Airline Network-Structure Change and Consumer Welfare

Airline Network-Structure Change

and Consumer Welfare

Dan Luo ∗

Abstract

This paper examines the effect of a change in an airline’s network structure on

consumer welfare. Specifically, it studies the de-hubbing of the Cincinnati airport

immediately following the Delta-Northwest merger and its impact on consumer welfare

in markets with Cincinnati as one endpoint and markets with Delta connections at

Cincinnati pre-merger. Empirical analysis finds that there is an increase in consumer

welfare for both types of markets. Lower post-merger fares due to Delta’s pricing

strategy and higher frequencies contribute to the consumer welfare gain for these two

types of markets, respectively.

Keywords: Airline merger; Hub-and-spoke network; De-hubbing; Consumer welfare;

1 Introduction

The airline industry has gone through a major wave of mergers in recent years, including

the Delta-Northwest merger, the AirTran-Southwest merger, the United-Continental merger

and the recently announced US Airways-American merger. Among the mergers that include

∗Address: Department of Economics, 3151 Social Science Plaza, University of California, Irvine, CA

92697-5100. E-mail: [email protected].

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two legacy carriers,1 merger partners both operate their own hub-and-spoke networks, with

each airline operating multiple hubs. In these networks, travelers change planes at connecting

airports (usually hubs) to reach their destinations. The adoption of a hub-and-spoke network

enables a legacy carrier to exploit economies of scope and economies of density. After the

merger, the newly formed airline will reorganize its hub-and-spoke network by adjusting

traffic in and out of different hubs to cut down redundancy and lower cost, which will affect

a large number of passengers whose trips involve certain hubs, resulting in a change in

consumer welfare.

Small hubs usually fear being de-hubbed as a result of a merger or other reorganization,

since losing hub status would greatly reduce the number of flights in and out of the airport,

resulting in abandoned terminals and airport facilities, job losses and profit reductions.

De-hubbing also hurts frequent flyers and the local business community, since convenient

direct flights to many cities are no longer provided. In the past decade, St. Louis has suffered

from de-hubbing after American Airlines bought TransWorld Airlines out of bankruptcy.

Pittsburgh also faced the same fate when US Airways made immense flight cuts there.

Prior to the Delta-Northwest airline merger, Cincinnati was the only Delta hub in the

Midwest, and it was the second largest Delta hub in terms of the number of flights per

quarter. However, after the merger, because of its proximity to Minneapolis, Memphis and

Detroit (Northwest’s hubs), Delta found it optimal to downsize Cincinnati. According to the

Economic Impact Report published by the Economics Center at the University of Cincinnati

and the Center for Economic Analysis and Development at Northern Kentucky University,

“Cincinnati (CVG) activity in 2010 contributed to more than 23,000 total jobs within the

metro area; $1 billion in total labor income; and $3.4 billion in total output in the Cincinnati

MSA. CVG also generated $92 million in tax revenue for Kentucky and Ohio.” Even though

capturing the business-climate change from de-hubbing is beyond the scope of this paper,

considering that de-hubbing Cincinnati might have great impact on consumers, this paper

looks into the effect of such a change in Delta’s network structure on consumer welfare.

1Depending on the operating cost structure, airlines can be categorized into low-cost carriers (LCCs) and

legacy carriers. Compared to LCCs that emphasize point-to-point service, legacy carriers are more oriented

toward hub-and-spoke network structures.

2

Retrospective analyses of mergers disproportionately focus on assessing their price effects.

Such merger analyses include Borenstein (1990), Werden et al. (1991), Kim and Singal

(1993), Morrison (1996), Kwoka and Shumilkina (2010), and Luo (2013). Few papers study

the non-price impacts of airline mergers, without which assessment of the overall effect

of an airline merger is incomplete. Bilotkach et al. (2012) analyzes the effects of airline

consolidation on the distribution of traffic between primary and secondary hubs and finds

that there is an increased number of flights being channeled through the primary hub after

consolidation.

Few papers have analyzed the effect of the change of airline’s hub status on capacity and

fares. Redondi, Malighetti, and Paleari (2012) find that de-hubbing results in a significant

and permanent decrease in the number of scheduled flights and seats offered. Tan (2013)

finds that average airfares increase after a legacy carrier de-hubs an airport, which ultimately

leads to softer competition between airlines at the airport. However, neither paper studies

the consumer welfare change. This paper is the first to analyze the effect of de-hubbing on

consumer welfare.

Specifically, the paper uses a random-coefficients discrete-choice model to analyze the

hub-and-spoke network-structure changes resulting from the Delta-Northwest airline merger

and its impact on consumer welfare, where consumer welfare is mainly measured in terms

of fare, frequency and travel distance. Recent development in methods of estimating

random-coefficients discrete-choice models follows Berry, Levinsohn and Pakes (1995), who

use market-level price and quantity data to estimate demand. Other applications include

Nevo (2000a, 2000b), who studies the ready-to-eat cereal industry, and Petrin (2002), who

uses a BLP-type model to estimate the consumer welfare change from the introduction of

the minivan. There are also several discrete choice applications in the airline literature,

including Peters (2006), Berry, Carnall and Spiller (2006), Armantier and Richard (2008),

and Berry and Jia (2010).

This paper focuses on welfare changes for passengers in two types of markets, where the

change is measured by the compensating variation. The first type of market includes all

the nonstop and one-stop flights with Cincinnati as one endpoint. The paper finds that,

even though Cincinnati lost direct flight service to many cities after de-hubbing, a large

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reduction in post-merger fares resulted in an increase in consumer welfare, where the total

compensating variation is $10.8 million after adjustment for the recession and seasonality.

The reduction in fares appears to be mainly due to Delta’s pricing strategy to compete with

nearby airports for local passengers. This finding is somewhat surprising because welfare is

expected to be lower after de-hubbing for passengers originating or terminating at Cincinnati.

Instead, these consumers are better off due to lower post-merger fares.

The second type of market includes all the airport-pairs where Delta provided connecting

service via Cincinnati pre-merger. Consumer welfare also increased for these markets,

with a total compensating variation of $12.9 billion after adjustment for the recession and

seasonality, which is mainly a result of an increase in frequency in the post-merger period.

Considering that the post-merger period was a recession year, and that pre-merger

and post-merger periods belong to different quarters, post-merger fares and frequencies are

adjusted for recession and seasonality. Admittedly, there might be other confounding factors

contributing to the welfare change between the periods. However, considering the short time

frame that is adopted in this study, it is likely that other factors do not change much, with

the ones that do change (recession and seasonality) being controlled for. Thus, the current

methodology is reasonable in measuring the welfare effect of de-hubbing.

The remainder of the paper is organized as follows. Section 2 provides background on

the Delta-Northwest merger and discusses the de-hubbing of Cincinnati. Section 3 presents

the model and illustrates the estimation process. Section 4 describes the data and section 5

presents the empirical specification. Section 6 presents the result, including the demand

parameter estimates, the welfare calculation, and recession and seasonality adjustment.

Section 7 discusses de-hubbing and employment, and section 8 concludes.

2 Merger and De-hubbing

2.1 Merger Background

On April 15, 2008, Delta Airlines and Northwest Airlines announced a merger agreement,

which formed the largest airline in the world at that time. The merger was approved by

4

Delta and Northwest’s shareholders on September 26, 2008 and approved by the Department

of Justice (DOJ) on October 29, 2008. It took more than one year for Northwest Airlines to

be integrated with Delta Airlines, and on December 31, 2009, the operating certificates of

the two airlines were merged. On January 31, 2010, the reservation systems were merged,

which officially completed the merger.

2.2 De-hubbing

Figure 1 shows that prior to the merger, Delta had four hubs: John F. Kennedy (JFK),

Cincinnati (CVG), Atlanta (ATL) and Salt Lake City (SLC). Cincinnati was the only Delta

hub in the Midwest. However, Cincinnati is in close proximity to three hubs previously used

by Northwest: Minneapolis (MSP), Detroit (DTW) and Memphis (MEM) and Delta found

it optimal to downsize Cincinnati.2

Figure 2 presents the number of quarterly DL and NW flights on routes where an endpoint

is a hub from the first quarter of 2006 to the first quarter of 2012, showing flights on merged

carrier following the merger. While the number of flights in and out of other hubs remained

mostly constant, the number of flights in and out of Cincinnati has been decreasing rapidly.

