Gravity With Lasso: Global Cereal Trade With Factors of Conflicts, COVID-19, Currency, China, Climate Change and Income ('6C')

Abstract

Global and open trade fosters peace, economic prosperity and food security worldwide, but it faces growing instability due to multidimensional factors. A range of new developments threaten the resilience of trade networks, with direct implications for food security. These include escalating conflicts along cereal trade routes, supply-chain disruptions such as COVID-19, high food inflation affecting exchange terms, China's increasing dominance in global trade, fluctuating yields due to climate change and rising food demand in high-growth countries. Appropriate policy responses are difficult to implement and typical short-term responses such as export bans, quotas or higher import tariffs tend to transfer vulnerabilities to other countries and to amplify global market volatility rather than to stabilise it. To address these challenges, policymakers need to first evaluate if and when to respond to these disruptions, and hence need to assess the relative importance of these threats. This paper first identifies six major influences on the world's economy that have the potential to disrupt trade flows: Conflicts, COVID-19, Currency, China, Climate change and inCome (the 6Cs). Second, it seeks to identify which of these factors are most relevant to cereal trade, using a gravity model of trade combined with the machine learning LASSO and Random Forest techniques. Results show that high income growth, China's weight and climate change are more consistently associated with trade fluctuations than abrupt events like COVID-19 or conflicts. Trade participation is more vulnerable to abrupt events than trade intensities, while instability from exporter countries matters more than from importer countries. Results differ by region, with Africa and Asia being more subject to global risk. These insights guide policymakers in assessing the importance of disruptions on trade fluctuations and prioritising responses that enhance trade resilience and food security globally.

Keywords

cereals; machine learning; resilience; trade

Published in

The World Economy
2025
Publisher: WILEY

SLU Authors

• Höschle, Lisa

  • Department of Economics, Swedish University of Agricultural Sciences
    

UKÄ Subject classification

Economics

Publication identifier

• DOI: https://doi.org/10.1111/twec.70033

Permanent link to this page (URI)

https://res.slu.se/id/publ/145518