The effect of the entry of low-cost airlines on price and passenger traffic
Master Thesis
Master in Economics and Business
Specialisation Urban, Port and Transport Economics
Yaxian WuStudent number: 332639
Thesis supervisor:Dr. Peran van Reeven
Department of Applied EconomicsErasmus School of EconomicsErasmus University Rotterdam
Abstract
Many researches about the impact of low-cost airlines are documented since
it is one of the most popular topics in airline transport. Generally, the previous
findings convince that the entry of low-cost airlines significantly depresses
price while increases the passenger traffic. This paper extends to investigate
how the low-cost airline impacts the pricing and passenger traffic currently.
The sample covers factors in terms of demand, cost and market
concentration, using quarterly figures from 1997 to 2010. Moreover, a series
of methods including OLS, Fixed effect model and IV estimation, are
performed stepwise to explore the most reliable estimation. The low-cost
airlines’ entrance does reduce price but at a lower level while the passenger
traffic is indirectly affect by the low-cost carriers through price.
i
Acknowledged
First of all, I really appreciate my thesis supervisor, Dr. Peran van Reeven for
all the help and guidance that he provided. The many thought-provoking
discussions and his detailed comments and suggestions were essential in the
completion of this work. Without his inspiring and encourage, I would not be
able to finish this paper.
Furthermore, I would also like to thank some friends who gave me many helps
during my writing: Fei Yu, Theun van Vliet, Lu Sun and Wei Li. Thanks for
your support both in knowledge and mentality.
My deepest thanks are given to my parents for their dedication!
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Table of ContentsAbstract.............................................................................................................i
Acknowledged...............................................................................................ii
List of Tables and Figures.........................................................................iii
1. Introduction...............................................................................................1
2. Literature review......................................................................................2
3. Methodology..............................................................................................7
3.1 Sample construction....................................................................................7
3.2 Variables description...................................................................................8
3.3 The estimating equation..........................................................................10
3.3.1 Pooled OLS model................................................................................11
3.3.2 Fixed effects model..............................................................................11
3.3.3 IV estimation........................................................................................12
3.3.4 Panel IV estimation..............................................................................12
4. Descriptive Analysis..............................................................................13
5. Model results...........................................................................................19
5.1 Pooled OLS model.......................................................................................19
5.2 Fixed effects model....................................................................................21
5.3 IV estimation.................................................................................................22
5.4 Panel IV estimation....................................................................................23
6. Conclusion................................................................................................25
6.1 Comparison with other researches....................................................26
6.2 Implications for low-cost airlines........................................................27
6.3 Limitation and further research...........................................................28
References....................................................................................................29
Appendix 1 Price movement pro and post entry.............................31
Appendix 2 Passengers movement pro and post entry.................32
iii
List of Tables and Figures
Tables:
Table 2.1 Literature summary 7
Table 3.1 Vacation cities in model 10
Table 3.2 Low-cost carriers in model (with carrier code) 10
Table 4.1 Descriptive statistics 14
Table 5.1 OLS regression results 20
Table 5.2 Fixed effect model results 22
Table 5.3 Correlation matrix 23
Table 5.4 2SLS regression results 24
Table 6.1 Results comparison 27
Figures:
Figure 4.1 The movements of price, passengers and low-cost airlines 14
Figure 4.2 The distance distribution on top 100 routes / average number of
LCC per route 15
Figure 4.3 One-way prices pro and post entry 17
Figure 4.4 The passenger traffic pro and post entry 18
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1. Introduction
Many factors influence the pricing and passenger traffic in air industry. The
presence of the low-cost airlines is one of the most popular determinants.
Consequently, the impact of low-cost airlines has been much documented.
The main conclusions drawn from previous literatures are: the entry of low-
cost airlines significantly depresses the price associated with the increase in
passenger traffic on the specific routes they joined.
Since the first low-cost airline, Southwest Airline, established in 1978, low-cost
airlines have captured a huge success in the US as well as European
countries. Many famous low-cost airlines like Southwest Airline, AirTran
Airways, JetBlue, JetBlue, Ryanair and Virgin, etc. show that the low-cost
airlines have grown to be the new strength of development. The Southwest
effect, firstly named in 1993 the U.S. Department of Transportation (DOT),
indicates the huge impact of the entry of Southwest airline on incumbents in
the same serving region. This term is subsequently inferred to describe the
general low-cost airlines’ effect. Later on, DOT defined the “low-cost airline
service revolution” considering dramatic boost of low-cost airlines
(Hüschelrath & Müller, 2011). Most of researches are all theoretically based
on the Southwest effects, extending to investigate of the effect of low-cost
revolution.
Summarised by Wang (2005), the Southwest effects consist of three
principals. First of all, the Southwest airline’s entrance brings remarkable
passenger enhance. Additionally, the entry of Southwest airline declines
passengers travelling on other routes in the same serving region.
Furthermore, incumbents attempt to retain the market share on the specific
route Southwest airline joined by depressing their price (Ritter, 1993). Taking
the Oakland-Ontario airport pair on California corridor as an example, Bennett
& Craun (1993) drew several charts illustrating the pricing and traffic
movements before and after Southwest’s entry from the second quarter of
1
1982 to the third quarter of 1992. It was convinced that price declines by 60
percent associated with triple traffic account. Therefore, competitors leave so
that Southwest replaces their capacities with the increasing load.
This paper extends the research to investigate how the low-cost airline
impacts the pricing and passenger traffic under the new circumstance.
Considering both time-series and cross-sectional effects, a panel dataset is
occupied. The sample covers factors in terms of demand, cost and market
concentration, using quarterly figures from 1997 to 2010. Moreover, a series
of methods are performed step by step to avoid the systemic drawbacks of
previous researches. It starts with the normal OLS regression which gives
inadequate explanation about the relations among variables due to the
heterogeneity bias. The second step is the fixed effect mode with a better
output but still do not sufficiently convince the impact of the low-cost airlines
on pricing and passenger traffic. On the top of two basic models, the following
step extends to the instrumental variable model. According to the test for
endogeneity, the instrumental variable is much more suitable for this dataset.
Finally, the panel IV model is installed to deal with the omitted variable bias
and causality problem between pricing and demand.
The rest of paper is structured as follows: section 2 reviews the literatures
relevant to the research question, followed by the sample construction and
modelling description in section 3. Then, section 4 illustrates the initial findings
descriptively while the results of empirical model are discussed in section 5.
