Livelihood Changes, Spatial Anticontagion Policy Effects, and Structural Resilience of National Food Systems in a Sub-Saharan African Country Context: A Panel Machine Learning Approach

Abstract

The livelihood changes due to the COVID-19 policies in low-income and transitional economies serve as a lever for gauging the structural resilience of national food systems. Yet, few studies have addressed the cascading effects of the pandemic policies on the livelihood changes of farming system actors or modeled and provided coherent hypotheses about the transitory structural shifts at the micro-level. Other studies on the subject have either captured the early impacts of the pandemic on food systems with limited or no insight into the sub-Saharan African context or have used macro-level data, due to sparsely available micro-level data. These early insights are relevant for the design of early warning systems. However, an ongoing and deeper insight into the effects of pandemic policies is critical, since new and more comprehensive policies are needed to address the economic fallout and the extenuating effects of COVID-19 on food supply chain disruptions. The overriding questions are as follows: what are the effects of the pandemic policies on the livelihoods of food system actors and are there spatial-economic variations in the effects of the pandemic policies on the livelihoods of the farming system actors? Using 2019 and 2020 primary data from 836 farming system actors in Ghana, we offer fresh insights into the transitory micro-level livelihood changes caused by the COVID-19 anticontagion policies. We analyzed the data using the generalized additive, subset regression, classical linear, and logistic regression models in a machine learning framework. We show that the changes in the livelihood outcomes of the food system actors in Ghana coincide with the nature of pandemic mitigation policies adopted in the spatial units. We found that the lockdown policies had a negative and significant effect on the livelihoods of the farming system actors in the lockdown areas. The policies also negatively affected the livelihoods of the farming system actors in distant communities that shared no direct boundary with the lockdown areas. On the contrary, the lockdown policies positively affected the livelihoods of the farming system actors in the directly contiguous communities to the lockdown areas. We also document the shifts in the livelihood outcomes of the farming system actors, such as income, employment, food demand, and food security in the different spatial policy areas.

Keywords

• food system
• structural effects
• sustainable livelihoods
• machine learning
• pandemic