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ON THE EFFECTS OF SUGGESTED PRICES IN GASOLINE MARKETS

Riemer P. Faber1 and Maarten C.W. Janssen2

September 2017

Abstract: There is a widespread suspicion that suggested prices act as a focal point for

individual firms when setting their prices. Oil companies announce suggested prices for

gasoline stations in the Dutch retail market. We show that, compared to the gasoline spot

market price, suggested prices contain additional information that explains retail price

changes. We conclude that suggested prices have a horizontal coordinating effect in the sense

that retail prices react to information that suggested prices contain and that is unrelated to

firms’ costs (i.e., the information that firms use under normal competitive conditions).

JEL classification: L11; L42; L81

Keywords: Suggested prices; Price setting; Coordination; Gasoline markets

1 Corresponding author. Charles River Associates, European Competition Practice. 8 Finsbury Circus, London EC2M 7EA, United Kingdom. Phone: +44-20-7959-1411, E-mail: rfaber@crai.com. Views expressed are those of the authors and do not necessarily reflect the official positions of Charles River Associates. 2 University of Vienna and Higher School of Economics, Moscow. Oskar-Morgenstern-Platz 1, A-1090 Vienna, Austria. Phone: +43-1-4277-37438, E-mail: maarten.janssen@univie.ac.at. Janssen acknowledges financial support from the Austrian Science Fund FWF, under project number P 27995-G27.

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1. Introduction

The boundary between price setting under normal competitive conditions and price

coordination is often not clear. This issue is particularly prominent regarding the

interpretation of suggested prices (Buehler and Gärtner (2013) and Lubensky (2017)).

Suggested prices are prices that are announced with the recommendation for firms to follow

them. Suggested prices are not binding in any legal way and firms are free to deviate and

charge higher or lower prices as they wish.

There is a widespread suspicion that suggested prices distort the normal functioning of

markets as they may act as a focal point (see, e.g., Kühn (2001), Motta (2004), and Buccirossi

(2008)). A suggested price stands out among all possible prices that a firm can choose and

therefore relatively many firms may choose it. As a result, suggested prices may have a

coordinating effect on prices set by individual firms. The evidence for this hypothesis is either

anecdotal or experimental (see, for example, Holt (1995) and Cason (2008)).1

In this paper, we empirically study the effects of suggested prices, using data from the Dutch

gasoline market.2 In many countries, such as the Netherlands, Norway, Sweden, Austria,

Spain, and Italy, large oil companies provide stations that use their brand with a suggested

price for gasoline on a daily basis.3 Often these oil companies are both wholesalers and

retailers as they own a substantial number of the stations that use their brand.

Despite the little empirical evidence on the effects of suggested prices, there are quite a few

1 Knittel and Stango (2003) quantify the impact of focal points on price setting via a study on government imposed nonbinding price ceilings in the American credit card market. See also Albæk, Møllgaard, and Overgaard (1997), Marshall, Marx, and Raiff (2008), and Sen, Clemente, and Jonker (2011). 2 Foros and Steen (2013b) is the closest to our paper and we extensively discuss the differences and similarities with that study below. De los Santos, Kim, and Lubensky (2013) study the effects of suggested prices in South Korean retail markets. Other empirical studies that use suggested prices include Gripsrud (1982), Hofstetter and Tovar (2010), and Foros and Steen (2013a). 3 Oil companies often also publish their suggested price on their website (e.g., BP in the Netherlands, Statoil in Norway and Sweden, and OMV in Austria). The Spanish government prohibited suggested prices for gasoline in 2013 and oil companies in Italy stopped publishing suggested prices after an investigation of the competition authority (Boletín Oficial del Estado, No. 47, Sec. I, p. 15226, 2013, CNC (2012), and AGCM press release 5/2007 and 92/2007).

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cases where different competition authorities have argued that by setting suggested prices,

firms or professional organizations have violated competition law. For example, the Dutch

competition authority (ACM) decided that several professional organizations for

psychologists and psychiatrists were guilty of violating competition law as they advised their

members via their website how much to charge per hour given the costs members typically

would encounter.4 What, according to the organizations, was meant as an aid to their

members, was judged by the ACM as a way to coordinate pricing decisions of individual

entrepreneurs above competitive levels. The ACM argued that suggested prices helped to

considerably reduce the uncertainty concerning competitors’ price setting behavior.5 A similar

ruling was made in a private U.S. case on petroleum products.6 The complaint was that oil

companies conspired to raise or stabilize prices by disseminating information concerning

wholesale and retail prices, with the purpose of quickly informing competitors so that they

could follow suit. The court mentioned in its judgment that it did not see any other business

purpose than to facilitate interdependent or collusive interaction.

Whether or not suggested prices have a coordinating effect depends on the counterfactual

price setting behavior in case suggested prices would not exist. In case of the Dutch gasoline

market, the cost of a liter of gasoline (i.e., the gasoline spot market price) is the main relevant

factor for price setting that changes for all firms on a daily basis. Under normal competitive

conditions, we expect that firms change their prices in line with these cost changes. Therefore,

the main question we address is whether the daily national suggested prices of oil companies

contain information that is not present in the spot market price and that explains retail price

changes of gasoline stations. If this is the case, suggested prices have a coordinating effect on

prices in the sense that the same information (which is unrelated to the information that firms 4 See ACM (formerly NMa) decision case 3309/NIP, LVE, NVP, and NVVP, 2004. The court of appeal cancelled the decision on the basis that it was not clear that the price was a decisive factor in the decision process of consumers while choosing a psychologist (CBb, decision case AWB 06/667, ECLI:NL:CBB:2008:BF8820, 2008). 5 The ACM reiterated its concerns in a case involving nonbinding public announcements of future price strategies of telecom firms (decision case 13.0612.53, 2014). 6 See In re Coordinated Pretrial Proceedings in Petroleum Products Antitrust Legislation 906 F.2d 432 1990 and Hay (1999). Borenstein (2004) studies another related case.

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use under normal competitive conditions) influences the price setting behavior of all firms.7

We have collected a panel data set consisting of daily retail prices of almost all gasoline

stations in the Netherlands and suggested prices of the five largest oil companies over more

than two years. We distinguish between (i) stations that use the brand of an oil company but

are owned by an independent dealer (“dealer-owned and dealer-operated” (dodo) stations), (ii)

stations that an oil company owns and operates (“company-owned and company-operated”

(coco) stations), and (iii) stations that an oil company owns and that an independent dealer

operates (“company-owned and dealer-operated” (codo) stations). Dealers of dodo and codo

stations are in principle free to set their own retail prices.

We find that suggested prices contain information that is not present in the spot market price

and that explains retail price changes of all three types of stations. Since suggested prices may

differ across oil companies, we inquire into the general effect of suggested prices across

brands and the effect of the brand-specific suggested price. We find that both the information

that is common to all suggested prices and the information that is only present in the brand-

specific suggested price explain retail price changes.

An important question is whether this coordinating effect has a horizontal nature (affecting

competition at the wholesale or retail level) or whether it has a vertical nature (restricting

price changes across the supply chain). Given that coco stations do not buy from a wholesaler,

there is certainly a horizontal coordinating effect on the retail prices of these stations. That is,

retail prices of stations that are owned and operated by the five largest oil companies all react

in the same way to changes in suggested prices and this reaction cannot be explained by

changes in their costs. Whether this horizontal coordinating effect extends to retail prices of

dealer-operated stations or wholesale contracts between dealer-operated stations and oil

companies depends on wholesale contracts. Interviews and public sources suggest that

7 Some oil companies claim to announce suggested prices to assist stations by informing them how they could price their product in view of the frequent changes in the spot market price (see, e.g., the Shell website and Shell (2001)). If suggested prices only reflect changes in the spot market price, then suggested prices would not have a coordinating effect.

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wholesale prices often depend on suggested prices. If this is the case, then suggested prices

have a horizontal coordinating effect on wholesale contracts as all oil companies introduce the

same information in their wholesale prices, even though this information is not related to their

costs. The final section gives further detail on the interpretation of our results.

It is ambiguous whether suggested prices make consumers worse off. On one hand, horizontal

coordinating effects typically lead to higher retail prices. On the other hand, the way retailers

use suggested prices informs consumers about where they can buy the cheapest gasoline in

their neighborhood. Individual stations typically have a banner outside their station stating

how many cents their retail price differs from the suggested price. These banners rarely

change and act as a commitment device: absolute retail price differences between stations are

more or less constant over time (as suggested prices are very similar across oil companies).

Given the frequent changes in retail prices, this information helps price sensitive consumers to

save on search costs as they do not have to drive around to find out which station has the

lowest retail price on a particular day. Other consumers may not find it worthwhile to spend

time on getting cheaper gasoline and buy for convenience at more expensive stations.

In addition to the horizontal coordinating effects described above, suggested prices can also

have a vertical coordinating effect. Foros and Steen (2013b) study the Norwegian gasoline

market in which weekly price cycles and a price support system exist. Oil companies switch

off the price support on Monday afternoon, which de facto forces all stations to increase their

retail prices to the suggested prices (which are set by the oil companies). Retail prices start

decreasing again when oil companies restart the price support on Monday evening. Thus the

combination of suggested prices and this price support system leads in Norway to a shift in

price control from many independent retailers to a few wholesalers.

On the basis of interviews and public sources we understand that also in the Netherlands

wholesale contracts may contain elements that in combination with suggested prices can limit

the freedom of some (codo) stations to set their own retail prices. Thus, although the

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institutional features of the Dutch and Norwegian gasoline market differ substantially,

suggested prices may have in the Netherlands a similar vertical coordinating effect as Foros

and Steen (2013b) describe.

Hence, it is possible that there are both horizontal and vertical coordinating effects in the

Dutch market. These are separate effects. We focus on the horizontal coordinating effects as

these impact directly or indirectly all retail prices and do not depend on the interplay with

other specific institutional features of the market. To the best of our knowledge, this paper is

the first to examine the horizontal coordinating effects of suggested prices. It complements

Foros and Steen (2013b) who focus on the vertical coordinating effect.

The rest of the paper is organized as follows. Section 2 describes the relevant aspects of the

Dutch gasoline market and the way oil companies set and communicate suggested prices.

Section 3 provides details on the data set and gives descriptive statistics. Section 4 discusses

our methodology. Section 5 presents the results. Section 6 discusses the results.

2. The Dutch gasoline market and suggested prices8

There are around 4,300 gasoline stations in the Netherlands. Around 60% of the stations use

the brand of one of the five largest oil companies (BP, Esso, Shell, Texaco, and Total). Shell

has the largest market share with 14% of the stations and 22% of all sold liters. The other four

large oil companies each sell around 12% of all sold liters. As discussed in the introduction,

there are roughly three types of ownership models. Coco stations, which include almost all the

larger stations along highways, are operated by oil companies that set the retail prices. Dealers

of codo stations rent the station from an oil company, but are in principle free to set their own

retail prices. Finally, dodo stations operate most independently from the oil companies and in

principle also set their own retail prices. Approximately 60% of the stations are dealer-owned

(dodo). We do not know exactly how many stations are dealer-operated (dodo and codo), but

8 Unless mentioned otherwise, the source of the data in this section is BOVAG (2006).

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a rough estimation indicates that about 80% are. During our sample period, almost all stations

set retail prices on a daily basis.

