Alliances, Codesharing, Antitrust Immunity and International Airfares: Do Previous Patterns Persist?

Transcription

1 Alliances, Codesharing, Antitrust Immunity and International Airfares: Do Previous Patterns Persist? by Jan K. Brueckner University of California, Irvine Darin Lee LECG, LLC and Ethan Singer LECG, LLC July 2010 Abstract This paper revisits the effect of airline cooperation on international airfares, using a panel data set from The findings mostly confirm previous results, showing that full airline cooperation lowers the fares paid by interline passengers, and that incremental improvements to cooperation individually lead to fare reductions. The results, which show that codesharing, alliance service, and antitrust immunity each separately reduce fares below the traditional interline level, overturn contrary and counterintuitive findings in recent US Department of Justice studies.

2 Alliances, Codesharing, Antitrust Immunity and International Airfares: Do Previous Patterns Persist? by Jan K. Brueckner, Darin Lee and Ethan Singer 1. Introduction International alliances have become a permanent fixture in the airline industry. Spurred by a desire to provide seamless international service in a world that prohibits most cross-border airline mergers, alliances first emerged in the early 1990s. With a dramatic expansion and some reshuffling of memberships, alliances now carry the great majority of international passengers, with traditional interline trips on nonaligned carriers fading in importance, especially across the Atlantic. 1 But despite the key role that alliances play in international travel, they are still frequently embroiled in regulatory controversy. The recent bid for antitrust immunity (ATI) by American Airlines (AA), British Airways (BA) and Iberia (IB), for example, was praised by its advocates for the better integration of the oneworld alliance, while simultaneously being criticized by other interested parties as anticompetitive. Such controversies arise because the impact of alliances on airfares is potentially complex, with both positive and negative elements. On the one hand, alliances can lead to lower fares for interline passengers, who must fly on two airlines to make their trip. When the two airlines are nonaligned, the fare setting process for such trips involves double marginalization, 1 For example, during 2009, approximately 84% of U.S. transatlantic and 72% of U.S. transpacific passengers were transported by members of one of the three global alliances: oneworld, Star and SkyTeam. Source: ConCRS MIDT database. 1

3 with each carrier introducing a separate markup over cost in determining the overall fare. 2 By contrast, when the two airlines are allied and enjoy ATI, each takes account of the fact that its own markup hurts the other carrier. While a higher markup may raise the carrier s own profit, the resulting decline in traffic (a consequence of the higher fare) reduces profit for the partner airline. When maximization of total profit is the goal, both airlines limit their markups, which leads to a lower overall fare and higher interline traffic. 3 Another type of passenger, who flies between the international gateway cities of the alliance partners, could in principle experience a different outcome. On such gateway-togateway routes, the alliance partners typically provide overlapping service, with each separately serving the route. Since ATI gives the airlines license for full cooperation, they could in theory choose to reduce the total number of seats offered to gateway-to-gateway passengers and charge a higher fare. This effect could arise even if total capacity on the hub-to-hub route expands as a result of increased interline traffic, consisting of passengers traveling between endpoints that are respectively behind and beyond the carriers hub gateways. This potential anticompetitive effect has been a concern in a number of alliance cases, including the AA/BA/IB case, and it has to the potential to work in the opposite direction to the beneficial impact on interline passengers. The resulting trade-off was explored theoretically by Brueckner (2001) and Brueckner and Proost (2010), and the earlier paper provided a conceptual framework for the subsequent empirical studies of Brueckner and Whalen (2000), Brueckner (2003) and Whalen (2007). Each 2 Fares on itineraries involving two non-aligned carriers are prorated according to the IATA Multilateral Proration Agreement. See 3 The ability to fully internalize the double marginalization problem occurs when partner carriers engage in revenue or profit sharing joint ventures, as is in the case with many of today s immunized alliances. 2

4 of these empirical studies showed that cooperative pricing by alliances led to lower fares for interline passengers over their sample periods. The measured fare discount relative to an interline trip on two nonaligned carriers was as large as 27 percent, confirming the theoretical prediction. 4 The three studies reflected successive methodological improvements. The 1997 US Department of Transportation (DOT) data used by Brueckner and Whalen (2000) did not permit fine distinctions between levels of cooperation, so that carriers were separated into allied and nonaligned groups based solely on announced alliance agreements. The improved 1999 US DOT data used by Brueckner (2003) permitted codesharing to be identified at the ticket level, allowing interline itineraries to be more finely categorized according to whether the carriers had ATI, practiced codesharing on the route, or exhibited neither form of cooperation. While both of these studies were based on data for a single quarter, Whalen (2007) offered a further substantial improvement by using a decade-long panel covering the period This data structure allowed the use of route fixed effects, which control for unobservable route characteristics that may affect fares, eliminating a possible source of bias. Unlike the earlier studies, Whalen also included online itineraries (those provided by a single carrier) in his sample, allowing a comparison between fares for various types of interline travel and fares for single-carrier trips. In addition to looking for an alliance discount in interline fares, Brueckner and Whalen (2000) also attempted to measure the potential anticompetitive effect of alliances on gateway-togateway routes. The results, however, did not reveal such an effect, showing that fares would be 4 For additional theoretical and empirical work on alliances, see Armantier and Richard (2008), Barla and Constantatos (2006), Bilotkach (2005, 2007), Chen and Gayle (2007), Flores-Fillol and Moner-Colonques (2007), Hassin and Shy (2004), Oum, Park and Zhang (1996), Oum, Yu and Zhang (2001), Park (1997), Park, Park and Zhang (2003), Park and Zhang (1998, 2000) and Park, Zhang and Zhang (2001). See Bamberger, Carlton and Newman (2004), Ito and Lee (2007), and Gayle (2007, 2008) for studies of domestic airline alliances. 5 Although the dataset used by Whalen (2007) spanned an entire decade, like the previous studies, it relied only on data from the third quarter of each year between 1990 and

