Editorials/Opinions Analysis For UPSC 30 July 2025

Content

1. Adopt formalisation to power productivity growth
2. Why the Gini Index is wrong about India

Adopt formalisation to power productivity growth

Structural Shift in Formal Manufacturing Employment

• Contract labour share rose from 20% (1999-2000) to 40.7% (2022-23) across industries, per Annual Survey of Industries (ASI).
• Indicates a doubling in informalisation within the formal sector — a core concern among academics and policymakers.
• Marks a structural transformation where cost-saving trumps productivity, contrary to the intended flexibility rationale.

Relevance : GS 3(Economy – Labour Force)

Practice Question :

Plight of Contract Workers

• Hired via third-party contractors, contract workers are often excluded from core protections under the Industrial Disputes Act, 1947.
• No safeguards against arbitrary dismissal, retrenchment, or fair layoff procedures.
• Weak bargaining power and lack of union representation enable systemic exploitation.

Wage Disparities (2018–19)

• Contract workers earned 14.47% less than regular workers.
• Wage gaps sharper in larger enterprises:
  • Large firms: 31% lower
  • Medium firms: 23% lower
  • Small firms: 12% lower
• Daily labour cost for contract workers was 24% lower on average.
• In 9 industry segments, contract worker costs were <50% of regular worker costs; in some cases, up to 85% lower.

Impact on Labour Productivity

• While contract labour offers operational flexibility, excessive reliance leads to:
  • High labour turnover
  • Reduced investment in skill development
  • Moral hazard and worker disengagement due to principal-agent misalignment.

Empirical Findings (1999–2019, plant-level ASI data)

• Labour productivity in Contract Labour-Intensive (CLI) enterprises is 31% lower than in Regular Labour-Intensive (RLI) units.
  • Small firms (<100 workers): 36% productivity gap
  • Medium firms (100–300 workers): 23%
  • Labour-intensive sectors: 42%
• Negative differentials persist even after controlling for firm- and state-level factors.

Positive Deviations

• High-skill CLI enterprises: 5% higher productivity than low-skill CLI firms.
• Large, high-skill CLI enterprises: 20% productivity advantage.
• Large, capital-intensive CLI firms: 17% productivity gain.
• However, these account for only ~20% of formal manufacturing; 80% suffer due to misuse of contract labour.

Policy Landscape & Critique

 Labour Code on Industrial Relations (2020)

• Aims to permit direct hiring of fixed-term workers (bypassing contractors).
• Mandates basic benefits (e.g., gratuity, leave).
• Yet to be implemented; unions fear further erosion of job security.

Suggested Reforms

• Incentivise longer-term contracts with:
  • Subsidies in social security contributions (e.g., EPFO/ESIC)
  • Access to government skilling programs
• Could improve workforce retention, enable skill accumulation, and reduce exploitative short-termism.

Revival of PMRPY Needed

• Pradhan Mantri Rojgar Protsahan Yojana (PMRPY) (2016–2022):
  • Government paid 12% employer contribution to EPF/EPS.
  • 1 crore+ employees benefited.
• Discontinuation in March 2022 stalled momentum toward formalisation.
• Revival could deter misuse of contract labour and incentivise formal hiring.

Conclusion

• Contractualisation, when driven by cost-cutting, has long-term costs: low productivity, weak skills, and poor job quality.
• Reform should balance flexibility with fairness, aligning with long-term industrial productivity and worker dignity.
• Formalisation is not just a social goal—it is also a strategic economic imperative for sustained manufacturing competitiveness.

Why the Gini Index is wrong about India

Context – Gini Paradox: Statistical Equality vs Lived Inequality

• India ranked among the most equal societies globally (Gini Index: 25.5) in 2025.
• Contradiction: Ground-level realities expose deep structural inequalities across economic, gender, digital, and social dimensions.
• Raises critical questions on the methodology and representativeness of the Gini Index, especially in contexts with large informal economies.

Relevance : GS 2 (Inequalities ,Poverty)

Economic & Wealth Inequality

• Income concentration: Top 1% earned 22.6% of national income in 2022–23 (Source: Income and Wealth Inequality in India, 1922–2023).
• Wealth inequality under-reported due to:
  • Informal employment dominance.
  • Low tax base (only ~10% of adults pay income tax).
• Real-life contrast: A chauffeur earning ₹3 lakh/year drives a ₹30 lakh luxury car — underscores systemic income disparity.

Gender Inequality

• Workforce participation: Women form only 35.9% of the workforce.
• Leadership gap: Only 12.7% of mid- and senior-level leadership roles held by women in 2024.
• Start-up landscape: Women-run start-ups form only 7.5% of all active ventures, despite India having the 3rd largest startup ecosystem.
• Social factors: Norms around inheritance, education spending on girls, and domestic roles continue to limit women’s access to opportunity.

Digital Divide

• Despite national efforts, access to functional technology remains unequal:
  • Only 52.7% of schools have working computers.
  • 53.9% of schools have internet access.
• Broadband penetration (urban + rural households): Just 41.8%.
• Education in emergencies (e.g., Delhi’s air pollution shutdowns) further isolates those without digital infrastructure.

Educational Inequality

• Students from tech-access schools gain digital fluency, reinforcing class advantages.
• Lack of access leads to lower-skilled employment, perpetuating intergenerational poverty.
• Digital infrastructure inequality severely limits upward mobility for rural and low-income youth.

Inequality Is Interconnected

• Multilayered inequalities reinforce each other:
  • Digital inequality ➡ limits access to banking ➡ restricts financial independence ➡ aggravates gender inequality.
  • Example: Only 25% of rural women have internet access vs 49% of rural men.
  • Internet is a gateway to financial inclusion, education, and employment opportunities.

Critique of the Gini Index Methodology

• Gini Index captures income distribution, but:
  • Misses informal sector realities.
  • Does not account for non-income-based inequality (e.g., gender, caste, digital access).
• India’s low Gini score masks high inequality outside the formal economic sphere.

Conclusion: A Call for Grounded Equality

• True equality goes beyond numerical scores — requires universal access to opportunity.
• Without inclusive policies to address structural inequalities, rankings like the Gini Index offer a misleading sense of equity.
• India must invest in education, digital access, women’s empowerment, and labour formalisation to move toward real equality, not just statistical parity.

Gini Index

What is the Gini Index?

• The Gini Index is a statistical measure of income or wealth inequality within a country or group.
• Developed by Corrado Gini, an Italian statistician, in 1912.

How does it work?

• Scale: Ranges from 0 to 100 (or sometimes 0 to 1 in academic texts).
  • 0 = Perfect equality (everyone has the same income).
  • 100 = Perfect inequality (one person has all the income, others have none).

Interpretation Example

How is it Calculated?

• Based on the Lorenz Curve, which plots:
  • % of population (X-axis)
  • vs % of income or wealth owned (Y-axis)
• Gini = Area between line of equality and Lorenz curve, divided by total area under the line of equality.

Limitations

• Doesn’t capture non-income inequalities (e.g., gender, caste, digital access).
• Sensitive to data quality — especially weak in economies with large informal sectors (like India).
• A low Gini score doesn’t always mean a fair society.