Euro95 and Diesel are the most important gasoline products with respectively 38% and 54%

of all sold liters. There is a close relation between input prices for gasoline and international

spot market prices. Although the large oil companies are fully integrated from extracting oil to

selling gasoline, the Dutch gasoline sales divisions mainly buy their gasoline at the

Amsterdam-Rotterdam-Antwerp (ARA) spot market which supplies large parts of Western

Europe. A price for this spot market is published once a day. On its website, Shell claims that

even if the Dutch sales division buys gasoline from a production division, it uses this spot

market price as internal price.

2.1 Suggested prices

For each gasoline type, the larger oil companies quote one national suggested price that they

calculate on a daily basis. Oil companies differ in the way they announce these suggested

prices. Some publish them on their website, others do not.9 Nevertheless, all suggested prices

are publicly available since they are published every day on the websites of the ANWB (the

Dutch automobile club with almost 4 million members) and United Consumers.10 In practice,

suggested prices act as reference prices. Stations decide whether to give a “discount”

compared to this reference price. They advertise this “discount” explicitly (whatever the level

of the suggested price may be), for example, via banners along the road (see Figure A1 in

online Appendix A for a photo). These banners rarely change and hence stations often charge

the suggested price minus a constant (see Section 3.1). Consumers therefore typically know

which local station has the lowest retail price.

9 BP and Total publish suggested prices on their website. Shell published its suggested prices on its website until 15 March 2006. Esso and Texaco do not publish their suggested prices on their website. 10 This latter site also links the suggested prices to other websites like the website of a car magazine and nu.nl, a news website that is the tenth most visited website in the Netherlands.

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Shell has explicitly claimed that it uses the suggested price as a way to make changes in the

gasoline spot market price transparent to its dealers. It also published detailed descriptions on

how it calculates the suggested price (see, e.g., Shell (2001) and the Shell website). Shell

claims that every morning it takes the spot market price of the previous day (which is the most

recent price available) and that it adds different taxes and margins (for transport, sales costs,

etc.) to come up with a price it thinks gasoline stations should charge for their gasoline. If this

price differs from the current suggested price, a pricing committee meets to determine

whether it changes the suggested price and by how much. Thus, the committee partly bases

the suggested price on its subjective judgment. If the committee decides to change the

suggested price, the oil company communicates the new suggested price (which is valid from

the next day onward) via fax and e-mail to all dealers in the evening. Dealers can then update

their retail prices the next morning. If oil companies indeed follow this decision process, the

delay between changes in the spot market price and retail prices is exactly two days.

The Shell website contains examples of breakdowns of the suggested price. For example, the

suggested price for a liter of Euro95 excluding taxes was 41.5 cents on 5 May 2004 (in euros,

taxes were 87 cents). Shell claims that the spot market price represented 28 cents, the costs for

the oil company represented 7 cents, the gross profit of the oil company represented 1.5 cents,

and the costs and margin for the gasoline station represented 5 cents.

2.2 Wholesale contracts

Wholesale contracts between oil companies and gasoline stations differ between individual

stations and are not publically available. However, based on public sources and informal

interviews with market participants, we are able to provide the following information on the

nature of these contracts during the sample period.

Coco stations are part of the oil company and thus do not have wholesale contracts. Dodo

stations can enter into a wholesale contract with any oil company. Dealers of codo stations

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can only enter into a wholesale contract with the oil company that owns their station.

The interviews and public sources indicate that wholesale contracts often oblige dodo stations

to exclusively purchase all their gasoline products from one oil company for a period of five

years. For codo stations the duration of the wholesale contract can be much longer (e.g.,

several decades). We understand that the wholesale price often equals the suggested price

minus a station-specific constant. This station-specific constant is in general substantially

higher for dodo stations than for codo stations, in line with their bargaining power.11

Oil companies invoice stations on the basis of the wholesale price at the day of delivery. Oil

companies deliver new stock at their own initiative based on the remaining stock of stations

that they monitor remotely and known turnover patterns. Trucks deliver the gasoline at the

stations.12 The delivery frequency of a station depends on its capacity to stock gasoline and its

turnover, but most stations get new stock once or twice a week.

We understand that many wholesale contracts contain a price support clause, which is almost

identical for the five largest oil companies. Price support entails that a station can ask its oil

company to reduce the wholesale price if a station in its neighborhood lowers its retail price.

The oil company can decide to provide this support. For each number of cents that the

adjusted retail price is below the suggested price, the contract determines how the oil

company and the station split the difference. The oil company recommends during the support

period a station-specific retail price (which may differ from the national suggested price) and

in practice can decide to withdraw the support at any time.13 Price support occurs regularly

and for long(er) continuous periods. Shell has indicated in 2002 that approximately a third of

its stations receive price support.14

11 See ACM (2006), BETA (brochure “BETA eist een eerlijk speelveld voor alle brandstofinkopers!”, 2014), Parket bij de Hoge Raad (decision case 14/04982, ECLI:NL:PHR:2016:7, 2016), Van den Berg (2012, 2013a, 2016), BOVAG (2006), and Volkskrant (“Pomphouders in de houdgreep”, 18 November 2000). 12 See Parket bij de Hoge Raad (decision case 14/04982, ECLI:NL:PHR:2016:7, 2016) and Nijhof-Wassink Transport (2007). 13 There is discussion on whether wholesale contracts allow oil companies to unilaterally decide on the support. 14 See ACM (formerly NMa, press release 01-46 and 03-09), ACM (formerly NMa, decision case 2498-166,

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According to the Dutch competition authority (ACM), the existence of a price support system

can discourage a station to lower its retail price as it knows that competitors can easily follow

suit if their oil companies contribute to their retail price decreases. However, it has decided

not to ban price support as there is “insufficient evidence that banning price support would

lead to lower retail prices”. The European Commission also allows the price support system.15

Van den Berg (a lawyer specialized in wholesale contracts between oil companies and

stations) points out that all oil companies increased the difference between the suggested price

and the spot market price since the late 1980s / early 1990s, while for many codo stations the

difference between the suggested price and their wholesale price (i.e., the station-specific

constant) remained the same. As a result, coco and dodo stations sometimes have a retail price

that is lower than the wholesale price of codo stations. Van den Berg argues that many codo

stations are therefore reliant on price support.16

So, the combination of a suggested price and price support can imply that some (codo)

stations might in practice not be able to set their own retail price (see also Foros and Steen

(2013b)). In addition, we understand that in a limited number of wholesale contracts of codo

stations oil companies guarantee the dealer a minimum and maximum income. Such dealers

therefore have a relatively limited influence on their income. The ACM suggests that this

clause discourages these codo stations to have an independent pricing policy.17

Hence, wholesale contracts between oil companies and stations seem to have a few clauses

that possibly limit the freedom of in particular codo stations to set their own retail price.

Therefore we separately study dealer-owned stations and company-owned stations.

2003, Footnote 44 contains an excerpt from a contract), European Commission (1999), BETA (website “Margebijdragesysteem”, 2009), Van den Berg (2013a, 2013b, 2015), Volkskrant (“Effect aanpak steun pompen onzeker”, 5 July 2002, based on a leaked confidential ACM report), and Court of First Instance (decision case T-354/99, ECLI:EU:T:2006:137, 2006). 15 See ACM (formerly NMa, press release 01-46 and 03-09) and European Commission (press release “Commission Rejects Complaint about Shell’s Petrol Retailing System in the Netherlands”, 19 December 1986). 16 See Van den Berg (2013a, 2013b, 2015). 17 See ACM (formerly NMa, press release 01-46), European Commission (1999), Rechtbank Maastricht (decision case 82747, ECLI:NL:RBMAA:2002:AD9504, 2002), and Volkskrant (“Esso geeft afromen pompsubsidies toe”, 1 March 2001).

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3. Data

Gasoline retail prices are published daily on the website of Athlon Car Lease. This company

leases cars to other firms including a so-called “fuel card”. If the driver fills up his car with

gasoline, he shows the card and the station electronically sends the bill with price and quantity

information to the lease company at the same time. As a result, Athlon Car Lease obtains

retail price quotations from 120,000 drivers, who fill up on average twice a week, from all

over the Netherlands. It publishes the data on retail prices on its website. The website also

shows the transaction date and only contains data that are three to four days old. Hence,

stations cannot use the website to obtain real-time information on competitors’ retail prices.

We downloaded the data on a daily basis over the period 30 May 2006 - 20 July 2008. The

data set includes about 3,600 of the approximately 4,300 gasoline stations in the Netherlands.

Stations that the data set does not include seem to be mostly smaller and nonactive stations

that are randomly distributed over the country. Data are available for seven different types of

gasoline. We focus our analysis on Euro95 and Diesel since these are most commonly used.

The whole data set contains approximately 6,000 unique retail price observations per day.18

For each combination of station and gasoline type, we have a maximum of one retail price

observation per day. Since less busy stations have a lower probability of being visited by a

driver of Athlon Car Lease, the data set contains more observations for busier stations. This

fact does not impact our analysis, however, as there is no indication that the pricing decisions

of larger gasoline stations differ significantly from that of smaller ones (after correction for

ownership structure). Finally, drivers do not have to pay for the gasoline (because the firm

that employs the driver does). So these drivers do not avoid more expensive stations. Casual

observation shows that they also do not avoid cheaper stations.19

18 Only a couple of observations in the data set are suspicious. There seem to be a few cases where a certain type of gasoline is reported as another kind of gasoline. We deleted these observations. 19 There are no Edgeworth cycles in the market. Following the methodology of Lewis (2009), we calculate the median price change of each station and search for price jumps. If there would be Edgeworth cycles, the median price change of a station would be negative and a station would have large price jumps that are independent of

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We matched individual stations in the data set to lists of station-specific characteristics,

namely owner and brand of a station and whether a station is located along a highway.20 We

do not have data on the operator of a station. However, since we do know the ownership

structure, we are able to filter out stations where an oil company decides on the retail price.

Table A1 and A2 in online Appendix A present for different groups of stations the total

number of retail price observations and the average number of retail price observations per

station (for both Euro95 and Diesel). The tables also show the distribution of the number of

observations per station. There are many observations for all groups. The tables confirm that

the data set contains more observations for busier stations. Company-owned stations have

more observations than dealer-owned stations, stations with the brand of one of the five

largest oil companies have more observations than stations with a different brand, and stations

that are located along a highway have more observations than stations that are not located

along a highway. In general, these stations have a busy location. There are also more

observations for Diesel than for Euro95. The last two rows of the tables contain the

observations for dealer-owned and company-owned stations with the brand of one of the five

largest oil companies because we focus our analysis on these stations. There are relatively

many observations for these stations.

The number of stations in the data set (and hence in Table A1 and A2) is not exactly identical

to the number of physical stations. Some gasoline stations changed their name during the

sample period and we do not merge the series of these stations. A name change may reflect a

change in management, brand, or type of service (e.g., manned versus unmanned) and

therefore a change in price setting. Unless mentioned otherwise, in the remainder of the paper

we refer to the number of stations in the data set and not the number of physical stations.

Table A3 in online Appendix A shows that the shares of the different types of gasoline

the spot market price. We do not observe this pattern. 20 We obtained data on the ownership structure and brand of a station from Catalist (a company collecting data on gasoline stations) and a list with highway stations from the Dutch Ministry of Finance. There are a few highway stations that are not on a state-owned location. We used address information to select these stations.

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stations in the data set are more or less the same as their market shares.

