TI 2008-117/1 Tinbergen Institute Discussion Paper

Do Auctions and Forced Divestitures increase Competition?

Adriaan R. Soetevent1,3

Marco A. Haan2

Pim Heijnen1

1 Amsterdam School of Economics, University of Amsterdam; 2 University of Groningen; 3 Tinbergen Institute.

Tinbergen Institute The Tinbergen Institute is the institute for economic research of the Erasmus Universiteit Rotterdam, Universiteit van Amsterdam, and Vrije Universiteit Amsterdam. Tinbergen Institute Amsterdam Roetersstraat 31 1018 WB Amsterdam The Netherlands Tel.: +31(0)20 551 3500 Fax: +31(0)20 551 3555 Tinbergen Institute Rotterdam Burg. Oudlaan 50 3062 PA Rotterdam The Netherlands Tel.: +31(0)10 408 8900 Fax: +31(0)10 408 9031 Most TI discussion papers can be downloaded at http://www.tinbergen.nl.

Do auctions and forced divestitures increase

competition? Evidence for retail gasoline markets

Adriaan R. Soetevent∗

University of Amsterdam (ASE) and Tinbergen Institute

Marco A. HaanUniversity of Groningen

Pim HeijnenUniversity of Amsterdam (ASE)

December 8, 2008

Abstract

Where markets are insufficiently competitive, governments can intervene byauctioning licenses to operate or by forcing divestitures. The Dutch governmenthas done exactly that, organizing auctions to redistribute tenancy rights for high-way gasoline stations and forcing the divestiture of outlets of four majors. Weevaluate this policy experiment using panel data containing detailed price infor-mation. Accounting for non-randomness of the sites are auctioned, we find thatan obligation to divest lowers prices by over 2% while the auctioning of licenseswithout such an obligation has no discernible effect. We find no evidence for priceeffects on nearby competitors.

JEL classification: D43, D44, L11Keywords: Divestitures, Auctions, Entry, Policy Evaluation

∗Corresponding author: University of Amsterdam, Amsterdam School of Economics/AE/IO,Roetersstraat 11, 1018 WB Amsterdam, The Netherlands, Ph: +31 - (0) 20 - 525 73 51;a.r.soetevent@uva.nl. Haan: m.a.haan@rug.nl; Heijnen: p.heijnen@uva.nl. Soetevent’s gratefullyacknowledges financial support by the Netherlands Organisation for Scientific Research under grant451-07-010. We thank Hessel Oosterbeek, Erik Plug, Sander Onderstal, Dirk Stelder and seminarparticipants at EARIE 2008, ACLE, Groningen and Tilburg University for valuable comments.

1

1 Introduction

Governments can play an active role in shaping markets. For example, to kickstart

competition on new markets, they can auction licenses to operate. This practice

is particularly prevalent in telecoms.1 One problem in the design of such auctions

is that established firms already have an advantage vis-a-vis new entrants.2 As an-

other example, when merging firms are deemed to obtain excessive market power, an-

titrust authorities can impose divestitures as a remedy. Recent examples in the EU

include Air France/KLM3, Skandinavisk Tobakskompagni/British American Tobacco4,

and Arcelor/Mittal5. Without any doubt, the most renowned divestiture in the US

is the separation of AT&T from the Regional Bell Operating Companies (RBOCs) in

1984.6 With the mergers of large oil companies, US antitrust authorities also required

the divestment of a number of assets, including retail gasoline stations.7

Such auctions and forced divestitures are supposed to foster competition. But em-

pirical analyses of their effectiveness are scarce.8 Lack of appropriate market data is

the prime explanation for this paucity of empirical work. Ideally, to evaluate the com-

petitive effect of these instruments, one would need a case in which some outlets are

sold, either through an auction or due to a forced divestiture. Then, one would need

to be able to observe how prices are affected at such outlets, both in absolute terms

and relative to outlets that were not put up for sale. In this paper, we analyze a Dutch

policy experiment that amounted to exactly that.

From 2002 to 2024, the Dutch government is organizing annual auctions to redis-

tribute the tenancy rights to operate gasoline stations along public highways. Moreover,

the four major firms had to divest a substantial share of their highway outlets by Jan-

uary 1, 2006. They could use the auctions to this end, but also had the ability to

sell outlets privately. The aim of both the auctions and the obligation to divest is to

increase competition among highway retail gasoline outlets. Using a new panel data1See e.g. Borgers and Dustmann (2003) for an overview of the European experience, or McAfee and

McMillan (1996) for the early US auctions.2See Hoppe et al. (2006) or Klemperer (2002).3See European Commission press release IP/04/194.4European Commission press release IP/08/1053.5European Commission press release IP/06/725.6For an evaluation of the effects of this breakup, see e.g. Hausman et al. (1993).7These cases include the BP/Amoco merger (http://www.ftc.gov/opa/1998/12/bpamoco.shtm),

a joint venture between Shell and Texaco (http://www.ftc.gov/opa/1997/12/shell.shtm) and theExxon/Mobil merger (http://www.ftc.gov/opa/1999/11/exxonmobil.shtm)

8Exceptions we are aware of include Elzinga (1969), Ellert (1976) and Rogowsky (1986). Thesepapers study the effects of divestitures, but do so in an indirect way.

2

set containing detailed price information for almost all Dutch gasoline stations, this pa-

per gives an empirical assessment of this natural experiment. We study whether prices

have decreased. Moreover, we study to what extent a possible price decrease can be

attributed to forced divestitures, and to what extent it is due to the auctions. We also

look at the effect on prices of nearby sites.

There is little reason to expect that auctions in themselves have an effect on retail

prices. When an outlet is auctioned and the current owner can also bid, he is likely to

win the auction: the current owner has superior information on local market conditions

and also has more at stake in the auction9. If the current owner does win the auction,

he is unlikely to change prices much. This is indeed what we find.

When auctions are combined with an obligation to divest, the story is different. As

the current owner cannot bid, the outlet has to change ownership. If forced divestitures

indeed increase competition, or when the new owner operates more efficiently, prices will

fall. If the new owner operates much less efficiently, however, prices may rise. We find

that when gasoline outlets are auctioned with an obligation to divest, prices decrease

by some 2% on average.

In some more detail, our analysis proceeds as follows. First, we establish that prices

at auctioned sites are 1% lower on average, controlling for local demand and competitive

conditions. This decrease is entirely driven by sites that change ownership, which are

1.6% cheaper on average. Yet, there may be a selection effect, as owners could change

the order in which sites were auctioned. They overwhelmingly chose to do so before 2006,

when the majors had not yet fulfilled their obligations to divest. Sites that were put

forward by oil companies were smaller than the average highway station and much more

likely to change ownership. Once we control for this endogeneity, the price difference

between auctioned and non-auctioned sites is no longer significant.

To avoid this selection effect, we also compare prices before and after the auction at

sites auctioned in 2005 (when there still was an obligation to divest) and 2006 (when

there was no longer such an obligation). Prices at sites auctioned in 2005 decrease with

1.2% on average. For sites that change ownership, the average drop amounts to 2.3%.

Because of the non-randomness in the auction schedule, this estimate is a lower bound

on the price effects of the obligation to divest. No effect is found for the 2006 auction.

We also do not find any price effects for nearby competitors.9See e.g. Bulow et al (1999), who show that in common value auctions a bidder with a small

advantage has a much higher probability to win.

3

The paper proceeds as follows. In the next section we describe the specifics of Dutch

market for retail gasoline. We also provide details of the auction design that are relevant

for our analysis, summarize the outcomes of past auctions, and provide a schedule for

which sites will be auctioned in the next eight years. The data set is introduced in

Section 3. Section 4 outlines our estimation procedure, focusing especially on non-

randomness in the auction schedule. Sections 5 and 6 present our results. Section 7

concludes.

2 The auctions and forced divestitures

In this section, we describe the Dutch gasoline auctions. In these auctions, the rights are

sold to operate a gasoline station on a given plot along the highway for a 15-year period.

