Editorials/Opinions Analysis For UPSC 15 July 2025

Content :

1. Women, STEM careers and a more receptive industry
2. Two Unequal

Women, STEM careers and a more receptive industry

Source : The Hindu

Context: Why This Matters

• World Youth Skills Day (15 July) highlights the role of skilling in employment and economic development.
• India shows a paradox: high share of women STEM graduates (43%), yet only 27% of the STEM workforce is female.
• Despite rising Female Labour Force Participation Rate (FLFPR), there is a persistent education-to-employment gap, especially in technical sectors.

Relevance : GS 1(Society ) ,GS 2(Social Issues)

Practice Question : Despite a high share of female STEM graduates, women remain underrepresented in India’s STEM workforce. Discuss the structural barriers and suggest a multi-stakeholder strategy to close the education-to-employment gap for women in STEM. (15 marks)

Key Data Points

Government Efforts on STEM & Skilling

1. Policy Framework

• NEP 2020: Integrated academics with life skills & technical education.
• Skill India, Digital India, PM Vishwakarma Yojana: Expanding access to vocational training.
• Gender Budget (2025–26): Increased from 6.8% to 8.8%; ₹4.49 lakh crore towards gender-focused schemes.

2. Infrastructure Expansion

• New National Skill Training Institutes (NSTIs) and revitalised ITIs, especially in rural areas.

3. Budgetary Incentives

• Term loans for women entrepreneurs.
• Technology-driven training support for high-tech careers.

Challenges & Barriers

1. Education-to-Workforce Disconnect

• STEM education doesn’t translate to jobs due to workplace culture, societal roles, and lack of structured transitions.

2. Cultural & Social Norms

• Deep-rooted stereotypes: “Mechanical is masculine”, “Coding is for boys”.
• Women exit STEM not due to capability, but due to unwelcoming environments and lack of family awareness.

3. Urban-Rural Divide

• Rural FLFPR is higher, but urban areas face formal sector barriers and low representation in high-paying STEM roles.

4. Life-Cycle Career Transitions

• Lack of workplace policies for:
  • Maternity & caregiving support.
  • Career breaks and re-entry pathways.

Industry as the Missing Link

• Current scenario: Industry is passive recruiter, not active enabler.
• Industry must:
  • Provide mentorship, internships, and direct hiring pathways.
  • Ensure workplace safety, flexible policies, and gender-sensitive HR practices.
  • Partner with communities to challenge stereotypes and build aspirational role models.

Good Practices: Case Study – WeSTEM

• UN Women’s WeSTEM, in partnership with Micron Foundation and state govts (MP & Gujarat):
  • Provides STEM training to young women.
  • Engages families, conducts safety sessions, introduces women role models.
  • Aims to shift mindsets along with skill-building.

Way Forward: Blueprint for Inclusive STEM Skilling

• For Government:
• Launch dedicated STEM transition schemes for women post-graduation.
• Incentivize private companies hiring women in core technical roles.

• For Industry:
• Integrate skilling with employment pipelines.
• Institutionalise return-to-work programmes.
• Invest in gender-sensitive design of workplaces (infrastructure, safety, flexibility).

• For Society:
• Promote community sensitisation around non-traditional roles for women.
• Break the stigma of technical/vocational jobs for women.

Two Unequal

Source : The Indian Express

Context

• The article critiques recent claims that India is both:
  • The most equal country in consumption inequality, and
  • One of the most unequal in income inequality.
• These claims arise from data by World Bank (via PIP) and World Inequality Database (WID).

Relevance : GS 2(Social Issues)

Practice Question : India is ranked among the most equal countries in consumption inequality, yet highly unequal in income estimates. Critically examine this paradox, and discuss the implications for welfare and taxation policy. (15 marks)

Key Terms and Data

Key Insights from the Article

1. India’s Gini of 25.5 is based on consumption, not income

• It reflects relative equality in expenditure, not earnings.
• Consumption inequality is generally lower than income inequality in all countries.

