Assessing The Level of Manufacturing Value Added of G-20 and Its Relation to Innovation Inputs and Outputs

Yıl 2024, Cilt: 17 Sayı: 2, 592 - 605, 17.03.2024

Murat Unanoglu , Çiğdem Özarı

https://doi.org/10.35674/kent.1417436

Öz

The primary objective of this study is to forecast the manufacturing value added levels of G-20 countries by leveraging the fundamental dimensions extracted from the Global Innovation Index and ascertain the extent to which innovation indicators contribute to variations in manufacturing value added. The Random Forest algorithm, known for its versatility and precision in dealing with complex datasets, has been employed as a prominent machine learning technique to predict the manufacturing value added levels of G-20 countries during the period 2013-2022. The MVA levels of G-20 countries, obtained using average and standard deviation, were predicted with a 54.14% error rate through the assistance of innovation input and output indicators. The level predicted with the highest accuracy is the one closely aligned with the average. This study's uniqueness lies in its utilization of the Random Forest algorithm to predict value added levels based on innovation inputs and outputs, which constitute the fundamental dimensions of the Global Innovation Index.

Anahtar Kelimeler

Global Innovation Index, G-20 Countries, Random Forest, Innovation, Manufacturing Value Added

G-20 Ülkelerinin İmalat Sanayi Katma Değerinin Değerlendirilmesi ve Yenilik Girdi ve Çıktı Göstergeleriyle İlişkisi

Öz

Bu çalışmanın temel amacı, Küresel İnovasyon Endeksi'nden elde edilen temel boyutları kullanarak G-20 ülkelerinin imalat sanayi katma değer düzeylerini tahmin etmektir ve aynı zamanda inovasyon göstergelerinin imalat sanayi katma değerindeki farklılıklara ne ölçüde katkı sağladığını belirlemektir. Karmaşık veri kümeleriyle başa çıkma yeteneği ve hassasiyetiyle bilinen makine öğrenme yöntemlerinden rassal orman algoritması, 2013-2022 döneminde G-20 ülkelerinin katma değer düzeylerini tahmin etmek için kullanılmıştır. Ortalama ve standart sapma kullanılarak elde edilen G-20 ülkelerinin MVA seviyeleri, inovasyon girdi ve çıkış göstergelerinin yardımıyla %54,14 hata oranıyla tahmin edilmiştir. En iyi tahmin edilen seviye ise ortalamaya yakın olan gruptur. Bu çalışmanın benzersizliği, rassal orman algoritmasının kullanılarak Küresel İnovasyon Endeksi'nin temel boyutlarını oluşturan inovasyon girdi ve çıktı göstergeleriyle ülkelerin katma değer düzeylerini tahmin etmektir.

Anahtar Kelimeler

Küresel İnovasyon Endeksi, G-20 Ülkeleri, Rassal Orman, Sanayi Katma Değeri, İnovasyon

