2005_2

Leisure Travel in Europe and Airline Business Models: A

Study of Regional Airports in Britain

Dr Andreas Papatheodorou, University of the Aegean

Mr Zheng Lei, University of Surrey

2005/2

Abstract

Tourism and air transport are explicitly linked especially in the context of leisure traffic.

This paper aims at highlighting this relation by focusing on the impact of the three main

airline business models (traditional scheduled, charter and low cost) on regional airports

using Britain as a case study. The panel data econometric results show that despite the

current perception, low cost carriers are not the only ones to contribute significantly to

airport aeronautical and non-aeronautical revenue. This observation has important

policy implications and calls for transparency in airport subsidies as argued in the

conclusion.

Keywords: tourism, regional airports, airline business models, panel data analysis

Leisure Travel in Europe and Airline Business Models: A Study of Regional Airports in Britain 2

1. Introduction

Tourism is a global phenomenon of major economic importance. It comprises the

activities of persons travelling to and staying in places outside their usual environment

for not more than one consecutive year for leisure, business and other purposes (WTO,

1994: 5). The latter include activities such as visiting friends and relatives (VFR), health

treatment and pilgrimage. The World Tourism Organization estimates that international

tourism arrivals reached 760 million in 2004, an increase of about 10% compared to

2003 (WTO, 2005). This is a very encouraging sign given the stagnation that prevailed

over the previous years due to the 9/11 terrorist attacks, the war in Iraq and SARS.

International tourism receipts were about 460 billion in 2003 (WTO, 2004) and are

expected to exhibit significant growth in the following years especially as China is

gradually becoming a dominant player in terms of both inbound and outbound tourism.

The Asian tsunami disaster is likely to have a negative impact in the short-run but the

longer-term still looks very promising especially when domestic tourism is also taken

into consideration.

Air travel is intrinsically related to tourism mainly in terms of international flows but also

for domestic movements in larger countries. The tourism ratio (which shows the

percentage of demand attributed to tourism over total industrial supply) of air

transportation can exceed 90% (Smith, 1998). Even if we consider only leisure and VFR

traffic, many destinations in the Mediterranean and the Caribbean region are still highly

dependent on air transport. Leisure travellers have been traditionally regarded as time-

rich, highly price sensitive people with preferences exhibiting a strong seasonal pattern

peaking during holidays and festive periods of the year. Trips may last for one or two

weeks usually with the family and involve both short and long-haul destinations with in-

flight service considered more important on the latter. The gradual emergence of the

short-break trend, however, has created new peaks during weekends and is essentially

associated with people who are better-off but time-constrained. Singles and people

Dr Andreas Papatheodorou, University of the Aegean and Mr Zheng Lei, University of Surrey.


belonging to the DINK (Double Income No Kids) family category are the typical

representatives in this case. VFR tourists have similar attitudes to leisure travellers but

are more likely to make last minute travel arrangements with limited flexibility,

especially in urgency (e.g. attendance of a funeral). Expatriate communities established

as a result of previous (South to North in the 1950s and 60s) and current (East to West)

immigration waves in Europe have enhanced VFR traffic; foreign students provide a

further boost to such travelling.

Charter carriers were the primary air service provider of leisure travel in Europe until the

early 1990s. The seasonal and/or occasional character of such flights matched well the

pattern of leisure travellers. These airlines acted as the original low cost ones by

emphasising cost reduction: dense seat configuration and high passenger load factors

allowed economies of density and low unit costs; irregular and sometimes inconvenient

schedules plus the choice of somewhat secondary airports reduced airport fees; basic in-

flight servicing cut operation expenses; and the explicit linkage with the travel

distribution system in the context of an integrated tourist package led to very low

publicity and distribution outlays. As a result of these low costs, charter carriers were

able to offer low prices and satisfy the conscious leisure travellers. On the other hand,

VFR traffic was a more active user of traditional scheduled carriers, as these fly

throughout the year directly to major cities, where immigrants and students are likely to

be based. Still, the relatively high cost of such air services discouraged frequent VFR

travelling; in this context, charter carriers could offer a seasonal alternative.

The completion of the European air transport liberalisation in 1997 and the creation of a

Single Aviation Market had major implications for leisure and VFR travel

(Papatheodorou, 2002). The abolition of any legal distinction between scheduled and

charter carriers and the gradual emergence of low cost carriers (LCC) as a third, robust

player in the market were among the most important developments. Within a short

period of time, LCC became leaders in cost reduction: they provided a basic quality



service in a single class cabin of dense configuration; they flew to secondary airports

and outsourced many activities to lower staff; and they instigated disintermediation by

favouring the direct contact between the airline and the customer over the Internet.