Figure 3 shows the total number of DL and NW passengers making connections via their

hubs. Aside from the seasonal effect, where the traffic volume peaks in the summer and

drops in the winter, the number of passengers connecting via Cincinnati trends downward

while the number of passengers connecting via Atlanta trends upward. Figure 4 focuses on

markets with DL connections at Cincinnati in the first quarter of 2006 and shows that the

percentage of passengers making connections via Cincinnati has been decreasing, while this

percentage has been increasing for Atlanta.

To evaluate the immediate post-merger effect of de-hubbing on consumer welfare, this

paper chooses the first quarter of 2008 (one quarter before the announcement of the

merger) and the second quarter of 2010 (one quarter after the completion of the merger)

as the pre-merger and post-merger periods.3 To understand what actually happened after

2In June, 2013, Delta announced that it would also eliminate Memphis as one of its hubs by September,

2013.

3Ideally, the first quarter of 2008 and the first quarter of 2010 should be used to avoid the seasonal effect.

5

de-hubbing, additional descriptive data analysis is carried out.

Prior to the merger, Delta provided nonstop service in 109 markets with CVG as an

endpoint. De-hubbing resulted in the loss of nonstop service in 39 of these markets. Table 1

displays the traffic split for markets where DL connecting service via CVG was observed in

the pre-merger period. This split shows the share of passengers in these markets connecting

at other hubs, along with the share connecting at CVG. In the first quarter of 2008, 49.2% of

DL connecting flights in these markets were channeled through Atlanta while 17% channeled

through Cincinnati. In the second quarter of 2010, the percentage of passengers connecting

via Cincinnati decreased to 6.2%. Instead of connecting via Cincinnati, passengers were

redirected to make connections via Detroit, Memphis and Minneapolis.

Data analysis also suggests that among 2292 markets with observed DL connections via

CVG pre-merger, only 1228 markets (54%) were still connected via CVG post-merger. 1886

markets were connected via Detroit, 981 via Minneapolis and 2046 via Atlanta. Among

1064 markets (46%) that were no longer connected via CVG, passengers in 770 markets

were channeled through Detroit and passengers in 341 markets went through Minneapolis.

In addition, there were 123 markets with feasible but unused CVG connections in the

post-merger period, meaning that Delta still operated direct flights on two CVG spokes

connecting the endpoints but did not provide the option of connecting via CVG.4

Overall, Delta has been decreasing the number of flights in and out of CVG and the

number of passengers making connections via CVG. Among all the markets where DL

connections at CVG were available pre-merger, only 54% of these markets still had CVG

connecting service post-merger, with the rest of the markets connecting mainly at Detroit

and Minneapolis.

However, the merger was not finalized until January 31, 2010. Thus, the second quarter of 2010 is used as

the post-merger period and the seasonality adjustment is carried out to exclude a possible seasonal effect

(discussed later in detail).

4Since DB1B is a 10% sample of all airline tickets, it is possible that some actual connections that were

used were missed in the DB1B sampling process if such markets are very thin. Therefore, the actual number

of markets of this type might be slightly smaller than 123.

6

3 Model

3.1 Demand Model

The demand model is a random-coefficients discrete-choice model in the spirit of Berry,

Levinsohn and Pakes (BLP, 1995). US airlines offer a set of differentiated products in

each “origin-and-destination” market, where products are differentiated by price, frequency,

carrier, travel distance, connecting airport, and so forth. Consumers are assumed to purchase

one product that gives them the highest utility.

Suppose we observe m = 1, ...,M markets, with consumers in the market designated

i = 1, ..., Im, and products designated j = 1, ..., Jm. A market is defined as a non-directional

“origin-destination” pair. A product is defined as a route with specific origin, connecting,

destination airports operated by a particular carrier. There are two periods, with t = 1, 2

representing the pre-merger and post-merger periods, respectively. For product j in market

m in period t, the utility of consumer i is given by

uijmt = λic+ xjmtβ + αipjmt + ξjmt + εijmt, (1)

where

• c is constant.

• xjmt is the observed characteristics of product j in market m in period t.5

• pjmt is the price of product j in market m in period t.

• ξjmt is the unobserved characteristics of product j in market m in period t, which are

unmeasured by researchers.

• εijmt is a mean-zero error term, assumed to be i.i.d. and to follow a type-I extreme

value distribution.

5Boldfaced symbols represent vectors and matrices throughout the paper.

7

This paper assumes that price coefficient αi and the constant coefficient λi are randomly

distributed across consumers, so that αi and λi can be rewritten as

αi = α + σpriceνpricei , νpricei ∼ LN(0, 1)

λi = λ+ σconstantνconstanti , νconstanti ∼ N(0, 1). (2)

α, β and λ measure the common effect of price, observable characteristics and constant on all

consumers, and σprice and σconstant are the coefficients of the stochastic parts of the price and

constant, which capture the consumers’ heterogenous tastes. νi include random draws for the

stochastic parts of the price and constant, where νpricei ∼ LN(0, 1) and νconstanti ∼ N(0, 1).

Let θ = (θ1,θ2) be a vector containing all the parameters of the model. The vector

θ1 =

α

β

λ

captures consumers’ common tastes for price, product characteristics and

constant, and the vector θ2 = σ =

σprice

σconstant

captures consumers’ idiosyncratic tastes

for price and the idiosyncratic constant. Combining (1) and (2), utility can be rewritten as

uijmt = δjmt + µijmt + εijmt, (3)

where

δjmt = λc+ xjmtβ + αpjmt + ξjmt, (4)

µijmt = σpricepjmtνpricei + σconstantcνconstanti . (5)

δjmt is the mean utility, which is common to all consumers. µijmt + εijmt is a heteroskedastic

deviation from the mean utility.

To complete the specification of the demand system, the model includes an outside

product option (the consumers may decide to use other travel modes), which yields a utility

that is normalized to zero.

Consumers are assumed to purchase one unit of the product that gives the highest utility.

Following Nevo (2001), by assuming that εijmt follows i.i.d. type-I extreme value distribution,

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the probability that a consumer i chooses alternative j in market m in period t is

P (uijmt > uikmt, k 6= j) =exp(δjmt + µijmt)

1 + Σq∈Jmexp(δqmt + µiqmt),

where q represents a particular product.

Assuming that the distributions of ν and ε are independent, the predicted market share

is the integral of the logit probability over the distribution of ν. The predicted market share

of product j in market m in period t is then

sjmt =

∫exp(δjmt + µijmt)

1 + Σq∈Jmexp(δqmt + µiqmt)dPν(ν), (6)

where Pν(ν) denotes population distribution function and ν is used to calculate µ (see

equation (5)).

For the random-coefficients model, the integral in (6) has to be computed by simulation.

For each draw of ν, a predicted market share is calculated. The simulated market share is

then the average of these predicted market shares over the number of draws. Specifically,

the simulated market shares are given by

sjmt =1

n

n∑i=1

sijmt

=1

n

n∑i=1

exp(δjmt + µijmt)

1 + Σq∈Jmexp(δqmt + µiqmt)(7)

where n is the number of draws for each observation.

3.2 Elasticities

The price elasticities of the market shares sjmt are calculated as the percentage change

in the market share of product j in market m in period t divided by the percentage change

in the price of product j (own-price elasticity) or product k (cross-price elasticity) in market

m in period t. Specifically, The price elasticities of the market shares sjmt are

ηjkmt =∂sjmt

∂pkmt

pkmt

sjmt

=

pjmt

sjmt

∫αisijmt(1− sijmt)dPν(ν), if j = k,

−pkmt

sjmt

∫αisijmtsikmtdPν(ν), otherwise.

The first equation gives the own-price elasticities and the second equation gives the

cross-price elasticities.

9

As explained in Nevo (2000b), compared to the simple logit model, the

random-coefficients models allow for more flexible substitution patterns, which can generate

elasticities that are no longer constrained by the property of independence from irrelevant

alternatives (IIA).

3.3 Welfare Calculation

The compensating variation is used to measure the change in consumer’s welfare after

Delta’s de-hubbing of the Cincinnati airport. The compensating variation refers to the

amount of money a consumer would need to give up following a change in prices or product

quality in order to reach his pre-change utility level. The compensating variation for

consumer i in market m may be written as (see Small and Rosen, 1981)

CVim = − 1

αi

[ln(

Jpostm∑q=0

eVpostiqm )− ln(

Jprem∑q=0

eVpreiqm)], (8)

where the superscripts pre and post refer to the pre-merger and post-merger periods

respectively. −αi is the marginal utility of income, which is equal to the negative of the

price coefficient. V is the observed portion of the utility (δ + µ), q represents product and

Jm represents the Jmth product in market m.

