India’s unemployment is predominantly structural, arising from deep-rooted issues such as a mismatch between available skills and market demand, low productivity sectors, and informal employment. Tackling this requires a clear understanding of how unemployment is measured and how data quality can be improved to formulate targeted policies.

Structural Nature of Unemployment in India

1. Skill-Job Mismatch: A large segment of the youth, though educated, lacks market-relevant skills.

   Example: Only ~45% of Indian graduates are considered employable in sectors like IT and data science.

2. Dominance of Informal Sector: Over 90% of the Indian workforce is employed informally, with limited social security and low productivity.

   Example: Daily wage labourers or self-employed vendors with no stable income.

3. Disguised and Seasonal Unemployment in Agriculture: Agriculture still absorbs ~45% of the workforce but contributes only ~18% to GDP.

   Example: Multiple family members working on the same small farm even when not needed.

4. Technological Disruption: Automation and AI are displacing low-skilled jobs, especially in manufacturing and services.

   Example: Digital banking reducing clerical jobs.

5. Low Female Labour Force Participation Rate (FLFPR): Patriarchal norms, lack of safety, and poor childcare infrastructure keep women's participation below 25%.

6. Urban-Rural Divide: Rural areas lack adequate non-farm employment, leading to seasonal migration.

   Example: Migrant workers from Bihar or Odisha working in metro cities’ construction sector.

7. Education-Industry Disconnect: Curricula often lack vocational and applied learning.

   Example: Arts graduates struggling to find employment in a tech-driven market.

Methodology of Measuring Unemployment in India

India primarily relies on the Periodic Labour Force Survey (PLFS) conducted by the NSSO, which uses three key methods:

1. Usual Principal and Subsidiary Status (UPSS):

   • Captures long-term unemployment over a 365-day reference period.
   • Suitable for understanding chronic unemployment patterns.

2. Current Weekly Status (CWS):

   • Based on whether a person was employed for at least one hour during the last 7 days.
   • Used for international comparability and urban employment trends.

3. Current Daily Status (CDS):

   • Measures daily activity during the survey week.
   • Captures underemployment better, especially in informal and casual work.

Limitations of Current Methodology

1. Lag in data publication: PLFS data is often published with significant delay, limiting policy responsiveness.

2. Inadequate representation of gig and platform workers: Gig, platform, and home-based work often go unrecorded or misclassified.

3. Lack of high-frequency, real-time employment data: Absence of high-frequency, dynamic labour market data.

4. Inability to fully capture disguised and underemployment: Especially in rural areas and agriculture, where people are technically employed but underutilised.

5. Poor granularity: Data often lacks granularity for district-level or sector-specific policymaking.

Suggestions for Improvement

1. Enhance frequency and timeliness of PLFS data: Move to quarterly surveys for both rural and urban areas.

2. Integrate real-time digital platforms: like EPFO, e-Shram, and gig economy platforms for dynamic tracking.

3. Disaggregate data for targeted policy: Provide district-wise, gender-wise, and skill-level employment statistics.

4. Improve capture of informal and gig work: Include modules in PLFS focused on freelancers, delivery workers, and platform-based workers.

5. Use AI/ML for analytics: Leverage technology to improve trend prediction, forecasting labour market shifts.

6. Promote skill-mapping surveys: Identify existing skill pools and their alignment with industry demand.

Structural unemployment in India reflects deeper socioeconomic and policy failures that cannot be resolved by cyclical boosts alone. It requires sustained interventions in education, skills, labour reforms, and improved measurement methodologies. A data-driven, inclusive approach is essential to enable India’s demographic dividend to translate into productive employment.