2009-25
Low-Cost Entry, Inter-Firm Rivalry, andWelfare Implications in US Large Air Markets
Hideki Murakami

Low-Cost Entry, Inter-Firm Rivalry, and Welfare Implications in US Large Air Markets1

Hideki MURAKAMI

Abstract

This paper empirically analyses the patterns of inter-firm rivalry between low-cost and fullservice carriers by carrier and airport bases, and demonstrate welfare implication of LCC, using 1163 US

cross-sectional data of 1998 when LCCs were purely no-frilled carriers. Our main findings are: (1) thatboth LCC and full service carriers keep higher price-cost margins when LCCs enter in the secondary

airport, while especially full service carriers suffer from low price-cost margin when LCCs enter the same

markets, (2) that total gains of welfare are 25.5 million USD for our dataset, and 90% of welfare gains

come from the gain in consumer’s surplus. LCCs’ cumulative profit is 4.45 million USD, but fullservice carriers lost 1.92 million USD in total due to the competition by LCCs, (3) that LCCs sometimes

provide unreasonably small (i.e, less-than-monopoly) capacities instead of profit-maximizing ones when

they have no information about own demand curves.

Key Words: low-cost carrier, inter-firm rivalry, social welfare

1

Please do not quote or cite without author’s permission.
11. Introduction

There have been many studies on the economic impact of the US low-cost carrier (LCC)’s

entry into the air transportation market.

Morrison and Winston (1995) empirically showed that

Southwest Airlines reduces the fare of every carrier it competes with.2

Dresner et al. (1996) andMorrison (2001) showed the airfare-reducing effect of low-cost entry in the primary and adjacent markets

by incorporating LCC dummy variables. Pitfield (2005, 2008) studied the airfare change after low-costentry by time series analysis. Goolsbee and Syverson (2005) and Oliveira and Huse (2009) studied theentry effect of LCCs on the responding behavior of incumbents. In studies on inter-firm rivalry among

airlines, Brander and Zhang (1990, 1993), Oum, Zhang, and Zhang (1993), Fischer and Kamerschen(2003), and Fageda (2006) empirically estimated the conduct parameters of airline industries in the

United States (the first three of four studies) and Spain (the last study). Furthermore, Fu, Lijensen andOum (2006) bridged the studies of LCCs vs. full-service carriers (FSCs) and duopolistic inter-firm rivalry,

and also incorporated the effect of pricing behavior of unregulated-monopoly airports on the competition

between LCCs and FSCs.

In this study we attempt to bridge the issues of the low-cost competition and inter-firm rivalry

measured by conduct parameters, following the line of research initiated by Brander and Zhang (1993).

We highlight not only the duopoly but also the larger markets where more than two carriers operate,

2

Morrison and Winston (1995), pp.132-156.

2

whereas previous studies have dealt with duopoly cases.

Furthermore, we will demonstrate, fromseveral aspects, not only the consumer welfare but also the total welfare derived from simultaneous

demand, airfare, and profit equations, using a carrier-specific dataset with 1163 data observations.

The

next section derives the conduct parameter and simultaneous equations theoretically and then converts

them to the econometric model. In Section 3 we present the data and in Section 4 we show the empiricalresults and perform several analyses of inter-firm rivalry between LCCs and FSCs. In Section 5 we

present welfare implications and Section 6 is the summary and concludsions.

2. The model

Early studies on estimating conjectural variation by CPM (conduct parameter method) toanalyze inter-carrier rivalry are Iwata (1979), Applebaum (1982), and Bresnahan (1981)(1989) and they

are followed by the studies on airline industries listed up at Section I.

Those studies on airline industry

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

and American Airlines, compete.

Our analysis uses, like many studies do, the cross-sectional data. Our choice of year is 1998.
Now not a few LCCs, such as ATA3 and Jetblue, have entered long distance markets and provide limited

frills. However, around 1998, LCCs still persisted in their original business domains such as providing

3

ATA was filed in Chapter eleven and quitted its operation in April 2008.

3

no frills, serving for the markets of short or medium distance, issuing no mileage service and so on.

Therefore, we expect economic impacts such as the degree of airfare-discounting may be stronger thanpicking up the recent year for our analysis.

This study has the following distinguishing features:(1) We incorporate triopoly and larger markets where multiple carriers enter as well as duopoly markets,

and we cover a wider range of the industry than was covered in previous studies. We analyze theinter-firm rivalry of 21 carriers, and 9 out of the 21 carriers are LCCs,(2) We compute airport-specific conduct parameters as well as carrier-specific ones.

(3) We estimate not only LCCs’ own capacity-expanding and consecutive airfare discounting effects but

also their impact on capacities and airfares of FSCs.

(4) We investigate how the airfares change after an LCC enters as the result of carriers’ behaviors of

capacity-expansion, though the number of cases for this analysis is low.

(5) We investigate how the output and airfare changed from the first year to the second year of the newentry by LCCs.

(6) We derive the total welfare, whereas previous studies focused on the consumer welfare only.

In subsection 2-1 we derive the conduct parameters, and in subsection 2-2 we explain simultaneous

systems to compute the total welfare effect.

42-1 Conduct Parameter and route-specific simultaneous equations

Our dataset consists of 180 duopoly markets, 138 triopoly markets, 56 four-carrier-operating

markets, 19 five-carrier-operating markets, 7 six-carrier-operating markets, and 4 seven-carrier-operating

markets.

Therefore, the route-specific dataset consists of 405 and the carrier-specific dataset has 1163

sample observations. As for the route-specific data, the entire data are aggregate, so airfares of thisdataset mean the market-share-weighted average airfares.

To estimate the conduct parameters, we use

both carrier- and route-specific dataset. In the carrier-specific dataset, we derive the conduct parameterassuming the n-firm case with one LCC.4
n 1

n

k 1

L 1

The market demand of route i is denoted as follows:

Qi   q ik  qij   q iL

( j  k , k  1,  , n  1, L  1,  , n)(1)where both of the superscripts k denotes a full service carrier and j denote an LCC.

The profit

function of each carrier is denoted as follows: iL  q iL p i Qi   TC iL q iL  (2) where TC ik    TC i j  
Taking the first-order condition of (2), we have:

 iL
p Q  Qi
 pi Qi   q iL i i
 MC iL qiL  0 (3)LL
Qi q i
qi 

We then define the conduct parameter as (4) and (5): im dq k 1  qij
m  i
dq i

 (m  k ) (4)4 ij 



d
qk
j  i
dq i

 (5)

We also have the case of “n-2” full service carriers and two LCCs. Even for this case we can derive
the conduct parameter without losing generality.

