Structural unemployment occurs when workers lack the skills, education, or geographic mobility required to match available jobs. In India, it reflects a mismatch between the workforce’s capabilities and the evolving needs of a modern economy.
Why Unemployment is Structural in India
Skill Mismatch – Majority of workforce is low-skilled; only ~4.7% formally skilled (NSDC).
Agriculture Dependence – 49% workforce in agriculture producing 16-17% of GDP
Slow Growth of Labour-Intensive Industries – Manufacturing unable to absorb labour at scale.
Automation and Digitalisation – Eg- AI, Robotics leading to job losses
Low Female Labour Participation – FLFPR at 41.7% (PLFS 2023-24) due to social norms, skill gaps, and lack of suitable jobs.
Regional Imbalances – Job clusters in southern/western India vs labour concentration in BIMARU states.
Informalization of economy – 89% of workforce in informal sector.
Methodology to Compute Unemployment in India
NSSO (under MOSPI) is the principal body responsible for estimating unemployment.
Periodic Labour Force Survey (PLFS) – NSO measures unemployment through three indicators:
Usual Status (US/PS+SS) – Based on activity over 365 days
Current Weekly Status (CWS) – If not worked for 1 hour in the last 7 days.
Current Daily Status (CDS) – Records activity for each day of last week – best for informal/underemployment.
Household Surveys – Annual (rural + urban) and quarterly (urban) surveys.
Establishment Surveys
QES for formal sector
ASI for organised manufacturing
Administrative Data – EPFO, ESIC, NPS payrolls used to estimate formal job creation.
Unemployment rate = No. of unemployed persons / Total labour force
Issues with Current Methodology
Underestimation of Informal Sector – ~90% workforce informal. PLFS & enterprise surveys do not capture home-based, gig, or platform work fully.
Surveys don’t map job requirements vs worker skills, essential for assessing structural unemployment.
Low Frequency – Eg- PLFS rural data is measured annually
Urban Bias – Quarterly surveys are confined to urban areas. Rural distress is under-measured.
Limited Coverage – Gig economy, digital services, start-ups, and EV/green jobs not adequately represented.
Way Forward
Use Big Data Analytics to gather real-time analysis.
Incorporate ‘underemployment’ into the definition of unemployment.
Timely release of data.
Increase Frequency – Monthly or quarterly surveys for rural areas
Align with International Standards (ILO + SNA 2025)- Update definitions to include multi-job holders, remote workers, freelancers, and platform-based workers.
Improving methodology is essential to generate accurate employment estimates and design stronger job creation policies.