Flexible statistical modelling in food insecurity risk assessment.

Abstract

Food insecurity has remained a persistent problem in Sub-Saharan Africa. Conflict and other protracted crisis have rendered a significant proportion of Africa’s populations to suffer the risk of food insecurity, as their resilience to livelihood shocks weakens. A significant and immense body of research in the past two decades has largely centred on describing the incidence of food insecurity and vulnerability. Limited research was done using statistical methods to determine the likelihood of food insecurity risk. The use of flexible statistical techniques for a sound and purposive monitoring, evaluation, planning and decision making in food security and resilience was limited. The study aimed to extend the use of statistics into the expanding field of food security and resilience, and also to provide new direction for future research involving applications of the methods explored, such as adjustments in statistical methods, sampling and data collection. The study specifically aims at helping food security analysts with tested and statistically robust tools for use in the analyses of the likelihood of food insecurity risk in settings with structural food insecurity issues. Moreover, it aimed to inform practice, policy and analysis in monitoring and evaluation of food insecurity risk in protracted crisis; thus helping in improving risk aversion measures. Utilising secondary data, the research examines relevant statistical techniques for determining predictors of food insecurity risk, namely; Principal Component Analysis; Multiple Correspondence Analysis; Classification and Regression Tree Analysis; Survey Logistic Regression, Generalized Linear Mixed Models for Ordered Categorical Data; and Joint Modelling. The study was conducted in the form of structured analysis of different datasets vi collected in the conflict-ridden South Sudan. Assets owned by households, as well as availability of livelihood endowments, was used as proxy for determining the level of resilience in particular demographic unit or geographical setting. The study highlighted the strengths and weaknesses of the techniques explored in the analysis as identifying or classifying potential predictors of food insecurity outcomes. Each technique is capable of generating a unique composite index for measuring the amount of resilience and predicting and classifying households according to food insecurity phase based on factor loadings. In general, the study determined that each method explored has peculiar strengths as well as limitations. However, a noteworthy implication observed is that asset-based statistical analysis, whether based on composite index that can be used as proxy for measuring the amount of resilience to food insecurity eventualities or on regression modelling approaches, does assure sufficient rigour in drawing conclusions about the wellbeing of households or populations under study and how they might withstand food insecurity and livelihood shocks. As food insecurity and malnutrition continue to attract substantial attention, such flexible analytical approaches exert potential usefulness in determining food insecurity risks, especially in protracted crisis settings.