Price Discrimination and Concentration in European Airline Markets

Price Discrimination and Concentration in European Airline Markets Stephanie GIAUME University of Nice-Sophia Antipolis, IDEFI-LATAPSES-CNRS. giaume@idefi.cnrs.fr Sarah GUILLOU University of Nice-Sophia Antipolis, IDEFI-LATAPSES-CNRS guillou@idefi.cnrs.fr Abstract. This article deals with the empirical question of the relationship between price discrimination and concentration in intra-european routes. We estimate the impact of purchase restrictions on price dispersion in order to evaluate price discrimination. Ticket restrictions are used as proxies for price discrimination. We use data on ticket prices of all flights from Nice Airport (France) to European destinations on October 16, 2002. Our empirical analysis shows that concentration measured by the variability of market shares affects positively the sensitivity of airline prices to purchase restrictions. Additionally, we show that second and third degree price discrimination verify the inverse relationship between price discrimination and concentration but market shares significantly explain price discrimination only when third-degree price discrimination is concerned. Key words: Concentration, price discrimination, price dispersion, airline markets. I. Introduction Since liberalization, European Airlines have been confronted to a new competitive environment through a series of three "packages" of legislation and with the Open Sky process proposed by the United States. Compared to the US market, the European airline market has two major specific features. First, a smaller geographical space and shorter distances between major agglomerations allow a greater competition from alternative transportation, notably the high-speed train. Second, airports do not control the slot allocation process. Within the European Union, slot allocation is governed by a regulation based on International Air Transportation Association (IATA) guidelines (European Regulation 95/93). This slots allocation system introduces barriers to entry by carrier incumbents who abuse of dominant position. Then, these differences may induce different results about market structure analysis. As far as European market is concerned, the liberalization process has not induced a homogeneous competition at the route level. Thus, to evaluate concentration 1

in the airline market, it is necessary to define each route as a specific market, which has its own market structure and its concentration index. The purpose of this paper is to explain the phenomenon of multiple prices offered in the intra- European routes. A common approach of pricing strategy in airline market is in line with the theory of price discrimination as airlines apply price discrimination by offering discount fares to consumers who satisfy various restrictions. This discriminatory behavior has become pervasive with the European Liberalization. Is persistent and growing price discrimination related to increased competition expected from liberalization? In others words, how price discrimination is affected by concentration? Recent theoretical analysis of price discrimination have investigated how the competitive dimension affects the price discrimination and the price dispersion in imperfect competition environment (Borenstein (1985); Holmes (1989); Stole (1995); Valletti (2000);(2002)). These studies show that price discrimination persists and may increase as a market moves from monopoly to imperfect competition. Price discrimination behavior must be precised when a competitive dimension is present. Actually, in monopoly markets, firms sort consumers on the basis of their willingness-topay (monopoly-type discrimination). When competition is introduced, firms must consider that consumers differ not only as regards the utility they derive from a good, but also as regards their preferences among different brands (competitive-type discrimination). The expected effect of market structure on price dispersion depends on whether monopoly-type or competitive-type discrimination dominates. Borenstein (1985) and Valletti (2000) emphasize these different aspects of price discrimination under spatial competition. Borenstein develops third-degree price discrimination, while Valletti deals with second-degree price discrimination in a context of imperfect information. Specifically, dealing with airline market Gale (1993), Gale et al., (1992);(1993), and Dana (1998) use advance purchase discounts as discriminatory practice and conclude that there is more price discrimination and then more price dispersion in a competitive environment. This practice disperses prices and allows carriers to sort consumers in order to face airlines specific constraints 1. Some empirical studies are consistent with these theoretical results establishing that the more competitive markets are, the greater price dispersion is (Borenstein et al., (1994); Morrisson et al., (1995); Hayes et al., (1998)). However, these contributions of airline pricing focus on the average fare dispersion in competitive environment and do not develop precisely on price discrimination behavior. Stavins (2001) s analysis is original as she uses data on individual airline ticket prices and ticket restrictions across various routes within the United States to estimate the relationship of the marginal effect of the restrictions across markets with various concentration levels. She finds a negative relationship between price discrimination and concentration. 1 Carriers are confronted to a problem of allocating limited capacities as airline service is not storable and as demand is uncertain. 2

