2. Home
  3. Archive
  4. Articles
  5. ECONOMICS AND MANAGEMENT

The Bulletin of the Institute of Economics of the Russian Academy of Sciences № 5/2025. Economics and Management.

Olga N. Gutnikova

Cand. Sci.(Econ.), Associate Professor of the Department of Marketing, Trade and Customs Aff airs, Institute of Economics and Management, V.I. Vernadsky Crimean Federal University, Simferopol, Russia

 

Natalia N. Kalkova

Cand. Sci. (Econ.), Associate Professor of the Department of Marketing, Trade and Customs Aff airs, Institute of Economics and Management, V.I. Vernadsky Crimean Federal University, Simferopol, Russia

 

ASSESSMENT OF FACTORS AFFECTING PERCEPTION OF GOODS PACKAGING BY CONSUMERS

Размер файла146-166 Размер файла  1.17 MB Размер файла Full text

This article examines various factors influencing consumer product perception and assesses the impact of individual aspects of product information on product packaging. Various brands of fruit juice served as the object of the study. Neuromarketing, specifically eye-tracking technology, was used as the research methodology. The paper characterizes the requirements of national standards of the Russian Federation regarding the rules for applying product information. An attempt was made to determine the level of influence of cognitive dissonance in the buyer, which occurs when focusing on the name printed on the product packaging with incorrectly placed hyphens, as well as to determine the degree to which this marketing ploy triggers a desire to make a purchase or the formation of negative perceptions. The neuromarketing study did not reveal a positive effect of intentional spelling errors in the product name on the formation of consumer interest. Oculographic analysis confirmed that incorrect hyphens in names have an insignificant impact on product perception. It was found that the key factors in attracting attention are the area of the name placement and its cognitive accessibility due to brand recognition, while minor typographic errors are offset by the integrity of perception and top-down processing of visual information, as well as the location of the product on the shelf.

Keywords: product information, product packaging, information parameters, cognitive dissonance, neuromarketing research, consumer perception, spelling errors, eye-tracking technology.

JEL: L15, D87, С93

EDN: IHNKDM

DOI: https://doi.org/10.52180/2073-6487_2025_5_146_166

This research was funded by the Russian Science Foundation (Project No. 25-28-20286. https://rscf.ru/project/25-28-20286/).

References

  1. Anderson J.R. The architecture of cognition. Harvard University Press, 1983.
  2. Zhalolov Sh.U. Grammatical norms of the use of words, phrases and sentences in the modern Russian language // Economy and Society. 2021. No. 11 (90). Pр. 995–998. (In Russ.).
  3. Plassmann H., O’Doherty J., Shiv B., & Rangel A. Marketing actions can modulate neural representations of experienced utility // Proceedings of the National Academy of Sciences. 2008. Vol. 105 (3). Рр. 1050–1054. DOI: 10.1073/pnas.0706929105.
  4. Aaker D.A. Creating Strong Brands/ Translated from English by S.A. Starov. M.: ID Grebennikov, 2003. (In Russ.).
  5. Stepantsova A.F. Research of product packaging in terms of its perception by potential and actual consumers (on the example of TM “Svitlogorye”) // In the collection: Development of science and practice in a globally changing world under risk conditions. collection of materials of the XXIII International scientific and practical conference. Moscow. 2023. Pр. 555–561. (In Russ.).
  6. Zherlitsyn O.M. Neuromarketing study of the attractiveness of packaging branding // Marketing and marketing research. 2024. No. 3. Pр. 176–182. (In Russ.).
  7. Losevskaya E.N. Neuromarketing as a tool for increasing the visibility of brand information in advertising and on packaging // Brand management. 2021. No. 3. Pр. 214–220. (In Russ.).
  8. Visual neuromarketing: fundamental and applied research / edited by O.B. Yarosh, V.E. Reutov. Simferopol: IT “ARIAL”, 2020. (In Russ.).
  9. Keller K.L. Strategic brand management: Building, measuring, and managing brand equity. Pearson Education, 2013.
  10. Alter A.L., Oppenheimer D.M. Uniting the tribes of fluency to form a metacognitive nation // Personality and Social Psychology Review. 2009. Vol. 13 (3). Рp. 219–235. DOI: 10.1177/1088868309341564.
  11. Kahneman D. Thinking, fast and slow. New York: Farrar, Straus and Giroux, 2011.
  12. Duchowski A. Eye-Tracking Methodology. Theory and Practice. (Second Edition). Springer-Verlag London Limited, 2007.
  13. Information asymmetry [Electronic resource]: methods and algorithms of neuromarketing / O.B. Yarosh, N.N. Kalkova, E.A. Mitina. Simferopol: IT «ARIAL», 2023. (In Russ.).
  14. Kalkova N.N. Neurobranding Research: Ethical Issues // Scientific Bulletin: Finance, Banks, Investments. 2022. No. 4. Pр. 100–113. (In Russ.).
  15. Kerzina E.A. Neuromarketing: Methodological Foundations and Practical Applications in Business // Marketing in Russia and Abroad. 2019. No. 3. Pр. 13–18. (In Russ.).
  16. Treisman A.M. Gelade G. A feature-integration theory of attention // Cognitive Psychology. 1980. Vol. 12 (1). Рp. 97–136. DOI: 10.1016/0010-0285(80)90005-5.
  17. Faggi L. et al. Loca l propagation of visual stimuli in focus of attention // Neurocomputing. 2023. Vol. 560. Article 126775. Рp. 1–18. DOI: 10.1016/j.neucom.2023.126775.
  18. Roelfsema P.R., Houtkamp R. Incremental grouping of image elements in vision // Attention, Perception, & Psychophysics. 2011. Vol. 73. Рp. 2542–2572. DOI: 10.3758/s13414-011-0200-0.
  19. Wagemans J. et al. A century of Gestalt psychology in visual perception: Perceptual grouping and figure-ground organization // Psychological Bulletin. 2012. Vol. 138 (6). Рp. 1172–1217. DOI: 10.1037/a0029333.
  20. Katsuki F., Constantinidis C. Bottom-Up and Top-Down Attention: Different Processes and Overlapping Neural Systems // The Neuroscientist. 2013. Vol. 20 (5). Рp. 509–521. DOI: 10.1177/107385841351413.
  21. Krishna A. An integrative re view of sensory marketing: Engaging the senses to affect perception, judgment and behavior // Journal of Consumer Psychology. 2012. Vol. 22 (3). Рр. 332–351. DOI: 10.1016/j.jcps.2011.08.003.
  22. Itti L., Koch C. A sali ency-based search mechanism for overt and covert shifts of visual attention // Vision Research. 2000. Vol. 40 (10–12). Рp. 1489–1506. DOI: 10.1016/S0042-6989(99)00163-7.

