An assessment of food security and vulnerability among female-headed households in South Africa
Hannah M Dunga
International Journal of Business Ecosystem & Strategy · 2026 · Vol. 8 / Issue 2 · 226-237
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Abstract
Food security remains a persistent challenge in South Africa, disproportionately affecting vulnerable groups such as female-headed households. While the country is nationally food secure, many households continue to experience inadequate access to sufficient, safe, and nutritious food due to structural inequalities, labour-market exclusion, and spatial disparities. Using data from the 2023 General Household Survey (GHS), this study assesses levels and determinants of food security among female-headed households in South Africa. A Food Insecurity Experience Index (FIEI) is constructed from three indicators capturing household experiences of limited food variety, inability to consume balanced meals, and reliance on monotonous diets due to financial constraints. An ordered logit model is estimated to examine how demographic, socioeconomic, and geographic factors shape household food security. The results reveal that female-headed households face significantly higher risks of food insecurity compared to male-headed households, even after controlling for income, education, employment status, and household composition. Provincial disparities are pronounced, with households in historically disadvantaged provinces exhibiting worse food-security outcomes. Education and employment are associated with lower food insecurity, while older household heads and larger households experience higher vulnerability. These findings highlight the deeply structural and gendered nature of food insecurity in South Africa. The study underscores the need for targeted, nutrition-sensitive social protection policies, improved market access, and gender-responsive interventions to enhance food security among women-led households.
Keywords: {'name': 'Dietary Diversity; Female-Headed Households; Food Security; General Household Survey (GHS); Ordered Logistic Regression'}
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Hannah M Dunga
University of South Africa
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