5 unaffected if two previously nonaligned carriers serving the route became alliance partners. Recently, the empirical results from this literature have been challenged in new work carried out as part of regulatory proceedings. The U.S. Department of Justice (DOJ), in two studies (2009a, 2009b) produced as part of its review of expanded ATI for the Star Alliance to include Continental and the subsequent AA/BA/IB antitrust-immunity case, argues that the beneficial effect of alliance cooperation on interline fares is no longer present. Instead of finding that successive increments to airline cooperation (codesharing, alliance membership, addition of ATI) each reduce interline fares, a DOJ study (2009a) contained in its Comments in the Star Alliance/Continental ATI matter shows a different ordering. Using third quarter data from the period, the DOJ study finds that unimmunized alliance fares (those where the carriers lack ATI) are indistinguishable from online fares, while immunized alliance fares are 3.5 percent higher than online fares. 6 A subsequent DOJ study (2009b) contained in its Comments in the AA/BA/IB ATI matter also based on third quarter data from found that immunized fares for each of the three main global alliances were significantly higher (by percent) than unimmunized fares of the same alliance. 7 These findings were used to argue that ATI is not needed to secure the fare benefits of cooperation, with unimmunized alliance membership and codesharing sufficient by itself. While DOJ s results diverge from those in the previous literature, they are also difficult to explain theoretically. In other words, it is logically 6 See Appendix B to Public Comments of the Department of Justice on the Show Cause Order, Joint Application of Air Canada, The Austrian Group, British Midland Airways Ltd, Continental Airlines, Inc., Deutsche Lufthansa Ag, Polskie Linie Lotniecze Lot S.A., Scandinavian Airlines System, Swiss International Air Lines Ltd., Tap Air Portugal, United Air Lines, Inc. to Amend Order under 49 U.S.C and so as to Approve and Confer Antitrust Immunity, Docket OST , (June 26, 2009). 7 See Appendix B to Public Comments of the Department of Justice, Joint Application of (June 26, 2009), Regarding the Joint Application of American Airlines, British Airways, Iberia, Finnair, Royal Jordanian Airlines for antitrust immunity, in DOT Docket #OST (2009). Although DOJ reports higher immunized fares for all three alliances, they note that in their sample, very few immunized oneworld tickets exist since during the period of their study, the only immunized oneworld partners across the Atlantic were American and Finnair. 4

6 unclear why unimmunized alliance partners, who lack the legal ability to fully cooperate, would set fares as if they were a single carrier. A second counterintuitive DOJ finding contained in its comments in the AA/BA/IB matter is that online fares of a single alliance member are between 3.1% and 5.4% higher than non-immunized alliance/codeshare fares. Willig, Israel and Keating (2009, 2010), working on behalf of the ATI applicants in the AA/BA/IB matter, argue that the DOJ s methodology is flawed, and that correcting the alleged errors re-establishes the traditional results. Their findings show that online fares of a single carrier are the least expensive type of ticket, and that ATI fares are 5.3 percent lower than traditional interline fares, reaffirming the earlier findings that additional airline cooperation is beneficial. 8 Two rounds of interaction between the DOJ and Willig, Israel and Keating have made it clear that the source of their conflict is an apparently small methodological difference. The issue is whether the fare regression equation should control for the individual identities of the carriers providing the service on an itinerary, along with their alliance status. The previous literature has controlled for these identities, and Willig, Israel and Keating (2009, 2010) do so as well. But the DOJ believes that this approach is improper. The notion that alliances generate fare benefits for interline passengers, coupled with actual dollar measures of these benefits, has played a prominent role in regulatory actions on alliances, both in the US and in Europe. 9 For example, the current AA/BA/IB application for 8 The DOJ studies also claim to overturn Brueckner and Whalen s (2000) finding of no anticompetitive alliance effect on gateway-to-gateway routes. Their results show a substantial fare increase when two previously competing airlines serving such a route become immunized alliance partners. Once again, however, Willig, Israel and Keating (2009, 2010) dispute this finding, showing that a different methodology reestablishes the absence of a competitive effect. 9 In addition to the comments of the US DOJ discussed above, the European Commission has likewise launched formal investigations into the competitive impact of ATI joint ventures ( JVs ), including the JV between Air France/KLM and Delta as well as the Star Alliance s A++ JV. 5

