Airport Pricing, Concession Revenues and Passenger Types

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Journal of Transport Economics and Policy, Volume 47, Part 1, January 2013, pp. 71–89

Airport Pricing, Concession Revenues and Passenger Types Tiziana D’Alfonso, Changmin Jiang, and Yulai Wan

Address for correspondence: Tiziana D’Alfonso, Department of Economics and Technology Management, University of Bergamo, Dalmine, Italy; also at Department of Informatics, Control and Management Engineering, Sapienza Universita` di Roma, Rome, Italy ([email protected] unibg.it). Changmin Jiang and Yulai Wan are at Sauder School of Business, University of British Columbia, Vancouver, BC, Canada. We thank the editor, David Starkie, and an anonymous referee for their constructive comments on a previous draft of this paper. We are also very grateful to Anming Zhang, Achim Czerny, and seminar participants at CTS, University of British Columbia, for further insightful suggestions.

Abstract We study airport pricing with aeronautical and concession activities, incorporating a positive relationship between delay and consumption of concession goods, and the effect of passenger types. We assume that as congestion increases, dwell time increases — and the money spent in concession activities — and we find: (i) there is a downward correction on the congestion toll due to the positive externality of delay; (ii) the component relevant to the per-passenger benefit from concessions may be a mark-up depending on delay and the passengers’ values of time. Furthermore, a welfare-maximising airport may have more incentives to induce congestion than a profit-maximising airport.

Date of receipt of final manuscript: December 2011

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1.0 Introduction Air traffic delay has been growing dramatically since the end of the 1990s. The delay problem has been widely discussed in policy circles: increasing the capacity of congested airports by investing in new runways or improving air traffic control technology is one possible remedy. Another solution is the imposition of congestion pricing, according to which the landing fees paid by airlines would vary with the level of congestion at the airport. Meanwhile, non-aeronautical revenues have been growing significantly to the point that they have become the main income source for many airports (Graham, 2009; Morrison, 2009). For these reasons, the impact of non-aeronautical revenues on airport pricing is of increasing concern for airport and airline management. With respect to the issue of airport congestion pricing, literature finds a negative relationship between the socially optimal airport charge and airlines’ market concentration (Brueckner, 2002; Pels and Verhoef, 2004; Zhang and Zhang, 2006; Basso and Zhang, 2007; Basso, 2008; Brueckner and Van Dender, 2008). The socially optimal charge should include only the residual share of the marginal congestion cost1 that is not internalised by monopoly or oligopoly carriers, and it should be reduced to correct for market power of airlines. On the other hand, concession revenues exert a downward pressure on the aeronautical charge (Starkie, 2002, 2008; Zhang and Zhang, 2003, 2010; Oum et al., 2004; Yang and Zhang, 2011). Commercial operations tend to be more profitable than aeronautical operations (Jones et al., 1993; Starkie, 2001); therefore, the aeronautical charge should be reduced so as to induce a higher volume of passengers and increase the demand for concessions. However, in order to have a more complete picture of optimal airport pricing, two more aspects of the air transport business should be incorporated into the analysis. First, passengers may not be a homogeneous group of individuals. Literature finds that, in the case of a single passenger type, the socially optimal charge never exceeds the residual share of the marginal congestion cost (Brueckner, 2002; Zhang and Zhang, 2006; Basso and Zhang, 2007). Czerny and Zhang (2010) find that, in the case of two types of passengers with different values of time, the socially efficient airport charge may exceed the residual share of the marginal congestion cost. Intuitively, their result implies that it can be useful to increase airport charge so as to protect business passengers with higher time value from excessive congestion caused by leisure passengers with lower time value. Second, there is a positive correlation between the expenditure in the concessions area and the dwell time (that is, the time available between the security check and the boarding): it is during that time that passengers will have a higher chance to shop. This follows the common sense that more spare time gives more opportunity for browsing in the shops and induces the need to buy refreshment. Hence, the expenditure increases as the dwell time increases. Congestion levels may have an impact on the dwell time, and therefore on the expenditure in the commercial area; but, without solid empirical studies in the literature, it is unclear whether increased congestion has a negative or positive effect. The higher the volume of passengers, the longer the time needed for check-in and 1

The residual share is equal to ð1  1=nÞ, where n represents the number of airlines.

