Tian Luo, Vikrant Vaze

1

Impacts of Airline Mergers on Passenger Welfare

Tian Luo

Thayer School of Engineering at Dartmouth College

14 Engineering Drive, Hanover, NH 03755

Tel: 603-277-0804; Email: tian.luo.th@dartmouth.edu

Vikrant Vaze, Corresponding Author

Thayer School of Engineering at Dartmouth College

14 Engineering Drive, Hanover, NH 03755

Tel: 603-646-9147; Fax: 603-646-3856; Email: vikrant.s.vaze@dartmouth.edu

Word count: 7221 words text + 1 tables x 250 words = 7471 words

Submission Date: July 31, 2016

Tian Luo, Vikrant Vaze

2

ABSTRACT

Over the last decade, US domestic airline industry has undergone a series of consolidations. We

provide a comprehensive assessment of the overall effects of each of the five major recent mergers

on passenger welfare as evaluated through consumer surplus changes. We develop discrete choice

models with fare, nonstop and one-stop service frequency, travel time, and other carrier and route

attributes as parameters. The consumer surplus, as a function of these parameters, is calculated for

each market as the measure of passengers’ welfare. By using the markets not affected by the

mergers as a control group, we are able to separate out the welfare effects of mergers from those

of other extrinsic factors such as oil price changes, changes in economic conditions, etc. Several

new insights are obtained. We find that mergers of legacy network carriers with a significant

proportion of overlapping markets are generally accompanied by flight reallocation and network

reorganization, which in turn, contribute to an increase in passenger welfare. However, overall

passenger welfare for very small communities declined after the mergers. Also, overall passenger

welfare in markets with many competitors declined, consistent with the classic economic theory

of consolidation-induced welfare losses. We also find that the welfare gain from mergers of legacy

network carriers with significant proportion of overlapping markets progressively decreased as the

number of existing major domestic carriers decreased, and that after the most recent mergers, any

further potential mergers of legacy network carriers are likely to result in welfare losses.

Key words: Airline Merger, Consumer Welfare, One-stop Service Frequency, Multinomial Logit,

Consumer Surplus

Tian Luo, Vikrant Vaze

3

1. INTRODUCTION

Since its deregulation in 1978, U.S. airline industry has experienced two major waves of airline

consolidations. During the first wave of mergers and bankruptcies, starting right after deregulation

and lasting until the early 1990s, the number of major domestic carriers dropped from 23 to 8 (1).

The second wave of mergers started with the US Airways – America West Airlines merger in 2005

and ended with the most recent American Airlines – US Airways merger, during which the number

of mega-carriers (defined as carriers that carry at least 5% of all U.S. domestic passengers)

decreased from seven to four. The overall effects of airline consolidations on air travelers are of

considerable interest to researchers and policy makers alike. There is a large amount of literature

studying the impacts of mergers on passengers. However, most previous studies (2,3,4,5,6) have

almost exclusively focused on the fare changes caused by the mergers, and most have focused on

the impacts of only one or two of these mergers in any single study. Comparative analysis of

multiple mergers has not been performed before. Moreover, those previous studies which did focus

on service frequency changes due to the mergers have been limited to assessing the merger impact

on only the nonstop service frequency. In actuality, passengers also consider flights/routes with

one or more stops when making travel decisions. For example, approximately 30% of the domestic

U.S. passengers traveling within the 48 contiguous states in the year 2015 chose a one-stop

itinerary (7). In this study, for the first time, we analyze the impacts on overall passenger welfare

due to each of the five mergers of major carriers, starting with the US Airways – America West

Airlines merger in 2005 and ending with the American Airlines – US Airways merger in 2013.

We develop a discrete choice model with fare, service frequency (both nonstop and one-stop),

travel time, and other carrier and route attributes as parameters. The consumer surplus, also

incorporating fare, service frequency, travel time and other attributes, is calculated for each market

as a measure of passengers’ welfare. In order to evaluate the welfare impacts of these mergers, we

compare the difference between consumer surplus before and after the merger in markets affected

by the merger with the difference between consumer surplus before and after the merger in markets

not affected by the merger. In other words, we use the markets not affected by the merger as a

control group, and therefore can separate out the effects of mergers from those of changes in other

extrinsic factors such as oil price changes, changes in economic conditions, etc.

Our study makes three main contributions. 1) We provide a holistic assessment of the

overall passenger impact of the mergers by capturing not only fare changes but also changes in

service quality as measured by both nonstop and one-stop service frequency, travel times, and

other attributes of the routes and carriers. We demonstrate that in addition to the effects of fare

changes, the effects of frequency changes also play a very important role in determining the

welfare consequences. The welfare consequences in our study are calculated by incorporating

these multidimensional attributes. 2) Ours is the first study to define, calculate and use the changes

in one-stop service frequency as a part of passenger welfare changes. 3) Previous studies focused

on only one or two mergers at a time. However, we analyze the impact of all five major mergers

in the second wave of mergers and study general impacts that the mergers bring to the passengers,

by observing the similarities and differences in the effects of these five mergers.

The rest of this paper is organized as follows. Section 2 describes the data sources used in

our analyses and provide details of some key data pre-processing steps. Section 3 discusses the

passenger choice model used in this study and summarizes the passenger choice model estimation

results. Section 4 describes the calculation of consumer surplus as well as the difference-in-

Tian Luo, Vikrant Vaze

4

differences estimator used to measure the welfare change caused by the merger. Section 5 presents

a series of key findings of this research and Section 6 provides the conclusions and discussion.

