Digital Transformation in the Manufacturing Industries of Russia: an Analysis of the Business Tendencies Observations Results

Сергеевна, Лола Инна; Bakeev, Murat

doi:10.21638/spbu05.2019.407

Cited by 6 publications

(1 citation statement)

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“…For instance, W. Huws, referring to the term "digital labor, " examined important trends in the transformation of the labor market under the influence of digitalization [Huws, 2014]. I. S. Lola and M. B. Bakeev established the importance and significance of criteria for digital transformations and the digitalization of labor in manufacturing [Lola, Bakeev, 2019]. S. D. Bodrunov, D. S. Demidenko and V. A. Plotnikov proved the complementarity of the trends of reindustrialization and the development of the digital economy [Bodrunov, Demidenko, Plotnikov, 2018].…”

Section: Introductionmentioning

confidence: 99%

The impact of digitalization on the demand for labor in the context of working specialties: Spatial analysis

Dubrovskaya¹,

Kosonogova²

2021

SUJES

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The spread of digital technologies, as well as the expansion of remote work practices, have a direct impact on the transformation of the labor market. At the same time, there is no wellestablished scholarly consensus about the nature and consequences of this influence. The key premise of the article is that there are relatively few empirical studies on the labor market that account for the influence of the location of regions relative to each other. This article tests a hypothesis about the significance of location and neighborhood of territories on labor demand in the context of economic digitalization, based on the calculation of coefficients of demand localization for specialists of different profiles and methods of spatial econometrics. The assessment is based on the evaluation of the registered unemployment rate heterogeneity for municipalities in the Perm region of the Russian Federation, using the global and local Moran’s indexes. The studies revealed a positive spatial autocorrelation among neighboring municipalities that proved the high spatial heterogeneity of the registered unemployment rate. Centers of localization and development of labor resources (“growth poles”) and territories affected by spillover effects were also identified. This demonstrates that spatial inter-territorial relations have a significant impact on the demand for labor, which makes it necessary to account for spatial effects when modeling dynamics of employment indicators. The method of labor market analysis based on the assessment of spatial heterogeneity of the unemployment rate and the calculation of localization coefficients is universal and can be applied to various taxonomic units.

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Section: Introductionmentioning

confidence: 99%