2007-45
An Empirical Analysis of Inter-Firm Rivalry
between Japanese Legacy and Low-Cost Carriers
Hideki Murakami

An Empirical Analysis of Inter-firm Rivalry between Japanese Full-Service and
Low-Cost Carriers
Hideki MURAKAMI∗
Abstract
This paper empirically analyzes dynamic change in inter-firm rivalry between Japanese
low-cost carriers (hereafter, LCCs) and full-service carriers 1 (hereafter, FSCs), by
estimating each carrier’s conduct parameters, and it deduces the dynamic change in
consumer surplus after an LCC enters a market by estimating structural demand andairfare equations using unbalanced carrier-specific panel data of two to four carriers onnine routes for four to eight years (130 samples).

Our findings are that (1) the conduct

parameters of LCCs and reacting FSCs were extraordinarily low during that period,
such that the Federal Trade Commission of Japan (FTCJ) was about to intervene; (2) the
conduct parameters were restored to, or even exceeded, the pre-entry level in the third∗

Associate Professor, Graduate School of Business, Kobe University, Rokkodai, Nada,

Kobe,

Hyogo,

Japan.

hidekim@panda.kobe-u.ac.jp

Phone:+81-78-803-6941-6977),

E-mail:The author is grateful to Anming Zhang and anonymousreferees for helpful comments．
1

(fax:

Japan Airlines (JAL) and All Nippon Airways (ANA).

1

year of LCC entry; (3) gains in total welfare were recognized for five of the ninemarkets, whereas in three markets only the airline industry benefited, and in one market,
total welfare decreased.

On the basis of result (3), we conclude that Japanese

regulatory sectors, which have allowed FSCs to engage in behavior that drives LCCs
out of competitive markets while also allowing collusive code-shares between ANA and
LCCs, seem to stand by the airline industry instead of consumers.
Key words: low-cost carrier, conduct parameter, economic welfare

I

Introduction
In 1996, responding to the worldwide tide of deregulation, Japan’s Ministry ofTransport (MoT) allowed for the foundation of two air carriers and let them enter
domestic markets.

One was Hokkaido International Airlines (called Air Do, with thecode ADO), founded by bankers and farm entrepreneurs in Hokkaido who had argued
that expensive airfares were damaging the economy in Hokkaido.
Skymark Airlines (SKY), founded by a travel agency, H.I.S.

The other is

H.I.S. wanted to create

new demand for package tours by issuing much cheaper tickets than FSCs did. In
1998, ADO and SKY entered Tokyo-Sapporo and Tokyo-Fukuoka, respectively, thelargest and second largest city-pair routes in the world in terms of demand.

2

SKY also

entered Osaka-Sapporo and Osaka-Fukuoka.
In 2000, MoT deregulated airfares and domestic market entry/exit, and twoother airlines were founded: Skynet Asia (SNA) and Star Flyer (SFJ). In 2002, SNA
entered Tokyo-Miyazaki, followed by Tokyo-Kumamoto and Tokyo-Nagasaki, all ofwhich are long-distance city-pair routes with few surface transportation modes tocompete with airlines. SFJ started operating in Tokyo-Kitakyushu in 2006, and thenentered Tokyo-Kansai (Osaka) in 2007.
This paper analyzes the dynamic change in inter-firm rivalry between new andlegacy Japanese carriers by modeling oligopolistic competition and estimating the
conduct parameter derived from the oligopoly model.

Our study focuses on the

pre-entry strategy of FSCs, the strategies of new entrants and FSCs during the fare war,and FSCs’ “price-recovery” behaviors after the new carriers exited.

Furthermore, weinvestigate how the fares dynamically change from pre-entry to post-exit situations
throughout the fare-war periods and compute the welfare effects by estimating asimultaneous demand and fare equation system.
characteristics of new Japanese carriers.

The next section overviews theSection III reviews previous studies, models

the oligopolistic competition, and derives the conduct parameter and demand and fareequation system.

Section IV explains our dataset and econometric method,3

demonstrates our empirical results, and discusses the welfare effects based on theempirical results.

II

Finally, we provide concluding remarks in Section V.

An overview of new Japanese carriers: Are they really low-cost carriers?
Although the Japanese Ministry of Land, Infrastructure, and Transport(MLITT2), some Japanese academics, and mass media call ADO, SKY, SNA, and SFJ
low-cost carriers, it seems doubtful that they belong in the same category as Southwest,
Ryan, or Jet Blue.

We first summarize their service characteristics in Table 1 andcompare them with those of US and European LCCs.
One characteristic is that none of the new carriers can choose to makesecondary airports their base.

Only SKY appears to be closest to the LCCs of theUnited States, such as Southwest Airlines in the 1990s, in terms of no-frills service, highdiscount ratios, very limited mileage service, and independence from FSCs. In addition,
all of these new carriers except for SKY offer more-frilled service like the new-generation
LCCs such as Jet Blue, even though the need for frills is lower than Jet Blue’s, since most

2

The Ministry of Transport merged with the Ministry of Construction in 2001 and waslater reorganized as the Ministry of Land, Infrastructure, Transport, and Tourism(MLITT).

4

Japanese routes are less than two in-flight hours.

