2013-31
Dynamic effect of inter-airline rivalry on airfares
and consumers’ welfare:Japan’s full-service vs. new air carriers
Hideki Murakami Yoshihisa Amano

Dynamic effect of inter-airline rivalry on airfares and consumers’ welfare:Japan’s full-service vs. new air carriers
Hideki MURAKAMI and Yoshihisa AMANO
Abstract. We analyzed the dynamic changes in carriers’ airfares and outputs and computed thechanges in the consumers’ surplus year by year after new Japanese carriers entered thriving
routes and started to compete with Japanese full-service airlines (FSAs). Using unbalanced
panel data of 222 route-and-carrier-specific sample observations, we found that new carriers
discounted airfares significantly as soon as they entered new markets, but two early-comers,
Skymark Airlines and AIRDO that had entered with very low airfares raised their price year by
year. The two FSAs All Nippon Airways and Japan Airlines responded to the new entrants andlowered their airfares to a much lesser extent than new entrants did, and kept their airfare levels
almost unchanged for at least four years from the first entry, although a tiny fluctuation of
airfares was recognized. The consumers’ surplus increased significantly in the first year of new
entries but gradually reduced as new entrants raised their airfares.
Key words: Japanese airlines, entry, dynamic change in airfare, consumers’ surplus
1. Introduction
In 1996, the Ministry of Transport of Japan authorized Skymark Airlines (SKY) and AIRDO(ADO) to commence operations. These new entries were followed by Skynet Asia Airways(SNA, now renamed Solaseed Air) and Star Flyer Inc. (SFJ), founded in 2000 when theJapanese Ministry of Land, Infrastructure, and Transport deregulated airlines’ entry and exit andairfares.
These new airlines were initially referred to as low-cost carriers (LCCs). However,
although new Japanese carriers have some features of LCCs in common with those of othercountries in terms of route-by-route service and low airfares, the services and cost structures ofJapan’s new entrants differ from those of foreign LCCs. The most salient difference between theforeign LCCs and new Japanese carriers is that, unlike the situation in the U.S., Japan’s
metropolitan areas do not have secondary commercial airports available for LCCs. In addition,
the cost structures of the new Japanese carriers are almost the same as those of full-service
airlines (FSAs) except for the new carriers’ low labor cost. The airplane-maintenance cost of the
new carriers could be more expensive than those of the FSAs, however. The reason is that new
carriers do not have their own maintenance subsidiary company or division, and they had toarrange to have the maintenance performed by the FSAs. Since FSAs charge the new carriers
high maintenance fees in order to weaken the new carriers’ cost competitiveness, the new

carrieers are com
mpelled to lower their input prices such as labor,lin-fligght servicess, andmainttenance costs. In additionn, new Japannese airlines offer some frequentfflyerr programs (FFPs)
withoout simplifying the servicce, and costs such as land
ding fees, fueel prices andd taxes are unniformamonng Japanese airlines; thuus, there is nnot much diffference betw
ween the new
w airlines annd theFSAss. However, the Japanesee governmennt imposes a tax for the fixed assets on each airrplane,
the coosts of the neew Japanese new airliness are higher thantthose off oversea LC
CCs.
Figure 1 deepicts the aveerage cost off seven Japannese airlines over a recentt 10-year perriod.
The aaverage costss of the new carriers did not differ grreatly from thhose of the FFSAs (ANA,, JAL,
and JJAS). In factt, ADO wen
nt bankrupt iin 2002, andd in 2004 SN
NA/Solaseed Airlines recceived
finanncial support from the Ind
dustrial Revittalizing Corp
poration.

Fig. 1. Changes in Japanese airlines’ average costs from 1998 to 22008.
Sourcce: JAA Civil Aviation
A
Dataabook and finaancial statemeents of each airline, 1998–20008.
O; SKY,
JAL: Japan Airliness; ANA: All Nippon
N
Airwayys; JAS: Japann Air System; ADO: AIRDO
Skymmark Airlines; SNA: Solaseeed Air; SFJ, Sttar Flyer Inc.

