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Multiway clustering in tourism research

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  • David Boto-García

Abstract

The importance of controlling for intragroup correlation in clustered samples is largely acknowledged in applied econometrics. However, this issue has remained underexplored in tourism research. In many instances, the observation units are naturally grouped, either geographically or due to the sampling scheme, and therefore the iid assumption of the error term in linear regression is broken. This paper presents two case studies to show how default standard errors overstate the estimator precision when the error terms are independent across clusters but correlated within clusters. First, hedonic pricing functions for the Airbnb rental market are revisited using data for almost 225,000 listings in 14 countries. Second, destination choice modelling is reconsidered exploiting monthly household microdata for Spain involving 115,937 tourism trips between 2015 and 2019. Practical implications for research practice are derived.

Suggested Citation

  • David Boto-García, 2022. "Multiway clustering in tourism research," Current Issues in Tourism, Taylor & Francis Journals, vol. 25(3), pages 363-378, February.
    • Handle: RePEc:taf:rcitxx:v:25:y:2022:i:3:p:363-378
    DOI: 10.1080/13683500.2021.1965552
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