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Article Excerpt
This article investigates the consumer welfare consequences of the recent code-share agreement between Continental Airlines and Northwest Airlines. We develop a discrete choice model based on individual flight characteristics. This structural model recognizes that consumers (i) may have heterogeneous preferences for flight attributes, and (ii) may face different prices for the same flight. The empirical methodology also deals with the measurement error problem stemming from the absence of consumer-level data on prices. The estimation results suggest that, whereas the code-share agreement did not impact consumers significantly on average, it increased the average surplus of connecting passengers but decreased the average surplus of nonstop passengers. Interestingly, the magnitude of our welfare results may be attributed in large part to changes in product characteristics other than prices.
1. Introduction
* Code-share agreements, whereby an airline can market seats on some of its partners' flights, have been a common practice in the airline industry for the past 30 years. Yet, recent alliances among major domestic carriers in the United States represent a significant development in code-share practices. (1) Airline executives have publicly emphasized that their "customers are the beneficiaries" because these new alliances "deliver more choices, more frequencies, and more destinations to the traveling public." (2) Consumer advocates, however, are concerned that these agreements may reduce competition and consumer welfare. Because alliances may be challenged by policymakers if they harm consumers, it is important to evaluate the precise impact of this new form of code-share agreements on consumers. In the present article, we apply a discrete choice model to an original set of data, and we analyze the consumer welfare consequences of the first significant domestic code-share agreement among major U.S. carriers, the 1999 alliance between Continental Airlines (CO) and Northwest Airlines (NW). (3)
Code-share agreements have been traditionally implemented to enable an airline to sell tickets in new markets without having to operate any additional aircraft. For instance, major airlines have long-standing regional code-share agreements at their hub airports with commuter carriers that serve smaller markets. Likewise, U.S. airlines faced with restrictions on entry in foreign markets (cabotage laws) have formed international alliances with foreign carriers that allow them to market flights within their partners' domestic network. These alliances have been shown to benefit consumers, as they not only allow the partner airlines to market new destinations but they also typically lead to lower prices and higher passenger volumes. (4) These findings, however, may not extend to recent agreements between U.S. carriers, such as CO and NW, as they present distinctive features. In contrast with regional agreements, the CO-NW alliance spans the entire United States and involves major airlines competing across similar networks. In contrast with international alliances, CO and NW face no restrictions on entry in the United States, and they must compete in prices as they do not have antitrust immunity.
Although it generated much controversy at policy levels, the CO-NW code-share agreement was implemented in 1999 without being challenged by the U.S. Department of Transportation, or the U.S. Department of Justice. (5) Arguably, this agreement, as well as the other domestic code-share agreements that followed, remains subject to additional investigation under the antitrust laws, should evidence of significant consumer harm be brought forward. Few studies, however, have examined how the CO-NW agreement affected consumers. (6) Armantier and Richard (2006) provide evidence suggesting that the CO-NW alliance had mixed effects on consumers. In particular, they find that the implementation of the code-share agreement in a market was accompanied by a drop in average prices, but also by an increase in the average price paid by nonstop passengers. Armantier and Richard (2006), however, are unable to draw unambiguous conclusions because their reduced form analysis (i) cannot formally aggregate gains and losses across passengers and markets, and (ii) focuses exclusively on prices and passenger volumes, which prevents them from taking into consideration additional benefits stemming from (e.g.) the introduction of new flights or the improvements in the attributes of existing flights.
To measure adequately the multidimensional implications of the CO-NW code-share agreement on consumer welfare, we propose in the present article a mixed logit discrete choice approach for the decision problem of the airline consumer. There are few comparable discrete choice applications in the airline literature, with the notable exceptions of Peters (2006) and Berry, Carnall, and Spiller (2006). (7) These articles analyze a passenger's decision to purchase a ticket on any one of the flights proposed by an airline on a specific itinerary (e.g., a seat on any one of NW's nonstop flights between JFK, and LAX). Consumers are therefore assumed to value the aggregate characteristics of an airline's flights within an itinerary (e.g., the number of flights in the itinerary), rather than the characteristics of the specific flight on which the passenger actually travels (e.g., the actual price paid, the time of departure, and the duration of travel). As discussed in Almantier and Richard (2006), the CO-NW agreement may affect the number as well as the characteristics of individual flights in a market. Therefore, we need a model of consumer decisions at the flight level if we are to measure properly the various effects of the agreement on consumer welfare. We develop a model of consumer utility in which a consumer decides to purchase a seat on a specific flight based on that flight's attributes. In doing so, we recognize that consumers may have heterogeneous and possibly correlated preferences for flight attributes. Finally, unlike most discrete choice models developed for market-level data, our model accounts for the fact that the price of a flight may differ across consumers (depending, e.g., on the date of purchase).