We analyzeed the dynammic price com
mpetition bettween Japan’s FSAs andd the new Jap
paneseairlinnes (i.e., SKY
Y, ADO, SNA
A, and SFJ).. The carrierrs have differrent scales anand characterristics.
The aanalysis incluuded not onlly the duopooly market (i.e., served byy JAL and A
ANA), but also themarkket in which severals
new airlinesahavee entered.
Having obsserved the airrline regulatoory reforms and the founnding of new
w carriers, wee were
motivvated to anallyze the markket performaance for the routesrin whhich new carrriers have enntered,and hhow the maarket perform
mance has cchanged overr time. To measuremthe performancce, we
estim
mated the strructural dem
mand and quuasi-supply equations, anda we introoduced entry
y-yeardumm
my variabless for the new
w entrants annd for the FS
SAs to investtigate the dyynamic changes in
airfarres and passeenger volumee after the neew carriers en
ntered the market.
m
In Section 2, we revieew the literaature on thee competition between FFSAs and carrierscproviding heterogeneous service such as LCCs, mainly concerning the U.S. In Section 3 we model

the entry-effect of a firm on the market price and output in a “one-shot game” case, assumingthat two firms produce heterogeneous products. We discuss the results in relation to the dynamic
competition issue, and we describe the econometric model. In Section 4 we demonstrate ourdataset, and in Section 5 we present and discuss the empirical results and the consumer surplus.
Section 6 provides our concluding remarks.
2. Literature Review
Although Japanese new carriers cannot be classified in the LCC category, but it is useful to review
literatures on LCCs for our research1. There have been many studies on the economic impact of theentry of the U.S. LCCs into the air transportation market. Morrison and Winston (1996)
empirically showed that Southwest Airlines forces its competitors to reduce their fares.2 Dresner
et al. (1996) and Morrison (2001) measured the airfare-reduction effect of LCC entry in the
primary and adjacent markets by incorporating LCC dummy variables in their econometric work.
In an empirical analysis of the U.S. domestic air markets that included a number of LCCs, Vowles(2000) found that Southwest Airlines, other LCCs, and the market share of LCCs had statisticallysignificant effects on the decrease in carriers’ airfares. Alderighi et al. (2004) estimated the priceequations derived from oligopoly theories and found that competition between European LCCs
and FSAs reduced all classes of the FSAs’ airfares. The airfare changes after LCC entry were
investigated by Pitfield (2005, 2008) in time series analyses. Goolsbee and Syverson (2005) andOliveira and Huse (2009) studied the effects of LCC entries on the incumbents’ responses.
Fu et al. (2006) explicitly incorporated a duopolistic inter-firm rivalry into their LCCs
versus FSA competition study, and they incorporated the effect of the pricing behavior of an
unregulated-monopoly airport on the downstream competition between LCCs and FSAs.
Murakami (2011a) empirically analyzed the effect of LCC entry on airfares and market welfare
in the Japanese domestic airline industry. According to Fu et al. (2011), service differentiation
between FSAs and LCCs leads to the cartelized behavior of FSAs.

Murakami and Asahi (2011)

studied the effect of multimarket contact on LCCs and FSAs and LCCs keep their airfares low
despite the fact that they frequently contact with each other.
Despite the number of studies on the effect of LCC entry on airfares, few researchers haveanalyzed the dynamic effect of LCC entry on both airfares and market welfare using data thathave a time-series dimension. Here we explain our econometric analysis of these untried issues,
using panel data from 1998 to 2008 and focusing on the markets where new carriers entered.
1

Why we reviewed U.S. LCCs is that originally, Japan’s new carriers were called “LCC” and theirpricing behavior, the quality of services (no or little frilled services) were similar to the U.S. LCCs’.
2
Morrison and Winston, The Evolution of the Airline Industry, Brookings Institution (1996), pp.132-156.

For the time-series dimension of our dataset, we chose to discard the samples beyond 2008
since Japan Airlines’ data were inconsistent before and after the year 2008 and we found it
impossible to adjust the data to be consistent; Japan Airlines changed their manner of financial
disclosure from an unconsolidated to consolidated statement of accounting and then wentbankrupt in 2010.
3. The ModelIn this section we explain the model and derive the demand and quasi-supply functions,demonstrate the method of approximating marginal cost, and show what happens to market
output if FSAs increase the degree of service homogeneity.
3.1. Quasi-Supply and Demand Equations
In a perfect competition, a firm’s supply function can be derived by taking thefirst-order condition of profit function with regard to price, according to Hoteling’s lemma.
However, since we focus on the oligopolistic airline industry of Japan, we must assume thecarrier-specific “quasi-supply” instead of an ordinary supply function. Assume the following
general profit functions of Carrier 1 (a new carrier) and Carrier 2 (an incumbent FSA) thatengage in Cournot competition with each other.

