Relevance. The paper explores interregional cooperation, examining the challenges of aligning spatial and innovation development in macro-regions, with a focus on two federal districts of Russia. The study assesses the potential of interregional cooperation among neighboring regions within a single federal district, as well as among more distant regions across different federal districts. Research Objective. The study aims to test two hypotheses: the first deals with the viability of imitation innovation strategies in peripheral regions of both intra and inter-federal districts. The second hypothesis concerns the presence of innovation interdependence (autocorrelation) among regions from different federal districts, influenced by the level and industrial compatibility of innovation outputs. Data and methods. The study employs the DEA method to identify central and peripheral regions (imitator regions) by calculating technical efficiency indicators. It also uses coupling interregional complementarity indexes to assess the potential for interregional cooperation in innovation and technological import substitution, considering the industrial profiles of the regions. Spatial autocorrelation is evaluated by using Moran’s Index to estimate the level of regional interdependence, factoring in the level and industry conformity of innovation output. The novelty of the proposed methodological approach lies in the application of interregional indexes of innovation complementarity as weighting coefficients in Moran’s Index calculation. Results. The study reveals a rise in spatial inequality, competition among regions, and constrained interregional innovation cooperation across federal districts. Geographical proximity currently plays a pivotal role in cooperation, with initial indications of a macro-regional space evolving through knowledge exchange. However, both hypotheses concerning imitation strategies and autocorrelation are only confirmed for regions within a single federal district. Conclusions. The findings of this study regarding spatial autocorrelation offer valuable insights for policymakers in the sphere of regional innovation.
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Идентификаторы и классификаторы
The study highlights the inequality between remote and bordering regions in accessing resources and innovation and emphasizes the need for new strategies for interregional cooperation (Kolomak, 2012; Chistyakova et al., 2023; Zemtsov & Kotsemir, 2019). In the Russian Federation, western regions experience increased agglomeration effects, while eastern regions face divergent trends. The administrative structure aligns well with the geographical and economic realities, which means that macro-regions (federal districts) are essentially clusters of geographically proximate regions with interconnected trade networks (Boschma, 2005; Torre & Rallet, 2008).
Список литературы
1. Ackerman E. N., Mikhalchuk A. A., Spitsyn V. V., Chistyakova N. O. (2019). Assessing the imitation potential of IT companies using the Cobb-Douglas production function, Bulletin of NSUEU, 4, 130-142. (In Russ.). DOI: 10.34020/2073-6495-2019-4-130-142 EDN: JFKGPR
2. Anisimkova N. D. (2017). Rol’ sektora vysshego obrazovaniya v stimulirova-nii innovatsionnogo protsessa v promyshlennosti, Omskogo universiteta. Seriya “Ekonomika”, 1, 57, 5-12. (In Russ.).
3. Anokhin S., Wincent J., Ylinenpää H. (2015). Technological Expansions, Catching-Up Innovations and Technological Shifts at the Regional Level: Conceptual Considerations and Empirical Illustration, Taylor & Francis Journals, 50, 8, 1433-1448,. DOI: 10.1080/00343404.2015.1027886
4. Averina L. M., Sirotin, D. V. (2020). Assessment of spatial effects from innovative activity of industrialized regions of the Russian Federation, Regional Economics, 16, 1, 268-282. 10.17059/2020-1-20 (In Russ.). DOI: 10.17059/2020-1-20(InRuss.)
5. Barro R., Sala-i-Martin X. (1990). Economic growth and convergence across the United States, NBER Working paper, 272.
6. Batrasova A. D., Konovalova T. V., Komarov P. I. (2022). Assessing the effectiveness of innovative activities of the subjects of the Federation. Bulletin of the Altai Academy of Economics and Law, 11-3, 389-395. (In Russ.). DOI: 10.17513/vaael.2578 EDN: ECCBAF
7. Becattini G. (1962). Il concetto d’industria e la teoria del valore. Torino: Boringhieri. 194.
8. Berkowitz D., DeJong D. N. (2005). Entrepreneurship and Post-Socialist Growth, Oxford Bulletin of Economics and Statistics, 67, 1, 25-46. DOI: 10.1111/j.1468-0084.2005.00108.x EDN: LXRVKR
9. Borts G. H., Stein J. I. (1964). Economic Growth in a Free Market. New York, London, 235.
10. Boschma R. A. (2010). Proximity and Innovation: a Critical Assessment, Regional Studies, 39, 61-74. DOI: 10.1080/0034340052000320887
11. Boudeville J.-R. (1966). Problems of Regional Economic Planning. Edinburgh: Edinburgh University Press, 192.
12. Burets Y. S. (2018). Assessment of innovative complementarity of regions, Economic environment, 3, 25, 40-49. (In Russ.).