We apply the model to a primary sample consisting of flight schedule and ticket price data for the period 1998-2001 that precisely identifies code-share flights. In this application, we encounter a measurement error problem, as the prices of the different flights in a market are not observed perfectly at the consumer level. To address this problem empirically, we acquired an auxiliary sample of airline tickets that provides detailed price, flight, and passenger information (e.g., dates of purchase and travel, flight schedule, and Saturday night stayover). The primary and auxiliary samples are then used in conjunction to estimate jointly the distribution of the measurement error and the discrete choice model.
The results suggest that the implementation of the code-share agreement did not impact consumers significantly on average. This finding contrasts with (e.g.) Brueckner and Whalen (2000) and Bamberger, Carlton, and Neuman (2004), who show that international and regional code-share alliances benefit consumers. We also find that, although neutral on average, the CO-NW code-share agreement did not impact all consumers equally. In particular, whereas the alliance increased the average surplus of passengers on connecting flights, the average surplus of nonstop passengers dropped significantly. Finally, our results highlight the importance of taking into consideration factors other than prices when analyzing consumer welfare. Indeed, our analysis reveals that, after the CO-NW code-share in a market, consumers benefit from lower average prices but are harmed by changes in other flight attributes, such as the duration of travel or whether the flight is nonstop and takes off during peak hours.
The article is structured as follows. We outline in Section 2 the basics of the CO-NW code-share agreement. The discrete choice model is introduced in Section 3, and we discuss in Section 4 its estimation in the presence of measurement errors in prices. In Section 5, we describe the primary and auxiliary samples. We discuss the estimation results in Section 6, and their economic implications in Section 7. In Section 8, we present the consumer welfare results. We test in Section 9 the robustness of the results to alternative specifications. Finally, we conclude in Section 10 with a discussion of the implications of our analysis for antitrust reviews of airline alliances and mergers.
2. The CO-NW code-share agreement
* In January 1998, CO and NW announced their intention to form a code-share agreement that included the U.S. market. Under the terms of the agreement, each airline is able to market seats on some of its partner's flights. The code-share flights are then listed twice in schedules, once by each airline with its own flight number and airline code. Moreover, the partners agree to coordinate flight schedules and operations to provide seamless service on code-share flights (e.g., one-stop check-in, automatic baggage transfers). The carrier operating the code-share flight determines seat availability for the marketing partner, but each airline commits to set prices competitively. All sales revenues go to the operating carrier. The marketing partner gets only a booking fee to cover handling costs (as travel agents do). Finally, the airlines agree to implement linkages in their frequent-flyer programs. (8)
Executives at CO-NW emphasized that their alliance would benefit consumers by (i) expanding the number of flights offered, (ii) opening new markets to their consumers, and (iii) improving the attributes of existing flights in markets in which they already operated. They claimed that their alliance would promote competition over the United States by creating "a fourth network to compete with the existing 'Big Three' airlines in the U.S.... Over 150 cities, 2,000 city-pairs, and three million passengers will gain a new airline competitor and new online connections through the alliance." (9)
To illustrate these claims, and to understand how the code-share agreement can affect consumers, consider three airports A, B, and C. Assume that CO (respectively, NW), operates flights in market A-B (respectively, B-C), but not in market B-C (respectively, A-B). The alliance enables CO-NW to pair their existing flights, and thereby offer code-share flights in market A-C that connect through airport B. The partners can therefore expand the number of flights they offer in market A-C without having to operate a new aircraft. In particular, if market A-C was not served by either CO or NW before the agreement, then the alliance enables CO-NW to open this market to their consumers. Of course, traveling between A and C was previously possible by purchasing two different tickets, one from CO and one from NW These so-called interline flights, however, are rare in practice (see Morrison and Winston, 1995), as they typically entail unfavorable features and, in particular, higher prices due to double marginalization. (10) In contrast, CO-NW can propose code-share flights in market A-C at a lower price and with seamless service. Finally, consumers may also benefit from shorter transit and travel times on code-share flights, as CO-NW were allowed to coordinate their flight schedules.
The economic evidence available at the time seemed to support the claim that code-share alliances benefit consumers. Morrison and Winston (1995), for instance, provide evidence that customers dislike interline flights, whereas Park (1997) explains how airline alliances might enhance flight options and social welfare. Park and Zhang (1998, 2000), as well as Brueckner and Whalen (2000), then show how international alliances between U.S. and foreign carriers had allowed the alliance airlines to expand flight options and markets served. They also provide evidence of lower prices in transatlantic markets in which these alliances competed. Lastly, Bamberger, Carlton, and Neuman (2004) give evidence that regional alliances in the United States allowed the partner airlines to expand the number of markets in which these airlines competed, resulting in lower average fares for consumers in those markets.
Nevertheless, the CO-NW proposal, given its distinctive scope and its focus on the entire U.S. market, generated much controversy at policy levels, prompting numerous hearings on its competitive implications. Concerns were primarily expressed about the possibility for the agreement to lower the incentives of CO and NW (i) to enter markets in which only one of the partners already operated, (ii) to maintain competing flights in markets in which they jointly operated, and (iii) to compete in prices. In October 1998, the U.S. Congress granted the Department of Transportation (DOT) the authority to delay the implementation of domestic alliances pending a review of their effects. In November 1998, the DOT decided to allow the implementation of the alliance without a formal investigation, after CO and NW consented not to code-share flights in markets between their respective hub airports. The DOT, as well as the U.S. Department of Justice, presumably retained the right, however, to challenge the agreement after data became available, to ensure that the alliance does not harm the public and is not anti-competitive.
