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This article estimates a count-data model on the flight behaviour of Austrian holiday-makers based on information from a large representative quarterly survey spanning the years 2014–2016. On average, the number of holiday flights ranges between 0.6 and 1.2 per year for residents in the least populated region and the capital, respectively. Results of the estimations reveal that the number of holiday flights is highest for persons with tertiary degrees, of a young age (16–24 years) and capital city residents, while it is lowest for individuals with children and large households. Residents of the capital city fly 78 percent more often in a given quarter than those living in Carinthia, the most rural region. The Oaxaca-Blinder decomposition analysis reveals that the difference is rather related to location than to variations in individual characteristics. Socio-demographic aspects such as age, household size and travelling with children are of no relevance for the holiday flying behaviour of capital residents.
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Urban Planning (ISSN: 2183–7635)
2021, Volume 6, Issue 2, Pages 246–256
DOI: 10.17645/up.v6i2.3972
Article
Characteristics of Middle European Holiday Highfliers
Martin Thomas Falk 1,* and Eva Hagsten 2
1School of Business, Department of Business and IT, University of South‐Eastern Norway, 3800 Bø, Norway;
E‐Mail: martin.falk@usn.no
2School of Social Sciences, University of Iceland, 102 Reykjavik, Iceland; E‐Mail: evamarie@hi.is
* Corresponding author
Submitted: 28 December 2020 | Accepted: 16 February 2021 | Published: 9 June 2021
Abstract
This article estimates a count‐data model on the flight behaviour of Austrian holiday‐makers based on information from
a large representative quarterly survey spanning the years 2014–2016. On average, the number of holiday flights ranges
between 0.6 and 1.2 per year for residents in the least populated region and the capital, respectively. Results of the esti‐
mations reveal that the number of holiday flights is highest for persons with tertiary degrees, of a young age (16–24 years)
and capital city residents, while it is lowest for individuals with children and large households. Residents of the capital
city fly 78 percent more often in a given quarter than those living in Carinthia, the most rural region. The Oaxaca‐Blinder
decomposition analysis reveals that the difference is rather related to location than to variations in individual characteris‐
tics. Socio‐demographic aspects such as age, household size and travelling with children are of no relevance for the holiday
flying behaviour of capital residents.
Keywords
count data model; holiday travel; tourist air travel; travel frequency
Issue
This article is part of the issue “Cities, Long‐Distance Travel, and Climate Impacts” edited by Jukka Heinonen (University of
Iceland, Iceland) and Michał Czepkiewicz (University of Iceland, Iceland / Adam Mickiewicz University in Poznań, Poland).
© 2021 by the authors; licensee Cogitatio (Lisbon, Portugal). This article is licensed under a Creative Commons Attribu‐
tion 4.0 International License (CC BY).
1. Introduction
Travelling by air is considered particularly harmful for
the environment (Becken, Friedl, Stantic, Connolly, &
Chen, 2021; Gössling & Peeters, 2007; Gössling & Upham,
2009). Despite this, long distance air travel is the fastest
growing passenger mobility segment in the pre‐Covid‐19
world (Gössling & Humpe, 2020).
Present discussions encompass the sustainability of
not only frequent flyers (Young, Higham, & Reis, 2014), but
increasingly also “unnecessary” leisure and holiday travel
(Alcock et al., 2017; Cohen, Higham, & Reis, 2013; Graham
& Metz, 2017; Hares, Dickinson, & Wilkes, 2010; Holden &
Norland, 2005; McDonald, Oates, Thyne, Timmis, & Carlile,
2015; Morten, Gatersleben, & Jessop, 2018). Since the
deregulation of the aviation market and the emergence of
low‐cost airlines in Europe, the share of leisure travellers
is increasing (O’Connell & Williams, 2005).
Air travel for purposes of business, migration, edu‐
cation as well as to visit friends and relatives might be
difficult to avoid. Many firms, institutions and organi‐
sations are active in the international arena and long‐
distance relationships are not uncommon. There are also
national as well as European members of parliament,
who are expected to have a close relationship with their
constituencies, for instance. Yet, holiday travel by air
might to a certain extent be prevented because there
are environmentally friendly transportation modes avail‐
able for short‐ or medium‐long distances. There is, how‐
ever, no detailed information available on the role played
by socio‐demographic aspects in the flight behaviour of
holiday‐makers.
The aim of this study is to gain more insights into the
determinants of air travel for holiday purposes. For this
objective, the frequency of flights is estimated by use of
a count data model. Socio‐demographic characteristics
Urban Planning, 2021, Volume 6, Issue 2, Pages 246–256 246
are employed to explain the flight behaviour of resi‐
dents in both rural and urban areas. In addition, evidence
of the flight destinations for holiday purposes is pro‐
vided. The analysis is based on a representative survey
of Austrian residents (17,400 observations) who travel at
least once per quarter for holiday purposes during the
period 2014–2016.
