Model Answer
0 min readIntroduction
Unemployment in India is a persistent socio-economic challenge, with a significant portion attributed to structural factors. Structural unemployment arises from a mismatch between the skills possessed by the workforce and the skills demanded by employers, often due to technological changes, shifts in industry, or geographical imbalances. Recent data from the Periodic Labour Force Survey (PLFS) indicates an unemployment rate of 4.2% in 2022-23 (MoSPI, 2023), but this figure often understates the true extent of the problem due to the prevalence of underemployment and the informal sector. Examining the methodologies used to calculate these figures and suggesting improvements is crucial for effective policy formulation.
Understanding Structural Unemployment in India
India’s structural unemployment is deeply rooted in several factors:
- Skill Gaps: A significant portion of the workforce lacks the skills required for modern industries, leading to a mismatch.
- Regional Disparities: Uneven economic development across states creates imbalances in job opportunities.
- Technological Advancements: Automation and technological changes displace workers in traditional sectors.
- Rigid Labor Laws: Restrictive labor laws can discourage firms from hiring, particularly in the formal sector.
- Informal Sector Dominance: The large informal sector often lacks job security and adequate wages, masking true unemployment levels.
Methodology for Computing Unemployment in India
Currently, India primarily relies on two major surveys to measure unemployment:
- Periodic Labour Force Survey (PLFS): Conducted by the National Statistical Office (NSO), MoSPI, annually since 2017-18. It provides quarterly estimates for urban areas and annual estimates for rural areas. It uses the ‘usual status’ approach, defining unemployment based on activity status for a major part of the year.
- National Sample Survey Office (NSSO) Employment-Unemployment Surveys: Conducted periodically (typically every five years). These surveys provide a more comprehensive picture but are less frequent.
However, these methodologies have limitations:
- Underestimation of Underemployment: Both surveys struggle to accurately capture underemployment – individuals working below their potential or earning insufficient wages.
- Challenges in Capturing Informal Employment: The informal sector, which employs a large majority of the workforce, is difficult to survey accurately.
- Definition of Unemployment: The ‘usual status’ approach may not reflect short-term unemployment or seasonal variations in employment.
- Data Collection Issues: Response bias and logistical challenges in reaching remote areas can affect data quality.
Suggested Improvements in Methodology
To improve the accuracy and relevance of unemployment data, the following improvements are suggested:
- Expanding the Scope of PLFS: Increase the frequency of data collection (e.g., monthly) and expand the sample size to improve representativeness.
- Incorporating a ‘Time-Use’ Survey: A time-use survey can provide insights into how individuals allocate their time, helping to identify underemployment and non-participation in the labor force.
- Developing a Dedicated Survey for the Informal Sector: A specialized survey focusing on the informal sector, with tailored questions on earnings, working conditions, and job security, is needed.
- Adopting a Multi-Dimensional Approach: Move beyond simple unemployment rates and consider indicators like the labor force participation rate, the employment-to-population ratio, and the quality of employment (wages, social security benefits).
- Leveraging Big Data: Utilize administrative data from sources like EPFO, ESIC, and MGNREGA to supplement survey data and provide real-time insights into employment trends.
- Improving Data Analysis Techniques: Employ advanced statistical techniques to account for biases and improve the accuracy of estimates.
Example: The ‘Labour Force Survey’ in the UK utilizes a continuous, household panel survey, providing detailed information on employment status, earnings, and job satisfaction. India could draw inspiration from this model.
Table: Comparison of Unemployment Measurement Methodologies
| Methodology | Frequency | Scope | Limitations |
|---|---|---|---|
| PLFS | Quarterly (Urban), Annual (Rural) | Household-based | Underestimates underemployment, challenges in capturing informal sector |
| NSSO Employment-Unemployment Surveys | Periodic (5 years) | Household-based | Less frequent, similar limitations to PLFS |
Conclusion
Addressing structural unemployment requires a multi-pronged approach, and accurate data is fundamental to effective policy-making. While the current methodologies provide valuable insights, they fall short of capturing the complexities of the Indian labor market. Implementing the suggested improvements – expanding the scope of surveys, incorporating new data sources, and adopting a more nuanced approach to measurement – will provide a more realistic assessment of unemployment and underemployment, enabling policymakers to design targeted interventions and promote inclusive growth.
Answer Length
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