R-ECONOMY

Relevance. The paper explores interregional cooperation, examining the challenges of aligning spatial and innovation development in macro-regions, with a focus on two federal districts of Russia. The study assesses the potential of interregional cooperation among neighboring regions within a single federal district, as well as among more distant regions across different federal districts. Research Objective. The study aims to test two hypotheses: the first deals with the viability of imitation innovation strategies in peripheral regions of both intra and inter-federal districts. The second hypothesis concerns the presence of innovation interdependence (autocorrelation) among regions from different federal districts, influenced by the level and industrial compatibility of innovation outputs. Data and methods. The study employs the DEA method to identify central and peripheral regions (imitator regions) by calculating technical efficiency indicators. It also uses coupling interregional complementarity indexes to assess the potential for interregional cooperation in innovation and technological import substitution, considering the industrial profiles of the regions. Spatial autocorrelation is evaluated by using Moran’s Index to estimate the level of regional interdependence, factoring in the level and industry conformity of innovation output. The novelty of the proposed methodological approach lies in the application of interregional indexes of innovation complementarity as weighting coefficients in Moran’s Index calculation. Results. The study reveals a rise in spatial inequality, competition among regions, and constrained interregional innovation cooperation across federal districts. Geographical proximity currently plays a pivotal role in cooperation, with initial indications of a macro-regional space evolving through knowledge exchange. However, both hypotheses concerning imitation strategies and autocorrelation are only confirmed for regions within a single federal district. Conclusions. The findings of this study regarding spatial autocorrelation offer valuable insights for policymakers in the sphere of regional innovation.

Relevance. As interregional competition intensifies, state support for innovation in regional economies becomes increasingly important. To improve the effectiveness of such support, it is necessary to gain a better understanding of the link between innovation and economic development. Research Objective. The study aims to assess the impact of innovation activity on regional economic growth, focusing on the case of a large industrial region in Russia. Data and Methods. The analysis uses socio-economic data for Sverdlovsk region from 2000 to 2023 and applies an autoregressive distributed lag (ARDL) model to assess the relationship between innovation and economic performance. The study introduces an Innovation Activity Index, which incorporates several key components, such as the number of research personnel, internal R&D expenditures, and other relevant indicators, for a more comprehensive evaluation. Results. The study traces the region’s innovation activity, revealing a general upward trend. However, while innovation clearly has a positively influence on economic growth in the short term, its effects over medium- and long-term periods are less obvious, likely due to structural factors in the regional economy. Conclusion. The study proposes recommendations to enhance regional innovation support within the «smart specialization» framework, including backing innovation projects, developing a sustainable innovation ecosystem, and investing in human capital.