Finally, section 6 concludes the paper with comparison as well as suggestions
for the low-cost airlines.
2. Literature review
Various factors may affect pricing and traffic on air transport industry, such as
the efficiency of hub and spoken operational system, the entry of low-cost
airlines, the extent of market concentration, and competition from other modes
of transportation, etc. (Vowles, 2000 and Wang, 2005). Among these
2
determinants, the entry of low-cost carriers captured most interest of
researchers. There are many empirical papers focusing on the effect of low-
cost airlines’ entrance on airfare and passenger traffic on US domestic air
transport and extensive results have been drawn from them.
Since the beginning of the deregulation period, many researchers have being
studied the impact of the entry of low-cost carriers in airfare pricing. Bailey et
al. concluded that there is the negative significant relation between the new
entry and US domestic yield in their book published in 1985. Strassmann used
a structural model involved variables of prices, entry and concentration. It is
convinced that entry and price are mutually affected. This finding is supported
by the fact that decreases in concentration, caused by entry, are associated
with a substantial decrease in fares (Strassmann, 1990). In 1992, Whinston
and Collins investigated the effect of the entry of People Express to the
airfare. They found significant evidence for the negative relationship between
price and People Express’ presence, in general low-cost carriers, using the
stock data from 1984 to 1985. Bennett and Graun (1993) studied the case of
Southwest Airline, and then defined “The Southwest Effect”. In sum, the
Southwest effect implies that the Southwest airline’s presence increases the
passenger count and lowers the airfare in that particular route with the
decrease of passenger traffic in competing routes (Wang, 2005). The common
result from these reviewed papers is that the presence of low-cost carriers
significantly lowers the price while increasing passenger traffic.
Following the previous researches, Windle & Dresner kept on exploring the
effect of low-cost carriers’ entrance on the specific market they joined. Windle
& Dresner analysed the short and long term effect of the entry of low-cost
airlines. They performed both descriptive analysis and econometric models,
which give the theoretical basis as well as the methodological basis to this
paper. Top 200 US domestic routes data from the third quarter of 1991 to the
second quarter of 1994 have been filtered from Origin and Destination Survey
published by US Department of Transportation. First, changes in the route
after the presence of low-cost airlines have been analysed by the time series
in terms of the market concentration which indicated by Herfindahl Index (HI), 3
price and passenger traffic. Southwest taken as representation of low-cost
carriers captured the biggest difference among three categories including
deregulation and other carriers. It lowers 25% of the market concentration of
the route joined, whilst the average decline is 15%. As for the airfare, the price
on the route Southwest entered depresses by 48% to pre-entry price and
keeps still while the average decrease is 19%. The passenger traffic
increases by 300% on the routes Southwest airline enters and 74% on
average. Furthermore, two empirical models have been performed to interpret
the mathematic relation between selected variables and price. When involved
the carrier dummy variable, the presence of low-cost airlines significantly
decreases the price on routes, however, the market concentration and route
density variables do not impact on price as much as authors expected.
Vowles (2000) performed a regression model to explain the variance in airfare
pricing. The regression involved several variables assumed have effect on the
airfare pricing such as distance, resort, southwest factor, hub, low, market
share of low-cost airlines and the market share of the largest carrier in the
market. According to the coefficients of model results, each additional
presence of low-cost airline is predicted to depress the average airfare by
45.47% which implies that low-cost carriers do have a remarkable effect in
pricing. However, Vowles considers that this outcome is not sufficient,
because this variable does not measure the percentage of schedule flights
offered by low-cost airlines. So the Southwest variable and the market share
of the low-cost carriers have been added in the model to compensate the
weakness. It convinced that the entry of Southwest airline immediately
decrease the price on the specific route by 77.61%.
To analyse the extent of the “Southwest Effect” on the pricing of fares in the
airline industry, Christine Wang (2005) played a regression using the data in
the first quarter of 2004. In the conclusion, the Southwest airline got a
significant negative relation with the fare which convinces the expectation of
author. To further interpret effect of Southwest presence, Wang performed
another regression, however, all data involving Southwest as the reporting
carrier was taken out of the dataset. It is interesting that even without the 4
ticket data directly from Southwest, Southwest variable still has one of the
biggest impacts in pricing. This finding convinced the Southwest’s entrance do
affect the price in the specific airline market it joined.
Other related papers extend the research to other impacts of the entry of the
low-cost airlines, such as the effect on competitors on the market the low-cost
airlines joined, the impact of alternative market in the same serving region, the
consumer welfare, the reaction from the established airlines in the market the
low-cost airlines entered, and the geographical competition in the whole air
transport industry. Although investigations about the impact of the entry of low-
cost airlines have been installed in different aspects, they all get the final
conclusion related to the pricing effect. That is the low-cost carriers’ entrance
definitely decreases the price with the increase of passenger traffic, resulting
in the gain of consumer welfare.
Dresner et al. (1996) examined the impact of low-cost carriers’ entrance on
airfare regarding to alternative routes at the same airport as well as other
airports with the same serving area. As a result, the outcome of the regression
consolidates the previous conclusion. They further addressed that the
presence of low-cost airlines reduces the yields while increases consumer
welfare. In addition, to test if the consumer welfare exaggerated, Windle &
Dresner (1998) extended their research by analysing the price change after
ValuJet airline entering into the hub, Delta. Authors found that Delta lowered
the fare on the routes ValuJet has joined without raising the price on the
routes involved no low-cost airline which implies that the low-cost carriers’
entrance indeed enhances consumer welfare.
Goolsbee and Syverson (2006) found that, on the routes Southwest joined,
those incumbents decrease prices considerably. It is interesting that
established carriers usually react to the threat of Southwest’s entrance in
advance. In other words, they lower airfare on those routes as long as
Southwest announced going to join in. This happens because they want to
deter Southwest by pre-emptively depressing the price. But the reduction of
price increases the passenger traffic on the specific route so that it hardly 5
refuses all the new entrance. Based on the research of Goolsbee and
Syverson, Daraban (2007) further examined the incumbent responses and
spatial competition regarding to the entry of low-cost airlines and the
conclusion totally demonstrated the previous studies. Likewise, Alderighi, et
al. (2004) investigated how the full service carriers respond to the entry of low-
cost airlines in terms of airfare pricing. Authors used monthly data in the first
quarter of 2004 within the whole Europe, they corroborated that incumbents
tend to depress the airfare to against the entry of low-cost carriers. And they
further discover that the weak and strong interdependent which implies that
direct competition of low-cost airlines also affecting on the established
carriers.
