2011-35
An empirical analysis of the effect of multimarket contacts
on air carriers’ pricing behaviors: A case of the U.S.
Hideki Murakami Ryota AsahiAn empirical analysis of the effect of multimarket contacts on air carriers’ pricing
behaviors: A case of the U.S.
by

Hideki MURAKAMI
Associate Professor
Kobe University, Graduate School of BusinessPhone: +81-78-803-6941
FAX: +81-78-803-6977
E-mail: hidekim@panda.kobe-u.ac.jp

andRyota ASAHI
Ph.D. student
Kobe University, Graduate School of BusinessPhone: +81-78-803-6941
FAX: +81-78-803-6977
E-mail: ryota.net1011@hotmail.co.jp

Abstract
A number of empirical studies have shown that multimarket contacts facilitate collusive behaviors
between full-service carriers (FSCs) in the U.S. airline industry. This paper empirically investigates theeffects of multimarket contacts on air carriers’ pricing behaviors and highlights those of low-cost carriers(LCCs) and FSCs as well as those among LCCs. We estimated the simultaneous demand and price(pseudo-supply) equations to derive these impacts of multimarket contacts by using cross-sectional dataof the year 2006 (top 30 U.S. air markets with 4484 sample observations). We found that having
multimarket contacts among FSCs leads to collusive setting of high airfares. However, the effect ofmultimarket contacts is lowered among LCCs, and the degree depends on the number of LCCs in amarket. We also found that LCCs’ airfares stay at low levels, even though they repeat multimarket
contacts among LCCs. These results suggest that LCCs’ behaviors are not affected by multimarket
contacts, and that these behaviors have strong impacts on lowering airfares.

11. Introduction“Multimarket contact” describes the situation where there are many inter-carrier
rivalries between a limited number of carriers in multiple markets (cross-sectionally anddynamically, and both). Since Edwards first described this situation (1955), it has been
said that this setup blunts the edge of the airlines’ competition. Many authors haveempirically analyzed the effect of multimarket contact and support the notion thatmultimarket contacts have a collusive effect (for example, Heggestad and Rhoades 1978,
Scott 1982, Jan and Rosenbaum 1996, Parker and Roller 1997, Fernandes and Marin
1998, and Fu 2003).
In the field of airline industries, the authors of many studies have also tried to analyze
collusive effects due to multimarket contact. Evans and Kessides (1994), Singal (1996),
Gimeno and Woo (1996, 1999), Gimeno (2002), and Zou et al. (2011) showed thatmultimarket contacts statistically increased carriers’ airfares. Baum and Korn (1999)
found that airline behaviors, such as entry and exit, decreased as multimarket contacts
increased.
Although most studies have revealed that multimarket contacts have collusive effects
in the airline industry, in all cases the authors used the data of the 1980s and did notconsider the presence of low-cost carriers (LCCs). There have been many studies on theeconomic impact of LCCs. Dresner et al. (1996) and Windle and Dresner (1999)
analyzed the effect of LCCs’ entries and found that they significantly decreased airfares.
Morrison (2001) also showed that entries of LCCs influenced airfares in the LCCs’
potential routes. Goolsbee and Syverson (2008) found that incumbents significantly cut
airfares when threatened by Southwest’s entry. Murakami (2011b) studied whether
price-lowering effects due to LCCs entries lasted over time and estimated the change insocial welfare in accordance with the change in airfares of full-service carriers (FSCs)
and LCCs. However, few previous studies have applied the idea of the competitiveeffect of multimarket contact to examining airline competitions with LCCs.
In this paper we empirically investigate whether multimarket contacts increase airfare,whether LCCs influence the collusive effect through multimarket contact behaviors, andwhether multimarket contact effects exist among LCCs. We estimated the simultaneous
demand and price (pseudo-supply) equations to derive these effects of multimarket
contacts by using cross-sectional data of the year 2006 for the top 30 U.S. air markets,with 4484 sample observations. We found that multimarket contacts among FSCs leadto collusive setting of high airfares. However, the effect of multimarket contact isdecreased by competition with LCC(s), and the degree depends on the number of LCCs
2

