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A DEA Travel–Tourism Competitiveness Index

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Abstract

Travel and tourism competitiveness has been paramount in the research agenda for transport, tourism and economics over the last decades because a larger number of destinations and businesses have entered into the international tourism market. Different approaches have been postulated to measuring, modeling and managing competitiveness in tourism. The present study aims to create a composite index of the travel and tourism competitiveness to rank 139 countries worldwide. Our sample is based on some of the data collected in “The Travel & Tourism Competitiveness Report 2011”, and the method is based on the virtual efficiency DEA model. An analysis of the competitiveness by geographical area and income is also analyzed. Finally some policy implications are discussed.

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Notes

  1. ICT is an acronym that means “Information and Communication Technology”.

  2. As a referee has pointed out, this variable could also been treated as a bad or an undesirable output. In this case, we have preferred to use a well-known and conventional method that treats this variable as an input, as it is known that this method is equivalent to other proposals which are based on a translation of these variables to treat them as outputs (Ali and Seiford 1990). Nowadays, there are new DEA models that can differentiate inputs, desirable outputs and undesirable outputs (Seiford and Zhu 2002). Nevertheless, it is out of the scope of this paper to discuss the robustness of the results using different DEA models, but this could be an interesting area for future research.

  3. The score refers to the percentage of UN countries whose citizens require a visa to enter each country. Each country that requires no visa at all receives a “1” and each country for which it is possible to obtain a visa upon arrival receives “0.5”. Those countries for which a visa is required prior to departure would receive a “0”. The sum across all UN countries produces the final score.

  4. This variable is the ratio of the sum of international tourism expenditures and receipts to GDP. “International tourism expenditures” are expenditures of international outbound visitors in other countries, including payments to foreign carriers for international transport. “International tourism receipts” are expenditures of international inbound visitors in other countries, including payments to foreign carriers for international transport.

  5. Different envelopment surfaces may be obtained considering additional constraints about the scalars. For example, variable returns to scale models (VRS) are obtained imposing that the sum of scalars is equal to one; and non-increasing return to scale models (NIRS) are characterized by the restriction of the sum of scalars being less or equal to one.

  6. This discussion is very close to the definition of Pareto–Koopmans efficiency. The unit o is considered fully efficient if and only if the performance of other DMUs does not provide evidence that some of the inputs or outputs of the unit o could have been improved without worsening off some of its other inputs or outputs. This definition of relative performance has its origin in Farrell (1957).

  7. We note here that all the DEA-TTCI scores are >1. The values have been calculated according to the formulation of DEA-LP program described by Eq. 2.

  8. GNI per capita based on purchasing power parity (PPP). PPP GNI is gross national income (GNI) converted to international dollars using purchasing power parity rates. An international dollar has the same purchasing power over GNI as a U.S. dollar has in the United States. GNI is the sum of value added by all resident producers plus any product taxes (less subsidies) not included in the valuation of output plus net receipts of primary income (compensation of employees and property income) from abroad.

References

  • Adler, N., Friedman, L., & Sinuany-Stern, Z. (2002). Review of ranking methods in the data envelopment analysis context. European Journal of Operational Research, 140, 249–265.

    Article  Google Scholar 

  • Ali, A. I., & Seiford, L. M. (1990). Translation invariance in data envelopment analysis. Operations Research Letters, 9(6), 403–405.

    Article  Google Scholar 

  • Ali, A., & Seiford, L. M. (1993). The mathematical programming approach to efficiency analysis. In H. O. Fried, C. A. K. Lovell, & S. S. Schmidt (Eds.), The measurement of productive efficiency: Techniques and applications (pp. 120–159). New York: Oxford University Press.

    Google Scholar 

  • Alonso, V. (2010). Factores críticos de éxito y evaluación de la competitividad de los destinos turísticos. Estudios y perspectivas en turismo, 19(2), 201–220.

    Google Scholar 

  • Andersen, P., & Petersen, N. C. (1993). A procedure for ranking efficient units in data envelopment analysis. Management Science, 39, 1261–1294.

    Article  Google Scholar 

  • Bandura, R. (2008). A survey of composite indices measuring country performance: 2008 update. Office development studies. United Nations Development Programme (UNDP).

