创新经济领域区域间经济合作的空间优化模式

IF 0.5 Q3 AREA STUDIES
A. Mosalev
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引用次数: 0

摘要

在俄罗斯,企业在创新经济领域的国内合作似乎是一个很有前景的研究领域,特别是考虑到最近由制裁引起的宏观经济事件,特别是对高科技产品进口的限制。因此,本研究考察了区域间创新合作的最优空间尺度。本文分析了确定创新活跃区域间关系k矩阵最优个数的方法。假设一个地区的商业部门的创新活动不会影响其邻国的创新活动,反之亦然。采用逐步回归方法确定核心解释变量。在空间权重矩阵的基础上,利用最小二乘法建立了空间计量模型。此外,采用全球Moran’s I来检验空间相关性,特别是利用邻近标准的空间关联局部指标(LISA)来确定相邻区域之间创新活动的依赖关系。该分析使用了2010年至2019年期间俄罗斯所有地区的面板数据,以及空间计量经济学模型来确定最佳空间尺度的副作用。结果表明,区域创新活动水平、市场规模、企业制度支持等方面均存在空间相关性。此外,研究还发现了区域国内收入、创新过程参与者数量和基础设施设施等对区域和邻近地区创新活动规模产生积极影响的因素。已经确定的是,以许多创新过程参与者(至少100个单位)以及创新基础设施设施(至少810个单位)的存在为特征的区域邻近将被视为区域合作的最佳规模。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Optimal Spatial Models of Interregional Economic Cooperation in the Field of Innovative Economy
Domestic cooperation of companies in the field of innovative economy seems to be a promising research area in Russia, especially considering recent macroeconomic events caused by sanctions, in particular, restrictions on the import of high-tech goods. Thus, the present study examines the optimal spatial scale of interregional innovation cooperation. The article presents an analysis of approaches to determining the optimal number of k-matrices of relations between innovation active regions. It is hypothesised that the innovative activity of the business sector in one region does not influence the innovative activity of its neighbours and vice versa. Stepwise regression was applied to identify the core explanatory variable. Based on the spatial weights matrices, a spatial econometric model was constructed using the least squares method. Further, the global Moran’s I was employed to test the spatial correlation, in particular, local indicators of spatial association (LISA) using the queen criterion of contiguity were utilised to determine the dependencies of innovative activity between neighbouring regions. The analysis used panel data from all Russian regions for the period from 2010 to 2019, as well as spatial econometric modelling to identify the side effects of the optimal spatial scale. As a result, the study revealed the presence of spatial correlation in the levels of regional innovative activity, the size of markets, as well as institutional support for enterprises in individual regions. Additionally, the research identified factors positively affecting the scale of innovative activity of the regions and adjacent territories, such as regional domestic income, the number of participants in innovation processes and infrastructure facilities. It has been established that the neighbourhood of regions characterised by the presence of many participants in innovation processes (at least 100 units), as well as innovation infrastructure facilities (at least 810 units) will be seen as the optimal scale of regional cooperation.
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来源期刊
CiteScore
1.80
自引率
20.00%
发文量
23
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