Macroeconomic Factors Determining Economic Complexity: An Empirical Analysis for E7 Countries

Year 2025, Volume: 10 Issue: 3, 1318 - 1335, 30.09.2025

Selin Zengin Taşdemir

https://doi.org/10.30784/epfad.1702620

Abstract

This study aims to examine the possible relationships between the Economic Complexity Index (ECI), which reflects the knowledge base and technology intensity in countries' production structures, and various macroeconomic variables. Economic complexity provides important insights into countries' long-term growth potential based on the diversity and sophistication levels in their production structures. The study analyzes the E-7 (Emerging-7) countries, which are among the developing countries and stand out with their high growth potential. Using an annual panel data set for the period 2000-2009, the study empirically tests the possible determinants of economic complexity. To mitigate the risk of bias in estimation results due to potential endogeneity issues among the determinants, the Instrumental Variables (IV) approach based on Two-Stage Least Squares (2SLS) was applied. The findings were evaluated comparatively with the Driscoll-Kraay estimator, which disregards the endogeneity assumption. The findings show that the economic growth rate, foreign direct investment, and terms of trade increase economic complexity, while low human capital levels and high dependence on natural resource rents have a reducing effect. The findings emphasize the importance of considering the endogeneity problem in policy implications.

Keywords

Economic Complexity , E-7 Countries , Panel Data Endogeneity

Ekonomik Karmaşıklığı Belirleyen Makroekonomik Faktörler: E7 Ülkeleri Üzerine Ampirik Bir Analiz

Abstract

Bu çalışma, ülkelerin üretim yapılarında yer alan bilgi birikimi ve teknoloji yoğunluğunu yansıtan Ekonomik Karmaşıklık Endeksi (ECI) ile çeşitli makroekonomik değişkenler arasındaki olası ilişkileri incelemeyi amaçlamaktadır. Ekonomik karmaşıklık, ülkelerin üretim yapılarındaki çeşitlilik ve sofistikasyon düzeyine bağlı olarak, uzun vadeli büyüme potansiyelleri hakkında önemli bilgiler sunmaktadır. Araştırmada, gelişmekte olan ülkeler arasında yer alan ve yüksek büyüme potansiyeliyle öne çıkan E-7 (Emerging-7) ülkeleri analiz edilmiştir. Çalışmada, 2000-2009 dönemine ait yıllık panel veri seti kullanılarak, ekonomik karmaşıklığın olası belirleyicileri ampirik olarak test edilmiştir. Belirleyiciler arasında ortaya çıkabilecek olası içsellik probleminin tahmin sonuçlarını saptırma riskine karşı Araç Değişkenler yaklaşımına dayalı İki Aşamalı En Küçük Kareler (IV-2SLS) yöntemi uygulanmıştır. Elde edilen bulgular, içsellik varsayımını göz ardı eden Driscoll-Kraay tahmincisi ile karşılaştırmalı olarak değerlendirilmiştir. Bulgular, ekonomik büyüme oranı, doğrudan yabancı sermaye yatırımları ve ticaret hadlerinin ekonomik karmaşıklığı artırdığını; düşük beşeri sermaye düzeyi ile doğal kaynak rantına yüksek bağımlılığın ise azaltıcı etkiler yarattığını göstermektedir. Bulgular, politika çıkarımlarında içsellik probleminin dikkate alınmasının önemini vurgulamaktadır.

