Abstract
To identify determinants of labor productivity, correlation relationships were estimated for indicators reflecting the influence of 30 socio-economic and innovative factors in the regions for 2015-2017. Of the 30 factors, for some factors, a significant correlation was found, characterizing their indicators and labor productivity. For these indicators, models of linear production functions were constructed. Modeling made it possible to establish that the following factors have a significant impact on labor productivity: capital productivity, investment, foreign investment, the number of government employees, wages, income inequalities, the number of university faculty, the number of advanced production technologies used, and the consumer price index. The instability of assessing the impact of indicators characterizing the determinants of labor productivity can be explained by two reasons of a different nature. Firstly, the development of the country's economic system at present may in fact be unstable. This problem determines the need for additional research. Secondly, the models obtained by the standard inclusion-exclusion method without taking into account and eliminating the multicollenarity effect can significantly reduce the reliability of estimates obtained by the least common square method. This determines the need to continue work using a more advanced modeling technique.
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