On these plots, gasoline stations are already active. Hence, the rights that are auctioned

are currently held by some private party that can also participate in the auction. This

is a crucial difference with most other license auctions, where newly created rights to

operate on some market are being sold.

In total, there are some 250 gasoline stations operating along the Dutch highways.

Over a 22-year period, tenancy rights for all these stations will be auctioned. Also,

the four major players (Esso, Shell, Texaco and BP) had the obligation to divest a

substantial number of their highway sites before 2006. This requirement could be met

through the auctions or via private sales. The obligation to divest will influence the

outcome of the auctions. To fully appreciate this effect it is important to be familiar

with some details of the auction. In this section we discuss these issues. We describe

how the auction schedule (i.e. the order in which the stations are to be auctioned)

was originally determined and how current tenants could influence this schedule. We

also discuss the auction design. But first, we provide some historical background of the

auctions.

The MDW-covenant Towards the end of the 20th century, both politicians and

the general public in the Netherlands had the strong impression that gasoline prices at

highway outlets were too high, due to a lack of competition. The impossibility to enter

the market was seen as the main culprit: at the time tenants held 99-year concessions

and the creation of new sites along existing highways was ruled out.

A long period of discussion and negotiations led to the covenant Market Forces,

4

Deregulation and Legislative Quality10 (MDW), signed in 2000 by the Dutch govern-

ment and representatives of current tenants. The covenant aims at creating more entry

possibilities and enhancing price competition. Current concessions are cancelled and re-

placed by concessions for only a 15-year period. New concessions are allocated through

a sequence of annual auctions which started in 2002. Subsequent auctions are scheduled

until 2024, when tenancy rights for all highway sites on state-owned land will have been

sold.11

Forced divestitures At the time the covenant was signed, some 225 out of roughly

250 stations on public highways carried one of the four major brands (Esso, Shell,

Texaco or BP). In 72% of these cases the oil company was the concession holder, while

the other cases concerned privately-owned stations supplied by one of the majors (NMa,

2006a p. 29). Bilateral agreements with the Ministry of Finance obliged the four majors

to divest a total of 48 stations (25 for Shell, 10 for Esso, 8 for Texaco and 5 for BP)

before March 15, 2006.12 Next to private sales, the auctions provided an opportunity

to fulfill this obligation. After 2006, companies were again allowed to accumulate sites.

The auction schedule In December 2001 a list of sites to be auctioned in the years

2002-2008 was published in the Staatscourant, which is the official Dutch Government

Gazette. Each December, the Staatscourant publishes an updated list of sites to be

auctioned in the next 7.5 years. For example, the Staatscourant of December 2007 lists

the sites to be auctioned from 2008 to 2014. Care is taken that the auction schedule is

‘balanced’ in the sense that each year sites of different size (in terms of volume) and in

different geographic areas are auctioned. Sites currently owned by private parties that

do not have a network of outlets at their disposal, only enter the auction schedule in its

17th year.13 In the auctions of 2002 and 2003 only stations operated by the concession

holder were auctioned. Some sites are operated by third-party tenants that do not own

the concession. The Act for the sale of certain outlets for motor fuels14 was necessary

to give a legal basis for the auction of such sites. As this Act only came into effect in10Alternatief Traject MDW Benzine Hoofdwegennet11Ten to fifteen station on public highways are located on private land; these stations are not auc-

tioned.12Due to the 2004 auction being postponed to December 15, 2005, the original deadline of January

1, 2005, was correspondingly changed to four months after the delayed 2004 auction.13For this reason, such sites are somewhat overrepresented in our control group. However, results do

not change when we restrict attention to company owned sites.14Wet tot veiling van bepaalde verkooppunten van motorbrandstoffen.

5

Table 1: Example of changes in the auction scheme.Staatscourant 2001 Staatscourant 2002 Staatscourant 2003

Name Year Current owner Year Current owner Year Current ownerHendriks 2008 Total 2008 Total 2008 Total

Shell Bergh-Z 2009 Shell 2009 ShellBP Witte Paarden 2009 BP

BP Hoefplan 2009 BPEsso Wons 2009 Esso

July 2005, no auction took place in 2004 (Ministry of Finance, 2004) and the entire

auction schedule was delayed by one year.

Endogenous changes in the auction schedule An important condition in the

covenant is that an oil company can make a written request to the government to

interchange the position in the auction schedule of two of its concessions. Two firms

may also request the government to exchange the position of two of their concessions.

The covenant stipulates that the government will grant such a request if it is made at

least one year before the auction and if the change does not affect the balance of the

auction schedule. How that balance is measured, is unclear.

In our analysis, we therefore make a distinction between unchanged sites (for which

the position in the auction schedule did not change), inserted sites (that are auctioned

earlier than originally scheduled because of an interchange request by the oil companies)

and postponed sites (that are auctioned later than originally scheduled because of an

interchange request by the oil companies). Table 1 provides an example. The site called

Hendriks, owned by Total, was initially announced in 2001 to be auctioned in 2008.

This planned auction date did not change in subsequent editions of the Staatscourant.

We therefore label Hendriks as an unchanged site. Shell Bergh-Z is also classified as

unchanged: its auction is still planned for 2009, consistent with the first announcement

in 2002, some 7.5 years before the auction. BP Witte Paarden also first appears in the

2002 announcement for auction in 2009. But in the 2003 announcement, it is no longer

listed. The site has been removed from the schedule and is labelled ‘postponed’ as it

will now be auctioned at some unknown future date. The 2003 announcement includes

two sites to be auctioned in 2009 that did not appear in the previous announcement:

BP Hoefplan and Esso Wons. As these stations are first announced less than 7.5 years

before the planned auction, they must be ‘inserted’ sites. BP Hoefplan may well replace

6

BP Witte Paarden that has disappeared from the schedule.

Table 12 of the Appendix gives a similar list for all highway stations in our data.

Because of the delay in the 2004 auction, the list of sites in the December 2003 and

December 2004 announcement are ceteris paribus the same except that the auction date

of each site is delayed for one year. The auction result (if any) is also given in this table.

Auction design The tenancy rights are sold through a first-price sealed-bid auction.

Bidders submit their bid by walking to the auctioneer with their sealed envelope. This

implies that all parties can observe who submits a bid for a site, but not the value

of the bid. In this first round of auctions for all stations, the proceeds go to the

current concession holder except when he is the highest bidder. In that case the current

concession holder wins the auction and has to pay the Dutch State the difference between

his bid and the second highest bid, up to a maximum of 15%15 of his own bid.

Exactly three months after the auction the site is transferred from the current to

the new concession holder. After the auction of a site a so-called territory restriction

(‘gebiedscriterium’) comes into force. This restriction prohibits two stations to operate

under the same brand name if they are within a distance of 25 kilometer on the same

highway in the same driving direction.16 The participation fee for the auction is e1,500.

Paying this amount allows participants to submit bids for all sites that are auctioned

in a particular year.

3 Data

For our analysis, we use a new fleet card data set which contains regular price quotes for

3,585 gasoline retail outlets in the Netherlands. For comparison, the Dutch competition

authority NMa (2006a, p. 8) cites a total number of 3,625 outlets in 2004.17 All but

five highway outlets are in our data. Price data were downloaded from the website of

Athlon, the largest independent car leasing company in the Netherlands with a fleet of

over 125,000 cars. At August 10, 2005, Athlon started to publish the gasoline prices

paid by its fleet card owners on its website on a daily basis. We limit attention to the

period October 1, 2005 - August 4, 2007. A detailed description of the specifics of both1530% as of 2007.16Note that this does not rule out that a company owns more stations in such an area.17The source of this number is VNPI. An estimate of Bovag (the Dutch industry association for the

automotive sector) mentions 4,319 outlets in 2005.

7

the Athlon data set and our data collection method is provided in the Appendix.18 We

collected price data for all grades, but in this paper we restrict attention to regular

unleaded gasoline.

