Pablo Arocena Garro

Alejandro Bello Pintado

Ignacio Contín-Pilart

Dpto. Gestión de Empresas

Universidad Pública de Navarra

� Motivation and contribution

� Theoretical background

� Data and model

� Empirical results

� Discussion

• The analysis of retail gasoline prices has been subject of numerous studies since the early 1980s. Most of them consist of cross-section analysis of price variation at the service station level

• This paper contributes to this literature on several ways:

• Firstly, we focus on how the number of nearby competitors influences prices. Economic models, like monopolistic competition or search consumers’ models, often dramatically differ in their predictions on how the extent of competition (number of sellers) influences equilibrium prices; hence that empirical examination may be particularly valuable (Barron et al, 2004).

• This paper contributes to this literature on several ways:

• Secondly, this paper provides further evidence on the effect

of independent marketers on retail gasoline and diesel

pricing behaviour, and issue barely addressed in previous

empirical studies (Hasting, 2004).

• Finally, we analyze the impact of local market structures and

independent service stations not only on gasoline prices but

also on diesel prices.

ModelModelModelModel Predicted correlation between: Predicted correlation between: Predicted correlation between: Predicted correlation between: number of sellers and average number of sellers and average number of sellers and average number of sellers and average price.price.price.price.

Monopolisitic competition model (Perloff and Salop, 1985)

negative

“consumer search models with heterogeneity in consumers’ search costs and sellers’ costs” (Carlson and MacAffe, 1983).

negative

“consumer search models with heterogeneity in consumers’ search costs” (Varian, 1980; Stahl, 1989).

positive

Janssen and Moraga-González (2004) (uninformed consumers differ in their search intensity).

moderately: positive with low intensity: noneintensively: non-linear association

• Reduction in the number of refining companies from 8 to 3:

Repsol, Cepsa, and BP Spain

• Repsol owns 57.5% of the Spanish refining capacity; Cepsa,

33.3% and BP, 9.2%.

• These refining companies are forward integrated both in the

transport and distribution of oil products (CLH) and in the

retail sector.

� Market shares in 2006 (8,382 service stations)

- Repsol, 43.7 %

- Cepsa, 18.3 %

- BP Spain, 7%

- Other brands, 17%

- Independents, 14%

� Retail prices for diesel and gasoline are totally liberalized since 1996 and 1998, respectively

� We use a unique dataset of weekly prices for a sample

representative of both national and regional networks of

service stations in Spain (485 service stations in total)

� Catalist España provided information about their brands,

location, the services they offered and identified the local

market characteristics in which they operate dated February

2007.

� Retail prices for unleaded gasoline Eurosuper 95 and for

automotive diesel are those provided by the Ministry of

Industry for all Mondays over the period 1 February- 30

November 2007

Figure 1: Monthly average gasoline and diesel retail prices before taxes in Spain

(euro-cents/liter).

Source: Ministry of Industry and our date set

Figure 2: Weekly average retail prices before taxes for gasoline (euro-cents/liter).

Source: Our data set

Table 1. Descriptive statistics (Number of observations = 18,915)

Variables Description Mean SD Min. Max.

pg Gasoline price (euro cents per liter) 105.21 4.56 89.9 117.2

pd Diesel price (euro cents per liter) 96.24 3.60 83.5 106.5

Wholesale prices

Spot priceg Spot price of gasoline in Rotterdam 38.68 3.43 30.27 44.03

(euro cents per liter)

Spot priced Spot price of diesel in Rotterdam 38.00 2.83 32.77 45.00

(euro cents per liter)

Regional taxes Taxesg Regional taxes for gasoline after VAT 1.38 1.34 0 2.78

(euro cents per liter)

Taxesd Regional taxes for diesel after VAT 1.04 1.15 0 2.78

(euro cents per liter)

Local market proxies:

Density Total number of stations in 2 km radius 2.97 2.37 1 10

Propb Proportion of station that carry the same brand .716 .305 .1 1

Distance Distance to closet station 2.93 4.00 .01 32.7

Table 1. Descriptive statistics (Number of observations = 18,915)

Variables Description Mean SD Min. Max.

