**AWARDS ISSUED PRIOR TO JANUARY 20, 2025, WERE FUNDED UNDER PREVIOUS ADMINISTRATIONS AND MAY NOT REFLECT THE PRIORITIES AND POLICIES OF THE CURRENT ADMINISTRATION.** THIS RESEARCH WILL GENERATE NEW KNOWLEDGE REGARDING THE FORMATION OF PREFERENTIAL TRADE AGREEMENTS (PTAS), THEIR IMPACT ON GLOBAL TRADE, AND THE CONSEQUENCES FOR U.S. AGRICULTURAL AND FOOD BUSINESSES AND EMPLOYMENT. TO ACCOMPLISH THIS GOAL, WE WILL RELY ON MODERN STATISTICAL MODELING TECHNIQUES TO THOROUGHLY INVESTIGATE THE FACTORS THAT INFLUENCE THE FORMATION OF PTAS. THIS ANALYSIS BUILDS ON NEWLY COLLECTED PTA DATA CAPTURED WITH THE HELP OF NEURAL MACHINE TRANSLATION AND NATURAL LANGUAGE PROCESSING SYSTEMS. TO DETERMINE FACTORS THAT INFLUENCE THE FORMATION OF PTAS, WE WILL ADOPT THE RANDOM FOREST ALGORITHM. THIS STATISTICAL ANALYSIS WILL PROVIDE NEW INSIGHTS REGARDING THE ROLE OF ECONOMIC, SOCIAL, AND POLITICAL FACTORS IN FORMING PTAS WITH AGRICULTURAL AND FOOD PROVISIONS. WE WILL USE THE NEWLY CREATED DATASET TO INVESTIGATE THE IMPACT OF PTA PROVISIONS ON AGRICULTURAL AND FOOD TRADE IN THE SECTORAL THREE-WAY GRAVITY MODEL CONTEXT RELYING ON AN ADAPTATION OF THE PRIOR LEAST ABSOLUTESHRINKAGE AND SELECTION OPERATOR TO THE POISSON PSEUDO-MAXIMUM LIKELIHOOD ESTIMATOR. THIS INNOVATIVE MACHINE LEARNING APPROACH WILL ENABLE US TO INCORPORATE PRIOR INFORMATION, REDUCE OVER-FITTING, AND FACILITATE FEATURE SELECTION IN A HIGH-DIMENSIONAL CONTEXT. WE WILL ALSO ASSESS THE IMPACT OF PTA PROVISIONS ON THE STRUCTURE AND CONDUCT OF THE U.S. AGRICULTURAL AND FOOD SECTOR AND EVALUATE EMPLOYMENT EFFECTS. A BETTER UNDERSTANDING OF THESE TRADE POLICY CONSEQUENCES WILL SHED LIGHT ON A CRITICAL DRIVER OF STRUCTURAL CHANGE. SUCH KNOWLEDGE IS ESSENTIAL FOR THE FUNCTIONING OF GLOBAL SUPPLY CHAINS. THE PROJECT WILL HELP TO INFORM FEDERAL POLICIES THAT AIM TO FOSTER THE COMPETITIVENESS OF U.S. FARMERS AND RANCHERS AND INCREASE THEIR PARTICIPATION AND SUCCESS IN INTERNATIONAL MARKETS.