Causal analysis of the interinfluence of workforce productivity and rail freight intensity in the regions of the Ural Federal District

Mikhail B. Petrov, Leonid A. Serkov, Kseniya A. Zavyalova


Relevance. The development of the railway industry has a significant positive impact on socio-economic dynamics at both the state and regional levels, which has been confirmed by numerous domestic and foreign studies. However, the issue of mutual influence of such categories as regional labour productivity and rail freight intensity has been little studied. At the same time, the most important task today is to find effective incentives for the growth of regional labour productivity.

Research Objective. This study aims to econometric analysis of the relationship between rail freight intensity and workforce productivity in the Ural Federal District (UFD).

Data and Methods. The study uses official statistical data on Russian regions provided by the Federal State Statistics Service. The methods of Vector Error Correction Models (VECM) and pooled mean group estimates (PMG method) formed the methodological basis of the study.

Results. The study has shown that there is a relationship between workforce productivity and rail freight intensity. At that point, in a short-term period growth of rail freight intensity leads to an increase in workforce productivity, which in a long-term period itself becomes an incentive to increase the shipped commodity mass and rail freight intensity.

Conclusions. The findings can be of interest to public authorities at the national and regional levels, for heads of industrial structures and functional institutions, representatives of business and scientific communities interested in the development and modernization of transport infrastructure, being a basic condition for the increased intensity of cargo transportation in the region.


regional development, regional transportation system, workforce productivity, rail freight intensity, vector error correction models (VECM), method of pooled mean group estimates (PMG method)

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Copyright (c) 2023 Mikhail B. Petrov, Leonid A. Serkov, Kseniya A. Zavyalova

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