Clustering Emerging Economies by Broadband Diffusion Trajectories

This study utilizes machine learning (ML) techniques to identify critical factors affecting broadband diffusion and its impact on economic growth in emerging economies. A K-means clustering algorithm has been applied to classify 29 emerging and developing economies using factors such as Information and Communication Technology (ICT) infrastructure, broadband adoption, and foreign direct investment (FDI). Several predictive ML models, such as Extreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM), have been employed to assess the economic effects of broadband adoption and the key drivers of digitaldriven growth. The analysis reveals the significant correlations between ICT development, broadband penetration, FDI, and economic growth, highlighting the critical role of digital infrastructure and targeted policy interventions in fostering sustainable economic development in emerging economies.