Most of papers reviewed above are published in the early 2000’s. At that
moment, the boost of low-cost carriers was considered as a main growth
power for the air transport industry. Nevertheless, the business environment
all over the world has changed a lot after decades, especially after the
financial crisis. Is the model still fit for the current situation? Is the price effects
of low-cost airlines sustained past the initial promotional period? Abda et al.
(2011) summarised a new trend for the impact of low-cost airlines growth on
domestic traffic using the data of the top 200 largest US airports. They
concluded that although low-cost carriers’ market share keeps growing, the
extent is much less than before. Generally, the prices on routes with low-cost
airlines depress less than those without entries. Meanwhile, people travelling
on routes with low-cost airlines’ entrance are more elastic than those without
entries, implying that people increase and decrease much more in good years
and bad years, respectively.
In this paper, empirical model will be performed, using the latest data, to
investigate the effect of low-cost carriers’ presence in airfare and passenger
traffic in terms of specific routes they entered under the new environment.
Overall, to clarify these previous researches, the main literatures reviewed
above are listed in a summary table below.
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Table 2.1 Literature summary
Research Dataset Method The impact of the entry of low-cost airlinesPrice Passenger traffic
W&D (1995)
Panel Descriptive decrease 19% on averagedecrease 48% (WN effect)
increase 182% on averageincrease 300% (WN effect)
W&D (1998)
Panel 3SLS decrease 53.3% (WN only)decrease 38% (multiple carriers)
Vowles (2000)
Panel OLS decrease 45.47% on average decrease 77.61% (WN effect)
Alderighi et al. (2004)
Cross section
OLS decrease 42.58%
Wang (2005)
Cross section
OLS decrease 18.25% (WN effect)
G&S (2006)
Time series
OLS decrease 18.6% at the entry year and keep depressing afterwards
the magnitude of the quantity response is roughly twice of the fare changes
Daraban (2007)
Time series
OLS WN’s entry decrease average fare by 22% while depressing legacy carriers’ price by 17.6%
Abda et al. (2011)
Panel Descriptive significantly depress 5% more on routes with low-cost airlines’ entrance in 2005
people are more elastic on routes with low-cost airlines’ entrance, implying increase and decrease more in good years and bad years, respectively
3. Methodology
Previously, many researches focusing on the impact of the entry are
documented. This paper extends the study using panel data, which consider
both cross-sectional and time series factors, to evaluate whether the effect of
the presence of low-cost airlines on pricing and traffic has changed with time.
3.1 Sample construction
The main data in this analysis sources from Domestic Airfare Consumer
Report which is originally based on the Origin and Destination Traffic Survey
conducted by the US Department of Transportation Bureau of Transportation
Statistics. This report was first published in June, 1997 by the Department’s
Office of Aviation Analysis. The information involves the 1,000 largest
domestic city-pair routes covering 75% of all 48 states passengers and 70%
7
of total domestic passengers (Domestic Airfare Consumer Report, 2010). This
paper filters the top 100 city-pairs in the domestic US market ranked by the
number of passengers in the third quarter of 2010 and matched to other
quarters. Besides, data has been also collected from the Bureau of Economic
Analysis and previous researches.
The panel data consist of repeated observations on certain variables for a
number of O-D pairs N at a number of points in time T. Here 100 O-D pairs for
54 points in time are selected from the second quarter of 1997 to the fourth
quarter of 20101. Data include price, passengers, distance, income, largest
market share, vacation and the presence of low-cost airlines.
3.2 Variables description
The construction of variables is described below. To avoid the bias caused the
heteroskedasticity as well as the huge volume variance between different
variables and get the sufficient coefficients, all variables with large positive
numbers have been transformed into natural log pattern, as seen with ln-
prefix such as lnprice, lnpassenger, lndistance and lnincome.
Lnprice: Average one-way fares are average prices paid by all fare paying
passengers. Therefore, they cover first class fares paid to carriers offering
such service but do not cover free tickets, such as those awarded by carriers
offering frequent flyer programs (Domestic Airfare Consumer Report, 2010).
Lnpassenger: This variable describes the number of passengers travelling on
the specific route per day. And it counts both directions into one city-pair, for
example, no matter travelling from Chicago to New York or from New York to
Chicago, the person will be record in the city-pair of Chicago-New York. The
expected relation between price and passenger is negative which implies that
the more people travelling, the lower the price is.
1 The data for the first quarter of 2009 are entirely missing due to some reporting issues, Domestic Airfare Consumer Report, 2009
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Lndistance: It shows the non-stop distance between two cities. The numbers
used in the analysis are chosen from the fourth quarter of 2010, the latest
report. Apparently, distance has the positive predictive relation with the
dependent variable, price.
Lnincome: The quarterly personal income for states has been collected from
the Bureau of Economic Analysis. In order to match the figure of passenger
variable, personal income level in both origin and destination cities have been
summarised. Then, all figures are adjusted by quarterly inflation rates. It is
assumed that people with high income are less affected by the level of price.
In other words, the relationship between two variables is supposed as
positive.
Lg_mktshare: Largest market share represents the market share of the
largest carrier on the specific route. This variable is reported in the Domestic
Airfare Consumer Report with the name of the largest carrier. The largest
market share, at some extent, indicates the concentration of a particular
market. The particular market is intensive when the largest carrier takes a
high proportion of market share, leaving other small airlines sharing little rest
of market. In general, to compensate the loss on other market with fierce
competition, the monopolist tends to set up the high price on the market due
to the lack of competitor. Consequently, it is assumed that this variable is
positive related to price.
Vacation: The variable of vacation is a dummy variable to check if a specific
city-pair is vacation route. It will be coded 1 when the origin or destination is
considered as the vacation place and 0, otherwise. According to previous
studies by Windle and Dresner in 1995, vacation cities almost centralised in
four regions, including Florida, Hawaii, Nevada and Puerto Rico. Markets
between vacation cities usually charge lower airfares implying a negative
relationship between two variables. Cities considered as vacation places are
listed in Table 3.1.
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Table 3.1 Vacation cities in model
Region Cities
Florida Fort Lauderdale, Fort Myer, Miami, Orlando, Tampa, West
Palm Beach
Hawaii Hilo, Honolulu, Kahylui, Kona
Nevada Las Vegas, Reno
Puerto Rico San Juan
Low: The last independent variable is the presence of low-cost airline.
