The spatial modification effect of predictors on household level food insecurity in Ethiopia

Abstract

Household food insecurity remains highly prevalent in developing countries (including in Ethiopia) and it has been recognized as a serious public health problem. Several factors such as demographic, economic, social, and clinical factors influence household food insecurity, and these vary geographically. In this work, we investigate the geographical modification of the effect of several factors on chronic food insecurity. The data is from the Ethiopia socioeconomic survey conducted by the Ethiopia Central Statistics Agency (ECSA) in collaboration with the World Bank. Ethiopia socioeconomic survey is a long-term project to collect nationally representative panel survey of over 6500 households. A geo-additive model which accounts the structured and unstructured special effect was adopted to estimate household food insecurity risk factors. The study also revealed significant spatial variations on household food insecurity among administrative zones. Mainly, household living in the Sidama, Gamo Gofa, Shinille, Basketo, Wolyita, Wag Hemira, Liben, Awi, Eastern Tigray and West Harerghe zones, having higher food insecurity than the other zones in Ethiopia. Moreover, the analysis also showed that availability of credit services, proximity to service centers, average years of schooling of members of the household, and household assets are negatively associated with household food insecurity, whereas shocks, age, and dependency ratio increase the odds of a household to be food insecured. The generalized geo-additive mixed-effects model enables simultaneous modeling of spatial correlation, heterogeneity and possible nonlinear effects of covariates. Our study investigated the spatial heterogeneity of household level food insecurity, and its association with shocks, age, dependency ratio, availability of credit services, average years of schooling, and household assets. Our findings have also an important implication for planning as well as in the search for the variables that might account for the residual spatial patterns.

The spatial effects of the household's food insecurity levels in Ethiopia: by ordinal geo-additive model

“…Though several researches assessed the determinants of Ethiopian household food insecurity and supported the associated factors obtained in this study ( 31 , 34 , 80 , 81 ), only few researches consider the spatial pattern in Ethiopia ( 22 , 82 , 83 ) with better literature on developing country ( 18 , 84 – 90 ). However, none of literature labeled as food insecure, vulnerable, and secured based on cut-points corrected for feeding cultural difference and provided the determinants specific to the hot spot zones of food insecurity and vulnerability.…”

“…In addition to vast intra and extra country conflicts in Africa, the frequent occurrence of drought in the region has been the major cause of crop and livestock losses (89%) ( 19 ), which leads the continent to take a larger proportion of severe food insecure population ( 2 ). The prevalence of food insecurity is high (42%) in East Africa ( 20 ), especially Ethiopia faced food insecurity for many years; from 1979/80 and 1995/96 ( 21 ), in recent years ( 22 ) especially from 2019 to 2021 more than half (56%) of the total population suffered from moderate or severe food insecurity ( 23 ).…”

“…The food insecurity is not yet evaluated using the recommended locally adopted cut-points for the food consumption score (FCS) measure of WFP, considering that the local factors effect resulted in a divers feeding pattern (e.g., exclusion of small amounts of food items from the diet during measurement extends to include sugar and oil consumption) ( 37 , 38 ). Furthermore, very few studies in the country evaluated the non-linear effects of the covariates ( 22 , 39 ) without considering the spatial effects. Moreover, even though similar comprehensive global studies, such as spatial correlation of food security in developing countries ( 18 ) and in China ( 39 ), have been conducted, the effect of local factors on food insecurity level cut-point is not considered.…”

Ethnicity, social connectedness, and the rural-urban food continuum: Food security among urban informal settlement dwellers in Kenya

“…Notably, the associations between dependency ratio, income, savings, social cohesion, rural visits, and dietary knowledge emerge as significant factors. The established positive relationship between dependency ratio and food insecurity is reiterated by earlier studies, indicating that households with a higher proportion of dependents are more susceptible to food insecurity due to increased resource pressure and limited income [ [40] , [41] , [42] , [43] , [44] ] (Aidoo et al, 2013; Ahmed & Abah, 2014; Agidew & Singh, 2018; Sisha, 2022; Dessie et al, 2022). Moreover, the negative correlation between monthly income and food insecurity has been documented in past research, suggesting that households with higher incomes enjoy better food security access, as they can afford a more diverse and nutritionally adequate diet [ 45 , 46 ] (Carson & Boege, 2020; Mei et al, 2020).…”

Mapping the Provincial Food Security Conditions in Indonesia Using Cluster Ensemble-Based Mixed Data Clustering-Robust Clustering with Links (CEBMDC-ROCK)

“…However, the availability of food that is smaller than the need can create economic instability. As a result, various social problems and political stability can occur if food security is disturbed [4]. Critical food conditions can disrupt economic and national stability [5].…”