مجلة الدراسات التجارية والاقتصادية المعاصرة
Volume 9, Numéro 2, Pages 332-346
2026-07-20
International Digital Competitiveness And Economic Growth In Emerging Economies: A Comparative Empirical Study Using Advanced Machine Learning And Modern Panel Data Techniques
Authors : Hadjadj Mustapha . Melouah Mohammed .
Abstract
This paper investigates whether and how international digital competitiveness contributes to economic growth in emerging economies. Building on the expanding literature on the digital economy and development, we assemble a multi-country panel dataset for 20–30 emerging economies over the period 2010–2024, combining macroeconomic indicators from the World Bank with digital competitiveness metrics from the IMD World Digital Competitiveness Ranking and the Digital Evolution Index (IMD World Competitiveness Center, 2024; Digital Planet, 2025). We estimate a series of fixed-effects, random-effects, and dynamic System GMM models to assess the impact of digital competitiveness on real GDP per capita growth, while controlling for investment, trade openness, human capital, and institutional quality (M'hamdi et al., 2025; Zhang et al., 2022). In addition, we apply machine-learning algorithms (Random Forest and Gradient Boosting) to explore potential non-linearities and heterogeneous effects across countries. This paper investigates whether and how international digital competitiveness contributes to economic growth in emerging economies. Building on the expanding literature on the digital economy and development, we assemble a multi-country panel dataset for 20–30 emerging economies over the period 2010–2024, combining macroeconomic indicators from the World Bank with digital competitiveness metrics from the IMD World Digital Competitiveness Ranking and the Digital Evolution Index (IMD World Competitiveness Center, 2024; Digital Planet, 2025). We estimate a series of fixed-effects, random-effects, and dynamic System GMM models to assess the impact of digital competitiveness on real GDP per capita growth, while controlling for investment, trade openness, human capital, and institutional quality (M'hamdi et al., 2025; Zhang et al., 2022). In addition, we apply machine-learning algorithms (Random Forest and Gradient Boosting) to explore potential non-linearities and heterogeneous effects across countries. The paper also examines cross-border e-commerce and ICT service exports as channels through which digital competitiveness influences growth (Gupta & Bansal, 2019; UNCTAD, 2024). The study is expected to show that higher digital competitiveness is associated with stronger and more resilient economic growth, particularly in upper-middle-income emerging economies, and that machine-learning models outperform traditional econometric models in predictive performance.
Keywords
Digital competitiveness; digital economy; emerging economies; economic growth; cross-border e-commerce; panel data; machine learning.
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