Endogenous price commitment and sticky pricing: Evidence from the Italian petrol market

 
Endogenous price commitment and sticky pricing: Evidence
              from the Italian petrol market
                                     Patrick Andreoli-Versbach
                International Max Planck Research School for Competition and Innovation
                                     & Ludwig Maximilian University

                                          December 11, 2011

     This paper analyses dynamic pricing strategies in the Italian wholesale gasoline market and highlights
the importance of endogenous price commitment. Using daily …rm level prices over 5 years I show how the
commitment of the market leader to keep prices …x for a long time period while costs were rising led to a
market outcome with sticky and leadership pricing. To show this I evaluate the consequences of two shocks:
…rst the publicly announced price policy change by the market leader, ENI, who started changing its retail
price less frequently but in bigger and less predictable amounts. Second, the investigation of the Antitrust
Authority after a buyer’s complaint of high and aligned prices. The e¤ect of the announced price policy
change was twofold: …rst, competitors adopted the same sticky pricing as the leader and second, they changed
prices after the leader did. This pricing mechanism was then broken after the Antitrust Authority announced
its investigation, which signi…cantly decreased margins and increased price variance. Once the investigation
ended the companies adopted cost-based pricing with very small and frequent price changes and margins
rose again. Sticky pricing and coordination through price leadership facilitated price alignment and helped
maintain higher margins as mean of conscious parallel behavior.

   Keywords: tacit collusion, price leadership, sticky pricing, endogenous price commitment
   JEL codes: K 21, L13, L41, L71

    I would like to thank John Sutton, who advised me on the …rst version of this paper and helped me to
highlight the important points. I greatly bene…tted from the comments of Rosa Abrantes-Metz, who discussed
this paper at the ZEW conference on "Quantitative assessment of Antitrust Analysis" in Mannheim. I am
also very grateful to all the seminar participants at the Italian Antitrust Authority in Rome, the German
Antitrust Authority in Bonn, the EDGE conference in Bocconi, the CISS workshop in Turkey, the Max
Planck Institute conference in Wildbad Kreuth and PhD workshops at the Ludwig Maximilian University for
their numerous comments. I am especially grateful to David Abrams, Carlo Bardini, Tomaso Duso, Fabio
Massimo Esposito, Valentina Giuliani, Andrea Güster, Dietmar Harho¤, Justus Haucap, Michael Meurer,
Hans Müller, Alessandro Noce, Martin Pesendorfer, Fabio Pinna, Stephen Ryan, Pierluigi Sabbatini, Monika
Schnitzer, Simcoe Timothy and Joachim Winter. I gratefully acknowledge …nancial support from the Deutsche
Forschungsgemeinschaft through GRK 801.

                                                     1
1      Introduction
This paper analyses dynamic pricing strategies in the Italian wholesale gasoline market after the
announcement by the market leader, ENI, of a new pricing policy. ENI commited to keep sticky
prices for a long and unspeci…ed time period irrespective of short run cost changes. Using di¤erent
shocks I will evaluate the e¤ects of the new pricing and, most importantly, how this sticky pricing
strategy facilitated parallel pricing by competitors and sustained a tacit collusive equilibrium. In the
seminal paper by Maskin and Tirole (1988), henceforth MT, the short run commitment to keep prices
…x leads to a dynamic equilibrium of two sorts, either a kinked demand curve (sticky and focal price)
or with Edgeworth cycles. While their paper establishes the types of equilibria that can emerge as
a result of a non-cooperative game, an unanswered question is how they emerge. This paper shows
that the sticky-focal pricing equilibrium can emerge endogenously in a market. More speci…cally it is
shown how the announced commitment of sticky pricing by the market leader generated MT’s focal
pricing and that the focal price adjusted to cost changes through leadership pricing. While MT’s paper
assumes that the short run price commitment is exogenous, deriving for example from menu costs, I
empirically show the relevance of endogenous price commitment1 . An additional departure from their
setting is that input costs, such as crude oil prices, vary over time and thus …rms must coordinate
on how to respond to exogenous costs changes while keeping the focal pricing equilibrium. In MT
the kinked demand curve equilibrium is reached through a mixed strategy which ultimately leads to
a unique focal price, the monopoly price, as …rms are symmetric. I show that instead of using mixed
strategy, the market leaders’ price is used as a focal price and coordination is reached through the
alignment of competitors to the leader’s price. Through their behavior, oligopolistic …rms can impact
the market outcome and tacitly coordinate price changes by reaching a focal pricing. While a large
literature of game theoretic papers show the market conditions under which a collusive strategy can
be sustained in equilibrium this is one of the few empirical papers showing how …rm speci…c strategic
behavior impacts the market outcome.
    One notable exception are the papers on the Joint Executive Committee (JEC) by Porter (1983)
and Ellison (1994) and the analysis of pricing with mixed strategies by Wang (2009). In JEC the
authors use a structural approach to show switches between collusive and non-collusive periods. They
show empirical evidence of …rms tacitly colluding using a trigger strategy which eventually reverses
and goes back to collusion providing support for the theoretical analysis of Green and Porter (1984).
Wang (2009) shows empirically that …rms indeed play mixed strategy in MT’s Edgeworth cycle. He
analyses the Australian petrol market after the introduction of a policy which required …rms to change
their prices only once a day. In an Edgeworth cycle this policy intervention increases the costs for the
…rst …rm to raise prices when prices equal costs. While both …rms have an incentive to raise prices,
when prices equal costs, each …rm wants to be the second to raise prices by slightly undercutting the
…rst …rm. While before the policy the same …rm raised prices and the other followed within a few
hours during the time of the policy …rms played mixed strategy and took turns in price increases.
    Using a unique dataset of daily …rm level prices over 5 years from 2004 to 2008 I show the means
and e¤ects of collusive pricing. Speci…cally I show how the commitment to keep sticky pricing led to
the coordination of …rms’ behavior through pricing leadership and …nally to higher margins. Price
leadership refers to the situation where the market leader sets a price and the competitors follow,
while sticky pricing refers to infrequent price changes. In the empirical analysis I will show both how
the sticky price equilibrium emerged and what impact it had on the industry’s pro…tability.
    On the 6th of October 2004 the CEO of ENI2 , Mr. Mincato, the market leader in Italian gasoline,
publicly announced a price policy change that consisted in rigid pricing and slow adjustment to the
    1 While  commitment has usually been modeled as one player having the opportunity to commit to a binding action,
little is said about how this commitment emerges. In line with Caruana and Einav (2008) who model a framework
where commitment stems endogenously form the model, I highlite how sticky pricing in the gasoline industry emerges
endogenously throgh the leader’s actions.
    2 ENI is one of the world’s major integrated energy companies. It is active on …nding, producing, transporting,

