Economic Analysis: Theory and Practice

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Neural network modeling of trends in Russia's higher education development from the perspective of human capital formation

Vol. 18, Iss. 4, APRIL 2019

Received: 20 November 2018

Received in revised form: 22 January 2019

Accepted: 7 March 2019

Available online: 26 April 2019


JEL Classification: C38, C45, I23, I25, O15

Pages: 642–662

Perova V.I. National Research Lobachevsky State University of Nizhny Novgorod (UNN), Nizhny Novgorod, Russian Federation

ORCID id: not available

Mamaeva N.A. Nizhny Novgorod State Agricultural Academy (NNSAA), Nizhny Novgorod, Russian Federation

ORCID id: not available

Zakharenko E.S. National Research Lobachevsky State University of Nizhny Novgorod (UNN), Nizhny Novgorod, Russian Federation

ORCID id: not available

Subject The article considers the specifics of higher education development in the Russian Federation as a factor of human capital formation, which is strategically important for the socio-economic growth of the country.
Objectives Our purpose is to investigate changes in the development of the first and second stages of higher education in Russia, using neural network modeling.
Methods The study rests on the multivariate data analysis. We analyzed the indicators of changes in the higher education development by training program for 2013–2017 with the help of Kohonen self-organizing maps using the STATISTICA software package.
Results Through the neural network modeling, we performed a cluster analysis of eight indicators, characterizing changes in activity of public higher education institutions of the Russian Federation in the field of bachelor's and master's degree programs for 2013–2017. We obtained the distribution of training programs in three clusters. The paper presents the characteristics of each cluster and socio-economic conclusions.
Conclusions The paper shows the impact of the said indicators on human capital. The findings are of practical importance for strategic planning of training programs in higher education institutions of the Russian Federation.

Keywords: human capital, higher education, cluster analysis, neural networks, Kohonen self-organizing maps


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