Small area estimation of food insecurity prevalence for the state of uttar pradesh in India

Article type: Research Article

Authors: Chandra, Hukum^* | Verma, Bhanu^*

Affiliations: ICAR-Indian Agricultural Statistics Research Institute, New Delhi, India

Correspondence: [*] Corresponding author: Bhanu Verma, ICAR-Indian Agricultural Statistics Research Institute, New Delhi, India. E-mail: [email protected].

Abstract: The 2nd Sustainable Development Goal (SDG) of the United Nations attempt to eliminate the potential hunger and food insecurity issues by 2030, but in the plight of COVID19 pandemic it has become far more critical and persistent issue globally as well as in India. The nation-wide socio-economic surveys of National Sample Survey Office (NSSO) in India are designed to produce reliable and representative estimates of important food insecurity parameters at state and national level for both rural and urban sectors separately but these surveys cannot be used directly to generate reliable district level estimates. Whereas, efficient and representative disaggregated level estimates for the extent (or incidence) of food insecurity prevalence has direct impact on strategizing effective policy plans and monitoring progress towards eliminating food insecurity. In this backdrop, the paper outlines small area estimation approach to estimate the incidence of food insecurity across the districts of rural Uttar Pradesh in India by linking data from the 2011–12 Household Consumer Expenditure Survey of NSSO, and the 2011 Indian Population Census. A spatial map has been generated showing spatial disparity for the incidence of food insecurity in Uttar Pradesh. These disaggregated level estimates are relevant and purposeful for SDG indicator 2.1.2 – severity of food insecurity. The estimates and map of food insecurity incidences are expected to deliver invaluable information to the policy-analysts and decision-makers.

Keywords: Food insecurity, sustainable development goal, small area estimation, precise, representative, rural districts

DOI: 10.3233/MAS-220011

Journal: Model Assisted Statistics and Applications, vol. 17, no. 2, pp. 73-85, 2022