{"title":"俄罗斯对数字技能的需求:地区差异","authors":"S. Kapelyuk, I. Karelin","doi":"10.14530/se.2023.1.070-092","DOIUrl":null,"url":null,"abstract":"This paper examines regional differences in the demand for digital skills based on an analysis of 9 million vacancies posted on the Unified Digital Platform ‘Work in Russia’ in 2018–2022. We examine approaches used in the literature to classify digital skills and using it develop our own classification. The paper studies the advantages and limitations of various indicators of the demand for digital skills. We suggest that the ratio between the share of vacancies requiring digital skills of a certain group in the region and the labor force population should be used as the most appropriate one. The results of the study show that in Russia there is still a significant regional differentiation in the employer’s demand for all selected groups. Differentiation increased with the beginning of the Covid-19 pandemic, and decreased slightly in 2021–2022. We reveal that regions with a higher level of economic development have higher requirements for digital skills. Digital skills are more often required in regions specialized on primary production and less often in agricultural regions. Of the federal districts, a slightly higher level of demand for digital skills is observed in the Ural and Far Eastern federal districts, while a significantly lower level is observed in the North Caucasus federal district","PeriodicalId":54733,"journal":{"name":"Networks & Spatial Economics","volume":null,"pages":null},"PeriodicalIF":1.6000,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Demand on Digital Skills in Russia: Regional Differences\",\"authors\":\"S. Kapelyuk, I. Karelin\",\"doi\":\"10.14530/se.2023.1.070-092\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper examines regional differences in the demand for digital skills based on an analysis of 9 million vacancies posted on the Unified Digital Platform ‘Work in Russia’ in 2018–2022. We examine approaches used in the literature to classify digital skills and using it develop our own classification. The paper studies the advantages and limitations of various indicators of the demand for digital skills. We suggest that the ratio between the share of vacancies requiring digital skills of a certain group in the region and the labor force population should be used as the most appropriate one. The results of the study show that in Russia there is still a significant regional differentiation in the employer’s demand for all selected groups. Differentiation increased with the beginning of the Covid-19 pandemic, and decreased slightly in 2021–2022. We reveal that regions with a higher level of economic development have higher requirements for digital skills. Digital skills are more often required in regions specialized on primary production and less often in agricultural regions. Of the federal districts, a slightly higher level of demand for digital skills is observed in the Ural and Far Eastern federal districts, while a significantly lower level is observed in the North Caucasus federal district\",\"PeriodicalId\":54733,\"journal\":{\"name\":\"Networks & Spatial Economics\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":1.6000,\"publicationDate\":\"2023-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Networks & Spatial Economics\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://doi.org/10.14530/se.2023.1.070-092\",\"RegionNum\":3,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"OPERATIONS RESEARCH & MANAGEMENT SCIENCE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Networks & Spatial Economics","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.14530/se.2023.1.070-092","RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"OPERATIONS RESEARCH & MANAGEMENT SCIENCE","Score":null,"Total":0}
Demand on Digital Skills in Russia: Regional Differences
This paper examines regional differences in the demand for digital skills based on an analysis of 9 million vacancies posted on the Unified Digital Platform ‘Work in Russia’ in 2018–2022. We examine approaches used in the literature to classify digital skills and using it develop our own classification. The paper studies the advantages and limitations of various indicators of the demand for digital skills. We suggest that the ratio between the share of vacancies requiring digital skills of a certain group in the region and the labor force population should be used as the most appropriate one. The results of the study show that in Russia there is still a significant regional differentiation in the employer’s demand for all selected groups. Differentiation increased with the beginning of the Covid-19 pandemic, and decreased slightly in 2021–2022. We reveal that regions with a higher level of economic development have higher requirements for digital skills. Digital skills are more often required in regions specialized on primary production and less often in agricultural regions. Of the federal districts, a slightly higher level of demand for digital skills is observed in the Ural and Far Eastern federal districts, while a significantly lower level is observed in the North Caucasus federal district
期刊介绍:
Networks and Spatial Economics (NETS) is devoted to the mathematical and numerical study of economic activities facilitated by human infrastructure, broadly defined to include technologies pertinent to information, telecommunications, the Internet, transportation, energy storage and transmission, and water resources. Because the spatial organization of infrastructure most generally takes the form of networks, the journal encourages submissions that employ a network perspective. However, non-network continuum models are also recognized as an important tradition that has provided great insight into spatial economic phenomena; consequently, the journal welcomes with equal enthusiasm submissions based on continuum models.
The journal welcomes the full spectrum of high quality work in networks and spatial economics including theoretical studies, case studies and algorithmic investigations, as well as manuscripts that combine these aspects. Although not devoted exclusively to theoretical studies, the journal is "theory-friendly". That is, well thought out theoretical analyses of important network and spatial economic problems will be considered without bias even if they do not include case studies or numerical examples.