2. DATA SOURCES AND PRE-PROCESSING

In this study we examined all five mergers of major carriers that happened between 2005 and 2015,

including US Airways (US)-America West Airlines (HP), Delta Airlines (DL)-Northwest Airlines

(NW), United Airlines (UA)-Continental Airlines (CO), Southwest Airlines (WN)-AirTran

Airways (FL) and American Airlines (AA)-US Airways (US). The pre-merger and post-merger

periods for each merger are selected as follow: US-HP: 2005 Q1-Q4 and 2008 Q1-Q4, DL-NW:

2008 Q1-Q4 and 2010 Q1-Q4, UA-CO: 2010 Q1-Q4 and 2012 Q1-Q4, WN-FL: 2012 Q1-Q2 and

2015 Q1-Q2, AA-US: 2013 Q3-Q4 and 2015 Q3-Q4. The selection of these periods is based on a

number of considerations. First, the post-merger period should begin after the merging of the FAA

operating certificate as reflected in the Airline On-Time Performance (AOTP) database (8) and

also in the Airline Origin Destination Survey (DB1B) database (7) on the Bureau of Transportation

Statistics (BTS) website. Second, the pre-merger period should be no later than the year when the

DOJ approved the merger. Third, the overlap of the pre- and post-merger periods across different

mergers should be as little as possible. Fourth, the gap between pre- and post-merger periods

should not be too long. Note that the case of the WN–FL merger is somewhat different than the

rest. Although they received an FAA single operating carrier certificate as early as in March 2012,

the merging carriers operated separately (ticketing systems had not merged and joint itineraries

were not sold) until the first quarter of 2015. This difference is reflected in our choice of pre- and

post-merger periods. Because of the overlap between the AA-US and WN-FL merger timelines,

we also performed a joint analysis of these two mergers. Through this joint analysis, we obtained

results that were very similar to those obtained from a separate analysis of each merger. Therefore,

for simplicity of presentation, we only present the results of the separate analyses in this paper.

2.1 Data Sources

Two databases from the Bureau of Transportation Statistics (BTS) website were used to acquire

all the data required for our analysis. The Airline Origin and Destination Survey (DB1B) database

provides a 10% sample of the U.S. domestic passenger tickets for each quarter of a year (with

flight date and time information removed) (9). The DB1B Market table in this database contains

the origin and destination of each directional market. This table provides information on number

of passengers, fares and ticketing carrier of each sampled ticket, but does not include scheduling

information beyond the year and quarter of a flight. By aggregating this data we get the total

quarterly number of passengers and average fares for each combination of carrier and route (where

a route is defined as a combination of origin airport, connection airport (if any), and destination

airport). More importantly, this dataset provides information on connection airport codes for all

one-stop passengers. The second database we depend on is the Airline On-Time Performance

(AOTP) database, which provides schedule and operational information about individual U.S.

domestic flights by major carriers (8). Reporting to this dataset is mandatory for carriers that have

at least 1% of the total U.S. domestic scheduled-service passenger revenues. AOTP is particularly

useful to us because, combined with the DB1B datasets, it allows us to calculate the nonstop and

one-stop service frequency and average total travel time. The process used for calculation of these

and other attributes is described next in Section 2.2.

Tian Luo, Vikrant Vaze

5

2.2 Data Pre-processing Before describing the data pre-processing steps, we first define two terms that we will use

repeatedly in this section and throughout the rest of the paper. A carrier-route is defined as a

combination of carrier, origin airport, connection airport (only for one-stop routes), and destination

airport, representing the flight path a passenger could select to travel from his/her origin to

destination on a specific carrier. An itinerary is defined as a sequence of connecting flight(s)

representing a one-way trip. According to our definitions, each valid itinerary is associated with

exactly one carrier-route but each carrier-route is typically associated with several valid itineraries

across that quarter. Note that our analysis ignores all one-stop itineraries that use two different

carriers that are not the mainline-regional partners of each other. However, the total passenger

share of such itineraries is very small, accounting for approximately 0.34% of all passengers.

Three major data pre-processing steps are performed sequentially. The first step is to match

the mainline carriers to their regional partners that help the mainline carriers in taking passengers

from smaller airports to their major hubs, and back. The flights of the regional carriers are

appended to the flight list of their respective mainline partners for all subsequent analyses. The

second step is to generate the (nonstop and one-stop) itineraries for each carrier-route for each

quarter. The third step is to calculate the values of the attributes for each carrier-route in each

market.

2.2.1 Mainline-Regional Carrier Match

From the aforementioned AOTP database, we generated lists of flights belonging to each carrier

for each quarter. Since the regional partners help in taking passengers to and from hub airports of

the mainline carriers, we need to include the regional carriers’ flights to avoid underestimation of

service frequency especially for those small communities which depend heavily on the regional

carrier service. In order to match the mainline carriers with their regional partners, we performed

the following two steps:

1. We used the DB1B market dataset to calculate the number of passengers each regional

(operating) carrier delivered for the mainline (ticketing) carrier, and we ranked the regional carriers

by the number of passengers that they delivered in each quarter.

2. The regional carriers that delivered at least the minimum of 10,000 (10% sampled)

passengers per quarter or 5% of the mainline carrier’s number of passengers in that quarter were

labeled as partner regional carriers. If the partner regional carrier reported to the AOTP database

that quarter, then their flights are added to the flight list of their mainline partner.

