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

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

Notes

  1. 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. 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. 3.

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

  4. 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. 5.

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

  6. 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. 7.

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

  8. 8.

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

  9. 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. 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. 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. 12.

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

  13. 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. 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. 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. 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. 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. 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. 19.

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

  20. 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).

References

  1. Alderighi, M., & Lorenzini, E. (2012). Cultural goods, cultivation of taste, satisfaction and increasing marginal utility during vacations. Journal of Cultural Economics, 36, 1–26.

    Article  Google Scholar 

  2. Assaf, A. G., & Josiassen, A. (2012). Identifying and ranking the determinants of tourism performance: A global investigation. Journal of Travel Research, 51(4), 388–399.

    Article  Google Scholar 

  3. Assaf, A. G., & Tsionas, E. G. (2015). Incorporating destination quality into the measurement of tourism performance: A Bayesian approach. Tourism Management, 49, 58–71.

    Article  Google Scholar 

  4. Ateca-Amestoy, V. (2008). Determining heterogeneous behavior for theatre attendance. Journal of Cultural Economics, 32, 127–151.

    Article  Google Scholar 

  5. Ateca-Amestoy, V., & Prieto-Rodriguez, J. (2013). Forecasting accuracy of behavioural models for participation in the arts. European Journal of Operational Research, 229, 124–131.

    Article  Google Scholar 

  6. Badin, L., Daraio, C., & Simar, L. (2010). Optimal bandwidth selection for conditional efficiency measures: A data-driven approach. European Journal of Operational Research, 201(2), 633–640.

    Article  Google Scholar 

  7. Barros, C. P., Botti, L., Peypoch, N., Robinot, E., Solonandrasana, B., & Assaf, A. G. (2011). Performance of French destinations: Tourism attraction perspectives. Tourism Management, 32, 141–146.

    Article  Google Scholar 

  8. Benito, B., Solana, J., & Lopez, P. (2014). Determinants of Spanish regions’ tourism performance: A two-stage, double-bootstrap data envelopment analysis. Tourism Economics, 20(5), 987–1012.

    Article  Google Scholar 

  9. Bonet, L. (2003). Cultural Tourism. In R. Towse (Ed.), A handbook of cultural economics (pp. 187–193). Northampton: Edward Elgar.

    Google Scholar 

  10. Borowiecki, K. J., & Castiglione, C. (2014). Cultural participation and tourism flows: An empirical investigation of Italian provinces. Tourism Economics, 20(2), 241–262.

    Article  Google Scholar 

  11. Bosetti, V., Cassinelli, M., & Lanza, A. (2007). Benchmarking in tourism destinations: Keeping in mind the sustainable paradigm. In A. Matias, P. Nijkamp, & P. Neto (Eds.), Advances in modern tourism research (pp. 165–180). Heidelberg, New York: Physica-Verlag.

    Google Scholar 

  12. Botti, L., Peypoch, N., Robinot, E., & Solonadrasana, B. (2009). Tourism destination competitiveness: The French regions case. European Journal of Tourism Research, 2(1), 5–24.

    Google Scholar 

  13. Bourdieu, P. (1984). Distinction: A social critique of the judgment of taste. London: Routledge.

    Google Scholar 

  14. Brida, J. G., Dalle Nogare, C., & Scuderi, R. (2016). Frequency of museum attendance: Motivation matters. Journal of Cultural Economics, 40(3), 261–283.

    Article  Google Scholar 

  15. Caserta, S., & Russo, A. P. (2002). More means worse: Asymmetric information, spatial displacement and sustainable heritage tourism. Journal of Cultural Economics, 26(4), 245–260.

    Article  Google Scholar 

  16. Cazals, C., Florens, J. P., & Simar, L. (2002). Nonparametric frontier estimation: A robust approach. Journal of Econometrics, 106, 1–25.

    Article  Google Scholar 

  17. Charnes, A., Cooper, W. W., & Rhodes, E. (1978). Measuring the efficiency of decision making units. European Journal of Operational Research, 2, 429–444.

    Article  Google Scholar 

  18. Cordero, J. M., Alonso-Moran, E., Nuno-Solinis, R., Orueta, J. F., & Arce, R. S. (2015). Efficiency assessment of primary care providers: A conditional nonparametric approach. European Journal of Operational Research, 240, 235–244.

    Article  Google Scholar 

  19. Cracolici, M. F., Nijkamp, P., & Cuffaro, M. (2007). Efficiency and productivity of Italian tourist destinations: A quantitative estimation based on data envelopment analysis and the Malmquist method. In A. Matias, P. Nijkamp, & P. Neto (Eds.), Advances in modern tourism research (pp. 325–343). Heidelberg, New York: Physica-Verlag.