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security checks. As a result, on one hand, it would be obvious that dwell time decreases as congestion goes up, since passengers spend more time in queues. However, on the other hand, higher congestion may force travellers to arrive in advance at airport terminals because they anticipate longer waiting time in queues (Appold et al., 2006; Buendia and de Barros, 2008). This can happen when air travellers are risk-averse, especially when the cost of missing a flight is relatively high: business passengers may miss important business opportunities; leisure passengers may have to cancel hotel and trip reservations whose costs cannot be fully recovered. In this context, if this amount of extra time they spend in the airport is disproportionally longer than the expected extra time they need to go through check-in and security checks, dwell time will increase: passengers will have more captive time in terminals and more time to spend money in shops. Specifically, in this paper, we assume that passengers will exaggerate waiting time and therefore dwell time increases. In other words, we assume that as congestion increases, dwell time increases and so the money spent in concession activities; equivalently, that there is a positive externality of congestion on concession activities. Hence, under this assumption, when concessions are taken into account, there can be some incentives for the airport to increase congestion in order to drive up the expenditure in the commercial area. There is a stream of empirical literature trying to explore this issue. Geuens et al. (2004) find that waiting time influences consumption of concession goods. CastilloManzana (2010) finds that the dwell time prior to embarking is positively correlated with the decisions of consuming food/beverages and making a purchase at a significance level of 99 per cent in both cases. Besides, he finds that being on vacation increases the likelihood of consuming concession goods. Moreover, the average expenditure of these passengers is greater than that of business passengers. Torres et al. (2005) show that the more time spent in the airport, the more consumption made by passengers. In addition, he finds that those flying on business consume more than those on vacation, if they are in the airport for less than 45 minutes. In the range of 45–170 minutes, leisure travellers consume more. When staying longer than 170 minutes, business travellers consume more. Graham (2008) finds that young leisure passengers are high spenders, while business passengers are unlikely shoppers. However, to the best of our knowledge, there is no contribution in literature analysing, from a theoretical point of view, the effects of congestion and passenger types on consumption of concession goods. This paper adds to literature on airport pricing as it takes into account the positive externality of congestion on concessions, through its impact on dwell time, while incorporating the effect of passenger types. Specifically, we consider a model with one congestible airport serving a number of competing airlines and two types of passengers — business and leisure — with the former having a higher time value than the latter. We consider two types of airports, namely private airports maximising their profits and public airports maximising social welfare. We assume that only the extra surplus generated by airport concession services not attainable elsewhere is counted into the social welfare function. In other words, we only include a proportion of the surplus from concession services. This reconciles two approaches to modelling the social welfare function in airport pricing literature: if the proportion is equal to one, all the surplus from concession activities is counted into social welfare (Zhang and Zhang, 2003, 2010; Yang and Zhang, 2011); if the proportion is equal to zero, surplus from concession activities is excluded (Czerny, 2011; Kratzsch and Sieg, 2011).

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We find that for both profit- and welfare-maximising airports there is a downward correction for the congestion toll, equal to the marginal airport concession profit and passengers’ concession surplus, respectively, due to the positive externality of delay. Furthermore, as the passenger volume changes when the airport charge increases, there is a correction on the optimal airport charge equal to the average concession profit and expected concession surplus for profit- and welfare-maximising, respectively, weighted for different passenger types. For some levels of delay this correction may not be a traditional mark-down but a mark-up. Finally, the comparison between privately and socially optimal airport charges shows that: (i) when concessions generate a sufficiently high proportion of extra surplus to total concession surplus, the welfare-maximising airport can have more incentives than the profit-maximising airport to decrease the congestion toll and induce delay; and (ii) depending on the difference in the passengers’ values of time and the proportion of extra surplus generated by airport concessions, the profitmaximising airport may or may not impose a higher charge than the welfare-maximising airport. The structure of the paper is as follows. Section 2 sets up the model. Sections 3 and 4 discuss, respectively, airlines’ and airports’ equilibrium behaviours. Section 5 contains concluding remarks.