As Stavins (2001), our study use purchase restrictions in order to evaluate price discrimination in airline markets. However, an estimation of different price discrimination practice should be done as it suggested in theoretical development as well as to understand the relationship between market structure and price discrimination. Unlike previous studies of price discrimination, we include second and third degree price discrimination. We add to purchase restrictions intending to foster selfselection 2, purchase restrictions intending to segment the market on the basis of exogenous passenger's characteristics. Moreover, as European airlines market is concerned, we take into account of European market structure particularities by using the Herfindahl decomposed index. In the second section, we present the empirical framework based on Stavins method extending by taking into account different discrimination practices and decomposed Herfindahl Index to measure concentration. The third section provides the results and we conclude in the fourth and final section. II. Empirical Framework 1. The data Our data contain all ticket prices of flights from Nice (France) 3 Airport to European destinations on Wednesday, October 16, 2002. Our 2592 ticket prices sample on 20 routes 4 was collected at the office of Amadeus system 5. The routes cover 9 European countries and 17 carriers. Among the routes sample, four routes are monopolistic which represents 20% of the sample and 12 are duopoly route, that is 60%. This sample is representative of European market. For all intra- European routes, the Civil Aviation Authority study establishes that in 1997, 77% of routes are serviced by one or two airlines (CAA, 1998). Ticket prices were collected 22, 14, 7, and 2 days before the departure day. This sequence allows taking into account the rationing device and the limited supply of the cheapest tickets. For each ticket, price depends on flight characteristics and on purchase restrictions. We evaluate price discrimination with the effect of ticket restriction on ticket price. If restrictions imply a greater discount on the ticket price, so the price discrimination increases. Before analyzing price discrimination by the mean of restrictions, it is also interesting to appreciate price dispersion in the sample. Since dispersion is measured for an only date of departure chosen in the week, excluding summer and holiday peaks, dispersion caused by high demand period 2 These restrictions on discount airline tickets introduce a quality differentiation (see Mussa and Rosen (1978), Salop (1977) and, Chiang and Spatt (1982)). 3 Nice airport is the second airport in France by passenger numbers. 4 We do not take into account Corsica routes because they are subsidised routes. We take account of all regular carriers on each route even if they have a code sharing. 5 Amadeus is a Global Distribution System. 3

and low demand period is excluded. Consequently, measure of dispersion by route and by carrier is an approximate measure of discrimination here. We choose the coefficient of variation, which measures the variability in term of the mean. The calculation of coefficient of variation by routes and by carrier for all carriers shows that there is a lot of dispersion of prices around the mean. It proves pervasive discrimination from carriers. On a same route, price dispersion is not equal between carriers, which means that carriers discriminate differently (appendix A). The following table shows the mean, the standard deviation and the range of the coefficient of variation for different groups of routes depending on market structure. Table 1: Mean, standard deviation and range of coefficient of variation All routes Oligopolistic routes 0.63 (0.21) 0.52 (0.19) Duopolistic routes 0.53 (0.13) 0.49 (0.11) Monopolistic routes 0.48 (0.11) 0.45 (0.10) Mean of CV (day-22)(s.d) 0.56 (0.16) 0.50 (0.14) Mean of CV (day-1) (s.d) Max min on day-22 1.01 0.83 0.54 0.27 Correlation with HHI -0.31 - - - The mean of coefficient of variation depending on the market structure shows that dispersion is negatively related with the number of carriers on the route. But the coefficient of correlation between the coefficient of variation and concentration measured by the Herfindahl index (HHI) indicates weak negative linear relationship between the two variables. Moreover, the coefficient of variation decreases as it comes closer to the departure date whatever market structure. Discretionary travelers tend to have more elastic demand for air travel and a lower value in their time. Then, cheaper fares disappear, leaving only more expensive tickets for sale. Indeed, the average price, for all carriers, increases from 424.4 to 478 euros between day-22 and day-1. These statistical descriptive results suggest a negative relationship between price dispersion and market structure. Here, the number of carriers on a route measures market structure. But counting the total number of carriers does not well evaluate the level of competition in the airline market. It does not take into account in the market share of firms and in the variability of market share of firms. In the econometric estimation below, we consider the Herfindahl index usually used in the oligopoly theory and in the airline literature. This index is defined as the sum of squared market shares of all firms in the market. It allows a representative measure of market structure and of competition on the route. This index may be manipulated to reveal the real market share of firms on a market (Adelman, 1969). On one hand, the inverse of the HHI can be interpreted as the number of equally sized firms that an equally competition market would have. On the other hand, the HHI can be decomposed into two parts to show the contribution of the number of firms and the firm size inequality contribution on 4

