2012-25
Dynamic Effect of Low-Cost Entry on the
Conduct Parameter: An Early-Stage Analysis
of Southwest Airlines and America West Airlines
Hideki Murakami

Dynamic Effect of Low-Cost Entry on the Conduct Parameter: An Early-Stage Analysis ofSouthwest Airlines and America West Airlines
By

Hideki Murakami
Associate Professor of Transportation Economics and Logistics
Graduate School of Business, Kobe University, Japan
2-1, Rokkodai, Nada, Kobe, Hyogo. 657-8501
Phone: +81-78-803-6941

FAX: +81-78-803-6977

Email: hidekim@panda.kobe-u.ac.jp

Abstract
The purpose of this research is to investigate the dynamic changes in the competition between air
carriers by applying a revised conduct parameter method. We examined the cases of Southwest Airlines
and America West Airlines due to the availability of data. Our interest is in what fashion a low-costcarrier (LCC) entered the market, how the rival reacted, and whether the fashions of competition between
two types of air carrier remained stable as time passed. Our empirical results obtained by econometricmethods using 894 sample observations show that the fashions of competition fell between Cournot
competition and “P=MC (price equals marginal cost)” competition, and sometimes the fashions werestable and sometimes not. Beyond four or five years after new entry by an LCC, these two fashions ofcompetition reached a state of equilibrium. An implication for industrial policy is that an LCC’s entry
improves consumer surplus but it seems not to maximize social welfare.
Key words: LCC, new entry, conduct parameter, dynamic analysis

Introduction
As the market share of the low-cost carriers (LCCs) in the airline industry has grown to about 30% of thetotal revenue passengers worldwide, many academic studies on the issues of LCCs have been published.
Among these studies, not a few have focused on the effect of an LCC’s entry on airfares and welfare
issues, but only a little attention has been paid to the fashions of competition between LCCs and fullservice airlines (FSAs) using the conduct parameter method (CPM).
The recent contributions to the study of inter-firm rivalry among air carriers are as follows: Goolsbee andSyverson (2005) and Oliveira and Huse (2009) studied the effects of LCC entries on the incumbents’
responses. Fu et al (2006) incorporated duopolistic inter-firm rivalry explicitly into their LCCs vs. FSAs
competition study, as well as incorporating the effect of pricing behavior of an unregulated-monopoly
airport on the downstream competition between LCCs and FSAs. Fu et al. (2011) found that the service
differentiation between FSAs and LCCs leads to the cartelized behavior of FSAs.
In analyses that used CPM, Fischer and Kamerschen (2003), Fageda (2006), and Murakami (2011)
empirically estimated the conduct parameters of airline industries in the United States (the first two of
three studies), Spain (the fourth study), and Japan. The earliest contributions of applying CPM to the1airline industry are those of Brander and Zhang (1990, 1993) and Oum et al.(1993), who analyzed theinter-firm rivalry between FSAs.
Our research will also apply the newly revised CPM to the analysis of the competition between LCCs and
FSAs, and the distinguishing feature of our analysis is that we focus on the dynamic aspects ofcompetitions. We will discuss the basic concept of CPM and also review the pros and cons of thismethod for analyzing competition in the next section, highlighting the studies of Corts (1999) and Puller(2009). In section 3 we will show how to overcome the drawbacks of CPM by quoting the methodologyof Puller (2009). In section 4 we construct an econometric model of demand and the pseudo-supply
equation system to derive the conduct parameters. In section 5 we will show the dataset for our empiricalanalysis. Section 6 demonstrates the dynamic change in the conduct parameter after the entry ofSouthwest Airlines, and we discuss the results. In section 7 (the final section), we will demonstrate thecontributions of our research and discuss the implications of industrial policy. Finally, we will mention
the limitations of this study, which will guide us in improving our analyses in the future.

2 CPM and dynamic competition
Reviewing the pertinent literature, we find that there have been two ways to estimate the conduct
parameter. One method was proposed by Brander and Zhang (1990, 1993) and Oum et al. (1993), whoestimated the non-liner pseudo-supply equation and the demand equation jointly. The alternative method
was proposed by Iwata (1974) and Bresnahan (1981), who estimated the linear inverse demand and thepseudo-supply equation jointly. The major difference between the two methods is that the former method
directly estimates the conduct parameter, while the latter method derives it indirectly from the parametersof output variables in the inverse demand and the pseudo-supply equations.
In a recent analysis of the estimation of market power using CPM, Fisher and Kamerschen (2003) pointout that in a static environment, the notions of expectation and conjectural variation are not well defined.
For example, if we start our analysis by modeling a one-shot Cournot competition and try to estimate theconjectural variation by CPM, we face the problem that we cannot describe a 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.”1 Also, Corts (1999) pointed out, “CPM estimates ofmarket power can be seriously misleading. In fact, the conduct parameter need not even be positively
correlated with the true measure of the elasticity-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.”2
CPM is also criticized by Lindh (1992), Reiss and Wolak (2005), and Kim and Knittel (2006). We canclassify the pitfalls of CPM into two parts. The first one is the problem with the link between theoreticaland empirical methods. Lindh (1992) points out that the estimated conduct parameter can represent the1

Bresnahan (1989), p.129.

