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Neuro-Fuzzy Modeling Techniques in Economics

Neuro-Fuzzy Modeling Techniques in Economics

ISSN 2415-3516
 
Anatolii Kolot  
 
Oksana Herasymenko  
 
Anna Shevchenko  
 
Ivan Ryabokon  

Employment in the coordinates of digital economy: current trends and foresight trajectories

DOI:

10.33111/nfmte.2022.078

Анотація:
Abstract: The article presents a scientific and applied argumentation of current trends and the authors’ vision of foresight trajectories of employment in the coordinates of digital economy. A critical synthesis of existing scientific research has been carried out, which has shown the dynamic scaling of digital economy in global economic space with profound multi-vector shifts in employment at various levels.
The authors’ hypothesis that the scale and structure of employment would change intensively in both constructive and destructive dimensions under the influence of digitalization has been suggested and proved. The nature of constructivism and destructiveness of such changes has been disclosed.
Trends in the level of employment in global coordinates, in Europe as a whole and in Ukraine have been analyzed with the use of the International Labour Organization’s information resource.
It was clustered eight analyzed industries (in the field of high-tech manufacturing, high-tech services and certain export-oriented industries) using Kohonen self-organizing maps toolset, which allowed, based on a set of characteristic socio-labour and socio-economic indicators of the State Statistics Service of Ukraine for the period from 2013 to 2020, to analyze the state of development of each industry by the structure of employed and economic development, as well as to study trends in changes in state of industries in dynamics under conditions of digitalization.
The analysis of the clustering results showed that despite very serious economic challenges and real problems in most industries, 2014 and 2020 can be considered the years of rapid development of digital technologies in high-tech industries, which were accompanied by a reduction in the number of employees and a decrease in salaries. The least digital transformations mainly concerned industrial sectors during the years of economic recovery of Ukraine in the period from 2015 to 2018.
Results of the study of the impact of employment in high-tech industries and high-tech services sectors on the dynamics of gross domestic product and gross value added in the Ukrainian economy are presented. Results of forecasting these indicators with allocation of upper and lower confidence limits in a trend have been presented, which allowed to model optimistic, realistic and pessimistic scenarios of the abovementioned macroeconomic indicators development. A hypothesis regarding increase in the probability of implementing an optimistic scenario of gross domestic product and gross value-added dynamics under conditions of digitalization by optimizing the number and the structure of those employed in the high-tech segment has been proposed and proved.
Ключові слова:
Key words: world of work digitalization, destruction of employment, constructivism of transformations in employment, future of employment, human-centered sustainable development, economic sectors clustering, high-tech industries
УДК:
UDC:

JEL: C38 E24 J21 J23

To cite paper
In APA style
Kolot, A., Herasymenko, O., Shevchenko, A., & Ryabokon, I. (2022). Employment in the coordinates of digital economy: current trends and foresight trajectories. Neuro-Fuzzy Modeling Techniques in Economics, 11, 78-123. http://doi.org/10.33111/nfmte.2022.078
In MON style
Kolot A., Herasymenko O., Shevchenko A., Ryabokon I. Employment in the coordinates of digital economy: current trends and foresight trajectories. Нейро-нечіткі технології моделювання в економіці. 2022. № 11. С. 78-123. http://doi.org/10.33111/nfmte.2022.078 (дата звернення: 21.05.2024).
With transliteration
Kolot, A., Herasymenko, O., Shevchenko, A., Ryabokon, I. (2022) Employment in the coordinates of digital economy: current trends and foresight trajectories. Neuro-Fuzzy Modeling Techniques in Economics, no. 11. pp. 78-123. http://doi.org/10.33111/nfmte.2022.078 (accessed 21 May 2024).
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