Agents of digitalization: gendered employment patterns and broadband access across Asian economies

While much of the literature treats broadband infrastructure as a catalyst for economic growth and in-novation, this study reverses the causal lens by analyzing broadband diffusion as a structural outcome of socioeconomic readiness. Drawing on a unique dataset covering 37 Asian economies from 2002 to 2008, this study develops a mixed analytical framework that integrates macroeconomic drivers and meso-regional structures, specifically the sectoral composition of female employment, as latent indica-tors of digital demand and institutional capacity. This approach merges classical econometric tech-niques – including Robust Least Squares (RLS) and Method of Moments Quantile Regression (MMQR), with advanced Machine Learning (ML) models, such as Generalized Additive Model with Boosting (GAMBoost), GAMBoost for Location, Scale, and Shape (GAMBoostLSS), and Shapley value decomposition. Moreover, Artificial Neural Networks (ANNs) are employed for robustness checks. The empirical findings show that female employment patterns are not merely socioeconomic consequences, but are also crucial in shaping digitalization trajectories. In particular, higher rates of female labor participation in agriculture are negatively associated with broadband penetration. In con-trast, greater female employment in public services and knowledge-intensive sectors predicts stronger adoption of digital infrastructure. These results also interrogate the marginal role of basic education and the paradoxical association between mortality and broadband expansion. By bridging gendered labor patterns, spatial inequality, and digital infrastructure, this study suggests a tailored, ecosystemic perspective on the social structuring of broadband diffusion.