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Power laws and the market structure of tourism industry

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  • Davide Provenzano

Abstract

In this article, we use both graphical and analytical methods to investigate the market structure of one of the world’s fastest growing industries. For the German and Italian datasets, we show that the size distribution of tourism industry is heavy-tailed and consistent with a power-law behavior in its upper tail. Such a behavior seems quite persistent over the time horizon covered by our study, provided that during the period 2004–2009, the shape parameter is always in the vicinity of 2.5 for Germany and 2.6 for Italy. Size of the tourism industry has been proxied by the lodging capacity of hotel establishments: hotels, boarding houses, inns, lodging houses, motels, apartment hotels, tourist villages, and tourist apartments. Data belonging to the EUROSTAT and ISTAT databases have been used for Germany and Italy, respectively. Our aim is not to provide the best fit to the data but simply to focus our attention on the right tail of the size distribution of tourism industry. Understanding the behavior of the upper tail is indeed fundamental to capture the structure of the market. This study adds a new evidence to the list of empirical phenomena for which power laws hold. Copyright Springer-Verlag Berlin Heidelberg 2014

Suggested Citation

  • Davide Provenzano, 2014. "Power laws and the market structure of tourism industry," Empirical Economics, Springer, vol. 47(3), pages 1055-1066, November.
  • Handle: RePEc:spr:empeco:v:47:y:2014:i:3:p:1055-1066
    DOI: 10.1007/s00181-013-0769-3
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    References listed on IDEAS

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    1. Mehmet A. Ulubaşoğlu & Bharat R. Hazari, 2004. "Zipf's law strikes again: the case of tourism," Journal of Economic Geography, Oxford University Press, vol. 4(4), pages 459-472, August.
    2. Baggio, Rodolfo, 2007. "The web graph of a tourism system," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 379(2), pages 727-734.
    3. M. Goldstein & S. Morris & G. Yen, 2004. "Problems with fitting to the power-law distribution," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 41(2), pages 255-258, September.
    4. Miguéns, J.I.L. & Mendes, J.F.F., 2008. "Travel and tourism: Into a complex network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 387(12), pages 2963-2971.
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    Cited by:

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    2. Baggio, Rodolfo, 2020. "Tourism destinations: A universality conjecture based on network science," Annals of Tourism Research, Elsevier, vol. 82(C).
    3. Maria Kuklina & Andrey Trufanov & Tuyana Bayaskalanova & Nina Urazova & Alexei Tikhomirov & Olga Berestneva & Olga Marukhina & Igor Vidyaev & Oksana Fisochenko & Ivan Lyzin & Elena Berestneva & Nadezh, 2020. "Network Platform for Tourism Sector: Transformation and Interpretation of Multifaceted Data," Sustainability, MDPI, vol. 12(16), pages 1-15, August.
    4. Xiaohui Xu, 2022. "The Impacts of COVID-19 on the Rank-Size Distribution of Regional Tourism Central Places: A Case of Guangdong-Hong Kong-Macao Greater Bay Area," Sustainability, MDPI, vol. 14(19), pages 1-21, September.
    5. Koo, Tay T.R. & Lau, Pong-Lung, 2019. "Impact of aviation on spatial distribution of tourism: An experiment," Annals of Tourism Research, Elsevier, vol. 78(C), pages 1-1.
    6. Yongrui Guo & Jie Zhang & Honglei Zhang, 2016. "Rank–size distribution and spatio-temporal dynamics of tourist flows to China’s cities," Tourism Economics, , vol. 22(3), pages 451-465, June.

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    More about this item

    Keywords

    Heavy-tailed distribution; Power-law behavior; Shape parameter; Tourism industry; Market structure; C1; C12; L8;
    All these keywords.

    JEL classification:

    • C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General
    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • L8 - Industrial Organization - - Industry Studies: Services

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