EC 460: Theory of Industrial Organization
Summer 2018
Homework 5
Due September 8, 11:59PM
In this assignment you’ll read two academic papers. Clearly answer all parts of the
questions and be prepared to discuss the papers on Monday. PLEASE TYPE YOUR SUBMISSIONS.
Question 1
Read the paper titled Pricing and Firm Conduct in California’s Deregulated Electricity
Market. Apart from some technical terms to describe energy markets, the paper is a pretty
easy read. Respond to the following questions:
a) What is the point of this paper?
b) Why does the author only focus on fossil-fuel generating units in the Cournot game?
c) Describe the auction process to determine which firms are awarded contracts in the dayahead market.
d) How does the author calculate the residual demand for the fossil fuel generating units?
e) Look closely at the equation on page 79, and notice that the author says θit ηDsit
=
strat t
LernerIndex for each firm. How does the author say that θit relates to market power,
and how does it affect the Lerner Index?
f) On the top of page 80, the author says he needs to only consider prices on the PX
day-ahead market because “a simple arbitrage argument suggests that day-ahead and
real-time prices should be equal in expectation.” Describe what arbitrage is and why he
can claim this.
g) Describe the main results.
1
h) Did you find this paper interesting or important? Why or why not?
Question 2
Read the paper titled The impact of mergers on fares structure: Evidence from European
low-cost airlines. Respond to the following questions:
a) Summarize the market that the paper analyzes.
b) Which firms were acquired and why?
c) According to the author, the merging firms can be thought of as a merger between
differentiated products rather than products that are in direct competition. Why is this?
d) What efficiency gains from the mergers does the author describe?
e) What are some possible ways that the mergers could have decreased consumer welfare?
f) Tables 5-7 detail how the merger affected airline ticket fares. Which groups of consumers
appear to be hurt by the mergers and which groups appear to be helped by the mergers?
2
THE IMPACT OF MERGERS ON FARES STRUCTURE: EVIDENCE FROM
EUROPEAN LOW-COST AIRLINES
PAUL W. DOBSON and CLAUDIO A. PIGA∗
This paper examines mergers that lead to an almost immediate replacement of
the target firm’s business model in favor of that of the acquiring firm. We examine
the post-merger behavior of the two leading European dedicated low-cost airlines,
EasyJet and Ryanair, each acquiring another low-cost airline, Go Fly and Buzz,
respectively. We find that both takeovers had an immediate and sustained impact on
both the pricing structures and the extent of intertemporal price schedules used on
the acquired routes, with early booking fares noticeably reduced and only very late
booking fares increased. The analysis suggests that the takeovers had a net beneficial
effect for consumers, at least in price terms, as a consequence of the introduction of the
acquiring firms’ business models and associated yield management pricing systems.
(JEL L11, L13, L93)
I.
service airlines (FSAs) to respond by adapting
their own operations and prices to compete
more effectively.2 As a consequence, passengers
appear to have been the real winners from this
revolution, enjoying a wider choice of routes,
more frequent flights, and lower prices.
Nevertheless, as the sector matures and consolidates, there is a concern that price competition might diminish. In particular, it is recognized that mergers between airlines may allow
efficiencies to be realized, but will this be at
the expense of higher prices and less choice
for consumers? The 2007 decision by the European Commission to block the proposed merger
of Ryanair and Aer Lingus highlights how
seriously this concern is taken.3 The central
INTRODUCTION
In Europe, the rapid growth of low-cost airlines (LCAs) has been made possible by the civil
aviation industry being fully liberalized in 1997,
allowing any airline registered in any European
Union (EU) member state to serve any city-pair
inside the EU.1 In the process, the industry has
been radically shaken up as LCAs expanded
their operations, opening up new routes with
new destinations and greatly extending demand
with their low prices, forcing the traditional full
*We are extremely grateful to Steve Davies, Maria
Gil-Molto, Steve Thompson, Mike Walker, Mike Waterson, and an anonymous referee for their helpful comments
and suggestions. We are also grateful for helpful comments and feedback received from participants at Centre
for Competition and Regulatory Policy Research Workshop, Birmingham, July 2007; the Royal Economic Society
Conference, Warwick, March 2008; and European Association for Research in Industrial Economics Conference,
Toulouse, September 2008. C.A.P. gratefully acknowledges
receipt of the British Academy Research Grants LRG-35378
and SG-45975.
Dobson: Professor, Norwich Business School, University of
East Anglia, Norwich NR4 7TJ, UK. Phone +44 (0)1603
597270, Fax +44 (0)1603 593343, E-mail p.w.dobson@
gmail.com
Piga: Reader, School of Business and Economics, Loughborough University, Loughborough LE11 3TU, UK. Phone
+44 (0)1509 222755, Fax +44 (0)1509 222739, E-mail
c.a.g.piga@Lboro.ac.uk; claudio.piga@gmail.com
1. A city-pair is used as synonymous with the airline
market for two cities (e.g., London and Rome). It generally
includes more than one route, each identified by a unique
airport-pair combination (e.g., London Heathrow/Rome Fiumicino and London Stansted/Rome Ciampino). In such markets, products are thus differentiated.
2. As Gagnepain and Marin (2006) show, greater
competition in the wake of deregulation may also have
brought about productivity improvements and other efficiency benefits.
3. See “Commission prohibits Ryanair’s proposed
takeover of Aer Lingus,” European Commission press
release IP/07/893, 27 June 2007. See Gaggero and Piga
(2010) for an analysis of the Ryanair-Aer Lingus case.
ABBREVIATIONS
APD: Advance-Purchase Discounts
CAA: U.K. Civil Aviation Authority
DID: Differences-in-Differences
EU: European Union
FSA: Full Service Airline
LCA: Low-Cost Airline
OLS: Ordinary Least Square
1196
Economic Inquiry
(ISSN 0095-2583)
Vol. 51, No. 2, April 2013, 1196–1217
doi:10.1111/j.1465-7295.2011.00392.x
Online Early publication June 28, 2011
© 2011 Western Economic Association International
DOBSON & PIGA: PRICE EFFECTS OF LOW-COST AIRLINES MERGERS
question examined by this paper is whether
previous mergers involving LCAs have had such
an effect. Specifically, this paper assesses the
impact on prices of the first two important
mergers involving European LCAs: EasyJet’s
acquisition of Go Fly in 2002 and Ryanair’s
acquisition of Buzz in 2003.
Although other mergers among LCAs have
occurred in the past (e.g., Southwest’s acquisition of both Morris Air and Muse Air), previous
studies of airline takeovers have largely focused
on FSAs in the United States (Borenstein
1990; Werden, Joskow, and Johnson 1991;
Kim and Singal 1993; Morrison 1996; Richard
2003; Peters 2006). With the exception of
the TWA/Ozark merger analyzed by Borenstein
(1990), these studies generally report significant
price effects, with increases by both the merging parties and their rival airlines (Weinberg
2008). However, they do not address a central issue in this paper, that is, how the
acquiring firms’ business model and associated approach to yield management (i.e., the
means of selling seats among differentiated customers with a view to maximizing profit for each
flight) may have impacted different consumer
types. Specifically, we examine how the mergers affected airlines’ temporal pricing profile in
order to compare the effects on “early bookers”
as opposed to “late bookers.” We can thereby
assess, among other things, whether the mergers
resulted in the application of a new segmentation strategy in the acquired routes (Alderighi
2010).
More generally, there has been a large number of studies examining the airline industry
because of its distinctive features and availability of detailed data, but again largely from
the perspective of FSAs. For instance, previous studies have considered effects relating to multimarket competition (Evans and
Kessides 1993, 1994), frequent flyer programs
(Lederman 2008), price dispersion and discrimination (Borenstein and Rose 1994; Stavins 2001;
Gerardi and Shapiro 2009; Puller, Sengupta, and
Wiggins 2009), dynamic pricing (McAfee and
te Velde 2006), and general trends (Borenstein
and Rose 2007). Existing studies on LCAs
have mostly focused on their entry patterns
and effects on FSA incumbents (Whinston and
Collins 1992; Windle and Dresner 1999; Goolsbee and Syverson 2008). An exception is the
study of Koenigsberg, Vilcassim, and Muller
(2008), which examines intertemporal pricing
by LCAs to consider whether offering discounts
1197
for very late bookers as well as early bookers
would enhance profits.
This paper seeks to extend this literature by
examining the impact of the aforementioned
two airline mergers on quoted on-line fares, the
key means by which tickets are purchased for
LCAs.4 Drawing on a novel and very extensive
data set of posted prices taken at frequent intervals over a prolonged period before each flight
departs, we are able to build up a very detailed
picture of the pattern of prices facing consumers
for each route and each flight operated by each
airline serving routes from the United Kingdom
to other parts of Europe. These data cover all
main LCAs as well as competing FSAs providing return flights over a 37-month period (from
the start of June 2002 through to the end of June
2005).5
We provide some illustrative cases to show
the effects at a very microlevel before moving on to present more general empirical evidence using “propensity score matching” and
“differences-in-differences” estimation techniques to compare the fares in the acquired
routes in the pre- and post-merger periods.6
Four key findings emerge from our analysis.
First, straight after concluding the takeovers,
the acquiring firms reduced most types of
posted fares, especially early booking fares. The
only notable exception was a sharp increase in
Ryanair’s posted fares for the day immediately
before departure on the routes taken over. Second, in the 24 months after the takeovers, the
fares of the acquiring firms remained largely
stable, with only minor upward adjustments of
EasyJet’s late booking fares. Third, and related
to the two previous findings, the acquiring firms
altered pricing in a consistent manner for the
acquired routes, indicating that they each introduced their own specific approach to yield management involving a more intense intertemporal
pricing strategy with early bookers paying lower
prices than previously but very late bookers
4. For instance, EasyJet reported that by 2003 around
97% of purchases were made on-line, moving to 98% by
2005 (see http://www.easyjet.com/common/img/UBSTrans
portConference19thSept05.pdf).
5. Unlike the basis of U.S. studies (with data available
from the Department of Transport Databank), there is
no sample available of actual ticket prices paid in Europe;
hence the focus and novelty of using posted prices in the
present study. See Section IV for further details.
6. See Cameron and Trivedi (2005) for a discussion of
these methodologies.
1198
ECONOMIC INQUIRY
paying more (Gale and Holmes 1993).7 Fourth,
given that only a small proportion of seats are
sold by LCAs in the last week before departure,8 the general price reductions suggest that,
despite higher prices for some consumers, both
takeovers may have significantly benefited consumers in aggregate through lower fares. This
benign view is supported by the fact that after
the takeovers very few routes were terminated
and Ryanair increased the number of flights it
operated on its acquired routes, while EasyJet maintained them at approximately the same
level as prior to the merger.