During the sample period, we downloaded the suggested prices from the website of United

Consumers. This website contains daily suggested prices of the five largest oil companies. The

spot market price that we use is the daily Platts Barges FOB Rotterdam High (series: Premium

Gasoline 10 PPM for Euro95 and Diesel 10 PPM for Diesel). Shell uses the same quotation

for calculating the suggested price (Shell (2001)). The websites of the European Central Bank

and the Dutch Ministry of Finance provided respectively the dollar-euro exchange rate and

data on taxes. We converted all gasoline prices to prices per liter (excluding taxes) in euros.

3.1 A first look at the data

Figure 1 shows the development over time of the two-day lagged spot market price, the retail

price of a representative Shell station, and the Shell suggested price for one liter Euro95

during the sample period.21 Figure A2 in online Appendix A shows the same data for Diesel.

The upper parts of Table A4 and A5 in online Appendix A present descriptive statistics for

the price series.

For illustration purposes, we plot some figures with Euro95 prices over the last 100 days of

the sample. Figure 2 shows the difference between the retail price and the Shell suggested

price for four selected dealer-owned Shell gasoline stations. The figure suggests that the retail

price of these four stations equals the suggested price minus a constant on almost every day.

Figure 3 shows the difference between the retail price of the same four gasoline stations and

the two-day lagged spot market price. Taken together, Figure 2 and 3 show that the difference

between the suggested price and retail price is more stable than the difference between the

retail price and spot market price. This observation gives some indication that the Shell

suggested price contains information that is not present in the spot market price but that helps

to explain the retail prices of these four gasoline stations.

21 We use the two-day lagged spot market price as this is the input for the suggested price (see Section 2.1).

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Figure 1 Shell suggested price, retail price of a representative Shell station, and spot market price for one liter Euro95

0.25

0.35

0.45

0.55

0.65

0.75

Pi,t Spott-2SugShell,t Notes: T=783, prices in euros per liter (excluding excise duty and VAT).

We are interested in whether suggested prices have a coordinating effect, both across brands

and within brands. This would be the case if (i) all suggested prices contain, next to the two-

day lagged spot market price, the same information, and (ii) this additional information

explains retail prices. In that case, suggested prices have a coordinating effect in the sense that

all retail prices contain the same information which is unrelated to costs (i.e., the information

that firms use under normal competitive conditions).

Figure 5 shows the difference between the suggested price and the spot market price for all

five oil companies. The figure also contains for each oil company the difference between the

average of the suggested prices of the other four oil companies and the spot market price. The

figure shows that oil companies often introduce the same additional information in their

suggested prices. Over the whole sample, the suggested price of at least four (or all five) oil

companies differs in exactly the same way from the two-day lagged spot market price on 61%

(32%) of the days. So it seems that there is a strong common component in the suggested

prices, explaining why suggested prices may have a coordinating effect across brands.

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Figure 2 Difference between retail price and Shell suggested price (Euro95, for four Shell stations)

-0.10

-0.08

-0.06

-0.04

-0.02

0.00

0.02

0.04

12-4-2008 12-5-2008 12-6-2008 12-7-2008

Reta

il pric

e -S

hell

sugg

este

d pr

ice

Figure 3 Difference between retail price and spot market price (Euro95, for four Shell stations)

0.06

0.08

0.10

0.12

0.14

0.16

0.18

0.20

12-4-2008 12-5-2008 12-6-2008 12-7-2008

Reta

il pric

e -s

pot m

arke

t pric

e

Figure 4 Difference between retail price and average of the suggested prices of the other four oil companies (Euro95, for four Shell stations)

-0.10

-0.08

-0.06

-0.04

-0.02

0.00

0.02

12-4-2008 12-5-2008 12-6-2008 12-7-2008

Reta

il pric

e -a

v. o

f oth

er su

g. p

rices

Notes: T=100, differences in euros per liter (excluding excise duty and VAT, rounded at two digits).

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Figure 4 shows the difference between the retail price of the four selected dealer-owned Shell

stations and the average of the suggested prices of the other four oil companies. This average

is a proxy for the information that is common to all suggested prices. Taken together, Figure 3

and 4 show that the difference between the retail price and the common information in all

suggested prices is more stable than the difference between the retail price and the spot

market price. The common information in all suggested prices explains the retail prices of the

four stations better than the spot market price does. This first look at the data suggests that

suggested prices have a coordinating effect across brands.

In combination with Figure 2, Figure 4 shows that the difference between the retail price and

the Shell suggested price is more stable than the difference between the retail price and the

common information in all suggested prices. Thus, the Shell suggested price explains the

retail prices of the selected Shell stations even better.

We study these observations in a more structured way in the next sections. Online Appendix

B generalizes the analysis in this section for all dealer-owned stations with the brand of one of

the five largest oil companies and for the full sample period. Online Appendix C provides

more descriptive statistics on the similarity of changes in suggested prices and retail prices.

The previous figures show how prices change over time. Figure 6 presents differences

between stations. It contains for all dealer-owned stations with the brand of one of the five

largest oil companies the average difference between the retail price and the suggested price

of the oil company of the station (for Euro95). For 17% of the stations the retail price is on

average equal to the suggested price. The retail prices of the other stations are distributed

around five cents below the suggested price. The figure also contains the average difference

for company-owned stations with the brand of one of the five largest oil companies. The

distribution is broadly the same as for dealer-owned stations, although retail price differences

are a bit larger within the group of company-owned stations. Oil companies own unmanned

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Figure 5 Difference between suggested price and spot market price; Difference between average of the suggested prices of the other four oil companies and spot market price (Euro95)

0.120.140.160.180.20

12-4-2008

12-5-2008

12-6-2008

12-7-2008

Sug(i,t) - Spot Sug(-i,t) - Spot ______

Sug-i,t - Spott-2Sugi,t - Spott-2

0.12

0.14

0.16

0.18

0.20

12-4-2008 12-5-2008 12-6-2008 12-7-2008

BP

0.12

0.14

0.16

0.18

0.20

12-4-2008 12-5-2008 12-6-2008 12-7-2008

Esso

0.12

0.14

0.16

0.18

0.20

12-4-2008 12-5-2008 12-6-2008 12-7-2008

Shell

0.12

0.14

0.16

0.18

0.20

12-4-2008 12-5-2008 12-6-2008 12-7-2008

Texaco

0.12

0.14

0.16

0.18

0.20

12-4-2008 12-5-2008 12-6-2008 12-7-2008

Total

Notes: T=100, differences in euros per liter (excluding excise duty and VAT, rounded at two digits).

stations which often have relatively low retail prices. In addition, they own most highway

stations which often have a retail price that equals the suggested price. Figure A3 in online

Appendix A shows that a highway location is an important determinant for the retail price

level of a station.

4. Empirical methodology

As discussed in the previous sections, we investigate whether suggested prices have a

18

Figure 6 Average difference between retail price and suggested price for dealer-owned stations and company-owned stations (Euro95)

0

5

10

15

20

-0.10 -0.08 -0.06 -0.04 -0.02 0.00 0.02 0.04

Perc

enta

ge o

f sta

tions

Average of (retail price - suggested price)

Dealer-owned Company-owned

Notes: T=783, stations with the brand of one of the five largest oil companies (N=1,220 for dealer-owned stations, N=1,098 for company-owned stations), difference in euros per liter (excluding excise duty and VAT, rounded at two digits).

coordinating effect. Under normal competitive conditions, we expect that firms change their

prices in line with cost changes. The cost of a liter of gasoline (i.e., the gasoline spot market

price) is the main relevant factor that changes for all firms on a daily basis. Therefore,

suggested prices have a coordinating effect if they contain, next to the spot market price,

information that explains retail price changes.22

We divide the suggested price of an oil company into three parts: (i) the spot market price, (ii)

a common component that is present in the suggested prices of all oil companies (next to the

spot market price), and (iii) a brand-specific part.

Whether suggested prices have a coordinating effect, depends on the part of the suggested

price that explains retail price changes. If changes in retail prices only reflect changes in the

part of the suggested price that represents the spot market price, then retail price changes are

in line with cost changes and suggested prices do not have a coordinating effect. If changes in 22 We do not focus on the relation between suggested prices and retail price levels as it is unclear what retail price levels would be in a situation without suggested prices (i.e., there is no clear counterfactual).

19

retail prices reflect changes in the common component of the suggested prices, then suggested

prices have a coordinating effect across brands since all retail prices change in the same way

as suggested prices, but in a different way than the spot market price. Finally, if changes in

retail prices reflect changes in the brand-specific part of the suggested price, then suggested

prices have a coordinating effect within brands since retail prices of stations with the same

brand change in the same way as the suggested price of their oil company. Section 6 discusses

the interpretation in more detail. An alternative methodology would be to assess whether the

spot market price, the common information in all suggested prices, or the brand-specific

suggested price explains the retail price best. Online Appendix B shows that this alternative

methodology results in the same conclusions.

We model the relation between the retail price and the three different parts of the suggested

price via a conditional error correction model:

=

−−−−=

−−=

− −Δ+Δ+Δ=Δr

kktktik

r

kktk

q

kktikti SpotSugSpotPP

02,

02

1,, )(γβα

][)( 31,1

0,, −−

=−−− −+−Δ+ tti

r

kktiktik SpotPSugSug φλ

tiititititi SugPSugP ,1,1,31,1,2 ][][ εηφφ ++−+−+ −−−−− (1)

where tiP , is the retail price of one liter of gasoline at station i on day t, ktSpot −−2 is the

gasoline spot market price on day t-2-k, ktiSug −− , is the average of the suggested prices of all

oil companies other than the oil company of the brand of station i on day t-k, and ktiSug −, is

the suggested price of the oil company of station i on day t-k.23 Finally, iη is a station-specific

effect and ti,ε is the error term which is allowed to be heteroskedastic (both across stations

and time), serially correlated, and cross-sectionally correlated (contemporaneous and

23 We choose a linear relationship instead of a log specification because the data show that the absolute differences between retail prices, suggested prices, and the spot market price are stable and independent of the level of the spot market price (see Figure 1 and also Borenstein, Cameron, and Gilbert (1997)).

20

lagged).24 All prices are measured in euros per liter and are excluding excise duty and VAT.

As mentioned in Section 2.1, we delay the spot market price with two days, since this is the

relevant input price for the suggested price.25

First, we motivate why we choose a conditional error correction model. All four price series

( tiP , , 2−tSpot , tiSug ,− , and tiSug , ) are integrated of order 1.26 From Figure 1, it seems that

there is one single stochastic trend driving the four price series. In fact, given the strict order

in which the prices are set, it seems appropriate to consider the spot market price as the

underlying stochastic trend. In other words, there seem to be three stationary cointegrating

relations between these four price series. These three linear combinations of price series are

not uniquely defined, but can be expressed in many ways. Therefore we choose an expression

that is easy to interpret.

