Establishing a robust technique for monitoring and early warning of food insecurity in post-conflict south Sudan using ordinal logistic regression

Abstract

The lack of a “gold standard” to determine and predict household food insecurity is well documented. While a considerable volume of research continues to explore universally applicable measurement approaches, robust statistical techniques have not been applied in food security monitoring and early warning systems, especially in countries where food insecurity is chronic. This study explored the application of various Ordinal Logistic Regression techniques in the analysis of national data from Southern Sudan. Five Link Functions of the Ordinal Regression model were tested. Of these techniques, the Probit Model was found to be the most efficient for predicting food security using ordered categorical outcomes (Food Consumption Scores). The study presents the first rigorous analysis of national food security levels in postconflict Southern Sudan and shows the power of the model in identifying significant predictors of food insecurity, surveillance, monitoring and early warning.