SDG 1: No Poverty | SDG 10: Reduced Inequalities
Institutions: Ministry of Statistics & Programme Implementation (MoSPI) | National Statistics Office (NSO)
The National Statistics Office (NSO), MoSPI, has released the pilot study report, Model-based District-level Estimates based on the Household Consumption Expenditure Survey (HCES) 2022–23 for Uttar Pradesh, marking a significant step toward data-driven governance. This initiative addresses the critical demand for granular socio-economic data needed to support decentralized planning and the monitoring of welfare schemes.
The Study’s Focus: Bridging the District Data Gap
The primary objective of this report is to develop statistically sound, granular estimates of Monthly Per Capita Consumption Expenditure (MPCE) for all districts of Uttar Pradesh (UP), disaggregated by rural and urban areas, utilizing data from the HCES 2022-23 survey.
The study was necessitated by a core limitation in traditional large-scale surveys:
Problem Statement: While the HCES is renowned globally for providing robust data on consumption patterns and living standards at the national and state levels, the sample size is generally insufficient for producing reliable estimates at smaller domains, such as the district level. Directly computing estimates for these smaller areas often results in statistically unstable (unreliable) figures.
Top Findings (Illustrative): Using the finalized methodology, the report generated reliable MPCE estimates for all 75 districts. Illustratively, the top five rural districts by MPCE estimates were Bagpat, Saharanpur, Gautam Buddha Nagar (Gb Nagar), Meerut, and Ghaziabad. The top five urban districts were Gautam Buddha Nagar, Gonda, Ghaziabad, Bagpat, and Lucknow.
Policy Implications and National Significance
The study demonstrates a cost-effective method for transforming the use of official statistics in governance, directly supporting India’s push toward decentralized development:
Targeted Welfare: The availability of accurate district-level data is essential for targeting subsidies and determining the scope of social welfare initiatives, poverty alleviation programs, and other schemes. Without this data, welfare scheme targeting is limited.
Decentralized Planning: MPCE estimates are fundamental for guiding localized policy planning, fiscal capacity evaluations, and making public investment choices at the micro-level (districts, blocks, villages).
Cost-Effectiveness: The methodology provides a unique, cost-effective, and scalable approach to obtain stable estimates without incurring the extra time and budget constraints of significantly increasing the overall survey sample size.
Data Equity: This approach is critical for data equity, ensuring that “no district is statistically invisible” due to low sample size, thereby advancing data-driven decision-making at all sub-state levels.
Methodology and Statistical Proof
The report validates the feasibility of using model-based estimation to overcome sampling deficiencies, establishing statistical rigor through the use of established international methodologies. The study relied on Small Area Estimation (SAE), a robust statistical technique that works by “borrowing strength” from related small areas or domains to increase the “effective sample size” of each district.
Follow the full report here: Harnessing Statistical Models: A Study Towards Inclusive and Targeted Development