Second, we discuss how to interpret Equation (1). In Equation (1) the change in the retail

price depends on the previous change in the retail price, the current (and previous) change in

the different parts of the suggested price, and the error correction terms. We first discuss the

direct impact of changes in the different parts of the suggested price on retail price changes. In

Equation (1), the second term denotes the part of the suggested price that reflects spot market

price changes. The variable ktiSug −− , proxies the information that is common in all suggested

prices. So the third term, )( 2, ktkti SpotSug −−−− − , is a proxy for the information that all oil

companies introduce in their suggested price over and above the spot market price (the

common component of the suggested prices). If the coefficients γ are positive and

significant, retail price changes contain information that is present in the suggested prices of

24 Contemporaneous correlation might, for example, exist because of errors in the suggested price data. If the brand-specific suggested price is important for explaining the retail price of each station and if there is an error in the data for the suggested price of a certain oil company on a certain day, then the price of all stations with that brand will deviate from the predicted retail price in the same manner. 25 The suggested price has the highest correlation with the spot market price if we use a two-day lag. 26 Augmented Dickey-Fuller unit root tests indicate that the two-day lagged spot market price, the suggested prices of the five oil companies, and the averages of the suggested prices are integrated of order 1 (for both Euro95 and Diesel). Augmented Dickey-Fuller Fisher panel unit root tests (Maddala and Wu (1999) and Choi (2001)) indicate that both Euro95 and Diesel retail prices are integrated of order 1. Augmented Dickey-Fuller unit root tests on individual price series of gasoline stations indicate that 98% of both Euro95 and Diesel retail price series are integrated of order 1. We treat all Euro95 and Diesel retail price series as integrated of order 1.

21

all oil companies and that is additional to the spot market price. In that case, suggested prices

have a coordinating effect across brands. Finally, the fourth term, )( ,, ktikti SugSug −−− − ,

reflects the brand-specific part of the suggested price and proxies the information that is only

present in the suggested price of the oil company of station i and not in the suggested prices of

other oil companies. If the parameters λ are positive and differ significantly from zero, then

suggested prices have a coordinating effect within brands.

The error correction terms are the terms between square brackets and define the long-run or

“equilibrium” relationships between the retail price and the other three price series. We

assume that the cointegrating relations can be specified as the difference between two price

series (see also Footnote 23). In the long run, changes in the underlying stochastic trend of the

three price series (which is the spot market price) should be fully reflected in retail prices.

Moreover, if we do estimate the long-run impact of 3−tSpot , 1, −− tiSug , and 1, −tiSug on 1, −tiP ,

we find, in line with our assumption, that their coefficients almost equal 1 (see online

Appendix B and Faber (2015)).27 The parameters φ show to what extent retail price changes

are influenced by deviations in the equilibrium relation between the retail price and

respectively the spot market price, the common information in all suggested prices, and the

brand-specific suggested price. Differences between the parameters φ show of which of the

three long-run relations the deviations have the largest impact on retail price changes.

Third, we discuss possible endogeneity issues. Simultaneity is not an issue due to the strict

order in which the prices are set: on day t-2 the spot market price, on day t-1 suggested prices,

and on day t retail prices (see Section 2.1). In theory, it could be that an omitted variable will

drive our results and that a correlation between retail prices and the additional information in

suggested prices exists because an unobserved factor impacts both retail prices and suggested

prices. For example, firms could pass through an unobserved cost that suggested prices also

contain. However, such an unobserved factor would have to change on a daily basis and in the 27 The panel cointegration test of Kao (1999) reveals homogeneous cointegration between retail prices and the two-day lagged spot market price, the common information in all suggested prices, and brand-specific suggested prices (for both Euro95 and Diesel).

22

same way for all firms to explain common daily retail price changes. Moreover, oil companies

should know this information one day in advance to be able to include it in their suggested

prices (see Section 2.1).

In breakdowns of suggested prices and retail prices, the spot market price is the only cost that

changes on a daily basis and in the same way for all firms (see the Shell website and BOVAG

(2006) which presents detailed examples of stations’ balance sheets). Other costs, like storage,

transportation, marketing, overhead of the oil company, staff of the station, investments in the

station, and compliance with environmental regulation, do not meet these requirements. For

example, transportation costs do not change in the same way for all firms on a daily basis as

we understand that oil companies generally outsource the transportation of gasoline.28 29

It also seems unlikely that the additional information in suggested prices reflects predictable

common daily changes in demand. People drive 21% fewer kilometers on Sundays compared

to Saturdays and 45% more kilometers on Mondays compared to Sundays.30 It is hard to

imagine any other factor that has a stronger impact on daily changes in demand. However, oil

companies never change their suggested prices on Sundays and rarely on Mondays. Given the

limited impact of these large predictable common daily changes in demand, we expect that

smaller changes also have a limited impact. All in all, we therefore have good reasons to

believe that endogeneity issues will not drive our results.

Fourth, we present some descriptive statistics on the three parts of the suggested price. Figure

7 presents the three parts of the suggested price and the difference between the retail price and

the suggested price for a dealer-owned Shell station over the last 100 days of the sample

28 Shell, for instance, fully outsources the delivery of stations’ gasoline to transportation companies (Nijhof-Wassink Transport (2007)). Transportation companies usually use longer-term contracts with a monthly or quarterly adjustment for changes in the cost of Diesel. 29 The breakdowns do not include biofuels. However, law requires the addition of 2% biofuels from 1 January 2007 onwards and 3.25% from 1 January 2008 onwards. This requirement applies to the total annual energy content of sold gasoline (Ministry of Housing, Spatial Planning and the Environment (2006)). Gasoline therefore does not necessarily contain for each day, oil company, station, and gasoline type the same percentage of biofuels. As a robustness check we have looked at all results for the period before 2007. All results are the same as for the full sample period. 30 Source: Statistics Netherlands, Statline (data for 2007).

23

Figure 7 Decomposition of the retail price of a dealer-owned Shell station (Euro95)

-0.10

0.00

0.10

0.20

0.30

0.40

0.50

0.60

12-4-2008 12-5-2008 12-6-2008 12-7-2008

Pi,t - SugShell,t

_____

Sug-Shell,t - Spott-2_____

SugShell,t - Sug-Shell,t

Spott-2

Notes: T=100, in euros per liter (excluding excise duty and VAT, differences are rounded at two digits). The three parts of the suggested price and the difference between the retail price and the suggested price add up to the retail price of the station: )()()( ,,,,2,2, tShelltitShelltShellttShelltti SugPSugSugSpotSugSpotP −+−+−+= −−−− .

(Euro95). The four series add up to the retail price of the station. The retail price equals the

Shell suggested price minus 3 cents on almost every day (see also Figure 2). All three parts of

the suggested price change over time and contribute to changes in the retail price. As the

suggested prices of the five oil companies are relatively similar, changes in the common

component of the suggested prices are relatively large compared to changes in the brand-

specific component (see also Figure 5). The lower parts of Table A4 and A5 in online

Appendix A present more descriptive statistics on the three parts of the suggested price for all

five oil companies.

We estimate Equation (1) two times: once for our subsample of Euro95 prices and once for

our subsample of Diesel prices. Our sample only contains stations with the brand of one of the

five largest oil companies, since for these stations we have data on brand-specific suggested

prices.31 We use the Ordinary Least Squares (OLS) Within estimator.32 Moreover, we use

31 Retail prices are not available for all stations for all days. We can only use an observation of a station’s retail price for estimation when the previous q+1 observations of this station are also available. 32 Although this estimator is inconsistent for dynamic models if the number of observations over time is fixed

24

Driscoll-Kraay standard errors that are robust to heteroskedasticity, serial correlation, and

cross-sectional correlation (see Driscoll and Kraay (1998) and also Hoechle (2007) and

Vogelsang (2012)).

5. Results

5.1 Dealer-owned stations

We first focus on dealer-owned gasoline stations that use the brand of one of the five largest

oil companies. These stations are in principle free to set their own retail prices. The first

column of Table 1 contains the estimation results of Equation (1) with q=0 and r=0 for

Euro95.

The table shows that the coefficient for the immediate impact of the part of the suggested

price that reflects the spot market price equals 0.88.33 So a change in this part of the suggested

price is largely reflected in the change in the retail price. However, we also find a strong

impact of the part of the suggested price that reflects the information that is, next to the spot

market price, common in the suggested prices of all oil companies. The coefficient of this

term is, with a value of 0.83, quite substantial and highly significant. This result indicates that

the common component of the suggested prices contains information that explains retail

prices over and above the fluctuations in the spot market price. The additional information in

the brand-specific suggested price, the third part of the suggested price, is also important for

explaining the retail price. The value of the coefficient is 0.67. So retail prices also contain

information that is only present in the brand-specific suggested price.

(even if the number of groups go to infinity), it is consistent if the number of observations over time also go to infinity. Our data set contains many stations over a relatively long period (783 days), so using the OLS Within estimator should not be a problem. 33 The specification includes the change in the spot market twice, namely as the part of the suggested price that reflects the spot market price and in the part of the suggested price that reflects the information that is, next to the spot market price, common in the suggested prices of all oil companies. Therefore, the impact of the change in the spot market on the change in the retail price is 0.05 (0.88-0.83). This outcome shows that suggested prices contain most of the information in the spot market price that is also present in retail prices.

25

Table 1 Estimation results dealer-owned stations Euro95

Equation (1), Dependent variable: ∆Pi,t q=0, r=0 Sample DO & Brand

Method Panel Individual time series

∆Spott-2 0.88 (0.01) 0.89 (99%) ______

∆(Sug-i,t - Spott-2) 0.83 (0.01) 0.85 (98%) ______

∆(Sugi,t - Sug-i,t) 0.67 (0.02) 0.69 (94%)

(Pi,t-1 - Spott-3) -0.04 (0.01) -0.03 (36%) ______

(Pi,t-1 - Sug-i,t-1) -0.04 (0.01) -0.09 (40%)

(Pi,t-1 - Sugi,t-1) -0.21 (0.01) -0.39 (79%)

Observations (average) 372,450 451 Stations 1,176 782

Notes: T=783. The first column contains the coefficient and the Driscoll-Kraay standard error (the lag length that is considered in the serial correlation structure is determined via the Newey-West procedure (6 lags)). Station-specific effects are not reported. The second column contains the average coefficient and the percentage of stations for which the coefficient is statistically significant at the 5% significance level (based on the Newey-West standard error, lag length is set via the Newey-West procedure). Stations for which fewer than 150 observations are used for estimation are excluded. The average constant is not reported. DO = dealer-owned stations, Brand = stations with the brand of one of the five largest oil companies.

Descriptive statistics show that changes in the common component of the suggested prices are

in general larger than changes in the brand-specific part of the suggested price (see, for

example, Figure 7). The estimated coefficient is also somewhat larger for this term. Hence,

changes in the common component of the suggested prices contribute in general more to retail

price changes than changes in the brand-specific part of the suggested price. Online Appendix

B quantifies this effect and shows that the brand-specific suggested price explains the retail

price better than the common information in all suggested prices, although its additional

explanatory power is relatively small.

All estimated parameters φ are negative, indicating that in all three cases the retail price is

corrected in the direction of the equilibrium retail price when it deviates from what the

equilibrium relation implies. However, the estimated values of the parameters 1φ and 2φ are

close to 0 (both are -0.04), while the estimated value of 3φ is -0.21. These values indicate that

26

retail prices respond mostly to deviations in the long-run relation between the retail price and

the brand-specific suggested price. Deviations in the long-run relation between the retail price

and the spot market price and between the retail price and the common information in all

suggested prices are less important for explaining retail price changes.34 Figure A4 in online

Appendix A shows the number of observations per station that we use for estimation. The

average number of observations per station is 317. Results do not change if we exclude

stations for which we use fewer than 150 observations for estimation.35

To take into account possible heterogeneity of stations, we estimate Equation (1) with q=0

and r=0 separately for each individual station. We use OLS and Newey-West standard errors.

As there are relatively many dealer-owned stations with a limited number of observations (see

Figure A4), we exclude stations for which we use fewer than 150 observations for estimation.

The qualitative results do not change if we use a lower threshold.