2.0 The Model Consider a single airport, n competing airlines, and two types of passengers, one of which has a higher time value than the other. For the sake of convenience, in our analysis we refer to them as business and leisure passengers, because Morrison (1987) and Pels et al. (2003), among others, provide empirical evidence that business passengers have a greater value of time than leisure passengers. We denote the business and leisure passengers’ value of time as vB and vL , respectively, with vB 5 vL > 0. Let QB and QL be the number of business and leisure passengers at the airport. Moreover, BB ðQB Þ and BL ðQL Þ represent the gross benefit from travelling, for business and leisure passengers, respectively, where B 0h > 0 for h 2 fB, Lg. For analytical tractability, we assume linear demand functions, which give: B 0h ðQh Þ ¼ ah  bh Qh ,

ð1Þ

where aB 5 aL > 0 (that is, the willingness of business passengers to pay for air travel is greater than that of leisure passengers); and bB 5 bL > 0 (that is, leisure passengers are more price-sensitive than business passengers). The airport is congestible: the average ~, KÞ, depends on the total number of flights, Q ~, and the airport’s congestion delay, DðQ capacity, K. With these specifications, we have: ~ , KÞ, B 0h ðQh Þ ¼ ph þ vh DðQ

ð2Þ

where ph is the airline ticket price for type h passengers. We use the same linear delay function as the one in Basso and Zhang (2007) and De Borger and Van Dender (2006).2 2

Such a linear delay function makes the analytical work more feasible, but it may lead to the problem that an interior solution may not exist – that is, we may have a corner solution. Nevertheless, we assume an interior solution.

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~, KÞ ¼ yðQ ~=KÞ, where y is a positive parameter. Specifically, let Q be the That is, DðQ number of passengers of all airlines. We assume, as is common in the airport pricing literature, a fixed proportion condition. That is, all the flights use identical aircraft and have the same load factor (Basso, 2008; Basso and Zhang, 2007; Brueckner, 2002; Pels and Verhoef, 2004; Zhang and Zhang, 2006, 2010). Therefore, each flight has an equal ~ and we obtain: number of passengers, denoted by S. Then, Q=S ¼ Q   ~; K ¼ y Q . D Q KS

ð3Þ

Furthermore, without loss of generality,   we normalise KS ¼ 1. Therefore, we can use, in ~, K . From equations (1)–(3), it follows that: what follows, DðQÞ instead of D Q ph ðQB , QL Þ ¼ ah  bh Qh  vh yQ.

ð4Þ

Carriers are ex-ante symmetric and offer a homogeneous good/service — the flight. Let qih denote the number of type h passengers served by airline i, for h 2 fB, Lg and i ¼ 1, 2, . . . , n. Let qi be airline i ’s P output — the totalPnumber of passengers who fly i i n i with Pairline i. Therefore, q ¼ h 2 fB;Lg qh , Qh ¼ i ¼ 1 qh , for h 2 fB, Lg and P Pn i Q ¼ h 2 fB;Lg , Qh ¼ h 2 fB;Lg i ¼ 1 qh . Next, we specify the passengers’ demand for concessions. In particular, we assume that demand for retail services depends on travel activities. In other words, we suppose that passengers make two separate decisions sequentially. First, they book the air tickets from the airlines, based on their perceived full prices; second, after arriving at the airport, they make decisions on purchasing concession goods. Our specification of the concession demand is related to, but different from, Yang and Zhang (2011), according to whom a passenger will consume one unit of the concession goods if their valuation is greater than the concession price. We suppose that the passengers’ valuation for the concession goods has a positive support on the interval ½0, u, where u is the highest valuation for the concession goods. We consider two random variables, uB and uL , representing, respectively, the valuations for the concession goods of business passengers and leisure passengers. We assume that uh is distributed with probability density function gh ðu; TÞ, given a specific level of dwell time, T. As we noted in the introduction, we assume that as congestion increases, dwell time increases as well, because passengers will exaggerate waiting time. Equivalently, we assume that the dwell time, T ¼ TðDÞ, is an increasing function of congestion. Therefore, we can use, in what follows, gh ðu; DÞ instead of gh ðu; T ðDÞÞ. Let Gh ðu; DÞ be the cumulative distribution function of type h passengers’ valuation. In this scenario, the probability that a type h passenger buys the concession goods at the price pc isR equal to the probability that their valuation for the good is greater than pc ; that is, puch gh ðp; DÞdp ¼ Gh ð pc ; DÞ, where Gh ðu; DÞ ¼ 1  Gh ðu; DÞ. With this setup we want to catch the relationship between congestion and the probability of purchasing, through the dwell time. As noted by an anonymous referee, it is possible that at some point concession revenues are adversely affected by congestion and waiting time: first, congestion may reduce the comfort level of shopping, affecting patronage of shops and restaurants; second, it may increase the stress level of passengers — passengers may get unnerved by waiting. A congested airport may simply not make the passengers relaxed enough to shop (Graham, 2009). On the other hand, for some people waiting may cause annoyance, leading them to search for comfort from shopping. In this paper,