2

Corts (1999), p. 299.

2

theoretically derived conduct parameter only under a specific information assumption. We repeat Corts’s(1999) critique of CPM that, since it assumes a static model, CPM does not explain the reaction of firms
or the incentive compatibility constraint that indicates why a firm stays in a collusion situation or deviatesfrom it. Therefore, CPM may yield an inconsistent estimator of the market power when firms areengaged in tacit collusion in a dynamic competition. The second type of pitfall is the problem with thefunctional form: earlier literature such as the studies by Bresnahan (1989) and Lau (1982) point out thatthe estimation of the conduct parameter varies widely depending on the functional form. Wolfram(1999), and Kim and Knittel (2006) studied the conduct parameter of the electricity market in Britain (theformer) and California (the latter) using the directly measured and the estimated marginal costs, andconcluded that the latter method (NEIO method) overstated the market power.
Taking these critics’ statements into account, Fisher and Kamerschen (2003) nonetheless stressed theusefulness of conjectural variation. They insisted, following Brander and Zhang (1993) and Oum et al.
(1993), that “one can view the conjectural variation as a parameter of market conduct that can capture thewhole range of market performance, from perfect competition to monopolistic behavior, rather thantaking it as an indicator for the firm’s expectation.” 3 Fageda (2006) also computed the conduct
parameters of Spanish air markets by estimating the demand and pseudo-supply equation system usingsemi-annual (summer and winter) data of the years 2000 and 2001 by three stage least square methods(3SLS), not stressing the problem with the dynamic features of conduct parameters but regarding conduct
parameters as the set of static equilibria.4
Kim and Knittel (2006) summarized the conjectural variation model to state that it is a proxy for a
dynamic model, and the folk theorem tells us that a range of conducts are Nash equilibria in a dynamic
game. Therefore, one can view the conjectural variation not as an estimate of a theoretical model, but as ameasure of the elasticity-adjusted Lerner index, measuring the static-equivalent level of an industry’s
competitiveness. Provided the technique yields accurate estimates of the elasticity-adjusted Lerner index,
it is a useful exercise.
Other research such as the studies by Genesove and Mullin (1998) and Clay and Troesken (2003) pointout that the estimates of conjectural variation are robust across functional form. The most recent remedy
for CPM is that by Puller (2009), which tries to answer the critique of Corts (1999). In summary, theestimate of conduct parameter could overstate, understate, or be close to the theoretical value.
The next section focuses on reviewing the critique by Corts (1999) and the remedy for Corts’ critique
proposed by Puller (2009).

3 The application of CPM to deduce the degree of competition: critique by Corts (1999)
and counterproposal by Puller (2009)
Corts (1999) assumed that we must estimate the following linear demand and supply system using timeseries data (t denotes time period):Pt   0  1 xt   2 Qt   t (1)3Fisher and Kamerschen (2003), p. 234.

4

Fageda (2006), pp. 388-395.

3

Pt   0  1 wit   2 qit   it (2)In equation (1), xt is a vector of demand shifters, qit is firm i’s output, and Qt  Nqit (N is the number ofsymmetric firms). Thus, equation (1) represents an inverse demand function, with  t being the random
disturbance. In equation (2), wt is the vector of cost shifters, and thus equation (2) represents a pseudosupply function, with  it being the random disturbance term. The 2SLS estimator of  2 is as follows:ˆ2  qˆ i' M w qˆ i  qˆ i' M w P   qˆ i' M w qˆ i  qˆ i' M w xa1  qˆ i' M wQa 2  qˆ i' M w ea2 1 qˆ i' M w qˆ i1

 qˆ M xa  qˆ M ea   Na
1

'
i

w

1

'
i

2

w

(3)

2

where M w is the instrumental variable such that p lim1 nM w ' q   0 and p lim1 nM '  t   0 .
The asymptotic estimator of ˆ 2 is ˆ 2  a1 /   Na 2 , where  is the parameter obtained by regressing
qi on xi . Let ˆ be the asymptotically consistent estimator of conduct parameter, such thatˆ  dQt dqit  1  d  q jt dqit  ˆ 2 ˆ 2  a1 a 2   N (4)i j

We see that the estimated conduct parameter is a function of only the demand parameters and  , theresponsiveness of equilibrium quantity to the demand shifter. Theoretically, the conduct parametermeasures something having to do with the slope of the supply relation. Assuming a firm’s optimum
supply on the pseudo-supply curve is linear in xi , i.e.,   dq * dx , then the estimated conduct
parameter measures the slope of the price-cost margin with respect to demand driven fluctuations inquantity, as follows:ˆ  1 P'd P  MC  dx dq* dx(5)
On the other hand, the conduct parameter, which is theoretically derived from the first-order condition(the so-called “as-if” conjectural variation), is depicted as a static form, as follows:   P  MCi  P' qi    1 P'P  MCi  x qi x (6)*Therefore, Corts proposed that for any underlying supply process generating q , the estimated conduct
parameter would accurately measure market power ˆ   if and only if(7)P  MC i x  dqi x   d P  MC i  dx dqi dx Otherwise, the estimated theta is not a consistent estimator of  . Therefore, the essence of Corts’ critiqueis that the conduct parameter derived from CPM does not capture any dynamic aspect, even in the case ofa dataset that has a time-series dimension.
Puller (2009) reviewed Corts’ critique and suggested the following theoretical model to obtain aconsistent estimate of  in a dynamic game. Note that the following discussion is taken from Puller(2009), pp. 1497-1500. In this model, a firm maximizes the joint profit so that no firm will deviate fromcollusion at t in the infinite game (and also in a finite game with many repetitions), as follows:Nmax   it Qt N 
Qt

i 1

4

 



