Three Essays on Competition and Productivity in the U.S. Airline Industry

Home , ATA Airlines, Legend Airlines

THREE ESSAYS ON COMPETITION AND PRODUCTIVITY IN THE U.S. AIRLINE INDUSTRY

A dissertation presented

Tuvshintulga Bold

The Department of Economics

Submitted in partial fulfillment of the requirements for the degree of

Doctor of Philosophy

in the field of

Economics

Northeastern University

Boston, MA

November, 2013

THREE ESSAYS ON COMPETITION AND PRODUCTIVITY IN THE U.S. AIRLINE INDUSTRY

Tuvshintulga Bold

ABSTRACT OF DISSERTATION

Submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Economics in the Graduate School of Social Sciences and Humanities of Northeastern University November, 2013

Abstracts

Chapter 1: The Effect of Wright Amendment on Consumer Welfare

This paper analyses effect of the Wright Amendment on airline ticket price and ultimately consumer welfare for passengers flying to and from the Dallas metropolitan area. The Wright Amendment is a law that was implemented in 1979 to restrict passenger air travel to and from Dallas Love Field airport in order to encourage growth at then the newly constructed Dallas Fort-Worth airport. Today, Dallas Fort-Worth has become one of the busiest airports in the U.S., but the Wright Amendment continues to suppress competition by prohibiting long distance flights to and from Dallas Love Field airport. While supporters and opponents of the Wright Amendment have been debating for some time, to date no economic study has measured the effect of the law on air fares and consumer welfare. I use data from the U.S. Department of Transportation’s Ticket and Origin and Destination Survey from 1996 to 2011 to produce estimations of the effect of the Wright Amendment. Series of three relaxations to the amendment created an opportunity to use the difference-in-difference econometric method to precisely measure the fare distortion brought by the law. Of the three relaxations, the third and the last change introduced a major alteration in the law by allowing airlines to fly anywhere in the country from Dallas Love Field airport. As a result, fares decreased on average by 13.88% while certain destinations experienced as much as 36% of fare decrease during the five years following the implementation of the change. Consequently, passengers of the Dallas area saved $1.31 billion from 2007 to 2011 on flights to and from the Dallas region.

Chapter 2: The Effect of Bankruptcy on Productivity in the Airline Industry

This study tracks major airlines in the U.S. during the past 20 years to determine whether bankruptcies of the biggest airlines affect their productivity under financial stress. The U.S. airline industry has seen an incredible amount of volatility ever since the deregulation of 1978 with every major carrier declaring bankruptcy at least once. Business cycles surely affect airlines’ health, but not evenly. During economic expansionary periods industry profits can be modest, but during recessions the biggest

airlines start declaring bankruptcies one after another. Yet after so many bankruptcies, the industry still remains very vulnerable. In common practice, Chapter 7 bankruptcy declaration is a way for an organization to reorganize and is a golden opportunity to improve by getting rid of its inefficiency. However, to date there has been no previous study in the airline industry looking into the relationship between bankruptcy and productivity. Using data of the 14 biggest airlines in the U.S., I empirically explore how bankruptcy affects productivity. Both partial and total factor productivity methods were used to provide a detailed presentation of the evolution of airline productivity. I find that bankruptcy does not have any impact on productivity as some of the major airlines declare bankruptcy multiple times indicating lack of improvement in employee and aircraft productivity. The results were consistent under different variations of post- bankruptcy periods where short term and long term effects were tested.

Chapter 3: The Effect of Mergers on Productivity in the Airline Industry

In this study, I examine the effect of mergers in the U.S. airline industry on productivity. Chapter 7 bankruptcies and mergers are the two major types of strategies a vulnerable airline can pursue in order to secure its survival in its immediate future. A merger deal can offer several major benefits including increased market power, availability of new financing source by merging with a healthier airline, reduced cost and improved productivity through synergy. The airline industry has witnessed a significantly increased level of mergers in the last decade, especially among its biggest players who enter mega-sized mergers to create the world’s biggest airlines one after another. Airlines highlight cost savings and improved efficiency as their primary merger motivations. Yet, to date no study exists has examined the relationship between merger and productivity in the airline industry. Similar to bankruptcies mergers can offer short term survival solutions, but long term viability comes from improvements in productivity. I use data of the 14 biggest airlines in U.S. during the past 20 years to track productivity of airlines going through mergers. For the 14 airlines, I construct partial and total factor productivity in order to estimate merger effects. Using the available data, I identify three mergers where is there is sufficient ex-ante and ex-post data exist. Using the difference-in-difference econometric technique, I find that mergers

do improve productivity as promised by the airlines. However, the extent of improvements depended on whether the acquiring airline was more productive than the target airlines prior to the merger. The results were consistent with previous findings of studies on mergers and productivity outside the airline industry.

Acknowledgements

I express my deepest gratitude to my dissertation committee. Without their contribution, encouragement and dedication, this dissertation would not have been possible. I thank my advisor Professor Steven Morrison for his invaluable advice, patience and support. I feel most fortunate to have met him, to be able to benefit from his constant encouragement and mentorship. I am thankful to Professor John Kwoka and Professor James Dana whose insightful comments, points of view enriched my thinking and whose ever present support made this work possible.

I thank Prof. Neil Alper for patiently mentoring me to deal with students, carrying a course load and teaching me how to teach. I am also indebted to the other faculty for their advice, the staff and my fellow graduate students at the Department of Economics for their support and kindness.

I am thankful to my father Kh.Bold and my mother M.Tsetsegmaa for dedicating themselves so that I could pursue my education in America.

Finally, I dedicate this dissertation to my daughter, T.Nandin, who puts a smile on my face every single day.

TABLE OF CONTENTS

Abstract 2

Acknowledgments 5

Table of Contents 6

Chapter 1: The Effect of Wright Amendment

on Consumer Welfare 8

Introduction 8

History of Airport Restrictions 11

Econometric Identification 17

Data 19

Regression Results 22

Conclusion 28

References 30

Tables 32

Chapter 2: The Effect of Bankruptcy on Productivity

in the Airline Industry 44

Introduction 44

Literature Review 46

Data 48

Airline bankruptcy background 51

Measuring Productivity 57

Econometric Methodology 64

Regression Results 67

Conclusion 70

References 72

Tables 75

Figures 85

Appendix 97

Chapter 3: The Effect of Mergers on Productivity in

the Airline Industry 99

Introduction 99

Literature Review 101

Data 102

Background on Mergers in the U.S. Airline Industry 105

Measuring Productivity 112

Econometric Methodology 118

Regression Results 121

Conclusion 124

References 127

Tables 130

Figures 148

Chapter 1: The effect of Wright Amendment on Consumer Welfare

I. Introduction

Deregulation of the airline industry in 1978 marked the beginning of an era of market competition for commercial aviation. Ever since, airlines have been competing fiercely using all their resources by any means without the kind of regulation that restricted their action before 1978: some have prospered, some ceased to exist and the rest are still trying to find ways to survive. While airlines may have differing opinions on whether deregulation has brought them prosperity, one particular stakeholder in the industry who has benefited substantially from deregulation is consumers. Market competition based pricing in the airline industry has made flying so inexpensive that the gain in consumer welfare has been large.1

Today airlines can enter and exit to serve any airport pair market at their will and set the level of fares and frequency of flights as they see fit. As such, passenger fares have become heavily dependent on two aspects of market structure: the level of competition that exists on a particular route and the extent of hub dominance at origin or destination airports of a specific flight. Though most routes are open to free entry and exit, laws that suppress market competition still exist, causing a significant decrease in consumer welfare in affected regions.

These laws exist in the form of limiting airlines’ ability to fly to and from certain airports. The two major airport restrictions are the perimeter rules and slot

1 See Morrison and Winston (1995) Estimation of annual benefits to the consumers were $12.4 billion in 1993 dollars, which is $19.9 billion in 2012 dollars.

rules.2 This paper aims to measure the effect of Wright Amendment, which is a form of a perimeter rule, on consumer welfare for passengers who travel to and from the Dallas

Fort-Worth metropolitan area.

The Wright Amendment directly suppresses airline competition at the fourth busiest airport in the United States, puts restrictions on flights out of Dallas Love Field

(DAL), which is the other major airport for commercial service in metropolitan Dallas besides Dallas Fort-Worth (DFW).3 DAL is one-third as far from downtown Dallas as

DFW airport.

The Wright Amendment has gone through several changes, but the original

Wright Amendment prohibited any airline serving DAL to sell long-haul flight tickets that used airplanes with more than 56 seats. Specifically, an airline at DAL could not sell tickets, connecting or direct, to any destinations beyond Texas and its neighboring states, Louisiana, Arkansas, Oklahoma and New Mexico. For example, if passengers wished to fly to Los Angeles from DAL, not only were they not able to fly directly to

Los Angeles but they could not even purchase a flight ticket with a connection either in

Texas or its four neighboring states. They would have to purchase two tickets separately, one for a flight to a connecting city within the Wright Amendment perimeter and another one for the flight between the connecting city and final

2 A fourth, long-term exclusive gate leases are another form of restriction on competition at the airport level. Such long-term leases allow incumbent airlines to employ a majority of the gates at a certain airport for 20 years at a time. If an entrant wishes, it has to purchase the rights to use those gates usually at undesired hours for higher prices. (“Slot-Controlled Airports” United States General Accounting Office Report, 2012). 3 DFW ranks fourth in passenger enplanement and deplanement after Hartsfield-Jackson Atlanta (92 million), Chicago O’Hare (66 million) and Los Angeles International (61 million) according to Airport Council International North America as of Q3 of 2012 (http://aci-na.org/).

destination. A more detailed background on the Wright Amendment comes in Section

II.

Because of the Wright Amendment, fares for flights to and from the Dallas metropolitan area (DFW + DAL) have been potentially higher than what they would have been due to the following factor. The higher the route level competition between the airlines on a particular route, the lower the fares have been for passengers. In the last three decades, Southwest has been the pioneer of increased route-level competition that results in low fares wherever it chooses to serve. When Southwest enters a route

(airport pair) fares have declined on average by 46% (Morrison 2001). This is known as the actual effect of Southwest. When Southwest serves a route by serving airports adjacent to a specific airport pair, fares have declined on average by 26% (Morrison

2001). This is termed the effect of adjacent competition. As such, passengers flying to or from the Dallas metropolitan area on routes outside of Texas and its neighboring states have been paying higher for fares due to Southwest’s inability to serve out of

DAL.4 This paper aims to measure the effect of the Wright Amendment on the consumer welfare of the Dallas metropolitan area.

The paper is organized as follows. Section II provides background on the

Wright Amendment and one other form of airport restriction. Section III discusses the econometric model used to capture the effect of Wright Amendment. Section IV

4 Another potential cause for higher fares at DFW is the hub premium effect. Borenstein (1989) finds that a carrier with at least 50% of the traffic at an airport charged about 12% higher fares than those with about 10% traffic. Currently, Dallas Fort-Worth is American Airline’s hub airport as it handles 82% of all flights originating or ending at the airport. Naturally, on routes outside of Texas and its contiguous states American Airlines aims to charge a hub-premium on fares on its customers which it will not be able to if faced with competition from Southwest out of DAL. Again, the Wright Amendment has preserved American Airlines’ ability to charge a hub-premium fare by prohibiting Southwest from serving the affected markets.

provides a description of the data used for the estimates. Section V presents and interprets the results. Section VI provides the paper’s summary.

II. History of airport restrictions

Currently there are two types of airport restrictions that legally limit competition in the airline industry. They are the slot rule and the perimeter rule.5 The

Wright Amendment is a form of a perimeter rule where the perimeter is defined by state borders instead of a constant distance.

Slot rules were introduced in 1969 as means of controlling rapidly increasing traffic at four airports: New York LaGuardia, New York JFK, Chicago O’Hare and

Washington National (now Reagan National). Slot controls functions by putting limits on the number of landings and take offs within a given hour mainly during peak periods. The limitation on the number of take offs and landings translates into reduced competition, but only if the traffic is high enough that the limitations are binding, which is indeed the case generally.

The general perimeter rules and Wright Amendment were implemented to encourage growth at nearby newly built airports, while the slot rules were targeted at reducing airport congestion. A perimeter rule at an airport restricts all carriers serving that airport from offering flights outside the indicated perimeter. For example, La

Guardia Airport’s (LGA) perimeter, formalized in 1984, is 1,500 miles and it was instituted to reduce congestion at LGA by forcing long-haul flights to John F. Kennedy

Airport (JFK). If someone wishes to fly non-stop from New York to Los Angeles, for

5 Slot rules are also known as “High Density Rule.”

example, the passenger would have to fly either from Newark or JFK since Los

Angeles lies outside of La Guardia’s 1,500-mile perimeter. For Reagan National in

Washington D.C., the perimeter rule was instituted in 1986.

The origin of the Wright Amendment dates back to the pre-deregulation era of the airline industry and is a very interesting case of a law that has come to suppress competition at one of the nation’s busiest metropolitan areas. As passenger traffic in the airline industry grew in the 1960s in the metropolitan Dallas area, competition for air service among airports surrounding the city intensified. These airports included Love

Field, Greater Southwest Airport, Red Bird Airport and Meacham Field. Concerned with duplication of services, Federal officials drafted a proposal to build a single airport to serve both the regions surrounding Dallas and Fort-Worth6. The proposal was accepted by the relevant local government bodies and all the airlines agreed to relocate to the new regional airport once construction was complete, except Southwest Airlines.

Southwest Airlines began service on June 18, 1971 operating intra-state flights within the state of Texas. As Southwest flourished at Love Field, it expressed its intention to remain at Love Field even after completion of construction at Dallas Fort-Worth

International Airport. As a result, Southwest Airlines was sued by the DFW Airport board and by the cities of Dallas and Fort-Worth, who tried to decommission DAL and force Southwest to move to DFW. Southwest Airlines won in court and was allowed to operate from DAL offering intrastate flights while DFW officially opened in 1974.

Southwest continued to grow successfully out of DAL offering intrastate services until

1978. However, the beginning of airline industry deregulation in 1978 opened a whole

6 Love Terminal Partners, et al., Plaintiffs, v. THE UNITED STATES, Defendant. No. 08-536 L.

new level of opportunities for the airline to capitalize on its successful business model on national level. In 1979, Southwest received a ruling from the Civil Aeronautics

Board that gave it permission to offer interstate services from DAL. Naturally, the

CBA’s ruling was not welcomed at all by the supporters of DFW as they quickly took counter measures by turning to U.S. House of Representatives Speaker Jim Wright (D-

Texas) to include an amendment to the International Air Transportation Competition

Act of 1979 that would protect DFW from any competition by DAL. The result, after some modifications, became what is known as the Wright Amendment of 1980, which enforced three major points: a) it became illegal for any airline at DAL to offer flights to destinations beyond Texas and its four neighboring states, Louisiana, Arkansas,

Oklahoma and New Mexico (the Wright perimeter), b) airlines were prohibited to offer or advertise the availability of any connecting flights between DAL and any city outside the Wright perimeter and c) airlines at DAL may not use aircraft with more than 56 seats for commercial purposes to destinations outside the Wright perimeter.7

Today the annual enplanements and deplanements at DFW are 57.7 million passengers, making them fourth largest in the country.8 At DAL it had stayed constant around 6 million until 2006 and grew to 7.9 million by 2011.9 Considering the traffic had reached 6.3 million in 1973 the growth at the airport was severely constrained by the Wright Amendment. For many of the 57.7 million passengers who are forced to use

DFW, the Wright Amendment is potentially a major setback preventing them from the choice of experiencing lower fares. As such, over the years there has been heavy

7 Love Terminal Partners, et al., Plaintiffs, v. THE UNITED STATES, Defendant. No. 08-536 L. 8 Dallas Fort-Worth Airport statistics of 2011 (http://www.dfwairport.com/stats/P1_058942.php). 9 Dallas Love Field Airport statistics of 2011 (http://www.dallas-lovefield.com/pdf/statistics).

campaigning resulting in a series of relaxations and an agreement to fully repeal the amendment in 2014 was reached in 2006 among the stake holders. These sequences of relaxations present an opportunity to measure what fares could be in the absence of the amendment. Arguably, the Wright Amendment has outlived its original purpose and stands in the way of all the benefits that can be brought by increased competition to the

Dallas metropolitan area consumers of air service.

The first exemption, passed in October of 1997, was sponsored by Senator

Richard Shelby of Alabama and was consequently named the Shelby Amendment.10

The Shelby Amendment allowed for non-stop flights to Alabama, Mississippi and

Kansas from DAL.11 Citing lack of demand at DAL, Southwest did not begin non-stop service immediately, though Southwest did take the opportunity to start selling tickets for connecting flights to two major cities in these states: Birmingham, Alabama and

Jackson, Mississippi. As Southwest did not increase its service due to low demand, it is expected that no major statistically significant change in fare would take place. Table 1 provides a comparison of fares and passenger quantity before and after the Shelby

Amendment took effect.

Even though Southwest did not begin non-stop service immediately, fares decreased by 23% for the Birmingham route, but increased by 12% over the next four

10 “Dallas Love field: The Wright and Shelby Amendments” CRS Report for Congress (2005). 11 The Shelby Amendment also introduced a more relaxed version of the 56-seat restriction stating that as long as the airplane contained, including reconfigured or originally manufactured, fewer than 56-seats and weighted less than 300,000 pounds, it could be flown anywhere in the country. The previous version of the 56-seat rule stipulated that the aircraft originally must have been produced with fewer than 56 seats. Legend Airlines used the opportunity to offer service using re-configured 56-seat airplanes to long- distance destinations such as Washington D.C. and New York. American Airlines immediately began offering the same service to the same destinations even occasionally at lower prices while at the same time suing Legend Airlines to halt their service out of DAL. Just a few months after beginning operation, Legend Airlines went out of business.

quarters for the Jackson route. The increase in Jackson fares will be explained in the results section. In addition, the number of passengers who flew to these destinations saw a big increase for the Birmingham route and a mild 10% increase for the Jackson route.

The second relaxation came in December 2005 when Senator Christopher ‘Kit’

Bond of Missouri successfully added another exemption (Bond Amendment) to the

Wright Amendment to allow flights to his state from DAL.12 Southwest immediately began service to Kansas City and St. Louis. At the same time, American Airlines also started serving those two cities from DAL. Table 2 provides a comparison of fares and passenger traffic before and after the Bond Amendment took effect.

Here, we observe much bigger changes in both fare and traffic due to

Southwest’s immediate entrance into the new markets. The market concentration level drops significantly as well.

The third and the most significant alteration was realized in 2006 after years of heavy campaigning by Southwest to repeal the amendment in its entirety. An agreement, which eventually became a public law named the Wright Amendment

Reform Act of 2006, was reached between American Airlines, the city of Dallas, the city of Fort-Worth and Southwest. The agreement was that the original Wright

Amendment would be partially repealed beginning in October 2006 and fully repealed in 2014. There were two major conditions that were agreed upon for the Reform-Act to be realized. First, beginning October 2006 until October 2014, airlines operating at

12 Wright Amendment Reform Act (2006).

DAL were now permitted to sell tickets to any destination in the country as long as the flights make a stopover (or a connection) within the Wright perimeter. Second, once the stop-over requirement expires and airlines would be allowed to make non-stop flights from DAL to anywhere in the country, the number of gates at DAL would be reduced from 32 to 20.13 Under the Reform-Act from October 2006 till October 2014, an airline could begin selling tickets from DAL to fly anywhere in the country, unlike previously, but the itinerary would have to make a stop-over (or a connection) within the Wright perimeter. Within a few days of passing of the Reform-Act, Southwest announced its plan to immediately start selling tickets to 25 metropolitan areas from Love Field with connections at various points inside the Wright perimeter. Table 3 presents a comparison of fares before and after the third relaxation was introduced allowing

Southwest to begin selling tickets to 25 cities:14

The simple comparison of before-and-after average fares for the 25 markets show significant reduction in fares for the most part as expected. 16 out of 25 markets experience fare decreases of more than 10%. Of the remaining nine markets, seven experience fare decrease, one shows no change and another one shows an increase in

13 Even though by 2014 all airlines could begin selling non-stop tickets to anywhere from Dallas Love Field, the cap of twenty gates at Dallas Love Field will remain and has been causing some controversy. The gate usage has been divided as 16 for Southwest, 2 for American and 2 for Continental (now United). JetBlue has opposed the reform act. The cap will become a binding restriction for further growth at DAL if demand increases significantly, which is expected. To remedy the situation at least partially, the airport is investing in modernizing the airport’s twenty gates to handle passenger traffic more efficiently. (http://www.dallas-lovefield.com/) 14 It must be noted that flights to these cities had been already available from Love Field prior to the reform act. However, it was illegal for Southwest and other airlines at Love Field to sell tickets to these long-haul destinations. There are reports of consumers who went through the task of booking two roundtrip tickets on their own, one to a destination within the Wright perimeter and second to the final destination from the connecting airport. Considering the additional task passengers had to complete to fly on Southwest, their fares must have been low enough to offset the extra hurdle. For example, if a passenger needs to fly from DAL to Chicago, he/she would comb through all the possible connecting points within the Wright perimeter using Southwest’s website. Then, he/she would need to determine flights with best matching connection times. Only, then he/she could begin to compare prices. It was known as the “Texas two-step fare and ticketing” among the local fliers (flyerguide.com, flyertalk.com).

fares of 7%. Aggregate calculation of all the 25 markets show a 14% fare decrease and a 13% increase in traffic.

III. Econometric Identification

The main estimation used for capturing the airport restriction effect is the OLS difference-in-difference (DD) model. It has become a common practice to employ the difference-in-differences methodology to estimate the effect of a natural experiment by observing changes in two groups: treatment and control. To employ the difference-in- differences method, we must have observations on both groups before and after the event. Once the timing of the event and the two groups are identified, the DD model works by observing the differences within the simultaneous changes in two groups as both groups evolve through time.

