The impact of mergers on fares structure: evidence from European low-cost airlines.

Dobson, Paul W. ; Piga, Claudio A.

I. 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 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 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 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 Easy Jet'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 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 Easy Jet, 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 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-and-spoke 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 cost-effectiveness does not necessarily imply a poor, unreliable service. Indeed, both Rayanair and Easy Jet 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 Easy Jet, 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 Easy Jet, and despite access to the parents' expertise and strong financial backing, the spin-off nature of the FSA-led LCAs 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 [pounds sterling] 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 Easy Jet). In May 2002, EasyJet announced its intention to buy Go Fly, whose largely complementary operations would enable Easy Jet 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) Easy Jet completed the acquisition for 374 million [pounds sterling] in August 2002. Go Fly continued to operate flights independently until mid-December 2002, after which its website was shut down and Easy Jet 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 [euro] 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 [pounds sterling], consequently increasing its share of slots at Stansted airport from 33% to 49.5% (see below). (10)

III. 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/Easy Jet and the Buzz/Ryanair takeovers, either full or partial overlap characterized about one-third of the routes operated by the target companies. (13) 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 city-pair 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 short-run 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 Easy Jet).

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 Easy Jet 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)

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 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. 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 [pounds sterling]. (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 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 city-pair 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 [pounds sterling] and 90 [pounds sterling] for both mergers, with fares increasing non-monotonically 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 [pounds sterling] for a ticket purchased 35 days prior to departure, while Ryanair's fare is about 20 [pounds sterling] cheaper a year later; but for tickets purchased the day before departure, Ryanair appears to have charged about 22 [pounds sterling] 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/Easy Jet takeover. All of Easy Jet'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", Easy Jet 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 [pounds sterling] 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 city-pair 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, Ryanair's fares show very little change across all types of routes and across booking days, while Easy Jet 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.

[FIGURE 1 OMITTED]

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 [pounds sterling]), 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 informative to take a detailed look at fares on a sample route affected by each takeover.

[FIGURE 2 OMITTED]

As an illustration, Figure 3 compares two late and two early booking days on the Stansted-Bergerac 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 pre-takeover 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 post-takeovers 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.

[FIGURE 3 OMITTED]

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 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 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.

[FIGURE 4 OMITTED]

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 post-takeover 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 Easy Jet. 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 first comprises the routes sharing the same city-pair 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 city-pair. 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 city-pair 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 novel aspect of our approach consists in the distinction between fares according to their booking days.

A. Propensity Score Matching

Let [A.sub.r] [member of] {0, 1} be an indicator of whether route r is taken over, and denote [DELTA] [P.sup.1.sub.rc] 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 "Pre-Post Period" (in which case [DELTA] [P.sup.1.sub.rc] 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) [DELTA][P.sub.[tau]] = E{[DELTA][P.sup.1.sub.rc][absolute value of [A.sub.r] = 1, b = [tau]} - E{[DELTA][P.sup.0.sub.rc]][A.sub.r] = 1, b = [tau]}

where [DELTA] [P.sup.0.sub.rc] denotes the year-to-year percentage 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, received the treatment and therefore [DELTA] [P.sup.0.sub.rc] 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 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 matches for each [DELTA] [P.sup.1.sub.rc]. (24) 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, 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 [[alpha].sub.b] in the ordinary least square (OLS) hedonic pricing regression on a sample including only observations for the same booking day:

(2) [P.sub.ribm] = [X'.sub.rm][[beta].sub.b] [[delta].sub.b] D P + [[gamma].sub.b] [D.sub.A] + [[alpha].sub.b] D P x [D.sub.A] + [u.sub.i]

where [P.sub.ribm] 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; [X'.sub.rm] 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 post-takeover period; [D.sub.A] 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 and the level of fares can only be ascribed to a variation in the pricing schemes over a 12-month 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 Easy Jet 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 Easy Jet's enhanced competitive position 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 [pounds sterling] as a consequence of the takeover, while prices for earlier booking fell by between 11.80 [pounds sterling] and 34.20 [pounds sterling] 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 [pounds sterling], the evidence obtained by applying the DID indicates that the post-merger fares exhibit a steeper temporal profile, which was maintained also in the second year of operation.

