The Effects of Code-sharing on Airline Fares and Traffic: The Case of
Delta-Continental-Northwest Code-share Alliance1
Adile TAMGUICHT2
Preliminary Version
Abstract
Code sharing allows an airline to sell seats on partners' flights as if they were its own. In
1999, a code-sharing agreement was formed between Continental and Northwest, and four
years later Continental, Delta, and Northwest started a new code-sharing agreement. The U.S.
Department of Transportation expressed concerns that Continental-Delta-Northwest may take
advantage of the previous Continental-Northwest code-sharing alliance to collude on prices
and service levels. The paper conducts an empirical analysis with original data to investigate
the effect of the Continental-Delta-Northwest agreement on fares and traffic, and to test
whether the effect of alliance between the three airlines depends on the existence of the
Continental-Northwest code-sharing agreement in the pre-alliance period. Following this
analysis, we will examine how variations of the number of operating carriers and various
types of code-sharing formats affect fares and traffic. Results suggest that average fares
decrease and traffic increases in markets in which there was a high level of competition in the
pre-alliance period after the creation alliance between the three airlines in those markets. We
also found that the extent of the effect of the alliance depended on the existence of the
Continental-Northwest alliance in the pre-alliance period.
JEL Classifications: L1, L9
Keywords: Airlines, Code-sharing, Alliances
Section 1: Introduction
Before the deregulation3 of the airline sector in the U.S., fares, routes, and schedules were
regulated by the U.S. federal aviation agency, but after deregulation these controls were
removed. Thus, each airline can choose its fares, enter into any route or market, and choose its
schedules and travel frequency. The airline industry has adopted several major innovations.
For example, airlines have reorganized their route structures into hub-and-spoke networks4,
used frequent-flyer programs, and employed computerized reservations systems (CRS). But
the most significant developments in the airline industry following deregulation of the sector
1
The author is infinitely indebted to Abraham Hollander for research guidance and fruitful discussions.
Department of Economics, University of Montreal, C.P. 6128, succursale Centre-ville, Montreal QC H3C3J7,
Canada. E-mail address: [email protected]. Tel: 1- (514) 678-6588
3
The Airline Deregulation Act of 1978 removed many controls in the U.S.
4
The major airlines adopt some cities as centers for their operations; these cities served as stops for most flights,
even if they were not on a direct route between two other end points.
2
1
are the different forms of cooperation, including alliances, in which the carriers have become
engaged. The number of these alliances has been increasing in recent years, especially in the
U.S. market.
Alliances between airlines vary from a limited marketing arrangement, such as sharing
frequent-flyer programs, to more complex agreements such as code sharing. Code sharing
forms the basis of most airline alliances and allows airlines to sell seats on partners’ flights as
if these flights were their own. Firms use code-sharing agreements for different reasons, such
as indirect entry into markets where costs and regulatory barriers would make direct entry
impossible, the expansion of networks, and an increase in service quality. To illustrate that,
section 2 uses examples to present the benefits of using code-sharing agreements. Section 2
also presents how code sharing works. Code-sharing agreements were first observed in
international aviation − for example, the code-sharing agreement among American Airlines
and KLM. In recent years, code sharing among domestic airlines in the U.S. has become very
common- for examples, the code-sharing agreement among US Airways and United Airlines
and the code-sharing agreement among Continental, Delta and Northwest.
In this paper, we examine the effect of the code-sharing agreement between Continental,
Delta, and Northwest5 implemented in June 2003. We analyze the effect of code sharing
among the three airlines on fares and traffic. We also analyze the effect of the three airlines’
code sharing with respect to the number of operating6 partners and the type7 of code sharing
in each market. Finally, we examine whether there is collusive behaviour between the three
airlines.
Code sharing may have beneficial and harmful effects on consumers. A code-sharing
agreement could benefit consumers in several ways. These include creating new online8
destinations that consumers prefer, increasing flight frequencies, providing better connections,
and giving consumers more frequent-flyer award destinations and other spin-offs. However,
code sharing can also disadvantage consumers. For example, competition may be reduced,
which may harm consumers, especially in markets in which both airlines previously competed
and in markets where code sharing gives the partners a dominant market position.
The alliance partners could also use operating and marketing strategies to create entry barriers
for other airlines. These could include operating strategies such as slot controls and gate
constraints, and marketing barriers such as frequent-flyer programs and travel agent
commission overrides, particularly in markets dominated by a given airline. The following
example will help to understand how these exclusionary strategies are helped by code sharing.
Suppose a case in which Continental dominates operations in an airport. Continental may give
its partners fewer gate constraints and more additional services than it gives to its rivals,
which, as a result, may find that it is not optimal to enter the market. This is an example of an
operating strategy that may be used as an entry barrier.
The announcement of a code-sharing agreement between airlines has always generated
considerable concern because of the possibility that competition decreases among partners.
For example, in 1998, a code-sharing agreement between Continental and Northwest was
5
Continental, Delta, and Northwest are among the five biggest domestic airlines in the U.S.
The carrier whose aircraft passengers board is called operating carrier.
7
In this paper, we consider two types of code sharing: traditional and virtual. Section 2 presents these types of
code sharing.
8
Online flight refers to all flights on an indirect route with the same airline.
6
2
announced; many experts opposed the formation of this alliance because they thought
competition between alliance partners would decline. The Department of Transportation
(DOT) allowed the implementation of the alliance between Continental and Northwest on two
conditions. The first condition was that Continental and Northwest could not code-share
flights in markets between their hub airports because it might give a dominant market position
to alliance partners and create barriers to entry. The second condition was that the DOT could
challenge the code-sharing agreement after the data became available if there was collusion
between both airlines or if they used anti-competitive practices. These conditions were
introduced as a response to the concern and as a way to prevent possible collusion between
airlines. Finally, code sharing between Continental and Northwest was initiated in 1999.
In August 2002, Delta Airlines, Continental, and Northwest, which are three of the five
largest carriers in the U.S., submitted code-sharing and frequent-flyer reciprocity agreements
to the U.S. Department of Transportation (DOT) for review. The fact that the three airlines
had large networks and that there was already a code-sharing agreement between Continental
and Northwest raised questions about the expected negative effects of this proposed alliance.
In fact, the DOT expressed concerns that the proposed alliance might reduce competition and
facilitate collusion between partners. The DOT imposed some restrictions on the three airlines
before giving approval for the alliance. One of the restrictions was that the airlines could not
coordinate between them to determine fares and capacity. In other words, even if airlines had
a code-sharing agreement, each airline would have to choose its capacity independently. It is
clear that the DOT imposed this condition to eliminate the possibility of collusion between
airlines. Another restriction was that the code-shared flights could not be listed more than
twice in the computer reservations systems (CRS). This condition, as will be explained later,
was imposed so as not to crowd out rivals’ flights from the CRS. Also, the alliance partners
could not code share flights in markets between their hub airports because it might give a
dominant market position to alliance partners. The DOT imposed many other restrictions of
the three airlines before approval of the alliance9.