The second column of Table 1 shows that for almost all stations changes in the retail price

reflect changes in all three parts of the suggested price. The means of the estimated individual

time series coefficients are for the three parts of the suggested price similar to the estimated

panel coefficients. The change in the part of the suggested price that reflects the spot market

price is for 99% of the stations significant at the 5% significance level. The change in the part

of the suggested price that is common in all suggested prices is significant for 98% of the

stations. The change in the brand-specific part of the suggested price is significant for 94%.

Since the coefficients of the individual regressions are statistically significant for (almost) all

stations, the conclusion that all three parts of the suggested price are important for explaining

retail prices is also valid for subgroups of the sample, such as stations with a specific brand,

stations in a specific region (e.g., cities or close to the border), and stations with specific local

market conditions (e.g., no nearby competitors).

34 All qualitative conclusions are the same if we use cluster-robust standard errors. These standard errors are robust to cross-sectional heteroskedasticity and within-station (serial) correlation, but require the residuals to be uncorrelated across stations. 35 All estimates in the remainder of this paper are also robust to this change in the sample.

27

Table A6 in online Appendix A shows that estimation results are similar for Diesel and Figure

A5 in online Appendix A shows the number of observations per station in case of panel

estimation for Diesel (the average is 400 observations). Online Appendix D checks the

robustness of the findings by estimating alternative specifications. These alternative

specifications confirm our findings. Among others, we estimate Equation (1) with q=3 and

r=3 and find that our estimates are robust to changes in the lag specification. We also check

whether the results differ if we include the one-day lagged change in the spot market price as

an additional variable. We find that this variable has a limited impact on the change in the

retail price when we control for the additional information in the suggested price, in line with

the observation that many stations use banners to communicate that the retail price equals the

suggested price minus a constant.

5.2 Company-owned stations

We now focus on the company-owned gasoline stations with the brand of one of the five

largest oil companies. The group of company-owned stations includes both coco (“company-

owned and company-operated”) stations and codo (“company-owned and dealer-operated”)

stations. Oil companies set the retail prices of coco stations (there are no wholesale contracts).

Dealers of codo stations set in principle their own retail prices (although in practice there may

be restrictions, see Section 2.2). As their price setting behavior may differ, we first assess the

effect of suggested prices for these two subgroups separately. Since we do not know whether

a station is company-operated or dealer-operated, we estimate a separate equation for each

individual company-owned station. If the coefficients of the individual regressions are

statistically significant for (almost) all individual company-owned stations, then all three parts

of the suggested price are important for the two subgroups.36

36 Approximately 50% of the company-owned stations are coco stations (see Section 2). The data set includes almost all company-owned stations. BOVAG (2006) reports that there are 1,613 company-owned stations. Table A1 and A2 in online Appendix A report that the data set contains 1,603 company-owned stations (of all brands)

28

Table 2 Estimation results company-owned stations Euro95

Equation (1), Dependent variable: ∆Pi,t q=0, r=0 Sample CO & Brand

Method Individual time series Panel

∆Spott-2 0.92 (99%) 0.92 (0.01) ______

∆(Sug-i,t - Spott-2) 0.88 (99%) 0.87 (0.01) ______

∆(Sugi,t - Sug-i,t) 0.74 (97%) 0.73 (0.02)

(Pi,t-1 - Spott-3) -0.03 (33%) -0.03 (0.00) ______

(Pi,t-1 - Sug-i,t-1) -0.08 (38%) -0.04 (0.01)

(Pi,t-1 - Sugi,t-1) -0.32 (75%) -0.15 (0.01)

Observations (average) 534 579,202 Stations 1,084 1,093

Notes: T=783. The first column contains the average coefficient and the percentage of stations for which the coefficient is statistically significant at the 5% significance level (based on the Newey-West standard error, lag length is set via the Newey-West procedure). The average constant is not reported. The second column contains the coefficient and the Driscoll-Kraay standard error (the lag length that is considered in the serial correlation structure is determined via the Newey-West procedure (6 lags)). Station-specific effects are not reported. CO = company-owned stations, Brand = stations with the brand of one of the five largest oil companies.

For each individual company-owned station with the brand of one of the five largest oil

companies, we estimate Equation (1) with q=0 and r=0 for Euro95. The first column of Table

2 shows that for almost all individual company-owned stations all three parts of the suggested

price are important for explaining the retail price. The change in the part of the suggested

price that reflects the spot market price is for 99% of the company-owned stations significant

at the 5% significance level. The same is true for the change in the part of the suggested price

that is common in all suggested prices. The change in the brand-specific part of the suggested

price is for 97% of the company-owned stations significant at the 5% significance level. The

parameter that measures the impact of deviations in the long-run relation between the retail

price and the brand-specific suggested price is significant at the 5% significance level for 75%

of the company-owned stations. Hence, for both coco and codo stations the part of the

for Euro95 and 1,609 for Diesel (these numbers might not be exactly identical to the number of physical stations, see Section 3).

29

suggested price that is common in all suggested prices and the part of the suggested price that

is brand-specific are important for explaining retail prices in addition to the part of the

suggested price that reflects the spot market price.

We also estimate Equation (1) with q=0 and r=0 for the whole group of company-owned

stations. The second column of Table 2 shows that for the three parts of the suggested price

the estimated panel coefficients are similar to the mean of the estimated individual time series

coefficients. The descriptive statistics and estimated coefficients show that changes in the

common component of the suggested prices contribute in general more than changes in the

brand-specific part of the suggested price. Figure A4 in online Appendix A shows the number

of used observations per station (the average is 530).

The panel coefficients are also similar to the coefficients for dealer-owned stations. The

immediate impact of the three parts of the suggested price is a bit larger for company-owned

stations than for dealer-owned stations.37 However, differences between the coefficients are

small. The impact of changes in suggested prices is broadly similar for the two groups. In

combination with Figure 6, this result shows that retail price behavior of company-owned

stations and dealer-owned stations is not very different.

Table A7 in online Appendix A shows that also for company-owned stations all estimation

results are similar for Diesel. Figure A5 in online Appendix A presents the number of

observations per station that we use in the panel estimation for Diesel (the average is 559).

The qualitative conclusions are the same when we estimate Equation (1) with q=3 and r=3.

37 If we include in Equation (1) separate coefficients for the two groups of stations and estimate it for the combined sample of dealer-owned and company-owned stations (such that the estimated coefficients are identical to the coefficients in Table 1 and 2), then a joint Wald test rejects the null hypothesis that the coefficients for the three parts of the suggested price are equal for both groups at the 1% significance level.

30

6. Discussion

The previous section shows that retail prices of “company-owned and company-operated”

(coco) stations, “company-owned and dealer-operated” (codo) stations, and “dealer-owned

and dealer-operated” (dodo) stations contain the information that is, next to the spot market

price, common to all suggested prices and the information that is only present in the brand-

specific suggested price. How should we interpret these findings: do suggested prices have a

coordinating effect on retail prices of stations or on wholesale contracts of oil companies?

What may be the effect on consumer welfare?

The interpretation of our findings depends on whether wholesale contracts between oil

companies and dealer-operated stations, in particular wholesale prices, depend on suggested

prices. Based on interviews and public sources, we understand that wholesale prices often

equal the suggested price minus a station-specific constant (see Section 2.2). However, we do

not observe wholesale contracts ourselves. Therefore, we discuss both cases.

If wholesale contracts do not depend on suggested prices, then stations themselves introduce

the additional information that suggested prices contain in their retail prices. The common

component of the suggested prices has in this case a coordinating effect on retail prices of all

stations (coco, codo, and dodo). Stations introduce this additional information that all

suggested prices contain in their retail prices, even though this information is not related to

their costs. In this case the brand-specific component of the suggested prices has an additional

coordinating effect on retail prices of stations with the same brand.38

If wholesale contracts between oil companies and dealer-operated stations do depend on

suggested prices, then oil companies introduce the additional information. For example, if

wholesale prices equal the suggested price minus a station-specific constant (as suggested by

the interviews and public sources), then the additional information that suggested prices

38 Both the common component and the brand-specific component do not have an intra-brand coordinating effect on the retail prices of coco stations as a single firm (the oil company) sets these retail prices.

31

contain reflects a cost for dealer-operated stations that they subsequently pass on in their retail

prices.39 In this case the common component of the suggested prices has a coordinating effect

on the wholesale contracts of oil companies as they all introduce the additional information

that all suggested prices contain, even though this information is not related to their costs.

Moreover, there is a coordinating effect on the retail prices of coco stations. These stations do

not buy from a wholesaler, but introduce the common component of the suggested prices in

their retail prices nevertheless. At the same time, coco stations also harmonize their retail

prices with dealer-operated stations (for which the wholesale contracts contain the additional

information). Due to the brand-specific component, coco stations harmonize their retail prices

most with dealer-operated stations with the same brand.

Therefore, suggested prices have a coordinating effect on retail prices of coco stations (that is,

retail prices of oil companies) irrespective of whether wholesale contracts depend on these

suggested prices. Dependent on the conditions in wholesale contracts, suggested prices also

have a coordinating effect on retail prices of dealer-operated stations or, as suggested by the

interviews and public sources, on wholesale contracts of oil companies. In both cases, the

suggested price that an oil company publishes has a coordinating effect on the behavior of the

oil company and its competitors.

Note that most stations do not charge the suggested price, but give a firm-specific “discount”

on the suggested price. What we show is that this “discount” is almost constant so that changes

in retail prices are similar to changes in suggested prices. Many retailers commit to this fixed

“discount” via clearly visible banners along the road. Local consumers and competitors know

this “discount”. Local price dispersion may still prevail as some consumers are less price

sensitive than others (e.g., because their car lease contracts include gasoline purchases) like in

consumer search models à la Stahl (1989) and Janssen, Pichler, and Weidenholzer (2011).

39 Alternatively, wholesale contracts could possibly contain other conditions that incentivize all retailers to change their retail prices in line with suggested prices (see Foros and Steen (2013a, 2013b)). Interviews and public sources suggest that this is not the case in the Netherlands as only the wholesale contracts of some (codo) stations may contain elements that could limit their freedom to set their own retail prices (see Section 2.2).

32

Potentially, there could be alternative explanations for the evidence that we provide, but the

fact that stations use banners to fix their retail price strategies relative to the suggested price

reduces the likelihood of these alternatives. For example, given the banners, it is not likely

that only some stations follow suggested prices, whereas others copy the retail prices of their

(local) competitors. Moreover, price leadership cannot explain that retail prices contain the

brand-specific part of the suggested price as most stations would have a different brand than

their local price leader.40 Another alternative explanation for the correlation between retail

prices and the additional information in suggested prices could be that suggested prices

contain information on unobserved factors that are relevant for price setting. We have argued

that it is unlikely that such an unobserved factor drives our results (see Section 4).

On the basis of our study, we cannot unambiguously assess the overall effect of suggested

prices for consumers. Normally, horizontal coordinating effects harm consumers as they lead

to higher average retail prices. However, some consumers may benefit as suggested prices

assist stations to communicate to consumers that they have low retail prices at any particular

day independent of their frequently changing retail prices. So suggested prices may help price

sensitive consumers to save on search costs.

Moreover, the interplay with other institutional features of the market is important for

assessing the effect of suggested prices for consumers. For example, in Sweden oil companies

combine suggested prices with a rebate system for business customers that de facto implies

price discrimination (Foros and Steen (2013a)) and in Norway oil companies combine

suggested prices with a price support system that de facto implies a shift in price control from

stations to oil companies (Foros and Steen (2013b)). This may also be the case in the

Netherlands as some wholesale contracts may contain elements that in combination with

suggested prices could limit the freedom of some (codo) stations to set their own retail prices.