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we assume that the impact of people finding relaxation in shopping is enough to offset that of unnerved passengers; that is, the extra dwell time leads to more retail activity. This is equivalent to assume that the probability of purchasing increases as the delay increases. In other words, Gh ðu; DÞ satisfies the first-order stochastic dominance property (FOSD) with respect to D; that is, @Gh ðu; DÞ[email protected] 4 0, with a strict inequality for some ~Þ, 8D > D ~; the value of u.3 From the FOSD property, we have that Gh ðpc ; DÞ 5 Gh ðpc ; D probability of purchasing a unit of concession goods increases with the delay. We further assume that the positive externality of delay decreases when the concession price increases: @ 2 Gh ð pc ; DÞ[email protected] @D < 0. Therefore, the concession demand function of the type h passengers, xh , is given by: xh ð pc , Qh , Qh Þ ¼ Qh Gh ð pc ; DðQh , Qh ÞÞ. ð5Þ In other words, the demand for non-aviation activities of type h passengers depends on the number of type h travellers, Qh , the concession price, pc , and the delay, DðQh , Qh Þ: The airport charges airlines a price per passenger, denoted as pa . For simplicity of presentation, the case where the airport has zero fixed costs is considered (that is, the only cost the airport bears is the operating cost per passenger, ca ).4 Since we consider  ex-ante symmetric carriers, the cost function of carrier i is given by C i qi , qi ¼ ðc þ pa þ bDðQÞÞqi , where c is the (constant) marginal operating cost and b is the value of time of carriers. Suppose that the airport provides concessions to (homogeneous) retailers and that the airport itself determines the concession price pc . Finally, we assume that the airport captures all the rents from the retailers and that the unit cost of the concession goods is constant and denoted by cc : The airport-airline vertical structure is modelled as a two-stage game. In the first stage, the airport decides both the aeronautical charge, pa , and the concession price, pc . In the second stage, taking pa as given, airlines compete in Cournot fashion5 and simultaneously choose their outputs: the number of passengers.

3.0 Airlines’ Equilibrium Behaviour In the second stage, each airline chooses its output to maximise its profit: X pi ¼ qih ½ ph ðQB , QL Þ  c  pa  bDðQÞ.

ð6Þ

h 2fB;Lg

3

This property means that for all u~ 2 ½0, u, the probability that u 4 u~ is weakly and sometimes strictly decreasing in delay; that is, gh ð ; DÞ shifts rightward when delay increases. 4 The qualitative results of this analysis, however, are unchanged since we assume there are no economies of scale as well as economies of scope. 5 Earlier studies that model a congestible airport serving air carriers with market power assume Cournot behaviour (Brueckner, 2002; Pels and Verhoef, 2004; Czerny, 2006; Zhang and Zhang 2006, 2010; Basso and Zhang, 2007). Brander and Zhang (1990, 1993) find that the Cournot model seems much more consistent with the data than either the Bertrand or the cartel model. On the other hand, Neven et al. (1999) provide evidence that the estimated conduct in the airline market is consistent not with Cournot, but with Bertrand. However, there is a theoretical justification for assuming Cournot behaviour: if firms first make pre-commitment of quantity, and then compete in prices, the equilibrium outcome will be equivalent to that of Cournot competition (Kreps and Scheinkman, 1983).

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To focus on the effect of the positive externality of congestion, we abstract away the possibility of price discrimination: all passengers pay a uniform airfare, p. Therefore, at the equilibrium, the condition pB ¼ pL ¼ p must be satisfied. That is: pðQB , QL Þ ¼ ah  bh Qh  vh yQ. The equilibrium outputs are determined by the first-order conditions:   @pi @p  by qi  byQ  c  pa ¼ 0, ¼ p þ 8i; h: @Q @qih Symmetry implies that: pþ

  1p 1  1 þ byQ  c  pa ¼ 0, ne n

ð7Þ

ð8Þ

ð9Þ

where Q=n ¼ qi and e ¼ ð@[email protected]Þð p=QÞ is the elasticity of demand for airline services with respect to the ticket price. The effect of the ticket price p on Q, QB and QL is summarised in Lemma 1. Lemma 1.

dQ=dp < 0, dQL =dp < 0, while the sign of dQB =dp is ambiguous.

