Pricing and time-differentiation: The two aspects of airline
competition
Evidence from the Greek market
Abstract
Airlines compete on multiple aspects. Besides the traditional competition on air fares, airlines take decisions on the time differentiation between their flights and those of their competitors. I examine the Greek airline market from 2009 to 2014. The multiple changes in the competition level of the market let me examine how a transition from a duopoly to i) an oligopoly, ii) a monopoly following a merger or iii) a duopoly with a low-cost airline did affect prices and departure times. The merger of the two leading Greek airlines did not have any significant effects on prices but it increased differentiation on departure times in an attempt cannibalization of products to be prevented. The entry of a third airline in the market had similar results on the incumbent’s competitive behavior as that of a low-cost airline. While departure time differentiation was not altered in both cases, the changes in the competitive framework of the market led to a significant price reduction.
Natasha Kalara
10425985
Statement of Originality
This document is written by Student Anastasia Xanthi Kalara who declares to take full responsibility for the contents of this document. I declare that the text and the work presented in this document are original and that no sources other than those mentioned in the text and its references have been used in creating it. The Faculty of Economics and Business is responsible solely for the supervision of completion of the work, not for the contents.
Table of contents
Table of contents ... 2
Introduction ... 4
Theory of spatial differentiation ... 5
The market ... 8
Data ... 12
Measure of departure-time differentiation ... 18
The Model ... 19 Results ... 24 Conclusion ... 26 Further research ... 29 References ... 30 Annex I ... 35 Annex II ... 36 Annex III ... 37 Annex IV... 39 Annex V ... 40 Annex VI... 41 Annex VII... 42
Introduction
The Greek airline market went through significant changes during the last years. For a long period, various airlines1 repeatedly attempted to break the duopoly of Olympic Air and Aegean Airlines. Following the privatization of Greek flag carrier Olympic Air (formerly Olympic Airlines), the two largest firms of the market tried to merge twice before the European Commission finally approve their plan on the ground of failing firm defense rather than a procompetitive effect reasoning. Even though, Ryanair had previously claimed to have no interest in the Greek market, the low-cost airline entered in 2014. However, its market presence was limited only in certain rather than the total number of routes. Thus, it did not fully restore the previous duopolistic status of the market. These adjustments in the state of the market provided the opportunity to examine how different levels of competition affect the airlines’ behavior.
Typically as in other type of markets, airlines compete through the quantity and prices they set. The firms choose the quantity they are offering and simultaneously the prices for their flights. Multiple aspects can affect this process, one of the most important being the market structure and the number of airlines performing in the market.
The number of companies that perform in a market plays a crucial role on the level of prices. The importance of an airline’s entry in a market has been examined during the first years of deregulation of the national markets. This action led to increased competition in international and national routes. Various studies have indicated that the entry of a new airline leads to lower prices. Joskow, Werden and Johnson (1994) confirm that the entry reduces airline fares and increases output. However, the incumbents do not increase their output. Hurdle et al. (1989) suggest that the best model for explaining fares is one that takes into account the market concentration for incumbents as well as the number of potential entrants. Graham et al. (1983) examines data from the U.S. airline market and reject the hypothesis that fares are independent of market concentration.
Indeed, market concentration affects price levels in multiple ways. Not only an increase in the number of competitors reduces airfares, but also the takeover of a rival can lead to reverse results. Kim and Singal (1993) confirm that prices increased after a merger when comparing a group of routes served by merging firms with a group of unaffected routes.
1 Athens Airways was active in the Greek domestic market in 2009-2010 while Cyprus Airways in 2012-2013.
Borenstein (1990) concludes that in addition to increased fares on routes which both merging parts were serving before, increases were further observed on routes with no previous active competition. Various studies examined the results of specific airline mergers and acquisitions (Werden et al, 1991 and Morrison, 1996). Their findings are in accordance with the theory.
Nonetheless, the level of fares is not only determined by the presence of potential entrants but also by the operational model of these firms. Low – cost airlines have proven to significantly reduce the level of fares in the markets they enter (Vowles, 2000). Goolsbee and Syverson (2005) study the effects of Southwest Airlines’ entry – the world’s largest low-cost airline – on various city pairs. They confirm that incumbents cut fares significantly when threatened by Southwest’s entry. While firms reduce their prices on the routes where a low-cost carrier entry occurred, they do not increase fares on non-competitive routes to compensate for the lost revenues (Windle & Dresner, 1999). In addition, competition with low-cost airlines reduces significantly all fare types, while competition with conventional carriers leads to only small reductions in the leisure segment and significant reductions on fares for business purposes (Alderighi et al, 2012). The presence of a low-cost airline not only affects indiscriminately types of fares but also types of markets. The effects are significant not only for airport pairs but also for adjacent airports and as potential competition (Brueckner et al, 2013).
During the recent years a new aspect of airline competition has come to light: the time of departure and how airlines differentiate on that dimension. Beyond just a price rivalry, airlines have another dimension in which they can set their strategy in comparison to their competitors. In the next chapter I will examine the theoretical framework of spatial differentiation and then I will provide an overview of the studies that examine the concept in respect to airline competition and airlines’ competitive behavior.
Theory of spatial differentiation
Spatial differentiation is a thoroughly examined aspect of competition. According to Hotelling’s law, firms decide to allocate their shops close to their competitors in order to achieve higher profits through capturing more consumers. Later research indicated that more intense competition can lead in fact to product differentiation (d’Aspremont et al,
1979). Various studies examined the phenomenon for different types of markets such as gasoline stations (Netz and Taylor, 2002).