Therefore competition authorities should consider the overall effect of suggested prices.

40 Note that suggested prices still have a coordinating effect on retail prices of the local leaders in case of price leadership.

33

References

ACM (NMa) (2006) “Benzinescan 2005/2006”.

Albæk, Svend, Peter Møllgaard, Per B. Overgaard (1997) “Government-Assisted Oligopoly

Coordination? A Concrete Case” Journal of Industrial Economics, 45(4), 429-443.

Borenstein, Severin (2004) “Rapid Price Communication and Coordination: The Airline

Tariff Publishing Case (1994)” In The Antitrust Revolution, edited by John E. Kwoka Jr.,

Lawrence J. White, 4th ed., 233-251. New York: Oxford University Press.

Borenstein, Severin, A. Colin Cameron, Richard Gilbert (1997) “Do Gasoline Prices Respond

Asymmetrically to Crude Oil Price Changes?” Quarterly Journal of Economics, 112(1),

305-339.

BOVAG (2006) Tankstations in cijfers 2006-2007. Bunnik.

Buccirossi, Paolo (2008) “Facilitating Practices” In Handbook of Antitrust Economics, edited

by Paolo Buccirossi, 305-351. Cambridge: The MIT Press.

Buehler, Stefan, Dennis L. Gärtner (2013) “Making Sense of Nonbinding Retail-Price

Recommendations” American Economic Review, 103(1), 335-359.

Cason, Timothy N. (2008) “Price Signaling and “Cheap Talk” in Laboratory Posted Offer

Markets” In Handbook of Experimental Economics Results Volume 1, edited by Charles R.

Plott, Vernon L. Smith, 164-169. Amsterdam: North-Holland.

Choi, In (2001) “Unit Root Tests for Panel Data” Journal of International Money and

Finance, 20(2), 249-272.

CNC (2012) “Report on the Consultation Request Submitted by the Secretary of State for the

Economy and Support for Enterprises regarding the Automotive Fuel Market in Spain”.

De los Santos, Babur, In Kyung Kim, Dmitry Lubensky (2013) “Estimating the Effect of the

Manufacturer’s Suggested Retail Price” Indiana University, Mimeo.

Driscoll, John C., Aart C. Kraay (1998) “Consistent Covariance Matrix Estimation with

Spatially Dependent Panel Data” Review of Economics and Statistics, 80(4), 549-560.

European Commission (1999) “Commission Decision of 20 July 1999 on the State Aid

Implemented by the Netherlands for 633 Dutch Service Stations Located near the German

34

Border” Official Journal of the European Communities, 42(L 280), 87-121.

Faber, Riemer P. (2015) “More New Evidence on Asymmetric Gasoline Price Responses”

Energy Journal, 36(3), 287-307.

Foros, Øystein, Frode Steen (2013a) “Retail Pricing, Vertical Control and Competition in the

Swedish Gasoline Market” Report for the Swedish Competition Authority.

Foros, Øystein, Frode Steen (2013b) “Vertical Control and Price Cycles in Gasoline

Retailing” Scandinavian Journal of Economics, 115(3), 640-661.

Gripsrud, Geir (1982) “Price Behavior and Store Size in Grocery Retailing” Scandinavian

Journal of Economics, 84(3), 471-482.

Hay, George A. (1999) “Facilitating Practices: The Ethyl Case (1984)” In The Antitrust

Revolution, edited by John E. Kwoka Jr., Lawrence J. White, 3rd ed., 182-201. New York:

Oxford University Press.

Hoechle, Daniel (2007) “Robust Standard Errors for Panel Regressions with Cross-Sectional

Dependence” Stata Journal, 7(3), 281-312.

Hofstetter, Marc, Jorge Tovar (2010) “Common Knowledge Reference Price and Asymmetric

Price Adjustments. Evidence from the Retail Gasoline Market in Colombia” Review of

Industrial Organization, 37(2), 141-159.

Holt, Charles A. (1995) “Industrial Organization: A Survey of Laboratory Research” In The

Handbook of Experimental Economics, edited by John H. Kagel, Alvin E. Roth, 349-443.

Princeton: Princeton University Press.

Janssen, Maarten C.W., Paul Pichler, Simon Weidenholzer (2011) “Oligopolistic Markets

with Sequential Search and Production Cost Uncertainty” RAND Journal of Economics,

42(3), 444-470.

Kao, Chihwa (1999) “Spurious Regression and Residual-Based Tests for Cointegration in

Panel Data” Journal of Econometrics, 90(1), 1-44.

Knittel, Christopher R., Victor Stango (2003) “Price Ceilings as Focal Points for Tacit

Collusion: Evidence from Credit Cards” American Economic Review, 93(5), 1703-1729.

Kühn, Kai-Uwe (2001) “Fighting Collusion by Regulating Communication between Firms”

Economic Policy, 16(32), 167-204.

35

Lewis, Matthew S. (2009) “Temporary Wholesale Gasoline Price Spikes Have Long-Lasting

Retail Effects: The Aftermath of Hurricane Rita” Journal of Law and Economics, 52(3),

581-605.

Lubensky, Dmitry (2017) “A Model of Recommended Retail Prices” RAND Journal of

Economics, 48(2), 358-386.

Maddala, G.S., Shaowen Wu (1999) “A Comparative Study of Unit Root Tests with Panel

Data and a New Simple Test” Oxford Bulletin of Economics and Statistics, 61(S1), 631-652.

Marshall, Robert C., Leslie M. Marx, Matthew E. Raiff (2008) “Cartel Price Announcements:

The Vitamins Industry” International Journal of Industrial Organization, 26(3), 762-802.

Ministry of Housing, Spatial Planning and the Environment (2006) “Besluit biobrandstoffen

wegverkeer 2007” Staatsblad van het Koninkrijk der Nederlanden, 2006(542), 1-15.

Motta, Massimo (2004) Competition Policy. Cambridge: Cambridge University Press.

Nijhof-Wassink Transport (2007) “It’s Better to Leave Transport to the Professionals” De

Schakel, September, 6-8.

Sen, Anindya, Anthony Clemente, Linda Jonker (2011) “Retail Gasoline Price Ceilings and

Regulatory Capture: Evidence from Canada” American Law and Economics Review, 13(2),

532-564.

Shell (2001) “Het is echt heel transparant. Hoe ontstaan de adviesprijzen voor benzine?” Shell

Venster, July/August, 16-18.

Stahl, Dale O., II (1989) “Oligopolistic Pricing with Sequential Consumer Search” American

Economic Review, 79(4), 700-712.

Van den Berg, Dirk (2012) “Pomphouders aller merken verenigt u!” Pompshop, June, 25.

Van den Berg, Dirk (2013a) “Adviesprijzen exit?” Pompshop, June, 13.

Van den Berg, Dirk (2013b) “Exploitanten exit?” Pompshop, July/August, 13.

Van den Berg, Dirk (2015) “Kortingen. Waarop eigenlijk? Schaf ze af!” Pompshop, June, 11.

Van den Berg, Dirk (2016) “De knellende marges van de codo’s” Pompshop, March, 13.

Vogelsang, Timothy J. (2012) “Heteroskedasticity, Autocorrelation, and Spatial Correlation

Robust Inference in Linear Panel Models with Fixed-Effects” Journal of Econometrics,

166(2), 303-319.

36

Online Appendix A: Extra figures and tables

Figure A1 Standard sign at a Shell gasoline station

Translation from Dutch: Discount!* Euro95: 7.0 cents per liter Diesel: 5.0 cents per liter LPG: not applicable (the station does not sell LPG) *) Discount with respect to the Shell suggested price.

37

Table A1 Number of retail price observations Euro95

Sample Number of obs.

Number of stations

Av. number of obs. Percentage of stations

1-149 obs.

150-299 obs.

300-449 obs.

450-599 obs.

600-783 obs.

All 1,635,653 4,004 409 22% 16% 14% 16% 33% DO 806,631 2,401 336 30% 19% 16% 16% 19% CO 829,022 1,603 517 9% 11% 11% 15% 53% Brand 1,139,512 2,318 492 13% 11% 11% 15% 49% No Brand 496,141 1,686 294 33% 22% 18% 16% 11% Highway 182,999 315 581 6% 7% 8% 10% 69% Nonhighway 1,452,654 3,689 394 23% 16% 15% 16% 30% DO & Brand 493,636 1,220 405 21% 15% 15% 18% 30% CO & Brand 645,876 1,098 588 4% 7% 7% 12% 70%

Table A2 Number of retail price observations Diesel

Sample Number of obs.

Number of stations

Av. number of obs. Percentage of stations

1-149 obs.

150-299 obs.

300-449 obs.

450-599 obs.

600-783 obs.

All 1,886,929 4,013 470 15% 12% 13% 17% 43% DO 993,502 2,404 413 20% 15% 16% 19% 30% CO 893,427 1,609 555 7% 9% 9% 14% 62% Brand 1,262,154 2,327 542 9% 8% 10% 14% 59% No Brand 624,775 1,686 371 23% 18% 17% 21% 21% Highway 192,789 315 612 5% 4% 7% 9% 76% Nonhighway 1,694,140 3,698 458 16% 13% 13% 18% 40% DO & Brand 591,545 1,224 483 13% 10% 14% 19% 44% CO & Brand 670,609 1,103 608 4% 6% 5% 9% 76%

Notes: DO = dealer-owned stations, CO = company-owned stations, Brand = stations with the brand of one of the five largest oil companies.

38

Table A3 Market share and share in the data set

Sample Market share Share in data set Euro95 Diesel

DO 62.4% 60.0% 59.9% Brand 58.0% 57.9% 58.0% BP 9.1% 9.0% 9.1% Esso 8.3% 9.0% 9.0% Shell 14.4% 15.8% 15.7% Texaco 12.6% 11.5% 11.5% Total 13.5% 12.6% 12.6% Highway 5.9% 7.9% 7.8%

Notes: DO = dealer-owned stations, Brand = stations with the brand of one of the five largest oil companies. The source of the market shares is BOVAG (2006), except for the number of highway stations for which the source is the Dutch Ministry of Finance (the reported market share for highway stations is a bit too low as not all highway stations are on a state-owned location, see Footnote 20).

Figure A2 Shell suggested price, retail price of a representative Shell station, and spot market price for one liter Diesel

0.30

0.40

0.50

0.60

0.70

0.80

0.90

Pi,t Spott-2SugShell,t Notes: T=783, prices in euros per liter (excluding excise duty and VAT).

39

Table A4 Descriptive statistics Euro95

Price series (sample) Average Std. dev. Min. 10th percentile Median 90th

percentile Max. Number of obs.

