BazEkon - The Main Library of the Cracow University of Economics

BazEkon home page

Main menu

Author
Olejnik Alicja (University of Lodz, Poland), Żółtaszek Agata (University of Lodz, Poland)
Title
Tracing the Spatial Patterns of Innovation Determinants in Regional Economic Performance
Określenie przestrzennych wzorców determinant innowacji w regionalnych wynikach gospodarczych
Source
Comparative Economic Research, 2020, vol. 23, nr 4, s. 87-108, rys., tab., bibliogr. 38 poz.
Keyword
Innowacyjność regionu, Innowacje, Modele panelowe
Regional innovation, Innovations, Panel model
Note
JEL Classification: O30, O33, C21, C23, R12
summ., streszcz.
Abstract
Niniejszy artykuł analizuje rolę czynników innowacyjności w rozwoju regionalnym 261 regionów UE w latach 2009-2012. Analiza przestrzenna wskazała, że regionalna innowacyjność, a dalej rozwój regionalny, zależą nie tylko od położenia geograficznego regionu, ale i jego sąsiadów. Pociąga to za sobą szczególnie poważne konsekwencje dla Europy Środkowo-Wschodniej. Za pomocą przestrzennego modelu panelowego Durbina ze stałymi efektami grupowymi (dla krajów), oceniliśmy wpływ czynników innowacji i ich przestrzennych odpowiedników na regionalne wyniki ekonomiczne. Pokazał on, że regiony czerpią korzyści ekonomiczne ze swoich efektów lokalizacyjnych pod względem kapitału społecznego, jednak w przypadku wydatków na badania i rozwój ujawniono efekt konkurencji między regionami. (abstrakt oryginalny)