 



s.t.  itbr Qt     s t Et  isp   it Qt N     s t Et  is*s t 1i (8)s t 1

where  it is the one-shot-game profit of a deviated firm,  is the discount factor,  is is the firm’spoptimal profit under collusion,  is is the firm’s profit after it has deviated, and E  is the expectation.
The first-order condition with respect to Qt yielding the condition that each firm in a collusive regime
satisfies is as follows:
*

br

 

 

  

P Qt*  MCit qit*   i Pt ' qit*  ut* 1  ut* Nd

br

dQt  0  i(9)is the conduct
where u t* is the Lagrange multiplier on the incentive compatibility constraint andparameter. According to Puller (2009), equation (9) has a simple interpretation. In a collusive
equilibrium, the firm internalizes the effect of price changes on the revenue for all firms’ inframarginal
output (θq∗ ). When the incentive compatibility constraint is not binding (u∗0) , the last term will be
zero and we get the firm-level first-order condition for joint monopoly pricing. When the constraint
binds, the joint output must rise and the price must fall, so no firm will deviate, i.e., firms collude between
the monopoly and Cournot levels.
It is apparent that equation (9) can explain the dynamic situation as well as the situation where firms playa one-shot game. In the one-shot-game, the last term on the right-hand side of equation (9) is zero, sinceU * = 0. This equation captures the following three common oligopoly models:H1: “P=MC”:  it 
H2: Cournot:  it 

 dq

 dq

jt

dqit   1 and U∗ = 0.

jt

dqit   0 and U* = 0.

H3: Efficient tacit collusion:  it 

 dq

jt

dqit   N  1 and u∗ ≧0where  it    1 and i  j.
If we regard the last term on the left-hand side of equation (9) as a random disturbance of an econometricmodel, we can rewrite equation (9) as follows:
P q it*  q  it  MC it q it*   Pt ' q it*   it (10)If ε is non-zero and correlated with q , the conduct parameter to be estimated is biased and inconsistent
due to the simultaneous-equation bias. This problem can be avoided by using two-stage least squares if
there is no heteroskedasticity. However, we have another problem to solve.
The last term in equation (10) is equal across firms in the collusive regime for a given period. Based on
this observation, Puller (2009) stated that although one does not have the data on the last term in equation(10), this term can be conditioned out by including a time fixed effect. Moreover, as discussed above, if
firms are playing a static game, this term is zero, so equation (10) can be generalized to both static anddynamic pricing, and it yields the consistent estimator of θ. The important point is that the conduct
parameter in our analysis does not capture the reaction behavior such as Stackelberg competition, but it
does demonstrate the series of “degree of market power” in one-shot games after an LCC’s entry. `In





 

5

addition, as will be shown in Section 4, our dataset is made up of the series of yearly sample observations,and each year’s estimate means the average Nash equilibria of a number of competitions performed in theyear. The next section shows the structural pseudo-supply and demand model incorporating time fixed
effect dummy variables.

4 Structural equation for deriving dynamic change in the conduct parameter
The empirical model to be estimated is the following simultaneous equation system consisting of thelinear inverse-demand and the pseudo-supply equations, which follow the reports of Iwata (1974) andBresnahan (1981) that were discussed in Corts (1999):(Inverse demand)
∑
P
α
A Q
α INC
α POP
α FDISTεk 1,2,3, 1,3,4
(11)
where
A

α
γ

DWN1
γ
γ DHP1
γ

DWNR1
γ
DWN2
DHPR1
γ DFL1γ DFL2
γ
DFLR2

γ

DWNR2
DFL1R

γ

(pseudo-supply)
P

β

B Q

β MC

β HERF

β DTR

μ

k

1,2,3

(12)

where
B

β
δγ

DWN1
δ
δ DHP1
δ

DWNR1
δ
DWN2
δ
DWNR2
DHPR1
δ DFL1δDFLR1δ DFL2δ
DFLR2where P is the year-average airfare of carrier k at route in year , Q is the number of passengers
carried by carrier at route in year , INC is the population-weighted average per-capita income ofroute ′s origin and destination areas in year , POP is the arithmetic average population of route ′sorigin and destination areas in year , FDIST is the distance flown by carrier at route ,5 MC is theroute marginal cost of carrier at route in year , HERF is the Herfindahl index of route in year ,and ε and u are the random disturbance terms. All the other variables starting with D are binary
variables, and A and B denote the sets of parameter dummy variables introduced to the output variableof each demand and pseudo-supply equation. The explanations of these dummy variables are shown in
Table 1.

5 The distance flown by each carrier is not equal across carriers, since LCCs flown from a secondary airport take different flightcourses than do FSAs flown from a primary airport, though the difference of distance may be small.

