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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">2411-1406</issn><issn publication-format="electronic">2411-1406</issn></journal-meta><article-meta><article-id pub-id-type="doi">10.17059/ekon.reg.2025-2-3</article-id><title-group xml:lang="en"><article-title>Cluster-Econometric Analysis of Russian Regions: Implications  for Differentiated Economic Policys</article-title></title-group><title-group xml:lang="ru"><article-title>Кластерно-эконометрический анализ российских регионов: выводы для дифференцированной экономической политики</article-title></title-group><contrib-group><contrib contrib-type="author"><contrib-id contrib-id-type="orcid">https://orcid.org/0000-0003-3042-7550</contrib-id><name-alternatives><name xml:lang="en"><surname>Gamidullaeva </surname><given-names>Leyla A. </given-names></name><name xml:lang="ru"><surname>Гамидуллаева </surname><given-names>Лейла Айваровна</given-names></name></name-alternatives><email>gamidullaeva@gmail.com</email><xref ref-type="aff" rid="aff1"/></contrib><contrib contrib-type="author"><contrib-id contrib-id-type="orcid">https://orcid.org/0000-0002-7511-2141</contrib-id><name-alternatives><name xml:lang="en"><surname>Roslyakova </surname><given-names>Natalya A.</given-names></name><name xml:lang="ru"><surname>Рослякова</surname><given-names>Наталья Андреевна </given-names></name></name-alternatives><email>na@roslyakova24.ru</email><xref ref-type="aff" rid="aff2"/></contrib></contrib-group><aff-alternatives id="aff1"><aff><institution xml:lang="en">Penza State University</institution></aff><aff><institution xml:lang="ru">Пензенский государственный университет</institution></aff></aff-alternatives><aff-alternatives id="aff2"><aff><institution xml:lang="en">Institute for Regional Economic Studies RAS</institution></aff><aff><institution xml:lang="ru">Институт проблем региональной экономики РАН</institution></aff></aff-alternatives><pub-date date-type="pub" iso-8601-date="2024-12-17" publication-format="electronic"/><volume>21</volume><issue>2</issue><fpage>283</fpage><lpage>300</lpage><history><date date-type="received" iso-8601-date="2024-05-06"/><date date-type="accepted" iso-8601-date="2024-08-08"/></history><permissions><copyright-statement xml:lang="ru">Copyright © 2025  </copyright-statement><copyright-year>2025</copyright-year><copyright-holder xml:lang="ru"> </copyright-holder><ali:free_to_read/><license license-type="open-access" xlink:href="https://creativecommons.org/licenses/by/4.0/"><ali:license_ref>https://creativecommons.org/licenses/by/4.0/</ali:license_ref></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/880">https://www.economyofregions.org/ojs/index.php/er/article/view/880</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/880/446">https://www.economyofregions.org/ojs/index.php/er/article/download/880/446</self-uri><abstract xml:lang="en"><p>In a rapidly changing economic landscape, a crucial challenge for state economic policy is the need for a differentiated approach to regional development. Despite its importance, this aspect has been insufficiently explored within the framework of macroeconomic regulation. This study investigates regional identity through the unique configurations of economic, innovative, technological, and transport potentials, and their influence on economic growth. At the first stage, a cluster analysis was performed to classify Russian regions into eight groups, based on 27 economic and structural indicators. These indicators encompass various dimensions such as economic capacity, innovation, technological readiness, and transport infrastructure. In the second stage, multiple regression analysis was used to evaluate the effect of these factors on per capita gross regional product. The econometric modelling