University of the Basque Country

Faculty of Economics and Business StudiesDepartment of Economic Analysis II

Master in Economics:Empirical Applications and Policies

Competition in the Spanish Gasoline MarketCollusion and Market Dominance

MASTER THESIS

Submitted on September 14th, 2012

Submitted by

Konrad Benze

konrad.benze@gmail.com

Advisors

María Paz Espinosa, Ph.D.

Aitor Ciarreta Antuñano, Ph.D.

University of the Basque Country

Faculty of Economics and Business StudiesDepartment of Economic Analysis II

Chair of Energy Markets

Contents

Contents I

List of Figures III

List of Tables IV

List of Abbreviations V

1 Introduction and Literature 1

1.1 The Spanish Gasoline Market . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1

1.1.1 Market Structure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1

1.1.2 Price Policies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2

1.2 Comparison within the European Union . . . . . . . . . . . . . . . . . . . . . . . . . 2

1.2.1 Overview Gasoline 95 EU . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2

1.2.2 Overview EU Gross Prices Gasoline 95 . . . . . . . . . . . . . . . . . . . . . . 4

1.2.3 Overview EU Net Prices Gasoline 95 . . . . . . . . . . . . . . . . . . . . . . . 5

1.2.4 Overview Taxes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6

1.2.5 Di�erences in Net Prices . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8

1.3 Motivation and Literature . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8

1.3.1 The Concept of “Rockets and Feathers” . . . . . . . . . . . . . . . . . . . . . 8

1.3.2 The “Usual Approach” . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9

1.3.3 Searching for Collusion and Market Dominance . . . . . . . . . . . . . . . . . 10

2 Data, Taxation and Market Concentration 10

2.1 Data Source . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10

2.2 Taxes per Region in Spain . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12

2.3 Market Concentration by Brand . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13

2.3.1 Market Shares per Brand . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14

2.3.2 Herfindahl Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15

2.4 Price Pattern: Average Daily Prices . . . . . . . . . . . . . . . . . . . . . . . . . . . 15

I

3 Variance Filter Approach 16

3.1 Empirical Strategy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16

3.2 Results of the Samples . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18

3.2.1 Average Net Prices and Standard Deviation - Plot . . . . . . . . . . . . . . . 18

3.2.2 Results Overall Sample . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18

3.2.3 Results Quadrant IV . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20

3.3 Conclusions from the Variance Filter Approach . . . . . . . . . . . . . . . . . . . . . 21

4 Market Concentration in the Province of Biscay 22

4.1 Service Stations in the Province of Biscay . . . . . . . . . . . . . . . . . . . . . . . . 22

4.2 The Data Base: Density of Service Station Grid in the Province of Biscay . . . . . . 23

4.3 Methodology and Analysis of Market Dominance and Concentration . . . . . . . . . 24

4.4 Conclusion regarding Market Dominance and Concentration in the Province of Biscay 27

5 Conclusion 28

References 29

6 Annex: Tables and Graphs A

II

List of Figures

1 Consumer Gross Price of Gasoline 95 Inclusive of Duties and Taxes . . . . . . . . . . 3

2 EU Comparison of Average Gross Prices from 21/05/2012 until 18/06/2012 . . . . . 4

3 EU Comparison of Average Net Prices from 21/05/2012 until 18/06/2012 . . . . . . 5

4 Taxation Share in the End Consumer Price For Gasoline 95 and Diesel Oil . . . . . . 7

5 Data Overview - National Service Station Grid . . . . . . . . . . . . . . . . . . . . . 11

6 Gross and Net Average Daily Prices . . . . . . . . . . . . . . . . . . . . . . . . . . . 16

7 Net Prices - Standard Deviation and Average Value . . . . . . . . . . . . . . . . . . . 18

8 Service Stations for Gasoline 95 operating in the Province of Biscay . . . . . . . . . 22

9 Gross Price - Standard Deviation and Average Value . . . . . . . . . . . . . . . . . . A

III

List of Tables

1 Net Average Price Di�erence of the Spanish Price of Gasoline 95 . . . . . . . . . . . 8

2 Tax Rates per Spanish Region - from Gross to Net Price . . . . . . . . . . . . . . . . 12

3 Market Shares per Brand - Overall Sample . . . . . . . . . . . . . . . . . . . . . . . 14

4 Pooled Market Shares of the 4 Biggest Brands . . . . . . . . . . . . . . . . . . . . . . 14

5 Net Price, Overall Sample - Descriptive Statistics by Region . . . . . . . . . . . . . . 19

6 Net Price, Overall Sample - Descriptive Statistics by Brand . . . . . . . . . . . . . . 19

7 Net Price, Quadrant IV - Descriptive Statistics by Region . . . . . . . . . . . . . . . 20

8 Net Price, Quadrant IV - Descriptive Statistics by Brand . . . . . . . . . . . . . . . 21

9 Comparison of the Province of Biscay with the Overall Sample . . . . . . . . . . . . 23

10 Distance to the Closest Competing S.S. . . . . . . . . . . . . . . . . . . . . . . . . . 23

11 Econometrical Results for Eq.(2), Eq.(3) and Eq.(4) . . . . . . . . . . . . . . . . . . 25

12 Econometrical Results for Eq.(5), Eq.(6) and Eq.(7) . . . . . . . . . . . . . . . . . . 27

13 Gross Price, Overall Sample - Descriptive Statistics by Region . . . . . . . . . . . . . B

14 Gross Price, Overall Sample - Descriptive Statistics by Brand . . . . . . . . . . . . . B

15 Gross Price, Quadrant IV - Descriptive Statistics by Region . . . . . . . . . . . . . . C

16 Gross Price, Quadrant IV - Descriptive Statistics by Brand . . . . . . . . . . . . . . C

IV

List of Abbreviations

PA

. . . . . . . . . . . Average Price

S.D.A

. . . . . . . Average Standard Deviation

B.C. . . . . . . . . . Basque Country

BP . . . . . . . . . . Public Limited Company, British Petroleum, now Beyond Petroleum

CAMPSA . . . Compañía Arrendataria del Monopolio de Petróleos, SA

CNC . . . . . . . . Comisión Nacional de Competencia/Spanish National Commission of Competition

CPI . . . . . . . . . Consumer Price Index

CR . . . . . . . . . . Concentration Ratio

EC EU 17 . . . European Community as the Euro Area with its 17 EU member states that haveadopted the Euro as their common currency

EC EU 27 . . . European Community as the European Union with its current 27 member states

ECM . . . . . . . . Error Correction Model

Eq. . . . . . . . . . . Equation

EU . . . . . . . . . . European Union

IE . . . . . . . . . . . Impuesto Especial o Impuesto de Hidrocarburos

IVA . . . . . . . . . Impuesto al Valor Agregado

IVMa . . . . . . . . Impuesto Venta Minoristas Autonómico

IVMe . . . . . . . . Impuesto Venta Minoristas Estatal

Q IV . . . . . . . . Quadrant IV

S.S. . . . . . . . . . . Service Station

U.S. . . . . . . . . . United States of America

VAT . . . . . . . . . Value Added Tax

V

1 Introduction and Literature

This paper supplies a general overview regarding the state of competition in the Spanish gasolinemarket. Focus is laid on gasoline 95. Two di�erent empirical approaches are used.

1.1 The Spanish Gasoline Market

1.1.1 Market Structure

During the greater time of the 20th century there was a state-monopoly on the distribution andmarketing of all oil products including gasoline in Spain. The monopoly was operated by a pri-vate concessionaire, which still operates nowadays – “Compañía Arrendataria del Monopolio dePetróleos, SA” or better known as CAMPSA. Under the long years of dictatorship 1, Spanish mar-kets at large were protected by high tari�s, closed markets and general restrictions to free trade[Closa and Heywood, 2004].

Public and private Spanish refiners supplied the monopoly with oil products such as gasoline.CAMPSA, refiners and the government negotiated over quantities and prices at which the oil prod-ucts were delivered to the markets. The overall processing capacity of the refiners and the supplyof crude oil both were controlled by the state [Contín-Pilart et al., 2009].

There were several reasons for Spain to reschedule and liberalize its oil industry during the endof the 1970s. In the Spanish oil market there were not only internal problems such as an excesscapacity, deficient yields of oil products and the inability to recapture production costs, but alsoexternal issues such as the anticipated entry of Spain into the EU with its clear competition rulesand requirements. The latter cause led to a sort of preparation phase within the Spanish oil industryto comply with the necessities and constrains of a competitive market [Correljé, 1990].

With the entry of Spain into the EU (1986), private domestic and foreign operators were allowedto enter the three di�erent trade segments contained in the oil industry consisting of refining,distribution and retail. The final result was the dismantlement of the state monopoly by 1993 afterthe “transitional period”, which was overviewed by the refiners, the Spanish government and theEuropean Commission [Correljé, 1994].