Our findings thus point to an interesting
aspect regarding the nature of potential efficiency benefits arising from a merger. Most previous studies of mergers point to efficiency benefits in terms of organizational and production
restructuring, often taking considerable time to
be realized (Focarelli and Panetta 2003; Paulter
2003). However, our findings suggest that efficiency and pro-consumer benefits can be quickly
realized because of the acquiring firm immediately imposing its own business model and yield
management system on the acquired routes’
operations in order to maximize the productivity
of its assets (i.e., airplanes’ capacity utilization)
and revenues. This is indicated in our analysis
not just by the use of a more intense intertemporal pricing profile but also through its effects
in serving to improve load factors and increasing the average numbers of passengers carried
on each flight. In other words, a merger might
allow for a different and perhaps superior business model to be quickly implemented which
may then immediately start providing consumer
benefits.
II.
TWO CONTRASTING LCA MERGERS
Ryanair and EasyJet, as two of the pioneers
of LCA travel in Europe, have also become two
of Europe’s largest airlines. Founded in 1985,
Ryanair expanded its route network rapidly
following liberalization of intra-EU air services, increasing its passenger numbers from
7. A sharp increase in fares is often empirically found in
the period immediately preceding a flight’s departure; see,
for instance, McAfee and te Velde (2006) or Gillen and
Mantin (2009).
8. For instance, Barlow (2000) suggests that less than
20% of tickets are sold in the final week before departure.
Similarly, working with data provided by EasyJet, the
examples provided by Koenigsberg, Vilcassim, and Muller
(2008) show less than 15% of tickets sold before the final
week.
2.25 million in 1995 to over 60 million by
2009. EasyJet, established in 1995, has similarly
expanded rapidly, taking its passenger numbers
from 3.1 million in 1999 to 46 million in 2009.
The low-cost carrier business model that
Ryanair and EasyJet share is based on the
“no frills” concept advanced by Southwest Airlines in the United States, centered on stripping out and avoiding all the complexity costs
associated with traditional FSAs. This business
model has several notable features: (i) using
a simple pricing structure with one passenger
class and fares only covering basic transportation (with optional paid-for in-flight food and
drink); (ii) relying on direct selling through
Internet bookings with electronic tickets and
no seat reservations; (iii) operating simplified
routes to often cheaper, less congested airports (with point-to-point rather than hub-andspoke networks); (iv) employing intensive aircraft usage (typically with 25-minute turnaround
times) and highly standardized fleets (with a
maximum of two different aircraft types); and
(v) having employees working in multiple roles
(e.g., flight attendants cleaning the aircrafts and
acting as gate agents). The emphasis on costeffectiveness does not necessarily imply a poor,
unreliable service. Indeed, both Rayanair and
EasyJet feature prominently in the 2005 and
2006 league tables of the most punctual airlines operating in the United Kingdom based
on U.K. Civil Aviation Authority (CAA) official
data (see www.flightontime.info/index.html). As
far as safety standards are concerned, they are
directly regulated in Europe (as well as in the
United States), hence the airlines are left with
little discretion in this matter. Furthermore, airlines also perceive the incentive to build and
maintain strong safety reputations as a prerequisite to attracting any passengers (Borenstein and
Rose 2007). This may actually confer a competitive advantage to both Ryanair and EasyJet, as
they operate a very young (and therefore likely
safer) fleet.
Faced with the need to compete with LCAs
(principally on short-haul flights) and hoping to curtail their growth, many FSAs opted
to launch their own no-frills airlines. In particular, British Airways launched Go Fly in
1998 and KLM launched Buzz in 2000. Yet,
unlike the dedicated and highly effective LCA
business model used by specialist LCAs like
Ryanair and EasyJet, and despite access to the
parents’ expertise and strong financial backing, the spin-off nature of the FSA-led LCAs
DOBSON & PIGA: PRICE EFFECTS OF LOW-COST AIRLINES MERGERS
tended to compromise (or at least restrict) their
operations. As a result, profitability generally
suffered.
Nevertheless, within 2 years of its launch, Go
Fly achieved a modest profit. Yet, in June 2001,
British Airways opted to sell the business for
£110 million as a private-equity backed management buyout. As a stand-alone business, Go
Fly grew quickly and profitably the following
year, becoming the third largest LCA in Europe
(after Ryanair and EasyJet). In May 2002, EasyJet announced its intention to buy Go Fly, whose
largely complementary operations would enable
EasyJet to nearly double its size in terms of
routes covered and quickly enter new U.K. bases
(see below). Following merger clearance from
the U.K. authorities in July 2002,9 EasyJet completed the acquisition for £374 million in August
2002. Go Fly continued to operate flights independently until mid-December 2002, after which
its website was shut down and EasyJet started
to operate on all of Go Fly’s former routes.
In contrast to the relative premerger success of Go Fly, Buzz was incurring significant losses (estimated at ¤1 million per week)
by early 2003 and its parent, KLM, was seeking to sell the “financially distressed” operation, even though by then it had become the
third largest LCA in Europe (but still considerably smaller than Ryanair and the merged
EasyJet/Go Fly enterprise). In February 2003,
Ryanair announced its intention to acquire
Buzz and fundamentally restructure the business—making 440 job redundancies (out of a
total staff of 610), retaining only 13 of the
24 routes operated (including three substituted
routes), and cancelling all operations for the
month of April 2003, while retraining Buzz
personnel and agents in Ryanair policies and
procedures. With regulatory approval granted
in April 2003, Ryanair purchased Buzz for
£15.1 million, consequently increasing its share
of slots at Stansted airport from 33% to 49.5%
(see below).10
9. In advising the U.K. Secretary of State for Trade
and Industry, the Office of Fair Trading noted that while
the merger would create a substantial market share for
the merged entity on some overlapping routes (e.g., Edinburgh/Belfast at 90% with 31% incremental rise), it took the
view that all overlapping routes would remain contestable,
with competitive choice across destinations and among carriers along with low barriers to entry sufficient to ensure that
the merger would not substantially lessen competition. See
http://www.oft.gov.uk/advice_and_resources/resource_base/
Mergers_home/mergers_fta/mergers_fta_advice/easyjet.
10. Details of the routes operated by Buzz, in respect
of which ones were continued, substituted, or terminated
III.
1199
MERGER EFFECTS IN THE SHORT AND LONG
RUN
In examining how the takeovers affected the
pricing structures of the acquired firms and
the routes operated, we seek to shed light on
whether the takeovers facilitated the acquiring
firms’ ability to unilaterally exercise market
power and raise fares.11 From a theoretical
perspective, in oligopolistic markets a merger
among directly competing firms is likely to
result in raised prices unless there are significant
efficiency gains associated with the merger (see
Farrell and Shapiro 1990, 2001 for the Cournot
case and Denekere and Davidson 1985 for the
Bertrand case with product differentiation).
An important exception to the latter theoretical result is where not all firms in an oligopoly
are direct competitors with each other. Following Levy and Reitzes (1992), only a merger
that involves neighboring products in the characteristics’ space may raise prices. Accordingly,
the manner in which airlines differentiate from
each other and whether they compete directly
(“head to head”) may take on some importance
in respect of the price effects resulting from
their merger. In practice, airlines differentiate
their products along a number of dimensions,
the most notable of which is the choice of a
route’s endpoints, that is, the geographical differentiation of an airline’s network.12 Thus, two
airlines can be perceived as highly differentiated if their networks do not overlap, that is,
they operate in independent city-pairs markets.
In principle, this would mean that their merger
leaves the competitive situation unaltered.
With regard to the Go Fly/EasyJet and
the Buzz/Ryanair takeovers, either full or partial overlap characterized about one-third of
the routes operated by the target companies.13
after the takeover, are available on request. For the
sake of ensuring like-for-like pre- and post-merger price
comparisons, in the evaluation of the takeover we only use
the routes that were continued.
11. That is, we do not address the issue of coordinated
or collusive effects.
12. Another strategically important characteristic is the
time of the day at which a flight departs, which can influence
whether airlines pursue a strategy of minimum or maximum
differentiation (Borenstein and Netz 1999). It may also
affect an airline’s ability to engage in second-degree price
discrimination (Gale and Holmes 1992, 1993). Furthermore,
the frequency of flights on a route may directly influence
the departure time of flights, which in turn affects travelers’
welfare (Richard 2003).
13. Ryanair continued only one of Buzz’s overlap
routes; all the others were substituted with Ryanair’s routes.
This accounts for why we carry out no specific analysis on
the price effects of the overlap routes in this merger.
1200
ECONOMIC INQUIRY
Although, in this situation, it would seem probable that the mergers could facilitate the exercise
of market power by the acquiring firms, the decision to allow both mergers could still be justified
on at least two grounds. First, the overlapping
routes could be positioned in competitive citypair markets where many other options are available to passengers willing to travel, say, from
London to Rome; an aspect we develop and
discuss in Section V.C. Second, the takeovers
may bring about cost-saving synergies that are
revealed by a drop in fares in the post-merger
period: an issue that is central to this paper.
The following analysis also identifies shortrun and longer-term effects of the takeovers by
distinguishing between a “Pre-Post Period” and
a “2 Years Post Period.” The former comprises
a sub-period with the months for which we
have fare data posted by the acquired carriers
(June 2002 to March 2003 for Buzz, and June
2002 to December 2002 for Go Fly) and another
sub-period with the same months 1 year later,
after each takeover had been completed. The
“2 Years Post Period” tracks the behavior of the
acquiring companies in two post-takeover subperiods, each identified, respectively, by the first
and the second year of operation in the acquired
routes (respectively, May 2003 to April 2004
and May 2004 to May 2005 for Ryanair, and
January 2003 to December 2003 and January
2004 to December 2004 for EasyJet).
Focarelli and Panetta (2003) argue that a
short post-merger period might fail to account
for a merger’s long-run efficiency gains because
of the harmonization of the organizational practices between the two merging firms. Considering that Ryanair needed just a month to
retrain Buzz’s retained workforce, and that
EasyJet presumably did the same without stopping the services it took over from Go Fly,
a 25-month post-merger period is likely to be
more than sufficient to capture each merger’s
full effect on fares. Indeed, previous studies
in the airline industry have considered even
shorter periods. In evaluating the impact of the
Northwest/Republic and TWA/Ozark mergers in
the United States, Borenstein (1990) looks at
the fares 1 year after the mergers took place,
while Kim and Singal (1993) analyze the price
changes one quarter after the two mergers’
completion.14
14. A notable exception is the study by Morrison (1996),
which examines the impact of U.S. airline mergers 8 to 9
years after they occurred. However, he acknowledges that
IV.
DATA COLLECTION
Our analysis is based on primary data on
fares and secondary data on routes traffic. Starting in May 2002, an “electronic spider,” which
connected directly to the websites of the main
LCAs in the United Kingdom (namely, Ryanair,
EasyJet, Go Fly, Buzz, Bmibaby, and MyTravelLite), collected all the fares and the associated flights’ characteristics used in this study.
