2021 (18), №2

Modelling of the Cause and Effect Relationship Between the Indicators of Railway Efficiency and Labour Productivity in the Russian Economy

For citation: 

Petrov, M. B., Serkov, L. A., & Kozhov, K. B. (2021). Modelling of the Cause and Effect Relationship Between the Indicators of Railway Efficiency and Labour Productivity in the Russian Economy. Zhurnal Economicheskoj Teorii [Russian Journal of Economic Theory], 18(2), 308-322, https://doi.org/10.31063/2073-6517/2021.18-2.12


The article analyzes the causal relationship between normalized economic indicators characterizing the efficiency of the railway and labour productivity in the Russian economy in 2000–2019. Two models were tested for this purpose. The first model investigates the relationship between the transport output as a dependent variable and the variables of cargo output and transport mobility of the population as independent variables. The second model deals with the relationship between the freight output of the railway network and labor productivity in the Russian economy. With the help of the tool vector model of error correction, it was found that among all the variables of the first model, there is a joint bilateral short — and long-term causal relationship. In the second model, this causal relationship is one-sided. At the same time, the change in the freight output of the railway network does not affect the change in labor productivity in the short- and long-term. On the contrary, the change in labor productivity affects the change in the load capacity of the network both in the short- and long-term. As a result, the equations for two models that determine the long-term equilibrium between the technical and economic indicators are estimated. The proposed approach creates additional instrumental and methodological opportunities for analyzing multi-factor causal relationships between the development of the transport network and the key indicators of a national economy. Such approach can increase the reliability and validity of assessments of the socio-economic efficiency of schemes and projects for the development of the railway network. The results can be used by public authorities, specialized research and engineering organizations, transport companies and local unions for strategic planning, designing of railway transport networks and similar.

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Mikhail B. Petrov — Doctor of Technical Sciences, Head of the Center for the Development and Location of the Productive Forces, Institute of Economics of the Ural Branch of the Russian Academy of Sciences (Ekaterinburg, Russian Federation; e-mail: michpetrov@mail.ru).

Leonid A. Serkov — PhD in Physics and Mathematics, Associate Professor, Senior Researcher of the Center for Development and Location of Productive Forces, Institute of Economics of the Ural Branch of the Russian Academy of Sciences (Ekaterinburg, Russian Federation; e-mail: dsge2012 @ mail.ru).

Konstantin B. Kozhov — PhD in Economics, Senior Researcher of the Center for Development and Location of Productive Forces, Institute of Economics of the Ural Branch of the Russian Academy of Sciences (Ekaterinburg, Russian Federation; e-mail: jefytt11@mail.ru).

Dubovik, V. O. (2013). Metody otsenki transportnoy dostupnosti territorii [Methods of territory transport accessibility estimation].  Regional’nye issledovaniya [Area studies], 4(42),  11–18. (In Russ.)

Kazakov, A. L., Petrov, M. B., & Maslov, A. M. (2014). Mnogokriterial’naya optimizatsiya transportnoy sistemy regiona na osnove ee gipergrafa [Multiobjective optimization of the region’s transport system on the basis of its hypergraph].  Ekonomika regiona [Economy of region], 4,  199–208. (In Russ.)

Martynenko, A. V., & Petrov, M. B. (2016). Vliyanie nachertaniya transportnoy seti na pokazateli dostupnosti (na primere Sverdlovskoy oblasti) [Influence of surface transportation network on accessibility (by the example of Sverdlovsk oblast)].  Regional’nye issledovaniya [Regional studies], 2(52),  21–30. (In Russ.)

Macheret, D. A. (2016). O chem svidetel’stvuet stoletnyaya dinamika pokazateley krupneyshikh zheleznodorozhnykh setey [As Evidenced by Theage-old Dynamics of Indicators of Major Rail System].  Ekonomicheskaya politika [Economic Policy], 11(6),  138–169. (In Russ.)

Popov, P. V., & Miretskij, I. Yu. (2019). Metodologiya postroeniya logisticheskoy infrastruktury na territorii regiona [Methodology for constructing the region’s logistics infrastructure].  Ekonomika regiona [Economy of region], 15(2),  483–492. DOI: 10.17059/2019–2-13. (In Russ.)

