Vertical collusion between airports and airlines: An empirical test for the European case

Transportation Research Part E 57 (2013) 3–15

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Vertical collusion between airports and airlines: An empirical test for the European case Cristina Barbot a, Tiziana D’Alfonso b,⇑, Paolo Malighetti c, Renato Redondi d a

CEF.UP, Faculty of Economics, University of Porto, Rua Dr. Roberto Frias, 4200 Porto, Portugal Department of Computer, Control and Management Engineering, Sapienza Università di Roma Via Ariosto 25, 00185 Rome, Italy c Department of Economics and Technology Management, University of Bergamo, Viale Marconi 5, 24044 Dalmine (BG), Italy d Department of Mechanical Engineering, University of Brescia, Via Branze 38, 25123 Brescia, Italy b

a r t i c l e Keywords: Vertical collusion Airport competition Airline competition

i n f o

a b s t r a c t We develop a test for vertical collusion between airports and airlines in the case of two different scenarios. In the first scenario there is one airport and one airline; this intends to depict the case of airports that do not compete with any other one. In the second, we consider two competing airports and one airline that uses the airport as a base or a hub. In the case of non competing airports we find that gross margins are lower when there is vertical collusion. In the case of competing airports, we find that gross margins are equal when both pairs collude or do not collude. But in the case in which only one pair colludes, a merger between them brings a lower margin. We tested 36 pairs of airports–airlines in the case of non competing airports and we find evidence for vertical collusion with respect to: (i) main national carriers in small airports (ii) low cost carriers in secondary airports. Ó 2013 Elsevier Ltd. All rights reserved.

1. Introduction Air transport deregulation led to stronger motivations for vertical agreements, including vertical collusion. On one hand, deregulation allowed for the entry of new airlines in the industry, namely low cost carriers, which often use the so-called secondary airports. These were former regional airports or military bases, with plenty of spare capacity. The operation of low cost carriers in these airports impressively increased traffic and led to a high growth in employment and income. On the other hand, recent dynamics in the industry have been outlining an increase in the degree of concentration in the supply of air services and a market polarization all around few carries with a relevant market share. As a consequence, dominance has been allowing a carrier to achieve higher bargaining power and to turn the airport–airline relation into a bilateralmonopoly (monopoly–monopsony). Moreover, before deregulation traditional carriers were public firms operating (and dominating) public airports, so that prices were set by governments and transactions occurred between two public firms. Once the deregulation process was finished, these (often privatized) airlines needed to establish contracts with the (often privatized) airports so that they could go on operating in the former conditions. In this paper we aim to empirically analyze vertical collusion between airports and airlines. We first develop two theoretical models, one for non competing pairs of airport–airline and one for competing pairs. We then perform empirical tests in order to detect the situations where it is probable that collusion has occurred. We find that collusion is clearly more likely to occur in two situations: (i) national airlines in their national hubs and (ii) low cost airlines in secondary airports. Though our empirical sample is too small to derive generalized conclusions, our paper is innovative since it provides a simple test ⇑ Corresponding author at: Department of Computer, Control and Management Engineering, Sapienza Università di Roma Via Ariosto 25, 00185 Rome, Italy. Tel.: +39 067 7274105; fax: +39 067 7274073. E-mail address: [email protected] (T. D’Alfonso). 1366-5545/$ - see front matter Ó 2013 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.tre.2013.01.002