  • Barzegarinegad, A., Jahanshahloo, G., & Rostamy-Malkhalifeh, M. (2014). A full ranking for decision making units using ideal and anti-ideal points in DEA. The Scientific World Journal. doi:10.1155/2014/282939

  • Bazargan, M., & Vasigh, B. (2003). Size versus efficiency: A case study of US commercial airports. Journal of Air Transport Management, 9, 187–193.

    Article  Google Scholar 

  • Buhalis, D. (2000). Marketing the competitive destination of the future. Tourism Management, 21(1), 97–116.

    Article  Google Scholar 

  • Charnes, A., Cooper, W. W., & Rhodes, E. (1978). Measuring the efficiency of decision making units. European Journal of Operational Research, 2(6), 429–444.

    Article  Google Scholar 

  • Charnes, A., Cooper, W., Lewin, A. Y., & Seiford, L. M. (1994). Data envelopment analysis. Theory, methodology and applications. Boston: Kluwer Academic.

    Book  Google Scholar 

  • Coelli, T. (1996), A guide to DEAP version 2.1: A data envelopment analysis (computer) program. CEPA Working Paper 96/08. Centre for Efficiency and Productivity Analysis, University of New England, Armidale.

  • Coelli, T., Rao, D. S. P., & Battese, G. E. (1998). An introduction to efficiency and productivity analysis. Boston: Kluwer Academic.

    Book  Google Scholar 

  • Cook, W. D., & Seiford, L. M. (2009). Data envelopment analysis (DEA)—Thirty years on. European Journal of Operational Research, 192(1), 1–17.

    Article  Google Scholar 

  • Cooper, W., Sieford, L., & Tone, K. (2000). Date envelopment analysis. A comprehensive text with models, applications, reference and DEA–Solver software. Norwell: Kluwer Academic Publishers.

    Google Scholar 

  • Cooper, W. W., Seiford, L. M., & Zhu, J. (2011). Handbook on data envelopment analysis (Vol. 164). New York: Springer Science & Business Media.

  • Cracolici, M. F., Nijkamp, P., & Rietveld, P. (2008). Assessment of tourism competitiveness by analysing destination efficiency. Tourism Economics, 14(2), 325–342.

    Article  Google Scholar 

  • Crouch, G. I. (2011). Destination competitiveness: An analysis of determinant attributes. Journal of Travel Research, 50(1), 27–45.

    Article  Google Scholar 

  • Doyle, J. R., & Green, R. (1994). Efficiency and cross-efficiency in data envelopment analysis: Derivatives, meanings and uses. Journal of the Operational Research Society, 45(5), 567–578.

    Article  Google Scholar 

  • Dwyer, L., & Kim, C. (2003). Destination competitiveness: A models and determinants. Current Issues in Tourism, 6(5), 369–414.

    Article  Google Scholar 

  • Emrouznejad, A., Parker, B. R., & Tavares, G. (2008). Evaluation of research in efficiency and productivity: A survey and analysis of the first 30 years of scholarly literature in DEA. Socio-Economic Planning Sciences, 42(3), 151–157.

    Article  Google Scholar 

  • Enright, M., & Newton, J. (2004). Tourism destination competitiveness: A quantitative approach. Tourism Management, 25(6), 777–788.

    Article  Google Scholar 

  • Farrel, M. J. (1957). The measurement of productive efficiency. Journal of the Royal Statistical Society, A120, 253–290.

    Article  Google Scholar 

  • Gooroochurn, N., & Sugiyarto, G. (2005). Competitiveness indicators in the travel and tourism industry. Tourism Economics, 11(1), 25–43.

    Article  Google Scholar 

  • Hassan, S. S. (2000). Determinants of market competitiveness in an environmentally sustainable tourism industry. Journal of Travel Research, 38(3), 239–245.

    Article  Google Scholar 

  • Kaufmann D., Kraay A., & Zoido-Lobatón P. (1999). Aggregating governance indicators. Policy Research Working Papers. World Bank.

  • Martín, J. C., & Román, C. (2006). A benchmarking analysis of Spanish commercial airports. A comparison between SMOP and DEA ranking methods. Networks and Spatial Economics, 6(2), 111–134.

