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

Structural unemployment in India

Structural unemployment arises from a mismatch between the skills workers possess and those the economy demands — a persistent, non-cyclical problem distinct from seasonal or cyclical joblessness. In India it is visible in low female participation, agrarian underemployment, jobless growth in capital-intensive sectors and a stream of unemployable graduates. Because policy responses (skilling, industrial and education reform) differ from those for cyclical joblessness, measuring it accurately is the first step to correcting it.

Methodology adopted

Since 2017-18 the Periodic Labour Force Survey (PLFS) of the NSO has replaced the quinquennial NSSO Employment-Unemployment Survey, giving annual rural data and quarterly urban data through the Current Weekly Status. It uses three reference concepts:

• Usual Status (365-day reference), capturing chronic, long-term joblessness;
• Current Weekly Status (7-day reference); and
• Current Daily Status (day-based), best at capturing intermittent and seasonal work.

The key derived indicators are the Labour Force Participation Rate, Worker Population Ratio and the Unemployment Rate, disaggregated by age, sex and education.

Limitations

• Disguised unemployment and underemployment in agriculture are poorly captured, since anyone working even briefly counts as employed.
• The vast informal sector, self-employment and unpaid family labour blur the line between employed and unemployed.
• Gig and platform workers fall outside traditional categories, and survey lags reduce timeliness.
• A worker doing low-productivity, low-wage work is counted as fully employed, masking the real problem.

Suggested improvements

• Blend survey data with administrative sources — EPFO, ESIC, e-Shram and NPS — for near real-time estimates.
• Add quality-of-employment metrics: wages, social security, hours worked, and a distinct gig/platform-work category.
• Use higher-frequency, larger urban and district-level samples for granular, policy-usable insight.
• Track skill mismatch directly by mapping vacancies against the skill profile of job-seekers.

Sharper measurement will let skilling and industrial policy target the true structural gaps rather than headline rates.