Therefore, an increase in the ticket price leads to a decrease in the total number of passengers and the number of leisure passengers, but it can lead to an increase or a decrease in the number of business passengers. The proof of Lemma 1 is given in the Appendix. Let Q ðpa Þ denote the equilibrium total number of passengers, QB ðpa Þ the equilibrium number of business passengers, QL ðpa Þ the equilibrium number of leisure passengers, and p ð pa Þ the equilibrium airlines ticket price. The comparative static of these equilibrium outcomes with respect to the airport charge, pa , is summarised in Lemma 2. Lemma 2. dQ =dpa < 0, dQL =dpa < 0, dp =dpa > 0, while the sign of dQB =dpa is ambiguous. Therefore, an increase in the airport charge leads to a decrease in the equilibrium total number of passengers and the number of leisure passengers, and an increase in the equilibrium airlines ticket price, but it can lead to an increase or a decrease in the equilibrium number of business passengers. The proof of Lemma 2 is given in the Appendix.

4.0 Airport Pricing Taking the second-stage airlines’ behaviour into account, the airport chooses pc , the concession price, and pa , the charge for airlines. We consider two types of airports, namely a private airport which maximises its profit and a public airport which is a welfare-maximiser. 4.1 Profit-maximising airport Consider a private airport maximising its profit:

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pA ¼ ð pa  ca ÞQ þ ðpc  cc Þ

X

Qh Gh ð pc ; DðQÞÞ:

ð10Þ

h 2fB;Lg

The optimal concession price is characterised by the first-order condition with respect to pc :          QB GB p c ; DðQ Þ þ QL GL pc ; DðQ Þ  ð11Þ pc ¼ c c    ,    @ GL ðpc ; DðQ ÞÞ  @ GB ðpc ; DðQ ÞÞ QB þ QL @pc @pc where the superscript  represents the profit-maximisation case. Since @ Gh ð pc ; DðQÞÞ= @pc < 0 with h 2 fB, Lg, a profit-maximising airport sets the optimal concession price above the marginal concession cost and, in particular, equal to the monopoly price. The profit-maximising airport charge is characterised by the first-order condition with respect to pa :      1 1 vB bL þ vL bB   p  c ¼ y 1 þ þ 1 þ Q bQ a a n n bL þ bB    X 1 bL bB @ Gh ðp c ; DðQ ÞÞ Q  yð p Qh þ 1þ c  cc Þ n bL þ bB @D h 2fB;Lg  ð p c  cc Þ

dp X dQh   G ðp ; DðQ ÞÞ: dQ h 2fB;Lg dp h c

ð12Þ

The first line on the right-hand-side (RHS) of equation (12) can be reduced to the results in earlier literature where only one passenger type is considered (Zhang and Zhang, 2006). The second line consists of two terms which are the focus of this paper. The first term is a correction for the congestion toll equal to the marginal airport concession profit due to the positive externality of congestion on concession activities. Since @ Gh ð pc ; DðQÞÞ[email protected] > 0, this term is negative. Therefore, the airport has incentives to reduce the congestion toll so as to increase the passenger volume and the passengers’ waiting time. This means that, in contrast with previous literature, the congestion toll may become a ‘subsidy’, when the positive externality of congestion is taken into account. The above discussion leads to Proposition 1. Proposition 1. In the case of a profit-maximising airport, there is a downward correction for the congestion toll which is equivalent to the marginal concession profit due to the positive externality of delay. Therefore, the airport has incentives to reduce the aeronautical charge so as to increase passengers’ waiting time and thus their consumption of concession goods. The last term is a correction on the optimal airport charge equal to the per passenger concession profit weighted for different passenger types, where the weight is the ratio of the marginal change in the number of type h passengers over the marginal change in the total number of passengers. This term takes into account the change in the passenger volume and hence the pool of potential consumers of concession services when the airport charge increases. When passengers have the same value of time, this term is

