Italy, Spain and UK. The 5
economies together represent 71 percent of the EU GDP10, in 2011, while the
manufacturing sector in the five countries observed represents on average 12.5
percent of a countryâ??s GDP11. The primary sources for data are National
Accounts, Structural Business Statistics and International Trade databases.
The aggregate statistics were compiled by Euromonitor12. The market features
variables contained in our database are: total production, value added, gross
operating surplus, market size, imports, exports, production and number of
firms by employment size, production value and production shares of up to five
top companies (all monetary data is recorded in euro)13. Using Eurostat NACE
2-digit classification14, the manufacturing sector can be split in 22
categories: Food products, Tobacco, Textiles, Wearing apparel, Leather
products, Wood and wood products, Paper and paper products, Reproduction of
recorded media, Chemicals, Pharmaceuticals, Rubber and Plastics, Other
non-metallic mineral products, Basic metals, Fabricated metal products,
Computers and electronics, Electrical equipment, Machinery and equipment,
Motor vehicles, Other transport equipment, Furniture, Other manufacturing15.
The 4-digit disaggregation results in 92 sub-categories.
The below table
provides an overview of the database with few key descriptive statistics
relative to 2010 for 2-digit sectors aggregated across the five economies. As
it can be noted the total manufacturing production for our database amounted
to â?¬3.5 trillion, with the Food, Motor vehicles and Fabricated metal sectors
topping the list in terms of production and value added. As for the
demand-side, the five economies consumed â?¬3.9 trillion with the Food and
Motor vehicles sectors again on the top 3 by market size, and Computers and
electronics coming third. The latter sector is ranked first also in terms of
imports. Noticeably, imports and exports are originally defined at country
level and therefore these aggregates include intra-group trade. The smallest
sectors are Tobacco, Electrical equipment and Wood16 by either production or
value added. The highest numbers of companies are in the Fabricated metal and
Food sectors, with more than 180 thousands firms.
3.
Results
3.1 Sector ranking â?? Antitrust Risk Indicators
Table 2 and Table 3
above report the ranking of all sectors according to each of the ARI
indicators (table 2 reports ranking based on 2-digit aggregation data, table 3
on 4-digit). In terms of market concentration, there is a general consistency
between the three indicators, price-cost margins, C4 and HHI4, particularly in
pointing to the most concentrated sectors: Tobacco, Beverages and
Pharmaceuticals. Reproduction of recorded media and Chemicals, Motor vehicles
and Electrical equipment score high respectively in terms of price cost
margins and HHI(4) and C4. Divergences between indicators are possibly due to
differences in cost structures (this should be the case for Motor vehicles and
Other transport equipment for example)17 or differences in the size of
antitrust markets. For example, Reproduction of recorded media scores very low
for HHI(4) and C4. That is possibly due to the fact that products in these
sectors tend to be more heterogeneous and therefore less substitutable to each
other. Therefore, even if several players are active in the sector (hence
market shares at sector level are low), each player can still enjoy a certain
degree of market power (hence price-cost margins are high), because the
products sold may not have immediate close substitutes, or be perceived as
such by customers. The opposite holds for Electrical equipment and Basic
metals: if price-margins are relatively low despite high market shares, that
may be due to a higher degree of substitutability between products.
Table 3
provides a more disaggregated insight by ranking 2-digit sectors according to
the highest score reached by any of their 4-digit sub-sectors. No great
difference is noted with the NACE-2 results. Tobacco, Pharmaceuticals and
Beverages (Spirits and Beer) still rank high. Interestingly, Food climbs up
the concentration ranking thanks to the low level of competition detected in
the Sugar market. Other transport equipment (Locomotives and Aircrafts) scores
high in terms of market share concentration.
Concerning entry, we note that,
consistently with intuition, the firm size indicator is highly correlated with
concentration. Tobacco, Motor Vehicles, Pharmaceutical, Chemicals, Beverages,
Electric equipment, Basic metals are in the upper half of the ranking. This is
not surprising given the relevance of research and development or high fixed
entry costs and economy of scale featuring most of the products manufactured
in these sectors. The NACE-4 analysis confirms Sugar (Food category) as a
potentially problematic market, together with Tobacco, Aircraft and Spacecraft
(Motor Vehicles), Plastic (Chemicals). The other entry indicator we use,
â??import penetrationâ??, scores low for sectors were production tends to have
a more narrow geographic scope (Reproduction of recorded media and in
particular at 4-digit level, Printing) or has a stronger local dimension
(Tobacco, Fabricated/Coated Metals, Other Non-metalic/Cement, Beverages/Soft
drinks), while import penetration is high where multinational companies tend
to be more present: Computer and electronics, Pharmaceuticals, Chemicals,
Motor vehicles.