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that distinguishes pairs of airline–airport where collusion may occur from those where this does not happen. Thus, we provide a sufficient condition for policy makers to detect cases where there can be a suspicion of collusion. In what regards air transport literature, few papers have addressed vertical collusion between airports and airlines. Gillen and Morrison (2003), using a model of spatial competition, conclude that: (i) when only one integrated pair of airport–airline covers the market, the merger firm will only charge its maximizing profit price if retail revenues per passenger are greater than the airport charges; (ii) this result holds for two competing pairs of airport–airline with symmetric airside costs. The authors also find that there is a clear incentive for airports and airlines to engage in vertical contracts. Barbot (2009) analyses competition between pairs of airline-airport that may vertically collude or not. The Nash equilibrium of a repeated game depends on the behavior of each pair, on the similarities of catchment areas and on the business model of each airline (low cost or full service). D’Alfonso and Nastasi (2012) investigate three different types of agreements between airports and their main carrier, in the context of two competing facilities and multiple airlines. Specifically, they find that both the two competing pairs of airport–airline have incentive to vertical collusion, when they share the same market and the market itself is not covered. As for empirical papers, airlines’ horizontal collusion has been tested by Brander and Zhang (1995), but, to the best of our knowledge, vertical collusion has not been empirically tested yet. Though there is a wide theoretical literature on vertical relations and vertical restraints drawing on Industrial Organization, few papers have addressed this theme on empirical grounds. Martin et al. (2001) use experimental tests to check some results with respect to vertical integration and market foreclosure. Villas-Boas (2007) tests the vertical market relations between manufacturers and retailers in one area of the US, using the case of yogurts. She conducts a pairwise comparison of supply models and concludes that non-linear pricing with manufacturers’ zero price cost margins rejects all the other models (except one) and is only rejected by the vertical merger model. Thus, results rule out double marginalization and allow for evidence on market power from the buyers’ side, as well as for vertical collusion. Bonnet and Dubois (2010) examine the case of bottled water in France to analyze the relations between manufacturers and retailers. They find that manufacturers use two-part tariff contracts with resale price maintenance. The paper is organized as follows. Section 2 presents the model. Section 3 describes the empirical study. In Section 4 we discuss the results. Section 5 contains some concluding remarks. 2. The model 2.1. General framework We consider two situations. In the first one there is one airport and one airline. This intends to depict the case of airports that do not compete with any other one, which we call NCA (Non Competing Airports). NCA’s are isolated airports, with no intersections with other airports’ catchment areas, and so may be considered as monopolies. The second situation is for competing airports (CA). In this situation we consider two airports and one airline that uses that airport as a base or a hub. We rule out the possibility of airlines switching between airports. It can be argued that airlines could, actually, decamp all or part of their operations to an alternative site. This is particularly true once we take into account non-networked air services operated by charter or low cost carriers which have more scope for switching operations between airports in order to reduce costs.1 Nevertheless, especially when air services are concentrated, the significance of the agglomeration economies/network externalities may be such that they tie the individual dominant airline to the airport even for low cost carriers. In the case of scheduled carriers, with a high level of transfer passengers to and from other airlines, to choose to forego the revenue and cost advantages of the hub by substituting a proximate, even adjacent, alternative airport, would seem most unlikely (Starkie, 2002). British Airways or British Midland at Heathrow, Air France at Paris Charles De Gaulle or Alitalia at Rome Fiumicino provide an example in this sense. Moreover, some airlines own or control airport facilities: Lufthansa has invested in terminals in Frankfurt airport and Munich airport; Latvia’s Riga Airport has offered a contract to the national airline Air Baltic to build and operate a 92 euro million terminal for seven million passengers per annum by 2014. This means that the costs of switching airports are higher for the dominant airlines, which is an essential condition for them to make long term commitment to the airport itself. Finally, there are limitations imposed by slots grandfathering systems.2 We assume that only one airline operates at each facility. Indeed, airports are used by a certain number of airlines, but usually with a dominant airline, which is the one that has the largest share at the airport. This dominance is historical in the sense that it results from grandfathering rules which determine an entry barrier with strong anticompetitive implications especially at hub airports (Gillen and Morrison, 2003; Starkie, 2008). In Europe, dominant airlines are usually flag carriers at main airports. In the US, airlines have chosen hubs (such as Delta at Atlanta, where it has a share of 72%, or Southwest Airlines at Chicago Midway, with a share of 81%, in 2008). Moreover, European low cost airlines established at the so-called secondary airports, which had excess capacity and are now dominated by these carriers. As an example, in 2010 Ryanair had a share of 72.6% at Bergamo Orio al Serio airport. A Stackelberg game would be more appropriate to model the airlines (downstream) market when considering a finite number of airlines, with a dominant one at each airport. But this would bring additional complications that are hardly tractable in empirical tests, mainly as they involve the determination of cross price elasticities of demand. 1 2

Competition between Luton and Stansted in the early 1990s for the custom of Ryanair provides an example in this sense. Our model is more applicable to the European case, but has limited application in the US, where the slots system is not so binding.

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2.2. The case of one airport and one airline In the first scenario one airline operates in one airport but this airport does not compete with any other. Let P be the final price of the tickets sold by airline to consumers, q(P) the quantity of tickets, T the input price (per passenger) paid by airlines for the use of the airport, cB the airline’s constant marginal cost (where airport charges are not included) and cS the airport’s constant marginal cost. We assume that there are no fixed costs, but this does not make any difference for our purposes. In what follows, we will use the subscript B to indicate the ‘‘Buyer’’, i.e. the carrier, and S to indicate the ‘‘Seller’’, i.e. the airport. The subscript M will be used to indicate ‘‘Merger’’, that is the airport and the airline collude Consider first the case of non-collusion. In the downstream market the airline is a monopolist. The upstream market, however, is a case of bilateral monopoly. To solve it, we use Bowley’s first case, where the upstream firm (the seller) has the power to set the price. The game is then developed in two stages. In the first stage the airport decides the input price T, according to the airline’s derived demand. In the second stage, the airline sets the ticket price, P. The airline’s profit can be written as:

pa ¼ qðPÞðp  cB  TÞ

ð1Þ

The carrier maximizes its profit with respect to P and the first-order condition is:

@q ðP  cB  TÞ þ q ¼ 0 @P

ð2Þ

Arranging terms, we get:

P  cB  T 1 ¼ P e

ð3Þ

where e is the price elasticity of demand in the downstream market. Solving Eq. (2) for q, we get the airport’s derived demand, q(T). In the first stage, the airport maximizes its profits:

pA ¼ qðTÞðT  cS Þ

ð4Þ

The first order condition is expressed as follows:

@q ðT  cS Þ þ q ¼ 0 @T

ð5Þ

which can be changed to:

T  cS 1 ¼ E T

ð6Þ

where E is the airport price elasticity of demand. If we consider a linear downstream demand, E = e.3 Then, by solving the airport’s first order condition for T and inserting in the airline first order condition, we get: e P  cB  cS e1 1 ¼ e P

ð7Þ

Eq. (7) can be arranged as:

 P  cB  c S 1 cS  e 1 ¼ þ e P e1 P

ð8Þ

so that if demand is elastic, i.e. e > 1, we have:

P  cB  c S 1 > e P

ð9Þ

If the airport and the airline collude, there is a single monopoly (a merger) in the downstream market which maximizes its profit:

pM ¼ qðPÞðP  cB  cS Þ

ð10Þ

The merger maximizes its profit and the first order condition is:

@q ðP  cB  cS Þ þ q ¼ 0 @P which becomes the usual monopoly condition:

3

For a proof, see Alves and Barbot (2010).