In addition to the conventional spatial differentiation of store allocation, the aspect of differentiation can be expanded to various characteristics of the products. For instance, Gentzkow, Shapiro and Sinkinson (2012) study the ideological diversity of US newspapers and conclude that as competition intensifies, newspapers have greater incentives to diversify. That is a positive consequence for customers since they provide them more distinctive options to choose from; it enhances product variety. Competition has proved to affect product variety. After a merger, firms reposition their products away from each other in order to avoid cannibalization (Gandhi et al, 2008). The aforementioned case can be found in industries such as television and music radio. After a merger, owners differentiate their stations from each other (Sweeting, 2010). In addition, mergers of radio stations increase programming variety (Berry & Waldfogel, 2001).
Another important application of product differentiation is that on time dimension. Firms strategically choose the time of their product release in order to compete more effectively. We can find applications of the time aspect differentiation again in the entertainment industry. Sweeting (2006) examines the timing of radio commercials and how radio stations choose to differentiate. Fewer stations and more concentrated ownership lead to great overlapping of commercials. Distributors in the U.S. motion picture industry cluster their release dates around big holiday weekends and prefer to follow industry trends (Einav, 2001 & 2010). Industries that care about time differentiation do so, because their products have a more intense competition with others released at the same period, e.g. a tv-show airing on Saturday night will compete with the other shows at the same time zone. The same happens with films releasing on Christmas. Consumers have a finite number of movies they will watch during the holidays.
Participants in airline industry also care about departure times through the competition between neighboring flights. When a consumer chooses his flight he not only makes a decision based on the airfare and date but also on how convenient the departure and arrival times are. Thus, when making his decision he sets a specific ideal timeframe for his flight. Flights inside this timeframe compete more intensively than flights that depart during the rest of the day, e.g. a passenger that wants to depart at 4 P.M. and cannot find a ticket on that departure time, will more likely buy a ticket at 5 P.M. rather than 10 P.M. Airlines face a dilemma between minimum and maximum departure time differentiation. Flights that
depart closer to each other can “steal” customers from competitors while flights that do not face severe competition in departure time can charge higher fares. The levels of competition in a market can affect the time differentiation of the flights. Borensteind and Netz (1999) find an inverse relation between competition and time differentiation. Additionally, Salvanes et al (2005) confirm that monopoly leads to less clustering of departure times than duopoly. Indeed, different aspects of competition have been examined and have shown that affect flight frequency. Richard (2003) concludes that airline mergers lead to increases in flight frequency especially in the smaller markets. At the same time, the entry of low cost airlines in a market also leads to a higher flight frequency (Schipper, Nijkamp & Rietveld, 2007). Brueckner and Luo (2013) confirm that flight frequencies of their competitors affect the airlines’ frequency on a route.
The aforementioned studies enrich the conventional wisdom of the ways that airlines compete. In addition to that, several studies examine the airline competition on multiple aspects combining effectively the study on pricing and time differentiation. For instance, Carlsson (2004) concludes that Herfindahl index does not have a significant effect on ticket prices but he confirms that it affect airline departures. Nero (1998) verifies a relationship between time allocation of flights and airfares by suggesting that price competition is reduced when firms do not compete intensively on departure times.
In my study, I also examine airline competition on multiple aspects, combining models from different studies. Examining the differentiation of departure times in accordance with the pricing behavior let me study the overall concept of airline competition. Competition is multi- rather than one-dimensional and examining multiple aspects of it draws a more robust picture of the competitive framework of a market.
My study makes use of real market data from various reliable sources. I examine the Greek airline market over which very few reports have been written over the past years due to a lack of data. Moreover, I effectively combine two different models in order to provide the overall view. In such a way, I can better understand the consequences of the changes in the competitive framework of the airline market. In the next chapters I will first describe the Greek airline market in detail and report my data. Then I will present my model and its results.
The market
The Greek domestic aviation market consists of thirty nine commercial airports, with Athens and Thessaloniki being the largest ones. Given the idiosyncratic topography of Greece, airline market holds an important role in the domestic transportations. Greece consists of 227 inhabited islands2 with many more deserted ones. Greek islands are mainly accessible by airplanes or ferries, but due to the significant difference in travel times and frequencies, ferry services do not constitute a close substitute. As an example, the travel time by ferry from Athens3 to Heraklion and Rhodes would be seven and 13,6 hours respectively, while the flight time would only last fifty and sixty minutes respectively. Only in Athens – Mykonos route, the ferry services could be considered a close substitute to air services, as the European Commission concluded in its decision4.
Greece consists of a hilly, peninsular mainland. Eighty percent of its territory consists of mountains or hills, making the country one of the most mountainous in Europe. Therefore, the country did not develop a proper functioning rail network. Railway transportation plays a lesser role in Greece and the rest Balkan countries than in other Western European countries according to the Transportation Statistical Pocketbook of the European Commission. Hellenic Railways operates at a loss and due to financial reforms imposed by the Greek state from 2011 and onwards, regional services were suspended in many parts of its already small network. Train services on the Greek mainland differ substantially in travel time from the flight duration. The Athens – Thessaloniki and the Athens – Alexandroupoli itineraries last 5,3 and 12,3 hours by train5, while a flight would last only fifty and sixty five minutes respectively. These differences in travel time remain significant even when we take into account the travel time to and from the airport as well as the waiting time before a flight.
Greeks use more frequently the public transport bus service rather than the railway network. According to European Commission6 only 0.9% of the passenger transport on land in Greece refers to railways, while 17.6% is performed by buses and coaches. There are 62 bus companies, each operating as a monopolist in a specific region of Greece and many of them
2
According to the Greek National Tourism Organization.
3 The times refer to ferry for itineraries from Piraeus, Athens’ port as they were reported by the ferry companies. The times indicate the shortest time travel per destination as of July 2015.