The collection of fares for flights operated by
FSAs (covering British Airways, BMI British
Midland, Air France, Lufthansa, KLM, Alitalia,
Iberia, and Czech Airlines) started in March
2003. These data cover fares only for the flights
that the FSAs operated on routes similar or identical to those where a LCA also flew.15
It is important to stress that our reference to
fares, and as a key difference with previous airline price studies, is to on-line posted prices and
not samples of actual transaction prices.16 The
advantages of this approach are manifold. First,
LCAs almost exclusively sell their tickets online; therefore our extensive data set is highly
representative of the pricing behavior the LCAs
adopt. Second, posted fares allows the determination of the departure times and of how far
in advance a fare is posted, which is not usually possible with transacted fares (Peters 2006,
p. 629). A possible disadvantage of posted fare,
that is, that they do not capture the evolution of
demand prior to a flight’s departure, is tackled
in two manners. One, we control for changes in
the capacity of the carriers’ operation on a route
by using monthly data on an airlines’ number
of flights and passengers, under the assumption
that capacity reflects underlying demand conditions (see below). Two, we compare fares posted
with such lengths of time determining whether subsequent
directions of prices were directly because of the mergers
or other market developments (e.g., entry/exit patterns,
changes in consumer demand, or cost conditions) is highly
problematic.
15. The fares of the traditional companies were collected
from the website www.opodo.co.uk, which is owned and
managed by British Airways, Air France, Alitalia, Iberia,
KLM, Lufthansa, Aer Lingus, Austrian Airlines, Finnair, and
the global distribution system Amadeus. Thus, fares listed on
Opodo represent the official prices of each airline, although
Opodo may not report promotional offers that an airline may
post on its own website.
16. Notably, this is a key difference with the U.S.
studies using the Databank of the U.S. Department of
Transportation’s Origin and Destination Survey, which is
a 10% yearly random sample of all tickets that originate in
the United States on U.S. carriers (Borenstein 1990; Evans
and Kessides 1993, 1994; Kim and Singal 1993; Borenstein
and Rose 1994, 2007; Lederman 2008; inter alia). Such data
are not available in Europe.
DOBSON & PIGA: PRICE EFFECTS OF LOW-COST AIRLINES MERGERS
in the same months of two consecutive years,
to exploit the fact that demand is seasonal and
follows similar yearly patterns.
To account for the heterogeneity of fares
offered by the airlines at different times prior
to departure, the spider collected the fares for
departures due, respectively, 1, 4, 7, 10, 14,
21, 28, 35, 42, 49, 56, 63, and 70 days from
the date of the query. Henceforth, these will
be referred to as “booking days.” Thus, for
every daily flight, we managed to obtain up
to 13 prices, one for each of these booking
days.17 However, given the website characteristics of Opodo, only fares from 49 to 7 days
prior to departure were available for the FSAs.
This is not going to affect the analysis, because
comparisons of prices in all periods are carried out by considering each booking day in
isolation.
The daily fares data set spans a 37-month
period running from June 2002 to June 2005.
The countries whose routes were directly
affected by the takeovers were France, Italy,
Germany, Netherlands, Portugal, Spain, the
Czech Republic, and the United Kingdom.
For consistency, collection of the airfares
took place at the same time every day. The
queries for the LCA were bidirectional, with
each leg priced independently. The return flight
was scheduled 1 week after the departure. When
a LCA operated more than one pair of flights
per day, the fares for every flight pairs were
collected.
Posted fares for FSAs were for a round trip
and were halved to determine the single leg
price. They belonged to the cheapest available
fare class and were chosen to facilitate comparison with the fares by LCAs; specifically, like
those of the LCA, the quoted prices were for
nonchangeable and nonrefundable tickets.18
Because of the websites content, we collected
fares before tax and handling fees for the case
of LCAs, but inclusive of them for the FSAs.
17. For instance, if we consider London StanstedBergerac as the route of interest, and assume the query
for the flights operated by a given airline was carried out
on March 1, 2004, the spider would retrieve the prices
for both the London Stansted-Bergerac and the BergeracLondon Stansted routes for departures on March 2, 2004,
March 5, 2004, March 8, 2004, March 11, 2004, and so on.
The return would be on March 8, 2004, March 11, 2004,
and so on.
18. Toward the end of our sample period, Ryanair and
EasyJet introduced the possibility to change a ticket, subject
to a fixed penalty and the payment of any fare difference.
This new strategy, though, does not impinge on the analysis
of the takeovers’ effects.
1201
Even so, this is not too much of a shortcoming
in our context because, as discussed below, the
analysis focuses on the changes made by each
airline on the fares posted in the same months
of two consecutive years. Thus, differencing
would generally cancel out the taxes and fees
included in the FSAs’ fares as long as these
have not deviated too much year on year.
However, we are aware that this would not
capture any upward changes in fixed charges
that the LCAs may have introduced during the
period.19 Having examined the different taxes
and fixed charges levied over the period study,
we estimate that any bias between LCA and
FSA fares would likely be less than £4.20 Also,
the amount would be negligible in the price
comparisons of the acquiring and target airlines
(given that any fixed charges, while possibly
different across the two types of firms, would be
part of the final price paid by their customers).
Other charges were introduced after our sample
period. For example, Ryanair was the first to
introduce the charges for checked-in luggage on
March 13, 2006. Finally, the credit card charges
have always been of similar magnitude across
all the LCAs as well as Opodo, and thus do not
have any differential role.
Secondary data on the traffic for all the
routes and all the airlines flying to the countries indicated above was obtained from the
CAA (see www.caa.co.uk). For each combination of company, route, flight code, and departure period (i.e., month/year), the CAA provided
19. Specifically, fixed charges introduce a wedge
between the price posted by the LCAs (which we collected) and the actual price paid by the consumers. Failing
to take account of increased LCA charges would underestimate, relative to the FSA’s fares, the possible increases the
LCAs may have introduced, or equivalently overestimate
any reduction in their fares.
20. The spider could not track the evolution of the
LCAs’ levels of fixed charges, but it is instructive to look
at what type of taxes and charges were imposed upon the
travellers, as these did not change over the sample period.
The Government tax and the Airport tax are exogenously
determined by such institutions and can only contribute to
the LCAs’ revenues in the case of no-shows. There is a
charge if a traveller applies for a refund of such taxes. Also,
Opodo tickets were nonrefundable. Accordingly, any bias is
likely to be a direct function of the level set by the airlines
for the following two charges: the Aviation Insurance Levy,
a post 9/11 surcharge to cover for the extra insurance costs
because of acts of terrorism; and the Wheelchair Levy,
which amounts to £0.33 and is only imposed by Ryanair.
Noting that the former has been generally applied by airlines
worldwide (e.g., the level set by Ryanair in September 2007
was £3.47), the bias when we compare LCAs and FSAs
should not exceed the £3.80 for Ryanair, and a similar level
for EasyJet.
1202
ECONOMIC INQUIRY
traffic statistics such as the number of monthly
seats, the number of monthly passengers and the
monthly load factors, which were used to derive
market structure variables.
V.
DESCRIPTIVE ANALYSIS OF THE MERGERS
To provide an overview of the impact on
fares resulting from each takeover, the acquired
routes are contrasted against the other routes
that form the same city-pair to assess how
fares evolved before and after the merger. For
instance, one possible comparison route for the
acquired route “London Stansted-Naples” would
be “London Gatwick-Naples.” The city-pairs
routes comprise both other LCAs and the FSAs
operating these routes. While the use of an
independent comparison group is postponed to
the Difference-in-Difference analysis in Section
VI.B, the reference to routes in the same citypair is done here for the specific purpose to shed
light on the possible impact of the mergers on
the routes that were directly affected, including
the routes where the rival airlines operated.
Standard merger analysis would predict that the
prices of both merged entities and the rival
airlines should move in parallel; on the one
hand, an increase in market power should induce
a rise in all fares; on the other, if the merger
entails efficiency gains that outweigh the market
power effect, then rivals should respond by
lowering their prices, too.
A. Impact of Takeovers on Average Fares
across Booking Days
Table 1 reports the mean fare, by booking day and period, for the acquired routes
and the other routes in the same city-pair.
The mean fares range between £30 and £90
for both mergers, with fares increasing nonmonotonically as the departure date approaches.
Taking first the Buzz/Ryanair takeover and
its “Pre-Post Period,” the descriptive evidence
points to the following aspects. First, relative to
Buzz, Ryanair appears to have cut all fares with
the exception of the fare for the day immediately
before departure. For instance, Buzz charged
about £48 for a ticket purchased 35 days prior
to departure, while Ryanair’s fare is about £20
cheaper a year later; but for tickets purchased the
day before departure, Ryanair appears to have
charged about £22 more than what Buzz used
to a year earlier. Second, Ryanair’s prices in the
“2 Years Post Period” remained highly stable on
the acquired routes across booking days. In contrast to the prediction from merger theory, the
fares of the other airlines in the city-pair have
generally tended to increase in all periods, unlike
those in the merged routes.
Similar findings appear to apply to the Go
Fly/EasyJet takeover. All of EasyJet’s fares turn
out to be lower on average than the ones posted
by Go Fly a year earlier and lower than those in
the same city-pair group. Yet, in the “2 Years
Post Period”, EasyJet raised its late booking
fares (i.e., for the 1, 4, 7, 10, and 14 booking
days) on these acquired routes, which in some
cases were higher than in the city-pair group
(last two columns in Table 1). Such increases
are, however, of limited magnitude (about £10
or less) and well below the decreases observed
in the first period. Similar to Ryanair, EasyJet maintained its lower early booking fares,
although the fares for the latest booking days
became higher than those in the same city-pair
group. No significant variation is observed in
the fares offered by the rival airlines in the same
city-pair.
Figures 1 and 2 plot the mean change in
fares for each booking day and route type. For
example, for the two groups of routes in Table 1,
the plotted values in each panel correspond
to the row difference in the values in the
“Before” and “After” columns and in the “1st
Year” and “2nd Year” columns, respectively.
Therefore, as far as the analysis of fare change
in the “Acquired Routes” and the “Other Firms
in Same City-Pair” routes are concerned, the
previous comments apply. In addition, Figures 1
and 2 consider two extra sets of routes of
the acquiring firms: those in the same citypair of the acquired routes and all their routes
except the acquired ones. The former is included
to evaluate whether the merger, by possibly
enhancing the acquiring firms’ market power,
enabled them to raise fares in the markets where
a competitor was being acquired. The latter
is included to assess possible merger effects
propagating across the network operated by the
acquiring firms.
