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 Wu
Student number: 332639
Thesis supervisor:
Dr. Peran van Reeven
Department of Applied Economics
Erasmus School of Economics
Erasmus 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!
ii
Table of Contents
Abstract ............................................................................................................. i
Acknowledged................................................................................................ii
List of Tables and Figures....................... Error! Bookmark not defined.
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 ................................................................................. 10
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
iv
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 1982 to the third quarter of 1992.
It was convinced that price declines by 60 percent associated with triple traffic
1
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
determinants, the entry of low-cost carriers captured most interest of
2
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), price and passenger
traffic. Southwest taken as representation of low-cost carriers captured the
3
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 ticket data directly
from Southwest, Southwest variable still has one of the biggest impacts in
4
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
refuses all the new entrance. Based on the research of Goolsbee and Syverson,
5
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.
Table 2.1 Literature summary
Research
Dataset
Method
The impact of the entry of low-cost airlines
6
Panel
Descriptive
Panel
3SLS
Panel
OLS
Cross
section
OLS
Price
decrease 19% on average
decrease 48% (WN effect)
decrease 53.3% (WN only)
decrease 38% (multiple carriers)
decrease 45.47% on average
decrease 77.61% (WN effect)
decrease 42.58%
Cross
section
Time
series
OLS
decrease 18.25% (WN effect)
OLS
Daraban
(2007)
Time
series
OLS
Abda et
al. (2011)
Panel
Descriptive
decrease 18.6% at the entry year the magnitude of the quantity
and keep depressing afterwards response is roughly twice of the
fare changes
WN’s entry decrease average
fare by 22% while depressing
legacy carriers’ price by 17.6%
significantly depress 5% more people are more elastic on routes
on routes with low-cost airlines’ with low-cost airlines’ entrance,
entrance in 2005
implying increase and decrease
more in good years and bad
years, respectively
W&D
(1995)
W&D
(1998)
Vowles
(2000)
Alderighi
et al.
(2004)
Wang
(2005)
G&S
(2006)
Passenger traffic
increase 182% on average
increase 300% (WN effect)
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% 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
7
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.
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.
1
The data for the first quarter of 2009 are entirely missing due to some reporting issues, Domestic Airfare
Consumer Report, 2009
8
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.
Table 3.1 Vacation cities in model
Region
Cities
9
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.
3.3.1 Pooled OLS model
Generally, the starting point for panel data analysis is Pooled OLS model, so is
10
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 +∑kk=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):
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.
11
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 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
12
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.
Table 4.1 Descriptive statistics
price
mean
sd
min
max
173.99
73.63
56.0
550
13
passenger
distance
income
lg_mktshare
vacation
low
N
2506.62
991.43
586497.54
53.69
0.24
0.68
5500
1506.29
642.66
321291.94
17.88
0.43
0.47
206.0
200.0
75717.2
18.8
0.0
0.0
12034
2704
1645968
100
1
1
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.
400
350
300
250
200
150
100
50
0
1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2010
Numbers of low-cost airlines
Average one-way price
Numbers of passengers per day
Figure 4.1 The movements of price, passengers and low-cost airlines
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
14
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.
50
45
40
35
30
25
City-pairs
20
Avg Nr.low
15
10
5
0
250 500 750 1000 1250 1500 1750 2000 2250 2500 2750
Figure 4.2 The distance distribution on top 100 routes / average number of LCC per route
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
2
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
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
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
16
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).
500
450
400
350
300
250
200
150
100
50
0
-4
-3
-2
-1
Entry
Quarter
1
2
3
4
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 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
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
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.
8000
7000
6000
5000
4000
3000
2000
1000
0
-4
-3
-2
-1
Entry
Quarter
1
2
3
4
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 formal
statistical models.
18
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 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.
