Enhancing understanding of tourist spending using unconditional quantile regression

Simon Rudkin, Abhijit Sharma

Research output: Contribution to journalArticlepeer-review

10 Citations (Scopus)


An important policy goal for governments is to increase expenditures by inbound tourists, requiring appropriate statistical analysis to correctly identify important drivers of spending. In 2014 the UK received 34.4 million visits from overseas residents, netting £5.5 billion (ONS, 2015), underscoring the importance of accurate analysis. Using hitherto underutilised data from the United Kingdom International Passenger Survey (ONS, 2015) we show that past emphasis on promoting longer tourist stays misses key factors such as reason for travel (business or leisure) mainly due to inappropriate methodologies employed previously. Our central contribution is to demonstrate that conventional use of ordinary least squares (OLS) and standard quantile regressions (QR) Koenker and Bassett (1978) can lead to incorrect inferences and suboptimal decisions in relation to expenditure promoting activities.
Original languageEnglish
Pages (from-to)188-191
Number of pages4
JournalAnnals of Tourism Research
Early online date22 Jun 2017
Publication statusPublished - 1 Sep 2017
Externally publishedYes


Dive into the research topics of 'Enhancing understanding of tourist spending using unconditional quantile regression'. Together they form a unique fingerprint.

Cite this