The compensating variation for market m is the market size (measured as the geometric

mean of the origin and destination city population) times the integral of the compensating

variation over the distribution of ν, and it is given by

CVm = Nm

∫CV imdPν(ν),

where Nm is the market size.

For the random-coefficients model, the compensation variation is calculated by

simulation. For each draw of ν, the compensating variation for market m is calculated.

The simulated compensating variation is then the average of these compensating variations

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over the number of draws. Specifically,

CVm = Nm1

n

n∑i=1

CVim

= Nm1

n

n∑i=1

{− 1

αi

[ln(

Jpostm∑q=0

eVpostiqm )− ln(

Jprem∑q=0

eVpreiqm)]} (9)

= Nm1

n

n∑i=1

{− 1

αi

[ln(1 +∑

q∈Jpostm

exp(δpostqm + µpostiqm ))− ln(1 +

∑q∈Jpre

m

exp(δpreqm + µpreiqm))]}

where αi = α + σpriceνpricei . The total compensating variation is then the sum of the

compensating variations across markets.

3.4 Estimation

Following BLP (1995), General Methods of Moments (GMM) is used to estimate the

demand parameters, where the moments are expectations of the structural error term ξjmt

interacted with instruments. Estimation details are provided in Appendix A.

In this paper, price and frequency are treated as endogenous and instrumented. Typical

instruments for price seek to exploit rival product attributes and the competitiveness of

the market environment. Variables that affect cost and markup but not demand are also

potential candidates. Following Berry and Jia (2010) and Gayle (2012), the instruments used

in this paper include: average distance for competing products, the squared deviation of a

product’s itinerary distance from the average distance of competing products,6 the number

of competing products, the total number of carriers within each market,7 and a dummy

variable for transferring at the hub (costs are lower if the flight connects at a hub).

Following Berry and Jia (2010), the instrument for frequency is the fitted value of

6This instrument measures the substitutability between an airline’s products and its rival products. The

smaller the deviation of a product’s distance from the average distance of competing products, the more

competitive the environment, and the smaller is the markup for the product.

7As usual, in the discrete-choice demand literature, the set of available products and the number of

carriers across markets is assumed to be exogenous in the short run. That is, even though the number of

carriers can change from pre-merger to post-merger period, this number is assumed to be fixed within each

period.

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frequencies, which is obtained from the regression of frequencies on the following exogenous

variables: carrier dummies, distance, tourist dummy, dummy for slot-controlled airports,

geometric mean of endpoint populations, hub dummy and the number of competitors. 8

4 Data

The main data source is the Department of Transportation’s Airline Origin and

Destination Survey (DB1B), which is a 10% quarterly sample of all airline tickets. The

DB1B includes information on fares, origin, connecting and destination airports, carrier,

distance, and number of passengers. Data on flight frequency is from the US Department of

Transportation’s (DOT) T-100 service-segment database, which contains domestic non-stop

segment data reported by U.S. carriers. Demographic information including the MSA

population comes from the U.S. Census Bureau. To evaluate the change in consumer welfare,

the first quarter of 2008 and the second quarter of 2010 are chosen as the pre-merger and

post-merger periods, as explained above.

4.1 Sample Selection

A market is defined as a non-directional “origin-destination” pair. Thus, LAX-JFK (Los

Angeles - John F. Kennedy) and JFK-LAX are counted as one market. As noted earlier, the

market size is the geometric mean of the MSA population of the endpoint cities. A product

is defined at the origin-connect-destination-carrier level. For example, LAX-CLT-JFK-US is

a product operated by US Airways that departs from Los Angeles, connects via Charlotte

and arrives at John F. Kennedy.

Several filters are employed in the data work. Only US travel is included. Both roundtrip

and one-way itineraries are used, but itineraries can have no more than two coupons in either

direction. Using data from the Official Airline Guide, direct flights that include a stop with

no change in flight number are recoded as connecting flights. Itineraries with segments that

8The overidentification test indicates that the instruments are valid in both types of markets. J-statistics

are 0.0097 and 0.0015 for type 1 and type 2 markets, respectively, which are less than the 0.95 quantile of

the chi-squared distribution (degree of freedom = 4).

12

are operated by a foreign carrier are removed. Itineraries with multiple marketing carriers

are excluded. The lower bound for the airfares is $25, and for roundtrip itineraries, the fares

are divided by two.

Two distinctive types of markets are analyzed. As shown in figure 5, the first type of

market includes all the nonstop and one-stop flights that have Cincinnati as one endpoint.

Possible products in type 1 market include nonstop flights operated by DL and other carriers,

and one-stop flights operated by DL and other carriers with CVG as an endpoint. The

welfare analysis targeting these markets seeks to find the change in consumer welfare of local

passengers and travelers whose destination is Cincinnati.

The second type of market includes all the airport-pairs with DL connecting service via

Cincinnati in the pre-merger period. As seen in figure 5, competing products within these

markets include nonstop flights operated by Delta, connecting flights operated by Delta that

connect at airports other than Cincinnati, and nonstop or connecting flights operated by

other carriers. After the merger, Delta redirected passengers to make connections mainly at

Detroit and Minneapolis, resulting in a change in consumer welfare due to changes in fares,

frequencies and distances.

A market size restriction is applied to the second type of market. Nonstop markets require

at least 10 passengers per day each way (PPDEW) across all carriers, which equals 1,800

passenger per quarter. Any carriers with less than 180 passengers per quarter (one tenth

of the minimum total) are excluded. Considering that connecting markets are thinner, the

restriction for these markets is relaxed to at least 5 PPDEW, which equals 900 passengers

per quarter. Accordingly, a carrier-size cutoff of 90 passengers per quarter is applied in

connecting markets. The market-size restriction is not applied to the first type of market

because Delta is more likely to discontinue nonstop service in very small markets, which might

be missed via a restriction. Excluding these markets through a market-size restriction might

underestimate the welfare loss for passengers whose trips have Cincinnati as one endpoint.

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5 Empirical Model

5.1 Model Specification

Demand is assumed to be affected by the following product attributes: fare, frequency,

distance, distance squared, a tourist dummy for airports in Florida and Las Vegas, a dummy

for slot-controlled airports,9 the geometric mean of the number of destinations a carrier offers

from origin and destination airports,10 a dummy variable for a nonstop route, and carrier

dummies.

Detailed ticket information, including the time of purchase, length-of-stay requirement,

the departure and arrival time are not observed in the data. Thus, unobservable product

characteristics are allowed to be correlated with price and frequency. In this paper, price

and frequency are treated as endogenous and instrumented.

Fares are calculated at the origin-connect-destination-carrier level. That is, for each

product within each market, the mean airfare is calculated as total revenue for the product

divided by its passenger count. Frequencies for nonstop routes are the total number of

quarterly departures performed for each carrier in a particular market. Since flight frequency

is a segment characteristic, frequency for a connecting route is set to equal the minimum of

the total number of departures on the two route segments, recognizing that overall frequencies

of the connecting routes are restricted by the segment with lower frequencies. As suggested by

Hansen (1990), minimum frequency is more critical to connecting service because “flight on

the minimum frequency link would be coordinated with a flight on the maximum frequency

link to achieve minimum layover time.”

In markets with DL service connecting at Cincinnati pre-merger, carrier dummies include

American (AA), Alaska (AS), Continental (CO), Delta (DL), Northwest (NW), United (UA),

9A slot-control dummy variable equals 1 if either the origin or the destination is slot-controlled, meaning

that the number of take off and landing per hour is limited at the airports. Slot-controlled airports in 08Q1 are

LGA (LaGuardia), JFK(John F.Kennedy), EWR(Newark), DCA(Washington National) and ORD(Chicago

O’Hare). ORD was no longer slot-controlled in 10Q2.

10The number of destinations is the total number of cities to which a carrier operates direct flights from

the airport. This variable captures the hub status of the endpoints.

14

US Airways (US), Southwest (WN), and a dummy variable for all other carriers (JetBlue

(B6), Frontier (F9), AirTran (FL), Allegiant Air (G4), Spirit (NK), Sun Country (SY), ATA

(TZ), USA 3000 (U5) and Midwest (YX)). In markets with CVG as an endpoint, carrier

dummies include American (AA), Continental (CO), Delta (DL), Northwest (NW), United

(UA) and US Airways (US).