19
Table 5.1 OLS regression results4
(1)
lnprice
lnpassenger
-0.0650***
(-9.81)
lndistance
0.433***
(71.16)
lnincome
-0.0528***
(-8.96)
lg_mktshare
-0.000468*
(-2.26)
vacation
-0.276***
(-44.85)
low
-0.211***
(-25.63)
lnprice
_cons
N
G
3.506***
(36.18)
5361
100
(2)
lnpassenger
-0.0809***
(-5.29)
0.246***
(22.60)
-0.0109***
(-29.68)
0.129***
(8.02)
-0.0808***
(-4.96)
-0.250***
(-9.70)
6.768***
(40.79)
5361
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 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
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
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
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
21
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)
lnprice
lnpassenger
-0.361***
(-4.97)
lnincome
0.965***
(3.92)
lg_mktshare
0.00145
(1.89)
low
-0.0600**
(-3.26)
lnprice
_cons
N
G
-4.621
(-1.52)
5361
100
(2)
lnpassenger
1.366***
(3.93)
-0.00315**
(-2.80)
0.0381
(1.95)
-0.730***
(-13.50)
-6.108
(-1.37)
5361
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 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,
22
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
lnpassenger
lndistance
lnincome
lg_mktshare
vacation
low
*
lnprice
lnpas~r
lndis~e
lninc~e
lg_mk~e
vacat~n
low
1
*
-0.14
0.72*
0.08*
-0.28*
*
-0.23
-0.32*
1
-0.05*
0.20*
-0.27*
*
0.12
0.01
1
0.08*
-0.42*
*
0.11
-0.12*
1
0.12*
*
-0.22
-0.03*
1
*
-0.12
-0.01
1
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 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.
23
Table 5.4 2SLS regression results
1st Stage
lnprice
lnprice
low
lndistance
lnincome
lg_mktshare
vacation
_cons
N
G
-0.102***
(-16.37)
0.470***
(9.84)
0.187***
(3.93)
0.003***
(13.89)
-0.211**
(-2.79)
-0.573
(-0.86)
5361
100
2nd Stage
lnpassenger
-1.066***
(-13.57)
0.359***
(4.97)
0.685***
(10.85)
-0.00206***
(-4.54)
0.0452
(0.46)
1.826*
(2.13)
5361
100
t 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 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
24
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%.
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
25
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 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
26
more reliable than others.
Table 6.1 Results comparison
Research
Dataset
Method
W&D
(1995)
W&D
(1998)
Vowles
(2000)
Alderighi
et al.
(2004)
Wang
(2005)
G&S
(2006)
Panel
Descriptive
Panel
3SLS
Panel
OLS
Cross
section
OLS
The impact of the entry of low-cost airlines
Price
Passenger traffic
decrease 19% on average
increase 182% on average
decrease 48% (WN effect)
increase 300% (WN effect)
decrease 53.3% (WN only)
decrease 38% (multiple carriers)
decrease 45.47% on average
decrease 77.61% (WN effect)
decrease 42.58%
Cross
section
Time
series
OLS
decrease 18.25% (WN effect)
OLS
Daraban
(2007)
Time
series
OLS
Abda et
al. (2011)
Panel
Descriptive
This
paper
Panel
OLS
decrease 18.6% at the entry year the magnitude of the quantity
and keep depressing afterwards response is roughly twice of the
fare changes
WN’s entry decrease average
fare by 22% while depressing
legacy carriers’ price by 17.6%
significantly depress 5% more people are more elastic on routes
on routes with low-cost airlines’ with low-cost airlines’ entrance,
entrance in 2005
implying increase and decrease
more in good years and bad
years, respectively
one more entry decreases 21.1% one additional entry decreases
of price on average
passengers by 8.08%
This
paper
Panel
FE
one more entry decreases 6% of one additional entry increases
price on average
passengers by 3.81%
This
paper
Panel
2SLS
one additional entry decreases 1% price reduction leads to
10.2% of price
1.07% more passengers
one more entry increases
passengers by 10.9%
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
27
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.
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.