Note that the thresholds of 10,000 sampled passengers and 5% of the mainline carrier’s

passengers are both arbitrary. However, upon conducting several additional experiments while

varying these two thresholds we found that our main results remained unaltered. Since in our data,

on an average, the proportion of passengers on code-share flights accounted for less than 1% of

the total passengers flying with that ticketing carrier, we found that this does not have any

significant effect on our results. Therefore, we did not treat code-share agreement flights any

differently than other flights in our datasets.

2.2.2 Itinerary Generation

In this study we only consider the nonstop and one-stop carrier-routes, and those with more than

one stop are excluded from both the pre- and post-merger data for all mergers. This simplification

makes our analysis considerably easier to perform and understand, and is not expected to affect

the main results of our study significantly, since itineraries with more than one stop account for

Tian Luo, Vikrant Vaze

6

less than 3% of all passengers in the DB1B database for the time period considered in this analysis.

Generation of nonstop itineraries is straightforward, since each flight in AOTP is associated with

a single nonstop itinerary. A one-stop itinerary is generated for each pair of flights in the AOTP

satisfying the following rules:

1. The carrier-route associated with the flight pair should exist in the DB1B Market table,

and that carrier-route should have, on an average, at least 2 passengers per day, that is, 18 (10%

sampled) passengers each quarter.

2. The planned connection time (that is, the difference between the scheduled departure

time of the second flight and the scheduled arrival time of the first flight) should be no less than

30 minutes and no longer than 5 hours.

3. The date of the two flights (defined based on the planned departure times of the two legs

of the one-stop itinerary) should be the same (we do not consider the overnight connections) and

both flights should belong to the flight list of the same mainline carrier associated with the carrier-

route (thus flights from regional partner carriers are also included).

Our main results were found to be highly insensitive to small variations in these data pre-

processing assumptions.

2.2.3 Carrier-Route Attributes Calculation

Following the generation of itineraries, we calculated the average fare, total travel time and service

frequency at the carrier-route level. Average fare was calculated by aggregating the tickets prices

in the DB1B Market table. The average value of the total travel time for each carrier-route was

obtained by averaging the total travel time of all generated itineraries belonging to that carrier-

route. The total travel time of a nonstop itinerary is the scheduled elapsed time (that is, the

difference between the schedule arrival time and the scheduled departure time) of that flight. Total

travel time of a one-stop itinerary is defined as the sum of the scheduled elapsed time for both

flights in that itinerary plus the planned connection time (that is, the difference between the

scheduled departure time of the second flight and the scheduled arrival time of the first flight). For

a nonstop carrier-route, service frequency is the number of nonstop flights (itineraries) associated

with that carrier-route in each quarter. For a one-stop carrier-route, service frequency is defined as

the number of distinct first-leg flights from the origin airport to the connection airport such that

there exists at least one second-leg flight (from the connection airport to the destination airport)

operated by the same carrier or its regional partners, and the planned connection time is between

30 minutes and 5 hours.

3. PASSENGER CHOICE MODEL

In our model a market is defined as an ordered pair of airports in a specific quarter, such that, on

an average, at least 30 passengers travel from origin to destination of this market per day. In other

words, there should be at least 270 (10% sampled) DB1B-listed passengers traveling in that market

in that quarter. Market is directional in our model. For example, Boston to Los Angeles and Los

Angeles to Boston in the same quarter are treated as two different markets. Products are carrier-

routes that link the origin and destination of that market, and a valid carrier-route in a market

should have, on an average, at least 2 passengers per day, and should have, on an average, a service

frequency of at least 1 per week. In our model, we only consider carrier-routes with at most one

connection, which means that the carrier-routes are either nonstop or one-stop. The choice set of a

passenger in a market is the set of all products (valid carrier-routes) in that market.

Tian Luo, Vikrant Vaze

7

The indirect or random utility derived by a passenger from a product (carrier-route) j in market m

can be expressed as

Ujm = Vj

m + ϵjm

where Vjm represents the deterministic part of the utility, and the error term ϵj

m is the random or

disturbance component of the utility, which represents the unobserved preferences of passengers

for product j in market m . We assume that the ϵjm terms are independently and identically

distributed (i.i.d.) and follow the type-I extreme value (Gumbel) distribution. Each passenger in a

market selects the product corresponding to the largest random utility across all available products

in that market, which leads to the well-known multinomial logit formula for the probability that

product j is selected by a passenger in market m given by

Prm(j) = Pr (Ujm ≥ Uj′

m, ∀j′ ∈ Jm, j′ ≠ j) = exp(Vjm) ∑ exp (Vj′

m)

j′∈Jm

⁄

where Jm denotes the choice set, i.e., the set of all products in market 𝑚. The deterministic utility

of product j is given by

Vjm = β0Pj

m + β1 ln(Fjm) + β2 ln(Fj

m) ∗ Inon−stop,jm + β3Inon−stop,j

m + β4Tjm + ∑ βh

h∈H

Ih,jm

+ ∑ βaIa,jm

a∈A

Vjm depends on the following attributes.

Pjm: average fare of carrier-route j in market m, calculated as the weighted average (weighted by

the number of passengers) of all tickets prices corresponding to that carrier-route and that quarter.

Fjm: quarterly total service frequency of product j in market m. This variable denotes the total

service frequency of the carrier-routes aggregated quarterly.

Tjm: average value of the total travel time of product j in market m, calculated by taking the

average of the scheduled travel time of all itineraries associated with carrier-route j in market m.

Imnon−stop,j: binary dummy variable that identifies the non-stop carrier-routes. This variable is set

to 1 for non-stop carrier-routes and is 0 otherwise.