7 ATI states that immunity will generate annual benefits of $92 million for interline passengers, and this number would ordinarily be weighed in the decision. But the recent controversy over whether such benefits even exist calls this number into question, while casting a shadow over ATI approvals in previous cases. As a result, it is important to revisit the issue of the fare benefits of alliances, doing so in a dispassionate fashion removed from the regulatory battle of the AA/BA/IB case. The purpose of the present paper is to carry out such an exercise. The inquiry combines the best features of the previous existing studies. Like Whalen (2007), the DOJ (2009a, 2009b) and Willig, Israel and Keating (2009, 2010), the paper uses a panel data set, but a much longer one than the one used in the latter studies, covering the years Like Brueckner and Whalen (2000) and Brueckner (2003) (but absent in Whalen (2007) and the recent DOJ and Willig, Israel and Keating studies), our study includes measures of competition on interline routes. Like the previous published literature and unlike the DOJ s work, the study controls for the identities of the carriers providing service, on the belief that failing to do so risks generating bias in the coefficient estimates. 11 The study will thus answer the question raised in its title. Do the previously measured interline fare benefits from alliances persist in the current era, where alliances are well established and traditional nonaligned travel has waned in significance? The answer to this question is important given that the alliance structure is still evolving despite its maturity, which means that many regulatory decisions remain to be made in future years. 10 Unlike all previous studies, our data set is comprised of all four quarters of data, rather than only the third quarter. 11 For a detailed explanation of how such a bias arises, see Appendix A to Brueckner, Lee and Singer (2010). 6

8 2. Empirical Model 2.1. Sample The structure of the sample and the specification of the empirical model are mostly familiar from earlier work. The US Department of Transportation s Origin and Destination survey, which consists of a 10 percent ticket sample of all U.S.-international passengers who fly at least one route segment on a US carrier, is the data source. Attention is restricted to itineraries (consisting of a routing and one or more carriers) where one endpoint is in the US and the other is in foreign country. To focus on transoceanic markets, itineraries with foreign endpoints in Canada, Mexico, or the Caribbean are excluded. 12 While the itinerary must involve a roundtrip with the same starting and ending points, the round-trips are broken into separate outbound and inbound itineraries, with each assigned half the round-trip fare. This fare is the passenger weighted average of the different fares observed on the itinerary. 13 Each one-way itinerary must have 3 or fewer ticket coupons, and service on the itinerary must be provided by no more than 2 carriers. 14 When 2 airlines are present, one must be a US carrier and one a foreign carrier. When a single carrier is present (indicating an online itinerary), it is necessarily a US carrier. Although some previous studies view the airport-pair as the relevant airline market, we use city-pairs instead, with airports in most multiple-airport metro areas grouped and treated as a single endpoint. For US airports, the metro-area airport groupings are the same as those used in 12 We exclude Canada, Mexico and the Caribbean from our analysis because their relatively short distance from the U.S. mainland has resulted in significant presence by low cost carriers on many routes. Thus, the competitive structures of city-pairs between the U.S. and Canada, Mexico and the Caribbean are more similar in many respects to those of U.S. domestic markets than to long haul transoceanic international markets. See, for example, the US DOJ s (2009a) comments in the expanded Star ATI/Continental matter (at page 27) which found that DOJ agrees with the [DOT] Order that the competitive structure of transborder routes is very similar to U.S. domestic routes. 13 Itineraries with round-trip fares less than $100 are dropped. 14 The requirement is that the itinerary has no more than two operating carriers, whose aircraft are used to provide the service. See the discussion of codesharing below for information on the distinction between operating and marketing carriers. 7

9 Brueckner, Lee and Singer (2010), while the grouping of foreign airports follows the convention used in the Official Airline Guide ( OAG ). 15 Given that airline alliances exist mainly to facilitate travel that requires one or more connections at intermediate airports, we exclude city-pair markets where nonstop service is available on a US carrier. 16 A related exclusion involves US endpoints with foreign carrier service. Given that foreign trips to or from US endpoints that are flown entirely on foreign carriers are mostly unmeasured in the O&D survey data, endpoints with such service are excluded. Specifically, using US DOT T100 data, US endpoints with nonstop foreign-carrier service to non-us cities are identified, and itineraries involving these endpoints are discarded. 17 For the US endpoints remaining in the sample, travel to a foreign city must therefore involve at least one route segment on a US carrier, making the itineraries observable in the O&D survey. This type of exclusion, which is standard in previous studies, is meant to facilitate the measurement of competition at the market level. If some service was unobservable, being provided solely by foreign airlines, proper competition counts could not be computed. 18 International codesharing is a major focus of the empirical work, but the data also shows extensive codesharing between regional feeder airlines and their major partners. Since this codesharing has a different nature, with the feeders acting as an extension of the major airline s domestic network, it is suppressed in the empirical model. To understand the required procedure, note that the data shows both the airline code of the operating carrier for each route 15 The US airport groupings are similar to those used in other U.S. DOT studies such as The Low-Fare Evolution, Part II Third Quarter 2002, U.S. Department of Transportation, Office of Aviation and International Affairs, Aviation Analysis Domestic Aviation Competition Issue Brief Number If the route has 60 or more roundtrips in any quarter during the sample period, it is excluded. 17 If the endpoint has 60 or more roundtrips in any quarter during the sample period, it is excluded. The exclusion of itineraries is actually carried out separately for each foreign region. For example, European itineraries whose US endpoint has nonstop foreign-carrier service to Europe are excluded, but itineraries involving this same endpoint and Asian destinations remain in the sample as long as the endpoint has no foreign-carrier nonstop service to Asia. 18 Itineraries where the US endpoint is in Hawaii, Alaska, or a US territory are also excluded. 8

