The impact of low cost carriers on

passenger growth at hub airports in Europe

Erasmus School of Economics – Erasmus University Rotterdam

August 19, 2016

M.C. van Witzenburg

386185

Supervisor: mr.dr. P.A. van Reeven

Abstract

This study examines the effect of low cost carriers on hub airports in Europe. First, hub

airports are defined as airports that handle many passengers. Also, the number of connecting

passengers must be relatively large. Second, low-fare airlines are defined as airlines that focus

on reducing costs in order to lower fares. A regression analysis is executed to determine the

impact of low cost carriers on passenger growth rates at hub airports. The results show that

airports where low-fare airlines are based have significantly larger passenger growth numbers.

The impact is unaffected by the number of low-fare airline bases and changes in population.

Further, it is concluded that only bases of low-fare airlines that are no subsidiaries of network

carriers are able to attract extra passengers.

Contents

Page

1 Introduction 3

2 Hub airports and demand for air transport 5

2.1 Derived demand 5

2.2 Market segments 6

2.3 Factors that influence demand 7

2.4 Defining hub airports 10

2.5 Demand and hub airports 13

3 Low cost carriers 15

3.1 Defining low-fare airlines 15

3.2 Reduce operating costs 15

3.3 Increasing revenues 19

3.4 Carriers within carriers 21

3.5 Low cost carriers at hub airports 23

4 Data and methodology 25

4.1 Hypotheses 25

4.2 Data 27

4.3 Methodology 29

5 Results 32

6 Conclusion 36

7 References 39

Appendix A: Table used to make the dataset 47

Appendix B: Data description 48

2

1 Introduction

The European aviation market changed a lot in recent years. The market share of low cost

carriers (LCC) on the intra-EU airline market increased from 47 per cent in 2005 to 57 per

cent in 2013. An even larger increase is shown on routes operating between EU and non-EU

countries. The market share of low cost carriers almost doubled from 16 per cent in 2005 to

30 per cent in 2013. Popular destinations are countries in Eastern Europe and the

Mediterranean region (European Commission, 2015).

These market shares were not a threat to legacy carriers like KLM and British Airways

in the past because low cost airlines were flying mainly to secondary airports. Examples of

such airports are London Stansted and Frankfurt Hahn. Most of these airports are located

more than one hour driving from the city-centre. Consequently, passengers heading to areas

near the secondary airport and low yield leisure traffic were the major customers of low cost

carriers (Dobruszkes, 2013).

However, low cost carriers are not focussing on secondary airports only nowadays.

Ryanair has opened routes to hub airports like Amsterdam Schiphol and plans to open more.

In addition, the carrier has opened bases on major airports like Brussels Zaventem and Rome

Fiumcino (Ryanair, 2014). Also other carriers are opening bases at hub airports (Transavia,

2015; Vueling, 2013). These airports are attractive because they are mostly well connected

with nearby cities. Good infrastructure enables a more convenient journey for passengers to or

from the airport, resulting in higher yield traffic compared to secondary airports (Dobruszkes,

2013).

Hub airports focus on low cost carriers as well. A major reason is that low cost carriers

attract new market segments so that the airport is able to grow faster (Cohen, 2016). Ryanair

has reported that most airports are only capable to grow because of the new routes opened by

low cost carriers. Consequently, airports would incentivise these carriers to open new routes

(Ryanair, 2015).

Do airports attract more passengers when they facilitate budget carriers? The main

question of the thesis is: What is the impact of low cost carriers on passenger growth at hub

airports in Europe?

In order to answer the main question the thesis is divided in three parts. The methodology of

the first two parts is reviewing literature. First, demand for air transport at hub airports will be

analysed. In order to do this, hub airports will be defined and factors that may impact demand

3

will be discussed. Second, the concept of low cost carriers will be explained so that a

definition can be formed. This makes it possible to discuss the role of hub airports for low

cost carriers.

Third, a regression analysis will be made on the annual passenger growth at European

airports. The presence of low cost carriers will be an exogenous variable. Other exogenous

variables that may influence are demand will be discussed in the first part and are used as

control variables. All data will be collected by analysing annual reports or via Eurostat.

The thesis consists of six chapters. Chapter two describes the demand for air travel at hubs.

Chapter three will define low cost carriers and discuss their presence at hub airports. In

chapter four the data and methodology of the regression analysis will be discussed. The

results of the regression are given in chapter five. Lastly, chapter six concludes the thesis.

Also limitations and recommendations for further research will be described in this chapter.

4

2 Hub airports and demand for air transport

2.1 Derived demand

Demand for transport is different compared to other goods. Most people do not benefit from

transport directly. Accordingly, direct demand for transport is low. However, people have to

travel to reach activities at other places that they want to consume. In other words, they use

transport in order to reach and do other activities. Correspondingly, demand for transport

depends on the demand of other goods. This is called derived demand. People take the costs

of travelling in account when they decide whether they will consume something at a place to

which they need to travel. As a result, the demand for the other good depends on the costs of

transportation and the demand for transport is determined by characteristics of other goods

(Kawamura, 2016; Stopher and Stanley, 2014).

For example, someone wants to go to a festival in another city that can be reached by

train. This person will only visit the festival if his marginal utility of attending the festival

(MU) is higher than the costs of the ticket and the costs of the train trip (MC t), as it is

impossible to attend without a transfer to the festival. Consequently, high costs of the train

trip will lower the demand for festival tickets. Equally, high prices for festival tickets will

result in less demand for train transfers.

Figure 1: Marginal costs and benefits for festival visits per year.

5

Figure 1 shows a hypothetical marginal utility function and the marginal costs for the festival

ticket (MCf), train ticket (MCt) and total marginal costs (MCf+t). This person does not attach

any value on being transported. It would go to four festivals per year when it does not have to

use transport. Despite, since this person has to use transport to reach festivals, total marginal

costs are higher and the individual will only attend two festivals. As a result, he will make

two train trips. However, he would make four trips when the ticket price of the festival was so

low that total marginal costs would be equal to MCf.

2.2 Market Segments

Also air transport has a derived demand. As a result it is possible to distinguish different

purposes to travel. Holloway (2008) differentiates two categories: Leisure and business

traffic. Both segments react different to aspects that affect demand. Accordingly, it is useful to

define these two categories.

Business travellers

Business travellers are people that fly because they have a work related event in another city.

Employers pay the ticket fare because these travellers fly for business purposes. As a

consequence, many business travellers are less affected by price. Especially travellers that

work at large companies do not take price into account. In spite of this, smaller companies

like family businesses are much more price-sensitive, although less sensitive than leisure

passengers on average. Correspondingly, most business travellers on low cost carrier flights

were employed by small or medium-size businesses (Holloway, 2008).

Leisure travellers

All passengers that do not travel for business purposes are called leisure traffic. These

passengers pay the ticket themselves, and consequently are more price-sensitive. Doganis

(2010) differentiates two under segments: Visiting friends and relatives (VFR) and holiday

traffic. Demand for air transport of VFR-traffic is very price-sensitive, as the largest expenses

of this group are often the fares of flight tickets. These people sleep and eat at their friends or

relatives and therefore have lower accommodation and food costs. Although to less extent

than VFR traffic, holiday traffic is called price-sensitive as well. Most leisure travellers

choose between places and expensive flights can encourage them to book a trip to another

destination. Further, holiday traffic is influenced by season and weather.

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2.3 Factors that influence demand

Because air transport has a derived demand, it is influenced by many external factors. These

influence the reason why people travel and thus can make a route, destination or airport

attractive or not. Besides, internal factors impact attractiveness too. Important factors are

described below.

Price

The fare a passenger has to pay is an important factor that influences demand despite the

derived demand. Airline fares affect the price of the total trip ‘package’ significantly in most

cases. In 2013, transport expenditures accounted for 32 per cent of total spending on holidays

in the European Union (Eurostat, 2015). As a result, a significant price drop in airline tickets

can influence the total demand for holidays by plane heavily and increase demand for air

transport. Also it can make flying more competitive to other travel modes like road and rail

(Doganis, 2010).

Price elasticity of demand is a method used to show the reaction of consumers on a

change of price. It is calculated by dividing the percentage change in demand by the

percentage price change. Price elasticity can be elastic (below -1.0), which means that the

change in quantity demand is higher than the change in price. As a result, revenues will

decrease when fares increase. Further, price elasticity can be inelastic (between 0.0 and -1.0).