6

Table 1 Explanations of binary variables
Table 1 is coming here.
All these dummy variables are introduced as “parameter dummy variables” so as to compute the dynamicchange in conduct parameters. Recalling equation (4) in the last section, we can derive the conduct
parameter of the benchmark carriers θ. The benchmark carriers are FSAs that did not compete with
LCC(s) for the period from 1997 to 2000 in their operating routes (for example, the case of thecompetition between AA and UA in a certain route). This is shown in the following equation:

1   BM  ˆBM  ˆ1 ˆ1  BM  ˆ1 ˆ1  1(13)This means the conduct parameter is computed by dividing the estimated parameter of the output variablein the pseudo-supply equation by that of the output variable in the inverse demand equation, and this is aconsistent estimator of the conduct parameter by introducing the time fixed effect dummy variables∑. Similarly, the conduct parameter of Southwest’s rival in the pre-entry year iscomputed as follows:

1  v0DWNR  ˆ0DWNR  ˆ1  ˆ0DWNR v0DWNR  ˆ0DWNR  1  ˆ1  ˆ0DWNR ˆ  ˆ ˆ  ˆ
1

1

DWNR
0

DWNR
0

1


(14)

The image of this computation is shown in Figure 1.

Figure 1 Graphical explanation of equation (14)Figure 1 is coming here.
Comparing equation (14) with equation (13), it is apparent that the angles of demand and pseudo-supply
curves are different, although those of intercepts are the same. Assume X is the “benchmarking” demandand pseudo-supply equilibrium, and Y is the demand and pseudo-supply equilibrium that Southwest’s
rivals had reached before Southwest Airlines entered the market. The conduct parameters that arecomputed from X and Y equilibria will be different from each other. We will do the same computation
for all the cases of the carrier dummy variables. The dummy variables and methods of computation are
shown in Table 2.

Table 2 Computing method to derive a carrier-specific conduct parameter7

Table 2 is coming here.
Since the thetas in the right columns of Table 2 contain carriers’ own behavior (that is,⁄1), weneed to deduce one I order to derive v, which stands for the sum of other carriers’ (j’s) reactions to carrier
i’s behavior. One feature of the structural model is that we have information about the marginal cost. We
used the route-specific marginal cost, which can be empirically derived, a method that was used in
Brander and Zhang (1990), Oum et al. (1993), and Murakami (2011). In addition, as one of the proxies ofthe marginal cost, we also used “distance flown by carriers,” since this proxy was used in recent reports
such as that by Fageda (2006). In addition, following the standard literature on airline costs, we assumed
that economies of density exist.
To estimate this structural model, we had to determine which method of estimation would best suit our
purposes. Theoretically, price and quantity are both endogenous, so it is natural to estimate this equation
system by 2SLS. The 2SLS method is, like OLS, efficient when the random disturbance follows normal
distribution; however, if we recognize the heteroskedastic distribution of random disturbance, 3SLS isbetter than 2SLS in terms of estimation. To diagnose the hetroskedasiticity, we performed the WhiteKornker test for the demand and the pseudo-supply equations. The test results were that for the demand
equation  (2d .o. f . 20 )  89.62 and for the pseudo-supply equation  (2d .o. f .9)  131.01 . These two values
are large enough to allow us to reject the null hypothesis that we have no hetroskedasiticity at the 1%
level. Therefore, we used 3SLS for our estimation method. We used alternative methods such as iterative
3SLS or G2SLS, each of which is more efficient than 2SLS under heteroskedasticity.

5 The data
As in our first study of this subject, we collected operational data observations from DB1A, which
included the available U.S. domestic flight data for carriers that had 10% market share in duopoly markets.
We omitted the data of carriers with less than 10% market share in duopoly markets, or 5% share in
triopoly markets or markets served by more carriers. Carriers whose codes are not reported in DB1A
(reported as XX) were also omitted, but, for example, a triopoly market with one XX carrier was notregarded as a duopoly market, since the XX carrier probably had competitive effects on the other carriersin that market. The flight data are outbound and non-connecting routes from the seven largest U.S.
airports and their regions: New York/Newark area, (JFK, LaGuardia, Newark), Washington, D.C., area
(Ronald Reagan (National), Dulles, Baltimore), Chicago area (O’Hare and Midway), Atlanta/Hartsfield
area, Dallas/Fort Worth area (DFW and Love Field), and Los Angeles.
The cost and input price data are from the Air Carrier Financial Reports, Form 41 Financial Data.
Income and population data are from the Regional Accounts Data, Bureau of Economic Analysis. We
8

used the Primary Metropolitan Statistical Area data (PMSA, an urbanized county or set of counties thathave strong social and economic links to neighboring communities) for each city.
We used data from 199 city-pairs that are duopoly markets and 166 triopoly markets, which gave us 894
sample observations. We did not extend the time dimension beyond year 2000, because we would have
had to remove the effect of the “9-11” terrorist attack in 2001. The descriptive statistics used for the
analyses of this chapter are shown in Table 3.

Table 3 Descriptive statistics of continuous variables

Table 3 is coming here.

6 Empirical results of the conduct parameter
The detailed estimated results of equations (11) and (12) are shown in Table 8 in the Appendix. Figure 2
and Figure 3 graphically show the dynamic changes in conduct parameters.