5Substituting (4) and (5) into (3), respectively, we obtain:

 iL
p Q  pi Qi   qiL i i 1  viL  MCiL qiL  0
L
Qi
qi





 

(6)

where MC i    MC i   .
k3

For example, the conduct parameter (4) means the marginal change in the output of other carriers (other

FSCs plus carrier j) against the marginal change in the output of carrier k. If all of them move in thesame direction and have the same volume, the result is L  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 airfare

equals the marginal cost.

In our model, if the airfare equals the marginal cost of an LCC, FSCs would have to exit themarket, since MC i    MC i   as long as carriers operate at the minimum efficient scale wherekjaverage cost equals marginal cost.

As in the previous studies, the equations (6) can be inverted to (7) by using the route-specific 

airfare elasticity of demand  i  and that the market shares of each carrier s i .
Lp Q   MC q    1 (7)
v 
L
i

i

i

p i Qi 

L
i

L
i

s

i
L
i

L

As for the variables and parameters in (7), we already have information on p i and s i , but
the route-specific marginal cost for each carrier and the route-specific airfare elasticity of demand are
unknown. Therefore, we need to estimate these two unknown variables and parameters in advance tocompute the conduct parameters.

L

To obtain v i , we use the following proxy to approximate

route-specific marginal cost for each carrier, as proposed by Brander and Zhang (1990),(1993) and Oum6et al. (1993)5.
 Dist i 
MC  AC  Dist i
L AFL Liwhere AC

L

L

(8)

L

is the aggregate average cost of carrier L , Dist i is the distance of route i , AFL

is the average distance flown by airline L 6.

Many studies on airline costs, such as Caves, Christensen,

and Tretheway (CCT, 1984), Gillen, Oum, Tretheway (1990), and Fischer and Kamerschen (2003),

show that economies of density exist in the airline industry.

This means that the total cost function isstrictly concave. Therefore,  in (8) ranges between 0 and 1. It is apparent that if  is 0, thecarrier’s marginal cost is proportional to distance, while if  is 1, the marginal cost is indifferent todistance.

According to previous empirical research such as CCT (1984), Borenstein (1990), Brander andZhang (1990)(1993), Oum, Zhang, and Zhang (1993),



may range between 0.15 and 0.67.

Armantier and Richard (2003) predict that the route-specific marginal cost of an airline is just equal to the
product of “cost per mile” and distance (this means   0 ) .
7

the equation (9) to obtain  .

Oum, Zhang, and Zhang (1993) estimatedThis equation (9) is derived from the first order condition of carrier’s

profit function, that is, a pseudo-inverse supply function under oligopoly.

AC Dist AFL  Dist   
p 
L 

L

L
i

i

  1   s

5

i

L
i

L
i

(9)

To estimate the route-specific marginal cost for each carrier, Fischer and Kamerschen (2003) jointly
estimate a translog total cost function and then approximate the route-specific marginal cost for eachcarrier. See Fischer and Kamerschen (2003), pp.235-237.
6
See Brander and Zhang (1990), pp. 572-575, Brander and Zhang (1993), pp.417-420, and Oum, Zhang,
and Zhang (1993), pp. 175-178.
7
Armantier and Richard (2003), pp. 468-469.

7

The system-wide conduct parameter can also be estimated in equation (9).

By substituting estimated into (8), we can approximate the route specific marginal cost.
However, we have yet to know the (positive) route-specific price elasticity of demand  .

Therefore,

we estimate the Marshallian demand function. We might as well simultaneously estimate demand
equation and pseudo-supply equation (9) at the same time, but what we need is the route-specific priceelasticity of demand, not the carrier-specific one. In other words, the dataset for estimating (9) isdifferent from the one for estimating the demand equation.

Considering the demand and supply system,

we detect the airfare on the right hand side of the demand equation and the output on the right hand sideof pseudo-supply equation may be correlated with the error term of each equation. Therefore, we carryout Hausman specification test to test the null hypotheses that neither airfare nor output is asymptoticallycorrelated with the error term of demand and the pseudo-supply equations, respectively.

 p' u  p' u   0 , H 1 : p lim
0
 n 
 n 
 q'   q'  
Hausman test for pseudo-supply equation: H 0 : p lim  0 , H 1 : p lim
0
 n 
 n 
Hausman test for demand equation: H 0 : p lim

where p is the average airfare in a market which is weighted by the number of traffic carried by airlines, q

is the market aggregate traffic, e is the error term of demand equation and u is also the error term of thepseudo-supply equation. Under the null hypothesis, OLS estimator is unbiased, consistent, and more

efficient than 2SLS estimator, while under H1 2SLS estimator is unbiased, asymptotically consistent, andmore efficient than OLS estimator.

The test result is that the second null hypothesis is rejected at 1%

level (  ( d .o. f .1)  30.49   ( 0.01 / d .o. f .1)  6.64 ), and the first null hypothesis is rejected at 7.1%
2

2level (  ( d .o. f .1)  3.30 ). Considering these results, we may have to estimate the structural demand28and pseudo-supply equation together either two stage or three stage least squares.

In addition, Bailey,

Graham, and Kaplan (1985) suggest that the market concentration is also an endogenous variable which isdetermined by output, distance and other exogenous factors such as the existence of slot controls, and thisis followed by our analysis. Our route-specific simultaneous equation system is as follows.
[Route-specific demand equation] 87ln Qi    0   ln pi   1 ln INC i   2 ln DISTi   3 ln POPi    4m MKTm   i

10

m 3

[Route-specific pseudo-supply equation]ln  p i    0   1 ln Qi   2 ln DISTi   3 ln HERFi   4 ln MCDi   5 DLCC1
  6 DLCC 2   i

11

where p i is the average airfare at route i weighted by market share, INC i is the arithmetic averageof per-capita income of route i , DISTi is the distance of route i . This variable enters both indemand and pseudo-supply equation, because it controls market demand, and also plays a role of proxy ofthe marginal cost9. POPi is the arithmetic average of O/D population, MKTm is a binary variable
that takes 1 for the market where m carriers compete.

For example, MKT3 is the dummy variable

that takes 1 for triopoly markets and zero otherwise.

HERFi is the Herfindahl index, and higher

HERFi means that the market is more concentrated. Since high concentration may lead to strong

8

Since we will compute the consumer welfare in Section 5 by using this demand equation, the functional
form must be carefully chosen. To determine whether the form of each equation is a simple linear, a
log-linear or others, we carried out the Box-Cox transformation. According to the result of LR test, wecannot reject the hypothesis that the best functional form is the log-linear functional form at 5% level.

 2 ( d .o. f .5)  0.12   (2 0.05 / d .o. f .5)  11.07
9

Since our data is the route-specific data, we have to use “distance” as the proxy on behalf of the “route
marginal cost”.

9market power, the parameter will be positive.

represent the presence of LCC(s).

DLCC1 and DLCC2 are binary variables that

The former takes 1 if at least one LCC serves the route and 0

otherwise. The latter takes 1 if at least 1 LCC serves the adjacent route and 0 otherwise.

