Open AccessStatistical Data Research on Staff Training for the Digital Economy in the Russian Federation
Author(s) -
Yu. V. Frolov,
T. M. Bosenko
Publication year - 2021
Publication title -
vysšee obrazovanie v rossii
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.292
H-Index - 8
eISSN - 2072-0459
pISSN - 0869-3617
DOI - 10.31992/0869-3617-2021-30-11-29-41
Subject(s) - staffing , matching (statistics) , digital economy , russian federation , relevance (law) , affect (linguistics) , national economy , vocational education , artificial neural network , economy , regression analysis , computer science , business , economics , economic system , statistics , artificial intelligence , economic growth , political science , psychology , management , mathematics , machine learning , communication , world wide web , law , economic policy
The article analyzes the statistical data relating to training specialists for digitalized economy by secondary vocational and higher education institutions. The purpose of the study was to develop and test personnel support indices for digitalization of the economy, as well as to identify social and economic factors that significantly affect the level of personnel support for the processes of digital transformation of the economy. The authors applied data from the official statistical reporting of the Russian Federation. The proposed staffing indices were modeled as objective functions depending on socio-economic factors characterizing the development of the economy in different dimensions. At the same time, the indices themselves were calculated as values in which the parameters of the output of digital specialists and their relevance in the economy were correlated. In the course of the study, a comparison of statistical and neural network data modeling methods and generalizing indices was performed. An analysis of the obtained regression models and an analysis of the sensitivity of trained neural networks made it possible to evaluate their accuracy in predicting the trends in the staffing of the digital economy and to identify factors that significantly affect the achievement of the goal of matching the output of specialists and the demands of economic sectors.