The transformation process of the Spanish oil industry was accompanied by several main adjust-ments. First, of eight refining companies in the beginning of the 1970s, only three remained atthe dismantlement stage. The intention was an e�ciency increase and the adjustment of supplyto the market’s demand. Second, a “national champion” evolved from several public companiesand was structured according to the model of other international oil companies – Repsol. Repsolwas privatized step by step and was renamed as Repsol YPF when it took over the Argentineancompany YPF2.

1Miguel Primo de Rivera (1923-1930) and Francisco Franco (1936-1975)2see media discussion lately, e.g. [TheEconomist, 2012]

1

The respectively large international oil companies Elf Aquitaine (Elf) and British Petroleum (BP)acquired the Spanish retailers Cepsa and Petromed in 1991, integrating them into the productionchains of their vast international grid and giving Spain access to crude oil supply from internationalmarkets [Contín-Pilart et al., 2009].

By 1992 the refineries took over the general retail network. Distribution was handled from now onby CAMPSA, turning the state owned company into a private transport company with its sharesspread over Spanish-based refiners and Shell [Contín et al., 1999].

1.1.2 Price Policies

The former administratively set prices for gasoline were regulated by a system of price ceilingsfrom 1990 onwards but supposedly free to market influences. The intention was to “protect” finalconsumers from exaggerated free market prices. Interestingly, the price ceiling scheme remainedvalid until 1998. [Contín-Pilart et al., 2009] found prices for unleaded gasoline below (and notequal to) the o�cial ceiling prices only from September 1998 to October 1998.

[Contín et al., 2000] highlight the close cooperation between the refiners on the one side and thegovernment on the other side in relation to the gasoline price setting. With the end of the ceilingprice scheme, the Spanish government integrated the price formation of gasoline into its policyto control the Consumer Price Index (CPI) by asking oil operators to collaborate. Due to anunexpected sharp increase of the CPI in the first quarter of 1999 the government pled Repsol,Cepsa-Elf and BP not to pass the full increase of the spot prices for crude oil into the Spanishgasoline prices. With the main operators at hand the rest followed. Since the possible retail marginwas endangered, only compensation in form of a slower rate of decrease in end customer retail priceswith simultaneous faster decrease of the crude oil spot prices made market operators eager to acceptthe deal. This behavior was reflected in a comparison of annual average gasoline prices before taxesin Spain and the EU for the periods 1999-2000 and 2001-2003 [Contín-Pilart et al., 2009].

1.2 Comparison within the European Union

1.2.1 Overview Gasoline 95 EU

Starting with an overview, a map of all EU member countries and corresponding gross prices forgasoline 95 is given. As shown below, Spain’s average gasoline price including taxes and duties islocated on the lower EU average. Since one would expect a higher degree of competition correlatingwith a lower gasoline price, the question arises whether Spain really faces a lack of competition inthe gasoline market.

The graph and corresponding data stems from the European Commission for Energy and is pub-lished on a weekly basis in the Oil Bulletin. All EU member countries are asked to report theiraverage gross and net prices for gasoline and other fuels every Monday of the corresponding week.

2

The data below was gathered on June 11th, 2012 which lies in between the time span of data usedin this study further below.

Figure 1: Consumer Gross Price of Gasoline 95 Inclusive of Duties and TaxesMeasured on: 11/06/2012, EU Weighted Average 1,59 Ä/liter

Source: European Commission for Energy, Oil Bulletin 11/06/2012

The graph makes it clear that the average gasoline price is higher in central and northern Europeancountries with the exception of Portugal, Italy and Greece. Interestingly, Spain ranges on the sameaverage as eastern European countries. But most importantly, one must keep in mind that grossprices are shown. To clarify the di�erence of gross to net prices, explanatory figures are addedlisting all the EU member countries by the amount of their average prices.

3

1.2.2 Overview EU Gross Prices Gasoline 95

Again, the data used in the following two listings stems from the Oil Bulletin of the EuropeanCommission for Energy. But this time the average values reported on Mondays from May 21st,2012 until June 18th, 2012 are shown. Once more, this time period corresponds to the one used inthe studies below.

Interestingly, the highest gross average price reported during this time span is not one from central ornorthern European countries but rather from Italy ranking first. France and Germany are locatedaround the average EU values. Importantly and once again Spain’s gross average price lies waybelow the average, indicating a su�cient level of competition on the Spanish gasoline market.

1,200% 1,300% 1,400% 1,500% 1,600% 1,700% 1,800%

Bulgaria%

Romania%

Cyprus%

Poland%

Estonia%

Luxembourg%

Latvia%

Spain%

Lithuania%

Slovenia%

Austria%

Hungary%

Czech%Republic%

Malta%

Slovakia%

France%

EC%EU%27%

Germany%

EC%Euro%Area%17%

Belgium%

Portugal%

Sweden%

Finland%

United%Kingdom%

Denmark%

Ireland%

Greece%

Netherlands%

Italy%

Gross%Average%Price%in%[€/liter]%

Gasoline%95.%EU%Comparison.%Gross%Average%Prices.%21/05/2012%@%18/06/2012%

Figure 2: EU Comparison of Average Gross Prices from 21/05/2012 until 18/06/2012Data Source: European Commission for Energy, Oil Bulletin 21/05/2012 - 18/06/2012

Own elaboration

After analyzing the EU price patterns with taxes and duties included, it is crucial to repeat thisstudy without the disguising e�ect of duties and taxes corresponding only to the particular EUcountry. It is impossible to compare gasoline price between countries on a gross price averagesince the actual price charged for gasoline is reflected exclusively by the net price. All statementsregarding the degree of competition done so far are therefore misleading and inaccurate. They aremerely an indication of the di�erences in end consumer prices between EU member countries. Fromnow on prices will be depicted as net prices only, leaving duties and taxes out of consideration.

4

1.2.3 Overview EU Net Prices Gasoline 95

Focusing on net average prices only for the same time period, the picture from above changes.Without taxes and duties, the prices shown below may now indicate the level of competition in theparticular market since the net price reflects the cost of production on the one side but also the oilindustries’ margins on the other. A higher net price might therefore reflect a higher margin andhence a lower degree of competition following microeconomic theories.

0,650% 0,670% 0,690% 0,710% 0,730% 0,750% 0,770% 0,790% 0,810%

Bulgaria%Austria%

United%Kingdom%Romania%France%

Slovenia%Sweden%Estonia%

Czech%Republic%Latvia%Poland%

EC%Euro%Area%17%Germany%Hungary%Greece%

EC%EU%27%Netherlands%

Lithuania%Finland%Spain%

Slovakia%Luxembourg%

Belgium%Portugal%Cyprus%Italy%

Denmark%Ireland%Malta%

Net$Average$Price$in$[€/liter]$

Gasoline$95.$EU$Comparison.$Net$Average$Prices.$21/05/2012$A$18/06/2012$

Figure 3: EU Comparison of Average Net Prices from 21/05/2012 until 18/06/2012Data Source: European Commission for Energy, Oil Bulletin 21/05/2012 - 18/06/2012

Own elaboration

Italy’s net average price still remains high but not as high as the one of the first listed country withan excessively high net price. Malta seems to have a very low degree of taxation but a very highnet price. This might be reflected through the relatively higher production and transportation costsMalta faces as an island country, but also by the higher margins the oil companies might acquire.

Germany’s net average price again ranges around the EU average values while the relatively lownet average price in France seems to reflect a rather competitive market.

The important and interesting case here is Spain’s net average price. This time it ranges clearlyabove the EU average indicating an uncompetitive gasoline market since production and transporta-tion costs shouldn’t be so di�erent from other EU continental countries. A higher net average pricemight therefore indeed reflect higher margins for the Spanish oil industry. This di�erent pictureresults from the relatively low degree of taxation on Spanish gasoline 95. Hence, an indication foran insu�cient level of competition in the Spanish gasoline market is found. This initial evidence

5

for a low level of competition will be further discussed below and analyzed by di�erent scientificapproaches.

Another interesting case and recently frequently discussed is the gasoline price in Greece. Netaverage prices seem to gather around the EU average while gross average prices were far higherthan the EU average. High duties and taxes in Greece are the answer to the resulting di�erence.Following the media lately regarding the European debt crisis, Greece seems to have problemscollecting taxes from its citizens in reliable ways. Therefore the Greek government appears to beusing taxes and duties on gasoline as a dependable taxation scheme, not giving any opportunity toevade tax payments since the final gross price has to be paid at the service station.

1.2.4 Overview Taxes

The di�erences in the amount of taxes and duties in all of the EU member countries are illustratedin the following and last map of Europe. Once again the illustration and data is taken from the OilBulletin. As above, all data was gathered on June 11th, 2012. The share of the tax component inthe final retail or gross price for each particular EU member country is constituted in percentagevalues.

Indeed Spain is located in the below average group with a value of 49% in respect to the EU averageof 56% for gasoline 95. Greece and Italy lie above the average value with a high tax componentwhile Portugal’s tax share is slightly below the average with a value of 55%. It seems that Portugalhas quite high net prices as well, reflecting an uncompetitive market not only in Spain but on thewhole Iberian Peninsula3.