For example,

DLCC2 takes 1 for both the cases as follows: the case to connect two secondary airports such as
Southwest’s Houston/Hobby-Chicago/Midway, and the case to connect the primary and secondary

airports such as Air Tran’s Atlanta/Hertsfield-Chicago Midway. If a quality-distinguished firm enters amarket and tries to distinguish itself further, the market output decrease and airfare rises. Therefore, thesign of

DLCC1 and DLCC2 would be positive if both carriers perfectly distinguish themselvesfrom each other. If they are not completely distinguished, both will compete and the signs of

and

DLCC1

DLCC2 would be negative.
Mason et al. (1992) discuss that if the firm’s structure is asymmetric (e.g., high-cost andlow-cost), it needs longer time for the market to reach cooperative equilibrium than if the firms are

symmetric.

Their discussions are followed by our variable MCDi , which is the standard deviation of

marginal cost. If this is sufficiently large, there may be at least one LCC, and carriers in a market will

hardly agree on colluding. Therefore, the parameter will have negative effect on the market airfare, and,vise-versa, positive effect on market output.

(Discussion on the use of conjectural variation to change a major firm’s conduct)

In a recent analysis of the estimation of market power using the conduct parameter method

10

(CPM), Fisher and Kamerschen (2003) point out that in a static environment, the notions of expectationand conjectural variation are not well defined. For example, if we start our analysis by modeling a

one-shot Cournot competition and try to estimate the conjectural variation by CPM, we face the problem

that we cannot describe the firm’s response or any dynamic change in the firm’s behavior. As

Bresnahan (1989) states, “The estimated parameters tell us about airfare- and quantity-setting behavior; if

the estimated ‘conjectures’ are constant over time, and if breakdowns in the collusive arrangements are
infrequent, we can safely interpret the parameters as measuring the average collusiveness of conduct.”10

Also, Corts (1999) pointed out, “CPM estimates of market power can be seriously misleading. In fact,

the conduct parameter need not even be positively correlated with the true measure of theelasticity-adjusted price-cost margin, so that some markets are deemed more competitive than a Cournot

equilibrium even though the price-cost margin approximates the fully collusive joint-profit maximizing
price-cost margin.”11

Taking these critics’ statements into account, Fisher and Kamerschen (2003) nonethelessstressed the usefulness of conjectural variation.

They insisted, following Brander and Zhang (1993) andOum et al. (1993), that “one can view the conjectural variation as a parameter of market conduct that cancapture the whole range of market performance, from perfect competition to monopolistic behavior,

10
11

Bresnahan (1989), p.129.
Corts (1999), p.299.

11rather than taking it as an indicator for the firm’s expectation.”12

Fageda (2006) also computed the

conduct parameters of Spanish air markets by estimating the demand and pseudo-supply equation systemusing semi-annual (summer and winter) data of the years 2000 and 2001 by 3SLS, not stressing the

problem with the dynamic features of conduct parameters but regarding conduct parameters as the set of
static equilibrium.13 Our analysis in the present essay follows this line of research and does not include

a discussion of the dynamic aspect of competition.

The problems pointed out by Corts (1999) arise

when we model the dynamic competition and use the panel data, and Puller (2008) suggested aprescription for addressing this problem.

2-2

We will discuss this issue further in the future.

Carrier-Specific Simultaneous Equations to Derive Total Welfare

Next, we try to construct the carrier-specific simultaneous demand and pseudo-supply equation

system.

As we did in 2-1, we assume that not only demand but also airfare is also an endogenous

variable. As for the new entry and the response by incumbent carriers, Joskow et al.(1994) find that new

entrants enter with low airfare, and incumbents respond by cutting airfares to keep their traffic maintained.

Dresner et al. (1996) estimated the simultaneous and demand- and price (pseudo-supply) equations thatincorporate the directly- and indirectly-competing LCC dummy variables. In order to ascertain the12
13

Fisher and Kamerschen (2003), p.234.
Fageda (2006), pp. 388-395.

12consumer welfare effect, we will follow the method performed by Dresner et al. and need to estimate thecarrier-specific demand equation as well as the pseudo-supply equation in log-linear forms.

Ourempirical model to obtain the effects of low-cost entry on consumers’ welfare is as follows.

For

example, a duopoly market contains four equations (demand and pseudo-supply equations for two

carriers), and the largest market does fourteen equations, which means we have seven carriers in thelargest market.

[Demand equation] 

7ln QiL   0   1 ln p iL   2 ln INC i   3 ln DISTi   4 ln POPi    5N MKTm  u iL

12

N 3

  0,   0,   0,   0,   0
1

2

3

N
5

4

[Pseudo-Supply equation] 

ln piL   0   1 ln QiL   2 ln MC iL   3 ln HERFi   4 D1LCC1i   5 D1LCC 2 i   6 D1LCR1i
2  7 D1LCR 2 i   8 D 2 LCC1i   9 D 2 LCC 2 i   10 D 2 LCR1i   11 D 2 LCR 2 i   12t LCCPE (WN ) tDt 12   13t LCRPE (WN ) tD  14 LCCPE (WN )1T  15 LCRPE (WN )1T  16 LCCPE ( HP) 2Dt 1

 17 LCRPE ( HP) 2D   iL

(13)

 ,   0,  ,  ,  ,  ,  ,  ,  ,  ,  ,  ,  ,  ,  ,   0
2

L

where u i , andrespectively.

3

4

6

7

8

9

10

11

t
12

t
13

14

15

16

17

 iL are error terms of carrier-specific demand, and pseudo-supply equations,

D1LCC1 , D1LCC 2 , D 2LCC1 , and D 2LCC 2 are binary variables that

represent the presence of LCC(s).

otherwise, and

5

D1LCC1 takes 1 if an LCC originates the primary airport and 0

D1LCC 2 takes 1 if two LCCs exist in the primary route, for example, the case to13

connect two secondary airports such as Southwest’s Houston/Hobby-Chicago/Midway, and the case toconnect the primary and secondary airports such as Air Tran’s Atlanta/Hertsfield-Chicago Midway..

Similarly,

D 2LCC1 takes 1 if an LCC enters the adjacent route and 0 otherwise, and D2LCC 2

takes 1 if two LCCs enter. We assume the positive price elasticity of demand is larger for LCCs thanFSCs, since FSCs usually have tools to prevent passengers from switching from FSCs to LCCs, such as

mileage services.

As for the pseudo-supply equation, the sign of

D1LCC1 , D1LCC 2 ,D 2LCC1 , and D2LCC 2 would be positive if both carriers perfectly distinguish themselves fromeach other. If they are not completely distinguished, both will compete and the signs of these fourbinary variables would be negative.

1 can take either negative, positive, or zero.