For both takeovers, in all the acquiring firms’
routes and the routes they operated in the
same city-pair of the acquired ones, their pricing profiles resemble those on the acquired
routes where, however, fares decreased by a
larger amount in the “Pre-Post Period,” which
is likely because of the large cost differential between the acquiring and the target firms
(see below). In the 2 years post-takeover period,
1
1
4
4
7
7
10
10
14
14
21
21
28
28
35
35
42
42
49
49
56
56
63
63
70
70
Same city-pair
Acquired
Same city-pair
Acquired
Same city-pair
Acquired
Same city-pair
Acquired
Same city-pair
Acquired
Same city-pair
Acquired
Same city-pair
Acquired
Same city-pair
Acquired
Same city-pair
Acquired
Same city-pair
Acquired
Same city-pair
Acquired
Same city-pair
Acquired
Same city-pair
Acquired
77.8
68.5
58.2
62.1
54.4
64.3
47.6
60.6
51.2
61.2
46.2
55.7
46.2
50.4
45.3
48.0
40.0
46.6
38.5
45.9
33.0
41.5
33.2
45.5
31.6
42.4
83.0
91.0
60.7
57.7
65.3
49.2
59.0
44.1
55.1
33.9
51.0
33.0
51.4
28.9
49.7
27.6
46.2
25.9
43.8
27.1
33.7
27.9
32.5
27.8
31.6
28.4
83.8
93.2
62.4
66.2
63.9
51.7
57.6
46.4
55.1
39.3
52.4
38.7
54.1
34.3
52.5
32.2
50.2
29.2
48.7
28.6
37.3
29.7
36.4
29.9
34.1
30.6
93.2
92.9
73.6
68.4
71.5
53.6
65.3
47.5
61.2
37.3
54.5
32.8
52.2
30.6
51.2
28.4
48.3
28.2
45.1
25.7
33.6
25.3
32.5
24.5
31.7
24.7
2nd Year
May 2004/
May 2005
Ryanair → Ryanair
1st Year
May 2003/
April 2004
77.6
79.9
60.5
66.8
56.9
71
50.8
65.6
52.1
66.5
46.0
61.7
43.1
56.6
41.5
56.0
37.9
53.3
36.7
53.1
32.1
48.7
33.6
51.7
31.4
48.0
81.1
79.3
59.0
55.9
59.9
50.6
53.1
42.4
49.8
44.6
45.9
42.4
45.9
47.3
45.4
46.8
43.1
43.9
41.5
41.1
33.5
38.6
33.0
36.8
31.9
35.5
After
June 2003/
December 2003
Go Fly → EasyJet
Before
June 2002/
December 2002
78.9
77.2
59.8
58.9
59.0
50.6
53.1
43.0
50.0
45.9
45.5
44.8
44.8
50.1
44.0
50.2
42.2
47.1
40.8
44.6
33.6
42.6
33.3
41.5
32.5
40.3
80.3
89.7
61.0
70.0
57.2
59.5
52.2
51.1
47.8
49.7
45.2
47.4
42.6
48.0
41.6
47.7
39.9
45.2
38.5
41.4
30.6
38.6
30.1
37.4
29.7
36.8
2nd Year
January 2004/
December 2004
EasyJet → EasyJet
1st Year
January 2003/
December 2003
Note: The “Same city-pair” routes fall into the same city-pairs of the “Acquired” routes. The “Same city-pair” group comprises such companies as Bmibaby, EasyJet, Ryanair,
MyTravelLite, Alitalia, BMI, British Airways, Czech Airlines, Iberia, and Lufthansa.
Days
Route types
After
June 2003/
March 2004
Buzz → Ryanair
Before
June 2002/
March 2003
TABLE 1
Mean Values of Fares in the Pre- and Post-takeovers Periods
DOBSON & PIGA: PRICE EFFECTS OF LOW-COST AIRLINES MERGERS
1203
1204
ECONOMIC INQUIRY
FIGURE 1
Ryanair/Buzz—Difference in Mean Monthly Fares on the Routes Directly Affected by the
Takeovers and on the Comparison Routes, by Booking Day and Period
Acquired routes
In same city-pair
In all routes except acquired
Other firms in city-pair
5 10 15 20 25
2 Years Post Period
-25 -20 -15 -10 -5 0
Difference in Mean Fares
Pre-Post Period
70 63 56 49 42 35 28 21 14107 4 1
70 63 56 49 42 35 28 21 14107 4 1
Days before Departure
Note: The “Other firms in city-pair” group comprises such companies as Bmibaby, EasyJet, British Airways, and Iberia
operating in routes that are part of the same city-pairs of the Acquired routes. The “Pre-Post Period” comprises the pretakeover months June 2002 to March 2003 and the same months 1 year later, after the takeover was completed, and thus
the analysis includes only data from LCCs. The “2 Years Post Period” includes the sub-period May 2003 to April 2004 and
the sub-period May 2004 to May 2005. The “In same city-pair” sample includes routes in the same city-pair of the acquired
routes in which Ryanair operated prior to the takeover.
Ryanair’s fares show very little change across
all types of routes and across booking days,
while EasyJet increased late booking fares
across all types of routes (Figure 2). Considering that it would seem highly unlikely that the
acquisition of a limited number of routes determined a widespread (and very fast) alteration in
the pricing strategy followed by the acquiring
firms in all the routes they were previously operating, the evidence seems to suggest the opposite
direction of causation: following the takeovers,
the pricing rule applied by the acquiring firms
on their wider network were likely used on the
acquired routes.
Interestingly, the change in fares of the
other companies in the same city-pair of the
acquired routes appears to be generally positive, but of small magnitude (less than £9), and
restricted to very late booking days, although,
in Figure 1, we can also observe decreases of
similar sizes for early booking fares in the
“2 Years Post Period.” Despite the significant
post-takeover reduction in most fares on the
acquired routes, the rival airlines seem to have
responded by maintaining the fare profile they
had used a year before. This suggests that the
city-pairs of the acquired routes might consist of largely independent routes with little
interdependence among each sub-market, possibly because the airlines may differentiate their
flights along a number of characteristics (see
Section III) so as to weaken the incentive to
engage in price competition (Borenstein and
Netz 1999).
B. Intertemporal Pricing Profile
The above discussion has highlighted that
the acquiring firms appear to have lowered
the posted prices for most booking days but at
some point increased their late booking fares
on the acquired routes. To provide some further insight on this latter aspect, it may be
DOBSON & PIGA: PRICE EFFECTS OF LOW-COST AIRLINES MERGERS
1205
FIGURE 2
EasyJet/Go Fly—Difference in Mean Monthly Fares on the Routes Directly Affected by the
Takeovers and on the Comparison Routes, by Booking Day and Period
Acquired routes
In same city-pair
In all routes except acquired
Other firms in city-pair
Difference in Mean Fares
2 Years Post Period
-21-18-15-12 -9 -6 -3 0 3 6 9 12
Pre-Post Period
70 63 56 49 42 35 28 21 14107 4 1
70 63 56 49 42 35 28 21 14107 4 1
Days before Departure
Note: The “Other firms in city-pair” group comprises such companies as Bmibaby, Ryanair, MyTravelLite, Alitalia, BMI,
British Airways, Czech Airlines, Iberia, and Lufthansa operating in routes that are part of the same city-pairs of the Acquired
routes. The “Pre-Post Period” comprises the pre-takeover months June 2002 to December 2002 and the same months 1 year
later, after the takeover was completed, and thus the analysis only includes data from LCCs. The “2 Years Post Period”
includes the sub-period January 2003 to December 2003 and the sub-period January 2004 to December 2004. The “In same
city-pair” sample includes routes in the same city-pair of the acquired routes in which EasyJet operated prior to the takeover.
informative to take a detailed look at fares on a
sample route affected by each takeover.
As an illustration, Figure 3 compares two late
and two early booking days on the StanstedBergerac route operated by Buzz (until March
2003) and then by Ryanair (from May 2003),
showing the mean weekly fares for the 1, 4,
49, and 56 booking days, normalized by the
fares posted 10 days prior to departure. The
pre-takeover period clearly shows a smaller dispersion of fares across all four of these booking days. Indeed, in the pre-takeover period, all
the ratios alternate around the value of 1 (i.e.,
fares for different booking days are not very different from the fares available 10 days before
departure), but in the post-takeover period the
late booking fares for 1 and 4 days prior to
departure are generally two to three times larger
than the base price. However, the early booking fares continue to fluctuate around the pretakeover values. This suggests that Ryanair,
unlike Buzz, is committed to a pricing policy characterized by large price hikes a few
days prior to departure. It is also noteworthy that the lowest dispersion in Figure 3 is
observed for August each year, when all the
fares for all the booking days tend to have
more similar high levels (presumably because
the yield management model takes account of
the high anticipated demand in that particular
month).
The increase in fare dispersion in the posttakeovers period also appears consistent with
the evidence in Figure 4, which uses all the
fares available for each airline. Compared with
Buzz, Ryanair operates with a much steeper
price profile for the days immediately preceding a flight’s departure, thereby engendering the
observed increase in price dispersion. Taking
together the evidence in Figures 1, 3, and 4, we
can surmise that Ryanair introduced its own specific yield management system to fare setting
in the routes it took over, which was substantially different from the one Buzz adopted, and
that this system was consistently followed in the
2 years after the takeover.
1206
ECONOMIC INQUIRY
FIGURE 3
Buzz/Ryanair—Evolution of Weekly Fares on an Acquired Route (Normalized by the Fares
Posted 10 Days from Departure)
A similar increase in the price dispersion
in the post-takeover period was found in the
Go Fly/EasyJet takeover.21 For both takeovers,
the data indicate a clear tendency for both
acquiring firms to raise late booking fares.
This is consistent with an attempt to pursue a
more intense intertemporal price discrimination
strategy aimed at extracting more surplus from
the consumers that have a low price elasticity,
presumably those that indeed book a flight late,
while offering lower fares to early bookers that
are more price sensitive and have more elastic
demand (Section VII.B).
C. A Possible Source of Efficiency Gains
Airline mergers may enjoy scale economies
from increased market share at the airport level
(given fixed costs of supporting flight operations) as well as network economies from
increased national and international presence.
The evolution of the acquiring firms’ market
shares in some of the main U.K. airports where
they (or the target) operated before and after the
21. Details are available from the authors on request.
takeovers is reported in Table 2. The table shows
that Ryanair’s main gain in market share arose
at London-Stansted airport, which was Buzz’s
only airport in the United Kingdom, while EasyJet enhanced its share in some airports it was
already operating from as well as gained new
presence at additional airports to extend its flight
network.
Table 2 also indicates significant entry activity registered by the acquiring firms in those airports, where the impact of the takeovers on their
airport market shares was largest. For example,
Ryanair started 21 new routes departing from
London-Stansted in the 25 months after the
takeover. EasyJet’s entry activity was particularly noticeable in those airports where it did
not operate prior to the takeover, as well as in
some of its existing bases. In both cases, the
new routes offered an increased number of travel
options for customers.
D. Other Effects
Apart from prices, consumer welfare will
be affected by other variables, notably the frequency, capacity, and choice of flights as well
DOBSON & PIGA: PRICE EFFECTS OF LOW-COST AIRLINES MERGERS
1207
FIGURE 4
Comparison of the Time Profile of Fare Levels between the Acquiring and the Acquired Company
Note: The Booking Day on the vertical axis indicates the number of days from take-off. The distribution of fares for
Ryanair and EasyJet is drawn from the routes not directly affected by the takeovers. The two extremes represent the lowest
and highest adjacent values, while the box reports the 25th percentile, the median, and the 75th percentile values.
as choice through competing airlines, all of
which can be indicative of effective competition
prevailing post-merger. Table 3 provides some
summary statistics on these aspects pertaining
to the routes and the markets directly affected
by the takeovers.