4 European Commission IP/11/68.
5 As reported by TrainOSE, the state-owned rail company. 6
serve a route connecting the main city of their region with Athens. Bus companies do not operate only in the mainland according to common sense. Many of them serve routes from Athens to specific islands such us Zakynthos, Corfu, Kefallonia and Lefkada – performing part of the trip with the use of a ferry. Thus they could theoretically constitute a substitute for flight services in certain routes. However the travel duration by bus is significantly longer than that of air services (a flight from Athens to Zakynthos lasts 1 hour, while travelling by bus takes 5 hours). Travelling from Athens to Thessaloniki by bus, is even more time consuming than travelling by train with a total travel time of 6 hours. Hence, it is evident that air services are a vital mean of transport, as no close substitute exist, and travelling by other means consist a time consuming process7.
There were 39 airports in Greece with more than 15.000 passenger movements in 2013. That corresponds to a ratio of 3.54 per one million inhabitants. Only Estonia and Finland have a higher ratio in the European Union. Furthermore, there were 6.3 million passengers on board domestic flights in 2014. That corresponds to 0.57 flights per capita, making Greece the third EU country with the highest per capita flight rate after Sweden and Spain8. Given the importance of air transportation in Greece, a lot of routes are part of a universal service obligation. Initially, these routes were served by the flag carrier but after its privatization, airlines of all European Union Member States can participate in tender procedures to serve one of the routes. The Greek State announces tenders every few years in order to guarantee that routes which are not making enough profit are constantly and uninterruptedly served. The State set criteria on the number of flights, the number of available seats per week, and the maximum one – way rates needed to serve public interest910. It does so, in order to guarantee a certain quality of life for the citizens of isolated areas, such as access to medical services. Following the tender’s publication, airlines can submit the quarterly amount needed to serve each route with the abovementioned criteria. The bidder with the lowest tender is responsible to serve the route as a monopolist, thus no other airline is allowed to enter the line for the specified period. Given the special conditions
7
A map with the aforementioned routes, that were used to draw conclusions on the travelling times with various means of transport, can be found in Annex I.
8
For a detailed overview of EU countries on the aforementioned indexes, see Annex II. 9
Extension and amendment of public service obligations imposed in respect of nine scheduled air services on routes within Greece in accordance with Council Regulation (ECC) No 2408/92 – Official Journal of the European Union 2008/C 249/06.
10
Operation of scheduled air services: Invitation to tender for contracts for the operation of ten scheduled air routes subject to public service obligations, issued by the Hellenic Republic under Article (4)(1)(d) of Council Regulation (EEC) No 2408/92 – Official Journal of the European Union 2008/C 250/09.
on these routes and the fact that they are considered a de facto monopoly, I decided to leave them out of my analysis. In fact, I am focusing on the routes that competition could be developed and I examine how specific factors affected this competition.
Competition Theory and the Greek Airline Market
The Greek airline market facilitated collusion prior the merger in many ways. The market was highly concentrated, consisting by a very small number of companies. The two firms were symmetrical regarding the market shares for domestic routes. As for the international flights, Aegean and Olympic gradually stopped serving common routes. Aegean Airlines directed its services to West European airports while Olympic Airlines focused its interest to Balkan and Middle East routes. Thus, no conflict of interest existed between the two in international flights.
Products were homogenous as both firms were well-known and respected in the market, however Aegean Airlines was perceived to offer better services due to its newer fleet by some passengers. The frequency of ticket orders is high, thus this is another fact that can facilitate collusion as it allows for a constant observation of competitor’s behavior and a timely punishment. Punishment can occur both in price and departure-times terms. Regarding the second aspect, Greek airports facilitate the rescheduling of departure times. After all, they do not suffer from high congestion and a lack of time slots as most of the biggest international airports in Europe. Thus a change in departure-times is easier and can be planned as a punishment for a deviation from the collusive behavior. The only factor that would deter collusion was the future steady decrease of demand in the Greek market due to the recession. As economic theory suggests, collusion is less likely when firms are facing a constant decrease of market demand.
Entry in an airline market is a difficult task due to the higher entry barriers such as sunk costs for aircrafts, personnel, infrastructure and marketing campaigns. When new entrants did achieve to gain access to the Greek market, incumbents’ behavior seemed to alter. The intense competition was either the result of the mavericks’ aggressive behavior or a way to force the firm out of the market by the use of predatory pricing. This practice is indeed a way that a monopolist can get rid of new entrants. However, it can also be the case of two firms that are taking part in collusion thus they are functioning as a monopolist. By lowering their prices in an extensive way and by offering similar flight services – meaning with the
same departure time, incumbents did not provide new firms with enough space in order to grow as strong competitors.
Timeline of the Greek Airline market after liberalization
The State owned flag carrier was the monopolist of the market until its liberalization. Following the foundation of Aegean Airlines, the company initiated commercial flights to domestic destinations. Third airlines11 later entered the market, yet serving a limited number of routes. Aegean airlines was the only firm that accomplished to compete successfully with Olympic Airlines leading to an established duopoly in the Greek domestic aviation market.
In 2008, following a conviction by the Court, the Greek State decided to privatize Olympic Airlines. Since September 2009, privatized Olympic Air and Aegean Airlines are both serving the Greek market undisturbedly – this happens as even after their merger the airlines continued their operation under different names. On the meantime, a third airline – Athens Airways – began its operations in early 2009. Following the privatization of Olympic Airlines, an intense competition in schedule times began between the three companies. This intense competition seemed to come to an end when in the summer of 2010 Aegean and Olympic expressed their intention to merge. Later that year, Athens Airways ceased its operations. The European Commission blocked the attempted merger on the ground of the creation of a quasi-monopolistic status-quo in the Greek airline market12. However, the two companies expressed their disappointment and their intention to appeal on the decision. At the same time, the two airlines seemed to start an informal sharing of the market yet both remaining present to the most active routes.