Likewise, the variable of low is a dummy variable coded 1 if any low-cost
carrier participates on the route while 0 on contrary. Table 3.2 shows the list of
low-cost airlines involved in this paper (Wikipedia, 2011 & Abda, et al., 2011).
Table 3.2 Low-cost carriers in model (with carrier code)
Allegiant Air (G4) AirTran Airways (FL) Southwest Airlines (WN)
Spirit Airlines (NK) Frontier Airlines (F9) Sun Country Airlines (SY)
ProAir Service(P9) Vanguard Airlines (NJ) America West Airlines (HP)
Virgin America (VX) American Trans Air (TZ) Western Pacific Airlines(W7)
JetBlue Airways (B6) USA3000 Airlines (U5)
3.3 The estimating equation
In this paper, the panel dataset has been analysed in four models step by
step: Pooled OLS model, Fixed effects model, Instrumental variable (IV)
estimation and Panel IV estimation. In first two approaches, two regressions
are estimated with the lnprice and lnpassenger as dependent variable,
respectively. The independent variables are lnpassenger, lndistance,
lnincome, lg_mktshare, vacation and low with the data variable of each
quarter and year. The independent variables have been chosen from various
related aspects representing a combination of demand, cost and market
concentration which influence airlines pricing. The last two approaches use
the two stages least squares (2SLS) regression. The construction details will
be discussed later in this part.
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3.3.1 Pooled OLS model
Generally, the starting point for panel data analysis is Pooled OLS model, so
is it in this paper. The pooled OLS estimator treats all the individuals for all
time points as a single sample so that the sample gains much bigger size
compared to the simple cross-sectional data set. When the sample is
sufficiently big, the coefficients of different variables will be assumed infinitely
close to the true value. A common equation of pooled OLS model given below
(Podestà, 2002):
yit = β1 +∑k=k
k
βk xkit+eit.
yit represents the dependent variable while xit is independent variable. i=1,
…,N indicates the number of cross sections while t=1,…,T means the different
point of time. k=1,…,K in this case representing the specific explanatory
variable. However, when there are differences existing among cross-sectional
observations, this model becomes improper on account of the heterogeneity
bias caused by the variance of coefficient (Heyman, 2010).
3.3.2 Fixed effects model
Considering the drawbacks of Pooled OLS model, a panel data model is
performed as well. The three common approaches are fixed effects model,
random effects model and mixed model. To use which one is naturally depend
on different given situations. The fixed effects model imposes time
independent effects for each entity that are possibly correlated with the
dependent variable. In short, the difference between fixed effect and random
effect is that the intercept is constant or not to the independent variables’
intercepts. Hausman test is the post-estimation test usually used to sort out
which effects model to choose. In this case, the result of Hausman test
indicates that the data collected fits the fixed effects model. The hypothesis of
Hausman test is that the estimates for fixed effects model and random effects
model have no significant difference. This hypothesis is rejected that implies
these two models differ a lot resulting in the selection of using fixed effects
model. The generic equation gives as follows (Paap, 2011):
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yit = α + x’itβ +εit.
In the equation, yit represents the dependent variable while xit is independent
variable. i=1,…,N indicates the number of cross sections while t=1,…,T
means the different point of time.
3.3.3 IV estimation
Although the fixed effect approach recovers the heterogeneity bias in OLS
model, it cannot deal with the endogenous problem. In addition, the fixed
effect model may cause the omitted variable bias when it automatically
ignores time-invariant variables. Moreover, both previous two regressions
analysis the relation between price and passenger in one direction while, in
fact, the relation is mutually affected. To further extend the model, the IV
estimation using 2SLS regression is performed in the third step.
Before use the IV estimation, it is necessary to make sure the correlation
among variables by using test for endogeneity before installing the model
(Shepherd, 2008). If the hypothesis is rejected which infers that the problem of
endogeneity exists, the IV estimation gains its advantage, on contrary, may
get even worse results than OLS models.
The simplest equation for the basic IV method is (Cameron and Trivedi,
2009):
y1i= y’2iβ1 + x’1iβ2 + ui, i=1, …, N
In the equation, y1i is the dependent variable while independent variables are
consist of endogenous variables (y’2i) and exogenous variables (x’1i). It implies
that the errors ui are uncorrelated with x’1i but correlated with y’2i which leads to
the inconsistence of β. To fix this endogenous problem, the instrumental
variable zi is required. It is assumed that zi fits the restriction that E(ui|zi)=0.
3.3.4 Panel IV estimation
Furthermore, because the dataset is in the panel pattern, the fourth step of
Panel IV approach is undertaken. Commonly, the genetic equation for the 12
2SLS regression is:
yit = x’itβ + αi + εit.
Likewise, an instrumental variable, zit is required. It assumed that zit meets two
assumptions. One is exogeneity while the other is correlated with the time-
invariant error (αi) but uncorrelated with the time-varying component of errors
implying E(εit | zit)=0. So the equation represent a consistent estimation
regressed of yit on xit with instruments zit (Cameron and Trivedi, 2009). The
strength of the instruments impacts the quality of the model as a whole. In
other words, the stronger the correlation between the instrumental variable
and regressors is, the smaller the IV standard errors are. Once are the
instruments too weak, the model is possible to lose the precision as well as
get incorrect inference.
4. Descriptive Analysis
The two sections above demonstrate economic evidences about the effect of
the entry of low-cost airlines from documentary and modelling respects.
However, after decades, both economic and industry environments have
considerably changed. Furthermore, most researches before used cross-
sectional model ignoring the effect in time series. Before generating a formal
model, descriptive statistics of variables will be analysed in section 5.
First of all, Table 4.1 summarised the means of all variables. The average
one-way price on a route is 173.99 dollars. The mean distance of top 100 city-
pairs is 991.43 miles, implying that the yield per miles is 0.175 dollars.
Although the price and distance of average level both increase, the yield is as
the same as the result got by Windle & Dresner in 1995. Every day, 2506.62
individuals on average travel between the origin and destination in both
directions. The mean level of personal income considering both origin and
destination states adjusted by inflation is 586497.54 dollars. As for two
dummy variables, 24 of 100 top routes travel between vacation places while
68 percent of city-pairs, on average, have low-cost airlines involved.