ð11Þ

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P  c B  cS 1 ¼ e P

ð12Þ

Then, the test can be expressed as follows:

P  cB  cS 1 ¼ e P B S Pc c 1 > e P

collusion

ð13Þ

no collusion

In order to confirm results, we perform a second test. Eq. (1) may be changed into:

P  c B  cS 1 T  c S ¼ þ e P P Thus, we also test:

T  cS ¼ 0 collusion P S T c > 0 no collusion P

ð14Þ

Notice that T  cS is the airport margin over marginal cost. But the airport charge, T, is often subject to regulation. However, regulation, while setting a price cap, states the maximum price and the airport may choose any price below this latter. Alves and Barbot (2010) found that, for a sample of the 106 largest airports in the world, in 97% of the cases price is above marginal cost. In the same paper, some arguments are provided to explain this possibility. Also, it is frequent that airports have negative profits and that profits are earned mostly through concession revenues. S Whenever this happens, we obtain Tc < 0. Given that 1e > 0 in the case of non-collusion we obtain: P

P  c B  cS 1 < e P So, for airports that have negative margins over operational costs, we test4:

P  cB  cS 1 ¼ e P B S Pc c 1 < e P

collusion

ð15Þ

no collusion

2.3. The case of two airports and two airlines In our second situation we consider two competing airports. These airports have common catchment areas, in its whole or in part of them. In other words, for simplification, we assume that the two airports compete in the same market or have the same catchment area. Airports are mainly differentiated by locations (Pels and Nijkamp, 2003). Therefore, in order to analyze competition between airports, it is adequate to use a spatial model, i.e. the Hotelling model. Specifically, we assume there is an infinite linear city where C potential consumers are uniformly distributed with a density of one consumer per unit length and a transportation cost of t. There are two airports, A1 and A2, respectively located at the left and right extremes of this line, 0 and 1, and locations of the facilities are exogenous. The assumption may seem restrictive when considering the case of three or four facilities competing on the same catchment area, for instance Paris Orly, Paris Charles De Gaulle and Paris Beauvais, although they are very rare. On the other hand, modeling more than two competing airports would bring additional complications that are hardly tractable in empirical tests, mainly as they involve asymmetry of demand.5 We assume all consumers fly, i.e., the market is covered. There is one airline in each airport. Let Ci stand for the carrier which operates in airport A1, with i = 1, 2. The airlines are bound to a certain airport6: Ci cannot switch from airport Ai, with i = 1, 2. Airports 4 Airlines may also have negative profits, and, when this happens, P  cB < 0. However, in this case, there is no need to change the test. In fact, in this situation, for the case of collusion, P  cB  cs < 0, and the aim of the merged firm is to minimize losses, or to maximize L(P) = q(P)(cB + cS  P). The first order condition is (P  cB  cs) = q/(oq/oP) which becomes (P  cB  cS)/P = 1/e. 5 Indeed, in our model consumers are uniformly distributed along a line of unitary length. If N airports are competing over this market, each of them will be located at the distance 1/(2N) from the two neighboring airports and will have a potential demand of 1/N. At the same time, the facilities located at 0 and 1 will have demand of 1/(2N). For instance, consider C consumers over a line with three airports, two at the extremes and one situated at the middle. The latter gets C/ 4 consumers on each side, i.e. C/2, and the airports at the extremes get C/4 consumers each. Besides, this is not a Nash equilibrium, as the airports at the extremes would move towards the center of the line. 6 The assumption of not allowing one airline to serve two airports may seem restrictive when considering the case of different facilities dominated by the same carrier. For inter-metropolitan case, for instance, Roma Fiumicino and Milano Malpensa are both dominated by Alitalia. For intra-metropolitan case, London Heathrow and London Gatwick are both dominated by British Airways. Our framework implies a perfect alignment between the interests of the airport and the airlines operating at the airport itself: in this sense our results are restricted to the cases just discussed, that is when the carries have no incentives to shift form one facility to another (high switching costs, etc.). If this is not case, the equilibrium of the game may change, given that airports would compete between each other for airlines and not through airlines to get passengers.

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compete through airlines to get passengers: consumers will choose either A1 and C1 or A2 and C2. For instance, a Londoner flying in a low cost airline to Marseille decides between Ryanair from Stansted and Easyjet from Gatwick. Moreover, we assume airlines compete in prices à la Bertrand.7 Let Pi be the final price of the tickets the airline Ci sells to consumers, qi(P1, P2) the quantity of passengers served by airline i, Ti the input price (per passenger) the airline Ci pays to Ai for the use of the facility, cBi the airline Ci ’s constant marginal cost (where airport charges are not included) and cSi the airport Ai’s constant marginal cost, for i = 1, 2. We suppose there are no fixed costs, but this does not make any difference for our purposes. The vertical structure of airports–airlines behavior is represented by a multistage game, and we analyze three cases: (i) none of the pairs collude; (ii) both A1 and C1 and A2 and C2 collude; (iii) only A1 and C1 collude (or only A2 and C2 collude, that leads to symmetric results). The game is solved by backward induction. For this purpose, we first focus on airlines’ demand. Potential consumers have unit demand for flights and they care for their ‘‘full price’’. Indeed, passengers may not necessarily choose the airport with cheaper fare, but may go to an airport that is nearer and has a shorter total travel time. Therefore, the full price is the sum of the ticket price and the travel cost to the facility and is given by:

