COMPETITION AND WELFARE IN THE U.S. AIRLINE INDUSTRY

Transcription

1 Please do not quote. COMPETITION AND WELFARE IN THE U.S. AIRLINE INDUSTRY Steven A. Morrison Northeastern University Clifford Winston 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

2 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.

3 2 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

4 3 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.

5 4 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 nondirectional 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.

6 5 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, time t as: D it D it D ( it it ijt it D Q it, on route i at Q D p, X, freq ; u ), (1) where p is the average fare in the market, X contains exogenous socioeconomic and route characteristics that affect demand, freqijt contains the service frequencies on route i it offered by each airline j, and u it is an error term. 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.

7 6 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: k p 1 MC( qk D ), 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 network development and are unlikely to be influenced by the fares and passengers on a given route.

8 7 where k is firm k s conduct parameter, MC is its marginal cost function, and q k 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: p S it S ( it it it ijt it S Q, F, Z, freq ; ), (2) where the fare is a function of the quantity of travelers who are flown, ; variables that affect the cost of air transportation, F ; other exogenous supply characteristics, Z ; the it flight frequencies of each airline j on route i, freqijt ; 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 S Q it it 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.

9 8 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 Slots at O Hare were eliminated in 2002, but airlines have agreed to trim their schedules during The airlines that entered into a code-share alliance during our sample period are Northwest and Alaska, Northwest and Continental, and Continental and Alaska.

10 9 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 service frequency, f i : Q Q( p, fi ) i. Carrier j s service frequency is a function of market demand, market price, and all other carriers frequencies: f j f ( Q, p, fi ) i j. And market price is a function of market demand and service frequencies: p p( Q, fi ) i. Substituting price into the frequency equation, we can express ~ f j f ( Q, p( Q, fi ), fi ) f ( Q, p( Q), fi ) i f ( Q, fi ) i j. ~ Thus, frequency denoted by f ( Q, fi ) i j is explicitly a function of market demand and other carriers frequencies and implicitly a function of price.

11 10 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: freqkit E( Ykit, Qit, freqmit, X it ; kit ), (3) where Y kit contains network variables that are specific to both the carrier and route, Qit is the total number of passengers on the route, freqmit are the frequencies for each airline m on the route for all m k, is an error term. X it contains relevant exogenous influences, and kit 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

12 11 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 e e p number of airports served by the carrier (nodes); number of nonstop routes served by the carrier (edges); number of nonstop routes served by the carrier from a particular airport p, for all airports p in the network; d p,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: 1 max( d p, q ) p q d p, q, 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. 1 e d p, q p q, 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.

13 12 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. 1 p d p, q, indicating the airport p analog to equal to the average length e p p q 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: e C, the standard definition of connectivity (the number of nonstop routes per n 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. C p e p n, indicating the airport p analog to C. Airports with high values of C p are likely to be hubs that are characterized by frequent operations that sustain an airline s

14 13 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.

15 14 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 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 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 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.

16 15 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

17 16 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 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-toface 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.

18 17 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.

19 18 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 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.

20 19 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 lowcost 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

21 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 80 th percentile, and the lowest fares, as indicated by the 20 th 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.

22 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: freq Total = freq Direct + freq Connecting, 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.

23 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 hubs during our sample period.

24 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

25 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 wellpublicized 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).

26 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

27 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