Preferential trading in agriculture: New insights from a structural gravity analysis and machine learning

Abstract Preferential trade agreements (PTAs) contain various non‐tariff provisions, yet identifying their trade effects remains challenging because these commitments are high‐dimensional and strongly correlated within agreements. We estimated a theory‐consistent structural gravity model with domestic flows for 26 agricultural subsectors over 1988–2017 and employed a plug‐in Elastic Net‐penalized PPML approach to isolate the provisions that systematically shape agricultural trade. The procedure identifies 13 provisions that are particularly important for agricultural trade, spanning trade facilitation, SPS/TBT disciplines, and rules on competition and subsidies. After accounting for the impact of preferential tariff rates, a generic PTA indicator is still estimated to increase trade by 20%. This effect is absorbed once the identified provision bundle is included, demonstrating that trade patterns under PTAs are primarily shaped by specific non‐tariff commitments. The results point to clear channels: facilitation and transparency provisions are trade‐promoting, whereas stronger reliance on international standards is associated with lower trade, consistent with compliance‐cost mechanisms.