13. Caniëls M., Verspagen B. (2003). Spatial Distance in a Technology Gap Model. In B. Fingleton (Eds.), European Regional Growth. Advances in Spatial Science (pp. 159-182). Springer, Berlin, Heidelberg. DOI: 10.1007/978-3-662-07136-6_6
14. Carluer F., Sharipova E. (2004). The Unbalanced Dynamics of Russian Regions: Towards a Real Divergence Process, East-West Journal of Economics and Business, 7, 1, 11-37.
15. Chistyakova N., Mikhalchuk A., Bocharova Y., Akerman E., Tatarnikova V. (2023). Catching-up regional strategy as a tool to reduce spatial inequality, Journal of the Geographical Institute “Jovan Cvijić” SASA, 73, 1, 33-48,. DOI: 10.2298/IJGI2301033C EDN: NZKPKM
16. Combes P.-P., Lafourcade M., Thisse J.-F., Toutain J.-C. (2011). The Rise and Fall of Spatial Inequalities in France: A Long-run Perspective, Exploration of Economic History, 48, 2, 243-271. DOI: 10.1016/j.eeh.2010.12.004
17. Demidova O. A., Ivanov D. S. (2016). Modeli ekonomicheskogo rosta s neodnorodnymi prostranstvennymi effektami (na primere rossiyskikh regionov) [Models of Economic Growth with Heterogenous Spatial Effects: The Case of Russian Regions], Ekonomicheskiy zhurnal VShE [The HSE Economic journal], 20, 1, 52-75. (In Russ.).
18. Dzyubenko I. B. (2021). Assessing the effectiveness of innovative systems in Russian regions using the method of shell data analysis (DEA). Economics and Entrepreneurship, 11(136), 442-450. DOI: 10.34925/EIP.2021.11.136.088 EDN: KUKEGA
19. EMISS (2024) Federal State Statistic Base of Data, https://www.fedstat.ru.
20. Enright M. J. (1992). Why Clusters are the Way to Win the Game? Word Link, 5, July/August, 24-25.
21. Fiani R. (1984). Increasing Returns, Non-Traded Inputs and Regional Development, Economic Journal, 94, 374, 308-323.
22. Friedmann J. (2001). Regional Development and Planning: The Story of a Collabo-ration, International Regional Science Review, 24, 3, 386-395. EDN: JMSDTX
23. Fritsch M. (2003). Does R&D-cooperation behavior differ between regions? Industry and Innovation, 10, 1, 25-39.
24. Fritsch M., Lukas R. (1999). Innovation, cooperation, and the region, In D. B. Audretsch, R. Thurik (Eds.), Innovation, Industry Evolution and Employment (pp. 157-181). Cambridge: Cambridge University Press.
25. Gaddy C. G., Hill F. (2003). The Siberian Curse: How Communist Planners Left Russia Out in the Cold. Washington, DC: Brookings Institution Press, 240.
26. Gareev T. R., Peker I. Yu., Kuznetsova T. Yu., Eliseeva N. A.(2023). Dynamic analysis of the operating environment in assessing the effectiveness of the research sector of Russian regions. Baltic region. 15, 2, 82-102. (In Russ.). DOI: 10.5922/2079-8555-2023-2-5 EDN: LSBOOV
27. Giersch H. (1979). Aspects of growth, structural change, and employment a schumpeterian perspective, Review of World Economics (Weltwirtschaftliches Archiv), 115, 4, 629-652. EDN: YAEVIK
28. Gohberg L. M. (2017) Raiting innovazionnogo razvititya subiektov rossoiskoy federazii, 260 с (In Russ.). ISBN: 978-5-7598-1591-4
29. Granberg A. G. (2006). Idei Avgusta Losha v Rossii, Prostranstvennaya ekonomika, 2, 5-22. (In Russ.).
30. Granberg A. G. (2009). O programme fundamental’nykh issledovaniy prostran-stvennogo razvitiya Rossii, Region: ekonomika i sotsiologiya, 12, 166-178. (In Russ.).
31. Griliches Z. (1979). Issues in assessing the contribution of R&D to productivity growth, Bell Journal of Economics, 10, 1, 92-116.
32. Hagerstrand T. (1967). Innovation Diffusion as a Spatial Process. Chicago: University of Chicago Press, 334.
33. Hall R., Jones Ch. (1999). Why Do Some Countries Produce So Much More Output per Woker than Others? Quarterly Journal of Economics, CXIV, 83-116.
34. Herzfeld T. (2008).Interregional Income Distribution: A Comparison of Russian and Chinese Experience, Post-Communist Economies, 20, 4, 431-447.
