ÇOK BOYUTLU EKONOMİK KARMAŞIKLIK VE EKONOMİK BÜYÜME: EN KARMAŞIK 10 ÜLKEDEN KANITLAR

Yıl 2025, Cilt: 21 Sayı: 4, 1372 - 1404, 25.12.2025

Seyhun Tutgun

https://doi.org/10.17130/ijmeb.1596858

Öz

Toplu bilgi birikimini ve ulusal yetenek setlerinin derinliğini yansıtan ekonomik karmaşıklık, ekonomik büyüme literatüründe önemli bir kavram olarak öne çıkmaktadır. Bu çalışma, ekonomik büyüme, bilgi üretme kapasitesini yansıtan Ekonomik Araştırma Karmaşıklık Endeksi (ECIR), bir ülkenin ihracat yapısının sofistikasyonunu gösteren Ekonomik Ticaret Karmaşıklık Endeksi (ECITRA) ve teknolojik ilerlemeyi işaret eden Ekonomik Teknoloji Karmaşıklık Endeksi (ECITEC) arasındaki etkileşimi analiz ederek ekonomik karmaşıklığın çok boyutlu doğasını ele almaktadır. Analiz, özellikle ekonomik karmaşıklık sıralamasında en karmaşık ülkeler arasında yer alan dünyanın en karmaşık 10 ekonomisine odaklanmaktadır: Avusturya, Çekya, Almanya, Japonya, Singapur, Slovenya, Güney Afrika, İsveç, İsviçre, Birleşik Krallık analiz edilmektedir. 1999-2021 dönemine ait yıllık verileri kullanılarak ekonomik büyüme ile çok boyutlu ekonomik karmaşıklık ilişkisi analiz edilmiştir. Panel veri analiz tekniklerinin kullanıldığı çalışmada sırasıyla yatay kesit bağımlılığı testi, homojenite testi, birim kök testi ve eşbütünleşme testi uygulanmıştır. Seriler arasında eşbütünleşme ilişkisinin olduğu bulgusuna ulaştıktan sonra, yatay kesit bağımlılığına ve heterojenliğe dayanıklı olan Ortak Korelasyonlu Etkiler (CCE) ve Genişletilmiş Ortalama Grup (AMG) tahmincileri kullanılarak uzun dönem katsayıları tahmin edilmiştir. Ampirik bulgular, hem panel ortalama düzeyinde hem de daha çarpıcı bir şekilde, tek tek ülkeler için ECIR, ECITRA ve ECITEC katsayılarında önemli bir heterojenlik olduğunu göstermektedir. Gözlemlenen ülke düzeyindeki heterojenlik, tek tip bir politika reçetesinin optimal olmayacağını, bunun yerine ulusal bağlamı, mevcut endüstriyel ve araştırma güçlerini ve kurumsal yapıyı dikkate alan ülkeye özgü stratejilerin gerekliliğini vurgulamaktadır. Nihayetinde bulgular, tüm ülkeler için karmaşık yetenekler inşa etmenin ve beşeri sermayeyi bilgi ekonomisinin talepleriyle uyumlu hale getirmenin uzun vadeli kalkınma açısından önemini pekiştirmektedir.