revealed key drivers of regional growth and identified the factors most significantly influencing development. Analysis of each cluster highlighted the varying degrees of factor potential and future development prospects. The findings suggest that many regions are not fully exploiting their available resources, while some are facing significant constraints to growth, underscoring the need for technological transformation. The study proposes an interregional cooperation policy aimed at redistributing technological capabilities and fostering technology transfer. This approach can help policymakers design more effective economic policies that promote sustainable growth, improve technological development, and balance regional potential.</p></abstract><abstract xml:lang="ru"><p>В условиях динамично меняющегося экономического ландшафта важным направлением государственной экономической политики является необходимость дифференцированного подхода к развитию отдельных региональных пространств. Данный аспект в теории и практике макроэкономического регулирования является недостаточно изученным. Настоящее исследование посвящено анализу региональной идентичности, проявляющейся через уникальные конфигурации факторных потенциалов и их влияние на экономический рост. На первом этапе работы был проведен кластерный анализ, в результате которого выделены восемь групп российских регионов, отличающихся по 27 параметрам, характеризующим их экономический, инновационно-технологический и транспортный потенциалы. На втором этапе использованы методы множественной регрессии для оценки влияния этих факторов на валовой региональный продукт на душу населения. Авторами проведено эконометрическое моделирование, позволившее идентифицировать факторы, значимо влияющие на динамику роста российских регионов. Для каждого кластера был проведен экономический анализ с точки зрения сложившейся в нем конфигурации факторных потенциалов и возможных перспектив развития. Показано, что в каждой группе регионов конструируется уникальная конфигурация факторных потенциалов, которые часто недостаточно эффективно используются, а в некоторых случаях имеет место исчерпание ресурсов для роста, что обусловливает необходимость в технологической трансформации. Авторы предлагают реализацию скоординированной политики межрегионального сотрудничества, направленной на перераспределение технологических потенциалов и трансфер технологий, что может повысить эффективность использования производственных факторов. Предложенная методика анализа и полученные результаты могут быть полезны для органов государственной власти при разработке дифференцированных мер экономической политики, способствующих устойчивому экономическому росту, повышению технологического уровня региональной экономики и гармонизации потенциалов территорий на системной основе.</p></abstract><kwd-group xml:lang="en"><kwd>cluster econometric analysis</kwd><kwd>region</kwd><kwd>factor potentials</kwd><kwd>spatial development</kwd><kwd>economic structure</kwd><kwd>diversification</kwd><kwd>economic growth</kwd></kwd-group><kwd-group xml:lang="ru"><kwd>кластерно-эконометрический анализ,</kwd><kwd>регион</kwd><kwd>факторные потенциалы</kwd><kwd>пространственное развитие</kwd><kwd>структура экономики</kwd><kwd>диверсификация</kwd><kwd>экономический рост</kwd></kwd-group></article-meta></front><body/><back><ack xml:lang="en"><p>The research was supported by the grant of the Russian Science Foundation No. 25-28-20328 ”Models and Mechanisms for Optimizing the Structure of the Regional Economy to Ensure Sustainable Industrial Development.”