Please do not quote.

COMPETITION AND WELFARE IN THE

U.S. AIRLINE INDUSTRY

Steven A. Morrison Clifford Winston Northeastern University Brookings Institution

Vikram Maheshri Brookings Institution

Abstract: We develop a structural econometric model of competition in U.S. airline markets to assess how traveler welfare may be affected by the exit of an industry competitor. We find that Southwest provides the greatest welfare to travelers and that other low-cost carriers also contribute significant benefits to the flying public. Of the legacy carriers, only United and Delta substantially raise traveler welfare while American, US Airways, and Continental actually reduce traveler welfare. We conclude that the market is efficiently determining which airlines remain in the industry and that financial assistance to carriers that may otherwise be liquidated is unwarranted.

October 2004

Introduction

Since its deregulation in 1978, the U.S. airline industry has evolved into a highly

competitive industry that has struggled to align its capacity with consumer demand over

the business cycle. For the most part, too many seats have been chasing too few

passengers. Morrison and Winston (1995) suggest that a major cause of the problem is

that airlines must make their capacity decisions years in advance because of the time it

takes them to acquire new aircraft. Overcapacity occurs when unanticipated changes in

the macroeconomy or external shocks, such as a war, reduce passenger demand. Indeed,

industry losses have totaled more than $30 billion during the economic downturns and

international conflicts that have marked the beginning of each of the past three decades.1

In such situations, unprofitable firms would be expected to exit the industry,

reducing overcapacity. In fact, airlines such as Eastern, Pan Am, and Braniff have been

liquidated, but in some cases the federal government—perhaps with cause—has been

reluctant to fully trust the market to determine whether carriers should exit. For instance,

several U.S. airlines have entered into bankruptcy since the early 1980s—some more than

once. During his tenure as CEO of American Airlines, Robert L. Crandall complained

that the bankruptcy laws were protecting carriers from their creditors for too long and

argued that the laws should be changed to limit the time that a carrier can remain in

bankruptcy to forestall liquidation. Morrison and Winston (2000) found that a significant

fraction of mergers are proposed because carriers in financial distress seek a merger

partner. But the U.S. Department of Justice has opposed a few mergers involving a

1 This figure is based on accounting (operating) profits, not economic profits. In any case, there is no doubt that the industry has lost billions of dollars during the early 1980s, 1990s, and 2000s.

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financially weak carrier because the mergers were alleged to be anti-competitive. Justice

recently opposed a merger between two carriers, US Airways and United, both of which

subsequently filed for bankruptcy protection. Finally, Justice and the U.S. Department of

Transportation have at various times put large carriers on notice that they would

prosecute them for pricing and capacity decisions that were allegedly designed to drive

smaller carriers out of the industry. No U.S. airline has ever been found guilty of such

predatory behavior, but policymakers continue to scrutinize larger competitors’ strategies

that may threaten the survival of smaller airlines.

Since the terrorist attacks of September 11, the federal government has made a

much greater effort to reduce the likelihood of a carrier exiting the industry. Under the

auspices of the Air Transportation Stabilization Board (ATSB), U.S. airlines have

received $5 billion in grants and six carriers have received $1.6 billion in guaranteed

loans. Defenders of the ATSB claim that it is providing social insurance against an

unforeseen shock, enabling airlines that might otherwise undergo liquidation to emerge as

viable competitors.

Critics of the ATSB characterize it as picking winners and losers. More to the

point, they claim it is impeding efficient structural changes in the industry by turning

losers into temporary winners. During the past decade, low-cost carriers such as

Southwest, JetBlue, and AirTran have developed a growing market share that now

exceeds 25 percent of the domestic market. In contrast to the so-called legacy (that is,

pre-deregulation) carriers such as United, American, and Delta, these low-cost carriers

are profitable. Moreover, because legacy carriers now face low-cost competition on

nearly three-quarters of their domestic routes, low-cost carriers are poised to fill in the

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gaps created by the exit of a legacy carrier experiencing financial distress.2 Indeed, the

competitive pressure supplied by low-cost carriers has greatly increased the likelihood

that a few legacy carriers may be forced to liquidate despite governmental assistance.

In this paper, we determine the net benefits of each U.S. airline to assess how

traveler welfare may be affected by the exit of a specific industry competitor. We use the

findings to shed light on whether policymakers could benefit consumers by aiding a

carrier that may otherwise exit the industry. We accomplish this by developing a

structural econometric model of competition in U.S. airline markets treating price,

passenger demand, and each carrier’s frequency of service as endogenous. We use the

parameter estimates to simulate a given airline’s effect, through its fares and service

frequencies, on travelers’ welfare by accounting for the changes in fares and frequencies

offered by other carriers should the airline leave the industry.

By 2000, we find that Southwest Airlines provides the greatest welfare to

travelers and that other low-cost carriers also contribute significant benefits to the flying

public. Of the legacy carriers, only United and Delta substantially raise traveler welfare.

In fact, the presence of American, US Airways, and Continental actually lowers travelers’

welfare, implying that unless these carriers have substantially improved their fares and

frequencies in the post-9/11 environment relative to other carriers’ offerings, travelers

would be better off in the long run if any one of them exited the industry because other

carriers that generate higher welfare would serve their markets. We conclude that the

2 Levine (2003) points out that low-cost carriers have always competed effectively for price-sensitive pleasure travelers. Recently, low-cost carriers have been able to attract business travelers by expanding route coverage and flight frequency and offering much lower “walk-up” fares than legacy carriers offer.

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market is efficiently determining which airlines remain in the industry because those

airlines that generate the greatest welfare to travelers are also the most profitable. The

federal government is not likely to significantly enhance consumer welfare by providing

financial assistance to carriers that may otherwise be liquidated.

A Structural Econometric Model of Airline Competition

We model competition over time among airlines at the route level defined by non-

directional airport pairs. Following standard models of empirical industrial organization

(e.g., Porter (1983)), we begin by specifying the demand and supply of air transportation.

However, we expand the basic model to include airlines’ service frequency as an

endogenous influence on both demand and supply. As discussed later, we define

frequency as the number of available seats, instead of flights, to account for differences in

airlines’ aircraft.

From a traveler’s viewpoint, frequency is an important component of service

quality because it determines a major source of delay.3 From an airline’s viewpoint, a

positive (i.e., non-zero) value of service frequency indicates that it has entered a market

while the actual value affects the supply price. Our analysis differs from previous studies

3 In theory, air travelers may be affected by two types of delay, schedule delay and traffic delays caused by congestion, poor weather, and so on. Schedule delay is the difference between a travelers’ desired departure time and the actual departure time (Douglas and Miller (1974)). It is composed of frequency delay, the difference between a traveler’s preferred departure time and the closest scheduled departure time, and stochastic delay, the difference between the time of the most-convenient flight, if unavailable due to capacity constraints, and the next flight with an available seat. Service frequency is obviously important to travelers because it affects frequency delay. Stochastic delay depends on frequency and load factor. We control for load factor by including passenger demand and available seat capacity. Traffic delays are difficult for an individual traveler to predict. Moreover, travelers are interested in the delay to their particular flight, which they cannot know until it is completed.

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such as Reiss and Spiller (1989), Morrison and Winston (1990, 1995), Berry (1992), and

Ciliberto and Tamer (2003) that focus on an airline’s decision to enter a route but do not

account for the extent of its entry—that is, how many seats it offers during a given period

of time.

Demand. Our empirical analysis is conducted on a panel of U.S. domestic airline

routes. We specify airline travelers’ demand for air transportation, , on route i at

time t as:

DitQ

);,,( itijtitDit

Dit ufreqXpDQ , (1)

where is the average fare in the market, contains exogenous socioeconomic and

route characteristics that affect demand, freq

Ditp itX

ijt contains the service frequencies on route i

offered by each airline j, and is an error term.itu

The exogenous socioeconomic characteristics of the Metropolitan Statistical

Areas that comprise the origin and destination of a route that we included are their

average incomes and populations. We also included as separate influences elderly

(defined as 65 years of age or older), professional employee, and hospitality employee

populations. Larger incomes and all the population measures should increase the demand

for air travel with the exception that elderly populations’ discretionary income must be

balanced against their possible immobility due to illness; thus, the a priori effect of this

variable is ambiguous. Finally, we specified the share of travelers on a route who

indicated that their primary trip purpose was business, which would be expected to

increase demand, and controlled for airport, seasonal, and yearly travel preferences with

dummy variables.

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The exogenous route characteristics that we included are the distance from the

origin to the destination and intra-regional dummy variables based on U.S. Census

regions, to control for total travel by all modes and intermodal competition; the

percentage of available seats on connecting flights, to control for an important dimension

of service quality; the difference in the average temperature at the origin and destination

(in absolute value), an important determinant of pleasure travel; and the number of

adjacent routes served by each carrier. We defined an adjacent route as having origin and

destination airports that are within 50 miles of the given route’s origin and destination

airports.4 Greater temperature differences should increase passenger demand on a given

route, while connecting service and alternative adjacent routings should decrease

demand. The effect of trip distance on demand is ambiguous a priori. A greater distance

reduces the demand for all modes, but increases air’s market share.

By specifying each carrier’s frequency in the demand equation, we allow for the

possibility that travelers perceive airlines as offering differentiated products. For

example, low-cost carriers offer lower fares but fewer in-flight and promotional amenities

than legacy carriers offer. Thus, airlines may generate different responses by travelers

when they change their available seats on a route.