Across booking days, Easy Jet's takeover led to average savings for passengers of about 14.00 [pounds sterling]-33.00 [pounds sterling] in the "Pre-Post Period," with median fares falling by about 16.00 [pounds sterling]-32.00 [pounds sterling]. Again, such values are well above the possible increases in fixed charges. With regards to the longer-run effects of Easy Jet's takeover, the findings suggest an increase of about 7.70 [pounds sterling]-9.40 [pounds sterling] 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 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 lower-priced 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 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 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 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 [pounds sterling]-24 [pounds sterling] 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 [pounds sterling] 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 Easy Jet'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," EasyJet's passengers paid on average between 19 [pounds sterling] and 25 [pounds sterling] 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 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.

doi: 10.1111/j.1465-7295.2011.00392.x

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

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PAUL W. DOBSON and CLAUDIO A. PIGA *

* 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 7T J, 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 LE 11 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.

(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/UBSTransportConference19thSept05.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.

(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 Easy Jet. the examples provided by Koenigsberg, Vilcassim, and Muller (2008) show less than 15% of tickets sold before the final week.

(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 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.

(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 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.

(17.) For instance, if we consider London Stansted-Bergerac 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 Bergerac-London 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.

(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 [pounds sterling] 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 [pounds sterling]), the bias when we compare LCAs and FSAs should not exceed the 3.80 [pounds sterling] for Ryanair, and a similar level for Easy Jet.

(21.) Details are available from the authors on request.

(22.) This is a common approach to examining pricing effects of mergers. See Weinberg (2008) for a survey.

(23.) Thus, the "Pre-Post" period does not include any observation for any of the Full Service Airlines.

(24.) More formally, let [P.sub.A] and [P.sub.C] 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 [M.sub.A](P) = {C | [min.sub.C] [parallel] [P.sub.A] - [P.sub.C][parallel]}. We set n = 2 to minimize the risk of spurious associations.

(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/common/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.

TABLE 1
Mean Values of Fares in the Pre- and Post-takeovers Periods

 Buzz [right arrow] Ryanair

Route types Days Before After
 June 2002/ June 2003/
 March 2003 March 2004

Same city-pair 1 77.8 83.0
Acquired 1 68.5 91.0
Same city-pair 4 58.2 60.7
Acquired 4 62.1 57.7
Same city-pair 7 54.4 65.3
Acquired 7 64.3 49.2
Same city-pair 10 47.6 59.0
Acquired 10 60.6 44.1
Same city-pair 14 51.2 55.1
Acquired 14 61.2 33.9
Same city-pair 21 46.2 51.0
Acquired 21 55.7 33.0
Same city-pair 28 46.2 51.4
Acquired 28 50.4 28.9
Same city-pair 35 45.3 49.7
Acquired 35 48.0 27.6
Same city-pair 42 40.0 46.2
Acquired 42 46.6 25.9
Same city-pair 49 38.5 43.8
Acquired 49 45.9 27.1
Same city-pair 56 33.0 33.7
Acquired 56 41.5 27.9
Same city-pair 63 33.2 32.5
Acquired 63 45.5 27.8
Same city-pair 70 31.6 31.6
Acquired 70 42.4 28.4

 Ryanair [right arrow]
 Ryanair

Route types Days 1st Year 2nd Year
 May 2003/ May 2004/
 April 2004 May 2005

Same city-pair 1 83.8 93.2
Acquired 1 93.2 92.9
Same city-pair 4 62.4 73.6
Acquired 4 66.2 68.4
Same city-pair 7 63.9 71.5
Acquired 7 51.7 53.6
Same city-pair 10 57.6 65.3
Acquired 10 46.4 47.5
Same city-pair 14 55.1 61.2
Acquired 14 39.3 37.3
Same city-pair 21 52.4 54.5
Acquired 21 38.7 32.8
Same city-pair 28 54.1 52.2
Acquired 28 34.3 30.6
Same city-pair 35 52.5 51.2
Acquired 35 32.2 28.4
Same city-pair 42 50.2 48.3
Acquired 42 29.2 28.2
Same city-pair 49 48.7 45.1
Acquired 49 28.6 25.7
Same city-pair 56 37.3 33.6
Acquired 56 29.7 25.3
Same city-pair 63 36.4 32.5
Acquired 63 29.9 24.5
Same city-pair 70 34.1 31.7
Acquired 70 30.6 24.7

 Go Fly [right arrow] EasyJet

Route types Days Before After
 June 2002/ June 2003/
 December 2002 December 2003