P1 þ tx for a consumer located at 0 6 z 6 1 and who goes to facility 0. If the consumer decides to fly, she derives a net utility:

U 1 ¼ U  P1  tx

ð16Þ

with U denoting the gross benefit. Similarly, if the consumer goes to facility 1, then she derives a net benefit:

U 2 ¼ U  P2  tð1  xÞ

ð17Þ

Assuming that everyone in the [0, 1] interval decides to fly the indifferent consumer ~ x 2 ð0; 1Þ can be expressed by the Hotelling condition and it is determined by U1 = U2, or:

~x ¼

P2  P1 þ t 2t

ð18Þ

Therefore, the passengers’ demand for C1 and A1 can be expressed as:

q1 ðP1 ; P2 Þ ¼ ~x ¼

P2  P1 þ t 2t

ð19Þ

while demand for C2 and A2 as:

q2 ðP1 ; P2 Þ ¼ 1  ~x ¼

P1  P2 þ t 2t

ð20Þ

We first analyze the case in which none of the pairs collude. In the first stage airports set input charges. In the second stage, airlines set prices for consumers. Solving airlines’ best response functions, we get solutions for prices, that is P1(T1, T2) and P2(T1, T2). Airlines i = 1, 2 maximize their profit with respect to the ticket price Pi:

pBi ¼

ðPi  cBi  T i ÞðPj  Pi þ tÞ 2t

ð21Þ

First order conditions for airline i = 1, 2 lead to:

Pi ¼

1 2½Pj þ t þ cBi þ T i 

ð22Þ

Inverting these functions, we get the airports’ derived demands, that is q1(T1, T2) and q2(T1, T2):

qi ¼

B B 1 ðT j  T i Þ þ ðcj  ci Þ þ 2 6t

ð23Þ

In the first stage airports i = 1, 2 maximize their profits with respect to Ti: S i

p ¼ ðT i 

" # B B 1 ðT j  T i Þ þ ðcj  ci Þ þ 2 6t

cSi Þ

ð24Þ

First order conditions for airports i = 1, 2 are:

T j  2T i ¼ ðcBi  cBj Þ  cSi  3t

ð25Þ

Substituting Eq. (25) into Eq. (22) we obtain:

5 4 Pi ¼ 4t þ ðcBi þ cSi Þ þ ðcBj þ cSj Þ 9 9

ð26Þ

7 Flights (offered by Ci in Ai) are homogeneous. Nevertheless, given that airports compete through airlines for consumers and airports are differentiated by location, final services to consumers are differentiated. Therefore, the Bertrand competition is feasible.

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that leads to:

P1  P2 ¼

1 B 1 ðc þ cS1 Þ  ðcB2 þ cS2 Þ 9 1 9

ð27Þ

We then analyze the case in which both C1 and A1 and C2 and A2 collude. The two mergers compete directly in final prices. They maximize their profits with respect to the ticket price, Pi:



pMi ¼ ½Pi  ðcSi þ cBi Þ

Pj  Pi þ t 2t



ð28Þ

First order conditions for mergers i = 1, 2 are:

Pj  2Pi þ t þ ðcSi þ cBi Þ ¼ 0 that lead to:

P1  P2 ¼

1 B 1 ðc þ cS1 Þ  ðcB2 þ cS2 Þ 3 1 3

ð29Þ

Finally, we consider the case in which only C1 and A1 collude (the case in which only C2 and A2 collude leads to symmetric results). In the first stage, airport A2 sets its input charge, while in the second stage merger 1 and airline C2 set prices for consumers. They maximize their profits:

  P2  P1 þ t 2t   P1  P2 þ t B ¼ ðP 2  c2  T 2 Þ 2t

pM1 ¼ ½P1  cS1  cB1 

ð30Þ

pB2

ð31Þ

First order conditions are:

1 3ð2cS1 þ 2cB1 þ cB2 þ T 2 Þ 1 P2 ¼ t þ 3ðcS1 þ cB1 þ þ2cB2 þ 2T 2 Þ

P1 ¼ t þ

ð32Þ ð33Þ

Inverting these functions, we get the airport A2’s derived demand, q2(T2):

q2 ¼

1 cS1 þ cB1  cB2  T 2 þ 2 6t

ð34Þ

In the first stage airport A2 maximizes its profits with respect to T2:

pS2 ¼ ðT 2  cS2 Þ

  1 cS1 þ cB1  cB2  T 2 þ 2 6t

ð35Þ

From the first order condition we derive:

T2 ¼

1 2ð3t þ cS1  cB2 þ cS2 Þ

ð36Þ

Substituting Eq. (36) into Eqs. (32) and (33) we obtain:

3 5 1 t þ ðcB1 þ cS1 Þ þ ðcB2 þ cS2 Þ 2 6 6 1 B 2 B S P2 ¼ 2t þ ðc2 þ c2 Þ þ ðc1 þ cS1 Þ 3 3

P1 ¼

ð37Þ ð38Þ

that leads to:

1 1 1 P1  P2 ¼  t þ ðcB1 þ cS1 Þ  ðcS2 þ cS2 Þ 2 6 6 Rearranging Eqs. (27), (29), and (39) the test is:

