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Clustering methodology of the Russian Federation regions with account of sectoral structure of GRP


  • Aivazian, Sergei

    () (CEMI RAS, Moscow, Russia)

  • Afanasiev, Mikhail

    () (CEMI RAS, Moscow, Russia)

  • Kudrov, Alexander

    () (CEMI RAS, Moscow, Russia)


The methodology to identify similar groups of Russian regions is presented, each of which has its own model of productive capacity, which determines the dependence of the GRP from the fixed assets and number of employees. It is carried out to test the hypothesis that the parameters of the functions describing the production potential of the regions included in the different groups, different by reason of the sectoral structure of GRP. The procedure for constructing the auxiliary integral indicators based on component analysis is offered, it is reflected the specialization of regions. Developed and tested on data for the period 2009–2013 algorithm of forming a similar group of regions. It is revealed the presence of a greater sensitivity to changes in GRP value of fixed assets for the group of regions with a specialization in mining and mixed type specialty than for groups of regions specializing in manufacturing and agriculture. At the same time, last two groups of regions and the group of regions with emerging economies are more sensitive to changes in the number of employees. The developed methodology is an element of forecasting procedures and planning options for sustainable socio-economic development of regions of the Russian Federation.

Suggested Citation

  • Aivazian, Sergei & Afanasiev, Mikhail & Kudrov, Alexander, 2016. "Clustering methodology of the Russian Federation regions with account of sectoral structure of GRP," Applied Econometrics, Publishing House "SINERGIA PRESS", vol. 41, pages 24-46.
  • Handle: RePEc:ris:apltrx:0283

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    References listed on IDEAS

    1. Anil Bera & Subhash Sharma, 1999. "Estimating Production Uncertainty in Stochastic Frontier Production Function Models," Journal of Productivity Analysis, Springer, vol. 12(3), pages 187-210, November.
    2. Efthymios G. Tsionas, 2006. "Inference in dynamic stochastic frontier models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 21(5), pages 669-676.
    3. Efthymios G. Tsionas & Subal C. Kumbhakar, 2004. "Markov switching stochastic frontier model," Econometrics Journal, Royal Economic Society, vol. 7(2), pages 398-425, December.
    4. Meeusen, Wim & van den Broeck, Julien, 1977. "Efficiency Estimation from Cobb-Douglas Production Functions with Composed Error," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 18(2), pages 435-444, June.
    5. van den Broeck, Julien & Koop, Gary & Osiewalski, Jacek & Steel, Mark F. J., 1994. "Stochastic frontier models : A Bayesian perspective," Journal of Econometrics, Elsevier, vol. 61(2), pages 273-303, April.
    6. 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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    Cited by:

    1. Tetyana Odintsova, 2018. "Influence of Tax Burden on Economic Development of Regions: The Cluster Approach," Accounting and Finance, Institute of Accounting and Finance, issue 1, pages 114-123, March.
    2. Aivazian, Sergei & Afanasiev, Mikhail & Kudrov, Alexander, 2019. "Indicators of the main directions of socio-economic development in the space of characteristics of regional differentiation," Applied Econometrics, Publishing House "SINERGIA PRESS", vol. 54, pages 51-69.
    3. Aivazian, Sergei & Afanasiev, Mikhail & Kudrov, Alexander, 2018. "Indicators of economic development in the basis of the characteristics of regional differentiation," Applied Econometrics, Publishing House "SINERGIA PRESS", vol. 50, pages 4-22.
    4. Айвазян С.А. & Афанасьев М.Ю. & Кудров А.В., 2016. "Модели Производственного Потенциала И Оценки Технологической Эффективности Регионов Рф С Учетом Структуры Производства," Журнал Экономика и математические методы (ЭММ), Центральный Экономико-Математический Институт (ЦЭМИ), vol. 52(1), pages 28-44, январь.

    More about this item


    regional economy; production potential; econometric modeling; hypothesis testing;

    JEL classification:

    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • R15 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General Regional Economics - - - Econometric and Input-Output Models; Other Methods


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