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TOURISM ISSUES: SEASONALITY AND ECONOMIC STRUCTURE
Albert Vancells Farraró
ADVERTIMENT. L'accés als continguts d'aquesta tesi doctoral i la seva utilització ha de respectar els drets
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TOURISM ISSUES: SEASONALITY AND ECONOMIC STRUCTURE
ALBERT VANCELLS FARRARÓ
TESI DOCTORAL – TESIS DOCTORAL- DOCTORAL THESIS
2023
UNIVERSITAT ROVIRA I VIRGILI
TOURISM ISSUES: SEASONALITY AND ECONOMIC STRUCTURE
Albert Vancells Farraró
UNIVERSITAT ROVIRA I VIRGILI
TOURISM ISSUES: SEASONALITY AND ECONOMIC STRUCTURE
Albert Vancells Farraró
Faculty of Geography and Tourism
Department of Geography
TOURISM ISSUES: SEASONALITY AND ECONOMIC STRUCTURE
PhD Dissertation by
ALBERT VANCELLS FARRARÓ
Supervised by Juan Antonio Duro and Mónica Oviedo
Vilaseca, March 2023
UNIVERSITAT ROVIRA I VIRGILI
TOURISM ISSUES: SEASONALITY AND ECONOMIC STRUCTURE
Albert Vancells Farraró
UNIVERSITAT ROVIRA I VIRGILI
TOURISM ISSUES: SEASONALITY AND ECONOMIC STRUCTURE
Albert Vancells Farraró
WE STATE that the present study, entitled “Tourism Issues: Seasonality and Economic structure”,
presented by Albert Vancells Farraró for the award of the degree of Doctor, has been carried out
under our supervision at the Department of Geography of this university.
Vilaseca, 28th March 2023
Doctoral Thesis Supervisor/s
Dr. Juan Antonio Duro Dra. Mónica Oviedo
UNIVERSITAT ROVIRA I VIRGILI
TOURISM ISSUES: SEASONALITY AND ECONOMIC STRUCTURE
Albert Vancells Farraró
UNIVERSITAT ROVIRA I VIRGILI
TOURISM ISSUES: SEASONALITY AND ECONOMIC STRUCTURE
Albert Vancells Farraró
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CONTENTS
CONTENTS ............................................................................................................................................. 3
ACKNOWLEDGMENTS ............................................................................................................................ 7
ABSTRACT …………………………………………………………………………………………………………………………………………..9
CHAPTER 1 INTRODUCTION ................................................................................................................. 13
CHAPTER 2 LITERATURE REVIEW OF SEASONALITY. ............................................................................. 27
2.1. GLOBAL ISSUES ............................................................................................................................. 27
2.2. THE ECONOMIC DETERMINANTS ...................................................................................................... 41
2.3. GEOGRAPHICAL ANALYSIS ............................................................................................................... 47
CHAPTER 3. RADIOGRAPHY OF EUROPEAN TOURIST SEASONALITY: A TERRITORIAL ANALYSIS ........... 55
3.1. INTRODUCTION AND METHODOLOGICAL ASPECTS................................................................................ 55
3.1.1. Introduction ...................................................................................................................... 55
3.1.2. Methodological Aspects ................................................................................................... 64
3.2. SEASONALITY IN EU-15 .................................................................................................................. 73
3.2.1. Data .................................................................................................................................. 73
3.2.2. Global Results .................................................................................................................. 75
3.2.3. European Seasonality and Countries ................................................................................ 85
3.3. EUROPEAN SEASONALITY AND PRODUCT-TYPE REGIONS ...................................................................... 112
3.3.1. Preliminary considerations ............................................................................................. 112
3.3.2 Results .............................................................................................................................. 116
3.3.3 Discussion and policy implications ................................................................................... 128
UNIVERSITAT ROVIRA I VIRGILI
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3.4. CONCLUDING REMARKS ................................................................................................................ 130
CHAPTER 4. ECONOMIC DETERMINANTS OF INTERNATIONAL ARRIVALS AND TOURISM SEASONALITY:
AN EUROPEAN UNION ANALYSIS ....................................................................................................... 141
4.1 INTRODUCTION ............................................................................................................................ 141
4.2 PREVIOUS EVIDENCE ..................................................................................................................... 144
4.3 METHODOLOGY AND DATA ............................................................................................................ 152
4.3.1. Estimation Methodology ................................................................................................ 157
4.4 RESULTS ..................................................................................................................................... 161
4.5. NEW MACROECONOMIC DETERMINANTS.......................................................................................... 165
4.6 DISCUSSION AND IMPLICATIONS ...................................................................................................... 175
4.7. CONCLUDING REMARKS ................................................................................................................ 178
CHAPTER 5. CATALAN TOURISM SUBSYSTEM: APPLYING THE METHODOLOGY OF SUBSYSTEMS IN THE
TOURISM SECTOR. ............................................................................................................................. 183
5.1 INTRODUCTION ............................................................................................................................ 183
5.2 METHODOLOGY ........................................................................................................................... 190
5.3 RESULTS ..................................................................................................................................... 198
5.4 IMPLICATIONS AND FUTURE RESEARCH ............................................................................................. 203
CHAPTER 6. EMPIRICAL FINDINGS AND THEIR IMPLICATIONS. ........................................................... 206
6.1. EMPIRICAL FINDINGS ................................................................................................................... 206
6.2 FUTURE RESEARCH........................................................................................................................ 210
7. REFERENCES ................................................................................................................................... 213
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Albert Vancells Farraró
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UNIVERSITAT ROVIRA I VIRGILI
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Albert Vancells Farraró
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ACKNOWLEDGMENTS
Vull començar amb l’agraïment a les persones que m’han guiat i acompanyat en l’elaboració
d’aquesta tesis. Primer al director de la tesi en Juan Antonio Duro, amb el qual sense ell, amb el
seu suport, guiatge i lideratge no hagués pogut presentar aquesta tesi. La segona persona a qui
he d’agrair haver arribat fins aquí és a la Mónica Oviedo, amb un ajut inestimable a l’apartat
numèric i metodològic d’aquesta tesis. Moltes gràcies a tots dos!!
Voldria continuar amb un agraïment a la Universitat Rovira i Virgili i a la seva Facultat de Turisme
i Geografia, pel seu suport i paciència, el temps s’ha allargat més del previst però sempre he
gaudit de la confiança i comprensió del Dr. Antón i el Dr. Russo. També al personal
d’administració, on en Rubén Aguado sempre ha estat amatent a les meves consultes.
També voldria agrair a la Universitat Autònoma de Barcelona per la formació rebuda, que ha
permès el meu desenvolupament professional i acadèmic. Voldria agrair a unes quantes
persones el seu suport en tot aquest procés i al seu ajut inestimable en la meva carrera
professional: a en Paco Uroz, a l’Albert Martí i, (allà on siguis) a l’Agustí Quer.
Finalment volia agrair a la meva família tot el seu suport i comprensió, als meus pares Dolors i
Albert, que van fer confiança en uns moments difícils a l’inici dels mus estudis, i que sempre han
estat al meu costat. Als meus fills, Laura i Albert, que quan comentàvem el tema de la tesis a
casa, “papa, encara no estàs??”. Doncs ja estic!!
I la persona més important, la Maria, sempre recolzant i al meu costat al llarg de tots aquests
anys. Sense tu, no estaria aquí ni seria la persona que soc ara, ni gaudiria d’aquesta vida plena i
feliç! T’estimo!
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UNIVERSITAT ROVIRA I VIRGILI
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ABSTRACT
Tourism is one of the major economic sectors in recent decades. It represents
more than 10% of the GDP and of those employed worldwide, so understand we are
dealing with one of the activities with the greatest economic and social impact, and
which requires preferential attention in research, especially in our territory: Europe,
Spain and Catalonia.
The increasing importance of this sector is demonstrated by the increasing
scientific production in recent years. And this production should help us to have more
information about the tourism activity, such as the benefits it generates, but also to
gather information to be able to deal with the problems generated by it, with pertinent
and effective policies.
In this sense, the research work that is presented here pursues two clear
objectives: first, to contribute to the understanding of seasonality, one of the biggest
problems facing the tourism sector and its agents. The second, to improve the
knowledge about the relationships that are generated between the different tourism
subsectors and the rest of the economic sectors.
Seasonality is one of the main challenges facing the tourism sector. This is one of
the traditional problems of this activity, especially in those destinations that have grown
and continue to have based on the tourist products developed in a specific season. In
the Spanish and Catalan case, we are talking about the seasonality that occurs, mainly,
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in summer and winter tourist destinations, but also at European level these are some of
the destinations with highest seasonality.
The problem of tourist seasonality has been described by some authors such as
BarOn (1975), Butler (1994), Hartmann (1986), Hylleberg (1992), Manning and Powers
(1984), Moore (1989) and Sutcliffe and Sinclair (1980). We have four documents that
focus on tourist seasonality: Koenig-Lewis and Bischoff (2005), Cannas (2012), Chung
(2009) and Corluka (2019). Recently, research carried out by Duro (2016), Rosselló et al.
(2004) and Turrión-Prats and Duro (2019), have deepened the analysis of this
phenomenon, the methodology of measurement, and the macroeconomic
determinants that can affect or explain the evolution of seasonality.
However, seasonality remains a problem that needs to be investigated. It
continues to generate negative impacts on the environment, employment, the
profitability of both public and private investments, in short, calling into question the
sustainability of the sector. It is important, therefore, to improve the analysis of the
phenomenon in order to develop political and managerial initiatives that allow us to
reduce the degree of seasonality and its impact. It can also allow us, especially with the
analysis of its relationship with macroeconomic variables, to improve the forecast of the
evolution of tourist demand and seasonality.
The second aspect examined in this dissertation, the economic structure of the
tourism sub-sector, allows us to make a double analysis. First, the effect of tourism
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demand over the own sector and on the rest of the economic sectors, but also of the
relationships that are established between the different subsectors.
In this case, the methodology used is the traditional one of Input-Output analysis,
initiated among others by Archer (1977), Archer and Fletcher (1990), Briassoulis (1991),
and Fletcher (1989), but with the version of the subsectors proposed by Alcántara (1995)
and specifically that of Alcántara and Padilla (2009).
The results obtained show us that seasonality does not have a significant value
at European level, except when we analyze some specific countries, especially those
around the Mediterranean Sea, and the same happens when analyzing the regions at
NUTS2 level. Clearly, sun and beach destinations suffer from significant seasonality.
However, what our results tell us that in recent years, prior to the Covid
pandemic, the increase in the number of tourists did not lead to a better temporal
distribution throughout the year, and neither the impetus for some deseasonalization
policies, nor those promoting alternative products had this effect. In fact, some regional
destinations that had not suffered from seasonality before, with the increase in the
arrival of tourists, are experiencing increases in seasonality.
One of the significant conclusions is that, despite the attempts and the policies
developed, seasonality remains very dependent on the type of products developed, the
demand for them, as well as institutional aspects, such as school holidays.
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Regarding the analysis of the macroeconomic determinants, some variables
happen to be significant, especially those related to income, which leads us to innovate
with the inclusion of the level of unemployment and the inequality index as conditioning
factors of seasonality. In relation to these results, the mixed evidence points to the need
to refine and deepen the research in this area, which can lead us to a better knowledge
of seasonality, its causes, and its consequences.
Finally, it can be seen how the Catalan tourism sector has subsectors that relate
to each other in a significant way, and that spread highly significant economic activity
towards the rest of the subsectors. The increase in tourism demand, therefore, can lead
to an economic growth, linking with the previous question, it is clear that an
improvement in seasonality can allow a better evolution of the economy of the area, as
well as of the economic sectors in the area that receive this demand.
Keywords: Seasonality, Inout-Output, European Union, Mediterraneant, Catalonia, Gini
Index, Decomposition, Macroeconomic Determinants, Tourism subsectors.
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CHAPTER 1 INTRODUCTION
The tourism sector in Catalonia, Spain and the European Union (EU) is one of the
most relevant economic sectors, as different authors have highlighted previously and as
we will reaffirm throughout this research. This significance of the tourism sector makes
research on it more than fundamental, both in terms of increasing the level of
knowledge and improving management of the sector. In this sense, this thesis is part of
an investigation of two aspects that significantly affect the tourism sector: seasonality
and the economic configuration of the sector.
