Araştırma Makalesi
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THE KEY ACTORS OF KNOWLEDGE PRODUCTION FUNCTION IN TURKEY: THE ROLE OF UNIVERSITIES AS A KNOWLEDGE PRODUCER

Yıl 2020, Cilt: 11 Sayı: 27, 344 - 354, 10.06.2020
https://doi.org/10.21076/vizyoner.672133

Öz

By taking the projects which are carried out in the universities in Turkey and are funded by the Scientific and Technological Research Council (TÜBİTAK) as an output, the paper tries to estimate the knowledge production function of Turkey. In the study, an OLS analysis is made with cross-section data by utilizing the datum of state universities operating in Turkey at the NUTS III level. The dependent variable of the knowledge production function that is developed by using Cobb-Douglas production function is the annual R&D fund transferred to universities by TÜBİTAK for the projects. Other variables in the analysis are the annual R&D expenditures of universities, the number of academic staff, number of postgraduate students and the existence of Technology Transfer Office and/or Technopolis. According to the analysis results there is a positive and significant relationship between R&D fund, and postgraduate students and technology transfer offices/technopolis. However, the relationship between R&D expenditures and the R&D funds transferred to the universities is found to be statistically insignificant. A negative and insignificant relationship is found when R&D funds and academic staff are estimated together, but in the models in which academic staff is decoupled, the results differ. 