10 segment of an itinerary as well as the code of the marketing (or ticketed ) carrier for the segment, whose flight number appears on the ticket. Under codesharing, these two codes are different, with one airline selling seats on its partner s aircraft using its own flight number. While this difference allows international codesharing to be identified, we suppress the difference for regional carrier segments by changing the code of the regional operating carrier to match that of its major partner. With this change, any difference on a route segment between the marketing and operating carrier codes indicates codesharing between major carriers, which is the desired focus. The last exclusion criterion involves the fare class of the ticket. In particular, we exclude all itineraries where the international segment is in first class. 19 Note that this exclusion is done before computing passenger-weighted average fares for the sample itineraries. Although one focus is on the average fare across all the remaining fare classes, we also create and retain average fares for restricted coach (X class) and business class (C and D classes) for each itinerary. Finally, as mentioned in the introduction, the sample is generated quarterly over the period Variables The dependent variable for the regressions is the log of the passenger weighted fare for the itinerary (on a one-way basis). The regression includes city-pair fixed effects, which are meant to control for unobservable market characteristics that may affect fares. In addition, separate fixed effects for both the year and quarter of the observation are included. 19 We exclude first class passengers from our dataset because not all international carriers offer a first class cabin. For example, among the large U.S. flag carriers, only United and American offer First Class, with Delta, Northwest, Continental and US Airways offering coach and business class. Similarly, while many foreign flag carriers such as British Airways, Lufthansa and Singapore Airlines offer first class, others (i.e., KLM, Iberia, EVA, SAS) do not. The remaining DOT fare classes for the international segments are X (restricted coach), Y (unrestricted coach), C (unrestricted business) and D (restricted business). 9

11 The number of ticket coupons is another control variable (COUPONS), intended to capture trip inconvenience. Itineraries with more connections, and thus more coupons, should be dispreferred by passengers, leading to a lower fare. Even though the city-pair fixed effects mostly control for the distance flown on itineraries in the market, we also include the natural log of the total one-way distance (LDIST) to capture the effects of longer-than-average itineraries. A US point-of-sale dummy variable for the round trip (US_POS) is also included in the regressions. US_POS equals one for each one-way portion of a US-originating round trip and zero for the each one-way portion of a foreign-originating round trip. Results from the earlier literature show that the fare is higher with a US point-of-sale. Following most earlier work, carrier effects are also included in the regressions, with the effects weighted by the operating carriers distance contributions on the itinerary. 20 The coefficients of these variables are not reported. The variables measuring the extent of airline cooperation are the main focus of the analysis. The baseline for such measurement is an itinerary where service is provided by two nonaligned carriers who do not codeshare. Such traditional interline service generally involves little or no cooperation between the carriers (beyond checked baggage transfer), with the fare determined according to IATA rules. Relative to this baseline, four separate dummy variables capture different possible degrees of cooperation between the carriers that provide the itinerary s service. 20 These variables are computed by first creating a set of dummy carrier variables for the first carrier on the itinerary (denoted D 1i, where i indexes the carriers) and a corresponding set of dummies (D 2i ) for the second carrier on the itinerary. If the itinerary is online (with service provided by a single carrier), then the same dummy from each set is equal to one; otherwise different dummies take unitary values. Next, two distance-share variables are created, equal to the fractions of the total distance flown by the first and second carriers, respectively (equal to S 1 and S 2 ). Finally, the dummies are multiplied by the share variables, yielding two sets of adjusted dummy variables, S 1 D 1i and S 2 D 2i. These variables appear in the regressions. Note that with an online itinerary, S 1 can be set to one and S 2 to zero. 10

12 The first such measure, the dummy variable CODESHARE, indicates a codeshare itinerary, where the marketing and operating carriers differ on a least one segment and a single carrier is the marketing carrier across all segments. The second measure, the dummy variable ALLIANCE, indicates that two airlines belonging to the same alliance provide the itinerary s service. 21 The third measure, the dummy variable ATI, indicates that two airlines who enjoy antitrust immunity provide the itinerary s service. 22 Clearly ATI = 1 implies that ALLIANCE = 1, although the reverse is not true; many allied carrier pairs are not immunized, so that the ALLIANCE = 1 can hold while ATI = Note also that an itinerary where the service is provided by immunized alliance partners who also codeshare on the route has ATI = ALLIANCE = CODESHARE = 1. The last measure is a dummy variable indicating that service on the itinerary is provided by a single carrier. This variable is denoted ONLINE, and if it equals 1, then ATI, ALLIANCE, and CODESHARE all equal 0. From the previous literature, we expect that successive increases in the extent of airline cooperation lead to reductions in the fare. The lowest level of cooperation, codesharing by carriers who are not alliance partners, is thus expected to reduce the fare relative to the traditional interline case, implying a negative coefficient for CODESHARE. 24 Alliance partnership combined with codesharing indicates additional cooperation between the carriers, which is expected to lead to a further reduction in the fare. The incremental fare decrease is given by the coefficient of ALLIANCE, which is thus expected to be negative. Since antitrust immunity gives even greater scope for cooperation, its presence is expected to yield an additional decrease in the fare beyond the effects of codesharing and alliance membership. Thus, ATI s 21 A list of alliances members by date is contained in Appendix B. 22 A list of ATI partners by date is contained in Appendix C. 23 For example, US Airways and Lufthansa are both Star Alliance members, but do not enjoy ATI. 24 Examples of this type of non-alliance codesharing include Continental/Virgin Atlantic, or United/Qatar Airways. 11

