Can Digital Skills Empower Disadvantaged Castes and Women? Evidence From India

Che-Wei Liu, Terence J.V. Saldanha, Sunil Mithas

Production and Operations Management · 2025 · Volume 34 · Issue 4

Abstract

How do digital skills influence individuals’ wages in contexts where caste-based and gender-based social stratification play an important role? We draw on sociology and economics literature to argue that the returns to digital skills in such contexts are shaped by caste and gender, and that digital skills empower disadvantaged individuals to increase their wages. Our empirical analysis of a rich micro-dataset on more than 20,000 individuals across all states in India from two waves of Indian Human Development Survey in 2005 and 2011 yields two key findings. First, we find that the positive returns to digital skills are greater for individuals from the Scheduled Castes and Scheduled Tribes in India than for individuals from other castes. Second, we find that the returns to digital skills are greater for women than for men. We also find that movement up the skilled occupation ladder is a mechanism that mediates the relationship between digital skills and wages. Our post hoc exploratory analyses suggest that among individuals from the lowest castes (Scheduled Castes and Scheduled Tribes), the returns to digital skills are greater for women than for men, and that returns to digital skills are lower in regions with less developed infrastructure and lower literacy rates than in other regions. We discuss the implications of our findings for diversity, equity, and inclusion research in operations management.

Keywords

Women empowerment, digital skills, caste, gender, India, wages, digitization, scheduled castes, scheduled tribes

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