LCC also innovated by offering one-way tickets priced according to efficient yield

management techniques based on simplicity and very restricted conditions.

By the late 1990s, the differences in the airline business models have become clear:

traditional scheduled carriers focused on service delivery offering a network-based

product, which could serve well the business passenger and the affluent leisure and VFR

clientele; LCC provided point-to-point flights of basic quality but priced so low that

attracted many leisure and VFR travellers; whereas, charter carriers were sitting

somewhere in-between with a rather uncertain future. This product differentiation

framework, however, did not last for long as the dynamics of competition gave rise to

osmotic phenomena: currently, it is becoming increasingly difficult to distinguish

between the three types of carriers. For example, many European traditional scheduled

airlines such as British Airways, Lufthansa, SAS, Iberia and Aer Lingus engaged in a

drastic cost-cutting exercise aiming to replicate many of the features of the LCC model

at least in short-haul flights. On the other hand, some LCC started upgrading their

services; easyJet, for example, serves also primary airports (e.g. Paris Orly) and offers

flexible tickets, while Jet Blue in the USA provides leather seats and a live satellite

television programme. Charter carriers decided to radicalise their product according to

the LCC prototype albeit in occasionally longer-haul destinations. In some cases,

charter carriers setup their own LCC affiliates as shown by the examples of My Travel

Airways and My Travel Lite in Britain and Hapag Lloyd and Hapag Lloyd Express in

Germany. As a result of this osmotic process and the acquisition of a new air travel

culture based on knowledge and bargaining, a clear-cut customer segmentation is also

difficult: illustratively, the results of the latest Barclaycard Business Travel Survey reveal

that 70% of business travellers used LCC with satisfaction rates over 95% (Davies,

2005). Conversely, a recent report by the British Air Transport Users Council stressed



the importance of considering the time of booking a flight as well as the hidden

monetary and time transfer costs associated with secondary airports: interestingly,

traditional scheduled carriers can occasionally offer a better-value-for-money than their

low-cost counterparts (AUC, 2003).

Still, the various airline business models retain some differences regarding network

structure and airport choice. In particular, the traditional scheduled airlines aim at

adding value to the passenger by offering an extensive network based on own hub-and-

spoke services and interlining agreements; its appeal is enhanced by airline participation

in one of the three major strategic alliances (Oneworld, Star Alliance and Sky Team) and

the existence of sophisticated frequent flyer programmes. To deliver these network

services, traditional carriers use primary airports with all necessary facilities. Regional

airports play only a minor role in their business model as the majority of related services

act as feeders to major hubs. On the other hand, low cost carriers focus exclusively on

basic point-to-point flights. This does not mean that LCC abstain completely from

connecting traffic: the latter amounts to 30% of Southwests market, while in London

Stansted Airport 14% of passengers engaged in do-it-yourself connections in 2003

(Mosner, 2005). However, LCC are not prepared to offer these extra services

themselves to avoid complication and an increase in their cost base. They choose

primarily secondary regional airports (Orly is one of the few exceptions) and benefit

substantially from low airport charges and station costs. For example, Frankfurt Hahn

costs Ryanair 4.25 per departing passenger and there is no landing fee; in contrast a

B737 operator at Frankfurt Main pays 13 per departing passenger and a landing fee of

about 1.75 (Button et al., 2002). In other words, the sustainable competitive

advantage of LCC is mainly derived from the adoption of point-to-point services

operating from regional airports (Lei et al., 2004). Charter carriers have always offered

point-to-point services and they increasingly fly also from secondary airports.

These different airport choices have important implications for the airline-airport

relationship. Traditionally, this has been characterised as love-and-hate; despite their



common future and the complementarity of their operations, airlines have often accused

airports of abusing their market power, while the latter justified any charge increase on

the need to improve and expand a risky and sunk piece of infrastructure. The Ryanair

experience has revealed that secondary airports are prepared not only to charge less but

even to offer subsidies to attract traffic in the context of wider regional economic

development (Papatheodorou, 2003; Starkie, 2002). Demand for air transport services

is essentially derived from tourism activities. When all potential destinations are

considered, tourism choice is discrete, i.e. go to place X and not to Y; lower airport

charges and direct airline services signify a substantial improvement in accessibility if

the destination is finally chosen, profits from consumption of local hospitality and other

tourism services may more than compensate any losses at the airport level. Similar

results hold when an airport acts as an origin rather than a destination gateway.