market concentration 6. The analysis of firm size inequality appears to be essential in order to evaluate concentration on European market. Indeed, intra-european routes can not be differentiated by the number of competitors because they are essentially served by two or three carriers. Consequently, it is necessary to precise the measure of concentration and of discrimination as to the relationship between concentration and discrimination. Following econometric estimations allow it by introducing restrictions and Herfindahl Index. 2. The econometric equation Two models are estimated. In the first model, price discrimination is assumed not to vary with market concentration. In the second model, price discrimination depends on market concentration. The first model is: P = β + βr + β H1 + β H 2 + β S + β DIST + β AVGPO + βoneway (1) h 0 1 2 i 3 i 4 ij 5 i 6 i 7 + β DAYS + β BUSS + ε 8 9 P is the price of round trip tickets of the route i by the company j with the fare k. So, i denote destinations from Nice to European cities, subscript j a airline company and subscript k a particular ticket for company on the route. Explicative variables vary with i or j or k, couple of them and/or the three. Ticket restrictions are used as proxies for price discrimination. We retain three ticket restrictions ( R h, h = 1, 3 ). First, R 1, the number of days in advance that purchase is required; second, R 1, whether or not a Saturday-night stay-over is required; and third, R 2, exogenous segmentation (family, age, student, events). R 1 and R 2 represent second-degree price discrimination. These restrictions permit to associate a "time cost" to a ticket leading to travelers to report their private information about their value of time. This mechanism facilitates surplus extraction by carriers. R 3 represents third degree price discrimination. Exogenous segmentation permits carriers to separate customers and to distinguish among them with respect to price elasticity of demand. As some restrictions are correlated, we avoid including them all together in the estimation. DAYS is a variable controlling for the number of days before the departure day ticket prices were available. This variable represents implicitly the rationing device as for each date we have selected available fares. 6 Barla (2000) and Carlsson (2002) argue that there is some restrictiveness of using HHI only as a concentration measure. 5

Market share, S ij, is the ratio of direct flights of the carrier j for the route i on the total of direct flights for the route i. The number of direct scheduled flights is a good measure of market share if the number of passengers for flights of different carriers is similar. 7 Market concentration is measured by 2 the Herfindahl decomposed index. We note H1 the effect of firm inequality ( cv N ) and H2 represents the value of Herfindahl index when all the firms are equal ( 1 N )8. Four more explicative variables are included, the distance between Nice and the destination (DIST i ), the average population in the two cities (AVGPO i ), a dummy variable, ONEWAY, equals to 1 for one-way tickets and a dummy variable, BUSS equals to one for business-class tickets. Last, to isolate price discrimination from costs and other factors affecting airfares, a dummy for each carrier is introduced which permits to estimate costs and others carrier-specific characteristics influencing ticket price. In this first model, we evaluate the effect of the restrictions on prices along with the effect of concentration and market share. We expect β 1 to be negative as ticket restrictions reduce the ticket price and we expect β 2 and β 3 to be positive because carriers with larger market share practice higher price. We estimate a second model with the same explicative variables but with a possible relationship between price discrimination and concentration of the routes. The second model allows price discrimination to vary with market concentration. ( ) 5 P = α + R γ + γ H1 + γ H2 + γ S + αh1 + α H2 + α S + α DIST + α AVGPO (2) h 0 0 11 i 12 i 2 ij 1 i 2 i 3 ij 4 i i + αoneway + α DAYS + α BUSS + ε 6 7 8 P R = γ + γ H 1 + γ H 2 + γ S (3) 0 11 i 12 i 2 ij We obtain a relationship between the sensitivity of price to restrictions and the concentration. Actually, γ 11 and γ 12 measure the effect of market concentration on price discrimination and γ 2 the effect of carrier s market share on price discrimination. 7 The number of passengers is not available on each flight. Literature (see for example Borenstein and Rose, 1994) shows that using either the number of flights or the number of passengers give no significantly different results. 8 HHI is the sum of H1 and H2. 6