In terms of market stability, Tobacco, Food, Beverages and
Pharmaceutical are amongst the sectors where demand varied the least during
the period of observation (beside Wearing apparel, a result driven by the
stability of the Clothing sector, as the 4-digit analysis shows). Import
penetration is stable the most in Rubber and plastics, Wearing apparel,
Electrical equipment, Wood and wood products. The lack of overtime variability
may be due to the relevance of products where demand is notoriously less
elastic (Meat products, Clothing, Tobacco, Beer and Footwear, Clothing, Pulp,
paper and paper board, Plastic products, respectively for market size and
import penetration variance at 4-digit level). Finally, the least
â??asymmetricâ?? sectors according to our Gini-indicator seem to be Rubber and
plastic, Textile, Electrical equipment and Tobacco.
3.2 Sector ranking â??
European Commission Merger and Anti-Cartel Decisions
Table 4 above
reports the ranking of manufacturing sectors on the basis of European
Commissionâ??s merger and cartel investigations during the period 2000 -
2013.18 The database was assembled downloading the decisionsâ?? record from
the Commissionâ??s website and allocating them to sectors according to the
reported economic classification. If more than one sector was reported, all
indicated sectors were compiled as affected by the decision.
For merger
investigations we collected three types of information: the number of mergers
that were unconditionally cleared in â??first phaseâ?? ie after a preliminary
inquiry usually requiring 1 month of investigation; the number of mergers that
were cleared in first phase but did instead require the parties to commit to
certain conditions; the number of mergers for which a deeper investigation
(â??second phaseâ??, usually lasting approximately 4 months) was deemed
necessary. We define as â??potentially problematicâ?? a merger that was deemed
as such at the end of the first phase investigation by the European Commission
either imposing conditions or requiring further scrutiny in second phase.19
The ratio between potentially problematic mergers and the total number of
scrutinised cases is the likelihood indicator used to rank sectors.
Sectors
display a high heterogeneity in terms of incidence of merger control. The
sector where merger scrutiny took place most often is Chemicals with an
overall count of 259 decisions, while only 6 mergers were scrutinised in the
Tobacco and the Leather sectors during the period of observation.
Since most
of mergers are cleared without conditions, the likelihood that a merger is
deemed potentially problematic by the European Commission is on average low
(approximately 11 percent for the manufacturing sector as a whole). The index
however varies substantially across sectors. Sectors where the index scores
higher are Paper and paper products (25.4 percent), Pharmaceuticals (25
percent), Chemicals (15.1 percent), Other manufacturing (14.6 percent). At the
other end, the risk of a finding of problematic merger by the European
Commission is lower in Motor vehicles (1.9 percent), Wearing apparel (5.6
percent), Electric equipment (6.5 percent). Tobacco (50 percent) and Furniture
and Leather (0 percent) are clearly outliers (these results are due to
idiosyncratic factors and the small number of observations).
As for hard-core
cartels, the Commission took decisions concerning 16 of the 22 sectors during
the period of analysis. Chemicals account for the majority of rulings, 27 out
of 65. Sectors with no uncovered cartels are Leather, Wood, Recorded media,
Other transport equipment, Furniture and Other Manufacturing. To rank the
sectors, we weighed the number of cartels to the size of the market as a share
of total production in manufacturing. In the resulting ranking the sectors
where the incidence of anti-cartel action was stronger in the period of
observation are Chemicals, Beverages, Electrical equipment and Other
non-metallic mineral products. Tobacco scores high as well, but again this
might as well be due to the very small size of the sector compared to the
other sectors, since just one cartel in Tobacco was sanctioned by the EC
during the period of observation.
It is interesting to note that the
likelihood that a merger is deemed problematic and the weighed incidence of
anti-cartel enforcement are highly and significantly correlated: 51.5 percent
(5 percent significance level). This provides comfort that economic sectorsâ??
features affecting the probability of collusion play a role in determining the
outcome of merger decisions.
3.3 Sector ranking - comparative exercise
We
now proceed with an illustrative comparative exercise. Figure 1 below
attributes colours to sectors according to their performance with respect to
the different computed indicators. The idea is to give a graphical glimpse of
the consistency between Antitrust Risk Indicators and the action of the
European Commission. As explained above, this exercise is useful to check
whether antitrust intervention is more frequent where it is expected to
according to from a macro-economic perspective. It is important to keep in
mind, though, that this exercise cannot provide indications as regards the
quality of antitrust intervention, given the fact that sector data are not
disaggregated enough to capture the boundaries of product markets as defined
in the course of antitrust investigations.
