According to MoSPI, the study leverages statistical innovation to strengthen evidence-based policymaking and enhance the precision of welfare planning.
The initiative stems from the recommendations of the National Statistical Commission’s Steering Committee, which advocated a pilot study to explore the feasibility of generating model-based district-level estimates. Led by Dr. Mausumi Bose, former Professor at the Indian Statistical Institute (ISI), Kolkata, the committee collaborated with NSO and the Directorate of Economics and Statistics (DES), Government of Uttar Pradesh, to develop this study.
While the HCES provides dependable state and national-level insights, local governance requires equally robust district-level data. The small survey sample sizes in individual districts make it statistically challenging to derive accurate figures. This model-based approach aims to resolve that gap.
How the Model Works
The study employed the Small Area Estimation (SAE) technique a modern statistical method that combines survey findings with administrative data. This helps produce stable and accurate district-level figures, even when sample sizes are limited.
Auxiliary data sources used in the model include:
- Number of old-age pension beneficiaries
- Patients enrolled under the Ayushman Bharat (PM-JAY) scheme
- Number of domestic LPG connections
- Beneficiaries under the Antyodaya food distribution scheme
Two primary statistical models the Fay–Herriot (FH) and the Spatial Fay–Herriot (SFH) were applied to validate and refine the district estimates.
Key Findings
The report highlights the top-performing districts in terms of monthly per capita consumption expenditure (MPCE):
- Rural areas: Bagpat, Saharanpur, Gautam Buddha Nagar, Meerut, and Ghaziabad.
- Urban areas: Gautam Buddha Nagar, Gonda, Ghaziabad, Bagpat, and Lucknow.
These findings indicate how statistical modelling can effectively generate reliable district-level insights, allowing policymakers to tailor development initiatives with precision. Moreover, this approach is scalable and can be replicated across other states and indicators such as employment or poverty estimation.
Implications for Policy and Governance
This model-based estimation framework strengthens India’s data ecosystem, making localized planning more inclusive and evidence-driven. It provides decision-makers with actionable insights for monitoring living standards, reducing regional disparities, and evaluating welfare programmes with greater accuracy.
By leveraging advanced statistical techniques, the NSO and MoSPI have taken a decisive step toward a data-driven governance model that promotes transparency, efficiency, and targeted development at the grassroots level.
The study reaffirms the government’s commitment to using data as a public good one that informs social welfare, economic planning, and sustainable development across the country.