If a carrier

supplies at its short run marginal cost curve,  1 will be positive, and if it does on its declining average
cost curve, it will be negative. In addition, if a carrier supplies at minimum efficient scale, it will be

zero.

LCCPE and LCRPE are dummy variables that are intended for measuring how the

airfares of a newly entered LCC and its rival’s airfares change over time.

In 1998, Southwest Airlines

entered the Chicago (Midway) – Manchester (New Hampshire), Chicago (Midway) – Birmingham(Alabama), and Chicago (Midway) – St. Louis markets.

Chicago-Manchester.

LCCPE (WN )1D takes 1 for Southwest in

The superscript and the subscript stand for the market type and the year from the

new entry, respectively.

For example, the superscript “D” means that this route is a duopoly market

and the subscript 1 means the first year of Southwest’s entry.

14

LCCPE (WN )1T takes 1 for Southwest

in Chicago – Birmingham and Chicago – St. Louis markets. The superscript “T” means that these

two routes are bigger than triopoly.

LCRPE (WN )1D and LCRPE (WN )1T take 1 for all the othercarriers in these three routes. The year 1998 is the second year of Southwest’s entry in Chicago(Midway) – Jackson (Mississippi) and America West’s entry in Dallas/Fort Worth – Long Beach.
D

D

Therefore, LCCPE (WN ) 2 and LCRPE (WN ) 2 take 1 for Southwest and all the other carriers in
Chicago – Jackson, respectively.

way.

LCCPE ( HP ) 2D and LCRPE ( HP ) 2D are created in the same

The effect of these “time dummy” variables is removed from the LCC dummy variables such as

D1LCC1 described previously.
HERFi is the Herfindahl index, and higher HERFi means that the market is moreconcentrated, since high concentration may lead to strong market power, the parameter will be positive.
k

In addition, HERFi and the route-basis marginal cost of a carrier, MC i , are also endogenous
variables.

The marginal cost is the function of output and also the independent variable ofpseudo-supply equation, so theoretically we have to use the instrument variable of marginal cost. To testk

the null hypothesis that neither Ln( HERFi ) nor ln MC i

is correlated with the error term  i , wecarried out Hausman test for each variable, and reject both of the null hypotheses at 1% level ofsignificance (  (1)  7.41 and 23.38 , respectively).
2

In total, our structural equations have fiveendogenous variables, but we show the demand, and the pseudo-supply, because the estimated results of

the rest equations are out of the scope of this paper. We also computed the carrier’s average cost in15

order to deduce producers’ profits and loss and derive the total welfare by adding producers’ profits andconsumers’ surplus.

3

The Data
We use the data of the scheduled operations by city-pair route by firm: they are 1998

cross-sectional data collected from DB1A, 10% samples of the US domestic flight data. Airfares are“fared passenger fares” and are described in terms of the US dollar. Omitted are the carriers which do
not have 10% market share in duopoly markets, and 5% share at triopoly or markets more carriers serving.
Carriers whose codes are not reported in DB1A (reported as XX) are also omitted, but, for example, a
tripoly market with one XX carrier are not regarded as a duopoly market, since XX carrier is thought tohave competitive effects on others. Flight data are outbound and non-connecting ones from the US six
large airports and their regions: New York/Newark area, (JFK, LaGuardia, Newark), Washington Ronald
Reagan (National), Atlanta Hartsfield, Dallas/Fort Worth area (DFW, and Love Field), and Los Angeles.
The source of cost data are from Air Carrier Financial Reports ,Form 41 Financial Data.
Income and population data are from Regional Accounts Data, Bureau of Economic Analysis.

We usethe Primary Metropolitan Statistical Area data (PMSA, an urbanized county or set of counties that havestrong social and economic links to neighboring communities) for each city.

The descriptive statistics ofcontinuous variables are shown in Table 9 in Appendix 3.

4.

Empirical Results

4-1. Conduct Parameter

Appendix 2 shows the estimated result of route-specific demand and pseudo-supply equations

16

by Generalized two stage least squares (G2SLS) and three stage least squares (3SLS). We obtain  1.544 with t-statistics = -3.711 by G2SLS and   1.756 with t-statistics = -4.27714.
Both of them are acceptable value according to the survey study by Oum, Waters and Yong (1992) whichsurveys that the price elasticity of demand of air travel estimated by cross-sectional data ranges from

-0.53 to -1.90.

To estimate the tapering effect of route marginal cost and system-wide conduct parameter,

we use the result obtained by G2SLS, since by our simulation we have found that conduct parameters of each carriers fall in the theoretical interval (that is,   0,1 ) better when using   1.544 thanusing   1.756 . Using the positive value of price elasticity, we estimate equation (9) by Non

Linear Least Squares. The result is shown in Table 1.

Table 1 Estimated result of non-linear pseudo-supply equation (9)Statistics

ParameterSEt-stat.
0.271
0.097
27.894-0.0530.030-1.782Log likelihood=-6454.86, n=1163,

P-Value
0.000
0.075

Maximum Likelihood of estimated ˆ =3875.7
2

According to Table 1, the tapering effect of marginal cost is 0.271 which falls between Oum, Zhang andZhang (1993) and Borenstein (1990).

The system-wide conduct parameter is -0.053 which is not

rejected the null hypothesis that   0 at 5% level. Therefore, we conclude that Cournot competitionis performed in the US air markets that experienced low-cost entry.

Figure 114

Market share and Distribution of Conduct Parameter

See Table 7 in Appendix 1.

17

Share and distribution of Conduct Parameter
1
0.9
0.8
0.7
0.6
0.5
0.4
0.3
0.2
-4

-3

-2

-1

0

1

2

3

4

Figure 1 shows the distribution of conduct parameter of each carrier which has at least 20%

market share.

The horizontal axis is the conduct parameters and vertical axis is carrier’s market share.

The figure shows that carriers which have large market share (about 65% or more) conduct in accordancewith the economic theory, but “fringe” carriers do not (their airfares conduct parameters are incredibly

low or high). The low outliers of conduct parameters can be regarded as the outcome of increasing too

much capacity to try to increase market share. Then as a result, equilibrium airfares go down. The

high outliers may take place when a carrier does not know the demand curve for itself, and limits its

capacity.

In such a case it may happen that its amount of capacity is smaller than the profit maximizing

level, so is its airfare higher than the monopoly level.

Figure 2 demonstrates the average value of each carrier’s conduct parameter and its 95%

confidence interval. According to Figure 2, full service carriers perform Cournot competition or morecollusively than Cournot competition. On the other hand, LCC’s behave more variously than full service

carriers. The most interesting sample is Southwest Airline (WN)’s behavior. Its conduct parameters at18

the secondary airports (Dallas Love and Chicago Midway) are higher than those at primary airport (LosAngeles), probably because WN can regionally build more monopolistic situation at the secondary

airports than at primary airport, although it must be competing with full service carries at the primary

airport. This implies that Southwest earns its profit mainly at the secondary airport. Contrarily withthe case of Southwest, America West Airlines (HP) and Spirit Airlines (NK) ,in most cases, competes withfull service carriers directly at the primary airports and this leads to the result that its conduct parameters

are lowest among LCCs. Other LCCs such as Air Tran (FL) and ATA (TZ), which also enter and basesecondary airports like Southwest, have higher conduct parameters, but their conduct parameters vary

more widely than other carriers.