35. Hirschman A. (1958). The Strategy of Economic Development, Yale University Press, New Haven, 217. DOI: 10.2307/1235188
36. Jackson M. C., Huang L., Xie Q., Tiwari R. C. (2010). A modified version of Moran’s I.International Journal of Health Geographics, 9. DOI: 10.1186/1476-072X-9-33
37. Kaneva M. A., Untura G. A. (2017). Vzaimosvyaz’ NIOKR, peretokov znaniy i dinamiki ekonomicheskogo rosta regionov Rossii, Region: ekonomika i sotsiologiya, 1, 93, 78-100. (In Russ.). DOI: 10.15372/REG20170104 EDN: YGFNZB
38. Kazakova S. M., Klimanov V. V. (2018). Zarubezhnyy opyt primeneniya differentsirovannogo podkhoda v regulirovanii regional’nogo razvitiya, Regional’naya ekonomika. Yug Rossii., 4. 55-68. (In Russ.).
39. Kirzner I. M. (1997). Entrepreneurial discovery and the competitive market process: An Austrian approach, Journal of Economic Literature, 35, 60-85. EDN: CLXKDF
40. Коломак Е. Неравномерное пространственное развитие в России: объяснения новой экономической географии // Вопросы экономики. - 2013. - № 2. - С. 132-150. DOI: 10.32609/0042-8736-2013-2-132-150 EDN: PVDKRX
41. Kolomak Ye. A. (2010). Prostranstvennyye eksternalii kak resurs ekonomicheskogo rosta //Region: ekonomika i sotsiologiya. 4. 73-87. (In Russ.). EDN: NCZCNT
42. Krugman P. R., Masahisa F., Anthony J. V. (1999). The Spatial Economy: Cities, Regions and International Trade. London The MIT Press,. DOI: 10.7551/mitpress/6389.001.0001
43. Kutsenko Ye. S. (2012). Zavisimost’ ot predshestvuyushchego razvitiya v sfere pro-stranstvennogo razmeshcheniya proizvoditel’nykh sil - plokhaya novost’ dlya empiri-cheskikh issledovaniy aglomeratsionnykh effektov, Zhurnal Novoy ekonomicheskoy assotsiatsii, 2. 10-27. (In Russ.).
44. Lavrovsky B. L., Shiltsin E. A. (2009).Russian regions: convergence or stratification? // Economics and Mathematical Methods, 45, 2, 31-36. (In Russ.). EDN: JXOYBX
45. Leamer E., Storper M. (2001). The economic geography of the Internet age, Journal of International Business Studies, 32, 4, 641-665. EDN: DQAVMR
46. Luo Q., Griffith D., Wu H. (2019). Spatial autocorrelation for massive spatial data: verification of efficiency and statistical power asymptotics, Journal of Geographical Systems, 21, 237-269. DOI: 10.1007/s10109-019-00293-3 EDN: NUFVHA
47. Marshall A., Paley Marshall M. (1879). The Economics of Industry. London: Macmillian and Co., XIV, 232.
48. Maskell P., Larenzen M. (2003). The Claster as Market Organizantion. DRUID Working Paper 03-14, 29 p.
49. McCann P. (2011). Smart Specialisation, Regional Growth and Applications to EU Cohesion Policy, URL.
50. Mosalov A. I. (2022). Optimal’nyye prostranstvennyye formaty mezhregional’nogo ekonomicheskogo sotrudnichestva v ramkakh innovatsionnoy ekonomiki, Ekonomika regiona, 18, 3, 638-652. DOI: 10.17059/ekon.reg.2022-3-2 EDN: EEPJWN
51. Murdal G. (1972). Modern problems of “third world”. Drama of Asia. Translation from English. M.: Publishing house “Progress”, 767.
52. Myrdal G. (1957). Economic Theory and Underdeveloped Regions. London: Duckworth, 167.
53. Porter M. E. (1988). Clusters and the New Economics of Competition, Harward Business Revies, 77-90.
54. Rastvortseva S. N., Ternovskiy D. S. (2016). Faktory kontsentratsii ekonomicheskoy aktivnosti v regionakh Rossii, Ekono-micheskiye tendentsii i perspektivy: fakty, tendentsii, prognoz, 2, 44, 153-170. (In Russ.).
55. Ratner S. W. (2017). Dynamic problems of assessing the environmental and economic efficiency of regions based on basic models for analyzing the operating environment, Management of large systems: collection of proceedings, 67, 81-106.
56. Friedman J., Alonso W. (1975) Regional Policy; Readings in Theory and Applications, Cambridge MIT Press, 1975.
57. Reid,A., Miedzinski M. (2014) A smart specialisation platform for advanced manufacturing. Technopolis Group, Brussels. DOI: 10.13140/RG.2.2.12261.19680
58. Reiterer M. (2006).Interregionalism as a new diplomatic tool: the EU and East Asia, EFAR, 11, 223-242.
59. Reshiyev S. S. (2010). Osnovnyye napravleniya razvitiya i podkhody v teorii regional’noy ekonomiki v razvitykh zapadnykh stranakh, Regional’naya ekonomika: teoriya i praktika, 33, 59-
68. (In Russ.).