H : set of three hub-related identifiers, namely the hub_connection , hub_origin and

hub_destination. In other words, H = {hub_connection, hub_origin, hub_destination}.

Imh,j : binary dummy variables corresponding to subscript h , namely Im

hub−connection,j ,

Imhub−origin,j , and Im

hub−dest,j , which respectively identify whether the connection, origin or

destination airport is the main hub of the carrier. It is set to 1 in case of a hub airport and is 0

otherwise. For all the nonstop carrier-routes Imhub−connection,j = 0.

A: set of carriers. The merged carrier is considered to be different from either of the merging

carriers. For example, UA-CO is used to designate the post-merger carrier in case of the merger

between UA and CO.

Ima,j: binary dummy variable corresponding to carrier a. For example, for the carrier-route DL-

BOS-LAX, the dummy for Delta Airlines ImDL,j equals to 1 and all other Im

a,j dummies are set to

0.

We estimated a separate model for each merger. Table 1 summarizes coefficient estimates

in the multinomial logit model. All estimates were found to be statistically significant at 0.5% or

lower level, and their signs and relative magnitudes were found to be consistent with our intuition

(10).

Tian Luo, Vikrant Vaze

8

4. CONSUMER SURPLUS MODEL

After estimating the discrete choice model coefficients, we calculated the expected values of the

pre-merger and post-merger consumer surplus in each market as the measure of passenger welfare.

This approach enables us to aggregate the merger’s impacts on fare, service frequency, travel time

and other attributes into a single metric, thus making it possible for us to analyze the overall

passenger effects of each merger.

The expected value of the consumer surplus in a market can be written as (11,12):

𝔼(CSm) = 𝔼 [1

αmmax

j(Uj

m, ∀j ∈ Jm)] = 𝔼 [1

|β0|max

j(Uj

m, ∀j ∈ Jm)]

where CSm is the consumer surplus of a passenger in market m, αm is the marginal utility of

income in market m (equal to the absolute value of the coefficient of Pjm, |β0|, in our model), Uj

m

is the random utility of product j in market m, and Jm denotes the choice set in market m. The

expectation is taken over all possible values of ϵjm . Although the random utility Uj

m is not

observable, it is possible to calculate the expected consumer surplus using the observable utility

Vjm. Hanemann (13) and Small and Rosen (12) demonstrated that, if all ϵj

m are independently and

identically distributed (i.i.d.) and follow type-I extreme value distributions, and utility is linear in

income, then the expected consumer surplus can be expressed as

𝔼(CSm) =1

|β0|ln (∑ exp(Vj

m)

j∈Jm

) + C

where C is an unknown constant which represents the fact that the absolute value of utility cannot

be measured. This is the well-known logsum formula for expected consumer surplus under the

multinomial logit model assumptions. The change in consumer surplus for a specific market is

then calculated as the difference between the E(CSm) value after the merger and the E(CSm) value

before the merger:

Δ𝔼(CSm) =1

|β0|[ln ( ∑ exp (Vj

post,m)

j∈Jpost,m

) − ln ( ∑ exp (Vjpre,m

)

j∈Jpre,m

)]

where the superscripts pre and post refer to before and after the merger respectively. The constant

C is canceled out (14) since it appears in expressions both before and after the merger. Note that

this expression accounts for not only the change in product attributes, but also for any changes in

choice sets themselves. Thus, the effects of entry and exit of carriers in a market, new carrier-

routes added to a market and old ones removed from a market are captured in our model.

To analyze the impacts of mergers, Difference-In-Differences (DID) estimators are generated to

remove the temporal trends as well as effects of events other than the mergers. The Basic DID

framework can be expressed as the following simple components-of-variance process (15):

ym,t = ωm + dt + γ ∙ Dm,t + υm,t (1)

ym,t represents the outcome variables of interest (namely, ECS, fare, frequency) in market m and

during period t. t is a binary variable which equals 0 in the pre-merger period and 1 in the post-

merger period. Dm,t is a binary dummy variable which equals 1 only when market m is in the

treatment group and t = 1, and 0 otherwise. ωm and dt are market-specific and time-specific

components respectively. υm,t is a market-and-time-specific component with mean equal to zero

at each time period. The coefficient γ measures the impact of the merger (treatment).

Tian Luo, Vikrant Vaze

9

The only observable variables are ym,t and Dm,t. Equation (1) can be rewritten (16) for t = 0,1 as:

ym,t = μ + τ ∙ Dm,1 + δ ∙ t + γ ∙ Dm,t + ζm,t (2)

where

ζm,t = ωm − 𝔼(ωm|Dm,1) + υm,t

δ = d1 − d0

μ = 𝔼(ωm|Dm,1 = 0) + d0

τ = 𝔼(ωm|Dm,1 = 1) − 𝔼(ωm|Dm,1 = 0)

All of the four parameters in Equation (2), namely μ, τ, δ and γ, can be estimated by

Ordinary Least Square (OLS). Specifically, the estimate of parameter γ can be written (16):

γ̂ = (𝔼(ym,1|Dm,1 = 1) − 𝔼(ym,0|Dm,1 = 1)) − (𝔼(ym,1|Dm,1 = 0) − 𝔼(ym,0|Dm,1 = 0)), and

hence the term “difference-in-differences”. This is the mathematical expression for “differencing

out” the effects of temporal trends and events that are not results of the merger. We estimated the

treatment impact γ with an OLS estimator, with each observation (market m) associated with a

weight equal to the average (over the pre-merger and post-merger periods) number of passengers

in that market.