Each of the three stages of the Wright Amendment serves as the external event that affects the treatment group. With each relaxation, new routes that are no longer subject to the Wright Amendment open up for service. These new routes will serve as the treatment group, while all the other routes from the Dallas metropolitan area will serve as the control group.

Furthermore, we implement a market-level fixed effect to account for all the different routes that are being regressed at the same time.

The main assumption of the DD method is that no major change takes place between the treatment and the control group other than the event in question. That is to say if there were some other external causes affecting the treatment group differently than the control group besides the event in question, then the control group can no

longer serve the purpose it is designed for. No event has been detected that could influence the routes affected by the Shelby, Bond and the Reform-Act other than the rest of the routes to/from the Dallas metropolitan area.

As I am only concerned with flights to and from the Dallas metropolitan area, I make no distinction between flights to and from DFW or DAL. Therefore, flights to and from DFW and DAL are regarded to be the same.

The following regression is used for the estimation:

ln (fare)it  0  * X it  2 *ex.post  3 *treatment  4 *(ex.post*treatment) it

Dependent variable: Log of fare: For one-way trip flights: log of the fare for passengers in route i in time t (year- quarter); For roundtrip flights: log of half of the fare for a single passenger on a flight to or from the Dallas metropolitan area in a given year-quarter; Explanatory variables X: Distance: Distance between DAL and either the originating or the destination airport; the coefficient is expected to be positive as longer trips are more expensive;

Quarterly Effect dummies: 1st quarter: 1 for all 1st quarter observations regardless of year; 0 otherwise; 2nd quarter: 1 for all 2nd quarter observations regardless of year; 0 otherwise; 3rd quarter: 1 for all 3rd quarter observations regardless of year; 0 otherwise;

Roundtrip dummy: 1 for roundtrips flights and 0 otherwise; the coefficient is expected to be negative as roundtrip flights are usually much cheaper than one way flights distance held constant;

Time dummy:

1 for all observations after the event and 0 otherwise;

Treatment dummy: The treatment dummy is 1 for routes where the Wright Amendment restriction previously applied but was removed due to a particular relaxation and 0 otherwise. A route is consider a specific airport pair, but because this paper aims to measure effect on consumers of the Dallas metropolitan area, containing both DFW and DAL, DFW and DAL are considered to be the same point of origin/destination.

Treatment Regional dummy: This dummy further extends the Treatment Dummy by including other airports in the nearby region, where a route is defined to exist between the Dallas metropolitan area and another metropolitan area; for example, under this definition, Dallas-Chicago, Midway and Dallas-Chicago, O’Hare are regarded as one route; while it is expected that the Wright Amendment caused higher fares when routes are defined at the airport level, it is interesting to see how far the effect extends when the routes are defined at the metropolitan level.

Time*Treatment dummy (or Treatment Regional): 1 for all observations that are in the treatment group for the time period after each relaxation and 0 otherwise; this is the main coefficient that will indicate the impact of the Wright Amendment and it is expected to be negative as we expect that restriction on competition results in higher fares; Table 4 presents the treatment groups for each case of the exemptions.

Once a relaxation is introduced, I wait till the beginning of the next quarter to implement the time dummy since the quarter in which the event takes place will have undistinguishable mix of affected and unaffected fares by the treatment. The same goes for the ex-ante time period that excludes the quarter in which the event takes place.

IV. Data

The data for this study come from the U.S. Department of Transportation’s

Ticket Origin and Destination Survey (Databank 1B), which consists of a 10% sample of the tickets provided by domestic airlines to the Department of Transportation on a

quarterly basis. The data contains collection of tickets where one observation (a row) is a ticket containing the following information:

 Fare paid for one passenger

 Number of passengers with the same itinerary and same fare

 Number of segments in a given ticket15

 Segment-specific variables: carrier airline, distance of segment, beginning and

ending airports,

 Origin and destination airports of a ticket

 Fare basis: first, business or coach economy class;16

 Reporting year and quarter

 Change in trip direction (used for identifying round trip tickets)

The following filters were applied to the 10 percent ticket sample to obtain relevant observations for this study:

 Only the tickets with trips either beginning or ending at DFW/DAL were

selected; this means all tickets that do not involve DFW/DAL as origins or

destinations were dropped.

 Tickets with more than four segments (5% of the sample), which means tickets

that involve two or more stop-overs, were dropped.

 Tickets with more than two trip breaks (1% of the sample), which means multi-

destination tickets, were dropped. A trip break indicates either a turn-around

15 One segment represents one leg of a flight. 16 In addition, the complete class of service types include variations of the above three which are first class discounted, coach class discounted, business class discounted, thrift, thrift discounted, first class premium, supersonic, standard class and coach economy premium.

point or the final destination of a ticket. The destination airport becomes

ambiguous for a ticket with more than two trip breaks when the ticket is for a

round-trip flight.

Table 5 presents the number of observations in each quarter for each relaxation event after all irrelevant parts of the data have been filtered out. In addition, the remaining data have been expanded by passenger number. In the original data set one observation meant one itinerary at a specific price with one or multiple passengers in the same itinerary. In other words, if multiple people paid the same and traveled exactly the same itinerary, during the given quarter, all the passengers were grouped into one observation. Expanding by passengers means converting that one observation into multiple observations where one observation represents one person the same as one enplanement. In doing so, the number of observations directly translates into number of enplanements, which means it now reports 10% of traffic on a given route. We will use traffic information to calculate the welfare effect in Section V.

Additional control variable data consist of population at destination airports where the origin is the Dallas metropolitan area and quarterly percentage change in

U.S. GDP. The population data comes from the U.S. Census Bureau and the GDP data comes from the U.S. Bureau of Economic Analysis. Table 5B provides descriptive statistics of the final data set.

V. Regression Results

1. Shelby Amendment

Table 6A presents the regression results for the routes affected by the Shelby

Amendment. The Shelby Amendment took place in the fourth quarter of 1997, thus that particular quarter is excluded from regression data. Three different regressions have been run covering three different frames of time. The first result covers the third quarter of 1997 and the first quarter of 1998 which are the immediate quarter before and after the Shelby relaxation takes effect. The second result covers the first quarter of 1997 and the first quarter of 1998 which are three quarters before and the quarter after the relaxation event, allowing for same quarter comparisons. The third result covers the twelve month period prior the relaxation, from 1996:4 to 1997:3 and the twelve month period after the event, from 1998:1 to 1998:4. This helps us to look at the effect of the relaxation on a twelve-month aggregated time frame. The treatment dummy includes both of the cities of Birmingham, AL and Jackson, MS for which Southwest began selling tickets for connecting flights following the enactment of the Shelby

Amendment.

We observe that for all three instances, all signs of the coefficients are the same.

However, we find the coefficients to be insignificant for the Time*Treatment dummy, the main variable capturing the change in fares, for the immediate before and after quarter and the twelve-month period regressions. It is significant for the same quarter analysis, which confirms that the fares decreased by 25% compared with the previous

year’s same quarter.17 For the immediate quarter before and after analysis, fares decreased by 11% and for the twelve month period fares decreased by 7%; however, since both of them are insignificant when the errors are clustered at the route level, they will not be used for calculating the welfare effect.

For the twelve-month period, the time dummy indicates a 4% increase in fares, statistically significant, as is the case for the same quarter analysis. All three regressions agree that round-trip flights cost 37% less than one-way flights when controlled for distance. Connecting flights do not display statistically different figures when everything else is the same as direct flights. This makes sense for the Shelby amendment as Southwest announced no intention of offering direct flights from DAL citing lack of demand and only began to sell connecting tickets on services that were already available.

An inquiry into the changes in traffic in Table 6B reveals that majority of the increase in traffic is attributable to Southwest’s connecting services to these destinations. It is likely that as Southwest offered only connecting services to compete against American and Delta’s non-stop services to the destinations affected by the

Shelby Amendment, Southwest’s entry into these markets did not result in statistically significant major reductions in fares.

17 The percentage change in fares is calculated by the formula of (e^(coefficient)-1)*100. It is roughly equal to the coefficient when the value is small enough, but the difference grows drastically for bigger values. Thus, 0.224 in percentage is (e^0.224-1)*100=25%.

2. Bond Amendment

Table 7A presents the effect of changes caused by Senator Bond’s amendment to the Wright Amendment. Once the changes took effect, Southwest immediately began non-stop service to Kansas City and St. Louis as they were lucrative markets in which

American had enjoyed a great deal of market power. The Bond amendment was enacted in 2005:4 and thus that period has been excluded from the data.

We observe some major differences and similarities between the Bond and the

Shelby Amendment regressions.

First, the Time*Treatment dummies across regressions, which capture the change in fares due to Southwest’s entrance allowed by the Bond Amendment, are not only much larger than in the Shelby Amendment but all are significant. The coefficients for one quarter before-and-after, same quarter and four quarter regressions are -0.45, -

0.53 and -0.49 respectively. In percentage terms these translate to fare reduction of

56%, 69% and 63% respectively. Second, the time dummy coefficient displays around

4% increase in fares over the twelve-month period captured in the regressions, the same as in the Shelby Amendment. Third, just as in the Shelby Amendment, the value the coefficients of the dummies on the connecting flights are small and statistically insignificant.

The increase in traffic is 53% for the Bond effect while for the Shelby effect the increase was 24%, signaling a much larger entry. During the four quarters after the enactment of the Bond Amendment, Southwest’s market share rose from 4% to 32%.

Using the data on change in traffic and fares from the regressions, the total gain in welfare brought to passengers can be calculated as follows. The mean fare for twelve-months prior to the amendment on the affected routes was $219. The twelve- month period following the amendment saw a 63% decrease in fares, which translates to $137. The number of passengers flying on the affected routes prior to the amendment was 489,980, with an increase of 261,640 passengers during the following twelve-month period. Lower bound of the total gain to the passengers then would be

$137*489,980=$67,127,260 or $67 million per year.18 This is the total savings realized for the existing passengers. The approximate total gain in welfare is $137*489,980 +

$137*261,640/2 = $85,049,600 or $85 million for the twelve months following the

Bond amendment.

3. The Reform Act

Table 8 presents results from the Reform-Act regression. Contrary to the time frames of the Shelby and Bond Amendment regressions results, the Reform-Act regression looks at a long term change in fares on the 25 destinations, represented by the five year ex-ante and ex-post periods. Additional set of regressions for each three different time periods have been conducted where the destinations are considered to be metropolitan areas rather than airports to see the extent to which Southwest’s entry impacts as implied in Morrison (2000). The results of airport destinations fare nearly identical with the results of metropolitan destinations.

18 This is the welfare gain to the existing number of passengers, not including any gains brought by new passengers who are flying due to fare decrease. With an assumption that there has been no shift in the demand curve and that these passengers have a linear demand curve, the additional gain in consumer surplus from new passengers can be calculated as (∆p * ∆q) / 2. We will make these assumptions for future calculations for approximately assessing total gain in welfare.

Distance and market concentration level have positive effect on fares as longer flights cost more and bigger concentration also results in higher fares. GDP and

Population have no effect on fares as GDP is significant only in the one year time frame and Population is dropped due to lack of variation.

Fares at airport with slots are on average 5% (the coefficient is 0.05) lower than non-slot airports and fares at hubs are about 13% (the coefficient is 0.12) higher than non-hub airports. Hub premiums are consistent with previous empirical studies

(Borenstein (1989)). The negative effect of the slot controlled airport could be logical if the extent of competition at these airports outweighs the degree to which traffic is constrained by the slot control. Roundtrip fares are on average 13% cheaper than one- way fares when controlled for distance. Quarter dummies bear significance when there is enough variation and get dropped otherwise. Where they are significant we observe that fares in Q2 and Q3 are slightly more expensive than fares in Q1 and Q4. This difference is related to the high tourism season.

The main variable of interest that captures the effect of the Wright Amendment is Time*Treatment. The coefficient of this variables is big at first when 1 quarter before and after fares are compared, 17%, but decreases to about 11%-13% when 1 year and 5 year periods are introduced. The decrease of fare difference between Wright affect and non-affected markets from 17% to 13% is plausible as American Airlines and those serving out of DFW cannot continuously keep charging higher amount for fares. What is surprising is that while the 13% difference is reached within a year, it doesn't change much from that level during the next four years. There are two possible explanations

that complement each other for airlines at DFW that continue to charge somewhat higher fares in the long run. First, DAL is capacity constrained where it has only 20 gates. Even though Southwest can offer lower fares, compared to DFW, capacity constraint will limit the extent to which consumers can benefit Southwest's new service.

Second, many consumers could be willing to pay 13% more for fares just to experience the non-stop feature offered from DFW. Even though Southwest now can fly to any destinations it wants from DAL, the law still enforces all flights out of DAL to have a connection inside the Wright perimeter.

For the final result, this study focuses on the 13.88% of fare reduction affecting the 25 metropolitan consumers for the five years following the Reform-Act.

The consumer welfare is calculated as follows. During the five years prior to the

Reform-Act, average fare between Dallas and the 25 affected metropolitan areas was

$215 per enplanement. 13.88% change in fare translates to $29.85. Total number of traffic to the five metropolitan areas prior to the Reform-Act was 41,730,860. This translates into $1,245,586,983 or $1.24 billion in savings to existing passengers alone.

Assuming the price elasticity of demand of -0.7 as the lower bound as done in

Morrison (1994), we can calculate the additional benefit to consumer surplus brought by new passengers who are flying from Dallas due to the lower price. We already know that price decreased by 14%, which means quantity demanded increased by

(-14)*(-0.7)=9.8 or 9.8% due to the lower price. If we apply the 9.8% increase in quantity demanded to the 41,730,860 enplanements five years preceding the Reform-

Act, then increase in quantity is 4,086,624 or about 4 million enplanements. Assuming

a linear demand curve, the added consumer surplus brought by new passengers is

(4,086,624*$29.85)/2 or $61 million. The combined gain in consumer surplus is

$1,245+$61 million or $1.31 billion.

The higher bound assumption of price elasticity of demand at -1.5 brings the added consumer surplus brought by new passengers to $131 million. The total gain in consumer surplus in this case is $1,245+$131=$1.37 billion which is not very different from the previous result.

VI. Conclusion.

In this essay, I measured the effect of airport competition restrictions on consumer welfare by identifying changes in fares and traffic. A law that prohibits long distance flights out of Dallas Love Field experienced three changes during its lifetime has made it possible to estimate its effect on fares using the difference-in-difference model. The regressions from three different time frames have produced fairly similar results all pointing in the same direction if not with equal weight. The Shelby amendment caused a decrease in fares, though statistically insignificant, with an increase in quantity. This was expected as Southwest announced its intention to not offer any new services and instead simply begin selling tickets on existing flights. The

Bond amendment saw a significant decrease in fares as Southwest immediately began non-stop service to two destinations, Kansas City and St. Louis. The Reform-Act finally allowed Southwest and other carriers at DAL to sell tickets to any destination outside the Wright perimeter from DAL on the condition that the flights make a stop- over. A 13.88% decrease in fares has been observed due to Southwest’s entry into

multiple markets. Based on the fare reduction and number of passengers flying long- distance from DAL, the gain in consumer surplus over the five years following the last change to the Wright Amendment has been estimated to be $1.31 billion.

It is worthy to note that the 13.88% reduction is occurring even though

Southwest is still being forced to make a connection within the Wright perimeter while having to compete against those carriers at DFW that offer non-stop flights. Thus, the price could fall even further once Southwest becomes eligible to offer the same service from DAL.

The above results clearly display the scale of the negative welfare impact the

Wright Amendment brings to passengers of the Dallas metropolitan area. After 2014, the Wright Amendment will be lifted, but not entirely due to gate capping, to allow

Southwest to offer non-stop flights and the region’s passengers and the economy can finally begin enjoy everything that is brought by low airfares.

References:

Airport Council International – North American Q3, 2012 Traffic statistics (2012) http://aci-na.org.

Borenstein, S. (1989) “Hubs and High Fares: Dominance and Market Power in the US Airline Industry,” RAND Journal of Economics, 20, 344-65.

Cherny, A.I., D.Gillen, H.M. Niemeier and P.Forsyth (2008) “Airport Slots,” Ashgate Publishing Company, London.

Dallas Fort-Worth International Airport Traffic report (2012) http://www.dfwairport.com/stats/index.php.

Dallas Love Field Airport Traffic report (2012) http://www.dallas- lovefield.com/pdf/statistics.

Dallas Love Field: The Wright and Shelby Amendments (2005) Congressional Research Service report for Congress, 109th Congress; H.R. 2932, H.R. 2646, H.R. 3058, H.R. 3383, S. 1424, and S. 1425.

Farris, M.T.II., and S.M.Swartz (2005) “Repeal or Retain? The Wright Amendment Debate” University of North Texas.

Morrison, S.A. and C. Winston (1994) “The Evolution of the Airline Industry,” The Brookings Institution Press, Washington, D.C.

Morrison, S.A. (2001) “Actual, Adjacent, and Potential Competition: Estimating the Full Effect of Southwest Airlines,” Journal of Transport Economics and Policy, Volume 35, Part 2, May 2001, pp. 239-256.

LOVE TERMINAL PARTNERS, et al., Plaintiffs, v. THE UNITED STATES, Defendant. (2011) United States Court of Federal Claims, No. 08-536 L.

Reforming the Wright Amendment (2006) Hearing before the Subcommittee on Aviation of the Committee on Transportation and Infrastructure House of Representations, 109th Congress, 2nd session.

Slot-Controlled Airports: Report to the Committee on Commerce, Science, and Transportation, U.S. Senate (2012) United States Government Accountability Office, GAO-12-902.

Southwest Airlines New Release (2006) “Wright Amendment Reform Act of 2006 Enacted Into Law; Southwest Airlines Offers Customers $99 One-Way Fares and Increased Travel Options From Dallas Love Field,” http://www.southwest.com.

“That Long Drive Out to the Airport: Why the Wright Amendment is bad for Dallas” (2005) Dallas Magazine, August, 2005.

The Repeal of the Wright Amendment (2005) The Legacy Center for Public Policy

Wright Amendment Reform Act (2006) Public Law 109-352, 109th Congress

“We’re talking about the Wright amendment and short-haul flights,” (2011) Dallas Morning News, Jan 11, 2011.

Tables:

Table 1 Comparison of average fares and traffic for the Shelby Amendment affected routes for four quarters before (1996:4 - 1997:3) and four quarters after (1998:1 - 1998:4) the event quarter (1997:4)* Before Relaxation Percentage Change after relaxation Average Traffic HHI Average Traffic HHI Fare) (Enplanements) Fare (Enplanements) Birmingham, - $213 67,380 3754 -23% +37% AL 12% Jackson, MS - $120 59,660 5210 12% +10% 10% Source: Author’s calculation from Databank 1B * The event quarter is the quarter in which the law changes takes place and is always excluded from analysis due to having some fares that were affected by the law and some that are not.

Table 2 Changes to the Bond Amendment affected routes; four quarters before (2004:4 - 2005:3) and after (2006:1 - 2006:4) the event quarter (2005:4) Percentage Change after Before Relaxation relaxation Average Traffic Average Traffic HHI HHI Fare (Enplanements) Fare (Enplanements) Kansas, 788 $220 241,370 -50% +52% -32% MO 0 St. Louis, 733 $217 248,510 -50% +55% -23% MO 4 Source: Author’s calculation from Databank 1B

Table 3 Comparison of average fares and traffic for the 25 Reform-Act affected routes 1 year before (2005:4 – 2006:3) and 1 year after (2007:1 - 2007:4)* Before Relaxation Percentage Change after relaxation Averag Traffic HHI Averag Traffic HHI e Fare (Enplanement e Fare (Enplanement s) s) Louisville $268 70,280 3281 -34% 59% 14% Omaha $239 81,830 6753 -30% 50% 2% Columbus $269 132,690 5077 -29% 30% 21% Detroit $252 271,660 3923 -27% 24% 2% Nashville $236 146,180 7612 -26% 46% - 13% Philadelphia $264 350,920 3954 -26% 22% 2% Phoenix $232 394,410 3673 -23% 31% -2% Oakland $274 117,910 5338 -22% 20% - 13% San Diego $267 242,970 5062 -21% 33% -6% Cleveland $263 131,190 3371 -20% -11% 30% Denver $194 574,910 3208 -20% 8% 0% Tampa $226 230,210 7044 -19% 24% - 15% Salt Lake City $241 18,230 2713 -18% 19% -3% Tucson $240 85,900 6714 -16% 3% 3% Jacksonville $210 122,420 5997 -13% 15% -4% Portland $253 161,360 3777 -10% 11% 5% Sacramento $244 151,600 3841 -9% 8% 6% Seattle/Tacoma $245 334,910 4281 -8% 8% -2% Los Angeles, $227 590,490 5017 -7% 11% -1% Indianapolis $204 169,580 6312 -7% 5% 11% Baltimore/Washingto $187 372,810 5776 -7% -1% -9% n D.C. Orlando $174 504,560 5108 -2% 14% -8% Las Vegas $186 587,460 3781 -2% 8% 2% Chicago Midway $131 328,220 4003 0% -25% 45% Fort $175 271,840 7005 7% -11% -9% Lauderdale/Hollywo od All 25 markets $217 6,608,610 4671 -14% 13% -1% combined Source: Author’s calculation from Databank 1B *the quarter in which the law takes place, 2006:4, is excluded

Table 4 Date of relaxations and affected treatment groups Relaxation Date of Control Treatment routes name Event routes To/From Dallas Shelby metropolitan area to 1997:4 Amendment Birmingham, AL and Jackson, MS all other To/From Dallas routes to/from Bond metropolitan area to 2005:4 the Dallas Amendment Kansas and St. Louis, metropolitan MO area To/From Dallas Reform-Act metropolitan area to the 2006:4 repeal 25 new routes Southwest entered Source: Author’s calculation

Table 5A Total number of observations (10% of total enplanements) for each relaxation event by quarter Reform-Act Repeal Shelby period Bond period Data period Data size size Data size 1996:4 364,814 2004:4 492,867 2005:4 562,663

1997:1 339,338 2005:1 435,226 2006:1 511,201 Ex-ante 1997:2 393,805 2005:2 499,175 2006:2 582,468

1997:3 389,264 2005:3 498,.557 2006:3 559,328

Event 1997:4 - 2005:4 - 2006:4 - quarter 1998:1 363,753 2006:1 472,729 2007:1 518,939

1998:2 399,211 2006:2 534,694 2007:2 605,671 Ex-post 1998:3 440,960 2006:3 509,927 2007:3 603,336

1998:4 469,380 2006:4 542,744 2007:4 616,030

Source: Author’s calculation from Databank 1B

Table 5B Descriptive statistics Variable Mean Std. Dev. Min Max Dependent: Fare 203 153 10 2,000 Distance 969 496 89 5,553 HHI metro 4,574 1,490 741 10,000 GDP 1.64% 2.73% -8.90% 6.90% Population 4,303,418 4,762,123 13,005 19,100,000 slot 0.06 0.24 0 1.00 hub 0.88 0.33 0 1.00 Source: Databank 1B, U.S. Census Bureau, U.S. Bureau of Economic Analysis

Table 6A Shelby Amendment regression results* 1 quarter before Four quarters before Same quarter, (1997:4) and 1 (1996:4 – 19997:3) (1997:1) to quarter after and four quarters (1998:1) (1998:1) after (1998:1 – 1998:4)

Clustered Clusered Coeff.** Coeff. Coeff. Clustered t-stat*** t-stat t-stat Time 0.070 (4.78) 0.044 (1.91) 0.048 (3.7) dummy Time * -0.106 (-0.89) -0.224 (-7.73) -0.073 (-0.83) Treatment Roundtrip -0.307 (-9.15) -0.322 (-10.6) -0.328 (-11.37)

Connecting -0.024 (-0.84) -0.002 (-0.09) -0.031 (-1.35)

1st quarter 0.033 (2.1) effect 2nd quarter 0.027 (2.53) effect 3rd quarter 0.020 (2.48) effect R-sq 0.23 0.24 0.25

Observations 753,017 703.091 3,160,525

* For all three regressions (Shelby, Bond and the Reform-Act), Hausman test has been performed to test for random fixed effects. The results indicate there is no statistically significant difference between the fixed and random effect approaches in this case. ** For the Shelby and Bond amendments, control variables HHI, GDP, population, slot dummy and hub dummy have been excluded due to lack of variation as the treatment group consists only of two destinations. The Reform- Act regression includes these variables. *** Given the large number of observations, a regression clustered at the route level has been performed to ensure robust results.