5.2 Data Summary

Table 2 and 4 report summary statistics for two types of markets. The top panel displays

the means and standard deviations of the variables used in the estimation, and the bottom

panel shows the market average summary statistics.

There are several noticeable changes between the first quarter of 2008 and the second

quarter of 2010. According to table 2, the average fares in markets with CVG as an endpoint

decreased from $315 to $269.7, a reduction of 14 percent. One reason for lower post-merger

fares is the recession. However, even after adjusted for the recession (discussed later), the

average fare is still lower post-merger. A possible explanation for cheaper fares is Delta’s

own pricing strategy during the de-hubbing process. In February, 2009, Delta announced

that it had cut fares between 5% and 60% on both business and leisure tickets for flights with

CVG as an endpoint. The reason behind this move is that Delta closely watched customer

buying habits and found an increase in the number choosing to fly out of alternate airports

in close proximity.11 (Airports in Louisville, Indianapolis and Columbus are all less than a

two-hour drive away from CVG.)

In order to keep CVG running even during the process of de-hubbing, Delta apparently

decided to attract more local passengers using lower fares. Table 3 shows the intertemporal

fare pattern by listing across quarters average nonstop DL fares with CVG as an endpoint.

Lower fares began in the first quarter of 2009 and lasted for about two years, with the fares

bouncing back by the end of 2011.

Table 2 shows that the average frequency on routes with a CVG endpoint decreased from

11Source: The wall street journal blogs,“Delta Cuts Fares at High-Priced Cincinnati: Why Now?” February

6, 2009.

15

609.1 to 584.5, a reduction of 4 percent that might be a result of de-hubbing. Distance

increased from 1218.7 to 1238.2 miles since several nonstop routes were replaced with

connecting counterparts after de-hubbing, a pattern confirmed by a decrease of the nonstop

dummy from 0.112 to 0.069. As a result of de-hubbing, the geometric mean of the number

of destination cities served by direct flights out of the endpoints of the markets decreased

from 19.1 to 17.3. The presence of AA decreased in these markets and the presence of CO,

DL, UA and US increased. Both the average number of products and the number of carriers

declined slightly, which is the result of both the merger and de-hubbing.

In markets with DL service connecting at CVG pre-merger, Table 4 shows that the

average frequency increased from 607.5 to 613.5 and the average distance increased from

1627.9 to 1646.5 miles. The increase in distance is resulted from passengers being channeled

mainly through Detroit, Memphis and Minneapolis instead of Cincinnati (Table 1). In

these markets, there is a decrease in the presence of AA, UA and US, and an increase in

the presence of CO, DL and WN. The average number of products increased slightly and

the average number of carriers decreased from 6.1 to 5.5, a result of the loss of NW as a

competitor.

The estimation is carried out by combining the pre and post-merger data, where the

same origin-destination pairs in different quarters are treated as different markets.12

6 Results

6.1 Demand Parameters

As discussed in Section 5.1, demand is affected by fares, frequencies, distance, distance

squared, a dummy for nonstop route, a tourist dummy, a dummy for slot-controlled airports,

geometric mean of the number of destinations, and carrier dummies.

Consumer utility is expected to decrease in fares and increase in frequencies. Air-travel

12Markets in the post-merger period belong to a subset of markets that existed pre-merger. The decrease

in the number of markets post-merger might be due to the market-size restriction and the limitation of the

number of stops (products are restricted to nonstop and one-stop only).

16

demand is usually non-linear in distance, with demand initially growing with distance

because passengers are more likely to fly than drive or take a train as distance increases. As

distance increases further, travel becomes less pleasant and demand starts to decrease. As

a result, both distance and distance squared are included to capture the expected curvature

of demand.

Consumer utility should increase with a nonstop flight, and the tourist dummy variable

helps to fit the relatively high traffic volumes to tourist destinations. The dummy variable

for slot-controlled airports is expected to exert downward pressure on utility since congestion

is worse at these airports. The geometric mean of the number of destination cities at the

market endpoints (the total number of cities to which a carrier serves with direct flights from

the airports) captures the value of frequent flyer programs. The more destinations a carrier

provides, the better service consumers living in endpoint cities get. Thus consumer utility

should increase with the number of destinations a carrier offers.

Table 5 presents the demand parameter estimates for markets with CVG as an endpoint.

The first two columns are based on the simple logit specification for the utility function,

where the dependent variable is ln(sjmt)− ln(s0mt). The first column shows the OLS results,

and the second column shows the results from 2SLS, using the instruments discussed in

section 5.3.

For the OLS regression, coefficients of fare, frequency, nonstop and slot− control are

of the expected sign, meaning that utility increases with frequency and the nonstop dummy

and decreases with the fare and the slot-control dummy. The simple logit specification gives

an R2 of 0.4489, which implies that 55 percent of the variance in mean utility levels is due to

the unobserved characteristics. The second column presents the results using 2SLS, where

the coefficient on fare increases in absolute value from 0.191 to 1.504. The interpretation is

that products with higher unmeasured quality components sell at higher prices. Thus OLS

estimate is biased upward toward zero. The coefficient on frequency increases from 0.129

to 0.470. Unmeasured product characteristics such as flight delays and cancellations are

associated with higher frequency, which lower consumer utility, yielding a downward-biased

OLS estimate. The coefficients of tourist, distance2 and #destinations have the wrong

signs.

17

The third column of Table 5 shows the estimates from the full random-coefficients model.

For each market, 3000 individuals are drawn (n = 3000 in equation (7)) using Halton

sequences. All the demand parameters have the expected signs. Consumer utility decreases

in fare and increases with frequency, distance, nonstop flights and the number of destinations.

Utility decreases in distance2 since longer flights are less pleasant. The coefficients of the

tourist and slot-control dummy variables are of the expected signs but insignificant, and so

are the coefficients for carrier dummies.

The bottom panel of Table 5 presents the estimates of σ, which capture consumers’

heterogenous tastes. The coefficient of the stochastic part of price is statistically insignificant,

suggesting that there is not much heterogeneity in the price coefficient. The explanation

might be that, in reality, there is little consumer heterogeneity with respect to price, but

this information is unknown a priori without proper estimation using the random-coefficient

model.

Table 6 presents the demand parameter estimates for markets that have Delta service

connecting at Cincinnati pre-merger. For most of the coefficients, the OLS and IV estimates

are of the expected signs, except for #destinations. To estimate the full random-coefficients

model, 1500 individuals are drawn (n = 1500) for each market using Halton sequences. The

coefficients derived from the mixed logit model are all statistically significant and are of the

expected signs. Among all the carrier dummies, the other carriers (the omitted group) and

Alaska have the highest coefficients. Similar to Table 5, the coefficient of the stochastic part

of price is statistically insignificant, suggesting that there is little consumer heterogeneity

with respect to price.

6.2 Elasticities

Table 7 and 8 present the means of own-price elasticities of all the carriers for both types

of markets. The first column shows the own-price elasticities in the pre-merger period, and

the second column shows values for the post-merger period. The own-price elasticities are

higher than the ones in the previous literature. Berry and Jia (2010) found the aggregate

price elasticities ranging from -1.58 to -2.01 in 2006. Peters (2006) found mean own-price

elacticities ranging from -3.2 to -4.0 depending on the type of discrete choice demand model

18

used (nested logit or generalized extreme value (GEV)). Gayle (2012) found a mean own-price

elasticity of -4.72. This paper finds mean own-price elasticities of -5.98 and -5.09 for type 1

markets in the pre-merger periods, respectively, and -4.60 and -4.57 for type 2 markets in the

post-merger periods, respectively. One possible reason for larger own-price elasticities is that,

compared to most other airline studies that use big markets connecting large metropolitan

areas, the selected markets used in this study include small markets with Cincinnati as one

endpoint. Own-price elasticities might be higher for smaller markets.

For most carriers, the own-price elasticities decreased in the post-merger period. In

markets with CVG as an endpoint, the mean own-price elasticity across carriers decreased

by 14.9 percent, while in markets with DL service connecting at CVG pre-merger, the

mean own-price elasticity across carriers decreased by 0.7 percent. Such a decrease in

own-price elasticities can be explained by a decrease in competition resulting from the loss

of a competitor after the merger.