Supply. In a market where firms face a demand elasticity D, profit maximization

implies that firm k’s pricing behavior can be characterized as:

)(1 kD

k qMCp ,

4 It is reasonable to treat the total number of adjacent routes served by a given carrier as exogenous because such routes are the outcome of a carrier’s overall networkdevelopment and are unlikely to be influenced by the fares and passengers on a given route.

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where k is firm k’s conduct parameter, MC is its marginal cost function, and qk is its

output. Following Porter (1983), we aggregate this condition across firms such that the

relationship between market supply price, output, and frequency effectively characterizes

an industry supply curve. Thus, we specify the average fare on route i at time t as:

);,,,( itijtititSit

Sit freqZFQSp , (2)

where the fare is a function of the quantity of travelers who are flown, ; variables that

affect the cost of air transportation, ; other exogenous supply characteristics, ; the

flight frequencies of each airline j on route i, freq

SitQ

itF itZ

ijt ; and an error term, it .

The variables that we included that affect the cost of air travel are route distance,

the existence of nonstop service, and average traffic delay on the route. Longer distances

and higher delays raise costs and fares, while for a given aircraft nonstop operations are

less costly than flights with connections because connecting flights incur the costs

associated with additional takeoffs and landings and circuitous routings. Thus, all else

constant, fares on a route should fall if airlines introduce nonstop service.5 Of course,

airlines try to realize economies of density by offering connecting service on many routes

because passenger demand is not large enough to support nonstop service. We also

specified airport, intraregional, transcontinental, seasonal, and yearly dummy variables to

capture influences on costs and fares that may vary across airports, within and across

regions of the country, by season, and over time.

5 One often observes that fares on connecting flights are lower than fares on nonstop flights on a given route because connecting flights are less attractive to travelers than nonstop flights. However, we hold the quantity of passengers constant in the supply equation and therefore capture the lower costs of nonstop flights, which should reduce fares.

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We captured the impact of direct competition on fares with each airline’s service

frequency on a route, which we treat as endogenous. As would be expected in an

industry with differentiated products and firms whose costs vary significantly, airlines are

likely to affect average fares on a route differently when they change their available seats.

Other competitive influences on fares that may plausibly be treated as exogenous

include the number of adjacent routes served by each carrier, whether a carrier supplies

potential competition (that is, serves the origin and destination airports but does not serve

the route), the extent of multimarket contact between carriers, whether the origin or

destination airport is a dominated hub based on the General Accounting Office’s (GAO)

criteria, whether the origin or destination airport is subject to slot controls, and whether

two carriers serving the route have a code-share alliance.6 We expect that adjacent and

potential competition will tend to reduce fares and that a dominated hub and slot controls

will tend to raise fares. Evans and Kessides (1994) argue that carriers that encounter each

other in many markets have an incentive to engage in tacit collusion and avoid fare wars

but Morrison and Winston (1995) find that such collusion—to the extent that it exists—

breaks down in hard economic times and leads to fare wars as carriers compete fiercely

for passengers. Thus, the effect of multimarket contact may vary with the business

6 Based on GAO (1990), an airport is considered to be dominated if one airline enplanes at least 60 percent of the airport’s passengers or two carriers together account for at least 85 percent of the airport’s enplaned passengers, the airport is located in the contiguous 48 states, is one of the 75 largest in the country based on enplanements, and is not located in a metropolitan area with multiple major commercial airports. Airports subject to slot controls are Washington DC Reagan National, Chicago O’Hare, and New York Kennedy and LaGuardia. Based on the AIR 21 legislation, slots at LaGuardia were relaxed partially in 2001 and, along with slots at Kennedy, will be eliminated in 2007. Slots at O’Hare were eliminated in 2002, but airlines have agreed to trim their schedules during 2004-2005. The airlines that entered into a code-share alliance during our sample period are Northwest and Alaska, Northwest and Continental, and Continental and Alaska.

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cycle.7 Finally, an alliance that enables the allied carriers to reduce costs and become

more effective competitors would be expected to lower fares, but if the alliance enables

the carriers to gain market power, it would be expected to raise fares.

Frequency. We complete our model of airline competition by specifying the

service frequency (i.e., available seats) each airline offers on a given route, including

frequencies of zero for routes that an airline does not serve. (We will distinguish between

direct and connecting service frequency when we discuss our data.) Airlines do not

develop their networks to serve all domestic markets or to offer the same level of service

in the markets that they do serve; thus, it is appropriate to treat service frequency as

endogenous because it is undoubtedly determined by the number of travelers on a route,

which implicitly determines the market price, the flight frequencies offered by other

carriers on the route, and a carrier’s profit-maximizing network development.8 By

7 Brander and Zhang (1993) find that American and United’s rivalry in Chicago may be influenced by changes in the business cycle.

8 Assuming that airlines are price setters and ignoring other exogenous influences, we can specify market demand, Q, as a function of the fare, p, and each carrier’s servicefrequency, fi :

ifpQQ i ),( .

Carrier j’s service frequency is a function of market demand, market price, and all other carriers’ frequencies:

jifpQff ij ),,( .

And market price is a function of market demand and service frequencies: ifQpp i ),( .

Substituting price into the frequency equation, we can expressjifQfifQpQfffQpQff iiiij ),(

~)),(,()),,(,( .

Thus, frequency denoted by jifQf i ),(~

is explicitly a function of market demand

and other carriers’ frequencies and implicitly a function of price.

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specifying other carriers’ flight frequencies, we account for the strategic interactions

among airline competitors.

A priori, it is not clear what sign we should expect these interactions to take. For

example, some airlines may respond to another carrier’s service on a route by avoiding

that route; others may act similarly to that carrier’s changes in flight frequency.

Symmetric responses may arise in two situations. When an airline increases its frequency

in response to a competitor’s increase in frequency, it is employing an aggressive strategy

to maintain market share. When an airline decreases its frequency in response to a

competitor’s reduction in frequency, it is increasing profits by raising its load factor on

the route thereby reducing costs and by shifting some of its aircraft to other routes.

Given the preceding considerations, we specify carrier k’s flight frequency on

route i at time t as:

);,,,( kititmititkitkit XfreqQYEfreq , (3)

where contains network variables that are specific to both the carrier and route,

is the total number of passengers on the route, are the frequencies for each

airline m on the route for all m k, contains relevant exogenous influences, and

kitY

itQ mitfreq

itX kit

is an error term.

An airline’s network is composed of its airports and routes. The shape and size of

its network are determined by the spatial distribution of the airports that it serves, and the

connectedness of its network is determined by the structure of its routes. We would

expect airports that are densely embedded in a carrier’s network (e.g., Atlanta in Delta’s

network) to be served frequently. And we would expect routes that are embedded closely

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in a carrier’s network (e.g., Houston to Dallas in Southwest’s network) to be served

frequently.

We use five metrics from graph theory to capture the effect of a carrier’s entire

network on the frequency with which it serves a given route. These parameters are

derived from the following components of an airline’s network:9

n number of airports served by the carrier (nodes);

e number of nonstop routes served by the carrier (edges);

ep number of nonstop routes served by the carrier from a particular airport

p, for all airports p in the network;

dp,q distance of a particular route from airport p to airport q, for all airports

p and q in the network served by a nonstop flight.

Network shape and size variables include:

qpqp

qp

dd ,

, )max(

1, indicating the “linearity” of the network. The measure

takes on a minimum value of one, characterizing a perfectly linear network. Larger

values characterize less linearity and thus more coverage of a given geographical area. If

a network becomes less linear, i.e., increases, then it may contain more alternative

routings between the origin and destination thereby enabling frequency on a given route

to increase. However, if increases for an overbuilt network, then airline frequency may

decrease.

qpqpd

e ,

1, indicating the average length of all routes in the network. As

pointed out by Douglas and Miller (1974) among others, airlines make the most efficient

9 Hagget and Chorley (1969) provide a complete discussion of the measures.

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use of their fleet by maintaining higher load factors on longer routes; thus, holding

passengers constant, we expect that airlines will reduce frequency as the average route

length in their network increases.

qpqp

pp d

e ,

1 , indicating the airport p analog to equal to the average length

of routes serving airport p. A given airport will have a greater average route length than

other airports because it is either located in an outlying area in the carrier’s network or

serves as a connecting airport for routes that emanate from different regions of the

country. We expect that frequencies will be lower if the airport is in a remote location

but that frequencies will be higher if the airport is an interregional hub. However, given

that an interregional hub serves more routes than an outlying airport, we expect that the

overall effect of an airport’s average route length on frequency will tend to be positive.

For a given route, we use the product of the measures for the origin and destination

airports.

Network connectivity variables include:

neC , the standard definition of connectivity (the number of nonstop routes per

airport in a network). In general, networks that are more connected provide more routing

options from one airport to another. We therefore expect that greater connectivity leads

to greater frequency on a given route for carriers with adequate capacity.

ne

C pp , indicating the airport p analog to C. Airports with high values of

are likely to be hubs that are characterized by frequent operations that sustain an airline’s

pC

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entire network. For a given route, we use the product of their measures at the origin and

destination airports and expect it to have a positive effect on frequency.