1 1 P1  ðcB1 þ cS1 Þ ¼ P2  ðcB2 þ cS2 Þ None of the pairs collude 9 9 1 B 1 S P1  ðc1 þ c1 Þ ¼ P2  ðcB2 þ cS2 Þ Both pairs collude 3 3 1 B 1 1 S P1  ðc1 þ c1 Þ ¼ P2  ðcS2 þ cS2 Þ  t Only one pair collude 6 6 2

ð39Þ

C. Barbot et al. / Transportation Research Part E 57 (2013) 3–15

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We can draft these conclusions. Gross margins are equal when both pairs collude or do not collude. But in the case in which only one pair colludes, the merger has a lower margin than the pair that does not merge. Comparing (27) with (39), the pair C1 and A1 decreases its gross margin from the case in which both the pairs collude to the case in which only one pair collude. The same happens with the second pair. This confirms the results of Barbot (2009), though profits are compared there. Comparing (28) with (38), both pairs decrease their gross margins when they both collude. This also confirms the results of Barbot (2009). Generally speaking, both gross margins are lower when both pairs collude and, comparing with the case in which both pairs collude, even the case in which only one pair colludes is worse for both of them. The reason is that double marginalization is eliminated: a vertical collusion decreases downstream prices. Moreover, these pairs of firms are also horizontally competing, i.e. one pair competes with the other: downstream competition enhances the decrease of prices and leads to a fall of margins. Nevertheless, vertical collusion increases quantities, so that joint profits may increase. In the case in which only one pair collude, the colluded firm’s demand, q1 (or q2) would increase by a larger amount and the left-alone firm’s demand q2 (or q1) might also increase, depending on the price elasticities of demands. Then, pS1 (or pS2 ), and pB1 (or pB2 ) might increase. The same applies to the case in which both the pairs collude, with both merged firms disputing in identical conditions the demand from the consumers that did not fly before the collusion, i.e., for whom the sum of the flight’s price plus the total transportation costs would exceed their reservation price. 3. Methodology 3.1. Choice of observations and data The whole sample contains up to 514 pairs of airport–airline. Some pairs have been eliminated, specifically those such that: (i) the airport has a number of passengers less than 500,000 per year, in order to withdraw very small airports; and (ii) the dominant airline has less than 40% share in the airport traffic. Then, two subsamples have been considered: one for CA’s and one for NCA’s. For an airport to be included in the CA’s subsample three further conditions must be met. The first condition is that there is another dominated airport in a ray of 100 km offering more than 500,000 seats per year. The distance threshold is coherent with previous literature (Fewings, 1999; Fuellhart, 2003; Malighetti et al., 2008). The second condition is that there must be two different dominant carriers in the two nearby airports. If the same dominant carrier is operating in both the airports, competition is not effective. The third condition is that the two dominant carriers in the nearby airports must offer at least 10 alternative routes. We assume that two nearby airports offer an alternative route when they connect to the same destination airport or to two different airports whose distance is less than 100 km (ICCSAI, 2010). So, the third condition is essential to select pairs of airport-carrier that significantly compete for the same origin–destination markets. If the three conditions are not jointly met, the airport is classified in the NCA’s subsample. We obtain 95 pairs in the NCA’s subsample. However, for the empirical analysis we considered only 36 pairs for which all data is available. Table 1 shows the list of airports considered and the reason for inclusion in the NCA’s sample. In most of the cases there are other potential competing airports in a ray of 100 km but those airports are not dominated by an airline. Airports considered are located with the following distribution: 1 in Austria, 1 in Malta, 1 in Iceland, 1 in France, 2 in Portugal, 2 in Ireland, 2 in Germany, 3 in Italy, 2 in the UK, 8 in Spain and 13 in Scandinavia. We decided to limit the empirical analysis to the case of non competing airports, where the number of observations allows some statistical analyses. Indeed, CA’s, observations sum up to only 24 potential cases within the limits defined above.8 Furthermore, in some of these cases there were no available data, which reduced still more our sample at less than 10 observations. Preliminary data appeared heterogeneous, with inferences without statistical power, and potentially misleading. Fares offered by carriers from dominated airports were collected directly from the airlines’ web sites. In particular, for each route, we collected fares information related to all flights offered in 2 months from 30th March to 30th May, 2011. For each flight, we considered fares offered from 30 days before departure to the day before the departure. For each pair airport–airline we then calculated the average fare, weighted by the seats capacity of the offered routes. All other data is from 2010 and has been collected from airlines’ and airports’ websites. So we have a gap of a few months between data for costs and airports’ revenues (of December 2010) and data for airlines’ prices (flights for May 2011). 3.2. Choice of variables 3.2.1. Airports’ and airlines’ marginal costs, revenues and fares In order to find airlines’ and airports’ marginal costs, it is adequate to estimate a cost function. On account of lack of the needed data, we shall use operating costs as a proxy for marginal cost, as it has been done in other studies (Brander and Zhang, 1990). These authors argue that there are a priori reasons for a concave relationship between operating cost and distance. However, Swan and Adler (2006), with a comprehensive sample for 5 years and 18 aircraft models, find out that the operating costs’ relationship is linear either with distance or with seat capacity and this happens for both long and short-haul flights. Furthermore, according to Swan and Adler (2006) it happens that some reasons 8

Some of them were competitive trios.