In some cases in our dataset, their market shares are very small, so they

seems to create new demand that results in low airfares, and to counterbalance the losses generated by

low airfares with higher airfares than their average at other thriving markets.

Figure 2 Average conduct parameters with 95% confidence interval

Conduct Parameter

1.5
1
0.5
0
AA

CO

DL

NW

TW

UA

US

FL2

HP

TZ2 WN1 WN2LB(95%)Ave.
UB(95%)-0.5-1
Carriers

Note: AA: American Airlines, CO: Continental Airlines, DL: Delta Airlines, NW: Northwest Airlines,
TW: TWA, UA: United Airlines, US: US airways

Figure 3 Average Conduct Parameter at Origin Airport

19

Conduct Parameter

0.8
0.6
0.4
0.2
0
-0.2

ATL

ORD

MDW

DFW

DAL

LAX

NY

WDC

LB(95%)Ave.
UB(95%)-0.4-0.6
Airports

Note: LB and UB means lower bound and upper bound, respectively.

Figure 3 shows the average conduct parameter and its 95% confident interval of all the airlines

which originate in ATL ‘(Atlanta), ORD (Chicago O’Hare), MDW (Chicago Midway), DFW(Dallas=Fortworth), DAL (Dallas Lovefield), LAX (Los Angeles), NY (average of JFK, LaGuardia, andNewark), and WAS (Washington Dulles and Ronald Regan).

It is apparent that the conduct parametersat Los Angeles International Airport, where multiple numbers of LCCs enter, are lower than any otherairport and those at Washington D.C., where there are multiple numbers of LCCs enters, airfares are also

low. On the contrary, these results are not followed by the following two cases: one is the case in whichan LCC entered but its presence is weak, and the other is the case in which LCCs entered the adjacentsecondary airport. In the latter case, LCCs as well as full service carriers must have regional market

power and can keep their conduct parameters higher than the level of Cournot competition. This implies

that both full service carriers and LCCs benefit, despite the rivalry between carriers at the primary airports

and those at the secondary airport.

20

Conduct Parameter at LAX

Figure 4 Average Conduct Parameter of each airline at Los Angeles
1
0.5
0
-0.5

AA

CO DL

HP NW TWUA US WN

LB(95%)Ave
UB(95%)-1-1.5
Carrier

Note: LB and UB means lower bound and upper bound, respectively.

Figure 4 shows the average conduct parameter of each airline with 95% confidence interval.

On average, conduct parameters of full service carriers are lower than the level of Cournot competition

except for TWA.

Two LCCs, America West and Southwest, keep their conduct parameters a little higher

than full service carriers. This fact implies that these big LCCs are better off than full service carriers.

Table 2 demonstrates the partial correlations selected variables used in our analysis. Focusing

on the correlation between the conduct parameter and other variables, we find that the conduct parameteris negatively correlated with market share and distance at 1% level of significance. These results are

consistent with Oum, Zhang, and Zhang (1993): a carrier with high market share tries to expel fringe

carriers by expanding their supply, and then market airfare goes down. Goolsbee and Syverson (2005) ,and Oliveira and Huse (2009) point out these capacity-expanding behaviors of carriers. We conclude
that this type of competition takes place in long haul markets than in short haul ones.15 The positive

15

There are many dimensions to the discussion about the relationship between distance and degree ofcompetition. One may insist that the competition becomes softer in long-distance markets because the21correlation between conduct parameter and profit is also consistent with the theory of economics.

Table 2 Partial Correlations between Conduct Parameter and Other Variables
Conduct Parameter
Conduct Parameter

Market Share

Distance

Herfindahl Index

Profit-Market Share-0.359**-Distance-0.515**-0.079*-Herfindahl Index-0.0370.607**-0.172**-

Profit0.272**

0.242**-0.389**0.157**-

Note: ** and * means it is significant at 1% and 5% level, respectively.

4-2. Carrier-Specific impact of Low-Cost Entry
This subsection investigates how the impacts of low-cost entry on full service carrier’s

behaviors differ when (an) LCC(s) enter(s) in the primary or secondary airport, and whether the number

of LCC’s affects the full service carrier’s behaviors. We simultaneously estimated equation (12) and(13) by iterated 3SLS method and the results are shown in Table 8 in Appendix 4.

Table 3 is thesummary parameters of LCC’s and their rival’s dummy variables which are picked up from Table 8.

Table 3 Summary of parameters of LCC’s and their rival’s dummy variables

Parameters

Standard
error

Difference
between one andmulti carrier(s) 16

number of surface competitors declines and because LCCs tend to enter short- and medium-distancemarkets. Another viewpoint is that short-distance air markets are normally small markets. Therefore,
in many cases we observe more carriers entering long-distance markets than short-distance markets, andthis is supported by the fact that the partial correlation between distance and the Herfindahl index is-0.172, which is statistically significant at the 1% level (see Table 2). Considering these offsetting
effects, it is an empirical issue whether the competition is softer in long-distance markets.
16
The Wald tests that test the hypothesis that two parameters are equal are not rejected at all at 5% level.

22One LCC at Primary-0.3400.031

Multiple LCCs at Primary-0.2990.064

FSC at Primary competing with one LCC-0.1720.028

FSC at Primary competing with multiple LCCs

-0.1550.045

One LCC at Secondary-0.377

0.073

Multiple LCCs at Secondary-0.1740.058

FSC at Primary competing with one LCC at Secondary-0.4030.077

FSC at Primary competing with multiple LCCs at Secondary-0.1900.067

None at 5% level
None at 5% level
None at 5% level
None at 5% level

Table 3 tells us that the quantity competition between LCCs and full service carriers leads tosignificant discount in their airfares comparing with the benchmarked full service carriers.

The number

of LCCs does not statistically affect LCC’s and rival’s airfares, though it appears it does at the secondary

airports. In other words, the first entry leads to significantly low market airfares, but the second or later

entry does not. These results are almost consistent with the results of Dresner et al.(1996) which also

introduce the dummy variables that reflect the number of competitive carriers. While they do notstatistically test the difference of parameters of these dummy variables, our analysis reveals that theadditional entries do not affect the rival’s capacity-expanding behaviors that may affect their airfares. In

the perfectly contestable markets, the number of firm does not affect the capacity or airfare. Since thefirst entry significantly affects the airfare, we can reject the hypothesis of perfect contestability.