Another maverick firm – Cyprus Airways – entered the busiest routes in spring of 2012 and remained active for a year. One year later, Olympic and Aegean submitted a second request for merger which this time was approved by the Commission in October 2013. The merger was accepted on the ground of failing firm defense for Olympic Air13. The companies demonstrated evidence that Olympic Air would exit the market if it was not sold to Aegean
11
For instance, Cronus Airlines was founded in 1994 and performed scheduled flights to domestic and international destinations. It was acquired by Aegean Airlines in 2001.
12 European Commission, IP/11/68. 13
Airlines. The merger created a monopoly in the unregulated domestic routes for the winter season 2013-2014.
Even though Ryanair has expressed its objections on entering the Greek domestic market during the investigation for the first merger attempt, it did so in spring 2014. Ryanair entered the three busiest routes creating again a partly duopolistic domestic market. Ryanair is known for its aggressive pricing behavior, which led in many instances its competitors out of the market14. In the summer program of 2015, more airlines joined specific routes from Athens such as Ellinair flying to Thessaloniki, Mykonos, Heraklion and Santorini. Nevertheless, my research period ends in November 2014 thus I was not able to examine the effect of the new mavericks’ entrance in the market. Yet, I believe that the effect of their entry in the market would be in accordance with the effect that other maverick firms had in the previous years, the impact of which I examine in my dataset. In the next chapter, I will describe the data I used in my regressions, the process I followed in order to give them the right format to work with them and I will explain how I collected them.
Data
I examine the Greek airline market in two aspects. My data regarding the competition on departure times refer to the period from December 2009 to November 2014. The reasoning behind this choice is the fact that before 2009 Olympic Air (then Olympic Airlines) was a public company and that was affecting its strategic planning. European Commission has found that the Greek State had offered illegal state aid to the company, an act that was incompatible with the common market. Even though, the decision of the Commission goes back to 200215, the State had not complied until 200516 and was convicted by the Court of Justice in 200817. It is possible that parts of this illegal behavior continued until the privatization. Thus it is of no use to examine the market behavior of a company that was not private at the time and did not fully relied on its own funds and profits to plan a competitive
14 For instance, EasyJet tried in many occasions to enter certain Irish routes that Ryanair was already serving but failed. Virgin Express served the Brussels-Shannon route but withdrew on the
announcement of Ryanair’s entry. A detailed description of Ryanair’s potential competitors and its competitors’ decision see “Commission Decision of 27/06/2007 declaring a concentration to be incompatible with the common market and the EEA Agreement (Case No COMP/M.4439 – Ryanair/Aer Lingus)”.
15 Commission decision C(2002) 4831. 16 European Commission, IP/05/1139. 17
schedule. I obtained the time schedules of the flights from Athens International Airport. The airport issues biannual timetables with the schedules of all flights. From October 2010 the timetables are available only in electronic format. From these timetables, I also derived information regarding the competition in each route namely the number and names of companies serving each market. Furthermore, they provided me with information regarding the number of flights serving each destination. In my analysis, I used only the flights that were occurring frequently. As a criterion, I kept in my analysis only the flights that the airlines served more than three times in a weekly basis. I paired every flight with the rest that occurred on the same date either they were performed by a competitor or not. The procedure created more than 95,000 pairs of flights for all the destinations in the dataset. I followed the measuring procedure of Borenstein and Netz (1999) on departure-time differentiation. Then I used the average monthly departure-time differentiation of all pairs to construct my independent variable. I describe in detail the procedure I followed to construct the time differentiation variable in the next chapter.
Regarding the competition on prices my reference period is from November 2010 to November 2014. The Greek Civil Aviation Authority had no available data regarding the airfares in the Greek Market. Collecting historical data of airfares seem a fairly difficult task no matter which the focus market would be. A thorough online search reveals that some historical data exist for a limited number of airports and/or only for a limited time period18. It is in fact a burdensome process for every researcher to collect airfare data outside USA. This creates a problematic situation for researchers that want to focus their analysis on the airline market. Data exist but only Authorities on national or European level (National Competition Authorities or Directorates of the European Commission) can obtain them effortlessly.
Given the fact that I wanted to examine an older time period, it was impossible to collect the airfares by myself – namely check daily the airfares in various travel agencies or travel search engines. I contacted various online travel agencies in Greece in order to ask them to provide
18 The U.S. Department of Transportation offers a wide range of airfare data for airport pairs within the USA. DWU Consulting offers historical data for flights which both departure and arrival airport are located within USA. Fare Detective offers historical data for various pairs of international airports around the globe but only for a limited period of time – a maximum period of 1 year applies – and the graph representation of data is not helpful for researchers. Passport premier has publically available historical data for flights between the busiest international airports but again only within a limited timeframe. Travel search engines such as Kayak, Google flights or the Flight Search Engine of MSN alert users on the best time to buy tickets, based on trends and historical data that again are hardly available for users.
me with prices over time. It was indeed a hard task to accomplish. Greece is falling behind according to the Digital Economy and Society Index of the European Commission, a tool to measure the progress of EU Member States towards a digital economy and society19. DESI takes into account various aspects such as connectivity, use of internet and integration of digital economy. Greece has one of the lowest scores on the individuals ordering services online as a percentage of the country’s population and the number of internet users’20. There was a hesitation regarding agencies providing me with data. In the end, I managed to obtain my airfare data from a Greek online travel agency which asked to remain anonymous. Due to the fact that online services are a fairly new process in the Greek market, the agency had available data only starting from the last quarter of 2010.