Kaynakça

  • Abdih, Y. and Joutz, F. (2006). Relating the knowledge production function to total factor productivity: An endogenous growth puzzle. IMF Staff Papers, 53(2), 242-271.
  • Acs, Z. J., Anselin, L. and Varga, A. (2002). Patents and innovation counts as measures of regional production of new knowledge. Research Policy, 31(7), 1069-1085.
  • Acs, Z. J., Audretsch, D. B. and Feldman, M. P. (1992). Real effects of academic research: Comment. The American Economic Review, 82(1), 363-367.
  • Acs, Z. J., Audretsch, D. B. and Feldman, M. P. (1994). R & D spillovers and recipient firm size. The Review of Economics and Statistics, 76(2), 336-340.
  • Andersson, M. and Ejermo, O. (2003). Knowledge production in Swedish functional regions 1993-1999 (No. 139). Kites, Centre for Knowledge, Internationalization and Technology Studies, Universita'Bocconi, Milano, Italy.
  • Anselin, L., Varga, A. and Acs, Z. (1997). Local geographic spillovers between university research and high technology innovations. Journal of Urban Economics, 42(3), 422-448.
  • Anselin, L., Varga, A. and Acs, Z. (2000). Geographical spillovers and university research: A spatial econometric perspective. Growth and Change, 31(4), 501-515.
  • Bartuševičienė, I. and Šakalytė, E. (2013). Organizational assessment: Effectiveness vs. efficiency. Social Transformations in Contemporary Society, 1(1), 45-53.
  • Baumann, J. and Kritikos, A. S. (2016). The link between R&D, innovation and productivity: Are micro firms different?. Research Policy, 45(6), 1263-1274.
  • Bilbao‐Osorio, B. and Rodríguez‐Pose, A. (2004). From R&D to innovation and economic growth in the EU. Growth and Change, 35(4), 434-455.
  • Buesa, M., Heijs, J., Pellitero, M. M. and Baumert, T. (2006). Regional systems of innovation and the knowledge production function: The Spanish case. Technovation, 26(4), 463-472.
  • Carlsson, B.(1995). Technological systems and economic performance: The case of factory automation. Kluwer Academic Publishers, Dordrecht.
  • Charlot, S., Crescenzi, R. and Musolesi, A. (2014). Augmented and unconstrained: Revisiting the regional knowledge production function (No. 2414). SEEDS. Sustainability Environmental Economics and Dynamics Studies.
  • Chatterjee, D., Dinar, A. and González-Rivera, G. (2016). A knowledge production function of agricultural research and extension: The case of the university of California cooperative extension. UCR SPP Working Paper Series (September 2016, WP# 16-06).
  • Chen, S. H. and He, W. (2014). Study on knowledge propagation in complex networks based on preferences, taking WeChat as example. In Abstract and Applied Analysis, 2014, Hindawi.
  • COHE. (2018). Council of higher education of Turkey. Statistics.
  • Conte, A. and Vivarelli, M. (2005). One or many knowledge production functions. Mapping Innovative Activity Using Microdata IZA Discussion Paper, 1878.
  • Conti, A. and Liu, C. C. (2014). The (changing) knowledge production function: Evidence from the MIT department of biology for 1970–2000. In The Changing Frontier: Rethinking Science and Innovation Policy (pp. 49-74), University of Chicago Press.
  • Czarnitzki, D., Kraft, K. and Etro, F. (2008). The effect of entry on R&D investment of leaders: Theory and empirical evidence. ZEW-Centre for European Economic Research Discussion Paper (08-078).
  • Edquist, C. (Ed.) (1997). Systems of innovation. Frances Pinter, London.
  • Egbu, C. O. (2004). Managing knowledge and intellectual capital for improved organizational innovations in the construction industry: An examination of critical success factors. Engineering, Construction and Architectural Management, 11(5), 301-315.
  • Feldman, M. P. and Florida, R. (1994). The geographic sources of innovation: Technological infrastructure and product innovation in The United States. Annals of the Association of American Geographers, 84(2), 210-229.
  • Fischer, M. M. and Varga, A. (2003). Spatial knowledge spillovers and university research: Evidence from Austria. The Annals of Regional Science, 37(2), 303-322.
  • Freeman, C. (1988). Japan: A new national system of innovation?. Technical Change and Economic Theory.
  • Fritsch, M. (2002). Measuring the quality of regional innovation systems: A knowledge production function Approach. International Regional Science Review, 25(1), 86-101.
  • Griliches, Z. (1979). Issues in assessing the contribution of research and development to productivity growth. The Bell Journal of Economics, 10(1), 92-116.
  • Griliches, Z. (1985). Productivity, R&D, and basic research at the firm level in the 1970s. NBER Working Paper No. 1547.
  • Gurmu, S., Black, G. C. and Stephan, P. E. (2010). The knowledge production function for university patenting. Economic Inquiry, 48(1), 192-213.
  • Jaffe, A. B. (1989). Real effects of academic research. The American Economic Review, 79(5), 957-970.
  • Jones, C. I. (1995). R & D-based models of economic growth. Journal of Political Economy, 103(4), 759-784.
  • Lorber, L. (2017). Universities, knowledge networks and local environment for innovation-based regional development: Case study of the University of Maribor. Geografický Časopis/Geographical Journal, 69, 361-383.
  • Lundvall, B. A. (1988). Innovation as an interactive process: From user-producer interaction to the national system of innovation. 349-369.
  • Nelson, R. R. (1993). National systems of innovation: A comparative study. University of Illinois at Urbana-Champaign's Academy for Entrepreneurial Leadership Historical Research Reference in Entrepreneurship.
  • Nelson, R. R. (2002). Bringing institutions into evolutionary growth theory. In Social Institutions and Economic Development (9-12), Springer, Dordrecht.
  • Óhuallacháin, B. and Leslie, T. F. (2007). Rethinking the regional knowledge production function. Journal of Economic Geography, 7(6), 737-752.
  • Perret, J. (2016). An Alternative approach towards the knowledge production function on a regional level: Applications for the USA and Russia. Schumpeter Discussion Papers 2016-003.
  • Ponds, R., Oort, F. V. and Frenken, K. (2009). Innovation, spillovers and university–industry collaboration: An extended knowledge production function approach. Journal of Economic Geography, 10(2), 231-255.
  • Ramani, S. V., El-Aroui, M. A. and Carrère, M. (2008). On estimating a knowledge production function at the firm and sector level using patent statistics. Research Policy, 37(9), 1568-1578.
  • Ranga, L., Debackere, K. and Tunzelmann, N. (2003). Entrepreneurial universities and the dynamics of academic knowledge production: A case study of basic vs. applied research in Belgium. Scientometrics, 58(2), 301-320.
  • Riddel, M. and Schwer, R. K. (2003). Regional innovative capacity with endogenous employment: Empirical evidence from the US. The Review of Regional Studies, 33(1), 73.
  • Romer, P. M. (1990). Endogenous technological change. Journal of Political Economy, 98(5, Part 2), 71-102.
  • Varga, A. (1997). Regional economic effects of university research: A survey. Unpublished Manuscript, West Virginia University, Regional Research Institute, Morgantown, WV.

TÜRKİYE'DEKİ BİLGİ ÜRETİM FONKSİYONUNUN TEMEL AKTÖRLERİ: BİLGİ ÜRETİCİSİ OLARAK ÜNİVERSİTELERİN ROLÜ

Yıl 2020, Cilt: 11 Sayı: 27, 344 - 354, 10.06.2020
https://doi.org/10.21076/vizyoner.672133

