This study examines how artificial intelligence (AI) and digital adoption reshape labor-market transitions in Pakistan and whether these changes widen or narrow gender gaps through human-capital channels. We integrate household microdata with firm-linked sector-region indicators to estimate how exposure to AI-relevant task content predicts transitions across employment states, occupation and sector switching, formalization, and earnings. The empirical strategy combines task-exposure indices mapped to Pakistan’s occupational structure, sectoral digital intensity proxies, and decomposition methods that separate endowment from return effects. Results indicate that AI exposure is associated with higher mobility toward non-routine analytical and interactive work for workers with secondary and tertiary education, but the gains are uneven: women face higher transition frictions, especially in urban services and export-linked manufacturing. Counterfactual simulations suggest that closing female human-capital gaps and reducing care-related constraints could materially increase female labor-force participation and household welfare. Policy implications emphasize skills certification, targeted reskilling, safe commuting, childcare, and firm incentives for inclusive technology adoption.
Hadia Majid (Sat,) studied this question.
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