This means that the relative change in price is larger than the relative change in demand,

increasing revenues when prices rise. Another stage is unitary-price-elasticity (-1.0), which

means that an increase of fares will result in an even large decrease in demand.

Correspondingly, revenues are unaffected by price changes (Holloway, 2008). 12

Flight Elasticity Business Elasticity Leisure

Long-haul international -0.27 -1.04

Long-haul domestic -1.15 -1.10

Short-haul -0.70 -1.52

Table 1: Price elasticity for different routes and different segments. Source: Gillen, Morrison

and Stewart (2008).

7

Leisure and business travellers react differently to price changes. A study conducted by

Gillen, Morrison and Stewart (2008) in Canada found that business traffic had an inelastic

demand, while leisure travellers had an elastic demand. Only Business passengers on long-

haul domestic flights were price-sensitive. However, flights with comparable characteristics

do not exist in Europe. Furthermore, the report presents that price matters much more on

short-haul than on long-haul routes. This could imply a substitution-effect, which is described

in the section below. All results of the study are shown in table 1.

Availability of substitutes

Substitutes for air travel can be traditional methods of transport, like road networks and high-

speed rail links, and technology such as video meetings. Passengers make a trade-off between

convenience, speed and price and choose the best option (McCann, 2013). Alternatives are

often only available on short-haul routes as air travel is very competitive regarding travel time

on long distances.

A study of Kopsch (2012) found a positive cross-price elasticity between rail and air

travel in Sweden, which means that both modes of transport are substitutes. In the same

report, a regression model to measure airport traffic demand shows a positive relationship

between prices of train tickets, the availability of high-speed rail networks and the price

elasticity of air transport demand. This shows that travellers are more price-sensitive to air

travel services when substitutes are available.

An example of a rail connection that has substituted air transport is LGV-Nord/HSL 1,

a high-speed rail link between Paris and London through a tunnel under the English Channel.

Before the rail link all traffic between France and the United Kingdom had to be made by boat

or plane. About eight million passengers travelled between the two countries in 1994 by

plane, the year before the tunnel opened. Over a half of these passengers travelled between

London and Paris. In comparison, about twelve million people travelled between the two

countries by air in 2008. Although less than twenty per cent between the two capitals, a

decline of approximately fifty per cent. In contrast, Eurostar carried about ten million people

on its London-Paris route. Similar figures are seen on the Paris-Brussels and London-Brussels

rail links (Dobruszkes, 2011).

Income, Trade and Gross Domestic Product (GDP)

Another factor that influences demand for air travel is income. People spend more money on

luxury products like vacations when their income increases. As a result, people make more

8

trips per year or spend more on the trips that they already made, for instance by choosing

faster ways of transport like airliners (Brons, Pels, Nijkamp & Rietveld, 2002). A method to

measure the impact of income on vacation spending is income elasticity. It is calculated by

dividing the relative change in demand by the relative change in income (Holloway, 2008).

Song, Kim and Yang (2010) examined the income elasticity of several tourist

destinations. They found a positive correlation between income and the distance of holiday

trips. In other words, people tend to travel further when they earn more money. Furthermore,

Graham (2000) states that people with high incomes are making more trips a year than people

with lower incomes. This results in increased demand for short- and long-haul travel in the

long run.

A way to measure income is gross domestic product (GDP). GDP is the sum of all

incomes in a country. Consequently, increases in GDP mean that more money can be spend in

a country. Nevertheless, it does not explain anything about disposable income per capita for

three reasons. First, price levels can differ between countries. Accordingly, higher incomes do

not mean that people can buy more goods in a particular place compared to other regions.

Second, a large country with many inhabitants can have a large GDP, but per capita GDP can

be low because many people with low incomes can earn a large total income. Likewise, a

small country with few inhabitants can have a significantly lower GDP, but a higher GDP per

person. Third, distribution of incomes in a country can be skewed. This means that a small

portion of total residents earn a lot while a large part is poor.

As regions have different standards to calculate disposable income per capita, it is

difficult to use this parameter to compare countries. Consequently, a lot of studies use GDP

growth as a method to measure the growth of individual incomes in a particular region

(Holloway, 2008).

Moreover, GDP is not only used to measure incomes. It is also a method to measure

trade. Income is a factor that influences mainly leisure traffic, as most people have to pay

those trips themselves. Conversely, trade influences business travellers heavily. Business

travellers negotiate with other companies or have conversations with international

departments of the same firm. In times of economic contraction, companies have a hard time

to make profit. Consequently they will delay expenses, resulting in less trade. Also, they will

sooner opt for cheaper solutions in order to decrease costs. As a result, it is more likely that

meetings will take place through video calling or that meetings will be held less frequently.

These cost savings result in less demand for air travel (Doganis, 2010; Holloway, 2008;

O’Connell & Williams, 2011).

9

Population

Not only price and economic factors affect the demand for air travel. Also the number of

residents in the service area of an airport influence the number of passengers handled. A large

population means many potential customers. This makes it is more attractive for carriers to

open routes to these locations. Accordingly, it is not surprising that London, Frankfurt, Paris

and Amsterdam are the largest airports in the EU. These four airports are located in the centre

of the EU, which is densely populated. Furthermore, good infrastructure in this area increases

the service range of the airport. Correspondingly, the potential number of travellers is

relatively larger than airports in other regions of Europe (Graham, 1998).

In addition, population has a positive correlation with the number of businesses in a

certain area. Because of the strong link between trade and the amount of companies, more

trade will take place in high densely populated areas. As a result, the demand of business

traffic for transport to densely populated regions is larger than sparsely populated areas.

Consequently, services to more densely populated areas have higher yields, which makes

routes to these destinations more attractive to airlines (Doganis, 2010; Holloway, 2008).

Besides the number of residents in an area, the composition of the population can

influence demand also. Cities that have a lot of internationals or ethnic diversity attract a lot

of VFR traffic because family and friends want to see their relatives (Graham, 2006). Lehto,

Morrison and O’Leary (2001) estimated that about 13 per cent of total traffic to the United

States was VFR traffic. This figure shows that VFR passengers are a significant amount of

traffic that influences demand heavily.

Other factors

Besides, other factors can impact demand for air transport as well. An example of such a

factor is safety. It influences in particular leisure traffic. Traffic decreased significantly after

the 9/11 terroristic attacks in New York on 11 September 2001 (Franke, 2004). Equally, in

Paris tourism declined dramatically after the November 2015 attacks (Coldwell, 2016).

2.4 Defining hub airports

The United States Federal Aviation Administration (FAA) categorizes airports as hubs based

on market share. They define hubs as airports at which at least 0.05% of the annual passenger

boardings in the US take place. Also, at least 10,000 boarded passenger aircraft must depart

10

every year. All hubs are categorized as small, medium or large by the FAA. Medium sized

hub airports must have at least 0.25% of the total annual passenger boardings and large hubs

are airfields at which at least 1% of the nationwide enplanements take place (Rodríguez-

Déniz, Suau-Sanchez & Voltes-Dorta, 2013).

In contrast, Dennis (1994) describes hubs as airfields that host an airline that offers

connections and is based at the particular airport. Airlines that offer connections are called

network carriers because they operate a hub-and-spoke network. This means that the carrier

sells tickets between two cities that are not directly linked. Passengers travel through an

airport to which many flights are operated and where passengers can transfer between flights,

the so-called hub. For instance, no direct flights are offered between Innsbruck (Austria) and

Dublin (Ireland) (Flughafen Innsbruck, 2016). However, Lufthansa offers multiple flights per

day from both, Innsbruck and Dublin, to Frankfurt. The airline sells tickets between Innsbruck

and Dublin. People fly first from Innsbruck to Frankfurt, where they can transfer to another

flight to Dublin (Lufthansa, 2016). Accordingly, passengers are able to fly between cities that

could not withstand direct flights. A consequence for the network carrier is that it transports

more passengers, increasing revenues. Likewise, hubs handle more passengers because

passengers that do not start or end their journey at the airfield use the airport (Dennis, 1994).

In order to specify the definition of hub airports, Costa, Lohmann and Oliveira (2010)

describe two additional conditions that a hub airport must fulfil. First, connecting traffic must

be a significant number of the total amount of passengers. In other words, network carriers

based at the airport must attract many transfer passengers and be a major player at the airport.

Second, the airport should handle a decent number of passengers compared to major airports.