I focus my research only in the domestic airline market since this will allow me to obtain more concrete results. In international routes, Greek companies face competition from foreign airlines. In this case it would be impossible to effectively measure the effects of the economic cycles of two different countries. However, having to examine routes between Greek cities will allow me to accurately examine all the relevant determinants. In addition, European Commission in its first merger decision concluded that a possible merger would create no problem on international routes. Only the domestic airline competition would be affected. Thus, my analysis is consistent with the observations of the Commission.
I decided to focus my research on flights departing from the same airport and heading to one of the domestic destinations. In this way, I manage to examine the effect of various characteristics on the final destination e.g. whether it is a hub, keeping constant the characteristics of the departure airport. I chose the Athens Airport as the constant departure point for all flights. Athens Airport is the biggest one in Greece in terms of flight and passenger numbers and this would let me examine the maximum of the available data. The Athens International Airport is serving 32 domestic destinations as of December 2014. Seventeen of them are part of universal service obligations. Regarding the rest, as of the summer program of 2014 – which was the last period before the merger of Olympic Air and Aegean Airlines – only ten out of the fifteen were served by both airlines. Aegean Airlines gradually withdrew from these routes in the period after the privatization of Olympic Air21.
19
European Commission, MEMO/16/385.
20 The performance of Greece compared to other EU countries can be found in Annex III, derived from the Digital Scoreboard of the European Commission.
21
Due to the fact that this period coincides with the Greek economic crisis, it is hard to tell whether this action regards a perception of a profitless duopoly market by the firms or it is part of a market sharing act. It is a fact that during the period 2009 – 2014, Olympic focused its services in domestic and short-distance European routes (such as Balkan destinations), while Aegean mainly concentrated its services on Central and Western European destinations. Nevertheless, I decided to leave these routes out of my research because the act of service withdrawal indicated a lack of competition in these routes.
I was able to acquire data only for the seven out of the ten locations that both Airlines were present in the period before the merger, namely routes to Thessaloniki, Chania, Heraklion, Santorini, Mykonos, Rhodes and Mytilini22. The travel agency provided me with the average monthly prices of the airfares per route before taxes. In order to exclude outliers, I requested the agency to provide me with the average prices only when five or more tickets have been booked per each destination and month. The prices regard the month that the flight was operated, not the date that it was booked. This allows me to build a better model since I can count on time specific variables such as seasonality. The agency provided me with both one-way and round-trip fares for Olympic Air and Aegean Airlines separately. In order to have a balanced data I would need Destinations x Months = 7 x 49 = 343 observations per airline. Unfortunately regarding the one-way fares, the agency was able to provide me with 311 observations for Aegean Airlines and only 134 for Olympic Air. Similarly for round-trip fares, I obtained 293 observations for Aegean Airlines and only 81 for Olympic Air. The reason for the low number of available fare prices for Olympic Air is not clear and the agency could not justify it. Nevertheless, I assume this is related with the different customer target groups of the two airlines. Olympic Air, as the former flag carrier, is trusted by older people that chose to purchase their fares in conventional ways, meaning without the use of internet. On the other hand, Aegean Airlines was launched as an alternative more modern firm, making online offers for its customers regularly available, hence targeting younger customers who were capable of using online services. Thus, I decided to focus my research only on the second airline.
22
Table 3.1 Descriptive Statistics
VARIABLES
N
mean
sd
min
max
Month
420
19,130
527.8
18,232
20,028
Fare one-way
311
60.21
19.31
13.02
143.8
Fare round-trip
293
115.7
42.36
30.01
340.3
Fuel cost
420
2.869
0.358
2.110
3.340
Cost per km per passenger
420
6.260
0.207
5.840
6.540
GDP
420
50,057
4,193
46,024
59,990
GDP Growth
420
-4.871
3.592
-9.930
1.500
Flight time difference
400
0.0597
0.0796 0.00284
0.363
Passengers
311
57,434
38,880
2,786
208,500
Number of airlines
415
2.067
0.622
1
3
Program
420
0.583
0.494
0
1
Hub
420
0.448
0.498
0
1
Frequency
415
51.67
29.75
7
157
Number of routes
7
7
7
7
7
Regarding the independent variables of my model I obtained data from various sources. I derived information on Greek GDP and GDP growth from the Statistical Data Warehouse of the European Central Bank. I used this variable in both regressions for the departure-time differentiation and the airfares price differences in the second case as an instrument. In order to build the supply – side equation, I needed information regarding the costs and the market conditions. As mentioned above, I gathered the information regarding market competition from the schedules of Athens International Airport. There, I had the chance to find information on the number of companies and the number of flights per week on each route on a biannual basis. I furthermore gathered information on the entry and exit of competitors in the Greek airline market from the news. As the flight schedules of Athens
International Airport are issued bi-annually, they could not capture changes in the market that happened after the issue of the one and before the issue of the next flight schedule23. I obtained the annual costs per seat per kilometer from the annual reports of Aegean Airlines, which were available in the corporate website. Since no monthly airline fuel costs data were available for the Greek or the European market, I obtained monthly data of the fuel costs from the Bureau of Transportation Statistics of the United States. Even if the data regard the American rather than the European market, I assume that they can capture the changes in the price of aviation fuel internationally. A supply or demand shock in the airline fuel market would correspondingly affect the prices worldwide, as it is happening in other segments of the oil market. To justify this choice, I used data over the diesel retail prices in USA and European Union to calculate the price correlation of fuel between the two markets. The correlation was 0.94 indicating a very strong relationship between the two markets even though the geographical distance between them is considerable24. Although the US prices were constantly higher than the European Union average, the prices moved in parallel as time went by. This indication makes me confident that US aviation fuel data would capture changes that affected markets globally.