Previous studies indicate that air travel behaviour
depends significantly on age, education, income, city of
residence and accessibility to airports (Graham & Metz,
2017; Reichert, Holz‐Rau, & Scheiner, 2016). Most stud‐
ies focus on total air travel and do not distinguish travel
for holiday purposes from travel for business or visit‐
ing friends and relatives. The few exceptions include
research on the holiday air travel behaviour of residents
in Helsinki and Reykjavik as well as students in Sweden
(Czepkiewicz, Heinonen, Næss, & Stefansdóttir, 2020;
Czepkiewicz, Klaas, & Heinonen, 2020; Gössling, Hanna,
Higham, Cohen, & Hopkins, 2019). Research based on
official representative surveys are rare (Schubert, Sohre,
& Ströbel, 2020) and the use of count‐data models,
that allows to explain the number of holiday flights,
are seldom employed so far. Exceptions to this are
Czepkiewicz, Heinonen, et al. (2020) relating to the
approach, Gössling, Lohmann, Grimm, and Scott (2017),
Dargay and Clark (2012), Alcock et al. (2017) and
Bruderer Enzler (2017) concerning the dataset as well as
Schubert et al. (2020) regarding both aspects.
The structure of this study is as follows: Section 2 out
lines the conceptual background; Section 3 describes the
empirical approach; and Section 4 introduces the dataset
and the descriptive statistics. The results are presented
and discussed in Section 5, while the conclusion is pre‐
sented in Section 6.
2. Conceptual Background
Investigations on flight behaviour can be found in travel
and transportation as well as in tourism literature. Many
studies explore the determinants of international travel,
air travel or long‐distance travel with a focus on socio‐
demographic characteristics. Common features analysed
are age, gender, household type, education, occupa‐
tion and income. However, air trips for holiday pur‐
poses are seldom treated separately (exceptions include
Czepkiewicz, Heinonen, et al., 2020; Czepkiewicz, Klaas,
& Heinonen, 2020). Graham and Metz (2017) discusses
the distinction between “discretionary” leisure travel
(including holiday travel) and “non‐discretionary” busi‐
ness travel where air travels motivated by visiting friends
and relatives are in principle voluntary but in practice
often indispensable. Based on the latter argument, and
on the fact that two out of three flights by Austrian resi‐
dents are holiday‐oriented, this study focuses specifically
on the segment that is considered dispensable.
Several studies show that the probability and num‐
ber of air travels depend on socio‐demographic fac‐
tors (Bruderer Enzler, 2017; Czepkiewicz, Klaas, &
Heinonen, 2020; LaMondia, Aultman‐Hall, & Greene,
2014). Proximity to the airport and residency in large
metropolitan areas or in the capital region is also
regarded as important factors for the likelihood of air
travel (Bruderer Enzler, 2017; Graham & Metz, 2017;
Holden & Norland, 2005; LaMondia et al., 2014; Schubert
et al., 2020; for a review of the literature see Czepkiewicz,
Heinonen, & Ottelin, 2018). Holden and Norland (2005)
demonstrate that individuals living in dense, centrally
located neighbourhoods in Oslo take the plane for leisure
purposes more often than the average holiday trav‐
eller. Næss (2006a, 2006b) suggests that air travel has
become an integral part of the urban and cosmopolitan
lifestyle of inner‐city residents, particularly so among
young students and academics (see also Große, Fertner,
& Carstensen, 2019). The high urban density constrains
the quality of life by frequent traffic jams and restricted
access to nature and thus creates demand for regular
weekend trips or other short breaks. This phenomenon is
referred to as “escape travel” or “compensation hypothe‐
sis” (Holden & Norland, 2005; Holz‐Rau, Scheiner, & Sicks,
2014; Muñiz, Calatayud, & Dobaño, 2013; Næss, 2006a,
2006b; Reichert et al., 2016). Czepkiewicz et al. (2018)
show that the positive relationship between urban den‐
sity and long‐distance travel behaviour is still significant
when demographic and socio‐economic variables are con‐
trolled for. Correspondingly, Heinonen, Jalas, Juntunen,
Ala‐Mantila, and Junnila (2013) report that air travel by
urban residents in Finland (especially in the Helsinki
Metropolitan region) is more frequent. The rebound
effect of consumption is also used as a possible explana‐
tion behind the higher level of flying by individuals living
in urban areas. In such areas you may not need to own
a car for local transportation. Giving up car‐ownership
saves a significant amount of money, which can then be
used for other purposes, such as holiday travel. Literature
indicates that car‐free people fly more frequently than
car‐owners (Ornetzeder, Hertwich, Hubacek, Korytarova,
& Haas, 2008; Ottelin, Heinonen, & Junnila, 2017).