After the introduction of code sharing between the three airlines, we can distinguish between
the three types of markets that will be considered in our paper. The first type, called New
Alliance market, is a market where there was no code-sharing agreement between Continental
and Northwest before June 2003, but there was a code-sharing agreement between the three
airlines after June 2003. The second type, called Pre-existing Alliance market, is a market
where there was a code-sharing agreement between Continental and Northwest before 2003
and a code-sharing agreement between the three airlines after 2003. Finally, the last type,
called Disappearing Alliance market, is a market where there was a code-sharing agreement
between Continental and Northwest before 2003, but there was no code sharing between the
three airlines after 2003. To study the effects of the three airlines’ code sharing on fares and
traffic, we will focus on results related to New Alliance and Pre-existing Alliance markets.
However, to find out whether the effect of the three airlines’ alliance depends on the existence
of the code-sharing agreement between Continental and Northwest before June 2003, we will
examine whether the three airlines code sharing affects fares and traffic in New Alliance
markets differently than in Pre-existing Alliance markets.
Four years have passed since the code-sharing agreement was introduced between
Continental, Delta, and Northwest. However, no formal study analyzes the effect of this code
9
Examples of restrictions are: the three airlines did not have the ability to offer joint bids to corporate customers
and travel agencies, the carriers must respect that their services be listed under no more than two codes in
computer reservation systems (CRS).
3
sharing on prices and traffic. Several questions need to be answered. What is the effect of the
Continental-Delta-Northwest code-sharing agreement on prices and traffic? Has there been
any collusive behaviour between these airlines after the introduction of the code-sharing
agreement? What is the effect of the code sharing on prices and traffic in markets where there
was a code-sharing agreement between Continental and Northwest before 2003?
This paper contains four main segments. First, we study the overall effects of the three
airlines’ code sharing-agreement on fares and traffic. After that, we use three interactive
analyses. The first one is to examine whether the effect of code sharing depends on the level
of competition in the pre-alliance period. In fact, the use of a code-sharing agreement may
give incentive to the partners to increase fares. However, the use of code sharing in the case of
a high level of competition in the pre-alliance period may give incentive to rivals to compete
more aggressively, which decreases fares. The second interactive analysis is to analyze
whether the effects of code sharing on fares and traffic depend on the variation of the number
of operating partners in the post-alliance period with respect to the pre-alliance period. In fact,
the existence of numerous operating partners in a market gives the possibility to partners to
increase the traffic. However, it may also give incentive to partners to coordinate among
themselves. Finally, the last interactive analysis consists of examining whether the effect of
code-sharing depends on the type of code sharing used by partners themselves. Following Ito
and Lee (2007), several types of code sharing exist in the U.S. domestic airline market.
Section 2 presents detailed explanations of these different types of code sharing and also the
effect of each of them on fares and traffic.
The overall market effects of code sharing and the overall effects of different interactive
analyses are exposed by estimating a series of before-and-after regression models, using a
sample of 21,640 airports-pair over the pre- and post-alliance periods. From the first part of
the analysis we find that the effects of the Continental-Delta-Northwest code sharing depend
on whether there was a code-sharing agreement between Continental and Northwest in the
pre-alliance period. Moreover, the effects of the three airlines’ alliance depend on the level of
competition in the pre-alliance period.
This paper is organized as follows: Section 2 details how a code-sharing agreement works, the
effects of code sharing, and the code-sharing agreement between Continental, Delta, and
Northwest; the paper also presents a literature review. Section 3 presents the data used in our
paper. Section 4 presents the baseline model and its associated results. The results of the three
interactive analyses are presented in section 5. Finally, section 6 contains the conclusion.
Section 2: Code sharing
2-1 How code sharing works
Before explaining the process of code sharing between airlines, we give some definitions to
facilitate an understanding of the process. We start by defining a coupon, which represents
travel on a particular flight segment between two airports. A flight itinerary consists of one or
more flight coupons. For example, if a flight itinerary between Detroit and California consists
of one flight coupon, that means there is at least one carrier offering a non-stop service
between Detroit and California. However, if a flight itinerary between Detroit and California
consists of two flights coupons, this means there is a single carrier offering a one-stop flight
between Detroit and California through Dallas, or two operating carriers − one offering a
service between Detroit and Dallas, and the other offering a service between Dallas and
4
California. The term ‘online flight’ refers to all flights on an indirect route with the same
airline. A flight on an indirect route that is hosted by at least two airlines is known as an
‘interline flight’. Passengers prefer online to interline flights. This is why airlines try to offer
online flights as much as possible.
Code sharing is part of most airline alliances and allows airlines to sell seats on partners’
flights as if these flights were their own. An example of code sharing is the Continental
service from Detroit to California. Delta, which has a code-sharing agreement with
Continental in that market, sells tickets to its passengers on Continental’s flights. The service
is operated with Continental’s fleet and crew, but both carriers can book the flight as their
own for their customers. The airline whose aircraft passengers board is called the operating
airline, while the airline that sells the flight is referred to as the marketing airline. In other
words, a marketing carrier is defined as the carrier that markets seats to its customers, sets its
fares independently, and does not use its own aircraft to operate the flight; it uses its partners’
aircraft − the operating carriers. For each flight coupon, there is a single operating carrier and
at least one marketing carrier. Code sharing between carriers requires the presence of a
marketing carrier different from the operating carrier.
Each airline is assigned flight designator codes used for reservations, schedules, and other
purposes. The code, which consists of two letters followed by a number, refers to a flight and
destination. Code sharing is a practice in which one particular flight receives the designations
of its code-sharing partner’s (or partners’) codes in the computerized reservation systems
(CRS). In other words, a code-shared flight is assigned the code of the operating carrier and
the code of the marketing carrier(s), which are the operating carrier’s alliance partners. For
example, before the code-sharing agreement between Continental (CO) and Delta (DL), the
flight between Detroit and California operated and marketed by Continental Airways was
coded ‘CO’ on CRS. After code sharing was instituted between Continental and Delta, that
flight also appeared on CRS with the Delta code ‘DL.’ Therefore, a single flight appears twice
on a CRS, once with the operating carrier’s code and once with the codes of the operating
carrier’s partners. The US Department of Transportation requires the addition of a star ‘*’
beside the codes of marketing carriers which don’t operate the flight. For example, given the
above, on the CRS we will have two codes: one will be ‘CO’ and the other will be ‘DL*’.
Airlines set prices independently, even if a carrier sells tickets on its partner’s flights, and the
operating carriers don’t affect prices fixed by marketing carriers.
Code-sharing agreements operate under either the blocked space system or the free-sale
system. Under the blocked space system, aircraft capacity is shared between marketing
carriers and the operating carrier. The marketing carrier buys a block of seats from the
operating carrier, sells them to its passengers as its own seats, and keeps all the revenue from
those sales. The operating carrier can’t sell any of the seats earned by the marketing carrier,
and both carriers fix fares independently. Under the free-sale model, all partners have free,
real-time access to the operating carrier’s seats, and there is no fixed limit on how many seats
the marketing carriers can sell. Moreover, the marketing carrier determines its fares
independently from the operating carrier. All revenue from seats the marketing carrier sells
under the free-sale system is kept by the operating carrier. For example, suppose a passenger
buys an indirect ticket from A to C through B from Continental, where the flight from A to B
is operated by Continental and the flight between B and C is operated by Delta. We also
suppose that both airlines have a code-sharing agreement between them and operate under a
free-sale system. Continental would keep all the revenue generated from A to B, and Delta
would keep all the revenue generated from B to C. The question that arises is why Continental
5
would accept this kind of code-sharing agreement. To answer this question, let us imagine a
scenario where there is no code-sharing agreement between Continental and Delta. Then, the
passenger who is looking for a flight from A to C will not buy his ticket from Continental
because it doesn’t offer the service and it doesn’t have any code-sharing agreement. As a
result, the passenger will buy his ticket from another carrier and Continental will lose this
passenger. Therefore, it is preferable for Continental to accept the code-sharing agreement to
earn positive revenue from A to B, rather than losing the passengers altogether and having no
revenue between A and B.