6.3 Welfare calculation

6.3.1 Markets with CVG as an Endpoint

Prior to the merger, Delta provided nonstop service in 109 markets with CVG as an

endpoint. De-hubbing resulted in the loss of nonstop service in 39 markets, which was

replaced by connecting service, resulting in loss of consumer welfare in terms of travel distance

and convenience. To appraise fare changes, note that after de-hubbing, Delta decreased

the number of flights out of Cincinnati, resulting in a reduction of traffic densities on the

remaining spoke routes (measured by the total number of quarterly passengers on a spoke

with CVG as an endpoint). The data show that average spoke traffic density decreased from

35,347 to 24,356 passengers per quarter. As a result, higher nonstop fares might be expected

because of reduced economies of density (see Brueckner and Spiller (1994)). However, Table

3 suggests that average DL nonstop fares with CVG as a endpoint decreased after the first

quarter of 2009 due to Delta’s pricing strategy (explained in section 5.2), yielding overall

post-merger fares that were lower despite the reduced traffic density.

As a point of reference, Table 9 presents the summary statistics for markets with CVG

19

as an endpoint for products that existed in both pre-merger and post-merger periods. The

average fare decreased from $305.9 to $264.1. The average frequency decreased from 667.8

to 641.3. Since the overall consumer welfare change depends on the combination of fares,

frequencies, distances and other factors, the consumer welfare change for these markets

would be positive if the effect of fare reductions outweighs other factors. Table 10 presents

the summary statistics for products that only existed post-merger (new products) and the

ones that only existed pre-merger (old products). Even though the new products have

lower fares and higher frequencies on average, new products have longer distances, fewer

direct destinations and fewer nonstop routes because some nonstop routes are replaced with

inferior connecting routes.

The compensating variation is used to measure the change in consumer welfare from

de-hubbing. The compensating variation is the amount of money a consumer would need

to give up following a change in prices or product quality in order to reach his pre-change

utility level. Table 11 presents the total compensating variation for markets with CVG as an

endpoint. According to equation (9), the total compensating variation is calculated as the

sum of the compensating variation in each market multiplied by the market size (geometric

mean of the origin and destination population). The total compensating variation is $14.8

million, meaning that consumers need to be compensated $14.8 million to reach the same

utility before and after de-hubbing. The average compensating variation is calculated as

total compensating variation divided by the sum of market sizes for all markets, and it

equals $0.05 for each consumer. This result suggests that consumers are better off after

de-hubbing, although by a very small amount.

To analyze the sources of this consumer welfare change, the total compensating variation

is decomposed into three parts, with the results presented in Table 12. The first part

captures the compensating variation for products that existed in both pre-merger and

post-merger periods. The second and third parts capture the compensating variation for

products that only existed post-merger (new products) and the ones that only existed

pre-merger (old products). The consumer welfare loss from disappearance of the old products

(-$22.69 million) outweighs the consumer welfare gain from the new products ($17.52 million)

Recalling Table 10, this suggests that the consumer welfare loss from longer distance, fewer

20

direct destinations and fewer nonstop routes is greater than the welfare gain from lower

fares and higher frequencies associated with the new products. The overall compensating

variation is positive because of the big consumer welfare gain from the products that existed

in both periods ($10.61 million), and this gain is mainly due to lower fares post-merger.13

To further analyze the positive compensating variation for products that existed in both

periods, counterfactual analysis is carried out for the following five scenarios:

1. Allow fares to change, keep everything else constant in pre and post-merger periods.

2. Allow frequencies to change, keep everything else constant.

3. Allow the number of destinations to change, keep everything else constant.

4. Allow carrier dummies to change, keep everything else constant.

5. Allow slot-control dummy to change, keep everything else constant.

Table 13 shows the results for the counterfactual analysis. Allowing fares to change

while keeping everything else constant, the total compensating variation is positive ($30.90

million) due to the decrease in average post-merger fares. Allowing frequencies to change

while keeping everything else constant, the total compensating variation is negative (-$10.49

million) , a result of decrease in average post-merger frequencies. The change in the number of

destinations results in a negative compensating variation (-$1.19 million) because the number

of cities to which Delta operates direct flights from CVG decreased after de-hubbing. The

change in slot-control dummy results in a positive compensating variation ($0.99 million),

which can be explained by the fact that one fewer airport (ORD) is slot-controlled after the

merger. The change in carrier dummies results in a positive compensating variation ($1.66

million), which indicates that consumers enjoy Delta more than Northwest in markets with

CVG as an endpoint in the post-merger period.

Overall, consumer welfare increased for markets with CVG as an endpoint despite the fact

that Delta discontinued nonstop service in many markets after de-hubbing. The main driving

13Note that the compensating variations for these three parts (products existed both pre and post-merger,

new products and old products) do not sum up to the total compensating variation. Assuming that the

observed portion of the utility function for products in these three parts are denoted by V1, V2 and V3,

respectively. When calculating the compensating variation according to equation (8), ln(eV1 + eV2 + eV3) 6=

ln(eV1) + ln(eV2) + ln(eV3).

21

force behind this welfare gain is the lower fares in the post-merger period, which reflect

Delta’s pricing strategy to attract local passengers in competition with nearby airports.

6.3.2 Markets with DL Service Connecting at CVG Pre-merger

In markets with Delta service connecting at CVG pre-merger, the predicted change in

consumer welfare is ambiguous. On the one hand, compared to connecting via CVG, the

distance for connecting via Detroit and Minneapolis in the post-merger period might be

longer. On the other hand, the change in fares, frequencies and other factors might offset

the welfare loss due to longer distances.

Table 14 presents the summary statistics for products in this group that existed in both

periods. The average fare increased from $231.7 to $233.1. The average frequency decreased

slightly. The slot-control dummy decreased from 0.139 to 0.107 since one fewer airport

(ORD) is slot-controlled after the merger. The number of destinations increased from 10.7

to 11.6. Table 15 presents the summary statistics for old and new products. As seen in

Table 15, compared to the old products, new products have lower fares, higher frequencies

and longer distances, rendering consumer welfare change ambiguous.

Table 16 shows that the total compensating variation for markets with DL service

connecting at CVG pre-merger is $21.38 billion, which corresponds to $5.0067 for each

consumer, indicating a consumer welfare gain post-merger. As is done for markets with CVG

as an endpoint, the total compensating variation is decomposed into three parts, with the

results listed in Table 17. The consumer welfare gain from the new products ($41.49 billion)

outweighs the consumer welfare loss from the disappearance of the old products (-$35.92

billion) . Recalling Table 15, the consumer welfare gain from lower fares, higher frequencies,

fewer slot-controlled airports and the higher number of destinations associated with the

new products is greater than the welfare loss from longer travel distances and fewer nonstop

routes associated with the old products. Note that the new products include flights channeled

through Detroit, Minneapolis and Memphis, which have higher frequencies than old products

with flights channeled through Cincinnati. The reason for higher frequencies might be

that the merged airline has more efficient operations over its hub-and-spoke network. The

compensating variation for products that existed in both periods is positive and equal to

22

$7.67 billion . To see the reason behind this positive compensating variation, counterfactual

analysis is carried out for products that existed in both periods.

Table 18 shows the results of the counterfactual analysis. Allowing fares to change while

keeping everything else constant, the total compensating variation is negative (-$0.46 billion)

due to an increase in average post-merger fares (Table 14). Allowing frequencies to change

while keeping everything else constant, the total compensating variation is positive ($6.35

billion) despite the fact that the average post-merger frequency decreased slightly (Table

14). The reason might be that on average, the markets with a consumer welfare gain due

to increased frequencies (average market size = 1,830,426) are larger than markets with a

consumer welfare loss due to decreased frequencies (average market size = 1,767,818). So

the overall compensating variation is positive even though the average frequency decreased.

The change in the number of destinations results in a negative compensating variation

(-$0.31 billion) even though the average number of destinations increased somewhat (Table

14). Again, an explanation is that, on average, markets with a consumer welfare loss due

to a decreased number of destinations (average market size = 1,877,781) are larger than

markets with a consumer welfare gain due to an increased number of destinations (average

market size = 1,764,195). So the overall compensating variation is negative even though

the average number of destinations increased. The change in the slot-control dummy results

in a positive compensating variation ($0.92 billion) due to one fewer slot-controlled airport

(ORD) after the merger. The change in carrier dummies results in a negative compensating

variation (-$0.38 billion) , which indicates that consumers liked Northwest more than Delta

in markets with DL service connecting at CVG pre-merger.