max  1 Pq1 , q 2 , TC1 q1 , q1 ; 1 

(1)max  2 Pq1 , q2 , TC2 q2 , q2 ;  2 

(2)

q1

q2

where 1 and  2 are the coefficients of rival’s output in the inverse demand function
usually assumed in a Cournot model.
For convenience, let 1 be the numeraire, and  2 be  *  0,1 . We can rewrite
Eqs. (1) and (2) as follows:(3)
1

max  Pq1 , q2 , TC1 q1 , q1 
q1max  2 Pq1 , q2 , TC2 q2 , q2 ;  *
q2



(4)

Taking the first-order condition of Eqs. (3) and (4) with regard to each output, we obtain thebest reply functions; these are also the inverse quasi-supply functions of Carrier 1 [Eq. (5)] andCarrier 2 [Eq. (6)] with the theoretically expected sign ahead of each variable:

P  f1 ()q1 , () MC1 , ()m1 P  f 2 ( )q 2 , () MC 2 , () m2 ; ()

*



(5)
(6)

where m1 and m2 are the price mark-up factors.
As for the carrier-specific demand functions, ours is ordinary Marshallian demand,where demand is explained by own price, cross-price, income, population, and other control
variables. The demand function of Carrier 1 is written as follows:

q1  g1 ()q1 , () q 1 , () INC , () POP, Control (7)

These models explain the one-shot equilibria. As we see later, we will analyze thedynamic effect of new carriers’ entries, so we need to explain how to relate these one-shot
equilibria to the dynamic issue. One possible method is that we assume the finite game with along time dimension or an infinite game. In this case, our solution is to derive the series of thesubgame-perfect equilibrium at each stage with the discount factor. Although this method does
not explain the true dynamic competition such as in Stackelberg fashion, our assumption of a“series of one-shot games” is supported by a carrier.3
3.2. Approximating Marginal Costs
To approximate route- and carrier-specific marginal costs, the most commonly usedmethod is to estimate the translog total cost function together with the shared equations derived
from Shephard’s lemma, such as that reported by Caves et al. (1984), Gillen et al.(1990), andJohnston and Ozment (2013).and Fischer and Kamerschen (2003). However, since we cannot

obtain a sufficient number of observations to use the translog functional form that requires many
numbers of variables, we use the following formula of approximation that was proposed by
Brander and Zhang (1990, 1993) and Oum et al. (1993)4 and used by Murakami (2011a,
2011b):(8)
whereis the aggregate average cost of carrier k in year t,

andis the average distance flown by airline k in year t.

is the distance of route i,

The aggregate average cost is calculated by dividing operating costs by the available
ton-kilometer. And

was derived by dividing total distance flown by the number of flightsof the year.
is a parameter that denotes the degree of taper. Caves et al. (1984) and Fischer andKamerschen (2003) showed that the total cost function was strictly concave. Therefore,(8) ranges between 0 and 1. If

in Eq.

is 0, the carrier’s marginal cost is proportional to the distance

3Source: The authors’ interview of Mr. Go Nishimura and Katsuhiko Okamura, who worked in the pricing

division in All Nippon Airways and Solaseed Air, respectively. According to them, carriers do not react tothe previous day’s airfares of their rivals, but they refer to last year’s airfares from the same season. Theinterviews were conducted on May 24 and July 26, 2013, respectively.
4
See Brander and Zhang (1990, pp.572-575), Brander and Zhang (1993, pp.417-420), Oum et al. (1993,
pp.175-178).

flown by the carrier, whereas in the case of= 1, the marginal cost of each airline isindifferent to distance.
Studies such as those by Brander and Zhang (1990, 1993) and Murakami (2011a, 2011b)
demonstrated that

ranges between 0.15 and 0.67.5 We also estimate the unknown parameterwith the following equation:
/

(9)

1

To obtain , we estimate the price Eq. (9) above by the nonlinear least-squares method.
The system-wide conduct parameter ? is also obtained from Eq. (9).6
Before we estimate the price Eq. (9), we need the information about the price elasticity ofdemand . Therefore, we will estimate the following Marshallian demand function by usingroute-specific unbalanced panel data.
ln

where

ln

ln

∗

ln

is the lowest airfare at route i in year t.

ln

is the arithmetic average of per-capita

income of the cities/counties around route i in year t. Bothretail price index.