As a result, airports that do not handle much traffic are not defined as hubs. This is in

accordance with the FAA, which excludes smaller airports (Rodríguez-Déniz, Suau-Sanchez

& Voltes-Dorta, 2013). The restrictions exclude airports that do not rely on transfer traffic and

airfields that are too small to be a major hub. For instance, several airlines at London City

Airport (United Kingdom) offer connections. Nevertheless, less than 2.5% of the passengers

changed flights at the airport in 2014. This is remarkably less than the percentage of

connecting passengers at London Heathrow, where 35.2% was transfer traffic (Civil Aviation

Authority, 2015). As a result of the first condition, London City is not regarded as hub.

Another example is Jyvaskyla airport (Finland). Transfer traffic exceeded 38% of total

passengers in 2015, which would make it without conditions a hub airport. However, less than

45,000 passengers were handled in 2015, which makes it one of the smallest airports in

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Finland. Hence it is not considered as an important hub for continental European traffic

(Eurostat, 2016).

AirportIATA Code Passengers

TransferTraffic Hub Airline Alliance

London Heathrow LHR 73,374,825 35.2% British Airways Oneworld

Paris Charles de Gaulle CDG 63,648,676 30.6% Air France Skyteam

Frankfurt FRA 59,571,802 55.0% Lufthansa Star Alliance

Amsterdam Schiphol AMS 54,459,000 40.5% KLM Skyteam

Madrid-Barajas MAD 41,833,686 24.3% IberiaAir Europa

OneworldSkyteam

Munich MUC 39,700,000 37.0% Lufthansa Star Alliance

Rome Fiumcino FCO 38,288,519 13.0% Alitalia Skyteam

Copenhagen CPH 25,627,093 24.6% SAS Star Alliance

Zürich ZRH 25,477,622 30.3% Swiss International Star Alliance

Dublin DUB 23,856,443 3.1% Aer Lingus Oneworld

Vienna VIE 22,500,000 29.0% Austrian Airlines Star Alliance

Brussels BRU 21,933,190 15.8% Brussels Airlines Star Alliance

Dusseldorf DUS 21,850,000 10.6% Air Berlin Oneworld

Berlin Tegel TXL 20,688,016 7.9% Air Berlin Oneworld

Lisbon LIS 18,145,631 - TAP Portugal Star Alliance

Helsinki HEL 15,900,000 15.7% Finnair Oneworld

Athens ATH 15,196,369 20.0% Aegean Airlines Star Alliance

Prague PRG 11,129,966 2.0% Czech Airlines Skyteam

Warsaw-Chopin WAW 10,590,473 42.0% LOT Polish Airlines Star Alliance

Table 2: European airports that served network carriers in 2014.

Table 2 shows nineteen European airports that are a base for at least one network carrier. The

figure shows the number of passengers handled in 2014, the percentage of transfer passengers

and the airline and alliance for which the particular airport is a hub. Frankfurt, Warsaw-

Chopin, Amsterdam Schiphol, London Heathrow and Paris Charles de Gaulle were the five

airports with the highest percentage of transfer passengers in 2014. With the exception of

Warsaw-Chopin, these airports are the four largest in Europe.

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2.5 Demand and hub airports

Hub airports are impacted differently by the factors described in section 2.3 than smaller

secondary airports because of several characteristics. One such characteristic is the supply of

long-haul flights. Customers on long-haul flights are in general less price-sensitive than

passengers on short-haul flights. This applies to business and leisure traffic as can be seen in

table 1. Consequently, demand for long-haul flights is less dependent on the expensiveness of

fares (Dennis, 1994; Gillen, Morrison & Stewart, 2008). On the other side, Song, Kim and

Yang (2010) found a strong positive correlation between long-haul vacations and incomes.

Correspondingly, demand for long-haul flights can be affected more heavily by changes in

income compared to short-haul traffic.

Also competition between network carriers/hubs can influence passenger numbers at

hub airports heavily. As shown in table 2, transfer traffic at hub airports can be as large as 55

per cent (Frankfurt airport). This means that 55 per cent of the traffic is travelling further to

another destination and does not have to travel through this airfield. Other network

carriers/hubs with similar routes are substitutes. As a consequence, competition between

network carriers/hub airports with a comparable network is fierce, which means that changes

in charges can impact the attractiveness of a hub airport and airline for transfer passengers,

especially price-sensitive traffic. Accordingly, increases in airfield duties can lead to

significant lower demand (Dennis, 1994)

Further, business travellers are more likely to use network carriers and hence hub

airports. Three reasons are higher frequencies, direct flights and frequent flyer programmes.

First, demand is higher on routes to or from hubs because of transfer passengers.

Consequently, airlines are able to enlarge capacity. Often frequencies are increased to cope

demand as this makes more connections attractive. As a result, even more traffic is attracted.

In addition, passengers can choose between more flights and consequently they are more

flexible. Especially business travellers appreciate flexibility (Holloway, 2008; Mason, 2000).

Second, network airlines are able to offer direct flights that would not be possible without

transfer passengers. Business travellers are more time sensitive in general. Hence, direct

flights are more attractive as they save time that would be necessary to transfer between

flights (Fujii, Im & Mak, 1992; Lijesen, Rietveld & Nijkamp, 2001). Third, business

passengers are more likely to use network carriers because of frequent flyer programs. A

frequent flyer program is a reward program that allocates a certain amount of points for every

flight to a passenger. Travellers can use the points to get discounts, free flights or upgrades to

more expensive classes. Business passengers prefer to travel with network carriers to collect

13

these points because they often can be used for personal trips as well. Consequently, business

passengers save personal money that could be spend otherwise (Doganis, 2010; Holloway,

2008; Lederman, 2008; Nako, 1992).

Furthermore, most hub airports are connected to national road and rail networks. As a

result they have good connections with nearby regions. Correspondingly, travellers can access

the airport easily, reducing time and costs to reach the airport (Dobruszkes, 2013; O’Connell

& Williams, 2005). Moreover, hub airports offer lounges. These places are often quiet and

offer facilities such as Internet connections that can be used as work places in case a business

traveller arrives early at the airport (Han, Ham, Yang & Baek, 2012).

Network carriers, good hinterland connections and the availability of lounges are

important reasons for (time sensitive) business passengers to use hub airports. As a result, the

percentage of business traffic is larger at these airports than at other airports (Innes & Doucet,

1990; Lian & Rønnevik, 2011). Consequently, the average passenger at hubs is less price-

sensitive and passenger numbers depend less on income. Therefore these factors impact

demand at hubs less heavily than at other airports. Despite, more traffic is related to trade.

Economic volatility is thus a relatively more important factor that influences demand.

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3 Low cost carriers

3.1 Defining low-fare airlines

Low cost carriers distinguish themselves by offering airline trips for lower fares than

traditional airlines. It is only possible to reduce prices and still make sufficient profit by

increasing efficiency or decreasing service levels (Porter, 1980). Low cost carriers use both

methods. They optimize efficiency and do not offer all services or charge a fee for these

services. This enables low cost carriers to charge a lower fare for the core product of an

airline, namely transporting people to a certain destination (Lawton, 2003).

It is difficult to indicate a carrier as low cost. All airlines offer a different product,

including low cost carriers. EasyJet for example flies mostly to primary airports while

Ryanair focuses on secondary airports. Still, both are categorised as low cost carrier

(European Commission, 2015). Furthermore, traditional service carriers like British Airways

and Lufthansa have introduced cheaper fares with restrictions comparable to those offered by

low cost carriers (Calder, 2013; Deutsche Lufthansa, 2015). These fares make differences

between service carriers and low cost carriers smaller.

Several papers describe some key characteristics of low cost carriers. These are

described in sections 3.2 and 3.3.

3.2 Reduce operating costs

Increase aircraft utilization

Costs can be categorized as fixed costs that do not differ when output changes, and variable

costs, which increase when output is enlarged (Tsai & Kuo, 2004). The fixed costs per flight

hour are calculated by dividing total fixed costs by total flight hours (Caves, Christensen &

Tretheway, 1984). In order to lower fixed costs per flight hour, low cost carriers use their

aircraft more than full-service competitors (Dobruszkes, 2006). One method used to optimize

utilization is to shorten turnaround times, the time between arrival and departure. These

turnaround times can be as short as 25 minutes. In comparison, most traditional service

carriers have a turnaround time of about an hour (O’Connell & Williams, 2005).

