Points to Remember:
- India’s unemployment problem is largely structural.
- Understanding the methodology of unemployment calculation is crucial.
- Improvements in methodology are needed for accurate policymaking.
- Structural unemployment requires targeted interventions.
Introduction:
Unemployment is a significant socio-economic challenge in India. While the overall unemployment rate fluctuates, a substantial portion is attributed to structural unemployment â a mismatch between the skills possessed by the workforce and the skills demanded by the economy. Accurate measurement of unemployment is paramount for effective policy interventions. India primarily relies on the Periodic Labour Force Survey (PLFS) for unemployment data, but its methodology has faced criticism. This necessitates an examination of the current methodology and suggestions for improvement.
Body:
1. Methodology for Computing Unemployment in India:
The PLFS, conducted by the National Sample Survey Office (NSSO), now the National Statistical Office (NSO), is the primary source for unemployment data in India. It employs a sample survey methodology, collecting data on employment and unemployment across various demographic groups. The key concepts used are:
- Workforce (or Labour Force): Individuals aged 15 years and above who are either employed or seeking employment.
- Employed: Individuals engaged in any economic activity for at least one hour during the reference period (usually a week).
- Unemployed: Individuals who are part of the workforce but are not employed and are actively seeking work.
The PLFS calculates unemployment rates using various measures:
- Usual Principal Status (UPS) Unemployment Rate: This measures unemployment based on the principal or main activity of individuals during the reference period.
- Usual Status (US) Unemployment Rate: This considers the activity status of individuals over a longer period (365 days).
- Current Weekly Status (CWS) Unemployment Rate: This measures unemployment based on the activity status during the previous week.
Limitations of the Current Methodology:
- Underestimation: Critics argue that the PLFS underestimates unemployment due to its reliance on self-reported data and the definition of “actively seeking work.” Many discouraged workers who have given up searching for jobs are not included.
- Regional Disparities: The survey’s sample size might not adequately capture regional variations in unemployment, leading to inaccurate representations of specific areas.
- Informal Sector Bias: The informal sector, a significant part of the Indian economy, is often underrepresented in the data due to difficulties in tracking employment in this sector.
- Skill Mismatch: The PLFS doesn’t comprehensively capture the skill mismatch that contributes significantly to structural unemployment.
2. Suggested Improvements:
- Enhanced Data Collection: Improving data collection methods by incorporating administrative data from various government schemes and leveraging technology (e.g., mobile surveys) can enhance accuracy.
- Broader Definition of Unemployment: A more inclusive definition of unemployment should consider discouraged workers and those working less than desired hours (underemployment).
- Improved Sampling Techniques: Employing more sophisticated sampling techniques to better represent regional and sectoral variations is crucial.
- Skill Mapping: Integrating skill mapping exercises with the survey to identify skill gaps and mismatches will provide valuable insights into structural unemployment.
- Regularity and Timeliness: More frequent surveys with timely data releases are needed for effective policy responses.
- Qualitative Data: Incorporating qualitative data through interviews and focus groups can provide a richer understanding of the reasons behind unemployment.
Conclusion:
The current methodology for computing unemployment in India, while providing valuable data, has limitations that lead to underestimation and an incomplete picture of the unemployment situation. Improvements are necessary to accurately reflect the complex reality of unemployment, particularly structural unemployment. By enhancing data collection methods, broadening the definition of unemployment, improving sampling techniques, and incorporating skill mapping, India can gain a more accurate understanding of its unemployment challenge. This improved data will enable the government to design and implement targeted policies to address structural unemployment, fostering inclusive and sustainable economic growth, aligning with the constitutional goal of providing equal opportunities to all citizens. A holistic approach, combining improved data with effective skill development programs and job creation initiatives, is crucial for tackling this multifaceted problem.