The price at a particular station on a given day is observed only if at least one

fleet card owner bought gasoline there. For highway outlets this will almost always be

the case. The use of fleet card data fits into a general trend in empirical studies on

retail gasoline markets. The first such studies appeared in the early 1990s and used

low-frequency price data (monthly or weekly) at a relatively high level of aggregation

(city or state averages) over a long period of time (typically 5 to 10 years). These papers

studied issues such as asymmetric price adjustments, and price-cost margins.19 More

recently, attention has shifted towards higher frequency (daily or even bi-hourly) price

data of individual stations for shorter periods of time (up to one year).20 Doyle and

Samphantharak (2008) and Abrantes-Metz et al. (2006) use fleet-card data similar to

ours.21

Using POI-data and Google Earth, we append our station data with geographic

coordinates that enable us to calculate for each station the number of other stations

in its direct neighborhood. For simplicity, we use distances as the crow flies instead of

driving distances. The effects on the aggregate results seem minor.

Highway vs. non-highway sites We distinguish between stations in the data set

based on whether they are located along the ‘main road network’ (hoofdwegennet). This

is the network of public highways numbered from 1 to 99 with either a prefix ‘A’ or

‘N’ depending on whether the type of road is an expressway or a highway. These are

also the roads to which the MDW covenant applies. Gasoline stations located along

this network are denoted ‘highway stations’, the remaining stations are ‘non-highway

stations’. Compared to other countries, the Netherlands has a highly connected network18The Appendix also discusses possible biases due to changes in how we downloaded data (no data

were downloaded during weekends before February 2007) and in how data were posted on the website(initially a price quote remained at the website for about eight days if a more recent price was unavail-able, later this period was reduced to four days). We do not believe that these changes have a materialimpact on our results.

19See for example Bacon (1991), Castanias and Johnson (1993), Borenstein and Shepard (1996) andBorenstein et al. (1997).

20Examples include Atkinson (2007), Eckert et al. (2004), Noel (2007a, 2007b, 2007c) and Wang(2005).

21Doyle and Samphantharak report that in the region they consider some 6,000 stations are surveyed,but it is unclear how many of these are included in their sample, and how often price quotes forindividual stations are observed. Abrantes-Metz et al. (2006) study price movements of 279 individualgasoline stations in Louisville to find indications for collusive practices.

8

of highways. The fraction of stations that is located on highways is over 5%, much higher

than in surrounding countries where it is only 1-3%. Highway stations are estimated to

account for 25% of all gasoline sold. As part of the covenant, no new highway locations

except those already planned will become available before the end of the first round of

auctions in 2024 (NMa, 2006a). The number of unmanned “express” stations increased

rapidly, from 14% in 2002 to 22% in 2005. Most of these are non-highway stations.

Hardly any supermarket has permission to sell gasoline. Only 0.9% of all gasoline is

sold through a supermarket, far less than in France (54%) or the UK (29%).22

The primary motivation for the MDW covenant was the conviction that gasoline

prices at highway stations were excessively high. It is therefore interesting to analyze

whether our data indeed identify such a price difference.23 Figure 1 gives the average

Figure 1: Development average relative price difference between highway and non-highway stations. Only days with at least 10 highway price observations and 20 non-highway price quotes have been included.

price difference between highway and non-highway stations over time. It shows that

prices at highway stations are some 3% higher on average. This relative difference is

significant and slightly increasing over time. It amounts to an absolute price difference22Other empirical papers on the Dutch gasoline market include Bettendorf et al. (2003) who study

price asymmetries based on recommended prices by perceived market leader Shell, Hassink and Schut(2004) who use 613 individual price observations over a time span of five years to try to assess whetherprice competition close to the German or Belgian border is more intense than in non-border regions,and Faber and Janssen (2008) who also use Athlon data to study the effect of recommended prices onretail prices.

23Price differences of about 12 eurocent per liter have been mentioned (Algemeen Dagblad, 2007a).We are not aware of price data to support this claim.

9

of about 4.5 eurocent.24 Possible explanations include cost differences (for example,

highway stations may have to pay higher rent), or the possibility that highway stations

are not perceived by consumers as a perfect substitute for non-highway stations. For

commuters, frequenting a highway station may be more convenient as it saves time.

Moreover, highway stations often have much larger on-site convenience stores, and con-

sumers may be willing to pay a little extra for their gasoline because of this.

Local market concentration Figure 2a depicts the sites in our data set. Clearly,

station density is highest in the more densely populated western part of the country.

Table 2 shows a tendency for (non-)highway sites to cluster with other (non-)highway

sites. Whereas the majority of non-highway sites does not have a highway site within a

five kilometer radius, the majority of highway sites has one within just one kilometer.

Column (1) in Table 3 provides a similar picture: 124 highway sites have at least one

highway site within 1 kilometer while only 39 non-highway stations do. The reason is

that many Dutch highway gasoline sites come in pairs, with stations located at opposite

sides of the highway, one in each driving direction (see Figure 2b for an example).

However, whereas non-highway sites have on average 3.6 non-highway sites within two

kilometer, highway sites only have 1.4. This reflects that quite some highway stations

are located in otherwise more rural areas.

For highway sites, the increase in the number of neighboring highway sites is rela-

tively small when one increases the radius from 1 to 10 kilometer. The ratio of averages

is 4.3 (2.39/0.55) for highway sites, but 38 (≈ 42.79/1.14) for non-highway sites. This

reflects that highway sites in the same driving direction along the same highway are

some 20 kilometer apart. Higher concentrations of highway sites at shorter distances

are due to the presence of highway intersections; each may have its own sites, with

short mutual distances as a result. In our analysis, we will incorporate two local market

concentration measures: one describing the local density of highway stations and the

other the local density of non-highway stations.24We also compared differences between the minimum price charged by highway stations at a given

day, and the minimum price charged by stations on the underlying road network. A large discount byjust one non-highway stations then translates into a spike in the observed difference. On average, thedifference between minima is about 6 eurocent.

10

Table 2: Local market concentration: number of (non-)highway sites within Y kilometer.number of other sites within. . .

0.1 km 1 km 2 km 5 km 10 km 20 kmother non-highway sites

min. 0 0 0 0 0 1mean 0.05 1.14 3.56 13.77 42.85 141.87

median 0 1 3 11 38 132Non-highway max. 2 10 18 55 142 389

sites(3348) highway sites

min. 0 0 0 0 0 0mean 0.00 0.02 0.10 0.73 2.72 9.39

median 0 0 0 0 2 9max. 1 4 4 6 11 22

non-highway sitesmin. 0 0 0 0 0 7

mean 0.00 0.20 1.42 10.41 39.34 136.19median 0 0 1 8 34 129

Highway max. 1 3 10 34 119 356sites

(237) other highway sitesmin. 0 0 0 0 0 1

mean 0.02 0.53 0.60 0.98 2.40 8.98median 0 1 1 1 2 9

max. 1 2 2 4 6 21

Table 3: Number of sites with z highway sites within Y kilometer.Y 1 2 5 10

(1) (2) (3) (1) (2) (3) (1) (2) (3) (1) (2) (3)

Non-highway sitesz0 3308 - - 3107 - - 1981 - - 539 - -1 29 13 5 153 42 13 595 178 62 394 224 662 8 5 2 74 45 19 565 342 151 758 598 2793 1 1 1 9 8 8 116 76 49 551 416 2284 1 0 0 4 1 0 74 38 20 435 320 1635 12 6 6 403 259 986 4 0 0 152 114 567 74 62 498 17 16 109 15 15 15

10 7 7 611 2 2 0

total 3347 19 8 3347 96 40 3347 640 288 3347 2033 970

Highway sites0 114 - - 100 - - 64 - - 17 - -1 121 53 20 132 57 20 135 58 19 61 30 122 3 1 1 6 3 3 19 9 5 51 33 133 19 14 8 52 32 94 1 1 1 39 30 155 14 6 56 4 4 3

total 238 54 21 238 60 23 238 82 33 238 135 57(1) number of sites with z highway sites within Y kilometer;(2) number of (1) with at least one auctioned site within Y kilometer at the end of 2007;(3) number of (1) with at least one announced site within Y kilometer at the end of 2007.

11

Figure 2: (a) Overview of the retail stations in the data set; (b) Example of a pair ofhighway sites, one in each driving direction.