An airline’s flight frequencies may also be affected by how many travelers on a

route make connections and whether the route includes a dominated hub. We expect a

carrier to provide greater service frequency as the share of connecting traffic increases

and if it has a dominated hub at the origin or destination airport. However, we expect a

carrier to provide fewer flights if another airline has a dominated hub at the origin or

destination airport. Finally, we control for the effect of regional traffic density, airport

infrastructure, and FAA slot restrictions on flight frequency with intraregional dummies

defined by Census region, airport hub classification dummies, and slot-controlled airport

dummies defined for Washington DC Reagan National, Chicago O’Hare, and New York

Kennedy and LaGuardia airports.10

Estimation. The simultaneous equations model of airline demand (1), (inverse)

supply (2), and frequency (3) can be jointly estimated by three-stage least squares

(3SLS), accounting for both endogenous and exogenous influences and the correlation of

the errors across the equations. The network characteristics of the m carriers that provide

service in a market are used to identify the effect that each of their flight frequencies has

on carrier k’s flight frequency, m k. In our estimations, the demand, supply, and

10 The FAA classifies commercial airports into one of four “hub” classifications based on an airport’s percentage of U.S. enplaned passengers. Airports enplaning 1 percent or more of the nation’s passengers are categorized as Large hubs (L); those enplaning between 0.25 percent and 0.99 percent are classified as Medium hubs (M); those enplaning between 0.05 percent and 0.24 percent are classified as Small hubs (S); and those enplaning fewer than 0.05 percent are classified as Non hubs (N). Based on these four categories, we constructed ten dummy variables to reflect the hub status of each route’s endpoints: LL, LM, LS, LN, MM, MS, MN, SS, SN, and NN. Our estimations are based on the top 1,000 routes, none of which were classified as NN, SN, and SS.

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frequency equations assume a logarithmic functional form, which is plausible and fits the

data better than a linear functional form.

Data

Our empirical analysis uses quarterly data for 1990-2000 for airline travel on the

1,000 most heavily traveled non-directional domestic routes as of 2000, resulting in a

panel of roughly 44,000 observations. Trips with an international component are not

included. We also exclude travel on any domestic portion of an international trip should

it occur because it is not explicitly priced. The basic data set from the U.S. Department

of Transportation Data Bank 1A contains quarterly observations of a running 10 percent

sample of tickets issued by “large” (that is, noncommuter) U.S. carriers. We initially

used data from 35 carriers that accounted for 98 percent of domestic passengers.

However, many of these now-defunct carriers had modest operations with statistically

insignificant effects on supply, demand, and other carriers’ frequencies. We therefore

included airlines that were classified as “major” carriers (that is, reported annual revenue

greater than one billion dollars) at some point during the decade and JetBlue, which

entered the sample in 2000. The final data set consisted of fourteen airlines including the

legacy network carriers, American, Continental, Delta, Eastern (until its liquidation in

1991), Northwest, TWA, United, and US Airways, other network carriers, Alaska, and

America West, and low-cost carriers, AirTran, ATA, JetBlue, and Southwest. These

fourteen carriers accounted for 91 percent of the passengers in our sample.11

11 The data include passengers carried on regional affiliates when the ticket is marketed by a partner that is among the fourteen carriers in our sample.

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The data sources for the variables used in our analysis and their sample means are

presented in table 1. Airlines provide both nonstop and connecting service. Frequency of

service on a route can be measured either by the number of flights or by the number of

seats available per quarter. Because aircraft have different seating capacities, seats are a

more appropriate measure of frequency than flights, especially when comparing

frequencies across airlines. We therefore used available seats to measure frequency.

We computed nonstop and connecting frequencies by using the Back Aviation

data base that contained the carriers’ timetables for each date in our sample and

aggregating direct and connecting frequencies for each year and quarter. Connecting

frequencies were constructed under the assumption that a traveler made only one

connection that required a layover between forty-five minutes and two hours. In our base

case, we summed nonstop and connecting frequencies to obtain total frequency.

However, we will report sensitivity analyses that placed greater weight on nonstop

service than on connecting service because travelers are likely to value such service more

highly. We also test the sensitivity of our results by using flights instead of seats to

measure frequency.

Estimation Results

We used 3SLS to estimate the demand, (inverse) supply, and fourteen airline

frequency equations. We conducted alternative structural tests to see if the estimated

coefficients varied over time, by the extent of route competition, and by hub

classification and found that we could reject the hypothesis that the coefficients were

statistically different in these dimensions. Thus, we estimated the model on the entire

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data set. Given so many coefficients were estimated, it is difficult to absorb the findings

in a single table. Because we are primarily interested in analyzing the effects of airline

competition on travelers’ welfare, we organize the results in separate tables for the

coefficients of the background variables that affect demand and supply, measures of

airline competition that affect demand and supply, background variables that affect

airline frequency, and measures of competition that affect airline frequency.

Demand and Supply

We begin with the background variables in the demand and supply equations

(table 2). Generally, the socioeconomic variables and route characteristics affecting

demand are statistically significant.12 Although the price elasticity of demand, -0.64, and

the income elasticity of demand, 0.08, are lower than most previous estimates, the price

elasticity increased in magnitude to -1.13 and the income elasticity increased to 0.50

when we did not include the year dummy variables in the model. Apparently, the

dummies are capturing a broader measure of economic activity than personal income at

the origin and destination and perhaps capturing the growing and more price sensitive

segment of the population that was attracted to air travel during the 1990s by overall

12 As indicated previously, our initial demand specification included the elderly, professional, and hospitality populations at the origin and destination and fixed effects for airport preferences, but these variables were statistically insignificant. We also found that the share of travelers on the route who indicated that their primary trip purpose was business was statistically insignificant, which may be due to firms increasingly substituting tele-conferencing and other forms of high speed communication for face-to-face meetings that require air travel. Indeed, the business travel coefficient became more statistically insignificant over the decade. Finally, we argued that average flight delay was unlikely to affect demand because travelers were primarily concerned with how much their particular flight was delayed. In fact, we found that average flight delay was statistically insignificant in the demand equation.

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economic growth, the growth of low-cost carriers, and the competitive responses by

legacy carriers.

We find that the demand for air travel is greater for longer routes, indicating that

as distance increases air’s competitive advantage over other modes of transportation

offsets the decline in total travel.13 Demand is also greater on routes connecting cities

with greater populations and larger temperature differences, but is less on routes that

offer a greater share of (less convenient) connecting flights.14

The elasticity of airline fares with respect to passengers in the inverse supply

equation, 0.01, indicates that carriers are operating at very close to constant returns to

scale, which is consistent with previous literature on airline scale economies (Braeutigam

(1999)).15 Generally, the cost variables and route characteristics affecting fares are

statistically significant.16 Air fares increase with distance, although less than

13 Of course, passenger demand also increases with distance because the average fare is held constant.

14 It is possible that the percentage of available seats on connecting flights is endogenous; thus, we performed a Hausman specification test using route characteristics in the supply equation as instruments (e.g., whether the origin or destination is a hub) and found that we could not reject the exogeneity of this variable at high levels of confidence.

15 Given that we hold service frequencies (seat capacity) constant in the supply equation, our estimate of the passengers’ coefficient may capture economies of density instead of economies of scale. We re-estimated the model without the frequency variables and found that the passengers’ coefficient became statistically insignificant, which is consistent with constant returns to scale.

16 Our initial inverse supply specification attempted to account for the effect of transcontinental service using a dummy variable for transcontinental routes and slot controls using airport dummies for National, LaGuardia, Kennedy, and O’Hare, but these dummy variables were statistically insignificant. The effect of slot controls is undoubtedly reduced because we control for airline frequency. We also specified fixed effects for each airport, but they tended to be statistically insignificant.

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proportionally because of the fixed costs of takeoff and landing, and increase with greater

average delays because delays increase carriers’ operating costs. Fares are also higher on

routes that are served by a carrier with a dominated hub at the origin or destination.

Finally, ceteris paribus, fares are lower on routes where most passengers fly nonstop and

on routes that are served by carriers that have entered into an alliance. Evidently,

alliances enable airlines to become more effective competitors instead of helping them to

acquire market power. Bamberger, Carlton, and Neumann (2004) report a similar

finding.

Airlines may provide competition on a route through direct service (measured

here by available seats), adjacent competition, or potential competition.17 One general

finding of this study, previewed in table 3, is that airlines have varying impacts on

variables that affect travelers’ welfare. As expected, airline frequency tends to increase

passenger demand and, with the exception of America West and TWA, the effect is

statistically significant. We do find that direct competition (additional seats) provided by

Alaska and Eastern actually reduces demand. Alaska’s effect may result from

idiosyncrasies in its operations that we were unable to capture. Eastern’s effect may be

related to its financial decline that culminated in its liquidation in 1991. When we

compare the low-cost and legacy carriers’ frequency coefficients, it does not appear that

17 As noted, our initial specification of supply also included multimarket contact between carriers. For each carrier, multimarket contact was defined as the percentage of revenue that it generated on routes that another given carrier served. We specified the maximum multimarket contact for all airlines serving a route and, as an alternative measure, a passenger weighted average of multimarket contact, but both measures were statistically insignificant. Zou, Dresner, and Windle (2004) find that multimarket contact affects the pricing behavior of legacy carriers but not the behavior of low-cost carriers. Thus the growth of low-cost competition may have weakened the effect that multimarket contact has on average fares.

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travelers view their products as differentiated. Rather, travelers’ responses to a change in

frequency vary for carriers in each group.

When airlines offer travelers alternative routings through adjacent competition,

passenger demand on a given route is usually reduced. Although we find that JetBlue

increases traffic on a given route when it provides more adjacent service, we view this

result with caution because JetBlue was starting to develop its core network as it entered

the sample. Our finding could change with data capturing JetBlue’s current network. On

the other hand, United may in fact increase traffic on a given route because it offers

higher fares on its adjacent routes. For example, United’s (higher) fares on certain routes

out of Washington Dulles airport may encourage some travelers to depart from BWI-

Washington airport to obtain lower fares.