(10)
and

are adjusted by theis the arithmetic average of the population of route i in year t, andis the Herfindahl index of route i at year t calculated from the share of each airline. “Time” is thetime trend variable. As we multiply

and, parameter 2 stands for the “grossregional income elasticity of demand.” The reason why we did this multiplication is that if we
separate these two variables, the estimate coefficient becomes negative (but not statisticallysignificant) against the assumption of microeconomics. Okinawa’s per-capita income is much
lower than Japan’s average, but the demand for air transportation is large since Okinawa is an
island isolated from the mainland (Honshu). This seems to affect the estimation, and when we
substitute per-capita income with gross regional income, this problem is eliminated as will beshown in Table 1.
3.3. Entry effect of a new carrier when firms produce heterogeneous services

5

Oum et al. (1993) obtained λ=0.43, and Murakami (2011a,2011b) obtained λ=0.374, 0.271.
⁄/) means the prediction of changes in the supply amount of the

6The conduct parameter “v”(=

third airline when Airline k increases its supply. If all of the airlines move in the same direction at thesame rate, the result is N−1 (N = number of carriers), indicating collusion. If the conduct parameter is 0, it
implies the Cournot competition. If it is −1, the price equals the marginal cost, and we interpret it ashomogeneous Bertrand competition. See Brander and Zhang (1990), Oum et al. (1993), and Fischer andKamerchen (2003).

This subsection investigates the following question: if an FSA successfully separates its
markets from its rivals’ markets, what happens to the FSA’s output and that of new carriers.
When airlines provide homogeneous services, the model is rather simpler than what we havepresented, but it is natural that Japan’s FSAs provide higher-quality service than new carriers.
Considering this fact, we assume the following general profit functions of firm 1 (a new carrier)and firm 2 (an incumbent FSA) that engage in Cournot competition with each other. There arealternative versions of Eqs. (1) and (2) in subsection 3.1; functions (1) and (2) are written ascomposite

functional forms, and if we write (1) and (2) as function of outputs only, they will be:

max  1 q1 , q2 ; 1 

(11)

max  2 q1 , q2 ;  2 

(12)

q1

q2

where 1 and  2 are the coefficients of own output in the inverse demand function usuallyassumed in a Cournot model.

For convenience, let 1 be the numeraire, and  2 be  *  0,1 . We can rewrite (11)and (12) as follows:max  1 q1 , q2 

(13)

q1



max  2 q1 , q 2 ;  *
q2



(14)

By our assumption, the smaller  * is, the more independent an FSA’s markets, and the
FSA becomes a monopolist.
Taking the first-order condition of Eqs. (13) and (14) with regard to each output, we
obtain the best reply function described as the form of implicit function:

 11 q1 , q 2   0
 22 q1 , q 2 ;  *   0
 iwhere  ii 
.

(15)
(16)

qi

  and q   , and substituting q   andSolving for each output, we obtain q1*  *  q  , q    0
 q  , q  ;    0

*
2

*

*
1

*

q 2*  * into Eqs. (3) and (4), we obtain:
1
1
2
2

*
1
*
1

*

*

*
2
*
2

*

*

*

(17)
(18)

In order to see the effect of the increase in heterogeneity, we totally differentiate Eqs. (17) and(18).

 111q1
1 q 2
  12
0
*

 *

(19)

 212

q1
2 q 2  22  22 *  0
*

 *

(20)

Rewriting Eqs. (19) and (20) into a matrix from, we obtain: q1 1
  11
 121   *   0 


 2

2 
2 


 21  22  q 2     2 *   * 1
  11

Let H  (21)

 121  , and since this is the symmetric nonsingular matrix, H can be inverted.
 222   21
2

Then we obtain: q1 2
 *
  221
    1 2

1
 212    212
 q 2   11 22   12
 *
  

1
 0 
  12
2
1  11    2 * 

(22)From (22) we obtain: 12 2 *
q1
1
 222   121  212
 *  11

(23)

  *
q 2
  1 211 2 1 2*
11 22   12 21


(24)