Table 1

Characteristics of new Japanese carriers
ADO

SKY

SNA

SFJ

Seating class

Economy*1

Economy andCygnus class*2

Economy*6

Economy*6

Discount ratio at its entry
against FSC’s airfare
Fleet configuration

36%
B767 and B737

50％
B767 and B737

32.3%
B737

17%
A320

Frequent Flyer Program

Yes

Limited*3

Limited*6

Yes

Method for booking
tickets

Internet, Tel, Mobile
Ticket desk,,
Travel Agent*4,
Nothing (even for
Cygnus class)Internet,
Toll-free tel, Mobile

Code-share/Partnership

Internet,
Toll-free tel, Mobile,,
Ticket desk
Nothing for B737;
radio & music for
B767
ANA, SNA

None*5

ANA, ADO

Internet, Tel, Mobile
Ticket desk,,
Travel Agent*4,
Sweets,Beverages(coffee, drinks)
ANA

Base airport

SapporoTokyo

Miyazaki

Kita-Kyushu-69.51-70.18(2002-06)-20.31(2005-06)

Free in-flight service and/or
amenity

Profit/loss (in total of 2000-06),
million USD (1 USD=120 yen)

-14.23

Beverages(coffee, soft drinks)

Note:*1: Discount tickets are available for pre-purchasing, students, the handicapped, and for inhabitants of Hokkaido andbusinesspeople working for companies in Hokkaido.
*2: Discount tickets are available for pre-purchasing, students, and the handicapped. Cygnus class offers more wide-pitched,comfortable seats than economy class for 1,000 yen more than economy fare.
*3: Available for buyers who pay with Skymark Visa/Master Card.
*4: Designated travel agents.
*5: SKY once allied with JAL in 2005 in Tokyo-Osaka (Kansai) and Tokyo-Kobe, but terminated this alliance quickly.
*6: Discount tickets are available for pre-purchasing, round-trip, students, and the handicapped.
*7: One free ticket for every 10 flights.

As for costs, Figure 1 shows the difference in RPI-adjusted unit costs between
FSCs and LCCs as well as the change in those costs over time.

JPN Yen (RPI adjusted)

Figure 1

Changes in carriers’ unit costs from 1998 to 2005

22
20

JAL
ANA
JAS
ADO
SKY

18
16
14
12
1998 1999 2000 2001 2002 2003 2004 2005
Year

Source: JAA Civil Aviation Databook (Koku Tokei Yoran), Japan Aeronautic Association, 1998-2005.

5

The reasons why these new carriers cannot achieve low cost are twofold: one isthat the government tax, fuel prices, maintenance costs, and airport charges are the sameamong all Japanese airlines; the other is that Japan does not have secondary airports in
its metropolitan areas, such as Chicago Midway, which would charge cheaper landingfees.

New carriers can choose to relegate maintenance to low-cost foreign companies,

but due to the problems with the quality of those foreign services, they currently havetheir aircraft maintained by their rivals, FSCs.

Only in labor costs can new carriers

spend less than FSCs, and indeed this labor cost difference equals the total costdifference.
As we see in Table 1, all the LCCs were deficit-ridden during the study period.
In addition to the high cost structure of new airlines, what makes them worse off is thepricing strategy of FSCs, which seem to have tried to drive their new competitors out of
the market by matching their fares.

For example, when ADO entered theTokyo-Sapporo market, FSCs matched its fares almost exactly.

As a result, ADO’s

cumulative deficit reached 61 million USD for its first four years, and it filed forprotection under Japan’s Corporate Reorganization Law (Minji Saisei Hou) in 2002.

It

was later reorganized by code-sharing with ANA and paid off its debt in 2005, a year
earlier than scheduled. SNA’s fortunes were very similar to ADO’s, and it is now

6code-shares with ANA.

Only SKY was able to reduce its costs to 20% lower than thehighest-cost carrier, JAS, between 2001 and 2002, but it made a profit only in 2004.

III

The model
As stated above, Japanese FSCs have responded to low-cost entrants by cutting

fares, and seem to have tried to expel them.

This section models oligopolisticcompetition between FSCs and their new rivals, and derives the conjectural variation(conduct parameter) to investigate rigidly what types of competition FSCs and LCCs
have engaged in.

We also investigate how much of an impact such competition has onsocial welfare by constructing and estimating carrier-specific simultaneous equations ofdemand and price.

1. Conduct parameter
Many previous studies have used the conduct parameter to analyze inter-firm
rivalry: Iwata (1979), Appelbaum (1982), Brander and Zhang (1990)(1993), Oum,
Zhang, and Zhang (1993), and Fischer and Kamerschen (2003). In particular, the last
three sets of authors have focused on the US airline industry.

Those studies use

cross-sectional data and focus on duopoly, in which two “symmetric” carriers, such as

7

United Airlines and American Airlines, compete. This paper has two distinguishingfeatures: (1) it focuses on asymmetric carriers (FSCs vs an LCC), and (2) it derives the
conduct parameters of the Japanese airline industry on route-by-route and year-by-yearbases, using the panel data.
The reason why we use panel data instead of cross-sectional data is that we do
not have a sufficient number of samples for each year.

Therefore, the variables to be

used in our model have superscripts, L and k, which denote a carrier, and subscripts i ,which denotes a market, and t , which denotes a fiscal year. Our model assumes thateach market has one LCC and three FSCs.

This scenario was found more often before

2002, when JAL and JAS merged, than after.
(k=1, 2, 3) and the LCC as carrier 4.