Domestic airlines, in contrast with international carriers, use different types of code-sharing
agreements. To illustrate those types of code-sharing agreements, consider the service
between cities A, B, and C. Assume that there are two carriers, Continental (CO) and Delta
(DL). We assume both carriers offer no non-stop flights between A and C, and each offers
one-stop flights between A and C through B. In the case where no code-sharing agreements
exist between Continental and Delta, passengers have four choices to travel from A to C.
There are two online flights and two interline flights. We denote a flight between A and C by
‘X1/X2’-‘Y1/Y2’, where X1 and Y1 denote operating carriers between A and B and between
B and C, respectively; and X2 and Y2 denote marketing carriers between A and B and
between B and C, respectively. Therefore, in our example, the two online flights are denoted
by ‘CO/CO’-‘CO/CO’ and by ‘DL/DL’-‘DL/DL’, and the two interline flights are denoted by
‘CO/CO’-‘DL/DL’ and by ‘DL/DL’-‘CO/CO’. In the case of code sharing between
Continental and Delta, passengers have additional choices to travel from A to C. The first one
is a flight denoted by ‘CO/CO’-‘DL/CO*’. In this case, there are two operating carriers, CO
and DL, and one marketing carrier, CO. This is a case of traditional code sharing that was first
used in international flights. In general, traditional code sharing is when there are two
operating carriers and one code-shared segment. The second one is a flight denoted by
‘CO/CO’-‘CO/DL*’. In this case, there are one operating carrier, two marketing carriers, and
one code-share segment. This case is called a semi-virtual case. Generally, semi-virtual cases
are those in which there are one operating carrier, two marketing carriers (one of which is also
the operating carrier), and one code-sharing segment. Finally, the last type of code sharing is a
flight denoted by ‘CO/DL*’-‘CO/DL*’, where there are one operating carrier, one marketing
carrier different from the operating carrier, and two code-sharing segments. This last case is
called a virtual case. Ito and Lee (2007) were the first to introduce these different types of
code-sharing agreements.
2-2 The Continental-Delta-Northwest Alliance
During the first half of 1998, three airline alliances that involved the six largest domestic
carriers in the United States were announced. The first one, involving Continental Airlines
and Northwest Airlines, was announced in January 1998. The second, involving American
Airlines and US Airways, and the third, involving Delta Airlines and United Airlines, were
announced in April 1998. The alliances’ objectives were to set code-sharing agreements
between partners.
The announcement of these alliances generated considerable controversy. While the airlines
claimed that the proposed alliances would benefit consumers, others, including experts and
politicians, argued that the alliances would lead to a reduction in competition among domestic
carriers and then a decrease of services offered and an increase in prices. As a result,
Congress granted the Department of Transportation the authority to delay alliances that the
Department believed could have an anti-competitive impact on consumers. The
6
Continental/Northwest alliance went forward from January 1999, but the other proposed deals
were abandoned. The DOT allowed the implementation of the alliance between Continental
and Northwest on two conditions: The first was that Continental and Northwest could not
code share flights in markets between their hub airports. A carrier’s hub airport is known by
the fact that the carrier is the dominant airline in the airport and controls all the services and
operations. Thus, the first condition was designed to prevent the carriers’ ability to prevent
new entry and future competition on the market between their hub airports. The second
condition was that the DOT could challenge the code-sharing agreement after data become
available if there was collusion between both airlines or if they used anti-competitive
practices.
The Continental-Northwest alliance includes an agreement to code-share on specific routes. In
other words, each airline allows its partner to sell seats on its flights. Therefore, each airline
can offer additional choices and services to potential travelers. Continental and Northwest
started by practicing the traditional code-sharing agreement, but in time they started using
other types of code sharing as well, such as semi-virtual and virtual code sharing.
In August 2002, Delta Airlines, Continental, and Northwest, which are the largest carriers in
the U.S., submitted code-sharing and frequent-flyer reciprocity agreements to the DOT for
review. The fact that the three airlines had large networks and that there was already a codesharing agreement between Continental and Northwest raised questions about the expected
negative effects of this proposed alliance. In fact, the DOT stated that the proposed alliance
could reduce competition and facilitate collusion between the partners. The DOT gave
conditional approval of the alliance subject to acceptance of the conditions. One of the
restrictions the DOT imposed was that the airlines were not allowed to coordinate fares and
capacity. It is clear that the DOT imposed this condition to eliminate the possibility of
collusion between the airlines. Another restriction was that code-shared flights must not be
listed more than twice in the computer reservations systems (CRS). This condition, as already
explained, was imposed so that rivals’ flights wouldn’t be crowded out from the CRS. Also,
the alliance partners were not allowed to code-share flights in markets between their hub
airports. The DOT imposed many other restrictions of the three carriers before approval of the
code sharing agreement10.
The main ideas behind those conditions were to ensure that competition would not be reduced
between the alliance’s partners, and that consumers would still benefit from a greater choice
of flights and frequent-flyer programs. The three carriers agreed to the DOT restrictions and
started their code-sharing alliance in June 2003.
The alliance between Continental, Delta, and Northwest has become the most important
alliance in the U.S. aviation market. In fact, the combined domestic share of this alliance is
about 31% of the whole market. In contrast, the combined domestic shares of the United/US
Airways alliance and that between American/Alaska, two other important alliances in the
market, are about 17% and 16%, respectively.
2-3 Effects of code sharing
Code sharing helps carriers expand their route network without large resource allocations.
Carriers can cover a larger network through their partners in the alliance, and it is much more
10
See note 9.
7
extensive than either carrier would be able to cover on its own. Also, the service provided
through code sharing replicates the seamless travel a single airline would offer. Passengers
generally prefer this type of service because it offers the convenience of single ticketing and
check-in. At the same time, code sharing provides airline companies with the opportunity to
capture new customers. To illustrate this, assume Continental offers a flight between two
airports, A and B. Delta doesn’t offer a service between A and B, but has a code-sharing
agreement with Continental whereby Delta can sell tickets on Continental’s flights. If Delta
and Continental didn’t have any code-sharing agreement, Delta wouldn’t have any passengers
between A and B. However, the fact that Delta has a code-sharing agreement with Continental
gives it the opportunity to sell tickets between A and B and then to have new consumers.
Code sharing can generate pro-competitive effects in a variety of ways. For example, it could
increase competition by allowing two carriers or more to market the same seat. This is due to
the creation of new online services offered by code-sharing partners. Therefore, a carrier may
compete in a market even if it doesn’t offer a service in the market.
