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From equality to diversity: Classifying Russian universities in a performance oriented system

Author

Listed:
  • Abankina, Irina
  • Aleskerov, Fuad
  • Belousova, Veronika
  • Gokhberg, Leonid
  • Kiselgof, Sofya
  • Petrushchenko, Vsevolod
  • Shvydun, Sergey
  • Zinkovsky, Kirill

Abstract

Over the last few decades, performance-based funding models of universities have been introduced and have made universities build and implement different strategies to enable them to compete and be viable in changing circumstances. In turn, national governments are focused on providing universities with more opportunities to run efficient programmes that advance higher education. This paper includes a detailed review of various taxonomies for structuring university. More importantly, it develops a typology of higher education institutions that is relevant for the Russian context. The Ward method is used to cluster universities on the basis of university distinctions in terms of the availability of resources, education, and research and development. This typology of universities is verified by assessing their efficiency score gained from modified Data Envelopment Analysis, incorporating universities' heterogeneity. Finally, the paper gives a decision tree for classifying universities bearing in mind their diversity. It might be expanded for a broader set of inputs and outputs, namely external project-based research funding modes and cooperation between universities and industry to pursue the development of innovation. The results can be used for shaping targeted policies aimed at particular university groups.

Suggested Citation

  • Abankina, Irina & Aleskerov, Fuad & Belousova, Veronika & Gokhberg, Leonid & Kiselgof, Sofya & Petrushchenko, Vsevolod & Shvydun, Sergey & Zinkovsky, Kirill, 2016. "From equality to diversity: Classifying Russian universities in a performance oriented system," Technological Forecasting and Social Change, Elsevier, vol. 103(C), pages 228-239.
    • Handle: RePEc:eee:tefoso:v:103:y:2016:i:c:p:228-239
    DOI: 10.1016/j.techfore.2015.10.007
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    Cited by:

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    2. Centobelli, Piera & Cerchione, Roberto & Esposito, Emilio & Shashi,, 2019. "Exploration and exploitation in the development of more entrepreneurial universities: A twisting learning path model of ambidexterity," Technological Forecasting and Social Change, Elsevier, vol. 141(C), pages 172-194.
    3. Telcs, András & Kosztyán, Zsolt Tibor & Banász, Zsuzsanna & Csányi, Vivien Valéria, 2019. "Felsőoktatási ligák, parciális rangsorok képzése biklaszterezési eljárásokkal [How to rate higher education systems partial rankings using bi-clustering methods]," Közgazdasági Szemle (Economic Review - monthly of the Hungarian Academy of Sciences), Közgazdasági Szemle Alapítvány (Economic Review Foundation), vol. 0(9), pages 905-931.
    4. Berbegal-Mirabent, Jasmina & Gil-Doménech, Dolors & de la Torre, Rocío, 2019. "Dealing with heterogeneity: An analysis of Spanish universities," TEC Empresarial, School of Business, Costa Rica Institute of Technology (ITCR), vol. 13(3), pages 58-77.
    5. Renato Bruni & Giuseppe Catalano & Cinzia Daraio & Martina Gregori & Henk F. Moed, 2019. "Studying the Heterogeneity of European Higher Education Institutions," DIAG Technical Reports 2019-12, Department of Computer, Control and Management Engineering, Universita' degli Studi di Roma "La Sapienza".
    6. Tommaso Agasisti & Ekaterina Shibanova & Daria Platonova & Mikhail Lisyutkin, 2018. "The Russian Excellence Initiative For Higher Education: An Econometric Evaluation Of Short-Term Results," HSE Working papers WP BRP 201/EC/2018, National Research University Higher School of Economics.
    7. Renato Bruni & Giuseppe Catalano & Cinzia Daraio & Martina Gregori & Henk F. Moed, 2020. "Studying the heterogeneity of European higher education institutions," Scientometrics, Springer;Akadémiai Kiadó, vol. 125(2), pages 1117-1144, November.

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    More about this item

    Keywords

    Higher education institutions (HEIs); Typology; Research and education; Hierarchical clustering; Data envelopment analysis; Efficiency;
    All these keywords.

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C38 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Classification Methdos; Cluster Analysis; Principal Components; Factor Analysis
    • D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search; Learning; Information and Knowledge; Communication; Belief; Unawareness
    • O32 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Management of Technological Innovation and R&D

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