Differentiation of Small Towns by Knowledge Localisation Factors
DOI:
https://doi.org/10.17059/ekon.reg.2023-2-3Abstract
The ambiguity of the causal relationship between knowledge creation and regional growth does not indicate its insignificance, as proven by numerous empirical studies. However, such works rarely examine small towns, characterised by uncertainty of knowledge sources. The article aims to identify and compare groups of similar small towns in the Central, Ural and Southern Federal Districts by using a set of knowledge localisation factors. A two-stage clustering was performed by the k-means method according to the following criteria: interactions between actors, specific knowledge stock and financial resources for commercialisation. The resulting cluster centres were divided into quartiles according to the grading system (good, satisfactory or poor). First, the study revealed 10 clusters in the Central Federal District, 7 clusters in the Ural Federal District and 5 clusters in the Southern Federal District. In 35 % of the towns of the Southern Federal District, 35 % of the Central Federal District and 38 % of the Ural Federal District, the estimated specific knowledge stock exceeded the availability of financial resources. Second, towns were differentiated by population and divided into two groups depending on the agglomeration impact of larger cities. Clusters were formed within each group and federal district. 50 % of Ural towns with a population of 10,000 to 20,000 people unaffected by the agglomeration, as well as 62 % of towns with more than 20,000 people have the advantage of specific knowledge stock over financial resources. These values are 18 % and 8 %, respectively, for the Central Federal District, 36 % and 30 % for the Southern Federal District. The findings can help extend the analytical framework for making decisions on the small towns development.
References
Andersson, M. & Larsson J. (2021). Mysteries of the trade? Skill-specific local agglomeration economies. Regional Studies, 56(9), 1538-1553. DOI: 10.1080/00343404.2021.1954611
Antonelli, C. & Link, A. (2015). Routledge Handbook of the Economics of Knowledge. Routledge, 282. DOI: 10.4324/9780203082324.ch3
Antonelli, C., Patrucco, P. & Quatraro, F. (2008). The governance of localized knowledge externalities. International Review of Applied Economics, 22(4), 479–498. DOI: 10.1080/02692170802137661
Audretsch, D. & Feldman, M. (1996). R&D Spillovers and the Geography of Innovation and Production. American Economic Review, 86(3), 630–640.
Capello, R. & Nijkamp, P. (Eds.). (2009). Handbook of Regional Growth and Development Theories. Cheltenham, UK: Edward Elgar Publishing, 529. DOI: 10.4337/9781848445987
Ciołek, D, Golejewska, A. & Zabłocka-Abi Yaghi, A. (2021). Regional Innovation Systems in Poland: How to classify them? Ekonomika regiona [Economy of region], 17(3), 987-1003. DOI: 10.17059/ekon.reg.2021-3-19
Davis, C. (2009). Interregional knowledge spillovers and occupational choice in a model of free trade and endogenous growth. Journal of Regional Science, 49(5), 855–876. DOI: 10.1111/j.1467-9787.2009.00612.x
Gaffeo, E. (1999). Competition-led endogenous growth with localized technological change. Economics of Innovation and New Technology, 8(3), 225–251. DOI: 10.1080/10438599900000010
Gambardella, A. & Giarratana, M. (2010). Localized knowledge spillovers and skill-biased performance. Strategic Entrepreneurship Journal, 4, 323–339. DOI: 10.1002/sej.99
Giltman, M. A., Pit, V. V., Batyreva, M. V. & Sumik, E. A. (2020). Which cities do we like to live in? Empirical analysis of employees’ attitude to cities . Zhurnal Novoy ekonomicheskoy assotsiatsii [Journal of the New Economic Association], 1(45), 111–130. DOI: 10.31737/2221-2264-2020-45-1-4. (In Russ.)
Gumbau-Albert, M. & Maudos, J. (2009). Patents, technological inputs and spillovers among regions. Applied Economics, 41(12), 1473–1486. DOI: 10.1080/00036840601032250
Hendrickx-Candéla, C. (2001). Externalités de connaissance et localisation des activités: une revue des analyses empiriques. Revue d’Économie Régionale & Urbaine, 1, 11–37. DOI: 10.3917/reru.011.0011. (In French)
Kaneva, M. & Untura, G. (2019). The impact of R&D and knowledge spillovers on the economic growth of Russian regions. Growth and Change, 50, 301–334. DOI: 10.1111/grow.12281
Koo, J. (2007). Determinants of Localized Technology Spillovers: Role of Regional and Industrial Attributes. Regional Studies, 41(7), 995–1011. DOI: 10.1080/00343400601142746
Kutsenko, E. & Eferin, Y. (2019). “Whirlpools” and “Safe Harbors” in the Dynamics of Industrial Specialization in Russian Regions. Foresight and STI Governance, 13(3), 24–40. DOI: 10.17323/2500-2597.2019.3.24.40
Kwon, H-S., Lee, J., Lee, S. & Oh, R. (2020). Knowledge spillovers and patent citations: trends in geographic localization, 1976–2015. Economics of Innovation and New Technology, 1–25. DOI: 10.1080/10438599.2020.1787001
Kyllingstad, N. (2021). Overcoming barriers to new regional industrial path development: The role of a center for research-based innovation. Growth and Change, 52, 1312–1329. DOI: 10.1111/grow.12485
Limonov L. E. & Nesena M. V. (2019). Disparity of “large” and “small” cities of Russia: A comparative analysis of indicators of economic development and social survey data. Zhurnal Novoy ekonomicheskoy assotsiatsii [Journal of the New Economic Association], 4(44), 163–188. DOI: 10.31737/2221-2264-2019-44-4-6. (In Russ.)
Mkrtchyan, N. V. & Florinskaya, Yu. F. (2019). Residents of Small and Mid-Size Towns of Russia: Labor Migration as an Alternative to Permanent Transfer. Zhurnal Novoy ekonomicheskoy assotsiatsii [Journal of the New Economic Association], 3(43), 78–94. DOI: 10.31737/2221-2264-2019-43-3-4. (In Russ.)
Parent, O. & LeSage, J. (2008). Using the variance structure of the conditional autoregressive spatial specification to model knowledge spillovers. Journal of Applied Econometrics, 23, 235–256. DOI: 10.1002/jae.981
Pereira dos Santos, U. & Scherrer Mendes, P. (2021). Regional spillovers of knowledge in Brazil: evidence from science and technology municipal indicators. Innovation and Development, 1–20. DOI: 10.1080/2157930X.2021.1978723
Tolmachev, D. Ye., Kuznetsov, P. D. & Ermak, S. V. (2021). Methodology for Identifying the Boundaries of Agglomerations based on Statistical Data. Ekonomika regiona [Economy of region], 17(1), 44–58. DOI: 10.17059/ekon.reg.2021-1-4. (In Russ.)
Torre, A. & Rallet, A. (2005). Proximity and Localization. Regional Studies, 39(1), 47–59. DOI: 10.1080/0034340052000320842
Varga, A. & Schalk, H. (2004). Knowledge Spillovers, Agglomeration and Macroeconomic Growth: An Empirical Approach. Regional Studies, 38(8), 977–989. DOI: 10.1080/0034340042000280974
Vukovic, N. A., Larionova, V. A. & Morganti, P. (2021). Smart Sustainable Cities: Smart Approaches and Analysis. Ekonomika regiona [Economy of region], 17(3), 1004–1013. DOI: 10.17059/ekon.reg.2021-3-20
Zahra, S. & George, G. (2002). Absorptive Capacity: A Review, Reconceptualization, and Extension. Academy of Management Review, 27(2), 185–203. DOI: 10.5465/AMR.2002.6587995