Anahtar Kelimeler

Ekonomik karmaşıklık , Ekonomik büyüme , Panel veri analizi

MULTIDIMENSIONAL ECONOMIC COMPLEXITY AND ECONOMIC GROWTH: EVIDENCE FROM THE 10 MOST COMPLEX COUNTRIES

Öz

Economic complexity, reflecting the collective know-how and the depth of national capability sets, features prominently as a key concept within the economic growth literature. This study addresses the multidimensional nature of economic complexity by analyzing the interplay between economic growth (GDP per capita), the Economic Research Complexity Index (ECIR) reflecting knowledge generation capacity, the Economic Trade Complexity Index (ECITRA) capturing the sophistication of a nation's export structure, and the Economic Technology Complexity Index (ECITEC) indicating technological advancement. To provide a more robust analysis, population (POP) and human capital (HUM) are included as control variables. The study investigates these relationships through three distinct models, each focusing on one dimension of complexity. The analysis focuses specifically on the world's 10 most complex economies: Austria, Czechia, Germany, Japan, Singapore, Slovenia, South Africa, Sweden, Switzerland, and the United Kingdom. The relationship between economic growth and multidimensional economic complexity is analyzed using annual data for the period 1999-2021. Second-generation panel data analysis techniques are used in the study, where for each of the three models, cross-section dependence, homogeneity/heterogeneity, unit root, and cointegration tests are applied respectively. After finding that there is a cointegration relationship between the series, the long-run coefficients are estimated using the Common Correlated Effects (CCE) and Augmented Mean Group (AMG) estimators, which are robust to cross-sectional dependence and heterogeneity. Furthermore, the causal relationships between the variables are examined using the Dumitrescu-Hurlin panel causality test. The empirical findings indicate significant heterogeneity in the coefficients of ECIR, ECITRA, and ECITEC, both at the panel-average level and, more strikingly, for individual countries. Ultimately, the findings reinforce the importance, relevant for all nations, of cultivating complex capabilities and aligning human capital with the demands of the knowledge economy as crucial elements for achieving long-term development.

