Introduction

As India moves toward an AI-intensive economic model, access to time for learning and self-development has become a decisive factor in labour market outcomes. Time Use Survey (2019) data reveals that working women in India spend 10 hours less per week on self-development than men, primarily due to disproportionate unpaid care responsibilities. This time deficit risks excluding women from AI-enabled productivity gains, reinforcing occupational segregation and low-wage employment.

Why in the News?

The article highlights a first-order structural risk: while AI adoption accelerates, women’s ability to upskill is constrained by time poverty rather than lack of intent or capability. This marks a departure from earlier debates that focused on access to education or labour participation. The scale of the issue is substantial, women work longer total hours per day than men (9.6 vs 8.6 hours) when paid and unpaid work are combined. Yet, women lose out on rest, leisure, and learning time. This creates a persistent disadvantage in an economy increasingly driven by algorithmic efficiency and skill intensity.

What does India’s Time Use Data reveal about gendered work patterns?

1. Combined Workload: Working women spend 9.6 hours/day on paid and unpaid work compared to 8.6 hours/day for men.
2. Unpaid Care Work: Women undertake nearly double the unpaid work of men, especially in childcare, eldercare, cooking, and cleaning.
3. Age-Specific Burden: The gender gap peaks in the 30-39 age group, coinciding with prime career years and child-rearing responsibilities.

Why does unpaid work translate into an upskilling disadvantage?

1. Time Deficit: Women spend 10 fewer hours per week on self-development activities than men.
2. Opportunity Cost: Reduced time for skill acquisition limits transition to high-value, AI-complementary roles.
3. Cumulative Effect: Persistent time poverty compounds across years, reinforcing occupational stagnation.

How does AI intensify existing labour market inequalities for women?

1. Algorithmic Bias: AI performance metrics penalise career breaks and irregular work histories.
2. Occupational Traps: Women are overrepresented in low-paid, automation-prone jobs and unpaid family work.
3. Invisible Labour: Care work remains uncaptured by productivity metrics, excluding women from AI-led recognition systems.

Why are women more vulnerable to exclusion from AI-led productivity gains?

1. Skill Transition Barriers: AI rewards continuous learning, which women lack time to pursue.
2. Sectoral Segregation: Women’s concentration in informal and care-intensive sectors limits AI exposure.
3. Labour Force Exit: Over 40% of women outside the labour force cite household responsibilities as the primary reason.

Why is this a macroeconomic and governance challenge, not just a gender issue?

1. Productivity Loss: Underutilisation of women’s human capital reduces aggregate growth.
2. Demographic Dividend Risk: Exclusion of women weakens India’s long-term workforce potential.
3. Inclusive Growth Failure: AI-led growth without gender equity risks widening income and skill inequalities.

Policy Implications 

1. Workplace Redesign
   1. Time Recognition: Integrates unpaid care work into productivity assessments.
   2. Flexibility: Supports hybrid work models aligned with care responsibilities.
2. Infrastructure Support
   1. Care Services: Expands childcare, eldercare, and safe public transport.
   2. Utilities Access: Reduces time spent on water, fuel, and energy collection.
3. Skill Policy Reorientation
   1. Time-Saving Learning Models: Encourages modular, flexible, and remote upskilling formats.
   2. Targeted AI Skilling: Prioritises women-centric AI and digital training initiatives.
4. Budgetary Prioritisation
   1. Gender Budgeting: Aligns public expenditure with time-saving social infrastructure.
   2. Outcome Metrics: Tracks women’s skill mobility and wage progression.

Conclusion:

An AI-driven growth strategy that overlooks women’s time poverty and unpaid care work risks deepening structural inequalities and weakening India’s human capital base. Integrating care responsibilities into economic planning, skill policy, and public expenditure is essential to ensure that technological progress translates into inclusive, equitable, and sustainable development.

PYQ Relevance

[UPSC 2023] Distinguish between ‘care economy’ and ‘monetized economy’. How can care economy be brought into monetized economy through women empowerment?

Linkage: The question addresses structural issues of inclusive growth, gender inequality, and human capital formation, which are recurring themes in GS-III (Economy) and GS-I (Society).

Get an IAS/IPS ranker as your 1: 1 personal mentor for UPSC 2024

Attend Now