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

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Structural Unemployment in India

• Skill Mismatch:
  • Education-Industry Gap: A significant portion of the workforce lacks industry-relevant skills. The India Skills Report 2022 highlighted that only about 46.2% of Indian youth are employable, indicating a gap between academic curricula and market demands.
  • Technological Displacement: Rapid automation and digitalization are making traditional roles obsolete. The World Bank estimates that automation could impact nearly 69% of jobs in India.
• Sectoral Shifts:
  • Agricultural Dependence: While agriculture contributes only about 18% to India's GDP, it still employs nearly 42.6% of the workforce (Economic Survey 2022-23), indicating high disguised and seasonal unemployment.
  • Urbanization and Regional Disparities: Rapid urban migration has strained urban labor markets. According to the 2011 Census, 31.2% of India's population was urban, and the service and manufacturing sectors have not expanded fast enough to absorb the influx.
• Demographic Factors:
  • Youth Unemployment: India's youth unemployment rate was estimated at 28.3% in 2020 by the World Bank, reflecting structural barriers in entry-level hiring.
  • Gender Disparities: The female labor force participation rate was low at 19.2% in 2021 (World Bank), constrained by cultural norms, safety concerns, and lack of flexible work options.

Methodology Adopted to Compute Unemployment in India

• Periodic Labour Force Survey (PLFS): Conducted by the National Statistical Office (NSO), the PLFS is the primary official data source. It uses three key measurement approaches:
  • Usual Status (UPS): Measures employment status based on the individual's activity over a 365-day reference period. This is effective for capturing long-term structural trends.
  • Current Weekly Status (CWS): Classifies a person as employed if they worked for at least one hour on any day during a 7-day reference period. This captures short-term seasonal fluctuations.
  • Current Daily Status (CDS): Records activity on a daily basis within the reference week, offering insights into underemployment and the intensity of work.
• Annual Employment-Unemployment Surveys (EUS): Previously conducted by the Labour Bureau, these surveys provided historical data on employment patterns before being succeeded by the PLFS.

Improvements in Measuring Unemployment

• Better Data Collection and Frequency:
  • More Frequent Surveys: Transitioning from annual to quarterly PLFS reporting for rural areas (similar to urban areas) would provide more responsive policy inputs.
  • Enhanced Data Granularity: Capturing detailed regional, sectoral, and demographic variations would help design targeted regional employment policies.
• Incorporation of Informal Sector Employment:
  • Recognizing Informal Work: Since a vast majority of India's workforce is in the informal sector, statistics should better capture gig workers, home-based workers, and self-employed individuals to avoid underestimating underemployment.
• Focus on Skill Gaps and Education:
  • Skill Mapping Surveys: Conduct regular industry-specific surveys to map emerging skill requirements against vocational training outputs.
  • Integration of Educational Data: Link educational databases with labor market registries to track employment outcomes of graduates.
• Use of Technology and Big Data:
  • Leveraging Big Data: Analyze real-time hiring trends from online job portals, professional networks, and EPFO registration data to complement traditional household surveys.
  • AI and Predictive Analytics: Use machine learning models to forecast labor demand in emerging sectors, helping align skill development programs proactively.