2. No official data exists for income Gini in India

• India hasn’t conducted a national income distribution survey in recent years.
• All income inequality estimates for India are modelled or inferred from limited sources.

3. WID uses synthetic methods for income estimation

• Constructs income inequality from tax data, surveys, and assumptions.
• These methods are not directly comparable with survey-based consumption data.

4. Comparison between income and consumption Gini is flawed

• Comparing Gini values across different metrics (income vs. consumption) or sources (survey vs. synthetic) leads to misleading conclusions.
• Gini indices are only meaningful within the same category.

Why the Confusion?

• The same term “Gini Index” is used for both consumption and income, though they capture different dimensions.
• The public and media often interpret Gini values without distinguishing the source or category.

Conceptual Clarification for UPSC

• Consumption Inequality: Affects access to goods and services; reflects actual standard of living.
• Income Inequality: Captures earnings and wealth gaps; often higher than consumption inequality.
• Gini Index (0 to 1 or 0 to 100): Closer to 0 = more equal; closer to 1/100 = more unequal.

Limitations of Global Comparisons

• WID estimates for India are not based on complete income data—rely on assumptions due to lack of official data.
• Cross-country inequality rankings vary based on whether income or consumption is measured.

Implications for India

1. India is indeed low on consumption inequality — based on credible, survey-based data.
2. Income inequality remains unclear — due to absence of official nationwide income surveys.
3. Policy relevance:
   1. Need for official income distribution surveys to assess inequality accurately.
   1. Better distinction and communication between types of inequality in public discourse.
   1. Reliable inequality metrics are crucial for targeting welfare and taxation policy.

Additional Dimensions to consider

1. Triangulating Inequality: Income, Wealth, and Consumption

• Consumption inequality (lowest globally for India) measures day-to-day spending—less volatile, but doesn’t capture savings or asset accumulation.
• Income inequality measures earnings—subject to fluctuations and underreported in surveys.
• Wealth inequality (not discussed in the article) is often higher and more persistent.
  • For example, Oxfam 2024 report estimated top 10% Indians own ~77% of wealth.

2. Implications for Welfare Policy

• Low consumption inequality may reflect effective welfare distribution (PDS, PM-KISAN, Ujjwala, etc.).
• But without income data, it’s hard to assess:
  • Labour market inequality
  • Effectiveness of direct benefit transfers (DBTs)
• Policy implication: Targeting may be suboptimal if based on outdated or indirect income estimates.

3. Data Governance Gap

• India lacks a regular, dedicated income and wealth distribution survey.
• NSSO collects consumption data, not detailed income data.
• Suggestion for reform:
  • Conduct a Periodic Income Distribution Survey (PIDS).
  • Integrate with e-Shram, Aadhaar-seeded economic profiles for targeted welfare design.

4. Global Comparability Challenges

• Different countries use different methodologies—some tax-based, some survey-based.
• For example:
  • Nordic countries use income-tax records.
  • Developing countries like India depend on household surveys.
• Implication: Global inequality rankings should be taken with caution.

5. Urban-Rural and Regional Inequality

• Even if national Gini is low, intra-state disparities (e.g., Bihar vs Kerala) may be high.
• Similarly, urban India may show rising income inequality due to informal sector stress and gig economy precarity.

6. Inequality and Growth

• Traditional view: Some inequality spurs growth by incentivising productivity.
• Modern consensus: High inequality hurts growth by reducing demand and increasing social conflict (OECD, IMF studies).
• India’s case: Balancing growth with equity remains key for inclusive development.

Conclusion :

While India’s low consumption inequality is backed by credible data, the lack of official income distribution surveys makes cross-metric comparisons misleading. A clear distinction between income, consumption, and wealth inequality is essential for designing equitable and evidence-based welfare policies.

Disclaimer : The views and opinions expressed here are based on the original article published in THE INDIAN EXPRESSand do not reflect the official stance of Legacy IAS Academy. This content is provided solely for Academic purposes.