For each takeover, Table 3 reports statistics
for the “Pre-Post Period” and the “2 Years Post
Period.” These figures provide a direct measure
of the changes brought about by the acquiring
companies and enable a first assessment of the
nonprice effects of the two takeovers. On its
acquired routes, Ryanair increased the mean
number of flights in a route by about 22%, from
63 to 77. This is reflected in the increase from
5,282 to 9,504 in the mean monthly number
of passengers. This implies an increase in the
average number of passengers per flight from 84
to 123. In contrast, EasyJet slightly reduced the
flight frequency that Go Fly had scheduled on
its routes, and managed to maintain very similar
passengers’ load factors, in the immediate posttakeover period.
Comparing the same variables over the
2-year period following the takeovers shows a
steady increase in flight frequency, passenger
numbers and load factors for the case of Ryanair
and a generally stable situation for EasyJet. The
remaining variables in Table 3 indicate that the
competitive scenarios in the two takeovers were
quite similar, but with Buzz/Ryanair operating
in slightly more concentrated routes and smaller
city-pairs. However, the relevant measures of
market structures pertaining to the acquired
routes tended to remain largely stable, in particular in the 2 years following the acquisitions.
VI.
ECONOMETRIC ASSESSMENT OF PRICING
EFFECTS
To evaluate in a more formal manner how
fares changed in the acquired routes, we consider the takeover as a treatment that the routes
received, and compare the fares in such a treated
group with those from a comparison group of
routes that did not receive the treatment.22 We
consider two types of comparison groups. The
22. This is a common approach to examining pricing
effects of mergers. See Weinberg (2008) for a survey.
1208
ECONOMIC INQUIRY
TABLE 2
Evolution of Acquiring Firms’ Market Shares in U.K. Airports
Ryanair
U.K. Airports
Bristol
Cardiff
East Midlands
Edinburgh
Glasgow-Prestwick
Liverpool
London-Gatwick
London-Luton
London-Stansted*
Newcastle
Teesside
March
2003
June
2003
June
2004
June
2005
Routes
entereda
0.07
0.06
0.00
0.05
1.00
0.13
0.03
0.12
0.50
0.04
0.18
0.06
0.06
0.00
0.05
0.87
0.14
0.02
0.11
0.68
0.07
0.18
0.06
0.06
0.12
0.03
0.93
0.11
0.03
0.11
0.65
0.05
0.11
0.05
0.07
0.20
0.03
0.94
0.27
0.04
0.21
0.65
0.06
0.19
1
—
5
—
8
12
1
10
21
1
1
November
2002
January
2003
June
2004
June
2005
Routes
entereda
0.54
0.00
0.03
0.12
0.13
0.00
0.75
0.14
0.80
0.00
0.74
0.34
0.25
0.20
0.21
0.07
0.72
0.15
0.76
0.24
0.77
0.41
0.24
0.16
0.15
0.32
0.74
0.21
0.78
0.24
0.70
0.39
0.26
0.13
0.13
0.33
0.48
0.26
0.66
0.22
4
7
2
—
—
12
3
15
8
7
EasyJet
Belfast Intl.∗
Bristol∗
East Midlands∗
Edinburgh∗
Glasgow∗
Newcastle∗
Liverpool
London-Gatwick
London-Luton
London-Stansted∗
Notes: The shares are obtained using the number of flights to the following European countries: United Kingdom
(domestic), Italy, France, Spain, Austria, Holland, Germany, Belgium, Greece, Ireland, Portugal, Switzerland, Sweden,
Norway, Czech Republic. The first two columns refer to a pre- and post-takeover period, respectively. The airports denoted
with an asterisk are those where Buzz and Go Fly operated before the takeover.
a Routes entered in period May 2003 to June 2005 for Ryanair, and January 2003 to June 2005 for EasyJet.
first comprises the routes sharing the same citypair with the treated routes. Using comparison
routes from the same markets of the directly
affected routes allows a more direct evaluation
of the mergers’ effects, because the treated and
the comparison group automatically share similar structural characteristics (e.g., route length),
as well as some unobserved idiosyncratic shocks
that may have occurred at the city-pair level.
Furthermore, as discussed above, it provides
an immediate assessment of how rival airlines responded to the merger. The descriptive evidence previously presented in Table 1
and Figures 1 and 2, which reveals no significant changes in the pricing strategies of rival
airlines, suggests that the routes in the same
city-pair of the treated routes were not particularly affected by the mergers, thereby justifying a further investigation of the impact of the
mergers relative to the other routes in the citypair. However, such a comparison group is not
necessarily independent, that is, the fares set by
the rival airlines may be jointly determined with
those set by the acquiring firms; in this case,
the estimates of price effects could be biased.
Therefore, we also consider a second comparison group, which is made up of completely
independent routes that are not part of the citypair markets of the mergers’ routes.
Formally, we use propensity score matching methods and a Differences-in-Differences
(henceforth, “DID”) approach to study whether
the takeovers resulted in lower or higher fares
for passengers. Both methodologies enable us to
control for route specific factors that could not
be taken into account in the above descriptive
analysis. Furthermore, given the significance of
intertemporal pricing in the airline industry, a
62.8
5,282
84.0
0.96
0.96
1.08
0.83
0.83
0.08
1.63
1.94
June 2002–
March 2003
77.3
9,504
123.0
0.94
0.94
1.13
0.83
0.83
0.08
1.81
1.81
June 2003–
March 2004
77.5
9,558
123.3
0.94
0.94
1.13
0.83
0.83
0.08
1.81
1.81
May 2003–
April 2004
87.9
13,064
148.6
0.92
0.93
1.15
0.78
0.78
0.09
2.20
2.06
May 2004–
May 2005
Ryanair → Ryanair
114.3
13,875
121.4
0.85
0.86
1.31
0.46
0.47
0.26
3.24
3.50
June 2002–
December 2002
101.3
12,024
118.7
0.86
0.87
1.30
0.42
0.43
0.23
3.79
3.43
June 2003–
December 2003
Go Fly → EasyJet
100.5
11,851
117.9
0.86
0.87
1.30
0.43
0.44
0.24
3.64
3.38
January 2003–
December 2003
99.7
12,142
121.8
0.87
0.88
1.30
0.41
0.42
0.23
3.91
3.44
January 2004–
December 2004
EasyJet→EasyJet
Note: For the Buzz-Ryanair case, the routes discontinued by Ryanair were not taken into account in the calculation of the mean values in the June 2002–March 2003 period.
a Market shares calculated using either the number of monthly flights per company or the number of monthly passengers per company.
b To obtain “Relative city-pair size” the United Kingdom, Italy, France, Germany, and Spain were each divided into three sub-country regions: north, center, and south. The variable
is calculated as the share of total flights in a city-pair (say, London to Rome) over the total flights connecting the sub-area in the United Kingdom with the sub-area in the country of
the other city-pair endpoint (i.e., from the south of the United Kingdom to the center of Italy as the sub-areas where London and Rome are respectively located). For smaller countries,
the denominator is given by taking the whole country.
Source: U.K. Civil Aviation Authority.
Flights per company in route
Passengers per company in route
Mean number of passengers per flight
Route Herfindahl (flights)a
Route Herfindhal (passengers)a
Companies in route
City-pair (flights) Herfindahla
City-pair (passengers) Herfindahla
Relative city-pair sizeb
Number of routes in city-pair
Number of companies in city-pair
Mean Values
Buzz → Ryanair
TABLE 3
Routes and Market (City-Pairs) Characteristics for the Routes and City-Pairs Involved in the Takeovers
DOBSON & PIGA: PRICE EFFECTS OF LOW-COST AIRLINES MERGERS
1209
1210
ECONOMIC INQUIRY
TABLE 4
Covariates Used to Calculate Propensity Scores
Variable
Description
Route Herfindahl
Route length
Number of U.K. airports connected to the
arrival airport
Relative city-pair size
Number of routes in city-pair
Herfindahl Index with route’s shares calculated using a company’s number
of flights (see Table 3)
Expressed in miles (airport to airport)
The number of U.K. origin airports offering flights to the arrival airport.
Size of regional market (see Table 3 for statistics)
Number of routes within a city-pair (see Table 3)
novel aspect of our approach consists in the distinction between fares according to their booking days.
A. Propensity Score Matching
Let Ar ∈ {0, 1} be an indicator of whether
1
route r is taken over, and denote Prc
as the
observed year-to-year difference in the log of
the monthly mean (or median) fares on route
r for flights with characteristics c in either the
1
“Pre-Post Period” (in which case Prc
captures the percentage change in the fares posted
by the acquiring firms relative to the target) or
the “2 Years Post Period” (so that the change
is between the fares posted by the acquiring firms over a 12-month period).23 Following the microeconometric evaluation approach
(Cameron and Trivedi 2005), the average effect,
conditioned by booking day b, of a takeover
on the fares in the acquired routes can be
defined as:
(1)
1
Pτ = E{Prc
|Ar = 1, b = τ}
0
|Ar = 1, b = τ}
− E{Prc
0
denotes the year-to-year percentage
where Prc
difference in the monthly mean (or median)
fares on route r, had route r not been taken over.
That is, the actual price effect of the takeover
corresponds to what we actually observe in
terms of price changes minus the change that
we would have observed in the absence of
the takeover. However, no individual route can
be observed as both having, and not having,
0
received the treatment and therefore Prc
is
unobservable.
To confront this missing data problem,
matching techniques employ a counterfactual
based on the selection of a valid comparison
group from the data. The purpose of matching
23. Thus, the “Pre-Post” period does not include any
observation for any of the Full Service Airlines.
is to pair, for a given booking day, each acquired
route with a counterfactual made up of a route
that has not undergone any ownership change
but that shares similar characteristics with the
acquired routes. In this case, we use the routes
that are part of the same city-pairs of the mergers’ routes.
To pair an observation in the treated group
with one (or more) in the comparison group
(the counterfactuals) we use the “propensity
score” proposed by Rosenbaum and Rubin
(1983). It provides a measure of “closeness”
encompassing the information for route and
city-pair characteristics. The propensity score is
calculated from the covariates listed in Table 4
and is used within the nearest-neighbor matching algorithm to identify two counterfactual
1 24
matches for each Prc
. The analysis is carried out independently for each booking day.
To further improve the reliability of our counterfactual, exact matching is imposed for the
following characteristics c: “Period” (i.e., observations from the same month and year), “Direction” (indicating whether the flight goes from the
United Kingdom to Continental Europe or vice
versa); “Week-End” (if the flight departs during the week days Friday to Monday); “Time
of Departure” (a three values discrete variable
identifying flights that depart before 7.30 a.m.,
between 7.30 a.m. and 7.30 p.m., and after 7.30
p.m.). Because we consider fare changes over
a 12-month period, the inclusion of the latter characteristic appears crucial, as it prevents
the possibility of mistakenly comparing fares
for an early morning flight with fares a year
later for a late evening flight. Furthermore, to
base our analysis on reliable monthly statistics,
24. More formally, let PA and PC denote the propensity
score in an acquired and nonacquired route, respectively.