Table 6B Changes in market share and traffic on routes affected by the Shelby Amendment American Delta Continental Southwest Remaining Combined Traffic* 1996:3 – 44% 44% 8% 1% 3% 127,040 1997:3 1998:1 – 41% 41% 2% 14% 2% 157,480 1998:4 Source: Author’s calculation from Databank 1B * Birmingham and Jackson traffic data are combined.

Table 7A Bond Amendment regression results 1 quarter before Four quarters before (2005:3) and 1 Same quarter, (2004:4 – 2005:3) and quarter after (2005:3) to (2006:3) four quarters after (2006:1) (2006:1 – 2006:4) Clustered Clustered Clustered t- Coeff. Coeff. Coeff. t-stat. t-stat. stat. Time 0.030 (4.73) 0.046 (5.77) 0.047 (6.08) dummy Time * -0.454 (-71.72) -0.539 (-27.86) -0.491 (-25.54) Treatment Roundtrip -0.022 (-2.46) -0.017 (-2.10) -0.119 (-1.55) Connecting -0.018 (-1.62) -0.015 -(1.35) -0.017 (-1.85) 1st quarter 0.033 (5.52) effect 2nd quarter 0.049 (9.57) effect 3rd quarter 0.052 (10.24) effect R-sq 0.27 0.25 0.27

Observations 971,286 907,955 3,985.919

Table 7B Changes in market share and traffic on routes affected by the Bond Amendment American Southwest Remaining Combined Traffic Q3,04 – Q3,05 87% 4% 9% 489,980 Q1,06 – Q4,06 66% 32% 2% 751,620 Source: Author’s calculation from Databank 1B

Table 8 Reform-Act Regression results for three different time frames to airport and metropolitan destinations Dependent: Ln Fare Treatment=25 airport destinations Treatment=25 metropolitan destinations Before/After time length 1 quarter 1 year 5 years 1 quarter 1 year 5 years Ln Distance 0.43*** 0.44*** 0.42*** 0.43*** 0.44*** 0.42*** (0.05) (0.04) (0.04) (0.05) (0.04) (0.04) Ln HHI metro (0.16) 0.04 0.13*** -0.14 0.03 0.13*** (0.08) (0.03) (0.04) (0.08) (0.04) (0.04) GDP - 0.34* 0.03 - 0.35* 0.03 (.) (0.15) (0.09) (.) (0.15) (0.09) Ln Population ------(.) (.) (.) (.) (.) (.) Slot dummy -0.06*** -0.04*** -0.05*** -0.06*** -0.04*** -0.05*** 0.00 0.00 0.00 0.00 0.00 0.00 Hub dummy 0.18*** 0.18*** 0.12*** 0.18*** 0.18*** 0.12*** (0.02) (0.02) (0.02) (0.02) (0.02) (0.02) Roundtrip dummy -0.12*** -0.12*** -0.12*** -0.12*** -0.12*** -0.12*** (0.01) (0.01) (0.01) (0.01) (0.01) (0.01) Q1 dummy -0.05** 0.00 0.01*** -0.02 0 0.01*** (0.01) (0.01) 0.00 (0.01) (0.01) 0.00 Q2 dummy - 0.04*** 0.02*** - 0.04*** 0.02*** (.) (0.01) 0.00 (.) (0.01) 0.00 Q3 dummy - 0.04*** 0.02*** - 0.04*** 0.02*** (.) (0.01) 0.00 (.) (0.01) 0.00 Time dummy - -0.02 0.13*** - - 0.15*** (.) (0.01) (0.02) (.) (0.01) (0.02) Time*Treatment -0.16*** -0.13*** -0.11*** -0.17*** -0.12*** -0.13*** (0.03) (0.03) (0.03) (0.03) (0.02) (0.02) Observation 1,005,367 4,259,388 19,992,921 1,005,367 4,259,388 19,992,921 R-squared 0.07 0.06 0.06 0.07 0.06 0.06 * p<0.05, ** p<0.01, *** p<0.001 Note 1: Robust standard clustered errors are reported.

Note 2: Hausman test rejects the random effects model

Chapter 2: The Effect of Bankruptcy on Productivity in the Airline Industry

1. Introduction

Rising costs due to over expansion and increasing input prices combined with stagnant revenue growth due to market competition is a theme that has plagued the U.S. airline industry ever since deregulation in 1978. The vulnerable states of the biggest airlines manifest themselves in waves of bankruptcies during economic downturns. The latest of such waves occurred from

2002 to 2007 following the recession caused by the tech-bubble and the September 11 attacks.19

During that time, four of the seven largest airlines in the country filed and were approved for

Chapter 11 reorganization bankruptcy protection plans.20

In the last decade, the majority of firms in the airline industry have gone through a bankruptcy procedure. Every time an airline announces a bankruptcy, it cites the reason is to become a more competitive and leaner organization.21 Can airlines really become more competitive and efficient by declaring a bankruptcy? It is evident that at least in the short to medium term the airlines reduce their costs (Government Accountability Study (2005)) by getting out of contracts and renegotiating better terms through means of leveraging their bankruptcy status. However, airline could benefit greatly in the long run from improvement in efficiency and productivity rather than a mere short term cost reduction. Ultimately, increased productivity of an organization is what surviving amid competition demands in the long term.

Judging from the industry’s fragile state, this paper hypothesizes that bankruptcies do not

19 The first wave took place just after the deregulation in the 80s. The second wave took place after the 1990 recession and the gulf war in 1991 that hurt air travel. 20TWA in 2001, US Airways in 2002, United Airlines in 2003, Delta Air Lines and Northwest Airlines in 2005. 21 “US Airways Enters bankruptcy to emerge as a leaner, more competitive airlines” (http://www.usairways.com/en- US/aboutus/pressroom/history/chronology.html); “AMR Files for Bankruptcy to Achieve Industry Competitiveness” (http://www.aa.com/i18n/amrcorp/newsroom/fp_restructuring.jsp)

improve airline productivity and aims to empirically test this hypothesis using multiple cases of airline bankruptcies.

Indeed, some industry insiders claim that Chapter 11 protection does not help firms become more productive in the long run.22 Such claims are not without merit given the behavior of repetitively declaring bankruptcy is observed. For instance, Trans World Airlines (TWA) went into bankruptcy in 1992, 1995 andother one again in 2001. Also, U.S. Airways came out of bankruptcy in 2003 only to go back in 2004. It could be that airlines simply use bankruptcy protection to get out of unpleasant financial obligations and that is all. That, of course, is not desirable market equilibrium for the long term as the inefficient incumbents remain by declaring bankruptcy over and over again whenever they are under financial stress. Of course, as long as there are enough lenders who are willing to keep the fund flowing into the airline industry regardless of its state, airlines can maintain their status quo and simply keep declaring bankruptcy. From the policy side, it becomes a question of how the bankruptcy courts can go about imposing requirements as far as productivity and efficiency improvements go to ensure that the bankrupt airlines are not simply “gaming” the system and that they are actually becoming better organizations as a whole. Hence, this paper sets out to empirically determine what happens to an airline’s overall productivity when it goes through a bankruptcy protection.

This paper uses bankruptcies of four legacy airlines to examine the effect of bankruptcy on airline productivity. I use quarterly input and output data for each airline and use them to

22“What is wrong with Chapter 11? It may keep ailing businesses going, but it distorts the airline industry: Chapters 11 businesses end up with unfair competitive advantages over competitors, thanks to their ability to renegotiate contracts, cut costs and dump debts.” A quote by Simon Wilson in MoneyWeek, Dec 12, 2005.

create the total-factor-productivity index for each airline. After running fixed-effects regressions,

I find that bankruptcy does not improve productivity.

The paper is organized as follow. Section 2 looks at the literature on the subject of bankruptcy and productivity in the airline industry. Section 3 discusses source of data and provides a summary of descriptive statistics. Section 4 provides discussion on bankruptcies in the airline industry since the deregulation. Section 5 elaborates on the ways in which productivity can be measured. Section 6 discusses the econometric method used for identifying the effect of bankruptcy on productivity. Section 7 covers regressions results and Section 8 provides concluding remarks.

2. Literature Review

Many articles on the topic of bankruptcy in the airline industry exist where the focus is mainly on the effect of bankruptcy on pricing, quality or capacity. Many articles also involve various aspects of airline productivity, but none involve bankruptcy effects. The following section provides a brief summary of the literature in bankruptcy and productivity respectively.

Bankruptcy and financial stress literature concerning the airline industry:

Borenstein and Rose (1995) focus on airline bankruptcies between 1989 and 1992 to examine the effect of bankruptcy on pricing behavior. Their study finds that bankrupt airlines do not change their own price dramatically and do not force competitors to reduce their price.

Borenstein and Rose (2003) look at whether airline bankruptcies affect supply quantity at the airport level. The paper finds no evidence of significant effects of bankruptcy on flight quantity at large and small airports. The effect on medium sized airports was small. The government

Accountability Office (2005) study investigates the effect of bankruptcy on aggregate costs of

airlines. The study determined that only some were able to reduce costs while others could not.

Hofer (2009) finds that financial distress causes airlines to reduce prices based on lowered costs and demands. Waite (2009) focuses on airline bankruptcy and contracts between airlines and airports to discover that airlines use bankruptcy status to avoid airport fees while continuing to use airport facilities. Jayanti (2009) finds that airline bankruptcies result in decreasing own market share and increasing rivals market presence. Lee (2010) finds that LCC capacity grew by

16% as legacy carriers when legacies reduce their capacity while undergoing a bankruptcy procedure. Ciliberto and Schenone (2012a) find that bankrupt airlines on average drop 25% of their pre-bankruptcy capacity. In addition, Ciliberto and Schenone (2012b) find that service quality increases during bankruptcy and return to pre-bankruptcy lower levels once airlines exit bankruptcy status.

Airline productivity literature:

The literature on the topic of airline productivity is rich. Caves et al. (1982a) was one of the first studies to look at productivity aspect of the airline industry using the Total Factor

Productivity (TFP) indexing method. A more detailed look into this method comes in Section 5 as this section will provide findings of empirical productivity studies. Cave et al. (1982b), using

TFP, produces a comparison of airline industry productivity between 1970-1975 and 1976-1980.

They find that airline industry productivity increased by about 80% between those two periods.

Windle (1991) compares productivity of global airlines. The study uses TFP methodology to determine that US airlines were 19% more productive than European airlines, but Asian airlines were 45% more productive than US airlines in 1983. Good et al. (1993) conducts productivity comparisons of eight US airlines to four European airlines from 1976 to 1986 and determines that while the productivity growth rate between 1976 and 1986 were almost identical between

US and European carriers, US carriers were more productive. Distexhe and Perelman (1994) finds that the biggest carriers, on a global scale, were better positioned to take advantage of technological development and in turn showed more technical efficiency than the others between

1977 and 1986. Oum and Yo (1997) provide an extensive study of productivity and cost competitiveness of world airlines employing the TFP index methodology. Ng and Seabrigth

(2001) perform productivity comparison of twelve European and seven major US airlines to find that state ownership has a large impact on costs. Specifically the study finds that privatization could reduce costs by as much as 20% for European airlines. Oum, et al. (2001) shows that horizontal alliances of international airlines induce a significantly positive effect on productivity, but there was no effect on profit. Swelbar (2007) compares partial productivity of legacy and low

-cost carriers US carriers from 1995 to 2006. The study shows that both aircraft and employee productivity for legacy carriers declined sharply for three to four years and began improving while low cost carrier partial productivity kept steadily increasing. Mark Greer (2008) looks at changes in productivity of major US airlines from 2000 to 2004 using the Malmquist productivity index, which is the same as the TFP index used in Caves et al. (1982), and finds that there was significant improved in productivity during this period. The study discusses the overall productivity trend in the industry from 1970 to 1980.

The main findings of the above studies were that over the years airlines’ productivity have been increasing significantly as output grew much faster than input.

3. Data

The data for this study come from several different sources. The final database includes detailed domestic US airline data on input/output quantity, cost/revenue statistics and market concentration data for 11 airlines in Table 3.1 from 1992 to 2011 on quarterly basis. The

procedure for selecting these airlines is related to finding airline bankruptcy cases where pre and post-bankruptcy data were available and determining suitable control group airlines for each instance. This procedure is discussed in detail in Section 4. All data used in this study only include the domestic portion of the 11 airlines.

The first part of the database consists of constructing necessary data for calculating the

TFP index and partial productivity ratios both of which come from the Department of

Transportation Form 41. Form 41 is a financial reports data where air carrier submit detailed revenue, cost and operational statistics to the Department of Transportation. The TFP index uses four input (labor, fuel, aircraft and miscellaneous) and three output (revenue passenger mile, revenue-ton mile and incidental) to calculate productivity. As such, detailed database containing both quantities of input/output components and respective dollar amount spent/earned on them was assembled on quarterly basis between 1992 and 2011.

Input data: The quantitative part of the input index includes data on the number of employees, gallons consumed for fuel and the average number of aircraft. Data on miscellaneous quantity of inputs (all other material input besides aircraft and fuel such as amount of passenger meals, various equipment, etc.) is not available. Instead, the miscellaneous material quantity index was calculated by dividing miscellaneous materials cost by the quarterly US GDP deflator as done in Oum, et al. (2001). The cost sharing part includes data on labor cost, fuel cost and aircraft cost. Miscellaneous was cost was calculated by subtracting the previous three main costs from total cost. The cost-share part of the input data was available from Form 41 on a quarterly

basis. It includes the dollar amount spent on labor, aircraft and fuel. The material cost (or miscellaneous cost) was computed by subtracting labor, aircraft and fuel cost from Total Cost.23

Output data: The quantitative section of the output index includes revenue passenger miles, revenue-ton miles and miscellaneous revenue earning quantity index (such as baggage fees, meal service, etc.). While the former two are available, the miscellaneous revenue earning quantity index is not available. Instead, it was replaced by an index computed by dividing miscellaneous revenue by the quarterly US GDP deflator as was done previously. The revenue- share section of the output index was computed by dividing each of ticket sale revenue, mail and freight revenue and incidental revenue by the total revenue.

Once the output and input data indexed are calculated, the TFP index is calculated by dividing output by input. Table 3.2 illustrates a sample of input, output and TFP index data.

The second part of the database consists of creating the independent variable data. The data for fleet size, fleet age and fleet type were calculated come from Department of

Transportation’s data on airline fleet statistics section from its website which is available only post 1992. Quarterly data for stage length and load factor come from Form 41. Quarterly GDP growth and GDP deflator data were collected from the U.S. Bureau of Economic Analysis. The quarterly HHI index, market share and network size for each airline was computed from

Databank 1B, the U.S. Department of Transportation’s Ticket Origin and Destination Survey, on quarterly basis.

23 Total Cost and Total Revenue excludes Transport Related Costs and Transport Related Revenues. Transport related revenues/costs report the amount earned/spent from purchasing airline service from regional feeder airlines and thus do not directly take part in an airline’s own production of goods and services.

The finished database consists of an unbalanced panel consisting of 612 combined observations where each observation is quarter-carrier unique data consisting of ln(TFP) as the dependent variable along with the aforementioned independent variables (unbalanced set of

11airlines for 80 quarters combined, but strongly balanced when each bankruptcy and its control groups are considered separately according to their time periods surrounding individual bankruptcies). Each cross-section contains a total of 14 input and output variables used for constructing the TFP index and nine potential independent variables for the panel regression.

Section 6 will provide a detailed discussion of the independent variables. The next section,

Section 4, discusses the background history of bankruptcy in the U.S. airline industry.

4. Airline Bankruptcy Background

There are two types of bankruptcies in the U.S., Chapter 7 and Chapter 11 of the

Bankruptcy Code of Title 11 of the United States Code. The Chapter 7 bankruptcy is a liquidation procedure where firms cease operations and are forced exit the market. Chapter 11 bankruptcy helps firms reorganize while continuing operation. This paper will focus on firms going through Chapter 11 bankruptcy since the focus is on evaluating their post-bankruptcy conditions.

In the government-regulated era of the airline industry prior to 1978, airline bankruptcy was not a very common phenomenon. However, after deregulation the number of bankruptcies in the industry exploded, as did the number of start-up airlines, as surviving in the new environment proved to be an extremely challenging task for most, both for the new entrants and well- established legacy carriers. Since 1979 there have been around 180 recorded cases (including

multiple bankruptcies by the same airline) of bankruptcies in the airline industry.24 This number includes every type of airline service providers including scheduled passenger carriers, cargo, chartered and specialized contract (such as military, mail, etc.) carriers. Even though, most of the bankruptcies were by non-standard service offering airlines, the list still included the industry’s biggest players. For the purpose of this study and based on availability of data, this paper will only consider bankruptcies of major scheduled passenger carriers between 1992 and 2011 excluding bankruptcies by regional feeder carriers whose main function is to provide local feeder service to the major airlines.25

Historically, the frequency of bankruptcies can be divided into four time frames. The first wave, beginning in the late 1980s, included Braniff International Airways from the majors and a number of smaller carriers. It was the result of shake-up in the immediate aftermath of deregulation as big and small airlines scrambled to establish their footing in the new environment.

The second wave began in the late 1980s and carried into early 1990s. This wave saw some of the biggest carriers declare bankruptcy one after another, including Eastern Air Lines

(1989), Pan American World Airways (1991), America West Airlines (1991) and TransWorld

Airlines (1992). Bankruptcies from the first two waves are not included in this study because (a) many ended up ceasing operation and (b) data prior to 1992 are limited.

24 For a complete list see Airline For America (www.airlines.org) which currently is the only source of its kind. 25 There are three types of carrier classifications categorized by the Department of Transportation based on annual revenue. Group I, called “regionals,” includes carriers with less than $100 million of revenue annually. Group II, “nationals,” includes those with revenues between $100 million and $1 billion annually. Group III, “majors,” covers carriers with revenues of over $1 billion. Once again, regional carriers are not to be mixed with regional-feeder carriers whose size can range from $100 million to over $1 billion in revenue but they are excluded from the study as they operate under a totally different system by receiving contracts from the major airlines.

The third wave began in early 2000s and continued till 2005 and is historically the biggest wave that hit the industry. During that time we saw extinction of TWA (2001), multiple bankruptcies by US Airways (2002, 2004), bankruptcies by United, Delta and Northwest. From the smaller airlines, Allegiant, Hawaiian, ATA and Aloha declared bankruptcy during this era.

This wave was caused by the recession of 2001 compounded with significantly diminished air travel due to 9/11 attacks.

The fourth wave began in 2008 with re-appearance in bankruptcies of Aloha, ATA, and first time cases of Frontier and Sun Country that all took place in the same year. Even though

America’s bankruptcy occurred several year after 2008, in 2011, this wave can be characterized as mostly affecting the smaller airlines. Arguably, this wave was the result of economic downturn of 2008.

This study will focus on the third and fourth waves that include bankruptcies by major and national carriers.

Table 4.1 lists all Chapter 11 bankruptcy occurrences by major, national and regional carriers with annual revenues of at least $100 million between 1992 and 2011. As mentioned previously, this study excludes all carriers offering other non-standard types of services (such as chartered service, cargo, military contract, etc.).