The coloured squares in
figure 1 reflect the ranking of the sectors ordered according to their
anticompetitive risk or the intensity of antitrust action: red corresponds to
the seven sectors at the top, green to the seven sectors at the bottom, and
yellow to the eight sectors in the middle. Red sectors in terms of
â??problematic merger riskâ?? are, as described above: Tobacco,
Pharmaceuticals, Chemical, Food and Paper; in terms of risk of cartel
conviction, red sectors are: Tobacco, Beverages, Other non-metallic,
Chemicals, Electric equipment, Rubber and plastic, Wearing apparel.
Figure 1
suggests a significant degree of consistency between European Commissionâ??s
action both in terms of merger control and anti-cartel enforcement and ARIs
related to market concentration and firmâ??s average size (simple correlation
analysis point to significant correlation coefficients between 45 percent and
75 percent). A much lower degree of consistency is observed as regards the
other ARIs and correlation results are all not statistically significant. The
variance of market size (a negative proxy of market stability) is however
broadly consistent with merger decisions for what concerns negative decisions
ie: sectors such as Tobacco, Food products, Pharmaceuticals, Paper and paper
products are ranked top both in terms of lack of market variance and of
probability of negative merger decision. Cartels discovery seems also overall
consistent in the top ranking for what concern import penetration (Tobacco,
Other non-metallic mineral products and Beverages), variance of market size
(Wearing apparel, Tobacco and Beverages), variance of import penetration and
market symmetry (Rubber and plastic, Wearing apparel, Electrical equipment and
Other non-metallic mineral products).
4. Conclusions
In this paper we have
analysed features of European manufacturing sectors. We ranked sectors
according to their performance based on indicators that economic wisdom
suggests positively affect the likelihood of collusive behaviour by market
players.
At 2-digit level, sectors that appear more exposed to collusion risk
are Tobacco, Pharmaceuticals, Beverages, Chemicals. The 4-digit analysis
suggests higher anticompetitive risk in Tobacco products, Spirits, Sugar,
Railway Locomotives and Aircrafts (high concentration and fixed costs),
Coating of Metals and Printing (low import penetration), Tobacco products,
Meat products, Footwear and Clothing (high market stability), Plastic products
and Spinning/Weaving of textiles (high symmetry of market leaders).
We also
have ranked sectors according to the distribution of Europeanâ??s
Commissionâ??s antitrust intervention between 2000 and 2013 in terms of merger
control and anti-cartel enforcement. Tobacco, Paper and paper products,
Pharmaceuticals, Food products, are the sectors in which a notified merger has
a greater likelihood of being deemed problematic by the Commission. The
incidence of anti-cartel action has been higher in Chemicals, Tobacco,
Beverages, Electric equipment and Rubber and plastic.
We then checked the
consistency of the European Commissionâ??s action with the prediction of
economic theory based on sector data, bearing in mind that sector data cannot
provide for indications on the quality of antitrust intervention given the
fact that antitrust investigations are based on very narrow product market
definitions. The comparison exercise suggests that, by and large, both merger
control and anti-cartel action have been focusing on sectors displaying a
higher level of market concentrations and economic rents or economy of scale.
This paper has a descriptive nature and should be taken as a starting point
for a deeper reflection on the choice of appropriate instruments to foster
competition in European manufacturing sectors and the definition of
intervention priorities. Without appropriate regulatory intervention, ex-ante
monitoring by the antitrust authority is warranted. The action of the European
Commission is sometimes considered to be too much 'case-driven'. Cartels are
discovered through whistle-blowers, abuse of dominance or anti-competitive
agreementsâ?? investigations are prompted by complaints. Because of such an
approach, the restoration of normal competitive conditions that antitrust
intervention is supposed to bring comes often with a significant delay with
respect to the starting of the infringement. Uncovered cartelsâ?? duration,
for example, fluctuates between 6 to 14 years (see Mariniello, 2013) from
their commencement. During that time, cartels affect the economy through a
higher burden on customers and ultimately on consumers. It would thus be more
efficient to anticipate the breaking down of cartels by investing resources in
uncovering cartels to monitor markets in which infringements are more likely.
The European Commission already has the tools to perform such a job through
so-called 'sector inquiries'; an appropriate use of those tools in the
identified sectors could yield significant social benefit.
***
1 Symeonidis
(2003) uses agreements between competitors that were formally registered in
compliance with UK Restrictive Trade Practice Act of 1956 as indication of an
industryâ??s propensity to collusion; those agreements were at the time
considered lawful.