Code-sharing agreements also allow a carrier to increase its frequency of service between two
cities. To illustrate this, we suppose the case of two carriers, 1 and 2. Carrier 1 has four flights
per day between cities A and B, and only one flight per day between cities B and C. Thus,
Carrier 1 can offer only one flight per day between cities A and C through B. If Carrier 2 has
three flights per day between cities B and C and those flights are code shared with Carrier 1,
the latter can offer up to four flights per day from A to C. Therefore, the frequency of service
between A and C increases due to code sharing. The increase in frequency increases the
service quality, which is another reason why an alliance between carriers works. Moreover,
the alliance partners may coordinate their schedules to create better connections at airport B,
and therefore passengers looking to travel from A to C, through B, spend less waiting time at
airport B.
Because of the double marginalization, interline itineraries are more expensive than
comparable online itineraries. The double marginalization that arises from pricing A to B and
B to C separately disappears, due to the alliance and coordination between carriers; thus, the
price should fall. Double marginalization is due to the fact that each airline in the interline
itinerary maximizes its profit from its own segment independently from other carriers.
Therefore, code-sharing agreements are expected to decrease airfares because of the existence
of one single airline controlling prices over the entire itinerary. This decrease in airfares will
benefit passengers and represents another example of how code sharing benefits consumers.
Carriers are always looking for ways to reduce their costs, and code-sharing agreements may
help them achieve this goal. Expansion of a carrier’s network through code sharing potentially
provides the carrier with shared facilities. These shared facilities are due to the fact that code
sharing enables the airline to serve new markets without having to expand other parts of its
operations system. There are also economies of scope in terminal operations. In fact, a code
sharing which increases the partner carrier’s traffic on code-shared routes will result in
economies of scope since there will be more intensive use of aircraft and services offered.
Code sharing may facilitate a carrier’s entry into new markets by reducing the costs involved,
especially the sunk costs associated with launching additional services using its own aircraft.
This result is easy to understand in the case of thin markets, where it may not be profitable for
a carrier to serve on its own because of the size of the market and the high level of sunk cost
required. As a consequence, it will be more profitable for the carrier to have a code-sharing
agreement with a small carrier that serves the thin market instead of serving the market with
8
its own aircraft − for example, the alliance between Northwest and Alaska Airlines and
between Continental and America West.
The partners of an alliance benefit from having greater bargaining power with aircraft and
aero engine manufacturers for the purchase of aircraft and equipment than individual airlines
acting on their own could ever conclude. However, the increase of bargaining power is not a
part of code sharing between airlines, but may be a form of alliance between carriers. Some
other possible ways to reduce costs include the joint use of airport facilities, such as lounges
and gates, and sharing advertising and promotion programs. Finally, another reason carriers
enter into alliances is to overcome numerous national and international restrictions. For
example, if only US carriers have the right to sell tickets for US domestic travel, a non-US
carrier doesn’t have access to that market. However, a code-sharing agreement between a US
carrier and a non-US carrier may give the latter access to US domestic airlines and allow it to
sell tickets on the US domestic market.
In spite of all the benefits to consumers, domestic alliances have the potential to decrease
competition by reducing the incentive for alliance partners to compete with each other.
Therefore, airfares may increase and services may decrease. Also, code sharing may crowd
out the itineraries of other carriers on the computer screens of travel agents or travellers trying
to book tickets, as shown by Bamberger, Carlton, and Neumann (2004). To illustrate this
situation, suppose two carriers, 1 and 2, offer a non-stop service between two cities. In the
case of no code sharing between Carriers 1 and 2, each carrier’s flight appears once on the
CRS. However, in the case of code sharing between Carriers 1 and 2, each carrier can sell
tickets on the other airline’s flight; thus, each carrier has the capability of offering two
different flights, and both carriers’ flights will appear four times on the CRS. This situation
may favour code-sharing carriers and may result in less competition and higher fares.
Finally, an alliance may increase the multi-contact between code-sharing partners and other
carriers, which could reduce the competition between them. When firms compete with each
other simultaneously in several markets, interdependence among them may be created in such
a way that a desirable competitive process could be reduced tacitly or in a coordinated way.
This can be explained by the fact that firms that meet each other in several markets may have
incentives to relax competition because they will recognize the interests of their rivals in some
markets and will respect them in the expectation that their rivals will reciprocate their interest
and will recognize and respect them. There have been numerous empirical works that test the
effect of multi-market competition on competition, such as those by Evans and Kessides
(1994), Parker and Roller (1997), and Pilloff (1999).
2-4 Literature
In the theoretical literature, Park (1997) examined the consequences of parallel and
complementary11 strategic alliances on output levels, profits, and social welfare, which is the
sum of the surplus of consumers and the profits of firms. He used a Cournot competition
model and assumed that alliance partners equally share the profit from the joint operation. He
showed that parallel alliances reduce social welfare, and complementary alliances raise social
11
The complementary alliance refers to the case where two firms link up their existing networks and build a new
complementary network to provide improve services for connecting passengers. The parallel alliance refers to
collaboration between two firms competing on the same routes.
9
welfare. He explained his result by the fact that parallel alliances reduce competition between
partners, leading to an increase in prices and then to welfare losses. However, a
complementary alliance gives partners the possibility of entering into markets they did not
serve before, in which case their competition increases, leading to a decrease in prices and an
increase in social welfare.
To study the effect of code sharing on surplus of passengers, Hassin and Shy (2004) used a
Hotelling model and assumed that passengers’ preferences for airlines are heterogeneous.
They showed that code-sharing agreements generate additional travel opportunities for
passengers, which enhance welfare. Specifically, passengers who have high preferences for an
airline that does not serve a connection entirely can benefit from code sharing because they
get the opportunity to use a flight entirely marketed by the preferred airline. As a
consequence, they concluded that no passengers are worse off, but some passengers are
strictly better off with code-sharing agreements.
In the empirical literature, Gellman Research Associates (1994) examined the effect of codesharing agreements between British Airways and USAIR, and between KLM and Northwest.
They found that the market share of the contracted carriers and the surplus of passengers
increased.
Park and Zhang (1998) studied the effects of regional alliances on output. They looked at
changes in traffic (number of passengers) on the relevant routes and compared them to routes
that were excluded from the alliance. They were interested in four major North Atlantic
alliances in the period 1992-94. They found a significant increase in the number of passengers
traveling on the alliance routes, compared with routes that were excluded from the alliance.
Park and Zhang (2000) examined the effects of code sharing on airfares, number of
passengers, and consumers’ surplus by looking at four major international alliances. To do so,
they compared the periods before and after alliances were formed. They also took into
account structural changes such as the presence of an alliance or not, the size of the
population, and income. They found that parallel alliances tend to decrease output and the
surplus of passengers. However, complementary alliances tend to increase output and the
surplus of passengers.
Brueckner and Whalen (2000) examined the effect of an alliance among partners based in
different countries. They showed that alliance partners charge interline fares that are
approximately 25 percent below those charged by non-allied carriers. They showed that this
reduction is due to the internalization of a negative externality that arises from the
uncoordinated choice of interline fares in the absence of an alliance. They also showed that an
alliance between two previously competitive carriers would raise fares by about 5%; however,
the effect is not statistically significant.
Armantier and Richard (2006) studied the effects on prices and traffic of the alliance between
Continental and Northwest that was formed in 1999. They found lower prices and higher
traffic across markets in which Continental and Northwest code shared. They also found
significantly higher prices across markets with Continental and Northwest’s non-stop flights
compared with the rest of the markets. They explain that as Continental and Northwest have
used their code-sharing agreement to expand the pool of passengers to whom they can sell
seats on their aircrafts, they have in turn extracted a higher average price.