A number of studies discover that education and
income are important drivers of air travel (Bruderer
Enzler, 2017; Czepkiewicz, Klaas, & Heinonen, 2020;
Dargay & Clark, 2012; Graham & Metz, 2017; Holden &
Norland, 2005; LaMondia et al., 2014; Ornetzeder et al.,
2008). Randles and Mander (2009) suggest that flying
remains an activity that is used disproportionately by
higher income and higher social class groups, and Graham
and Metz (2017) find that the proportion of highly skilled
air travellers is twice as large as that of unskilled persons.
Czepkiewicz, Klaas, and Heinonen (2020) show that per
sons in the highest income class and those with a univer‐
sity degree in the larger Reykjavik area have a significantly
higher number of non‐work‐related flights.
The freedom to travel independently of transporta‐
tion mode seems to attract young adults in particular.
Shaw and Thomas (2006) conclude that environmental
awareness among young adults is relatively high, such
as sustainable local transportation and waste recycling.
Urban Planning, 2021, Volume 6, Issue 2, Pages 246–256 247
However, this does not necessarily apply to air travel.
The phase of life appears to be important for the deci‐
sion to travel by air (Davison & Ryley, 2013). Dargay
and Clark (2012) document that United Kingdom fami‐
lies with children and those living in large households
fly less often. Based on the Swiss environmental survey,
Bruderer Enzler (2017) finds that household characteris‐
tics and family size are important, while the role of gen‐
der is less obvious. To the contrary, Dargay and Clark
(2012) exhibit that women in the United Kingdom under‐
take less air travel.
Because of marked differences in sample designs and
sizes (time period, reference period for survey questions;
individual or trip level and representativeness), defini‐
tions of air travel (probability of flying, number of flights),
travel distances as well as methods used (multivariate or
bivariate) results in recent literature are difficult to com‐
pare. There are also few studies that distinguish between
air travel for leisure, visiting friends or relatives and work
among groups of residents. The few common denomina‐
tors available indicate that contextual (purpose, destina‐
tion, length of stay, etc.) and socio‐demographic factors
are of importance. However, the contextual part is less
prominent here since the analysis focuses on one specific
context: holidays. Based on the determinants highlighted
in the literature, the first hypothesis is formulated:
H1: The number of holiday flights depends on individ‐
ual socio‐demographic characteristics.
Although residents in urban and rural areas may exhibit
different characteristics, literature is less clear on how
this aspect affects their flying behaviour, leading to the
second hypothesis:
H2: The importance of individual socio‐demographic
characteristic for the number of holiday flights varies
between residents in the capital city and those living
in other regions.
3. Empirical Approach
The specification of the number of holiday flights per
person and quarter builds on count data models similar
to those employed by Czepkiewicz, Klaas, and Heinonen
(2020) on urbanite leisure travel and by Falk and Hagsten
(2021) on emissions caused by air travel. The flight
frequency is modelled as a function of several socio‐
demographic factors:
gitlnit   05
A1
jAAGECATA
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E1
jEEDUE
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 jWWOMENit  jK CHILDRENit
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S1
jSLABOURSTATUSS
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jQQUARTERQ
it  it
it g1X
it
with ias the individual, tas the quarter in a given year
of travel, vector Xrepresenting a set of covariates and
is the corresponding group of coefficients. The link
function g  transforms the probability of the categori‐
cal variable to a continuous scale that can be modelled
by linear regression. The explanatory variables in vec‐
tor Xencompass AGECAT denoting age‐class, EDU indi‐
cating the level of education and WOMEN if the trav‐
eller is female. CHILDREN is a dummy variable for trav‐
elling with children, HHSIZE is a set of dummy variables
measuring household size and LABOURSTATUS is a group
of dummy variables reflecting the labour market status
(employed, unemployed, student or retired). Variable
REGION relates to the region where the traveller resides.
Macroeconomic factors such as price effects and fluc‐
tuations of the business cycle are captured by annual
year dummy variables YEAR, indicating the year of travel
and QUARTER controls for calendar effects within the
year. To uncover the possible differences between urban
and rural agglomerations, separate estimations are con‐
ducted for the capital (Vienna) and non‐capital regions,
the latter consisting of eight federal states.
Since the dependent variable is a highly skewed
count with values ranging from zero to four and a few
above, the Poisson or Negative Binomial models are suit‐
able. The Poisson model is a special case of the Negative
Binomial regression model where the dispersion param‐
eter alpha is constrained to zero (Cameron & Trivedi,
2010). A Likelihood ratio test can be used to test the
Negative Binomial regression model against the Poisson
model. Besides the count data model, the Pearson‐Chi‐
Square and G tests are used to identify if the different
holiday flight destinations are independent of residence
(Cochran, 1954; McDonald, 2009).
4. Data and Descriptive Statistics
Data for this analysis originate from the official Austrian
Travel Survey (Statistics Austria, 2017). This is a quarterly
representative survey on holiday and business travels
with at least one overnight stay, undertaken by persons
living in Austria aged 15 years or older. The survey is strat‐
ified by federal state, age of the individual and gender.