2 See Amelio and Donath, 2009.
3 There are other factors
which may be relevant to explain the likelihood of collusion in a certain
market: for example, the existence of cross-ownership links between players or
the frequency of their multi-market contacts. However, to our knowledge those
factors are not available at sector level and are therefore excluded from our
analysis.
4 We presume that the average marketsâ?? performance across the 5
countries reported in our dataset is a good approximation of the average
performance of a cross-border market within the European Union. For the sake
of illustration, consider the following example: we assume that averaging out
the concentration ratio within the tobacco sector in UK, France, Germany,
Italy and Spain yields a good approximation of the average concentration ratio
of a market within the tobacco sector that has an international dimension
(that is: it is not confined to just one European country and therefore falls
in the competence of the European Commission). The validity of this
presumption crucially depends on the degree of commonality that sectors have
across countries in Europe. If the tobacco sector is very open to competition
in UK while little competition in the same sector occurs in Italy, then the
cross-country average may bear little indication as to the level of
competition of a hypothetical tobacco market affecting Italy and UK. Instead,
if cross-country variability is limited, this would suggest that sectors have
intrinsic characteristics that, despite idiosyncratic country characteristics
(such as domestic regulatory policy) are conducive to similar market
structures. For example: a production process typically implemented in a
certain sector may give raise to sector-specific economies of scale, resulting
in more concentrated markets. Strong and highly significant pairwise
correlations between EU-wide and national indicators in our dataset support
such presumption. Confirmations are also found in the empirical literature.
Hollis (2003) for example finds that concentration ratios in 82 sectors are
very similar across five European economies (Belgium, France, Germany, Italy
and the UK), the US and Japan.
5 Veugelers (2004) analyses 67 manufacturing
sectors in the EU15, finding that concentration ratios tend to be quite stable
over time. Persistency checks ran on our database point to strong and highly
significant cross-year correlations for price-cost margins, import penetration
and firm size.
6 We implement Griffithâ??s methodology except that we do not
subtract for the capital costs because of data availability.
7 Alternative
measures could be used to proxy entry (such as â??businessâ?? churn rateâ?? ie
the sum of firmsâ?? birth and date rate) using Eurostat and OECD datasets.
However, we believe that using average firm size as an indication of barriers
to entry is a better option. First, because the data on firm size are reported
at a higher level of disaggregation (up to 4-digit in our dataset, while
businessâ?? churn rate is limited to 2-digit in the Eurostat/OECD dataset).
Second, because the number of companies that enter or exit a sector is less
informative about the disruptive power that those firms can exert on potential
collusive agreements. A high number of small firms entering small markets
within a sector affect positively the sectorâ??s businessâ?? churn rate, but
this is unlikely to represent a threat to collusive agreements between bigger
companies in wider markets. An extended discussion on alternative indicators
to measure entry likelihood is reported in the Appendix.
8 The Gini index
expresses inequality among values of a frequency distribution and ranges from
0 (complete equality) to 100 (extreme inequality).
9 Formally, we compute the
Gini index as follows: Index = 1- (7*x4 + 5*x3 + 3*x2 + x1)/4; where x1 is the
production share of the top company normalized to the production share of the
four companies (or concentration ratio).
10 Source: Eurostat.
11 Source: The
World Bank.
12 Euromonitor International (link) is a research and data
company that collects and aggregate data at sector level from official sources
as well as through market research. The data obtained through market research
in our dataset consists of production value and production shares for the year
2010 of up to five top companies for all manufacturing sectors in the 5 target
economies for our analysis.
13 Total production is the total revenue of all
locally-registered companies, excluding taxes and subsidies on products like
VAT; valued added equals total production minus intermediate consumption; the
gross operating surplus equals value added minus labour costs and taxes less
subsidies on production and therefore includes the remuneration of equity and
the depreciation of capital; market size consists of the value of all goods
and services sold, either from local or foreign producers and recorded at
purchaser prices; imports consist of the value of goods delivered at the
frontier and consumed in the country; exports consist of the value of goods
shipped out of the country, excluding re-exports; the number of firms is made
up by all locally-registered companies, including 0 employees enterprises and
single-employed; production values and shares of top companies refer to the
revenues made by companies from industry-specific products.
14
http://epp.eurostat.ec.europa.eu/portal/page/portal/nace_rev2/introduction
15
Two 2-digit sectors â?? Coke and refined petroleum products, and Repair and
installation of machinery and equipment â?? are left out of our analysis.