Manuscript submission date: 30.04.2025

Manuscript acceptance date: 13.10.2025

 

For citation:

Gutnikova O.N., Kalkova N.N. Assessment of factors affecting perception of goods packaging by consumers // Vestnik Instituta Ekonomiki Rossiyskoy Akademii Nauk. 2025. № 5. Pp. 146-166. (In Russ.). https://doi.org/10.52180/2073-6487_2025_5_146_166 EDN: IHNKDM

  Creative Commons 4.0

The Bulletin of the Institute of Economics of the Russian Academy of Sciences № 5/2025. Economics and Management.

Vladimir L. Bersenev

Dr. Sci. (Hist.), Professor, Leading Researcher, Institute of Economics of the Urals Branch of the RAS, Yekaterinburg, Russia

ORCID: 0000-0002-3554-6965

 

Olga N. Buchinskaia

Cand. Sci. (Econ.), Senior Researcher, Institute of Economics of the Urals Branch of the RAS, Yekaterinburg, Russia

ORCID: 0000-0002- 5421-2522

 

ANALYSIS OF THREATS TO THE RESILIENCE OF THE RUSSIAN ECONOMY USING MACHINE LEARNING ALGORITHMS

Размер файла121-145 Размер файла  577.92  KB Размер файла Full text

The increasing number of exogenous and endogenous shocks faced by national economies, as well as various measures of external negative impact on the country’s economy, necessitate a diagnosis of threats to the viability of the economy. Based on the statistical data of the Russian Federation for the period 1990–2023, eight different types of shocks were identified, which were classified as external or internal, natural or artificial in origin. Some of these became bifurcation points, changing the vector of Russian economic development. Since the impact of artificially induced shocks cannot be analyzed through deterministic models, it becomes necessary to use current statistical data instead of analyzing long time series to diagnose threats to the resilience of the Russian economy. To this end, the authors used machine learning algorithms, including identifying specific machine learning algorithms that allow for a fairly accurate diagnosis of the impact of shocks on the resilience of our country’s economic system. The AdaBoost and decision tree algorithms demonstrated the best results. Machine learning was used to identify the variables the algorithms selected for shock impact diagnostics, as well as to determine the threshold values for the indicators used by the algorithms to diagnose economic shocks.

Keywords: economic shocks, economic crises, resilience, economic stability, economic diagnostics, machine learning.