Same city-pair 1 77.6 81.1
Acquired 1 79.9 79.3
Same city-pair 4 60.5 59.0
Acquired 4 66.8 55.9
Same city-pair 7 56.9 59.9
Acquired 7 71 50.6
Same city-pair 10 50.8 53.1
Acquired 10 65.6 42.4
Same city-pair 14 52.1 49.8
Acquired 14 66.5 44.6
Same city-pair 21 46.0 45.9
Acquired 21 61.7 42.4
Same city-pair 28 43.1 45.9
Acquired 28 56.6 47.3
Same city-pair 35 41.5 45.4
Acquired 35 56.0 46.8
Same city-pair 42 37.9 43.1
Acquired 42 53.3 43.9
Same city-pair 49 36.7 41.5
Acquired 49 53.1 41.1
Same city-pair 56 32.1 33.5
Acquired 56 48.7 38.6
Same city-pair 63 33.6 33.0
Acquired 63 51.7 36.8
Same city-pair 70 31.4 31.9
Acquired 70 48.0 35.5

 EasyJet [right arrow] EasyJet

Route types Days 1st Year 2nd Year
 January 2003/ January 2004/
 December 2003 December 2004

Same city-pair 1 78.9 80.3
Acquired 1 77.2 89.7
Same city-pair 4 59.8 61.0
Acquired 4 58.9 70.0
Same city-pair 7 59.0 57.2
Acquired 7 50.6 59.5
Same city-pair 10 53.1 52.2
Acquired 10 43.0 51.1
Same city-pair 14 50.0 47.8
Acquired 14 45.9 49.7
Same city-pair 21 45.5 45.2
Acquired 21 44.8 47.4
Same city-pair 28 44.8 42.6
Acquired 28 50.1 48.0
Same city-pair 35 44.0 41.6
Acquired 35 50.2 47.7
Same city-pair 42 42.2 39.9
Acquired 42 47.1 45.2
Same city-pair 49 40.8 38.5
Acquired 49 44.6 41.4
Same city-pair 56 33.6 30.6
Acquired 56 42.6 38.6
Same city-pair 63 33.3 30.1
Acquired 63 41.5 37.4
Same city-pair 70 32.5 29.7
Acquired 70 40.3 36.8

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, BM1, British
Airways, Czech Airlines, Iberia, and Lufthansa.

TABLE 2
Evolution of Acquiring Firms' Market Shares in U.K. Airports

 Ryanair

 March June June June Routes
U.K. Airports 2003 2003 2004 2005 entered (a)

Bristol 0.07 0.06 0.06 0.05 1
Cardiff 0.06 0.06 0.06 0.07 --
East Midlands 0.00 0.00 0.12 0.20 5
Edinburgh 0.05 0.05 0.03 0.03 --
Glasgow-Prestwick 1.00 0.87 0.93 0.94 8
Liverpool 0.13 0.14 0.11 0.27 12
London-Gatwick 0.03 0.02 0.03 0.04 1
London-Luton 0.12 0.11 0.11 0.21 10
London-Stansted * 0.50 0.68 0.65 0.65 21
Newcastle 0.04 0.07 0.05 0.06 1
Teesside 0.18 0.18 0.11 0.19 1

 EasyJet

 November January June June Routes
 2002 2003 2004 2005 entered (a)

Belfast Intl. * 0.54 0.74 0.77 0.70 4
Bristol * 0.00 0.34 0.41 0.39 7
East Midlands * 0.03 0.25 0.24 0.26 2
Edinburgh * 0.12 0.20 0.16 0.13 --
Glasgow * 0.13 0.21 0.15 0.13 --
Newcastle * 0.00 0.07 0.32 0.33 12
Liverpool 0.75 0.72 0.74 0.48 3
London-Gatwick 0.14 0.15 0.21 0.26 15
London-Luton 0.80 0.76 0.78 0.66 8
London-Stansted * 0.00 0.24 0.24 0.22 7

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.

