Knowledge Externalities in Turkish High-Technology Manufacturing Industries: Spatial Panel Data Analyses

Year 2015, Volume: 10 Issue: 40, 6711 - 6729, 04.11.2015

Burcu Turkcan

https://doi.org/10.19168/jyu.66147

Cited By: 2

Abstract

Knowledge externalities are defined as positive spillover outcomes of new and innovative knowledge among firms. They create positive impacts on firms’ productions, which did not participate in the production process of innovative knowledge and hence the costs of new knowledge creation. Knowledge externalities have critical importance especially for regional economies due to the fact that firms locating in the same region create networks in which knowledge disseminates quickly. This paper presents empirical evidences about the existence, types and impacts of knowledge externalities in Turkish regions for 1989 – 2008 time period. The main focus of this study is medium-high and high-technology industries due to the fact that these industries produce innovative outcomes and hence affect regional economies by creating knowledge externalities. In this context empirical evidences indicate that dynamic knowledge externalities occur more frequently than static ones and in general they affect regional economies positively in Turkey. Also, Marshall-Arrow-Romer knowledge externalities most frequently occurred before 2001 while after this year, Porter knowledge externalities have most frequently occurred in medium-high and high technology industries in Turkish regions.

Keywords

Knowledge Externalities, High Technology Industries, Spatial Econometrics

Türkiye’nin Yüksek Teknolojili İmalat Sanayi Endüstrilerinde Bilgi Dışsallıkları: Mekansal Panel Veri Analizleri

Abstract

Bilgi dışsallıkları, firmalar arasındaki yeni ve yenilikçi bilginin pozitif yayılma çıktıları olarak tanımlanmaktadır. Bilgi dışsallıkları, yenilikçi bilginin üretim sürecine ve dolayısıyla da yeni bilgi yaratımının maliyetine ortak olmayan firmalar üzerinde pozitif etkiler yaratmaktadır. Belli bir mekansal alanda odaklanmış firmalar arasındaki ağ ekonomilerinin bilgi akışını hızlandırması nedeniyle ise bilgi dışsallıkları, özellikle bölgesel ekonomiler için kritik öneme sahip olmaktadır. Bu çalışma, Türkiye’de bölgeler itibariyle 1989 – 2008 zaman periyodunda bilgi dışsallıklarının varlığını, ortaya çıkan bilgi dışsallıklarının türlerini ve ekonomik etkilerini ampirik bulgular ile ortaya koymaktadır. Bu çalışmanın temel odak noktası, yenilikçi çıktılar üretmeleri ve dolayısıyla bilgi dışsallıkları üreterek bölgesel ekonomileri etkilemeleri açısından orta-yüksek ve yüksek teknolojili endüstrilerdir. Bu bağlamda ampirik bulgular, Türkiye’de dinamik bilgi dışsallıklarının static bilgi dışsallıklarından daha sık ortaya çıktığını ve bölgesel ekonomileri de genellikle pozitif etkilediklerini göstermektedir. Ayrıca, Marshall – Arrow – Romer bilgi dışsallıklarının 2001 yılı öncesinde bölgelerde en sık ortaya çıkan bilgi dışsallığı türü olduğu gözlenirken, 2001 sonrasında Porter bilgi dışsallıklarının en sık görülen bilgi dışsallıkları haline geldiği tespit edilmiştir