Overall, consumer welfare increased in markets with DL service connecting at CVG

pre-merger. Despite the fact that travel distance is longer after the merger, higher frequencies

contribute to the overall consumer welfare gain.

6.3.3 Recession and Seasonality Adjustment

Considering that the post-merger period was a recession year, the change in consumer

welfare might not only be the result of the merger, but also the result of the recession. To

isolate the effect of the merger from the recession, post-merger fares and frequencies are

23

adjusted for the recession. Specifically, this adjustment is done by multiplying post-merger

fares and frequencies by a particular ratio. The ratio equals the average fare (frequency)

of all the routes across carriers (excluding products operated by Delta and Northwest) in

the pre-merger period divided by the average fare (frequency) in the post-merger period.

Considering that the ratios for fares and frequencies are similar for similar products, products

are grouped into different categories according to flight distance and the number of stops,

and a ratio is calculated for each group. Table 19 shows the ratio for each product category.

Since the pre-merger and post-merger periods belong to different quarters, fares and

frequencies of the second quarter of 2010 are adjusted for a possible seasonal effect. The

seasonality adjustment is done in the same way as the recession adjustment. Specifically, the

adjustment ratio is calculated as the average fare (frequency) of all the routes across carriers

in the first quarter of 2010 divided by the average fare (frequency) in the second quarter of

2010. Table 20 shows the adjustment ratio for the seasonal effect.

Table 21 shows the summary statistics for products that existed in both periods for type 1

markets with the recession adjustment, seasonality adjustment and both. The third column

shows that after adjustment for the recession, the average post-merger fare increased from

$264.1 to $268. The average frequency increased from 641.3 to 655.2. The fourth column

shows the average post-merger fare and frequency adjusted for the seasonal effect. The

average fare adjusted for seasonality decreased slightly and the average frequency decreased

from 641.3 to 613.4, which can be explained by the fact that traffic volume is the lowest

in the winter, leading to lower frequencies. As a result, the seasonality adjusted frequency

in the second quarter of 2010 is lower than the one without adjustment. The fifth column

presents the statistics adjusted for both recession and seasonality. Compared to the second

column, the average fare increased slightly and average frequency decreased from 641.3 to

626.9. Note that with or without adjustment, the post-merger fares and frequencies are

lower than the ones in the pre-merger period.

Table 22 shows the summary statistics for type 2 markets with adjustment. The average

fare did not change much after different adjustments. The average frequency increased

from 654.1 to 670.3 after adjusted for the recession, but decreased from 654.1 to 628.9 after

adjustment for seasonality. The average frequency adjusted for both effects is 644.8. Even

24

though the post-merger frequency adjusted for both effects is lower than before the merger

for products that existed in both periods, the post-merger frequency adjusted for both effects

for new products (493.553) is still higher than pre-merger frequency (462.479).

Table 23 and 24 present the total and average compensating variation with and without

different adjustments for type 1 and type 2 markets, respectively. Compared to the first

column (without any adjustment), the total compensating variation for both types of markets

increased after adjustment for the recession. The explanation might be that the gain from

increase in frequencies outweighs the loss from higher fares, resulting in an even bigger

consumer welfare gain. The third columns of both tables show that, compared to the

first column, seasonality adjustment yields lower compensating variation for both types of

markets, which can be explained by lower post-merger frequencies after adjusting for the

seasonal effect. Column 4 presents the compensating variation adjusted for both recession

and seasonality. The compensating variation for markets with CVG as an endpoint decreases

from $14.8 million without adjustment to $10.8 million with adjustment. In markets with

DL service connecting at CVG pre-merger, the compensating variation decreases to $12.89

billion from $21.38 billion without adjustment.

To sum up, after adjustment for the recession and seasonal effect, the compensating

variations are smaller, but still positive and similar in magnitude to the unadjusted values.

7 De-hubbing and Employment

So far, this paper only captures the de-hubbing effect on consumer welfare immediately

after the merger. De-hubbing also affected the overall business climate in Cincinnati. A

reduction in airline service quality may inhibit local business growth as existing firms relocate

to other cities with more direct flight options and new businesses find the city unattractive

to enter. In the long run, de-hubbing will not only result in the loss of airport-related jobs,

but also service-related employment that requires frequent business travel.

Brueckner (2003) quantifies the relationship between airline traffic and employment

and finds that a 10 percent increase in passenger enplanements (the number of passengers

boarding aircraft) in a metro area leads to approximately a 1 percent increase in employment

25

in service-related industries.

To carry out such a job-loss calculation for Cincinnati, the 10-to-1 effect from above

is combined with the decrease in passenger enplanements caused by the flight cutbacks

at Cincinnati. Data show that total passenger enplanements at CVG decreased from

3,193,255 in the first quarter of 2008 to 1,940,665 in the second quarter of 2010, a reduction

of 39 percent. Since a 10 percent enplanement cut leads to a 1 percent job loss, a 39

percent enplanement cut leads to a 3.9 percent reduction in service-related jobs. With total

service-related jobs in Cincinnati equaling to 908,640 in 2008,14 a 3.9 percent reduction

amounts to 35,437 jobs. Note that this job-impact would be expected to occur gradually.

The national employment trend will also have an effect. But, in the long run, Cincinnati’s

service-related job total is predicted to be 35,000 lower than if its airport not been de-hubbed.

8 Conclusion

This paper uses a random-coefficients discrete-choice model to analyze the welfare impacts

of the hub-and-spoke network-structure changes resulting from the Delta-Northwest airline

merger, in particular the de-hubbing of the Cincinnati airport. Consumer welfare mainly

depends on fares, frequencies and travel distances, all of which changed for passengers using

CVG. The paper studies two types of markets, with the first type including markets with

Cincinnati as an endpoint and the second type including markets with Delta connecting

service via Cincinnati pre-merger.

The findings suggest that the consumer welfare gain is $10.8 million (after recession and

seasonality adjustment) for markets with CVG as an endpoint despite the fact that Delta

discontinued nonstop service on many spoke routes after de-hubbing. The main driving force

behind this welfare gain is lower post-merger fares, which reflected Delta’s price strategy to

compete with nearby airports for local passengers. In markets with DL service connecting at

CVG pre-merger, the consumer welfare gain is a much larger $12.89 billion (after recession

and seasonality adjustment). Even though travel distances are longer after the merger since

passengers are connected mainly via Detroit and Minneapolis instead of Cincinnati, higher

14Source: Bureau of Labor Statistics, Occupational Employment Statistics (OES) survey.

26

frequencies contribute to the overall consumer welfare gain.

Note that this study only focuses on the de-hubbing effect on consumer welfare

immediately after the merger. In the long run, lower fares on CVG-endpoint routes bounced

back and consumer welfare could have declined. In addition, the business-climate change

from de-hubbing is not captured in this paper. An estimate of the number of job loss is

calculated, using the methodology of Brueckner (2003), which predicts that in the long run,

the metro area’s service-related job total will be 35,000 lower than if Cincinnati had not

been de-hubbed.

Overall, the short-run effect of de-hubbing is not so detrimental for Cincinnati as

described in the media. In fact, consumers were better off immediately after the

Delta-Northwest merger because of lower fares, although the consumer welfare gains mostly

go to passengers who make connections at Cincinnati. However, de-hubbing does not only

affect passengers, but also the whole local business climate. In addition, the long-run effect

of de-hubbing could differ from the short-run effect, and specific hubs being de-hubbed are

different from one another in terms of geographic location and the airline’s pricing strategy.

In DOJ’s report against the American-US Airways merger, they stated that “American

expects that the merger will lead to capacity reductions that would negatively impact

‘communities,’ ‘people,’ ‘customers,’ and ‘suppliers.’ Higher fares would be right around

the corner.” To verify this speculation, future research on the long-run effect of de-hubbing

and other de-hubbing cases is needed.

Acknowledgements

The author gratefully acknowledges comments from Jan Brueckner, Miguel Angel

Alcobendas, Achim Czerny, Jiawei Chen, Linda Cohen, Ethan Singer, Philip Gayle, Fan

Jiang, Kevin Roth, Amihai Glazer and David Neumark. Any errors or shortcomings in the

paper, however, are my responsibility.

