Most Of The Unemployment In India Is Structural In

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.