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Application of spatial econometric methods to analyze factors attracting young faculty to Russian universities

Author

Listed:
  • Byvaltseva-Stankevich, Anastasia

    (HSE University, Moscow, Russian Federation)

  • Panova, Anna

    (HSE University, Moscow, Russian Federation)

Abstract

The aging of university faculty is an important problem. This studies the issue of attracting young faculty to leading and non-leading Russian universities in 2014–2022. This is the first work that applies spatial econometric methods using data from the Russian higher education sector, allowing for the consideration of interdependence among universities. Three different weight matrices are used — neighborhood matrix, five nearest neighbors matrix, and inverse distances matrix. An important methodological aspect is the adaptation of matrices built on macrodata (regions) to mesodata (universities). The results of the regression analysis are robust to the change of spatial matrices. There is a spatial dependence between Russian universities, it is especially sound within two separate clusters: leading and non-leading universities, and leading universities appear to be in a situation of competition with each other for young faculty. In order to attract young faculty, Russian universities may increase the remuneration level and decrease the pressure regarding young scholars’ publishing activity.

Suggested Citation

  • Byvaltseva-Stankevich, Anastasia & Panova, Anna, 2025. "Application of spatial econometric methods to analyze factors attracting young faculty to Russian universities," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 77, pages 91-115.
    • Handle: RePEc:ris:apltrx:0517
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    References listed on IDEAS

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    More about this item

    Keywords

    spatial econometrics; Russian universities; young faculty;
    All these keywords.

    JEL classification:

    • C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Spatio-temporal Models
    • I23 - Health, Education, and Welfare - - Education - - - Higher Education; Research Institutions

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