transforming and marketing oil and gas. In this paper I will restrict my attention on ENI’s wholesale gasoline activities
in Italy.

                                                           2
major cost factor, Platt’s Cif Med (Platts)3 , which is the wholesale Mediterranean price of gasoline.
The price policy change was called “New Method Mincato” and was designed to maintain sticky
prices by committing not to change the retail price following the Mediterranean quotation of wholesale
gasoline, the Platts. The average time interval between price changes went from 6 days before the
new pricing policy was introduced, to 19 days after its introduction. The average absolute percentage
change on the price increased from 1% to 4.5%. ENI publicly committed to change prices less often,
but when they changed them, they did so in bigger amounts.
    The competitors didn’t react immediately to the leaders’announcement, represented by the …rst
vertical line in Figure 1, and kept following the main costs, the Platts. After about two weeks
without price changes, but increasing costs, the price di¤erential between competitors and leader
grew dramatically, as shown in Figure 1. Then, as costs decreased, competitors started to align to
the market leaders’ price and since then followed its changes, the second vertical line in Figure 1.
Most of the competitors changed their pricing policies as well following the leader’s sticky pricing.
When costs changed most …rms did not change their price and waited for the leader’s change. This
leadership pricing is evident in Figure 1, where the dashed-dotted line is ENI’s price. After the second
vertical linerepresenting the pricing alignment by competitors, all big price changes are preceded by
ENI’s changes. This price leadership is far from perfect, as competitors still apply small changes to
their price, but all the big price variation happens after ENI’s change. The e¤ect of this behavior was
twofold: …rst, industry level margins increased with respect to the previous period and second, the
price dispersion within …rms considerably dropped4 .
    After 5 months, on the 25th of March 2005, the Italian Trucker Association, FITA, publicly com-
plained about high, rigid and perfectly aligned prices. This eventually triggered an Antitrust In-
vestigation (AI), which was communicated to companies on 23rd January 2007 and ended on 20th
December 2007 with the acceptance of behavioral remedies jointly proposed by the Antitrust Author-
ity (AA) and by companies, see Figure 2. The initial accusation of collusion was not investigated by
AA, rather as soon as the companies proposed behavioral remedies these were accepted without fur-
ther investigation of an infringement of Art. 1015 . After the AA announced an investigation margins
dropped and the price variance increased.
    The empirical part of this paper consists of two sections. First, I will show that pricing strategies
are consistent with price leadership and that competitors changed their pricing in response to the
leader’s new pricing. Second, I will show the consequences of such behavior on margins, the most
relevant indicator of pro…tability, and price dispersion.
    While in this analysis I will focus on dynamic pricing behavior my …ndings are potentially important
for Antitrust policy. The results of this paper might contribute to the growing concerns by authorities
that given the oligopolistic nature of some markets …rms can easily collude without the need of an
explicit and thus punishable communication. A central question to the problem of whether tacit
collusion should be punished is if an agreement can be inferred only by looking at …rms’behavior, but
with no proof of communication. This analysis provides an insight in this discussion: in oligopolistic
markets …rms can tacitly coordinate using sticky pricing and leadership pricing. If e¤ective competition
has to be preserved methods to discourage this conscious parallel behavior must be implemented.
    The rest of the paper proceeds as follows. Section II reviews the related literature with a special
focus on MT’s model. Section III describes the Italian gasoline industry and the dataset. The
empirical analysis of the paper is in Section III, where I …rst show the coordination mechanism,
leadership pricing, and then the consequences it had on margins and price dispersion. Finally Section
VI concludes.