JEL: E37

EDN: CBAPHB

DOI: https://doi.org/10.52180/2073-6487_2025_5_121_145

References

  1. Ricardo D. On the Principles of Political Economy and Taxation. Moscow: Eksmo, 2007. (In Russ.).
  2. Sey J.B. A treatise on political economy // Library of Economists. Issue VII. Moscow: Publishing house of K.T. Soldatenkov, 1896. Pp. 1–112. (In Russ.).
  3. Tugan-Baranovsky M.I. Selections. Periodic industrial crises. The history of the English crises. The General Theory of Crises, Moscow: Nauka, The Russian Political Encyclopedia (ROSSPAN), 1997. (In Russ.).
  4. Kondratiev N.D. Large cycles of the market: the report // N.D. Kondratiev. Problems of economic dynamics, Moscow: Ekonomika, 1989. Pp. 170–411. (In Russ.).
  5. Glazyev S.Y. Some patterns of technical and economic development and the possibility of accelerating scientific and technological progress. // Izvestia of the USSR Academy of Sciences: Economic Series. 1987. No. 3. Pp. 3–15. (In Russ.).
  6. Toynbee A. J. A Study of History. Moscow: Progress, 1996. (In Russ.).
  7. Senyavsky A.S. Big challenges in the imperial and soviet history of Russia: comparative analysis // Ural Historical Journal. 2018. No. 2. Pp. 39–48. (In Russ.). DOI: 10.30759/1728-9718-2018-2(59)-39-48. EDN: UQVEQA.
  8. Nefedov S.A. “Challenges” and “Responses” in the history of Russia (using the example of the pre-Petr and Petr reforms, 1615-1725) // Sociological Research. 2017. No. 9. Pp. 78–87. (In Russ.). DOI: 10.7868/S0132162517090094. EDN: ZFTRNP.
  9. Nefedov S.A. “Challenges” and “responses” of the 18th century Russia // Sociological research. 2018. No. 10. Pp. 130–139. (In Russ.) DOI: 10.31857/S013216250002165-4. EDN: YOUXVZ.
  10. Nefedov S.A. “Challenges” and “responses” in Russian history: the first half of the 19th century // Sociological research. 2019. No. 6. Pp. 86–96. (In Russ.). DOI: 10.31857/S013216250005484-5. EDN: KHTCVI.
  11. Giddens E. The Consequences of Modernity // Philosophy of history: An anthology. Moscow: Aspect Press, 1995. Pp. 340–347. (In Russ.).
  12. Ochkin R.O. Theoretical and methodological aspects of the study of external shocks as a determinant factor of national and state economic interests // Theoretical economics. 2018. No. 2 (44). Pp. 161–167. (In Russ.). EDN: YPTVML.
  13. Akberdina V.V. Factors of resistance in the Russian economy: a comparative analysis for the period 2000-2020 // National interests: priorities and security. 2021. Vol. 7. No. 8. Pp. 1412–1432. (In Russ.). DOI: 10.24891/ni.17.8.1412. EDN: UTWWQM.
  14. Fukuyama F. The end of history? // Philosophy of History: An Anthology. Moscow: Aspect Press, 1995. Pp. 200–310. (In Russ.).
  15. Chatterjee S. From cycles to shocks: Progress in business cycle theory // Business Review. 2000. Vol. 3. Pp. 27–37. https://core.ac.uk/download/pdf/6648921.pdf (accessed: 20.05.2025).
  16. Gallegati M. Irregular business cycles // Structural Change and Economic Dynamics. 1994. Vol. 5. No. 1. Pp. 73–79. DOI: 10.1016/S0954-349X(05)80020-7.
  17. Ng S. Modeling macroeconomic variations after COVID-19. National Bureau of Economic Research, 2021. No. w29060. https://www.nber.org/system/files/working_papers/w29060/w29060.pdf (accessed: 20.05.2025).
  18. Hurduzeu G., Lupu R., Lupu I., Filip R. I. Evolving Economic Relationships: A TVPVAR Analysis of Trade, World Uncertainty, and Stock Market Volatility in Europe // Economic Computation & Economic Cybernetics Studies & Research. 2024. Vol. 58. No. 4. Pp. 20–41. DOI: 10.24818/18423264/58.4.24.02.
  19. Chen K., Di Q., Chen X. The resilience of island economy in the face of adverse shocks and its influencing factors: A case study from county-level islands in China // Marine Policy. 2024. Vol. 165. Pp. 106178. DOI: 10.1016/j.marpol.2024.106178.
  20. Šťastná S., Ženka J., Krtička L. Regional economic resilience: insights from five crises // European Planning Studies. 2024. Vol. 32. No. 3. Pp. 506–533. DOI: 10.1080/09654313.2023.2267250.
  21. Nchofoung T. N., Ngouhouo I. Determinants of economic resilience response in sub-Saharan Africa to a common exogenous shock: roles of demographic differences, exchange rate regimes and institutional quality // Journal of Social and Economic Development. 2024. Vol. 26. No. 1. Pp. 186-213. DOI: 10.1007/s40847-023-00258-9.
