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Determinants of Individual Tourist Expenditure as a Network: Empirical Findings from Uruguay

Author

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  • Antonio Abbruzzo

    (DSEAS, University of Palermo)

  • Juan Gabriel Brida

    (School of Economics and Management and TOMTE (Competence Centre in Tourism Management and Tourism Economics), Free University of Bozen/Bolzano)

  • Raffaele Scuderi

    (Free University of Bolzano‐Bozen, School of Economics and Management.)

Abstract

Past literature investigating the determinants of tourist expenditure have made a wide use of econometric models to assess conditional relationships of a set of regressors in predicting individual spending. However such techniques provide little or no information about the relationships within the total set of regressors as determinants of such expenditure. This paper applies graphical models to investigate the links that occur within a set of variables derived from an official survey of Uruguayan statistics of visitor expenditures. Symmetric conditional dependence structures within socio-demographic and trip-related variables are first investigated. Then a chain graph assessment of asymmetric conditional dependencies of these two categories of variables is used to explain individual expenditure items. The output is displayed through graphs that allow for an easy interpretation of the data. The results highlight the marginal role of socio-demographic variables and the direct importance of accommodation type and destination as determinants of tourist expenditure.

Suggested Citation

  • Antonio Abbruzzo & Juan Gabriel Brida & Raffaele Scuderi, 2013. "Determinants of Individual Tourist Expenditure as a Network: Empirical Findings from Uruguay," BEMPS - Bozen Economics & Management Paper Series BEMPS09, Faculty of Economics and Management at the Free University of Bozen.
    • Handle: RePEc:bzn:wpaper:bemps09
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    References listed on IDEAS

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    1. Juan Brida & Juan Pereyra & Raffaele Scuderi, 2014. "Repeat tourism in Uruguay: modelling truncated distributions of count data," Quality & Quantity: International Journal of Methodology, Springer, vol. 48(1), pages 475-491, January.
    2. Zhang, Hui & Zhang, Junyi & Kuwano, Masashi, 2012. "An integrated model of tourists’ time use and expenditure behaviour with self-selection based on a fully nested Archimedean copula function," Tourism Management, Elsevier, vol. 33(6), pages 1562-1573.
    3. Thrane, Christer & Farstad, Eivind, 2011. "Domestic tourism expenditures: The non-linear effects of length of stay and travel party size," Tourism Management, Elsevier, vol. 32(1), pages 46-52.
    4. Joaquín Alegre & Magdalena Cladera, 2010. "Tourist Expenditure and Quality: Why Repeat Tourists Can Spend Less than First-Timers," Tourism Economics, , vol. 16(3), pages 517-533, September.
    5. Carl H. Marcussen, 2011. "Determinants of Tourist Spending in Cross-Sectional Studies and at Danish Destinations," Tourism Economics, , vol. 17(4), pages 833-855, August.
    6. Alegre, Joaquín & Mateo, Sara & Pou, Llorenç, 2010. "An analysis of households' appraisal of their budget constraints for potential participation in tourism," Tourism Management, Elsevier, vol. 31(1), pages 45-56.
    7. Brida, Juan Gabriel & Scuderi, Raffaele, 2012. "Determinants of tourist expenditure: a review of microeconometric models," MPRA Paper 38468, University Library of Munich, Germany.
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    Cited by:

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    2. 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.
    3. 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.
    4. 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.
    5. 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.
    6. Olya, Hossein GT & Mehran, Javaneh, 2017. "Modelling tourism expenditure using complexity theory," Journal of Business Research, Elsevier, vol. 75(C), pages 147-158.
    7. 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.
    8. 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.
    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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    More about this item

    Keywords

    tourist expenditure; graphical model; chain graph; log-linear graphical model;
    All these keywords.

    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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