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always negative as shown in previous literature ( for example, Zhang and Zhang, 2010; Yang and Zhang, 2011), but the sign of this term is no longer clear-cut when more than one type of passenger is considered. In particular, when dQB =dp > 0 (that is ðvB  vL Þy > bL ) and: dQB   ; D ð Q Þ GL p dp c 0. Therefore, equation (17) is satisfied if and only if @[email protected] ¼ 0, that is: 2 3 Z u   X @ G ð z; D ð Q Þ Þ h dz5 p ¼ ðc þ ca Þ þ y4vB QB þ vL QL þ 2bQ  d Qh W @D p c h 2fB;Lg  dð pc  c c Þ

X h 2fB;Lg

Qh

   @ Gh pW c ; D ðQ Þ @D

2

3 Z  W  d 4 X dQh u zgh ðz; DÞdz  cc Gh pc ; DðQÞ 5:  W dQ dp p c h 2fB;Lg dp

ð18Þ

Substituting equation (9) into equation (18), we derive the optimal airport charge, pW a :    1 1 vB bL þ vL bB      c ¼ y 1  þ v Q þ v Q  Q pW bQ a B B L L a n n bL þ bB Z u  X 1 bL bB @ Gh ðz; DðQ ÞÞ dp dz  d  Q  yd Qh n bL þ bB @D dQ cc h 2 fB;Lg X dQh Z u  ðz  cc Þgh ðz; DÞdz: dp cc h 2fB;Lg

ð19Þ

The first line on the RHS of equation (19) is the sum of the uninternalised marginal congestion cost for airlines, the marginal congestion cost for passengers, a correction for the internalised marginal congestion cost for passengers, and a correction for airlines’ market power. Similar to the case of a profit-maximising airport, the second line of equation (19) also contains two terms of interest when d > 0. The first term is again a downward correction for the congestion toll to internalise the positive externality of congestion on concessions, but this time it stems from the marginal increase in passenger concession surplus rather than the marginal increase in profit. Therefore, the airport can have incentives to reduce the congestion toll so as to increase the passenger volume and their waiting time. The above discussion can be summarised in Proposition 2. Proposition 2. In the case of a welfare-maximising airport, when concession services generate extra surplus, there is a downward correction for the congestion toll, which is equal to the marginal passenger concession surplus due to the positive externality of delay. Therefore, it can be useful to decrease the airport charge so as to increase passengers’ waiting time and thus their consumption of concession goods. The last term accounts for the per-passenger expected concession surplus, weighted for different passenger types. Unlike previous literature where this term is always negative, this is again no longer clear-cut when more R of passenger is considered. P than one type This can be seen as follows. Let ðDÞ ¼ h 2fB;Lg dQh =dp cuc ðz  cc Þgh ðz; DÞdz:

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Consider the case in which dQB =dp > 0. Since dQ=dp < 0, from Lemma 2, we have dQB =dp < dQL =dp. It follows that ðDÞ > 0 when: R u ðDÞ ¼

c ðz R uc cc ð z

dQB dp 0. Since vB > vL , we obtain dQL =dp < 0, while the sign of dQB =dp is undetermined. Since bB > 0 and bL > 0, we obtain dQ=dp < 0: Proof of Lemma 2 Differentiating equation (8) on both sides, we have: dQ ¼ dpa

n ð 1 þ nÞ

dp d2p þ Q 2  ð1 þ nÞby dQ dQ

< 0,

where d 2 p=dQ2 ¼ 0 as the inverse demand for air travel is linear and dp=dQ < 0 from Lemma 1. Therefore: dp dp dQ ¼ > 0: dpa dQ dpa From equations (7) and (8) we derive: ðaL  c  pa Þðð1 þ nÞðbB þ yðb þ vB ÞÞÞ  ðaB  c  pa Þðnyðb þ vL Þ þ yðb þ vB ÞÞ ; H ðaB  c  pa Þðð1 þ nÞðbL þ yðb þ vL ÞÞÞ  ðaL  c  pa Þðnyðb þ vB Þ þ yðb þ vL ÞÞ qi , B ¼ H qi L ¼