  22. Chen P. Empirical and theoretical evidence of economic chaos // System Dynamics Review. 1988. Vol. 4. No. 1–2. Pp. 81–108. DOI: 10.1002/sdr.4260040106.
  23. Akhmet M., Akhmetova Z., Fen M. O. Chaos in economic models with exogenous shocks // Journal of Economic Behavior & Organization. 2014. Vol. 106. Pp. 95–108. DOI: 10.1016/j.jebo.2014.06.008.
  24. Alexeeva T. A., Kuznetsov N. V., Mokaev T. N. Study of irregular dynamics in an economic model: attractor localization and Lyapunov exponents // Chaos, Solitons & Fractals. 2021. Vol. 152. Pp. 111365. DOI: 10.1016/j.chaos.2021.111365. EDN: CYHAVX.
  25. Zhang W. B. Chaos, complexity, and nonlinear economic theory/ World Scientific, 2023.
  26. Orlando G., Sportelli M. On business cycles and growth // Nonlinearities in Economics: An Interdisciplinary Approach to Economic Dynamics, Growth and Cycles. 2021. Pp. 153–168.
  27. Farazmand M., Sapsis T. P. Extreme events: Mechanisms and prediction // Applied Mechanics Reviews. 2019. Vol. 71. No. 5. Pp. 050801. DOI: 10.1115/1.4042065.
  28. Chowdhury S.N., Ray A., Dana S.K., Ghosh D. Extreme events in dynamical systems and random walkers: A review // Physics Reports. 2022. Vol. 966. Pp. 1–52. DOI: 10.1016/j.physrep.2022.04.001.
  29. Ray R.K., Sumsuzoha M., Faisal M.H., Chowdhury S.S., Rahman Z., Hossain E., Rashid M.M., Hossain М., Rahman M.S. Harnessing Machine Learning and AI to Analyze the Impact of Digital Finance on Urban Economic Resilience in the USA // Journal of Ecohumanism. 2025. Vol. 4. No. 2. Pp. 1417–1442. DOI: 10.62754/joe.v4i2.6515.
  30. Wu Z. Evaluation of Provincial Economic Resilience in China Based on the TOPSISXGBoost-SHAP Model // Journal of Mathematics. 2023. Vol. 2023. No. 1. Pp. 6652800. DOI: 10.1155/2023/6652800.
  31. Villacis A. H., Badruddoza S., Mishra A. K. A machine learning-based exploration of resilience and food security // Applied Economic Perspectives and Policy. 2024. Vol. 46. No. 4. Pp. 1479–1505. DOI: 10.1002/aepp.13475.
  32. Ahmed M.K., Bhuiyan M.M. R., Saimon A.S. M., Hossain S., Hossain S., Manik M.M.T.G., Rozario E. Harnessing Big Data for Economic Resilience the Role of Data Science in Shaping US Economic Policies and Growth // Journal of Management. 2025. Vol. 2. Pp. 26–34. DOI: 10.53935/jomw.v2024i4.865.
  33. Danilov Yu.A., Pivovarov D.A., Davydov I.S. On the issue of anticipating global financial and economic crises // Finance: theory and practice. 2020. Vol. 24. No. 1. Pp. 87–104. (In Russ.). DOI: 10.26794/2587-5671-2020-24-1-87-104. EDN: YBOVDS.
  34. Freund Y., Schapire R., Abe N. A short introduction to boosting // Journal-Japanese Society For Artificial Intel-ligence. 1999. Vol. 14. No. 5 Pp. 771–780.
  35. Quinlan J. R. Induction of decision trees // Machine learning. 1986. Vol. 1. Pp. 81–106. DOI: 10.1007/BF00116251.
  36. Breiman L. Random forests // Machine learning. 2001. Vol. 45. Pp. 5–32. DOI: 10.1023/A:1010933404324.
  37. Hosmer Jr/D.W., Lemeshow S., Sturdivant R.X. Applied logistic regression. John Wiley & Sons, 2013. DOI: 10.1002/9781118548387.
  38. Demšar J., Curk T., Erjavec A., Gorup C., Hočevar T., Milutinovič V., Možina M., Matija Polajnar M., Toplak M., Starič A., Štajdohar M., Umek L., Žagar L., Žbontar J., Žitnik M., Zupan B. Orange: data mining toolbox in Python // The Journal of machine Learning research. 2013. Vol. 14. No. 1. Pp. 2349–2353.
  39. Senchagov V. K., Mityakov S.N. Using the index method to assess the level of economic security // Bulletin of Economic Security. 2011. No. 5. Pp. 41–50. (In Russ.). EDN: OGXLJL.
  40. Krivorotov V.V., Kalina A.V., Belik I.S. Threshold values of indicative indicators for diagnostics of economic security of the Russian Federation at the present stage // Bulletin of UrFU. Economics and Management series. 2019. Vol. 18. No. 6. Pp. 892–910. (In Russ.). DOI: 10.15826/vestnik.2019.18.6.043. EDN: JRQDEP.
  41. Dubinina Je.V., Zhilina E.V. Diagnostics of threats to the economic security of the Russian Federation // Actual problems of science and education in the context of modern challenges. 2023. Pp. 498–508. (In Russ.). DOI: 10.34755/IROK.2023.29.97.092. EDN: QNVSUR.