Conditional on obtaining an exact matching for the chosen
characteristics, the set of n counterfactual matches satisfy
MA (P ) = {C| minC PA − PC }. We set n = 2 to minimize
the risk of spurious associations.
DOBSON & PIGA: PRICE EFFECTS OF LOW-COST AIRLINES MERGERS
the mean and the median fares were not calculated unless, for each month and each company, the group route-characteristics included
at least seven observations for prices in each
booking day.
B. DID Estimator
Following Cameron and Trivedi (2005), the
DID estimator can be shown to be equivalent
to the estimate of αb in the ordinary least
square (OLS) hedonic pricing regression on a
sample including only observations for the same
booking day:
(2)
Pribm =
Xrm
βb
+ δb DP + γb DA
+ αb DP ·DA + ui
where Pribm denotes company i’s monthly mean
(median) fare posted b days before a flight’s
departure for flights in route r departing in
month m; Xrm includes a constant, the flight’s
characteristics “Direction,” “Week-End,” and
“Time of Departure,” plus the last three variables in Table 4; DP equals one in the posttakeover period; DA equals one in the treated
routes. The nontreated routes are part of city-pair
markets not directly affected by the mergers, that
is, in the DID we use the second comparison
group.
Given the differing characteristics of the markets involved in the two takeovers (see Section
V.D), regression (2) is run separately for each
takeover. Furthermore, given the strong seasonality exhibited by airline fares, the “Pre-Post
Period” and the “2 Years Post Period” are also
studied in separate regressions.
Finally, bearing in mind that data are from
posted fares, in the application of both methodologies it is essential to control for the change
in the capacity offered by an airline on a route.
Indeed, the decision by the acquiring firm to,
say, double the number of flights in a route
is also likely to have obvious repercussions on
its fare setting decisions. Therefore, in applying
Equations (1) and (2), we only considered those
routes where the yearly percentage change in
the total number of flights operated by an airline remained below or equaled 25%. Given the
high correlation between number of flights and
number of passengers, imposing such a threshold reinforces the results obtained using posted
fares. Assuming that monthly demand conditions remain sufficiently stable year on year,
controlling for an airline capacity in a route
implies that a change in both the time profile
1211
and the level of fares can only be ascribed to
a variation in the pricing schemes over a 12month period. Such a variation may be a direct
consequence of the takeovers, when we compare
the fares of the target and the acquiring firms; or
of their longer-term effects, when we consider
the fares posted by the acquiring firms in the
25 months following the takeovers.
VII.
EVALUATION OF PRICING EFFECTS
A. Econometric Results
Tables 5 and 6 report the average effect of
the takeover on the sample of treated routes
for both mean and median yearly fare changes.
Following Borenstein (1990) and Kim and Singal (1993), these tables include, in parentheses, the same estimates weighted by the number
of monthly passengers flown by an airline on
a route.
With regard to Ryanair’s “Pre-Post Period”
(shown in the first half of Table 5), the previous comments relating to Table 1 appear
to be supported in respect of the takeover’s
impact. Indeed, the percentage change in fares
posted one day from take-off from Buzz to
Ryanair was between 28% and 34.6% larger
than in the comparison group, while weighted
mean fares changes for bookings between 28
and 70 days were 43%–67% smaller. The
effect is even stronger, for both increases and
decreases, on median fares, which are included
to control for possible effects induced by the
aggregation procedure by outliers. Interestingly,
the marked increase in late booking fares
observed in the “Pre-Post Period” is only partly
reabsorbed in the 2 years after the takeover (see
second half of Table 5), with Ryanair’s weighted
“1 Day” fares changing similarly to the comparison group, but with “4 Days” fares decreasing
in relative terms by 14%–20%. More generally,
the long-run effects suggest a relatively smaller
decrease in all fares, with the estimates for the
weighted median fares generally appearing to be
nonsignificant.
In contrast, the first half of Table 6 suggests that the takeover by EasyJet led to
a direct, short-run decrease across all fares,
which are particularly conspicuous for very
early (56–70 days) and late (1–10 days) booking days. Critically, a similar pattern is revealed
by the estimates for the overlap routes, although
the decrease is of a smaller magnitude, indicating that EasyJet’s enhanced competitive position
1212
ECONOMIC INQUIRY
TABLE 5
Buzz/Ryanair Takeover—Nearest-Neighbor Matching Estimates for Percentage Change in Monthly
Mean and Median Fares (Average Treatment Effect for the Treated with Weighted Estimates in
Parentheses)
Buzz → Ryanair
Starting Period/
End Period
Booking Day
1 day
4 days
7 days
10 days
14 days
21 days
28 days
35 days
42 days
49 days
56 days
63 days
70 days
N
Ryanair → Ryanair
June 2002–March 2003/
June 2003–March 2004
May 2003–April 2004/
May 2004–May 2005
Mean
Median
Mean
Median
34.6a
(28.0)a
9.0b
(2.9)
−7.5c
(−13.1)a
−11.0b
(−15.0)a
−24.5a
(−22.8)a
−28.9a
(−23.8)a
−43.4a
(−43.5)a
−48.3a
(−48.9)a
−49.9a
(−58.4)a
−49.6a
(−59.6)a
−50.3a
(−66.7)a
−56.2a
(−65.6)a
−54.4a
(−67.3)a
36.3a
(28.9)a
4.5
(3.5)
−15.4a
(−18.6)a
−29.9a
(−29.1)a
−47.2a
(−43.2)a
−46.4a
(−38.4)a
−59.5a
(−54.7)a
−61.8a
(−61.8)a
−48.0a
(−63.5)a
−41.3a
(−53.7)a
−37.3a
(−51.0)a
−49.3a
(−59.8)a
−45.3a
(−54.2)a
−10.5a
(−1.8)
−20.8a
(−14.5)a
−19.4a
(−15.6)a
−17.2a
(−12.7)a
−23.4a
(−17.4)a
−21.1a
(−15.5)a
−8.3b
(−6.2)
−11.8a
(−10.3)c
−7.7
(−7.8)
−9.5c
(−9.0)
−12.0b
(−12.7)b
−17.6a
(−14.4)a
−6.6
(−9.3)c
−9.2c
(−0.2)
−20.6a
(−18.0)a
−14.5a
(−6.8)
−10.2b
(−4.5)
−21.6a
(−8.3)
−25.2a
(−24.8)a
−7.4
(−8.7)
−11.2c
(−11.2)c
−6.8
(−4.9)
−14.0c
(−15.2)c
−20.0a
(−18.9)b
−16.2a
(−16.0)b
−7.5
(−7.5)
2,138
5,439
Notes: Propensity score evaluated using the covariates in Table 4. Exact matching variables: “Period,” “Direction,” “WeekEnd,” and “Time of Departure.” Weights: Number of company i’s monthly passengers on a route. The analysis in the “Pre-Post
Period” does not include data from FSAs.
a
Significant at 1% level; b significant at 5% level; c significant at 10% level.
in those routes may have led to smaller downward adjustments for fares.
In the 2 years following the takeover (see
second half of Table 6), EasyJet’s weighted
median fares for late booking days in acquired
routes increased, relative to the counterfactuals,
by about 7%–11%, while no noticeable change
is observed for all the earlier fares. For the
overlapping routes, the increase for late booking
fares is lower, while the early booking fares
exhibit a tendency to fall (although by only
about 5%).
Table 7 shows the DID estimates, which,
despite the use of a different comparison group,
are largely consistent with the results in Table 5
and Table 6. In the “Pre-Post Period,” Ryanair’s
“1 Day” unweighted median fares increased by
about £20.10 as a consequence of the takeover,
while prices for earlier booking fell by between
£11.80 and £34.20 depending on the booking
day. Also, as far as the long-term effects are
concerned, we observe a co-movement of the
fares in the treated and the comparison groups,
because the price adjustments are smaller in
magnitude and often nonsignificant, especially
for the weighted median case. In any case, even
taking into account a possible increase in fixed
charges of about £4.00, the evidence obtained
by applying the DID indicates that the postmerger fares exhibit a steeper temporal profile,
DOBSON & PIGA: PRICE EFFECTS OF LOW-COST AIRLINES MERGERS
1213
TABLE 6
Go Fly/EasyJet Takeover—Nearest-Neighbor Matching Estimates for Percentage Change in
Monthly Mean and Median Fares (Average Treatment Effect for the Treated with Weighted
Estimates in Parentheses)
Go Fly → EasyJet
Starting Period/
End Period
Booking Day
1 day
4 days
7 days
10 days
14 days
21 days
28 days
35 days
42 days
49 days
56 days
63 days
70 days
N
EasyJet → EasyJet
June 2002–December 2002/
June 2003–December 2003
Mean
Mean
Overlap
Median
−22.8a
(−22.3)a
−25.8a
(−28.6)a
−22.8a
(−22.6)a
−28.2a
(−28.8)a
−23.7a
(−25.0)a
−21.7a
(−21.1)a
−13.5a
(−14.5)a
−17.3a
(−19.3)a
−21.5a
(−23.5)a
−30.8a
(−29.8)a
−33.7a
(−34.7)a
−40.1a
(−41.1)a
−46.6a
(−49.6)a
−26.4a
(−28.8)a
−24.5a
(−31.3)a
−15.8a
(−17.8)a
−20.2a
(−23.5)a
−19.7a
(−22.1)a
−18.2a
(−19.7)a
−12.4b
(−16.3)a
−17.7a
(−21.5)a
−24.4a
(−30.9)a
−37.7a
(−39.4)a
−42.4a
(−43.3)a
−52.0a
(−56.6)a
−58.0a
(−66.7)a
8,893
January 2003–December 2003/
January 2004–December 2004
−11.9a
(−10.2)a
−22.5a
(−18.4)a
−25.5a
(−24.1)a
−32.4a
(−31.8)a
−20.7a
(−17.6)a
−19.5a
(−16.7)a
−13.2a
(−10.3)a
−10.2a
(−6.3)c
−14.7a
(−9.7)a
−16.0a
(−8.0)b
−27.9a
(−23.6)a
−30.4a
(−25.3)a
−34.2a
(−27.9)a
3,866
Mean
12.7a
(10.7)a
11.8a
(9.3)a
9.8a
(6.3)a
9.8a
(6.1)a
7.0a
(3.2)b
3.8a
(0.8)
−2.3c
(−4.2)a
−1.1
(−1.7)
1.2
(1.4)
1.8
(1.8)
4.0a
(4.0)b
3.6b
(2.8)c
3.5b
(2.3)
Median
12.6a
(10.8)a
15.0a
(11.2)a
12.8a
(7.0)a
11.8a
(6.8)a
10.3a
(4.0)c
3.9c
(0.8)
1.6
(−0.5)
1.9
(0.2)
3.1
(0.1)
3.3c
(2.1)
5.2a
(3.8)c
4.7b
(−0.4)
4.9b
(1.6)
39,925
Mean
Overlap
5.6a
(6.0)a
3.7c
(3.4)
1.7
(1.8)
2.3
(2.9)b
0.3
(−1.5)
1.1
(−0.5)
−2.1
(−3.6)a
−1.5
(−2.2)
−4.1a
(−4.9)a
−4.7a
(−5.0)a
−5.0a
(−5.0)a
−4.7a
(−4.6)a
−4.9a
(−4.8)a
10,772
Notes: Propensity score evaluated using the covariates in Table 4. Exact matching variables: “Period,” “Direction,” “WeekEnd,” and “Time of Departure.” Overlap routes are shown in Table 1. Weights: Number of company i’s monthly passengers
on a route. The analysis in the “Pre-Post Period” does not include data from FSAs.
a Significant at 1% level; b significant at 5% level; c significant at 10% level.
which was maintained also in the second year
of operation.
