Spatial variations and determinants of receiving cash and food from the productive safety net program among households in Ethiopia: spatial clustering and multilevel analyses.

Background: There is a global struggle with food insecurity and undernutrition among women, and Ethiopia has been particularly impacted by these issues. To address this challenge, Ethiopia has implemented a cash and food safety net program over many years. However, there is limited information available regarding the program's factors and spatial distributions, with no recent national evidence from Ethiopia. Consequently, the objective of this study is to investigate the spatial clustering and determinants of the Productive Safety Net Program (PSNP) in Ethiopia. Method: This study utilized data from the Ethiopian Demographic and Health Survey. The sample included 8,570 weighted households. Given the hierarchical nature of the data, a multilevel logistic regression model was employed to identify factors influencing the outcome variable. Geographical clusters of individuals receiving assistance from the PSNP were examined using SaTScan software and the Bernoulli model, along with the Kulldorff methods. The nationwide distribution of the program beneficiaries was visualized using ArcGIS version 10.8. Variables were considered statistically significant if their Results: The overall coverage of the PSNP was 13.54% [95% confidence interval (CI): 12.84-14.29] among households in Ethiopia. The study revealed that people from richer households adjusted odds ratio [AOR = 0.46 (95% CI: (0.33, 0.64))], those from the richest households [AOR = 0.26 (95% CI:(0.17,0.41))], and those with educated household heads [AOR = 0.45 (95% CI:(0.28, 0.71))] have a lower likelihood of utilizing the PSNP compared to their counterparts. Conversely, a unit increase in household heads' age [AOR = 1.02 (95% CI:(1.01, 1.02))] and family size [AOR = 1.05 (95% CI:1.021.10)] showed a higher likelihood of joining the PSNP, respectively. Household heads who have joined community health insurance [AOR = 3.21 (95% CI:(2.58, 4.01))] had significantly higher odds of being included in the PSNP than their counterparts. Heads who belong to a community with a high poverty level [AOR = 2.68 (95% CI:(1.51, 4.79))] and community health insurance [AOR = 2.49 (95% CI:(1.51, 4.11))] showed more inclination to utilize the PSNP compared to their counterparts. Conclusion: PSNP was judged to have a low implementation status based on the findings gathered regarding it. We found factors such as age, sex, region, wealth, education, family size, regions, and health insurance to be statistically significant. Therefore, encouraging women empowerment, community-based awareness creation, and coordination with regional states is advisable.