Anahtar Kelimeler

Economic complexity , Economic growth , Panel data analysis

Kaynakça

• Aghion, P., & Howitt, P. (1992). A model of growth through creative destruction. Econometrica, 60(2), 323-351. https://doi.org/10.2307/2951599
• Akyol, H., & Gül, K. (2021). Sürdürülebilir kalkınma, ekonomik karmaşıklık ve uluslararası turizmin panel veri analizi ile incelenmesi. Erciyes Akademi, 35(4), 1721-1740. https://doi.org/10.48070/erciyesakademi.1001890
• Balsalobre, S. J. P., Verduras, C. L., & Lanchas, J. D. (2017). Measuring the economic complexity at the sub-national level using international and interregional trade. Nineteenth Annual Conference of European Trade Study Group.
• Bond, S., & Eberhardt, M. (2013). AMG: An estimator for dynamic heterogeneous panels. Economics Letters, 119(3), 299-302.
• Boschma, R. (2005). Proximity and innovation: A critical assessment. Regional Studies, 39(1), 61-74. https://doi.org/10.1080/0034340052000320887
• Breusch, T. S., & Pagan, A. R. (1980). The Lagrange multiplier test and its applications to model specification in econometrics. The Review of Economic Studies, 47(1), 239-253. https://doi.org/10.2307/2297111
• Chirwa, T. G., & Odhiambo, N. M. (2016). Macroeconomic determinants of economic growth: A review of international literature. South East European Journal of Economics and Business, 11(2), 33-47. https://doi.org/10.1515/jeb-2016-0009
• Cohen, W. M., & Levinthal, D. A. (1989). Innovation and learning: The two faces of R&D. The Economic Journal, 99(397), 569-596.
• Doğan, B., Balsalobre-Lorente, D., & Nasir, M. A. (2020). European commitment to COP21 and the role of energy consumption, FDI, trade and economic complexity in sustaining economic growth. Journal of Environmental Management, 273, 111146. https://doi.org/10.1016/j.jenvman.2020.111146
• Domini, G. (2022). Patterns of specialization and economic complexity through the lens of universal exhibitions, 1855-1900. Explorations in Economic History, 83, 101421. https://doi.org/10.1016/j.eeh.2021.101421
• Dosi, G., Llerena, P., & Labini, M. S. (2006). The relationships between science, technologies and their industrial exploitation: An illustration through the myths and realities of the so-called "European Paradox". Research Policy, 35(10), 1450-1464. https://doi.org/10.1016/j.respol.2006.09.012
• Dumitrescu, E. I., & Hurlin, C. (2012). Testing for Granger non-causality in heterogeneous panels. Economic Modelling, 29(4), 1450-1460. https://doi.org/10.1016/j.econmod.2012.02.014
• Felipe, J., Kumar, U., & Abdon, A. (2012). Product complexity and economic development. Structural Change and Economic Dynamics, 23(1), 36-63. https://doi.org/10.1016/j.strueco.2011.08.003
• Gao, J., & Zhou, T. (2018). Quantifying China’s regional economic complexity. Physica A: Statistical Mechanics and its Applications, 492, 1591-1603. https://doi.org/10.1016/j.physa.2017.11.084
• Gómez-Zaldívar, M., Fonseca, F., Mosqueda, M., & Gómez-Zaldívar, F. (2020). Spillover effects of economic complexity on the per capita GDP growth rates of Mexican states, 1993-2013. Estudios de Economía, 47(2), 221-243.
• Grossman, G. M., & Helpman, E. (1991). Innovation and growth in the global economy. MIT Press.
• Güneri, B. (2019). Economic complexity and economic performance (Yüksek lisans tezi). https://tez.yok.gov.tr sayfasından erişilmiştir. Erişim Tarihi: 10.10.2024
• Haldane, A. (2018). The UK’s productivity problem: Hub no spokes. Academy of Social Sciences Annual Lecture.
• Hausmann, R., & Klinger, B. (2007). The structure of the product space and the evolution of comparative advantage. CID Working Paper No. 146, Harvard University.
• Hausmann, R., Hidalgo, C. A., Bustos, S., Coscia, M., Simoes, A., & Yildirim, M. A. (2014). The atlas of economic complexity: Mapping paths to prosperity. MIT Press.
• Hausmann, R., Hwang, J., & Rodrik, D. (2007). What you export matters. Journal of Economic Growth, 12, 1-25. https://doi.org/10.1007/s10887-006-9009-4
• Helpman, E. (2011). Understanding global trade. Harvard University Press.
• Hidalgo, C. A., & Hausmann, R. (2009). The building blocks of economic complexity. Proceedings of the National Academy of Sciences, 106(26), 10570-10575. https://doi.org/10.1073/pnas.0900943106