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Table 1 List of airport included in NCA sample.

a

Airport code

Airline code

Country code

2010 traffic passenger (‘000)

Share 1st airline (%)

Reason for inclusion in NCA sample

MAD MUC CPH VIE OSL DUB ARN LIS HEL BGY BGO GRO CIA EMA PSA TRD SVG MLA NRN FNC KEF PIK TOS BOO SNN REU OVD MJV VGO LCG LLA BIQ SDR KRS AES OUL

IB LH SK OS SK FR SK TP AY FR SK FR FR FR FR SK SK KM FR TP FI FR SK SK FR FR IB FR IB IB SK AF FR SK SK AY

ES DE DK AT NO IE SE PT FI IT NO ES IT GB IT NO NO MT DE PT IS GB NO NO IE ES ES ES ES ES SE FR ES NO NO FI

49,786 34,722 21,452 19,691 19,091 18,431 16,988 14,045 12,843 7675 4860 4846 4532 4157 4062 3521 3468 3305 2896 2233 1791 1662 1649 1612 1476 1407 1351 1349 1093 1085 992 989 917 839 832 699

48 59 45 49 44 45 42 61 61 80 51 98 76 48 62 47 49 59 98 47 75 94 47 42 65 88 52 41 45 44 52 63 62 67 72 63

x

No close airportsa

Competing airports not dominated

Same dominant airlines

Not enough overlapping routes

x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x

No airport bigger than threshold chosen (500,000 annual seats).

that could cause concavity (such as higher fuel consumption while landing and taking-off) may be offset by other factors that lead to a linear relationship with distance. Using a ‘‘planar’’ form, the authors regress operating costs on distance and seat density and find noticeably different estimated parameters for long and short haul. Airlines’ and airports’ marginal costs are operational costs net of airport charges in order to match the variables cB and cS in our model and have been collected from the companies’ annual reports displayed in their websites. Data for the number of passengers, airlines’ revenue passenger kilometer (RPK), aeronautical and other revenues are from the same sources. We considered the following variables for our empirical test: (i) airlines operational costs net of airport charges per passenger, cB; (ii) airports’ operational costs per passenger, cS; (iii) airports’ aeronautical revenues per passenger, T; (iv) average price for each particular route, P. As some airlines have a higher share of long haul flights than others and long haul flights have higher operational costs per passenger, we multiplied, in each case, the operational cost per Available Seat per Kilometer (ASKs) of a particular airline by the average distance of the routes we analyze, for each airport from where that airline departs. So, we obtain the average operational cost per passenger, net of airports charges, for that particular airline and for that particular airport.

3.2.2. Airlines’ price elasticities of demand There are many estimates of price elasticity of demand, which vary much across countries or regions, types of passengers, stage length and even over time. It is widely recognized that price elasticity is lower for business passengers and for long haul flights. Also, some authors point out that demand is getting more price sensitive over time (Brons et al., 2002; DFC, 2008). In this paper we are dealing with European airports. In Europe, international flights are mostly short and medium haul. Only intercontinental flights are long haul. Thus, international flights also have higher elasticities9, and we should focus on 9 For instance, passengers traveling through France, Belgium, Germany, and many other countries, as well as domestic passengers, have high speed rail’s, or simply fast trains’ alternatives, which increases the price elasticity of flying.