10
Ito and Lee (2007) studied code sharing in the domestic airline sector. They observed that in
contrast with international code sharing, domestic code sharing involves one operating carrier
in most cases. This is why they disaggregated itineraries into six main types: pure online, nonallied interline, allied interline, traditional code share, semi-virtual code share, and virtual
code share. They used original data to determine the effect of each type of code sharing on
fares. They found that virtual code sharing itineraries are priced lower than itineraries
operated and marketed by a single carrier in the same market.
Our paper contributes to the literature by studying the specific Continental-Delta-Northwest
code-sharing alliance on fares and traffic, which started in June 2003, and examines whether
the effects of the three airlines’ alliance depend on where a prior code sharing between
Continental and Northwest was in effect. It also studies the effects of the three airlines’
alliance with respect to the variation in the number of operating carriers present in the market
and the type of code sharing the partners use.
Section 3: Data
The data for our analysis were obtained from the Origin-Destination Survey DataBank 1B of
the US Department of Transportation (DOT), which is a 10% random sample of tickets sold
by US domestic airlines for travel in a specific quarter. The time periods used for the analysis
are the first quarter of 2003 (pre-alliance period) and the first quarter of 2004 (post-alliance
period). The raw DOT data consist of a unique airline itinerary, including the origin and
destination airports of each flight coupon; the operating, marketing and the reporting carrier12
for each flight coupon; as well as the price and the number of passengers traveling on that
particular itinerary. There was other information included in the DOT data, but we did not
make use of it in our analysis13.
We have applied a number of filters to the original data to obtain the final sample that was
used for our analysis. First, we selected airport-pairs (markets) that have at least one of the
three alliance partners − Continental, Delta and Northwest − offering service between them
either pre-alliance or post-alliance. Second, we eliminated all itineraries with missing data or
prices equal to zero. The final sample contains roughly six million itineraries contained in
21,640 markets over the pre-alliance and the post-alliance periods.
Summary statistics of all variables are reported in Table 1. The first three columns give the
mean, the minimum, and the maximum values for each variable for the entire sample of
airports-pair. For the overall market, the mean percent price change for the entire sample is
positive, which means that the prices increase in the post-alliance period as compared with the
pre-alliance period. The mean percent price change for the alliance partners, at 3.3%, is higher
than the mean percent price change for the rivals, which is 1.9%. In addition, the percent
change of total traffic is positive. However, the percent change of total traffic for the alliance
partners, about 0.4%, is lower than the percent change of total traffic of the rivals, which is
about 9.5%.
12
The reporting carrier is the carrier that gives the itinerary information to the DOT.
The other information included in the DOT data are: Origin airport and country, Destination airport and
country, Itinerary geography type, Market geography type.
13
11
The remaining columns present summary statistics in alliance and non-alliance markets.
Alliance markets are markets where the three airlines have a code-sharing agreement in the
post-alliance period. The ranking of percent variations of prices and traffic are the same as for
the entire sample. The percent change of price for alliance partners is higher than the percent
price variation of rivals in both the alliance markets and non-alliance markets. However, the
percent change of total traffic for rivals is higher than the percent change of total traffic for
alliance partners in both the alliance markets and non-alliance markets.
The sample also reveals that there were code-shared services between Continental and
Northwest in about 18.2% of total markets. Moreover, about 45.2% of all markets in which
there is a code-shared service between the three airlines in the post-alliance period are
markets in which Continental and Northwest offered code-shared services in the pre-alliance
period.
There are 3534 New Alliance markets, 2919 Pre-existing Alliance markets, and 1035
Disappearing Alliance markets, which represent, respectively, 16.3%, 13.5% and 4.8% of all
markets introduced in our final sample data. There are 6543 markets in which Continental,
Delta and Northwest practice code sharing. Among them, they engage in traditional code
sharing in 3950 markets, which is about 60% of all alliance markets. Virtual code sharing is
practiced in 4470 markets, which is about 68% of all alliance markets. Finally, semi-virtual
code sharing is practiced in only 13 markets. There are markets in which the three airlines
engage in more than one type of code sharing. Table 2 indicates that in our final sample data
there are three common situations: the three airlines use only traditional code-sharing, only
virtual code-sharing, and a combination of traditional and virtual code-sharing agreements.
12
Table 2 reveals that the percent of markets where the semi-virtual code sharing is used is very
small. This is why in our analysis, we don’t distinguish between virtual and semi-virtual code
sharing. Appendix presents additional data statistics.
Section 4: Empirical Model
We use a series of before-and-after regressions to study the effects of the Continental-DeltaNorthwest code-sharing alliance. In fact, pre- and post-alliance periods are used to compute
the change in average price and total traffic in a market and then compare the price and traffic
changes of markets in which the partners have code-sharing agreements to markets in which
they don’t. We also examine whether or not Continental-Northwest code-sharing alliances
were present in the pre-alliance period, and the effect of this alliance on the three airlines’
alliance.
4-1 Market
A market is defined as a pair of airports. Markets are directional. This means that DetroitCalifornia and California-Detroit are different markets. In addition, an airports pair may
contain different flight itineraries that are distinguished by the number of stops. A flight
itinerary is defined by a specific airport of origin, a specific destination airport, and a specific
sequence of airport stops in traveling between specific origin and destination airports. The
flight itinerary is called a non-stop itinerary when it doesn’t contain any intermediate airport
stop. If the flight itinerary contains at least one stop at an intermediary airport, the flight
itinerary is called a connecting itinerary. In our analysis, all itineraries that have identical
origin and destination airports are considered as belonging to the same market. Thus, a market
contains different types of itineraries: non-stop and connecting itineraries.
4-2 Dependent variables
We estimate two models. The first explains the variation in prices; the second explains the
change in traffic volumes.
The percent change in average market price is constructed in three steps. First, we take the
average price of all itineraries in a market for the pre-alliance period. Next, we take the
average price of all itineraries in a market for the post-alliance period. Finally, we take the log
of the ratio of the two average prices for each market. The log represents the percentage
change in average market price.
The percent change in total market traffic is also constructed in three steps. First, we take the
sum of passengers over all itineraries in a market for the pre-alliance period. Next, we take the
sum of passengers over all itineraries in a market for the post-alliance period. Finally, we take
the log of the ratio of the two total passengers for each market.
13
4-3 Baseline model
In our baseline model, we regress the two dependent variables on a vector of market
characteristics (M) and a vector of alliance characteristics (A). The form of the regression
equation is defined as follows:
Regression model: Y= α + β*M + γ*A +ε
Where Y is a dependent variable, which may be one of the two dependent variables, β is the
vector of coefficient associated with market characteristics variables (M), and γ is the vector
of coefficient associated with alliance characteristics variables (A). The next subsections
present whole explanatory variables used in the vectors M and A.
4-4 Market characteristics (vector M)
Changes in fares and traffic depend on a whole slew of factors. To understand the effect of the
alliance on fares and traffic properly, we have to introduce other factors to our models. We
have listed five factors that on a priori grounds could influence price and traffic variation.
Variations in prices and traffic may be related to the level of competition in the pre-alliance
period. For example, if there is only a monopolistic firm in a market in the pre-alliance period,
the entrance of another airline into that market decreases prices because of the competition.
