A Nonparametric Panel Data Model for Examining the Contribution of Tourism To Economic Growth
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Date
2023
Authors
Journal Title
Journal ISSN
Volume Title
Publisher
Elsevier
Open Access Color
HYBRID
Green Open Access
No
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Publicly Funded
No
Abstract
We apply a nonparametric panel data model with cross-sectional and time-varying coefficients to examine the relationship between tourist arrivals and economic growth in the Schengen area from 1995 to 2019. In contrast to the parametric models employed in other studies, our nonparametric model makes no assumption about functional form and, hence, allows us to model the relationship nonlinearly. We find that the tourism-economic growth relationship in the Schengen area is nonlinear and time-varying. While the relationship between tourism and economic growth was positive and significant during 1995-2003, it was negative and significant during the Global Financial Crisis (2007-2008) and the European recession of 2012-2013. One additional contribution of the study is the finding that total factor productivity (TFP) has been growing at 1.45% per year. The results also show that country-level TFP growth was disrupted during the aforementioned negative economic shocks.
Description
Zhang, Xibin/0000-0001-5813-3066
ORCID
Keywords
Eurozone Crisis, Global Financial Crisis, Nonparametric Estimation, Time-Varying Panel Data Model, Total Factor Productivity, Tourism-Led Economic Growth Hypothesis
Fields of Science
Citation
Doğan, Ergun; Zhang, Xibin (2023). "A nonparametric panel data model for examining the contribution of tourism to economic growth", Economic Modelling, Vol. 128.
WoS Q
Q1
Scopus Q
Q1

OpenCitations Citation Count
14
Source
Economic Modelling
Volume
128
Issue
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End Page
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CrossRef : 16
Scopus : 11
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Mendeley Readers : 94
SCOPUS™ Citations
12
checked on Feb 23, 2026
Web of Science™ Citations
15
checked on Feb 23, 2026
Page Views
1
checked on Feb 23, 2026
Google Scholar™

OpenAlex FWCI
20.14009739
Sustainable Development Goals
8
DECENT WORK AND ECONOMIC GROWTH

10
REDUCED INEQUALITIES