Across booking days, EasyJet’s takeover led
to average savings for passengers of about
£14.00–£33.00 in the “Pre-Post Period,” with
median fares falling by about £16.00–£32.00.
Again, such values are well above the possible increases in fixed charges. With regards to
the longer-run effects of EasyJet’s takeover, the
findings suggest an increase of about £7.70–
£9.40 for late booking fares which partly counteracts the fall in the first months after the
takeover. For instance, observe that in the
January 2003 to December 2004 period, the
estimates for the late booking fares (up to
“10 Days”) are positive, while they are negative for early booking days. Taking into account
the possible bias introduced by increases in fixed
charges would not change the basic result that
in the second year of EasyJet’s operation, late
booking fares slightly increased (after they had
fallen in the first year), while early booking fares
remained largely stable relative to those posted
in the comparison group.
B. Impact on Pricing Policy
Drawing on these results, we can make some
observations in relation to the pricing strategy
1214
ECONOMIC INQUIRY
TABLE 7
Difference-in-Difference Estimates for Change in Fare Levels (£’s) between the Starting and End
Period (Weighted Estimates in Parentheses)
Buzz → Ryanair
Starting Period/
End Period
Booking Day
1 day
4 days
7 days
10 days
14 days
21 days
28 days
35 days
42 days
49 days
56 days
63 days
70 days
June 2002–
March 2003/
June 2003–
March 2004
Mean
15.9a
(5.1)
−2.9
(−12.0)a
−18.9a
(−31.2)a
−19.3a
(−29.8)a
−24.7a
(−32.8)a
−23.3a
(−29.4)a
−21.6a
(−26.5)a
−22.9a
(−28.9)a
−23.5a
(−28.7)a
−20.3a
(−28.5)a
−13.0a
(−16.9)a
−15.6a
(−23.1)a
−12.7a
(−20.2)a
Median
20.1a
(9.0)b
−4.6
(−11.8)a
−21.3a
(−31.2)a
−22.0a
(−30.9)a
−27.0a
(−34.2)a
−25.6a
(−29.7)a
−23.1a
(−26.4)a
−24.7a
(−30.4)a
−24.3a
(−28.9)a
−20.6a
(−28.2)a
−13.9a
(−16.6)b
−15.9a
(−22.7)a
−13.2a
(−20.0)a
Ryanair → Ryanair
May 2003–
April 2004/
May 2004–
May 2005
Go Fly → EasyJet
EasyJet → EasyJet
June 2002–
December 2002/
June 2003–
December 2003
January 2003–
December 2003/
January 2004–
December 2004
Mean
Median
Mean
−3.2
(−1.9)
−3.5
(−6.0)b
−1.0
(−7.0)a
−2.4
(−8.2)b
−5.9a
(−11.7)a
−8.2a
(−13.9)b
−4.9b
(10.9)b
−5.1b
(−10.3)b
−1.9
(−7.2)b
−2.0
(−8.3)a
−3.4b
(−6.5)b
−3.9b
(−7.2)a
−4.8a
(−8.3)a
−4.6
(−3.1)
−3.6b
(−7.3)b
−2.0
(−8.8)a
−1.8
(−8.4)b
−5.5a
(−12.1)a
−9.1a
(−14.2)a
−4.5
(−10.8)a
−5.0a
(−10.1)a
−2.0
(−7.3)b
−1.8
(−8.1)b
−3.8
(−6.8)b
−5.7a
(−8.9)a
−5.4a
(−8.7)a
−14.2a
(−19.3)a
−17.2a
(−19.4)a
−27.0a
(−30.9)a
−29.3a
(−33.1)a
−24.7a
(−29.4)a
−22.9a
(−26.2)a
−15.2a
(−19.1)a
−16.7a
(−20.0)a
−17.6a
(−20.5)a
−20.4a
(−23.1)a
−15.6a
(−18.0)a
−15.1a
(−19.1)a
−16.8a
(−20.8)a
Median
−16.6a
(−23.0)a
−18.5a
(−21.7)a
−28.5a
(−32.4)a
−28.2a
(−32.4)a
−24.4a
(−29.3)a
−23.2a
(−26.8)a
−15.7a
(−19.5)a
−17.8a
(−20.5)a
−18.8a
(−21.4)a
−21.7a
(−24.2)a
−16.3a
(−18.2)a
−17.1a
(−21.3)a
−18.2a
(−21.9)a
Mean
9.4a
(5.7)a
7.7a
(4.2)a
6.1a
(0.2)
4.6a
(−1.3)
1.1
(−5.0)a
−1.7c
(−7.9)a
−4.7a
(−10.4)a
−4.9a
(−10.4)a
−4.2b
(−10.0)a
−5.3a
(−10.7)a
−3.1a
(−4.2)a
−3.0a
(−3.9)a
−2.8b
(−3.6)a
Median
8.7a
(5.5)a
7.2a
(3.8)a
5.5a
(−0.5)
3.5a
(−1.8)
0.3b
(−5.4)a
−2.0
(−7.8)a
−4.4a
(−9.9)a
−4.8a
(−10.1)a
−3.7a
(−9.1)a
−5.5a
(−10.2)a
−3.3a
(−4.1)a
−3.1a
(−3.8)a
−2.7a
(−3.8)a
Notes: For each merger, the comparison sample includes only the routes that were not part of the same city-pairs of the
directly affected routes. The DID estimates derive from OLS regressions including a number of regressors that are detailed
in the paper. The full set of estimates is available upon request. Weights: Number of company i’s monthly passengers on
a route.
a Significant at 1% level; b significant at 5% level; c significant at 10% level.
used by the acquiring airlines, and specifically
the possible theoretical reasons behind the intensification of the temporal pricing profile induced
by the takeovers, and the impact on prices resulting from the two takeovers.
On pricing strategy, the theoretical literature
on intertemporal price discrimination suggests
various reasons why airlines might offer lowerpriced seats to earlier purchasers. For instance,
Gale and Holmes (1993) study the adoption of
Advance-Purchase Discounts (APD) in monopolistic markets when off-peak flights can be identified with certainty. They show that setting a
low fare for the off-peak flight at an early, but
not a late, stage induces travellers to self-select
according to their preference for a peak or an
off-peak flight. With demand uncertainty, Gale
and Holmes (1992) show that APD can promote efficiency by spreading consumers evenly
across flights before timing of the peak period
is known. The implication is that, ex post, both
the peak and the off-peak flight will exhibit a
monotonically increasing time profile.
In addition to being an efficient device the
airlines use to shift demand from peak to
off-peak flights, APD has been found to be
an optimal pricing strategy for more general
market conditions. For both competitive and
imperfectly competitive markets where firms set
prices before the demand for a single flight is
known, Dana (1998, 1999) shows that firms
may offer APD because travellers with more
DOBSON & PIGA: PRICE EFFECTS OF LOW-COST AIRLINES MERGERS
certain demand and weaker departure time preferences are better off buying in advance because
of the presence of other consumers with higher
valuations and more uncertain demand. Indeed,
in Dana’s analysis the airlines commit to a
rationing rule that limits the number of cheaper
seats and thus reduces the incentive of consumers with more (less) certain demand to postpone (bring forward) purchase.
In our context, more certain demand and
weaker preferences for the schedule convenience usually denote characteristics associated
with the leisure travellers segment, to which
the lower early fares posted by the LCAs seem
to be mostly directed. Furthermore, the rapid
expansion of travel possibilities in the European
market has created a situation where leisure travellers, once they have decided to travel, may
have a more elastic demand because they can
substitute across a sizable number of equally
attractive destinations, and choose those that
are more competitively priced. In contrast, route
substitutability may not matter so much for business customers, for whom traveling needs may
arise quite unexpectedly and at short notice.
Given their strong preference for schedule convenience, the high late booking fares appear
to be meant for business customers (or the
presumably rare, price insensitive leisure traveler). Gerardi and Shapiro (2009) present evidence from the U.S. market supporting the
notion of larger price dispersion in routes with a
1215
more heterogeneous customer base. Their analysis also reveals a positive correlation between
price dispersion and route concentration, which
is consistent with the present results from the
two mergers, where most affected routes were
monopolistic.
Both the theoretical and empirical literature
thus seem to provide support to Ryanair’s decision to replace Buzz’s flat intertemporal pricing
profile with a much steeper one. Accordingly,
the most straightforward explanation for the
change in prices on the acquired routes is that
these simply followed the pricing formulae the
acquiring firm used on its existing routes, that
is, the price changes simply reflect the acquiring
firm imposing its pricing model on the acquired
routes rather than exploiting any enhanced market power.
C. Aggregate Impact on Consumers
With the mergers having potentially different effects on early and late bookers, the net
effect may depend critically on when seats are
actually sold (i.e., when posted prices become
transaction prices). To see how the distribution
of sales over time may impact consumers overall we examine four distributions of seats sold
across the booking days. The results of these
simulations are shown in Table 8, using the DID
estimates in Table 7 to work out a measure of
the possible changes in the actual mean and
TABLE 8
Simulated Fare Changes in Mean and Median Fares
Starting Period/
End Period
Distribution 1
Distribution 2
Distribution 3
Distribution 4
Buzz → Ryanair
Ryanair → Ryanair
Go Fly → EasyJet
EasyJet → EasyJet
June 2002–
March 2003/
June 2003–
March 2004
May 2003–
April 2004/
May 2004–
May 2005
June 2002–
December 2002/
June 2003–
December 2003
January 2003–
December 2003/
January 2004–
December 2004
Mean
−16.6
(−25.1)
−16.3
(−25.0)
−15.4
(−24.5)
−15.2
(−24.4)
Median
−17.8
(−24.4)
−17.5
(−24.2)
−16.5
(−23.4)
−16.3
(−23.3)
Mean
Median
−3.9
(−6.5)
−4.0
(−6.6)
−3.9
(−6.5)
−3.9
(−6.5)
−4.2
(−9.1)
−4.3
(−9.2)
−4.2
(−8.9)
−4.2
(−8.9)
Mean
−19.9
(−23.5)
−20.2
(−23.8)
−20.2
(−23.9)
−20.3
(−23.9)
Median
−20.8
(−24.5)
−21.0
(−24.8)
−21.1
(−24.9)
−21.2
(−25.0)
Mean
Median
0.1
(−4.8)
0.4
(−4.5)
0.9
(−4.0)
1.1
(−3.8)
−0.2
(−4.8)
0.1
(−4.5)
0.5
(−4.0)
0.8
(−3.8)
Note: The numbers are derived using the estimates from the DID estimates reported in Table 7. The simulations from
weighted estimations are in parentheses. The four distributions assume that, respectively: (a) 30%, 25%, 26%, and 23% of
seats are cumulatively sold 42 days from departure; (b) 59%, 55%, 52%, and 48% of seats are cumulatively sold 21 days
from departure; (c) 41%, 45%, 48%, and 52% of seats sold in the last two weeks before departure, with 14%, 16%, 17%,
and 20% sold in the last 4 days.
