SavedAdd your journal

Journals / Erciyes Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi / 2026 / Sayı: 74

The effects of economic growth, trade and demographic transformation on disposable income inequality: CS-ARDL analyses for Asian countries

Seçilmiş Asya ülkelerinde ekonomik büyüme, ticaret ve demografik dönüşümün harcanabilir gelir eşitsizliği üzerindeki etkileri: CS-ARDL analizi

Ömer Yılmaz(GAZİANTEP ÜNİVERSİTESİ, NİZİP MESLEK YÜKSEKOKULU)0000-0002-2325-6135
Mehmet Ali Orhan(KAHRAMANMARAŞ SÜTÇÜ İMAM ÜNİVERSİTESİ)0000-0001-7818-0433
Ali İmran Tatlıbadem(GAZİANTEP ÜNİVERSİTESİ)0000-0001-6512-7828
Pages
1–8
Publication Date
2026-08-31
🏛️Bibliographic metadata and full text access for this article are provided via DergiPark.
DergiPark Record ↗
✨

AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

The empirical findings reveal that real income per capita, trade openness, and old-age dependency ratio increase significantly and both in the short run and long run decrease disposable income inequality.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:This study empirically investigates the effects of economic growth, trade openness, and demographic transition on disposable income inequality in China, Japan, South Korea, Indonesia, India, and Türkiye using annual data covering the period 1990-2022.
📊
Key Findings & Data:The empirical findings reveal that real income per capita, trade openness, and old-age dependency ratio increase significantly and both in the short run and long run decrease disposable income inequality.
💡
Conclusion & Impact:These are significant findings for developing and emerging economies in their quest to reduce income inequality while maintaining sustainable economic development.
🔬Design:Araştırma Makalesi
📍Scope:2022 Verileri
⚙️Variables:CS-ARDL · demografik geçiş · ekonomik büyüme

Abstract

This study empirically investigates the effects of economic growth, trade openness, and demographic transition on disposable income inequality in China, Japan, South Korea, Indonesia, India, and Türkiye using annual data covering the period 1990-2022. It uses the disposable income Gini coefficient as a measure of disposable income inequality, real income per capita to represent economic growth, and the trade openness ratio for trade integration. Old-age dependency ratio is used to represent demographic transition. Because there might be cross-sectional dependence and slope heterogeneity across countries, the Panel Cross-Sectionally Augmented Autoregressive Distributed Lag (CS-ARDL) approach is used to estimate both short-run and long-run relationships. The direction of causality among the variables is also examined using the Dumitrescu Hurlin panel causality test. The empirical findings reveal that real income per capita, trade openness, and old-age dependency ratio increase significantly and both in the short run and long run decrease disposable income inequality. The estimated error-correction coefficient is negative and statistically significant, which means that short-run disequilibria gradually converge toward the long-run equilibrium path. Also, the causality results indicate predominantly bidirectional causal relationships between economic growth, trade openness, demographic transition, and income inequality. In other words, this study infers that economic growth which is inclusive and has deeper trade integration, combined with effective social transfer mechanisms related to demographic transformation, would result in a more equitable income distribution. These are significant findings for developing and emerging economies in their quest to reduce income inequality while maintaining sustainable economic development.

Özet

Bu çalışma, 1990-2022 dönemini kapsayan yıllık verileri kullanarak Çin, Japonya, Güney Kore, Endonezya, Hindistan ve Türkiye’de ekonomik büyüme, ticaret açıklığı ve demografik geçişin kullanılabilir gelir eşitsizliği üzerindeki etkilerini ampirik olarak araştırmaktadır. Kullanılabilir gelir eşitsizliğinin ölçüsü olarak kullanılabilir gelir Gini katsayısı, ekonomik büyümeyi temsil etmek için kişi başına reel gelir ve ticaret entegrasyonunu temsil etmek için ticaret açıklığı oranı kullanılmıştır. Demografik geçişi temsil etmek için yaşlılık bağımlılık oranı kullanılmıştır. Ülkeler arasında kesitsel bağımlılık ve eğim heterojenliği olabileceği için, hem kısa hem de uzun vadeli ilişkileri tahmin etmek için Panel Kesitsel Olarak Genişletilmiş Otoregresif Dağıtılmış Gecikme (CS-ARDL) yaklaşımı kullanılmıştır. Değişkenler arasındaki nedensellik yönü de Dumitrescu-Hurlin panel nedensellik testi kullanılarak incelenmiştir. Ampirik bulgular, kişi başına reel gelir, ticaret açıklığı ve yaşlılık bağımlılık oranının önemli ölçüde arttığını ve hem kısa hem de uzun vadede kullanılabilir gelir eşitsizliğini azalttığını ortaya koymaktadır. Tahmin edilen hata düzeltme katsayısı negatif ve istatistiksel olarak anlamlıdır; bu da kısa vadeli dengesizliklerin kademeli olarak uzun vadeli denge yoluna doğru yakınsadığı anlamına gelir. Ayrıca, nedensellik sonuçları, ekonomik büyüme, ticaret açıklığı, demografik geçiş ve gelir eşitsizliği arasında ağırlıklı olarak çift yönlü nedensel ilişkiler olduğunu göstermektedir. Başka bir deyişle, bu çalışma, kapsayıcı ve daha derin ticaret entegrasyonuna sahip ekonomik büyümenin, demografik dönüşümle ilgili etkili sosyal transfer mekanizmalarıyla birleştiğinde, daha adil bir gelir dağılımına yol açacağını öne sürmektedir. Bunlar, sürdürülebilir ekonomik kalkınmayı korurken gelir eşitsizliğini azaltma arayışındaki gelişmekte olan ve yükselen ekonomiler için önemli bulgulardır.

