India’s Ministry of Statistics and Programme Implementation (MoSPI) has released the first monthly bulletin of the revamped Periodic Labour Force Survey (PLFS) for April 2025. This new format marks a significant milestone in India’s labour data ecosystem, providing high-frequency, rural and urban employment-unemployment indicators under the Current Weekly Status (CWS) framework.
Why in News?
The PLFS Monthly Bulletin for April 2025, published on 15 May 2025, is the first in the new series of monthly bulletins under the revamped PLFS design, effective from January 2025. This shift enables monthly updates on key employment indicators, improving policy responsiveness and statistical transparency.
New Methodology & Framework
- Revamped in January 2025 to generate high-frequency data.
- Introduced a monthly rotational panel sampling approach.
- Each household is surveyed 4 times across 4 consecutive months.
- 75% of sampling units are retained month-to-month for better consistency.April 2025 Labour Market Indicators (Age 15+)
1. Labour Force Participation Rate (LFPR) – CWS
- Overall (India): 55.6%
- Rural Areas: 58.0%
- Urban Areas: 50.7%
- Male LFPR: 79.0% (rural), 75.3% (urban)
- Female LFPR (rural): 38.2%
2. Worker Population Ratio (WPR) – CWS
- Overall (India): 52.8%
- Rural Areas: 55.4%
- Urban Areas: 47.4%
- Female WPR: 36.8% (rural), 23.5% (urban)
- Overall Female WPR: 32.5%
3. Unemployment Rate (UR) – CWS
- Overall: 5.1%
- Male: 5.2%
- Female: 5.0%
Sample Design and Size
- Sample FSUs (April 2025): 7,511
- Rural: 4,140
- Urban: 3,371
- Households surveyed: 89,434 (Rural: 49,323 | Urban: 40,111)
- Persons surveyed: 3,80,838
Concept Definitions (for clarity in exams)
- LFPR: % of people working or seeking work.
- WPR: % of employed people in the population.
- UR: % of unemployed in the labour force.
- CWS: Status based on the activity in the last 7 days.
Significance
- Offers timely and granular data critical for policy decisions, especially in rural areas.
- Helps monitor employment gender disparities.
- Enhances comparability and statistical accuracy through a modern survey design.
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