Manuscript submission date: 30.05.2025

Manuscript acceptance date: 13.10.2025

 

For citation:

Bersenev V.L., Buchinskaia O.N. Analysis of threats to the resilience of the Russian economy using machine learning algorithms // Vestnik Instituta Ekonomiki Rossiyskoy Akademii Nauk. 2025. № 5. Pp. 121-145. (In Russ.). https://doi.org/10.52180/2073-6487_2025_5_121_145 EDN: CBAPHB

  Creative Commons 4.0

The Bulletin of the Institute of Economics of the Russian Academy of Sciences № 5/2025. Economics and Management.

Svetlana S. Patrakova

Cand. Sci. (Econ.), Researcher, Laboratory of Spatial Development of Territorial Systems and Intersectoral Complexes, Center for the Study of Spatial Development of Socio-Economic Systems, Vologda Research Center of the RAS, Vologda, Russia

ORCID: 0000-0002-4834-3083

 

Sergey A. Kozhevnikov

Cand. Sci. (Econ.), Leading Researcher, Head of the Center for the Study of Spatial Development of Socio-Economic Systems, Vologda Research Center of the RAS, Vologda, Russia

ORCID: 0000-0001-9063-6587

 

ASSESSMENT OF THE DEVELOPMENT LEVEL OF SECOND-TIER URBAN AGGLOMERATIONS IN RUSSIA

Размер файла81-104 Размер файла  442.39 KB Размер файла Full text

The article is devoted to the problems of development of second-tier urban agglomerations in Russia (with a population of less than 500 thousand people), which are formed around a number of large and big cities. The author’s approach to assessing development is proposed, which is based on understanding the essence of an agglomeration as a self- developing socio-economic system that is internally integrated and produces positive agglomeration effects. The approach has been tested on the data of eight agglomerations with cores in the cities of Arkhangelsk, Vologda, Kaluga, Norilsk, Surgut, Tambov, Khanty-Mansiysk, Yuzhno-Sakhalinsk for the period 2014–2023. It was revealed that only five out of these eight agglomerations are classified as such: the agglomerations with an average level of integration include Yuzhno-Sakhalinsk, Arkhangelsk, with a low level – Tambov, Surgut and Vologda.

Keywords: urban agglomeration, development level, internal integration, agglomeration effect, core, satellite area.

JEL: R11, R58

EDN: DZJEBC

DOI: https://doi.org/10.52180/2073-6487_2025_5_81_104

This research was funded by the Russian Science Foundation (Project No. 23-78-10054. https://rscf.ru/ project/23-78-10054/).

