Assessment of the Regional Labor Market, Taking into Account the Territorial Effect (Case Study: The Republic of Belarus)

L. A. Soshnikava, A. V. Kishkovich
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Abstract

The relevance of the study concerning evaluation problems and a specific comparative analysis of regional labor markets is explained by the growing importance of improving macroeconomic regulation in almost all CIS countries. The purpose of the work was to demonstrate the capabilities of spatial econometrics tools in the analysis of regional labor markets on the example of the Republic of Belarus. The authors consider methodological approaches to analyzing labor market indicators based on modern statistical and econometric tools.The authors substantiated the necessity of using spatial econometrics methods for a more accurate assessment of the specific characteristics of the regions of the Republic of Belarus. A spatial autoregressive model was built using panel data. Here, integral block indicators were used as factors, covering only 40 primary characteristics of the region. This article briefly discusses the provisions used in spatial data analysis. It also presents the results of building a mixed model of spatial autoregression. For calculations, the authors used data for 2016–2019, which are freely available in the interactive business intelligence system for distribution of official statistical information of the National Statistical Committee of the Republic of Belarus (Belstat, 2021).As predictors, the regression model included integral indicators of the regional labor market, weighted using a matrix of distances between the centers of regions. Here, were used forty initial indicators. According to the authors, the results of the study can have practical application when planning programs for the development of regional labor markets.
考虑地域效应的区域劳动力市场评估(案例研究:白俄罗斯共和国)
关于评价问题和对区域劳动力市场进行具体比较分析的研究之所以有意义,是因为改善几乎所有独联体国家的宏观经济管制日益重要。这项工作的目的是以白俄罗斯共和国为例,展示空间计量经济学工具在分析区域劳动力市场方面的能力。作者考虑了基于现代统计和计量经济学工具分析劳动力市场指标的方法方法。作者证实了使用空间计量经济学方法对白俄罗斯共和国各地区的具体特征进行更准确评估的必要性。利用面板数据建立空间自回归模型。在这里,积分块指标作为因子,只涵盖了该地区的40个主要特征。本文简要讨论了空间数据分析中使用的条款。并给出了建立空间自回归混合模型的结果。为了进行计算,作者使用了2016-2019年的数据,这些数据可以在白俄罗斯共和国国家统计委员会发布官方统计信息的交互式商业智能系统中免费获得(Belstat, 2021)。作为预测指标,回归模型包括区域劳动力市场的积分指标,使用区域中心之间的距离矩阵加权。这里,我们使用了40个初始指标。根据作者的说法,研究结果可以在规划区域劳动力市场发展计划时具有实际应用价值。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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