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Table 4.1 Descriptive statistics
mean sd min max
price 173.99 73.63 56.0 550passenger 2506.62 1506.29 206.0 12034distance 991.43 642.66 200.0 2704income 586497.54 321291.94 75717.2 1645968lg_mktshare 53.69 17.88 18.8 100vacation 0.24 0.43 0.0 1low 0.68 0.47 0.0 1N 5500
On the top of the average level, all variables with positive numbers, such as
one-way price, the number of passenger travelled per day, personal income
by states and the largest market share, vary a lot and much more than it did in
1995. It insinuates that the market is getting competitive and differentiation,
resulting in the offset of yields. Moreover, other factors like the sharp increase
of oil price are right to explain this counteraction.
To investigate the particular impact of the presence of low-cost airlines,
several figures are performed below, showing the historical changes of the
low-cost carriers and the relationship with other key variables.
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Numbers of low-cost airlines Average one-way priceNumbers of passengers per day
Figure 4.1 The movements of price, passengers and low-cost airlines
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Figure 4.1 shows three key variables, the average one-way price, the
passengers travelling per day and the number of low-cost airlines participating
on the top 100 routes per year between 1998 and 2010. Figure 4.1 skips the
numbers for 1997 and 2009, because the data are collected from the second
quarter of 1997 and the data of first quarter of 2009 are entirely missed due to
the reporting issues, respectively. In that case, these two figures are not
comparable with those in other points of time. The line graph representing the
presence of low-cost airlines illustrates a gently increase over 12 years which
confirms the background discussed in the second section. In total, 360 low-
cost airlines operate in the whole year of 2010. On one hand, the red line
indicating the average one-way air fare almost keeps stable, fluctuating
between 150 dollars and 200 dollars. Unexpectedly, the price does not go
against the increase of low-cost airlines. On the other hand, the line
representing the passengers travelling per day is not entirely increasing with
the movement of low-cost airlines. Actually, it mildly waves between 22522 and
2740 people per day, reaching the bottom at 2002 and the peak at 2006. It
appears to sum up that the price as well as passenger traffic effects of low-
cost airlines have sustained past the initial promotional period. However, it is
not entirely certain, considering that many other external and internal factors
have changed like the sharp increase of oil price.
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66716
0tan1aa
566710tan28aa566028
0tan4aa566040tan9aa56609
0tan14aa5660140tan19aa5660190tan24aa5660240tan29aa566029
0tan4aa566040tan9aa56609
0tan14aa5660140tan19aa566019
City-pairsAvg Nr.low
Figure 4.2 The distance distribution on top 100 routes / average number of LCC per route2 The numbers of passengers travelling per day showing in the figure have been adjusted by 10 times less to fit the volume level of other two variables.
15
After vertical investigation by time series, Figure 4.2 presents the two
variables horizontally based on the distance. On one hand, the blue bars
representing the number of city-pairs at that level of miles show the
distribution of top 100 flying routes. For instance, there are 2 of the top 100
routes between 2000 miles and 2250 miles, which are Las Vegas-New York
and New York-Phoenix. From the bar chart, it is obvious that routes
concentrate in relative short distance between 250 miles and 1250 miles with
the most of 20 routes scattered in the range of 250 miles to 500 miles. On the
other hand, the average numbers of low-cost airlines operating on a route for
different distances are illustrated by red bars. Unlikely, Figure 5.2 does not
support the negative relationship between the entry of low-cost carriers and
the distance concluded in the early research (Windle & Dresner, 1995). Low-
cost carriers no longer sorely participate in the short distance routes, but
spread to long and popular city-pairs. The route involved the most low-cost
airlines distribute around the distance of 1750 miles, at the average level of 47
carriers together operating on one market. However, the time points those
low-cost airlines entering the long-distance routes are generally late than they
do on the short-distance routes.
Unfortunately, the two figures above seem to provide unexpected indications
refusing the impact of the entry of low-cost airlines as a whole. To further
explore influence of low-cost carriers, the variable of presence of low-cost
airlines is deeply analysed. First, 45 of 100 top routes had already got low-
cost airlines participating since second quarter of 1997, the beginning of the
data collection in this paper. In the meanwhile, there are other 6 city-pairs
having no low-cost involved over the whole sample period or the existence of
low-cost carriers are too short to be taken into account. For these routes, it is
hardly to indicate the entry impact on pricing and passenger traffic. After
skipping this kind of routes, 49 city-pairs are left. Then, two line graphs
representing the change of price and passengers after the low-cost airlines
entering the route are performed.
First, according to the Appendix 1, 22 out of 49 routes prove that when the 16
low-cost airlines enter a particular market, the price on such route declines
and keep the low level. Moreover, among 22 city-pairs, routes with long-
distance capture the deeper impact than those short-distance routes do. In
other words, the air fares on the long-distance routes decline more than those
on the short routes. The graph (Appendix 1) shows the movements of the
price in the four quarters before and after the entry3 including all 22 city-pairs.
Here, Figure 4.3 only takes the route between New York and Seattle as an
example. As seen in the figure, at the entry quarter, the price decreases by
40% of the highest pre-entry price, from 441 to 265 dollars. Then, the price
keeps stable in the trend of declining although there is a slight stage back at
the second quarter after entry. Nevertheless, this result is less than the
outcome got by Windle & Dresner in 1995, almost 50% decrease after entry.
Besides, the price decline of the city-pair of New York-Seattle is already the
largest among the 22 routes depending on the graph (Appendix 1).
-4 -3 -2 -1
Entry
Quart
er 1 2 3 40tan28aa5660280tan19aa5660190tan10aa566010
0tan1aa566010tan21aa5660210tan12aa566012
0tan3aa566130tan23aa5661230tan14aa566114
0tan5aa566150tan25aa566125
New York-Seattle
Figure 4.3 One-way prices pro and post entry
Furthermore, the situation of the passenger traffic is illustrated in Appendix 2.
It is unpleasant to see that only 16 routes amongst 49 routes are considerably
increasing after the low-cost enter such routes. In addition, there is no clue
that the entry of low-cost carriers impacts the passenger traffic depending on
the distance. How much that the passenger traffic influenced by the entry is
random walk among these 16 routes. However, 14 of 16 routes are the routes 3 It has been proved by Windle & Dresner in their paper in 1995 that 4 quarters pro and post the entry are sufficient to explain the impact of the entry.