4 Empirical strategy

4.1 Identification

At each point in time, there are three groups of highway gasoline outlets. The first

group comprises the stations that have not been announced to be auctioned. This is

our control group. The second group is the set of sites that have been auctioned.25 The

third group comprises sites that have been announced, but for which the actual auction

has not (yet) taken place. We refer to the latter two groups as the treatment group.

Due the rolling horizon of auction announcements the control and treatment group

change over time. Figure 3 shows how, from December 2001 onwards, sites have grad-

ually moved from the control into the treatment group. In September 2008, 130 sites

(54%) were still in the control group. Note that there are two types of auctioned highway

sites in our data: sites that were already auctioned before data collection started, and

sites auctioned during data collection. The latter group, consisting of sites auctioned in

2005 and 2006, is of particular interest: for this group we have price observations both

before and after the auction. We will use that property in Section 6.

We want to identify the average treatment effect on auctioned sites.26 Let DSi,t be

25Throughout this paper, we define the “auction date” as the date at which the transfer of ownershipfrom the previous to the next licensee takes place, which is normally three months after the actualauction date. Stations that are ‘auctioned’ are therefore stations for which both the auction and thepotential ownership transfer has already taken place.

26To be precise, this treatment effect estimates the aggregate of the direct effect on prices at theauctioned site (i.e. due to the transfer of ownership) plus the indirect effects caused by feedback loopsfrom competitors (i.e. the change in ownership at site i urges its local competitors to decrease prices

12

Figure 3: Control and treatment groups.

a dummy that is 1 if site i has been auctioned at time t′ ≤ t and 0 otherwise. The

average treatment effect then is

ATE ≡ E(

ln pDS

i,t=1

i,t − ln pDS

i,t=0

i,t |DSi,t = 1

)= E

(ln p

DSi,t=1

i,t |DSi,t = 1

)− E

(ln p

DSi,t=0

i,t |DSi,t = 1

), (1)

with E

(ln p

DSi,t=0

i,t |DSi,t = 1

)a counterfactual outcome. Under the assumption that

E

(ln p

DSi,t=0

i,t |DSi,t = 1

)= E

(ln p

DSi,t=0

i,t |DSi,t = 0

),

the difference

E(ln pi,t|DS

i,t = 1)− E

(ln pi,t|DS

i,t = 0)

(2)

identifies the ATE. A random assignment of sites into the treatment and control group

would ensure that this assumption holds. In the next subsection, however, we will show

that non-randomness cannot be ruled out.

We follow a similar approach when studying the indirect price effects of auctioned

sites on nearby competitors. DN,Yi,t is a dummy that is 1 if there is a site within distance

Y of site i that has been auctioned at time t′ ≤ t, and 0 otherwise. We allow the radius

which again triggers a price response by the new operator at site i). Our results however show thatthese indirect effects can be neglected.

13

Y to differ for highway (YH) and non-highway (YNH) sites. The indirect treatment

effect27

ITE ≡ E(

ln pDN,Y

i,t =1

i,t − ln pDN,Y

i,t =0

i,t |DN,Yi,t = 1

)= E

(ln p

DN,Yi,t =1

i,t |DN,Yi,t = 1

)− E

(ln p

DN,Yi,t =0

i,t |DN,Yi,t = 1

). (3)

is identified by the difference

E(

ln pi,t|DN,Yi,t = 1

)− E

(ln pi,t|DN,Y

i,t = 0)

(4)

under the assumption that

E

(ln p

DN,Yi,t =0

i,t |DN,Yi,t = 1

)= E

(ln p

DN,Yi,t =0

i,t |DN,Yi,t = 0

).

Again, this assumption holds if the auction schedule is fully randomized. However, if

e.g. sites in areas with lower average prices are more likely to be auctioned, the indirect

treatments effects that we estimate are unlikely to apply to areas where no sites have

been auctioned. With violations of this type, the difference E(

ln pi,t|DN,Yi,t = 1

)−

E(

ln pi,t|DN,Yi,t = 0

)is an upper bound for the true ITE.

4.2 Non-randomness in the auction schedule

This section investigates whether we can indeed consider the set of auctioned sites as a

random draw from the full population of highway sites. Non-randomness may especially

arise due to the possibility for firms to request changes in the auction schedule, as

mentioned in Section 2. The four majors had the obligation to divest a number of sites

before 2006. It is likely that each firm had some less-preferred sites which it wanted

to divest, and some more-preferred sites which it wanted to keep. If one if its more-

preferred sites was scheduled to be auctioned before 2006, a firm obviously had an

incentive to interchange that site with a less-preferred one.28

Table 4 relates the status of sites in the auction schedule (unchanged, postponed, or

inserted) to the outcome of the auction (ownership transfer, no ownership transfer, and

27See Angelucci and Di Giorgi (forthcoming) for a similar approach to identifying indirect treatmenteffects for food and non-food consumption using data on an aid program which targets poor Mexicanhouseholds and is randomized at the village level. Similar to our approach in estimating the ITEs, theycompare consumption of non-eligible households in treatment villages with consumption of non-eligiblehouseholds in control villages.

28It could also choose to sell the less-preferred station outside the auction and bid aggressively inthe auction for the more-preferred site. But even when winning that auction, it still would have to paysome amount to the Dutch government. That payment is avoided by changes in the schedule.

14

Table 4: Movements in the scheduled year of auction and auction outcome.Year of Auction average

Position in schedule. . . 2002 2003 2005 2006 2007 total highestbid

(mln. e)

No change in ownership. . . unchanged 5 3 6 5 5 24 4.33. . . postponed 1 1 2.31. . . inserted 1 1 10.00

Change in ownership. . . unchanged 4 3 1 1 9 3.95. . . postponed 0 –. . . inserted 4 6 1 1 12 1.06

Change in ownership from. . .. . . major to major 1 1 1 3 7.04. . . major to minor 3 6 4 1 1 15 1.68. . . minor to major 0 –. . . minor to minor 1 2 3 0.51

Change in ownership among. . .. . . all auctioned sites 44% 70% 54% 17% 22% 45%. . . unchanged sites 44% 50% 14% 0% 17% 27%. . . postponed sites 0% 0%. . . inserted sites 100% 100% 100% 50% 92%

Sites inserted among. . .. . . all auctioned sites 0% 40% 46% 14% 22% 28%. . . sites that changed ownership 0% 57% 86% 100% 50% 57%

the average highest bid). Especially the auctions in 2003 and 2005 saw a high number

of inserted sites; 4 out of 10 and 6 out of 13, respectively.29 Most strikingly, all inserted

sites changed ownership in the auction, while only 25% of the unchanged sites did. Most

ownership changes involved a transfer from a major to a minor firm. These observations

strongly suggest that majors have indeed made changes to the auction schedule in order

to have their less-preferred sites auctioned while the obligation to divest was still in

place.

If inserted sites really are less attractive than unchanged sites, one would also expect

them to fetch a lower price in the auction. A comparison of average highest bids in

Table 4 shows that this is indeed the case.30 Also, one would expect the inserted sites

to be smaller. The second column of Table 5 shows that this is true as well: inserted

sites are significantly smaller than those in the control group in terms of both number29Requests for changes had to be made at least on year before the auction. This rules out the

auctioning of inserted sites in 2002.30Using the data in the table, it is easy to calculate that average highest bid for the 13 inserted

stations was 1.75 mln, while that for the 33 unchanged stations was 4.23 mln.

15

of pumps and volume sold. Inserted sites also have significantly fewer pumps than

postponed sites.31

In Table 6 we compare the characteristics of sites in the control group with those in

the treatment group. The second column shows that treated stations are smaller than

those in the control group in terms of plotsize area of the site and number of pumps.

They are less likely to carry a major brand. The latter difference may be a result of

the auction. Column (3) shows that except for the number of pumps, these effects

are no longer significant when we exclude the inserted sites from the treatment group.

Columns (4) and (5) show that more generally, sites that have been auctioned so far were

relatively small in terms of plot size and number of pumps, whereas the characteristics

of sites announced but not yet auctioned are roughly comparable to those of the control

group.