IV. Results The following table shows the estimations of both models for each restriction. Both models were estimated using OLS. Table 2: Results of estimations of model 1 and model 2 First Model Second Model R 1 R 2 R 3 R 1 R 2 R 3 R -11.48-69.02-104.4-20.36 H1-695.8-723.44-629.04-743.3 HHI H2-37.92-22.12-27.9 12.5 (0.76) (0.86) (0.82) (0.9) Share -167.42-188.2-148.36-198.53 (0.06) (0.03) (-0.10) (0.03) γ 11 - - - 56.106 (0.18) γ 12 - - - -11.26 (0.63) γ 2 - - - 24.96 (0.2) DIST 0.02 0.01 0.05 0.015 (0.7) (0.85) (0.28) (0.77) AVGPO (0.04) (0.2) (0.27) (0.03) SGL 104.88 97.26 103.4 102.1 DAY -0.64-1.29-1.75-0.63 (0.56) (0.24) (0.19) (0.57) BUSS 418.8 399.9 406.55 417.72-221 -996.6 3.6 (0.97) -254 831.7 55.5 (0.7) 167.4 (0.17) (0.94) 86.08 (0.01) -1.25 (0.25) 389.58-220.8-929.3 104.5 (0.45) -291.5 810.8-209.2 (0.18) 329.3 0.07 (0.19) (0.6) 99.3-1.69 (0.12) 416.65 Adj R 2 0.37 0.37 0.38 0.37 0.37 0.38 LogL-Ratio -14832-14835 -14828.21-14831.75-14829.57-14821.61 Fisher 47.67 47.28 48.1 44.25 44.5 45.41 Critical probability between parentheses Whatever the model, restrictions always have a negative impact on the level of prices. As expected, the more the tariff is bound to restriction, the lowest price is. Unlike Stavins' regression, when we use HHI index, carriers' market share and concentration is not significant (or slightly significant) whatever the retained restrictions (Appendix B). Whereas by using decomposed index, the effects of market shares and of concentration on price are negative and statistically significant. Among both components of concentration indicator, only the indicator of market share variability H1 is significant. In the first model, adding a Saturday-night stay-over requirement (R 2 ) results on average in 7

a 69.02 euros drop in the ticket price. Increasing the advance-purchase requirement (R 1 ) by a day results in a 11.48 euros decrease in the price ticket. For instance, a ticket with a 14-day advance purchase requirement costs 161 euros less than a similar ticket on the same route without the requirement. It appears that the exogenous segmentation (R 3 ) is more influential on the decrease of the price. A student, for example, would pay his ticket 104.4 euros less than a passenger non-included in the segmentation. As far as concentration is concerned, only H1 is significant and negatively alters prices. With R 3, a 10 per cent increase in concentration level of H1 results in a 63 euros decrease in ticket price. Market shares are negatively significant. Single tickets (SGL) as well as business class tickets (BUSS) command significantly higher fares than round-trip and coach tickets. Business tariffs are on average, 408.5 euros more than economic tariff and single tickets are 102 euros more expensive. The distance and population coefficients are not significant. The coefficient on the DAY variable is always negative but is not significant. In the second model, price discrimination is supposed to depend on concentration and market shares. Restrictions are still negatively significant and explicative variables others than concentration and market shares have the same coefficients. About the relationship between concentration and price discrimination, the following equations show that concentration affects significantly and positively the sensitivity of price to restrictions. P R2 = 20.36 + 56.1H1 11.26H2 + 24.9 S (4 a) i i ij P R3 = 221 + 831.7H1 + 55.5H2 + 167.4 S (4 b) i i ij P R5 = 220.8 + 810.8H1 209.2H2 + 329.3 S (4 c) i i ij As sensitivity of price to restrictions is negative, then positive influence of concentration leads to lower price discrimination. Under competition, carriers compete only on passengers with a low willingness to pay the ticket. Carriers attract them increasing the discounts offered in exchange for the restrictions. They do not discriminate on passengers who both suffer "time cost" and prefer pay a full ticket without restriction. So, airlines attract and establish this type of customer by frequent-flyer plans. Consequently, these practices allow price discrimination to increase with competition. Market shares do not contribute to explain price discrimination when R 1 and R 2 are retained unlike when R 3 is retained. The firms' ability to implement exogenous segmentation depends on their market power. It is a necessary condition to practice a third degree discrimination. Alternatively, 8