   3 The Italian Petrol Association estimates that the Platts accounts for about 67% of the Italian wholesale price, while

the rest is attributable to storage, administration and delivery costs.
   4 This is in line with Abrantes-Metz, Froeb, Geweke and Taylor (2006), who analyse the price distribution of a bid-

rigging conspiracy. After the cartel break the mean decreased by 16% and the standard deviation increased by over
200%. For a review of the literature on collusion and price dispersion see Connor (2004).
   5 Article 101 of the Treaty on the Functioning of the European Union prohibits cartels and other agreements that

could distort free competition in the European Economic Area’s common market.

                                                            3
Figure 1

                                              Cartel Formation

            .5  .45
     Firms' Prices
    .4

4
            .35
                      01may2004   01aug2004        01nov2004        01feb2005    01may2005

                                       ENI                  Q8           Shell
                                       Tamoil               Total        Erg
                                       Esso                 Api          IP
Figure 2

                                       Cartel Break

    .5      .6  .55
    Firms' Prices

5
            .45
               01oct2006   01jan2007                01apr2007           01jul2007

                             ENI                   Q8           Shell
                             Tamoil                Total        Erg
                             Esso                  Api          IP
2    Literature review
The idea of commitment plays a crucial role in many game theoretic models. As Shelling (2006) states
it:

     "Commitment means becoming bound, or obligated to some course of action or inaction
     or to some constraint on future action. It is relinquishing some options, eliminating some
     choices, surrendering some control over one’s future behavior. And it is doing so deliber-
     ately, with a purpose. The purpose is to in‡uence someone else’s choices".

    The …rst to formalize the importance of commitment in pricing strategies when two …rms compete
repeatedly were MT. Starting from the observation that Bertrand’s result have virtually no empirical
justi…cation, they develop a repeated game with two …rms, homogeneous good and short run price
commitment. In their model …rms take turns in choosing prices and once a …rm sets its price it
remains …x for a short time period. The inability to change their price continuously due for example
to menu costs creates (an exogenous) commitment by …rms after they chose their price to keep it.
Using Markov strategies MT show that two kinds of equilibria might arise, both bounded away from
perfect competition. Either Edgeworth cycles or a focal, stable, price can be sustained in equilibrium.
    Edgeworth cycles emerge when each …rm slightly undercuts the rival, thus gaining market shares,
until the price equals the marginal cost and both …rms earn zero pro…ts. At p = M C both …rms
would like to increase their price, but no one would like to be the …rst, as the follower will again
slightly undercut and gain most. In equilibrium …rms play mixed strategies and each raises the price
…rst with a certain probability. These cycles have been documented in di¤erent countries including
the US, Canada and Australia [Eckert (2003), Lewis (2009), Noel (2007) and Wang (2008)].
    In one of the few paper empirically showing the use of mixed strategies empirically Wang (2009)
uses a policy change in Australia which increases the "commitment costs" by allowing …rms to change
their price only once per day. Prior to the policy change the …rst …rm raising the price was followed
within a few hours by the competitors, while after the introduction it would take an entire day, thus
signi…cantly increasing the costs of being the …rst …rm to raise the price. While before the law a single
large …rm appeared to be the price leader and was consistently the …rst to raise prices, under the law,
…rms played mixed strategies and took turns in being the …rst to raise prices.
    Less attention was spent on the second possible equilibrium in MT, the kinked demand type, where
the market price converges to a focal price which is unique and sticky for both …rms. As price changes
might be indicative for a price war which lowers pro…ts signi…cantly, …rms charge the monopoly price
and have no incentive to undercut. In order to be an equilibrium price, wars must be long and costly
enough in order to deter …rms from undercutting. In MT costs are constant and …rms take turns
choosing prices. In reality costs are volatile and the coordination problem might be solved by the
leader as was the case in Wang(2009) before the policy intervention. The theoretical justi…cation for
sticky pricing under cost volatility and the endogenous pricing leadership stem from Athney, Bagwell
and Sanchirico (2004) and Damme and Hurkens (1999 and 2004) respectively.
    Athney, Bagwell and Sanchirico (2004) analyze collusive pricing schemes in an in…nitely repeated
Bertrand game when costs are iid across …rms and time, but …rms only know their costs and prices
(…rms’actions) are publicly observed. This model is closely related to my analysis as it highlights the
importance of price rigidity in collusive oligopolistic industries. They show that in the repeated-game
model multiple symmetric perfect public equilibria (SPPE) arise, one of which is of special interest for
the purpose of this paper. If …rms are patient enough and the distribution of cost types is log concave
the optimal SPPE is characterized by a rigid pricing scheme. In this equilibrium …rms select the same
price at each period whatever their cost level is. This provides a formal justi…cation for the empirical
observation that collusion is associated with rigid prices and stable market shares over time.
    The issue of endogenous quantity and price leadership was analyzed by van Damme and Hurkens
(1999 and 2004). In their …rst paper they show how in an undi¤erentiated product market with a
linear demand curve and constant marginal costs, the low cost …rm would endogenously emerge as the
quantity leader in the market. In their second paper they address the question with respect to price
competition and di¤erentiated goods. Even though each …rm has the incentive to wait and undercut,