The results presented in the next section are based on the estimation of γ parameter. The

percentage changes in frequency are calculated by dividing by the corresponding pre-merger

frequency values. Calculation of the percentage change in frequency is consistent with the utility

expression in our passenger choice model which is linear in the logarithm of frequency.

5. RESULTS

In this section, we describe our major findings summarizing the effects of the mergers on the

passenger welfare. The average per-trip value of consumer surplus change for each market due to

each merger is calculated as per the methodology described in Section 5. To get the overall

weighted average and weighted sums across the markets, we first calculated the average (over the

pre- and post-merger periods) annual number of passengers in each market affected by the mergers

by simply multiplying the DB1B-reported 10% sampled number of passengers by 10. Then we

used these numbers of passengers as the weights to calculate the weighted averages and weighted

sums of consumer surplus changes. Following each merger the total consumer surplus change is

as follows: US-HP: +$0.10 Billion [+$0.50], DL-NW: + $9.76 Billion [+$42.80], UA-CO:

+$1.99Billion [+$10.40], WN-FL: -$0.12 Billion [-$1.30] (Q1-Q2 only) and AA-US: +$0.53

Billion [+$4.10] (Q3-Q4 only). The numbers listed in square brackets are those on a per-trip

average basis.

The five most significant findings of this research are summarized below as Key Findings

1 through 5.

Key Finding 1 – The passenger welfare increases after the mergers of the legacy network

carriers (DL-NW, UA-CO and AA-US), and remains almost unchanged when at least one of the

merging carriers is a low-cost carrier (US-HP, and WN-FL), owing primarily to the percentage

change in service frequency.

We categorized US, DL, NW, UA, CO and AA as legacy network carriers; and HP, WN

and FL as low-cost carriers. We defined the merger markets as the markets with operations of at

least one of the merging airlines. We find that following the DL-NW, UA-CO and AA-US mergers,

the average per-trip consumer surplus increased by $42.8, $10.4 and $4.1 respectively. In contrast,

consumer surplus remained almost unchanged after the US-HP and WN-FL mergers, with merely

Tian Luo, Vikrant Vaze

10

a $0.5 increase and $1.3 drop respectively. While seasonality does play a role in affecting these

exact values, the positive effects of the DL-NW, UA-CO and AA-US mergers and the mixed

effects of the US-HP and WN-FL mergers were found to be consistent across all quarters of the

data used in this analysis. The reasons for such changes become clear when we look at the

percentage change in service frequency. After the UA-CO merger, the total service frequency

(nonstop and one-stop combined) per market increased by 4.6% on average. Same service

frequency change (4.6%) was found after the DL-NW merger, while after the AA-US merger,

service frequency increased by 2.5%. In contrast, US-HP merger resulted in only a 1.2% service

frequency increase and the WN-FL merger was followed by a 2.5% decrease in service frequency.

Change in airfare also played an important role in determining the passenger welfare after these

mergers. Although the DL-NW and UA-CO mergers experienced the same service frequency

increase of 4.6% after merger, consumer surplus increased by $42.8 after the DL-NW merger,

which is approximately 4 times as large as that after the UA-CO merger. This can be explained by

the difference in fare changes after these two mergers, with an average $5.5 increase after the UA-

CO merger compared to an average $12.4 drop after the DL-NW merger.

The welfare consequences following the WN-FL merger of low-cost carriers are not surprising

given their point-to-point mode of operations, which made it relatively difficult for them to

increase service frequency through reorganization of their joint network. The almost unchanged

welfare following the US-HP merger likely stems from their highly segregated pre-merger

networks, with US largely operating in the eastern part of the United States, while HP operating

mostly in the western part. We calculated the percentage of overlapping markets (defined as the

number of markets in the treatment group with pre-merger operations of both merging airlines

divided by the total number of markets in the treatment group) for each merger. US-HP had only

8.6% overlapping markets, but DL-NW, UA-CO, WN-FL and AA-US had 35.2%, 42.2%, 22.7%

and 52.7%, respectively. This low overlap in their networks rendered the mechanism of service

frequency increases through network reorganizations (described in more details in the next Key

Finding) inapplicable for the US-HP merger, thus causing no welfare gains. We observe that the

same argument holds, to less extent, for the WN-FL merger as well.

Key Finding 2 – Hub-and-spoke carriers reorganized their joint networks, after a merger,

by reinforcing the hub airports of the primary carriers, with more nonstop flights diverted to these

hubs. Aside from an increase in service frequency, this network reorganization also resulted in

significant welfare gain for passengers using these hubs as origins or destinations.

We defined hub airports as the former designated hubs of each merging carrier before the merger.

The primary carriers (as defined by those with larger number of flights) in the US-HP, DL-NW,

UA-CO, WN-FL and AA-US mergers are HP, DL, UA, WN and AA, respectively. We find that

hubs of primary carriers were considerably strengthened after the mergers. Specifically, average

quarterly nonstop service frequency in markets with primary carrier’s hubs as origin and/or

destination increased by 40, 90, 51 and 66 after the US-HP, DL-NW, UA-CO and AA-US mergers

respectively. The corresponding numbers for secondary carrier’s hub markets were -74, 77, 46,

and -15 respectively. Previous research has shown that this increased concentration of traffic

to/from hubs of primary carriers potentially improves the efficiency of the joint network (17). We

found that it also led to an increase in service frequency.