Öz

Bu çalışmada Türkiye'deki üniversitelerde yürütülen ve Bilimsel ve Teknolojik Araştırma Kurumu (TÜBİTAK) tarafından finanse edilen projeler çıktı olarak ele alınarak, Türkiye'nin bilgi üretim işlevi tahmin edilmeye çalışılmıştır. Çalışmada, Türkiye'de faaliyet gösteren devlet üniversitelerinin NUTS III düzeyinde verileri kullanılarak kesit verilerle OLS analizi yapılmıştır. Cobb-Douglas üretim fonksiyonu kullanılarak geliştirilen bilgi üretim fonksiyonunun bağımlı değişkeni, TÜBİTAK tarafından projeler için üniversitelere aktarılan yıllık araştırma ve geliştirme fonudur. Analizdeki diğer değişkenler, üniversitelerin yıllık Ar-Ge harcamaları, akademik personel sayısı, lisansüstü öğrenci sayısı ve Teknoloji Transfer Ofisi ve / veya Teknokent'in varlığıdır. Analiz sonuçlarına göre Ar-Ge fonu ile lisansüstü öğrenciler ve teknoloji transfer ofisleri / Teknokent arasında pozitif ve anlamlı bir ilişki vardır. Ancak Ar-Ge harcamaları ile üniversitelere aktarılan Ar-Ge fonları arasındaki ilişki istatistiksel olarak anlamlı bulunmamıştır. Ar-Ge fonları ve akademik personel birlikte değerlendirilirken negatif ve önemsiz bir ilişki görülmektedir ancak akademik personelin ayrıştığı modellerde sonuçlar farklıdır.

