Culture, space, and tourism Spillovers in silk road

Fatemeh Rafiei

Annals of Tourism Research Empirical Insights2026https://doi.org/10.1016/j.annale.2026.100211article
ABDC B
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0.50

Abstract

This study investigates spatial interactions in tourism across 11 Silk Road countries (2002–2019) by combining a nested Constant Elasticity of Substitution utility function with Bayesian Spatial Durbin Models. It compares three spatial structures: inverse-distance, cultural proximity, and trade-intensity matrices. The results reveal competition and destination substitution among geographically and culturally proximate countries, while trade-based linkages play a relatively limited role. An increase in tourism arrivals in one destination tends to reduce tourism flows to neighboring destinations. At the same time, improvements in economic fundamentals—GDP and rule of law—in surrounding countries lead to an increase in inbound tourism to the country under consideration. This indicates that although destination choice is competitive, regional institutional improvements can yield mutual benefits. • Integrates a nested CES utility framework with a Bayesian Spatial Durbin Model to analyze tourism spillovers across 11 Silk Road countries. • Compares geographic, cultural, and economic channels, finding that spatial and cultural proximity outweigh trade links in driving interdependence. • Demonstrates that while direct tourism competition exists, institutional quality and GDP growth in one country generate positive regional externalities for neighbors. • Provides a behavioral micro-foundation linking individual consumer choices to macro-level spatial interdependence.

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https://doi.org/https://doi.org/10.1016/j.annale.2026.100211

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@article{fatemeh2026,
  title        = {{Culture, space, and tourism Spillovers in silk road}},
  author       = {Fatemeh Rafiei},
  journal      = {Annals of Tourism Research Empirical Insights},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.1016/j.annale.2026.100211},
}

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R · text relevance †0.50 × 0.4 = 0.20

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