Introduction
Unemployment in India is predominantly structural, arising from a persistent mismatch between labour supply, skills and sectoral demand. In such a context, accurate measurement of unemployment becomes crucial for designing effective labour market and skill policies.
1. Nature of Structural Unemployment in India (Context)
- Sectoral shifts from agriculture → industry/services without adequate skill transition.
- Educated unemployment due to education–market mismatch.
- Technological change and automation displacing low-skilled labour.
- Dominance of informal sector with disguised and under-employment.
Thus, capturing the true nature of unemployment requires a robust measurement framework.
2. Methodology Adopted to Compute Unemployment in India
India’s unemployment estimates are primarily generated by the National Statistical Office (NSO) through the Periodic Labour Force Survey (PLFS), which replaced earlier NSSO employment surveys.
(A) Key Approaches Used
- Usual Status (US): Person unemployed if not working for a major part of the previous year and actively seeking work; captures long-term unemployment.
- Current Weekly Status (CWS): Activity during the preceding 7 days; captures short-term/intermittent unemployment.
- Current Daily Status (CDS): Daily activity during reference week; best for disguised and under-employment.
(B) Key Features of PLFS
- Annual rural–urban employment estimates.
- Quarterly urban unemployment data.
- Rotational Panel Sampling.
- Generates LFPR, WPR and Unemployment Rate.
3. Critical Evaluation of Existing Methodology
Strengths
- Multi-dimensional measurement through US, CWS and CDS.
- High-frequency urban data helps track business cycle shocks.
- International comparability with ILO standards.
Major Limitations
- Inadequate capture of informal sector: Irregular work treated as full employment.
- Skill mismatch not reflected: Overqualified workers counted as employed.
- Seasonality in rural employment: Off-season joblessness not fully captured.
- Disguised unemployment underestimated: Family labour treated as employed.
- Urban bias in quarterly data: Most structural unemployment is rural.
- Migration & gig economy gaps: Short-term migrants and platform workers under-represented.
4. Structural Nature of Unemployment & Measurement Gaps
| Structural Feature | Why Existing Method Fails |
| Skill mismatch | Measures job presence, not skill-appropriateness |
| Informality | Irregular work treated as full employment |
| Disguised unemployment | Family workers counted as employed |
| Labour mobility barriers | Sectoral rigidity not captured |
| Technological displacement | No dynamic tracking of job losses |
5. Suggestions for Improving Unemployment Measurement
- Introduce skill-adjusted employment indicators.
- Expand quarterly rural labour surveys to reflect seasonality.
- Create a separate category for gig & platform workers.
- Strengthen informal sector data via GST, EPFO–ESIC and PM-Shram linkage.
- Develop district-level real-time employment dashboards.
- Measure quality of employment (job security, hours, income stability, social security).
6. Policy Significance of Improved Measurement
- Better targeting of Skill India and education reforms.
- Improved design of MSME and industrial policy.
- Evidence-based expansion of urban employment schemes.
- Shift from job counting to productivity and job quality assessment.
Conclusion
Refining India’s unemployment measurement system to capture informality, skill mismatch and job quality is indispensable for addressing the fundamentally structural nature of unemployment and for designing future-ready employment policy.