🔖

Cite This Article

Yılmaz, Ö., Orhan, M. A., Tatlıbadem, A. İ. (2026). The effects of economic growth, trade and demographic transformation on disposable income inequality: CS-ARDL analyses for Asian countries. Erciyes Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, Sayı: 74, 1–8. https://doi.org/10.18070/erciyesiibd.1882163

📄 Full Text PDF Viewer

📄

PDF görüntüleyici hazırlanıyor...

References (37)

  1. Autor, D. H., Dorn, D., & Hanson, G. H. (2016). The China shock: Learning from labor market adjustment to large changes in trade. Annual Review of Economics, 8, 205–240. https://doi.org/10.1146/annurev-economics-080315-015041
  2. Bloom, D. E., Canning, D., & Fink, G. (2010). Implications of population aging for economic growth. Oxford Review of Economic Policy, 26(4), 583–612. https://doi. org/10.1093/oxrep/grq038
  3. Bourguignon, F. (2018). World changes in inequality: An overview of facts, causes, consequences, and policies. CESifo Economic Studies, 64(3), 345–370. https://doi. org/10.1093/cesifo/ifx028
  4. 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
  5. Bucak, Ç., & Saygılı, F. (2022). The relationship between trade openness, income inequality, and human development: Panel data analysis. Ege Strategic Research Journal, 13(2), 23–41. https://doi.org/10.18354/esam.1089120
  6. Cengiz, V., & Demir, S. (2023). Trade openness, financial openness and income inequality: Empirical evidence for MIST countries. International Journal of Economics, Business and Policy, 7(1), 16–27. https://doi.org/10.29216/ueip.1181188
  7. Chudik, A., & Pesaran, M. H. (2015). Common correlated effects estimation of heterogeneous dynamic panel data models with weakly exogenous regressors. Journal of Econometrics, 188(2), 393–420. https://doi.org/10.1016/j.jeconom.2015.03.007
  8. Dorn, F., Fuest, C., & Potrafke, N. (2021). Trade openness and income inequality: New empirical evidence. Economic Inquiry, 60(1), 202–223. https://doi.org/10.1111/ecin.13018 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
  9. Feenstra, R. C., & Hanson, G. H. (1999). The impact of outsourcing and high-technology capital on wages: Estimates for the United States, 1979–1990. The Quarterly Journal of Economics, 114(3), 907–940.
  10. Gao, X., & Fan, M. (2023). The effect of income inequality and economic growth on carbon dioxide emissions. Environmental Science and Pollution Research, 30(24), 65149 65159. https://doi.org/10.1007/s11356-023-27009-x
  11. Han, V. (2022). Examining the relationship between trade openness and income inequality in BRICS-T countries: A panel VAR approach. Journal of Economic Policy and Finance Research, 7(3), 677–696. https://doi.org/10.30784/epfad.1062453
  12. Kanbur, R., & Zhuang, J. (2013). Urbanization and inequality in Asia. Asian Development Review, 30(1), 131–147. https://doi.org/10.1162/ADEV_a_00006
  13. Kavya, T. B., & Shijin, S. (2020). The nexus of economic development, financial development, and income inequality. Borsa Istanbul Review, 20(1), 80–93. https://doi. org/10.1016/j.bir.2019.12.002
  14. Khan, M., Destek, M., & Khan, Z. (2024). Income inequality and artificial intelligence: Globalization and age dependency for developed countries. Social Indicators Research, 176, 1207–1233. https://doi.org/10.1007/s11205-024-03493-7
  15. Kuznets, S. (1955). Economic growth and income inequality. The American Economic Review, 45(1), 1–28.
  16. Lazar, D., & Litan, C. M. (2024). Inequality, growth, and structural transformation: New evidence from a post-communist economy. Comparative Economic Studies, 66(2), 236–260. https://doi.org/10.1057/s41294-023-00214-w
  17. Lee, K., & Vu, T. V. (2020). Economic complexity, human capital and income inequality: A cross-country analysis. The Japanese Economic Review, 71(4), 695–718. https://doi. org/10.1007/s42973-019-00026-7