Summarizing, the evidence in this section suggests that the order in which sites are

auctioned is not random, due to the changes firms could make in the auction schedule.

Inserted sites were smaller and less profitable than those they replaced, and the balance

of the auction schedule has been affected by these changes.

5 Empirical results

5.1 Price determinants

Before investigating the price effects of the auctions, we first study how gasoline prices

are affected by demand and supply factors. We estimate the following baseline reduced

form equation:

ln pi,t = αt + θXi,t + φZi + εi,t. (5)

Here, pi,t is the price charged by station i at time t. The αt’s are day dummies which

absorb daily price fluctuations common to all outlets, for example those due to fluctu-

ations in the price of crude oil. Time-dependent station-specific explanatory variables

are captured by Xi,t. These include dummies reflecting whether the station carries one

of the four major brands, the Total brand, or the Q8 brand (the other two companies

with considerable market share), and whether it is an unmanned station. The vector

Zi includes a dummy for whether the station is located along a highway, and border31As noted, ownership of many inserted sites is transferred from a major to a minor which explains the

negative coefficient for this variable in column (2). The fraction of company owned sites is significantlyhigher in the treatment group due to the fact that sites currently operated by private parties will onlyenter the schedule in the 17th year of the auctions.

16

Table 5: Descriptive statistics: site characteristics of control group and the set of in-serted and postponed sites (as measured per end of sample period). Standard errorswithin parentheses.

Control Treatment - Control(1) (2) (3)

Inserted Postponed

Structural characteristicsGerman border 0.0231 0.1019* -0.0231

(0.0132) (0.0854) (0.0000)Belgian border 0.0154 0.0471 -0.0153

(0.0108) (0.0625) (0.0000)

Site characteristicsCompany owned 0.8605 -0.0480 0.13954

(0.0306) (0.1008) (0.0000)Major brand1 0.7077 -0.3952** -0.0713

(0.0400) (0.1197) (0.1521)Car wash 0.0308 -0.0308 -0.0308

(0.0152) (0.000) (0.0000)Plotsize area 4094 -1740† -916

(318) (426) (393)# pumps 4.1032 -1.6032** -0.1941‡‡‡

(0.2022) (0.2041) (0.5127)volume sold 5082 -1798* -1064

(295) (439) (625)hotdrinks 0.9308 -0.1183 0.0692

(0.0223) (0.1008) (0.0000)shop area 86.0 -8.7 0.4

(2.5) (8.2) (9.3)shop sales 1185 8 -427

(102) (341) (164)

# sites 130 16 11

Information on site characteristics provided by Catalist Ltd.;†: Significant different from control at the 10-percent level;∗ : idem 5-percent level; ∗∗ : idem 1-percent level;‡: Significant different from inserted at the10-percent level; ‡‡ : idem 5-percent level;‡‡‡ : idem 1-percent level.1 : for auctioned stations: post auction value.

17

Table 6: Descriptive statistics: site characteristics control and treatment group (asmeasured per end of sample period). Standard errors within parentheses.

Control Treatment - ControlAll excl. Ins. Announced Auctioned

(1) (2) (3) (4) (5)Structural characteristics

German border 0.0231 0.0232 0.0095 0.0198 0.0296(0.0132) (0.0203) (0.0186) (0.0244) (0.0367)

Belgian border 0.0154 0.0494* 0.0498† 0.0275 0.0899**(0.0108) (0.0238) (0.0259) (0.0244) (0.0505)

Site characteristicsCompany owned 0.8605 0.0741† 0.0956* 0.0967* 0.0314

(0.0306) (0.0240) (0.0216) (0.0244) (0.0518)Major brand1 0.7077 -0.1244* -0.0773 -0.0077 -0.3393**

(0.0400) (0.0477) (0.0506) (0.0552) ( 0.0793)Car wash 0.0308 -0.0308† -0.0308† -0.0308 -0.0308

(0.0152) (0.0000) (0.0000) (0.0000) (0.0000)Plotsize area 4094 -756† -582 -500 -1238*

(318) (180) (193) (225) (284)# pumps 4.1032 -0.7107** -0.5537† -0.5175† -1.0761**

(0.2022) (0.1598) (0.1797) (0.1999) (0.2585)volume sold 5082 -141.5 153 92.1 -595.9

(295) (319) (359) (397) (532)hotdrinks 0.9308 0.0044 0.0258 0.0549† -0.0887†

(0.0223) (0.0238) (0.0214) (0.0143) (0.0599)shop area 86.0 -0.1 1.2 1.6 -3.6

(2.5) (2.6) (2.7) (3.1) (4.9)shop sales 1185 -69 -80 -59 -89

(102) (99) (102) (114) (195)

# sites 130 108 92 70 38

Information on site characteristics provided by Catalist Ltd.;†: Significant different from control at the 10-percent level;∗ : idem 5-percent level; ∗∗ : idem 1-percent level.1 : for auctioned stations: post auction value.

18

dummies that are 1 if the station is located in a zip code adjacent to the German or

Belgian border. It also contains measures of local market concentration: the log of the

number of highway and non highway sites within 1 kilometer, and at a 1-2, 2-5 and 5-10

kilometer distance. We interact these with the highway-dummy to allow for a different

impact of local market concentration on highway and non-highway stations.32 We al-

low for local differences in demand by including the log of the number of private cars

owned within a radius of 20 kilometer.33 Price observations for a given station are not

independent: we account for this by clustering disturbances εi,t at the station level.

Table 7 reports the results. In Model A1, we only include a constant and highway-

year interaction dummies. Consistent with Figure 1, the estimates show that prices

at highway stations are some 3% higher. This difference is significant and increases

somewhat over time. When we add a major brand dummy (A2), we find that the four

majors charge somewhat higher (1.4%) prices on average.

In Models A3 and A4 we add border dummies, our local demand measure, and

the local concentration measures. Prices are higher in areas with higher local demand.

They are some 0.8% higher close to the Belgian border,34 but no effect is found for

the German border. The concentration of non-highway stations does affect prices. For

non-highway stations, we find significant effects up to five kilometer, with the effects

being strongest for distances between one and two kilometer. Doubling the number of

non-highway stations within a radius of 5 kilometer decreases price levels by a total of

0.8%. Highway sites are only affected by non-highway stations if these are within one

kilometer, which implies that they must be near a highway entry or exit, or at a road

running parallel to the highway. This limited dependence lends some support to the

claim that highway outlets should be considered a separate product market.35

32In calculating local concentration measures, we assume that no sites are closed or newly openedduring the data collection period. For highway stations, this is one of the conditions of the covenant.Hence this assumption does not affect our results.

33We used information at the 4-digit zip code level (8.7 km2 on average) provided by StatisticsNetherlands on the number of private cars in 2006, using the midpoint of the zipcode as point ofreference. Taking a radius of 5 or 10 kilometer does not affect the results. Given the size of the zipcodes, smaller radii are not informative.

34This is surprising as gasoline prices in Belgium are somewhat lower than in the Netherlands. Onepossible explanation is that by choosing not to buy their gasoline in Belgium, the costumers of thesestations reveal themselves as not being too price sensitive.

35As noted e.g. by Motta (2004, p. 82), on the basis of price differences alone one cannot con-clude that fuel retailing on highways constitutes a separate product market. In its decision on theExxon/Mobil merger, the European Commission stated that “In some countries, it is possible to con-sider fuel retailing on motorways as a separate product market.” (European Commission, 1999). TheCommission mentioned the limited familiarity of motorists travelling on motorways and the payment oftoll as factors which “discourages motorists to exit motorways and buy fuel on off-motorway stations”.There are no toll roads in the Netherlands.

19

Table 7: Regression of ln(p) on explanatory variables.Model Model Model

A1 A2 A3

highway*2005 0.0282** 0.0209** 0.0246**(0.0013) (0.0028) (0.0060)

highway*2006 0.0300** 0.0227** 0.0265**(0.0012) (0.0028) (0.0060)

highway*2007 0.0317** 0.0243** 0.0280**(0.0013) (0.0028) (0.0060)

Major 0.0143** 0.0148**(0.0008) (0.0008)

Major*highway 0.0053†

(0.0031)Express -0.0320** -0.0315**

(0.0012) (0.0013)Total 0.0147** 0.0143**

(0.0012) (0.0011)Q8 0.0098** 0.0118**

(0.0019) (0.0018)ln(# non-highway sites+1) at...