Figure 2 Dynamic changes in Southwest’s and its rivals’ conduct parameters

Figure 2 is coming here.

Overall, for both the case of Southwest Airlines and that of America West Airlines, in the single-yeartime span, carriers competed between the Cournot and the competitive level (that is,zero and -1).

distributes between

Southwest Airlines’ conduct parameter is lower by 14% than those of the incumbent(s),and this means the incumbents reacted very competitively against Southwest Airlines’ entry.

The

incumbents’ conduct parameters dropped by 6.3% after Southwest Airlines entered, so it appears that
Southwest Airlines also reacted more competitively than did other carriers that had operated before
Southwest’s entry.
The case of America West Airlines is a little different from that of Southwest Airlines, except for the
long-run result. Unlike Southwest Airlines, America West Airlines seems not to have carefully targeted

9which markets to enter: sometimes it entered markets where another LCC had already entered.6 For
example, America West entered the Chicago-Sacramento market in 1999, where Southwest Airlines andUnited Airlines were already competing. Since competitions had already started in that market,
Southwest Airlines and other incumbents reacted by implementing tough strategies, even though AmericaWest’s market share in the beginning was small. The reason for the blue wavy line, which shows thereactions of Southwest and United Airlines, did not drop after America West Airlines entered is that theseincumbent carriers including Southwest Airlines had already started competition before America West
Airlines entered the market. In the fourth year of America West Airline’s operation in this market, when
its market share increased, its rivals reacted even more competitively than they had in the former years.

Figure 3 Dynamic changes in America West’s and its rivals’ conduct parameters
Figure 3 is coming here.
In the case where we cannot identify when LCCs entered (i.e., in the long run), the incumbent(s)’ conduct
parameter drops by 34% from the pre-entry level, and Southwest’s conduct parameter drops by 48.7%.
Therefore, in the case of Southwest Airlines, the competition seems to last and become fierce after morethan five years have passed. This is also true for the case of America West Airlines.
Our next goal was to determine whether the competitions between LCCs and FSAs are a series of “P=MC”
competitions or a Cournot game in terms of statistics. To do this, we tested the hypotheses that the
conduct parameters are -1 (the case of “P=MC” competition) and zero (Cournot case), while theparameters of entry-year time fixed effect dummy variables are simultaneously zero for “P=MC”
competitions and Cournot cases. Table 4 shows the results of these joint tests of the two hypotheses forSouthwest Airlines, and Table 5 shows the results for America West Airlines.

Table 4 The results of joint tests of hypothesis for Southwest Airlines

Table 4 is coming here.

According to Tables 4 and 5, we can reject the hypothesis that a carrier performs Cournot competitions
for the first year and the third year of Southwest Airline’s entry and the cases where competitions last for
more than five years by carrying out the Wald

test with a degree of freedom equal to two.

6

Such a case rarely happens to Southwest Airlines. It carefully targets the monopoly or joint-monopoly market from which it
potentially can make abundant profits.

10

Table 5 The results of joint tests of hypothesis for America West Airlines

Table 5 is coming here.

We cannot reject the hypothesis that the conduct parameter and the parameters of the entry-year time
fixed effect dummy variable are equal to minus one at the 5% level for all the other cases of Southwest
Airlines. As for other than the first year and the third year of Southwest’s entry, we cannot reject the
hypothesis that the conduct parameter of Southwest Airlines is either minus one or zero. Considering
these values of the conduct parameters, FSAs competed very fiercely, especially when Southwest Airlines
entered the market, then softened competition in the second year, and again adopted a tough strategy inthe third year. FSAs’ strategies were softened after the third year, and the fashion of competition fell
between Cournot and “P=MC” competition.
Considering the conduct parameters of FSAs, Southwest’s fashion of competition fell between Cournot
and “P=MC” competition. The data indicate those patterns had wide variation, except for the fourth yearof Southwest’s entry: in the fourth year of entry, the competition was softer than in the previous years,
and Southwest’s competition was of the Cournot type rather than the “P=MC” type.
When competitions lasted for more than five years for Southwest Airlines or four years for America West
Airlines, it can be firmly stated that the patterns of competition were neither at the Cournot nor the“P=MC” competition level; that is, they were between the two. This result may have come from the factthat we have abundant sample observations for these cases, so thevalue asymptotically became stable.

As for the rivals of these two LCCs, Southwest’s rivals stayed at an in-between level, but America West’s
rivals took a closer strategy to Cournot than to “P=MC” competition.
It appears the average value of America West’s conduct parameter is higher than that of Southwest
Airlines, but there are no significant differences from a statistical viewpoint.7

7 Concluding Remarks

7 We tested the null hypothesis that the conduct parameters of Southwest Airlines and those of America West Airlines are equal(θθ , t=1,⋯,4) by the Wald test. The
statistics with degree of freedom equal to one are 0.170, 0.0001, 0.021, and
0.004, respectively. Since thestatistics at the 5% level of significance with d.o.f=1 is 3.84, we cannot reject the null
hypothesis.

