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.