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Introduction

Unemployment refers to the share of the labor force that is without work but available for and seeking employment. India’s unemployment rate for 2022 was 7.33% affecting millions of people. One of the main causes of unemployment is structural unemployment, which occurs when there is a mismatch between the skills of the workers and the skills demanded by the employers.

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Reasons why most of the unemployment in India is structural in nature

• Skill Gap: The education system in India is often critiqued for being more theoretical than practical. Reports like the ASER indicate that a large number of graduates are not employable due to skill deficiencies. Initiatives like Skill India are still not sufficient to bridge this gap.
• Technology Shifts: Rapid technological changes, especially automation and AI, have rendered certain jobs obsolete. Eg: the closure of Nokia’s manufacturing plant in Chennai displaced thousands of workers who lacked the technical skills for jobs in emerging industries.
• Jobless growth: Machine learning and robotics create structural changes in industrial functioning creating concern for Jobs.
• Industrial Changes: Traditional sectors like agriculture are declining, both in GDP contribution and employment opportunities. The near-collapse of the handloom industry in places like Varanasi left many artisans unemployed, highlighting the failure to transition workers into new roles.
• Geographical Imbalance: Metropolitan cities like Bangalore and Mumbai are hubs for tech and finance jobs, respectively, but employment opportunities are sparse in rural regions. The regional focus of Special Economic Zones (SEZs) also exacerbates this imbalance.
• Outdated Economic Policies: Policies that do not align with contemporary economic conditions can create structural unemployment. For example, the focus on traditional manufacturing over service sectors can cause misalignment in job skills.

Methodology for Computing Unemployment in India: An In-depth Look

• Periodic Labour Force Survey (PLFS): Conducted annually by the Ministry of Statistics and Programme Implementation, PLFS is considered the most comprehensive survey for measuring unemployment.
• Census: Conducted every decade, the Census provides a macroscopic view of employment trends. While it’s exhaustive, it is outdated almost as soon as it’s published due to the long intervals between each survey.
• NSSO Surveys: The National Sample Survey Office conducts employment-unemployment surveys roughly every five years. These surveys are based on samples, which may or may not capture the real state of employment accurately.
• Tertiary Sources: Reports from private research organizations, think-tanks, and international bodies like the World Bank provide alternate viewpoints. However, the data may not be as rigorous as government-sponsored surveys.
• Online Portals: Government job portals like the National Career Service do collect some data, but they are not exhaustive as they only capture formal sector employment and those actively seeking jobs online.

Suggestions for Improving Unemployment Data Methodology in India

• Real-time Data: The government should leverage Big Data analytics and Internet of Things (IoT) to gather real-time unemployment statistics for timely interventions and policy adjustments. Eg: real-time job portal analytics can be used to track demand and supply in the job market.
• Skill Mapping: Regular skill mapping surveys could be conducted across various sectors. Such surveys could identify the mismatch between education and industry requirements, thereby addressing structural unemployment effectively.
• Transparency: All collected data and subsequent reports should be made publicly available in an easy-to-understand format. Dashboards could be created for real-time public monitoring of employment statistics.
• Policy Feedback Loop: A system should be created where this enhanced data is immediately used to impact policy decisions. Eg: if data shows high unemployment in a specific sector, immediate policy interventions, such as skill development programs, could be initiated.
• Incorporate Underemployment: The concept of underemployment, where people work below their skill level or part-time, should be integrated into official statistics. Countries like Australia have already adopted such metrics to provide a more nuanced understanding of the labour market.

Conclusion

Understanding structural unemployment is crucial for formulating effective policy measures. Current methodologies for computing unemployment in India offer a starting point but have room for improvement. Adopting innovative data collection and analysis methods can provide more accurate insights, thereby enabling targeted interventions for alleviating structural unemployment.

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