11This paper analyzed the dynamic change in the fashions of competition between Southwest Airlines and
FSAs, and between America West Airlines and FSAs by using CPM. This method might have been a“dead end” method but for the proposals made by Puller (2009). The methodological contribution of thispaper is to re-vitalize CPM and apply Puller’s proposals for modeling the simultaneous equation system
to deduce the fashions of dynamic competitions between FSAs and LCCs for the first time. The
empirical contributions are that we were able to estimate the consistent conduct parameters for the
analyses of dynamic competition and found the facts summarized in Table 6 and Table 7.

Table 6 The fashion of dynamic competition between Southwest Airlines and its rivals
Table 6 is coming here.

Table 7 The fashion of dynamic competition between Southwest Airlines and its rivals
Table 7 is coming here.
The implication for the policy of the U.S. airline industry is that the competitions between LCCs and
FSAs never reached the equilibrium state where social welfare is maximized (i.e., the “P=MC” level),even after 5 or more years. One possible explanation of this fact is that the market segments of FSAs
were partly separated from those of LCCs, and both FSAs and LCCs had market power to increase theirprice-cost margins.

These situations may have taken place where either FSAs succeeded indifferentiating their services against those of LCCs or vise-versa, and eventually the airlines
differentiating services gained the power to be able to control their price-cost margins. There may beroom for further discussion about whether government sectors have to intervene to remove these market
powers or let the industry be “as-is” as long as consumers have several choices of carriers and behave on
the basis of their willingness to pay.
Of course, our paper has limitations. One is that the data used here are “dated,” although we intended to
avoid the effect of 9-11 terrorism and wars in the Middle East following 9-11. The second limitation isthat we did not widen the range of our analyses to include other LCCs such as Air Tran and Jet Blue.
This will be possible if we extend the time dimension of the dataset in future analyses. A third possible
limitation is that we did not try to determine how the welfare changed over time in accordance with thechange in the fashion of competitions, due to the limitation of the length of article. We will analyze thesethree issues in the future.

12Appendix
Table 8 Demand and Pseudo-supply equations to derive conduct parameters

Table 8 is coming here.

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Oliveira, A.V.M., and C. Huse (2009), “Localized competitive advantage and price reactions to entry: Full-service vs. low-costairlines in recently liberalized emerging markets,” Transportation Research Part E, Vol. 45, No. 2, pp. 307-320.
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No. 4, pp. 805-826.

Table 1 Explanations of binary variables
DWN
DWNR
DHP
DHPR
DWN1
DWNR1
DWN2
DWNR2
DHP1
DHPR1
DFL1
DFLR1
DFL2
DFLR2
α DYEARDTR

Dummy variable that takes 1 for Southwest Airlines that operates for T year in the route T
1, ⋯ ,5 .
Dummy variable that takes 1 for FSA(s) that compete(s) with Southwest Airlines for T year in theroute T 0, ⋯ ,5 . “T=0” means the year preceding Southwest’s entry.
Dummy variable that takes 1 for America West Airlines that operates for T year in the route
T 1, ⋯ ,4 .
Dummy variable that takes 1 for FSA(s) that compete(s) with America West Airlines for T year in
the route T 0, ⋯ ,4 . “T=0” means the year preceding America West’s entry.
Dummy variable that takes 1 for Southwest Airlines that operates at a primary airport. The entry
year cannot be recognized in our dataset (subscript “L” means “Longer than five years”).
Dummy variable that takes 1 for FSA(s) that compete(s) with Southwest Airlines at a primary
airport. The entry year cannot be recognized in our dataset.
Dummy variable that takes 1 for Southwest Airlines that operates at a secondary airport. The
entry year cannot be recognized in our dataset.
Dummy variable that takes 1 for FSA(s) at a primary airport that compete(s) with Southwest
Airlines operating at an adjacent secondary airport. The entry year cannot be recognized in ourdataset.
Dummy variable that takes 1 for America West Airlines that operates at a primary airport. The
entry year cannot be recognized in our dataset.
Dummy variable that takes 1 for FSA(s) at a primary airport that compete(s) with America West
Airlines at a primary airport. The entry year cannot be recognized in our dataset.
Dummy variable that takes 1 for Air Tran that operates at a primary airport. The entry year
cannot be recognized in our dataset.
Dummy variable that takes 1 for FSA(s) that compete(s) with Air Tran at a primary airport. The
entry year cannot be recognized in our dataset.
Dummy variable that takes 1 for Air Tran that operates at a secondary airport. The entry year
cannot be recognized in our dataset.
Dummy variable that takes 1 for FSA(s) at a primary airport that compete(s) with Air Tran
operating at an adjacent secondary airport. The entry year cannot be recognized in our dataset.
Time fixed effect dummy variables. The benchmark year is 1998, when the sample observations
are richest. “ τ 1” is year 1997 and “ τ 4” is year 2000.
Dummy variable that controls the market size. This takes 1 for carriers in triopoly markets.