</p></ack><ack xml:lang="ru"><p>Исследование выполнено за счет гранта Российского научного фонда № 25-28-20328 "Модели и механизмы оптимизации структуры региональной экономики в целях устойчивого развития промышленности"</p></ack><ref-list><ref id="ref1"><label>1</label><citation-alternatives><mixed-citation xml:lang="ru">Гамидуллаева, Л. А., Рослякова, Н. А. (2023). Комплексный методический подход к структурной трансформации региональной экономики. Труды III Гранберговской конференции: Сборник докладов Всероссийской конференции с международным участием, посвященной памяти академика А. Г. Гранберга, Новосибирск, 11–13 октября 2023 года (С. 106–112). Новосибирск: Институт экономики и организации промышленного производства СО РАН. </mixed-citation><mixed-citation xml:lang="en">Akberdina, V. V., &amp; Romanova, O. A. (2021). Regional Industrial Development: Review of Approaches to Regulation and Determining of Priorities. Economy of region, 17 (3), 714–736. https://doi.org/10.17059/ekon.reg.2021-3-1</mixed-citation></citation-alternatives></ref><ref id="ref2"><label>2</label><citation-alternatives><mixed-citation xml:lang="ru">Демичев, В. В., Маслакова, В. В., Нестратова, А. А. (2020). Кластеризация регионов России по уровню эффективности сельского хозяйства. Бухучет в сельском хозяйстве, (12), 58–66. https://doi.org/10.33920/sel-11-2012-06</mixed-citation><mixed-citation xml:lang="en">Balland, P. A., &amp; Boschma, R. (2021). Complementary interregional linkages and Smart Specialisation: an empirical study on European regions. Regional Studies, 55 (6), 1059–1070. https://doi.org/10.1080/00343404.2020.1861240</mixed-citation></citation-alternatives></ref><ref id="ref3"><label>3</label><citation-alternatives><mixed-citation xml:lang="ru">Еферин, Я. Ю., Куценко, Е. С. (2021). Адаптация концепции умной специализации для развития регионов России. Вопросы государственного и муниципального управления, (3), 75–110.</mixed-citation><mixed-citation xml:lang="en">Blanco, E., Elosegui, P., Izaguirre, A., &amp; Montes-Rojas, G. (2019). Regional and state heterogeneity of monetary shocks in Argentina. The Journal of Economic Asymmetries, 20, e00129. https://doi.org/10.1016/j.jeca.2019.e00129</mixed-citation></citation-alternatives></ref><ref id="ref4"><label>4</label><citation-alternatives><mixed-citation xml:lang="ru">Кетова, К. В., Касаткина, Е. В., Вавилова, Д. Д. (2021). Кластеризация регионов Российской Федерации по уровню социально-экономического развития с использованием методов машинного обучения. Экономические и социальные перемены: факты, тенденции, прогноз, 14 (6), 70–85. https://doi.org/10.15838/esc.2021.6.78.4</mixed-citation><mixed-citation xml:lang="en">Demichev, V. V., Maslakova, V. V., &amp; Nestratova, A. A. (2020). Clustering Russian regions by the level of agricultural efficiency. Bukhuchet v sel'skom khozyaystve [Accounting in Agriculture], (12), 58–66. https://doi.org/10.33920/sel-11-2012-06 (In Russ.)</mixed-citation></citation-alternatives></ref><ref id="ref5"><label>5</label><citation-alternatives><mixed-citation xml:lang="ru">Лавровский, Б. (2015). Государственная политика регионального развития. Вопросы теории. Федерализм, (4), 121–130. https://doi.org/10.21686/2073–1051-2015-4-121-130</mixed-citation><mixed-citation xml:lang="en">Dzemydaitė, G. (2021). The Impact of Economic Specialization on Regional Economic Development in the European Union: Insights for Formation of Smart Specialization Strategy. Economies, 9 (2), 76. https://doi.org/10.3390/economies9020076</mixed-citation></citation-alternatives></ref><ref id="ref6"><label>6</label><citation-alternatives><mixed-citation xml:lang="ru">Локосов, В. В., Рюмина, Е. В., Ульянов, В. В. (2019). Кластеризация регионов России по показателям качества жизни и качества населения. Народонаселение, 22 (4), 4–17. https://doi.org/10.19181/1561–7785-2019–00035</mixed-citation><mixed-citation xml:lang="en">Eferin, Ya. Yu., &amp; Kutsenko, E. S. (2021). Adjusting smart specialization concept for Russian regions. Voprosy gosudarstvennogo i munitsipal'nogo upravleniya [Public Administration Issues], (3), 75–110. (In Russ.)