Unemployment in India is predominantly structural, highlighting a mismatch between skills and available jobs. The current methodologies for measuring unemployment, such as the Periodic Labour Force Survey (PLFS), provide valuable data but require improvements to address emerging challenges.

Q.11 Most of the unemployment in India is structural in nature. Examine the methodology adopted to compute unemployment in the country and suggest improvements.

Model Answer:

Introduction

Unemployment refers to the situation where individuals who are capable and willing to work do not find suitable employment. As of the latest data, the unemployment rate in India stands at approximately 7.8% (CMIE, June 2024).

Body

Reasons for Structural Unemployment in India:

  • Lack of Skills: A significant portion of the workforce lacks the necessary skills required by modern industries, leading to a mismatch between job seekers and job requirements.
  • Technological Changes: Rapid technological advancements render many traditional jobs obsolete, and the workforce often struggles to adapt quickly.
  • Geographical Immobility: Many workers are unable or unwilling to relocate to areas where jobs are available due to social, cultural, or economic reasons.
  • Educational Mismatch: The education system often does not align with industry needs, producing graduates who are not readily employable.

Methodology to Compute Unemployment in India:

  • Periodic Labour Force Survey (PLFS): Conducted by the National Statistical Office (NSO), it provides estimates of various labour force indicators.
  • Usual Status Approach: Measures the activity status of an individual over a reference period of one year.
  • Current Weekly Status (CWS): Measures the activity status of an individual over a reference period of one week.
  • Current Daily Status (CDS): Measures the activity status of an individual on each day of the reference week.

Suggestions to Improve the Methodology:

  • Incorporate CPHS Data: The Consumer Pyramids Household Survey (CPHS) by CMIE provides more frequent and granular data, which can complement PLFS data.
  • Use of Technology: Leveraging big data analytics and AI can provide real-time insights into employment trends.
  • Enhanced Sample Size and Frequency: Increasing the sample size and frequency of surveys can provide more accurate and timely data.

Way Forward:

To improve the methodology for computing unemployment, integrating CPHS data with PLFS, adopting advanced data analytics, and increasing survey frequency and sample size are crucial steps. This will ensure more accurate, timely, and actionable data, aiding policymakers in addressing structural unemployment effectively.

Conclusion

Therefore , while structural unemployment in India is driven by factors like skill gaps, technological changes, and geographical immobility, refining the methodology to compute unemployment through modern techniques and comprehensive data sources can significantly enhance our understanding and response to this critical issue.

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