Taking into account that there is a relatively inelastic nature of demand for gasoline at least inthe short run, the tax component in the final retail price for gasoline is an e�ective source ofrevenue for governments. In the long run or a longer time span in general, however, the demandfor gasoline might become more elastic considering that citizens consume less gasoline as the priceincreases further. They adjust their consumption of gas or switch to more fuel-e�cient cars, publictransportation, car sharing, cycling or other means of transportation.

Hence many EU member countries such as France, the United Kingdom or Germany use the taxcomponent of the final price not only for tax revenues but also to reduce tra�c, pollution and thedependence on fossil fuels and to support by these means new technologies and products which aresubstitutes to fossil fuels.

3Recently the Portuguese Galp Energia Group is following an aggressive expansion strategy in the Spanish gasolinemarket, already controlling over 50% in the Portuguese gasoline market and all the refining capacities there.

6

Figure 4: Taxation Share in the End Consumer Price For Gasoline 95 and Diesel OilMeasured on: 11/06/2012; EU Weighted Average: 56% (Gasoline 95), 49% (Diesel Oil)

Source: European Commission for Energy, Oil Bulletin 11/06/2012

The next table emphasizes the Spanish gasoline net price, relating it to the gasoline price in Ger-many, France and to the European averages. Once more the descriptive importance of the netgasoline price regarding production and transportation costs but also the national oil companies’margins must be taken into account.

7

1.2.5 Di�erences in Net Prices

The data stems from a report published by Spain’s antitrust authority named National Commissionof Competition or “Comisión Nacional de la Competencia” (CNC) in June 2012 [CNC, 2012]. Inthis report, the CNC concludes that the automotive fuel distribution market has an insu�cient levelof e�ective competition and urges the implementation of the recommendations issued by the CNCin a report published in March 2009. The data used for this yearly evaluation displayed below from2005 until 2011 was delivered again by the Oil Bulletin of the European Commission for Energy.

Here, the foreign net average prices for gasoline 95 serves as a benchmark and only the di�erencesof the Spanish average net price are shown. More detailed, the net average price di�erence of theSpanish net price for gasoline 95 in respect to the net average prices of Germany, France, the EuroArea and the European Union are displayed. All di�erences are denoted as cÄ per liter.

Year Germany France EC Euro Area 17 EC EU 272005 2,87 4,44 0,38 1,072006 3,31 3,87 0,51 1,282007 2,98 3,44 0,77 1,342008 5,18 3,22 1,09 2,002009 3,77 3,88 1,73 3,162010 4,05 3,85 2,00 3,012011 4,53 3,23 2,01 3,11

Table 1: Net Average Price Di�erence of the Spanish Price of Gasoline 95all values in [cÄ/liter]Source: [CNC, 2012]

Data Source: European Commission for Energy, Oil Bulletin

First and foremost, all price di�erences of the Spanish net average gasoline price are positive,indicating a higher than average gasoline price in Spain from 2005 onwards up to 2011 in comparisonto all the other gasoline markets. Furthermore, a development of an increasingly positive di�erencein respect to the other markets is shown. This pattern actually reflects a decrease of competitionin the Spanish market while relating it to the other European markets, taking into account thatproduction and transportation costs increased for all countries considered. Contrary to the aimof promoting more competition in the Spanish gasoline market, these figures indicate a deficient,uncompetitive industry sector.

1.3 Motivation and Literature

1.3.1 The Concept of “Rockets and Feathers”

The pricing behavior aforementioned in part 1.1.2 Price Policies is partly captured by the conceptof “rockets and feathers” examined by [Bacon, 1991] for the UK whereby in Spain the rocket israther substituted by a hot-air balloon with less hot-air the stronger the inflationary e�ects are.

8

The feather, in reverse, stays in place as a compensational measure. [Bacon, 1991] describes theconcept of “rockets and feathers” the following way: While faced with cost increases, oil companiesrapidly adjusted their prices upwards. But when faced with cost decreases, they adjusted their pricesdownwards more slowly, hereby permitting a temporary level of high profits with the consequentharm to the end consumer. This asymmetrical pattern of gasoline retail price adjustment to gasolinewholesale spot price changes may be found with the help of descriptive and graphical analysis ofweekly or daily data. Importantly, the relevant cost for gasoline retailers is the spot price of gasolineat the spot market in Rotterdam, rather than the price of crude oil. [Bacon, 1991] considers it tobe the best available measure of the marginal cost since it is related to the transfer price betweenthe refining and the selling division for refiners who operate their own service stations. Using theeconometrical procedure of a quadratic quantity adjustment function with fortnightly data for theUK from 1982 to 1989, [Bacon, 1991] finds evidence that the upward adjustment process of retailgasoline prices was slightly faster than was the case when costs fell.

Besides the work done by Bacon there is sizable literature evaluating empirical evidence in respectof such asymmetries.

1.3.2 The “Usual Approach”

The “usual approach” followed by many scientists evaluating competition, regulation, and pricesetting in retail gasoline markets is characterized by a benchmark and the actual net price. Asmentioned above, the wholesale spot price at the spot market in Rotterdam4 or an average netprice of a multitude of EU countries is usually set as benchmark while the net price for gasoline ofa particular country is compared over time. Econometrically, a multivariate error correction modelor ECM is used. [Contín-Pilart et al., 2009] take this approach to evaluate competition and pricingbehavior in the Spanish market. They arrive at the conclusion that after the abolishment of theceiling price regulation, gasoline retail prices increased at a slower rate than elsewhere, probablydue to the control for inflation as was mentioned earlier. Furthermore, retail margins were identifiedto be well above the European average ones in the first years after the abolishment of the statemonopoly in Spain.

[Kirchgässner and Kuebler, 1992] evaluate the gasoline market in Germany over the time period1972-1989 using monthly data. With two periods at hand, before and after January 1980, theyconsider the response of both consumer and producer wholesale gasoline prices to spot price changesusing an ECM and finding a considerable short-run asymmetry in the former period but not in thelatter.

[Johnson, 2002] investigates retail price responses in gasoline and diesel markets to wholesale pricechanges in fifteen U.S. cities using weekly data and an ECM approach. [Asplund et al., 2000] usean ECM method as well to analyze the Swedish gasoline market. Relating their work to Bacon’soriginal paper, [Galeotti et al., 2003] apply an ECM using monthly data to conduct an international

4as a key international market among others as New York for the U.S.

9

comparison of asymmetries in the transmission of shocks to crude oil prices onto the retail priceof gasoline among five European countries for a five-year time span. Far more researchers like[Bettendorf et al., 2003] follow these methods. [Bettendorf et al., 2003] explore the gasoline retailprice adjustments in the Dutch gasoline market to changes in the Rotterdam spot prices by applyingan asymmetric error correction model. In sum, there is vast literature on price (a)symmetry applyingECMs with sometimes contradictory results. From now on, the focus will be on two other approachesto gain new insights in the outcome of the process of deregulation and liberalization in Spain. Thefinal question remaining is: How far-reaching is the development when it comes to competition inthe Spanish gasoline market?

1.3.3 Searching for Collusion and Market Dominance

This work will follow two papers written by Juan Luis Jiménez and Jordi Perdiguero. In thefirst paper, [Jiménez and Perdiguero, 2009] analyze the price levels in the Spanish gasoline marketwith the basic hypothesis that in uncompetitive markets the prices are higher and the standarddeviation is lower. Using weekly retail gasoline price data from the ten biggest Spanish cities,they employ a variance filter. In their second paper, [Jiménez and Perdiguero, 2011b], the conceptsof accessibility and the firm’s pricing behavior in the regional retail market in Spain are linked.Using an outstanding database for the region Galicia in Spain the uncompetitive e�ect of marketdominance is proven econometrically. To summarize, the accessibility of own-brand gas stationscauses price increases while the accessibility of rival gas stations causes price reductions. Themethodologies are used again below, taking a di�erent data base and applying them to di�erentareas. Particularly the data base turned out to be a hard nut to crack.

2 Data, Taxation and Market Concentration

2.1 Data Source

The data sample used in this study stems from the Spanish Ministry of Industry, Energy andTourism or rather “Ministerio de Industria, Energía y Turismo” from now on called the Ministry.The Ministry provides an online application called Geoportal

5. The application provides dailydata of 8711 service stations (S.S.) o�ering unleaded gasoline with 95 octane in the time spanconsidered. All S.S. operators and their personnel responsible are obliged by law to send in theirprice settings of their selling point every Monday. Furthermore, every price change during the weekshould be reported as well. This data is collected and processed by the Ministry and providedto the public via the Geoportal application. In 2009 the Ministry was even rewarded for thisonline application by the European Commission as a finalist of the European eGovernment Awardin the subcategory of best practices in the use of information and communication technology inpublic service. Indeed this application provides a certain market transparency for final consumers

5Geoportal de Hidrocarburos: http://geoportal.mityc.es/hidrocarburos/

10

comparing prices and is quite unique. Only few countries such as Austria or Australia providesimilar tools for their citizens. Below all 8711 S.S. considered are displayed as they appear in theapplication. Not only the daily price but also the location of the sales point, the brand and theproducts sold are reported.