Consequently, low cost carriers are able to fly more trips per day than their competitors,

lowering the fixed costs per flight. So flew Ryanair aircraft 9.71 hours per day on average in

15

2007, while Air France short-haul aircraft flew only 6.46 hours per day. That is a difference

of about 33 per cent (figure 2) (German Aerospace Center, 2008).

Low-fare airlines are able to achieve these faster turnarounds by several factors. An

important aspect is the use of secondary airports. Secondary or regional airports are in general

small and only served by a few airlines (Graham & Shaw, 2008). Therefore they are less

complex and less congested than larger airports (Barbot, 2006). As a result, planes do not

have to queue till a stand is available and ground handling can be performed in a shorter

timeframe.

Figure 2: Aircraft utilization by airline in 2007 for Airbus A320-family and Boeing 737

aircraft. Source: German Aerospace Center (2008).

Another characteristic that enables faster turnaround times is solely offering point-to-point

traffic. Full service airlines offer connections at their hubs. Connections make it easier to

operate large networks as flights feed each other. In spite of this, ground-handling processes

are more complex because luggage has to be transferred between aircrafts in a short period.

Connections lead in particular to a complex situation when planes are delayed. This may

result in lower aircraft utilization (Doganis, 2010).

Operate a common fleet

Operating a common fleet has several cost advantages. First, less reserve crews are required,

because no reserve crews that are certified for different aircraft types are needed. Every crew

can operate all airplanes, resulting in more flexibility. Second, maintenance costs are lower.

Maintenance workers are familiar with the aircraft type so that they can work more efficient.

British MidlandAir France

British Airways

KLM

Lufthansa

Germanwings

EasyJet

Ryanair

0 1 2 3 4 5 6 7 8 9 10

6.096.46

6.82

7.7

8.26

9.23

9.24

9.71

16

In addition, it is possible for a worker to specialise in a specific part of the aeroplane or

engine, which results in even more efficiency and therefore higher production. Furthermore,

less spare parts are needed per aeroplane as all parts can be used on all aircraft, reducing

storage costs. Third, ground handling is less complex because it can be standardized.

Consequently, expenses of ground handling may decrease because less equipment is needed

and the equipment used can be optimized for the aircraft type. Finally, operating more aircraft

of a particular type often results in lower capital costs. Airplanes can be bought in larger

volumes, resulting in a larger quantity discounts and thus lower capital costs (Brüggen &

Klose, 2010). As a result of all these cost advantages, about 74 per cent of all low-fare airlines

operate a common fleet (figure 3) (Gross, Lück & Schröder, 2013).

1 Type: 74, 68%

2 Types: 26, 24%

3-4 Types: 9, 8%

B737 Family: 33, 30%

A320 Family: 32, 29%

Other: 9, 8%

Figure 3: Number of aircraft types that low cost carriers use and the most popular models

among the airliners that used only one type of aircraft. Source: Gross, Lück & Schröder

(2013).

Tickets and distribution

Full service carriers sell tickets directly through their website and call centre, and indirectly

via travel agencies and other intermediaries. Indirect sales happen in a lot of cases through

global distribution systems (GDSs). Those are online systems that enable travel agencies to

compare and book flights. In the early 2000s, offering flights in GDS systems cost about three

dollars per booking in addition to the commission of travel agencies. These commissions were

about eight per cent of the total ticket price. In order to decrease costs to enable lower fares,

easyJet decided to sell tickets only directly when they commenced flying in 1995. Most low

17

cost carriers followed this decision and stopped distributing tickets via intermediaries and

GDSs in the early 2000s, lowering costs per ticket.

Nevertheless, easyJet and other low cost carriers reintroduced indirect ticket

distribution through GDSs after a statement of Jetblue in 2007. This American low-fare

airline claimed that revenues per ticket sold through GDSs were $35 higher than tickets sold

directly. As a result, some low cost airlines reintroduced selling tickets indirectly through

intermediaries. However, two differences were made. One, middleman have to pay the GDS

fees. Two, the contribution margin of intermediaries has to be paid by the customer. These

two requirements result in lower costs for the airline compared to the past (Doganis, 2010).

In addition, 79 per cent of the low cost carriers sell only one cabin: economy class

(Gross, Lück & Schröder, 2013). The reason is that business class seating makes the product

more complex as passengers expect a better seat and better services. To meet these

requirements, extra staff with better training would be required. This would result in higher

costs. Likewise, only one or two ticket types with little or no flexibility are sold. The

advantage of a non-flexible ticket is that the airline will receive revenue for every seat sold

because passengers cannot change flights or cancel their trip. Moreover, non-flexible tickets

reduce costs because fewer employees are required to rebook tickets. Further, often only one-

way tickets are sold as this simplifies the booking system (Doganis, 2010).

Use of secondary airports

As stated earlier, low cost carriers use secondary airports that are in general small and less

complicated than large airports. It is cheaper to operate from these airports. Already

mentioned is the advantage of the airport being less congested, so that faster turnarounds can

be made more easily. However, the benefits of smaller airports are not limited to that

advantage. Some other characteristics make it also attractive to operate flight to these

airfields.

First, smaller airports charge lower fees than large airports because they offer fewer

facilities compared to larger airports. In addition, often only a few airlines serve them. As a

result charges are often low in order to attract new carriers. Second, plenty of slots are

available because the airports are often underserved. These slots often include slots on times

which suite carriers best (Barbot, 2006). Consequently, low cost carriers can optimize their

flight schedule and aircraft utilization. Third, carriers can negotiate with the airport about

charges. Often only a few airlines serve the secondary airport; therefore the withdrawal of a

carrier will result in a significant decrease in passenger numbers (Graham & Shaw, 2008).

18

Fourth, some secondary airports lack infrastructure like check-in desks because they were

military bases or too small to accommodate such facilities in the past. Accordingly, the newly

build infrastructure at these airports may be optimized for quick and efficient services that are

desired by low-fare airlines (Barbot, 2006).

Offering no connections

Full service carriers operate a hub-and-spoke network. All flights go to one or several hubs

where transfers between flights are offered. Conversely, low cost carrier offer only point-to-

point flights. Those are flights between two non-hub airports that do not stop at a major hub.

Despite the fact that some airports have many flights and thus a lot of traffic, no connections

are offered. The reason is that operating a hub-and-spoke network requires a more

complicated business model. This would result in higher costs (Barrett, 2004; Gross, Lück &

Schröder, 2013).

Examples of higher costs caused by connections are more frequencies, ground

handling costs and booking costs. Low cost carriers offer lower frequencies on short-haul

routes compared to network carriers. For instance, Ryanairs average frequency was five

flights a week in 2010. This would result in long stopovers that make the connection very

unattractive. In order to make connections attractive high frequencies must be offered. This

would only be possible by adapting flight schedules to each other, which may result in longer

turnaround times and less utilization (Gillen & Morrison, 2003; de Wit & Zuidberg, 2012).

Further, connections require a more complex booking system. Full service carriers

have multiple rates that are dependent on several factors, including connecting traffic. Seats

are sometimes sold for lower fares to transfer passengers compared to origin and destination

(O&D) traffic in order to fill the connecting flight. A consequence of this policy is that the

booking system becomes more complicated and therefore more expensive to build, operate

and maintain (Doganis, 2010).

3.3 Increasing revenues

Low-fare airline are not only able to lower fares because of their lower costs, they have found

methods to maximise revenues as well. Effective yield management, selling ancillary services

on board and selling related services are examples of methods used to collect as much

revenues as possible. Some methods are discussed below.

19

Effective yield management

Low-fare airlines have a less complex business model. This allows them to execute more

effective yield management. Network carriers must take transfer passengers into account

when they set fares. In comparison, low cost carriers focus only on O&D traffic by offering

solely point-to-point traffic, thereby optimizing yields for each passenger. Furthermore,

services that may make the booking system more complex, like multiple travel classes and the

option to book in several currencies are not offered. Consequently, the system is simpler,

which makes it able to manage yields better (Doganis, 2010).

Ancillary revenues

Another way that low-fare airlines use to increase revenues is offering services for a fee.

These services can be segmented in two categories. One, services that are part of the

traditional airline product, but are unbundled by low cost carriers. Two, services provided by

third parties that airlines sell on a commission base (Holloway, 2008).

Examples of services belonging to the first segment are on-board catering and checked

luggage. Passengers have to pay a charge with high margins for on-board food and drinks.