Enhanced tourism consumption in this case is substituted by new employment

opportunities at the airport and the wider local economy and the subsequent generation

of additional expenditure and income through a multiplier-accelerator process.

As expected, this subsidisation strategy raised adverse reaction from the part of various

airlines such as Air France, which accused Ryanair of benefiting from unfair competition.

The issue has reached the European Commission which, in its recent verdict on Charleroi

Airport, espoused partly the rationale behind cross-subsidisation stressing, however, the

need for transparency and fair trade: an airport should not offer secret concessions to

specific airlines but adopt a tender-like procedure open to all carriers. The private

investor principle should also be met; in other words, the longer-term financial

sustainability and profitability of an airport as a stand-alone investment should be

guaranteed (European Commission, 2004). In this context, it is essential for an airport

to know what type of airlines and passengers are likely to be more beneficial. This is

admittedly a very difficult exercise as the benefits or losses of the airport as such should

be compared with the impact on the business turnover of the wider local tourism

activities. Nonetheless, exploring differences in consumption patterns according to a



market segmentation based solely on the choice of airline may be somewhat bizarre;

what matters primarily is demographic and vacation habit criteria (Holloway, 2004).

Moreover, the private investor principle necessitates focusing on the airport side by

providing a break-down in terms of aeronautical and non-aeronautical revenue. The

empirical part of this paper undertakes such an econometric exercise aiming at proving

useful to airport planners, regional economists and tourism destination policymakers.

2. Empirical Study

The study focuses on the British experience essentially due to data availability and the

need to focus on airports served by all three airline business models for comparison

reasons. Twenty-one regional airports are considered over an eight-year period ranging

from 1995-96 to 2003-04. The airports exhibit considerable differences in size: London

Gatwick, which is the largest in the sample, recorded 32.1 million passengers in 2000-01

while Blackpool, which is the smallest, handled only 46,820 passengers in 2002-03. For

this reason, the data set is further subdivided into two groups - large and small airports

using three million passengers per year as the cutting benchmark. Ten large-size

airports are included in the sample namely Belfast, Birmingham, Bristol, Edinburgh,

Glasgow, London Gatwick, London Luton, London Stansted, Manchester and Newcastle

(in alphabetic order). The remaining eleven are classified as small-size airports,

namely, Aberdeen, Blackpool, Bournemouth, Cardiff, East Midlands, Exeter, Humberside,

Leeds & Bradford, Liverpool, Southampton and Teesside. Airport revenue data are

collected from the Annual Airport Statistics published by the Centre for the Studies of

Regulated Industries and deflated using the British Consumer Price Index (CPI). Airline

flight and passenger data are obtained from the UK Civil Aviation Authority. All three

airline business models are considered; data are adjusted to the British financial year,

ranging from 1st of April to 31st of March. Table 1 shows relevant descriptive statistics.



Table 1 Descriptive Statistics

Variables All Airports (N=21) Large Airports (N=10) Small Airports (N=11)

Mean St. Dev. Mean St. Dev. Mean St. Dev.

Total pax 4,821,583 6,879,189 8,943,011 8,137,319 1,074,830 931,398

Non Aeronautical Revenue (in 000 pounds)

25,355 43,827 47,707 55,510 5,034 3,227

LCC pax 858,651 2,152,222 1,566,028 2,919,122 215,582 519,606

Charter pax 1,697,978 2,776,110 3,110,572 3,497,314 413,802 421,964

Full-service pax 2,264,954 3,841,623 4,266,412 4,811,512 445,446 513,425

Aeronautical Revenue (in 000 pounds)

26,478 32,227 46,766 36,878 8,035 5,976

LCC flights 8,096 18,892 14,572 25,414 2,210 4,962

Charter flights 15,073 14,674 20,421 15,535 10,211 11,989

Full-service flights 36,458 42,270 62,027 48,617 13,214 11,616

Aeronautical charges (AR) in Britain are levied according to aircraft size and passenger

numbers. As it has been difficult to obtain aircraft size data, air transport movements,

i.e. number of flights is used as a proxy for aircraft size. Non-aeronautical revenue

