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FIFA 2018 World Cup and efficiency of collective accommodation facilities in Russian regions

Author

Listed:
  • Kirill Furmanov

    (CEMI RAS, Moscow, Russian Federation)

  • Marina Predvoditeleva

    (HSE University, Moscow, Russian Federation)

  • Olga Balaeva

    (HSE University, Moscow, Russian Federation)

Abstract

The paper studies changes in technical efficiency of the hotels, hostels, and other accommodation facilities in regions of Russia that possibly took place due to the 2018 FIFA World Cup. The production frontier for the accommodation facilities is estimated using 2015–2021 data on Russian regions, regional technical efficiency before and after the World Cup is computed and dynamics of facilities’ technical efficiency indices is compared between the regions involved in the organization of the World Cup and other regions. Our analysis employs the approach to production frontier modeling introduced by Schmidt & Sickles. In contrast with widely used parametric stochastic frontier models, this approach does not impose distributional assumptions on the components of the regression error. It is found that although the relative technical efficiency level has changed, the change is not significantly related to regions’ involvement in the World Cup. Considering this effect by government agencies and local communities when planning mega-events, will help better assess the return on investment and expectations.

Suggested Citation

  • Kirill Furmanov & Marina Predvoditeleva & Olga Balaeva, 2026. "FIFA 2018 World Cup and efficiency of collective accommodation facilities in Russian regions," Applied Econometrics, Russian Academy of National Economy and Public Administration (RANEPA), vol. 82, pages 67-85.
  • Handle: RePEc:ris:apltrx:022710
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    File URL: http://pe.cemi.rssi.ru/pe_2026_82_067-085.pdf
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    References listed on IDEAS

    as
    1. John C. Driscoll & Aart C. Kraay, 1998. "Consistent Covariance Matrix Estimation With Spatially Dependent Panel Data," The Review of Economics and Statistics, MIT Press, vol. 80(4), pages 549-560, November.
    2. William Greene, 2004. "Distinguishing between heterogeneity and inefficiency: stochastic frontier analysis of the World Health Organization's panel data on national health care systems," Health Economics, John Wiley & Sons, Ltd., vol. 13(10), pages 959-980, October.
    3. Battese, George E. & Coelli, Tim J., 1988. "Prediction of firm-level technical efficiencies with a generalized frontier production function and panel data," Journal of Econometrics, Elsevier, vol. 38(3), pages 387-399, July.
    4. Schmidt, Peter & Sickles, Robin C, 1984. "Production Frontiers and Panel Data," Journal of Business & Economic Statistics, American Statistical Association, vol. 2(4), pages 367-374, October.
    5. Heather Mitchell & Mark Fergusson Stewart, 2015. "What should you pay to host a party? An economic analysis of hosting sports mega-events," Applied Economics, Taylor & Francis Journals, vol. 47(15), pages 1550-1561, March.
    6. Ageev, V. & Altukhov, S., 2018. "Comparative Analysis of Costs and Economic Effects of the FIFA World Cups (1998-2018)," Journal of the New Economic Association, New Economic Association, vol. 40(4), pages 158-167.
    7. Aigner, Dennis & Lovell, C. A. Knox & Schmidt, Peter, 1977. "Formulation and estimation of stochastic frontier production function models," Journal of Econometrics, Elsevier, vol. 6(1), pages 21-37, July.
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    JEL classification:

    • L83 - Industrial Organization - - Industry Studies: Services - - - Sports; Gambling; Restaurants; Recreation; Tourism

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