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Determinants of individual tourist expenditure as a network: Empirical findings from Uruguay

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  • Abbruzzo, Antonino
  • Brida, Juan Gabriel
  • Scuderi, Raffaele

Abstract

This paper introduces the use of graphical models for assessing the determinants of individual tourist spending. These models have the advantage of synthesizing and visualizing the relationships occurring within large sets of random variables, through an easy to interpret output. To this end, individual data from a large official survey of international tourists in Uruguay are used. Symmetric conditional independence structures are first investigated. Then subgraphs of each expenditure item's neighbourhood are extracted in order to assess the impact of main effects and interactions through proportional ordinal logistic regression. Results highlight the marginal role of socio-demographic variables and direct importance of accommodation type, destination and length of stay.

Suggested Citation

  • Abbruzzo, Antonino & Brida, Juan Gabriel & Scuderi, Raffaele, 2014. "Determinants of individual tourist expenditure as a network: Empirical findings from Uruguay," Tourism Management, Elsevier, vol. 43(C), pages 36-45.
    • Handle: RePEc:eee:touman:v:43:y:2014:i:c:p:36-45
    DOI: 10.1016/j.tourman.2014.01.014
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    Cited by:

    1. Xiang Wei & Songshan (Sam) Huang & Ghialy Yap & Xinfang Wu & Ariuna Taivan, 2018. "The influence of national holiday structure on domestic tourism expenditure," Tourism Economics, , vol. 24(7), pages 781-800, November.
    2. Olya, Hossein GT & Mehran, Javaneh, 2017. "Modelling tourism expenditure using complexity theory," Journal of Business Research, Elsevier, vol. 75(C), pages 147-158.
    3. Usamah F Alfarhan & Khaldoon Nusair & Fevzi Okumus & SR Nikhashemi, 2024. "Identifying structural asymmetries by jointly estimating tourism expenditure intensity and extensity," Tourism Economics, , vol. 30(5), pages 1281-1305, August.
    4. María Redondo-Carretero & Carmen Camarero-Izquierdo & Ana Gutiérrez-Arranz & Javier Rodríguez-Pinto, 2017. "Language tourism destinations: a case study of motivations, perceived value and tourists’ expenditure," Journal of Cultural Economics, Springer;The Association for Cultural Economics International, vol. 41(2), pages 155-172, May.
    5. Genjin Sun & Qi Qian & Yanxiu Liu & Bo Pu & Dan Wang, 2022. "Social network and tourism consumption by households: Evidence from China," PLOS ONE, Public Library of Science, vol. 17(9), pages 1-18, September.
    6. Mohammad Nur Nobi & A. H. M. Raihan Sarker & Biswajit Nath & Eivin R{o}skaft & Ma Suza & Paul Kvinta, 2021. "Economic valuation of tourism of the Sundarban Mangroves, Bangladesh," Papers 2110.00182, arXiv.org.
    7. Hamaguchi, Yoshihiro, 2021. "Does the trade of aviation emission permits lead to tourism-led growth and sustainable tourism?," Transport Policy, Elsevier, vol. 105(C), pages 181-192.
    8. Julio Vena-Oya & José-Alberto Castañeda-García & Miguel-à ngel Rodríguez-Molina, 2022. "Determinants of the Likelihood of Tourist Spending in Cultural Micro-Destinations: Type, Timing, and Distance of the Activity as Predictors," SAGE Open, , vol. 12(3), pages 21582440221, September.
    9. Juan D Montoro-Pons & Manuel Cuadrado-García, 2021. "Analyzing online search patterns of music festival tourists," Tourism Economics, , vol. 27(6), pages 1276-1300, September.

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    Keywords

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    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
    • L83 - Industrial Organization - - Industry Studies: Services - - - Sports; Gambling; Restaurants; Recreation; Tourism

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