References

  1. Kuznetsova O.V., Druzhinin A.G. Towards a new strategy for the spatial development of Russia // Studies on Russian Economic Development. 2024. Vol. 205. No. 4. Pp. 36–45. (In Russ.). DOI: 10.47711/0868-6351-205-36-45. EDN: VVXHSP.
  2. Shvetsov A.N. Urban agglomerations in the transformation of urban space // Russian Economic Journal. 2018. No. 1. Pp. 45–65. (In Russ.). EDN: NTOLQB.
  3. Melnikova L.V. The size of cities, efficiency and economic growth // ECO. 2017. No 7. Pp. 5–19. (In Russ.). EDN: YUSMFZ.
  4. Stroev P.V. Transformations of the spatial structure of Russia // Bulletin of the Institute of Economics of the Russian Academy of Sciences. 2014. No. 4. Pp. 61–70. (In Russ.). EDN: SMLIUF.
  5. Dmitriev M.E., Chistyakov P.A., Romashina A.A. The role of the spatial factor in accelerating economic growth // Social sciences and modernity. 2018. No. 5. Pp. 31–47. (In Russ.). DOI: 10.31857/S086904990001496-7. EDN: YPOJGH.
  6. Puzanov A., Popov R. Approaches to assessing the development of urban agglomerations. Moscow: Fund “Institute for Urban Economics”, 2017. https://www.urbaneconomics.ru/sites/default/files/iue_press.pdf (In Russ.). EDN: UVJUKO.
  7. Urban agglomerations in modern Russia: problems and prospects of development / A.S. Puzanov, R.A. Popov, T.D. Polidi, A.Ya. Gershovich; edited by Candidate of Geographical Sciences A.S. Puzanov. Moscow: Fund “Institute for Urban Economics”, 2023. (In Russ.).
  8. Polyan P.M. Territorial structures – urbanization – settlement: theoretical approaches and methods of study. Moscow: New Chronograph, 2014. (In Russ.). EDN: TAYFBZ.
  9. Zaslavsky I.N., Naimark N.I., Polyan P.M. Problems of delimitation of urban agglomerations: comparison and synthesis of leading methods // Problems of studying urban agglomerations: Collection of articles. Moscow: Institute of Geography of the USSR Academy of Sciences, 1988. Pp. 27–41. (In Russ.).
  10. Uchida H., Nelson A. Agglomeration index: Towards a new measure of urban concentration // World Institute for Development Economics Research. 2010. No. 29. Pp. 1–16.
  11. Trunova N.A. Improvement of methodological approaches to the analysis and assessment of factors influencing the development of urban agglomerations // Economic sciences. 2011. No. 3. Pp. 205–208. (In Russ.). EDN: OYUBNH.
  12. Volchkova I.V. Methodological aspects of indicative analysis of the state and development of agglomerations // Economic sciences. 2014. Vol. 110. No. 1. Pp. 73–77. (In Russ.). EDN: SHDIMB.
  13. Glazychev V., Starodubrovskaya I. [et al.]. Chelyabinsk agglomeration: development potential. Chelyabinsk. 2008. (In Russ.).
  14. Zhang S., Wei H. Identification of Urban Agglomeration Spatial Range Based on Social and Remote-Sensing Data–For Evaluating Development Level of Urban Agglomeration // ISPRS International Journal of Geo-Information. 2022. No. 11. Pp. 1–19. DOI: 10.3390/ijgi11080456.
  15. Fang Ch., Yu X., Zhang X., Fang J., Liu H. Big data analysis on the spatial networks of urban agglomeration // Cities. 2020. Vol. 102. 102735. DOI: 10.1016/j.cities.2020.102735.
  16. Roy B., Kasemi N. Monitoring urban growth dynamics using remote sensing and GIS techniques of Raiganj Urban Agglomeration, India // The Egyptian Journal of Remote Sensing and Space Science. 2021. Vol. 24. Pp. 221–230. DOI: 10.1016/j.ejrs.2021.02.001.
  17. Shi Q., Yan X., Jia B., Gao Z. Freight Data-Driven Research on Evaluation Indexes for Urban Agglomeration Development Degree // Sustainability. 2020. Vol. 12. No. 11. Pp. 1–16. DOI: 10.3390/su12114589.
  18. Voroshilov N.V. Development of urban agglomerations in the European North of Russia // Federalism. 2021. No. 4. Pp. 54–74. (In Russ.). DOI: 10.21686/2073-1051-2021-4-54-74. EDN: INUFHK.
  19. Tiwari A.K., Antoniuk, V.S., Lapo A.S., Vansovich E.R. Managing urban agglomeration processes in Russia in the context of agglomerative and socio-economic development // Heliyon. 2024. No. 7. Pp. 1–11. DOI: 10.1016/j.heliyon.2024.e28654
  20. Kozhevnikov S.A., Voroshilov N.V. Agglomeration processes in Russian regions: features and problems of activating positive effects // Economic and social changes: facts, trends, forecast. 2024. Vol. 17. No. 1. Pp. 91–109. (In Russ.). DOI: 10.15838/esc.2024.1.91.5. EDN: OGARTF.
  21. Lavrinenko P.A., Mikhailova T.N., Romashina A.A., Chistyakov P.A. Agglomeration effects as a tool for regional development // Studies on Russian Economic Development. 2019. No. 3 (174). Pp. 50–59. (In Russ.). EDN: SFXHGX.
  22. Kolomak E.A., Sherubneva A.I. Assessing the influence of agglomeration factors on economic activity (microeconomic analysis) // Economy of Regions. 2023. No. 3. Pp. 766–781. (In Russ.). DOI: 10.17059/ekon.reg.2023-3-12. EDN: ZCZFDD.
  23. Makhrova A.G. Features of the stadium development of the Moscow agglomeration // Bulletin of the Moscow University. Series 5. Geography. 2014. No. 4. Pp. 10–16. (In Russ.). EDN: TAVMWZ.
  24. Kozhevnikov S.A., Patrakova S.S., Kopytova E.D. Promising centers of economic growth in Russian regions and agglomeration effects: an empirical analysis // Creative Economy. 2024. Vol. 18. No. 5. Pp. 1075–1090. (In Russ.). DOI 10.18334/ce.18.5.121016. EDN: HBPLAN.

Manuscript submission date: 27.05.2025

Manuscript acceptance date: 13.10.2025

 

For citation:

Patrakova S.S., Kozhevnikov S.A. Assessment of the development level of second-tier urban agglomerations in Russia // Vestnik Instituta Ekonomiki Rossiyskoy Akademii Nauk. 2025. № 5. Pp. 81-104. (In Russ.).  https://doi.org/10.52180/2073-6487_2025_5_81_104 EDN: DZJEBC

  Creative Commons 4.0

The Bulletin of the Institute of Economics of the Russian Academy of Sciences № 5/2025. Economics and Management.

Svetlana V. Kozlova

Dr. Sci. (Econ.), Chief Researcher, Head of the Sector, Institute of Economics of the RAS, Moscow, Russia

ORCID: 0009-0009-1708-4945

 

PROPERTY TREASURY MANAGEMENT: TRANSFORMATION OF APPROACHES IN NEW SOCIO-ECONOMIC CONDITIONS

Размер файла105-120  Размер файла  309.99 KB Размер файла Full text

The article analyses the process of managing the transformation of the institute of property treasury in a dynamically changing institutional environment in Russia. The study was conducted taking into account the current geopolitical and socio-economic conditions, with an emphasis on current trends and innovations. The directions of improvement and development of the state management of the property treasury for the medium and long term are proposed.