The frequency coefficients in the supply equation clearly suggest that low-cost

carriers and legacy carriers are affecting travelers’ welfare in different ways. As the low-

cost carriers, Southwest, JetBlue, AirTran, and ATA, increase their presence in a market

(measured by available seats), average fares decline. Continental and America West, two

carriers that emerged from bankruptcy with lower costs, also reduce fares, but the

magnitude of their coefficients is roughly half that of the coefficients for the low-cost

carriers. TWA and Eastern are the only other carriers that put downward pressure on

fares, possibly due to price cuts prior to their acquisition and liquidation, respectively.

Alaska, Delta, and United have a statistically insignificant effect on fares.

American, Northwest, and US Airways actually raise fares as they expand their

presence in a market, primarily because their high operating costs and large capacity

20

provide an umbrella for other carriers to raise fares.18 Evidence also indicates that

American and US Airways have been price leaders in a significant fraction of their

competitive interactions with other carriers (Morrison and Winston (1995)), while

Northwest has developed a reputation of taking strong retaliatory actions against carriers

that sharply reduce their fares.

We explored the effect that individual airlines had on fares in more detail by

analyzing the effect of carrier frequency at points of the fare distribution besides the

mean. We focused on the highest fares, as indicated by the 80th percentile, and the lowest

fares, as indicated by the 20th percentile. We found that the highest fares declined to

varying extents as carriers increased their frequencies with the exception that American,

Northwest, and US Airways had a positive and statistically significant effect on these

fares—a finding that is consistent with the umbrella effect. All of the legacy carriers

tended to increase the lowest fares but low-cost carriers reduced fares even at this level

by increasing their frequency.

All of the low-cost carriers also reduce fares through adjacent competition. The

effect of the few legacy carriers that decrease fares in this manner and that are still

operating is often much smaller than the low-cost carriers’ effect. Finally, most carriers’

threat of entry as potential competitors helps lower fares. In sum, our findings indicate

that low-cost carriers have consistently reduced fares through direct and adjacent

competition and have been a critical force for lower fares in the domestic industry during

18 Although airlines’ costs are affected by their average stage length and other factors, it is useful to point out that in the year 2000, based on data from U.S. Department of Transportation, Form 41, US Airways’ average cost per passenger mile was 18 cents, American’s was 14 cents, and Northwest’s was 13 cents. In comparison, Southwest’s average costs were 11 cents per passenger mile.

21

the past decade. Legacy carriers have had mixed effects on fares, raising questions about

their contribution to travelers’ welfare.

Service Frequency

Travelers’ welfare is also affected by airlines’ competitive interactions through

service frequency. We identified these interactions by specifying an airline’s service

frequency on a route as a function of the attributes of the routes, the basic characteristics

of the airline’s network, and other airlines’ frequencies.19 The signs of the coefficients

of the route and network variables presented in table 4 are broadly consistent with

expectations but also reveal idiosyncrasies among carriers pertaining to their network

development, fleet composition, and operating strategies.

For instance, most airlines increase frequencies on a route as passenger traffic

increases. The opposite effects that we find for America West and TWA, which were in

bankruptcy for part of the sample period, may reflect their cuts in service despite

exogenous traffic growth. The negative effects for Alaska and Northwest are more

difficult to explain and may reflect aspects of their operations that we were not able to

capture. For example, Alaska’s network is susceptible to significant seasonal changes

19 We report estimation results in this section that are based on the assumption that direct and connecting frequencies, as measured by available seats, are of equal importance and that airlines’ total service frequency can be calculated as the sum of the two. We explored the sensitivity of the findings to alternative ways of calculating frequency, freq,by using the parametric expression:

freqTotal = freqDirect + ·freqConnecting ,where is a parameter. We re-estimated our model of airline demand, supply, and frequency conducting a grid search that placed relatively greater importance on direct frequencies by setting = 0.1, 0.2,…, 1.0. We found that the coefficient estimates were generally quite close to the estimates reported in this section that were based on = 1.0.We also re-estimated the model defining frequency based on departures rather than available seats, but this had little effect on the main findings.

22

because many of the airports that it serves are closed in the winter. We also find that

most airlines increase flight frequency when they provide a greater share of connecting

service on a route. The exceptions, Alaska and Northwest, may use their smaller aircraft

to provide such service, while America West’s coefficient may again reflect adjustments

it was making during bankruptcy.

The increase in flight frequency following deregulation can be largely attributed

to the accelerated development of hub-and-spoke operations (Morrison and Winston

(1986)). It is therefore not surprising that airlines with a dominated hub at a route’s

origin or destination generally provide more frequency.20 Southwest is an exception to

this finding because it temporarily dominated the airport at El Paso, which does not have

much (connecting) traffic. Conversely, the majority of carriers reduce frequency on

routes where another carrier has a dominated hub at the origin or destination. The

exceptions occur in cases where the airline in question together with another carrier

dominate a hub: AirTran (with Delta at Atlanta), Northwest (with Delta at Memphis),

TWA (with Southwest at St. Louis), United (with American at Miami), and US Airways

(with Delta at Dayton).

Our conceptual discussion of airlines’ network characteristics identified broad

expectations of their effects on frequency, but also speculated that they may vary in

accordance with the structure of an airline’s network and operations. For example, less

linearity (i.e., larger values of ) should increase a carrier’s frequency if its network is

not overbuilt in relation to its traffic and fleet. We find this to be the case for Alaska,

20 We could not estimate a dominant hub dummy for Alaska, JetBlue, Continental,Eastern, AirTran, America West, and ATA because they did not have any dominant hubsduring our sample period.

23

Eastern, America West, ATA, United, and US Airways. In contrast, we find that larger

values of decrease service frequency provided by American and Delta—the carriers

with the most nonlinear networks in the industry—which may indicate that their networks

are overbuilt and a potential source of their financial problems. Indeed, Delta recently

announced plans to pare its network by closing its Dallas hub and expanding flights at

Atlanta.

We find for several carriers that increases in average route length in a network

tend to decrease flight frequency. Opposite effects were found for carriers that were

making adjustments to their networks and fleets during the 1990s that caused them to

offer more (less) frequency as their average route length increased (decreased).

Specifically, JetBlue initially developed its network mainly to serve transcontinental

routes; TWA’s financial decline caused its average route length and frequency to fall;

United invested in larger (Boeing 777) aircraft that it used to increase frequency (as

measured by seats) on its longer domestic routes; and US Airways’ fleet contained a

significant fraction of large aircraft, making it profitable for the airline to fly those planes

more frequently on longer routes than on shorter routes.

We suggested that the product of average route lengths for airports that comprise

a given city pair would have a positive effect on frequency because such routes were

likely to be major travel destinations. This expectation is empirically verified for all but

American and TWA. However, American has increasingly segmented its operations into

distinct geographical areas with limited connections between them, which may have led it

to reduce frequencies for cities serving longer routes; TWA responded to its financial

24

distress by selling its largest planes while abandoning many of its longer routes to

provide service on shorter routes.

We expected that greater network connectivity would increase frequency given

that carriers had available capacity to accommodate greater traffic. We find this to be

consistent with the signs of the coefficients for the larger carriers and AirTran (whose

capacity in its former incarnation as ValuJet exceeded demand in the wake of its well-

publicized 1996 crash in Florida). Apparently, the low-cost and regional carriers’

additions to capacity lagged behind the growing connectedness of their networks.

Finally, we find that greater route-specific connectivity led all carriers to provide more

frequency.

An airline’s non-price strategic interactions are primarily characterized by how it

affects and is affected by other airlines’ service frequencies. A wide range of outcomes is

possible. By increasing its service frequency, a particular carrier may encourage,

discourage, or have no effect on the service offered by another carrier depending on the

operating strategies, fleet, and network development of the carriers in question.

We report the service frequency elasticities (i.e., the percentage change in a given

carrier’s frequency in response to a one percent change in a competitor’s frequency) in

the appendix. Figure 1 graphically depicts the results, classifying behavior as either

unresponsive (a small elasticity, less than 0.03 in absolute value), symmetrically

responsive (a positive elasticity greater than 0.03 and statistically significant), or

asymmetrically responsive (a negative elasticity greater than 0.03 in absolute value and

statistically significant).

25

The responses are especially important for assessing how other carriers will adjust

their frequencies if a given airline exits the industry. Airlines that respond symmetrically

to that airline’s service frequency will curtail their service, but those that respond

asymmetrically to its service frequency will expand their service. Because airlines’

frequency elasticities vary greatly in sign and magnitude, the change in traveler welfare

will depend on the identity of the airlines that make particular frequency adjustments.

For example, reading down the appropriate column in figure 1, if United curtails its

service in a market, Delta, US Airways, AirTran, and Southwest will expand their

frequencies, but American, Alaska, and Continental (among others) will reduce their

frequencies. Subsequent responses will follow, to lesser extents, and we must assess their

iterative effect upon equilibration to determine how travelers in all markets would be

affected by United’s reduction in service. As United’s frequency goes to zero, we can

use this procedure to assess how travelers would be affected by its exit from the industry.

The figure suggests that the airline industry can be characterized by at least two

broad competitive interactions. Southwest and, to some extent, ATA respond

asymmetrically to other airlines’ frequency changes. In all likelihood, this finding

reflects low-cost carriers’ tendency to increase their service frequencies in a market as

legacy carriers cut back their service. (We found JetBlue and AirTran to be unresponsive,

primarily because they have been in the industry for only a short period.) Asymmetric

responses are also consistent with the view that low-cost carriers are careful to avoid

markets where they may be drawn into a capacity war.