                                                   6
the e¢ cient low cost …rm will emerge as the price leader, as the less e¢ cient …rm is more committed
to wait and set a lower price than the more e¢ cient leader. As a general result these papers establish
that no matter which one is the strategic variable, price or quantity, the low cost …rm always emerges
as the leader. As is usually assumed, the market leader is the low cost …rm while the competitors are
the less e¢ cient …rms.
    If these …ndings are combined they show in a rigorous way, that under general conditions the more
e¢ cient …rm emerges endogenously as the price leader and coordinates price changes (if needed) and
that optimal collusion is characterized by a rigid-pricing scheme, in which …rms select the same price.
    While theoretical work on cartels o¤ers a rich set of models and predictions the empirical evidence is
often missing. This is due to a fundamental problem of data unavailability as companies don’t disclose
sensible information, especially not if it could hint to collusion. In addition even if a researcher knew
which industry to analyze, often a full structural model is needed in order to evaluate the welfare
e¤ects of …rms’behavior which is computationally challenging and time consuming.
    Most of the work on econometric detection of collusion stems from empirical studies on auctions6 .
In this context bids are made available along with …rms’characteristics which makes the identi…cation
of collusive behavior possible. Porter and Zona (1993, 1999) and Bajari and Ye (2003) estimate the
bidders’ pricing equation and compare the residuals. In a competitive model, controlling for costs,
the unexplained part of the bids should be independent and exchangable.
    Aspects regarding the coordination of (explicit) cartels were analyzed by Pesendorfer (2000) who
shows how two types of cartel design, …rst, dividing market shares among members and second, side
payments are both e¢ cient. Röller and Steen (2006) analyze the welfare e¤ects of the sharing rule
in the (legal) Norvegian cement cartel and …nd that a sharing rule which is proportional to capacity
creates an incentive to overproduce and thus is not optimal for the cartel. Genesove and Mullin (2001)
present narrative evidence on the working of the sugar cartel and emphasize the role of the association
of sugar producers as a mechanism for governance and a forum for communication among the re…ners.
Marshall, Marx and Rai¤ (2008) show the role of price announcements during the vitamin cartel.
During the cartel price announcements were driven by the length of time between announcements,
rather than cost or demand factors, suggesting that they stem from cartel meetings.

3        Market Setting and Dataset
The structure of the Italian gasoline market is similar to the setting of MT’s model. It is an olipopolistic
market with nine …rms, prices and the costs are publicly observable and a measure of pro…tability,
margins, is readily available. In addition demand is stable in the short term, the interaction between
…rms is repeated and gasoline is a homogeneous product.
    Given the similarities there are two key di¤erences to the MT setting: …rst, …rms are asymmetric
with respect to market shares and second, costs vary daily. These di¤erences have relevant implications
on the market setting. As will be discussed later the prices need to adjust to cost changes, which
introduces the scope for a coordination mechanism between …rms with respect to the focal price
in MT’s model. While in the MT setting the symmetry of …rms led to change prices using mixed
strategies, in this asymmetric setting the leader’s price is the focal price.
    The industry under examination is the wholesale gasoline in Italy. More than 95% of the market
share is allocated among nine big players. Most of the …rms are partially vertically integrated, with
respect to the upstream market they either own a re…nery or have access to crude oil. With respect
to the downstream market they either distribute gasoline to petrol stations which are privately owned
(30%) and committed to a single brand or own the stations (70%).
    In comparison with the EU Italy has 22,000 retail sites, which is by far the highest number of sites
in the EU, resulting in a low average throughput per site. This has been explained by the demographic
and traditional situation in Italy with many small villages all wanting their own petrol station. In
addition the hypermarkets own considerably more stations in the rest of Europe than in Italy. Only
    6A   theoretical justi…cation for the vulnerability of auctions to collusion can be found in Marx and Marshall (2009).

                                                             7
.2% of Italian stations are owned by hypermarkets, in contrast to 10% in Germany and 51.9% in
France, the country with European lowest margins on fuel.
    Until 1991 the pricing of petrol was controlled by the state through two institutions: the Italian
ministry of industry and the Inter-ministry committee on prices. The …nal price was set in relation
to the crude oil price, the European price and other “political” variables such as unemployment and
in‡ation. From 1991 the liberalization process began with the “supervised”regime, where the ministry
participated in the price setting. In September 1993 a resolution by the Inter-ministry committee
stated that “the prices of all petrol products [...] are freely determined by the companies”7 . The
only obligation for companies was to communicate their price to the ministry. In the time period of
interest, going from January 2004 to December 2008 prices were freely determined by the market.
    Since then the prices have followed the major cost component for wholesale companies, the Platts
Cif MED (Platts), the re…nery premium unleaded gasoline price reference for the Mediterranean area.
The Platts company is a leading global provider of energy information that collects and publishes on
a daily basis details on the prices of bids and o¤ers for speci…c oil products and regions from traders
and exchange platforms. In Italy the reference cost of buying gasoline in the re…nery market based in
Genoa (north-west Italy) and Lavera (southern France) is the Platts Cif MED. The Platts is widely
regarded as the major costs for wholesalers and is used by market speci…c newspapers and industry
insiders to calculate industrial margins, the di¤erence between the wholesale price and the Platts.
    The retail price has two components, a …scal and an industrial. It has been estimated by the
Italian Union of Petrol Producers that the Platt’s re‡ects 67% of the industrial price, while the rest
is attributable to distribution, storage, administrative steps and the petrol stations’ margin. Taxes
account for approximately 58% of the …nal retail price in Italy.
    The distribution and price setting works as follows: The companies transmit to the manager of
the station the so called “suggested price”. This price is a non-binding indication of what the …nal
price for consumers should be. The owner of the station receives a discount on the suggested price
and is allowed to charge up to a certain percentage more of the suggested price. So even though he
…xes the …nal retail price, his range is within his purchase price and the maximum price he is allowed
to charge, as stipulated by the company. The suggested price represents a very good approximation
of the …nal price charged to customers.