second degree discrimination is consistent with principle of self-selection, where carriers are confronted to imperfect information. V. Conclusions Our empirical analysis shows that concentration affects significantly and positively the sensitivity of airline prices to purchase restrictions. Consequently we can conclude that price discrimination decreases with market concentration as Stavins s (2001) has shown for the US airline market. Additionally, we precise the relationship between price discrimination and market structure by two more essential results. First, we find that only the indicator of market shares variability (H1) contributes to explain the effect of concentration on price discrimination. This result is consistent with European airline markets structure where two or three carriers serve routes. Second, both types of price discrimination verify the inverse relationship between price discrimination and concentration but market shares significantly explain price discrimination only when third-degree price discrimination is concerned. These results should lead to theoretical investigations on the relationship of different type of discrimination with concentration. Empirically, given the recent evolution of the European airlines markets, it will be interesting to introduce the code shares practices of carriers and the low-cost companies for which data were not available at the moment. As low cost companies entrance should lead to growing discrimination and code shares practices should lead to less discrimination, evolution of price discrimination is not finished. 9

Appendix A. Summary statistics ROUTES Carriers mean day-22 mean day-1 CV (Day 22) CV (Day 1) KLM 581 656 0,53 0,46 Amsterdam HV 232,5 304 0,85 0,71 FU 390,4 452 0,54 0,45 Barcelona AF 435 485,5 0,48 0,44 IB 431,5 523 0,59 0,48 FU 252 264 0,56 0,56 Bordeaux AF 306 323 0,44 0,41 TV 220,5 240 0,46 0,4 Brussels SN 487 487 0,71 0,71 LH 520 618 0,59 0,55 Dusseldorf EW 616 711 0,55 0,48 Frankfurt LH 501 593,5 0,55 0,5 Geneva LX 301,6 358 0,57 0,54 FU 308 298 0,47 0,44 Lille AF 293 319 0,37 0,32 NI 508 508 0,59 0,59 Lisbon TP 618 737 0,62 0,53 BA 533,5 687 0,7 0,5 London AF 584 680 0,57 0,43 BD 1480 1735 1,31 1,17 Lyon AF 200 211 0,49 0,48 Madrid Milan Munich Nantes Paris FU 503 526 0,62 0,6 AF 550 626 0,57 0,51 IB 541 668 0,62 0,48 AZ 359 379 0,31 0,28 AF 497 548 0,54 0,52 FU 522 506 0,51 0,58 LH 445,5 513,5 0,6 0,58 FU 285 290 0,47 0,4 AF 270 284 0,39 0,42 IW 181 226 0,78 0,6 AF 268,5 273 0,69 0,68 Rennes AF 345 358 0,3 0,29 AZ 450,5 519 0,48 0,4 Rome Strasbourg Toulouse AF 619 786 0,62 0,46 FU 390 445 0,56 0,45 AP 376 445 0,57 0,45 FU 255 250 0,43 0,42 AF 253 260 0,44 0,43 FU 254 259 0,48 0,42 AF 237 248 0,41 0,39 10

Appendix B. Results of estimation of model 1 and model 2 with not decomposed Herfindahl index First Model Second Model R 1 R 2 R 3 R 1 R 2 R 3 R -11.35-63.16-106.74-20.01 HHI -321.53-319.5-285.58-288.3 (0.01) (0.01) Share 47.46 38.80 47.7 33.95 (0.52) (0.60) (0.52) (0.65) γ 12 3.23 - - - (0.88) γ 2 - - - 14.81 DIST 0.04 (0.38) AVGPO (0.04) SGL 107.1 DAY -0.69 (0.53) BUSS 416.05 0.03 (0.47) (0.02) 100.78-1.36 (0.21) 399 0.08 (0.13) (0.26) 105.23-1.79 (0.10) 403.28 (0.41) 0.04 (0.03) 105.70-0.68 (0.53) 413.8-201.5-355.9 27.23 (0.73) 202 (0.15) 65.1 (0.57) 0.03 (0.49) (0.01) 96.4-1.3 (0.22) 389.03-20.01-288.3 (0.01) 33.95 (0.65) 3.23 (0.88) 14.81 (0.41) 0.04 (0.43) (0.03) 105.70-0.68 (0.53) 413.8 Adj R 2 0.37 0.38 0.39 0.39 0.37 0.37 LogL-Ratio -14840.60-14832.2-14836.81-14839 -14831.7-14838.34 Fisher 48.5 49.6 48.9 46.7 47.7 46.9 11

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