Consequently, revenues are increased and basic fares can be reduced. In addition, costs are

diminished because aircrafts can be provisioned quickly due to lower demand. Also, lower

demand makes some galleys unnecessary, which can be replaced by more seats so that fixed

costs per seat are reduced (Doganis, 2010). Another unbundled service is baggage. Basic fares

include hand luggage only and supplements must be paid for checked baggage. Ryanair

started this policy as first airline in Europe in 2005 and many others followed. A consequence

of the policy is that fewer passengers transport luggage. So saw Ryanair the amount

passengers transporting hold luggage decreasing from eighty per cent to below forty per cent

(O’Connell & Williams, 2011). As a result, ground-handling costs decreased and short

turnaround times could be met more easily (Doganis, 2010). Other charges introduced by low

cost carriers are priority boarding and seat selection (O’Connell & Warnock-Smith, 2013).

Further, airlines sell third-party services on a commission bases. These services are

primarily related to the airline product. Examples are offering accommodations, hiring cars

and selling insurance. O’Connell and Williams (2011) report that Ryanairs commission

revenues of hiring cars exceeded 32 million euros in 2009 and that these revenues were

increasing faster than passenger growth.

Ancillary revenues are important for low-fare airlines. So were Ryanairs ancillary

revenues a quarter of total earnings in 2015, equal to about 1.4 billion euros (Ryanair, 2015).

20

Likewise, Wizz Airs ancillary revenues were about 433 million euros, equal to 35 per cent of

the airlines’ revenues (Wizz Air, 2015). These figures have encouraged service carriers to also

sell ancillary services. As a result, these airlines could reduce their standard fares, increasing

competitiveness. Especially since most passengers compare airlines only on their basic fares

and do not take additional charges into account (de Wit & Zuidberg, 2012).

3.4 Carriers within carriers

Low cost carriers can be distinguished in two categories: Independent low cost carriers and

subsidiaries of network carriers. Independent low cost carriers are not affiliated with network

carriers. Consequently they are able to apply cost saving management among all levels in the

company. Also, they do not have to take competition with profitable sister companies into

account. As a consequence, costs at these airlines are at its lowest. This helps them to achieve

their main objective, namely maximising profits (Conrady, 2013; Graham & Vowles, 2006).

A list of independent low cost carriers that operate more than fifteen aeroplanes in Europe is

shown in table 3.

AirlineStart LCC operations Country of origin

Base at hub airports from table 2

Air Berlin 2002 Germany Yes

easyJet1 1995 United Kingdom Yes

Flybe 2002 United Kingdom No

Jet2.com 2003 United Kingdom No

Monarch 2004 United Kingdom No

Norwegian Air Shuttle 2002 Norway Yes

Ryanair 1992 Ireland Yes

Smartwings 2004 Czech Republic Yes

Volotea 2012 Spain Yes

Wizz Air 2004 Hungary Yes

Table 3: Independent low cost carriers in Europe. Source: European Commission (2015) and

ICAO (2014)

Due to the success of low cost carriers, network carriers have launched subsidiaries that

operate a low-fare business model. These subsidiaries are called carriers within carriers. The

1 Including easyJet Switzerland.

21

main goal of these carriers is often to retain market share of the parent organisation

(Dobruszkes, 2006; Graham & Vowles, 2006; Homsobat, Lei & Fu, 2014). Three different

methods are used to achieve this object in Europe. First, subsidiaries are used to expand the

hub function. In this case the low cost subsidiary operates flights to markets that lack

sufficient premium traffic to be served efficiently by the main airline. An example is Iberia

Express, which operates low cost flights from Madrid-Barajas to markets that are not served

by parent Iberia. As a result, the two airlines do not compete. Instead, they complement each

other, as passengers are able to transfer between full service and low cost flights.

Accordingly, Iberia Express is integrated in the Iberia product (Fageda, Suau-Sanchez &

Mason, 2015; Graham & Vowles, 2006). Second, low cost carriers can be used to retain

market share without operating flights to hubs of the mainline carrier. So operates Lufthansa’s

subsidiary Eurowings flights from various bases in Germany. Nevertheless, no flights are

operated to hubs of Lufthansa. Consequently, Lufthansa Group can retain market share in

Germany without direct competition with the mainline (Fageda, Suau-Sanchez & Mason,

2015; Homsobat, Lei & Fu, 2014). Third, low-fare airlines can be used to obtain market share

in other market segments than those of the mainline company. So operates Air France/KLM

subsidiary Transavia flights between bases in the Netherlands, including hub airport

Amsterdam Schiphol, and holiday destinations like Malaga and Gran Canaria. The subsidiary

does not compete on most routes with the parent company, which does not focus on the short-

haul sun holiday market (Air France, 2016; Fageda, Suau-Sanchez & Mason, 2015; Graf,

2005).

To summarize, the purpose of low cost subsidiaries of network carriers is to retain

market share without competing with sister and parent companies. Consequently, it is likely

that carriers within carriers make different decisions than independent low cost carriers. A list

of low cost airlines that are subsidiaries of network carriers is given in Table 4.

22

AirlineStart LCC operations

Country of origin Parent Company

Eurowings2 2002 Germany Lufthansa

Iberia Express 2012 Spain Iberia

Transavia 2005 The Netherlands KLM

Transavia France 1995 France Air France

Vueling 2002 Spain International Airlines Group3

Table 4: Low cost carriers of network carriers in Europe. Source: European Commission,

(2015) and ICAO (2014).

3.5 Low cost carriers at hub airports

It is more difficult to operate a low fare business model from hubs compared to secondary

airports for two reasons. One, hub airports are often less efficient and airports fees are

frequently higher. As a consequence costs are higher. A characteristic of low cost carriers is

higher aircraft utilization, which is achieved by faster turnaround times. However, it is

difficult to achieve short turnarounds for two reasons. First, hub airports are often large.

Therefore, it takes more time to travel between aircraft stand and runway. This may result in

longer journey times (de Jong, 2006). Second, hub airports are busier than secondary airports.

As a result, airplanes often have to wait till a gate or runway is available. Further, hub airports

charge higher fees than secondary airports. Explanations for the higher fees are better

infrastructure, less bargaining power of airlines and high demand for slots. Finally, peak-hour

slots are scarce at several hub airports. As a consequence, carriers that do not have these slots

have to adjust flights to off-peak times, which may reduce aircraft utilization and thus

efficiency.

Two, demand is different as hub airports attract high yield (business) traffic that is not

price-sensitive. These passengers are willing to pay high fares for more comfort, high

frequencies and direct flights. Low cost carriers do not offer these services except direct

flights. Frequencies are low since they do not offer connections that enlarge demand.

Moreover, low cost carriers do not operate different aircraft models. This makes it difficult to

exploit high frequencies at all destinations as the only way to adjust supply is changing

frequencies. Next, low-fare airlines offer less comfort in order to lower costs and fares.

2 Lufthansa acquired Eurowings low cost department in 2012.3 International Airlines Group (IAG) is the parent company of Aer Lingus, British Airways and Iberia.

23

However, business travellers appreciate comfort since it facilitates better work conditions.

Further, many firms use travel agencies to book flights. These intermediaries book flights

using GDSs. Nevertheless, many low-fare airlines do not offer flights in those systems.

Consequently, it is less likely that business passengers will book flights of low cost carriers

instead of full-service competitors.

Hub airports attract higher yield traffic that is less likely to use low-fare airlines. In

addition, operating costs are higher at these airfields. Despite these facts, low cost carriers

have opened bases at hubs. A list of these bases is given in Appendix A. The openings of new

bases show that low fare airlines expect that hubs are attractive to price-sensitive passengers

as well. Low-fare airlines can attract people that benefit from good infrastructure but do not

want to pay high fares for services on board, like VFR traffic. Consequently, low cost carriers

may attract new market segments to hub airports.

24

4 Data and methodology

4.1 Hypotheses

Low cost carriers distinguish from full service carriers by offering flights for lower fares.

They are able to offer these fares because of their lower costs, which are partly lower at the

expense of services. As a result, these carriers are able to attract price-sensitive passengers,

which travel often for leisure purposes. It is debatable to which extent low-fare airlines

compete with full service carriers. Network carriers focus on people that are willing to pay

higher fares for more service. Many of these passenger travel for business purposes.

Consequently, both categories of airlines serve different market segments.