  18. Lee, R., & Mason, A. (2011). Generational economy: A global analysis of consumption, income, and transfers. Edward Elgar Publishing.
  19. Michael, C., & Roupakias, S. (2020). The effect of military spending on income inequality: Evidence from NATO countries. Empirical Economics, 58(3), 1305–1337. https://doi. org/10.1007/s00181-018-1576-7
  20. Milanovic, B. (2000). The median-voter hypothesis, income inequality, and income redistribution: An empirical test with the required data. European Journal of Political Economy, 16(3), 367–410. https://doi.org/10.1016/S0176-2680(00)00014-8
  21. Nguyen, L., Taisei, K., & Tomoo, I. (2025). The causal relationship between income inequality and economic growth: Evidence from panel data for Vietnam. Applied Economics, 57(19), 2333–2347. https://doi.org/10.1080/00036846.2024.2323551
  22. Organisation for Economic Co-operation and Development (OECD). (2023). Income distribution database (IDD). https://www.oecd.org/social/income-distribution database.htm
  23. Pesaran, M. H. (2004). General diagnostic tests for cross-section dependence in panels (IZA Discussion Paper No. 1240). Institute for the Study of Labor. https://dx.doi. org/10.2139/ssrn.572504
  24. Pesaran, M. H. (2006). Estimation and inference in large heterogeneous panels with a multifactor error structure. Econometrica, 74(4), 967–1012. https://doi.org/10.1111/ j.1468-0262.2006.00692.x
  25. 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
  26. 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
  27. Piketty, T. (2014). Capital in the twenty-first century (A. Goldhammer, Trans.). Belknap Press.
  28. Rodrik, D. (1998). Why do more open economies have bigger governments? Journal of Political Economy, 106(5), 997–1032. https://doi.org/10.1086/250038
  29. Seabela, M., Ogujiuba, K., & Eggink, M. (2024). Determinants of income inequality in South Africa: A vector error correction model approach. Economies, 12(7), 169. https:// doi.org/10.3390/economies12070169
  30. Solt, F. (2020). Measuring income inequality across countries and over time: The standardized world income inequality database. Social Science Quarterly, 101(3), 1183–1199. https://doi.org/10.1111/ssqu.12795
  31. Soyhan, S., & Mollavelioğlu, M. Ş. (2025). The effect of trade openness and foreign direct investment on income inequality: An examination of Türkiye. Pamukkale Journal of Eurasian Socioeconomic Studies, 12(2), 100–116. https://doi.org/10.34232/ pjess.1836802
  32. Swamy, P. A. V. B. (1970). Efficient inference in a random coefficient regression model. Econometrica, 38(2), 311–323. https://doi.org/10.2307/1913012
  33. Tabash, M. I., Elsantil, Y., Hamadi, A., & Drachal, K. (2024). Globalization and income inequality in developing economies: A comprehensive analysis. Economies, 12(1), Article 23. https://doi.org/10.3390/economies12010023
  34. Tekbaş, M. (2022). The relationship between economic growth, financial development, and income inequality in ASEAN-5 countries. Journal of Mehmet Akif Ersoy University Economics and Administrative Sciences Faculty, 9(2), 717–741. https://doi. org/10.30798/makuiibf.691416
  35. Topcu, M., & Tugcu, C. T. (2020). The impact of renewable energy consumption on income inequality: Evidence from developed countries. Renewable Energy, 151, 1134–1140. https://doi.org/10.1016/j.renene.2019.11.103
  36. Wolde, M., Sera, L., & Merra, T. M. (2022). Causal relationship between income inequality and economic growth in Ethiopia. Cogent Economics & Finance, 10(1), 2087299. https://doi.org/10.1080/23322039.2022.2087299
  37. World Bank. (2022). World development indicators. https://databank.worldbank.org/source/ world-development-indicator