Station characteristics:

Repsol Station brand: Repsol .439 0 1

Cepsa Station brand: Cepsa .189 0 1

BP Station brand: BP .072 0 1

Agip Station brand: Agip .039 0 1

Avia Station brand: Avia .012 0 1

Erg Station brand: Erg .014 0 1

Esso Station brand: Esso .010 0 1

Galp Station brand: Galp .020 0 1

Meroil Station brand: Meroil .022 0 1

Petrocat Station brand: Petrocat .008 0 1

Q8 Station brand: Q8 .004 0 1

Shell Station brand: Shell .035 0 1

Tamoil Station brand: Tamoil .008 0 1

Unbranded Station unbranded .123 0 1

Hg Station is located in a highway .022 0 1

Service bay Station provides repair service .086 0 1

Car wash Station has a car wash .602 0 1

C-Store Station has a convenience store .187 0 1

Trafiff flow Station traffic level 3.20 .84 1 4

Location type: Rural Station is located in a rural area .336 0 1

Urban Station is located in a urban area .325 0 1

Commercial Station is located in an commercial area .338 0 1

Local demographic characteristics: Unemp. rate Unemployment rate 4.19 1.68 1.3 12.3

� To test the relationship between the expected price and the

number of stations in a local market, we estimate the

following equation by ordinary least square,

� To investigate whether the relationship between density and price

at unbranded stations is different than that at branded stations, we

estimate the following equation by ordinary least square,

Table 2. Price specification by type of automotive fuels for Spain

Gasoline Diesel

Dependent variable (1) (2) (3) (4)

Spot 1.048***

(.004)

1.048***

(.004)

1.076***

(.002)

1.076***

(.002)

Tax 1.008***

(.027)

1.007***

(.027)

1.024***

(.029)

1.023***

(.029)

Density .077**

(.031) .096***

(.033)

.075***

(.026)

.096***

(.028)

Density*Unbranded -.172***

(.064)

-.177***

(.060)

Brandshare .407* (.208) .477

** (.220) .378

* (.203) .518

** (.204)

Brandshare*Unbranded -.628 (.534) -1.117*(.622)

Distance .001 (.009) .0007 (.009) .0003 (.008) .0007 (.008)

Service bay .112 (.145) .122 (.142) -.049 (.173) -.050 (.172)

Car wash .127 (.093) .119 (.091) .212* (.089)

.206

** (.088)

C-Store .003 (.110) -.006 (.111) -.079 (.099) -.088 (.099)

Urban .193* (.101) .165 (.101) .351

*** (.102)

.333

*** (.096)

Rural .172* (.099)

.152

(.098)

.260

** (.103)

.248

** (.102)

Highway .473* (.264)

.483

* (.264)

.617

*** (.229)

.616

*** (.229)

Traffic .118**

(.048)

.115**

(.048)

.042 (.056) .036 (.058)

Unemployment rate -.050* (.028)

-.051

* (.028)

-.029

(.022)

-.028

(.022)

Repsol -.046 (.141) .158 (.170) .416***

(.138)

.621***

(.150)

Cepsa .353**

(.143)

.568***

(.174)

.559***

(.143)

.774***

(.155)

BP .509***

(.159)

.716***

(.188)

.443***

(.162)

.663***

(.172)

Agip .031 (.170) .239 (.197) .210 (.254) .434* (.257)

Avia -.106 (.282) .097 (.288) .433* (.238) .627

** (.242)

Erg .094 (.280) .304 (.306) -.013(.289) .214 (.298)

Esso -.200 (.671) .020 (.678) -.298 (.914) -.052 (.909)

Galp .340 (.172) .234 (.194) .230 (.212) .452**

(.209)

Meroil -.554 (.391) -.327 (.404) -.277 (.303) -.031 (.307)

Petrocat -.187 (.227) .040 (.256) -.121 (.220) .122 (.233)

Q8 .870 (.532) 1.098**

(.543) .612**

(.239) .852***

(.237)

Shell -.161 (.212) 0.038 (.230) .278 (.229) .503 **

(.227)

Tamoil -.188 (.543) .007 (.578) -.004 (.577) 0.200 (.600)

constant 62.28***

(.452)

62.73***

(.393)

53.09***

(.463)

53.508***

(.364)

R2 .7196 .7200 .8330 .8336

Number of observations 18,915 18,915 18,915 18,915

* , ** and *** respectively denote statistical significance at the 10%, 5% and 1% level.

Standard errors clustered by stations are in parenthesis.

The excluded brand category is unbranded independent stations.

Number of observations: 485 stations observed for 39 weeks.

Table 2. Price specification by type of automotive fuels for Spain

Gasoline Diesel

Dependent variable (1) (2) (3) (4)

� Our main findings are consistent with the predictions of

theoretical search models that typically divide the market into

informed and uninformed buyers (e.g. Varian, 1980;

Rosenthal, 1980; Stahl, 1989; Janssen and Moraga-González,

2004) while showing that the relationship between the number

of sellers and prices varies across different types of stations

� Our results suggest that unbranded stations would attract a

larger share of informed consumers, with lower search costs

and sensitive to retail prices, whereas branded station would

attract relatively more uninformed consumers with higher

search costs and less sensitive to prices.

� Our results also suggest that diesel consumers search more

intensively than gasoline consumers.

� Policy implications: Promoting the entry of independent

stations in local markets may be more effective for enhancing

price competition than simple promoting per se the increase in

the number of competitor in local markets (this latter has been

suggested several times by the Spanish antitrust authority)