TABLE 3
Routes and Market (City-Pairs) Characteristics for the Routes and
City-Pairs Involved in the Takeovers

 Buzz [right arrow] Ryanair

 June 2002- June 2003-
Mean Values March 2003 March 2004

Flights per company in route 62.8 77.3
Passengers per company in route 5,282 9,504
Mean number of passengers per flight 84.0 123.0
Route Herfindahl (flights) (a) 0.96 0.94
Route Herfindhal (passengers) (a) 0.96 0.94
Companies in route 1.08 1.13
City-pair (flights) Herfindahl (a) 0.83 0.83
City-pair (passengers) Herfindahl (a) 0.83 0.83
Relative city-pair size (b) 0.08 0.08
Number of routes in city-pair 1.63 1.81
Number of companies in city-pair 1.94 1.81

 Ryanair [right arrow] Ryanair

 May 2003- May 2004-
Mean Values April 2004 May 2005

Flights per company in route 77.5 87.9
Passengers per company in route 9,558 13,064
Mean number of passengers per flight 123.3 148.6
Route Herfindahl (flights) (a) 0.94 0.92
Route Herfindhal (passengers) (a) 0.94 0.93
Companies in route 1.13 1.15
City-pair (flights) Herfindahl (a) 0.83 0.78
City-pair (passengers) Herfindahl (a) 0.83 0.78
Relative city-pair size (b) 0.08 0.09
Number of routes in city-pair 1.81 2.20
Number of companies in city-pair 1.81 2.06

 Go Fly [right arrow] Easyjet

 June 2002- June 2003-
Mean Values December 2002 December 2003

Flights per company in route 114.3 101.3
Passengers per company in route 13,875 12,024
Mean number of passengers per flight 121.4 118.7
Route Herfindahl (flights) (a) 0.85 0.86
Route Herfindhal (passengers) (a) 0.86 0.87
Companies in route 1.31 1.30
City-pair (flights) Herfindahl (a) 0.46 0.42
City-pair (passengers) Herfindahl (a) 0.47 0.43
Relative city-pair size (b) 0.26 0.23
Number of routes in city-pair 3.24 3.79
Number of companies in city-pair 3.50 3.43

 Easyjet [right arrow] Easyjet

 January 2003- December 2003
Mean Values January 2004 December 2004

Flights per company in route 100.5 99.7
Passengers per company in route 11,851 12,142
Mean number of passengers per flight 117.9 121.8
Route Herfindahl (flights) (a) 0.86 0.87
Route Herfindhal (passengers) (a) 0.87 0.88
Companies in route 1.30 1.30
City-pair (flights) Herfindahl (a) 0.43 0.41
City-pair (passengers) Herfindahl (a) 0.44 0.42
Relative city-pair size (b) 0.24 0.23
Number of routes in city-pair 3.64 3.91
Number of companies in city-pair 3.38 3.44

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.

TABLE 4
Covariates Used to Calculate Propensity Scores

Variable Description

Route Herfindahl Herfindahl Index with route's shares
 calculated using a company's number of
 flights (see Table 3)
Route length Expressed in miles (airport to airport)
Number of U.K. airports The number of U.K. origin airports offering
 connected to the flights to the arrival airport.
 arrival airport
Relative city-pair size Size of regional market (see Table 3 for
 statistics)
Number of routes in Number of routes within a city-pair (see
 city-pair Table 3)

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 [right arrow] Ryanair

Starting Period/ June 2002-March 2003/
End Period June 2003-March 2004

Booking Day Mean Median

1 day 34.6 (a) 36.3 (a)
 (28.0) (a) (28.9) (a)
4 days 9.01 (b) 4.5
 (2.9) (3.5)
7 days -7.5 (c) -15.4 (a)
 (-13.1) (a) (-18.6) (a)
10 days -11.0 (b) -29.9 (a)
 (-15.0) (a) (-29.1) (a)
14 days -24.5 (a) -47.2 (a)
 (-22.8) (a) (-43.2) (a)
21 days -28.9 (a) -46.4 (a)
 (-23.8) (a) (-38.4) (a)
28 days -43.4 (a) -59.5 (a)
 (-43.5) (a) (-54.7) (a)
35 days -48.3 (a) -61.8 (a)
 (-48.9) (a) (-61.8) (a)
42 days -49.9 (a) -48.0 (a)
 (-58.4) (a) (-63.5) (a)
49 days -49.6 (a) -41.3 (a)
 (-59.6) (a) (-53.7) (a)
56 days -50.3 (a) -37.3 (a)
 (-66.7) (a) (-51.0) (a)
63 days -56.2 (a) -49.3 (a)
 (-65.6) (a) (-59.8) (a)
70 days -54.4 (a) -45.3 (a)
 (-67.3) (a) (-54.2) (a)
N 2,138