Each quarter, around 3,500 randomly selected persons
are interviewed by telephone. Participation in the sur‐
vey is voluntary and the non‐response rate is on average
29 percent.
The dataset encompasses information on actual
domestic as well as international (outbound) flights by
destination country or region (42 international destina‐
tions) and travel purpose, length of stay, accommodation
Urban Planning, 2021, Volume 6, Issue 2, Pages 246–256 248
type, departure month, transportation mode and expen‐
ditures. In this analysis a distinction between the capital
and non‐capital regions are made by use of population
density measures (Thrall, 1988). Vienna has a population
density of 4,600 inhabitants per square metre, which
is a factor 55 higher than in the non‐capital regions
(Statistics Austria, 2017). The non‐capital regions show
a spread between 59 and 153 inhabitants per square
metre (Carinthia and Vorarlberg).
The travel data are accompanied by a wide range
of socio‐demographic factors such as educational attain‐
ment, labour market status and travel company size.
Although data are available from 2012 onwards, method‐
ological changes of the travel survey, restrict the esti‐
mation sample to the period 2014–2016. In this study,
the sample is confined to holiday trips, which amounts
to two‐thirds of total travels, of which 18 percent are
undertaken by air transportation (Table 1). Descriptive
Table 1. Proportion of persons flying to their holiday destinations 2014–2016 (percentage).
Other transportation Flying 1 Flight 2 Flights 3 Flights
All residents
2014 Q1 85.9 14.1 12.9 1.2 0.1
2014 Q2 80.2 19.8 18.2 1.5 0.1
2014 Q3 75.6 24.3 22.1 2.1 0.1
2014 Q4 84.4 15.6 14.6 1.0 0.1
2015 Q1 81.8 18.1 17.0 1.1 0.0
2015 Q2 80.3 19.7 18.0 1.5 0.2
2015 Q3 78.7 21.3 19.4 1.7 0.2
2015 Q4 87.6 12.3 11.3 0.9 0.1
2016 Q1 86.8 13.2 12.4 0.6 0.2
2016 Q2 80.7 19.3 18.0 1.2 0.0
2016 Q3 79.6 20.3 18.0 2.2 0.2
2016 Q4 86.2 13.8 12.3 1.4 0.1
2014–2016 mean 82.3 17.7 16.2 1.4 0.1
Capital residents
2014 Q1 77.8 22.2 19.3 2.8 0.0
2014 Q2 72.8 27.2 24.9 2.3 0.0
2014 Q3 62.6 37.4 33.9 3.5 0.0
2014 Q4 78.2 21.8 20.3 1.5 0.0
2015 Q1 72.5 27.1 24.9 2.2 0.0
2015 Q2 75.2 24.8 21.7 2.3 0.8
2015 Q3 71.4 28.6 24.0 4.4 0.3
2015 Q4 77.0 22.6 20.4 1.9 0.4
2016 Q1 79.9 20.1 17.9 1.8 0.4
2016 Q2 73.6 26.4 23.2 3.3 0.0
2016 Q3 66.9 32.8 28.0 4.2 0.5
2016 Q4 80.2 19.8 15.4 4.0 0.4
2014–2016 mean 74.0 25.9 22.8 2.9 0.2
Residents in other regions
2014 Q1 87.2 12.8 11.8 0.9 0.1
2014 Q2 81.8 18.2 16.7 1.3 0.1
2014 Q3 77.2 22.7 20.7 1.9 0.1
2014 Q4 86.1 13.9 13.0 0.8 0.1
2015 Q1 84.4 15.6 14.7 0.8 0.0
2015 Q2 81.6 18.4 17.1 1.3 0.0
2015 Q3 80.4 19.6 18.4 1.1 0.2
2015 Q4 90.7 9.3 8.7 0.7 0.0
2016 Q1 88.6 11.4 10.9 0.3 0.2
2016 Q2 82.2 17.7 16.9 0.8 0.0
2016 Q3 82.6 17.4 15.6 1.7 0.1
2016 Q4 87.7 12.3 11.5 0.8 0.0
2014–2016 mean 84.2 15.8 14.7 1.0 0.1
Source: Austrian Travel Survey (Statistics Austria, 2017).
Urban Planning, 2021, Volume 6, Issue 2, Pages 246–256 249
statistics also reveal that only 16 percent of Austrian res‐
idents outside the capital region travel by air for holiday
purposes in a given quarter, compared with more than
one fourth of those living in the capital region. This latter
group also flies more than once per quarter.