:

One interesting finding is that capacity-expanding and consecutive airfare-reducing effects at thesecondary airport look greater than those at the primary airport, although they are not statisticallysignificant.

For example, an LCC’s airfare at the secondary airport is -0.377 while that at the primary23

airport is -0.34017.

To explain this phenomenon, we replace all the carrier-related dummy variables(D1LCC1, D1LCC2, D2LCC1, D2LCC2, D1LCR1, D1LCR2, D2LCR1, and D2LCR2) in Table 6 witheach carrier’s dummy variable, and re-estimate the carrier-specific structural equations using the samedata and the same estimation method. The result is shown Table 9 in Appendix 5, and the summary of

the parameters of carrier dummy variables is shown in Table 4.

The methods to introduce each carrier’s dummy variable are as follows: for example,

speaking of Southwest (WN), we introduce WN1, WNR1, WN2, and WNR2.

WN1 takes 1 forSouthwest operating in the primary airport, and WNR1 takes 1 for the full service carriers that arecompeting with Southwest at the primary airport. Similarly, WN2 takes 1 for Southwest operating in thesecondary airport, and WNR2 takes 1 for the full service carriers that are competing with Southwest at the

primary airport. This method of introducing carrier dummy variables is followed by all the othercarriers shown in Table 3, except for Spirits Airlines and Tower Airlines. They did not operate thesecondary airports in our dataset, so we have neither NK2, NKR2, FF2, nor FFR2.

Table 4 Carrier-specific impacts on airfare at primary and secondary airports
LCC’s airfare
Entry byPrimary

FSC’s airfare

SecondaryPrimary

Secondary

KP (Kiwi Int’l)-0.560(0.179a)-0.368(0.252)-0.132(0.120)0.109(0.183)TZ (ATA)-0.200(0.870b)-0.081(0.067)0.007(0.052)0.032(0.047)HP (America West)-0.042(0.042)-0.451(0.254)0.008(0.029)-0.015(0.129)FL (Air Tran)-0.537(0.047a)-0.406(0.146a)-0.310(0.043a)-0.105(0.091)-0.495(0.147a)-0.158(0.069)0.249 (0.099b)-0.255(0.127b)

NJ (Vanguard)-0.554(0.090a)NK (Spirit Air)

-0.556(0.146a)

17

The Wald test cannot reject the hypothesis that these two values are equal at 10% (Chi-square withd.o.f.=1 is 2.01).

24

WN (Southwest)-0.414(0.050a)FF (Tower Air)

-0.754(0.121a)-0.375(0.042a)-0.296(0.033a)-0.106(0.080)-0.247

Note: Values are the parameters of carrier dummy variables, and standard errors are in parentheses.

(0.029a)“a”, “b”, and “c”

mean that they are significant at 1% and 5%, respectively.

Table 4 tells us that Southwest’s capacity-expanding behavior at the primary airports leads tovery low airfares, and so does in the secondary airports with a little higher airfares than in the primary

airports. Full service carriers also expand their capacities to cope with Southwest, and subsequent

airfares reduction is greater in the primary airport than in the secondary airports. These results as forSouthwest are quite comprehensive, but are not necessarily followed by other LCCs.

It is, indeed,almost common for LCCs to operate with very low airfare at the primary airports, but even this pricing

strategy is fallacy for America West (HP). Competitions caused by Vanguard Airlines (NJ), which used

to hub Chicago Midway Airport, and Air Tran (FL) lead to low airfares both at the primary and thesecondary airports, but the impact of their presence on rival’s airfares at the secondary airport are much

weaker than that of Southwest’s. Only does Southwest give competitive pressure to the full service

carriers at remote airports.

The results in Table 4 that shows the entry-impact is stronger in the primary airport than in thesecondary airports for most LCCs seem to contradict with the results in Table 3. One reason is that theeffect of America West, the entry of which exceptionally leads to lower airfares at the secondary airports

than at the primary airport, is strongly reflected for the estimation of Table 3. America West can not be

regarded as a true LCC, because its average airfares are almost the same as those of typical full service

25carriers. However, since America West bases Chicago Midway which is less convenient than O’Hareinternational Airport in terms of access, it may have to discount the airfare to offset the inconvenience ofaccess. On the other hand, Southwest and Air Tran, which also base Chicago Midway, are true LCCs

and their entry consistently leads to low airfares anywhere, and discount effects are much more at the

primary airports than at the secondary airports.

About relation between the conduct parameters and airfare level, we find that, as may be

expected, full service carriers are better off without any entry by LCC, but they achieve higher conduct

parameter with LCC(s) at the secondary airport than for the benchmark case where full service carriers

are competing with each other. (See Table 5).

Table 5 Conduct parameters and airfares of FSCs and LCC(s) at the primary and secondary airports

No LCCOne LCC at Primary Airport
One LCC at Seconday Airport
Two LCCs at Primary Airport
Two LCCs at Secondary Airpo

Full Service Carrier

Low Cost Carrier

Conduct Lower Bound Upper BoundParameter
of CP
of CP

Conduct Lower Bound Upper BoundParameter
of CP
of CP

0.289
0.100
0.575
0.011
0.317

-1.250-2.205-0.977-2.012-1.572

1.828
2.405
2.127
2.034
2.206

Airfarenbenchmark 413-0.217 169-0.16181-0.187
11-0.297
16

0.394
0.501
0.486
0.245

-1.497-0.843-0.853-2.078

2.285
1.845
1.825
2.568

Airfaren-0.347-0.393-0.348-0.447

56
41
13
15

In Table 5, leaving out the outliers of conduct parameters, we take the average of average of conduct

parameters and 95% confidence intervals for full service and low-cost carriers for the cases in which (an)LCC(s) enter(s) in the primary and secondary airports

When an LCC enters in the adjacent market, full service carriers may quit competition within

the primary airport and try to win the competition with the LCC. In addition, both full service carriers

26and the LCC can keep, although not strong, the regional monopolistic power comparing with the case ofhead to head competition at the primary airports. This is why conduct parameters at the primary airport

is comparatively high for the case in which one LCC enters in the secondary airport. One interestingfinding is that LCCs at the secondary airport can keep a little large price-cost margin despite their

low-airfares. One reason is that since the airport charges at the secondary airport are not so expensive as

those at the primary airport, LCC can benefit though average airfares are low.

Our last findings from pseudo-supply equations are that the degree of competition started bySouthwest is significant in the entry year and is continued to the second year of entry for some duopolyDcases, whereas this does not hold for the other cases (See the parameters of LCCPE (WN )1 ,LCCPE (WN ) 2D , LCRPE (WN )1D , and LCRPE (WN ) 2D ). The factor that causes these four results
is the market share an LCC gains in the entry year. If Southwest can gain a large market share in theentry year, the incumbent pays close attention to the competitor’s entry and increases its capacity, and as aresult airfares are kept as low as, or much lower than the level in the entry year. On the other hand, if

Southwest cannot gain much share in the entry year, the incumbent expects the competition will end soon

and it actually ends in the second year. Another way of explaining the latter case may be the tacit

collusion due to multi-market contacts suggested by Bernheim and Whinston (1990), and empiricallytested by Evans and Kessides (1994) and Morrison and Winston (1995).