Figure 5: Data Overview - National Service Station Gridall 8711 Service Stations for Gasoline 95 operating in June 2012 in Spain

Source: Geoportal - Ministry of Industry, Energy and Tourism

Unfortunately there is a crucial drawback when it comes to using the data delivered for researchproposes. Only current data is available. No historical data is released even though the data waspublished at some point in time and must be available somewhere within the Ministry. Requestingthe data for scientific purposes the Ministry answers as follows.

La información que no aparece, bien porque ya no aparece, porque todavía no apareceo porque nunca ha aparecido ni va a aparecer, es confidencial y sólo se proporciona encaso de peticiones judiciales.

The information which doesn’t appear, doesn’t appear because it hasn’t appeared yet or because ithas never appeared and will never appear, is confidential and will only be handed out on judicialdemand. This is a clear answer and reason for a time-consuming, grossly ine�cient daily downloadtask in order to be able to collect a useful data sample for a certain span of time.

In this work, focus is laid on unleaded gasoline with 95 octane since it is characterized by domesticuse and a rather inelastic demand scheme at least in the short run. The data sample for gasoline95 was taken from May 21st, 2012 until June 21st, 2012 on a daily basis. Of the displayed 8711

11

S.S. only 8454 S.S. had delivered the necessary data. This means that 2.95% of all S.S. considereddidn’t report their prices. Excluding those stations with a significant number of missing valuescould lead to a sample bias. Since the low share of 2.95% S.S. doesn’t show any specific pattern,either in terms of brand or location, the final results shouldn’t be a�ected to a noticeable degree.The sample may be considered representative of the total population at least for the time spanconsidered. The S.S. of the Canary Islands, Ceuta and Melilla (410 S.S.) are not covered in thesample due to their special status when it comes to taxation6 and transportation costs. This yieldsa final sample of 8044 stations considered. Due to di�culties obtaining the data, the time periodis regretfully short. A longer time span would be desirable to gather more consistent results. Forinstance a time horizon of one year would have been preferred. Nevertheless, the data provides avaluable insight into the Spanish gasoline market and at some point in time the analysis was dueto meet the given deadline.

2.2 Taxes per Region in Spain

The aforementioned procedure to compare gasoline prices within the EU by considering net pricesis valid for Spain as well. There is a di�erence in taxation even between the Spanish regions. Facinggross retail prices delivered by the Ministry, the net prices per region may be attained discountingfour di�erent tax schemes in four steps displayed below.

No. Region 4. IVA 18% 3. IE [Ä/liter] 2. IVMe [Ä/liter] 1. IVMa [Ä/liter]1 Andalusia /1,18 -0,40069 -0,024 -0,0242 Aragon /1,18 -0,40069 -0,024 03 Asturias /1,18 -0,40069 -0,024 -0,0244 Balearic Islands /1,18 -0,40069 -0,024 -0,0485 Basque Country /1,18 -0,40069 -0,024 06 Cantabria /1,18 -0,40069 -0,024 -0,0487 Castile - La Mancha /1,18 -0,40069 -0,024 -0,0488 Castile and Leon /1,18 -0,40069 -0,024 -0,0489 Catalonia /1,18 -0,40069 -0,024 -0,04810 Extremadura /1,18 -0,40069 -0,024 -0,02411 Galicia /1,18 -0,40069 -0,024 -0,02412 Madrid /1,18 -0,40069 -0,024 -0,01713 Murcia /1,18 -0,40069 -0,024 -0,02414 Navarra /1,18 -0,40069 -0,024 015 Rioja /1,18 -0,40069 -0,024 016 Valencia /1,18 -0,40069 -0,024 -0,048

Table 2: Tax Rates per Spanish Region - from Gross to Net Price

Starting with the last tax applied, the relative value added tax (VAT) of 18% or “IVA”7 must be6VAT of only 7% on the Canary Islands instead of 21% on the mainland7published by the Ministry in “PRECIOS DE CARBURANTES Y COMBUSTIBLES DATOS DE JUNIO DE

2012”; in September 2012 the VAT was increased to 21% points by the current Spanish government due to thedelicate financial situation in Spain

12

discounted. Interestingly, the VAT is charged on top of all the other taxes added and not solely onthe net price. The next two absolute taxes are the “Impuesto Especial (IE)” of 40,069 euro cents8

and “Impuesto Venta Menoristas estatal (IVMe)” of 2,4 euro cents9 which have to be discounted.Both are fixed amounts and charged by the federal government. Up to now the taxation systemis homogenous. The value of the last absolute tax listed is called “Impuesto Venta Minoristasautonómico (IVMa)”10 and di�ers between the Spanish regions as displayed above. Each regionsets this tax on its own with a given maximum value. Due to this di�erence in taxation, the shareof the price before taxes in the final retail price and thereby the margin of market operators isconcealed. This leads to a certain degree of market non-transparency within Spain and between theregions.

2.3 Market Concentration by Brand

When it comes to market concentration in economics, the number of firms and their respectiveshares of total production in a market is analyzed. Following [Contín-Pilart et al., 2009], in 1993,Spanish-based refiners and the biggest three operators in the gasoline market (Repsol, Cepsa-Elfand BP) controlled 85% of the 5983 S.S.. In 2003, these Spanish-based refiners still controlled 70%of the 8155 S.S. What stands out is the overall increase in available S.S. between 1993 and 2003.Obviously, this increase slowed down with today’s available S.S. ranging around 8711 stations.

Interestingly, the final price set by the S.S. is controlled by the large oil companies. Nearly allof the S.S. (95%) not owned and managed directly by the oil companies are operated throughexclusive selling contracts with their suppliers. By establishing prices and fees for the indepen-dent stations’ operators, the large oil companies in Spain at least have the possibility of settinga minimum price range and influencing pricing behavior of independent gas stations indirectly[Contín-Pilart et al., 2009].

This market control stems from the limited refining capacities on Spanish soil which are concentratedand run by three companies in Spain (Repsol YPF, Cepsa-Elf and BP) and by one company inPortugal (GALP Energia). An aforementioned recent report by Spain’s antitrust authority CNC[CNC, 2012] urges the implementation of the recommendations of the 2009 report where thesedeficiencies were already pointed out.

The result of a high degree of market concentration and price control is expected to lead to a lesscompetitive market. Only hypermarkets may reflect an exception, maintaining their more aggressiveprice strategy and continuously setting prices below the average but only holding a small percentageof the overall market share and being located mainly in urban areas.

8according to the law “Ley 38/1992, de 28 de diciembre, de Impuestos Especiales”9according to the law “Ley 24/2001, de 27 de diciembre, de Medidas Fiscales, Administrativas y del Orden Social”

10published by the Ministry in “PRECIOS DE CARBURANTES Y COMBUSTIBLES DATOS DE JUNIO DE2012”

13

2.3.1 Market Shares per Brand

To check whether there is market concentration in the Spanish gasoline market, the data sampledescribed above is used. All S.S. operators supplying gasoline 95 via more than fifty S.S. in thecountry are taken into account. All other S.S. are pooled under “Others”. Below all S.S. and theiroperators are displayed as well as the corresponding market share.

No. Brand # Service Stations % Market Share1 Repsol YPF 3081 38,30%2 Cepsa 1282 15,94%3 Galp 537 6,68%4 BP 307 3,82%5 Shell 278 3,46%6 Saras 100 1,24%7 Carrefour 95 1,18%8 Meroil 81 1,01%9 Avia 76 0,94%10 Petrocat 62 0,77%11 Eroski 59 0,73%

Others 2086 25,93%All Brands 8044 100,00%

Table 3: Market Shares per Brand - Overall Sample

Calculating the Concentration Ratio (CR) for the three biggest market operators, it becomes clearthat over 60% of all S.S. are still operated by Spanish or Portuguese refiners. Taking into accountthe four biggest market operators, nearly 65% of the S.S. fall under their control. To summarize,there is a considerable amount of market concentration in Spain. The Spanish gasoline market maytherefore reflect an oligopoly with one leading company - Repsol YPF.

Concentration Pooled Brands CRi

C1 Repsol YPF 38,30%C2 Repsol YPF + Cepsa 54,24%C3 Repsol YPF + Cepsa + Galp 60,91%C4 Repsol YPF + Cepsa + Galp + BP 64,73%

Table 4: Pooled Market Shares of the 4 Biggest Brands

The market concentration is not only reflected by the Concentration Ratio but also by the HerfindahlIndex which is computed below.