In contrast, a given legacy airline tends to respond symmetrically to other

airlines’ frequency changes, especially when the change is initiated by another legacy

26

airline. Thus, we find that American offers more service frequency in response to

increases in frequency by Continental, Delta, Eastern, America West, TWA, and United;

Continental offers more service frequency in response to increases in frequency by

American, Delta, Northwest, United, and US Airways; and so on. With the exception of

Northwest and TWA, which was struggling to survive in the industry, legacy carriers

tend to respond asymmetrically to changes in low-cost carriers’ frequencies.

Airline Identity and Traveler Welfare

Two central findings have emerged from our empirical analysis of domestic

airline competition that concern travelers’ welfare. First, low-cost carriers generally

reduce fares in markets that they serve and in markets where they provide adjacent

competition. Second, although legacy carriers offer more service frequency to domestic

travelers than low-cost carriers offer, their competitive interactions with low-cost carriers

are generally asymmetric—which means that they may be discouraging low-cost carriers

from entering some routes where they have to compete intensely with frequency. It also

means, however, that low-cost carriers would provide more service in markets that legacy

carriers exited.

We sharpen the implications of these findings by estimating how much each

airline contributes to travelers’ welfare. The conceptual experiment that we perform is to

“eliminate” an airline from the industry by setting its service frequency, adjacent

competition, and potential competition equal to zero for all routes in the sample. We then

predict new frequencies for the remaining airlines as indicated by equation (3), adjust

27

demand (1) and supply (2) accordingly, and compute the change in traveler welfare. The

process is repeated until equilibrium of all the endogenous variables is achieved.

Figure 2 depicts the case when an airline’s exit leads to higher fares, as indicated

by an inward shift of the supply function, and lower frequency, as indicated by an inward

shift of the demand function, in the markets that it served. The welfare loss to travelers

from the airline’s exit is indicated by the shaded area (i.e., the airline’s presence in the

industry benefited travelers by that amount). Note that most of the welfare change, W,

will accrue to travelers because we found that the inverse supply equation was almost

perfectly elastic.21

Using the inverse of equation (1) and equation (2), the expression for the welfare

change associated with airline j exiting the industry at time t is given by:

,)exits()exits()()(exits|

00

**

r

jQSrt

Drt

QSrt

Drtjt

rtrt

dQjQpjQpdQQpQpW (4)

where we sum over all routes r, and are route-level inverse demand and

supply functions determined endogenously by the quantities and frequencies of all

airlines, and asterisks denote market equilibrium quantities.

)(Drtp )(S

rtp

22 If an airline is increasing

(decreasing) traveler welfare through its effect on fares and frequencies relative to the

effects that other carriers would have in its absence, then traveler welfare will fall (rise) if

it exits the industry.

21 We deduct airline taxes from the ticket price so that we do not include the welfare effect of an airline’s exit on the government.

22 In our estimation, we use a log-linear specification of demand, which has no price-intercept. The integral of demand taken from Q=0 to Q=Q* is undefined for elasticities that are less than -1. Our estimate of the elasticity of demand is roughly -0.6; hence, we can calculate the difference in welfare defined by the two separate demand curves.

28

We have estimated our model under conditions where one or more carriers exits a

route in one period and other carriers enter the route in subsequent periods. Thus, our

experiments of eliminating a carrier from the industry and calculating the welfare effects

are not beyond the range of the coefficients because we are effectively summing route-

level “experiments” that have occurred repeatedly in our sample. To be sure, one should

not focus on more than one airline exit at a time, but the individual experiments offer

valid comparisons of each carrier’s contribution to welfare. Our estimated equations also

implicitly capture changes in assets that occur at the route level when one carrier enters

and another exits. When an airline exits the industry, we assume that its assets will be

absorbed by the remaining carriers in accordance with their changes in service frequency.

Finally, by specifying the effect of each carrier on market demand, fares, and

other carriers’ frequencies, we have allowed for the possibility that airlines provide

differentiated products. Generally, it could be argued that business travelers prefer

legacy over low-cost carriers, but we could not discern any distinct impacts of legacy and

low cost-carriers on demand. (Recall, we did not find that trip purpose influenced

demand on a route.) In addition, the major difference between business and pleasure

travelers, which we control for, is that business travelers tend to value flight frequency

and network coverage more than pleasure travelers value these dimensions of service.

The estimates of net-benefits, presented in figure 3, crystallize previous inferences

that traveler welfare is highly dependent on the identity of the airlines in a market. In

general, we find that the value of a carrier is aligned with the business cycle. When the

U.S. economy was experiencing a downturn in the early 1990s and the industry was

characterized by considerable excess capacity, only a few carriers would have harmed

29

travelers had they exited. But as the economy rebounded and the low-cost carriers in

particular expanded their operations, the distinct value of each airline has come into

sharper focus.

As expected, Southwest contributes the greatest benefits to travelers—valued at

roughly $20 billion by 2000 and vastly exceeding the benefits provided by any other

airline. (Note that each estimate reflects an airline’s contribution to welfare, assuming all

other carriers remain in the industry; thus, it would be misleading to interpret the sum of

the individual estimates as the total benefits that the industry provides to travelers.)

Despite their smaller size relative to the legacy carriers, the other low-cost carriers, ATA,

JetBlue, and AirTran, are included among the second most valuable group of airlines to

travelers, contributing some $5 billion to $10 billion in benefits in 2000. In fact, we have

probably underestimated JetBlue’s value today because it has grown significantly since

the last year of our sample.

In contrast, only two legacy carriers, United and Delta, are included in the second

group. Two others, Northwest and TWA, fall into a group with Alaska and America West

that provides small or negligible benefits to travelers, while the remaining legacy carriers,

American, Continental, and US Airways, actually harm travelers by remaining in the

industry.

As noted, competition provided by low-cost carriers has been the primary source

of reduced fares for domestic travelers, so it is not surprising that their presence in the

industry contributes a great deal to traveler welfare. Also recall that American,

Northwest, and US Airways raised fares as they expanded their presence in a market.

Nonetheless, given that legacy carriers do provide a great deal of service frequency, it

30

may be somewhat surprising that they fare so poorly in our assessment and that travelers

would actually be better off if at least one of them exited the industry. The basis for this

finding partly stems from the legacy carriers’ asymmetric competitive interactions with

low-cost carriers. Low-cost carriers have partly achieved success by steadily growing

but, in most of their markets, by not trying to compete with legacy carriers on frequency.

In the process, some legacy carriers have effectively reduced traveler welfare by

discouraging low-cost carriers from expanding operations into routes they serve. If a

legacy airline exited the industry, it is plausible that traveler welfare could rise because

the airline would be replaced by low-cost carriers that collectively provide comparable

service frequency at lower fares. For example, as shown in figure 1, Southwest and ATA

would respond to Continental’s exit by expanding their service frequency while most

other legacy carriers would curtail theirs. This response would generate additional

frequency provided by low-cost carriers.

It is useful to give examples of how some specific airlines’ exit would affect fares

and service frequency (figure 4). If Southwest left the industry, then other lower-cost

carriers would enter its routes, as shown in figure 1, and still charge low fares. However,

they would not offer the same flight frequency as Southwest, which would greatly hurt

travelers. They would also not be as effective as Southwest is at providing adjacent

competition. Some legacy carriers, such as American and United, would be inclined to

serve Southwest’s routes ceteris paribus, but the entry by other low-cost carriers would

discourage them from doing so. Morrison and Winston (1986) estimated that, as of 1983,

nearly $8 billion (2000 dollars) of benefits, particularly from additional flight frequency,

could be generated under deregulation by entry into routes other than those in the large

31

hub-large hub classification. By filling in significant gaps in service for medium and low

density routes during the past decade, Southwest has substantially benefited travelers.

American and United appear to have similar behavior because they have

responded symmetrically to legacy carriers and asymmetrically to low-cost carriers. But

as shown in the appendix, other carriers respond more dramatically to changes in

United’s presence than to changes in American’s presence, perhaps because American is

a more aggressive competitor than United. In addition, American tends to elevate the

average fare in the markets it serves and its average fares (yields) have been greater than

United’s throughout most of the sample period. Thus, if American exited the industry,

low-cost carriers would collectively provide comparable service frequency on its routes

while benefiting travelers with lower fares. On the other hand, if United exited,

frequencies would substantially decrease in equilibrium and fares would rise because

most other legacy carriers would substantially cut back their service in United’s markets

while US Airways, which has the highest yields in the industry, would substantially

expand its service in United’s markets.

Discussion and Conclusions

We have found considerable variation in the benefits that the flying public derives

from each U.S. airline. Based on their fares, service frequency, and competitive

interactions in the year 2000, all of the low-cost carriers and Delta and United provide

significant benefits, while the other legacy and network carriers provide modest or in

some cases negative benefits.

32

Although economic theory does not suggest that our findings are implausible, it

does not shed much light on why airlines may have positive or negative effects on

consumer welfare because it does not focus on idiosyncratic aspects of competition

between firms such as legacy and low-cost carriers. New theoretical developments may

be called for to explain how heterogeneity among firms in imperfectly competitive

markets affects consumers.

From an empirical perspective it could be argued that we have analyzed only

domestic air travel and not accounted for the international service by U.S. airlines that is

currently mostly provided by legacy carriers.23 However, entry into international

markets is generally constrained by bilateral treaties. This makes it difficult to assess

how consumers benefit from legacy carriers in these markets because we cannot model

how other airlines would adjust their operations if a legacy carrier exited the industry.