≤ 1 km*(1-highway) -0.0023*(0.0007)

1− 2 km*(1-highway) -0.0044**(0.0006)

2− 5 km*(1-highway) -0.0011*(0.0005)

5− 10 km*(1-highway) -0.0001(0.0009)

≤ 1 km*highway -0.0155**(0.0056)

1− 2 km*highway -0.0023(0.0023)

2− 5 km*highway -0.0008(0.0018)

5− 10 km*highway -0.0011(0.0024)

ln(# highway sites+1) at...≤ 1 km*(1-highway) 0.0052

(0.0051)1− 2 km*(1-highway) 0.0007

(0.0018)2− 5 km*(1-highway) 0.0013†

(0.0008)5− 10 km*(1-highway) -0.0004

(0.0006)≤ 1 km*highway 0.0001

(0.0039)1− 2 km*highway 0.0100†

(0.0058)2− 5 km*highway 0.0052*

(0.0025)5− 10 km*highway -0.0004

(0.0021)German border -0.0020

(0.0018)Belgian border 0.0083**

(0.0021)ln(Nprivatecars ≤ 20km) 0.0070**

(0.0010)Day dummies YES YES YES

R2 0.1411 0.2404 0.2911obs 1039363 1039363 1039363

Standard errors are clustered at the station level;†: Significant at the 10-percent level;∗ : Significant at the 5-percent level;∗∗ : Significant at the 1-percent level.

20

The local concentration of highway stations seems to have little effect. For highway

stations, we find a significant positive correlation between price and the number of other

highway stations between 1 and 5 kilometer. Most probably this picks up the positive

demand effect of being close to an intersection of highways. In our estimates of the

indirect price effects of the auctions, we will use a YH = 5 kilometer radius around

highway stations and a YNH = 1 kilometer radius around non highway-stations.

5.2 Price effects of the auctions

This section assesses whether the auctions of tenancy rights have succeeded in their

objective to increase price competition. We estimate the reduced form equation

ln pi,t = αt + βDSi,t + γHIH

i ·DN,5i,t + γNH(1− IH

i ) ·DN,1i,t

+ θXi,t + φZi + εi,t. (6)

Under the assumptions in the previous section, β identifies the ATE and the γ’s the

ITE. The dummy IHi is 1 if station i is located at a highway site and 0 otherwise. This

allows for different ITE’s for highway and non-highway stations. We add day dummies

and the conditioning variables Xi,t and Zi discussed in Section 5.1 to increase precision

of the estimates. Throughout, errors are clustered at the station level.

Table 8 reports the results. We first discuss the direct effects. Columns (1) and

(2) present naive OLS regressions that neglect possible endogeneities in the auction

schedule. We regress the natural log of prices on the post-auction dummies. Column (1)

shows that auctioned sites are on average some 0.8% cheaper. This effect is significant.

The estimate may pick up some selection effects if the number of major sites selected for

auction is either very low or very high. To account for this, Column (2) includes brand

name dummies and a dummy that reflects whether the site was operated by a major at

the time of auction. The estimated price difference is slightly larger at 1.0%. To improve

our understanding of what causes the price difference, Column (3) distinguishes between

auctioned sites that changed ownership and those that did not. It clearly shows that

the price difference is fully driven by the sites that did change ownership. At these sites,

prices are some 1.6% (= 0.3 + 1.3) lower. From Table 4 we know that most ownership

transfers were from a major to a minor oil company.

However, estimating (6) using OLS leads to inconsistent estimates of β and the γ’s if

the decision which sites are auctioned is correlated with the disturbance εi,t. Such a cor-

relation is likely to occur as firms were able to influence the auction schedule. We solve

21

Table 8: Direct and indirect price effects auctions. Dependent variable: ln(p)OLS IV

(1) (2) (3) (4) (5) (6)

Own price effectPost auction (β) -0.0083* -0.0097* -0.0031 -0.0112** -0.0050 -0.0038

(0.0038) (0.0038) (0.0054) (0.0040) (0.0043) (0.0049)Post auction - change -0.0131†

(0.0075)Post announcement -0.0031

(0.0026)

Effect on price competitorsNon-highway stations ≤ 1 km

Post auction (γNH) 0.0122 0.0086 0.0173† 0.0116 0.0075 -0.0034(0.0090) (0.0089) (0.0101) (0.0100) (0.0103) (0.0154)

Post auction - change -0.0145(0.0121)

Post announcement 0.0158(0.0139)

Highway stations ≤ 5 kmPost auction (γH) -0.0054† -0.0044 -0.0022 -0.0059† -0.0036 -0.0045

(0.0029) (0.0030) (0.0039) (0.0033) (0.0035) (0.0039)Post auction - change -0.0023

(0.0054)

Post announcement 0.0029(0.0027)

Major1 0.0145** 0.0145** 0.0145** 0.0144** 0.0144**(0.0008) (0.0008) (0.0008) (0.0008) (0.0008)

Express -0.0315** -0.0315** -0.0314** -0.0315** -0.0314**(0.0013) (0.0013) (0.0013) (0.0013) (0.0013)

Total 0.0142** 0.0142** 0.0142** 0.0142** 0.0142**(0.0011) (0.0011) (0.0011) (0.0011) (0.0011)

Q8 0.0113** 0.0114** 0.0114** 0.0112** 0.0112**(0.0018) (0.0018) (0.0018) (0.0018) (0.0018)

Day dummies YES YES YES YES YES YESInstruments All Ann. Unch Ann. Unch Ann.

Explanatory variables A4 A4 A4 A4 A4 A4R2 0.1932 0.2898 0.2908 0.2897 0.2894 0.2905obs 1039363 1039363 1039363 1039363 1039363 1039363

Standard errors are clustered at the station level; 1 for auctioned stations: value at time announcement;†: Significant at the 10-percent level;∗ : Significant at the 5-percent level;∗∗ : Significant at the 1-percent level.

22

this potential endogeneity problem by taking an instrumental variable approach. We

need an instrument that is correlated with the ‘Post auction’ dummy but uncorrelated

with the disturbance. A natural candidate is the auction announcement.

We thus construct a dummy yi,t that is 1 if, according to any announcement, site

i would be auctioned at time t′ ≤ t, irrespective of the whether the site was actually

auctioned at that time. In the example in Table 1, for all sites except Hendriks, yi,t

would be 0 for dates t before the date of the 2009 auction and 1 for all dates after

that. Column (4) of Table 8 reports the results of the second stage of a two-stage least

squares regression using yi,t as an instrument for DSi,t. The estimates for β are roughly

comparable to the OLS estimates, indicating a close correspondence between the initial

auction announcement and the probability that the site is indeed auctioned at that

time.

The use of auction announcements as an instrument may still yield two problems

however. First and foremost, announcements are also not random as firms have the

option to request changes in the schedule. Second, as announcements are public, both

the current lessee and its competitors can respond strategically to them. Next to their

effect via the auctions, announcements may thus also have a direct effect on prices,

inducing a correlation between the instrument and the disturbance εi,t.

To address the first problem, we split auction announcements in two groups: ‘initial

announcements’ that were included in the initial published schedule for a particular

year,36 and ‘inserted announcements’ that were added later. Our modified instrument

yi,t is 1 if, according to its initial announcement, site i would be auctioned at time t′ ≤ t.

Under the assumption that the initial schedules were not influenced by the sector,37 the

use of the modified instrument yi,t gives unbiased estimates of the price effects of the

auctions. We tackle the second problem as follows. As there is always at least one year

between the announcement and the actual auction, we can control for potential strategic

effects by including an announcement dummy ri,t that is 1 for any time t following the

announcement of the auction of site i. Thus, while the instrument yi,t is only 1 after

the date of auction, the dummy ri,t is already 1 after the announcement.

