Mains Marathon 2024

Approach Provide a brief definition of unemployment. Give an overview of the current methodology for computing unemployment in India. Mention the limitations of the current methodology Propose enhancements to address structural unemployment effectively. Conclude Suitably

Introduction

Unemployment is defined as a condition in which individuals who are capable of working are actively seeking employment but are unable to find suitable jobs. An unemployed person is part of the labor force, and possesses the necessary skills, but currently lacks gainful employment.

Structural unemployment arises when there is a mismatch between the skills and qualifications of the workforce and the demands of the job market. This situation often results from changes in the economy that alter the requirements for certain jobs, leaving workers without the necessary skills to secure employment.

Body

Current Methodology for Computing Unemployment in India:

• The methodology for computing unemployment in India primarily relies on periodic labor force surveys conducted by the National Statistical Office (NSO).
• The National Sample Survey Office (NSSO) launched the Periodic Labour Force Survey (PLFS) in April 2017.
• The objective of PLFS is primarily twofold:
  • to estimate the key employment and unemployment indicators (viz. Worker Population Ratio, Labour Force Participation Rate, Unemployment Rate) in the short time interval of three months for the urban areas only in the ‘Current Weekly Status’ (CWS).
  • To estimate employment and unemployment indicators in both ‘Usual Status’ (ps+ss) and CWS in both rural and urban areas annually.
• Sample Design of PLFS:
  • A rotational panel sampling design has been used in urban areas. In this rotational panel scheme, each selected household in urban areas is visited four times, in the beginning with ‘First Visit Schedule’ and thrice periodically later with a ‘Revisit Schedule’.
  • The scheme of rotation ensures that 75% of the first-stage sampling units (FSUs) are matched between two consecutive visits.
• Sample Size :
  • At the all-India level, in the urban areas, a total number of 5,706 FSUs (urban sampling unit curved out from Urban Frame Survey) have been surveyed during the quarter January – March 2024.
  • The number of urban households surveyed was 44,598 and the number of persons surveyed was 1,69,459 in urban areas.
• Key Employment and Unemployment Indicators : The Periodic Labour Force Survey (PLFS) gives estimates of key employment and unemployment Indicators like the Labour Force Participation Rate (LFPR), Worker Population Ratio (WPR), Unemployment Rate (UR), etc. These indicators, and ‘Current Weekly Status’ are defined as follows:
  • Labour Force Participation Rate (LFPR): LFPR is defined as the percentage of persons in the labour force (i.e. working or seeking or available for work)in the population.
  • Worker Population Ratio (WPR): WPR is defined as the percentage of employed persons in the population.
  • Unemployment Rate (UR): UR is defined as the percentage of persons unemployed among the persons in the labour force.
    • Unemployment Rate in urban areas decreased from 6.8% in January – March 2023 to 6.7% in January – March2024
  • Current Weekly Status (CWS): The activity status determined on the basis of a reference period of last 7 days preceding the date of survey is known as the current weekly status (CWS) of the person.

Limitations of the Current Methodology

• Narrow Definition of Unemployment: The current definitions primarily capture individuals actively seeking jobs, excluding discouraged workers and those engaged in informal employment.
  • According to the latest data from the Centre for Monitoring Indian Economy (CMIE), an independent think tank, the unemployment rate in India was recorded at 9.2 percent in June 2024, contrasting with the figures reported by the Periodic Labor Force Surveys (PLFS)
• Static Nature of Data: The reliance on periodic surveys may not accurately capture real-time changes in the labor market.
• Underrepresentation of Informal Sector: A significant portion of the workforce operates in the informal sector, which is often underreported in official statistics.
• Lack of Insight into Skills Mismatch: Without skill mapping, there is limited understanding of the specific skills available in the labor force compared to those required by employers. This gap can lead to a misalignment between job seekers and job opportunities, contributing to structural unemployment

Proposed Enhancements to the Methodology

• Comprehensive Unemployment Definitions: Expand the definition of unemployment to include discouraged workers, underemployed individuals, and those engaged in informal employment. This will provide a more accurate representation of the labor market.
• Continuous Monitoring and Real-Time Data: Establish mechanisms for ongoing data collection using technology, such as mobile applications and online surveys. This will enable real-time monitoring of employment trends and fluctuations.
• Focus on Skills and Mismatches: Incorporate skill mapping and labor market demand assessments into the methodology. Collaboration with industry stakeholders can help identify skills gaps and inform education and training programs.
• Regional Disaggregation: Enhance the granularity of data collection to reflect regional disparities in unemployment. Tailored strategies can be developed to address specific challenges faced by different regions.
• Integrating Informal Sector Data: Develop methodologies to capture data from the informal sector more effectively. This could include regular surveys and partnerships with local organizations to gather insights on informal employment patterns.
• Longitudinal Studies: Conduct longitudinal studies to track employment patterns over time. This can provide valuable insights into the long-term effects of economic policies and labor market interventions.

Conclusion

Enhancing the methodology for computing unemployment in India is crucial for effectively addressing structural unemployment. By broadening definitions, incorporating real-time data, focusing on skills mismatches, and ensuring regional representation, India can develop a more comprehensive understanding of its labor market. These enhancements will empower policymakers to formulate targeted interventions that promote inclusive employment opportunities, ultimately leading to sustainable economic growth.