14

Table 2 Computing method to derive a carrier-specific conduct parameter
Parameter

Explanations
Conduct parameter of Southwest’s rival before Southwest Airlines’
entry into the secondary airport.
Conduct parameter of Southwest’s rival in the n-th year of Southwest’s
entry into the secondary airport. (n=1…5)
Conduct parameter of Southwest Airlines in the m-th year of its entry
into the secondary airport. (n=1…5)
Conduct parameter of Southwest’s rival in the case where theinformation of entry year is not available. This is the case when therival airline and Southwest Airlines compete when the rival uses a
primary airport and Southwest Airlines uses a secondary airport.
Conduct parameter of Southwest Airlines in the case where theinformation of entry year is not available. This is the case when therival airline and Southwest Airlines compete when the rival uses a
primary airport and Southwest Airlines uses a secondary airport.
Conduct parameter of Southwest’s rival in the case where theinformation of entry year is not available. This is the case when therival and Southwest Airlines compete at a primary airport.
Conduct parameter of Southwest Airlines in the case where theinformation of entry year is not available. This is the case when therival and Southwest Airlines compete at a primary airport.
Conduct parameter of America West’s rival before America West
Airlines entered in the secondary airport.
Conduct parameter of America west’s rival in the n-th year of AmericaWest’s entry into the primary airport. (n=1…4)
Conduct parameter of America West’s rival in the long run. This is thecase when the rival and America West Airlines compete between
primary and secondary airport.
Conduct parameter of America West Airlines in the long run. This is
the case when the rival and America West Airlines compete between
primary and secondary airports.
Conduct parameter of America West’s rival in the long run. This is thecase when the rival and America West Airlines compete at a primary
airport.
Conduct parameter of America West Airlines in the long run. This is
the case when the rival and America West Airlines compete at a
primary airport.

Derivation
β

δ

α

γ

－1

β

δ

⁄ α

γ

－1

β

δ

⁄ α

γ

－1

β

δ

α

γ

－1

β

δ

α

γ

－1

β

δ

α

γ

－1

β

δ

α

γ

－1

β

δ

α

γ

－1

β

δ

⁄ α

γ

－1

β

δ

α

γ

－1

β

δ

α

γ

－1

β

δ

α

γ

－1

β

δ

α

γ

－1

Table 3 Descriptive statistics of continuous variables
Mean

S.E.

MedianMinimum

Maximum

Distance

903.5122.50

673.0094.00

4917.00

Passengers

48457.93

2480.06

23230.00

850.00613380.00

Airfare147.832.18

142.4226.26

392.55Herfindahl Index

544.884.35

520.14292.471000.00

MC

84.67

1.77

71.60

14.35

472.74

Population

295.784.88

263.4617.91

889.94

Income31594.13

97.87

31725.28

24924.77

37637.41

15

Table 4 The results of joint tests of hypothesis as for Southwest Airlines
Carriers
Southwest’s
CP

CP of rivalsofSouthwest

Status
1st year
2nd year
3rd year
4th year
5th yearLong run
1st year
2nd year
3rd year
4th year
5th yearLong run

Wald χ
562.865
0.242
6.113
0.581
1.479
65.251
2.710
0.414
2.954
1.892
0.590
78.176

P-value
0.000
0.886
0.047
0.748
0.477
0.000
0.258
0.813
0.228
0.388
0.745
0.000

Carriers
Southwest’s
CP

CP of rivalsofSouthwest

Status
1st year
2nd year
3rd year
4th year
5th yearLong run
1st year
2nd year
3rd year
4th year
5th yearLong run

Wald χ
1.947
0.094
0.142
0.210
0.071
11.093
1.748
1.876
4.050
6.153
2.642
6.571

P-value
0.379
0.954
0.931
0.900
0.965
0.004
0.417
0.620
0.132
0.046
0.267
0.037

Wald χ
8.909
1.179
0.474
3.242
6.526
5.357
4.078
10.3300.132
4.789

P-value
0.011
0.555
0.789
0.198
0.038
0.069
0.130
0.006
0.936
0.091

Note: Left: test of Cournot hypothesis, Right: P=MC hypothesis

Table 5 The results of joint tests of hypothesis as America West Airlines
Carriers
America West’s
CP

CP of rivalsofAmerica West

Status
1st year
2nd year
3rd year
4th yearLong run
1st year
2nd year
3rd year
4th yearLong run

Wald χ
8.929
2.730
4.505
3.242
55.5955.474
4.144
10.3255.072
161.543

P-value
0.012
0.255
0.105
0.198
0.000
0.065
0.125
0.001
0.079
0.000

Carriers
AmericaWest’sCP

CP of rivalsofAmericaWest

Status
1st year
2nd year
3rd year
4th yearLong run
1st year
2nd year
3rd year
4th yearLong run

Note: Left: test of Cournot hypothesis, Right: P=MC hypothesis

16

Table 6 The fashion of dynamic competition of Southwest Airlines and its rivals
Southwest Airlines
Status Cournot Hypothesis P=MC hypothesisst
1 yearrejected
not rejectednd
2 yearnot rejected
not rejectedrd
3 yearrejected**
not rejectedth
4 yearnot rejected
not rejectedth
5 yearnot rejected
not rejectedlong runrejected***rejected***Southwest Airlines' rivals (FSA)
Status Cournot Hypothesis P=MC hypothesisst
1 yearnot rejected
not rejectednd
2 yearnot rejected
not rejectedrd
3 yearnot rejected
not rejectedth
4 yearnot rejected
rejected**th
5 yearnot rejected
not rejectedlong runrejected***rejected**

Sign of v-

Fashion of competition
Close to P＝MC
Various between Cournot and P＝MC
Close to P＝MC
Various between Cournot and P＝MC
Various between Cournot and P＝MC
Stable between Cournot and P=MC

Sign of v-

Fashion of competition
Various between Cournot and P＝MC
Various between Cournot and P＝MC
Various between Cournot and P＝MC
Close to Cournot
Various between Cournot and P＝MC
Stable between Cournot and P=MC

Note that ***shows the hypothesis was rejected at 1%, ** at 5%, and * at 10%, respectively.