The representative sample holds data on 3,471 hol‐
iday flights over the period 2014–2016, of which less
than one percent is domestic. The average number of
holiday flights per person and year is 0.8, with the
capital residents flying somewhat more frequently, 1.2
times (Figure 1; see also Table A1 in the Supplementary
File). For Germany, Aamaas, Borken‐Kleefeld, and Peters
(2013) report that total trips by plane occurs with a
spread of 0.6–6.6 per person and year on average, span‐
ning from low to high‐income groups.
Both the proportion of Austrians flying to their holi‐
day destinations and the number of flights are larger for
highly skilled individuals (tertiary degrees), residents of
the capital city (Vienna) and young people, while trav
ellers with children and those living in large households
exhibit the opposite pattern (Table 2). Individuals with a
tertiary degree undertake 0.24 holiday flights per quarter
on average as compared to those without degrees (0.16
flights). Young persons (aged 15–24) fly the most while
middle aged (35–44) persons the least. Individuals who
mainly travel with children fly less. Residents of the cap‐
ital Vienna show an average of 0.30 holiday flights per
quarter, while inhabitants of Carinthia, the least popu‐
lated region, exhibit the lowest number of flights (0.13).
It should be noted that this region is the Austrian lake
district, with both the Alps and the Mediterranean Sea
within driving proximity. The highest number of holiday
flights can be observed in the second and third quarters.
Additional descriptive statistics reveal that the vast
majority of holiday flights (78 percent) goes to European
destinations, followed by Asia, the American continent
and Africa (Table 3). Given the dominance of intra‐
European flights and data limitations, the empirical
analysis does not distinguish between European and
non‐European destinations. The most common destina‐
tions are Spain, Greece, Italy and Turkey, but there are
differences across residence of the travellers. Viennese
residents show a stronger preference for overseas trips
(to North and South America) and for holiday flights to
expensive destinations in Europe (France, Sweden and
Switzerland) than residents of the non‐capital region.
5. Empirical Results and Discussion
The Poisson estimations show that the number of
quarterly holiday trips by air relates to individual
socio‐demographic factors, implying that H1 cannot
be rejected (Table 4). Socio‐demographic factors are
relevant not only for the total sample but also for
the sub‐sample of residents living in the less popu‐
lated non‐capital regions. As a contrast, holiday flying
behaviour of residents in the capital city area is less
dependent on these aspects except the level of educa‐
tion, coinciding with H2. Capital city residents are also
not particularly dependent on the season since only the
third quarter renders significant estimates.
Younger persons, those with a tertiary degree and
residents of the capital city (Vienna) show significantly
higher number of air travels. The number of holiday
flights are also significant and more pronounced for
women than for men. Persons travelling with children
and those living in larger households take the plane less
often. The labour market status is not or only weakly
related to the number of holiday flights. Season is also
important with the largest number of flights in the sum‐
mer quarter followed by spring. The Incidence Rate Ratio
(IRR) coefcient reveals that residents of Vienna travel
79 percent more often by air than individuals in the
region with the lowest population density (Carinthia).
This difference is large given the average number of hol‐
iday flights of 0.2 per quarter (equal to 0.8 per year).
0.5
0.4
0.3
0.2
0.1
0.0
2014
Q1 Q2 Q3 Q4
2015
Q1 Q2 Q3 Q4
2016
Q1 Q2 Q3 Q4
All residents Capital residents Residents in other regions
Figure 1. Evolution over time, average number of holiday flights per person and quarter. Source: Statistics Austria (2017)
and own calculations.
Urban Planning, 2021, Volume 6, Issue 2, Pages 246–256 250
Table 2. Individual holiday flying behaviour by character‐
istics (per quarter).
Number
Proportion of flights
Percent Mean
Age 15–24 20.5 0.23
Age 25–34 18.2 0.20
Age 35–44 14.5 0.16
Age 45–54 19.0 0.20
Age 55–64 18.1 0.20
Age 6518.5 0.20
Education low level 14.9 0.16
Education medium level 17.6 0.19
Education tertiary level 21.6 0.24
Men 16.8 0.19
Women 19.3 0.21
Travellers (all) no children 19.0 0.21
Travellers (all) with children 14.0 0.15
Employed 18.0 0.20
Unemployed 18.3 0.23
Student 20.7 0.23
Pensioner/out of labour force 17.9 0.20
Household size 1 20.7 0.23
Household size 2 20.2 0.23
Household size 3 18.8 0.21
Household size 4 16.0 0.17
Household size 5 14.7 0.16
Household size 6 11.6 0.12
Burgenland 17.0 0.18
Lower Austria 18.0 0.20
Vienna 26.1 0.30
Carinthia 12.1 0.13
Styria 14.7 0.16
Upper Austria 15.5 0.17
Salzburg 17.3 0.18
Tyrol 16.5 0.18
Vorarlberg 20.2 0.22
Travel year 2014 18.9 0.21
Travel year 2015 18.4 0.20
Travel year 2016 17.2 0.19
Quarter 1 15.1 0.16
Quarter 2 19.6 0.21
Quarter 3 21.9 0.24
Quarter 4 14.0 0.15
Source: Statistics Austria (2017) and own calculations.
