区域人均收入差异:空间和等级依赖关系

Q3 Economics, Econometrics and Finance REconomy Pub Date : 2022-01-01 DOI:10.15826/recon.2022.8.1.003
V. Timiryanova, K. Yusupov, I. Lakman, Aleksandr F. Zimin
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引用次数: 0

摘要

的相关性。由于许多原因,人均收入的区域差异是许多国家关注的问题,包括这种区域差异对国家安全构成的威胁。使用多种工具和方法来调查和修复这些差异。因此,使用较低层次的汇总数据和考虑到空间相互作用的分析变得尤为重要,因为它使我们能够揭示微观层面上相互作用的多样性。研究目标。本研究旨在确定不同数据聚集水平和层级依赖的空间关系对人均收入的重要性,并突出对人均收入影响最大的行政区划水平(区域或市)。方法与数据。该分析基于俄罗斯85个地区2270个城市的数据。采用层次空间自回归模型(HSAR)区分空间效应和层次效应。我们使用了模型的三种规格:在较高水平(区域水平的空间误差),在较低水平(城市水平的空间滞后),以及在两个水平上的空间相互作用的估计。结果。空间相互作用解释了高(区域)和低(市)两级数据中观测到的人均收入变化,但在高(区域)水平上估算的空间相互作用模型更好。结论。尽管低层的空间相互作用很重要,但在某些情况下,只考虑上层空间相互作用的模式可能更好地解释所观察到的差异。我们的研究结果对行政区划空间关系的研究文献有所贡献。我们已经表明,对于每个具体案例,重要的是不仅要确定因素,还要确定与这个或那个领土等级水平相关的空间效应。
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Regional per capita income differences: Spatial and hierarchical dependencies
Relevance. Regional differences in per capita income are a matter of concern for many countries for many reasons, including the threat that such regional disparities pose to national security. Multiple tools and methods are used to investigate these disparities and fix them. The use of lower level aggregated data and the analysis that takes into account spatial interactions thus become particularly relevant because it allows us to reveal the diversity of interactions at the micro-level. Research objective. This study aims to determine the significance of spatial relationships at different levels of data aggregation and hierarchical dependencies in per capita income and highlight the level of administrative division (regional or municipal) that has the greatest impact on per capita income. Methods and data. The analysis relies on the data from 2,270 municipalities in 85 Russian regions. The Hierarchical Spatial Autoregressive Model (HSAR) was used to distinguish both spatial and hierarchical effects. We used three specifications of the model: with estimates of the spatial interaction on the higher level (spatial error at the regional level), on the lower level (spatial lag at the municipal level), and on both levels. Results. Spatial interactions explain the observed variation of per capita income at the municipal level data at both the higher (regional) and lower (municipal) levels but the model with the estimated spatial interaction on the higher level was better. Conclusion. Despite the importance of spatial interactions at the lower level, models that take into account spatial interactions only at the upper level may better explain the observed differences in some cases. Our findings contribute to the rather scarce research literature on spatial relationships on several levels of administrative division. We have shown that for each specific case it is important to identify not only the factors but also the spatial effects in relation to this or that level of the territorial hierarchy.
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来源期刊
REconomy
REconomy Economics, Econometrics and Finance-General Economics, Econometrics and Finance
CiteScore
1.60
自引率
0.00%
发文量
8
审稿时长
14 weeks
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