16
The great difference between market size and production for the Tobacco sector
is given by secondary production, i.e. production of Tobacco products made by
companies falling in other categories.
17 Profit margins are calculated with
respect to estimation of marginal costs that includes intermediate goods and
services. As explained above, this is a standard methodology in the
literature, although alternative measures could rely on labour costs only â??
depending on what is considered a better approximation of total marginal
costs. The methodology used in this paper therefore tends to bias downwards
profit margins of sectors that rely heavily on intermediate goods and
services, such as motor vehicles or other transport equipment.
18 Data were
retrieved from the website of the European Commissionâ??s Directorate-General
of Competition through the case search tool: link.
19 We opted for this
definition in order to guarantee the maximum degree of statistical
compatibility between merger decisions, since the ones used for the indicators
are taken all at the end of a first phase investigation. Alternative
definitions could also be possible. For example it could be possible to
further segment mergers that were investigated in â??second phaseâ?? in
mergers cleared with conditions, mergers cleared with no conditions and
blocked mergers. A problematic merger could then be defined as a merger for
which conditions were imposed at the end of either first or second phase
investigation or a blocked merger. However, this would have implied mixing
decisions taken after different administrative processes and with different
depth of scrutiny. It should be said in any case that the ranking of sectors
is not affected by the choice between the two different definitions.
20
According to the Eurostat definition, â??the enterprise is the smallest
combination of legal units that is an organisational unit producing goods or
services, which benefits from a certain degree of autonomy in decision-making,
especially for the allocation of its current resourcesâ??. Births and deaths
account for the creation or dissolution of entreprise units, thus excluding
mergers, break-ups or restructuring of a set of enterprises.
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Appendix 1 â??
Alternative ways to measure likelihood of entry
In this paper we have used
firm size as an indication of entry costs. Firms in sectors with higher
barriers to entry are expected on average to be bigger in size. Another way to
proxy likelihood of entry consists in measuring the actual number of
enterprise births and deaths using the Business Demography datasets of
Eurostat and the OECD20. A summary indicator for firmsâ?? turnover is the
business churn, obtained as the sum of the birth rate and the death rate over
the number of active enterprises in a given year. The higher is the churn
rate, the easier is for firms to enter or exit a sector. Table A below reports
the indicator used in this paper, firm size, and business churn in two
separate columns at two-digits NACE 2.
As it can be noted, the sectoral
disaggregation of the two indicators differs. In particular, the Eurostat
Business Demography/OECD dataset provides data at a more aggregated level than
the level of analysis used in this paper. This makes the comparison between
the two indicators difficult as sectors included in the same group in the
Business Demography dataset may have very heterogeneous firmsâ?? size. For
example, the Tobacco sector has the highest average firm size but Tobacco is
aggregated with Food and Beverages in Eurostat and OECD datasets, which have
average firm size about 10 times smaller. A rough comparison yields mixed
results. Sectors with the highest business churn (ie Textiles, Wearing
apparel, and Leather products) have very low firm sizes â?? consistently with
the approach adopted in our analysis. However, sectors with higher firm sizes
(eg Motor vehicles and Transport Equipment) also display relatively high churn
rates. A possible explanation for this divergence is that high entry and exit
rates may be due to high flows of small companies in narrow markets within a
sector. If a high number of small companies enter or exit small markets in a
sector, this significantly increases the sectorâ??s reported average churn
rate. However, the â??disruptiveâ?? effect on collusion brought about by these
companies can be very limited, given their small size. For that reason, we
believe that using firm size is a better measure to indicate the exposure of
the sector to external competition for the purposes of the analysis reported
in this paper.
Another way to measure barriers to entry is to use sector
capital and R&D intensity as in Gual and Mas (2011) and Symeonidis (2003). A
high capital intensity, as measured by investment in tangible goods over value
added, might imply that firms need to make expensive investments in order to
operate at an efficient scale. Similarly, a high R&D intensity, as measures by
R&D spending over value added, may point to high costs incurred to
differentiate or improve their products. Thus, capital and R&D costs may
represent fixed or sunk costs that reduce likelihood of entry. The two
indicators are also displayed in Table A.
Again testing the similarity
between these alternative measures and firm size is difficult due to the
different level of aggregation of the sectors. Nevertheless, a rough
comparison suggests a higher degree of consistency compared to what observed
in the case of business churn rate. Excluding Tobacco, the correlations
between capital intensity and firm size and between R&D intensity and firm
size are respectively as high as 44 percent and 72 percent. Taking the sum of
the capital and the R&D intensity the correlation with firm size reaches 89
percent. |