C. Barbot et al. / Transportation Research Part E 57 (2013) 3–15

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short and medium haul elasticities. We need some justification to neglect long haul elasticities. According to the ATRS Airport Benchmark Report (2009), only Heathrow has a share of intercontinental passengers (which for Europe is long haul) that is higher than 50% and in many airports of the sample this share is smaller than one third. ICCSAI (2010) confirms these data, showing than only Heathrow surpasses the 50% share of intercontinental passengers and other airports present much smaller shares. Moreover, 50% of airlines’ ASKs were, in 2007, for intra-Europe flights (AEA, 2007), which confirms the focus on short and medium haul flights’ price elasticity. Our task is to find appropriate elasticities for passengers flying in European airlines. In what regards existing estimates and studies, DFC (2008) is the most comprehensive study of existing research in price elasticity of demand. This paper provides a survey of 21 studies and 254 estimates of airlines’ demand price elasticity. Specifically, DFC (2008)’s survey includes estimates mostly for countries or regions outside Europe (mainly for the US and Australia), while our work should focus on European estimates. However, Brons et al. (2002) find that European price elasticity is not significantly different from other regions of the world. A priori, there may not be a large difference in consumers’ behavior, but the difference may lay on the nature of flights. In the US and Australia, long haul flights may be domestic, while this does not happen in Europe. Also, some authors point out that demand is getting more price sensitive over time (Brons et al., 2002; DFC, 2008), which lends more relevance to recent studies. DFC (2008), while surveying several estimates of demand price elasticity, gives strong arguments in favor of using the median (and not the mean) of the values found in the different studies. Their findings for the median of elasticities and for the cases that we are covering are, for all studies, equal to: (1) 0.857 in the case of long haul routes; 1.150 in the case of short and medium haul. DFC (2008) scores studies according to several factors, which include, among others, the use of intermodal competition, age or methodology and find medians, for the highest scored studies, equal to: (1) 1.52 in the case of short and medium haul routes and leisure passengers; (2) 0.7 in the case of short and medium haul routes and business passengers; (3) 0.265 in the case of long haul and international routes and business passengers; (4) 1.040 in the case of long haul and international routes and leisure passengers. Other studies have found not very different values. Brons et al. (2002) develop a meta-analysis of airlines price elasticity estimates (from 37 studies) and find a mean value of price elasticity of demand equal to 1.146 for all types of passengers. However, the mean value for business passengers equal to 0.8. Castelli et al. (2003) introduce a multilevel analysis using data for 9 routes – of which only one route is domestic – served by an Italian regional airline. These authors find a price elasticity of demand equal to -1.058. Kontas and Mylonakis (2009), using data for European airlines related to the period 1993–2007, find a value for demand elasticity equal to 1.24 for their one-stage game and 1.28 for their two-stage game. Ernst and Young (2007) is, to the authors’ knowledge, the only study based on a review of existing research that finds a value for low cost airlines’ price elasticity of demand. Specifically, this value is equal to of 1.5. Moreover, price elasticity is equal to 0.8 for business passengers and 1.5 for short haul leisure passengers. In face of these estimates, it seems reasonable to use an elasticity equal to 1.5 (Ernst and Young, 2007) for low cost airlines, which carry mostly leisure passengers. Moreover, this value is confirmed by the findings of DFC (2008) for short and medium haul leisure, that is equal to 1.52. The main problem is to find the adequate value for full service carriers’ short and medium haul flights. DFC (2008) finds a median value equal to 1.15 for this stage length and for all studies. But we may suspect that this elasticity could be higher, as there is evidence, as referred above, that consumers are getting more price sensitive, switching to low cost carriers. Moreover, in Europe there are more railway alternatives than in other countries, as in the case of Australia, which provides an important source of DFC (2008)’s studies. In other words, the level of intermodal competition is higher. We first should have data on the percentage of business and leisure passengers choosing full service carriers. These data is scarce and so it is difficult to find. We have found only indirect evidence. Njegovan (2005) reports data from the United Kingdom Civil Aviation Authority, which shows that, in 2003, business travelers, both from the United Kingdom and from foreign countries, totalized 18% of all United Kingdom airports’ passengers. In 2005, the United Kingdom registered the largest share, equal to 25%, of all low-cost movements (Eurocontrol, 2005). According to the same study, low cost carriers had a market share of 13.1% in Europe, in the same year. A simple exercise, using these data, leads to a percentage of business travelers in FSCs of 23% (and of 77% for leisure passengers) in the UK. Applying these weights to the elasticity values found in DFT (2008)’s most recent studies, we get a price elasticity of demand of 1.33 for short and medium haul leisure and business segments. This value is higher than those found in the more recent studies for Europe that we have referred above. But, though it is computed only using the United Kingdom market segmentation between leisure and business passengers, it is based on a European country and on the median value of a very comprehensive study (DFC, 2008). Considering all the values mentioned above, we use Kontas and Mylonakis’s (2009) lowest value, equal to 1.24, as it uses data computed for a well calibrated, quite general and very recent sample: European airlines and for the period 1993–2007. Additionally, this value is near other estimates. Concluding, our selected values for the price elasticity of demand are: 1.5 in the case of low cost carriers and 1.24 in the case of full service carriers. We then perform a sensitivity analysis which will bring more consistency to our results. 3.3. Comparative evaluation of the two tests Our first test (Test I) is:

P  cB  cS 1 ¼ e P B S Pc c 1 > e P

collusion no collusion

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C. Barbot et al. / Transportation Research Part E 57 (2013) 3–15 Table 2 Results for Test II. Airport

Airline

Test II

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36

A B C D E E F F F F G H H H H H H H H H H H H I I I I I I I I I I I J J

0.02582 0.00764 0.01419 0.00649 0.00012 0.00005 0.01381 0.01673 0.00469 0.01553 0.02753 0.05295 0.07376 0.16429 0.07337 0.12647 0.05717 0.04470 0.00841 0.00463 0.04128 0.04651 0.10591 0.03513 0.03795 0.03715 0.04213 0.04336 0.04064 0.02738 0.03913 0.00720 0.04156 0.05102 0.02220 0.00650

The second test (Test II) is:

T  cS ¼ 0 collusion P S Tc > 0 no collusion P

Notice that Test II does not depend on some assumptions: (i) E = e. (ii) On the chosen value for the price elasticity of demand. Thus, Test II may be more accurate. In particular, there are no a priori problems – from an empirical point of view – in assuming a linear demand, but results depend much on the selected value for price elasticity. Oum et al. (1992) provide a valuable list of pitfalls that occur when demand models are estimated and therefore affect the interpretation of the elasticity estimates from these empirical studies. Specifically, they find that one of these is just the choice of the functional form. Anyway most studies of estimation of air travel demand use a linear or log-linear functional specification (Gillen et al., 2003).10 Then, assuming E = e in addition to linear approximation does not seem consistent to literature. Thus, though we perform both tests, we must bear in mind the problems associated with Test I and that Test II is more reliable. 10 Some estimations are linear: Andrikopoulos and Terovitis (1983), Bureau of Transport Communications and Economics, Australia (1995), Battersby and Oczkowski (2001); while others are log-linear: Ippolito (1981), Agarwal and Talley (1985), Oum et al. (1986), Talley and Schwarz-Miller (1988) and Hamal (1998).

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C. Barbot et al. / Transportation Research Part E 57 (2013) 3–15 Table 3 Comparison evaluation of the test. Test II Values

Case 1 NA/small

Case 2 NA/hub

Case 3 LCC/SEC

Case 4 LCC/small

Number of cases <1% <3%

1 6

6 9

3 3

0 0

% of Cases <1% <3%

7% 43%

60% 90%

100% 100%

0% 0%

NA: National Airline; LCC: Low Cost Carrier; small: Small Airport; hub: Hub Airport; SEC: Secondary Airports.