Seasonality is one of the most significant problems in the tourism sector, both
because of its effects on the sector’s sustainability, and because of the difficulty in
finding solutions that allow a reduction of this issue, as indicated by, Cannas (2012),
Corluka (2019) and Koenig and Bischoff (2005) in their reference articles.
This problem implies the need to expand the knowledge we have on this subject,
so in the following chapters (from Chapters 2 to 5) a study is conducted on the evolution
of seasonality and the economic determinants that affect this seasonality at the level of
the European Union, concentrating on the most significant countries in terms of tourism.
The second relevant aspect to highlight in this thesis is the choice of geographical
area. There are few studies that focus on a detailed analysis of the main countries in
terms of tourism in the European Union (EU). The existing studies, such as that of
Ferrante et al. (2018) relate to the EU as a whole. This choice has an economic logic
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because of the knowledge it will provide us about the relationships established between
these countries, and the tourist flows that occur between them.
In the last chapter, a more specific study is made, both in terms of the
geographical area, Catalonia, and in methodological terms of the application of input–
output subsystems in the tourism sector. The application of this methodology allows us
to increase our knowledge of the relationships established between the subsectors
themselves, and about the rest of the economy.
The choice of these areas of study is justified by the need to respond to some
gaps detected in the research, such as:
First, the expansion of knowledge about seasonality in the most significant
tourist countries at EU level, which may involve investments, both economic and policy
to reduce its impact. The descriptive analysis developed at different geographical levels,
but also in terms of economic determinants, expands this knowledge and we hope that
it can lead to new political and management strategies.
Second, the need to open new fields, both in terms of economic determinants
and other hidden (or not so visible) aspects in the evolution of this seasonality, such as
the issue of economic inequality.
Thirdly, the knowledge of the relationships that are established between the
tourism subsectors at the Catalan level and that shape this tourism sector for us, as well
as increasing knowledge of the spillover effects of this sector on other economic sectors.
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The growing research in tourism is justified as this activity has been, and
continues to be, one of the fundamental economic sectors in the economic
development of Spain and Catalonia. Larrinaga and Vallejo (2013) present the
importance of tourism as a development factor in the Spanish economy, since the
beginning of the ‘Plan de Estabilización Económica’ of 1959, which was considered a key
actor for economic growth. The inflow of currency was supposed to allow some
economic problems of the moment to be resolved in relation to the outside world.
However, they also allowed the growth of other economic sectors in the country, and
for these to gain in importance, above all, thanks to the indirect and induced effects,
and their multiplier effect (Goeldner, Ritchie & McIntosh, 2000).
This importance of the sector can be seen in the figures it represents in the
economy, both in Catalonia and in Spain. In this sense, we find that in Spain and
Catalonia tourism accounted for 12% of Gross Domestic Product (GDP) in 2019.
Additionally, according to data from the Tourism Satellite Account (2020) and the
Generalitat de Catalunya, those employed in the tourism sector represent 12% of
Spain’s total and 14% of Catalans. Unfortunately, the impact of Covid-19 has caused a
drop to 5.5% of GDP in 2020, and the value of employment in the tourism sector was
reduced to 11.8%, saved by the aid programmes of the Government of Spain. At
European level, the importance is relatively lower, but it is still above 10% of GDP and
more than 11% of those employed, according to the 2018 data.
According to the United Nations World Tourism Organisation (UNWTO), in 2019,
Spain was in second position in the number of international arrivals with 84 million, only
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behind France with 89 million, and in terms of income, with 80 billion dollars, behind
the USA with 214 billion dollars. At a global level, the European region remains the one
that receives the most international tourists globally with 744 million international
tourists, more than double the number of arrivals compared to the second region (Asia
and the Pacific).
This importance of the tourism sector has meant that in the set of large tourist
receivers, Spain is where tourism is more important in the overall economy, for example
in France this sector only reaches 8% of GDP (UNWTO, 2020). The importance of tourist
activity at European level and its problems justify the continuous research in tourism. In
this framework, this research begins by investigating seasonality, since it remains a
problem where the different administrations and tourism managers apply significant
effort and public budget, such as the IMSERSO-type programmes of the Government of
Spain.
Seasonality, following Butler (1994), is the temporal inequality in the arrivals of
tourists, their expenses, the traffic that occurs, as well as in other issues related to this
problem such as some businesses’ activity or, simply, employment. This problem has
increased the research produced in recent years, especially around the papers of
Koenig-Lewis and Bischoff (2005) and Cannas (2012), but also by that developed by Duro
(2016), Turrión-Prats (2018) or Turrión-Prats and Duro (2019).
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In this sense, the research on seasonality in this thesis is based on two questions:
the analysis of the evolution of seasonality and the economic determinants that
influence this seasonality.
Regarding the first question, the analysis of the evolution of seasonality, this is
subdivided into two studies. First, the evolution of seasonality for the EU of 15, where
the evolution of seasonality at a global level is analysed for these countries. In the
second part, groups of EU regions are identified by the chosen countries, according to
the main tourist product.
These two studies make it possible to analyse seasonality based on demand, but
also the supply developed in the analysed area. This is an important decision in terms of
political implications, focusing seasonality solutions on demand or on supply.
Traditionally, both solutions have been promoted, boosting demand in off-season
periods, such as Spain's IMSERSO programmes or the recommendation to develop off-
season products. The analysis from the point of view of demand or supply will lead us to
policy proposals with different implications.
The geographical decision of the EU-15 is justified by the touristic importance of
the countries that comprise it. According to the UNWTO (2020), five of the top ten world
tourist destinations, both in terms of arrivals and income, are in the European area:
France, Spain, Italy, Germany and the UK. Although in recent years the exit of the UK
from the EU has taken place, we continue to consider this as an important tourism unit
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in the EU, and which continues to take advantage in terms of mobility at the level of the
EU and which continues to make the traditional movements of the last decades.
This descriptive analysis of the evolution of seasonality at EU level,
corresponding to the first part of Chapter 2, was presented at the XXII Congress of
Asociación Española de Expertos Científicos en Turismo (AECIT), October 2022, where it
received the ‘Award for the Best Communication in the Area of Tourism and
Sustainability’, and was published in the book of abstracts of the congress.
As we have previously commented, the second question discussed was the
seasonality economic determinants, which can show us some of the economic reasons
that could explain the evolution of seasonality. In this sense, the article by Rosselló et
al. (2004) is taken as a reference. The GDP, the price index and the exchange rate are
analysed in relation to the evolution of seasonality of the Balearic Islands, counting the
arrivals of tourists from the UK and Germany. This research allows us to develop a first
analysis of the economic determinants that give us information on the evolution of
seasonality and that also provide us with a certain possibility of predicting what the
future situation of seasonality may be in a certain area. The EU is once again the
geographical reference for making this analysis; this being a very interesting innovation
as it allows us to see the great differences that occur between the destinations closest
to the Mediterranean area and those that are not.
To close this question of economic determinants, the research focuses on one
of the factors that emerge from the previous analysis, the effects of unemployment and
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inequality on seasonality. Little research has focused on this aspect, and that existing
has focused on the analysis of how unemployment affects demand, not having much
literature concerning seasonality and unemployment. The analysis presented in this
thesis makes it possible to carry out a double exercise, first to analyse the effects of
unemployment on the evolution of seasonality and to see which areas are most
affected. The second approach tries to consider inequality as one of the causes of
seasonality.
Therefore, based on what we have previously proposed, the basic objectives of
this thesis in terms of seasonality would focus on:
- First, to present a descriptive analysis of the evolution of seasonality:
o To analyse the evolution of seasonality at European level, concentrating on the
most significant touristic countries.
o To observe whether similar patterns can be identified between countries and
be able to group countries and thus develop more surgical policies to reduce seasonality
and its negative effects.
o To assess seasonality at NUTS-2 level and assess the effect of product supply
on seasonality.
o To identify the countries that cause seasonality or those that allow its
reduction.
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- The second group of objectives is based on assessing the economic
determinants that affect seasonality:
o To analyse whether the economic determinants traditionally considered are
confirmed at European level.
o To observe if there is any difference in the importance of the economic
determinants between the analysed countries. Or, if there are different effects
depending on the grouping by product; in this case if the countries are located around
the Mediterranean or not.
o To identify if unemployment and inequality can be significant economic
determinants in the explanation of seasonality.
To carry out this research, the other decision related to the measure of
seasonality. The application of the best methodology to calculate this variable was not
a matter of research in this thesis. There is a lot of literature on the calculation of
seasonality and the methodology to follow, such as in Rosselló and Sansó (2017) or Duro
(2016) and Duro and Turrión-Prats (2019) where calculations are presented. In this case,
the choice was to follow Gini index, although calculations have also been carried out
with other options, such as Theil index or coefficient of variance, with which no
significant differences were found.
Similarly, given its validity, the econometric methodologies applied in the
different chapters of this thesis follow classic research, with structural break calculations
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in Chapter 3 to observe the possibility of changes in trends due to external shocks, or
the dynamic linear panel data model in the chapter on economic determinants.
The application of the Gini decomposition is considered an important numerical
exercise to understand the evolution of seasonality and allow a refinement of the
policies, both public and private, to be applied to reduce this issue. This methodology
allows the identification of the territories which are more important when explaining
the evolution of seasonality, and what causes a growth in these data. We applied this
methodology at all geographical levels followed in this thesis: countries, groups of
countries, regions and groups of regions.
In each of the chapters, the decision regarding the chosen model is justified.
Regarding the last chapter, as previously mentioned, the main objective was to
have a greater knowledge of the relationships that are established within the different
tourism subsectors and with other sectors.
To do this we used the traditional input–output methodology, with its
subsystem’s version. Archer (1977), Archer and Fletcher (1990), Briassoulis (1991) and
Fletcher (1989) are the papers considered initiators of the use of the input–output
method as an instrument to analyse the economic impacts of tourism. This methodology
can be optimised with the application of subsystems, developed by Sraffa (1960) and
continued by Pasinetti (1980) or Sinisalco (1982), among others. Later, we also find
applications by Alcántara (1995) and by Alcántara and Padilla (2009), which will be the
methodology followed. The advantage of using this methodology allows us to know the
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relationship and economic impacts that occur between the different sectors that make
up the tourism subsystem but also the relationships that are established with the rest
of the sectors of the economy.
As some researchers have shown (Alcantara & Padilla, 2009), this methodology
will allow us to expand research on tourism in a subject as sensitive as sustainability and
the environmental impacts of the sector. The big problem with this methodology is
being able to differentiate the part that corresponds to the tourism sector itself, and the
part of the activity that does not specifically correspond to tourism. In this case, we
follow the definition of the subsectors proposed by the Tourism Satellite Account of the
Instituto Nacional de Estadística (INE). To reduce the issues, the touristic subsectors are
refined in the research. For example, sectors such as transport, restaurants, bars or
leisure activities, include an important economic activity that is not properly touristic or
not generated by tourists. Some of the economic impacts are generated by residents or
by activities that are not related to tourism. Refining the statistics can improve the data
and therefore, the reality of the findings.
The research questions raised in this chapter are:
- What is the economic importance of the tourism sector and its subsectors?
- Is it possible to identify a proper tourism subsector in Catalonia?
- What activities can this subsector collect? Which activities defined by the INE
Tourism Satellite Account can be considered non-tourist?
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It should be noted that this chapter has already been published in the
international journal, Research in Hospitality Management. This methodology continues
to arouse interest among researchers, although recently, other methodologies have
been imposed on the traditional input–output approach.
To finish, we present a brief summary of each of the chapters that make up this
thesis:
Brief summary of Chapter 2 ‘A seasonality’s review of literature’.
In this chapter, we present a brief review of seasonality’s literature in the main
different questions treated in this thesis: definitions, effects, methodologies, economic
determinants and unemployment.
Brief summary of Chapter 3 ´Radiography of European Tourist seasonality: A
Territorial Analysis’.
This chapter is divided into two important sections. The first describes the
evolution of seasonality at EU level, and the countries where seasonality is more
significant. A possible grouping of countries is presented for a more accurate analysis,
to help policy managers to prepare policies to reduce this problem. The decomposition
of the Gini Index is carried out, which allows us to identify the countries that have more
importance in terms of seasonality, and those that have a positive impact, in terms of
reducing seasonality or negative, in terms of its expansion.