We denote the three FSCs as carrier k

The market demand of route i in year t isdenoted as follows:
3

4

k =1

L =1

Qit = ∑ qitk + qit4 ≡ ∑ qitL

(k = 1,2,3, L = 1,2,3,4)(1)

where superscripts k and L each denote a carrier; L includes an LCC ( L ≡ 1,2,3,4 )while k denotes FSCs only. The profit function of each carrier at route i in year t
is denoted as follows:

π itL = qitL pit (Qit ) − TC itL (qitL ) (2) where TCitk (•) > TC it4 (•)
Taking the first-order condition of (2), we have:8∂π itL∂p (Q ) ∂Qit
= pit (Qit ) + qitL it it− MCitL qitL = 0
L
L∂Qit ∂qit
∂qit

( )

(3)

We then define the conduct parameters as (4) and (5):

ν itk ≡d
( q j + qit4 )k ∑ itdqitj≠kν it4 ≡

(4)d
( qk )
4 ∑ itdqit(5)

Substituting (4) and (5) into (3), respectively, we obtain:∂π itL∂p (Q )
= pit (Qit ) + qitL it it 1 + vitL − MCitL qitL = 0
L
∂Qit
∂qit

(

)

( )

(6)

where MCitk (•) > MCit4 (•) .
For example, the conduct parameter (4) means the marginal change in the output ofother carriers (two FSCs except for k plus carrier 4) against the marginal change in the
output of carrier k. If both of them move in the same direction and have the same
volume, the result is 1 and this means collusion. If the conduct parameter is 0, (6)equals the first-order conditions for Cournot competition. If it is –1, the price equals
the marginal cost, and this is considered Bertrand competition.
In our model, if the price equals the marginal cost of an LCC, FSCs would
have to exit the market, since MCitk (•) > MCit4 (•) as long as carriers operate at theminimum efficient scale where average cost equals marginal cost. In Japan’s case, we
may have observed this scenario once with Tokyo-Asahikawa (the second-largest city in
Hokkaido), from which ANA and JAS (just before it merged with JAL) exited and only
one LCC (ADO) stayed. But in most cases, FSCs stay, so we expect that the market

9price rarely falls close to an LCC’s marginal cost level.
As in the previous studies, equation (6) can be inverted to (7) by using the price( )elasticity of demand (η it ) and the market share of each carrier sitL .

{p (Q ) − MC (q )}η − 1 (7)
v =
L
it

it

L
it

it

L
it

it
L
it

pit (Qit )s
As for the variables and parameters in (7), we already have information on pit
and s itL , but the route-specific marginal cost for each carrier and the route-specific priceelasticity of demand are unknown. Therefore, we need to estimate these two unknown
variables and parameters in advance to compute the conduct parameters.
To estimate the route-specific marginal cost for each carrier, Fischer andKamerschen (2003) jointly estimate a translog total cost function and then approximate
the route-specific marginal cost for each carrier3. The estimation of translog total costfunction requires a sufficient number of samples. However, since Japan had only threemajor airlines until 2002 and now has only two, the number of samples of ourunbalanced panel dataset, which take 20 years for the time-series dimension, would beless than 60. In addition, since no Japanese LCCs have officially disclosed their costs
for labor, capital materials, and so on, we cannot incorporate these LCCs into the dataset
for our cost analysis. Therefore, it is hardly possible to arrive at marginal cost by
estimating translog cost function.

Alternatively, we use the following proxy toapproximate route-specific marginal cost for each carrier, as proposed by Brander andZhang (1990), (1993) and Oum et al. (1993).

3

See Fischer and Kamerschen (2003), pp.235-237.

10

−λ

⎛ Dist i ⎞⎟ Dist i
MC = AC ⎜⎜
L ⎟
AFL
t ⎠
⎝
L
it

L
t

(8)

where ACtL is the aggregate average cost of carrier L at year t, Dist i is the distanceof route i regardless of time, AFLLt is the average distance flown by airline L at year
t 4. Studies on airline costs, such as Caves, Christensen, and Tretheway (1984), andGillen, Oum, Tretheway (1990), show that economies of density exist in the airline
industry, and this means that the total cost function is strictly concave. Therefore, λin (8) ranges between 0 and 1. It is apparent that if λ is 0, the carrier’s marginal costis proportional to distance, while if λ is 1, the marginal cost is indifferent to distance.
Oum, Zhang, and Zhang (1993) statistically estimated that λ = 0.43 .

Armantier andRichard (2003) predict that the route-specific marginal cost of an airline is just equal tothe product of “cost per mile” and distance (this means λ = 0 )5. Among these studies,
the most comprehensive way to approximate the marginal cost with small samples
seems to be that proposed by Oum, Zhang, and Zhang (1993). They construct thefollowing nonlinear price equation to obtain λ and the system-wide conduct
parameter ν .
4

See Brander and Zhang (1990), pp. 572-575, Brander and Zhang (1993), pp.417-420,

and Oum, Zhang, and Zhang (1993), pp. 175-178.
5

Armantier and Richard (2003), pp. 468-469.

11

{AC (Dist AFL ) Dist }η + ε
p =
L
it

L
t

i

L −λ
t
L
it

η − (1 + ν )s

i

L
it

(9)

Before we can estimate the price equation (9), we need the route-specific price elasticity
of demand η .

