Abstract
The second UN Sustainable Development Goal establishes food security as a priority for governments, multilateral organizations, and NGOs. These institutions track national-level food security performance with an array of metrics and weigh intervention options considering the leverage of many possible drivers. We studied the relationships between several candidate drivers and two response variables based on prominent measures of national food security: the 2019 Global Food Security Index (GFSI) and the Food Insecurity Experience Scale’s (FIES) estimate of the percentage of a nation’s population experiencing food security or mild food insecurity (FI<mod). We compared the contributions of explanatory variables in regressions predicting both response variables, and we further tested the stability of our results to changes in explanatory variable selection and in the countries included in regression model training and testing. At the cross-national level, the quantity and quality of a nation’s agricultural land were not predictive of either food security metric. We found mixed evidence that per-capita cereal production, per-hectare cereal yield, an aggregate governance metric, logistics performance, and extent of paid employment work were predictive of national food security. Household spending as measured by per-capita final consumption expenditure (HFCE) was consistently the strongest driver among those studied, alone explaining a median of 92% and 70% of variation (based on out-of-sample R2) in GFSI and FI<mod, respectively. The relative strength of HFCE as a predictor was observed for both response variables and was independent of the countries used for model training, the transformations applied to the explanatory variables prior to model training, and the variable selection technique used to specify multivariate regressions. The results of this cross-national analysis reinforce previous research supportive of a causal mechanism where, in the absence of exceptional local factors, an increase in income drives increase in food security. However, the strength of this effect varies depending on the countries included in regression model fitting. We demonstrate that using multiple response metrics, repeated random sampling of input data, and iterative variable selection facilitates a convergence of evidence approach to analyzing food security drivers.
Highlights
Recent data indicate that more than two billion people lack regular access to safe, nutritious, and sufficient food (FAO 2019), and an estimated 821 million people are not able to acquire enough food to meet minimum dietary energy requirements (FAO 2018a)
This study analyzed the relationships between two measures of national food security and a dataset of explanatory variables that characterizes 65 nations in terms of agricultural land quality and quantity, agricultural production, governance and infrastructure, and household income
We used linear regression models to quantify the contribution of each explanatory variable to the variation in both metrics
Summary
Recent data indicate that more than two billion people lack regular access to safe, nutritious, and sufficient food (FAO 2019), and an estimated 821 million people are not able to acquire enough food to meet minimum dietary energy requirements (FAO 2018a). A widely used definition from the FAO states that “food security exists when all people, at all times, have physical and economic access to sufficient, safe and nutritious food to meet their dietary needs and food preferences for an active and healthy life” (FAO 1996) This definition has been critiqued and refined (Barrett 2010; Coates 2013; Dilley and Boudreau 2001; Pinstrup-Andersen 2009; Tendall et al 2015), and many food security measurement methodologies have been developed (Cafiero 2016; Carletto et al 2013; EIU 2019; IPC Global Partners 2019; Jones et al 2013; Leroy et al 2015; Russell et al 2018). The official SDG indicator framework designates the FIES-based estimate of the prevalence of moderate or severe food insecurity in a nation’s population as SDG Indicator 2.1.2 (UN General Assembly 2017)
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have