In Armantier and Richard (2006), we report on some of the changes that followed the January 1999 implementation of the CO-NW code-share agreement. We summarize here the findings most relevant to the present analysis. By 2000, CO and NW code-shared in 26% of their combined markets. They chose to code-share mainly in markets they already served prior to the alliance. More specifically, at least one of CO or NW was present in 1998 in 88% of the code-shared markets. In that regard, the CO-NW alliance differs notably from traditional regional and international agreements, in which the partners essentially code-share flights in markets where none of them would otherwise operate. The alliance also appears to have enabled the partners to exploit the geographical complementary in the location of their hubs. In particular, 64% of NW's code-share passengers connect through CO's southern hub (Houston), whereas 64% of CO's code-share passengers connect through NW's northern hubs (Minneapolis and Detroit).
When CO-NW code-shared in a market in 2000, (i) an average of 9% of their passengers traveled with a code-share ticket, (ii) virtually all code-share passengers (96%) traveled on connecting itineraries, and (iii) the number of connecting flights offered in the market increased by 15%, on average, whereas the number of nonstop flights remained essentially unchanged. This increase in connecting flights is mostly attributable to CO-NW (+29% on average), although their competitors also increased their number of connecting flights by 5% on average. Lastly, we found that the alliance had mixed effects on prices. Indeed, after the implementation of the agreement in a market, average fares for connecting flights declined by 5%, whereas average fares for passengers traveling nonstop increased by 11%. These variations in prices seem consistent with the conjecture that CO-NW have used the introduction of code-share connecting flights as a way to price discriminate more effectively between passengers with different willingness to pay for nonstop and connecting flights (see Ito and Lee, 2007 for a similar conjecture).
The mixed results in Armantier and Richard (2006) did not allow us to draw any consumer welfare conclusions for the CO-NW alliance. Indeed, the reduced form analysis adopted does not provide the means to compare the relative gains and losses to consumers across markets, and it does not account for additional potential benefits, such as the introduction of new products or the improvement of existing products. (11) In the present article, we propose a discrete choice model of consumer decisions that quantifies the multidimensional welfare implications of the CO-NW code-share agreement.
3. A discrete choice model
* We start by formalizing some of the concepts on which we build our model. Following Berry, Carnall, and Spiller (2006), we define a market as a round-trip travel from an origin airport to a destination airport, with a departure date within a specific quarter. (12) Markets are defined directionally. For instance, a round-trip in a given quarter from Pittsburgh to Miami, and a roundtrip from Miami to Pittsburgh in the same quarter, are two different markets. A product in a market is a ticket for a seat on a sequence of flights offered daily that link the origin to the destination, and the destination to the origin. The product is nonstop if it consists of a single nonstop flight each way. If the product requires at least one transfer at an intermediate airport, then the product is said to be connecting. A product belongs to an airline itinerary, where the airline is the carrier selling the ticket, and the itinerary is the sequence of airports that are part of the round-trip (origin, destination, and intermediate transfer airports, if any). When the airline marketing the product differs from the airline actually operating one of the flights in the product, then the product is a code-share. In contrast, the flights in an interline product are not only operated but also marketed by two different airlines.
As explained below, the consumer's choice set, denoted T, is composed of products j = 0, ..., J, where j = identifies an outside good representing the decision of the consumer not to purchase any of the J airline products in the market. The outside good is assumed to encompass all means of transportation between the origin and destination airports other than airlines. Following convention, the mean indirect utility of the outside good will be normalized to when we estimate the discrete choice model. Following Berry (1990) and Berry, Carnall, and Spiller (2006), we assume that the market size N is proportional to [POP.sub.t], the geometric mean of the population in quarter t at the metropolitan areas for the airports in the market (source: U.S. Census data for 1998-2001). In addition, we specify the proportionality factor to allow for exogenous variations in the market size over time. In other words, we define N = ([[phi].sub.0] + [[phi].sub.1]t) [POP.sub.t], where ([[phi].sub.0], [[phi].sub.1]) are parameters to be estimated.
To define a manageable choice set T, we assume that a consumer is initially endowed with an exogenous type [tau] characterizing in particular her time of purchase, dates of travel, class of travel, and airports of origin and destination. For each product, a consumer is then quoted a specific price consistent with her type. (13) A market therefore consists of N heterogeneous consumers who choose among the same set of J + 1 products, but each consumer faces a different vector of prices. In other words, a consumer in our model does not choose her time of purchase, travel dates, class of travel, and market. She only selects one of the J + 1 alternatives based on their characteristics and the prices specifically quoted to her.
We recognize that our model imposes some restrictions on the consumers' possible choices. In particular, a passenger with a given type (e.g., a business passenger) cannot purchase certain tickets...
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