We estimate the following Marshallian demand function by usingroute-specific unbalanced panel data to obtain the information on η .
ln (Qit ) = A − η ln pit + β ln INC it + γ ln Dist i + δ ln POPit + ρ ln HI it + μ it

(10)

where pit is the lowest price of each airline at route i in year t , and INC i is thearithmetic average of per-capita income of route i in year t . Both pit and INC i
are adjusted by the RPI index. POPi is the arithmetic average of the population ofroute i in year t ,and HI it is the Herfindahl index of route i at year t .

The

Herfindahl index is expected to have a negative effect on the number of passengers
when price elasticity of demand is relatively small, since carriers with monopolistic
power will reduce output to maximize profit.

2. Structural equations of demand and price

Our second interest is in how much the route-specific social welfare gain has been
since the new entry of LCCs and, for a couple of routes where competition ended, howmuch the welfare loss might be after the LCC exited. To know this, we need to know(a) how the price and demand have dynamically changed from the pre-entry situation to12

the years of the fare-war; (b) in the route from which the LCC exited, whether or not (orby how much) the price recovered after the competition ended; and (c) the yearly
profit/loss of each carrier.
The effect of the entry of low-cost carriers on carrier's airfare at primary andsecondary airports has been analyzed empirically by Dresner et al. (1996) and Morrison(2001). We build upon the method proposed by Dresner et al. (1996) who estimatedthe simultaneous demand and airfare equations using three stage least squares. To
ascertain the consumer welfare effect, we also need to know the demand for low-costcarriers as well as the airfare, both of which are simultaneously related to each other inthe demand and supply system. To incorporate the simultaneous relations of airfare
and demand, we construct the following structural equation system for FSCs.
ln q itk = α 0 + α 1 ln p itk + α 2 ln p it4 + α 3 ln POPit + α 4 ln INC it + α 5 ln Dist i + α 6 FRQitk
4

4

3

n =1

n =1

m =1

+ α 7 MSHEitk + α 8 MJJ + ∑ α 9 n ADOl + ∑ α 10 n SKYl + ∑ α 11m SNAm + eitk(k = 1,2,3, n = 1,2,3,4, m = 1,2,3)

(11)

4

4

n =1

n =1

ln pitk = β 0 + β 1 ln qitk + β 2 ln MCitk + β 3 FRQitk + β 4 MSHEitk + ∑ β 5 n JALn + ∑ β 6 n ANAn
4

+ ∑ β 7 n JAS n + β 8 EXJ + β 9 EXA + β10 EXD + β 11 EXH + u itk(n = 1,2,3,4)

(12)

n =1

where FRQitk is the number of departures of carrier k at route i in year t, MJJ is
the JAL-JAS merger’s dummy variable. For MJJ , the three elements of JAL’s years
2003, ‘04, and ‘05 take 1, and all the other elements take 0. JALn , ANAn , JAS n , ADOn ,13

SKYn , and SNAm are the dummy variables showing the dynamic effect of an LCC’s
entry. For example, the elements of ADO1 are 1 for ADO’s first year of entry and 0
for the other year of ADO and the other carriers, and the observation of JAL1 is 1 forthe first year of any LCC’s entry.

Therefore, JALn ANAn , and JAS n reflect thestrategy FSCs took against LCCs.

MSHEitk shows the market share of carrier k atroute i in year t.

This structural equation has five endogenous variables.

Considering the demand and supply system, it would make sense to assume thatdemand, own price, cross price, and marginal cost are endogenous. In addition, acarrier’s market share is determined by the market structure and is also assumed to beendogenous. Similarly, the structural equation system for carrier 4 is obtained byreplacing α 2 ln pit4 with α 2 ln pitj in the demand equation where j is the FSC

having the lowest airfare among JAL, ANA, and JAS, and replacing all the othersuperscript k with superscript 4.
Since we assume a case of four-carrier oligopoly, we have to have threecross-price terms in the demand equation. However, FSCs set almost the same airfares
as each other. Therefore, we introduce one cross term, and by doing this we mean thatthree FSCs pay attention to the LCC’s fare6 while the LCC sets its fare below the6

According to G. Nishimura who experienced the director of yield management of14

lowest fare or the collusion fare of the FSC(s).
As for the dummy variables related to exit, we have created the dummy
variables EXJ , EXA ,and EXD to see the effect of the FSCs’ fare-restoring behavior.
These variables are each 1 for the legacies’ elements in the year after an LCC’s exit.

EXH is the dummy variable to show ADO’s fare-restoring behavior after the FSCs
have exited. The element of EXH is 1 for the year after an FSC’s exit.

3.

Implications for market welfareThis section demonstrates how to compute the change in market welfare,referring to each carrier’s price and output after an LCC enters. Our method is simply
to compute the triangle surrounded by the intercept of the demand function, output, andprice before and after an LCC’s entry, and compare. The “benchmark” for computing
consumer welfare is the triangle surrounded by the horizontal intercept of the demand
curve, Int 0K ,i , the pre-entry price, p 0K ,i , and the corresponding market output q 0K ,i .
Both p 0K ,i and q 0K ,i are estimated values computed from the simultaneous equations.
The superscript i denotes the market, that is, i = Tokyo-Sapporo, Tokyo-Fukuoka,
Tokyo-Asahikawa,Tokyo-Aomori,Tokyo-Tokushima,

ANA, this practice actually occurs.