Column (5) of Table 8 shows that, when we use initial announcements as an instru-

ment, the direct price effect is smaller and no longer significant (p = 0.242). This is

36For example, Shell Bergh-Z and BP Witte Paarden were included in the initial auction scheme for2009 first announced in 2002.

37Note that if that influence would have been substantial, we would not observe so many changes inthe schedule.

23

consistent with the observations that the price difference is fully caused by sites that

change ownership (Column (3) in Table 8), and that most sites that changed ownership

were inserted (Table 4). In Column (6) we include the announcement dummy to test

for strategic effects of the auction announcement. We do not find such effects.

The price difference that we initially found thus seems primarily due to lower average

prices at inserted sites. Those sites were explicitly selected by the companies to be

auctioned. Hence, we cannot tell whether lower prices at those sites are due to the

auctions, or merely reflect that these sites are intrinsically less attractive. We return

to this selection issue in Section 6 where we analyze the effects of the 2005 and 2006

auctions at the site level. For sites auctioned in these years, we have price observations

both before and after the auction, enabling us to to circumvent selection issues.

Indirect treatment effects We now turn to possible spillover effects of the auctions

to nearby competitors. Column (5) of Table 8 shows that once we properly account for

endogenous changes in the auction schedule, the auction does not affect prices of nearby

non-highway competitors. Interestingly, Column (3) suggests that non-highway sites

near an auctioned site that did not change ownership, charge prices that are on average

1.7% higher than those near a highway site in the control group. Such a difference does

not occur when the nearby auctioned site did change ownership. This again suggests

that the current tenant is more likely to win the auction if the site is located in an area

where for some reason competition is less fierce, and all stations thus charge a higher

price. In turn, this suggests that firms have primarily selected sites for auction in areas

that are more competitive.

Column (4) further reports that highway sites within 5 kilometer of an auctioned

site charge prices that are on average 0.6% lower. But as for non-highway competitors,

once we account for endogenous changes in the auction scheme, the auction does not

have an discernible impact on prices of nearby highway competitors.

6 Effects of the 2005 and 2006 auctions

In the previous section, we observed lower prices at sites that changed ownership in the

auction. However, we could not identify whether these were genuine price decreases,

or that they were due to less attractive sites being put up for auction. Fortunately,

for the auctions in 2005 and 2006 we have price observations both before and after the

24

Table 9: Own price effect auctions: Average percentage deviation from market average.after 2005

# sites before 2005 auction after 2006auction before 2006 auction

auction

Control group144 0.0260 0.0274 0.0293

(0.0015) (0.0016) (0.0017)

Announced63 0.0258 0.0267 0.0288

(0.0024) (0.0025) (0.0025)

Already auctioned18 0.0117 0.0109 0.0138

(0.0055) (0.0061) (0.0061)

Auctioned in 200513 0.0250 0.0146 0.0160

(0.0069) (0.0096) (0.0098)

Auctioned in 20067 0.0345 0.0361 0.0339

(0.0006) (0.0005) (0.0052)

TOT 2005 -0.0118 -0.0124TOT 2006 -0.0041

auction. If lower prices are entirely driven by selection bias, we should not observe any

price change for these sites after the auction. If lower prices are caused by a change in

ownership, we should observe lower prices after the auction at sites where such a transfer

takes place. Moreover, by comparing the effects of the 2005 auction (with obligation

to divest) and the 2006 auction (without obligation to divest), we can identify whether

the auction or the obligation to divest was the driving force behind any price decreases.

We divide our price data into three groups: prices observed before the 2005 auction;

prices observed after the 2005 but before the 2006 auction, and prices observed after the

2006 auction. At each point in time, highway sites are now divided into five groups; the

‘control group’ of sites not (yet) announced to be auctioned, ‘announced sites’ that are

announced but not yet auctioned, ‘already auctioned sites’ that were already auctioned

before 2005, ‘sites auctioned in 2005’, and ‘sites auctioned in 2006’. Table 9 summarizes

how average prices in each of these five groups differs from the national average for the

three time intervals.38 For example, in the control group prices are on average 2.6% to

2.9% higher than the market average (which also includes non-highway sites).

38For each group and time interval, the reported number is the sample analogue ofEi {Et [pi,t/pt − 1]} with pt the average price at time t.

25

Figure 4 depicts the price differences over time of the announced sites and the

already auctioned sites, relative to the control group. It provides a number of insights.

First, there is no price difference between control sites and announced sites. This again

indicates that announcements themselves do not trigger a price response. Second, prices

of sites already auctioned are 1.5% lower than those in the control group (p < 0.0001).39

This can be due to the auctions, or to a selection bias. Third, for sites auctioned in

2005, pre-auction prices are equal to prices in the control group, but post-auction prices

are close to those at sites auctioned before 2005. Prices drop considerably but not

significantly (p = 0.32). This within-site price variation can only attributed to the

auction, not to selection effects. Fourth, prices at sites auctioned in 2006 are 0.9%

higher (p < 0.0001) than those in the control group. The auction does not have much

of an impact on this difference. After the auction, prices decreased somewhat towards

those in the control group (p = 0.43) but are still well above those at sites auctioned

before or in 2005 (p = 0.02 and 0.12, respectively).

Figure 4: Treatment effect on treated.

In 2005, the majors still had to divest concessions, while in 2006 this obligation was

no longer in place. This may explain the different price effects of the 2005 and 2006

auctions. If that is the explanation, then the observed effect of the 2005 auction should

be driven by changes in ownership, which in turn are due to the obligation to divest.

Figure 5 therefore separates the treatment effect of the 2005 auction conditional on

whether a site changed ownership. The blue dashed line is the total effect and identical

to that in Figure 4. The figure shows no treatment effect for sites that did not change39All p-values reported in this section are based on two-tailed t-tests of group means.

26

Figure 5: Treatment effect on treated: 2005 auction.

ownership (p = 0.77) but a significant decrease of some 2.3% relative to the control

group for sites that did (p = 0.060, one-sided t-test). Combined with the absence of any

effect for the 2006 auction, this lends support to the hypothesis that the ultimate cause

for the price effects at auctioned sites is the obligation to divest rather than the auctions

themselves. Figure 5 also shows that before the auction, prices at sites that did change

ownership were already some 1.7% lower than those at sites that did not (p = 0.111,

one-sided t-test). This suggests that our estimate of a 2.3% is a lower bound for the

true treatment effect in the case majors have to divest sites chosen by the government.

Figure 6: ITE on other highway sites within 5 km. auctioned site.

27

Indirect treatment effects To determine the effect of the 2005 and 2006 auctions

on prices of nearby highway competitors, we conduct an analysis similar to the one

above, but now for highway sites within 5 kilometers of an auctioned site. This allows

us to distinguish between genuine spillover effects and selection effects. We look at the

same time intervals and restrict attention to the 148 non-auctioned highway sites within

5 kilometer of another highway site. We split this set into four subsets: the ‘control

group’ with sites that have no auctioned site within 5 km., ’close to already auctioned’

with sites within 5 km. of a site auctioned before 2005, ’close to auctioned in 2005’ with

sites within 5 km. of a site auctioned in 2005, and ’close to auctioned in 2006’ with

sites within 5 km. of a site auctioned in 2006.

Figure 6 shows prices of the three treated groups relative to the control group. Most

notably, prices at sites close to a site auctioned before 2005 are significantly lower than

those in the control group (p = 0.014). Again, as we only observe prices for these sites

after the auction, we cannot identify whether this is a competitive effect or a selection

effect. If there are any competitive effects, however, we would expect prices at sites

close to sites auctioned in 2005 and 2006 to decrease after the auction. Figure 6 reveals

that that is not the case; prices at such sites hardly change.

7 Summary and conclusions

Governments often try to influence the competitiveness of markets by auctioning licences

to operate, or by forcing divestitures. This paper set out to empirically identify the

effects of these policies on Dutch market prices for retail gasoline. All licences to operate

gasoline stations along public highways will be auctioned. In addition, the four major

companies had to divest 48 of their licences before 2006.