We have controlled for the most important ways that airlines differentiate their

product; thus, we have not significantly overstated the benefits provided by low-cost

carriers and understated the benefits provided by legacy carriers. In fact, the distinction

between the type of service offered by these carriers is blurring. Legacy carriers have

either eliminated meals or begun to charge for them on all but long distance flights

thereby approaching low-cost carriers’ minimalist in-flight service. In an effort to follow

low-cost carriers’ policy of attaching few restrictions to their fares, legacy carriers have

also largely eliminated the Saturday night stay as a requirement to obtain a discount

23 Low-cost carriers are starting to offer international service. JetBlue serves Puerto Rico and has expanded its service to the Bahamas and other parts of the Caribbean. ATA serves Mexico and plans to serve some European destinations in the near future.

33

fare.24 For their part, low-cost carriers are offering more attractive frequent flier

programs as their networks grow and making a greater effort to appeal to business

travelers by providing leather seating (JetBlue), account managing systems (Southwest),

business class service (ATA), and so on.

Our findings depart from conventional assessments of airline competition by

indicating that consumer welfare is affected by the identity rather than simply a count of

the airlines in a market. From a policy perspective if airline competition has been an

effective custodian of traveler welfare, then we would expect that those airlines that

provide the greatest benefits to travelers are among the most profitable while those that

provide the fewest benefits are among the least profitable. Otherwise, government

intervention may be justified to keep an airline from exiting the industry if it is

experiencing financial distress but providing substantial benefits to travelers.

We assess the effectiveness of industry competition by classifying each U.S.

airline according to its effect on traveler welfare and its profitability. Table 5 shows that

in the year 2000 all the airlines that produced significant welfare gains were also

profitable. Thus, the market was working effectively with the (temporary) exception that

American and Continental were profitable but not benefiting consumers.

Since September 11 the only carriers that have been able to remain profitable for

at least part of the period were producing significant welfare gains. Indeed, despite a

significant reduction in air travel, most low-cost airlines have developed a strategy for

satisfying consumer preferences and avoiding financial distress. In the process, they

24 Morrison and Winston (1995) find that the Saturday night stay is by far the most onerous travel restriction for business travelers.

34

appear to be gradually abandoning their asymmetric competitive interactions by

expanding into markets served by legacy airlines.25

Legacy airlines that are now unprofitable are trying to cut costs dramatically so

that they can compete more effectively with low-cost carriers that continue to capture an

ever greater share of passenger traffic. Those airlines that reduce their costs and improve

their operations will become more profitable and in the process benefit travelers. Those

that do not will probably undergo liquidation. Therefore, in the post 9/11 environment,

competitive forces are continuing to spur airlines to maximize benefits to travelers or face

severe consequences. Efforts by the ATSB or any other governmental body to forestall

an airline’s exit by providing financial assistance may benefit shareholders and labor but

will hinder the competitive process and do little to improve—and may possibly harm—

traveler welfare.

25 Southwest entered Philadelphia in 2004 and may expand its presence as US Airways cuts back its operations there. Similarly, with Delta electing to leave Dallas Fort Worth airport, Southwest may enter even though it operates out of Dallas Love field.

35

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Table 1. Variables, Sample Means, and Data Sources*

Variable Units Mean Data Source**Average fare on route Dollars 194.7 U.S. Department of

aa Transportation, Data Bank 1ATotal sampled passengers on route 4978 U.S. Department of

aa Transportation, Data Bank 1AAvailable seats on connecting flights Percent 36.80 BACK Aviation, Aviation Link

aa Connections BuilderDominated hub at origin or aa destination

Dummy 0.363 FAA, Airport Activity Statistics

Hub classification of airports at aa origin and destination, by hub size

Dummy FAA, Airport Activity Statistics

Distance from origin to destination Miles 1057 U.S. Department ofaa Transportation, Data Bank 1A

Product of populations of MSAs at origin and destination

Millions2 0.210 U.S. Department of Commerce, aa Bureau of Economic Analysis

Product of incomes of MSAs at origin and destination

($1000)2 867.7 U.S. Department of Commerce, aa Bureau of Economic Analysis

Share of travelers whose primary aaaa purpose is business

Percent 41.96 U.S. Department of Transportation American Travel Survey

Average temperature difference at aa origin and destination (abs. value)

°Fahrenheit 11.06 National Weather Service, aaClimatography of the US, No. 81

Average delay on routea Minutes 9.876 U.S. Department of Transportation A, T100 segment data

Presence of carrier alliance on route Dummy 0.017 Carriers’ websites Intra-regional route classification by aa census region

Dummy U.S. Census Bureau

Total taxes and fees on aa routeb

Percent of total ticket price

15.95 Air Transport Association

Carrier-Specific Variables Potential competition on route Dummy U.S. Department of

aa Transportation, Data Bank 1AAdjacent competition on route Routes U.S. Department of

aa Transportation, Data Bank 1AService frequencies on route Available seats

per quarter BACK Aviation, Aviation Link aa Connections Builder

Number of routes in carrier’s aa network

U.S. Department of aa Transportation, Data Bank 1A

Number of cities served by carrier U.S. Department of aa Transportation, Data Bank 1A

Number of routes served by carrier aa from each city

U.S. Department of aa Transportation, Data Bank 1A

* The primary source of our data, U.S. Department of Transportation Data Bank 1A, is a 10% sample of aa all ticketed passengers. The basic unit of observation is an origin-destination pair (route) for a given aaaa quarter between 1990-2000. All monetary values are in 2000 dollars. ** In most cases, the variables are constructed by the authors using the raw data from the source aa aa aa aa identified.a To calculate delay we use 1977 as a base year and then for each route calculate the difference between the average travel time in a given quarter and the average travel time in 1977. b Total taxes and fees vary over routes because airports have different passenger facility charges and because taxes are based on a percentage of the ticket price and, beginning in 1997, on the number of flight segments.

Table 2. 3SLS Coefficients for Demand and Supply:Route-Level Variables (Huber-White robust standard errors are provided in parentheses)

Variable Demand Supply

Total sampled passengers* Dependent Variable 0.013 (0.004)

Average fare* -0.638(0.030)

Dependent Variable

Distance between origin and destination* 0.150 (0.013)

0.293 (0.004)

Product of average incomes of MSAs at origin and destination*

0.039 (0.018)

--

Product of populations of MSAs at origin and destination*

0.067 (0.004)

--

Difference between average temperatures at origin and destination (absolute value)

0.002 (0.0003)

--

Percentage of available seats on connecting flights -2.467(0.020)

--

Average Delay -- 0.018 (0.001)

Dominated Hub Dummy (1 if the origin or destination airport is a dominated hub, 0 otherwise)

-- 0.032 (0.004)

Nonstop Dummy (1 if fewer than 5% of passengers on the route make a connection, 0 otherwise)

-- -0.079(0.004)

Alliance Dummy (1 if two carriers in an alliance both serve the route, 0 otherwise)

-- -0.070(0.010)

Intra-Regional Dummies (1 if origin and destination are in the same census region, 0 otherwise)

Included Included

Seasonal Dummy variables Included Included

Year Dummy variables Included Included

Number of Observations 42,587 42,587 R2 0.64 0.74*Indicates that variable has been transformed by natural logarithm.

Table 3. 3SLS Coefficients for Demand and Supply:Competition Variables (Huber-White robust standard errors are provided in parentheses)

Carrier Demand SupplyService

Frequency*aAdjacent

Competition*Service

Frequency* aAdjacent

Competition*Potential

Competition

American 0.023 (0.002)

-0.031(0.008)

0.020 (0.001)

0.023 (0.005)

-0.002(0.004)

Alaska -0.032(0.003)

-0.059(0.019)

0.001 (0.001)

-0.0003 (0.011)

0.092 (0.017)

JetBlue** 0.091 (0.019)

0.159 (0.073)

-0.044(0.008)

-0.176(0.043)

---

Continental 0.041 (0.002)

-0.021(0.009)

-0.016(0.001)

-0.008(0.005)

-0.053(0.006)

Delta 0.090 (0.002)

-0.067(0.010)

-0.001(0.001)

-0.059(0.005)

0.010 (0.004)

Eastern -0.041(0.022)

0.001 (0.025)

-0.327(0.010

-0.099(0.015)

-0.112(0.032)

AirTran** 0.053 (0.004)

-0.045(0.047)

-0.033(0.001)

-0.037(0.028)

---

America West 0.001 (0.002)

-0.000(0.013)

-0.017(0.001)

0.082 (0.008)

-0.062(0.012)

Northwest 0.025 (0.002)

-0.186(0.013)

0.008 (0.001)

0.050 (0.007)

-0.030(0.005)

TWA 0.002 (0.002)

-0.097(0.013)

-0.006(0.001)

-0.066(0.008)

-0.101(0.015)

ATA 0.066 (0.004)

-0.032(0.022)

-0.028(0.001)

-0.109(0.013)

-0.060(0.048)

United 0.128 (0.002)

0.019 (0.009)

-0.0001 (0.001)

0.013 (0.005)

-0.013(0.004)

US Airways 0.121 (0.001)

-0.177(0.009)

0.011 (0.001)

-0.045(0.006)

-0.045(0.006)

Southwest 0.093 (0.002)

-0.083(0.012)

-0.038(0.001)

-0.048(0.010)

-0.048(0.010)

* These variables were expressed in natural logarithms. Because they were occasionally equal to zero, aa we used the transformation ln(1+x) where x was the variable in question. ** Potential competition coefficients could not be estimated for JetBlue and AirTran because they aa offered potential competition on only a few routes. a Service frequency is measured by the number of available seats on direct and connecting flights.