27

Figure 1: DL & NW Hubs

28

Figure 2: DL & NW Flights on Routes Where an Endpoint is a Hub (Source: T-100)

Figure 3: Number of Passengers Connecting at DL & NW Hubs (Source: DB1B)

29

Figure 4: Percentage of DL & NW Passengers Connecting at Hubs (Source: DB1B)

Figure 5: Market Types

30

Table 1: Traffic Split for Markets with DL connections via CVG Pre-merger

2008Q1 2010Q2

Connection Hub # Passengers % Passengers # Passengers % Passengers

via ALL 3,709,990 100% 4,402,370 100%

via CVG 629,910 17.0% 273,000 6.2%

via ATL 1,824,900 49.2% 2,206,980 50.1%

via SLC 78,010 2.1% 129,140 2.9%

via JFK 50,560 1.4% 62,640 1.4%

via DTW 473,750 12.8% 768,810 17.5%

via MEM 204,220 5.5% 349,360 7.9%

via MSP 337,730 9.1% 480,360 10.9%

via other 110,910 2.9% 132,080 3.1%

31

Table 2: Summary Statistics for Markets with CVG as an Endpoint

2008Q1 2010Q2

Variables Mean Std.Dev. Mean Std.Dev.

Fare 315.035 161.407 269.722 114.137

Frequency 609.095 290.688 584.468 328.994

Distance 1218.670 759.155 1238.169 790.709

Nonstop 0.112 0.316 0.069 0.254

Tourist place 0.154 0.361 0.129 0.335

Slot-control 0.057 0.231 0.049 0.216

# Destinations 19.100 30.063 17.281 22.895

AA 0.139 0.346 0.099 0.299

CO 0.059 0.237 0.073 0.260

DL 0.441 0.497 0.536 0.499

NW 0.113 0.317 - -

UA 0.127 0.333 0.167 0.373

US 0.121 0.326 0.126 0.332

Product share 3.266E-04 1.080E-03 4.027E-04 1.289E-03

Observations 1059 1126

Market average

# Products 10.928 10.322 9.542 6.646

# Carriers 4.025 1.712 3.396 1.301

# markets 242 230

1 # Destinations is the geometric mean of the number of

destinations from the origin and destination airports.

32

Table 3: Average Nonstop DL Fares of Markets with CVG as an Endpoint

08Q1 08Q2 08Q3 08Q4 09Q1 09Q2 09Q3 09Q4

Average Fare 254.8 264.2 278.1 259.5 223.5 193 186.8 197.5

10Q1 10Q2 10Q3 10Q4 11Q1 11Q2 11Q3 11Q4 12Q1 12Q2

Average Fare 216.7 223.4 226.6 214.1 240.2 242.6 248.7 261.9 286.5 280.5

Table 4: Summary Statistics for Markets with DL Service Connecting at CVG Pre-merger

2008Q1 2010Q2


Fare 236.152 114.164 234.054 102.837

Frequency 607.491 369.595 613.497 393.588

Distance 1627.852 693.686 1646.496 693.697

Nonstop 0.040 0.197 0.039 0.193

Tourist place 0.306 0.461 0.291 0.454

Slot-control 0.144 0.351 0.104 0.305

# Destinations 6.838 11.711 7.518 11.472

AA 0.138 0.345 0.107 0.309

AS 0.001 0.034 0.001 0.038

CO 0.075 0.264 0.092 0.289

DL 0.212 0.409 0.310 0.462

NW 0.111 0.315 - -

UA 0.131 0.337 0.126 0.332

US 0.155 0.362 0.132 0.339

WN 0.113 0.316 0.168 0.374

Other 0.063 0.243 0.064 0.245

Product share 7.133E-04 3.0400E-03 6.814E-04 2.607E-03

Observations 28260 28456

Market average

# Products 17.831 12.132 18.577 11.593

# Carriers 6.087 1.962 5.524 1.701

# markets 2418 2316

33

Table 5: Demand Parameter Estimates for Markets with CVG as an Endpoint

α, β OLS logit IV logit Mixed logit

Fare -0.1905** -1.5035** -2.008**(0.0188) (0.1217) (0.7896)

Frequency 0.1287** 0.1578** 0.4698**(0.0067) (0.0238) (0.1728)

Distance -0.0292 0.7086** 2.8735**(0.1038) (0.2099) (0.5471)

Distance2 0.0468** 0.0387 -0.2970**(0.0145) (0.0246) (0.1033)

Nonstop 3.1447** 2.8658** 4.1597**(0.1110) (0.2149) (0.6581)

Tourist place -0.0832 -0.7243** 0.3548(0.0874) (0.1550) (0.3064)

Slot-control -0.9993** -0.7297** -0.5774(0.1336) (0.2188) (0.4091)

# Destinations -0.0202** -0.0119** 0.0139*(0.001) (0.0016) (0.0081)

AA -0.3681** -0.1945 -0.1823(0.1088) (0.1816) (0.2523)

CO -0.9394** -0.3180 -0.1386(0.1351) (0.2286) (0.3914)

DL 0.1736** 0.3864** -0.0138(0.0890) (0.1473) (0.2010)

NW -0.8084** -0.6776** -0.6061(0.1430) (0.2378) (0.4697)

UA -0.3225** -0.2597 0.3192(0.1008) (0.1866) (0.4876)

Constant -9.6812** -7.1616** -20.4245**(0.1358) (1.0662) (2.7926)

R2 0.4489 - -

Function Value - - 21.1435

Observations 2185 2185 2185

σ

Fare - - -0.0058- - (0.8475)

Constant - - 5.5121**- - (0.5701)

1 Standard errors are in parentheses.

2 ** p<0.05, * p<0.10.

3 US is omitted to avoid multicollinearity.

34

Table 6: Demand Parameter Estimates for Markets with

DL Service Connecting at CVG Pre-merger

α, β OLS logit IV logit Mixed logit

Fare -0.2733** -0.3956** -2.0769**(0.0056) (0.0383) (0.8970)

Frequency 0.0953** 0.6533** 1.0605**(0.0013) (0.0060) (0.0627)

Distance 0.0726** 3.6815** 7.3058**(0.0369) (0.0963) (0.3791)

Distance2 -0.1232** -0.9144** -1.5585**(0.0049) (0.0109) (0.0944)

Nonstop 4.2666** 5.3146** 6.3446**(0.0328) (0.0613) (0.1572)

Tourist place 0.1675** 0.1730** 0.7769**(0.0137) (0.0247) (0.0622)

Slot-control -0.6482** -0.9753** -0.4573**(0.0195) (0.0361) (0.1410)

# Destinations -0.0228** -0.0243** 0.0148*(0.0003) (0.001) (0.0086)

AA -0.3892** -0.6031** -0.9753**(0.0252) (0.0530) (0.2237)

AS 0.0953 1.1664** 2.7782**(0.1760) (0.3068) (0.8640)

CO -0.5220** -0.4485** -0.4775**(0.0282) (0.0547) (0.1994)

DL -0.3532** -0.2460** -0.7080**(0.0205) (0.0409) (0.1623)

NW -0.6487** 0.0332 0.0434(0.0324) (0.0581) (0.1016)

UA -0.3188** -0.1799** -0.3258**(0.0248) (0.0488) (0.1801)

US -0.2083** -0.0704 -0.1053(0.0239) (0.0493) (0.2537)

WN -0.6781** -0.2689** -0.5572**(0.0242) (0.0430) (0.0933)

Constant -8.2103** -14.9241** -27.1086**(0.0383) (0.2886) (0.9205)

R2 0.3826 - -

Function Value - - 82.9337

Observations 56716 56716 56716

σ

Fare - - 0.0155- - (0.1876)

Constant - - 5.5345**- - (0.3364)

1 The dummy variable for other carriers is omitted to avoid

multicollinearity. 35

Table 7: The Mean of Own-price Elasticities of Markets with CVG as an Endpoint

08Q1 10Q2

Across Carriers -5.98 -5.09

AA -6.15 -4.80

CO -8.59 -4.91

DL -6.05 -5.16

NW -5.35 -

UA -5.59 -5.12

US -5.22 -5.07

Table 8: The Mean of Own-price Elasticities of Markets

with DL Service Connecting at CVG Pre-merger

08Q1 10Q2

Across Carriers -4.60 -4.57

AA -4.96 -4.96

AS -6.13 -5.57

CO -5.05 -4.69

DL -4.66 -4.50

NW -4.33 -

UA -4.99 -4.86

US -4.62 -4.92

WN -4.16 -4.34

Other -3.52 -3.40


(Products that Existed Both Periods)

