Vol. 11 No. 4 (2022)
BAE 10th Anniversary papers

Vulnerability and resilience to food and nutrition insecurity: A review of the literature towards a unified framework

Pierluigi Montalbano
La Sapienza, Università di Roma
Donato Romano
Università di Firenze

Published 2023-05-03

Keywords

  • vulnerability,
  • resilience,
  • food security,
  • nutrition

How to Cite

Montalbano, P., & Romano, D. (2023). Vulnerability and resilience to food and nutrition insecurity: A review of the literature towards a unified framework. Bio-Based and Applied Economics, 11(4), 303–322. https://doi.org/10.36253/bae-14125

Abstract

Current approaches to measuring food and nutrition security (FNS) mainly consider past access to food, while assessing vulnerability and resilience to food insecurity requires a dynamic setting and sound predictive models, conditional to the entire set of food-related multiple-scale shocks and stresses as well as households’ characteristics. The aim of this work is twofold: i) to review the state of the relevant literature on the conceptualization and the empirical measurement of vulnerability and resilience to food insecurity; ii) to frame the main coordinates of a possible unifying framework aiming at improving ex-ante targeting of policy interventions and resilience-enhancing programs. Our argument is that clarifying the relationships existing between vulnerability and resilience provides a better understanding and a more comprehensive picture of food insecurity that includes higher-order conditional moments and non-linearities. Furthermore, adopting the proposed unified framework, one can derive FNS measures that are: scalable and aggregable into higher-level dimensions (scale axiom); inherently dynamic (time axiom); conditioned to various factors (access axiom); applicable to various measures of food and nutrition as dependent variables (outcomes axiom). Unfortunately, the proposed unified framework shows some limitations. First, estimating conditional moments is highly data-demanding, requiring high-quality and high-frequency micro-level panel data for all the relevant FNS dimensions, not mentioning the difficulty of measuring risks/shocks and their associated probabilities using short panel data. Hence, there is a general issue of applicability of the proposed approach to typically data-scarce environments such as developing contexts. Second, there is an inherent tradeoff between the proposed approach in-sample precision and out-of-sample predictive performance. This is key to implement effective early warning systems and foster resilience-building programs.

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