Analysis of Factors Influencing Labor Resources in the Kyiv Region, and Their Dynamics

Анотація

The article in question continues the authors’ study of the refitting model for the Kyiv region aa for searching for and analyzing the factors that affect a number of labor resources in the region, their intra-regional movement, outflow and inflow. The primary assumption in the refitting model for modeling the number of labor resources in economic sectors was the constant proportions of the number of employees in each sector to the total labor force and the functional dependence of the total number of employees on the total population in the Kyiv region using the logistics function. Such assumptions were sufficient for carrying out the initial modeling and analysis of the results, but they are not insufficient to obtain more accurate results that could be relied on to make decisions in the economic policy in the region. Therefore, there is an urgent need for a more detailed analysis of the labor resources dynamics, and for the search of new factors affecting them. Thus, we have analyzed socio-economic indicators of various activities at the regional level and balanced them against the number of full-time employees involved in the main industries represented in the region. Mutually conditioned influence was found between indicators in education and the two main economic sectors: agriculture and health. Highly correlated connections have been found, which are determined by the number of employees in health care, transportation and environment. The potential impact of sociodemographic and migration processes on the number of employees in the abovementioned industries is analyzed. The main reasons why tax burden indicators and local budget expenditures influence the agricultural sector, as well as the region as a whole, are enumerated. Options for improving the refitting model for the Kyiv region are suggested, namely the introduction of a production institutional function with tax burden factors. A multifactor regression model has been developed to predict the number of full-time employees in the region based on statistical indicators of education, health care, and environment, as well as sociodemographic factors.

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