Another related finding is that, primary carriers’ hubs benefited more than the secondary

carriers’ hubs. In the US-HP merger, passengers using HP hubs as origin and/or destination had a

consumer surplus increase by $5.3, whereas for US hubs it declined by $3.4. Similarly, UA-CO

merger resulted in a $33.1 increase in consumer surplus for passengers with origin and/or

Tian Luo, Vikrant Vaze

11

destination at UA hubs but a $25.8 decrease for CO hubs. Even though both DL and NW hubs

experienced welfare increases, for passengers to/from the primary carrier’s (DL) hubs gained

$52.7 whereas those to/from the secondary carrier’s (NW) hubs gained only $36.4. Similarly, for

the AA-US merger, the corresponding numbers were +$24 for AA hubs but only +$4.3 for US

hubs. Since WN does not have designated hubs, we did not include the WN-FL merger in this

particular analysis.

Key finding 3 – When comparing the mergers of hub-and-spoke carriers with significant

proportion of pre-merger overlapping markets, the consumer welfare gains decreased as the

number of existing major domestic carriers decreased.

There is a monotonic relationship between the welfare increases following the DL-NW,

UA-CO and AA-US mergers and the number of major domestic carriers just before the time of the

merger. From the merger of DL-NW, to that of UA-CO, to the most recent merger of AA-US, the

welfare gains declined from $42.8 to $10.4 to $4.1. At the same time, the number of major

domestic carriers (defined as mega carriers in Section 1) before each merger, decreased from 7 to

6 to 5. This positive relationship between the number of major domestic carriers and the welfare

gain due to the merger implies that passengers gained more from mergers of hub-and-spoke

airlines when there were more major domestic carriers. We note that this finding should be treated

with some caution because it relies only on 3 data points. However, it is consistent with previous

theoretical work (1), which suggests that as the number of carriers decreases, the welfare gains

decline, and finally reach an equilibrium where any further mergers would result in zero or even

negative welfare gains.

Key finding 4 – For very small communities, the DL-NW and UA-CO mergers resulted in

welfare losses to passengers.

Before defining very small communities, we first categorized the airports within the 48

contiguous (i.e., excluding Alaska and Hawaii) U.S. states into 4 classes: large, medium, small and

very small, as per the definitions of large hub, medium hub, small hub, and non-hub airports

respectively as defined by the Federal Aviation Administration (FAA) (18). As per the FAA, a

large hub is defined as an airport accounting for at least 1% of national annual passenger boardings.

We simply call them ‘large airports’ (to avoid confusion over the term “hub”). Airports

contributing between 0.25% and 1% of the national annual passenger boardings are categorized as

medium hubs (‘medium airports’ in our terminology), while those accounting for between 0.05%

and 0.25% are denoted as small hubs (‘small airports’ in our terminology). Finally, airports

accounting for less than 0.05% of the national annual passenger boardings are defined as non-hubs

(‘very small airports’ in our terminology). Using the above criteria, 14 of the total 141 airports in

our study are categorized as very small airports, and the number of small, medium and large

airports is 56, 40 and 31, respectively. For our analysis, markets with origin and/or destination at

a very small airport are denoted as markets serving very small communities.

We observed that the merging airlines in only the DL-NW, UA-CO and AA-US mergers operated

in any markets serving very small communities and the number of markets serving very small

communities in our AA-US merger data is too small (18 total markets including 6 in the control

group) to draw any reliable conclusions. In comparison, for the UA-CO merger, there were 94

total markets serving very small communities including 70 in the control group, and for the DL-

NW merger there were 140 total markets serving very small communities including 38 in the

control group. Therefore, our analyses of merger impacts on very small communities were limited

to the DL-NW and UA-CO mergers. In the markets serving the very small communities, the DL-

NW and UA-CO mergers lead to per-trip welfare losses of $69.2 and $27.0, respectively.

Tian Luo, Vikrant Vaze

12

Interestingly, very different welfare consequences were observed in markets serving slightly larger

communities. We define as markets serving small communities those markets which have origin

and/or destination at a small airport. All three mergers between two legacy network carriers,

namely DL-NW, UA-CO and AA-US, were followed by a per-trip welfare gain of $70, $18 and

$39.3, respectively, in the markets serving small communities in contrast to per-trip welfare losses

of $35.2 and $29.7 after the US-HP and WN-FL mergers, respectively. Welfare change in markets

serving large communities (defined as those markets where the origin and/or destination is a large

airport) is similar to the overall impacts after each merger. Per-trip welfare in markets serving large

communities increased by $43.6, $9.9 and $3.6 respectively after the DL-NW, UA-CO and AA-

US mergers, and remained almost unchanged following US-HP (increased by $0.8) and WN-FL

(decreased by $1.7) mergers.

These results for the small and very small communities are mostly driven by the fare

changes in those respective markets. The welfare losses in markets serving very-small

communities were accompanied by fare increases following both the DL-NW and UA-CO mergers.

In markets serving small communities, for four out of the five mergers, the welfare consequences

were mostly driven by fares changes, with fare increases causing welfare losses and fare decreases

causing welfare gains. The only exception to this was the welfare gain despite the fare increase

following the AA-US merger. This welfare gain was driven by a significant service frequency

increase (22.1%), which was at least twice as big as that for any other of the five mergers.

Key finding 5 – Passengers from the markets with low or moderate market concentration

experienced welfare losses after all five of the mergers.

We used the Herfindahl–Hirschman Index (HHI) as the measure of market concentration

for each market, following the conventional definition of HHI as the square of sums of the market

shares of all carriers in that market (19). We define market share as the number of passengers of

the carrier in the market divided by the total number of passengers in that market. The markets

were then characterized as high concentration (with post-merger HHI of at least 0.18) or

moderate/low concentration (with post-merger HHI smaller than 0.18), which includes markets

with both low and moderate concentrations as per the definition by the Department of Justice (19).