In addition, I obtained information regarding the number of passengers travelling to each destination from the Greek Civil Aviation Authority. The numbers refer to the sum of persons that arrived to and departed from each specific destination in relation to all domestic airports. In Greece, Athens International Airport plays a significant role in domestic flights as it serves the market as a hub. Direct flight connection between the rest city pairs is limited. Only Thessaloniki Airport has a significant amount of direct flights to a number of domestic destinations. Thus, the sum of passengers obtained from the Civil Aviation Authority regards mainly people travelling to and from Athens. Besides, factors that would influence the travelling preferences of passengers (for instance, the economic crisis) affect simultaneously all parts of the domestic market. Furthermore, foreigners that visit Greece usually prefer to travel straight to their final destination from their country of origin rather than make a stop in Athens and board another flight. For all these reasons, I am convinced that the variable captures the changes of passengers travelling monthly from Athens to various domestic airports in the most appropriate way.
23
This was the case of Athens Airways exit from the market. The flight schedule captured this change on its next issue, as the airline ceased its operation abruptly.
24 For an overview of the oil prices and the correlation between the US and European market, see Annex VI.
I also extracted information from the press releases of Aegean Airlines regarding its hubs and the changes of its focus cities over time. Last, I collected various constant variables over time regarding specific unique characteristics of the observed cities that would improve my model. Such examples include the cities’ population with reference to the last census of 2011 and their distance from Athens. However, since I favored a panel data analysis, such variables were dropped as they were time invariant.
Measure of departure-time differentiation
I follow the measurement of time differentiation of Borenstein and Netz (1999). I expect that every flight in a day compete not only with its closest neighbors but with all flights departing on the same day. Thus, I pair a flight with all flights departing at the same day with the same destination regardless the operator. Flights of an airline not only compete with flights of its competitors but also the flights of the same operator. The time of each departure is represented in minutes away from 12 o’clock midnight. Thus, a flight departing at 00:10 will have 10 as departure time, while a flight departing at 23:50 will have 1430 as departure time. I first measure the average time difference between each flight pair using:
Where n is the number of daily flights and d represents the time of the departure of each flight paired with the rest of flights departing on the same day. The difference is raised to the power ½ for a weighted measure. In this way, changes for flights that are closer together weigh more in the final result. AVGDIFF is maximized when all flights are equally spaced during the day. I then calculate this maximum value of AVGDIFF as MAXDIFF25. The final
25
measure is the average difference of flights normalized by the maximum difference the flights could have. Doing so, allow me to compare routes with different number of flights.
DIFF has an interval [0,1] and the closer it is to 1, the more equally spaced the flights are.
The Model
My model consists of two parts. First, I examine the competition of the Greek airlines regarding their departure times and then I focus my research on the price competition. Thus, I have the opportunity to compare how different levels of competition affect both aspects of airlines behavior.
Regarding the first part of my analysis referring to the departure time differentiation, I use as basic model the one of Borenstein and Netz (1999) adjusting it for a domestic airline market. I expect that changes in the level of the competition will have an effect on the departure times. More airlines in a market would lead to a more intense competition on departure times as was the case in their findings. Borenstein and Netz use the Herfindahl-Hirschman Index while I use dummies to examine the effect of a transition from a duopoly to an oligopoly or monopoly. In such a way, I can examine the effects of the transition to a new competitive market state, rather than draw conclusions on the overall competition effect. I expect that a transition to a monopoly will lead to larger difference between departure times of flights since a monopolist will try not to cannibalize its products. In contrast, oligopoly will lead to smaller time differences as the firms will compete more intensively. In my model, I also have the opportunity to examine the effect of a low-cost firm entry. It is not clear whether the entry of a low-cost firm will have a significant effect on the departure time-differentiation of the flights. It is known that Ryanair has an aggressive price policy for its services but it is unknown whether this aggressive behavior extends to other aspects of airline competition.
In addition, I expect that the function of an airport as a hub for an airline might have a significant effect on the scheduling of flights. When an airport is serving as a hub the operator has to take into account the scheduling of domestic flights to match that of international ones in order to allow for transfers. This will allow less space for intense departure competition in routes serving hubs. The issue of time slots does not play a significant role in this analysis as it does in the research of Borenstein and Netz. As the European Commission ruled in its first decision considering the merger of Olympic and Aegean Airlines, Greek airports do not suffer from the congestion problem observed at other European Airports in previous mergers.
Greece has entered a period of severe recession since the Greek government-debt crisis started in the late 2009, thus I count for it by using the Greek GDP as an instrument. I expect that as the crisis exacerbated and the economy was falling deeper in the recession, airlines would start to intensify their competition. After all, during a recession competition gets fiercer since competitors are fighting over a shrinking market. I finally decided to use the weekly frequency of flights serving a route as their number is affecting the placement of flights on the 24-hour clock. Borenstein and Netz did not use the flight frequency in their model as they only compared routes with the same number of flights. Since my observations are not enough to perform a similar analysis, I used the frequency of flights per route in order to be able to compare routes with different frequency levels.
The final model regarding the time differentiation is: 0 .0 5 .1 .1 5 .2 .2 5 T ime D if fe re n ti a ti o n
Jul-09 Jul-10 Jul-11 Jul-12 Jul-13 Jul-14
Month
Hubs Other Airports
Hubs and Departure-Time Differentiation
4 5 0 0 0 5 0 0 0 0 5 5 0 0 0 6 0 0 0 0 G D P
Jul-09 Jul-10 Jul-11 Jul-12 Jul-13 Jul-14
Time
Where monopoly, oligopoly and lowcost are dummies indicating the competition in the market and hub is a dummy that indicates that the destination airport is a hub or a focus city as defined by the airline. GDP is the quarterly Greek Gross Domestic Product and frequency is the weekly number of flights per route.