 Ryanair [right arrow] Ryanair

Starting Period/ May 2003-April 2004/
End Period May 2004-May 2005

Booking Day Mean Median

1 day -10.5 (a) -9.2 (c)
 (-1.8) (-0.2)
4 days -20.8 (a) -20.6 (a)
 (-14.5) (a) (-18.0) (a)
7 days -19.4 (a) -14.5 (a)
 (-15.6) (a) (-6.8)
10 days -17.2 (a) -10.2 (b)
 (-12.7) (a) (-4.5)
14 days -23.4 (a) -21.6 (a)
 (-17.4) (a) (-8.3)
21 days -21.1 (a) -25.2 (a)
 (-15.5) (a) (-24.8) (a)
28 days -8.3 (b) -7.4
 (-6.2) (-8.7)
35 days -11.8 (a) -11.2 (c)
 (-10.3) (c) (-11.2) (c)
42 days -7.7 -6.8
 (-7.8) (-4.9)
49 days -9.5 (c) -14.0 (c)
 (-9.0) (-15.2) (c)
56 days -12.0 (b) -20.0 (a)
 (-12.7) (b) (-18.9) (b)
63 days -17.6 (a) -16.2 (a)
 (-14.4) (a) (-16.0) (b)
70 days -6.6 -7.5
 (-9.3) (c) (-7.5)
N 5,439

Notes: Propensity score evaluated using the covariates in Table 4.
Exact matching variables: "Period," "Direction," "Week-End," 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.

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 [right arrow] EasyJet

Starting Period/ June 2002-December 2002/
End Period June 2003-December 2003

 Mean
Booking Day Mean Median Overlap

1 day -22.8 (a) -26.4 (a) -11.9 (a)
 (-22.3) (a) (-28.8) (a) (-10.2) (a)
4 days -25.8 (a) -24.5 (a) -22.5 (a)
 (-28.6) (a) (-31.3) (a) (-18.4) (a)
7 days -22.8 (a) -15.8 (a) -25.5 (a)
 (-22.6) (a) (-17.8) (a) (-24.1) (a)
10 days -28.2 (a) -20.2 (a) -32.4 (a)
 (-28.8) (a) (-23.5) (a) (-31.8) (a)
14 days -23.7 (a) -19.7 (a) -20.7 (a)
 (-25.0) (a) (-22.1) (a) (-17.6) (a)
21 days -21.7 (a) -18.2 (a) -19.5 (a)
 (-21.1) (a) (-19.7) (a) (-16.7) (a)
28 days -13.5 (a) -12.4 (b) -13.2 (a)
 (-14.5) (a) (-16.3) (a) (-10.3) (a)
35 days -17.3 (a) -17.7 (a) -10.2 (a)
 (-19.3) (a) (-21.5) (a) (-6.3) (c)
42 days -21.5 (a) -24.4 (a) -14.7 (a)
 (-23.5) (a) (-30.9) (a) (-9.7) (a)
49 days -30.8 (a) -37.7 (a) -16.0 (a)
 (-29.8) (a) (-39.4) (a) (-8.0) (b)
56 days -33.7 (a) -42.4 (a) -27.9 (a)
 (-34.7) (a) (-43.3) (a) (-23.6) (a)
63 days -40.1 (a) -52.0 (a) -30.4 (a)
 (-41.1) (a) (-56.6) (a) (-25.3) (a)
70 days -46.6 (a) -58.0 (a) -34.2 (a)
 (-49.6) (a) (-66.7) (a) (-27.9) (a)
N 8,893 3,866

 EasyJet [right arrow] EasyJet

Starting Period/ January 2003-December 2003/
End Period January 2004-December 2004