</mixed-citation></citation-alternatives></ref><ref id="ref7"><label>7</label><citation-alternatives><mixed-citation xml:lang="ru">Марченко, Е. М., Белова, Т. Д. (2016). Кластеризация регионов с учетом показателей энергоэффективности. Реги¬ональная экономика: теория и практика, (1(424)), 51–60. </mixed-citation><mixed-citation xml:lang="en">Ehrlich, M., &amp; Overman, H. G. (2020). Place-Based Policies and Spatial Disparities across European Cities. Journal of Economic Perspectives, 34 (3), 128–149. http://dx.doi.org/10.1257/jep.34.3.128</mixed-citation></citation-alternatives></ref><ref id="ref8"><label>8</label><citation-alternatives><mixed-citation xml:lang="ru">Орлова, И. В., Филонова, Е. С. (2015). Кластерный анализ регионов Центрального федерального округа по социально-экономическим и демографическим показателям. Статистика и экономика, (5), 111–115. </mixed-citation><mixed-citation xml:lang="en">Gamidullaeva, L. A., &amp; Roslyakova, N. A. (2023). An integrated methodological approach to the structural transformation of the regional economy. Trudy III Granbergovskoi konferentsii: Sbornik dokladov Vserossiiskoi konferentsii s mezhdunarodnym uchastiem, posvyashchennoi pamyati akademika A. G. Granberga, Novosibirsk, 11–13 oktyabrya 2023 goda [Proceedings of III Granberg Conference: Collected papers of National Conference dedicated to the memory of Academician A. G. Granberg] (pp. 106-112). Novosibirsk: Institut ekonomiki i organizatsii promyshlennogo proizvodstva SO RAN. (In Russ.)</mixed-citation></citation-alternatives></ref><ref id="ref9"><label>9</label><citation-alternatives><mixed-citation xml:lang="ru">Панкова, Ю. В. (2022). Проблемы дифференцированного воздействия мер макрорегулирования на социально-экономическое пространство. Экономическая политика России в межотраслевом и пространственном измерении: Материалы IV Всероссийской научно-практической конференции ИЭОПП СО РАН и ИНП РАН по межотраслевому и региональному анализу и прогнозированию, Белокуриха, 24–25 марта 2022 года. Том 4. Отв. редакторы А. О. Баранов, А. А. Широв (C. 163–166). Новосибирск: Институт экономики и организации промышленного производства СО РАН. https://doi.org/10.36264/978-5-89665-367-7-2022-005/33-180</mixed-citation><mixed-citation xml:lang="en">Gamidullaeva, L., &amp; Roslyakova, N. (2024). A methodological approach to complex territorial development based on agglomeration effects: “Smart” specialization perspective. Journal of Infrastructure, Policy and Development, 8 (7), 4986. https://doi.org/10.24294/jipd.v8i7.4986</mixed-citation></citation-alternatives></ref><ref id="ref10"><label>10</label><citation-alternatives><mixed-citation xml:lang="ru">Петрыкина, И. Н. (2013). Кластерный анализ регионов Центрального федерального округа по уровню развития человеческого капитала. Вестник Воронежского государственного университета. Экономика и управление, (1), 72–80.</mixed-citation><mixed-citation xml:lang="en">Golova, I. M., &amp; Sukhovey, A. F. (2019). Differentiation of innovative development strategies considering specific characteristics of the Russian regions. Economy of Region, 15 (4), 1294–1308. https://doi.org/10.17059/2019-4-25</mixed-citation></citation-alternatives></ref><ref id="ref11"><label>11</label><citation-alternatives><mixed-citation xml:lang="ru">Пискун, Е. И., Хохлов, В. В. (2019). Экономическое развитие регионов Российской Федерации. Факторно-кластерный анализ. Экономика региона, 15 (2), 363–376. https://doi.org/10.17059/2019-2-5</mixed-citation><mixed-citation xml:lang="en">Hewings, G. J. D. (2014). Spatially blind trade and fiscal impact policies and their impact on regional economies. The Quarterly Review of Economics and Finance, 54 (4), 