Cross-national analysis of food security drivers: comparing results based on the Food Insecurity Experience Scale and Global Food Security Index

• Published in: Food security Vol. 13; no. 5; pp. 1245 - 1261
• Main Authors: Allee, Andrew, Lynd, Lee R., Vaze, Vikrant
• Format: Journal Article
• Language: English
• Published: Dordrecht Springer Netherlands 01-10-2021; Springer Nature B.V; Springer Science + Business Media
• Subjects: Agricultural land; Agriculture; Biomedical and Life Sciences; development; ENGINEERING; Environment; food insecurity experience scale; Food Science; Food security; global food security index; Independent variables; Iterative methods; Life Sciences; Logistics; Model testing; national food security; Original Paper; Plant Sciences; Random sampling; Regression models; Social Policy; Social Sciences; Statistical sampling; Sustainable development; Training; Variables; Development; National food security; Food Insecurity Experience Scale; Global Food Security Index
• ISSN: 1876-4517; 1876-4525
• DOI: 10.1007/s12571-021-01156-w

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 R
2
) 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.