Household food insecurity levels in Ethiopia: quantile regression approach - Wubetie, Habtamu T.; Zewotir, Temesgen; Mitku, Aweke A.; Dessie, Zelalem G. (Frontiers in Public Health, 2023)
Introduction: Numerous natural and man-made factors have afflicted Ethiopia, and millions of people have experienced food insecurity. The current cut-points of WFP food consumption score (FCS) have limitations in measuring the food insecurity level of different feeding patterns due to the diversified culture of the society. The aim of this study is to adapt the WFP food security score cut-points corrected for the different feeding culture of the society using effect-driven quantile clustering. Method: The 2012, 2014, and 2016 Ethiopian socio-economic household-based panel data sets with a sample size of 3835 households and 42 variables were used. Result: Household food insecurity is reduced trough time across the quintiles of food security score distribution, mainly in the upper quantiles. The leveling based on effect-driven quantile clustering brings 35.5 & 49 as FCS cut-points corrected for cultural diversity. The corrected FCS brings wider interval for food insecure households and with the same interval range for vulnerable households, though the WFP FCS cut-points under estimate by 7 score. Education level, employment, fertilizer usage, farming type, agricultural package, infrastructure-related factors, and environmental factors are found to be the significant contributing factors in the food security. On the other hand, household’s head age, dependency ratio, shock and no irrigation in households make significant contributions to food insecurity. Moreover, households live in rural areas and farming crops on small lands are comparatively vulnerable and food insecure. Conclusion: Measuring the food insecurity in Ethiopia using WFP FCS cut off points underestimates the household’s food insecurity levels. Since the WFP FCS cut-points have universality and comparability limitation, there is a need for a universally accepted local-threshold, corrected for local factors those resulted in different consumption patterns in the standardization of food security score. Accordingly, the quantile regression approach adjusts the WFP-FCS cut-points by adjusting for local situations. Applying WFP cut points will wrongly assign households on each level, so the proportion of households will inflate for secured level & under estimated for insecure level, and the influence of factors also will wrongly recommend to the levels of food security score.