The Hindu Editorial Summary

Editorial Topic : Unemployment in India: A Maze of Numbers and Mounting Frustration

 GS-3 Mains Exam : Economy

Revision Notes

Question : Critically analyze the conflicting data on unemployment in India provided by the RBI report (KLEMS database) and the Centre for Monitoring Indian Economy (CMIE). How do these differing perspectives impact the perception of the unemployment situation in India?

India’s unemployment situation has become a heated debate, fueled by conflicting data and a disconnect between government claims and ground realities.

The Spark:

• Prime Minister referenced a recent RBI report (KLEMS database) to counter the narrative of high unemployment. This report supposedly showed 8 crore jobs created in the last 3-4 years.

The Confusion:

• This optimistic outlook clashes with reports from the Centre for Monitoring Indian Economy (CMIE), a well-respected private data agency. CMIE paints a bleaker picture, with India’s unemployment rate hitting a concerning 9.2% in June 2024, the highest in eight months.
• The public is left bewildered by such contrasting data, unsure of the true extent of the problem.

Dissecting the Data Debate:

• KLEMS Data Under Scrutiny: Experts point out flaws in the KLEMS data used by the government.
  • It relies on existing NSSO and PLFS data, not acting as an independent source.
  • The complex structure of the Indian economy, with a massive unorganized sector (94% of workforce), creates challenges in data collection.
  • Traditional methods like the Census (conducted every 10 years) and ASUSE surveys (every 5 years) provide outdated information, failing to capture the dynamic job market.
  • Further complicating matters is the lack of recent data, with the Census stuck in 2011 and ASUSE potentially using data from 2012-17.
  • Additionally, the report ignores major economic shocks like demonetization, GST implementation, the NBFC crisis, and the COVID-19 pandemic, all of which significantly impacted job creation, particularly in the unorganized sector.

The Discrepancy Between PLFS and CMIE:

• Another layer of confusion arises from the different methodologies used by PLFS (Periodic Labour Force Survey) and CMIE.
  • CMIE follows the International Labour Organization (ILO) definition, where only those earning income from work are considered employed.
  • PLFS paints a rosier picture, counting anyone actively working (even without income) as employed. This includes those offering free labor or waiting in fields for potential work.
  • Consequently, PLFS data shows a lower unemployment rate, potentially including disguised unemployed and under-employed individuals. CMIE data, on the other hand, reflects the harsh reality of those who have given up searching for work altogether, a group not recognized as unemployed by official data.

The Bottom Line:

• Ground reports paint a grim picture, with young people struggling to find jobs. Their frustration is palpable, highlighted by frequent news stories.
• The government’s denial of the problem creates a sense of disconnect with the lived experiences of many Indians.
• Acknowledging the true extent of unemployment and taking concrete action is crucial to address this growing concern and prevent potential social unrest due to youth frustration.

 

 

The Hindu Editorial Summary

Editorial Topic : Parametric Insurance

 GS-3 Mains Exam : Economy

Revision Notes

Question : Analyze the challenges and key factors that governments must consider to ensure the effective use of parametric insurance for disaster resilience.

The Problem:

• 2023: Warmest year on record.
• $250 billion disaster losses, only $100 billion insured (huge gap, especially in developing economies).
• Traditional indemnity insurance requires physical damage assessment, difficult in large-scale disasters.

The Solution: Parametric Insurance

• Triggers payouts based on pre-defined parameters (e.g., rainfall exceeding 100 mm/day for 2 days).
• Faster payouts, no need for physical verification.
• Example: Disaster-prone islands successfully use parametric insurance for climate adaptation.

Early Examples in India:

• Pradhan Mantri Fasal Bima Yojana (crop insurance) – verification based.
• Restructured Weather Based Crop Insurance Scheme – threshold limits, no field verification.

Rise of Parametric Products in India:

• Customized products for states, corporations, self-help groups, micro-finance institutions.
• Examples:
  • Nagaland: Parametric cover for extreme precipitation (2021). Improved version with lower thresholds & maximized payouts.
  • Kerala: Parametric insurance for dairy farmers (lower milk yields due to heat stress).

Ensuring Effective Use

• 5 Key Factors for Governments:
  1. Precise thresholds & proper monitoring mechanisms.
  2. Experience sharing between governments.
  3. Mandatory bidding process for transparent pricing.
  4. Widespread payout dissemination system.
  5. Encouraging long-term premium payment by households.

India’s Advantage:

• Aadhaar-based payment system facilitates payouts.

The Future:

• South Asia – world’s most climate-vulnerable zone.
• India and its neighbors can:
  • Use parametric products.
  • Pool risks collaboratively.
  • Negotiate better deals with global insurance companies.