Bernheim and Whinston

suggested that collusive behavior due to multi-market contacts can take place even though the cost level27differs between firms and the products are differentiated in the repeated game. We will investigate thisdynamic issue further in a future study.

5. Welfare Effect

Our final analysis is to compute the consumer’s, producer’s, and total welfare.

Since we do

not have the supply curve under the imperfect competition, we do not compute the true producer’s surplus.

Instead, we compute the carrier’s profit calculated by the carrier’s route average cost, carrier’s average

yields, and the number of passengers for a carrier.

The route average cost is computed by the product of

the route distance and carrier’s unit cost (total cost / aggregate RPM). The consumer’s surplus iscomputed by the following method: we compute the area of “trapezoids” of our demand equation (11)which are surrounded by the benchmark airfare, lowered airfare computed from the carrier-related

dummy variables, benchmark output and increased output due to low-cost competition.

illustrates the change in consumer’s surplus in a simple way.

Figure 5

The trapezoid A is the gain in consumer’s

surplus due to LCC’s entry in the primary airport, and trapezoid C is also the gain in consumer’s surplus

due to full service carrier’s reaction to LCCs at the primary airport (FSC’s airfare is higher than LCC’s).

Similarly, the trapezoids B and D are those for the cases of secondary airports.

Since the market demandis the sum of the demands for each carrier, the total welfare is the sum of the trapezoids of LCCs and28those of full service carriers for the entry in the primary and the secondary airports (that is, A+B+C+D).18

Figure 5 Gains in Consumer’s Surplus

P
Benchmark Price

A

B

C

D

FSC’s price at the secondary airport
FSC’s price at the primary airport
LCC’s price at the secondary airport
LCC’s price at the primary airport

0
Carrier specific demand for LCC

qMarket demandat primary airport

Table 6 demonstrates the gain in consumer’s surplus, newly-entered LCC’s profit, the change in fullservice carrier’s profit, and the gain in total welfare. Since we limit the number of sample observations

by selecting only six airport groups, the amounts themselves may not be important. However, the results

imply, in overall, that the gain in consumer’s surplus is very large, and LCCs also benefits by entry. On

the other hand, full service carriers are losing their profits due to the low-cost entry, and especially theirlosses due to the competition from the adjacent airport are mostly caused by the entry by Southwest.

However, since the losses of full service carriers are much smaller than the sum of the gain in consumer’s

surplus and LCC’s profits, the gain in total welfare is apparently large. Considering the results in Figure

2, full service carriers are suffering from the decrease in profits, although they keep the conduct

parameters at more than Cournot level, while LCCs are benefiting at smaller conduct parameters than full18

Since we introduce LCC dummy variables, the intercepts of LCCs have to be lower than those of fullservice carriers, but for convenience we depict them as shown in Figure 5.

29

service carriers.

Table 6 Summary of welfare effect of LCC’s entry
Gain in
Cons. Welf.

Newly entered
LCC's Profit

Change in
FSC's Profit

Gain in Total
Welf.

An LCC's entry into Primary Airport

5.33

1.28

-0.48

6.13

An LCC's entry into Secondary Airport

5.05

1.27

-0.34

5.98

Two LCCs' entry into Primary Airport

5.31

1.08

-0.22

6.17

Two LCCs' entry into Secondary Airport

7.29

0.82

-0.88

7.23

Sum of the Gain in Welfare

22.98

4.45

-1.92

25.51

Due to:Million USD (Cons, Welf. = Consumer's Welfare). Total Welf. = Total Welfare

6. Summary and conclusion

Our findings on the competitive behaviors of full service- and low-cost carriers and welfares

are as follows:(1) Full service carriers suffer from the competition with LCC(s) at the primary airports: they expand

their capacities and their airfares drop statistically low and so are the conduct parameters, although

the differences of conduct parameters are not statistically significant. This fact implies that fullservice carriers do not benefit when LCC(s) enters in the same airports.

(2) The conduct parameters of LCCs are, on average, higher than those of full service carriers, and thisfact implies that LCCs do not necessarily performing the cut-throat competition with thin price-cost

margin but they make reasonable profits in spite of their low airfares, especially when they stay their

dominant secondary airport such as Southwest’s Dallas Love Field.

30

(3) As for the impact of the first entry of an LCC and the consecutive entry, the first entry has great

impact on output and airfare, but the second and more additional entry have not much impact than thefirst one.

(4) Full service carriers are better off without any entry by LCC, but they achieve higher conduct

parameter with LCC(s) at the secondary airport than for the benchmark case where full service

carriers are competing with each other.

(5) It is recognized that an LCC’s low-airfare entry has capacity-expanding and airfare-reducing effects

on its rival at an initial stage, but whether these effects are maintained in the long run seems to be

dependent on each carrier.

(6) It sometimes happens that competition between LCCs and full service carriers ends in the second

year of a new entry, especially when a newly entered LCC cannot gain sufficient market share. We

need to further investigate the issues of dynamic competition in the future.

(7) It is implied that total gains of welfare due to LCCs’ entries seems to be substantial, and 90% of

welfare gains come from the gain in consumer’s surplus and the rest comes from the profit of LCCs.

It is also implied that full service carriers do not earn profits although their price-cost margins are notnecessarily low.

This fact seems to be due to their high average cost level.

Furthermore, our important finding is that LCC’s conduct parameters sometimes take unreasonably highvalue. In such a case, LCCs may not know their own demand curve and determine capacities and31

airfares “the left” of profit-maximizing” point. This fact may imply that the conduct parameters willtake wider range than the range that the economic theory assumes.

