Agglomeration, innovation, productivity and tourism competitiveness:a spatial econometric analysis on European regionsJoão Romão - Hokkaido University
Abstract
Tourism is a place-based activity, while in many advanced economies it is increasingly
becoming a knowledge-based activity. Recent developments and innovations in Information
and Communication Technologies (ICT) have radically transformed the tourism business. The
broad interpretation of the tourism sector adopted in our work follows this comprehensive
approach, including ICT services. This analysis for 237 European NUTS 2 regions aims tocontribute to the understanding of whether and how the development of regional systems ofinnovation has influenced the competitiveness of tourism destinations.
Against the background of existing relevant and comparable data - appropriate for aninternational analysis of the tourist sector - this study examines the impacts of both traditional
production factors (physical and human capital) and contextual variables related to theterritorial capital of each region (such as level of specialisation in tourism, intensity ofinnovation, labour qualification or productivity) on regional tourism competitiveness(expressed by the evolution of the growth of gross value added generated by tourism in eachregion) over a period of 8 years (including a period of unprecedented expansion of tourism
activities, negative economic effects caused by the international crisis and the gradual effectsof the processes of recovery).
The results reveal lower levels of productivity in those regions where tourism services are
more labour-intensive, suggesting that the supply of tourism services in those regions is basedon traditional products and services with a low value added. On the other hand, regions where
education, innovation and productivity demonstrate higher levels are also those where grossvalue added in tourism is less important for the regional economy. Different patterns ofspatial development and agglomeration, with important policy and managerial implications
for the tourist sector, were identified, thus confirming the great potential of spatial
econometric analysis – and, in particular, spatial autocorrelation methods – for advanced
tourism studies.
	
  

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Agglomeration, innovation, productivity and tourism competitiveness:a spatial econometric analysis on European regionsJoão Romão - Hokkaido University
Tourism is clearly a place-based activity, while in many advanced economies it isincreasingly becoming a knowledge-based activity. Co-terminality, spatiality and temporality
distinguish tourism from other economic activities, enhancing its potential for thedevelopment of practice and place-based innovation strategies based on information andknowledge, while the spatial agglomeration of tourism activities can generate externalities forthe regional economy. Recent developments and innovations in Information andCommunication Technologies have radically transformed the tourism business, increasing its
potential for interactivity and cooperation, making information a key element to match supply
and demand of tourism services. New practices oriented towards personalized needs andbased on new internet services enhance the potential for the development of processes of cocreation of services or may even to become “tools of mass collaboration”, with significant
impacts on innovation processes, collaborative governance models or education and training.
The broad interpretation of the tourism sector adopted in our work follows thiscomprehensive approach.
As most of the tourism services are provided by small and medium-sized companies - oftenwith limited structured formal learning processes -, the information obtained through theirinteraction with tourists tends to be tacit and localised, enhancing the importance of regionalinnovation networks for innovation. The regional analysis developed in this work aims tocapture this regional innovation dynamics and its implications for tourism competitiveness.
Against the background of existing relevant and comparable data, this study examines
whether and how the development of regional systems of innovation influence thecompetitiveness of tourism destinations. The impacts of both traditional production factors
and contextual variables related to the territorial capital of each region on regional tourism
competitiveness over a period of 8 years will be analysed.
The research includes a general inventory and overview of the data for the different variables
included in the model. Next, an exploratory spatial analysis based on local Moran-I indicatorsof spatial autocorrelation between tourism competitiveness and the explanatory variables
	
  

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taken into consideration is offered. Finally, an advanced econometric approach based on a
spatial panel data model is presented and developed. A discussion of the main results of ouranalysis and its policy and managerial implications will be offered. This study covers almost
all NUTS-2 regions (237) in the European Union. NUTS-2 regions are relevant for the
purposes of this analysis, as they generally have a specific institutional framework forregional and tourism policies and exhibit also some territorial coherence, although, in a strict
sense, they can not exactly to be considered a tourism destination.
The dependent variable considered in the spatial panel model is tourism competitiveness,measured on the basis of the regional gross value added (at current prices) by tourism
activities in each region. Tourism is clearly more important in South and West European
regions but a shift to the Eastern side of Europe can be noticed in the last 8 years. The
determinants of the regional tourism competitiveness considered are related to the importanceof tourism in the regional economy (share of tourism in regional employment and gross value
added; tourism demand, measured by the nights spent at regional tourism accommodation
establishments), human capital (education of the work force), physical capital (gross fixed
capital formation in tourism) and immaterial regional resources (productivity and regional
investment in research and development). These indicators reflect the territorial capital ofeach region and its ability to transform resources into competitive products and services.
Tourism regions are normally not islands in isolation and tourism flows are often crossing
regional borders. The existence of such spatial effects can be tested using Global and Local
Indicators of Spatial Autocorrelation by introducing spatially lagged variables. For the
purposes of this analysis, a geographic neighbourhood is defined according to the second
level rook contiguity criteria. This information is used to build a spatial weight matrix thatdefines the impacts of each region on its neighbours. Two additional conditions are defined:
the spatial weight matrix is row normalized and the impact on the direct first-level neighbour
is twice as large as the impact on the second level.
Clusters of high values of regional competitiveness in tourism and high tourism demand wereidentified in the Southern European regions from Spain, France and Italy. It is also noticeable
that there exists a large number of Southern regions (in the same countries) where low value
added in tourism has a clear spatial autocorrelation with high tourism demand, suggesting theexistence of a tourism supply based on low value-added products and services, with relatively
	
  