Airports that are destinations of both types of airlines are attractive for price-sensitive

and price-insensitive travellers. As a result, passengers should be more diverse at these

airports. Hence, growth rates could be higher at these airports compared to hub airports at

which no low-fare airlines are based. Therefore the first hypothesis is:

Hypothesis 1: Passenger growth rates are higher at hubs that are bases of low cost carriers.

So it is expected that bases of low cost carrier attract passengers that would otherwise not use

the airport. Likewise, the opening of a base of a second low cost carrier may increase

passenger numbers. Second low-fare airlines will open routes in addition to the already

operated flights, increasing capacity and supply. Besides, economies of synergy could arise,

as it is more attractive for the airport to open facilities enhanced for low-fare airlines. This

makes it easier to operate the low cost business model so that lower fares can be offered while

the company remains profitable. Nevertheless, it is debatable to what extent the base of a

second low cost carrier provides the same number of additional passenger growth. Carriers

may open routes already operated by the other low-fare airline. Accordingly, the airlines will

compete with each other. As a result the number of additional customers attracted could be

small. Also, airlines that are the second low-fare airline to open a base can decide to not start

offering particular routes that are already operated by established carriers in order to avoid

competition. Therefore, the effect of a base of a second low-fare airline on passenger growth

is expected to be smaller.

Hypothesis 2: Passenger growth rates are less influenced by bases of additional low cost

carriers compared to the effect of the first base.

25

So low-fare airlines focus on price-sensitive passengers. A market segment to which many of

these passengers belong is VFR traffic. In general, the costs of flight tickets are relatively

large for these travellers because many sleep and eat at their relatives, reducing the costs for

accommodation and food. As a consequence the total costs of the trip are lower. Accordingly,

lower airfares can increase demand for VFR trips and thus flights. Another factor that can

determine the demand for flights of this segment is the size of population. VFR-passengers

travel to visit family and friends and a larger population means that a city has more

inhabitants that other people can visit. Likewise, more citizens would like to visit relatives in

other cities when the population increases. As a result, VFR traffic is affected by the size of

population.

Low cost carriers are relatively more attractive to VFR passengers than network

carriers because of their lower fares. It is expected that the number of passengers that travel to

visit relatives increase when low-fare airlines are based at an airport. Correspondingly,

passenger growth at hub airports where low-fare airlines are based could be more positively

affected by population growth compared to hubs that are no bases of those carriers. Hence, the

following hypothesis is formed:

Hypothesis 3: Population growth has greater impact on passenger growth rates at airports

where low-fare airlines are based.

So the effect of bases of low cost carriers on passenger growth rates may differ between hubs

at which one and multiple low-fare airlines are based. Another factor that may influence the

size of the additional passenger growth is the type of the low cost carrier. The main goal of

independent airlines is to maximise profits, whereas the goal of subsidiaries of network

carriers is to sustain market share for the airline group. This difference between the two

categories of airlines may influence their decisions. Further, independent carriers are able to

minimise costs at all levels of the company, while carriers within carriers cannot because of

their ties with the mother company. As a result, the characteristics of the low-fare airline

differ between the two types of carriers. Therefore, choices that may influence passenger

growth could be made differently, resulting in dissimilar effects on traffic growth. Hence the

fourth hypothesis is formed:

Hypothesis 4: Independent low-fare airlines and carriers within carriers affect passenger

growth rates differently.

26

4.2 Data

A regression analyses will be executed to test the hypotheses. In order to perform this

analysis, a cross-sectional dataset is composed. Cross-sectional data is suitable to measure the

impact of a factor on another variable. In order to create the dataset, airports had to be defined

as hub. In section 2.4 is described that hub airports must handle decent amount of passengers

and relatively many transfer passengers. In the dataset the corresponding minimum

requirements were set at one million passengers per month (twelve million per year) and 15%

transfer traffic in 2014. Consequently, twelve airports are selected. Table 5 shows these

airports.

AirportPassengers

2014Transfertraffic Included

London Heathrow 73,374,825 35.2% Yes

Paris Charles de Gaulle 63,648,676 30.6% Yes

Frankfurt 59,571,802 55.0% Yes

Amsterdam Schiphol 54,459,000 40.5% Yes

Madrid-Barajas 41,833,686 24.3% Yes

Munich 39,700,000 37.0% Yes

Rome Fiumcino 38,288,519 13.0% No

Copenhagen 25,627,093 24.6% Yes

Zürich 25,477,622 30.3% Yes

Dublin 23,856,443 3.1% No

Vienna 22,500,000 29.0% Yes

Brussels 21,933,190 15.8% Yes

Dusseldorf 21,850,000 10.6% No

Berlin Tegel 20,688,016 7.9% No

Lisbon 18,145,631 - No

Helsinki 15,900,000 15.7% Yes

Athens 15,196,369 20.0% Yes

Prague 11,129,966 2.0% No

Warsaw-Chopin 10,590,473 42.0% No

Table 5: Airports that are included in the dataset.

The data is longitudinal, which means that it is collected for several years. This has been done

in order to improve the reliability of the dataset, as only twelve observations may be sensitive

27

to external factors. The data concerns the time period 2005-2014 because those were the ten

most recent years over which all data was available.

Several variables that may influence passenger numbers at hubs are included in the set.

These factors are whether low cost carriers have a base at the particular airport, GDP per

capita and population. GDP is measured per capita because absolute GDP is correlated with

population. How GDP and population may influence passenger numbers at airports is

discussed in section 2.3. All data on GDP and population comes from Eurostat. Further, the

dataset contains data of the number of low cost carriers based at a particular hub airport. This

data corresponds with the data in table 10 in appendix A. This information is included for

independent airlines, carriers within carriers and both categories together. It has been

compiled by analysing annual reports of airports and airlines.

Passengers

Low cost

carrierIndepen-dent LCC

Carrier within carrier

National GDP / capita

National population

Passengers 0.2922 0.1343 0.3779 -0.1545 0.7564

Low cost carrier 0.2922 0.8751 0.8696 -0.2050 0.0501

Independent LCC 0.1343 0.8751 0.5220 -0.2089 0.0396

Carrier within carrier

0.3779 0.8696 0.5220 -0.1481 0.0479

National GDP / capita

-0.1545 -0.2050 -0.2089 -0.1481 -0.3244

National population 0.7564 0.0501 0.0396 0.0479 -0.3244

Table 6: Correlations between variables of the dataset.

Table 7 shows some statistics of the data. So was the largest amount of passengers measured

over 73.4 million, while the smallest observation was 11.3 million passengers. The average

amount of passengers handled at European airports was 35.3 million per year. Next, the table

shows that the smallest national income per capita measured equals 16,301 euros while the

largest equals 64,734. Further, the table shows that on average 0.625 low cost carriers were

based at hub airports in the timeframe 2005-201

28

Passengers

National income /

capitaNational

populationBase of

LCC

Base of independen

t LCCBase of CWC

Mean 35,302,549 34,000 33,527,619 0.625 0.308 0.317

Minimum 11,130,589 16,301 5,246,096 0 0 0

Maximum 73,405,330 64,734 82,469,422 4 2 2

Observations 120 120 120 120 120 120

Table 7: Descriptive statistics of several variables in the dataset.

Additional figures and tables that show statistics of the data are included in appendix B. So

shows figure 4a that during 21 observations independent low cost carriers were based at hub

airports. Further, table 11 shows that independent low cost carriers are on average based at

smaller airports than carriers within carriers.

4.3 Methodology

The data will be used to perform a linear regression analysis. The method used to estimate the

model is least squares (LS). LS minimises the squared residuals of the estimation. As a result,

the estimated linear regression model is as close as possible to the observed data. Passenger

growth will be the dependent variable in all regression models. As the dataset contains only

data of total passenger numbers, this data has to be transformed into logarithmic values. As a

result, changes of independent variables are expressed in percentages and values can be

compared (Stock & Watson, 2015). Also GDP per capita and population are converted to

logarithmic terms in order to make all observations comparable. LS can only be used if the

data is stationary. Therefore all variables are controlled for unit roots first by executing Levin-

Lin-Chu unit-root tests. The null hypothesis of this test is that the data contains a unit-root and

has to be adjusted. One method to correct non-stationary data is using the differences between

observations (difference in difference) (Levin, Lin & Chu, 2002; Stock & Watson, 2015).

This method will be used to correct the data if it contains unit-roots.

A significance level of 5% is used to interpret significance tests. This means that the

probability of a type I error, the rejection of a correct null hypotheses, is below 5%. Further, it

is possible to conclude with at least 95% certainty that the dependent variable is affected by

the independent factor in the regression analysis.