    Google Scholar 

  20. Cracolici, M. F., Nijkamp, P., & Rietveld, P. (2008). Assessment of tourism competitiveness by analysing destination efficiency. Tourism Economics, 14, 325–342.

    Article  Google Scholar 

  21. Cuccia, T., & Cellini, R. (2007). Is cultural heritage really important for tourists? A contingent rating study. Applied Economics, 39(2), 261–271.

    Article  Google Scholar 

  22. Cuccia, T., Guccio, C., & Rizzo, I. (2016). The effects of UNESCO World Heritage list inscription on tourism destinations performance in Italian regions. Economic Modelling, 53, 494–508.

    Article  Google Scholar 

  23. Cuccia, T., Guccio, C., & Rizzo, I. (2017). UNESCO sites and performance trend of Italian regional tourism destinations: A two-stage DEA window analysis with spatial interaction. Tourism Economics. doi:10.1177/1354816616656266.

    Google Scholar 

  24. Cuccia, T., & Rizzo, I. (2013). Seasonal tourism flows in UNESCO sites: The case of Sicily. In J. Kaminsky, A. M. Benson, & D. Arnold (Eds.), Contemporary issues in cultural heritage tourism (pp. 179–199). London: Routledge.

    Google Scholar 

  25. Daouia, A., Florens, J. P., & Simar, L. (2012). Regularization of nonparametric frontier estimators. Journal of Econometrics, 168(2), 285–299.

    Article  Google Scholar 

  26. Daouia, A., & Simar, L. (2007). Non-parametric efficiency analysis: A multivariate conditional quantile approach. Journal of Econometrics, 140, 375–400.

    Article  Google Scholar 

  27. Daraio, C., & Simar, L. (2005). Introducing environmental variables in nonparametric frontier models: A probabilistic approach. Journal of Productivity Analysis, 24, 93–121.

    Article  Google Scholar 

  28. Daraio, C., & Simar, L. (2007a). Conditional nonparametric frontier models for convex and nonconvex technologies: A unifying approach. Journal of Productivity Analysis, 28, 13–32.

    Article  Google Scholar 

  29. Daraio, C., & Simar, L. (2007b). Advanced robust and nonparametric methods in efficiency analysis—Methodology and applications. New York: Springer Science.

    Google Scholar 

  30. De Cantis, S., Ferrante, M., & Vaccina, F. (2011). Seasonal pattern and amplitude–a logical framework to analyse seasonality in tourism: An application to bed occupancy in Sicilian hotels. Tourism Economics, 17(3), 655–675.

    Article  Google Scholar 

  31. De Witte, K., & Kortelainen, M. (2013). What explains performance of students in a heterogeneous environment? Conditional efficiency estimation with continuous and discrete environmental variables. Applied Economics, 45(17), 2401–2412.

    Article  Google Scholar 

  32. Debreu, G. (1951). The coefficient of resource utilization. Econometrica, 19, 273–292.

    Article  Google Scholar 

  33. Deprins, D., Simar, L., & Tulkens, H. (1984). Measuring labor efficiency in post offices. In M. Marchand, P. Pestieau, & H. Tulkens (Eds.), The performance of public enterprises: Concepts and measurement (pp. 243–267). Amsterdam, NH: Elsevier.

    Google Scholar 

  34. Di Lascio, F. M. L., Giannerini, S., Scorcu, A. E., & Candela, G. (2011). Cultural tourism and temporary art exhibitions in Italy: A panel data analysis. Statistical Methods and Applications, 20, 519–542.

    Article  Google Scholar 

  35. Dwyer, L., & Kim, C. (2003). Destination competitiveness: Determinants and indicators. Current Issues in Tourism, 6(5), 369–414.

    Article  Google Scholar 

  36. Eurostat. (2016). Culture statistics 2016 edition. Luxemburgo.

  37. Falk, M., & Katz-Gerro, T. (2016). Cultural participation in Europe: Can we identify common determinants? Journal of Cultural Economics, 40, 127–162.

    Article  Google Scholar 

  38. Farrell, M. (1957). The measurement of productive efficiency. Journal of the Royal Statistical Society, 120, 253–290.

    Article  Google Scholar 

  39. Federculture. (2015). Cultura, identita’ e innovazione la sfida per il futuro—11° rapporto annuale Federculture 2015. 24 Ore Cultura, Milano.

