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Regional Tourism Competition in the Baltic States: a Spatial Stochastic Frontier Approach

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  • Pavlyuk, Dmitry

Abstract

This paper aimed at a statistical analysis of competition for tourists between regions within Baltic states (Estonia, Latvia, Lithuania) and estimation relative efficiency levels of regions. We apply a modern approach called Spatial Stochastic Frontier and corresponded to spatial modification of a stochastic frontier model. We specify two alternative spatial stochastic frontier models – distance and travel-time based to identify an influence of existing transport network on research results. Using the model we analyse region-specific factors (tourism infrastructure, employment, geographical position and natural attractors) having an effect on a number of visitors and estimate regions' efficiency values. We discover a significant level of inefficiency of Baltic states regions and propose some ways to improve the situation.

Suggested Citation

  • Pavlyuk, Dmitry, 2010. "Regional Tourism Competition in the Baltic States: a Spatial Stochastic Frontier Approach," MPRA Paper 25052, University Library of Munich, Germany.
    • Handle: RePEc:pra:mprapa:25052
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    File URL: https://mpra.ub.uni-muenchen.de/25052/1/MPRA_paper_25052.pdf
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    References listed on IDEAS

    as
    1. Wei-Chiang Hong, 2008. "Competitiveness in the Tourism Sector," Contributions to Economics, Springer, number 978-3-7908-2042-3, December.
    2. Jondrow, James & Knox Lovell, C. A. & Materov, Ivan S. & Schmidt, Peter, 1982. "On the estimation of technical inefficiency in the stochastic frontier production function model," Journal of Econometrics, Elsevier, vol. 19(2-3), pages 233-238, August.
    3. Maria Francesca Cracolici & Peter Nijkamp & Miranda Cuffaro, 2007. "Efficiency and Productivity of Italian Tourist Destinations: A Quantitative Estimation Based on Data Envelopment Analysis and the Malmquist Method," Springer Books, in: Álvaro Matias & Peter Nijkamp & Paulo Neto (ed.), Advances in Modern Tourism Research, chapter 0, pages 325-343, Springer.
    4. Erniel B. Barrios & Rouselle F. Lavado, 2010. "Spatial Stochastic Frontier Models," Microeconomics Working Papers 23091, East Asian Bureau of Economic Research.
    5. Kumbhakar,Subal C. & Lovell,C. A. Knox, 2003. "Stochastic Frontier Analysis," Cambridge Books, Cambridge University Press, number 9780521666633.
    Full references (including those not matched with items on IDEAS)

    Citations

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    Cited by:

    1. Thomas Graaff, 2020. "On the estimation of spatial stochastic frontier models: an alternative skew-normal approach," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 64(2), pages 267-285, April.
    2. Ricardo Oliveira & Maria Isabel Pedro & Rui Cunha Marques, 2014. "Cost Efficiency of Portuguese Hotels in the Algarve: A Comparative Analysis Using Mathematical and Econometric Approaches," Tourism Economics, , vol. 20(4), pages 797-812, August.

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    More about this item

    Keywords

    spatial stochastic frontier; efficiency; competition; regional tourism; transport network;
    All these keywords.

    JEL classification:

    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • O18 - Economic Development, Innovation, Technological Change, and Growth - - Economic Development - - - Urban, Rural, Regional, and Transportation Analysis; Housing; Infrastructure
    • R15 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General Regional Economics - - - Econometric and Input-Output Models; Other Methods
    • C31 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models; Quantile Regressions; Social Interaction Models
    • L83 - Industrial Organization - - Industry Studies: Services - - - Sports; Gambling; Restaurants; Recreation; Tourism
    • C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Spatio-temporal Models

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