Analysis of Labor Market Transformation in the Context of Industry 4.0

Olena Stryzhak
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Abstract

Abstract The digitalization of the economy and society in the context of Industry 4.0 affects the labor market. New jobs are being created with new personnel requirements. At the same time, digitalization processes affect countries and sectors of the economy in different ways. The number of employed is declining in some sectors and increasing in others. The ratio of people employed in different industries also differs from country to country. As the digitalization of production relations intensifies, transformation processes in the labor market will increase. In the context of the actualization of digital changes in modern society, the aim of the article is to analyze the labor market under the digitalization of economic relations. The World Development Indicators (WDI) and Digital Intelligence Index (DII) were used to realize the purpose of the study. The study covers 89 countries for 2019. Correlation analysis, standardization, cluster analysis and analysis of variance were used as methods in the research. As a result of the study, the following conclusions were obtained. A strong relationship was found between Gross Domestic Product (GDP) and DII for the whole sample. The cluster analysis process found that the variables form three clusters with values of most indicators by group: at the high, the average and the low levels. Plots of mean and confidence intervals for the variables in each cluster were also plotted. Correlation coefficients were calculated for each cluster. It was found that each group has its own internal patterns.
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工业4.0背景下的劳动力市场转型分析
工业4.0背景下经济和社会的数字化影响着劳动力市场。新的工作岗位不断涌现,对人员也有新的要求。与此同时,数字化进程以不同的方式影响着国家和经济部门。一些部门的就业人数在下降,而另一些部门的就业人数在增加。不同行业的就业人口比例也因国而异。随着生产关系数字化的加强,劳动力市场的转型过程将会增加。在现代社会实现数字化变革的背景下,本文旨在分析数字化经济关系下的劳动力市场。采用世界发展指标(WDI)和数字智能指数(DII)来实现研究目的。该研究涵盖了2019年的89个国家。研究采用了相关分析、标准化分析、聚类分析和方差分析等方法。通过研究,得出以下结论。在整个样本中,国内生产总值(GDP)和DII之间存在很强的关系。聚类分析过程发现,变量组成了三个聚类,大多数指标的值按组划分:高、平均和低水平。还绘制了每个簇中变量的均值和置信区间图。计算每个聚类的相关系数。研究发现,每个群体都有自己的内部模式。
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来源期刊
CiteScore
2.00
自引率
42.90%
发文量
20
审稿时长
8 weeks
期刊最新文献
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