Rakhmangulov, A. N., & Kopylova, O. A. (2014). Otsenka sotsial’no-ekonomicheskogo potentsiala regiona dlya razmeshcheniya ob”ektov logisticheskoy infrastruktury [Assessment of socio-economic potential of regions for placement of the logistic infrastructure objects].  Ekonomika regiona [Economy of region], 2,  254–263. DOI: 10.17059/2014–2-25. (In Russ.)

Shcherbanin, Yu. A. (2011). Transport i ekonomicheskiy rost: vzaimosvyaz’ i vliyanie [Transport and Economic Growth: Connectivity and Impact].  Evraziyskaya ekonomicheskaya integratsiya [Journal of Eurasian Economic Integration], 3(12),  65–78. (In Russ.)

Akaike, H. (1974). A new look at the statistical model identification.  IEEE Transactions on Automatic Control, AC-19(6),  716–723.

Alleman, J., Hunt, C., Michaels, D., Mueller, M., Rappoport, P., & Taylor, L. Telecommunications and economic development: Empirical evidence from Southern Africa.  International Telecommunications Society.  Retrieved from: https://www.academia.edu/6950978/Telecommunications_and_Economic_Development_Empirical_Evidence_from_Southern_Africa (Date of access: 20.11.2020).

Aushauer, D. (1989). Is Public Expenditure Productive?  Journal of Monetary Economics, 23(2),  177–200. DOI: https://doi.org/10.1016/0304-3932(89)90047-0.

Cai, D. P. (2006). Statistical analysis of the modern logistics industry and the national economy.  Logistics, 21(1),  74–75.

Dickey D. A., & Fuller, W. A. (1981). Likelihood ratio statistics for autoregressive time series with a unit root.  Econometrica, 49(4),  1057 -1072.

Holtz-Eakin, D. H., & Schwartz, A. E. (1995). Infrastructure in a structural of economic growth.  Regional Science and Urban Economics, 25(2),  131–151.

Engle, R. F., & Granger, C. W. J. (1987). Cointegration and error correction: representation, estimation and testing.  Econometrica, 55(2),  251–276.

Granger, C. W. (1981). Some Properties of Time Series Data and Their Use in Econometric Model Specification.  Journal of Econometrics, 16(1),  121–130.

Granger, C. W. J. (1986). Developments in the study of cointegrated economic variables.  Oxford Bulletin of Economics and Statistics, 48,  213–228. DOI: https://doi.org/10.1111/j.1468-0084.1986.mp48003002.x.

Bliemer, M. C. J., Mulley C., & Moutou, C. J. (Eds.). (2016).  Handbook on Transport and Urban Planning in the Developed World.  University of Sydney: Edward Elgar Pub, 544.

Johansen, S., & Juselius, K. (1990). Maximum likelihood estimation and inference on cointegration with applications to the demand for money.  Oxford Bulletin of Economics and Statistics, 52(2),  169–210.

Johansen, S. (1988). Statistical analysis for cointegration vectors.  Journal of Economic Dynamics and Control, 12(2–3),  231–254. DOI: https://doi.org/10.1016/0165-1889(88)90041-3.

Liu, W., Li, W., & Huang, W. (2006). Analysis of the dynamic relation between logistics development and GDP growth in China.  Proceedings of IEEE International Conference on Service Operations and Logistics, and Informatics.  153–157. DOI: 10.1109/SOLI.2006.329054.

Arvin, M. B., Pradhan, R. P., & Norman, N. R. (2015). Transportation intensity, urbanization, economic growth, and CO2 emissions in the G-20 countries.  Utilities Policy, 35,  50–66. DOI: https://doi.org/10.1016/j.jup.2015.07.003.

Pradhan, R. P., & Bagchi, T. P. (2013). Effect of transportation infrastructure on economic growth in India: The VECM approach.  Research in Transportation Economics, 38,  139–148. DOI: https://doi.org/10.1016/j.retrec.2012.05.008.

Rietveld, P. (1989). Infrastructure and regional development: a survey of multi- regional economic models.  The Annals of Regional Science, 23(4),  255–274. DOI: https://doi.org/10.1007/BF01579778.