4. Results As we anticipated, we found that Test I is rather inconclusive11. Therefore, we do not show the results of Test I here as they S may be misleading when compared to those of Test II. Results with respect to Test II are obtained for Tc and they are presented P in Table 2. Instead of revealing their real names, we represent airports by numbers and airlines by letters. In 2010, only 4 airports had a positive difference ‘‘aeronautical revenues – operational cost’’. However, whether the sign is positive or negative does not make difference, as we are looking for values that are near zero (collusion) or that are significantly different from zero (non collusion). In fact, we need to have a limit margin of the difference between the resulting values for each pair of airport – airline and zero, so that we may distinguish collusion from non collusion cases. There is no theoretical limit for this value. Strictly following our theoretical results, we would have the values of ‘‘zero’’ and ‘‘non zero’’. However, in the empirical study, values exactly equal to zero are not obtained, but only values near zero. As for Test II, we analyzed results with two different criteria: the value of the critical variable of Test II smaller than 1% and smaller than 3%. We then divided our pairs of airport – airline in 4 subsets: (i) Main national carriers in small towns of their own countries, i.e.14 cases. This situation depicts domestic regional markets of national carriers (or their regional subsidiaries) which operate, often as monopolies, services between small towns and one of the countries’ hubs. Examples can be Iberia linking small towns to Madrid and Barcelona or Air France linking Paris to other French towns.12 (ii) Main national carriers in their main hubs, i.e. 10 cases. In Europe, national carriers were the first to operate their main hubs and usually keep in those airports a large share of slots, so that this situation is still quite common. An example can be TAP in Lisbon, where the airline carried 56% of all Lisbon airport’s passengers in 2011. (iii) Low cost carriers in secondary airports, which is the case of low cost airports serving metropolitan areas or large towns where they compete with main hub airports., i.e.3 cases; This could be the case of Rome Ciampino or Frankfurt Hahn airports. (iv) Low cost carriers in small airports, i.e. 10 cases. Low cost carriers often use regional airports – or former military bases changed into regional airports – that had excess capacity and adapted these airports to their own needs, obtaining there a large share of flights and passengers. Moreover, low cost carriers’ network strategy includes ‘‘point to point’’ flights between small towns. Table 3 shows the distribution of results with respect to the four subsets. Though our sample is small and results should be carefully interpreted within this limitation, they suggest that collusion is more likely to occur in cases 2 and 3, with national airlines in their hubs and with low cost carriers in secondary airports. Values suggest the possibility of collusion, respectively, in 90% and 100% of the airport – airline pairs and for a limit of 3%. Small airports seem to be less favoring collusion, probably because the volume of traffic is much lower and so the change in profits is not so important. Our values are only lower than 3% in 7% of the airport–airline pairs for national airlines and never for low cost carriers. 5. Conclusion In this paper, we develop a test to identify vertical collusion between airports and airlines, based on the evaluation of price–costs margins. We both model the case of competing and non competing airports. In the empirical study, we test the case of non competing airports and find that two situations are more likely to be favoring collusion: national flag airlines in their hub airports, e.g. Lufthansa in Munich, and low cost carriers in secondary airports, e.g. Ryanair at Rome Ciampino. Indeed, an increasing number of minor and regional airports rely, now, on the operations of low-cost carriers, which use a 11 This may be due to the fact that this test depends on elasticity values, which are different for each particular flight and it is impossible to estimate values for all flights. 12 These examples do not necessarily match our sample, where we keep airports and airlines unnamed.

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business model that has a relevant cost driver in airport costs and enables them to shop around airports. This allows LCCs to achieve higher bargaining power and to turn the airport–airline relation into a bilateral-monopoly. The paper adds to literature as it provides a sufficient condition to detect vertical collusion: the cases we identify as favoring ‘‘collusion’’ can be or not real situations of collusion between the airline and the airport, while in the non collusion cases the situation may be taken as granted. The results raise some policy issues and avenues for future research. In particular, a merger implies a downstream market foreclosure through a price-squeeze strategy. On one hand, a vertical collusion is anti-competitive. On the other hand, consumers’ surplus and welfare increase with respect to the case in which no agreement occurs: indeed, final quantities increase and final prices for consumers decrease because of the internalization of vertical externalities due to a double-marginalization effect. Therefore, the agreement exhibits a trade-off between competitiveness and welfare. Then, the problem of vertical collusion constitutes a fundamental issue because of the ensuing regulatory requirements. In this context, our paper provides a method for authorities to identify the situations where collusion may be occurring and this particularly helps in the investigation of these cases. In fact, it is difficult to detect vertical collusion and authorities usually start an investigation whenever there is a denunciation from some party that has suffered damages on account of illegal agreements or when observed prices clearly show price discrimination. Further developments of the present work may go along within the scope of policy implications. In particular, the aforementioned effects of anti-competitiveness should be evaluated, even in the light of an increasing competition between airports. Acknowledgments We are very grateful to two anonymous referees for their helpful suggestions and to David Gillen, Nicole Adler and Anming Zhang for their perceptive comments on earlier version(s) of the paper. We also thank seminar participants at Center for Transportation Studies, University of British Columbia and at the 15th ATRS – Air Transport Research Society, Annual Conference for further insightful comments and discussions. References Association of European Airlines (AEA), 2007. Operating Economy of AEA Airlines: Summary Report. . Agarwal, V., Talley, W.K., 1985. The demand for international air passenger service provided by U.S. air carriers. International Journal of Transport Economics 12 (1), 63–70. 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