What matters for agricultural trade? Assessing the role of trade deal provisions using machine learning

Stepan Gordeev, Jeremy Jelliffe, Dongin Kim and Sandro Steinbach

Applied Economic Perspectives and Policy, 2025, vol. 47, issue 4, 1469-1506

Abstract: This paper employs machine learning to determine which preferential trade agreement (PTA) provisions are relevant to agricultural trade patterns and the factors that may influence their adoption. Utilizing the three‐way gravity model, we apply plug‐in Lasso regularized regression to pinpoint predictive PTA provisions for agricultural trade. Our findings underscore the importance of competition policies, export taxes, intellectual property rights, capital movement, state enterprises, and technical trade barriers. Subsequently, we use Random Forests to reveal the economic, political, social, and geographic factors associated with the inclusion of those provisions in PTAs. The findings highlight the roles of contagion, governance quality, energy use, and geographic proximity. Our analysis provides new insights that can aid in formulating strategies to support agricultural trade.

Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
https://doi.org/10.1002/aepp.13525

Related works:
Working Paper: What Matters for Agricultural Trade? Assessing the Role of Trade Deal Provisions using Machine Learning (2023)
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:wly:apecpp:v:47:y:2025:i:4:p:1469-1506

Access Statistics for this article

More articles in Applied Economic Perspectives and Policy from John Wiley & Sons
Bibliographic data for series maintained by Wiley Content Delivery ().