Articles
DOI DOI: 10.5281/zenodo.18435770

Gender, Human Capital, and AI-Driven Labor Market Transitions in Pakistan Firm-Level and Household Evidence

Burhan Reshat Rexhepi1
  1. 1 Lahore University of Management Sciences (LUMS), Lahore, Pakistan;

Abstract

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 laborforce participation and household welfare. Policy implications emphasize skills
certification, targeted reskilling, safe commuting, childcare, and firm incentives
for inclusive technology adoption.

How to Cite

Rexhepi, B. R. (2026). Gender, Human Capital, and AI-Driven Labor Market Transitions in Pakistan Firm-Level and Household Evidence. Transnational Academic Journal of Economics, 3(1), 80–89. https://doi.org/10.5281/zenodo.18435770
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