Keywords

Bilgi Dışsallıkları, Yüksek Teknolojili Endüstriler, Mekansal Ekonometri

References

• Anselin, L. (1988). Spatial econometrics: Methods and models. Dordrecht: Kluwer
• Antonelli, C., Patrucco, P.P. & Quatraro, F. (2011). Productivity growth and pecuniary knowledge externalities: An empirical analysis of agglomeration economies in European regions. Economic Geography, 87(1): 23 – 50.
• Antonelli, C. (2003). Knowledge complementarity and fungeability: Implications for regional strategy. Regional Studies, 37: 595-606.
• Arrow, K.J. (1962). Economic welfare and the allocation of resources for invention, In:Nelson RR (ed), The rate and direction of inventive activity, Princeton, NJ: Princeton University Press, 609- 626.
• Asheim, B.T., Boschma, R. & Cooke, P. (2011). Constructing regional advantage: Platform policies based on related variety and differentiated knowledge bases. Regional Studies, 45(7): 893 – 904.
• Asheim, B.T. (2002). Temporary organisations and spatial embeddedness of learning and knowledge creation. Human Geography, 84(2): 111 – 124.
• Autant-Bernard, C. & LeSage, J.P. (2011). Quantifying knowledge spillovers using spatial econometric models. Journal of Regional Science, 51(3): 471-496.
• Batisse, C. (2002). Dynamic externalities and local growth: A panel data analysis applied to Chinese provinces. China
• Economic Review, 13(2-3): 231 – 251. Brakman, S., Garretsen, H. & Van Marrewijk, C. (2003). An introduction to geographical economics, Cambridge University Press.
• Bun, M.J.G. & El Makhlouf, A. (2007). Dynamic externalities, local industrial structure and economic development: panel data evidence for Morocco. Regional Studies, 41(6): 823-837.
• Combes, P-P. (2000). Economic structure and local growth: France, 1984 – 1993. Journal of Urban Economics, 47: 329 – 3
• Dalum, B., Laursen, K. & Verspagen, B. (1999). Does specialization matter for growth? Industrial and Corporate Change, 8(2): 267 – 288.
• De Groot, H.L.F., Poot, J. & Smit, M. J. (2007). Agglomeration, innovation and regional development. Tinbergen Institute
• Discussion Paper No TI 2007 – 079/3.
• De Lucio, J.J., Herce, J.A. & Goicolea, A. (2002). The effects of externalities on productivity growth in Spanish industry.
• Regional Science and Urban Economics, 32: 241 – 258. Elhorst, J.P. (2003). Specification and estimation of spatial panel data models. International Regional Science Review, 26(3): 244 – 268.
• EUROSTAT. (1996). NACE rev1.1 statistical classification of economic activities in the European community, Luxembourg.
• Feldman, M.P. & Audretsch, D.B. (1999). Innovation in cities: Science-based diversity, specialization and localized competition. European Economic Review, 43: 409 – 429.
• Fischer, M.M., Scherngell, T. & Reismann, M. (2009). Knowledge spillovers and total factor productivity: Evidence using a spatial panel data model. Geographical Analysis, 41: 204-220.
• Frenken, K., Van Oort, F. & Verburg, T. (2007). Related variety, unrelated variety and regional economic growth.
• Regional Studies, 41(5): 685 – 697. Gao, T. (2004). Regional industrial growth: Evidence from Chinese industries. Regional Science and Urban Economics, 32: 101 – 124.
• Glaeser, E.L., Kallal, H.D., Scheinkman, J.A. & Schleifer, A. (1992). Growth in cities. Journal of Political Economy, 100(6): 1126 – 1152.
• Hatzichranoglou, T. (1997). Revision of the high-technology sector and product classification. OECD Science,
• Technology and Industry Working Papers, 1997/2, OECD Publishing.
• Henderson, V. (2003). Marshall’s scale economies. Journal of Urban Economics, 53: 1-28.
• Henderson, V. (1997). Externalities and industrial development. Journal of Urban Economics, 42: 449-470.
• Hulten, C.R. (2001). Total factor productivity – A short biography. in New developments in productivity analysis, Hulten,
• C.R., Dean, E.R. & Harper, M.J. (eds), University of Chicago Press, 1-54. Jacobs, J. (1969). The economy of cities, New York: Vintage.