Kaynakça

  • Abdih, Y. and Joutz, F. (2006). Relating the knowledge production function to total factor productivity: An endogenous growth puzzle. IMF Staff Papers, 53(2), 242-271.
  • Acs, Z. J., Anselin, L. and Varga, A. (2002). Patents and innovation counts as measures of regional production of new knowledge. Research Policy, 31(7), 1069-1085.
  • Acs, Z. J., Audretsch, D. B. and Feldman, M. P. (1992). Real effects of academic research: Comment. The American Economic Review, 82(1), 363-367.
  • Acs, Z. J., Audretsch, D. B. and Feldman, M. P. (1994). R & D spillovers and recipient firm size. The Review of Economics and Statistics, 76(2), 336-340.
  • Andersson, M. and Ejermo, O. (2003). Knowledge production in Swedish functional regions 1993-1999 (No. 139). Kites, Centre for Knowledge, Internationalization and Technology Studies, Universita'Bocconi, Milano, Italy.
  • Anselin, L., Varga, A. and Acs, Z. (1997). Local geographic spillovers between university research and high technology innovations. Journal of Urban Economics, 42(3), 422-448.
  • Anselin, L., Varga, A. and Acs, Z. (2000). Geographical spillovers and university research: A spatial econometric perspective. Growth and Change, 31(4), 501-515.
  • Bartuševičienė, I. and Šakalytė, E. (2013). Organizational assessment: Effectiveness vs. efficiency. Social Transformations in Contemporary Society, 1(1), 45-53.
  • Baumann, J. and Kritikos, A. S. (2016). The link between R&D, innovation and productivity: Are micro firms different?. Research Policy, 45(6), 1263-1274.
  • Bilbao‐Osorio, B. and Rodríguez‐Pose, A. (2004). From R&D to innovation and economic growth in the EU. Growth and Change, 35(4), 434-455.
  • Buesa, M., Heijs, J., Pellitero, M. M. and Baumert, T. (2006). Regional systems of innovation and the knowledge production function: The Spanish case. Technovation, 26(4), 463-472.
  • Carlsson, B.(1995). Technological systems and economic performance: The case of factory automation. Kluwer Academic Publishers, Dordrecht.
  • Charlot, S., Crescenzi, R. and Musolesi, A. (2014). Augmented and unconstrained: Revisiting the regional knowledge production function (No. 2414). SEEDS. Sustainability Environmental Economics and Dynamics Studies.
  • Chatterjee, D., Dinar, A. and González-Rivera, G. (2016). A knowledge production function of agricultural research and extension: The case of the university of California cooperative extension. UCR SPP Working Paper Series (September 2016, WP# 16-06).
  • Chen, S. H. and He, W. (2014). Study on knowledge propagation in complex networks based on preferences, taking WeChat as example. In Abstract and Applied Analysis, 2014, Hindawi.
  • COHE. (2018). Council of higher education of Turkey. Statistics.
  • Conte, A. and Vivarelli, M. (2005). One or many knowledge production functions. Mapping Innovative Activity Using Microdata IZA Discussion Paper, 1878.
  • Conti, A. and Liu, C. C. (2014). The (changing) knowledge production function: Evidence from the MIT department of biology for 1970–2000. In The Changing Frontier: Rethinking Science and Innovation Policy (pp. 49-74), University of Chicago Press.
  • Czarnitzki, D., Kraft, K. and Etro, F. (2008). The effect of entry on R&D investment of leaders: Theory and empirical evidence. ZEW-Centre for European Economic Research Discussion Paper (08-078).
  • Edquist, C. (Ed.) (1997). Systems of innovation. Frances Pinter, London.
  • Egbu, C. O. (2004). Managing knowledge and intellectual capital for improved organizational innovations in the construction industry: An examination of critical success factors. Engineering, Construction and Architectural Management, 11(5), 301-315.
  • Feldman, M. P. and Florida, R. (1994). The geographic sources of innovation: Technological infrastructure and product innovation in The United States. Annals of the Association of American Geographers, 84(2), 210-229.
  • Fischer, M. M. and Varga, A. (2003). Spatial knowledge spillovers and university research: Evidence from Austria. The Annals of Regional Science, 37(2), 303-322.
  • Freeman, C. (1988). Japan: A new national system of innovation?. Technical Change and Economic Theory.
  • Fritsch, M. (2002). Measuring the quality of regional innovation systems: A knowledge production function Approach. International Regional Science Review, 25(1), 86-101.
  • Griliches, Z. (1979). Issues in assessing the contribution of research and development to productivity growth. The Bell Journal of Economics, 10(1), 92-116.
  • Griliches, Z. (1985). Productivity, R&D, and basic research at the firm level in the 1970s. NBER Working Paper No. 1547.
  • Gurmu, S., Black, G. C. and Stephan, P. E. (2010). The knowledge production function for university patenting. Economic Inquiry, 48(1), 192-213.
  • Jaffe, A. B. (1989). Real effects of academic research. The American Economic Review, 79(5), 957-970.
  • Jones, C. I. (1995). R & D-based models of economic growth. Journal of Political Economy, 103(4), 759-784.
  • Lorber, L. (2017). Universities, knowledge networks and local environment for innovation-based regional development: Case study of the University of Maribor. Geografický Časopis/Geographical Journal, 69, 361-383.
  • Lundvall, B. A. (1988). Innovation as an interactive process: From user-producer interaction to the national system of innovation. 349-369.
  • Nelson, R. R. (1993). National systems of innovation: A comparative study. University of Illinois at Urbana-Champaign's Academy for Entrepreneurial Leadership Historical Research Reference in Entrepreneurship.
  • Nelson, R. R. (2002). Bringing institutions into evolutionary growth theory. In Social Institutions and Economic Development (9-12), Springer, Dordrecht.
  • Óhuallacháin, B. and Leslie, T. F. (2007). Rethinking the regional knowledge production function. Journal of Economic Geography, 7(6), 737-752.
  • Perret, J. (2016). An Alternative approach towards the knowledge production function on a regional level: Applications for the USA and Russia. Schumpeter Discussion Papers 2016-003.
  • Ponds, R., Oort, F. V. and Frenken, K. (2009). Innovation, spillovers and university–industry collaboration: An extended knowledge production function approach. Journal of Economic Geography, 10(2), 231-255.
  • Ramani, S. V., El-Aroui, M. A. and Carrère, M. (2008). On estimating a knowledge production function at the firm and sector level using patent statistics. Research Policy, 37(9), 1568-1578.
  • Ranga, L., Debackere, K. and Tunzelmann, N. (2003). Entrepreneurial universities and the dynamics of academic knowledge production: A case study of basic vs. applied research in Belgium. Scientometrics, 58(2), 301-320.
  • Riddel, M. and Schwer, R. K. (2003). Regional innovative capacity with endogenous employment: Empirical evidence from the US. The Review of Regional Studies, 33(1), 73.
  • Romer, P. M. (1990). Endogenous technological change. Journal of Political Economy, 98(5, Part 2), 71-102.
  • Varga, A. (1997). Regional economic effects of university research: A survey. Unpublished Manuscript, West Virginia University, Regional Research Institute, Morgantown, WV.
Toplam 42 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Ekonomi
Bölüm Araştırma Makaleleri
Yazarlar

Selen Işık Maden 0000-0002-3998-855X

Aykut Sezgin 0000-0001-7039-8032

Ayşegül Baykul 0000-0002-7581-9972

Sevim Unutulmaz 0000-0002-2286-9458

Murat Dulupçu 0000-0001-9269-5978

Yayımlanma Tarihi 10 Haziran 2020
Gönderilme Tarihi 8 Ocak 2020
Yayımlandığı Sayı Yıl 2020 Cilt: 11 Sayı: 27

Kaynak Göster

APA Işık Maden, S., Sezgin, A., Baykul, A., Unutulmaz, S., vd. (2020). THE KEY ACTORS OF KNOWLEDGE PRODUCTION FUNCTION IN TURKEY: THE ROLE OF UNIVERSITIES AS A KNOWLEDGE PRODUCER. Süleyman Demirel Üniversitesi Vizyoner Dergisi, 11(27), 344-354. https://doi.org/10.21076/vizyoner.672133

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