in a market. We also found that LCCs’ airfares stay at low levels, even though they
repeat multimarket contacts among LCCs. These results suggest that LCCs’ behaviors
are not affected by multimarket contacts.
In Section 2 we model the simultaneous demand and pseudo-supply equation-system
to measure the effect of multimarket contacts, highlighting the impact of LCCs presence.
In Section 3 we describe the data, and in Section 4 we show the empirical results. In
Section 5 we present concluding remarks.
2. Econometric model
In this section we construct a simultaneous demand and pseudo-supply equation
system to estimate the effect of multimarket contact. Dresner et al. (1996) and
Murakami (2011a, 2011b) applied the simultaneous demand and pseudo supply
equation model to the analysis of the competition between FSCs and LCCs, and wefollow their methods. In addition, in terms of measuring the effect of multimarket
contact, we follow the ideas proposed by Jan and Rosenbaum (1996), who studied thecement industry. Jan and Rosenbaum (1996) also estimated simultaneous equation
systems by nonlinear 3SLS (three stage least squares), adding a multimarket contact
variable to right-hand side of the pseudo-supply equation.
Our empirical model to investigate the effect of multimarket contact is as follows:[Demand equation]logQαα logP

α logDistα logPOPα logINC

α MKTu[Pseudo-supply equation]logPββ logQββ logDist β logHHI
β LCC β VSLCC∑θ INLCCn θ INLCCA logMMC
e ,where P and Q are the average airfare and output of route j of carrier k,respectively. INC is the arithmetic per capita income of route j. Dist is the distance
between a city pair of route j. POP is the arithmetic average of the O/D population.
MKT are binary variables that take 1 for the market where m carriers compete, andthe benchmark market of this binary variable is duopoly markets. This MKT variable3

is introduced to control the market size in the demand equation. The parameters of thisvariable could be positive or negative. In the negative case, for example, if too many
carriers enter a market and compete for limited demand, the demand that each carrier
faces could be smaller than what each carrier would face in a duopoly market.
Dist in the pseudo-supply equation is used as the proxy variable of marginal cost.
This variable will have a positive effect on airfares. HHI is the Herfindahl index, andhigher HHI means that the market is more concentrated. Since high concentration may
lead to strong market power, the parameter will be positive. LCC is a binary variable
that takes 1 if firm k is an LCC. VSLCC is a binary variable that takes 1 if firm kcompetes with LCC(s) in a market.
To analyze whether LCCs have a collusive effect through multimarket contact
behaviors, we set the case “between FSC competition” as the benchmark, andstatistically test the three hypotheses that the effect of multimarket contact on airfares isequal between (a) an FSC vs. an LCC, (b) FSCs vs. LCCs, and (c) an LCC vs. an LCC.
To test these hypotheses, we use the coefficients of “slope dummy” variables, INLCCn
and INLCCA . INLCCn are a binary variable that takes 1 for a route if n LCC(s) operateand compete with FSC(s) in route j, and INLCCA takes 1 for the cases where there
exist only LCCs in route j. The parameter-signs of these two binary variables will be
negative if LCCs’ behaviors are not affected by multimarket contact. Especially in themarkets where we observe “between-LCCs” competition, the slope-angle could be steep(i.e., the absolute value of
could be large). MMC is firm k’s multimarket contact
on route j. The sign of parameterof MMC will be positive if multimarket
contacts have collusive effects. u and e are random error terms of the demand
equation and pseudo-supply equation, respectively.
Multimarket contacts have been measured by several methods. Our method is to countthe number of overlapping markets in which firms compete with one another. Thismeasurement is defined as follows:[Multimarket contact (MMC) measurement]

MMC

∑

a

a D D
f 1
D D

4

where D

is a binary variable that takes 1 if firm k operates in route j. f is thenumber of firms that operate in route j. This measurement has been used in many
previous studies.
As noted above, HHI measures the degree of market concentration. Bailey et al.
(1985) suggested that the market concentration is an endogenous variable determined byoutput, distance, and other exogenous factors. In addition, Waldfogel and Wulf (2006)
suggest that the variable of multimarket contact may be endogenous. Thus we must
check the endogeneity of these variables. To test the null hypothesis that logHHI and
logMMC are not correlated with the error term e , we carried out the Hausman testfor each variable. The test result was that we rejected the null hypotheses for both cases
at the 1% level of significance (χ188.37 and 11.67, respectively).