14

2.3.2 Herfindahl Index

The Herfindahl Index11 is a measure of the market share of firms in relation to the whole industryand an indicator of the amount of competition among them. It is defined as the sum of the squaresof the market shares of all firms within the industry, where the market shares are expressed asfractions. The result is proportional to the average market share, weighted by the overall marketshare. As such, it can range from 0 to 1, moving from a huge number of very small firms toa single monopolistic producer. Increases in the Herfindahl index generally indicate a decrease incompetition and an increase of market power, whereas decreases indicate the opposite. Alternatively,if whole percentages are used, the index ranges from 0 to 10,000. The major benefit of the HerfindahlIndex in relationship to such measures as the concentration ratio is that it gives more weight tolarger firms. The Herfindahl Index is computed below for the data sample described above and usedin this work.

H =I=8044ÿ

i=1s2

i

= 0, 1799 or 1799 (1)

with si

=q

K

k=1 S.S.kq

I=8044i=1 S.S.

i

and K µ I

The U.S. Department of Justice and the Federal Trade Commission consider markets in whichthe Herfindahl Index is below 1500 points to be unconcentrated, markets in which the HerfindahlIndex is between 1500 and 2500 points to be moderately concentrated and markets in which theHerfindahl Index is in excess of 2500 points to be highly concentrated. This is described in theHorizontal Merger Guidelines § 5.2 (2010) of the U.S. Department of Justice and the Federal TradeCommission. Transactions or mergers that increase the Herfindahl Index by more than 100 points inmoderately concentrated markets are presumed likely to enhance market power under the HorizontalMerger Guidelines and would therefore potentially raise significant competitive concerns. This wouldin turn often warrant scrutiny measures.

Given these interpretations, the Spanish gasoline market may be considered moderately concen-trated with a Herfindahl Index of 1799 points. We must take into account that many independentS.S. operators (nearly all of them) only run one particular S.S. and therefore hold a very low marketshare. The very low squared values might be the reason why in sum the Herfindahl Index doesn’tshow a highly concentrated market.

2.4 Price Pattern: Average Daily Prices

Comparing the average daily values of gross and net prices for gasoline 95 using the database asdescribed above, the following results were obtained. The outcome is quite interesting and may befound in other countries as well, e.g. Germany.

11named after the economists Orris C. Herfindahl and Albert O. Hirschman (Herfindahl-Hirschman Index)

15

1,3905'

1,3912'

1,3900'

1,3904'

1,3938'

1,3918'

1,3911'

0,7179'

0,7185'

0,7174'

0,7178'

0,7207'

0,7190'

0,7184'

0,7140'

0,7150'

0,7160'

0,7170'

0,7180'

0,7190'

0,7200'

0,7210'

0,7220'

1,3890'

1,3900'

1,3910'

1,3920'

1,3930'

1,3940'

1,3950'

1,3960'

1,3970'

Monday' Tuesday' Wednesday' Thursday' Friday' Saturday' Sunday'

Average'Da

ily'Net'Pric

e'[€/lite

r]'

Average'Da

ily'Gross'Pric

e'[€/lite

r]'

Gasoline'95.'Gross'and'Net'Average'Daily'Prices.'Overall'Sample.'

Gross'Prices'

Net'Prices'

Figure 6: Gross and Net Average Daily PricesSource: own elaboration

It seems that gasoline prices follow a cyclical pattern within the week. Every Friday prices reachtheir highest value while on Wednesday the lowest values are reached. Final domestic consumersusually take longer trips during the weekend. The above average price during the weekend continuesto be at a high level until Sunday. On Monday and during the week the average prices are far lower.Since this pricing behavior is found everywhere in Spain, it is a certain kind of proof for the pricingpower of the oligopoly and its leader. Given this outcome, Section 3 searches for possible collusivebehavior in the gasoline market.

3 Variance Filter Approach

3.1 Empirical Strategy

The variance filter methodology stems from the idea that the price dynamics of a market wherecompanies collude di�er from those of a competitive industry. Average prices and the variance ofthese prices serve as measurements for collusion. As pointed out and explained in the work done by[Jiménez and Perdiguero, 2009], both the economic theory and the available empirical evidence oncartel behavior and collusion agreements suggest that average prices will be higher in uncompetitivemarkets and, moreover, a significant reduction in the dispersion or standard deviation of the priceswould also be expected. There is a wide range of models that show a positive relationship betweencollusion and price rigidity, and they also indicate that there is lower price dispersion under the

16

terms of collusive behavior. The introduction of laws to prevent sales at a loss in the petrol marketof the U.S. state of Wisconsin was analyzed by [Brannon, 2003]. The preventive measures for saleof any item below cost resulted in collusive agreements; higher prices and the average mark-up ofretail gasoline over the wholesale price increased significantly in Wisconsin. It was also found thatprice dispersion was significantly lower in the protected Wisconsin market than in the unprotectedmarkets.

[Jiménez and Perdiguero, 2009] conclude that despite the existence of many theoretical argumentsas empirical evidence of collusive markets showing higher prices and lower variations, the variancefilter methodology is rarely used to detect such behavior. As an exception and standing alone[Abrantes-Metz et al., 2006] use a variance filter approach to find out which S.S. in the U.S. state ofKentucky have higher than average prices and low variance, in order to detect possible indicators ofcollusion in the retail gasoline market. Nevertheless, with all points of sale showing similar pricingbehavior there were no conclusive results.

Following the footsteps of [Jiménez and Perdiguero, 2009] who applied the methodology for the firsttime to the gasoline market in Spain, an average and variance filter is computed, trying to detectquestionable price dynamics indicating uncompetitive behavior in the overall gasoline 95 market byusing daily retail data from the data set gathered.

Based on the indications above of uncompetitive tendency encountered in the Spanish gasolinemarket, average net prices are expected to be higher and there should be a significant reduction inprice dispersion or standard deviation as an indication of collusive agreements.

The average net price and the standard deviation for each of the 8044 S.S. included in the dataset for the 28 days of study have been calculated and plotted against each other (p

iA

vs. ‡i

) in thefollowing way:

station’s average net price piA

= 1T

qT

t

pit

station’s standard deviation ‡i

=Ò

1T

qT

t

(pit

≠ piA

)2

The aim is to identify those S.S. with higher net average prices and lower standard deviations thanthe respective mean value. Contrary to [Jiménez and Perdiguero, 2009], who reviewed the Spanishgasoline and diesel market for the ten largest Spanish cities measured in terms of population in 2007and using a data base consisting of 544 S.S. this analysis here will use the data base of 8044 S.S.,giving a broader picture of the whole Spanish gasoline market. Furthermore and as described above,focus is laid on gasoline 95 only, being mostly or even exclusively used by domestic households. Alower absolute price elasticity of demand for gasoline 95 than for diesel is expected to result in ahigher probability of levels of collusion for gasoline with respect to diesel. Hence the results forgasoline 95 are shown below, including only the net price to permit the comparison of regions andbrands as outlined above. For readers interested, the analysis was done for gross prices as well. Endconsumers might want to know where the highest and lowest final retail prices appeared within theSpanish regions or brands. In section 6 Annex the results are shown.

17

3.2 Results of the Samples

3.2.1 Average Net Prices and Standard Deviation - Plot

As the results exhibit below, the average net price of gasoline was 0,718 Ä/liter with an averagestandard deviation of 0,019. Figure 7 includes all the 8044 S.S. analyzed, and at first sight it isobvious that the majority of them lie within a cloud of points around the average values. Question-able S.S. are those located in Quadrant IV at the bottom right of the graph with higher averageprices and lower standard deviation. They will be analyzed in section 3.2.3 below.

Figure 7: Net Prices - Standard Deviation and Average ValueSource: own elaboration

3.2.2 Results Overall Sample

In the following tables for the overall sample the data was briefly summarized, both for regions andfor brands, by using the price average and standard deviation. Table 5 shows the regions sorted bythe highest average price to the lowest average price. The highest net price was found in Cantabriawhile the lowest was found in Murcia. The figures of each single station range from 0,603 Ä/liter to0,845Ä/liter. The average value for Spain as a whole is 0,718 Ä/liter. Most of the S.S. are locatedin Andalusia, taking a share of 18,09%.

18

No. Region # Service Stations % Service Stations PA [Ä/liter]« S.D.A

Maximum Values 0,845 0,0691 Cantabria 126 1,57% 0,748 0,0162 Balearic Islands 170 2,11% 0,727 0,0203 Extremadura 292 3,63% 0,722 0,0194 Basque Country 259 3,22% 0,721 0,0205 Andalusia 1455 18,09% 0,721 0,0196 Rioja 63 0,78% 0,720 0,0207 Galicia 581 7,22% 0,720 0,0208 Madrid 564 7,01% 0,718 0,0209 Asturias 193 2,40% 0,718 0,01910 Valencia 957 11,90% 0,718 0,01911 Castile and Leon 693 8,62% 0,718 0,01912 Castile - La Mancha 631 7,84% 0,717 0,02013 Navarra 186 2,31% 0,717 0,01914 Aragon 339 4,21% 0,715 0,01915 Catalonia 1210 15,04% 0,714 0,01916 Murcia 325 4,04% 0,713 0,020

Minimum Values 0,603 0,002Total Spain 8044 100,00% 0,718 0,019

Table 5: Net Price, Overall Sample - Descriptive Statistics by Region

Table 6 reflects the same descriptive statistics but makes distinctions between the brands. Again,the brands are sorted by their price average from highest to lowest. Interestingly, Carrefour askedfor the highest net price. This might be due to the fact that Carrefour S.S. are often located inregions with the highest price averages, mainly in the Northwest. On the other hand Eroski, anotherhypermarket, charged the lowest net price for gasoline. Minimum, maximum and overall averagevalues remained the same, of course. Independent S.S. seem to charge lower net prices on average.