    The dataset consists of a collection of daily …rm speci…c pre-tax wholesale prices and costs as
reported by the Platts, summarized in Table 1. The wholesale prices refer to the "suggested prices" of
gasoline described above from the nine major companies: Agip, Api, Erg, Esso, IP, Q8, Shell, Tamoil
and Total from the 1st of January 2004 until the 31st of December 2008. Even though I do not
observe …rm speci…c costs, the main source of cost for …rms is the Platts. As companies are subject
to the same opportunity cost it is easy to build a variable representing the industry’s pro…tability, in
this case industrial margin, which is de…ned as the di¤erence between the mean Italian price and the
Platts’.
    The data about the di¤erence in market shares of independently owned petrol stations and the
petrol stations owned by hypermarkets, e.g. Auchan or Carrefour, across countries was published by
the Italian Union of Petrol and Pöyry Energy Consulting.

  7 Gazzetta   U¢ ciale, 8 October 1993, Nr. 237

                                                   8
Table 1: Summary Statistic

                    Variable     Mean     Standard Dev.     Obs.        Min   Max

                    ENI          0.483           .095       1,827    0.311    0.738
                    Api          0.487           .096       1,732    0.314    0.737
                    ERG          0.485           .095       1,732   0.312     0.733
                    ESSO         0.484           .095       1,754   0.314     0.737
                    IP           0.486           .096       1,732    0.314    0.738
                    Q8           0.486           .094       1,472    0.312    0.732
                    Shell        0.485           .096       1,751    0.313    0.738
                    Tamoil       0.485           .095       1,751    0.311    0.736
                    Total        0.486           .095       1,827    0.314    0.736
                    Platts       0.336           .093       1,827    0.175    0.576
                    Margin       0.149           .029       1,827   0.0150    0.253
                    Alignment    2.162           2.01       1,827      0        8

4      Empirical Pricing
4.1     New pricing policy
From the 6th of October 2004 onwards a new pricing policy was introduced by ENI. The aim of the
new pricing policy, summarized in Table 2, was to make ENI’s prices less responsive to the Platt’s
and carry out a pricing strategy where price changes were less predictable, on a longer term basis
and the percentage change bigger than before. In the press release preceding the policy introduction
ENI argued that this policy would counter speculation on oil prices and maintain the buying power
of consumers constant in times of volatile indices.

                        Table 2: Price Policy Change and Dummy Variables

    Period                  Pre Sticky    Sticky    Investigation An.    Remedies An.   Post Invest.

    Days                        280        828            171                 161           377
    Mean time with               6         19             1.7                  2            2.1
    no price changes
    Mean price change (%)       1          4.5             .35                .34           .55
    if pEN I;t 6= 0

    As shown Table 2 there were two main features of the policy: sticky pricing and larger price
changes. The average time interval between price changes went from 6 days before the new pricing
policy was introduced, to 19 days after its introduction. The average absolute percentage change on
the price increased from 1% to 4.5%. ENI publicly committed to change prices less often, but when
they changed them, they did in bigger amounts. While this are descriptive statistics on the new
pricing the next section will analyze the responses by competitors and show how they adapted to the
policy by copying the pricing scheme.
    Throughout the rest of the paper the dummy variables used will be the ones described in terms
of ENI’s pricing policy in Table 2. P reSticky refers to the period before the sticky pricing was
introduced, Sticky to the period where ENI adopted the new pricing policy, InvestigationAn: refers
to the period between the Antitrust announcement of investigation to the communication of the
proposed remedies. RemediesAn: goes from the announcement of behavioral remedies to the actual

                                                    9
end of the investigation with the acceptance of these remedies by the Antitrust. Sometimes I will refer
to the entire period where the Antitrust was investigating as Antitrust, going from the beginning of
InvestigationAn: to the end of RemediesAn. P ostInv: refers to the period after the end of the
investigation to the end of the dataset in December 2008.