We constructed a new panel data set based on two years of fleet-card price data,

supplemented by information on the characteristics of individual sites. We showed that

auctioned sites charge lower prices but that this effect can partly be attributed to non-

randomness in the auction schedule: sites that have now been auctioned are not a

random draw from the set of all highway sites. The problem of selection bias can be

circumvented for the sites auctioned in 2005 or 2006: for these we have price quotes

both before and after the auction. For the 2005 auctions we find a considerable post-

auction price decrease of 1.1%. This is fully driven by sites that changed ownership in

the auction, and for which prices decreased by 2.3%. For sites auctioned in 2006, prices

28

before and after the auction are roughly the same. As majors still had to divest sites

in 2005 and no longer in 2006, this suggests that not the auction itself, but rather the

obligation to divest ultimately drove the price effects. We do not find any competitive

spillovers between auctioned sites and price levels at nearby competitors.

Auctions for tenancy rights of Dutch highway gasoline outlets thus have no dis-

cernible effect on prices when there is no obligation to divest. With such an obligation,

however, prices decrease by at least 2%. Our results thus indicate that auctioning ex-

isting rights to operate on an established market does not increase competition, unless

these auctions are augmented with an obligation for incumbent firms to divest some of

their rights.

References

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29

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31

Data collection procedure

This appendix explains more in detail the Athlon price comparison site and our data

collection. During the two years of data collection, both the website and our collec-

tion procedure saw some changes. A detailed description of these changes helps to

understand the strengths and weaknesses of this particular data set.

The Athlon price comparison site started on August 10, 2005. A consumer can enter

her zip code, choose her type of fuel, choose a radius of 1, 5, 10, 15 or 25 kilometers,

and then obtain price quotes for all gasoline outlets in that area, from cheapest to

most expensive. Quotes include the full name and address of the outlet, the date of

observation, and the distance to the zip code.

When Athlon started this service, some gasoline station operators were not amused

and threatened to no longer supply gasoline to Athlon-lessees. Others claimed that the

quoted prices were wrong (De Telegraaf, 2005). After some weeks, the unrest among

station owners died down. Our data collection started August 11, 2005. As price

observations are reported with a lag, our first observation dates from Aug 8. In the first

two months, data were collected once a week and not the entire country was covered.

As from September 28, 2005, data were collected automatically on an almost daily basis

and the entire country was covered. This is reflected in the increase in the number

of unique observations per month which rose to about 35,000 in October 2005 (see

Figure 7).

Table 10 gives for each day the number of new observations and the average lag with

which data were reported on the site. A station’s price quote may disappear from the

site for two reasons: it is either replaced with a more recent price observation or the

quote is no longer up-to-date. Table 10 shows that on average, price quotes on the site

are some three days old. Some notable changes have taken place in the reporting of the

data. In the initial months of operation, prices up to eight days old were available if

they had not been replaced by more recent price quotes. Nowadays, all quotes concern

prices of either three or at most four days old. This reduction in backlog of data implies

that not downloading the data at a day certain implies a greater loss of information.

For example, initially, we did not download data on Saturday and Sunday. With a

minimum lag of three and a maximum lag of four days, this implies that one completely

misses out on the Wednesday gasoline prices. The left panel of figure 8 shows that data

were downloaded between 82 and 95 times at the weekdays Monday till Friday but only

32

Table 10: Summary statistics data collection

Reporting lag Athlon siteyear month Obs/query Mean sd Min Max

2005 8 6852 2.00 0.82 1 59 16401 2.87 1.06 1 7

10 35384 2.63 1.06 2 811 38436 2.59 0.82 2 512 35907 2.29 0.58 2 4

2006 1 49133 3.01 0.23 2 42 44041 3.05 0.22 3 43 43830 3.05 0.22 3 44 35807 3.37 0.89 3 75 43738 3.10 0.30 3 46 40942 3.13 0.53 3 67 42644 3.06 0.25 3 48 50646 3.03 0.17 3 49 39996 3.08 0.26 3 4

10 40129 3.05 0.21 3 411 37444 3.23 0.79 3 812 47770 3.05 0.23 3 4

2007 1 36366 3.15 0.63 3 72 58698 3.06 0.24 3 53 61711 3.08 0.28 3 54 51175 3.05 0.22 3 45 59814 3.03 0.16 3 46 66456 3.06 0.24 3 47 63128 3.03 0.17 3 48 13471 3.23 0.42 3 4

33

Figure 7: Observations per month (based on date downloads).

about 30 times at Saturday and Sunday. As a result, we do not have price observations

for a significant number of Wednesdays (right panel). As of February 2007, we started

to structurally download data also during weekends, which explains the increase in

monthly observations in Figure 7.

Figure 8: Number of times data were downloaded per day of week (left panel) andnumber of times data set contains price a positive number of observations, per weekday(right panel).

34

Table 11: By month and weekday: average number of download price quotes

year month Sun Mon Tue Wed Thu Fri Sat average

2005 8 124 584 450 231 206 227 502 3329 347 676 717 690 1377 272 122 600

10 652 1836 1792 2014 1514 513 320 123411 1236 1983 1509 1624 1709 600 500 130912 1191 1799 1598 1625 1705 492 722 1304

2006 1 1451 2201 2230 1241 995 2279 812 16012 1395 1825 2293 359 846 2387 2182 16123 1815 2346 1697 1374 865 2354 2247 18144 1657 1740 1639 2374 291 2022 2252 17115 1879 2082 1676 377 348 2382 1894 15206 980 2264 2333 0 875 2021 2254 15327 1402 2294 2303 0 386 2367 2164 15598 1809 2291 2234 0 775 2327 2189 16619 655 1855 2334 0 388 2022 2190 1349

10 1887 1579 2356 365 365 2421 1952 156111 1059 2378 2357 2366 878 2430 1877 190612 1494 2380 1854 1401 1012 2015 1752 1701

2007 1 1459 2142 2392 1893 1024 2394 2281 19412 1863 1847 1871 2425 2398 2427 2258 21563 1920 2391 1894 2431 766 2459 2281 20204 1516 2244 1436 2423 881 2466 2311 18975 1902 2318 2008 2437 1897 1945 2258 21096 1943 2415 1885 1896 1888 2474 2281 21117 1856 1961 2333 2258 1873 2401 2311 2142

35

Figure 9: Development monthly average number of new observations per day.

36

Table

12:

Sche

me

ofpa

stan

dfu

ture

auct

ions

nam

e1st

obs

last

sc2001

sc2002

sc2003

sc2004

sc2005

sc2006

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nB

id(e106)

date

date

sell

Yr

1st

2nd

win

n.

Sti

enkam

p3-1

0-5

377

5-8

-7’0

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385

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414

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422

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o’0

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315

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37

Table

12:

(conti

nued)

nam

e1st

obs

last

sc2001

sc2002

sc2003

sc2004

sc2005

sc2006

sc2007

Auctio

nB

id(e106)

date

date

sell

Yr

1st

2nd

win

n.

van

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t3-1

0-5

449

5-8

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4T

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51.5

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de

Kooi

2-1

0-5

426

5-8

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6Shell

’06

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’06

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’07

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’07

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’07

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’07

7.4

12.3

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Shell

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at

3-1

0-5

425

5-8

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6Shell

’06

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’06

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’07

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Born

heim

2-1

0-5

423

5-8

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6Shell

’08

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3-1

0-5

412

5-8

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6Shell

de

Lokkant

3-1

0-5

443

5-8

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6Tex.

’06

Tex.

’06

Tex.

’07

Tex.

’07

Tex.

’07

Tex.

Tex.

’07

2.4

0#

N/A

TO

T.

BP

de

Kro

on

3-1

0-5

452

5-8

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7B

P’0

7B

P’0

7B

P’0

8B

P’0

8B

P’0

8B

P’0

8B

PK

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hoorn

Bv

3-1

0-5

445

5-8

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3-1

0-5

243

4-8

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7B

P’0

7B

P’0

7B

P’0

8B

P’0

8B

P’0

8B

P’0

8G

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n&

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3-1

0-5

421

5-8

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4Tex.

’04

Tex.

’04

Tex.

’05

Tex.

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