Most of the unemployment in India is structural in nature. Examine the methodology adopted to compute unemployment in the country and suggest improvements.

Introduction

Unemployment in India, particularly structural unemployment, arises due to a mismatch between the skills of the workforce and the demands of the economy. According to the Periodic Labour Force Survey (PLFS) 2021-22, India’s unemployment rate stood at 4.1%, but this figure often underrepresents the true extent of the problem due to methodological limitations. Addressing these gaps is critical for effective policymaking.

Key Dimensions of Unemployment Measurement in India

Methodology Adopted to Compute Unemployment in India

1. Periodic Labour Force Survey (PLFS)

• Conducted by: National Statistical Office (NSO).
• Key Indicators:
  • Usual Status (Principal and Subsidiary Status): Measures employment over a year.
  • Current Weekly Status (CWS): Measures employment over a week.
  • Daily Status: Captures daily variations in employment.
• Strengths:
  • Provides comprehensive data on employment, unemployment, and labour force participation.
  • Covers both formal and informal sectors.
• Limitations:
  • Underreporting of disguised unemployment in rural areas.
  • Exclusion of gig and platform workers.
  • Lag in data release, reducing its relevance for real-time policymaking.

2. Census and Economic Surveys

• Census: Provides decadal data on workforce participation.
• Economic Surveys: Offer insights into employment trends but lack granular data.
• Limitations:
  • Census data is infrequent.
  • Economic Surveys rely on secondary sources, leading to inconsistencies.

3. Employment-Unemployment Surveys (EUS)

• Conducted by: Labour Bureau (discontinued post-2016).
• Strengths:
  • Focused on labour market dynamics.
• Limitations:
  • Overlap with PLFS led to its discontinuation.

4. Other Sources

• EPFO, ESIC, and NPS Data:
  • Tracks formal sector employment.
  • Limitation: Excludes informal workers, who constitute ~90% of India’s workforce.
• CMIE’s Consumer Pyramids Household Survey:
  • Provides real-time data but lacks official recognition.

Challenges in Current Methodology

• Underestimation of Structural Unemployment:
  • Focus on employment status rather than skill mismatches.
• Informal Sector Exclusion:
  • Inadequate representation of gig, platform, and self-employed workers.
• Regional and Gender Disparities:
  • Limited focus on rural-urban divides and female workforce participation.
• Data Timeliness:
  • Delayed release of PLFS data hampers policy responsiveness.
• Lack of Real-Time Monitoring:
  • Absence of dynamic tools to track unemployment trends.

Suggested Improvements in Methodology

1. Incorporate Real-Time Data Collection

• Leverage big data analytics and AI-based tools to track employment trends dynamically.
• Collaborate with platforms like Aadhaar, GSTN, and EPFO for real-time insights.

2. Expand Coverage to Informal and Gig Workers

• Include gig economy workers in surveys to reflect the changing nature of employment.
• Use self-reporting mobile apps for informal sector data collection.

3. Focus on Skill Mapping

• Conduct sector-specific skill surveys to identify mismatches between workforce skills and industry demands.
• Integrate findings with Skill India Mission for targeted interventions.

4. Enhance Regional and Gender Representation

• Design region-specific surveys to address rural-urban disparities.
• Introduce gender-sensitive indicators to capture female workforce participation accurately.

5. Timely and Frequent Data Release

• Reduce the lag in PLFS data publication by adopting digital data collection methods.
• Conduct quarterly surveys for more frequent updates.

6. Strengthen Institutional Coordination

• Establish a National Employment Data Authority to streamline data collection and analysis across agencies like NSO, Labour Bureau, and CMIE.

Way Forward

To address structural unemployment, India must adopt a multi-pronged approach that combines robust data collection with targeted policy interventions. Initiatives like Skill India, PMKVY, and Digital India should be aligned with real-time labour market data to ensure workforce readiness for emerging sectors like green energy and digital services.

Conclusion

A robust and inclusive unemployment measurement framework is essential for addressing India’s structural unemployment challenge. By leveraging technology, real-time data, and skill mapping, India can ensure that its workforce is equipped to meet the demands of a rapidly evolving economy, aligning with SDG 8 (Decent Work and Economic Growth) and the vision of a $5 trillion economy.