[Model Answer QP2023 GS3] Most of the unemployment in India is structural in nature. Examine the methodology adopted to compute unemployment in the country and suggest improvements.
India has a diverse labor market, influenced by factors ranging from geography to educational attainment.
Structural Unemployment is a form of unemployment is deeply rooted in the mismatches between the demand for labor and the skills (or locations) of the available workforce. It arises due to fundamental changes in an economy.
Most of the unemployment in India is structural in nature:
1. Agricultural Dominance: Despite agriculture’s share in India’s GDP dropping to around 15%, it still employs approximately 40% of the country’s workforce. This indicates an over-dependence on agriculture and an under-representation in higher productivity sectors like manufacturing and services. The shift of labor from agriculture to manufacturing, as seen in developed countries, didn’t materialize to the expected extent in India.
2. Education and Skills Mismatch: Indian educational institutions produce millions of graduates every year. However, industry reports frequently highlight that a large portion of these graduates isn’t immediately employable without further training. The skills taught in academic settings often don’t match industry requirements. For instance, while there’s a burgeoning IT industry in cities like Bengaluru, there’s a shortage of skilled coders and data scientists.
3. Rapid Technological Advancements: Automation and digitization are transforming industries at a swift pace. Sectors that used to be major employers, like textiles, are now increasingly automated, leading to reduced manual job opportunities.
4. Regional Disparities: States like Maharashtra and Karnataka, with major urban centers, have relatively lower unemployment rates compared to Bihar or Uttar Pradesh, which are largely agrarian. People often aren’t in places where the jobs are, leading to structural mismatches.
Methodology Adopted to Compute Unemployment in India:
1. Periodic Labour Force Survey (PLFS): This survey captures various metrics, with the Usual Status being a key metric to identify structural unemployment by comparing year-long activities of the population.
2. Population Census: Although conducted once every decade, it gives an insight into long-term shifts in employment sectors and migration patterns.
3. Employment Exchanges: Their limited scope often misses out on capturing structural shifts but still provides a glimpse of job-seeker preferences and regional demands.
Suggested Improvements:
1. Focused Surveys: Conduct specialized surveys targeting sectors undergoing major structural changes, like agriculture and manufacturing.
2. Skill Mapping and Gap Analysis: Understand the specific skills the unemployed possess versus what the market demands.
3. Collaboration with Industry: Forge ties with industry associations to get real-time feedback on skills in demand, facilitating early curriculum changes in educational institutions.
4. Regional Analysis: Dive deeper into state-wise data to understand and address regional disparities in unemployment, considering migration patterns, urbanization rates, and sectoral growth.
Conclusion:
We need Long-term Projections to incorporate economic models and predictions to anticipate future sectors of growth and decline. For instance, as India pushes for more renewable energy, what will be the job implications in the coal sector?
