Modeling the Spatial Effects of the Impact of Innovation on Regional Economic Growth
- DOI
- 10.2991/ahcs.k.191206.020How to use a DOI?
- Keywords
- regional disparity, convergence, spatial autocorrelation, spatial econometrics, spatial spillovers
- Abstract
In this paper, we analyze σ- and β-convergence using data from the socioeconomic development of the Russian regions and reveal the role of spatial autocorrelation in regional economic development. We consider 80 regions of Russia for the period 2010–2017. We estimate spatial autocorrelation based on Moran’s coefficients. We construct a Moran scatter plot of GDP per capita and the growth rate of GDP per capita in 2017 compared to 2014. We investigate the impact on investment growth in fixed capital and the expenditure on technological innovation. We evaluate a wide range of specifications of spatial econometric models for different weight matrices. It is shown that according to the results of estimation of conditional β-convergence models, the models of 2010–2014 and 2014–2017 differ significantly. There is a statistically significant β-convergence for the period 2010–2014, as well as the presence of spatial autocorrelation. However, according to the results of estimation models constructed from data after 2014, the estimates of the coefficients for the explanatory variables are not significantly different from zero and there is no trend toward regional convergence in terms of economic development. All conclusions obtained in the work are resistant to the choice of spatial weights matrices and model specifications.
- Copyright
- © 2019, the Authors. Published by Atlantis Press.
- Open Access
- This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
Cite this article
TY - CONF AU - Vladimir A. Balash AU - Olga S. Balash AU - Alexey Faizliev AU - Elena V. Chistopolskaya PY - 2019 DA - 2019/12/12 TI - Modeling the Spatial Effects of the Impact of Innovation on Regional Economic Growth BT - Proceedings of the Fourth Workshop on Computer Modelling in Decision Making (CMDM 2019) PB - Atlantis Press SP - 108 EP - 114 SN - 2589-4900 UR - https://doi.org/10.2991/ahcs.k.191206.020 DO - 10.2991/ahcs.k.191206.020 ID - Balash2019 ER -