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On the role of cultural participation in tourism destination performance: an assessment using robust conditional efficiency approach

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Abstract

The relationship between culture and tourism has been widely investigated from different perspectives. A large strand of literature studies the role of cultural heritage to attract tourists, while a rich bulk of studies on cultural participation investigates the impact of tourism flows on the demand for culture. Another aspect worth investigating relates to the link between cultural participation and the performance of tourism destinations (TDs), as a higher cultural participation in an area could boost the performance in the management of tourism resources. However, so far, this issue has been disregarded in the literature, and this paper aims at filling this gap. Specifically, it studies the effect of cultural participation on TDs’ performance using a conditional efficiency approach that ensures robust inference on the role of environmental factors. We employ data on the Italian regions for the period 2004–2010, and we explore the role of cultural participation for tourism by using several indicators. Our findings offer empirical support to the positive role of cultural participation and, thus, suggest that public cultural policies might also boost the efficiency of the tourism sector.

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Fig. 1

Source: our computation on data provided by ISTAT

Fig. 2

Source: our computation on data provided by ISTAT

Fig. 3

Source: our computation on data provided by ISTAT

Fig. 4

Source: our computation on data provided by ISTAT

Fig. 5

Source: our computation on data provided by ISTAT

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Notes

  1. It is difficult to define cultural tourism (Richards 1996); however, its role within tourism is widely acknowledged as showed by the first World Conference on Tourism and Culture organised by the United Nations World Tourism Organisation and UNESCO (UNWTO 2016).

  2. Household expenditures for culture can be considered also an indicator of the cultural capital of an area, which following Bourdieu (1984) can be used to explain cultural tourism production (Richards 1996).

  3. Examples of efficiency assessment in a dynamic perspective are Cracolici et al. (2007), Peypoch and Solonandrasana (2007), and Cuccia et al. (2017).

  4. Note that the order-m efficiency scores are not bounded by 1 as it is the case under DEA or FDH. In these cases, values equal to 1 correspond to efficient DMUs, whereas values higher than 1 correspond to inefficient DMUs.

  5. The debate focuses on the more appropriate estimator for the second stage (see Simar and Wilson 2011).

  6. The separability condition requires that the environmental variables \(z \left( {z \in {\mathbb{R}}_{ + }^{k} } \right)\) do not influence the input–output space \(\Psi\) (Simar and Wilson 2007, 2011).

  7. In what follows, we use an output-oriented model assuming that TDs maximise their outputs for given inputs.

  8. See Daraio and Simar (2005, 2007a) for a detailed formal description of the model.

  9. Note that for a sample size \(N\), Daouia et al. (2012) proposed a simple rule \(m = \sqrt[3]{N}\) that in our sample corresponds to \(m \approx 6\).

  10. Furthermore, yearly unconditional efficiency estimates reported in “Table 6 in the Appendix” clearly show that in our case the TDs’ performance and rank are quite stable in the time span period.

  11. The Trentino Alto Adige region has two autonomous provinces, Bolzano and Trento, which are usually assimilated to regions in empirical works about Italy, because of their institutional autonomy.

  12. In the geographical maps, for the autonomous provinces Bolzano and Trento, it is reported the average regional value.

  13. For the sake of completeness, we use all the items from the “Indagine Multiscopo” that include also some variables related to leisure activities. In general, despite the attempt to homogenise cultural statistics (UNESCO 2009), there are still differences among countries. In fact, the Italian national survey we use adopts a different definition of cultural participation compared, for instance, to Eurostat (2016). The Italian definition includes two items (z 6 and z 7) adopting a wider definition that contains some leisure activities. We believe that this “extended” definition allows us to get a wider overview of regional liveliness.

  14. The curse of dimensionality implies that, for a given sample size, a small dimensionality space (i.e. the number of input and output variables in the efficiency analysis) tends to produce better estimates for the efficient frontier than large dimensionality space. For a numerical example of the trade-off between sample size and the number of inputs and outputs used for consistency of efficiency estimates, see Simar and Wilson (2008).

  15. It is worth to recall that in such a context, an efficient TD, which is located on the best practice frontier, obtains an efficiency score \(\hat{\lambda }_{m}\) = 1, while inefficient TDs are denoted by efficiency scores higher than 1. The inefficiency measure (i.e. \(1 - \hat{\lambda}_{m}\)) indicates the potential percentage increase in the output that an inefficient TD could achieve performing as efficiently as its references m. Finally, efficiency scores lower than 1 represent super-efficient TDs. Furthermore, it must be noticed that the order-m estimator is robust with respect to the presence of outliers.

  16. This aspect would require further and deeper investigation, which would also require information about prices to assess the potential role of sunk cost in the performance. However, it is out of this paper’s scope.

  17. Notice that, while this may have minor effects on rankings, it should have no effect on frontier estimation as well as on the measurement of efficiency for the other regions as the order-m estimator is robust with respect to outliers and extreme points, as underlined in the previous sections.

  18. Furthermore, results reported in “Table 6 in the Appendix” seem to show that time dependency is not a major problem in our efficiency estimates.

  19. For a more general setting including both discrete and continuous variables, see De Witte and Kortelainen (2013).

  20. Since we are examining an output-oriented case, an increasing regression line indicates that the environmental variable is favourable to DMU’s efficiency (Daraio and Simar 2005).

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Authors and Affiliations

  1. Department of Economics and Business, University of Catania, Corso Italia 55, 95129, Catania, Italy

    Calogero Guccio, Domenico Lisi, Marco Martorana & Anna Mignosa

  2. Erasmus School of History, Culture and Communication, Rotterdam, The Netherlands

    Anna Mignosa

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  1. Calogero Guccio
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  2. Domenico Lisi
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Correspondence to Domenico Lisi.

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Appendix

Appendix

See Table 6.

Table 6 Unconditional efficiency scores by year.
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Guccio, C., Lisi, D., Martorana, M. et al. On the role of cultural participation in tourism destination performance: an assessment using robust conditional efficiency approach. J Cult Econ 41, 129–154 (2017). https://doi.org/10.1007/s10824-017-9295-z

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