Industrial Robotization in Russia: Methodological Approaches to Statistical Measurement
This article addresses the challenges of accounting for and monitoring industrial robotization processes in Russia. Industrial robotization is viewed as one of the key instruments for increasing labor productivity and achieving the country’s technological sovereignty, including in the context of national development goals. The authors focus on the indicator of robot density and its role in the system for monitoring strategic policy targets.The study presents a comparative analysis of national and international methodologies for calculating indicators, including the practices of the International Federation of Robotics (IFR) and Eurostat. It highlights differences in the approaches to defining industrial robots, the scope of data coverage, and the analytical capacity of the respective indicators. Based on statistical data for 2023–2024, the article examines the deployment of industrial robots across Russia’s regions and identifies territorial disparities in their distribution.The authors propose measures to improve the monitoring system for industrial robotization aimed at enhancing the accuracy and reliability of statistical data. These include refining calculation methodologies, increasing the analytical content of indicators, and shifting from purely quantitative measurements to a more comprehensive evaluation of robotization effects. The practical significance of the study lies in outlining directions for enhancing the statistical monitoring of robotization processes and in supporting the development of evidence-based strategic benchmarks for national industrial policy.
Authors
- Е. V. Potaptseva (ORCID: https://orcid.org/0000-0001-8670-0304)
- О. S. Bryantseva (ORCID: https://orcid.org/0000-0003-0195-8694)
Institutions
- Ural Federal University (RU)
- Institute of Economics (RU)
Publication Details
- Journal
- Voprosy statistiki
- Published
- 2025-09-02
- DOI
- https://doi.org/10.34023/2313-6383-2025-32-4-5-20
- Primary Topic
- Economic Development and Digital Transformation
- Type
- article
- Field-Weighted Citation Impact
- 0.00