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Cee-16: A Cluster Analysis Based on Tourism Competitiveness and Correlations With Major Determinants

Author

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  • Delia Popescu

    (The Bucharest University of Economic Studies, Bucharest, Romania)

  • Iulia Monica Oehler-Sincai

    (Institute for World Economy - Romanian Academy, Bucharest, Romania)

  • Daniel Bulin

    (The Bucharest University of Economic Studies, Bucharest, Romania)

  • Ion Alexandru Tanase

    (The Bucharest University of Economic Studies, Bucharest, Romania)

Abstract

The purpose of this paper is to identify the main groups of CEE-16 countries in terms of tourism competitiveness. Competitiveness, as part of the supply side, is addressed in view of the supply-demand correlations, and we consider the attractiveness of tourist destinations from the tourists’ perspective as the link between the two sides. For the analysis are selected exactly the 16 countries participating in the 16+1 platform initiated by China in 2011, tourism being one of the components of sectoral cooperation in this framework. We demonstrate that tourism infrastructure (its upgrading and development) is one of the main determinants of tourism competitiveness, even surpassing in importance the tourism assets. In this context, the “China factor” might play a significant role for the CEE-16 in terms of spurring competitiveness, as it is not antagonistic, but complementary with other determinants of competitiveness, including the EU funds. From the perspective of attractiveness, 16+1 cooperation can boost Chinese tourists’ arrivals in the region. In order to test these hypotheses, as part of the methodology, the authors calculate a specific travel and tourism competitiveness index (STTCI), starting from the main objectives of the 16+1 cooperation platform, namely prioritization, openness, environmental sustainability, transport infrastructure and specific infrastructure for tourism services. The authors correlate this STTCI with other qualitative indicators and obtain two categories of clusters among the CEE-16 countries. In Romania’s case, one can remark the following paradox. The cumulative amount of investment in tourism during 2000-2017 is the largest among the CEE-16 countries, corresponding to its tourism assets and the necessity to connect them; however, Romania lags behind most of the analyzed countries in terms of qualitative indicators such as revealed comparative advantage, direct productivity and multiplier effect of GDP. Therefore, the cluster it belongs to does not reflect its real tourism assets.

Suggested Citation

  • Delia Popescu & Iulia Monica Oehler-Sincai & Daniel Bulin & Ion Alexandru Tanase, 2018. "Cee-16: A Cluster Analysis Based on Tourism Competitiveness and Correlations With Major Determinants," The AMFITEATRU ECONOMIC journal, Academy of Economic Studies - Bucharest, Romania, vol. 20(S12), pages 833-833, November.
    • Handle: RePEc:aes:amfeco:v:20:y:2018:i:s12:p:833
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    Cited by:

    1. Anna V. VERSHININA & Elena R. ORLOVA, 2019. "The tourism industry in Russia: Problems of public administration," Upravlenets, Ural State University of Economics, vol. 10(1), pages 16-27, March.
    2. Mirea Cosmin Nicolae & Sarbu Alexandra Maria & Ionescu Andra Maria, 2021. "The Relationship Between The Number Of Tourists Arrived In Romania'S Development Regions And The Gross Domestic Product," Cactus - The tourism journal for research, education, culture and soul, Bucharest University of Economic Studies, vol. 3(1), pages 16-23.
    3. Yue Pan & Zhaolong An & Jianpu Li & Gangmin Weng & Lingyan Li, 2023. "Spatiotemporal Characteristics and Determinants of Tourism Cooperation Network in Beijing–Tianjin–Hebei Region," Sustainability, MDPI, vol. 15(5), pages 1-20, February.

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    More about this item

    Keywords

    CEE; CEE-16; China; 16+1; Romania; tourism competitiveness; tourism indicators; cluster analysis;
    All these keywords.

    JEL classification:

    • L83 - Industrial Organization - - Industry Studies: Services - - - Sports; Gambling; Restaurants; Recreation; Tourism
    • C83 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Survey Methods; Sampling Methods
    • F02 - International Economics - - General - - - International Economic Order and Integration

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