13 coefficient is also expected to be negative. The highest level of cooperation between two carriers occurs when all three dummies equal one, and the resulting fare impact relative to traditional interline service is given by the sum of the CODESHARE, ALLIANCE, and ATI coefficients. The fare impact of this level of cooperation can be compared to the ONLINE case, where cooperation is perfect in the sense that a single carrier provides the itinerary s service. To make the comparison, the ONLINE coefficient is compared to the sum of the CODESHARE, ALLIANCE, and ATI coefficients. 25 The regression also includes measures of competition in the itinerary s city-pair market. The measure is a count of competing online carriers or carrier pairs, which is subject to a marketshare threshold. To be included under the most stringent threshold, an online carrier or a carrier pair must carry at least 10 percent of the traffic in the city-pair market. Another count variable based on lower 3 percent share threshold is also created. In creating the count measure, each distinct carrier pair is usually counted as a separate competitor, even if two pairings involve a single common carrier. For example, if American-Finnair service and American-British Airways service are both observed in a city-pair market (with an endpoint at Helsinki, say), then both pairings are counted as competitors. The treatment is different, however, if the pairings involve carriers that all belong to the same immunized grouping. For example, if United-Lufthansa service and United-SAS service are both observed in a city-pair market, the fact that both carrier pairings enjoy Star-alliance ATI means that, together, they count as only one competitor, not 25 It should be noted that other combinations of the cooperation dummies are possible. ALLIANCE could equal one while ATI = CODESHARE = 0. Or ALLIANCE and ATI could equal one while CODESHARE equals zero (i.e., for a variety of reasons, including a limitation on the four-digit flight numbers and restrictions contained in the collective bargaining agreements between some carriers and their pilots union, codesharing is not universally applied to every flight even for carriers able to fully cooperate under ATI). 12

14 two. 26 By the same logic, if United online service were also observed in the same market along with these two United pairings, that service would also not count as an additional competitor. Note that, while the same avoidance of double counting might be desirable in dealing with pairings like the American ones from above, any such adjustment would be more arbitrary than in the group ATI case. 27 Thus, we did not attempt to impose one. As noted above, the regressions are run using the average fare across all fare classes (other than first class) as the dependent variable, while also looking at subgroups of fare classes. We also present results for a subsample where the reaching the itinerary s foreign endpoint requires a transatlantic trip. While the world sample includes itineraries with endpoints in all parts of the world (excluding Canada, Mexico, and the Caribbean, as noted above), this transatlantic sample includes only itineraries with endpoints in Europe, Africa or the Middle East. Even though the regressions contain city-pair fixed effects, we include time-varying measures of market population and income. The variable POP equals the geometric mean of two populations: the population of the MSA containing the US endpoint and the population of the country containing the foreign endpoint. Similarly, the variable INCOME equals the geometric mean of the US-city and foreign-country per-capita incomes AA and BA did not have antitrust immunity over the sample period, even though they will soon enjoy it. 27 For example, although the presence of American in both pairings may make them less than full competitors, the extent of the shortfall is not clear and must be assumed. Note also that itineraries differing only in their codesharing status can generate separate competitors. For example, a European itinerary where United is the operating carrier for all segments may also appear as separate codeshare itineraries, with the transatlantic segment marketed by, say, Lufthansa and also by US Airways. In this situation, the United-operated service generates two distinct competitors since US Airways is a non-immunized member of Star Alliance. 28 Country populations are used since consistently measured foreign-city populations are difficult to obtain, while city-level incomes are not always available outside the US. Foreign-country populations and incomes per-capita are from the Penn World Tables. For the U.S. data, per capita income is measured at the MSA level. If the metro area contains multiple airports in different MSAs, the population is summed across these MSAs and income is a weighted average of the MSA incomes 13