3. The Data
We used the carrier-specific data of the scheduled operations in city-pair routes. They
are cross-sectional data of the year 2006 collected from DB1A, which files a 10%
random choice of samples from all the U.S. domestic flight operations. Per-capita
individual income and demographic data were collected from Regional Accounts Data,
Bureau of Economic Analysis. We omitted monopoly markets, carriers that did not have
10% market share in duopoly markets, and carriers that did not have 5% share in
triopoly or greater markets. Carriers reported as carrier XX (carriers that are not filed in
IATA codes) in DB1A were also omitted. There are 4484 observations. These dataconsist of non-connecting flights from the top 30 largest U.S. airports and their regions
and include 487 duopoly markets, 460 tripoly markets, 195 four-carrier operating
markets, 101 five-carrier operating markets, 87 six-carrier operating markets, 41
seven-carrier operating markets, 2 eight-carrier operating markets, and 2 ten-carrier
operating markets. The descriptive statistics of continuous variables are shown in Table
1.
To classify the sampled nineteen carriers into FSCs and LCCs, we calculated
the carrier’s unit cost with the cost data from the Air Carrier Financial Reports, Form 41,
Financial Data, and estimated the 95% confidence interval of carriers’ unit costs.
We found a number of carriers that operated at very low unit cost level, and thenexcluded carriers with very small networks, carriers that were allied with anotherairline, and carriers that went bankrupt around 2006. As a result, we defined
Airtran Airways, Spirit Airlines, Jet Blue Airways, and Southwest Airlines as LCCs.
5

There are 714 one-LCC-operating-markets, 71 two-operating-markets, and 5
three-operating-markets in this sample.
4. Empirical results
We estimated demand and price (pseudo-supply) equations simultaneously by an
iterative 3SLS method to measure the effect of multimarket contact. Table 2 presents
the results. Model 1 provides estimated parameters of the system equation withoutcoefficient binary variables of price equation, and Model 2 provides estimatedparameters of the system equation with those variables. The results indicate that theparameters of variables meet the expected signs and are statistically significant except
for the case of the Herfindahl index. The reason for the unexpected sign of the
Herfindahl index parameter is that there are lots of markets where LCCs have a large
market share. In such a case, the more concentrated a markets is, the lower the level of
average airfares in the market.
The results for both cases show that the coefficient of multimarket contact issignificantly positive. These results suggest that multimarket contacts have collusive
effects and are consistent with previous studies that indicate multimarket contacts have
led to collusive establishment of high airfares in the airline industry.
The coefficients of INLCC1, INLCC2, and INLCC3 are significantly negative. These
results show that the collusive effects are decreased by competition with LCC(s),indicating that LCCs compete aggressively with FSCs, regardless of the multimarket
contacts. The parameter of INLCCA is significantly negative, and the absolute value of.is almost the same as . This result implies that multimarket contacts among LCCs
do not influence LCC ticket prices (we cannot reject the hypothesis that0 by the Wald test, and
0.056
with
degreeoffreedom(d.o.f.)=1,
P-value¬=0.813). Boguslaski et al. (2004) indicated that the presence of an LCC in amarket did not influence other LCC’s entry behavior and this implies that LCCs tend tocompete with each other aggressively. Therefore, it is inferred that our finding is
consistent with their study.
An interesting finding in our study is that the degree of completion is not strengthenedin “slope-up” way. To examine whether each parameter of INLCCn is significantly
different, we test following hypotheses respectively: (1) H : θθ , (2) H : θθand (3) H : θ
θ . We reject hypotheses (1), suggesting that the effect of multimarket
contacts on carrier’s air-ticket price is statically different between
one-LCC-operating-markets and two-LCCs-operating-markets. The difference between
6INLCC1 and INLCC2 could be explained in the following ways. In
one-LCC-operating-markets, FSCs could induce an LCC to work in collusion,
especially when the LCC with a small market share is deficit-ridden. However, in
two-LCCs-operating-markets, competition between FSCs and two LCCs is accelerated
due to the increase in the number of LCCs.
We also rejected hypothesis (2) and did not reject hypothesis (3). These two resultsmean that competition among carriers is softened when the number of LCCs increases
from two to three. These results imply that even LCCs may tend to choose
comparatively collusive behavior when they face competition with “well-known rivals,”
to avoid too fierce a competition and to try to coexistent and co-prosper.
5. Conclusion
The hypothesis that multimarket contact blunts the edge of competition was suggested
by Edwards (1955) and has been theoretically supported by Bernheim and Whinston(1990). Based on the idea of multimarket contact, many researchers have empiricallyshowed that multimarket contact has a collusive effect in certain industries, including
the airline industry.
While there are many studies indicating that airlines set higher fares when there ismultimarket contact in the airline industry, these studies do not take into account thepresence of LCC(s). The distinguishing feature of this paper is that we investigatedwhether the collusive effects of multimarket contact were observed when LCC(s)existed in a market.
By using cross-sectional data of the year 2006 with 4484 sample observations, wefound that : (1) multimarket contacts among FSCs led to high airfares due to collusion
among carriers; (2) in the case where LCC(s) exist(s) in a market, multimarket contacts
have less collusive impacts, and the degree of impacts on airfares depends on thenumber of LCCs in a market; (3) in the case where there are only LCCs in a market (theLCC vs. LCC case), multimarket contacts have no price-lowering effects and airfares
stay at low levels, even though there are repeated multimarket contacts among LCCs.
Our finding (1) is consistent with those of previous literatures. In addition, ourcontributions are that we found facts (2) and (3) by using rigid econometric models andlarge number of sample observations in an attempt to avoid data selection bias.
Of course, this study has some limitations. First, although we used abundant sample
observations, we may need to update the dataset to panel data. A lot of previous studies
use panel data to measure the effect of multimarket contact. Second, we may have to7

