Innovation in Company Labor Productivity Management: Data Science Methods Application

The article considers the challenge of labor productivity growth in a company using objective data about economic, demographic and social factors and subjective information about an employees’ health quality. We propose the technology for labor productivity management based on the phased data processing and modeling of quantitative and qualitative data relations, which intended to provide decision making when planning trajectories for labor productivity growth. The technology is supposed to use statistical analysis and machine learning, to support management decision on planning health-saving strategies directed to increase labor productivity. It is proved that to solve the problem of employees’ clustering and design their homogeneous groups, it is properly to use the k-means method, which is more relevant and reliable compared to the clustering method based on Kohonen neural networks. We also test different methods for employees’ classification and predicting of a new employee labor productivity profile and demonstrate that over problem with a lot of qualitative variables, such as gender, education, health self-estimation the support vector machines method has higher accuracy.

Intangible Assets and Labor Productivity Growth

We examine the contribution to labor productivity growth in the manufacturing sector of investment in different intangible asset categories—computerized information, innovative property, and economic competencies—for a set of 18 European countries between 1995 and 2017, as well as whether this contribution varies between different groups of countries. The motivation is to go a step further and identify which single or combination of intangible assets are relevant. The main findings can be summarized as follows. Firstly, all the three different categories of intangible assets contribute to labor productivity growth. In particular, intangible assets related to economic competences together with innovative property assets have been identified as the main drivers; specifically, advertising and marketing, organizational capital, research and development (R&D) investment, and design. Secondly, splitting the sample of European Union (EU) member states into three groups—northern, central and southern Europe—allows for the identification of a significant differentiated behavior between and within groups, in terms of the effects of investment in intangible assets on labor productivity growth. We conclude that measures promoting investment in intangibles at EU level should be accompanied by specific measures focusing on each country’s needs, for the purpose of promoting labor productivity growth. The obtained evidence suggests that the solution for the innovation deficit of some European economies consist not only of raising R&D expenditure, but also exploiting complementarities between different types of assets.

Labor productivity growth factors

The modern strategy of economic development of Russia, which is aimed at restructuring the economy, requires, first of all, the search for new and improvement of existing ways of increasing labor productivity at any domestic enterprise. That is why the issues of creating effective motives and incentives for increasing labor productivity as an important tool for the development of an industrial enterprise are relevant.

Econometric Analysis of Factors of Labor Productivity Growth in Constituent Entities of the Russian Federation

In the current context of recovery growth in Russia, the urgent task of identifying factors of growth of labor productivity can be solved using econometric methods. Preliminary examination of interregional comparative analysis of dynamics of this crucial economic efficiency index, in the author’s opinion, showed low information content of this approach due to the presence of a low base effect. The author recommends a more realistic approach to the interregional comparative analysis of the labor productivity dynamics and its growth factors on the basisof econometric panel data models.It was revealed that for the period 2010–2018, the growth of labor productivity in the regions of the Russian Federation strongly correlated with dynamic characteristics of industrial production, real wages and physical volume of investments in fixed assets, growth intensity in the share of added value of high-tech and knowledge-intensive industries in GRP. It has been empirically proven that the growth of labor productivity in the regions in the considered interval correlates with a decrease in the number of employees. In addition, an increase in labor productivity is typical for constituent entities of the Russian Federation with high rates of industrial production.The influence of the structure of employed in the economy on the level of education on the growth of labor productivity has not been established, which may indicate the presence of inefficient jobs. Also, the hypothesis that the export-oriented regions of the Russian Federation are highly productive has not been confirmed.In conclusion, taking into account the results of modeling, the author formulated recommendations adjusting focal points of structural changes in the economy, which could boost labor productivity growth.

Assessment of macroeconomic factors and internal conditions of labor productivity growth in the functioning of economic entities

Changes in labor productivity in enterprises occur due to various factors that are external macroeconomic factors, as well as due to changes in internal conditions that could lead to changes within the enterprise itself. The article analyzes macroeconomic factors and internal conditions of labor productivity growth in the functioning of economic entities.