Abstract
Purpose: The purpose of the article is to compare the models of state regulation of economic growth, connected to market optimization and counter-cyclical regulation, through the prism of their influence on population’s living standards and to determine the most optimal one according to the criterion of maximization of positive influence. Methodology: The authors use the method of regression analysis for determining the dependence of the life quality index in 2018 on the 2007 level, growth rate of GDP (model of market optimization), and depth of decline of GDP in the crisis period (2010) (model of counter-cyclical regulation). The research objects include 16 developed and developing countries that are peculiar for the highest living standards as of 2018–8 countries implement the model of market optimization and 8 countries implement the model of counter-cyclical regulation. Results: It is concluded that maximization of advantages for the global economy, connected to increase of population’s living standards, requires the model of counter-cyclical regulation, which envisages limitation of growth rate of economy in the period of its rise for leveling the following cyclical fluctuations–minimization of crisis in the period of decline. The alternative model of market optimization is undesired, as it hinders the achievement of global goals in the sphere of sustainable development, reducing population’s living standards. Recommendations: It is shown that practical implementation of global goals in the sphere of sustainable development envisages unification of the practice of national state regulation of economic growth for optimizing the growth of the modern global economy. The authors’ recommendations for this are offered.
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Bogoviz, A.V., Bolonin, A.I., Kletskova, E.V., Romantsova, T.V., Karp, M.V. (2020). Growth of the Modern Global Economy: Market Optimization Versus Counter-Cyclical Regulation. In: Popkova, E. (eds) Growth Poles of the Global Economy: Emergence, Changes and Future Perspectives. Lecture Notes in Networks and Systems, vol 73. Springer, Cham. https://doi.org/10.1007/978-3-030-15160-7_22
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DOI: https://doi.org/10.1007/978-3-030-15160-7_22
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