Türkiye’de Bölgesel İnovasyon Etkinliği: Bootstrap VZA Analizi / Regional Innovation Efficiency in Turkey: Analysis of Bootstrap DEA

Year 2023, Volume: 7 Issue: 1, 118 - 131, 15.04.2023

Özlem Topçuoğlu

https://doi.org/10.29216/ueip.1249926

Abstract

Sürdürülebilir ekonomik büyümenin en önemli unsurlarından birinin inovasyon olduğu bilinmektedir. Bu bağlamda ülkeler mevcut rekabet şartlarında güçlerini artırabilmek için Ar-Ge yatırımlarına ayrılan kaynağı yükseltmeye başlamışlardır. Bölgesel gelişmişlik düzeyinde farklılıkların giderilmesi açısından bakıldığında da yine inovasyonun en önemli girdisi olan Ar-Ge yatırımları, hedefleri doğru belirlemek için dikkat edilmesi gereken bir noktadır. Özellikle gelişmekte olan ülkelerde teknolojik gelişim ve ticarileşme süreçlerinin başarıyla sonuçlanabilmesinde inovasyon performans ölçümleri büyük önem taşımaktadır. Bu çalışmada, Türkiye’de 2013-2020 dönemi için İstatistiki Bölge Birimleri Sınıflaması (İBBS) Düzey 1’de yer alan 12 bölge kapsamında inovasyon etkinliğinin belirlenmesi amaçlanmaktadır. Bu amaçla temel inovasyon girdi ve çıktıları kullanılarak Veri Zarflama Analizi (VZA) ile Orijinal ve Bootstrap etkinlik analizi yapılmıştır. Ayrıca çalışmada, kullanılan girdi değişkenlerinin etkinlik üzerine etki dereceleri belirlenmeye çalışılmıştır. Elde edilen inovasyon performas sonuçları bölgesel durumun ve farklılıkların ortaya konulması yönünde değerlendirilmiştir.

Keywords

İnovasyon, Bölgesel Etkinlik, Bootstrap VZA

Regional Innovation Efficiency in Turkey: Analysis of Bootstrap DEA / Türkiye’de Bölgesel İnovasyon Etkinliği: Bootstrap VZA Analizi

Abstract

It is known that one of the most important elements of sustainable economic growth is innovation. In this context, countries have started to increase the resources allocated to R&D investments in order to increase their power in the current competitive conditions. R&D investments, which are also the most important input of innovation, are a point to be considered in order to determine the targets correctly when it is considered in terms of eliminating the differences in the level of regional development. The measurement of innovation performances is of great importance in the successful conclusion of technological development and commercialization processes, especially in developing countries. In this study, it is aimed to determine the innovation efficiency within the scope of 12 regions in the Statistical Regional Units Classification (NUTS) Level 1 for the period 2013-2020 in Turkey. For this purpose, with Data Envelopment Analysis (DEA), Original and Bootstrap efficiency analysis were carried out using the inputs and outputs of the main innovation. In addition, the effect of the input variables used on the efficiency was tried to be determined in the study. The innovation performance results obtained were evaluated in terms of revealing the regional situation and differences.