60. Rodriguez-Pose A (2011). Economists as Geographers and Geographers as Something Else: On the Changing Conception of Distance in Geography and Economics, Journal of Economic Geography, 11, 2, 347-356.
61. Rosenfeld S. A. (1997). Bringing Business Clusters into the Mainstream of Economic Development, European Planning Studies, 5, 3-23.
62. Saxenian A.-L. (1994). Regional advantage. Culture and Competition on Silicon Valley and Route 128. Harvard Univ. Press, 226.
63. Schymik C. (2011). Blueprint for a macro-region. EU Strategies for the Baltic Sea and Danube regions, Berlin, RP 10, 31. URL: https://www.swp-berlin.org/publications/products/research_papers/2011_RP10_shy_ks.pdf.
64. Shane, S. (2000). Prior knowledge and the discovery of entrepreneurial opportunities, Organization Science: A Journal of the Institute of Management Sciences, 11, 448-469. EDN: DSHXVF
65. Siebert H. (1969). Regional Economic Growth; Theory and Policy. Scranton, International Textbook Company, XIII, 217.
66. Smirnyagin L. V. (2012). Place Instead of Location? (About Shifts in Basic Geographical Notions, in Geogra-ficheskoe polozhenie i territorial’nye struktury: Pamya- ti I. M. Maergoiza (Geographical Position and Territorial Structures, In Memory of I. M. Maergoiz), P. M. Polyan, A. I. Treivish (Eds.). Moscow: Novyi Khronograf Publ., 421-456. (In Russ.).
67. Smirnyagin L. V. The Future Of Geographical Space In Social Sciences. Izvestiya Rossiiskoi Akademii Nauk. Seriya Geograficheskaya. 2016; (4): 7-19. (In Russ.). DOI: 10.15356/0373-2444-2016-4-7-19 EDN: WGYBSD
68. Solow R. (1956). Contribution to the Theory of Economic Growth, Quarterly Journal of Economics, 70, 65-94.
69. Storper M. (1997). The Regional World: Territorial Development in Global Economy. New York: Guilford Press, 338.
70. Suvorova A. V. (2019). Development of growth poles in the Russian Federation: direct and reverse effects, Economic and social changes: facts, trends, forecast, 12, 6, 110-128. (In Russ.). DOI: 10.15838/esc.2019.6.66.6
71. Swan T. (1956). Economic Growth and Capital Accumulation, Economic Record, 32, 2, 334-361.
72. Tolbert P., Zucker L. (1996). The institutionalization of institutional theory, Handbook of organization studies, 175-190.
73. Torre A. (2008). On the role played by temporary geographical proximity in knowledge transmission, Regional Studies, 42, 6, 869-889.
74. Torre A., Rallet A. (2005). Proximity and localization, Regional Studies, 39, 47-59.
75. Troutt M. D., Ehie I. C., Brandyberry A. A. (2007). Maximally productive input-output units, European Journal of Operational Research, 2007, 178, 359-373.
76. Waldhor T. (1996). The spatial autocorrelation coefficient Moran’s I under heteroscedasticity, Statistics in Medicine, 15, 7-9, 887-892. :7/9. DOI: 10.1002/(SICI)1097-0258(19960415)15
77. Weil D. (1992). Contribution to the Empirics of Economic Growth, Quarterly Journal of Economics, 107, 407-438.
78. Yan Y., Wu Z. (2020). Regional innovation distribution and its dynamic evolution: Policy impact and spillover effect Based on the perspective of innovation motivation, PLoS ONE, 15. DOI: 10.1371/journal.pone.0235828 EDN: RNYCGV
79. Zemtsov S., Kotsemir, M. (2019). An assessment of regional innovation system efficiency in Russia: the application of the DEA approach, Scientometrics, 120, 375-404. (In Russ.). DOI: 10.1007/s11192-019-03130-y EDN: QTJQFR
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Издательство
- Издательство
- УрФУ
- Регион
- Россия, Екатеринбург
- Почтовый адрес
- 620002, Свердловская область, г. Екатеринбург, ул. Мира, д. 19
- Юр. адрес
- 620002, Свердловская область, г. Екатеринбург, ул. Мира, д. 19
- ФИО
- Кокшаров Виктор Анатольевич (Ректор)
- E-mail адрес
- rector@urfu.ru
- Контактный телефон
- +7 (343) 3754507
- Сайт
- https://urfu.ru/ru