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The second part of the chapter focuses on the analysis of the evolution of
seasonality at regional level. Eurostat statistics from regions defined as NUTS-2 at EU
level are taken. With these regions, a grouping proposal is made according to the main
offer identified in each region. Once the descriptive analysis wis carried out, two
elements are identified, the first if it is significant that this region belongs to the
Mediterranean area, and second, the decomposition of the Gini Index. As in the previous
case, the decomposition exercise allows us to see which regions are most important in
the evolution of seasonality at European level, and which groups of regions explain this
evolution.
Brief summary of Chapter 4 ‘Economic determinants of international arrivals and
tourism seasonality: A macroeconomic approach’.
This chapter is based on the research of Rosselló et al. (2004) on the economic
determinants that can affect seasonality and its evolution. What is interesting in this
case are: first, the geographical decision: the EU-15, never applied in previous research.
Second, the signs that we found in our study, that were slightly different to those found
by previous research. Finally, we grouped the countries according to their proximity to
the Mediterranean or not. This allowed us to continue with the thread established in
the previous chapter and which indicated a clear pattern of seasonality behaviour at EU
level. The seasonal activity of ‘sun and sea’, as well as the strong appeal of these
destinations, together with the institutional factor of the concentration of family
holidays in the summer months, can explain a good part of the seasonality, both at
European level, as well as regionally, but we should focus on the economic factors that
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determine this problem in the tourism field, because they are the main questions that
policymakers could use to prevent and solve seasonality.
In this chapter, we propose an innovative element in the research proposed
above on economic determinants. In this case, unemployment and inequality as
economic variables are considered to explain seasonality. The approach of the proposed
numerical exercise allows us to consider a continuation in the search for the relationship
between inequality and seasonality. In this case, we found a significant relationship
between unemployment and the evolution of seasonality and concentrated in the
Mediterranean countries. These destinations have a very high degree of tourism
maturity and a type of tourism that, on many occasions, is based on economic prices
and closed packages. The research allowed us to point out that, a worsening of economic
inequality would imply a lower concentration of tourist activity in more economic areas,
with cheaper prices, and where the number of trips would increase in periods where
institutional issues were more significant such as family holidays.
These exercises opened a new research line on seasonality. Chapter 4 is related
to the effects of income changes over seasonality. The inclusion of GDP, Unemployment
and Income Inequality lead seasonality research to the effects of the income over
seasonality. Results obtained lead to define and to apply different methodologies to
obtain better information to help managers to deal with seasonality problems.
Brief summary Chapter 5 ‘Catalan tourism subsystem: Applying the methodology
of subsystems in the tourism sector’.
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As noted previously, this chapter starts from the use of the methodology
developed by Sraffa (1960) and with the application proposed by Alcántara (1995),
where the identification of economic subsystems based on input–output tables is
proposed. This application enables an exercise to identify the tourism economic
subsystem and the relationships established between these subsectors at the Catalan
level. This application allows us to see these relationships but also the indirect effects
that can occur in other economic sectors from tourism demand, and which continue to
confirm that the tourism sector is highly significant in the economic growth of the area.
Brief summary of Chapter 6 ‘Empirical findings and their implications’.
In this last chapter, we present the main conclusions of the thesis, but also the
implications both at the policy level and at the level of future research in the tourism
field.
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CHAPTER 2 LITERATURE REVIEW OF SEASONALITY.
2.1. Global Issues
The importance of tourism as an economic sector has been steadily increasing
since 1960 and is nowadays one of the principal sources of economic activity. From the
beginning of this spike, seasonality has been one of the main problems faced by firms
and local politicians. BarOn (1975) produced early work on the role of seasonality and
was followed by other researchers such as Butler (1994), Hartmann (1986), Hylleberg
(1992), Manning and Powers (1984), Moore (1989) and Sutcliffe and Sinclair (1980).
There are four seminal papers which provide a comprehensive study on seasonality
within tourism: Cannas (2012), Chung (2009), Corluka (2019) and Koenig-Lewis and
Bischoff (2005). Using these works, among others, we present a short review of the
seasonality literature, in Table 2.1. is presented the main authors by theme.
Koenig-Lewis and Bischoff (2005) produced a seminal paper in which we find a
wealth of information on the main issues surrounding seasonality: the definitions of
seasonality, causes and impacts, policy implications, studies into consumer behaviour
and approaches to measuring seasonality. In other works, for example Cannas (2012)
and Goulding (2006), a range of questions and methodologies related to seasonality
have been analysed.
We can find different definitions of seasonality in Allock (1994), BarOn (1975),
Butler (1994), Hartmann (1986), Mitchell & Murphy (1991) among others. According to
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Butler (1994, p.332), seasonality is “a temporal imbalance in the phenomenon of
tourism, [which] may be expressed in terms of dimensions of such elements as numbers
of visitors, expenditure of visitors, traffic on highways and other forms of transportation,
employment, and admissions to attractions”.
As stated by Turrion-Prats (2018), there are few differences among the
definitions provided by Allcock (1994), Butler (1994), Cooper et al. (2005), Grainger and
Judge (1996), Higham and Hinch (2002), Hylleberg (1992), Lundtorp (2001) and Moore
(1989).
Abstracting from this formal definition, we can reduce the dimensionality of
seasonality by focusing on variation over time, since many factors, for example those
related to social, economic and institutional determinants, cause tourist arrivals to be
concentrated in a specific period.
Beyond simply stating definitions, we should go deeper into the reasons for
seasonality. A second and significant section of the literature, in addition to the
definition field, is based on understanding the causes of seasonality. In this area we
found a wide range of literature targeting such questions and catalogued under
“Different Structural Reasons” in Table 2.1.
BarOn (1975) classified the causes of seasonality into two types: natural and
institutional seasonality. Natural causes stem from the geography of the local area, for
example local weather or climate conditions, and institutional reasons arise from social
factors such as school holidays, fashion or religious periods. Kessler (1990) calculated
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that 50% of the population plan their trips based on school holidays. Butler and Mao
(1997) provide further information on the causes of seasonality and its principal factors,
and they extend their analysis of seasonality to include supply and demand side
concerns.
Butler (2001) describes five different forms of seasonality: natural, institutional,
social, climatic, and traditional or inertia. Budyko (1974), Mauss and Beuchat (1979), and
Smith (1973) showed that seasonality increases with distance from the equator,
increasing the importance of natural channels. This result is significant for our research
when analysing patterns across the EU. Other authors talking about structural reasons
are; Baum (1999), Baum & Hagen (1999), Connell et al. (2015), Frechtling (1996),
Frechtling (2001), López-Bonilla and López-Bonilla (2006), among others.
But, literature can be defined in three specific set of factors which determine
seasonality: natural factors, institutional factors and push and pull factors. Such set of
factors are not exclusive, for instance: natural factors, as weather, determine some
institutional activities as scholar or labour holidays.
Natural factors, as stated in Allcock (1989), BarOn (1973), Baum and Lundtrop
(2001), Bender et al. (2005), Butler (1994), Butler and Mao (1997), Koenig and Bischoff
(2005), Luntrop (2001) or in Turrión-Prats and Duro (2019), describe casual channels
which cover a range of climatic effects which include natural phenomena such as
sunlight, snowfall, storms and rainfall. Climatic phenomena determine which types of
tourist activities are feasible in which periods of the year. For example, will tourists have
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access to sunny beaches, skiing, outdoor activities or other weather-dependent tourism
goods? Turrión-Prats (2018) provided in-depth and relevant analysis of recent research
into the role of climate factors.
The institutional factors are well presented in BarOn (1972), BarOn (1975), Butler
(1994), Hinch and Hickey (1996), Murphy (1985), and Rosselló and Sanso (2017).
Institutional factors refer to the seasonality caused by activities due to religious, cultural,
social or organizational factors. For instance, school vacations are one of the principal
components of institutional factors. School holidays occur in specific and regular periods
of the year: Christmas, Easter or summer. Families will organize their vacations during
these periods, and this implies that tourism activity peaks during these specific times of
the year. In the same way, labour holidays are another institutional factor. Companies
provide holidays to the employees in some specific moments of the year, for instance:
tourism companies allow the employees to take vacations when the tourism demand is
low. Another example of this is teachers’ holidays. Government decides the academic
calendar and teachers should go on holiday during the periods indicated.
Butler and Mao (1997), Cannas (2012), Corluka (2019), Kolomiets (2010), and
Lundtorp et al. (1999), discussed push and pull factors. Push factors are those which
drive people to make certain choices around their tourism. These include upcoming
school holidays and climate concerns, among other trends, whereas pull factors are
those which attract tourists to certain destinations, which again include climate
awareness and local events and activities.
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Another interesting body of literature is that which examines different
approaches about seasonality measurement. We analyse two subgroups in this field. We
first look at data collection and then the methods used to calculate seasonality. As stated
in Turrion (2018), there are different schools of thought on which variables are best to
capture seasonality within tourism: tourist arrivals (e.g., Duro, 2016; Rosselló et al.,
2004), overnight stays (Cuccia & Rizzo, 2011; Duro, 2016; Ferrante et al., 2018) or
average spending per person (Koc & Altinay, 2007).
The other subgroup looks at the methods used by researchers to measure in a
synthetic way seasonality. We can identify four leading indices: the Gini index, the
coefficient of variation, the Theil index and the Atkinson indices. Following Koenig-Lewis
and Bischoff (2005), a combination of different methodologies, as Theil indices or
Coefficient of Variation (CV), is often the best way in which to analyse seasonality, but
the methodology most commonly used is the Gini index. Rosselló and Sansó (2017)
present a broad analysis of the literature on measuring seasonality. They focus on
research which comes under the category of longitudinal analysis or that which utilises
the older statistic, the “seasonality ratio” or the “coefficient of seasonal variation”
alongside the leading research examples which use the Gini index. Additionally, along
with Duro (2016), Rosselló and Sansó (2017) aimed to derive new indexes to calculate
seasonality and they proposed a number of new methods which include entropy and
relative redundancy. These measures are derivatives of the Theil index.
Wanhill (1980) provides the justification for why we should move on from old
methodologies such as the seasonality ratio or the coefficient of seasonal variation.
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Wanhill’s work was developed by Cannas (2012), Koenig-Lewis and Bischoff (2005),
Lundtrop (2001), and in particular Karamustafa and Ulama (2010), who apply each of
the different methodologies to data on Turkish tourism. This literature concludes that
the main reason to leave behind old methodologies is that those types of index present
severe deficiencies when used to calculate inequality because they are influenced by
extreme values and are held back by this inherent weakness.
The Gini index is the most commonly used statistic in seasonality research, as
stated in Roselló and Sansó (2017). Other authors who applied Gini Index are
Fernández-Morales and Mayorga-Toledano, (2008), Fernández-Morales et al. (2016),
Koenig-Lewis and Bischoff (2005), Lau et al., (2017), Lundtorp, (2001), Martín Martín et
al., (2014), Rosselló et al. (2004), or Wanhill (1980), among others.
Duro (2016, p.54) defended the use of the Theil index as an alternative to the
Gini index, and stated that it “is more sensitive to changes in months with a lower
demand”. Rosselló and Sansó (2017) and Duro (2018) used this Index in other research
papers.
The coefficient of variation was used by Duro (2016) and Duro and Turrion-Prats
(2019) as an alternative to the Gini and Theil indices. Because the current thesis is not
an attempt to check the validity of the different methods, we decided to apply the Gini
index, as it is the measure most accepted by researchers1.
1 We should highlight the work done by Gil Alana, who has developed some research to adjust the
seasonality of time series. The aim of the methodology applied by this author is to improve the time series
available and to provide better data to study tourism demand, and therefore seasonality. Some of his
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Finally, we want to highlight another methodology, the Gini decomposition.
Following Shorrocks (1982) and Lerman and Yitzaki (1985), we used this methodology to
go deeper into the investigation of seasonality’sources, including its origin and
evolution. In geographical terms, we will identify the countries and regions where
seasonality is significant, and the marginal effects created by tourist arrivals. This
methodology has been widely used by many authors (Duro, 2016; Duro, 2018;
Fernández-Morales, 2003; Fernández-Morales et al., 2016; Fernández-Morales &
Mayorga-Toledano, 2008; Rosselló & Sansó, 2017; Turrion-Prats & Duro, 2019; Duro &
Turrion-Prats, 2022). Decomposition methodology will be explained in the next
paragraphs.