Keywords

Ekonomik Karmaşıklık , E-7 Ülkeleri , Panel Veri İçsellik

References

• Acemoglu, D. (2002). Technical change, inequality, and the labor market. Journal of Economic Literature, 40(1), 7-72. https://doi.org/10.1257/0022051026976
• Agosin, M., Alvarez, R. and Baravo-Ortega, C. (2011). Determinants of export diversification around the world: 1962-2000. The World Economy, 35(3), 295-315. https://doi.org/10.1111/j.1467-9701.2011.01395.x
• Arellano, M. and 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), 277-297. https://doi.org/10.2307/2297968
• Auty, R.M. (2000). How natural resources affect economic development. Development Policy Review, 18(4), 347-364. Retrieved from https://library.fes.de/
• Badinger, H. and Tondl, G. (2003). Trade, human capital and innovation: the engines of European regional growth in the 1990s. In B. Fingleton (Ed.), European regional growth (pp. 215-239). Berlin: Springer. https://doi.org/10.1007/978-3-662-07136-6_8
• Balasubramanyam, V.N. and Mahambare, V. (2003). FDI in India. Transnational Corporations, 12(2), 45-72. Retrieved from https://unctad.org/
• Banson, K.E., Nguyen, N.C., Bosch, O.J. and Nguyen, T.V. (2015). A systems thinking approach to address the complexity of agribusiness for sustainable development in Africa: A case study in Ghana. Systems Research and Behavioral Science, 32(6), 672–688. https://doi.org/10.1002/sres.2270
• Baum, C.F., Schaffer, M.E. and Stillman, S. (2003). Instrumental variables and GMM: Estimation and testing. The Stata Journal, 3(1), 1-31. https://doi.org/10.1177/1536867X0300300101
• Bound, J., Jaeger, D.A. and Baker, R.M. (1995). Problems with instrumental variables estimation when the correlation between the instruments and the endogenous explanatory variable is weak. Journal of the American Statistical Association, 90(430), 443-450. https://doi.org/10.1080/01621459.1995.10476536
• Brambilla, I. (2009). Multinationals, technology, and the introduction of varieties of goods. Journal of International Economics, 79(1), 89–101 https://doi.org/10.1016/j.jinteco.2009.06.004
• Brückner, M. (2010). Natural resource dependence, non-tradables, and economic growth. Journal of Comparative Economics, 38(4), 461-471. https://doi.org/10.1016/j.jce.2010.06.002
• Costinot, A. and Rodríguez-Clare, A. (2014). Trade theory with numbers: Quantifying the consequences of globalization. In G. Gopinath, E. Helpman and K. Rogoff (Eds.), Handbook of international economics (pp. 197-261). https://doi.org/10.1016/B978-0-444-54314-1.00004-5
• Crespo, N. and Fontoura, M.P. (2007). Determinant factors of FDI spillovers–what do we really know? World Development, 35(3), 410-425. https://doi.org/10.1016/j.worlddev.2006.04.001
• Docquier, F. and Rapoport, H. (2012). Globalization, brain drain, and development. Journal of Economic Literature, 50(3), 681-730. https://doi.org/10.1257/jel.50.3.681
• Driscoll, J.C. and Kraay, A.C. (1998). Consistent covariance matrix estimation with spatially dependent panel data. Review of Economics and Statistics, 80(4), 549-560. https://doi.org/10.1162/003465398557825
• Fang, Z. (2016). Data on examining the role of human capital in the energy-growth nexus across countries. Data in Brief, 9, 540-542. https://doi.org/10.1016/j.dib.2016.09.027
• Felipe, J., Kumar, U., Abdon, A. and Bacate, M. (2012). Product complexity and economic development. Structural Change and Economic Dynamics, 23(1), 36–68. https://doi.org/10.1016/j.strueco.2011.08.003
• Gala, P., Camargo, J. and Magacho, G. (2017). The resource curse reloaded: revisiting the Dutch disease with economic complexity analysis. Real World Economics Review, 81, 118-134. Retrieved from https://rwer.wordpress.com/
• Gujarati, D.N. and Porter, D.C. (2009). Basic econometrics (5th ed.). Ohio: McGraw-Hill.
• Hakimi, A. and Hamdi, H. (2016). Trade liberalization, FDI inflows, environmental quality and economic growth: A comparative analysis between Tunisia and Morocco. Renewable and Sustainable Energy Reviews, 58, 1445–1456. https://doi.org/10.1016/j.rser.2015.12.280
• Hartmann, D., Guevara, M.R., Jara-Figueroa, C., Aristarán, M. and Hidalgo, C.A. (2017). Linking economic complexity, institutions, and income inequality. World Development, 93, 75-93. https://doi.org/10.1016/j.worlddev.2016.12.020
• Hausmann, R., Hwang, J. and Rodrik, D. (2007). What you export matters. Journal of Economic Growth, 12, 1-25. https://doi.org/10.1007/s10887-006-9009-4
• Hausmann, R., Yildirim, M.A., Chacua, C., Hartog, M. and Matha, S.G. (2024). Innovation policies under economic complexity (WIPO Economic Research Working Paper Series No. 79). Retrieved from https://www.wipo.int/edocs/pubdocs/en/wipo-pub-econstat-wp-79-en-innovation-policies-under-economic-complexity.pdf
• Herzer, D. and Nowak-Lehnmann, D.F. (2006). What does export diversification do for growth? An econometric analysis. Applied Economics, 38(15), 1825-1838. https://doi.org/10.1080/00036840500426983