Between 1992 and 2011, there were 18 cases of Chapter 11 bankruptcies by major, national and regional carriers. Six of these (marked by *) are re-entry into Chapter 11 by the same carriers. Table 4.2 illustrates the history of re-entering bankruptcy by the same airlines. The level of frequency of re-entering bankruptcy protection could be an indication of airlines’ poor health post-bankruptcy and what airlines are able to achieve (or not achieve) by declaring bankruptcy. Even though they are able to shed costs (Government Accountability Study (2005))

by getting bankruptcy protection, ideally an organization could be using the protection as an opportunity to improve its fundamental structure by becoming more productive and efficient to guarantee long term viability.

In this study, I will focus on four instances of bankruptcies between 1992-2011 involving a total of 11 airlines based on availability of data and compatibility of control group airlines.26

Two additional bankruptcies, that of Hawaiian’s in 2003 and Frontier’s in 2008, were also considered and their results are displayed in the appendix due to compatibility of their control group. Previously, we saw the list of 11 airlines considered in this study in Table 3.1. Table 4.3 lists the six instances of bankruptcies with control groups of each bankruptcy. The following criteria were applied in determining airlines for a specific control group. First, during the entire pre, mid and post-bankruptcy period, a control group airline must not itself be involved in a bankruptcy procedure as that would conflict with being a control entity. Second, treatment and control group airlines should be similar in size and network coverage to each other.27 The major airlines were all much bigger in size than the non-major carriers and typically offer nationwide network coverage where they compete against each other throughout the country and thus being

26 The 18 instances of bankruptcies in Table 4.1 were reduced to 6 as the following 12 exclusions took place in a chronological order: (1)TWA’s 1992 bankruptcy didn’t have pre-bankruptcy data, (2) Hawaiian’s 1993 bankruptcy does not have proper control group data (3) TWA’s 1995 bankruptcy was not truly a reorganization procedure (though it was Chapter 11) as the sole purpose of it was to break a discounted ticketing contract made with Carl Icahn as a condition of his departure in 1993, (4) Allegiant’s 2000 bankruptcy was excluded as only post 2004 data is available for the airline, (5) Trans World Airline’s Chapter 11 in 2000 ended with a sale/merger with American Airlines and thus contains no post-bankruptcy data, (6) US Airways’ 2004 bankruptcy ended after merging with America West and thus the post-bankruptcy productivity would contain ambiguous merger and bankruptcy effects, (7) ATA entered bankruptcy in 2004 and exited in 2006:1 only to go back into liquidation bankruptcy after 3 quarters and thus not having sufficient post-bankruptcy data,(8) and (9) Aloha Airline’s two instances of bankruptcies have been excluded as (a) Aloha was too small to be compared to any other airline and (b) it entered liquidation process soon after it exited its first bankruptcy, (10) ATA Airlines 2008 bankruptcy was followed by dismantling the airline and selling remainder of its assets to Southwest, (11) Sun Country lost all of its fleet during bankruptcy and as such, the post-bankruptcy airline was effectively a new organization that simply kept the name and (12) American Airline is still in bankruptcy. 27 It is possible to question whether a particular control airline is the ideal match for the given treatment airline. However, such a perfect match is hard to achieve and thus, the current selection is a best-possible scenario given the situation.

subject to similar macro shocks. However, the non-major carriers are much smaller in size and their service is mostly limited to destinations from their single local hub. As such, creating a proper control group for them becomes increasingly difficult even though they may be similar in size. For instance, Frontier and Hawaiian are comparable in size in terms of revenue passenger miles and their bankruptcies are many years apart. Thus, it can be tempting use one as a control group for the other and those results are displayed in the appendix. However, it can be argued that due to their localized network concentration, Frontier in Denver and Hawaiian in Hawaii, they become subject to entirely different local market conditions, which asks the question whether they are good comparisons with each other. Therefore, the results are displayed in the appendix and not included with the major airline results.

In addition to the control group airlines, Table 4.3 details entry and exit points as announced by each airline and as well as pre and post-bankruptcy periods. The length of the pre and post-bankruptcy periods were determined as the longest possible continuation of time without having one of the control group carriers overlap into a bankruptcy period. For the main results of this study, each airline’s announcement of exit from bankruptcy is used to mark beginning of their post-bankruptcy period. Additionally, in the results section I post regression results with an alternative version of the post-bankruptcy period where each airline is given a longer time to spend in bankruptcy in case it took beyond the exit announcements dates for the bankruptcy effects to be fully integrated into productivity.

Descriptive statistics of bankrupt airlines:

This section will discuss the descriptive statistics as airlines enter and exit bankruptcy.

For each of the four legacy bankruptcies, four descriptive measurements are illustrated. They are

(1) employee size, (2) aircraft quantity, which are inputs and (3) revenue passenger miles and (4) network size, which are outputs.

Figure 4.1 looks at employee size patterns of the four airlines as they go through bankruptcy. All the four airlines were experiencing a reduction in labor size for some time prior to bankruptcy. During bankruptcy the trend continues except the magnitude of labor reduction seems to have increased a bit as we see steeper downward trending slopes during bankruptcy.

The exception here is United, which sees a major increase in labor size right before exiting bankruptcy. This was a result recalling thousands of furloughed workers, a normal process when airlines go through difficult times, but in this case much larger in quantity for United than the other airlines.28 Post-bankruptcy, airlines continue cut labor for some time except US Airways and Delta, which entered merger deals that added to their existing work force.

Figure 4.2 looks at aircraft quantity trend. US Airways and United had a pre-bankruptcy reduction in number of aircraft they operate, which continued during bankruptcy. Delta and

Northwest had maintained a stable quantity pre-bankruptcy but during bankruptcy we see their fleet become smaller at a very fast rate. Post-merger, we see that the aircraft quantity stabilize for some time until airlines begin engaging in mergers (for Delta, US Airways and Northwest).

Figure 4.3 illustrates the revenue-passenger-mile trend for each airline. The common pattern here is the post-bankruptcy downward trend in output as compared with its pre and mid- bankruptcy trends until an airline becomes involved in a merger, which increases their output.

Surprisingly, during bankruptcy, we see a relatively flat trend in that amid all the reduction in

28 United Airlines to recall pilots and other labor force (http://www.aviationpros.com/news/10405905/united- airlines-to-recall-300-pilots)

labor and other input sources airlines manage to maintain their output until they exit bankruptcy, at which point the downward trend begins.

Figure 4.4 illustrates the network size of each airline. Pre-bankruptcy, US Airways and

United had decreasing trends in network size, where Delta and Northwest had increasing trends.

By going into bankruptcy, Delta and Northwest enter a downward trend in decreasing network size and US Airways continues its downward trend, United begins to expand its network until it takes a sharp downward turn in 2008 in relation so the recession of that year.

In conclusion, the surprising common pattern from the above four airlines is that while they make significant cuts in labor and aircraft quantity, and thus reducing their inputs, their output levels, illustrated by RPM and network size, are not decreasing as fast as their inputs.

However, the above is only part of a bigger story as we need to take the partial and TFP productivity measurements in conjunction.

5. Measuring productivity

This study will focus on two most used approaches to measure productivity: partial and total factor productivity (TFP).29 Partial productivity is a simple ratio of one input to one output and is widely used in the finance and mainstream economic literature to have a quick look at a specific productivity. While partial productivity is simple to calculate, it can only focus on one input/output at a time and therefore it lacks capacity to indicate a firm’s overall productivity. On the other hand, TFP is an index based system that includes all inputs/outputs and enables

29 Similar to TFP, there is also data envelopment analysis and stochastic frontier estimation methods that employ multiple input and outputs to asses a firm’s overall productivity. However, the study chose TFP as it is the most commonly used method for looking at productivity in the airline industry. A full extent treatment and comparison of all the three methods is beyond the scope of this paper.

comparison of productivity across different firms. This paper will analyze airlines’ partial and total factor productivity in the style of Oum (2001).

5.1 Partial Productivity:

Partial productivity is a ratio of an input to output. In the airline industry, depending on one’s research interest, it is possible to produce a variety of ratios using the many different input and output variables available. This study will focus on the most four widely used ratios that consider productivity of two inputs: employees ((a) and (b) below) and aircraft ((c) and (d) below). The two productivities are presented through four ratios:

a. ASM/employee: output (available seat miles) per employee

b. Total passengers/employee: number of passengers enplaned per employee

c. ASM/aircraft day: output per aircraft day

d. Block hours/aircraft day: block hours per aircraft day which indicates on average

how many hours in a 24-hour frame one aircraft spends between gate-close and

gate-open.

Figures 5.1-5.4 illustrate comparisons of airlines’ partial productivity in the order listed above.

Figure 5.1 compares employee productivity in terms of ASM for each airline. We see all the airlines’ employee productivity increase in terms of ASM as in Figure 4.1 we saw that airline cutting work force significantly. Reduction in employee size is much faster than reduction in

ASM output pre-bankruptcy. Post-bankruptcy, however, presents a story where the reduction in

ASMs have caught up with fluctuation in employee size where we see a decrease in employee productivity as the number of employees keep decreasing from Figure 4.1.

Figure 5.2 has been considered to test a different aspect of employee productivity. While airlines can maintain ASM, it is possible that being in bankruptcy could hurt passenger traffic greatly and thus reducing the number of passengers handled by each employee. However, we see a much similar pattern to the one in Figure 5.1 as the change in passenger traffic moves very similarly to change in ASM as airlines enter and exit bankruptcy.

Figure 5.3 presents a measurement of how many hours per day on average each aircraft is used. From Figure 4.2, we know that airlines continuously kept reducing their fleet before, during and after bankruptcy. For US Airways, Delta and Northwest, we don’t see a major shift in pre and post-bankruptcy behavior either increased or decreased. On the contrary, United’s aircraft usage for post-bankruptcy increases steadily compared with its pre-bankruptcy trend.

Figure 5.4 compares aircraft productivity in terms of ASM. From Figure 4.2, United and

US Airways had experienced a sudden drop in aircraft quantity right before entering bankruptcy.

That reduction in quantity has affected the ASM productivity positively before bankruptcy for the two airlines. Post-bankruptcy, the two airlines’ aircraft productivity remains somewhat flat as their output reduction catches up with aircraft reduction.

Delta and Northwest both had very gradual declines in aircraft quantity and accordingly, we observe a very gradual increase in aircraft productivity till they exit bankruptcy. However, in post-bankruptcy, Delta’s productivity begins to drop slowly at the beginning and then drops significantly in 2009 at which point it merged with Northwest increasing its aircraft quantity dramatically. Northwest’s productivity keeps increasing post-bankruptcy as they keep reducing their fleet size much faster than they cut their ASM.

5.2. Productivity indexing background

Although simple to calculate and widely used, the partial productivity method has two main shortfalls. First, it cannot offer measurement at the firm level when multiple inputs are used to produce multiple outputs. Second, partial productivity measurements do not account for rational trade-off choices firms make between multiple inputs. An index numbering system solves both of them.

One of the most widely used index numbering system for measuring productivity is the

Total Factor Productivity (TFP) index, which is a trans-log multilateral index formula. The TFP index was advocated by Tornqvist (1936) and Theil (1965) and was later extended by Caves,

Christensen and Diewert in their 1982 paper. The extension by Caves et al. (1982) consisted of modifying TFP to improve its transitivity property. Since then, as stated in Oum and Tretheway

(1986) and Windle (1991), the TFP has become “the single most useful measure of productive efficiency.” Indeed, the majority of the empirical studies of productivity in the airline industry employee TFP as their main tool to measure productivity. Such studies include Caves et al.

(1982b), Caves et al. (1983), Windle (1991), Oum et al. (1992), Good et al. (1993), Oum et al.

(1997), Duke (2005), Apostolides (2006), Honsombat et al. (2010) and also other studies in the transportation industry.

The Thornqvist-Theil index formula is:

lnTFPk

where aggregate productivity of firm k with j outputs and i inputs are calculated. Full explanation of the formula is provided below. Caves et al. (1982) further extended the above into

the following, to calculate difference in productivity of either between two firms or same firm’s performance over two different periods:

where:

- lnTFPkj is the productivity index difference between firm k and m;

- Yk and Ym are output and Xk and Xm are input indexes for firm k and m respectively;

- i is set of inputs and j is set of outputs;

- Rjk is the revenue share from output j for firm k (if j was output representing

passengers, then Rjk=0.8 would mean 80% of firm k’s entire revenue came from

passenger ticket sales during the given time frame, which is one quarter in this

study);

- is the arithmetic average of all output revenue shares for firm k;

- Yjk is the quantity of output j for firm k (if j was output in terms of passengers, then

Yjk=8,000,000 means firm k served 8 million passenger during the relevant quarter);

- is the geometric average of all output quantities;

- is the cost share of input i for firm k (if i was an input representing fuel, Wik=.25

mean 25% of k’s entire costs was fuel expense);

- is the quantity if input i for firm k;

- is the geometric mean of input quantities;

The extended version of TFP index calculates percentage change of productivity between two firms (or two points of time). Both indexes use revenue (cost) shares as weights for aggregating firm’s output (input) quantities to produce an output and an input index.

This study uses the extended version of the TFP index by Caves et al. as is the standard in most empirical work.

As was stated earlier, the extension of Caves, et al. (1982) to the Tornqvist-Theil index was to improve its transitivity property. Such transitivity property becomes important when simultaneous multilateral (more than two firms at once) comparisons are made across multiple firms in cross section and/or over multiple time periods.30

In order to calculate the TFP indices, this study uses four input and three output variables of 11 airlines from 1992 to 2011 on a quarterly basis to measure the TFP for each scenario in the same way done in Oum (2001). The inputs include (i) number of employees, (ii) gallons of fuel consumed, (iii) number of aircraft used and (iv) miscellaneous material input that consist of everything else. For each input, its share of total cost was calculated separately.31 The outputs include (i) RPM, (ii) Revenue-ton mile and (iii) incidental output, which includes all other material outputs that earn revenue such as catering, ground handling, sales of technology, consulting services, hotel businesses, etc. Both miscellaneous input quantity and incidental

30 In their empirical paper, Caves et al. (1983) demonstrates how the extended version of TFP index can be used by aggregating productivity indices from 1970 to 1975 to compare with aggregated indices from1976 to 1980. For an extended discussion see Coelli et al. (2005) and Caves et al. (1982). 31 Total Cost and Total Revenue excludes Transport Related Costs and Transport Related Revenues as was explained previously in Footnote 4 on p.6.

output quantity are not available as there are no data for many different types of inputs/outputs that take only small fraction of the entire operation. Instead, as done in Oum (2001), the dollar amount spent on miscellaneous inputs and earned from incidental services was divided by the

U.S GDP deflator to be used as proxies.

Figures 5.5 through 5.8 illustrate output, input and TFP index comparisons of bankrupt airlines with their control group airlines. The indices all have been normalized to American

Airline’s 1992 output, input and TFP index.

Figure 5.5 illustrates the rate of change in input, output and TFP index each of the four bankruptcy airlines enter and exit bankruptcy. The index is created by pooling from four different kinds of inputs taking into account individual cost shares. We see that unlike the output and the TFP indices, there is less quarterly effect on input as it stays flat with a downward trajectory. The exception is a sharp drop in the input index right before bankruptcy and a sharp increase right before they exit bankruptcy. This is related to United furloughing thousands of employees and recalling them when they exited bankruptcy as we saw in Figure 4.1. This change visibly affects productivity positively as United’s output stays flat relative to its input. The pattern of continuously reducing inputs while trying to maintain output at the same level is also observed for Delta and United. US Airways’ output however declines as fast as its input.

Figure 5.6 and 5.7 separates each airline’s input and output indices and compares them with their respective control group airlines’ indices. In each graph, the name of the airline that is going through bankruptcy is outlined in a box and the vertical lines representing bankruptcy entry and exit dates as usual. The common pattern observed in Figure 5.6 and 5.7 is that both input and output indices decrease faster than their control group airlines’ respective indices. This

is natural as an organization decreases its overall input and output by going through bankruptcy.

However, the final effect on productivity cannot be told here as the rate of change in input and output eventually determine the level of TFP. This is what Figure 5.8 achieves, which compares each bankrupt airline’s TFP with its control group.

First, we see US Airway’s TFP history from 2000 to 2004 as it enters and exits bankruptcy. There are five control group airlines and US Airways is the worst performer among the six airlines. If we look at US Airways’ TFP on its own, visibly there is no point of defection around bankruptcy entry and exit dates as the airlines mostly follows a similar pattern its control group is following.

United’s TFP comparison reveals a slightly different story. Before bankruptcy it was doing slightly better than American and we see sharp increase in productivity caused by the change in employee size which comes down eventually. Post-bankruptcy, United is doing worse than Southwest and American and Southwest continue to do better in the long run while

American eventually comes down to United’s level of productivity.

Delta’s productivity history differs from the above two in that it manages to the top performer before and after bankruptcy, but post-bankruptcy it shares that top spot with

Southwest. American and Continental appear to be following the same pattern during Delta’s post-bankruptcy era as it in the pre-bankruptcy period. We see a much similar story with

Northwest’s bankruptcy.

6. Econometric methodology

The econometric identification method used in this follows the methodology used in Oum et al. (2001). The regression is a fixed effects difference-in-difference method that captures the

difference between the treatment and control groups using bankruptcy dates point of difference between the two groups after controlling for exogenous variations. The treatment group in this case is the bankrupt airline and the control group consists of other airlines that were selected based on criteria in Section III. The panel regression is for each of the four bankruptcy case is:

where:

- is the lnTFP productivity index dependent variable for i (i=1….11) firm at quarter t

(t=1…80);

- is the firm level fixed effect for firm i;

- measures quarter effect where Quarter Dummyl (l=1,2,3 for quarter 1, 2 and 3) is 1 for

Quarter 1, 0 otherwise, etc;

- measures individual airline level fixed effect where (m=1,2…11) is 1

for each individual airline and 0 otherwise;

- is 0 for all quarters pre-bankruptcy and 1 for all quarters post-bankruptcy;

- is 1 for the firm going through bankruptcy and 0 for the firms that are in control group for

each bankruptcy case;

- is interaction of the Time dummy and the Treatment dummy which captures the effect of

bankruptcy on productivity, the coefficient of interest;

- is a vector of independent variables that consist of the following for each firm i:

1. Employee size: the more workers a firm has, the less productive it will be all else

constant. The hypothesis is that a firm under bankruptcy is could to reduce work

force and thus affecting productivity;

2. Stage length: the longer the stage length, the productive a firm will be;

3. Average fleet age: older fleet age could harm productivity as more resources will

be dedicated to keep the planes running all else equal;

4. Number of different types of aircraft: the more number of varieties of aircraft an

airline used, the less productive it is expected to be. The classical example is

Southwest’s 2-3 different types of usage of the Boeing 737 aircrafts as opposed to

a typical legacy carrier that used on average 14 different aircrafts.

5. Load factor: higher load factor is expected to result in higher productivity as more

people board a flight at a fixed level of input;

6. Quarterly GDP growth rate: as GDP growth increases productivity is expected to

increase as output can increase faster than input regardless of firm’s bankruptcy

7. Average-HHI of participating markets of airline i (airport pair or metropolitan

area pair): It is calculated as follows. Suppose firm i flew only on two markets A

and B. Let’s assume market A has HHI of 3000 and market B has HHI of 5000,

and 40% of i’s total flights were on market A and 60% were on market B. Then

the Average-HHI equals 3000*40%+5000*60%=4200. This variables measures

how frequently an airline serves highly competitive (and thus increasing pressure

on productivity) or highly non-competitive routes (and thus decreasing pressure

on productivity).

8. Average market share on participating markets (airport pair or metropolitan area

pair): this variables measures firm’s own presence on routes it serves. If most of

its flights are on markets where it has huge dominance, there is less competitive

pressure to be productive and vice versa. Continuing with the previous example, if

firm i’s market share on route A is 80% and on B is 90%, then its Average-

market-share equals 0.8*40%+0.9*60%=.86, a firm with very a dominant

advantage and thus less incentive to improve its productivity.

9. Network size (airport pair): this variables measures number of markets served in

a given quarter. As a firm goes through bankruptcy, its network size is known to

decrease significantly, because it is cutting back on service. The network size

needs to be controlled for because the study aims capture variation in productivity

not caused by decrease in network size.

Table 6 provides summary statistics of the TFP index and the ten independent variables. The next section provides discussion of regression results.

7. Regression Results

Table 7.1 presents the fixed effects regression results for each of the four bankruptcies considered in this study. The dependent variable is ln(TFP). The second row from the top indicates the length of pre and post-bankruptcy periods along with the number of quarters each airline spent in bankruptcy. For each bankruptcy, US Airways has a control group consisting of five airlines where there are five dummies for each. For the other three bankruptcy cases, the control group airlines are American, Continental and Southwest. Of the four regressions, United has the longest pre/post and bankruptcy period. The airline spent 3.5 years in bankruptcy and data for 4.25 years pre and after are available. Thus, having far more observations, the United

regression could have had much more significant variables but the results in Table 7.1 show that the same significant variables are also significant across the other regressions, even for US

Airways where only 1.5 years of pre/post data was available.

Our main variable of interest that measures the effect of bankruptcy on firm productivity,

Time*Treatment, is insignificant for all cases of bankruptcies, confirming bankruptcies do not result in any change in productivity. This confirms our observation from Figures 5.5 to 5.8 where a detailed look into the source of change in productivity was provided. Since the post-bankruptcy period began when each airline announced its exit in Table 7.1, another version of the regressions were performed to test whether giving airlines more time to adjust for its post- bankruptcy life would produce different results. In the alternative version, each airline’s post- bankruptcy periods began after a certain time they had announced their exit and thus lending them more time for adjustment. The new post-bankruptcy date for US Airways began 2 quarters after their exit announcement date. This means US Airways actually exited bankruptcy on

2003:1 and previously for Table 7.1 regression their post-bankruptcy period began in 2003:2, the very next quarter after their exit quarter. However, for Table 7.2, the post-bankruptcy period is set to begin in 2003:4, giving them additional two quarters for adjustment. The extensions for the

United, Delta and Northwest bankruptcies were all four quarters each.