    Article  Google Scholar 

  • Martín, J. C., & Román, C. (2007). Political opportunists and mavericks? A typology of Spanish airports. International Journal of Transport Economics, 34(2), 245–269.

    Google Scholar 

  • OECD (2008). Handbook on constructing composite indicators. Methodology and user guide.

  • Puolamaa, M., Kaplas, M., & Reinikainen, T. (1996). Index of economic friendliness. A methodological study. Helsinki: Official Statistics of Finland.

  • Ritchie, B., & Crouch, G. (2003). The competitive destination: A sustainable tourism perspective. Reino Unido: CABI Publishing.

    Book  Google Scholar 

  • Rogerson, C. M. (2004). Regional tourism in South Africa: A case of ‘mass tourism of the South’. GeoJournal, 60(3), 229–237.

    Article  Google Scholar 

  • Rogerson, C. M. (2011). Urban tourism and regional tourists: Shopping in Johannesburg, South Africa. Tijdschrift voor Economische en Sociale Geografie, 102, 316–330.

    Article  Google Scholar 

  • Rogerson, C. M. (2012). The tourism-development nexus in sub-Saharan Africa: Progress and prospects. Africa Insight, 42(2), 28–45.

    Google Scholar 

  • Rogerson, C. M., & Kiambo, W. (2007). The growth and potential of regional tourism in the developing world: The South African experience. Development Southern Africa, 24, 505–521.

    Article  Google Scholar 

  • Rogerson, C. M., & Visser, G. (2011). African tourism geographies: Existing paths and new directions. Tijdschrift voor Economische en Sociale Geografie, 102(3), 251–259.

    Article  Google Scholar 

  • Saltelli, A. (2007). Composite indicators between analysis and advocacy. Social Indicators Research, 81, 65–77.

    Article  Google Scholar 

  • Seiford, L. M. (1996). Data envelopment analysis: The evolution of the state of the art (1978–1995). The Journal of Productivity Analysis, 7, 99–137.

    Article  Google Scholar 

  • Seiford, L. M., & Thrall, R. M. (1990). Recent developments in data envelopment analysis: The mathematical programming approach to frontier analysis. Journal of Econometrics, 46, 7–38.

    Article  Google Scholar 

  • Seiford, L. M., & Zhu, J. (1998). Stability regions for maintaining efficiency in data envelopment analysis. European Journal of Operational Research, 108, 127–139.

    Article  Google Scholar 

  • Seiford, L. M., & Zhu, J. (2002). Modeling undesirable factors in efficiency evaluation. European Journal of Operational Research, 142(1), 16–20.

    Article  Google Scholar 

  • Sexton, T. R., Silkman, R. H., & Hogan, A. J. (1986). Data envelopment analysis: Critique and extensions. In R. H. Silkman (Ed.), Measuring efficiency: An assessment of data envelopment analysis (pp. 73–105). San Francisco: Jossey-Bass.

    Google Scholar 

  • UNWTO. (2009). World Tourism Barometer (Vol. 7). N. 1. January 2009.

  • World Centre of Excellence for Destinations. (2011). www.ced.travel/es/noticias-de-los-destinos/132-international-tourism-2010-multi-speed-recovery.html. Accessed 22 January, 2012.

  • World Economic Forum. (2011). The travel and tourism competitiveness report 2011. Beyond the downturn.

  • World Travel & Tourism Council. (2015). Travel & Tourism Econimic Impact 2015.

  • Zhu, J. (1996). Robustness of the efficient DMUs in data envelopment analysis. European Journal of Operational Research, 90, 451–460.

    Article  Google Scholar 

  • Zhu, J. (2014). Quantitative models for performance evaluation and benchmarking: Data envelopment analysis with spreadsheets (Vol. 213). New York: Springer.

    Google Scholar 

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Acknowledgments

This work benefits from a research project “La calidad del servicio en la industria hotelera. ECO2011-23852” funded by the Ministry of Science and Innovation of the Spanish Government. We also want to thank many colleagues’ comments, suggestions, discussions and assistance. The usual disclaimer applies.

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Correspondence to Juan Carlos Martín.

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Martín, J.C., Mendoza, C. & Román, C. A DEA Travel–Tourism Competitiveness Index. Soc Indic Res 130, 937–957 (2017). https://doi.org/10.1007/s11205-015-1211-3

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