15 A final point concerns passenger weighting of the regressions. Such weighting may be appropriate given that the thinness of many city-pair markets in the sample, which is reflected in a passenger count that ranges from 10 passengers per quarter to over 20,000 (see Table 1 below). This pattern may generate heteroscedasticity in the passenger-weighted fares, which can be corrected by a further passenger weighting of the regression. However, given that key papers in the prior literature do not use passenger weighting, we likewise focus on unweighted regressions, presenting the weighted results for comparison purposes. Sample means are shown in Table 1 for the world and transatlantic samples, all of which are based on 48 quarters of data. 29 The world sample represents over 90 million passengers, and it contains 2,716,706 observed itineraries in 61,567 city-pair markets. The transatlantic sample contains 1,484,456 itineraries in 36,460 city-pair markets, while the average (quarterly) passenger count, denoted PAX_ALL, is 34 for itineraries in the world sample and 30 for the transatlantic sample, with the minimums equal to 10 in all cases. 30 The maximum counts are, of course, much larger. The table also shows passenger means for restricted coach (X class) and business class (C and D classes). In addition, the table shows the average number of passengers at the city-pair level (PAX_MKT), equal to 758 for the world sample and 541 for the transatlantic sample. In the transatlantic sample, 34 percent of the itineraries are online, being provided by a single carrier, while the remaining 66 percent have two operating carriers. Within this group, itineraries that involve codesharing represent 25 percent of the overall sample, those where the carriers are alliance partners represent 47 percent, and those with ATI represent 37 percent. 29 It should be noted that our panel is not balanced. Travel in thin city-pair markets may not be observed in every quarter. 30 Since the passenger counts in the US DOT survey data are multiplied by 10 to adjust for the 10 percent sampling rate, this number indicates one sampled passenger during a quarter. 14

16 Traditional interline itineraries represent 16 percent of the sample. Note that overlaps among the CODESHARE, ALLIANCE and ATI categories mean that the shares of all four non-online categories (including traditional interline) sum to more than the 66 percent share of this group. TABLE 1: SUMMARY STATISTICS Variable Mean (Weighted) Mean (Unweighted) Std. Dev. (Weighted) Min Max Observations Passengers City-Pairs US-World FARE_ALL ,404 2,716,706 91,925,990 61,567 FARE_BIZ 2,678 2,653 1, , ,067 5,683,870 19,812 FARE_X ,404 2,564,270 84,966,440 60,204 ONLINE ,564,270 84,966,440 60,204 ATI ,564,270 84,966,440 60,204 ALLIANCE ,564,270 84,966,440 60,204 CODESHARE ,564,270 84,966,440 60,204 COUPONS ,564,270 84,966,440 60,204 US_POS ,564,270 84,966,440 60,204 COMP_ ,564,270 84,966,440 60,204 COMP_ ,564,270 84,966,440 60,204 interline ,564,270 84,966,440 60,204 DIST 6,150 6,373 2, ,434 2,564,270 84,966,440 60,204 PAX_ALL ,300 2,716,706 91,925,990 61,567 PAX_BIZ ,067 5,683,870 19,812 PAX_X ,130 2,564,270 84,966,440 60,204 PAX_MKT 1, , ,390 2,564,270 84,966,440 60,204 Transatlantic FARE_ALL ,258 1,484,456 44,342,000 36,460 FARE_BIZ 2,363 2, , ,675 2,151,700 11,424 FARE_X ,258 1,415,910 41,601,910 35,741 ONLINE ,415,910 41,601,910 35,741 ATI ,415,910 41,601,910 35,741 ALLIANCE ,415,910 41,601,910 35,741 CODESHARE ,415,910 41,601,910 35,741 COUPONS ,415,910 41,601,910 35,741 US_POS ,415,910 41,601,910 35,741 COMP_ ,415,910 41,601,910 35,741 COMP_ ,415,910 41,601,910 35,741 interline ,415,910 41,601,910 35,741 DIST 5,146 5,427 1,044 2,508 16,067 1,415,910 41,601,910 35,741 PAX_ALL ,850 1,484,456 44,342,000 36,460 PAX_BIZ ,675 2,151,700 11,424 PAX_X ,740 1,415,910 41,601,910 35,741 PAX_MKT 1, , ,800 1,415,910 41,601,910 35,741 Table 1 also presents passenger-weighted means. For each sample, the share of passengers using online itineraries is larger than the actual share of such itineraries (64 percent vs. 34 percent in the transatlantic sample). Conversely, the shares of passengers on 15

17 CODESHARE, ALLIANCE, ATI and traditional interline itineraries are smaller than the actual shares of such itineraries. Both differences reflect the fact that markets where online service is available tend to be larger than markets where two carriers must be used to make the trip, thus generating higher itinerary passenger counts. Table 1 shows means for the additional variables, with weighted values typically being more relevant. The average transatlantic fare on a one-way basis is $616, while the average restricted coach and business class fares are $522 and $2,363, respectively. The average number of coupons is somewhat larger than 2 in both samples, indicating that most trips involve a single connection, with fewer having double connections. More than 60 percent of tickets have a US point of sale. As with the online share, the competition variables take larger values with passenger weighting, reflecting the presence of more competition in large markets. With a 10 percent market-share threshold for competitors, the average transatlantic passenger travels in a market with 2.66 competitors. Reducing the threshold to 3 percent raises this count to 3.94 competitors. The passenger weighted mean flight distance is 6,150 miles for the world sample and 5,146 miles for the transatlantic sample. 3. Results The empirical results are presented for three different fare-class categories, with the dependent variable being the log of the relevant fare. In the first set of regressions (shown in Table 2), the fare variable is the average across all passenger classes (X, Y, C and D). The second set of regressions (Table 3) uses the restricted economy-class fare (class X), and the third set of regressions (Table 4) uses the business-class fare (classes C and D). Note that the all- 16

