Analysis of Factors Affecting Innovation Outputs of Developed and Developing G20 Countries Using the Knowledge Production Function
Yıl 2020,
Cilt: 9 Sayı: 5, 3874 - 3900, 29.12.2020
Ferhat Özbay
,
Bekir Sami Oguzturk
,
Aykut Sezgin
Öz
Today innovation is one of the most significant policy tools. From this perspective, the factors affecting innovation are receiving increasing attention. Despite the fact that innovation has the same theoretical meaning for developed and developing countries, its nature differs in terms of the factors affecting it. At the same time, the factors and environment that affect innovation, which is a dynamic process, are changing rapidly. In this respect, these changes and differences should be well observed and analysed.
This study investigates how the knowledge production function works on different country groups by comparing the developed and developing G20 countries with similar market size. In this respect, factors affecting innovation output on both developed and developing G20 countries are discussed. For the countries discussed, the study is carried out with panel data analysis covering 1995-2018 period. Driscoll-Kraay and Arellano-Bond GMM methods are used for panel data analysis. According to the empirical findings of the study, it has been observed that R-D has a positive effect on patent output both in case of a model with prior classification of countries with similar market size into developed and developing and in case of a model with no prior classification. It is also well understood that these effects vary according to the country group. Another important result is that unlike R-D other innovation inputs seem to have different effects according to the development classification of countries. While it is observed that different employment sectors have a significant effect on innovative output for all country groups it is understood that this effect differs according to country groups.
Kaynakça
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Gelişmiş ve Gelişmekte Olan G20 Ülkelerinin İnovasyon Çıktılarını Etkileyen Faktörlerin Bilgi Üretim Fonksiyonu ile Analizi
Yıl 2020,
Cilt: 9 Sayı: 5, 3874 - 3900, 29.12.2020
Ferhat Özbay
,
Bekir Sami Oguzturk
,
Aykut Sezgin
Öz
İnovasyon, günümüzün en önemli politika araçlarından biridir. Bu bakımdan inovasyonu etkileyen faktörler günümüzde ön plana çıkmaktadır. Özellikle gelişmiş ve gelişmekte olan ülkeler için inovasyon teorik olarak aynı anlamı ifade etmesine rağmen onu etkileyen etmenler farklılık göstermektedir. Aynı zamanda dinamik bir sürece de sahip olan inovasyonu etkileyen etmenler ve çıktıları, büyük bir hızla değişmektedir. Bu bakımdan bu değişiklikler ve farklılıklar iyi gözlenmeli ve incelenmelidir.
Bu çalışmada, benzer pazar büyüklüğüne sahip G20 ülkeleri ile yine G20 içinde; gelişmiş ve gelişmekte olarak sınıflandırılan ülkeler karşılaştırarak bilgi üretim fonksiyonunun farklı ülke grupları üzerinde nasıl çalıştığını araştırılmaktadır. Bu bakımdan, gerek G20 gerekse de gelişmiş ve gelişmekte olan ülkeler üzerinde inovasyon çıktısını etkileyen etmenler tartışılmıştır. Ele alınan ülkeler için 1995-2018 dönemini kapsayan panel veri analizi yöntemiyle çalışma yürütülmüştür. Panel analizi için Driscoll-Kraay ile Arellano Bond GMM yöntemi kullanılmıştır. Çalışmanın ampirik bulgularına göre, benzer pazar büyüklüğüne sahip tüm örneklem ile benzer pazar büyüklüğüne sahip fakat gelişmiş ve gelişmekte olarak sınıflandırılan ülkelerde Ar-Ge’nin patent çıktısı üzerinde pozitif bir etkiye sahip olduğu gözlenmiş ayrıca bu etkilerin ülke grubuna göre de değiştiği anlaşılmıştır. Diğer önemli bir sonuç Ar-Ge’nin yanında diğer inovasyon girdilerinin, ülkelerin gelişmişlik sınıflandırmasına göre farklı etkilere sahip olduğu da ortaya çıkmıştır. Tüm ülke grupları için farklı istihdam alanlarının inovatif çıktı üzerinde önemli bir etkiye sahip olduğu görülürken; bu etkinin, ülke gruplarına göre farklılaştığı anlaşılmıştır.
