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

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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

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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

Appendix 2 Passengers movement pro and post entry

-4 -3 -2 -1 Entry Quarter 1 2 3 40

1000

2000

3000

4000

5000

6000

7000

Nr.4 Nr.18 Nr.25 Nr.26 Nr.28 Nr.35 Nr.38 Nr.42Nr.53 Nr.59 Nr.61 Nr.65 Nr.76 Nr.77 Nr.85 Nr.96