17
those also sufficiently affected by the entry in the one-way price figure. It
seems that the entry of low-cost airlines has simultaneous effect on both price
and passenger traffic. Taking Ft. Lauderdale-New York route as an instance,
Figure 4.4 shows the movement of passenger traffic pro and post entry. This
route has the most obvious react to the entry of low-cost airlines. The
passengers travelling on this route increases 54.2% upon entry, from 4114 to
6343 people per day. Although there is a slight downturn, it gets the peak at
the fourth quarter after entry at the number of 6707 people per day, which is
63.4% higher than the lowest point. Also look backwards to the results in the
Windle & Dresner’s paper (1995), the entry of Southwest Airline brought 300
percent more traffic to the specific route while all carriers increase 74% of the
passenger traffic on the average level.
-4 -3 -2 -1 Entry Quarter
1 2 3 40
1000
2000
3000
4000
5000
6000
7000
8000
Ft. Lauderdale-New York
Figure 4.4 The passenger traffic pro and post entry
After the descriptive analysis, several initial conclusions can be drawn. First,
the low-cost airlines keep increasing over the sample period. However, the
boost is associated with neither the price declining nor the traffic increasing on
average level of the top 100 city-pairs. Second, the figure also rejects the
outcome from previous researchers that the low-cost airlines tend to
participate only in the short-distance market. Finally, it should be admitted that
the presence of low-cost airlines does influence the pricing and traffic on the
particular route they entered but the impact is levelling off. However, the deep
interpretation will be performed in the next section using several steps of
18
formal statistical models.
5. Model results
Depending on the descriptive statistics, the impact of the presence of low-cost
carriers still exists but on a lower level. In details, only 22 percent routes
illustrate that the entry of low-cost airlines is associated with the price
decrease. And the extent of price decline varies based on the distance of
routes resulting in the longer routes have deeper decrease. Using the
quarterly data from 1997 to 2010, this section will interpret the statistical
meaning of data and analysis the result of panel data models. As described in
the modelling section, the regressions for panel data will be performed in four
steps.
5.1 Pooled OLS model
The beginning step of analysis is the Pooled OLS model. Table 5.1 lists the
results of two OLS models. The first column is the regression with the
variables of lnprice as the dependent variable while the second column model
regresses the variable of lnpassenger. The standard errors have been
adjusted for both regressions by the cluster of group number. R2 which
interprets how much of the variability in the actual values explained by the
model are 69.36% and 22.94%, respectively.
In particular of the first regression, four of the six cross sectional independent
variables are significant, including lnpassenger, lndistance, vacation and low.
It is logical that the price increases with the flight distance. The price
decreases when the route involves the vacation origin or destination whilst the
number of passengers negatively influences the price on the particular route.
Moreover, the presence of low-cost airlines sufficiently depresses the price on
the route been joined. Usually, rich people are less elastic to price, so it is
assumed that the airlines may set higher price in states those with higher
personal income. Nevertheless, this sort of price discrimination is not existed
19
as the lnincome is not significant related to lnprice. Meanwhile, the monopoly
power is neither inferred according to the non-significant relation between the
indicator of largest market share and price.
Table 5.1 OLS regression results4
(1) (2)lnprice lnpassenger
lnpassenger -0.0650***
(-9.81)lndistance 0.433*** -0.0809***
(71.16) (-5.29)lnincome -0.0528*** 0.246***
(-8.96) (22.60)lg_mktshare -0.000468* -0.0109***
(-2.26) (-29.68)vacation -0.276*** 0.129***
(-44.85) (8.02)low -0.211*** -0.0808***
(-25.63) (-4.96)lnprice -0.250***
(-9.70)_cons 3.506*** 6.768***
(36.18) (40.79)N 5361 5361G 100 100
t statistics in parentheses* p < 0.05, ** p < 0.01, *** p < 0.001
When it comes to the results of second column, only three cross sectional
variables are significant, including lnincome, lg_mktshare and lnprice. It is
apparently that low price attracts more passengers. Rich people travel more
since they have more disposable personal income. Furthermore, people
prefer travelling on the routes with less concentration which offer them more
choices. Unfortunately, the rest variables such as lndistance, vacation and low
are not significantly related to the number of passengers travelling. The
expected relation between flight distance and the number of passengers is
negative. People prefer short travels considering the comfort, time as well as
cost. Conventionally, the vacation places appear to attract more passengers
inferring a positive relation between them. In addition, it has been convinced
in the descriptive analysis section that the entry of low-cost airlines increases
4 Coefficients for all date dummies have been excluded from the result table considering: first, they are not variables concerned by the research question; second, the paper layout. And it is same with the two results tables following.
20
the passenger traffic. Here, to explain the non-significant relations, the travel
objective change might be a reason. Currently, with the globalisation and
liberalisation of sky, people increasingly travel for business. In that case, the
impacts of distance, vacation and low become ambiguous.
5.2 Fixed effects model
Nevertheless, the results of OLS regression are likely to be biased depending
on the inherent drawbacks of the model which discussed in the previous
section of modelling. To check the reliability of the results, a further step is
undertaken using the fixed effects model. Likewise, two fixed effects models
with dependent variables as lnprice and lnpassenger, respectively, are
performed with robust standard errors as follows. This adjustment of the
standard errors prevents the inaccurate individual variances misleading the
result by weighting them less. After the modification, all coefficients keep the
same sign, however, with smaller t estimations. Two variables, lndistance and
vacation, are skipped leaving rest independent variables all significant. It is
due to the limitation of the fixed effects model. The model assists in controlling
for unobserved heterogeneity when this heterogeneity is constant over time
and correlated with independent variables. This constant can be removed
from the data through differencing, for example by taking a first difference
which will remove any time invariant components of the model (Wikipedia,
2011). Variables of distance and vacation are such constants that do not
change over time. So the model omitted these two variables automatically.
