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

Introduction Structural unemployment refers to a mismatch between the jobs available in the market and the skills possessed by the workforce. In India, despite economic growth, job creation has not kept pace with the demographic dividend, making structural unemployment the most prominent feature of the Indian labor market.

Structural Nature of Unemployment in India The structural nature of India’s unemployment is driven by several underlying systemic factors:

• Skill-Deficit and Education-Industry Gap: According to the India Skills Report 2023, only about 50% of Indian graduates are highly employable. The education system remains largely degree-centric rather than skill-centric.
• Sectoral Asymmetry (Premature De-industrialization): India transitioned directly from an agrarian economy to a services-led economy. The manufacturing sector, which traditionally absorbs low-to-medium skilled surplus agricultural labor, has remained stagnant at around 16-17% of GDP.
• Technological Disruption: The rapid adoption of automation, Artificial Intelligence, and capital-intensive production methods has reduced the labor-elasticity of growth, leading to "jobless growth."
• Geographical Mismatch: High-growth clusters are concentrated in western and southern India, while the highest addition to the working-age population is occurring in northern and eastern states, creating friction in labor mobility.

Methodology Adopted to Compute Unemployment in India Historically conducted quinquennially (every 5 years) by the NSSO, the methodology was revamped in 2017 with the introduction of the Periodic Labour Force Survey (PLFS) by the National Statistical Office (NSO).

The PLFS computes unemployment based on two primary reference periods:

1. Usual Principal and Subsidiary Status (UPSS): Reference period of 365 days. A person is considered unemployed if they were available for work but did not find work for the major part of the year. This captures chronic unemployment.
2. Current Weekly Status (CWS): Reference period of 7 days preceding the survey. A person is considered unemployed if they did not work for even one hour on any day in the week but sought work. This captures short-term fluctuations and underemployment.

Secondary sources of employment data include:

• EPFO/ESIC Payroll Data: Used to estimate formal sector job creation.
• Private Data (CMIE): The Centre for Monitoring Indian Economy conducts high-frequency household surveys to release monthly unemployment data.

Critical Examination of the Methodology (Limitations) While the shift to PLFS improved data frequency, the methodology suffers from significant blind spots:

• Inadequate Capture of the Informal Sector: Over 80% of India's workforce is informal. The current methodology struggles to capture the volatile, transient nature of informal jobs, often categorizing casual laborers as "employed" even if they work intermittently.
• Masking Disguised Unemployment: In UPSS, anyone working in agriculture (even if their marginal productivity is zero) is counted as employed. This drastically underreports the severe disguised unemployment in rural India.
• Gender Bias in Unpaid Care Work: Women engaged in domestic duties (Code 92/93 in PLFS) are considered "out of the labor force." However, many perform unremunerated economic activities (e.g., poultry, tailoring at home), leading to a skewed Female Labour Force Participation Rate (FLFPR).
• Quality vs. Quantity of Jobs: The binary metric of 'employed' vs. 'unemployed' fails to capture "working poverty" or income-based underemployment. A person earning ₹200/day and a person earning ₹2000/day are both merely counted as 'employed'.
• Frequency and Granularity Constraints: While urban data is released quarterly, rural data is only annual. Furthermore, district-level granularity, which is essential for micro-level policy interventions, is missing.

Suggested Improvements in Computation Methodology To design targeted interventions for structural unemployment, India needs a more dynamic and multi-dimensional labor market data ecosystem:

• Measuring Time-Related and Income-Related Underemployment: The PLFS should introduce supplementary indicators that track the number of hours worked versus hours desired to work, and cross-reference employment status with basic minimum wage thresholds.
• High-Frequency Rural Surveys: Urban quarterly surveys should be expanded to rural areas to capture the deep seasonal variations of the agrarian economy.
• Capturing the Gig and Platform Economy: With millions joining the gig economy (Zomato, Uber, Urban Company), a separate classification code must be introduced in the PLFS to track platform workers, as they blur the lines between formal and informal employment.
• Triangulation of Big Data (LMIS): Create a robust Labour Market Information System (LMIS) by integrating traditional PLFS survey data with administrative datasets like EPFO (formal jobs), e-Shram portal (informal workers), Udyam portal (MSME jobs), and GSTN registrations.
• Redefining Women’s Economic Contribution: Adopt the International Labour Organization’s (ILO) latest standards (19th ICLS) that distinguish between "employment" (work for pay/profit) and "work" (including unpaid domestic services), to bring visibility to women's hidden economic contributions.

Conclusion Accurate diagnosis is the prerequisite for effective treatment. Shifting from a purely binary metric of employment to a holistic measurement of 'quality of work', 'underemployment', and 'skill-matching' will empower policymakers to bridge the structural gaps in the economy. This is imperative for India to convert its demographic transition into a true demographic dividend during the Amrit Kaal.