The study investigates the predictors of food insecurity among households in Eswatini given the 2015/16 El Niño induced drought. To identify the geographic and socioeconomic factors that predict food insecurity during a drought in Eswatini, the study uses a logistic regression. The logistic regression results show that households that have a deteriorated health and disability status are three times more likely to be food insecure during a drought than households that have no health or disability impacts. In contrast, high quality vegetables, meat, and fish can be considered luxury food items that significantly predict food security among households in the country. The study also finds that the prices of maize and rice are good predictors of food insecurity among households given that maize is a staple food in Eswatini. A major finding on the predictors of food insecurity is that all incomes above E1,000 significantly reduce the chances of food insecurity among households compared to those households that have no form of income. The regression reveals that E3,500 is the optimal level of monthly income to cushion households from severe food insecurity. Therefore, the study recommends that Government (Ministry of Labour and Social Security) should investigate the suitability and sustainability of a E3,500 monthly minimum income (wage) in Eswatini. Furthermore, in the event of drought, the Government of Eswatini should prioritise intervention programmes such as food distribution on households living with disabilities and those with deteriorated health status. In terms of building drought preparedness and mitigation for future droughts, implementation of the 2005 Food Security Policy should deliberately target the following constituencies; Lomahasha, Mthongwaneni, Matsanjeni North, Ngudzeni, Sigwe, Hlane, Mandlangempisi, Sandleni, Mkhiweni, Sithobela, Ntontozi, Lubuli, Dvokodvweni, Mayiwane, Siphofaneni, Mafutseni, Ndzingeni, Mahlangatane, Matsanjeni South, Mahlangatja, and Nkwene

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Keywords

Drought, Food Insecurity, Predictors, Food Insecurity Vulnerability