Subsequently, I study the effects of the competition changes on the airfares. To do so, I combine the models of Peteraf and Reed (1994) and Joesch and Zick (1994) utilizing the supply equation. I suppose that the number of passengers flying to a destination is positively correlated with the ticket price. Regarding my competition dummies, I expect that the transition from a duopoly to an oligopoly or monopoly will affect the airfares. Monopoly will lead to higher fares while more intense competition will most likely lead prices down. In addition, I predict that the entry of a low-cost airline in the market will have a negative and significant effect on the fares.
Whether an airport is a hub or not might also have an effect on pricing. As the graph shows, hubs have in general lower prices that the rest of the airports that were examined due to its function as a transfer point for international flights. To complete the supply equation I use
0 50 1 0 0 1 5 0 2 0 0
Jul-10 Jul-11 Jul-12 Jul-13 Jul-14
month
Hub one-way fares Other airports one-way fares
Hub round-trip fares Other airports round-trip fares
Hubs and Fares
factors of cost for the airlines. I expect that cost will have a positive effect on the airfares, as raises in fuel prices or other cost factors will be absorbed in the final airfares.
I use a two-stage least squares (2SLS) regression. Due to simultaneous causality bias, I treat the number of passengers travelling to each destination as endogenous. To accurately build my model, I need instruments that shift demand but not supply. Thus, I use as instruments the GDP, the local population as was recorded in the 2011 census and monthly dummies. Passengers alternate their travelling behavior during holidays; hence there is a greater demand for tickets during summer months. In addition, there is a greater demand during the Christmas and Easter period than the rest of the year. At the meantime, airlines do not have any reason to alter their supply of flights and seats during these periods other than to serve the excess demand. Thus, I fully attribute the increased prices due to seasonality on changes of demand rather than supply.
The final model regarding ticket prices is the following:
Where passengers is the number of airline passengers arriving per month to each destination and monopoly, oligopoly and lowcost refer again to competition dummies. Fuel
50 1 0 0 1 5 0 2 0 0
Jul-10 Jul-11 Jul-12 Jul-13 Jul-14
Month
One-way fares Round-trip fares
refers to the average monthly price of airline fuel and cost indicates the average airline cost per passenger per kilometer.
2SLS Regression
I performed multiple tests to confirm that the 2SLS model was the most appropriate for my analysis. The endogeneity test rejected the hypothesis that my endogenous variable passengers could be treated as exogenous, thus the use of instrumental variables is appropriate. Sargan Hansen test confirmed that the chosen instruments are valid and uncorrelated with the error term. Cragg- Donald test showed that my instruments are not weak. In addition, my model is not under-identified as given by the Anderson Canonical correlation LM statistic26.
Results
My data consist of an unbalanced panel of seven destinations for a period of 49 months. Thus, I am able to perform panel data analysis, eliminating the effect of omitted variables that differ across routes but stay constant over time. An example of such omitted factors is the population and the level of unemployment in each destination. In both models, I first perform a Wu-Hausman-Durbin test to choose between a fixed- and a random-effects model. In both cases, the null hypothesis is rejected thus fixed effects model is consistent and preferred.
(1)
VARIABLES
lDIFF
monopoly
0.175***
(0.0523)
oligopoly
-0.00159
(0.0493)
lowcost
0.0403
(0.0964)
26hub
-0.0302
(0.161)
GDP
2.69e-05***
(5.23e-06)
frequency
-0.0417***
(0.00139)
Constant
-2.663***
(0.252)
Observations
415
Number of
destinations
7
R-squared
0.763
Standard errors in parentheses
*** p<0.01, ** p<0.05, * p<0.1
Regarding the regression on the time difference of flight departures, I use the log of departure time-differentiation since heteroscedasticity is present in the regression. The transition from a duopoly effect has a positive effect on time differentiation, as expected from the theory. Monopolists position their products further from each other, in an attempt to avoid cannibalization. In contrast, the entry of a third airline in a route has no significant effect on the time difference. Thus it is not evident from the data that when the market transforms to an oligopoly, airlines intensifies their departure time competition by placing their departure times closer to that of their competitors. Furthermore, the entry of the low cost firm has also no effect on the time difference. Whether the arrival airport is a hub plays no significant role on the departure times either. Thus, Greek domestic market does not fall in the case of the sizable International European and American Airports where congestion affect competitive behavior. Finally, the crisis leads to a more intense competition. As the Greek Gross Domestic Product fell, airlines started competing more on departure times which comes in accordance with what theory of competition policy suggests.
I used data for two different types of airfares to determine the effects of competition on prices. First, I ran the regression using data on single airfares and then performed it again on round tickets. The results are quite identical for the two regressions, in respect to the effect and significance of variables. There is a positive relationship between prices and the number of passengers travelling to a destination. Fuel prices have no significant effect on prices which at first might seem strange. A possible explanation for it is that airline fuel prices for Greece or Europe were unavailable. Instead, I used fuel data from the US Bureau of
Transportation Statistics. It is possible that the small differences between the prices of the two regions might have affected the results of the model. Cost has a significant positive effect on single airfares. The transition to a monopoly has no effect on ticket pricing. However, the entry of a third airline or the entry of a low-cost airline has a negative and significant effect on prices. The entry of the low-cost airline had a significantly larger effect on the price reduction than the entry of a third airline in the market. Finally, the status of an airport as a hub has no effect on prices. The last observation is in accordance with the results of the departure-time differentiation regression.