[2009.6.12 929]

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34

Appendix 1

Table 7

Estimated result of route-specific structural equations
Generalized 2SLSDemand Equation

COEF

SE

t-RATIO

3SLS
t-testCOEF

SE

t-RATIO

t-testConstanta0-15.287

7.326-2.087b-19.226

6.481-2.967aMarket airfare

a1-1.5440.416-3.711a-1.7560.411-4.277aIncome

a2

1.574

0.678

2.323

b

1.960

0.621

3.157

a

Population

a3

0.963

0.109

8.806

a

0.997

0.091

10.902aDistance

a4

0.195

0.205

0.950

0.272

0.181

1.499

Triopoly Dummy

a5

0.330

0.097

3.396

a

0.333

0.098

3.399

a

4-firm Dummy

a6

0.699

0.145

4.828

a

0.632

0.145

4.365

a

5-firm Dummy

a7

0.989

0.234

4.225

a

0.931

0.222

4.199

a

6-firm Dummy

a8

0.350

0.268

1.309

0.344

0.336

1.022

7-firm Dummy
Pseudo supply
Equation
Constant

a9

0.571

0.426

1.340

0.561

0.431

1.301

COEF

SE

t-RATIO

COEF

SE

t-RATIO

b0

0.825

0.820

1.006

0.308

0.839

0.367

Output

b1

0.053

0.049

1.087

0.079

0.050

1.567

Distance

b2

0.572

0.073

7.801

Herfindahl Index

b3

0.079

0.129

0.612

t-test

a

0.622

0.073

8.472

0.075

0.131

0.575

t-testaDiversity of MC

b4-0.0260.011-2.337b-0.0310.011-2.838

a

DLCC1

b5

-0.2060.067-3.084a-0.2060.068-3.016

a

DLCC2

b6

-0.1900.092-2.070b-0.1550.093-1.670R-Square of demand equation: G2SLS=0.458, 3SLS=0.441
R-Square of pseudo-supply equation: G2SLS=0.430,

Statistics

3SLS=0.218

R-Square of structure equation: G2SLS=0.546, 3SLS=0.549
n=405, "a”: significant at 1%, “b”: 5%Appendix 2

Table 8Estimated result of carrier-specific structural equations
Demand Equation
Model 1VARIABLES

Parameter

SE

Model 2
P-VALUE

ParameterSE

P-VALUE

Airfare-1.387

0.334

0.000

-1.260

0.271

0.000

Distance

0.092

0.138

0.501

0.029

0.113

0.801

Per-Capita Income

1.883

0.430

0.000

1.648

0.392

0.000

Average Population

0.783

0.069

0.000

0.844

0.067

0.000

Dummy for Triopoly Market-0.092

0.082

0.265

-0.112

0.079

0.157

Dummy for 4-firm Market-0.194

0.113

0.085

-0.186

0.105

0.075

35

Dummy for 5-firm Market

0.072

0.147

0.627

0.063

0.136

0.645

Dummy for 6-firm Market-0.736

0.213

0.001

-0.674

0.199

0.001

Dummy for 7-firm Market-0.641

0.274

0.019

-0.565

0.260

0.029

Dummy for LCCs at Primary AP-0.582

0.159

0.000

-0.567

0.140

0.000

Dummy for LCCs at Secondary AP-0.350

0.197

0.075

-0.283

0.170

0.097

Constant

2.458

1.282

0.055

3.013

1.266

0.017

Pseudo-Supply Equation
VARIABLES

ParameterSE

P-VALUE

ParameterSE

P-VALUE

Output

0.120

0.023

0.000

0.079

0.021

0.000

Route Marginal Cost

0.609

0.053

0.000

0.504

0.023

0.000

Diversity of MC-0.055

0.051

0.276

-0.022

0.013

0.086

Herfindahl Index

0.141

0.045

0.002

0.183

0.035

0.000

D1LCC1-0.340

0.031

0.000

D1LCC2-0.299

0.064

0.000

D1LCR1-0.172

0.028

0.000

D1LCR2-0.155

0.045

0.001

D2LCC1-0.377

0.073

0.000

D2LCC2-0.174

0.058

0.003

D2LCR1-0.403

0.077

0.000

D2LCR2-0.190

0.067

0.005

KP at Primary Airport-0.560

0.179

0.002

KP at Secondary Airport-0.368

0.252

0.144

KP's Rival at Primary Airport-0.132

0.120

0.270

KP's Rival at Secondary Airport

0.109

0.183

0.550

TZ at Primary Airport-0.200

0.087

0.021

TZ at Secondary Airport-0.081

0.067

0.230

TZ's Rival at Primary Airport

0.007

0.052

0.898

TZ's Rival at Secondary Airport

0.032

0.047

0.503

HP at Primary Airport-0.042

0.042

0.324

HP at Secondary Airport-0.451

0.254

0.076

HP's Rival at Primary Airport

0.008

0.029

0.792

HP's Rival at Secondary Airport-0.015

0.129

0.907

FL at Primary Airport-0.537

0.047

0.000

FL at Secondary Airport-0.406

0.146

0.005

FL's Rival at Primary Airport-0.310

0.043

0.000

36

FL's Rival at Secondary Airport-0.105

0.091

0.251

NJ at Primary Airport-0.554

0.090

0.000

NJ at Secondary Airport-0.495

0.147

0.001

NJ's Rival at Primary Airport-0.128

0.069

0.064

NJ's Rival at Secondary Airport

0.249

0.099

0.012

NK at Primary Airport-0.556

0.146

0.000

NK's Rival at Primary Airport-0.255

0.127

0.044

WN at Primary Airport-0.414

0.050

0.000

WN at Secondary Airport-0.375

0.042

0.000

WN's Rival at Primary Airport-0.296

0.033

0.000

WN's Rival at Secondary Airport-0.247

0.029

0.000

FF at Primary Airport-0.754

0.121

0.000

FF's Rival at Primary Airport-0.106

0.080

0.186

F9 at Primary Airport-0.392

0.114

0.001

F9's Rival at Primary Airport-0.051

0.073

0.482

LCCPE(WN)1 Duopoly-0.363

0.298

0.222

-0.431

0.251

0.086

LCRPE(WN)1 Duopoly-0.077

0.294

0.794

-0.116

0.251

0.644

LCCPE(WN)1 Triopoly-0.365

0.213

0.086

-0.409

0.180

0.023

LCRPE(WN)1 Triopoly-0.326

0.134

0.015

-0.351

0.114

0.002

LCCPE(WN)2 Duopoly-0.351

0.292

0.229

-0.410

0.251

0.102

LCRPE(WN)2 Duopoly-0.079

0.291

0.786

-0.145

0.252

0.566

LCCPE(HP)2 Duopoly-0.613

0.292

0.035

-0.616

0.251

0.014

LCRPE(HP)2 Duopoly-0.497

0.294

0.091

-0.469

0.254

0.065

Constant

1.601

0.235

0.000

1.980

0.208

0.000

System R-Square=0.552
Statistics of Model 1

Test of overall significance: χ=933.1 (P-value=0.000)

System R-Square=0.677
Statistics of Model 2

Test of overall significance: χ 51

2=1312.7 (P-value=0.000)

Appendix 3

Table 9 The descriptive statistics of continuous variables used for the computation of consumer’s
welfareAverage
Output155010.0

S.E.

Minimum

191486.3

10660.037

Maximum
1143550.0

Median82185.0

AirfarePopulation
IncomeHerfindahl index

175.3

62.6

49.4

354.4

172.7

3274440.1

1701878.1

161757.0

11792430.2

2958991.6

31907.3

2933.526047.838346.732238.4

49.8

13.7

15.3

81.9

50.4

38