590–602. http://dx.doi.org/10.1016/j.qref.2014.04.007</mixed-citation></citation-alternatives></ref><ref id="ref12"><label>12</label><citation-alternatives><mixed-citation xml:lang="ru">Серков, Л. А., Петров, М. Б., Кожов, К. Б. (2021). Кластерно-эконометрический инструментарий для исследования неоднородности регионов России. Journal of New Economy, 22 (4), 78–96. https://doi.org/10.29141/2658–5081-2021-22-4-5</mixed-citation><mixed-citation xml:lang="en">Ketova, K. V., Kasatkina, E. V., &amp; Vavilova, D. D. (2021). Clustering Russian Federation regions according to the level of socio-economic development with the use of machine learning methods. Ekonomicheskiye i sotsial'nyye peremeny: fakty, tendentsii, prognoz [Economic and Social Changes: Facts, Trends, Forecast], 14 (6), 70–85. https://doi.org/10.15838/esc.2021.6.78.4 (In Russ.)</mixed-citation></citation-alternatives></ref><ref id="ref13"><label>13</label><citation-alternatives><mixed-citation xml:lang="ru">Соболева, Т. С. (2009). Кластерный анализ диспропорций инновационно-инвестиционного развития регионов. Управление общественными и экономическими системами, (1), 56–66.</mixed-citation><mixed-citation xml:lang="en">Lavrovskiy, B. L. (2015). The state policy of regional development: questions of the theory. Federalizm [Federalism], (4), 121–130. https://doi.org/10.21686/2073–1051-2015-4-121-130 (In Russ.)</mixed-citation></citation-alternatives></ref><ref id="ref14"><label>14</label><citation-alternatives><mixed-citation xml:lang="ru">Akberdina, V. V., &amp; Romanova, O. A. (2021). Regional Industrial Development: Review of Approaches to Regulation and Determining of Priorities. Economy of region, 17 (3), 714–736. https://doi.org/10.17059/ekon.reg.2021-3-1</mixed-citation><mixed-citation xml:lang="en">Lokosov, V. V., Ryumina, E. V., &amp; Ulyanov, V. V. (2019). Clustering of regions by indicators of quality of life and quality of population. Narodonaseleniye [Population], 22 (4), 4–17. https://doi.org/10.19181/1561–7785-2019–00035 (In Russ.)</mixed-citation></citation-alternatives></ref><ref id="ref15"><label>15</label><citation-alternatives><mixed-citation xml:lang="ru">Balland, P. A., &amp; Boschma, R. (2021). Complementary interregional linkages and Smart Specialisation: an empirical study on European regions. Regional Studies, 55 (6), 1059–1070. https://doi.org/10.1080/00343404.2020.1861240</mixed-citation><mixed-citation xml:lang="en">Marchenko, E. M., &amp; Belova, T. D. (2016). Clustering of regions taking into account the energy efficiency. Regional'naya ekonomika: teoriya i praktika [Regional Economics: Theory and Practice], (1(424)), 51–60. (In Russ.)</mixed-citation></citation-alternatives></ref><ref id="ref16"><label>16</label><citation-alternatives><mixed-citation xml:lang="ru">Blanco, E., Elosegui, P., Izaguirre, A., &amp; Montes-Rojas, G. (2019). Regional and state heterogeneity of monetary shocks in Argentina. The Journal of Economic Asymmetries, 20, e00129. https://doi.org/10.1016/j.jeca.2019.e00129</mixed-citation><mixed-citation xml:lang="en">Orlova, I. V., &amp; Filonova, E. S. (2015). Cluster analysis of the regions of the central federal district socio-economic and demographic indicators. Statistika i ekonomika [Statistics and Economics], (5), 111–115. (In Russ.)