• Hidalgo, C. A., Klinger, B., Barabási, A. L., & Hausmann, R. (2007). The product space conditions the development of nations. Science, 317(5837), 482-487. https://doi.org/10.1126/science.1144581
• Ikram, M., Xia, W., Fareed, Z., Shahzad, U., & Rafique, M. Z. (2021). Exploring the nexus between economic complexity, economic growth and ecological footprint: Contextual evidences from Japan. Sustainable Energy Technologies and Assessments, 47, 101460. https://doi.org/10.1016/j.seta.2021.101460
• Keynes, J. M. (2003). The general theory of employment, interest and money (1936). Project Gutenberg Literary Archive Foundation.
• Koch, P. (2021). Economic complexity and growth: Can value-added exports better explain the link? Economics Letters, 198, 109682. https://doi.org/10.1016/j.econlet.2020.109682
• Lee, J. (2011). Export specialization and economic growth around the world. Economic Systems, 35(1), 45-63. https://doi.org/10.1016/j.ecosys.2010.11.002
• Li, H. S., Geng, Y. C., Shinwari, R., Yangjie, W., & Rjoub, H. (2021). Does renewable energy electricity and economic complexity index help to achieve carbon neutrality target of top exporting countries? Journal of Environmental Management, 299, 113386. https://doi.org/10.1016/j.jenvman.2021.113386
• Lucas, R. E. (1988). On the mechanics of economic development. Journal of Monetary Economics, 22(1), 3-42. https://doi.org/10.1016/0304-3932(88)90168-7
• Malthus, T. R. (1986). An essay on the principle of population (1798). Pickering & Chatto.
• McMillan, M., Rodrik, D., & Verduzco-Gallo, Í. (2014). Globalization, structural change and productivity growth, with an update on Africa. World Development, 63, 11-32. https://doi.org/10.1016/j.worlddev.2013.10.012
• Mighri, Z., & AlSaggaf, M. I. (2023). Asymmetric impacts of renewable energy consumption and economic complexity on economic growth in Saudi Arabia: Evidence from the NARDL model. Environmental Science and Pollution Research, 30(3), 7446-7473. https://doi.org/10.21203/rs.3.rs-1594202/v1
• Nelson, R. R. (1959). The simple economics of basic scientific research. Journal of Political Economy, 67(3), 297-306. https://doi.org/10.1086/258177
• Nelson, R. R., & Winter, S. G. (1982). An evolutionary theory of economic change. Belknap Press.
• OECD. (2022). OECD economic surveys: Germany 2022. OECD Publishing.
• Pesaran, M. H. (2004). General diagnostic tests for cross section dependence in panels (IZA Discussion Paper No. 1240). Institute for the Study of Labor.
• Pesaran, M. H. (2006). Estimation and inference in large heterogeneous panels with cross-section dependence. Econometrica, 74(4), 967-1012.
• Pesaran, M. H. (2007). A simple panel unit root test in the presence of cross-section dependence. Journal of Applied Econometrics, 22(2), 265-312. https://doi.org/10.1002/jae.951
• Pesaran, M. H., & Smith, R. P. (1995). Estimating long-run relationships from dynamic heterogeneous panels. Journal of Econometrics, 68(1), 79-113. https://doi.org/10.1016/0304-4076(94)01644-F
• Pesaran, M. H., & Yamagata, T. (2008). Testing slope homogeneity in large panels. Journal of Econometrics, 142(1), 50-93. https://doi.org/10.1016/j.jeconom.2007.05.010
• Pesaran, M. H., Shin, Y., & Smith, R. P. (1999). Pooled mean group estimation of dynamic heterogeneous panels. Journal of the American Statistical Association, 94(446), 621-634. https://doi.org/10.1080/01621459.1999.10474156
• Pesaran, M. H., Ullah, A., & Yamagata, T. (2008). A bias-adjusted LM test of error cross-section independence. The Econometrics Journal, 11(1), 105-127. https://doi.org/10.1111/j.1368-423X.2007.00227.x
• Poncet, S., & de Waldemar, F. S. (2013). Economic complexity and growth: Evidence from China. Revue Economique, 64(3), 495-503.
• Porter, M. E. (1990). The competitive advantage of nations. Free Press.
• Rafique, M. Z., Doğan, B., Husain, S., Huang, S., & Shahzad, U. (2021). Role of economic complexity to induce renewable energy: Contextual evidence from G7 and E7 countries. International Journal of Green Energy, 18(7), 745-754. https://doi.org/10.1080/15435075.2021.1880912