  40. Fondazione Symbola—Unioncamere. (2014). Io sono CulturaRapporto 2014, Quaderni di Symbola.

  41. Fuchs, M. (2004). Strategy development in tourism destinations: A DEA approach. Poznam University Economic Review, 4(1), 52–73.

    Google Scholar 

  42. Gapinski, J. (1988). Tourism contribution to the demand for London’s lively arts. Applied Economics, 20, 957–968.

    Article  Google Scholar 

  43. Guccio, C., Lisi, D., Mignosa, A., & Rizzo, R. (2016). Has cultural heritage monetary value an impact on visits? An assessment using Italian official data. Paper presented at the Biannual Conference of the Association of Cultural Economics International, Valladolid.

  44. Kneip, A., Park, B., & Simar, L. (1998). A note on the convergence of nonparametric DEA efficiency measures. Econometric Theory, 14, 783–793.

    Article  Google Scholar 

  45. Koopmans, T. C. (1951). An analysis of production as an efficient combination of activities. In T. C. Koopmans (Ed.), Activity analysis of production and allocation, Cowles Commission for Research in Economics (pp. 33–37). New York: Wiley.

    Google Scholar 

  46. Mastromarco, C., & Simar, L. (2015). Effect of FDI and time on catching up: New insights from a conditional nonparametric frontier analysis. Journal of Applied Econometrics, 30(5), 826–847.

    Article  Google Scholar 

  47. Nicolau, J. L. (2010). Culture-sensitive tourists are more price insensitive. Journal of Cultural Economics, 34, 181–195.

    Article  Google Scholar 

  48. Peypoch, N., & Solonandrasana, B. (2007). On E-attraction tourism destination: Extension and application. In A. Matias, P. Nijkamp, & P. Neto (Eds.), Advances in modern tourism research (pp. 293–306). Heidelberg, New York: Physica-Verlag.

    Google Scholar 

  49. Prieto-Rodriguez, J., & Gonzalez-Díaz, M. (2008). Is there an economic rent for island hotels? Tourism economics, 14(1), 131–154.

    Article  Google Scholar 

  50. Richards, G. (1996). Production and consumption of European cultural tourism. Annals of Tourism Research, 23(2), 261–283.

    Article  Google Scholar 

  51. Simar, L., & Wilson, P. (2007). Estimation and inference in two-stage, semi-parametric models of production processes. Journal of Econometrics, 136, 31–64.

    Article  Google Scholar 

  52. Simar, L., & Wilson, P. (2008). Statistical inference in nonparametric frontier models: Recent developments and perspectives. In H. O. Fried, C. A. Knox Lovell, & S. S. Schmidt (Eds.), The measurement of productive efficiency and productivity growth (pp. 421–521). New York: Oxford University Press.

    Google Scholar 

  53. Simar, L., & Wilson, P. (2011). Two stage DEA: Caveat emptor. Journal of Productivity Analysis, 36, 205–218.

    Article  Google Scholar 

  54. Simar, L., & Wilson, P. (2013). Estimation and inference in nonparametric frontier models: Recent developments and perspectives. Foundations and Trends in Econometrics, 5(3–4), 183–337.

    Google Scholar 

  55. Simar, L., & Wilson, P. (2015). Statistical approaches for nonparametric frontier models: A guided tour. International Statistical Review, 83(1), 77–110.

    Article  Google Scholar 

  56. Tsionas, E. G., & Assaf, A. G. (2014). Short-run and long-run performance of international tourism: Evidence from Bayesian dynamic models. Tourism Management, 42, 22–36.

    Article  Google Scholar 

  57. UNESCO. (2009). Measuring cultural participation. 2009 FRAMEWORK FOR CULTURAL STATISTICS HANDBOOK NO. 2. http://www.uis.unesco.org/culture/Documents/fcs-handbook-2-cultural-participation-en.pdf.

  58. UNWTO. (2015). Tourism highlightsEdition 2015. http://www.e-unwto.org/doi/pdf/10.18111/9789284416899.

  59. UNWTO. (2016). UNWTO/UNESCO world conference on tourism and culture: Building a new partnership Siem Reap, Cambodia, 46 February 2015. http://www.e-unwto.org/doi/pdf/10.18111/9789284417360.

  60. World Economic Forum. (2015). The travel and tourism competitiveness report 2015, Geneva.

  61. Zieba, M. (2016). Tourism flows and the demand for regional and city theatres in Austria. Journal of Cultural Economics, 40, 191–221.

    Article  Google Scholar 

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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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Keywords

  • Tourism destination
  • Cultural participation
  • Efficiency
  • Conditional FDH

JEL Classification

  • Z11
  • L83
  • D21