where: H ¼ ð1 þ nÞ2 ðbB þ yðb þ vB ÞÞðbL þ yðb þ vL ÞÞ  ðnyðb þ vB Þ þ yðb þ vL ÞÞðnyðb þ vL Þ þ yðb þ vB ÞÞ: Therefore, we obtain: dqi 1 L ¼ ½nyðvL  vB Þ  bB ð1 þ nÞ, dpa H dqi 1 B ¼ ½nyðvB  vL Þ  bL ð1 þ nÞ: dpa H From the concavity condition of airlines’ profit function, we derive: piBB piLL  piLB piBL ¼ 4½bB þ yðb þ vB Þ½bL þ yðb þ vL Þ  ½yðb þ vB Þ þ yðb þ vL Þ2 > 0, with piBB piLL  piLB piBL jn ¼ 1 ¼ Hjn ¼ 1 ¼ @[email protected] ¼ 1 : Therefore, when n ¼ 1, H > 0. Moreover: d 2H ¼ 2½bB þ yðb þ vB Þ½bL þ yðb þ vL Þ > 0; dn2 that is, @[email protected] is an increasing function of n. Therefore, we derive that H > 0 8n 5 1. i Since vB > vL , we have that dqi L =dpa < 0, but the sign for dqB =dpa is undetermined. Proof of Proposition 3 (1) Let 1 be the difference between the first terms of the second line of equations (12) and (19):

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1 ¼



p c

  Z u  X @ Gh p @ Gh ðz; DðQÞÞ c ; D ðQ Þ d dz:  cc Qh Qh @D @D cc h 2fB;Lg h 2fB;Lg  X

If d ¼ 1, we have: X

1 ¼

Z Qh

h 2fB;Lg



p c

cc

Z

X

Qh

Z

X

p c

cc

h 2fB;Lg



  Z u  X @ Gh p @ Gh ðz; DðQÞÞ c ; DðQÞ dz Qh dz   @D @D pc h 2fB;Lg

Qh

h 2fB;Lg

p c

cc

 Z pc    X @ Gh pc ; DðQÞ @ Gh ðz; DðQÞÞ dz dz < Qh @D @D cc h 2fB;Lg @ Gh ðz; DðQÞÞ dz: @D

Given @ 2 Gh ð pc ; DÞ[email protected] @D < 0, we have that:  Z pc    Z pc  X X @ Gh pc ; DðQÞ @ Gh ðz; DðQÞÞ dz < dz: Qh Qh @D @D cc cc h 2fB;Lg h 2fB;Lg Therefore, 1 < 0. If d ¼ 0, we have: 1 ¼



p c

  @ Gh p c ; DðQÞ > 0:  cc Qh @D h 2fB;Lg  X

Therefore, since 1 is linear in d, there must exist some d 2 ð0, 1Þ such that 1 ¼ 0: (2) Let 2 be the difference between the second terms of the second line of equations (12) and (19): X dQh Z u     X dQh    G p ; DðQÞ  d  2 ¼ pc  c c ðz  cc Þgh ðz; DÞdz: dp h c dp cc h 2fB;Lg h 2fB;Lg If d ¼ 1, we have: X dQh Z u  X dQh Z u  pc  cc gh ðz; DÞdz  ðz  cc Þgh ðz; DÞdz: dp pc dp cc h 2fB;Lg h 2fB;Lg

2 ¼ Let Eh ¼

R u  p c

 R u W p c  cc gh ðz; DÞdz and Eh ¼ cc ðz  cc Þgh ðz; DÞdz. Then: 2 ¼

 dQL    dQB   EB  EBW þ EL  ELW : dp dp

Since: EhW ¼

88

Z

u

p c

ðz  cc Þgh ðz; DÞdz þ

Z

p c

cc

ðz  cc Þgh ðz; DÞdz >

Z

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ðz  cc Þgh ðz; DÞdz > Eh ,

Airport Pricing, Concession Revenues and Passenger Types

D’Alfonso, Jiang, and Wan

when dQB =dp < 0 (that is, ðvB  vL Þy < bL ), we have 2 > 0. When dQB =dp > 0, 2 > 0, if and only if: dQB E W  EL dp :  < LW dQL EB  EB dp If d ¼ 0, we have: 2 ¼

dQB  dQL  E þ E ; dp B dp L

when dQB =dp < 0, we have 2 < 0. When dQB =dp > 0, 2 < 0, if and only if: dQB E dp  < L : dQL EB dp Therefore, when dQB =dp < 0, since 2 is linear in d, there must exist some ^d 2 ð0, 1Þ such that 2 ¼ 0:

89

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