With the extended time in bankruptcy, all signs and statistical significances in Table 7.2 mimic Table 7.1 including the Time*Treatment dummy which all remain statistically insignificant.

In terms of the control variables, we see that labor size has a negative impact on productivity across the board. The weight of the effect ranges from -0.49% to -0.89% on productivity as labor size increases by 1%. This means that as far as the legacy carriers go, they

are at a point where additional labor impacts productivity negatively or they got too big in terms of labor size.

Load factor and stage length impact productivity positively expectedly as observed in

Oum (2001) and is a norm in the industry.

The additional variables used in this study that were not used in any of the previous studies were the aircraft age, aircraft type and the market concentration variables which include the Average HHI metro, average individual market share and average network size. Aircraft type does not affect productivity. Surprisingly, we see that the age of fleet is positively related with productivity. This means while controlled for everything else, the older planes increase productivity compared to the younger planes. This could be explained by pre-existing trained workers and equipment to work with older planes that improve productivity.

In addition, we see that airlines are productive in Q2 and Q3 than in Q4 which is most likely related with the summer travel season. There is no statistical difference between Q1 and

Q4.

The airline dummies are consistent with what we observed from Figure 5.8. The two noteworthy trends are that Southwest and Delta have been consistently leading the industry in terms of productivity. Across the four regressions we see Southwest being at least 60% more productive than its competitors which is attributed to their explosive growth especially during the last decade while the rest of the pack were scrambling to just maintain their positions.32

32 The appendix contains the regression results for Hawaiian and Frontier. These results have not been included as part of the main results due to the weak control group as discussed previously on pg. 10. Figure A compares each of Hawaiian and Frontier bankruptcies in terms of TFP to their control group which are Alaska/Frontier and Alaska/Hawaiian respectively. While there is no obvious elevation or a defecting point pre and post-bankruptcy, the fact that Hawaiian’s productivity remain flat while the other two experience major gains during the same period signals the possibility of Hawaiian lagging behind due to bankruptcy. Indeed, in Table A where the regression results are displayed, we observe Hawaiian to be experiencing a 63% (ln(.49)-1) drop in productivity as a result of the bankruptcy. However, this number could be so high is because (a) Hawaiian had only two airlines in its control

8. Conclusion

After an extensive assembly of quarterly input and output data for 11 airlines, this study focused on four airline bankruptcies to measure the effect of bankruptcy on productivity. The study used the fixed effects model to look at pre and post-bankruptcy behavior of airlines closely following the methodology of Oum (2001) and introduced new control variables such as aircraft age, type, market concentration, individual market share and network size.

A detailed look partial productivity revealed that productivity increased/decreased based on a particular input/output selected and thus creating ambiguous effects for assessing overall firm health. Indeed, the main descriptive indicators used in conjunction with the TFP indices confirm that the TFP method successfully represents an airline’s overall productivity level.

For its main finding, this study discovers that declaring bankruptcy does not impact firm productivity in the airline industry either negatively or positively. The results were consistent across four airlines and were further strengthened when regressions with extended time in bankruptcy for each airline produced very similar results. Even though the airlines reduce costs by declaring bankruptcy, it has been decidedly determined that they do not improve productivity.

This could be one of the reasons why airlines continue to enter bankruptcy one after another and why the industry remains fragile as even the most urgent measure of restricting an organization by the threat of extinction, declaring bankruptcy, does not help them become more productive.

We know that improvement in productivity means either output grows faster than input or input reduces faster than output. During bankruptcy, an organization is more likely to be cutting back

group and (b) both of those airlines happened to have experienced major gains during the period Hawaiian went through bankruptcy. Frontier displays no change in productivity as a result of the bankruptcy.

on all fronts and thus to improve productivity during bankruptcy, they would need to make sure to decrease output as little as possible while cutting back on input.

From the policy perspective, one way to prevent another round of bankruptcy wave is to stipulate a stricter condition that enforces improvement in productivity by the bankruptcy courts.

However, so long as investors are willing to keep pouring money into the industry, the airlines will not have any increased incentive to become more efficient. In that regard, there is very little the bankruptcy courts can do.

References

Apostolides, A.D. (2006) “Impact of Productivity Changes in Air Transportation of Profits, Prices and Labor Compensation: 1990-2001.” Journal of Transportation Statistics, 9(1).

Borenstein, S., Rose, N.L. (1995) “Bankruptcy and Pricing Behavior in the U.S. Airline Markets.” The American Economic Review, 85(2).

Borenstein, S., Rose, N.L. (2003) “Do Bankruptcies Reduce Air Service?” NBER Working Paper Series,9636.

Borenstein, S. (2011) “On the Persistent Financial Losses of the U.S. Airlines: A Preliminary Exploration” NBER Working Paper Series, 16744.

Caves, D.W., Christensen, L.R., Diewert, W.E. (1982) “Airline productivity under deregulation” AIE Journal on Government and Society, November/December Edition.

Caves, D.W., Christensen, L.R., Diewert, W.E. (1982) “Multilateral Comparisons of Output, Input and Productivity using Superlative Index numbers.”Economic Journal, 92.

Caves, D.W., Christensen, L.R., Tretheway, M.W. (1983) “Productivity Performance and U.S. Trunk and Local Service Airlines in the Era of Deregulation” Economic Inquiry, 21(3).

Ciliberto, F., Schenone, C. (2012) “Bankruptcy and Product Market Competition: Evidence from the Airline Industry” MPRA, 38624.

Distexhe, V., Perelman, Sergio.. (1994) “Technical Efficiency and Productivity Growth in an Era of Deregulation: the case of Airlines” Swiss Journal of Economics and Statistics, 130(4).

Duke, J., Torres, V. (2005) “Multifactor Productivity change in the Air Transportation Industry.” Monthly Labor Review, March Edition.

Good, D.H., Nadiri, M.I., Roller, L., Sickles, R. (1993) “Efficiency and Productivity Growth Comparisons of European and U.S. Air Carriers: A First Look at the Data.” The Journal of Productivity Analysis, (4):115-125.

Greer, M.R. (2008) “Nothing focuses the mind on productivity quite like the fear of liquidation: Changes in airline productivity in the United States, 2000–2004” Transportation Research, Part A(42).

Hendel, I. (1996) “Competition under Financial Stress” The Journal of Industrial Economics, 44(3).

Hofer, C., Dresner. M., Windle, R. (2009) “Financial Distress and Airline Fares” IFSPA, Hong Kong.

Homsombat, W., Fu, X., Sumalii, A. (2010) “Policy Implications of Airline Performance Indicators: Analysis of Major North American Airlines.” Transportation Research Record, 2177:41-48.

Jayanti, R.K., Jayanti, S.V. (2009) “Effects of Airlines Bankruptcies: An Event Study” Journal of Services Marketing, 25(6).

Kennedy, R.E. (2000) “The Effect of Bankruptcy Filing’s on Rivals’ Operating Performance: Evidence from 51 Large Bankruptcies.” Journal of the Economic Business, 7(1).

Lee, H.R. (2010) “Bankruptcy and Low Cost Carrier Expansion in the Airline Industry” Institute for Empirical Research in Economics, University of Zurich Working Paper Series, 1424-0459.

McGeehan, H. (1995) “Measuring Airline Productivity in Ireland: A Note.” The Economic and Social Review, 26(3).

Ng, C.K., Seabright, P. (2001) “Competition, Privatization and Efficiency: Evidence from the Airline Industry” The Economics Journal, 111(July).

Opler, T.C., Titman, S.(1994) “Financial Distress and Corporate Performance.” The Journal of Finance, XLIX(3).

Oum, T.H., Tretheway, M.W. (1992) “Concepts, Methods and Purposes of Productivity Measurement in Transportation.” Transport Research Journal, 26(6)

Oum, T.H., Yu, C. (1998) “Winning Airlines: Productivity and Cost Competitiveness of the World’s Major Airlines.” Kluwer Academic Publishers, 6th Ed.

Oum, T.H., Park, J., Kim, K., Yu, C. (2001) “The Effect of Horizontal Alliances on Firm Productivity and Profitability: Evidence from the Global Airline Industry.” Journal of Business Research, 57.

Swelbar, W.S. (2007) “Cost and Productivity Convergence” MIT.

Törnqvist, L. (1936) “The Bank of Finland’s Consumption Price Index.” Bank of Finland Monthly Bulleting, 10:1-8.

Theil, H. (1965) “The Information Approach to Demand Analysis” Econometrica, 33(1). Vennet, R.V. (1996) “The Effects of Mergers and Acquisitions on the Efficiency and Profitability of E.C. Credit Institutions.” Journal of Banking and Finance, 20:1531-1558.

Waite, J.K., McDaniel, J.B. (2009) “The Impact of Airlines Bankruptcies on Airports” Legal Research Digest, 6.

Windle, R.J. (1991) “The World’s Airlines: A Cost and Productivity Comparison.” Journal of Transport and Economics Policy, 25(1).

Worthington, A.C. ( 2001) “Efficiency in Pre-Merger and Post-Merger Non-Bank Financial Institutions.” Managerial and Decision Economics, 22(8).

United States Government Accountability Office (2004) “Legacy Airlines Must Further Reduce Costs to Restore Profitability”

United States Government Accountability Office (2005) “Bankruptcy and Pension Problems are Symptoms of Underlying Structural Issues”

Tables:

Table 3.1 List of Airlines categorized by size Major carriers Non-major carriers 1 American Airlines (American) 9 Alaska Airlines (Alaska) 2 America West Airlines (American 10 Frontier Airlines (Frontier) West) 3 Continental Airlines (Continental) 11 Hawaiian Airlines (Hawaiian) 4 Delta Air Lines (Delta) 5 Northwest Airlines (Northwest) 6 Southwest Airlines (Southwest) 7 United Airlines (United) 8 US Airways

Table 3.2 Sample of input and output variables used for TFP index calculation.

Table 4.1 Bankruptcies of major, regional and national carrier in the US: 1992-2011 Airline Name Entry Exit Date Course of Action Carrier Data Group 1 Trans World Airlines 1/31/1992 10/7/1993 Chapter 11 Major 2 Hawaiian Airlines 9/21/1993 9/20/1994 Chapter 11 Major 3 Trans World Airlines* 6/30/1995 8/1/1995 Chapter 11 Major 4 Allegiant Air 2/13/2000 3/1/2002 Chapter 11 National 5 Trans World Airlines* 4/1/2000 - Chapter 11 and a Major merger with American Airlines 6 US Airways 8/11/2002 3/31/2003 Chapter 11 Major 7 United Airlines 12/9/2002 2/1/2006 Chapter 11 Major 8 Hawaiian Airlines* 3/21/2003 6/2/2005 Chapter 11 Major 9 US Airways* 9/12/2004 9/27/2005 Chapter 11 and a merger with America West 10 ATA Airlines 10/26/2004 2/8/2005 Chapter 11 National 11 Aloha Airlines 12/30/2004 2/7/2006 Chapter 11 National 12 Delta Air Lines 9/14/2005 4/30/2007 Chapter 11 Major 13 Northwest Airlines 9/14/2005 5/30/2007 Chapter 11 Major 14 Aloha Airlines* 3/20/2008 - Chapter 11 followed by National liquidation process 15 ATA Airlines* 4/2/2008 - Chapter 11 followed by National sale to Southwest 16 Frontier Airlines 4/11/2008 8/3/2009 Chapter 11 Major 17 Sun Country 10/6/2008 9/10/2008 Chapter 11 National 18 American Airlines 11/19/2009 Currently Chapter 11 and Major under proposed merger with bankruptcy US Airways Source: Author’s research from airlines.org and individual airline’s website.

Table 4.2 Post Chapter 11 bankruptcy behavior Airline Entry Exit Procedure 1 Trans World Airlines 1/31/1992 10/7/1993 Chapter 11 Trans World Airlines 6/30/1995 8/1/1995 Chapter 11 Trans World Airlines 4/1/2000 - Chapter 11 and a merger with American Airlines Hawaiian Airlines 9/21/1993 9/20/1994 Chapter 11 2 Hawaiian Airlines 3/21/2003 6/2/2005 Chapter 11

US Airways 8/11/2002 3/31/2003 Chapter 11 US Airways 9/12/2004 9/27/2005 Chapter 11 and a merger with America West 3 ATA Airlines 10/26/2004 2/8/2005 Chapter 11 ATA Airlines 4/2/2008 - Chapter 11 followed by sale to Southwest

4 Aloha Airlines 12/30/2004 2/7/2006 Chapter 11 Aloha Airlines 3/20/2008 - Chapter 11 followed by liquidation process

Source: Author’s research from airlines.org and individual airline’s website.

Table 4.3 The list of six bankruptcies considered in this study, corresponding control group airlines, pre and post-bankruptcy periods, entry and exit dates; Bankruptcies of Control Group Pre-merger Entry Date Length of time Exit Date Post-merger major carriers Airlines period spent in bankruptcy period 1 US Airways American 6 quarters: 8/11/2002 3 quarters 3/31/2003 6 quarters, Continental 2001:1-2002:2 (2002:3) (2003:1) 2003:2-2004:3 Southwest Delta Northwest 2 United American 17 quarters: 12/9/2002 14 quarters 2/1/2006 17 quarters: Major Continental 1998:3-2002:3 (2002:4) (2006:1) 2006:2-2010:2 Carriers Southwest 3 Delta American 10 quarters: 9/14/2005 6 quarters 4/30/2007 10 quarters: Continental 2003:1-2005:2 (2005:3) (2007:1) 2007:3-2009:3 Southwest 4 Northwest American 10 quarters: 9/14/2005 6 quarters 4/30/2007 10 quarters: Continental 2003:1-2005:2 (2005:3) (2007:1) 2007:3-2009:3 Southwest

Non- 5 Hawaiian 2003 Alaska 11 quarters: 3/21/2003 10 quarters 6/2/2005 11 quarters: major Frontier 2000:2-2002:4 (2003:1) (2005:2) 2005:3-2008:1 carriers 6 Frontier Alaska 9 quarters: 4/11/2008 6 quarters 8/3/2009 9 quarters: Hawaiian 2006:1-2008:1 (2008:2) (2009:3) 2009:4-2011:4

Table 6 Summary statistics of the dependent and independent variables; Variable Obs Mean Std. Dev. Min Max TFP 612 1.36 0.38 0.59 2.64

Employee size 612 37,137 16,577 10,118 76,031 Load factor 612 73% 8% 53% 89% Stage length 612 820 207 373 1287 GDP 612 2.7% 2.6% -8.9% 8.0%

Aircraft age 608 11.2 3.4 6.3 19.9 Aircraft type 608 11.0 4.7 3.0 24.0 Aircraft quantity 612 356 134 84 681

Network size 612 6,922 3,410 493 13,796 Avg. HHI Metro 612 4,027 671 2,708 5,788 Avg. Ind Mkt Share 612 41% 9% 20% 61% Source: Compilation of data from Databank 1B, F41 and Bureau of Transportation Fleet Statistics.

Table 7.1 Fixed effects regression results where post-bankrutpcy period starts at the exit announcement. Dependent Variable: Ln(TFP) US Airways United Delta Northwest Pre - during bankruptcy – post1 6-3-6. 17-14-17. 10-6-10. 10-6-10. Ln (Employee Size) -0.49** -0.65*** -0.71*** -0.89*** (0.16) (0.06) (0.11) (0.08) Load factor (percentage) 1.28*** 0.63** 0.80*** 1.00*** (0.31) (0.22) (0.21) (0.20) Ln(Stage length) 0.99*** 0.46*** 1.10*** 1.43*** (0.20) (0.10) (0.19) (0.18) Aircraft age (years) 0.04* 0.01** 0.02* -0.02 (0.01) 0.00 (0.01) (0.01) Type (numbers) 0 0.01*** 0 -0.01 0.00 0.00 0.00 0.00 Ln (Aircraft quantity) 0.76*** 0.55*** 0.58*** 0.53*** (0.14) (0.05) (0.07) (0.07) GDP (percentage) 0.21 0.2 -0.1 -0.22 (0.30) (0.13) (0.14) (0.14) Ln (Avg. hhi metro) -0.71 -0.04 0.34 0.47 (0.47) (0.24) (0.26) (0.25) Ln (Avg. indmkt share) 0.47 0.29 0.12 -0.06 (0.32) (0.17) (0.15) (0.17) Ln(Network size) 0.11 0.26*** 0.29*** 0.16* (0.14) (0.08) (0.07) (0.07) Q1 dummy 0.01 -0.01 -0.02* -0.02* (0.02) (0.01) (0.01) (0.01) Q2 dummy 0.01 0.04** 0.03 0.01 (0.02) (0.01) (0.01) (0.01) Q3 dummy -0.01 0.05*** 0.03* 0.02 (0.02) (0.01) (0.01) (0.01) American dummy -0.33 -0.11*** -0.19*** -0.05 (0.22) (0.02) (0.06) (0.08) Continental dummy -0.11 -0.20*** -0.31** -0.59** (0.19) (0.04) (0.10) (0.17) Southwest dummy 0.63** 0.40** 0.60*** 0.55** (0.22) (0.13) (0.14) (0.17) Northwest dummy -0.35*** - - - (0.09) - - - Delta dummy 0.07 - - - (0.13) - - - Time dummy -0.07* 0.08* -0.03 0.02 -0.03 (0.03) (0.02) (0.03) Time*Treatment dummy 0.04 -0.04 0.02 -0.1 -0.07 (0.03) (0.03) (0.05) F_stats 180 210 240 222 Observations 48 136 84 80 R-sqr 0.99 0.97 0.99 0.98 81

* p<0.05, ** p<0.01, *** p<0.001 Note 1: This row indicates the number of pre and post-bankruptcy quarters and number of quarters spend in bankruptcy. For instance, 6-3-6 means 6 quarters of data pre and post with 3 quarters spent in bankruptcy. Note 2: Robust standard errors in brackets. Hausman test rejects the random effects model.

Table 7.2 Fixed effects regression results where post-bankruptcy period begins past the exit date announcement giving airlines additional time spent in bankruptcy. US Airways United Delta Northwest Pre - during bankruptcy - post 4-5-4. 13-18-13. 6-10-6. 6-10-6. Ln (Employee Size) -0.58*** -0.65*** -0.76*** -0.87*** (0.09) (0.07) (0.14) (0.08) Load factor (percentage) 1.16** 0.41 0.87*** 0.92*** (0.35) (0.29) (0.21) (0.21) Ln(Stage length) 0.74*** 0.40* 1.23*** 1.24*** (0.17) (0.16) (0.31) (0.19) Aircraft age (years) 0.05*** 0.01 -0.01 -0.01 (0.01) (0.01) (0.01) (0.01) Type (numbers) - 0.01*** 0 0 - 0.00 (0.01) 0.00 Ln (Aircraft quantity) 0.88*** 0.53*** 0.51*** 0.58*** (0.11) (0.07) (0.11) (0.08) GDP (percentage) 0.56 0.27 -0.24 -0.2 (0.34) (0.16) (0.14) (0.14) Ln (Avg. hhi metro) -1.12*** -0.11 0.34 0.37 (0.28) (0.39) (0.32) (0.26) Ln (Avg. indmkt share) 0.68*** 0.28 0.1 0 (0.17) (0.25) (0.22) (0.18) Ln(Network size) -0.05 0.27* 0.16 0.15* (0.10) (0.10) (0.11) (0.07) Q1 dummy 0 -0.01 -0.01 -0.01 (0.02) (0.01) (0.01) (0.01) Q2 dummy -0.01 0.05** 0.04* 0.02 (0.02) (0.02) (0.01) (0.01) Q3 dummy -0.01 0.06*** 0.04* 0.03* (0.02) (0.02) (0.02) (0.01) American dummy -0.27*** -0.10** -0.25** 0 (0.04) (0.03) (0.08) (0.09) Continental dummy -0.02 -0.18*** -0.68*** -0.46* (0.06) (0.05) (0.18) (0.19) Southwest dummy 0.14*** 0.41 0.28 0.50** (0.02) (0.21) (0.23) (0.18) Northwest dummy -0.37*** - - - (0.08) - - - Delta dummy 0.32 - - - (0.16) - - - Time dummy -0.08** 0.1 0.03 0.02 -0.03 (0.06) (0.04) (0.03) Time*Treatment dummy 0.07 -0.06 -0.1 -0.06 -0.05 (0.04) (0.06) (0.05) F_stats 166 132 185 184 Observations 48 104 52 72 R-sqr 0.99 0.97 0.99 0.98 * p<0.05, ** p<0.01, *** p<0.001

Note 1: This row indicates the number of pre and post-bankruptcy quarters and number of quarters spend in bankruptcy. For instance, 6-3-6 means 6 quarters of data pre and post with 3 quarters spent in bankruptcy. Note 2: Robust standard errors in brackets. Hausman test rejects the random effects model.

Figures: Figure 4.1 Quarterly history of work force size among bankrupt carriers.

Bankruptcy exit date

Bankruptcy entry date

Figure 4.2 Quarterly history of aircraft quantity size among bankrupt carriers.

Bankruptcy exit date

Bankruptcy entry date

Figure 4.3 Quarterly history of Revenue-Passenger-Mile among bankrupt carriers.

Bankruptcy entry date

Bankruptcy exit date

Figure 4.4 Quarterly history of network size among bankrupt carriers.

Bankruptcy exit date

Bankruptcy entry date

Figure 5.1 Comparison of ASM output per employee among bankrupt carriers.

Bankruptcy entry date

Bankruptcy exit date

Figure 5.2 Comparison of total passengers served by per employee among bankrupt carriers.

Bankruptcy entry date

Bankruptcy exit date

Figure 5.3 Comparison of aircraft productivity in terms of block-hour usage among bankrupt carriers.