No. Brand # Service Stations % Service Stations PA [Ä/liter]« S.D.A

Maximum Values 0,845 0,0691 Carrefour 95 1,18% 0,729 0,0212 Avia 76 0,94% 0,725 0,0183 Petrocat 62 0,77% 0,724 0,0204 BP 310 3,85% 0,723 0,0195 Repsol 3081 38,30% 0,721 0,0206 Galp 537 6,68% 0,721 0,0197 Campsa 675 8,39% 0,720 0,0208 Shell 278 3,46% 0,720 0,0199 Cepsa 1282 15,94% 0,719 0,01910 Meroil 81 1,01% 0,718 0,01711 Saras 100 1,24% 0,715 0,01912 Others 2082 25,88% 0,713 0,01813 Eroski 59 0,73% 0,703 0,021

Minimum Values 0,603 0,002All Brands 8044 100,00% 0,718 0,019

Table 6: Net Price, Overall Sample - Descriptive Statistics by Brand

19

Most importantly, the average net prices and standard deviation for all regions and brands arequite similar and don’t reflect a very heterogenous pricing behavior. The aim of the analysis was toidentify those S.S. with higher average prices than the mean for all S.S. and low standard deviations.In order to establish a predetermined value, a possible benchmark could be an initial threshold witha standard deviation at least 50% lower than the combined average of the S.S. in the sample thathave a higher price average.

3.2.3 Results Quadrant IV

Now focusing on Quadrant IV only, with all S.S. showing a higher than average price and a lowstandard deviation, the following results were gathered. 2018 S.S. or 25,09% are situated in thisquadrant and distributed all over Spain. A very high share of 84,92% are located in Cantabria, ascan be seen in Table 7, sorting regions by their share of stations in Quadrant IV. Only few of theS.S. in Galicia, the Basque Country and Murcia are found in Quadrant IV. Maximum values for aparticular S.S. in Spain amount to the net average price of 0,813 Ä/liter (found in Aragon) and astandard deviation of 0,019 as the average value. The minimum values for a S.S. are 0,718 Ä/liter(the net average price of the whole sample) and 0,002 for the standard deviation.

No. Region # S.S. Q IV # S.S. % S.S. Q IV « PA [Ä/liter] S.D.A

Maximum Values 0,813 0,0191 Cantabria 107 126 84,92% 0,749 0,0152 Balearic Islands 61 170 35,88% 0,730 0,0183 Andalusia 494 1455 33,95% 0,726 0,0174 Extremadura 98 292 33,56% 0,727 0,0165 Navarra 55 186 29,57% 0,727 0,0166 Valencia 252 957 26,33% 0,724 0,0187 Rioja 15 63 23,81% 0,724 0,0178 Castile - La Mancha 143 631 22,66% 0,725 0,0179 Catalonia 271 1210 22,40% 0,725 0,01810 Aragon 70 339 20,65% 0,728 0,01711 Madrid 104 564 18,44% 0,726 0,01712 Asturias 35 193 18,13% 0,724 0,01613 Castile and Leon 125 693 18,04% 0,724 0,01714 Galicia 100 581 17,21% 0,724 0,01715 Basque Country 43 259 16,60% 0,726 0,01816 Murcia 45 325 13,85% 0,724 0,017

Minimum Values 0,718 0,002Total Spain QIV 2018 8044 25,09% 0,727 0,017Total Spain - 8044 - 0,718 0,019

Table 7: Net Price, Quadrant IV - Descriptive Statistics by Region

Sorting all brands by their share of S.S. in Quadrant IV, Petrocat, BP and Avia stand out withmost of their S.S. being located there, while Carrefour, interestingly Repsol, and Eroski only havevery few S.S. with higher than average prices and lower standard deviation.

20

No. Brand # S.S. Q IV # S.S. % S.S. Q IV « PA [Ä/liter] S.D.A

Maximum Values Others by PA 0,813 0,0191 Petrocat 41 62 66,13% 0,726 0,0182 BP 196 307 63,84% 0,725 0,0183 Avia 42 76 55,26% 0,730 0,0164 Others 749 2086 35,91% 0,727 0,0165 Galp 190 537 35,38% 0,727 0,0176 Shell 91 278 32,73% 0,727 0,0177 Meroil 26 81 32,10% 0,727 0,0168 Cepsa 403 1282 31,44% 0,725 0,0189 Saras 28 100 28,00% 0,724 0,01610 Carrefour 23 95 24,21% 0,733 0,01811 Repsol 229 3081 7,43% 0,730 0,01612 Eroski 0 59 0% - -

Minimum Values Others by PA 0,718 0,002All Brands QIV 2018 8044 25,09% 0,727 0,017All Brands - 8044 - 0,718 0,019

Table 8: Net Price, Quadrant IV - Descriptive Statistics by Brand

Most importantly, none of the average values for the S.S. sorted by region or brand in QuadrantIV breach the lower than standard deviation threshold established earlier. The combined averageof the standard deviation for the population that have a higher price average and are located inQuadrant IV is 0,017. The respective value for the overall sample is 0,019. The average standarddeviations for the pooled groups roam very close to these values, regardless of whether they aretaken from Quadrant IV or the whole sample. With the initially imposed and probably problematicbenchmark the existence of collusion can’t be proven.

3.3 Conclusions from the Variance Filter Approach

Concluding the third section, it must be said that no explicit evidence of anomalous pricing behaviorhas been found in any of the regions nor within the di�erent brands. Even though the initialthreshold of a 50% lower standard deviation might be excessive, there is no accentuated di�erencebetween average values, neither for communities nor brands. Again, a larger data base with agreater time span and a higher price movement would have been preferred. While interpreting theresults, one should keep in mind the following very relevant aspect of the gasoline industry: Due tothe large volumes and high frequencies of consumption the gasoline industry is not characterizedby large margins from few sales, which would probably result in larger price di�erences. On thecontrary, huge profits are gained through large volumes of sales. Hence, small price changes maysignificantly a�ect profits even though prices seem very similar. Another aspect to think aboutwould be the very symmetrical behavior demonstrated in the homogeneity of prices and standarddeviation, allowing for the dynamics of a highly competitive situation or a perfect collusion equally.All in all, the possibility of a low degree of competition in the sector remains, even though theaspect of collusion can’t be proven by means of a variance filter approach with the given data base.

21

4 Market Concentration in the Province of Biscay

4.1 Service Stations in the Province of Biscay

The empirical application in this section is done with data from the autonomous community ofthe Basque Country, more specifically in the Province of Biscay12, one of the most prosperousand important provinces of Spain as a result of massive industrialization in the last years of the19th century and first half of the 20th century. The whole data sample was narrowed to 108 S.S.operating in the Province of Biscay since the data formation for the econometrical analysis was donemanually by calculating distances between each S.S. and recording the corresponding brand as well.[Jiménez and Perdiguero, 2011b] used a similar econometrical approach but with an outstandingdatabase for the autonomous region of Galicia in Spain. Their database for each S.S. not onlycontained the sale price for gasoline 95, the type of brand operating the station, tra�c intensity ofthe road and exact location, but also more importantly the distance to other stations in minutesdepending on the road speed limitations and meters as well as the type of brand of the other stations.This allowed them to demarcate the possibility for competition with other stations for each pointof sale. Similar to the data base of Galicia, the Province of Biscay is an area with a high degree ofboth rural and urban areas. This is shown by Figure 8 below.

Figure 8: Service Stations for Gasoline 95 operating in the Province of BiscaySource: Geoportal - Ministry of Industry, Energy and Tourism

12besides the other two provinces of Álava and Gipuzkoa

22

Due to the di�culties in obtaining a similar data base as used by [Jiménez and Perdiguero, 2011b],only the S.S. in the Province of Biscay are analyzed. The characteristics of the stations with respectto the Basque Country and Spain are compared below. Interestingly, the average retail sales pricesfor gasoline 95 are lower in the Province of Biscay and the Basque Country than in Spain. But whenfocusing on the net price this property changes. The Basque Country and the Province of Biscayhave higher than average net prices. The average standard deviation of the S.S. is higher in bothcases when compared to Spain. While checking the tax schemes in Table 2 above, it becomes obviousthat there is a lower tax charged, with the so-called tax “Impuesto Venta Minoristas autonómico(IVMa)” or also named “céntimo sanitario” being zero. This gives the operators in the BasqueCountry the opportunity of charging higher net prices and consequently gaining higher mark-ups.

