Public Policymaking for International Agricultural Trade using Association Rules and Ensemble Machine Learning

TL;DR

This paper introduces novel AI-based methods, including association rules and ensemble machine learning, to improve predictions and analysis of international agricultural trade flows, aiding policymakers in decision-making amid trade disruptions.

Contribution

It presents new application of association rules and ensemble machine learning to analyze and predict international agricultural trade, addressing recent trade shocks and disruptions.

Findings

Improved prediction accuracy for trade flows.

Identification of key trade association patterns.

Enhanced understanding of trade disruptions' impacts.

Abstract

International economics has a long history of improving our understanding of factors causing trade, and the consequences of free flow of goods and services across countries. The recent shocks to the free trade regime, especially trade disputes among major economies, as well as black swan events, such as trade wars and pandemics, raise the need for improved predictions to inform policy decisions. AI methods are allowing economists to solve such prediction problems in new ways. In this manuscript, we present novel methods that predict and associate food and agricultural commodities traded internationally. Association Rules (AR) analysis has been deployed successfully for economic scenarios at the consumer or store level, such as for market basket analysis. In our work however, we present analysis of imports and exports associations and their effects on commodity trade flows. Moreover, Ensemble Machine Learning methods are developed to provide improved agricultural trade predictions, outlier events' implications, and quantitative pointers to policy makers.