Decomposability—i.e. the possibility of calculating the contribution of different
components to the total concentration. The literature on inequality measurement
emphasizes different possibilities to decompose concentration indexes (Cowell, 1999):
Decomposition by group: identifying an intra-group component and an
inter-group component (Shorrocks, 1984), where groups are defined according to a
specific characteristic such as gender, nationality, geographic location, etc. The intra-
group element captures inequality due to variation in a selected variable (income,
arrivals, overnight stays, for instance) within each group, while the inter-group
component captures inequality due to variation in this variable across different
papers are Gil-Alana, L.A. Perez, F. and Cuñado, J (2004) for Spain or Gil-Alana, L.A., Mudida, R. and Perez,
F. (2014) for Kenya, Gil-Alana, L.A. and Huijbens, E.H. (2018) for Iceland, Payne, J.E., Gil-Alana, J.A., Mervar,
A., and Goenenchea, M. (2022) for Croatia, among others.
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groups. Application to seasonality in tourism usually defines groups formed of
consecutive months or tourism seasons. For instance, Fernández-Morales (2003)
uses this approach to decompose the Gini index to analyse differences in the
concentration of hotel demand in three Spanish provinces between 1980 and 2001
and shows that the between-seasons component is the most important source of
seasonality. In a related study, Duro (2016) uses a Theil index decomposition to
analyse the case of the main Spanish provinces for the period 1999–2012 to test the
reliability of monthly aggregates to explain the global concentration of overnight
stays and assess their use as a tool for public planning.
Decomposition by factor: when factors can be expressed additively
(Shorrocks, 1982)—i.e. understanding each factor as an additive part of the global
component. In the literature on income inequality, this approach refers generally to
the contribution of different sources of income. Applications to tourism seasonality
analyse the role of different factors, mostly defined in terms of origin markets for a
particular destination. An example is Fernández-Morales and Mayorga-Toledano
(2008), who use a Gini decomposition by factor components to examine the effect
of different markets on the annual level of seasonal concentration in Costa del Sol,
Spain. More recently, Fernández-Morales et al. (2016) applied this methodology to
the analysis of seasonality in the United Kingdom and tourists’ place of origin and
their main travelling motivation. Duro (2016) also applies this approach to analyse
the contribution of domestic and non-resident tourists to overall seasonality, to
identify the main role played by the foreign component. In the same research
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project, Duro (2016) presents a broad explanation of the reasons to apply this form
of decomposition to analyse the distributional imbalance, that, as defined by Butler
(1994), seems to be seasonality’s principle phenomenon. As we go on to explain in
the subsequent paragraphs, decomposition by factor gives us the best option to
analyse the reasons behind the evolution of seasonality observed in the data. Our
main objective is to understand the importance of each factor to EU seasonality,
individual country arrival rates, and the trends observed in seasonality in each
region.
In a recent paper by Turrión-Prats and Duro (2022), we find a deeper analysis of
the existing decomposition methodologies and their properties. In this chapter, we
followed the Lerman and Yitzhaki (1985) proposal, focusing on two concepts. Firstly, the
proposal allow us to know the weights of every market in an effective way; secondly,
the authors propose an interesting calculation of the marginal effects when seasonality
changes. This gives us the chance to know the weight of every market and the effect in
relation to seasonality. Decomposition, then, is a good exercise to know where
seasonality has greater importance and how to act specifically in that area.
Turrión-Prats and Duro (2022) explained and applied two other decomposition
methods following Shorrocks (1982) and the Shapley value, proposed by Shapley (1953).
In Shorrocks (1982), the index used to calculate inequality it is not significant.
Decomposition calculations use any proposed index. Nevertheless, Duro (2016, p.3)
indicates that we obtained different data depending on the index used: “The Gini index
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is sensitive to central observations; the Theil to those in the last positions of the ranking
and the CV is neutral.”
The decomposition proposed by Shorrocks (1982) is based on the relative weight
of every market and is the result of the concentration of every relative weight and the
correlation with other markets. Following the analysis by Turrión-Prats and Duro (2022),
the problem of this method is that it works better with absolute data than with relative
data, and the data used are mostly relative.
Meanwhile, the Shapley value allow us to calculate the percentage of arrivals and
the absolute and relative contribution of every market using the Gini index. The use of
the Gini index exclusively is a limitation in comparison with the previous method;
however, the Shapley value allows us to reduce duplications and we obtain better values
for marginal effects.
As stated by many researchers, seasonality creates numerous problems for the
touristic sector due to the imbalance of tourist activity, and over the next sections we
present research on the impact of seasonality. As presented in Yan and Wall (2003), the
impacts of seasonality have increased with the growth in the number of tourists
travelling each year. It is clear that not only economic impacts are significant but often
ecological and socio-cultural impacts lead to sizable effects (Cannas, 2012; Koenig-Lewis
& Bischoff, 2005). Obviously, the range of seasonality impacts is huge and covers a wide
range of questions. Despite this, we are able to concentrate our analysis on four
different impacts: economic, employment, ecological and sociocultural impacts.
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Duro and Farré (2015) and Fernández-Morales and Mayorga-Toledano (2008)
reproduce the consequences of seasonality in Spain, which we can extend to other
countries. They find the following set of outcomes:
Labour market problems including unemployment during the off-peak
season, low salaries, lower job quality.
Economic inefficiency, due to the saturation or underutilisation of
resources or the deficit in public and private resources.
Social and environment effects such as traffic problems, security or
queues.
Other problems are related to the fact that income during the off-peak season
does not cover fixed costs, particularly in the family business accommodation sector
(Koenig-Lewis & Bischoff, 2005). The same authors, following Grant et al. (1997), argue
that there are some positive impacts of seasonality as it provides the time and
opportunity to conduct necessary maintenance or to offer training to employees.
Butler (2001) and Cannas (2012) wrote in-depth research papers on seasonality
and presented some of the economic impacts and problems that seasonality causes in
the tourism sector. These include difficulties obtaining capital, full time workers, issues
with returns, and reduced and/or overused facilities.
In economic terms, one clear problem is the inefficient use of resources and
assets during periods of low activity, as stated in many reports, like those of Sutcliffe
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and Sinclair (1980) and Butler (1994), or the overuse of infrastructure or costs to public
services in moments of high activity (Duro, 2016 and Roselló et al., 2004). Seasonality
creates many problems for companies due to cash-flow discontinuities or questions
about the number of rooms to offer during peak season compared to off-peak season.
Employment within the sector and its evolution is another issue related to
seasonality. The imbalance of tourism activity creates employment problems due to the
instability which spreads throughout the sector. Conditions make it hard for employers
to contract workers throughout the whole year or to maintain high-skilled workers in
places with high seasonality. This problem, as stated in Turrion-Prats (2018, p. 33), leads
employers “to employ staff with a low level of professional qualification and offer them
temporary contracts.” This compounds the underlying problem and leads to a lower
standard of quality for the products offered (Corluka, 2019).
Ecological impacts were clearly defined by Manning and Powers (1984), who
emphasized the strain tourism activities have on the ecological capacity of certain
destinations, especially as a result of heavy usage during the peak season. Ecological
impacts are one of the main and most urgent problems within tourism, but the situation
becomes worse when we focus on destinations which suffer from high seasonality. Peak
seasons mean high numbers of visitors and spikes in pressure being applied to fragile
environments (Butler, 1994). One of the questions to solve by local tourism planners is
how to handle the negative effects seasonality has on the environment.
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Lastly, there is a strand of literature which has looked at sociocultural impacts.
Some authors, like Allcock (1989), Butler (1994), Chung (2009), Commons and Page
(2001), Koenig and Bischoff (2005), Manning and Powers (1984), Mathieson and Wall
(1982) or Murphy (1985) have pointed out that large numbers of visitors during peak
seasons increase the number of services which are required during this period—which
stresses infrastructure, health services and other vital provisions. In addition, prices can
rise during peak seasons, which translates into increasing costs for some social services.
Even though local populations can enjoy such services during the off-peak season,
sometimes these services with higher level in standards in comparison to other places
with no tourist activities, as stated in Murphy (1985).
Strategies to solve seasonality are another vibrant research question. The main
strategies, which were stated by Turrion-Prats (2018), include product diversification,
market segmentation and differential pricing strategies. The first aims at trying to
expand the number of tourist activities or locations on offer so as to reduce the pressure
placed on highly popular destinations, as stated by Getz (2008), events are one of the
most common activities developed to reduce seasonality. The second strategy is to
identify different demand-side causes. Spotts and Mahoney (1993) stated that matching
seasonal motivation with tourism products and services offered is needed to increase
visitors during the off-season period. This permits the supply side of the sector to
prepare products during the off-season period or to look for new places with different
attractions, again aimed at expanding the number of destinations on offer to visit. For
instance, tourism agents develop marketing strategies to know new market niches and
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new tourism products are created to attract new tourist during off-season period or to
diversify the tourist destinations. Finally, the price diversification strategy is founded on
finding ways to offer products with lower prices during the off-season period (Jang,
2004, Manning & Powers, 1984, O’Driscoll, 1985 or Sasser, 1976).
Lee et al. (2008) added another one, facilitation by state, but this strategy is
about to provide facilities to destinations to help to increase the number of tourists, for
instance the development of transports infrastructures.
Recently, some authors like Senbeto and Hon (2021) and Medina et al. (2022)
presented some interesting strategies about management culture, innovation and
efficiency in management. This is a different point of view, focusing the question in
improving the management in business, more than other questions.
Finally, we want to present the list of strategies proposed by different
researchers outlined by Corluka (2019):
Introduction or development of festivals and events
Diversifying into niche products
Offering off-season holiday packages
Business travel
Multiple use schemes
Circuits’ attractions, twin attractions or two-centre holidays
Special price offers, such as seasonal pricing
Group booking offers
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Marketing campaigns to attract different markets in different seasons
Staggering of holidays over a longer period
Improved and expanded regional infrastructure
Development of local business networks and partnerships
In these strategies, we can see the effort of the different local planners and
business owners to develop strategies to reduce seasonality and, at the same time, to
solve one of the biggest issues in the touristic sector. Unfortunately, as stated in Corluka
(2019) p.19, “The literature is missing empirical studies with evaluation of the outcomes
of applied strategies.”. Recently, Rico et al. (2021) presented a paper asking about the
Senior programs developed in Spain (Imserso program) or Senbeto and Hon (2021)
presented a qualitative research about the strategies applied and its results in Ethiopia.
Another interesting question done by Corluka (2019) is which of the strategies are useful
in one place and can be transferred to another.
2.2. The Economic Determinants
To contextualize this issue in this doctoral thesis, we will focus on the leading
examples from this body of research which are related to the economic determinants of
seasonality. We use as a reference paper that of Rosselló et al. (2004), which was the
first to use a dynamic model including economic variables to explain the evolution of
seasonality.
The link between economic activity and tourism activity has been analysed by
many researchers in a variety of ways. Copeland (1991), Hazari and Sgro (1995) and
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Lanza and Pigliaru (1999), among others, have all presented research addressing this
question. For an in-depth literature review, consult Chatziantoniou et al. (2013), who
provide an overview of the main hypotheses and references on the link between tourism
and economic activity. Four hypotheses are formulated: tourism-led economic growth
(TLEG), economic-driven tourism growth (EDTG), bidirectional causality (BC), and no
causality hypothesis (NC). These different hypotheses demonstrate the importance, as
well as the complexity, of this question for the tourism industry.
These four hypotheses are based on the sequence established to
understand the relation between tourism and economic development.
According to Chatziantoniou et al. (2013), there is evidence that causality
indeed runs from the tourism sector to the broader economy — a hypothesis
known as the TLEG hypothesis.
There is the view that economic growth is instead a crucial factor to the
increase in tourism income — the so-called EDTG hypothesis.
A third strand of literature provides evidence that there is BC between
tourism and economic growth.
Finally, some authors report no significant evidence for causality (NC).