4.1.1   Alignment
The aim of this section is to show how after ENI’s commitment to keep sticky prices the competitors
aligned to the leader’s price and followed his pricing. To show that the price policy change by ENI
was used to create a focal point which facilitates alignment I will show how the pricing behavior of
competitors di¤ers in the periods before, during and after the pricing policy took place. In particular
I will show that during the collusive period each time the leader changed its price the competitors
reacted and aligned to it, a practice referred as leadership pricing. This pricing behavior refers to
the situation where the market leader sets the price …rst and everyone else follows. If such behavior,
which is not per se illegal, was present during ENI’s sticky pricing period but not before or afterwards
then the competitors’pricing behavior, and especially the level of price alignment, should di¤er across
time. If during the collusive period the market leader acted as cartel leader, then within a few
days after his price changes the followers should change their price as well and align. To show that
competitors’alignment followed market leader’s price changes I will construct two variables alignmentt
and P changeEnit . The …rst variables measure the number of competitors charging exactly the same
price as the market leader at time t. As there are eight competitors, alignmentt is a count variable
2 (0; :::; 8) as at most every competitor will charge the leader’s price. Using alignment has the bene…t
that with only one variable we can detect both the timing and the amount of competitors’ price
changes. The downside of this parallel pricing measure is that this variable will miss some degree of
coordination, as a competitor charging a price a millicent di¤erent from the leader would not count
as aligned. In addition as there were fewer price changes during the sticky pricing period we have less
days with price changes. For this reason we would expect the coe¢ cient to be rather low.
    The other variable, P changeEnit is a dummy variable indicating days where ENI changed its price
and 0 when no price change occurred.

                                                   1;   if   pEN I;t =
                                                                     6 0
                               P changeEnit =
                                                   0;   if   pEN I;t = 0
    Before running the regressions some preliminary …ndings are presented to show the signi…cant
di¤erence between alignment in various periods. In Figure 3 alignment is divided in two periods,
when the sticky pricing policy was active and when it wasn’t. As can be seen the distribution of
aligned competitors is signi…cantly di¤erent for the two periods. During the sticky pricing period
signi…cantly more competitors, on average 3, charged exactly the same price as the market leader.

                                                  10
Figure 3

                                          All peri ods                            Sticky pricing

                           .6
                           .4
               Alignment
                           .2
                           0

                                0     2        4       6          8       0       2     4      6   8

                                                                  Histogram
                                                                  Kernel Density

    The same analysis can be performed econometrically with two sets of regressions, in the …rst only
period dummy variables will be included. These show if the average number of aligned competitors
changed signi…cantly over time similarly as in the distributional graph, while the second regression will
include ENI’s price changes as exogenous variable and relate current and lagged ENI’s price changes
to the number of aligned competitors. This regression model will show the dynamics of competitors
alignment after a leader’s price change
    The …rst speci…cation is:
                                                                 n
                                                                 X
                                    alignmentt =         0   +         r dummyperiodr   + ut
                                                                 r=1

   In the second regression the leader’s price changes with lags is added. The interaction between
time periods and ENI’s price changes will capture whether during the collusive period ENI’s price
changes caused the competitors to align their prices to ENI. If this is the case we should observe that
within a few days of a price change by the leader, the alignmentt variable signi…cantly increases,
meaning that competitors changed their prices and aligned to ENI.
                                               n
                                               X        XL
                    alignmentt =          0+         r (   P changeEnit       l    dummyperiodr ) + ut
                                               r=1     l=0

   In every regression the omitted dummy variable is the 10 month time period before the pricing
change. A negative binomial regression model is used to estimate the set of coe¢ cients, as alignment

                                                                  11
is a count variable. In Table 3 the results of the two regression models, with the marginal e¤ect
computed at the mean, are presented. While over the whole period, excluding the sticky pricing one,
on average 1 competitor has same price as the leader, this number increases to 3 during the sticky
pricing period and then decreases to .6 during the Antitrust investigation. This shows a clear and
signi…cant break in the alignment relation over time.
    In addition to the mean alignment over time it is very interesting to capture the dynamic e¤ect of
a leader’s price change on followers’alignment. When P chnageEni is added with lags we see that the
interaction with the sticky pricing dummy is negative and signi…cant on the day of the change and
then positive subsequently. This clearly indicates that competitors’ reactions were not independent
of the leader’s price changes during this period. On days with price changes the average alignment
decreased, because competitors kept sticky prices. After observing the leader’s move they reacted
with 1 to 3 days lag and aligned their prices to the leader’s and thus alignment grew. This shows how
the followers’pricing was not independent over the sticky pricing period, while in the other periods
most of the alignment lags are highly insigni…cant.

                                              [Table 3]

4.2     E¤ects
While the previous section focused on the pricing mechanism that was adopted by the competitors
after ENI’s pricing change, this section investigates the consequences of such behavior on prices and
margins. Even though pro…ts are the most important aim for …rms, this analysis does not take into
account other potential bene…ts of sticky pricing such as the reduction in monitoring and menu costs.
    In order to model the dynamic processes that describe the pricing-pro…tability relationship I use
an error correction model (ECM) following Engle and Granger (1987) and Stock (1987). The ultimate
goal of this analysis is to show that oligopolistic …rms can in‡uence the market outcome trough their
behavior and earn extrapro…ts due to a conscious (but tacit) price coordination. The data used are
daily industry-average prices without taxes of the nine companies over the period 1st January 2004
to 31st December 2008.
    The three main variables used in this section are price, platts and margins, de…ned as the di¤er-
ence between the …rst two variables. Performing a Dickey–Fuller test of prices and cost, platts, the
nonstationarity cannot be rejected and thus I will use …rst di¤erences of prices and platts throughout
the rest of the analysis. On the other side a Dickey–Fuller test on margins rejects nonstationarity and
thus I will use the level of margins rather than the …rst di¤erence.
    To indicate the di¤erent time periods a series of dummy variables will be used, see Table 2 for a
description, along with their interaction with the platts. While the dummy variable will show whether
in that particular period prices and margins were higher or lower than in the pre policy period, the
interaction terms will control for di¤erent level of costs and prices.