Bankruptcy entry date

Bankruptcy exit

Figure 5.4 Comparison of aircraft output productivity in terms of ASM across bankrupt airlines.

Bankruptcy entry date Bankruptcy exit

Figure 5.5 Input and Output index of bankrupt airlines*

*This graph illustrates the relationship between input, output and the TFP index. Recall that TFP Index=Output/Input 93

Figure 5.6 Input indexof each bankrupt airline compared with its control group airlines’ input indices

Figure 5.7 Output index of each bankrupt airline compared with its control group airlines’ output indices

Bankruptcy entry date Figure 5.8 Bankruptcy exit date Quarterly TFP comparison of legacy bankruptcies against their control groups (normalized at American 1992=1)

Appendix Hawaiian and Frontier’s bankruptcy

Figure A Quarterly TFP index comparisons of Alaska, Frontier and Hawaiian from 2000-2011 (normalized at Alaska 2000=1)

Hawaiian’s entry

Frontier’s entry Hawaiian’s exit Frontier’s exit

Appendix Hawaiian and Frontier’s bankruptcy Table A Hawaiian and Frontier bankruptcy regressions Hawaiian Frontier Pre - during bankruptcy - post 11-10-11. 9-6-9. Ln (Employee Size) -0.50*** -0.56 (0.14) (0.55) Load factor (percentage) 1.03* 2.42** (0.43) (0.74) Ln (Stage length) -0.2 0.3 (0.24) (0.48) Aircraft age (years) 0.01 -0.01 (0.01) (0.01) Type (numbers) -0.01 0 (0.02) (0.02) Ln (Aircraft quantity) -0.01 1.37** (0.03) (0.38) GDP (percentage) -0.72 -1.92 (0.55) (1.04) Ln (Avg. hhi metro) 0.2 -0.33 (0.44) (0.45) Ln (Avg. indmkt share) 0 0 (.) (.) Ln (Network size) 0.39** -0.17 (0.11) (0.29) Q1 dummy -0.05 -0.04 (0.03) (0.05) Q2 dummy -0.01 -0.06 (0.04) (0.05) Q3 dummy -0.02 -0.06 (0.03) (0.05) Alaska dummy -0.77*** 0.09 (0.19) (0.39) Frontier dummy -1.35*** - (0.22) - Hawaiian dummy - 1.08* - (0.47) Time dummy 0.25** -0.03 (0.08) (0.08) Time*Treatment dummy -0.49*** 0.01 (0.11) (0.14) F_stats 104 26 Observations 66 54 R-sqr 0.97 0.92 * p<0.05, ** p<0.01, *** p<0.001 98

---

Chapter 3: The Effect of Mergers on Productivity in the Airline Industry

1. Introduction

This paper aims to measure the effect of mergers on firm productivity in the airline industry. The level of consolidation in the airline industry has been increasing at a level never seen before where every single one of the top carriers has been involved in mergers with another mega carrier33. Such industry wide consolidation has attracted a lot of attention from the academics, the public media and the government which has allowed to every single major- merger among airlines in the last two decade except the very last one, US Airways and American

Airlines.34

Traditionally, airline mergers have been well studied on antitrust grounds where common concerns include effect of mergers on market power35, price36 and service quality.37 However, the amount of empirical research on the effect of mergers on productivity within the airline industry has been very limited to date and yet, merging airlines like to cite increased synergy resulting in improved efficiency as one of their reasons to merge.38

Indeed, the Horizontal Merger Guidelines of 2010 by the Department of Justice note in

Section 10 that “a primary benefit of mergers to the economy is their potential to generate significant efficiencies and thus enhance the merged firm’s ability and incentive to compete, which may result in lower prices, improved quality, enhanced service, or new products.” Given

33 Southwest/Airtran merger is an exception. However, AirTran was the third biggest carrier after Southwest and jetBlue having served 3.66% of domestic passengers in the 3rd quarter of 2012. 34 American/Trans World in 2001, US Air and America West in 2005, 35 See Borenstein (1990). 36 See Morrison (1996) and Kwoka, J. and E. Shumilkina (2010). 37 See Mazzeo (2003). See Greenfield (2011) for a more recent study. 38Kole and Lehn (2000) is an exception which is a study that approaches the topic of efficiency and mergers in the airline industry by analyzing the case of USAir’s acquisition of Piedmont Aviation. The study, however, solely focuses on financial performance indicators with special attention given to increased labor costs as result of the merger.

the increased level of consolidation during the last two decades, this study looks into each merger to determine if any change in productivity has occurred due to mergers.

Outside the airline industry, empirical studies completed on other industries are rich and diverse. While there are varieties of studies focusing on diverse industries, most of the studies exploring the relationship between productivity and mergers focus either on hospital or financial industries. Overall, the empirical studies produce very mixed results where some confirm a positive relationship between mergers and productivity (see Harris, et al. (2000)and Brooks, et al, (1992)) and others find no significant relationship between the two (see Alexander (1995)).

This study closely follows the methodology used in Oum (2001) using data of 8 airlines from 1992 to 2012 to evaluate the effect of mergers on productivity. I define productivity both at the partial level, where a single input to a single output ratio is used, and at the aggregate level by employing the Total Factor Productivity (TFP) methodology. After determining that partial productivity measures did not all point in the same directions pre and post-merger, I find that the mergers do not bear any causal change on TFP productivity for the mergers considered in this study.

The remainder of the paper is organized as follows. Section 2 looks at the previously exiting literature and explains the sources of data. Section 3 covers the background of merger activity in the airline industry between 1992 and 2012. Section 4 discusses the various methods in which productivity is measured and illustrates the productivity trend of the merging airlines.

Section 5 explains the regressions used for econometric identification. Section 6 contains regressions results and Section 7 provides concluding remarks.

100

2. Literature Review and Data

2.1 Literature Review

As previously mentioned, the two industries that have attracted empirical studies in the past on the topic of mergers and productivity are the financial and the hospital service industry.

However, within each industry, the existing literature presents mixed results with no clear trend in either direction. In the financial industry, for instance, Hayens and Thompson (1998),

Cummins and Xie (2007), Worthington (2001) find productivity gains as result of mergers in the financial services industry. On the contrary, Dickerson, et al. (1997), Berger and Humphrey

(1992) and Vander and Vennet (1996) find no evidence of ex-post improvement in efficiency for banking and credit institutions mergers. In addition, Cummins and Xie (2007) find post-merger improvement in cost efficiency in the property liability insurance industry.

In the hospital industry, Ferrier and Valdmanis (2004) and Alexander et, al. (1996) found no to little impact of mergers on operating efficiencies. However, Sinay (1998) and Harris, et al.

(2000) found gains of operational efficiencies to be one of the major impacts of mergers.

There are also several notable studies outside the finance and health industry, which are the two industries that attract major attention on the topic of mergers and productivity.39 Kwoka and Pollitt (2007) offer a comprehensive look at the relationship between mergers and productivity in the electric power industry. They find that acquiring firms tended to seek out better performing firms to merge. As result of mergers, acquiring firms saw their ex-post productivity improve while target firms saw their productivity decrease. Nguyen and Ollinger

(2006) focus on the effect of merges on labor productivity in the meat products industry. The study finds that labor productivity increased as result of mergers. Rajanet, et al. (1997) discusses

39 See Kaplan (2000) for a survey of empirical studies on mergers and productivity.

101

the effect of mergers in the US tire industry and they find productivity did not increase as result of mergers.

Section 3 provides background history on the airline mergers that took place in the last two decades as well as analysis on descriptive statistics of select mergers.

2.2 Data

The data for this study come from several different sources. The final database includes domestic US Airline data on input/output quantity, cost/revenue statistics and market concentration data for the following eight airlines from 1992 to 2011 on quarterly basis:

1. America West Airlines (America West) 2. American Airlines (American) 3. Continental Airlines (Continental) 4. Delta Air Lines (Delta) 5. Northwest Airlines (Northwest) 6. Southwest Airlines (Southwest) 7. United Airlines (United) 8. US Airways All data used in this study only include the domestic portion of the above eight airlines.

The above eight were chosen based on their comparability among each other in that when one goes through a merger the rest could be used as a control group.

The data used for constructing the TFP index and partial productivity ratios come from the Department of Transportation Form 41. Form 41 Schedule is a publicly available data presented by Bureau of Transportation statistics that contains detailed categorized information on revenues, expenses and other operating statistics.40 The TFP index uses four inputs (labor, fuel, aircraft and miscellaneous) and three outputs (revenue passenger mile, revenue-ton mile and

40 Form 41 schedule at Bureau of Transportation Statistics: http://www.transtats.bts.gov/DataIndex.asp

102

incidental) to calculate productivity. As such, a detailed database containing both quantities of input/output components and respective dollar amount spent/earned on them was assembled.

Input data: The quantitative part of the input index includes data on number of employees, amount of gallons consumed and average number of aircraft. Data on miscellaneous quantity of inputs (all other material input besides aircraft and fuel, such as amount of passenger meals, various equipment, etc.) is not available. Instead, the miscellaneous material quantity index was calculated by dividing miscellaneous materials cost by the quarterly US GDP deflator and then normalized as done in Oum et al. (2001). The cost sharing part includes data on labor cost, fuel cost and aircraft cost. Miscellaneous was cost was calculated by subtracting the previous three main costs from total cost. The cost-share part of the input data was available from Form 41 on a quarterly basis. It includes the dollar amount spent on labor, aircraft and fuel.

The material cost (or miscellaneous cost) was computed by subtracting labor, aircraft and fuel cost from Total Cost.41

41 Total Cost and Total Revenue excludes Transport Related Costs and Transport Related Revenues. Transport related revenues/costs report the amount earned/spent from purchasing airline service from regional feeder airlines and thus not directly taking part in an airline’s own production of goods and services.

103

The independent variable section consists of nine variables that are being used in the same style as in Oum (2001). In addition to Oum (2001), due to availability of data this study is able to include a list of new additional variables such as aircraft age, quantity and market concentration level. The data for fleet size, fleet age and fleet type come from Department of

Transportation’s fleet statistics published on its website.42 Data for stage length and load factor come from Form 41 database. Quarterly GDP growth and GDP deflator data was collected from the U.S. Bureau of Economic Analysis. The HHI index, market share and network size for each airline was computed from Databank 1B, the U.S. Department of Transportation’s Ticket Origin and Destination Survey, on quarterly basis.

The final database consists of unbalanced panel consisting of 612 observations where each observation is year-quarter-carrier unique (unbalanced set of 8 airlines for 80 quarters, but strongly balanced when each merger and its control groups are considered separately according to their time period surrounding individual merger). A single observation consists of a TFP index as the dependent variable and 9 different independent variables for the panel regression.

Table 2.1 provides summary statistics of TFP index and the nine independent variables.

Employee size is the number of workers employed by an airline. Load factor is the average fill rate of flights. For instance, 0.7 of load factor means on average the flights are 70% full. Stage lengths are the average distance travelled per flight of an airline. Aircraft age indicates the average fleet age of an airline in a given quarter. Aircraft type indicates the number of different types of aircraft being used by the airline. Avg. HHI Metro indicates the weighted average market concentration level across all markets where an airline serves. Avg. Ind Market share

42 Department of Transportation’s publication of airline fleet statistics: https://ntl.custhelp.com/app/answers/detail/a_id/223/~/airline-fleet-size-statistics

104

indicates the weighted average market share of all markets an airline serves. Finally, network size indicates the number of unique airport pairs that an airline served in a given quarter.

3. Background on Mergers in the U.S. Airline Industry

During last two decades, from 1992 to 2012, the airline industry has become more consolidated than ever. While during the 1990s we barely witnessed any noteworthy mergers, the tech-bubble era of 2001 combined with September 11 events marked beginning of decade long mega-merger frenzy in the airline industry. The 9 biggest airlines and AirTran, outlined in Table 3.1, that operated independently at the beginning of 2001 had turned into the 5 biggest airlines, United,

Delta, US Airways, American and Southwest, by the end of 2012.In addition, two of those five airlines, US Airways and American, have been in talks of entering a merger deal since late 2012.

With the recent objection to US Airways and American Airline’s merger by the Department of

Justice, some suggest that the industry is at such a high level of consolidation that any further mergers could harm the consumers (see Moss and Mitchell (2012)). Table 3.1 presents all mergers involving major airlines from 1992 to 2012.

In the 90s there were only three mergers that were minor in scale. The first one was in

1993 where a relatively small Southwest Airlines purchased even smaller Morris Air. The other two were simply big carriers purchasing much smaller regional airlines for purposes of regional feed services. However, we can see that beginning with American and TransWorld’s merger in

2001, the big carriers begun to start merging with each other. This latest wave of mega mergers can be viewed as a method of survival in an increasingly hostile post 9/11 environment for the airlines where the industry faced sharp rise in fuel costs and excess capacity combined with ever increasing competition from the low cost carriers that put downward pressure on fares. Table 3.2

105

illustrated the evolution of the big 10 carriers’ market shares between 1992 and 2012 as they engaged mergers.

Table 3.2 provides a detailed history of how the 10 airlines evolved from 1992 till 2012 in terms of individual and as well as industry-wide statics covering total combined passenger load share, average industry market concentration, passenger load share, individual market share, market concentration level and network size. Each of these six variables are discussed below.

First of all, the 10 airlines’ combined traffic share fell from being 95% in 1992 to 64% in

2012. There are two main reasons for the reduction of passengers carried by these10 majorcarriers. First, beginning around 2001 a new wave of low-cost carriers have been successfully taking away market shares from the legacy carriers. Such new airlines include jetBlue (carrying 5.4% of total domestic passengers as of 2012), Spirit, Frontier, Virgin,

Allegiant and Sun Country. Combined share of domestic passengers carried by these six low cost carriers in 2012 was 12%. Second, the feeder carrier have been reporting to have carried more and more passengers as of late as legacy carriers are relying more on their services for regional service. The four biggest of such regional airlines include Atlantic Southwest (4.01% of domestic passengers as feeder for Delta and United), Skywest (3.48% as feeder for United, US

Airways, Delta, American and Alaskan), American Eagle (2.59% as a feeder for American) and

Pinnacle (1.83% and a feeder for Delta). Their combined traffic in 2012 was 12% of all domestic passengers.

Second, the weighted average industry-wide HHI has been decreasing steadily as competition increases.43

Third, the domestic share of passengers carried by each airline steadily decrease over the

20 years unless an airline is involved in a merger where a temporary uptick is observed before

43The weighted average HHI of each domestic metropolitan-pair markets.

106

reentering a downward trajectory. The sole exceptions are Southwest and AirTran, both of which start with 2% and 1% domestic share respectively. By 2012, Southwest had captured an amazing

22% of market share (in terms of passengers served) while AirTran had achieved 4%. In the first merged considered in this study, the merger of American and Trans World, we see American’s domestic share decline from 17% in 1992 to 10% in 2001 when it bought out Trans World.

Immediately, its domestic share increases to 13% only to begin another decade long decrease. In the second merger, which concerns US Airways and America West, U.S Air’s speedy loss of domestic share from 14% in 1992 to 5% in 2007 is rescued by acquiring America West under its wings to bring up US Airways’ domestic share to 7% 2008. As a result of buying a much healthier airline, US Airways is able to hold on to its 7% domestic share from 2008 to 2012. In the merger, involving Delta and Northwest, we see a story similar to that of US Airways. Delta’s domestic share goes from a commanding 18% in 1992 to a mere 8% 2009 which is brought up to

13% the very next year by buying Northwest. In United and Continental’s merger, almost the same pattern is observed.

Fourth, as the big airlines serve less and less passengers domestically, consequently we observe the same airline’s weighted average of its own route-market shares decrease. The hardest hit airlines are United and US Airways. For example, US Airways’ average weighted market share peaked at 59% 1997 only to end up at 29% in 2012 even with the purchase of America

West. United’s market share peaked at 50% in 1995 before bottoming out at 25% in 2011. It rose to 29% in 2012 with the purchase of Continental.

Fifth, with exception of Southwest and AirTran, for all the other airlines we see their average HHI in markets served decrease dramatically. This is because the highly concentrated markets the legacy carriers served in the early 90s have been successfully raided by low cost

107

carriers led by the likes of Southwest and jetBlue bringing down the HHI level. However, the story is not the same for Southwest and AirTran because we see that they continue to operate in high-level HHI markets throughout the years even though they are causing reduction in HHI by stealing from the legacy carriers. Southwest’s HHI of markets served peaks at 4500 in 2005 before beginning to decline for the next seven years. This can be partially explained by

Southwest’s strategy of locating and entering niche markets and successfully protecting its lead against any potential competition. However, with the rise of new low-cost airlines, its market leadership is also being contested as new players enter the very same markets Southwest has been serving.

Sixth, network size is a variable of great interest because it shows how an airline’s national network coverage changes throughout the years as it faces harsh times and competition.

We see that American cut its network coverage almost in half where it went from serving 13,058 airport pairs in 1992 to 6,864 airport pairs in 2001. It went up to 8,385 through purchase of

TransWorld in 2001. Delta is the other behemoth whose network size dipped below 10,000 only one time during the past 20 years, in 2009, but that was quickly solved by purchasing Northwest to bring it back up to 13,000 in 2010. As usual, the odd pair in this group is Southwest and

AirTran, which have much smaller network sizes that keeps growing. An interesting observation is to compare Southwest’s passenger-to-network size ratio to a typical legacy carrier’s passenger- to- network size ratio. In 2012, Southwest carried 22% of all domestic passengers on a 2,640 unique leg network while United carried 10% of all domestic passengers on a 10,427 unique leg network. This shows how serious Southwest is about choosing a niche market (a) with high volume and (b) which at minimum must be served by 737s. It seems legacy carriers could have been stuck serving thousands and thousands of the smaller markets forcing them to deal with

108

massive network management and associated costs. If that is the case, the rise of regional feeder carriers is justifiable as legacy carrier try to outsource serving smaller markets and concentrate on the bigger markets.

Of the nine mergers presented in Table 3.1, three mergers have been qualified for productivity analysis as six of the above mergers are dropped primarily due to missing data and other reasons.44

Table 3.3 outlines the three mergers for productivity analysis including pre and post- merger periods.

In Table 3.3, we see three important dates. First, the beginning of a merger signifies a date when both airlines have received approval from shareholders and regulatory government officials to merge. Second, joint report date is the first time in which both airlines report jointly filings under a single certificate. Joint report dates are of importance because this is the first time when productivity statistics of two airlines are combined to one airline. These dates are usually several quarters later than the official announcement of successful merger closing date.

Therefore, it is safe to assume that by the time they begin jointly reporting, merger integration process would have been well under way for several months and sometime integration could be in its later stages by that time. Third, merger integration end dates are approximations for all three airlines to give them between 1.25-2.25 years to merge the most fundamental operations.45

44 (1)Southwest’s purchase of Morris Air does not have pre-merger as fleet data in general prior to 1992 are not available. Similarly, (2) U.S Airways and American airlines merger is still pending and hence does not have post- merger data. (3) United’s merger with Continental was not completed until 2011:4 which means post-merger data is not available. (4) American’s 1998 merger with Reno Air and (5) Delta’s 1999 merger with several smaller airlines are seen as purchase of regional feeder airlines that do not impact each airline’s operational productivity at the national level. (6) Southwest’s purchase of AirTran has been complete but the two airlines are still in the midst of integration as AirTran will continue operate as a separate airline towards late 2014. 45Even though, a 100% full integration can possibly take much longer than two years to complete, based on available announcements, this study assumes that main operations are merged within the period indicated. In case of American and Trans World, Trans World had been facing major financial problems by early 2001 when American started acquiring its assets to integrate into its fleet. Eventually, as part of a buyout deal by American, Trans World declared bankruptcy and ended all booking activity in November of 2001. At that time, most of its assets were already transferred and being utilized by American. Delta and

109

Table 3.4 lists control group airlines for each of the three mergers. The primary criteria for control group airlines were that (a) they were similar in size, operation and network coverage and (b) that a control group’s airlines must not have overlapping pre- and post-merger periods with the airline at question in case a control group airline was also involved in a merger recently.

Even though there were other airlines such as jetBlue, Alaska and Frontier with available data, they were excluded for being much smaller and regionally focused airlines. In Table 3.4 all airlines have a history of competing against each other throughout the country because of their nationwide network coverage.

Figures 3.1, 3.2 and 3.3 illustrate the main airline descriptive parameters of American,

US Airways and Delta for the 20 year period of 1992-2011 where pre, mid and post-merger dates are marked. Tables 3.5, 3.5 and 3.7 display percentage changes in those values during pre, mid and post-merger period for the three airlines.

Figure 3.1 in conjunction with Table 3.5, illustrates changes to American’s main descriptive statistics. American’s numbers can be divided into pre-merger and post-merger trends. The had airline had been relatively healthy prior to the merger as we see it was adding labor by 11% and capacity by 3% during two years leading up to the merger. The merger brought in additional 16% of labor, 12% of network size and 7% of capacity where these numbers peak.

However, the merger marks a change of direction for American as the numbers begin a decade long downward trajectory. In the two years following the merger, labor size is down by 20%, network size down by 8%. Surprisingly, capacity is at the same level, but from Figure 3.1 we can see beyond the post-merger two year period, the capacity starts decreasing continuously till the present time.

Northwest’s merger began in 2008:4 and they posted a similar announcement in 2010:1 which confirmed the last part of integration of by merging ground operations and reservation systems.

110

In Figure 3.2 and Table 3.6, US Airways’ statistics are presented. Prior to the merger, we see a different story for US Airways than American’s. Up till 2001, the airline has been steadily increasing its ASM while cutting down on network size. It had an expanding labor force that peaked 1 year prior to September 11. However as US Airways was the hardest hit airline in the wake of September 11 attacks, we see its numbers take a sharp decrease following 2001.

Ultimately US Airways was never able to reverse its steep downward trajectory of its vital statistics including labor size, ASM and network until it merged with America West in 2006:1.

