Announcing changes to the Periodic Labour Force Survey, the Ministry of Statistics and Programme Implementation (MoSPI) will increase its publication of labour force data, extending quarterly employment estimates to rural areas for the first time.

The first monthly bulletin for April is scheduled for release in May, the ministry said, adding that the quarterly results of the survey will be published for rural, urban, and combined areas.

"The revamped PLFS sample design will enable generation of the monthly estimates of key labour market indicators viz. 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," said the ministry.

At present PLFS provides quarterly labour market indicators for the urban areas only. With the updation 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.

Additionally, the statistics' ministry announced that from the year 2025, the annual PLFS results will be brought out based on the calendar year, that is, 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 timely updation of India’s labour market statistics in the databases maintained by the international agencies," said MoSPI's statement.

The MoSPI has also decided to make district as the the primary geographical unit, called basic stratum within a state/UT separately for rural and urban sectors for selecting FSUs for most part of the geography covered. In the remaining parts, NSS region has been made the basic stratum. This will ensure presence of sample observations from most of the districts in the PLFS sample, which will improve the representativeness of the estimates generated.