In this paper, we investigate innovation factors and their role in regional economic performance for a sample of 261 EU NUTS 2 regions over the period 2009-2012. In our study, we identify regions with spillover as well as drain effects of innovation factors on economic performance. The spatial analysis indicates that both regional innovativeness and regional development are strongly determined by the region's location and "neighbourhood", with severe consequences for Central and Eastern Europe. We assessed the impact of innovation factors and their spatial counterparts on economic performance using a spatial Durbin panel model. The model is designed to test the existence and strength of the country-effect of innovativeness on the level of regional economic status. This allows for controlling the country-specific socio-economic factors, without reducing the number of degrees of freedom. Our model shows that regions benefit economically from their locational spillovers in terms of social capital. However, the decomposition of R&D expenditures revealed competition effect between internal R&D and external technology acquisition, favouring in-house over outsourced research. (original abstract)
Accessibility
The Library of University of Economics in Katowice
Full text
Show
Bibliography
Show
  1. Anselin, L. (1998), Spatial Econometrics: Methods and Models, Kluwer, Dordrecht.
  2. Anselin, L., Le Gallo, J., Jayet, H. (2008), Spatial panel econometrics, [in:] L. Mátyás, P. Sevestre (eds.) The econometrics of panel data, fundamentals and recent developments in theory and practice, 3rd ed., Kluwer, Dordrecht. https://doi.org/10.1007/978-3-540-75892-1_19
  3. Anselin, L., Varga, A., Acs, Z. (1997), Local Geographic Spillovers between University Research and High Technology Innovations, "Journal of Urban Economics", 42 (3), pp. 422-448. https://doi.org/10.1006/juec.1997.2032
  4. Bilbao-Osorio, B., Rodríguez-Pose, A. (2004), From R&D to Innovation and Economic Growth in the EU, "Growth and Change", 35 (4), pp. 434-455. https://doi.org/10.1111/j.1468-2257.2004.00256.x
  5. Boschma, R. (2005), Proximity and innovation - a critical assessment, "Regional Studies", 39 (1), pp. 61-74. https://doi.org/10.1080/0034340052000320887
  6. Brouwer, E., Kleinknecht, A. (1999), Innovative output, and a firm's propensity to patent. An exploration of CIS micro data, "Research Policy", 28 (6), pp. 615-624. https://doi.org/10.1016/S0048-7333(99)00003-7
  7. Cabrer-Borrás, B., Serran-Domingo, G. (2007), Innovation and R&D spillover effects in Spanish regions: A spatial approach, "Research Policy", 36, pp. 1357-1371. https://doi.org/10.1016/j.respol.2007.04.012
  8. Caragliu, A., Nijkamp, P. (2012), The impact of regional absorptive capacity on spatial knowledge spillovers: the Cohen and Levinthal model revisited, "Applied Economics", 44, pp. 1363-1374. https://doi.org/10.1080/00036846.2010.539549
  9. Cliff, A.D., Ord, J.K. (1981), Spatial processes: models and applications, Taylor & Francis, London.
  10. Corrado, C., Haskel J., Jona-Lasinio, C. (2017), Knowledge Spillovers, ICT and Productivity Growth, "Oxford Bulletin of Economics and Statistics", 79 (4), pp. 592-618. https://doi.org/10.1111/obes.12171
  11. Corrado, C., Hulten, Ch., Sichel, D. (2009), Intangible capital and U.S. economic growth, "The Review of Income and Wealth", 55 (3), pp. 661-685. https://doi.org/10.1111/j.1475-4991.2009.00343.x
  12. Crescenzi, R., Rodrígue-Pose, A., Storper, M. (2007), The territorial dynamics of innovation: a Europe - United States comparative analysis, "Journal of Economic Geography", 7 (6), pp. 673-709. https://doi.org/10.1093/jeg/lbm030
  13. Dominicis, L. de, Florax, R.J.G.M., Groot, H.L.F. de (2013), Regional clusters of innovative activity in Europe: are social capital and geographical proximity key determinants?, "Applied Economics", 45 (17), pp. 2325-2335. https://doi.org/10.1080/0003 6846.2012.663474
  14. Educational attainment statistics. http://ec.europa.eu/eurostat/statistics-explained/index.php/Educational_attainment_statistics (accessed: 23.02.2020).
  15. Elhorst, J.P. (2014), Spatial Panel Models, [in:] M. Fischer, P. Nijkamp (eds.) Handbook of Regional Science, Berlin, Springer. https://doi.org/10.1007/978-3-642-23430-9_86
  16. Elhorst, J.P., Gross M., Tereanu E. (2018), Spillovers in space and time: where spatial econometrics and Global VAR models meet, European, Central Bank, Frankfurt, Working Paper Series, No. 2134.
  17. European Innovation Scoreboard 2016. https://op.europa.eu/en/publication-detail/-/publication/693eaaba-de16-11e6-ad7c-01aa75ed71a1/language-en/format-PDF /source-31233711 (accessed: 23.02.2020).
  18. Eurostat Regional Database. https://ec.europa.eu/eurostat/web/regions/data/database (accessed: 23.02.2020).
  19. Frascati Manual (2002). https://www.oecd-ilibrary.org/science-and-technology/frascati-manual-2002_9789264199040-en (accessed: 23.02.2020).
  20. Global Innovation Index 2016 report. http://www.wipo.int/edocs/pubdocs/en/wipo _pub_gii_2016.pdf (accessed: 23.02.2020).
  21. Global Innovation Index 2017 report. https://www.globalinnovationindex.org/userfiles/file/reportpdf/gii-full-report-2017.pdf (accessed: 23.02.2020).
  22. Global Innovation Index 2018 report. https://www.globalinnovationindex.org/userfiles/file/reportpdf/gii_2018-report-new.pdf (accessed: 23.02.2020).
  23. Global Innovation Index 2019 report. https://www.globalinnovationindex.org/userfiles/file/reportpdf/GII2019-keyfinding-E-Web3.pdf (accessed: 23.02.2020).
  24. Gonçalves, E., Almaida, E.S. (2009), Innovation and Spatial Knowledge Spillovers: Evidence from Brazilian Patent Data, "Regional Studies", 43 (4), pp. 513-528. https://doi.org/10.1080/00343400701874131
  25. Granovetter, M. (2005), The impact of social structure on economic outcomes, "Journal of Economic Perspectives", 19 (1), pp. 33-50. https://doi.org/10.1257/0895330053147958
  26. Griliches, Z. (1979), Issues in assessing the contribution of research and development to productivity growth, "Bell Journal of Economics", 10 (1), pp. 92-116. https://doi.org/10.2307/3003321
  27. Halleck, V.S., Elhorst J.P. (2016), A regional unemployment model simultaneously accounting for serial dynamics, spatial dependence and common factors, "Regional Science and Urban Economics", 60, pp. 85-95. https://doi.org/10.1016/j.regsciurbeco.2016.07.002
  28. Jaffe, A.B. (1989), Real effects of academic research, "American Economic Review", 79 (5), pp. 957-970.
  29. Kelejian, H.H., Prucha, I.R. (1998), A Generalised Spatial Two-Stage Least Squares Procedure for Estimating a Spatial Autoregressive Model with Autoregressive Disturbances, "The Journal of Real Estate Finance and Economics", 17 (1), pp. 99-121.
  30. Kelejian, H.H., Prucha, I.R. (2010), Specification and estimation of spatial autoregressive models with autoregressive and heteroskedastic disturbances, "Journal of Econometrics", 157 (1), pp. 53-67. https://doi.org/10.1016/j.jeconom.2009.10.025
  31. LeSage, J., Pace, R.K. (2009), Introduction to Spatial Econometrics, Taylor & Francis Group, New York. https://doi.org/10.1201/9781420064254
  32. Moran, P.A.P. (1948), The Interpretation of Statistical Maps, "Journal of the Royal Statistical Society", Series B (Methodological), 10 (2), pp. 243-251. https://doi.org/10.1111/j.2517-6161.1948.tb00012.x
  33. Olejnik, J., Olejnik, A. (2020), QML estimation with non-summable weight matrices, "Journal of Geographical Systems", 22, pp. 469-495. https://doi.org/10.1007/s10109-020-00326-2
  34. Ord, K. (1975), Estimation Methods for Models of Spatial Interaction, "Journal of the American Statistical Association", 70, pp. 120-126. https://doi.org/10.1080/016214 59.1975.10480272
  35. Regional Innovation Scoreboard 2016 report. https://op.europa.eu/en/publication-detail/-/publication/693eaaba-de16-11e6-ad7c-01aa75ed71a1/language-en/format-PDF /source-31233711 (accessed: 23.02.2020).
  36. Regional Innovation Scoreboard 2017 report. https://op.europa.eu/en/publication-detail/-/publication/ce38bc9d-5562-11e7-a5ca-01aa75ed71a1/language-en/format-PDF /source-99532255 (accessed: 23.02.2020).
  37. Regional Innovation Scoreboard 2019 report. https://ec.europa.eu/growth/sites/growth/files/ris2019.pdf (accessed: 23.02.2020)
  38. Shi, W., Lee, L.F. (2017), Spatial dynamic panel data model with interactive fixed effects, "Journal of Econometrics", 197, pp. 323-347. https://doi.org/10.1016/j.jeconom.2016.12.001
Cited by
Show
ISSN
1508-2008
Language
eng
URI / DOI
http://dx.doi.org/10.18778/1508-2008.23.29
Share on Facebook Share on Twitter Share on Google+ Share on Pinterest Share on LinkedIn Wyślij znajomemu