India’s unemployment scenario is complex, with structural factors playing a significant role.
Structural Unemployment in India:
- Definition: Structural unemployment arises from a mismatch between the skills that workers offer and the skills demanded by employers.
- Industrial Shift: As India transitions from an agrarian to a service-based economy, many lack the skills required for newer jobs.
- Technological Advancements: Automation and digitization have reduced the demand for manual labor.
- Rigid Labor Laws: Stringent laws make it challenging for employers to hire and lay off workers, leading to reluctance in hiring.
- Education System: The education system often doesn’t equip students with skills aligned with industry demands.
Methodology to Compute Unemployment:
- Periodic Labour Force Survey (PLFS): The main source of unemployment data. The 2019-20 PLFS showed a decrease in unemployment to 4.8% from 6.1% in 2017-18.
- Usual Status (UPSS): Classifies individuals based on their activity for a longer period in the previous year. This can under-represent unemployment as even short-term work is counted.
- Current Weekly Status (CWS): Measures economic activity over the previous week. It showed unemployment remained around 8.8% from 2017 to 2020.
- Challenges: Seasonal employment, informal sector prominence, and varying reference periods complicate accurate measurement.
- Shorter Reference Periods: To capture dynamic changes in employment. copyright©iasexpress.net
- Skill Development Programs: Align education with industry needs to reduce the skill mismatch.
- Labor Law Reforms: Make laws flexible to encourage employers to hire.
- Promote Female Workforce Participation: Address societal norms and provide safe working conditions to encourage more women to join the workforce.
- Inclusive Data Collection: Ensure representation from all sectors, including the informal sector.
- Regular Updates: Frequent surveys to capture real-time data.
In conclusion, while India has made strides in measuring unemployment, there’s room for improvement. A multi-pronged approach addressing structural issues, refining data collection methods, and promoting skill development can pave the way for a more accurate and actionable unemployment metric.