4.2.1   Prices & Margins
In the …rst speci…cation I focus on prices. The main di¤erence between this speci…cation and the one
on margins is the use of the lagged long run relation between costs and prices which I include here but
not in the next regression. The reason for this is that prices and costs are cointegrated, while their
di¤erence, margins, isn’t. Using the lagged error term, which can be estimated superconsistently, adds
the long run dependency structure to the regression model while at the same time allowing for short
run deviations. To proceed with the estimation I will apply a two-step approach.

                                                  12
First the long run relationship between prices and costs implied in the error correction term is
estimated from:

                                            P ricet =       0   +    1 P lattst   +    t

   In the second step the lagged error term,               t 1,     will be added to the ECM regression model. The
…nal equation to be estimated is:

         P ricet   =
                                 n
                                 X                                  n
                                                                    X        L
                                                                             X
                         0   +            r dummyperiodr        +         r(   P lattst           l   dummyperiodr ) +
                                 r=1                                r=1        l=1
                       n
                       X            L
                                    X                                                L
                                                                                     X                     L
                                                                                                           X
                               r(     P ricet       l   dummyperiodr ) +                   r P ricer +           r P lattsr
                       r=1          l=1                                              r=1                   r=1

                             t 1   + ut

    The advantage of the ECM is that while the regression model allows for short term deviations, it
controls for the long run relation between prices and costs. The only concern now is the endogeneity
of costs. This might arise if shocks on prices impact future platts changes. In line with most of studies
on dynamic pricing I will estimate the coe¢ cients using OLS. The main reason for this is that costs,
the platts, is determined on the international market and follows the cost of crude oil.
    In the second speci…cation I focus on margins. The gasoline market has a simple and consistent
measure of pro…tability, margins. With this speci…cation we can immediately relate the pricing be-
havior to the industry’s pro…tability. The reason for this is that gasoline is a homogeneous good and
the demand is inelastic in the short term. Thus we expect that an increase in margins re‡ects an
increase in pro…ts.
    There are two econometric di¤erences with respect to the previous model. First, levels of margins
are used as a Dickey–Fuller test on margins rejects nonstationarity. Second, the lagged long run
relation is omitted. Similarly as before lagged prices and costs will be used in order not to omit
relevant information to the equation. The price and platts lag length chosen for both regression
models is 14 days and was chosen using the SBIC criterion, other lag length criteria as AIC yielded
similar results.

      M argint     =
                               n
                               X                                    n
                                                                    X        XL

                        0+               r dummyperiodr +                 r (   P lattst      l       dummyperiodr +
                               r=1                                  r=1     l=1
                       n
                       X           L
                                   X                                                 n
                                                                                     X                     n
                                                                                                           X
                              r(     P ricet    l       dummyperiodr ) +                   r P ricer   +         r P lattsr   + ut
                       r=1         l=1                                               r=1                   r=1

   The results of regression 1 and 2 are presented in Table 4. In line with the collusive hypothesis in
both speci…cations the prices and margins were higher during the sticky leadership pricing.

                                                          [Table 4]

   During Sticky industry-average prices and margins rose controlling for costs, while during Antitrust
they went back to pre-policy levels or even lower in the case of margins. The announcement of an in-
vestigation did not only have a signi…cant impact on the …rst moment of prices, but also on the second
moment, price variance, as is evident from Figure 4. In addition to lower average margins the price

                                                                13
dispersion rose signi…cantly indicating a break in the coordination mechanism. The di¤erent levels of
price dispersions are plotted in Figure 4. The lines show that price dispersion was highest after the
announcement a common …nding in periods when cartels break down as shown by Abrantes-Metz et
al. (2006).

                                              Figure 4

                                          Price Dispersion
               400  300
       Kernel Density
          200  100
               0

                          0   .005               .01               .015               .02

                                     Sticky pricing             All other periods
                                     Antitrust Inv.

   Given the evidence on prices and margins and the impact of the investigation on price dispersion
there is very strong evidence that …rms were tacitly colluding using sticky and leadership pricing and
that the investigation by the Antitrust broke this conscious parallel pricing.

5    Conclusion
This paper examines dynamic pricing in the Italian wholesale gasoline market. The analysis highlights
the importance of endogenous price commitment in tacit collusion. Through its behavior the market
leader was able to change the pricing of competitors and coordinate prices. Sticky and leadership
pricing were used as facilitating practice by …rms to consciously coordinate on the leader’s (focal)
price and resulted in higher margins. This is the …rst paper to show that the kinked demand (sticky
pricing) equilibrium in Maskin and Tirole (1988) can emerge endogenously in an oligopolistic market
through speci…c …rm behavior as opposed to exogenous short run menu costs.
    On the 6th of October 2004 the CEO of ENI, Mr. Mincato, the market leader in Italian gasoline,
publicly announced a price policy change that consisted in rigid pricing and slow adjustment to the
major cost factor, Platt’s Cif Med (Platts), which is the wholesale Mediterranean price of gasoline.
The time lag between price changes increased from 6 days before the pricing policy change to 19 days
after the new pricing was introduced. In addition the average absolute change was 4.5% instead of