Region # S.S. % S.S. Spain % S.S. B.C. PGr,A [Ä/liter] S.D.Gr,A PNet,A [Ä/liter] S.D.Net,A

Biscay 108 1,34% 41,70% 1,356 0,024 0,720 0,020B.C. 259 3,22% 100,00% 1,357 0,023 0,721 0,020Spain 8044 100,00% 1,391 0,023 0,718 0,019

Table 9: Comparison of the Province of Biscay with the Overall Sample

Only 1,34% or 108 of all S.S. in Spain are located within the Province of Biscay, unfortunatelyminimizing the data sample. Nevertheless, 108 observation points for 28 days still is a considerableamount for the usage of econometric tools, keeping in mind the degrees of freedom.

4.2 The Data Base: Density of Service Station Grid in the Province of Biscay

Directing the attention to the data sample of the 108 S.S. and their respective characteristicsalready gathered, it becomes clear that besides the average price for gasoline 95, the brand andthe location of the station, other attributes become relevant. The type of road the S.S. is locatedon (i.e. a city road, a national road or a highway) was added to the data base. Furthermore, thesize of the population and the GDP per capita were added for each municipality where the S.S. aresituated. The last and important point missing for the analysis was the actual distance betweenthe stations. This was calculated manually by focusing on one particular S.S. and searching for theclosest competing S.S. in its surroundings. The distance was measured in kilometers. The resultsobtained are displayed in Table 10 below.

Distance d [km] to closest S.S. % to closest rival S.S. % to closest own S.S. %d Æ 0, 5 55 51% 21 32% 34 81%

0, 5 < d Æ 1, 5 22 20% 20 30% 2 5%1, 5 < d Æ 5 26 24% 20 30% 6 14%

d > 5 5 5% 5 8% 0 0%Total 108 100% 66 100% 42 100%

Table 10: Distance to the Closest Competing S.S.

The whole data base of all S.S. and their distances to the closest competing S.S. is divided between

23

the ones being confronted with a rival brand and those facing a station operated by their own brand.The respective shares are denoted above. Furthermore, the distances are reduced into four intervalsfor illustration purposes in Table 10. The actual distance is kept available. With the new andextended data base for the Province of Biscay at hand, everything stood open for the econometricalanalysis.

4.3 Methodology and Analysis of Market Dominance and Concentration

The methodology deals with the price applied by a station with competitors in its surroundingsbelonging to a rival or own brand. To a minor degree, it follows the linear city model and theunitary circular spatial market theories firstly developed by Hotelling (1929) and Salop (1979). Asdescribed by [Jiménez and Perdiguero, 2011b], there is a long history of competition analysis inindustrial economics, especially when companies are located in di�erent points of the territory. Themodels by Hotelling and Salop and several contributions to them have already demonstrated theimportance, which proximity of the competitors can have on prices applied by the di�erent pointsof sale. Furthermore, it is pointed out that competition in terms of space is by its very nature aparadigm of product di�erentiation. Each operator first chooses the physical location and given thecompetitors’ location it then sets the price at which to o�er its products.

Therefore, the importance of the proximity of competitors and the e�ect on prices applied by thedi�erent points of sale is incorporated into the empirical estimations below. The elasticities ofprices of each singular S.S. in the Province of Biscay are estimated by calculating their respectivedemand equations. Having a relatively close rival should lower the price, as the demand elasticitywill be higher. This is the so-called competitive e�ect. Conversely, having a same brand competitornearby should a�ect prices positively resulting in possible collusion and market dominance. This isdescribed as the domino e�ect.

The first simple empirical estimate is the following:

PGross,A,i

= –0 + –1rivali

+ Ái

(2)

A dummy takes value one if the nearest rival is of a particular brand and zero otherwise. Then,the initial estimation is extended step by step. First a set of dummies that determine the type ofmotorway on which the station is located are added in case of a national road or highway. Cityroads serve as the benchmark case.

PGross,A,i

= –0 + –1rivali

+ –2NationalRoadi

+ –3Highwayi

+ Ái

(3)

Then the size of demand is represented by the population of the municipality the particular station

24

is situated in as well as the respective GDP per capita of the municipality, holding for the influenceof the income e�ect on pricing behavior.

PGross,A,i

= –0 + –1rivali

+ –2NationalRoadi

+ –3Highwayi

+ –4Populationi

+ –5GDP p.c.i

+ Ái

(4)

All in all, three consecutive estimations are computed. Regarding multicollinearity, the correlationbetween the explanatory variables is not high, with a maximum of 30%. This indicates that theproblem of multicollinearity is not a�ecting the outcome. In the following Table 11 the econometricresults are shown. The respective p-values are below the estimated values in parentheses.

PGross,A,i

Eq. (2) Eq. (3) Eq. (4)Constant 1.360*** 1.355*** 1.353***

(0.000) (0.000) (0.000)rival -0.00452** -0.00289 -0.00285

(0.018) (0.147) (0.142)National Road 0.00571*** 0.00610***

(0.004) (0.002)Highway 0.00502 0.00637*

(0.169) (0.076)Population 2.36e-08***

(0.007)GDP p.c. 9.68e-09

(0.537)S.S. 108 108 108R2 0.052 0.127 0.187

p-Values in Parentheses* p < 0.1, ** p < 0.05, *** p < 0.01

Table 11: Econometrical Results for Eq.(2), Eq.(3) and Eq.(4)

In all three cases the expected negative e�ect of a close rival station on the price set is obtainedin form of a negative sign. But only in the first equation a significant result is obtained. Sincethe dummy variables of being a rival or own brand station are perfectly collinear, entering the ownbrand station as a dummy in all three equations would result in the same values but with a positivesign instead. The first e�ect seen above is the competitive e�ect, while the second e�ect describedis the domino e�ect.

In all cases, a significant value for the influence of the location on a national road is obtained.Consequentially, S.S. located on a national road tend to have higher prices than S.S. on city roads.The same positive sign is reached for the dummy variable of highways, but only in the last estimationis this outcome significant on a 10% significance level.

25

The income e�ect denoted by the explanatory variable GDP per capita doesn’t explain any ofthe variance in price setting to a su�cient degree. Contrary, the size of the population of themunicipality the S.S. is located in explains part of the pricing behavior for each station of the sampleon all generally-used significance levels. The e�ect is very small but the higher the population thehigher prices seem to be on a very diminutive scale.

The second approach takes the estimations from the first part but with interacted variables for thedistance. An interaction variable is a variable constructed from an original set of variables to tryto represent either all of the interaction present or some part of it. Here the dummy variables offacing an own or rival brand as first competitor surrounding the station are interacted with theactual distance to this first competitor. The idea is that same brand stations should a�ect pricespositively (domino e�ect) even to a higher degree if the station is less accessible or isolated fromother stations. Hence, an increase in distance is expected to have a positive impact on the price set.

PGross,A,i

= “0 + “1(rivali

ú distancei

) + “2(owni

ú distancei

) + Ái

(5)

Again the dummy variables for being located on a national road or highway are added.

PGross,A,i

= “0 + “1(rivali

ú distancei

) + “2(owni

ú distancei

)

+ “3NationalRoadi

+ “4Highwayi

+ Ái

(6)

And finally, as a last step, the population and income e�ect are attached to the estimation.

PGross,A,i

= “0 + “1(rivali

ú distancei

) + “2(owni

ú distancei

)

+ “3NationalRoadi

+ “4Highwayi

+ “5Populationi

+ ”6GDP p.c.i

+ Ái

(7)

The new econometric results of the equations (5), (6) and (7) are presented in Table 12 below.Once more, the respective p-values are shown in parentheses. Regarding multicollinearity, thecorrelations between the explanatory variables again don’t exceed the value of 30%. With theexception of equation (6), an increase in the distance between S.S. seems to increase the price of aS.S. the more isolated it is. In equation (6) only, the competitive e�ect appears to overthrow theisolation e�ect. However, looking more closely at the estimation results, it must be pointed out thatall values gathered are not significant. The respective null hypothesis cannot be rejected on anysignificance level, leaving the influence of distance at an inconsiderable importance. The varianceof the the price setting is not a�ected significantly by the interacted variables in the data sample.

26

PGross,A,i

Eq. (5) Eq. (6) Eq. (7)Constant 1.356*** 1.353*** 1.350***

(0.000) (0.000) (0.000)rival ú distance 0.000344 -0.0000621 0.000293

(0.459) (0.898) (0.547)own ú distance 0.00196 0.000684 0.00138

(0.108) (0.585) (0.268)National Road 0.00616*** 0.00587***

(0.004) (0.006)Highway 0.00670* 0.00820**

(0.054) (0.017)Population 2.59e-08***

(0.005)GDP p.c. 1.15e-08

(0.473)S.S. 108 108 108R2 0.025 0.113 0.180

p-Values in Parentheses* p < 0.1, ** p < 0.05, *** p < 0.01

Table 12: Econometrical Results for Eq.(5), Eq.(6) and Eq.(7)

The results for price variation of gas stations being located on national roads or highways areconsistent with the ones reached above. Prices tend to be higher. As before, the size of thepopulation has a significant positive but minuscule e�ect on the price setting and again the incomee�ect is insignificant.