(NAR) is related to the number of passengers. Although meeters, greeters and airport

personnel can also affect the magnitude of NAR, the primary drive is passengers (Gillen

and Hinsch, 2001). Two empirical models are estimated in log-linear form to find out

the rate of growth with respect to flight and passenger numbers. Passengers are highly

correlated with flights and thus dropped from the AR model. On the other hand, the

number of passengers carried by LCC, charter and full service carriers are used as

explanatory variables in the NAR model.

log ARit= i + 1 Lccflightit +2 Charterflightit +3 Fullserviceflightit + it (Model 1)

log NARit= i + 1 Lccpaxit +2 Charterpaxit +3 Fullservicepaxit + uit (Model 2)

where the subscripts i and t indicate the airport and time period (year) respectively;

and are coefficients that vary across airports but are constant over time; the and

are coefficients which are constant across airports and time; , u are the error terms,



which are identical and independently distributed for all i and t; AR and NAR are

aeronautical and non-aeronautical revenue in airport i in year t; Lccflight, Charterflight

and Fullserviceflight show the total number of flights of the low-cost, charter and full

service airlines respectively in airport i and year t; Lccpax, Charterpax and

Fullservicepax are the total number of terminal passengers carried by the above three

types of airlines respectively in airport i and year t. All and are expected to have

positive signs.

Models 1 and 2 have been estimated using panel data techniques to account for

differences across airports and time. In principle, estimation can be done in three ways

depending on whether the individual cross-section effects are considered to be constant,

fixed (parametric shifts of the regression function) or random. Following Greene

(2003), Breusch and Pagan Lagrange multiplier (LM) and Hausman tests were applied to

choose the appropriate model formulation. The LM test is used first to compare the

random effects specification against the simple linear model. Acceptance of the null

hypothesis means that the classical regression model with a single constant term is

appropriate, i.e. the model can be estimated by Pooled Ordinary Least Squares (POLS).

Conversely, rejection of the null hypothesis is in favour of the random effects

specification. The Hausman test has been subsequently used to determine the choice

between fixed and random effects model. Under the null hypothesis of the Hausman

test, both models are consistent but the random effects specification is more efficient;

acceptance of the alternative hypothesis suggests that the fixed effects model is more

appropriate. Moreover, a F test was performed to examine whether there are airport-

specific effects across the different groups of airports (large and small). The F statistic

for testing the joint significance of the airport-specific effects was F(20, 165)=170.08 for

Model 1; this suggests strong evidence of airport-specific effects in the data. Similar

results were derived from Model 2. On these grounds, both fixed and random effect

models were estimated.



Table 2 reports the results of Model 1; all variables are measured in thousands. The

significant LM test statistic suggests that a common slope for the different panel groups

in the pooled regression cannot be assumed: a simple OLS regression of a

straightforward pooling of all observations without considering heterogeneity would

report biased results. The insignificance of the Hausman test statistic at the 5% level

suggests that the random effects model is more efficient both for the whole sample and

two airport subgroups. Therefore, only the results of the random effects models are

reported here (the others are available from the authors upon request). A Chow test is

performed to see whether the coefficients of the large airports group are equal to those

of the small ones. The significant F(4,181)=51.4 means that the coefficients of the two

groups are different and justifies the classification of the airports into two separate

groups.

All explanatory variables are highly significant across the three samples and have

expected signs. Given the log-linear form, the coefficients may be interpreted as the

percentage impact of an incremental thousand flights on aeronautical revenue. For

example, an extra thousand LCC flights result in a 1.25% AR growth in the all airports

group. For the whole sample, charter flights have the largest impact on airport

aeronautical revenue, followed by low-cost and full service. This may be explained by

the use of different types of aircraft. Charter carriers often use larger aircraft while full

service carriers operate turbo-props or regional jets; as for low-cost carriers, B737 or

A320 is the standard aircraft type. Similar results hold for large airports. On the other

hand, LCC flights emerge as the largest contributor in the small airports group with an

impact of 3.79% per additional thousand flights. This finding is very interesting and

suggests that the small regional airports in Britain should become more oriented towards

low-cost traffic to increase their aeronautical revenue.