Table 5.2 shows the details of the results. On one hand, in the estimation for
lnprice, most of the expectations are fulfilled. All variables except lg_mktshare
are significant. As expected, the variable of lnpassenger has negative relation
with the lnprice, implying that more passengers travelling on a specific route
decreases the price. It is kind of promotion so that airlines earn small profit but
with quick turnover in order to compensate other unpopular routes. Lnincome
is positively related to the lnprice which hints that the people living in the
richer region are willing to pay higher price for travelling. Finally, the variable
representing the presence of low-cost airlines is negatively related to the
21
dependent variable, lnprice. In the previous descriptive analysis, the impact of
the entry of low-cost carriers has appeared to be proved that is getting weak
and ambiguous. The statistical evidence given by the model convinces that,
all other things being equal, the entry of low-cost carriers on a specific route
sufficiently decline the price. As for the lg_mktshare variable, it has no
significant relation with the lnprice when considering the date variables. On
the other hand, the estimation for lnpassenger also gets much more
significative results than OLS model does. Most variables get significant
results, however, the presence of low-cost airlines which is the main research
objective, keeps showing no influence to the passenger traffic. Generally,
coefficients are too small to give sufficient interpretation.
Table 5.2 Fixed effect model results(1) (2)
lnprice lnpassengerlnpassenger -0.361***
(-4.97)lnincome 0.965*** 1.366***
(3.92) (3.93)lg_mktshare 0.00145 -0.00315**
(1.89) (-2.80)low -0.0600** 0.0381
(-3.26) (1.95)lnprice -0.730***
(-13.50)_cons -4.621 -6.108
(-1.52) (-1.37)N 5361 5361G 100 100
t statistics in parentheses* p < 0.05, ** p < 0.01, *** p < 0.001
5.3 IV estimation
The two steps before do not give satisfactory outcomes. As described in the
modelling section, an IV model with the 2SLS regression is installed. At the
beginning, it is very important to select the instruments. Table 5.3 shows the
correlation matrix. Suppose that lnprice is correlated with the time-varying
component of the error, then the simple fixed effect model becomes
inconsistent and the variable of lnprice is needed to be instrumented. In this
case, the instrumental variable is needed to be highly correlated with pricing
22
but not directly determine the number of passenger travelling. Therefore, there
are two choices among variables according to the correlation matrix. One is
the variable of lndistance while the other is low. However, in the fixed effect
model, the variable of lndistance has been dropped for the time-invariance. as
for the variable of low, its correlation with lnprice and lnpassenger are -0.32
and 0.01, respectively. They differ a lot. Moreover, the correlation with
lnpassenger is not significant under 95% confidence interval. Hence, the
variable of low representing the presence of low-cost airlines is the most
proper instrumental variable. It implies that the low-cost airlines impacts
passenger traffic by influencing the pricing level.
Table 5.3 Correlation matrix
lnprice lnpas~r
lndis~e lninc~e lg_mk~e
vacat~n
low
lnprice 1 lnpassenger
-0.14* 1 lndistance 0.72* -0.05* 1 lnincome 0.08* 0.20* 0.08* 1 lg_mktshare
-0.28* -0.27* -0.42* 0.12* 1 vacation -0.23* 0.12* 0.11* -0.22* -0.12* 1 low -0.32* 0.01 -0.12* -0.03* -0.01 0.10* 1
* p < 0.05
Since the instrumental variable has been selected, a basic IV estimation is
performed. Considering that the main purpose of this step is to make sure
which method (OLS or 2SLS) is more suitable for the database, the
regression results are not reported. Besides, a test for endogeneity is
performed after running 2SLS regression. And the hypothesis that the
variables are exogenous has been rejected implying the IV estimation is much
more reliable than OLS method.
5.4 Panel IV estimation
The three steps above are exploring the most suitable approach for the
dataset. Finally, the panel IV method is considered as the best choice which
can either fix the endogenous problem or interpret the mutual causality
23
between price and demand. Furthermore, p-value of Hausman test is 0.9727
which accept the hypothesis. In other words, the panel IV estimation should
be run under the random effect model. Hence, it avoids the omitted variable
bias in the second step caused by the fixed effect model. Table 5.4 presents
the outcomes.
Table 5.4 2SLS regression results
1st Stage 2nd Stagelnprice lnpassenger
lnprice -1.066***
(-13.57)low -0.102***
(-16.37)lndistance 0.470*** 0.359***
(9.84) (4.97)lnincome 0.187*** 0.685***
(3.93) (10.85)lg_mktshare 0.003*** -0.00206***
(13.89) (-4.54)vacation -0.211** 0.0452
(-2.79) (0.46)_cons -0.573 1.826*
(-0.86) (2.13)N 5361 5361G 100 100t statistics in parentheses* p < 0.05, ** p < 0.01, *** p < 0.001
Similarly, the first column indicates the results of first stage while the second
stage outputs are recorded in the second column. And the R2 is equal to 9%
which is considered as quite acceptable in panel IV estimation. In the first
stage, all independent variables are significant with expected sigh. Especially,
it is pleasant to see that the largest market share has significant positive effect
on pricing. Although the coefficient (0.003) is lower than other variables, the
market concentration does affect the air fare sufficiently. The more intensive
the market is, the higher price is. It is fit for the theoretical evidence that when
the monopolist controls a specific market, they have power to increase the
price to compensate their business on other depressing markets (Wang,
2005). The presence of low-cost airline keeps the negative relation with the
price. Because low is a dummy variable, it can be concluded in details that
24
one more low-cost airline enters the specific route, the price decreases by
10.2%.
As for the second stage, lnprice which is instrumented by low negatively
influence the number of passengers. Passengers are elastic so that they tend
to switch from expensive routes to cheap ones. The mathematical relation is 1
dollar decrease on price leads to 1.07 more passengers. However, the
vacation loses the significant relation with passenger traffic. There are several
reasons may explain it. On one hand, from the statistic aspect, lnprice from
the current period is instrumented. However, it is not an external variable
added especially for IV estimation, but one of variables in previous models. If
the time-varying errors are independent, then it is not suitable to be a valid
instrument (Cameron & Trivedi, 2009). On the other hand, in terms of reality,
the purpose of travelling changes a lot recently. People fly to different places
not only for travelling but also for business. Passengers cannot choose
destination when they travel on business. Meanwhile, those traditional resorts
are dropping attraction for tourists pursue diversification and customisation
nowadays.
In sum, four econometric methods have been used step by step in this
section. Beginning with the simplest OLS model, the results are not
acceptable due to the inherent method drawback. The fixed effect model
solves the problem of heterogeneity but leaves the omitted variable bias.