In both approaches the total variation of the price setting explained is quite low reaching a maximumof 18,7%. Section 4.4 concludes the analysis done in the last section of this work.

4.4 Conclusion regarding Market Dominance and Concentration in the Provinceof Biscay

In the final empirical approach realized above, the relationship between local market dominanceand the level of competition in the gasoline market of the Province of Biscay was highlighted. Theresults clarify that having competitors of the same brand in a local market increases prices whilefacing competitors of a rival brand has the opposite e�ect. Prices for gasoline 95 tend to be higheron highways or national roads where less concentration may be expected in contrast to urban areas.Furthermore, Spanish gasoline operators tend to construct same-brand gasoline stations in bothdirections of national roads and highways13. The larger the population, the higher the price tendsto be, since with a higher demand the price equilibrium might be higher. But this e�ect is ofdiminutive importance.

13Interacting the type of road with the respective brand doesn’t yield any significant results though.

27

A major drawback in the econometrical analysis above is the following aspect: The influence of allother S.S. surrounding the particular point of sale analyzed except the closest competitor is nottaken into account when estimating the equations. This might indeed influence the results andpricing behavior in general to a considerable degree. To hold for this deficiency, a larger data basewith all competitors considered is needed. Of course and again, this has to be done manually forthe Province of Biscay. Further research could be done here taken into account all the S.S. of theBasque Country for instance.

In summary and as a conclusion it can be stated that the competition in the Spanish gasolinemarket should be promoted by encouraging new market entrants (hypermarkets e.g.) to reducelocal market dominance and concentration since a su�cient level of competition is not reached yet.

5 Conclusion

Starting with a general overview regarding the development and state of competition as well as theprice setting in the Spanish gasoline market and comparing taxation and pricing schemes of Spain inrelation to the EU, two empirical approaches were applied, using a self-developed data base explainedand analyzed in section 2. In section 3, a variance filter approach was implemented to search forcollusive behavior, taking into account that high average prices with low standard deviations mightbe indicators for such pricing agreements. The variance filter methodology turned out to be unfitfor the pricing behavior encountered. Nevertheless, the empirical results show that all S.S. in Spainset very homogeneous prices and that standard deviations are similar. Consequently, the resultinghomogeneity of prices and standard deviations equally allow for the dynamics of competition orcollusion. These results stand in line with those obtained by [Jiménez and Perdiguero, 2009] for theten biggest Spanish cities.

In the second empirical approach in section 4, the focus was on the relationship between marketconcentration and level of competition in a local market. A data base was compiled for the Provinceof Biscay in the Basque Country, possessing both urban and rural areas. The econometric resultsexhibit a competitive e�ect in lowering the price if a gas station is confronted by a rival brand in itssurroundings, as well as a domino e�ect increasing the price set if the station faces an own-brandpoint of sale as closest competitor. The positive influence of an increasing distance between S.S. onthe pricing behavior resulting in isolated local markets couldn’t be proven.

In summary, taking into account all the di�erent parts of this work regarding the degree of com-petition in the Spanish gasoline market, it can be stated that market concentration remains highand competition rather low. Consequently, the uncompetitive market and the resulting higher pricelevels harm the Spanish economy. These exaggerated prices work as an additional consumption tax,since domestic consumers of unleaded gasoline 95 may spend less on other products. The generalwelfare is not being promoted the way it should. Government actions are required. Interestinglyand stated by [Balbe and Padros, 2001], “Mediterranean liberalization” although formally impres-sive, has only slightly transformed the former national firms, sometimes simply converting them

28

from public to private monopolies or at least to oligopolies with one leader, the national champion.

The other side of the coin is that gasoline sales have been declining for years and the Spanish oilindustry of course tries to hold its historical yields. This represents an even greater challenge asthere are more fuel-e�cient cars being introduced into the market.

The positive aspect of the high gasoline prices might be the chance to detach from oil and searchfor other alternatives keeping in mind that oil is a finite resource. In general, the constraints ofshortage can be translated into the most e�ective instrument to foster innovations. The higher theprice of gasoline, the more worthwhile investments in the alternative energy sector become. So far,the infinite alternative energy sources represent the one and only solution in the long run.

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6 Annex: Tables and Graphs

Variance Filter Approach - all with Gross Prices

Average Gross Price

Figure 9: Gross Price - Standard Deviation and Average Value

A

Overall Sample

No. Region # Service Stations % Service Stations PA [Ä/liter]« S.D.A

Maximum Values 1,518 0,0821 Balearic Islands 170 2,11% 1,421 0,0232 Valencia 957 11,90% 1,410 0,0233 Castile and Leon 693 8,62% 1,410 0,0234 Castile - La Mancha 631 7,84% 1,409 0,0235 Catalonia 1210 15,04% 1,405 0,0236 Cantabria 126 1,57% 1,389 0,0197 Extremadura 292 3,63% 1,387 0,0238 Andalusia 1455 18,09% 1,385 0,0229 Galicia 581 7,22% 1,384 0,02310 Asturias 193 2,40% 1,382 0,02211 Murcia 325 4,04% 1,376 0,02312 Madrid 564 7,01% 1,374 0,02413 Basque Country 259 3,22% 1,357 0,02314 Rioja 63 0,78% 1,356 0,02315 Navarra 186 2,31% 1,352 0,02216 Aragon 339 4,21% 1,350 0,023

Minimum Values 1,251 0,002Total Spain 8044 100,00% 1,391 0,023

Table 13: Gross Price, Overall Sample - Descriptive Statistics by Region

No. Brand # Service Stations % Service Stations PA [Ä/liter]« S.D.A

Maximum Values 1,518 0,0821 Petrocat 62 0,77% 1,417 0,0232 Carrefour 95 1,18% 1,404 0,0253 BP 307 3,82% 1,399 0,0224 Meroil 81 1,01% 1,398 0,0205 Galp 537 6,68% 1,396 0,0236 Saras 100 1,24% 1,394 0,0227 Repsol 3081 38,30% 1,393 0,0248 Shell 278 3,46% 1,391 0,0239 Cepsa 1282 15,94% 1,391 0,02210 Others 2086 25,93% 1,386 0,02211 Avia 76 0,94% 1,373 0,02112 Eroski 59 0,73% 1,359 0,024

Minimum Values 1,251 0,002All Brands 8044 100,00% 1,391 0,023

Table 14: Gross Price, Overall Sample - Descriptive Statistics by Brand

B

Quadrant IV

No. Region # S.S. Q IV # S.S. % S.S. Q IV « PA [Ä/liter] S.D.A

Maximum Values 1,495 0,0231 Cantabria 69 126 54,76% 1,395 0,0192 Valencia 405 957 42,32% 1,412 0,0213 Balearic Islands 61 170 35,88% 1,424 0,0214 Castile - La Mancha 217 631 34,39% 1,414 0,0205 Catalonia 412 1210 34,05% 1,414 0,0216 Castile and Leon 217 693 31,31% 1,411 0,0207 Andalusia 211 1455 14,50% 1,398 0,0198 Extremadura 37 292 12,67% 1,402 0,0179 Galicia 29 581 4,99% 1,396 0,01810 Asturias 9 193 4,66% 1,395 0,01711 Murcia 9 325 2,77% 1,397 0,01612 Madrid 11 564 1,95% 1,400 0,02013 Aragon 3 339 0,88% 1,437 0,01014 Navarra 1 186 0,54% 1,409 0,01215 Basque Country 1 259 0,39% 1,412 0,01616 Rioja 0 63 0% - -

Minimum Values 1,391 0,003Total Spain QIV 1692 8044 21,03% 1,410 0,020total spain - 8044 - 1,391 0,023

Table 15: Gross Price, Quadrant IV - Descriptive Statistics by Region

No. Brand # S.S. Q IV # S.S. % S.S. Q IV « PA [Ä/liter] S.D.A

Maximum Values 1,495 0,0231 Petrocat 45 62 72,58% 1,418 0,0222 Meroil 40 81 49,38% 1,408 0,0193 BP 119 307 38,76% 1,413 0,0214 Cepsa 416 1282 32,45% 1,409 0,0215 Others 622 2086 29,82% 1,410 0,0196 Saras 29 100 29,00% 1,408 0,0197 Galp 152 537 28,31% 1,410 0,0208 Shell 61 278 21,94% 1,406 0,0209 Carrefour 19 95 20,00% 1,408 0,02110 Avia 15 76 19,74% 1,408 0,01811 Repsol 174 3081 5,65% 1,407 0,01912 Eroski 0 59 0% - -

Minimum Values 1,391 0,003All Brands QIV 1692 8044 21,03% 1,410 0,020All Brands - 8044 - 1,391 0,023

Table 16: Gross Price, Quadrant IV - Descriptive Statistics by Brand

C