Kaynakça

• Anyanwu, J. C. (2017). Manufacturing Value Added Development in North Africa: Analysis of Key Drivers, Asian Development Policy Review, 5(4), 281-298. https://doi.org/10.18488/journal.107.2017.54.281.298
• Baldwin, R., & Ito, T. (2021). The Smile Curve: Evolving Sources of Value Added in Manufacturing, Canadian Journal of Economics/Revue Canadienne D'économique, 54(4), 1842-1880. https://doi.org/10.1111/caje.12555.
• Baba, B., & Sevil, G. (2020). Predicting IPO Initial Returns Using Random Forest, Borsa Istanbul Review, 20(1), 13-23. https://doi.org/10.1016/j.bir.2019.08.001.
• Boudt, K., Todorov, V., & Upadhyaya, S. (2009). Nowcasting Manufacturing Value Added for Cross-Country Comparison, Statistical Journal of the IAOS, 26(1,2), 15-20.
• Breiman, L. (2001). Random Forests, Machine Learning, 45, 5-32.
• Calderoni, L., Ferrara, M., Franco, A., & Maio, D. (2015). Indoor Localization in a Hospital Environment Using Random Forest Classifiers, Expert Systems with Applications, 42(1), 125-134. https://doi.org/10.1016/j.eswa.2014.07.042.
• Cantore, N., Clara, M., Lavopa, A., & Soare, C. (2017). Manufacturing as an Engine Of Growth: Which is The Best Fuel?, Structural Change and Economic Dynamics, 42, 56-66. https://doi.org/10.1016/j.strueco.2017.04.004.
• Chen, D.; Zheng, S., & Gou, L. (2015). The Impact of Science and Technology Policies on Rapid Economic Development in China, The Global Innovation Index 2015, chapter 6, https://www.wipo.int/edocs/pubdocs/en/wipo_pub_gii_2015-chapter6.pdf.
• Chen, S. (2023). Predicting Lung Cancer Stage by Expressions of Protein-Encoding Genes. Advances in Bioscience and Biotechnology, 14(8), 368-377.
• Cındık, Z., & Armutlulu, I. H. (2021). A Revision of Altman Z-Score Model and a Comparative Analysis of Turkish Companies’ Financial Distress Prediction, National Accounting Review, 3(2), 237-255. https://doi.org/10.3934/NAR.2021012
• Coutinho, E. M. O., & Au-Yong-Oliveira, M. (2023). Factors Influencing Innovation Performance in Portugal: A Cross-Country Comparative Analysis Based on the Global Innovation Index and on the European Innovation Scoreboard. Sustainability, 15(13), 10446. https://doi.org/10.3390/su151310446
• Çemberci, M., Civelek, M. E., & Cömert, P. N. (2022). The Role of Foreign Direct Investment in The Relationship Between Global Innovation Index and Gross Domestic Product. GURUKUL BUSINESS REVIEW-GBR, 18., 101-111. https://doi.org/10.48205/gbr.v18.8
• Dutta, S., Lanvin, B., & Wunsch-Vincent, S. (Eds.). (2020). Global Innovation Index 2020. Johnson Cornell University.
• Dziallas, M., & Blind, K. (2019). Innovation Indicators Throughout the Innovation Process: An Extensive Literature Analysis. Technovation, 80-81, 3-29. https://doi.org/10.1016/j.technovation.2018.05.005.
• Eisavi, V., & Homayouni, S. (2016). Performance Evaluation of Random Forest and Support Vector Regressions in Natural Hazard Change Detection, Journal of Applied Remote Sensing, 10(4), 046030-046030. https://doi.org/10.1117/1.JRS.10.046030
• Erciş, A., & Ünalan, M. (2016). Innovation: A Comparative Case Study of Turkey and South Korea. Procedia-Social and Behavioral Sciences, 235, 701-708. https://doi.org/10.1016/j.sbspro.2016.11.071
• Farnaaz, N., & Jabbar, M. A. (2016). Random Forest Modeling for Network Intrusion Detection System, Procedia Computer Science, 89, 213-217. https://doi.org/10.1016/j.procs.2016.06.047.
• Feng, W., Sui, H., Tu, J., Huang, W., & Sun, K. (2018). A Novel Change Detection Approach Based on Visual Saliency and Random Forest from Multi-Temporal High-Resolution Remote-Sensing Images, International Journal of Remote Sensing, 39(22), 7998-8021. https://doi.org/10.3390/rs8110888.
• Ganguly, S., Das, S., & Pandya, S. (2022). Influence of Strategy Typology on Innovation: Evidence from The Manufacturing Sector, International Journal of Electronic Government Research (IJEGR), 18(2), 1-16. http://doi.org/10.4018/IJEGR.298156.
• Ghosh, P., Neufeld, A., & Sahoo, J. K. (2022). Forecasting Directional Movements of Stock Prices for Intraday Trading Using LSTM and Random Forests, Finance Research Letters, 46, 1-8. https://doi.org/10.1016/j.frl.2021.102280.
• Haraguchi, N., Cheng, C. F. C., & Smeets, E. (2017). The Importance of Manufacturing in Economic Development: Has This Changed?, World Development, 93, 293-315. https://doi.org/10.1016/j.worlddev.2016.12.013.
• Hlazova, A. (2021). Researching The Problems of Digital Economy Development as an Indicator of The Information Society: Potential Threats and Prospects, Technology Audit and Production Reserves, 6(4(62)). https://doi.org/10.15587/2706-5448.2021.248124.
• Huarng, K. H., & Yu, T. H. K. (2022). Analysis of Global Innovation Index by Structural Qualitative Association, Technological Forecasting and Social Change, 182, 121850. https://doi.org/10.1016/j.techfore.2022.121850.
• Kaczmarczyk, K., & Hernes, M. (2020). Financial Decisions Support Using the Supervised Learning Method Based on Random Forests, Procedia Computer Science, 176, 2802-2811. https://doi.org/10.1016/j.procs.2020.09.276.
• Karami, M., Elahinia, N., & Karami, S. (2019). The Effect of Manufacturing Value Added on Economic Growth: Empirical Evidence from Europe, Journal of Business Economics and Finance, 8(2), 133-147. http://doi.org/10.17261/Pressacademia.2019.1044.