                                                 14
1% in days with price changes. To show how the pricing behavior of competitors changed during the
sticky pricing period I related competitors’alignment to the market leader’s price changes. The results
show that the competitors changed prices 1-3 days after the leader did and aligned to the leaders’
price. This relation between the leader’s and competitors’ pricing behavior occurs only during the
sticky pricing period. After the Antitrust investigation was announced on the 23rd of January 2007
the sticky pricing ended, margins fell and the price dispersion signi…cantly increased.
    The evidence presented here strongly supports the hypothesis that the announcement of a new
pricing policy could have been used as a facilitating practice for aligned and higher prices. On the
contrary, the Antitrust investigation which followed the buyer’s complaint caused a break in the cartel
due to an uncoordinated reaction by cartel members. After the policy change prices were more aligned
than before and after a few days the leader had changed its price the followers aligned to the leader’s
price. The same relation can be found in margins, which increased after ENI’s policy change and
decreased after the investigation was announced.
    These …ndings pose a serious problem to the e¤ectiveness of Competition Policy. How can "e¤ective
competition" be reconciled with tacit collusive pricing? My results show that even in the absence of
communication, price commitment is su¢ cient to coordinate market conduct. Tacit collusion seems
to be a natural way in which oligopolistic markets work, how to stop this without limiting the freedom
of price setting remains an open question.

                                                  15
6     Appendix
                                          Table 3

Variable           Alignment   Marginal E¤ects        Variable        Alignment   Marginal E¤ects
                        (1)           (2)                                 (3)           (4)
EcP                   -0.194        -0.305        Sticky               1.005***      1.769***
                     (0.156)       (0.235)                             (0.0766)       (0.150)
L1_EcP              -0.360**      -0.546**        Investigation An.   -0.585***     -0.758***
                     (0.161)       (0.225)                              (0.132)       (0.134)
L2_EcP             -0.455***     -0.677***        Remedies An.          -0.0510       -0.0809
                     (0.163)       (0.218)                              (0.118)       (0.183)
L3_EcP             -0.565***     -0.821***        Post Investig.        0.203**       0.350**
                     (0.168)       (0.216)                             (0.0903)       (0.165)
L4_EcP             -0.518***     -0.759***
                     (0.169)       (0.220)
EcP_Sticky         -0.678***     -0.833***
                     (0.217)       (0.193)
L1_EcP_Sticky        0.438**        0.896*
                     (0.200)       (0.499)
L2_EcP_Sticky       0.898***      2.333***
                     (0.200)       (0.770)
L3_EcP_Sticky       1.048***      2.957***
                     (0.202)       (0.896)
L4_EcP_Sticky       1.009***      2.785***
                     (0.202)       (0.864)
EcP_Antitrust        -0.352*       -0.505*
                     (0.209)       (0.259)
L1_EcP_Antitrust      0.0206        0.0343
                     (0.213)       (0.359)
L2_EcP_Antitrust     -0.0669        -0.108
                     (0.216)       (0.337)
L3_EcP_Antitrust       0.268         0.497
                     (0.220)       (0.455)
L4_EcP_Antitrust       0.117         0.203
                     (0.218)       (0.398)
EcP_Post Inv.        0.0280         0.0468
                     (0.193)       (0.327)
L1_EcP_Post Inv.     0.332*          0.632
                     (0.200)       (0.434)
L2_EcP_Post Inv.       0.198         0.355
                     (0.200)       (0.389)
L3_EcP_Post Inv.       0.333         0.632
                     (0.204)       (0.444)
L4_EcP_Post Inv.       0.314         0.592
                     (0.202)       (0.431)
Constant            0.812***                                          -0.863***
                    (0.0321)                                           (0.0928)

Observations         1,822          1,822              1,827            1,827          1,827
                                Standard errors in parentheses
                               *** p
Table 4
                   Variables                                 Price        Margin
                                                              (1)           (2)
                   Sticky                                0.00224***     0.0411***
                                                         (0.000756)     (0.00386)
                   LD(1/14)Sticky_Price                        X             X
                   LD(1/14)Sticky_Platts                       X             X
                   Investigation Announcement              4.64e-05    -0.00360***
                                                         (0.000262)     (0.00140)
                   LD(1/14)Investigation An._Price             X             X
                   LD(1/14)Investigation An._Platts            X             X
                   Remedies An.                         0.000947***     0.0203***
                                                         (0.000293)     (0.00141)
                   LD(1/14)Remedies An._Price                  X             X
                   LD(1/14)Remedies An._Platts                 X             X
                   Post Investigation                    0.00127***     0.0258***
                                                         (0.000264)     (0.00112)
                   LD(1/14)Post Inv._Price                     X             X
                   LD(1/14)Post Inv._Platts                    X             X
                   L.Error Correction                    -0.0470***
                                                          (0.00555)
                   Constant                             -0.000439***    0.142***
                                                         (0.000169)    (0.000840)

                   Observations                            1,811          1,811
                   R-squared                               0.337          0.794
                                   Standard errors in parentheses
                                  *** p
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                                                 18
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