Two years prior to the merger US Airways saw its work force cut by 30%, network size by 10% and ASM by 5% from previous years. The merger substantially lifts US Airways’ size in all aspects where labor size increases by 52%, network size by 14% and capacity by 47%. The post- merger story is, however, similar to American’s as we see work force, network size and ASM get reduced.

In Figure 3.3 and Table 3.7, Delta’s descriptive numbers are presented. Similar to US

Airways and unlike American, Delta had been experiencing a steady decline in its work force, network and capacity sharply prior to its merger as it had been spending some time in bankruptcy. The merger decidedly puts Delta on an upward direction as it peaks in 2010:1 where labor size increased by 58%, network size by 10% and capacity by a whopping 34%. Unlike

American and U.S. Air, Delta managed to successfully maintain the upward trajectory in the post-merger era where all of its vital statistics continue to grow.

In summary, naturally we see each airline’s size and capacity increase dramatically as they enter mergers. While American and US Airways were not able to hold on to their lead in in capacity in the long run, Delta on the contrary so far has done very well at maintaining its lead.

While capacity is expected to grow, what remains to be determined is what happens to

111

productivity as the airlines graduate through a merger process. Section 4 discusses various ways to measure airline productivity and provides a preliminary analysis of each of the three merger’s productivity statistics during mergers.

4. Measuring Productivity

This study will focus on two most used approaches to measure productivity in the airline industry: partial and total factor productivity (TFP).46 Partial productivity is a simple ratio of one input to one output that is widely used and easy to grasp. While partial productivity’s simplicity is its advantage, it falls short on measuring a firm’s overall productivity with multiple inputs/outputs as it can do only one input/output at a time. On the other hand, TFP is an index based system that includes all inputs/outputs and enables comparison of productivity across different firms at different points in time. This paper will offer analysis of airlines’ both types of productivity measures: partial through industry standard ratios and total factor productivity (in the style of Oum (2001)).

4.1 Partial Productivity:

A partial productivity measure consists of a ratio of an input to output. In the airline industry, depending on one’s focus area, it is possible to produce a variety of ratios using the many different input and output variables available. This study will focus on the most widely used ratios that include three inputs: employees, aircraft and fuel utilization productivity. The five types of partial productivity included in this analysis are:

e. ASM/employee: output (available seat miles) per employee

f. Total passengers/employee: number of passengers enplaned by per employee

46 Besides TFP, the two other most commonly used productivity measurement methods for multi-input and multi- output scenario are data envelopment analysis and stochastic frontier estimation. However, the study chose TFP as it has been the most commonly used method for measuring productivity in the airline industry. [citation of previous usage of TFPs]

112

g. ASM/aircraft day: output per aircraft day

h. Block hours/aircraft day: block hours per aircraft day which indicates on average

how many hours in a 24-hour frame one aircraft spends between gate-close and

gate-open.

i. ASM/per gallon of fuel: output per gallon of fuel

The above list includes productivities associated with the three single biggest costs for airlines: labor, aircraft and fuel. Labor productivity is measured by (a) and (b), aircraft productivity is measured by (c) and (d) and fuel efficiency is measured by (e).

Figures 4.1, 4.2 and 4.3 display evolution of American, US Airways and Delta’ partial productivity change from 1992 to 2011. Table 1.4 combines all three airline’s immediate pre- merger and post-merger partial productivity into one table.

From Figure 4.1[tables section] and Table 4.1, we can see that American experienced a modest increase in partial productivity following the merger. In the long run, its employee productivity increased until 2006 and stays flat from there on. Aircraft productivity decreases by around 2% and fuel efficiency increases by 3.8% as the airline replaces its older planes with newer fleet. The main pattern observed from Figure 4.1 is that the merger looks to have reversed the direction of employee productivity from decreasing trend pre-merger to an increasing trend post-merger. While aircraft usage per day remains relatively flat, capacity output by per aircraft day also changes its downward slope from pre-merger to an upward slope post-merger.

From Figure 4.2 [appendix], for US Airways, the story is not as straightforward as

American’s as its employee productivity decreases post-merger compared to pre-merger, we see that its aircraft utilization has increased significantly. Its aircraft usage per day stays constant.

113

From Figure 4.3 [appendix] , for Delta, all post-mergers number are big negatives with the exception of fuel efficiency. Employee productivity is down by as much as 12.25% and aircraft usage is down by as much as 11.25%.

In summary, the above story illustrates how difficult it is to asses airline’s overall health using partial productivity when different productivity ratios begin pointing in different directions. This problem however is solved by employing the Total Factor Productivity method which is discussed in detail the next section.

4.2. Productivity indexing background

Although simple to calculate and widely used, the partial productivity method has two main shortfalls. First, it cannot offer measurement at the firm level when multiple inputs are used to produce multiple outputs. Second, partial productivity measurements do not account for trade- offs between multiple inputs as individual weights shift among inputs. An index numbering system solves both of those problems.

One of the most widely used index numbering system for measuring productivity is Total

Factor Productivity (TFP) index which is a trans-log multilateral index formula. The TFP index was developed by Tornqvist (1936) and Theil (1965) which was later extended by Caves,

Christensen and Diewert in their 1982 paper. The extension by Caves et, al. (1982) consisted of modifying TFP to improve its transitivity property. Since then, as stated in Oum and Tretheway

(1986) and Windle (1991), the TFP has become “the single most useful measure of productive efficiency.” Indeed, majority of the empirical studies of productivity in the airline industry employee TFP as their main tool to measure productivity. Such studies include Caves et, al.

(1982b), Caves et, al. (1983), Windle (1991), Good et, al. (1993), Oum et al. (1998), Duke

114

(2005), Apostolides (2006), Honsombat et al. (2010) and also other studies in the transportation industry outside the airline industry.

The Thornqvist-Theil index formula is:

lnTFPk

where aggregate productivity of firm k with j outputs and i inputs are calculated. Full explanation of denomination is provided below. Caves et al. (1982) further extended the above into the following, to calculate difference in productivity of either between two firms or same firm’s performance over two different periods:

where:

- lnTFPkj is the productivity index difference between firm k and m (or two period k

and m);

- Yk and Ym are output and Xk and Xm are input indexes for firm k and m respectively;

- i is set of inputs and j is set of outputs;

- Rjk is revenue share from output j for firm k (if j was output representing passengers,

then Rjk=.8 would mean 80% of firm k’s entire revenue came from passenger ticket

sales during the given time frame, which is one quarter in this study);

- is the arithmetic average of all output revenue shares for firm k;

115

- Yjk is quantity of output j for firm k (if j was output in terms of passengers, then

Yjk=8,000,000 means firm k served 8 million passenger during the relevant quarter);

- is the geometric average of all output quantities;

- is cost share of input i for firm k (if i was an input representing fuel, Wik=0.25

means 25% of k’s entire costs was fuel expense);

- is quantity if input i for firm k;

- is geometric mean of input quantities;

This study uses the extended version of the TFP index by Caves et al. as is the standard in most empirical work.

The extension of Caves et al. (1982) to the Tornqvist-Theil index was to improve its transitivity property from bilateral comparison to multilateral comparison. Such transitivity property becomes important when simultaneous multilateral (more than two firms at once) comparisons are made across multiple firms in cross section and/or over multiple time periods.47

In order to calculate the TFP indices, this study uses four inputs and three outputs data of eight airlines from 1992 to 2011 on a quarterly basis to measure the TFP for each scenario in the same way done in Oum (2001). The inputs consist of:

(i) number of employees,

47 In their empirical paper, Caves et, al. (1983) demonstrates how the extended version of TFP index can be used by aggregating productivity indices from 1970 to 1975 to compare with aggregated indices from1976 to 1980. For an extended discussion see Coelli et, al. (2005) and Caves et, al. (1982).

116

(ii) gallon of fuel consumed

(iii) number of aircraft used

(iv) miscellaneous material input that consist of everything else

For each input, its share of total cost was calculated separately.48 The outputs include:

(i) RPM

(ii) Revenue-ton miles

(iii) Incidental output which includes all other material outputs that earn revenue

such as catering, ground handling, sales of technology, consulting services,

hotel businesses, etc.

Both miscellaneous input quantity and incidental output quantity are not available as there is no data for many different types of inputs/outputs that take only small fraction of the entire operation. Instead, as done in Oum (2001), the dollar amount spent on miscellaneous inputs and earned from incidental services were divided by the U.S GDP deflator to be used as proxies.

From Table 3.4, we saw that there are eight unique airlines, including all the airlines in the control group that will be included in this study. 49 For each of the eight airlines, using the above input and output data, I generated a quarterly TFP index from 1992 to 2011, which will serve as dependent variables in the regression.

The TFP index as the independent variable and its dependent variables are discussed in detail in Section 6.

Figure 4.4 illustrates TFP indices of the three merging airlines from 1992 to 2011 and

Table 4.2 illustrates pre an post-merger percentage comparisons.

48 Total Cost and Total Revenue excludes Transport Related Costs and Transport Related Revenues. Transport related revenues/costs report the amount earned/spent from purchasing airline service from regional feeder airlines and thus not directly taking part in an airline’s own production of goods and services. 49 America West, American, Continental, Delta, Northwest, Southwest, United and US Airways;

117

From Table 4.2 we see numbers that for all three airlines, post-merger productivity is up unlike the partial productivities that pointed in different directions. Figure 4.4, which looks at the long term evolution of TFP for each three airlines including the mergers periods, presents a pattern of increasing productivity over the long term which is consistent with Oum (1998).

Even though the two year pre and post-merger partial productivity averages were not consistent with TFP averages, we see from the figures that the TFP long term pre and post- merger patterns have a good resemblance to the long term partial productivity patterns for all the three airlines: American’s decreasing productivity pre-merger begins to increase, US Airways’ post-merger productivity is elevated and flat and Delta having a good increase in the early 2000s.

However, the averages cannot provide accurate measurement. The real change in productivity taking into account the control variables and control groups will be estimated using the difference-in-difference estimation which will be presented in the section 6.

5. Econometric Methodology

As mentioned previously, the econometric identification method used in this study follows closely the methodology used in Oum et al. (2001). The regression is a difference-in- difference method that captures the difference between treatment and control groups using merger entry and exit dates to group data into ex-ante and ex-post time frames. There are nine dependent variables that are used to control for exogenous variations. The treatment group in this case is the acquirer airline and the control group consists of other airlines that were selected based on similarity in size and operational characteristics. The panel regression applied for each of the three mergers consider is:

118

where:

- is the lnTFP productivity index dependent variable for i (i=1….14) firm at quarter t

(t=1…80);

- measures quarter effect where Quarter Dummyl (l=1,2,3 for quarter 1, 2 and 3) is 1 for

Quarter 1, 0 otherwise, etc;

- measures individual airline level fixed effect where (m=1,2…14) is 1

for each individual airline and 0 otherwise;

- is 0 for all quarters pre-bankruptcy and 1 for all quarters post-bankruptcy;

- is 1 for the firm going through bankruptcy and 0 for the firms that are in control group for

each bankruptcy case;

- is interaction of the Time dummy and the Treatment dummy which captures the effect of

bankruptcy on productivity, the coefficient of interest;

- is a vector of independent variables that consist of the following for each firm i:

10. Employee size: the more workers a firm has, the less productive it will be all else

constant. The hypothesis is that a firm under bankruptcy is could to reduce work

force and thus affecting productivity;

11. Stage length: the longer the stage length, the productive a firm will be;

12. Average fleet age: older fleet age could harm productivity as more resources will

be dedicated to keep the planes running all else equal;

119

13. Number of different types of aircraft: the more number of varieties of aircraft an

airline used, the less productive it is expected to be. The classical example is

Southwest’s 2-3 different types of usage of the Boeing 737 aircrafts as opposed to

a typical legacy carrier that used on average 14 different aircrafts.

14. Load factor: higher load factor is expected to result in higher productivity

considering if more people fly using the same resources, then output per resource

should increase.

15. Quarterly GDP growth rate: as GDP growth increases productivity is expected to

increase as output can increase faster than input regardless of firm’s bankruptcy

16. Average-HHI of participating markets of airline i (airport pair or metropolitan

area pair): It is calculated as follows. Suppose firm i flew only on two markets A

and B. Let’s assume market A has HHI of 3000 and market B has HHI of 5000,

and 40% of i’s total flights were on market A and 60% were on market B. Then

the Average-HHI equals 3000*40%+5000*60%=4200. This variables measures

how frequently an airline serves highly competitive (and thus increasing pressure

on productivity) or highly non-competitive routes (and thus decreasing pressure

on productivity).

17. Average market share on participating markets (airport pair or metropolitan area

pair): this variables measures firm’s own presence on routes it serves. If most of

its flights are on markets where it has huge dominance, there is less competitive

pressure to be productive and vice versa. Continuing with the previous example, if

firm i’s market share on route A is 80% and on B is 90%, then its Average-

120

market-share equals 0.8*40%+0.9*60%=0.86, a firm with very a dominant

advantage and thus less incentive to improve its productivity.

18. Network size (airport pair or metropolitan pair): this variables measures number

of markets served in a given quarter. As a firm goes through bankruptcy, its

network size is known to decrease significantly, because it is cutting back on

service. The network size needs to be controlled for because the study aims

capture variation in productivity not caused by decrease in network size.

The next section provides discussion on regression results.

6. Regression Results

Using the independent variables in Table 2.1 and the control groups in Table 3.4, there are multiple regressions performed for each of the three mergers considered in this study. The regressions differ from each other by the length of pre and post-merger periods they cover as well as lengths of period airlines spent for merger integration. The different values of time have been added to have a short-term and a long-term perspective in assessing the productivity change. All regressions have been tested for Hausman test with all of them selecting the fixed effects model over random effects model. At the bottom of each table, the pre and post-merger period and merger integration time lengths are provided. For example, at the bottom of the first column of Table 6.1A, which says “American 1”, indicates this regression compares performance of two years pre-merger period with a two years post-merger period having a one year merger integration window in the middle.

The regression tables have been split into two parts: non-dummy and dummy variables due to space limitation. The first sections of each airline regressions, Table 6.1A, 6.2A and 6.3A present the non-dummy variable section of regressions for American, US Airways and Delta

121

respectively. Across the board, we see some very consistent results regardless of airline and time period chosen if we focus only on the statistically significant variables. Some of the consistent results are employee size impacting productivity negatively, load factor and stage lengths improving productivity. Aircraft age and type did not affect productivity while aircraft quantity affected productivity positively.

We see that the weighted average of individual market share impacts productivity positively. Higher market share could mean higher capacity to keep the airlines “busier.” For

American, in Table 6.1A, in the short run time period in regression “American 1”, market concentration level, Ln (Avg. hhi metro), and network size have negative impact on productivity.

However, the signs switch for both in the long run regressions “American 4” while maintaining its statistical significance. This could be explained by the fact that immediately after the merger

American may have been serving many of the markets previously served by Trans World where

American was not achieving good economies of scale. Over time, as the integration progresses,

American figures out which of Trans World’s markets offer better synergy with its own market, it begins to serve selectively and thus arriving at a point where increased network size results in improved productivity. We see a similar result for US Airways in Table 6.2A where there is no effect of network size on productivity in the short run, in regression “US Airways 1”, but in the long run, in regression “US Airways 4”, we observe a statistically significant positive relationship between network size and productivity.

The second parts of the regression tables for each airline, Table 6.1B, Table 6.2B and

Table 6.3B, present all the dummy variable results. This section contains the coefficient of the primary variable of interest for this study, the Time*Treatment dummy for each airline.

122

For American, we see a consistent improvement in productivity regardless of time frame fluctuations. For American, the increase in productivity due to merger consistently stays around

10% in the long run while having no effect in the short run. Figure 4.5 offers a look into the pre- merger comparison of TFP among American, TWA and their productivity weighted by respective Revenue Passenger Mile. We can see that immediately before it merger with

American, TWA was a much more productive airline than American. However, their weights are factored in, the difference becomes small. In addition, TWA is observed to more productive than

American only after 1998, the year when they switched from being mostly an international carrier to mostly domestic carrier, significantly increasing their domestic output. As result of this merger, we see that American’s productivity continues to climb in the long run.

For US Airways, increase is 28% in the long run while having a 35% increase in the short term which are both very high.50If we consult Figure 4.4 for the source of this high increase in productivity, we see that while US Airways maintained its input flat after a slight increase during merger integration, it was able to boost its output by a big margin. Its output index jumps from

.40 to .70 from 2007:3 to 2007:4, the quarter of first joint reporting, clearly indicating the amount of additional capacity that was brought by America West’s addition. From there on, it maintains its output levels while slowly decreasing input.

Delta does not report a statistically significant increase in productivity resulting from its merger with Northwest. On one hand, this could be regarded as a lack of post-merger data range for Delta and as with more time, it possibly could have posted statistically significant results like

American or US Airways in the future. On the other hand however, its two year post-merger coverage is the same as American and US Airways’ two year coverage where both of which posted statistically significant results. Furthermore, Figure 4.3, which details Delta’s partial

50 25% converts to (e.25-1)*100=.28

123

productivity, and Figure 4.4, which presents Delta’s TFP, both suggest that by the time Delta’s post-merger period began in 2010:1, its productivity levels were beyond any abnormal spikes related with its merger and only saw “smooth” curves during the next two years. In addition,

Figure 4.7 shows that Delta was already more productivity than Northwest pre-merger. In other words, it is reasonable to argue that Delta’s integration was over by 2010:1 and the period

2010:1-2011:4 presents a sufficient post-merger on which statistical conclusions can made. Thus, it may be concluded that Delta did not experience statistically significant improvement in productivity during from its merger with Northwest.

7. Conclusion

As the industry has gone under extensive consolidation, many interesting questions have been asked and answered, except the issue of post-merger productivity. As such this study aimed to help shed light on the subject by closely following the methodology of Oum (2001), but with additional variables to examine merging airlines. The study carefully explores all major mergers in the airline industry from 1992 to 2011. After an initial listing of nine major mergers that took place during the stated period, it was decided that only three of those fit the criteria for proper post-merger analysis. With the aim to illustrate all aspects of the three merging airlines’ health from 1992 to 2011 as they enter and exit mergers, this study provided a look at overall evolution of market shares, descriptive statistics, partial productivity and Total Factor Productivity indicators.

In terms of shift in market concentration, of the nine major carriers and one smaller carrier (AirTran, which did become a major carrier eventually) that operated in 1992, only five remained in 2015 after a series of high level mergers. We saw the legacy carriers consistently lost individual market shares (in terms of passengers) and consequently their combined market

124

share as it decreased from 95% in 1992 to 64% by 2011. The big winner has been Southwest

Airlines whose domestic market share went from 2% to 22%.

In terms of descriptive statistics, we see that while American’s aggregate indicators returned to their downward outlook soon after it completed its merger, US Airways and Delta, on the contrary, have been able to use the merger to stabilize their previously decreasing output levels.

In terms of productivity, we saw that for a specific agenda, such as determining labor productivity, the partial productivity method maybe more fitting, but for assessing an airline’s overall health Total Factor Productivity is the better method. The Total Factor Productivity indexing system was used in conjunction with a difference-in-difference estimation method to measure post-merger productivity changes.

For its main finding, the study determined that American and US Airways experienced significant gains in productivity, 10% and 28%, respectively from their mergers while Delta did not make any gain. This is an interesting finding as there is good history of airlines terribly suffering from badly timed and orchestrated mergers (for example, see history of Texas Air’s purchase of Eastern Air, and People’s Express’ purchase of Frontier). In the case of US Air, the improvement in productivity is consistent with the common speculation that at the time America

West was a much healthier airline that really helped stabilize US Airways’ downward outlook.

In terms of policy implications, the regulatory body can indeed expect improved efficiency and synergy from mega-mergers. Airline mergers themselves are so complicated procedures that they do not always guarantee improved performance. In addition, often times mergers are orchestrated by managers and/or CEOs who have motivations more related to

125

market power, empire building, bargain hunting, survival, and access to new market entry than to increase economies of scale and productivity. Yet, against the odds, the mega-mergers are successfully producing improved efficiency.

126

References

Alexander, J.A., Halpern, M.T., Lee, S.Y. (1995) “The Short-Term Effects of Merger on Hospital Operations.” Health Services Research, 30(6).

Apostolides, A.D. (2006) “Impact of Productivity Changes in Air Transportation of Profits, Prices and Labor Compensation: 1990-2001.” Journal of Transportation Statistics, 9(1).

Berger, A.N., Humphrey, D.B. (1992) “Megamergers in banking and the use of cost efficiency as an antitrust defense.” Antitrust Bulletin, 37.

Borenstein, S. (1990) “Airline Mergers, Airport Dominance, and Market Power." American Economic Review Papers and Proceedings, 80.

Brooks, G.R., Jones V.G. (1997) “Hospital Mergers and Market overlap.” Health Services Research, 31(6).

Caves, D.W., Christensen, L.R., Diewert, W.E. (1982) “Airline productivity under deregulation” AIE Journal on Government and Society, November/December Edition.

Caves, D.W., Christensen, L.R., Diewert, W.E. (1982) “Multilateral Comparisons of Output, Input and Producitivity using Superlative Index numbers.” Economic Journal, 92.

Caves, D.W., Christensen, L.R., Tretheway, M.W. (1983) “Productivity Performance and U.S. Trunk and Local Service Airlines in the Era of Deregulation” Economic Inquiry, 21(3).

Cummins, J.D., Xie, X. (2007) “Mergers and Acquisitions in the U.S. Property-Liability Insurance Industry: Productivity and Efficiency Effect.” Journal of Banking and Finance, 32(1).

Dickerson, A., Gibons, H.D., Tsakalotos, E., (1997) “The Impact of Acquisitions on Company Performance: Evidence from a Large Panel of UK firms.” Oxford Economic Papers, 49(3).