2008Q1 2010Q2


Fare 305.919 130.143 264.054 108.126

Frequency 667.832 279.011 641.260 344.829

Slot-control 0.058 0.344 0.053 0.224

# Destinations 16.246 27.714 16.189 21.800

# Products 1548 1548

36


(Products that Existed Either Pre-merger or Post-merger)

Old Products New Products


Fare 339.793 223.33 282.185 125.618

Frequency 449.579 260.701 459.591 249.593

Distance 1238.354 728.158 1296.980 831.346

Slot-control 0.053 0.224 0.040 0.196

# Destinations 26.852 34.553 19.682 25.001

Nonstop 0.147 0.355 0.003 0.053

# Products 285 352

Table 11: The Compensating Variation (in dollars) for Markets with CVG as an Endpoint

Total Compensating Variation 1.4791E+07

Average Compensating Variation 0.0504

Table 12: The Compensating Variation Decomposition for Markets with CVG as an Endpoint

Products existed pre-merger and post-merger 1.0605E+07

Products existed pre-merger only (old products) -2.2690E+07

Products existed post-merger only (new products) 1.7520E+07

Table 13: Counterfactual Analysis for Products that Existed Both Periods

(Markets with CVG as an Endpoint)

Change Fares 3.0900E+07

Change Frequencies -1.0489E+07

Change #Destinations -1.1944E+06

Change Carrier dummy 1.6638E+06

Change Slot− control 9.9157E+05

37



2008Q1 2010Q2


Fare 231.660 99.195 233.113 92.467

Frequency 655.885 356.316 654.124 390.856

Slot-control 0.139 0.346 0.107 0.309

# Destinations 10.695 16.897 11.562 16.846

# Products 42370 42370


(Products that Existed Either Pre-merger or Post-merger)

Old Products New Products


Fare 249.603 149.525 236.796 128.33

Frequency 462.479 370.721 494.296 376.45

Distance 1689.054 711.759 1760.354 702.417

Slot-control 0.160 0.366 0.095 0.293

# Destinations 11.594 17.057 13.247 15.887

Nonstop 0.032 0.176 0.025 0.157

# Products 7075 7271

Table 16: The Compensating Variation for Markets with DL Connections at CVG Pre-merger

Total Compensating Variation 2.1377E+10

Average Compensating Variation 5.0067

38

Table 17: The Compensating Variation Decomposition for Markets with

DL Service Connecting at CVG Pre-merger

Products existed pre-merger and post-merger 7.6674E+09

Products existed pre-merger only (old products) -3.5915E+10

Products existed post-merger only (new products) 4.1492E+10

Table 18: Counterfactual Analysis for Products that Existed Both Periods

(Markets with DL Service Connecting at CVG Pre-merger)

Change Fares -4.6164E+08

Change Frequencies 6.3529E+09

Change #Destinations -3.1434E+08

Change Carrier dummy -3.7725E+08

Change Slot− control 9.1872E+08

Table 19: Recession Adjustment Ratio

Price Ratio Frequency Ratio

Distance Bins (miles) Nonstop Connecting Nonstop Connecting

Distance < 500 0.9759 1.0378 1.0446 1.0121

500 ≤ Distance < 1000 0.9921 1.0026 1.0094 1.0164

1000 ≤ Distance < 1500 0.9777 1.0012 1.0482 1.0258

1500 ≤ Distance < 2000 0.9702 1.0263 1.0114 1.0276

2000 ≤ Distance < 2500 1.0446 1.0434 1.0435 1.0136

2500 ≤ Distance < 3000 1.0684 1.0375 1.0126 1.0393

Distance ≥ 3000 1.1163 1.0745 1.0990 1.1207

39

Table 20: Seasonality Adjustment Ratio

Price Ratio Frequency Ratio

Distance Bins (miles) Nonstop Connecting Nonstop Connecting

Distance < 500 0.9946 0.9658 0.9639 0.9417

500 ≤ Distance < 1000 0.9949 0.9615 0.9572 0.9489

1000 ≤ Distance < 1500 0.9769 0.9888 1.0026 0.9612

1500 ≤ Distance < 2000 0.9952 1.0178 0.9047 0.9684

2000 ≤ Distance < 2500 0.9516 1.0353 0.9849 0.9396

2500 ≤ Distance < 3000 1.0204 1.0342 0.9665 0.9745

Distance ≥ 3000 1.0599 1.0659 1.4935 1.1886

Table 21: Summary Statistics for Markets with CVG as an Endpoint with Adjustment


08Q1 10Q2 10Q2 Recession 10Q2 Seasonality 10Q2 Recession

Adjusted Adjusted & Seasonality Adjusted

Variables (1) (2) (3) (4) (5)

Fare 305.919 264.054 267.958 261.936 265.938

(130.143) (108.126) (112.444) (110.966) (115.864)

Frequency 667.832 641.260 655.154 613.428 626.949

(279.011) (344.829) (351.054) (327.920) (334.243)

1 Standard deviations are in parentheses.

40


with Adjustment (Products that Existed Both Periods)

08Q1 10Q2 10Q2 Recession 10Q2 Seasonality 10Q2 Recession

Adjusted Adjusted & Seasonality Adjusted

Variables (1) (2) (3) (4) (5)

Fare 231.660 233.113 237.992 234.535 239.571

(99.195) (92.467) (96.830) (96.206) (101.088)

Frequency 655.885 654.124 670.325 628.870 644.769

(356.316) (390.856) (399.537) (374.506) (383.655)

1 Standard deviations are in parentheses.

Table 23: Compensating Variation for Markets with CVG as an Endpoint with Adjustment

No Recession Seasonality Recession

Adjustment Adjusted Adjusted & Seasonality Adjusted

(1) (2) (3) (4)

Total CV 1.4791E+07 1.8818E+07 7.0723E+06 1.0780E+07

Average CV 0.0504 0.0642 0.0241 0.0368

Table 24: Compensating Variation for Markets with DL Service

Connecting at CVG Pre-merger with Adjustment

No Recession Seasonality Recession

Adjustment Adjusted Adjusted & Seasonality Adjusted

(1) (2) (3) (4)

Total CV 2.1377E+10 3.3532E+10 1.4497E+09 1.2887E+10

Average CV 5.0067 7.8533 0.3395 3.0182

41

A Appendix: Estimation

Following Rasmusen (2007), the estimation takes the following steps.

(0) Select arbitrary values of δ (δ is the vector of mean utility for each of the products),

θ2 and θ1 as a starting point.

(1) Using the starting values of θ2 and δ, compute the predicted market shares implied

by equation (7).

(2) Using the starting values of θ2, compute δ that equates the predicted market shares

computed in step 1 to the observed market shares by a contraction mapping:

δh+1 = δh + ln(S)− ln(s), h = 0, ..., H (10)

where S is the vector of observed market shares, s is the vector of predicted market shares

from step 1 based on δh. H is the smallest integer such that ||δh+1 − δh|| is smaller than

some tolerance level.15

(3) Compute the value of the GMM objective function, using the starting value for θ1

from step 0 and the δ estimate from step 2.

(3a) Calculate the structural error term.

ξjmt = δjmt − λc− xjmtβ − αpjmt. (11)

(3b) Compute the value of the GMM objective function

ξ′ZΦ−1Z′ξ, (12)

where Z is a vector of instruments and Φ−1 is a weighting matrix, where Φ is a consistent

estimate of E(Z′ξξ′Z).

(4) Compute better estimates of θ1 and θ2.

(4a) Find an estimate of θ1 using the GMM estimator:

θ̂1 = (X1′ZΦ−1Z′X1)−1X1

′ZΦ−1Z′δ. (13)

X1 is the matrix of explanatory variables common to all consumers.

15Tolerance level of 1E-14 is used in the contraction mapping.

42

(4b) Use a search algorithm to find new values for θ2 (This paper uses the interior-point

method). Take the new values and return to step 1, and keep iterating until the GMM

objective function is minimized. 16

To sum up, an inner loop is nested within an outer loop in the estimation process. In

the inner loop, for a given value of θ2, δ is computed by solving the contraction mapping.

The outer loop then searches for the values of θ1 and θ2 that minimize the GMM objective

function.

16Tolerance level of 1E-6 is used for the GMM function.

43

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Airline Network-Structure Change and Consumer Welfare

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