In order to identify the major factors responsible for
the differences in holiday flying behaviour between resi‐
dents in the capital and those in the non‐capital regions,
the Oaxaca‐Blinder decomposition translated to the case
of count data models is used (Stata command “nldecom‐
pose”; Bauer & Sinning, 2008; Sinning, Hahn, & Bauer,
2008). This technique decomposes the variation in the
holiday air travel behaviour into a coefficient (or resid‐
ual) effect and a characteristic effect. The decomposition
is important if the characteristics of the residents diverge
Table 3. Choice of holiday flight destination by residence,
pooled 2014–2016 (percent).
Other
Total Vienna regions
Belgium 0.4 0.7 0.3
Denmark 0.6 0.5 0.6
Germany 7.6 7.7 7.6
Finland 0.1 0.0 0.2
France 4.1 5.1 3.8
Greece 11.0 11.0 11.0
United Kingdom 5.6 5.8 5.5
Ireland 1.3 1.3 1.4
Italy 8.0 9.3 7.5
Luxembourg 0.1 0.2 0.0
Netherlands 2.0 2.5 1.9
Portugal 3.1 3.0 3.2
Sweden 1.2 1.6 1.1
Spain 17.7 14.4 19.0
Iceland 0.6 0.0 0.8
Norway 0.8 0.7 0.9
Switzerland 0.6 0.9 0.5
Baltic states 0.3 0.3 0.3
Croatia 0.7 0.4 0.8
Malta 0.6 0.5 0.6
Poland 0.5 0.8 0.4
Romania 0.5 0.6 0.4
Slovakia 0.0 0.0 0.0
Slovenia 0.0 0.0 0.0
Turkey 7.8 6.9 8.2
Czech Republic 0.0 0.0 0.0
Hungary 0.0 0.0 0.0
Cyprus 1.1 1.8 0.9
Bosnia Herzegovina 0.0 0.0 0.0
Serbia 0.1 0.1 0.0
Bulgaria 0.9 0.6 1.0
Russia 0.8 0.9 0.8
Other Europe 0.5 0.8 0.3
Egypt 3.4 2.7 3.6
Tunisia 0.4 0.4 0.4
Rest of Africa 2.9 3.1 2.8
United States 3.5 4.3 3.2
Canada 0.5 0.3 0.6
Middle and South America 3.1 3.9 2.8
China 0.3 0.2 0.4
Other Asia 6.6 5.9 6.9
Australia, New Zealand etc. 0.5 0.5 0.5
Total 100 100 100
Regions:
Europe 78.8 78.6 78.8
America 7.1 8.4 6.6
Africa 6.6 6.3 6.7
Asia and Pacific 7.5 6.7 7.8
Number of holiday flights 3,465 960 2,505
Source: Statistics Austria (2017) and own calculations.
Urban Planning, 2021, Volume 6, Issue 2, Pages 246–256 251
Table 4. Determinants of holiday flights 2014–2016, Poisson estimations.
Total sample Vienna Other regions
IRR z‐stat IRR z‐stat IRR z‐stat
Age 15–24 (ref cat.: 45–54) 1308 *** 348 1028 018 1388 *** 373
Age 25–34 0925 128 0970 027 0910 130
Age 35–44 0804 *** 349 0894 095 0768 *** 359
Age 55–64 0920 137 0994 006 0907 133
Age 65+ 0924 100 0876 089 0947 059
Education medium (ref.: low) 1234 *** 377 1344 ** 213 1210 *** 312
Education tertiary level 1512 *** 660 1526 *** 297 1525 *** 591
Women 1147 *** 394 1101 147 1167 *** 376
Travellers with children 0808 *** 348 0915 079 0779 *** 339
Unemployed (ref.: employed) 1213 * 173 1030 016 1297 * 188
Student 1235 * 166 1120 052 1321 * 180
Pensioners/out of labour force 1128 098 1081 036 1154 095
Household size 2 (ref. 1) 1107 * 181 1161 * 171 1079 102
Household size 3 0963 058 0967 031 0954 058
Household size 4 0849 ** 238 0832 144 0844 ** 199
Household size 5 0797 *** 264 0762 154 0789 ** 230
Household size 6 0644 *** 398 0829 072 0609 *** 394
Burgenland (ref.: Lower Austria) 0940 056 0944 052
Vienna 1445 *** 727
Carinthia 0664 *** 430 0666 *** 426
Styria 0820 *** 299 0822 *** 295
Upper Austria 0869 ** 246 0869 ** 245
Salzburg 0930 096 0931 093
Tyrol 0931 096 0928 101
Vorarlberg 1132 151 1133 151
Quarter 2 (ref.: quarter 1) 1304 *** 509 1101 100 1396 *** 540
Quarter 3 1545 *** 900 1431 *** 399 1596 *** 815
Quarter 4 0912 158 0940 059 0900 149
Year 2015 (ref.: year 2014) 0924 * 191 1001 001 0899 ** 220
Year 2016 0890 *** 276 0994 007 0858 *** 308
Constant 0118 *** 1487 0182 *** 677 0113 *** 1247
Number of observations 17,381 3,216 14,165
Log pseudo likelihood 9032 2182 6836
Pseudo R20.030 0.014 0.025
LR‐test alpha 0, p‐value 0.292 0.500 0.200
Notes: ***, ** and * represent significance at the 1, 5 and 10 percent levels; dy/dx denotes the marginal effects and IRR is the inci‐
dence rate ratio. A likelihood ratio test indicates that the negative binomial regression model is rejected in favour of the Poisson model.