15

Tokyo-Miyazaki,Tokyo-Kagoshima, Osaka-Sapporo, and Osaka-Fukuoka; and K is the carrier in the∧ Kmarket including the LCC. Letting this benchmark consumer surplus be CS 0 , we can∧describe the CS 0 of a market as:
∧ K

∧

∧ K

Int 0K , i()

CS 0 = ∑ CS 0 , where CS 0 = ∫ K ,i f p 0K ,i dp 0K ,i
p0

K

(13)

where f (•) is the carrier-specific demand function defined as (11) in the last section.
Then we compute the size of the triangle surrounded by ptK ,i , qtK ,i and the horizontal
intercept of the demand curve adjusted by a carrier-entry dummy variable, Int tK ,i . The
subscript t denotes the years after the LCC entry, and the subscript 0 denotes the years∧

before LCC entry. Letting the post-entry consumer surplus be CS t , we can describe∧

CS t as:
∧ K

∧

∧ K

InttK , i()

CS t = ∑ CS t , where CS t = ∫ K ,i f ptK ,i dptK ,i
pt

K

∧

(14)

∧

Then we take the ratio Wˆ t ≡ CS t / CS 0 and show the change in consumer surplus
graphically. After we compute the consumer surplus, we deduce the total welfare bysumming consumer surplus and the route-specific profits of carriers. We use carrier
profit as a proxy of producer surplus because we may not be able to find the true
producer surplus, since the Japanese airline market is not perfectly competitive.

IV Empirical Results

16

1. Conduct parameter

The former half of this section derives each carrier’s conduct parameter per
route per year. The data sources for carrier costs, passengers, and flight frequency are
Koku Tokei Yoran (JAA Civil Aviation Handbook) published by the Japan Aeronautic

Association, and Koku Yuso Tokei Nempo (Yearly Statistical Survey of Japanese
Aviation) published by MLITT. The fare information is obtained from Jikoku Hyo (amonthly published timetable of railways and airlines). The demographic data sources
are Kakei Chosa Hokoku (Family Income and Expenditure Survey), which is publishedby the Japan Statistics Bureau, and web pages of related prefectures and cities. For
each year studied, the statistics on fares, population, flight frequency, and income aredata from April, when airline demand is lowest. The reason why we use April data isthat we can recognize a carrier’s fare strategies best in that month, since carriers issue
many varieties of discount tickets to convert potential demand to actual.
We estimated the demand equation (10), using the route-specific unbalanced
panel data of nine routes for four to eight years. Values in parentheses are t-values
computed using heteroskedasticity-robust standard errors, and “a” means that theparameters are significant at the 1% level.
ln (Qit ) = − 41.479− 1.252 ln pit + 2.254 ln INC it + 0.604 ln DISTi + 1.894 ln POPit − 1.299 ln HI it( −1.441)( −4.269 a )(1.197 )(1.272 )17( 3.692 a )( 3.291a )R 2 = 0.503 σˆ 2 = 0.536 n = 43

The price elasticity of demand (η ) is －1.252, and we will use this information toestimate λ and ν in equation (9). The dataset for estimating (9) is different fromthe one used for estimating the demand equation (10).

It is the carrier-specificunbalanced panel data of two to four carriers on nine routes for four to eight years(n=130). Using the nonlinear least-squares method, we obtain the estimated results
shown in Table 2.

Table 2 The estimated parameters of price equation (9)Parameter
Parameter value
Asymptotic t-stat
λ
0.374
7.274
ν
-0.242-6.119Test resultaaNote: Log-likelihood = –1317.63 n = 130 “a”: significant at 1% level

According to Table 2, we recognize that economies of density also may exist inJapan’s airline industry, though we do not treat all the domestic markets.

Thesystem-wide conduct parameter is –0.242, so the markets where an LCC enters are
regarded as more competitive than the Cournot competition level.
First we discuss two major LCC markets: Tokyo-Sapporo and Tokyo-Fukuoka.
Figure 2 (a) shows the dynamic change in conduct parameters of the four airlines
operating in Tokyo-Sapporo.
Since the Tokyo-Sapporo market has few alternative surface transportation18

modes that can compete with airlines, three airlines could have colluded and shared amonopoly profit. But rather, it seems that before ADO entered, two big FSCs, JAL
and ANA, engaged in Cournot competition to try to obviate intervention by the Japan
Fair Trade Commission (FTCJ, Kosei Torihiki Iinkai). Since JAS’s marginal cost was
lower than those of the big two, while its fares were almost the same as theirs, itsconduct parameter is closer to the collusion level. Then, in 1998, ADO entered with anincredibly low conduct parameter that can theoretically be impossible in the equilibrium.
ADO, which had only less than 10% market share, adopted such behavior because it
wanted to create new demand by reducing fares, and subsidized the loss in the
low-demand month by the profit earned in high-demand months. At that time, FTCJ
was about to intervene, as it considered this behavior as predatory pricing, butultimately it did not act, since “predatory pricing is difficult to establish unless fares
increase after new entrants exit the market.”7 Three FSCs quickly matched their fares
to the marginal cost level, and the fierce fare war lasted till 2001, by which time ADO
had accumulated serious deficits.