Pi,t (Brand) 0.521 0.074 0.286 0.417 0.524 0.614 0.785 1,139,512

Pi,t (DO & Brand) 0.523 0.073 0.333 0.421 0.526 0.617 0.785 493,636

Pi,t (CO & Brand) 0.519 0.074 0.286 0.412 0.518 0.614 0.751 645,876 ___

Pt (Brand) 0.522 0.069 0.393 0.423 0.527 0.602 0.686 745

SugBP,t 0.561 0.070 0.429 0.461 0.567 0.641 0.730 783

SugEsso,t 0.560 0.071 0.429 0.459 0.567 0.641 0.733 783

SugShell,t 0.559 0.070 0.429 0.459 0.565 0.641 0.725 783

SugTexaco,t 0.561 0.070 0.433 0.462 0.567 0.646 0.733 783

SugTotal,t 0.563 0.070 0.433 0.462 0.568 0.649 0.733 783

Spott-2 0.403 0.069 0.268 0.307 0.408 0.484 0.572 783 _______

Sug-BP,t - Spott-2 0.158 0.007 0.135 0.150 0.158 0.167 0.185 783 _______

Sug-Esso,t - Spott-2 0.158 0.007 0.132 0.149 0.158 0.167 0.187 783 _______

Sug-Shell,t - Spott-2 0.159 0.007 0.134 0.150 0.159 0.168 0.191 783 _______

Sug-Texaco,t - Spott-2 0.158 0.007 0.134 0.149 0.158 0.167 0.191 783 _______

Sug-Total,t - Spott-2 0.158 0.007 0.135 0.149 0.158 0.167 0.189 783 _______

SugBP,t - Sug-BP,t 0.000 0.004 -0.015 -0.004 0.000 0.005 0.019 783 _______

SugEsso,t - Sug-Esso,t -0.001 0.005 -0.017 -0.006 0.000 0.004 0.017 783 _______

SugShell,t - Sug-Shell,t -0.002 0.004 -0.020 -0.007 -0.002 0.002 0.012 783 _______

SugTexaco,t - Sug-Texaco,t 0.000 0.005 -0.026 -0.004 0.000 0.006 0.018 783 _______

SugTotal,t - Sug-Total,t 0.002 0.004 -0.012 -0.003 0.002 0.008 0.017 783 Notes: T=783, in euros per liter (excluding excise duty and VAT). Brand = stations with the brand of one of the five largest oil companies, DO = dealer-owned stations, CO = company-owned stations. tP is the average retail price on day t.

40

Table A5 Descriptive statistics Diesel

Price series (sample) Average Std. dev. Min. 10th percentile Median 90th

percentile Max. Number of obs.

Pi,t (Brand) 0.559 0.101 0.342 0.459 0.532 0.719 0.955 1,262,154

Pi,t (DO & Brand) 0.561 0.100 0.363 0.459 0.535 0.719 0.955 591,545

Pi,t (CO & Brand) 0.557 0.101 0.342 0.456 0.532 0.719 0.879 670,609 ___

Pt (Brand) 0.561 0.099 0.428 0.468 0.537 0.720 0.828 745

SugBP,t 0.592 0.101 0.455 0.497 0.568 0.752 0.861 782

SugEsso,t 0.591 0.100 0.455 0.497 0.568 0.752 0.861 782

SugShell,t 0.590 0.100 0.455 0.494 0.568 0.752 0.861 782

SugTexaco,t 0.593 0.099 0.460 0.497 0.569 0.752 0.861 782

SugTotal,t 0.592 0.100 0.460 0.497 0.568 0.746 0.861 782

Spott-2 0.456 0.097 0.319 0.365 0.435 0.616 0.723 783 _______

Sug-BP,t - Spott-2 0.135 0.007 0.114 0.128 0.135 0.144 0.161 782 _______

Sug-Esso,t - Spott-2 0.136 0.007 0.114 0.128 0.135 0.143 0.165 782 _______

Sug-Shell,t - Spott-2 0.136 0.007 0.114 0.128 0.135 0.144 0.167 782 _______

Sug-Texaco,t - Spott-2 0.135 0.007 0.115 0.127 0.135 0.143 0.167 782 _______

Sug-Total,t - Spott-2 0.135 0.007 0.114 0.128 0.135 0.144 0.167 782 _______

SugBP,t - Sug-BP,t 0.000 0.004 -0.014 -0.005 0.000 0.005 0.023 782 _______

SugEsso,t - Sug-Esso,t 0.000 0.004 -0.011 -0.005 0.000 0.004 0.014 782 _______

SugShell,t - Sug-Shell,t -0.002 0.004 -0.019 -0.006 -0.002 0.002 0.013 782 _______

SugTexaco,t - Sug-Texaco,t 0.001 0.004 -0.023 -0.003 0.001 0.006 0.018 782 _______

SugTotal,t - Sug-Total,t 0.001 0.005 -0.015 -0.005 0.001 0.006 0.019 782 Notes: T=783, in euros per liter (excluding excise duty and VAT). Brand = stations with the brand of one of the five largest oil companies, DO = dealer-owned stations, CO = company-owned stations. tP is the average retail price on day t. Suggested price data are missing for one day.

41

Figure A3 Average difference between retail price and suggested price for highway stations and nonhighway stations (Euro95)

0

10

20

30

40

50

60

70

-0.10 -0.08 -0.06 -0.04 -0.02 0.00 0.02 0.04

Perc

enta

ge o

f sta

tions

Average of (retail price - suggested price)

Highway Nonhighway

Notes: T=783, stations with the brand of one of the five largest oil companies (N=253 for highway stations, N=2,065 for nonhighway stations), difference in euros per liter (excluding excise duty and VAT, rounded at two digits).

42

Figure A4 Number of observations per station used for estimation Euro95

0

50

100

150

200

250

300

350

400

450

50 100 150 200 250 300 350 400 450 500 550 600 650 700

Num

ber o

f sta

tions

Number of observations

Company-owned

0

25

50

75

100

125

150

175

200

225

250

50 100 150 200 250 300 350 400 450 500 550 600 650 700

Num

ber o

f sta

tions

Number of observations

Dealer-owned

Figure A5 Number of observations per station used for estimation Diesel

0

50

100

150

200

250

300

350

400

450

500

550

50 100 150 200 250 300 350 400 450 500 550 600 650 700

Num

ber o

f sta

tions

Number of observations

Company-owned

0

25

50

75

100

125

150

175

200

225

250

50 100 150 200 250 300 350 400 450 500 550 600 650 700

Num

ber o

f sta

tions

Number of observations

Dealer-owned

Notes: The figures relate to Equation (1) with q=0 and r=0 (the first column of Table 1 and the second column of Table 2 for Euro95 and the first column of Table A6 and the second column of Table A7 for Diesel). The first number on the horizontal axis refers to 1-50 observations, the second number to 51-100 observations, etc..

43

Table A6 Estimation results dealer-owned stations Diesel

Equation (1), Dependent variable: ∆Pi,t q=0, r=0 Sample DO & Brand

Method Panel Individual time series

∆Spott-2 0.89 (0.01) 0.89 (98%) ______

∆(Sug-i,t - Spott-2) 0.85 (0.01) 0.87 (97%) ______

∆(Sugi,t - Sug-i,t) 0.63 (0.02) 0.65 (93%)

(Pi,t-1 - Spott-3) -0.03 (0.01) -0.02 (26%) ______

(Pi,t-1 - Sug-i,t-1) -0.06 (0.01) -0.12 (44%)

(Pi,t-1 - Sugi,t-1) -0.20 (0.01) -0.35 (74%)

Observations (average) 483,469 490 Stations 1,209 957

Table A7 Estimation results company-owned stations Diesel

Equation (1), Dependent variable: ∆Pi,t q=0, r=0 Sample CO & Brand

Method Individual time series Panel

∆Spott-2 0.94 (99%) 0.94 (0.01) ______

∆(Sug-i,t - Spott-2) 0.92 (99%) 0.90 (0.01) ______

∆(Sugi,t - Sug-i,t) 0.73 (97%) 0.70 (0.02)

(Pi,t-1 - Spott-3) -0.02 (31%) -0.03 (0.01) ______

(Pi,t-1 - Sug-i,t-1) -0.10 (40%) -0.04 (0.01)

(Pi,t-1 - Sugi,t-1) -0.31 (74%) -0.17 (0.01)

Observations (average) 563 614,142 Stations 1,091 1,099

44

Online Appendix B: Explaining the retail price by the spot market price, the common

information in all suggested prices, and the brand-specific suggested price

This appendix generalizes the figures in Section 3.1 for all dealer-owned stations that use the

brand of one of the five largest oil companies and for the full sample period. We explain the

difference between the retail price and the two-day lagged spot market price (see Section 2.1),

the difference between the retail price and the common information in all suggested prices,

and the difference between the retail price and the brand-specific suggested price by a station-

specific constant (fixed effect).

For each regression, we calculate the Sum of Squared Residuals (SSR). The SSR indicates to

what extent the retail price ( tiP , ) differs from respectively the spot market price ( 2−tSpot ) plus

or minus a station-specific constant, the common information in all suggested prices ( tiSug ,− )

plus or minus a station-specific constant, and the brand-specific suggested price ( tiSug , ) plus

or minus a station-specific constant. A lower value of the SSR indicates that the price series

explains the retail price better. A low SSR is equivalent to a stable difference between the

retail price and the price series in Figure 2-4.

Table B1 presents the results. The table confirms the conclusions of the analysis for the four

dealer-owned Shell stations in the main text. For both Euro95 and Diesel, the SSR is lower for

the common information in all suggested prices than for the spot market price. The common

information in all suggested prices explains the retail price better than the spot market price.

The brand-specific suggested price explains the retail price best, as for both Euro95 and

Diesel the SSR is the lowest for the brand-specific suggested price. However, the difference

between the SSRs for the brand-specific suggested price and the common information in all

suggested prices is much smaller than the difference between the SSRs for the common

45

Table B1 Stability of the difference between the retail price and the three price series

Sum of Squared Residuals (SSR) Sample DO & Brand

Gasoline type Euro95 Diesel

Dependent variable:

(Pi,t - Spott-2) 52.32 62.10 ______

(Pi,t - Sug-i,t) 35.03 40.94

(Pi,t - Sugi,t) 30.65 37.10 Observations (all regressions) 493,636 590,657 Stations (all regressions) 1,220 1,224

Notes: T=783, all Euro95 (Diesel) regressions use the same observations. We use the OLS Within estimator. DO = dealer-owned stations, Brand = stations with the brand of one of the five largest oil companies.

information in all suggested prices and the spot market price. Hence, the additional

explanatory power of the brand-specific suggested price is relatively small.41

The analysis above assumes that changes in the three price series are fully reflected in retail

prices in the long run. To check this assumption, we estimate the long-run impact of the three

price series. We explain the retail price by a station-specific constant (fixed effect) and

respectively the two-day lagged spot market price, the common information in all suggested

prices, and the brand-specific suggested price.

All four price series are integrated of order 1 (see Footnote 26). Moreover, there is a

homogeneous cointegrating relation between the retail price and the two-day lagged spot

market price, the common information in all suggested prices, and the brand-specific

suggested price (see Footnote 27). We use a panel Dynamic OLS (DOLS) estimator and

include in the regression 6 leads and lags of changes in the explanatory variable.42

41 The SSRs can be expressed as R-squareds by moving the spot market price, the common information in all suggested prices, and the brand-specific suggested price to the right-hand side of the equation such that all three equations get the same dependent variable (while the SSRs do not change). The R-squareds for Euro95 are respectively 0.980, 0.987, and 0.988 (and for Diesel 0.990, 0.993, and 0.994). 42 Results are similar if we use a different number of leads and lags. To limit the number of coefficients that we have to estimate, the impact of the leads and lags is homogeneous across stations.

