New Delhi, May 14 The National Statistics Office (NSO) announced on Wednesday that the sampling design of the Periodic Labour Force Survey (PLFS) has been revamped from January 2025 to address the requirement of high-frequency labour market indicators with enhanced coverage to include the rural areas as well.
The revamped PLFS is envisaged to estimate the key employment and unemployment indicators comprising Labour Force Participation Rate, Worker Population Ratio and Unemployment Rate, on a monthly basis for rural and urban areas at the all-India level in the Current Weekly Status (CWS).
Usual Status and Current Weekly Status (CWS) refers to frameworks for determining the activity status of persons surveyed based on reference periods of the last 365 days and the last seven days preceding the date of survey, respectively.
The revamped PLFS sample design will enable generation of the monthly estimates of key labour market indicators-- Labour Force Participation Rate (LFPR), Worker Population Ratio (WPR) and Unemployment Rate (UR) at the all-India level following the Current Weekly Status (CWS) approach. The monthly estimates will help in timely policy interventions. The first monthly bulletin of PLFS for the month of April 2025 is scheduled to be released in May 2025, according to the NSO statement
At present, PLFS provides quarterly labour market indicators for the urban areas only. With the update in the PLFS sample design, quarterly estimates of employment-unemployment indicators will be available for both rural and urban areas and hence for the entire country. The first quarterly bulletin of PLFS covering both rural and urban areas for the quarter April-June, 2025, is slated to be released in August 2025, the statement said.
From the year 2025, the annual PLFS results will be brought out based on the calendar year, i.e. survey period of January – December of a specific year (e.g. January 2025 - December 2025). This change in disseminating the PLFS annual results and unit-level data will facilitate comprehensive analysis of labour market performance through review of key employment-unemployment indicators and also assist in the timely updation of India’s labour market statistics in the databases maintained by the international agencies.
The sampling methodology encompassing various aspects of the PLFS sampling design has been revamped to realise the objective of releasing the high-frequency labour force indicators from PLFS.
In the revamped PLFS sample design, a larger total sample size of 22,692 FSUs is planned to be surveyed (12,504 FSUs in the rural areas and 10,188 in the urban areas) in each year of the two-year panel, with a first visit schedule as compared to 12,800 FSUs surveyed in PLFS up to December 2024.
A total of 12 households will be surveyed from each of the selected FSUs, which implies an overall sample size of around (22,692 x 12) = 2,72,304 households. This marks a 2.65 time increase in sample households to be covered in PLFS as compared to the number of sample households covered up to December 2024 (which was around 1,02,400). The enhanced sample size is expected to provide reliable estimates of labour market indicators with improved precision.
In the revamped PLFS sample design, the district has been made the primary geographical unit, called basic stratum, within a state/UT separately for rural and urban sectors for selecting FSUs for most of the geography covered. In the remaining parts, the NSS region has been made the basic stratum. This will ensure the presence of sample observations from most of the districts in the PLFS sample, which will improve the representativeness of the estimates generated.
For rural areas, provision for stratification has been made within the district/NSS region (as the case may be) based on the distance of the villages within 5 km from the district headquarters or from a city/town with more than 5 lakh population. In the case of urban areas, million-plus cities within the district/NSS region have been formed as a stratum. This will also improve the reliability of the estimates by incorporating sample observations from spatial segments having different nature of labour market dynamics, the statement added.
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