District and Social Group-wise Estimation and Spatial Mapping of Food Insecurity in the State of Odisha in India

Abstract

The Sustainable Development Goal of Zero Hunger is a bold commitment towards 795 million undernourished people to end all forms of hunger and malnutrition by 2030 (http://www.undp.org/sustainable-development-goals/goal-2-zero-hunger/). India, sharing a quarter of the global hunger burden, has set a comprehensive action against the food insecurity and hunger issue through microscopic identification of food insecure mass followed by decentralized level planning and effective monitoring. Availability of reliable disaggregate level statistics using Small Area Estimation (SAE) approach for measuring the prevalence of food insecurity can be a potential key to the Governmental organization to take consistent steps towards framing strategic plans eyeing zero hunger. A pragmatic approach in SAE is to consider Hierarchical Bayes (HB) framework, which provide an added flexibility of using complex models without concerning much about known design variance or traditional normality assumption. However, this approach does not incorporate the survey weights that are essential for valid inference given the informative samples that are produced by complex survey designs. In this paper, involving survey design information a number of model specifications are discussed in area level HB version to generate reliable and representative district and district by social groupwise estimates of food insecurity incidence for rural areas of the State of Odisha in India by combining the Household Consumer Expenditure Survey 2011-2012 data of National Sample Survey Office and with the Population census 2011. Spatial maps have been produced to observe the inequality in food insecurity distribution among the districts as well as districts cross classified by socio-economic categories. Such maps are definitely useful for policy formulation, fund disbursement purpose and for the Government in taking effective administrative decisions targeting zero hunger.