</mixed-citation></citation-alternatives></ref><ref id="ref17"><label>17</label><citation-alternatives><mixed-citation xml:lang="ru">Dzemydaitė, G. (2021). The Impact of Economic Specialization on Regional Economic Development in the European Union: Insights for Formation of Smart Specialization Strategy. Economies, 9 (2), 76. https://doi.org/10.3390/economies9020076</mixed-citation><mixed-citation xml:lang="en">Pankova, Yu. V. (2022). The problems of the differentiated impact of macro-regulatory measures on the socio-economic space. In A. O. Baranov, A. A. Shirov (Eds.), Ekonomicheskaya politika Rossii v mezhotraslevom i prostranstvennom izmerenii: Materialy IV Vserossiyskoy nauchno-prakticheskoy konferentsii IEOPP SO RAN i INP RAN po mezhotraslevomu i regional’nomu analizu i prognozirovaniyu, Belokurikha, 24–25 marta 2022 goda. Tom 4. [Russia’s economic policy in the intersectoral and spatial dimension: Proceedings of the IV All-Russian Scientific and Practical Conference of INP SB RAS on intersectoral and regional analysis and forecasting, Belokurikha, March 24-25, 2022. Vol. 4] (pp. 163–166). Novosibirsk: Institute of Economics and Industrial Engineering, Siberian Branch of the Russian Academy of Sciences. https://doi.org/10.36264/978-5-89665-367-7-2022-005/33-180 (In Russ.)</mixed-citation></citation-alternatives></ref><ref id="ref18"><label>18</label><citation-alternatives><mixed-citation xml:lang="ru">Ehrlich, M., &amp; Overman, H. G. (2020). Place-Based Policies and Spatial Disparities across European Cities. Journal of Economic Perspectives, 34 (3), 128–149. http://dx.doi.org/10.1257/jep.34.3.128</mixed-citation><mixed-citation xml:lang="en">Petrykina, I. N. (2013). Cluster analysis of regions of the Central Federal District in terms of human capital development. Vestnik Voronezhskogo gosudarstvennogo universiteta. Ekonomika i upravleniye [Eurasian Journal of Economics and Management], (1), 72–80. (In Russ.)</mixed-citation></citation-alternatives></ref><ref id="ref19"><label>19</label><citation-alternatives><mixed-citation xml:lang="ru">Gamidullaeva, L., &amp; Roslyakova, N. (2024). A methodological approach to complex territorial development based on agglomeration effects: “Smart” specialization perspective. Journal of Infrastructure, Policy and Development, 8 (7), 4986. https://doi.org/10.24294/jipd.v8i7.4986</mixed-citation><mixed-citation xml:lang="en">Piskun, E. I., &amp; Khokhlov, V. V. (2019). Economic development of the Russian Federation’s regions: factor-cluster analysis. Ekonomika regiona [Economy of region], 15 (2), 363–376. https://doi.org/10.17059/2019-2-5 (In Russ.)</mixed-citation></citation-alternatives></ref><ref id="ref20"><label>20</label><citation-alternatives><mixed-citation xml:lang="ru">Golova, I. M., &amp; Sukhovey, A. F. (2019). Differentiation of innovative development strategies considering specific characteristics of the Russian regions. Economy of Region, 15 (4), 1294–1308. https://doi.org/10.17059/2019-4-25</mixed-citation><mixed-citation xml:lang="en">Regal, A., Gonzalez-Feliu, J., &amp; Rodriguez, M. (2023). A spatio-functional logistics profile clustering analysis method for metropolitan areas. Transportation Research Part E: Logistics and Transportation Review, 179, 103312. https://doi.org/10.1016/j.tre.2023.103312</mixed-citation></citation-alternatives></ref><ref id="ref21"><label>21</label><citation-alternatives><mixed-citation xml:lang="ru">Hewings, G. J. D. (2014). Spatially blind trade and fiscal impact policies and their impact on regional economies. The Quarterly Review of Economics and Finance, 54 (4), 590–602. http://dx.doi.org/10.1016/j.qref.2014.04.007</mixed-citation><mixed-citation xml:lang="en">Rhoden, I., Weller, D., Voit, A-K. (2022). Spatio-Temporal Dynamics of European Innovation — An Exploratory Approach via Multivariate Functional Data Cluster Analysis. Journal of Open Innovation: Technology, Market, and Complexity, 8 (1), 6, https://doi.org/10.3390/joitmc8010006</mixed-citation></citation-alternatives></ref><ref id="ref22"><label>22</label><citation-alternatives><mixed-citation xml:lang="ru">Regal, A., Gonzalez-Feliu, J., &amp; Rodriguez, M. (2023). A spatio-functional logistics profile clustering analysis method for metropolitan areas. Transportation Research Part E: Logistics and Transportation Review, 179, 103312. https://doi.org/10.1016/j.tre.2023.103312</mixed-citation><mixed-citation xml:lang="en">Serkov, L. A., Petrov, M. B., &amp; Kozhov, K. B. (2021). Сluster-based econometric analysis to study the heterogeneity of Russian regions. Journal of New Economy, 22 (4), 78–96. https://doi.org/10.29141/2658–5081-2021-22-4-5 (In Russ.)