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The effects of World Heritage Sites on domestic tourism: a spatial interaction model for Italy

Journal of Geographical Systems volume 15, pages 369–402 (2013)Cite this article

Abstract

Culture is gaining increasing importance in the modern tourism industry and represents a significant force of attraction for tourists (both domestic and international). Cultural tourism allows destinations and regions to expand their customer base, diversify their offer, extend the stay of the tourist, and reduce seasonality. Great efforts are made, by national governments and regions, in order to obtain official designation regarding the relevance of their historical/cultural attractions, for example through UNESCO’s World Heritage Sites (WHS) list. Such an aspect seems particularly relevant for a country like Italy, which has a high number of entries in the WHS list and where regions take an active role in promoting tourism. Using an 12-year panel of domestic tourism flows, we investigate the importance of the regional endowment in terms of WHS from two perspectives: (a) by separately estimating the effects, on tourism flows, of WHS located in the residence region of tourists and in the destination region; and (b) by taking into account potential spatial substitution/complementarity effects between regions due to their WHS endowment. Finally, a sensitivity analysis is offered to evaluate the spatial extent of the latter.

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Notes

  1. The UNWTO also derived different categories of tourism by combining the three basic forms of tourism: ‘internal tourism’, which comprises domestic tourism and inbound tourism; ‘national tourism’, which comprises domestic tourism and outbound tourism; and ‘international tourism’, which consists of inbound tourism and outbound tourism.

  2. Further specifications in the literature have used population (Linnemann 1966) in order to capture size effects.

  3. ‘Cultural heritage’ is defined in Article 1 of the Convention concerning the Protection of the World Cultural and Natural Heritage (adopted by UNESCO in 1972) as monuments, groups of buildings and sites that are of ‘outstanding universal value from the point of view of history, art or science’ and form the ‘aesthetic, ethnological or anthropological point of view’.

  4. A further (binary) variable simply indicating a relationship of spatial contiguity (shared border) between the origin and destination regions could be employed, if it is of interest to parcel this component out from the average effect of distance. We choose not to follow this approach, so to maintain the most general estimate for distance deterrence.

  5. One would prefer to use regional power-purchasing-parity (PPP) price indices to account for relative consumption prices. However, such indices are not available from the Italian National Statistical Agency and have been computed only in one study (ISTAT, Unioncamere, Istituto Tagliacarne 2010). Additionally, the FE estimators used in this paper would render the long-run levels of relative price irrelevant (they are absorbed into the FE), so that only short-run inflation trends would be identified (as for the variable used here).

  6. The variable for the share of coast unsuitable for bathing should ideally be complemented by a variable for the length of the coast, in order to account for landlocked regions. As for other time-invariant variables (e.g., indicator variables for regions bordering with other countries), it is not possible to include them in our models (unless interacted with time-varying variables), as their effect is accounted for by the FE.

  7. When a contiguity rule is applied to define proximity, two regions are defined as neighbours if they share a border. In rook contiguity, the common border has to have length greater than zero, while in queen contiguity, common borders of length zero are allowed as well.

  8. Internal distances are computed as \( \sqrt {{{\text{area}} \mathord{\left/ {\vphantom {{\text{area}} \pi }} \right. \kern-0pt} \pi }} \) (see, e.g., Leamer 1997; Nitsch 2000).

  9. Because the implementation of a panel spatial filtering model is not the main focus of this paper, we refer to Chun and Griffith (2011) and Lionetti and Patuelli (2009) for methodological and implementation details.

  10. Alternatively, we could hypothesize \( T_{2.2}^{\prime \prime } (k) < 0 \). We consider the case of the intersecting functions more interesting, and we limit ourselves to discussing the latter.

  11. The results concerning GDP and population are stable independently of the number of contextual variables added to the basic framework of the spatial interaction model (supply and demand size variables and distance as a deterrence variable). Only if the individual FE are excluded from the model GDP and population appear with the usual (expected) coefficient values between 0 and 1, which leads us to assume that in our panel specifications the ‘size’ effects are picked up by the FE.

  12. A sensitivity analysis testing polynomial specifications for the distance term shows that a cubic specification provides slight fitting advantages (for example, in terms of BIC). The negative-positive-negative signs for the three terms of the polynomial suggest that a destination’s distance from the tourist’s residence region becomes a positive tourism reinforcing factor only after a certain threshold (after which the destination appears to be ‘exotic’), and up to a second threshold level, after which the distance deterrence effect again becomes dominant.

  13. Our results might not carry over to international tourism.

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Acknowledgments

The authors wish to thank Roberto Cellini (University of Catania) and two anonymous referees for useful comments, as well as session participants and seminar attendees at: the 2011 NECTAR Conference (Antwerp), the 51st European Congress of the Regional Science Association International (Barcelona), the Fifth European Workshop on Applied Cultural Economics (Dublin), the SNSF International Exploratory Workshop on ‘Advances in the Statistical Modelling of Spatial Interaction Data’ (Lugano), the 2nd International Conference on the Measurement and Economic Analysis of Regional Tourism (Bilbao), the Toulouse School of Economics of the Université Toulouse 1 Capitole (Toulouse), the Workshop on ‘The Economics and Management of Leisure, Travel and Tourism’ (Rimini), and the Workshop on ‘UNESCO World Heritage: Economic and Policy Issues’ (Turin).

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Authors and Affiliations

  1. Department of Economics, University of Bologna, Rimini Campus, via Angherà 22, 47921, Rimini, Italy

    Roberto Patuelli & Maurizio Mussoni

  2. Department of Economics, University of Bologna, piazza Scaravilli 2, 40126, Bologna, Italy

    Guido Candela

  3. The Rimini Centre for Economic Analysis (RCEA), Rimini, Italy

    Roberto Patuelli & Maurizio Mussoni

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  1. Roberto Patuelli
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  2. Maurizio Mussoni
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  3. Guido Candela
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Correspondence to Roberto Patuelli.

Appendix

Appendix

See Table 6.

Table 6 Correlation matrix
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Patuelli, R., Mussoni, M. & Candela, G. The effects of World Heritage Sites on domestic tourism: a spatial interaction model for Italy. J Geogr Syst 15, 369–402 (2013). https://doi.org/10.1007/s10109-013-0184-5

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Keywords

  • Cultural tourism
  • Domestic tourism
  • World Heritage Sites
  • Spatial interaction model
  • Italy
  • Spatial competition

JEL Classification

  • C23
  • L83
  • R12
  • Z10