The effect is less if several firms served the market in the pre-alliance period. The first
explanatory variable is therefore the pre-alliance Herfindahl-Hirschman index (HHI) based on
O&D14 passengers.
The presence of low-cost carriers in a market generates a high level of competition to the
other regular carriers. In our case, low-cost carriers were present in some markets, both in the
pre- and post-alliance periods. As a consequence, to introduce the effect of low-cost carriers,
we have to focus on markets where the presence of low-cost carriers was observed only in the
post-alliance period and not in the pre-alliance period. This is why our analysis concentrates
on markets where low-cost carriers weren’t present or had a very small share in the prealliance period and a significant share in the post-alliance period. Then, the second
explanatory variable (Southwest) introduced in our analysis is a zero-one dummy variable that
equals one only if Southwest, the largest low-cost carrier in United States, had more than 5%
market share in the post-alliance period and less than 5% in the pre-alliance period. In our
analysis, we take only Southwest as a low-cost carrier because it is the largest and the likeliest
to influence prices and traffic.
Finally, we introduce average distance, which is the average distance (in hundreds of miles)
travelled by passengers in the market in the post-alliance period. A market may contain
different types of itineraries corresponding to a different distance between the origin and the
destination airport, depending on whether the itinerary is non-stop or connecting. Longer
distances resulting from more circuitous routings may be considered less desirable, which
could have the effect of lowering fares. On the other hand, more circuitous routings also cost
more to provide, which could result in higher fares.
14
Origin and Destination
14
4-5 Alliance characteristics (Vector A)
The introduction of the Continental-Northwest-Delta alliance allows us to distinguish between
four types of change of code-sharing arrangements. The first type is where there was no codesharing agreement between Continental and Northwest in the pre-alliance period, but a codesharing agreement exists between at least two of the three airlines in the post-alliance period.
The zero-one dummy introduced to represent these markets is called the New Alliance. The
second type of market is where there was a code-sharing agreement between Continental and
Northwest in the pre-alliance period and a code-sharing agreement still exists between at least
two of the three airlines in the post-alliance period. The zero-one dummy introduced to
represent these markets is called the Pre-existing Alliance. The third type of market is where
there was a code-sharing agreement between Continental and Northwest in the pre-alliance
period, but no code-sharing agreements exist between the three airlines in the post-alliance
period. The zero-one dummy introduced to represent these markets is called the Disappearing
Alliance. Finally, there are markets where there were no code-sharing agreements in both the
pre- and post-alliance periods. No zero-one dummy variable is introduced to represent these
markets15. The vector A is composed of these three dummy variables.
We start with the aggregated case, where we use only the three dummy variables defined
above. Results related to our baseline model are presented in the following subsection.
4-6 Baseline model: Results
Table 3 reports regression results for the regression baseline models. The intercept results
presented in column 1 show that fares are affected positively in markets where there was no
code-sharing agreement in both the pre- and post-alliance periods, but are affected negatively
in the rest of the markets. Moreover, coefficients are statistically significant. Specifically, we
find that fares in New Alliance markets fell 1.7% as compared with the rest of the markets
between the first quarter of 2003 and 2004. Fares in Pre-existing Alliance and Disappearing
15
This is because they weren’t affected by the code-sharing agreements and because we can get results in those
markets by using the dummies introduced so far.
15
Alliance markets fell 2.4% and 1.3%, respectively, between the first quarter of 2003 and
2004.
Results presented in column 2 show that total traffic is affected negatively in markets where
there was no code-sharing agreement in both the pre- and post-alliance periods and in
Disappearing Alliance markets, but is affected positively in the rest of markets. Moreover,
coefficients are statistically significant except for the Pre-existing Alliance dummy
coefficient. For example, we find that total traffic in New Alliance markets increased 1.7% as
compared with the rest of the markets between the first quarter of 2003 and 2004. Total traffic
in Disappearing Alliance markets fell 1.9% as compared with the rest of the markets between
the first quarter of 2003 and 2004. However, total traffic remained the same in Pre-existing
Alliance markets in the post-alliance period as compared with the pre-alliance period.
To summarize the results in the case of the baseline model, code-sharing agreements
negatively affect fares and positively affect total traffic. In addition, the effect of code sharing
depends on the type of alliance market. In fact, the effect of code sharing on fares in Preexisting Alliance markets is more intensive than in New-alliance markets. The effect of code
sharing on total traffic is more intensive in New-alliance markets than in Pre-existing markets.
Many explanations can be used to interpret the decrease of fares in code-shared markets (New
Alliance and Pre-existing Alliance markets). The level of competition is one of the reasons
that can explain the decrease of prices. On one hand, the fact that the three airlines started
code-sharing agreements among them in June 2003 gives them the opportunity to increase
their service quality16. Thus, rivals who are not able to increase the service quality of their
flights respond by a severe price competition, which involves a price decrease in the market.
Also, the introduction of code-sharing agreements in markets with a high level of competition
could not greatly help the partners to gain higher market share. On the other hand, the fact that
the three airlines started the code-sharing agreements among them may give them the
incentive to coordinate among themselves, which may involve a price increase − specifically,
in markets where the alliance partners had higher market shares in the pre-alliance period.
Therefore, we may expect that the introduction of code sharing in markets where the level of
competition is very high may negatively affect fares and positively affect total traffic.
However, the introduction of code sharing in markets where the level of competition is very
low may positively affect fares and negatively affect total traffic − specifically, in the case
where the alliance partners coordinate among themselves. The introduction of code sharing in
markets where the level of competition is in between the two extreme cases presented before
is ambiguous. But the level of competition in the pre-alliance period may explain the
difference that exists between the effect of the introduction of code sharing in New Alliance
and Pre-existing Alliance markets. Hence, to examine whether the introduction of a codesharing agreement depends on the level of competition in the pre-alliance period, we realize
an interactive analysis which introduces the interaction between the level of competition and
the introduction of code sharing as a new explanatory variable. Results associated with this
interactive analysis are presented in subsection 5-1.
The effects of code sharing on fares and traffic can also be affected by the number of
operating partners offering services in a market. When the number of operating partners in a
market is higher, partners are able to offer more choices to customers and increase their
16
The increase of service quality can be due to the increase of the number of online flights, more interesting
frequent flyer programs, and many other examples, as was shown in section 3.
16
service quality. For example, the case of three operating partners having a code-sharing
agreement among them and offering services in a market has a higher probability of
increasing total traffic and reducing fares than in the case of the existence of two operating
partners. On the other hand, the existence of numerous operating partners in a market in the
pre-alliance period may give them an incentive to collude in the post-alliance period and to
use practices to deter entry, which may result in an increase of prices and a reduction of
services. Moreover, this collusion between operating partners may be more probable where
there are three operating partners than where there are two operating partners in the prealliance period. Finally, the existence of numerous partners in the pre-alliance period in a
market may give incentive to some of them to disappear from the market after the
introduction of a code-sharing agreement among partners. There are many reasons why some
partners choose to disappear from the market after the introduction of a code-sharing
agreement, such as to avoid a harsh price competition with partners and to benefit from a cost
reduction because it will be cheaper for some carriers to use its partners’ plans than to use its
own. To this end, the variation of the number of operating partners may affect the results of
code sharing in a market. To examine that, we introduce an interactive analysis where we
examine the interaction between the introduction of code sharing and the variation of the
number of operating partners. In this interactive analysis, we introduce three dummy variables
to represent cases where the number of operating partners increases, decreases, and remains
the same in the post-alliance period as compared with the pre-alliance period. Results
associated with this interactive analysis are presented in subsection 5-2.