Table 2: Impact on Regional Airports Aeronautical Revenue

Dependent variable: log

AR All Airports Large Airports Small Airports

Constant 8.8232 (47.41)***

9.5020 (66.67)***

7.8499 (32.11)***

LCC Flights 1 0.0125 (12.73)***

0.0108 (13.56)***

0.0379 (8.20)***

Charter Flights 2 0.0144 (2.39)**

0.0135 (2.34)**

0.0280 (3.37)***

Full Service Flights 3 0.0109 (7.31)***

0.0090 (7.47)***

0.0343 (4.12)***

No. of observations 189 90 99

R2 0.68 0.80 0.50

F test of group effects F(20,165)=170.08 F(9,77)=46.31 F(10, 85)=123.02

LM Test 655.68 185.41 309.97

Hausman Test 6.84 5.01 1.64

Notes: Figures in parentheses are t values. One, two and three stars indicate significance at the 10%, 5% and 1% levels respectively.

Based on the same econometric procedure, table 3 reports the results of Model 2

concerning the impact on the airports non-aeronautical revenue; all variables are again

measured in thousands and similar interpretation holds for the coefficients. The LM and

Hausman test statistics indicate that the fixed effect specification is appropriate for the

whole sample and the large airports group, while the random effect model is more

suitable for the small airports group. Table 3 shows only the benchmark parameter ;

the constant terms for individual airports and all the alternative model specifications are

available upon request. All coefficients have the expected signs. The results show that

the LCC passengers contribution is highly significant at 1% level across all three

samples, albeit smaller for the large airports group than for the small one, where an

incremental thousand passengers generate an increase of 0.0554% in non-aeronautical

revenue. The contribution of charter and full service passengers is insignificant for the

whole sample and the large airport group but highly significant in the small one. In fact,

their contribution is higher compared to LCC travellers! The results are not surprising as



charter passengers usually travel for leisure purposes and have more desire to purchase

goods and/or services at airports. Moreover, they usually stay longer at airports, thus,

providing more commercial revenue opportunities for airports. Similarly, full service

passengers may belong to higher-expenditure groups than their low-cost counterparts;

alternatively, they may travel on business and wish to make some last-minute purchases

(for personal use or gifts) from the airport.

Table 3: Impact on Regional Airports Non-Aeronautical Revenue

Dependent variable: log

NAR All Airports Large Airports Small Airports

Constant 9.0060 (100.05)***

10.0092 (89.50)***

7.6269 (56.41)***

LCC Passengers 1 8.44E-05 (6.77)***

6.17E-05 (7.27)***

5.54E-04 (11.34)***

Charter Passengers 2 8.46E-05 (1.91)*

2.16E-05 (0.71)

7.65E-04 (5.17)***

Full Service Passengers

3 1.53E-05

(0.47) 2.14E-05

(0.98) 6.26E-04 (4.71)***

No. of observations 189 90 99

R2 0.76 0.69 0.67

F test of group effects F(20,165)=50.53 F(9,77)=33.93 F(10, 85)=28.69

LM Test 412.84 96.10 182.17

Hausman Test 13.85 26.62 4.67

Notes: Figures in parentheses are t values. One, two and three stars indicate significance at the 10%, 5% and 1% levels respectively.

3. Conclusions

The emergence of low-cost carriers as a viable business model has revolutionised the

airline industry since the early 1990s. Leisure and VFR passengers in Europe have now a

wider choice and are able to discover the European regions at lower fares than in the

past. No matter whether a regional airport operates as an origin or destination gateway,

notable improvements in accessibility can play a significant role in economic and/or

tourism development. The results of the empirical study in this paper, however, belie the



perception that LCC are the only way forward for regional airports. Full service carriers

and charter airlines can also have a significant if not higher contribution to both

aeronautical and non-aeronautical airport revenue. A replication of the present

econometric exercise using different data sets is required of course to test the robustness

of these conclusions. A potential validation would provide support for the European

Commission argument by calling for the removal of obscure airport subsidies and the

adoption of a new transparent framework open to all carriers. Despite the existence of

victims, LCC are undoubtedly the major fashion in the contemporary airline industry; by

no means, however, does this mean that the evolved full service and charter carrier

models are necessarily dead!...

Acknowledgements

The authors are grateful to the UK Civil Aviation Authority and Mr Nenad Njegovan for

providing relevant air traffic data. Mr Zheng Lei would also like to thank Dr Edit Szivas

for her valuable guidance and support and Dr Chenggang Wang for his insightful

comments on the econometric analysis.

References

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Davies, P. (2005) 70% of Biz Travellers Fly Low Cost. Travel Mole, 16th May, www.travelmole.com

European Commission (2004) The Commissions decision on Charleroi airport promotes the activities of low-cost airlines and regional development. IP/04/157,

Brussels: European Commission.




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