18 passenger regression includes class Y, unrestricted economy class. 31 Within each passenger group, separate regressions are run for the world and transatlantic samples. In digesting the results, a key task is to compare the coefficient magnitudes to those from the previous literature. However, to maintain an orderly presentation, this comparison is carried out in section 3.4, after all the current regression results have been described All-passenger results Consider the all-passenger results in Table 2, where the first column contains the estimates using the COMP_10 competition variable. The ONLINE coefficient shows that the fare for an online itinerary is 14.4 percent lower than the traditional interline fare, the omitted category, with the coefficient significant at the 1 percent level. The coefficients of the airline cooperation measures are each negative and statistically significant, indicating that the different forms of cooperation lead to fare reductions. The CODESHARE coefficient indicates that the fare for a multiple-carrier itinerary that involves codesharing is 3.6 percent lower than the traditional interline fare. 32 The ALLIANCE coefficient shows that, when alliance partners provide the service, an itinerary s fare is 2.7 percent lower than the traditional interline fare. Therefore, if the itinerary has alliance service with codesharing, the fare is 6.3 percent ( ) below the traditional fare. Turning to the ATI coefficient, the estimate shows that antitrust immunity generates an additional 4.9 percent fare reduction. Thus, the fullest possible cooperation short of online service (immunized alliance service with codesharing) yields an We do not include Y class in the economy-class regressions because some carriers (such as United, British Airways, Air France, Singapore Airlines and SAS) offer premium economy cabins, with greater seat pitch than standard coach class seating. Since passengers purchasing unrestricted economy tickets are typically provided seats in the premium economy cabin of carriers offering such service, aggregating X and Y fare classes does not provide and apples to apples comparison of coach class service across carriers. 32 When dummy coefficients in a regression are relatively large, as is the ONLINE coefficient, the numerical value overstates the percentage impact on the dependent variable by a non-trivial factor. However, to facilitate a simple discussion, this discrepancy is ignored, a point that should be borne in mind in digesting the results. 17

19 percent ( ) fare reduction relative to the traditional interline fare. This value is about 3 percentage points less than the reduction from online service, and the difference is statistically significant. VARIABLES TABLE 2: ALL PASSENGER RESULTS (1) (2) (3) (4) US-World US-World Transatlantic Transatlantic (all passengers) (all passengers) (all passengers) (all passengers) ONLINE ** ** ** ** ATI ** ** ** ** ALLIANCE ** ** ** ** CODESHARE ** ** ** ** COMP_ ** ** COMP_ ** ** COUPONS ** ** ** ** US_POS ** ** ** ** LDIST 0.445** 0.445** 0.208** 0.208** POP -1.23e-06** -1.22e-06** -1.23e-06** -1.22e-06** INCOME 3.32e-06** 3.27e-06** -3.48e-06** -3.47e-06** Constant 3.080** 3.080** 4.969** 4.967** Observations 2,716,706 2,716,706 1,484,456 1,484,456 Markets 61,567 61,567 36,460 36,460 Passengers 91,925,990 91,925,990 44,342,000 44,342,000 Adjusted R-squared Dati+Dalliance+CS SIGNIFICANCE TESTS (p-values) Dati+Dalliance+CS = singlecarrieronline ** p<0.01, * p<0.05 Market, year, and quarter fixed effects suppressed Carrier controls supressed Robust Standard Errors Dependent variable: lfare Sample is from Unweighted Regressions Table 2 s second column shows the regression coefficients when COMP_3 replaces COMP_10 as the competition measure. As can be seen, the coefficients of ONLINE and the three cooperation measures exhibit negligible changes. As for the competition coefficients themselves, they are similar in magnitude and small, indicating that an extra competitor reduces the fare by less than 1 percent (by 0.2 percent), regardless of which competition measure is used. Brueckner (2003) found a similar result, with competitive effects on the order of 1 percent in 18

20 various specifications. The domestic US study by Brueckner, Lee and Singer (2010) showed that, in domestic markets requiring a connection (like those in present international sample), the fare reduction from an extra (connecting) competitor is on the order of 2 percent. The reason for the smaller competitive effect on international routes is not entirely clear. But the fact that much of the competition in these markets comes from pairings of two different carriers, rather a single carrier providing connecting service, may weaken its effect, despite the airlines efforts to make such service as seamless as online travel. Before considering the fare impacts of the remaining covariates, it is useful to explore how a change in the sample affects the impacts of airline cooperation. Columns 3 and 4 of Table 1 show the regression results from the transatlantic sample, where the key coefficients are again virtually identical across the two competition specifications. Now, the fare reduction associated with online service is larger, at 18.9 percent in both specifications. Codesharing has a slightly larger impact than in the world sample, leading to a 3.9 percent fare reduction, while the impact of alliance service is much larger, at 7.6 percent. The effect of immunity, however, is slightly smaller, leading to a fare reduction of 4.4 percent. When combined, the three forms of cooperation reduce the fare by 15.9 percent, an effect that is about 3 percentage points less than the online effect. This difference, which is close to that from the world sample, is again statistically significant. The impact of competition is again slight. Turning to the effects of the remaining covariates, an additional coupon (indicating an additional connection and less convenience) reduces the fare, as expected, with the effect equal to percent. A US point of sale reduces the fare by 2.3 percent in the world sample while raising the fare by 3.6 percent in the transatlantic sample, matching the positive effect found by 19