The threshold of 0.18 is chosen for consistency with the definition of Department of Justice and to

have slightly more balanced number of markets in the two categories created due to the threshold.

After the mergers, the welfare consequences for high concentration markets were very

similar to the corresponding trend in overall welfare changes, with increases of $2, $44.2, $12.5,

$0.1 and $4.5 for US-HP, DL-NW, UA-CO, WN-FL and AA-US mergers, respectively. However,

for the markets with low and moderate concentrations, the five mergers resulted in welfare losses

of $44, $61.4, $74.1, $35.7 and $9.1, respectively.

From the results, we see that for highly concentrated markets the changes in welfare, fares

and service frequency are very similar to the general changes for all markets as analyzed previously

in this section. All three of them, namely, passenger welfare, average fares and average service

frequencies, almost always go up following a merger. This similarity is not a coincidence, since

approximately 95% of all passengers travel in markets with high concentration. This implies that

the impacts of, as well as the possible mechanisms behind the mergers discussed in this section so

far are essentially for the highly concentrated markets, and passengers on these markets account

for the majority of the total traveling population.

Also noticeable is that for the low and moderate concentration markets, the consequences

for welfare, fares as well as service frequency are very different. Following the mergers,

passengers in these markets experienced fare increases (in all cases), service frequency decreases

Tian Luo, Vikrant Vaze

13

(in all but one cases), and consequently welfare losses in all cases. The low and moderate

concentration markets are very dense markets being served by a large number of competitors. Their

characteristics, as compared to those of the high concentration markets, are much closer to the

classic definition of perfectly competitive markets. It appears that in these moderate and low

concentration markets with a lot of competitors, the reduction in competition due to the mergers

does have significant negative effects on prices and service quality as expected by classic economic

theories. Moreover, many carriers in these major markets generally offer non-stop services, the

frequency of which may not be significantly increased through network reorganizations, unlike

one-stop frequency increases. Combining these two factors, these markets experience a significant

welfare loss following all five mergers.

6. CONCLUSIONS

In this study, we examined the welfare consequences of mergers of major airlines by incorporating

effects of fare, service frequency, travel time as well as other relevant attributes of the carriers and

routes. Previous research on impacts of mergers focused primarily on the fare effects while

ignoring other attributes such as service frequency. We hypothesized that service frequency (for

both nonstop and one-stop carrier-routes) plays a very important role in determining the welfare

of passengers. Consistent with our hypothesis, we find that service frequency, along with fare,

travel time and other attributes, determines the change in consumer welfare following a merger. In

general, the merging carriers that operate in a hub-and-spoke mode, are likely to reorganize their

joint network following the merger. The network reorganization results in the strengthening of the

hub airports of the primary carriers, with more flights channeled through them. For merging

airlines with significant percentage (more than 30%) of overlapping markets, the network

reorganization (diversion of more flights to the primary merging carrier’ hubs) leads to a

significant increase in service frequency, especially the one-stop service frequency, for the merged

carrier. As a result, the effects of increasing service frequency outweigh those brought about by

increased fares, leading to net welfare gains for passengers. Compared to the theoretical work of

Brueckner and Spiller (20,21), which concluded that consumers may benefit from the efficiency

gains and cost reductions achieved by the merging carriers, we stress that the post-merger

reorganization of hub-and-spoke networks was found to especially benefit the passengers through

increased service frequency.

As for the mergers between legacy network carriers, aside from the attributes of the

merging airlines such as fare and service frequency, the amount of welfare gain also depends on

the existing number of major domestic carriers. From the DL-NW to UA-CO to AA-US merger,

the welfare gains shrank along with the decrease in the number of major domestic U.S. carriers.

The welfare increase due to the most recent AA-US merger was merely $4.1, only about 10% of

that of the DL-NW merger. This seems to indicate that the number of major domestic U.S. carriers

after the most recent merger is close to the equilibrium number and further consolidation may not

lead to welfare increases or may even result in welfare losses.

The mergers of US-HP and WN-FL, where at least one merging carrier was a low-cost carrier, had

a negligible effect on passenger welfare. One possible reason is that the networks of US and HP

before the merger were segregated with a very small overlap, with HP network mostly in the west

and US network mostly in the east, sharing only 8.6% overlapping markets before merger. This

segregation of networks likely renders the mechanism of channeling more traffic through main

hubs inapplicable for US-HP merger (and to a lesser extent for the WN-FL merger with 22.7%

Tian Luo, Vikrant Vaze

14

overlapping markets); while for the other three mergers, it is this mechanism that significantly

increased the service frequency, therefore contributing to an increase is passenger welfare.

Finally, we note that most aviation markets are highly concentrated in that they are

dominated by a few carriers. Mergers in such markets typically yield service frequency increases

that more than compensate for any fare increases and result in net welfare gains. However, in the

few markets that are fiercely competed for by multiple major carriers using non-stop service, a

situation sharing some characteristics with the perfect competition setting, mergers led to fare

increases and service frequency reductions, resulting in welfare losses.

Tian Luo, Vikrant Vaze

15

REFERENCES

1. Bailey, E., Liu, D., Airline consolidation and consumer welfare. Eastern Economic

Journal, Vol. 21, No. 4, 1995, pp. 463-476.

2. Borenstein, S., Airline mergers, airport dominance, and market power. American

Economic Review, Vol. 80, No. 2, 1990, pp. 400-404.