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low productivity. On the other hand, clusters of low values for both variables can generally befound in Eastern European regions, despite the existence of a few exceptions within this area
(and also in the United Kingdom), where a reverse pattern occurs.
It is also possible to observe a high dispersion of clusters of regions with a positive spatial
autocorrelation. Some regions from Portugal, Spain and Italy, but also in the United Kingdom
or North Eastern Europe, show high values for both variables, linking high competitiveness tohigh specialisation in tourism. Clusters of regions with low competitiveness and low
specialisation are located in Central or Eastern European regions. Central Europe is the area
where clusters of regions with high competitiveness and low specialisation can be identified(mostly in the North of France and Germany), suggesting high productivity in tourism
services, although this sector is not predominant in the regional economy. The other type ofnegative spatial autocorrelation occurs in important parts of the territories from Portugal,
Spain, Greece or North Eastern regions, revealing a high importance of tourism for theregional economy, but low achievements in terms of value added (low productivity).
A large number of clusters of regions with low levels of competitiveness in tourism and low
levels for productivity, education and R&D (mostly in Eastern European regions) is also
found. On the contrary, very few clusters of regions exist where high levels of tourism
competitiveness are related to high scores achieved in any of those three factors. We may also
observe the existence of a large number of clusters of regions with low values for gross value
added in tourism, despite the high scores registered for productivity, education and R&D in
the area where they are located. Again, this confirms the abundance of regions where thesefactors are more developed and tourism is less important within the regional economy. On thecontrary, there is a low number of regions registering the reverse situation (high value added
in tourism with low scores for productivity, innovation and education).
A spatial panel data model is an adequate tool to provide a model explanatory analysis, as itallows the estimation of a model including a time series and cross-sectional dataset, withinformation related to a large number of units over 8 years, taking into consideration thepotential spatial effects. For computational purposes, natural logarithms were applied to thedata related to the dependent variable, measuring tourism competitiveness (gross value addedin the tourism sector - “logGVAT”) and to the independent variables related to tourism
demand (nights spent in tourism accommodation establishments - “logNIT”) and investments
	
  

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in tourism (gross fixed capital formation in the tourism sector - “logINV”); the importance of
tourism in the regional gross value added (GVATS) and employment (EMPTS) are expressedin percentage, which is also the case for the immaterial factors of competitiveness (work force
with tertiary education – “EDUC” – and investment in research and development as a share of
the regional GDP – “ID”). Productivity (“PROD”) is expressed in thousands of euros per
worker.
(1) logGVATit = ß0 + ß1 logNITit + ß2 logINVit + ß3 EMPTSit + ß4 GVATSit + ß5 EDUCit ++ ß6 IDit + ß7 PRODit + it uit
where i is an index for the regions, t is an index for the time period and u is the error term. All
the scores obtained for a Variance Inflation Test were clearly below the threshold of 5(O’Brien, 2007), revealing the absence of problems caused by multicollinearity. The
computation of a spatial panel model regression implies the inclusion of a spatially lagged
endogenous variable, measuring the impact of the growth of tourism competitiveness in one
region on its neighbours. A spatial multiplier is also included in order to capture un-modelled
spatial effects potentially existing in the distribution of the error term (spatial error).
(2) Yit = ρWYit + Xitβ + uit,
(3) uit = λWuit + εit
In this case, Y represents the dependent variable, X is a vector that represents the independent
variables, W is a nonnegative N×N matrix of known contiguity parameters describing the
spatial impacts among the regions considered in this study, WY represents the endogenousinteraction effects among the dependent variable, Wu shows the interaction effects among the
disturbance terms of the different units, ρ is called the spatial autoregressive coefficient(identifying the spatial lag), λ the spatial autocorrelation coefficient (identifying the spatial
error), i is an index for the regions and t is an index for the time period concerned.
Considering the existence of both types of spatial effects (identified after the performance of
relevant tests), a general spatial Cliff-Ord type model (Cliff and Ord, 1981) is computed andthe option between a random or fixed effects model has been supported by the result of a
Spatial Hausmann test (p-value < 2.2e-16), leading to the choice for a fixed effects model.

	
  

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The results of the model confirmed the expected positive correlation between tourism
competitiveness, tourism demand, investment in the tourism sector and the level of
specialisation in tourism, when measured by the share of the gross value added by this sector
within the overall regional gross value added. Nevertheless, when specialisation is measured
taking into consideration the share of the work force employed in tourism, a negative
correlation with tourism competitiveness can be found. This suggests that regions where thetourism sector is more labour-intensive achieve lower levels of productivity and also
relatively lower levels of gross value added in the tourism sector. It was also possible toobserve that productivity in tourism, qualifications of the regional work force and regional
investment in research and development have the expected positive impacts on thecompetitiveness on tourism. Time effects clear identify a negative impact on the gross value
added in the tourism sector after 2008, reflecting the impact of the international financial
crisis on the tourism sector. The existence of spatial effects among regions is also clear. These
include positive spillover effects, expressed in the value of the spatial autoregressive
coefficient (suggesting that tourism dynamics in one region has positive impacts on thecontiguous regions) and also positive unmodelled effects, expressed in the value of the spatial
autocorrelation coefficient).
The exploratory spatial analysis presented had revealed the existence of a large number ofSouthern European regions (where the major mass tourism sun-and-sea destinations arelocated) with high levels of specialisation in tourism and relatively low achievements in termsof value added. This has important implications for those destinations, as it reveals theexistence of a high pressure on local resources (normally ecologically sensitive), withrelatively low impact on the regional economy. Adequate policies oriented to increase thevalue added of these services may lead to the achievement of a higher economic performance,
reducing the negative impacts on the environment and landscapes.

	
  

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