investigate whether the behaviors of airlines under multimarket contact lead to morecollusive behavior such as mergers or alliances. Knowing this would be important for
determining entry and exit policy. Using panel data and investigating whether
multimarket contact leads to mergers or alliances will be done in our future research.
[2011.6.10 1052]

Table 1The descriptive statistics of continuous variables.
Name

Mean

SE

Minimum

Maximum

Airfare

167.1

55.5

55.5

563.4

Passengers

4113.95598.2157.045144.0

Population

3778600.0

2511000.0

556430.017161000.0

Per-capita income

40426.04098.827000.055101.0

Herfindahl index

386.6

146.7

108.1

813.4

Distance

1452.6

819.0

177.0

5095.0Multimarket contact

156.1

91.2

1.5

416.0

8

Table 2 Estimated results
Model 1Model 2Standard
VariableStandard

Parameter

p-ValueParameter

Error

p-Value
Error

Demand equation
Airfare-0.497

0.074

0.000

-0.523

0.074

0.000

Distance

0.142

0.043

0.001

0.163

0.043

0.000

Per-capita income

0.284

0.092

0.002

0.290

0.093

0.002

Average population

0.158

0.021

0.000

0.164

0.021

0.000

Tripoly market-0.511

0.025

0.000

-0.519

0.026

0.000

4-firm market-1.095

0.036

0.000

-1.115

0.036

0.000

5-firm market-1.589

0.044

0.000

-1.613

0.044

0.000

6-firm market-2.388

0.049

0.000

-2.425

0.049

0.000

7-firm market-2.834

0.060

0.000

-2.877

0.060

0.000

8-firm market-3.404

0.146

0.000

-3.482

0.148

0.000

10-firm market-4.041

0.138

0.000

-4.095

0.140

0.000

Constant

4.684

0.939

0.000

4.537

0.948

0.000

Output

0.587

0.021

0.000

0.244

0.011

0.000

Herfindahl index-1.791

0.017

0.000

-0.741

0.017

0.000

Distance

0.224

0.026

0.000

0.278

0.013

0.000

LCCI

-0.290

0.009

0.000

-0.189

0.020

0.000

VSLCCI-0.151

0.007

0.000

-0.038

0.022

0.082

0.059

0.005

0.000

0.072

0.005

0.000

Pseudo-supply equation

MMC

β

INLCC1θ-0.021

0.005

0.000

INLCC2

θ

-0.048

0.007

0.000

INLCC3

θ-0.023

0.011

0.031

INLCCAθ

-0.076

0.016

0.000

5.392

0.166

0.000

Constant

9.427

System R-square
Test

of

0.316

0.000

0.966

the

overallχ

0.966

15175.000

0.000

χ

15206.000

0.000

significance
H :θ

θ

0.027

0.000

H :θ

θ

‐0.025

0.004

H :θ

θ

0.002

0.820

9

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