Model Answer

Introduction

Unemployment in India, particularly structural unemployment, arises from a mismatch between workers’ skills and employer demands. In 2022, the unemployment rate was 7.33%, affecting millions. Understanding the methodology for computing unemployment is essential for addressing this issue.

Structural Unemployment in India

Reasons for Structural Unemployment

1. Skill Gap: The Indian education system is often criticized for its theoretical focus. Reports like the Annual Status of Education Report (ASER) reveal that many graduates lack employable skills, and initiatives like Skill India have not sufficiently bridged this gap.
2. Technology Shifts: Advances in automation and AI have made certain jobs obsolete. For instance, the closure of Nokia’s manufacturing plant in Chennai displaced thousands of workers unprepared for new tech roles.
3. Jobless Growth: Industries increasingly rely on robotics, creating structural changes that do not translate into job creation.
4. Industrial Changes: The decline of traditional sectors, such as agriculture, has reduced employment opportunities. The near-collapse of the handloom industry in Varanasi illustrates the challenges of transitioning workers to new roles.
5. Geographical Imbalance: Job opportunities are concentrated in metropolitan areas like Bangalore and Mumbai, while rural regions remain underserved.
6. Outdated Economic Policies: Policies focused on traditional manufacturing hinder adaptation to modern service sectors, exacerbating skill mismatches.

Methodology for Computing Unemployment in India

Current Approaches

1. Periodic Labour Force Survey (PLFS): Conducted annually, it provides comprehensive unemployment data (Source: Ministry of Statistics and Programme Implementation).
2. Census: Conducted every decade, it offers a broad view of employment trends but becomes outdated quickly.
3. NSSO Surveys: Conducted approximately every five years, these surveys rely on samples that may not accurately reflect employment conditions.
4. Tertiary Sources: Reports from private organizations and international bodies provide alternate data but lack the rigor of government surveys.
5. Online Portals: Government job portals collect data but focus primarily on formal sector employment.

Suggestions for Improving Unemployment Data Methodology

1. Real-time Data: Utilize Big Data and IoT for real-time unemployment statistics, enabling timely policy adjustments.
2. Skill Mapping: Conduct regular surveys to identify mismatches between education and industry needs.
3. Transparency: Make all data publicly accessible through user-friendly dashboards for real-time monitoring.
4. Policy Feedback Loop: Implement a system to use enhanced data for immediate policy action, such as launching skill development programs where needed.
5. Incorporate Underemployment: Include underemployment metrics to provide a more nuanced view of the labor market.

Conclusion

Addressing structural unemployment in India requires improved methodologies for computing unemployment. By adopting innovative data collection and analysis techniques, policymakers can gain better insights and implement targeted interventions to alleviate unemployment.