We can identify research following these four hypotheses:
TLEG: Ballaguer & Cantavella-Jorda, 2002; Blake & Sinclair, 2003; Brida
et al., 2016; Chingarande & Saayman, 2018; Croes & Vanegas, 2008; Fayissa
et al., 2011; Lee & Chang, 2008; Pan et al., 2014; Paramati et al., 2017;
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Salifou & Haq, 2017; Schubert & Brida, 2011; Soukiazis & Proença,
2008; Tang & Tan, 2015; Vanegas & Croes, 2003; Vita & Kyaw 2017; Zhang
& Cheng, 2019; Zortuk, 2009
EDTG: Antonakakis et al., 2015; Hussain-Shahzad et al., 2017; Narayan,
2004; Oh, 2005; Payne & Mervar, 2010; Pulido-Fernandez & Cárdenas-
García, 2021
BC: Antonakakis et al., 2019; Apergis & Payne, 2012; Bojanic & Lo, 2016;
Chingarande & Saayman, 2018; Hussain-Shahzad et al., 2017; Ridderstaat et
al., 2013; Tang & Tan, 2018
NC: Eugenio-Martin et al., 2004; Katircioglu, 2009; Po & Huang, 2008;
Tang, 2013.
Usually, the TLEG hypothesis is the most supported, In fact, Song and Wu (2022)
identified more than 100 papers using this hypothesis as the basis of the research.
Nevertheless, in this research, Song and Wu (2022) criticised this hypothesis and the
methodologies used to sustain this hypothesis. Our paper, however, is not focused on
the causes of tourism growth, and we do not explain those hypotheses in detail.
Another key question within the tourism literature looks at how demand within
tourism sectors responds to business cycle fluctuations. Gonzalez and Moral (1995,
1996) presented two papers on the relationship between the co-evolution of the wider
economy and tourism demand. The analysis looks to discover trends present in the
Spanish tourism sector, which are derived from economic data on the years 1979–1994.
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Another seminal paper is by Gouveia and Rodrigues (2005), who analysed the
synchronisation between tourism demand and the business cycle for different European
countries. Another paper showing the correlation between the economic and tourism
cycles is by Guizzardi and Mazzocchi (2010), who used a Structural Time Series model to
analyse the business cycle effects on tourism seasonality rates. Both research projects
conclude that tourism demand is correlated with the business cycle; however, the
response is lagged.
Smeral (2012 p.381), states, “The business cycle affects tourism import demand
because of the fluctuations in the overall economic activity as well as changes in people’s
expectations about their future income and job situations”. In addition, the author finds
that income and price elasticity are quite sensitive.
More recently, a body of work has begun linking tourism and economic crises.
For example, Hall (2010), Papatheodorou et al. (2010), and Eugenio-Martin and Campos-
Soria (2014) highlighted that economic trends have a negative impact on tourist arrivals
and expenditure rates. Sala et al. (2014) examine how tourist demand reacts to poor
economic perspectives or indicators, as well as other indicators like family debt levels or
unemployment. These papers showed that the general economy has a strong impact on
tourist demand.
Eugenio-Martin and Campos-Soria (2014) present a broad review of the
literature on tourism demand and economic crises. Frechtling (1982) analysed changes
in real travel and economic activity for the United States during the economic crisis of
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the 1980s. Henderson (1999), Law (2001) and Prideaux (1999) analysed the Asian
financial crisis of the 1990s, and other authors like Alegre et al (2013), Page et al. (2012),
Sheldon and Dwyer (2010) and Smeral (2009) examined the effects of the previous
financial economic crisis.
Authors have formed hypotheses on how economic crises affect tourism activity.
Eugenio-Martin and Campos-Soria (2014), Page et al. (2012), and Smeral (2009) pointed
out that tourists cut back their demand when experiencing an economic crisis. Others,
such as Stabler et al. (2009), linked tourism activity to income elasticity. Smeral (2010)
was one of the first to analyse the effects of the downturn in tourist activity caused by
the global financial crisis. In closer relation to the work presented here, Papatheodouro
et al. (2010) and Page et al. (2012) analysed the response in the number of arrivals
caused by economic fluctuations.
In addition, Perles et al. (2016) analysed the effects of unemployment in the
Spanish tourism sector. Zaharia et al. (2014) analysed the effect of unemployment, and
Cafiso et al. (2018) wrote an article about the effects of economic crises and highlighted
the fact that in such situations, Italian tourists opt for destinations closer to Italy, thus
showing a change in tourist behaviour.
Smeral (2009) and Smeral (2010) presented two reference papers linking the
evolution of tourism demand and economic evolution. These two papers analysed the
evolution of tourism demand after the 2008 economic crisis, and they presented GDP as
one of the economic determinants. Those are not the only papers linking tourism
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demand and GDP, Eugenio-Martin and Campos-Soria (2014) wrote a document with
similar research, linking tourism expenditure cutback and GDP, GDP Growth and tourist
origin. Garín-Muñoz and Moreno (2007), Ledesma-Rodriguez et al. (2001) and Song and
Witt (2000) used GDP as economic variable to analyse tourism demand.
Eugenio-Martin and Campos-Soria (2014) introduced us to a new important
question in tourism literature, as Campos-Soria, García-Pozo and Marchente-Mera
(2018), Sheldon and Dyer (2010) or Tse, R.Y.C (2001), the importance of micro and macro
data to understand the tourist attitudes. As stated in p.55 “ideally, microdata and
macrodata should be combined in the analysis. This approach may be of interest for
tourism and hospitality decision-makers who need to understand and anticipate the
linkage between GDP and tourists’ behaviour.”
Focusing in the research done by Rosselló et al. (2004), they used microdata as,
gross domestic product (GDP), the price index and exchange rates as key data variables.
GDP, or income, is one of the most informative variables when analysing demand, as
stated in. In addition, we place particular emphasis on two pieces of research from
Turrión-Prats (2018) and Turrión-Prats and Duro (2019).
We follow the lead established by these papers and analyse the economic
determinants of tourism seasonality at the European Union (EU) level. One reason to
use these variables is that they have been shown to be economically and statistically
significant. In addition, we have access to such data through statistical organizations
such as EUROSTAT, OCDE, the World Bank or various national statistics institutes.
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One economic determinant which few previous researchers have used to analyse
seasonality evolution is unemployment rates, and for that reason, as stated in the
introduction, we wanted to include this data in our analysis in chapter 5. In that case,
Koenig and Bischoff (2010) present a clear overview of state-of-the-art modelling and
understanding within the seasonality and unemployment literature. Alegre et al. (2013
and 2019) explained the effects of unemployment over demand attitudes.
Finally, the link between seasonality and inequality are presented. Alam and
Paramati (2016) presented a relation between the economic determinants, tourism and
inequality. But other authors presented some research in that sense: Incera and
Fernandez (2015), Llorca-Rodríguez et al. (2017), Lv (2019), Mahadevan and Suardi
(2019), or Raza and Shah (2017).
2.3. Geographical Analysis
Lastly, we want to explain our decision to include specific geographical analysis.
We decided to continue the practice outlined in the work of Duro (2016), Duro and
Turrión-Prats (2019) and Turrión-Prats and Duro (2019). They analysed different
geographical locations across Spanish provinces (e.g., Catalonia) or worldwide regions;
however, we have observed that this research is not uniform, and there are areas which
are relatively under-studied. In our case we decided to analyse seasonality across EU
countries by focusing on a group of central countries to the bloc. In a second round of
analysis, we examine these regions at the NUTS 2 level. This level of analysis gives us a
broad perspective of seasonality in Europe.
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It is clear that the impacts vary by location, which implies that seasonality is a
place-specific issue as well as temporal problem. Urban destinations are less affected by
seasonality than other places, for example beach or mountains destinations (Cannas,
2012).
Tourism in urban cities is less affected because the supply of tourism goods and
services is less correlated to weather or school holidays. Obviously, tourism demand
peaks during school holidays, but throughout the rest of the year the fact that these
locations offer cultural heritage, and congress and events activities lead to a more linear
and stable level of tourism activity. Lopez-Bonilla and Lopez-Bonilla (2006) produced a
seminal research project looking at supply side concerns and the respective impacts of
seasonality for a range of Spanish regions. However, these authors concentrated their
analysis on the supply of accommodation and omitted the analysis of alternative goods
and services. This article is highlighted because the authors identified two questions as
a significant angle from which to analyse seasonality: regional and supply side effects in
tourism, which are two of the principle research aims of this doctoral thesis.
For most mature destinations whose tourism activities are basically
concentrated around weather, such as sun and sand or winter destinations, seasonality
is an important issue and the planners try to resolve it in different ways, but not with
clear success. Therefore, while urban tourism appears less affected, it should be noted
that there is a lack of specific research on differences in seasonality patterns between
cities and other destinations, or, as posited in Butler and Mao (1997), the non-peak type
of seasonality. Duro and Farré (2015) observed that some Spanish provinces’ higher
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rates of urban tourism suffer from less seasonality, or indicate better forecasts for future
seasonality, than the mature tourism destinations based on the weather conditions.
Those conclusions give us some basic points for seasonality research. One is that the
supply of goods and services within tourism is a key point when analysing seasonality.
From the literature review presented above, we draw the conclusion that we need more
research on the issue of seasonality, and this research should be policy driven. Doing so
enables us to pass understanding on to local leaders within the tourism sector in order
to produce solutions to real world problems.
As stated previously, first we obtain the evolution of seasonality trends at the
country level, which can highlight differences between European countries. We then
create groups according to the location of each country: North, Centre and South. This
gives us a clearer picture of the differences between these groups and provides some
clear results on why seasonality has evolved as observed. In that section we focus on
the demand side, but in following that we present a set of conclusions about the tourist
products offered by each group. A similar research project was completed by Ferrante
et al. (2018), analysing European countries beyond those that form the EU. The clusters
of countries in our work in some part mirror those of Ferrante et al. (2018). Instead of
using international arrivals, however, Ferrante and colleagues took hotel overnight stays
as their key data variable, as it enabled them to gather data from a wider range of
countries. However, the weakness of this method is that it excludes those tourists who
did not choose this accommodation option.
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The second level of analysis uses variation at the NUTS 2 level. Previously, Duro
(2016) examined seasonality at the Spanish province level (NUTS 3), but to the best of
our knowledge no existing attempts have exploited the NUTS 2 level at EU level. Our
research focuses on both the supply and demand side of analysis. This gives us a
complete picture of the evolution of seasonality at this territorial level. We obtained
relevant information for local planners to solve problems and tackle the impacts of
seasonality we have previously outlined.
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Table 2.1 Main contributions in Seasonality
Question Main Contributions
State of the art Koenig & Bischoff, 2005; Cannas, 2012;
Chung (2009); Corluka, 2019
First definitions BarOn, 1975; Sutcliffe & Sinclair, 1980;
Manning & Powers, 1984; Hartmann,
1986; Moore, 1989; Hylleberg, 1992;
Butler, 1994; Grainger & Judge, 1996;
Baum & Lundtorp, 2001; Lundtorp, 2001;
Higham & Hinch, 2002; Cooper et al.,
2005
Different structural reasons BarOn, 1975; Hylleberg, 1992; Butler,
1994; Butler, 2001; Butler & Mao, 1997;
Frechtling, 1996; Baum, 1998; Baum &
Hagen, 1999; Frechtling, 2001; López-
Bonilla & López-Bonilla, 2006; Connell et
al., 2015
Natural factors BarOn, 1973; Hartmann, 1986; Allcock,
1989; Butler, 1994; Butler & Mao, 1997;
Baum & Lundtrop, 2001; Koenig &
Bischoff, 2005; Bender et al., 2005;
Turrion-Prats, 2018; Turrión-Prats &
Duro, 2019
Institutional factors BarOn, 1972; BarOn, 1975; Murphy,
1985; Butler, 1994; Hinch & Hickey, 1996;
Rosselló & Sansó, 2017
Push and pull factors Butler & Mao, 1997; Lundtorp et al.,
1999; Kolomiets, 2010; Cannas, 2012;
Goran, 2017
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Table 2.2 Research in Seasonality—Seasonality impacts
Question Main Contributions
Effects of seasonality Sutcliffe & Sinclair, 1980; Yacoumis, 1980;
Butler, 1994; Grant, 1997; Baum & Hagen,
1999; Jang, 2004; Cooper et al., 2005;
Koenig & Bischoff, 2005; Wall & Yan,
2003; Fernandez-Morales & Mayorga-
Toledano, 2008; Chung, 2009; Duro &
Farré, 2015
Economic impacts BarOn, 1975; Sutcliffe & Sinclair, 1980;
Manning & Powers, 1984; Williams &
Shaw, 1991; Butler, 1994; Jang, 2004;
Goeldner & Ritchie, 2003; Rosselló et al.
