District unemployment data needs granular analysis for policymaking

National Sample Survey findings reveal significant divergence between combined and demographic-specific joblessness figures across key districts

The National Sample Survey (NSS) under the National Statistics Office (NSO) recently released a report compiling district-wise data on the labour market for 2025. In the preface of that report, NSS Director General Geeta Singh Rathore writes that the estimates are expected to be useful to policymakers, state and district administrations, researchers, academics and other stakeholders for policy formulation, planning, monitoring and analytical work.

 

The data on unemployment rates is indeed useful for policy and planning. However, the task is not that simple and requires careful assessment of the data.

 

For instance, if we take a sample of 10 districts with the highest unemployment rates in combined urban and rural areas for both males and females and juxtapose it with separate figures on joblessness specific to urban, rural, males or females, we will not always get the same districts for these parameters.

 

For instance, Papum Pare in Arunachal Pradesh had the highest combined unemployment rate among 584 districts surveyed in 2025. It is the only district to have figured among the 10 districts with the highest urban unemployment rate, rural joblessness, male unemployment rate and female joblessness as well.

 

However, the same is not true for Kohima in Nagaland which figured among the 10 districts having the highest combined unemployment rate but not the highest rural unemployment rate.

 

Similarly, Jaipur was among the 10 districts having the highest combined joblessness but not in urban and rural areas. Pathankot in Punjab is one of the 10 districts having the highest combined unemployment rate but not among those having the highest urban unemployment rate. Srinagar is among the 10 districts having the highest combined joblessness and female unemployment rate but not the highest urban, rural and male unemployment rate.

 

The other five districts — Jodhpur in Rajasthan, Patna and Munger in Bihar, South Goa and Kalaburagi in Karnataka — figured in the top 10 districts with the highest combined unemployment rate but did not figure in all of the other four parameters.

 

This makes the task of state and district administrations complicated since policies targeting combined joblessness must differ from those specifically targeting urban and rural parts or males and females.