28
References
[1] Bailey , Graham & Kaplan (1985), Deregulating the Airlines, London, England
[2] Doganis, R. (2006), The Airline Business, Bodmin, Great Britain
[3] Cameron & Trivedi (2009), Microeconometrics using Stata, Texas, United States
[4] Strassmann (1990), Potential competition in the deregulated airlines, The Review of
Economics and Statistics, Vol. 72, No. 4 (Nov., 1990), pp. 696-702
[5] Whinston & Collins (1992), Entry and Competitive Structure in Deregulated Airline
Markets: An Event Study Analysis of People Express, The RAND Journal of
Economics, Vol. 23, No. 4 (Winter, 1992), pp. 445-462
[6] Bennett & Craun (1993), The airline deregulation evolution continues: The
Southwest effect, presented by Office of Aviation Analysis, United States
[7] Windle & Dresner (1995), The short and long run effects of entry on U.S. domestic
air routes, Transportation Journal, Winter 1995, 35, 2, pp. 14
[8] Windle & Dresner (1999), Competitive responses to low cost carrier entry,
Transportation Research, Part E 35 (1999), pp. 59-75
[9] Dresner, Lin & Windle (1996), The impact of low-cost carriers on airport and route
competition, Paper presented at American Economic Association Meetings, Maryland,
United States
[10] Vowles (2000), The effect of low fare air carriers on airfares in the US, Journal of
Transport Geography, 8 (2000), pp. 121-128
[11] Wang (2005), The effect of a low cost carrier in the airline industry, Paper
presented at MMSS Honors Seminar, United States
[12] Goolsbee & Syverson (2008), How do incumbents respond to the threat of entry?
Evidence from the major airlines, The Quarterly Journal of Economics, Retrieved from
http://www.qje.oxfordjournals.org
[13] Abda (2011), Impacts of LCC growth on domestic traffic and fares at largest US
airports, Journal of Air Transport Management, 18 (2011) pp. 21-25
[14] Daraban (2007), Low cost carrier entry, incumbent responses and spatial
competition in the US airline industry (PhD Thesis, The Florida State University,
United States)
[15] Alderighi, Cento, Nijkamp & Rietveld (2004), The entry of low-cost airlines,
Unpublished paper, Tinbergen Institute Discussion Paper TI 2004-074/3.
29
[16] Bundgaard, Bejjani & Helmer (2006), Strategic report for Southwest airlines, Out
of the Box Consulting, Pandora Group
[17] Paap, R. (2011), Lecture 4: Applied Microeconometrics [PowerPoint slides],
Rotterdam, The Netherlands, Erasmus University Rotterdam
[18] US Department of Transportation (2011), Airline Origin and Destination Survey
(DB1B), Retrieved from
http://www.transtats.bts.gov/databases.asp?Mode_ID=1&Mode_Desc=Aviation&Subject_ID2
=0
[19] Bureau of Economic Analysis (2011), Regional Data, Quarterly State Personal
Income: Personal income (SQ1), Retrieved from
http://www.bea.gov/iTable/iTable.cfm?reqid=70&step=1&isuri=1&acrdn=3
30
Appendix 1 Price movement pro and post entry
500.00
450.00
400.00
350.00
300.00
250.00
200.00
150.00
100.00
50.00
0.00
-4
-3
-2
-1
Entry Quarter
1
2
3
4
Nr.7
Nr.16
Nr.25
Nr.28
Nr.31
Nr.35
Nr.38
Nr.40
Nr.42
Nr.52
Nr.53
Nr.59
Nr.61
Nr.62
Nr.64
Nr.65
Nr.74
Nr.76
Nr.77
Nr.85
Nr.96
Nr.98
Appendix 2 Passengers movement pro and post entry
7000
6000
5000
4000
3000
2000
1000
0
-4
-3
-2
-1
Entry Quarter
1
2
Nr.4
Nr.18
Nr.25
Nr.26
Nr.28
Nr.35
Nr.38
Nr.42
Nr.53
Nr.59
Nr.61
Nr.65
Nr.76
Nr.77
Nr.85
Nr.96
3