Furthermore, to deal with the endogenous problem and mutual causality, IV
estimation is used in the third step. In the end, the panel IV estimation
resolves all problems and gives the final output of this paper. The impact of
the entry of low-cost airlines on pricing is significant negative. Nevertheless,
the presence of low-cost airlines does not directly affect the passenger traffic
any more. However, it shows additive effect among the presence of low-cost
airlines, price and passenger traffic through the recurrence relation. One
additional low-cost airline enter the route reduce 10.2% of price. 1 dollar
depressing of price brings 1.07 more passengers to the route. Consequently,
it infers an additional presence of low-cost airline increases passengers
travelling on that route by 10.9%. 25
6. Conclusion
As one of the most popular topic in the airline transport, the impact of the
entry of low-cost airlines on pricing and passenger traffic has been
documented a lot. This paper extends the previous researches to evaluate the
influence in the current circumstance. Having the descriptive as well as the
modeling analysis above, this section will conclude the findings with
comparison with other researches and advices for the low-cost airlines. Some
limitations and further research suggestion are given in the end of this paper.
6.1 Comparison with other researches
In the literature review section, a summary table has been drawn to briefly
demonstrate results found by previous researchers. To clarify the comparison,
the table is restored with findings of this paper (Table 6.1).
Obviously, the conclusion drawn from this paper generally supports the
previous findings. Initially, the low-cost airlines’ entrance does reduce price
associated with the increase of the number of passengers. However, the
differences are in two main aspects. First, the impact is sufficient level off.
From the 38% to 10.2%, the negative influence on price is apparent shrink.
Meanwhile, the entry of low-cost airlines does not directly affect the number of
passengers any more, but mediately affect through price.
The reasons leading to these differences might be as follows. First, the data
collected in this paper is from the second quarter of 1997 to the fourth quarter
of 2010. It covers 14 years with 54 quarters which is much longer and newer
than any other literatures reviewed in section 2. The papers reviewed are
mostly published in the early 2000’s. At that moment, the boost of low-cost
airlines was considered as a main growth power for the air transport industry.
Nonetheless, the business environment all over the world has changed,
especially after the financial crisis. Hence, the new data gives the new trend of
the impact of low-cost airlines. Depending on Table 6.1, given the same
26
method (OLS), the impact on price has about halved from 1998 to 2007. On
top of the time changes, method is another reason for the different conclusion.
As seen in the Table 6.1, although some of them used the panel data, they
only used OLS method which has inherent drawbacks when analysis the
panel dataset. In this paper, this kind of drawbacks has been resolved
stepwise by advanced approaches, including fixed effect model and IV
estimation. Based on the results of IV estimation, the impact on pricing has
been halved again, from 22% to 10.2%. As indicated in previous sections, the
conclusions of this paper are more reliable than others.
Table 6.1 Results comparison
Research Dataset Method The impact of the entry of low-cost airlinesPrice Passenger traffic
W&D (1995)
Panel Descriptive decrease 19% on averagedecrease 48% (WN effect)
increase 182% on averageincrease 300% (WN effect)
W&D (1998)
Panel 3SLS decrease 53.3% (WN only)decrease 38% (multiple carriers)
Vowles (2000)
Panel OLS decrease 45.47% on average decrease 77.61% (WN effect)
Alderighi et al. (2004)
Cross section
OLS decrease 42.58%
Wang (2005)
Cross section
OLS decrease 18.25% (WN effect)
G&S (2006)
Time series
OLS decrease 18.6% at the entry year and keep depressing afterwards
the magnitude of the quantity response is roughly twice of the fare changes
Daraban (2007)
Time series
OLS WN’s entry decrease average fare by 22% while depressing legacy carriers’ price by 17.6%
Abda et al. (2011)
Panel Descriptive significantly depress 5% more on routes with low-cost airlines’ entrance in 2005
people are more elastic on routes with low-cost airlines’ entrance, implying increase and decrease more in good years and bad years, respectively
This paper
Panel OLS one more entry decreases 21.1% of price on average
one additional entry decreases passengers by 8.08%
This paper
Panel FE one more entry decreases 6% of price on average
one additional entry increases passengers by 3.81%
This paper
Panel 2SLS one additional entry decreases 10.2% of price
1% price reduction leads to 1.07% more passengersone more entry increases passengers by 10.9%
27
6.2 Implications for low-cost airlines
According to the conclusion, low-cost airlines are losing impact on pricing and
passenger traffic. It convinces the conventional strategy for growth may not
continue to optimistically work in the future. Hence, how to maintain cost
advantages and capture new opportunities become the most important tasks
for low-cost carriers (Bundgaard et al,. 2006). To keep cost advantage, low-
cost airlines need increase the efficiency of using fuel. Most of low-cost
carriers are still using very old and simplex fleet which are very low efficient of
using oil. The dramatically increasing fuel price no doubt burdens the low-cost
airlines. Since it is not possible to control the fuel price, the only way to reduce
the cost is to utilize fuel more efficient. As for the new growth opportunities,
low-cost airlines have already entered the most highly profitable city pair
routes. Competition on those routes is getting fierce and expensive. So they
are supposed to seek new growth points on those hub cities served by
weakened legacy carriers, or international destinations might be another
choice.
6.3 Limitation and further research
Compared with previous researches, this paper gives a more reliable
interpretation for the impact of entry of low-cost airlines on pricing and
passenger traffic. However, there is still a limitation and further researches are
required.
Looking backward to the descriptive analysis (Section 4), Figure 4.3 shows
the price changes pro and post entry. The dummy variable of low is taken into
account at the entry quarter. However, the depressing impact has already
existed from two quarters before the entry. It is the same with Figure 4.4 which
indicates the movements of passengers pro and post entry. The number of
passengers enhances since two quarters before the entry. These two figures
support the conclusion of Goolsbee & Syverson (2008) that incumbents tend
to deter low-cost airlines by pre-emptively depressing the price, however, the
entry cannot be avoided due to the increase on passenger flow.28
This lagged dummy variable appears to influence the model results. So for the
further researches, they are supposed to consider taking into account the
entry point in advance, for example, put the dummy at the point of two quarter
before the entry.
29
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31
Appendix 1 Price movement pro and post entry
-4 -3 -2 -1 Entry Quarter 1 2 3 40.00
50.00
100.00
150.00
200.00
250.00
300.00
350.00
400.00
450.00
500.00
Nr.7 Nr.16 Nr.25 Nr.28 Nr.31 Nr.35 Nr.38 Nr.40 Nr.42 Nr.52 Nr.53Nr.59 Nr.61 Nr.62 Nr.64 Nr.65 Nr.74 Nr.76 Nr.77 Nr.85 Nr.96 Nr.98