2004; Koenig & Bischoff, 2005; Cooper et
al., 2005; Chung, 2009; Duro, 2016;
Employment impacts Yacoumis, 1980; Mathieson & Wall, 1982;
Murphy, 1985; Mill & Morrison, 1998;
Baum, 1999; Common & Page, 2001;
Szivas et al., 2003; Cooper et al., 2005;
Koenig & Bischoff, 2005; Chung, 2009;
Koenig & Bischoff, 2010
Ecological impacts Manning & Powers, 1984; Butler 1994;
Grant et al., 1997; Bender et al., 2005;
Chung, 2009
Sociocultural impacts Mathieson & Wall, 1982; Manning &
Powers, 1984; Murphy, 1985; Allcock,
1989; Butler, 1994; Common & Page,
2001; Koenig & Bischoff, 2005; Chung,
2009
Strategies to reduce impacts Sutcliffe & Sinclair, 1980; Kotler, 1984;
Middleton, 1992; Butler, 1994; Moutinho
& Witt, 1995; Getz & Nilsson, 2004; Getz,
2008; Lee et al., 2008; Wang, 2011;
Corluka, 2019
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Table 2.3 Research in Seasonality—Measurement
Question Main Contributions
Variables used (arrivals, overnights,
average spending)
Duro, 2016; Cuccia & Rizzo, 2011;
Ferrante et al. 2018; Koc & Altinay, 2007;
Rosselló et al., 2004
Gini index Wanhill, 1980; Lundtorp, 2001; Rosselló
et al., 2004; Koenig-Lewis & Bischoff,
2005; Fernández-Morales & Mayorga-
Toledano, 2008; Martín Martín et al.,
2014; Fernández-Morales et al., 2016; Lau
et al., 2017
Theil index Duro, 2016; Rosselló & Sansó, 2017; Duro,
2018
Other measurements Duro, 2016; Turrión-Prats & Duro, 2019
Decomposition in tourism seasonality Cisneros-Martínez & Fernández-Morales,
2016; Duro, 2016; Duro, 2018;
Fernández-Morales, 2003;
Fernández-Morales et al., 2016;
Fernández-Morales & Mayorga-
Toledano, 2008; Roselló & Sansó, 2017;
Turrión-Prats & Duro, 2019, Duro &
Turrión-Prats, 2022; Vergori & Arima
(2022)
Table 2.4 Research in Seasonality—Thesis aims
Question Main Contributions
Economic determinants Rosselló et al., 2004; Turrión-Prats &
Duro, 2017; Turrión-Prats & Duro, 2018;
Turrión-Prats & Duro, 2019; Xie, 2020;
Geographical dimension Cisneros-Martinez & Fernández-Morales,
2013; Duro, 2016; Radic, 2017; Ferrante
et al., 2018; Turrión-Prats & Duro, 2018;
Šegota & Mihalič, 2018; Turrión-Prats &
Duro, 2019, Duro & Turrión-Prats, 2022
Unemployment Koenig & Bischoff, 2010; Alegre et al.,
2013; Incera and Fernandez, 2015; Alam
and Paramati, 2016; Llorca- Rodríguez et
al., 2017; Raza and Shah, 2017; Alegre et
al., 2019, Lv, 2019; Mahadevan and
Suardi, 2019
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CHAPTER 3. RADIOGRAPHY OF EUROPEAN TOURIST
SEASONALITY: A TERRITORIAL ANALYSIS
3.1. Introduction and Methodological Aspects.
3.1.1. Introduction
As stated previously, seasonality is one of the key issues that tourism planners
are seeking to resolve. Since the start of the global boom in tourism, particularly from
the 1970s, the “Fordism period”, the issue of severe fluctuations in tourism rates has
been significant. It has been shown that most economic activities have some form of
seasonality patterns, for instance agricultural activities (Kuznets, 1933), but the problem
is particularly acute and inherent to tourism.
To summarize, the main reason for an increased interest came thanks to an
increase in climate concerns, seasonal activities, and other activities which, when
concentrated in a certain period of time, lead to problems which are difficult to solve.
As we will see over the next few pages, the rate of seasonality fluctuates from high to
low. To help agents in the tourism sector, it is imperative to obtain and provide more
information about what is driving these changes.
EU has taken this issue as significant in terms of tourism policy. As indicated by
Ferrante et al. (2018), the European Commission and the European Parliament have
launched various programmes to combat seasonality by developing off-season tourism
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activity and encouraging off-season tourism among older people, young people and
people with fewer resources.
In the next paragraphs, a brief review of literarure at geographic level is
presented. Some attempts to understand seasonality focus on a single country such as
Austria or Germany (Bender et al, 2005) or regional provinces such as in Spain by Duro
(2016) or Andalusia in Martin Martin et al. (2014), among others.
As previously outlined, Ferrante et al. (2018) produced a similar paper to ours in
that they measured tourism seasonality across European countries. They used
hospitality overnight stays as a key variable to capture tourist activity. This gave them
the chance to obtain data on all target countries; however, in doing so, they lost some
information of interest. One strength of their paper was that using two similar groups
of countries—a Mediterranean group and North and Central countries—they found
similar results. Another similar conclusion presented in Ferrante et al. (2018) is that,
along with characteristics of the product on offer, climatic and institutional factors are
the main drivers behind seasonality patterns across Europe.
Sustar and Azic (2020) analyse seasonality across a range of Mediterranean
countries. In their case, the authors examine seasonality in Croatia, France, Greece, Italy,
Portugal and Spain. They chose this set of countries because of the similarities between
the types of tourism product these countries offer. For example, the authors highlight
that these countries have plenty of sun and activities based around the sea, which leads
them to have comparable tourism sectors. Niavis (2020) studied spatiotemporal and
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tourism destinations, the key contribution being the analysis of coastal destinations
throughout the Mediterranean.
Finally, Radic (2017) produce a research about EU and its seasonality excluding
institutional factors. Again. their findings show a high seasonality around the
Mediterranean. The authors follow the Coefficient of Variation (CV) option to calculate
seasonality and only analyse the fact if exist differences between seasonality caused by
natural reasons.
Other examples of current literature include Turrión-Prats and Duro (2019), who
examine seasonality determinants for Spain’s main markets; Fernández-Morales and
Cisneros-Martínez (2019), who provides a seasonal decomposition of cruise tourism,
Martínez et al. (2019), who analyses the different regions in Spain using a DP2 indicator,
and Duro and Turrión-Prats (2022) for Catalonia.
In geographical terms, we chose to analyse seasonality at two levels. The first is
at the national level for EU-15 countries. This gave us a clear picture of the trends
observed in the data across different countries—for example, identifying which
countries show similar patterns of evolution. Along these lines, another valuable piece
of information obtained is that some countries can be grouped. Overall, the data provide
a rich source from which to extract information on seasonality.
In fact, the EU has taken this issue on as significant in terms of tourism policy. As
indicated by Ferrante et al. (2018), the European Commission and the European
Parliament have launched different programmes to combat seasonality, trying to
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develop off-season tourism by encouraging off-season tourism through tourism
programmes for old people, young people, or people with few resources. This is
evidenced by the interest of these institutions in knowing first-hand the evolution of
seasonality at the EU level through the study done in 2019, although the analysis is at a
more general or provincial level and its study is limited to some descriptive conclusions
about the seasonality or intensity of tourist activity (European Commission, 2019). The
second level is the regional level, NUTS-2. Through this exercise we obtain significant
information on the supply and demand effects within seasonality. Both levels of analysis
are valuable because they each provide new information about tourist behaviour
patterns, tourism activities present in these regions, and their influence on seasonality.
As previously mentioned, in the analysis at the regional level, we used the
product supply and demand side, whereas for the country analysis we used only the
demand side. Data on the supply of tourism goods and services gave us a better basis
on which to compare seasonality in different EU regions. This is because these products
provide more information than using only tourist arrivals. Obviously, we used the
arrivals to calculate the Gini values for every region, but when deciding the groups and
analysing the differences between them, variation in the goods and services offered by
the tourism sector provided us with key results. These results show that seasonality is
quite different when we focus on the supply side of the economy, validating the
importance we have placed on this analysis. Regions with more seasonal touristic
activity present higher seasonality values compared to regions that offer activities
related to urban activities with no seasonal patterns, like events and congresses.
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We asked ourselves different questions when planning the thesis and this
research. Seasonality is raised as a serious problems of tourist activity. Its negative
effects, from an economic, social, and ecological point of view, affect the sustainability
of the sector.
There are a number of dedicated political, management and economic efforts to
reduce levels of seasonality. Deep knowledge of this problematic area can allow us to
rationalize the resources and dedicate them to those problems that they really require
priority attention
Therefore, the first question we asked ourselves was whether seasonality at the
European level, specifically in the European Union (EU), was high or whether it did not
imply worrying levels. Obviously, knowledge of seasonality at the EU level can allow
different EU bodies and the Member States to clarify their actions and prioritize their
resources. To follow this logic, the next question we asked ourselves was whether levels
of seasonality and their evolution were homogeneous between the different countries
that are part of the EU. This information is significant in terms of tourism management
and policy. More accurate knowledge of seasonality in terms of the different Member
States, as we have said, would allow the greatest precision in terms of policies to be
applied by managers. Greater seasonality in countries where the type of tourism that
exists is due more to the demand produced by the climate than by the product itself
would require greater attention from managers. Areas with sun and beach tourism or
areas where tourist activity is produced by a product such as congresses or businesses
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related to urban tourism should have a different policy according to the analyses carried
out.
This approach becomes even more significant when the analysis takes place at
the regional level. This is again an innovative and important approach. Knowledge of
regional seasonality at the EU level has been little analysed, and where it has, this has
often been a partial analysis, only fetauring regions in a particular state or area as is
explained in previous sections.
Knowing more precisely what seasonality is at the regional level will lead us to
have a more concrete idea of what aspects need to be addressed to reduce seasonality,
if necessary. The first questions we ask ourselves are as follows:
- Can we identify group of regions with similar seasonality?
- Which regions and groups of regions have higher seasonality and which
lower?
- Can we identify seasonality evolution following supply patterns?
- Can similar patterns can be identified between regions, and similar behavior?
We can understand the evolution of seasonality if we treat seasonality when we
speak of the behavior of demand in relation to the offered product. Therefore, in
regional research, there is a double grouping, one around the demand that reaches the
analysed regions of the EU, and a second grouping around the most important products
of those regions. This grouping by product will allow us to identify patterns of
seasonality according to the tourist activity developed in each region. This grouping can
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allow us to identify products that may have a better attitude towards seasonality.
Although it is already known that some products have better behavior in the face of
seasonality, confirmation of this allows to identify possible policies to be developed in
each of the regions to reduce the level of seasonality and therefore reduce the negative
impacts.
Continuing with the topic of product analysis, identifying the climate issue as a
main cause of seasonality, and the fact that Mediterranean regions accumulate a higher
level of tourist entry into Europe as a whole, has made us wonder if there is a big
difference between the regions that develop their product around the three Ss, sun,
sand and sea, and other regions. We explore whether those who have developed other
products in addition to sun and sand are able to differentiate themselves clearly from
the areas with only the sun, sand and sea product. Possible success stories or good
practices identified in the development of these products may identify patterns to apply
to other regions with worse seasonal evolution.
A second group of questions are presented, leading to various exercises in
analysing and decomposing seasonality data, a common practice to identify the weights
and marginalities that occur in the evolution of seasonality, even at country and regional
level.
- Does the evolution of European seasonality explain the seasonality of
different states and regions?
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- What weight does each state and region place on seasonality at the European
level?
- How does the explanatory weight change according to the evolution of
tourist arrivals in each state and region?
Throughout the chapter, these questions will be answered, but new ones will
also be generated that will lead us to new research and to the implications in terms of
policies to be applied by managers and the private sector.
