Household income and tourism expenditure: an unconditional quantile regression approach

Wanglin Ma; Puneet Vatsa; Xiaoshi Zhou; Huanyu Zhu

doi:10.1080/00036846.2023.2267823

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Household income and tourism expenditure: an unconditional quantile regression approach

Journal article

Open access

Peer reviewed

Household income and tourism expenditure: an unconditional quantile regression approach

Wanglin Ma, Puneet Vatsa, Xiaoshi Zhou and Huanyu Zhu

Applied Economics, Vol.56(50), pp.6144-6157

2024

DOI: https://doi.org/10.1080/00036846.2023.2267823

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Abstract

income growth

tourism expenditure

UQR model

China

Although a growing number of studies have investigated the nexus between income and tourism demand, little is known about the heterogeneous income elasticity of tourism expenditure. This study fills this gap by analysing the 2018 China Family Panel Studies household survey data using an unconditional quantile regression model. The findings show that tourism expenditure is income inelastic regardless of the tourism expenditure quantile. Specifically, a 1% increase in per capita household income increases per capita tourism expenditure by 0.514-0.637%. In addition, household income affects tourism expenditure heterogeneously; the effects are larger for households at the higher tourism expenditure quantiles. Furthermore, the income elasticity of tourism expenditure is larger in relatively affluent and advanced regions - it is greater in urban areas relative to rural areas and in the eastern region of China relative to its western region.

Details

Title: Household income and tourism expenditure: an unconditional quantile regression approach
Authors: Wanglin Ma - Lincoln University
Puneet Vatsa - Lincoln University
Xiaoshi Zhou (Corresponding Author) - China Agricultural University
Huanyu Zhu - Lincoln University
Publication details: Applied Economics, Vol.56(50), pp.6144-6157
Publisher: Routledge
Date published: 2024
DOI: 10.1080/00036846.2023.2267823
ISSN: 1466-4283; 0003-6846
Copyright note: © 2023 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The terms on which this article has been published allow the posting of the Accepted Manuscript in a repository by the author(s) or with their consent.
Data Availability: The data that support the findings of this study are available from Xiaoshi Zhou upon request.
Grant note: This work was supported by the National Natural Sciences Foundation of China [NSFC No. 72003089].
Organisation Unit: School of Business and Creative Industries
Language: English
Record Identifier: 991148738302621
Output Type: Journal article

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