The intensity of the effects of the code-sharing agreement on fares and total traffic changed
from one market to another; this may be due to the type of code sharing (traditional or virtual)
used by the partners. We have presented above in section 3 that there are many differences
between these two types of code sharing. First, there is a difference with respect to the
quality; a traditional code-shared flight has a higher level of quality than a virtual code-shared
flight because some carriers offer fewer services with the latter than with the former. In
addition, some carriers use the virtual code-shared flights to discriminate among their
customers. Therefore, we have to expect that the price of virtual code-shared flights is lower
than the price of traditional code-shared flights. Hence, the effect of code sharing on fares is
negative and more intensive in markets where virtual code sharing is practiced than in
markets where traditional code sharing is practiced. This is why it will be interesting to
examine the interaction of the effect of code sharing with respect to the type of code sharing
used by the partners and to examine whether the effect of code sharing on fares and traffic
depends on the type of code sharing. This analysis will be done in section 5-3.
Table 3 also reports interesting results about the effect of Southwest and the average distance.
Fares fell 5.1% and total traffic increased 5.3% in markets where Southwest had a significant
market share in the post-alliance period as compared to the pre-alliance period. This is an
expected result because the presence of Southwest generated high level of competition and
then a decrease of prices. We also have a negative (positive) effect of average distance on
fares (total traffic). Passengers prefer non-stop flights with lower distance between the
departure and destination airports. The presence of code-sharing agreement gives the
opportunity to airlines to offer this kind of non-stop flights. However, passengers will switch
from indirect flights to direct flights. Thus, to face the competition of direct flights and to
encourage passengers to choose indirect flights, airlines have to reduce prices of indirect
flights. As a consequence, the increase of average distance on a market will result in a
reduction of fares as found in our baseline model.
17
Section 5: Interactive analysis
5-1 Interactive analysis 1: Pre-alliance competition level
In this subsection, we allow for the possibility that the alliance effect depended on the prealliance level of competition, which is measured by HHI. To do so, we added the following
regressors to the baseline model: New Alliance*HHI pre-alliance, Pre-existing Alliance*HHI
pre-alliance and Disappearing Alliance*HHI pre-alliance. In Table 4, column 1 presents
regression results for the fares baseline model, and column 2 presents results for the fares
interactive analysis 1 regression model.
Results presented in column 2 suggest that the effect of the three airlines’ alliance may
depend on the level of the pre-alliance competition in New Alliance markets because the
coefficient of New Alliance*HHI pre-alliance is statistically significant. However, the effect
of the alliance doesn’t depend on the level of pre-alliance competition in Pre-existing Alliance
markets because the coefficient of Pre-existing Alliance*HHI pre-alliance is not statistically
significant. We also found that the average price decrease is less intense in New Alliance
markets as there is less competition in the pre-alliance period. In other words, as the alliance
partners have a higher market share in the pre-alliance period, the decrease of prices in the
post-alliance period will be less intensive. The same result is true in Pre-existing Alliance and
Disappearing Alliance markets, but the coefficients are not statistically significant.
18
In Table 5, column 1 presents regression results for the traffic baseline model, and column 2
presents results for the traffic interactive analysis 1 regression model. Coefficients associated
with variables Pre-existing Alliance, New Alliance*HHI pre-alliance and Pre-existing
Alliance*HHI pre-alliance are not statistically significant, which means that total traffic
change in New Alliance markets didn’t depend on the level of pre-alliance competition and
total traffic in Pre-existing Alliance markets remained the same in the post-alliance period as
compared with the pre-alliance period. However, the coefficient associated with Disappearing
Alliance*HHI pre-alliance is statistically significant; that means that the total traffic change
depends on the level of pre-alliance competition in Disappearing Alliance markets. In fact, for
Disappearing Alliance markets with the pre-alliance HHI higher (lower) than 0.20, the total
traffic in the post-alliance period decreased by a maximum of 6.2% (increased by a maximum
of 1.6%) as compared with the pre-alliance period. This last result can be explained by the
decrease of services offered in these markets because of the disappearing alliances in the postalliance period.
This first interactive analysis shows that the effect of Continental-Delta-Northwest code
sharing on fares depends on the level of competition in the pre-alliance period; specifically, in
New Alliance markets. However, there is no clear evidence that the effect of the three airlines
code sharing on total traffic depends on the level of competition in the pre-alliance period.
5-2 Interactive analysis 2: Number of operating partners
To allow for the possibility that the market effects of code sharing depend on the change in
the number of operating partners in a market between the pre- and the post-alliance periods,
we introduce the interaction of the three dummies introduced in our baseline model with
Variation of the number of operating partners, which is a variable that represents the change
of the number of operating partners in a market in the post-alliance period with respect to the
19
pre-alliance period. Variation of the number of operating partners equals 1, 2, 3, respectively,
in the case where the number of operating partners decreased, remained the same, or
increased in the post-alliance period as compared with the pre-alliance period. Thus, we
introduce the following interaction variables to the baseline model: New Alliance*Variation
of number of partners, Pre-existing Alliance* Variation of number of partners, Disappearing
Alliance* Variation of number of partners.
Table 6 presents results of this interactive analysis on fares. In order to compare this case with
the baseline mode, column 1 presents the results for the baseline model, and column 2
presents results for the interactive case with respect to the number of operating partners. We
have coefficients associated with interactive variables New Alliance*Variation of number of
partners and Disappearing Alliance* Variation of number of partners that are not statistically
significant, which means that the effect of alliance on the corresponding markets did not
depend on the change of the number of operating partners. However, the coefficient
associated with Pre-existing Alliance* Variation of number of partners is statistically
significant and suggests that the effect of the three airlines’ alliance on Pre-existing Alliance
markets depends on the change of the number of operating partners between the pre- and postalliance periods. In fact, the decrease of prices is lower in the case of a decrease of the number
of operating partners than in the case where the number of operating partners remained the
same. Also, the decrease of prices in the case where the number of operating partners
remained the same is lower than the decrease of prices in the case where the number of
operating partners increased in the post-alliance period as compared with the pre-alliance
period. In other words, as the number of operating partners is higher in the post-alliance
period compared with the pre-alliance period, the decrease of prices is more intensive. This
last result may be explained by the increase of level of competition between partners or by the
increase of level of competition of rivals as a response to the high quality of services offered
by the alliance partners.
20
In Table 7, results for this interactive analysis on traffic are presented in column 2, while
results for the baseline model are presented in column 1. In this case, we have the effect of
code sharing in New Alliance and Pre-existing Alliance markets depending on the change of
the number of operating partners because the coefficients of the variables New
Alliance*Variation of number of partners and Pre-existing Alliance* Variation of number of
partners are statistically significant. Results suggest that the total traffic decreases in both
New Alliance (0.6%) and Pre-existing Alliance (0.7%) markets where the number of
operating partners decreases. However, total traffic increases in both New Alliance markets
and Pre-existing Alliance markets where the number of operating partners remained the same
or increased, but the case where the number of operating partners increased generated a higher
increase of total traffic in the post-alliance period as compared with the pre-alliance period.