The next year ADO filed for protection underJapan’s Corporate Reorganization Law (Minji Saisei Hou) and reorganized under ANA’s
support.

7

Since ADO code-shared with ANA, its conduct parameter substantially

Ito (2003), p. 27.

19

increased and now appears to be converging to the collusion level. In Figure 2 (b), we
observe almost the same behavior in Tokyo-Fukuoka as was seen in Tokyo-Sapporo in
Figure 2 (a). The logic of SKY’s behavior seems to be almost the same as ADO’s, andit had become deficit-ridden by the end of 2000.

Figure 2 Conduct parameter change in Tokyo-Sapporo (a) and Tokyo-Fukuoka (b)
(a)

4
3

2

2
1

0

0
-1

(b)

4

1998 1999 2000 2001 2002 2003 2004 2005

-2
-3
-4

JAL
ANA
JAS
ADO

-2

1998 1999 2000 2001 2002 2003 2004 2005

-4

JAL
ANA
JAS
SKY

-6
-8

-5
-6

-10

Note: The vertical axis is the conduct parameter, and the horizontal is the fiscal year.

Figures 2 (a) and (b) also show that the conduct parameter of JAL moves in thesame direction as that of ANA. The correlation coefficient between JAL’s and ANA’s
conduct parameters is 0.955 for Tokyo-Sapporo and 0.984 for Tokyo-Fukuoka, and bothare statistically significant at the 5% level. These results imply a higher probability
that JAL and ANA are colluding. However, it appears that they have been exhibiting
Bertrand competition between 1999 and 2000 and then Cournot competition after 2001,
at least during the off-season, rather than colluding, probably due to the anti-trust
consideration mentioned above.
A similar finding is that SNA entered Tokyo-Miyazaki in 2003 with a conduct20

parameter equal to －0.118, and it always kept the parameter at low levels: －0.050
and －0.186 between 2003 and ‘05. JAL tried to match its fare in the first year ofentry but restored it when the year was over, while ANA matched its fare for the firsttwo years. During the competition period, SNA’s deficit had grown to 61.6 million
USD by 2005. Although SNA made efforts to keep its fares low a little longer than ADO
and SKY it finally resorted to code-sharing with ANA, and market-averaged airfares
increased. Tokyo-Kagoshima, where low-cost competition lasted more than four years,
experienced SKY’s entry in 2002. It is interesting that the conduct parameter movement
is quite similar to the right-hand side of Figure 2 (b); that is, the high conduct parameterof SKY and the low conduct parameters of the FSCs. SKY was already suffering fromdeficits in Tokyo-Fukuoka, so it had to set airfares high to make profits, while it seems
that FSCs were trying to expel SKY from the market. This scenario also holds forTokyo-Tokushima, where low-cost competition lasted three years.
The only market where an LCC seems to have won a fare war against FSCs isTokyo-Asahikawa, as mentioned in section III(1). Before ADO entered this route, the
JAL-JAS group had an 85% market share while ANA had a 15% share, with theirconduct parameters －0.422 and 1.868, respectively. Then in 2004 ADO entered with
its conduct parameter 0.810, after its code-share partner, ANA, exited. This value is21

close to a monopoly level, but ADO’s fare was 16.4% less than JAL-JAS’s. By doing
this, the ANA-ADO group increased its market share from 15% to 30%, and JAL exited
due to the low profitability of the route, even though it still had a 70% market share.
Three other markets (Osaka-Sapporo, Osaka-Fukuoka, and Tokyo-Aomori)experienced only one- or two-year price wars initiated by SKY. The pre-entry conduct
parameter of the FSCs was 0.148, and the FSCs kept that parameter just above theCournot competition level even after the fare wars ended, while SKY entered with avery low conduct parameter and then exited the next year.

The FSCs may haveanticipated that SKY would exit soon without any price-matching strategy, since theyknew that potential demand was too low for SKY to achieve profitability.
Next we focus on the relationship between conduct parameters and market
share.

Usually, firms with strong market power will increase their price-to-cost

margins as their market share increases. However, firms with higher market sharesometimes choose low price (and as a result, low conduct parameters) in order to expel
rivals. Oum, Zhang, and Zhang (1993) empirically recognize this behavior through ananalysis of duopolistic competition between American Airlines and United Airlines.
Japanese FSCs appear to have chosen almost the same behaviors as AA and UA. The
correlation coefficient between an FSC’s conduct parameter and its market share is －22

0.517, which is statistically significant at the 1% level by t-test, while that of an LCC is
0.072, which is not statistically significant. This implies that LCCs behave as theoligopolistic theory predicts.
Oum, Zhang, and Zhang (1993) also argue that an airline’s conduct parameter
inversely correlates with market distance. Usually, US long-haul markets are so large
and thriving that more entrants try to enter and compete in them than in short-haulmarkets. As a result, conduct parameters decrease. This situation also occurred inJapan. By carrying out a simple regression of the conduct parameter on distance, we
observe a negative, though weak, relationship between conduct parameter and distance8.
Furthermore, this result tends to be stronger for LCCs than FSCs. These results seem
attributable to the “seemingly predatory” prices in the first and second years of ADO’s
and SKY’s entry into Tokyo-Sapporo and Tokyo-Fukuoka.