29

It is expected that the data contain fixed effects. Passenger numbers may be influenced

by decisions in the past that cannot be changed in the short-term, like terminal capacity and

accessibility. In addition, certain characteristics of the location of the airport can impact

passenger numbers. For instance, legislation could be different so that the airfield is less

attractive for transfer passengers. Another example is culture, variations in attitude as the

general opinion about strikes may impact the attractiveness of a certain country and impact

demand of hubs in that particular country. These factors are characteristics that are very

difficult to change in the short-run. As a result, fixed effect will be included in the regression

analysis.

The first regression model will have logarithmic annual passenger growth as dependent

variable and whether a low cost carrier is based at the airport as independent variable of

interest. In addition, GDP per capita and population growth will be added as control variables

in order to reduce bias. The variable whether low-fare airlines are based at the airport is a

dummy-variable, which means that it has value 0 when no low cost carriers are based at the

airport and 1 when these airlines have a basis at the airfield.

The second hypothesis states that passenger growth caused by a second or higher

number of low-fare airlines bases is significantly smaller than the effect of the first low cost

base. Whether at least two low cost carriers are based at the airport will be added to the model

as additional dummy variable to test this hypothesis. A positive but significantly smaller

effect is expected.

To test the third hypothesis, an interaction effect between population growth and

whether the airport is a base for low-fare airlines will be added to the model. This makes it

possible to determine whether passenger growth at bases of low cost carriers is affected more

heavily by population growth.

Finally, regression models that distinguish independent low cost carriers and carriers

within carriers will be estimated to test hypothesis four. The variables of interest in these

models are whether the airfield is a base of independent low-fare airlines and whether carriers

within carriers are based at the hub airport. Passenger growth will remain the dependent

variable. Likewise, increases in population and GDP per capita will remain control variables.

After the estimation, a significance test will be conducted in order to assess whether the

estimated coefficient of independent low-fare airlines and carrier within carriers differ

significantly.

30

5 Results

First, Levin-Lin-Chu unit-root tests were executed in order to determine whether the data

consists unit-roots. The results show that national population is the only variable that has a

unit-root (t = -1.45, p = 0.074). Consequently, the first difference is used in order to make this

variable stationary so that it can be used to estimate regressions with the least squares method.

For all other variables, the null-hypothesis of the unit-root test was rejected. This means that

these variables could be used without any restrictions. All results of the test are shown in table

8.

Variable t-statistic p-value Conclusion

Passenger numbers -2.2216 0.0132 Reject null-hypothesis: variable is stationary

Low cost carrier -3.3625 0.0004 Reject null-hypothesis: variable is stationary

Independent LCC -1.8455 0.0325 Reject null-hypothesis: variable is stationary

Carrier within carrier -2.0408 0.0206 Reject null-hypothesis: variable is stationary

National GDP/capita -3.6786 0.0001 Reject null-hypothesis: variable is stationary

National population -1.4500 0.0735 Adopt null-hypothesis: variable is non-stationary

Table 8: Results of the Levin-Lin-Chu unit-root test.

After determining whether the data consists of unit-roots, the regression analysis could

be estimated. All estimations are based on the first model, which estimates the effect of

whether a low cost carrier is based at the airport, national population and national income per

capita. All estimations are given in table 9.

The first hypothesis is that passenger growth rates are higher at hubs that are bases of

low cost carriers. Model 1 is estimated to test this hypothesis. The first model shows a highly

significant (p < 0.01) positive effect of 0.087 for variable ‘low fare airlines based at the

airport’. This means that on average passenger numbers at hub airports at which low cost

carriers are based grow 0.087 percentage point faster than other hubs. Consequently, the first

hypothesis is adopted. This was expected and in accordance with the theory. The lower

airfares of low cost carriers may be able to attract price-sensitive passengers that would not

fly with more expensive network carriers. Consequently, hubs at which these airlines are

based have higher passenger growth rates than comparable airports where no low-fare airlines

are based.

31

National Fixed Model 1 Model 2 Model 3 Model 4

Constant 8.581 ** 8.631 ** 8.355 ** 8.646 **

(0.621) (0.629) (0.650) (0.612)

Low cost carrier (LCC)0.087 ** 0.083 ** 0.927

(0.018) (0.019) (0.673)

More than one LCC0.011

(0.019)

Independent LCC0.115 **

(0.020)

Carrier within carrier (CWC)0.011

(0.042)

Independent LCC * CWC-0.079

(0.041)

Log national GDP/capita0.828 ** 0.823 ** 0.849 ** 0.822 **

(0.060) (0.061) (0.063) (0.059)

Dlog national population2.736 2.868 3.313 2.990

(1.506) (1.528) (2.517) (1.516)

Log national GDP/capita * LCC-0.080

(0.065)

Dlog national population * LCC-0.313

(3.050)

R-squared 0.7196 0.7205 0.7245 0.7380

Table 9: Results of the regression analysis. Standard errors are given between brackets. * =

significant (p < 0.05), ** = highly significant (p < 0.01)

Model 2 tests the second hypothesis, which states that the impact of bases of second low cost

carriers on passenger growth is smaller than the effect of the first low-fare base. Variable

‘more than one low cost carrier based at the airport’ is added to regression model 1. The

regressor of this variable is positive but insignificant (p = 0.56). This means that the expected

effect of the opening of more low-fare airline bases at airports at which already another low

cost carrier is based is negligible. A significance test was conducted in order to determine

whether the variable differs significantly from variable ‘low cost carrier based at airport’. The

result of this test shows that the effects of both variables differ significantly (p = 0.021).

32

Although, the total effect of low cost carrier bases does not differ significantly compared to

the first model. The estimated coefficient of variable ‘low cost carriers based’ declines from

0.087 to 0.083. It seems that one established low cost carrier already meets the needs of

people that are price sensitive. Bases of additional airlines do not result in additional

passengers, which could be the result of increased competition between all carriers at the

airport. Network carriers may reduce frequencies, resulting in less transfer passengers.

Equally, the already established low cost carrier may decide to reduce capacity on certain

routes to reduce excess capacity. The results are in line with the second hypothesis, which

states that the effect of the second low cost base is lower. Accordingly, the second hypothesis

is adopted.

Interaction variable ‘population growth and low cost carrier based at airport’ is added

to model 1 to test the third hypothesis. As a result, model 3 is created. It is expected that the

added variable is positive. Nevertheless, the estimated coefficient is highly insignificant (p =

0.919). This means that the effect of low cost carrier bases on passenger growth is probably

not affected by changes in population. This is unexpected and not in accordance with the third

hypothesis, which is rejected. An explanation for the unexpected effect could be that the

composition of new inhabitants differs significantly from the general population structure. For

example, the natural birth rate can be positive, so that the number of children is increasing.

Parents of these young children may prefer to spend free time at home instead of extra trips to

European destinations. Organising a trip with kids is more difficult as children could have

other preferences than their parents. Moreover, children may have more fun by playing with

local friends than going on a trip. The money saved by not going on additional trips could be

spent on other things, like local days out and toys. Furthermore, the proportion of young

people may be larger among immigrants. Retired people may be emotionally attached to

regions where they have lived their whole lives. In addition, these people can benefit for a

shorter period while the costs of moving are not lower compared to younger people.

Consequently, retired people may be less likely to move to other countries. On the other hand,

young people may be more likely to move to other countries as their remaining life

expectancy and therefore benefits are higher. In addition, moving to other countries can

improve the lives of their children, so that total benefits are even larger. However, younger

people may be less likely to increase their number of air trips when airfares are lower. People

that move to another country to make career could have a lack of spare time. These people

may work many hours in order to get the best chance of promotion. Consequently, they may

33

not have enough days off to increase their number of trips. Therefore lower airfares could not

result in an increase in the number of annual trips these people make.

Finally, the fourth hypothesis is tested. This hypothesis states that independent low-

fare airlines and carriers within carriers affect passenger growth rates differently. It is tested

by model 4, which is also based on the first model. In this model variable ‘low cost carrier’ is

replaced by variables ‘base of independent low-fare airline’, ‘carrier within carrier’ and an

interaction effect between the two types of airlines. The estimated coefficient of independent

low cost carriers is 0.115 and highly significant (p < 0.01). This means that airports at which

only independent low fare airlines are based have an additional passenger growth of 0.12

percentage point compared to hub airports at which no low cost carriers are based. In contrast,

‘carrier within carrier’ has an insignificant coefficient (p = 0.796). This means that airports at

which carriers within carriers are the only low-fare airlines do not have larger passenger

growth than airports at which no low cost carriers are based at all. A significance test is

executed in order to determine whether the effects of independent low cost carriers and

carriers within carriers differ significantly. The results of the test are a t-statistic of 2.339 and

a p-value of 0.021. Consequently, it is concluded that the effects of independent low cost

carriers and carrier within carriers on passenger growth differ significantly. Therefore the

fourth hypothesis is adopted.