Keywords: state property, management institutions, conceptual approaches to state property management, strategic goals, transformation of state property management institutions.

JEL: H82, H19

EDN: LNJFLY

DOI: https://doi.org/10.52180/2073-6487_2025_5_105_120

References

  1. Kozlova S.V. Transformation of state property management institutions: results for 2022–2024 // Bulletin of the Institute of Economics of the Russian Academy of Sciences. 2024. No. 5. Рp. 42–57. (In Russ.). DOI: 10.52180/2073-6487_2024_5_42_57. EDN: HWKQIA.
  2. Malginov G.N., Radygin A.D. Problems of managing the property of the state treasury and the implementation of the state program “Federal Property Management” // Economic development of Russia. 2014. Vol. 21. No. 8. Pp. 55–61. (In Russ.). EDN: SNOPEZ.
  3. Zgonnikov P.P., Gladneva E.P. Problems of legal regulation of the formation and development of the state treasury: history and development paths // FES: Finance. Economy. Strategy. 2017. № 1. Рp. 9–14. (In Russ.).
  4. Kozlova S.V., Zvyagintsev P.S. Transformation of the state program “Federal Property Management” // Bulletin of the Institute of Economics of the Russian Academy of Sciences. 2023. No. 1. Pр. 90–105. (In Russ.). DOI: 10.52180/2073-6487_2023_5_90_105. EDN: PCLKGJ.
  5. Kozlova S.V., Gribanova O.M. Formation of the institutional environment for treasury management in modern Russia // Bulletin of the Institute of Economics of the Russian Academy of Sciences. 2019. No. 4. Рp. 66–81. (In Russ.).
  6. Savina A.V. Certain categories and features of property relations in the sphere of public financial activity // Legal Bulletin. 2024. No. 1. Vol. 9. Рp. 42–51. (In Russ.).]
  7. Makarskova V.N. Accounting of property of the municipal treasury Financial Control. 2025. No. 2. (In Russ.).
  8. Khamalinskaya V.V., Khamalinsky I.V., Kuznetsova E.K. Modern problems of state property management in the Russian Federation // Bulletin of the Moscow University named after S.Yu. Witte. Series 1. Economics and Management. 2023. No. 3 (46). Pр. 86–93. (In Russ.).
  9. Kozlova S.V., Gribanova O.M. Institute of property treasury and its role in the public administration system // Management and business administration. 2019. No. 3. P. 30–39. (In Russ.).
  10. Bratchenko S.A. Goal setting in public administration: issues of achievability, relevance and consistency of goals // Bulletin of the Institute of Economics of the Russian Academy of Sciences. 2024. No. 1. Pр. 24–46. (In Russ.). DOI: 10.52180/2073-6487_2024_5_24_46. EDN: FXOLFG.

Manuscript submission date: 18.07.2025

Manuscript acceptance date: 13.10.2025

 

For citation:

Kozlova S.V. Property treasury management: transformation of approaches in new socio-economic conditions // Vestnik Instituta Ekonomiki Rossiyskoy Akademii Nauk. 2025. № 5. Pp. 105-120. (In Russ.). https://doi.org/10.52180/2073-6487_2025_5_105_120 EDN: LNJFLY

  Creative Commons 4.0

The Bulletin of the Institute of Economics of the Russian Academy of Sciences № 5/2025. Economics and Management.

Irina N. Domnina

Cand. Sci (Econ), Associate Professor, Leading Researcher, Center for Federal Relations and Regional Development, Institute of Economics of the RAS, Moscow, Russia

ORCID: 0000-0002-6377-2265

 

GEOSTRATEGIC TERRITORIES IN STRATEGIC PLANS FOR SPATIAL TRANSFORMATION OF THE RUSSIAN ECONOMY

Размер файла65-80 Размер файла  451.47 KB Размер файла Full text

The article examines a new stage of strategic planning and management of economic space, presented by the Strategy for Spatial Development of the Russian Federation for the period up to 2030 with a forecast up to 2036. It is shown that the transformation of the spatial model of the economy affects, among other things, the so-called geostrategic territories, significantly changing their quantitative composition and qualitative content. Particular attention is paid to geostrategic territories, including conceptual issues of their formation and management. The main trends in the evolution of the place of these territories in strategic plans for the development of economic space are determined. The directions for increasing the effectiveness of the policy for managing geostrategic territories are substantiated, taking into account the new opportunities of the economic space in implementing national development goals.

Keywords: geostrategic territories, macroterritories, spatial development, strategic planning, economic space.