8

Since we detect the heteroskedasticity for this regression (BPG test: χ (23) = 25.45

with P-Value 0.000), we use the ML method. “a”, “b”: significant at 1% and 5%..
L
CPiAv
= 1.314− (0.00083 + 0.00447 DLCC ) Dist i + 4.301 DLCC

(3.122 a )( −2.159 b )(3.191a )( −3.474 a )Log − likelihood = −68.206

where DLCC is the dummy variable, each of which is 1 for LCCs’ elements. The
subscript “Av” beside CP means that the conduct parameter used here is the averagevalue of each firm at route i after an LCC enters. n=30.

23

2.

Market welfare

The latter half of this section discusses the effect of an LCC’s entry on
consumer surplus. Information for computing consumer surplus is obtained from thefollowing simultaneous equations of carrier-specific demand and price. Table 3 showsthe estimated results.

Table 3 Empirical results of demand and price equations
Parametert-stat.

Parametert-stat.

Own Price Elasticity

Demand Equation-1.089-3.709a

Output-0.464-4.696a

Cross Price Elasticity

0.383

2.043bRoute Marginal Cost

1.196

8.520aDistance

0.775

3.958aFrequency

0.537

4.486aPopulation

0.249

1.921cMarket Share

0.212

4.325aFrequency

1.186

25.540a

1st year of JAL-0.150-2.263bMarket Share

0.245

2.768a2nd year of JAL-0.058-1.019

1st year of ADO-0.057-0.375

3rd year of JAL

0.093

1.451

2nd year of ADO

0.079

0.382

4th and further years of JAL-0.129-2.040b

Price Equation

3rd year of ADO-0.184-0.852

1st year of ANA-0.080-1.137

4th and further years of ADO-0.345-2.637a

2nd year of ANA

0.047

0.587

1st year of SKY-0.212-0.952

3rd year of ANA-0.143-1.964b

2nd year of SKY-0.155-1.021

4th and further years of ANA-0.101-1.4493rd year of SKY-0.342-2.673a

1st year of JAS

0.008

0.077

4th and further years of SKY-0.396-2.699a

2nd year of JAS-0.213-2.843a

1st year of SNA-0.568-2.931a

3rd year of JAS-0.060-0.912

2nd year of SNA-0.450-2.433b

4th year of JAS

0.179

1.721

3rd year of SNA-0.523-2.508b

Post-exit Price of JAL

0.072

0.857

JAL-JAS Merger-0.143-2.515b

Post-exit Price of ANA

0.202

2.214bIncome-247.130-2.277b

Post-exit Price of JAS

0.408

3.769a9.460

2.263bPost-exit Price of ADO

0.208

0.992

1614.400

2.292b

CONSTANT-0.240-0.186

Income*Income
CONSTANTOur estimation method is the three-stage least squares. The empirical statistic
is that the system R 2 is 0.9911. The letter “a” beside a t-statistic means the parameteris significant at the 1% level, and “b” indicates significance at the 5% level.

24

Figure 3 Dynamic change in market average price before, during, and after
competition between LCCs and FSCs
Price/Dist
34
32
30
28

Price/Dist

26
24
22
20
Preent

1

2

3

4<

exit

Note: The vertical axis is airfare in Japanese yen per mile, and the horizontal axis is the n-th year of LCC entry(“Preent” is the pre-entry average fare per distance, “4<” denotes the average fare per distance in the fourth year andafter, and “exit” denotes the average fare of FSCs after an LCC has exited).

Before performing welfare analysis using the results of the econometric model,
we do a preliminary analysis of how the price per distance has changed from the LCC
pre-entry year to their post-exit year.

Figure 3 describes the change in fares per

distance. Each is the passenger-weighted average of six carriers. We have nine routes
for the pre-entry year and for the first year of LCC entry. The low-cost competition
ended within a year in Osaka-Sapporo and Tokyo-Aomori, and within two years in
Osaka-Fukuoka and Tokyo-Asahikawa. For these four routes, we observe the FSC’s
fares after an LCC has exited. For the other five routes, the cost competition continued
for more than three years. Therefore, we don’t observe a post-exit price.
Figure 3 shows the fare drops by 25% after LCC entry. In the routes in which
low-cost competition ended within one or two years, the initial significant price drop25

reversed, eventually exceeding the pre-entry price, probably because the FSCs tried tocompensate for the loss incurred during the fare war (see the dotted and dashed lines,
which jump from 1 to exit and 2 to exit). In the routes in which the wars lasted morethan four years, the fare was restored close to the pre-entry fare in the third year.
Figures 4 (a) and (b) describe the change in two kinds of consumer surplus
using the real values of price and output ( Wt ), as well as using estimated output andprice in our simultaneous equation ( Ŵt ). Looking at the graphs in those figures, ourestimated results ( Ŵt ), which predict that consumer-surplus curves will slope down in
the long run, look “pessimistic” compared with the case using the real values.

Figure 4 Change in consumer surplus in Tokyo-Sapporo (a) and Tokyo-Fukuoka (b)
1.15

1.04

(a)

1.02
1

(b)

1.1
1.05

0.98

Wt
Wt hat

0.96
0.94

0.95

0.92

0.9

0.9
Preent

1

2

3

4<

Wt
Wt hat1

0.85
Preent

1

2

3

4<

However, for Tokyo-Sapporo and Tokyo-Fukuoka, consumer surplus increased in thefirst year and first two years, respectively, of LCC entry, but decreased from the second
and third years. The total consumer surpluses from the first years can be summarized
as follows: the surplus decreased in Tokyo-Sapporo even from the optimistic viewpoint26

(1% decrease) as well as the pessimistic viewpoint (6% decrease).