This result is easy to understand; the fact that the number of operating partners increased is
the case where the partners offer more services in the markets and then generated more traffic
in the market. This result can also be used to show that partners did not practice any collusive
behaviour, because if that had been the case, partners might have reduced services and
increased prices.
5-3 Interactive analysis 3: Types of Code sharing
In the baseline model, we analyzed the effect of the Continental-Delta-Northwest codesharing alliance on prices and traffic without specifying the type of code sharing practiced by
the partners. In this subsection, we are interested in the effect of each type of code sharing on
prices and traffic. We have introduced above two different types of code sharing: traditional
and virtual. In traditional cases, there are at least two different operating carriers and only one
marketing carrier, which is one of the operating carriers. In virtual code sharing, there is one
operating carrier and one marketing carrier, which is different from the operating carrier.
21
To study the effect of each type of code sharing, we introduce the following variables to the
baseline model: New Alliance*Traditional, New Alliance*Virtual, Pre-existing
Alliance*Traditional and Pre-existing Alliance*Virtual. Thus, we have a regression model
with five dummy variables: four of them represent different types of code sharing practiced in
New Alliance and Pre-existing Alliance markets, and the fifth is Disappearing Alliance
markets.
22
Table 8 shows regression results for this interactive regression model on average fares, and
Table 9 shows the types of code-sharing interactive regression model on total traffic. The first
column of both tables presents results of the baseline model in order to make a comparison
with the interactive analysis results. Column 2 in Table 8 shows that the effects of code
sharing on fares depended on the type of code sharing practiced by the three airlines in New
Alliance markets; however, it didn’t depend on the type of code sharing practiced in Preexisting Alliance markets. This is due to the fact that coefficients of variables New
Alliance*Traditional and New Alliance*Virtual are statistically significant and the
coefficients of variables Pre-existing Alliance*Traditional and Pre-existing Alliance*Virtual
are not statistically significant. In addition, the decrease in prices is more intensive in markets
where virtual code sharing is practiced as compared with markets where traditional code
sharing is practiced. This result is a confirmation of the literature and can be explained by the
fact that airlines consider virtual code-shared tickets as low-quality substitutes for their
products and use them to discriminate between their customers.
Column 2 in Table 9 shows results of the types of code sharing interactive regression model.
We see that the effect of code sharing didn’t depend on the type of code sharing practiced by
the three airlines in the post-alliance period. This is due to the fact that coefficients of
interactive variables are not statistically significant. However, we see that the traditional codeshared alliance generated a higher increase of total traffic as compared with the virtual codeshared alliance.
To this end, we can see that the fare regression suggests that traditional and virtual code
sharing are associated with a decrease of fares in both New Alliance and Pre-existing Alliance
markets. Moreover, the extent of the decreases in fares is higher in virtual code sharing than
in traditional code sharing. On the other hand, the traffic regression suggests that traditional
and virtual code sharing are associated with increased traffic in both New Alliance and Pre23
existing Alliance markets. Moreover, the extent of traffic increases is higher in the case of
virtual code sharing as compared with traditional code sharing.
These results are consistent with the results found by Ito and Lee (2007), which suggest that
virtual code sharing negatively affects fares and positively affects total traffic more
intensively than traditional code sharing. Airlines use virtual code sharing as an imperfect
substitute for their own flights. Moreover, airlines always choose a low quality level for its
virtual code-shared flights as compared with its regular flights. For example, some airlines do
not permit passengers to earn frequent flyer points or to benefit from other services offered by
carriers. The use of virtual code sharing helps airlines to discriminate between their
consumers. Therefore, it is preferable for an airline to offer a virtual code-shared ticket to
some of its consumers rather than losing them. The effect of semi-virtual code sharing is not
studied in this part because airlines rarely use this type of code sharing. To conclude this part,
the effect of different types of code sharing may be explained by quality level, which is
reduced in the case of virtual code sharing as compared with traditional code sharing.
Section 6: Conclusion
The objective of this paper is to empirically investigate the effects of the Continental-DeltaNorthwest code-share alliance on fares and traffic, with a particular focus on whether or not
the alliance benefited from the former Continental-Northwest code-sharing agreements and
whether or not the alliance facilitated collusion behaviour between the three partners.
Our baseline model shows that the alliance is associated with a marginal decrease in average
city pair fares and with a marginal increase in total traffic in the majority of markets in which
the partners code share. However, the effects of the three airlines alliance depends on the type
of market among the three types introduced in our analysis: New Alliance, Pre-existing
Alliance and Disappearing Alliance markets. For example, we found that fares decrease by a
maximum of 1.7% in New Alliance markets and by a maximum of 2.4% in Pre-exiting
markets. We also found that total traffic increases in New Alliance markets by a maximum of
2.5%, but there is no effect of code-sharing on total traffic in the rest of markets. The effects
of code sharing on fares and total traffic could be related to other factors, such as the level of
competition in the pre-alliance period, the number of operating partners and the type of code
sharing used by the three airlines.
We then explored three interactive analyses. The first one examines whether the effect of code
sharing depends on the level of competition in the pre-alliance period. We found that the
effect of Continental-Delta-Northwest code sharing agreement depends on the level of
competition in the pre-alliance period; specifically, in New Alliance markets. However, there
is no clear evidence that the effect of code sharing on traffic depends on the level of
competition in the pre-alliance period. The Second interactive analysis examines whether the
market effects of code sharing depend on the number of operating partners offering services
in a particular market. To do so, we introduced dummy variables that represent the variations
of the number of operating partners between the pre- and the post-alliance periods. The
findings for this second interactive analysis suggest that the effect of the three airlines code
24
sharing on fares and total traffic depends on the variation of the number of operating partners
in the post-alliance period with respect to the pre-alliance period. This last result reflects the
existence of collusive behaviour among partners in some markets
Finally, we explored a third interactive analysis to examine whether the market effects of code
sharing depend on the type of code sharing practiced by Continental, Delta, and Northwest in
the post-alliance period. We have focused on two types of code sharing practiced by the three
airlines: traditional and virtual. The results show that both traditional and virtual code sharing
are associated with decreased fares and increased traffic. Moreover, the effect of virtual code
sharing is more intense than the effect of traditional code sharing. The decreased fares,
increased traffic, and intensity difference are probably due to different reasons: (1) the
difference in product quality where the product quality of virtual code-shared tickets is lower
than the product quality of traditional code-shared tickets and (2) the higher quality of codeshared tickets, which provide better connections, a high variety of flights, and more
interesting frequent-flyer programs.
The paper examines the effect of the Continental-Delta-Northwest alliance on fares and traffic
and provides some interesting results that may be useful for analyzing future airline alliance
announcements. However, there are other important aspects that we didn’t explore and that
may be interesting to analyze for future research. For example, how has the ContinentalDelta-Northwest alliance affected consumers’ surplus and welfare? Second, how has the
alliance affected the entry and exit of rivals? Finally, the three airlines presented in our paper
offer both domestic and international flights. Therefore, it will be interesting to examine the
effect of the Continental-Delta-Northwest alliance on the three airlines’ behaviour in
international markets. Structural econometric models may be required to analyze some of
these issues.
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Appendix
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