1

2

1

2

Matrix H must be a negative definite matrix due to the second-order condition for profit
1
1
maximization. Therefore,  11
 0 and  11
 222   121  212  0 . In addition, since we assume
1
2
Cournot competition,  12
 0 , and  21
 0 due to the “strategic substitute” effect.  * isthe index of the degree of heterogeneity and comes to the right-hand side of the inverse demandwith a negative effect on price. As for the sign of  2 * , we take the derivative of Eq. (18) with2

regard to  * . Since it is obvious that  * has a negative effect on price and therefore profit[see Eq. (6)],  2 *  0 . Substituting this result into Eqs. (23) and (24), we obtain:21() 12() 22 *
q1

0
1
 222   121  212
 * ( )  11(25)1() 11() 22*
q 2

0
1 2
()  11
 22   121  212
 *

(26)









And we can suggest the following proposition.
Proposition: If an FSA distinguishes its service and creates a new market against new carriers(that is, smaller  * ), its output increases and a new carrier’s output decreases.
This proposition implicitly states that the fixed effect dummy variable for FSAs in the
demand equation would be significantly positive as long as the FSAs provide differentiatedservice against new carriers.

In addition, the effect of  * on airfares might not be specified;theoretically,

of

⁄

⁄

the
⁄

effect∗ * onairfare

is

⁄

⁄

⁄

∗

0

and

0, but the creation of new market by FSAs could lead to the “shift-up”

of the quasi-supply and the demand curves, that is, pushing the equilibrium point to the
up-and-right direction.

We will discuss this issue in the next subsection.

3.4. Structural Equations to Estimate
This subsection models the structural equations based on the prior subsections 3.1 to 3.3. Our
quasi-inverse supply function in the econometric model goes as follows:
ln

ln

ln

ln
(27)

where,

is the airfare of each carrier k at route i in year t,

route i in year t, andis the route-specific marginal cost of each carrier at route i in year t

calculated from Eq. (8).
,,is the number of passengers atshows the market share of carrier k at route i in year t.
,

are the carrier-fixed effect dummy variables for new carriers, andsubscript “n” stands for the year after their entries. By these dummy variables we see thedynamic effect of entries.

,

, and

are also the dummy variables used to see

the effect of the FSA’s airfare-restoring behavior. These variables are each 1 for the FSA’s
elements in the year after a new Japanese airline’s exit, and otherwise they are 0.
We will also estimate the following demand function together with the inverse
quasi-supply function.

ln

β

β ln

β ln

β ln

β

β

β ln
β

β

β

β

β

(28)

β

where

is the origin-destination distance that is not affected by carrier or time.

thefrequency

frightofeachcarrier

k

at

route

population-weighted-average of per-capita income, and

i

in

year

is

t.

is

the

is the weighted average of

populations of origin-destination cities or county areas around the main cities. The functional
form of these two variables are quadratic, and these forms were selected by comparing
log-likelihood functions of log-linear, log-linear of gross regional income (=ln(and linear-quadratic.

,

,

, and

∗

)

are the carrier-specific fixed-effectdummy variables to demonstrate the dynamic behavior of FSAs against new carriers’ entries.
The benchmark markets of these carrier-specific dummy variables are those without new
carriers’ entries; that is, one-year before new carriers’ entries and one-year after their exits.
Therefore, we expect the parameter of FSAs’ carrier-specific dummy variables will be positive.
According to our proposition in section 3.3, the effect of increasing  * on new carriers’
outputs is negative, as long as FSAs successfully create their markets.
increasing *

However, the effect ofon airfares are not necessarily negative due to the effect of “shift-up” ofdemand curve, that is, the positive sign of the parameters of carrier-specific dummy variables inthe demand function.
4. The Data
We used the data of Japanese domestic routes from 1998 to 2008. The total number ofroutes is 14. The period of data for each route depends on the timing at which new carriers
entered. We chose the period from one year before the new entry to one year after the exit years.
For example, Tokyo-Miyazaki’s data starts from the year 2002 to 2008 because SNA had
entered in 2003 and it still continues to serve. The total number of samples is 222.
The data sources for available ton-kilometer, the operating costs of each carrier, the flight
distances, and the number of flights are the Koku Tokei Yoran (JAA Civil Aviation Handbook)issued by the Japan Aeronautic Association and the website of each airline. The passenger data
and the distance of each route were obtained from the Koku Yuso Tokei Nempo (Yearly
Statistical Survey of Japanese Aviation) published by the Ministry of Land, Infrastructure andTransport and Tourism. The fare information was obtained from the Jikoku Hyo (a timetable ofrailways and airlines that is issued monthly by the Japan Tourist Bureau).
The demographic data sources such as population, income, and retail price index are fromthe Kakei Chosa Hokoku (Family Income and Expenditure Survey), which is published by theJapan Statistics Bureau, and the websites of the relevant prefectures and cities.
Passengers, numbers of flights, population and income are monthly data; we used April’s
data from each year, when the airline demand is the lowest in Japan. When evaluating thelowest-demand month, we observed that the carriers issue various types of discount airfares togenerate demand. By choosing April, then, we can analyze the carriers that were mostcompetitive each year.