JEL: R1, R50

EDN: CHIARA

DOI: https://doi.org/10.52180/2073-6487_2025_5_65_80

References

  1. Buchwald E. M. Strategic spatial planning: macro-regions and subjects of the Russian Federation // Journal of Russian Law. 2020. No. 3. Pp. 31–44. (In Russ.). EDN: ODIHCE. DOI: 10.12737/jrl.2020.028.
  2. Domnina I.N. «Geostrategic territory» as a form of spatial regulation of the economy // Bulletin of the Institute of Economics of the Russian Academy of Sciences. 2020. No. 6. Pр. 126–141. (In Russ.). EDN: VELKJM. DOI: 10.24411/2073-6487-2020-10074.
  3. Domnina I.N. Macro-regional regulation of the economic space: functional tasks and problems of implementation // Business. Education. Right. 2024. No. 3 (68). Pp. 91–97. (In Russ.). DOI: 10.25683/VOLBI.2024.68.1065.
  4. Ivanov O.B., Buchwald E.M. “Geostrategic territories” and “growth points” in strategizing the spatial development of the Russian Federation // STAGE: economic theory, analysis, practice. 2019. No. 4. Pp. 7–23. (In Russ.). EDN: MVVQYQ. DOI: 10.24411/2071-6435-2019-10098.
  5. Kryukov A.V., Seliverstov V.E. Strategic planning of spatial development of Russia and its macroregions: in captivity of old illusions // Russian Economic Journal. 2022. No. 5. Pp. 22–40. (In Russ.). EDN: STMBMA. DOI: 10.33983/0130-9757-2022-5-22-40.
  6. Kuvalin D.B. Selection of priorities of spatial development and geostrategic regions of Russia. Moscow: Institute of Economic Forecasting.18.06.2024. https://ecfor.ru/wp-content/uploads/2025/04/prioritety-prostranstvennogo-razvitiya-igeostrategicheskie-regiony-rossii.pdf (accessed: 31.07.2025).
  7. Mikheeva N.N. Priority geostrategic regions of Russia’s spatial development strategy // ECO. 2025. Vol. 55. No. 3. Pp. 40–55. (In Russ.). DOI: 10.29003/m252.sp_ief_ras2018/32-52.
  8. Senin V. Geostrategic Territories of the Far East: a new theoretical approach as a response to “big challenges” for society, Government and science // Regional economy. The South of Russia. Vol. 12. No. 4. Pp. 47–57. (In Russ.). EDN: PJGVLO. DOI: 10.15688/re.volsu.2024.4.5.
  9. Orlov S.L. Modern problems of socioeconomic development of priority geostrategic territories of Russia // Bulletin of Economics, Law and Sociology. 2022. No. 1. Рp. 28–34. (In Russ.). EDN TCQPCM.
  10. Russia 2035: space of development: Scientific report / A.A. Shirov, B. N. Porfirev, A. V. Petrikov [et al.]. M.: Institute of National Economic Forecasting of the Russian Academy of Sciences, 2025. (In Russ.). EDN DKVIKY. DOI: 10.47711/sr1-2025.
  11. Khmeleva G.A., Kostromin K.O., Skreblov N.I. Current state and risks of development of border geostrategic territories // Bulletin of Eurasian Science. 2023. Vol. 15. No. 1. (In Russ.). DOI: 10.15862/64ECVN123.

Manuscript submission date: 15.07.2025

Manuscript acceptance date: 13.10.2025

 

For citation:

Domnina I.N. Geostrategic territories in strategic plans for spatial transformation of the Russian economy // Vestnik Instituta Ekonomiki Rossiyskoy Akademii Nauk. 2025. № 5. Pp. 65-80. (In Russ.). https://doi.org/10.52180/2073-6487_2025_5_65_80 EDN: CHIARA

  Creative Commons 4.0

  1. Vilenskiy A.V. UNITY OF THE NATIONAL ECONOMIC SPACE AND PREFERENTIAL ECONOMIC ZONES
  2. Potapenko V.V. POPULATION AGING AND THE DEMOGRAPHIC BURDEN ON THE RUSSIAN PENSION SYSTEM
  3. Soboleva I.V., Sobolev E.N. EMPLOYEE WELL-BEING IN A LABOR-DEFICIT ECONOMY
  4. Gribanova O.M. TRANSFORMATION OF PUBLIC ADMINISTRATION INSTITUTIONS IN NEW SOCIO-ECONOMIC CONDITIONS: GOALS AND CURRENT TASKS

© Vestnik Instituta Ekonomiki Rossiyskoy Akademii Nauk, 2021 - 2025

32, Nakhimovskiy Prospekt, Moscow, Russia 117218, Institute of Economics of the Russian Academy of Sciences.

Phone.: +7 (499) 724-13-91, E-mail: vestnik-ieran@inecon.ru

  

Russian version

HOME

JOURNAL DESCRIPTION

General information

Editorial policies

Author guidelines

LATEST ISSUE

ARCHIVE

Editorial advisory board

Editorial team

Author guidelines

Submit article