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

Unemployment in India: A Structural Conundrum

India, with its diverse and vast economy, faces structural unemployment challenges rooted in factors like technological changes, socio-economic transformations, and changes in business practices. Most of these challenges arise from the mismatch of skills with the requirements of a modernizing economy.

Methodology to Compute Unemployment in India:

1. Periodic Labour Force Survey (PLFS): Conducted by the National Sample Survey Office (NSSO), PLFS replaced the erstwhile quinquennial employment-unemployment surveys. It provides quarterly data for urban areas and annual data for rural areas. Unemployment is computed based on the Usual Status (adjusted), Weekly Status, and Daily Status methods.
2. Census Data: Held decennially, the Census provides data related to workforce participation and unemployment, although it is not as frequent or detailed as specialized surveys.
3. Employment Exchanges: While not exhaustive, data from government employment exchanges provide insights into unemployment, especially urban unemployment.

Issues with the Current Methodology:

1. Lack of Timely Data: The data is often published with considerable time lags, making it less relevant for immediate policy decisions.
2. Under-representation: Informal sector employment, forming a significant portion of India’s workforce, is not effectively captured.
3. Over-reliance on Self-reporting: Subjectivity in individual responses can sometimes skew the actual scenario.

Suggested Improvements:

1. Real-time Data Collection: Leveraging technology can help gather real-time data, allowing for timely interventions.
2. Skill Mapping: Regular surveys mapping skills and industries can provide insights into where skill development initiatives are required, addressing structural unemployment head-on.
3. Collaboration with Private Job Portals: Private employment portals have vast datasets on employment, skill requirements, and job vacancies. Collaborative efforts can provide a more comprehensive view.
4. Regional and Sectoral Analysis: Instead of a blanket nationwide approach, regional and sectoral analyses can provide more nuanced insights, guiding targeted policy decisions.
5. International Best Practices: Studying and implementing best practices from countries with similar challenges can be insightful.

Facts:

1. According to the PLFS report for 2019-20, the unemployment rate stood at 5.8% in rural areas and 9.7% in urban areas.
2. The pandemic exacerbated the unemployment scenario with CMIE data suggesting that the unemployment rate peaked at around 23% in April and May 2020.

In conclusion, while India’s structural unemployment poses significant challenges, a more robust, timely, and nuanced data collection methodology can drive better-informed policy decisions to address the issue effectively.