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Tourism and regional development: a spatial econometric model for Portugal at municipal level

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Abstract

This study examines the importance of tourism as a factor for regional economic development in Mainland Portugal, emphasizing the inter-regional spatial spillover effects. A spatial analysis of the main variables of the tourism sector revealed strong evidence of positive spatial autocorrelation across the municipalities of Portugal. A significant spatial clustering of these activities on coastal locations was identified, leading to the formation of hot spots in coastal regions and cold spots in inland regions. Furthermore, this work specifies spatial econometric models aiming to estimate the relevance of the tourism sector in regional economic development, on a municipal level. The econometric model, which highlights the role performed by interregional spatial spillovers, regresses the regional gross value added against a group of variables, which reflect the contribution of the tourism sector and, furthermore, control variables for the classic determinants of income, for the 278 municipalities of Portugal. The results show that tourism is a significant driver of regional economic development. Moreover, they revealed the presence of positive and significant inter-regional spillover effects, which strongly enhance tourism’s economic impact.

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Notes

  1. Mainland Portugal (hereafter referred to simply as Portugal) is divided into 278 municipalities, with an average surface area of 371 km2, corresponding to the administrative division of the country at the level of LAU 1 (Local Administrative Unit level 1, former NUTS 4).

  2. In Portugal there is no data on “Gross Domestic Product” at the municipal level, therefore GVA was selected. The suitability of its use stems from the strong correlation between the two variables: for 2012, at NUTS 3 level, these two variables have a correlation coefficient of 0.9935.

  3. All the statistical analyses and econometric estimations were performed using the software GeoDa and SpaceGeoDa, Copyright © 2011–2015 by Luc Anselin.

  4. It is possible to express the degree of spatial proximity of N geographic units through a spatial weight matrix W (NxN). For each location i (in line), wij = 1 if locations i and j are neighbours and wij = 0 otherwise. Additionally, it is assumed that wii = 0, that is, that a location is never its own neighbour. Typically, the criterion used to define whether two geographic units are neighbours or not is based on the geographic arrangement of the observations, more specifically on the contiguity or the geographical distance between them. On the definition of matrix W, particularly on the range of criteria that can be used to define neighbourhood and on the relevance of the matrix, see Getis and Aldstadt (2004), Getis (2009) and Harris et al. (2011).

  5. With a value of 0.114 for overnight stays (p value: 0.013) and of 0.159 for accommodation capacity (p value: 0.003).

  6. The National Strategic Reference Framework (QREN) was the framework for the application of Community economic and social cohesion policy in Portugal for the period 2007–2013.

  7. Regarding Moran’s I test see, for instance, Cliff and Ord (1972) or Anselin (1999).

  8. The alternative spatial econometric models used were the spatial lag, the mixed-regressive, the spatial error, the combo spatial lag + spatial error and the combo mixed-regressive + spatial error models (see Anselin 2014) but as mentioned the best results were obtained for the mixed regressive-spatial cross-regressive.

  9. For an in-depth discussion of the various models tested, the estimation methods and the statistical tests used to compare and choose between the different specifications, see Vieira (2016); on the models and estimation methods, see also Anselin (1988 and 1999) and Kelejian and Prucha (1998).

  10. See, for instance, Banco de Portugal (2014).

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Funding

This research has been financed by Portuguese public funds through FCT - Fundação para a Ciência e a Tecnologia, I.P., in the framework of the project with reference UIDB/04105/2020.

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Authors and Affiliations

  1. CEF.UP and Faculdade de Economia, Universidade do Porto, Porto, Portugal

    Luis Delfim Santos

  2. Faculdade de Economia, Universidade do Porto, Porto, Portugal

    Ana Catarina Vieira

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  1. Luis Delfim Santos
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  2. Ana Catarina Vieira
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Correspondence to Luis Delfim Santos.

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Santos, L.D., Vieira, A.C. Tourism and regional development: a spatial econometric model for Portugal at municipal level. Port Econ J 19, 285–299 (2020). https://doi.org/10.1007/s10258-020-00179-z

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