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A panel data quantile regression analysis of the impact of corruption on tourism

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  • Zhike Lv
  • Ting Xu

Abstract

Employing a data set of 62 nations over the period of 1998–2011, we adopt the quantile regression model to provide a broad description of the relationship between tourism demand and corruption across the demand distribution. Our results confirm some findings in the literature, and also provide some new conclusions. More specifically, our empirical results indicate that the nonlinear relationship between corruption and tourism demand is only significant at the 50th and 75th quantiles. Moreover, we also find a significant positive relationship between income and tourism demand across various quantiles, and the strength of the relationship is larger at lower demand levels. These findings may suggest that the existing level of demand is as important as other determinants of the tourism demand, and thereby this paper opens up new insights for national tourism administration policy-makers as well as for managerial purposes.

Suggested Citation

  • Zhike Lv & Ting Xu, 2017. "A panel data quantile regression analysis of the impact of corruption on tourism," Current Issues in Tourism, Taylor & Francis Journals, vol. 20(6), pages 603-616, April.
    • Handle: RePEc:taf:rcitxx:v:20:y:2017:i:6:p:603-616
    DOI: 10.1080/13683500.2016.1209164
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    Cited by:

    1. Hatice Jenkins & Ezuldeen Alshareef & Amer Mohamad, 2023. "The impact of corruption on commercial banks' credit risk: Evidence from a panel quantile regression," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 28(2), pages 1364-1375, April.
    2. Joey Pek U Sou & Thea Vinnicombe, 2023. "Does governance quality matter for FDI-led tourism development? A supply-side perspective," Tourism Economics, , vol. 29(2), pages 392-408, March.

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