 Mean
Booking Day Mean Median Overlap

1 day 12.7 (a) 12.6 (a) 5.6 (a)
 (10.7) (a) (10.8) (a) (6.0) (a)
4 days 11.8 (a) 15.0 (a) 3.7 (c)
 (9.3) (a) (11.2) (a) (3.4)
7 days 9.8 (a) 12.8 (a) 1.7
 (6.3) (a) (7.0) (a) (1.8)
10 days 9.8 (a) 11.8 (a) 2.3
 (6.1) (a) (6.8) (a) (2.9) (b)
14 days 7.0 (a) 10.3 (a) 0.3
 (3.2) (b) (4.0) (c) (-1.5)
21 days 3.8 (a) 3.9 (c) 1.1
 (0.8) (0.8) (-0.5)
28 days -2.3 (c) 1.6 -2.1
 (-4.2) (a) (-0.5) (-3.6) (a)
35 days -1.1 1.9 -1.5
 (-1.7) (0.2) (-2.2)
42 days 1.2 3.1 -4.1 (a)
 (1.4) (0.1) (-4.9) (a)
49 days 1.8 3.3 (c) -4.7 (a)
 (1.8) (2.1) (-5.0) (a)
56 days 4.0 (a) 5.2 (a) -5.0 (a)
 (4.0) (b) (3.8) (c) (-5.0) (a)
63 days 3.6 (b) 4.7 (b) -4.7 (a)
 (2.8) (c) (-0.4) (-4.6) (a)
70 days 3.5 (b) 4.9 (b) -4.9 (a)
 (2.3) (1.6) (-4.8) (a)
N 39,925 10,772

Notes: Propensity score evaluated using the covariates in Table 4.
Exact matching variables: "Period," "Direction," "Week- End," 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.

TABLE 7
Difference-in-Difference Estimates for Change in Fare Levels ([pounds
sterling]'s) between the Starting and End Period (Weighted Estimates
in Parentheses)

 Buzz [right arrow] Ryanair

Starting Period/ June 2002-March 2003/
End Period June 2003-March 2004

Booking Day Mean Median

1 day 15.9 (a) 20.1 (a)
 (5.1) (9.0) (b)
4 days -2.9 -4.6
 (-12.0) (a) (-11.8) (a)
7 days -18.9 (a) -21.3 (a)
 (-31.2) (a) (-31.2) (a)
10 days -19.3 (a) -22.0 (a)
 (-29.8) (a) (-30.9) (a)
14 days -24.7 (a) -27.0 (a)
 (-32.8) (a) (-34.2) (a)
21 days -23.3 (a) -25.6 (a)
 (-29.4) (a) (-29.7) (a)
28 days -21.6 (a) -23.1 (a)
 (-26.5) (a) (-26.4) (a)
35 days -22.9 (a) -24.7 (a)
 (-28.9) (a) (-30.4) (a)
42 days -23.5 (a) -24.3 (a)
 (-28.7) (a) (-28.9) (a)
49 days -20.3 (a) -20.6 (a)
 (-28.5) (a) (-28.2) (a)
56 days -13.0 (a) -13.9 (a)
 (-16.9) (a) (-16.6) (b)
63 days -15.6 (a) -15.9 (a)
 (-23.1) (a) (-22.7) (a)
70 days -12.7 (a) -13.2 (a)
 (-20.2) (a) (-20.0) (a)

 Ryanair [right arrow] Ryanair

Starting Period/ May 2003-April 2004/
End Period May 2004-May 2005

Booking Day Mean Median

1 day -3.2 -4.6
 (-1.9) (-3.1)
4 days -3.5 -3.6 (b)
 (-6.0) (b) (-7.3) (b)
7 days -1.0 -2.0
 (-7.0) (a) (-8.8) (a)
10 days -2.4 -1.8
 (-8.2) (b) (-8.4) (b)
14 days -5.9 (a) -5.5 (a)
 (-11.7) (a) (-12.1) (a)
21 days -8.2 (a) -9.1 (a)
 (-13.9) (b) (-14.2) (a)
28 days -4.9 (b) -4.5
 (10.9) (b) (-10.8) (a)
35 days -5.1 (b) -5.0 (a)
 (-10.3) (b) (-10.1) (a)
42 days -1.9 -2.0
 (-7.2) (b) (-7.3) (b)
49 days -2.0 -1.8
 (-8.3) (a) (-8.1) (b)
56 days -3.4 (b) -3.8
 (-6.5) (b) (-6.8) (b)
63 days -3.9 (b) -5.7 (a)
 (-7.2) (a) (-8.9) (a)
70 days -4.8 (a) -5.4 (a)
 (-8.3) (a) (-8.7) (a)

