. Н.Н.КуницынаiD, А. В. Д. I. С. Ф. университет, Российская Ставрополь, Федерация, СОЦиА льНОе РАЗВитие РеГиОНА, . NataliaN.KunitsynaiD, Aleksandr V. Dzhioev
{"title":"Dependence of Informal Employment on Population Income in Russian Regions: Lessons from the Pandemic","authors":". Н.Н.КуницынаiD, А. В. Д. I. С. Ф. университет, Российская Ставрополь, Федерация, СОЦиА льНОе РАЗВитие РеГиОНА, . NataliaN.KunitsynaiD, Aleksandr V. Dzhioev","doi":"10.17059/ekon.reg.2023-2-11","DOIUrl":null,"url":null,"abstract":"The coronavirus spread transformed the economy and social order, and dealt a crushing blow to the labour market. Considering the worsening unemployment, it becomes important to reduce informal employment, which leads to an increase in the shadow economy. It is hypothesised that the decline in official income is accompanied by an increase in informal employment differentiated across Russian region. The study aims to theoretically justify and empirically confirm the relationship between the consequences of the pandemic, decline in population income and dynamics of informal employment in regions, as well as to develop ways to reduce their negative impact on the labour market. The study utilised an approach of the Federal State Statistics Service (Rosstat) to determining employment criteria; additionally, expert and analytical methods, analysis of statistical series, clustering and cartography were applied. The regions were clustered according to Ward’s hierarchical method based on weighted standardised data. To this end, official data from Rosstat, the United Nations, and the World Bank were examined. As a result, the analysis of informal employment in Russian regions during the pandemic did not confirm the hypothesis, showing that informal employment actually decreased in most constituent entities; the largest decrease was observed in the North Caucasus republics. The performed clustering revealed groups of Russian regions in terms of the dependence of informal employment on average per capita income and gross regional product per capita. The obtained findings can be used to develop standard solutions for establishing long- and short-term support measures for employees at the national, regional and micro-level aimed at reducing the negative impact of the identified reasons for the growth of informal employment.","PeriodicalId":47897,"journal":{"name":"Cambridge Journal of Regions Economy and Society","volume":"57 1","pages":""},"PeriodicalIF":5.6000,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Cambridge Journal of Regions Economy and Society","FirstCategoryId":"96","ListUrlMain":"https://doi.org/10.17059/ekon.reg.2023-2-11","RegionNum":2,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"DEVELOPMENT STUDIES","Score":null,"Total":0}
引用次数: 0
Abstract
The coronavirus spread transformed the economy and social order, and dealt a crushing blow to the labour market. Considering the worsening unemployment, it becomes important to reduce informal employment, which leads to an increase in the shadow economy. It is hypothesised that the decline in official income is accompanied by an increase in informal employment differentiated across Russian region. The study aims to theoretically justify and empirically confirm the relationship between the consequences of the pandemic, decline in population income and dynamics of informal employment in regions, as well as to develop ways to reduce their negative impact on the labour market. The study utilised an approach of the Federal State Statistics Service (Rosstat) to determining employment criteria; additionally, expert and analytical methods, analysis of statistical series, clustering and cartography were applied. The regions were clustered according to Ward’s hierarchical method based on weighted standardised data. To this end, official data from Rosstat, the United Nations, and the World Bank were examined. As a result, the analysis of informal employment in Russian regions during the pandemic did not confirm the hypothesis, showing that informal employment actually decreased in most constituent entities; the largest decrease was observed in the North Caucasus republics. The performed clustering revealed groups of Russian regions in terms of the dependence of informal employment on average per capita income and gross regional product per capita. The obtained findings can be used to develop standard solutions for establishing long- and short-term support measures for employees at the national, regional and micro-level aimed at reducing the negative impact of the identified reasons for the growth of informal employment.