Kaynakça
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- Abramovitz, M. (1956), “Resource And Output Trends İn The United States Since 1870”. In Resource And Output Trends İn The United States Since 1870, (ss: 1-23), NBER.
- Acs, Z. J., Braunerhjelm, P., Audretsch, D. B. - Carlsson, B. (2009). The knowledge spillover theory of entrepreneurship, Small business economics, 32(1), ss: 15-30.
- Acs, Z. J., Anselin, L. - Varga, A. (2002). Patents and innovation counts as measures of regional production of new knowledge, Research policy, 31(7) ss: 1069-1085.
- Aghion, P. – Howitt, P. (1992). A Model of Growth Through Creative Destruction, Econometrica, 60 (2), ss: 323-351.
- Aghion, P. – Howitt, P. (1998). Endogenous Growth Theory, Cambridge, MA: MIT Press.
- Akay, Ç. E. (2015). Dinamik Panel Veri Modelleri, Editör: Selahattin Güriş, İçinde: Stata ile Panel Veri Modelleri (ss.81-104) , İstanbul: DER Yayınevi.
- Anderson, N., Potočnik, K. - Zhou, J. (2014). Innovation And Creativity İn Organizations: A State-Of-The-Science Review, Prospective Commentary, And Guiding Framework. Journal of management, 40(5), ss: 1297-1333.
- Arellano, M. - Bond, S. (1991). Some Tests of Specification for Panel Data: Monte Carlo Evidence and An Application to Employment Equations. The Review of Economic Studies, 58(2), ss: 277-297.
- Arrow, K. J. (1961). The Economıc Implıcatıons Of Learnıng By Doıng (No. Tr-101). Stanford Unıv Ca Applıed Mathematıcs And Statıstıcs Labs.
- Arrow, K. (1962). “Economic welfare and the Allocation of resources for invention”. In N. Bureau, The Rate and Direction of inventive Activity: Ecnomic and social factors (ss. 609-626). Princeton University press.
- Autant-Bernard, C. - LeSage, J. P. (2019). A heterogeneous coefficient approach to the knowledge production function. Spatial Economic Analysis, 14(2), ss: 196-218.
- Aytaç, D. (2015). Yeniliğin Finansmanı: Girişim Sermayesi. Cumhuriyet Üniversitesi İktisadi ve İdari Bilimler Dergisi, 16(1), ss: 59-80.
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- Baldwin, R. E. (1956). Patterns Of Development İn Newly Settled Regions. The Manchester School, 24(2), ss: 161-179.
- Baltagi, B. H. - Wu, P. X. (1999). Unequally Spaced Panel Data Regressions with AR (1) Disturbances. Econometric Theory, 15(6), ss: 814-823.
- Barro, R. J. (1990). Government Spending İn A Simple Model Of Endogeneous Growth. Journal of political economy, 98(5, Part 2), ss: 103-125.
- Bhargava, A.- Franzini, L., Narendranathan, W. (1982). Serial Correlation and The Fixed Effects Model. The Review of Economic Studies, 49(4), ss: 533-549.
- Blaug, M. (1963). A Survey Of The Theory Of Process-İnnovations. Economica, 30(117), ss: 13-32.
- Bloss, R. (2014). Robot İnnovation Brings to Agriculture Efficiency, Safety, Labor Savings and Accuracy By Plowing, Milking, Harvesting, Crop Tending/Picking and Monitoring. Industrial Robot: An International Journal. 41(6), ss: 493–499.
- Breusch, T. S. - Pagan, A. R. (1980). The Lagrange Multiplier Test and İts Applications to Model Specification in Econometrics. The Review of Economic Studies, 47(1), ss: 239-253.
- Chakraborty, S. K. - Mazzanti, M. (2019). Modelling the Green Knowledge Production Function with Latent Group Structures for OECD countries (No. 0719).
SEEDS, Sustainability Environmental Economics and Dynamics Studies.
- Champernowne, D. G. (1953). The Production Function And The Theory Of Capital: A Comment. The Review of Economic Studies, 21(2), ss: 112-135.
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