A nonparametric analysis of household-level food insecurity and its determinant factors: exploratory study in Ethiopia and Nigeria

Given the fundamental importance of food to human well-being, understanding food insecurity is crucial for sustainable development. However, due to the complex nature of food insecurity, traditional linear methods of empirical analysis may mask critical relationships between food insecurity and demographic, agricultural, and environmental factors. Here we show, using two years of household-level survey data from Ethiopia and Nigeria, that nonparametric regression (random forest, in this study) enables enhanced insight into the factors associated with self-reported food security and household dietary diversity score. We observe nonlinearities and thresholds in the relationships between the measures of food security, livestock ownership, and climatic conditions. The threshold-based relationships suggest that policies aimed at increasing agricultural productivity (e.g., livestock holdings) may only be beneficial up to an extent. While it is intuitive that some level of diminishing returns will exist, our nonparametric analysis could be used as a first step to discern the levels to which policies may be beneficial. Additionally, our results indicate that the random forest (and perhaps nonparametric regression and classification methods more generally) may be especially well-positioned to uncover nuances in these relationships in years with suboptimal climatic conditions (such as during the 2015 drought in Ethiopia). Ultimately, we argue that nonparametric approaches, when informed by existing theory, provide an insightful complement to inform the analysis of agricultural and development policy.

Automated summary

This study uses nonparametric regression methods to analyze the relationship between food insecurity and household survey data, revealing complex nonlinear and threshold relationships between food security measures, livestock ownership, and climatic conditions. The findings suggest that policy decisions should take into account nonlinearity, and that random forest and other nonparametric methods may be particularly useful in uncovering nuances in these relationships during suboptimal climatic conditions.