An added value of this research, as Ferrante et al. (2018) explained, is the fact
that there is little research analysing similar patterns and identifying seasonality
clusters. The main differences between our research and the research conducted by
Ferrante et al. are that they use overnight stays and we use arrivals, and they analyse
EU-28 as opposed to our EU-15. In addition, they have included Norway and Switzerland,
and faced similar problems with the analyse of UK and Ireland due to the lack of data.
As we stated previously, in the second section of this chapter we will try to
provide new information about seasonality in Europe and seasonality across some of
the European Union’s regions at the NUTS 2 level. This research offers the opportunity
to analyse seasonality at the EU level, allows us to see trends at the EU and country level,
and gives us the opportunity to identify different development depending on the
touristic activity developed in the countries analysed. The analysis at NUTS 2 level
reinforces the importance of the touristic product to understand the evolution of
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seasonality. This offers the opportunity to talk about the common policies applied by
the EU and the success of these policies.
Governments see this situation of seasonality as a difficulty in terms of possible
sustainability imbalances. As mentioned in the introduction, the negative economic,
social and environmental effects that result from excessive concentration of tourism
activity have led governments to establish policies to reduce seasonality. Thus,
programmes such as the European Commission’s Calypso, or IMSERSO in Spain, try to
develop off-season tourism activities to reduce seasonality and mitigate the negative
impacts. However, different governments’ efforts do not seem to have reduced
seasonality, as we will see in our analysis. The research we present here should allow
politicians to see that this is a problem that is still very present in the European tourism
sector, and that a higher level of research and new programmess are needed to reduce
the problem.
In this chapter, as we have presented in the introduction and in the literature
review chapter, one of the main questions to highlight is the question of the
geographical area to analyse. In this research, we have chosen the EU-15. As we stated
in previous paragraphs, an added value of this chapter is the decision to choose EU-15
because these countries are some of the more significant countries in terms of
international arrivals2, and the evolution of seasonality in this geographical zone could
2 According to the UNWTO, in 2019, Spain was in second position in the number of international
arrivals with 84 million, only behind France with 89 million. Also in terms of income, with 80 billion dollars,
behind the USA with 214 billion dollars. At global level, the European region remains the one that receives
the most international tourists globally with 744 million international tourists, more than double the
number of arrivals compared to the second region (Asia and the Pacific).
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offer some interesting patterns. In addition, these countries have some common policies
applied by the European Commission and it is interesting to check the validity of these
common policies.
Usually, we found analysis at country or regional level as presented by these
recent analyses done by Cisneros-Martinez and Fernández-Morales (2013), Duro (2016),
Ferrante et al. (2018), and Turrión-Prats (2018), Turrión-Prats and Duro (2019). Where
do not attempt to analyse a global area like the EU, but instead research a more specific
area, such as Spanish provinces (Duro, 2016) or Mediterranean cruise harbour as in
Fernández-Morales & Cisneros-Martínez, 2019. Therefore, it is true that European
countries are analysed by Ferrante et al. (2018) and global countries in Duro and Turrión-
Prats (2019), but the question of common policies is not applied in these two papers.
The second decision is the analysis at EU regional level. As stated in the chapter
2, it is important to focus the analysis at double level, at demand and at supply level.
Strategies to reduce seasonality are applied at both level but, sometimes, more
information and data is needed to reach a more successful policies and strategies. At
this level only few attempts are found, as stated in the previous paragraph, but none of
them at EU level.
3.1.2. Methodological Aspects
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The first pertinent question to ask on how to conduct research into seasonality
is around measurement. According to Duro (2016), despite there being a significant
body of research into methodologies for measuring seasonality, there is little which
specifies the best methodology (Fernandez-Morales 2003; Fernández-Morales and
Mayorga-Toledano 2008; Lundtorp 2001; Martín Martín et al., 2014; Wanhill 1980).
Koenig-Lewis and Bischoff (2005) refer to many other sources of current understanding
about the measurement of seasonality (Baron, 1975; Donatos and Zairis; 1991;
Drakatos, 1987; Sutcliffe & Sinclair, 1980; Yacoumis, 1980, Yan & Wall 2003; among
others). The conclusion of those authors is similar to of Duro (2016) in that it is difficult
to decide the best methodology to calculate seasonality.
There exists a range of methodologies that can be applied to measure
seasonality. In recent years, the most commonly used methodology has been the Gini
index, but there are other methodologies such as the Theil index and the coefficient of
variation (CV). These different methodologies are applied to calculate seasonality (Duro,
2016), and the main reason to calculate the different indexes is to try to resolve a range
of problems observed in each index. The option to calculate seasonality using different
indexes gives more consolidated results and more robust information about seasonality.
In this paper, we use the Gini index.3 Recently, authors such as Lo Magno et al. (2017)
have proposed alternative methodologies to study transport issues, alongside other
authors who utilized the CV, as in Rosselló and Sansó (2017) or Radic (2017).
3 We calculated the other indexes, Theil and CV, but the results are very similar to Gini and we decided to
use the latter.
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Nevertheless, and following Duro (2016), Lundtrop (2001) and Wanhill (1980),
the Gini index can be the best approximation for seasonality for three reasons:
The reduced dependence on the changes in the peak months
It is highly stable.
Its low sensitivity to extreme values.
After estimating our model using the Gini index, we provide a decomposition of
the results. The aim of the decomposition analysis is to give more information to
planners about seasonality and to demonstrate the underlying channels behind the
data. In that sense, we chose decomposition by factors, following Duro (2016),
Fernández-Morales et al. (2016), and Fernández-Morales and Mayorga-Toledano
(2008). Decomposing seasonality is a common practice beyond these papers (for good
examples, see Duro, 2018; Fernández-Morales & Cisneros-Martínez, 2019; Rosselló &
Sansó, 2017). This analysis gives us the chance to highlight in detail the evolution of Gini
values across the EU over recent years and indicates which countries contribute most to
the evolution of EU seasonality. We believe that this is one of the most valuable
exercises presented by this chapter.
By taking advantage of the time variation in our data, we demonstrate a set of
different results over the period studied. The evolution prior to the economic crisis in
2008 is clear, with a stark reduction in seasonality across the EU; after that year, the
seasonality increases, and during the last years analysed the evolution is not clear. The
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process of decomposition gave us the opportunity to clarify our understanding of the
origin of the increasing seasonality rates. We identify the countries responsible for the
observed evolution and we propose three subsets of countries, North, Center and South,
based on the similarity of their tourism products and seasonality evolution.
The next step is to present the Gini Index methodologies.
In mathematical terms, assuming two extreme cases, one in which arrivals in all
months are the same (represented by the equidistributional line) and the other in which
all the arrivals occur in a single month, representing the actual distribution of arrivals
across months with the Lorenz curve, the Gini index measures the ratio of the
concentration area, i.e. the area between the Lorenz Curve and the equidistributional
line to the area of maximum concentration. There are different ways of measuring such
ratio. One way of calculating it is as follows:
𝐺 =
∑ ∑ 𝑝𝑝𝑦−𝑦
(1)
Where i and j can be any two months in the year and p and p are the relative
weights of the observations (months); y is the variable measuring tourists flows (in this
case, non-resident tourist arrivals), and μ is the annual mean of y. Usually when applied
to the measurement of seasonality, the weights for all the observations are equal and
equivalent to 1/n, where “n” is the number of months under analysis (therefore, n = 12).
The interpretation of this index is intuitive: the higher the Gini index, the greater
the degree of concentration of tourist arrivals. One of its properties is that it gives
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greater weight to distributional changes occurring at the centre of the distribution (i.e.
in the months of annual average demand) and gives asymmetrical weight to changes in
the tails (i.e. months with higher and lower demand). Therefore, besides its utility as a
synthetic annual measure to compare the evolution of seasonality across years and/or
regions or countries, these properties have been highlighted to justify its extended use:
the Gini coefficient has little dependence on changes in the peak months; it is more
stable than other measures; and it has low sensitivity to extreme values (Baum &
Lundtrop, 2001).
Duro (2016) describes the main problem with the Gini index and the option of
using the Theil index as a second or alternative methodology. The author highlights that
the Gini index gives greater importance to distributional changes taking place in the
centre of the distribution and gives asymmetrical weight to changes in the tails.
A very important feature of inequality and concentration measures is their
decomposability—i.e. the possibility of calculating the contribution of different
components to the total concentration. The literature on inequality measurement
emphasizes different possibilities to decompose concentration indexes (Cowell, 1999):
We decided to employ decomposition by factor in which we assess the
contribution of each country to total seasonality seen in the EU, understanding the total
arrivals to the EU as the sum of arrivals in each of the member countries, and defining
each factor as a specific destination (a country or a group or countries). There are several
approaches to the decomposition by additive sources, depending on the concentration
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index chosen to analyse the concentration level. For instance, to analyse the
contribution of domestic versus international markets, Duro (2016) uses the natural
variance decomposition, which measures the total contribution of each factor as the
addition of its individual variance and the cross-covariance. On the other hand,
Fernandez-Morales and Cisneros-Martinez (2019), Fernández-Morales et al. (2016) and
Fernandez-Morales and Mayorga-Toledano (2008) use the methodology to decompose
the Gini index proposed by Lerman and Yitzaki (1985). As the Gini index is not additive,
to decompose it by additive components, Lerman and Yitzaki propose to view each
source’s contribution to the total concentration as the product of the source’s own Gini,
its share of the total variable under analysis (income in their study), and the correlation
of each source with the total rank of this variable4.
Following the example of the these cited studies, we also decided to use the
methodology proposed by Lerman and Yitzaki (1985) because it applies the Gini index,
the most widely used indicator of seasonality in tourism studies and it provides an
intuitive interpretation of the Gini index component. It not only allows for the estimation
of the contribution of each source to total annual seasonality, but also a measure of the
4 However, and according to Shorrocks (1982), there is no unique rule to conduct a factor distribution
of the correlation effects and thus, the contribution of the factors to the total inequality. As a result, the
contribution assigned to each component strictly depends on the way the interaction effects are allocated
among contributions. This is why the literature does not consider Gini as a decomposable index in these
terms (Goerlich, 1998). Therefore, this kind of decomposition, contrary to what happens in the group
decomposition, is not quite clear (Duro, 2016).
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marginal effect of changes in each destination’s share5, which helps us to understand
how changes in arrivals in a particular country affects overall seasonality in the EU.
The decomposition proposed by Lerman and Yitzaki (1985) is based on the
covariance approach to calculate the Gini index. Let Y be the variable measuring tourism
demand, in our case the number of monthly arrivals of non-resident tourist in Europe.
The annual Gini index would be calculated as follows:
G =
Cov(Y,F) (2)
where Y
is the mean of Y, F is its cumulative distribution function, and
Cov (Y, F) stands for the covariance between Y and F. Different from the definition of
the Gini coefficient explained previously (1), where each month had the same weight,
with this method it is possible to allocate weights to the months according to their
length, i.e. 31/365 for January or 28/365 for February and so on. The Gini coefficient
equals zero when arrivals are the same for all the 12 months, which means no seasonal
concentration. On the other hand, unlike continuous variables where the maximum is 1,
the restriction of 12 observations per year reduces the range of the Gini index to (0, 1 –
28/365), which is reached when Y is different from zero in February but zero in the rest
of the months (Fernández-Morales & Mayorga-Toledano, 2008).
5 This decomposition provides an easier interpretation of the Gini index components compared to
other decompositions of this index, as the one carried out by Shorrocks (1982) or by Dagum (1997), which
do not allow for the obtaining of relative and marginal effects
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The variable Y can be represented additively as the sum of the arrivals in each
country: Y = Y+ Y+ ⋯ + Y (therefore in our case, k = 1,.., 13). The Lerman and
Yitzaki (1985) approach implies the following decomposition:
𝐆 = ∑𝐒𝐤
𝐊
𝐤𝟏 𝐑𝐤𝐆𝐤 (3)
where G is the level of seasonality in the EU as a whole, G is the annual Gini
index of the country k, S is county k’s annual share of the total annual value of Y, and
R represents the correlation between arrivals in country k with the distribution of total
arrivals in Europe, this is, the correlation between Y and Y defined by: (,)
, , where
F and F are the cumulative distribution functions of Y and Y, respectively. Therefore,
the contribution of each country to the overall seasonality in Europe depends on these
three components: S represents the relative importance of the country k as a tourist
destination in the EU, G measures the annual seasonality in country k, and R gives us
an idea of the strength and direction of the linear relationship between the destination
k and the distribution of arrivals in Europe. The higher any of these components is in the
country k, the higher k’ contribution to overall seasonality.
