India, Nov. 3 -- The Government of India has issued a release:
The report titled "Model-based District-level Estimates based on the Household Consumption Expenditure Survey (HCES) 2022-23 for Uttar Pradesh" is available for download in the following link :
Scan the below QR code to access the Publications/ Reports
https://new.mospi.gov.in/publications-reports
The National Statistics Office (NSO), Ministry of Statistics and Programme Implementation (MoSPI) has conducted a study on the Model-based District-level Estimates based on the Household Consumption Expenditure Survey (HCES) 2022-23 for the State of Uttar Pradesh. The Report of the study has been released and is available in MoSPI website:
https://new.mospi.gov.in/uploads/publications_reports/publications_reports1761641209612_6875c53f-d8eb-4458-be3e-1e12a9e528c9_Compiled_Report_final17092025.pdf.
NSO, MoSPI has been regularly conducting large-scale household surveys on diverse social and economic subjects to provide comprehensive statistical information for evidence-based policymaking. Among these surveys, the Household Consumption Expenditure Survey (HCES) is one of the most important. It provides information on consumption patterns, household characteristics, and living standards at national and state levels.
Background
The Steering Committee constituted by the National Statistical Commission (NSC) for National Sample Survey (NSS) recommended to undertake a pilot study to assess the feasibility of generating model-based district-level estimates. Accordingly, a committee was constituted under the Chairpersonship of Dr. Mausumi Bose, Former Professor, Indian Statistical Institute (ISI), Kolkata to explore estimation of district-level Monthly Per Capita Consumption Expenditure (MPCE) figures for the districts of Uttar Pradesh utilizing data from the Household Consumption Expenditure Survey (HCES): 2022-23. NSO and the Directorate of Economics and Statistics (DES), Government of Uttar Pradesh rendered technical support to the Committee.
Need for this study
While the HCES gives reliable estimates at the national and state levels, there has been a growing demand for similar information at district level to support local planning and monitoring of welfare schemes. However, as the survey sample in each district is relatively small, it is difficult to get statistically reliable district-level estimates directly from the survey. To find out an alternative way of addressing this data gap, a model-based approach was taken up as a pilot for the State of Uttar Pradesh.
Objective of the study
The main objective was to develop district-level estimates of Monthly Per Capita Consumption Expenditure (MPCE) for all districts of Uttar Pradesh using a model-based approach that could supplement the direct survey results. The idea was to test whether statistical modelling could help fill in the information gaps where survey data was limited or not available.
How the model-based method works
The study used a statistical method known as Small Area Estimation (SAE). This technique combines survey data with information from other sources to get better and more stable results for smaller areas like districts. Essentially, this approach borrows strength incorporating auxiliary information, like administrative data to improve precision of estimates where direct sampling fails to produce reliable estimates.
Some of the auxiliary information used in this exercise include:
Two types of models: the Fay-Herriot (FH) and the Spatial Fay-Herriot (SFH); were used in this pilot study.
Key findings
Conclusion
This study highlights how statistical models can help fill data gaps where direct survey results are limited, providing policymakers and planners with valuable insights to design and evaluate welfare programmes, monitor living standards, and reduce regional inequalities. The approach adopted in this study can be extended to other states and other socio-economic indicators such as employment or poverty, thereby advancing data-driven governance and local-level planning. It reaffirms the potential of model-based estimation as an effective tool for supporting targeted interventions and sustainable development.
Disclaimer: Curated by HT Syndication.