Duke, J., Torres, V. (2005) “Multifactor Productivity change in the Air Transportation Industry.” Monthly Labor Review, March Edition.

Ferrier, G.D., Valdmanis.V.G.(2004) “Do Mergers Improve Hospital Productivity?” The Journal of Operational Research Society, 55(10).

Mazzeo, M.J. (2003) “Competition and Service Quality in the U.S. Airline Industry.” Review of Industrial Organization, 22.

127

Harris II, J.,Ozgen, H., Ozcan, Y. (2000) “Do mergers enhance the performance of hospital industry?” The Journal of the Operational Research Society, 51, 801-811.

Haynes, M., Thompson, S. (1999)“The Productivity effects of bank mergers: Evidence from the UK building societies.” Journal of Banking and Finance, 23.

Homsombat, W., Fu, X., Sumalii, A. (2010) “Policy Implications of Airline Performance Indicators: Analysis of Major North American Airlines.” Transportation Research Record, 2177:41-48.

Kole, S., Lehn, K.M. (2010) “Workforce Integration and the Dissipation of Value in Mergers.” Mergers and Productivity: National Bureau of Economic Research Conference Report.239-286.

Kwoka, J., Pollitt, M. (2007) “Do Mergers Improve Efficiency? Evidence from the Restructuing the U.S. Electric Power Sector.” International Journal of Industrial Organization, 28(6).

Morrison, S.A. (1996) “Airline Mergers: A Longer View.” Journal of Transport Economics and Policy, 30(3).

Nguyen, S.V., Ollinger, M. (2006) “Mergers and Acquisitions and Productivity in the U.S. Meat Products Industries: Evidence from the Micro Data.” American Journal of Agricultural Economcis, 88(3).

Oum, T.H., Tretheway, M.W. (1992) “Concepts, Methods and Purposes of Productivity Measurement in Transportation.” Transport Research Journal, 26(6)

Oum, T.H., Yu, C. (1998) “Winning Airlines: Productivity and Cost Competitiveness of the World’s Major Airlines.” Kluwer Academic Publishers, 6th Ed.

Rajan, R., Volpin, P., Zingales, L. (1997) “The Eclipse of the U.S. Tire Industry.” Mergers and Productivity, 51-92.

Sinay, U.A., Campbell, C.R. (1995) “Scope and scale economies in merging hospitals prior to merger.” Journal of Economics and Finance, 19(2).

Törnqvist, L. (1936) “The Bank of Finland’s Consumption Price Index.” Bank of Finland Monthly Bulleting, 10:1-8.

Theil, H. (1965) “The Information Approach to Demand Analysis” Econometrica, 33(1).

128

Vennet, R.V. (1996) “The Effects of Mergers and Acquisitions on the Efficiency and Profitability of E.C. Credit Institutions.” Journal of Banking and Finance, 20:1531-1558.

Windle, R.J. (1991) “The World’s Airlines: A Cost and Productivity Comparison.” Journal of Transport and Economics Policy, 25(1).

Worthington, A.C. ( 2001) “Efficiency in Pre-Merger and Post-Merger Non-Bank Financial Institutions.” Managerial and Decision Economics, 22(8).

129

Tables: Table 2 Sample of Output and Input data for index calculations

130

---

Table 2.1 Summary of statistics for Independent and Dependent variables; Data for 8 airlines from 1992 to 2011. Variable Obs Mean Std. Dev. Min Max TFP Index 612 1.37 0.39 0.59 2.63 Employee size 612 37,136 16,576 10,118 76,031 Load factor 612 0.7 0.1 0.5 0.9 Stage length (miles) 612 819 207 373 1,287 Aircraft age (years) 608 11.2 3.4 6.3 19.9 Aircraft type 608 11.0 4.7 3.0 24.0 Aircraft quantity 612 355.8 134.2 83.7 680.9 Avg. HHI Metro 612 4,027 671 2,708 5,788 Avg. Ind Market 612 40 9.4 20.2 60.6 share Network size 612 6,922 3,410 493 13,796

131

---

Table 3.1 All mergers involving a major airline between 1992 and 2012 in the airline industry* Acquiring Share of Target Airline Share of Announce Purchase Airline domestics domestics ment date close date passengers** passengers 1 Southwest 2.28% Morris Airlines N/A 12/13/1993 12/31/1993 Airlines 2 American 13.20% Reno Air 1.09% 11/19/1998 2/1/1999 Airlines 3 Delta Air 14.70% Atlantic 1.94% 2/16/1999 10/22/1999** Lines Southwest * Airlines & Comair 4 American 11.14% Trans World 4.24% 1/10/2001 4/9/2001 Airlines Airlines 5 US Airways 7.21% America West 3.48% 5/19/2005 1/1/2006 Airlines 6 Delta Air 7.91% Northwest 4.79% 4/14/2008 12/31/2009 Lines Airlines 7 United 6.41% Continental 4.60% 5/3/2010 10/1/2010 Airlines Airlines 8 Southwest 20.94% AirTran Airways 4.65% 9/27/2010 5/2/2011 Airlines 9 US Airways 6.80% American Airlines 8.97% 8/31/2012 pending

Source: Author’s research from Usatoday.com, Newyorktimes.com and Airlines.org *Major airlines are Carrier Group III airlines that have revenue of at least $1 billion. **Share of all domestic passengers served in the quarter of merger announcement. ***In the same annual year 1999, Delta bought two other regional carriers. Such purchases can characterized mostly as financial transactions as Delta continued to operate them separately from its main operation after acquisition only to sell them off later on.

132

Table 3.2 Evolution of mergers involving the 10 biggest airlines the airline industry from 1992 to 2012

133 ---

1 Total Combined Share: Combined percentage of passengers carried for indicated year. 95% in 1992 signifies the 10 carriers carried 95% of all domestic passengers in the U.S. 2Avg Industry HHI: Weighted (weighted by passengers carried) industry average of HHI. The average taken over HHI of all metropolitan-pair markets where service exists. 3% of Domestic Passg: Percentage of domestic passengers served by indicated airline in a given year. 18% for American in 1992 signifies American carried 18% of all domestic passengers in the U.S. in 1992. 4Own Avg Market Share: Weighted average (weighted by passengers carried) of market shares of each metropolitan-pair market for a given airline in a given year. For instance, suppose airline XYZ served only two metropolitan-pair markets, NY-LA and LA-Chicago flying 10 passengers total in one year. If airline XYZ served 60% of the market flying 3 passengers in NY-LA market, but served only 30% of the market in LA-Chicago market flying 7 passengers, its “Own Avg Market Share” equals 60%*3/10+30%*7/10=39%. 5 Avg HHI of Markets served: Weighted average of HHI of every market a given airline serves. 6Network Size: number of airport-pairs a given airline served during indicated year.

134

Table 3.3 List of mergers analyzed with pre and post-merger dates Pre- Merger Joint Merger Integration Post- merger integration report integration length merger begins begins ends American/Trans pre 2001:2* 2002:1 2002:2 1.25 years post World 2001:1 2002:3 US pre 2006:1** 2007:4 2008:1 2.25 years post Airways/America 2005:4 2008:2 West Delta/Northwest pre 2008:4*** 2010:1 2010:2 2 years post 2008:3 2010:3 *For American and Trans World merger, 2001:2 was the quarter in which they announced successfully closing the merger deal marking the start of integration and by the beginning of 2002 the majority bulk of integration process was over. **For US Airways and America West, 2006:1 was the quarter in which they announced successfully closing the merger deal marking the start of integration. ***For Delta and Northwest, even though the purchase closure date was in late 2009, by October of 2008 they had received approvals from shareholders, the U.S. Department of Justice and European Union’s regulatory authority at which point they began integrating.

135

---

Table 3.4 List of control groups for each merger American/Trans US Airways/America Delta/Northwest World West America West American American Continental Continental Continental Delta Delta Southwest Northwest Northwest United Southwest Southwest United United US Airways

136

Table 3.5 Descriptive Statistics for American’s merger with TWA Pre 2 year avg: First joint quarter: Post 2 year avg: 1999:2-2001:1 2002:1 2002:3-2004:2 Quantity % change Quantity % change Quantity % change Work force 64,776 11% 74,853 16% 60,034 -20% Load Factor .70 1% .68 -2% .75 9% Network 7,265 -13% 8,117 12% 7,431 -8% Size ASM(000s) 27,416,000 3% 29,286,000 7% 29,224,000 0% Source: Author’s calculation from F41 and Databank 1B

137

Table 3.6 Descriptive Statistics for U.S. Air’s merger with America West Pre 2 year avg: First joint quarter: Post 2 year avg: 2004:1-2005:4 2007:4 2008:2-2010:1 Quantity % change Quantity % change Quantity % change Work force 19,086 -30% 30,451 52% 27,373 -10% Load Factor .74 5% .79 6% .83 5% Network 6,409 -10% 7,309 14% 6,906 -6% Size ASM(000s) 10,067,000 -5% 14,752,000 47% 13,632,000 -8% Source: Author’s calculation from F41 and Databank 1B

138

Table 3.7 Descriptive Statistics for Delta’s merger with Northwest Pre 2 year avg: First joint quarter: Post 1.25 year avg: 2006:4-2008:3 2010:1 2010:4-2011:4 Quantity % change Quantity % change Quantity % change Work force 31,689 -18% 50,113 58% 51,826 3% Load Factor .82 7% .81 -1% .84 3% Network 10,336 -4% 11,325 10% 12,388 9% Size ASM(000s) 19,181,000 -18% 25,672,000 34% 27,166,000 6% Source: Author’s calculation from F41 and Databank 1B

139

Table 4.1 Percentage changes for two years prior to merger start and two year following merger close dates. Pre 2 year Post 2 year % change avg. avg. American ASM/Employee 423 458 8.3% (000s) Total Passg/Employee 265 288 8.7% Blockhours/Aircraft 9.85 9.58 -2.7% day ASM/Aircraft day 546 536 -1.8% (000s) ASM/Gallon of fuel 53 55 3.8%

U.S. Air ASM/Employee 510 498 -2.4% (000s) Total Passg/Employee 476 424 -10.9% Blockhours/Aircraft 9.15 9.18 0.3% day ASM/Aircraft day 440 484 10.0% (000s) ASM/Gallon of fuel 58 64 10.3%

Delta ASM/Employee 609 535 -12.2% (000s) Total Passg/Employee 488 452 -7.4% Blockhours/Aircraft 10.52 9.86 -6.3% day ASM/Aircraft day 660 586 -11.2% (000s) ASM/Gallon of fuel 66 66 0.0% Source: Author’s calculation from F41.

140

Table 4.2 Percentage change in TFP pre and post-merger Pre 2 Post 2 % year year change avg. avg. American 1.33 1.41 6%

US Airways 1.10 1.48 35%

Delta 2.04 2.26 11% Source: Author’s calculation from F41.

141

Table 6.1 Part -1 (continued on the next page with dummy variables) American and TWA merger regression: TFP is the weighted average of the two airlines’ TFP pre-merger and American TFP post-merger; Dependent: Ln (TFP) American American American American 1 2 3 4 Ln (Employee Size) -0.36*** -0.58*** -0.78*** -0.51*** (0.07) (0.06) (0.11) (0.03) Load factor (percentage) 0.04 0.68** 0.45 1.28*** (0.24) (0.23) (0.33) (0.12) Ln(Stage length) 0.50*** 0.83*** 0.93*** 0.92*** (0.14) (0.13) (0.23) (0.07) Aircraft age (years) 0.02** 0.01 0.01 0 (0.01) (0.01) (0.01) 0.00 Type (numbers) 0.01 0 0 0.01*** 0.00 0.00 (0.01) 0.00 Ln (Aircraft quantity) 0.47*** 0.48*** 0.62*** 0.56*** (0.08) (0.08) (0.12) (0.04) GDP (percentage) 0.58*** -0.06 -0.03 -0.1 (0.16) (0.22) (0.29) (0.13) Ln (Avg. hhi metro) -0.87*** -0.16 0.36 0.39*** (0.25) (0.15) (0.39) (0.07) Ln (Avg. indmkt share) 0.51*** 0.27*** 0.21 -0.07 (0.14) (0.06) (0.23) (0.04) Ln(Network size) -0.38*** 0.04 0.09 0.14** (0.10) (0.09) (0.17) (0.05) F-stats 143 127 73 379 Observations 128 240 128 192 R-sqr 0.97 0.93 0.94 0.98 Pre and post-merger period 2 years 4 years 2 years 4 years length Merger integration length 1 year 1 year 3 years 3 years * p<0.05, ** p<0.01, *** p<0.001 Note 1: The regression table was divided into two sections due to space limitation. Note 2: Robust standard errors in brackets. Note 3: Hausman test rejects the random effects model.

142

Table 6.1 Part 2 – Dummy variables American and TWA merger regression: TFP is the weighted average of the two airlines’ TFP pre-merger and American’s TFP post-merger; Dependent: Ln (TFP) American 1 American 2 American 3 American 4 Q1 dummy -0.03** -0.05*** -0.07*** -0.03*** (0.01) (0.01) (0.02) (0.01) Q2 dummy 0.05*** 0.02 0.01 -0.01 (0.01) (0.02) (0.02) (0.01) Q3 dummy 0.06*** 0.03* 0.04* 0.00 (0.01) (0.01) (0.02) (0.01) America West dummy -0.36*** -0.23** -0.28 - (0.10) (0.07) (0.16) - Continental dummy -0.15** -0.19*** -0.18* -0.12*** (0.05) (0.05) (0.08) (0.02) Delta dummy 0.38*** 0.27*** 0.15 0.24*** (0.07) (0.06) (0.09) (0.04) Northwest dummy 0.04 0 -0.2 0.06 (0.13) (0.09) (0.16) (0.05) Southwest dummy -0.2 0.43** 0.36 0.77*** (0.18) (0.15) (0.27) (0.08) United dummy 0.28*** 0.19*** 0.15** 0.15*** (0.03) (0.03) (0.05) (0.02) US Airways dummy 0.06 -0.06 -0.25 (0.11) (0.08) (0.14) Time dummy -0.05** -0.09*** -0.08 -0.05** (0.02) (0.02) (0.04) (0.02) Treatment *Time dummy 0.04 0.09* 0.06 0.10*** (0.03) (0.04) (0.06) (0.02) F-stats 143 127 73 379 Observations 128 240 128 192 R-sqr 0.97 0.93 0.94 0.98 Pre and post-merger period 2 years 4 years 2 years 4 years length Merger integration length 1 year 1 year 3 years 3 years * p<0.05, ** p<0.01, *** p<0.001 Note 1: The regression table was divided into two sections due to space limitation. Note 2: Robust standard errors in brackets. Note 3: Hausman test rejects the random effects model.

143

Table 6.2 Part - 1 (continued on the next page with dummy variables) US Airways and America West merger regression: merger regression: TFP is the weighted average of the two airlines’ TFP pre-merger and US Airways’ TFP post-merger; Dependent: Ln (TFP) US Airways 1 US Airways 2 US Airways 3 US Airways 4 Ln (Employee Size) -0.75*** -0.74*** -0.86*** -0.83*** (0.06) (0.05) (0.11) (0.07) Load factor (percentage) 0.65** 0.78*** 0.82** 0.79*** (0.24) (0.20) (0.27) (0.21) Ln(Stage length) 0.68*** 1.06*** 1.21*** 1.27*** (0.17) (0.14) (0.23) (0.16) Aircraft age (years) 0 -0.01 -0.01 -0.01 (0.01) (0.01) (0.02) (0.01) Type (numbers) - - - - 0.00 0.00 0.00 0.00 Ln (Aircraft quantity) 0.63*** 0.58*** 0.64*** 0.58*** (0.07) (0.05) (0.08) (0.06) GDP (percentage) -0.22 -0.26 -0.54* -0.4 (0.15) (0.16) (0.27) (0.23) Ln (Avg. hhimetro) -1.00*** -0.47 -0.73 -0.59* (0.28) (0.27) (0.38) (0.28) Ln (Avg. indmkt share) 0.92*** 0.75*** 0.75** 0.84*** (0.19) (0.19) (0.25) (0.19) Ln(Network size) 0.13 0.11 -0.02 0.13 (0.09) (0.07) (0.11) (0.07) F-stats 220 266 265 283 Observations 111 145 80 120 R-sqr 0.98 0.98 0.99 0.98 Pre and post-merger 2 years 4 years 2 years 3 years period length Merger integration 2 years 2 years 3 years 3 years length * p<0.05, ** p<0.01, *** p<0.001 Note 1: The regression table was divided into two sections due to space limitation. Note 2: Robust standard errors in brackets. Note 3: Hausman test rejects the random effects model.

144

Table 6.2 Part 2 – Dummy variables US Airways and American West merger regression: merger regression: TFP is the weighted average of the two airlines’ TFP pre-merger and US Airways’ TFP post-merger; Dependent: Ln (TFP) US Airways 1 US Airways 2 US Airways 3 US Airways 4 Q1 dummy -0.03* -0.04*** -0.04** -0.04** (0.01) (0.01) (0.02) (0.01) Q2 dummy 0.02 0.01 0 0.01 (0.01) (0.01) (0.02) (0.01) Q3 dummy 0.03* 0.03** 0.02 0.03* (0.01) (0.01) (0.02) (0.01) American dummy 0.28** 0.16 0.18 0.17* (0.09) (0.08) (0.15) (0.08) Continental dummy 0.1 -0.07 -0.18 -0.14 (0.08) (0.07) (0.10) (0.07) Delta dummy 0.37*** (0.06) Northwest dummy 0.08 (0.10) Southwest dummy 0.64*** 0.60*** 0.55** 0.72*** (0.15) (0.11) (0.16) (0.11) United dummy 0.38*** 0.26*** 0.24 0.24*** (0.09) (0.07) (0.13) (0.07) Time dummy 0.01 0.03 -0.01 0 (0.03) (0.02) (0.05) (0.02) Treatment *Time dummy 0.32*** 0.23*** 0.22** 0.25*** (0.07) (0.05) (0.08) (0.05) F-stats 220 266 265 283 Observations 111 145 80 120 R-sqr 0.98 0.98 0.99 0.98 Pre and post-merger 2 years 4 years 2 years 3 years period length Merger integration length 2 years 2 years 3 years 3 years * p<0.05, ** p<0.01, *** p<0.001 Note 1: The regression table was divided into two sections due to space limitation. Note 2: Robust standard errors in brackets. Note 3: Hausman test rejects the random effects model.

145

Table 6.3 Part 1 - (continued on the next page with dummy variables) Delta and Northwest merger regression: TFP is the weighted average of the two airlines’ TFP pre-merger and Delta’s TFP post-merger; Dependent: Ln (TFP) Delta 1 Delta 2 Ln (Employee Size) -0.65*** -1.00* (0.19) (0.36) Load factor 0.67* 1.14* (percentage) (0.28) (0.44) Ln(Stage length) 1.13*** 1.02* (0.29) (0.45) Aircraft age (years) (0.01) (0.02) (0.01) (0.02) Type (numbers) - - 0.00 (0.01) Ln (Aircraft quantity) 0.86*** 1.23** (0.20) (0.36) GDP (percentage) (0.30) (0.21) (0.31) (0.43) Ln (Avg. hhi metro) (0.03) (0.02) (0.35) (0.66) Ln (Avg. indmkt share) 0.03 (0.15) (0.29) (0.46) Ln(Network size) (0.05) (0.13) (0.07) (0.12) F-stats 101 59 Observations 76 46 R-sqr 0.97 0.98 Pre and post-merger 2 years 1.25 year period length Merger integration 1.25 year 2 years length

146

Table 6.3 Part 2 – Dummy variables Delta Air Lines regressions: dummy variables Dependent: Ln (TFP) Delta 1 Delta 2 Q1 dummy -0.04** (0.02) (0.01) (0.02) Q2 dummy 0.02 0.01 (0.01) (0.02) Q3 dummy 0.03 0.01 (0.02) (0.03) American dummy -0.31*** -0.27* (0.06) (0.11) Continental dummy -0.44*** -0.50* (0.12) (0.20) Southwest dummy 0.12 -0.18 (0.22) (0.37) United dummy -0.24** -0.19 (0.08) (0.15) Time dummy 0.01 0 (0.02) (0.05) Treatment *Time dummy 0.06 0.21 (0.11) (0.21) F-stats 101 59 Observations 76 46 R-sqr 0.97 0.98 Pre and post-merger 2 years 1.25 year period length Merger integration length 1.25 year 2 years

147

Figures: Figure 3.1 American’s descriptive parameters as it merges with Trans World: 1992-2011

The first line indicates the date of merger closure announcement which marks the start of merger The second line indicates the date of The third line indicates the integration. first joint reporting. date for merger completion.

148

Figure 3.2 U.S. Airways’ descriptive parameters as it merges with America West: 1992-2011

149

Figure 3.3 Delta’s descriptive parameters as it mergers with Northwest: 1992-2011

150

Figure 4.1 Partial productivity for American as it mergers with TWA: 1992-2011 The second line indicates the date of The first line indicates the date of merger first joint reporting. closure announcement which marks the start of merger integration.

The third line indicates the date for merger completion.

151

Figure 4.2 Partial productivity of US Airways as it mergers with American West: 1992-2011

152

Figure 4.3 Partial productivity for Delta as it merges with Northwest: 1992-2011

153

Figure 4.4 Evolution of TFP productivity for American, US Airways and Delta (normalized at American 1992=1) (The three vertical lines mark beginning of merger, first joint report and completion of merger, respectively)

154

Figure 4.5 Comparison of pre and post-merger TFP indices of American (as the acquirer), TWA (as the target airline) and their average (weighted by RPM)

155

Figure 4.6 Comparison of pre and post-merger TFP indices of US Airways (as the acquirer), America West (as the target airline) and their average (weighted by RPM)

156

Figure 4.7 Comparison of pre and post-merger TFP indices of US Airways (as the acquirer), America West (as the target airline) and their average (weighted by RPM)

157