(1)
(2)
VARIABLES
fare_one
fare_round
total
0.000500***
0.00145***
(6.40e-05)
(0.000139)
fuel
-3.389
-18.99
(4.774)
(11.93)
cost
15.66**
28.04
(7.894)
(17.19)
monopoly
-0.0391
-12.96
(3.732)
(8.012)
oligopoly
-8.295***
-12.42**
(2.580)
(5.387)
lowcost
-34.96***
-87.56***
(5.590)
(11.88)
hub
4.570
21.13
(6.460)
(13.38)
Constant
-53.89
-85.14
(52.05)
(111.8)
Observations
311
290
Number of destinations
7
7
Standard errors in parentheses
*** p<0.01, ** p<0.05, * p<0.1
Conclusion
Airlines compete on multiple aspects. In addition, to the conventional pricing competition that has been thoroughly examined, during the last years other forms of competition are being studied. One important form of airline competition is that on the times of flight
departures. Departure times set closer to that of competitors’ indicate a stronger intention to compete and to steal customers.
To summarize my study, airline transportation in Greece holds a significant role. Traveling by train, bus or ferry does not constitute a close substitute due to the significantly longer traveling time. I examined the Greek Airline market from 2009 to 2014. During this time period, multiple changes in the competitive framework occurred. The entry of a maverick firm in the market occurred in two instances, but the firms did not manage to stay in the market for long. The two incumbent firms tried twice to merge and they achieved it on the ground of failing firm defense. In the meantime, firms shared the market and remained active in parallel only in a few routes. Market conditions such as the high entry barriers and the symmetry among the firms could facilitate collusion, but a foreseen decrease in demand would deter it.
I collected data from various sources in order to examine the two aspects of competition. I created all possible flight pairs regarding flights that departed on the same date from the same airport. I then used the average time differentiation of all flights performed per airport and month in order to create my final dataset of departure-time differentiation among flights. I adopted the model of Borenstein and Netz in order to build an instrument that would not only count for the time-differentiation but would also normalize my variable. I also used the weekly frequencies per destination. In such a way I was able to compare different time periods and routes with different levels of congestion. Regarding the price model, a normal regression of prices on quantities would fall short of its role. Set of prices and quantities observed over time represent equilibrium points rather than the supply or the demand curve. To avoid a miscalculation of the effects, I performed an instrumental variable regression and used demand shifters.
The transition of the Greek market from a duopoly to a monopoly with the merger of Aegean Airlines and Olympic Air had no significant effects on pricing but it led in bigger time differentiation between flight departures. Regarding the time differentiation behavior, the monopolist tried to efficiently avoid cannibalization of its products by changing the placement of flights in the 24-hour clock. The lack of significant price increases after the merger indicates the fear of the monopolist that a change in the duopolistic pricing behavior might lead in the entry of new airlines. Even though, it would be more profitable for a monopolist to set the monopolistic price for his products in order to maximize his profits, this poses a threat on the viability of his monopolistic status. High prices can attract new
firms in the market, in the hope to capture part of it with their lower prices. For such a reason, it is possible a monopolist will not set monopolistic prices in the fear of potential competition and entry of new firms in the market.
The absence of significant price increases after the market transformed into a monopoly can also be explained by another economic theory. It is possible that a monopolistic behavior already existed even when the market was served by two airlines. The two companies would benefit more if they managed to function as a monopoly – with lower quantities and higher prices – and then split the profits. This could have been achieved either by tacit or explicit collusion; however the first is a not punishable act while the second is an illegal practice. It is a very difficult process for competition authorities to prove an explicitly collusive behavior. This can usually be proved only with the use of hard evidence (emails that prove communication between the parties with the intention to discuss their pricing behavior etc). Thus it is impossible for this research to prove the actual reasoning behind the lack of price increases after the merger.
However by looking at the overall picture of the study, there is another indication that supports the fear of potential entrants reasoning. The significant positive effect GDP had on departure-time differentiation can support the first explanation regarding airlines behavior rather than a collusive theory. Firms are supposed to compete more intensively during a recession as the market over which they are competing is shrinking. This was also the case for the Greek airline market; airlines were intensifying their departure-time competition while the market was falling deeper in the recession. Thus firms did not show any indication of collusive behavior regarding their departure-time strategy as the results of the empirical analysis show. These results support the idea that the two firms were not participating in collusion.
The entry of a third airline in a route led only to a significant reduction of prices while it had no significant effect on departure time differentiation. Oligopoly had a positive effect for consumers through the lowering of prices. The entry of Ryanair in the Greek market had similar results. It led to lower prices for the incumbent firm without intensifying the competition on departure times. However, the effect on prices of the low-cost firm entry was larger than the effect of the third airline. The incumbent significantly lowered its prices in an attempt to avoid its customers’ being stolen by the low-cost firm.
Further research
In my analysis, I had the chance to examine the dataset until 2014. After that more airlines entered the market. It would be interesting to further expand the examined period in order to study whether the results remain the same and draw a more robust conclusion regarding the effects competition had on the airlines’ behavior.
In addition, the use of another model in order to pair the flights that compete with each other would be closer to reality. Even though, a Hotelling model is helpful and easier to use, a Salop’s circle model would represent the departure time in a more realistic manner. After all, flights do not only compete with the ones that depart on the same date. For instance, it is equally likely that passengers would prefer to take a flight early on the same or the next morning when they cannot travel late at night.
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Annex I
Map of selected routes used to examine substitute means of transportation
Annex II
Airports and passengers on board domestic flights in European Union Countries
The importance of air transportation in Greece can be seen in the following graphs. Greece has the third highest ration of airports per 1,000,000 inhabitants in the European Union. It also has the third highest ratio of passengers on board domestic flights per capita.
0 1 2 3 4 5 EE FI EL SE MT CY FR IE LU DK HR PT SI LT SK ES LV DE UK IT AT RO BG HU CZ BE NL PL
Number of airports per 1,000,000 inhabitants
Source: Eurostat 0 .2 .4 .6 .8 SE ES EL FI IT FR DK UK DE PT HR AT PL RO BG IE EE CZ SK BE LU NL LT LV HU CY MT SI
Number of passengers on board domestic flights per capita