Therefore, the interpretation of the results focuses on the Poisson estimations. Source: Statistics Austria (2017) and own calculations.
between the capital city and the other regions. Vienna
has, for instance, the highest share of persons with ter‐
tiary degrees among all regions. The characteristic effect
measures the difference in the predicted number of hol‐
iday flights by air for the total sample when the parame‐
ter vector is held constant. On the other hand, the coef‐
ficient effect is the variation in predicted number of hol‐
iday flights by air when the characteristics of capital city
residents are held constant. Results of the decomposi‐
tion show that the coefficient effect account for between
88 and 90 percent of the total capital resident effect
(given the 59 percent higher flight intensity when only
the Vienna dummy variable is included) indicating that
deviations in the characteristics between the capital and
non‐capital regions are negligible. In other words, if resi‐
dents in the capital city region would have the same char‐
acteristics as those in the non‐capital areas, the observed
difference in the flying behaviour would only be reduced
from 59 to 53 percent.
Besides location, education is a major variable of
influence. Persons with tertiary education fly on aver‐
age 51 percent more often to their holiday destina‐
tions ((1.51–1) 100 51 percent) than people without
degrees. This means that the average number of holi‐
day flights by tertiary educated individuals is 0.10 larger
compared with those without, given a sample mean of
0.2 holiday flights per quarter. Young people fly 31 per‐
cent more often than middle‐aged persons (45–54) while
Urban Planning, 2021, Volume 6, Issue 2, Pages 246–256 252
persons aged 35–44 show the lowest number of flights
(minus 20 percent).
In general, the results coincide to some extent with
recent, but fragmented literature in that educated indi‐
viduals (Graham & Metz, 2017) living in urban areas fly
more (Bruderer Enzler, 2017; Czepkiewicz et al., 2018).
Schubert et al. (2020) use both a similar approach to
the present study and a representative (Swiss) dataset,
although their explanatory variables expand beyond
socio‐demographic aspects. In line with this study,
Schubert et al. (2020) find that residents in urban areas
have a higher probability to travel by air, but only short
and middle distances. As a contrast, education and age
are variables of no importance for the Swiss travellers,
while gender is the only significant socio‐demographic
factor for long distance flights. The differences in results
could originate from variations in travel behaviour across
neighbouring countries as well as from the survey or
the modelling itself, where certain lifestyle questions in
the Swiss study also implicitly capture educational level,
income and age, for instance.
As compared to other studies, the present approach
also allows a ranking of the importance of the explana‐
tory variables, where young persons, those with higher
degrees or residents of the capital city both have a
higher probability to take the plane for their holiday and
use this transportation mode more often than others.
The two latter variables may also be indications of a cer‐
tain income level. Thus, the suggestion of escape or com‐
pensation travel by inhabitants in urban areas cannot be
dismissed (Czepkiewicz et al., 2018), although it should
not be forgotten that capital cities attract individuals
with certain characteristics. This could mean preference
for a lifestyle without car ownership, for instance, but use
of other transportation modes, including air (Ornetzeder
et al., 2008; Ottelin et al., 2017). An alternative expla‐
nation, that closeness to an international airport leads
to more flying (Bruderer Enzler, 2017; Graham & Metz,
2017), is not convincing in this case since the neighbour‐
ing provinces of Vienna show significantly lower number
of holiday air travel despite the fact that the travel time
is less than two hours for the majority of these residents.
Given the geographical location of Austria, in the mid‐
dle of Europe, several European holiday destinations are
easily reached by car, bus or train. The same is valid for a
large group of countries around Austria. Thus, the results
are expected to be representative beyond the Austrian
borders, but not necessarily for the countries in the out
skirts of Europe or the islands, where the probability of
flying to a holiday destination might be higher.
