Comparative Analysis of Various Approaches to Calculating Labour Productivity

The article is devoted to the problem of analyzing the methodological foundations for assessing the dynamics of labour productivity in the regions of Russia in the context of the digital transformation of the economy. The identification of differences in the level of labour productivity and employment and the influencing factors is a necessary requirement for building a competent regional socio-economic policy in the new institutional conditions. Calculations of labour productivity are carried out by various international and national organizations that use a variety of methods for measuring this indicator. The presence of a sufficiently large number of methods for measuring productivity leads to the fact that each one gives its own result and different dynamics. The authors set the task to consider the existing methodological approaches to calculating labor productivity and to analyze how much they affect the results. The authors statistically tested the hypothesis of heterogeneity of ratings obtained by various methods. The calculations were made on the basis of data from Rosstat for the regions of Russia for the period from 2013 to 2018. To study the uniformity of the series, various statistical criteria were used. The analysis showed that in all years of the period under review the samples are heterogeneous. Consequently, the development of managerial decisions substantially depends on the methodology used to calculate the labour productivity index. The authors concluded that it is necessary to build an index of labour productivity based on regression dependencies on indicators reflecting the main factors affecting regional labor productivity. It helps to get a more effective assessment of regional differences in its level and dynamics. This index should be built for each region taking into account its industry specifics and the level of development of new digital technologies.

labour productivity, regions, regional economy, labour market

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