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<!DOCTYPE article PUBLIC "-//NLM//DTD JATS (Z39.96) Journal Archiving and Interchange DTD v1.4 20241031//EN" "https://jats.nlm.nih.gov/archiving/1.4/JATS-archive-oasis-article1-4-mathml3.dtd">
<article xmlns:ali="http://www.niso.org/schemas/ali/1.0/" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" article-type="research-article" xml:lang="ru"><front><journal-meta><issn publication-format="print">2072-6414</issn><issn publication-format="electronic">2411-1406</issn></journal-meta><article-meta><article-id pub-id-type="doi">10.17059/ekon.reg.2023-2-11</article-id><title-group xml:lang="en"><article-title>DEPENDENCE OF INFORMAL EMPLOYMENT ON POPULATION INCOME IN RUSSIAN REGIONS: LESSONS FROM THE PANDEMIC</article-title></title-group><title-group xml:lang="ru"><article-title>ЗАВИСИМОСТЬ НЕФОРМАЛЬНОЙ ЗАНЯТОСТИ  ОТ УРОВНЯ ДОХОДОВ НАСЕЛЕНИЯ РОССИЙСКИХ РЕГИОНОВ: УРОКИ ПАНДЕМИИ</article-title></title-group><contrib-group><contrib contrib-type="author"><contrib-id contrib-id-type="orcid">https://orcid.org/0000-0001-9336-8100</contrib-id><name-alternatives><name xml:lang="en"><surname>Kunitsyna </surname><given-names>Natalia N. </given-names></name><name xml:lang="ru"><surname>Куницына</surname><given-names>Наталья Николаевна </given-names></name></name-alternatives><email>nkunitcyna@ncfu.ru</email><xref ref-type="aff" rid="aff1"/></contrib><contrib contrib-type="author"><contrib-id contrib-id-type="orcid">https://orcid.org/0000-0002-4958-2860</contrib-id><name-alternatives><name xml:lang="en"><surname>Dzhioev </surname><given-names>Aleksandr V. </given-names></name><name xml:lang="ru"><surname>Джиоев </surname><given-names>Александр Валерьевич </given-names></name></name-alternatives><email>dzhioevsasha@gmail.com</email><xref ref-type="aff" rid="aff1"/></contrib></contrib-group><aff-alternatives id="aff1"><aff><institution xml:lang="en">North-Caucasus Federal University</institution></aff><aff><institution xml:lang="ru">Северо-Кавказский федеральный университет</institution></aff></aff-alternatives><pub-date date-type="pub" iso-8601-date="2023-06-29" publication-format="electronic"/><volume>19</volume><issue>2</issue><fpage>437</fpage><lpage>450</lpage><history><date date-type="received" iso-8601-date="2021-05-12"/><date date-type="accepted" iso-8601-date="2023-03-24"/></history><permissions><copyright-statement xml:lang="en">Copyright © 2023 Natalia N. Kunitsyna, Aleksandr V. Dzhioev</copyright-statement><copyright-statement xml:lang="ru">Copyright © 2023 Наталья Николаевна Куницына, Александр Валерьевич Джиоев</copyright-statement><copyright-year>2023</copyright-year><copyright-holder xml:lang="en">Natalia N. Kunitsyna, Aleksandr V. Dzhioev</copyright-holder><copyright-holder xml:lang="ru">Наталья Николаевна Куницына, Александр Валерьевич Джиоев</copyright-holder><ali:free_to_read/><license license-type="open-access" xlink:href="https://creativecommons.org/licenses/by/4.0/"><license-p>CC BY 4.0</license-p></license></permissions><self-uri content-type="html" mimetype="text/html" xlink:title="article webpage" xlink:href="https://www.economyofregions.org/ojs/index.php/er/article/view/613">https://www.economyofregions.org/ojs/index.php/er/article/view/613</self-uri><self-uri content-type="pdf" mimetype="application/pdf" xlink:title="article pdf" xlink:href="https://www.economyofregions.org/ojs/index.php/er/article/download/613/209">https://www.economyofregions.org/ojs/index.php/er/article/download/613/209</self-uri><abstract xml:lang="en"><p>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.</p></abstract><abstract xml:lang="ru"><p>Распространение новой коронавирусной инфекции трансформировало экономику и общественный уклад, пандемия нанесла сокрушительный удар по рынку труда. На фоне обострения проблем безработицы актуализировались задачи сокращения уровня неформальной занятости, обусловливающей прирост объемов теневой экономики. Гипотезой исследования стало предположение, что падение официальных доходов населения в период пандемии сопровождалось ростом неформальной занятости, дифференцированным по регионам России. Целью научного поиска явилось теоретическое обоснование и эмпирическое подтверждение взаимосвязи последствий пандемии, снижения уровня доходов населения и неформальной занятости в региональном разрезе и формулирование направлений нивелирования их негативного влияния на рынок труда. В основу методологии исследования положен подход Росстата к определению критериев занятости, поставленные задачи решались с использованием экспертно-аналитических методов, анализа статистических рядов, кластеризации и картографии. Группировка регионов выполнена на основе иерархического метода Уорда, кластеры построены по взвешенным стандартизированным данным. Эмпирическая база сформирована из официальной информации Федеральной службы государственной статистики РФ, Организации Объединенных Наций, Всемирного банка. Результаты исследования масштабов неформальной занятости в регионах России в условиях пандемии, вопреки гипотетическим ожиданиям, показали, что в период кризиса неформальная занятость населения в большинстве субъектов Федерации сократилась, при этом набольшее ее снижение отмечено в республиках Северного Кавказа. Кластеризация территорий позволила выявить группы российских регионов по степени зависимости неформальной занятости от доходов населения и валового регионального продукта на душу населения. Практическое значение результатов обусловлено возможностью их использования для выработки типовых решений в части формирования комплекса мер долгосрочной и краткосрочной поддержки работников на уровне государства, субъектов Федерации и на микроуровне в направлении снижения негативного влияния выявленных причин на рост неформальной занятости.</p></abstract><kwd-group xml:lang="en"><kwd>regional development</kwd><kwd>formal employment</kwd><kwd>informal employment</kwd><kwd>average per capita income</kwd><kwd>pandemic</kwd><kwd>crisis</kwd><kwd>coronavirus</kwd><kwd>clustering of regions</kwd></kwd-group><kwd-group xml:lang="ru"><kwd>региональное развитие</kwd><kwd>формальная занятость</kwd><kwd>неформальная занятость</kwd><kwd>среднедушевые доходы населения</kwd><kwd>пандемия</kwd><kwd>кризис</kwd><kwd>коронавирус</kwd><kwd>кластеризация регионов</kwd></kwd-group></article-meta></front><body/><back><ack xml:lang="en"><p>The article has been prepared with the support of the Russian Foundation for Basic Research, the project No. 20-010-00243, and North-Caucasus Federal University.</p></ack><ack xml:lang="ru"><p>Исследование выполнено при финансовой поддержке РФФИ в рамках научного проекта № 20-010-00243 и Северо-Кавказского федерального университета.</p></ack><ref-list><ref id="en-ref1"><label>1</label><mixed-citation xml:lang="en">Adair, P. 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