Regulations of government policy in financing education: identification based on neural networks

Abstract

Purpose. The aim of the article is a progress in addressing the problem of identifying educational funding rules and harnessing the potential of artificial neural networks in the process of identifying rules. Methodology of research. The following specific methods have been used in the research process: a systematic approach – to classify forms of identification of policy rules and to determine the levels at which educational funding rules should be implemented; artificial neural network analysis – to substantiate and identify educational funding rules; formalization and graphical presentation of the results obtained – to refine the results of the study. Findings. It has been determined that government policy rules are a tool of public administration, and to be used in this capacity they must be identified and institutionalized. It is substantiated that the use of artificial neural network method, which allows to analyze and predict nonlinear processes with high uncertainty, creates new opportunities for identification of policy rules. Two funding indicators for education, the formation of which should be governed by government rules, are based on two neural networks. Originality. An innovative approach to modelling and forecasting of Ukrainian education funding indicators, subject to certain government rules, using artificial neural network models, is proposed. This made it possible to obtain graphs of the relationship between input and output variables and to predict the share of education expenditure in GDP and the share of education expenditure in public spending, provided that the five governmental variables envisaged by the government, international institutions and research institutions of the country, namely: labour productivity, share of innovative activity, share of budget deficit and public debt in GDP, share of able-bodied population in the country's population. Practical value. Practical approaches to the identification of educational funding rules developed and proposed by the authors can help to achieve a higher level of institutionalization of the rules and be used in the work of the Ministry of Education and Science. Key words: rules of government policy; rules for financing education; public administration; artificial neural networks.