The larger impact of independent low cost carriers compared to subsidiaries of

network carriers is not surprising. Independent low-fare airlines like easyJet and Ryanair are

able to reduce costs at all levels of the company, which makes it easier to operate the low cost

business model. Furthermore, these airlines do not have to take the effect of their business on

the mother airline into account, so that the airline is able to make the best decisions.

34

6 Conclusion

Demand for air transport is determined by several factors. Important factors are population

size, the state of the economy and the price of airline tickets. Low cost carriers are airlines

that focus on low fares. They are able to offer these low fares by reducing costs. Optimizing

utilization and efficiency, economies of scale and the reduction of services are methods used

to decrease costs. The low prices of low cost carriers are especially attractive for people that

are price-sensitive. Many of these passengers travel for leisure purposes. Consequently, low

cost carriers focus mostly on leisure passengers. In contrast, business travellers care more

about flexibility and services and less about price. They often prefer service carriers for their

good services and flexibility. Service carriers are able to provide some services because they

offer connections, which increase demand.

Large airports at which many passengers change flights are called hub airports. They

are very attractive to business travellers because of good hinterland connections and the

availability of many flights of service carriers. In contrast, it is more difficult to operate the

low cost carrier business model from these airports because of their size, complex

infrastructure needed for transfer passengers and high charges. Consequently, low cost

carriers focussed mostly on secondary airports in the past. However, several low-fare airlines

have recently opened bases at hub airports, which seems to be a success. Because of their

relatively large attractiveness to price-sensitive passengers compared to service carriers, low

cost carriers are able to attract additional passengers that would otherwise not use the airport.

The executed regression analysis shows that hub airports where low-fare airlines are based

have significantly larger passenger growth rates than hubs at which no low cost carriers have

opened a base. This effect is unaffected by the number of low-fare airline bases and the size

of the population.

Low cost carriers can be distinguished in independent low-fare airlines and carriers

within carriers. Independent low cost carriers have no connections with service carriers. As a

result they can make route choices without restrictions and are able to reduce costs at all

levels of the company. Consequently, they are able to attract passengers that would otherwise

not use the airport. Passenger growth numbers at hubs where independent low-fare airlines are

based are significantly larger than airports at which no low-fare airlines were based. In

contrast, carriers within carriers are subsidiaries of service airlines and therefore may be less

able to make the best decisions. Hubs at which these carriers were based have no additional

passenger growth compared to hub airports without low cost carriers.

35

To conclude, European hub airports at which low cost carriers are based have larger

passenger growth numbers than other hubs. This growth is only caused by independent low

cost carriers and not by low-fare subsidiaries of network carriers. The magnitude of the effect

seems to be fixed, as it is not affected by the number of low-fare airlines and changes in

population.

Airport managers can use the results of this thesis for short-term decisions. They can

increase passenger numbers by encouraging an independent low cost carrier to open a base at

the airport. Also, the results can be used to negotiate with low-fare airlines. The base of a

second low cost carrier does not result in additional passengers. This information could be

used in negotiations to enforce higher airport charges.

In spite of this, it is difficult to give proper recommendations for policymakers and

airport managers in the long run due to some limitations of this thesis. First, the effect of low

cost carriers on network carriers is not discussed. It may be harder for these carriers to operate

a profitable network, as competition on short-haul flights may be fierce, resulting in lower

prices on those particular routes. Accordingly, service carriers can be forced to reduce

frequencies or suspend routes. This makes the airline less attractive for transfer passengers

and consequently can result in a further reduction of operational activities. This could not

only harm the airline, also the hub airport may be affected. The route network of the hub can

be less attractive, so that passenger numbers will decrease in the long run. Further research

could focus on the impact of low-fare airlines on network carriers and passenger numbers at

hub airports in the long run. This makes it possible to conclude whether encouraging the

advent of low cost carriers may result in larger passenger numbers in the long run and which

policy is the most suitable to increase passenger numbers.

Second, the thesis has focussed on the effect of low cost carriers on passenger

numbers. Nevertheless, the impact on society has not been taken into account. Passengers that

are attracted by low-fare airlines are more price-sensitive and therefore may spend less in the

local economy than passengers that use service carriers. It could be that the negative effects of

these passengers, like extra pollution caused by flights and crowdedness, outweigh the

benefits to local society. Furthermore, competition of low-fare airlines may impact the

operations of network carriers, which may decide to reduce routes, operations and the amount

of jobs. This effect could be very disadvantageous to society, as the attractiveness of the

region to investors will be reduced due to the loss of connections. This effect can outweigh

the benefits of low-fare airlines to society. Future research could focus on societal effects of

36

low cost carriers. This will make it possible to give proper recommendations about whether

policymakers should encourage the advent of low-fare airlines.

37

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Appendix A: Table used to make the dataset

Airport IATA Code

Low cost carrier

Base opened

Base closed

Paris Charles de Gaulle CDG easyJetVueling

20082007

Amsterdam Schiphol AMS easyJetTransaviaVueling

201520052011

Madrid-Barajas MAD easyJetIberia ExpressNorwegianRyanairVueling

20072012201420062005

2013

Munich MUC Transavia 2016Rome Fiumcino FCO easyJet

NorwegianRyanairVueling

2009201620142012

2016

Copenhagen CPH Norwegian 2008Zürich ZRH Vueling 2016Dublin DUB Ryanair 1992Vienna VIE Eurowings 2015

Brussels BRU RyanairVueling

20142014

Dusseldorf DUS Air BerlinGermanwings

20022012

Berlin Tegel TXL Air BerlinGermanwings

20022013

Lisbon LIS easyJetRyanair

20122014

Helsinki HEL Norwegian 2011Athens ATH Ryanair

Volotea20142015

Prague PRG RyanairSmartwingsWizz Air

201620042010

Warsaw-Chopin WAW Wizz Air 2005

Table 10: List of hub airports defined by ICAO (2014) and European Commission (2015) that

are bases of low cost carriers including year that the base opened and year that the base

closed.

46

Appendix B: Data description

(a)

No Yes0

20406080

100

91 29

Independent Low Cost Carries

Based at hub airport

Num

ber o

f ob

serv

atio

ns

(b)

No Yes0

20406080

100

89 31

Carriers within Carriers

Based at hub airport

Num

ber o

f ob

serv

atio

ns

Figures 4: Number of observations in which an independent low cost carrier or a carrier

within carrier was based at a hub airport.

<10 10-20 20-30 30-40 40-50 50-60 60-70 70-80 >800

10

20

30

40

0 39 22 10 14 20 12 3 0

Passenger Numbers

Passenger number in millions

Num

ber

of o

bser

vatio

ns

Figure 5: Number of passengers per observation in millions.

47

All low-fare airlines Independent LCC Carrier within CarrierNo base Base No base Base No Base Base

Pass

enge

rsMean 32,221,035 41,240,102 34,393,460 38,155,210 30,774,110 48,303,552 Min 11,130,589 14,858,215 11,130,589 14,858,215 11,130,589 19,715,451 Max 73,405,330 63,813,756 73,405,330 63,813,756 73,405,330 63,813,756 Obs 79 41 91 29 89 31

Nat

iona

l In

com

e /

capi

ta

Mean 34,366 33,296 34,532 32,332 34,883 31,468Min 16,451 16,301 16,451 16,301 16,301 21,317Max 64,734 46,174 64,734 46,174 64,734 43,535Obs 79 41 91 29 89 31

Nat

iona

l po

pula

tion Mea

n 35,394,519 29,930,422 33,729,701 32,893,500 32,097,486 37,633,484Min 5,246,096 5,388,272 5,246,096 5,388,272 5,246,096 5,523,095Max 82,469,422 66,152,155 82,469,422 66,152,155 82,469,422 66,152,155Obs 79 41 91 29 89 31

Table 11: Descriptive statistics of variables based on whether a low cost carrier is based on

the hub airport.

48