3. Kim, E., Singal, V., Mergers and market power: evidence from the airline industry.

American Economic Review, Vol. 83, No.3, 1993, pp. 549-569.

4. Morrison, S., Airline mergers: A longer view. Journal of Transport Economics and

Policy, Vol. 30, 1996, pp. 237-250.

5. Veldhius, J., Impacts of the Air France-KLM merger for airlines, airports and air

transport users. Journal of Air Transport Management, Vol. 11, 2005, pp. 9-18.

6. Peters, C., Evaluating the performance of merger simulations: evidence from the U.S.

airline industry. The Journal of Law and Economics, Vol. 49, 2006, pp. 627-649.

7. BTS, RITA | BTS | Transtats., 2016.

http://www.transtats.bts.gov/DL_SelectFields.asp?Table_ID=247 Accessed June 28,

2016.

8. BTS, RITA | BTS | Transtats., 2016.

http://www.transtats.bts.gov/DatabaseInfo.asp?DB_ID=125 Accessed June 28, 2016.

9. BTS, RITA | BTS | Transtats., 2016.

http://www.transtats.bts.gov/DL_SelectFields.asp?Table_ID=236 Accessed June 28,

2016.

10. Luo, T., Impacts of airline mergers on passenger welfare. M.S. Thesis, Thayer School of

Engineering, Dartmouth College, 2016

11. McFadden, D., Modelling the choice of residential location. In Karlqvist, A., Lundqvist,

L., Snickars, F. and Weibull, J. (eds) Spatial Interaction Theory and Residential

Location. North-Holland, Amsterdam. 1978,

12. Small, K., Rosen, H., Applied welfare economics with discrete choice models.

Econometrica, Vol. 49, No. 1, 1981, pp. 105-130.

13. Hanemann, W., A methodological and empirical study of the recreation benefits from

water quality improvement. Ph.D. Dissertation, Department of Economics, Harvard

University, 1978,

14. Train, K. Discrete Choice Methods with Simulation, Cambridge, New York, 2003.

15. Ashenfelter, O., Card, D., Using the longitudinal structure of earnings to estimate the

effect of training programs. The Review of Economics and Statistics, Vol. 67, 1985, pp.

648-660.

16. Abadie, A., Semiparametric difference-in-differences estimators. Review of Economic

Studies Rev Econ Studies, Vol. 72, 2005, pp. 1-19.

17. Bilotkach, V., Fageda, X., Flores-Fillol, R., Airline consolidation and the distribution of

traffic between primary and secondary hubs. Regional Science and Urban Economics,

Vol. 43, No. 6, 2013, pp. 951-963.

18. FAA, Airport Categories., 2016.

http://www.faa.gov/airports/planning_capacity/passenger_allcargo_stats/categories/

Accessed June 26, 2016.

19. DOJ, 1.5 Concentration And Market Shares., 2016

https://www.justice.gov/atr/15-concentration-and-market-shares Accessed July 02, 2016.

Tian Luo, Vikrant Vaze

16

20. Brueckner, J., Spiller, P., Competition and mergers in airline networks. International

Journal of Industrial Organization, Vol. 9, No. 3, 1991, pp. 323-342.

21. Brueckner, J., Spiller, P., 1994. Economies of traffic density in the deregulated airline

industry. Journal of Law and Economics, Vol. 37, No. 2, 1994, pp. 379-415.

LIST OF TABLES

TABLE 1 Multinomial Logit Estimation Results Coefficients\Mergers US-HP DL-NW UA-CO WN-FL AA-US

FARE -0.0028594 -0.0019749 -0.0019146 -0.0025055

FARE_Q2 -0.0034523 -0.0021000 -0.0023171 -0.0028105

FARE_Q3 -0.0040167 -0.0029841 -0.0030622 -0.0041397

FARE_Q4 -0.0032757 -0.0027521 -0.0028321 -0.0032000

LOG_FREQ 0.6494200 0.5975540 0.5435646 0.5229924 0.5630661

LOG_FREQ_NONSTOP 0.3728665 0.3858614 0.4095097 0.3837923 0.3565542

NONSTOP 0.3062835 0.2468040 0.0840086 0.1871312 0.3386226

TRAVEL_TIME -0.3529548 -0.3449595 -0.3522767 -0.3637937 -0.3478442

HUB_CONNECTION 0.2435818 0.3332108 0.4329411 0.5058625 0.3865461

HUB_ORIGIN 0.0335122 0.0539540 0.0704936 0.1343206 0.1287028

HUB_DESTINATION 0.0518227 0.0633010 0.0758849 0.1383652 0.1264823

DL -0.2262614 -0.2389686 -0.2511143 -0.2274947 -0.2876646

NW -0.1311213 -0.1242487

WN 0.1511855 0.2009348 0.1247180 0.1123365 -0.0475741

AA -0.2719276 -0.2943930 -0.4252073 -0.4609919 -0.4867530

US -0.2137411 -0.0479177 -0.1661698 -0.3186210 -0.3066448

HP -0.0335062

UA -0.2260532 -0.3036222 -0.4536810 -0.3575683 -0.3667093

CO -0.0978422 -0.0839999 -0.1919434

FL -0.0778299 -0.0664204 -0.2127837 -0.3367957 -0.6118024

B6 0.1132982 0.1473113 0.0439044 0.0224074 -0.0950362

AS 0.2498063 0.3932927 0.2497390 0.1801938 0.1054130

VX 0.0745454 -0.0185114

US+HP -0.0261209

DL+NW -0.2099403

UA+CO -0.3598630

WN+FL 0.1271550

AA+US -0.3449208