46

Table B2 Estimation results long-run impact of the three price series on the retail price

Panel DOLS specification, Dependent variable: Pi,t Sample DO & Brand

Gasoline type Euro95 Diesel

Price series

Spott-2 ______

Sug-i,t

Sugi,t

Spott-2 ______

Sug-i,t

Sugi,t

Coefficient 1.018 (0.000) 0.991 (0.000) 0.991 (0.000) 1.024 (0.000) 0.992 (0.000) 0.994 (0.000) Observations 493,636 489,485 489,485 591,545 575,782 575,782 Stations 1,220 1,215 1,215 1,224 1,219 1,219

Notes: T=783, Newey-West standard errors are in parentheses (the lag length that is considered in the serial correlation structure is determined via the Newey-West procedure (6 lags)). The DOLS terms and station-specific effects are not reported. DO = dealer-owned stations, Brand = stations with the brand of one of the five largest oil companies.

Table B2 presents the results. For both Euro95 and Diesel, the estimated coefficients for the

long-run impact of the price series are close to 1. Hence, in the long run there is almost a full

pass-through of changes in the three price series into the retail price (see also Faber (2015)).

The SSRs provide the same conclusions as in the analysis where we assume a full pass-

through, but we do not report them here as their interpretation is less straightforward due to

the inclusion of the leads and lags in the regressions.

47

Online Appendix C: Similarity of changes in suggested prices and retail prices

Figure 2 suggests that the retail prices of the four selected gasoline stations and the suggested

price change in a similar way on almost every day. To corroborate this observation for our

entire data set, we pool all our price data across time and stations.43 Table C1 shows how

often a retail price changes, given that the suggested price of the oil company of the gasoline

station changes. It shows that if the suggested price changes, the retail price also changes in

73% of all cases. Moreover, if the suggested price does not change, the retail price also does

not change in 77% of all cases. We split the upper left cell of Table C1 for the direction of the

suggested price change. If the suggested price increases (decreases), then 97% (96%) of all

changing retail prices increase (decrease) as well.

Next, we analyze the size of the differences. The first column of Table C2 shows that in 72%

of all observations, the size of the change in the retail price equals the size of the change in

the suggested price. If the suggested price changes, 56% of all changes in the retail price are

equal to the change in the suggested price. The fourth column of Table C2 shows that if both

the suggested price and the retail price change, then in 77% of all cases the change in the

retail price equals the change in the suggested price.

Table C1 Frequency of a retail price change conditional on a suggested price change

if ∆Sugi,t ≠ 0 if ∆Sugi,t = 0 ∆Pi,t ≠ 0 73% 23% ∆Pi,t = 0 27% 77% 100% 100%

Table C2 Frequency of a similar change in the retail price and suggested price

if ∆Sugi,t ≠ 0 if ∆Sugi,t = 0 if ∆Sugi,t ≠ 0 and ∆Pi,t ≠ 0 ∆Pi,t = ∆Sugi,t 72% 56% 77% 77% ∆Pi,t ≠ ∆Sugi,t 28% 44% 23% 23% 100% 100% 100% 100%

43 This appendix contains the calculations for Euro95 observations of dealer-owned stations with the brand of one of the five largest oil companies. We only report results for Euro95 since results for Diesel are similar.

48

Online Appendix D: Robustness of the results for dealer-owned stations

We estimate alternative specifications to check the robustness of the results for dealer-owned

stations. All alternative specifications underline the qualitative aspects of the results that we

report in the main text.

First, we estimate Equation (1) with 1−Δ tSpot as an additional control variable. Platts

publishes 1−tSpot at the end of day t-1. Hence, stations can know this spot market price at the

moment that they set their retail prices (day t). However, Shell does not know 1−tSpot at the

moment that it sets its suggested price (the morning of day t-1) and indicates that it does not

use it (see Section 2.1). However, possibly oil companies can to some extent predict 1−tSpot

and take it into account in their suggested prices.44

Table D1 presents the results with q=0 and r=0 for dealer-owned stations with the brand of

one the five largest oil companies. The table shows that the one-day lagged change in the spot

market price has a limited impact on the retail price when we control for the additional

information in the suggested price. Its coefficient equals 0.05 for Euro95 and 0.04 for Diesel.

The other coefficients do not substantially change. Hence, the additional information in

suggested prices remains important for explaining retail prices.

Second, we estimate Equation (1) with q=3 and r=3. The first column of Table D2 shows that

for Euro95 our estimates are robust to changes in the lag specification. Again, changes in the

part of the suggested price that reflects the spot market price are important for explaining

changes in retail prices, but so are the parts that represent the common component of the

suggested prices and the brand-specific component. For each lag, the estimated coefficients

for the three parts of the suggested price are positive, broadly similar to each other, and

broadly similar to the absolute value of the negative coefficient for the lagged retail price.

44 For example, oil companies can possibly look at trades during the day or prices of related products for which real-time price data are available.

49

Table D1 Estimation results when the one-day lagged change in the spot market price is included

Equation (1) with ∆Spott-1, Dependent variable: ∆Pi,t q=0, r=0 Sample DO & Brand

Gasoline type Euro95 Diesel

∆Spott-1 0.05 (0.01) 0.04 (0.01)

∆Spott-2 0.85 (0.02) 0.86 (0.01) ______

∆(Sug-i,t - Spott-2) 0.78 (0.02) 0.82 (0.01) ______

∆(Sugi,t - Sug-i,t) 0.66 (0.02) 0.62 (0.02)

(Pi,t-1 - Spott-3) -0.05 (0.01) -0.04 (0.01) ______

(Pi,t-1 - Sug-i,t-1) -0.03 (0.01) -0.06 (0.01)

(Pi,t-1 - Sugi,t-1) -0.21 (0.01) -0.19 (0.01)

Observations 372,450 483,469 Stations 1,176 1,209

Hence, the contribution of the lagged change in the retail price and the contribution of the

lagged change in the three parts of the suggested price approximately cancel each other out.

Thus, current values are more important for explaining the retail price than lagged values. The

estimation results concerning the long-run relations confirm our previous findings. The

second column of the table shows that results are similar for Diesel.

Third, to study whether the levels of the coefficients differ between stations with certain

characteristics, we group gasoline stations based on location (highway or nonhighway) and

brand. Table D3 contains for dealer-owned stations with the brand of one the five largest oil

companies for each of these respective cases the panel estimation results of Equation (1) with

q=0 and r=0 for Euro95. The upper part of the table shows the estimation results for stations

that are located along a highway and stations that are not located along a highway. In both

cases, we confirm the general conclusion that changes in retail prices reflect changes in all

three parts of the suggested price. In the lower part of Table D3, we group the gasoline

50

Table D2 Estimation results with lags dealer-owned stations

Equation (1), Dependent variable: ∆Pi,t q=3, r=3 Sample DO & Brand

Gasoline type Euro95 Diesel

∆Pi,t-1 -0.39 (0.01) -0.37 (0.01)

∆Pi,t-2 -0.25 (0.01) -0.22 (0.01)

∆Pi,t-3 -0.13 (0.01) -0.11 (0.01)

∆Spott-2 0.92 (0.01) 0.93 (0.01)

∆Spott-3 0.37 (0.02) 0.34 (0.01)

∆Spott-4 0.25 (0.01) 0.20 (0.01)

∆Spott-5 0.13 (0.01) 0.11 (0.01) ______

∆(Sug-i,t - Spott-2) 0.87 (0.02) 0.89 (0.01) ______

∆(Sug-i,t-1 - Spott-3) 0.36 (0.02) 0.33 (0.02) ______

∆(Sug-i,t-2 - Spott-4) 0.25 (0.02) 0.20 (0.02) ______

∆(Sug-i,t-3 - Spott-5) 0.13 (0.02) 0.11 (0.01) ______

∆(Sugi,t - Sug-i,t) 0.69 (0.02) 0.66 (0.02) ______

∆(Sugi,t-1 - Sug-i,t-1) 0.27 (0.02) 0.24 (0.02) ______

∆(Sugi,t-2 - Sug-i,t-2) 0.19 (0.02) 0.16 (0.01) ______

∆(Sugi,t-3 - Sug-i,t-3) 0.10 (0.01) 0.07 (0.01)

(Pi,t-1 - Spott-3) -0.04 (0.01) -0.03 (0.01) ______

(Pi,t-1 - Sug-i,t-1) -0.02 (0.01) -0.02 (0.01)

(Pi,t-1 - Sugi,t-1) -0.11 (0.01) -0.11 (0.01)

Observations 219,221 310,726 Stations 970 1,108

Notes: T=783, Driscoll-Kraay standard errors are in parentheses (the lag length that is considered in the serial correlation structure is determined via the Newey-West procedure (5 lags)). Station-specific effects are not reported. DO = dealer-owned stations, Brand = stations with the brand of one of the five largest oil companies.

stations by brand. The results show that the brand-specific part of the suggested price is

important for all brands, although for some brands the coefficient is somewhat larger than for

other brands.45

45 We perform the same robustness checks as reported in Table D3 for our subsample of Diesel observations. We do not report the results here as the qualitative conclusions are similar to the conclusions of the checks reported in Table D3. The conclusions of the checks are also similar if we estimate Equation (1) with q=3 and r=3.

51

Table D3 Estimation results for groups of dealer-owned stations Euro95

Equation (1), Dependent variable: ∆Pi,t q=0, r=0

Sample DO & Brand & Highway DO & Brand & Nonhighway

∆Spott-2 0.90 (0.01) 0.88 (0.01) ______

∆(Sug-i,t - Spott-2) 0.85 (0.02) 0.83 (0.01) ______

∆(Sugi,t - Sug-i,t) 0.60 (0.02) 0.67 (0.02)

(Pi,t-1 - Spott-3) -0.03 (0.01) -0.04 (0.01) ______

(Pi,t-1 - Sug-i,t-1) -0.12 (0.02) -0.04 (0.01)

(Pi,t-1 - Sugi,t-1) -0.25 (0.02) -0.21 (0.01)

Observations 17,265 355,185 Stations 40 1,136

Equation (1), Dependent variable: ∆Pi,t q=0, r=0

Sample DO & BP DO & Esso DO & Shell DO & Texaco DO & Total

∆Spott-2 0.91 (0.02) 0.83 (0.02) 0.92 (0.01) 0.85 (0.02) 0.83 (0.01) ______

∆(Sug-i,t - Spott-2) 0.85 (0.02) 0.75 (0.02) 0.87 (0.02) 0.84 (0.03) 0.81 (0.01) ______

∆(Sugi,t - Sug-i,t) 0.64 (0.04) 0.50 (0.04) 0.85 (0.02) 0.57 (0.05) 0.65 (0.02)

(Pi,t-1 - Spott-3) -0.06 (0.01) -0.07 (0.01) -0.05 (0.01) 0.03 (0.01) -0.01 (0.01) ______

(Pi,t-1 - Sug-i,t-1) -0.05 (0.02) -0.12 (0.02) 0.03 (0.01) -0.11 (0.03) -0.06 (0.02)

(Pi,t-1 - Sugi,t-1) -0.24 (0.02) -0.09 (0.02) -0.26 (0.02) -0.18 (0.02) -0.24 (0.02)

Observations 58,286 67,141 128,014 64,096 54,913 Stations 144 190 282 270 290

We also estimate an alternative version of Equation (1) by replacing the average suggested

price by the Shell suggested price (as some consider Shell to be the price leader in the Dutch

gasoline market). These estimations show that information in the brand-specific suggested

prices that is additional to the Shell suggested price is important for explaining retail prices of

stations with the brand of one of the other four oil companies.