Keywords

Innovation, Regional Efficiency, Bootstrap DEA

References

• Banker, R. D., Charnes, A. and Cooper, W. W. (1984). Some Models for Estimating Technical and Scale Inefficiencies in Data Envelopment Analysis. Management Science, 30(9), 1078-1092.
• Broekel, T., Rogge, N. and Brenner, T. (2013). The Innovation Efficiency of German Regions-A Shared-Input DEA Approach (No. 08.13). Working Papers on Innovation and Space.
• Büyüköztürk, Ş., Çokluk, Ö. ve Köklü, N. (2018). Sosyal Bilimler İçin İstatistik. Ankara: Pegem Akademik Yayıncılık.
• Çakın, E. and Özdemir, A. (2015). Bölgesel Gelişmişlikte Ar-Ge ve İnovasyonun Rolü: DEMATEL Tabanlı Analitik Ağ Süreci (DANP) ve TOPSIS Yöntemleri ile Bölgelerarası Bir Analiz. Eylül Üniversitesi İktisadi İdari Bilimler Fakültesi Dergisi, 30(1), 115-144.
• Charnes, A., Cooper, W. W. And Rhodes, E. (1978). Measuring the Efficiency of Decision Making Units. European Journal of Operational Research, 2(6), 429-444.
• Dökmen, G. (2012). Bölgesel Yenilik Sistemlerinde Devlet Rolü: Düzey 2 Bölgelerine İlişkin Ampirik Bir Analiz. Yönetim ve Ekonomi Dergisi, 19 (2), 143-163.
• Efron, B. (1992). Bootstrap Methods: Another Look at The Jackknife (pp. 569-593). New York: Springer. Efron, B. And Tibshirani, R. J. (1994). An Introduction to the Bootstrap. CRC Press.
• Ferrier, G. D. and Hirschberg, J. G. (1997). Bootstrapping Confidence Intervals for Linear Programming Efficiency Scores: With an Illustration Using Italian Banking Data. Journal of Productivity Analysis, 8, 19-33.
• Firsova, A. and Chernyshova, G. (2020). Efficiency Analysis of Regional Innovation Development Based on DEA Malmquist Index. Information, 11(6), 294.
• Hajek, P. and Henriques, R. (2017). Modelling Innovation Performance of European Regions Using Multi-Output Neural Networks. Plos one, 12(12), e0189746, Access address: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0185755
• Hansen, M. T. and Birkinshaw, J. (2007). The Innovation Value Chain. Harvard Business Review, 85(6), 1-13.
• Kleinknecht, A., Van Montfort, K. and Brouwer, E. (2002). The Non-Trivial Choice Between Innovation Indicators. Economics of Innovation and New Technology, 11(2), 109-121.
• Kneip, A., Simar, L. and Wilson, P. W. (2008). Asymptotics and Consistent Bootstraps for DEA Estimators in Nonparametric Frontier Models. Econometric Theory, 24(6), 1663-1697.
• Kocakalay, Ş. ve Işık, A. (2003). Veri Zarflama Analizi ve Uygulamasına Yönelik Bir Araştırma. (Yayınlanmamış Yüksek Lisans Tezi), Dumlupınar Üniversitesi Sosyal Bilimler Enstitüsü, Afyon.
• Lim, J. D. (2006). Regional Innovation System and Regional Development: Survey and a Korean Case. Working Paper, Access address: http://en.agi.or.jp/workingpapers/WP2006-05.pdf
• Min, S., Kim, J. and Sawng, Y. W. (2020). The Effect of Innovation Network Size and Public R&D Investment on Regional Innovation Efficiency. Technological Forecasting and Social Change, 155, 1-13.
• Nan, Y. and Tian, Y. (2011, December). Performance Evaluation on Regional Innovation System Based on AHP-TOPSIS Methodology. In Proceedings of 2011 International Conference on Computer Science and Network Technology (Vol. 2, pp. 1140-1143). IEEE.
• OECD (2002). Proposed Standard Practice for Surveys on Research and Experimental Development Frascati Manual, Access address: https://www.oecd-ilibrary.org/science-and-technology/ frascati -manual-2002_9789264199040-en
• Ramanathan, R. (2003). An Introduction to Data Envelopment Analysis: A Tool for Performance Measurement. New Delhi: Sage Publications.
• Simar, L. and Wilson, P. W. (1998). Sensitivity Analysis of Efficiency Scores: How to Bootstrap in Nonparametric Frontier Models. Management Science, 44(1), 49-61.
• Simar, L. and Wilson, P. W. (1999). Of Course We Can Bootstrap DEA Scores! But Does It Mean Anything? Logic Trumps Wishful Thinking. Journal of Productivity Analysis, 11, 93-97.
• Simar, L. and Wilson, P. W. (2000a). A General Methodology for Bootstrapping in Non-Parametric Frontier Models. Journal of Applied Statistics, 27(6), 779-802.
• Simar, L. and Wilson, P. W. (2000b). Statistical Inference in Nonparametric Frontier Models: The State of the Art. Journal of Productivity Analysis, 13, 49-78.
• Smeekes, S. (2009). Bootstrapping Nonstationary Time Series. Universitaire Pers Maastricht.
• Tutar, F., Kocabay, M. Ve Halil, A. (2007). Firmaların Yenilik (İnovasyon) Yaratma Sürecinde Serbest. Karamanoğlu Mehmetbey Üniversitesi Sosyal Ve Ekonomik Araştırmalar Dergisi, 3, 195-203.
• Usman, K. and Liu, Z. (2015). Innovation Index Framework to Measure the Innovation Capacity and Efficiency of SAARC Countries. European Journal of Social Sciences, 46(3), 325-338.
• WorldBank (2021) Access address: https://databank.worldbank.org/source/world-development-indicators
• Wu, J., Zhou, Z. and Liang, L. (2010). Measuring The Performance of Chinese Regional Innovation Systems with Two-Stage DEA-Based Model. Int. J. Sustainable Society, 2(1), 85- 99.
• Yun, Y. B., Nakayama, H. and Arakawa, M. (2004). Multiple Criteria Decision Making with Generalized DEA and an Aspiration Level Method. European Journal of Operational Research, 158(3), 697-706.
• Zabala-Iturriagagoitia, J. M., Voigt, P., Gutiérrez-Gracia, A. and Jiménez-Sáez, F. (2007). Regional Innovation Systems: How to Assess Performance. Regional Studies, 41(5), 661-672.
• Zemtsov, S. and Kotsemir, M. (2019). An Assessment of Regional Innovation System Efficiency in Russia: The Application of the DEA Approach. Scientometrics, 120(2), 375-404.