It is important to emphasize that a destination with a relatively high degree of
seasonal concentration of arrivals might in fact reduce overall seasonality in the EU if its
arrivals are not concentrated in the other countries’ peak months because it will show a
negative R.
Finally, this decomposition method allows for the estimation of the marginal
effects of changes in arrivals in each country (Lopez-Feldman, 2006). Consider a change
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in the number of arrivals in country k equal to εY (i.e., ε is the percentage change
and it is assumed to be equally distributed throughout the year). It can be shown that
the partial derivative of the overall Gini with respect to this percentage change is:
∂G/∂ε= S (RG−G). Dividing by G gives the country’s marginal effect relative to
the overall Gini:
RME= /
= S
−1 (4)
In other words, the percentage change in arrivals in country K can increase (or
decrease) the overall seasonality in a proportion equal to the RME. Which can be
written as the country’s contribution as a percentage of the overall Gini minus the
country’s share of total arrivals. The sum of relative marginal effects of all countries is
zero and if all countries’ arrivals are multiplied by the same ε, the overall Gini is left
unchanged.
With this measure, it is possible to estimate the impact that a 1% change in
arrivals of non-resident tourists in country k will have on total tourism seasonality in the
EU. The estimation of these marginal effects can be particularly useful when evaluating
the effect of changes in arrivals patterns related to changes in economic conditions in
origin and destination markets, changes in the supply characteristics of particular
countries, or even natural disasters and terrorism attacks, among other factors.
Chapter is structured as follows. In the next section results and implications at
EU-15 level. The third section NUTS2 level results are presented. The final section
provides the mainimplications and future investigations .
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3.2. Seasonality in EU-15
3.2.1. Data
Duro (2016) outlines various perspectives on the data sources most often used
when studying seasonality. In particular, he uses the number of overnight stays at hotels,
as do Cuccia and Rizo (2011), Fernández-Morales (2003), Fernández-Morales and
Mayorga-Toledano (2008), and Martín Martín et al. (2014). We want to highlight these
references used to be oriented to analyse seasonality through the tourism demand and
in a less number using the supply offer. As previously discussed, our aim with this
research project is to further our understanding on both the supply and demand effects
of seasonality. We begin by analysing seasonality using country arrivals and present a
set of empirical facts about the evolution of seasonality across the EU. We then proceed
with the analysis of regional seasonality and present our conclusions on supply side
effects. To analyse the questions presented above, we use monthly data provided by
Eurostat on arrivals of non-residents at tourist accommodation establishments for the
period 1996–2019, for which complete monthly data were available for 13 EU member
countries: Austria, Belgium, Denmark, Finland, Germany, Greece, Italy, Luxembourg, the
Netherlands, Portugal, Spain, Sweden, and the United Kingdom. Unfortunately, monthly
data for France and Ireland are not complete for this period. Finally, we are able to
recover data on regions at the NUTS 2 level from 12 countries, excluding the UK and
Ireland and we have decided to consider Luxembourg as one region, beside the Belgian’s
regions.
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As indicated in the previous paragraph, we were unable to obtain monthly data
pertaining to France, which is the leading country for tourism in Europe. The Institut
National de la Statistique et des Études Économiques (INSEE) only provides monthly
information at NUTS 1 and NUTS 2 levels from 2010 onwards. In previous years, INSEE
provided this information for the peak tourism period, April to September, though this
cannot be used to make general conclusions about seasonality throughout the year.
From 2010 onwards, even the data are not complete, authors like Rico et al. (2021) or
Sustar and Anzic (2019) offer some results on seasonality in France. France has the
largest tourism market in Europe and it offers a very broad range of activities and
products for tourists. Tourists can enjoy the beaches, mountains, urban life and the
countryside. According to data from Eurostat, in 2018, France enjoyed 15 per cent of the
arrivals and 11 per cent of the overnight stays recorded across all EU-15 countries. These
data show the importance of France within European tourism. Despite this, we proceed
with the countries listed above. These countries cover more than 80 per cent of total
arrivals in the EU-15 and provide a complete time series at monthly intervals.
Nevertheless, Ferrante et al. (2018) and Sustar and Anzic (2019) offer guidelines about
seasonality in France and we comment on their conclusions in the results section.
Other important countries to highlight at the NUTS 2 level are the Netherlands
and Italy. In the case of the Netherlands, data are available over a long period, but the
data are not homogeneous. In the end, we decided to use the four years 2012–2015.
For Italy, we found some problems with specific data variables; the period available is
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short, at only five years, and we observed some minor problems which led us to ignore
data recorded for the Lazio region.
Other minor but relevant points about the data are as follows.
Some countries provide data at the NUTS 3 level, so we aggregate the
different NUTS 3 data to obtain the NUTS 2 data.
Denmark gave information about occupied rooms rather than arrivals,
but both statistics provide similar information.
3.2.2. Global Results
As we said, we want to present an analysis of seasonality across the EU as a
whole. We believe that the results are of interest to us as researchers but also to
practitioners since they give us an initial description of seasonality over recent years.
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Figure 3.1. Arrivals of non-residents and seasonality in the EU (1996–2019)
Source: Compiled by author based on Eurostat
Figure 3.1 shows that the number of arrivals in the EU increased over the 25 years
analysed. Tourism was one of the most dynamic economic activities during that period.
After the economic crisis of 1993–1994, the world’s economy improved and we saw the
emergence of the BRIC6 countries. Growth was especially impressive in China and Russia,
6 BRIC countries are Brazil, Russia, India and China. These countries experienced significant economic
improvement from early 2000 to 2015. There are some common factors amongst these countries: for
example, they are highly populous and very large geographically. Despite this, Brazil and Russia suffered
some economic difficulties and their evolution had been less positive in recent years.
6
0
0
0
0
0
0
8
0
0
0
0
0
0
1
0
0
0
0
0
0
0
1
2
0
0
0
0
0
0
1
4
0
0
0
0
0
0
Arrivals
.
1
9
.
2
.
2
1
.
2
2
Gini Arrivals
1995
2000
2005 2010 2015 2020
Year
Gini Arrivals
Arrivals
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which led to these countries becoming large sources of outgoing tourists. Tourism has
grown across the international market, and Europe is a main destinations, as we can see
in the increasing number of arrivals. The trend is increasing, except for in 2001 due to
the 9/11 terrorist attacks. Importantly, following the severe financial crisis of 2008–
2009, the number of arrivals recovered quickly, and in 2011 the number was larger than
in 2008.
Between 1996 and 2019, we can distinguish two different periods which show
two contradictory trends in seasonality. The economic recovery after the 1993 economic
crisis led to a great reduction in seasonality. This reduction is linked to some economic
and social changes related to tourism, such as the emergence of new tourism products
developed during those early years. These include an increase in city breaks or green
and rural tourism combined with the emergence of low-cost airlines. As we can see, the
Gini index reduced in value from 0.22 to 0.19 between 1996 and 2008. Despite this, the
financial economic crisis in 2008/2009 sent the Gini figure back to the level recorded in
1996. People reacted to the economic crisis by reducing the number of trips they took
due to a lack of confidence in the wider economic situation. The effect was that people
concentrated the number of trips in specific periods of the year, and as a result
seasonality increased. During the last years, 2015-2018 it seems that seasonality
recovers a positive trend, except the last year analysed, 2019, with a higher seasonality
value than previous years.
New tourism products developed in the late 1990s, and the interest of
governments and companies in taking advantage of the resources held by the tourism
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sector, will lead to further reductions in seasonality. Obviously, the coronavirus
pandemic of 2020 and 2021 has put a stop to the downward trend in seasonality. It is
too soon to calculate the overall effect that the pandemic will have on seasonality, but
a large fraction of tourist activity during 2020 was recorded in summer. In addition,
tourism increased again in 2021 compared to 2020. Both years will record a high level
of monthly seasonality.
Gini values obtained in this research are around 0.2. This value, as an average
level, could be considered a medium value and is similar to that obtained by Ferrante et
al. (2018) (but some areas analysed are around 0.5, specifically in Mediterranean
regions, as we will present in the next chapters). During the first period, we can see a
seasonality reduction of 15 per cent: this means a reduction of 1.5 per cent annually.
We can see a clear demand and tourist arrivals increase. With this situation, we could
say that the good economic data, the increasing offseason holiday trips, and the
increasing arrivals led to a reduction in seasonality. But with the financial crisis of 2008–
2009 this situation changed: we observe a return to the highest values, but with a
significant change—the arrivals continue the growing trend. During the last five years,
seasonality has shown little reduction.
Turrión-Prats and Duro (2019) found similar results when analysing Spain’s
seasonality in a similar period. In the first years analysed, seasonality falls with an
increase in demand; in a second period, the authors find an increasing seasonality
alongside the demand. The same authors, in Duro and Turrión-Prats (2019), found a
similar pattern when they analysed developed economies worldwide. A possible
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explanation is that the traditional and most important tourist destinations suffer more
when demand rises. This increase in demand tends to happen during the traditional
periods related to climatic conditions (summer or winter) and social factors (school
holidays). The impact is that some tradional areas with important tourist activity based
on the climate who developed an important effort to reduce seasonality during the first
years of the data found that the increasing demand showed a high concentration during
months with high demand, resulting in increased seasonality.
As we highlighted previously, Gini values for the EU are around 0.2. In fact, as an
average level this is not too high, however it conceals the fact that there are some
regions which show relatively much higher, and therefore concerning, seasonality rates.
Over the following sections, we show that some countries, particularly in southern
Europe, where tourist arrivals are higher than other areas in the north, the Gini index is
higher than 0.2. As seen in Figure 3.1, arrivals grew towards the end of the period
studied, however seasonality evolved differently over the same period.
As we can see in Figure 3.1, seasonality follows a U-shaped curve until 2016.
Clearly decreasing over the years 1996 until 2008, after which it trends upwards. We can
state that, before the economic crisis, seasonality had decreased. This leads us to the
following hypothesis: the state of the wider economy is the principal driver of
seasonality in tourism, however it is not the only cause. We observe that tourist
attitudes changed during this time which led to a reduction in seasonality.
UNIVERSITAT ROVIRA I VIRGILI
TOURISM ISSUES: SEASONALITY AND ECONOMIC STRUCTURE
Albert Vancells Farraró
80
It is true that seasonality is highly correlated with the health of the general
economy, but to what extent this drives seasonality will be treated in the next chapter.
First, we will deal with seasonality variation across EU countries and regions.
Our first method of analysis is to check the validity of the time series used. To do
so, we check if we can find a structural break in the period analysed. We apply a test of
structural change (the Chow test) with the aim of observing whether or not a significant
change occurred in 2008. The Chow test allows us to observe whether a structural
change has occurred, based on verification of the errors of two separate estimates of
the structural change point, and the third model includes all the period.
Firstly we apply an AR(1) model7 with the following form:
ln(𝐺𝑖𝑛𝑖)=∝+𝛽ln(𝐺𝑖𝑛𝑖)+ 𝛽ln (𝑎𝑟𝑟𝑖𝑣𝑎𝑙𝑠)+𝛽ln (𝑎𝑟𝑟𝑖𝑣𝑎𝑙𝑠) + 𝜀 (5)
where ln is natural logarithm and the standard error
Table 3.1 try to establish the relationship between Gini and arrivals considering all the
European countries under consideration (13 countries).
7 An autoregressive (AR) model is a representation of a type of random process that describes certain
variable processes in time either in nature, the economy, etc. The autoregressive model specifies that the
output variable depends linearly on its previous own values (Garcia-Alvarado, 2014). Gi-Alana et al. (2004)
presented some interesting works, as stated in the chapter 2, where they used some AR(1) methodologies
to time series in a similar way that is presented in this thesis.
UNIVERSITAT ROVIRA I VIRGILI
TOURISM ISSUES: SEASONALITY AND ECONOMIC STRUCTURE
Albert Vancells Farraró
81
Table 3.1 Model AR (1)
(1)
(2)
(3)
lngini
lngini
lngini
Ln(Gini
t
-
1
)
0.800***
0.802***
0.769***
(6.21)