As forTokyo-Fukuoka, consumer surplus may have increased by 1.5% from the optimistic
computation using real value, but decreased by 7.5% from the pessimistic computation
by estimated output and price. Among the other seven markets, those where low-costcompetition lasted more than three years saw relatively large gains in consumer welfare,
while other markets did not. Table 4 summarizes the percent change in consumer
welfare on a route-by route basis.

Table 4 Percent change in consumer welfare by low-cost competition
O/D
Sapporo Fukuoka Asahikawa Aomori Tokushima Miyazaki Kagoshima
Tokyo
-1.00
1.50
0.04
-2.60
3.52
6.98
4.23
Osaka
-2.17
-7.50
Finally, we comment on issues of social welfare. The industry’s profit in the
pre-entry year (1998) was 42 million USD for Tokyo-Sapporo and 44 million forTokyo-Fukuoka.

Profit significantly dropped in the first year of new entry, but

recovered and even surpassed the previous levels in the third year (61 and 72 million
USD, respectively) and stabilized from the fourth year on.
Jointly considering consumer welfare as shown in Table 4, it is apparent that
total welfare increased in five of the nine markets but decreased in Tokyo-Aomori, athin demand route. Meanwhile, only the industry-side benefited in Tokyo-Sapporo,

27

Osaka-Sapporo, and Osaka-Fukuoka.

Table 5 Cumulative industry profit during low-cost competition (million USD)
O/D
Sapporo Fukuoka Asahikawa Aomori Tokushima Miyazaki KagoshimaTokyo
374.00289.679.27-28.03
61.25
26.56
71.57
Osaka
26.19
24.01
Note: We assume 1 USD=120 yen.

V Concluding Remarks

Summarizing the findings of empirical analyses, the first and second years of
LCC entry saw very fierce fare wars in the two biggest markets, such that the antitrust
sector was about to intervene.

Despite the fare wars, consumer welfare did notincrease very much in any of the markets, probably due to the limited size of the market
pie in the off-peak season. To recover the losses incurred during that period, theindustry quickly tried to collude to restore prices to the pre-entry level or higher. At
the same time, the FSCs tried to expel the LCCs by financially battering them, andeventually succeeded in establishing code-share arrangements with them. The industry
succeeded in making a profit even during the fare ware period, and now the profits
continue through such collusive behaviors, while the gain in consumer welfare has been
relatively small and may actually decrease from now, especially in the two biggest
markets. Therefore, our conclusion is that Japanese regulatory sectors seem to stand28

by the industry rather than protecting consumers. Because this analysis was performed
using off-peak data, our next focus will be on the analysis of high-season data.

References(1) Appelbaum, E. (1982), “The Estimation of the Degree of Oligopoly Power,” Journalof Econometrics Vol.19, pp.287-299.

(2) Armantier, O., and O. Richard (2003), “Exchanges of Cost Information in the
Airline Industry,” RAND Journal of Economics Vol.34 No.3, pp.461-477.
(3) Brander, J. A., and A. Zhang (1990), “Market Conduct in the Airline Industry: An
Empirical Investigation,” RAND Journal of Economics Vol.21, pp.567-583.
(4) Brander, J. A., and A. Zhang (1993), “Dynamic Oligopoly Behavior in the Airline
Industry,” International Journal of Industrial Organization Vol.11, pp.407-435.
(5) Caves, D. W., L. R. Chistensen, and M. W. Tretheway (1984), “Economies ofDensity versus Economies of Scale: Why Trunk and Local Service Airline Costs
Differ,” RAND Journal of Economics Vol.15, pp.471-489.
(6) Dresner, M., J. S. C. Lin, and R. Windle (1996), “The Impact of Low-Cost
Carriers on Airport and Route Competition,” Journal of Transport Economics andPolicy Vol.30, No.3, pp.309-328.

29

(7) Endo, N. (2004), “Estimation of Conjectural Variation in the Japanese Domestic
Airline Market,” Journal of Logistics and Shipping Economics (Kaiun Keizai
Kenkyu), No.38, pp.41-50 (in Japanese).

(8) Fischer T. and Kamerschen D. R. (2003), “Price-Cost Margins in the US Airline
Industry Using a Conjectural Variation Approach,” Journal of Transport Economics
and Policy Vol.37 No.2, pp.227-259.

(9) Ito, T. (2003), Political Economy of Competition Policy in Japan: Case of Airline
Services, FTCJ.

(10) Iwata, G. (1974), “Measurement of Conjectural Variation in Oligopoly,”
Econometrica Vol.42, pp.947-966.

(11) Morrison, S. A. (2001), “Actual, Adjacent, and Potential Competition: Estimating
the Full Effect of Southwest Airlines,” Journal of Transport Economics and Policy
Vol.35, No.2, pp.239-256.
(12) Oum, T. H., A. Zhang., and Y. Zhang (1993), “Inter-Firm Rivalry andFirm-Specific Price Elasticities in the Deregulated Airline Markets”, Journal ofTransport Economics and Policy Vol.27 No.2, pp.171-192.
[2007.11.14 836]30