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Insights from analysing tourist expenditure using quantile regression

Author

Listed:
  • António Almeida

    (University of Madeira, Portugal)

  • Brian Garrod

    (Aberystwyth University, UK)

Abstract

Mature tourism destinations are increasingly needing to diversify their products and markets. To be successful, such strategies require a very detailed understanding of potential tourists’ levels and patterns of spending. Empirical studies of tourist expenditure have tended to employ ordinary least squares regression for this purpose. There are, however, a number of important limitations to this technique, chief among which is its inability to distinguish between tourists who have higher- and lower-than-average levels of spending. As such, some researchers recommend the use of an alternative estimation technique, known as quantile regression, which does allow such distinctions to be made. This study uses a single data set, collected among rural tourists in Madeira, to analyse the determinants of tourist expenditure using both techniques. This enables direct comparison to be made and illustrates the additional insights to be gained using quantile regression.

Suggested Citation

  • António Almeida & Brian Garrod, 2017. "Insights from analysing tourist expenditure using quantile regression," Tourism Economics, , vol. 23(5), pages 1138-1145, August.
    • Handle: RePEc:sae:toueco:v:23:y:2017:i:5:p:1138-1145
    DOI: 10.1177/1354816616668108
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    References listed on IDEAS

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    Cited by:

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    2. Luojia Wang & Kerui Du & Bin Fang & Rob Law, 2023. "Escape from air pollution: How does air quality in the place of residence shape tourism consumption?," Tourism Economics, , vol. 29(4), pages 1074-1099, June.
    3. Qiu, Wanling & Rudkin, Simon & Sharma, Abhijit, 2017. "An Analysis of the Impact of Low Cost Airlines on Tourist Stay Duration and Expenditures," MPRA Paper 81428, University Library of Munich, Germany.
    4. Rudkin, Simon & Sharma, Abhijit, 2017. "The Impact of Football Attendance on Tourist Expenditures for the United Kingdom," MPRA Paper 81427, University Library of Munich, Germany.
    5. Yuanyuan Mao & Xiyuan Ren & Ling Yin & Qingying Sun & Ke Song & De Wang, 2021. "Investigating Tourists’ Willingness to Walk (WTW) to Attractions within Scenic Areas: A Case Study of Tongli Ancient Town, China," Sustainability, MDPI, vol. 13(23), pages 1-15, November.

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