Tab

le 4

. 3S

LS

Coe

ffic

ient

s fo

r Se

rvic

e F

requ

ency

: R

oute

and

Net

wor

k C

hara

cter

isti

cs*

(Hub

er-W

hite

rob

ust s

tand

ard

erro

rs a

re p

rovi

ded

in p

aren

thes

es)

Dep

ende

nt V

aria

ble:

Air

line

Fre

quen

cy**

Am

eric

an

Alas

kaJe

tBlu

eC

ontin

enta

lD

elta

East

ern

AirT

ran

Am.

Wes

tN

orth

wes

tTW

AAT

AU

nite

dU

S Ai

rway

sSo

uthw

est

Tot

al n

umbe

r of

sam

pled

pa

ssen

gers

(al

l air

lines

)**

0.29

0(0

.056

)-0

.371

(0.0

21)

0.01

3(0

.003

)0.

558

(0.0

54)

2.48

6(0

.054

)0.

053

(0.0

10)

0.19

7(0

.016

)-0

.483

(0.0

52)

-1.0

80(0

.052

)-0

.460

(0.0

53)

0.19

7(0

.019

)2.

199

(0.0

48)

4.02

2(0

.052

)2.

419

(0.0

35)

Perc

ent o

f av

aila

ble

seat

s on

con

nect

ing

flig

hts

1.61

4(0

.162

)-1

.096

(0.0

61)

0.03

6(0

.008

)0.

664

(0.1

59)

7.13

2(0

.159

)0.

211

(0.0

28)

0.51

5(0

.047

)-1

.616

(0.1

50)

-2.4

56(0

.154

)0.

172

(0.1

53)

0.38

4(0

.055

)6.

390

(0.1

42)

12.0

2(0

.156

)6.

511

(0.1

05)

Car

rier

’s d

omin

ated

hub

du

mm

y 1.

299

(0.0

87)

--

----

4.11

7(0

.076

)--

----

6.23

9(0

.071

)5.

432

(0.1

40)

--1.

909

(0.0

80)

1.70

1(0

.093

)-0

.439

(0.1

81)

Oth

er c

arri

er’s

dom

inat

ed

hub

dum

my

-0.6

64(0

.053

)-0

.594

(0.0

20)

-0.0

06(0

.003

)-2

.210

(0.0

48)

-0.3

16(0

.055

)-0

.124

(0.0

10)

0.08

4(0

.015

)-1

.488

(0.0

44)

0.42

7(0

.049

)0.

185

(0.0

49)

-0.4

74(0

.166

)0.

201

(0.0

46)

0.29

6(0

.055

)-0

.537

(0.0

32)

Net

wor

k V

aria

bles

: N

etw

ork

linea

rity

-0

.045

(0.0

07)

0.01

1(0

.003

)0.

003

(0.0

08)

-0.0

02(0

.002

)-0

.033

(0.0

06)

0.02

4(0

.013

)-0

.005

(0.0

03)

0.01

5(0

.005

)-0

.002

(0.0

05)

-0.0

09(0

.011

)0.

026

(0.0

03)

0.02

3(0

.006

)0.

043

(0.0

03)

-0.0

01(0

.002

)

: Ave

rage

rou

te le

ngth

in

netw

ork

(x 1

00)

-0.3

67(0

.024

)0.

058

(0.0

38)

0.03

6(0

.009

)-0

.023

(0.0

24)

-0.1

18(0

.132

)-0

.195

(0.0

65)

-0.0

53(0

.004

)-0

.065

(0.0

43)

0.05

7(0

.082

)0.

069

(0.0

35)

-0.0

26(0

.006

)0.

067

(0.0

32)

0.17

2(0

.059

)-0

.106

(0.0

43)

p: P

rodu

ct o

f av

erag

e le

ngth

of

rout

es s

ervi

ng

orig

in a

nd d

estin

atio

n

-2.1

9(0

.283

)26

.61

(0.6

00)

3.06

(0.1

64)

0.71

2(0

.245

)4.

282

(0.4

40)

1.90

(0.1

66)

80.1

(0.7

75)

2.85

(0.1

92)

0.88

1(0

.252

)-1

.152

(0.1

93)

11.2

6(0

.135

)0.

823

(0.1

98)

1.19

(0.3

14)

82.8

1(0

.708

)

C: N

etw

ork

conn

ectiv

ity

2.23

3(0

.203

)-0

.291

(0.1

19)

-0.4

10(0

.078

)-0

.001

(0.1

16)

-0.3

65(0

.428

)0.

826

(0.3

60)

0.51

7(0

.077

)-0

.788

(0.1

27)

0.19

6(0

.463

)0.

707

(0.1

63)

-0.2

05(0

.039

)0.

015

(0.2

74)

-0.1

67(0

.166

)-0

.161

(0.0

58)

Cp:

Pro

duct

of

conn

ectiv

ity a

t ori

gin

and

dest

inat

ion

78.8

8(3

.19)

150.

8(1

.30)

49.4

1(0

.288

)61

.89

(2.8

23)

76.1

5(4

.644

)14

.19

(2.0

64)

76.9

7(1

.153

)10

2.3

(2.0

63)

44.6

8(3

.191

)14

8.9

(8.5

97)

60.4

7(0

.776

)28

.16

(2.3

98)

127.

0(8

.055

)25

1.9

(7.0

09)

Con

stan

t 2.

388

(0.7

35)

0.88

6(0

.396

)-0

.069

(0.0

34)

-3.6

10(0

.783

)-1

2.21

(0.8

15)

-0.4

97(0

.117

)-1

.316

(0.1

87)

6.15

2(0

.718

)5.

696

(0.8

35)

1.09

4(0

.737

)-0

.144

(0.2

29)

-16.

96(0

.855

)-2

7.46

(0.8

31)

-12.

62(0

.426

)

*

Intr

a-re

gion

al tr

avel

dum

mie

s an

d hu

b cl

assi

fica

tion

dum

mie

s ar

e in

clud

ed in

all

freq

uenc

y eq

uatio

ns.

** T

hese

var

iabl

es w

ere

expr

esse

d in

nat

ural

loga

rith

ms.

Bec

ause

they

wer

e oc

casi

onal

ly e

qual

to z

ero,

we

used

the

tran

sfor

mat

ion

ln(1

+x)

whe

re x

was

the

vari

able

in q

uest

ion.

Tab

le 5

. So

cial

and

Mar

ket

Val

uati

on o

f C

arri

ers:

Bef

ore

and

Aft

er S

epte

mbe

r 11

th*

Bef

ore

Sept

embe

r 11

th

Pro

fita

ble

Not

Pro

fita

ble

Indu

cing

Sig

nifi

cant

Wel

fare

Gai

ns

Sout

hwes

t, A

TA

, Uni

ted,

D

elta

, Air

Tra

n, J

etB

lue

Indu

cing

Sm

all o

r N

eglig

ible

Wel

fare

Gai

ns

Nor

thw

est

Am

eric

a W

est,

Ala

ska,

TW

A

Indu

cing

Wel

fare

Los

ses

Con

tinen

tal,

Am

eric

anU

SAir

way

s

Aft

er S

epte

mbe

r 11

th

Pro

fita

ble

Not

Pro

fita

ble

Indu

ced

Sign

ific

ant

Wel

fare

Gai

ns

Sout

hwes

t, A

TA

**, A

irT

ran,

Je

tBlu

eU

nite

d, D

elta

Indu

ced

Smal

l or

Neg

ligib

le W

elfa

re G

ains

N

orth

wes

t, A

mer

ica

Wes

t, A

lask

a, T

WA

Indu

ced

Wel

fare

Los

ses

Con

tinen

tal,

USA

irw

ays,

Am

eric

an

*Ope

ratin

g pr

ofit

was

com

pute

d fr

om U

.S. D

epar

tmen

t of

Tra

nspo

rtat

ion

Form

41

data

. W

e us

ed th

e fo

ur q

uart

ers

befo

re S

epte

mbe

r 11

th a

nd th

e fo

ur q

uart

ers

dire

ctly

aft

er S

epte

mbe

r 11

th to

det

erm

ine

prof

itabi

lity.

* *

AT

A h

ad a

slig

htly

neg

ativ

e op

erat

ing

prof

it fo

r th

e fo

ur q

uart

ers

dire

ctly

aft

er S

epte

mbe

r 11

th, b

ut it

s op

erat

ing

prof

it w

as p

ositi

ve if

we

took

in

toac

coun

t an

addi

tiona

l qua

rter

of

data

. T

his

was

not

the

case

for

any

oth

er a

irlin

e. I

n 20

03 a

nd 2

004,

AT

A h

as v

acill

ated

bet

wee

n pe

riod

s of

aa

aa

prof

itabi

lity

and

unpr

ofita

bilit

y.

App

endi

x.

3SL

S C

oeff

icie

nts

for

Serv

ice

Fre

quen

cy:

Com

peti

tive

Int

erac

tion

s (E

last

icit

ies)

(H

uber

-Whi

te r

obus

t sta

ndar

d er

rors

are

pro

vide

d in

par

enth

eses

)

Dep

ende

nt V

aria

ble:

Air

line

Fre

quen

cy*

Amer

ican

Al

aska

JetB

lue

Con

tinen

tal

Del

taEa

ster

nAi

rTra

nAm

.W

est

Nor

thw

est

TWA

ATA

Uni

ted

US

Airw

ays

Sout

hwes

t

Ava

ilabl

e Se

ats*

Am

eric

an

---0

.027

-0.0

01(0

.004

)(0

.001

)0.

074

(0.0

12)

0.13

7(0

.012

)0.

026

(0.0

02)

-0.0

10(0

.003

)0.

073

(0.0

10)

0.00

8(0

.011

)0.