The lists of routes and descriptive statistics are shown in Appendix 1

and Appendix 2, respectively.
5. Empirical Results and discussin
The estimation results of Eq. (7) are shown in Table 1. We estimated them by OLS
with heterocedasticity robust standard errors. The data are the route-specific unbalanced panel
data of 14 routes for 2–11 years.
Table 1. The estimated parameters of Eq. (10)Variable
Price elasticity (η)
Per-capita income*population (β）
Distance (γ)
Herfindahl index (δ)
Time trend (ρ)
Constant (A)Log likelihood function= −78.670, n = 76

Estimated Coefficient−0.8391.381
2.038−1.140−0.041−25.692

T-Ratio−2.562*
4.124**
4.659**−3.409**−1.486−1.936

Note: ** and * = significant at the 1% and 5% levels, respectively.
The price elasticity of demand (η) is −0.839, which was significant at the 5% level. Then,
using the estimated η, we further estimate λ and the system-wide conduct parameter ν. The data
used to estimate the Eq. (9) are different from those used for the estimation of Eq. (10). They are
the carrier-specific unbalanced panel data of 2 to 5 carriers in 14 routes for 2–11 years.

Using

the nonlinear least-squares method, we obtain the estimated results shown in Table 2.
Table 2. The estimated parameters of Eq. (9)Coefficient
Estimated Coefficient
λ
0.216
ν
0.018
Log-likelihood function= −2548.48, n = 222

T-Ratio13.6716.820

Note: Both parameters are significant at the 1% level.
The parameter λ is 0.216, which rejects the null hypothesis that λ equals 0 at the 1% level.

Thesystem-wide conduct parameter ν is 0.018. This value is very close to the Cournot-competition

hypothesis, but is rejected at the 1% level of significance. Therefore, the system-wide fashion ofcompetition in Japanese domestic air markets falls between the Cournot fashion and collusion.
Comparing this result with Murakami (2011a), who found that ν was −0.242, Japanese airlines
are less competitive than before, since the “latecomer” carriers, SNA and SFJ, chose to soften
their competition compared to the first comers, that is, ADO and SKY.
Next, we discuss the empirical results of the structural equation model in Table 3.

Table 3. Estimation results of the structural equations
Inverse quasi-supply (=airfare) function
Estimated
Variable Name
T-Ratio
Coefficient
Passenger−0.038−2.474*
Marginal cost
0.656
12.170**
Share
0.111
4.359**JAL−JAS merger
0.178
5.450**JAL exit
0.017
0.209
−0.055−0.525
ANA exit−0.142−1.098JAS exit
ADO exit
0.524
2.079*ADO 1st year
−0.658−3.644**
ADO 2nd year
−0.404−3.157**rd
ADO 3 year
−0.269−2.113*th
ADO 4 year
−0.169−2.647**st
SKY 1 year
−0.532−7.419**nd
SKY 2 year
−0.418−5.801**rd
SKY 3 year
−0.290−3.557**th
SKY 4 year
−0.249−4.015**
SNA 1st year
−0.215−1.998*nd
SNA 2 year
−0.243−1.883
SNA 3rd year
−0.141−1.086
SNA 4th year
−0.311−2.843**−0.205−1.604
SFJ 1st year
SFJ 2nd year
−0.168−0.927
SFJ 3rd year
−0.004−0.021
3.877
7.054
Constant
Note:

**

and

*

Demand function
Estimated
Variable Name
CoefficientAir fare−0.762
Distance
0.605
Frequency
1.133
Share
0.177
Income−97.077
Income2
3.725
Population−21.976
Population2
0.735
JAL 1st year
0.170
JAL 2nd year
0.165
JAL 3rd year
0.086
JAL 4th year
0.122
ANA 1st year
0.234
nd
ANA 2 year
0.250
ANA 3rd year
0.250
ANA 4th year
0.247
JAS 1st year
0.176
JAS 2nd year
0.056
JAS 3rd year
0.192
JAS 4th year
0.257
JTA 1st year
−0.076
JTA 2nd year
−0.026
Constant

T-Ratio−5.594**4.033**
30.94**
3.907**−0.7380.734−2.135*2.137*
2.041*
1.866
0.834
1.557
2.676**
2.521*
2.094*
3.049**
1.345
0.387
0.954
1.247
−0.250−0.086803.9900.950
2= significant at the 1% and 5% levels, respectively. System R =0.973, R2 ofquasi-supply=0.622, R2 of demand=0.916, n=222, estimated by Iterated 3SLS.

