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How to understand high global food price? Using SHAP to interpret machine learning algorithm

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Listed:
  • Xiao Han
  • Tong Yuan
  • Donghui Wang
  • Zheng Zhao
  • Bing Gong

Abstract

The global food prices have surged to historical highs, and there is no consensus on the reasons behind this round of price increases in academia. Based on theoretical analysis, this study uses monthly data from January 2000 to May 2022 and machine learning models to examine the root causes of that period’s global food price surge and global food security situation. The results show that: Firstly, the increase in the supply of US dollars and the rise in oil prices during pandemic are the two most important variables affecting food prices. The unlimited quantitative easing monetary policy of the US dollar is the primary factor driving the global food price surge, and the alternating impact of oil prices and excessive US dollar liquidity are key features of the surge. Secondly, in the context of the global food shortage, the impact of food production reduction and demand growth expectations on food prices will further increase. Thirdly, attention should be paid to potential agricultural import supply chain risks arising from international uncertainty factors such as the ongoing Russia-Ukraine conflict. The Russian-Ukrainian conflict has profoundly impacted the global agricultural supply chain, and crude oil and fertilizers have gradually become the main driving force behind the rise in food prices.

Suggested Citation

  • Xiao Han & Tong Yuan & Donghui Wang & Zheng Zhao & Bing Gong, 2023. "How to understand high global food price? Using SHAP to interpret machine learning algorithm," PLOS ONE, Public Library of Science, vol. 18(8), pages 1-20, August.
    • Handle: RePEc:plo:pone00:0290120
    DOI: 10.1371/journal.pone.0290120
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    References listed on IDEAS

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