Table 7 The fashion of dynamic competition of Southwest Airlines and its rivals
Ame ric a West Airlin es
StatusCournot Hypothesis P=MC Hypothesisst
1 yearrejected*
rejected**nd
2 yearnot rejected
not rejectedrd
3 year(Almost) rejected*
not rejectedth
4 yearnot rejected
not rejectedlong runrejected***rejected**Ame ric a West Airlin es' rivals (FSA)
StatusCournot Hypothesis P=MC Hypothesisst
1 yearrejected*
rejected*nd
2 yearnot rejected
not rejectedrd
3 yearrejected***rejected***th
4 yearrejected*
not rejectedlong runrejected***rejected*

Sign of v-

Fashion of competition
Stable between Cournot and P=MC
Various between Cournot and P＝MC
Close to Cournot
Various between Cournot and P＝MC
Stable between Cournot and P=MC

Sign of v-

Fashion of competition
Stable between Cournot and P=MC
Various between Cournot and P＝MC
Stable between Cournot and P=MC
Close to P=MC
Stable between Cournot and P=MC

Note that ***shows the hypothesis was rejected at 1%, ** at 5%, and * at 10%, respectively.

17

Table 8 Demand and Pseudo-supply equations to derive conduct parameters
Inverse-Demand
Parameter

Pseudo-supply.Standardized

AsymptCoefficient

T-RATIO

P-VALUE

Parameter.Standardized

AsymptCoefficient

T-RATIO

P-VALUEα-2.964-3.850

0.000

β

0.487

3.584

0.000

α

0.812

4.412

0.000

β

0.373

12.450

0.000

α

0.513

6.402

0.000

β

-0.462-2.618

0.009

α

0.041

0.418

0.676

β

-0.769-6.3810.000

γ-0.047-0.8280.407

δ-0.030-1.244

0.213

γ

0.018

0.283

0.777

δ

-0.024-0.9100.363

γ-0.013-0.2260.821

δ-0.012-0.4780.633

γ-0.016-0.280

0.780

δ

0.012

0.497

0.619

γ

-0.020-0.352

0.725

δ

0.018

0.722

0.470
0.000

γ

1.883

3.653

0.000

δ

-0.402-4.787

γ

0.140

2.276

0.023

δ

0.011

0.450

0.653

γ

0.066

1.146

0.252

δ

-0.009-0.372

0.710

γ

0.270

3.087

0.002

δ

-0.034-1.329

0.184

γ

0.242

2.917

0.004

δ

-0.031-1.200

0.230

γ

0.242

2.839

0.005

δ

-0.041-1.596

0.111

γ

0.228

2.765

0.006

δ

-0.032-1.246

0.213

γ

0.247

2.414

0.016

δ

-0.173-5.9280.000

γ-0.004-0.068

0.946

δ

0.043

1.617

0.106

γ

-0.041-0.713

0.476

δ

0.007

0.282

0.778

γ

-0.045-0.779

0.436

δ

0.006

0.241

0.809

γ

0.002

0.034

0.973

δ

0.020

0.795

0.427

γ

0.113

1.601

0.109

δ

-0.094-3.565

0.000

γ

0.021

0.373

0.709

δ

0.069

2.709

0.007

γ

0.034

0.602

0.547

δ

0.086

3.515

0.000

γ

0.051

0.901

0.368

δ

0.076

3.139

0.002

γ

0.056

0.997

0.319

δ

0.087

3.594

0.000

γ

-0.037-0.6380.523

δ-0.007-0.2570.797

γ-0.004-0.0680.946

δ-0.053-1.859

0.063

γ

1.237

3.672

0.000

δ

-0.254-4.070

0.000

γ

0.117

1.227

0.220

δ

0.267

7.907

0.000

γ

0.024

0.366

0.714

δ

0.087

3.260

0.001

γ

0.014

0.218

0.828

δ

0.052

1.834

0.067

γ

-0.008-0.1340.893

δ

-0.046-1.902

0.057

γ

0.094

1.515

0.130

δ

-0.030-1.1720.241

α-0.240-3.2530.001

α-0.264-3.5380.000

R of the demand equation
R of the pseudo-supply equation

α-0.251-3.3640.001

Estimated by I3SLS, n=894.

0.124
0.508

Note: The intercept terms were omitted, since they are zero when standardized.

18

Figure 1 Graphical explanation of equation (14)
P
Y
X

0

q

Figure 2 Dynamic changes in Southwest’s and its rival’s conduct parameters‐0.81‐0.815Pre‐entry

1st year

2nd year

3rd year

4th year

5th year‐0.82‐0.825
‐0.83

Rival
WN

‐0.835‐0.84‐0.845‐0.85

19

Figure 3 Dynamic changes in America West’s and its rival’s conduct parameters[2012.7.4 1092]

20