21 Brueckner (2003) in his world sample. Even though most of the variation in route distance is capture by the city-pair fixed effects, the LDIST coefficient is nevertheless positive and significant across all the regressions, reflecting the higher carrier costs of providing longer trips. The POP coefficient is negative and significant in each case, possibly reflecting the benefits of economies of density, which may be passed on in lower fares on thick routes. The INCOME coefficient is positive in the world sample and negative in the transatlantic sample, for reasons that are not apparent Economy-class results Table 3 shows the regression results when the log of the restricted economy-class fare is used as the dependent variable. For the world sample, the economy fare reduction associated with online service is smaller than Table 2 s all-passenger value, at 10.5 percent. At 9.5 percent, the sum of the cooperation coefficients is now just one percentage point smaller than the online effect (though still significantly different). The fare reduction due to ATI (at 2.7 percent) makes a relatively smaller contribution than before, with the codeshare effect relatively more important (at 4.2 percent). These patterns persist in the transatlantic sample, shown in columns 3 and 4. The economy fare reduction from online service ( percent) is again smaller than the allpassenger online effect. The sum of the cooperation coefficients is correspondingly smaller for the economy case (at 12.6 percent), but it is now a bit closer to the online effect, with the 2 percentage-point difference again statistically significant. Antitrust immunity again makes a smaller contribution to the combined cooperation effect, with the ATI coefficient equal to percent. Thus, the bulk of the economy fare reduction from cooperation comes from codesharing and alliance service. Codesharing reduces the fare by 3.6 percent, while alliance service leads to 20

22 a reduction of percent, with both numbers closely matching the values in the allpassengers case. TABLE 3: ECONOMY CLASS RESULTS VARIABLES (1) (2) (3) (4) US-World Transatlantic (Economy) (Economy) US-World (Economy) Transatlantic (Economy) ONLINE ** ** ** ** ATI ** ** ** ** ALLIANCE ** ** ** ** CODESHARE ** ** ** ** COMP_ ** COMP_ ** ** COUPONS ** ** ** ** US_POS ** ** ** ** LDIST 0.398** 0.398** 0.211** 0.211** POP -1.48e-06** -1.47e-06** -7.59e-07** -7.55e-07** INCOME 2.41e-06** 2.36e-06** -1.29e-06** -1.28e-06** Constant 3.275** 3.275** 4.491** 4.488** Observations 2,564,270 2,564,270 1,415,910 1,415,910 Markets 60,204 60,204 35,741 35,741 Passengers 84,966,440 84,966,440 41,601,910 41,601,910 Adjusted R-squared Dati+Dalliance+CS SIGNIFICANCE TESTS (p-values) Dati+Dalliance+CS = singlecarrieronline ** p<0.01, * p<0.05 Market, year, and quarter fixed effects suppressed Carrier controls supressed Robust Standard Errors Dependent variable: lfare Sample is from Unweighted Regressions The coefficients of the remaining covariates are qualitatively similar to those in the allpassenger case. One notable difference is the smaller impact of an extra coupon, which now reduces the fare by percent Business-class results Table 4 presents the regression results where the log of the business-class fare is the 21

23 dependent variable. The online effects are larger than in Tables 2 and 3. Relative to traditional interline service, online service generates a percent reduction in the business-class fare in the world sample and a percent reduction in the transatlantic sample. Antitrust immunity also generates larger fare reductions than in the all-passengers and economy cases, with the effect similar in size between the world and transatlantic samples, at percent. VARIABLES TABLE 4: BUSINESS CLASS RESULTS (1) (2) (3) (4) US-World US-World Transatlantic Transatlantic (Business) (Business) (Business) (Business) ONLINE ** ** ** ** ATI ** ** ** ** ALLIANCE ** ** CODESHARE ** ** ** ** COMP_ COMP_ ** COUPONS ** ** ** ** US_POS 0.101** 0.101** 0.177** 0.177** LDIST 0.323** 0.323** 0.152** 0.151** POP 1.47e-06** 1.47e-06** -2.44e-06** -2.43e-06** INCOME 1.52e-05** 1.52e-05** -2.27e-05** -2.28e-05** Constant 4.073** 4.059** 7.853** 7.850** Observations 285, , , ,675 Markets 19,812 19,812 11,424 11,424 Passengers 5,683,870 5,683,870 2,151,700 2,151,700 Adjusted R-squared Dati+Dalliance+CS SIGNIFICANCE TESTS (p-values) Dati+Dalliance+CS = singlecarrieronline ** p<0.01, * p<0.05 Market, year, and quarter fixed effects suppressed Carrier controls supressed Robust Standard Errors Dependent variable: lfare Sample is from Unweighted Regressions In the world sample, the fare reductions from codeshare service drop to 1 percent and the fare reductions from alliance service drop below 1 percent, with alliance coefficients now insignificant. As a result, the sum of the cooperation coefficients, which equals percent, is only about half as large as the ONLINE coefficient, indicating that full airline cooperation 22