• Hidalgo, C.A. and 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. and Hausmann, R. (2007). The product space conditions the development of nations. Science, 317(5837), 482-487. https://doi.org/10.1126/science.1144581
• Hoechle, D. (2007). Robust standard errors for panel regressions with cross-sectional dependence. The Stata Journal, 7(3), 281-312. https://doi.org/10.1177/1536867X0700700301
• Ikram, M., Xia, W., Fareed, Z., Shahzad, U. and 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
• Javorcik, B., Lo Turco, A. and Maggioni, D. (2017). New and improved: Does FDI boost production complexity in host countries? The Economic Journal, 128(614), 2507 2537. https://doi.org/10.1111/ecoj.12530
• Javorcik, B.S. (2004). Does foreign direct investment increase the productivity of domestic firms? In search of spillovers through backward linkages. American Economic Review, 94(3), 605–627. https://doi.org/10.1257/0002828041464605
• Kharas, H. and Gertz, G. (2010). The new global middle class: A cross-over from West to East. In C. Li (Ed.), China’s emerging middle class: Beyond economic transformation (pp. 1-14). Washington DC: Brookings Institution Press.
• Lapatinas, A. (2019). The effect of the internet on economic sophistication: An empirical analysis. Economics Letters, 174, 35–38. https://doi.org/10.1016/j.econl et.2018.10.013
• Lederman, D. and Maloney, W. (2012). Does what you export matter? In search of empirical guidance for industrial policies. Washington DC: World Bank Publications.
• Levin, A., Lin, C.F. and Chu, C.S.J. (2002). Unit root tests in panel data: asymptotic and finite-sample properties. Journal of Econometrics, 108(1), 1-24. https://doi.org/10.1016/S0304-4076(01)00098-7Get rights and content
• Liu, X. and Wang, C. (2003). Does foreign direct investment facilitate technological progress?: Evidence from Chinese industries. Research Policy, 32(6), 945-953. https://doi.org/10.1016/S0048-7333(02)00094-X
• Ndoya, H. and Bakouan, P. (2023). Does tax revenue improve economic complexity in Africa? Journal of Economic Integration, 38(2), 278-301. https://doi.org/10.11130/jei.2023.38.2.278
• Nguyen, C.P., Schinckus, C. and Su, T.D. (2023). Determinants of economic complexity: A global evidence of economic integration, institutions, and internet usage. Journal of the Knowledge Economy, 14(4), 4195-4215. https://doi.org/10.1007/s13132-022-01053-3
• OEC. (2019). Economic complexity legacy rankings (ECI). Retrieved from https://oec.world/en/rankings/eci/hs6/hs96?tab=ranking
• OEC. (2020). Economic complexity legacy rankings (ECI). Retrieved from https://oec.world/en/rankings/legacy/eci
• OECD. (2021). OECD science, technology and innovation outlook 2021: Times of crisis and opportunity. Paris: OECD Publishing.
• Ourens, G. (2013). Can the method of reflections help predict future growth? (IRES Discussion Paper No. 2013-8). Retrieved from https://sites.uclouvain.be/econ/DP/IRES/2013008.pdf
• Rodrik, D. (2011). The future of economic convergence (NBER Working Paper No. w17400). Retrieved from http://www.nber.org/papers/w17400
• Rodrik, D. (2016). Premature deindustrialization. Journal of Economic Growth, 21(1), 1-33. https://doi.org/10.1007/s10887-015-9122-3
• Romer, P.M. (1990). Endogenous technological change. Journal of Political Economy, 98(5, Part 2), S71-S102. https://doi.org/10.1086/261725
• Rosser, J.B. (2010). Is a transdisciplinary perspective on economic complexity possible? Journal of Economic Behavior & Organization, 75(1), 3–11. https://doi.org/10.1016/j.jebo.2010.03.012
• Sachs, J.D. and Warner, A.M. (1999). The big push, natural resource booms and growth. Journal of Development Economics, 59(1), 43-76. https://doi.org/10.1016/S0304-3878(99)00005-X
• Söderbom, M. (2009). Applied econometrics lecture 2: Instrumental variables, 2SLS and GMM. Retrieved from https://www.soderbom.net/lec2n_final.pdf
• Tabash, M.I., Mesagan, E.P. and 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
• Tacchella, A., Cristelli, M., Caldarelli, G., Gabrielli, A. and Pietronero, L. (2013). Economic complexity: Conceptual grounding of a new metrics for global competitiveness. Journal of Economic Dynamics and Control, 37(8), 1683–1691. https://doi.org/10.1016/j.jedc.2013.04.006
• Tebaldi, E. (2011). The determinants of high-technology exports: A panel data analysis. Atlantic Economic Journal, 39, 343-353. https://doi.org/10.1007/s11293-011-9288-9
• Tsui, K.K. (2011). More oil, less democracy: Evidence from worldwide crude oil discoveries. The Economic Journal, 121(551), 89-115. https://doi.org/10.1111/j.1468-0297.2009.02327.x
• Wooldridge, J.M. (2010). Econometric analysis of cross section and panel data. Cambridge: MIT Press.
• World Bank. (2019). World development indicators. Retrieved from https://databank.worldbank.org/source/world-development-indicators#
• Yalta, A.Y. and Yalta, T. (2021). Determinants of economic complexity in MENA Countries. JOEEP: Journal of Emerging Economies and Policy, 6(1), 5-16. Retrieved from https://dergipark.org.tr/en/pub/joeep/
• Zhu, S. and 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