The first robustness check, where the flight intensity
is estimated by a Probit model, confirms that low age,
high education, being a woman, large household size,
travelling with children and region of residence give the
largest marginal effects (Table A2 in the Supplementary
File). Among the predictors, location of residence has the
largest effect.Persons living in the capital city region have
a 12.7 percentage points higher probability to fly per
quarter than those living in Carinthia. The decomposition
analysis for Probit models developed by Fairlie (2005)
shows that the characteristics effect explains 90 per‐
cent of the difference in the number of holiday flights
between the capital and the other regions. In other
words, if capital residents share the same characteristics
as those in rural areas, then the observed difference in
the number of holiday flights would be reduced from 8.8
to 7.8 percentage points.
The third robustness check uses Chi‐square and
G‐tests to establish whether the choice of destinations
differs between the residents in capital and non‐capital
regions. Due to the features of the tests, which do not
allow small number of observations in the cells, 35 out
of 42 destinations need to be excluded. This reduces the
number of holiday flights with eleven percent to 3,437.
The results of the Pearson Chi square test show that
the null hypothesis is rejected, implying that there is evi‐
dence of a statistical association between different flight
destinations and place of residence at the five percent
level (Table 5). The G‐test (or Likelihood ratio test) comes
to a similar conclusion.
Table 5. Test of interdependence for choice of holiday
destination and residence in the capital city 2014–2016.
chi2(34) p‐value
Pearson Chi square test 51.536 0.027
Likelihood‐ratio (G) test 50.222 0.036
Notes: The number of holiday flights used in the test is 2,477
and 960, for capital and non‐capital residents, respectively.
Slovakia, Hungary, Czech Republic Bosnia and Herzegovina,
Finland, Iceland as well as Slovenia are excluded because there
is no flight in at least one of the two groups. The test is recom‐
mended for large sample sizes with 1000 or more observations.
The Pearson Chi‐square test and the G‐test require not only a
large sample size, but also that no more than 20 percent of the
cells in the expected frequency table contains fewer than five
observations and that no cell has less than one (Cochran, 1954).
6. Conclusion
This study estimates a count‐data model on the flight
behaviour of Austrian holiday‐makers, based on a
large quarterly representative dataset for the years
2014–2016. In general, flying to a holiday destination is
a rare event and the majority of holiday‐makers do not
even fly once per year (0.8 holiday flights), although capi‐
tal city residents use this transportation mode somewhat
more often (1.2 flights per year). Those who fly twice
or more for holiday purposes amounts to almost two
percent per quarter on average. This means that exces‐
sive holiday flying is not a general trend, even if two out
of three flights have holiday purposes. A presumptive
explanation behind this could be the central location of
Austria, where several sun and beach as well as winter
sport destinations can be reached within a few hours by
car, bus or train.
Urban Planning, 2021, Volume 6, Issue 2, Pages 246–256 253
Persons with higher education, those who live in
the capital city and young people, fly more regu‐
larly. This coincides with the existing, somewhat frag‐
mented literature and could be related to the idea of
escape travel. Alternatively, people who habitually travel
often, may search for dwelling in larger cities. While
the average results indicate the importance of several
socio‐demographic aspects, the flying by residents in
the capital region is mainly driven by individuals with
higher education.
The findings imply that only a small group of Austrian
residents engage in extensive holiday flying. In light of
this, presumptive policy measures to reduce flying need
to be customised. Some limitations of the study should
be noted. The central location of Austria in the mid‐
dle of Europe means that the conclusions may not be
fully representative for countries at the outskirts where
flying might be a necessity to reach a holiday destina‐
tion. Income level is an important variable in determin‐
ing the demand for air travel. This variable is not avail‐
able in the dataset at hand. Instead, income effects are
expected to be captured by the variables for region,
employment status and education. There are several
ideas for future work. One idea is to investigate the
determinants of holiday destination choice by using a
Multinomial Logit model. Another is to focus on the sup‐
ply side of air travelling.
Acknowledgments
The authors would like to thank Tess Landon for careful
proofreading of the manuscript.
Conflict of Interests
The authors declare no conflict of interests.
Supplementary Material
Supplementary material for this article is available online
in the format provided by the authors (unedited).
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Urban Planning, 2021, Volume 6, Issue 2, Pages 246–256 255
About the Authors
Martin Thomas Falk is Professor of Innovation and Entrepreneurship at the University of Southeast
Norway (Campus Bø). He holds a PhD in Economics from the University of Regensburg. His research
interests include innovation, sustainability, tourism and climate change. Since 2016 he is visiting pro‐
fessor at the Shanghai Lixin University of Accounting and Finance (School of economics and trade).
ORCID: 0000–0003‐0518–6513
Eva Hagsten holds a PhD in Economics from the University of Iceland and a master’s degree in eco‐
nomics from Örebro University (Sweden). Her research interests encompass applied economics ori‐
ented towards firm behaviour, firm performance, tourism economics, ICT and international economics.
She also has experience from leadership of large EU‐funded research projects. ORCID: 0000–0001‐
7091–1449
Urban Planning, 2021, Volume 6, Issue 2, Pages 246–256 256
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