The endogenous variables are specified as airfare and price, so the model isover-identified. As for the quasi-supply, the parameter ‘passengers’ is slightly negative. Thisimplies that economies of density exist. The first two new carriers discounted their airfares
significantly for the first four years of entries, unlike the two latecomers, but airfare levels have
gradually risen, as shown in Figure 2.
Testing the hypothesis that the first year’s airfare levels equal the fourth year’s for ADO
and SKY, the hypotheses were rejected at the 1% level of significance (the Wald chi-squarevalues with a degree of freedom equal to one are 6.864 and 11.400, respectively). ADO’s
price-adjusting behavior can be explained by the following fact; when it entered theTokyo-Sapporo market in 1998, it was a budget carrier fully independent of the FSAs, but after
it went bankrupt and then revived under the codeshare agreement with ANA, ADO stopped
offering aggressive discounts. SKY has been independent of FSAs since its founding in 1996
(the first entry was in 1998) as of now, but its price-adjusting behavior is quite similar to
ADO’s.
0.000-0.100

1st year

2nd year

3rd year

4th year-0.200ADO-0.300SKY-0.400SNA-0.500SFJ-0.600-0.700Fig. 2. Overtime changes in the airfares of new Japanese carriers.
Note: The airfares were adjusted by retail price index (chain of average).
During the period we analyzed, SKY seems to have faced the same situation as ADO, but
unlike the case of ADO, SKY was saved by a large investment from an individual entrepreneur
who managed an internet service provider.7
Regarding the carrier-exit dummy variables, the FSAs did not raise airfares after theirrivals exited, but ADO did. This means that ADO is not a U.S.-type LCC like Southwest
Airlines in the 1990s that kept airfares at low levels, but rather is similar to FSAs.
In the demand function, the signs of ANA’s fixed-effect dummy variables are consistently
positive, and JAL’s first year is also positive and significant. Considering our Proposition, ANA
successfully created different markets against the entry of new carriers, but other FSAs did notnecessarily do so. When we derive the reduced form of demand function, new carriers’
parameters are also positive, and this result is contrary to the assumption of FSAs’ creation of
new markets. Considering this result and the unstable parameters of FSAs, only ANA succeeded
in distinguishing itself compared with other FSAs.
Our final analysis is how the consumers’ surplus changed from the first year of entries by
new carriers to the fourth year. Table 4 shows the increase or decrease in the consumers’ surplus
on year by year and carrier by carrier bases. The method of computation is to use the estimateddemand function and calculate the change in the triangle surrounded by the intercept of inverse
7The Nikkei, September 24th, 2003.

demand, the average airfare of each carrier, and the corresponding quantities of each carrier.
Therefore, what is derived was is the size of trapezoids (=increase or decrease in consumers’
surplus).
Table 4. Change in consumers’ surplus after new entries of carriers
JAL
ANA
JAS
JTA
ADO
SKY
SNA
SFJ
TOTAL

1st year362.51583.82229.73−12.57553.68359.11
96.54
156.212329.02

2nd year351.77624.1772.71−4.31
337.12281.10109.21127.86
1899.63

3rd year
182.72624.17250.794th year259.61616.60336.64223.47194.2063.15
3.03
1541.53

139.93166.52140.081659.38

Total
1156.612448.75
889.88−16.881254.20
1000.93408.98287.097429.57

Note: 1,000USD (1 USD = 100 Yen).