• Ketelhohn, N.W. (2006). The role of clusters as sources of dynamic externalities in the US semiconductor industry.
• Journal of Economic Geography, 6: 679-699. Kiymalioglu, U. & Ayoglu, D. (2006). Dynamic agglomeration economies and the case of Turkey: Panel data analysis.
• Journal of Iktisat, Isletme ve Finans, 21(249). King III, C., Silk, A.J. & Ketelhohn, N. (2003). Knowledge spillovers and growth in the disagglomeration of the US advertising-agency industry. Journal of Economics&Management Strategy, 12(3): 327-362.
• Krugman, P. (1990). Increasing returns and economic geography, NBER Working Paper Series, Working Paper No 3275.
• LeSage, J.P. & Fischer, M.M. (2012). Estimates of the impact of static and dynamic knowledge spillovers on regional factor productivity. International Regional Science Review, 35(1): 103-127.
• LeSage, J.P. & Pace, R.K. (2010). Spatial econometric models. Handbook of Applied Spatial Analysis: Software Tools,
• Methods and Applications, in Fischer, M.M. & Getis, A. (Eds). Springer-Verlag Berlin Heidelberg. Lim, U. (2007). Knowledge externalities, spatial dependence and metropolitan economic growth in the United States.
• Environment and Planning A, 39: 771-788. Marshall, A. (1890) Principles of economics. (8th ed. 1920, later reprints), London: Macmillan.
• Neffke, F., Henning, M., Boschma, R., Lundquist, K.J. & Olander, L.O. (2011). The dynamics of agglomeration externalities along the life cycle of industries. Regional Studies, 45(1): 49-65.
• OECD. (2009). How regions grow: Trends and analysis, OECD Press, Paris.
• OECD. (2001). Measuring productivity: Measurement of aggregate and industry-level productivity growth. OECD Press, Paris.
• Onder, A.O. & Lenger, A. (2003). Productivity in Turkish manufacturing industry: A comparative analysis on the basis of selected provinces. Yapı Kredi Economic Review, 14(1).
• Paci, R. & Usai, S. (1999). Externalities, knowledge spillovers and the spatial distribution of innovation. GeoJournal, 49: 381-3
• Romer, P.M. (1986). Increasing returns and long run growth. The Journal of Political Economy, 94(5): 1002-1037.
• Scitovsky, T. (1954). Two concepts of external economies. Journal of Political Economy, 62(2): 143-51.
• Solow, R. (1957). Technical change and the aggregate production function. Review of Economics and Statistics, 39: 312– 3
• Suedekum, J. & Blien, U. (2005). Local economic structure and industry development in Germany, 1993 – 2001.
• Economics Bulletin, 15(17): 1−8. Turkish Patent Institute, Patent applications as to cities, http://www.turkpatent.gov.tr Turkish Statistical Institute. (2001). International standard classification of all economic activities, second revision (ISIC
• Rev.2) Manufacturing Classification, http://www.tuik.gov.tr Van Der Panne, G. (2004). Agglomeration externalities: Marshall versus Jacobs. Journal of Evolutionary Economics, 14: 593–604.
• Van Soest, D.P., Gerking, S. & Van Oort, F.G. (2006). Spatial impacts of agglomeration externalities. Journal of Regional Science, 46(5): 881-899.
• Van Stel, A.J. & Nieuwenhuijsen, H.R. (2004). Knowledge spillovers and economic growth: An analysis using data of
• Dutch regions in the period 1987 – 1995. Regional Studies, 38(4): 393 – 407.
• WDI. GDP deflator, http://data.worldbank.org/indicator/ Zheng, X.P. (2010). A cointegration analysis of dynamic externalities. Japan and the World Economy, 22: 130–140. Appendix
• Table 1A: Medium-High and High-Technology Industries According to ISIC Rev2 Industrial Classification (1989 – 2001) CODE 3511 3512 3513 3521 3522 3523 3529 3821 3822 3823 3824 3825 3829 3831 3832 3833 3839 3842 3843 3844 3845 3849 3851 INDUSTRY NAME
• Source: TSI (2001) International standard classification of all economic activities, second revision (ISIC rev.2) manufacturing classification. http://www.tuik.gov.tr
• Table 2A: Analysed Medium-High and High-Technology Industries According to NACE Rev.1.1 Industrial Classification (2004 – 2008) CODE 22 24 25 28 29 31 34 36 INDUSTRY NAME