• Romer, P. M. (1990). Endogenous technological change. Journal of Political Economy, 98(5, Part 2), S71-S102. https://doi.org/10.1086/261725
• Romer, P. M. (1994). The origins of endogenous growth. Journal of Economic Perspectives, 8(1), 3-22. Saviotti, P. P., & Pyka, A. (2004). Economic development by the creation of new sectors. Journal of Evolutionary Economics, 14(1), 1-35. https://doi.org/10.1007/s00191-003-0182-x
• Schumpeter, J. A. (2003). Capitalism, socialism and democracy (1942). Taylor & Francis e-Library.
• Schwab, K. (2017). The Fourth Industrial Revolution. Crown Business.
• Smith, A. (2002). An inquiry into the nature and causes of the wealth of nations (1776).
• Smith, L. V., Leybourne, S., Kim, T. H., & Newbold, P. (2004). More powerful panel data unit root tests with an application to mean reversion in real exchange rates. Journal of Applied Econometrics, 19(2), 147-170. https://doi.org/10.1002/jae.723
• Solow, R. (1956). A contribution to the theory of economic growth. The Quarterly Journal of Economics, 70(1), 65-94. https://doi.org/10.2307/1884513
• Stojkoski, V., & Kocarev, L. (2017). The relationship between growth and economic complexity: Evidence from Southeastern and Central Europe. https://mpra.ub.uni-muenchen.de/87432/
• Stojkoski, V., Koch, P., & Hidalgo, C. A. (2023). Multidimensional economic complexity and inclusive green growth. Communications Earth & Environment, 4, 130. https://doi.org/10.1038/s43247-023-00770-0
• Stojkoski, V., Utkovski, Z., & Kocarev, L. (2016). The impact of services on economic complexity: Service sophistication as route for economic growth. PLOS ONE, 11(8), e0161633. https://doi.org/10.1371/journal.pone.0161633
• Swamy, P. A. (1970). Efficient inference in a random coefficient regression model. Econometrica, 38, 311-323. https://doi.org/10.2307/1913012
• Tabash, M. I., Mesagan, E. P., & Farooq, U. (2022). Dynamic linkage between natural resources, economic complexity and economic growth: Empirical evidence from Africa. Resources Policy, 78, 102865. https://doi.org/10.1016/j.resourpol.2022.102865
• Teece, D. J., Pisano, G., & Shuen, A. (1997). Dynamic capabilities and strategic management. Strategic Management Journal, 18(7), 509-533. https://doi.org/10.1002/(SICI)1097-0266(199708)18:7<509::AID-SMJ882>3.0.CO;2-Z
• The Growth Lab at Harvard University. (2019). The Atlas of Economic Complexity. https://atlas.hks.harvard.edu/ (Accessed: 10.01.2025)
• The Observatory of Economic Complexity (OEC). https://oec.world/en/data-availability.(Accessed: 10.01.2025)
• Uçar, M., Soyyiğit, S., & Nişancı, M. (2019). Ülkelerin iktisadi gelişmişlik ve iktisadi karmaşıklık düzeyleri arasındaki ilişki: G8 ülkeleri örneği. Cumhuriyet Üniversitesi İktisadi ve İdari Bilimler Dergisi, 20(1). https://doi.org/10.37880/cumuiibf.524670
• Udeogu, E., Roy-Mukherjee, S., & Amakom, U. (2021). Does increasing product complexity and diversity cause economic growth in the long-run? A GMM panel VAR evidence. Sage Open, 11(3), 21582440211032918. https://doi.org/10.1177/21582440211032918
• Vinci, G. V., & Benzi, R. (2018). Economic complexity: Correlations between gross domestic product and fitness. Entropy, 20(10), 766. https://doi.org/10.3390/e20100766
• Westerlund, J. (2007). Testing for error correction in panel data. Oxford Bulletin of Economics and Statistics, 69(6), 709-748.
• Westerlund, J. (2007). Testing for panel cointegration with multiple structural breaks. Oxford Bulletin of Economics and Statistics, 69(6), 709-748.
• Xu, M., & Lybbert, T. J. (2019). Innovation-adjusted economic complexity and growth: Does product-specific patenting reveal enhanced economic capabilities? Review of Development Economics. https://doi.org/10.1111/rode.12816
• Yıldız, G., & Yıldız, B. (2019). Ekonomik karmaşıklık ile ekonomik büyüme arasındaki ilişki: Panel bootstrap Granger nedensellik analizi. Uluslararası Yönetim İktisat ve İşletme Dergisi, 15(2), 329-340. https://doi.org/10.17130/ijmeb.2019252097
• Zhu, S., & Li, R. (2017). Economic complexity, human capital and economic growth: Empirical research based on cross-country panel data. Applied Economics, 49(38), 3815-3828. https://doi.org/10.1080/00036846.2016.1270413