 Go Fly [right arrow] EasyJet

Starting Period/ June 2002-December 2002/
End Period June 2003-December 2003

Booking Day Mean Median

1 day -14.2 (a) -16.6 (a)
 (-19.3) (a) (-23.0) (a)
4 days -17.2 (a) -18.5 (a)
 (-19.4) (a) (-21.7) (a)
7 days -27.0 (a) -28.5 (a)
 (-30.9) (a) (-32.4) (a)
10 days -29.3 (a) -28.2 (a)
 (-33.1) (a) (-32.4) (a)
14 days -24.7 (a) -24.4 (a)
 (-29.4) (a) (-29.3) (a)
21 days -22.9 (a) -23.2 (a)
 (-26.2) (a) (-26.8) (a)
28 days -15.2 (a) -15.7 (a)
 (-19.1) (a) (-19.5) (a)
35 days -16.7 (a) -17.8 (a)
 (-20.0) (a) (-20.5) (a)
42 days -17.6 (a) -18.8 (a)
 (-20.5) (a) (-21.4) (a)
49 days -20.4 (a) -21.7 (a)
 (-23.1) (a) (-24.2) (a)
56 days -15.6 (a) -16.3 (a)
 (-18.0) (a) (-18.2) (a)
63 days -15.1 (a) -17.1 (a)
 (-19.1) (a) (-21.3) (a)
70 days -16.8 (a) -18.2 (a)
 (-20.8) (a) (-21.9) (a)

 EasyJet [right arrow] EasyJet

Starting Period/ January 2003-December 2003/
End Period January 2004-December 2004

Booking Day Mean Median

1 day 9.4 (a) 8.7 (a)
 (5.7) (a) (5.5) (a)
4 days 7.7 (a) 7.2 (a)
 (4.2) (a) (3.8) (a)
7 days 6.1 (a) 5.5 (a)
 (0.2) (-0.5)
10 days 4.6 (a) 3.5 (a)
 (-1.3) (-1.8)
14 days 1.1 0.3 (b)
 (-5.0) (a) (-5.4) (a)
21 days -1.7 (c) -2.0
 (-7.9) (a) (-7.8) (a)
28 days -4.7 (a) -4.4 (a)
 (-10.4) (a) (-9.9) (a)
35 days -4.9 (a) -4.8 (a)
 (-10.4) (a) (-10.1) (a)
42 days -4.2 (b) -3.7 (a)
 (-10.0) (a) (-9.1) (a)
49 days -5.3 (a) -5.5 (a)
 (-10.7) (a) (-10.2) (a)
56 days -3.1 (a) -3.3 (a)
 (-4.2) (a) (-4.1) (a)
63 days -3.0 (a) -3.1 (a)
 (-3.9) (a) (-3.3) (a)
70 days -2.8 (b) -2.7 (a)
 (-3.6) (a) (-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.

TABLE 8
Simulated Fare Changes in Mean and Median Fares

 Buzz [right arrow] Ryanair [right
 Ryanair arrow] Ryanair

 June 2002- May 2003-
 March 2003/ April 2004/
 June 2003- May 2004-
 March 2004 May 2005
Starting Period/
End Period Mean Median Mean Median

Distribution 1 -16.6 -17.8 -3.9 -4.2
 (-25.1) (-24.4) (-6.5) (-9.1)
Distribution 2 -16.3 -17.5 -4.0 -4.3
 (-25.0) (-24.2) (-6.6) (-9.2)
Distribution 3 -15.4 -16.5 -3.9 -4.2
 (-24.5) (-23.4) (-6.5) (-8.9)
Distribution 4 -15.2 -16.3 -3.9 -4.2
 (-24.4) (-23.3) (-6.5) (-8.9)

 Go Fly [right EasyJet [right
 arrow] EasyJet arrow] EasyJet

 June 2002- January 2003-
 December 2002/ December 2003/
 June 2003- January 2004-
 December 2003 December 2004
Starting Period/
End Period Mean Median Mean Median

Distribution 1 -19.9 -20.8 0.1 -0.2
 (-23.5) (-24.5) (-4.8) (-4.8)
Distribution 2 -20.2 -21.0 0.4 0.1
 (-23.8) (-24.8) (-4.5) (-4.5)
Distribution 3 -20.2 -21.1 0.9 0.5
 (-23.9) (-24.9) (-4.0) (-4.0)
Distribution 4 -20.3 -21.2 1.1 0.8
 (-23.9) (-25.0) (-3.8) (-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.