Introduction. Currently, Ethiopia, in particular, the rural areas of Ethiopia, faces high levels of food insecurity. In spite of the fact that there have been many studies on food security, most of them have been conducted in specific national settings. Hence, the determinants of food insecurity should be assessed at the national level. Therefore, this study was primarily aimed to identify the determinant factors of household food insecurity in rural Ethiopia. Method. A cross-sectional Ethiopian socioeconomic survey (ESS) data collected from September 2018 to August 2019 was utilized. A sample of 3115 households was selected from 316 clusters across rural Ethiopia using a two-stage probability sampling technique. To identify the determinants of food insecurity, logistic regression was applied. Results. Among 3,115 households, 50.05% of them were food insecure. Factors such as the household head being aged from 30 to 64 (AOR = 0.786, 95% CI: [0.635, 0.973]), widowed, divorced, or separated (AOR = 1.588, 95%CI: [1.001, 2.518]), literate (AOR = 0.702, 95%CI: [0.590, 0.834]), household aid (AOR = 1.339, 95%CI: [1.089, 1.648]), drought-affected (AOR = 0.640, 95%CI: [0.507, 0.808]), nonagricultural business (AOR = 0.655, 95%CI: [0.472, 0.908]), dependency ratio from 50 to 75% (AOR = 0.680, 95%CI: [0.534, 0.867]), having 6 to 10 livestock (AOR = 0.644, 95%CI: [0.496, 0.836]), and more than 10 livestock (AOR = 0.362, 95% CI: [0.284, 0.461]) were found to be significantly associated with the household’s food insecurity at 5% level of significance. Conclusion. The household head’s age from 30 to 64, being literate, drought-affected, having nonagricultural business, dependency ratio from 50 to 75%, and owning more than 10 livestock have been negatively affecting food insecurity. While supporting households, a “widowed, divorced, or separated” household head has had a positive effect on food insecurity in rural Ethiopia positively influencing food insecurity in rural Ethiopia. Policymakers need to pay special attention to very young and old-aged household heads, adult education, household self-help, livestock improvement, and entrepreneurship while implementing poverty reduction programs.

Currently, around 36% of the rural Ethiopian population is accessing drinking water from unimproved sources and it is unevenly distributed through time and geographic regions. Therefore, this study aimed to analyze the spatiotemporal patterns of unimproved drinking water sources and identify hotspot areas in rural Ethiopia. Ethiopian Socioeconomic Survey (ESS) data obtained from the Central Statistical Agency were used. It was conducted in four waves from 2011 to 2019. A two-stage probability sampling design was applied. The sample of enumeration areas and households were taken as the first and second stages of sampling, respectively. A total of 3912, 3775, 3698, and 3115 sample households with complete information on drinking water sources were taken in each wave of ESS data, respectively. Weighted proportions, autocorrelation (Moran’s “I”) statistic, and hotspot analyses were applied to estimate the prevalence, test the presence of clustering, and identify vulnerable areas with unimproved drinking water sources. The STATA version 14, Excel, and ArcGIS 10.6 were used to manage and analyze data. The proportions of households with unimproved drinking water sources were 0.497, 0.385, 0.298, and 0.363 in consecutive waves of ESS data. The results also revealed the existence of geographical and temporal variations of access to drinking water from unimproved sources, and the most recent vulnerable (hotspot) areas in the borders of the West and East Gojjam zones in the western Amhara region, Zone one in southern Afar region, and Liben, Afder, Shebelle, Korahe, and Nobob zones in Somali region were identified. In conclusion, this study reveals significant geographic inequalities in the use of improved drinking water sources. This may be necessary for policies and coverage targeting the most vulnerable regions. The presented map and analytical approaches can provide a mechanism to monitor future reductions in inequality within countries by reflecting resource allocation priorities.