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

A panel data quantile regression analysis of the impact of corruption on tourism 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. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Current Issues in Tourism Taylor & Francis

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

Current Issues in Tourism , Volume 20 (6): 14 – Apr 26, 2017

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

Current Issues in Tourism , Volume 20 (6): 14 – Apr 26, 2017

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.

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References (38)

Publisher
Taylor & Francis
Copyright
© 2016 Informa UK Limited, trading as Taylor & Francis Group
ISSN
1747-7603
eISSN
1368-3500
DOI
10.1080/13683500.2016.1209164
Publisher site
See Article on Publisher Site

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.

Journal

Current Issues in TourismTaylor & Francis

Published: Apr 26, 2017

Keywords: corruption; tourism; quantile regression; panel data; heterogeneity

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