1216
ECONOMIC INQUIRY
median ticket paid by the passengers flying with
Ryanair and EasyJet. Note that these simulated
distributions are intentionally loaded toward late
purchases, that is, where fare increases were
recorded. For instance, Distribution 4 assumes
that 52% (20%) of seats are sold within a fortnight (4 days) before departure. Even so, the
simulations indicate a significant reduction of
about £15–£24 on average per passenger flying
with Ryanair on the acquired routes immediately after the takeover. A back of the envelope
calculation suggests that for all distributions,
the merger would have no price effect only if
about 53% of passengers booked their flights
the day before departure and the others bought
uniformly across the previous booking periods.
In the 24 months after the takeover, mean and
median simulated fares remained stable, with a
slight downward adjustment. Overall, the simulated results point strongly toward consumers
in aggregate benefiting from lower fares on the
routes directly affected by this takeover.
The short-run effects of EasyJet’s acquisition are more straightforward: there were fare
savings for all types of travellers. However, a
less clear-cut conclusion can be reached for the
longer-term effects because an increase in the
set of late booking fares has to be weighed
against the decrease in early booking fares. Nevertheless, notice how fare decreases are larger,
and increases smaller for the weighted estimates,
suggesting that larger decreases were observed
when an airline transported a high number of
passengers. According to Barlow (2000), EasyJet sells about one-fifth of seats within the last
5 days from take-off, while about two-fifths
of its load factor is realized between 45 and
10 days from departure.25 Marginal increases of
£1 or less in the simulated fares are recorded in
Table 8, but only for distributions that attach a
much greater weight to late booking sales. Interestingly, the simulations based on weighted estimates continue to yield negative changes. Thus,
it is very unlikely that the EasyJet’s takeover
determined a significant, sustainable increase in
the fares paid by passengers on the acquired
routes, especially considering that the simulated
fares suggest that, in the “Pre-Post Period,”
25. More generally on the pattern of typical booking
profile of EasyJet ticket sales, see EasyJet’s 2003 annual
report and accounts (p. 13) (http://www.easyjet.com/com
mon/img/FY2003EZJAnnualReportandAcconts.pdf). This
indicates that around half of tickets sold occur between
6 weeks and 1 week prior to departure and around 15%
occur in the final week.
EasyJet’s passengers paid on average between
£19 and £25 less than they would have paid
with Go Fly.
Finally, as a further argument suggesting why
both mergers may have had a beneficial effect in
more general welfare terms through the change
in temporal price schedules, notice that late
booking fares are usually related to more inelastic demand. Their increase therefore has smaller
total welfare effects, as it largely corresponds
to a direct transfer from the consumers to the
firm. Correspondingly, the lower fares for early
bookers, who presumably are more price elastic, can represent a significant net increase in
welfare as they afford an expansion in demand
(as evidenced by the high post-merger loading
factors and generally increased capacity).
VIII.
CONCLUSION
In this study, we argue that LCAs, which
have become key players in Europe after the
civil aviation industry was fully liberalized in
1997, do not constitute a homogeneous strategic group as their business models can differ
markedly. A source of this difference may lie in
the history of each airline. In this respect, both
acquiring firms in this study operated as independent companies since their inception, pioneering in their own specific way the Southwest
“no-frills” business approach in Europe, that is,
unlike both acquired firms, which were launched
as subsidiaries of full service airlines.
In our analysis of the two takeovers, we have
focused primarily on fare structures as a critical
differentiator in the firms’ business models. The
evidence reveals that the acquiring firms have
generally kept most fares below the pre-takeover
period—the exception being for the fares posted
only a few days before departure. Yet, we have
also looked at some other aspects, beyond fares,
that might have impinged on consumer welfare.
Notably, a possible concern with the takeovers
might have been that they would afford the
acquiring firms increased market power that
would have allowed them to reduce capacity
and flight frequency on the acquired routes (in
order to drive up prices). However, our findings show the acquiring firms either increasing
or keeping the capacity and frequency of the
flights operated on the acquired routes stable.
Ryanair, for example, succeeded in increasing
capacity and flight frequency while also raising
the load factors on the acquired routes (suggesting both allocative and productive efficiency
DOBSON & PIGA: PRICE EFFECTS OF LOW-COST AIRLINES MERGERS
gains). Moreover, all these effects were realized
within the first post-takeover year, suggesting
that the takeovers led to the almost immediate
assimilation of the target firms’ business models
in favor of those of the acquiring firms and that
consumers gained as a consequence.
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Pricing and Firm Conduct in California's Deregulated Electricity Market
Author(s): Steven L. Puller
Source: The Review of Economics and Statistics, Vol. 89, No. 1 (Feb., 2007), pp. 75-87
Published by: The MIT Press
Stable URL: https://www.jstor.org/stable/40043075
Accessed: 04-09-2018 16:58 UTC
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PRICING AND FIRM CONDUCT IN CALIFORNIA'S DEREGULATED
ELECTRICITY MARKET
Steven L. Puller*
Abstract - This paper analyzes the pricing behavior of electricity withholding
generby specific generating firms in summer 2000
ating firms in the restructured California market from its inception in April
(Joskow & Kahn, 2002). *
there is evidence of some form of market
1998 until its collapse in late 2000. Using detailed firm-level data, I find
Although
that conduct is fairly consistent with a Cournot pricing game for much
of
the sample. In summer and fall 2000, the market was slightly
less there is less understanding of the type of oligopoly
power,
competitive, yet the dramatic rise in prices was more driven by changes in
pricing that led to the exercise of market power. Price-cos
costs and demand than by changes in firm conduct. The five large
margins vary due to both demand- and supply-side
nonutility generators raised prices slightly above unilateral market-power
levels in 2000, but fell far short of colluding on the joint monopoly
price.
tors
- demand can become more or less elastic, or firms
fa
can
engage in a more or less competitive oligopoly pricing
I. Introduction
game. For example, the rise in price-cost margins from 199
to 2000 could have resulted from firms behaving le
competitively or firms behaving similarly on a less elast
restructuring of the electricity industry in California
demand
function. Several oligopoly pricing models coul
and the subsequent meltdown of the market
raised
apply
to
this
many questions about the feasibility of competitive electric- market, including models of unilateral marke
power
and tacit collusion. Individual firms were likely t
ity markets. In 1998 California opened electricity
generaface
relatively
inelastic residual demand, which allowe
tion to competition by restructuring the method of procuring
electricity. Incumbent regulated utilities divestedthem
manytoofraise prices unilaterally.2 In addition, collusion was
because electricity was traded through daily re
their plants to private firms, which bid in daily possible
auctions to
peated
auctions between a small set of firms with ver
supply power to the grid. Wholesale prices averaged
$31 per
accurateto
information about rivals' costs. Understanding the
megawatt-hour from 1998 to May 2000 but skyrocketed
underlying
$141 during summer and fall 2000, with prices
in some pricing game is important for the optimal design
of restructured electricity markets. Depending upon th
hours reaching $750. By the end of 2000, the incumbent
game, the market designer can change the competutilities were required to purchase power at high pricing
wholesale
itiveness
of the market outcome by altering the structure o
prices and to sell to customers at substantially lower
prices.
ownership,
The utilities eventually lost their creditworthiness,
the or- the method and frequency of procurement, and
the information
available to market participants.
ganized market broke down, and the state government
was
This
paper analyzes the extent to which higher prices
required to step in to purchase power. This paper
investi-
resulted from less competitive pricing behavior rather than
gates the nature of the competition that led to skyrocketing
less elastic demand or higher costs. I test whether firm-leve
wholesale prices.
production
behavior was more consistent with unilatera
Studies have found empirical evidence that firms
in the
market
power
or tacit collusion. This paper decomposes the
California market exercised market power. Adopting the
demandand supply-side effects that contributed to th
Wolfram (1999) methodology, Borenstein, Bushnell,
and
variation
Wolak (2002) simulate a perfectly competitive market
from in price-cost margins over time. I use hourly
1998 to 2000 and compare the resulting prices firm-level
with actualdata on output and marginal cost and show that
the five large generating firms withheld output whe
prices. They find high price-cost margins duringeach
the of
highprice very
exceeded marginal cost: all these firms exercised some
demand summer months, with the margins becoming
degreeover
of market power.
large in 2000. Notably, these margins vary significantly
Next, the
I compare the observed prices to simulated price
the three years of the market. Higher prices during
under
three
benchmark models of competition - compet
summer months of 1999 and 2000 can be partially explained
tive,
Cournot,
and joint monopoly pricing. I model the
by the smaller forward contract positions of the various
market
as five large strategic producers competing against
market participants (Bushnell, Mansur, & Saravia,
2005;
firms that either are relatively small or do not face
Bushnell, 2005). Finally, there is strong evidence other
of quantity
strong incentives to influence the price. I estimate the suppl
Received for publication July 9, 2004. Revision accepted for publication
October 3, 2005.
1 Evidence exists of market power in other restructured electricit
* Texas A&M University.
markets, including Australia (Wolak, 2000), Pennsylvania-New Jersey
I thank Severin Borenstein, James Bushnell, Greg Crawford,
Carlos
Maryland
(Mansur, forthcoming), New England (Bushnell & Saravi
Dobkin, Richard Gilbert, James Griffin, Bronwyn Hall, 2002),
Ali Hortacsu,
England and Wales (Wolak & Patrick, 1997; Sweeting, 2005), Ne
Edward Kahn, Erin Mansur, Aviv Nevo, Julio Rotemberg, Anjali
YorkSheffrin,
(Saravia, 2003), Spain (Fabra & Toro, 2005), and Texas (Hortacsu
2005).
Steve Wiggins, Catherine Wolfram, anonymous referees,Puller,
and seminar
2 WolakI (2003b)
finds that individual firms in California faced residu
participants at various universities for their helpful comments.
am
grateful for funding from the California Public Utilities Commission
demand in and
the real-time market that created a potential for substantia
University of California Energy Institute to support this work.
unilateral market power.
The Review of Economics and Statistics, February 2007, 89(1): 75-87
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