</mixed-citation></citation-alternatives></ref><ref id="ref23"><label>23</label><citation-alternatives><mixed-citation xml:lang="ru">Rhoden, I., Weller, D., Voit, A-K. (2022). Spatio-Temporal Dynamics of European Innovation — An Exploratory Approach via Multivariate Functional Data Cluster Analysis. Journal of Open Innovation: Technology, Market, and Complexity, 8 (1), 6, https://doi.org/10.3390/joitmc8010006</mixed-citation><mixed-citation xml:lang="en">Soboleva, T. S. (2009). The analysis of disproportions of is innovative-investment development of regions of Russia. Upravleniye obshchestvennymi i ekonomicheskimi sistemami [Management in social and economic systems], (1), 56–66. (In Russ.)</mixed-citation></citation-alternatives></ref><ref id="ref24"><label>24</label><citation-alternatives><mixed-citation xml:lang="ru">Uitermark, J., Hochstenbach, C., &amp; Groot, J. (2023). Neoliberalization and urban redevelopment: the impact of public policy on multiple dimensions of spatial inequality. Urban Geography, 45 (4), 541–564. https://doi.org/10.1080/02723638.2023.2203583</mixed-citation><mixed-citation xml:lang="en">Uitermark, J., Hochstenbach, C., &amp; Groot, J. (2023). Neoliberalization and urban redevelopment: the impact of public policy on multiple dimensions of spatial inequality. Urban Geography, 45 (4), 541–564. https://doi.org/10.1080/02723638.2023.2203583</mixed-citation></citation-alternatives></ref><ref id="ref25"><label>25</label><citation-alternatives><mixed-citation xml:lang="ru">World Bank. (2009). World Development Report. Reshaping economic geography. 383. https://hdl.handle.net/10986/5991 (дата обращения: 17.09.2024).</mixed-citation><mixed-citation xml:lang="en">World Bank. (2009). World Development Report. Reshaping economic geography. 383. https://hdl.handle.net/10986/5991 (Date of access: 17.09.2024).</mixed-citation></citation-alternatives></ref><ref id="ref26"><label>26</label><citation-alternatives><mixed-citation xml:lang="ru">World Bank. (2018). Re-mapping Opportunity. Making best use of the economic potential of Russia’s regions. World Bank Group.</mixed-citation><mixed-citation xml:lang="en">World Bank. (2018). Re-mapping Opportunity. Making best use of the economic potential of Russia’s regions. World Bank Group.</mixed-citation></citation-alternatives></ref><ref id="ref27"><label>27</label><citation-alternatives><mixed-citation xml:lang="ru">Yang, J., Yang, C., &amp; Hu, X. (2021). Economic policy uncertainty dispersion and excess returns: Evidence from China. Finance Research Letters, 40, 101714. https://doi.org/10.1016/j.frl.2020.101714</mixed-citation><mixed-citation xml:lang="en">Yang, J., Yang, C., &amp; Hu, X. (2021). Economic policy uncertainty dispersion and excess returns: Evidence from China. Finance Research Letters, 40, 101714. https://doi.org/10.1016/j.frl.2020.101714</mixed-citation></citation-alternatives></ref><ref id="ref28"><label>28</label><citation-alternatives><mixed-citation xml:lang="ru">Zhu, S., &amp; Yu, G. (2022). The impact of economic policy uncertainty on industrial output: The regulatory role of technological progress. Sustainability, 14 (16), 10428. https://doi.org/10.3390/su141610428</mixed-citation><mixed-citation xml:lang="en">Zhu, S., &amp; Yu, G. (2022). The impact of economic policy uncertainty on industrial output: The regulatory role of technological progress. Sustainability, 14 (16), 10428. https://doi.org/10.3390/su141610428</mixed-citation></citation-alternatives></ref></ref-list></back></article>