188

(0.0

10)

-0.0

56(0

.004

)0.

424

(0.0

10)

-0.2

38(0

.013

)-0

.100

(0.0

07)

Ala

ska

-0.1

40(0

.016

)--

-0.0

00(0

.001

)-0

.372

(0.0

16)

0.24

1(0

.016

)-0

.029

(0.0

03)

0.01

6(0

.004

)-0

.575

(0.0

13)

0.07

1(0

.015

)0.

176

(0.0

14)

-0.0

43(0

.005

)0.

432

(0.0

15)

0.23

2(0

.018

)-0

.056

(0.0

10)

JetB

lue

-0.3

17(0

.107

)-0

.037

(0.0

40)

---0

.270

(0.1

12)

0.03

7(0

.113

)-0

.009

(0.0

20)

-0.0

79(0

.029

)-0

.255

(0.0

91)

-0.0

45(0

.100

)0.

460

(0.0

98)

-0.0

95(0

.034

)-0

.163

(0.1

07)

-0.5

89(0

.124

)-0

.411

(0.0

68)

Con

tinen

tal

0.14

2(0

.013

)-0

.098

(0.0

05)

-0.0

01(0

.001

)--

0.11

4(0

.013

)-0

.053

(0.0

02)

-0.0

03(0

.004

)-0

.118

(0.0

12)

0.29

8(0

.012

)-0

.080

(0.0

13)

-0.0

95(0

.004

)-0

.007

(0.0

13)

0.26

5(0

.014

)-0

.181

(0.0

08)

Del

ta0.

089

(0.0

09)

0.06

7(0

.004

)0.

0001

(0.0

00)

0.21

4(0

.010

)--

0.01

5(0

.002

)0.

027

(0.0

03)

0.13

0(0

.008

)0.

136

(0.0

09)

0.20

7(0

.009

)-0

.020

(0.0

03)

-0.2

86(0

.009

)-0

.513

(0.0

10)

-0.3

30(0

.006

)E

aste

rn

0.72

3(0

.095

)-0

.419

(0.0

39)

0.00

1(0

.005

)-1

.617

(0.0

95)

0.87

4(0

.104

)--

-0.0

61(0

.025

)-1

.186

(0.1

04)

0.06

2(0

.088

)-0

.653

(0.0

96)

0.05

2(0

.037

)-0

.261

(0.0

93)

1.49

3(0

.115

)-0

.247

(0.0

64)

Air

Tra

n-0

.187

-0.0

24(0

.021

)(0

.008

)0.

001

(0.0

01)

-0.0

26(0

.022

)0.

038

(0.0

22)

-0.0

16(0

.004

)--

-0.0

76(0

.018

)0.

167

(0.0

20)

-0.0

45(0

.019

)0.

031

(0.0

07)

-0.1

53(0

.021

)-0

.302

(0.0

24)

-0.1

72(0

.013

)A

mer

ica

Wes

t 0.

056

(0.0

12)

-0.2

05(0

.004

)-0

.000

(0.0

01)

-0.0

89(0

.013

)0.

016

(0.0

13)

-0.0

27(0

.002

)0.

004

(0.0

03)

--0.

155

(0.0

11)

0.23

5(0

.011

)-0

.039

(0.0

04)

0.11

4(0

.012

)-0

.061

(0.0

15)

-0.1

36(0

.008

)N

orth

wes

t-0

.075

0.03

20(0

.010

)(0

.004

)-0

.002

(0.0

00)

0.23

9(0

.010

)0.

043

(0.0

10)

0.01

1(0

.002

)-0

.000

(0.0

03)

0.11

5(0

.008

)--

0.14

3(0

.009

)0.

057

(0.0

03)

0.14

1(0

.009

)-0

.045

(0.0

11)

-0.0

09(0

.006

)T

WA

0.26

10.

040

(0.0

12)

(0.0

05)

0.00

1(0

.001

)-0

.034

(0.0

13)

0.16

3(0

.013

)-0

.008

(0.0

02)

-0.0

03(0

.003

)0.

075

(0.0

11)

0.24

8(0

.012

)--

0.06

0(0

.004

)-0

.069

(0.0

12)

0.11

8(0

.014

)0.

110

(0.0

08)

AT

A-0

.095

-0.0

02(0

.020

)(0

.008

)-0

.001

(0.0

01)

-0.4

56(0

.020

)-0

.199

(0.0

21)

-0.0

12(0

.004

)0.

016

(0.0

05)

0.01

5(0

.017

)0.

576

(0.0

18)

0.15

1(0

.018

)--

-0.2

19(0

.020

)-0

.350

(0.0

23)

-0.3

47(0

.012

)U

nite

d0.

534

0.16

8(0

.012

)(0

.005

)-0

.001

(0.0

00)

0.14

6(0

.014

)-0

.479

(0.0

15)

-0.0

12(0

.002

)-0

.046

(0.0

04)

0.17

4(0

.011

)0.

468

(0.0

12)

0.21

5(0

.013

)-0

.018

(0.0

04)

---0

.773

(0.0

14)

-0.4

20(0

.008

)U

S A

irw

ays

-0.1

32(0

.009

)0.

635

(0.0

03)

-0.0

01(0

.000

)0.

249

(0.0

09)

-0.3

49(0

.009

)0.

023

(0.0

02)

-0.0

23(0

.002

)0.

039

(0.0

08)

0.23

2(0

.008

)0.

127

(0.0

08)

-0.0

29(0

.003

)-0

.358

(0.0

08)

---0

.398

(0.0

05)

Sout

hwes

t-0

.105

0.06

7(0

.009

)(0

.003

)-0

.002

(0.0

00)

0.02

3(0

.009

)-0

.411

(0.0

09)

0.00

2(0

.002

)-0

.033

(0.0

02)

0.18

5(0

.008

)0.

146

(0.0

08)

0.13

5(0

.008

)-0

.045

(0.0

03)

-0.3

58(0

.008

)-0

.689

(0.0

09)

--

* T

hese

var

iabl

es w

ere

expr

esse

d in

nat

ural

loga

rith

ms.

Bec

ause

they

wer

e oc

casi

onal

ly e

qual

to z

ero,

we

used

the

tran

sfor

mat

ion

ln(1

+x)

whe

re x

was

the

vari

able

in q

uest

ion.

Fig

ure

1.

Air

line

Fre

quen

cy E

last

icit

ies

(The

ela

stic

ity is

the

perc

enta

ge c

hang

e in

the

base

air

line’

sfr

eque

ncy

in r

espo

nse

to a

one

per

cent

cha

nge

in a

com

petit

or’s

freq

uenc

y.)

CO

MP

ET

ITO

RA

AA

SC

OD

LE

AH

PN

WT

WU

AU

SB

6F

LT

ZW

NA

mer

ican

AA

XX

XA

lask

aA

SX

XX

BA

SE

Con

tinen

tal

CO

XX

XA

IRL

INE

Del

taD

LX

XX

*E

aste

rnE

AX

XX

Am

eric

a W

est

HP

XX

XN

orth

wes

tN

WX

XX

TW

AT

WX

XX

Uni

ted

UA

XX

XU

S A

irway

sU

SX

XX

JetB

lue

B6

XX

XA

irTra

nF

LX

XX

AT

AT

ZX

XX

Sou

thw

est

WN

XX

X

Sym

met

ric R

espo

nse

(ela

stic

ity g

reat

er th

an 0

.03)

Unr

espo

nsiv

e (e

last

icity

less

than

0.0

3 in

abs

olut

e va

lue)

Asy

mm

etric

Res

pons

e (e

last

icity

less

than

-0.

03)

Figure 2. Airline Exit and Total Welfare

exitsjp Drt

Drtp

Fare

exitsjp Srt

Srtp

*rtQexits* jQrt Passengers

Fig

ure

3.

Net

Ben

efit

s P

rovi

ded

by A

irlin

es:

1990

-200

0

-10-50510152025

1990

1995

2000

Ye

ar

Total Loss in Net Benefits if Airline Exited theIndustry($Billions, 2000)

So

uth

we

st (

$19.

6)

ATA

($7.

8)

Un

ited

($7.

2)

De

lta ($

6.1

)

AirT

ran

($5

.5)

JetB

lue

($5

.5)

No

rthw

est

($1

.0)

Am. W

est (

$0.

6)

Ala

ska

(-$0

.03

)

TWA

(-$0

.2)

Co

ntin

enta

l (-$

2.4

)

US

Air

wa

ys (-

$3.

6)

Amer

ica

n (-

$3.7

)

Ea

ste

rn

Leg

end

*

*F

igur

es in

par

enth

eses

indi

cate

tota

l net

ben

efits

in b

illio

ns o

f do

llars

in th

e ye

ar 2

000.

Figure 4. Decomposition of Fare and Frequency Effects on Total Welfare for Selected Airlines

A. Southwest Airlines' Welfare Loss Decomposition

-10

-5

0

5

10

15

20

1990 1995 2000

Year

Wel

fare

Loss

by

Sou

rce

($B

illio

ns, 2

000)

Frequency

Fare

Adjacent Competition

B. American Airlines' Welfare Loss Decomposition

-6

-4

-2

0

2

4

6

1990 1995 2000

Year

Wel

fare

Lo

ss b

y S

ou

rce

($B

illio

ns,

200

0)

Frequency

Fare

C. United Airlines' Welfare Loss Decomposition

0

2

4

6

8

10

12

14

1990 1995 2000

Year

Wel

fare

Lo

ss b

y S

ou

rce

($B

illio

ns,

200

0)

Frequency

Fare