• Liu, Y., Wang, Y., & Zhang, J. (2012). New Machine Learning Algorithm: Random Forest, In Information Computing and Applications: Third International Conference, ICICA 2012, Chengde, China, September 14-16, 2012. Proceedings 3, 246-252. Springer Berlin Heidelberg.
• Lohrmann, C., & Luukka, P. (2019). Classification of Intraday S&P500 Returns with a Random Forest, International Journal of Forecasting, 35(1), 390-407. https://doi.org/10.1016/j.ijforecast.2018.08.004.
• Luken, R. A., Saieed, A., & Magvasi, M. (2022). Industry-related Sustainable Development Goal-9 Progress and Performance Indices and Policies for Sub-Saharan African Countries, Environmental Development, 42, 1-14.
• https://doi.org/10.1016/j.envdev.2021.100694
• Nasir, M. H., & Zhang, S. (2024). Evaluating Innovative Factors of The Global Innovation Index: A Panel Data Approach. Innovation and Green Development, 3(1), 100096. https://doi.org/10.1016/j.igd.2023.100096.
• Onea, I. A. (2020). Innovation Indicators and the Innovation Process- Evidence from The European Innovation Scoreboard, Management & Marketing, 15(4), 605-620. https://doi.org/10.2478/mmcks-2020-0035
• Oturakci, M. (2021). Comprehensive Analysis of The Global Innovation Index: Statistical and Strategic Approach. Technology Analysis & Strategic Management, 35(6), 676-688. https://doi.org/10.1080/09537325.2021.1980209.
• Pallathadka, H., Ramirez-Asis, E. H., Loli-Poma, T. P., Kaliyaperumal, K., Ventayen, R. J. M., & Naved, M. (2023). Applications of Artificial Intelligence in Business Management, E-Commerce And Finance, Materialstoday: Proceedings, 80, 2610-2613.
• https://doi.org/10.1016/j.matpr.2021.06.419.
• Quitzow, R. (2013). Towards an Integrated Approach to Promoting Environmental Innovation And National Competitiveness, Innovation and Development, 3(2), 277-296. https://doi.org/10.1080/2157930X.2013.825070.
• Roos, G. (2016). Design-based Innovation for Manufacturing Firm Success in High-Cost Operating Environments, She Ji: The Journal of Design, Economics, and Innovation, 2(1), 5-28. https://doi.org/10.1016/j.sheji.2016.03.001
• Sekuloska, J. D. (2015). Innovation Oriented FDI As a Way of Improving the National Competitiveness, Procedia-Social and Behavioral Sciences, 213, 37-42. https://doi.org/10.1016/j.sbspro.2015.11.400.
• Shaikhina, T., Lowe, D., Daga, S., Briggs, D., Higgins, R., & Khovanova, N. (2019). Decision Tree and Random Forest Models for Outcome Prediction In Antibody Incompatible Kidney Transplantation. Biomedical Signal Processing and Control, 52, 456-462. https://doi.org/10.1016/j.bspc.2017.01.012.
• Sıcakyüz, Ç. (2023). How Does the Global Innovation Index Score Affect Income? A Policy for Innovativeness. Journal of Research in Business, 8(1), 1-19. https://doi.org/10.54452/jrb.1022938.
• Singh, S. & Paliwal, M. (2017). Unleashing The Growth Potential of Indian MSME Sector, Comparative Economic Research. Central and Eastern Europe, 20(2), 35–52. https://doi.org/10.1515/cer-2017-0011
• Stojanović, I., Puška, A., & Selaković, M. (2022). A Multi-Criteria Approach to The Comparative Analysis of The Global Innovation Index on The Example of The Western Balkan Countries. Economics, 10(2), 9-26.
• Swamynathan, M. (2017). Mastering Machine Learning with Python In Six Steps: A Practical Implementation Guide To Predictive Data Analytics Using Python, Apress, Almanya.
• Szopik-Depczyńska, K., Kędzierska-Szczepaniak, A., Szczepaniak, K., Cheba, K., Gajda, W., & Ioppolo, G. (2018). Innovation in Sustainable Development: An Investigation of The EU Context Using 2030 Agenda Indicators, Land Use Policy, 79, 251-262. https://doi.org/10.1016/j.landusepol.2018.08.004
• Thakur, M. & Kumar, D. (2018). A Hybrid Financial Trading Support System Using Multi-Category Classifiers and Random Forest, Applied Soft Computing, 67, 337-349. https://doi.org/10.1016/j.asoc.2018.03.006
• Yu, T. H. K., Huarng, K. H., & Huang, D. H. (2021). Causal Complexity Analysis of The Global Innovation Index. Journal of Business Research, 137, 39-45. https://doi.org/10.1016/j.jbusres.2021.08.013
• Yönkul, N. G., & Ünlü, H. (2022). How Does The Effect of Absorptive Capacity on Innovation Capacity Change According To Countries’ Technology Manufacturing Value-Added Levels?, In Strategic Innovation: Research Perspectives on Entrepreneurship and Resilience, 127-164, Springer International Publishing.
• Yüregir, O. H., Sıcakyüz, Ç., & Güler, S. (2022). Comparison of Relationship Between Global Innovation Index Achievements and University Achievements in Terms of Countries. International Research Journal of Social Sciences, 11(2), 1-12.
• Wang, J., Sun, X., Cheng, Q., & Cui, Q. (2021). An Innovative Random Forest-Based Nonlinear Ensemble Paradigm of Improved Feature Extraction and Deep Learning for Carbon Price Forecasting, Science of the Total Environment, 762, 143099. https://doi.org/10.1016/j.scitotenv.2020.143099
• Wessels, K. J., Van den Bergh, F., Roy, D. P., Salmon, B. P., Steenkamp, K. C., MacAlister, B., Swanepoel D., & Jewitt, D. (2016). Rapid land Cover Map Updates Using Change Detection and Robust Random Forest Classifiers, Remote Sensing, 8(11), 1-24. https://doi.org 10.3390/rs8110888
• WIPO. (2023). Global Innovation Index (GII). Global Innovation Index (GII). Retrieved August 15, 2023, from https://www.wipo.int/global_innovation_index/en/index.html
• World Bank. (2010). Innovation Policy: A guide for Developing Countries. The World Bank.