Overall, the first-year impacts were the largest, and the impacts gradually got weaker.
However, the consumers’ surplus was apparently increased by the new entries. From theviewpoint of consumers’ protection, the introduction of the policy allowing new carriers’ entries
was successful. However, it must be noted that this result may have come from the large
increase in consumes’ surplus in two large markets, Tokyo-Sapporo and Tokyo-Fukuoka.
Looking at the results of JTA and SFJ (which are comparatively small carriers), our concern isthat small communities might not have benefited or benefited only slightly from the policy.8
Another concern is that the merger of JAL and JAS generated a $230,000 USD loss of
consumers’ surplus in the merger year. More attention should be paid to merging carriers, andforecasts of the welfare gain or loss will be needed.
6. Conclusions
The outstanding features of our analyses are that we modeled the effect of market
separation on the outputs of carriers and empirically found that the border of market separation
was ambiguous, that new entries improved the consumers’ surplus of large markets, seemingly,
and that the first-year impact on the improvement of the consumers’ surplus was largest andthen gradually declined. From the perspective of consumers’ welfare, it would be better to leave
ambiguous the border of market separation between FSAs and new carriers and to let

8JTA connects the mainland (Honshu) and Okinawa and flies between islands in Okinawa. SFJ was based
at Kita-Kyushu Airport during this examination period and connected Kita-Kyushu City and Tokyo.
Kita-Kyushu City is in Fukuoka Prefecture, but the Tokyo-Kitakyushu market is much smaller than theTokyo-Fukuoka market.

passengers move between FSAs and new carriers, since the market separation enforces theincrease in FSAs’ output; this would lead to enhancement of the monopolistic power of FSAs.
We also need to let new entrants maintain their airfare levels in the long run. To do this, we needto open the slots of large airports to new entrants and bring up the new entrants so that they cansurvive the airfare competition with FSAs.
The limitations of our paper are that (1) we could not cover the period during which JALchanged their disclosure method, and (2) we did not include Japanese LCCs such as Peach
Aviation, Jetstar Japan and Air Asia Japan due to the unavailability of data. These drawbacks
will be eliminated by our future research.
Appendix 1. The route list used for estimation
Route
PeriodDistance (km)Tokyo-Sapporo
1998-2008
894
Tokyo-Fukuoka
1998-2008
1041
Osaka-Sapporo
1998-2001
1161
Osaka-Fukuoka
1998-2001
578
Tokyo-Asahikawa
2003-2008
1052
Tokyo-Aomori
2002-2004
690
Tokyo-Tokushima
2002-2007
703
Tokyo-Miyazaki
2002-2008
1023
Tokyo-Kagoshima
2001-2008
1111
Tokyo-Kobe
2006-2008
695
Tokyo-Kitakyushu
2005-2008
958
Tokyo-Osaka
2007-2008
678
Tokyo-Nagasaki
2005-2008
1143
Tokyo-Okinawa
2006-2008
1687
Number of Routes = 14, Number of Samples = 222

Carrier (period)
JAL(98-08) ANA(98-08) JAS(98-02) ADO(99-08) SKY(06-08)
JAL(98-08) ANA(98-08) JAS(98-02) SKY(99-08)
JAL(98-01) ANA(98-01) JAS(98-01) SKY(00)
JAL(98-01) ANA(98-01) JAS(99-01) SKY(99-00)
JAL(03-08) ANA(03) ADO(04-08) SKY(08)JAL(03-04) ANA(02-03) JAS(02) SKY(03)JAL(03-07) ANA(02-03) JAS(02) SKY(03-06)
JAL(02-08) ANA(02-08) JAS(02) SNA(03-08)
JAL(01-08) ANA(01-08) JAS(01-02) SKY(02-06) SNA(08)JAL(06-08) ANA(06-08) SKY(06-08)
JAL(05-08) SFJ(06-08)
JAL(07-08) ANA(07-08) SFJ(08)JAL(05-08) ANA(05-08) SNA(06-08)
JAL(06-08) ANA(06-08) JTA(06-08) SKY(07-08)

Sample
40
37
13
13
13
6
12
21
24
9
7
5
11
11

Appendix 2. Descriptive statistics
Variable Name
Distance
Passenger
Price
Price (lowest)
Poplation
IncomeShare

Mean
985.45088433.982
26787.838
23946.396
3944138.636
2010057.529
34.234Standard Error
15.5595731.941
497.385490.32154251.184
127487.364
1.290

Minimum
578.000
1599.000
9600.000
9600.000
1966125.798
369972.647
1.463

Maximum
1109.000
324299.000
40800.000
40800.000
4936713.245
4578925.465
100.000

Median1023.000
53069.000
28000.000
23000.000
4100038.640
432980.034
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