Household Resilience to Food Insecurity in Southern Ethiopia

This research examines the rural household resilience to food insecurity using cross-sectional data in four Kebeles of Damot Pulasa district of Wolaita Zone, Southern Ethio-pia. This research aimed to assess the current levels of rural household resilience to food insecurity and analyse the critical determinants of rural household resilience to food insecurity in the study area. A multi-stage sampling technique was employed to gener-ate data from 187 sampled household heads. Data were collected using the Key Inform-ant Interview, Focus Group Discussion, and interview schedule. The collected data were analyzed using descriptive statistics, a household resilience index, and an econometric model. Categorical and continuous variables were analyzed using x2-test and one-way ANOVA, respectively. The dependent variable, Household resilience to Food Insecurity, was measured by Household Resilience Index using Principal Component Analysis re-sulting from five variables, i.e., Income, Food expenditure, Crop held in stock, Precau-tionary assets, and Investment in cash savings. The study employed 18 Independent variables. Moreover, after computing the descriptive statistics, the Ordered Probit Re-gression was used to identify the key determinants of household resilience to food inse-curity. The survey results revealed that 57.2% were Non-resilient, 18.2% were Moderate-ly Resilient, 15.0% Resilient, and 9.6% Highly Resilient. Ordered Probit Model estima-tion result showed family size, diversity of income source, livestock ownership, use of improved seed with fertilizer, access to credit service, and assistance received were the six significant explanatory variables and determinants of the household resilience to food insecurity at 95% CI or P<0.05. Based on this study's significant explanatory varia-bles findings, stakeholders should focus on household resilience to food insecurity as many residents were Non-resilient in the study area. Moreover, this study should focus on critical predictors highly linked with resilience to food insecurity.